"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
"AlphaFold","oist/BioinfoUgrp","Nextflow.md",".md","4676","168","Nextflow
========

A DSL for data-driven computational pipelines <http://nextflow.io>.

In 2022, _Nextflow_ had a backwards-incompatible change that depreacated an old
syntax called _DSL1_ in favor of the _DSL2_ syntax.  We still provide the old
version `21.10.6` in the `Nextflow` module on _deigo_.  Newer versions are in
the `Nextflow2` module.  To use a _DSL1_ pipeline with the `Nextflow2` module,
pass the `-dsl1` flag to the `nextflow` program.

The `Nextflow2` module also limits the use of memory by setting the environment
variable `NXF_OPTS` to `-Xms500M -Xmx2G`.  Please contact us or open an issue
if you hit the limit.


Installation on deigo
---------------------

See also <https://groups.oist.jp/scs/install-software-your-unit>.

### Release or edge versions from GitHub

Source: https://github.com/nextflow-io/nextflow

```
APP=Nextflow2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
VER=25.10.2
mkdir -p $APPDIR/$VER/bin
cd $APPDIR/$VER
wget https://github.com/nextflow-io/nextflow/releases/download/v${VER}/nextflow-${VER}-dist
mv nextflow-${VER}-dist bin/nextflow
chmod 775 bin/nextflow
mkdir -p $MODROOT/$APP/modulefiles/
cd $MODROOT/$APP/modulefiles/
cat <<'__END__' > $VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp/Other""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/nextflow-io/nextflow/releases"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pipeline, SLURM"")
whatis(""Description: "".."" Data-driven computational pipelines."")

help([[Nextflow version downloaded from GitHub

See https://www.nextflow.io/ for help.]])

-- Package settings
depends_on(""singularity"")
depends_on(""java-jdk/17"")
setenv(""NXF_OPTS"", ""-Xms500M -Xmx2G"")
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

nf-test
=======

Testing framework for Nextflow pipelines (<https://www.nf-test.com/>).

Installation on deigo
---------------------

```
APP=nf-test
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
VER=0.9.2
mkdir -p $APPDIR/$VER/bin
cd $APPDIR/$VER
wget https://github.com/askimed/nf-test/releases/download/v${VER}/nf-test-${VER}.tar.gz
pushd bin
tar xvfz ../nf-test-${VER}.tar.gz
chmod 775 nf-test
chmod 664 nf-test.jar
popd
rm nf-test-${VER}.tar.gz
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/askimed/nf-test/releases"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pipeline, SLURM"")
whatis(""Description: ""..""Testing framework for Nextflow pipelines."")

help([[nf-test version downloaded from GitHub

See https://www.nf-test.com/ for help.]])

-- Package settings
depends_on(""Nextflow2"")
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

nf-core
=======

Python package with helper tools for the nf-core community (<https://nf-co.re/docs/nf-core-tools>).

To install a new latest version, update the `VER` variable below and run the commands.

```
ml purge
ml bioinfo-ugrp-modules Nextflow2
ml python/3.11.4
APP=nf-core
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
VER=3.5.1
mkdir -p $APPDIR/$VER
cd $APPDIR/$VER
PYTHONUSERBASE=$(pwd) pip3 install --no-warn-script-location --user $APP 
PYTHONUSERBASE=$(pwd) pip3 install --no-warn-script-location --user pre-commit
cd $MODROOT/$APP/modulefiles/
cp 3.1.2.lua ${VER}.lua
```

Contents of 3.1.2.lua:

```
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp/Other""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())
-- setenv(""Nextflow_MOD_HOME"", apphome)
-- setenv(""Nextflow_MOD_VERSION"", appversion)

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://nf-co.re/docs/nf-core-tools"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pipeline, SLURM"")
whatis(""Description: "".."" Python package with helper tools for the nf-core community."")

help([[nf-core tools installed with pip

See https://nf-co.re/ for help.]])

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PYTHONPATH"", apphome..""/lib/python3.11/site-packages/"")
depends_on(""Nextflow2"")
depends_on(""python/3.11.4"")
depends_on(""Other/nf-test"")
```
","Markdown"
"AlphaFold","oist/BioinfoUgrp","DebianMedModules.md",".md","6201","156","Debian Med modules
==================

The [Debian Med](https://www.debian.org/devel/debian-med/) project packages
bioinformatics and other medically relevant software for the
[Debian](https://www.debian.org/intro/about) operating system.  We collect
many of them in a [Singularity](https://sylabs.io/) container and create one
module per package on Deigo.

For users
---------

```
ml bioinfo-ugrp-modules DebianMed
ml av
```

Enjoy !


For developers
--------------

### Docker image

See [oist/BioinfoUgrp_DebianMed_Images](https://github.com/oist/BioinfoUgrp_DebianMed_Images)
on how to create a Docker image with [GitHub actions](https://docs.github.com/en/actions).

### Create a new Singularity image

Pull the Docker image built on GitHub.

```
export DEBVERSION=13.0-1
singularity pull DebianMed_${DEBVERSION}.sif docker://ghcr.io/oist/bioinfougrp_debianmed_images:${DEBVERSION}
```

### Generate all the modules

Make sure the module and modulefiles created are writable for the group:

```
umask 002
```

Generate a list of all the bioinformatics packages recommended by the med-bio and med-cloud metapackages:

```
PKGLIST=$(./DebianMed_$DEBVERSION.sif apt show med-bio med-cloud |
  grep Recommends                   | 
  sed 's/ |/,/'g                    |  # Sanitize pipe symbols representive alternatives
  cut -d' ' -f2-                    |  # Remove 'Recommends: '
  sed -e 's/, /\n/g'                |  # Make it a space-separated pacakge list 
  sort -u                           |  # Remove duplicates and in the next command, remove R packages
  grep -v -e r-cran -e r-bioc -e r-other )
```

Create the modules

```
ln -s DebianMed_${DEBVERSION}.sif debalamod.sif
debalamod.sif cp /debalamod .
chmod 775 debalamod
for package in $PKGLIST source-highlight
do
  ./debalamod $package
done
```

After creating a new module, if it is not seen by `ml av`, delete the cache:

```
rm -rf ~/.lmod.d/.cache
```

### `biosyntax-less` module

This module was generated by hand as it needs complex setting of environment variables.

```
mkdir -p /bucket/BioinfoUgrp/DebianMed/$DEBVERSION/modules/biosyntax-less/1.0.0b-2
cd /bucket/BioinfoUgrp/DebianMed/$DEBVERSION/modules/biosyntax-less/1.0.0b-2
cat > src-hilite-lesspipe-bio.sh <<__END__
#!/bin/sh
LC_ALL=C singularity exec /bucket/BioinfoUgrp/DebianMed/$DEBVERSION/DebianMed_$DEBVERSION.sif /usr/share/source-highlight/src-hilite-lesspipe-bio.sh ""\$@"" 
__END__
chmod 775 src-hilite-lesspipe-bio.sh
mkdir -p /bucket/BioinfoUgrp/DebianMed/$DEBVERSION/modulefiles/biosyntax-less/
cat > /bucket/BioinfoUgrp/DebianMed/$DEBVERSION/modulefiles/biosyntax-less/1.0.0b-2.lua <<__END__
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp/DebianMed/$DEBVERSION""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, ""modules"", ""biosyntax-less"", appversion)

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/oist/BioinfoUgrp"")
whatis(""Category: "".."" bioinformatics"")
whatis(""Keywords: "".."" Bioinformatics, Debian"")
whatis(""Description: "".."" Syntax Highlighting for Computational Biology"")

help([[This module runs from Singularity image of Debian Med.
Please contact the Bioinfo user group for details.]])

-- Package settings
depends_on(""singularity"", ""source-highlight"")

-- Adatpted from /usr/share/doc/biosyntax-less/rc_append.txt

pushenv(""HIGHLIGHT"", ""/usr/share/source-highlight"")
pushenv(""LESSOPEN"", ""| "" .. apphome .. ""/src-hilite-lesspipe-bio.sh %s"")
pushenv(""LESS"", "" -R "")

set_alias(""less"", ""less -NSi -# 10"")
--  -N: add line numbers
--  -S: don't wrap lines (force to single line)
--  -# 10: Horizontal scroll distance

-- Explicit call of  <file format>-less for piping data
-- i.e:  samtools view -h aligned_hits.bam | sam-less
-- Core syntaxes (default)
set_alias(""clustal-less"", ""source-highlight -f esc --lang-def=\$HIGHLIGHT/clustal.lang --outlang-def=\$HIGHLIGHT/bioSyntax.outlang     --style-file=\$HIGHLIGHT/fasta.style | less"")
set_alias(""bed-less"", ""source-highlight     -f esc --lang-def=\$HIGHLIGHT/bed.lang     --outlang-def=\$HIGHLIGHT/bioSyntax.outlang     --style-file=\$HIGHLIGHT/sam.style   | less"")
set_alias(""fa-less"", ""source-highlight      -f esc --lang-def=\$HIGHLIGHT/fasta.lang   --outlang-def=\$HIGHLIGHT/bioSyntax.outlang     --style-file=\$HIGHLIGHT/fasta.style | less"")
set_alias(""fq-less"", ""source-highlight      -f esc --lang-def=\$HIGHLIGHT/fastq.lang   --outlang-def=\$HIGHLIGHT/bioSyntax.outlang     --style-file=\$HIGHLIGHT/fasta.style | less"")
set_alias(""gtf-less"", ""source-highlight     -f esc --lang-def=\$HIGHLIGHT/gtf.lang     --outlang-def=\$HIGHLIGHT/bioSyntax-vcf.outlang --style-file=\$HIGHLIGHT/vcf.style   | less"")
set_alias(""pdb-less"", ""source-highlight     -f esc --lang-def=\$HIGHLIGHT/pdb.lang     --outlang-def=\$HIGHLIGHT/bioSyntax-vcf.outlang --style-file=\$HIGHLIGHT/pdb.style   | less"")
set_alias(""sam-less"", ""source-highlight     -f esc --lang-def=\$HIGHLIGHT/sam.lang     --outlang-def=\$HIGHLIGHT/bioSyntax.outlang     --style-file=\$HIGHLIGHT/sam.style   | less"")
set_alias(""vcf-less"", ""source-highlight     -f esc --lang-def=\$HIGHLIGHT/vcf.lang     --outlang-def=\$HIGHLIGHT/bioSyntax-vcf.outlang --style-file=\$HIGHLIGHT/vcf.style   | less"")
set_alias(""bam-less"", ""sam-less"")

-- Auxillary syntaxes (uncomment to activate)
set_alias(""fai-less"", ""source-highlight      -f esc --lang-def=\$HIGHLIGHT/faidx.lang    --outlang-def=\$HIGHLIGHT/bioSyntax.outlang   --style-file=\$HIGHLIGHT/sam.style   | less"")
set_alias(""flagstat-less"", ""source-highlight -f esc --lang-def=\$HIGHLIGHT/flagstat.lang --outlang-def=\$HIGHLIGHT/bioSyntax.outlang   --style-file=\$HIGHLIGHT/sam.style   | less"")
__END__
```

### Finally make the modules available

Test them before !

```
cat > /apps/.bioinfo-ugrp-modulefiles81/DebianMed/$DEBVERSION.lua <<__END__
-- Setup Modulepath for packages built by this compiler
-- local mroot = os.getenv(""MODULEPATH_ROOT"")
prepend_path(""MODULEPATH"", ""/bucket/BioinfoUgrp/DebianMed/$DEBVERSION/modulefiles"")
__END__
```

## TODO

 - Parse description from Debian package
 - Export man pages and add MANPATH to the Lua module.
","Markdown"
"AlphaFold","oist/BioinfoUgrp","DataBases.md",".md","6966","163","# Databases

## Taxified BLAST databases
The following databases were constructed using ncbi-blast v2.10.0+. The module `ncbi-blast/2.10.0+` has to be loaded in order to use these databases.
- NCBI NT and NR databases (release 238) : `ml DB/blastDB/ncbi/238`. To be used with the arguments `nt` or `nr` supplied to `-db` in the commands of your scripts. Example script to get a taxified blast report:
```
module load ncbi-blast/2.10.0+
module load DB/blastDB/ncbi/238
WORKDIR=""$PWD""
FASTA=FULL/PATH/TO/YOUR/FASTA/FILE
blastn -task megablast -db nt -query $FASTA -num_threads ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/megablastn.out \
	-outfmt '6 qseqid bitscore evalue length qlen qcovs pident sseqid sgi sacc staxid ssciname scomname stitle sseq' \
	-max_target_seqs 1
```
- Swiss-Prot (version 2020_06): `ml DB/blastDB/sprot/2020_06`
- UniRef90 (version 2020_06): `ml DB/blastDB/uniref90/2020_06`

## Taxified DIAMOND databases
The following databases were constructed using DIAMOND v2.0.4.142. The module `Other/DIAMOND/2.0.4.142` has to be loaded in order to use them.
- the NCBI-NR database (release 238): `ml DB/diamondDB/ncbi/238`
- Swiss-Prot (version 2020_06): `ml DB/diamondDB/sprot/2020_06`
- UniRef90 (version 2020_06): `ml DB/diamondDB/uniref90/2020_06`

Unlike ncbi-blast, DIAMOND requires full path of the databases. The database module automatically create an environment variable ""DIAMONDDB"" which specifies full path to the DIAMOND database. So you need to prepend `${DIAMONDDB}` to the name of database. 
Example script to run diamond with the database module:
```
# load ncbi database for DIAMOND (proper version of DIAMOND is automatically loaded)
module load DB/diamondDB/ncbi/238

# check the loaded DIAMOND version and ${DIAMONDDB} variable
diamond --version
echo ${DIAMONDDB}

# run diamond search
WORKDIR=""$PWD""
FASTA=FULL/PATH/TO/YOUR/FASTA/FILE
diamond blastp -db ${DIAMONDDB}/nr -q $FASTA -p ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/diamond.blastp.out -outfmt 6
```

## GTDB DIAMOND Database (r226)

The Genome Taxonomy Database (GTDB) DIAMOND database was constructed using DIAMOND v2.1.11. The ```bioinfo-ugrp-modules``` module needs to be loaded in order to use it. 

### Available as Module

Load the module to access the database path: `module load DB/diamondDB/GTDB/r226`


**Use case**: Fast sequence similarity searches against GTDB reference genomes (BLAST-like functionality)

##### Example script

```bash
# Load bioinfo-user group modules
module load bioinfo-ugrp-modules
# Load GTDB database module
module load DB/diamondDB/GTDB/r226

# Optional: set variables for output directory and fasta files path
OUTDIR=/path/to/ouput/directory
FASTA=/path/to/fasta/files

# Create output directory if it does not exist
mkdir -p ${OUTDIR}

# Run search
diamond blastp -d ${DIAMONDDB}/gtdb.dmnd -q ${FASTA} -p 4 -o ${OUTDIR}

```

## GTDB-Tk r226 Reference Data

The GTDB-Tk r226 reference data package contains the auxiliary files required for taxonomic classification of bacterial and archaeal genomes. This is **the database only** - the GTDB-Tk software itself is not installed as a module, but is available through nf-core pipeline containers.The ```bioinfo-ugrp-modules``` module needs to be loaded in order to use it. 


**Use case**: Provides the reference database path for GTDB-Tk when running nf-core pipelines (e.g., nf-core/mag)


### Available as Module

Load the module to access the database path: ```module load DB/GTDBTK/ref_data/226.0```

This module sets `$GTDBTK_DATA_PATH` & `GTDBTK_DATA_ARCHIVED` environment variables pointing to the uncompressed and compressed reference data location.

### Usage with nf-core Pipelines

The GTDB-Tk r226 reference data is available for use with nf-core pipelines (e.g., nf-core/mag, nf-core/taxprofiler). Instead of downloading the ~100GB database, you simply specify the path to the pre-installed data.

The nf-core pipeline will use its own containerized version of the GTDB-Tk software and access your local reference data.

##### Example nf-core/mag pipeline

```bash

# Load bioinfo-user group modules
module load bioinfo-ugrp-modules
# Load the module to get database path
module load bioinfo/gtdbtk/r226-full

# Optional: set variables for output directory 
OUTDIR=/path/to/ouput/directory

# Run nf-core pipeline specifying the compressed database path
# Note: for uncompressed use GTDBTK_DATA_PATH
nextflow run nf-core/mag \
  --profile oist \
  --input samplesheet.csv \
  --outdir ${OUTDIR} \
  --gtdb_db ${GTDBTK_DATA_ARCHIVED} 
```

---

## Pfam

Version 34.0:  Use `ml DB/Pfam/34.0` to invoke it in your scripts.

## Dfam

Version 3.9 downloaded from <https://www.dfam.org/releases/Dfam_3.9/families/> and <https://www.dfam.org/releases/Dfam_3.9/families/FamDB>.  HDF5 files were uncompressed after checking their MD5 sums.

The command `ml DB/Dfam/3.9` exposes two environment variables
 - `$BioinfoUgrp_Dfam` containing the path to the directory containing the HMM files, that can be passed to RepeatMasker through its `-libdir` argument.
 - `$BioinfoUgrp_FamDB` containing the path to the directory containing the HDF5 files, to be bind-mounted on `/opt/RepeatMasker/Libraries/famdb/` when using `TETools`.
 - `SINGULARITY_BINDPATH`, with a value to replace `/opt/RepeatMasker/Libraries/famdb` in the Singularity containers with the local full copy of Dfam.

This is done with the following Lua module file.

```
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://dfam.org"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""annotation, transposon, repeat"")
whatis(""Description: ""..""The Dfam open collection of Transposable Element DNA sequence alignments"")

-- Package settings
LmodMessage(""You can find the Dfam database HMMs in the directory pointed to\nby the environment variable $BioinfoUgrp_Dfam, and the\nHDF5 files in $BioinfoUgrp_FamDB."")
setenv(""BioinfoUgrp_Dfam"", apphome)
setenv(""BioinfoUgrp_FamDB"", apphome .. ""/FamDB"")
setenv(""SINGULARITY_BINDPATH"", apphome..""/FamDB:/opt/RepeatMasker/Libraries/famdb"")
```

## Dfam for RepeatMasker

The command `ml DB/Dfam_RepeatMasker/3.6__4.1.3` will set an environmental variable that changes the behaviour of the `repeatmodeler` module, so that it will use the full Dfam database provided by us instead of the “_curated only_” version provided by default.

### Developer details

The RepeatMasker program does not follow symbolic links and the Dfam database is large (160 Gb), so I had to use hard links to the files of the `Dfam` module instead.  Also, the modulefile contains:

```
setenv(""BioinfoUgrp_Dfam_Rmsk_4_1_3"", apphome..""/RepeatMasker_4.1.3/Libraries"")
setenv(""SINGULARITY_BINDPATH"", apphome..""/Libraries:/opt/RepeatMasker/Libraries"")
```
","Markdown"
"AlphaFold","oist/BioinfoUgrp","mkDebMedModule.sh",".sh","1711","63","#!/bin/bash -e
ml singularity
APP=$1
if [ -z ""$DEBVERSION"" ]
then
	printf 'Error: $DEBVERSION is not set and exported\n'
	exit 1
fi

printf ""==== $APP ====\n""

MODROOT=/bucket/BioinfoUgrp/DebianMed/$DEBVERSION
DEBMEDIMAGE=$MODROOT/DebianMed_$DEBVERSION.sif
APPDIR=$MODROOT/modules/$APP
VER=$(singularity exec $DEBMEDIMAGE dpkg-query -W -f='${Version}' $APP | perl -pe 's/^.*?://')
PROGLIST=$($DEBMEDIMAGE dpkg -L $APP | grep -v -e 'package diverts' -e 'diverted by' | grep /bin/) || true
if [ -z ""$PROGLIST"" ]
then
	printf ""Module not created: package $APP has no executables\n""
	exit 0
fi

printf ""   creating module in $APPDIR/$VER\n""

mkdir -p $APPDIR/$VER/bin
cd $APPDIR/$VER

for prog in $(basename -a $PROGLIST)
do
cat <<__END__ > bin/$prog
#!/bin/sh
LC_ALL=C singularity exec $DEBMEDIMAGE $prog ""\$@"" 
__END__
chmod 775 bin/$prog
done
mkdir -p $MODROOT/modulefiles/$APP
cd $MODROOT/modulefiles/$APP

printf ""   creating modulefile $VER in $MODROOT/modulefiles/$APP\n""

cat <<__END__ > $VER.lua
-- Default settings
local modroot    = ""$MODROOT""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, ""modules"", ""$APP"", appversion)

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/oist/BioinfoUgrp"")
whatis(""Category: "".."" bioinformatics"")
whatis(""Keywords: "".."" Bioinformatics, Debian"")
whatis(""Description: "".."" Module automatically generated by the Bioinfo user group."")

help([[This module runs from Singularity image of Debian Med.
Please contact the Bioinfo user group for details.]])

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome..""/bin"")
__END__
","Shell"
"AlphaFold","oist/BioinfoUgrp","RStudio/launch_rserver.sh",".sh","1801","57","#!/bin/sh -e

if [ ""$1"" = ""desktop"" ]; then
  # Replace the shell process with the rstudio process
  exec rstudio
fi

RSTUDIO_TEMP=$(mktemp -d -p ~)
trap ""{ rm -rf $RSTUDIO_TEMP; }"" EXIT

cat <<__DBCONF__ > $RSTUDIO_TEMP/dbconf
provider=sqlite
directory=$RSTUDIO_TEMP/db.sqlite3
__DBCONF__


export RSTUDIO_PASSWORD=${RSTUDIO_PASSWORD-`date -R | md5sum | cut -c-16`}

if [ -z ""$RSTUDIO_PORT"" ]
then
	# Taken from https://gitlab.oit.duke.edu/chsi-informatics/containers/singularity-rstudio-base/-/blob/master/port_and_password_1_3.sh
	LOWERPORT=50000
	UPPERPORT=65535
	for RSTUDIO_PORT in $(seq $LOWERPORT $UPPERPORT);
	do
	    RSTUDIO_PORT=""`shuf -i $LOWERPORT-$UPPERPORT -n 1`""
	    # echo ""Testing port: $RSTUDIO_PORT""
	    ss -lpn | grep -q "":$RSTUDIO_PORT "" || break
	done
fi

RSTUDIO_HOST=$(hostname -A | cut -f1 -d' ')
RSTUDIO_HOST=${RSTUDIO_HOST:-localhost}

printf ""\nRStudio URL:\t\thttp://${RSTUDIO_HOST}:${RSTUDIO_PORT}/\n""
printf ""RStudio Username:\t$USER\n""
printf ""RStudio Password:\t$RSTUDIO_PASSWORD\n""
if [ ! $RSTUDIO_HOST = ""localhost"" ]
then
	printf ""\nIf you remote work, you can forward the server to port 1664 on your computer with:\n""
	printf ""ssh ${USER}@${RSTUDIO_HOST} -J ${USER}@login.oist.jp,${USER}@deigo.oist.jp -L 1664:localhost:${RSTUDIO_PORT}\n""
fi
printf ""\nYou may need to clean your temporary files by yourself:\n""
printf ""RStudio temporary files:\t$RSTUDIO_TEMP\n""
printf ""\nThis image will build its packages in the following directory if it exists:\n""
grep ^R_LIBS_USER /etc/R/Renviron.site
printf ""\n""

/usr/lib/rstudio-server/bin/rserver \
	--server-working-dir $RSTUDIO_TEMP \
	--server-data-dir $RSTUDIO_TEMP \
	--database-config-file $RSTUDIO_TEMP/dbconf \
	--server-user=$USER \
	--www-port=$RSTUDIO_PORT \
	--auth-none 0 \
	--auth-pam-helper rstudio_auth
","Shell"
"AlphaFold","oist/BioinfoUgrp","RStudio/rstudio_auth.sh",".sh","569","28","#!/usr/bin/env bash

# Confirm username is supplied
if [[ $# -lt 1 ]]; then
  echo ""Usage: auth USERNAME""
  exit 1
fi
USERNAME=""${1}""

# Confirm password environment variable exists
if [[ -z ""${RSTUDIO_PASSWORD}"" ]]; then
  echo ""The environment variable RSTUDIO_PASSWORD is not set""
  exit 1
fi

# Read in the password from user
read -s -p ""Password: "" PASSWORD
echo """"

# Authenticate user
if [[ ""${USER}"" == ""${USERNAME}"" && ""${PASSWORD}"" == ""${RSTUDIO_PASSWORD}"" ]]; then
  echo ""Successful authentication""
  exit 0
else
  echo ""Invalid authentication""
  exit 1
fi
","Shell"
"AlphaFold","oist/BioinfoUgrp","AlphaFold/AlphaFold2.md",".md","6034","106","# Alphafold 2.0 and 2.1 @ SAION

*Disclaimer: this implementation was made for general use with the help of Jan Moren of SCS. I do take no responsibility for bugs or errors, but if you could report them, I will gladly try to fix on my extra time. Nonetheless, the AF2 predictions appear to be working. Feel free but not obligated to acknowledge me or at least the SCS if this work was helpful for your research.*

In order to use AF2, you need [SAION access](https://oist.service-now.com/sp?id=sc_category&sys_id=9c71871fdbcdeb806885f00ebf961928) and have basic knowledge of the [terminal](https://groups.oist.jp/scs/basic-linux-commands) and [slurm submission commands](https://groups.oist.jp/scs/use-slurm).  
AF2 is part of the OIST [Bioinfo User group](https://github.com/oist/BioinfoUgrp).  
AF2 was implemented using the [docker image](https://hub.docker.com/r/uvarc/alphafold) made by the University of Virginia, that here we gratefully acknowledge.
## AF2 content
### AF 2.0
* 'alphafold_2.0.0.sif': singularity image created from the previously mentioned docker file
* 'run_alphafold.sh': AF2 run script. Contains the default parameters (except input file and output folder). DO NOT MODIFY
* 'alphafold_example_script.sh': AF2 sbatch example. Copy to your folder and modify (add input file and output folder paths).
* 'get_plddt_attribfile.py': Chimera attribute file generator. Automatically generates an attribute file for plddt coloring in chimera.
### AF 2.1
* 'alphafold_2.1.1.sif': singularity image created from the previously mentioned docker file
* 'run_alphafold.sh': AF2 run script. Contains the default parameters (except input file and output folder). DO NOT MODIFY
* 'run_alphafold_multimer.sh': AF2 run script specialized in multimer run. Contains the default parameters (except input file and output folder). DO NOT MODIFY
* 'alphafold_example_script.sh': AF2 sbatch example. Copy to your folder and modify (add input file and output folder paths).
* 'get_plddt_attribfile.py': Chimera attribute file generator. Automatically generates an attribute file for plddt coloring in chimera.

MULTIMER RUN [instructions](https://github.com/deepmind/alphafold#running-alphafold-multimer)
TL;DR: provide a multi fasta file as input.

## Example run
Assume to have a folder in /work with the input single protein sequence: example.fasta and a test output folder: test_out/.  
Copy the example script:
> cp /bucket/BioinfoUgrp/alphafold/2.1.1/bin/alphafold_example_script.sh .

Change the file name and modify the input and output folder paths, using your favourite text editor:  

> mv alphafold_example_script.sh AF2_testrun.sh  
> vim AF2_testrun.sh 
> (change input file and output folder, save and exit)  

OPTIONAL: CHECK IF THE RESOURCES YOU ARE REQUESTING VIA SLURM ARE ENOUGH (by default 4cpu cores, 100GB, 1 GPU)    
   
Submit the job: 

> sbatch AF2_testrun.sh 

The job is expected to take 2 to 12+ hours depending on sequence lenght.

## Output
A description of the output can be found in [AlphaFold 2 Github page](https://github.com/deepmind/alphafold/blob/main/README.md#alphafold-output).  
However, for lazy people, ranked_0.pdb is the highest scoring model.

## Example script
For convenience, we report here the example script content for version 2.1.1. 

> #!/bin/bash  
> #SBATCH --job-name=test_af  
> #SBATCH -c 4  
> #SBATCH --mem=100G  
> #SBATCH --partition=gpu  
> #SBATCH -t 4-0  
> #SBATCH --gres=gpu:1  
>   
> module load bioinfo-ugrp-modules  
> module load alphafold/2.1.1 
>   
> \# Uncomment the monomer or multimer depending on the use case  
> \# Monomer  
> \# run_alphafold.sh --fasta_paths test.fa --output_dir test_output  
> \# Multimer  
> \# run_alphafold_multimer.sh --fasta_paths multifasta_test.fa --output_dir test_output  


## Attrib file
If you use Chimera to visualize the pdb file, you might be interested in per-residue plddt coloring. plddt is the self-evaluated confidence measure of the prediction of alphafold2. It goes from 0 (low confidence) to 100 (high confidence). A low value indicates either badly predicted regions or disordered regions.   
To automatically generate a chimera attribute file for coloring by plddt, you can use the python script I made:
> module load python/3.7.3   
> python /bucket/BioinfoUgrp/alphafold/2.0.0/bin/get_plddt_attribfile.py <result_model_X.pkl> <outfile.txt>
The text file generated can be used in Chimera (Tools > Structure Analysis > Define Attribute) after opening the same pdb model.   

## Appendix: parameters
This is a list of all the default parameters in the run_alphafold script. If you want to modify them, do not change the default script. Instead, provide them to the example run script just like the input fasta and output folder.  
Default parameters (v2.0):
> MAX_TEMPLATE_DATE=2030-09-09   
> MODEL_NAMES=model_1,model_2,model_3,model_4,model_5   
> DATA_DIR=/bucket/BioinfoUgrp/alphafold/database/
> UNIREF90_PATH=$DATA_DIR/uniref90/uniref90.fasta   
> MGNIFY_PATH=$DATA_DIR/mgnify/mgy_clusters.fa   
> UNICLUST30_PATH=$DATA_DIR/uniclust30/uniclust30_2018_08/uniclust30_2018_08   
> BFD_PATH=$DATA_DIR/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt   
> PDB70_PATH=$DATA_DIR/pdb70/pdb70   
> TEMPLATE_PATH=$DATA_DIR/pdb_mmcif/mmcif_files/   
> OBSOLETE_PATH=$DATA_DIR/pdb_mmcif/obsolete.dat   

Default parameters (v2.1):
> MAX_TEMPLATE_DATE=2030-09-09  
> MODEL_PRESET=multimer  
> DATA_DIR=/bucket/BioinfoUgrp/alphafold/database/
> UNIREF90_PATH=$DATA_DIR/uniref90/uniref90.fasta  
> MGNIFY_PATH=$DATA_DIR/mgnify/mgy_clusters.fa  
> UNICLUST30_PATH=$DATA_DIR/uniclust30/uniclust30_2018_08/uniclust30_2018_08  
> BFD_PATH=$DATA_DIR/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt  
> PDB70_PATH=$DATA_DIR/pdb70/pdb70   
> TEMPLATE_PATH=$DATA_DIR/pdb_mmcif/mmcif_files/  
> OBSOLETE_PATH=$DATA_DIR/pdb_mmcif/obsolete.dat  
> PDB_SEQRES_PATH=$DATA_DIR/pdb_seqres/pdb_seqres.txt  
> UNIPROT_PATH=$DATA_DIR/uniprot/  


## Appendix: contact
Stefano Pascarelli, PhD, Laurino unit
","Markdown"
"AlphaFold","oist/BioinfoUgrp","Other/Assembly.md",".md","16928","630","# Bioinformatics modules for genome assembly

To use the modules, load the `bioinfo-ugrp-modules` metamodule first.

```bash
ml bioinfo-ugrp-modules
ml av Other
ml Other/<your-favorite-module>
```

## assembly-stats

- Home page: https://github.com/sanger-pathogens/assembly-stats
- Source code: https://github.com/sanger-pathogens/assembly-stats

### Installation on Deigo

```bash
APP=assembly-stats
VER=1.0.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/sanger-pathogens/assembly-stats/archive/refs/tags/v1.0.1.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER && mkdir build && cd build
cmake -DINSTALL_DIR:PATH=$APPDIR/$VER ..
make test
make install
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/sanger-pathogens/assembly-stats"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly-stats"")
whatis(""Description: ""..""Get assembly statistics from FASTA and FASTQ files."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running assembly-stats on Deigo

```bash
ml Other/assembly-stats
srun -p compute -c 1 --mem 10G -t 00:10:00 --pty \
    assembly-stats contigs.fasta
```

## BandageNG

- Home page: https://github.com/asl/BandageNG
- Source code: https://github.com/asl/BandageNG

### Installation on Deigo

I created a Singularity image with the following definition file on my Linux laptop.

```
Bootstrap: docker
From: debian:bookworm


%runscript
    exec /squashfs-root/AppRun ""$@""

%post
    # Update the image
    apt update
    apt upgrade -y
    
    # Add a package needed to suppress some debconf error messages
    apt install -y whiptail
    
    # Install all locales
    apt install -y locales-all

    # Install FUSE to extract and run the AppImage
    apt install -y fuse3
    # X11 and other dependencies
    apt install -y x11-apps mesa-utils libgl1 libglu1-mesa
    apt install -y dbus
    dbus-uuidgen > /etc/machine-id

    # Download BandageNG
    apt install -y wget
    wget https://github.com/asl/BandageNG/releases/download/continuous/BandageNG-Linux-827c5cb.AppImage
    chmod 775 BandageNG-Linux-827c5cb.AppImage
    ./BandageNG-Linux-827c5cb.AppImage --appimage-extract

    # Clean downoladed package cache.  Yes I know about /var/libs.
    apt clean
```

Then, I copied it in my home directory on _deigo_.

```bash
APP=BandageNG
VER=2025-03-28
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER
cd $APPDIR/$VER
cp ~/BandageNG.sif BandageNG
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/chaoszhang/ASTER"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""BandageNG"")
whatis(""Description: "".."" a Bioinformatics Application for Navigating De novo Assembly Graphs Easily"")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)
__END__
```

## pbgzip

- Home page: https://github.com/nh13/pbgzip
- Source code: https://github.com/nh13/pbgzip

### Installation on Deigo

```bash
APP=pbgzip
VER=20160804
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/nh13/pbgzip
mv pbgzip $VER && cd $VER 
sh autogen.sh && ./configure --prefix=$APPDIR/$VER && make 
make install
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/nh13/pbgzip"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pbgzip"")
whatis(""Description: ""..""This tool and API implements parallel block gzip."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running pbgzip on Deigo

```bash
module load Other/pbgzip
srun -p compute -c 128 --mem 100G -t 1:00:00 --pty \
    pbgzip <arguments>
```

## SMARTdenovo

```bash
APP=SMARTdenovo
VER=2021-02-24
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $MODROOT/$APP
cd $MODROOT/$APP
git clone https://github.com/ruanjue/smartdenovo $VER
ml gcc
cd $VER
make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/ruanjue/smartdenovo"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""PacBio, Nanopore, genome, assembler"")
whatis(""Description: ""..""de novo assembler for PacBio and Oxford Nanopore (ONT) data"")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

## BUSCO

- Home page: https://busco.ezlab.org/
- Source code: https://gitlab.com/ezlab/busco

### Installation on Deigo

```bash
module load singularity
APP=BUSCO
VER=5.8.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
singularity pull busco.sif docker://ezlabgva/busco:v${VER}_cv1
echo '#!/bin/sh' > busco && echo ""singularity exec $APPDIR/busco.sif busco \$*"" >> busco && chmod +x busco
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://busco.ezlab.org/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (BUSCO)."")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)

LmodMessage([[
================================================================================
The BUSCO module runs a read-only Singularity image.

If you need to change AUGUSTUS configuration, you can do a local copy with the
following command that will place it in $HOME/augustus_config.

    singularity exec /bucket/BioinfoUgrp/Other/BUSCO/5.8.2/busco.sif cp -r /usr/local/config $HOME/augustus_config

You can then pass the information by exporting the AUGUSTUS_CONFIG_PATH variable.

    export AUGUSTUS_CONFIG_PATH=""$HOME/augustus_config""
================================================================================
]])
__END__
```

### Example commands for running BUSCO on Deigo

The following command executes `$ busco` using the Singularity image:

```bash
module load Other/BUSCO
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    busco.sif <arguments> 
```

We also provide a command `busco` that is effectively the same as above:

```bash
module load Other/BUSCO
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    busco <arguments>
```

**IMPORTANT: Using Augustus for BUSCO running via its Singularity image requires the Augustus' config directory to be copied (from inside of Singularity container environment) to your own environment (outside Singularity in Deigo). The module we provide automatically copy the config directory to `$HOME/augustus_config` if it does not exist yet, and also set the `AUGUSTUS_CONFIG_PATH` varieble as `$HOME/augustus_config`, when the module is loaded.**

## gfatools

- Home page: https://github.com/lh3/gfatools
- Source code: https://github.com/lh3/gfatools/releases

### Installation on Deigo

```bash
APP=gfatools
VER=0.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/lh3/gfatools/archive/refs/tags/v$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/lh3/gfatools"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""gfa, assembly"")
whatis(""Description: ""..""Tools for manipulating sequence graphs in the GFA and rGFA formats."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running gfatools on Deigo

```bash
module load Other/gfatools
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    gfatools <arguments>
```

## hifiasm

- Home page: https://github.com/chhylp123/hifiasm
- Source code: https://github.com/chhylp123/hifiasm/releases

### Installation on Deigo

```bash
APP=hifiasm
VER=0.25.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/chhylp123/hifiasm/archive/refs/tags/$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER
ml purge && ml gcc/11.2.1
make CXXFLAGS=""-g -O3 -mavx2 -mpopcnt -fomit-frame-pointer -Wall""  # Replace SSE with AVX.  When updating, check that the other flags did not change.
cd /bucket/BioinfoUgrp/Other/modulefiles/hifiasm
cp 0.20.0.lua ${VER}.lua
```

### Example commands for running hifiasm on Deigo

```bash
module load Other/hifiasm
srun -p compute -c 64 --mem 500G -t 24:00:00 --pty \
    hifiasm <arguments>
```

## Canu (and HiCanu)

- Home page: https://github.com/marbl/canu
- Source code: https://github.com/marbl/canu/releases

### Installation on Deigo

```bash
APP=canu
VER=2.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/marbl/canu/releases/download/v$VER/$APP-$VER.Linux-amd64.tar.xz | tar Jxvf -
mv $APP-$VER $VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/marbl/canu"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""A single molecule sequence assembler for genomes large and small."")

-- Package settings
depends_on(""samtools"")
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running Canu on Deigo

```bash
module load Other/canu
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    canu <arguments>
```

## purge_dups

- Home page: https://github.com/dfguan/purge_dups
- Source code: https://github.com/dfguan/purge_dups

### Installation on Deigo

**WARNING: This module currently does not support the optional runner script nor k-mer comparision plot.**

```bash
APP=purge_dups
VER=1.2.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/dfguan/purge_dups
mv purge_dups $VER && cd $VER/src && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/dfguan/purge_dups"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""haplotypic duplication identification tool."")

-- Package settings
depends_on(""Other/minimap2"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PATH"", apphome..""/scripts"")
__END__
```

### Example commands for running purge_dups on Deigo

To load the module, run:

```bash
module load Other/purge_dups
```

Then, the following commands become available:

- `calcuts`
- `get_seqs`
- `ngscstat`
- `pbcstat`
- `purge_dups`
- `split_fa`

See the [README](https://github.com/dfguan/purge_dups) written by the developer for details about how to use the programs.

## Asset

- Home page: https://github.com/dfguan/asset
- Source code: https://github.com/dfguan/asset

### Installation on Deigo

**WARNING: This module currently does not support the optional runner script.**

```bash
APP=asset
VER=1.0.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/dfguan/asset
mv asset $VER && cd $VER/src && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/dfguan/asset"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""assembly evaluation tool."")

-- Package settings
depends_on(""samtools"", ""bedtools"", ""bwa"", ""Other/minimap2"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PATH"", apphome..""/scripts"")
__END__
```

### Example commands for running Asset on Deigo

To load the module, run:

```bash
module load Other/asset
```

Then, the following commands become available:

- `10x`
- `acc`
- `ast_10x`
- `ast_bion`
- `ast_bion_bnx`
- `ast_hic`
- `ast_hic2`
- `ast_pb`
- `col_conts`
- `detgaps`
- `pchlst`
- `pchlst0`
- `split_fa`
- `union`
- `union_brks`

See the [README](https://github.com/dfguan/asset) written by the developer or [another workflow](https://github.com/VGP/vgp-assembly/tree/master/pipeline/asset) written by researchers of the Vertebrate Genome Project for details about how to use the programs.

## SPAdes
- Home page: https://github.com/ablab/spades
- Source code: http://cab.spbu.ru/files/release3.15.1/SPAdes-3.15.1-Linux.tar.gz

### Installation on Deigo
```bash
APP=SPAdes
VER=3.15.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - http://cab.spbu.ru/files/release3.15.1/SPAdes-3.15.1-Linux.tar.gz | tar xzvf -
mv $APP-$VER-Linux $VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://github.com/ablab/spades

## Short description of package:
module-whatis  ""SPAdes is an assembly toolkit containing various assembly pipelines.""

## Load any needed modules:
module load python/3.7.3

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome
prepend-path    PATH            $apphome/bin
__END__
```

## Flye
- Home page: https://github.com/mikolmogorov/Flye/tree/flye
- Installation: https://github.com/mikolmogorov/Flye/tree/flye

### Installation on Deigo
```bash
APP=Flye
VER=2.9.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/mikolmogorov/Flye/archive/refs/tags/$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER
make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/mikolmogorov/Flye/tree/flye"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""flye"")
whatis(""Description: ""..""Flye is a de novo assembler for single-molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies.."")

-- Package settings
depends_on(""python/3.7.3"")
prepend_path(""PATH"", apphome..""/bin"")
__END__
```
","Markdown"
"AlphaFold","oist/BioinfoUgrp","Other/Other.md",".md","60648","1963","# A collection of general bioinformatics modules

To use the modules, load the `bioinfo-ugrp-modules` metamodule first.

```bash
ml bioinfo-ugrp-modules
ml av Other
ml Other/<your-favorite-module>
```

Here are installation snippets to help the members of the bioinfo user group to update the modules.

## ASTRAL
- Home page: https://github.com/smirarab/ASTRAL
- Source code: https://github.com/smirarab/ASTRAL

### Installation on Deigo
```
APP=astral
VER=5.7.8
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget https://github.com/smirarab/ASTRAL/raw/master/Astral.$VER.zip && unzip Astral.$VER.zip && rm Astral.$VER.zip
mv Astral $VER && cd $VER
chmod g+r * && chmod g+x *
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://github.com/smirarab/ASTRAL

## Short description of package:
module-whatis  ""ASTRAL is a tool for estimating an unrooted species tree given a set of unrooted gene trees. To be called in script with: java -jar dollarASTRAL. Replace dollar by the actual symbol""

## Load any needed modules:

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome    
prepend-path    PATH $apphome/lib
prepend-path    ASTRAL      $apphome/astral.5.7.8.jar
__END__
```

## ASTER

- Home page: https://github.com/chaoszhang/ASTER
- Source code: https://github.com/chaoszhang/ASTER

### Installation on Deigo

```bash
APP=aster
VER=1.20.4.6
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget https://github.com/chaoszhang/ASTER/archive/refs/heads/Linux.zip
unzip Linux.zip
cd ASTER-Linux
make
cd ..
mv ASTER-Linux/bin $VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/chaoszhang/ASTER"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""ASTER"")
whatis(""Description: "".."" Accurate Species Tree EstimatoR: a family of optimation algorithms for species tree inference (including ASTRAL & CASTER)."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

## pbgzip

- Home page: https://github.com/nh13/pbgzip
- Source code: https://github.com/nh13/pbgzip

### Installation on Deigo

```bash
APP=pbgzip
VER=20160804
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/nh13/pbgzip
mv pbgzip $VER && cd $VER 
sh autogen.sh && ./configure --prefix=$APPDIR/$VER && make 
make install
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/nh13/pbgzip"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pbgzip"")
whatis(""Description: ""..""This tool and API implements parallel block gzip."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running pbgzip on Deigo

```bash
module load Other/pbgzip
srun -p compute -c 128 --mem 100G -t 1:00:00 --pty \
    pbgzip <arguments>
```


- Home page: https://www.ncbi.nlm.nih.gov/datasets/docs/v2/download-and-install/

### Installation on Deigo

```bash
APP=ncbi-datasets-cli
VER=18.10.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER
cd $APPDIR/$VER
wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets
chmod 775 datasets
wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/dataformat
chmod 775 dataformat
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://www.ncbi.nlm.nih.gov/datasets/docs/v2/download-and-install/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""NCBI"")
whatis(""Description: ""..""Use datasets to download biological sequence data across all domains of life from NCBI. Use dataformat to convert metadata from JSON Lines format to other formats."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running pairtools on Deigo

```bash
module load Other/pairtools
srun -p compute -c 128 --mem 20G -t 1:00:00 --pty \
    pairtools parse -c example.chrom.sizes -o example_R1.pairs.gz --drop-sam example_R1.bam 
```


## pairtools

- Home page: https://pairtools.readthedocs.io/en/latest/
- Source code: https://github.com/open2c/pairtools

### Installation on Deigo

```bash
module load python/3.7.3
APP=pairtools
VER=0.3.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/open2c/pairtools/archive/refs/tags/v0.3.0.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER
mkdir -p lib/python3.7/site-packages
PYTHONUSERBASE=$(pwd) python setup.py install --user
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://pairtools.readthedocs.io/en/latest/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""pairtools"")
whatis(""Description: ""..""pairtools is a simple and fast command-line framework to process sequencing data from a Hi-C experiment."")

-- Package settings
depends_on(""python/3.7.3 "", ""samtools"", ""pbgzip"", ""htslib"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PYTHONPATH"", apphome..""/lib/python3.7/site-packages"")
__END__
```

### Example commands for running pairtools on Deigo

```bash
module load Other/pairtools
srun -p compute -c 128 --mem 20G -t 1:00:00 --pty \
    pairtools parse -c example.chrom.sizes -o example_R1.pairs.gz --drop-sam example_R1.bam 
```

## preseq

- Home page: http://smithlabresearch.org/software/preseq/
- Source code: https://github.com/smithlabcode/preseq

### Installation on Deigo

```bash
APP=preseq
VER=3.1.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/smithlabcode/preseq/releases/download/v3.1.2/preseq-3.1.2.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER && mkdir build && cd build
../configure --prefix=$APPDIR/$VER && make && make install && cd .. && rm -r build
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://smithlabresearch.org/software/preseq/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""preseq"")
whatis(""Description: ""..""The preseq package is aimed at predicting and estimating the complexity of a genomic sequencing library, equivalent to predicting and estimating the number of redundant reads from a given sequencing depth and how many will be expected from additional sequencing using an initial sequencing experiment"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running preseq on Deigo

```bash
module load Other/preseq
srun -p compute -c 128 --mem 20G -t 1:00:00 --pty \
    preseq c_curve -o output.txt input.sort.bed
```


## seqkit

- Home page: https://bioinf.shenwei.me/seqkit/
- Source code: https://github.com/shenwei356/seqkit

### Installation on Deigo

```bash
APP=seqkit
VER=2.10.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
wget -qO- https://github.com/shenwei356/seqkit/releases/download/v$VER/seqkit_linux_amd64.tar.gz | tar xzvf -
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/shenwei356/seqkit"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""fasta, fastq, sequence analysis"")
whatis(""Description: ""..""A cross-platform and ultrafast toolkit for FASTA/Q file manipulation in Golang"")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running seqkit on Deigo

```bash
module load Other/seqkit
srun -p compute -c 1 --mem 40G -t 24:00:00 --pty \
    seqkit <subcommand> <arguments>
```

## GNU parallel

- Home page: https://www.gnu.org/software/parallel
- Source code: https://ftp.gnu.org/gnu/parallel

### Installation on Deigo

```bash
APP=parallel
VER=20210622
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER
cd $APPDIR
wget -O - https://ftp.gnu.org/gnu/parallel/parallel-$VER.tar.bz2 | tar xjvf -
cd $APP-$VER && ./configure --prefix=$APPDIR/$VER && make && make install && cd .. && rm -r $APP-$VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://www.gnu.org/software/parallel"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""GNU, parallel"")
whatis(""Description: ""..""A shell tool for executing jobs in parallel using one or more computers."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running GNU parallel on Deigo

```bash
module load Other/parallel
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    parallel <arguments>
```

## mosdepth

- Home page: https://github.com/brentp/mosdepth
- Source code: https://github.com/brentp/mosdepth/releases

### Installation on Deigo

```bash
APP=mosdepth
VER=0.3.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
wget https://github.com/brentp/mosdepth/releases/download/v$VER/mosdepth && chmod +x mosdepth
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/brentp/mosdepth"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running mosdepth on Deigo

```bash
module load Other/mosdepth
srun -p compute -c 1 --mem 100G -t 24:00:00 --pty \
    mosdepth <arguments>
```

## Meryl

- Home page: https://github.com/marbl/meryl
- Source code: https://github.com/marbl/meryl/releases

### Installation on Deigo

```bash
APP=meryl
VER=1.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/marbl/meryl/releases/download/v$VER/meryl-$VER.Linux-amd64.tar.xz | tar Jxvf -
mv $APP-$VER $VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/marbl/meryl"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""A genomic k-mer counter (and sequence utility) with nice features."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running Meryl on Deigo

```bash
module load Other/meryl
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    meryl <arguments>
```

## Merqury

- Home page: https://github.com/marbl/merqury
- Source code: https://github.com/marbl/merqury/releases

### Installation on Deigo

```bash
module load R/4.0.4
APP=merqury
VER=1.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/marbl/merqury/archive/refs/tags/v$VER.tar.gz | tar zxvf -
mv $APP-$VER $VER && cd $VER
sed -i 's/echo $?/echo 1/' util/util.sh
for FILE in plot/*.R; do sed -i 's|#!/usr/bin/env Rscript|#!/usr/bin/env -S Rscript --vanilla|'
mkdir -p lib/R && Rscript -e 'install.packages(c(""argparse"",""R6"",""jsonlite"",""findpython"",""ggplot2"",""scales""), lib=""./lib/R"")'
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/marbl/merqury"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""k-mer based assembly evaluation."")

-- Package settings
depends_on(""R/4.0.4"", ""samtools/1.12"", ""bedtools/v2.29.2"", ""Other/meryl/1.3"")
prepend_path(""PATH"", apphome)
prepend_path(""R_LIBS"", apphome..""/lib/R"")
setenv(""MERQURY"", apphome)
__END__
```

### Example commands for running Merqury on Deigo

```bash
module load Other/merqury
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    merqury.sh <arguments>
```

## Peregrine

- Home page: https://github.com/cschin/Peregrine
- Source code: https://hub.docker.com/r/cschin/peregrine/tags

### Installation on Deigo

```bash
module load singularity
APP=peregrine
VER=1.6.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
singularity pull $APP.sif docker://cschin/$APP:$VER
echo '#!/bin/sh' > $APP && echo ""echo yes | singularity run $APPDIR/$APP.sif asm \$*"" >> $APP && chmod +x $APP
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/cschin/Peregrine"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly"")
whatis(""Description: ""..""Fast Genome Assembler Using SHIMMER Index."")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running Peregeine on Deigo

The following command executes `$ pg_run.py asm` using the Singularity image:

```bash
module load Other/peregrine
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    peregrine.sif asm <arguments> 
```

For simplicity and to skip the interactive prompt message, we also provide a command `peregrine` that is effectively the same as above:

```bash
module load Other/peregrine
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    peregrine <arguments>
```

## IPA (Improved Phased Assembler)

- Home page: https://github.com/PacificBiosciences/pbipa
- Source code: https://anaconda.org/bioconda/pbipa

IPA was installed with conda before and we had to remove the module for that reason.  Please let us know if you want IPA back.

## DeepVariant

- Home page: https://github.com/google/deepvariant
- Source code: https://hub.docker.com/r/google/deepvariant/tags

### Installation on Deigo

```bash
module load singularity
APP=deepvariant
VER=1.1.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
singularity pull $APP.sif docker://google/$APP:$VER
for CMD in run_deepvariant make_examples call_variants postprocess_variants; do echo '#!/bin/sh' > $CMD && echo ""singularity exec $APPDIR/$APP.sif $CMD \$*"" >> $CMD && chmod +x $CMD; done
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/google/deepvariant"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""variant call"")
whatis(""Description: ""..""DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data."")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running DeepVariant on Deigo

The following command executes `$ run_deepvariant` using the Singularity image:

```bash
module load Other/deepvariant
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    deepvariant.sif <arguments> 
```

We also provide a command `run_deepvariant` that is effectively the same as above:

```bash
module load Other/deepvariant
srun -p compute -c 128 --mem 500G -t 24:00:00 --pty \
    run_deepvariant <arguments>
```

## arima_pipeline (ArimaGenomics Hi-C mapping pipeline)

- Home page: https://github.com/ArimaGenomics/mapping_pipeline
- Source code: https://github.com/ArimaGenomics/mapping_pipeline

### Installation on Deigo

```bash
APP=arima_pipeline
VER=2019.02.08
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/ArimaGenomics/mapping_pipeline
mv mapping_pipeline $VER && cd $VER
chmod +x *.pl
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/ArimaGenomics/mapping_pipeline"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Hi-C, mapping"")
whatis(""Description: ""..""Mapping pipeline for data generated using Arima-HiC."")

-- Package settings
depends_on(""samtools"", ""picard"", ""bwa"")
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running arima_pipeline on Deigo

To load the module, run:

```bash
module load Other/arima_pipeline
```

Then, the following executables become available as commands:

- `filter_five_end.pl`
- `two_read_bam_combiner.pl`
- `get_stats.pl`

See the pipeline script ([01_mapping_arima.sh](https://github.com/ArimaGenomics/mapping_pipeline/blob/master/01_mapping_arima.sh)) written by the developer for details about how to use the Perl scripts.

## SALSA

- Home page: https://github.com/marbl/SALSA
- Source code: https://github.com/marbl/SALSA/releases

### Installation on Deigo

```bash
APP=SALSA
VER=2.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/marbl/SALSA/archive/refs/tags/v$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER && cd $VER && make
wget https://s3.amazonaws.com/hicfiles.tc4ga.com/public/juicer/juicer_tools_1.22.01.jar
cat <<'__END__' > convert.sh
#!/bin/bash
JUICER_JAR=/bucket/BioinfoUgrp/Other/SALSA/2.3/juicer_tools_1.22.01.jar
SALSA_OUT_DIR=$1

samtools faidx ${SALSA_OUT_DIR}/scaffolds_FINAL.fasta
cut -f 1,2 ${SALSA_OUT_DIR}/scaffolds_FINAL.fasta.fai > ${SALSA_OUT_DIR}/chromosome_sizes.tsv
alignments2txt.py -b ${SALSA_OUT_DIR}/alignment_iteration_1.bed -a ${SALSA_OUT_DIR}/scaffolds_FINAL.agp -l ${SALSA_OUT_DIR}/scaffold_length_iteration_1 > ${SALSA_OUT_DIR}/alignments.txt
awk '{if ($2 > $6) {print $1""\t""$6""\t""$7""\t""$8""\t""$5""\t""$2""\t""$3""\t""$4} else {print}}'  ${SALSA_OUT_DIR}/alignments.txt | sort -k2,2d -k6,6d -T $PWD --parallel=16 | awk 'NF'  > ${SALSA_OUT_DIR}/alignments_sorted.txt
java -jar -Xmx500G ${JUICER_JAR} pre ${SALSA_OUT_DIR}/alignments_sorted.txt ${SALSA_OUT_DIR}/salsa_scaffolds.hic ${SALSA_OUT_DIR}/chromosome_sizes.tsv
__END__
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/marbl/SALSA"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly, scaffolding"")
whatis(""Description: ""..""SALSA: A tool to scaffold long read assemblies with Hi-C data."")

-- Package settings
depends_on(""python/2.7.18"", ""bedtools"")
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running SALSA on Deigo

The main routine is executed as follows:

```bash
module load Other/SALSA
srun -p compute -c 1 --mem 500G -t 48:00:00 --pty \
    run_pipeline.py <arguments>
```

Some hard-coded values specific to the developers' environment in (`convert.sh`; see SALSA's README for what the script itself does) are replaced so that it works in our environment:

```bash
module load Other/SALSA
srun -p compute -c 16 --mem 500G -t 48:00:00 --pty \
    convert.sh <salsa-out-dir>
```

## Juicer

- Home page: https://github.com/aidenlab/juicer
- Source code: https://github.com/aidenlab/juicer/releases

### Installation on Deigo

NOTE: Only the ""CPU"" mode is installed, as the codes for HPCs are hard to use.

```bash
APP=juicer
VER=1.6
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/aidenlab/juicer/archive/refs/tags/$VER.tar.gz | tar xzvf -
mv $APP-$VER/CPU scripts && mv $APP-$VER/misc/* . && rm -rf scripts/README.md $APP-$VER
sed -i 's|juiceDir=""/opt/juicer""|juiceDir="".""|' scripts/juicer.sh
CMD=juicer_copy_scripts_dir
echo '#!/bin/sh' > $CMD && echo ""ln -sf $APPDIR/scripts/ ."" >> $CMD
chmod +x * scripts/common/*
cd scripts/common && wget https://s3.amazonaws.com/hicfiles.tc4ga.com/public/juicer/juicer_tools_1.22.01.jar && ln -sf juicer_tools_1.22.01.jar juicer_tools.jar
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/aidenlab/juicer"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Hi-C, scaffolding"")
whatis(""Description: ""..""A One-Click System for Analyzing Loop-Resolution Hi-C Experiments."")

-- Package settings
depends_on(""java-jdk/14"", ""bwa"")
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running Juicer on Deigo

Juicer (and other tools by Aiden lab) adopts a very unique approach to run programs. Read carefully e.g. [their wiki](https://github.com/aidenlab/juicer/wiki/Installation) to understand their assumptions.

To copy the `scripts/` directory (of the CPU mode), we provide the following command:

```bash
module load Other/juicer
juicer_copy_scripts_dir   # A symlink `scripts` will be generated in the current directory
```

Note that `scripts/common/juicer_tools.jar` is already downloaded and put in the appropriate directory in the module.
Then, after creating some directories required, you will need to run the main shell script as follows:

```bash
module load Other/juicer
srun -p compute -c 128 --mem 500G -t 48:00:00 --pty \
    ./scripts/juicer.sh <arguments>
```

Note that the default path of the option `-D` is replaced from `/opt/juicer` to `.` (i.e. the directory you ran `juicer.sh`).

## 3D-DNA

- Home page: https://github.com/aidenlab/3d-dna
- Source code: https://github.com/aidenlab/3d-dna

### Installation on Deigo

**IMPORTANT: Currently only the normal mode is supported by this module. That is, the diploid mode, phasing mode, etc. are NOT supported.**

```bash
APP=3d-dna
VER=180922
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/aidenlab/3d-dna
mv 3d-dna $VER && cd $VER
ln -sf run-asm-pipeline.sh 3d-dna
ln -sf run-asm-pipeline-post-review.sh 3d-dna-post-review
cd visualize/ && ln -sf run-assembly-visualizer.sh 3d-dna-run-assembly-visualizer && cd ..
CMD=3d-dna-fasta2assembly
echo '#!/bin/sh' > $CMD && echo ""awk -f $APPDIR/$VER/utils/generate-assembly-file-from-fasta.awk \$*"" >> $CMD
chmod +x *.sh */*.sh $CMD
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/aidenlab/3d-dna"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Hi-C, scaffolding"")
whatis(""Description: ""..""3D de novo assembly (3D DNA) pipeline."")

-- Package settings
depends_on(""bwa"", ""Other/bioawk"", ""Other/parallel"", ""Other/juicer"")
prepend_path(""PATH"", apphome)
prepend_path(""PATH"", apphome..""/visualize"")
__END__
```

### Example commands for running 3D-DNA on Deigo

3D-DNA (and other tools by Aiden lab) adopts a very unique approach to run programs. Read carefully e.g. [their manual](http://aidenlab.org/assembly/manual_180322.pdf) to understand their assumptions.

By loading the module, the following main commands become available via `PATH`. Additionally, we provide alias names for these programs to make it easier to remember the commands:

- `run-asm-pipeline.sh` (alias: `3d-dna`)
- `run-asm-pipeline-post-review.sh` (alias: `3d-dna-post-review`)
- `run-assembly-visualizer.sh` (alias: `3d-dna-run-assembly-visualizer`)

These commands can be executed like this:

```bash
module load Other/3d-dna
srun -p compute -c 128 --mem 500G -t 48:00:00 --pty \
    run-asm-pipeline.sh <arguments>
```

or using their alias names:

```bash
module load Other/3d-dna
srun -p compute -c 128 --mem 500G -t 48:00:00 --pty \
    3d-dna <arguments>   # This is exactly same as the previous example
```

We also provide a command `3d-dna-fasta2assembly`, which is an alias of `$ awk -f /path/to/utils/generate-assembly-file-from-fasta.awk`. A usage example of this command is as follows:

```bash
module load Other/3d-dna
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    3d-dna-fasta2assembly ${YOUR_ASSEMBLY}.fasta > ${YOUR_ASSEMBLY}.assembly
```

## hic2cool

- Home page: https://github.com/4dn-dcic/hic2cool
- Source code: https://github.com/4dn-dcic/hic2cool/releases

### Installation on Deigo

```bash
module load python/3.7.3
APP=hic2cool
VER=0.8.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/4dn-dcic/hic2cool/archive/refs/tags/$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER && cd $VER
mkdir -p lib/python3.7/site-packages
sed -i 's|    name = ""hic2cool"",|    name = ""hic2cool"",\n    zip_safe = False,|' setup.py
PYTHONUSERBASE=$(pwd) python setup.py install --user
PYTHONUSERBASE=$(pwd) pip install -r requirements.txt --force-reinstall --user
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/4dn-dcic/hic2cool"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Hi-C"")
whatis(""Description: ""..""Lightweight converter between hic and cool contact matrices."")

-- Package settings
depends_on(""python/3.7.3"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PYTHONPATH"", apphome..""/lib/python3.7/site-packages"")
__END__
```

### Example commands for running hic2cool on Deigo

```bash
module load Other/hic2cool
srun -p compute -c 128 --mem 500G -t 12:00:00 --pty \
    hic2cool <arguments>
```


## make_telomere_bed

- Home page: https://github.com/yoshihikosuzuki/make_telomere_bed
- Source code: https://github.com/yoshihikosuzuki/make_telomere_bed

### Installation on Deigo

```bash
module load python/3.7.3
APP=make_telomere_bed
VER=2021.05.20
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/yoshihikosuzuki/make_telomere_bed
mv make_telomere_bed $VER && cd $VER
mkdir -p lib/python3.7/site-packages
PYTHONUSERBASE=$(pwd) python setup.py install --user
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/yoshihikosuzuki/make_telomere_bed"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""telomere"")
whatis(""Description: ""..""Make a .bed file for telomeres in a contig/scaffold file."")

-- Package settings
depends_on(""python/3.7.3"", ""Other/trf"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""PYTHONPATH"", apphome..""/lib/python3.7/site-packages"")
__END__
```

### Example commands for running make_telomere_bed on Deigo

```bash
module load Other/make_telomere_bed
srun -p compute -c 1 --mem 100G -t 12:00:00 --pty \
    make_telomere_bed <arguments>
```


- Home page: https://github.com/thegenemyers/GENESCOPE.FK
- Source code: https://github.com/thegenemyers/GENESCOPE.FK

### Installation on Deigo

```bash
module load R/4.0.4
APP=genescope
VER=2021.03.26
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/thegenemyers/GENESCOPE.FK
mv GENESCOPE.FK $VER && cd $VER
mkdir -p lib/R && cat <<'__END__' > install.R
local_lib_path = ""./lib/R""
install.packages('minpack.lm', lib=local_lib_path)
install.packages('argparse', lib=local_lib_path)
install.packages('.', repos=NULL, type=""source"", lib=local_lib_path)
__END__
Rscript install.R
Rscript -e 'install.packages(c(""R6"", ""jsonlite"", ""findpython""), lib=""./lib/R"")'
sed -i 's|#!/usr/bin/env Rscript|#!/usr/bin/env -S Rscript --vanilla|' GeneScopeFK.R
mkdir -p bin && mv GeneScopeFK.R bin/
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/thegenemyers/GENESCOPE.FK"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""sequencing"")
whatis(""Description: ""..""A derivative of GenomeScope2.0 modified to work with FastK."")

-- Package settings
depends_on(""R/4.0.4"", ""Other/FASTK"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""R_LIBS"", apphome..""/lib/R"")
__END__
```

### Example commands for running GeneScope on Deigo

```bash
module load Other/genescope
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    GeneScopeFK.R <arguments>
```

## KMC (for GenomeScope 2.0 and Smudgeplot)

- Home page: https://github.com/tbenavi1/KMC
- Source code: https://github.com/tbenavi1/KMC

### Installation on Deigo

```bash
APP=KMC
VER=genomescope
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/tbenavi1/KMC
mv KMC $VER && cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/tbenavi1/KMC"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""k-mer counter"")
whatis(""Description: ""..""Fast and frugal disk based k-mer counter (version for GenomeScope and Smudgeplot)."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running KMC (for GenomeScope/Smudgeplot) on Deigo

To load the module, run:

```bash
module load Other/KMC/genomescope
```

Then, the following commands become available:

- `kmc`
- `kmc_dump`
- `kmc_tools`
- `smudge_pairs`

Visit the [GitHub page](https://github.com/tbenavi1/KMC) for details about how to use the programs.

## GenomeScope 2.0

- Home page: https://github.com/tbenavi1/genomescope2.0
- Source code: https://github.com/tbenavi1/genomescope2.0

### Installation on Deigo

```bash
module load R/4.0.4
APP=genomescope
VER=2.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/tbenavi1/genomescope2.0
mv genomescope2.0 $VER && cd $VER
mkdir -p lib/R && sed -i 's|local_lib_path = ""~/R_libs/""|local_lib_path = ""./lib/R""|' install.R && Rscript install.R
Rscript -e 'install.packages(c(""R6"", ""jsonlite"", ""findpython""), lib=""./lib/R"")'
sed -i 's|#!/usr/bin/env Rscript|#!/usr/bin/env -S Rscript --vanilla|' genomescope.R
mkdir -p bin && mv genomescope.R bin/
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/tbenavi1/genomescope2.0"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""sequencing"")
whatis(""Description: ""..""Reference-free profiling of polyploid genomes."")

-- Package settings
depends_on(""R/4.0.4"", ""Other/KMC/genomescope"")
prepend_path(""PATH"", apphome..""/bin"")
prepend_path(""R_LIBS"", apphome..""/lib/R"")
__END__
```

### Example commands for running GenomeScope 2.0 on Deigo

```bash
module load Other/genomescope/2.0
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    genomescope.R <arguments>
```

## Smudgeplot

- Home page: https://github.com/KamilSJaron/smudgeplot
- Source code: https://github.com/KamilSJaron/smudgeplot

### Installation on Deigo

```bash
module load R/4.0.4
APP=smudgeplot
VER=0.2.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/KamilSJaron/smudgeplot
mv smudgeplot $VER && cd $VER
mkdir -p lib/R && Rscript -e 'install.packages(c(""devtools"",""argparse"",""R6"",""jsonlite"",""findpython"",""viridis""), lib=""./lib/R"")'
sed -i '1ilocal_lib_path = ""./lib/R""' install.R && sed -i 's|install.packages(""."", repos = NULL, type=""source"")|install.packages(""."", repos = NULL, type=""source"", lib=local_lib_path)|' install.R
R_LIBS=./lib/R Rscript install.R && unset R_LIBS
sed -i 's|#!/usr/bin/env Rscript|#!/usr/bin/env -S Rscript --vanilla|' exec/smudgeplot_plot.R
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/KamilSJaron/smudgeplot"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""qc, sequencing"")
whatis(""Description: ""..""Inference of ploidy and heterozygosity structure using whole genome sequencing data."")

-- Package settings
depends_on(""R/4.0.4"", ""Other/KMC/genomescope"", ""Other/genomescope/2.0"")
prepend_path(""PATH"", apphome..""/exec"")
prepend_path(""R_LIBS"", apphome..""/lib/R"")
__END__
```

### Example commands for running Smudgeplot on Deigo

```bash
module load Other/smudeplot
srun -p compute -c 1 --mem 100G -t 24:00:00 --pty \
    smudgeplot.py <arguments>
```

## iqtree2
- Home page: http://www.iqtree.org
- Source code: https://github.com/iqtree/iqtree2/releases

### Installation on Deigo

```bash
DIS=iqtree2
APP=iqtree
VER=2.2.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/iqtree/iqtree2/archive/refs/tags/v2.2.0.tar.gz | tar xzvf -
mv $DIS-$VER $VER
cd $VER && mkdir build && cd build
cmake ..
make -j
cd $MODROOT/$APP/$VER
mkdir -p bin
cd bin
ln -s $MODROOT/$APP/$VER/build/iqtree2 .
ln -s iqtree2 iqtree
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://www.iqtree.org"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""iqtree"")
whatis(""Description: ""..""A fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

```bash
DIS=iqtree2
APP=iqtree
VER=2.2.2.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
## getting submodules for dating
# https://github.com/Cibiv/IQ-TREE/issues/151
# https://github.com/Cibiv/IQ-TREE/issues/151
# Submodule 'lsd2' (https://github.com/tothuhien/lsd2.git) registered for path 'lsd2'
# git checkout latest #VER=2.2.2.6
# git submodule init 
# git submodule update
### leads to compiling errors
# /bucket/BioinfoUgrp/Other/iqtree/2.2.2.6/alignment/superalignment.cpp:28:10: erreur fatale: boost/container_hash/hash.hpp : Aucun fichier ou dossier de ce type
# /bucket/BioinfoUgrp/Other/iqtree/2.2.2.6/alignment/alignment.cpp:24:10: erreur fatale: boost/container_hash/hash.hpp : Aucun fichier ou dossier de ce type
## switch to older distrib
wget -O - https://github.com/iqtree/iqtree2/archive/refs/tags/v2.2.2.5.tar.gz | tar xzvf -
mv $DIS-$VER $VER && cd $VER
git clone https://github.com/tothuhien/lsd2
mkdir build && cd build
## enabling dating
cmake -DUSE_LSD2=ON ..
make -j
cd $MODROOT/$APP/$VER
mkdir -p bin
cd bin
ln -s $MODROOT/$APP/$VER/build/iqtree2 .
ln -s iqtree2 iqtree
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://www.iqtree.org"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""iqtree"")
whatis(""Description: ""..""A fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

## FastTree
- Home page: http://www.microbesonline.org/fasttree/
- Source code: http://www.microbesonline.org/fasttree/#Install

### Installation on Deigo

```bash
APP=fasttree
VER=2.1.11
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER/bin
cd $APPDIR/$VER/bin
wget -O - http://www.microbesonline.org/fasttree/FastTreeMP > fasttree
chmod a+x fasttree
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://www.microbesonline.org/fasttree/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""fasttree"")
whatis(""Description: ""..""FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

## bcftools
- Home page: http://www.htslib.org
- Source code: https://github.com/samtools/bcftools/releases/download/1.15.1/bcftools-1.15.1.tar.bz2

### Installation on Deigo
```bash
APP=bcftools
VER=1.15.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/samtools/bcftools/releases/download/1.15.1/bcftools-1.15.1.tar.bz2 | tar xjvf -
mv $APP-$VER $VER
cd $VER
./configure --prefix=$APPDIR/$VER && make && make install
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://www.htslib.org"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""bcftools"")
whatis(""Description: ""..""contains all the vcf* commands which previously lived in the htslib repository (such as vcfcheck, vcfmerge, vcfisec, etc.) and the samtools BCF calling from bcftools subdirectory of samtools."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

## prokka
- Home page: https://github.com/tseemann/prokka
- Source code: https://hub.docker.com/r/staphb/prokka

### Installation on Deigo
```bash
module load singularity
APP=prokka
VER=1.14.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
singularity build prokka.sif docker://staphb/prokka:${VER}
echo '#!/bin/sh' > prokka && echo ""singularity exec $APPDIR/prokka.sif prokka \$*"" >> prokka && chmod +x prokka
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://hub.docker.com/r/staphb/prokka"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""annotation"")
whatis(""Description: ""..""rapid prokaryotic genome annotation"")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)
__END__
```

## BEAST1
- Home page: https://beast.community/index.html
- Source code: https://github.com/beast-dev/beast-mcmc/releases/download/v1.10.4/BEASTv1.10.4.tgz

### Installation on Deigo
```bash
APP=BEAST
VER=1.10.4
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/beast-dev/beast-mcmc/releases/download/v1.10.4/BEASTv1.10.4.tgz | tar xzvf -
mv ${APP}v${VER} $VER
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://beast.community

## Short description of package:
module-whatis  ""BEAST is a cross-platform program for Bayesian analysis of molecular sequences using MCMC.""

## Load any needed modules:
module load java-jdk/1.8.0_20
module load beagle/3.1.2

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome
prepend-path    PATH            $apphome/bin
setenv			JAVA_HOME		/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.252.b09-2.el8_1.x86_64
setenv			JRE_HOME		/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.252.b09-2.el8_1.x86_64/jre
__END__
```

## mugsy
- Home page: https://mugsy.sourceforge.net
- Source code: https://sourceforge.net/projects/mugsy/files/

### Installation on Deigo
```
APP=mugsy
VER=1r2.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://sourceforge.net/projects/mugsy/files/${APP}_x86-64-v${VER}.tgz | tar xzvf - && mv ${APP}_x86-64-v${VER} ${VER} && cd ${VER}
sed -i 's/export MUGSY_INSTALL/#export MUGSY_INSTALL/g' mugsyenv.sh
echo ""export MUGSY_INSTALL=$PWD"" >> mugsyenv.sh
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://mugsy.sourceforge.net

## Short description of package:
module-whatis  ""Mugsy is a multiple whole genome aligner.""

## Load any needed modules:

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome
prepend-path    PATH            $apphome/MUMmer3.20
eval [exec /usr/bin/env -i /usr/share/Modules/bin/createmodule.sh $apphome/mugsyenv.sh]
__END__
```

## TCSF_IMRA
- Home page: https://github.com/Yukihirokinjo/TCSF_IMRA

### Installation on Deigo
```
APP=TCSF_IMRA
VER=2.7.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/Yukihirokinjo/TCSF_IMRA.git
mv $APP $VER && cd $VER
chmod u+x *.bash
chmod u+x *.R
echo '#!/bin/sh' > tcsf && echo ""TCSF-$VER.bash \$*"" >> tcsf && chmod +x tcsf
echo '#!/bin/sh' > imra && echo ""IMRA-$VER.bash \$*"" >> imra && chmod +x imra
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/Yukihirokinjo/TCSF_IMRA"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""TCSF_IMRA"")
whatis(""Description: ""..""tools developed for improving de novo assembly of endosymbiont genomes"")

-- Package settings
depends_on(""seqtk/1.3"", ""R/4.2.1"", ""ncbi-blast/2.10.0+"", ""bowtie2/2.2.6"", ""samtools/1.12"", ""SPAdes/3.13.0"", ""idba/1.1.3-7"")
prepend_path(""PATH"", apphome, apphome..""/Rlib"")
setenv(""TCSF_IMRA"", apphome)
__END__
```

## InterProScan
- Home page: https://interproscan-docs.readthedocs.io/en/v5/
- Source code: 

### Installation on Deigo
```bash
APP=interproscan
VER=5.76-107.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget https://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/${VER}/interproscan-${VER}-64-bit.tar.gz
wget https://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/${VER}/interproscan-${VER}-64-bit.tar.gz.md5
md5sum -c interproscan-${VER}-64-bit.tar.gz.md5
tar -pxvzf interproscan-${VER}-64-bit.tar.gz
chmod -R g+w interproscan-${VER}
mv interproscan-${VER} ${VER}
cd ${VER}
python3 setup.py -f interproscan.properties
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://interproscan-docs.readthedocs.io/en/v5/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""InterPro"")
whatis(""Description: "".."" InterproScan to run the scanning algorithms from the InterPro database in an integrated way."")

-- Package settings
prepend_path(""PATH"", apphome)
if os.getenv(""IPRDIR"") then
    prepend_path(""IPRDIR"", apphome)
else
    setenv(""IPRDIR"", apphome)
end

-- Load dependencies
-- begin ChatGPT
-- Load dependencies (Ensure at least one version is loaded)
local python_versions = {""python/3.11.4"", ""python/3.10.2""}
local java_versions = {""java-jdk/21"", ""java-jdk/17"", ""java-jdk/14"", ""java-jdk/11""}

local function ensure_one_loaded(versions)
    for _, v in ipairs(versions) do
        if isloaded(v) then
            return  -- If one is already loaded, no need to load another
        end
    end
    depends_on(versions[1])  -- Load the first option if none are loaded
end

ensure_one_loaded(python_versions)
ensure_one_loaded(java_versions)
-- end ChatGPT

-- Display note when the module is loaded
if (mode() == ""load"") then
    LmodMessage([[
An environment variable ""IPRDIR"" has been set to the InterProScan directory.
InterProScan software has been installed.

USAGE EXAMPLE:
  ${IPRDIR}/interproscan.sh -i input.fasta --output-file-base IPRresult -cpu 4
]])
end
__END__
```

## RAxML NG (RAxML Next Generation MPI)
- Home page: https://github.com/amkozlov/raxml-ng/
- Source code: https://github.com/amkozlov/raxml-ng/releases/download/1.2.0/raxml-ng_v1.2.0_linux_x86_64_MPI.zip

### Installation on Deigo
```
APP=raxml-ng-mpi
VER=1.2.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
mkdir -p $APPDIR/$VER
cd $APPDIR/$VER
wget -O - https://github.com/amkozlov/raxml-ng/releases/download/1.2.0/raxml-ng_v1.2.0_linux_x86_64_MPI.zip > $APP-$VER.zip && unzip $APP-$VER.zip && rm $APP-$VER.zip
sh install.sh
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/amkozlov/raxml-ng"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""raxml-ng"")
whatis(""Description: ""..""RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

## PAML
- Home page: http://abacus.gene.ucl.ac.uk/software/#paml-for-unixlinux
- Source code: https://github.com/abacus-gene/paml

### Installation on Deigo
```
APP=paml
VER=4.10.6
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/abacus-gene/paml # this reads as version 4.9j on GitHub, which prints out as 4.10.6 when executed
mv $APP $VER && cd $VER/src
## allow higher number of taxa in some programs
sed -i ""s/#define NS            500/#define NS            5000/g"" mcmctree.c
sed -i ""s/#define NS            1000/#define NS            5000/g"" yn00.c
sed -i ""s/#define NS          10/#define NS          5000/g"" basemlg.c
## compile
make -f Makefile
rm *.o
mkdir ../bin
mv baseml basemlg chi2 codeml evolver infinitesites mcmctree pamp yn00 ../bin
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/abacus-gene/paml"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""PAML"")
whatis(""Description: ""..""PAML is a program package for model fitting and phylogenetic tree reconstruction using DNA and protein sequence data. The programs are written in ANSI C"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

## PHYLUCE
- Home page: https://phyluce.readthedocs.io/en/latest/
- Source code: https://phyluce.readthedocs.io/en/latest/installation.html
- - Note: switched to miniforge installation on 2024/12/11

### Installation on Deigo
```
APP=phyluce
VER=1.7.3
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
curl -L -O ""https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh""
bash Miniforge3-$(uname)-$(uname -m).sh -b -p $APPDIR/$VER && rm Miniforge3-$(uname)-$(uname -m).sh && cd $VER
source ./bin/activate
./bin/mamba env create -p $MODROOT/$APP/$VER/envs/phyluce-1.7.3 --file=https://raw.githubusercontent.com/faircloth-lab/phyluce/v1.7.3/distrib/phyluce-1.7.3-py36-Linux-conda.yml
### modulefile
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://phyluce.readthedocs.io/en/latest/index.html

## Short description of package:
module-whatis  ""Phyluce installed using Miniforge3.""

## Load any needed modules:

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome/bin
prepend-path    PATH            $apphome/envs/phyluce-1.7.3
prepend-path    PATH            $apphome/envs/phyluce-1.7.3/bin
prepend-path    PATH $apphome/lib
prepend-path    PYTHONPATH	$apphome/bin
__END__

```

## VeryFastTree
- Home page: https://github.com/citiususc/veryfasttree

### Installation on Deigo

```bash
APP=veryfasttree
VER=4.0.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/ && cd $APPDIR/
git clone https://github.com/citiususc/veryfasttree
mv veryfasttree $VER && cd $VER/
cmake -DINSTALL_DIR:PATH=$APPDIR/$VER
make
chmod a+x VeryFastTree
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/citiususc/veryfasttree"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""veryfasttree"")
whatis(""Description: ""..""VeryFastTree is a new tool designed for efficient phylogenetic tree inference, specifically tailored to handle massive taxonomic datasets."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```
## phyloflash
- Home page: http://hrgv.github.io/phyloFlash/
- Note: switched to miniforge installation on 2024/12/12
### Installation on Deigo
```bash
APP=phyloflash
VER=3.4.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
curl -L -O ""https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh""
bash Miniforge3-$(uname)-$(uname -m).sh -b -p $APPDIR/$VER && rm Miniforge3-$(uname)-$(uname -m).sh && cd $VER
source ./bin/activate
# add the channel to the configuration for the active environment
# https://stackoverflow.com/questions/40616381/can-i-add-a-channel-to-a-specific-conda-environment
./bin/conda config --env --add channels defaults
./bin/conda config --env --add channels bioconda
./bin/conda config --env --add channels conda-forge
# Create new environment named ""pf"" with phyloflash
#./bin/mamba env create -p $MODROOT/$APP/$VER/envs/pf $APP #### needs yml file
./bin/conda create -n pf $APP
# ./bin/conda activate $MODROOT/$APP/$VER/envs/pf #### CondaError: Run 'conda init' before 'conda activate'
# export PATH=$MODROOT/$APP/$VER/envs/pf/bin:$PATH
# ./envs/pf/bin/phyloFlash.pl -check_env
### modulefile
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://hrgv.github.io/phyloFlash/"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""rRNA, phylogeny, illumina, transcriptome"")
whatis(""Description: ""..""phyloFlash is a pipeline to rapidly reconstruct the SSU rRNAs and explore phylogenetic composition of an Illumina (meta)genomic or transcriptomic dataset."")

-- Package settings
prepend_path(""PATH"", apphome)
prepend_path(""PATH"", apphome..""/envs/pf"")
prepend_path(""PATH"", apphome..""/envs/pf/bin"")
__END__
```


## QIIME2-amplicon
- Home page: https://github.com/qiime2/qiime2
- Installation: https://docs.qiime2.org/2024.5/install/native/#miniconda
- Note: switched to miniforge installation on 2024/12/12

### Installation on Deigo
```bash
APP=qiime2-amplicon
VER=2024.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
curl -L -O ""https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh""
bash Miniforge3-$(uname)-$(uname -m).sh -b -p $APPDIR/$VER && rm Miniforge3-$(uname)-$(uname -m).sh && cd $VER
source ./bin/activate
./bin/mamba env create -p $MODROOT/$APP/$VER/envs/$APP-$VER --file=https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2024.5-py39-linux-conda.yml
# conda activate /bucket/BioinfoUgrp/Other/qiime2-amplicon/2024.5/envs/qiime2-amplicon-2024.5
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/qiime2/qiime2"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""DNA, amplicon, metagenome"")
whatis(""Description: ""..""QIIME 2 is a powerful, extensible, and decentralized microbiome bioinformatics platform that is free, open source, and community developed."")

-- Package settings
prepend_path(""PATH"", apphome)
prepend_path(""PATH"", apphome..""/envs/qiime2-amplicon-2024.5"")
prepend_path(""PATH"", apphome..""/envs/qiime2-amplicon-2024.5/bin"")
__END__
```

## BAMM
- Home page: https://github.com/macroevolution/bamm
- Installation: https://github.com/macroevolution/bamm

### Installation on Deigo
```bash
APP=bamm
VER=2.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER
cd $APPDIR
git clone https://github.com/macroevolution/bamm
mv $APP $VER
cd $VER
# compile
mkdir build
cd build
cmake ..
make -j
cd $MODROOT/$APP/$VER
mkdir -p bin
cd bin
ln -s $MODROOT/$APP/$VER/build/bamm .
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/macroevolution/bamm"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""bamm"")
whatis(""Description: ""..""A program for multimodel inference on speciation and trait evolution."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```
","Markdown"
"AlphaFold","oist/BioinfoUgrp","Other/Repeats.md",".md","5088","188","# A collection of general bioinformatics modules

To use the modules, load the `bioinfo-ugrp-modules` metamodule first.

```bash
ml bioinfo-ugrp-modules
ml av Other
ml Other/<your-favorite-module>
```

Here are installation snippets to help the members of the bioinfo user group to update the modules.

## DfamTEBrowser

```
APP=DfamTEBrowser
VER=1.0.0
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget https://github.com/Dfam-consortium/DfamTEBrowser/archive/refs/tags/${VER}.tar.gz
tar xvfz ${VER}.tar.gz
mv DfamTEBrowser-$VER $VER
rm ${VER}.tar.gz
cd ${VER}/Libraries
ln -s /bucket/.deigo/BioinfoUgrp/DB/Dfam/3.9/RepeatPeps.lib
ln -s /bucket/.deigo/BioinfoUgrp/DB/Dfam/3.9/Dfam-RepeatMasker.lib Dfam-curated.fa
/bucket/BioinfoUgrp/Other/TETools/1.95/bin/makeblastdb -in /bucket/BioinfoUgrp/Other/DfamTEBrowser/1.0.0/Libraries/Dfam-curated.fa -dbtype nucl
/bucket/BioinfoUgrp/Other/TETools/1.95/bin/makeblastdb -in /bucket/BioinfoUgrp/Other/DfamTEBrowser/1.0.0/Libraries/RepeatPeps.lib  -dbtype prot
```

### Module file template

```
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/Dfam-consortium/DfamTEBrowser"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Dfam, RepeatMasker, RepeatModeller"")
whatis(""Description: ""..""Dfam Transposable Element Family Genome Browser"")

-- Package settings
depends_on(""Other/TETools"", ""Other/ULTRA"")
prepend_path(""PATH"", apphome)
```

## TETools

Downloaded singularity image from <https://github.com/Dfam-consortium/TETools>:

```
APP=TETools
VER=1.95
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/$VER/bin
cd $APPDIR/$VER
singularity pull docker://dfam/tetools:$VER
CMDS=""BuildDatabase calcDivergenceFromAlign.pl createRepeatLandscape.pl famdb.py generateSeedAlignments.pl RepeatMasker RepeatModeler rmblastn makeblastdb blastx""
for cmd in $CMDS; do
  cat > ""bin/$cmd"" <<EOF
#!/bin/sh
VER=${VER}
COMMAND=${cmd}
LC_ALL=C singularity exec /bucket/.deigo/BioinfoUgrp/Other/TETools/\${VER}/tetools_\${VER}.sif \$COMMAND ""\$@""
EOF
  chmod +x ""bin/$cmd""
done
cd $MODROOT/modulefiles/
mkdir -p $APP
cd $APP
cp 1.94.lua ${VER}.lua
```

### Module file template

```
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/Dfam-consortium/TETools"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""Dfam, RepeatMasker, RepeatModeller"")
whatis(""Description: ""..""Dfam Transposable Element Tools Container."")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome..""/bin"")
```

## Tandem Repeat Finder (TRF)

- Home page: https://github.com/Benson-Genomics-Lab/TRF
- Source code: https://github.com/Benson-Genomics-Lab/TRF/releases

### Installation on Deigo

```bash
APP=trf
VER=4.09.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
wget https://github.com/Benson-Genomics-Lab/TRF/releases/download/v$VER/trf409.linux64 && mv trf409.linux64 $APP && chmod +x $APP
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/Benson-Genomics-Lab/TRF"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""DNA, repeat"")
whatis(""Description: ""..""Tandem Repeats Finder: a program to analyze DNA sequences."")

-- Package settings
prepend_path(""PATH"", apphome.."""")
__END__
```

### Example commands for running Tandem Repeat Finder on Deigo

```bash
module load Other/trf
srun -p compute -c 1 --mem 40G -t 10:00:00 --pty \
    trf <arguments>
```

## ULTRA

```
APP=ULTRA
VER=1.2.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/TravisWheelerLab/ULTRA $VER
cd $VER
git checkout $VER
cmake .
make
cd $MODROOT/modulefiles/$APP
cp 1.2.1.lua ${VER}.lua
```

### Module file template

```
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/TravisWheelerLab/ULTRA"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""DNA, repeat"")
whatis(""Description: ""..""ULTRA Locates Tandemly Repetitive Areas"")

-- Package settings
prepend_path(""PATH"", apphome)
```
","Markdown"
"AlphaFold","oist/BioinfoUgrp","Other/Alignment.md",".md","14530","559","# A collection of general bioinformatics modules

To use the modules, load the `bioinfo-ugrp-modules` metamodule first.

```bash
ml bioinfo-ugrp-modules
ml av Other
ml Other/<your-favorite-module>
```

Here are installation snippets to help the members of the bioinfo user group to update the modules.

## BWA

- Home page: https://github.com/lh3/bwa
- Source code: https://github.com/lh3/bwa

### Installation on Deigo

```bash
APP=bwa
VER=0.7.17
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/lh3/bwa/releases/download/v0.7.17/bwa-0.7.17.tar.bz2 | tar xjvf -
mv $APP-$VER $VER
cd $VER
make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/lh3/bwa"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""bwa"")
whatis(""Description: ""..""BWA is a software package for mapping DNA sequences against a large reference genome, such as the human genome."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running bwa on Deigo

```bash
module load Other/bwa
srun -p compute -c 128 --mem 100G -t 1:00:00 --pty \
    bwa mem <arguments>
```


## samblaster

- Home page: https://academic.oup.com/bioinformatics/article/30/17/2503/2748175
- Source code: https://github.com/GregoryFaust/samblaster

### Installation on Deigo

```bash
APP=samblaster
VER=0.1.26
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/GregoryFaust/samblaster/releases/download/v.0.1.26/samblaster-v.0.1.26.tar.gz | tar xzvf -
mv $APP-v.$VER $VER
cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/GregoryFaust/samblaster"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""samblaster"")
whatis(""Description: ""..""samblaster is a fast and flexible program for marking duplicates in read-id grouped paired-end SAM files"")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running samblaster on Deigo

```bash
module load Other/samblaster
srun -p compute -c 1 --mem 40G -t 1:00:00 --pty \
    bwa mem <idxbase> samp.r1.fq samp.r2.fq | samblaster | samtools view -Sb - > samp.out.bam
```

## hal

 - Homepage: https://github.com/ComparativeGenomicsToolkit/hal
 - Source code: https://github.com/ComparativeGenomicsToolkit/hal/releases

### Installation on Deigo

```bash
APP=hal
VER=2.2
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR/${VER}
cd $APPDIR/${VER}
wget https://github.com/ComparativeGenomicsToolkit/hal/archive/refs/tags/release-V${VER}.tar.gz
tar xvfz release-V${VER}.tar.gz
git clone https://github.com/ComparativeGenomicsToolkit/sonLib.git
pushd sonLib
git checkout a4d45c46c6b9ee580fbac89db10894bac8844fd9 # As of January 23 2023
make
popd
cd hal-release-V${VER}
make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/ComparativeGenomicsToolkit/hal"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""hal, assembly, cactus"")
whatis(""Description: ""..""Tools for manipulating sequence graphs in the HAL formats."")

-- Package settings
prepend_path(""PATH"", apphome..""/hal-release-V""..appversion..""/bin"")
__END__
```

## k8 (Javascript shell)

- Home page: https://github.com/attractivechaos/k8
- Source code: https://github.com/attractivechaos/k8/releases

This module is installed just to satisfy the requirements by paftools.js in the minimap2 module.

### Installation on Deigo

```bash
APP=k8
VER=0.2.5
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/attractivechaos/k8/releases/download/$VER/$APP-$VER.tar.bz2 | tar xjvf -
mv $APP-$VER $VER && cd $VER
ln -sf k8-Linux k8
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/attractivechaos/k8"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""javascript"")
whatis(""Description: ""..""k8 Javascript shell."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running minimap2 on Deigo

```bash
module load Other/k8
srun -p compute -c 1 --mem 40G -t 10:00:00 --pty \
    k8 <arguments>
```

## minimap2

- Home page: https://github.com/lh3/minimap2
- Source code: https://github.com/lh3/minimap2/releases

### Installation on Deigo

```bash
APP=minimap2
VER=2.20
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/lh3/minimap2/archive/refs/tags/v$VER.tar.gz | tar xzvf -
mv $APP-$VER $VER
cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/lh3/minimap2"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""alignment"")
whatis(""Description: ""..""A versatile pairwise aligner for genomic and spliced nucleotide sequences."")

-- Package settings
depends_on(""Other/k8"")
prepend_path(""PATH"", apphome)
prepend_path(""PATH"", apphome..""/misc"")
__END__
```

### Example commands for running minimap2 on Deigo

```bash
module load Other/minimap2
srun -p compute -c 64 --mem 500G -t 24:00:00 --pty \
    minimap2 <arguments>
```

The `paftools.js` command is also available:

```bash
module load Other/minimap2
srun -p compute -c 1 --mem 100G -t 24:00:00 --pty \
    paftools.js <arguments>
```

## winnowmap

- Home page: https://github.com/marbl/Winnowmap
- Source code: https://github.com/marbl/Winnowmap/releases

### Installation on Deigo

```bash
APP=winnowmap
VER=2.03
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/marbl/Winnowmap/archive/refs/tags/v$VER.tar.gz | tar xzvf -
mv Winnowmap-$VER $VER
cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/marbl/Winnowmap"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""alignment"")
whatis(""Description: ""..""Long read / genome alignment software"")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```

### Example commands for running winnowmap on Deigo

```bash
module load Other/winnowmap
srun -p compute -c 64 --mem 500G -t 24:00:00 --pty \
    winnowmap <arguments>
```


## DAZZ_DB

- Home page: https://github.com/thegenemyers/DAZZ_DB
- Source code: https://github.com/thegenemyers/DAZZ_DB

### Installation on Deigo

```bash
APP=DAZZ_DB
VER=2021.03.30
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/thegenemyers/DAZZ_DB
mv DAZZ_DB $VER && cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/thegenemyers/DAZZ_DB"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""assembly, sequencing"")
whatis(""Description: ""..""The Dazzler Data Base."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running DAZZ_DB on Deigo

To load the module, run:

```bash
module load Other/DAZZ_DB
```

Then the following commands become available:

- `arrow2DB`
- `Catrack`
- `DAM2fasta`
- `DB2arrow`
- `DB2fasta`
- `DB2quiva`
- `DBa2b`
- `DBb2a`
- `DBdump`
- `DBdust`
- `DBmv`
- `DBrm`
- `DBshow`
- `DBsplit`
- `DBstats`
- `DBtrim`
- `DBwipe`
- `fasta2DAM`
- `fasta2DB`
- `quiva2DB`
- `rangen`
- `simulator`

See the [README](https://github.com/thegenemyers/DAZZ_DB) written by the developer for details about how to use the programs.

## FASTK

- Home page: https://github.com/thegenemyers/FASTK
- Source code: https://github.com/thegenemyers/FASTK

### Installation on Deigo

```bash
APP=FASTK
VER=2021.05.27
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
git clone https://github.com/thegenemyers/FASTK
mv FASTK $VER && cd $VER && make
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/thegenemyers/FASTK"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""k-mer counter"")
whatis(""Description: ""..""A fast K-mer counter for high-fidelity shotgun datasets."")

-- Package settings
prepend_path(""PATH"", apphome)
__END__
```

### Example commands for running FASTK on Deigo

To load the module, run:

```bash
module load Other/FASTK
```

Then the following commands become available:

- `Fastcp`
- `FastK`
- `Fastmv`
- `Fastrm`
- `Haplex`
- `Histex`
- `Homex`
- `Logex`
- `Profex`
- `Symmex`
- `Tabex`
- `Vennex`

See the [README](https://github.com/thegenemyers/FASTK) written by the developer for details about how to use the programs.


## SAMtools
- Home page: http://www.htslib.org
- Source code: https://github.com/samtools/samtools/releases/download/1.15.1/samtools-1.15.1.tar.bz2

### Installation on Deigo
```bash
APP=samtools
VER=1.15.1
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
cd $APPDIR
wget -O - https://github.com/samtools/samtools/releases/download/1.15.1/samtools-1.15.1.tar.bz2 | tar xjvf -
mv $APP-$VER $VER
cd $VER
./configure --prefix=$APPDIR/$VER && make && make install
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""http://www.htslib.org"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""samtools"")
whatis(""Description: ""..""mpileup and other tools for handling SAM, BAM, CRAM."")

-- Package settings
prepend_path(""PATH"", apphome..""/bin"")
__END__
```


## DIAMOND
- Home page: https://github.com/bbuchfink/diamond
- Source code: https://github.com/bbuchfink/diamond/releases/download/v2.0.15/diamond-linux64.tar.gz

### Installation on Deigo
```bash
APP=DIAMOND
VER=2.0.15.153
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP
mkdir -p $APPDIR
mkdir $VER
cd $APPDIR/$VER
wget -O - https://github.com/bbuchfink/diamond/releases/download/v2.0.15/diamond-linux64.tar.gz | tar xzvf -
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER
#%Module1.0##################################################################
set approot    [lrange [split [module-info name] {/}] 0 0]
set appname    [lrange [split [module-info name] {/}] 1 1]
set appversion [lrange [split [module-info name] {/}] 2 2]
set apphome    /bucket/BioinfoUgrp/$approot/$appname/$appversion

## URL of application homepage:
set appurl     https://github.com/bbuchfink/diamond

## Short description of package:
module-whatis  ""DIAMOND is a sequence aligner for protein and translated DNA searches, designed for high performance analysis of big sequence data.""

## Load any needed modules:

## Modify as needed, removing any variables not needed.
## Non-path variables can be set with ""setenv VARIABLE value""
prepend-path    PATH            $apphome
__END__
```

## cactus
- Home page: (https://github.com/ComparativeGenomicsToolkit/cactus)
- Source code: quay.io/comparative-genomics-toolkit/cactus:v2.9.7

### Installation on Deigo
```
ml singularity
APP=cactus
VER=2.9.7
MODROOT=/bucket/BioinfoUgrp/Other
APPDIR=$MODROOT/$APP/$VER
mkdir -p $APPDIR
cd $APPDIR
singularity build $APP.sif docker:quay.io/comparative-genomics-toolkit/${APP}:v${VER}
echo '#!/bin/sh' > $APP && echo ""singularity exec $APPDIR/$APP.sif $APP \$*"" >> $APP && chmod +x $APP
echo '#!/bin/sh' > cactus-pangenome && echo ""singularity exec $APPDIR/$APP.sif cactus-pangenome \$*"" >> cactus-pangenome && chmod +x cactus-pangenome
cd $MODROOT/modulefiles/
mkdir -p $APP
cat <<'__END__' > $APP/$VER.lua
-- Default settings
local modroot    = ""/bucket/BioinfoUgrp""
local appname    = myModuleName()
local appversion = myModuleVersion()
local apphome    = pathJoin(modroot, myModuleFullName())

-- Package information
whatis(""Name: ""..appname)
whatis(""Version: ""..appversion)
whatis(""URL: ""..""https://github.com/ComparativeGenomicsToolkit/cactus"")
whatis(""Category: ""..""bioinformatics"")
whatis(""Keywords: ""..""alignment"")
whatis(""Description: ""..""reference-free whole-genome alignment program"")

-- Package settings
depends_on(""singularity"")
prepend_path(""PATH"", apphome)
__END__
```
","Markdown"
"AlphaFold","alisiafadini/ROCKET","test/conftest.py",".py","341","18","import sys
from unittest.mock import MagicMock

to_mock = [
    ""openfold"",
    ""openfold.model"",
    ""openfold.utils"",
    ""openfold.config"",
    ""openfold.data"",
    ""openfold.model.model"",
    ""openfold.np"",
    ""openfold.utils.import_weights"",
    ""openfold.utils.script_utils"",
]

for mod in to_mock:
    sys.modules[mod] = MagicMock()
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_cryo_structurefactors.py",".py","2503","82","import pytest
import torch


class DummyCryoSFCalc:
    def __init__(self, *a, **k):
        self.Fprotein_HKL = torch.tensor([5.0])
        self.Fo = torch.tensor([2.0])
        self.bins = torch.tensor([0])
        self.SigF = ""should be overwritten""
        self.called = []

    def calc_fprotein(self):
        self.called.append(""calc_fprotein"")

    def calc_Ec(self, x):
        self.called.append((""calc_Ec"", x))
        # Return dummy normalized value
        return torch.tensor([1.0])

    def get_scales_adam(self, sub_ratio=1.0):
        self.called.append((""get_scales_adam"", sub_ratio))


@pytest.fixture(autouse=True)
def patch_cryo_SFcalculator(monkeypatch):
    import SFC_Torch

    import rocket.cryo.structurefactors

    monkeypatch.setattr(rocket.cryo.structurefactors, ""SFcalculator"", DummyCryoSFCalc)
    monkeypatch.setattr(SFC_Torch, ""SFcalculator"", DummyCryoSFCalc)


def test_initial_cryoSFC_sets_attributes():
    from rocket.cryo.structurefactors import initial_cryoSFC

    result = initial_cryoSFC(
        ""model.pdb"", ""map.mtz"", ""Emean"", ""PHIEmean"", device=""cpu"", n_bins=11
    )

    # Instance of DummyCryoSFCalc
    assert isinstance(result, DummyCryoSFCalc)
    assert result.SigF is None
    assert torch.allclose(result.Fprotein_HKL, torch.tensor([1.0]))
    called = result.called
    # Should be three calls
    assert len(called) == 3
    assert called[0] == ""calc_fprotein""
    assert called[2] == (""get_scales_adam"", 1.0)
    name, tensor_arg = called[1]
    assert name == ""calc_Ec""
    assert torch.allclose(tensor_arg, torch.tensor([5.0]))


def test_initial_cryoSFC_device_and_bins(monkeypatch):
    # Patch to record init args
    recorded_kwargs = {}

    class DummyCryoSFCalcRecord(DummyCryoSFCalc):
        def __init__(self, *a, **k):
            recorded_kwargs.update(k)
            super().__init__(*a, **k)

    import SFC_Torch

    import rocket.cryo.structurefactors

    monkeypatch.setattr(
        rocket.cryo.structurefactors, ""SFcalculator"", DummyCryoSFCalcRecord
    )
    monkeypatch.setattr(SFC_Torch, ""SFcalculator"", DummyCryoSFCalcRecord)

    from rocket.cryo.structurefactors import initial_cryoSFC

    _ = initial_cryoSFC(""x.pdb"", ""y.mtz"", ""E"", ""PHIE"", device=""cuda:1"", n_bins=3)
    assert recorded_kwargs[""device""] == ""cuda:1""
    assert recorded_kwargs[""n_bins""] == 3
    assert recorded_kwargs[""mode""] == ""cryoem""
    assert recorded_kwargs[""freeflag""] == ""None""
    assert recorded_kwargs[""expcolumns""] == [""E"", ""PHIE""]
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_xtal_structurefactors.py",".py","6818","228","import numpy as np
import pytest
import torch

from rocket.xtal.structurefactors import (
    calculate_Sigma_atoms,
    compute_sigmaA_true,
    ftotal_amplitudes,
    ftotal_phis,
    initial_SFC,
    normalize_Fs,
)

# ---------- MOCKS for initial_SFC ----------


class DummySFCalc:
    def __init__(self, *a, **k):
        self.Fprotein_HKL = torch.tensor([1.0])
        self.Fprotein_asu = torch.tensor([1.0])
        self.Fmask_HKL = torch.tensor([1.0])
        self.solventpct = 0.5

    def inspect_data(self, **k):
        pass

    def calc_fprotein(self):
        pass

    def calc_fsolvent(self):
        self.Fmask_HKL = torch.zeros(1)

    def get_scales_adam(self):
        pass


@pytest.fixture(autouse=True)
def patch_SFcalculator(monkeypatch):
    import SFC_Torch

    import rocket.xtal.structurefactors

    monkeypatch.setattr(rocket.xtal.structurefactors, ""SFcalculator"", DummySFCalc)
    monkeypatch.setattr(SFC_Torch, ""SFcalculator"", DummySFCalc)
    # Patch SFC.utils.try_gpu
    monkeypatch.setattr(""SFC_Torch.utils.try_gpu"", lambda: ""cpu"")


# ---------- TESTS ----------


def test_initial_SFC_device_default():
    result = initial_SFC(""pdb"", ""mtz"", ""F"", ""SigF"", device=None)
    assert isinstance(result, DummySFCalc)


def test_initial_SFC_added_chain():
    result = initial_SFC(
        ""pdb"",
        ""mtz"",
        ""F"",
        ""SigF"",
        added_chain_HKL=torch.tensor([2.0]),
        added_chain_asu=torch.tensor([3.0]),
        total_chain_copy=2.0,
    )
    assert torch.allclose(result.Fprotein_HKL, torch.tensor([3.0]))  # 1 + 2
    assert torch.allclose(result.Fprotein_asu, torch.tensor([4.0]))  # 1 + 3
    # solventpct should update: 1 - (1-0.5)*2 = 0.0
    assert pytest.approx(result.solventpct) == 0.0


def test_initial_SFC_solvent_false():
    result = initial_SFC(""pdb"", ""mtz"", ""F"", ""SigF"", solvent=False)
    assert torch.all(result.Fmask_HKL == 0)


def test_initial_SFC_custom_device(monkeypatch):
    # Should not call try_gpu if device given
    device = ""cuda""
    called = []
    monkeypatch.setattr(""SFC_Torch.utils.try_gpu"", lambda: called.append(True))
    result = initial_SFC(""pdb"", ""mtz"", ""F"", ""SigF"", device=device)
    assert result is not None
    assert not called  # try_gpu should not be called


# ----------- ftotal_amplitudes -------------


def test_ftotal_amplitudes_sorted():
    Ftotal = torch.tensor([3 + 4j, 1 + 1j, 0 + 2j])
    dHKL = np.array([2.0, 3.0, 1.0])
    result = ftotal_amplitudes(Ftotal, dHKL, sort_by_res=True)
    expected = torch.abs(Ftotal)[torch.tensor([1, 0, 2])]
    torch.testing.assert_close(result, expected)


def test_ftotal_amplitudes_nosort():
    Ftotal = torch.tensor([3 + 4j, 1 + 1j, 0 + 2j])
    dHKL = np.array([2.0, 3.0, 1.0])
    result = ftotal_amplitudes(Ftotal, dHKL, sort_by_res=False)
    expected = torch.abs(Ftotal)
    torch.testing.assert_close(result, expected)


def test_ftotal_amplitudes_empty():
    Ftotal = torch.tensor([], dtype=torch.cfloat)
    dHKL = np.array([])
    result = ftotal_amplitudes(Ftotal, dHKL)
    assert result.shape == (0,)


# ----------- ftotal_phis -------------


def test_ftotal_phis_sorted():
    Fc = torch.tensor([1 + 0j, 0 + 1j, -1 + 0j])
    dHKL = np.array([1.0, 3.0, 2.0])
    result = ftotal_phis(Fc, dHKL, sort_by_res=True)
    PI_on_180 = 0.017453292519943295
    phases = torch.angle(Fc) / PI_on_180
    sorted_indices = np.argsort(dHKL)[::-1]
    sorted_indices = sorted_indices.copy()  # ensures positive strides
    expected = phases[torch.tensor(sorted_indices)]

    torch.testing.assert_close(result, expected)


def test_ftotal_phis_nosort():
    Fc = torch.tensor([1 + 0j, 0 + 1j])
    dHKL = np.array([1.0, 2.0])
    result = ftotal_phis(Fc, dHKL, sort_by_res=False)
    PI_on_180 = 0.017453292519943295
    expected = torch.angle(Fc) / PI_on_180
    torch.testing.assert_close(result, expected)


def test_ftotal_phis_empty():
    Fc = torch.tensor([], dtype=torch.cfloat)
    dHKL = np.array([])
    result = ftotal_phis(Fc, dHKL)
    assert result.shape == (0,)


# ----------- compute_sigmaA_true -------------


def test_compute_sigmaA_true_single_bin():
    Eobs = np.array([1, 2])
    phiobs = np.array([0, np.pi / 2])
    Ecalc = np.array([1, 2])
    phicalc = np.array([0, np.pi / 2])
    bin_labels = np.array([0, 0])
    result = compute_sigmaA_true(Eobs, phiobs, Ecalc, phicalc, bin_labels)
    # cos(0)=1, so: 1*1*1, 2*2*1 => mean(1,4)=2.5
    assert np.allclose(result, [2.5])


def test_compute_sigmaA_true_multiple_bins():
    Eobs = np.array([1, 2, 2])
    phiobs = np.array([0, np.pi / 2, np.pi])
    Ecalc = np.array([2, 1, 3])
    phicalc = np.array([0, np.pi / 2, 0])
    bin_labels = np.array([0, 1, 0])
    # For bin 0: (1*2*cos(0), 2*3*cos(pi)) = (2, -6) => mean(-2)
    # For bin 1: (2*1*cos(0)) = 2
    result = compute_sigmaA_true(Eobs, phiobs, Ecalc, phicalc, bin_labels)
    assert np.allclose(result, [-2, 2])


def test_compute_sigmaA_true_empty():
    result = compute_sigmaA_true(
        np.array([]), np.array([]), np.array([]), np.array([]), np.array([])
    )
    assert result == []


# ----------- calculate_Sigma_atoms -------------


def test_calculate_Sigma_atoms_simple():
    Fs = torch.tensor([2.0, 4.0])
    eps = torch.tensor([1.0, 2.0])
    bin_labels = torch.tensor([0, 0])
    result = calculate_Sigma_atoms(Fs, eps, bin_labels)
    expected = torch.mean(torch.tensor([4.0 / 1.0, 16.0 / 2.0]))
    torch.testing.assert_close(result, torch.tensor([expected]))


def test_calculate_Sigma_atoms_multiple_bins():
    Fs = torch.tensor([2.0, 4.0, 3.0])
    eps = torch.tensor([1.0, 2.0, 3.0])
    bin_labels = torch.tensor([0, 1, 0])
    # bin 0: [4/1, 9/3] = [4, 3.0], mean=3.5
    # bin 1: [16/2]=8
    result = calculate_Sigma_atoms(Fs, eps, bin_labels)
    torch.testing.assert_close(result, torch.tensor([3.5, 8.0]))


def test_calculate_Sigma_atoms_empty():
    Fs = torch.tensor([], dtype=torch.float32)
    eps = torch.tensor([], dtype=torch.float32)
    bin_labels = torch.tensor([], dtype=torch.int64)
    with pytest.raises(ValueError, match=""All input tensors must be non-empty""):
        calculate_Sigma_atoms(Fs, eps, bin_labels)


# ----------- normalize_Fs -------------


def test_normalize_Fs_all_one_bin():
    Fs = torch.tensor([2.0, 4.0])
    eps = torch.tensor([1.0, 2.0])
    Sigma_atoms = torch.tensor([6.0])
    bin_labels = torch.tensor([0, 0])
    E = normalize_Fs(Fs, eps, Sigma_atoms, bin_labels)
    assert round(torch.mean(E**2).item()) == 1


def test_normalize_Fs_raises_on_bad_scale():
    Fs = torch.tensor([1.0, 1.0])
    eps = torch.tensor([1.0, 1.0])
    Sigma_atoms = torch.tensor([0.1])  # Will cause mean(E**2) != 1
    bin_labels = torch.tensor([0, 0])
    with pytest.raises(AssertionError):
        normalize_Fs(Fs, eps, Sigma_atoms, bin_labels)
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_coordinates.py",".py","41954","1278","from unittest.mock import MagicMock, patch

import numpy as np
import pytest
import torch

from rocket.coordinates import (
    align_tensors,
    calculate_mse_loss_per_residue,
    construct_SO3,
    decompose_SO3,
    extract_allatoms,
    extract_atoms_and_backbone,
    extract_bfactors,
    find_rigidbody_matrix_lbfgs_quat,
    fractionalize_torch,
    kabsch_align_matrices,
    pose_train_lbfgs_quat,
    quaternions_to_SO3,
    rigidbody_refine_quat,
    select_CA_elements,
    select_CA_from_craname,
    weighted_kabsch,
    write_pdb_with_positions,
)


@pytest.fixture
def mock_setup():
    """"""Create mock data for rigid body refinement tests.""""""
    # Create mock coordinates
    xyz = torch.tensor(
        [
            [1.0, 2.0, 3.0],
            [2.0, 3.0, 4.0],
            [3.0, 4.0, 5.0],
            [4.0, 5.0, 6.0],
            [5.0, 6.0, 7.0],
        ],
        dtype=torch.float32,
    )

    # Create mock CRA names
    cra_name = [""A-0-GLY-CA"", ""A-1-ALA-CA"", ""A-2-LEU-CA"", ""A-3-VAL-CA"", ""A-4-PHE-CA""]

    # Create mock LLGloss
    llgloss = MagicMock()
    llgloss.device = ""cpu""

    return xyz, cra_name, llgloss


@patch(""rocket.coordinates.find_rigidbody_matrix_lbfgs_quat"")
def test_rigidbody_refine_quat_single_domain(mock_find_rigidbody, mock_setup):
    """"""Test rigidbody_refine_quat with a single domain.""""""
    xyz, cra_name, llgloss = mock_setup

    # Mock the quaternions and translation vectors returned by find_rigidbody_matrix_lbfgs_quat  # noqa: E501
    trans_vecs = [torch.tensor([0.1, 0.2, 0.3])]
    qs = [torch.tensor([0.9, 0.1, 0.1, 0.1])]
    mock_find_rigidbody.return_value = (trans_vecs, qs, [10.0, 9.0, 8.0])  # Loss track

    # Call the function
    result_xyz, loss_track = rigidbody_refine_quat(
        xyz=xyz, llgloss=llgloss, cra_name=cra_name, lbfgs=True, verbose=False
    )

    # Verify find_rigidbody_matrix_lbfgs_quat was called correctly
    mock_find_rigidbody.assert_called_once()
    args, kwargs = mock_find_rigidbody.call_args
    assert args[0] == llgloss
    assert torch.equal(args[1].detach(), xyz.detach())
    assert args[2] == llgloss.device
    # The domain_bools would be one array of all True
    assert len(args[3]) == 1
    assert np.all(args[3][0])

    # Check that result has correct shape
    assert result_xyz.shape == xyz.shape
    assert loss_track == [10.0, 9.0, 8.0]

    # Check that the transformation is applied correctly
    propose_rmcom = xyz - torch.mean(xyz, dim=0)
    propose_com = torch.mean(xyz, dim=0)
    expected_rotation = quaternions_to_SO3(qs[0]).detach()
    optimized_xyz = (
        torch.matmul(propose_rmcom, expected_rotation)
        + propose_com
        + trans_vecs[0].detach()
    )
    assert torch.allclose(result_xyz, optimized_xyz)


@patch(""rocket.coordinates.find_rigidbody_matrix_lbfgs_quat"")
def test_rigidbody_refine_quat_multiple_domains(mock_find_rigidbody, mock_setup):
    """"""Test rigidbody_refine_quat with multiple domains.""""""
    xyz, cra_name, llgloss = mock_setup

    # Domain segmentation at residue 2 (so residues 1-2 and 3-5 are two domains)
    domain_segs = [3]  # Split between residues 2 and 3

    # Mock the quaternions and translation vectors returned by find_rigidbody_matrix_lbfgs_quat  # noqa: E501
    trans_vecs = [torch.tensor([0.1, 0.2, 0.3]), torch.tensor([0.4, 0.5, 0.6])]
    qs = [torch.tensor([0.9, 0.1, 0.1, 0.1]), torch.tensor([0.8, 0.2, 0.2, 0.2])]
    mock_find_rigidbody.return_value = (trans_vecs, qs, [10.0, 9.0])  # Loss track

    # Call the function with domain segmentation
    result_xyz, loss_track = rigidbody_refine_quat(
        xyz=xyz,
        llgloss=llgloss,
        cra_name=cra_name,
        lbfgs=True,
        verbose=False,
        domain_segs=domain_segs,
    )

    # Verify find_rigidbody_matrix_lbfgs_quat was called correctly
    mock_find_rigidbody.assert_called_once()
    args, kwargs = mock_find_rigidbody.call_args

    # There should be 2 domain boolean arrays
    assert len(args[3]) == 2

    # First domain: residues 1-2
    expected_domain1 = np.array([True, True, False, False, False])
    assert np.array_equal(args[3][0], expected_domain1)

    # Second domain: residues 3-5
    expected_domain2 = np.array([False, False, True, True, True])
    assert np.array_equal(args[3][1], expected_domain2)

    # Check that result has correct shape
    assert result_xyz.shape == xyz.shape
    assert loss_track == [10.0, 9.0]

    # Each domain should have coordinates transformed separately
    # So the transformation for first three atoms is different from last two
    domain_bools = args[3]
    optimized_xyz = torch.ones_like(xyz)
    for i in range(2):
        propose_rmcom = xyz[domain_bools[i]] - torch.mean(xyz[domain_bools[i]], dim=0)
        propose_com = torch.mean(xyz[domain_bools[i]], dim=0)
        transform_i = quaternions_to_SO3(qs[i]).detach()
        optimized_xyz[domain_bools[i]] = (
            torch.matmul(propose_rmcom, transform_i)
            + propose_com
            + trans_vecs[i].detach()
        )
    assert torch.allclose(result_xyz, optimized_xyz)


@patch(""rocket.coordinates.find_rigidbody_matrix_lbfgs_quat"")
def test_rigidbody_refine_quat_with_added_chains(mock_find_rigidbody, mock_setup):
    """"""Test rigidbody_refine_quat with additional chain parameters.""""""
    xyz, cra_name, llgloss = mock_setup

    # Mock additional chain parameters
    added_chain_HKL = torch.tensor([1.0, 2.0, 3.0])
    added_chain_asu = torch.tensor([4.0, 5.0, 6.0])

    # Mock return values
    trans_vecs = [torch.tensor([0.1, 0.2, 0.3])]
    qs = [torch.tensor([0.9, 0.1, 0.1, 0.1])]
    mock_find_rigidbody.return_value = (trans_vecs, qs, [8.0, 7.0])

    # Call the function with additional chain parameters
    result_xyz, loss_track = rigidbody_refine_quat(
        xyz=xyz,
        llgloss=llgloss,
        cra_name=cra_name,
        lbfgs=True,
        added_chain_HKL=added_chain_HKL,
        added_chain_asu=added_chain_asu,
        lbfgs_lr=100.0,
        verbose=False,
    )

    # Verify find_rigidbody_matrix_lbfgs_quat was called with correct parameters
    mock_find_rigidbody.assert_called_once()
    args, kwargs = mock_find_rigidbody.call_args
    assert torch.equal(kwargs[""added_chain_HKL""], added_chain_HKL)
    assert torch.equal(kwargs[""added_chain_asu""], added_chain_asu)
    assert kwargs[""lbfgs_lr""] == 100.0
    assert kwargs[""verbose""] is False

    # Check results
    assert result_xyz.shape == xyz.shape
    assert loss_track == [8.0, 7.0]


def test_rigidbody_refine_quat_integration():
    """"""Integration test for rigidbody_refine_quat using actual computation.""""""
    # Create simple test data
    xyz = torch.tensor(
        [
            [1.0, 2.0, 3.0],
            [2.0, 3.0, 4.0],
            [3.0, 4.0, 5.0],
        ],
        dtype=torch.float32,
        requires_grad=True,
    )
    cra_name = [""A-0-GLY-CA"", ""A-1-ALA-CA"", ""A-2-LEU-CA""]

    # Create a mock llgloss that returns a simple loss value
    class MockLLGLoss:
        def __init__(self):
            self.device = torch.device(""cpu"")
            self.call_count = 0

        def __call__(self, positions, **kwargs):
            # Simple loss function: sum of squared distances from origin
            self.call_count += 1
            return -torch.sum(torch.sum(positions**2, dim=1))

    llgloss = MockLLGLoss()

    # We'll use a patch here to avoid testing the full LBFGS optimization
    with patch(""rocket.coordinates.find_rigidbody_matrix_lbfgs_quat"") as mock_find:
        # Mock quaternion and translation that makes a simple transformation
        q = torch.tensor([1.0, 0.0, 0.0, 0.0], dtype=torch.float32)  # Identity rotation
        trans = torch.tensor([1.0, 1.0, 1.0], dtype=torch.float32)  # Simple translation
        mock_find.return_value = ([trans], [q], [-100.0])

        # Call the function
        optimized_xyz, loss_track = rigidbody_refine_quat(
            xyz=xyz, llgloss=llgloss, cra_name=cra_name, lbfgs=True, verbose=False
        )

        # For an identity rotation and translation of [1,1,1],
        # each point should be shifted by [1,1,1]
        expected_xyz = xyz + torch.tensor([1.0, 1.0, 1.0])
        assert torch.allclose(optimized_xyz, expected_xyz)
        assert loss_track == [-100.0]


@pytest.fixture
def mock_optimizer():
    """"""Creates a mock LBFGS optimizer.""""""
    mock_opt = MagicMock()
    mock_opt.step.return_value = torch.tensor(-10.0)
    return mock_opt


@patch(""torch.optim.LBFGS"")
def test_find_rigidbody_matrix_lbfgs_quat(mock_lbfgs_class, mock_setup, mock_optimizer):
    """"""Test find_rigidbody_matrix_lbfgs_quat function.""""""
    xyz, cra_name, llgloss = mock_setup

    # Make just 2 points for simplicity
    xyz = xyz[:2]
    device = ""cpu""

    # Create domain booleans
    domain_bools = [np.array([True, True])]

    # Configure the mock optimizer
    mock_lbfgs_class.return_value = mock_optimizer

    # Call the function
    trans_vecs, qs, loss_track = find_rigidbody_matrix_lbfgs_quat(
        llgloss=llgloss,
        xyz=xyz,
        device=device,
        domain_bools=domain_bools,
        lbfgs_lr=200.0,
        verbose=False,
    )

    # Check that the LBFGS optimizer was created with correct parameters
    mock_lbfgs_class.assert_called_once()
    args, kwargs = mock_lbfgs_class.call_args
    assert len(args[0]) == 2  # qs + trans_vecs
    assert kwargs[""lr""] == 200.0
    assert kwargs[""line_search_fn""] == ""strong_wolfe""

    # Check that the optimizer step was called 15 times (default n_steps)
    assert mock_optimizer.step.call_count == 15

    # Check return values
    assert len(trans_vecs) == 1
    assert len(qs) == 1
    assert len(loss_track) == 15
    assert all(x == -10.0 for x in loss_track)

    # Check that q is initialized as identity quaternion
    assert torch.allclose(qs[0], torch.tensor([1.0, 0.0, 0.0, 0.0], device=device))

    # Check that translation vector is initialized as zeros
    assert torch.allclose(trans_vecs[0], torch.tensor([0.0, 0.0, 0.0], device=device))


def test_pose_train_lbfgs_quat():
    """"""Test pose_train_lbfgs_quat function with mock optimizer.""""""

    # Create mock data
    llgloss = MagicMock()
    llgloss.return_value = torch.tensor(-5.0)

    # Create quaternions and translation vectors
    qs = [torch.tensor([1.0, 0.0, 0.0, 0.0], requires_grad=True)]
    trans_vecs = [torch.tensor([0.0, 0.0, 0.0], requires_grad=True)]

    # Mock coordinates
    xyz = torch.tensor(
        [
            [1.0, 2.0, 3.0],
            [2.0, 3.0, 4.0],
            [3.0, 4.0, 5.0],
        ],
        dtype=torch.float32,
    )

    # Mock domain booleans
    domain_bools = [np.array([True, True, True])]

    # Use patch to mock the optimizer
    with patch(""torch.optim.LBFGS"") as mock_lbfgs_class:
        # Create a mock optimizer that returns a specified loss
        mock_optimizer = MagicMock()
        mock_optimizer.step.return_value = torch.tensor(-5.0)
        mock_lbfgs_class.return_value = mock_optimizer

        # Call the function with few steps to speed up test
        loss_track = pose_train_lbfgs_quat(
            llgloss=llgloss,
            qs=qs,
            trans_vecs=trans_vecs,
            xyz=xyz,
            domain_bools=domain_bools,
            lr=100.0,
            n_steps=3,
            verbose=False,
        )

        # Check that optimizer was created with correct parameters
        mock_lbfgs_class.assert_called_once()
        args, kwargs = mock_lbfgs_class.call_args
        assert args[0] == qs + trans_vecs
        assert kwargs[""lr""] == 100.0

        # Check that optimizer.step was called the expected number of times
        assert mock_optimizer.step.call_count == 3

        # Check the loss track
        assert len(loss_track) == 3
        assert all(x == -5.0 for x in loss_track)


def test_pose_train_lbfgs_quat_with_added_chains():
    """"""Test pose_train_lbfgs_quat function properly passes added chains to llgloss.""""""
    import torch

    from rocket.coordinates import pose_train_lbfgs_quat

    # Track calls to llgloss
    call_args_list = []

    # Create a mock llgloss that records its arguments and returns a tensor that requires gradients  # noqa: E501
    def mock_llgloss_function(
        xyz, added_chain_HKL=None, added_chain_asu=None, **kwargs
    ):
        call_args_list.append({
            ""xyz"": xyz,
            ""added_chain_HKL"": added_chain_HKL,
            ""added_chain_asu"": added_chain_asu,
            ""kwargs"": kwargs,
        })
        # Create a tensor that requires gradients
        result = torch.tensor(-5.0, requires_grad=True)
        return result

    llgloss = MagicMock(side_effect=mock_llgloss_function)

    # Create quaternions and translation vectors
    qs = [torch.tensor([1.0, 0.0, 0.0, 0.0], requires_grad=True)]
    trans_vecs = [torch.tensor([0.0, 0.0, 0.0], requires_grad=True)]

    # Mock coordinates
    xyz = torch.tensor(
        [
            [1.0, 2.0, 3.0],
            [2.0, 3.0, 4.0],
            [3.0, 4.0, 5.0],
        ],
        dtype=torch.float32,
    )

    # Mock domain booleans
    domain_bools = [np.array([True, True, True])]

    # Create added chain tensors
    added_chain_HKL = torch.tensor([1.0, 2.0, 3.0])
    added_chain_asu = torch.tensor([4.0, 5.0, 6.0])

    # Use patch to mock the optimizer with a simpler approach
    with patch(""torch.optim.LBFGS"") as mock_lbfgs_class:
        # Create a mock optimizer that just returns a fixed loss value when step is called  # noqa: E501
        mock_optimizer = MagicMock()

        # Make step actually call the closure function once and return its value
        def mock_step(closure):
            return closure()

        mock_optimizer.step.side_effect = mock_step
        mock_lbfgs_class.return_value = mock_optimizer

        # Call the function with just 1 step
        loss_track = pose_train_lbfgs_quat(
            llgloss=llgloss,
            qs=qs,
            trans_vecs=trans_vecs,
            xyz=xyz,
            domain_bools=domain_bools,
            lr=100.0,
            n_steps=1,  # Use a single step for simplicity
            added_chain_HKL=added_chain_HKL,
            added_chain_asu=added_chain_asu,
            verbose=False,
        )

        # Verify that llgloss was called at least once
        assert len(call_args_list) > 0

        # Verify that the most recent call included the added_chain parameters
        last_call = call_args_list[-1]
        assert torch.equal(last_call[""added_chain_HKL""], added_chain_HKL)
        assert torch.equal(last_call[""added_chain_asu""], added_chain_asu)

        # Check the loss track has expected values
        assert len(loss_track) == 1
        assert loss_track[0] == 5.0


def test_quaternions_to_SO3_identity():
    """"""Test quaternions_to_SO3 with identity quaternion.""""""

    # Identity quaternion [r,i,j,k] = [1,0,0,0]
    q = torch.tensor([1.0, 0.0, 0.0, 0.0])

    # Apply the function
    rotation_matrix = quaternions_to_SO3(q)

    # Check that the result is the identity matrix
    expected = torch.eye(3)
    assert torch.allclose(rotation_matrix, expected)


def test_quaternions_to_SO3_rotation():
    """"""Test quaternions_to_SO3 with a 90-degree rotation around z-axis.""""""

    # Quaternion for 90-degree rotation around z-axis
    # [r,i,j,k] = [cos(45°),0,0,sin(45°)]
    # 45° is half of the desired rotation angle
    import math

    angle = math.pi / 4  # 45 degrees
    q = torch.tensor([math.cos(angle), 0.0, 0.0, math.sin(angle)])

    # Apply the function
    rotation_matrix = quaternions_to_SO3(q)

    # Expected: 90-degree rotation around z-axis
    expected = torch.tensor([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])

    assert torch.allclose(rotation_matrix, expected, atol=1e-6)


def test_quaternions_to_SO3_normalization():
    """"""Test that quaternions_to_SO3 properly normalizes the quaternion.""""""
    from rocket.coordinates import quaternions_to_SO3

    # Non-normalized quaternion
    q = torch.tensor([2.0, 0.0, 0.0, 0.0])

    # Apply the function
    rotation_matrix = quaternions_to_SO3(q)

    # Should still produce the identity matrix
    expected = torch.eye(3)
    assert torch.allclose(rotation_matrix, expected)


def test_quaternions_to_SO3_batch():
    """"""Test quaternions_to_SO3 with batched quaternions.""""""
    from rocket.coordinates import quaternions_to_SO3

    # Batch of quaternions: identity and 180-degree rotation around x-axis
    qs = torch.tensor([
        [1.0, 0.0, 0.0, 0.0],  # identity
        [0.0, 1.0, 0.0, 0.0],  # 180° around x
    ])

    # Apply the function
    rotation_matrices = quaternions_to_SO3(qs)

    # Expected matrices
    expected = torch.tensor([
        [  # Identity
            [1.0, 0.0, 0.0],
            [0.0, 1.0, 0.0],
            [0.0, 0.0, 1.0],
        ],
        [  # 180° around x
            [1.0, 0.0, 0.0],
            [0.0, -1.0, 0.0],
            [0.0, 0.0, -1.0],
        ],
    ])

    assert torch.allclose(rotation_matrices, expected)


def test_construct_SO3():
    """"""Test construct_SO3 function.""""""

    # Test with orthogonal vectors
    v1 = torch.tensor([1.0, 0.0, 0.0])
    v2 = torch.tensor([0.0, 1.0, 0.0])

    R = construct_SO3(v1, v2)

    # Should be identity matrix
    expected = torch.eye(3)
    assert torch.allclose(R, expected)

    # Test with non-orthogonal vectors
    v1 = torch.tensor([1.0, 1.0, 0.0])
    v2 = torch.tensor([0.0, 1.0, 1.0])

    R = construct_SO3(v1, v2)

    # Check that R is orthogonal (R^T * R = I)
    identity = torch.matmul(R.T, R)
    assert torch.allclose(identity, torch.eye(3), atol=1e-6)

    # Check that det(R) = 1 (proper rotation)
    det = torch.det(R)
    assert torch.isclose(det, torch.tensor(1.0))


def test_decompose_SO3():
    """"""Test decompose_SO3 function.""""""

    # Create a rotation matrix
    theta = torch.tensor(0.5)  # some angle
    c, s = torch.cos(theta), torch.sin(theta)
    R = torch.tensor([[c, -s, 0], [s, c, 0], [0, 0, 1]])

    # Decompose it
    v1, v2 = decompose_SO3(R)

    # Reconstruct the rotation matrix
    R_recon = construct_SO3(v1, v2)

    # Check they match
    assert torch.allclose(R, R_recon, atol=1e-6)

    # Test with custom a, b, c parameters
    v1, v2 = decompose_SO3(R, a=2.0, b=1.5, c=0.5)

    # The reconstructed matrix should still be the same
    R_recon = construct_SO3(v1, v2)
    assert torch.allclose(R, R_recon, atol=1e-6)

    # Test exception with c=0
    with pytest.raises(AssertionError):
        decompose_SO3(R, c=0.0)


def test_select_CA_elements():
    """"""Test select_CA_elements function.""""""

    # Test with mixed atom types
    cra_names = [""A-1-GLY-CA"", ""A-1-GLY-N"", ""A-2-ALA-CA"", ""A-2-ALA-CB""]

    result = select_CA_elements(cra_names)

    expected = [True, False, True, False]
    assert result == expected


def test_select_CA_from_craname():
    """"""Test select_CA_from_craname function.""""""

    # Test with mixed atom types
    cra_names = [""A-1-GLY-CA"", ""A-1-GLY-N"", ""A-2-ALA-CA"", ""A-2-ALA-CB""]

    ca_list, mask = select_CA_from_craname(cra_names)

    expected_list = [""A-1-GLY-CA"", ""A-2-ALA-CA""]
    expected_mask = [True, False, True, False]

    assert ca_list == expected_list
    assert mask == expected_mask


def test_calculate_mse_loss_per_residue():
    """"""Test calculate_mse_loss_per_residue function.""""""

    # Create two tensors with atom coordinates
    tensor1 = torch.tensor([
        [1.0, 1.0, 1.0],  # residue 1
        [2.0, 2.0, 2.0],  # residue 1
        [3.0, 3.0, 3.0],  # residue 2
        [4.0, 4.0, 4.0],  # residue 3
    ])

    tensor2 = torch.tensor([
        [1.1, 1.1, 1.1],  # residue 1
        [2.1, 2.1, 2.1],  # residue 1
        [3.1, 3.1, 3.1],  # residue 2
        [4.1, 4.1, 4.1],  # residue 3
    ])

    # Residue numbers
    residue_numbers = [1, 1, 2, 3]

    # Calculate MSE loss per residue
    mse_losses = calculate_mse_loss_per_residue(tensor1, tensor2, residue_numbers)

    # Expected results:
    # Residue 1: sqrt(mean(sum((1,1,1)-(1.1,1.1,1.1))^2 + sum((2,2,2)-(2.1,2.1,2.1))^2)) = sqrt(3*0.1^2 + 3*0.1^2) = sqrt(0.06)  # noqa: E501
    # Residue 2: sqrt(sum((3,3,3)-(3.1,3.1,3.1))^2) = sqrt(0.03)
    # Residue 3: sqrt(sum((4,4,4)-(4.1,4.1,4.1))^2) = sqrt(0.03)

    expected = [
        np.sqrt(0.06),  # residue 1 with 2 atoms
        np.sqrt(0.03),  # residue 2 with 1 atom
        np.sqrt(0.03),  # residue 3 with 1 atom
    ]

    assert len(mse_losses) == 3
    for actual, expecte in zip(mse_losses, expected, strict=False):
        assert np.isclose(actual, expecte)


@patch(""builtins.open"", new_callable=MagicMock)
def test_write_pdb_with_positions(mock_open):
    """"""Test write_pdb_with_positions function.""""""

    # Create mock file handles
    mock_file_in = MagicMock()
    mock_file_out = MagicMock()

    # Configure mock_open to return different file handles for input and output
    mock_open.side_effect = [mock_file_in, mock_file_out]

    # Set up mock file content
    mock_file_in.__enter__.return_value.readlines = lambda: [
        ""HEADER    TEST FILE\n"",
        ""ATOM      1  N   ALA A   1      10.000  20.000  30.000  1.00 10.00           N\n"",  # noqa: E501
        ""ATOM      2  CA  ALA A   1      11.000  21.000  31.000  1.00 10.00           C\n"",  # noqa: E501
        ""TER\n"",
    ]
    mock_file_in.__enter__.return_value.__iter__ = lambda self: iter(self.readlines())

    # Positions to write
    positions = [[12.345, 23.456, 34.567], [45.678, 56.789, 67.890]]

    # Call function
    write_pdb_with_positions(""input.pdb"", positions, ""output.pdb"")

    # Check that files were opened correctly
    mock_open.assert_any_call(""input.pdb"")
    mock_open.assert_any_call(""output.pdb"", ""w"")

    # Check that write was called with correct content
    calls = mock_file_out.__enter__.return_value.write.call_args_list

    # First line should be header (unchanged)
    assert calls[0][0][0] == ""HEADER    TEST FILE\n""

    # Second line should be first ATOM with updated coordinates
    expected_line1 = ""ATOM      1  N   ALA A   1      12.345  23.456  34.567  1.00 10.00           N\n""  # noqa: E501
    assert calls[1][0][0] == expected_line1

    # Third line should be second ATOM with updated coordinates
    expected_line2 = ""ATOM      2  CA  ALA A   1      45.678  56.789  67.890  1.00 10.00           C\n""  # noqa: E501
    assert calls[2][0][0] == expected_line2

    # Fourth line should be TER (unchanged)
    assert calls[3][0][0] == ""TER\n""


@pytest.fixture
def mock_unit_cell():
    """"""Create a mock unit cell with known fractionalization matrix.""""""
    mock_cell = MagicMock()
    # Create a simple fractionalization matrix
    # This matrix converts from orthogonal to fractional coordinates
    mock_cell.fractionalization_matrix = np.array([
        [0.1, 0.0, 0.0],  # a* = 0.1
        [0.0, 0.1, 0.0],  # b* = 0.1
        [0.0, 0.0, 0.1],  # c* = 0.1
    ])
    return mock_cell


def test_fractionalize_torch_simple_matrix(mock_unit_cell):
    """"""Test fractionalize_torch with a simple fractionalization matrix.""""""

    # Create a simple set of orthogonal coordinates
    atom_pos_orth = torch.tensor([
        [10.0, 0.0, 0.0],  # 10 Å along x-axis
        [0.0, 20.0, 0.0],  # 20 Å along y-axis
        [0.0, 0.0, 30.0],  # 30 Å along z-axis
        [5.0, 5.0, 5.0],  # 5 Å along each axis
    ])

    # Call the function
    atom_pos_frac = fractionalize_torch(atom_pos_orth, mock_unit_cell, device=""cpu"")

    # With our simple fractionalization matrix (0.1 on diagonal),
    # orthogonal coordinates are simply multiplied by 0.1
    expected_frac = torch.tensor([
        [1.0, 0.0, 0.0],  # 10 * 0.1 = 1.0 along a
        [0.0, 2.0, 0.0],  # 20 * 0.1 = 2.0 along b
        [0.0, 0.0, 3.0],  # 30 * 0.1 = 3.0 along c
        [0.5, 0.5, 0.5],  # 5 * 0.1 = 0.5 along each axis
    ])

    assert torch.allclose(atom_pos_frac, expected_frac)


def test_extract_allatoms_with_mocking():
    """"""Test extract_allatoms with mocked residue_constants.""""""

    # Create minimal test data
    cra_name_sfc = [
        ""A-0-GLY-N"",
        ""A-0-GLY-CA"",
        ""A-0-GLY-C"",
        ""A-0-GLY-O"",
        ""A-1-ALA-N"",
        ""A-1-ALA-CA"",
        ""A-1-ALA-C"",
        ""A-1-ALA-O"",
        ""A-1-ALA-CB"",
    ]

    # Let's see key parts of the function
    # 1. It expects residue_constants.atom_types to be a list of atom names
    # 2. It expects feats[""aatype""] to contain residue type indices
    # 3. It expects outputs[""final_atom_mask""] to be [n_res, N_atom_types]
    # 4. It expects outputs[""final_atom_positions""] to be [n_res, N_atom_types, 3]

    # We'll patch more of the function's internal calls to isolate the test

    with (
        patch(
            ""rocket.coordinates.residue_constants.atom_types"",
            [""N"", ""CA"", ""C"", ""O"", ""CB""],
        ),
        patch(
            ""rocket.coordinates.residue_constants.restype_1to3"",
            {0: ""GLY"", 1: ""ALA"", -1: ""UNK""},
        ),
    ):  # noqa: E501
        # Create expected outputs for different stages of the function
        atom_type_indices = {  # noqa: F841
            ""N"": 0,
            ""CA"": 1,
            ""C"": 2,
            ""O"": 3,
            ""CB"": 4,
        }

        # Mock atom_positions that will be returned at the end
        expected_positions = torch.tensor([
            [1.0, 2.0, 3.0],  # GLY-N
            [2.0, 3.0, 4.0],  # GLY-CA
            [3.0, 4.0, 5.0],  # GLY-C
            [4.0, 5.0, 6.0],  # GLY-O
            [5.0, 6.0, 7.0],  # ALA-N
            [6.0, 7.0, 8.0],  # ALA-CA
            [7.0, 8.0, 9.0],  # ALA-C
            [8.0, 9.0, 10.0],  # ALA-O
            [9.0, 10.0, 11.0],  # ALA-CB
        ])

        # Mock plddt that will be returned
        expected_plddt = torch.tensor([
            90.0,
            90.0,
            90.0,
            90.0,
            80.0,
            80.0,
            80.0,
            80.0,
            80.0,
        ])  # noqa: E501

        # Now instead of mocking np.char.add, we'll mock the whole extraction process
        with (
            patch(""torch.cat"", return_value=expected_positions),
            patch(""torch.repeat_interleave"", return_value=expected_plddt),
        ):
            # Create minimal outputs and feats that will pass through the mocked parts
            outputs = {
                ""final_atom_mask"": torch.tensor([
                    [1.0, 1.0, 1.0, 1.0, 0.0],  # GLY
                    [1.0, 1.0, 1.0, 1.0, 1.0],  # ALA
                ]),  # Just needs correct shape
                ""final_atom_positions"": torch.concat(
                    [
                        expected_positions[:5].unsqueeze(0),
                        expected_positions[4:].unsqueeze(0),
                    ],
                    axis=0,
                ),  # Will be mocked
                ""plddt"": torch.tensor([90.0, 80.0]),  # Per-residue scores
            }
            feats = {""aatype"": torch.tensor([[0], [1]])}  # GLY and ALA

            # Call the function
            positions, plddt = extract_allatoms(outputs, feats, cra_name_sfc)

    # Verify the results
    assert torch.allclose(positions, expected_positions)
    assert torch.allclose(plddt, expected_plddt)


def test_extract_atoms_and_backbone():
    """"""Test extract_atoms_and_backbone function with minimal mocking.""""""

    # Create mock inputs
    outputs = {
        ""final_atom_mask"": torch.tensor([
            # First residue (GLY) - only backbone atoms
            [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0],
            # Second residue (ALA) - backbone + CB
            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
        ]),
        ""final_atom_positions"": torch.tensor([
            # GLY residue
            [
                [1.0, 2.0, 3.0],  # N
                [2.0, 3.0, 4.0],  # CA
                [3.0, 4.0, 5.0],  # C
                [4.0, 5.0, 6.0],  # O
                [0.0, 0.0, 0.0],  # CB (not present in GLY)
                [0.0, 0.0, 0.0],  # CG (not present)
                [0.0, 0.0, 0.0],  # CD (not present)
            ],
            # ALA residue
            [
                [5.0, 6.0, 7.0],  # N
                [6.0, 7.0, 8.0],  # CA
                [7.0, 8.0, 9.0],  # C
                [8.0, 9.0, 10.0],  # O
                [9.0, 10.0, 11.0],  # CB
                [0.0, 0.0, 0.0],  # CG (not present in ALA)
                [0.0, 0.0, 0.0],  # CD (not present)
            ],
        ]),
    }

    feats = {
        ""aatype"": torch.tensor([[0], [1]])  # GLY and ALA
    }

    # Mock residue_constants.atom_types
    with patch(
        ""rocket.coordinates.residue_constants.atom_types"",
        [""N"", ""CA"", ""C"", ""O"", ""CB"", ""CG"", ""CD""],
    ):
        # Call the function
        positions, backbone_mask = extract_atoms_and_backbone(outputs, feats)

    # Expected positions - Only atoms with mask=1 should be included
    expected_positions = torch.tensor([
        [1.0, 2.0, 3.0],  # GLY-N
        [2.0, 3.0, 4.0],  # GLY-CA
        [3.0, 4.0, 5.0],  # GLY-C
        [4.0, 5.0, 6.0],  # GLY-O
        [5.0, 6.0, 7.0],  # ALA-N
        [6.0, 7.0, 8.0],  # ALA-CA
        [7.0, 8.0, 9.0],  # ALA-C
        [8.0, 9.0, 10.0],  # ALA-O
        [9.0, 10.0, 11.0],  # ALA-CB
    ])

    # Expected backbone mask - True for N, CA, C, O atoms
    expected_backbone_mask = torch.tensor(
        [
            True,  # GLY-N
            True,  # GLY-CA
            True,  # GLY-C
            True,  # GLY-O
            True,  # ALA-N
            True,  # ALA-CA
            True,  # ALA-C
            True,  # ALA-O
            False,  # ALA-CB
        ],
        dtype=torch.bool,
    )

    # Check results
    assert torch.equal(positions, expected_positions)
    assert torch.equal(backbone_mask, expected_backbone_mask)


def test_extract_atoms_and_backbone_all_atoms():
    """"""Test extract_atoms_and_backbone with all atom types present.""""""
    # Create mock inputs with a more complex amino acid (e.g., LYS)
    outputs = {
        ""final_atom_mask"": torch.tensor([
            # LYS residue with all atoms
            [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
        ]),
        ""final_atom_positions"": torch.tensor([
            # LYS residue
            [
                [1.0, 1.0, 1.0],  # N
                [2.0, 2.0, 2.0],  # CA
                [3.0, 3.0, 3.0],  # C
                [4.0, 4.0, 4.0],  # O
                [5.0, 5.0, 5.0],  # CB
                [6.0, 6.0, 6.0],  # CG
                [7.0, 7.0, 7.0],  # CD
            ]
        ]),
    }

    feats = {
        ""aatype"": torch.tensor([[3]])  # LYS (arbitrary residue type)
    }

    # Mock residue_constants.atom_types
    with patch(
        ""rocket.coordinates.residue_constants.atom_types"",
        [""N"", ""CA"", ""C"", ""O"", ""CB"", ""CG"", ""CD""],
    ):
        # Call the function
        positions, backbone_mask = extract_atoms_and_backbone(outputs, feats)

    # Expected positions - All atoms should be included
    expected_positions = torch.tensor([
        [1.0, 1.0, 1.0],  # N
        [2.0, 2.0, 2.0],  # CA
        [3.0, 3.0, 3.0],  # C
        [4.0, 4.0, 4.0],  # O
        [5.0, 5.0, 5.0],  # CB
        [6.0, 6.0, 6.0],  # CG
        [7.0, 7.0, 7.0],  # CD
    ])

    # Expected backbone mask - True for N, CA, C, O atoms
    expected_backbone_mask = torch.tensor(
        [
            True,  # N
            True,  # CA
            True,  # C
            True,  # O
            False,  # CB
            False,  # CG
            False,  # CD
        ],
        dtype=torch.bool,
    )

    # Check results
    assert torch.equal(positions, expected_positions)
    assert torch.equal(backbone_mask, expected_backbone_mask)


def test_extract_bfactors():
    """"""Test the extract_bfactors function.""""""

    # Create a mock protein structure
    class MockProtein:
        def __init__(self):
            # Create atom mask: 2 residues with different numbers of atoms
            self.atom_mask = np.array([
                [1.0, 1.0, 1.0, 1.0, 0.0],  # First residue has 4 atoms
                [1.0, 1.0, 1.0, 1.0, 1.0],  # Second residue has 5 atoms
            ])

            # Create aatype (residue types)
            self.aatype = np.array([
                0,  # GLY
                1,  # ALA
            ])

            # Create b-factors: assign different values to each atom
            self.b_factors = np.array([
                [10.0, 15.0, 20.0, 25.0, 0.0],  # First residue B-factors
                [30.0, 35.0, 40.0, 45.0, 50.0],  # Second residue B-factors
            ])

    # Create the mock protein
    mock_prot = MockProtein()

    # Call the function
    b_factor_array = extract_bfactors(mock_prot)

    # Expected B-factors (only for atoms with mask >= 0.5)
    expected_b_factors = np.array([
        10.0,  # First residue, first atom
        15.0,  # First residue, second atom
        20.0,  # First residue, third atom
        25.0,  # First residue, fourth atom
        30.0,  # Second residue, first atom
        35.0,  # Second residue, second atom
        40.0,  # Second residue, third atom
        45.0,  # Second residue, fourth atom
        50.0,  # Second residue, fifth atom
    ])

    # Check results
    assert np.array_equal(b_factor_array, expected_b_factors)
    assert b_factor_array.shape == (9,)


def test_extract_bfactors_with_masked_atoms():
    """"""Test extract_bfactors with partially masked atoms.""""""

    # Create a mock protein structure with some atoms masked out
    class MockProtein:
        def __init__(self):
            # Create atom mask with some atoms masked out
            self.atom_mask = np.array([
                [1.0, 0.4, 1.0, 1.0, 0.0],  # Second atom is masked out (below 0.5)
                [1.0, 1.0, 0.3, 1.0, 1.0],  # Third atom is masked out (below 0.5)
            ])

            # Create aatype (residue types)
            self.aatype = np.array([
                0,  # GLY
                1,  # ALA
            ])

            # Create b-factors
            self.b_factors = np.array([
                [10.0, 15.0, 20.0, 25.0, 0.0],  # First residue B-factors
                [30.0, 35.0, 40.0, 45.0, 50.0],  # Second residue B-factors
            ])

    # Create the mock protein
    mock_prot = MockProtein()

    # Call the function
    b_factor_array = extract_bfactors(mock_prot)

    # Expected B-factors (only for atoms with mask >= 0.5)
    # Note: second atom of first residue and third atom of second residue are masked out
    expected_b_factors = np.array([
        10.0,  # First residue, first atom
        # 15.0 is skipped (masked out)
        20.0,  # First residue, third atom
        25.0,  # First residue, fourth atom
        30.0,  # Second residue, first atom
        35.0,  # Second residue, second atom
        # 40.0 is skipped (masked out)
        45.0,  # Second residue, fourth atom
        50.0,  # Second residue, fifth atom
    ])

    # Check results
    assert np.array_equal(b_factor_array, expected_b_factors)
    assert b_factor_array.shape == (7,)  # 9 total atoms minus 2 masked atoms


def test_kabsch_align_matrices_identity():
    """"""Test kabsch_align_matrices with identical tensors.""""""

    # Create identical tensors (a simple cube)
    tensor1 = torch.tensor([
        [0.0, 0.0, 0.0],  # Origin
        [1.0, 0.0, 0.0],  # X-axis point
        [0.0, 1.0, 0.0],  # Y-axis point
        [0.0, 0.0, 1.0],  # Z-axis point
        [1.0, 1.0, 1.0],  # Diagonal point
    ])
    tensor2 = tensor1.clone()

    # Apply the function
    centroid1, centroid2, rotation_matrix = kabsch_align_matrices(tensor1, tensor2)

    # Expected results:
    # - Centroids should be the same
    # - Rotation matrix should be identity since tensors are identical
    expected_centroid = torch.tensor([[0.4, 0.4, 0.4]])
    expected_rotation = torch.eye(3)

    # Check results
    assert torch.allclose(centroid1, expected_centroid)
    assert torch.allclose(centroid2, expected_centroid)
    assert torch.allclose(rotation_matrix, expected_rotation, atol=1e-5)


def test_kabsch_align_matrices_translation():
    """"""Test kabsch_align_matrices with translated tensors.""""""

    # Create two tensors with the same shape but different positions
    # The second tensor is shifted by [1, 2, 3]
    tensor1 = torch.tensor([
        [0.0, 0.0, 0.0],
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
    ])

    translation = torch.tensor([1.0, 2.0, 3.0])
    tensor2 = tensor1 + translation

    # Apply the function
    centroid1, centroid2, rotation_matrix = kabsch_align_matrices(tensor1, tensor2)

    # Expected results:
    # - Centroids should reflect the translation
    # - Rotation matrix should be identity since there's only translation
    expected_centroid1 = torch.tensor([[0.4, 0.4, 0.4]])
    expected_centroid2 = expected_centroid1 + translation
    expected_rotation = torch.eye(3)

    # Check results
    assert torch.allclose(centroid1, expected_centroid1)
    assert torch.allclose(centroid2, expected_centroid2)
    assert torch.allclose(rotation_matrix, expected_rotation, atol=1e-5)


def test_kabsch_align_matrices_rotation():
    """"""Test kabsch_align_matrices with rotated tensors.""""""

    # Create a tensor representing a simple structure
    tensor1 = torch.tensor(
        [
            [0.0, 0.0, 0.0],
            [1.0, 0.0, 0.0],
            [0.0, 1.0, 0.0],
            [0.0, 0.0, 1.0],
            [1.0, 1.0, 1.0],
        ],
        dtype=torch.float32,
    )

    # Create a rotation matrix (90 degrees around Z axis)
    angle = np.pi / 2  # 90 degrees
    rotation = torch.tensor(
        [
            [np.cos(angle), -np.sin(angle), 0],
            [np.sin(angle), np.cos(angle), 0],
            [0, 0, 1],
        ],
        dtype=torch.float32,
    )

    # Apply rotation to create tensor2
    tensor2 = torch.matmul(tensor1, rotation.T)

    # Apply the function
    centroid1, centroid2, calculated_rotation = kabsch_align_matrices(tensor1, tensor2)

    # Expected results:
    # - Centroids should be the same in magnitude (the origin point is moved)
    # - Rotation matrix should match the original rotation

    # Check that the calculated rotation is close to the expected rotation
    # Note: The calculated rotation will align tensor1 to tensor2, so it should be
    # the transpose of the original rotation
    assert torch.allclose(calculated_rotation, rotation, atol=1e-5)

    # Check that the centroids are at the same distance from origin
    assert torch.allclose(torch.norm(centroid1), torch.norm(centroid2))


def test_kabsch_align_matrices_complex():
    """"""Test kabsch_align_matrices with a more complex transformation.""""""

    # Create a tensor representing a protein backbone fragment
    tensor1 = torch.tensor(
        [
            [0.0, 0.0, 0.0],  # N
            [1.5, 0.0, 0.0],  # CA
            [2.5, 1.2, 0.0],  # C
            [3.5, 1.5, 1.0],  # N
            [4.0, 2.5, 1.5],  # CA
        ],
        dtype=torch.float32,
    )

    # Create a transformation: rotation + translation
    angle = np.pi / 4  # 45 degrees
    rotation = torch.tensor(
        [
            [np.cos(angle), -np.sin(angle), 0],
            [np.sin(angle), np.cos(angle), 0],
            [0, 0, 1],
        ],
        dtype=torch.float32,
    )
    translation = torch.tensor([5.0, -3.0, 2.0])

    # Apply transformation manually
    centroid1_manual = torch.mean(tensor1, dim=0, keepdim=True)
    tensor1_centered = tensor1 - centroid1_manual
    tensor2 = (
        torch.matmul(tensor1_centered, rotation.T) + centroid1_manual + translation
    )  # noqa: E501

    # Apply the function
    centroid1, centroid2, calculated_rotation = kabsch_align_matrices(tensor1, tensor2)

    # Apply the calculated transformation to tensor1
    aligned_tensor1 = align_tensors(tensor1, centroid1, centroid2, calculated_rotation)

    # Check that the aligned tensor matches tensor2
    assert torch.allclose(aligned_tensor1, tensor2, atol=1e-6)

    # Check that the rotation matrix is orthogonal (R^T R = I)
    identity = torch.matmul(calculated_rotation.T, calculated_rotation)
    assert torch.allclose(identity, torch.eye(3), atol=1e-6)

    # Check that the determinant is 1 (proper rotation, not a reflection)
    assert torch.allclose(torch.det(calculated_rotation), torch.tensor(1.0))


def test_weighted_kabsch_basic():
    """"""Test weighted_kabsch function with mocked dependencies.""""""

    # Create test inputs
    moving_tensor = torch.tensor([
        [1.0, 2.0, 3.0],  # Atom 1
        [2.0, 3.0, 4.0],  # Atom 2
        [3.0, 4.0, 5.0],  # Atom 3
        [4.0, 5.0, 6.0],  # Atom 4
    ])

    ref_tensor = torch.tensor([
        [2.0, 3.0, 4.0],  # Atom 1 (moved)
        [3.0, 4.0, 5.0],  # Atom 2 (moved)
        [4.0, 5.0, 6.0],  # Atom 3 (moved)
        [5.0, 6.0, 7.0],  # Atom 4 (moved)
    ])

    # Create cra_name list with backbone atoms
    cra_name = [
        ""A-1-GLY-N"",
        ""A-2-ALA-CA"",
        ""A-3-VAL-C"",
        ""A-4-LEU-O"",
    ]

    # Create a mock Rotation class
    class MockRotation:
        def as_matrix(self):
            # Identity rotation
            return np.eye(3)

        @classmethod
        def align_vectors(cls, target, source, weights=None):
            # Return the mock rotation and a dummy residual
            return (MockRotation(), 0.0)

    # Expected result to return from align_tensors
    expected_aligned = torch.tensor([
        [2.0, 3.0, 4.0],  # Aligned with reference
        [3.0, 4.0, 5.0],
        [4.0, 5.0, 6.0],
        [5.0, 6.0, 7.0],
    ])

    # Patch both Rotation class and align_tensors function
    with (
        patch(""rocket.coordinates.Rotation"", MockRotation),
        patch(""rocket.coordinates.align_tensors"", return_value=expected_aligned),
    ):
        # Call the function
        aligned_tensor = weighted_kabsch(moving_tensor, ref_tensor, cra_name)

    # Check that we got the expected tensor back
    assert torch.equal(aligned_tensor, expected_aligned)
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_xtal_utils.py",".py","5044","179","import numpy as np
import torch

from rocket.xtal.utils import (
    Ak_approx,
    compute_sigmaA_true,
    find_bin_dHKL,
    interpolate_smooth,
    lb_Ak,
    llgIa_calculate,
    llgIa_firstdev,
    llgIa_seconddev,
    llgIc_calculate,
    llgIc_firstdev,
    llgIc_seconddev,
    llgItot_calculate,
    llgItot_firstdev,
    llgItot_with_derivatives2sigmaA,
    logcosh,
    newton_step,
    ub_Ak,
)


def test_newton_step_identity():
    x = torch.tensor([1.0, 2.0])
    grad = torch.tensor([0.1, -0.2])
    Hessian = torch.eye(2)
    result = newton_step(x, grad, Hessian)
    expected = x - grad
    assert torch.allclose(result, expected, atol=1e-6)


def test_newton_step_zero_grad():
    x = torch.tensor([1.0, 2.0])
    grad = torch.zeros(2)
    Hessian = torch.eye(2)
    result = newton_step(x, grad, Hessian)
    assert torch.allclose(result, x)


def test_llgIa_firstdev_simple():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    result = llgIa_firstdev(sA, d, Eeff, Ec)
    assert torch.isfinite(result).all()


def test_llgIc_firstdev_simple():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    result = llgIc_firstdev(sA, d, Eeff, Ec)
    assert torch.isfinite(result).all()


def test_llgItot_firstdev_mix():
    sigmaA = torch.tensor([0.5, 0.6])
    d = torch.tensor([1.0, 1.0])
    Eeff = torch.tensor([1.0, 1.0])
    Ec = torch.tensor([1.0, 1.0])
    centric = torch.tensor([True, False])
    total = llgItot_firstdev(sigmaA, d, Eeff, Ec, centric)
    part_c = llgIc_firstdev(sigmaA, d[centric], Eeff[centric], Ec[centric])
    part_a = llgIa_firstdev(sigmaA, d[~centric], Eeff[~centric], Ec[~centric])
    expected = part_c.sum() + part_a.sum()
    assert torch.allclose(total, expected)


def test_llgIa_seconddev_finite():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    out = llgIa_seconddev(sA, d, Eeff, Ec)
    assert torch.isfinite(out).all()


def test_llgIc_seconddev_finite():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    out = llgIc_seconddev(sA, d, Eeff, Ec)
    assert torch.isfinite(out).all()


def test_interpolate_smooth_linear_zero():
    sigmaAs = torch.tensor([1.0, 2.0, 3.0, 4.0])
    out = interpolate_smooth(sigmaAs)
    assert out >= 0


def test_llgItot_with_derivatives2sigmaA_matches_autodiff():
    sigmaA = torch.tensor([0.5], requires_grad=True)
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    centric = torch.tensor([False])
    l1, lp1, lpp1 = llgItot_with_derivatives2sigmaA(
        sigmaA, d, Eeff, Ec, centric, method=""autodiff""
    )
    l2, lp2, lpp2 = llgItot_with_derivatives2sigmaA(
        sigmaA, d, Eeff, Ec, centric, method=""analytical""
    )
    print((lp1 - lp2).abs())
    assert torch.allclose(l1, l2, atol=1e-5)
    assert torch.allclose(lp1, lp2, atol=1e-2)  # TODO this is too high!!!
    assert torch.allclose(lpp1, lpp2, atol=5e-1)  # TODO this is too high!!!


def test_Ak_approx_bounds():
    nu = torch.tensor([1.0])
    z = torch.tensor([2.0])
    lb = lb_Ak(nu, z)
    ub = ub_Ak(nu, z)
    ak = Ak_approx(nu, z)
    assert torch.all(lb <= ak)
    assert torch.all(ak <= ub)


def test_llgIa_calculate_simple():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    out = llgIa_calculate(sA, d, Eeff, Ec)
    assert torch.isfinite(out).all()


def test_llgIc_calculate_simple():
    sA = torch.tensor([0.5])
    d = torch.tensor([1.0])
    Eeff = torch.tensor([1.0])
    Ec = torch.tensor([1.0])
    out = llgIc_calculate(sA, d, Eeff, Ec)
    assert torch.isfinite(out).all()


def test_llgItot_calculate_simple():
    sA = torch.tensor([0.5, 0.7])
    d = torch.tensor([1.0, 1.0])
    Eeff = torch.tensor([1.0, 1.0])
    Ec = torch.tensor([1.0, 1.0])
    centric = torch.tensor([False, True])
    out = llgItot_calculate(sA, d, Eeff, Ec, centric)
    assert torch.isfinite(out).all()


def test_logcosh_matches_numpy():
    xs = torch.tensor([-2.0, 0.0, 2.0])
    out = logcosh(xs)
    np_out = np.log(np.cosh(xs.numpy()))
    assert np.allclose(out.numpy(), np_out, atol=1e-6)


def test_find_bin_dHKL_simple():
    dHKLs = torch.tensor([1.0, 2.0, 3.0, 4.0])
    bin_labels = torch.tensor([0, 0, 1, 1])
    out = find_bin_dHKL(dHKLs, bin_labels)
    assert out[0] == out[1]
    assert out[2] == out[3]
    # Check value is mean of min and max for each bin
    assert np.isclose(out[0], 1.5)
    assert np.isclose(out[2], 3.5)


def test_compute_sigmaA_true_all_phases_aligned():
    Eobs = np.array([1.0, 2.0])
    phiobs = np.array([0.0, 0.0])
    Ecalc = np.array([1.0, 2.0])
    phicalc = np.array([0.0, 0.0])
    bin_labels = np.array([0, 0])
    out = compute_sigmaA_true(Eobs, phiobs, Ecalc, phicalc, bin_labels)
    assert np.isclose(out[0], 2.5)
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_refinement_utils.py",".py","27696","807","from unittest.mock import MagicMock, patch

import numpy as np
import pytest
import torch

from rocket.refinement_utils import (
    EarlyStopper,
    generate_feature_dict,
    get_common_bb_ind,
    get_common_ca_ind,
    get_current_lr,
    get_identical_indices,
    get_pattern_index,
    init_bias,
    init_llgloss,
    init_processed_dict,
    number_to_letter,
    position_alignment,
    sigmaA_from_true,
    update_sigmaA,
)


@pytest.fixture
def mock_data_processor():
    """"""Create a mock data processor object with process_fasta method.""""""
    data_processor = MagicMock()
    data_processor.process_fasta.return_value = {""features"": ""test_features""}
    return data_processor


def test_generate_feature_dict(mock_data_processor):
    expected_result = {""features"": ""test_features""}

    fasta_path = ""/path/to/fasta""
    alignment_dir = ""/path/to/alignment""

    result = generate_feature_dict(
        fasta_path=fasta_path,
        alignment_dir=alignment_dir,
        data_processor=mock_data_processor,
    )

    # Verify process_fasta was called with correct parameters
    mock_data_processor.process_fasta.assert_called_once_with(
        fasta_path=fasta_path, alignment_dir=alignment_dir, seqemb_mode=False
    )
    assert result == expected_result


def test_number_to_letter():
    # Test valid cases (0-25 should map to A-Z)
    assert number_to_letter(0) == ""A""
    assert number_to_letter(1) == ""B""
    assert number_to_letter(25) == ""Z""

    # Test boundary cases
    assert number_to_letter(-1) is None
    assert number_to_letter(26) is None

    # Test some middle values
    assert number_to_letter(7) == ""H""
    assert number_to_letter(12) == ""M""
    assert number_to_letter(19) == ""T""


def test_get_identical_indices():
    # Test case from docstring
    A = ""EWTUY""
    B = ""E-RUY""
    ind_A, ind_B = get_identical_indices(A, B)
    assert ind_A.tolist() == [0, 3, 4]
    assert ind_B.tolist() == [0, 2, 3]

    # Test with identical strings
    A = ""ABCDE""
    B = ""ABCDE""
    ind_A, ind_B = get_identical_indices(A, B)
    assert ind_A.tolist() == [0, 1, 2, 3, 4]
    assert ind_B.tolist() == [0, 1, 2, 3, 4]

    # Test with no identical characters
    A = ""ABCDE""
    B = ""FGHIJ""
    ind_A, ind_B = get_identical_indices(A, B)
    assert ind_A.tolist() == []
    assert ind_B.tolist() == []

    # Test with gaps in both sequences
    A = ""A-BC-DE""
    B = ""-AB-CDE""
    ind_A, ind_B = get_identical_indices(A, B)
    assert ind_A.tolist() == [1, 3, 4]
    assert ind_B.tolist() == [1, 3, 4]

    # Test with empty strings
    A = """"
    B = """"
    ind_A, ind_B = get_identical_indices(A, B)
    assert ind_A.tolist() == []
    assert ind_B.tolist() == []


def test_get_pattern_index():
    # Test with more complex patterns
    pdb_like_list = [""A-1-ASP-CA"", ""A-2-GLY-N"", ""A-2-GLY-CA"", ""A-3-PHE-CA""]
    # Find the CA atom of residue 2
    pattern = r"".*-2-.*-CA$""
    assert get_pattern_index(pdb_like_list, pattern) == 2

    # Find any atom of PHE
    pattern = r"".*-PHE-.*""
    assert get_pattern_index(pdb_like_list, pattern) == 3


@pytest.fixture
def mock_pdb1():
    """"""Simple mock PDBParser for first protein.""""""
    pdb = MagicMock()
    pdb.sequence = ""GFTT""
    pdb.cra_name = [
        ""A-0-GLY-CA"",
        ""A-1-PHE-CA"",
        ""A-2-THR-CA"",
        ""A-3-THR-CA"",
        ""A-3-THR-N"",
    ]
    return pdb


@pytest.fixture
def mock_pdb2():
    """"""Simple mock PDBParser for second protein.""""""
    pdb = MagicMock()
    pdb.sequence = ""DGFTT""
    pdb.cra_name = [
        ""A-0-ASP-CA"",
        ""A-1-GLY-CA"",
        ""A-2-PHE-CA"",
        ""A-3-THR-CA"",
        ""A-4-THR-CA"",
        ""A-4-THR-N"",
    ]
    return pdb


def test_get_common_ca_ind(mock_pdb1, mock_pdb2):
    """"""Minimal test to verify the I/O pipeline of get_common_ca_ind.""""""

    # Call the function
    common_ind1, common_ind2 = get_common_ca_ind(mock_pdb1, mock_pdb2)

    # Verify the function returns numpy arrays
    assert isinstance(common_ind1, list)
    assert isinstance(common_ind2, list)

    assert common_ind1 == [0, 1, 2, 3]
    assert common_ind2 == [1, 2, 3, 4]


def test_get_common_bb_ind(mock_pdb1, mock_pdb2):
    """"""Minimal test to verify the I/O pipeline of get_common_ca_ind.""""""

    # Call the function
    common_ind1, common_ind2 = get_common_bb_ind(mock_pdb1, mock_pdb2)

    # Verify the function returns numpy arrays
    assert isinstance(common_ind1, list)
    assert isinstance(common_ind2, list)

    assert common_ind1 == [0, 1, 2, 3, 4]
    assert common_ind2 == [1, 2, 3, 4, 5]


def test_get_current_lr():
    """"""Test that get_current_lr returns the learning rate from optimizer.""""""
    # Create a mock optimizer with param_groups
    optimizer = MagicMock()

    # Set up the mock to have param_groups with a learning rate
    expected_lr = 0.001
    optimizer.param_groups = [{""lr"": expected_lr}]

    # Test that the function returns the expected learning rate
    result = get_current_lr(optimizer)
    assert result == expected_lr

    # Test with a different learning rate
    new_lr = 0.0001
    optimizer.param_groups = [{""lr"": new_lr}]
    result = get_current_lr(optimizer)
    assert result == new_lr


def test_early_stopper():
    """"""Test basic functionality of EarlyStopper.""""""
    # Create EarlyStopper with small patience for testing
    stopper = EarlyStopper(patience=2, min_delta=0.1)

    # First call should set the minimum loss and return False (don't stop)
    assert stopper.early_stop(10.0) is False
    assert stopper.min_loss == 10.0
    assert stopper.counter == 0

    # Significant improvement should reset counter and return False
    assert stopper.early_stop(9.8) is False
    assert stopper.min_loss == 9.8
    assert stopper.counter == 0

    # No significant improvement should increment counter and return False
    assert stopper.early_stop(9.75) is False
    assert stopper.min_loss == 9.8  # Unchanged
    assert stopper.counter == 1

    # Still no improvement - should hit patience limit and return True (stop)
    assert stopper.early_stop(9.85) is True
    assert stopper.counter == 2


@patch(""rocket.make_processed_dict_from_template"")
@patch(""glob.glob"")
@patch(""builtins.open"")
@patch(""pickle.load"")
def test_init_processed_dict(mock_pickle_load, mock_open, mock_glob, mock_make_dict):
    """"""Test init_processed_dict with both bias_version=4 and other values.""""""
    # Mock device
    device = torch.device(""cpu"")

    # Test with bias_version=4 (uses make_processed_dict_from_template)
    # Setup mock return value for rocket.make_processed_dict_from_template
    mock_processed_dict = {""template_torsion_angles_sin_cos"": torch.ones(5, 4, 2)}
    mock_make_dict.return_value = mock_processed_dict

    # Call the function
    result_dict, feature_key, features = init_processed_dict(
        bias_version=4,
        path=""/test/path"",
        device=device,
        template_pdb=""template.pdb"",
        target_seq=""ABCDEFG"",
    )

    # Verify the function called make_processed_dict_from_template
    mock_make_dict.assert_called_once_with(
        template_pdb=""/test/path/ROCKET_inputs/template.pdb"",
        target_seq=""ABCDEFG"",
        config_preset=""model_1_ptm"",
        device=device,
        msa_dict=None,
    )

    # Verify returned values
    assert result_dict == mock_processed_dict
    assert feature_key == ""template_torsion_angles_sin_cos""
    assert torch.equal(features, mock_processed_dict[""template_torsion_angles_sin_cos""])

    # Reset mocks for next test
    mock_make_dict.reset_mock()

    # Test with bias_version=1 (uses pickle loading)
    # Setup mock returns for glob, open, and pickle.load
    mock_glob.return_value = [""/test/path/predictions/test_processed_feats.pickle""]
    mock_pickle_dict = {""msa_feat"": torch.ones(5, 10, 23), ""aatype"": torch.zeros(10)}
    mock_pickle_load.return_value = mock_pickle_dict

    # Call the function with bias_version=1
    result_dict, feature_key, features = init_processed_dict(
        bias_version=1, path=""/test/path"", device=device
    )

    # Verify the glob pattern was correct
    mock_glob.assert_called_once_with(""/test/path/predictions/*processed_feats.pickle"")

    # Verify pickle.load was called
    mock_pickle_load.assert_called_once()

    # Verify returned values
    assert feature_key == ""msa_feat""
    assert torch.equal(features, mock_pickle_dict[""msa_feat""])
    assert ""msa_feat"" in result_dict
    assert ""aatype"" in result_dict


def test_init_llgloss():
    """"""Test init_llgloss creates LLGloss object with correct parameters.""""""
    # Mock the structure factor object
    mock_sfc = MagicMock()
    mock_sfc.dHKL = torch.tensor([1.5, 2.0, 2.5, 3.0, 3.5])
    mock_sfc.device = ""cpu""

    # Mock the tng_file path
    mock_tng_file = ""/path/to/test.tng""

    # Mock the LLGloss constructor
    with patch(""rocket.xtal.targets.LLGloss"") as mock_llgloss_class:
        # Create a mock LLGloss object to be returned
        mock_llgloss = MagicMock()
        mock_llgloss_class.return_value = mock_llgloss

        # Call the function with default resolution parameters
        result = init_llgloss(mock_sfc, mock_tng_file)

        # Verify LLGloss was created with correct parameters
        mock_llgloss_class.assert_called_once_with(
            mock_sfc,
            mock_tng_file,
            mock_sfc.device,
            min(mock_sfc.dHKL),
            max(mock_sfc.dHKL),
        )

        # Verify the function returns the LLGloss object
        assert result == mock_llgloss

        # Reset mock for next test
        mock_llgloss_class.reset_mock()

        # Test with custom resolution parameters
        min_res = 2.0
        max_res = 3.0
        result = init_llgloss(
            mock_sfc, mock_tng_file, min_resolution=min_res, max_resolution=max_res
        )  # noqa: E501

        # Verify LLGloss was created with custom resolution parameters
        mock_llgloss_class.assert_called_once_with(
            mock_sfc, mock_tng_file, mock_sfc.device, min_res, max_res
        )


@patch(""torch.optim.Adam"")
@patch(""torch.optim.AdamW"")
def test_init_bias(mock_adamw, mock_adam):
    """"""Test init_bias function with different bias versions.""""""
    # Mock the device and learning rates
    device = torch.device(""cpu"")
    lr_a = 1e-3
    lr_m = 1e-4

    # Create a mock device_processed_features with minimum required data
    mock_features = {
        ""aatype"": torch.zeros(10)  # Simulate a sequence with 10 residues
    }

    # Set up the mocked optimizers
    mock_optimizer = MagicMock()
    mock_adam.return_value = mock_optimizer
    mock_adamw.return_value = mock_optimizer

    # Test bias_version=3
    result_features, optimizer, bias_names = init_bias(
        device_processed_features=mock_features,
        bias_version=3,
        device=device,
        lr_a=lr_a,
        lr_m=lr_m,
    )

    # Check that msa_feat_bias was created with correct shape
    assert ""msa_feat_bias"" in result_features
    assert result_features[""msa_feat_bias""].shape == (512, 10, 23)
    assert result_features[""msa_feat_weights""].shape == (512, 10, 23)
    assert result_features[""msa_feat_bias""].requires_grad

    # Check optimizer was created with correct parameters
    mock_adam.assert_called_once()
    assert bias_names == [""msa_feat_bias"", ""msa_feat_weights""]

    # Reset mocks for next test
    mock_adam.reset_mock()
    mock_adamw.reset_mock()

    # Test bias_version=1
    result_features, optimizer, bias_names = init_bias(
        device_processed_features=mock_features,
        bias_version=1,
        device=device,
        lr_a=lr_a,
        lr_m=lr_m,
    )

    # Check that msa_feat_bias was created with correct shape
    assert ""msa_feat_bias"" in result_features
    assert result_features[""msa_feat_bias""].shape == (512, 10, 23)
    assert result_features[""msa_feat_bias""].requires_grad

    # Check optimizer was created with correct parameters
    mock_adam.assert_called_once()
    assert bias_names == [""msa_feat_bias""]

    # Reset mocks for next test
    mock_adam.reset_mock()
    mock_adamw.reset_mock()

    # Test bias_version=4
    result_features, optimizer, bias_names = init_bias(
        device_processed_features={
            ""aatype"": torch.zeros(10),
            ""template_torsion_angles_sin_cos"": torch.zeros(10, 5, 2),
        },
        bias_version=4,
        device=device,
        lr_a=lr_a,
        lr_m=lr_m,
    )

    # Check that template_torsion_angles_sin_cos_bias was created
    assert ""template_torsion_angles_sin_cos_bias"" in result_features
    assert result_features[""template_torsion_angles_sin_cos_bias""].shape == (10, 5, 2)
    assert result_features[""template_torsion_angles_sin_cos_bias""].requires_grad

    # Check bias names
    assert bias_names == [""template_torsion_angles_sin_cos_bias""]

    # Reset mocks for weight decay test
    mock_adam.reset_mock()
    mock_adamw.reset_mock()

    # Test with weight_decay
    result_features, optimizer, bias_names = init_bias(
        device_processed_features=mock_features,
        bias_version=2,
        device=device,
        lr_a=lr_a,
        lr_m=lr_m,
        weight_decay=0.01,
    )

    # Check AdamW was called instead of Adam
    mock_adamw.assert_called_once()
    mock_adam.assert_not_called()


@patch(""rocket.coordinates.extract_allatoms"")
@patch(""rocket.coordinates.iterative_kabsch_alignment"")
@patch(""rocket.utils.plddt2pseudoB_pt"")
@patch(""rocket.utils.weighting"")
@patch(""rocket.utils.assert_numpy"")
def test_position_alignment(
    mock_assert_numpy,
    mock_weighting,
    mock_plddt2pseudoB,
    mock_weighted_kabsch,
    mock_extract_allatoms,
):
    """"""Test position_alignment with mocked dependencies.""""""
    # Create mock inputs
    af2_output = {
        ""plddt"": torch.ones(10) * 90.0  # 10 residues with good plddt scores
    }
    device_processed_features = {}
    cra_name = [""A-1-GLY-CA"", ""A-1-GLY-N"", ""A-2-ALA-CA""]
    best_pos = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
    exclude_res = []

    # Configure mocks
    # extract_allatoms returns coordinates and plddts
    mock_extract_allatoms.return_value = (
        torch.ones((3, 3)),  # xyz coordinates
        torch.ones(3),  # plddts for atoms
    )

    # Configure assert_numpy to return numpy arrays
    mock_assert_numpy.side_effect = (
        lambda x: x.numpy() if isinstance(x, torch.Tensor) else x
    )  # noqa: E501

    # Configure plddt2pseudoB to return B-factors
    pseudo_bs = torch.ones(3) * 30.0
    mock_plddt2pseudoB.return_value = pseudo_bs

    # Configure weighting to return weights
    weights = np.ones(3)
    mock_weighting.return_value = weights

    # Configure weighted_kabsch to return aligned coordinates
    aligned_xyz = torch.tensor([[1.5, 2.5, 3.5], [4.5, 5.5, 6.5], [7.5, 8.5, 9.5]])
    mock_weighted_kabsch.return_value = aligned_xyz

    # Call the function
    result_xyz, result_plddts, result_bs = position_alignment(
        af2_output, device_processed_features, cra_name, best_pos, exclude_res
    )

    # Verify the function works as expected
    assert torch.equal(result_xyz, aligned_xyz)
    assert torch.equal(torch.tensor(result_plddts), af2_output[""plddt""])
    assert torch.equal(result_bs, pseudo_bs)

    # Check that extract_allatoms was called with correct arguments
    mock_extract_allatoms.assert_called_once_with(
        af2_output, device_processed_features, cra_name
    )

    # Check that weighted_kabsch was called with correct arguments
    mock_weighted_kabsch.assert_called_once()
    args, kwargs = mock_weighted_kabsch.call_args
    assert torch.equal(args[0], torch.ones((3, 3)))  # xyz_orth_sfc
    assert torch.equal(args[1], best_pos)  # best_pos
    assert args[2] == cra_name  # cra_name
    assert np.array_equal(kwargs[""weights""], weights)  # weights
    assert kwargs[""exclude_res""] == exclude_res  # exclude_res


@patch(""rocket.coordinates.extract_allatoms"")
@patch(""rocket.coordinates.iterative_kabsch_alignment"")
@patch(""rocket.utils.plddt2pseudoB_pt"")
@patch(""rocket.utils.weighting"")
@patch(""rocket.utils.assert_numpy"")
def test_position_alignment_with_reference_bfactor(
    mock_assert_numpy,
    mock_weighting,
    mock_plddt2pseudoB,
    mock_weighted_kabsch,
    mock_extract_allatoms,
):
    """"""Test position_alignment with reference_bfactor parameter.""""""
    # Create mock inputs
    af2_output = {""plddt"": torch.ones(10) * 90.0}
    device_processed_features = {}
    cra_name = [""A-1-GLY-CA"", ""A-1-GLY-N"", ""A-2-ALA-CA""]
    best_pos = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
    exclude_res = []
    reference_bfactor = torch.ones(3) * 20.0

    # Configure mocks as before
    mock_extract_allatoms.return_value = (torch.ones((3, 3)), torch.ones(3))
    mock_assert_numpy.side_effect = (
        lambda x: x.numpy() if isinstance(x, torch.Tensor) else x
    )  # noqa: E501
    mock_plddt2pseudoB.return_value = torch.ones(3) * 30.0
    mock_weighting.return_value = np.ones(3)
    mock_weighted_kabsch.return_value = torch.tensor([
        [1.5, 2.5, 3.5],
        [4.5, 5.5, 6.5],
        [7.5, 8.5, 9.5],
    ])

    # Call the function with reference_bfactor
    position_alignment(
        af2_output,
        device_processed_features,
        cra_name,
        best_pos,
        exclude_res,
        reference_bfactor=reference_bfactor,
    )

    # Check that weighting was called with reference_bfactor instead of pseudo_Bs
    mock_weighting.assert_called_once()
    args = mock_weighting.call_args[0]
    # Should be using reference_bfactor.numpy()
    assert np.array_equal(args[0], reference_bfactor.numpy())


def test_update_sigmaA():
    """"""Test update_sigmaA with mocked LLGloss objects.""""""
    # Create mock LLGloss objects
    llgloss = MagicMock()
    llgloss_rbr = MagicMock()

    # Create a mock for aligned_xyz
    aligned_xyz = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])

    # Set up return values for compute_Ecalc
    mock_ecalc = torch.tensor([0.5, 0.6, 0.7])
    mock_fc = torch.tensor([1.0 + 2.0j, 3.0 + 4.0j, 5.0 + 6.0j])
    llgloss.compute_Ecalc.return_value = (mock_ecalc, mock_fc)

    mock_ecalc_rbr = torch.tensor([0.4, 0.5, 0.6])
    mock_fc_rbr = torch.tensor([0.5 + 1.0j, 1.5 + 2.0j, 2.5 + 3.0j])
    llgloss_rbr.compute_Ecalc.return_value = (mock_ecalc_rbr, mock_fc_rbr)

    # Test with no constant_fp parameters
    result_llgloss, result_llgloss_rbr, result_ecalc, result_fc = update_sigmaA(
        llgloss, llgloss_rbr, aligned_xyz
    )

    # Verify that compute_Ecalc was called correctly
    llgloss.compute_Ecalc.assert_called_once()
    # Use ANY for tensor arguments to avoid tensor comparison issues
    args, kwargs = llgloss.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""update_scales""] is True
    assert kwargs[""added_chain_HKL""] is None
    assert kwargs[""added_chain_asu""] is None

    # Verify that compute_Ecalc was called correctly for llgloss_rbr
    llgloss_rbr.compute_Ecalc.assert_called_once()
    args, kwargs = llgloss_rbr.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""solvent""] is False
    assert kwargs[""update_scales""] is True
    assert kwargs[""added_chain_HKL""] is None
    assert kwargs[""added_chain_asu""] is None

    # Verify that refine_sigmaA_newton was called with correct parameters
    llgloss.refine_sigmaA_newton.assert_called_once()
    args, kwargs = llgloss.refine_sigmaA_newton.call_args
    assert torch.equal(args[0], mock_ecalc)
    assert kwargs[""n_steps""] == 5
    assert kwargs[""subset""] == ""working""
    assert kwargs[""smooth_overall_weight""] == 0.0

    # Verify that refine_sigmaA_newton was called correctly for llgloss_rbr
    llgloss_rbr.refine_sigmaA_newton.assert_called_once()
    args, kwargs = llgloss_rbr.refine_sigmaA_newton.call_args
    assert torch.equal(args[0], mock_ecalc_rbr)
    assert kwargs[""n_steps""] == 2
    assert kwargs[""subset""] == ""working""
    assert kwargs[""smooth_overall_weight""] == 0.0

    # Verify that the function returns the expected values
    assert result_llgloss == llgloss
    assert result_llgloss_rbr == llgloss_rbr
    assert torch.equal(result_ecalc, mock_ecalc)
    assert torch.equal(result_fc, mock_fc)

    # Reset mocks for the next test
    llgloss.reset_mock()
    llgloss_rbr.reset_mock()

    # Test with constant_fp parameters
    constant_fp_added_HKL = torch.tensor([0.1, 0.2, 0.3])
    constant_fp_added_asu = torch.tensor([0.4, 0.5, 0.6])

    update_sigmaA(
        llgloss,
        llgloss_rbr,
        aligned_xyz,
        constant_fp_added_HKL=constant_fp_added_HKL,
        constant_fp_added_asu=constant_fp_added_asu,
    )

    # Verify that compute_Ecalc was called with constant_fp parameters
    llgloss.compute_Ecalc.assert_called_once()
    args, kwargs = llgloss.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""update_scales""] is True
    assert torch.equal(kwargs[""added_chain_HKL""], constant_fp_added_HKL)
    assert torch.equal(kwargs[""added_chain_asu""], constant_fp_added_asu)

    # Verify that compute_Ecalc was called with constant_fp parameters for llgloss_rbr
    llgloss_rbr.compute_Ecalc.assert_called_once()
    args, kwargs = llgloss_rbr.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""solvent""] is False
    assert kwargs[""update_scales""] is True
    assert torch.equal(kwargs[""added_chain_HKL""], constant_fp_added_HKL)
    assert torch.equal(kwargs[""added_chain_asu""], constant_fp_added_asu)


def tensor_call_equal(call1, call2):
    """"""Helper function to compare calls containing tensors.""""""
    if len(call1) != len(call2):
        return False

    for arg1, arg2 in zip(call1, call2, strict=False):
        if isinstance(arg1, torch.Tensor) and isinstance(arg2, torch.Tensor):
            if not torch.equal(arg1, arg2):
                return False
        elif arg1 != arg2:
            return False
    return True


@patch(""rocket.xtal.utils.sigmaA_from_model"")
def test_sigmaA_from_true(mock_sigmaA_from_model):
    """"""Test sigmaA_from_true with mocked dependencies.""""""
    # Create mock LLGloss objects
    llgloss = MagicMock()
    llgloss_rbr = MagicMock()

    # Configure bin_labels and dHKL properties that will be accessed
    llgloss.bin_labels = torch.tensor([1, 2, 3])
    llgloss.sfc.dHKL = torch.tensor([2.0, 2.5, 3.0])

    # Create mock aligned_xyz, Etrue, and phitrue
    aligned_xyz = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
    Etrue = torch.tensor([1.1, 1.2, 1.3])
    phitrue = torch.tensor([0.1, 0.2, 0.3])

    # Set up return values for compute_Ecalc
    mock_ecalc = torch.tensor([0.5, 0.6, 0.7])
    mock_fc = torch.tensor([1.0 + 2.0j, 3.0 + 4.0j, 5.0 + 6.0j])
    llgloss.compute_Ecalc.return_value = (mock_ecalc, mock_fc)

    mock_ecalc_rbr = torch.tensor([0.4, 0.5, 0.6])
    mock_fc_rbr = torch.tensor([0.5 + 1.0j, 1.5 + 2.0j, 2.5 + 3.0j])
    llgloss_rbr.compute_Ecalc.return_value = (mock_ecalc_rbr, mock_fc_rbr)

    # Mock sigmaA_from_model return values
    mock_sigmas = torch.tensor([0.8, 0.7, 0.6])
    mock_sigmas_rbr = torch.tensor([0.85, 0.75, 0.65])
    mock_sigmaA_from_model.side_effect = [mock_sigmas, mock_sigmas_rbr]

    # Call the function
    result_llgloss, result_llgloss_rbr = sigmaA_from_true(
        llgloss, llgloss_rbr, aligned_xyz, Etrue, phitrue
    )

    # Verify compute_Ecalc was called correctly
    llgloss.compute_Ecalc.assert_called_once()
    args, kwargs = llgloss.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""update_scales""] is True
    assert kwargs[""added_chain_HKL""] is None
    assert kwargs[""added_chain_asu""] is None

    # Verify compute_Ecalc was called correctly for llgloss_rbr
    llgloss_rbr.compute_Ecalc.assert_called_once()
    args, kwargs = llgloss_rbr.compute_Ecalc.call_args
    assert torch.equal(args[0], aligned_xyz.detach())
    assert kwargs[""return_Fc""] is True
    assert kwargs[""solvent""] is False
    assert kwargs[""update_scales""] is True
    assert kwargs[""added_chain_HKL""] is None
    assert kwargs[""added_chain_asu""] is None

    # Check if sigmaA_from_model was called with the expected arguments
    assert mock_sigmaA_from_model.call_count == 2

    # Check first call arguments (for llgloss)
    args_llgloss = mock_sigmaA_from_model.call_args_list[0][0]
    assert torch.equal(args_llgloss[0], Etrue)
    assert torch.equal(args_llgloss[1], phitrue)
    assert torch.equal(args_llgloss[2], mock_ecalc)
    assert torch.equal(args_llgloss[3], mock_fc)
    assert torch.equal(args_llgloss[4], llgloss.sfc.dHKL)
    assert torch.equal(args_llgloss[5], llgloss.bin_labels)

    # Check second call arguments (for llgloss_rbr)
    args_llgloss_rbr = mock_sigmaA_from_model.call_args_list[1][0]
    assert torch.equal(args_llgloss_rbr[0], Etrue)
    assert torch.equal(args_llgloss_rbr[1], phitrue)
    assert torch.equal(args_llgloss_rbr[2], mock_ecalc_rbr)
    assert torch.equal(args_llgloss_rbr[3], mock_fc_rbr)
    assert torch.equal(args_llgloss_rbr[4], llgloss.sfc.dHKL)
    assert torch.equal(args_llgloss_rbr[5], llgloss.bin_labels)

    # Verify sigmaAs properties were updated correctly
    assert torch.equal(llgloss.sigmaAs, mock_sigmas)
    assert torch.equal(llgloss_rbr.sigmaAs, mock_sigmas_rbr)

    # Verify the function returns the modified llgloss objects
    assert result_llgloss is llgloss
    assert result_llgloss_rbr is llgloss_rbr


@patch(""rocket.xtal.utils.sigmaA_from_model"")
def test_sigmaA_from_true_with_constant_fp(mock_sigmaA_from_model):
    """"""Test sigmaA_from_true with constant_fp parameters.""""""
    # Create mock LLGloss objects
    llgloss = MagicMock()
    llgloss_rbr = MagicMock()

    # Configure necessary properties
    llgloss.bin_labels = torch.tensor([1, 2, 3])
    llgloss.sfc.dHKL = torch.tensor([2.0, 2.5, 3.0])

    # Mock input parameters
    aligned_xyz = torch.tensor([[1.0, 2.0, 3.0]])
    Etrue = torch.tensor([1.1, 1.2, 1.3])
    phitrue = torch.tensor([0.1, 0.2, 0.3])
    constant_fp_added_HKL = torch.tensor([0.1, 0.2, 0.3])
    constant_fp_added_asu = torch.tensor([0.4, 0.5, 0.6])

    # Configure compute_Ecalc return values
    llgloss.compute_Ecalc.return_value = (
        torch.tensor([0.5]),
        torch.tensor([1.0 + 2.0j]),
    )
    llgloss_rbr.compute_Ecalc.return_value = (
        torch.tensor([0.4]),
        torch.tensor([0.5 + 1.0j]),
    )  # noqa: E501

    # Configure sigmaA_from_model return values
    mock_sigmaA_from_model.side_effect = [torch.tensor([0.8]), torch.tensor([0.85])]

    # Call the function with constant_fp parameters
    sigmaA_from_true(
        llgloss,
        llgloss_rbr,
        aligned_xyz,
        Etrue,
        phitrue,
        constant_fp_added_HKL=constant_fp_added_HKL,
        constant_fp_added_asu=constant_fp_added_asu,
    )

    # Verify compute_Ecalc was called with constant_fp parameters
    args, kwargs = llgloss.compute_Ecalc.call_args
    assert torch.equal(kwargs[""added_chain_HKL""], constant_fp_added_HKL)
    assert torch.equal(kwargs[""added_chain_asu""], constant_fp_added_asu)

    # Verify compute_Ecalc was called with constant_fp parameters for llgloss_rbr
    args, kwargs = llgloss_rbr.compute_Ecalc.call_args
    assert torch.equal(kwargs[""added_chain_HKL""], constant_fp_added_HKL)
    assert torch.equal(kwargs[""added_chain_asu""], constant_fp_added_asu)
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_utils.py",".py","7274","268","# Mock external dependencies before importing utils
from unittest.mock import MagicMock, patch

import numpy as np
import pytest
import torch

from rocket.utils import (
    apply_resolution_cutoff,
    assert_numpy,
    assert_tensor,
    convert_feat_tensors_to_numpy,
    d2q,
    dict_map,
    get_params_path,
    load_mtz,
    load_pdb,
    move_tensors_to_device,
    move_tensors_to_device_inplace,
    plddt2pseudoB_np,
    tree_map,
    try_gpu,
    weighting,
    weighting_torch,
)


def test_plddt2pseudoB_basic():
    arr = np.array([0, 50, 100])
    out = plddt2pseudoB_np(arr)
    assert out.shape == arr.shape
    assert np.all(out > 0)
    assert np.isfinite(out).all()


def test_plddt2pseudoB_empty():
    arr = np.array([])
    out = plddt2pseudoB_np(arr)
    assert out.shape == arr.shape


def test_weighting_cutoffs():
    b = np.array([10.0, 11.5, 20.0, 30.0, 40.0])
    w = weighting(b)
    assert np.isclose(w[0], 1.0)
    assert np.isclose(w[1], 1.0)
    assert 0.5 < w[2] < 1.0
    assert np.isclose(w[3], 0.5)
    assert w[4] < 0.5


def test_weighting_torch_matches_numpy():
    b = torch.tensor([10.0, 11.5, 20.0, 30.0, 40.0])
    wn = weighting(b.numpy())
    wt = weighting_torch(b).numpy()
    np.testing.assert_allclose(wn, wt, rtol=1e-5)


def test_weighting_scalar():
    w = weighting(10.0)
    assert w == 1.0


def test_dict_map_exclude_keys():
    d = {""a"": 1, ""msa_feat_bias"": 2}

    def fn(x):
        return x + 1

    out = dict_map(fn, d, int)
    assert out[""a""] == 2
    assert out[""msa_feat_bias""] == 2  # should not be mapped


def test_tree_map_nested():
    tree = {""a"": [1, 2], ""b"": (3, 4)}
    out = tree_map(lambda x: x * 2, tree, int)
    assert out[""a""] == [2, 4]
    assert out[""b""] == (6, 8)


def test_tree_map_non_supported():
    class Dummy:
        pass

    with pytest.raises(ValueError):
        tree_map(lambda x: x, Dummy(), int)


def test_try_gpu_cpu():
    with patch(""torch.cuda.device_count"", return_value=0):
        dev = try_gpu()
        assert str(dev) == ""cpu""


def test_try_gpu_cuda():
    with patch(""torch.cuda.device_count"", return_value=2):
        dev = try_gpu(1)
        assert str(dev) == ""cuda:1""


def test_move_tensors_to_device():
    d = {""a"": torch.zeros(2), ""b"": 3}
    out = move_tensors_to_device(d, device=""cpu"")
    assert out[""a""].device.type == ""cpu""
    assert out[""b""] == 3


def test_move_tensors_to_device_inplace():
    d = {""a"": torch.zeros(2)}
    move_tensors_to_device_inplace(d, device=""cpu"")
    assert d[""a""].device.type == ""cpu""


def test_convert_feat_tensors_to_numpy():
    d = {""a"": torch.ones(2), ""b"": ""notensor""}
    out = convert_feat_tensors_to_numpy(d)
    assert isinstance(out[""a""], np.ndarray)
    assert out[""b""] == ""notensor""


def test_assert_numpy_from_tensor():
    arr = torch.ones(2)
    np_arr = assert_numpy(arr)
    assert isinstance(np_arr, np.ndarray)


def test_assert_numpy_from_list():
    lst = [1, 2, 3]
    np_arr = assert_numpy(lst)
    assert isinstance(np_arr, np.ndarray)


def test_assert_tensor_from_np():
    arr = np.ones(2)
    tensor = assert_tensor(arr, device=""cpu"")
    assert isinstance(tensor, torch.Tensor)
    assert tensor.device.type == ""cpu""


def test_assert_tensor_from_list():
    lst = [1, 2, 3]
    tensor = assert_tensor(lst, device=""cpu"")
    assert isinstance(tensor, torch.Tensor)
    assert tensor.device.type == ""cpu""


def test_d2q_scalar_and_array():
    assert np.isclose(d2q(2.0), np.pi)
    arr = np.array([1.0, 2.0])
    out = d2q(arr)
    assert np.allclose(out, 2 * np.pi / arr)


def test_load_mtz():
    with patch(""rocket.utils.rs.read_mtz"") as mock_read:
        mock_ds = MagicMock()
        mock_read.return_value = mock_ds
        load_mtz(""file.mtz"")
        mock_read.assert_called_once_with(""file.mtz"")
        mock_ds.compute_dHKL.assert_called_once_with(inplace=True)


def test_load_pdb():
    with patch(""rocket.utils.PDBParser"") as mock_pdb:
        load_pdb(""file.pdb"")
        mock_pdb.assert_called_once_with(""file.pdb"")


def test_get_params_path(monkeypatch):
    monkeypatch.setenv(""OPENFOLD_RESOURCES"", ""/tmp/resources"")
    path = get_params_path()
    assert path == ""/tmp/resources/params""


def test_get_params_path_missing(monkeypatch):
    monkeypatch.delenv(""OPENFOLD_RESOURCES"", raising=False)
    with pytest.raises(ValueError):
        get_params_path()


def test_apply_resolution_cutoff_with_dataset():
    """"""
    Test apply_resolution_cutoff with a DataSet object and explicit max_resolution.
    """"""

    # Setup mock dataset
    mock_ds = MagicMock()
    mock_ds.dHKL = np.array([1.0, 2.0, 3.0, 4.0, 5.0])

    # Mock the filtering and copy chain
    filtered_mock = MagicMock()
    mock_ds.__getitem__.return_value = filtered_mock

    # Call the function
    result = apply_resolution_cutoff(mock_ds, min_resolution=2.0, max_resolution=4.0)

    # Assertions
    mock_ds.compute_dHKL.assert_called_once_with(inplace=True)

    # Check that the filtering happened correctly
    args, _ = mock_ds.__getitem__.call_args
    mask = args[0]
    expected_mask = (mock_ds.dHKL >= 2.0) & (mock_ds.dHKL <= 4.0)
    np.testing.assert_array_equal(mask, expected_mask)

    # Check that copy() was called on the filtered result
    filtered_mock.copy.assert_called_once()

    # Check that the final result is the copied, filtered mock
    assert result == filtered_mock.copy.return_value


def test_apply_resolution_cutoff_with_filepath():
    """"""Test apply_resolution_cutoff with a file path string.""""""

    with patch(""rocket.utils.rs.read_mtz"") as mock_read_mtz:
        # Setup mock dataset
        mock_ds = MagicMock()
        mock_ds.dHKL = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
        mock_read_mtz.return_value = mock_ds

        # Mock the filtering and copy chain
        filtered_mock = MagicMock()
        mock_ds.__getitem__.return_value = filtered_mock

        # Call the function
        apply_resolution_cutoff(""test.mtz"", min_resolution=2.0, max_resolution=4.0)

        # Assertions
        mock_read_mtz.assert_called_once_with(""test.mtz"")
        mock_ds.compute_dHKL.assert_called_once_with(inplace=True)

        # Check filtering
        args, _ = mock_ds.__getitem__.call_args
        mask = args[0]
        expected_mask = (mock_ds.dHKL >= 2.0) & (mock_ds.dHKL <= 4.0)
        np.testing.assert_array_equal(mask, expected_mask)

        filtered_mock.copy.assert_called_once()


def test_apply_resolution_cutoff_no_max_resolution():
    """"""Test apply_resolution_cutoff when max_resolution is not provided.""""""

    # Setup mock dataset
    mock_ds = MagicMock()
    mock_ds.dHKL = np.array([1.0, 2.0, 3.0, 4.0, 5.0])  # .max() will be 5.0

    # Mock the filtering and copy chain
    filtered_mock = MagicMock()
    mock_ds.__getitem__.return_value = filtered_mock

    # Call the function
    apply_resolution_cutoff(mock_ds, min_resolution=3.0)

    # Assertions
    mock_ds.compute_dHKL.assert_called_once_with(inplace=True)

    # Check filtering logic
    args, _ = mock_ds.__getitem__.call_args
    mask = args[0]
    # max_resolution should be inferred as 5.0
    expected_mask = (mock_ds.dHKL >= 3.0) & (mock_ds.dHKL <= 5.0)
    np.testing.assert_array_equal(mask, expected_mask)

    filtered_mock.copy.assert_called_once()
","Python"
"AlphaFold","alisiafadini/ROCKET","test/unit/test_cryo_utils.py",".py","5819","191","import math
from unittest.mock import patch

import numpy as np
import pandas as pd
import pytest
import torch

from rocket.cryo import utils as cryo_utils


# Test downsample_data
@pytest.mark.parametrize(
    ""downsample_ratio, expected_vals"",
    [
        (2, {1, 2, 4}),  # Only rows where all H/K/L divisible by 2
        (1, {1, 2, 3, 4}),  # All rows should pass for ratio 1
    ],
)
def test_downsample_data_masking_and_no_downsampling(downsample_ratio, expected_vals):
    df = pd.DataFrame({
        ""H"": [0, 2, 3, 4],
        ""K"": [0, 2, 3, 4],
        ""L"": [0, 2, 3, 4],
        ""VAL"": [1, 2, 3, 4],
    })
    df.get_hkls = lambda: df[[""H"", ""K"", ""L""]].values

    with patch(""reciprocalspaceship.read_mtz"", return_value=df):
        result = cryo_utils.downsample_data(
            ""dummy.mtz"", downsample_ratio=downsample_ratio
        )
        assert set(result[""VAL""]) == expected_vals
        assert result.shape[0] == len(expected_vals)


# Test compute_sigmaA_for_bin is within clamped values
def test_compute_sigmaA_for_bin_typical():
    # Chosen so cosdphi=1, everything positive, no sqrt_arg issues
    eEsel = np.array([1.0, 2.0])
    Ecalc_sel = np.array([1.0, 2.0])
    dobssel = np.array([1.0, 1.0])
    expectE_phi = np.array([0.0, 0.0])
    Ecalc_phi = np.array([0.0, 0.0])
    result = cryo_utils.compute_sigmaA_for_bin(
        eEsel, Ecalc_sel, dobssel, expectE_phi, Ecalc_phi
    )
    assert 1e-6 <= result <= 0.999


def test_compute_sigmaA_for_bin_zero_division_or_nan():
    # Inputs that lead to denominator zero (all zeros)
    eEsel = np.array([0.0, 0.0])
    Ecalc_sel = np.array([0.0, 0.0])
    dobssel = np.array([0.0, 0.0])
    expectE_phi = np.array([0.0, 0.0])
    Ecalc_phi = np.array([0.0, 0.0])
    with pytest.raises((ZeroDivisionError, ValueError)):
        cryo_utils.compute_sigmaA_for_bin(
            eEsel, Ecalc_sel, dobssel, expectE_phi, Ecalc_phi
        )


# Test compute_sigmaA_error
def test_compute_sigmaA_error_finite():
    dobssel = np.array([0.5, 0.7])
    sigmaA = 0.5
    abseE = np.array([1.0, 2.0])
    absEc = np.array([2.0, 1.0])
    cosdphi = np.array([1.0, 1.0])
    over_sampling_factor = 1.0
    result = cryo_utils.compute_sigmaA_error(
        dobssel, sigmaA, abseE, absEc, cosdphi, over_sampling_factor
    )
    assert isinstance(result, float)
    assert result > 0


# Test fit_line
def test_fit_line_perfect():
    x = np.array([1, 2, 3])
    y = 2 * x + 1
    w = np.ones_like(x)
    slope, intercept = cryo_utils.fit_line(x, y, w)
    assert np.isclose(slope, 2.0)
    assert np.isclose(intercept, 1.0)


# Test combine_sigmaA
def test_combine_sigmaA_shape_and_type():
    slope = 1.0
    intercept = 0.0
    xdat = np.array([0.0, 1.0])
    ydat = np.array([0.1, 0.2])
    wdat = np.array([1.0, 2.0])
    sigma_linlog = 1.0
    linlogsiga, logsiga_combined = cryo_utils.combine_sigmaA(
        slope, intercept, xdat, ydat, wdat, sigma_linlog
    )
    assert len(linlogsiga) == 2
    assert len(logsiga_combined) == 2
    assert all(isinstance(v, float) for v in linlogsiga)
    assert all(isinstance(v, float) for v in logsiga_combined)


# Test llgcryo_calculate (torch-based)
def test_llgcryo_calculate_simple():
    E_amp = torch.tensor([1.0])
    E_phi = torch.tensor([0.0])
    Ec_amp = torch.tensor([1.0])
    Ec_phi = torch.tensor([0.0])
    sigmaA = torch.tensor([0.5])
    dobs = torch.tensor([0.5])
    result = cryo_utils.llgcryo_calculate(E_amp, E_phi, Ec_amp, Ec_phi, sigmaA, dobs)
    assert result.shape == (1,)
    assert torch.isfinite(result).all()


# Test load_tng_data with minimal mock
def test_load_tng_data_keys_and_types():
    df = pd.DataFrame({
        ""Emean"": [1.0, 2.0],
        ""PHIEmean"": [0.0, 90.0],
        ""Dobs"": [1.0, 2.0],
    })
    with (
        patch(""rocket.utils.load_mtz"", return_value=df),
        patch(""rocket.utils.try_gpu"", return_value=""cpu""),
    ):
        result = cryo_utils.load_tng_data(""dummy_file"")
        assert set(result.keys()) == {""Emean"", ""PHIEmean"", ""Dobs""}
        for k in result:
            assert isinstance(result[k], torch.Tensor)
            assert result[k].shape[0] == 2


# Test sigmaA_from_model_in_map with dummy sfc object
def test_sigmaA_from_model_in_map_dummy():
    class DummySFC:
        def __init__(self):
            self.n_bins = 2
            self.bins = np.array([0, 0, 1, 1])

    expectE_amp = np.array([1.0, 1.0, 2.0, 2.0])
    expectE_phi = np.array([0.0, 0.0, 0.0, 0.0])
    dobs = np.array([1.0, 1.0, 1.0, 1.0])
    Ecalc_amp = np.array([1.0, 1.0, 2.0, 2.0])
    Ecalc_phi = np.array([0.0, 0.0, 0.0, 0.0])
    dhkl = np.array([1.0, 1.0, 2.0, 2.0])
    sfc = DummySFC()
    over_sampling_factor = 1.0
    model_sigmaA = cryo_utils.sigmaA_from_model_in_map(
        expectE_amp,
        expectE_phi,
        dobs,
        Ecalc_amp,
        Ecalc_phi,
        sfc,
        over_sampling_factor,
        dhkl,
        plot=False,
    )
    assert model_sigmaA.shape == expectE_amp.shape
    assert np.all(model_sigmaA > 0)
    assert np.all(model_sigmaA < 1)


# Test combine_sigmaA logic (numerical, edge)
def test_combine_sigmaA_behavior():
    slope, intercept = 0.0, 0.0
    xdat = np.array([0.0])
    ydat = np.array([0.0])
    wdat = np.array([1.0])
    sigma_linlog = 1.0
    linlogsiga, logsiga_combined = cryo_utils.combine_sigmaA(
        slope, intercept, xdat, ydat, wdat, sigma_linlog
    )
    # With all zeros, log(0.999) and logsiga_combined = 0
    assert np.isclose(linlogsiga[0], math.log(0.999))
    assert np.isfinite(logsiga_combined[0])


# Test fit_line for zero slope
def test_fit_line_zero_slope():
    x = np.array([1, 2, 3])
    y = np.array([5, 5, 5])
    w = np.ones_like(x)
    slope, intercept = cryo_utils.fit_line(x, y, w)
    assert np.isclose(slope, 0.0)
    assert np.isclose(intercept, 5.0)
","Python"
"AlphaFold","alisiafadini/ROCKET","test/functional/dummy.py",".py","0","0","","Python"
"AlphaFold","alisiafadini/ROCKET","test/scripts/dummy.py",".py","0","0","","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/coordinates.py",".py","32989","1043","""""""
Functions relating model coordinates/PDB file modificaitons
""""""

import time

import numpy as np
import torch
from openfold.np import residue_constants
from scipy.spatial.transform import Rotation
from SFC_Torch import SFcalculator

from rocket import utils


def rigidbody_refine_quat(
    xyz,
    llgloss,
    cra_name,
    lbfgs=False,
    added_chain_HKL=None,
    added_chain_asu=None,
    lbfgs_lr=150.0,
    verbose=True,
    domain_segs=None,
):
    resid = [int(i.split(""-"")[1]) + 1 for i in cra_name]
    minid = min(resid)
    maxid = max(resid)

    if domain_segs is None:
        domain_ranges = [[minid, maxid + 1]]
    else:
        domain_ranges = []
        start = minid
        for i, seg in enumerate(domain_segs):
            domain_ranges.append([start, seg])
            start = seg
            if i == len(domain_segs) - 1:
                domain_ranges.append([start, maxid + 1])

    domain_bools = []
    for domain_start, domain_end_notin in domain_ranges:
        domain_bools.append(
            np.array([(i >= domain_start) and (i < domain_end_notin) for i in resid])
        )
    n_domains = len(domain_bools)

    # llgloss.sfc.get_scales_lbfgs()
    if lbfgs:
        trans_vecs, qs, loss_track_pose = find_rigidbody_matrix_lbfgs_quat(
            llgloss,
            xyz.detach(),
            llgloss.device,
            domain_bools,
            added_chain_HKL=added_chain_HKL,
            added_chain_asu=added_chain_asu,
            lbfgs_lr=lbfgs_lr,
            verbose=verbose,
        )
    else:
        pass
        # trans_vec, q, loss_track_pose = find_rigidbody_matrix_adam_quat(
        #     llgloss,
        #     propose_coms.clone().detach(),
        #     propose_rmcom.clone().detach(),
        #     llgloss.device,
        #     added_chain_HKL=added_chain_HKL,
        #     added_chain_asu=added_chain_asu,
        #     verbose=verbose
        # )
    optimized_xyz = torch.ones_like(xyz)
    for i in range(n_domains):
        propose_rmcom = xyz[domain_bools[i]] - torch.mean(xyz[domain_bools[i]], dim=0)
        propose_com = torch.mean(xyz[domain_bools[i]], dim=0)
        transform_i = quaternions_to_SO3(qs[i]).detach()
        optimized_xyz[domain_bools[i]] = (
            torch.matmul(propose_rmcom, transform_i)
            + propose_com
            + trans_vecs[i].detach()
        )

    return optimized_xyz, loss_track_pose


def find_rigidbody_matrix_lbfgs_quat(
    llgloss,
    xyz,
    device,
    domain_bools,
    added_chain_HKL=None,
    added_chain_asu=None,
    lbfgs_lr=150.0,
    verbose=True,
):
    n_domains = len(domain_bools)
    qs = [
        torch.tensor(
            [1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=device, requires_grad=True
        )
        for _ in range(n_domains)
    ]
    trans_vecs = [
        torch.tensor([0.0, 0.0, 0.0], device=device, requires_grad=True)
        for _ in range(n_domains)
    ]

    loss_track_pose = pose_train_lbfgs_quat(
        llgloss,
        qs,
        trans_vecs,
        xyz,
        domain_bools,
        loss_track=[],
        lr=lbfgs_lr,
        added_chain_HKL=added_chain_HKL,
        added_chain_asu=added_chain_asu,
        verbose=verbose,
    )
    return trans_vecs, qs, loss_track_pose


def find_rigidbody_matrix_adam_quat(
    llgloss,
    propose_com,
    propose_rmcom,
    device,
    added_chain_HKL=None,
    added_chain_asu=None,
    verbose=True,
):
    q = torch.tensor(
        [1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=device, requires_grad=True
    )
    trans_vec = torch.tensor([0.0, 0.0, 0.0], device=device, requires_grad=True)
    loss_track_pose = pose_train_adam_quat(
        llgloss,
        q,
        trans_vec,
        propose_com,
        propose_rmcom,
        loss_track=[],
        added_chain_HKL=added_chain_HKL,
        added_chain_asu=added_chain_asu,
        verbose=verbose,
    )
    return trans_vec, q, loss_track_pose


def pose_train_lbfgs_quat(
    llgloss,
    qs,
    trans_vecs,
    xyz,
    domain_bools,
    lr=150.0,
    n_steps=15,
    loss_track=None,
    added_chain_HKL=None,
    added_chain_asu=None,
    verbose=True,
):
    if loss_track is None:
        loss_track = []

    def closure():
        optimizer.zero_grad()
        temp_model = torch.zeros_like(xyz)
        for i in range(n_domains):
            temp_R = quaternions_to_SO3(qs[i])
            temp_model[domain_bools[i]] = (
                torch.matmul(propose_rmcoms[i], temp_R)
                + propose_coms[i]
                + trans_vecs[i]
            )
        loss = -llgloss(
            temp_model,
            bin_labels=None,
            num_batch=1,
            sub_ratio=1.0,
            solvent=False,
            return_Rfactors=False,
            added_chain_HKL=added_chain_HKL,
            added_chain_asu=added_chain_asu,
        )
        loss.backward()
        return loss

    n_domains = len(domain_bools)
    optimizer = torch.optim.LBFGS(
        qs + trans_vecs,
        lr=lr,
        line_search_fn=""strong_wolfe"",
        tolerance_change=1e-9,
        max_iter=1,
    )
    propose_rmcoms = []
    propose_coms = []
    for domain_bool in domain_bools:
        propose_rmcoms.append(xyz[domain_bool] - torch.mean(xyz[domain_bool], dim=0))
        propose_coms.append(torch.mean(xyz[domain_bool], dim=0))
    start_time = time.time()
    for _ in range(n_steps):
        temp = optimizer.step(closure)
        loss_track.append(temp.item())
    elapsed_time = time.time() - start_time
    if verbose:
        print(
            f""LBFGS RBR, {n_steps} steps, time taken: {elapsed_time:.4f} seconds"",
            flush=True,
        )
    return loss_track


def pose_train_adam_quat(
    llgloss,
    q,
    trans_vec,
    propose_com,
    propose_rmcom,
    lr=1e-3,
    n_steps=100,
    loss_track=None,
    added_chain_HKL=None,
    added_chain_asu=None,
    verbose=True,
):
    if loss_track is None:
        loss_track = []

    def pose_steptrain(optimizer):
        optimizer.zero_grad()
        temp_R = quaternions_to_SO3(q)
        temp_model = torch.matmul(propose_rmcom, temp_R) + propose_com + trans_vec
        loss = -llgloss(
            temp_model,
            bin_labels=None,
            num_batch=1,
            sub_ratio=1.0,
            solvent=False,
            return_Rfactors=False,
            added_chain_HKL=added_chain_HKL,
            added_chain_asu=added_chain_asu,
        )
        loss.backward()
        optimizer.step()
        return loss.item()

    start_time = time.time()
    optimizer = torch.optim.Adam([q, trans_vec], lr=lr)
    for _ in range(n_steps):
        temp = pose_steptrain(optimizer)
        loss_track.append(temp)
    elapsed_time = time.time() - start_time
    if verbose:
        print(
            f""Adam RBR, {n_steps} steps, time taken: {elapsed_time:.4f} seconds"",
            flush=True,
        )
    return loss_track


def pose_train_adam_matrix(
    llgloss,
    rot_v1,
    rot_v2,
    trans_vec,
    propose_com,
    propose_rmcom,
    lr=1e-3,
    n_steps=100,
    loss_track=None,
    added_chain=None,
):
    if loss_track is None:
        loss_track = []

    def pose_steptrain(optimizer):
        optimizer.zero_grad()
        temp_R = construct_SO3(rot_v1, rot_v2)
        temp_model = torch.matmul(propose_rmcom, temp_R) + propose_com + trans_vec
        loss = -llgloss(
            temp_model,
            bin_labels=None,
            num_batch=1,
            return_Rfactors=False,
            sub_ratio=1.0,
            solvent=False,
            added_chain=added_chain,
        )
        loss.backward()
        optimizer.step()
        return loss.item()

    start_time = time.time()
    optimizer = torch.optim.Adam([rot_v1, rot_v2, trans_vec], lr=lr)
    for k in range(n_steps):
        temp = pose_steptrain(optimizer)
        loss_track.append(temp)

        elapsed_time = time.time() - start_time
        print(f""Step {k + 1}/{n_steps} - Time: {elapsed_time:.4f} s, Loss: {temp:.3f}"")

    return loss_track


def pose_train_lbfgs(
    llgloss,
    rot_v1,
    rot_v2,
    trans_vec,
    propose_com,
    propose_rmcom,
    lr=0.005,
    n_steps=50,
    loss_track=None,
):
    if loss_track is None:
        loss_track = []

    def closure():
        optimizer.zero_grad()
        temp_R = construct_SO3(rot_v1, rot_v2)
        temp_model = torch.matmul(propose_rmcom, temp_R) + propose_com + trans_vec
        loss = -llgloss(
            temp_model,
            bin_labels=None,
            return_Rfactors=False,
            num_batch=1,
            sub_ratio=1.0,
        )
        loss.backward()
        return loss

    start_time = time.time()

    optimizer = torch.optim.LBFGS(
        [rot_v1, rot_v2, trans_vec],
        lr=lr,
        line_search_fn=""strong_wolfe"",
        tolerance_change=1e-3,
        max_iter=1,
    )

    for k in range(n_steps):
        temp = optimizer.step(closure)
        loss_track.append(temp.item())
        elapsed_time = time.time() - start_time
        print(f""Step {k + 1}/{n_steps} - Time by optimizer: {elapsed_time:.4f} s"")
    return loss_track


def find_rigidbody_matrix_adam(
    llgloss, propose_com, propose_rmcom, device, added_chain
):
    q = torch.tensor([1.0, 0.0, 0.0, 0.0], dtype=torch.float32)
    unit_R = quaternions_to_SO3(q)
    v1, v2 = decompose_SO3(unit_R)
    rot_v1 = torch.tensor(utils.assert_numpy(v1), device=device, requires_grad=True)
    rot_v2 = torch.tensor(utils.assert_numpy(v2), device=device, requires_grad=True)
    trans_vec = torch.tensor([0.0, 0.0, 0.0], device=device, requires_grad=True)

    loss_track_pose = pose_train_adam_matrix(
        llgloss,
        rot_v1,
        rot_v2,
        trans_vec,
        propose_com,
        propose_rmcom,
        loss_track=[],
        added_chain=added_chain,
    )
    return trans_vec, rot_v1, rot_v2, loss_track_pose


def find_rigidbody_matrix_lbfgs(llgloss, propose_com, propose_rmcom, device):
    q = torch.tensor([1.0, 0.0, 0.0, 0.0], dtype=torch.float32)
    unit_R = quaternions_to_SO3(q)
    v1, v2 = decompose_SO3(unit_R)
    rot_v1 = torch.tensor(utils.assert_numpy(v1), device=device, requires_grad=True)
    rot_v2 = torch.tensor(utils.assert_numpy(v2), device=device, requires_grad=True)
    trans_vec = torch.tensor([0.0, 0.0, 0.0], device=device, requires_grad=True)

    loss_track_pose = pose_train_lbfgs(
        llgloss,
        rot_v1,
        rot_v2,
        trans_vec,
        propose_com,
        propose_rmcom,
        loss_track=[],
    )
    return trans_vec, rot_v1, rot_v2, loss_track_pose


def construct_SO3(v1, v2):
    """"""
    Construct a continuous representation of SO(3) rotation with two 3D vectors
    https://arxiv.org/abs/1812.07035
    Parameters
    ----------
    v1, v2: 3D tensors
        Real-valued tensor in 3D space
    Returns
    -------
    R, A 3*3 SO(3) rotation matrix
    """"""
    e1 = v1 / torch.norm(v1)
    u2 = v2 - e1 * torch.tensordot(e1, v2, dims=1)
    e2 = u2 / torch.norm(u2)
    e3 = torch.cross(e1, e2)
    R = torch.stack((e1, e2, e3)).T
    return R


def decompose_SO3(R, a=1, b=1, c=1):
    """"""
    Decompose the rotation matrix into the two vector representation
    This decomposition is not unique
    a, b, c can be set as arbitrary constants you like
    C != 0
    Parameters
    ----------
    R: 3*3 tensors
        Real-valued rotation matrix
    Returns
    -------
    v1, v2: Two real-valued 3D tensors, continuous representation of the rotation matrix
    """"""
    assert c != 0, ""Give a nonzero c!""
    v1 = a * R[:, 0]
    v2 = b * R[:, 0] + c * R[:, 1]

    return v1, v2


def quaternions_to_SO3(q):
    """"""
    Normalizes q and maps to group matrix.
    https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Quaternion-derived_rotation_matrix
    https://danceswithcode.net/engineeringnotes/quaternions/quaternions.html
    """"""
    # q = assert_tensor(q, torch.float32)
    q = q / q.norm(p=2, dim=-1, keepdim=True)
    r, i, j, k = q[..., 0], q[..., 1], q[..., 2], q[..., 3]

    return torch.stack(
        [
            1 - 2 * j * j - 2 * k * k,
            2 * (i * j - r * k),
            2 * (i * k + r * j),
            2 * (i * j + r * k),
            1 - 2 * i * i - 2 * k * k,
            2 * (j * k - r * i),
            2 * (i * k - r * j),
            2 * (j * k + r * i),
            1 - 2 * i * i - 2 * j * j,
        ],
        -1,
    ).view(*q.shape[:-1], 3, 3)


def select_CA_elements(data):
    return [element.endswith(""-CA"") for element in data]


def select_CA_from_craname(cra_name_list):
    boolean_mask = select_CA_elements(cra_name_list)
    cra_CAs_list = [
        element
        for element, mask in zip(cra_name_list, boolean_mask, strict=False)
        if mask
    ]
    return cra_CAs_list, boolean_mask


def calculate_mse_loss_per_residue(tensor1, tensor2, residue_numbers):
    mse_losses = []

    for residue in set(residue_numbers):
        # Find indices of atoms with the current residue number in tensor1
        indices1 = [i for i, x in enumerate(residue_numbers) if x == residue]

        if len(indices1) > 0:
            # Extract coordinates for atoms with the current residue number in tensor1
            coords1 = tensor1[indices1, :]

            # Extract coordinates for atoms with the current residue number in tensor2
            coords2 = tensor2[indices1, :]

            # Calculate MSE loss for coordinates of atoms with the same residue number
            mse_loss = torch.sqrt(torch.sum((coords1 - coords2) ** 2))
            mse_losses.append(mse_loss.item())

    return mse_losses


def write_pdb_with_positions(input_pdb_file, positions, output_pdb_file):
    # positions here expected to be rounded to 3 decimal points

    with open(input_pdb_file) as f_in, open(output_pdb_file, ""w"") as f_out:
        for line in f_in:
            if line.startswith(""ATOM""):
                atom_info = line[
                    :30
                ]  # Extract the first 30 characters containing atom information
                rounded_pos = positions.pop(
                    0
                )  # Pop the first rounded position from the list
                new_line = (
                    f""{atom_info}{rounded_pos[0]:8.3f}{rounded_pos[1]:8.3f}{rounded_pos[2]:8.3f}""
                    + line[54:]
                )
                f_out.write(new_line)
            else:
                f_out.write(line)


def fractionalize_torch(atom_pos_orth, unitcell, device=None):
    """"""
    Apply symmetry operations to real space asu model coordinates

    Parameters
    ----------
    atom_pos_orth: tensor, [N_atom, 3]
        ASU model ccordinates

    Will return fractional coordinates; Otherwise will return orthogonal coordinates

    Return
    ------
    atom_pos_sym_oped, [N_atoms, N_ops, 3] tensor, fractional or orthogonal coordinates
    """"""
    if device is None:
        device = utils.try_gpu()
    atom_pos_orth.to(device=device)
    orth2frac_tensor = torch.tensor(
        unitcell.fractionalization_matrix.tolist(), device=device
    )
    atom_pos_frac = torch.tensordot(atom_pos_orth, orth2frac_tensor.T, 1)

    return atom_pos_frac


def extract_allatoms(outputs, feats, cra_name_sfc: list):
    N_atom_types = len(residue_constants.atom_types)  # by default 37
    atom_mask = outputs[""final_atom_mask""]  # shape [n_res, N_atom_types]
    n_res = atom_mask.shape[0]

    # get atom positions in vectorized manner
    positions_atom = outputs[""final_atom_positions""][atom_mask == 1.0]  # [n_atom, 3]

    # get plddt in vectorized manner
    plddt_atom = (
        outputs[""plddt""].reshape([-1, 1]).repeat([1, N_atom_types])[atom_mask == 1.0]
    )  # shape [n_atom,]

    # get cra_name from AF2, [chain-resid-resname-atomname,...]
    res_names = utils.assert_numpy(
        [i + ""-"" for i in list(residue_constants.restype_1to3.values())] + [""UNK-""]
    )
    aatype = feats[
        ""aatype""
    ]  # TODO: tackle the match between UNK and real non-standard aa name from SFC
    aatype_1d = res_names[utils.assert_numpy(aatype[:, 0], arr_type=int)]
    chain_resid = np.array([
        ""A-"" + str(i) + ""-"" for i in range(n_res)
    ])  # TODO: here we assume all residues in same chain A
    crname_repeats = (
        np.char.add(chain_resid, aatype_1d).reshape(-1, 1).repeat(N_atom_types, axis=-1)
    )  # [n_res, N_atom_types]
    crname_atom = crname_repeats[utils.assert_numpy(atom_mask) == 1]
    atom_types_repeats = (
        utils.assert_numpy(residue_constants.atom_types)
        .reshape(1, N_atom_types)
        .repeat(n_res, axis=0)
    )  # [n_res, N_atom_types]
    aname_atom = atom_types_repeats[utils.assert_numpy(atom_mask) == 1]
    cra_name_af = np.char.add(crname_atom, aname_atom).tolist()

    # reorder and assert the same topology
    reorder_index = utils.assert_numpy(
        [cra_name_af.index(i) for i in cra_name_sfc], arr_type=int
    )

    assert np.all(
        utils.assert_numpy(cra_name_af)[reorder_index]
        == utils.assert_numpy(cra_name_sfc)
    ), ""Mismatch topolgy between AF and SFC!""

    return positions_atom[reorder_index], plddt_atom[reorder_index]


def extract_atoms_and_backbone(outputs, feats):
    atom_types = residue_constants.atom_types
    atom_mask = outputs[""final_atom_mask""]
    pdb_lines = []
    aatype = feats[""aatype""]
    atom_positions = outputs[""final_atom_positions""]
    selected_atoms_mask = []

    n = aatype.shape[0]
    for i in range(n):
        for atom_name, pos, mask in zip(
            atom_types, atom_positions[i], atom_mask[i], strict=False
        ):
            if mask < 0.5:
                continue
            pdb_lines.append(pos)

            if atom_name in [""C"", ""CA"", ""O"", ""N""]:
                selected_atoms_mask.append(torch.tensor(1, dtype=torch.bool))
            else:
                selected_atoms_mask.append(torch.tensor(0, dtype=torch.bool))

    return torch.stack(pdb_lines), torch.stack(selected_atoms_mask)


def extract_bfactors(prot):
    atom_mask = prot.atom_mask
    aatype = prot.aatype
    b_factors = prot.b_factors

    b_factor_lines = []

    n = aatype.shape[0]
    # Add all atom sites.
    for i in range(n):
        for mask, b_factor in zip(atom_mask[i], b_factors[i], strict=False):
            if mask < 0.5:
                continue

            b_factor_lines.append(b_factor)

    return np.array(b_factor_lines)


def kabsch_align_matrices(tensor1, tensor2):
    # Center the atoms by subtracting their centroids
    centroid1 = torch.mean(tensor1, dim=0, keepdim=True)
    tensor1_centered = tensor1 - centroid1
    centroid2 = torch.mean(tensor2, dim=0, keepdim=True)
    tensor2_centered = tensor2 - centroid2

    # Calculate the covariance matrix
    covariance_matrix = torch.matmul(tensor2_centered.t(), tensor1_centered)

    # Perform Singular Value Decomposition (SVD) on the covariance matrix
    U, _, Vt = torch.linalg.svd(covariance_matrix)

    # Calculate the rotation matrix
    rotation_matrix = torch.matmul(U, Vt)

    # Ensure the determinant is positive
    if torch.det(rotation_matrix) < 0:
        Vt[:, -1] *= -1
        rotation_matrix = torch.matmul(Vt.t(), U.t())

    # Ensure the rotation matrix is not a reflection
    if torch.det(rotation_matrix) < 0:
        rotation_matrix[:, 0] *= -1

    return centroid1, centroid2, rotation_matrix


def select_confident_atoms(current_pos, target_pos, bfacts=None, b_thresh=400.0):
    if bfacts is None:
        # If bfacts is None, set mask to all True
        reshaped_mask = torch.ones_like(current_pos, dtype=torch.bool)
    else:
        # Boolean mask for confident atoms
        mask = bfacts < b_thresh
        reshaped_mask = mask.unsqueeze(1).expand_as(current_pos)

    # Select confident atoms using the mask
    current_pos_conf = torch.flatten(current_pos)[torch.flatten(reshaped_mask)]
    target_pos_conf = torch.flatten(target_pos)[torch.flatten(reshaped_mask)]

    N = current_pos_conf.numel() // 3

    return current_pos_conf.view(N, 3), target_pos_conf.view(N, 3)


def align_tensors(tensor1, centroid1, centroid2, rotation_matrix):
    """"""
    Apply rotation and translation to a tensor
    """"""
    tensor1_centered = tensor1 - centroid1
    # Apply the rotation and translation to align the first tensor to the second one
    aligned_tensor1 = torch.matmul(tensor1_centered, rotation_matrix.T) + centroid2

    return aligned_tensor1


def weighted_kabsch_svd(P, Q, weights=None):
    """"""
    Computes the optimal rotation matrix that minimizes the weighted RMSD
    between two sets of corresponding points P and Q using SVD.

    Args:
        P (np.ndarray): Reference points, shape (N, 3).
        Q (np.ndarray): Points to align to P, shape (N, 3).
        weights (np.ndarray, optional): Weights for each point, shape (N,).

    Returns:
        np.ndarray: Optimal rotation matrix (3, 3).
    """"""
    if P.shape[0] < 3:
        raise ValueError(""Need at least 3 points for stable alignment"")

    # Center the point clouds using weighted average
    centroid_P = np.average(P, axis=0, weights=weights)
    centroid_Q = np.average(Q, axis=0, weights=weights)
    P_centered = P - centroid_P
    Q_centered = Q - centroid_Q

    # Compute weighted covariance matrix
    H = Q_centered.T @ np.diag(weights if weights is not None else 1) @ P_centered

    # Perform SVD
    try:
        U, _, Vt = np.linalg.svd(H)
    except np.linalg.LinAlgError:
        return np.identity(3)  # Fallback for unstable SVD

    # Calculate rotation matrix R
    R = Vt.T @ U.T

    # Handle reflection case (ensures a right-handed coordinate system)
    if np.linalg.det(R) < 0:
        Vt[-1, :] *= -1
        R = Vt.T @ U.T

    return R


def iterative_kabsch_alignment(
    moving_tensor,
    ref_tensor,
    cra_name,
    weights=None,
    exclude_res=None,
    domain_segs=None,
    cutoff=2.0,
    cycles=5,
):
    """"""
    Performs Kabsch alignment with iterative outlier rejection on protein structures.

    Args:
        moving_tensor (torch.Tensor): Coords to move, shape [n_points, 3].
        ref_tensor (torch.Tensor): Reference coords, shape [n_points, 3].
        cra_name (List[str]): Chain-residue-atom identifiers, [n_points].
        weights (torch.Tensor|np.ndarray, optional): Weights for the alignment.
        exclude_res (List[int], optional): Residue IDs to exclude.
        domain_segs (List[int], optional): Residue IDs defining domain boundaries.
        cutoff (float): Distance cutoff in Angstroms for outlier rejection.
        cycles (int): Max number of refinement cycles. Set to 0 for a single
                      alignment without outlier rejection.

    Returns:
        torch.Tensor: The aligned moving_tensor, shape [n_points, 3].
    """"""
    # --- 1. Initial Filtering Setup ---
    backbone_bool = np.array([i.split(""-"")[-1] in [""N"", ""CA"", ""C""] for i in cra_name])
    resid = np.array([int(i.split(""-"")[1]) for i in cra_name])
    minid, maxid = resid.min(), resid.max()

    if exclude_res is None:
        residue_bool = (resid > minid + 4) & (resid < maxid - 4)
    else:
        residue_bool = np.isin(resid, exclude_res, invert=True)

    # --- 2. Define Domains ---
    if domain_segs is None:
        domain_ranges = [[minid, maxid + 1]]
    else:
        domain_ranges = []
        start = minid
        sorted_segs = sorted(domain_segs)
        for i, seg in enumerate(sorted_segs):
            domain_ranges.append([start, seg])
            start = seg
            if i == len(sorted_segs) - 1:
                domain_ranges.append([start, maxid + 1])

    # --- 3. Process Each Domain ---
    aligned_pos = moving_tensor.clone()
    for domain_start, domain_end_notin in domain_ranges:
        domain_bool = (resid >= domain_start) & (resid < domain_end_notin)
        working_set_mask = backbone_bool & residue_bool & domain_bool

        if not working_set_mask.any():
            continue

        moving_align_coords = utils.assert_numpy(moving_tensor)[working_set_mask]
        ref_align_coords = utils.assert_numpy(ref_tensor)[working_set_mask]
        if moving_align_coords.shape[0] < 3:
            continue

        align_weights = (
            utils.assert_numpy(weights)[working_set_mask]
            if weights is not None
            else None
        )  # noqa: E501

        # --- 4. Iterative Alignment for the Current Domain ---
        inlier_indices = np.arange(moving_align_coords.shape[0])
        final_R = np.identity(3)

        for cycle in range(cycles + 1):
            P_iter = ref_align_coords[inlier_indices]
            Q_iter = moving_align_coords[inlier_indices]
            w_iter = (
                align_weights[inlier_indices] if align_weights is not None else None
            )  # noqa: E501
            try:
                R_iter = weighted_kabsch_svd(P_iter, Q_iter, weights=w_iter)
            except ValueError:
                break
            final_R = R_iter

            if cycle == cycles:
                break

            # Apply transform and find new inliers
            centroid_Q = np.average(Q_iter, axis=0, weights=w_iter)
            centroid_P = np.average(P_iter, axis=0, weights=w_iter)
            t_iter = centroid_P - final_R @ centroid_Q

            transformed_align_coords = (final_R @ moving_align_coords.T).T + t_iter
            distances = np.linalg.norm(
                ref_align_coords - transformed_align_coords, axis=1
            )  # noqa: E501
            new_inlier_indices = np.where(distances < cutoff)[0]

            if len(new_inlier_indices) < 3 or np.array_equal(
                inlier_indices, new_inlier_indices
            ):  # noqa: E501
                break
            inlier_indices = new_inlier_indices
        # --- 5. Apply Final Transformation to the Entire Domain ---
        P_final = ref_align_coords[inlier_indices]
        Q_final = moving_align_coords[inlier_indices]
        w_final = align_weights[inlier_indices] if align_weights is not None else None

        # Calculate final centroids based on the final set of inliers
        final_centroid_moving = np.average(Q_final, axis=0, weights=w_final)
        final_centroid_ref = np.average(P_final, axis=0, weights=w_final)
        # Get all points in the domain and convert components to tensors
        moving_domain_tensor = moving_tensor[domain_bool]
        R_torch = torch.tensor(
            final_R, dtype=moving_tensor.dtype, device=moving_tensor.device
        )
        centroid1_torch = torch.tensor(
            final_centroid_moving,
            dtype=moving_tensor.dtype,
            device=moving_tensor.device,
        )
        centroid2_torch = torch.tensor(
            final_centroid_ref, dtype=moving_tensor.dtype, device=moving_tensor.device
        )

        # Use your function to apply the final transformation
        aligned_domain_tensor = align_tensors(
            moving_domain_tensor, centroid1_torch, centroid2_torch, R_torch
        )
        aligned_pos[domain_bool] = aligned_domain_tensor

    return aligned_pos


def weighted_kabsch(
    moving_tensor,
    ref_tensor,
    cra_name,
    weights=None,
    exclude_res=None,
    domain_segs=None,
):
    """"""
    Weighted Kabsch Alignment, using scipy implementation
    'scipy.spatial.transform.Rotation.align_vectors'

    Args:
        moving_tensor: torch.Tensor, [n_points, 3]
            coordinates you want to move

        ref_tensor: torch.Tensor, [n_points, 3]
            reference coordinates you want to align to

        cra_name: List[str], [n_points]
            chain-residue-atom name of each atom

        weights: torch.Tensor | np.ndarray, [n_points]
            weights used in the Kabsch Alignment

        exclude_res: List[int] or None
            list of resid you want to exclude from the alignment

        domain_segs: List[int] or None
            List of resid as boundary between different domains,
            i.e. domain_segs = [196] means there are two domains, [0-195] and [196-END]

    Returns:
        aligned_tensor: torch.Tensor, [n_points, 3]
    """"""
    # use only backbone atoms
    backbone_bool = np.array([i.split(""-"")[-1] in [""N"", ""CA"", ""C""] for i in cra_name])

    # exclude some residues, by default 5 residues on both ends
    resid = [int(i.split(""-"")[1]) + 1 for i in cra_name]
    minid = min(resid)
    maxid = max(resid)
    if exclude_res is None:
        residue_bool = np.array([(i > minid + 4) and (i < (maxid - 4)) for i in resid])
    else:
        residue_bool = np.array([i not in exclude_res for i in resid])

    if domain_segs is None:
        domain_ranges = [[minid, maxid + 1]]
    else:
        domain_ranges = []
        start = minid
        for i, seg in enumerate(domain_segs):
            domain_ranges.append([start, seg])
            start = seg
            if i == len(domain_segs) - 1:
                domain_ranges.append([start, maxid + 1])
    aligned_pos = torch.ones_like(moving_tensor)
    for domain_range in domain_ranges:
        domain_start, domain_end_notin = domain_range
        domain_bool = np.array([
            (i >= domain_start) and (i < domain_end_notin) for i in resid
        ])
        working_set = backbone_bool & residue_bool & domain_bool
        moving_tensor_np = utils.assert_numpy(moving_tensor)[working_set]
        ref_tensor_np = utils.assert_numpy(ref_tensor)[working_set]
        if weights is None:
            weights_np = None
        else:
            weights_np = utils.assert_numpy(weights)[working_set]

        com_moving = np.average(moving_tensor_np, axis=0, weights=weights_np)
        com_ref = np.average(ref_tensor_np, axis=0, weights=weights_np)
        C, _ = Rotation.align_vectors(
            ref_tensor_np - com_ref, moving_tensor_np - com_moving, weights=weights_np
        )

        rotation_matrix = torch.tensor(C.as_matrix()).to(moving_tensor)
        centroid1 = torch.tensor(com_moving).to(moving_tensor)
        centroid2 = torch.tensor(com_ref).to(moving_tensor)
        aligned_pos[domain_bool] = align_tensors(
            moving_tensor[domain_bool], centroid1, centroid2, rotation_matrix
        )
    return aligned_pos


def cutoff_kabsch(
    moving_tensor, ref_tensor, cra_name, pseudoB, threshB=None, exclude_res=None
):
    # use only backbone atoms
    backbone_bool = np.array([i.split(""-"")[-1] in [""N"", ""CA"", ""C""] for i in cra_name])

    # exclude some residues, by default 5 residues on both ends
    resid = [int(i.split(""-"")[1]) for i in cra_name]
    if exclude_res is None:
        minid = min(resid)
        maxid = max(resid)
        residue_bool = np.array([(i > minid + 4) and (i < (maxid - 4)) for i in resid])
    else:
        residue_bool = np.array([i not in exclude_res for i in resid])

    if threshB is None:
        bfactor_bool = np.ones_like(backbone_bool, dtype=bool)
    else:
        bfactor_bool = pseudoB < threshB

    working_set = backbone_bool & residue_bool & bfactor_bool
    centroid1, centroid2, rotation_matrix = kabsch_align_matrices(
        moving_tensor[working_set].detach(), ref_tensor[working_set].detach()
    )
    aligned_pos = align_tensors(moving_tensor, centroid1, centroid2, rotation_matrix)

    return aligned_pos


def set_new_positions(orth_pos, frac_pos, sfmodel, device=None):
    if device is None:
        device = utils.try_gpu()
    sfmodel.atom_pos_orth = torch.squeeze(orth_pos, dim=1).to(device)
    sfmodel.atom_pos_frac = torch.squeeze(frac_pos, dim=1).to(device)
    return sfmodel


def transfer_positions(aligned_pos, sfcalculator_model, device=None):
    if device is None:
        device = utils.try_gpu()
    # Transfer positions to sfcalculator
    frac_pos = fractionalize_torch(
        aligned_pos,
        sfcalculator_model.unit_cell,
        sfcalculator_model.space_group,
        device=device,
    )
    sfcalculator_model = set_new_positions(
        aligned_pos, frac_pos, sfcalculator_model, device=device
    )

    # add bfactor calculation based on plddt
    # sfcalculator_model.atom_b_iso = b_factors

    sfcalculator_model = update_sfcalculator(sfcalculator_model)
    return sfcalculator_model


def update_sfcalculator(sfmodel):
    sfmodel.inspect_data(verbose=False)
    sfmodel.calc_fprotein()
    sfmodel.calc_fsolvent()
    sfmodel.init_scales(requires_grad=True)
    sfmodel.calc_ftotal()
    return sfmodel


def initialize_model_frac_pos(model_file, tng_file, device=None):
    if device is None:
        device = utils.try_gpu()
    sfcalculator_model = SFcalculator(
        model_file,
        tng_file,
        expcolumns=[""FP"", ""SIGFP""],
        set_experiment=True,
        testset_value=0,
        device=device,
    )
    target_pos = sfcalculator_model.atom_pos_orth
    sfcalculator_model.atom_pos_frac = sfcalculator_model.atom_pos_frac * 0.00
    sfcalculator_model.atom_pos_orth = sfcalculator_model.atom_pos_orth * 0.00
    sfcalculator_model.atom_pos_frac.requires_grad = True

    return sfcalculator_model, target_pos
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/refinement_mse.py",".py","10695","323","import os
import time
import uuid
import warnings

import numpy as np
import torch
import yaml
from openfold.config import model_config
from pydantic import BaseModel
from SFC_Torch import PDBParser
from tqdm import tqdm

import rocket
from rocket import coordinates as rk_coordinates
from rocket import refinement_utils as rkrf_utils
from rocket import utils as rk_utils

PRESET = ""model_1""
# THRESH_B = None
EXCLUDING_RES = None


class RocketMSERefinmentConfig(BaseModel):
    path: str
    file_root: str
    bias_version: int
    iterations: int
    template_pdb: str | None = None
    cuda_device: int = (0,)
    num_of_runs: int = 1
    init_recycling: int = (1,)
    additive_learning_rate: float
    multiplicative_learning_rate: float
    weight_decay: float | None = 0.0001  # TODO: should default be 0.0?
    rbr_opt_algorithm: str
    rbr_lbfgs_learning_rate: float
    note: str = """"
    free_flag: str
    testset_value: int
    verbose: bool = False
    starting_bias: str | None = None
    starting_weights: str | None = None
    uuid_hex: str | None = None

    # intercept them upload load/save and cast to string as appropriate
    def to_yaml_file(self, file_path: str) -> None:
        with open(file_path, ""w"") as file:
            yaml.dump(self.dict(), file)

    @classmethod
    def from_yaml_file(self, file_path: str):
        with open(file_path) as file:
            payload = yaml.safe_load(file)
        return RocketMSERefinmentConfig.model_validate(payload)


def run_refinement_mse(*, config: RocketMSERefinmentConfig) -> str:
    ############ 1. Global settings ############
    # Device
    device = f""cuda:{config.cuda_device}""

    # Configure input paths
    input_pdb = f""{config.path}/{config.file_root}/{config.file_root}-pred-aligned.pdb""

    # Configure output path
    # Generate uuid for this run
    if config.uuid_hex is None:
        refinement_run_uuid = uuid.uuid4().hex
    else:
        refinement_run_uuid = config.uuid_hex
    output_directory_path = (
        f""{config.path}/{config.file_root}/outputs/{refinement_run_uuid}/{config.note}""
    )
    try:
        os.makedirs(output_directory_path, exist_ok=True)
    except FileExistsError:
        print(
            f""Warning: Directory '{output_directory_path}' already exists. Overwriting.""
        )
    print(
        f""System: {config.file_root}, run ID: {refinement_run_uuid!s}, Note: {config.note}"",  # noqa: E501
        flush=True,
    )
    if not config.verbose:
        warnings.filterwarnings(""ignore"")

    ############ 2. Initializations ############
    target_pdb = PDBParser(input_pdb)
    reference_pos = torch.tensor(
        target_pdb.atom_pos, dtype=torch.float32, device=device
    )
    mseloss = rocket.MSElossBB(target_pdb, target_pdb, device)

    # CA mask and residue numbers for track
    cra_calphas_list, calphas_mask = rk_coordinates.select_CA_from_craname(
        target_pdb.cra_name
    )

    # Use initial pos B factor instead of best pos B factor for weighted L2
    init_pos_bfactor = torch.tensor(
        target_pdb.atom_b_iso, dtype=torch.float32, device=device
    )
    bfactor_weights = rk_utils.weighting_torch(init_pos_bfactor, cutoff2=20.0)

    # Model initialization
    version_to_class = {
        1: rocket.MSABiasAFv1,
        2: rocket.MSABiasAFv2,
        3: rocket.MSABiasAFv3,
        4: rocket.TemplateBiasAF,
    }
    af_bias = version_to_class[config.bias_version](
        model_config(PRESET, train=True), PRESET
    ).to(device)
    af_bias.freeze()  # Free all AF2 parameters to save time

    # Optimizer settings and initialization
    lr_a = config.additive_learning_rate
    lr_m = config.multiplicative_learning_rate

    # Initialize best Rfree weights and bias for Phase 1
    best_loss = float(""inf"")
    best_run = None
    best_msa_bias = None
    best_feat_weights = None
    best_iter = None
    best_pos = reference_pos

    for n in range(config.num_of_runs):
        run_id = rkrf_utils.number_to_letter(n)

        # Initialize the processed dict space
        device_processed_features, feature_key, features_at_it_start = (
            rkrf_utils.init_processed_dict(
                bias_version=config.bias_version,
                path=config.path,
                file_root=config.file_root,
                device=device,
                template_pdb=config.template_pdb,
                PRESET=PRESET,
            )
        )
        # Initialize bias
        device_processed_features, optimizer, bias_names = rkrf_utils.init_bias(
            device_processed_features=device_processed_features,
            bias_version=config.bias_version,
            device=device,
            lr_a=lr_a,
            lr_m=lr_m,
            weight_decay=config.weight_decay,
            starting_bias=config.starting_bias,
            starting_weights=config.starting_weights,
        )

        # List initialization for saving values
        mse_losses = []
        time_by_epoch = []
        memory_by_epoch = []
        all_pldtts = []
        mean_it_plddts = []
        absolute_feats_changes = []

        early_stopper = rkrf_utils.EarlyStopper(patience=50, min_delta=0.1)

        progress_bar = tqdm(
            range(config.iterations),
            desc=f""{config.file_root}, uuid: {refinement_run_uuid[:4]}, run: {run_id}"",
        )

        ############ 3. Run Refinement ############
        for iteration in progress_bar:
            start_time = time.time()
            optimizer.zero_grad()

            # Avoid passing through graph a second time
            device_processed_features[feature_key] = (
                features_at_it_start.detach().clone()
            )

            # AF pass
            if iteration == 0:
                af2_output, prevs = af_bias(
                    device_processed_features,
                    [None, None, None],
                    num_iters=config.init_recycling,
                    bias=False,
                )
                prevs = [tensor.detach() for tensor in prevs]

            deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
            af2_output, __ = af_bias(
                device_processed_features, deep_copied_prevs, num_iters=1, bias=True
            )

            # Position Kabsch Alignment
            aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
                af2_output=af2_output,
                device_processed_features=device_processed_features,
                cra_name=target_pdb.cra_name,
                best_pos=best_pos,
                exclude_res=EXCLUDING_RES,
            )

            all_pldtts.append(plddts_res)
            mean_it_plddts.append(np.mean(plddts_res))

            # MSE loss
            loss = mseloss.forward(aligned_xyz, bfactor_weights)

            mse_losses.append(loss.item())

            # check if current Rfree is the best so far
            if mse_losses[-1] < best_loss:
                best_loss = mse_losses[-1]
                best_msa_bias = (
                    device_processed_features[""msa_feat_bias""].detach().cpu().clone()
                )
                best_feat_weights = (
                    device_processed_features[""msa_feat_weights""].detach().cpu().clone()
                )
                best_run = run_id
                best_iter = iteration
                best_pos = aligned_xyz.detach().clone()

            # Save postRBR PDB
            mseloss.pdb_obj.atom_b_iso = rk_utils.assert_numpy(pseudo_Bs)
            mseloss.pdb_obj.atom_pos = rk_utils.assert_numpy(aligned_xyz)
            mseloss.pdb_obj.savePDB(
                f""{output_directory_path!s}/{run_id}_{iteration}_aligned.pdb""
            )

            progress_bar.set_postfix(
                MSE=f""{loss.item():.2f}"",
                memory=f""{torch.cuda.max_memory_allocated() / 1024**3:.1f}G"",
            )

            loss.backward()

            if early_stopper.early_stop(loss.item()):
                break

            optimizer.step()

            time_by_epoch.append(time.time() - start_time)
            memory_by_epoch.append(torch.cuda.max_memory_allocated() / 1024**3)

            # Save the absolute difference in mean contribution from each residue
            # channel from previous iteration
            if config.bias_version == 4:
                features_at_step_end = (
                    device_processed_features[""template_torsion_angles_sin_cos""][..., 0]
                    .detach()
                    .clone()
                )
                mean_change = torch.mean(
                    torch.abs(features_at_step_end - features_at_it_start[..., 0]),
                    dim=(0, 2, 3),
                )
            else:
                features_at_step_end = (
                    device_processed_features[""msa_feat""][:, :, 25:48, 0]
                    .detach()
                    .clone()
                )
                mean_change = torch.mean(
                    torch.abs(
                        features_at_step_end - features_at_it_start[:, :, 25:48, 0]
                    ),
                    dim=(0, 2),
                )
            absolute_feats_changes.append(rk_utils.assert_numpy(mean_change))

        ####### Save data
        # Average plddt per iteration
        np.save(
            f""{output_directory_path!s}/mean_it_plddt_{run_id}.npy"",
            np.array(mean_it_plddts),
        )

        # MSE per iteration
        np.save(
            f""{output_directory_path!s}/MSE_it_{run_id}.npy"",
            rk_utils.assert_numpy(mse_losses),
        )

        np.save(
            f""{output_directory_path!s}/time_it_{run_id}.npy"",
            rk_utils.assert_numpy(time_by_epoch),
        )

        np.save(
            f""{output_directory_path!s}/memory_it_{run_id}.npy"",
            rk_utils.assert_numpy(memory_by_epoch),
        )

        # Absolute MSA change per column per iteration
        np.save(
            f""{output_directory_path!s}/MSA_changes_it_{run_id}.npy"",
            rk_utils.assert_numpy(absolute_feats_changes),
        )

        # Mean plddt per residue (over iterations)
        np.save(
            f""{output_directory_path!s}/mean_plddt_res_{run_id}.npy"",
            np.mean(np.array(all_pldtts), axis=0),
        )

    # Save the best msa_bias and feat_weights
    torch.save(
        best_msa_bias,
        f""{output_directory_path!s}/best_msa_bias_{best_run}_{best_iter}.pt"",
    )

    torch.save(
        best_feat_weights,
        f""{output_directory_path!s}/best_feat_weights_{best_run}_{best_iter}.pt"",
    )

    config.to_yaml_file(f""{output_directory_path!s}/config.yaml"")

    return refinement_run_uuid
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/refinement_cryoem.py",".py","25211","658","import glob
import os
import shutil
import time
import uuid

import numpy as np
import torch
from loguru import logger
from openfold.config import model_config
from openfold.data import data_pipeline, feature_pipeline
from tqdm import tqdm

import rocket
from rocket import coordinates as rk_coordinates
from rocket import refinement_utils as rkrf_utils
from rocket import utils as rk_utils
from rocket.cryo import structurefactors as cryo_sf
from rocket.cryo import targets as cryo_targets
from rocket.cryo import utils as cryo_utils
from rocket.refinement_config import RocketRefinmentConfig

PRESET = ""model_1_ptm""
EXCLUDING_RES = None
N_BINS = 20


def run_cryoem_refinement(config: RocketRefinmentConfig | str) -> RocketRefinmentConfig:
    if isinstance(config, str):
        config = RocketRefinmentConfig.from_yaml_file(config)
    assert config.datamode == ""cryoem"", ""Make sure to set datamode to 'cryoem'!""

    ############ 1. Global settings ############
    # Device
    device = f""cuda:{config.cuda_device}""

    # Using LBFGS or Adam in RBR
    if config.rbr_opt_algorithm == ""lbfgs"":
        RBR_LBFGS = True
    elif config.rbr_opt_algorithm == ""adam"":
        RBR_LBFGS = False
    else:
        raise ValueError(""rbr_opt only supports lbfgs or adam"")

    target_id = config.file_id
    path = config.path
    mtz_file = f""{path}/ROCKET_inputs/{target_id}-Edata.mtz""
    try:
        input_pdb = glob.glob(config.input_pdb)[0]
    except Exception as err:
        raise ValueError(""input_pdb path is not valid!"") from err
    note = config.note
    bias_version = config.bias_version
    iterations = config.iterations
    num_of_runs = config.num_of_runs

    if config.uuid_hex:
        refinement_run_uuid = config.uuid_hex
    else:
        config.paths.uuid_hex = uuid.uuid4().hex[:10]
        refinement_run_uuid = config.uuid_hex
    output_directory_path = f""{path}/ROCKET_outputs/{refinement_run_uuid}/{note}""
    try:
        os.makedirs(output_directory_path, exist_ok=True)
    except FileExistsError:
        logger.info(
            f""Warning: Directory '{output_directory_path}' already exists. Overwriting.""
        )
        logger.info(
            f""System: {target_id}, run ID: {refinement_run_uuid!s}, Note: {note}"",
        )

    ############ 2. Initializations ############
    # Do downsampling if specified
    if config.downsample_ratio:
        if config.downsample_ratio == 1:
            logger.info(""Downsampling ratio is 1. Skipping downsampling..."")
        else:
            logger.info(
                f""Downsampling reciprocal space, axis ratio: {config.downsample_ratio}""
            )
            mtz_file = cryo_utils.downsample_data(mtz_file, config.downsample_ratio)

    # Apply resolution cutoff to the reflection file
    if config.min_resolution is not None or config.max_resolution is not None:
        mtz_file = rk_utils.apply_resolution_cutoff(
            mtz_file,
            min_resolution=config.min_resolution,
            max_resolution=config.max_resolution,
        )
    if type(mtz_file) is str:
        mtz_file = rk_utils.load_mtz(mtz_file)

    # Prepare reusable variables for RSCC calculation
    dobs_values = mtz_file[""Dobs""].to_numpy()
    structure_factor = mtz_file.to_structurefactor(
        sf_key=""Emean"", phase_key=""PHIEmean""
    ).to_numpy()
    rscc_reference_Fmap = torch.tensor(dobs_values * structure_factor, device=device)

    # Initialize SFC
    cryo_sfc = cryo_sf.initial_cryoSFC(
        input_pdb, mtz_file, ""Emean"", ""PHIEmean"", device, N_BINS
    )

    sfc_rbr = cryo_sf.initial_cryoSFC(
        input_pdb, mtz_file, ""Emean"", ""PHIEmean"", device, N_BINS
    )
    # prepare reusable variables for RSCC calculation
    gridsize = mtz_file.get_reciprocal_grid_size(sample_rate=3.0)
    Rg = torch.tensor(
        rk_utils.g_function_np(2 * cryo_sfc.dmin, 1 / cryo_sfc.dHKL), device=device
    )
    uc_volume = cryo_sfc.unit_cell.volume

    reference_pos = cryo_sfc.atom_pos_orth.clone()
    target_seq = cryo_sfc._pdb.sequence
    cra_calphas_list, calphas_mask = rk_coordinates.select_CA_from_craname(
        cryo_sfc.cra_name
    )

    # Initialize RSCC Bfactor if phase1
    if ""phase1"" in config.note:
        Fprotein_plddt = cryo_sfc.calc_fprotein(Return=True)
        ccmap = rk_utils.get_rscc_from_Fmap(
            Fprotein_plddt.detach(),
            rscc_reference_Fmap,
            cryo_sfc.HKL_array,
            gridsize,
            Rg,
            uc_volume,
        )
        atom_cc = rk_utils.interpolate_grid_points(
            ccmap, cryo_sfc.atom_pos_frac.cpu().numpy()
        )
        rscc_bfactor = torch.tensor(
            rk_utils.get_b_from_CC(atom_cc, cryo_sfc.dmin),
            dtype=torch.float32,
            device=device,
        )

        cryo_sfc.atom_b_iso = rscc_bfactor.clone().detach()
        sfc_rbr.atom_b_iso = rscc_bfactor.clone().detach()

    # Use initial pos B factor instead of best pos B factor for weighted L2
    init_pos_bfactor = cryo_sfc.atom_b_iso.clone()
    # Ad hoc settings for B-factor weighting cutoffs
    cutoff1 = np.quantile(init_pos_bfactor.cpu().numpy(), 0.3)
    cutoff2 = cutoff1 * 1.5
    bfactor_weights = rk_utils.weighting_torch(init_pos_bfactor, cutoff1, cutoff2)
    bfactor_weights = bfactor_weights / torch.sum(bfactor_weights)

    # residue_numbers = [int(name.split(""-"")[1]) for name in cra_calphas_list]

    # LLG initialization
    cryo_llgloss = cryo_targets.LLGloss(cryo_sfc, mtz_file)
    cryo_llgloss_rbr = cryo_targets.LLGloss(sfc_rbr, mtz_file)

    # Model initialization
    version_to_class = {
        1: rocket.MSABiasAFv1,
        2: rocket.MSABiasAFv2,
        3: rocket.MSABiasAFv3,
        4: rocket.TemplateBiasAF,
    }
    af_bias = version_to_class[bias_version](
        model_config(PRESET, train=True), PRESET
    ).to(device)
    af_bias.freeze()

    # Optimizer settings and initialization
    if ""phase1"" in config.note:
        lr_a = config.additive_learning_rate
        lr_m = config.multiplicative_learning_rate
    elif ""phase2"" in config.note:
        lr_a = config.phase2_final_lr
        lr_m = config.phase2_final_lr

    best_loss = float(""inf"")
    best_msa_bias = None
    best_feat_weights = None
    best_run = None
    best_iter = None

    # MH edit @ Nov 8th, 2024: Support to use msa as input
    if config.msa_subratio is not None and config.input_msa is None:
        config.input_msa = ""alignments""  # default dir for alignment

    recombination_bias = None
    if config.input_msa is not None:
        fasta_path = [
            f
            for ext in (""*.fa"", ""*.fasta"")
            for f in glob.glob(os.path.join(config.path, ext))
        ][0]
        a3m_path = os.path.join(config.path, config.input_msa)
        if os.path.isfile(a3m_path):
            msa_name, ext = os.path.splitext(os.path.basename(a3m_path))
            alignment_dir = os.path.join(os.path.dirname(a3m_path), ""tmp_align"")
            os.makedirs(alignment_dir, exist_ok=True)
            shutil.copy(a3m_path, os.path.join(alignment_dir, msa_name + "".a3m""))
            tmp_align = True
        elif os.path.isdir(a3m_path):
            alignment_dir = a3m_path
            tmp_align = False
        data_processor = data_pipeline.DataPipeline(template_featurizer=None)
        feature_dict = rkrf_utils.generate_feature_dict(
            fasta_path,
            alignment_dir,
            data_processor,
        )
        # prepare featuerizer
        afconfig = model_config(PRESET)
        afconfig.data.common.max_recycling_iters = config.init_recycling
        del afconfig.data.common.masked_msa
        afconfig.data.common.resample_msa_in_recycling = False
        feature_processor = feature_pipeline.FeaturePipeline(afconfig.data)
        if tmp_align:
            shutil.rmtree(alignment_dir)

        # MH edits @ Oct 19, 2024, support MSA subsampling at the beginning
        if config.msa_subratio is not None:
            assert config.msa_subratio > 0.0 and config.msa_subratio <= 1.0, (
                ""msa_subratio should be None or between 0.0 and 1.0!""
            )
            # Do subsampling of msa, keep the first sequence
            if config.sub_msa_path is None:
                idx = np.arange(feature_dict[""msa""].shape[0] - 1) + 1
                sub_idx = np.concatenate((
                    np.array([0]),
                    np.random.choice(
                        idx, size=int(config.msa_subratio * len(idx)), replace=False
                    ),
                ))
                feature_dict[""msa""] = feature_dict[""msa""][sub_idx]
                feature_dict[""deletion_matrix_int""] = feature_dict[
                    ""deletion_matrix_int""
                ][sub_idx]
                # Save out the subsampled msa
                np.save(
                    f""{output_directory_path!s}/sub_msa.npy"",
                    feature_dict[""msa""],
                )
                np.save(
                    f""{output_directory_path!s}/sub_delmat.npy"",
                    feature_dict[""deletion_matrix_int""],
                )
            else:
                feature_dict[""msa""] = np.load(config.sub_msa_path, allow_pickle=True)
                feature_dict[""deletion_matrix_int""] = np.load(
                    config.sub_delmat_path, allow_pickle=True
                )
        processed_feature_dict = feature_processor.process_features(
            feature_dict, mode=""predict""
        )

        # Edit by MH @ Nov 18, 2024, use bias of fullmsa to realize the cluster msa
        if config.bias_from_fullmsa:
            fullmsa_dir = os.path.join(config.path, ""alignments"")
            fullmsa_feature_dict = rkrf_utils.generate_feature_dict(
                fasta_path,
                fullmsa_dir,
                data_processor,
            )
            fullmsa_processed_feature_dict = feature_processor.process_features(
                fullmsa_feature_dict, mode=""predict""
            )
            fullmsa_profile = fullmsa_processed_feature_dict[""msa_feat""][
                :, :, 25:48
            ].clone()
            submsa_profile = processed_feature_dict[""msa_feat""][:, :, 25:48].clone()
            processed_feature_dict[""msa_feat""][:, :, 25:48] = (
                fullmsa_profile.clone()
            )  # Use full msa's profile as basis for linear space -- higher rank (?)
            recombination_bias = (
                submsa_profile[..., 0] - fullmsa_profile[..., 0]
            )  # Use difference as the initial bias so we can start from desired profile
        elif config.chimera_profile:
            fullmsa_dir = os.path.join(config.path, ""alignments"")
            fullmsa_feature_dict = rkrf_utils.generate_feature_dict(
                fasta_path,
                fullmsa_dir,
                data_processor,
            )
            fullmsa_processed_feature_dict = feature_processor.process_features(
                fullmsa_feature_dict, mode=""predict""
            )
            full_profile = fullmsa_processed_feature_dict[""msa_feat""][
                :, :, 25:48
            ].clone()
            sub_profile = processed_feature_dict[""msa_feat""][:, :, 25:48].clone()
            processed_feature_dict[""msa_feat""][:, :, 25:48] = torch.where(
                sub_profile == 0.0, full_profile.clone(), sub_profile.clone()
            )

        device_processed_features = rk_utils.move_tensors_to_device(
            processed_feature_dict, device=device
        )
        feature_key = ""msa_feat""

        if config.msa_feat_init_path is None:
            features_at_it_start = (
                device_processed_features[feature_key].detach().clone()
            )
            np.save(
                f""{output_directory_path!s}/msa_feat_start.npy"",
                rk_utils.assert_numpy(features_at_it_start[..., 0]),
            )
        else:
            msa_feat_init_np = np.load(
                glob.glob(config.msa_feat_init_path)[0], allow_pickle=True
            )
            features_at_it_start_np = np.repeat(
                np.expand_dims(msa_feat_init_np, -1), config.init_recycling + 1, -1
            )
            features_at_it_start = torch.tensor(features_at_it_start_np).to(
                device_processed_features[feature_key]
            )
            device_processed_features[feature_key] = (
                features_at_it_start.detach().clone()
            )

    else:
        # Initialize the processed dict space
        device_processed_features, feature_key, features_at_it_start = (
            rkrf_utils.init_processed_dict(
                bias_version=config.bias_version,
                path=config.path,
                device=device,
                template_pdb=config.template_pdb,
                target_seq=target_seq,
                PRESET=PRESET,
            )
        )

    # MH edit @ Oct 2nd, 2024: Support optional template input
    if config.template_pdb is not None:
        device_processed_features_template = rocket.make_processed_dict_from_template(
            config.template_pdb,
            target_seq,
            device=device,
            mask_sidechains_add_cb=True,
            mask_sidechains=True,
            max_recycling_iters=config.init_recycling,
        )
        for key in device_processed_features_template:
            if key.startswith(""template_""):
                device_processed_features[key] = device_processed_features_template[key]
    # Write out config used, start the journey
    config.to_yaml_file(f""{output_directory_path!s}/config.yaml"")
    for n in range(num_of_runs):
        run_id = rkrf_utils.number_to_letter(n)
        best_pos = reference_pos

        # Initialize bias
        device_processed_features, optimizer, bias_names = rkrf_utils.init_bias(
            device_processed_features=device_processed_features,
            bias_version=config.bias_version,
            device=device,
            lr_a=lr_a,
            lr_m=lr_m,
            weight_decay=config.weight_decay,
            starting_bias=config.starting_bias,
            starting_weights=config.starting_weights,
            recombination_bias=recombination_bias,
        )

        # List initialization for saving values
        # sigmas_by_epoch = []
        llg_losses = []
        time_by_epoch = []
        memory_by_epoch = []
        all_pldtts = []
        mean_it_plddts = []

        progress_bar = tqdm(
            range(iterations),
            desc=f""{target_id}, uuid: {refinement_run_uuid[:4]}, run: {run_id}"",
        )

        # Prepare MDTraj PDB trajectory writer (open in append mode if exists)
        traj_writer = None
        md = None
        mdtraj_template = None
        try:
            import mdtraj as md  # type: ignore

            try:
                from mdtraj.formats import PDBTrajectoryFile  # type: ignore
            except Exception:
                PDBTrajectoryFile = None  # type: ignore
            traj_path_pdb = (
                f""{output_directory_path!s}/{run_id}_refinement_trajectory.pdb""
            )
            mdtraj_template = md.load_pdb(input_pdb)
            if PDBTrajectoryFile is not None:
                traj_writer = PDBTrajectoryFile(traj_path_pdb, mode=""w"")
            else:
                traj_writer = None
        except Exception as e:  # pragma: no cover - optional dependency
            logger.warning(
                f""mdtraj not available or failed to initialize writer ({e}); ""
                ""falling back to per-iteration PDB saves.""
            )

        # Run smooth stage in phase 1
        if ""phase1"" in config.note:
            w_L2 = config.l2_weight
        elif ""phase2"" in config.note:
            w_L2 = 0.0

        ######
        early_stopper = rkrf_utils.EarlyStopper(patience=200, min_delta=10.0)

        #### Phase 1 smooth scheduling ######
        if config.smooth_stage_epochs is not None:
            lr_a_initial = lr_a
            lr_m_initial = lr_m
            w_L2_initial = w_L2
            lr_stage1_final = config.phase2_final_lr
            smooth_stage_epochs = config.smooth_stage_epochs

            # Decay rates for each stage
            decay_rate_stage1_add = (lr_stage1_final / lr_a) ** (
                1 / smooth_stage_epochs
            )
            decay_rate_stage1_mul = (lr_stage1_final / lr_m) ** (
                1 / smooth_stage_epochs
            )

        ############ 3. Run Refinement ############
        for iteration in progress_bar:
            start_time = time.time()
            optimizer.zero_grad()

            # Avoid passing through graph a second time
            device_processed_features[feature_key] = (
                features_at_it_start.detach().clone()
            )

            # AF pass
            if iteration == 0:
                af2_output, prevs = af_bias(
                    device_processed_features,
                    [None, None, None],
                    num_iters=config.init_recycling,
                    bias=False,
                )

                prevs = [tensor.detach() for tensor in prevs]

            deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]

            af2_output, __ = af_bias(
                device_processed_features, deep_copied_prevs, num_iters=1, bias=True
            )
            L_plddt = -torch.mean(af2_output[""plddt""])

            # Position Kabsch Alignment
            aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
                af2_output=af2_output,
                device_processed_features=device_processed_features,
                cra_name=cryo_sfc.cra_name,
                best_pos=best_pos,
                exclude_res=EXCLUDING_RES,
                domain_segs=config.domain_segs,
                reference_bfactor=init_pos_bfactor,
            )

            cryo_llgloss.sfc.atom_b_iso = pseudo_Bs.detach().clone()
            cryo_llgloss_rbr.sfc.atom_b_iso = pseudo_Bs.detach().clone()
            all_pldtts.append(plddts_res)
            mean_it_plddts.append(np.mean(plddts_res))

            # Save the preRBR structure, for debugging
            cryo_llgloss_rbr.sfc.atom_pos_orth = aligned_xyz.detach().clone()

            # Compute RSCC, update the Bfacotrs
            Fprotein_plddt = cryo_llgloss_rbr.sfc.calc_fprotein(Return=True)
            ccmap = rk_utils.get_rscc_from_Fmap(
                Fprotein_plddt.detach(),
                rscc_reference_Fmap,
                cryo_llgloss_rbr.sfc.HKL_array,
                gridsize,
                Rg,
                uc_volume,
            )
            atom_cc = rk_utils.interpolate_grid_points(
                ccmap, cryo_llgloss_rbr.sfc.atom_pos_frac.cpu().numpy()
            )
            rscc_bfactor = torch.tensor(
                rk_utils.get_b_from_CC(atom_cc, cryo_llgloss_rbr.sfc.dmin),
                dtype=torch.float32,
                device=device,
            )
            cryo_llgloss_rbr.sfc.atom_b_iso = rscc_bfactor.detach().clone()
            cryo_llgloss.sfc.atom_b_iso = rscc_bfactor.detach().clone()

            # cryo_llgloss_rbr.sfc.savePDB(
            #     f""{output_directory_path!s}/{run_id}_{iteration}_preRBR.pdb""
            # )
            if config.sfc_scale:
                cryo_llgloss_rbr.sfc.calc_fprotein()
                cryo_llgloss_rbr.sfc.get_scales_adam(
                    lr=0.01,
                    n_steps=10,
                    sub_ratio=0.7,
                    initialize=False,
                )

            # Rigid body refinement (RBR) step
            optimized_xyz, loss_track_pose = rk_coordinates.rigidbody_refine_quat(
                aligned_xyz,
                cryo_llgloss_rbr,
                sfc_rbr.cra_name,
                domain_segs=config.domain_segs,
                lbfgs=RBR_LBFGS,
                lbfgs_lr=config.rbr_lbfgs_learning_rate,
                verbose=config.verbose,
            )

            # Save the postRBR structure
            cryo_llgloss.sfc.atom_pos_orth = optimized_xyz.detach().clone()

            # Save trajectory on the fly
            if traj_writer is not None:
                coords_nm = optimized_xyz.detach().cpu().numpy().reshape(-1, 3) / 10.0
                traj_writer.write(
                    coords_nm, mdtraj_template.topology, modelIndex=iteration
                )
                # Flush the underlying file handle to write data immediately
                if hasattr(traj_writer, ""_file"") and hasattr(
                    traj_writer._file, ""flush""
                ):
                    traj_writer._file.flush()
            else:
                pdb_path = f""{output_directory_path!s}/{run_id}_{iteration}_postRBR.pdb""
                cryo_llgloss.sfc.savePDB(pdb_path)

            # LLG loss
            L_llg = -cryo_llgloss(
                optimized_xyz,
                bin_labels=None,
                num_batch=config.number_of_batches,
                sub_ratio=config.batch_sub_ratio,
                update_scales=config.sfc_scale,
            )

            llg_estimate = L_llg.clone().item() / (
                config.batch_sub_ratio * config.number_of_batches
            )  # + 30 * L_plddt

            llg_losses.append(llg_estimate)

            # check if current loss is the best so far
            if llg_losses[-1] < best_loss:
                best_loss = llg_losses[-1]
                best_msa_bias = (
                    device_processed_features[""msa_feat_bias""].detach().cpu().clone()
                )
                best_feat_weights = (
                    device_processed_features[""msa_feat_weights""].detach().cpu().clone()
                )
                best_run = run_id
                best_iter = iteration
                best_pos = optimized_xyz.detach().clone()

            progress_bar.set_postfix(
                NEG_LLG=f""{llg_estimate:.2f}"",
                memory=f""{torch.cuda.max_memory_allocated() / 1024**3:.1f}G"",
            )

            #### add an L2 loss to constrain confident atoms ###
            if w_L2 > 0.0:
                # use
                L2_loss = torch.sum(
                    bfactor_weights.unsqueeze(-1) * (optimized_xyz - reference_pos) ** 2
                )  # / conf_best.shape[0]
                loss = L_llg + w_L2 * L2_loss + config.w_plddt * L_plddt
                loss.backward()
            else:
                loss = L_llg + config.w_plddt * L_plddt
                loss.backward()

                if early_stopper.early_stop(loss.item()):
                    break

            # Smooth in last part of phase 1 instead of beginning of phase 2
            if (""phase1"" in config.note) and (config.smooth_stage_epochs is not None):
                if iteration > (config.iterations - smooth_stage_epochs):
                    lr_a = lr_a_initial * (decay_rate_stage1_add**iteration)
                    lr_m = lr_m_initial * (decay_rate_stage1_mul**iteration)
                    w_L2 = w_L2_initial * (1 - (iteration / smooth_stage_epochs))

                # Update the learning rates in the optimizer
                optimizer.param_groups[0][""lr""] = lr_a
                optimizer.param_groups[1][""lr""] = lr_m
                optimizer.step()
            else:
                optimizer.step()

            time_by_epoch.append(time.time() - start_time)
            memory_by_epoch.append(torch.cuda.max_memory_allocated() / 1024**3)

        # Close the trajectory writer if it was opened
        if traj_writer is not None:
            traj_writer.close()

        # Average plddt per iteration
        np.save(
            f""{output_directory_path!s}/mean_it_plddt_{run_id}.npy"",
            np.array(mean_it_plddts),
        )

        # LLG per iteration
        np.save(
            f""{output_directory_path!s}/NEG_LLG_it_{run_id}.npy"",
            rk_utils.assert_numpy(llg_losses),
        )

    torch.save(
        best_msa_bias,
        f""{output_directory_path!s}/best_msa_bias_{best_run}_{best_iter}.pt"",
    )

    torch.save(
        best_feat_weights,
        f""{output_directory_path!s}/best_feat_weights_{best_run}_{best_iter}.pt"",
    )

    # Save best model as a single PDB (preserve input_pdb topology)
    try:
        if best_pos is not None:
            try:
                cryo_llgloss.sfc.atom_pos_orth = best_pos
                best_name = (
                    f""{output_directory_path!s}/best_model_{best_run}_{best_iter}.pdb""
                )
                cryo_llgloss.sfc.savePDB(best_name)
            except Exception:
                try:
                    import mdtraj as md  # type: ignore

                    topo = md.load_pdb(input_pdb).topology
                    coords_nm = best_pos.detach().cpu().numpy().reshape(1, -1, 3) / 10.0
                    traj = md.Trajectory(coords_nm, topo)
                    traj.save(
                        f""{output_directory_path!s}/best_model_{best_run}_{best_iter}.pdb""
                    )
                except Exception:
                    logger.warning(
                        ""Failed to write best model PDB with both SFC and mdtraj.""
                    )
    except NameError:
        pass

    return config
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/refinement_utils.py",".py","15167","478","import glob
import pickle
import re

import numpy as np
import skbio
import torch
from SFC_Torch import PDBParser

import rocket
from rocket import coordinates as rk_coordinates
from rocket import utils as rk_utils
from rocket.xtal import utils as llg_utils


def generate_feature_dict(
    fasta_path,
    alignment_dir,
    data_processor,
):
    feature_dict = data_processor.process_fasta(
        fasta_path=fasta_path,
        alignment_dir=alignment_dir,
        seqemb_mode=False,
    )
    return feature_dict


def number_to_letter(n):
    if 0 <= n <= 25:
        return chr(n + 65)
    else:
        return None


def get_identical_indices(A, B):
    """"""
    Get indices of aligned string A and B to produce identical sequence

    >>> A = 'EWTUY'
    >>> B = 'E-RUY'
    >>> get_identical_indices(A, B)
    [0,3,4], [0,2,3]

    So A[0,3,4] = 'EUY' = B[0,2,3]
    """"""
    ind_A = []
    ind_B = []
    ai = 0
    bi = 0
    for a, b in zip(A, B, strict=False):
        if a == ""-"":
            bi += 1
            continue
        if b == ""-"":
            ai += 1
            continue
        if a == b:
            ind_A.append(ai)
            ind_B.append(bi)
            ai += 1
            bi += 1
        else:
            ai += 1
            bi += 1
    return np.array(ind_A), np.array(ind_B)


def get_pattern_index(str_list, pattern):
    return next((i for i, s in enumerate(str_list) if re.match(pattern, s)), None)


def get_common_ca_ind(pdb1: PDBParser, pdb2: PDBParser):
    """"""
    A known bug: it can throw some residues out when the two pdbs have ideentical sequences
    for example, ""DFGTT"" for both, and it will only keep ""GTT""
    """"""  # noqa: E501
    seq1 = pdb1.sequence
    seq2 = pdb2.sequence
    alignment = skbio.alignment.StripedSmithWaterman(seq1)(
        seq2
    )  # Align sequence with Smith Waterman Algorithm
    subind_1 = np.arange(alignment.query_begin, alignment.query_end + 1)
    subind_2 = np.arange(alignment.target_begin, alignment.target_end_optimal + 1)
    subsubind_1, subsubind_2 = get_identical_indices(
        alignment.aligned_query_sequence, alignment.aligned_target_sequence
    )
    common_seq1 = subind_1[subsubind_1]
    common_seq2 = subind_2[subsubind_2]
    common_ca_ind_1 = [
        get_pattern_index(pdb1.cra_name, rf"".*-{j}-.*-CA$"") for j in common_seq1
    ]
    common_ca_ind_2 = [
        get_pattern_index(pdb2.cra_name, rf"".*-{i}-.*-CA$"") for i in common_seq2
    ]
    assert (
        np.array([i[-6:] for i in np.array(pdb1.cra_name)[common_ca_ind_1]])
        == np.array([i[-6:] for i in np.array(pdb2.cra_name)[common_ca_ind_2]])
    ).all()
    return common_ca_ind_1, common_ca_ind_2


def get_common_bb_ind(pdb1, pdb2):
    seq1 = pdb1.sequence
    seq2 = pdb2.sequence
    alignment = skbio.alignment.StripedSmithWaterman(seq1)(
        seq2
    )  # Align sequence with Smith Waterman Algorithm
    subind_1 = np.arange(alignment.query_begin, alignment.query_end + 1)
    subind_2 = np.arange(alignment.target_begin, alignment.target_end_optimal + 1)
    subsubind_1, subsubind_2 = get_identical_indices(
        alignment.aligned_query_sequence, alignment.aligned_target_sequence
    )
    common_seq1 = subind_1[subsubind_1]
    common_seq2 = subind_2[subsubind_2]
    common_ca_ind_1 = [
        get_pattern_index(pdb1.cra_name, rf"".*-{j}-.*-CA$"") for j in common_seq1
    ]
    common_N_ind_1 = [
        get_pattern_index(pdb1.cra_name, rf"".*-{j}-.*-N$"") for j in common_seq1
    ]
    common_C_ind_1 = [
        get_pattern_index(pdb1.cra_name, rf"".*-{j}-.*-C$"") for j in common_seq1
    ]
    common_ca_ind_2 = [
        get_pattern_index(pdb2.cra_name, rf"".*-{i}-.*-CA$"") for i in common_seq2
    ]
    common_N_ind_2 = [
        get_pattern_index(pdb2.cra_name, rf"".*-{i}-.*-N$"") for i in common_seq2
    ]
    common_C_ind_2 = [
        get_pattern_index(pdb2.cra_name, rf"".*-{i}-.*-C$"") for i in common_seq2
    ]

    filtered_ca_ind_1 = list(filter(lambda x: x is not None, common_ca_ind_1))
    filtered_N_ind_1 = list(filter(lambda x: x is not None, common_N_ind_1))
    filtered_C_ind_1 = list(filter(lambda x: x is not None, common_C_ind_1))

    filtered_ca_ind_2 = list(filter(lambda x: x is not None, common_ca_ind_2))
    filtered_N_ind_2 = list(filter(lambda x: x is not None, common_N_ind_2))
    filtered_C_ind_2 = list(filter(lambda x: x is not None, common_C_ind_2))

    # Now add only the valid lists
    common_bb_ind_1 = filtered_ca_ind_1 + filtered_N_ind_1 + filtered_C_ind_1
    common_bb_ind_2 = filtered_ca_ind_2 + filtered_N_ind_2 + filtered_C_ind_2
    assert (
        np.array([i[-6:] for i in np.array(pdb1.cra_name)[common_bb_ind_1]])
        == np.array([i[-6:] for i in np.array(pdb2.cra_name)[common_bb_ind_2]])
    ).all()
    return common_bb_ind_1, common_bb_ind_2


def get_current_lr(optimizer):
    for param_group in optimizer.param_groups:
        return param_group[""lr""]


class EarlyStopper:
    def __init__(self, patience=200, min_delta=0.1):
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.min_loss = float(""inf"")

    def early_stop(self, loss):
        if loss < (self.min_loss - self.min_delta):
            self.min_loss = loss
            self.counter = 0
        else:
            self.counter += 1
            if self.counter >= self.patience:
                return True
        return False


def init_processed_dict(
    bias_version,
    path,
    device,
    template_pdb=None,
    target_seq=None,
    PRESET=""model_1_ptm"",
    postfix=""processed_feats.pickle"",
):
    if bias_version == 4:
        device_processed_features = rocket.make_processed_dict_from_template(
            template_pdb=f""{path}/ROCKET_inputs/{template_pdb}"",
            target_seq=target_seq,
            config_preset=PRESET,
            device=device,
            msa_dict=None,
        )
        features_at_it_start = (
            device_processed_features[""template_torsion_angles_sin_cos""]
            .detach()
            .clone()
        )
        feature_key = ""template_torsion_angles_sin_cos""
    else:
        with open(glob.glob(f""{path}/predictions/*{postfix}"")[0], ""rb"") as file:
            # Load the data from the pickle file
            processed_features = pickle.load(file)

        device_processed_features = rk_utils.move_tensors_to_device(
            processed_features, device=device
        )
        features_at_it_start = device_processed_features[""msa_feat""].detach().clone()
        feature_key = ""msa_feat""
    return device_processed_features, feature_key, features_at_it_start


def init_llgloss(sfc, tng_file, min_resolution=None, max_resolution=None):
    resol_min = min(sfc.dHKL) if min_resolution is None else min_resolution
    resol_max = max(sfc.dHKL) if max_resolution is None else max_resolution
    llgloss = rocket.xtal.targets.LLGloss(
        sfc, tng_file, sfc.device, resol_min, resol_max
    )
    return llgloss


def init_bias(
    device_processed_features,
    bias_version,
    device,
    lr_a,
    lr_m,
    weight_decay=None,
    starting_bias=None,
    starting_weights=None,
    recombination_bias=None,
):
    num_res = device_processed_features[""aatype""].shape[0]
    device_processed_features[""msa_feat_bias""] = torch.zeros(
        (512, num_res, 23), requires_grad=True, device=device
    )

    if bias_version == 4:
        device_processed_features[""template_torsion_angles_sin_cos_bias""] = (
            torch.zeros_like(
                device_processed_features[""template_torsion_angles_sin_cos""],
                requires_grad=True,
                device=device,
            )
        )
        if weight_decay is None:
            optimizer = torch.optim.Adam([
                {
                    ""params"": device_processed_features[
                        ""template_torsion_angles_sin_cos_bias""
                    ],
                    ""lr"": lr_a,
                },
            ])
        else:
            optimizer = torch.optim.AdamW(
                [
                    {
                        ""params"": device_processed_features[
                            ""template_torsion_angles_sin_cos_bias""
                        ],
                        ""lr"": lr_a,
                    },
                ],
                weight_decay=weight_decay,
            )
        bias_names = [""template_torsion_angles_sin_cos_bias""]

    elif bias_version == 3:
        if starting_weights is not None:
            device_processed_features[""msa_feat_weights""] = (
                torch.load(glob.glob(starting_weights)[0])
                .detach()
                .to(device=device)
                .requires_grad_(True)
            )
        else:
            device_processed_features[""msa_feat_weights""] = torch.ones(
                (512, num_res, 23), requires_grad=True, device=device
            )

        if recombination_bias is not None:
            device_processed_features[""msa_feat_bias""] = (
                recombination_bias.detach().to(device=device).requires_grad_(True)
            )
        elif starting_bias is not None:
            device_processed_features[""msa_feat_bias""] = (
                torch.load(glob.glob(starting_bias)[0])
                .detach()
                .to(device=device)
                .requires_grad_(True)
            )

        if weight_decay is None:
            optimizer = torch.optim.Adam([
                {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
                {
                    ""params"": device_processed_features[""msa_feat_weights""],
                    ""lr"": lr_m,
                },
            ])
        else:
            optimizer = torch.optim.AdamW(
                [
                    {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
                    {
                        ""params"": device_processed_features[""msa_feat_weights""],
                        ""lr"": lr_m,
                    },
                ],
                weight_decay=weight_decay,
            )
        bias_names = [""msa_feat_bias"", ""msa_feat_weights""]

    elif bias_version == 2:
        device_processed_features[""msa_feat_weights""] = torch.eye(
            512, dtype=torch.float32, requires_grad=True, device=device
        )

        if weight_decay is None:
            optimizer = torch.optim.Adam([
                {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
                {
                    ""params"": device_processed_features[""msa_feat_weights""],
                    ""lr"": lr_m,
                },
            ])
        else:
            optimizer = torch.optim.AdamW(
                [
                    {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
                    {
                        ""params"": device_processed_features[""msa_feat_weights""],
                        ""lr"": lr_m,
                    },
                ],
                weight_decay=weight_decay,
            )
        bias_names = [""msa_feat_bias"", ""msa_feat_weights""]

    elif bias_version == 1:
        if weight_decay is None:
            optimizer = torch.optim.Adam([
                {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
            ])
        else:
            optimizer = torch.optim.AdamW(
                [
                    {""params"": device_processed_features[""msa_feat_bias""], ""lr"": lr_a},
                ],
                weight_decay=weight_decay,
            )

        bias_names = [""msa_feat_bias""]

    return device_processed_features, optimizer, bias_names


def position_alignment(
    af2_output,
    device_processed_features,
    cra_name,
    best_pos,
    exclude_res,
    domain_segs=None,
    reference_bfactor=None,
):
    xyz_orth_sfc, plddts = rk_coordinates.extract_allatoms(
        af2_output, device_processed_features, cra_name
    )
    plddts_res = rk_utils.assert_numpy(af2_output[""plddt""])
    pseudo_Bs = rk_utils.plddt2pseudoB_pt(plddts)

    # MH @ Sep 10 2024, temp edits to convert weighted kabsch to cutoff kabsch
    if reference_bfactor is None:
        pseudoB_np = rk_utils.assert_numpy(pseudo_Bs)
        cutoff1 = np.quantile(pseudoB_np, 0.3)
        cutoff2 = cutoff1 * 1.5
        weights = rk_utils.weighting(pseudoB_np, cutoff1, cutoff2)
    else:
        assert reference_bfactor.shape == pseudo_Bs.shape, (
            ""Reference bfactor should have same shape as model bfactor!""
        )
        reference_bfactor_np = rk_utils.assert_numpy(reference_bfactor)
        cutoff1 = np.quantile(reference_bfactor_np, 0.3)
        cutoff2 = cutoff1 * 1.5
        weights = rk_utils.weighting(reference_bfactor_np, cutoff1, cutoff2)
    # plddts_np = rk_utils.assert_numpy(plddts)
    # weights = np.ones_like(plddts_np)
    # weights[plddts_np < 85.0] = 1e-5

    aligned_xyz = rk_coordinates.iterative_kabsch_alignment(
        xyz_orth_sfc,
        best_pos,
        cra_name,
        weights=weights,
        exclude_res=exclude_res,
        domain_segs=domain_segs,
    )
    return aligned_xyz, plddts_res, pseudo_Bs.detach()


def update_sigmaA(
    llgloss,
    llgloss_rbr,
    aligned_xyz,
    constant_fp_added_HKL=None,
    constant_fp_added_asu=None,
):
    Ecalc, Fc = llgloss.compute_Ecalc(
        aligned_xyz.detach(),
        return_Fc=True,
        return_Rfactors=False,
        update_scales=True,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
    )
    Ecalc_rbr, _ = llgloss_rbr.compute_Ecalc(
        aligned_xyz.detach(),
        return_Fc=True,
        return_Rfactors=False,
        solvent=False,
        update_scales=True,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
    )
    llgloss.refine_sigmaA_newton(
        Ecalc, n_steps=5, subset=""working"", smooth_overall_weight=0.0
    )
    llgloss_rbr.refine_sigmaA_newton(
        Ecalc_rbr, n_steps=2, subset=""working"", smooth_overall_weight=0.0
    )
    return llgloss, llgloss_rbr, Ecalc, Fc


def sigmaA_from_true(
    llgloss,
    llgloss_rbr,
    aligned_xyz,
    Etrue,
    phitrue,
    constant_fp_added_HKL=None,
    constant_fp_added_asu=None,
):
    Ecalc, Fc = llgloss.compute_Ecalc(
        aligned_xyz.detach(),
        return_Fc=True,
        update_scales=True,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
    )
    Ecalc_rbr, Fc_rbr = llgloss_rbr.compute_Ecalc(
        aligned_xyz.detach(),
        return_Fc=True,
        solvent=False,
        update_scales=True,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
    )
    sigmas = llg_utils.sigmaA_from_model(
        Etrue,
        phitrue,
        Ecalc,
        Fc,
        llgloss.sfc.dHKL,
        llgloss.bin_labels,
    )
    llgloss.sigmaAs = sigmas
    sigmas_rbr = llg_utils.sigmaA_from_model(
        Etrue,
        phitrue,
        Ecalc_rbr,
        Fc_rbr,
        llgloss.sfc.dHKL,
        llgloss.bin_labels,
    )
    llgloss_rbr.sigmaAs = sigmas_rbr
    return llgloss, llgloss_rbr
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/__init__.py",".py","704","27","# Top Level API
# Submodules
from rocket import base, coordinates, cryo, refinement_utils, utils, xtal
from rocket.base import MSABiasAFv1, MSABiasAFv2, MSABiasAFv3, TemplateBiasAF
from rocket.helper import make_processed_dict_from_template
from rocket.mse import MSEloss, MSElossBB
from rocket.xtal.targets import LLGloss

__all__ = [
    # List submodules you want to expose
    ""base"",
    ""coordinates"",
    ""xtal"",
    ""cryo"",
    ""utils"",
    ""refinement_utils"",
    # List specific classes/functions you want to expose directly
    ""MSABiasAFv1"",
    ""MSABiasAFv2"",
    ""MSABiasAFv3"",
    ""TemplateBiasAF"",
    ""make_processed_dict_from_template"",
    ""LLGloss"",
    ""MSEloss"",
    ""MSElossBB"",
]
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/refinement_config.py",".py","13412","382","""""""
This module contains the configuration classes for the ROCKET refinement pipeline.
The configuration is stored in a human-readable YAML file and
can be loaded into a RocketRefinmentConfig object.
""""""

import os
import uuid
from enum import Enum
from typing import Any, ClassVar

import yaml
from pydantic import BaseModel, Field, field_validator


# Custom StrEnum implementation for Python < 3.11
class StrEnum(str, Enum):
    def __str__(self) -> str:
        return self.value

    def __repr__(self) -> str:
        return str(self)


class DATAMODE(StrEnum):
    XRAY = ""xray""
    CRYOEM = ""cryoem""


# Path and file configuration
class PathConfig(BaseModel):
    path: str = """"
    file_id: str = """"
    input_pdb: str = """"
    template_pdb: str | None = None
    input_msa: str | None = None
    sub_msa_path: str | None = None
    sub_delmat_path: str | None = None
    msa_feat_init_path: str | None = None
    starting_bias: str | None = None
    starting_weights: str | None = None
    uuid_hex: str | None = None


# Hardware and execution configuration
class ExecutionConfig(BaseModel):
    cuda_device: int = 0
    num_of_runs: int = 1
    verbose: bool = False


# Optimization parameters
class OptimizationParams(BaseModel):
    additive_learning_rate: float = 0.05
    multiplicative_learning_rate: float = 1.0
    weight_decay: float | None = 0.0001
    batch_sub_ratio: float = 0.7
    number_of_batches: int = 1
    rbr_opt_algorithm: str = ""lbfgs""
    rbr_lbfgs_learning_rate: float = 150.0
    smooth_stage_epochs: int | None = 50
    phase2_final_lr: float = 1e-3
    l2_weight: float = 1e-7


# Feature flags
class FeatureFlags(BaseModel):
    solvent: bool = True
    sfc_scale: bool = True
    refine_sigmaA: bool = True
    additional_chain: bool = False
    total_chain_copy: float = 1.0
    bias_from_fullmsa: bool = False
    chimera_profile: bool = False


# Algorithm parameters
class AlgorithmConfig(BaseModel):
    bias_version: int = 3
    iterations: int = 100
    init_recycling: int = 1
    domain_segs: list[int] | None = None
    optimization: OptimizationParams = Field(default_factory=OptimizationParams)
    features: FeatureFlags = Field(default_factory=FeatureFlags)


# Data-specific configuration
class DataConfig(BaseModel):
    datamode: DATAMODE = ""xray""
    free_flag: str = ""R-free-flags""
    testset_value: int = 1
    min_resolution: float | None = None
    max_resolution: float | None = None
    voxel_spacing: float = 4.5
    msa_subratio: float | None = None
    w_plddt: float = 0.0
    downsample_ratio: int | None = None

    @field_validator(""datamode"", mode=""before"")
    @classmethod
    def validate_datamode(cls, v: str) -> ""DATAMODE"":
        """"""
        Validates and converts the input to a DATAMODE enum member.

        Raises:
            ValueError: If `v` is not a valid DATAMODE value.
        """"""
        if isinstance(v, str):
            try:
                return DATAMODE(v)
            except ValueError as err:
                valid_values = [e.value for e in DATAMODE]
                raise ValueError(
                    f""Invalid datamode: {v}. Must be one of: {valid_values}""
                ) from err
        return v

    model_config = {""use_enum_values"": True}


# Main configuration class
class RocketRefinmentConfig(BaseModel):
    # Metadata
    note: str = """"

    # Nested configurations
    paths: PathConfig = Field(default_factory=PathConfig)
    execution: ExecutionConfig = Field(default_factory=ExecutionConfig)
    algorithm: AlgorithmConfig = Field(default_factory=AlgorithmConfig)
    data: DataConfig = Field(default_factory=DataConfig)

    model_config = {""use_enum_values"": True}

    # Mapping for flat to nested structure conversion
    _flat_to_nested_map: ClassVar[dict[str, str]] = {
        # Paths
        ""path"": ""paths.path"",
        ""file_id"": ""paths.file_id"",
        ""input_pdb"": ""paths.input_pdb"",
        ""template_pdb"": ""paths.template_pdb"",
        ""input_msa"": ""paths.input_msa"",
        ""sub_msa_path"": ""paths.sub_msa_path"",
        ""sub_delmat_path"": ""paths.sub_delmat_path"",
        ""msa_feat_init_path"": ""paths.msa_feat_init_path"",
        ""starting_bias"": ""paths.starting_bias"",
        ""starting_weights"": ""paths.starting_weights"",
        ""uuid_hex"": ""paths.uuid_hex"",
        # Execution
        ""cuda_device"": ""execution.cuda_device"",
        ""num_of_runs"": ""execution.num_of_runs"",
        ""verbose"": ""execution.verbose"",
        # Algorithm
        ""bias_version"": ""algorithm.bias_version"",
        ""iterations"": ""algorithm.iterations"",
        ""init_recycling"": ""algorithm.init_recycling"",
        ""domain_segs"": ""algorithm.domain_segs"",
        # Optimization
        ""additive_learning_rate"": ""algorithm.optimization.additive_learning_rate"",
        ""multiplicative_learning_rate"": ""algorithm.optimization.multiplicative_learning_rate"",  # noqa: E501
        ""weight_decay"": ""algorithm.optimization.weight_decay"",
        ""batch_sub_ratio"": ""algorithm.optimization.batch_sub_ratio"",
        ""number_of_batches"": ""algorithm.optimization.number_of_batches"",
        ""rbr_opt_algorithm"": ""algorithm.optimization.rbr_opt_algorithm"",
        ""rbr_lbfgs_learning_rate"": ""algorithm.optimization.rbr_lbfgs_learning_rate"",
        ""smooth_stage_epochs"": ""algorithm.optimization.smooth_stage_epochs"",
        ""phase2_final_lr"": ""algorithm.optimization.phase2_final_lr"",
        ""l2_weight"": ""algorithm.optimization.l2_weight"",
        # Features
        ""solvent"": ""algorithm.features.solvent"",
        ""sfc_scale"": ""algorithm.features.sfc_scale"",
        ""refine_sigmaA"": ""algorithm.features.refine_sigmaA"",
        ""additional_chain"": ""algorithm.features.additional_chain"",
        ""total_chain_copy"": ""algorithm.features.total_chain_copy"",
        ""bias_from_fullmsa"": ""algorithm.features.bias_from_fullmsa"",
        ""chimera_profile"": ""algorithm.features.chimera_profile"",
        # Data
        ""datamode"": ""data.datamode"",
        ""free_flag"": ""data.free_flag"",
        ""testset_value"": ""data.testset_value"",
        ""min_resolution"": ""data.min_resolution"",
        ""max_resolution"": ""data.max_resolution"",
        ""voxel_spacing"": ""data.voxel_spacing"",
        ""msa_subratio"": ""data.msa_subratio"",
        ""w_plddt"": ""data.w_plddt"",
        ""downsample_ratio"": ""data.downsample_ratio"",
        # Metadata
        ""note"": ""note"",
    }

    # Helper methods for backward compatibility
    def __getattr__(self, name):
        """"""Allow access to nested attributes directly for backward compatibility""""""
        if name in self._flat_to_nested_map:
            path = self._flat_to_nested_map[name].split(""."")
            value = self
            for part in path:
                value = getattr(value, part)
            return value
        raise AttributeError(
            f""'{type(self).__name__}' object has no attribute '{name}'""
        )

    def to_yaml_file(self, file_path: str) -> None:
        """"""Save configuration to YAML with fields in specific order""""""
        # Convert model to dict
        model_dict = self.model_dump()

        # Create an ordered dictionary with the desired field order
        ordered_dict = {}
        # Define the order of top-level fields
        field_order = [""note"", ""data"", ""paths"", ""execution"", ""algorithm""]

        # Add fields in the specified order
        for field in field_order:
            if field in model_dict:
                ordered_dict[field] = model_dict[field]

        # Add any remaining fields that weren't in our order list
        for key, value in model_dict.items():
            if key not in ordered_dict:
                ordered_dict[key] = value

        # Write to file
        with open(file_path, ""w"") as file:
            yaml.dump(ordered_dict, file, sort_keys=False)

    def to_flat_yaml_file(self, file_path: str) -> None:
        """"""Save configuration in the old flat format for backward compatibility""""""
        flat_dict = self.to_flat_dict()
        with open(file_path, ""w"") as file:
            yaml.dump(flat_dict, file)

    def to_flat_dict(self) -> dict[str, Any]:
        """"""Convert the nested structure to a flat dictionary""""""
        result = {}
        for flat_key, nested_path in self._flat_to_nested_map.items():
            path_parts = nested_path.split(""."")
            value = self
            for part in path_parts:
                value = getattr(value, part)
            result[flat_key] = value
        return result

    @classmethod
    def from_yaml_file(cls, file_path: str):
        with open(file_path) as file:
            payload = yaml.safe_load(file)

        # Try to determine if this is a flat or nested format
        if any(key in payload for key in [""paths"", ""algorithm"", ""data"", ""execution""]):
            # This appears to be a nested format
            return cls.model_validate(payload)
        else:
            # This appears to be a flat format
            return cls.from_flat_dict(payload)

    @classmethod
    def from_flat_dict(cls, flat_dict: dict[str, Any]):
        """"""Create an instance from a flat dictionary (old format)""""""
        # Initialize nested dictionaries
        nested_dict = {
            ""paths"": {},
            ""execution"": {},
            ""algorithm"": {""optimization"": {}, ""features"": {}},
            ""data"": {},
            ""note"": flat_dict.get(""note"", """"),
        }

        # Map flat keys to nested structure
        for flat_key, value in flat_dict.items():
            if flat_key in cls._flat_to_nested_map:
                nested_path = cls._flat_to_nested_map[flat_key].split(""."")

                # Navigate to the correct nested dictionary
                target_dict = nested_dict
                for part in nested_path[:-1]:
                    target_dict = target_dict[part]

                # Set the value in the nested structure
                target_dict[nested_path[-1]] = value

        # Create the instance
        return cls.model_validate(nested_dict)


class RUNMODE(StrEnum):
    PHASE1 = ""phase1""
    PHASE2 = ""phase2""
    BOTH = ""both""


def gen_config(
    mode: RUNMODE | None = RUNMODE.BOTH,
    datamode: DATAMODE | None = None,
    working_dir: str | None = None,
    file_id: str | None = None,
    pre_phase1_config: RocketRefinmentConfig | None = None,
):
    if mode == RUNMODE.PHASE1:
        phase1_config = gen_config_phase1(datamode, working_dir, file_id)
        phase1_config.to_yaml_file(
            os.path.join(working_dir, ""ROCKET_config_phase1.yaml"")
        )
        return phase1_config
    elif mode == RUNMODE.PHASE2:
        phase2_config = gen_config_phase2(pre_phase1_config)
        phase2_config.to_yaml_file(
            os.path.join(phase2_config.working_dir, ""ROCKET_config_phase2.yaml"")
        )
        return phase2_config
    else:
        phase1_config = gen_config_phase1(datamode, working_dir, file_id)
        phase1_config.to_yaml_file(
            os.path.join(working_dir, ""ROCKET_config_phase1.yaml"")
        )
        phase2_config = gen_config_phase2(phase1_config)
        phase2_config.to_yaml_file(
            os.path.join(working_dir, ""ROCKET_config_phase2.yaml"")
        )
        return phase1_config, phase2_config


def gen_config_phase1(datamode: DATAMODE, working_dir: str, file_id: str):
    phase1_config = RocketRefinmentConfig(
        note=""phase1_<your_note_here>"",
        paths=PathConfig(
            path=working_dir,
            file_id=file_id,
            input_pdb=os.path.join(
                working_dir, ""ROCKET_inputs"", f""{file_id}-pred-aligned.pdb""
            ),  # noqa: E501
            uuid_hex=uuid.uuid4().hex[:10],
        ),
        execution=ExecutionConfig(
            cuda_device=0,
            num_of_runs=3,
            verbose=False,
        ),
        algorithm=AlgorithmConfig(
            iterations=100,
            optimization=OptimizationParams(
                additive_learning_rate=0.05,
                multiplicative_learning_rate=1.0,
                l2_weight=1e-7,
                phase2_final_lr=1e-3,
                smooth_stage_epochs=50,
            ),
        ),
        data=DataConfig(
            datamode=datamode,
            min_resolution=3.0,
        ),
    )
    return phase1_config


def gen_config_phase2(phase1_config: RocketRefinmentConfig):
    phase2_config = phase1_config.model_copy()
    phase2_config.note = ""phase2_<your_note_here>""
    phase2_config.data.min_resolution = None

    output_directory_path = os.path.join(
        phase2_config.path, ""ROCKET_outputs"", phase2_config.uuid_hex
    )
    phase1_path = os.path.join(output_directory_path, phase1_config.note)
    input_pdb_path = os.path.join(phase1_path, ""best_model_*_*.pdb"")
    starting_bias_path = os.path.join(phase1_path, ""best_msa_bias*.pt"")
    starting_weights_path = os.path.join(phase1_path, ""best_feat_weights*.pt"")

    phase2_config.paths.input_pdb = input_pdb_path
    phase2_config.paths.starting_bias = starting_bias_path
    phase2_config.paths.starting_weights = starting_weights_path
    if phase1_config.input_msa is not None:
        msa_feat_init_path = os.path.join(phase1_path, ""msa_feat_start.npy"")
        phase2_config.paths.msa_feat_init_path = msa_feat_init_path

    phase2_config.algorithm.iterations = 500
    phase2_config.algorithm.optimization.smooth_stage_epochs = None
    phase2_config.execution.num_of_runs = 1

    return phase2_config
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/utils.py",".py","12823","389","import os
from functools import partial

import numpy as np
import reciprocalspaceship as rs
import torch
from scipy.interpolate import RegularGridInterpolator
from SFC_Torch import PDBParser
from SFC_Torch.mask import reciprocal_grid


def plddt2pseudoB_np(plddts):
    # Use Tom Terwilliger's formula to convert plddt to Bfactor
    deltas = 1.5 * np.exp(4 * (0.7 - 0.01 * plddts))
    b_factors = (8 * np.pi**2 * deltas**2) / 3
    return b_factors


def plddt2pseudoB_pt(plddts):
    # Use Terwilliger's formula to convert plddt to pseudoB
    deltas = 1.5 * torch.exp(4 * (0.7 - 0.01 * plddts))
    b_factors = (8 * torch.pi**2 * deltas**2) / 3

    return b_factors


def weighting(x, cutoff1=11.5, cutoff2=30.0):
    """"""
    Convert B factor to weights for L2 loss and Kabsch Alignment
    w(B) =
    1.0                                           B <= cutoff1
    1.0 - 0.5*(B-cutoff1)/(cutoff2-cutoff1)       cutoff1 < B <= cutoff2
    0.5 * exp(-sqrt(B-cutoff2))                   cutoff2 < B
    """"""
    a = np.where(x <= cutoff1, 1.0, 1.0 - 0.5 * (x - cutoff1) / (cutoff2 - cutoff1))
    b = 0.5 * np.exp(-np.sqrt(np.where(x <= cutoff2, 1.0, x - cutoff2)))
    return np.where(a >= 0.5, a, b)


def weighting_torch(x, cutoff1=11.5, cutoff2=30.0):
    """"""
    pytorch implementation of the weighting function:
    w(B) =
    1.0                                           B <= cutoff1
    1.0 - 0.5*(B-cutoff1)/(cutoff2-cutoff1)       cutoff1 < B <= cutoff2
    0.5 * exp(-sqrt(B-cutoff2))                   cutoff2 < B
    """"""
    a = torch.where(x <= cutoff1, 1.0, 1.0 - 0.5 * (x - cutoff1) / (cutoff2 - cutoff1))
    b = 0.5 * torch.exp(-torch.sqrt(torch.where(x <= cutoff2, 1.0, x - cutoff2)))
    return torch.where(a >= 0.5, a, b)


def dict_map(fn, dic, leaf_type, exclude_keys=None):
    if exclude_keys is None:
        exclude_keys = {""msa_feat_bias"", ""msa_feat_weights""}

    new_dict = {}
    for k, v in dic.items():
        if k in exclude_keys:
            new_dict[k] = v  # Keep the key-value pair as it is
        elif isinstance(v, dict):
            new_dict[k] = dict_map(fn, v, leaf_type, exclude_keys)
        else:
            new_dict[k] = tree_map(fn, v, leaf_type)

    return new_dict


def tree_map(fn, tree, leaf_type):
    if isinstance(tree, dict):
        return dict_map(fn, tree, leaf_type)
    elif isinstance(tree, list):
        return [tree_map(fn, x, leaf_type) for x in tree]
    elif isinstance(tree, tuple):
        return tuple([tree_map(fn, x, leaf_type) for x in tree])
    elif isinstance(tree, leaf_type):
        return fn(tree)
    else:
        print(type(tree))
        raise ValueError(""Not supported"")


tensor_tree_map = partial(tree_map, leaf_type=torch.Tensor)


def try_gpu(i=0):
    if torch.cuda.device_count() >= i + 1:
        return torch.device(f""cuda:{i}"")
    return torch.device(""cpu"")


def move_tensors_to_device_inplace(processed_features, device=None):
    """"""
    Moves PyTorch tensors in a dictionary to the specified device in-place.

    Args:
        processed_features (dict): Dictionary containing tensors.
        device (str): Device to move tensors to (e.g., ""cuda:0"", ""cpu"").
    """"""
    if device is None:
        device = try_gpu()
    # Iterate through the keys and values in the input dictionary
    for key, value in processed_features.items():
        # Check if the value is a PyTorch tensor
        if isinstance(value, torch.Tensor):
            # Move the tensor to the specified device in-place
            processed_features[key] = value.to(device)


def move_tensors_to_device(processed_features, device=None):
    """"""
    Moves PyTorch tensors in a dictionary to the specified device.

    Args:
        processed_features (dict): Dictionary containing tensors.
        device (str): Device to move tensors to (e.g., ""cuda:0"", ""cpu"").

    Returns:
        dict: Dictionary with tensors moved to the specified device.
    """"""
    if device is None:
        device = try_gpu()
    processed_features_on_device = {}

    for key, value in processed_features.items():
        if isinstance(value, torch.Tensor):
            device_value = value.clone().to(device)
        else:
            device_value = value
        processed_features_on_device[key] = device_value

    return processed_features_on_device


def convert_feat_tensors_to_numpy(dictionary):
    numpy_dict = {}
    for key, value in dictionary.items():
        if isinstance(value, torch.Tensor):
            numpy_dict[key] = value.detach().cpu().numpy()
        else:
            numpy_dict[key] = value
    return numpy_dict


def is_list_or_tuple(x):
    return isinstance(x, list | tuple)


def assert_numpy(x, arr_type=None):
    if isinstance(x, torch.Tensor):
        if x.is_cuda:
            x = x.cpu()
        x = x.detach().numpy()
    if is_list_or_tuple(x):
        x = np.array(x)
    assert isinstance(x, np.ndarray)
    if arr_type is not None:
        x = x.astype(arr_type)
    return x


def assert_tensor(x, arr_type=None, device=""cuda:0""):
    if isinstance(x, np.ndarray):
        x = torch.tensor(x, device=device)
    if is_list_or_tuple(x):
        x = np.array(x)
        x = torch.tensor(x, device=device)
    assert isinstance(x, torch.Tensor)
    if arr_type is not None:
        x = x.to(arr_type)
    return x


def d2q(d):
    return 2 * np.pi / d


def load_mtz(mtz: str) -> rs.DataSet:
    dataset = rs.read_mtz(mtz)
    dataset.compute_dHKL(inplace=True)
    return dataset


def load_pdb(pdb: str) -> PDBParser:
    model = PDBParser(pdb)
    return model


def get_params_path():
    resources_path = os.environ.get(""OPENFOLD_RESOURCES"", None)
    if resources_path is None:
        raise ValueError(""Please set OPENFOLD_RESOURCES environment variable"")
    params_path = os.path.join(resources_path, ""params"")
    return params_path


def apply_resolution_cutoff(
    dataset: rs.DataSet | str,
    min_resolution: float = None,
    max_resolution: float = None,
):
    """"""
    Apply resolution cutoff to a dataset.

    Args:
        dataset (rs.DataSet | str): The dataset to filter, can be a path to an MTZ file
            or an rs.DataSet object.
        min_resolution (float): Minimum resolution to keep.
        max_resolution (float): Maximum resolution to keep.

    Returns:
        rs.DataSet: Filtered dataset with applied resolution cutoff.
    """"""
    if isinstance(dataset, str):
        dataset = rs.read_mtz(dataset)

    dataset.compute_dHKL(inplace=True)
    if max_resolution is None:
        max_resolution = dataset.dHKL.max()
    if min_resolution is None:
        min_resolution = dataset.dHKL.min()
    filtered_dataset = dataset[
        (dataset.dHKL >= (min_resolution - 1e-4))
        & (dataset.dHKL <= (max_resolution + 1e-4))
    ].copy()

    return filtered_dataset


def g_function_np(R: float, s: np.ndarray) -> np.ndarray:
    """"""
    Calculates the Fourier transform of a sphere (g-function) using
    spherical Bessel function in NumPy.

    Args:
        R (float): The radius of the sphere.
        s (np.ndarray): An array of reciprocal space vector magnitudes (1/d).

    Returns:
        np.ndarray: The calculated g-function values.
    """"""
    # Ensure s is a numpy array
    if not isinstance(s, np.ndarray):
        s = np.array(s, dtype=np.float64)

    # Create an output array initialized with the value for s=0
    g_vals = np.ones_like(s)

    # Identify non-zero s values to avoid division by zero
    nonzero_mask = np.abs(s) > 1e-9

    # Calculate the g-function only for non-zero s
    s_nonzero = s[nonzero_mask]
    x = 2 * np.pi * R * s_nonzero
    g_vals_nonzero = 3 * (np.sin(x) - x * np.cos(x)) / np.power(x, 3)

    # Place the calculated values into the output array
    g_vals[nonzero_mask] = g_vals_nonzero

    return g_vals


def map2fourier(map_grid, HKL_arrays):
    rs_grid = torch.fft.ifftn(map_grid, dim=(-3, -2, -1), norm=""forward"")
    tuple_index = tuple(torch.tensor(HKL_arrays.T, device=rs_grid.device, dtype=int))
    Frs = rs_grid[tuple_index]
    return Frs


def fourier2map(Fs, HKL_arrays, gridsize):
    rs_grid = reciprocal_grid(HKL_arrays, Fs, gridsize)
    map_grid = torch.real(torch.fft.fftn(rs_grid, dim=(-3, -2, -1)))
    return map_grid


def interpolate_grid_points(map_grid, frac_coords_batch, method=""linear""):
    """"""
    Interpolates values on a 3D grid at a batch of fractional coordinates.

    Args:
        map_grid (np.ndarray): A 3D NumPy array representing the data grid.
        frac_coords_batch (np.ndarray): A 2D NumPy array of shape (N, 3),
                                        where N is the number of points and
                                        each row contains the [x, y, z]
                                        fractional coordinates.

    Returns:
        np.ndarray: A 1D NumPy array of shape (N,) containing the
                    interpolated values for each point in the batch.
    """"""
    # 1. Define the grid points (indices) for each dimension.
    x = np.arange(map_grid.shape[0])
    y = np.arange(map_grid.shape[1])
    z = np.arange(map_grid.shape[2])

    # 2. Create the interpolator object.
    interpolator = RegularGridInterpolator(
        (x, y, z), map_grid, method=method, bounds_error=False, fill_value=None
    )

    # 3. Convert the batch of fractional coordinates to grid coordinates.
    grid_shape = np.array(map_grid.shape)
    frac_coords_batch = frac_coords_batch % 1.0
    point_coords_batch = frac_coords_batch * (grid_shape - 1)

    # 4. Get the interpolated values for the entire batch of points.
    interpolated_values = interpolator(point_coords_batch)

    return interpolated_values


def get_rscc_from_Fmap(
    Fcalc: torch.Tensor,
    Fmap: torch.Tensor,
    HKL_arrays: np.ndarray,
    gridsize: list[int],
    Rg: torch.Tensor,
    unitcell_volume: float,
) -> float:
    assert len(Fcalc) == len(HKL_arrays)
    assert len(Fmap) == len(HKL_arrays)
    assert len(Rg) == len(HKL_arrays)

    map1 = fourier2map(Fcalc, HKL_arrays, gridsize)
    map1_normed = (map1 - map1.mean()) / map1.std()

    map2 = fourier2map(Fmap, HKL_arrays, gridsize)
    map2_normed = (map2 - map2.mean()) / map2.std()

    productmap = map1_normed * map2_normed
    Fproductmap = map2fourier(productmap, HKL_arrays)
    Fproductmap_smooth = Fproductmap * Rg

    productmap_smooth = fourier2map(Fproductmap_smooth, HKL_arrays, gridsize)
    productmap_smooth_norm = 2.0 * productmap_smooth / unitcell_volume

    variance1_map = map1_normed * map1_normed
    Fvmap1 = map2fourier(variance1_map, HKL_arrays)
    Fvmap1_smoothed = Fvmap1 * Rg
    vmap1_smoothed = fourier2map(Fvmap1_smoothed, HKL_arrays, gridsize)
    vmap1_smoothed_normed = 2.0 * vmap1_smoothed / unitcell_volume
    vmap1_smoothed_normed_offset = (
        vmap1_smoothed_normed - vmap1_smoothed_normed.min() + 1e-3
    )

    variance2_map = map2_normed * map2_normed
    Fvmap2 = map2fourier(variance2_map, HKL_arrays)
    Fvmap2_smoothed = Fvmap2 * Rg
    vmap2_smoothed = fourier2map(Fvmap2_smoothed, HKL_arrays, gridsize)
    vmap2_smoothed_normed = 2.0 * vmap2_smoothed / unitcell_volume
    vmap2_smoothed_normed_offset = (
        vmap2_smoothed_normed - vmap2_smoothed_normed.min() + 1e-3
    )

    ccmap = (productmap_smooth_norm - productmap_smooth_norm.mean()) / torch.sqrt(
        vmap1_smoothed_normed_offset * vmap2_smoothed_normed_offset
    )
    return ccmap.cpu().numpy()


def get_b_from_CC(CC_values, d_min):
    # Truncate very small or negative CC values
    CC_trunc = (CC_values + 0.001 + np.abs(CC_values - 0.001)) / 2

    # Flex arrays can't be used as exponents, so work around this with logs
    # TODO: verify the constants here
    # Randy's note: In case you’re wondering, the magic numbers and the magic formula
    # come from some things I did in Mathematica.
    # I worked out a formula that converts RMSD to an expected CC.
    u1 = np.exp(np.log(0.188779) * CC_trunc)
    u2 = np.exp(2.21742 * CC_trunc * np.log(CC_trunc))
    u3 = np.exp(0.777046 * np.tan(CC_trunc) * np.log(CC_trunc) * CC_trunc)
    b_values_from_CC = (
        np.power(d_min, 2) * 134.024 * np.power(0.213624 - u1 * u2 - 0.359452 * u3, 2)
    )
    # Previous Approach, now we dropped
    # Truncate B-values at a maximum of 350 above minimum
    # 350 was chosen semi-arbitrarily as a point at which the sigmaA curve drops
    # below 0.1 at 6A, so that data beyond 6A will have little influence
    # bmax = np.min(b_values_from_CC) + 350.0
    # b_values_from_CC = (b_values_from_CC + bmax - np.abs(b_values_from_CC - bmax)) / 2

    # Do normal truncation instead, as Randy Suggested
    bmin = np.min(b_values_from_CC)
    b_values_from_CC = b_values_from_CC - bmin
    bmax = 999.0
    b_values_from_CC = np.clip(b_values_from_CC, 0.0, bmax)
    return b_values_from_CC
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/base.py",".py","5091","161","""""""
Include modified subclasses of AlphaFold
""""""

import os
import re

import torch
from openfold.model.model import AlphaFold
from openfold.utils.import_weights import import_jax_weights_
from openfold.utils.script_utils import get_model_basename

# from openfold.utils.tensor_utils import tensor_tree_map
from rocket.utils import get_params_path, tensor_tree_map


class MSABiasAFv1(AlphaFold):
    """"""
    AlphaFold with trainable bias in MSA space
    """"""

    def __init__(
        self,
        config,
        preset,
        params_root=None,
        use_deepspeed_evo_attention=True,
    ):
        super().__init__(config)

        if params_root is None:
            params_root = get_params_path()

        # AlphaFold params
        params_path = os.path.join(params_root, f""params_{preset}.npz"")
        model_basename = get_model_basename(params_path)
        model_version = ""_"".join(model_basename.split(""_"")[1:])
        import_jax_weights_(self, params_path, version=model_version)
        config.globals.use_deepspeed_evo_attention = use_deepspeed_evo_attention
        # print(""DEEPSPEED IS "", config.globals.use_deepspeed_evo_attention)
        self.eval()  # without this, dropout enabled

        # self.train()
        # for m in self.modules():
        #    if m.__class__.__name__.startswith(""Dropout""):
        #        m.eval()

    def freeze(self, skip_str=None):
        """"""
        freeze AF2 parameters, skip those parameters with str match
        """"""
        if skip_str is None:
            for params in self.parameters():
                params.requires_grad_(False)
        else:
            for name, params in self.named_parameters():
                if re.match(skip_str, name) is None:
                    params.requires_grad_(False)

    def unfreeze(self, skip_str=None):
        """"""
        unfreeze AF2 parameters, skip those parameters with str match
        """"""
        if skip_str is None:
            for params in self.parameters():
                params.requires_grad_(True)
        else:
            for name, params in self.named_parameters():
                if re.match(skip_str, name) is None:
                    params.requires_grad_(True)

    def _bias(self, feats):
        feats[""msa_feat""][:, :, 25:48] = (
            feats[""msa_feat""][:, :, 25:48] + feats[""msa_feat_bias""]
        )
        return feats

    def iteration(self, feats, prevs, _recycle=True, bias=True):
        if bias:
            feats = self._bias(feats)
        return super().iteration(feats, prevs, _recycle)

    def forward(self, batch, prevs=None, num_iters=1, bias=True):
        if prevs is None:
            prevs = [None, None, None]
        is_grad_enabled = torch.is_grad_enabled()

        # Main recycling loop
        for cycle_no in range(num_iters):
            # Select the features for the current recycling cycle
            fetch_cur_batch = lambda t: t[..., cycle_no]  # noqa: E731, B023
            feats = tensor_tree_map(fetch_cur_batch, batch)

            is_final_iter = cycle_no == (num_iters - 1)

            with torch.set_grad_enabled(is_grad_enabled and is_final_iter):
                if is_final_iter and torch.is_autocast_enabled():
                    # Sidestep AMP bug (PyTorch issue #65766)
                    torch.clear_autocast_cache()

                # Run the next iteration of the model
                outputs, m_1_prev, z_prev, x_prev, _ = self.iteration(
                    feats, prevs, _recycle=(num_iters > 1), bias=bias
                )

                if not is_final_iter:
                    del outputs
                    prevs = [m_1_prev, z_prev, x_prev]
                    del m_1_prev, z_prev, x_prev

        # Run auxiliary heads
        outputs.update(self.aux_heads(outputs))

        return outputs, [m_1_prev, z_prev, x_prev]


class MSABiasAFv2(MSABiasAFv1):
    """"""
    AlphaFold with trainable bias + trainable linear combination in MSA space
    """"""

    def _bias(self, feats):
        feats[""msa_feat""][:, :, 25:48] = (
            torch.einsum(
                ""ijk,in->njk"",
                feats[""msa_feat""][:, :, 25:48],
                feats[""msa_feat_weights""],
            )
            + feats[""msa_feat_bias""]
        )
        return feats


class MSABiasAFv3(MSABiasAFv1):
    """"""
    AlphaFold with trainable bias + trainable linear combination in MSA space
    """"""

    def _bias(self, feats):
        feats[""msa_feat""][:, :, 25:48] = (
            feats[""msa_feat""][:, :, 25:48].clone() * feats[""msa_feat_weights""]
            + feats[""msa_feat_bias""]
        )
        return feats


class TemplateBiasAF(MSABiasAFv1):
    """"""
    AlphaFold with trainable bias in template representation
    """"""

    def _bias(self, feats):
        # TODO: make sure the following operations are valid,
        # Values in feature have to be mapped into -1.0 - 1.0

        feats[""template_torsion_angles_sin_cos""] = (
            feats[""template_torsion_angles_sin_cos""].clone()
            + feats[""template_torsion_angles_sin_cos_bias""]
        )
        return feats
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/helper.py",".py","6109","178","import numpy as np
from openfold import config
from openfold.data import data_pipeline, feature_pipeline
from openfold.np import protein, residue_constants

from rocket import utils as rk_utils


def make_decoy_seq(target_seq, template_resid, decoy_seq_ori):
    decoy_seq = """"
    j = 0
    for i in range(len(target_seq)):
        if i in template_resid:
            decoy_seq += decoy_seq_ori[j]
            j += 1
        else:
            decoy_seq += ""-""
    return decoy_seq


def check_sequence(decoy_seqence, target_sequence):
    assert len(decoy_seqence) == len(target_sequence)
    for m, n in zip(decoy_seqence, target_sequence, strict=False):
        if m == n or m == ""-"":
            pass
        else:
            print(m, n, ""do not match!"", flush=True)
            return False
    return True


def make_processed_dict_from_template(
    template_pdb,
    target_seq=None,
    config_preset=""model_1"",
    msa_dict=None,
    device=""cpu"",
    seq_replacement=""-"",  # gap string
    mask_sidechains_add_cb=True,
    mask_sidechains=True,
    deterministic=True,
    max_recycling_iters=0,  # no recycling
):
    """"""
    template_pdb           :  path to pdb file used as template
    config_dict            :  the name of the model to get the parameters from.
                              Options: model_[1-5]
    msa_dict               :  dictionary containing precomputed msa properties in numpy
    device                 :  ""cpu"" or ""cuda:i""
    mask_sidechains_add_cb :  mask out sidechain atoms except for C-Beta, add gly C-Beta
    mask_sidechains        :  mask out sidechain atoms except for C-Beta
    deterministic          :  make all data processing deterministic (no masking, etc.)
    """"""
    decoy_prot = protein.from_pdb_string(pdb_to_string(template_pdb))
    decoy_seq_ori = """".join([residue_constants.restypes[x] for x in decoy_prot.aatype])
    if target_seq is None:
        target_seq = decoy_seq_ori
        template_idxs = np.arange(len(decoy_prot.residue_index))
        decoy_seq = (
            seq_replacement * len(target_seq)
            if len(seq_replacement) == 1
            else target_seq
        )
    else:
        template_idxs = decoy_prot.residue_index - 1
        decoy_seq = make_decoy_seq(target_seq, template_idxs, decoy_seq_ori)
        assert check_sequence(decoy_seq, target_seq)
    template_idx_set = set(template_idxs)

    pos = np.zeros([1, len(target_seq), 37, 3])
    atom_mask = np.zeros([1, len(target_seq), 37])
    if mask_sidechains_add_cb:
        pos[0, template_idxs, :5] = decoy_prot.atom_positions[:, :5]
        backbone_modelled = np.asarray(
            np.all(decoy_prot.atom_mask[:, [0, 1, 2]] == 1, axis=1)
        )
        backbone_idx_set = set(template_idxs[backbone_modelled])
        projected_cb = [
            i
            for i, b, m in zip(
                template_idxs, backbone_modelled, decoy_prot.atom_mask, strict=False
            )
            if m[3] == 0 and b
        ]
        projected_cb_set = set(projected_cb)
        gly_idx = [i for i, a in enumerate(target_seq) if a == ""G""]
        assert all(
            k in projected_cb_set
            for k in gly_idx
            if k in template_idx_set and k in backbone_idx_set
        )
        cbs = np.array([
            extend(c, n, ca, 1.522, 1.927, -2.143)
            for c, n, ca in zip(pos[0, :, 2], pos[0, :, 0], pos[0, :, 1], strict=False)
        ])
        pos[0, projected_cb, 3] = cbs[projected_cb]
        atom_mask[0, template_idxs, :5] = decoy_prot.atom_mask[:, :5]
        atom_mask[0, projected_cb, 3] = 1
    elif mask_sidechains:
        pos[0, template_idxs, :5] = decoy_prot.atom_positions[:, :5]
        atom_mask[0, template_idxs, :5] = decoy_prot.atom_mask[:, :5]
    else:
        pos[0, template_idxs] = decoy_prot.atom_positions
        atom_mask[0, template_idxs] = decoy_prot.atom_mask
    template = {
        ""template_aatype"": residue_constants.sequence_to_onehot(
            decoy_seq, residue_constants.HHBLITS_AA_TO_ID
        )[None],
        ""template_all_atom_mask"": atom_mask.astype(np.float32),
        ""template_all_atom_positions"": pos.astype(np.float32),
        ""template_domain_names"": np.asarray([""None""]),
    }

    # Make feature_dict
    feature_dict = {}
    feature_dict.update(
        data_pipeline.make_sequence_features(target_seq, ""test"", len(target_seq))
    )
    feature_dict.update(template)
    if msa_dict is None:
        # Make dummy msa features
        msa = [data_pipeline.make_dummy_msa_obj(target_seq)]
        msa_dict = data_pipeline.make_msa_features(msa)

    feature_dict.update(msa_dict)

    # create a config
    cfg = config.model_config(config_preset)
    cfg.data.common.max_recycling_iters = max_recycling_iters  # no
    if deterministic:
        cfg.data.eval.masked_msa_replace_fraction = 0.0
        cfg.data.predict.masked_msa_replace_fraction = 0.0

    # process_dict
    feature_processor = feature_pipeline.FeaturePipeline(cfg.data)
    processed_feature_dict = feature_processor.process_features(
        feature_dict, mode=""predict""
    )
    if device != ""cpu"":
        processed_feature_dict = rk_utils.move_tensors_to_device(
            processed_feature_dict, device=device
        )

    return processed_feature_dict


""""""
Read in a PDB file from a path
""""""


def pdb_to_string(pdb_file):
    lines = []
    for line in open(pdb_file):  # noqa: SIM115
        if line[:6] == ""HETATM"" and line[17:20] == ""MSE"":
            line = ""ATOM  "" + line[6:17] + ""MET"" + line[20:]
        if line[:4] == ""ATOM"":
            lines.append(line)
    return """".join(lines)


""""""
Function used to add C-Beta to glycine resides
input:  3 coords (a,b,c), (L)ength, (A)ngle, and (D)ihedral
output: 4th coord
""""""


def extend(a, b, c, L, A, D):
    def N(x):
        return x / np.sqrt(np.square(x).sum(-1, keepdims=True) + 1e-08)

    bc = N(b - c)
    n = N(np.cross(b - a, bc))
    m = [bc, np.cross(n, bc), n]
    d = [L * np.cos(A), L * np.sin(A) * np.cos(D), -L * np.sin(A) * np.sin(D)]
    return c + sum([m * d for m, d in zip(m, d, strict=False)])
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/refinement_xray.py",".py","31407","813","import glob
import os
import shutil
import time
import uuid
import warnings

import numpy as np
import torch
from loguru import logger
from openfold.config import model_config
from openfold.data import data_pipeline, feature_pipeline
from SFC_Torch import SFcalculator
from tqdm import tqdm

import rocket
from rocket import coordinates as rk_coordinates
from rocket import refinement_utils as rkrf_utils
from rocket import utils as rk_utils
from rocket.refinement_config import RocketRefinmentConfig
from rocket.xtal import structurefactors as llg_sf

PRESET = ""model_1_ptm""
EXCLUDING_RES = None


def run_xray_refinement(config: RocketRefinmentConfig | str) -> RocketRefinmentConfig:
    if isinstance(config, str):
        config = RocketRefinmentConfig.from_yaml_file(config)
    assert config.datamode == ""xray"", ""Make sure to set datamode to 'xray'!""

    ############ 1. Global settings ############
    # Device
    device = f""cuda:{config.cuda_device}""

    # Using LBFGS or Adam in RBR
    if config.rbr_opt_algorithm == ""lbfgs"":
        RBR_LBFGS = True
    elif config.rbr_opt_algorithm == ""adam"":
        RBR_LBFGS = False
    else:
        raise ValueError(""rbr_opt only supports lbfgs or adam"")

    # Configure input paths
    tng_file = f""{config.path}/ROCKET_inputs/{config.file_id}-Edata.mtz""
    try:
        input_pdb = glob.glob(config.input_pdb)[0]
    except Exception as err:
        raise ValueError(""input_pdb path is not valid!"") from err

    # Configure output path
    # Generate uuid for this run
    if config.uuid_hex:
        refinement_run_uuid = config.uuid_hex
    else:
        config.paths.uuid_hex = uuid.uuid4().hex[:10]
        refinement_run_uuid = config.uuid_hex
    output_directory_path = (
        f""{config.path}/ROCKET_outputs/{refinement_run_uuid}/{config.note}""
    )
    try:
        os.makedirs(output_directory_path, exist_ok=True)
    except FileExistsError:
        logger.info(
            f""Warning: Directory '{output_directory_path}' already exists. Overwriting.""
        )
    logger.info(
        f""System: {config.file_id}, run ID: {refinement_run_uuid!s}, Note: {config.note}"",  # noqa: E501
        flush=True,
    )
    if not config.verbose:
        warnings.filterwarnings(""ignore"")

    ############ 2. Initializations ############

    # Apply resolution cutoff to the reflection file
    if config.min_resolution is not None or config.max_resolution is not None:
        tng_file = rk_utils.apply_resolution_cutoff(
            tng_file,
            min_resolution=config.min_resolution,
            max_resolution=config.max_resolution,
        )

    # If there are additional chain in the system
    if config.additional_chain:
        added_chain_pdb = (
            f""{config.path}/ROCKET_inputs/{config.file_id}_added_chain.pdb""
        )
        if not os.path.exists(added_chain_pdb):
            raise FileNotFoundError(
                f""Additional chain PDB file '{added_chain_pdb}' does not exist!""
            )
        # calculate the structure factors for the additional chain
        sfc_added_chain = SFcalculator(
            added_chain_pdb,
            tng_file,
            expcolumns=[""FEFF"", ""DOBS""],
            freeflag=config.free_flag,
            set_experiment=True,
            testset_value=config.testset_value,
            device=device,
        )
        sfc_added_chain.calc_fprotein()
        constant_fp_added_HKL = sfc_added_chain.Fprotein_HKL.clone().detach()
        constant_fp_added_asu = sfc_added_chain.Fprotein_asu.clone().detach()
        del sfc_added_chain

        phitrue_path = f""{config.path}/ROCKET_inputs/{config.file_id}_allchains-phitrue-solvent{config.solvent}.npy""  # noqa: E501
        Etrue_path = f""{config.path}/ROCKET_inputs/{config.file_id}_allchains-Etrue-solvent{config.solvent}.npy""  # noqa: E501

        if os.path.exists(phitrue_path) and os.path.exists(Etrue_path):
            SIGMA_TRUE = True
            phitrue = np.load(phitrue_path)
            Etrue = np.load(Etrue_path)
        else:
            SIGMA_TRUE = False
    else:
        constant_fp_added_HKL = None
        constant_fp_added_asu = None

        phitrue_path = f""{config.path}/ROCKET_inputs/{config.file_id}-phitrue-solvent{config.solvent}.npy""  # noqa: E501
        Etrue_path = f""{config.path}/ROCKET_inputs/{config.file_id}-Etrue-solvent{config.solvent}.npy""  # noqa: E501

        if os.path.exists(phitrue_path) and os.path.exists(Etrue_path):
            SIGMA_TRUE = True
            phitrue = np.load(phitrue_path)
            Etrue = np.load(Etrue_path)
        else:
            SIGMA_TRUE = False

    # Initialize SFC
    sfc = llg_sf.initial_SFC(
        input_pdb,
        tng_file,
        ""FEFF"",
        ""DOBS"",
        Freelabel=config.free_flag,
        device=device,
        testset_value=config.testset_value,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
        total_chain_copy=config.total_chain_copy,
        spacing=config.voxel_spacing,
    )
    reference_pos = sfc.atom_pos_orth.clone()
    target_seq = sfc._pdb.sequence

    # Use initial pos B factor instead of best pos B factor for weighted L2
    init_pos_bfactor = sfc.atom_b_iso.clone()
    bfactor_weights = rk_utils.weighting_torch(init_pos_bfactor, cutoff2=20.0)

    sfc_rbr = llg_sf.initial_SFC(
        input_pdb,
        tng_file,
        ""FEFF"",
        ""DOBS"",
        Freelabel=config.free_flag,
        device=device,
        solvent=False,
        testset_value=config.testset_value,
        added_chain_HKL=constant_fp_added_HKL,
        added_chain_asu=constant_fp_added_asu,
        total_chain_copy=config.total_chain_copy,
        spacing=config.voxel_spacing,
    )

    # LLG initialization with resol cut
    llgloss = rkrf_utils.init_llgloss(
        sfc, tng_file, config.min_resolution, config.max_resolution
    )
    llgloss_rbr = rkrf_utils.init_llgloss(
        sfc_rbr, tng_file, config.min_resolution, config.max_resolution
    )

    # Model initialization
    version_to_class = {
        1: rocket.MSABiasAFv1,
        2: rocket.MSABiasAFv2,
        3: rocket.MSABiasAFv3,
        4: rocket.TemplateBiasAF,
    }
    af_bias = version_to_class[config.bias_version](
        model_config(PRESET, train=True), PRESET
    ).to(device)
    af_bias.freeze()  # Free all AF2 parameters to save time

    # Optimizer settings and initialization
    # Run smooth stage in phase 1 instead
    if ""phase1"" in config.note:
        lr_a = config.additive_learning_rate
        lr_m = config.multiplicative_learning_rate
    elif ""phase2"" in config.note:
        lr_a = config.phase2_final_lr
        lr_m = config.phase2_final_lr

    # Initialize best Rfree weights and bias for Phase 1
    best_llg = float(""inf"")
    best_msa_bias = None
    best_feat_weights = None
    best_run = None
    best_iter = None

    # MH edit @ Nov 8th, 2024: Support to use msa as input
    if config.msa_subratio is not None and config.input_msa is None:
        config.input_msa = ""alignments""  # default dir for alignment

    recombination_bias = None
    if config.input_msa is not None:
        fasta_path = [
            f
            for ext in (""*.fa"", ""*.fasta"")
            for f in glob.glob(os.path.join(config.path, ext))
        ][0]
        a3m_path = os.path.join(config.path, config.input_msa)
        if os.path.isfile(a3m_path):
            msa_name, ext = os.path.splitext(os.path.basename(a3m_path))
            alignment_dir = os.path.join(os.path.dirname(a3m_path), ""tmp_align"")
            os.makedirs(alignment_dir, exist_ok=True)
            shutil.copy(a3m_path, os.path.join(alignment_dir, msa_name + "".a3m""))
            tmp_align = True
        elif os.path.isdir(a3m_path):
            alignment_dir = a3m_path
            tmp_align = False
        data_processor = data_pipeline.DataPipeline(template_featurizer=None)
        feature_dict = rkrf_utils.generate_feature_dict(
            fasta_path,
            alignment_dir,
            data_processor,
        )
        # prepare featuerizer
        afconfig = model_config(PRESET)
        afconfig.data.common.max_recycling_iters = config.init_recycling
        del afconfig.data.common.masked_msa
        afconfig.data.common.resample_msa_in_recycling = False
        feature_processor = feature_pipeline.FeaturePipeline(afconfig.data)
        if tmp_align:
            shutil.rmtree(alignment_dir)

        # MH edits @ Oct 19, 2024, support MSA subsampling at the beginning
        if config.msa_subratio is not None:
            assert config.msa_subratio > 0.0 and config.msa_subratio <= 1.0, (
                ""msa_subratio should be None or between 0.0 and 1.0!""
            )
            # Do subsampling of msa, keep the first sequence
            if config.sub_msa_path is None:
                idx = np.arange(feature_dict[""msa""].shape[0] - 1) + 1
                sub_idx = np.concatenate((
                    np.array([0]),
                    np.random.choice(
                        idx, size=int(config.msa_subratio * len(idx)), replace=False
                    ),
                ))
                feature_dict[""msa""] = feature_dict[""msa""][sub_idx]
                feature_dict[""deletion_matrix_int""] = feature_dict[
                    ""deletion_matrix_int""
                ][sub_idx]
                # Save out the subsampled msa
                np.save(
                    f""{output_directory_path!s}/sub_msa.npy"",
                    feature_dict[""msa""],
                )
                np.save(
                    f""{output_directory_path!s}/sub_delmat.npy"",
                    feature_dict[""deletion_matrix_int""],
                )
            else:
                feature_dict[""msa""] = np.load(config.sub_msa_path, allow_pickle=True)
                feature_dict[""deletion_matrix_int""] = np.load(
                    config.sub_delmat_path, allow_pickle=True
                )
        processed_feature_dict = feature_processor.process_features(
            feature_dict, mode=""predict""
        )

        # Edit by MH @ Nov 18, 2024, use bias of fullmsa to realize the cluster msa
        if config.bias_from_fullmsa:
            fullmsa_dir = os.path.join(config.path, ""alignments"")
            fullmsa_feature_dict = rkrf_utils.generate_feature_dict(
                fasta_path,
                fullmsa_dir,
                data_processor,
            )
            fullmsa_processed_feature_dict = feature_processor.process_features(
                fullmsa_feature_dict, mode=""predict""
            )
            fullmsa_profile = fullmsa_processed_feature_dict[""msa_feat""][
                :, :, 25:48
            ].clone()
            submsa_profile = processed_feature_dict[""msa_feat""][:, :, 25:48].clone()
            processed_feature_dict[""msa_feat""][:, :, 25:48] = (
                fullmsa_profile.clone()
            )  # Use full msa's profile as basis for linear space -- higher rank (?)
            recombination_bias = (
                submsa_profile[..., 0] - fullmsa_profile[..., 0]
            )  # Use the difference as the initial bias, to start from a desired profile
        elif config.chimera_profile:
            fullmsa_dir = os.path.join(config.path, ""alignments"")
            fullmsa_feature_dict = rkrf_utils.generate_feature_dict(
                fasta_path,
                fullmsa_dir,
                data_processor,
            )
            fullmsa_processed_feature_dict = feature_processor.process_features(
                fullmsa_feature_dict, mode=""predict""
            )
            full_profile = fullmsa_processed_feature_dict[""msa_feat""][
                :, :, 25:48
            ].clone()
            sub_profile = processed_feature_dict[""msa_feat""][:, :, 25:48].clone()
            processed_feature_dict[""msa_feat""][:, :, 25:48] = torch.where(
                sub_profile == 0.0, full_profile.clone(), sub_profile.clone()
            )

        device_processed_features = rk_utils.move_tensors_to_device(
            processed_feature_dict, device=device
        )
        feature_key = ""msa_feat""

        if config.msa_feat_init_path is None:
            features_at_it_start = (
                device_processed_features[feature_key].detach().clone()
            )
            np.save(
                f""{output_directory_path!s}/msa_feat_start.npy"",
                rk_utils.assert_numpy(features_at_it_start[..., 0]),
            )
        else:
            msa_feat_init_np = np.load(
                glob.glob(config.msa_feat_init_path)[0], allow_pickle=True
            )
            features_at_it_start_np = np.repeat(
                np.expand_dims(msa_feat_init_np, -1), config.init_recycling + 1, -1
            )
            features_at_it_start = torch.tensor(features_at_it_start_np).to(
                device_processed_features[feature_key]
            )
            device_processed_features[feature_key] = (
                features_at_it_start.detach().clone()
            )

    else:
        # Initialize the processed dict space
        device_processed_features, feature_key, features_at_it_start = (
            rkrf_utils.init_processed_dict(
                bias_version=config.bias_version,
                path=config.path,
                device=device,
                template_pdb=config.template_pdb,
                target_seq=target_seq,
                PRESET=PRESET,
            )
        )

    # MH edit @ Oct 2nd, 2024: Support optional template input
    if config.template_pdb is not None:
        device_processed_features_template = rocket.make_processed_dict_from_template(
            config.template_pdb,
            target_seq,
            device=device,
            mask_sidechains_add_cb=True,
            mask_sidechains=True,
            max_recycling_iters=config.init_recycling,
        )
        for key in device_processed_features_template:
            if key.startswith(""template_""):
                device_processed_features[key] = device_processed_features_template[key]

    # Write out config used, start the journey
    config.to_yaml_file(f""{output_directory_path!s}/config.yaml"")
    for n in range(config.num_of_runs):
        run_id = rkrf_utils.number_to_letter(n)
        best_pos = reference_pos

        # Initialize bias
        device_processed_features, optimizer, bias_names = rkrf_utils.init_bias(
            device_processed_features=device_processed_features,
            bias_version=config.bias_version,
            device=device,
            lr_a=lr_a,
            lr_m=lr_m,
            weight_decay=config.weight_decay,
            starting_bias=config.starting_bias,
            starting_weights=config.starting_weights,
            recombination_bias=recombination_bias,
        )

        # List initialization for saving values
        rbr_loss_by_epoch = []
        llg_losses = []
        rfree_by_epoch = []
        rwork_by_epoch = []
        time_by_epoch = []
        memory_by_epoch = []
        all_pldtts = []
        mean_it_plddts = []
        absolute_feats_changes = []

        progress_bar = tqdm(
            range(config.iterations),
            desc=f""{config.file_id}, uuid: {refinement_run_uuid[:4]}, run: {run_id}"",
        )

        # Prepare an MDTraj trajectory writer so we can append frames
        # to a single trajectory file instead of writing one PDB per iteration.
        # Prefer HDF5 (smaller, single-file) and fall back to a multi-model
        # PDB if HDF5 support isn't available. Import mdtraj lazily so the
        # rest of the code still runs if mdtraj isn't installed.
        traj_writer = None
        md = None
        mdtraj_template = None
        try:
            import mdtraj as md  # type: ignore

            try:
                from mdtraj.formats import PDBTrajectoryFile  # type: ignore
            except Exception:
                PDBTrajectoryFile = None  # type: ignore
            traj_path_pdb = (
                f""{output_directory_path!s}/{run_id}_refinement_trajectory.pdb""
            )
            mdtraj_template = md.load_pdb(input_pdb)
            if PDBTrajectoryFile is not None:
                traj_writer = PDBTrajectoryFile(traj_path_pdb, mode=""w"")
            else:
                traj_writer = None
        except Exception as e:  # pragma: no cover - optional dependency
            logger.warning(
                f""mdtraj not available or failed to initialize writer ({e}); ""
                ""falling back to per-iteration PDB saves.""
            )

        # Run smooth stage in phase 1
        if ""phase1"" in config.note:
            w_L2 = config.l2_weight
        elif ""phase2"" in config.note:
            w_L2 = 0.0

        ######
        early_stopper = rkrf_utils.EarlyStopper(patience=200, min_delta=10.0)

        #### Phase 1 smooth scheduling ######
        if config.smooth_stage_epochs is not None:
            lr_a_initial = lr_a
            lr_m_initial = lr_m
            w_L2_initial = w_L2
            lr_stage1_final = config.phase2_final_lr
            smooth_stage_epochs = config.smooth_stage_epochs

            # Decay rates for each stage
            decay_rate_stage1_add = (lr_stage1_final / lr_a) ** (
                1 / smooth_stage_epochs
            )
            decay_rate_stage1_mul = (lr_stage1_final / lr_m) ** (
                1 / smooth_stage_epochs
            )

        ############ 3. Run Refinement ############
        for iteration in progress_bar:
            start_time = time.time()
            optimizer.zero_grad()

            # Avoid passing through graph a second time
            device_processed_features[feature_key] = (
                features_at_it_start.detach().clone()
            )

            # AF pass
            if iteration == 0:
                af2_output, prevs = af_bias(
                    device_processed_features,
                    [None, None, None],
                    num_iters=config.init_recycling,
                    bias=False,
                )
                prevs = [tensor.detach() for tensor in prevs]

                # # MH @ June 19: Fix the iteration 0 for phase 2 running
                # print(""config.starting_bias"", config.starting_bias)
                # if (config.starting_bias is not None) or (
                #     config.starting_weights is not None
                # ):
                #     deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
                #     af2_output, __ = af_bias(
                #         device_processed_features,
                #         deep_copied_prevs,
                #         num_iters=1,
                #         bias=True,
                #     )
            deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
            af2_output, __ = af_bias(
                device_processed_features, deep_copied_prevs, num_iters=1, bias=True
            )

            # pLDDT loss
            L_plddt = -torch.mean(af2_output[""plddt""])

            # Position Kabsch Alignment
            aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
                af2_output=af2_output,
                device_processed_features=device_processed_features,
                cra_name=sfc.cra_name,
                best_pos=best_pos,
                exclude_res=EXCLUDING_RES,
                domain_segs=config.domain_segs,
                reference_bfactor=init_pos_bfactor,
            )
            llgloss.sfc.atom_b_iso = pseudo_Bs.detach().clone()
            llgloss_rbr.sfc.atom_b_iso = pseudo_Bs.detach().clone()
            all_pldtts.append(plddts_res)
            mean_it_plddts.append(np.mean(plddts_res))

            ##############################################

            # Calculate (or refine) sigmaA
            if config.refine_sigmaA is True:
                llgloss, llgloss_rbr, Ecalc, Fc = rkrf_utils.update_sigmaA(
                    llgloss=llgloss,
                    llgloss_rbr=llgloss_rbr,
                    aligned_xyz=aligned_xyz,
                    constant_fp_added_HKL=constant_fp_added_HKL,
                    constant_fp_added_asu=constant_fp_added_asu,
                )
            else:
                if SIGMA_TRUE:
                    llgloss, llgloss_rbr = rkrf_utils.sigmaA_from_true(
                        llgloss=llgloss,
                        llgloss_rbr=llgloss_rbr,
                        aligned_xyz=aligned_xyz,
                        Etrue=Etrue,
                        phitrue=phitrue,
                        constant_fp_added_HKL=constant_fp_added_HKL,
                        constant_fp_added_asu=constant_fp_added_asu,
                    )
                else:
                    raise ValueError(
                        ""No Etrue or phitrue provided! Can't get the true sigmaA!""
                    )

            # For record
            # if SIGMA_TRUE:
            #     true_sigmas = llg_utils.sigmaA_from_model(
            #         Etrue,
            #         phitrue,
            #         Ecalc,
            #         Fc,
            #         llgloss.sfc.dHKL,
            #         llgloss.bin_labels,
            #     )

            # Update SFC and save
            llgloss.sfc.atom_pos_orth = aligned_xyz.detach().clone()
            # llgloss.sfc.savePDB(
            #    f""{output_directory_path!s}/{run_id}_{iteration}_preRBR.pdb""
            # )

            # Rigid body refinement (RBR) step
            optimized_xyz, loss_track_pose = rk_coordinates.rigidbody_refine_quat(
                aligned_xyz,
                llgloss_rbr,
                sfc.cra_name,
                domain_segs=config.domain_segs,
                lbfgs=RBR_LBFGS,
                added_chain_HKL=constant_fp_added_HKL,
                added_chain_asu=constant_fp_added_asu,
                lbfgs_lr=config.rbr_lbfgs_learning_rate,
                verbose=config.verbose,
            )
            rbr_loss_by_epoch.append(loss_track_pose)

            # LLG loss
            llg, r_work, r_free = llgloss(
                optimized_xyz,
                bin_labels=None,
                num_batch=config.number_of_batches,
                sub_ratio=config.batch_sub_ratio,
                solvent=config.solvent,
                update_scales=config.sfc_scale,
                added_chain_HKL=constant_fp_added_HKL,
                added_chain_asu=constant_fp_added_asu,
                return_Rfactors=True,
            )
            L_llg = -llg

            llg_estimate = L_llg.clone().item() / (
                config.batch_sub_ratio * config.number_of_batches
            )
            llg_losses.append(llg_estimate)
            rwork_by_epoch.append(r_work.item())
            rfree_by_epoch.append(r_free.item())

            # check if current Rfree is the best so far
            if llg_losses[-1] < best_llg:
                best_llg = llg_losses[-1]
                best_msa_bias = (
                    device_processed_features[""msa_feat_bias""].detach().cpu().clone()
                )
                best_feat_weights = (
                    device_processed_features[""msa_feat_weights""].detach().cpu().clone()
                )
                best_run = run_id
                best_iter = iteration
                best_pos = optimized_xyz.detach().clone()
                # best_pos_bfactor = llgloss.sfc.atom_b_iso.detach().clone()

            llgloss.sfc.atom_pos_orth = optimized_xyz
            # Save postRBR frame: either append to a single PDB trajectory using
            # MDTraj if available, or fall back to writing one PDB per iteration.
            if traj_writer is not None:
                coords_nm = optimized_xyz.detach().cpu().numpy().reshape(-1, 3) / 10.0
                traj_writer.write(
                    coords_nm, mdtraj_template.topology, modelIndex=iteration
                )
                # Flush the underlying file handle to write data immediately
                if hasattr(traj_writer, ""_file"") and hasattr(
                    traj_writer._file, ""flush""
                ):
                    traj_writer._file.flush()
            else:
                pdb_path = f""{output_directory_path!s}/{run_id}_{iteration}_postRBR.pdb""
                llgloss.sfc.savePDB(pdb_path)

            progress_bar.set_postfix(
                NEG_LLG=f""{llg_estimate:.2f}"",
                r_feff_work=f""{r_work.item():.3f}"",
                r_feff_free=f""{r_free.item():.3f}"",
                memory=f""{torch.cuda.max_memory_allocated() / 1024**3:.1f}G"",
            )

            # if config.alignment_mode == ""B"":
            #     if loss < best_loss:
            #         best_loss = loss

            # Save sigmaA values for further processing
            # sigmas_dict = {
            #     f""sigma_{i + 1}"": sigma_value.item()
            #     for i, sigma_value in enumerate(sigmas)
            # }
            # sigmas_by_epoch.append(sigmas_dict)

            # if SIGMA_TRUE:
            #     true_sigmas_dict = {
            #         f""sigma_{i + 1}"": sigma_value.item()
            #         for i, sigma_value in enumerate(true_sigmas)
            #     }
            #     true_sigmas_by_epoch.append(true_sigmas_dict)

            #### add an L2 loss to constrain confident atoms ###
            if w_L2 > 0.0:
                # use
                L2_loss = torch.sum(
                    bfactor_weights.unsqueeze(-1) * (optimized_xyz - reference_pos) ** 2
                )  # / conf_best.shape[0]
                loss = L_llg + w_L2 * L2_loss + config.w_plddt * L_plddt
                loss.backward()
            else:
                loss = L_llg + config.w_plddt * L_plddt
                loss.backward()

                if early_stopper.early_stop(loss.item()):
                    break

            # Smooth last several iterations of phase 1 instead of beginning of phase 2
            if (""phase1"" in config.note) and (config.smooth_stage_epochs is not None):
                if iteration > (config.iterations - smooth_stage_epochs):
                    lr_a = lr_a_initial * (decay_rate_stage1_add**iteration)
                    lr_m = lr_m_initial * (decay_rate_stage1_mul**iteration)
                    w_L2 = w_L2_initial * (1 - (iteration / smooth_stage_epochs))

                # Update the learning rates in the optimizer
                optimizer.param_groups[0][""lr""] = lr_a
                optimizer.param_groups[1][""lr""] = lr_m
                optimizer.step()
            else:
                optimizer.step()

            time_by_epoch.append(time.time() - start_time)
            memory_by_epoch.append(torch.cuda.max_memory_allocated() / 1024**3)

            # Save the absolute difference in mean contribution
            # from each residue channel from previous iteration
            if config.bias_version == 4:
                features_at_step_end = (
                    device_processed_features[""template_torsion_angles_sin_cos""][..., 0]
                    .detach()
                    .clone()
                )
                mean_change = torch.mean(
                    torch.abs(features_at_step_end - features_at_it_start[..., 0]),
                    dim=(0, 2, 3),
                )
            else:
                features_at_step_end = (
                    device_processed_features[""msa_feat""][:, :, 25:48, 0]
                    .detach()
                    .clone()
                )
                mean_change = torch.mean(
                    torch.abs(
                        features_at_step_end - features_at_it_start[:, :, 25:48, 0]
                    ),
                    dim=(0, 2),
                )
            absolute_feats_changes.append(rk_utils.assert_numpy(mean_change))

        ####### Save data
        # Close mdtraj writer if used so file is flushed and closed properly
        try:
            if traj_writer is not None:
                traj_writer.close()
        except Exception:
            pass
        # Average plddt per iteration
        np.save(
            f""{output_directory_path!s}/mean_it_plddt_{run_id}.npy"",
            np.array(mean_it_plddts),
        )

        # LLG per iteration
        np.save(
            f""{output_directory_path!s}/NEG_LLG_it_{run_id}.npy"",
            rk_utils.assert_numpy(llg_losses),
        )

        # R work per iteration
        np.save(
            f""{output_directory_path!s}/rwork_it_{run_id}.npy"",
            rk_utils.assert_numpy(rwork_by_epoch),
        )

        # R free per iteration
        np.save(
            f""{output_directory_path!s}/rfree_it_{run_id}.npy"",
            rk_utils.assert_numpy(rfree_by_epoch),
        )

        np.save(
            f""{output_directory_path!s}/time_it_{run_id}.npy"",
            rk_utils.assert_numpy(time_by_epoch),
        )

        np.save(
            f""{output_directory_path!s}/memory_it_{run_id}.npy"",
            rk_utils.assert_numpy(memory_by_epoch),
        )

        # Absolute MSA change per column per iteration
        np.save(
            f""{output_directory_path!s}/MSA_changes_it_{run_id}.npy"",
            rk_utils.assert_numpy(absolute_feats_changes),
        )

        # Mean plddt per residue (over iterations)
        np.save(
            f""{output_directory_path!s}/plddt_res_{run_id}.npy"",
            np.array(all_pldtts),
        )

        # Iteration sigmaA dictionary
        # with open(
        #     f""{output_directory_path!s}/sigmas_by_epoch_{run_id}.pkl"",
        #     ""wb"",
        # ) as file:
        #     pickle.dump(sigmas_by_epoch, file)

        # if SIGMA_TRUE:
        #     with open(
        #         f""{output_directory_path!s}/true_sigmas_by_epoch_{run_id}.pkl"",
        #         ""wb"",
        #     ) as file:
        #         pickle.dump(true_sigmas_by_epoch, file)

    # Save the best msa_bias and feat_weights
    torch.save(
        best_msa_bias,
        f""{output_directory_path!s}/best_msa_bias_{best_run}_{best_iter}.pt"",
    )

    torch.save(
        best_feat_weights,
        f""{output_directory_path!s}/best_feat_weights_{best_run}_{best_iter}.pt"",
    )

    # Save best model as a single PDB (preserve input_pdb topology)
    try:
        if best_pos is not None:
            # best_pos is in Å, set it on the llgloss.sfc object and save
            try:
                llgloss.sfc.atom_pos_orth = best_pos
                best_name = (
                    f""{output_directory_path!s}/best_model_{best_run}_{best_iter}.pdb""
                )
                llgloss.sfc.savePDB(best_name)
            except Exception:
                # Fallback: try to write using mdtraj with the input topology
                try:
                    import mdtraj as md  # type: ignore

                    topo = md.load_pdb(input_pdb).topology
                    coords_nm = best_pos.detach().cpu().numpy().reshape(1, -1, 3) / 10.0
                    traj = md.Trajectory(coords_nm, topo)
                    traj.save(
                        f""{output_directory_path!s}/best_model_{best_run}_{best_iter}.pdb""
                    )
                except Exception:
                    logger.warning(
                        ""Failed to write best model PDB with both SFC and mdtraj.""
                    )
    except NameError:
        # best_pos not defined; nothing to save
        pass

    return config
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/msacluster_utils.py",".py","3399","109","""""""
Modified from https://github.com/HWaymentSteele/AF_Cluster/blob/main/scripts/utils.py
""""""

import os

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from Bio import SeqIO


def lprint(string, f):
    print(string)
    f.write(string + ""\n"")


def load_fasta(path):
    """"""
    Handle case where path is a folder with multiple a3ms from different databases
    """"""
    seqs, IDs = [], []
    seen_sequences = set()
    if os.path.isdir(path):
        for file in os.listdir(path):
            file_name, ext = os.path.splitext(file)
            if ext not in ["".a3m"", "".fasta""]:
                continue
            file_path = os.path.join(path, file)
            with open(file_path) as handle:
                for record in SeqIO.parse(handle, ""fasta""):
                    seq = """".join(list(record.seq))
                    if seq in seen_sequences:
                        continue
                    seen_sequences.add(seq)
                    IDs.append(record.id)
                    seqs.append(seq)
    elif os.path.isfile(path):
        file_name, ext = os.path.splitext(os.path.basename(path))
        if ext not in ["".a3m"", "".fasta""]:
            raise KeyError(""Input alignment should be a3m or fasta format"")
        with open(path) as handle:
            for record in SeqIO.parse(handle, ""fasta""):
                seq = """".join(list(record.seq))
                if seq in seen_sequences:
                    continue
                seen_sequences.add(seq)
                IDs.append(record.id)
                seqs.append(seq)
    return IDs, seqs


def write_fasta(names, seqs, outfile=""tmp.fasta""):
    with open(outfile, ""w"") as f:
        for nm, seq in list(zip(names, seqs, strict=False)):
            f.write(f"">{nm}\n{seq}\n"")


def encode_seqs(seqs, max_len=108, alphabet=None):
    if alphabet is None:
        alphabet = ""ACDEFGHIKLMNPQRSTVWY-""

    arr = np.zeros([len(seqs), max_len, len(alphabet)])
    for j, seq in enumerate(seqs):
        for i, char in enumerate(seq):
            for k, res in enumerate(alphabet):
                if char == res:
                    arr[j, i, k] += 1
    return arr.reshape([len(seqs), max_len * len(alphabet)])


def consensusVoting(seqs):
    ## Find the consensus sequence
    consensus = """"
    residues = ""ACDEFGHIKLMNPQRSTVWY-""
    n_chars = len(seqs[0])
    for i in range(n_chars):
        baseArray = [x[i] for x in seqs]
        baseCount = np.array([baseArray.count(a) for a in list(residues)])
        vote = np.argmax(baseCount)
        consensus += residues[vote]

    return consensus


def plot_landscape(x, y, df, query_, plot_type, args):
    plt.figure(figsize=(5, 5))
    tmp = df.loc[df.dbscan_label == -1]
    plt.scatter(tmp[x], tmp[y], color=""lightgray"", marker=""x"", label=""unclustered"")

    tmp = df.loc[df.dbscan_label > 9]
    plt.scatter(tmp[x], tmp[y], color=""black"", label=""other clusters"")

    tmp = df.loc[df.dbscan_label >= 0][df.dbscan_label <= 9]
    sns.scatterplot(
        x=x, y=y, hue=""dbscan_label"", data=tmp, palette=""tab10"", linewidth=0
    )

    plt.scatter(query_[x], query_[y], color=""red"", marker=""*"", s=150, label=""Ref Seq"")
    plt.legend(bbox_to_anchor=(1, 1), frameon=False)

    plt.xlabel(x)
    plt.ylabel(y)
    plt.tight_layout()

    plt.savefig(
        args.o + ""/"" + args.keyword + ""_"" + plot_type + "".pdf"", bbox_inches=""tight""
    )
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/mse.py",".py","2903","97","""""""
MSE target for a target PDB file
""""""

import numpy as np
import torch
from SFC_Torch import PDBParser

from rocket import refinement_utils as rkrf_utils


class MSElossBB:
    """"""
    Object_oreinted interface to calculate MSE loss for backbone or CA atoms
    """"""

    def __init__(
        self, target: PDBParser, moving: PDBParser, device: torch.device, selection=""BB""
    ):
        """"""
        selection: str
            ""BB"" for backbone, ""CA"" for Calpha
        """"""

        self.device = device
        self.target_cra = target.cra_name
        self.moving_cra = moving.cra_name
        self.pdb_obj = moving
        self.target_pos = torch.tensor(
            target.atom_pos, device=device, dtype=torch.float32
        ).clone()
        self.selection = selection
        if selection == ""BB"":
            self.ind1, self.ind2 = rkrf_utils.get_common_bb_ind(moving, target)
        elif selection == ""CA"":
            self.ind1, self.ind2 = rkrf_utils.get_common_ca_ind(moving, target)

    def forward(self, xyz_ort: torch.Tensor, weights: torch.Tensor):
        mse_loss = torch.mean(
            torch.sum(
                (xyz_ort[self.ind1] - self.target_pos[self.ind2]) ** 2,
                dim=-1,
            )
            * weights[self.ind1]
        )
        return mse_loss


class MSEloss:
    """"""
    Object_oreinted interface to calculate MSE loss
    """"""

    def __init__(self, target: PDBParser, moving: PDBParser, device: torch.device):
        assert target.sequence == moving.sequence, (
            ""target pdb has different sequence with moving pdb!""
        )
        # get the intersect atoms index
        self.target_pdb = target
        self.device = device
        index_target = []
        index_moving = []
        for i, name in enumerate(moving.cra_name):
            try:
                id_t = target.cra_name.index(name)
                index_moving.append(i)
                index_target.append(id_t)
            except ValueError:
                pass
        assert (
            np.array(target.cra_name)[index_target]
            == np.array(moving.cra_name)[index_moving]
        ).all()
        self.index_target = np.array(index_target)
        self.index_moving = np.array(index_moving)
        self.target_pos = torch.tensor(
            target.atom_pos, device=device, dtype=torch.float32
        )

    @property
    def sequence(self):
        return self.target_pdb.sequence

    def forward(self, xyz_ort: torch.Tensor, subratio=1.0):
        sub_boolean_mask = np.random.rand(len(self.index_moving)) < subratio
        mse_loss = torch.mean(
            torch.sum(
                (
                    xyz_ort[self.index_moving][sub_boolean_mask]
                    - self.target_pos[self.index_target][sub_boolean_mask]
                )
                ** 2,
                dim=-1,
            )
        )
        return mse_loss
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/xtal/targets.py",".py","15988","433","""""""
LLG targets for xtal data
""""""

import time
from functools import partial

import numpy as np
import torch
from SFC_Torch import SFcalculator

from rocket import utils
from rocket.xtal import structurefactors as llg_sf
from rocket.xtal import utils as llg_utils


class LLGloss(torch.nn.Module):
    """"""
    Object_oriented interface to calculate LLG loss

    # Initialization, only have to do it once
    sfc = llg_sf.initial_SFC(...)
    llgloss = LLGloss(sfc, tng_file, device)
    Ecalc = llgloss.compute_Ecalc(xyz_orth)
    llgloss.refine_sigmaA_adam(Ecalc.detach(), n_step=50)
    llgloss.freeze_sigmaA()

    # Loss calculation for each step
    loss = -llgloss(xyz_orth, bin_labels=[1,2,3], num_batch=10, sub_ratio=0.3)

    resol_min, resol_max: None | float
        resolution cutoff for used miller index. Will use resol_min <= dHKL <= resol_max

    TODO:
    Currently the initialization needs Eobs, Eps, Centric, Dobs, Feff, Bin_labels.
    We do so by loading the tng_data.
    Later we should be able to calculate everything from SFcalculator,
    all necesary information is ready there.

    """"""

    def __init__(
        self,
        sfc: SFcalculator,
        tng_file: str,
        device: torch.device,
        resol_min=None,
        resol_max=None,
    ) -> None:
        super().__init__()
        self.sfc = sfc
        self.device = device
        data_dict = llg_utils.load_tng_data(tng_file, device=device)

        self.register_buffer(""Eobs"", data_dict[""EDATA""])
        self.Eobs: torch.Tensor
        self.register_buffer(""Eps"", data_dict[""EPS""])
        self.Eps: torch.Tensor
        self.register_buffer(""Centric"", data_dict[""CENTRIC""])
        self.Centric: torch.Tensor
        self.register_buffer(""Dobs"", data_dict[""DOBS""])
        self.Dobs: torch.Tensor
        self.register_buffer(""Feff"", data_dict[""FEFF""])
        self.Feff: torch.Tensor
        self.register_buffer(""bin_labels"", data_dict[""BIN_LABELS""])
        self.bin_labels: torch.Tensor
        self.unique_bins = torch.unique(self.bin_labels)
        self.register_buffer(""bin_dHKL"", data_dict[""BIN_dHKLS""])
        self.bin_dHKL: torch.Tensor

        if resol_min is None:
            resol_min = min(self.sfc.dHKL)

        if resol_max is None:
            resol_max = max(self.sfc.dHKL)

        resol_bool = (self.sfc.dHKL >= (resol_min - 1e-4)) & (
            self.sfc.dHKL <= (resol_max + 1e-4)
        )
        self.working_set = (~self.sfc.free_flag) & (~self.sfc.Outlier) & (resol_bool)
        self.free_set = (self.sfc.free_flag) & (~self.sfc.Outlier) & (resol_bool)

    def init_sigmaAs(self, Ecalc, subset=""working"", requires_grad=True):
        if subset == ""working"":
            subset_boolean = (~self.sfc.free_flag) & (~self.sfc.Outlier)
        elif subset == ""free"":
            subset_boolean = (self.sfc.free_flag) & (~self.sfc.Outlier)

        Ecalc = Ecalc.detach().clone()
        self.sigmaAs = []
        for bin_i in self.unique_bins:
            index_i = self.bin_labels[subset_boolean] == bin_i
            Eobs_i = self.Eobs[subset_boolean][index_i]
            Ecalc_i = Ecalc[subset_boolean][index_i]

            # Initialize from correlation coefficient
            sigmaA_i = (
                torch.corrcoef(torch.stack([Eobs_i**2, Ecalc_i**2], dim=0))[1][0]
                .clamp(min=0.001, max=0.999)
                .sqrt()
                .to(device=self.device, dtype=torch.float32)
                .requires_grad_(requires_grad)
            )
            self.sigmaAs.append(sigmaA_i)

    def init_sigmaAs_nodata(self, frac=1.0, rms=0.7, requires_grad=True):
        self.sigmaAs = []
        for bin_i in self.unique_bins:
            index_i = self.bin_labels == bin_i
            s = self.bin_dHKL[index_i][0]
            sigmaA_i = torch.sqrt(torch.tensor(frac)) * torch.exp(
                -2 / 3 * torch.pi**2 * torch.tensor(rms) ** 2 * s**-2
            ).to(device=self.device, dtype=torch.float32).requires_grad_(requires_grad)
            self.sigmaAs.append(sigmaA_i)

    def freeze_sigmaA(self):
        self.sigmaAs = [sigmaA.requires_grad_(False) for sigmaA in self.sigmaAs]

    def unfreeze_sigmaA(self):
        self.sigmaAs = [sigmaA.requires_grad_(True) for sigmaA in self.sigmaAs]

    def refine_sigmaA_adam(
        self, Ecalc, n_steps=50, lr=0.01, sub_ratio=0.3, initialize=True, verbose=False
    ):
        def adam_opt_i(i, index_i, n_steps, sub_ratio, lr, verbose):
            def adam_stepopt(sub_boolean_mask):
                loss = -llg_utils.llgTot_calculate(
                    self.sigmaAs[i],
                    Eobs_i[sub_boolean_mask],
                    Ecalc_i[sub_boolean_mask],
                    centric_i[sub_boolean_mask],
                )
                adam.zero_grad()
                loss.backward()
                adam.step()
                self.sigmaAs[i].data = torch.clamp(self.sigmaAs[i].data, 0.015, 0.99)
                return loss

            Eobs = self.Eobs.detach().clone()
            Ecalc_cloned = Ecalc.detach().clone()
            Eobs_i = Eobs[index_i]
            Ecalc_i = Ecalc_cloned[index_i]
            centric_i = self.Centric[index_i]
            adam = torch.optim.Adam([self.sigmaAs[i]], lr=lr)
            for _ in range(n_steps):
                start_time = time.time()
                sub_boolean_mask = (
                    np.random.rand(
                        len(Eobs_i),
                    )
                    < sub_ratio
                )
                temp_loss = adam_stepopt(sub_boolean_mask)
                time_this_round = round(time.time() - start_time, 3)
                str_ = ""Time: "" + str(time_this_round)
                if verbose:
                    print(
                        f""SigmaA {i}"", utils.assert_numpy(temp_loss), str_, flush=True
                    )

        if initialize:
            self.init_sigmaAs(Ecalc, requires_grad=True)

        for i, bin_i in enumerate(self.unique_bins):
            index_i = self.bin_labels == bin_i
            adam_opt_i(
                i, index_i, n_steps=n_steps, sub_ratio=sub_ratio, lr=lr, verbose=verbose
            )

    def refine_sigmaA_newton(
        self,
        Ecalc,
        n_steps=2,
        initialize=True,
        method=""autodiff"",
        subset=""test"",
        edge_weights=0.25,
        smooth_overall_weight=200.0,
    ):
        """"""
        subset : str, ""working"" or ""free""

        method : str, ""autodiff"" or ""analytical""

        TODO: include smooth_constraint
        """"""
        if initialize:
            self.init_sigmaAs(Ecalc, subset=""working"", requires_grad=False)
            # self.init_sigmaAs_nodata()

        if subset == ""working"":
            subset_boolean = (~self.sfc.free_flag) & (~self.sfc.Outlier)
        elif subset == ""test"":
            subset_boolean = (self.sfc.free_flag) & (~self.sfc.Outlier)

        for _n in range(n_steps):
            lps = []
            lpps = []
            ls = []
            for i, label in enumerate(self.unique_bins):
                # for i in range(0, len(self.unique_bins) - 1, 2):
                # TODO: tackle the corner case where no freeset in a bin
                index_i = self.bin_labels[subset_boolean] == label

                Ecalc_i = Ecalc[subset_boolean][index_i]
                Eob_i = self.Eobs[subset_boolean][index_i]

                Centric_i = self.Centric[subset_boolean][index_i]
                Dobs_i = self.Dobs[subset_boolean][index_i]
                sigmaA_i = self.sigmaAs[int(i)].detach().clone()
                l, lp, lpp = llg_utils.llgItot_with_derivatives2sigmaA(  # noqa: E741
                    sigmaA=sigmaA_i,
                    dobs=Dobs_i,
                    Eeff=Eob_i,
                    Ec=Ecalc_i,
                    centric_tensor=Centric_i,
                    method=method,
                )
                ls.append(l)
                lps.append(lp)
                lpps.append(lpp)

            dL1 = torch.stack(lps).detach()
            H1 = torch.diag(torch.stack(lpps).detach())

            # Smooth terms
            sigmaAs_tensor = (
                torch.stack(self.sigmaAs).detach().clone().requires_grad_(True)
            )
            smooth_calculator = partial(
                llg_utils.interpolate_smooth,
                edge_weights=edge_weights,
                total_weight=smooth_overall_weight,
            )
            Ls = smooth_calculator(sigmaAs_tensor)
            dL2 = torch.autograd.grad(Ls, sigmaAs_tensor, create_graph=True)[0]
            H2 = torch.autograd.functional.hessian(smooth_calculator, sigmaAs_tensor)

            # Combine the two terms
            dL_total = dL1 - dL2.detach()
            Htotal = H1 - H2.detach()

            sigmaAs_updated = llg_utils.newton_step(sigmaAs_tensor, dL_total, Htotal)
            sigmaAs_new = torch.clamp(sigmaAs_updated, 0.015, 0.99)
            self.sigmaAs = [s.detach().requires_grad_(False) for s in sigmaAs_new]

    def compute_Ecalc(
        self,
        xyz_orth,
        solvent=True,
        return_Fc=False,
        return_Rfactors=False,
        update_scales=False,
        scale_steps=10,
        scale_initialize=False,
        added_chain_HKL=None,
        added_chain_asu=None,
    ) -> torch.Tensor:
        """"""
        Compute normalized structure factors (Ecalc).

        Args:
            xyz_orth: Orthogonal coordinates of atoms
            solvent: Whether to include solvent contribution
            return_Fc: Whether to return calculated structure factors Fc
            return_Rfactors: Whether to calculate and return R-work and R-free
            update_scales: Whether to update scaling factors
            scale_steps: Number of steps for scale refinement
            scale_initialize: Whether to initialize scales
            added_chain_HKL: Additional HKL contributions
            added_chain_asu: Additional ASU contributions

        Returns:
            If return_Rfactors=True and return_Fc=True: (Ecalc, Fc, R_work, R_free)
            If return_Rfactors=True and return_Fc=False: (Ecalc, R_work, R_free)
            If return_Rfactors=False and return_Fc=True: (Ecalc, Fc)
            If return_Rfactors=False and return_Fc=False: Ecalc

        Note:
            R-factors are calculated using:
            R_FEFF = Sum[DOBS^2 * |FEFF - FCALC|] / Sum[DOBS^2 * FEFF]
            R_work uses the working set (~free_flag), R_free uses the free set
            (free_flag)
        """"""
        self.sfc.calc_fprotein(atoms_position_tensor=xyz_orth)

        if added_chain_HKL is not None:
            self.sfc.Fprotein_HKL = self.sfc.Fprotein_HKL + added_chain_HKL
            self.sfc.Fprotein_asu = self.sfc.Fprotein_asu + added_chain_asu

        if solvent:
            self.sfc.calc_fsolvent()
            if update_scales:
                self.sfc.get_scales_adam(
                    lr=0.01,
                    n_steps=scale_steps,
                    sub_ratio=0.7,
                    initialize=scale_initialize,
                )
            Fc = self.sfc.calc_ftotal()
        else:
            # MH note: we need scales here, even without solvent contribution
            self.sfc.Fmask_HKL = torch.zeros_like(self.sfc.Fprotein_HKL)
            if update_scales:
                self.sfc.get_scales_adam(
                    lr=0.01,
                    n_steps=scale_steps,
                    sub_ratio=0.7,
                    initialize=scale_initialize,
                )
            Fc = self.sfc.calc_ftotal()

        Fm = llg_sf.ftotal_amplitudes(Fc, self.sfc.dHKL, sort_by_res=True)
        sigmaP = llg_sf.calculate_Sigma_atoms(Fm, self.Eps, self.bin_labels)
        Ecalc = llg_sf.normalize_Fs(Fm, self.Eps, sigmaP, self.bin_labels)

        if return_Rfactors:
            # Calculate R-work and R-free using the formula:
            # R_FEFF = Sum[DOBS^2 * |FEFF - FCALC|] / Sum[DOBS^2 * FEFF]

            # Calculate R-work (working set)
            working_mask = self.working_set
            dobs_work = self.Dobs[working_mask]
            feff_work = self.Feff[working_mask]
            fcalc_work = Fc[working_mask]

            numerator_work = torch.sum(dobs_work**2 * torch.abs(feff_work - fcalc_work))
            denominator_work = torch.sum(dobs_work**2 * feff_work)
            R_work = numerator_work / (denominator_work + 1e-8)  # avoid divide by zero

            # Calculate R-free (free set)
            free_mask = self.free_set
            dobs_free = self.Dobs[free_mask]
            feff_free = self.Feff[free_mask]
            fcalc_free = Fc[free_mask]

            numerator_free = torch.sum(dobs_free**2 * torch.abs(feff_free - fcalc_free))
            denominator_free = torch.sum(dobs_free**2 * feff_free)
            R_free = numerator_free / (denominator_free + 1e-8)  # avoid divide by zero

            if return_Fc:
                return Ecalc, Fc, R_work, R_free
            else:
                return Ecalc, R_work, R_free
        elif return_Fc:
            return Ecalc, Fc
        else:
            return Ecalc

    def forward(
        self,
        xyz_ort: torch.Tensor,
        bin_labels=None,
        num_batch=1,
        sub_ratio=1.0,
        solvent=True,
        update_scales=False,
        added_chain_HKL=None,
        added_chain_asu=None,
        return_Rfactors=False,
    ):
        """"""
        Args:
            xyz_orth: torch.Tensor, [N_atoms, 3] in angstroms
                Orthogonal coordinates of proteins, coming from AF2 model, send to SFC

            bin_labels: None or List[int]
                Labels of bins used in the loss calculation.
                If None, will use the whole miller indices.
                Serve as a proxy for resolution selection

            num_batch: int
                Number of batches

            sub_ratio: float between 0.0 and 1.0
                Fraction of mini-batch sampling over all miller indices,
                e.g. 0.3 meaning each batch sample 30% of miller indices

            return_Rfactors: bool
                Whether to calculate and return R-work and R-free factors.
                If True, returns (llg, r_work, r_free). If False, returns llg only.

        """"""

        if return_Rfactors:
            Ecalc, r_work, r_free = self.compute_Ecalc(
                xyz_ort,
                solvent=solvent,
                update_scales=update_scales,
                added_chain_HKL=added_chain_HKL,
                added_chain_asu=added_chain_asu,
                return_Rfactors=True,
            )
        else:
            Ecalc = self.compute_Ecalc(
                xyz_ort,
                solvent=solvent,
                update_scales=update_scales,
                added_chain_HKL=added_chain_HKL,
                added_chain_asu=added_chain_asu,
                return_Rfactors=False,
            )
        llg = 0.0

        if bin_labels is None:
            bin_labels = self.unique_bins

        for i, label in enumerate(bin_labels):
            index_i = self.bin_labels[self.working_set] == label
            # if sum(index_i) == 0:
            #    continue
            Ecalc_i = Ecalc[self.working_set][index_i]
            Eob_i = self.Eobs[self.working_set][index_i]
            Centric_i = self.Centric[self.working_set][index_i]
            Dobs_i = self.Dobs[self.working_set][index_i]

            sigmaA_i = self.sigmaAs[int(i)]
            for _j in range(num_batch):
                sub_boolean_mask = np.random.rand(len(Eob_i)) < sub_ratio
                llg_ij = llg_utils.llgItot_calculate(
                    sigmaA_i,
                    Dobs_i[sub_boolean_mask],
                    Eob_i[sub_boolean_mask],
                    Ecalc_i[sub_boolean_mask],
                    Centric_i[sub_boolean_mask],
                ).sum()
                llg = llg + llg_ij

        if return_Rfactors:
            return llg, r_work, r_free
        else:
            return llg
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/xtal/__init__.py",".py","99","4","from . import structurefactors, targets, utils

__all__ = [""structurefactors"", ""targets"", ""utils""]
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/xtal/utils.py",".py","13947","485","""""""
Functions relating to sigmaa calculation and refinement
""""""

import numpy as np
import reciprocalspaceship as rs
import torch

from rocket import utils
from rocket.xtal import structurefactors


def newton_step(tensor_pre_update, gradient, Hessian):
    # updated_tensor = torch.clamp(
    #    tensor_pre_update - derivatives_tensor,
    #    0.015,
    #    0.99,
    # )
    gamma = 1.0
    derivatives_tensor = torch.matmul(torch.linalg.inv(Hessian), gradient)
    updated_tensor = tensor_pre_update - gamma * derivatives_tensor

    # ds_tensor = torch.matmul(torch.linalg.inv(Htotal), dL_total)
    # sigmaAs_new = sigmaAs_tensor - ds_tensor
    #             sigmaAs_new = llg_utils.newton_step(
    #            sigmaAs_tensor, dL_total, torch.linalg.inv(Htotal)
    #        )
    return updated_tensor


def llgIa_firstdev(sigmaA, dobs, Eeff, Ec):
    """"""
    partial llgIa / partial sigmaA
    """"""

    bessel_argument = (2 * dobs * Ec * Eeff * sigmaA) / (1 - dobs**2 * sigmaA**2)
    bessel_ratio = Ak_approx(
        torch.tensor(1).to(bessel_argument), bessel_argument
    ).squeeze()

    dLa = (
        2
        * dobs
        * (
            Ec * Eeff * (dobs**2 * sigmaA**2 + 1) * bessel_ratio
            - dobs * sigmaA * (dobs**2 * sigmaA**2 + Ec**2 + Eeff**2 - 1)
        )
        / (dobs**2 * sigmaA**2 - 1) ** 2
    )

    return dLa


def llgIc_firstdev(sigmaA, dobs, Eeff, Ec):
    """"""
    partial llgIc / partial sigmaA
    """"""

    tanh_argument = (dobs * Ec * Eeff * sigmaA) / (1 - dobs**2 * sigmaA**2)

    dLc = (
        dobs
        * (
            Ec * Eeff * (dobs**2 * sigmaA**2 + 1) * torch.tanh(tanh_argument)
            - dobs * sigmaA * (dobs**2 * sigmaA**2 + Ec**2 + Eeff**2 - 1)
        )
        / (dobs**2 * sigmaA**2 - 1) ** 2
    )

    return dLc


def llgItot_firstdev(sigmaA, dobs, Eeff, Ec, centric_tensor):
    """"""
    partial llgI / partial sigmaA
    """"""
    llgIp_centric = llgIc_firstdev(
        sigmaA, dobs[centric_tensor], Eeff[centric_tensor], Ec[centric_tensor]
    )
    llgIp_acentric = llgIa_firstdev(
        sigmaA, dobs[~centric_tensor], Eeff[~centric_tensor], Ec[~centric_tensor]
    )

    return llgIp_centric.sum() + llgIp_acentric.sum()


def llgIa_seconddev(sigmaA, dobs, Eeff, Ec):
    """"""
    partial^2 llgIa / partial sigmaA^2
    """"""

    bessel_argument = (2 * dobs * Ec * Eeff * sigmaA) / (1 - dobs**2 * sigmaA**2)
    bessel_ratio = Ak_approx(
        torch.tensor(1).to(bessel_argument), bessel_argument
    ).squeeze()

    d2La = (
        2
        * dobs
        / (dobs**2 * sigmaA**2 - 1) ** 4
        * (
            dobs
            * (
                Ec**2
                * (
                    3 * dobs**4 * sigmaA**4
                    + 2 * (dobs**2 * Eeff * sigmaA**2 + Eeff) ** 2
                    - 2 * dobs**2 * sigmaA**2
                    - 1
                )
                + (dobs**2 * sigmaA**2 - 1)
                * (dobs**4 * sigmaA**4 + Eeff**2 * (3 * dobs**2 * sigmaA**2 + 1) - 1)
            )
            + Ec
            * Eeff
            * bessel_ratio
            * (
                bessel_ratio * (-2 * dobs * Ec * Eeff * (dobs**2 * sigmaA**2 + 1) ** 2)
                - (
                    (
                        dobs**6 * sigmaA**6
                        + 3 * dobs**4 * sigmaA**4
                        - 5 * dobs**2 * sigmaA**2
                        + 1
                    )
                    / sigmaA
                )
            )
        )
    )

    return d2La


def llgIc_seconddev(sigmaA, dobs, Eeff, Ec):
    """"""
    partial^2 llgIc / partial sigmaA^2
    """"""

    tanh_and_sech_argument = (dobs * Ec * Eeff * sigmaA) / (1 - dobs**2 * sigmaA**2)

    term_1 = (
        dobs**4 * sigmaA**4
        + 3 * dobs**2 * sigmaA**2 * (Ec**2 + Eeff**2)
        + Ec**2
        + Eeff**2
        - 1
    )
    term_2 = (
        Ec
        * Eeff
        * (dobs**2 * sigmaA**2 + 1) ** 2
        * torch.cosh(tanh_and_sech_argument) ** -2
    )
    term_3 = (dobs**4 * sigmaA**4 + 2 * dobs**2 * sigmaA**2 - 3) * torch.tanh(
        tanh_and_sech_argument
    )

    d2Lc = (dobs**2 * sigmaA**2 - 1) ** -4 * (
        dobs**2
        * (
            (dobs**2 * sigmaA**2 - 1) * term_1
            + Ec * Eeff * (term_2 - 2 * dobs * sigmaA * term_3)
        )
    )

    return d2Lc


def llgItot_seconddev(sigmaA, dobs, Eeff, Ec, centric_tensor):
    """"""
    partial^2 llgI / partial sigmaA^2
    """"""

    llgIpp_centric = llgIc_seconddev(
        sigmaA, dobs[centric_tensor], Eeff[centric_tensor], Ec[centric_tensor]
    )
    llgIpp_acentric = llgIa_seconddev(
        sigmaA, dobs[~centric_tensor], Eeff[~centric_tensor], Ec[~centric_tensor]
    )

    return llgIpp_centric.sum() + llgIpp_acentric.sum()


def interpolate_smooth(sigmaAs_tensor, edge_weights=0.25, total_weight=200.0):
    # Calculate loss for internal values
    internal_loss = (
        (sigmaAs_tensor[1:-1] - (sigmaAs_tensor[:-2] + sigmaAs_tensor[2:]) / 2) ** 2
    ).sum()

    # Extrapolate at the edges
    edge_loss_start = (
        sigmaAs_tensor[0] - 2 * sigmaAs_tensor[1] + sigmaAs_tensor[2]
    ) ** 2
    edge_loss_end = (
        sigmaAs_tensor[-1] - 2 * sigmaAs_tensor[-2] + sigmaAs_tensor[-3]
    ) ** 2

    # Total loss
    total_loss = (
        internal_loss + edge_weights * edge_loss_start + edge_weights * edge_loss_end
    ) / float(sigmaAs_tensor.shape[0])

    return total_weight * total_loss


def llgItot_with_derivatives2sigmaA(
    sigmaA, dobs, Eeff, Ec, centric_tensor, method=""autodiff""
):
    """"""
    sigmaA : torch.Tensor

    method : str, ""autodiff"" or ""analytical""
        calculate derivatives with autodiff or analytical expression
    """"""
    if method == ""autodiff"":
        sA = sigmaA.detach().clone().requires_grad_(True)
        l = llgItot_calculate(sA, dobs, Eeff, Ec, centric_tensor)  # noqa: E741
        lp = torch.autograd.grad(l, sA, create_graph=True)[0]
        lpp = torch.autograd.grad(lp, sA, create_graph=True, allow_unused=True)[0]
    elif method == ""analytical"":
        l = llgItot_calculate(sigmaA, dobs, Eeff, Ec, centric_tensor)  # noqa: E741
        lp = llgItot_firstdev(
            sigmaA, dobs.detach(), Eeff.detach(), Ec.detach(), centric_tensor.detach()
        )
        lpp = llgItot_seconddev(
            sigmaA, dobs.detach(), Eeff.detach(), Ec.detach(), centric_tensor.detach()
        )

    return l.detach().clone(), lp.detach().clone(), lpp.detach().clone()


def Ak_approx(nu, z):
    """"""
    Approximation of ratio of modified Bessel functions of 1st kind.
    https://arxiv.org/pdf/1902.02603.pdf

    Parameters
    ----------
    nu: tensor, shape (N0,)
            Order of modified Bessel functions of 1st kind.
    z: tensor, shape (N1,)
            Argument of Bessel function. Positive values only.

    Return
    ------
    tensor, shape (N1, N0)
    """"""
    return 0.5 * (lb_Ak(nu, z) + ub_Ak(nu, z))


def ub_Ak(nu, z):
    """"""
    Upper-bound for the ratio of modified Bessel functions of 1st kind.
    https://arxiv.org/pdf/1606.02008.pdf (Theorems 5 and 6).

    Return
    ------
    ub: tensor, shape (z.shape[0], nu.shape[0])
            Upper-bound for Ak(nu, z).
    """"""
    assert torch.all(nu >= 0)
    nu = nu.reshape(1, -1)
    z = z.reshape(-1, 1)

    ub = torch.zeros(z.shape[0], nu.shape[1]).to(z)
    ub[:, nu.reshape(-1) >= 0.5] = torch.min(
        B(0, nu[nu >= 0.5], z), B_tilde(2, nu[nu >= 0.5], z)
    )
    ub[:, nu.reshape(-1) < 0.5] = B_tilde(2, nu[nu < 0.5], z)
    return ub


def lb_Ak(nu, z):
    """"""
    Lower-bound for the ratio of modified Bessel functions of 1st kind.
    https://arxiv.org/pdf/1606.02008.pdf (Theorems 5 and 6).
    """"""
    assert torch.all(nu >= 0)
    nu = nu.reshape(1, -1)
    z = z.reshape(-1, 1)
    return B_tilde(0, nu, z)


def B_tilde(alpha, nu, z):
    """"""
    https://arxiv.org/pdf/1606.02008.pdf
    """"""
    nu = nu.reshape(1, -1)
    z = z.reshape(-1, 1)
    sigma = nu + float(alpha + 1) / 2.0
    delta_p = nu + 0.5 + sigma / (2 * torch.sqrt(sigma**2 + z**2))
    delta_m = nu - 0.5 - sigma / (2 * torch.sqrt(sigma**2 + z**2))
    return z / (delta_m + torch.sqrt(delta_p**2 + z**2))


def B(alpha, nu, z):
    """"""
    https://arxiv.org/pdf/1606.02008.pdf
    """"""
    nu = nu.reshape(1, -1)
    z = z.reshape(-1, 1)
    lamda = nu + float(alpha - 1) / 2.0
    delta = nu - 0.5 + lamda / (2 * torch.sqrt(lamda**2 + z**2))
    return z / (delta + torch.sqrt(delta**2 + z**2))


def llgIa_calculate(sigmaA, dobs, Eeff, Ec):
    # acentric reflections
    bessel_arg = (2 * dobs * sigmaA * Eeff * Ec) / (1 - dobs**2 * sigmaA**2)
    exp_bessel = torch.special.i0e(bessel_arg)

    llg = torch.sum(
        torch.log((1 - dobs**2 * sigmaA**2) ** (-1) * exp_bessel)
        + (
            sigmaA
            * dobs
            * (-dobs * sigmaA * Eeff**2 - dobs * sigmaA * Ec**2 + 2 * Eeff * Ec)
        )
        / (1 - dobs**2 * sigmaA**2)
    )
    return llg


def llgIc_calculate(sigmaA, dobs, Eeff, Ec):
    # centric reflections
    cosh_arg = (sigmaA * dobs * Ec * Eeff) / (1 - dobs**2 * sigmaA**2)
    expo_arg = (sigmaA**2 * dobs**2 * (Eeff**2 + Ec**2)) / (2 * dobs**2 * sigmaA**2 - 2)

    llg = torch.sum(
        torch.log((1 - dobs**2 * sigmaA**2) ** (-0.5)) + expo_arg + logcosh(cosh_arg)
    )

    return llg


def llgItot_calculate(sigmaA, dobs, Eeff, Ec, centric_tensor):
    # (1) Make a centric and acentric tensor
    acentric_tensor = ~centric_tensor

    # (2) Call respective llg targets with indexed data
    llg_centric = llgIc_calculate(
        sigmaA, dobs[centric_tensor], Eeff[centric_tensor], Ec[centric_tensor]
    )
    llg_acentric = llgIa_calculate(
        sigmaA, dobs[acentric_tensor], Eeff[acentric_tensor], Ec[acentric_tensor]
    )

    return llg_acentric + llg_centric


def llgA_calculate(sigmaA, E, Ec):
    # acentric reflections
    bessel_arg = (2 * sigmaA * E * Ec) / (1 - sigmaA**2)
    exp_bessel = torch.special.i0e(bessel_arg)
    llg = torch.sum(
        torch.log((1 - sigmaA**2) ** (-1) * exp_bessel)
        + (sigmaA * (-sigmaA * E**2 - sigmaA * Ec**2 + 2 * E * Ec)) / (1 - sigmaA**2)
    )

    return llg


def logcosh(x):
    # s always has real part >= 0

    s = torch.sign(x) * x
    p = torch.exp(-2 * s)
    return s + torch.log1p(p) - torch.log(torch.tensor(2.0))


def llgC_calculate(sigmaA, E, Ec):
    # centric reflections
    cosh_arg = (Ec * sigmaA * E) / (1 - sigmaA**2)
    expo_arg = (sigmaA**2 * Ec**2 + sigmaA**2 * E**2 - 2 * Ec * E * sigmaA) / (
        2 * sigmaA**2 - 2
    )
    cosh_exp = -Ec * E * sigmaA / (1 - sigmaA**2)

    llg = torch.sum(
        torch.log((1 - sigmaA**2) ** (-0.5)) + expo_arg + logcosh(cosh_arg) + cosh_exp
    )

    return llg


def llgTot_calculate(sigmaA, E, Ec, centric_tensor):
    # (1) Make a centric and acentric tensor
    acentric_tensor = ~centric_tensor

    # (2) Call respective llg targets with indexed data
    llg_centric = llgC_calculate(sigmaA, E[centric_tensor], Ec[centric_tensor])
    llg_acentric = llgA_calculate(sigmaA, E[acentric_tensor], Ec[acentric_tensor])

    return llg_acentric + llg_centric


def sigmaA_from_model(E_true, phi_true, E_model, Fcs, dHKL, bin_labels):
    # TODO naming Etrue vs Eobs

    phitrue_rad = np.deg2rad(phi_true)
    phimodel = utils.assert_numpy(
        structurefactors.ftotal_phis(Fcs, dHKL, sort_by_res=True)
    )
    phimodel_rad = np.deg2rad(phimodel)

    sigmaAs = structurefactors.compute_sigmaA_true(
        utils.assert_numpy(E_true),
        phitrue_rad,
        utils.assert_numpy(E_model),
        phimodel_rad,
        utils.assert_numpy(bin_labels),
    )

    return sigmaAs


def compute_sigmaA_true(Eobs, phiobs, Ecalc, phicalc, bin_labels):
    # TODO naming Etrue vs Eobs

    # Combine the absolute values and phase difference into sigmaA_true
    sigmaA_true = Eobs * Ecalc * np.cos(phiobs - phicalc)
    data = np.stack((sigmaA_true, bin_labels), axis=1)

    Sigma_trues = []
    for label in np.unique(bin_labels):
        F_in_bin = data[data[:, 1] == label][:, 0]
        bin_mean = np.mean(F_in_bin)
        Sigma_trues.append(bin_mean)

    return Sigma_trues


def find_bin_dHKL(dHKLs, bin_labels):
    unique_bins = bin_labels.unique()

    bin_dHKLs = []

    for bin_label in unique_bins:
        # Find indices corresponding to the current bin
        bin_indices = torch.where(bin_labels == bin_label)[0]
        # Extract distances for the current bin
        bin_distances = dHKLs[bin_indices]
        # Find the largest and lowest distances
        max_distance = torch.max(bin_distances)
        min_distance = torch.min(bin_distances)
        # Calculate the halfway distance
        halfway_distance = (max_distance + min_distance) / 2.0
        # Add the halfway distance for each row in the current bin
        bin_dHKLs.extend([halfway_distance.item()] * len(bin_indices))

    return torch.tensor(bin_dHKLs)


def load_tng_data(tng_file, device=None):
    if device is None:
        device = utils.try_gpu()
    if isinstance(tng_file, str):
        tng = utils.load_mtz(tng_file).dropna()
    elif isinstance(tng_file, rs.DataSet):
        tng = tng_file.dropna()
    # Generate PhaserTNG tensors
    eps = torch.tensor(tng[""EPS""].values, device=device)
    centric = torch.tensor(tng[""CENT""].values, device=device).bool()
    dobs = torch.tensor(tng[""DOBS""].values, device=device)
    feff = torch.tensor(tng[""FEFF""].values, device=device)
    bin_labels = torch.tensor(tng[""BIN""].values, device=device)
    resn = torch.tensor(tng[""RESN""].values, device=device)
    dHKLs = torch.tensor(tng[""dHKL""].values, device=device)
    bin_dHKLs = find_bin_dHKL(dHKLs, bin_labels).to(device=device)

    Edata = feff / resn

    data_dict = {
        ""EDATA"": Edata,
        ""EPS"": eps,
        ""CENTRIC"": centric,
        ""DOBS"": dobs,
        ""FEFF"": feff,
        ""BIN_LABELS"": bin_labels,
        ""BIN_dHKLS"": bin_dHKLs,
    }

    return data_dict
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/xtal/structurefactors.py",".py","3358","117","""""""
Functions relating model structure factor manipulation and normalization
""""""

import numpy as np
import SFC_Torch as SFC
import torch
from SFC_Torch import SFcalculator


def initial_SFC(
    pdb_file,
    mtz_file,
    Flabel,
    SigFlabel,
    Freelabel=None,
    device=None,  # <--- default to None
    testset_value=0,
    n_bins=10,
    solvent=True,
    added_chain_HKL=None,
    added_chain_asu=None,
    total_chain_copy=1.0,
    spacing=4.5,
):
    if device is None:
        device = SFC.utils.try_gpu()  # <-- assign here

    sfcalculator = SFcalculator(
        pdb_file,
        mtz_file,
        expcolumns=[Flabel, SigFlabel],
        freeflag=Freelabel,
        set_experiment=True,
        testset_value=testset_value,
        device=device,
        n_bins=n_bins,
    )
    sfcalculator.inspect_data(verbose=False, spacing=spacing)
    sfcalculator.calc_fprotein()

    if added_chain_HKL is not None:
        sfcalculator.Fprotein_HKL = sfcalculator.Fprotein_HKL + added_chain_HKL
        sfcalculator.Fprotein_asu = sfcalculator.Fprotein_asu + added_chain_asu
        sfcalculator.solventpct = 1 - (1 - sfcalculator.solventpct) * total_chain_copy

    if solvent:
        sfcalculator.calc_fsolvent()
    else:
        sfcalculator.Fmask_HKL = torch.zeros_like(sfcalculator.Fprotein_HKL)
    sfcalculator.get_scales_adam()
    return sfcalculator


def ftotal_amplitudes(Ftotal, dHKL, sort_by_res=True):
    F_mag = torch.abs(Ftotal)

    if sort_by_res:
        dHKL_tensor = torch.from_numpy(dHKL)
        sorted_indices = torch.argsort(dHKL_tensor, descending=True)
        return F_mag[sorted_indices]

    return F_mag


def ftotal_phis(Fc, dHKL, sort_by_res=True):
    PI_on_180 = 0.017453292519943295
    Fc_phase = torch.angle(Fc) / PI_on_180
    dHKL_tensor = torch.from_numpy(dHKL)
    if sort_by_res:
        sorted_indices = torch.argsort(dHKL_tensor, descending=True)
        Fc_phase = Fc_phase[sorted_indices]
    return Fc_phase


def compute_sigmaA_true(Eobs, phiobs, Ecalc, phicalc, bin_labels):
    # Combine the absolute values and phase difference into sigmaA_true
    sigmaA_true = Eobs * Ecalc * np.cos(phiobs - phicalc)
    data = np.stack((sigmaA_true, bin_labels), axis=1)

    Sigma_trues = []
    for label in np.unique(bin_labels):
        F_in_bin = data[data[:, 1] == label][:, 0]
        bin_mean = np.mean(F_in_bin)
        Sigma_trues.append(bin_mean)

    return Sigma_trues


def calculate_Sigma_atoms(Fs, eps, bin_labels):
    if Fs.numel() == 0 or eps.numel() == 0 or bin_labels.numel() == 0:
        raise ValueError(""All input tensors must be non-empty."")
    F_over_eps = Fs**2 / eps
    data = torch.stack((F_over_eps, bin_labels), dim=1)

    Sigma = []
    for label in torch.unique(bin_labels):
        F_in_bin = data[data[:, 1] == label][:, 0]
        bin_mean = torch.mean(F_in_bin)
        Sigma.append(bin_mean)

    return torch.stack(Sigma)


def normalize_Fs(Fs, eps, Sigma_atoms, bin_labels):
    # e.g. Ecalc = Fc / (eps*SigmaP)**(-0.5)
    data = torch.stack((Fs, eps, bin_labels), dim=1)

    for label in torch.unique(bin_labels):
        indices = data[:, 2] == label
        data[indices, 0] = Fs[indices] / torch.sqrt(
            eps[indices] * Sigma_atoms[label.item()].item()
        )
    assert round(torch.mean(data[:, 0] ** 2).item()) == 1

    return data[:, 0]
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/cryo/targets.py",".py","7816","239","""""""
LLG targets for cryoEM data
""""""

import numpy as np
import reciprocalspaceship as rs
import torch
from SFC_Torch import SFcalculator

from rocket import utils as rk_utils
from rocket.cryo import utils as cryo_utils


class LLGloss(torch.nn.Module):
    """"""
    Object_oriented interface to calculate LLG loss

    # Initialization, only have to do it once
    sfc = llg_sf.initial_SFC(...)
    llgloss = LLGloss(sfc, tng_file, device)
    Ecalc = llgloss.compute_Ecalc(xyz_orth)
    TODO: sigmaA calculation/initialization

    # Loss calculation for each step
    loss = -llgloss(xyz_orth, bin_labels=[1,2,3], num_batch=10, sub_ratio=0.3)

    resol_min, resol_max: None | float
        resolution cutoff for the used miller index.
        Will use resol_min <= dHKL <= resol_max

    TODO:
    Currently the initialization needs Eobs, Eps, Centric, Dobs, Feff, Bin_labels.
    We load them through tng_data.
    Later we should be able to calculate everything from SFcalculator,
    all necesary information is ready there.

    """"""

    def __init__(
        self,
        sfc: SFcalculator,
        mtz_file: str | rs.DataSet,
        resol_min=None,
        resol_max=None,
    ) -> None:
        super().__init__()
        self.sfc = sfc
        self.device = sfc.device
        assert isinstance(mtz_file, str | rs.DataSet), (
            ""mtz_file must be a path to mtz file or rs.DataSet""
        )

        data_dict = cryo_utils.load_tng_data(mtz_file, device=self.device)

        self.register_buffer(""Emean"", data_dict[""Emean""])
        self.register_buffer(""PHIEmean"", data_dict[""PHIEmean""])
        self.register_buffer(""Dobs"", data_dict[""Dobs""])

        if resol_min is None:
            resol_min = min(self.sfc.dHKL)

        if resol_max is None:
            resol_max = max(self.sfc.dHKL)

        resol_bool = (self.sfc.dHKL >= (resol_min - 1e-4)) & (
            self.sfc.dHKL <= (resol_max + 1e-4)
        )
        self.working_set = resol_bool

    def assign_sigmaAs_sfc(self, Ec_HKL):
        """"""
        This function is copied from SFC, compute sigmaA using corr(Eo, Ec)

        # TODO: make sure the correlation works for complex number

        Args:
            Ec_HKL (torch.tensor) : [N_HKL]

        Returns:
            sigmaAs [N_bins]
        """"""

        Ecalc = Ec_HKL.abs().detach().clone()
        sigmaAs = torch.zeros(self.sfc.n_bins, dtype=torch.float32, device=self.device)
        for i in range(self.sfc.n_bins):
            index_i = self.sfc.bins == i
            Eobs_i = self.Emean[index_i].square()  # [N_subset]
            Ecalc_i = Ecalc[index_i].square()  # [N_subset]
            # Compute correlation coefficient
            Eoi_centered = Eobs_i - Eobs_i.mean()
            Eci_centered = Ecalc_i - torch.mean(Ecalc_i, dim=-1, keepdims=True)
            Covi = (Eci_centered @ Eoi_centered) / (Eoi_centered.shape[0] - 1)
            Eoi_std = torch.std(Eoi_centered, correction=0)
            Eci_std = torch.std(Eci_centered, dim=-1, correction=0)
            sigmaAs[i] = (Covi / (Eoi_std * Eci_std)).clamp(min=0.001, max=0.999).sqrt()
        return sigmaAs

    def assign_sigmaAs_read(self, Ec_HKL):
        """"""
        This function is copied from SFC, compute sigmaA using corr(Eo, Ec)

        # TODO: make sure the correlation works for complex number

        Args:
            Ec_HKL (torch.tensor) : [N_HKL]

        Returns:
            sigmaAs [N_bins]
        """"""

        # Ecalc = Ec_HKL.abs().detach().clone()
        Ecalc = Ec_HKL.detach().clone()

        oversampling_factor = 4
        Eobs_amp = self.Emean
        Eobs_phi = self.PHIEmean

        PI_on_180 = 0.017453292519943295
        Ecalc_amp = torch.abs(Ecalc)
        Ecalc_phi = torch.angle(Ecalc) / PI_on_180

        sigmaAs = cryo_utils.sigmaA_from_model_in_map(
            rk_utils.assert_numpy(Eobs_amp),
            rk_utils.assert_numpy(Eobs_phi),
            rk_utils.assert_numpy(self.Dobs),
            rk_utils.assert_numpy(Ecalc_amp),
            rk_utils.assert_numpy(Ecalc_phi),
            self.sfc,
            oversampling_factor,
            self.sfc.dHKL,
            plot=False,
        )

        return sigmaAs

    def freeze_sigmaA(self):
        self.sigmaAs = [sigmaA.requires_grad_(False) for sigmaA in self.sigmaAs]

    def unfreeze_sigmaA(self):
        self.sigmaAs = [sigmaA.requires_grad_(True) for sigmaA in self.sigmaAs]

    def compute_Ecalc(
        self,
        xyz_orth,
        update_scales=False,
        scale_steps=10,
        scale_initialize=False,
    ) -> torch.Tensor:
        self.sfc.calc_fprotein(atoms_position_tensor=xyz_orth)

        if update_scales:
            # We used Emean to override the Fo attribute in SFC,
            # so the scales are optimized to match Ep and Emean
            self.sfc.get_scales_adam(
                lr=0.01,
                n_steps=scale_steps,
                sub_ratio=0.7,
                initialize=scale_initialize,
            )

        # We have normalized the Ep above, and the scales are optimized to match Emean,
        # so we what we got is already Ecalc
        # Note @ Aug 26 by MH, do scaling first, then normalizationg
        # Fc_HKL = self.sfc.calc_ftotal()
        # replace with its normalized Ep
        # Ec_HKL = self.sfc.calc_Ec(Fc_HKL)

        ## testing
        Ep = self.sfc.calc_Ec(self.sfc.Fprotein_HKL)
        self.sfc.Fprotein_HKL = Ep
        Fc_HKL = self.sfc.calc_ftotal()
        Ec_HKL = self.sfc.calc_Ec(Fc_HKL)
        self.sigmaAs = self.assign_sigmaAs_read(Ec_HKL)

        return Ec_HKL

    def forward(
        self,
        xyz_ort: torch.Tensor,
        bin_labels=None,
        num_batch=1,
        sub_ratio=1.0,
        update_scales=False,
        solvent=False,
        added_chain_HKL=None,
        added_chain_asu=None,
    ):
        """"""
        TODO: Use rfree label in the LLG calculation
        Args:
            xyz_orth: torch.Tensor, [N_atoms, 3] in angstroms
                Orthogonal coordinates of proteins, coming from AF2 model, send to SFC

            bin_labels: None or List[int]
                Labels of bins used in the loss calculation.
                If None, will use the whole miller indices.
                Serve as a proxy for resolution selection

            num_batch: int
                Number of batches

            sub_ratio: float between 0.0 and 1.0
                Fraction of mini-batch sampling over all miller indices,
                e.g. 0.3 meaning each batch sample 30% of miller indices

        """"""

        Ecalc = self.compute_Ecalc(
            xyz_ort,
            update_scales=update_scales,
        )
        llg = 0.0

        if bin_labels is None:
            bin_labels = self.sfc.n_bins

        # for i, label in enumerate(bin_labels):
        for i, label in enumerate(range(bin_labels)):
            index_i = self.sfc.bins[self.working_set] == label
            Ecalc_i = torch.abs(Ecalc[self.working_set][index_i])
            Ecalc_phi_i = torch.angle(Ecalc[self.working_set][index_i])
            Eob_i = self.Emean[self.working_set][index_i]
            Eob_phi_i = self.PHIEmean[self.working_set][index_i] * (torch.pi / 180)
            Dobs_i = self.Dobs[self.working_set][index_i]

            sigmaA_i = self.sigmaAs[int(i)]
            for _ in range(num_batch):
                sub_boolean_mask = np.random.rand(len(Eob_i)) < sub_ratio
                llg_ij = cryo_utils.llgcryo_calculate(
                    Eob_i[sub_boolean_mask],
                    Eob_phi_i[sub_boolean_mask],
                    Ecalc_i[sub_boolean_mask],
                    Ecalc_phi_i[sub_boolean_mask],
                    sigmaA_i,
                    Dobs_i[sub_boolean_mask],
                ).sum()
                llg = llg + llg_ij
        return llg
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/cryo/__init__.py",".py","0","0","","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/cryo/utils.py",".py","7137","238","""""""
Functions relating to sigmaa calculation and refinement for cryoEM
""""""

import math

import matplotlib.pyplot as plt
import numpy as np
import reciprocalspaceship as rs
import torch

from rocket import utils


def downsample_data(mtz_path, downsample_ratio: int):
    """"""
    Downsample the data in an mtz file by a given ratio
    """"""
    df = rs.read_mtz(mtz_path)
    # Get the h, k, l values
    hkls = df.get_hkls()
    # Create a boolean mask where all h, k, l values are divisible by the ratio
    mask = (
        (hkls[:, 0] % downsample_ratio == 0)
        & (hkls[:, 1] % downsample_ratio == 0)
        & (hkls[:, 2] % downsample_ratio == 0)
    )
    # Apply the mask to the DataFrame
    downsampled_df = df[mask].copy()

    return downsampled_df


def load_tng_data(tng_file, device=None):
    if device is None:
        device = utils.try_gpu()
    if isinstance(tng_file, str):
        tng = utils.load_mtz(tng_file).dropna()
    elif isinstance(tng_file, rs.DataSet):
        tng = tng_file.dropna()

    # Generate PhaserTNG tensors
    emean = torch.tensor(tng[""Emean""].values, dtype=torch.float32, device=device)
    phi_emean = torch.tensor(tng[""PHIEmean""].values, dtype=torch.float32, device=device)
    dobs = torch.tensor(tng[""Dobs""].values, dtype=torch.float32, device=device)

    data_dict = {
        ""Emean"": emean,
        ""PHIEmean"": phi_emean,
        ""Dobs"": dobs,
    }

    return data_dict


# sigmaA functions


def compute_sigmaA_for_bin(eEsel, Ecalc_sel, dobssel, expectE_phi, Ecalc_phi):
    abseE = np.abs(eEsel)
    absEc = np.abs(Ecalc_sel)
    p1 = np.radians(expectE_phi)
    p2 = np.radians(Ecalc_phi)
    cosdphi = np.cos(p2 - p1)

    sum0 = np.sum(2.0 * dobssel * abseE * absEc * cosdphi)
    sum1 = np.sum(2 * np.square(dobssel) * (1.0 - np.square(abseE) - np.square(absEc)))
    sum2 = np.sum(2 * np.power(dobssel, 3) * abseE * absEc * cosdphi)
    sum3 = np.sum(-2 * np.power(dobssel, 4))

    u1 = -2 * sum2**3 + 9 * sum1 * sum2 * sum3 - 27 * sum0 * sum3**2
    sqrt_arg = u1**2 - 4 * (sum2**2 - 3 * sum1 * sum3) ** 3

    if sqrt_arg < 0:
        raise ValueError(""Argument of square root in sigmaA calculation is negative"")

    third = 1.0 / 3
    x1 = (u1 + math.sqrt(sqrt_arg)) ** third
    denominator = 6 * sum3 * x1
    if denominator == 0:
        raise ZeroDivisionError(""Division by zero in sigmaA calculation"")
    sigmaA = (
        2 * 2.0**third * sum2**2
        - 6 * 2.0**third * sum1 * sum3
        - 2 * sum2 * x1
        + 2.0 ** (2 * third) * x1**2
    ) / denominator

    if not np.isfinite(sigmaA):
        raise ValueError(""sigmaA is NaN or inf in sigmaA calculation"")

    return max(min(sigmaA, 0.999), 1.0e-6)


def compute_sigmaA_error(dobssel, sigmaA, abseE, absEc, cosdphi, over_sampling_factor):
    denom = np.power((1 - np.square(dobssel) * sigmaA**2), 3)
    sum0 = np.sum(
        (2 * np.square(dobssel) * (1.0 - np.square(abseE) - np.square(absEc))) / denom
    )
    sum1 = np.sum((12 * np.power(dobssel, 3) * abseE * absEc * cosdphi) / denom)
    sum2 = np.sum(
        (-6 * np.power(dobssel, 4) * (np.square(abseE) + np.square(absEc))) / denom
    )
    sum3 = np.sum((4 * np.power(dobssel, 5) * abseE * absEc * cosdphi) / denom)
    sum4 = np.sum((-2 * np.power(dobssel, 6)) / denom)
    d2LLGbydsigmaA = (
        sum0 + sum1 * sigmaA + sum2 * sigmaA**2 + sum3 * sigmaA**3 + sum4 * sigmaA**4
    )
    d2LLGbydsigmaA /= over_sampling_factor
    return 1.0 / math.sqrt(abs(d2LLGbydsigmaA))


def fit_line(xdat, ydat, wdat):
    W = np.sum(wdat)
    Wx = np.sum(wdat * xdat)
    Wy = np.sum(wdat * ydat)
    Wxx = np.sum(wdat * xdat * xdat)
    Wxy = np.sum(wdat * xdat * ydat)
    slope = (W * Wxy - Wx * Wy) / (W * Wxx - Wx * Wx)
    intercept = (Wy * Wxx - Wx * Wxy) / (W * Wxx - Wx * Wx)
    return slope, intercept


def combine_sigmaA(slope, intercept, xdat, ydat, wdat, sigma_linlog):
    linlogsiga = []
    logsiga_combined = []

    for i, x in enumerate(xdat):
        linlog = min(math.log(0.999), (intercept + slope * x))
        linlogsiga.append(linlog)
        sigma_sigmaA = 1.0 / math.sqrt(wdat[i])
        sigma_lnsigmaA = math.exp(-ydat[i]) * sigma_sigmaA
        combined_logsiga = (ydat[i] / sigma_lnsigmaA**2 + linlog / sigma_linlog**2) / (
            1.0 / sigma_lnsigmaA**2 + 1.0 / sigma_linlog**2
        )
        logsiga_combined.append(combined_logsiga)

    return linlogsiga, logsiga_combined


def sigmaA_from_model_in_map(
    expectE_amp,
    expectE_phi,
    dobs,
    Ecalc_amp,
    Ecalc_phi,
    sfc,
    over_sampling_factor,
    dhkl,
    plot=False,
):
    n_bins = sfc.n_bins
    model_sigmaA = np.zeros_like(expectE_amp, dtype=np.float32)
    xdat = []
    ydat = []
    wdat = []

    for i_bin in range(n_bins):
        bin_mask = sfc.bins == i_bin
        if not np.any(bin_mask):
            continue

        eEsel = expectE_amp[bin_mask]
        Ecalc_sel = Ecalc_amp[bin_mask]
        dobssel = dobs[bin_mask]
        cosdphi = np.cos(
            np.radians(Ecalc_phi[bin_mask]) - np.radians(expectE_phi[bin_mask])
        )

        sigmaA = compute_sigmaA_for_bin(
            eEsel, Ecalc_sel, dobssel, expectE_phi[bin_mask], Ecalc_phi[bin_mask]
        )
        sigma_sigmaA = compute_sigmaA_error(
            dobssel,
            sigmaA,
            np.abs(eEsel),
            np.abs(Ecalc_sel),
            cosdphi,
            over_sampling_factor,
        )

        ssqr = np.mean(np.square(1 / dhkl[bin_mask]))
        xdat.append(ssqr)
        ydat.append(math.log(sigmaA))
        wdat.append(abs(1.0 / sigma_sigmaA**2))

    xdat = np.array(xdat)
    ydat = np.array(ydat)
    wdat = np.array(wdat)

    slope, intercept = fit_line(xdat, ydat, wdat)

    linlogsiga, logsiga_combined = combine_sigmaA(
        slope, intercept, xdat, ydat, wdat, math.log(1.5)
    )

    i_bin_used = 0
    for i_bin in range(n_bins):
        bin_mask = sfc.bins == i_bin
        if not np.any(bin_mask):
            continue

        combined_siga = math.exp(logsiga_combined[i_bin_used])
        model_sigmaA[bin_mask] = combined_siga
        i_bin_used += 1

    if plot:
        plot_sigmaA(xdat, ydat, linlogsiga, logsiga_combined)

    return model_sigmaA


def plot_sigmaA(xdat, ydat, linlogsiga, logsiga_combined):
    fig, ax = plt.subplots(2, 1)
    ax[0].plot(xdat, ydat, label=""ln(sigmaA)"")
    ax[0].plot(xdat, linlogsiga, label=""line fit"")
    ax[0].plot(xdat, logsiga_combined, label=""combined"")
    ax[0].legend(loc=""upper right"")
    ax[1].plot(xdat, np.exp(ydat), label=""sigmaA"")
    ax[1].plot(xdat, np.exp(linlogsiga), label=""line fit"")
    ax[1].plot(xdat, np.exp(logsiga_combined), label=""combined"")
    ax[1].legend(loc=""lower left"")
    plt.show()


def llgcryo_calculate(E_amp, E_phi, Ec_amp, Ec_phi, sigmaA, dobs):
    cos_phi_diff = torch.cos(Ec_phi - E_phi)
    term1 = (
        2 / (1 - dobs**2 * sigmaA**2) * dobs * sigmaA * E_amp * Ec_amp * cos_phi_diff
    )

    term2 = (dobs**2 * sigmaA**2 * (E_amp**2 + Ec_amp**2)) / (1 - dobs**2 * sigmaA**2)
    term3 = torch.log(1 - dobs**2 * sigmaA**2)

    llg_cryo = term1 - term2 - term3

    return llg_cryo
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/cryo/structurefactors.py",".py","1620","57","from SFC_Torch import SFcalculator


def initial_cryoSFC(
    pdb_file,
    mtz_file,
    Elabel,
    PHIElabel,
    device,
    n_bins=15,
):
    """"""
    Initialize a SFC for docking target in cryoEM problem.
    Note, we override the attribute .Fo to store Emean, to easily compute scales
    Also no freeflag is needed for cryoEM cases
    Args:
        pdb_file  : path to initial dock model
        mtz_file  : path to map.mtz file
        Elabel    : column name for Emean
        PHIElabel : column name for PHIEmean
        device    : torch device
        n_bins    : number of bins in the resolution binning
    Return:
        SFcalculator with attributes
        .Fo is Emean from map.mtz
        .bins is resolution binning label
    """"""
    # it will store Emean in attribute Fo
    sfcalculator = SFcalculator(
        pdb_file,
        mtz_file,
        expcolumns=[Elabel, PHIElabel],
        freeflag=""None"",
        set_experiment=True,
        testset_value=0,
        device=device,
        n_bins=n_bins,
        mode=""cryoem"",
    )

    # SigF is actually PHIEmean, don't need it here
    sfcalculator.SigF = None

    # calculate fprotein from default pdb file, no solvent is needed
    sfcalculator.calc_fprotein()

    Ep = sfcalculator.calc_Ec(sfcalculator.Fprotein_HKL)
    sfcalculator.Fprotein_HKL = Ep

    # normalize the Fp, use it to replace the Fp, initialize scales
    # note @ Aug 27 by MH: treat Emean as Fmean
    # Ep = sfcalculator.calc_Ec(sfcalculator.Fprotein_HKL)
    # sfcalculator.Fprotein_HKL = Ep
    sfcalculator.get_scales_adam(sub_ratio=1.0)

    return sfcalculator
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_preprocess.py",".py","17217","487","import argparse
import glob
import os
import shutil
import subprocess

from loguru import logger
from SFC_Torch import PDBParser

from ..refinement_config import gen_config_phase1, gen_config_phase2
from ..utils import plddt2pseudoB_np

### Phenix variables
phenix_directory = os.environ[""PHENIX_ROOT""]
phenix_source = os.path.join(phenix_directory, ""phenix_env.sh"")


def get_script_path(import_stmt: str) -> str:
    """"""Source Phenix and run phenix.python to get script path.""""""
    module_name = import_stmt.split(""import"")[-1].strip()
    python_code = f""{import_stmt}; print({module_name}.__file__)""

    bash_cmd = f'source {phenix_source} && phenix.python -c ""{python_code}""'

    try:
        result = subprocess.run(
            [""bash"", ""-c"", bash_cmd], check=True, capture_output=True, text=True
        )
        return result.stdout.strip()
    except subprocess.CalledProcessError as e:
        print(f""Error resolving script path for '{import_stmt}':\n{e.stderr}"")
        return None


# Phenix scripts paths
em_nodockedmodel_script = get_script_path(
    ""from New_Voyager.scripts import emplace_simple""
)
em_dockedmodel_script = get_script_path(
    ""from cctbx.maptbx import prepare_map_for_refinement""
)
xtal_edata_script = get_script_path(""from phasertng.scripts import mtz_generator"")


def run_command(command, env_source=None):
    """"""Runs a shell command with optional Phenix environment sourcing.""""""
    cmd_str = (
        f""bash -c 'source {env_source} && {' '.join(command)}'""
        if env_source
        else "" "".join(command)
    )

    logger.info(f""Executing: {cmd_str}"")

    subprocess.run(cmd_str, shell=True, check=True, executable=""/bin/bash"")


def run_openfold(
    file_id,
    output_dir,
    precomputed_alignment_dir,
    jax_param_path,
    max_recycling_iters,
    use_deepspeed_evoformer_attention,
):
    """"""Runs OpenFold inference using the specified parameters.""""""
    fasta_dir = f""{file_id}_fasta""
    predicted_model = os.path.join(
        output_dir, ""predictions"", f""{file_id}_model_1_ptm_unrelaxed.pdb""
    )

    if os.path.exists(predicted_model):
        logger.info(f""Skipping OpenFold: output {predicted_model} already exists."")
        return predicted_model

    openfold_cmd = [
        ""rk.predict"",
        fasta_dir,
        ""--output_dir"",
        f""{output_dir}"",
        ""--config_preset"",
        ""model_1_ptm"",
        ""--model_device"",
        ""cuda:0"",
        ""--save_output"",
        ""--data_random_seed"",
        ""42"",
        ""--skip_relaxation"",
        ""--max_recycling_iters"",
        f""{max_recycling_iters}"",
        ""--use_precomputed_alignments"",
        f""{precomputed_alignment_dir}"",
    ]
    if use_deepspeed_evoformer_attention:
        openfold_cmd.extend([""--use_deepspeed_evoformer_attention""])

    if jax_param_path:
        openfold_cmd.extend([""--jax_params_path"", jax_param_path])

    run_command(openfold_cmd)

    if not os.path.exists(predicted_model):
        raise FileNotFoundError(f""Expected output model {predicted_model} not found."")

    return predicted_model


def generate_seg_id_file(file_id, output_dir):
    """"""Generates seg_id.txt using chain changes and >20-residue continuous stretches.
    Skips first seg_id, outputs None if only one domain.""""""
    seg_id_path = os.path.join(output_dir, ""ROCKET_inputs"", ""seg_id.txt"")
    aligned_pdb_path = os.path.join(output_dir, ""ROCKET_inputs"", f""{file_id}-MRed.pdb"")

    if not os.path.exists(aligned_pdb_path):
        raise FileNotFoundError(f""Aligned PDB file not found at {aligned_pdb_path}"")

    # Collect residues per chain in order of appearance
    chain_residues = {}
    chain_order = []
    with open(aligned_pdb_path) as f:
        for line in f:
            if line.startswith(""ATOM""):
                try:
                    chain_id = line[21].strip()
                    res_num = int(line[22:26].strip())
                    if chain_id not in chain_residues:
                        chain_residues[chain_id] = set()
                        chain_order.append(chain_id)
                    chain_residues[chain_id].add(res_num)
                except ValueError:
                    continue

    domain_ranges = []
    seg_start_residues = []
    previous_chain = None

    for chain_id in chain_order:
        if chain_id == previous_chain:
            continue  # Only one domain per unique chain

        residues = sorted(chain_residues[chain_id])
        if not residues:
            continue

        # Find first continuous stretch >20
        current_stretch = [residues[0]]
        for i in range(1, len(residues)):
            if residues[i] == residues[i - 1] + 1:
                current_stretch.append(residues[i])
            else:
                if len(current_stretch) > 20:
                    domain_ranges.append((current_stretch[0], current_stretch[-1]))
                    seg_start_residues.append(current_stretch[0])
                    break
                current_stretch = [residues[i]]

        # Handle final stretch
        if len(current_stretch) > 20 and (
            not domain_ranges
            or domain_ranges[-1] != (current_stretch[0], current_stretch[-1])
        ):
            domain_ranges.append((current_stretch[0], current_stretch[-1]))
            seg_start_residues.append(current_stretch[0])

        previous_chain = chain_id

    # Write seg_id.txt
    with open(seg_id_path, ""w"") as out_f:
        for i, (start, end) in enumerate(domain_ranges, 1):
            out_f.write(f""domain{i}: {start}-{end}\n"")

        if len(seg_start_residues) > 1:
            seg_ids = "","".join(str(r) for r in seg_start_residues[1:])  # Skip first
            out_f.write(f'seg_id: ""{seg_ids}""\n')
            logger.info(f""Segment ID file written to {seg_id_path}"")
            return seg_start_residues[1:]
        else:
            out_f.write(""seg_id: None\n"")
            logger.info(""No segment, only one domain found."")
            return None


def run_process_predicted_model(file_id, input_dir, predicted_model):
    """"""Processes the predicted model using Phenix.""""""
    logger.info(""Looking for"", predicted_model)

    process_cmd = [
        ""phenix.process_predicted_model"",
        ""output_files.mark_atoms_to_keep_with_occ_one=True"",
        f""{predicted_model}"",
        ""minimum_domain_length=20"",
        ""b_value_field_is=plddt"",
        ""minimum_sequential_residues=10"",
        f""pae_file={os.path.join(input_dir, f'{file_id}_pae.json')}"",
        ""pae_power=2"",
        ""pae_cutoff=4"",
        ""pae_graph_resolution=0.5"",
    ]

    run_command(process_cmd, env_source=phenix_source)


def move_processed_predicted_files(output_dir):
    """"""Moves processed files into a 'processed_predicted_files' directory.""""""
    processed_dir = os.path.join(output_dir, ""processed_predicted_files"")
    os.makedirs(processed_dir, exist_ok=True)

    processed_files = glob.glob(""*processed*"") + glob.glob(""*.seq"")

    if not processed_files:
        logger.info(""No processed files found to move."")
        return

    for file_path in processed_files:
        if os.path.isfile(file_path):
            shutil.move(
                file_path, os.path.join(processed_dir, os.path.basename(file_path))
            )


def dock_into_data(
    file_id,
    method,
    resolution,
    output_dir,
    predicted_model,
    predocked_model,
    map1,
    map2,
    fixed_model=None,
    fasta_composition=None,
):
    """"""Handles molecular docking for Xray or CryoEM data.""""""
    docking_output_dir = os.path.join(output_dir, ""docking_outputs"")
    os.makedirs(docking_output_dir, exist_ok=True)

    if method == ""xray"":
        mtz_files = glob.glob(os.path.join(f""{file_id}_data"", ""*.mtz""))

        for mtz_file in mtz_files:
            if os.path.isfile(mtz_file):
                shutil.copy2(
                    mtz_file,
                    os.path.join(
                        output_dir,
                        ""processed_predicted_files"",
                        os.path.basename(mtz_file),
                    ),
                )

                # Always run Edata generation
                edata_cmd = [""phenix.python"", xtal_edata_script, ""-i"", mtz_file]
                run_command(edata_cmd, env_source=phenix_source)

        # If predocked_model is provided, skip MR and copy the model directly
        if predocked_model:
            print(""Predocked model provided for Xray: skipping MR step."")
            rocket_dir = os.path.join(output_dir, ""ROCKET_inputs"")
            os.makedirs(rocket_dir, exist_ok=True)

            aligned_pdb_path = os.path.join(rocket_dir, f""{file_id}-MRed.pdb"")
            shutil.copy2(predocked_model, aligned_pdb_path)
        else:
            # Proceed with MR step
            mr_cmd = [
                ""phasertng.picard"",
                f""directory={os.path.join(output_dir, 'processed_predicted_files')}"",
                f""database={os.path.join(output_dir, 'phaser_files')}"",
            ]
            run_command(mr_cmd, env_source=phenix_source)

    elif method == ""cryoem"":
        docking_script = (
            em_dockedmodel_script if predocked_model else em_nodockedmodel_script
        )
        docking_cmd = [""phenix.python"", docking_script]

        if predocked_model:
            docking_cmd += [map1, map2, predocked_model, resolution]
            if fixed_model:
                docking_cmd.append(f""--fixed_model={fixed_model}"")
        else:
            docking_cmd += [
                f""--d_min={resolution}"",
                f""--output_folder={docking_output_dir}"",
                f""--model_file={predicted_model}"",
                f""--map1={map1}"",
                f""--map2={map2}"",
                f""--sequence_composition={fasta_composition}"",
                ""--level=logfile"",
            ]
            if fixed_model:
                docking_cmd.append(f""--fixed_model={fixed_model}"")

        run_command(docking_cmd, env_source=phenix_source)

        if predocked_model:
            for file in [""weighted_map_data.mtz"", ""likelihood_weighted.map""]:
                src_path = os.path.join(""."", file)
                dest_path = os.path.join(docking_output_dir, file)
                if os.path.exists(src_path):
                    shutil.move(src_path, dest_path)

            # Move the predocked model
            model_filename = os.path.basename(predocked_model)
            model_dest_path = os.path.join(docking_output_dir, model_filename)
            if os.path.exists(predocked_model):
                shutil.copy(predocked_model, model_dest_path)


def prepare_rk_inputs(file_id, output_dir, method):
    """"""Creates ROCKET_inputs directory and moves necessary files.""""""
    rocket_dir = os.path.join(output_dir, ""ROCKET_inputs"")
    os.makedirs(rocket_dir, exist_ok=True)

    if method == ""xray"":
        best_pdb_src = os.path.join(
            output_dir, ""phaser_files"", ""best.1.coordinates.pdb""
        )
        mtz_files = glob.glob(""./*feff/*.data.mtz"")
    elif method == ""cryoem"":
        best_pdb_src = next(
            iter(glob.glob(os.path.join(output_dir, ""docking_outputs"", ""*.pdb""))), None
        )
        mtz_files = glob.glob(f""{output_dir}/docking_outputs/weighted_map_data.mtz"")
    else:
        raise ValueError(""Invalid method. Choose either 'xray' or 'cryoem'."")

    best_pdb_dst = os.path.join(rocket_dir, f""{file_id}-MRed.pdb"")
    if best_pdb_src and os.path.exists(best_pdb_src):
        shutil.copy2(best_pdb_src, best_pdb_dst)

    for mtz_src in mtz_files:
        mtz_dst = os.path.join(rocket_dir, f""{file_id}-Edata.mtz"")
        shutil.copy2(mtz_src, mtz_dst)


def prepare_pred_aligned(output_dir, file_id):
    mr_model_path = os.path.join(output_dir, ""ROCKET_inputs"", f""{file_id}-MRed.pdb"")
    assert os.path.exists(mr_model_path), f""MR model not found: {mr_model_path}""
    pred_model_path = os.path.join(
        output_dir, ""predictions"", f""{file_id}_model_1_ptm_unrelaxed.pdb""
    )
    assert os.path.exists(pred_model_path), (
        f""Predicted model not found: {pred_model_path}""
    )
    superpose_command = [
        ""phenix.superpose_pdbs"",
        f""{mr_model_path}"",
        f""{pred_model_path}"",
        f""output.file_name={os.path.join(output_dir, 'ROCKET_inputs', f'{file_id}-pred-aligned_unprocessed.pdb')}"",  # noqa: E501
    ]
    run_command(superpose_command, env_source=phenix_source)
    aligned_model_path = os.path.join(
        output_dir, ""ROCKET_inputs"", f""{file_id}-pred-aligned_unprocessed.pdb""
    )
    assert os.path.exists(aligned_model_path), (
        f""Failed to superpose models: {aligned_model_path}""
    )

    mr_model = PDBParser(mr_model_path)
    align_model = PDBParser(aligned_model_path)
    align_model.set_spacegroup(mr_model.spacegroup)
    align_model.set_unitcell(mr_model.cell)
    align_model.set_biso(plddt2pseudoB_np(align_model.atom_b_iso))
    align_model.savePDB(
        os.path.join(output_dir, ""ROCKET_inputs"", f""{file_id}-pred-aligned.pdb"")
    )


def symlink_input_files(file_id, output_dir, precomputed_alignment_dir):
    """"""Symlinks the sequence FASTA and alignment directory to the output folder.""""""
    fasta_src = os.path.join(f""{file_id}_fasta"", f""{file_id}.fasta"")
    fasta_dst = os.path.join(output_dir, f""{file_id}.fasta"")
    if os.path.exists(fasta_src):
        if not os.path.exists(fasta_dst):
            os.symlink(os.path.abspath(fasta_src), fasta_dst)
    else:
        raise FileNotFoundError(f""FASTA file not found: {fasta_src}"")

    alignments_dst = os.path.join(output_dir, ""alignments"")
    if not os.path.exists(alignments_dst):
        os.symlink(
            os.path.join(os.path.abspath(precomputed_alignment_dir), file_id),
            alignments_dst,
        )


def parse_args():
    parser = argparse.ArgumentParser(
        description=""Run OpenFold inference and dock into data""
    )

    parser.add_argument(""--file_id"", required=True)
    parser.add_argument(""--method"", choices=[""xray"", ""cryoem""], required=True)
    parser.add_argument(""--resolution"", default=None)
    parser.add_argument(""--output_dir"", default=""preprocessing_output"")
    parser.add_argument(""--precomputed_alignment_dir"", default=""alignments/"")
    parser.add_argument(""--max_recycling_iters"", type=int, default=4)
    parser.add_argument(
        ""--use_deepspeed_evoformer_attention"",
        action=""store_true"",
        default=False,
        help=""Whether to use the DeepSpeed evoformer attention layer. ""
        ""Must have deepspeed installed in the environment."",
    )
    parser.add_argument(""--jax_params_path"", default=None)
    parser.add_argument(""--predocked_model"", default=None)
    parser.add_argument(""--fixed_model"", default=None)
    parser.add_argument(""--map1"", default=None)
    parser.add_argument(""--map2"", default=None)
    parser.add_argument(""--full_composition"", default=None)

    args = parser.parse_args()

    if args.method == ""cryoem"":
        missing = [
            arg for arg in [""map1"", ""map2"", ""resolution""] if getattr(args, arg) is None
        ]
        if missing:
            parser.error(
                f""The following arguments are required for 'cryoem' method: {', '.join(missing)}""  # noqa: E501
            )

        # Require full_composition only if predocked_model is not provided
        if not args.predocked_model and args.full_composition is None:
            parser.error(
                ""--full_composition is required for cryoem when --predocked_model is not provided.""  # noqa: E501
            )

    return args


def cli_runpreprocess():
    args = parse_args()

    os.makedirs(args.output_dir, exist_ok=True)
    symlink_input_files(args.file_id, args.output_dir, args.precomputed_alignment_dir)

    predicted_model = run_openfold(
        args.file_id,
        args.output_dir,
        args.precomputed_alignment_dir,
        args.jax_params_path,
        args.max_recycling_iters,
        args.use_deepspeed_evoformer_attention,
    )
    run_process_predicted_model(args.file_id, args.output_dir, predicted_model)
    move_processed_predicted_files(args.output_dir)

    dock_into_data(
        args.file_id,
        args.method,
        args.resolution,
        args.output_dir,
        predicted_model,
        args.predocked_model,
        args.map1,
        args.map2,
        args.fixed_model,
        args.full_composition,
    )
    prepare_rk_inputs(args.file_id, args.output_dir, args.method)
    prepare_pred_aligned(args.output_dir, args.file_id)
    seg_id = generate_seg_id_file(args.file_id, args.output_dir)

    # Generate ROCKET configuration yaml files
    phase1_config = gen_config_phase1(
        datamode=args.method,
        file_id=args.file_id,
        working_dir=os.path.abspath(args.output_dir),
    )
    phase1_config.algorithm.init_recycling = args.max_recycling_iters
    if seg_id:
        phase1_config.algorithm.domain_segs = seg_id
    phase1_config.to_yaml_file(
        os.path.join(args.output_dir, ""ROCKET_config_phase1.yaml"")
    )
    phase2_config = gen_config_phase2(phase1_config)
    phase2_config.to_yaml_file(
        os.path.join(args.output_dir, ""ROCKET_config_phase2.yaml"")
    )


if __name__ == ""__main__"":
    cli_runpreprocess()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/__init__.py",".py","0","0","","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_pretrained_openfold.py",".py","20177","564","# Modified by Minhuan Li, for ROCKET, 2025
# Remove the requirement template, remove single seq mode, read resources from
# the system environment variables

# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import logging
import math
import os
import pickle
import random
import time

import numpy as np
import torch
from openfold.config import model_config
from openfold.data import data_pipeline, feature_pipeline, templates
from openfold.data.tools import hhsearch, hmmsearch
from openfold.np import protein
from openfold.utils.script_utils import (
    load_models_from_command_line,
    parse_fasta,
    prep_output,
    relax_protein,
    run_model,
)
from openfold.utils.tensor_utils import tensor_tree_map
from openfold.utils.trace_utils import (
    pad_feature_dict_seq,
    trace_model_,
)

# from scripts.precompute_embeddings import EmbeddingGenerator
from ..utils import get_params_path

logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(level=logging.INFO)

torch_versions = torch.__version__.split(""."")
torch_major_version = int(torch_versions[0])
torch_minor_version = int(torch_versions[1])
if torch_major_version > 1 or (torch_major_version == 1 and torch_minor_version >= 12):
    # Gives a large speedup on Ampere-class GPUs
    torch.set_float32_matmul_precision(""high"")

torch.set_grad_enabled(False)


TRACING_INTERVAL = 50


def precompute_alignments(tags, seqs, alignment_dir, args):
    for tag, seq in zip(tags, seqs, strict=False):
        tmp_fasta_path = os.path.join(args.output_dir, f""tmp_{os.getpid()}.fasta"")
        with open(tmp_fasta_path, ""w"") as fp:
            fp.write(f"">{tag}\n{seq}"")

        local_alignment_dir = os.path.join(alignment_dir, tag)

        if args.use_precomputed_alignments is None:
            logger.info(f""Generating alignments for {tag}..."")

            os.makedirs(local_alignment_dir, exist_ok=True)

            if ""multimer"" in args.config_preset:
                template_searcher = hmmsearch.Hmmsearch(
                    binary_path=args.hmmsearch_binary_path,
                    hmmbuild_binary_path=args.hmmbuild_binary_path,
                    database_path=args.pdb_seqres_database_path,
                )
            else:
                template_searcher = hhsearch.HHSearch(
                    binary_path=args.hhsearch_binary_path,
                    databases=[args.pdb70_database_path],
                )

            # In seqemb mode, use AlignmentRunner only to generate templates
            if args.use_single_seq_mode:
                # alignment_runner = data_pipeline.AlignmentRunner(
                #     jackhmmer_binary_path=args.jackhmmer_binary_path,
                #     uniref90_database_path=args.uniref90_database_path,
                #     template_searcher=template_searcher,
                #     no_cpus=args.cpus,
                # )
                # embedding_generator = EmbeddingGenerator()
                # embedding_generator.run(tmp_fasta_path, alignment_dir)
                # MH edit @ Mar 24 2025, remove the single seq mode for ROCKET
                raise NotImplementedError(
                    ""Single Seq Mode not supported for ROCKET use""
                )
            else:
                alignment_runner = data_pipeline.AlignmentRunner(
                    jackhmmer_binary_path=args.jackhmmer_binary_path,
                    hhblits_binary_path=args.hhblits_binary_path,
                    uniref90_database_path=args.uniref90_database_path,
                    mgnify_database_path=args.mgnify_database_path,
                    bfd_database_path=args.bfd_database_path,
                    uniref30_database_path=args.uniref30_database_path,
                    uniclust30_database_path=args.uniclust30_database_path,
                    uniprot_database_path=args.uniprot_database_path,
                    template_searcher=template_searcher,
                    use_small_bfd=args.bfd_database_path is None,
                    no_cpus=args.cpus,
                )

            alignment_runner.run(tmp_fasta_path, local_alignment_dir)
        else:
            logger.info(f""Using precomputed alignments for {tag} at {alignment_dir}..."")

        # Remove temporary FASTA file
        os.remove(tmp_fasta_path)


def round_up_seqlen(seqlen):
    return int(math.ceil(seqlen / TRACING_INTERVAL)) * TRACING_INTERVAL


def generate_feature_dict(
    tags,
    seqs,
    alignment_dir,
    data_processor,
    args,
):
    tmp_fasta_path = os.path.join(args.output_dir, f""tmp_{os.getpid()}.fasta"")

    if ""multimer"" in args.config_preset:
        with open(tmp_fasta_path, ""w"") as fp:
            fp.write(
                ""\n"".join([
                    f"">{tag}\n{seq}"" for tag, seq in zip(tags, seqs, strict=False)
                ])
            )
        feature_dict = data_processor.process_fasta(
            fasta_path=tmp_fasta_path,
            alignment_dir=alignment_dir,
        )
    elif len(seqs) == 1:
        tag = tags[0]
        seq = seqs[0]
        with open(tmp_fasta_path, ""w"") as fp:
            fp.write(f"">{tag}\n{seq}"")

        local_alignment_dir = os.path.join(alignment_dir, tag)
        feature_dict = data_processor.process_fasta(
            fasta_path=tmp_fasta_path,
            alignment_dir=local_alignment_dir,
            seqemb_mode=args.use_single_seq_mode,
        )
    else:
        with open(tmp_fasta_path, ""w"") as fp:
            fp.write(
                ""\n"".join([
                    f"">{tag}\n{seq}"" for tag, seq in zip(tags, seqs, strict=False)
                ])
            )
        feature_dict = data_processor.process_multiseq_fasta(
            fasta_path=tmp_fasta_path,
            super_alignment_dir=alignment_dir,
        )

    # Remove temporary FASTA file
    os.remove(tmp_fasta_path)

    return feature_dict


def list_files_with_extensions(dir, extensions):
    return [f for f in os.listdir(dir) if f.endswith(extensions)]


def main(args):
    # Create the output directory
    os.makedirs(args.output_dir, exist_ok=True)

    if args.config_preset.startswith(""seq""):
        args.use_single_seq_mode = True

    config = model_config(
        args.config_preset,
        long_sequence_inference=args.long_sequence_inference,
        use_deepspeed_evoformer_attention=args.use_deepspeed_evoformer_attention,
    )

    # MH @ Jun 21, 2024, support customized n_recycling for initial prediction
    config.data.common.max_recycling_iters = args.max_recycling_iters

    if args.experiment_config_json:
        with open(args.experiment_config_json) as f:
            custom_config_dict = json.load(f)
        config.update_from_flattened_dict(custom_config_dict)

    if args.experiment_config_json:
        with open(args.experiment_config_json) as f:
            custom_config_dict = json.load(f)
        config.update_from_flattened_dict(custom_config_dict)

    if args.trace_model and not config.data.predict.fixed_size:
        raise ValueError(
            ""Tracing requires that fixed_size mode be enabled in the config""
        )

    is_multimer = ""multimer"" in args.config_preset

    # MH @ Jun 21, 2024. Support case where no template mmcif
    if args.template_mmcif_dir is None:
        template_featurizer = None
    else:
        if is_multimer:
            template_featurizer = templates.HmmsearchHitFeaturizer(
                mmcif_dir=args.template_mmcif_dir,
                max_template_date=args.max_template_date,
                max_hits=config.data.predict.max_templates,
                kalign_binary_path=args.kalign_binary_path,
                release_dates_path=args.release_dates_path,
                obsolete_pdbs_path=args.obsolete_pdbs_path,
            )
        else:
            template_featurizer = templates.HhsearchHitFeaturizer(
                mmcif_dir=args.template_mmcif_dir,
                max_template_date=args.max_template_date,
                max_hits=config.data.predict.max_templates,
                kalign_binary_path=args.kalign_binary_path,
                release_dates_path=args.release_dates_path,
                obsolete_pdbs_path=args.obsolete_pdbs_path,
            )

    data_processor = data_pipeline.DataPipeline(
        template_featurizer=template_featurizer,
    )

    if is_multimer:
        data_processor = data_pipeline.DataPipelineMultimer(
            monomer_data_pipeline=data_processor,
        )

    output_dir_base = args.output_dir
    random_seed = args.data_random_seed
    if random_seed is None:
        random_seed = random.randrange(2**32)

    np.random.seed(random_seed)
    torch.manual_seed(random_seed + 1)

    feature_processor = feature_pipeline.FeaturePipeline(config.data)
    if not os.path.exists(output_dir_base):
        os.makedirs(output_dir_base)
    if args.use_precomputed_alignments is None:
        alignment_dir = os.path.join(output_dir_base, ""alignments"")
    else:
        alignment_dir = args.use_precomputed_alignments

    tag_list = []
    seq_list = []
    for fasta_file in list_files_with_extensions(args.fasta_dir, ("".fasta"", "".fa"")):
        # Gather input sequences
        fasta_path = os.path.join(args.fasta_dir, fasta_file)
        with open(fasta_path) as fp:
            data = fp.read()

        tags, seqs = parse_fasta(data)

        if not is_multimer and len(tags) != 1:
            print(
                f""{fasta_path} contains more than one sequence but ""
                f""multimer mode is not enabled. Skipping...""
            )
            continue

        # assert len(tags) == len(set(tags)), ""All FASTA tags must be unique""
        tag = ""-"".join(tags)

        tag_list.append((tag, tags))
        seq_list.append(seqs)

    seq_sort_fn = lambda target: sum([len(s) for s in target[1]])  # noqa: E731
    sorted_targets = sorted(zip(tag_list, seq_list, strict=False), key=seq_sort_fn)
    feature_dicts = {}
    model_generator = load_models_from_command_line(
        config,
        args.model_device,
        args.openfold_checkpoint_path,
        args.jax_param_path,
        args.output_dir,
    )

    for model, output_directory in model_generator:
        cur_tracing_interval = 0
        for (tag, tags), seqs in sorted_targets:
            output_name = f""{tag}_{args.config_preset}""
            if args.output_postfix is not None:
                output_name = f""{output_name}_{args.output_postfix}""

            # Does nothing if the alignments have already been computed
            precompute_alignments(tags, seqs, alignment_dir, args)

            feature_dict = feature_dicts.get(tag)
            if feature_dict is None:
                feature_dict = generate_feature_dict(
                    tags,
                    seqs,
                    alignment_dir,
                    data_processor,
                    args,
                )

                if args.trace_model:
                    n = feature_dict[""aatype""].shape[-2]
                    rounded_seqlen = round_up_seqlen(n)
                    feature_dict = pad_feature_dict_seq(
                        feature_dict,
                        rounded_seqlen,
                    )

                feature_dicts[tag] = feature_dict

            processed_feature_dict = feature_processor.process_features(
                feature_dict, mode=""predict"", is_multimer=is_multimer
            )

            processed_feature_dict = {
                k: torch.as_tensor(v, device=args.model_device)
                for k, v in processed_feature_dict.items()
            }

            if args.trace_model and rounded_seqlen > cur_tracing_interval:
                logger.info(f""Tracing model at {rounded_seqlen} residues..."")
                t = time.perf_counter()
                trace_model_(model, processed_feature_dict)
                tracing_time = time.perf_counter() - t
                logger.info(f""Tracing time: {tracing_time}"")
                cur_tracing_interval = rounded_seqlen

            out = run_model(model, processed_feature_dict, tag, args.output_dir)

            # MH @ Jun 21, 2024, save out the processed feature dict
            with open(
                f""{output_directory}/{output_name}_processed_feats.pickle"", ""wb""
            ) as file:
                from rocket import utils as rk_utils

                device_processed_features = rk_utils.move_tensors_to_device(
                    processed_feature_dict, device=""cpu""
                )
                pickle.dump(device_processed_features, file)

            # Toss out the recycling dimensions --- we don't need them anymore
            processed_feature_dict = tensor_tree_map(
                lambda x: np.array(x[..., -1].cpu()), processed_feature_dict
            )
            out = tensor_tree_map(lambda x: np.array(x.cpu()), out)

            unrelaxed_protein = prep_output(
                out,
                processed_feature_dict,
                feature_dict,
                feature_processor,
                args.config_preset,
                args.multimer_ri_gap,
                args.subtract_plddt,
            )

            unrelaxed_file_suffix = ""_unrelaxed.pdb""
            if args.cif_output:
                unrelaxed_file_suffix = ""_unrelaxed.cif""
            unrelaxed_output_path = os.path.join(
                output_directory, f""{output_name}{unrelaxed_file_suffix}""
            )

            with open(unrelaxed_output_path, ""w"") as fp:
                if args.cif_output:
                    fp.write(protein.to_modelcif(unrelaxed_protein))
                else:
                    fp.write(protein.to_pdb(unrelaxed_protein))

            logger.info(f""Output written to {unrelaxed_output_path}..."")

            if not args.skip_relaxation:
                # Relax the prediction.
                logger.info(f""Running relaxation on {unrelaxed_output_path}..."")
                relax_protein(
                    config,
                    args.model_device,
                    unrelaxed_protein,
                    output_directory,
                    output_name,
                    args.cif_output,
                )

            if args.save_outputs:
                output_dict_path = os.path.join(
                    output_directory, f""{output_name}_output_dict.pkl""
                )
                with open(output_dict_path, ""wb"") as fp:
                    pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)

                logger.info(f""Model output written to {output_dict_path}..."")


def cli_runopenfold():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        ""fasta_dir"",
        type=str,
        help=""Path to directory containing FASTA files, one sequence per file"",
    )
    parser.add_argument(
        ""--template_mmcif_dir"",
        type=str,
        default=None,
    )
    parser.add_argument(
        ""--use_precomputed_alignments"",
        type=str,
        default=None,
        help=""""""Path to alignment directory. If provided, alignment computation
                is skipped and database path arguments are ignored."""""",
    )
    parser.add_argument(
        ""--use_single_seq_mode"",
        action=""store_true"",
        default=False,
        help=""""""Use single sequence embeddings instead of MSAs."""""",
    )
    parser.add_argument(
        ""--output_dir"",
        type=str,
        default=os.getcwd(),
        help=""""""Name of the directory in which to output the prediction"""""",
    )
    parser.add_argument(
        ""--model_device"",
        type=str,
        default=""cpu"",
        help=""""""Name of the device on which to run the model. Any valid torch
             device name is accepted (e.g. ""cpu"", ""cuda:0"")"""""",
    )
    parser.add_argument(
        ""--config_preset"",
        type=str,
        default=""model_1"",
        help=""""""Name of a model config preset defined in openfold/config.py"""""",
    )
    parser.add_argument(
        ""--jax_param_path"",
        type=str,
        default=None,
        help=""""""Path to JAX model parameters. If None, and openfold_checkpoint_path
             is also None, parameters are selected automatically according to
             the model name from openfold/resources/params"""""",
    )
    parser.add_argument(
        ""--openfold_checkpoint_path"",
        type=str,
        default=None,
        help=""""""Path to OpenFold checkpoint. Can be either a DeepSpeed
             checkpoint directory or a .pt file"""""",
    )
    parser.add_argument(
        ""--save_outputs"",
        action=""store_true"",
        default=False,
        help=""Whether to save all model outputs, including embeddings, etc."",
    )
    parser.add_argument(
        ""--cpus"",
        type=int,
        default=4,
        help=""""""Number of CPUs with which to run alignment tools"""""",
    )
    parser.add_argument(
        ""--preset"", type=str, default=""full_dbs"", choices=(""reduced_dbs"", ""full_dbs"")
    )
    parser.add_argument(""--data_random_seed"", type=int, default=None)
    parser.add_argument(
        ""--output_postfix"",
        type=str,
        default=None,
        help=""""""Postfix for output prediction filenames"""""",
    )
    # MH @ Jun 21, 2024, support different n_recyclings for initial predictions
    parser.add_argument(""--max_recycling_iters"", type=int, default=4)
    parser.add_argument(
        ""--skip_relaxation"",
        action=""store_true"",
        default=False,
    )
    parser.add_argument(
        ""--multimer_ri_gap"",
        type=int,
        default=200,
        help=""""""Residue index offset between multiple sequences, if provided"""""",
    )
    parser.add_argument(
        ""--trace_model"",
        action=""store_true"",
        default=False,
        help=""""""Whether to convert parts of each model to TorchScript.
                Significantly improves runtime at the cost of lengthy
                'compilation.' Useful for large batch jobs."""""",
    )
    parser.add_argument(
        ""--subtract_plddt"",
        action=""store_true"",
        default=False,
        help=""""""""Whether to output (100 - pLDDT) in the B-factor column instead
                 of the pLDDT itself"""""",
    )
    parser.add_argument(
        ""--long_sequence_inference"",
        action=""store_true"",
        default=False,
        help=""""""enable options to reduce memory usage at the cost of speed, helps longer
        sequences fit into GPU memory, see the README for details"""""",
    )
    parser.add_argument(
        ""--cif_output"",
        action=""store_true"",
        default=False,
        help=""Output predicted models in ModelCIF format instead of PDB format (default)"",  # noqa: E501
    )
    parser.add_argument(
        ""--experiment_config_json"",
        default="""",
        help=""Path to a json file with custom config values to overwrite config setting"",  # noqa: E501
    )
    parser.add_argument(
        ""--use_deepspeed_evoformer_attention"",
        action=""store_true"",
        default=False,
        help=""Whether to use the DeepSpeed evoformer attention layer.""
        ""Must have deepspeed installed in the environment."",
    )
    # add_data_args(parser)
    args = parser.parse_args()

    if args.jax_param_path is None and args.openfold_checkpoint_path is None:
        args.jax_param_path = os.path.join(
            get_params_path(), ""params_"" + args.config_preset + "".npz""
        )

    if args.model_device == ""cpu"" and torch.cuda.is_available():
        logging.warning(
            """"""The model is being run on CPU. Consider specifying
            --model_device for better performance""""""
        )

    main(args)
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_msacluster.py",".py","10560","333","""""""
Cluster sequences in a MSA using DBSCAN algorithm and write .a3m file for each cluster.
Assumes first sequence in fasta is the query sequence.

rk.msacluster v1 -i alignments/ -o msaclusters --run_TSNE

This script is modified from AF_cluster repo:
https://github.com/HWaymentSteele/AF_Cluster/blob/main/scripts/ClusterMSA.py
""""""

import argparse
import os

import numpy as np
import pandas as pd
from polyleven import levenshtein
from sklearn.cluster import DBSCAN

from rocket.msacluster_utils import (
    consensusVoting,
    encode_seqs,
    load_fasta,
    lprint,
    plot_landscape,
    write_fasta,
)


def main():
    p = argparse.ArgumentParser(
        description=""""""
    Cluster sequences in a MSA using DBSCAN algorithm and write .a3m file for each cluster.
    Assumes first sequence in fasta is the query sequence.

    H Wayment-Steele, 2022

    Modified by Minhuan Li for ROCKET, 2024
    """"""  # noqa: E501
    )

    p.add_argument(
        ""keyword"", action=""store"", help=""Keyword to call all generated MSAs.""
    )
    p.add_argument(
        ""-i"",
        action=""store"",
        help=""fasta/a3m file of original alignment, or path containing fasta/a3m files"",
    )
    p.add_argument(
        ""-o"", action=""store"", help=""name of output directory to write MSAs to.""
    )
    p.add_argument(
        ""--n_controls"",
        action=""store"",
        default=10,
        type=int,
        help=""Number of control msas to generate (Default 10)"",
    )
    p.add_argument(
        ""--verbose"",
        action=""store_true"",
        help=""Print cluster info as they are generated."",
    )

    p.add_argument(
        ""--scan"", action=""store_true"", help=""Select eps value on 1/4 of data, shuffled.""
    )
    p.add_argument(
        ""--eps_val"",
        action=""store"",
        type=float,
        help=""Use single value for eps instead of scanning."",
    )
    p.add_argument(
        ""--resample"",
        action=""store_true"",
        help=""If included, will resample the original MSA with replacement before writing."",  # noqa: E501
    )
    p.add_argument(
        ""--gap_cutoff"",
        action=""store"",
        type=float,
        default=0.25,
        help=""Remove sequences with gaps representing more than this frac of seq."",
    )
    p.add_argument(
        ""--min_eps"",
        action=""store"",
        default=3,
        help=""Min epsilon value to scan for DBSCAN (Default 3)."",
    )
    p.add_argument(
        ""--max_eps"",
        action=""store"",
        default=20,
        help=""Max epsilon value to scan for DBSCAN (Default 20)."",
    )
    p.add_argument(
        ""--eps_step"",
        action=""store"",
        default=0.5,
        help=""step for epsilon scan for DBSCAN (Default 0.5)."",
    )
    p.add_argument(
        ""--min_samples"",
        action=""store"",
        default=10,
        help=""Default min_samples for DBSCAN (Default 3, recommended no lower than that)."",  # noqa: E501
    )

    p.add_argument(
        ""--run_PCA"",
        action=""store_true"",
        help=""Run PCA on one-hot embedding of sequences and store in output_cluster_metadata.tsv"",  # noqa: E501
    )
    p.add_argument(
        ""--run_TSNE"",
        action=""store_true"",
        help=""Run TSNE on one-hot embedding of sequences and store in output_cluster_metadata.tsv"",  # noqa: E501
    )

    args = p.parse_args()

    if args.run_PCA:
        from sklearn.decomposition import PCA

    if args.run_TSNE:
        from sklearn.manifold import TSNE

    os.makedirs(args.o, exist_ok=True)
    f = open(f""msacluster_{args.keyword}.log"", ""w"")  # noqa: SIM115
    IDs, seqs = load_fasta(args.i)

    seqs = [
        """".join([x for x in s if x.isupper() or x == ""-""]) for s in seqs
    ]  # remove lowercase letters in alignment

    df = pd.DataFrame({""SequenceName"": IDs, ""sequence"": seqs})

    query_ = df.iloc[:1]
    df = df.iloc[1:]

    if args.resample:
        df = df.sample(frac=1)

    L = len(df.sequence.iloc[0])
    N = len(df)  # noqa: F841

    df[""frac_gaps""] = [x.count(""-"") / L for x in df[""sequence""]]

    former_len = len(df)
    df = df.loc[df.frac_gaps < args.gap_cutoff]

    new_len = len(df)
    lprint(args.keyword, f)
    lprint(
        f""{former_len - new_len} seqs removed for containing more than {int(args.gap_cutoff * 100)}% gaps, {new_len} remaining"",  # noqa: E501
        f,
    )

    ohe_seqs = encode_seqs(df.sequence.tolist(), max_len=L)

    n_clusters = []
    eps_test_vals = np.arange(args.min_eps, args.max_eps + args.eps_step, args.eps_step)

    if args.eps_val is None:  # performing scan
        lprint(""eps\tn_clusters\tn_not_clustered"", f)

        for eps in eps_test_vals:
            testset = encode_seqs(df.sample(frac=0.25).sequence.tolist(), max_len=L)
            clustering = DBSCAN(eps=eps, min_samples=args.min_samples).fit(testset)
            n_clust = len(set(clustering.labels_))
            n_not_clustered = len(
                clustering.labels_[np.where(clustering.labels_ == -1)]
            )
            lprint(""%.2f\t%d\t%d"" % (eps, n_clust, n_not_clustered), f)  # noqa: UP031
            n_clusters.append(n_clust)
            if eps > 10 and n_clust == 1:
                break

        eps_to_select = eps_test_vals[np.argmax(n_clusters)]
    else:
        eps_to_select = args.eps_val

    # perform actual clustering

    clustering = DBSCAN(eps=eps_to_select, min_samples=args.min_samples).fit(ohe_seqs)

    lprint(""Selected eps={:.2f}"".format(eps_to_select), f)  # noqa: UP032

    lprint(""%d total seqs"" % len(df), f)  # noqa: UP031

    df[""dbscan_label""] = clustering.labels_

    clusters = [x for x in df.dbscan_label.unique() if x >= 0]
    unclustered = len(df.loc[df.dbscan_label == -1])

    lprint(
        ""%d clusters, %d of %d not clustered (%.2f)""  # noqa: UP031
        % (len(clusters), unclustered, len(df), unclustered / len(df)),
        f,
    )

    avg_dist_to_query = np.mean([
        1 - levenshtein(x, query_[""sequence""].iloc[0]) / L
        for x in df.loc[df.dbscan_label == -1][""sequence""].tolist()
    ])
    lprint(""avg identity to query of unclustered: %.2f"" % avg_dist_to_query, f)  # noqa: UP031

    avg_dist_to_query = np.mean([
        1 - levenshtein(x, query_[""sequence""].iloc[0]) / L
        for x in df.loc[df.dbscan_label != -1][""sequence""].tolist()
    ])
    lprint(""avg identity to query of clustered: %.2f"" % avg_dist_to_query, f)  # noqa: UP031

    cluster_metadata = []
    for clust in clusters:
        tmp = df.loc[df.dbscan_label == clust]

        cs = consensusVoting(tmp.sequence.tolist())

        avg_dist_to_cs = np.mean([
            1 - levenshtein(x, cs) / L for x in tmp.sequence.tolist()
        ])
        avg_dist_to_query = np.mean([
            1 - levenshtein(x, query_[""sequence""].iloc[0]) / L
            for x in tmp.sequence.tolist()
        ])

        if args.verbose:
            print(""Cluster %d consensus seq, %d seqs:"" % (clust, len(tmp)))  # noqa: UP031
            print(cs)
            print(""#########################################"")
            for _, row in tmp.iterrows():
                print(row[""SequenceName""], row[""sequence""])
            print(""#########################################"")

        tmp = pd.concat([query_, tmp], axis=0)

        cluster_metadata.append({
            ""cluster_ind"": clust,
            ""consensusSeq"": cs,
            ""avg_lev_dist"": ""%.3f"" % avg_dist_to_cs,  # noqa: UP031
            ""avg_dist_to_query"": ""%.3f"" % avg_dist_to_query,  # noqa: UP031
            ""size"": len(tmp),
        })

        write_fasta(
            tmp.SequenceName.tolist(),
            tmp.sequence.tolist(),
            outfile=args.o + ""/"" + args.keyword + ""_"" + ""%03d"" % clust + "".a3m"",  # noqa: UP031
        )

    print(f""writing {args.n_controls} size-10 uniformly sampled clusters"", flush=True)
    for i in range(args.n_controls):
        tmp = df.sample(n=10)
        tmp = pd.concat([query_, tmp], axis=0)
        write_fasta(
            tmp.SequenceName.tolist(),
            tmp.sequence.tolist(),
            outfile=args.o + ""/"" + ""U10-"" + args.keyword + ""_"" + ""%03d"" % i + "".a3m"",  # noqa: UP031
        )
    if len(df) > 100:
        print(
            f""writing {args.n_controls} size-100 uniformly sampled clusters"", flush=True
        )
        for i in range(args.n_controls):
            tmp = df.sample(n=100)
            tmp = pd.concat([query_, tmp], axis=0)
            write_fasta(
                tmp.SequenceName.tolist(),
                tmp.sequence.tolist(),
                outfile=args.o
                + ""/""
                + ""U100-""
                + args.keyword
                + ""_""
                + ""%03d"" % i  # noqa: UP031
                + "".a3m"",
            )

    if args.run_PCA:
        lprint(""Running PCA ..."", f)
        ohe_vecs = encode_seqs(df.sequence.tolist(), max_len=L)
        mdl = PCA()
        embedding = mdl.fit_transform(ohe_vecs)

        query_embedding = mdl.transform(
            encode_seqs(query_.sequence.tolist(), max_len=L)
        )

        df[""PC 1""] = embedding[:, 0]
        df[""PC 2""] = embedding[:, 1]

        query_[""PC 1""] = query_embedding[:, 0]
        query_[""PC 2""] = query_embedding[:, 1]

        plot_landscape(""PC 1"", ""PC 2"", df, query_, ""PCA"", args)

        lprint(""Saved PCA plot to "" + args.o + ""/"" + args.keyword + ""_PCA.pdf"", f)

    if args.run_TSNE:
        lprint(""Running TSNE ..."", f)
        ohe_vecs = encode_seqs(
            df.sequence.tolist() + [query_.sequence.tolist()], max_len=L
        )
        # different than PCA because tSNE doesn't have .transform attribute

        mdl = TSNE()
        embedding = mdl.fit_transform(ohe_vecs)

        df[""TSNE 1""] = embedding[:-1, 0]
        df[""TSNE 2""] = embedding[:-1, 1]

        query_[""TSNE 1""] = embedding[-1:, 0]
        query_[""TSNE 2""] = embedding[-1:, 1]

        plot_landscape(""TSNE 1"", ""TSNE 2"", df, query_, ""TSNE"", args)

        lprint(""Saved TSNE plot to "" + args.o + ""/"" + args.keyword + ""_TSNE.pdf"", f)

    outfile = args.o + ""/"" + args.keyword + ""_clustering_assignments.tsv""
    lprint(""wrote clustering data to %s"" % outfile, f)  # noqa: UP031
    df.to_csv(outfile, index=False, sep=""\t"")

    metad_outfile = args.o + ""/"" + args.keyword + ""_cluster_metadata.tsv""
    lprint(""wrote cluster metadata to %s"" % metad_outfile, f)  # noqa: UP031
    metad_df = pd.DataFrame.from_records(cluster_metadata)
    metad_df.to_csv(metad_outfile, index=False, sep=""\t"")

    print(""Saved this output to msacluster_%s.log"" % args.keyword)  # noqa: UP031
    f.close()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_config.py",".py","3025","91","import argparse
import os

from loguru import logger

from ..refinement_config import DATAMODE, RUNMODE, RocketRefinmentConfig, gen_config


def parse_args():
    parser = argparse.ArgumentParser(description=""Generate ROCKET configuration files"")
    parser.add_argument(
        ""--mode"",
        type=str,
        choices=[""phase1"", ""phase2"", ""both""],
        default=""both"",
        help=""Execution mode: phase1, phase2, or both (default: both)"",
    )
    parser.add_argument(
        ""--datamode"", type=str, choices=[""xray"", ""cryoem""], help=""Data mode""
    )
    parser.add_argument(""--working-dir"", type=str, help=""Working directory path"")
    parser.add_argument(""--file-id"", type=str, help=""File ID to use"")
    parser.add_argument(
        ""--pre-phase1-config"",
        type=str,
        help=""Path to pre-phase1 configuration file (required for phase2 mode)"",
    )

    args = parser.parse_args()

    # Convert string mode to RUNMODE enum
    run_mode = RUNMODE(args.mode)

    # Convert string datamode to DATAMODE enum if provided
    data_mode = None
    if args.datamode:
        data_mode = DATAMODE(args.datamode)

    # Load pre_phase1_config if provided and mode is phase2
    pre_phase1_config_obj = None
    if args.pre_phase1_config:
        if run_mode == RUNMODE.PHASE2 and not os.path.exists(args.pre_phase1_config):
            parser.error(f""Pre-phase1 config file not found: {args.pre_phase1_config}"")
        # Load the config file
        pre_phase1_config_obj = RocketRefinmentConfig.from_yaml_file(
            args.pre_phase1_config
        )  # noqa: E501
    elif run_mode == RUNMODE.PHASE2:
        parser.error(""--pre-phase1-config is required when mode is phase2"")

    return {
        ""mode"": run_mode,
        ""datamode"": data_mode,
        ""working_dir"": args.working_dir,
        ""file_id"": args.file_id,
        ""pre_phase1_config"": pre_phase1_config_obj,
    }


def cli_runconfig():
    args = parse_args()

    result = gen_config(
        mode=args.get(""mode""),
        datamode=args.get(""datamode""),
        working_dir=args.get(""working_dir""),
        file_id=args.get(""file_id""),
        pre_phase1_config=args.get(""pre_phase1_config""),
    )

    if args.get(""mode"") == RUNMODE.BOTH:
        phase1_config, phase2_config = result
        logger.info(
            f""Generated Phase 1 config at: {os.path.join(args.get('working_dir'), 'ROCKET_config_phase1.yaml')}""  # noqa: E501
        )
        logger.info(
            f""Generated Phase 2 config at: {os.path.join(args.get('working_dir'), 'ROCKET_config_phase2.yaml')}""  # noqa: E501
        )
    elif args.get(""mode"") == RUNMODE.PHASE1:
        logger.info(
            f""Generated Phase 1 config at: {os.path.join(args.get('working_dir'), 'ROCKET_config_phase1.yaml')}""  # noqa: E501
        )
    else:  # PHASE2
        logger.info(
            f""Generated Phase 2 config at: {os.path.join(result.working_dir, 'ROCKET_config_phase2.yaml')}""  # noqa: E501
        )


if __name__ == ""__main__"":
    cli_runconfig()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_msascore.py",".py","14245","414","#!/usr/bin/env python3

import argparse
import glob
import os
import shutil

import pandas as pd
import torch
from loguru import logger
from openfold.config import model_config
from openfold.data import data_pipeline, feature_pipeline
from tqdm import tqdm

import rocket
from rocket import coordinates as rk_coordinates
from rocket import refinement_utils as rkrf_utils
from rocket import utils as rk_utils

PRESET = ""model_1_ptm""


def main():
    parser = argparse.ArgumentParser(
        description=(
            ""Run LLG scoring for system with different msas, ""
            ""supporting both xray and cryoem modes""
        )
    )
    parser.add_argument(""path"", action=""store"", help=""Path to parent folder"")
    parser.add_argument(""system"", action=""store"", help=""file_id for the dataset"")
    parser.add_argument(
        ""-i"", action=""store"", help=""prefix for msas to use, path will prepend""
    )
    parser.add_argument(
        ""-o"",
        action=""store"",
        help=(
            ""name of output directory to write prediction and scoring to, ""
            ""path will prepend""
        ),
    )
    parser.add_argument(
        ""--datamode"",
        choices=[""xray"", ""cryoem""],
        required=True,
        help=""Choose between xray or cryoem mode"",
    )
    parser.add_argument(
        ""--domain_segs"",
        type=int,
        nargs=""*"",
        default=None,
        help=""A list of resid as domain boundaries"",
    )
    parser.add_argument(
        ""--additional_chain"", action=""store_true"", help=""Additional Chain in ASU""
    )
    parser.add_argument(
        ""--init_recycling"", default=4, type=int, help=""number of initial recycling""
    )
    parser.add_argument(
        ""--free_flag"", default=""R-free-flags"", type=str, help=""Column name of free flag""
    )
    parser.add_argument(
        ""--testset_value"", default=1, type=int, help=""Value for test set""
    )
    parser.add_argument(
        ""--voxel_spacing"",
        default=4.5,
        type=float,
        help=""Voxel spacing for solvent percentage estimation"",
    )
    parser.add_argument(
        ""--min_resolution"", default=3.0, type=float, help=""min resolution cut""
    )
    parser.add_argument(
        ""--chimera_profile"", action=""store_true"", help=""Use chimera profile""
    )
    parser.add_argument(
        ""--score_fullmsa"", action=""store_true"", help=""Also score the full msa""
    )
    config = parser.parse_args()

    device = rk_utils.try_gpu()
    RBR_LBFGS = True

    output_directory_path = os.path.join(config.path, config.o)
    os.makedirs(output_directory_path, exist_ok=True)

    logger.info(f""Working with system {config.system}"", flush=True)

    # Input paths
    tng_file = os.path.join(config.path, ""ROCKET_inputs"", f""{config.system}-Edata.mtz"")
    input_pdb = os.path.join(
        config.path, ""ROCKET_inputs"", f""{config.system}-pred-aligned.pdb""
    )

    # Handle additional chain
    constant_fp_added_HKL = None
    constant_fp_added_asu = None
    if config.additional_chain:
        constant_fp_added_HKL = torch.load(
            f""{config.path}/ROCKET_inputs/{config.system}_added_chain_atoms_HKL.pt""
        ).to(device=device)
        constant_fp_added_asu = torch.load(
            f""{config.path}/ROCKET_inputs/{config.system}_added_chain_atoms_asu.pt""
        ).to(device=device)

    # --- Data mode specific imports and objects ---
    if config.datamode == ""xray"":
        from rocket.xtal import structurefactors as sf_module

        def llgloss_init(sfc, mtz, minres, maxres):
            return rkrf_utils.init_llgloss(sfc, mtz, minres, maxres)

        initial_SFC = sf_module.initial_SFC
        SFC_kwargs = {
            ""Freelabel"": config.free_flag,
            ""device"": device,
            ""testset_value"": config.testset_value,
            ""added_chain_HKL"": constant_fp_added_HKL,
            ""added_chain_asu"": constant_fp_added_asu,
            ""spacing"": config.voxel_spacing,
        }
        LBFGS_LR = 150

    elif config.datamode == ""cryoem"":
        from rocket.cryo import structurefactors as sf_module
        from rocket.cryo import targets as cryo_targets

        def llgloss_init(sfc, mtz, *_):
            return cryo_targets.LLGloss(sfc, mtz)

        def initial_SFC(
            pdb,
            mtz,
            FP,
            SIGFP,
            Freelabel,
            device,
            testset_value,
            added_chain_HKL,
            added_chain_asu,
            spacing,
        ):
            return sf_module.initial_cryoSFC(pdb, mtz, ""Emean"", ""PHIEmean"", device, 20)

        SFC_kwargs = {
            ""Freelabel"": config.free_flag,
            ""device"": device,
            ""testset_value"": config.testset_value,
            ""added_chain_HKL"": constant_fp_added_HKL,
            ""added_chain_asu"": constant_fp_added_asu,
            ""spacing"": config.voxel_spacing,
        }
        LBFGS_LR = 0.1

    else:
        raise ValueError(f""Unknown datamode: {config.datamode}"")

    # --- SFC and LLGloss Initialization ---
    sfc = initial_SFC(input_pdb, tng_file, ""FEFF"", ""DOBS"", **SFC_kwargs)
    reference_pos = sfc.atom_pos_orth.clone()
    init_pos_bfactor = sfc.atom_b_iso.clone()

    sfc_rbr = initial_SFC(input_pdb, tng_file, ""FEFF"", ""DOBS"", **SFC_kwargs)

    llgloss = llgloss_init(sfc, tng_file, config.min_resolution, None)
    llgloss_rbr = llgloss_init(sfc_rbr, tng_file, config.min_resolution, None)

    # AF2 model initialization
    af_bias = rocket.MSABiasAFv3(model_config(PRESET, train=True), PRESET).to(device)
    af_bias.freeze()

    fasta_path = [
        f
        for ext in (""*.fa"", ""*.fasta"")
        for f in glob.glob(os.path.join(config.path, ext))
    ][0]

    fullmsa_dir = os.path.join(config.path, ""alignments"")
    data_processor = data_pipeline.DataPipeline(template_featurizer=None)
    fullmsa_feature_dict = rkrf_utils.generate_feature_dict(
        fasta_path,
        fullmsa_dir,
        data_processor,
    )

    afconfig = model_config(PRESET)
    afconfig.data.common.max_recycling_iters = config.init_recycling
    del afconfig.data.common.masked_msa
    afconfig.data.common.resample_msa_in_recycling = False
    feature_processor = feature_pipeline.FeaturePipeline(afconfig.data)
    fullmsa_processed_feature_dict = feature_processor.process_features(
        fullmsa_feature_dict, mode=""predict""
    )
    full_profile = fullmsa_processed_feature_dict[""msa_feat""][:, :, 25:48].clone()

    df = pd.DataFrame(
        columns=[
            ""msa_name"",
            ""depth"",
            ""mean_plddt"",
            ""llg"",
            #  ""rfree"",
            #  ""rwork""
        ]
    )
    df.to_csv(os.path.join(output_directory_path, ""msa_scoring.csv""), index=False)

    # --- (Optional) Score the full MSA ---
    if config.score_fullmsa:
        msa_name = ""fullmsa""
        device_processed_features = rk_utils.move_tensors_to_device(
            fullmsa_processed_feature_dict, device=device
        )
        af2_output, prevs = af_bias(
            device_processed_features,
            [None, None, None],
            num_iters=config.init_recycling,
            bias=False,
        )
        prevs = [tensor.detach() for tensor in prevs]
        deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
        af2_output, __ = af_bias(
            device_processed_features, deep_copied_prevs, num_iters=1, bias=False
        )
        plddt = torch.mean(af2_output[""plddt""])
        aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
            af2_output=af2_output,
            device_processed_features=device_processed_features,
            cra_name=sfc.cra_name,
            best_pos=reference_pos,
            exclude_res=None,
            domain_segs=config.domain_segs,
            reference_bfactor=init_pos_bfactor,
        )
        llgloss.sfc.atom_b_iso = pseudo_Bs.detach()
        llgloss_rbr.sfc.atom_b_iso = pseudo_Bs.detach()

        if config.datamode == ""xray"":
            llgloss, llgloss_rbr, Ecalc, Fc = rkrf_utils.update_sigmaA(
                llgloss=llgloss,
                llgloss_rbr=llgloss_rbr,
                aligned_xyz=aligned_xyz,
                constant_fp_added_HKL=constant_fp_added_HKL,
                constant_fp_added_asu=constant_fp_added_asu,
            )
        optimized_xyz, loss_track_pose = rk_coordinates.rigidbody_refine_quat(
            aligned_xyz,
            llgloss_rbr,
            sfc.cra_name,
            domain_segs=config.domain_segs,
            lbfgs=RBR_LBFGS,
            added_chain_HKL=constant_fp_added_HKL,
            added_chain_asu=constant_fp_added_asu,
            lbfgs_lr=LBFGS_LR,
            verbose=False,
        )
        llg = llgloss(
            optimized_xyz,
            bin_labels=None,
            num_batch=1,
            sub_ratio=1.0,
            solvent=True,
            update_scales=True,
            added_chain_HKL=constant_fp_added_HKL,
            added_chain_asu=constant_fp_added_asu,
        )
        llgloss.sfc.atom_pos_orth = optimized_xyz
        llgloss.sfc.savePDB(f""{output_directory_path!s}/{msa_name}_postRBR.pdb"")
        (
            plddt_i,
            llg_i,
        ) = (
            plddt.item(),
            llg.item(),
            # llgloss.sfc.r_free.item(),
            # llgloss.sfc.r_work.item(),
        )
        df_tmp = pd.DataFrame({
            ""msa_name"": [msa_name],
            ""depth"": [fullmsa_feature_dict[""msa""].shape[0]],
            ""mean_plddt"": [plddt_i],
            ""llg"": [llg_i],
            # ""rfree"": [rfree_i],
            # ""rwork"": [rwork_i],
        })
        df_tmp.to_csv(
            os.path.join(output_directory_path, ""msa_scoring.csv""),
            mode=""a"",
            header=False,
            index=False,
        )

    # --- Score all MSAs ---
    a3m_paths = glob.glob(os.path.join(config.path, config.i + ""*.a3m""))
    print(f""{len(a3m_paths)} msa files available..."", flush=True)
    a3m_paths.sort()

    for a3m_path in tqdm(a3m_paths):
        msa_name, ext = os.path.splitext(os.path.basename(a3m_path))
        data_processor = data_pipeline.DataPipeline(template_featurizer=None)
        temp_alignment_dir = os.path.join(os.path.dirname(a3m_path), ""tmp_align"")
        os.makedirs(temp_alignment_dir, exist_ok=True)
        shutil.copy(a3m_path, os.path.join(temp_alignment_dir, msa_name + "".a3m""))
        feature_dict = rkrf_utils.generate_feature_dict(
            fasta_path,
            temp_alignment_dir,
            data_processor,
        )
        afconfig = model_config(PRESET)
        afconfig.data.common.max_recycling_iters = config.init_recycling
        del afconfig.data.common.masked_msa
        afconfig.data.common.resample_msa_in_recycling = False
        feature_processor = feature_pipeline.FeaturePipeline(afconfig.data)
        processed_feature_dict = feature_processor.process_features(
            feature_dict, mode=""predict""
        )

        if config.chimera_profile:
            sub_profile = processed_feature_dict[""msa_feat""][:, :, 25:48].clone()
            processed_feature_dict[""msa_feat""][:, :, 25:48] = torch.where(
                sub_profile == 0.0, full_profile.clone(), sub_profile.clone()
            )

        device_processed_features = rk_utils.move_tensors_to_device(
            processed_feature_dict, device=device
        )

        af2_output, prevs = af_bias(
            device_processed_features,
            [None, None, None],
            num_iters=config.init_recycling,
            bias=False,
        )
        prevs = [tensor.detach() for tensor in prevs]
        deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
        af2_output, __ = af_bias(
            device_processed_features, deep_copied_prevs, num_iters=1, bias=False
        )
        plddt = torch.mean(af2_output[""plddt""])
        aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
            af2_output=af2_output,
            device_processed_features=device_processed_features,
            cra_name=sfc.cra_name,
            best_pos=reference_pos,
            exclude_res=None,
            domain_segs=config.domain_segs,
            reference_bfactor=init_pos_bfactor,
        )
        llgloss.sfc.atom_b_iso = pseudo_Bs.detach()
        llgloss_rbr.sfc.atom_b_iso = pseudo_Bs.detach()
        if config.datamode == ""xray"":
            llgloss, llgloss_rbr, Ecalc, Fc = rkrf_utils.update_sigmaA(
                llgloss=llgloss,
                llgloss_rbr=llgloss_rbr,
                aligned_xyz=aligned_xyz,
                constant_fp_added_HKL=constant_fp_added_HKL,
                constant_fp_added_asu=constant_fp_added_asu,
            )
        optimized_xyz, loss_track_pose = rk_coordinates.rigidbody_refine_quat(
            aligned_xyz,
            llgloss_rbr,
            sfc.cra_name,
            domain_segs=config.domain_segs,
            lbfgs=RBR_LBFGS,
            added_chain_HKL=constant_fp_added_HKL,
            added_chain_asu=constant_fp_added_asu,
            lbfgs_lr=LBFGS_LR,
            verbose=False,
        )
        llg = llgloss(
            optimized_xyz,
            bin_labels=None,
            num_batch=1,
            sub_ratio=1.0,
            solvent=True,
            update_scales=True,
            added_chain_HKL=constant_fp_added_HKL,
            added_chain_asu=constant_fp_added_asu,
        )
        llgloss.sfc.atom_pos_orth = optimized_xyz
        llgloss.sfc.savePDB(f""{output_directory_path!s}/{msa_name}_postRBR.pdb"")
        (
            plddt_i,
            llg_i,
        ) = (
            plddt.item(),
            llg.item(),
            # llgloss.sfc.r_free.item(),
            # llgloss.sfc.r_work.item(),
        )
        df_tmp = pd.DataFrame({
            ""msa_name"": [msa_name],
            ""depth"": [feature_dict[""msa""].shape[0]],
            ""mean_plddt"": [plddt_i],
            ""llg"": [llg_i],
            # ""rfree"": [rfree_i],
            # ""rwork"": [rwork_i],
        })
        df_tmp.to_csv(
            os.path.join(output_directory_path, ""msa_scoring.csv""),
            mode=""a"",
            header=False,
            index=False,
        )
        shutil.rmtree(temp_alignment_dir)


if __name__ == ""__main__"":
    main()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/run_refine.py",".py","757","23","import argparse

from ..refinement_config import RocketRefinmentConfig
from ..refinement_cryoem import run_cryoem_refinement
from ..refinement_xray import run_xray_refinement


def run_refinement(config: RocketRefinmentConfig | str) -> RocketRefinmentConfig:
    if isinstance(config, str):
        config = RocketRefinmentConfig.from_yaml_file(config)

    if config.datamode == ""xray"":
        return run_xray_refinement(config)
    elif config.datamode == ""cryoem"":
        return run_cryoem_refinement(config)


def cli_runrefine():
    parser = argparse.ArgumentParser(description=""Run ROCKET refinement"")
    parser.add_argument(""config"", type=str, help=""Path to the configuration file"")
    args = parser.parse_args()
    run_refinement(args.config)
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/archived_scripts/run_phase1andphase2.py",".py","15103","508","import argparse
import glob
import os

from rocket.refinement_xray import RocketRefinmentConfig, run_refinement


# WORKING_DIRECTORY = Path(""/net/cci/alisia/openfold_tests/run_openfold/test_cases"")
# ALL_DATASETS = [""6lzm""]
def int_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return int(value)
    except ValueError:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an integer or 'None'.""
        )


def float_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return float(value)
    except ValueError:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an float or 'None'.""
        )


def parse_arguments():
    """"""Parse commandline arguments""""""
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawTextHelpFormatter, description=__doc__
    )

    parser.add_argument(
        ""-p"",
        ""--path"",
        default=""./benchmark_data/"",
        help=(""Path to the parent folder""),
    )

    # Required arguments
    parser.add_argument(
        ""-sys"",
        ""--systems"",
        nargs=""+"",
        help=(""PDB codes or filename roots for the dataset""),
    )

    parser.add_argument(
        ""--note"",
        default="""",
        help=(""note""),
    )

    parser.add_argument(
        ""--template_pdb"",
        default=None,
        help=(""template model name in the path""),
    )

    parser.add_argument(
        ""--input_msa"",
        default=None,
        help=(
            ""path to msa file .a3m/.fasta for use, working path and system will prepend""
        ),
    )

    parser.add_argument(
        ""--domain_segs"",
        type=int,
        nargs=""*"",
        default=None,
        help=(""A list of resid as domain boundaries""),
    )

    parser.add_argument(
        ""--additional_chain"", action=""store_true"", help=(""Additional Chain in ASU"")
    )

    parser.add_argument(
        ""--only_phase1"",
        action=""store_true"",
        help=(""Turn off phase 2 refinement in the pipeline""),
    )

    parser.add_argument(
        ""--only_phase2"",
        action=""store_true"",
        help=(""Turn off phase 1 refinement in the pipeline""),
    )

    parser.add_argument(""--phase1_uuid"", default=None, help=(""uuid for phase 1 run""))

    parser.add_argument(""--mse_uuid"", default=None, help=(""uuid for mse run""))

    parser.add_argument(
        ""--init_recycling"",
        default=20,
        type=int,
        help=(""number of initial recycling""),
    )

    parser.add_argument(
        ""--phase1_add_lr"",
        default=0.05,
        type=float,
        help=(""phase 1 additive learning rate""),
    )

    parser.add_argument(
        ""--phase1_mul_lr"",
        default=1.0,
        type=float,
        help=(""phase 1 multiplicative learning rate""),
    )

    parser.add_argument(
        ""--phase1_w_l2"",
        default=1e-11,
        type=float,
        help=(""phase 1 weights of L2 loss""),
    )

    parser.add_argument(
        ""--phase2_final_lr"",
        default=1e-3,
        type=float,
        help=(""phase 2 final learning rate""),
    )

    parser.add_argument(
        ""--smooth_stage_epochs"",
        default=50,
        type=int_or_none,
        help=(""number of smooth stages in phase1""),
    )

    parser.add_argument(
        ""--phase1_min_resol"",
        default=3.0,
        type=float,
        help=(""phase 1 resolution cut""),
    )

    parser.add_argument(
        ""--free_flag"",
        default=""R-free-flags"",
        type=str,
        help=(""Column name of free flag""),
    )

    parser.add_argument(
        ""--testset_value"",
        default=1,
        type=int,
        help=(""Value for test set""),
    )

    parser.add_argument(
        ""--voxel_spacing"",
        default=4.5,
        type=float,
        help=(""Voxel spacing for solvent percentage estimation""),
    )

    parser.add_argument(
        ""--w_plddt"",
        default=0.0,
        type=float,
        help=(""Weights for plddt loss""),
    )

    parser.add_argument(
        ""--msa_subratio"",
        default=None,
        type=float_or_none,
        help=(
            ""MSA subsampling ratio, between 0.0 and 1.0. Default None, no subsampling.""
        ),
    )

    return parser.parse_args()


def generate_phase1_config(
    *,
    working_path: str,
    file_root: str,
    cuda_device: int = 0,
    free_flag: str = ""R-free-flags"",
    testset_value: int = 1,
    template_pdb: str | None = None,
    input_msa: str | None = None,
    additional_chain: bool = False,
    additive_learning_rate: float = 0.05,
    multiplicative_learning_rate: float = 1.0,
    init_recycling: int = 20,
    phase1_min_resol: float = 4.0,
    phase1_w_l2: float = 1e-3,
    phase2_final_lr: float = 1e-3,
    smooth_stage_epochs: int | None = 50,
    domain_segs: list[int] | None = None,
    note: str = """",
    mse_uuid: str | None = None,
    voxel_spacing: float = 4.5,
    msa_subratio: float | None = None,
) -> RocketRefinmentConfig:
    if mse_uuid is None:
        starting_bias_path = None
        starting_weights_path = None
    else:
        output_directory_path = f""{working_path}/{file_root}/outputs/{mse_uuid}""
        mse_path = glob.glob(f""{output_directory_path}/mse*/"")[0]
        starting_bias_path = glob.glob(os.path.join(mse_path, ""best_msa_bias*.pt""))[0]
        starting_weights_path = glob.glob(
            os.path.join(mse_path, ""best_feat_weights*.pt"")
        )[0]

    if template_pdb is not None:
        template_pdb = f""{working_path}/{file_root}/{template_pdb}""

    phase1_config = RocketRefinmentConfig(
        file_root=file_root,
        path=working_path,
        init_recycling=init_recycling,
        batch_sub_ratio=0.7,
        number_of_batches=1,
        rbr_opt_algorithm=""lbfgs"",
        rbr_lbfgs_learning_rate=150.0,
        additional_chain=additional_chain,
        template_pdb=template_pdb,
        input_msa=input_msa,
        domain_segs=domain_segs,
        verbose=False,
        bias_version=3,
        num_of_runs=3,
        iterations=100,
        cuda_device=cuda_device,
        solvent=True,
        sfc_scale=True,
        refine_sigmaA=True,
        additive_learning_rate=additive_learning_rate,
        multiplicative_learning_rate=multiplicative_learning_rate,
        phase2_final_lr=phase2_final_lr,
        smooth_stage_epochs=smooth_stage_epochs,
        free_flag=free_flag,
        testset_value=testset_value,
        l2_weight=phase1_w_l2,
        # b_threshold=10.0,
        min_resolution=phase1_min_resol,
        note=""phase1"" + note,
        uuid_hex=mse_uuid,
        starting_bias=starting_bias_path,
        starting_weights=starting_weights_path,
        voxel_spacing=voxel_spacing,
        msa_subratio=msa_subratio,
    )

    return phase1_config


def generate_phase2_config(
    *,
    phase1_uuid: str | None,
    working_path: str,
    file_root: str,
    cuda_device: int = 0,
    free_flag: str = ""R-free-flags"",
    testset_value: int = 1,
    additional_chain: bool = False,
    phase1_add_lr: float = 0.05,
    phase1_mul_lr: float = 1.0,
    phase1_w_l2: float = 1e-3,
    phase2_final_lr: float = 1e-3,
    input_msa: str | None = None,
    template_pdb: str | None = None,
    domain_segs: list[int] | None = None,
    voxel_spacing: float = 4.5,
    init_recycling: int = 20,
    note: str = """",
    w_plddt: float = 0.0,
    # msa_subratio: Union[float, None] = None,
) -> RocketRefinmentConfig:
    if phase1_uuid is None:
        starting_bias_path = None
        starting_weights_path = None
    else:
        output_directory_path = f""{working_path}/{file_root}/outputs/{phase1_uuid}""
        phase1_path = glob.glob(f""{output_directory_path}/phase1*/"")[0]
        starting_bias_path = glob.glob(os.path.join(phase1_path, ""best_msa_bias*.pt""))[
            0
        ]
        starting_weights_path = glob.glob(
            os.path.join(phase1_path, ""best_feat_weights*.pt"")
        )[0]

    if input_msa is not None:
        msa_feat_init_path = glob.glob(os.path.join(phase1_path, ""msa_feat_start.npy""))[
            0
        ]
    else:
        msa_feat_init_path = None
        # best_runid = os.path.basename(starting_bias_path).split(""_"")[-2]
        # if msa_subratio is not None:
        #     sub_msa_path = glob.glob(os.path.join(phase1_path, f""sub_msa_{best_runid}.npy""))[0]
        #     sub_delmat_path = glob.glob(os.path.join(phase1_path, f""sub_delmat_{best_runid}.npy""))[0]
        # else:
        #     sub_msa_path = None
        #     sub_delmat_path = None

    if template_pdb is not None:
        template_pdb = f""{working_path}/{file_root}/{template_pdb}""

    phase2_config = RocketRefinmentConfig(
        file_root=file_root,
        path=working_path,
        batch_sub_ratio=1.0,
        number_of_batches=1,
        init_recycling=init_recycling,
        rbr_opt_algorithm=""lbfgs"",
        rbr_lbfgs_learning_rate=150.0,
        smooth_stage_epochs=50,
        additional_chain=additional_chain,
        input_msa=input_msa,
        template_pdb=template_pdb,
        domain_segs=domain_segs,
        verbose=False,
        bias_version=3,
        iterations=500,
        cuda_device=cuda_device,
        solvent=True,
        sfc_scale=True,
        refine_sigmaA=True,
        additive_learning_rate=phase1_add_lr,
        multiplicative_learning_rate=phase1_mul_lr,
        phase2_final_lr=phase2_final_lr,
        weight_decay=None,
        free_flag=free_flag,
        testset_value=testset_value,
        l2_weight=phase1_w_l2,
        w_plddt=w_plddt,
        b_threshold=10.0,
        note=""phase2"" + note,
        uuid_hex=phase1_uuid,
        starting_bias=starting_bias_path,
        starting_weights=starting_weights_path,
        voxel_spacing=voxel_spacing,
        msa_subratio=None,
        msa_feat_init_path=msa_feat_init_path,
        # sub_msa_path=sub_msa_path,
        # sub_delmat_path=sub_delmat_path,
    )

    return phase2_config


def run_both_phases_single_dataset(
    *,
    working_path,
    file_root,
    note,
    free_flag,
    testset_value,
    additional_chain,
    phase1_add_lr,
    phase1_mul_lr,
    phase1_w_l2,
    w_plddt,
    phase2_final_lr,
    smooth_stage_epochs,
    init_recycling,
    phase1_min_resol,
    domain_segs,
    mse_uuid,
    voxel_spacing,
    template_pdb,
    msa_subratio,
    input_msa,
) -> None:
    phase1_config = generate_phase1_config(
        working_path=working_path,
        file_root=file_root,
        note=note,
        free_flag=free_flag,
        testset_value=testset_value,
        additional_chain=additional_chain,
        additive_learning_rate=phase1_add_lr,
        multiplicative_learning_rate=phase1_mul_lr,
        phase1_w_l2=phase1_w_l2,
        init_recycling=init_recycling,
        phase2_final_lr=phase2_final_lr,
        smooth_stage_epochs=smooth_stage_epochs,
        phase1_min_resol=phase1_min_resol,
        template_pdb=template_pdb,
        input_msa=input_msa,
        domain_segs=domain_segs,
        mse_uuid=mse_uuid,
        voxel_spacing=voxel_spacing,
        msa_subratio=msa_subratio,
    )
    phase1_uuid = run_refinement(config=phase1_config)

    phase2_config = generate_phase2_config(
        phase1_uuid=phase1_uuid,
        working_path=working_path,
        file_root=file_root,
        note=note,
        free_flag=free_flag,
        testset_value=testset_value,
        additional_chain=additional_chain,
        phase1_add_lr=phase1_add_lr,
        phase1_mul_lr=phase1_mul_lr,
        phase1_w_l2=phase1_w_l2,
        w_plddt=w_plddt,
        phase2_final_lr=phase2_final_lr,
        init_recycling=init_recycling,
        input_msa=input_msa,
        template_pdb=template_pdb,
        domain_segs=domain_segs,
        voxel_spacing=voxel_spacing,
    )
    phase2_uuid = run_refinement(config=phase2_config)


def run_both_phases_all_datasets() -> None:
    args = parse_arguments()
    datasets = args.systems

    for file_root in datasets:
        if args.only_phase1:
            phase1_config = generate_phase1_config(
                working_path=args.path,
                file_root=file_root,
                note=args.note,
                free_flag=args.free_flag,
                testset_value=args.testset_value,
                additional_chain=args.additional_chain,
                additive_learning_rate=args.phase1_add_lr,
                multiplicative_learning_rate=args.phase1_mul_lr,
                phase1_w_l2=args.phase1_w_l2,
                phase2_final_lr=args.phase2_final_lr,
                smooth_stage_epochs=args.smooth_stage_epochs,
                init_recycling=args.init_recycling,
                phase1_min_resol=args.phase1_min_resol,
                template_pdb=args.template_pdb,
                input_msa=args.input_msa,
                domain_segs=args.domain_segs,
                mse_uuid=args.mse_uuid,
                voxel_spacing=args.voxel_spacing,
                msa_subratio=args.msa_subratio,
            )
            phase1_uuid = run_refinement(config=phase1_config)

        elif args.only_phase2:
            phase2_config = generate_phase2_config(
                phase1_uuid=args.phase1_uuid,
                working_path=args.path,
                free_flag=args.free_flag,
                testset_value=args.testset_value,
                file_root=file_root,
                note=args.note,
                additional_chain=args.additional_chain,
                phase1_add_lr=args.phase1_add_lr,
                phase1_mul_lr=args.phase1_mul_lr,
                phase1_w_l2=args.phase1_w_l2,
                w_plddt=args.w_plddt,
                phase2_final_lr=args.phase2_final_lr,
                init_recycling=args.init_recycling,
                input_msa=arg.input_msa,
                template_pdb=args.template_pdb,
                domain_segs=args.domain_segs,
                voxel_spacing=args.voxel_spacing,
            )
            phase2_uuid = run_refinement(config=phase2_config)

        else:
            run_both_phases_single_dataset(
                working_path=args.path,
                file_root=file_root,
                note=args.note,
                free_flag=args.free_flag,
                testset_value=args.testset_value,
                additional_chain=args.additional_chain,
                phase1_add_lr=args.phase1_add_lr,
                phase1_mul_lr=args.phase1_mul_lr,
                phase1_w_l2=args.phase1_w_l2,
                w_plddt=args.w_plddt,
                smooth_stage_epochs=args.smooth_stage_epochs,
                phase2_final_lr=args.phase2_final_lr,
                init_recycling=args.init_recycling,
                phase1_min_resol=args.phase1_min_resol,
                template_pdb=args.template_pdb,
                domain_segs=args.domain_segs,
                mse_uuid=args.mse_uuid,
                voxel_spacing=args.voxel_spacing,
                msa_subratio=args.msa_subratio,
                input_msa=args.input_msa,
            )


if __name__ == ""__main__"":
    run_both_phases_all_datasets()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/archived_scripts/run_mse.py",".py","3222","140","import argparse

from rocket.refinement_mse import RocketMSERefinmentConfig, run_refinement_mse


def parse_arguments():
    """"""Parse commandline arguments""""""
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawTextHelpFormatter, description=__doc__
    )

    parser.add_argument(
        ""-p"",
        ""--path"",
        default=""./benchmark_data/"",
        help=(""Path to the parent folder""),
    )

    # Required arguments
    parser.add_argument(
        ""-sys"",
        ""--systems"",
        nargs=""+"",
        help=(""PDB codes or filename roots for the dataset""),
    )

    parser.add_argument(
        ""--add_lr"",
        default=1e-3,
        type=float,
        help=(""additive learning rate""),
    )

    parser.add_argument(
        ""--mul_lr"",
        default=1e-3,
        type=float,
        help=(""multiplicative learning rate""),
    )

    parser.add_argument(
        ""--num_of_runs"",
        default=2,
        type=int,
        help=(""number of trials""),
    )

    parser.add_argument(
        ""--n_step"",
        default=200,
        type=int,
        help=(""number of steps""),
    )

    parser.add_argument(
        ""--init_recycling"",
        default=20,
        type=int,
        help=(""number of initial recycling""),
    )

    parser.add_argument(
        ""--free_flag"",
        default=""R-free-flags"",
        type=str,
        help=(""Column name of free flag""),
    )

    parser.add_argument(
        ""--testset_value"",
        default=1,
        type=int,
        help=(""Value for test set""),
    )

    parser.add_argument(
        ""--note"",
        default="""",
        help=(""note""),
    )

    return parser.parse_args()


def generate_mse_config(
    *,
    working_path: str,
    file_root: str,
    cuda_device: int = 0,
    free_flag: str = ""R-free-flags"",
    testset_value: int = 1,
    num_of_runs: int = 1,
    n_step: int = 50,
    note: str = """",
    add_lr: float = 0.05,
    mul_lr: float = 1.0,
    init_recycling: int = 20,
) -> RocketMSERefinmentConfig:
    mse_config = RocketMSERefinmentConfig(
        file_root=file_root,
        path=working_path,
        init_recycling=init_recycling,
        rbr_opt_algorithm=""lbfgs"",
        rbr_lbfgs_learning_rate=150.0,
        bias_version=3,
        num_of_runs=num_of_runs,
        iterations=n_step,
        cuda_device=cuda_device,
        additive_learning_rate=add_lr,
        multiplicative_learning_rate=mul_lr,
        free_flag=free_flag,
        testset_value=testset_value,
        note=""mse"" + note,
    )

    return mse_config


def run_mse_all_datasets() -> None:
    args = parse_arguments()
    datasets = args.systems
    for file_root in datasets:
        mse_config = generate_mse_config(
            working_path=args.path,
            file_root=file_root,
            note=args.note,
            add_lr=args.add_lr,
            mul_lr=args.mul_lr,
            num_of_runs=args.num_of_runs,
            n_step=args.n_step,
            free_flag=args.free_flag,
            testset_value=args.testset_value,
            init_recycling=args.init_recycling,
        )
        phase1_uuid = run_refinement_mse(config=mse_config)


if __name__ == ""__main__"":
    run_mse_all_datasets()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/archived_scripts/run_phase1.py",".py","8418","319","import argparse
import glob
import os

from rocket.refinement_xray import RocketRefinmentConfig, run_refinement


def int_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return int(value)
    except ValueError:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an integer or 'None'.""
        )


def float_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return float(value)
    except ValueError:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an float or 'None'.""
        )


def parse_arguments():
    """"""Parse commandline arguments""""""
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawTextHelpFormatter, description=__doc__
    )

    parser.add_argument(
        ""-p"",
        ""--path"",
        default=""./benchmark_data/"",
        help=(""Path to the parent folder""),
    )

    # Required arguments
    parser.add_argument(
        ""-sys"",
        ""--systems"",
        nargs=""+"",
        help=(""PDB codes or filename roots for the dataset""),
    )

    parser.add_argument(
        ""--template_pdb"",
        default=None,
        help=(""template model name in the path""),
    )

    parser.add_argument(
        ""--domain_segs"",
        type=int,
        nargs=""*"",
        default=None,
        help=(""A list of resid as domain boundaries""),
    )

    parser.add_argument(
        ""--free_flag"",
        default=""R-free-flags"",
        type=str,
        help=(""Column name of free flag""),
    )

    parser.add_argument(
        ""--testset_value"",
        default=1,
        type=int,
        help=(""Value for test set""),
    )

    parser.add_argument(
        ""--additional_chain"", action=""store_true"", help=(""Additional Chain in ASU"")
    )

    parser.add_argument(
        ""--w_l2"",
        default=1e-11,
        type=float,
        help=(""weight for L2 loss""),
    )

    parser.add_argument(
        ""--sub_ratio"",
        default=0.7,
        type=float,
        help=(""Sub ratio of reflectios for each iteratio""),
    )

    parser.add_argument(
        ""--add_lr"",
        default=0.05,
        type=float,
        help=(""additive learning rate""),
    )

    parser.add_argument(
        ""--mul_lr"",
        default=1.0,
        type=float,
        help=(""multiplicative learning rate""),
    )

    parser.add_argument(""--mse_uuid"", default=None, help=(""uuid for mse run""))

    parser.add_argument(
        ""--phase2_final_lr"",
        default=1e-3,
        type=float,
        help=(""phase 2 final learning rate""),
    )

    parser.add_argument(
        ""--smooth_stage_epochs"",
        default=50,
        type=int_or_none,
        help=(""number of smooth stages in phase1""),
    )
    parser.add_argument(
        ""--num_of_runs"",
        default=3,
        type=int,
        help=(""number of trials""),
    )

    parser.add_argument(
        ""--n_step"",
        default=100,
        type=int,
        help=(""number of steps""),
    )

    parser.add_argument(""--refine_sigmaA"", action=""store_true"", help=(""refine sigmaAs""))

    parser.add_argument(
        ""--min_resolution"",
        default=3.0,
        type=float,
        help=(""min resolution cut""),
    )

    parser.add_argument(
        ""--init_recycling"",
        default=20,
        type=int,
        help=(""number of initial recycling""),
    )

    parser.add_argument(
        ""--note"",
        default="""",
        help=(""note""),
    )

    parser.add_argument(
        ""--voxel_spacing"",
        default=4.5,
        type=float,
        help=(""Voxel spacing for solvent percentage estimation""),
    )

    parser.add_argument(
        ""--msa_subratio"",
        default=None,
        type=float_or_none,
        help=(
            ""MSA subsampling ratio, between 0.0 and 1.0. Default None, no subsampling.""
        ),
    )

    parser.add_argument(
        ""--input_msa"",
        default=None,
        help=(
            ""path to msa file .a3m/.fasta for use, working path and system will prepend""
        ),
    )

    parser.add_argument(
        ""--bias_from_fullmsa"",
        action=""store_true"",
        help=(""Generate initial profile via biasing from full msa profile""),
    )

    parser.add_argument(
        ""--chimera_profile"", action=""store_true"", help=(""Use chimera profile"")
    )

    return parser.parse_args()


def generate_phase1_config(
    *,
    working_path: str,
    file_root: str,
    cuda_device: int = 0,
    free_flag: str = ""R-free-flags"",
    testset_value: int = 1,
    additional_chain: bool = False,
    w_l2: float = 1e-11,
    num_of_runs: int = 1,
    sub_ratio: float = 0.7,
    n_step: int = 50,
    init_recycling: int = 20,
    min_resol: float = 3.0,
    note: str = """",
    refine_sigmaA: bool = True,
    input_msa: str | None = None,
    bias_from_fullmsa: bool = False,
    chimera_profile: bool = False,
    template_pdb: str | None = None,
    domain_segs: list[int] | None = None,
    add_lr: float = 0.05,
    mul_lr: float = 1.0,
    phase2_final_lr: float = 1e-3,
    smooth_stage_epochs: int = 50,
    mse_uuid: str | None = None,
    voxel_spacing: float = 4.5,
    msa_subratio: float | None = None,
) -> RocketRefinmentConfig:
    if mse_uuid is None:
        starting_bias_path = None
        starting_weights_path = None
    else:
        output_directory_path = f""{working_path}/{file_root}/outputs/{mse_uuid}""
        mse_path = glob.glob(f""{output_directory_path}/mse*/"")[0]
        starting_bias_path = glob.glob(os.path.join(mse_path, ""best_msa_bias*.pt""))[0]
        starting_weights_path = glob.glob(
            os.path.join(mse_path, ""best_feat_weights*.pt"")
        )[0]

    if template_pdb is not None:
        template_pdb = f""{working_path}/{file_root}/{template_pdb}""

    phase1_config = RocketRefinmentConfig(
        file_root=file_root,
        path=working_path,
        init_recycling=init_recycling,
        batch_sub_ratio=sub_ratio,
        number_of_batches=1,
        rbr_opt_algorithm=""lbfgs"",
        rbr_lbfgs_learning_rate=150.0,
        additional_chain=additional_chain,
        domain_segs=domain_segs,
        verbose=False,
        bias_version=3,
        num_of_runs=num_of_runs,
        iterations=n_step,
        # iterations=2,
        input_msa=input_msa,
        bias_from_fullmsa=bias_from_fullmsa,
        chimera_profile=chimera_profile,
        template_pdb=template_pdb,
        cuda_device=cuda_device,
        solvent=True,
        sfc_scale=True,
        refine_sigmaA=refine_sigmaA,
        additive_learning_rate=add_lr,
        multiplicative_learning_rate=mul_lr,
        phase2_final_lr=phase2_final_lr,
        smooth_stage_epochs=smooth_stage_epochs,
        free_flag=free_flag,
        testset_value=testset_value,
        l2_weight=w_l2,
        min_resolution=min_resol,
        starting_bias=starting_bias_path,
        starting_weights=starting_weights_path,
        note=""phase1"" + note,
        voxel_spacing=voxel_spacing,
        msa_subratio=msa_subratio,
    )

    return phase1_config


def run_phase1_all_datasets() -> None:
    args = parse_arguments()
    datasets = args.systems
    for file_root in datasets:
        phase1_config = generate_phase1_config(
            working_path=args.path,
            file_root=file_root,
            note=args.note,
            free_flag=args.free_flag,
            testset_value=args.testset_value,
            additional_chain=args.additional_chain,
            domain_segs=args.domain_segs,
            init_recycling=args.init_recycling,
            w_l2=args.w_l2,
            sub_ratio=args.sub_ratio,
            add_lr=args.add_lr,
            mul_lr=args.mul_lr,
            num_of_runs=args.num_of_runs,
            n_step=args.n_step,
            input_msa=args.input_msa,
            bias_from_fullmsa=args.bias_from_fullmsa,
            chimera_profile=args.chimera_profile,
            template_pdb=args.template_pdb,
            refine_sigmaA=args.refine_sigmaA,
            min_resol=args.min_resolution,
            phase2_final_lr=args.phase2_final_lr,
            smooth_stage_epochs=args.smooth_stage_epochs,
            mse_uuid=args.mse_uuid,
            voxel_spacing=args.voxel_spacing,
            msa_subratio=args.msa_subratio,
        )
        phase1_uuid = run_refinement(config=phase1_config)


if __name__ == ""__main__"":
    run_phase1_all_datasets()
","Python"
"AlphaFold","alisiafadini/ROCKET","rocket/scripts/archived_scripts/run_plddtoptimize.py",".py","12294","405","import argparse
import glob
import os
import time
import uuid
import warnings

import numpy as np
import torch
from openfold.config import model_config
from SFC_Torch import PDBParser
from tqdm import tqdm

import rocket
from rocket import refinement_utils as rkrf_utils
from rocket import utils as rk_utils

PRESET = ""model_1""


def int_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return int(value)
    except ValueError as err:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an integer or 'None'.""
        ) from err


def float_or_none(value):
    if value.lower() == ""none"":
        return None
    try:
        return float(value)
    except ValueError as err:
        raise argparse.ArgumentTypeError(
            f""Invalid value: {value}. Must be an float or 'None'.""
        ) from err


def parse_arguments():
    """"""Parse commandline arguments""""""
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawTextHelpFormatter, description=__doc__
    )

    parser.add_argument(
        ""-p"",
        ""--path"",
        default=""./benchmark_data/"",
        help=(""Path to the parent folder""),
    )

    # Required arguments
    parser.add_argument(
        ""-sys"",
        ""--systems"",
        nargs=""+"",
        help=(""PDB codes or filename roots for the dataset""),
    )

    parser.add_argument(
        ""--note"",
        default="""",
        help=(""note""),
    )

    parser.add_argument(
        ""--domain_segs"",
        type=int,
        nargs=""*"",
        default=None,
        help=(""A list of resid as domain boundaries""),
    )

    parser.add_argument(
        ""--init_recycling"",
        default=20,
        type=int,
        help=(""number of initial recycling""),
    )

    parser.add_argument(
        ""--phase1_add_lr"",
        default=0.05,
        type=float,
        help=(""phase 1 additive learning rate""),
    )

    parser.add_argument(
        ""--phase1_mul_lr"",
        default=1.0,
        type=float,
        help=(""phase 1 multiplicative learning rate""),
    )

    parser.add_argument(
        ""--phase1_w_l2"",
        default=1e-11,
        type=float,
        help=(""phase 1 weights of L2 loss""),
    )

    parser.add_argument(
        ""--phase2_final_lr"",
        default=1e-3,
        type=float,
        help=(""phase 2 final learning rate""),
    )

    parser.add_argument(
        ""--smooth_stage_epochs"",
        default=50,
        type=int_or_none,
        help=(""number of smooth stages in phase1""),
    )

    return parser.parse_args()


def run_plddt_optim(
    working_path: str,
    file_root: str,
    note: str,
    lr_a: float,
    lr_m: float,
    iterations: int,
    init_recycling: int = 20,
    phase2_final_lr: float = 1e-3,
    weight_decay: float | None = 0.0001,
    smooth_stage_epochs: int | None = 50,
    l2_weight: float = 0.0,
    cuda_device: int = 0,
    starting_bias=None,
    starting_weights=None,
    uuid_hex=None,
    domain_segs=None,
):
    device = f""cuda:{cuda_device}""
    input_pdb = f""{working_path}/{file_root}/{file_root}-pred-aligned.pdb""

    run_uuid = uuid.uuid4().hex if uuid_hex is None else uuid_hex

    output_directory_path = f""{working_path}/{file_root}/outputs/{run_uuid}/{note}""
    try:
        os.makedirs(output_directory_path, exist_ok=True)
    except FileExistsError:
        print(
            f""Warning: Directory '{output_directory_path}' already exists. Overwriting...""  # noqa: E501
        )
    print(
        f""System: {file_root}, refinment run ID: {run_uuid!s}, Note: {note}"",
        flush=True,
    )
    warnings.filterwarnings(""ignore"")

    initial_model = PDBParser(input_pdb)
    reference_pos = rk_utils.assert_tensor(
        initial_model.atom_pos, arr_type=torch.float32, device=device
    ).clone()
    init_pos_bfactor = rk_utils.assert_tensor(
        initial_model.atom_b_iso, arr_type=torch.float32, device=device
    ).clone()
    bfactor_weights = rk_utils.weighting_torch(init_pos_bfactor, cutoff2=20.0)

    af_bias = rocket.MSABiasAFv3(model_config(PRESET, train=True), PRESET).to(device)
    af_bias.freeze()  # Free all AF2 parameters to save time

    device_processed_features, feature_key, features_at_it_start = (
        rkrf_utils.init_processed_dict(
            bias_version=3,
            path=working_path,
            file_root=file_root,
            device=device,
            template_pdb=None,
            target_seq=None,
            PRESET=PRESET,
        )
    )

    device_processed_features, optimizer, bias_names = rkrf_utils.init_bias(
        device_processed_features=device_processed_features,
        bias_version=3,
        device=device,
        lr_a=lr_a,
        lr_m=lr_m,
        weight_decay=weight_decay,
        starting_bias=starting_bias,
        starting_weights=starting_weights,
        recombination_bias=None,
    )

    L_plddt_it = []
    all_pldtts = []
    time_by_epoch = []
    memory_by_epoch = []
    best_plddt = 0.0
    progress_bar = tqdm(
        range(iterations),
        desc=f""{file_root}, uuid: {run_uuid[:4]}"",
    )

    # Run smooth stage in phase 1
    w_L2 = l2_weight
    early_stopper = rkrf_utils.EarlyStopper(patience=100, min_delta=1.0)

    if smooth_stage_epochs is not None:
        lr_a_initial = lr_a
        lr_m_initial = lr_m
        w_L2_initial = w_L2
        lr_stage1_final = phase2_final_lr

        # Decay rates for each stage
        decay_rate_stage1_add = (lr_stage1_final / lr_a) ** (1 / smooth_stage_epochs)
        decay_rate_stage1_mul = (lr_stage1_final / lr_m) ** (1 / smooth_stage_epochs)

    for iteration in progress_bar:
        start_time = time.time()
        optimizer.zero_grad()

        # Avoid passing through graph a second time
        device_processed_features[feature_key] = features_at_it_start.detach().clone()
        # working_batch = copy.deepcopy(device_processed_features)
        # for bias in bias_names:
        #     working_batch[bias] = device_processed_features[bias].clone()

        # AF pass
        if iteration == 0:
            af2_output, prevs = af_bias(
                device_processed_features,
                [None, None, None],
                num_iters=init_recycling,
                bias=False,
            )
            prevs = [tensor.detach() for tensor in prevs]

            # # MH @ June 19: Fix the iteration 0 for phase 2 running
            # print(""config.starting_bias"", config.starting_bias)
            # if (config.starting_bias is not None) or (
            #     config.starting_weights is not None
            # ):
            #     deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
            #     af2_output, __ = af_bias(
            #         device_processed_features,
            #         deep_copied_prevs,
            #         num_iters=1,
            #         bias=True,
            #     )
        deep_copied_prevs = [tensor.clone().detach() for tensor in prevs]
        af2_output, __ = af_bias(
            device_processed_features, deep_copied_prevs, num_iters=1, bias=True
        )

        # pLDDT loss
        L_plddt = -torch.mean(af2_output[""plddt""])

        # Position Kabsch Alignment
        aligned_xyz, plddts_res, pseudo_Bs = rkrf_utils.position_alignment(
            af2_output=af2_output,
            device_processed_features=device_processed_features,
            cra_name=initial_model.cra_name,
            best_pos=reference_pos,
            exclude_res=None,
            domain_segs=domain_segs,
            reference_bfactor=init_pos_bfactor,
        )
        all_pldtts.append(plddts_res)
        L_plddt_it.append(L_plddt.item())

        initial_model.set_positions(rk_utils.assert_numpy(aligned_xyz.detach().clone()))
        initial_model.set_biso(rk_utils.assert_numpy(pseudo_Bs.detach().clone()))
        initial_model.savePDB(f""{output_directory_path!s}/{iteration}_preRBR.pdb"")

        if L_plddt_it[-1] < best_plddt:
            best_plddt = L_plddt_it[-1]
            best_msa_bias = (
                device_processed_features[""msa_feat_bias""].detach().cpu().clone()
            )
            best_feat_weights = (
                device_processed_features[""msa_feat_weights""].detach().cpu().clone()
            )
            best_iter = iteration
            # best_pos = aligned_xyz.detach().clone()

        if w_L2 > 0.0:
            # use
            L2_loss = torch.sum(
                bfactor_weights.unsqueeze(-1) * (aligned_xyz - reference_pos) ** 2
            )  # / conf_best.shape[0]
            loss = w_L2 * L2_loss + L_plddt
            loss.backward()
        else:
            loss = L_plddt
            loss.backward()
            if early_stopper.early_stop(loss.item()):
                break

        # Do smooth in last several iterations of phase 1 instead of beginning of phase 2
        if (""phase1"" in note) and (smooth_stage_epochs is not None):
            if iteration > (iterations - smooth_stage_epochs):
                lr_a = lr_a_initial * (decay_rate_stage1_add**iteration)
                lr_m = lr_m_initial * (decay_rate_stage1_mul**iteration)
                w_L2 = w_L2_initial * (1 - (iteration / smooth_stage_epochs))
            # Update the learning rates in the optimizer
            optimizer.param_groups[0][""lr""] = lr_a
            optimizer.param_groups[1][""lr""] = lr_m
            optimizer.step()
        else:
            optimizer.step()

        time_by_epoch.append(time.time() - start_time)
        memory_by_epoch.append(torch.cuda.max_memory_allocated() / 1024**3)
        progress_bar.set_postfix(
            plddt=f""{L_plddt.item():.2f}"",
            memory=f""{torch.cuda.max_memory_allocated() / 1024**3:.1f}G"",
        )

    np.save(
        f""{output_directory_path!s}/mean_it_plddt.npy"",
        np.array(L_plddt_it),
    )

    np.save(
        f""{output_directory_path!s}/plddt_res.npy"",
        np.array(all_pldtts),
    )

    np.save(
        f""{output_directory_path!s}/time_it.npy"",
        rk_utils.assert_numpy(time_by_epoch),
    )

    np.save(
        f""{output_directory_path!s}/memory_it.npy"",
        rk_utils.assert_numpy(memory_by_epoch),
    )

    # Save the best msa_bias and feat_weights
    torch.save(
        best_msa_bias,
        f""{output_directory_path!s}/best_msa_bias_{best_iter}.pt"",
    )

    torch.save(
        best_feat_weights,
        f""{output_directory_path!s}/best_feat_weights_{best_iter}.pt"",
    )

    return run_uuid


def main():
    args = parse_arguments()
    datasets = args.systems

    for file_root in datasets:
        phase1_uuid = run_plddt_optim(
            working_path=args.path,
            file_root=file_root,
            note=""phase1"" + args.note,
            iterations=100,
            lr_a=args.phase1_add_lr,
            lr_m=args.phase1_mul_lr,
            init_recycling=args.init_recycling,
            weight_decay=0.0001,
            smooth_stage_epochs=args.smooth_stage_epochs,
            l2_weight=args.phase1_w_l2,
            cuda_device=0,
            phase2_final_lr=args.phase2_final_lr,
            starting_bias=None,
            starting_weights=None,
            uuid_hex=None,
            domain_segs=args.domain_segs,
        )

        output_directory_path = f""{args.path}/{file_root}/outputs/{phase1_uuid}""
        phase1_path = glob.glob(f""{output_directory_path}/phase1*/"")[0]
        starting_bias_path = glob.glob(os.path.join(phase1_path, ""best_msa_bias*.pt""))[
            0
        ]
        starting_weights_path = glob.glob(
            os.path.join(phase1_path, ""best_feat_weights*.pt"")
        )[0]

        phase2_uuid = run_plddt_optim(
            working_path=args.path,
            file_root=file_root,
            note=""phase2"" + args.note,
            iterations=500,
            lr_a=args.phase2_final_lr,
            lr_m=args.phase2_final_lr,
            init_recycling=args.init_recycling,
            weight_decay=None,
            smooth_stage_epochs=None,
            l2_weight=0.0,
            cuda_device=0,
            phase2_final_lr=args.phase2_final_lr,
            starting_bias=starting_bias_path,
            starting_weights=starting_weights_path,
            uuid_hex=phase1_uuid,
            domain_segs=args.domain_segs,
        )
","Python"
"AlphaFold","inductive-bio/strong-docking-baseline","evaluate_results.py",".py","3756","100","# Uses the PoseBusters library to evaluate the results of the baselines.
# It saves the results in the `results` directory.
import argparse
import multiprocessing
import os
from pathlib import Path

import pandas as pd
from posebusters import PoseBusters
from rdkit import RDLogger
from rdkit.Chem import PandasTools


def bust_complex(base_path, complex_id, suffix, use_vina_order=False):
    """"""
    Bust predictions for the top pose of a complex.

    The use_vina_order argument can be used to re-order the poses by the
    minimizedAffinity property before busting the top pose. This is useful since
    we've saved the poses in the gnina-ranked order.
    """"""
    RDLogger.DisableLog(""rdApp.*"")
    if use_vina_order:
        # save a copy of the poses with the vina order
        poses = PandasTools.LoadSDF(
            Path(base_path, complex_id, f""{complex_id}{suffix}.sdf"")
        )
        poses[""minimizedAffinity""] = poses[""minimizedAffinity""].astype(float)
        vina_order = poses.sort_values(""minimizedAffinity"").reset_index(drop=True)
        pred_file = Path(base_path, complex_id, f""{complex_id}{suffix}_vina_order.sdf"")
        PandasTools.WriteSDF(
            vina_order,
            pred_file,
            properties=list(poses.columns),
        )
    else:
        pred_file = Path(base_path, complex_id, f""{complex_id}{suffix}.sdf"")
    true_file = Path(base_path, complex_id, f""{complex_id}_ligand.sdf"")
    receptor_file = Path(base_path, complex_id, f""{complex_id}_protein.pdb"")
    buster = PoseBusters(top_n=1)
    df = buster.bust([pred_file], true_file, receptor_file)
    df = df.reset_index(drop=True).assign(complex_id=complex_id)
    return df


if __name__ == ""__main__"":
    RDLogger.DisableLog(""rdApp.*"")
    parser = argparse.ArgumentParser()
    parser.add_argument(
        ""--base_path"",
        type=str,
        help=""dir containing posebusters_paper_data and posebusters_308_ids.csv"",
    )
    args = parser.parse_args()
    base_path = args.base_path
    if base_path is None:
        base_path = os.path.dirname(os.path.abspath(__file__))
    posebusters_308 = pd.read_csv(os.path.join(base_path, ""posebusters_308_ids.csv""))
    posebusters_308[""complex_id""] = (
        posebusters_308[""pdb_id""] + ""_"" + posebusters_308[""ccd_id""]
    )
    num_processes = multiprocessing.cpu_count()
    os.makedirs(os.path.join(base_path, ""results""), exist_ok=True)
    for name, args in {
        ""vina_single_conformer"": (""_single_conf_poses"", True),
        ""gnina_single_conformer"": (""_single_conf_poses"", False),
        ""vina_multiple_conformers"": (""_multiple_confs_poses"", True),
        ""gnina_multiple_conformers"": (""_multiple_confs_poses"", False),
    }.items():
        # PoseBusters is a bit slow, so parallelize
        with multiprocessing.Pool(num_processes) as p:
            results = p.starmap(
                bust_complex,
                [
                    (
                        Path(
                            base_path,
                            ""posebusters_paper_data/posebusters_benchmark_set"",
                        ),
                        complex_id,
                        args[0],
                        args[1],
                    )
                    for complex_id in posebusters_308[""complex_id""]
                ],
            )
        full_df = pd.concat(results, ignore_index=True)
        full_df.to_csv(
            os.path.join(base_path, f""results/posebusters_results_{name}.csv""),
            index=False,
        )
        full_df = full_df.set_index(""complex_id"")
        print(""Method:"", name)
        print(
            ""PB-valid:"",
            full_df.all(axis=1).mean(),
            ""RMSD<2:"",
            full_df[""rmsd_≤_2å""].mean(),
        )
","Python"
"AlphaFold","inductive-bio/strong-docking-baseline","run_single_conformer_baseline.py",".py","3598","108","# runs smina/vina docking and gnina rescoring, using only a single
# starting conformation.
import argparse
import os
import shutil
import subprocess

import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, rdMolTransforms


def protonate_receptor_and_ligand(folder, complex_id):
    # This function is the same as in run_strong_baseline.py
    if complex_id[:4] == ""8CSD"":
        # 8CSD is already protonated and crashes reduce, so just copy it over
        shutil.copy(
            os.path.join(folder, f""{complex_id}_protein.pdb""),
            os.path.join(folder, f""{complex_id}_H.pdb""),
        )
    else:
        protein_in = os.path.join(folder, f""{complex_id}_protein.pdb"")
        protein_out = os.path.join(folder, f""{complex_id}_H.pdb"")
        with open(protein_out, ""w"") as f:
            subprocess.run(
                [""reduce"", ""-BUILD"", protein_in],
                stdout=f,
                stderr=subprocess.DEVNULL,
                check=False,
            )
    ligand_in = os.path.join(folder, f""{complex_id}_ligand_start_conf.sdf"")
    ligand_out = os.path.join(folder, f""{complex_id}_ligand_start_conf_H.sdf"")
    subprocess.run([""obabel"", ligand_in, ""-O"", ligand_out, ""-p"", ""7.4""], check=False)


def generate_conformer(folder, complex_id):
    mol = Chem.MolFromMolFile(
        os.path.join(folder, f""{complex_id}_ligand_start_conf_H.sdf"")
    )
    mol.RemoveAllConformers()
    mol = Chem.AddHs(mol)
    AllChem.EmbedMolecule(mol, randomSeed=1)
    AllChem.UFFOptimizeMolecule(mol)
    Chem.MolToMolFile(mol, os.path.join(folder, f""{complex_id}_ligand_single_conf.sdf""))


def run_docking(folder, complex_id):
    crystal_ligand = Chem.MolFromMolFile(
        os.path.join(folder, f""{complex_id}_ligand.sdf"")
    )
    center = rdMolTransforms.ComputeCentroid(crystal_ligand.GetConformer())
    subprocess.run(
        [
            ""gnina"",
            ""-r"",
            os.path.join(folder, f""{complex_id}_H.pdb""),
            ""-l"",
            os.path.join(folder, f""{complex_id}_ligand_single_conf.sdf""),
            ""-o"",
            os.path.join(folder, f""{complex_id}_single_conf_poses.sdf""),
            ""--center_x"",  # bounding box matching PoseBusters methodology
            str(center.x),
            ""--center_y"",
            str(center.y),
            ""--center_z"",
            str(center.z),
            ""--size_x"",
            ""25"",
            ""--size_y"",
            ""25"",
            ""--size_z"",
            ""25"",
            ""--scoring"",
            ""vina"",
            ""--exhaustiveness"",
            ""32"",
            ""--num_modes"",
            ""40"",
            ""--seed"",
            ""1"",
        ],
        check=False,
    )


if __name__ == ""__main__"":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        ""--base_path"",
        type=str,
        help=""dir containing posebusters_paper_data and posebusters_308_ids.csv"",
    )
    args = parser.parse_args()
    base_path = args.base_path
    if base_path is None:
        base_path = os.path.dirname(os.path.abspath(__file__))
    posebusters_308 = pd.read_csv(os.path.join(base_path, ""posebusters_308_ids.csv""))
    posebusters_308[""complex_id""] = (
        posebusters_308[""pdb_id""] + ""_"" + posebusters_308[""ccd_id""]
    )
    for complex_id in posebusters_308[""complex_id""]:
        folder = os.path.join(
            base_path, ""posebusters_paper_data/posebusters_benchmark_set"", complex_id
        )
        protonate_receptor_and_ligand(folder, complex_id)
        generate_conformer(folder, complex_id)
        run_docking(folder, complex_id)
","Python"
"AlphaFold","inductive-bio/strong-docking-baseline","run_strong_baseline.py",".py","4256","121","# Runs the full strong baseline, including smina/vina docking,
# gnina rescoring, and an input conformational ensemble.
import argparse
import os
import shutil
import subprocess

import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, PandasTools, rdMolTransforms


def protonate_receptor_and_ligand(folder, complex_id):
    # This function is the same as in run_single_conformer_baseline.py
    if complex_id[:4] == ""8CSD"":
        # 8CSD is already protonated and crashes reduce, so just copy it over
        shutil.copy(
            os.path.join(folder, f""{complex_id}_protein.pdb""),
            os.path.join(folder, f""{complex_id}_H.pdb""),
        )
    else:
        protein_in = os.path.join(folder, f""{complex_id}_protein.pdb"")
        protein_out = os.path.join(folder, f""{complex_id}_H.pdb"")
        with open(protein_out, ""w"") as f:
            subprocess.run(
                [""reduce"", ""-BUILD"", protein_in],
                stdout=f,
                stderr=subprocess.DEVNULL,
            )
    ligand_in = os.path.join(folder, f""{complex_id}_ligand_start_conf.sdf"")
    ligand_out = os.path.join(folder, f""{complex_id}_ligand_start_conf_H.sdf"")
    subprocess.run([""obabel"", ligand_in, ""-O"", ligand_out, ""-p"", ""7.4""])


def generate_conformers(folder, complex_id, num_confs=8):
    mol = Chem.MolFromMolFile(
        os.path.join(folder, f""{complex_id}_ligand_start_conf_H.sdf"")
    )
    mol.RemoveAllConformers()
    mol = Chem.AddHs(mol)
    AllChem.EmbedMultipleConfs(mol, numConfs=num_confs, randomSeed=1)
    AllChem.UFFOptimizeMoleculeConfs(mol)
    with Chem.SDWriter(
        os.path.join(folder, f""{complex_id}_ligand_multiple_confs.sdf"")
    ) as writer:
        for cid in range(mol.GetNumConformers()):
            writer.write(mol, confId=cid)


def run_docking(folder, complex_id):
    crystal_ligand = Chem.MolFromMolFile(
        os.path.join(folder, f""{complex_id}_ligand.sdf"")
    )
    center = rdMolTransforms.ComputeCentroid(crystal_ligand.GetConformer())
    subprocess.run(
        [
            ""gnina"",
            ""-r"",
            os.path.join(folder, f""{complex_id}_H.pdb""),
            ""-l"",
            os.path.join(folder, f""{complex_id}_ligand_multiple_confs.sdf""),
            ""-o"",
            os.path.join(folder, f""{complex_id}_multiple_confs_poses.sdf""),
            ""--center_x"",  # bounding box matching PoseBusters methodology
            str(center.x),
            ""--center_y"",
            str(center.y),
            ""--center_z"",
            str(center.z),
            ""--size_x"",
            ""25"",
            ""--size_y"",
            ""25"",
            ""--size_z"",
            ""25"",
            ""--scoring"",
            ""vina"",
            ""--exhaustiveness"",
            ""4"",
            ""--num_modes"",
            ""10"",
            ""--seed"",
            ""1"",
        ]
    )
    # sort the poses from the multiple conformation runs, so overall best is first
    poses = PandasTools.LoadSDF(
        os.path.join(folder, f""{complex_id}_multiple_confs_poses.sdf"")
    )
    poses[""CNNscore""] = poses[""CNNscore""].astype(float)
    gnina_order = poses.sort_values(""CNNscore"", ascending=False).reset_index(drop=True)
    PandasTools.WriteSDF(
        gnina_order,
        os.path.join(folder, f""{complex_id}_multiple_confs_poses.sdf""),
        properties=list(poses.columns),
    )


if __name__ == ""__main__"":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        ""--base_path"",
        type=str,
        help=""dir containing posebusters_benchmark_set and posebusters_308_ids.csv"",
    )
    args = parser.parse_args()
    base_path = args.base_path
    if base_path is None:
        base_path = os.path.dirname(os.path.abspath(__file__))
    posebusters_308 = pd.read_csv(os.path.join(base_path, ""posebusters_308_ids.csv""))
    posebusters_308[""complex_id""] = (
        posebusters_308[""pdb_id""] + ""_"" + posebusters_308[""ccd_id""]
    )
    for complex_id in posebusters_308[""complex_id""]:
        folder = os.path.join(
            base_path, ""posebusters_paper_data/posebusters_benchmark_set"", complex_id
        )
        protonate_receptor_and_ligand(folder, complex_id)
        generate_conformers(folder, complex_id)
        run_docking(folder, complex_id)
","Python"
"AlphaFold","CYP152N1/plddt2csv","align_ptm.py",".py","12131","334","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2.1_(2021Dec.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program align output pdb file  and extract pae plddt values from [result_model_ptm.pkl] to [.csv] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""You need to change model_names to model_?_ptm from model_? at /Docker/run_docker.py."")
print(""for Alphafold2.1.1"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(            description=""The program extract pae from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model',   help='input XXXXX if [XXXXX_Y_ptm.pkl]; ex) result_model (def.)')
parser.add_argument('-m', default='relaxed_model',  help='input ZZZZZ if [ZZZZZ_Y_ptm.pdb]; ex) result_model (def.)')
parser.add_argument('-n', default='5',              help='input Y if you want open [XXXXX_1_ptm.pkl,XXXXX_2_ptm.pkl ... ,XXXXX_5_ptm.pkl]; ex) 5 (def.)')
parser.add_argument('-d', default='',               help='ignored residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
parser.add_argument('-al1', default=0,              help='Center the mid point of al1 and al3, the point (al1) shift on x-axis')
parser.add_argument('-al2', default=0,              help='The point (al2) shift on xy-plane')
parser.add_argument('-al3', default=0,              help='Center the mid point of al1 and al3, the point (al3) shift on x-asis and opozit site of al1')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_ptm.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

mname=str(args.m)
print(""Input file name:""+str(mname)+""_?_ptm.pdb"")
print(""It can be changed by [-m ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Ignore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Ignore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        print(str(rein))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    # Model alignment
    import math
    o4=int(len(eres)/4)
    w4=int(o4*2)
    t4=int(o4*3)
    
    if int(args.al1)>0:
        if int(args.al1) < len(eres):
            o4=int(args.al1)
    if int(args.al1)>0:
        if int(args.al2) < len(eres):
            w4=int(args.al2)
    if int(args.al1)>0:
        if int(args.al3) < len(eres):
            t4=int(args.al3)
    
    xyz1=np.zeros((4,3),dtype=float)
    for i in range(namen):
        www=str(int(float(i)+1))
        with open(cpass+""/""+mname+""_""+www+""_ptm.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    if o4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[0,0] = float(line[30:38].strip())
                            xyz1[0,1] = float(line[38:46].strip())
                            xyz1[0,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif w4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[1,0] = float(line[30:38].strip())
                            xyz1[1,1] = float(line[38:46].strip())
                            xyz1[1,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif t4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[2,0] = float(line[30:38].strip())
                            xyz1[2,1] = float(line[38:46].strip())
                            xyz1[2,2] = float(line[46:54].strip())
                            pass
                        pass
                    else:
                        pass
                    pass
                else:
                    pass
                pass
            pass
        print(""------------"")
        xyz1[3,0] = round((xyz1[0,0]+xyz1[2,0])/2,3)
        xyz1[3,1] = round((xyz1[0,1]+xyz1[2,1])/2,3)
        xyz1[3,2] = round((xyz1[0,2]+xyz1[2,2])/2,3)
        xyzm=np.zeros((1,3),dtype=float)
        xyzm[0,0] = xyz1[3,0]
        xyzm[0,1] = xyz1[3,1]
        xyzm[0,2] = xyz1[3,2]
        print(""Initial xyz"")
        print(xyz1)
        xyz2=xyz1-xyzm
        print(""Center xyz"")
        #print(xyz2)
        #print(""------------"")
        # rotation z fit oy and ty to zero
        if xyz2[0,1]>0:
            pmy=-1
            pass
        else:
            pmy=1
            pass
        distaxy=math.sqrt(math.pow(xyz2[0,0], 2)+math.pow(xyz2[0,1], 2))
        radax=pmy*math.acos(xyz2[0,0]/distaxy)
        rot1=np.zeros((3,3),dtype=float)
        rot1[0,0]=math.cos(radax)
        rot1[1,0]=-math.sin(radax)
        rot1[0,1]=math.sin(radax)
        rot1[1,1]=math.cos(radax)
        rot1[2,2]=1
        xyz3=np.dot(xyz2,rot1)
        print(""rotation z fit 1y and 3y to zero"")
        #print(xyz3)
        #print(""------------"")
        # rotation y fit ox to zero
        if xyz3[0,2]>0:
            pmz=-1
            pass
        else:
            pmz=1
            pass
        dista1=math.sqrt(math.pow(xyz3[0,0], 2)+math.pow(xyz3[0,1], 2)+math.pow(xyz3[0,2], 2))
        radaxy=pmz*math.acos(xyz3[0,0]/dista1)
        rot2=np.zeros((3,3),dtype=float)
        rot2[0,0]=math.cos(radaxy)
        rot2[2,0]=-math.sin(radaxy)
        rot2[0,2]=math.sin(radaxy)
        rot2[2,2]=math.cos(radaxy)
        rot2[1,1]=1
        xyz4=np.dot(xyz3,rot2)
        print(""rotation y fit 1z and 3z to zero"")
        #print(xyz4)
        #print(""------------"")
        # rotation x fit wz to zero
        if xyz4[1,2]>0:
            pmyz=-1
            pass
        else:
            pmyz=1
            pass
        distbbyz=math.sqrt(math.pow(xyz4[1,1], 2)+math.pow(xyz4[1,2], 2))
        radbbyz=pmyz*math.acos(xyz4[1,1]/distbbyz)
        rot3=np.zeros((3,3),dtype=float)
        rot3[1,1]=math.cos(radbbyz)
        rot3[2,1]=-math.sin(radbbyz)
        rot3[1,2]=math.sin(radbbyz)
        rot3[2,2]=math.cos(radbbyz)
        rot3[0,0]=1
        xyz5=np.dot(xyz4,rot3)
        print(""rotation x fit 2z to zero"")
        #print(xyz5)
        #print(""------------"")
        # Check all shift & roation 
        xyz6=np.dot(np.dot(np.dot(xyz1-xyzm,rot1),rot2),rot3)
        print(""Check all shift & roation"")
        print(xyz6)
        print(""------------"")
        xyz=np.zeros((1,3),dtype=float)
        liii=""""
        with open(cpass+""/""+mname+""_""+www+""_ptm.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    xyz[::1]=0
                    xyz[0,0] = float(line[30:38].strip())
                    xyz[0,1] = float(line[38:46].strip())
                    xyz[0,2] = float(line[46:54].strip())
                    xyzn=np.dot(np.dot(np.dot(xyz-xyzm,rot1),rot2),rot3)
                    qxxl="" ""*(8-len(str(round(xyzn[0,0],4))))+str(round(xyzn[0,0],4))
                    qyyl="" ""*(8-len(str(round(xyzn[0,1],4))))+str(round(xyzn[0,1],4))
                    qzzl="" ""*(8-len(str(round(xyzn[0,2],4))))+str(round(xyzn[0,2],4))
                    liii+=line[0:30]+qxxl+qyyl+qzzl+line[54:]
                    pass
                else:
                    liii+=line
                pass
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_m""+www+""_ptm.pdb"", mode='w') as i:
            i.write(liii)
            i.close()
            pass
        pass


    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+""_ptm.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+str(lpass[-1])+'_pae_m'+www+'_ptm.csv',pae,delimiter=',',fmt=""%s"")
            print(""Save:""+cpass+'/'+str(lpass[-1])+'_pae_m'+www+'_ptm.csv')
            # Extract PAE
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        
        # Write pLDDT in the PDB_Bfactor
        print(""Save:""+cpass+""/align_""+str(lpass[-1])+""_m""+www+""_ptm.pdb"")
        pass
    print(""------------"")
    
    

    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+str(lpass[-1])+'_plddt.csv',twoda,delimiter=',',fmt=""%s"")
    print(""Save:""+cpass+'/'+str(lpass[-1])+'_plddt.csv')
    pass



#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","pae2png.py",".py","4006","119","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extract pae values from [result_model_?_ptm.pkl] to [.png] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""You need to change model_names to model_?_ptm from model_? at Docker/run_docker.py"")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='p2c', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-c', default='Greens_r', help='color of heat map (PAE; ex) Greans_r (def.)')
parser.add_argument('-s', default='None', help=""plt.show was skiped when -s 'Skip'"")
parser.add_argument('-m', default=30, help=""vmax of sns.heatmap ex) -m 30 (def.)"")
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_ptm.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")


# Color
cname=str(args.c)
print(""Output file color:""+str(cname))
print(""It can be changed by [-c ""+cname+""]"")
print("""")


#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        eres=np.array(list(str(cnn['sequence'][0])[2:-1]))
        cn.close()
        pass
    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?_ptm.pkl
        with open(cpass+""/""+iname+""_""+www+""_ptm.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+oname+'_pae_'+www+'.csv',pae,delimiter=',',fmt=""%s"")
            print(""Display:""+cpass+'/'+oname+'_pae_'+www+'.png')
            plt.figure(figsize=(15, 12))
            ax = sns.heatmap(pae, linewidth=0, cmap=cname, square=True, xticklabels=50, yticklabels=50, vmin=0, vmax=str(args.m))
            plt.savefig(cpass+'/'+oname+'_pae_'+www+'.png')
            if str(args.s)=='None':
                plt.show()
                pass
            # Extract PAE
            pass
        # Read pLDDT from result_model_?_ptm.pkl
        pass
    pass


#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","paem2png.py",".py","3774","111","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2.1_(2021Dec.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extract pae values from [result_model_?_ptm.pkl] to [.png] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""You need to change model_names to model_?_ptm from model_? at Docker/run_docker.py"")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y_multimer.pkl]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='p2c', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-c', default='Greens_r', help='color of heat map (PAE; ex) Greans_r (def.)')
parser.add_argument('-s', default='None', help=""plt.show was skiped when -s 'Skip'"")
parser.add_argument('-m', default=30, help=""vmax of sns.heatmap ex) -m 30 (def.)"")
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_multimer.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")


# Color
cname=str(args.c)
print(""Output file color:""+str(cname))
print(""It can be changed by [-c ""+cname+""]"")
print("""")


#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?_ptm.pkl
        with open(cpass+""/""+iname+""_""+www+""_multimer.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+oname+'_pae_'+www+'.csv',pae,delimiter=',',fmt=""%s"")
            print(""Display:""+cpass+'/'+oname+'_pae_'+www+'.png')
            plt.figure(figsize=(15, 12))
            ax = sns.heatmap(pae, linewidth=0, cmap=cname, square=True, xticklabels=50, yticklabels=50, vmin=0, vmax=str(args.m))
            plt.savefig(cpass+'/'+oname+'_pae_'+www+'.png')
            if str(args.s)=='None':
                plt.show()
                pass
            # Extract PAE
            pass
        # Read pLDDT from result_model_?_ptm.pkl
        pass
    pass


#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","ptm_docker.py",".py","2791","96","# Import
import os
import sys
import sys
import time
import argparse
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.1_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program make run_docker_ptm.py to extract pae value"")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program make run_docker_ptm for caliculate pae value in alphafold2"")
parser.add_argument('-i', default='run_docker', help='input .py file name ; ex) run_docker (def.)')
parser.add_argument('-o', default='run_docker_ptm', help='output .py file name ; ex) run_docker_ptm (def.)')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""py"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+""py"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    cowco=6
    liii=""""
    with open(cpass+""/docker/run_docker.py"", mode='r') as g:
        print(""Read:""+cpass+""/docker/run_docker.py"")
        for line in g:
            if cowco < 5.5:
                print(cowco)
                liii+=line[:12]+""_ptm""+line[12:]
                cowco+=1
                print(line)
                print(line[:12]+""_ptm""+line[12:])
                pass
            else:
                liii+=line
                if line[0:13]==""model_names ="":
                    cowco+=-5
                    print(""model_name was found"")
                pass
            pass
        g.close()
        pass
    with open(cpass+""/docker/run_docker_ptm.py"", mode='w') as i:
        i.write(liii)
        i.close()
        pass
    # Write pLDDT in the PDB_Bfactor
    print(""Save:""+cpass+""/docker/run_docker_ptm.py"")
    print(""------------"")
    
 
#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","plddt_align_ptm.py",".py","12914","356","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extracts alignd model with plddt calue in b-factor using [result_model_?_ptm.pkl] and [.pdb] "")
print(""The program extracts pae plddt values from [result_model_ptm.pkl] to [.csv] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""You need to change model_names to model_?_ptm from model_? at /Docker/run_docker.py."")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-m', default='relaxed_model', help='input ZZZZZ if [ZZZZZ_Y.pdb]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='plddt', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-d', default='', help='ignored residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_ptm.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

mname=str(args.m)
print(""Input file name:""+str(mname)+""_?_ptm.pdb"")
print(""It can be changed by [-m ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")

# Ignore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Ignore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    # Model alignment
    import math
    o4=int(len(eres)/4)
    w4=int(o4*2)
    t4=int(o4*3)
    xyz1=np.zeros((4,3),dtype=float)
    for i in range(namen):
        www=str(int(float(i)+1))
        with open(cpass+""/""+mname+""_""+www+""_ptm.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    if o4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[0,0] = float(line[30:38].strip())
                            xyz1[0,1] = float(line[38:46].strip())
                            xyz1[0,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif w4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[1,0] = float(line[30:38].strip())
                            xyz1[1,1] = float(line[38:46].strip())
                            xyz1[1,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif t4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[2,0] = float(line[30:38].strip())
                            xyz1[2,1] = float(line[38:46].strip())
                            xyz1[2,2] = float(line[46:54].strip())
                            pass
                        pass
                    else:
                        pass
                    pass
                else:
                    pass
                pass
            pass
        print(""------------"")
        xyz1[3,0] = round((xyz1[0,0]+xyz1[2,0])/2,3)
        xyz1[3,1] = round((xyz1[0,1]+xyz1[2,1])/2,3)
        xyz1[3,2] = round((xyz1[0,2]+xyz1[2,2])/2,3)
        xyzm=np.zeros((1,3),dtype=float)
        xyzm[0,0] = xyz1[3,0]
        xyzm[0,1] = xyz1[3,1]
        xyzm[0,2] = xyz1[3,2]
        print(""Initial xyz"")
        print(xyz1)
        xyz2=xyz1-xyzm
        print(""Center xyz"")
        #print(xyz2)
        #print(""------------"")
        # rotation z fit oy and ty to zero
        if xyz2[0,1]>0:
            pmy=-1
            pass
        else:
            pmy=1
            pass
        distaxy=math.sqrt(math.pow(xyz2[0,0], 2)+math.pow(xyz2[0,1], 2))
        radax=pmy*math.acos(xyz2[0,0]/distaxy)
        rot1=np.zeros((3,3),dtype=float)
        rot1[0,0]=math.cos(radax)
        rot1[1,0]=-math.sin(radax)
        rot1[0,1]=math.sin(radax)
        rot1[1,1]=math.cos(radax)
        rot1[2,2]=1
        xyz3=np.dot(xyz2,rot1)
        print(""rotation z fit 1y and 3y to zero"")
        #print(xyz3)
        #print(""------------"")
        # rotation y fit ox to zero
        if xyz3[0,2]>0:
            pmz=-1
            pass
        else:
            pmz=1
            pass
        dista1=math.sqrt(math.pow(xyz3[0,0], 2)+math.pow(xyz3[0,1], 2)+math.pow(xyz3[0,2], 2))
        radaxy=pmz*math.acos(xyz3[0,0]/dista1)
        rot2=np.zeros((3,3),dtype=float)
        rot2[0,0]=math.cos(radaxy)
        rot2[2,0]=-math.sin(radaxy)
        rot2[0,2]=math.sin(radaxy)
        rot2[2,2]=math.cos(radaxy)
        rot2[1,1]=1
        xyz4=np.dot(xyz3,rot2)
        print(""rotation y fit 1z and 3z to zero"")
        #print(xyz4)
        #print(""------------"")
        # rotation x fit wz to zero
        if xyz4[1,2]>0:
            pmyz=-1
            pass
        else:
            pmyz=1
            pass
        distbbyz=math.sqrt(math.pow(xyz4[1,1], 2)+math.pow(xyz4[1,2], 2))
        radbbyz=pmyz*math.acos(xyz4[1,1]/distbbyz)
        rot3=np.zeros((3,3),dtype=float)
        rot3[1,1]=math.cos(radbbyz)
        rot3[2,1]=-math.sin(radbbyz)
        rot3[1,2]=math.sin(radbbyz)
        rot3[2,2]=math.cos(radbbyz)
        rot3[0,0]=1
        xyz5=np.dot(xyz4,rot3)
        print(""rotation x fit 2z to zero"")
        #print(xyz5)
        #print(""------------"")
        # Check all shift & roation 
        xyz6=np.dot(np.dot(np.dot(xyz1-xyzm,rot1),rot2),rot3)
        print(""Check all shift & roation"")
        print(xyz6)
        print(""------------"")
        xyz=np.zeros((1,3),dtype=float)
        liii=""""
        with open(cpass+""/""+mname+""_""+www+""_ptm.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    xyz[::1]=0
                    xyz[0,0] = float(line[30:38].strip())
                    xyz[0,1] = float(line[38:46].strip())
                    xyz[0,2] = float(line[46:54].strip())
                    xyzn=np.dot(np.dot(np.dot(xyz-xyzm,rot1),rot2),rot3)
                    qxxl="" ""*(8-len(str(round(xyzn[0,0],4))))+str(round(xyzn[0,0],4))
                    qyyl="" ""*(8-len(str(round(xyzn[0,1],4))))+str(round(xyzn[0,1],4))
                    qzzl="" ""*(8-len(str(round(xyzn[0,2],4))))+str(round(xyzn[0,2],4))
                    liii+=line[0:30]+qxxl+qyyl+qzzl+line[54:]
                    pass
                else:
                    liii+=line
                pass
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+""_ptm.pdb"", mode='w') as i:
            i.write(liii)
            i.close()
            pass
        pass


    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+""_ptm.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+oname+'_pae_'+www+'.csv',pae,delimiter=',',fmt=""%s"")
            print(""Save:""+cpass+'/'+oname+'_pae_'+www+'.csv')
            # Extract PAE
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        
        ll1=""""
        ss1=""""
        # Write pLDDT in the PDB_Bfactor
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+""_ptm.pdb"", mode='r') as g1:
            for line in g1:
                if  line[0:4]==""ATOM"":
                    pdd1=pd1[int(float(line[22:26].strip())-1)]
                    ll1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    if int(eres[int(float(line[22:26].strip())-1)])==1:
                        ss1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    else:
                        pass
                    pass
                else:
                    ll1+=line
                    ss1+=line
                    pass
                pass
            g1.close()
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+""_ptm.pdb"", mode='w') as i:
            i.write(ll1)
            i.close()
            pass
        with open(cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+""_ptm.pdb"", mode='w') as i:
            i.write(ss1)
            i.close()
            pass
        # Write pLDDT in the PDB_Bfactor
        print(""Save:""+cpass+""/align_""+str(lpass[-1])+""_""+www+""_ptm.pdb"")
        print(""Save:""+cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+""_ptm.pdb"")
        pass
    print(""------------"")
    
    

    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+oname+'_plddt.csv',twoda,delimiter=',',fmt=""%s"")
    print(""Save:""+cpass+'/'+oname+'_plddt.csv')
    pass



#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","align_multi.py",".py","13481","358","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2.1_(2021Dec.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program align output pdb file and extract pae plddt values from [result_model_multimer.pkl] to [.csv] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""for Alphafold2.1.1"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(            description=""The program extract pae from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model',   help='input XXXXX if [XXXXX_Y_ptm.pkl]; ex) result_model (def.)')
parser.add_argument('-m', default='relaxed_model',  help='input ZZZZZ if [ZZZZZ_Y_ptm.pdb]; ex) result_model (def.)')
parser.add_argument('-n', default='5',              help='input Y if you want open [XXXXX_1_ptm.pkl,XXXXX_2_ptm.pkl ... ,XXXXX_5_ptm.pkl]; ex) 5 (def.)')
parser.add_argument('-d', default='',               help='!!this argument is not suport for chain number!!. ignored residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
parser.add_argument('-alc', default=""1"",            help='Change chain number from first chain to other chain defalt: 1')
parser.add_argument('-al1', default=0,              help='Center the mid point of al1 and al3, the point (al1) shift on x-axis')
parser.add_argument('-al2', default=0,              help='The point (al2) shift on xy-plane')
parser.add_argument('-al3', default=0,              help='Center the mid point of al1 and al3, the point (al3) shift on x-asis and opozit site of al1')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_multimer.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

mname=str(args.m)
print(""Input file name:""+str(mname)+""_?_multimer.pdb"")
print(""It can be changed by [-m ""+mname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
#oname=str(args.o)
#print(""Output file name:""+oname+"".csv"")
#print("""")

# Ignore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Ignore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))


# Number of models
chn=int(str(args.alc))


#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        print(str(cnn['asym_id']))
        rein=np.array(list(cnn['asym_id']))
        twoda=rein
        renn=np.array(list(cnn['residue_index']))
        twoda=np.column_stack((twoda,renn+1))
        aatp=np.array(list(cnn['aatype']))
        aadic={0: ""A"", 1: ""R"", 2: ""N"", 3: ""D"", 4: ""C"", 5: ""Q"", 6: ""E"", 7: ""G"", 8: ""H"", 9: ""I"", 10: ""L"", 11: ""K"", 12: ""M"", 13: ""F"", 14: ""P"", 15: ""S"", 16: ""T"", 17: ""W"", 18: ""Y"", 19: ""V"", 20: ""X"", 21: ""X"", 22: ""X"", 23: ""X""}
        aanp=np.array(list(aadic.values()))
        aala=np.searchsorted(list(aadic), aatp)
        twoda=np.column_stack((twoda,aanp[aala]))
        print(str(aanp[aala]))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    # Model alignment
    import math
    f4=int(np.count_nonzero(rein == chn))
    o4=int(f4/4)
    w4=int(o4*2)
    t4=int(o4*3)
    novch={""1"":""A"", ""2"":""B"", ""3"":""C"", ""4"":""D"", ""5"":""E"", ""6"":""F"", ""7"":""G"", ""8"":""H"", ""9"":""I"", ""10"":""J"", ""11"":""K"", ""12"":""L"", ""13"":""M"", ""14"":""N"", ""15"":""O"", ""16"":""P"", ""17"":""Q"", ""18"":""R"", ""19"":""S"", ""20"":""T"", ""21"":""U"", 22:""V"", ""23"":""W"", ""24"":""X"", ""25"":""Y"", ""26"":""Z""}
    cf=novch[args.alc]

    if int(args.al1)>0:
        if int(args.al1) < f4:
            o4=int(args.al1)
    if int(args.al1)>0:
        if int(args.al2) < f4:
            w4=int(args.al2)
    if int(args.al1)>0:
        if int(args.al3) < f4:
            t4=int(args.al3)

    xyz1=np.zeros((4,3),dtype=float)
    for i in range(namen):
        www=str(int(float(i)+1))
        with open(cpass+""/""+mname+""_""+www+""_multimer.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    if line[21:22]==cf:
                        if o4==int(float(line[22:26].strip())):
                            if ""CA""==line[12:16].strip():
                                print(line)
                                xyz1[0,0] = float(line[30:38].strip())
                                xyz1[0,1] = float(line[38:46].strip())
                                xyz1[0,2] = float(line[46:54].strip())
                                pass
                            pass
                        elif w4==int(float(line[22:26].strip())):
                            if ""CA""==line[12:16].strip():
                                print(line)
                                xyz1[1,0] = float(line[30:38].strip())
                                xyz1[1,1] = float(line[38:46].strip())
                                xyz1[1,2] = float(line[46:54].strip())
                                pass
                            pass
                        elif t4==int(float(line[22:26].strip())):
                            if ""CA""==line[12:16].strip():
                                print(line)
                                xyz1[2,0] = float(line[30:38].strip())
                                xyz1[2,1] = float(line[38:46].strip())
                                xyz1[2,2] = float(line[46:54].strip())
                                pass
                            pass
                        pass
                    else:
                        pass
                    pass
                else:
                    pass
                pass
            pass
        print(""------------"")
        xyz1[3,0] = round((xyz1[0,0]+xyz1[2,0])/2,3)
        xyz1[3,1] = round((xyz1[0,1]+xyz1[2,1])/2,3)
        xyz1[3,2] = round((xyz1[0,2]+xyz1[2,2])/2,3)
        xyzm=np.zeros((1,3),dtype=float)
        xyzm[0,0] = xyz1[3,0]
        xyzm[0,1] = xyz1[3,1]
        xyzm[0,2] = xyz1[3,2]
        print(""Initial xyz"")
        print(xyz1)
        xyz2=xyz1-xyzm
        print(""Center xyz"")
        #print(xyz2)
        #print(""------------"")
        # rotation z fit oy and ty to zero
        if xyz2[0,1]>0:
            pmy=-1
            pass
        else:
            pmy=1
            pass
        distaxy=math.sqrt(math.pow(xyz2[0,0], 2)+math.pow(xyz2[0,1], 2))
        radax=pmy*math.acos(xyz2[0,0]/distaxy)
        rot1=np.zeros((3,3),dtype=float)
        rot1[0,0]=math.cos(radax)
        rot1[1,0]=-math.sin(radax)
        rot1[0,1]=math.sin(radax)
        rot1[1,1]=math.cos(radax)
        rot1[2,2]=1
        xyz3=np.dot(xyz2,rot1)
        print(""rotation z fit 1y and 3y to zero"")
        #print(xyz3)
        #print(""------------"")
        # rotation y fit ox to zero
        if xyz3[0,2]>0:
            pmz=-1
            pass
        else:
            pmz=1
            pass
        dista1=math.sqrt(math.pow(xyz3[0,0], 2)+math.pow(xyz3[0,1], 2)+math.pow(xyz3[0,2], 2))
        radaxy=pmz*math.acos(xyz3[0,0]/dista1)
        rot2=np.zeros((3,3),dtype=float)
        rot2[0,0]=math.cos(radaxy)
        rot2[2,0]=-math.sin(radaxy)
        rot2[0,2]=math.sin(radaxy)
        rot2[2,2]=math.cos(radaxy)
        rot2[1,1]=1
        xyz4=np.dot(xyz3,rot2)
        print(""rotation y fit 1z and 3z to zero"")
        #print(xyz4)
        #print(""------------"")
        # rotation x fit wz to zero
        if xyz4[1,2]>0:
            pmyz=-1
            pass
        else:
            pmyz=1
            pass
        distbbyz=math.sqrt(math.pow(xyz4[1,1], 2)+math.pow(xyz4[1,2], 2))
        radbbyz=pmyz*math.acos(xyz4[1,1]/distbbyz)
        rot3=np.zeros((3,3),dtype=float)
        rot3[1,1]=math.cos(radbbyz)
        rot3[2,1]=-math.sin(radbbyz)
        rot3[1,2]=math.sin(radbbyz)
        rot3[2,2]=math.cos(radbbyz)
        rot3[0,0]=1
        xyz5=np.dot(xyz4,rot3)
        print(""rotation x fit 2z to zero"")
        #print(xyz5)
        #print(""------------"")
        # Check all shift & roation 
        xyz6=np.dot(np.dot(np.dot(xyz1-xyzm,rot1),rot2),rot3)
        print(""Check all shift & roation"")
        print(xyz6)
        print(""------------"")
        xyz=np.zeros((1,3),dtype=float)
        liii=""""
        with open(cpass+""/""+mname+""_""+www+""_multimer.pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    xyz[::1]=0
                    xyz[0,0] = float(line[30:38].strip())
                    xyz[0,1] = float(line[38:46].strip())
                    xyz[0,2] = float(line[46:54].strip())
                    xyzn=np.dot(np.dot(np.dot(xyz-xyzm,rot1),rot2),rot3)
                    qxxl="" ""*(8-len(str(round(xyzn[0,0],4))))+str(round(xyzn[0,0],4))
                    qyyl="" ""*(8-len(str(round(xyzn[0,1],4))))+str(round(xyzn[0,1],4))
                    qzzl="" ""*(8-len(str(round(xyzn[0,2],4))))+str(round(xyzn[0,2],4))
                    liii+=line[0:30]+qxxl+qyyl+qzzl+line[54:]
                    pass
                else:
                    liii+=line
                pass
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_m""+www+""_multi.pdb"", mode='w') as i:
            i.write(liii)
            i.close()
            pass
        pass


    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+""_multimer.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+str(lpass[-1])+'_pae_m'+www+'_multi.csv',pae,delimiter=',',fmt=""%s"")
            print(""Save:""+cpass+'/'+str(lpass[-1])+'_pae_m'+www+'_multi.csv')
            # Extract PAE
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
          
        # Write pLDDT in the PDB_Bfactor
        print(""Save:""+cpass+""/align_""+str(lpass[-1])+""_m""+www+""_multi.pdb"")
#        print(""Save:""+cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+""_ptm.pdb"")
        pass
    print(""------------"")
    
    

    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+str(lpass[-1])+'_plddt.csv',twoda,delimiter=',',fmt=""%s"")
    print(""Save:""+cpass+'/'+str(lpass[-1])+'_plddt.csv')
    pass



#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","plddt_align.py",".py","12198","347","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extract plddt values from [result_model.pkl] to [.csv] "")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-m', default='relaxed_model', help='input ZZZZZ if [ZZZZZ_Y.pdb]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='plddt', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-d', default='', help='ignoreed residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

mname=str(args.m)
print(""Input file name:""+str(mname)+""_?.pdb"")
print(""It can be changed by [-m ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")

# Igunore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Igunore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    # Model alignment
    import math
    o4=int(len(eres)/4)
    w4=int(o4*2)
    t4=int(o4*3)
    xyz1=np.zeros((4,3),dtype=float)
    for i in range(namen):
        www=str(int(float(i)+1))
        with open(cpass+""/""+mname+""_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    if o4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[0,0] = float(line[30:38].strip())
                            xyz1[0,1] = float(line[38:46].strip())
                            xyz1[0,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif w4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[1,0] = float(line[30:38].strip())
                            xyz1[1,1] = float(line[38:46].strip())
                            xyz1[1,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif t4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[2,0] = float(line[30:38].strip())
                            xyz1[2,1] = float(line[38:46].strip())
                            xyz1[2,2] = float(line[46:54].strip())
                            pass
                        pass
                    else:
                        pass
                    pass
                else:
                    pass
                pass
            pass
        print(""------------"")
        xyz1[3,0] = round((xyz1[0,0]+xyz1[2,0])/2,3)
        xyz1[3,1] = round((xyz1[0,1]+xyz1[2,1])/2,3)
        xyz1[3,2] = round((xyz1[0,2]+xyz1[2,2])/2,3)
        xyzm=np.zeros((1,3),dtype=float)
        xyzm[0,0] = xyz1[3,0]
        xyzm[0,1] = xyz1[3,1]
        xyzm[0,2] = xyz1[3,2]
        print(""Initial xyz"")
        print(xyz1)
        xyz2=xyz1-xyzm
        print(""Center xyz"")
        #print(xyz2)
        #print(""------------"")
        # rotation z fit oy and ty to zero
        if xyz2[0,1]>0:
            pmy=-1
            pass
        else:
            pmy=1
            pass
        distaxy=math.sqrt(math.pow(xyz2[0,0], 2)+math.pow(xyz2[0,1], 2))
        radax=pmy*math.acos(xyz2[0,0]/distaxy)
        rot1=np.zeros((3,3),dtype=float)
        rot1[0,0]=math.cos(radax)
        rot1[1,0]=-math.sin(radax)
        rot1[0,1]=math.sin(radax)
        rot1[1,1]=math.cos(radax)
        rot1[2,2]=1
        xyz3=np.dot(xyz2,rot1)
        print(""rotation z fit 1y and 3y to zero"")
        #print(xyz3)
        #print(""------------"")
        # rotation y fit ox to zero
        if xyz3[0,2]>0:
            pmz=-1
            pass
        else:
            pmz=1
            pass
        dista1=math.sqrt(math.pow(xyz3[0,0], 2)+math.pow(xyz3[0,1], 2)+math.pow(xyz3[0,2], 2))
        radaxy=pmz*math.acos(xyz3[0,0]/dista1)
        rot2=np.zeros((3,3),dtype=float)
        rot2[0,0]=math.cos(radaxy)
        rot2[2,0]=-math.sin(radaxy)
        rot2[0,2]=math.sin(radaxy)
        rot2[2,2]=math.cos(radaxy)
        rot2[1,1]=1
        xyz4=np.dot(xyz3,rot2)
        print(""rotation y fit 1z and 3z to zero"")
        #print(xyz4)
        #print(""------------"")
        # rotation x fit wz to zero
        if xyz4[1,2]>0:
            pmyz=-1
            pass
        else:
            pmyz=1
            pass
        distbbyz=math.sqrt(math.pow(xyz4[1,1], 2)+math.pow(xyz4[1,2], 2))
        radbbyz=pmyz*math.acos(xyz4[1,1]/distbbyz)
        rot3=np.zeros((3,3),dtype=float)
        rot3[1,1]=math.cos(radbbyz)
        rot3[2,1]=-math.sin(radbbyz)
        rot3[1,2]=math.sin(radbbyz)
        rot3[2,2]=math.cos(radbbyz)
        rot3[0,0]=1
        xyz5=np.dot(xyz4,rot3)
        print(""rotation x fit 2z to zero"")
        #print(xyz5)
        #print(""------------"")
        # Check all shift & roation 
        xyz6=np.dot(np.dot(np.dot(xyz1-xyzm,rot1),rot2),rot3)
        print(""Check all shift & roation"")
        print(xyz6)
        print(""------------"")
        xyz=np.zeros((1,3),dtype=float)
        liii=""""
        with open(cpass+""/""+mname+""_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    xyz[::1]=0
                    xyz[0,0] = float(line[30:38].strip())
                    xyz[0,1] = float(line[38:46].strip())
                    xyz[0,2] = float(line[46:54].strip())
                    xyzn=np.dot(np.dot(np.dot(xyz-xyzm,rot1),rot2),rot3)
                    qxxl="" ""*(8-len(str(round(xyzn[0,0],4))))+str(round(xyzn[0,0],4))
                    qyyl="" ""*(8-len(str(round(xyzn[0,1],4))))+str(round(xyzn[0,1],4))
                    qzzl="" ""*(8-len(str(round(xyzn[0,2],4))))+str(round(xyzn[0,2],4))
                    liii+=line[0:30]+qxxl+qyyl+qzzl+line[54:]
                    pass
                else:
                    liii+=line
                pass
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+"".pdb"", mode='w') as i:
            i.write(liii)
            i.close()
            pass
        pass


    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+"".pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        
        ll1=""""
        ss1=""""
        # Write pLDDT in the PDB_Bfactor
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if  line[0:4]==""ATOM"":
                    pdd1=pd1[int(float(line[22:26].strip())-1)]
                    ll1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    if int(eres[int(float(line[22:26].strip())-1)])==1:
                        ss1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    else:
                        pass
                    pass
                else:
                    ll1+=line
                    ss1+=line
                    pass
                pass
            g1.close()
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_""+www+"".pdb"", mode='w') as i:
            i.write(ll1)
            i.close()
            pass
        with open(cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+"".pdb"", mode='w') as i:
            i.write(ss1)
            i.close()
            pass
        # Write pLDDT in the PDB_Bfactor
        print(""Save:""+cpass+""/align_""+str(lpass[-1])+""_""+www+"".pdb"")
        print(""Save:""+cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+"".pdb"")
        pass
    print(""------------"")
    
    

    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+oname+'.csv',twoda,delimiter=',',fmt=""%s"")
    pass



#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","align_mono.py",".py","11561","326","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2.1_(2021Dec.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program align the output of AF2.1_monomer"")
print(""for Alphafold2.1.1"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(            description=""The program align the output of AF2.1_monomer"")
parser.add_argument('-i', default='result_model',   help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-m', default='relaxed_model',  help='input ZZZZZ if [ZZZZZ_Y.pdb]; ex) relaxed_model (def.)')
parser.add_argument('-n', default='5',              help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-d', default='',               help='ignoreed residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
parser.add_argument('-al1', default=0,              help='Center the mid point of al1 and al3, the point (al1) shift on x-axis')
parser.add_argument('-al2', default=0,              help='The point (al2) shift on xy-plane')
parser.add_argument('-al3', default=0,              help='Center the mid point of al1 and al3, the point (al3) shift on x-asis and opozit site of al1')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

mname=str(args.m)
print(""Input file name:""+str(mname)+""_?.pdb"")
print(""It can be changed by [-m ""+mname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Igunore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Igunore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        print(str(rein))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    # Model alignment
    import math
    o4=int(len(eres)/4)
    w4=int(o4*2)
    t4=int(o4*3)
    xyz1=np.zeros((4,3),dtype=float)
    
    if int(args.al1)>0:
        if int(args.al1) < len(eres):
            o4=int(args.al1)
    if int(args.al1)>0:
        if int(args.al2) < len(eres):
            w4=int(args.al2)
    if int(args.al1)>0:
        if int(args.al3) < len(eres):
            t4=int(args.al3)

    for i in range(namen):
        www=str(int(float(i)+1))
        with open(cpass+""/""+mname+""_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    if o4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[0,0] = float(line[30:38].strip())
                            xyz1[0,1] = float(line[38:46].strip())
                            xyz1[0,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif w4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[1,0] = float(line[30:38].strip())
                            xyz1[1,1] = float(line[38:46].strip())
                            xyz1[1,2] = float(line[46:54].strip())
                            pass
                        pass
                    elif t4==int(float(line[22:26].strip())):
                        if ""CA""==line[12:16].strip():
                            print(line)
                            xyz1[2,0] = float(line[30:38].strip())
                            xyz1[2,1] = float(line[38:46].strip())
                            xyz1[2,2] = float(line[46:54].strip())
                            pass
                        pass
                    else:
                        pass
                    pass
                else:
                    pass
                pass
            pass
        print(""------------"")
        xyz1[3,0] = round((xyz1[0,0]+xyz1[2,0])/2,3)
        xyz1[3,1] = round((xyz1[0,1]+xyz1[2,1])/2,3)
        xyz1[3,2] = round((xyz1[0,2]+xyz1[2,2])/2,3)
        xyzm=np.zeros((1,3),dtype=float)
        xyzm[0,0] = xyz1[3,0]
        xyzm[0,1] = xyz1[3,1]
        xyzm[0,2] = xyz1[3,2]
        print(""Initial xyz"")
        print(xyz1)
        xyz2=xyz1-xyzm
        print(""Center xyz"")
        #print(xyz2)
        #print(""------------"")
        # rotation z fit oy and ty to zero
        if xyz2[0,1]>0:
            pmy=-1
            pass
        else:
            pmy=1
            pass
        distaxy=math.sqrt(math.pow(xyz2[0,0], 2)+math.pow(xyz2[0,1], 2))
        radax=pmy*math.acos(xyz2[0,0]/distaxy)
        rot1=np.zeros((3,3),dtype=float)
        rot1[0,0]=math.cos(radax)
        rot1[1,0]=-math.sin(radax)
        rot1[0,1]=math.sin(radax)
        rot1[1,1]=math.cos(radax)
        rot1[2,2]=1
        xyz3=np.dot(xyz2,rot1)
        print(""rotation z fit 1y and 3y to zero"")
        #print(xyz3)
        #print(""------------"")
        # rotation y fit ox to zero
        if xyz3[0,2]>0:
            pmz=-1
            pass
        else:
            pmz=1
            pass
        dista1=math.sqrt(math.pow(xyz3[0,0], 2)+math.pow(xyz3[0,1], 2)+math.pow(xyz3[0,2], 2))
        radaxy=pmz*math.acos(xyz3[0,0]/dista1)
        rot2=np.zeros((3,3),dtype=float)
        rot2[0,0]=math.cos(radaxy)
        rot2[2,0]=-math.sin(radaxy)
        rot2[0,2]=math.sin(radaxy)
        rot2[2,2]=math.cos(radaxy)
        rot2[1,1]=1
        xyz4=np.dot(xyz3,rot2)
        print(""rotation y fit 1z and 3z to zero"")
        #print(xyz4)
        #print(""------------"")
        # rotation x fit wz to zero
        if xyz4[1,2]>0:
            pmyz=-1
            pass
        else:
            pmyz=1
            pass
        distbbyz=math.sqrt(math.pow(xyz4[1,1], 2)+math.pow(xyz4[1,2], 2))
        radbbyz=pmyz*math.acos(xyz4[1,1]/distbbyz)
        rot3=np.zeros((3,3),dtype=float)
        rot3[1,1]=math.cos(radbbyz)
        rot3[2,1]=-math.sin(radbbyz)
        rot3[1,2]=math.sin(radbbyz)
        rot3[2,2]=math.cos(radbbyz)
        rot3[0,0]=1
        xyz5=np.dot(xyz4,rot3)
        print(""rotation x fit 2z to zero"")
        #print(xyz5)
        #print(""------------"")
        # Check all shift & roation 
        xyz6=np.dot(np.dot(np.dot(xyz1-xyzm,rot1),rot2),rot3)
        print(""Check all shift & roation"")
        print(xyz6)
        print(""------------"")
        xyz=np.zeros((1,3),dtype=float)
        liii=""""
        with open(cpass+""/""+mname+""_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if line[0:4]==""ATOM"":
                    xyz[::1]=0
                    xyz[0,0] = float(line[30:38].strip())
                    xyz[0,1] = float(line[38:46].strip())
                    xyz[0,2] = float(line[46:54].strip())
                    xyzn=np.dot(np.dot(np.dot(xyz-xyzm,rot1),rot2),rot3)
                    qxxl="" ""*(8-len(str(round(xyzn[0,0],4))))+str(round(xyzn[0,0],4))
                    qyyl="" ""*(8-len(str(round(xyzn[0,1],4))))+str(round(xyzn[0,1],4))
                    qzzl="" ""*(8-len(str(round(xyzn[0,2],4))))+str(round(xyzn[0,2],4))
                    liii+=line[0:30]+qxxl+qyyl+qzzl+line[54:]
                    pass
                else:
                    liii+=line
                pass
            pass
        with open(cpass+""/align_""+str(lpass[-1])+""_m""+www+"".pdb"", mode='w') as i:
            i.write(liii)
            i.close()
            pass
        pass


    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+"".pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        
        print(""Save:""+cpass+""/align_""+str(lpass[-1])+""_m""+www+"".pdb"")
#        print(""Save:""+cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+"".pdb"")
        pass
    print(""------------"")
    
    

    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+str(lpass[-1])+'_plddt.csv',twoda,delimiter=',',fmt=""%s"")
    print(""Save:""+cpass+'/'+str(lpass[-1])+'_plddt.csv')
    pass


#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","plddt2csv.py",".py","6330","187","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extract plddt values from [result_model.pkl] to [.csv] "")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='plddt', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-d', default='', help='ignoreed residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")

# Igunore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Igunore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)
    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+"".pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        
        ll1=""""
        ss1=""""
        # Write pLDDT in the PDB_Bfactor
        with open(cpass+""/relaxed_model_""+www+"".pdb"", mode='r') as g1:
            for line in g1:
                if  line[0:4]==""ATOM"":
                    pdd1=pd1[int(float(line[22:26].strip())-1)]
                    ll1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    if int(eres[int(float(line[22:26].strip())-1)])==1:
                        ss1+=line[0:60]+"" ""*(6-len(pdd1))+pdd1+line[66:]
                    else:
                        pass
                    pass
                else:
                    ll1+=line
                    ss1+=line
                    pass
                pass
            g1.close()
            pass
        with open(cpass+""/relaxed_""+str(lpass[-1])+""_""+www+"".pdb"", mode='w') as i:
            i.write(ll1)
            i.close()
            pass
        with open(cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+"".pdb"", mode='w') as i:
            i.write(ss1)
            i.close()
            pass
        # Write pLDDT in the PDB_Bfactor
        print(""Save:""+cpass+""/relaxed_""+str(lpass[-1])+""_""+www+"".pdb"")
        print(""Save:""+cpass+""/""+str(lpass[-1])+""_""+www+""_pLDDT""+str(int(np.sum(na_mul)/np.sum(eres_f)))+"".pdb"")
        pass
    print(""------------"")
    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+oname+'.csv',twoda,delimiter=',',fmt=""%s"")
    pass

#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","CYP152N1/plddt2csv","pae2csv.py",".py","5720","170","# Import
import os
import sys
import pickle
import csv
import sys
import time
import numpy as np
import argparse
from scipy.stats import rankdata
# Import

# Header
now = time.ctime()
cnvtime = time.strptime(now)
print(""------------"")
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Start:""+str(sys.argv))
print(""Ver.2_(2021Jul.)"")
print(""by Hiroki Onoda, YCU"")
print(""------------"")
print(""The program extract pae and plddt values from [result_model_?_ptm.pkl] to [.csv] "")
print(""Thanks Dr. Yoshitaka Moriwaki (@Ag_smith) for initially showing how to export predicted aligned error in alphafold2."")
print(""You need to change model name to model_?_ptm from model_? at Docker/run_docker.py"")
print(""for Alphafold2"")
print(""------------"")
# Header

# Argument & Help
parser = argparse.ArgumentParser(description=""The program extract plddt from [result_model.pkl] to [.csv]"")
parser.add_argument('-i', default='result_model', help='input XXXXX if [XXXXX_Y.pkl]; ex) result_model (def.)')
parser.add_argument('-n', default='5', help='input Y if you want open [XXXXX_1.pkl,XXXXX_2.pkl ... ,XXXXX_5.pkl]; ex) 5 (def.)')
parser.add_argument('-o', default='p2c', help='input XXXXX if you want to name the output [XXXXX.csv]; ex) plddt (def.)')
parser.add_argument('-d', default='', help='ignored residue number for average pLDDTs caliculation to determined the ranking of models [1-5_56-75_105-135]; ex) """" (def.)')
args = parser.parse_args()
# Argument & Help

#l----------------------------------------------l
#l           Valiable information               l
#l----------------------------------------------l

# Input file name
iname=str(args.i)
print(""Input file name:""+str(iname)+""_?_ptm.pkl"")
print(""It can be changed by [-i ""+iname+""]"")
print("""")

# Number of models
namen=int(str(args.n))

# Output file name
oname=str(args.o)
print(""Output file name:""+str(oname)+"".csv"")
print(""It can be changed by [-o ""+oname+""]"")
print("""")

# Ignore residue
dres=str(args.d)
ures=dres.split(""_"")
print(""Ignore residues:""+str(ures))
sres=[]
for i in range(len(ures)):
    hres=[]
    hres=ures[i].split(""-"")
    sres.extend(hres)
fres=list(filter(None,sres))
mres=np.empty_like(fres)
mres[::1]=0
mres[::2]=1
fres_f = np.array(fres).astype(np.float32)
mres_f = mres.astype(np.float32)
ares=fres_f-mres_f
print(""ON/OFF pisition:""+str(ares))

#l-----------------------------------------------l
#l           Valiable information                l
#l-----------------------------------------------l


def read():    
    cpass=os.getcwd()
    lpass=cpass.split(""/"")
    
    # Get sequene from features.pkl 
    with open(cpass+""/features.pkl"", 'rb') as cn:
        cnn=pickle.load(cn)
        sequ=cnn['residue_index']
        rein=np.array(list(str(cnn['sequence'][0])[2:-1]))
        twoda=np.column_stack((sequ+1,rein))
        cn.close()
        pass
    # Get sequence from features.pkl
    
    # Make array to determine the caliculation range of pLDDT average
    eres=np.empty_like(rein)
    for i in range(len(eres)):
        cococo=0
        for j in range(len(ares)):
            if i > int(ares[j])-1:
                cococo+=1
                pass
            pass
        if cococo % 2 == 0:
            eres[i]=1
        else:
            eres[i]=0
        pass
    # Make array to determine the caliculation range of pLDDT average
    print(eres)
    eres_f = eres.astype(np.float32)

    np.set_printoptions(formatter={'float': '{:.1f}'.format})

    
    plddts=np.empty(namen)
    # Repeat for the caliculation of all models
    for i in range(namen):
        www=str(int(float(i)+1))
        # Read pLDDT from result_model_?.pkl
        with open(cpass+""/""+iname+""_""+www+""_ptm.pkl"", 'rb') as c1:
            c1n=pickle.load(c1)
            # Extract PAE
            pae = c1n['predicted_aligned_error'].astype(np.float32)
            np.savetxt(cpass+'/'+oname+'_pae_'+www+'.csv',pae,delimiter=',',fmt=""%s"")
            print(""Save:""+cpass+'/'+oname+'_pae_'+www+'.csv')
            # Extract PAE
            c1n_f = c1n['plddt'].astype(np.float32)
            # Caliculation of average pLDDT
            na_mul=c1n_f*eres_f
            plddts[i]=np.sum(na_mul)/np.sum(eres_f)
            # Caliculation of average pLDDT
            pld1=np.round(c1n['plddt'], decimals=2)
            twoda=np.column_stack((twoda,pld1))
            pd1=['{:.2f}'.format(n) for n in pld1.tolist()]
            c1.close()
            pass
        # Read pLDDT from result_model_?.pkl
        pass
    print(""------------"")
    
    # Caliculation of pLDDT rank without specificic range (-d argument)
    print(""number of calculated residues: ""+str(int(np.sum(eres_f)))+"" (Ignore: ""+str(ures)+"")"")
    np.set_printoptions(formatter={'float': '{:.0f}'.format})
    print(""Rank  :""+str(rankdata(-plddts)))
    np.set_printoptions(formatter={'float': '{:.1f}'.format})
    twoda=np.column_stack((twoda,eres))
    print(""plddts:""+str(plddts))
    print(""------------"")
    # Caliculation of pLDDT rank without specificic range (-d argument)
    
    # Write pLDDT in csv file  
    np.savetxt(cpass+'/'+oname+'_plddt.csv',twoda,delimiter=',',fmt=""%s"")
    print(""Save:""+cpass+'/'+oname+'_plddt.csv')
    pass



#l-----------------------------------------------l
#l                   Execution                   l
#l-----------------------------------------------l

print(""------------"")
read()
print(""------------"")
now = time.ctime()
cnvtime = time.strptime(now)
print(time.strftime(""%Y/%m/%d %H:%M"", cnvtime))
print(""Finish:""+str(sys.argv))
print(""------------"")
","Python"
"AlphaFold","jasperzuallaert/VIBFold","alphafold_job_script_multimer.sh",".sh","689","24","#!/bin/bash
#PBS -N AlphaFold_multimer_script
#PBS -l nodes=1:ppn=8:gpus=1
#PBS -l mem=64gb
#PBS -l walltime=24:0:0

PROTEIN=SARSCOV2_VHH72

module load AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310

WORKDIR=$VSC_DATA/alphafold/runs/$PBS_JOBID-$PROTEIN
mkdir -p $WORKDIR
cp -a $PBS_O_WORKDIR/fastas/$PROTEIN.fasta $WORKDIR/
cd $WORKDIR

echo Running $PROTEIN.fasta, output found at $WORKDIR
alphafold --fasta_paths=$PROTEIN.fasta \
          --max_template_date=2999-01-01 \
          --db_preset=full_dbs \
          --output_dir=$PWD \
          --model_preset=multimer \
          --num_multimer_predictions_per_model=1
","Shell"
"AlphaFold","jasperzuallaert/VIBFold","alphafold_job_script.sh",".sh","633","26","#!/bin/bash
#PBS -N AlphaFold_tutorial_script
#PBS -l nodes=1:ppn=8:gpus=1
#PBS -l mem=64gb
#PBS -l walltime=24:0:0

PROTEIN=rbd

module load AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310

WORKDIR=$VSC_DATA/alphafold/runs/$PBS_JOBID-$PROTEIN
mkdir -p $WORKDIR
cp -a $PBS_O_WORKDIR/fastas/$PROTEIN.fasta $WORKDIR/
cd $WORKDIR

echo Running $PROTEIN.fasta, output found at $WORKDIR
alphafold --fasta_paths=$PROTEIN.fasta \
          --max_template_date=2999-01-01 \
          --db_preset=full_dbs \
          --output_dir=$PWD \
          --model_preset=monomer_ptm



","Shell"
"AlphaFold","jasperzuallaert/VIBFold","VIBFold_adapted_functions.py",".py","13478","344","import random
from tqdm import tqdm
import dataclasses
import json
import copy
from alphafold.data import feature_processing, msa_pairing, msa_identifiers
from alphafold.data import pipeline as alpha_pipeline
from alphafold.data import parsers
from alphafold.common import residue_constants
from alphafold.data import pipeline_multimer
import numpy as np
import tarfile
import requests
import time
import os

# Copied from AlphaFold - should recheck where this comes from
def process_multimer_features(features_for_chain):
    all_chain_features = {}
    for chain_id, chain_features in features_for_chain.items():
        all_chain_features[chain_id] = pipeline_multimer.convert_monomer_features(
            chain_features, chain_id
        )

    all_chain_features = pipeline_multimer.add_assembly_features(all_chain_features)
    feature_processing.process_unmerged_features(all_chain_features)
    np_chains_list = list(all_chain_features.values())
    pair_msa_sequences = not feature_processing._is_homomer_or_monomer(np_chains_list)
    chains = list(np_chains_list)
    chain_keys = chains[0].keys()
    updated_chains = []
    for chain_num, chain in enumerate(chains):
        new_chain = {k: v for k, v in chain.items() if ""_all_seq"" not in k}
        for feature_name in chain_keys:
            if feature_name.endswith(""_all_seq""):
                feats_padded = msa_pairing.pad_features(
                    chain[feature_name], feature_name
                )
                new_chain[feature_name] = feats_padded
        new_chain[""num_alignments_all_seq""] = np.asarray(
            len(np_chains_list[chain_num][""msa_all_seq""])
        )
        updated_chains.append(new_chain)
    np_chains_list = updated_chains
    np_chains_list = feature_processing.crop_chains(
        np_chains_list,
        msa_crop_size=feature_processing.MSA_CROP_SIZE,
        pair_msa_sequences=pair_msa_sequences,
        max_templates=feature_processing.MAX_TEMPLATES,
    )

    np_example = feature_processing.msa_pairing.merge_chain_features(
        np_chains_list=np_chains_list,
        pair_msa_sequences=pair_msa_sequences,
        max_templates=feature_processing.MAX_TEMPLATES,
    )
    np_example = feature_processing.process_final(np_example)

    np_example = pipeline_multimer.pad_msa(np_example, min_num_seq=512)
    return np_example

# Copied from AlphaFold, adapted code so that duplicate rows in the MSA are not deleted. This gave problems
# when pairing MSAs
def my_make_msa_features_keep_duplicates(msas):
  if not msas:
    raise ValueError('At least one MSA must be provided.')

  int_msa = []
  deletion_matrix = []
  species_ids = []
  for msa_index, msa in enumerate(msas):
    if not msa:
      raise ValueError(f'MSA {msa_index} must contain at least one sequence.')
    for sequence_index, sequence in enumerate(msa.sequences):
      int_msa.append(
          [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence])
      deletion_matrix.append(msa.deletion_matrix[sequence_index])
      identifiers = msa_identifiers.get_identifiers(
          msa.descriptions[sequence_index])
      species_ids.append(identifiers.species_id.encode('utf-8'))

  num_res = len(msas[0].sequences[0])
  num_alignments = len(int_msa)
  features = {}
  features['deletion_matrix_int'] = np.array(deletion_matrix, dtype=np.int32)
  features['msa'] = np.array(int_msa, dtype=np.int32)
  features['num_alignments'] = np.array([num_alignments] * num_res, dtype=np.int32)
  features['msa_species_identifiers'] = np.array(species_ids, dtype=np.object_)
  return features

# Copied from AlphaFold datapipeline package: modified the process function to use a seq_to_features_cache across
# runs (multiple permutations), instead of within one run
class Cached_DataPipeline(pipeline_multimer.DataPipeline):  # copied from AlphaFold repo, adapted caching
    def __init__(self, monomer_data_pipeline, jackhmmer_binary_path, uniprot_database_path, use_precomputed_msas, seq_to_features_cache):
        super(Cached_DataPipeline, self).__init__(monomer_data_pipeline=monomer_data_pipeline,
                                                  jackhmmer_binary_path=jackhmmer_binary_path,
                                                  uniprot_database_path=uniprot_database_path,
                                                  use_precomputed_msas=use_precomputed_msas)
        self.seq_to_features_cache = seq_to_features_cache

    def process(self,
                input_fasta_path: str,
                msa_output_dir: str) -> alpha_pipeline.FeatureDict:
        """"""Runs alignment tools on the input sequences and creates features.""""""
        with open(input_fasta_path) as f:
            input_fasta_str = f.read()
        input_seqs, input_descs = parsers.parse_fasta(input_fasta_str)

        chain_id_map = pipeline_multimer._make_chain_id_map(sequences=input_seqs, descriptions=input_descs)
        chain_id_map_path = os.path.join(msa_output_dir, 'chain_id_map.json')
        with open(chain_id_map_path, 'w') as f:
            chain_id_map_dict = {chain_id: dataclasses.asdict(fasta_chain)
                                 for chain_id, fasta_chain in chain_id_map.items()}
            json.dump(chain_id_map_dict, f, indent=4, sort_keys=True)

        all_chain_features = {}
        is_homomer_or_monomer = len(set(input_seqs)) == 1
        for chain_id, fasta_chain in chain_id_map.items():
            if fasta_chain.sequence in self.seq_to_features_cache:
                all_chain_features[chain_id] = copy.deepcopy(
                    self.seq_to_features_cache[fasta_chain.sequence])
                continue
            chain_features = self._process_single_chain(
                chain_id=chain_id,
                sequence=fasta_chain.sequence,
                description=fasta_chain.description,
                msa_output_dir=msa_output_dir,
                is_homomer_or_monomer=is_homomer_or_monomer)

            chain_features = pipeline_multimer.convert_monomer_features(chain_features, chain_id=chain_id)
            all_chain_features[chain_id] = chain_features
            self.seq_to_features_cache[fasta_chain.sequence] = copy.deepcopy(chain_features)

        all_chain_features = pipeline_multimer.add_assembly_features(all_chain_features)
        np_example = feature_processing.pair_and_merge(all_chain_features=all_chain_features)
        # Pad MSA to avoid zero-sized extra_msa.
        np_example = pad_msa(np_example, 512)

        # need this to

        return np_example

# Copied from AlphaFold repo, to use in the pipeline class
def pad_msa(np_example, min_num_seq):
  np_example = dict(np_example)
  num_seq = np_example['msa'].shape[0]
  if num_seq < min_num_seq:
    for feat in ('msa', 'deletion_matrix', 'bert_mask', 'msa_mask'):
      np_example[feat] = np.pad(
          np_example[feat], ((0, min_num_seq - num_seq), (0, 0)))
    np_example['cluster_bias_mask'] = np.pad(
        np_example['cluster_bias_mask'], ((0, min_num_seq - num_seq),))
  return np_example


# Exact copy from ColabFold
##########################################
# call mmseqs2
##########################################

TQDM_BAR_FORMAT = '{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'

def run_mmseqs2(x, prefix, use_env=True, use_filter=True,
                use_templates=False, filter=None, use_pairing=False,
                host_url=""https://api.colabfold.com""):
  submission_endpoint = ""ticket/pair"" if use_pairing else ""ticket/msa""

  def submit(seqs, mode, N=101):
    n, query = N, """"
    for seq in seqs:
      query += f"">{n}\n{seq}\n""
      n += 1

    res = requests.post(f'{host_url}/{submission_endpoint}', data={'q':query,'mode': mode})
    try:
      out = res.json()
    except ValueError:
      print(f""Server didn't reply with json: {res.text}"")
      out = {""status"":""ERROR""}
    return out

  def status(ID):
    res = requests.get(f'{host_url}/ticket/{ID}')
    try:
      out = res.json()
    except ValueError:
      print(f""Server didn't reply with json: {res.text}"")
      out = {""status"":""ERROR""}
    return out

  def download(ID, path):
    res = requests.get(f'{host_url}/result/download/{ID}')
    with open(path,""wb"") as out: out.write(res.content)

  # process input x
  seqs = [x] if isinstance(x, str) else x

  # compatibility to old option
  if filter is not None:
    use_filter = filter

  # setup mode
  if use_filter:
    mode = ""env"" if use_env else ""all""
  else:
    mode = ""env-nofilter"" if use_env else ""nofilter""

  if use_pairing:
    mode = """"
    use_templates = False
    use_env = False

  # define path
  path = f""{prefix}_{mode}""
  if not os.path.isdir(path): os.mkdir(path)

  # call mmseqs2 api
  tar_gz_file = f'{path}/out.tar.gz'
  N,REDO = 101,True

  # deduplicate and keep track of order
  seqs_unique = []
  #TODO this might be slow for large sets
  [seqs_unique.append(x) for x in seqs if x not in seqs_unique]
  Ms = [N + seqs_unique.index(seq) for seq in seqs]
  # lets do it!
  if not os.path.isfile(tar_gz_file):
    TIME_ESTIMATE = 150 * len(seqs_unique)
    with tqdm(total=TIME_ESTIMATE, bar_format=TQDM_BAR_FORMAT) as pbar:
      while REDO:
        pbar.set_description(""SUBMIT"")

        # Resubmit job until it goes through
        out = submit(seqs_unique, mode, N)
        while out[""status""] in [""UNKNOWN"", ""RATELIMIT""]:
          sleep_time = 5 + random.randint(0, 5)
          print(f""Sleeping for {sleep_time}s. Reason: {out['status']}"")
          # resubmit
          time.sleep(sleep_time)
          out = submit(seqs_unique, mode, N)

        if out[""status""] == ""ERROR"":
          raise Exception(f'MMseqs2 API is giving errors. Please confirm your input is a valid protein sequence. If error persists, please try again an hour later.')

        if out[""status""] == ""MAINTENANCE"":
          raise Exception(f'MMseqs2 API is undergoing maintenance. Please try again in a few minutes.')

        # wait for job to finish
        ID,TIME = out[""id""],0
        pbar.set_description(out[""status""])
        while out[""status""] in [""UNKNOWN"",""RUNNING"",""PENDING""]:
          t = 5 + random.randint(0,5)
          print(f""Sleeping for {t}s. Reason: {out['status']}"")
          time.sleep(t)
          out = status(ID)
          pbar.set_description(out[""status""])
          if out[""status""] == ""RUNNING"":
            TIME += t
            pbar.update(n=t)
          #if TIME > 900 and out[""status""] != ""COMPLETE"":
          #  # something failed on the server side, need to resubmit
          #  N += 1
          #  break

        if out[""status""] == ""COMPLETE"":
          if TIME < TIME_ESTIMATE:
            pbar.update(n=(TIME_ESTIMATE-TIME))
          REDO = False

        if out[""status""] == ""ERROR"":
          REDO = False
          raise Exception(f'MMseqs2 API is giving errors. Please confirm your input is a valid protein sequence. If error persists, please try again an hour later.')

      # Download results
      download(ID, tar_gz_file)

  # prep list of a3m files
  if use_pairing:
    a3m_files = [f""{path}/pair.a3m""]
  else:
    a3m_files = [f""{path}/uniref.a3m""]
    if use_env: a3m_files.append(f""{path}/bfd.mgnify30.metaeuk30.smag30.a3m"")

  # extract a3m files
  if any(not os.path.isfile(a3m_file) for a3m_file in a3m_files):
    with tarfile.open(tar_gz_file) as tar_gz:
      tar_gz.extractall(path)

  # templates
  if use_templates:
    templates = {}
    #print(""seq\tpdb\tcid\tevalue"")
    for line in open(f""{path}/pdb70.m8"",""r""):
      p = line.rstrip().split()
      M,pdb,qid,e_value = p[0],p[1],p[2],p[10]
      M = int(M)
      if M not in templates: templates[M] = []
      templates[M].append(pdb)
      #if len(templates[M]) <= 20:
      #  print(f""{int(M)-N}\t{pdb}\t{qid}\t{e_value}"")

    template_paths = {}
    for k,TMPL in templates.items():
      TMPL_PATH = f""{prefix}_{mode}/templates_{k}""
      if not os.path.isdir(TMPL_PATH):
        os.mkdir(TMPL_PATH)
        TMPL_LINE = "","".join(TMPL[:20])
        os.system(f""curl -s -L {host_url}/template/{TMPL_LINE} | tar xzf - -C {TMPL_PATH}/"")
        os.system(f""cp {TMPL_PATH}/pdb70_a3m.ffindex {TMPL_PATH}/pdb70_cs219.ffindex"")
        os.system(f""touch {TMPL_PATH}/pdb70_cs219.ffdata"")
      template_paths[k] = TMPL_PATH

  # gather a3m lines
  a3m_lines = {}
  for a3m_file in a3m_files:
    update_M,M = True,None
    for line in open(a3m_file,""r""):
      if len(line) > 0:
        if ""\x00"" in line:
          line = line.replace(""\x00"","""")
          update_M = True
        if line.startswith("">"") and update_M:
          M = int(line[1:].rstrip())
          update_M = False
          if M not in a3m_lines: a3m_lines[M] = []
        a3m_lines[M].append(line)

  # return results

  a3m_lines = ["""".join(a3m_lines[n]) for n in Ms]

  if use_templates:
    template_paths_ = []
    for n in Ms:
      if n not in template_paths:
        template_paths_.append(None)
        #print(f""{n-N}\tno_templates_found"")
      else:
        template_paths_.append(template_paths[n])
    template_paths = template_paths_


  return (a3m_lines, template_paths) if use_templates else a3m_lines
","Python"
"AlphaFold","jasperzuallaert/VIBFold","submit_vibfold.py",".py","4401","110","
FASTA_FILE = 'fastas/test.fasta'        # location of fasta file between '' - absolute or relative path possible
IS_COMPLEX = True                       # True or False
MSA_MODE = 'mmseqs2_server'             # 'alphafold_default' or 'mmseqs2_server'
SAVE_DIR = 'results/test'               # location of results directory between '' - abs or rel path possible
DO_RELAX = 'best'                       # 'all', 'best' or 'none'
NUM_RUNS_PER_MODEL = 1                  # number of runs per model, with different random seed
USE_TEMPLATES = True                   # True, False
MAX_RECYCLES = 3                        # default == 3


import subprocess
import os
import time

timestamp = time.strftime('%Y%m%d_%H%M%S') + '_'

def submit(FASTA_FILE, IS_COMPLEX, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES):
    assert ' ' not in FASTA_FILE, 'The name of your FASTA file cannot contain any spaces'
    # automatically select accelgor/joltik based on output of 'ml'
    module_info = subprocess.check_output('ml',shell=True).decode('utf-8')
    cluster = 'accelgor' if 'accelgor' in module_info else \
              'joltik' if 'joltik' in module_info else \
              'litleo' if 'litleo' in module_info else \
              'donphan' if 'donphan' in module_info else ''
    if not cluster:
        raise NotImplementedError('Cluster joltik/accelgor/donphan not found in ""ml"" output. Did you use ""module swap cluster/joltik"" (or other)?')

    fasta_d = {}
    seq = ''
    ctr = 1
    for line in open(FASTA_FILE):
        if line.startswith('>'):
            if seq:
                fasta_d[prot_id] = seq
                seq = ''
            prot_id = f'{ctr}_{line.rstrip().lstrip("">"").replace("" "", ""_"").replace("":"", ""_"").replace(""("", """").replace("")"", """")}'
            if '|' in prot_id: prot_id = prot_id.split('|')[1]
            ctr+=1
        elif line:
            seq += line.rstrip()
    if seq:
        fasta_d[prot_id] = seq

    all_seqs = {}
    all_protnames = {}
    if not IS_COMPLEX: # create one new FASTA file per entry
        for prot_id, seq in fasta_d.items():
            all_seqs[prot_id] = seq
            all_protnames[prot_id] = prot_id
    else: # create a copy of the multi-entry FASTA file
        fasta_id = os.path.basename(FASTA_FILE).split('.')[0]
        seqs = fasta_d.values()
        all_protnames[fasta_id] = ':'.join(fasta_d.keys())
        all_seqs[fasta_id] = ':'.join(seqs)

    if not SAVE_DIR.startswith('/'):
        SAVE_DIR = f'$PBS_O_WORKDIR/{SAVE_DIR}'

        for prot_id, seq in all_seqs.items():
            prot_names = all_protnames[prot_id]
            run_save_dir = f'{SAVE_DIR}/{timestamp}_{prot_id}'
            script_content = f'''#!/bin/bash
#PBS -N VIBFold_{prot_id}
#PBS -l nodes=1:ppn={12 if (cluster == 'accelgor' or cluster == 'litleo') else 8}{"",gpus=1"" if (cluster == 'accelgor' or cluster == 'joltik' or cluster == 'litleo') else """"}
#PBS -l mem={125 if cluster == 'accelgor' else 64 if (cluster == 'joltik' or cluster == 'litleo') else 20}g
#PBS -l walltime=48:00:00

module load Python/3.11.3-GCCcore-12.3.0

module load tqdm/4.66.1-GCCcore-12.3.0 
module load matplotlib/3.7.2-gfbf-2023a
module load AlphaFold/2.3.2-foss-2023a{""-CUDA-12.1.1"" if (cluster == 'accelgor' or cluster == 'joltik' or cluster == 'litleo') else """"}
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310
PROTEIN={prot_id}

jobname=""$PROTEIN""_""$PBS_JOBID""

SAVEDIR={run_save_dir}
mkdir -p $SAVEDIR

cd $PBS_O_WORKDIR
python VIBFold.py \
 --seq {seq} \
 --prot_names ""{prot_names}"" \
 --jobname $jobname \
 --save_dir $SAVEDIR \
 --do_relax {DO_RELAX} \
 {""--no_templates"" if not USE_TEMPLATES else """"} \
 --msa_mode {MSA_MODE} \
 --num_runs_per_model {NUM_RUNS_PER_MODEL} \
 --max_recycles {MAX_RECYCLES} \
 --do_gather_best
'''

            scriptname = 'submit_new.sh'
            f = open(scriptname,'w')
            print(script_content,file=f)
            f.close()

            print()
            print(f'############# submitting {prot_id} #############')
            subprocess.Popen(['echo',f'{prot_id}'],shell=False)
            subprocess.Popen(['qsub',f'{scriptname}'],shell=False).wait()
            subprocess.Popen(['rm',f'{scriptname}'],shell=False).wait()
            print()

if __name__ == ""__main__"":
    submit(FASTA_FILE, IS_COMPLEX, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES)
","Python"
"AlphaFold","jasperzuallaert/VIBFold","VIBFold.py",".py","33906","626","import argparse
import itertools
import re
import logging
from alphafold.model import data as alpha_data, config as alpha_config
from alphafold.common import protein as alpha_protein
from alphafold.relax import relax
from alphafold.model import model as alpha_model
from random import randint
from alphafold.data import templates as alpha_templates
from alphafold.data.tools import hhsearch
from alphafold.data.tools import hmmsearch
from alphafold.data import pipeline as alpha_pipeline
from alphafold.data import templates
from alphafold.common import residue_constants
import numpy as np
from matplotlib import pyplot as plt
import alphafold
import json
import os
import shutil
import VIBFold_adapted_functions as adapted
from matplotlib.colors import ListedColormap

MAX_NUM_PERMUTATIONS = 6 # to limit computational time
ALPHAFOLD_DATA_DIR = os.environ['ALPHAFOLD_DATA_DIR']
MAX_TEMPLATE_HITS = 20 # default alphafold setting
MAX_UNIREF_HITS = 10000
MAX_UNIPROT_HITS = 50000 # for multimer
MAX_MGNIFY_HITS = 501
jackhmmer_binary_path = shutil.which('jackhmmer')
hhblits_binary_path=shutil.which('hhblits')
hmmsearch_binary_path=shutil.which('hmmsearch')
hmmbuild_binary_path=shutil.which('hmmbuild')
hhsearch_binary_path=shutil.which('hhsearch')
kalign_binary_path=shutil.which('kalign')
uniref90_database_path=ALPHAFOLD_DATA_DIR + '/uniref90/uniref90.fasta'
mgnify_database_path=ALPHAFOLD_DATA_DIR + '/mgnify/mgy_clusters_2022_05.fa'
bfd_database_path=ALPHAFOLD_DATA_DIR + '/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt'
template_mmcif_dir=ALPHAFOLD_DATA_DIR + '/pdb_mmcif/mmcif_files'
obsolete_pdbs_path=ALPHAFOLD_DATA_DIR + '/pdb_mmcif/obsolete.dat'
uniref30_database_path= ALPHAFOLD_DATA_DIR + '/uniref30/UniRef30_2021_03'
pdb70_database_path=ALPHAFOLD_DATA_DIR + '/pdb70/pdb70'
pdb_seqres_database_path=ALPHAFOLD_DATA_DIR + '/pdb_seqres/pdb_seqres.txt'
uniprot_database_path = ALPHAFOLD_DATA_DIR + '/uniprot/uniprot.fasta' # for multimer

def run_alphafold_advanced_complex(seq, prot_names, jobname, save_dir, use_templates, num_runs_per_model, do_relax, max_recycles=3, tolerance=0, msa_mode='alphafold_default', do_gather_best=False):
    # process input parameters
    sequences = seq.split(':')
    prot_names = [name[:20] for name in prot_names.split(':')]
    seq_idx = list(range(len(sequences)))
    jobname = """".join(jobname.split())
    jobname = re.sub(r'\W+', '', jobname)
    use_env = True if msa_mode.startswith('mmseqs2') else False
    model_used = 'monomer_ptm' if len(sequences) == 1 else 'multimer'
    # Load model parameters, same for all permutations
    model_params, model_runners_12, model_runners_345 = [], [], []
    for _ in range(num_runs_per_model):
        model_param, model_runner_12, model_runner_345 = prepare_models(model_used, max_recycles, tolerance)
        model_params.append(model_param)
        model_runners_12.append(model_runner_12)
        model_runners_345.append(model_runner_345)
    seq_to_msa_d = {}
    # if multiple sequences are present in the query, a single run is done for each of the permutations
    # Protein complexes with 'first A then B', will yield different results than 'first B then A'
    for perm_idx, indices in enumerate(set(itertools.permutations(seq_idx))):
        if perm_idx >= MAX_NUM_PERMUTATIONS:
            break
        query_sequences = [sequences[i] for i in indices]
        query_prot_names = [prot_names[i] for i in indices]
        # the output dir gets a permutation number if applicable
        out_dir = f'{save_dir}/{jobname}' if len(sequences) == 1 else f'{save_dir}/{jobname}_permutation{perm_idx}'
        out_dir = out_dir.rstrip('/')
        os.makedirs(out_dir,exist_ok=True)

        fasta_file = f'{out_dir}/query.fasta'
        write_to_fasta = open(fasta_file, 'w')
        for i, query_sequence in enumerate(query_sequences):
            print(f'>chain_{i}',file=write_to_fasta)
            print(query_sequence,file=write_to_fasta)
        write_to_fasta.close()
        print(open(fasta_file).read())

        # set logging
        logger = logging.Logger(f'perm{perm_idx}', level = logging.INFO)
        fh = logging.FileHandler(filename=f'{out_dir}/info.log', mode='w')
        fh.setFormatter(logging.Formatter(datefmt='%Y-%m-%d %H:%M:%S', fmt='%(asctime)s - %(message)s'))
        logger.addHandler(fh)
        logger.info(f'Parameters: num_sequences = {len(query_sequences)}')
        logger.info(f'Parameters: model used = {model_used}')
        logger.info(f'Parameters: use_amber = {do_relax}')
        logger.info(f'Parameters: msa_mode = {msa_mode}')
        logger.info(f'Parameters: use_templates = {use_templates}')
        logger.info(f'Parameters: max_recycles = {max_recycles}')
        logger.info(f'Parameters: recycling_tolerance = {tolerance}')
        logger.info(f'Parameters: num_runs_per_model = {num_runs_per_model}')
        logger.info(f'Started permutation # {perm_idx}')
        print(f'Parameters: num_sequences = {len(query_sequences)}')
        print(f'Parameters: model used = {model_used}')
        print(f'Parameters: use_amber = {do_relax}')
        print(f'Parameters: msa_mode = {msa_mode}')
        print(f'Parameters: use_templates = {use_templates}')
        print(f'Parameters: max_recycles = {max_recycles}')
        print(f'Parameters: recycling_tolerance = {tolerance}')
        print(f'Parameters: num_runs_per_model = {num_runs_per_model}')
        print(f'Started permutation # {perm_idx}')

        # Do MSA search
        logger.info('Starting MSA search (+ templates if req)')
        feature_dict = run_msa_search(msa_mode, query_sequences, fasta_file, seq_to_msa_d, use_templates, out_dir, jobname, logger)

        # pickle.dump(feature_dict,file=open(out_dir+'/features_for_debugging.pkl','wb'))
        if use_templates:
            try:
                log_template_info(feature_dict, logger)
            except Exception as exx:
                logger.info('Exception encountered during template logging. Feel free to contact jasper.zuallaert@vib-ugent.be about this. Error message:')
                logger.info(exx)
        else:
            logger.info('No templates used.')
        # Predict
        logger.info('Starting predictions...')

        unrelaxed_pdb_lines, unrelaxed_proteins, paes, plddts, ptms, iptms, model_names = predict_structures(logger,
                                                                                                  model_params,
                                                                                                  model_runners_12,
                                                                                                  model_runners_345,
                                                                                                  feature_dict,
                                                                                                  model_used == 'multimer',
                                                                                                  num_runs_per_model)
        # rank models, relax, write pdb files
        pae_plddt_per_model = rank_relax_write(logger, unrelaxed_pdb_lines, unrelaxed_proteins, plddts, paes, ptms, iptms, out_dir, jobname, do_relax, model_names, model_used=='multimer')
        # generate output images

        logger.info('Generating output images...')
        generate_output_images(query_sequences, query_prot_names, pae_plddt_per_model, feature_dict['msa'], num_runs_per_model, out_dir, jobname)
        # remove intermediate directories
        if msa_mode.startswith('mmseqs2'):
            os.popen(f'rm -rf {out_dir}/{jobname}_*{""env"" if use_env else """"}/')
        else:
            os.popen(f'rm -rf {out_dir}/{jobname}_*{""env"" if use_env else ""all""}/')
        logger.info(f'Permutation {perm_idx} finished!')
    # gather best prediction
    logger.info('Gathering best prediction...')
    if do_gather_best:
        gather_best_prediction(save_dir, jobname, len(sequences) > 1)

# Loads the models, and compiles them. Weights are loaded, but only at prediction time added to the model.
# There is distinction between model_1 and model_3, because 1-2 and 3-4-5 have a different number of parameters
def prepare_models(model_used, max_recycles, tolerance):
    model_params = {}
    model_runner_12, model_runner_345 = None, None
    model_extension = 'ptm' if model_used == 'monomer_ptm' else 'multimer_v3'
    for model_num in range(1,6):
        model_name = f'model_{model_num}_{model_extension}'
        model_params[model_name] = alpha_data.get_model_haiku_params(model_name=model_name, data_dir=ALPHAFOLD_DATA_DIR)
        if model_num in (1,3):
            model_config = alpha_config.model_config(model_name)
            model_config.model.recycle_tol = tolerance
            if model_extension == ""ptm"":
                model_config.data.eval.num_ensemble = 1
                model_config.data.common.num_recycle = max_recycles
                model_config.model.num_recycle = max_recycles
            else: # multimer
                model_config.model.num_ensemble_eval = 1
                model_config.model.num_recycle = max_recycles
            if model_num == 1:
                model_runner_12 = alpha_model.RunModel(model_config, model_params[model_name])
            elif model_num == 3:
                model_runner_345 = alpha_model.RunModel(model_config, model_params[model_name])
    return model_params, model_runner_12, model_runner_345

def run_msa_search(msa_type, query_sequences, query_fasta, seq_to_msa_d, use_templates, out_dir, jobname, logger):
    run_multimer_system = len(query_sequences) > 1
    max_template_hits = MAX_TEMPLATE_HITS if use_templates else 0
    if msa_type == 'alphafold_default':
        if run_multimer_system:
            template_searcher = hmmsearch.Hmmsearch(binary_path=hmmsearch_binary_path, hmmbuild_binary_path=hmmbuild_binary_path, database_path=pdb_seqres_database_path)
            template_featurizer = templates.HmmsearchHitFeaturizer(mmcif_dir=template_mmcif_dir, max_template_date='2100-10-28', max_hits=max_template_hits, kalign_binary_path=kalign_binary_path, release_dates_path=None, obsolete_pdbs_path=obsolete_pdbs_path)
        else:
            template_searcher = hhsearch.HHSearch(binary_path=hhsearch_binary_path, databases=[pdb70_database_path])
            template_featurizer = templates.HhsearchHitFeaturizer(mmcif_dir=template_mmcif_dir, max_template_date='2100-10-28', max_hits=max_template_hits, kalign_binary_path=kalign_binary_path, release_dates_path=None, obsolete_pdbs_path=obsolete_pdbs_path)
        logger.info('setting up data pipeline')
        monomer_data_pipeline = alpha_pipeline.DataPipeline(
            jackhmmer_binary_path=jackhmmer_binary_path,
            hhblits_binary_path=hhblits_binary_path,
            uniref90_database_path=uniref90_database_path,
            mgnify_database_path=mgnify_database_path,
            bfd_database_path=bfd_database_path,
            uniref30_database_path=uniref30_database_path,
            small_bfd_database_path=None,
            template_searcher=template_searcher,
            template_featurizer=template_featurizer,
            use_small_bfd=False,
            use_precomputed_msas=False)
        if run_multimer_system:
            data_pipeline = adapted.Cached_DataPipeline(
                monomer_data_pipeline=monomer_data_pipeline,
                jackhmmer_binary_path=jackhmmer_binary_path,
                uniprot_database_path=uniprot_database_path,
                use_precomputed_msas=False,
                seq_to_features_cache=seq_to_msa_d
            )
        else:
            data_pipeline = monomer_data_pipeline
        msa_dir = f'{out_dir}/{jobname}_seq_all'
        if not os.path.exists(msa_dir): os.mkdir(msa_dir)
        feature_dict = data_pipeline.process(input_fasta_path=query_fasta, msa_output_dir=msa_dir)

        return feature_dict
    elif msa_type == 'mmseqs2_server':
        if run_multimer_system:
            # special stuff
            if use_templates:
                unpaired_a3m_lines, template_paths = adapted.run_mmseqs2(query_sequences, f'{out_dir}/{jobname}', True, use_templates=True, use_pairing=False)
                paired_a3m_lines = adapted.run_mmseqs2(query_sequences, f'{out_dir}/{jobname}', True, use_templates=False, use_pairing=True)
                template_features = []
                for query_sequence, a3m_lines_unpaired, template_path in zip(query_sequences, unpaired_a3m_lines, template_paths):
                    tt = None
                    if template_path:
                        try:
                            tt = mk_template(query_sequence, a3m_lines_unpaired, template_path, logger)
                        except RuntimeError as ex:
                            print(f'Error in template construction for {template_path}')
                            print(ex)
                            logger.info(f'Error in template construction for {template_path}')
                            logger.info(ex)
                    if tt == None:
                        tt = mk_placeholder_template(1,len(query_sequence))
                    template_features.append(tt)
            else:
                unpaired_a3m_lines = adapted.run_mmseqs2(query_sequences, f'{out_dir}/{jobname}', True, use_templates=False, use_pairing=False)
                paired_a3m_lines = adapted.run_mmseqs2(query_sequences, f'{out_dir}/{jobname}', True, use_templates=False, use_pairing=True)
                template_features = [mk_placeholder_template(1,len(seq)) for seq in query_sequences]

            features_for_chain = {}
            chain_cnt = 0
            for chain_seq, chain_unp_msa, chain_p_msa, chain_temp_feat in zip(query_sequences, unpaired_a3m_lines, paired_a3m_lines, template_features):
                msa = alpha_pipeline.parsers.parse_a3m(chain_unp_msa)
                feature_dict = {
                    **alpha_pipeline.make_sequence_features(sequence=chain_seq, description=""none"", num_res=len(chain_seq)),
                    **alpha_pipeline.make_msa_features([msa]),
                    **chain_temp_feat
                }

                parsed_paired_msa = alpha_pipeline.parsers.parse_a3m(chain_p_msa)
                paired_feature_dict = {
                    f""{k}_all_seq"": v for k, v in adapted.my_make_msa_features_keep_duplicates([parsed_paired_msa]).items()
                }
                feature_dict.update(paired_feature_dict)

                features_for_chain[chr(ord('A')+chain_cnt)] = feature_dict
                chain_cnt += 1

            return adapted.process_multimer_features(features_for_chain)

        else:
            query_sequence = query_sequences[0]
            if use_templates:
                a3m_lines_unpaired, template_paths = adapted.run_mmseqs2(query_sequence, f'{out_dir}/{jobname}', True, use_templates=True)
                if template_paths[0] is not None:
                    template_features = mk_template(query_sequence, a3m_lines_unpaired[0], template_paths[0], logger)
                else:
                    template_features = mk_placeholder_template(1, len(query_sequence))
            else:
                a3m_lines_unpaired = adapted.run_mmseqs2(query_sequence, f'{out_dir}/{jobname}', True, use_templates=False)
                template_features = mk_placeholder_template(1, len(query_sequence))
                print('bbb template_aatype',template_features['template_aatype'].shape if 'template_aatype' in template_features else 'not present')

            msas = [alpha_pipeline.parsers.parse_a3m(a3m_lines_unpaired[0])]
            feature_dict = {
                **alpha_pipeline.make_sequence_features(sequence=query_sequence, description=""none"", num_res=len(query_sequence)),
                **alpha_pipeline.make_msa_features(msas=msas),
                **template_features
            }

            return feature_dict
    else:
        raise NotImplementedError(msa_type)

# returns a filled in template dict
# the dict contains:
#   - 'template_aatype' - shape (num_templates, num_residues, 22)
#   - 'template_all_atom_masks' - shape (num_templates, num_residues, 37)
#   - 'template_all_atom_positions' - shape (num_templates, num_residues, 37, 3)
#   - 'template_domain_names' - shape (num_templates, )
#   - 'template_sum_probs' - shape (num_templates, 1)
# Eventually, this dict is processed by AlphaFold code and turned into input features for AlphaFold
def mk_template(query_sequence, a3m_lines, template_paths, logger):
    # default settings
    template_featurizer = alpha_templates.HhsearchHitFeaturizer(
        mmcif_dir=template_paths,
        max_template_date=""2100-01-01"",
        max_hits=20,
        kalign_binary_path=""kalign"",
        release_dates_path=None,
        obsolete_pdbs_path=None)
    hhsearch_pdb70_runner = hhsearch.HHSearch(binary_path=""hhsearch"", databases=[f""{template_paths}/pdb70""])
    hhsearch_result = hhsearch_pdb70_runner.query(a3m_lines)
    hhsearch_hits = alphafold.data.parsers.parse_hhr(hhsearch_result)
    templates_result = template_featurizer.get_templates(query_sequence,
                                                         hhsearch_hits)

    logger.info('Templates found:')
    logger.info('\t'.join(['hit_id','query_start_idx', 'query_stop_idx', 'hit_start_idx', 'hit_stop_idx', 'hit_info']))
    for hit in hhsearch_hits:
        name = hit.name
        id = name.split(' ')[0]
        info = ' '.join(name.split(' ')[1:])
        query_start, query_stop = min(x for x in hit.indices_query if x != -1), max(hit.indices_query)
        hit_start, hit_stop = min(x for x in hit.indices_hit if x != -1), max(hit.indices_hit)
        logger.info('\t'.join(str(x) for x in [id, query_start, query_stop, hit_start, hit_stop, info]))

    # check for empty template - otherwise problems with shapes
    if len(templates_result.features['template_aatype']) == 0:
        return mk_placeholder_template(1, len(query_sequence))
    else:
        return templates_result.features

# returns a template placeholder, with size None in the first dimension
def mk_placeholder_template(num_templates_, num_res_):
    print(f'Dummy template created ({num_templates_})') ### TMP
    return {
        'template_aatype': np.zeros([num_templates_, num_res_, 22], np.float32),
        # 'template_all_atom_mask': np.zeros([num_templates_, num_res_, 37], np.float32),
        'template_all_atom_masks': np.zeros([num_templates_, num_res_, 37], np.float32),
        'template_all_atom_positions': np.zeros([num_templates_, num_res_, 37, 3], np.float32),  # 3d coords
        'template_sequence': np.zeros(20),
        'template_domain_names': np.zeros([num_templates_], np.float32),
        'template_sum_probs': np.zeros([num_templates_, 1], np.float32),
    }


# For each model to run, collect:
# - predicted structure (unrelaxed) in pdb lines
# - predicted structure (unrelaxed) as an object
# - predicted alignment error (PAE)
# - predicted local distance difference test (LDDT)
def predict_structures(logger, all_model_params, all_model_runners_12, all_model_runners_345, feature_dict, is_multimer, num_runs_per_model):
    plddts, paes, ptms, iptms = [], [], [], []
    model_names = []
    unrelaxed_pdb_lines = []
    unrelaxed_proteins = []
    for k in range(num_runs_per_model):
        model_runner_12 = all_model_runners_12[k]
        model_runner_345 = all_model_runners_345[k]
        model_params = all_model_params[k]
        for model_name, params in model_params.items():
            logger.info(f'running {model_name}')
            # swap params to avoid recompiling
            # note: models 1,2 have diff number of params compared to models 3,4,5
            if any(str(m) in model_name for m in [1, 2]): model_runner = model_runner_12
            if any(str(m) in model_name for m in [3, 4, 5]): model_runner = model_runner_345
            model_runner.params = params
            r = randint(0, 100000)
            processed_feature_dict = model_runner.process_features(feature_dict, random_seed=r)
            r = randint(0, 100000)
            prediction_result = model_runner.predict(processed_feature_dict,random_seed=r)
            if 'predicted_aligned_error' in prediction_result: paes.append(prediction_result['predicted_aligned_error'])
            if 'plddt' in prediction_result: plddts.append(prediction_result['plddt'])
            if 'ptm' in prediction_result: ptms.append(prediction_result['ptm'])
            if 'iptm' in prediction_result: iptms.append(prediction_result['iptm'])
            final_atom_mask = prediction_result[""structure_module""][""final_atom_mask""]
            b_factors = prediction_result[""plddt""][:, None] * final_atom_mask

            unrelaxed_protein = alpha_protein.from_prediction(processed_feature_dict, prediction_result, b_factors, remove_leading_feature_dimension = not is_multimer)
            unrelaxed_pdb_lines.append(alpha_protein.to_pdb(unrelaxed_protein))
            unrelaxed_proteins.append(unrelaxed_protein)
            model_names.append(f'{model_name}x{k+1}')
    return unrelaxed_pdb_lines, unrelaxed_proteins, paes, plddts, ptms, iptms, model_names

# Rerank based on pLDDT, relax best model if desired, and write pdb files
def rank_relax_write(logger, unrelaxed_pdb_lines, unrelaxed_proteins, plddts, paes, ptms, iptms, out_dir, jobname, do_relax, model_names, is_multimer):
    logger.info(f'Reranking based on {""pLDDT"" if not is_multimer else ""0.2*PTM+0.8*iPTM""}...')
    combined = []
    if iptms: combined = [0.2*_ptm+0.8*_iptm for _ptm, _iptm in zip(ptms, iptms)]
    if is_multimer:
        rank = np.asarray(ptms).argsort()[::-1]
    else:
        rank = np.mean(plddts, -1).argsort()[::-1]
    plddt_pae_per_model = {}
    for n, r in enumerate(rank):
        logger.info(f""rank_{n+1}_{model_names[r]} plddt={np.mean(plddts[r]):2.3f}, ptm={ptms[r]:2.3f}"" + (f"", iptm={iptms[r]:2.3f}, 0.2ptm+0.8iptm={combined[r]:2.3f}"" if iptms else """"))
        unrelaxed_pdb_path = f'{out_dir}/{jobname}_unrelaxed_rank_{n+1:02d}_{model_names[r]}.pdb' if len(model_names)>9 else f'{out_dir}/{jobname}_unrelaxed_rank_{n+1}_{model_names[r]}.pdb'
        with open(unrelaxed_pdb_path, 'w') as f:
            f.write(unrelaxed_pdb_lines[r])
        if (do_relax == 'best' and n == 0) or (do_relax == 'all'):
            try:
                amber_relaxer = relax.AmberRelaxation(max_iterations=0, tolerance=2.39, stiffness=10.0, exclude_residues=[], max_outer_iterations=20, use_gpu=True)
                relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_proteins[r])
                relaxed_pdb_path = f'{out_dir}/{jobname}_relaxed_rank_{n+1:02d}_{model_names[r]}.pdb' if len(model_names)>9 else f'{out_dir}/{jobname}_relaxed_rank_{n+1}_{model_names[r]}.pdb'
                with open(relaxed_pdb_path, 'w') as f:
                    f.write(relaxed_pdb_str)
            except ValueError:
                print('Error during relaxation - skipped!')
                logger.info('Error during relaxation - skipped!')
        plddt_pae_per_model[f""rank_{n+1:0{len(str(len(rank)))}d}_{model_names[r]}""] = {""plddt"": plddts[r], ""ptm"": ptms[r], ""pae"": paes[r]}
        if iptms: plddt_pae_per_model[f""rank_{n+1:0{len(str(len(rank)))}d}_{model_names[r]}""].update({'iptm':iptms[r], '0.2ptm+0.8iptm':combined[r]})
        # write json file
        if n == 0:
            with open(f'{out_dir}/{jobname}_rank_{n+1}_{model_names[r]}.json', 'w') as f:
                d = [{""predicted_aligned_error"": [[float(x) for x in l] for l in paes[r]],
                     ""max_predicted_aligned_error"": float(np.max(paes[r])),
                     }]
                json.dump(d, f)
    return plddt_pae_per_model

# Generates images, with beautiful visualizations of:
# - the MSA
# - the pLDDT per model
# - the PAE per model
def generate_output_images(seqs, query_prot_names, pae_plddt_per_model, msa, num_runs_per_model, out_dir, jobname):
    # gather MSA info
    if len(seqs) > 1:
        seq = ''.join(seqs)
        map_d = {c: i for i, c in enumerate(residue_constants.restypes)}
    else:
        seq = seqs[0]
        map_d = residue_constants.HHBLITS_AA_TO_ID
    seq_by_indices = [map_d[c] for c in seq]
    seqid = (np.array(seq_by_indices == msa).mean(-1))
    seqid_sort = seqid.argsort()
    non_gaps = (msa != 21).astype(float)
    non_gaps[non_gaps == 0] = np.nan
    final = non_gaps[seqid_sort] * seqid[seqid_sort, None]

    ##################################################################
    plt.figure(figsize=(14, 4), dpi=100)
    ##################################################################
    plt.subplot(1, 2, 1)
    plt.title(""Sequence coverage"")
    plt.imshow(final,
               interpolation='nearest', aspect='auto',
               cmap=""rainbow_r"", vmin=0, vmax=1, origin='lower')
    plt.plot((msa != 21).sum(0), color='black')
    plt.xlim(-0.5, msa.shape[1] - 0.5)
    plt.ylim(-0.5, msa.shape[0] - 0.5)
    plt.colorbar(label=""Sequence identity to query"", )
    plt.xlabel(""Positions"")
    plt.ylabel(""Sequences"")

    ##################################################################
    plt.subplot(1, 2, 2)
    plt.title(""Predicted LDDT per position"")
    for model_name, value in pae_plddt_per_model.items():
        plt.plot(value[""plddt""], label=model_name)
    plt.ylim(0, 100)
    plt.ylabel(""Predicted LDDT"")
    plt.xlabel(""Positions"")
    plt.savefig(f""{out_dir}/{jobname}_coverage_LDDT.png"")
    ##################################################################

    ##################################################################
    num_models = 5
    fig = plt.figure(figsize=(3 * num_models, 2*num_runs_per_model), dpi=100)
    for n, model_name in enumerate(sorted(pae_plddt_per_model.keys())):
        value = pae_plddt_per_model[model_name]
        if n == 0:
            best_pae = value[""pae""]
            best_name = model_name
        plt.subplot(num_runs_per_model, num_models, n + 1)
        plt.title(model_name)
        plt.imshow(value[""pae""], label=model_name, cmap=""bwr"", vmin=0, vmax=30)
        plt.colorbar()
    fig.tight_layout()
    plt.savefig(f""{out_dir}/{jobname}_PAE.png"")

    #### single best PAE
    print(f'best model: {best_name}')
    plddt, ptm = np.mean(pae_plddt_per_model[best_name][""plddt""]), pae_plddt_per_model[best_name][""ptm""]
    iptm = pae_plddt_per_model[best_name].get(""iptm"", -1)
    fig = plt.figure(figsize=(6, 3), dpi=100)
    plt.suptitle(f""{best_name}\npLDDT:{plddt:.2f} PTM:{ptm:.2f}""+(f"" iPTM:{iptm:.2f}"" if iptm != -1 else """"))

    plt.subplot(1, 2, 1)
    plt.imshow(best_pae, label=best_name, cmap=""bwr"", vmin=0, vmax=30)
    # draw lines on x and y according to the length of strings in seqs
    for i in range(1, len(seqs)):
        plt.axhline(len(''.join(seqs[:i])) + 0.5, color='black')
        plt.axvline(len(''.join(seqs[:i])) + 0.5, color='black')
    plt.colorbar()

    # add sequence labels in extra plot
    plt.subplot(1, 2, 2)
    new_np = np.zeros_like(best_pae)
    colors = ['white',
              '#FFDAB9',  # Peachpuff
              '#ADD8E6',  # Light blue
              '#FFD1DC',  # Pink
              '#90EE90',  # Light green
              '#FAFAD2',  # Light goldenrod yellow
              '#FFECDB',  # Cream
              '#E0FFFF',  # Light cyan
              '#FFE4E1',  # Misty rose
              '#F0FFF0',  # Honeydew
              '#FFF0F5'  # Lavender blush
              ][:len(seqs) + 1]

    for i in range(len(seqs)):
        before = len(''.join(seqs[:i]))
        seqlen = len(seqs[i])
        new_np[before:before + seqlen, before:before + seqlen] = i + 1
        plt.text(before + seqlen / 2, before + seqlen / 2, query_prot_names[i], color='black', ha='center', va='center',
                 fontsize=8)

    for i in range(1, len(seqs)):
        plt.axhline(len(''.join(seqs[:i])) + 0.5, color='black')
        plt.axvline(len(''.join(seqs[:i])) + 0.5, color='black')

    cmap = ListedColormap(colors)
    plt.imshow(new_np, cmap=cmap)

    fig.tight_layout()
    iptm_str = f""_iptm{int(iptm*100):02d}"" if iptm != -1 else """"
    plt.savefig(f""{out_dir}/best_PAE_plddt{int(plddt):02d}_ptm{int(ptm*100):02d}{iptm_str}.png"")
    ##################################################################

# gets a list of sequences from the fasta file (one for monomer, multiple for multimer)
def get_sequences_from_fasta(fasta_file):
    seqs = []
    seq = ''
    for line in open(fasta_file):
        if line.startswith('>'):
            if seq:
                seqs.append(seq)
                seq = ''
        elif line:
            seq += line.rstrip()
    if seq:
        seqs.append(seq)
    return seqs

def log_template_info(feature_dict, logger):
    if 'template_domain_names' in feature_dict: # monomer
        logger.info('Templates shown here are reduced to 4 later on in the pipeline.')

    residue_idx = feature_dict['residue_index']
    template_aatype = feature_dict['template_aatype']
    if len(template_aatype.shape) == 3: # extra last dimension, one-hot encoded
        template_aatype = template_aatype.argmax(axis=-1)
    chain_starts = [idx for idx in range(len(residue_idx)) if residue_idx[idx] == 0]
    chain_ids = np.zeros_like(template_aatype[0])
    for chain_start_idx in chain_starts:
        chain_ids[chain_start_idx:]+=1

    logger.info('Templates found:')
    for k, single_template_aatype in enumerate(template_aatype):
        fros, tos = [], []
        idx = 0
        while True:
            while idx < len(single_template_aatype) and single_template_aatype[idx] == 21:
                idx += 1
            if idx == len(single_template_aatype): # if this is the case, we are at the end of the sequence
                break
            chain_id = chain_ids[idx]
            fros.append(idx)
            while idx < len(single_template_aatype) and single_template_aatype[idx] != 21 and chain_id == chain_ids[idx]:
                idx += 1
            tos.append(idx-1)
            if idx == len(single_template_aatype): # if this is the case, we are at the end of the sequence
                break
        for fro, to in zip(fros, tos):
            chain_id = chain_ids[fro]
            assert chain_id == chain_ids[to]
            logger.info(f' ({k+1}) At chain #{chain_id}, positions [{residue_idx[fro]}, {residue_idx[to]}]')

def gather_best_prediction(run_dir, jobname, is_multimer):
    best_pdb, best_plddt, best_ptm = None, -1, -1
    rerank_by = 'ptm' if is_multimer else 'plddt'
    for subdir in os.listdir(run_dir):
        subdir = run_dir + '/' + subdir
        plddt, ptm = -1, -1
        for line in open(subdir + '/info.log'):
            if 'Reranking' in line and 'PTM' in line:
                rerank_by = 'ptm'
            if 'plddt=' in line:
                plddt = float(line.split('=')[1].split(',')[0])
                ptm = float(line.split('=')[2].split(',')[0].rstrip())
                break
        files = [file for file in os.listdir(subdir) if '_relaxed' in file]
        if not files:
            files = [file for file in os.listdir(subdir) if '_unrelaxed' in file]
        if files:
            pdb_file = subdir.rstrip('/') + '/' + files[0]
        else:
            pdb_file = f'pdb file missing from {subdir}'
        if (rerank_by == 'ptm' and ptm > best_ptm) or (rerank_by == 'plddt' and plddt > best_plddt):
            best_pdb, best_plddt, best_ptm = pdb_file, plddt, ptm

    best_directory = run_dir.rstrip('/') + f'/best_{jobname}/'
    if not os.path.exists(best_directory):
        os.mkdir(best_directory)
    os.system(f'cp {best_pdb} {best_directory}')
    os.system(f'cp {best_pdb[:best_pdb.rfind(""/"")]}/best_PAE*.png {best_directory}/')


if __name__ == ""__main__"":
    import sys
    print(sys.argv)
    parser = argparse.ArgumentParser()
    parser.add_argument('--seq',help='the input sequence or sequences (in case of multiple, separate with "":"")')
    parser.add_argument('--prot_names',help='the names of the proteins (in case of multiple, separate with "":"")')
    parser.add_argument('--jobname',help='the name for the job, used for file naming')
    parser.add_argument('--save_dir',help='the location where new directories (per permutation) will be created')
    parser.add_argument('--max_recycles',type=int,default=3,help='the maximum number of recycles',required=False)
    parser.add_argument('--tolerance',type=int,default=0,help='the tolerance level, decides when recycling stops',required=False)
    parser.add_argument('--do_relax',dest='do_relax',required=True)
    parser.add_argument('--num_runs_per_model',type=int,dest='num_runs_per_model',required=False,default=1)
    parser.add_argument('--no_templates',dest='use_templates',action='store_false')
    parser.set_defaults(use_templates=True)
    parser.add_argument('--msa_mode',type=str,default='mmseqs2_server',help='choose one of ""mmseqs2_server"", ""mmseqs2_local"", ""alphafold_default"", ""single_sequence""',required=False)
    parser.add_argument('--do_gather_best',dest='do_gather_best',action='store_true')
    parser.set_defaults(do_gather_best=False)
    args = parser.parse_args()
    run_alphafold_advanced_complex(seq=args.seq,
                                   prot_names=args.prot_names,
                                   jobname=args.jobname,
                                   save_dir=args.save_dir,
                                   max_recycles=args.max_recycles,
                                   tolerance=args.tolerance,
                                   use_templates=args.use_templates,
                                   num_runs_per_model=args.num_runs_per_model,
                                   do_relax=args.do_relax,
                                   msa_mode=args.msa_mode,
                                   do_gather_best=args.do_gather_best)
    print('Finished run!')","Python"
"AlphaFold","jasperzuallaert/VIBFold","visualize_alphafold_results.py",".py","3583","80","import glob
import math
import os
import numpy as np
from matplotlib import pyplot as plt
import argparse
import pickle

def get_pae_plddt(model_names):
    out = {}
    for i,name in enumerate(model_names):
        d = pickle.load(open(name,'rb'))
        basename = os.path.basename(name)
        basename = basename[basename.index('model'):]
        out[f'{basename}'] = {'plddt': d['plddt'], 'pae':d['predicted_aligned_error']}
    return out

def generate_output_images(feature_dict, out_dir, name, pae_plddt_per_model):
    msa = feature_dict['msa']
    seqid = (np.array(msa[0] == msa).mean(-1))
    seqid_sort = seqid.argsort()
    non_gaps = (msa != 21).astype(float)
    non_gaps[non_gaps == 0] = np.nan
    final = non_gaps[seqid_sort] * seqid[seqid_sort, None]

    ##################################################################
    plt.figure(figsize=(14, 4), dpi=100)
    ##################################################################
    plt.subplot(1, 2, 1)
    plt.title(""Sequence coverage"")
    plt.imshow(final,
               interpolation='nearest', aspect='auto',
               cmap=""rainbow_r"", vmin=0, vmax=1, origin='lower')
    plt.plot((msa != 21).sum(0), color='black')
    plt.xlim(-0.5, msa.shape[1] - 0.5)
    plt.ylim(-0.5, msa.shape[0] - 0.5)
    plt.colorbar(label=""Sequence identity to query"", )
    plt.xlabel(""Positions"")
    plt.ylabel(""Sequences"")

    ##################################################################
    plt.subplot(1, 2, 2)
    plt.title(""Predicted LDDT per position"")
    for model_name, value in pae_plddt_per_model.items():
        plt.plot(value[""plddt""], label=model_name)
    plt.ylim(0, 100)
    plt.ylabel(""Predicted LDDT"")
    plt.xlabel(""Positions"")
    plt.savefig(f""{out_dir}/{name+('_' if name else '')}coverage_LDDT.png"")
    ##################################################################

    ##################################################################
    num_models = 5 # columns
    num_runs_per_model = math.ceil(len(model_names)/num_models)
    fig = plt.figure(figsize=(3 * num_models, 2 * num_runs_per_model), dpi=100)
    for n, (model_name, value) in enumerate(pae_plddt_per_model.items()):
        plt.subplot(num_runs_per_model, num_models, n + 1)
        plt.title(model_name)
        plt.imshow(value[""pae""], label=model_name, cmap=""bwr"", vmin=0, vmax=30)
        plt.colorbar()
    fig.tight_layout()
    plt.savefig(f""{out_dir}/{name+('_' if name else '')}PAE.png"")
    ##################################################################

parser = argparse.ArgumentParser()
parser.add_argument('--input_dir',dest='input_dir',required=True)
parser.add_argument('--name',dest='name')
parser.set_defaults(name='')
parser.add_argument('--output_dir',dest='output_dir')
parser.set_defaults(output_dir='')
args = parser.parse_args()

feature_dict = pickle.load(open(f'{args.input_dir}/features.pkl','rb'))
# is_multimer = ('result_model_1_multimer.pkl' in [os.path.basename(f) for f in os.listdir(path=args.input_dir)])
# is_ptm = ('result_model_1_ptm.pkl' in [os.path.basename(f) for f in os.listdir(path=args.input_dir)])
# model_names = [f'{args.input_dir}/result_model_{f}{""_multimer"" if is_multimer else ""_ptm"" if is_ptm else """"}.pkl' for f in range(1,6)]
model_names = sorted(glob.glob(f'{args.input_dir}/result_*.pkl'))

pae_plddt_per_model = get_pae_plddt(model_names)
generate_output_images(feature_dict, args.output_dir if args.output_dir else args.input_dir, args.name, pae_plddt_per_model)","Python"
"AlphaFold","jasperzuallaert/VIBFold","submit_vibfold_interaction_candidates.py",".py","4810","111","MAIN_FASTA = 'fastas/test_main.fasta' # list of proteins, to interact with each of the proteins in CANDIDATES_FASTA
CANDIDATES_FASTA = 'fastas/test_candidates.fasta' # list of proteins, to interact with each of the proteins in MAIN_FASTA

MSA_MODE = 'mmseqs2_server'                # 'alphafold_default' or 'mmseqs2_server'
SAVE_DIR = 'results/test'                # location of results directory between '' - abs or rel path possible
DO_RELAX = 'none'                             # 'all', 'best' or 'none'
USE_TEMPLATES = True                          # True, False
MAX_RECYCLES = 3                              # default == 3
NUM_RUNS_PER_MODEL = 5                  # number of runs per model, with different random seed

# IS_COMPLEX = True # Fixed value, this is no longer considered

import subprocess
import os
import time

timestamp = time.strftime('%Y%m%d_%H%M%S') + '_'

def submit(MAIN_FASTA, CANDIDATES_FASTA, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES, NUM_RUNS_PER_MODEL):
    assert ' ' not in MAIN_FASTA, 'The name of your FASTA files cannot contain any spaces'
    assert ' ' not in CANDIDATES_FASTA, 'The name of your FASTA files cannot contain any spaces'
    # automatically select accelgor/joltik based on output of 'ml'
    module_info = subprocess.check_output('ml',shell=True).decode('utf-8')
    cluster = 'accelgor' if 'accelgor' in module_info else \
              'joltik' if 'joltik' in module_info else ''
    if not cluster:
        raise NotImplementedError('Cluster joltik/accelgor not found in ""ml"" output. Did you use ""module swap cluster/joltik"" (or other)?')

    def read_fasta(file):
        fasta_d = {}
        seq = ''
        ctr = 1
        for line in open(file):
            if line.startswith('>'):
                if seq:
                    fasta_d[prot_id] = seq
                    seq = ''
                prot_id = f'{ctr}_{line.rstrip().lstrip("">"").replace("" "", ""_"").replace("":"", ""_"").replace(""("", """").replace("")"", """")}'
                if '|' in prot_id: prot_id = prot_id.split('|')[1]
                ctr+=1
            elif line:
                seq += line.rstrip()
        if seq:
            fasta_d[prot_id] = seq
        return fasta_d

    proteins_1 = read_fasta(MAIN_FASTA)
    proteins_2 = read_fasta(CANDIDATES_FASTA)

    all_interactions = {}
    for prot1_id, seq1 in proteins_1.items():
        for prot2_id, seq2 in proteins_2.items():
            prot1_id = prot1_id.replace(' ', '_').replace(':', '_').replace('(', '').replace(')', '')
            prot2_id = prot2_id.replace(' ', '_').replace(':', '_').replace('(', '').replace(')', '')
            prot_names = f'{prot1_id}:{prot2_id}'
            all_interactions[prot_names] = f'{seq1}:{seq2}'

    if not SAVE_DIR.startswith('/'):
        SAVE_DIR = f'$PBS_O_WORKDIR/{SAVE_DIR}'

        for prot_names, seq in all_interactions.items():
            run_save_dir = f'{SAVE_DIR}/{timestamp}_{prot_names.replace("":"", ""_"")}'
            script_content = f'''#!/bin/bash
#PBS -N interactions_VIBFold_{prot_names.replace("":"", ""_"")}
#PBS -l nodes=1:ppn={12 if (cluster == 'accelgor' or cluster == 'litleo') else 8}{"",gpus=1"" if (cluster == 'accelgor' or cluster == 'joltik' or cluster == 'litleo') else """"}
#PBS -l mem={125 if cluster == 'accelgor' else 64 if (cluster == 'joltik' or cluster == 'litleo') else 20}g
#PBS -l walltime=48:00:00

module load Python/3.11.3-GCCcore-12.3.0

module load tqdm/4.66.1-GCCcore-12.3.0 
module load matplotlib/3.7.2-gfbf-2023a
module load AlphaFold/2.3.2-foss-2023a{""-CUDA-12.1.1"" if (cluster == 'accelgor' or cluster == 'joltik' or cluster == 'litleo') else """"}
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310

PROTEIN={prot_names.replace("":"", ""_"")}

jobname=""$PROTEIN""_""$PBS_JOBID""

SAVEDIR={run_save_dir}
mkdir -p $SAVEDIR

cd $PBS_O_WORKDIR
python VIBFold.py \
 --seq {seq} \
 --prot_names ""{prot_names}"" \
 --jobname $jobname \
 --save_dir $SAVEDIR \
 --do_relax {DO_RELAX} \
 {""--no_templates"" if not USE_TEMPLATES else """"} \
 --msa_mode {MSA_MODE} \
 --max_recycles {MAX_RECYCLES} \
 --num_runs_per_model {NUM_RUNS_PER_MODEL} \
 --do_gather_best
'''

            scriptname = 'submit_new.sh'
            f = open(scriptname,'w')
            print(script_content,file=f)
            f.close()

            print()
            print(f'############# submitting {prot_names.replace("":"", ""_"")} #############')
            subprocess.Popen(['echo',f'{prot_names.replace("":"", ""_"")}'],shell=False)
            subprocess.Popen(['qsub',f'{scriptname}'],shell=False).wait()
            subprocess.Popen(['rm',f'{scriptname}'],shell=False).wait()
            print()

if __name__ == ""__main__"":
    submit(MAIN_FASTA, CANDIDATES_FASTA, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES, NUM_RUNS_PER_MODEL)
","Python"
"AlphaFold","jasperzuallaert/VIBFold","submit_vibfold_with_reservation.py",".py","4260","109","
FASTA_FILE = 'fastas/NAME.fasta'        # location of fasta file between '' - absolute or relative path possible
IS_COMPLEX = True                       # True or False
MSA_MODE = 'mmseqs2_server'             # 'alphafold_default' or 'mmseqs2_server'
SAVE_DIR = 'results/naa10_practical'               # location of results directory between '' - abs or rel path possible
DO_RELAX = 'none'                       # 'all', 'best' or 'none'
NUM_RUNS_PER_MODEL = 1                  # number of runs per model, with different random seed
USE_TEMPLATES = True                   # True, False
MAX_RECYCLES = 3                        # default == 3


import subprocess
import os
import time

timestamp = time.strftime('%Y%m%d_%H%M%S') + '_'

def submit(FASTA_FILE, IS_COMPLEX, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES):
    assert ' ' not in FASTA_FILE, 'The name of your FASTA file cannot contain any spaces'
    # automatically select accelgor/joltik based on output of 'ml'
    module_info = subprocess.check_output('ml',shell=True).decode('utf-8')
    cluster = 'accelgor' if 'accelgor' in module_info else \
              'joltik' if 'joltik' in module_info else \
              'donphan' if 'donphan' in module_info else ''
    if not cluster:
        raise NotImplementedError('Cluster joltik/accelgor/donphan not found in ""ml"" output. Did you use ""module swap cluster/joltik"" (or other)?')

    fasta_d = {}
    seq = ''
    ctr = 1
    for line in open(FASTA_FILE):
        if line.startswith('>'):
            if seq:
                fasta_d[prot_id] = seq
                seq = ''
            prot_id = f'{ctr}_{line.rstrip().lstrip("">"").replace("" "", ""_"").replace("":"", ""_"").replace(""("", """").replace("")"", """")}'
            if '|' in prot_id: prot_id = prot_id.split('|')[1]
            ctr+=1
        elif line:
            seq += line.rstrip()
    if seq:
        fasta_d[prot_id] = seq

    all_seqs = {}
    all_protnames = {}
    if not IS_COMPLEX: # create one new FASTA file per entry
        for prot_id, seq in fasta_d.items():
            all_seqs[prot_id] = seq
            all_protnames[prot_id] = prot_id
    else: # create a copy of the multi-entry FASTA file
        fasta_id = os.path.basename(FASTA_FILE).split('.')[0]
        seqs = fasta_d.values()
        all_protnames[fasta_id] = ':'.join(fasta_d.keys())
        all_seqs[fasta_id] = ':'.join(seqs)

    if not SAVE_DIR.startswith('/'):
        SAVE_DIR = f'$PBS_O_WORKDIR/{SAVE_DIR}'

        for prot_id, seq in all_seqs.items():
            prot_names = all_protnames[prot_id]
            run_save_dir = f'{SAVE_DIR}/{timestamp}_{prot_id}'
            script_content = f'''#!/bin/bash
#PBS -N VIBFold_{prot_id}
#PBS -l nodes=1:ppn={12 if cluster=='accelgor' else 8}{"",gpus=1"" if cluster in ['accelgor','joltik'] else """"}
#PBS -l mem={125 if cluster=='accelgor' else 64 if cluster=='joltik' else 20}g
#PBS -l walltime=48:00:00

module load Python/3.11.3-GCCcore-12.3.0

module load tqdm/4.66.1-GCCcore-12.3.0 
module load matplotlib/3.7.2-gfbf-2023a
module load AlphaFold/2.3.2-foss-2023a{""-CUDA-12.1.1"" if cluster in ['accelgor','joltik'] else """"}
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310
PROTEIN={prot_id}

jobname=""$PROTEIN""_""$PBS_JOBID""

SAVEDIR={run_save_dir}
mkdir -p $SAVEDIR

cd $PBS_O_WORKDIR
python VIBFold.py \
 --seq {seq} \
 --prot_names ""{prot_names}"" \
 --jobname $jobname \
 --save_dir $SAVEDIR \
 --do_relax {DO_RELAX} \
 {""--no_templates"" if not USE_TEMPLATES else """"} \
 --msa_mode {MSA_MODE} \
 --num_runs_per_model {NUM_RUNS_PER_MODEL} \
 --max_recycles {MAX_RECYCLES} \
 --do_gather_best
'''

            scriptname = 'submit_new.sh'
            f = open(scriptname,'w')
            print(script_content,file=f)
            f.close()

            print()
            print(f'############# submitting {prot_id} #############')
            subprocess.Popen(['echo',f'{prot_id}'],shell=False)
            subprocess.Popen(['qsub',f'{scriptname}', '--pass=reservation=D013637_2023'],shell=False).wait()
            subprocess.Popen(['rm',f'{scriptname}'],shell=False).wait()
            print()

if __name__ == ""__main__"":
    submit(FASTA_FILE, IS_COMPLEX, MSA_MODE, SAVE_DIR, DO_RELAX, USE_TEMPLATES, MAX_RECYCLES)
","Python"
"AlphaFold","jasperzuallaert/VIBFold","boltz_job_script.sh",".sh","371","14","#!/bin/bash
#PBS -N Boltz_test
#PBS -l nodes=1:ppn=8:gpus=1
#PBS -l mem=32gb
#PBS -l walltime=6:0:0

# input file MUST have the file extension added here
input_file=RBD_VHHE.yaml
out_dir=results/RBD_VHHE_boltz_pred

ml Boltz-1/0.4.1-foss-2023a-CUDA-12.1.1
cd $PBS_O_WORKDIR
boltz predict $input_file --write_full_pae --cache $VSC_DATA --out_dir $out_dir --use_msa_server
","Shell"
"AlphaFold","jasperzuallaert/VIBFold","visualize_boltz_results.py",".py","2925","74"," 
import glob
import math
import os
import numpy as np
from matplotlib import pyplot as plt
import argparse

def get_pae_plddt(input_dir):
    out = {}
    
    # Look for PAE and pLDDT files recursively in predictions directory
    pae_files = sorted(glob.glob(f'{input_dir}/predictions/**/pae_*.npz', recursive=True))
    plddt_files = sorted(glob.glob(f'{input_dir}/predictions/**/plddt_*.npz', recursive=True))
    
    if not pae_files or not plddt_files:
        print(f""Warning: No PAE files found: {len(pae_files)}, No pLDDT files found: {len(plddt_files)}"")
        print(f""Looking in: {input_dir}/predictions/**/"")
        return out
    
    # For Boltz-1, there should be only one of each
    pae_file = pae_files[0]
    plddt_file = plddt_files[0]
    
    print(f""Loading PAE from: {pae_file}"")
    print(f""Loading pLDDT from: {plddt_file}"")
    
    pae_data = np.load(pae_file)['pae']
    plddt_data = np.load(plddt_file)['plddt']
    
    basename = os.path.basename(pae_file).replace('pae_', '').replace('.npz', '')
    out[f'boltz_model_{basename}'] = {'plddt': plddt_data, 'pae': pae_data}
    
    return out

def generate_output_images(out_dir, name, pae_plddt_per_model):
    ##################################################################
    plt.figure(figsize=(14, 4), dpi=100)
    ##################################################################
    plt.subplot(1, 1, 1)
    plt.title(""Predicted LDDT per position"")
    for model_name, value in pae_plddt_per_model.items():
        plt.plot(value[""plddt""], label=model_name)
    plt.ylim(0, 1)
    plt.ylabel(""Predicted LDDT"")
    plt.xlabel(""Positions"")
    plt.legend()
    plt.savefig(f""{out_dir}/{name+('_' if name else '')}LDDT.png"")
    plt.close()
    ##################################################################

    ##################################################################
    num_models = 1 # columns
    num_runs_per_model = math.ceil(len(pae_plddt_per_model)/num_models)
    fig = plt.figure(figsize=(3 * num_models, 2 * num_runs_per_model), dpi=100)
    for n, (model_name, value) in enumerate(pae_plddt_per_model.items()):
        plt.subplot(num_runs_per_model, num_models, n + 1)
        plt.imshow(value[""pae""], label=model_name, cmap=""bwr"", vmin=0, vmax=30)
        plt.colorbar()
    fig.tight_layout()
    plt.savefig(f""{out_dir}/{name+('_' if name else '')}PAE.png"")
    plt.close()
    ##################################################################

parser = argparse.ArgumentParser()
parser.add_argument('--input_dir',dest='input_dir',required=True)
parser.add_argument('--name',dest='name')
parser.set_defaults(name='')
parser.add_argument('--output_dir',dest='output_dir')
parser.set_defaults(output_dir='')
args = parser.parse_args()

pae_plddt_per_model = get_pae_plddt(args.input_dir)
generate_output_images(args.output_dir if args.output_dir else args.input_dir, args.name, pae_plddt_per_model)","Python"
"AlphaFold","jasperzuallaert/VIBFold","generate_alphapulldown_scripts.sh",".sh","5352","142","#!/bin/bash

# Check if the correct number of arguments is provided
if [ ""$#"" -ne 4 ]; then
  echo ""Usage: $0 output_directory fasta_location_1 fasta_location_2 number_of_predictions_per_model""
  exit 1
fi

# Create the output directory if it doesn't exist
mkdir -p ""$1""

# Assign arguments to variables
output_directory=`realpath $1`
fasta_location_1=`realpath $2`
fasta_location_2=`realpath $3`
pred_per_model=$4

# Function to count the number of entries in a fasta file
count_entries() {
  grep -c '^>' ""$1""
}

# Function to create a protein list from a fasta file
create_protein_list() {
  grep '^>' ""$1"" | sed 's/^>//' | sed 's/[|= #;]/_/g' > ""$2""
}

# Count entries in each fasta file
count1=$(count_entries ""$fasta_location_1"")
count2=$(count_entries ""$fasta_location_2"")
total_count=$((count1 + count2))
total_preds=$((count1 * count2))

# Create protein lists
protein_list_1=""${output_directory}/protein_list_1.txt""
protein_list_2=""${output_directory}/protein_list_2.txt""

create_protein_list ""$fasta_location_1"" ""$protein_list_1""
create_protein_list ""$fasta_location_2"" ""$protein_list_2""

# Create MSA job script
msa_job_script=""${output_directory}/msa_job_script.sh""
cat <<EOT > ""$msa_job_script""
#PBS -N msa_pulldown
#PBS -l nodes=1:ppn=4
#PBS -l walltime=12:00:00
#PBS -l mem=80gb

module load AlphaPulldown/2.0.3-foss-2023a
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310
cd $output_directory

create_individual_features.py \
  --fasta_paths=$fasta_location_1,$fasta_location_2 \
  --data_dir=\$ALPHAFOLD_DATA_DIR \
  --save_msa_files=False \
  --output_dir=$output_directory/msas \
  --use_precomputed_msas=False \
  --max_template_date=2050-01-01 \
  --skip_existing=True \
  --uniref30_database_path=/arcanine/scratch/gent/apps/AlphaFold/20230310/uniref30/UniRef30_2021_03 \
  --seq_index \${PBS_ARRAYID}
EOT

instr_file=$output_directory/instructions.txt
echo """" > $instr_file
# Print MSA job script command
echo ""######################################"" >> $instr_file
echo ""#####     STEP 1. MSA search     #####"" >> $instr_file
echo ""######################################"" >> $instr_file
echo "">>> To launch the MSA search, run: "" >> $instr_file
echo ""          module swap cluster/doduo"" >> $instr_file
echo ""          qsub -m ae $msa_job_script -t1-$total_count -o $output_directory/output_msa.log -e $output_directory/error_msa.log"" >> $instr_file
echo "">>> Note that you can change the doduo cluster to any CPU cluster you wish"" >> $instr_file
echo "">>> Wait until all jobs have been successfully completed - you will receive an email when finished."" >> $instr_file
echo "" "" >> $instr_file

# Create the GPU job script
predict_job_script=""${output_directory}/predict_job_script.sh""
cat <<EOT > ""$predict_job_script""
#PBS -N predict_pulldown
#PBS -l nodes=1:ppn=8,gpus=1
#PBS -l mem=64g
#PBS -l walltime=12:00:00

module load AlphaPulldown/2.0.3-foss-2023a-CUDA-12.1.1

cd $output_directory
export EBROOTCOLABFOLD=\$PBS_O_WORKDIR/$output_directory
export ALPHAFOLD_DATA_DIR=/arcanine/scratch/gent/apps/AlphaFold/20230310

run_multimer_jobs.py --mode=pulldown \
--num_cycle=3 \
--num_predictions_per_model=$pred_per_model \
--output_path=$output_directory/preds/ \
--data_dir=\$ALPHAFOLD_DATA_DIR \
--protein_lists=$protein_list_1,$protein_list_2 \
--monomer_objects_dir=$output_directory/msas/ \
--compress_result_pickles=True \
--remove_result_pickles=True \
--job_index=\${PBS_ARRAYID}
EOT

echo ""######################################"" >> $instr_file
echo ""#####     STEP 2. Prediction     #####"" >> $instr_file
echo ""######################################"" >> $instr_file
echo "">>> To launch the prediction search, run: "" >> $instr_file
echo ""          module swap cluster/joltik"" >> $instr_file
echo ""          qsub -m ae $predict_job_script -t1-$total_preds -o $output_directory/output_predict.log -e $output_directory/output_predict.log"" >> $instr_file
echo "">>> Note that you can change the joltik cluster to any GPU cluster you wish"" >> $instr_file
echo "">>> You will receive an email when finished."" >> $instr_file
echo "" "" >> $instr_file


gather_results_script=""${output_directory}/gather_results_script.sh""
cat <<EOT > ""$gather_results_script""
#PBS -N gather_results_pulldown
#PBS -l nodes=1:ppn=4
#PBS -l walltime=12:00:00
#PBS -l mem=16gb

cd $output_directory

apptainer exec /arcanine/scratch/gent/apps/AlphaPulldown/alpha-analysis_jax_0.4.sif run_get_good_pae.sh --cutoff=10 --output_dir=$output_directory/preds 
EOT

echo ""######################################"" >> $instr_file
echo ""#####      STEP 3. Results       #####"" >> $instr_file
echo ""######################################"" >> $instr_file
echo "">>> Finally, you can gather results in a csv by running"" >> $instr_file
echo ""          module swap cluster/doduo"" >> $instr_file
echo ""          qsub -m ae $gather_results_script -o $output_directory/output_gather.log -e $output_directory/output_gather.log"" >> $instr_file
echo "">>> Note that you can adjust the cutoff parameter in the file to include lower quality interactions as well"" >> $instr_file
echo "">>> Note that you can change the doduo cluster to any CPU cluster you wish"" >> $instr_file
echo "" "" >> $instr_file

echo "">>> Outputs can be found in $output_directory/outputs."" >> $instr_file

echo ""Instructions can be found at $instr_file as well""
cat $instr_file

","Shell"
"AlphaFold","allanchen95/ESMPair","model_ori.py",".py","15770","439","# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math

import torch
import torch.nn as nn
import torch.nn.functional as F

from .modules import (
    TransformerLayer,
    AxialTransformerLayer,
    LearnedPositionalEmbedding,
    SinusoidalPositionalEmbedding,
    RobertaLMHead,
    ESM1bLayerNorm,
    ContactPredictionHead,
)

from .axial_attention import RowSelfAttention, ColumnSelfAttention


class ProteinBertModel(nn.Module):
    @classmethod
    def add_args(cls, parser):
        parser.add_argument(
            ""--num_layers"", default=36, type=int, metavar=""N"", help=""number of layers""
        )
        parser.add_argument(
            ""--embed_dim"", default=1280, type=int, metavar=""N"", help=""embedding dimension""
        )
        parser.add_argument(
            ""--logit_bias"", action=""store_true"", help=""whether to apply bias to logits""
        )
        parser.add_argument(
            ""--ffn_embed_dim"",
            default=5120,
            type=int,
            metavar=""N"",
            help=""embedding dimension for FFN"",
        )
        parser.add_argument(
            ""--attention_heads"",
            default=20,
            type=int,
            metavar=""N"",
            help=""number of attention heads"",
        )

    def __init__(self, args, alphabet):
        super().__init__()
        self.args = args
        self.alphabet_size = len(alphabet)
        self.padding_idx = alphabet.padding_idx
        self.mask_idx = alphabet.mask_idx
        self.cls_idx = alphabet.cls_idx
        self.eos_idx = alphabet.eos_idx
        self.prepend_bos = alphabet.prepend_bos
        self.append_eos = alphabet.append_eos
        self.emb_layer_norm_before = getattr(self.args, ""emb_layer_norm_before"", False)
        if self.args.arch == ""roberta_large"":
            self.model_version = ""ESM-1b""
            self._init_submodules_esm1b()
        else:
            self.model_version = ""ESM-1""
            self._init_submodules_esm1()

    def _init_submodules_common(self):
        self.embed_tokens = nn.Embedding(
            self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx
        )
        self.layers = nn.ModuleList(
            [
                TransformerLayer(
                    self.args.embed_dim,
                    self.args.ffn_embed_dim,
                    self.args.attention_heads,
                    add_bias_kv=(self.model_version != ""ESM-1b""),
                    use_esm1b_layer_norm=(self.model_version == ""ESM-1b""),
                )
                for _ in range(self.args.layers)
            ]
        )

        self.contact_head = ContactPredictionHead(
            self.args.layers * self.args.attention_heads,
            self.prepend_bos,
            self.append_eos,
            eos_idx=self.eos_idx,
        )

    def _init_submodules_esm1b(self):
        self._init_submodules_common()
        self.embed_scale = 1
        self.embed_positions = LearnedPositionalEmbedding(
            self.args.max_positions, self.args.embed_dim, self.padding_idx
        )
        self.emb_layer_norm_before = (
            ESM1bLayerNorm(self.args.embed_dim) if self.emb_layer_norm_before else None
        )
        self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim)
        self.lm_head = RobertaLMHead(
            embed_dim=self.args.embed_dim,
            output_dim=self.alphabet_size,
            weight=self.embed_tokens.weight,
        )

    def _init_submodules_esm1(self):
        self._init_submodules_common()
        self.embed_scale = math.sqrt(self.args.embed_dim)
        self.embed_positions = SinusoidalPositionalEmbedding(self.args.embed_dim, self.padding_idx)
        self.embed_out = nn.Parameter(torch.zeros((self.alphabet_size, self.args.embed_dim)))
        self.embed_out_bias = None
        if self.args.final_bias:
            self.embed_out_bias = nn.Parameter(torch.zeros(self.alphabet_size))

    def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
        if return_contacts:
            need_head_weights = True

        assert tokens.ndim == 2
        padding_mask = tokens.eq(self.padding_idx)  # B, T

        x = self.embed_scale * self.embed_tokens(tokens)

        if getattr(self.args, ""token_dropout"", False):
            x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0)
            # x: B x T x C
            mask_ratio_train = 0.15 * 0.8
            src_lengths = (~padding_mask).sum(-1)
            mask_ratio_observed = (tokens == self.mask_idx).sum(-1).float() / src_lengths
            x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]

        x = x + self.embed_positions(tokens)

        if self.model_version == ""ESM-1b"":
            if self.emb_layer_norm_before:
                x = self.emb_layer_norm_before(x)
            if padding_mask is not None:
                x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))

        repr_layers = set(repr_layers)
        hidden_representations = {}
        if 0 in repr_layers:
            hidden_representations[0] = x

        if need_head_weights:
            attn_weights = []

        # (B, T, E) => (T, B, E)
        x = x.transpose(0, 1)

        if not padding_mask.any():
            padding_mask = None

        for layer_idx, layer in enumerate(self.layers):
            x, attn = layer(
                x, self_attn_padding_mask=padding_mask, need_head_weights=need_head_weights
            )
            if (layer_idx + 1) in repr_layers:
                hidden_representations[layer_idx + 1] = x.transpose(0, 1)
            if need_head_weights:
                # (H, B, T, T) => (B, H, T, T)
                attn_weights.append(attn.transpose(1, 0))

        if self.model_version == ""ESM-1b"":
            x = self.emb_layer_norm_after(x)
            x = x.transpose(0, 1)  # (T, B, E) => (B, T, E)

            # last hidden representation should have layer norm applied
            if (layer_idx + 1) in repr_layers:
                hidden_representations[layer_idx + 1] = x
            x = self.lm_head(x)
        else:
            x = F.linear(x, self.embed_out, bias=self.embed_out_bias)
            x = x.transpose(0, 1)  # (T, B, E) => (B, T, E)

        result = {""logits"": x, ""representations"": hidden_representations}
        if need_head_weights:
            # attentions: B x L x H x T x T
            attentions = torch.stack(attn_weights, 1)
            if self.model_version == ""ESM-1"":
                # ESM-1 models have an additional null-token for attention, which we remove
                attentions = attentions[..., :-1]
            if padding_mask is not None:
                attention_mask = 1 - padding_mask.type_as(attentions)
                attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2)
                attentions = attentions * attention_mask[:, None, None, :, :]
            result[""attentions""] = attentions
            if return_contacts:
                contacts = self.contact_head(tokens, attentions)
                result[""contacts""] = contacts

        return result

    def predict_contacts(self, tokens):
        return self(tokens, return_contacts=True)[""contacts""]

    @property
    def num_layers(self):
        return self.args.layers


class MSATransformer(nn.Module):
    @classmethod
    def add_args(cls, parser):
        # fmt: off
        parser.add_argument(
            ""--num_layers"",
            default=12,
            type=int,
            metavar=""N"",
            help=""number of layers""
        )
        parser.add_argument(
            ""--embed_dim"",
            default=768,
            type=int,
            metavar=""N"",
            help=""embedding dimension""
        )
        parser.add_argument(
            ""--logit_bias"",
            action=""store_true"",
            help=""whether to apply bias to logits""
        )
        parser.add_argument(
            ""--ffn_embed_dim"",
            default=3072,
            type=int,
            metavar=""N"",
            help=""embedding dimension for FFN"",
        )
        parser.add_argument(
            ""--attention_heads"",
            default=12,
            type=int,
            metavar=""N"",
            help=""number of attention heads"",
        )
        parser.add_argument(
            ""--dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--attention_dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--activation_dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--max_tokens_per_msa"",
            default=2 ** 14,
            type=int,
            help=(
                ""Used during inference to batch attention computations in a single ""
                ""forward pass. This allows increased input sizes with less memory.""
            ),
        )
        # fmt: on

    def __init__(self, args, alphabet):
        super().__init__()
        self.args = args
        self.alphabet_size = len(alphabet)
        self.padding_idx = alphabet.padding_idx
        self.mask_idx = alphabet.mask_idx
        self.cls_idx = alphabet.cls_idx
        self.eos_idx = alphabet.eos_idx
        self.prepend_bos = alphabet.prepend_bos
        self.append_eos = alphabet.append_eos

        self.embed_tokens = nn.Embedding(
            self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx
        )

        if getattr(self.args, ""embed_positions_msa"", False):
            emb_dim = getattr(self.args, ""embed_positions_msa_dim"", self.args.embed_dim)
            self.msa_position_embedding = nn.Parameter(
                0.01 * torch.randn(1, 1024, 1, emb_dim),
                requires_grad=True,
            )
        else:
            self.register_parameter(""msa_position_embedding"", None)

        self.dropout_module = nn.Dropout(self.args.dropout)
        self.layers = nn.ModuleList(
            [
                AxialTransformerLayer(
                    self.args.embed_dim,
                    self.args.ffn_embed_dim,
                    self.args.attention_heads,
                    self.args.dropout,
                    self.args.attention_dropout,
                    self.args.activation_dropout,
                    getattr(self.args, ""max_tokens_per_msa"", self.args.max_tokens),
                )
                for _ in range(self.args.layers)
            ]
        )

        self.contact_head = ContactPredictionHead(
            self.args.layers * self.args.attention_heads,
            self.prepend_bos,
            self.append_eos,
            eos_idx=self.eos_idx,
        )
        self.embed_positions = LearnedPositionalEmbedding(
            self.args.max_positions,
            self.args.embed_dim,
            self.padding_idx,
        )
        self.emb_layer_norm_before = ESM1bLayerNorm(self.args.embed_dim)
        self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim)
        self.lm_head = RobertaLMHead(
            embed_dim=self.args.embed_dim,
            output_dim=self.alphabet_size,
            weight=self.embed_tokens.weight,
        )

    def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
        if return_contacts:
            need_head_weights = True

        assert tokens.ndim == 3
        batch_size, num_alignments, seqlen = tokens.size()
        padding_mask = tokens.eq(self.padding_idx)  # B, R, C
        if not padding_mask.any():
            padding_mask = None

        x = self.embed_tokens(tokens)
        x += self.embed_positions(tokens.view(batch_size * num_alignments, seqlen)).view(x.size())
        if self.msa_position_embedding is not None:
            if x.size(1) > 1024:
                raise RuntimeError(
                    ""Using model with MSA position embedding trained on maximum MSA ""
                    f""depth of 1024, but received {x.size(1)} alignments.""
                )
            x += self.msa_position_embedding[:, :num_alignments]

        x = self.emb_layer_norm_before(x)

        x = self.dropout_module(x)

        if padding_mask is not None:
            x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))

        repr_layers = set(repr_layers)
        hidden_representations = {}
        if 0 in repr_layers:
            hidden_representations[0] = x
            

        if need_head_weights:
            row_attn_weights = []
            col_attn_weights = []

        # B x R x C x D -> R x C x B x D
        x = x.permute(1, 2, 0, 3)

        for layer_idx, layer in enumerate(self.layers):
            x = layer(
                x,
                self_attn_padding_mask=padding_mask,
                need_head_weights=need_head_weights,
            )
            if need_head_weights:
                x, col_attn, _ = x
                if layer_idx == 0:
                    col_attn_weights = symmetrize(col_attn.permute(2, 0, 1, 3, 4))
                else:
                    col_attn_weights += symmetrize(col_attn.permute(2, 0, 1, 3, 4))
            # if need_head_weights:
            #     x, col_attn, row_attn = x
            #     # H x C x B x R x R -> B x H x C x R x R
            #     col_attn_weights.append(col_attn.permute(2, 0, 1, 3, 4))
            #     # H x B x C x C -> B x H x C x C
            #     row_attn_weights.append(row_attn.permute(1, 0, 2, 3))
            if (layer_idx + 1) in repr_layers:
                hidden_representations[layer_idx + 1] = x.permute(2, 0, 1, 3)



        x = self.emb_layer_norm_after(x)
        x = x.permute(2, 0, 1, 3)  # R x C x B x D -> B x R x C x D

        # last hidden representation should have layer norm applied
        if (layer_idx + 1) in repr_layers:
            hidden_representations[layer_idx + 1] = x
        x = self.lm_head(x)

        result = {""logits"": x, ""representations"": hidden_representations}
        if need_head_weights:
            # # col_attentions: B x L x H x C x R x R
            # col_attentions = torch.stack(col_attn_weights, 1)
            # # row_attentions: B x L x H x C x C
            # row_attentions = torch.stack(row_attn_weights, 1)
            # result[""col_attentions""] = col_attentions
            # result[""row_attentions""] = row_attentions
            result[""col_attentions""] = col_attn_weights.sum(1)
            # if return_contacts:
            #     contacts = self.contact_head(tokens, row_attentions)
            #     result[""contacts""] = contacts

        return result

    def predict_contacts(self, tokens):
        return self(tokens, return_contacts=True)[""contacts""]

    @property
    def num_layers(self):
        return self.args.layers

    def max_tokens_per_msa_(self, value: int) -> None:
        """"""The MSA Transformer automatically batches attention computations when
        gradients are disabled to allow you to pass in larger MSAs at test time than
        you can fit in GPU memory. By default this occurs when more than 2^14 tokens
        are passed in the input MSA. You can set this value to infinity to disable
        this behavior.
        """"""
        for module in self.modules():
            if isinstance(module, (RowSelfAttention, ColumnSelfAttention)):
                module.max_tokens_per_msa = value

def symmetrize(x):
    ""Make layer symmetric in final two dimensions, used for contact prediction.""
    # print(x.size())
    # exit()
    return x + x.transpose(-1, -2)","Python"
"AlphaFold","allanchen95/ESMPair","msa_transformer.py",".py","8777","252","import torch
import torch.nn as nn

from ..modules import (
    AxialTransformerLayer,
    LearnedPositionalEmbedding,
    RobertaLMHead,
    ESM1bLayerNorm,
    ContactPredictionHead,
)

from ..axial_attention import RowSelfAttention, ColumnSelfAttention



class MSATransformer(nn.Module):
    @classmethod
    def add_args(cls, parser):
        # fmt: off
        parser.add_argument(
            ""--num_layers"",
            default=12,
            type=int,
            metavar=""N"",
            help=""number of layers""
        )
        parser.add_argument(
            ""--embed_dim"",
            default=768,
            type=int,
            metavar=""N"",
            help=""embedding dimension""
        )
        parser.add_argument(
            ""--logit_bias"",
            action=""store_true"",
            help=""whether to apply bias to logits""
        )
        parser.add_argument(
            ""--ffn_embed_dim"",
            default=3072,
            type=int,
            metavar=""N"",
            help=""embedding dimension for FFN"",
        )
        parser.add_argument(
            ""--attention_heads"",
            default=12,
            type=int,
            metavar=""N"",
            help=""number of attention heads"",
        )
        parser.add_argument(
            ""--dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--attention_dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--activation_dropout"",
            default=0.1,
            type=float,
            help=""Dropout to apply.""
        )
        parser.add_argument(
            ""--max_tokens_per_msa"",
            default=2 ** 14,
            type=int,
            help=(
                ""Used during inference to batch attention computations in a single ""
                ""forward pass. This allows increased input sizes with less memory.""
            ),
        )
        # fmt: on

    def __init__(self, args, alphabet):
        super().__init__()
        self.args = args
        self.alphabet_size = len(alphabet)
        self.padding_idx = alphabet.padding_idx
        self.mask_idx = alphabet.mask_idx
        self.cls_idx = alphabet.cls_idx
        self.eos_idx = alphabet.eos_idx
        self.prepend_bos = alphabet.prepend_bos
        self.append_eos = alphabet.append_eos

        self.embed_tokens = nn.Embedding(
            self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx
        )

        if getattr(self.args, ""embed_positions_msa"", False):
            emb_dim = getattr(self.args, ""embed_positions_msa_dim"", self.args.embed_dim)
            self.msa_position_embedding = nn.Parameter(
                0.01 * torch.randn(1, 1024, 1, emb_dim),
                requires_grad=True,
            )
        else:
            self.register_parameter(""msa_position_embedding"", None)

        self.dropout_module = nn.Dropout(self.args.dropout)
        self.layers = nn.ModuleList(
            [
                AxialTransformerLayer(
                    self.args.embed_dim,
                    self.args.ffn_embed_dim,
                    self.args.attention_heads,
                    self.args.dropout,
                    self.args.attention_dropout,
                    self.args.activation_dropout,
                    getattr(self.args, ""max_tokens_per_msa"", self.args.max_tokens),
                )
                for _ in range(self.args.layers)
            ]
        )

        self.contact_head = ContactPredictionHead(
            self.args.layers * self.args.attention_heads,
            self.prepend_bos,
            self.append_eos,
            eos_idx=self.eos_idx,
        )
        self.embed_positions = LearnedPositionalEmbedding(
            self.args.max_positions,
            self.args.embed_dim,
            self.padding_idx,
        )
        self.emb_layer_norm_before = ESM1bLayerNorm(self.args.embed_dim)
        self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim)
        self.lm_head = RobertaLMHead(
            embed_dim=self.args.embed_dim,
            output_dim=self.alphabet_size,
            weight=self.embed_tokens.weight,
        )

    def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
        if return_contacts:
            need_head_weights = True

        assert tokens.ndim == 3
        batch_size, num_alignments, seqlen = tokens.size()
        padding_mask = tokens.eq(self.padding_idx)  # B, R, C
        if not padding_mask.any():
            padding_mask = None

        x = self.embed_tokens(tokens)
        x += self.embed_positions(tokens.view(batch_size * num_alignments, seqlen)).view(x.size())
        if self.msa_position_embedding is not None:
            if x.size(1) > 1024:
                raise RuntimeError(
                    ""Using model with MSA position embedding trained on maximum MSA ""
                    f""depth of 1024, but received {x.size(1)} alignments.""
                )
            x += self.msa_position_embedding[:, :num_alignments]

        x = self.emb_layer_norm_before(x)

        x = self.dropout_module(x)

        if padding_mask is not None:
            x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))

        repr_layers = set(repr_layers)
        hidden_representations = {}
        if 0 in repr_layers:
            hidden_representations[0] = x

        if need_head_weights:
            row_attn_weights = []
            col_attn_weights = []

        # B x R x C x D -> R x C x B x D
        x = x.permute(1, 2, 0, 3)

        for layer_idx, layer in enumerate(self.layers):
            x = layer(
                x,
                self_attn_padding_mask=padding_mask,
                need_head_weights=need_head_weights,
            )
            # if need_head_weights:
            #     x, col_attn, row_attn = x
            #     # H x C x B x R x R -> B x H x C x R x R
            #     col_attn_weights.append(col_attn.permute(2, 0, 1, 3, 4))
            #     # H x B x C x C -> B x H x C x C
            #     row_attn_weights.append(row_attn.permute(1, 0, 2, 3))
            
            # directly sum col_attn_weights to reduce memory cost
            # col_attn_weights: B x H x C x R x R
            if need_head_weights:
                x, col_attn, _ = x
                if layer_idx == 0:
                    col_attn_weights = symmetrize(col_attn.permute(2, 0, 1, 3, 4))
                else:
                    col_attn_weights += symmetrize(col_attn.permute(2, 0, 1, 3, 4))
            if (layer_idx + 1) in repr_layers:
                hidden_representations[layer_idx + 1] = x.permute(2, 0, 1, 3)

        x = self.emb_layer_norm_after(x)
        x = x.permute(2, 0, 1, 3)  # R x C x B x D -> B x R x C x D

        # last hidden representation should have layer norm applied
        if (layer_idx + 1) in repr_layers:
            hidden_representations[layer_idx + 1] = x
        x = self.lm_head(x)

        result = {""logits"": x, ""representations"": hidden_representations}
        if need_head_weights:
            
            # # col_attentions: B x L x H x C x R x R
            # col_attentions = torch.stack(col_attn_weights, 1)
            # # row_attentions: B x L x H x C x C
            # row_attentions = torch.stack(row_attn_weights, 1)
            # result[""col_attentions""] = col_attentions
            # result[""row_attentions""] = row_attentions
            
            
            # B x H x C x R x R -> B x C x R x R
            result[""col_attentions""] = col_attn_weights.sum(1)
            
            # if return_contacts:
            #     contacts = self.contact_head(tokens, row_attentions)
            #     result[""contacts""] = contacts

        return result

    def predict_contacts(self, tokens):
        return self(tokens, return_contacts=True)[""contacts""]

    @property
    def num_layers(self):
        return self.args.layers

    def max_tokens_per_msa_(self, value: int) -> None:
        """"""The MSA Transformer automatically batches attention computations when
        gradients are disabled to allow you to pass in larger MSAs at test time than
        you can fit in GPU memory. By default this occurs when more than 2^14 tokens
        are passed in the input MSA. You can set this value to infinity to disable
        this behavior.
        """"""
        for module in self.modules():
            if isinstance(module, (RowSelfAttention, ColumnSelfAttention)):
                module.max_tokens_per_msa = value

def symmetrize(x):
    ""Make layer symmetric in final two dimensions, used for contact prediction.""
    return x + x.transpose(-1, -2)","Python"
"AlphaFold","allanchen95/ESMPair","colattn_pair.py",".py","3116","100","import os
import json
from tqdm import tqdm
import numpy as np
from msa_pair.data import (
    species_processing, row_processing, pairing_pipeline,
)
import esm


msa_transformer, msa_alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S()
msa_batch_converter = msa_alphabet.get_batch_converter()

def compute_scores(input_dir, dst_path, tag, max_num_msas, is_cpu=False):
    from msa_pair.data import esm_scoring

    species_dict, msas_dict, _, _ = species_processing.pair_species(
        input_dir, names=['uniref90.a3m'], chain_ids=['A', 'B']
        # input_dir, names=['uniprot.a3m'], chain_ids=['A', 'B']
        )
    esm_scorer = esm_scoring.EsmScoring(msa_transformer, msa_batch_converter, tag)
    sequences_scores = esm_scorer.score_sequences(species_dict, msas_dict, max_num_msas=max_num_msas)
    with open(dst_path, 'wt') as fh:
        json.dump(sequences_scores, fh, indent=4, sort_keys=True)


def pair_rows(input_dir, src_score_path, dst_pr_path, tag, overwrite=False):


    with open(src_score_path) as fh:
        sequences_scores = json.load(fh)

    species_dict, msas_dict, _, _ = species_processing.pair_species(
        input_dir, names=['uniref90.a3m'], chain_ids=['A', 'B']
        # input_dir, names=['uniprot.a3m'], chain_ids=['A', 'B']
    )
    paired_rows_dict = row_processing.create_paired_rows_dict(
        species_dict, msas_dict, sequences_scores
    )
    # print(paired_rows_dict)
    # print(paired_rows_dict[""A""])
    # print(paired_rows_dict[""A""][:10])
    # print(paired_rows_dict[""B""][:10])
    # exit()

    with open(dst_pr_path, 'wt') as fh:
        json.dump(paired_rows_dict, fh, indent=4)


def process(input_dir, src_pr_path, dst_path, overwrite=False):
    if not overwrite and os.path.exists(dst_path):
        return

    pipeline = pairing_pipeline.PairingPipeline()

    with open(src_pr_path) as fh:
        paired_rows_dict = json.load(fh)

    try:
        np_example = pipeline.process(input_dir, paired_rows_dict)
    except IOError as e:
        print(e)
        return

    np.savez(dst_path, **np_example)

if __name__ == '__main__':
    # python colattn_pair.py ./dataset/ 4 512 
    import sys
    import logging
    logging.basicConfig(level=logging.INFO)
    
    # use column attention for pairing
    tag = 'col'
    
    # .a3m path
    input_root = sys.argv[1]
    
    # device id
    device_id = sys.argv[2]
    
    # max_msa for each batch: default 512
    max_per_msa = 512
    total_dir_list = os.listdir(input_root)

    os.environ['CUDA_VISIBLE_DEVICES'] = str(device_id)
    err_dirs = []
    for name in tqdm(total_dir_list):
        input_dir = os.path.join(input_root, name)
        
        # calculate and save the column attention score
        score_path = os.path.join(input_dir, f'{tag}_scores_{max_per_msa}.json')
        if not os.path.exists(score_path):
            compute_scores(input_dir, score_path, tag, int(max_per_msa))
        
        # 
        pr_path = os.path.join(input_dir, f'{tag}_pr_{max_per_msa}.json')
        if not os.path.exists(pr_path):
            pair_rows(input_dir, score_path, pr_path, tag)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/setup.py",".py","232","13","from setuptools import find_packages
from setuptools import setup

setup(
    name='msa_pair',
    packages=find_packages(),
    install_requires=[
        'pandas',
        'scipy',
        'requests',
        'biopython',
    ],
)","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/__init__.py",".py","0","0","","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/model_preset_runner.py",".py","6309","191","import os
import time
import random
import logging
from typing import Mapping, Optional, Sequence, List

import jax
import tree
import numpy as np

from alphafold.data import pipeline_multimer, msa_pairing
from alphafold.model import config, data, model
# from msa_pair.runner import model, modules_multimer

logger = logging.getLogger(__file__)


def clear_mem(device=None):
    '''remove all data from device'''
    backend = jax.lib.xla_bridge.get_backend(device)
    if hasattr(backend,'live_buffers'):
        for buf in backend.live_buffers():
            buf.delete()


def _is_params_transferable(old_params, next_params):
    def _get_all_keys(params):
        all_keys = []
        for path in params.keys():
            for k in params[path].keys():
                all_keys.append(f'{path}/{k}')
        return tuple(sorted(all_keys))

    return _get_all_keys(old_params) == _get_all_keys(next_params)


class ModelPresetRunner:
    def __init__(
        self,
        database: str,
        model_preset: str = 'multimer',
        allow_params_transfer: bool = True,
        cache_params: bool = True,
        ignore_unpaired_sequences: bool = True,
    ):
        assert model_preset == 'multimer', 'Only supports preset multimer'

        self.model_preset = model_preset
        self.database = database
        self.allow_params_transfer = allow_params_transfer
        self.cache_params = cache_params

        self.params_dict = {}
        # Check model parameters
        first_params, first_model_name = None, None
        first_config = None

        self.avail_model_names = []
        for model_name in config.MODEL_PRESETS[model_preset]:
            try:
                params = data.get_model_haiku_params(
                    model_name, self.database
                )
                if first_params is None:
                    first_model_name = model_name
                    first_params = params
                    first_config = config.model_config(model_name)
                elif self.allow_params_transfer:
                    if not _is_params_transferable(first_params, params):
                        continue
                    if first_config != config.model_config(model_name):
                        continue
                self.avail_model_names.append(model_name)
            except FileNotFoundError as e:
                continue

        assert len(self.avail_model_names) >= 1

        # print(f'{len(self.avail_model_names)} models are available')
        # print(f'Build RunModel {first_model_name}')
        self.running_model = model.RunModel(first_config, first_params)

        self.ignore_unpaired_sequences = ignore_unpaired_sequences

    def predict(
        self,
        feat: Mapping[str, np.ndarray],
        rng_seed: Optional[int] = None,
        model_names: Optional[List[str]] = None,
        num_predictions_per_model: int = 5,
        model_index: str = None,
    ):
        if rng_seed is not None:
            rng = np.random.default_rng(rng_seed)
        else:
            rng = np.random.default_rng(random.randrange(1, 2**24))

        if model_names is None:
            model_names = self.avail_model_names
        else:
            model_names = [
                model_name for model_name in model_names if model_name in 
                self.avail_model_names
            ]

        if model_index is not None:
            model_names = [self.avail_model_names[int(model_index)]]


        print(f""#models: {len(model_names)} Used model {model_index} for predicting {num_predictions_per_model} number."")
        outputs = {}
        for model_name in model_names:
            for pred_num in range(num_predictions_per_model):
                seed = int(rng.integers(1, 2**24))
                result, timing = self.run_model(
                    feat, model_name, seed
                )
                outputs[f'{model_name}_{pred_num}'] = {
                    'result': result,
                    'timing': timing,
                    'seed': seed,
                }
            logger.info(tree.map_structure(lambda x: x.shape, result))

        return outputs

    def run_model(
        self,
        feat: Mapping[str, np.ndarray],
        model_name: str,
        random_seed: int,
    ):
        t0 = time.time()

        if not self.cache_params or model_name not in self.params_dict:
            params = data.get_model_haiku_params(model_name, self.database)
            if self.cache_params:
                self.params_dict[model_name] = params
        else:
            params = self.params_dict[model_name]

        if self.running_model is not None and self.allow_params_transfer:
            logger.info(f'Transfer {model_name} params to the running model')
            for path in params.keys():
                self.running_model.params[path] = params[path]

        logger.info(
            f'Start prediction using {model_name} with seed {random_seed}'
        )
        if self.ignore_unpaired_sequences:
            feat = self._remove_unpaired_sequences(feat)
        result = self.running_model.predict(feat, random_seed)
        t1 = time.time()

        logger.info(
            f'Finish prediction using {model_name} in {t1 - t0} seconds'
        )
        # exit()
        timing = t1 - t0

        return result, timing

    def _remove_unpaired_sequences(
        self,
        feat: Mapping[str, np.ndarray],
    ):
        paired_sequence_mask = 1
        for chain_id in np.unique(feat['asym_id']):
            if chain_id:
                chain_mask = feat['asym_id'] == chain_id
                paired_sequence_mask *= np.sum(
                    feat['msa'][:,chain_mask] != msa_pairing.MSA_GAP_IDX,
                    axis=-1,
                ) > 0
        paired_sequence_mask = paired_sequence_mask.astype(bool)
        for key, value in feat.items():
            if key in (
                'msa', 
                'deletion_matrix',
                'bert_mask',
                'msa_mask', 
                'cluster_bias_mask',
            ):
                feat[key] = value[paired_sequence_mask]
            elif key in ('num_alignments',):
                feat[key] = np.sum(paired_sequence_mask)

        feat = pipeline_multimer.pad_msa(feat, 512)

        return feat
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/model.py",".py","6673","180","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Code for constructing the model.""""""
from typing import Any, Mapping, Optional, Union

from absl import logging
from alphafold.common import confidence
from alphafold.model import features
from alphafold.model import modules
from msa_pair.runner import modules_multimer
import haiku as hk
import jax
import ml_collections
import numpy as np
import tensorflow.compat.v1 as tf
import tree


def get_confidence_metrics(
    prediction_result: Mapping[str, Any],
    multimer_mode: bool) -> Mapping[str, Any]:
  """"""Post processes prediction_result to get confidence metrics.""""""
  confidence_metrics = {}
  confidence_metrics['plddt'] = confidence.compute_plddt(
      prediction_result['predicted_lddt']['logits'])
  if 'predicted_aligned_error' in prediction_result:
    confidence_metrics.update(confidence.compute_predicted_aligned_error(
        logits=prediction_result['predicted_aligned_error']['logits'],
        breaks=prediction_result['predicted_aligned_error']['breaks']))
    confidence_metrics['ptm'] = confidence.predicted_tm_score(
        logits=prediction_result['predicted_aligned_error']['logits'],
        breaks=prediction_result['predicted_aligned_error']['breaks'],
        asym_id=None)
    if multimer_mode:
      # Compute the ipTM only for the multimer model.
      confidence_metrics['iptm'] = confidence.predicted_tm_score(
          logits=prediction_result['predicted_aligned_error']['logits'],
          breaks=prediction_result['predicted_aligned_error']['breaks'],
          asym_id=prediction_result['predicted_aligned_error']['asym_id'],
          interface=True)
      confidence_metrics['ranking_confidence'] = (
          0.8 * confidence_metrics['iptm'] + 0.2 * confidence_metrics['ptm'])

  if not multimer_mode:
    # Monomer models use mean pLDDT for model ranking.
    confidence_metrics['ranking_confidence'] = np.mean(
        confidence_metrics['plddt'])

  return confidence_metrics


class RunModel:
  """"""Container for JAX model.""""""

  def __init__(self,
               config: ml_collections.ConfigDict,
               params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None):
    self.config = config
    self.params = params
    self.multimer_mode = config.model.global_config.multimer_mode
    if self.multimer_mode:
      def _forward_fn(batch):
        model = modules_multimer.AlphaFold(self.config.model)
        return model(
            batch,
            return_representations=True,
            is_training=False)
    else:
      def _forward_fn(batch):
        model = modules.AlphaFold(self.config.model)
        return model(
            batch,
            is_training=False,
            compute_loss=False,
            ensemble_representations=True)

    self.apply = jax.jit(hk.transform(_forward_fn).apply)
    self.init = jax.jit(hk.transform(_forward_fn).init)

  def init_params(self, feat: features.FeatureDict, random_seed: int = 0):
    """"""Initializes the model parameters.

    If none were provided when this class was instantiated then the parameters
    are randomly initialized.

    Args:
      feat: A dictionary of NumPy feature arrays as output by
        RunModel.process_features.
      random_seed: A random seed to use to initialize the parameters if none
        were set when this class was initialized.
    """"""
    if not self.params:
      # Init params randomly.
      rng = jax.random.PRNGKey(random_seed)
      self.params = hk.data_structures.to_mutable_dict(
          self.init(rng, feat))
      logging.warning('Initialized parameters randomly')

  def process_features(
      self,
      raw_features: Union[tf.train.Example, features.FeatureDict],
      random_seed: int) -> features.FeatureDict:
    """"""Processes features to prepare for feeding them into the model.

    Args:
      raw_features: The output of the data pipeline either as a dict of NumPy
        arrays or as a tf.train.Example.
      random_seed: The random seed to use when processing the features.

    Returns:
      A dict of NumPy feature arrays suitable for feeding into the model.
    """"""

    if self.multimer_mode:
      return raw_features

    # Single-chain mode.
    if isinstance(raw_features, dict):
      return features.np_example_to_features(
          np_example=raw_features,
          config=self.config,
          random_seed=random_seed)
    else:
      return features.tf_example_to_features(
          tf_example=raw_features,
          config=self.config,
          random_seed=random_seed)

  def eval_shape(self, feat: features.FeatureDict) -> jax.ShapeDtypeStruct:
    self.init_params(feat)
    logging.info('Running eval_shape with shape(feat) = %s',
                 tree.map_structure(lambda x: x.shape, feat))
    shape = jax.eval_shape(self.apply, self.params, jax.random.PRNGKey(0), feat)
    logging.info('Output shape was %s', shape)
    return shape

  def predict(self,
              feat: features.FeatureDict,
              random_seed: int,
              ) -> Mapping[str, Any]:
    """"""Makes a prediction by inferencing the model on the provided features.

    Args:
      feat: A dictionary of NumPy feature arrays as output by
        RunModel.process_features.
      random_seed: The random seed to use when running the model. In the
        multimer model this controls the MSA sampling.

    Returns:
      A dictionary of model outputs.
    """"""
    self.init_params(feat)
    print('Running predict with shape(feat) = %s',
                 tree.map_structure(lambda x: x.shape, feat))
    # exit()
    result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
    # exit()
    # This block is to ensure benchmark timings are accurate. Some blocking is
    # already happening when computing get_confidence_metrics, and this ensures
    # all outputs are blocked on.
    jax.tree_map(lambda x: x.block_until_ready(), result)
    result.update(
        get_confidence_metrics(result, multimer_mode=self.multimer_mode))
    logging.info('Output shape was %s',
                 tree.map_structure(lambda x: x.shape, result))
    return result

","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/modules_multimer.py",".py","38002","1080","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Core modules, which have been refactored in AlphaFold-Multimer.

The main difference is that MSA sampling pipeline is moved inside the JAX model
for easier implementation of recycling and ensembling.

Lower-level modules up to EvoformerIteration are reused from modules.py.
""""""

import functools
from typing import Sequence

from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.model import prng
from alphafold.model import utils
import tree
from jax.experimental.host_callback import call
from jax.experimental.host_callback import id_print
from msa_pair.runner import modules

import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np


def reduce_fn(x, mode):
  if mode == 'none' or mode is None:
    return jnp.asarray(x)
  elif mode == 'sum':
    return jnp.asarray(x).sum()
  elif mode == 'mean':
    return jnp.mean(jnp.asarray(x))
  else:
    raise ValueError('Unsupported reduction option.')


def gumbel_noise(key: jnp.ndarray, shape: Sequence[int]) -> jnp.ndarray:
  """"""Generate Gumbel Noise of given Shape.

  This generates samples from Gumbel(0, 1).

  Args:
    key: Jax random number key.
    shape: Shape of noise to return.

  Returns:
    Gumbel noise of given shape.
  """"""
  epsilon = 1e-6
  uniform = utils.padding_consistent_rng(jax.random.uniform)
  uniform_noise = uniform(
      key, shape=shape, dtype=jnp.float32, minval=0., maxval=1.)
  gumbel = -jnp.log(-jnp.log(uniform_noise + epsilon) + epsilon)
  return gumbel


def gumbel_max_sample(key: jnp.ndarray, logits: jnp.ndarray) -> jnp.ndarray:
  """"""Samples from a probability distribution given by 'logits'.

  This uses Gumbel-max trick to implement the sampling in an efficient manner.

  Args:
    key: prng key.
    logits: Logarithm of probabilities to sample from, probabilities can be
      unnormalized.

  Returns:
    Sample from logprobs in one-hot form.
  """"""
  z = gumbel_noise(key, logits.shape)
  return jax.nn.one_hot(
      jnp.argmax(logits + z, axis=-1),
      logits.shape[-1],
      dtype=logits.dtype)


def gumbel_argsort_sample_idx(key: jnp.ndarray,
                              logits: jnp.ndarray) -> jnp.ndarray:
  """"""Samples with replacement from a distribution given by 'logits'.

  This uses Gumbel trick to implement the sampling an efficient manner. For a
  distribution over k items this samples k times without replacement, so this
  is effectively sampling a random permutation with probabilities over the
  permutations derived from the logprobs.

  Args:
    key: prng key.
    logits: Logarithm of probabilities to sample from, probabilities can be
      unnormalized.

  Returns:
    Sample from logprobs in one-hot form.
  """"""
  z = gumbel_noise(key, logits.shape)
  # This construction is equivalent to jnp.argsort, but using a non stable sort,
  # since stable sort's aren't supported by jax2tf.
  axis = len(logits.shape) - 1
  iota = jax.lax.broadcasted_iota(jnp.int64, logits.shape, axis)
  _, perm = jax.lax.sort_key_val(
      logits + z, iota, dimension=-1, is_stable=False)
  return perm[::-1]


def make_masked_msa(batch, key, config, epsilon=1e-6):
  """"""Create data for BERT on raw MSA.""""""
  # Add a random amino acid uniformly.
  random_aa = jnp.array([0.05] * 20 + [0., 0.], dtype=jnp.float32)

  categorical_probs = (
      config.uniform_prob * random_aa +
      config.profile_prob * batch['msa_profile'] +
      config.same_prob * jax.nn.one_hot(batch['msa'], 22))

  # Put all remaining probability on [MASK] which is a new column.
  pad_shapes = [[0, 0] for _ in range(len(categorical_probs.shape))]
  pad_shapes[-1][1] = 1
  mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
  assert mask_prob >= 0.
  categorical_probs = jnp.pad(
      categorical_probs, pad_shapes, constant_values=mask_prob)
  sh = batch['msa'].shape
  key, mask_subkey, gumbel_subkey = key.split(3)
  uniform = utils.padding_consistent_rng(jax.random.uniform)
  mask_position = uniform(mask_subkey.get(), sh) < config.replace_fraction
  mask_position *= batch['msa_mask']

  logits = jnp.log(categorical_probs + epsilon)
  bert_msa = gumbel_max_sample(gumbel_subkey.get(), logits)
  bert_msa = jnp.where(mask_position,
                       jnp.argmax(bert_msa, axis=-1), batch['msa'])
  bert_msa *= batch['msa_mask']

  # Mix real and masked MSA.
  if 'bert_mask' in batch:
    batch['bert_mask'] *= mask_position.astype(jnp.float32)
  else:
    batch['bert_mask'] = mask_position.astype(jnp.float32)
  batch['true_msa'] = batch['msa']
  batch['msa'] = bert_msa

  return batch


def nearest_neighbor_clusters(batch, gap_agreement_weight=0.):
  """"""Assign each extra MSA sequence to its nearest neighbor in sampled MSA.""""""

  # Determine how much weight we assign to each agreement.  In theory, we could
  # use a full blosum matrix here, but right now let's just down-weight gap
  # agreement because it could be spurious.
  # Never put weight on agreeing on BERT mask.

  weights = jnp.array(
      [1.] * 21 + [gap_agreement_weight] + [0.], dtype=jnp.float32)

  msa_mask = batch['msa_mask']
  msa_one_hot = jax.nn.one_hot(batch['msa'], 23)

  extra_mask = batch['extra_msa_mask']
  extra_one_hot = jax.nn.one_hot(batch['extra_msa'], 23)

  msa_one_hot_masked = msa_mask[:, :, None] * msa_one_hot
  extra_one_hot_masked = extra_mask[:, :, None] * extra_one_hot

  agreement = jnp.einsum('mrc, nrc->nm', extra_one_hot_masked,
                         weights * msa_one_hot_masked)

  cluster_assignment = jax.nn.softmax(1e3 * agreement, axis=0)
  cluster_assignment *= jnp.einsum('mr, nr->mn', msa_mask, extra_mask)

  cluster_count = jnp.sum(cluster_assignment, axis=-1)
  cluster_count += 1.  # We always include the sequence itself.

  msa_sum = jnp.einsum('nm, mrc->nrc', cluster_assignment, extra_one_hot_masked)
  msa_sum += msa_one_hot_masked

  cluster_profile = msa_sum / cluster_count[:, None, None]

  extra_deletion_matrix = batch['extra_deletion_matrix']
  deletion_matrix = batch['deletion_matrix']

  del_sum = jnp.einsum('nm, mc->nc', cluster_assignment,
                       extra_mask * extra_deletion_matrix)
  del_sum += deletion_matrix  # Original sequence.
  cluster_deletion_mean = del_sum / cluster_count[:, None]

  return cluster_profile, cluster_deletion_mean


def create_msa_feat(batch):
  """"""Create and concatenate MSA features.""""""
  msa_1hot = jax.nn.one_hot(batch['msa'], 23)
  deletion_matrix = batch['deletion_matrix']
  has_deletion = jnp.clip(deletion_matrix, 0., 1.)[..., None]
  deletion_value = (jnp.arctan(deletion_matrix / 3.) * (2. / jnp.pi))[..., None]

  deletion_mean_value = (jnp.arctan(batch['cluster_deletion_mean'] / 3.) *
                         (2. / jnp.pi))[..., None]

  msa_feat = [
      msa_1hot,
      has_deletion,
      deletion_value,
      batch['cluster_profile'],
      deletion_mean_value
  ]

  return jnp.concatenate(msa_feat, axis=-1)


def create_extra_msa_feature(batch, num_extra_msa):
  """"""Expand extra_msa into 1hot and concat with other extra msa features.

  We do this as late as possible as the one_hot extra msa can be very large.

  Args:
    batch: a dictionary with the following keys:
     * 'extra_msa': [num_seq, num_res] MSA that wasn't selected as a cluster
       centre. Note - This isn't one-hotted.
     * 'extra_deletion_matrix': [num_seq, num_res] Number of deletions at given
        position.
    num_extra_msa: Number of extra msa to use.

  Returns:
    Concatenated tensor of extra MSA features.
  """"""
  # 23 = 20 amino acids + 'X' for unknown + gap + bert mask
  extra_msa = batch['extra_msa'][:num_extra_msa]
  deletion_matrix = batch['extra_deletion_matrix'][:num_extra_msa]
  msa_1hot = jax.nn.one_hot(extra_msa, 23)
  has_deletion = jnp.clip(deletion_matrix, 0., 1.)[..., None]
  deletion_value = (jnp.arctan(deletion_matrix / 3.) * (2. / jnp.pi))[..., None]
  extra_msa_mask = batch['extra_msa_mask'][:num_extra_msa]
  return jnp.concatenate([msa_1hot, has_deletion, deletion_value],
                         axis=-1), extra_msa_mask


def sample_msa(key, batch, max_seq):
  """"""Sample MSA randomly, remaining sequences are stored as `extra_*`.

  Args:
    key: safe key for random number generation.
    batch: batch to sample msa from.
    max_seq: number of sequences to sample.
  Returns:
    Protein with sampled msa.
  """"""
  # Sample uniformly among sequences with at least one non-masked position.
  logits = (jnp.clip(jnp.sum(batch['msa_mask'], axis=-1), 0., 1.) - 1.) * 1e6
  # The cluster_bias_mask can be used to preserve the first row (target
  # sequence) for each chain, for example.
  if 'cluster_bias_mask' not in batch:
    cluster_bias_mask = jnp.pad(
        jnp.zeros(batch['msa'].shape[0] - 1), (1, 0), constant_values=1.)
  else:
    cluster_bias_mask = batch['cluster_bias_mask']

  logits += cluster_bias_mask * 1e6
  index_order = gumbel_argsort_sample_idx(key.get(), logits)
#   print(id_print(index_order))
#   call(lambda x: print(f""There are {x} true bools""), index_order[:6])
#   exit()
  sel_idx = extra_idx = index_order
#   extra_idx = index_order[index_order.shape[0]:]
#   print(extra_idx)

  for k in ['msa', 'deletion_matrix', 'msa_mask', 'bert_mask']:
    if k in batch:
      batch['extra_' + k] = batch[k][extra_idx]
      batch[k] = batch[k][sel_idx]

  return batch


def make_msa_profile(batch):
  """"""Compute the MSA profile.""""""

  # Compute the profile for every residue (over all MSA sequences).
  return utils.mask_mean(
      batch['msa_mask'][:, :, None], jax.nn.one_hot(batch['msa'], 22), axis=0)


class AlphaFoldIteration(hk.Module):
  """"""A single recycling iteration of AlphaFold architecture.

  Computes ensembled (averaged) representations from the provided features.
  These representations are then passed to the various heads
  that have been requested by the configuration file.
  """"""

  def __init__(self, config, global_config, name='alphafold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config
    # print(config)
    # print(global_config)
  def __call__(self,
               batch,
               is_training,
               return_representations=False,
               safe_key=None):

    if is_training:
      num_ensemble = np.asarray(self.config.num_ensemble_train)
    else:
      num_ensemble = np.asarray(self.config.num_ensemble_eval)

    # Compute representations for each MSA sample and average.
    embedding_module = EmbeddingsAndEvoformer(
        self.config.embeddings_and_evoformer, self.global_config)
    repr_shape = hk.eval_shape(
        lambda: embedding_module(batch, is_training))
    representations = {
        k: jnp.zeros(v.shape, v.dtype) for (k, v) in repr_shape.items()
    }

    print(representations)
    exit()
    def ensemble_body(x, unused_y):
      """"""Add into representations ensemble.""""""
      del unused_y
      representations, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      representations_update = embedding_module(
          batch, is_training, safe_key=safe_subkey)

      for k in representations:
        if k not in {'msa', 'true_msa', 'bert_mask'}:
          representations[k] += representations_update[k] * (
              1. / num_ensemble).astype(representations[k].dtype)
        else:
          representations[k] = representations_update[k]

      return (representations, safe_key), None

    (representations, _), _ = hk.scan(
        ensemble_body, (representations, safe_key), None, length=num_ensemble)

    self.representations = representations
    ret = {}
    ret['representations'] = representations
    print(representations)
    print(""embedding complete!"")
    exit()
    return ret


class AlphaFold(hk.Module):
  """"""AlphaFold-Multimer model with recycling.
  """"""

  def __init__(self, config, name='alphafold'):
    super().__init__(name=name)
    self.config = config
    self.global_config = config.global_config

  def __call__(
      self,
      batch,
      is_training,
      return_representations=False,
      safe_key=None):

    c = self.config
    impl = AlphaFoldIteration(c, self.global_config)
    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())
    elif isinstance(safe_key, jnp.ndarray):
      safe_key = prng.SafeKey(safe_key)

    assert isinstance(batch, dict)
    num_res = batch['aatype'].shape[0]

    def get_prev(ret):
      new_prev = {
          'prev_pos':
              ret['structure_module']['final_atom_positions'],
          'prev_msa_first_row': ret['representations']['msa_first_row'],
          'prev_pair': ret['representations']['pair'],
      }
      return jax.tree_map(jax.lax.stop_gradient, new_prev)

    def apply_network(prev, safe_key):
      recycled_batch = {**batch, **prev}
      return impl(
          batch=recycled_batch,
          is_training=is_training,
          safe_key=safe_key)

    prev = {}
    emb_config = self.config.embeddings_and_evoformer
    if emb_config.recycle_pos:
      prev['prev_pos'] = jnp.zeros(
          [num_res, residue_constants.atom_type_num, 3])
    if emb_config.recycle_features:
      prev['prev_msa_first_row'] = jnp.zeros(
          [num_res, emb_config.msa_channel])
      prev['prev_pair'] = jnp.zeros(
          [num_res, num_res, emb_config.pair_channel])
    self.config.num_recycle = 0
    # print(""recycle: cuurr"", self.config.num_recycle)
    if self.config.num_recycle:
      if 'num_iter_recycling' in batch:
        # Training time: num_iter_recycling is in batch.
        # Value for each ensemble batch is the same, so arbitrarily taking 0-th.
        num_iter = batch['num_iter_recycling'][0]

        # Add insurance that even when ensembling, we will not run more
        # recyclings than the model is configured to run.
        num_iter = jnp.minimum(num_iter, c.num_recycle)
      else:
        # Eval mode or tests: use the maximum number of iterations.
        num_iter = c.num_recycle

      def recycle_body(i, x):
        del i
        prev, safe_key = x
        safe_key1, safe_key2 = safe_key.split() if c.resample_msa_in_recycling else safe_key.duplicate()  # pylint: disable=line-too-long
        ret = apply_network(prev=prev, safe_key=safe_key2)
        return get_prev(ret), safe_key1

      prev, safe_key = hk.fori_loop(0, num_iter, recycle_body, (prev, safe_key))

    # Run extra iteration.
    ret = apply_network(prev=prev, safe_key=safe_key)

    if not return_representations:
      del ret['representations']
    return ret


class EmbeddingsAndEvoformer(hk.Module):
  """"""Embeds the input data and runs Evoformer.

  Produces the MSA, single and pair representations.
  """"""

  def __init__(self, config, global_config, name='evoformer'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def _relative_encoding(self, batch):
    """"""Add relative position encodings.

    For position (i, j), the value is (i-j) clipped to [-k, k] and one-hotted.

    When not using 'use_chain_relative' the residue indices are used as is, e.g.
    for heteromers relative positions will be computed using the positions in
    the corresponding chains.

    When using 'use_chain_relative' we add an extra bin that denotes
    'different chain'. Furthermore we also provide the relative chain index
    (i.e. sym_id) clipped and one-hotted to the network. And an extra feature
    which denotes whether they belong to the same chain type, i.e. it's 0 if
    they are in different heteromer chains and 1 otherwise.

    Args:
      batch: batch.
    Returns:
      Feature embedding using the features as described before.
    """"""
    c = self.config
    rel_feats = []
    pos = batch['residue_index']
    asym_id = batch['asym_id']
    asym_id_same = jnp.equal(asym_id[:, None], asym_id[None, :])
    offset = pos[:, None] - pos[None, :]

    clipped_offset = jnp.clip(
        offset + c.max_relative_idx, a_min=0, a_max=2 * c.max_relative_idx)

    if c.use_chain_relative:

      final_offset = jnp.where(asym_id_same, clipped_offset,
                               (2 * c.max_relative_idx + 1) *
                               jnp.ones_like(clipped_offset))

      rel_pos = jax.nn.one_hot(final_offset, 2 * c.max_relative_idx + 2)

      rel_feats.append(rel_pos)

      entity_id = batch['entity_id']
      entity_id_same = jnp.equal(entity_id[:, None], entity_id[None, :])
      rel_feats.append(entity_id_same.astype(rel_pos.dtype)[..., None])

      sym_id = batch['sym_id']
      rel_sym_id = sym_id[:, None] - sym_id[None, :]

      max_rel_chain = c.max_relative_chain

      clipped_rel_chain = jnp.clip(
          rel_sym_id + max_rel_chain, a_min=0, a_max=2 * max_rel_chain)

      final_rel_chain = jnp.where(entity_id_same, clipped_rel_chain,
                                  (2 * max_rel_chain + 1) *
                                  jnp.ones_like(clipped_rel_chain))
      rel_chain = jax.nn.one_hot(final_rel_chain, 2 * c.max_relative_chain + 2)

      rel_feats.append(rel_chain)

    else:
      rel_pos = jax.nn.one_hot(clipped_offset, 2 * c.max_relative_idx + 1)
      rel_feats.append(rel_pos)

    rel_feat = jnp.concatenate(rel_feats, axis=-1)

    return common_modules.Linear(
        c.pair_channel,
        name='position_activations')(
            rel_feat)

  def __call__(self, batch, is_training, safe_key=None):

    c = self.config
    gc = self.global_config

    batch = dict(batch)
    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    output = {}

    batch['msa_profile'] = make_msa_profile(batch)
    target_feat = jax.nn.one_hot(batch['aatype'], 21)

    preprocess_1d = common_modules.Linear(
        c.msa_channel, name='preprocess_1d')(
            target_feat)


    safe_key, sample_key, mask_key = safe_key.split(3)
    # print(""config: "", c.num_msa, c.masked_msa)
    # print(sample_key)
    # print(batch)
    # exit()

    # without msa sampling
    batch = sample_msa(sample_key, batch, c.num_msa)
    batch = make_masked_msa(batch, mask_key, c.masked_msa)
    (batch['cluster_profile'],
     batch['cluster_deletion_mean']) = nearest_neighbor_clusters(batch)

    msa_feat = create_msa_feat(batch)

    preprocess_msa = common_modules.Linear(
        c.msa_channel, name='preprocess_msa')(
            msa_feat)
    msa_activations = jnp.expand_dims(preprocess_1d, axis=0) + preprocess_msa
    left_single = common_modules.Linear(
        c.pair_channel, name='left_single')(
            target_feat)
    right_single = common_modules.Linear(
        c.pair_channel, name='right_single')(
            target_feat)
    pair_activations = left_single[:, None] + right_single[None]
    mask_2d = batch['seq_mask'][:, None] * batch['seq_mask'][None, :]
    mask_2d = mask_2d.astype(jnp.float32)

    if c.recycle_pos:
      prev_pseudo_beta = modules.pseudo_beta_fn(
          batch['aatype'], batch['prev_pos'], None)

      dgram = modules.dgram_from_positions(
          prev_pseudo_beta, **self.config.prev_pos)
      pair_activations += common_modules.Linear(
          c.pair_channel, name='prev_pos_linear')(
              dgram)
    if c.recycle_features:
      prev_msa_first_row = hk.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_msa_first_row_norm')(
              batch['prev_msa_first_row'])
      msa_activations = msa_activations.at[0].add(prev_msa_first_row)
    #   print(""adddd: "", msa_activations)

      pair_activations += hk.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_pair_norm')(
              batch['prev_pair'])

    if c.max_relative_idx:
      pair_activations += self._relative_encoding(batch)

    if c.template.enabled:
      template_module = TemplateEmbedding(c.template, gc)
      template_batch = {
          'template_aatype': batch['template_aatype'],
          'template_all_atom_positions': batch['template_all_atom_positions'],
          'template_all_atom_mask': batch['template_all_atom_mask']
      }
      # Construct a mask such that only intra-chain template features are
      # computed, since all templates are for each chain individually.
      multichain_mask = batch['asym_id'][:, None] == batch['asym_id'][None, :]
      safe_key, safe_subkey = safe_key.split()
      template_act = template_module(
          query_embedding=pair_activations,
          template_batch=template_batch,
          padding_mask_2d=mask_2d,
          multichain_mask_2d=multichain_mask,
          is_training=is_training,
          safe_key=safe_subkey)
      pair_activations += template_act

    # Extra MSA stack.
    # (extra_msa_feat,
    #  extra_msa_mask) = create_extra_msa_feature(batch, c.num_extra_msa)
    # extra_msa_activations = common_modules.Linear(
    #     c.extra_msa_channel,
    #     name='extra_msa_activations')(
    #         extra_msa_feat)
    # extra_msa_mask = extra_msa_mask.astype(jnp.float32)

    # extra_evoformer_input = {
    #     'msa': extra_msa_activations,
    #     'pair': pair_activations,
    # }
    # extra_masks = {'msa': extra_msa_mask, 'pair': mask_2d}

    # extra_evoformer_iteration = modules.EvoformerIteration(
    #     c.evoformer, gc, is_extra_msa=True, name='extra_msa_stack')

    # def extra_evoformer_fn(x):
    #   act, safe_key = x
    #   safe_key, safe_subkey = safe_key.split()
    #   extra_evoformer_output = extra_evoformer_iteration(
    #       activations=act,
    #       masks=extra_masks,
    #       is_training=is_training,
    #       safe_key=safe_subkey)
    #   return (extra_evoformer_output, safe_key)

    # if gc.use_remat:
    #   extra_evoformer_fn = hk.remat(extra_evoformer_fn)

    # safe_key, safe_subkey = safe_key.split()
    # extra_evoformer_stack = layer_stack.layer_stack(
    #     c.extra_msa_stack_num_block)(
    #         extra_evoformer_fn)
    # extra_evoformer_output, safe_key = extra_evoformer_stack(
    #     (extra_evoformer_input, safe_subkey))

    # pair_activations = extra_evoformer_output['pair']
    # Get the size of the MSA before potentially adding templates, so we
    # can crop out the templates later.
    num_msa_sequences = msa_activations.shape[0]
    # print(""num:"", num_msa_sequences)
    evoformer_input = {
        'msa': msa_activations,
        'pair': pair_activations,
    }
    evoformer_masks = {'msa': batch['msa_mask'].astype(jnp.float32),
                       'pair': mask_2d}

    if c.template.enabled:
      template_features, template_masks = (
          template_embedding_1d(batch=batch, num_channel=c.msa_channel))
      evoformer_input['msa'] = jnp.concatenate(
          [evoformer_input['msa'], template_features], axis=0)
      evoformer_masks['msa'] = jnp.concatenate(
          [evoformer_masks['msa'], template_masks], axis=0)

    evoformer_iteration = modules.EvoformerIteration(
        c.evoformer, gc, is_extra_msa=False, name='evoformer_iteration')
    def evoformer_fn(x):
      act, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      evoformer_output = evoformer_iteration(
          activations=act,
          masks=evoformer_masks,
          is_training=is_training,
          safe_key=safe_subkey)
      return (evoformer_output, safe_key)

    if gc.use_remat:
      evoformer_fn = hk.remat(evoformer_fn)

    safe_key, safe_subkey = safe_key.split()
    evoformer_stack = layer_stack.layer_stack(c.evoformer_num_block)(
        evoformer_fn)

    def run_evoformer(evoformer_input):
      evoformer_output, _ = evoformer_stack((evoformer_input, safe_subkey))
      return evoformer_output

    evoformer_output = run_evoformer(evoformer_input)

    msa_activations = evoformer_output['msa']
    pair_activations = evoformer_output['pair']
    single_activations = common_modules.Linear(
        c.seq_channel, name='single_activations')(
            msa_activations[0])

    output.update({
        'single':
            single_activations,
        'pair':
            pair_activations,
        # Crop away template rows such that they are not used in MaskedMsaHead.
        'msa':
            msa_activations[:num_msa_sequences, :, :],
        'msa_first_row':
            msa_activations[0],
    })
    return output


class TemplateEmbedding(hk.Module):
  """"""Embed a set of templates.""""""

  def __init__(self, config, global_config, name='template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_batch, padding_mask_2d,
               multichain_mask_2d, is_training,
               safe_key=None):
    """"""Generate an embedding for a set of templates.

    Args:
      query_embedding: [num_res, num_res, num_channel] a query tensor that will
        be used to attend over the templates to remove the num_templates
        dimension.
      template_batch: A dictionary containing:
        `template_aatype`: [num_templates, num_res] aatype for each template.
        `template_all_atom_positions`: [num_templates, num_res, 37, 3] atom
          positions for all templates.
        `template_all_atom_mask`: [num_templates, num_res, 37] mask for each
          template.
      padding_mask_2d: [num_res, num_res] Pair mask for attention operations.
      multichain_mask_2d: [num_res, num_res] Mask indicating which residue pairs
        are intra-chain, used to mask out residue distance based features
        between chains.
      is_training: bool indicating where we are running in training mode.
      safe_key: random key generator.

    Returns:
      An embedding of size [num_res, num_res, num_channels]
    """"""
    c = self.config
    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    num_templates = template_batch['template_aatype'].shape[0]
    num_res, _, query_num_channels = query_embedding.shape

    # Embed each template separately.
    template_embedder = SingleTemplateEmbedding(self.config, self.global_config)
    def partial_template_embedder(template_aatype,
                                  template_all_atom_positions,
                                  template_all_atom_mask,
                                  unsafe_key):
      safe_key = prng.SafeKey(unsafe_key)
      return template_embedder(query_embedding,
                               template_aatype,
                               template_all_atom_positions,
                               template_all_atom_mask,
                               padding_mask_2d,
                               multichain_mask_2d,
                               is_training,
                               safe_key)

    safe_key, unsafe_key = safe_key.split()
    unsafe_keys = jax.random.split(unsafe_key._key, num_templates)

    def scan_fn(carry, x):
      return carry + partial_template_embedder(*x), None

    scan_init = jnp.zeros((num_res, num_res, c.num_channels),
                          dtype=query_embedding.dtype)
    summed_template_embeddings, _ = hk.scan(
        scan_fn, scan_init,
        (template_batch['template_aatype'],
         template_batch['template_all_atom_positions'],
         template_batch['template_all_atom_mask'], unsafe_keys))

    embedding = summed_template_embeddings / num_templates
    embedding = jax.nn.relu(embedding)
    embedding = common_modules.Linear(
        query_num_channels,
        initializer='relu',
        name='output_linear')(embedding)

    return embedding


class SingleTemplateEmbedding(hk.Module):
  """"""Embed a single template.""""""

  def __init__(self, config, global_config, name='single_template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_aatype,
               template_all_atom_positions, template_all_atom_mask,
               padding_mask_2d, multichain_mask_2d, is_training,
               safe_key):
    """"""Build the single template embedding graph.

    Args:
      query_embedding: (num_res, num_res, num_channels) - embedding of the
        query sequence/msa.
      template_aatype: [num_res] aatype for each template.
      template_all_atom_positions: [num_res, 37, 3] atom positions for all
        templates.
      template_all_atom_mask: [num_res, 37] mask for each template.
      padding_mask_2d: Padding mask (Note: this doesn't care if a template
        exists, unlike the template_pseudo_beta_mask).
      multichain_mask_2d: A mask indicating intra-chain residue pairs, used
        to mask out between chain distances/features when templates are for
        single chains.
      is_training: Are we in training mode.
      safe_key: Random key generator.

    Returns:
      A template embedding (num_res, num_res, num_channels).
    """"""
    gc = self.global_config
    c = self.config
    assert padding_mask_2d.dtype == query_embedding.dtype
    dtype = query_embedding.dtype
    num_channels = self.config.num_channels

    def construct_input(query_embedding, template_aatype,
                        template_all_atom_positions, template_all_atom_mask,
                        multichain_mask_2d):

      # Compute distogram feature for the template.
      template_positions, pseudo_beta_mask = modules.pseudo_beta_fn(
          template_aatype, template_all_atom_positions, template_all_atom_mask)
      pseudo_beta_mask_2d = (pseudo_beta_mask[:, None] *
                             pseudo_beta_mask[None, :])
      pseudo_beta_mask_2d *= multichain_mask_2d
      template_dgram = modules.dgram_from_positions(
          template_positions, **self.config.dgram_features)
      template_dgram *= pseudo_beta_mask_2d[..., None]
      template_dgram = template_dgram.astype(dtype)
      pseudo_beta_mask_2d = pseudo_beta_mask_2d.astype(dtype)
      to_concat = [(template_dgram, 1), (pseudo_beta_mask_2d, 0)]

      aatype = jax.nn.one_hot(template_aatype, 22, axis=-1, dtype=dtype)
      to_concat.append((aatype[None, :, :], 1))
      to_concat.append((aatype[:, None, :], 1))

      # Compute a feature representing the normalized vector between each
      # backbone affine - i.e. in each residues local frame, what direction are
      # each of the other residues.
      raw_atom_pos = template_all_atom_positions

      atom_pos = geometry.Vec3Array.from_array(raw_atom_pos)
      rigid, backbone_mask = folding_multimer.make_backbone_affine(
          atom_pos,
          template_all_atom_mask,
          template_aatype)
      points = rigid.translation
      rigid_vec = rigid[:, None].inverse().apply_to_point(points)
      unit_vector = rigid_vec.normalized()
      unit_vector = [unit_vector.x, unit_vector.y, unit_vector.z]

      backbone_mask_2d = backbone_mask[:, None] * backbone_mask[None, :]
      backbone_mask_2d *= multichain_mask_2d
      unit_vector = [x*backbone_mask_2d for x in unit_vector]

      # Note that the backbone_mask takes into account C, CA and N (unlike
      # pseudo beta mask which just needs CB) so we add both masks as features.
      to_concat.extend([(x, 0) for x in unit_vector])
      to_concat.append((backbone_mask_2d, 0))

      query_embedding = hk.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='query_embedding_norm')(
              query_embedding)
      # Allow the template embedder to see the query embedding.  Note this
      # contains the position relative feature, so this is how the network knows
      # which residues are next to each other.
      to_concat.append((query_embedding, 1))

      act = 0

      for i, (x, n_input_dims) in enumerate(to_concat):

        act += common_modules.Linear(
            num_channels,
            num_input_dims=n_input_dims,
            initializer='relu',
            name=f'template_pair_embedding_{i}')(x)
      return act

    act = construct_input(query_embedding, template_aatype,
                          template_all_atom_positions, template_all_atom_mask,
                          multichain_mask_2d)

    template_iteration = TemplateEmbeddingIteration(
        c.template_pair_stack, gc, name='template_embedding_iteration')

    def template_iteration_fn(x):
      act, safe_key = x

      safe_key, safe_subkey = safe_key.split()
      act = template_iteration(
          act=act,
          pair_mask=padding_mask_2d,
          is_training=is_training,
          safe_key=safe_subkey)
      return (act, safe_key)

    if gc.use_remat:
      template_iteration_fn = hk.remat(template_iteration_fn)

    safe_key, safe_subkey = safe_key.split()
    template_stack = layer_stack.layer_stack(
        c.template_pair_stack.num_block)(
            template_iteration_fn)
    act, safe_key = template_stack((act, safe_subkey))

    act = hk.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='output_layer_norm')(
            act)
    return act


class TemplateEmbeddingIteration(hk.Module):
  """"""Single Iteration of Template Embedding.""""""

  def __init__(self, config, global_config,
               name='template_embedding_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, act, pair_mask, is_training=True,
               safe_key=None):
    """"""Build a single iteration of the template embedder.

    Args:
      act: [num_res, num_res, num_channel] Input pairwise activations.
      pair_mask: [num_res, num_res] padding mask.
      is_training: Whether to run in training mode.
      safe_key: Safe pseudo-random generator key.

    Returns:
      [num_res, num_res, num_channel] tensor of activations.
    """"""
    c = self.config
    gc = self.global_config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    dropout_wrapper_fn = functools.partial(
        modules.dropout_wrapper,
        is_training=is_training,
        global_config=gc)

    safe_key, *sub_keys = safe_key.split(20)
    sub_keys = iter(sub_keys)

    act = dropout_wrapper_fn(
        modules.TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                                       name='triangle_multiplication_outgoing'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    act = dropout_wrapper_fn(
        modules.TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                                       name='triangle_multiplication_incoming'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    act = dropout_wrapper_fn(
        modules.TriangleAttention(c.triangle_attention_starting_node, gc,
                                  name='triangle_attention_starting_node'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    act = dropout_wrapper_fn(
        modules.TriangleAttention(c.triangle_attention_ending_node, gc,
                                  name='triangle_attention_ending_node'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    act = dropout_wrapper_fn(
        modules.Transition(c.pair_transition, gc,
                           name='pair_transition'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    return act


def template_embedding_1d(batch, num_channel):
  """"""Embed templates into an (num_res, num_templates, num_channels) embedding.

  Args:
    batch: A batch containing:
      template_aatype, (num_templates, num_res) aatype for the templates.
      template_all_atom_positions, (num_templates, num_residues, 37, 3) atom
        positions for the templates.
      template_all_atom_mask, (num_templates, num_residues, 37) atom mask for
        each template.
    num_channel: The number of channels in the output.

  Returns:
    An embedding of shape (num_templates, num_res, num_channels) and a mask of
    shape (num_templates, num_res).
  """"""

  # Embed the templates aatypes.
  aatype_one_hot = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1)

  num_templates = batch['template_aatype'].shape[0]
  all_chi_angles = []
  all_chi_masks = []
  for i in range(num_templates):
    atom_pos = geometry.Vec3Array.from_array(
        batch['template_all_atom_positions'][i, :, :, :])
    template_chi_angles, template_chi_mask = all_atom_multimer.compute_chi_angles(
        atom_pos,
        batch['template_all_atom_mask'][i, :, :],
        batch['template_aatype'][i, :])
    all_chi_angles.append(template_chi_angles)
    all_chi_masks.append(template_chi_mask)
  chi_angles = jnp.stack(all_chi_angles, axis=0)
  chi_mask = jnp.stack(all_chi_masks, axis=0)

  template_features = jnp.concatenate([
      aatype_one_hot,
      jnp.sin(chi_angles) * chi_mask,
      jnp.cos(chi_angles) * chi_mask,
      chi_mask], axis=-1)

  template_mask = chi_mask[:, :, 0]

  template_activations = common_modules.Linear(
      num_channel,
      initializer='relu',
      name='template_single_embedding')(
          template_features)
  template_activations = jax.nn.relu(template_activations)
  template_activations = common_modules.Linear(
      num_channel,
      initializer='relu',
      name='template_projection')(
          template_activations)
  return template_activations, template_mask
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/evo_preset_runner.py",".py","6034","185","import os
import time
import random
import logging
from typing import Mapping, Optional, Sequence, List

import jax
import tree
import numpy as np

from alphafold.data import pipeline_multimer, msa_pairing
from alphafold.model import config, data
from msa_pair.runner import model, modules_multimer

logger = logging.getLogger(__file__)


def clear_mem(device=None):
    '''remove all data from device'''
    backend = jax.lib.xla_bridge.get_backend(device)
    if hasattr(backend,'live_buffers'):
        for buf in backend.live_buffers():
            buf.delete()


def _is_params_transferable(old_params, next_params):
    def _get_all_keys(params):
        all_keys = []
        for path in params.keys():
            for k in params[path].keys():
                all_keys.append(f'{path}/{k}')
        return tuple(sorted(all_keys))

    return _get_all_keys(old_params) == _get_all_keys(next_params)


class ModelPresetRunner:
    def __init__(
        self,
        database: str,
        model_preset: str = 'multimer',
        allow_params_transfer: bool = True,
        cache_params: bool = True,
        ignore_unpaired_sequences: bool = True,
    ):
        assert model_preset == 'multimer', 'Only supports preset multimer'

        self.model_preset = model_preset
        self.database = database
        self.allow_params_transfer = allow_params_transfer
        self.cache_params = cache_params

        self.params_dict = {}
        # Check model parameters
        first_params, first_model_name = None, None
        first_config = None

        self.avail_model_names = []
        for model_name in config.MODEL_PRESETS[model_preset]:
            try:
                params = data.get_model_haiku_params(
                    model_name, self.database
                )
                if first_params is None:
                    first_model_name = model_name
                    first_params = params
                    first_config = config.model_config(model_name)
                elif self.allow_params_transfer:
                    if not _is_params_transferable(first_params, params):
                        continue
                    if first_config != config.model_config(model_name):
                        continue
                self.avail_model_names.append(model_name)
            except FileNotFoundError as e:
                continue

        assert len(self.avail_model_names) >= 1

        print(f'{len(self.avail_model_names)} models are available')
        print(f'Build RunModel {first_model_name}')
        self.running_model = model.RunModel(first_config, first_params)

        self.ignore_unpaired_sequences = ignore_unpaired_sequences

    def predict(
        self,
        feat: Mapping[str, np.ndarray],
        rng_seed: Optional[int] = None,
        model_names: Optional[List[str]] = None,
        num_predictions_per_model: int = 5,
    ):
        if rng_seed is not None:
            rng = np.random.default_rng(rng_seed)
        else:
            rng = np.random.default_rng(random.randrange(1, 2**24))

        if model_names is None:
            model_names = self.avail_model_names
        else:
            model_names = [
                model_name for model_name in model_names if model_name in 
                self.avail_model_names
            ]

        outputs = {}
        for model_name in model_names:
            for pred_num in range(num_predictions_per_model):
                seed = int(rng.integers(1, 2**24))
                result, timing = self.run_model(
                    feat, model_name, seed
                )
                outputs[f'{model_name}_{pred_num}'] = {
                    'result': result,
                    'timing': timing,
                    'seed': seed,
                }
            logger.info(tree.map_structure(lambda x: x.shape, result))

        return outputs

    def run_model(
        self,
        feat: Mapping[str, np.ndarray],
        model_name: str,
        random_seed: int,
    ):
        t0 = time.time()

        if not self.cache_params or model_name not in self.params_dict:
            params = data.get_model_haiku_params(model_name, self.database)
            if self.cache_params:
                self.params_dict[model_name] = params
        else:
            params = self.params_dict[model_name]

        if self.running_model is not None and self.allow_params_transfer:
            logger.info(f'Transfer {model_name} params to the running model')
            for path in params.keys():
                self.running_model.params[path] = params[path]

        logger.info(
            f'Start prediction using {model_name} with seed {random_seed}'
        )
        if self.ignore_unpaired_sequences:
            feat = self._remove_unpaired_sequences(feat)
        result = self.running_model.predict(feat, random_seed)
        t1 = time.time()

        logger.info(
            f'Finish prediction using {model_name} in {t1 - t0} seconds'
        )
        # exit()
        timing = t1 - t0

        return result, timing

    def _remove_unpaired_sequences(
        self,
        feat: Mapping[str, np.ndarray],
    ):
        paired_sequence_mask = 1
        for chain_id in np.unique(feat['asym_id']):
            if chain_id:
                chain_mask = feat['asym_id'] == chain_id
                paired_sequence_mask *= np.sum(
                    feat['msa'][:,chain_mask] != msa_pairing.MSA_GAP_IDX,
                    axis=-1,
                ) > 0
        paired_sequence_mask = paired_sequence_mask.astype(bool)
        for key, value in feat.items():
            if key in (
                'msa', 
                'deletion_matrix',
                'bert_mask',
                'msa_mask', 
                'cluster_bias_mask',
            ):
                feat[key] = value[paired_sequence_mask]
            elif key in ('num_alignments',):
                feat[key] = np.sum(paired_sequence_mask)

        feat = pipeline_multimer.pad_msa(feat, 512)

        return feat
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/utils.py",".py","1673","57","import json
from time import time

from alphafold.common import protein
from alphafold.common import confidence

def move_to_tensor(feat):
    import torch

    new_feat = {}
    for key, value in feat.items():
        new_feat[key] = torch.from_numpy(value).cuda()
    return new_feat

def write_pdb(
    output_pdb: str, processed_feature_dict, ret, plddt=None, is_multimer=True,
):
    unrelaxed_protein = protein.from_prediction(
        features=processed_feature_dict,
        result=ret,
        b_factors=plddt,
        remove_leading_feature_dimension=not is_multimer,
    )

    pdb_string = protein.to_pdb(unrelaxed_protein)

    with open(output_pdb, 'wt') as fh:
        fh.write(pdb_string)

def write_ranking(output_ranking: str, ret, timing, random_seed=None):
    stat = {
        'confidence': float(ret['ranking_confidence']),
        'timing': timing,
    }
    if 'iptm' in ret:
        stat['iptm'] = float(ret['iptm'])
    if 'ptm' in ret:
        stat['ptm'] = float(ret['ptm'])
    if random_seed is not None:
        stat['seed'] = float(random_seed)

    with open(output_ranking, 'wt') as fh:
        json.dump(stat, fh, indent=2, sort_keys=True)

def get_iptm(prediction_result, asym_id):
    confidence_metrics = {}
    if 'predicted_aligned_error' in prediction_result:
        # Compute the ipTM only for the multimer model.
        confidence_metrics['iptm'] = confidence.predicted_tm_score(
            logits=prediction_result['predicted_aligned_error']['logits'],
            breaks=prediction_result['predicted_aligned_error']['breaks'],
            asym_id=asym_id,
            interface=True,
        )

    return confidence_metrics
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/runner/modules.py",".py","71765","2114","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Modules and code used in the core part of AlphaFold.

The structure generation code is in 'folding.py'.
""""""
import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import utils
import haiku as hk
import jax
import jax.numpy as jnp


def softmax_cross_entropy(logits, labels):
  """"""Computes softmax cross entropy given logits and one-hot class labels.""""""
  loss = -jnp.sum(labels * jax.nn.log_softmax(logits), axis=-1)
  return jnp.asarray(loss)


def sigmoid_cross_entropy(logits, labels):
  """"""Computes sigmoid cross entropy given logits and multiple class labels.""""""
  log_p = jax.nn.log_sigmoid(logits)
  # log(1 - sigmoid(x)) = log_sigmoid(-x), the latter is more numerically stable
  log_not_p = jax.nn.log_sigmoid(-logits)
  loss = -labels * log_p - (1. - labels) * log_not_p
  return jnp.asarray(loss)


def apply_dropout(*, tensor, safe_key, rate, is_training, broadcast_dim=None):
  """"""Applies dropout to a tensor.""""""
  if is_training and rate != 0.0:
    shape = list(tensor.shape)
    if broadcast_dim is not None:
      shape[broadcast_dim] = 1
    keep_rate = 1.0 - rate
    keep = jax.random.bernoulli(safe_key.get(), keep_rate, shape=shape)
    return keep * tensor / keep_rate
  else:
    return tensor


def dropout_wrapper(module,
                    input_act,
                    mask,
                    safe_key,
                    global_config,
                    output_act=None,
                    is_training=True,
                    **kwargs):
  """"""Applies module + dropout + residual update.""""""
  if output_act is None:
    output_act = input_act

  gc = global_config
  residual = module(input_act, mask, is_training=is_training, **kwargs)
  dropout_rate = 0.0 if gc.deterministic else module.config.dropout_rate

  if module.config.shared_dropout:
    if module.config.orientation == 'per_row':
      broadcast_dim = 0
    else:
      broadcast_dim = 1
  else:
    broadcast_dim = None

  residual = apply_dropout(tensor=residual,
                           safe_key=safe_key,
                           rate=dropout_rate,
                           is_training=is_training,
                           broadcast_dim=broadcast_dim)

  new_act = output_act + residual

  return new_act


def create_extra_msa_feature(batch):
  """"""Expand extra_msa into 1hot and concat with other extra msa features.

  We do this as late as possible as the one_hot extra msa can be very large.

  Arguments:
    batch: a dictionary with the following keys:
     * 'extra_msa': [N_extra_seq, N_res] MSA that wasn't selected as a cluster
       centre. Note, that this is not one-hot encoded.
     * 'extra_has_deletion': [N_extra_seq, N_res] Whether there is a deletion to
       the left of each position in the extra MSA.
     * 'extra_deletion_value': [N_extra_seq, N_res] The number of deletions to
       the left of each position in the extra MSA.

  Returns:
    Concatenated tensor of extra MSA features.
  """"""
  # 23 = 20 amino acids + 'X' for unknown + gap + bert mask
  msa_1hot = jax.nn.one_hot(batch['extra_msa'], 23)
  msa_feat = [msa_1hot,
              jnp.expand_dims(batch['extra_has_deletion'], axis=-1),
              jnp.expand_dims(batch['extra_deletion_value'], axis=-1)]
  return jnp.concatenate(msa_feat, axis=-1)


class AlphaFoldIteration(hk.Module):
  """"""A single recycling iteration of AlphaFold architecture.

  Computes ensembled (averaged) representations from the provided features.
  These representations are then passed to the various heads
  that have been requested by the configuration file. Each head also returns a
  loss which is combined as a weighted sum to produce the total loss.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 3-22
  """"""

  def __init__(self, config, global_config, name='alphafold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               ensembled_batch,
               non_ensembled_batch,
               is_training,
               compute_loss=False,
               ensemble_representations=False,
               return_representations=False):

    num_ensemble = jnp.asarray(ensembled_batch['seq_length'].shape[0])

    if not ensemble_representations:
      assert ensembled_batch['seq_length'].shape[0] == 1

    def slice_batch(i):
      b = {k: v[i] for k, v in ensembled_batch.items()}
      b.update(non_ensembled_batch)
      return b

    # Compute representations for each batch element and average.
    evoformer_module = EmbeddingsAndEvoformer(
        self.config.embeddings_and_evoformer, self.global_config)
    batch0 = slice_batch(0)
    representations = evoformer_module(batch0, is_training)

    # MSA representations are not ensembled so
    # we don't pass tensor into the loop.
    msa_representation = representations['msa']
    del representations['msa']

    # Average the representations (except MSA) over the batch dimension.
    if ensemble_representations:
      def body(x):
        """"""Add one element to the representations ensemble.""""""
        i, current_representations = x
        feats = slice_batch(i)
        representations_update = evoformer_module(
            feats, is_training)

        new_representations = {}
        for k in current_representations:
          new_representations[k] = (
              current_representations[k] + representations_update[k])
        return i+1, new_representations

      if hk.running_init():
        # When initializing the Haiku module, run one iteration of the
        # while_loop to initialize the Haiku modules used in `body`.
        _, representations = body((1, representations))
      else:
        _, representations = hk.while_loop(
            lambda x: x[0] < num_ensemble,
            body,
            (1, representations))

      for k in representations:
        if k != 'msa':
          representations[k] /= num_ensemble.astype(representations[k].dtype)

    representations['msa'] = msa_representation
    batch = batch0  # We are not ensembled from here on.

    heads = {}
    for head_name, head_config in sorted(self.config.heads.items()):
      if not head_config.weight:
        continue  # Do not instantiate zero-weight heads.

      head_factory = {
          'masked_msa': MaskedMsaHead,
          'distogram': DistogramHead,
          'structure_module': functools.partial(
              folding.StructureModule, compute_loss=compute_loss),
          'predicted_lddt': PredictedLDDTHead,
          'predicted_aligned_error': PredictedAlignedErrorHead,
          'experimentally_resolved': ExperimentallyResolvedHead,
      }[head_name]
      heads[head_name] = (head_config,
                          head_factory(head_config, self.global_config))

    total_loss = 0.
    ret = {}
    ret['representations'] = representations

    def loss(module, head_config, ret, name, filter_ret=True):
      if filter_ret:
        value = ret[name]
      else:
        value = ret
      loss_output = module.loss(value, batch)
      ret[name].update(loss_output)
      loss = head_config.weight * ret[name]['loss']
      return loss

    for name, (head_config, module) in heads.items():
      # Skip PredictedLDDTHead and PredictedAlignedErrorHead until
      # StructureModule is executed.
      if name in ('predicted_lddt', 'predicted_aligned_error'):
        continue
      else:
        ret[name] = module(representations, batch, is_training)
        if 'representations' in ret[name]:
          # Extra representations from the head. Used by the structure module
          # to provide activations for the PredictedLDDTHead.
          representations.update(ret[name].pop('representations'))
      if compute_loss:
        total_loss += loss(module, head_config, ret, name)

    if self.config.heads.get('predicted_lddt.weight', 0.0):
      # Add PredictedLDDTHead after StructureModule executes.
      name = 'predicted_lddt'
      # Feed all previous results to give access to structure_module result.
      head_config, module = heads[name]
      ret[name] = module(representations, batch, is_training)
      if compute_loss:
        total_loss += loss(module, head_config, ret, name, filter_ret=False)

    if ('predicted_aligned_error' in self.config.heads
        and self.config.heads.get('predicted_aligned_error.weight', 0.0)):
      # Add PredictedAlignedErrorHead after StructureModule executes.
      name = 'predicted_aligned_error'
      # Feed all previous results to give access to structure_module result.
      head_config, module = heads[name]
      ret[name] = module(representations, batch, is_training)
      if compute_loss:
        total_loss += loss(module, head_config, ret, name, filter_ret=False)

    if compute_loss:
      return ret, total_loss
    else:
      return ret


class AlphaFold(hk.Module):
  """"""AlphaFold model with recycling.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference""
  """"""

  def __init__(self, config, name='alphafold'):
    super().__init__(name=name)
    self.config = config
    self.global_config = config.global_config

  def __call__(
      self,
      batch,
      is_training,
      compute_loss=False,
      ensemble_representations=False,
      return_representations=False):
    """"""Run the AlphaFold model.

    Arguments:
      batch: Dictionary with inputs to the AlphaFold model.
      is_training: Whether the system is in training or inference mode.
      compute_loss: Whether to compute losses (requires extra features
        to be present in the batch and knowing the true structure).
      ensemble_representations: Whether to use ensembling of representations.
      return_representations: Whether to also return the intermediate
        representations.

    Returns:
      When compute_loss is True:
        a tuple of loss and output of AlphaFoldIteration.
      When compute_loss is False:
        just output of AlphaFoldIteration.

      The output of AlphaFoldIteration is a nested dictionary containing
      predictions from the various heads.
    """"""

    impl = AlphaFoldIteration(self.config, self.global_config)
    batch_size, num_residues = batch['aatype'].shape

    def get_prev(ret):
      new_prev = {
          'prev_pos':
              ret['structure_module']['final_atom_positions'],
          'prev_msa_first_row': ret['representations']['msa_first_row'],
          'prev_pair': ret['representations']['pair'],
      }
      return jax.tree_map(jax.lax.stop_gradient, new_prev)

    def do_call(prev,
                recycle_idx,
                compute_loss=compute_loss):
      if self.config.resample_msa_in_recycling:
        num_ensemble = batch_size // (self.config.num_recycle + 1)
        def slice_recycle_idx(x):
          start = recycle_idx * num_ensemble
          size = num_ensemble
          return jax.lax.dynamic_slice_in_dim(x, start, size, axis=0)
        ensembled_batch = jax.tree_map(slice_recycle_idx, batch)
      else:
        num_ensemble = batch_size
        ensembled_batch = batch

      non_ensembled_batch = jax.tree_map(lambda x: x, prev)

      return impl(
          ensembled_batch=ensembled_batch,
          non_ensembled_batch=non_ensembled_batch,
          is_training=is_training,
          compute_loss=compute_loss,
          ensemble_representations=ensemble_representations)

    prev = {}
    emb_config = self.config.embeddings_and_evoformer
    if emb_config.recycle_pos:
      prev['prev_pos'] = jnp.zeros(
          [num_residues, residue_constants.atom_type_num, 3])
    if emb_config.recycle_features:
      prev['prev_msa_first_row'] = jnp.zeros(
          [num_residues, emb_config.msa_channel])
      prev['prev_pair'] = jnp.zeros(
          [num_residues, num_residues, emb_config.pair_channel])

    if self.config.num_recycle:
      if 'num_iter_recycling' in batch:
        # Training time: num_iter_recycling is in batch.
        # The value for each ensemble batch is the same, so arbitrarily taking
        # 0-th.
        num_iter = batch['num_iter_recycling'][0]

        # Add insurance that we will not run more
        # recyclings than the model is configured to run.
        num_iter = jnp.minimum(num_iter, self.config.num_recycle)
      else:
        # Eval mode or tests: use the maximum number of iterations.
        num_iter = self.config.num_recycle

      body = lambda x: (x[0] + 1,  # pylint: disable=g-long-lambda
                        get_prev(do_call(x[1], recycle_idx=x[0],
                                         compute_loss=False)))
      if hk.running_init():
        # When initializing the Haiku module, run one iteration of the
        # while_loop to initialize the Haiku modules used in `body`.
        _, prev = body((0, prev))
      else:
        _, prev = hk.while_loop(
            lambda x: x[0] < num_iter,
            body,
            (0, prev))
    else:
      num_iter = 0

    ret = do_call(prev=prev, recycle_idx=num_iter)
    if compute_loss:
      ret = ret[0], [ret[1]]

    if not return_representations:
      del (ret[0] if compute_loss else ret)['representations']  # pytype: disable=unsupported-operands
    return ret


class TemplatePairStack(hk.Module):
  """"""Pair stack for the templates.

  Jumper et al. (2021) Suppl. Alg. 16 ""TemplatePairStack""
  """"""

  def __init__(self, config, global_config, name='template_pair_stack'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, pair_act, pair_mask, is_training, safe_key=None):
    """"""Builds TemplatePairStack module.

    Arguments:
      pair_act: Pair activations for single template, shape [N_res, N_res, c_t].
      pair_mask: Pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.
      safe_key: Safe key object encapsulating the random number generation key.

    Returns:
      Updated pair_act, shape [N_res, N_res, c_t].
    """"""

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    gc = self.global_config
    c = self.config

    if not c.num_block:
      return pair_act

    def block(x):
      """"""One block of the template pair stack.""""""
      pair_act, safe_key = x

      dropout_wrapper_fn = functools.partial(
          dropout_wrapper, is_training=is_training, global_config=gc)

      safe_key, *sub_keys = safe_key.split(6)
      sub_keys = iter(sub_keys)

      pair_act = dropout_wrapper_fn(
          TriangleAttention(c.triangle_attention_starting_node, gc,
                            name='triangle_attention_starting_node'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleAttention(c.triangle_attention_ending_node, gc,
                            name='triangle_attention_ending_node'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                                 name='triangle_multiplication_outgoing'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                                 name='triangle_multiplication_incoming'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          Transition(c.pair_transition, gc, name='pair_transition'),
          pair_act,
          pair_mask,
          next(sub_keys))

      return pair_act, safe_key

    if gc.use_remat:
      block = hk.remat(block)

    res_stack = layer_stack.layer_stack(c.num_block)(block)
    pair_act, safe_key = res_stack((pair_act, safe_key))
    return pair_act


class Transition(hk.Module):
  """"""Transition layer.

  Jumper et al. (2021) Suppl. Alg. 9 ""MSATransition""
  Jumper et al. (2021) Suppl. Alg. 15 ""PairTransition""
  """"""

  def __init__(self, config, global_config, name='transition_block'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, act, mask, is_training=True):
    """"""Builds Transition module.

    Arguments:
      act: A tensor of queries of size [batch_size, N_res, N_channel].
      mask: A tensor denoting the mask of size [batch_size, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      A float32 tensor of size [batch_size, N_res, N_channel].
    """"""
    _, _, nc = act.shape

    num_intermediate = int(nc * self.config.num_intermediate_factor)
    mask = jnp.expand_dims(mask, axis=-1)

    act = hk.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='input_layer_norm')(
            act)

    transition_module = hk.Sequential([
        common_modules.Linear(
            num_intermediate,
            initializer='relu',
            name='transition1'), jax.nn.relu,
        common_modules.Linear(
            nc,
            initializer=utils.final_init(self.global_config),
            name='transition2')
    ])

    act = mapping.inference_subbatch(
        transition_module,
        self.global_config.subbatch_size,
        batched_args=[act],
        nonbatched_args=[],
        low_memory=not is_training)

    return act


def glorot_uniform():
  return hk.initializers.VarianceScaling(scale=1.0,
                                         mode='fan_avg',
                                         distribution='uniform')


class Attention(hk.Module):
  """"""Multihead attention.""""""

  def __init__(self, config, global_config, output_dim, name='attention'):
    super().__init__(name=name)

    self.config = config
    self.global_config = global_config
    self.output_dim = output_dim

  def __call__(self, q_data, m_data, bias, nonbatched_bias=None):
    """"""Builds Attention module.

    Arguments:
      q_data: A tensor of queries, shape [batch_size, N_queries, q_channels].
      m_data: A tensor of memories from which the keys and values are
        projected, shape [batch_size, N_keys, m_channels].
      bias: A bias for the attention, shape [batch_size, N_queries, N_keys].
      nonbatched_bias: Shared bias, shape [N_queries, N_keys].

    Returns:
      A float32 tensor of shape [batch_size, N_queries, output_dim].
    """"""
    # Sensible default for when the config keys are missing
    key_dim = self.config.get('key_dim', int(q_data.shape[-1]))
    value_dim = self.config.get('value_dim', int(m_data.shape[-1]))
    num_head = self.config.num_head
    assert key_dim % num_head == 0
    assert value_dim % num_head == 0
    key_dim = key_dim // num_head
    value_dim = value_dim // num_head

    q_weights = hk.get_parameter(
        'query_w', shape=(q_data.shape[-1], num_head, key_dim),
        init=glorot_uniform())
    k_weights = hk.get_parameter(
        'key_w', shape=(m_data.shape[-1], num_head, key_dim),
        init=glorot_uniform())
    v_weights = hk.get_parameter(
        'value_w', shape=(m_data.shape[-1], num_head, value_dim),
        init=glorot_uniform())

    q = jnp.einsum('bqa,ahc->bqhc', q_data, q_weights) * key_dim**(-0.5)
    k = jnp.einsum('bka,ahc->bkhc', m_data, k_weights)
    v = jnp.einsum('bka,ahc->bkhc', m_data, v_weights)
    logits = jnp.einsum('bqhc,bkhc->bhqk', q, k) + bias
    # print(""init_q: "", q_data, ""k"", k, ""logits: "", logits)
    if nonbatched_bias is not None:
      logits += jnp.expand_dims(nonbatched_bias, axis=0)
    weights = jax.nn.softmax(logits)
    weighted_avg = jnp.einsum('bhqk,bkhc->bqhc', weights, v)

    if self.global_config.zero_init:
      init = hk.initializers.Constant(0.0)
    else:
      init = glorot_uniform()

    if self.config.gating:
      gating_weights = hk.get_parameter(
          'gating_w',
          shape=(q_data.shape[-1], num_head, value_dim),
          init=hk.initializers.Constant(0.0))
      gating_bias = hk.get_parameter(
          'gating_b',
          shape=(num_head, value_dim),
          init=hk.initializers.Constant(1.0))

      gate_values = jnp.einsum('bqc, chv->bqhv', q_data,
                               gating_weights) + gating_bias

      gate_values = jax.nn.sigmoid(gate_values)

      weighted_avg *= gate_values

    o_weights = hk.get_parameter(
        'output_w', shape=(num_head, value_dim, self.output_dim),
        init=init)
    o_bias = hk.get_parameter('output_b', shape=(self.output_dim,),
                              init=hk.initializers.Constant(0.0))

    output = jnp.einsum('bqhc,hco->bqo', weighted_avg, o_weights) + o_bias


    # logits: [Residues, head, num_msa, num_msa]
    return output, logits


class GlobalAttention(hk.Module):
  """"""Global attention.

  Jumper et al. (2021) Suppl. Alg. 19 ""MSAColumnGlobalAttention"" lines 2-7
  """"""

  def __init__(self, config, global_config, output_dim, name='attention'):
    super().__init__(name=name)

    self.config = config
    self.global_config = global_config
    self.output_dim = output_dim

  def __call__(self, q_data, m_data, q_mask):
    """"""Builds GlobalAttention module.

    Arguments:
      q_data: A tensor of queries with size [batch_size, N_queries,
        q_channels]
      m_data: A tensor of memories from which the keys and values
        projected. Size [batch_size, N_keys, m_channels]
      q_mask: A binary mask for q_data with zeros in the padded sequence
        elements and ones otherwise. Size [batch_size, N_queries, q_channels]
        (or broadcastable to this shape).

    Returns:
      A float32 tensor of size [batch_size, N_queries, output_dim].
    """"""
    # Sensible default for when the config keys are missing
    key_dim = self.config.get('key_dim', int(q_data.shape[-1]))
    value_dim = self.config.get('value_dim', int(m_data.shape[-1]))
    num_head = self.config.num_head
    assert key_dim % num_head == 0
    assert value_dim % num_head == 0
    key_dim = key_dim // num_head
    value_dim = value_dim // num_head

    q_weights = hk.get_parameter(
        'query_w', shape=(q_data.shape[-1], num_head, key_dim),
        init=glorot_uniform())
    k_weights = hk.get_parameter(
        'key_w', shape=(m_data.shape[-1], key_dim),
        init=glorot_uniform())
    v_weights = hk.get_parameter(
        'value_w', shape=(m_data.shape[-1], value_dim),
        init=glorot_uniform())

    v = jnp.einsum('bka,ac->bkc', m_data, v_weights)

    q_avg = utils.mask_mean(q_mask, q_data, axis=1)

    q = jnp.einsum('ba,ahc->bhc', q_avg, q_weights) * key_dim**(-0.5)
    k = jnp.einsum('bka,ac->bkc', m_data, k_weights)
    bias = (1e9 * (q_mask[:, None, :, 0] - 1.))
    logits = jnp.einsum('bhc,bkc->bhk', q, k) + bias
    weights = jax.nn.softmax(logits)
    weighted_avg = jnp.einsum('bhk,bkc->bhc', weights, v)

    if self.global_config.zero_init:
      init = hk.initializers.Constant(0.0)
    else:
      init = glorot_uniform()

    o_weights = hk.get_parameter(
        'output_w', shape=(num_head, value_dim, self.output_dim),
        init=init)
    o_bias = hk.get_parameter('output_b', shape=(self.output_dim,),
                              init=hk.initializers.Constant(0.0))

    if self.config.gating:
      gating_weights = hk.get_parameter(
          'gating_w',
          shape=(q_data.shape[-1], num_head, value_dim),
          init=hk.initializers.Constant(0.0))
      gating_bias = hk.get_parameter(
          'gating_b',
          shape=(num_head, value_dim),
          init=hk.initializers.Constant(1.0))

      gate_values = jnp.einsum('bqc, chv->bqhv', q_data, gating_weights)
      gate_values = jax.nn.sigmoid(gate_values + gating_bias)
      weighted_avg = weighted_avg[:, None] * gate_values
      output = jnp.einsum('bqhc,hco->bqo', weighted_avg, o_weights) + o_bias
    else:
      output = jnp.einsum('bhc,hco->bo', weighted_avg, o_weights) + o_bias
      output = output[:, None]
    return output


class MSARowAttentionWithPairBias(hk.Module):
  """"""MSA per-row attention biased by the pair representation.

  Jumper et al. (2021) Suppl. Alg. 7 ""MSARowAttentionWithPairBias""
  """"""

  def __init__(self, config, global_config,
               name='msa_row_attention_with_pair_bias'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               pair_act,
               is_training=False):
    """"""Builds MSARowAttentionWithPairBias module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      pair_act: [N_res, N_res, c_z] pair representation.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m].
    """"""
    c = self.config

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_row'

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = hk.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)

    pair_act = hk.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='feat_2d_norm')(
            pair_act)

    init_factor = 1. / jnp.sqrt(int(pair_act.shape[-1]))
    weights = hk.get_parameter(
        'feat_2d_weights',
        shape=(pair_act.shape[-1], c.num_head),
        init=hk.initializers.RandomNormal(stddev=init_factor))
    nonbatched_bias = jnp.einsum('qkc,ch->hqk', pair_act, weights)

    attn_mod = Attention(
        c, self.global_config, msa_act.shape[-1])
    msa_act, col_attn = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, bias],
        nonbatched_args=[nonbatched_bias],
        low_memory=not is_training)

    return msa_act


class MSAColumnAttention(hk.Module):
  """"""MSA per-column attention.

  Jumper et al. (2021) Suppl. Alg. 8 ""MSAColumnAttention""
  """"""

  def __init__(self, config, global_config, name='msa_column_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               is_training=False):
    """"""Builds MSAColumnAttention module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m]
    """"""
    c = self.config
    # print(""before: "", msa_act)

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_column'

    msa_act = jnp.swapaxes(msa_act, -2, -3)
    msa_mask = jnp.swapaxes(msa_mask, -1, -2)

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = hk.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)
    attn_mod = Attention(
        c, self.global_config, msa_act.shape[-1])
    # print(""self.global_config.subbatch_size,"", self.global_config.subbatch_size)
    self.global_config.subbatch_size = 256
    msa_act, col_attns = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, bias],
        nonbatched_args=[],
        low_memory=not is_training)

    msa_act = jnp.swapaxes(msa_act, -2, -3)
    # print(""attn: "", attn_mod)
    print(msa_act, col_attns)
    exit()
    return msa_act


class MSAColumnGlobalAttention(hk.Module):
  """"""MSA per-column global attention.

  Jumper et al. (2021) Suppl. Alg. 19 ""MSAColumnGlobalAttention""
  """"""

  def __init__(self, config, global_config, name='msa_column_global_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               is_training=False):
    """"""Builds MSAColumnGlobalAttention module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m].
    """"""
    c = self.config

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_column'

    msa_act = jnp.swapaxes(msa_act, -2, -3)
    msa_mask = jnp.swapaxes(msa_mask, -1, -2)

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = hk.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)

    attn_mod = GlobalAttention(
        c, self.global_config, msa_act.shape[-1],
        name='attention')
    # [N_seq, N_res, 1]
    msa_mask = jnp.expand_dims(msa_mask, axis=-1)
    msa_act = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, msa_mask],
        nonbatched_args=[],
        low_memory=not is_training)

    msa_act = jnp.swapaxes(msa_act, -2, -3)

    return msa_act


class TriangleAttention(hk.Module):
  """"""Triangle Attention.

  Jumper et al. (2021) Suppl. Alg. 13 ""TriangleAttentionStartingNode""
  Jumper et al. (2021) Suppl. Alg. 14 ""TriangleAttentionEndingNode""
  """"""

  def __init__(self, config, global_config, name='triangle_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, pair_act, pair_mask, is_training=False):
    """"""Builds TriangleAttention module.

    Arguments:
      pair_act: [N_res, N_res, c_z] pair activations tensor
      pair_mask: [N_res, N_res] mask of non-padded regions in the tensor.
      is_training: Whether the module is in training mode.

    Returns:
      Update to pair_act, shape [N_res, N_res, c_z].
    """"""
    c = self.config

    assert len(pair_act.shape) == 3
    assert len(pair_mask.shape) == 2
    assert c.orientation in ['per_row', 'per_column']

    if c.orientation == 'per_column':
      pair_act = jnp.swapaxes(pair_act, -2, -3)
      pair_mask = jnp.swapaxes(pair_mask, -1, -2)

    bias = (1e9 * (pair_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    pair_act = hk.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            pair_act)

    init_factor = 1. / jnp.sqrt(int(pair_act.shape[-1]))
    weights = hk.get_parameter(
        'feat_2d_weights',
        shape=(pair_act.shape[-1], c.num_head),
        init=hk.initializers.RandomNormal(stddev=init_factor))
    nonbatched_bias = jnp.einsum('qkc,ch->hqk', pair_act, weights)

    attn_mod = Attention(
        c, self.global_config, pair_act.shape[-1])
    pair_act, col_attn = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[pair_act, pair_act, bias],
        nonbatched_args=[nonbatched_bias],
        low_memory=not is_training)

    if c.orientation == 'per_column':
      pair_act = jnp.swapaxes(pair_act, -2, -3)

    return pair_act


class MaskedMsaHead(hk.Module):
  """"""Head to predict MSA at the masked locations.

  The MaskedMsaHead employs a BERT-style objective to reconstruct a masked
  version of the full MSA, based on a linear projection of
  the MSA representation.
  Jumper et al. (2021) Suppl. Sec. 1.9.9 ""Masked MSA prediction""
  """"""

  def __init__(self, config, global_config, name='masked_msa_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

    if global_config.multimer_mode:
      self.num_output = len(residue_constants.restypes_with_x_and_gap)
    else:
      self.num_output = config.num_output

  def __call__(self, representations, batch, is_training):
    """"""Builds MaskedMsaHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'msa': MSA representation, shape [N_seq, N_res, c_m].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * 'logits': logits of shape [N_seq, N_res, N_aatype] with
            (unnormalized) log probabilies of predicted aatype at position.
    """"""
    del batch
    logits = common_modules.Linear(
        self.num_output,
        initializer=utils.final_init(self.global_config),
        name='logits')(
            representations['msa'])
    return dict(logits=logits)

  def loss(self, value, batch):
    errors = softmax_cross_entropy(
        labels=jax.nn.one_hot(batch['true_msa'], num_classes=self.num_output),
        logits=value['logits'])
    loss = (jnp.sum(errors * batch['bert_mask'], axis=(-2, -1)) /
            (1e-8 + jnp.sum(batch['bert_mask'], axis=(-2, -1))))
    return {'loss': loss}


class PredictedLDDTHead(hk.Module):
  """"""Head to predict the per-residue LDDT to be used as a confidence measure.

  Jumper et al. (2021) Suppl. Sec. 1.9.6 ""Model confidence prediction (pLDDT)""
  Jumper et al. (2021) Suppl. Alg. 29 ""predictPerResidueLDDT_Ca""
  """"""

  def __init__(self, config, global_config, name='predicted_lddt_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds PredictedLDDTHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'structure_module': Single representation from the structure module,
             shape [N_res, c_s].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing :
        * 'logits': logits of shape [N_res, N_bins] with
            (unnormalized) log probabilies of binned predicted lDDT.
    """"""
    act = representations['structure_module']

    act = hk.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='input_layer_norm')(
            act)

    act = common_modules.Linear(
        self.config.num_channels,
        initializer='relu',
        name='act_0')(
            act)
    act = jax.nn.relu(act)

    act = common_modules.Linear(
        self.config.num_channels,
        initializer='relu',
        name='act_1')(
            act)
    act = jax.nn.relu(act)

    logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='logits')(
            act)
    # Shape (batch_size, num_res, num_bins)
    return dict(logits=logits)

  def loss(self, value, batch):
    # Shape (num_res, 37, 3)
    pred_all_atom_pos = value['structure_module']['final_atom_positions']
    # Shape (num_res, 37, 3)
    true_all_atom_pos = batch['all_atom_positions']
    # Shape (num_res, 37)
    all_atom_mask = batch['all_atom_mask']

    # Shape (num_res,)
    lddt_ca = lddt.lddt(
        # Shape (batch_size, num_res, 3)
        predicted_points=pred_all_atom_pos[None, :, 1, :],
        # Shape (batch_size, num_res, 3)
        true_points=true_all_atom_pos[None, :, 1, :],
        # Shape (batch_size, num_res, 1)
        true_points_mask=all_atom_mask[None, :, 1:2].astype(jnp.float32),
        cutoff=15.,
        per_residue=True)
    lddt_ca = jax.lax.stop_gradient(lddt_ca)

    num_bins = self.config.num_bins
    bin_index = jnp.floor(lddt_ca * num_bins).astype(jnp.int32)

    # protect against out of range for lddt_ca == 1
    bin_index = jnp.minimum(bin_index, num_bins - 1)
    lddt_ca_one_hot = jax.nn.one_hot(bin_index, num_classes=num_bins)

    # Shape (num_res, num_channel)
    logits = value['predicted_lddt']['logits']
    errors = softmax_cross_entropy(labels=lddt_ca_one_hot, logits=logits)

    # Shape (num_res,)
    mask_ca = all_atom_mask[:, residue_constants.atom_order['CA']]
    mask_ca = mask_ca.astype(jnp.float32)
    loss = jnp.sum(errors * mask_ca) / (jnp.sum(mask_ca) + 1e-8)

    if self.config.filter_by_resolution:
      # NMR & distillation have resolution = 0
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


class PredictedAlignedErrorHead(hk.Module):
  """"""Head to predict the distance errors in the backbone alignment frames.

  Can be used to compute predicted TM-Score.
  Jumper et al. (2021) Suppl. Sec. 1.9.7 ""TM-score prediction""
  """"""

  def __init__(self, config, global_config,
               name='predicted_aligned_error_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds PredictedAlignedErrorHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'pair': pair representation, shape [N_res, N_res, c_z].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * logits: logits for aligned error, shape [N_res, N_res, N_bins].
        * bin_breaks: array containing bin breaks, shape [N_bins - 1].
    """"""

    act = representations['pair']

    # Shape (num_res, num_res, num_bins)
    logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='logits')(act)
    # Shape (num_bins,)
    breaks = jnp.linspace(
        0., self.config.max_error_bin, self.config.num_bins - 1)
    return dict(logits=logits, breaks=breaks)

  def loss(self, value, batch):
    # Shape (num_res, 7)
    predicted_affine = quat_affine.QuatAffine.from_tensor(
        value['structure_module']['final_affines'])
    # Shape (num_res, 7)
    true_affine = quat_affine.QuatAffine.from_tensor(
        batch['backbone_affine_tensor'])
    # Shape (num_res)
    mask = batch['backbone_affine_mask']
    # Shape (num_res, num_res)
    square_mask = mask[:, None] * mask[None, :]
    num_bins = self.config.num_bins
    # (1, num_bins - 1)
    breaks = value['predicted_aligned_error']['breaks']
    # (1, num_bins)
    logits = value['predicted_aligned_error']['logits']

    # Compute the squared error for each alignment.
    def _local_frame_points(affine):
      points = [jnp.expand_dims(x, axis=-2) for x in affine.translation]
      return affine.invert_point(points, extra_dims=1)
    error_dist2_xyz = [
        jnp.square(a - b)
        for a, b in zip(_local_frame_points(predicted_affine),
                        _local_frame_points(true_affine))]
    error_dist2 = sum(error_dist2_xyz)
    # Shape (num_res, num_res)
    # First num_res are alignment frames, second num_res are the residues.
    error_dist2 = jax.lax.stop_gradient(error_dist2)

    sq_breaks = jnp.square(breaks)
    true_bins = jnp.sum((
        error_dist2[..., None] > sq_breaks).astype(jnp.int32), axis=-1)

    errors = softmax_cross_entropy(
        labels=jax.nn.one_hot(true_bins, num_bins, axis=-1), logits=logits)

    loss = (jnp.sum(errors * square_mask, axis=(-2, -1)) /
            (1e-8 + jnp.sum(square_mask, axis=(-2, -1))))

    if self.config.filter_by_resolution:
      # NMR & distillation have resolution = 0
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


class ExperimentallyResolvedHead(hk.Module):
  """"""Predicts if an atom is experimentally resolved in a high-res structure.

  Only trained on high-resolution X-ray crystals & cryo-EM.
  Jumper et al. (2021) Suppl. Sec. 1.9.10 '""Experimentally resolved"" prediction'
  """"""

  def __init__(self, config, global_config,
               name='experimentally_resolved_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds ExperimentallyResolvedHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'single': Single representation, shape [N_res, c_s].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * 'logits': logits of shape [N_res, 37],
            log probability that an atom is resolved in atom37 representation,
            can be converted to probability by applying sigmoid.
    """"""
    logits = common_modules.Linear(
        37,  # atom_exists.shape[-1]
        initializer=utils.final_init(self.global_config),
        name='logits')(representations['single'])
    return dict(logits=logits)

  def loss(self, value, batch):
    logits = value['logits']
    assert len(logits.shape) == 2

    # Does the atom appear in the amino acid?
    atom_exists = batch['atom37_atom_exists']
    # Is the atom resolved in the experiment? Subset of atom_exists,
    # *except for OXT*
    all_atom_mask = batch['all_atom_mask'].astype(jnp.float32)

    xent = sigmoid_cross_entropy(labels=all_atom_mask, logits=logits)
    loss = jnp.sum(xent * atom_exists) / (1e-8 + jnp.sum(atom_exists))

    if self.config.filter_by_resolution:
      # NMR & distillation examples have resolution = 0.
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


class TriangleMultiplication(hk.Module):
  """"""Triangle multiplication layer (""outgoing"" or ""incoming"").

  Jumper et al. (2021) Suppl. Alg. 11 ""TriangleMultiplicationOutgoing""
  Jumper et al. (2021) Suppl. Alg. 12 ""TriangleMultiplicationIncoming""
  """"""

  def __init__(self, config, global_config, name='triangle_multiplication'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, act, mask, is_training=True):
    """"""Builds TriangleMultiplication module.

    Arguments:
      act: Pair activations, shape [N_res, N_res, c_z]
      mask: Pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      Outputs, same shape/type as act.
    """"""
    del is_training
    c = self.config
    gc = self.global_config

    mask = mask[..., None]

    act = hk.LayerNorm(axis=[-1], create_scale=True, create_offset=True,
                       name='layer_norm_input')(act)
    input_act = act

    left_projection = common_modules.Linear(
        c.num_intermediate_channel,
        name='left_projection')
    left_proj_act = mask * left_projection(act)

    right_projection = common_modules.Linear(
        c.num_intermediate_channel,
        name='right_projection')
    right_proj_act = mask * right_projection(act)

    left_gate_values = jax.nn.sigmoid(common_modules.Linear(
        c.num_intermediate_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='left_gate')(act))

    right_gate_values = jax.nn.sigmoid(common_modules.Linear(
        c.num_intermediate_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='right_gate')(act))

    left_proj_act *= left_gate_values
    right_proj_act *= right_gate_values

    # ""Outgoing"" edges equation: 'ikc,jkc->ijc'
    # ""Incoming"" edges equation: 'kjc,kic->ijc'
    # Note on the Suppl. Alg. 11 & 12 notation:
    # For the ""outgoing"" edges, a = left_proj_act and b = right_proj_act
    # For the ""incoming"" edges, it's swapped:
    #   b = left_proj_act and a = right_proj_act
    act = jnp.einsum(c.equation, left_proj_act, right_proj_act)

    act = hk.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='center_layer_norm')(
            act)

    output_channel = int(input_act.shape[-1])

    act = common_modules.Linear(
        output_channel,
        initializer=utils.final_init(gc),
        name='output_projection')(act)

    gate_values = jax.nn.sigmoid(common_modules.Linear(
        output_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='gating_linear')(input_act))
    act *= gate_values

    return act


class DistogramHead(hk.Module):
  """"""Head to predict a distogram.

  Jumper et al. (2021) Suppl. Sec. 1.9.8 ""Distogram prediction""
  """"""

  def __init__(self, config, global_config, name='distogram_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds DistogramHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'pair': pair representation, shape [N_res, N_res, c_z].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * logits: logits for distogram, shape [N_res, N_res, N_bins].
        * bin_breaks: array containing bin breaks, shape [N_bins - 1,].
    """"""
    half_logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='half_logits')(
            representations['pair'])

    logits = half_logits + jnp.swapaxes(half_logits, -2, -3)
    breaks = jnp.linspace(self.config.first_break, self.config.last_break,
                          self.config.num_bins - 1)

    return dict(logits=logits, bin_edges=breaks)

  def loss(self, value, batch):
    return _distogram_log_loss(value['logits'], value['bin_edges'],
                               batch, self.config.num_bins)


def _distogram_log_loss(logits, bin_edges, batch, num_bins):
  """"""Log loss of a distogram.""""""

  assert len(logits.shape) == 3
  positions = batch['pseudo_beta']
  mask = batch['pseudo_beta_mask']

  assert positions.shape[-1] == 3

  sq_breaks = jnp.square(bin_edges)

  dist2 = jnp.sum(
      jnp.square(
          jnp.expand_dims(positions, axis=-2) -
          jnp.expand_dims(positions, axis=-3)),
      axis=-1,
      keepdims=True)

  true_bins = jnp.sum(dist2 > sq_breaks, axis=-1)

  errors = softmax_cross_entropy(
      labels=jax.nn.one_hot(true_bins, num_bins), logits=logits)

  square_mask = jnp.expand_dims(mask, axis=-2) * jnp.expand_dims(mask, axis=-1)

  avg_error = (
      jnp.sum(errors * square_mask, axis=(-2, -1)) /
      (1e-6 + jnp.sum(square_mask, axis=(-2, -1))))
  dist2 = dist2[..., 0]
  return dict(loss=avg_error, true_dist=jnp.sqrt(1e-6 + dist2))


class OuterProductMean(hk.Module):
  """"""Computes mean outer product.

  Jumper et al. (2021) Suppl. Alg. 10 ""OuterProductMean""
  """"""

  def __init__(self,
               config,
               global_config,
               num_output_channel,
               name='outer_product_mean'):
    super().__init__(name=name)
    self.global_config = global_config
    self.config = config
    self.num_output_channel = num_output_channel

  def __call__(self, act, mask, is_training=True):
    """"""Builds OuterProductMean module.

    Arguments:
      act: MSA representation, shape [N_seq, N_res, c_m].
      mask: MSA mask, shape [N_seq, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      Update to pair representation, shape [N_res, N_res, c_z].
    """"""
    gc = self.global_config
    c = self.config

    mask = mask[..., None]
    act = hk.LayerNorm([-1], True, True, name='layer_norm_input')(act)

    left_act = mask * common_modules.Linear(
        c.num_outer_channel,
        initializer='linear',
        name='left_projection')(
            act)

    right_act = mask * common_modules.Linear(
        c.num_outer_channel,
        initializer='linear',
        name='right_projection')(
            act)

    if gc.zero_init:
      init_w = hk.initializers.Constant(0.0)
    else:
      init_w = hk.initializers.VarianceScaling(scale=2., mode='fan_in')

    output_w = hk.get_parameter(
        'output_w',
        shape=(c.num_outer_channel, c.num_outer_channel,
               self.num_output_channel),
        init=init_w)
    output_b = hk.get_parameter(
        'output_b', shape=(self.num_output_channel,),
        init=hk.initializers.Constant(0.0))

    def compute_chunk(left_act):
      # This is equivalent to
      #
      # act = jnp.einsum('abc,ade->dceb', left_act, right_act)
      # act = jnp.einsum('dceb,cef->bdf', act, output_w) + output_b
      #
      # but faster.
      left_act = jnp.transpose(left_act, [0, 2, 1])
      act = jnp.einsum('acb,ade->dceb', left_act, right_act)
      act = jnp.einsum('dceb,cef->dbf', act, output_w) + output_b
      return jnp.transpose(act, [1, 0, 2])

    act = mapping.inference_subbatch(
        compute_chunk,
        c.chunk_size,
        batched_args=[left_act],
        nonbatched_args=[],
        low_memory=True,
        input_subbatch_dim=1,
        output_subbatch_dim=0)

    epsilon = 1e-3
    norm = jnp.einsum('abc,adc->bdc', mask, mask)
    act /= epsilon + norm

    return act


def dgram_from_positions(positions, num_bins, min_bin, max_bin):
  """"""Compute distogram from amino acid positions.

  Arguments:
    positions: [N_res, 3] Position coordinates.
    num_bins: The number of bins in the distogram.
    min_bin: The left edge of the first bin.
    max_bin: The left edge of the final bin. The final bin catches
        everything larger than `max_bin`.

  Returns:
    Distogram with the specified number of bins.
  """"""

  def squared_difference(x, y):
    return jnp.square(x - y)

  lower_breaks = jnp.linspace(min_bin, max_bin, num_bins)
  lower_breaks = jnp.square(lower_breaks)
  upper_breaks = jnp.concatenate([lower_breaks[1:],
                                  jnp.array([1e8], dtype=jnp.float32)], axis=-1)
  dist2 = jnp.sum(
      squared_difference(
          jnp.expand_dims(positions, axis=-2),
          jnp.expand_dims(positions, axis=-3)),
      axis=-1, keepdims=True)

  dgram = ((dist2 > lower_breaks).astype(jnp.float32) *
           (dist2 < upper_breaks).astype(jnp.float32))
  return dgram


def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
  """"""Create pseudo beta features.""""""

  is_gly = jnp.equal(aatype, residue_constants.restype_order['G'])
  ca_idx = residue_constants.atom_order['CA']
  cb_idx = residue_constants.atom_order['CB']
  pseudo_beta = jnp.where(
      jnp.tile(is_gly[..., None], [1] * len(is_gly.shape) + [3]),
      all_atom_positions[..., ca_idx, :],
      all_atom_positions[..., cb_idx, :])

  if all_atom_masks is not None:
    pseudo_beta_mask = jnp.where(
        is_gly, all_atom_masks[..., ca_idx], all_atom_masks[..., cb_idx])
    pseudo_beta_mask = pseudo_beta_mask.astype(jnp.float32)
    return pseudo_beta, pseudo_beta_mask
  else:
    return pseudo_beta


class EvoformerIteration(hk.Module):
  """"""Single iteration (block) of Evoformer stack.

  Jumper et al. (2021) Suppl. Alg. 6 ""EvoformerStack"" lines 2-10
  """"""

  def __init__(self, config, global_config, is_extra_msa,
               name='evoformer_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config
    self.is_extra_msa = is_extra_msa

  def __call__(self, activations, masks, is_training=True, safe_key=None):
    """"""Builds EvoformerIteration module.

    Arguments:
      activations: Dictionary containing activations:
        * 'msa': MSA activations, shape [N_seq, N_res, c_m].
        * 'pair': pair activations, shape [N_res, N_res, c_z].
      masks: Dictionary of masks:
        * 'msa': MSA mask, shape [N_seq, N_res].
        * 'pair': pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.
      safe_key: prng.SafeKey encapsulating rng key.

    Returns:
      Outputs, same shape/type as act.
    """"""
    c = self.config
    gc = self.global_config

    msa_act, pair_act = activations['msa'], activations['pair']
    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    msa_mask, pair_mask = masks['msa'], masks['pair']

    dropout_wrapper_fn = functools.partial(
        dropout_wrapper,
        is_training=is_training,
        global_config=gc)

    safe_key, *sub_keys = safe_key.split(10)
    sub_keys = iter(sub_keys)

    outer_module = OuterProductMean(
        config=c.outer_product_mean,
        global_config=self.global_config,
        num_output_channel=int(pair_act.shape[-1]),
        name='outer_product_mean')
    if c.outer_product_mean.first:
      pair_act = dropout_wrapper_fn(
          outer_module,
          msa_act,
          msa_mask,
          safe_key=next(sub_keys),
          output_act=pair_act)

    msa_act = dropout_wrapper_fn(
        MSARowAttentionWithPairBias(
            c.msa_row_attention_with_pair_bias, gc,
            name='msa_row_attention_with_pair_bias'),
        msa_act,
        msa_mask,
        safe_key=next(sub_keys),
        pair_act=pair_act)
    # print(""self.is_extra_msa:"", self.is_extra_msa)
    if not self.is_extra_msa:
      attn_mod = MSAColumnAttention(
          c.msa_column_attention, gc, name='msa_column_attention')
    else:
      attn_mod = MSAColumnGlobalAttention(
          c.msa_column_attention, gc, name='msa_column_global_attention')
    # msa_act = dropout_wrapper_fn(
    #     attn_mod,
    #     msa_act,
    #     msa_mask,
    #     safe_key=next(sub_keys))
    msa_act, col_attn = attn_mod(msa_act, msa_mask)
    print(msa_act, col_attn)
    exit()

    msa_act = dropout_wrapper_fn(
        Transition(c.msa_transition, gc, name='msa_transition'),
        msa_act,
        msa_mask,
        safe_key=next(sub_keys))

    if not c.outer_product_mean.first:
      pair_act = dropout_wrapper_fn(
          outer_module,
          msa_act,
          msa_mask,
          safe_key=next(sub_keys),
          output_act=pair_act)

    pair_act = dropout_wrapper_fn(
        TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                               name='triangle_multiplication_outgoing'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))
    pair_act = dropout_wrapper_fn(
        TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                               name='triangle_multiplication_incoming'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    pair_act = dropout_wrapper_fn(
        TriangleAttention(c.triangle_attention_starting_node, gc,
                          name='triangle_attention_starting_node'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))
    pair_act = dropout_wrapper_fn(
        TriangleAttention(c.triangle_attention_ending_node, gc,
                          name='triangle_attention_ending_node'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    pair_act = dropout_wrapper_fn(
        Transition(c.pair_transition, gc, name='pair_transition'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    return {'msa': msa_act, 'pair': pair_act}


class EmbeddingsAndEvoformer(hk.Module):
  """"""Embeds the input data and runs Evoformer.

  Produces the MSA, single and pair representations.
  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 5-18
  """"""

  def __init__(self, config, global_config, name='evoformer'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, batch, is_training, safe_key=None):

    c = self.config
    gc = self.global_config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    # Embed clustered MSA.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 5
    # Jumper et al. (2021) Suppl. Alg. 3 ""InputEmbedder""
    preprocess_1d = common_modules.Linear(
        c.msa_channel, name='preprocess_1d')(
            batch['target_feat'])

    preprocess_msa = common_modules.Linear(
        c.msa_channel, name='preprocess_msa')(
            batch['msa_feat'])

    msa_activations = jnp.expand_dims(preprocess_1d, axis=0) + preprocess_msa

    left_single = common_modules.Linear(
        c.pair_channel, name='left_single')(
            batch['target_feat'])
    right_single = common_modules.Linear(
        c.pair_channel, name='right_single')(
            batch['target_feat'])
    pair_activations = left_single[:, None] + right_single[None]
    mask_2d = batch['seq_mask'][:, None] * batch['seq_mask'][None, :]

    # Inject previous outputs for recycling.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 6
    # Jumper et al. (2021) Suppl. Alg. 32 ""RecyclingEmbedder""
    if c.recycle_pos:
      prev_pseudo_beta = pseudo_beta_fn(
          batch['aatype'], batch['prev_pos'], None)
      dgram = dgram_from_positions(prev_pseudo_beta, **self.config.prev_pos)
      pair_activations += common_modules.Linear(
          c.pair_channel, name='prev_pos_linear')(
              dgram)

    if c.recycle_features:
      prev_msa_first_row = hk.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_msa_first_row_norm')(
              batch['prev_msa_first_row'])
      msa_activations = msa_activations.at[0].add(prev_msa_first_row)

      pair_activations += hk.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_pair_norm')(
              batch['prev_pair'])

    # Relative position encoding.
    # Jumper et al. (2021) Suppl. Alg. 4 ""relpos""
    # Jumper et al. (2021) Suppl. Alg. 5 ""one_hot""
    if c.max_relative_feature:
      # Add one-hot-encoded clipped residue distances to the pair activations.
      pos = batch['residue_index']
      offset = pos[:, None] - pos[None, :]
      rel_pos = jax.nn.one_hot(
          jnp.clip(
              offset + c.max_relative_feature,
              a_min=0,
              a_max=2 * c.max_relative_feature),
          2 * c.max_relative_feature + 1)
      pair_activations += common_modules.Linear(
          c.pair_channel, name='pair_activiations')(
              rel_pos)

    # Embed templates into the pair activations.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-13
    if c.template.enabled:
      template_batch = {k: batch[k] for k in batch if k.startswith('template_')}
      template_pair_representation = TemplateEmbedding(c.template, gc)(
          pair_activations,
          template_batch,
          mask_2d,
          is_training=is_training)

      pair_activations += template_pair_representation

    # Embed extra MSA features.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 14-16
    extra_msa_feat = create_extra_msa_feature(batch)
    extra_msa_activations = common_modules.Linear(
        c.extra_msa_channel,
        name='extra_msa_activations')(
            extra_msa_feat)

    # Extra MSA Stack.
    # Jumper et al. (2021) Suppl. Alg. 18 ""ExtraMsaStack""
    extra_msa_stack_input = {
        'msa': extra_msa_activations,
        'pair': pair_activations,
    }

    extra_msa_stack_iteration = EvoformerIteration(
        c.evoformer, gc, is_extra_msa=True, name='extra_msa_stack')

    def extra_msa_stack_fn(x):
      act, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      extra_evoformer_output = extra_msa_stack_iteration(
          activations=act,
          masks={
              'msa': batch['extra_msa_mask'],
              'pair': mask_2d
          },
          is_training=is_training,
          safe_key=safe_subkey)
      return (extra_evoformer_output, safe_key)

    if gc.use_remat:
      extra_msa_stack_fn = hk.remat(extra_msa_stack_fn)

    extra_msa_stack = layer_stack.layer_stack(
        c.extra_msa_stack_num_block)(
            extra_msa_stack_fn)
    extra_msa_output, safe_key = extra_msa_stack(
        (extra_msa_stack_input, safe_key))

    pair_activations = extra_msa_output['pair']

    evoformer_input = {
        'msa': msa_activations,
        'pair': pair_activations,
    }

    evoformer_masks = {'msa': batch['msa_mask'], 'pair': mask_2d}

    # Append num_templ rows to msa_activations with template embeddings.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 7-8
    if c.template.enabled and c.template.embed_torsion_angles:
      num_templ, num_res = batch['template_aatype'].shape

      # Embed the templates aatypes.
      aatype_one_hot = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1)

      # Embed the templates aatype, torsion angles and masks.
      # Shape (templates, residues, msa_channels)
      ret = all_atom.atom37_to_torsion_angles(
          aatype=batch['template_aatype'],
          all_atom_pos=batch['template_all_atom_positions'],
          all_atom_mask=batch['template_all_atom_masks'],
          # Ensure consistent behaviour during testing:
          placeholder_for_undefined=not gc.zero_init)

      template_features = jnp.concatenate([
          aatype_one_hot,
          jnp.reshape(
              ret['torsion_angles_sin_cos'], [num_templ, num_res, 14]),
          jnp.reshape(
              ret['alt_torsion_angles_sin_cos'], [num_templ, num_res, 14]),
          ret['torsion_angles_mask']], axis=-1)

      template_activations = common_modules.Linear(
          c.msa_channel,
          initializer='relu',
          name='template_single_embedding')(
              template_features)
      template_activations = jax.nn.relu(template_activations)
      template_activations = common_modules.Linear(
          c.msa_channel,
          initializer='relu',
          name='template_projection')(
              template_activations)

      # Concatenate the templates to the msa.
      evoformer_input['msa'] = jnp.concatenate(
          [evoformer_input['msa'], template_activations], axis=0)
      # Concatenate templates masks to the msa masks.
      # Use mask from the psi angle, as it only depends on the backbone atoms
      # from a single residue.
      torsion_angle_mask = ret['torsion_angles_mask'][:, :, 2]
      torsion_angle_mask = torsion_angle_mask.astype(
          evoformer_masks['msa'].dtype)
      evoformer_masks['msa'] = jnp.concatenate(
          [evoformer_masks['msa'], torsion_angle_mask], axis=0)

    # Main trunk of the network
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 17-18
    evoformer_iteration = EvoformerIteration(
        c.evoformer, gc, is_extra_msa=False, name='evoformer_iteration')

    def evoformer_fn(x):
      act, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      evoformer_output = evoformer_iteration(
          activations=act,
          masks=evoformer_masks,
          is_training=is_training,
          safe_key=safe_subkey)
      return (evoformer_output, safe_key)

    if gc.use_remat:
      evoformer_fn = hk.remat(evoformer_fn)

    evoformer_stack = layer_stack.layer_stack(c.evoformer_num_block)(
        evoformer_fn)
    evoformer_output, safe_key = evoformer_stack(
        (evoformer_input, safe_key))

    msa_activations = evoformer_output['msa']
    pair_activations = evoformer_output['pair']

    single_activations = common_modules.Linear(
        c.seq_channel, name='single_activations')(
            msa_activations[0])

    num_sequences = batch['msa_feat'].shape[0]
    output = {
        'single': single_activations,
        'pair': pair_activations,
        # Crop away template rows such that they are not used in MaskedMsaHead.
        'msa': msa_activations[:num_sequences, :, :],
        'msa_first_row': msa_activations[0],
    }

    return output


class SingleTemplateEmbedding(hk.Module):
  """"""Embeds a single template.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9+11
  """"""

  def __init__(self, config, global_config, name='single_template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, batch, mask_2d, is_training):
    """"""Build the single template embedding.

    Arguments:
      query_embedding: Query pair representation, shape [N_res, N_res, c_z].
      batch: A batch of template features (note the template dimension has been
        stripped out as this module only runs over a single template).
      mask_2d: Padding mask (Note: this doesn't care if a template exists,
        unlike the template_pseudo_beta_mask).
      is_training: Whether the module is in training mode.

    Returns:
      A template embedding [N_res, N_res, c_z].
    """"""
    assert mask_2d.dtype == query_embedding.dtype
    dtype = query_embedding.dtype
    num_res = batch['template_aatype'].shape[0]
    num_channels = (self.config.template_pair_stack
                    .triangle_attention_ending_node.value_dim)
    template_mask = batch['template_pseudo_beta_mask']
    template_mask_2d = template_mask[:, None] * template_mask[None, :]
    template_mask_2d = template_mask_2d.astype(dtype)

    template_dgram = dgram_from_positions(batch['template_pseudo_beta'],
                                          **self.config.dgram_features)
    template_dgram = template_dgram.astype(dtype)

    to_concat = [template_dgram, template_mask_2d[:, :, None]]

    aatype = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1, dtype=dtype)

    to_concat.append(jnp.tile(aatype[None, :, :], [num_res, 1, 1]))
    to_concat.append(jnp.tile(aatype[:, None, :], [1, num_res, 1]))

    n, ca, c = [residue_constants.atom_order[a] for a in ('N', 'CA', 'C')]
    rot, trans = quat_affine.make_transform_from_reference(
        n_xyz=batch['template_all_atom_positions'][:, n],
        ca_xyz=batch['template_all_atom_positions'][:, ca],
        c_xyz=batch['template_all_atom_positions'][:, c])
    affines = quat_affine.QuatAffine(
        quaternion=quat_affine.rot_to_quat(rot, unstack_inputs=True),
        translation=trans,
        rotation=rot,
        unstack_inputs=True)
    points = [jnp.expand_dims(x, axis=-2) for x in affines.translation]
    affine_vec = affines.invert_point(points, extra_dims=1)
    inv_distance_scalar = jax.lax.rsqrt(
        1e-6 + sum([jnp.square(x) for x in affine_vec]))

    # Backbone affine mask: whether the residue has C, CA, N
    # (the template mask defined above only considers pseudo CB).
    template_mask = (
        batch['template_all_atom_masks'][..., n] *
        batch['template_all_atom_masks'][..., ca] *
        batch['template_all_atom_masks'][..., c])
    template_mask_2d = template_mask[:, None] * template_mask[None, :]

    inv_distance_scalar *= template_mask_2d.astype(inv_distance_scalar.dtype)

    unit_vector = [(x * inv_distance_scalar)[..., None] for x in affine_vec]

    unit_vector = [x.astype(dtype) for x in unit_vector]
    template_mask_2d = template_mask_2d.astype(dtype)
    if not self.config.use_template_unit_vector:
      unit_vector = [jnp.zeros_like(x) for x in unit_vector]
    to_concat.extend(unit_vector)

    to_concat.append(template_mask_2d[..., None])

    act = jnp.concatenate(to_concat, axis=-1)

    # Mask out non-template regions so we don't get arbitrary values in the
    # distogram for these regions.
    act *= template_mask_2d[..., None]

    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 9
    act = common_modules.Linear(
        num_channels,
        initializer='relu',
        name='embedding2d')(
            act)

    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 11
    act = TemplatePairStack(
        self.config.template_pair_stack, self.global_config)(
            act, mask_2d, is_training)

    act = hk.LayerNorm([-1], True, True, name='output_layer_norm')(act)
    return act


class TemplateEmbedding(hk.Module):
  """"""Embeds a set of templates.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-12
  Jumper et al. (2021) Suppl. Alg. 17 ""TemplatePointwiseAttention""
  """"""

  def __init__(self, config, global_config, name='template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_batch, mask_2d, is_training):
    """"""Build TemplateEmbedding module.

    Arguments:
      query_embedding: Query pair representation, shape [N_res, N_res, c_z].
      template_batch: A batch of template features.
      mask_2d: Padding mask (Note: this doesn't care if a template exists,
        unlike the template_pseudo_beta_mask).
      is_training: Whether the module is in training mode.

    Returns:
      A template embedding [N_res, N_res, c_z].
    """"""

    num_templates = template_batch['template_mask'].shape[0]
    num_channels = (self.config.template_pair_stack
                    .triangle_attention_ending_node.value_dim)
    num_res = query_embedding.shape[0]

    dtype = query_embedding.dtype
    template_mask = template_batch['template_mask']
    template_mask = template_mask.astype(dtype)

    query_num_channels = query_embedding.shape[-1]

    # Make sure the weights are shared across templates by constructing the
    # embedder here.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-12
    template_embedder = SingleTemplateEmbedding(self.config, self.global_config)

    def map_fn(batch):
      return template_embedder(query_embedding, batch, mask_2d, is_training)

    template_pair_representation = mapping.sharded_map(map_fn, in_axes=0)(
        template_batch)

    # Cross attend from the query to the templates along the residue
    # dimension by flattening everything else into the batch dimension.
    # Jumper et al. (2021) Suppl. Alg. 17 ""TemplatePointwiseAttention""
    flat_query = jnp.reshape(query_embedding,
                             [num_res * num_res, 1, query_num_channels])

    flat_templates = jnp.reshape(
        jnp.transpose(template_pair_representation, [1, 2, 0, 3]),
        [num_res * num_res, num_templates, num_channels])

    bias = (1e9 * (template_mask[None, None, None, :] - 1.))

    template_pointwise_attention_module = Attention(
        self.config.attention, self.global_config, query_num_channels)
    nonbatched_args = [bias]
    batched_args = [flat_query, flat_templates]

    embedding, col_attn = mapping.inference_subbatch(
        template_pointwise_attention_module,
        self.config.subbatch_size,
        batched_args=batched_args,
        nonbatched_args=nonbatched_args,
        low_memory=not is_training)
    embedding = jnp.reshape(embedding,
                            [num_res, num_res, query_num_channels])

    # No gradients if no templates.
    embedding *= (jnp.sum(template_mask) > 0.).astype(embedding.dtype)

    return embedding
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/common/pdb_utils.py",".py","8034","238","import io
import gzip
import dataclasses
from typing import Mapping, Set

import torch
import numpy as np
from Bio.PDB import PDBParser
from Bio.PDB.Model import Model
from Bio.PDB.Chain import Chain
from Bio.PDB.Residue import Residue
from Bio.PDB.Atom import Atom
from alphafold.common import protein, residue_constants


def trim_pdb_by_residue_index(query_pdb: str, target_pdb: str) -> str:
    tp = build_from_pdb_string(
        read_pdb_string(query_pdb),
        chain_residue_index=build_chain_residue_index(
            read_pdb_string(target_pdb)
        ),
        output_model=True
    )
    return tp

def assign_residue_index(assigning, query_chains, target_chains):
    new_query_chains = {}
    for qi, ti, qidx, tidx in assigning:
        qchain = dataclasses.asdict(query_chains[qi])
        tchain = dataclasses.asdict(target_chains[ti])
        for k in qchain:
            if k == 'residue_index':
                qchain[k] = tchain[k][tidx]
            elif k == 'chain_index':
                t = protein.PDB_CHAIN_IDS.index(ti)
                qchain[k] = np.full(len(tchain[k][tidx]), t)
            else:
                qchain[k] = qchain[k][qidx]
        new_query_chains[ti] = protein.Protein(**qchain)
    return new_query_chains

def merge_chains(
    chains: Mapping[str, protein.Protein],
    only_backbone: bool = False,
) -> protein.Protein:
    ca_atom_order = residue_constants.atom_order['CA']
    p = None
    for _, chain in chains.items():
        chain = dataclasses.asdict(chain)
        if p is not None:
            for k in p:
                p[k] = np.concatenate([p[k], chain[k]], axis=0)
        else:
            p = chain
    if only_backbone:
        ca_mask = p['atom_mask'][:,ca_atom_order]
        atom_mask = np.zeros(p['atom_mask'].shape)
        for atom_type in ('CA', 'N', 'C'):
            atom_order = residue_constants.atom_order[atom_type]
            atom_mask[:,atom_order] = p['atom_mask'][:,atom_order]
        p['atom_mask'] = atom_mask

    return protein.Protein(**p)

def read_pdb_string(path):
    if path.endswith('.gz'):
        with gzip.open(path) as fh:
            pdb_string = fh.read().decode()
    else:
        with open(path) as fh:
            pdb_string = fh.read()
    return pdb_string

def from_pdb_string(pdb_string):
    pdb_fh = io.StringIO(pdb_string)
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure('none', pdb_fh)
    models = list(structure.get_models())
    model = models[0]
    chains_result = {}
    chain_ids = []
    for chain in model:
        chains_result[chain.id] = from_chain(chain)
        chain_ids += chains_result[chain.id]['chain_ids']
    unique_chain_ids = np.unique(chain_ids)
    chain_id_mapping = {cid: n for n, cid in enumerate(unique_chain_ids)}

    chains = {}
    for chain_id, chain_result in chains_result.items():
        chains[chain_id] = protein.Protein(
            atom_positions=np.array(chain_result['atom_positions']),
            atom_mask=np.array(chain_result['atom_mask']),
            aatype=np.array(chain_result['aatype']),
            residue_index=np.array(chain_result['residue_index']),
            chain_index=np.array([
                chain_id_mapping[cid] for cid in chain_result['chain_ids']
            ]),
            b_factors=np.array(chain_result['b_factors'])
        )

    return chains

def build_chain_residue_index(pdb_string):
    pdb_fh = io.StringIO(pdb_string)
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure('none', pdb_fh)
    models = list(structure.get_models())
    model = models[0]
    chain_residue_index = {}
    for chain in model:
        if (chain_residue_index is not None and chain.id in chain_residue_index):
            continue
        chain_residue_index[chain.id] = set()
        for res in chain:
            if res.id[0] != ' ': # remove hetatom
                continue
            if res.id[2] != ' ':
                continue
            chain_residue_index[chain.id].add(res.id)
    return chain_residue_index

def build_from_pdb_string(
    pdb_string,
    chain_residue_index: Mapping[str, Set[int]] = None,
    min_length_cutoff=0,
    output_model=False,
):
    pdb_fh = io.StringIO(pdb_string)
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure('none', pdb_fh)
    models = list(structure.get_models())
    chains = {}
    chains_counter = {}
    if output_model:
        new_model = Model(0)
    for model in models:
        for chain in model:
            if (
                chain_residue_index is not None 
                and chain.id not in chain_residue_index
            ):
                continue
            new_chain = Chain(chain.id)
            for res in chain:
                if res.id[0] != ' ': # remove hetatom
                    continue
                if res.id[2] != ' ':
                    continue
                res_id = res.id
                if (
                    chain_residue_index is not None
                    and res_id not in chain_residue_index[chain.id]
                ):
                    continue
                if new_chain.has_id(res_id):
                    continue
                new_res = Residue(res.id, res.resname, res.segid)
                for atom in res:
                    new_atom = Atom(
                        atom.name,
                        atom.coord,
                        atom.bfactor,
                        atom.occupancy,
                        atom.altloc,
                        atom.fullname,
                        atom.serial_number,
                        atom.element
                    )
                    new_res.add(new_atom)
                new_chain.add(new_res)

            if len(new_chain) < min_length_cutoff:
                # too few experimentally resolved residues
                continue

            if output_model:
                new_model.add(new_chain)
            else:
                if chain.id not in chains_counter:
                    chains_counter[chain.id] = 0
                chains_counter[chain.id] += 1
                chains[f'{chain.id}{chains_counter[chain.id]}'] = new_chain

    if output_model:
        return new_model
    else:
        return chains

def from_chain(chain):
    atom_positions = []
    aatype = []
    atom_mask = []
    residue_index = []
    chain_ids = []
    b_factors = []
    for res in chain:
        if res.id[0] != ' ': # remove hetatom
            continue
        if res.id[2] != ' ':
            print(
                f'PDB contains an insertion code at chain {chain.id} and res'
                f' index {res.id[1]}. This insertion is skipped.'
            )
            continue
        res_shortname = residue_constants.restype_3to1.get(res.resname, 'X')
        restype_idx = residue_constants.restype_order.get(
            res_shortname, residue_constants.restype_num
        )
        pos = np.zeros((residue_constants.atom_type_num, 3))
        mask = np.zeros((residue_constants.atom_type_num,))
        res_b_factors = np.zeros((residue_constants.atom_type_num,))
        for atom in res:
            if atom.name not in residue_constants.atom_types:
                continue
            pos[residue_constants.atom_order[atom.name]] = atom.coord
            mask[residue_constants.atom_order[atom.name]] = 1.
            res_b_factors[residue_constants.atom_order[atom.name]] = \
                atom.bfactor
        if np.sum(mask) < 0.5:
            # If no known atom positions are reported for the residue then skip
            # it
            continue
        aatype.append(restype_idx)
        atom_positions.append(pos)
        atom_mask.append(mask)
        residue_index.append(res.id[1])
        chain_ids.append(chain.id)
        b_factors.append(res_b_factors)

    return {
      'atom_positions': atom_positions,
      'aatype': aatype,
      'atom_mask': atom_mask,
      'residue_index': residue_index,
      'chain_ids': chain_ids,
      'b_factors': b_factors,
    }
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/common/sequence_utils.py",".py","1375","47","import numpy as np

from Bio import pairwise2
from alphafold.common import protein, residue_constants

def aatype_to_sequence(aatype):
    sequence = ''.join(
        residue_constants.restypes_with_x_and_gap[aa] for aa in aatype)
    return sequence


x_order = residue_constants.restype_order_with_x['X']
def sequence_to_aatype(sequence):
    aatype = np.array(
        [residue_constants.restype_order_with_x.get(w, x_order) for w in 
         sequence],
        dtype=np.int64)
    return aatype


def global_align(srcseq, tgtseq, gap_penalty=-2.):
    def _get_alnidx(seqA, seqB):
        srcidx, tgtidx = [], []
        ai, bj = 0, 0
        for a, b in zip(seqA, seqB):
            if a != '-' and b != '-':
                srcidx.append(ai)
                tgtidx.append(bj)
            if a != '-':
                ai += 1
            if b != '-':
                bj += 1
        srcidx = np.array(srcidx, dtype=np.int64)
        tgtidx = np.array(tgtidx, dtype=np.int64)
        return srcidx, tgtidx
    srcseq = ''.join([_ for _ in srcseq if _ != '-'])
    tgtseq = ''.join([_ for _ in tgtseq if _ != '-'])

    aln = pairwise2.align.globalms(srcseq, tgtseq, 2, -1, gap_penalty, -0.5)

    if len(aln) >= 1:
        aln = aln[0]
        srcidx, tgtidx = _get_alnidx(aln.seqA, aln.seqB)
        return aln, srcidx, tgtidx
    else:
        return None, None, None
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/species_processing.py",".py","9961","314","import os
import json
import time
from tqdm import tqdm
from typing import Sequence, Dict
from collections import defaultdict

import numpy as np
import pandas as pd

from alphafold.data import parsers, pipeline, msa_identifiers, msa_pairing
from alphafold.common import residue_constants

from msa_pair.data import msa_processing



def get_uniref_species(description):
    temp = description[1:]
    if temp.find('TaxID=')!=-1:
        n_index = temp.index('n=')
        temp = temp[n_index:].split(maxsplit=1)[-1]
        TaxID_index = temp.index('TaxID=')
        Tax,[TaxID,RepID] = temp[:TaxID_index].strip(),temp[TaxID_index:].split()
        Tax = Tax.split('=')[1]
        TaxID = int(TaxID.split('=')[1])
        RepID = RepID.split('=')[1]
        return str(TaxID)
    else:
        return ''

def make_msa_features(msas: Sequence[parsers.Msa]):
    """"""Constructs a feature dict of MSA features.""""""
    if not msas:
      raise ValueError('At least one MSA must be provided.')

    int_msa = []
    deletion_matrix = []
    species_ids = []
    seen_sequences = set()
    sequences = []
    descriptions = []
    for msa_index, msa in enumerate(msas):
        if not msa:
            raise ValueError(
                f'MSA {msa_index} must contain at least one sequence.'
            )
        for sequence_index, sequence in enumerate(msa.sequences):
            if sequence in seen_sequences:
                continue
            seen_sequences.add(sequence)
            int_msa.append(
                [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence]
            )
            
            #--------------------------------------------------------------------
            # # for MSA searched from uniprotKB
            # identifiers = msa_identifiers.get_identifiers(
            #     msa.descriptions[sequence_index]
            # )
            # species_ids.append(identifiers.species_id.encode('utf-8'))

            
            # for MSA seached from uniref
            species_idf = get_uniref_species(msa.descriptions[sequence_index]) 
            species_ids.append(species_idf.encode('utf-8'))
            
            #--------------------------------------------------------------------
            
            deletion_matrix.append(msa.deletion_matrix[sequence_index])
            sequences.append(sequence)
            descriptions.append(msa.descriptions[sequence_index])

    processed_msa = parsers.Msa(
        sequences=sequences,
        descriptions=descriptions,
        deletion_matrix=deletion_matrix,
    )

    num_res = len(msas[0].sequences[0])
    num_alignments = len(int_msa)
    features = {}
    features['deletion_matrix_int'] = np.array(
        deletion_matrix, dtype=np.int32
    )
    features['msa'] = np.array(int_msa, dtype=np.int32)
    features['num_alignments'] = np.array(
        [num_alignments] * num_res, dtype=np.int32)
    features['msa_species_identifiers'] = np.array(
        species_ids, dtype=np.object_
    )

    return features, processed_msa


def make_msa_df(chain_features):
    """"""Construct DataFrame for species processing
    """"""
    chain_msa = chain_features['msa']
    # print(chain_msa)
    # exit()
    query_seq = chain_msa[0]
    per_seq_similarity = np.sum(
        query_seq[None] == chain_msa, axis=-1
    ) / float(len(query_seq))
    per_seq_gap = np.sum(chain_msa == 21, axis=-1) / float(len(query_seq))
    msa_df = pd.DataFrame({
        'msa_species_identifiers':
            chain_features['msa_species_identifiers'],
        'msa_row':
            np.arange(len(chain_features['msa_species_identifiers'])),
        'msa_similarity': per_seq_similarity,
        'gap': per_seq_gap
    })
    return msa_df


def create_species_dict(msa_df: pd.DataFrame) -> Dict[bytes, pd.DataFrame]:
    species_lookup = {}
    for species, species_df in msa_df.groupby('msa_species_identifiers'):
        species_lookup[species] = species_df
    return species_lookup


def parse(
    input_dir: str,
    names: Sequence[str],
    chain_ids=['A', 'B'], 
    pair_species=False,
):
    """"""Parse all MSAs in the directory
    """"""
    grouped_paths = {
        chain_id: 
            [ os.path.join(input_dir, chain_id, name) for name in names ]
        for chain_id in chain_ids
    }
    # print(grouped_paths)
    if any(
        not all(os.path.exists(_) for _ in paths) for paths in 
        grouped_paths.values()
    ):
        raise IOError('Some files do not exist')

    msas_dict = {}
    msa_feats_dict = {}
    if pair_species:
        all_species_dict = defaultdict(dict)
    for chain_id, paths in grouped_paths.items():
        msas = []
        for path in paths:
            with open(path) as fh:
                a3m_str = fh.read()
                msa = parsers.parse_a3m(a3m_str)
            msas.append(msa)
        msa_feat, processed_msa = make_msa_features(msas)

        if pair_species:
            msa_df = make_msa_df(msa_feat)
            species_dict = create_species_dict(msa_df)
            for spec, df in species_dict.items():
                if spec == b'':
                    continue
                all_species_dict[spec][chain_id] = df

        msas_dict[chain_id] = processed_msa
        msa_feats_dict[chain_id] = msa_feat
    
    if pair_species:
        return all_species_dict, msas_dict, msa_feats_dict
    else:
        return msas_dict, msa_feats_dict


def parse_pairs(
    pairs: list,
    pair_species=False,
):
    msas_dict = {}
    msa_feats_dict = {}
    chain_ids = [""A"", ""B""]

    if pair_species:
        all_species_dict = defaultdict(dict)
    for i in range(2): # 0 or 1
        chain_id = chain_ids[i]
        msas = []
        descriptions = [pair[i][0] for pair in pairs]
        sequences = [pair[i][1] for pair in pairs]

        # for desc, seq in zip(descriptions, sequences):
        msas.append(parsers.convert_seq_desc_to_Msa(sequences, descriptions))
        msa_feat, processed_msa = make_msa_features(msas)

        if pair_species:
            msa_df = make_msa_df(msa_feat)
            species_dict = create_species_dict(msa_df)
            for spec, df in species_dict.items():
                if spec == b'':
                    continue
                all_species_dict[spec][chain_id] = df

        msas_dict[chain_id] = processed_msa
        msa_feats_dict[chain_id] = msa_feat
    
    if pair_species:
        return all_species_dict, msas_dict, msa_feats_dict
    else:
        return msas_dict, msa_feats_dict


def pair_species(
    input_dir: str,
    names: Sequence[str] = ['uniprot.a3m'],
    chain_ids=['A', 'B'],
):
    all_species_dict, msas_dict, msa_feats_dict = parse(
        input_dir,
        names,
        chain_ids=chain_ids,
        pair_species=True,
    )

    matched_species_dict = {}
    for spec, dfs in all_species_dict.items():
        if len(dfs) < 2:
            continue
        matched_species_dict[spec] = dfs
    num_residues = {}
    for chain_id, msa_feat in msa_feats_dict.items():
        num_residues[chain_id] = msa_feat['msa'].shape[1]

    fixed = {}
    unfixed = {}
    for spec, dfs in matched_species_dict.items():
        num_seqs = [(chain_id, len(df)) for chain_id, df in dfs.items()]
        if max(_[1] for _ in num_seqs) == 1:
            fixed[spec.decode()] = dict(num_seqs)
        else:
            unfixed[spec.decode()] = dict(num_seqs)

    species_stats = {
        'num_species': {'fixed': len(fixed), 'unfixed': len(unfixed)},
        'num_sequences': {'fixed': fixed, 'unfixed': unfixed},
        'num_residues': num_residues,
    }
    print(f""matched_species: {len(matched_species_dict)} fixed: {len(fixed)} unfixed: {len(unfixed)}"")
    return matched_species_dict, msas_dict, msa_feats_dict, species_stats


def process_paired_msas(matched_species_dict, msas_dict):
    processed_msas_dict = {}
    for chain_id, msa in msas_dict.items():
        rows = []
        for spec, dfs in matched_species_dict.items():
            rows += list(dfs[chain_id].msa_row.values)
        rows = sorted(rows)
        if 0 not in rows:
            rows = [0] + rows
        msa = msa_processing._build_msa(msa, rows)
        processed_msas_dict[chain_id] = msa

    return processed_msas_dict

def dump_msa_to_a3m(dst_path, msa):
    with open(dst_path, 'wt') as fh:
        for desc, seq in zip(msa.descriptions, msa.sequences):
            fh.write(f'>{desc}\n{seq}\n')

def read_include_file(include_file):
    included_names = set()
    with open(include_file) as fh:
        for line in fh:
            line = line.strip()
            if line:
                name = line.split()[0]
                included_names.add(name)

    return included_names


def process_batch(src_root, dst_root, include_file=None):
    names = sorted(os.listdir(src_root))
    if include_file is not None:
        included_names = read_include_file(include_file)
        names = [name for name in names if name in included_names]

    for name in tqdm(names):
        input_dir = os.path.join(src_root, name)
        output_dir = os.path.join(dst_root, name)
        output_stats_path = os.path.join(output_dir, 'species_stats.json')
        if os.path.exists(output_stats_path):
            continue 
        result = pair_species(input_dir)
        if result is None:
            continue

        os.makedirs(output_dir, exist_ok=True)

        processed_msas_dict = process_paired_msas(result[0], result[1])
        for chain_id, msa in processed_msas_dict.items():
            dst_chain_dir = os.path.join(output_dir, chain_id)
            os.makedirs(dst_chain_dir, exist_ok=True)
            dump_msa_to_a3m(os.path.join(dst_chain_dir, 'uniprot.a3m'), msa)

        with open(output_stats_path, 'wt') as fh:
            json.dump(result[-1], fh, indent=2, sort_keys=True)


if __name__ == '__main__':
    import sys
    include_file = None if len(sys.argv) <= 3 else sys.argv[3]
    process_batch(sys.argv[1], sys.argv[2], include_file)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/msa_processing.py",".py","3132","106","import copy

from typing import Sequence, Mapping, List

from alphafold.data import parsers, msa_pairing, feature_processing
from alphafold.common import residue_constants


def _build_msa(input_msa, rows):
    return parsers.Msa(
        sequences = [input_msa.sequences[i] for i in rows],
        deletion_matrix = [input_msa.deletion_matrix[i] for i in rows],
        descriptions = [input_msa.descriptions[i] for i in rows],
    )

class _MsaSubset:
    def __init__(self, main_msa: parsers.Msa, rows: Sequence[int]):
        self.msa_subset = {}
        for r in sorted(set(rows)):
            self.msa_subset[r] = {
                key: getattr(main_msa, key)[r] for key in [
                    'sequences', 'descriptions', 'deletion_matrix'
                ]
            }


    def __add__(self, next_subset):
        this_subset = copy.deepcopy(self)
        for r, v in next_subset.msa_subset.items():
            if r in this_subset.msa_subset:
                assert v['sequences'] == this_subset.msa_subset[r]['sequences']
            else:
                this_subset.msa_subset[r] = v

        return this_subset

    def build_msa(self):
        rows_sorted = self.rows
        return parsers.Msa(
            sequences=[self.msa_subset[r]['sequences'] for r in rows_sorted],
            deletion_matrix=[
                self.msa_subset[r]['deletion_matrix'] for r in rows_sorted
            ],
            descriptions=[
                self.msa_subset[r]['descriptions'] for r in rows_sorted
            ],
        )

    @property
    def rows(self):
        return sorted(self.msa_subset)

    def __len__(self):
        return len(self.msa_subset)

class MsaBlock:
    def __init__(
        self,
        msas_dict: Mapping[str, parsers.Msa],
        rows_dict: Mapping[str, List[int]],
    ):
        assert len(set(msas_dict) ^ set(rows_dict)) == 0
        self.msa_subsets = {
            chain_id: _MsaSubset(msa, rows_dict[chain_id]) for chain_id, msa
            in msas_dict.items()
        }

    def __add__(self, next_block):
        this_block = copy.deepcopy(self)
        for chain_id, msa_subset in next_block.msa_subsets.items():
            if chain_id in self.msa_subsets:
                this_block.msa_subsets[chain_id] += msa_subset
            else:
                this_block.msa_subsets[chain_id] = msa_subset

        return this_block

    def get_msas(self):
        return {
            chain_id: msa_subset.build_msa() for chain_id, msa_subset in 
            self.msa_subsets.items()
        }

    def get_rows(self):
        return {
            chain_id: msa_subset.rows for chain_id, msa_subset in 
            self.msa_subsets.items()
        }

    def get_lengths(self):
        return {
            chain_id: len(msa_subset) for chain_id, msa_subset in 
            self.msa_subsets.items()
        }


def create_paired_rows(chains, rows_dict):
    paired_rows = []
    for chain in chains:
        chain_id = str(chain['auth_chain_id'])
        paired_rows.append(rows_dict[chain_id])
    
    paired_rows = np.concatenate(paired_rows, axis=0).T

    return paired_rows
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/row_processing.py",".py","6487","185","from contextlib import suppress
import os
import json
from typing import List, Mapping
from collections import defaultdict
import numpy as np
np.set_printoptions(threshold=np.inf)
np.set_printoptions(suppress = True)

from alphafold.data import parsers, msa_pairing, feature_processing
from alphafold.common import residue_constants
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import min_weight_full_bipartite_matching
from operator import itemgetter

def _upgrade_sequences_scores(sequence_scores):
    processed_scores = {}
    for result in sequence_scores.values():
        desc = result['description']
        score = result['score']
        processed_scores[desc] = score

    return processed_scores

def create_paired_rows_dict(
    species_dict,
    msas_dict,
    sequences_scores,
    chain_ids: List[str] = ['A', 'B'],
) -> Mapping[str, List[int]]:

    # upgrade sequences scores 
    for chain_id in chain_ids:
        sequences_scores[chain_id] = _upgrade_sequences_scores(
            sequences_scores[chain_id]
        )

    paired_rows_dict = {chain_id: [0] for chain_id in chain_ids}
    # pair_scores = [ np.full((1, len(chain_ids)), 1e6) ]
    pair_scores = [ np.full((1, len(chain_ids)), -1e6) ] # reverse
    

    spec2ind2score = defaultdict(list)
    for spec, dfs in species_dict.items():
        if spec == b'':
            continue
        # spec = b'CERCE'
        # dfs = species_dict[spec]

        # find and sort scores
        scores_ = {}
        for chain_id, df in dfs.items():
            chain_scores_ = []
            rows = df.msa_row.values
            # print(rows)
            for r in rows:
                desc = msas_dict[chain_id].descriptions[r].split()[0]
                if desc in sequences_scores[chain_id]:
                    s = sequences_scores[chain_id][desc]
                    chain_scores_.append((int(r), s))
                # if int(r) == 90:
                #     print(dfs['A'])
                #     print(dfs['B'])
                #     print(desc)
                #     print(chain_scores_)
                    # exit()
            # print(chain_scores_)
            if len(chain_scores_) >= 1:
                scores_[chain_id] = sorted(
                    chain_scores_, key=lambda v: v[-1], reverse=True,
                )
        # print(scores_)
        
        # exit()

        # match rows within the species
        if len(scores_) > 1:
            num_seqs = min(len(v) for v in scores_.values())
            pair_scores_ = []
            for chain_id in chain_ids:
                tmp = {}
                if chain_id in scores_:
                    chain_rows_ = [
                        _[0] for _ in scores_[chain_id][:num_seqs]
                    ]
                    chain_scores_ = [
                        _[-1] for _ in scores_[chain_id][:num_seqs]
                    ]
                else:
                    chain_rows_ = [-1] * num_seqs
                    chain_scores_ = [0.] * num_seqs
                tmp[chain_id] = scores_[chain_id][:num_seqs]
                spec2ind2score[spec].append(tmp)
                paired_rows_dict[chain_id] += chain_rows_
                pair_scores_ += [np.array(chain_scores_)[:,None]]
            pair_scores.append(np.concatenate(pair_scores_, axis=-1))
    # sort rows by their pair scores
    pair_scores = np.concatenate(pair_scores, axis=0)
    # inds_sorted = np.argsort(np.sum(pair_scores, axis=-1))[::-1]
    inds_sorted = np.argsort(np.sum(pair_scores, axis=-1)) # reverse
    # print(np.sum(pair_scores, axis=-1)[inds_sorted])
    # print(inds_sorted[:10])
    # # print(np.sum(pair_scores, axis=-1)[inds_sorted][:10])
    
    paired_rows_dict  = {
        chain_id: [rows[i] for i in inds_sorted] for chain_id, rows in 
        paired_rows_dict.items()
    }
    assert len(pair_scores) == len(paired_rows_dict[chain_ids[0]])
    # check the first row is always zero (that is, the first sequence is 
    # always the query sequence)
    assert all(rows[0] == 0 for rows in paired_rows_dict.values())

    return paired_rows_dict



def find_alignment(rowsA, rowsB, sims, tag):
    ori_sims = sims
    lenx, leny = sims.shape
    # print(sims.shape, len(rowsA), len(rowsB))
    assert (lenx == len(rowsA)) and (leny == len(rowsB))
    num_seqs = min(len(rowsA), len(rowsB))
    alignments = []
    if tag == 'local':
        sims = sims.reshape(-1)
        index = np.argsort(-sims)
        det_x = set()
        det_y = set()
        for each in index:
            row, col = int(each/leny), each % leny
            if row in det_x or col in det_y:
                continue
            det_x.add(row)
            det_y.add(col)
            alignments.append((rowsA[row], rowsB[col], sims[each]))
        assert len(alignments) == num_seqs
        # print(alignments)
        # exit()
    elif tag =='global':
        tmp_align = []
        sims = csr_matrix(sims)
        rows, cols = min_weight_full_bipartite_matching(sims, maximize = True)
        sims = sims.toarray()
        assert len(rows) == len(cols) == num_seqs
        for x, y in zip(rows, cols):
            tmp_align.append((rowsA[x], rowsB[y], sims[x][y]))
        alignments = sorted(tmp_align, key=itemgetter(2), reverse=True)
    else:
        raise ValueError(f""No such strategy: {tag}!"")
    
    return alignments, num_seqs
            



def create_inter_paired_rows_dict(
    sequences_scores,
    tag,
    chain_ids: List[str] = ['A', 'B'],
) -> Mapping[str, List[int]]:

    paired_rows_dict = {chain_id: [0] for chain_id in chain_ids}
    pair_scores = [1e6]
    for spec, info in sequences_scores.items():
        if spec == b'':
            continue
        rowsA, rowsB, sims = info['rowsA'], info['rowsB'], info['sims']

        alignments, num_seqs = find_alignment(rowsA, rowsB, sims, tag)        

        paired_rows_dict['A'] += [_[0] for _ in alignments]
        paired_rows_dict['B'] += [_[1] for _ in alignments]
        pair_scores += [_[2] for _ in alignments]
    inds_sorted = np.argsort(-np.array(pair_scores))
    paired_rows_dict  = {
        chain_id: [int(rows[i]) for i in inds_sorted] for chain_id, rows in 
        paired_rows_dict.items()
    }
    assert len(pair_scores) == len(paired_rows_dict[chain_ids[0]])
    # check the first row is always zero (that is, the first sequence is 
    # always the query sequence)
    assert all(rows[0] == 0 for rows in paired_rows_dict.values())

    return paired_rows_dict","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/pairing_pipeline_v2.py",".py","6218","154","import os
import copy
import json
import logging
import argparse

from typing import Mapping, List

from tqdm import tqdm

import tree
import numpy as np
from alphafold.data import (
    msa_pairing, pipeline, pipeline_multimer, feature_processing
)
from alphafold.common import residue_constants

from msa_pair.data import species_processing

logger = logging.getLogger(__file__)

def _make_all_seq_msa_features(msa_feats):
    return {
        f'{k}_all_seq': v for k, v in msa_feats.items() if k in (
            msa_pairing.MSA_FEATURES + ('msa_species_identifiers',)
        )
    }

def _make_empty_templates_features(num_res):
    """"""Construct a default template with all zeros.""""""                           
    return {
        'template_aatype':
            np.zeros(
                (1, num_res, len(residue_constants.restypes_with_x_and_gap)),
                np.float32
            ),
        'template_all_atom_masks':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num), np.float32
            ),
        'template_all_atom_positions':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num, 3), np.float32
            ),
        'template_domain_names': np.array([''.encode()], dtype=object),
        'template_sequence': np.array([''.encode()], dtype=object),
        'template_sum_probs': np.array([0], dtype=np.float32),
    }

def build_paired_rows(auth_chain_ids, paired_rows_dict):
    paired_rows = []
    all_rows = [paired_rows_dict[chain_id] for chain_id in auth_chain_ids]
    num_rows = len(all_rows[0])
    for i in range(num_rows):
        paired_rows.append([rows[i] for rows in all_rows])
    paired_rows = np.array(paired_rows)
    assert paired_rows.shape == (num_rows, len(auth_chain_ids)), paired_rows.shape
    return paired_rows


class PairingPipeline:
    def process(self, pairs):
        logger.info(f'Start building')
        
        # load paired msas
        # msa_feats_dict: dict_keys(['deletion_matrix_int', 'msa', 'num_alignments', 'msa_species_identifiers'])
        msas_all_seq_dict, msa_all_seq_feats_dict = species_processing.parse_pairs(
            pairs
        )

        # msas_dict: {A/B:{sequences:[n_seq, seq_length], descriptions:[n_seq, -1]}}
        # print('%s',tree.map_structure(lambda x: x.shape, msa_feats_dict))
        # build merged feature
        chains_dict = {}
        for chain_id, msa in msas_all_seq_dict.items():
            seq, desc = msa.sequences[0], msa.descriptions[0]  # parimary seq
            num_res = len(seq)
            chain = {
                **_make_all_seq_msa_features(msa_all_seq_feats_dict[chain_id]),  # 'deletion_matrix_int_all_seq', 'msa_all_seq', 'num_alignments_all_seq', 'msa_species_identifiers_all_seq'
                # **msa_feats_dict[chain_id],   # 'deletion_matrix_int', 'msa_all', 'num_alignments', 'msa_species_identifiers'
                **pipeline.make_sequence_features(seq, desc, num_res),  # 'aatype', 'between_segment_residues', 'domain_name', 'residue_index', 'seq_length', 'sequence'
                **_make_empty_templates_features(num_res),  # 'template_aatype', 'template_all_atom_masks', 'template_all_atom_positions', 'template_domain_names', 'template_sequence', 'template_sum_probs'
            }
            # sequence key is the primary sequence

            # 'auth_chain_id'
            # only change the feature 'aatype' 'template_aatype' 'template_all_atom_masks' 'template_all_atom_mask
            chains_dict[chain_id] = pipeline_multimer.convert_monomer_features(
                chain, chain_id=chain_id
            )

        # for heterodiamer (A[""sequence""] != B[""sequence""]), A,B -> A_1,B_1
        chains_dict = pipeline_multimer.add_assembly_features(chains_dict)
        np_example = self.pair_and_merge(chains_dict)
        np_example = pipeline_multimer.pad_msa(np_example, 512)
        logger.info(f""Done building"")
        return np_example

    def pair_and_merge(
        self,
        chains_dict: Mapping[str, pipeline.FeatureDict],
        # paired_rows_dict: Mapping[str, List[int]],
    ) -> pipeline.FeatureDict:

        feature_processing.process_unmerged_features(chains_dict)
        chains = list(chains_dict.values())

        # auth_chain_ids = [str(chain['auth_chain_id']) for chain in chains]
        # paired_rows = build_paired_rows(auth_chain_ids, paired_rows_dict)
        # chains = self.create_paired_features(paired_rows, chains)
        for k in range(len(chains)):
            chains[k]['num_alignments_all_seq'] = np.asarray(
                chains[k][""msa_all_seq""].shape[0]
            )

        # chains = msa_pairing.deduplicate_unpaired_sequences(chains)
        chains = feature_processing.crop_chains_v2(
            chains,
            msa_crop_size=feature_processing.MSA_CROP_SIZE,
            max_templates=feature_processing.MAX_TEMPLATES,
        )

        np_example = msa_pairing.merge_chain_features(
            np_chains_list=chains,
            pair_msa_sequences=True,
            max_templates=feature_processing.MAX_TEMPLATES,
        )

        np_example = feature_processing.process_final(np_example)
        logger.info(tree.map_structure(lambda x: x.shape, np_example))

        return np_example

    def create_paired_features(self, paired_rows, chains):
        chain_keys = chains[0].keys()
        updated_chains = []
        for chain_num, chain in enumerate(chains):
            new_chain = {k: v for k, v in chain.items() if '_all_seq' not in k}
            for feature_name in chain_keys:
                if feature_name.endswith('_all_seq'):
                    feats_padded = msa_pairing.pad_features(
                        chain[feature_name], feature_name
                    )
                    new_chain[feature_name] = \
                        feats_padded[paired_rows[:, chain_num]]
            new_chain['num_alignments_all_seq'] = np.asarray(
                len(paired_rows[:, chain_num])
            )
            updated_chains.append(new_chain)
        # print(""****************************"")
        # print('%s',tree.map_structure(lambda x: x.shape, updated_chains))
        # exit()
        return updated_chains
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/pairing_pipeline.py",".py","8127","198","import os
import copy
import json
import logging
import argparse

from typing import Mapping, List
from sympy import sequence

from tqdm import tqdm

import tree
import numpy as np
from alphafold.data import (
    msa_pairing, pipeline, pipeline_multimer, feature_processing
)
from alphafold.common import residue_constants

from msa_pair.data import species_processing
from copy import deepcopy

logger = logging.getLogger(__file__)

def _make_all_seq_msa_features(msa_feats):
    return {
        f'{k}_all_seq': v for k, v in msa_feats.items() if k in (
            msa_pairing.MSA_FEATURES + ('msa_species_identifiers',)
        )
    }

def _make_empty_templates_features(num_res):
    """"""Construct a default template with all zeros.""""""                           
    return {
        'template_aatype':
            np.zeros(
                (1, num_res, len(residue_constants.restypes_with_x_and_gap)),
                np.float32
            ),
        'template_all_atom_masks':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num), np.float32
            ),
        'template_all_atom_positions':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num, 3), np.float32
            ),
        'template_domain_names': np.array([''.encode()], dtype=object),
        'template_sequence': np.array([''.encode()], dtype=object),
        'template_sum_probs': np.array([0], dtype=np.float32),
    }

def build_paired_rows(auth_chain_ids, paired_rows_dict):
    paired_rows = []
    all_rows = [paired_rows_dict[chain_id] for chain_id in auth_chain_ids]
    num_rows = len(all_rows[0])
    for i in range(num_rows):
        paired_rows.append([rows[i] for rows in all_rows])
    paired_rows = np.array(paired_rows)
    assert paired_rows.shape == (num_rows, len(auth_chain_ids)), paired_rows.shape
    return paired_rows



# def make_fixed_length(np_example, min_seq_l=1024):
#     if np_example['seq_length'] >= min_seq_l:
#         return np_example
    
#     np_example = deepcopy(np_example)
#     for k, v in np_example.items():
#         if not isinstance(v, int):
#             v_shape = v.shape

#         if k in ['seq_length']:
#             np_example[k] = np.array(min_seq_l)
#         elif k in ['msa', 'deletion_matrix', 'bert_mask', 'msa_mask']:
#             np_example[k] = np.pad(v, ((0, 0), (0, min_seq_l - v_shape[-1])))
#         elif k in ['residue_index', 'asym_id', 'sym_id', 'entity_id', 'entity_mask', 'seq_mask', 'aatype']:
#             np_example[k] = np.pad(v, (0, min_seq_l - v_shape[-1]))
#         elif k in ['template_aatype']:
#             np_example[k] = np.pad(v, ((0, 0), (0, min_seq_l - v_shape[-1])))
#         elif k in ['template_all_atom_mask']:
#             np_example[k] = np.pad(v, ((0, 0), (0, min_seq_l - v_shape[1]), (0, 0)))
#         elif k in ['template_all_atom_positions']:
#             np_example[k] = np.pad(v, ((0, 0), (0, min_seq_l - v_shape[1]), (0, 0), (0, 0)))
#         elif k in ['all_atom_mask']:
#             np_example[k] = np.pad(v, ((0, min_seq_l - v_shape[0]), (0, 0)))
#         elif k in ['all_atom_positions']:
#             np_example[k] = np.pad(v, ((0, min_seq_l - v_shape[0]), (0, 0), (0, 0)))

#     return np_example



class PairingPipeline:
    def process(self, input_dir, paired_rows_dict, padding = True):
        logger.info(f'Build into {input_dir}')
        # load paired msas
        msas_all_seq_dict, msa_all_seq_feats_dict = species_processing.parse(
            input_dir, names=['uniprot.a3m'], pair_species=False
        )

        # print('%s',
        #         tree.map_structure(lambda x: x.shape, msa_all_seq_feats_dict))
        # exit()
        # load unpaired msas
        msas_dict, msa_feats_dict = species_processing.parse( 
            input_dir, names=['uniclust30.a3m'], pair_species=False
        )
        # msas_dict, msa_feats_dict = species_processing.parse( 
        #     input_dir, names=['uniref90.a3m'], pair_species=False
        # )

        # msas_dict: {A/B:{sequences:[n_seq, seq_length], descriptions:[n_seq, -1]}}
        # print('%s',tree.map_structure(lambda x: x.shape, msa_feats_dict))
        # build merged feature
        chains_dict = {}
        for chain_id, msa in msas_dict.items():
            seq, desc = msa.sequences[0], msa.descriptions[0]  # primary seq and desc
            num_res = len(seq)  # seq length
            chain = {
                **_make_all_seq_msa_features(msa_all_seq_feats_dict[chain_id]),  # 'deletion_matrix_int_all_seq', 'msa_all_seq', 'num_alignments_all_seq', 'msa_species_identifiers_all_seq'
                **msa_feats_dict[chain_id],   # 'deletion_matrix_int', 'msa_all', 'num_alignments', 'msa_species_identifiers'
                **pipeline.make_sequence_features(seq, desc, num_res),  # 'aatype', 'between_segment_residues', 'domain_name', 'residue_index', 'seq_length', 'sequence'
                **_make_empty_templates_features(num_res),  # 'template_aatype', 'template_all_atom_masks', 'template_all_atom_positions', 'template_domain_names', 'template_sequence', 'template_sum_probs'
            }

            chains_dict[chain_id] = pipeline_multimer.convert_monomer_features(
                chain, chain_id=chain_id
            )

        chains_dict = pipeline_multimer.add_assembly_features(chains_dict)

        np_example = self.pair_and_merge(chains_dict, paired_rows_dict)
        np_example = pipeline_multimer.pad_msa(np_example, 512)
        logger.info(f""Done building {input_dir}"")
        # if padding:
        #     np_example = make_fixed_length(np_example)
        return np_example

    def pair_and_merge(
        self,
        chains_dict: Mapping[str, pipeline.FeatureDict],
        paired_rows_dict: Mapping[str, List[int]],
    ) -> pipeline.FeatureDict:

        feature_processing.process_unmerged_features(chains_dict)
        chains = list(chains_dict.values())

        auth_chain_ids = [str(chain['auth_chain_id']) for chain in chains]
        paired_rows = build_paired_rows(auth_chain_ids, paired_rows_dict)  # [n_pairs, 2]
        # num_alignments_all_seq
        chains = self.create_paired_features(paired_rows, chains)
        chains = msa_pairing.deduplicate_unpaired_sequences(chains)
        chains = feature_processing.crop_chains(
            chains,
            msa_crop_size=feature_processing.MSA_CROP_SIZE,
            pair_msa_sequences=True,
            max_templates=feature_processing.MAX_TEMPLATES,
        )
        # print(feature_processing.MSA_CROP_SIZE)
        # print('-------------',tree.map_structure(lambda x: x.shape, chains))
        # exit()
        np_example = msa_pairing.merge_chain_features(
            np_chains_list=chains,
            pair_msa_sequences=True,
            max_templates=feature_processing.MAX_TEMPLATES,
        )
        np_example = feature_processing.process_final(np_example)
        logger.info(tree.map_structure(lambda x: x.shape, np_example))
        return np_example

    def create_paired_features(self, paired_rows, chains):
        # print(paired_rows)
        # print(chains)
        # print('%s',tree.map_structure(lambda x: x.shape, chains))
        # exit()
        
        chain_keys = chains[0].keys()
        updated_chains = []

        for chain_num, chain in enumerate(chains):
            new_chain = {k: v for k, v in chain.items() if '_all_seq' not in k}
            for feature_name in chain_keys:
                if feature_name.endswith('_all_seq'):
                    feats_padded = msa_pairing.pad_features(
                        chain[feature_name], feature_name
                    )
                    new_chain[feature_name] = \
                        feats_padded[paired_rows[:, chain_num]]  # only take paired seq
            new_chain['num_alignments_all_seq'] = np.asarray(
                len(paired_rows[:, chain_num])
            )
            updated_chains.append(new_chain)
        # print(""****************************"")
        # print('%s',tree.map_structure(lambda x: x.shape, updated_chains))
        # exit()
        return updated_chains
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/megatron_scoring.py",".py","7351","199","from asyncio import FastChildWatcher
from importlib.machinery import all_suffixes
import string
from tkinter.tix import Tree
from typing import List, Tuple, Mapping

from tqdm import tqdm
import torch
import pandas as pd
from alphafold.data import parsers
import torch.nn.functional as F

from msa_pair.data.msa_processing import MsaBlock

class EsmScoring:
    def __init__(self, msa_transformer, msa_batch_converter):
        self.msa_transformer = msa_transformer.eval()
        self.msa_batch_converter = msa_batch_converter
        deletekeys = dict.fromkeys(string.ascii_lowercase)
        deletekeys["".""] = None
        deletekeys[""*""] = None
        self.translation = str.maketrans(deletekeys)
        # self.inter_tag = inter_tag
        self.is_cpu = False
        # self.tag = tag

    def set_device(self, is_cpu):
        self.is_cpu = is_cpu
        return self.is_cpu


    def _read_msa(
        self,
        input_msa: parsers.Msa,
        nseq: int
    ) -> List[Tuple[str, str]]:
        def remove_insertions(sequence: str) -> str:
            """""" Removes any insertions into the sequence. Needed to load 
                aligned sequences in an MSA.
            """"""
            return sequence.translate(self.translation)
        input_msa = [
            (desc, seq) for desc, seq in zip(
                input_msa.descriptions[:nseq], input_msa.sequences[:nseq]
            )
        ]
        return [ (desc, remove_insertions(seq)) for desc, seq in input_msa ]

    def score(self, input_msa: parsers.Msa, max_num_msas: int, is_cpu = False, repr_layers=[]):
        """"""Compute the ColAttn scores
        """"""
        assert max_num_msas <= 1024
        msa_data = [ self._read_msa(input_msa, max_num_msas) ]
        msa_batch_labels, msa_batch_strs, msa_batch_tokens = \
            self.msa_batch_converter(msa_data)
        if not is_cpu:
            msa_transformer = self.msa_transformer.cuda()
            msa_batch_tokens = msa_batch_tokens.cuda()
        else:
            msa_transformer = self.msa_transformer.cpu()
            msa_batch_tokens = msa_batch_tokens.cpu()
        # print(repr_layers)
        # exit()
        with torch.no_grad():
            all_info = msa_transformer(
                msa_batch_tokens, repr_layers = repr_layers, need_head_weights=True
            )
            col_attention = all_info['col_attentions']
            B, C, R, R = col_attention.size()
            # B, C, R
            col_attention = col_attention[:, :, 0, :]
            col_attention = col_attention.mean(-2).cpu().numpy()

        return col_attention[0][1:] # R - 1

    def score_sequences(
        self,
        species_dict: Mapping[str, Mapping[str, pd.DataFrame]],
        msas_dict: Mapping[str, parsers.Msa],
        take_num_seqs: int = 128,
        max_num_msas: int = 512,
        repr_layers: list = [],
        max_num_species: int = -1,
        show_progress: bool = True,
    ):
        """"""Compute the scores of sequences that are paired by species
        Args:
            take_num_seqs: max number of sequence per species
            max_num_msas: max number of sequence in an MsaBlock
            max_num_species: max number of species to be processed
        """"""
        msa_blocks = self._build_msa_blocks(
            species_dict, msas_dict, take_num_seqs=take_num_seqs
        )

        cur_msa_block = MsaBlock(
            msas_dict, {chain_id: [0] for chain_id in msas_dict}
        )
        sequences_scores = {
            chain_id: {
                '0': {
                    'description': str(msa.descriptions[0]),
                    'score': 1e6,
                    'block_num': -1,
                }
            } for chain_id, msa in msas_dict.items()
        }
        # print(repr_layers)
        # exit()
        def _score_cur_block():
            if not hasattr(_score_cur_block, 'block_num'):
                _score_cur_block.block_num = 0
            _score_cur_block.block_num += 1
            chain_msas = cur_msa_block.get_msas()
            rows = cur_msa_block.get_rows()
            for chain_id, chain_msa in chain_msas.items():   
                try:
                    scores_ = self.score(chain_msa, max_num_msas, is_cpu = False, repr_layers=repr_layers)
                except:
                    scores_ = self.score(chain_msa, max_num_msas, is_cpu = True, repr_layers=repr_layers)
                rows_ = rows[chain_id]
                # print(len(scores_), len(rows_))
                assert len(scores_) + 1 == len(rows_)
                for i, (r, s) in enumerate(zip(rows_[1:], scores_), start=1):
                    r = str(int(r))
                    assert r not in sequences_scores[chain_id]
                    # if r in sequences_scores[chain_id]:
                    #     print(""orig:"", sequences_scores[chain_id][r])
                    #     print(""cur: "",  {
                    #     'description': 
                    #         str(chain_msa.descriptions[i]).split()[0],
                    #     'score': float(s),
                    #     'block_num': _score_cur_block.block_num,
                    #     })
                    #     print(r)

                    sequences_scores[chain_id][r] = {
                        'description': 
                            str(chain_msa.descriptions[i]).split()[0],
                        'score': float(s),
                        'block_num': _score_cur_block.block_num,
                    }

        all_msa_blocks = list(msa_blocks.items())
        if max_num_species > 0:
            all_msa_blocks = all_msa_blocks[:max_num_species]
        if show_progress:
            progress = tqdm(all_msa_blocks)
        else:
            progress = all_msa_blocks

        for spec, msa_block in progress:
            cur_lengths = cur_msa_block.get_lengths()
            next_lengths = {
                chain_id:
                this_length if chain_id not in cur_lengths else
                this_length + cur_lengths[chain_id]
                for chain_id, this_length in msa_block.get_lengths().items()
            }
            # print(next_lengths, max_num_msas)
            if max(next_lengths.values()) >= max_num_msas:
                _score_cur_block()
                cur_msa_block = MsaBlock(
                    msas_dict, {chain_id: [0] for chain_id in msas_dict}
                )
            cur_msa_block += msa_block

        if max(cur_msa_block.get_lengths().values()) > 1:
            _score_cur_block()

        return sequences_scores

    def _build_msa_blocks(
        self, species_dict, msas_dict, gap_cutoff=0.4, take_num_seqs=128,
    ):
        def _filter_rows(df):
            df = df.sort_values('msa_similarity', axis=0, ascending=False)
            rows = df.msa_row[df.gap <= gap_cutoff].iloc[:take_num_seqs].values
            return rows.astype(int)

        msa_blocks = {}
        for spec, dfs in species_dict.items():
            rows_dict = {}
            if spec == b'':
                continue

            for chain_id, df in dfs.items():
                rows = _filter_rows(df)
                if len(rows) < 1:
                    continue
                rows_dict[chain_id] = rows

            if len(rows_dict) < 2:
                continue

            msa_blocks[spec] = MsaBlock(msas_dict, rows_dict)

        return msa_blocks
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/esm_scoring.py",".py","12303","324","from asyncio import FastChildWatcher
from importlib.machinery import all_suffixes
import string
from tkinter.tix import Tree
from typing import List, Tuple, Mapping

from tqdm import tqdm
import torch
import pandas as pd
from alphafold.data import parsers
import torch.nn.functional as F

from msa_pair.data.msa_processing import MsaBlock

class EsmScoring:
    def __init__(self, msa_transformer, msa_batch_converter, tag, inter_tag=False):
        self.msa_transformer = msa_transformer.eval()
        self.msa_batch_converter = msa_batch_converter
        deletekeys = dict.fromkeys(string.ascii_lowercase)
        deletekeys["".""] = None
        deletekeys[""*""] = None
        self.translation = str.maketrans(deletekeys)
        self.inter_tag = inter_tag
        self.is_cpu = False
        self.tag = tag

    def set_device(self, is_cpu):
        self.is_cpu = is_cpu
        return self.is_cpu


    def _read_msa(
        self,
        input_msa: parsers.Msa,
        nseq: int
    ) -> List[Tuple[str, str]]:
        def remove_insertions(sequence: str) -> str:
            """""" Removes any insertions into the sequence. Needed to load 
                aligned sequences in an MSA.
            """"""
            return sequence.translate(self.translation)
        input_msa = [
            (desc, seq) for desc, seq in zip(
                input_msa.descriptions[:nseq], input_msa.sequences[:nseq]
            )
        ]
        return [ (desc, remove_insertions(seq)) for desc, seq in input_msa ]

    def sim_score(self, input_msa: parsers.Msa, max_num_msas: int, is_cpu = False, repr_layers=[12]):
        """"""Compute the Cosim scores
        """"""
        assert max_num_msas <= 1024
        msa_data = [ self._read_msa(input_msa, max_num_msas) ]
        msa_batch_labels, msa_batch_strs, msa_batch_tokens = \
            self.msa_batch_converter(msa_data)
        if not is_cpu:
            msa_transformer = self.msa_transformer.cuda()
            msa_batch_tokens = msa_batch_tokens.cuda()
        else:
            msa_transformer = self.msa_transformer.cpu()
            msa_batch_tokens = msa_batch_tokens.cpu()

        with torch.no_grad():
            repre = msa_transformer(
                msa_batch_tokens, 
                repr_layers=repr_layers
            )['representations'][repr_layers[0]]
            repre = repre.mean(-2)
            target_emb = F.normalize(repre[:, :1], p=2, dim=-1)
            msas_emb = F.normalize(repre[:,1:], p=2,dim=-1)
            # print(target_emb.size(), msas_emb.size())
            if self.inter_tag:
                return msas_emb
            else:
                target2sim = torch.bmm(target_emb, msas_emb.transpose(-1,-2)).reshape(-1).cpu().numpy()
                return target2sim


    def score(self, input_msa: parsers.Msa, max_num_msas: int, is_cpu = False, repr_layers=[]):
        """"""Compute the ColAttn scores
        """"""
        assert max_num_msas <= 1024
        msa_data = [ self._read_msa(input_msa, max_num_msas) ]
        msa_batch_labels, msa_batch_strs, msa_batch_tokens = \
            self.msa_batch_converter(msa_data)
        if not is_cpu:
            msa_transformer = self.msa_transformer.cuda()
            msa_batch_tokens = msa_batch_tokens.cuda()
        else:
            msa_transformer = self.msa_transformer.cpu()
            msa_batch_tokens = msa_batch_tokens.cpu()
        with torch.no_grad():
            all_info = msa_transformer(
                msa_batch_tokens, need_head_weights=True
            )
            col_attention = all_info['col_attentions']
            B, C, R, R = col_attention.size()
            # B, C, R
            col_attention = col_attention[:, :, 0, :]
            col_attention = col_attention.mean(-2).cpu().numpy()

        return col_attention[0][1:] # R - 1

    def score_sequences(
        self,
        species_dict: Mapping[str, Mapping[str, pd.DataFrame]],
        msas_dict: Mapping[str, parsers.Msa],
        take_num_seqs: int = 128,
        max_num_msas: int = 256,
        max_num_species: int = -1,
        show_progress: bool = True,
    ):
        """"""Compute the scores of sequences that are paired by species
        Args:
            take_num_seqs: max number of sequence per species
            max_num_msas: max number of sequence in an MsaBlock
            max_num_species: max number of species to be processed
        """"""
        msa_blocks = self._build_msa_blocks(
            species_dict, msas_dict, take_num_seqs=take_num_seqs
        )

        cur_msa_block = MsaBlock(
            msas_dict, {chain_id: [0] for chain_id in msas_dict}
        )
        sequences_scores = {
            chain_id: {
                '0': {
                    'description': str(msa.descriptions[0]),
                    'score': 1e6,
                    'block_num': -1,
                }
            } for chain_id, msa in msas_dict.items()
        }
        def _score_cur_block():
            if not hasattr(_score_cur_block, 'block_num'):
                _score_cur_block.block_num = 0
            _score_cur_block.block_num += 1
            chain_msas = cur_msa_block.get_msas()
            rows = cur_msa_block.get_rows()
            for chain_id, chain_msa in chain_msas.items():
                if self.tag == 'sim':
                    try:
                        scores_ = self.sim_score(chain_msa, max_num_msas, is_cpu = False)
                    except:
                        scores_ = self.sim_score(chain_msa, max_num_msas, is_cpu = True)
                elif self.tag == 'col':
                    try:
                        scores_ = self.score(chain_msa, max_num_msas, is_cpu = False)
                    except:
                        scores_ = self.score(chain_msa, max_num_msas, is_cpu = True)
                else:
                    raise ValueError(f""No such metrics {self.tag} !"")
                rows_ = rows[chain_id]
                # print(len(scores_), len(rows_))
                assert len(scores_) + 1 == len(rows_)
                for i, (r, s) in enumerate(zip(rows_[1:], scores_), start=1):
                    r = str(int(r))
                    assert r not in sequences_scores[chain_id]
                    # if r in sequences_scores[chain_id]:
                    #     print(""orig:"", sequences_scores[chain_id][r])
                    #     print(""cur: "",  {
                    #     'description': 
                    #         str(chain_msa.descriptions[i]).split()[0],
                    #     'score': float(s),
                    #     'block_num': _score_cur_block.block_num,
                    #     })
                    #     print(r)

                    sequences_scores[chain_id][r] = {
                        'description': 
                            str(chain_msa.descriptions[i]).split()[0],
                        'score': float(s),
                        'block_num': _score_cur_block.block_num,
                    }

        all_msa_blocks = list(msa_blocks.items())
        if max_num_species > 0:
            all_msa_blocks = all_msa_blocks[:max_num_species]
        if show_progress:
            progress = tqdm(all_msa_blocks)
        else:
            progress = all_msa_blocks

        for spec, msa_block in progress:
            cur_lengths = cur_msa_block.get_lengths()
            next_lengths = {
                chain_id:
                this_length if chain_id not in cur_lengths else
                this_length + cur_lengths[chain_id]
                for chain_id, this_length in msa_block.get_lengths().items()
            }
            # print(next_lengths, max_num_msas)
            if max(next_lengths.values()) >= max_num_msas:
                _score_cur_block()
                cur_msa_block = MsaBlock(
                    msas_dict, {chain_id: [0] for chain_id in msas_dict}
                )
            cur_msa_block += msa_block

        if max(cur_msa_block.get_lengths().values()) > 1:
            _score_cur_block()

        return sequences_scores

    def _build_msa_blocks(
        self, species_dict, msas_dict, gap_cutoff=0.4, take_num_seqs=128,
    ):
        def _filter_rows(df):
            df = df.sort_values('msa_similarity', axis=0, ascending=False)
            rows = df.msa_row[df.gap <= gap_cutoff].iloc[:take_num_seqs].values
            return rows.astype(int)

        msa_blocks = {}
        for spec, dfs in species_dict.items():
            rows_dict = {}
            if spec == b'':
                continue

            for chain_id, df in dfs.items():
                rows = _filter_rows(df)
                if len(rows) < 1:
                    continue
                rows_dict[chain_id] = rows

            if len(rows_dict) < 2:
                continue

            msa_blocks[spec] = MsaBlock(msas_dict, rows_dict)

        return msa_blocks

    def score_inter_sequences(
        self,
        species_dict: Mapping[str, Mapping[str, pd.DataFrame]],
        msas_dict: Mapping[str, parsers.Msa],
        take_num_seqs: int = 128,
        max_num_msas: int = 256,
        max_num_species: int = -1,
        show_progress: bool = True,
    ):
        """"""Compute the scores of sequences that are paired by species
        Args:
            take_num_seqs: max number of sequence per species
            max_num_msas: max number of sequence in an MsaBlock
            max_num_species: max number of species to be processed
        """"""
        msa_blocks = self._build_msa_blocks(
            species_dict, msas_dict, take_num_seqs=take_num_seqs
        )

        cur_msa_block = MsaBlock(
            msas_dict, {chain_id: [0] for chain_id in msas_dict}
        )
        cur_spec_len = []
        sequences_scores = {}
        def _score_cur_block():
            if not hasattr(_score_cur_block, 'block_num'):
                _score_cur_block.block_num = 0
            _score_cur_block.block_num += 1
            chain_msas = cur_msa_block.get_msas()
            rows = cur_msa_block.get_rows()
            chain2emb = {}
            for chain_id, chain_msa in chain_msas.items():
                try:
                    seqEmb = self.sim_score(chain_msa, max_num_msas)
                except:
                    seqEmb = self.sim_score(chain_msa, max_num_msas, True)
                chain2emb[chain_id] = seqEmb

            cur_sa = 0
            cur_sb = 0

            for each in cur_spec_len:
                spec, chainLen = each
                Alen, Blen = chainLen['A'], chainLen['B'] 
                seqA_emb = chain2emb['A'][:, cur_sa:(cur_sa+Alen)]
                seqB_emb = chain2emb['B'][:, cur_sb:(cur_sb+Blen)]

                inter_sims = torch.bmm(seqA_emb, seqB_emb.transpose(-1,-2)).squeeze(0).cpu().numpy()

                # print(inter_sims.shape)
                rowsA = rows['A'][(cur_sa + 1) : (cur_sa + Alen + 1)]
                rowsB = rows['B'][(cur_sb + 1) : (cur_sb + Blen + 1)]

                cur_sa += Alen
                cur_sb += Blen

                assert spec not in sequences_scores
                sequences_scores[spec]={
                    ""rowsA"": rowsA,
                    ""rowsB"": rowsB,
                    ""sims"": inter_sims
                }

        all_msa_blocks = list(msa_blocks.items())
        if max_num_species > 0:
            all_msa_blocks = all_msa_blocks[:max_num_species]
        if show_progress:
            progress = tqdm(all_msa_blocks)
        else:
            progress = all_msa_blocks

        for spec, msa_block in progress:
            cur_lengths = cur_msa_block.get_lengths()
            next_lengths = {
                chain_id:
                this_length if chain_id not in cur_lengths else
                this_length + cur_lengths[chain_id]
                for chain_id, this_length in msa_block.get_lengths().items()
            }
            if max(next_lengths.values()) >= max_num_msas:
                _score_cur_block()
                cur_msa_block = MsaBlock(
                    msas_dict, {chain_id: [0] for chain_id in msas_dict}
                )
                cur_spec_len = []
            cur_msa_block += msa_block
            cur_spec_len.append((spec, msa_block.get_lengths()))

        if max(cur_msa_block.get_lengths().values()) > 1:
            _score_cur_block()

        return sequences_scores","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/esm_pairing.py",".py","3024","89","import os
import json
from typing import List, Mapping

import numpy as np

from alphafold.data import parsers, msa_pairing, feature_processing
from alphafold.common import residue_constants

def _upgrade_sequences_scores(sequence_scores):
    processed_scores = {}
    for result in sequence_scores.values():
        desc = result['description']
        score = result['score']
        processed_scores[desc] = score

    return processed_scores

def create_paired_rows_dict(
    species_dict,
    msas_dict,
    sequences_scores,
    chain_ids: List[str] = ['A', 'B'],
) -> Mapping[str, List[int]]:

    # upgrade sequences scores 
    for chain_id in chain_ids:
        sequences_scores[chain_id] = _upgrade_sequences_scores(
            sequences_scores[chain_id]
        )

    paired_rows_dict = {chain_id: [0] for chain_id in chain_ids}
    pair_scores = [ np.full((1, len(chain_ids)), 1e6) ]
    for spec, dfs in species_dict.items():
        if spec == b'':
            continue

        # find and sort scores
        scores_ = {}
        for chain_id, df in dfs.items():
            chain_scores_ = []
            rows = df.msa_row.values
            for r in rows:
                desc = msas_dict[chain_id].descriptions[r].split()[0]
                if desc in sequences_scores[chain_id]:
                    s = sequences_scores[chain_id][desc]
                    chain_scores_.append((int(r), s))

            if len(chain_scores_) >= 1:
                scores_[chain_id] = sorted(
                    chain_scores_, key=lambda v: v[-1], reverse=True,
                )

        # match rows within the species
        if len(scores_) > 1:
            num_seqs = min(len(v) for v in scores_.values())
            pair_scores_ = []
            for chain_id in chain_ids:
                if chain_id in scores_:
                    chain_rows_ = [
                        _[0] for _ in scores_[chain_id][:num_seqs]
                    ]
                    chain_scores_ = [
                        _[-1] for _ in scores_[chain_id][:num_seqs]
                    ]
                else:
                    chain_rows_ = [-1] * num_seqs
                    chain_scores_ = [0.] * num_seqs

                paired_rows_dict[chain_id] += chain_rows_
                pair_scores_ += [np.array(chain_scores_)[:,None]]

            pair_scores.append(np.concatenate(pair_scores_, axis=-1))

    # sort rows by their pair scores
    pair_scores = np.concatenate(pair_scores, axis=0)
    # inds_sorted = np.argsort(np.sum(pair_scores, axis=-1))[::-1]
    inds_sorted = np.argsort(np.sum(pair_scores, axis=-1))
    paired_rows_dict  = {
        chain_id: [rows[i] for i in inds_sorted] for chain_id, rows in 
        paired_rows_dict.items()
    }
    assert len(pair_scores) == len(paired_rows_dict[chain_ids[0]])
    # check the first row is always zero (that is, the first sequence is 
    # always the query sequence)
    assert all(rows[0] == 0 for rows in paired_rows_dict.values())

    return paired_rows_dict
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/alphafold_pipeline.py",".py","3894","104","import os
import copy
import json
import logging
import dataclasses

import numpy as np
from alphafold.data import (
    pipeline, pipeline_multimer, parsers, msa_pairing, feature_processing
)

from msa_pair.data import pairing_pipeline

logger = logging.getLogger(__file__)

class AlphaFoldPipeline:
    def _all_seq_msa_features(self, hit_path, sequence):
        with open(hit_path, 'rt') as fh:
            a3m_str = fh.read()
        msa = parsers.parse_a3m(a3m_str)
        assert msa.sequences[0] == sequence
        all_seq_features = pipeline.make_msa_features([msa])
        valid_feats = msa_pairing.MSA_FEATURES + (
            'msa_species_identifiers',
        )
        feats = {
            f'{k}_all_seq': v for k, v in all_seq_features.items()
            if k in valid_feats
        }

        return feats

    def process(self, input_dir, chain_ids=['A', 'B']):
        input_fasta_path = os.path.join(input_dir, 'multimer.fasta')
        with open(input_fasta_path) as f:
            input_fasta_str = f.read()
        input_seqs, input_descs = parsers.parse_fasta(input_fasta_str)

        chain_id_map = pipeline_multimer._make_chain_id_map(
            sequences=input_seqs,
            descriptions=input_descs
        )
        chain_id_map_path = os.path.join(input_dir, 'chain_id_map.json')
        with open(chain_id_map_path, 'w') as f:
            chain_id_map_dict = {
                chain_id: dataclasses.asdict(fasta_chain)
                for chain_id, fasta_chain in chain_id_map.items()
            }
            json.dump(chain_id_map_dict, f, indent=4, sort_keys=True)

        all_chain_features = {}
        sequence_features = {}
        is_homomer_or_monomer = len(set(input_seqs)) == 1
        for chain_id, fasta_chain in chain_id_map.items():
            if fasta_chain.sequence in sequence_features:
                all_chain_features[chain_id] = copy.deepcopy(
                    sequence_features[fasta_chain.sequence]
                )
                continue
            num_res = len(fasta_chain.sequence)
            sequence_features = pipeline.make_sequence_features(
                sequence=fasta_chain.sequence,
                description=fasta_chain.description,
                num_res=num_res
            )
            uniclust_path = os.path.join(input_dir, chain_id, 'uniclust30.a3m')
            # uniclust_path = os.path.join(input_dir, chain_id, 'uniref90.a3m')
            with open(uniclust_path) as fh:
                a3m_str = fh.read()
                uniclust_msa = parsers.parse_a3m(a3m_str)
            assert uniclust_msa.sequences[0] == fasta_chain.sequence
            msa_features = pipeline.make_msa_features((uniclust_msa,))
            # uniprot_hits.a3m
            uniprot_path = os.path.join(input_dir, chain_id, 'uniprot.a3m')
            # uniprot_path = os.path.join(input_dir, chain_id, 'uniprot_hits.a3m')
            msa_all_seq_features = self._all_seq_msa_features(
                uniprot_path, fasta_chain.sequence
            )

            chain_features = {
                **msa_all_seq_features,
                **sequence_features,
                **msa_features,
                **pairing_pipeline._make_empty_templates_features(num_res),
            }
            chain_features = pipeline_multimer.convert_monomer_features(
                chain_features, chain_id=chain_id
            )
            all_chain_features[chain_id] = chain_features
            sequence_features[fasta_chain.sequence] = chain_features

        all_chain_features = pipeline_multimer.add_assembly_features(
            all_chain_features
        )

        np_example = feature_processing.pair_and_merge(
            all_chain_features=all_chain_features
        )

        # Pad MSA to avoid zero-sized extra_msa.
        np_example = pipeline_multimer.pad_msa(np_example, 512)

        return np_example
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/species_processing.py",".py","3253","97","import os
import string
from collections import defaultdict
from msa_pair.data import species_processing
from alphafold.data import parsers
from Bio import SeqIO
from typing import List, Tuple
import itertools
import string
import numpy as np
import json

deletekeys = dict.fromkeys(string.ascii_lowercase)
deletekeys["".""] = None
deletekeys[""*""] = None
translation = str.maketrans(deletekeys)


def convert_seq_desc_to_Msa(sequences, descriptions):
    deletion_matrix = []
    for msa_sequence in sequences:
        deletion_vec = []
        deletion_count = 0
        for j in msa_sequence:
            if j.islower():
                deletion_count += 1
            else:
                deletion_vec.append(deletion_count)
                deletion_count = 0
        deletion_matrix.append(deletion_vec)

    # Make the MSA matrix out of aligned (deletion-free) sequences.
    deletion_table = str.maketrans('', '', string.ascii_lowercase)
    aligned_sequences = [s.translate(deletion_table) for s in sequences]
    return parsers.Msa(sequences=aligned_sequences,
                        deletion_matrix=deletion_matrix,
                        descriptions=descriptions)


def read_sequence(filename: str) -> Tuple[str, str]:
    """""" Reads the first (reference) sequences from a fasta or MSA file.""""""
    record = next(SeqIO.parse(filename, ""fasta""))
    return record.description, str(record.seq)

def remove_insertions(sequence: str) -> str:
    """""" Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. """"""
    return sequence.translate(translation)

def read_msa(filename: str, nseq: int) -> List[Tuple[str, str]]:
    """""" Reads the first nseq sequences from an MSA file, automatically removes insertions.""""""
    return [(record.description, remove_insertions(str(record.seq)))
            for record in itertools.islice(SeqIO.parse(filename, ""fasta""), nseq)]

def parse_pairs(
    input_dir: str,
    paired_row_dict: list,
    chain_ids=['A', 'B'], 
    pair_species=False,
):

    grouped_paths = {
        chain_id: 
            os.path.join(input_dir, chain_id, 'uniprot.a3m')
        for chain_id in chain_ids
    }
    msas_dict = {}
    msa_feats_dict = {}
    chain_ids = [""A"", ""B""]

    if pair_species:
        all_species_dict = defaultdict(dict)
    for chain_id, paths in grouped_paths.items():
        msas = []
        msa = read_msa(paths, nseq=100000) 
        msa = [msa[_] for _ in paired_row_dict[chain_id]]
        descriptions = [_[0] for _ in msa]
        sequences = [_[1] for _ in msa]
        msas.append(convert_seq_desc_to_Msa(sequences, descriptions))
        msa_feat, processed_msa = species_processing.make_msa_features(msas)

        if pair_species:
            msa_df = species_processing.make_msa_df(msa_feat)
            species_dict = species_processing.create_species_dict(msa_df)
            for spec, df in species_dict.items():
                if spec == b'':
                    continue
                all_species_dict[spec][chain_id] = df

        msas_dict[chain_id] = processed_msa
        msa_feats_dict[chain_id] = msa_feat
    
    if pair_species:
        return all_species_dict, msas_dict, msa_feats_dict
    else:
        return msas_dict, msa_feats_dict

","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/model_preset_runner.py",".py","6167","189","import os
import time
import random
import logging
from typing import Mapping, Optional, Sequence, List

import jax
import tree
import numpy as np

from alphafold.data import pipeline_multimer, msa_pairing
from alphafold.model import config, data, model
# from msa_pair.runner import model, modules_multimer

logger = logging.getLogger(__file__)


def clear_mem(device=None):
    '''remove all data from device'''
    backend = jax.lib.xla_bridge.get_backend(device)
    if hasattr(backend,'live_buffers'):
        for buf in backend.live_buffers():
            buf.delete()


def _is_params_transferable(old_params, next_params):
    def _get_all_keys(params):
        all_keys = []
        for path in params.keys():
            for k in params[path].keys():
                all_keys.append(f'{path}/{k}')
        return tuple(sorted(all_keys))

    return _get_all_keys(old_params) == _get_all_keys(next_params)


class ModelPresetRunner:
    def __init__(
        self,
        database: str,
        model_preset: str = 'multimer',
        allow_params_transfer: bool = True,
        cache_params: bool = True,
        ignore_unpaired_sequences: bool = True,
    ):
        assert model_preset == 'multimer', 'Only supports preset multimer'

        self.model_preset = model_preset
        self.database = database
        self.allow_params_transfer = allow_params_transfer
        self.cache_params = cache_params

        self.params_dict = {}
        # Check model parameters
        first_params, first_model_name = None, None
        first_config = None

        self.avail_model_names = []
        for ind, model_name in enumerate(config.MODEL_PRESETS[model_preset]):
            try:
                params = data.get_model_haiku_params(
                    model_name, self.database
                )
                if first_params is None:
                    first_model_name = model_name
                    first_params = params
                    first_config = config.model_config(model_name)
                    print(ind)
                elif self.allow_params_transfer:
                    if not _is_params_transferable(first_params, params):
                        continue
                    if first_config != config.model_config(model_name):
                        continue
                self.avail_model_names.append(model_name)
            except FileNotFoundError as e:
                continue

        assert len(self.avail_model_names) >= 1

        print(type(first_config))
        # first_config[""num_recycle""] = 1

        print(f'{len(self.avail_model_names)} models are available')
        print(f'Build RunModel {first_model_name}')
        self.running_model = model.RunModel(first_config, first_params)

        self.ignore_unpaired_sequences = ignore_unpaired_sequences

    def predict(
        self,
        feat: Mapping[str, np.ndarray],
        rng_seed: Optional[int] = None,
        model_names: Optional[List[str]] = None,
        num_predictions_per_model: int = 5,
    ):
        if rng_seed is not None:
            rng = np.random.default_rng(rng_seed)
        else:
            rng = np.random.default_rng(random.randrange(1, 2**24))

        if model_names is None:
            model_names = self.avail_model_names
        else:
            model_names = [
                model_name for model_name in model_names if model_name in 
                self.avail_model_names
            ]

        outputs = {}
        for model_name in model_names:
            for pred_num in range(num_predictions_per_model):
                seed = int(rng.integers(1, 2**24))
                result, timing = self.run_model(
                    feat, model_name, seed
                )
                outputs[f'{model_name}_{pred_num}'] = {
                    'result': result,
                    'timing': timing,
                    'seed': seed,
                }
            logger.info(tree.map_structure(lambda x: x.shape, result))

        return outputs

    def run_model(
        self,
        feat: Mapping[str, np.ndarray],
        model_name: str,
        random_seed: int,
    ):
        t0 = time.time()

        if not self.cache_params or model_name not in self.params_dict:
            params = data.get_model_haiku_params(model_name, self.database)
            if self.cache_params:
                self.params_dict[model_name] = params
        else:
            params = self.params_dict[model_name]

        if self.running_model is not None and self.allow_params_transfer:
            logger.info(f'Transfer {model_name} params to the running model')
            for path in params.keys():
                self.running_model.params[path] = params[path]

        logger.info(
            f'Start prediction using {model_name} with seed {random_seed}'
        )
        if self.ignore_unpaired_sequences:
            feat = self._remove_unpaired_sequences(feat)
        result = self.running_model.predict(feat, random_seed)
        t1 = time.time()

        logger.info(
            f'Finish prediction using {model_name} in {t1 - t0} seconds'
        )
        # exit()
        timing = t1 - t0

        return result, timing

    def _remove_unpaired_sequences(
        self,
        feat: Mapping[str, np.ndarray],
    ):
        paired_sequence_mask = 1
        for chain_id in np.unique(feat['asym_id']):
            if chain_id:
                chain_mask = feat['asym_id'] == chain_id
                paired_sequence_mask *= np.sum(
                    feat['msa'][:,chain_mask] != msa_pairing.MSA_GAP_IDX,
                    axis=-1,
                ) > 0
        paired_sequence_mask = paired_sequence_mask.astype(bool)
        for key, value in feat.items():
            if key in (
                'msa', 
                'deletion_matrix',
                'bert_mask',
                'msa_mask', 
                'cluster_bias_mask',
            ):
                feat[key] = value[paired_sequence_mask]
            elif key in ('num_alignments',):
                feat[key] = np.sum(paired_sequence_mask)

        feat = pipeline_multimer.pad_msa(feat, 512)

        return feat
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/pair_process.py",".py","4836","124","import os
import copy
import json
import logging
import argparse

from typing import Mapping, List

from tqdm import tqdm

import tree
import numpy as np
from alphafold.data import (
   pipeline, pipeline_multimer
)
from alphafold.common import residue_constants

from msa_pair.data.evo_pipeline.species_processing import parse_pairs
from msa_pair.data.evo_pipeline import feature_processing
from msa_pair.data.evo_pipeline import msa_pairing

logger = logging.getLogger(__file__)

def _make_all_seq_msa_features(msa_feats):
    return {
        f'{k}_all_seq': v for k, v in msa_feats.items() if k in (
            msa_pairing.MSA_FEATURES + ('msa_species_identifiers',)
        )
    }


def _make_empty_templates_features(num_res):
    """"""Construct a default template with all zeros.""""""                           
    return {
        'template_aatype':
            np.zeros(
                (1, num_res, len(residue_constants.restypes_with_x_and_gap)),
                np.float32
            ),
        'template_all_atom_masks':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num), np.float32
            ),
        'template_all_atom_positions':
            np.zeros(
                (1, num_res, residue_constants.atom_type_num, 3), np.float32
            ),
        'template_domain_names': np.array([''.encode()], dtype=object),
        'template_sequence': np.array([''.encode()], dtype=object),
        'template_sum_probs': np.array([0], dtype=np.float32),
    }


class PairingPipeline:
    def process(self, input_dir, paired_rows_dict):
        logger.info(f'Start building')
        # load paired msas
        # msa_feats_dict: dict_keys(['deletion_matrix_int', 'msa', 'num_alignments', 'msa_species_identifiers'])
        msas_all_seq_dict, msa_all_seq_feats_dict = parse_pairs(
            input_dir, paired_rows_dict
        )

        # msas_dict: {A/B:{sequences:[n_seq, seq_length], descriptions:[n_seq, -1]}}
        # build merged feature
        chains_dict = {}
        for chain_id, msa in msas_all_seq_dict.items():
            seq, desc = msa.sequences[0], msa.descriptions[0]  # parimary seq
            num_res = len(seq)
            chain = {
                **_make_all_seq_msa_features(msa_all_seq_feats_dict[chain_id]),  # 'deletion_matrix_int_all_seq', 'msa_all_seq', 'num_alignments_all_seq', 'msa_species_identifiers_all_seq'
                # **msa_feats_dict[chain_id],   # 'deletion_matrix_int', 'msa_all', 'num_alignments', 'msa_species_identifiers'
                **pipeline.make_sequence_features(seq, desc, num_res),  # 'aatype', 'between_segment_residues', 'domain_name', 'residue_index', 'seq_length', 'sequence'
                **_make_empty_templates_features(num_res),  # 'template_aatype', 'template_all_atom_masks', 'template_all_atom_positions', 'template_domain_names', 'template_sequence', 'template_sum_probs'
            }
            # sequence key is the primary sequence

            # 'auth_chain_id'
            # only change the feature 'aatype' 'template_aatype' 'template_all_atom_masks' 'template_all_atom_mask
            chains_dict[chain_id] = pipeline_multimer.convert_monomer_features(
                chain, chain_id=chain_id
            )

        # for heterodimer (A[""sequence""] != B[""sequence""]), A,B -> A_1,B_1
        chains_dict = pipeline_multimer.add_assembly_features(chains_dict)
        np_example = self.pair_and_merge(chains_dict)
        # np_example = pipeline_multimer.pad_msa(np_example, 256)

        return np_example

    def pair_and_merge(
        self,
        chains_dict: Mapping[str, pipeline.FeatureDict],
    ) -> pipeline.FeatureDict:

        feature_processing.process_unmerged_features(chains_dict)
        chains = list(chains_dict.values())

        # auth_chain_ids = [str(chain['auth_chain_id']) for chain in chains]
        # paired_rows = build_paired_rows(auth_chain_ids, paired_rows_dict)
        # chains = self.create_paired_features(paired_rows, chains)
        for k in range(len(chains)):
            chains[k]['num_alignments_all_seq'] = np.asarray(
                chains[k][""msa_all_seq""].shape[0]
            )

        # chains = msa_pairing.deduplicate_unpaired_sequences(chains)
        chains = feature_processing.crop_chains(
            chains,
            msa_crop_size=feature_processing.MSA_CROP_SIZE,
            max_templates=feature_processing.MAX_TEMPLATES,
            pair_msa_sequences=True,
        )

        np_example = msa_pairing.merge_chain_features(
            np_chains_list=chains,
            pair_msa_sequences=True,
            max_templates=feature_processing.MAX_TEMPLATES,
        )

        np_example = feature_processing.process_final(np_example)
        logger.info(tree.map_structure(lambda x: x.shape, np_example))

        return np_example
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/inference.py",".py","2913","98","import pair_process

""""""AlphaFold protein structure prediction.""""""
import os
import sys
import time
import random
import logging
import argparse
from tqdm import tqdm

import torch
import numpy as np
from alphafold.common import residue_constants
from alphafold.model import model as alphafold_model

from msa_pair.runner import utils
import model_preset_runner

logger = logging.getLogger(__file__)


@torch.no_grad()
def run_alphafold(
    processed_feature_dict,
    model_runner,
    random_seed: int,
    max_num_res: int = -1,
    num_predictions_per_model: int = 1,
):
    num_res = len(processed_feature_dict['aatype'])
    if max_num_res >= 0 and num_res > max_num_res:
        logger.warning(f'Too many residues {num_res}\n')
        return

    all_outputs = model_runner.predict(
        processed_feature_dict,
        rng_seed=random_seed,
        num_predictions_per_model=num_predictions_per_model,
    )

    return all_outputs


""""""for testing""""""
from Bio import SeqIO
from typing import List, Tuple
import itertools
import string
import json

deletekeys = dict.fromkeys(string.ascii_lowercase)
deletekeys["".""] = None
deletekeys[""*""] = None
translation = str.maketrans(deletekeys)

def read_sequence(filename: str) -> Tuple[str, str]:
    """""" Reads the first (reference) sequences from a fasta or MSA file.""""""
    record = next(SeqIO.parse(filename, ""fasta""))
    return record.description, str(record.seq)

def remove_insertions(sequence: str) -> str:
    """""" Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. """"""
    return sequence.translate(translation)

def read_msa(filename: str, nseq: int) -> List[Tuple[str, str]]:
    """""" Reads the first nseq sequences from an MSA file, automatically removes insertions.""""""
    return [(record.description, remove_insertions(str(record.seq)))
            for record in itertools.islice(SeqIO.parse(filename, ""fasta""), nseq)]

def read_pair(filename: str):
    with open(filename) as file:
        json_dict = json.load(file)
        return json_dict[""A""], json_dict[""B""]

if __name__ == ""__main__"":
    input_dir = ""/root/dataset/Old_PLM_MSA/data/5xeq""
    msa_A = read_msa(input_dir + ""/A/uniprot.a3m"", nseq=5000)
    msa_B = read_msa(input_dir + ""/B/uniprot.a3m"", nseq=5000)

    msa_A, msa_B = msa_A[:100], msa_B[:100]
    msa_pair = [(A, B) for A, B in zip(msa_A, msa_B)]
    cur_pipeline = pair_process.PairingPipeline()
    np_example = cur_pipeline.process(msa_pair)
    print(np_example.keys())

    os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
    os.environ['CUDA_VISIBLE_DEVICES'] = ""7""

    model_runner = model_preset_runner.ModelPresetRunner(
        ""/root/dataset/af2_database/"",
        ignore_unpaired_sequences=False,
    )

    num_predictions_per_model = 1
    seed = random.randrange(1, 2**24)
    prediction = run_alphafold(np_example, model_runner, seed, num_predictions_per_model)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/msa_pairing.py",".py","16976","453","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Pairing logic for multimer data pipeline.""""""

import collections
import functools
import string
from typing import Any, Dict, Iterable, List, Sequence

from alphafold.common import residue_constants
from alphafold.data import pipeline
import numpy as np
import pandas as pd
import scipy.linalg
# from alphafold.data import (
#    msa_pairing
# )

MSA_GAP_IDX = residue_constants.restypes_with_x_and_gap.index('-')
SEQUENCE_GAP_CUTOFF = 0.5
SEQUENCE_SIMILARITY_CUTOFF = 0.9

MSA_PAD_VALUES = {'msa_all_seq': MSA_GAP_IDX,
                  'msa_mask_all_seq': 1,
                  'deletion_matrix_all_seq': 0,
                  'deletion_matrix_int_all_seq': 0,
                  'msa': MSA_GAP_IDX,
                  'msa_mask': 1,
                  'deletion_matrix': 0,
                  'deletion_matrix_int': 0}

MSA_FEATURES = ('msa', 'msa_mask', 'deletion_matrix', 'deletion_matrix_int')
SEQ_FEATURES = ('residue_index', 'aatype', 'all_atom_positions',
                'all_atom_mask', 'seq_mask', 'between_segment_residues',
                'has_alt_locations', 'has_hetatoms', 'asym_id', 'entity_id',
                'sym_id', 'entity_mask', 'deletion_mean',
                'prediction_atom_mask',
                'literature_positions', 'atom_indices_to_group_indices',
                'rigid_group_default_frame')
TEMPLATE_FEATURES = ('template_aatype', 'template_all_atom_positions',
                     'template_all_atom_mask')
CHAIN_FEATURES = ('num_alignments', 'seq_length')


def create_paired_features(
    chains: Iterable[pipeline.FeatureDict]) ->  List[pipeline.FeatureDict]:
  """"""Returns the original chains with paired NUM_SEQ features.

  Args:
    chains:  A list of feature dictionaries for each chain.

  Returns:
    A list of feature dictionaries with sequence features including only
    rows to be paired.
  """"""
  chains = list(chains)
  chain_keys = chains[0].keys()

  if len(chains) < 2:
    return chains
  else:
    updated_chains = []
    paired_chains_to_paired_row_indices = pair_sequences(chains)
    paired_rows = reorder_paired_rows(
        paired_chains_to_paired_row_indices)

    for chain_num, chain in enumerate(chains):
      new_chain = {k: v for k, v in chain.items() if '_all_seq' not in k}
      for feature_name in chain_keys:
        if feature_name.endswith('_all_seq'):
          feats_padded = pad_features(chain[feature_name], feature_name)
          new_chain[feature_name] = feats_padded[paired_rows[:, chain_num]]
      new_chain['num_alignments_all_seq'] = np.asarray(
          len(paired_rows[:, chain_num]))
      updated_chains.append(new_chain)
    return updated_chains


def pad_features(feature: np.ndarray, feature_name: str) -> np.ndarray:
  """"""Add a 'padding' row at the end of the features list.

  The padding row will be selected as a 'paired' row in the case of partial
  alignment - for the chain that doesn't have paired alignment.

  Args:
    feature: The feature to be padded.
    feature_name: The name of the feature to be padded.

  Returns:
    The feature with an additional padding row.
  """"""
  assert feature.dtype != np.dtype(np.string_)
  if feature_name in ('msa_all_seq', 'msa_mask_all_seq',
                      'deletion_matrix_all_seq', 'deletion_matrix_int_all_seq'):
    num_res = feature.shape[1]
    padding = MSA_PAD_VALUES[feature_name] * np.ones([1, num_res],
                                                     feature.dtype)
  elif feature_name == 'msa_species_identifiers_all_seq':
    padding = [b'']
  else:
    return feature
  feats_padded = np.concatenate([feature, padding], axis=0)
  return feats_padded


def _make_msa_df(chain_features: pipeline.FeatureDict) -> pd.DataFrame:
  """"""Makes dataframe with msa features needed for msa pairing.""""""
  chain_msa = chain_features['msa_all_seq']
  query_seq = chain_msa[0]
  per_seq_similarity = np.sum(
      query_seq[None] == chain_msa, axis=-1) / float(len(query_seq))
  per_seq_gap = np.sum(chain_msa == 21, axis=-1) / float(len(query_seq))
  msa_df = pd.DataFrame({
      'msa_species_identifiers':
          chain_features['msa_species_identifiers_all_seq'],
      'msa_row':
          np.arange(len(
              chain_features['msa_species_identifiers_all_seq'])),
      'msa_similarity': per_seq_similarity,
      'gap': per_seq_gap
  })
  return msa_df


def _create_species_dict(msa_df: pd.DataFrame) -> Dict[bytes, pd.DataFrame]:
  """"""Creates mapping from species to msa dataframe of that species.""""""
  species_lookup = {}
  for species, species_df in msa_df.groupby('msa_species_identifiers'):
    species_lookup[species] = species_df
  return species_lookup


def _match_rows_by_sequence_similarity(this_species_msa_dfs: List[pd.DataFrame]
                                       ) -> List[List[int]]:
  """"""Finds MSA sequence pairings across chains based on sequence similarity.

  Each chain's MSA sequences are first sorted by their sequence similarity to
  their respective target sequence. The sequences are then paired, starting
  from the sequences most similar to their target sequence.

  Args:
    this_species_msa_dfs: a list of dataframes containing MSA features for
      sequences for a specific species.

  Returns:
   A list of lists, each containing M indices corresponding to paired MSA rows,
   where M is the number of chains.
  """"""
  all_paired_msa_rows = []

  num_seqs = [len(species_df) for species_df in this_species_msa_dfs
              if species_df is not None]
  take_num_seqs = np.min(num_seqs)

  sort_by_similarity = (
      lambda x: x.sort_values('msa_similarity', axis=0, ascending=False))

  for species_df in this_species_msa_dfs:
    if species_df is not None:
      species_df_sorted = sort_by_similarity(species_df)
      msa_rows = species_df_sorted.msa_row.iloc[:take_num_seqs].values
    else:
      msa_rows = [-1] * take_num_seqs  # take the last 'padding' row
    all_paired_msa_rows.append(msa_rows)
  all_paired_msa_rows = list(np.array(all_paired_msa_rows).transpose())
  return all_paired_msa_rows


def pair_sequences(examples: List[pipeline.FeatureDict]
                   ) -> Dict[int, np.ndarray]:
  """"""Returns indices for paired MSA sequences across chains.""""""

  num_examples = len(examples)

  all_chain_species_dict = []
  common_species = set()
  for chain_features in examples:
    msa_df = _make_msa_df(chain_features)
    species_dict = _create_species_dict(msa_df)
    all_chain_species_dict.append(species_dict)
    common_species.update(set(species_dict))

  common_species = sorted(common_species)
  common_species.remove(b'')  # Remove target sequence species.

  all_paired_msa_rows = [np.zeros(len(examples), int)]
  all_paired_msa_rows_dict = {k: [] for k in range(num_examples)}
  all_paired_msa_rows_dict[num_examples] = [np.zeros(len(examples), int)]

  for species in common_species:
    if not species:
      continue
    this_species_msa_dfs = []
    species_dfs_present = 0
    for species_dict in all_chain_species_dict:
      if species in species_dict:
        this_species_msa_dfs.append(species_dict[species])
        species_dfs_present += 1
      else:
        this_species_msa_dfs.append(None)

    # Skip species that are present in only one chain.
    if species_dfs_present <= 1:
      continue

    if np.any(
        np.array([len(species_df) for species_df in
                  this_species_msa_dfs if
                  isinstance(species_df, pd.DataFrame)]) > 600):
      continue

    paired_msa_rows = _match_rows_by_sequence_similarity(this_species_msa_dfs)
    all_paired_msa_rows.extend(paired_msa_rows)
    all_paired_msa_rows_dict[species_dfs_present].extend(paired_msa_rows)
  all_paired_msa_rows_dict = {
      num_examples: np.array(paired_msa_rows) for
      num_examples, paired_msa_rows in all_paired_msa_rows_dict.items()
  }
  return all_paired_msa_rows_dict


def reorder_paired_rows(all_paired_msa_rows_dict: Dict[int, np.ndarray]
                        ) -> np.ndarray:
  """"""Creates a list of indices of paired MSA rows across chains.

  Args:
    all_paired_msa_rows_dict: a mapping from the number of paired chains to the
      paired indices.

  Returns:
    a list of lists, each containing indices of paired MSA rows across chains.
    The paired-index lists are ordered by:
      1) the number of chains in the paired alignment, i.e, all-chain pairings
         will come first.
      2) e-values
  """"""
  all_paired_msa_rows = []

  for num_pairings in sorted(all_paired_msa_rows_dict, reverse=True):
    paired_rows = all_paired_msa_rows_dict[num_pairings]
    paired_rows_product = abs(np.array([np.prod(rows) for rows in paired_rows]))
    paired_rows_sort_index = np.argsort(paired_rows_product)
    all_paired_msa_rows.extend(paired_rows[paired_rows_sort_index])

  return np.array(all_paired_msa_rows)


def block_diag(*arrs: np.ndarray, pad_value: float = 0.0) -> np.ndarray:
  """"""Like scipy.linalg.block_diag but with an optional padding value.""""""
  ones_arrs = [np.ones_like(x) for x in arrs]
  off_diag_mask = 1.0 - scipy.linalg.block_diag(*ones_arrs)
  diag = scipy.linalg.block_diag(*arrs)
  diag += (off_diag_mask * pad_value).astype(diag.dtype)
  return diag


def _correct_post_merged_feats(
    np_example: pipeline.FeatureDict,
    np_chains_list: Sequence[pipeline.FeatureDict],
    pair_msa_sequences: bool) -> pipeline.FeatureDict:
  """"""Adds features that need to be computed/recomputed post merging.""""""

  np_example['seq_length'] = np.asarray(np_example['aatype'].shape[0],
                                        dtype=np.int32)
  np_example['num_alignments'] = np.asarray(np_example['msa'].shape[0],
                                            dtype=np.int32)

  if not pair_msa_sequences:
    # Generate a bias that is 1 for the first row of every block in the
    # block diagonal MSA - i.e. make sure the cluster stack always includes
    # the query sequences for each chain (since the first row is the query
    # sequence).
    cluster_bias_masks = []
    for chain in np_chains_list:
      mask = np.zeros(chain['msa'].shape[0])
      mask[0] = 1
      cluster_bias_masks.append(mask)
    np_example['cluster_bias_mask'] = np.concatenate(cluster_bias_masks)

    # Initialize Bert mask with masked out off diagonals.
    msa_masks = [np.ones(x['msa'].shape, dtype=np.float32)
                 for x in np_chains_list]

    np_example['bert_mask'] = block_diag(
        *msa_masks, pad_value=0)
  else:
    np_example['cluster_bias_mask'] = np.zeros(np_example['msa'].shape[0])
    np_example['cluster_bias_mask'][0] = 1

    if np_example[""msa""].shape == np_example[""msa_all_seq""].shape:
        msa_masks_all_seq = [np.ones(x['msa_all_seq'].shape, dtype=np.float32) for
                                x in np_chains_list]
        msa_mask_all_seq = np.concatenate(msa_masks_all_seq, axis=1)
        np_example['bert_mask'] = msa_mask_all_seq

    else:
        # Initialize Bert mask with masked out off diagonals.
        msa_masks = [np.ones(x['msa'].shape, dtype=np.float32) for
                    x in np_chains_list]
        msa_masks_all_seq = [np.ones(x['msa_all_seq'].shape, dtype=np.float32) for
                            x in np_chains_list]

        msa_mask_block_diag = block_diag(
            *msa_masks, pad_value=0)
        msa_mask_all_seq = np.concatenate(msa_masks_all_seq, axis=1)
        np_example['bert_mask'] = np.concatenate(
            [msa_mask_all_seq, msa_mask_block_diag], axis=0)
  return np_example


def _pad_templates(chains: Sequence[pipeline.FeatureDict],
                   max_templates: int) -> Sequence[pipeline.FeatureDict]:
  """"""For each chain pad the number of templates to a fixed size.

  Args:
    chains: A list of protein chains.
    max_templates: Each chain will be padded to have this many templates.

  Returns:
    The list of chains, updated to have template features padded to
    max_templates.
  """"""
  for chain in chains:
    for k, v in chain.items():
      if k in TEMPLATE_FEATURES:
        padding = np.zeros_like(v.shape)
        padding[0] = max_templates - v.shape[0]
        padding = [(0, p) for p in padding]
        chain[k] = np.pad(v, padding, mode='constant')
  return chains


def _merge_features_from_multiple_chains(
    chains: Sequence[pipeline.FeatureDict],
    pair_msa_sequences: bool) -> pipeline.FeatureDict:
  """"""Merge features from multiple chains.

  Args:
    chains: A list of feature dictionaries that we want to merge.
    pair_msa_sequences: Whether to concatenate MSA features along the
      num_res dimension (if True), or to block diagonalize them (if False).

  Returns:
    A feature dictionary for the merged example.
  """"""
  merged_example = {}
  for feature_name in chains[0]:
    feats = [x[feature_name] for x in chains]
    feature_name_split = feature_name.split('_all_seq')[0]
    if feature_name_split in MSA_FEATURES:
      if pair_msa_sequences or '_all_seq' in feature_name:
        merged_example[feature_name] = np.concatenate(feats, axis=1)
      else:
        merged_example[feature_name] = block_diag(
            *feats, pad_value=MSA_PAD_VALUES[feature_name])
    elif feature_name_split in SEQ_FEATURES:
      merged_example[feature_name] = np.concatenate(feats, axis=0)
    elif feature_name_split in TEMPLATE_FEATURES:
      merged_example[feature_name] = np.concatenate(feats, axis=1)
    elif feature_name_split in CHAIN_FEATURES:
      merged_example[feature_name] = np.sum(x for x in feats).astype(np.int32)
    else:
      merged_example[feature_name] = feats[0]
  return merged_example


def _merge_homomers_dense_msa(
    chains: Iterable[pipeline.FeatureDict]) -> Sequence[pipeline.FeatureDict]:
  """"""Merge all identical chains, making the resulting MSA dense.

  Args:
    chains: An iterable of features for each chain.

  Returns:
    A list of feature dictionaries.  All features with the same entity_id
    will be merged - MSA features will be concatenated along the num_res
    dimension - making them dense.
  """"""
  entity_chains = collections.defaultdict(list)
  for chain in chains:
    entity_id = chain['entity_id'][0]
    entity_chains[entity_id].append(chain)

  grouped_chains = []
  for entity_id in sorted(entity_chains):
    chains = entity_chains[entity_id]
    grouped_chains.append(chains)
  chains = [
      _merge_features_from_multiple_chains(chains, pair_msa_sequences=True)
      for chains in grouped_chains]
  return chains


def _concatenate_paired_and_unpaired_features(
    example: pipeline.FeatureDict) -> pipeline.FeatureDict:
    """"""Merges paired and block-diagonalised features.""""""
    features = MSA_FEATURES
    for feature_name in features:
        if feature_name in example:
            feat = example[feature_name]
            feat_all_seq = example[feature_name + '_all_seq']
            merged_feat = np.concatenate([feat_all_seq, feat], axis=0)
            example[feature_name] = merged_feat
        elif feature_name + ""_all_seq"" in example:
            feat_all_seq = example[feature_name + '_all_seq']
            example[feature_name] = feat_all_seq
    example['num_alignments'] = np.array(example['msa'].shape[0],
                                        dtype=np.int32)
    return example


def merge_chain_features(np_chains_list: List[pipeline.FeatureDict],
                         pair_msa_sequences: bool,
                         max_templates: int) -> pipeline.FeatureDict:
    """"""Merges features for multiple chains to single FeatureDict.

    Args:
        np_chains_list: List of FeatureDicts for each chain.
        pair_msa_sequences: Whether to merge paired MSAs.
        max_templates: The maximum number of templates to include.

    Returns:
        Single FeatureDict for entire complex.
    """"""
    np_chains_list = _pad_templates(
        np_chains_list, max_templates=max_templates)
    np_chains_list = _merge_homomers_dense_msa(np_chains_list)
    # Unpaired MSA features will be always block-diagonalised; paired MSA
    # features will be concatenated.
    np_example = _merge_features_from_multiple_chains(
        np_chains_list, pair_msa_sequences=False)
    if pair_msa_sequences:
        np_example = _concatenate_paired_and_unpaired_features(np_example)
    np_example = _correct_post_merged_feats(
        np_example=np_example,
        np_chains_list=np_chains_list,
        pair_msa_sequences=pair_msa_sequences)

    return np_example

","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/evo_pipeline/feature_processing.py",".py","6331","168","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Feature processing logic for multimer data pipeline.""""""

from typing import Iterable, MutableMapping, List

from alphafold.common import residue_constants
from alphafold.data import msa_pairing
from alphafold.data import pipeline
import numpy as np

REQUIRED_FEATURES = frozenset({
    'aatype', 'all_atom_mask', 'all_atom_positions', 'all_chains_entity_ids',
    'all_crops_all_chains_mask', 'all_crops_all_chains_positions',
    'all_crops_all_chains_residue_ids', 'assembly_num_chains', 'asym_id',
    'bert_mask', 'cluster_bias_mask', 'deletion_matrix', 'deletion_mean',
    'entity_id', 'entity_mask', 'mem_peak', 'msa', 'msa_mask', 'num_alignments',
    'num_templates', 'queue_size', 'residue_index', 'resolution',
    'seq_length', 'seq_mask', 'sym_id', 'template_aatype',
    'template_all_atom_mask', 'template_all_atom_positions'
})

MAX_TEMPLATES = 4
MSA_CROP_SIZE = 2048


def crop_chains(
    chains_list: List[pipeline.FeatureDict],
    msa_crop_size: int,
    pair_msa_sequences: bool,
    max_templates: int) -> List[pipeline.FeatureDict]:
  """"""Crops the MSAs for a set of chains.

  Args:
    chains_list: A list of chains to be cropped.
    msa_crop_size: The total number of sequences to crop from the MSA.
    pair_msa_sequences: Whether we are operating in sequence-pairing mode.
    max_templates: The maximum templates to use per chain.

  Returns:
    The chains cropped.
  """"""

  # Apply the cropping.
  cropped_chains = []
  for chain in chains_list:
    cropped_chain = _crop_single_chain(
        chain,
        msa_crop_size=msa_crop_size,
        pair_msa_sequences=pair_msa_sequences,
        max_templates=max_templates)
    cropped_chains.append(cropped_chain)

  return cropped_chains


def _crop_single_chain(chain: pipeline.FeatureDict,
                       msa_crop_size: int,
                       pair_msa_sequences: bool,
                       max_templates: int) -> pipeline.FeatureDict:
    """"""Crops msa sequences to `msa_crop_size`.""""""
    msa_size_all_seq = chain['num_alignments_all_seq']
    msa_crop_size_all_seq = np.minimum(msa_size_all_seq, msa_crop_size // 2)

    include_templates = 'template_aatype' in chain and max_templates
    if include_templates:
        num_templates = chain['template_aatype'].shape[0]
        templates_crop_size = np.minimum(num_templates, max_templates)

    for k in chain:
        k_split = k.split('_all_seq')[0]
        if k_split in msa_pairing.TEMPLATE_FEATURES:
            chain[k] = chain[k][:templates_crop_size, :]
        elif k_split in msa_pairing.MSA_FEATURES:
            # if '_all_seq' in k and pair_msa_sequences:
            chain[k] = chain[k][:msa_crop_size_all_seq, :]

    chain['num_alignments'] = np.asarray(msa_crop_size, dtype=np.int32)
    if include_templates:
        chain['num_templates'] = np.asarray(templates_crop_size, dtype=np.int32)
    if pair_msa_sequences:
        chain['num_alignments_all_seq'] = np.asarray(
            msa_crop_size_all_seq, dtype=np.int32)
    return chain


def process_final(np_example: pipeline.FeatureDict) -> pipeline.FeatureDict:
    """"""Final processing steps in data pipeline, after merging and pairing.""""""
    np_example = _correct_msa_restypes(np_example)
    np_example = _make_seq_mask(np_example)
    np_example = _make_msa_mask(np_example)
    np_example = _filter_features(np_example)
    return np_example


def _correct_msa_restypes(np_example):
  """"""Correct MSA restype to have the same order as residue_constants.""""""
  new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
  np_example['msa'] = np.take(new_order_list, np_example['msa'], axis=0)
  np_example['msa'] = np_example['msa'].astype(np.int32)
  return np_example


def _make_seq_mask(np_example):
  np_example['seq_mask'] = (np_example['entity_id'] > 0).astype(np.float32)
  return np_example


def _make_msa_mask(np_example):
  """"""Mask features are all ones, but will later be zero-padded.""""""

  np_example['msa_mask'] = np.ones_like(np_example['msa'], dtype=np.float32)

  seq_mask = (np_example['entity_id'] > 0).astype(np.float32)
  np_example['msa_mask'] *= seq_mask[None]

  return np_example


def _filter_features(np_example: pipeline.FeatureDict) -> pipeline.FeatureDict:
  """"""Filters features of example to only those requested.""""""
  return {k: v for (k, v) in np_example.items() if k in REQUIRED_FEATURES}


def process_unmerged_features(
    all_chain_features: MutableMapping[str, pipeline.FeatureDict]):
  """"""Postprocessing stage for per-chain features before merging.""""""
  num_chains = len(all_chain_features)
  for chain_features in all_chain_features.values():
    
    if 'deletion_matrix_int' in chain_features:
        # Convert deletion matrices to float.
        chain_features['deletion_matrix'] = np.asarray(
            chain_features.pop('deletion_matrix_int'), dtype=np.float32)
        chain_features['deletion_mean'] = np.mean(
            chain_features['deletion_matrix'], axis=0)

    if 'deletion_matrix_int_all_seq' in chain_features:
      chain_features['deletion_matrix_all_seq'] = np.asarray(
          chain_features.pop('deletion_matrix_int_all_seq'), dtype=np.float32)

    # Add all_atom_mask and dummy all_atom_positions based on aatype.
    all_atom_mask = residue_constants.STANDARD_ATOM_MASK[
        chain_features['aatype']]
    chain_features['all_atom_mask'] = all_atom_mask
    chain_features['all_atom_positions'] = np.zeros(
        list(all_atom_mask.shape) + [3])

    # Add assembly_num_chains.
    chain_features['assembly_num_chains'] = np.asarray(num_chains)

  # Add entity_mask.
  for chain_features in all_chain_features.values():
    chain_features['entity_mask'] = (
        chain_features['entity_id'] != 0).astype(np.int32)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/ena/ena_pairing.py",".py","13116","381","import os
import json
import math
import string
import logging
import functools
import itertools
from typing import List, Any, Callable, Mapping, Set
from collections import defaultdict

from tqdm import tqdm
from alphafold.data import msa_identifiers, parsers

# from repair.msa import msa_df

logger = logging.getLogger(__file__)

def get_wgs_loci_path(ena_repo, wgs_name,):
    for name in [wgs_name, wgs_name[:6]]:
        path = os.path.join(ena_repo, f'{name}.json')
        if os.path.exists(path):
            return path
     
class EnaPairing:
    def __init__(self, targets_dirs: List[str], ena_repo=None):
        self.targets_dict = self._get_targets_lookup(targets_dirs)
        self.targets_lookup = {}
        self.ena_repo = ena_repo

    def lookup_loci(self, set_name):
        path = get_wgs_loci_path(self.ena_repo, set_name)
        if path is not None:
            with open(path) as fh:
                all_locus = json.load(fh)
            for name, locus in all_locus.items():
                name = name.split('.')[0]
                if name == set_name:
                    return {
                        loci_name.split('.')[0]: loc for loci_name, loc in 
                        locus.items()
                    }

    def _get_targets_lookup(self, targets_dirs):
        targets_dict = defaultdict(list)
        for targets_dir in targets_dirs:
            for filename in os.listdir(targets_dir):
                shortname = filename.split('.')[0].split('/')[-1]
                targets_dict[shortname].append(
                    os.path.join(targets_dir, filename)
                )
        return targets_dict


    def lookup_target(self, ac):
        shortname = ac[3:6]
        if shortname not in self.targets_dict:
            return

        # load target
        if shortname not in self.targets_lookup:
            self.targets_lookup[shortname] = {}
            for p in self.targets_dict[shortname]:
                with open(p) as fh:
                    targets_ = json.load(fh)
            self.targets_lookup[shortname].update(targets_)
        else:
            targets_ = self.targets_lookup[shortname]

        if ac not in targets_:
            return

        return targets_[ac]


    def read_msa(self, src_path):
        with open(src_path) as fh:
            a3m_str = fh.read()
        msa = parsers.parse_a3m(a3m_str)
        return msa


    def parse_paired_accessions(self, input_dir, chain_ids):
        msas_dict = {}
        for chain_id in chain_ids:
            msa = self.read_msa(
                os.path.join(input_dir, chain_id, 'uniprot.a3m')
            )
            msas_dict[chain_id] = msa

        grouped_paired_acs = defaultdict(lambda :defaultdict(set))
        ac_to_row = defaultdict(dict)
        ac_to_cds = {}
        for chain_id, msa in msas_dict.items():
            for r, desc in tqdm(list(enumerate(msa.descriptions))):
            # for r, desc in enumerate(msa.descriptions):
                if r == 0:
                    continue
                identifiers = msa_identifiers._extract_sequence_identifier(
                    desc
                ).split('|')
                ac = identifiers[1]
                ac_to_row[chain_id][ac] = r
                targets = self.lookup_target(ac)
                if targets is None:
                    continue
                set_names = targets['EMBL'].split(',')
                for set_name in set_names:
                    grouped_paired_acs[set_name][chain_id].add(ac)
                cds_names = targets['EMBL-CDS']
                cds_names = [_.split('.')[0] for _ in cds_names.split(',')]
                ac_to_cds[ac] = cds_names
        # clear targets
        self.targets_lookup = {}
        # filter unpaired
        for target in list(grouped_paired_acs.keys()):
            paired_acs = grouped_paired_acs[target]
            if len(paired_acs) <= 1:
                del grouped_paired_acs[target]

        return grouped_paired_acs, ac_to_row, ac_to_cds


    def find_paired_accessions(
        self,
        grouped_paired_acs: Mapping[str, Mapping[str, Set[str]]],
        ac_to_row: Mapping[str, Mapping[str, int]],
        ac_to_cds: Mapping[str, str],
        chain_ids: List[str],
        find_matches: Callable,
    ):
        # find rows
        paired_rows = {chain_id: [] for chain_id in chain_ids}
        request_paired_acs = {}
        request_ac_to_cds = {}
        for target, paired_acs in grouped_paired_acs.items():
            accession_id_lists = [
                list(paired_acs[chain_id]) for chain_id in chain_ids
            ]
            accession_tuples = find_matches(accession_id_lists)
            # update paired rows
            for paired_ac in accession_tuples:
                for i, (chain_id, ac) in enumerate(zip(chain_ids, paired_ac)):
                    paired_rows[chain_id].append(ac_to_row[chain_id][ac])
                    if ac in accession_id_lists[i]:
                        accession_id_lists[i].remove(ac)
            # update paired acs
            paired_acs = {
                chain_id: ids for chain_id, ids in
                zip(chain_ids, accession_id_lists) if len(ids) > 0
            }
            if len(paired_acs) > 1:
                request_paired_acs[target] = paired_acs
                for acs in paired_acs.values():
                    for ac in acs:
                        request_ac_to_cds[ac] = ac_to_cds[ac]

        return request_paired_acs, request_ac_to_cds, paired_rows


    def pair_rows(self, input_dir):
        chain_ids = ['A', 'B']
        grouped_paired_acs, ac_to_row, ac_to_cds = self.parse_paired_accessions(
            input_dir, chain_ids,
        )

        request_paired_acs, request_ac_to_cds, paired_rows = \
            self.find_paired_accessions(
                grouped_paired_acs,
                ac_to_row,
                ac_to_cds,
                chain_ids,
                _find_all_exact_accession_matches,
                # _find_all_accession_matches,
            )

        def get_num_rows(chain_id=chain_ids[0]) -> int:
            return len(paired_rows[chain_id])

        logger.info(f'Before loci pairing: {input_dir} {get_num_rows()}')

        def calc_locus_diff(id_a, id_b, wgs) -> int:
            v_a = [wgs[_] for _ in ac_to_cds[id_a] if _ in wgs]
            v_b = [wgs[_] for _ in ac_to_cds[id_b] if _ in wgs]
            if len(v_a) == 0 or len(v_b) == 0: 
                return math.inf
            return min(abs(a[0] - b[0]) for a, b in itertools.product(v_a, v_b))


        # wgs_names = tqdm(list(request_paired_acs.keys()))
        wgs_names = request_paired_acs.keys()
        for wgs_name in wgs_names:
            paired_acs = request_paired_acs[wgs_name]
            wgs = self.lookup_loci(wgs_name)
            if not wgs:
                continue
            wgs_paired_acs = {} 
            for chain_id, acs in paired_acs.items():
                acs = [ac for ac in acs if ac in ac_to_cds]
                if len(acs) > 0:
                    wgs_paired_acs[chain_id] = acs
            if len(wgs_paired_acs) <= 1:
                continue

            wgs_paired_acs = {wgs_name: wgs_paired_acs}
            _find_all_locus_matches = functools.partial(
                _find_all_accession_matches,
                diff_cutoff=20,
                _calc_id_diff=lambda a, b: calc_locus_diff(a, b, wgs),
            )
            _, _, wgs_paired_rows = self.find_paired_accessions(
                wgs_paired_acs,
                ac_to_row,
                ac_to_cds,
                chain_ids,
                _find_all_locus_matches,
            )
            for chain_id in paired_rows:
                paired_rows[chain_id] += wgs_paired_rows[chain_id]

        assert all(
            get_num_rows(chain_ids[i]) == get_num_rows() for i in 
            range(1,len(paired_rows))
        )

        logger.info(f'After loci pairing: {input_dir} {get_num_rows()}')

        return paired_rows

    def export_paired_accessions(self, input_dir):
        chain_ids = ['A', 'B']
        grouped_paired_acs, ac_to_row, ac_to_cds = self.parse_paired_accessions(
            input_dir, chain_ids
        )
        request_paired_acs, request_ac_to_cds, paired_rows = \
            self.find_paired_accessions(
                grouped_paired_acs,
                ac_to_row,
                ac_to_cds,
                chain_ids,
                _find_all_exact_accession_matches,
            )

        def get_num_rows(chain_id=chain_ids[0]) -> int:
            return len(paired_rows[chain_id])

        logger.info(f'{get_num_rows()}')

        format_paired_acs = {}
        for target, paired_acs in request_paired_acs.items():
            format_paired_acs[target] = {
                chain_id: ','.join(acs) for chain_id, acs in paired_acs.items()
            }
        logger.warning(len(request_paired_acs))

        return {
            'uniprot_accessions': format_paired_acs,
            'embl_cds': request_ac_to_cds,
        }


""""""From AlphaFold-v2.1.2
""""""
ALPHA_ACCESSION_ID_MAP = {x: y for y, x in enumerate(string.ascii_uppercase)}
ALPHANUM_ACCESSION_ID_MAP = {
    chr: num for num, chr in enumerate(string.ascii_uppercase + string.digits)
}  # A-Z,0-9
NUM_ACCESSION_ID_MAP = {str(x): x for x in range(10)}

@functools.lru_cache(maxsize=65536)
def encode_accession(accession_id: str) -> int:
    """"""Map accession codes to the serial order in which they were assigned.""""""
    alpha = ALPHA_ACCESSION_ID_MAP        # A-Z
    alphanum = ALPHANUM_ACCESSION_ID_MAP  # A-Z,0-9
    num = NUM_ACCESSION_ID_MAP            # 0-9

    coding = 0

    # This is based on the uniprot accession id format
    # https://www.uniprot.org/help/accession_numbers
    if accession_id[0] in {'O', 'P', 'Q'}:
        bases = (alpha, num, alphanum, alphanum, alphanum, num)
    elif len(accession_id) == 6:
        bases = (alpha, num, alpha, alphanum, alphanum, num)
    elif len(accession_id) == 10:
        bases = (
            alpha, num, alpha, alphanum, alphanum, num, alpha, alphanum,
            alphanum, num
        )

    product = 1
    for place, base in zip(reversed(accession_id), reversed(bases)):
        coding += base[place] * product
        product *= len(base)

    return coding


def calc_accession_diff(id_a, id_b) -> int:
    return abs(encode_accession(id_a) - encode_accession(id_b))

def _find_all_accession_matches(
    accession_id_lists: List[List[str]],
    diff_cutoff: int = 20, # **strictly** less than diff_cutoff
    _calc_id_diff: Callable = calc_accession_diff,
) -> List[List[Any]]:
    """"""Finds accession id matches across the chains based on their difference.
    """"""
    all_accession_tuples = []
    current_tuple = []
    tokens_used_in_answer = set()

    def _matches_all_in_current_tuple(inp, diff_cutoff) -> bool:
        return all((_calc_id_diff(s, inp) < diff_cutoff for s in current_tuple))

    def _all_tokens_not_used_before() -> bool:
        return all((s not in tokens_used_in_answer for s in current_tuple))

    def dfs(level, accession_id, diff_cutoff=diff_cutoff) -> None:
        if level == len(accession_id_lists) - 1:
            if _all_tokens_not_used_before():
                all_accession_tuples.append(list(current_tuple))
                for s in current_tuple:
                    tokens_used_in_answer.add(s)
            return

        if level == -1:
            new_list = accession_id_lists[level+1]
        else:
            new_list = [
                (_calc_id_diff(accession_id, s), s) for s in 
                accession_id_lists[level+1]
            ]
            new_list = sorted(new_list)
            new_list = [s for d, s in new_list]

        for s in new_list:
            if (
                _matches_all_in_current_tuple(s, diff_cutoff)
                and s not in tokens_used_in_answer
            ):
                current_tuple.append(s)
                dfs(level + 1, s)
                current_tuple.pop()

    dfs(-1, '')

    return all_accession_tuples

def _find_all_exact_accession_matches(accession_id_lists):
    return _find_all_accession_matches(
        accession_id_lists,
        diff_cutoff=1,
        _calc_id_diff=calc_accession_diff,
    )

def filter_names(names, include_file):
    included_names = set()
    with open(include_file) as fh:
        for line in fh:
            line = line.strip()
            if line:
                name = line.split()[0]
                included_names.add(name)

    return [name for name in names if name in included_names]


def batch_process(names, input_root, use_tqdm):
    if use_tqdm:
        names = tqdm(names)
    for name in names:
        ena_pairing = EnaPairing(['idmappings'], 'ena_repo')
        # logger.info(f'Start pairing {name}')
        sub_dir = os.path.join(input_root, name)
        pr_path = os.path.join(sub_dir, 'ena_pr.json')
        if os.path.exists(pr_path):
            continue 
        paired_rows = ena_pairing.pair_rows(sub_dir)
        with open(pr_path, 'wt') as fh:
            json.dump(paired_rows, fh, indent=2)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/ena/ena_downloader.py",".py","8913","307","import os
import re
import gzip
import json
import time
import ftplib
import logging
import functools
import itertools
import urllib.request
import multiprocessing as mp
from typing import Mapping, List, Any
from collections import defaultdict, OrderedDict

from tqdm import tqdm

from repair.ena import ena_pairing

EMBL_EXT = '.cds.gz'
FTP_BASE = 'ftp.ebi.ac.uk'
EMBL_VIEW_URL_BASE = 'https://www.ebi.ac.uk/ena/browser/api/embl'
BASE = '/pub/databases/ena/coding'
SET_TYPES = ['wgs', 'tsa', 'tls']
STATUSES = ['public', 'suppressed']

EMBL_WORDS = { 'ID', 'PA', 'OS', 'FT', '//' }

logger = logging.getLogger(__file__)


def get_basename(filename, stem=False):
    filename = filename.split('/')[-1]
    if stem:
        filename = filename.split('.')[0]
    return filename

def format_wgs_url(
    accession: str,
    prefices: List[str] = ['wgs/public', 'wgs/suppressed'],
):
    sub_dir = accession[:3].lower()
    filename = f'{accession[:6]}{EMBL_EXT}'
    for prefix in prefices:
        url = (
            f'https://{FTP_BASE}/{BASE.strip(""/"")}/{prefix}/{sub_dir}/{filename}'
        )
        try:
            response = urllib.request.urlopen(url)
        except Exception as e:
            continue
        return url

def format_sequence_url(accession):
    url = f'{EMBL_VIEW_URL_BASE}/{accession}'
    try:
        response = urllib.request.urlopen(url)
        if url == response.url: # ""sequence"" target
            return url
        elif not response.url.endswith('dat.gz'):
            print(url, response.url)
    except Exception as e:
        return

def batch_get_urls(is_master, acs):
    # if is_master:
    #     acs = tqdm(acs)
    urls = []
    for ac in acs:
        for g in [format_wgs_url, format_sequence_url]:
            url = g(ac)
            if url is not None:
                break
        else:
            logger.info(f'[W] Cannot find exact match to {ac} to download.')
        if url is not None:
            urls.append(url)
    return urls


def retrieve_url(url, dst_file):
    try:
        urllib.request.urlretrieve(url, dst_file)
        ret_code = 0
    except Exception as e:
        logger.error(f'[E] {e}')
        ret_code = -1

    return ret_code

def download_record(url: str, dst_dir: str):
    dst_dat_file = os.path.join(dst_dir, get_basename(url))
    ret_code = retrieve_url(url, dst_dat_file)
    if ret_code == 0:
        return dst_dat_file


START_SEGMENT_PATTERN = re.compile(""<(\d+)\.\.>?\d+"")
END_SEGMENT_PATTERN = re.compile(""<?\d+\.\.>(\d+)"")
SEGMENT_PATTERN = re.compile(""<?(\d+)\.\.>?(\d+)"")


def batch_download_and_parse(
    is_master: bool, urls: List[Any], dst_dir: str
):
    # if is_master:
    #     urls = tqdm(urls)

    for url in urls:
        _download_and_parse(url, dst_dir, remove_dat_file=True)


def _download_and_parse(
    url: str,
    dst_dir: str,
    remove_dat_file: bool = False,
):
    dst_file = os.path.join(
        dst_dir,
        os.path.basename(url).split('.')[0] + '.json'
    )
    if os.path.exists(dst_file):
        return
    dst_dat_file = download_record(url, dst_dir,)
    if dst_dat_file is None:
        logger.warning(f'[I] No exact match to {url} can be downloaded')
        return

    if os.path.exists(dst_dat_file):
        if dst_dat_file.endswith('.gz'):
            try:
                with gzip.open(dst_dat_file, 'rb') as fh:
                    src_str = fh.read().decode()
            except Exception as e:
                print(url, e)
                return -1
            cds_sorted = parse_wgs_cds(src_str)
        else:
            with open(dst_dat_file, 'rt') as fh:
                src_str = fh.read()
            cds_sorted = parse_sequence_cds(src_str)

        with open(dst_file, 'wt') as fh:
            json.dump(cds_sorted, fh, indent=2, sort_keys=False)

    if remove_dat_file:
        if os.path.exists(dst_dat_file):
            os.remove(dst_dat_file)


def parse_cds(value):
    start, end = None, None
    m = re.search(START_SEGMENT_PATTERN, value['CDS'])
    if m is not None:
        start = int(m.group(1))

    m = re.search(END_SEGMENT_PATTERN, value['CDS'])
    if m is not None:
        end = int(m.group(1))

    if start is None or end is None:
        m = re.findall(SEGMENT_PATTERN, value['CDS'])
        start = int(m[0][0])
        end = int(m[-1][-1])

    return start, end


def parse_sequence_cds(src_str: str):
    cds_data = defaultdict(dict)
    all_cds = []
    cds_flag = False
    sequence_id = None
    for line in src_str.split('\n'):
        line = line.strip()
        prefix = line[:2]
        fields = line.split()
        if prefix == 'ID':
            sequence_id = fields[1].split(';')[0].strip()
        elif prefix == 'FT':
            if fields[1] == 'CDS' and line.startswith(""FT   CDS""):
                all_cds.append({})
                all_cds[-1]['CDS'] = ''
                cds_flag = True
            elif fields[1].startswith('/'):
                cds_flag = False
                if fields[1].startswith('/protein_id'):
                    cds_id = fields[1].split('=')[-1].strip('""')
                    all_cds[-1]['ID'] = cds_id

            if cds_flag:
                all_cds[-1]['CDS'] += fields[-1]

    assert sequence_id is not None
    grouped_cds = {sequence_id: []}
    for value in all_cds:
        try:
            if 'ID' not in value:
                continue
            cds_id = value['ID']
            start, end = parse_cds(value)
            grouped_cds[sequence_id].append((cds_id, start, end))
        except Exception:
            raise ValueError(f'Cannot parse {m}\n : {value}\n')

    cds_sorted = OrderedDict()
    cds_list = grouped_cds[sequence_id]
    if len(cds_list) > 1:
        cds_sorted[sequence_id] = {
            v[0]: (i,) + v[1:] for i, v in 
            enumerate(sorted(cds_list, key=lambda v: v[1]))
        }

    return cds_sorted


def parse_wgs_cds(src_str: str, dst_file: str = None):
    cds_data = defaultdict(dict)
    cds_flag = False
    cds_id = None
    for line in src_str.split('\n'):
        line = line.strip()
        prefix = line[:2]
        fields = line.split()
        if prefix == 'ID':
            assert not cds_flag
            cds_id = fields[1].split(';')[0].strip()
            cds_data[cds_id] = {}
        elif prefix == 'PA':
            assert cds_id is not None
            cds_data[cds_id]['PA'] = fields[1].strip(';')
        elif prefix == 'FT':
            assert cds_id is not None
            if fields[1] == 'CDS' and line.startswith(""FT   CDS""):
                cds_flag = True
                cds_data[cds_id]['CDS'] = ''
            elif fields[1].startswith('/'):
                cds_flag = False

            if cds_flag:
                cds_data[cds_id]['CDS'] += fields[-1]
        elif prefix == '//':
            assert cds_id is not None
            cds_id = None

    grouped_cds = defaultdict(list)
    for cds_id, value in cds_data.items():
        try:
            set_id = value['PA']
            start, end = parse_cds(value)
            grouped_cds[set_id].append((cds_id, start, end))
        except Exception:
            raise ValueError(f'Cannot parse {m}\n{set_id}, {cds_id}: {value}\n')

    cds_sorted = OrderedDict()
    for set_id in sorted(grouped_cds.keys()):
        cds_list = grouped_cds[set_id]
        if len(cds_list) > 1:
            cds_sorted[set_id] = {
                v[0]: (i,) + v[1:] for i, v in 
                enumerate(sorted(cds_list, key=lambda v: v[1]))
            }

    return cds_sorted


def download_requests(src_file: str, dst_repo: str, max_ncpu: int = 20):
    with open(src_file) as fh:
        wgs_acs = json.load(fh)['uniprot_accessions']

    acs = []
    for ac in wgs_acs:
        path = ena_pairing.get_wgs_loci_path(dst_repo, ac)
        if path is None:
            acs.append(ac)

    ncpu = min(len(acs), max_ncpu)
    if ncpu > 1:
        urls = set()
        with mp.Pool(ncpu) as pool:
            results = []
            for i in range(ncpu):
                acs_ = [acs[j] for j in range(i, len(acs), ncpu)]
                result = pool.apply_async(
                    batch_get_urls, (i==0, acs_),
                )
                results.append(result)

            for result in results:
                urls.update(result.get())
    else:
        urls = batch_get_urls(True, acs)

    logger.warning(f'{len(urls)} to be downloaded')
    ncpu = min(len(urls), max_ncpu)
    if ncpu > 1:
        with mp.Pool(ncpu) as pool:
            results = []
            for i in range(ncpu):
                urls_ = [urls[j] for j in range(i, len(urls), ncpu)]
                result = pool.apply_async(
                    batch_download_and_parse, (i==0, urls_, dst_repo),
                )
                results.append(result)
            [ result.get() for result in results ]
    else:
        batch_download_and_parse(True, urls, dst_repo)
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/data/ena/ena_idmapping_parsing.py",".py","2807","94","import os
import json
import mmap
import time
import logging
from collections import defaultdict

from tqdm import tqdm

logger = logging.getLogger(__file__)


def parse_idmapping_file(
    idmapping_file: str,
    dst_file: str = None,
    use_mmap: bool = False,
):
    accessions = {}
    t0 = time.time()
    # Read accessions
    with open(idmapping_file) as fh:
        if use_mmap:
            mm = mmap.mmap(fh.fileno(), 0, prot=mmap.PROT_READ)
            lines = [line.decode() for line in mm.read().split(b'\n')]
            progress = tqdm(lines)
        else:
            progress = tqdm(fh)
        for line in iterator:
            line = line.strip()
            fields = line.split()
            accession = fields[0]
            key = fields[1]
            target = fields[2]
            if key in ('EMBL', 'EMBL-CDS',):
                if accession not in accessions:
                    accessions[accession] = {
                        'EMBL': [],
                        'EMBL-CDS': [],
                    }
                accessions[accession][key].append(target)

    t1 = time.time()

    if t1 - t0 > 3*60:
        logger.warning(
            f'[W] Read {len(accessions)} accessions in {t1-t0:.2f}s'
        )

    return accessions


def split_src_file(accessions, dst_dir: str):
    os.makedirs(dst_dir, exist_ok=True)
    split_accessions = defaultdict(dict)
    ac_keys = list(accessions.keys())
    for ac in tqdm(ac_keys):
        ac_dict = accessions[ac]
        split_accessions[ac[3:6]][ac] = {
            k: ','.join(vs) for k, vs in ac_dict.items()
        }

    logger.warning(len(split_accessions))
    prefices = list(split_accessions.keys())
    for prefix in tqdm(prefices):
        sub_accessions = split_accessions[prefix]
        output_path = os.path.join(dst_dir, f'{prefix}.json')
        with open(output_path, 'wt') as fh:
            json.dump(sub_accessions, fh, indent=2)


def update_legacy(src_dir: str):
    for src_filename in tqdm(os.listdir(src_dir)):
        src_path = os.path.join(src_dir, src_filename)
        if not src_filename.endswith('.json'):
            continue
        with open(src_path, 'rt') as fh:
            src_data = json.load(fh)
        dst_data = {}
        for key, values in src_data.items():
            if isinstance(values, str):
                dst_data[key] = values
                continue
            values = sorted([v['EMBL'] for v in values if 'EMBL' in v])
            if len(values) > 0:
                dst_data[key] = ','.join(values)
        with open(src_path, 'wt') as fh:
            src_data = json.dump(dst_data, fh, indent=2)

if __name__ == '__main__':
    import sys
    parser = IdMappingParser()
    accessions = parse_idmapping_file(sys.argv[1])
    split_src_file(accessions, sys.argv[2])
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/assess/multimer_assess.py",".py","11764","345","import os
import io
import sys
import copy
import json
import math
import logging
import argparse
import tempfile
import itertools
import subprocess
from typing import Mapping, Any
from collections import defaultdict

import numpy as np
from Bio.PDB.Model import Model
from Bio.PDB.Chain import Chain
from Bio.PDB.PDBIO import PDBIO
from Bio.PDB.PDBParser import PDBParser
from alphafold.common import residue_constants, protein

from msa_pair.common import sequence_utils, pdb_utils

logger = logging.getLogger(__file__)


def _decode_binary_output(output):
    msg = []
    for line in output.decode().split('\n'):
        msg.append(line)
    return '\n'.join(msg)

class MultimerAssess:
    def __init__(self, dockq_binary_path):
        self.dockq_binary_path = dockq_binary_path
        assert os.path.exists(self.dockq_binary_path)

    @classmethod
    def add_args(cls, parser):
        group = parser.add_argument_group('AlphaFold Evaluation')
        group.add_argument('--query-pdb', type=str)
        group.add_argument('--truth-pdb', type=str)

    def _dockq(
        self, query_pdb: str, truth_pdb: str, receptor_chain: str
    ) -> Mapping[str, Any]:
        cmd_paths = [query_pdb, truth_pdb]
        cmd = (
            [self.dockq_binary_path] + 
            cmd_paths +
            [
                '-native_chain1', receptor_chain, 
                '-model_chain1', receptor_chain, '-no_needle'
            ]
        )
        # print(cmd)
        # exit()
        process = subprocess.Popen(
            cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
        )
        output, stderr = process.communicate()
        retcode = process.wait()
        if retcode:
            msg = _decode_binary_output(stderr)
            if 'length of native is zero' in msg:
                logger.info(
                   ""###########################                           \n""
                   ""Error during running DockQ:                           \n""
                   f""{msg.strip()}                                        \n""
                   ""--------------------------                            \n""
                   ""the reason may be that the common_interface defined in\n"" 
                   ""DockQ is empty                                        \n""
                   ""##########################                              ""
                )
            else:
                raise RuntimeError(msg)

        def _get_score(line):
            return float(line.split()[1])

        result = {}
        lines = []
        # print(output)
        for line in output.decode().split('\n'):
            if line.startswith('Fnat'):
                result['fnat'] = _get_score(line)
            elif line.startswith('Fnonnat'):
                result['fnonnat'] = _get_score(line)
            elif line.startswith('iRMS'):
                result['irms'] = _get_score(line)
            elif line.startswith('LRMS'):
                result['lrms'] = _get_score(line)
            elif line.startswith('DockQ'):
                result['dockq'] = _get_score(line)
            lines.append(line)

        if 'dockq' not in result:
            result = None
            logger.warning(cmd + lines)

        return result


    def align_residue_index(
        self, query_pdb: str, truth_pdb: str, output_pdb_dir: str, suffix: str
    ):
        query_chains = pdb_utils.from_pdb_string(
            pdb_utils.read_pdb_string(query_pdb)
        )
        if len(query_chains) <= 1:
            raise ValueError(query_pdb)

        truth_chains = pdb_utils.from_pdb_string(
            pdb_utils.read_pdb_string(truth_pdb)
        )
        if len(truth_chains) <= 1:
            return

        # Align every chains
        alns = defaultdict(dict)
        alns_score = defaultdict(dict)
        for qchain in query_chains.keys():
            query_seq = sequence_utils.aatype_to_sequence(
                query_chains[qchain].aatype
            )
            for tchain in truth_chains.keys():
                truth_seq = sequence_utils.aatype_to_sequence(
                    truth_chains[tchain].aatype
                )
                aln, qidx, tidx = sequence_utils.global_align(
                    query_seq, truth_seq
                )
                if aln is None or aln.score < 5:
                    continue
                alns[qchain][tchain] = (aln.score, qidx, tidx)
                alns_score[qchain][tchain] = aln.score

        if len(alns_score) < 2:
            logger.info(
                f'Cannot align {query_pdb} to {truth_pdb}: {alns_score}'
            )
            return

        # Find unambiguous assignments
        final_assignment = defaultdict(dict)
        for qchain in list(alns):
            if len(alns[qchain]) == 1:
                for tchain in alns[qchain]:
                    if tchain in final_assignment:
                        logger.info(
                            f'Cannot align {query_pdb} to {truth_pdb}: '
                            f'{alns_score}'
                        )
                        return
                    final_assignment[tchain][qchain] = alns[qchain][tchain]
                del alns[qchain]

        # Resolve ambiguous assignments
        results = []
        if len(alns) > 0:
            # try all possible permutations
            for qchain in list(alns):
                for tchain in final_assignment:
                    if tchain in alns[qchain]:
                        del alns[qchain][tchain]
            qchains = [qchain for qchain in alns]
            tuples = [list(alns[qchain]) for qchain in qchains]
            n = 0
            for tchains in itertools.product(*tuples):
                if len(set(tchains)) < len(tchains): # ignore this combination
                    continue
                final_assignment_ = copy.deepcopy(final_assignment)
                for t, q in zip(tchains, qchains):
                    final_assignment_[t][q] = alns[q][t]
                output_pdb = os.path.join(
                    output_pdb_dir, f'model_{n}_to_{suffix}.pdb'
                )
                receptor_chain = self._build_new_pdb(
                    final_assignment_, query_chains, truth_chains, output_pdb
                )
                results.append((receptor_chain, output_pdb))
                n += 1
        else:
            output_pdb = os.path.join(
                output_pdb_dir, f'model_0_to_{suffix}.pdb'
            )
            receptor_chain = self._build_new_pdb(
                final_assignment, query_chains, truth_chains, output_pdb
            )
            results.append((receptor_chain, output_pdb))

        if len(results) < 1:
            logger.warning(f'Empty results, the alns_score is {alns_score}')
            return

        return results

    def _build_new_pdb(
        self,
        final_assignment, query_chains, truth_chains,
        output_pdb: str,
        only_backbone: bool = False
    ):
        assigning = []
        receptor_chain = None
        max_score = 0
        for tid in final_assignment:
            for qid, (s, qidx, tidx) in final_assignment[tid].items():
                if s > max_score:
                    max_score = s
                    receptor_chain = tid
                assigning.append((qid, tid, qidx, tidx))

        new_query_chains = pdb_utils.assign_residue_index(
            assigning, query_chains, truth_chains
        )

        if len(new_query_chains) < 2:
            raise RuntimeError(output_pdb, len(query_chains), len(truth_chains))

        new_query_structure = pdb_utils.merge_chains(
            new_query_chains, only_backbone=only_backbone
        )

        pdb_string = protein.to_pdb(new_query_structure)
        p = protein.from_pdb_string(pdb_string)
        with open(output_pdb, 'wt') as fh:
            fh.write(pdb_string)

        return receptor_chain

    def build_models(self, truth_pdb: str, output_prefix: str):
        pdb_str = pdb_utils.read_pdb_string(truth_pdb)
        pdb_fh = io.StringIO(pdb_str)
        parser = PDBParser(QUIET=True)
        structure = parser.get_structure('none', pdb_fh)
        models = list(structure.get_models())
        # print(models)

        build_full_model =  len(models) > 1
        if build_full_model:
            full_model = Model(0)

        output_models = {}
        num_chains = 0
        for model in models:
            pdb_io = PDBIO()
            pdb_io.set_structure(model)
            output_path = f'{output_prefix}_{model.id}.pdb'
            # print(list(pdb_io))
            # exit()
            with open(output_path, 'wt') as fh:
                pdb_io.save(fh)
            output_models[model.id] = output_path
            # build a model that includes all chains
            if build_full_model:
                chains = sorted(model, key=lambda chain: chain.id)
                PDB_CHAIN_IDS = sorted(protein.PDB_CHAIN_IDS)
                for chain in model:
                    chain.id = PDB_CHAIN_IDS[num_chains]
                    num_chains += 1
                    full_model.add(chain)

        if build_full_model:
            pdb_io = PDBIO()
            pdb_io.set_structure(full_model)
            output_path = f'{output_prefix}_full.pdb'
            with open(output_path, 'wt') as fh:
                pdb_io.save(fh)
            output_models['full'] = output_path

        return output_models


    def assess(
        self,
        query_pdb: str, truth_pdb: str, receptor_chain: str
    ):
        # print(query_pdb, truth_pdb)
        query_model, gt_model = trim_common_residues(query_pdb, truth_pdb)
        with tempfile.NamedTemporaryFile('w+t') as gt_fp:
            pdb_io = PDBIO()
            pdb_io.set_structure(gt_model)
            pdb_io.save(gt_fp)

            with tempfile.NamedTemporaryFile('w+t') as query_fp:
                pdb_io = PDBIO()
                pdb_io.set_structure(query_model)
                pdb_io.save(query_fp)
                try:
                    result = self._dockq(
                        query_fp.name, gt_fp.name, receptor_chain
                    )
                except RuntimeError as e:
                    raise RuntimeError(query_pdb, truth_pdb)
        return result

def get_residues(pdb_str):
    pdb_fh = io.StringIO(pdb_str)
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure('none', pdb_fh)
    models = list(structure.get_models())
    assert len(models) == 1
    model = models[0]
    residues = {}
    for chain in model:
        for res in chain:
            if res.id[2] != ' ' or res.id[0] != ' ':
                continue
            name = f'{chain.id}_{res.id[1]}'
            residues[name] = res

    return residues

def build_model_from_residues(residues):
    new_model = Model(0)
    chains = {}
    for res_id, res in residues.items():
        chain_id, res_id = res_id.split('_')
        if chain_id not in chains:
            chains[chain_id] = Chain(chain_id)
        chains[chain_id].add(res)

    for chain in chains.values():
        new_model.add(chain)

    return new_model


def trim_common_residues(query_pdb: str, truth_pdb: str):
    truth_residues = get_residues(pdb_utils.read_pdb_string(truth_pdb))
    query_residues = get_residues(pdb_utils.read_pdb_string(query_pdb))
    common_res_ids = set(query_residues.keys()) & set(truth_residues.keys())

    truth_model = build_model_from_residues(dict(
        (res_id, res) for res_id, res in truth_residues.items() if res_id in 
        common_res_ids
    ))
    query_model = build_model_from_residues(dict(
        (res_id, res) for res_id, res in query_residues.items() if res_id in 
        common_res_ids
    ))

    return query_model, truth_model
","Python"
"AlphaFold","allanchen95/ESMPair","msa_pair/msa_pair/assess/monomer_assess.py",".py","3296","112","import os
import io
import sys
import copy
import json
import math
import logging
import argparse
import tempfile
import itertools
import subprocess
from typing import Mapping, Any
from collections import defaultdict

import numpy as np
from Bio.PDB.Model import Model
from Bio.PDB.Chain import Chain
from Bio.PDB.PDBIO import PDBIO
from Bio.PDB.PDBParser import PDBParser
from alphafold.common import residue_constants, protein

logger = logging.getLogger(__file__)


def _decode_binary_output(output):
    msg = []
    for line in output.decode().split('\n'):
        msg.append(line)
    return '\n'.join(msg)

class MonomerAssess:
    def __init__(self, tmalign_binary_path):
        self.tmalign_binary_path = tmalign_binary_path

    @classmethod
    def add_args(cls, parser):
        group = parser.add_argument_group('AlphaFold Evaluation')
        group.add_argument('--query-pdb', type=str)
        group.add_argument('--truth-pdb', type=str)

    def _run_tmalign(
        self, query_pdb: str, truth_pdb: str
    ) -> Mapping[str, Any]:
        cmd_paths = [query_pdb, truth_pdb]
        cmd = (
            [self.tmalign_binary_path] + 
            cmd_paths
        )
        process = subprocess.Popen(
            cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
        )
        output, stderr = process.communicate()
        retcode = process.wait()
        if retcode:
            msg = _decode_binary_output(stderr)
            if 'Warning! Cannot parse file' in msg:
                logger.warning(msg)
                return
            raise RuntimeError(msg)

        def _get_score(line):
            return float(line.split()[1])

        result = {}
        for line in output.decode().split('\n'):
            if line.startswith('TM-score='):
                result['TMscore'] = float(line.split()[1])
                break

        return result


    def assess(
        self,
        query_pdb: str,
        truth_pdb: str,
    ):
        parser = PDBParser(QUIET=True)
        def _get_chains(pdb_file):
            chains = {}
            structure = parser.get_structure('', pdb_file)
            assert len(structure) == 1
            for chain in structure[0]:
                chains[chain.id] = chain
            return chains

        query_chains = _get_chains(query_pdb)
        truth_chains = _get_chains(truth_pdb)
        common_chains = set(list(query_chains)) & set(list(truth_chains))

        results = {}
        for chain_id in common_chains:
            with tempfile.NamedTemporaryFile('w+t') as gt_fp:
                pdb_io = PDBIO()
                gt_model = truth_chains[chain_id]
                pdb_io.set_structure(gt_model)
                pdb_io.save(gt_fp)

                with tempfile.NamedTemporaryFile('w+t') as query_fp:
                    pdb_io = PDBIO()
                    query_model = query_chains[chain_id]
                    pdb_io.set_structure(query_model)
                    pdb_io.save(query_fp)
                    try:
                        result = self._run_tmalign(query_fp.name, gt_fp.name)
                    except RuntimeError as e:
                        raise RuntimeError(query_pdb, truth_pdb)
                if result:
                    results[chain_id] = result

        return results
","Python"
"AlphaFold","Zuricho/AlphaFold3_Result_Visualize","vis_pae.py",".py","6207","171","#!/usr/bin/env python3

import json
import os
import sys

import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable

mpl.rcParams[""font.size""] = 12
mpl.rcParams[""font.family""] = ""Arial""



# helper functions

def hide_axes_frame(ax):
    """"""
    Hide the frame of the given matplotlib axis.
    
    Parameters:
    ax (matplotlib.axes.Axes): The axis object whose frame will be made invisible.
    """"""
    # Hide the axes frame
    for spine in ax.spines.values():
        spine.set_visible(False)
    
    # Optionally, you might also want to hide the ticks and labels if needed
    ax.set_xticks([])
    ax.set_yticks([])


def process_token_chains(token_chain_ids):
    """"""
    Process token chains to assign numerical indices and find start and end indices for each unique token.
    
    Parameters:
    token_chain_ids (list): List of characters representing token chains.
    
    Returns:
    dict, dict, dict, np.ndarray: Three dictionaries mapping characters to their numerical index, start index,
                                  and end index, plus a numpy array of numerical indices.
    """"""
    # Convert list to numpy array if not already
    token_chain_ids = np.array(token_chain_ids)
    
    # Find unique chains and create a mapping to numerical indices
    unique_chains = np.unique(token_chain_ids)
    chain_to_num = {char: i for i, char in enumerate(unique_chains)}
    
    # Convert token chains to numerical indices
    token_chain_nums = np.array([chain_to_num[char] for char in token_chain_ids]).reshape(1, -1)
    
    # Find the start and end index for each character
    chain_to_start_index = {}
    chain_to_end_index = {}
    
    for char in unique_chains:
        indices = np.where(token_chain_ids == char)[0]
        chain_to_start_index[char] = indices[0]
        chain_to_end_index[char] = indices[-1]

    return chain_to_num, chain_to_start_index, chain_to_end_index, token_chain_nums


def main(alphafold_prediction_name):
    # get the file names
    json_name_full = [os.path.join(alphafold_prediction_name, f""{alphafold_prediction_name}_full_data_{i}.json"") for i in range(5)]
    json_name_confidence = [os.path.join(alphafold_prediction_name, f""{alphafold_prediction_name}_summary_confidences_{i}.json"") for i in range(5)]


    # load the data
    json_data_full = [json.load(open(i)) for i in json_name_full]
    json_data_confidence = [json.load(open(i)) for i in json_name_confidence]

    for model_num in range(5):


        json_data_full[model_num].keys()
        # atom_chain_ids, atom_plddts, contact_probs, pae, token_chain_ids, token_res_ids

        json_data_confidence[model_num].keys()
        # chain_iptm, chain_pair_iptm, chain_pair_pae_min, chain_ptm, fraction_disordered, has_clash, iptm, num_recycles, ptm, ranking_score


        # processing the data
        # chain num
        chain_to_num, chain_to_start_index, chain_to_end_index, token_chain_nums = process_token_chains(json_data_full[model_num][""token_chain_ids""])

        # get xticks data
        token_res_ids = json_data_full[model_num][""token_res_ids""]
        xticks_loc = []
        xticks_present = []
        for i in range(len(token_res_ids)):
            if token_res_ids == 1 or token_res_ids[i]%200 == 0:
                xticks_loc.append(i)
                xticks_present.append(token_res_ids[i])


        # Assuming 'json_data_full' and 'model_num' are already defined and available
        fig, ax = plt.subplots(figsize=(4, 4))

        # Display the data
        pae_array = np.array(json_data_full[model_num][""pae""])
        image = ax.imshow(pae_array, cmap=""Greens_r"", vmin=0, vmax=30)

        # ax.set_xticks(xticks_loc, xticks_present)
        # ax.set_xticks([])
        # ax.set_xticks(np.arange(0, len(token_res_ids), 200))
        ax.set_yticks([])
        # set the frame to dashed line
        for spine in ax.spines.values():
            spine.set_linestyle(""--"")
            spine.set_linewidth(1)
            spine.set_color(""k"")


        # Create an axes on the right side of ax, which will match the height of ax
        divider = make_axes_locatable(ax)
        ax_colorbar = divider.append_axes(""right"", size=""5%"", pad=0.2)
        ax_topbar = divider.append_axes(""top"", size=""8%"", pad=0.03)
        ax_leftbar = divider.append_axes(""left"", size=""8%"", pad=0.03)


        # topbar
        ax_topbar.imshow(token_chain_nums, cmap=""tab10"", aspect=""auto"", alpha=0.7)
        hide_axes_frame(ax_topbar)


        # leftbar
        ax_leftbar.imshow(token_chain_nums.T, cmap=""tab10"", aspect=""auto"", alpha=0.7)
        hide_axes_frame(ax_leftbar)

        # colorbar
        colorbar = fig.colorbar(image, cax=ax_colorbar, label=""PAE (Å)"")


        # plot a axhline at the start and end of each token
        for char, start_index in chain_to_start_index.items():
            if start_index != 0:
                ax.axhline(start_index - 0.5, color=""k"", linewidth=1, linestyle=""--"")
                ax.axvline(start_index - 0.5, color=""k"", linewidth=1, linestyle=""--"")
                ax_topbar.axvline(start_index - 0.5, color=""w"", linewidth=1, linestyle=""-"")
                ax_leftbar.axhline(start_index - 0.5, color=""w"", linewidth=1, linestyle=""-"")

        # Adding text annotations at the center of each token chain
        for char in chain_to_start_index:
            start_index = chain_to_start_index[char]
            end_index = chain_to_end_index[char]
            center_index = (start_index + end_index) / 2
            # Add text to top bar
            ax_topbar.text(center_index, 0, char, color='#222222', ha='center', va='center')
            # Add text to left bar
            ax_leftbar.text(0, center_index, char, color='#222222', ha='center', va='center')

        # Show the plot
        plt.savefig(f""{alphafold_prediction_name}/figure_pae_{model_num}.png"", dpi=300, bbox_inches=""tight"", transparent=False)





if __name__ == ""__main__"":
    if len(sys.argv) < 2:
        print(""Usage: python vis_pae.py <alphafold_prediction_directory>"")
        sys.exit(1)
    alphafold_prediction_name = sys.argv[1]
    main(alphafold_prediction_name)
","Python"
"AlphaFold","prehensilecode/alphafold_singularity","example_slurm_job.sh",".sh","2552","81","#!/bin/bash

# Copyright 2023 David Chin
#
# This file is part of alphafold_singularity.
#
# alphafold_singularity is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# alphafold_singularity is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with alphafold_singularity. If not, see <https://www.gnu.org/licenses/>.

#SBATCH --partition=gpu
#SBATCH --time=2:00:00
#SBATCH --gpus=4
#SBATCH --cpus-per-gpu=12
#SBATCH --mem-per-gpu=36G

### NOTE
### This job script cannot be used without modification for your specific environment.

module load python/gcc/3.11
module load alphafold/2.3.2-1

### Check values of some environment variables
echo INFO: SLURM_GPUS_ON_NODE=$SLURM_GPUS_ON_NODE
echo INFO: SLURM_JOB_GPUS=$SLURM_JOB_GPUS
echo INFO: SLURM_STEP_GPUS=$SLURM_STEP_GPUS
echo INFO: ALPHAFOLD_DIR=$ALPHAFOLD_DIR
echo INFO: ALPHAFOLD_DATADIR=$ALPHAFOLD_DATADIR
echo INFO: TMP=$TMP

###
### README This runs AlphaFold 2.3.2 on the T1050.fasta file
###

# AlphaFold should use all GPU devices available to the job by default.
#
# To run the CASP14 evaluation, use:
#   --model_preset=monomer_casp14
#   --db_preset=full_dbs (or delete the line; default is ""full_dbs"")
#
# On a test system with 4x Tesla V100-SXM2, this took about 50 minutes.
#
# To benchmark, running multiple JAX model evaluations (NB this 
# significantly increases run time):
#   --benchmark
#
# On a test system with 4x Tesla V100-SXM2, this took about 6 hours.

# Create output directory in $TMP (which is cleaned up by Slurm at end
# of job).
output_dir=$TMP/output
mkdir -p $output_dir

echo INFO: output_dir=$output_dir

# Run AlphaFold; default is to use GPUs
python3 ${ALPHAFOLD_DIR}/singularity/run_singularity.py \
    --use_gpu \
    --output_dir=$output_dir \
    --data_dir=${ALPHAFOLD_DATADIR} \
    --fasta_paths=T1050.fasta \
    --max_template_date=2020-05-14 \
    --model_preset=monomer \
    --db_preset=reduced_dbs

echo INFO: AlphaFold returned $?

### Copy Alphafold output back to directory where ""sbatch"" command was issued.
mkdir $SLURM_SUBMIT_DIR/Output-$SLURM_JOB_ID
cp -R $output_dir $SLURM_SUBMIT_DIR/Output-$SLURM_JOB_ID

","Shell"
"AlphaFold","prehensilecode/alphafold_singularity","run_singularity.py",".py","11698","298","#!/usr/bin/env python3
# Copyright 2023 David Chin
#
# This file is part of alphafold_singularity.
#
# alphafold_singularity is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# alphafold_singularity is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with alphafold_singularity. If not, see <https://www.gnu.org/licenses/>.

""""""Singularity launch script for Alphafold Singularity image.""""""

import os
import sys
import pathlib
import signal
from typing import Tuple

from absl import app
from absl import flags
from absl import logging
from spython.main import Client

import tempfile
import subprocess


#### USER CONFIGURATION ####

# Path to AlphaFold Singularity image. This relies on
# the environment variable ALPHAFOLD_DIR which is the
# directory where AlphaFold is installed.
singularity_image = Client.load(os.path.join(os.environ['ALPHAFOLD_DIR'], 'alphafold.sif'))

# tmp directory
if 'TMP' in os.environ:
    tmp_dir = os.environ['TMP']
elif 'TMPDIR' in os.environ:
    tmp_dir = os.environ['TMPDIR']
else:
    tmp_dir = '/tmp'

# Default path to a directory that will store the results.
output_dir_default = tempfile.mkdtemp(dir=tmp_dir, prefix='alphafold')

logging.info(f'INFO: tmp_dir = {tmp_dir}')
logging.info(f'INFO: output_dir_default = {output_dir_default}')

#### END USER CONFIGURATION ####

### These flags correspond to the flags defined in ../run_alphafold.py
flags.DEFINE_bool(
    'use_gpu', True, 'Enable NVIDIA runtime to run with GPUs.')
flags.DEFINE_enum(
    'models_to_relax', 'best', ['best', 'all', 'none'],
    'The models to run the final relaxation step on. '
    'If `all`, all models are relaxed, which may be time '
    'consuming. If `best`, only the most confident model is '
    'relaxed. If `none`, relaxation is not run. Turning off '
    'relaxation might result in predictions with '
    'distracting stereochemical violations but might help '
    'in case you are having issues with the relaxation '
    'stage.')
flags.DEFINE_bool(
    'enable_gpu_relax', True, 'Run relax on GPU if GPU is enabled.')
flags.DEFINE_string(
    'gpu_devices', 'all',
    'Comma separated list of devices to pass to NVIDIA_VISIBLE_DEVICES.')
flags.DEFINE_list(
    'fasta_paths', None, 'Paths to FASTA files, each containing a prediction '
    'target that will be folded one after another. If a FASTA file contains '
    'multiple sequences, then it will be folded as a multimer. Paths should be '
    'separated by commas. All FASTA paths must have a unique basename as the '
    'basename is used to name the output directories for each prediction.')
flags.DEFINE_string(
    'output_dir', output_dir_default,
    'Path to a directory that will store the results.')
flags.DEFINE_string(
    'data_dir', None,
    'Path to directory with supporting data: AlphaFold parameters and genetic '
    'and template databases. Set to the target of download_all_databases.sh.')
flags.DEFINE_string(
    'docker_image_name', 'alphafold', 'Name of the AlphaFold Docker image.')
flags.DEFINE_string(
    'max_template_date', None,
    'Maximum template release date to consider (ISO-8601 format: YYYY-MM-DD). '
    'Important if folding historical test sets.')
flags.DEFINE_enum(
    'db_preset', 'full_dbs', ['full_dbs', 'reduced_dbs'],
    'Choose preset MSA database configuration - smaller genetic database '
    'config (reduced_dbs) or full genetic database config (full_dbs)')
flags.DEFINE_enum(
    'model_preset', 'monomer',
    ['monomer', 'monomer_casp14', 'monomer_ptm', 'multimer'],
    'Choose preset model configuration - the monomer model, the monomer model '
    'with extra ensembling, monomer model with pTM head, or multimer model')
flags.DEFINE_integer('num_multimer_predictions_per_model', 5, 'How many '
                     'predictions (each with a different random seed) will be '
                     'generated per model. E.g. if this is 2 and there are 5 '
                     'models then there will be 10 predictions per input. '
                     'Note: this FLAG only applies if model_preset=multimer')
flags.DEFINE_boolean(
    'benchmark', False,
    'Run multiple JAX model evaluations to obtain a timing that excludes the '
    'compilation time, which should be more indicative of the time required '
    'for inferencing many proteins.')
flags.DEFINE_boolean(
    'use_precomputed_msas', False,
    'Whether to read MSAs that have been written to disk instead of running '
    'the MSA tools. The MSA files are looked up in the output directory, so it '
    'must stay the same between multiple runs that are to reuse the MSAs. '
    'WARNING: This will not check if the sequence, database or configuration '
    'have changed.')
flags.DEFINE_string(
    'docker_user', f'{os.geteuid()}:{os.getegid()}',
    'UID:GID with which to run the Docker container. The output directories '
    'will be owned by this user:group. By default, this is the current user. '
    'Valid options are: uid or uid:gid, non-numeric values are not recognised '
    'by Docker unless that user has been created within the container.')

FLAGS = flags.FLAGS

_ROOT_MOUNT_DIRECTORY = '/mnt/'


def _create_bind(bind_name: str, path: str) -> Tuple[str, str]:
  """"""Create a bind point for each file and directory used by the model.""""""
  path = os.path.abspath(path)
  source_path = os.path.dirname(path) if bind_name != 'data_dir' else path
  target_path = os.path.join(_ROOT_MOUNT_DIRECTORY, bind_name)

  logging.info('Binding %s -> %s', source_path, target_path)

  # NOTE singularity binds are read-only by default
  if bind_name == 'data_dir':
    data_path = target_path
  else:
    data_path = f'{os.path.join(target_path, os.path.basename(path))}'

  return (f'{source_path}:{target_path}', f'{data_path}')


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  # You can individually override the following paths if you have placed the
  # data in locations other than the FLAGS.data_dir.

  # Path to the Uniref90 database for use by JackHMMER.
  uniref90_database_path = os.path.join(
      FLAGS.data_dir, 'uniref90', 'uniref90.fasta')

  # Path to the Uniprot database for use by JackHMMER.
  uniprot_database_path = os.path.join(
      FLAGS.data_dir, 'uniprot', 'uniprot.fasta')

  # Path to the MGnify database for use by JackHMMER.
  mgnify_database_path = os.path.join(
      FLAGS.data_dir, 'mgnify', 'mgy_clusters_2022_05.fa')

  # Path to the BFD database for use by HHblits.
  bfd_database_path = os.path.join(
      FLAGS.data_dir, 'bfd',
      'bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')

  # Path to the Small BFD database for use by JackHMMER.
  small_bfd_database_path = os.path.join(
      FLAGS.data_dir, 'small_bfd', 'bfd-first_non_consensus_sequences.fasta')

  # Path to the Uniref30 database for use by HHblits.
  uniref30_database_path = os.path.join(
      FLAGS.data_dir, 'uniref30', 'UniRef30_2021_03')

  # Path to the PDB70 database for use by HHsearch.
  pdb70_database_path = os.path.join(FLAGS.data_dir, 'pdb70', 'pdb70')

  # Path to the PDB seqres database for use by hmmsearch.
  pdb_seqres_database_path = os.path.join(
      FLAGS.data_dir, 'pdb_seqres', 'pdb_seqres.txt')

  # Path to a directory with template mmCIF structures, each named <pdb_id>.cif.
  template_mmcif_dir = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'mmcif_files')

  # Path to a file mapping obsolete PDB IDs to their replacements.
  obsolete_pdbs_path = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'obsolete.dat')

  alphafold_path = pathlib.Path(__file__).parent.parent
  data_dir_path = pathlib.Path(FLAGS.data_dir)
  if alphafold_path == data_dir_path or alphafold_path in data_dir_path.parents:
    raise app.UsageError(
        f'The download directory {FLAGS.data_dir} should not be a subdirectory '
        f'in the AlphaFold repository directory. If it is, the Singularity build is '
        f'slow since the large databases are copied during the image creation.')

  binds = []
  command_args = []

  # Mount each fasta path as a unique target directory.
  target_fasta_paths = []
  for i, fasta_path in enumerate(FLAGS.fasta_paths):
    bind, target_path = _create_bind(f'fasta_path_{i}', fasta_path)
    binds.append(bind)
    target_fasta_paths.append(target_path)
  command_args.append(f'--fasta_paths={"","".join(target_fasta_paths)}')

  database_paths = [
      ('uniref90_database_path', uniref90_database_path),
      ('mgnify_database_path', mgnify_database_path),
      ('data_dir', FLAGS.data_dir),
      ('template_mmcif_dir', template_mmcif_dir),
      ('obsolete_pdbs_path', obsolete_pdbs_path),
  ]

  if FLAGS.model_preset == 'multimer':
    database_paths.append(('uniprot_database_path', uniprot_database_path))
    database_paths.append(('pdb_seqres_database_path',
                           pdb_seqres_database_path))
  else:
    database_paths.append(('pdb70_database_path', pdb70_database_path))

  if FLAGS.db_preset == 'reduced_dbs':
    database_paths.append(('small_bfd_database_path', small_bfd_database_path))
  else:
    database_paths.extend([
        ('uniref30_database_path', uniref30_database_path),
        ('bfd_database_path', bfd_database_path),
    ])

  # NB for binds:
  #    - first arg = path on host
  #    - second arg = path in container
  for name, path in database_paths:
    if path:
      bind, target_path = _create_bind(name, path)
      binds.append(bind)
      command_args.append(f'--{name}={target_path}')

  output_target_path = os.path.join(_ROOT_MOUNT_DIRECTORY, 'output')
  binds.append(f'{FLAGS.output_dir}:{output_target_path}')
  logging.info('Binding %s -> %s', FLAGS.output_dir, output_target_path)

  tmp_target_path = '/tmp'
  binds.append(f'{tmp_dir}:{tmp_target_path}')
  logging.info('Binding %s -> %s', tmp_dir, tmp_target_path)

  use_gpu_relax = FLAGS.enable_gpu_relax and FLAGS.use_gpu

  command_args.extend([
      f'--output_dir={output_target_path}',
      f'--max_template_date={FLAGS.max_template_date}',
      f'--db_preset={FLAGS.db_preset}',
      f'--model_preset={FLAGS.model_preset}',
      f'--benchmark={FLAGS.benchmark}',
      f'--use_precomputed_msas={FLAGS.use_precomputed_msas}',
      f'--num_multimer_predictions_per_model={FLAGS.num_multimer_predictions_per_model}',
      f'--models_to_relax={FLAGS.models_to_relax}',
      f'--use_gpu_relax={use_gpu_relax}',
      '--logtostderr',
  ])

  options = [
    '--bind', f'{"","".join(binds)}',
    '--env', f'NVIDIA_VISIBLE_DEVICES={FLAGS.gpu_devices}',
    # The following flags allow us to make predictions on proteins that
    # would typically be too long to fit into GPU memory.
    '--env', 'TF_FORCE_UNIFIED_MEMORY=1',
    '--env', 'XLA_PYTHON_CLIENT_MEM_FRACTION=4.0',
  ]

  # Run the container.
  # Result is a dict with keys ""message"" (value = all output as a single string),
  # and ""return_code"" (value = integer return code)
  result = Client.run(
             singularity_image,
             command_args,
             nv=True if FLAGS.use_gpu else None,
             return_result=True,
             options=options
           )


if __name__ == '__main__':
  flags.mark_flags_as_required([
      'data_dir',
      'fasta_paths',
      'max_template_date',
  ])
  app.run(main)
","Python"
"AlphaFold","sbhakat/AF-cryptic-pocket","af-md.sh",".sh","1750","52","for i in {1..32}; do

mkdir Model-${i}
cp AF-structures/prot${i}.pdb Model-${i}/prot.pdb
cd Model-${i}
cp ../tleap.all .
cp ../subst .
subst 'CYS A  49' 'CYX A  49' prot.pdb 
subst 'CYS A  54' 'CYX A  54' prot.pdb
subst 'CYS A 251' 'CYX A 251' prot.pdb 
subst 'CYS A 287' 'CYX A 287' prot.pdb
subst 'ASP A 216' 'ASH A 216' prot.pdb
tleap -s -f tleap.all
sander -O -i ../inputs/min.in -o min.out -p com_solvated.top -c com_solvated.crd -ref com_solvated.crd -r Partial_Mini.rst
sander -O -i ../inputs/full-min.in -o full-min.out -p com_solvated.top -c Partial_Mini.rst -r Full_Mini.rst
ambpdb -p com_solvated.top -c Full_Mini.rst > Full_Mini.pdb
echo 0 | gmx trjconv -s Full_Mini.pdb -f Full_Mini.pdb -o Full-min.gro
acpype -p com_solvated.top -x com_solvated.crd -b gmx

cd gmx.amb2gmx

echo q | gmx make_ndx -f gmx_GMX.gro
echo 4 | gmx genrestr -f gmx_GMX.gro -fc 500 500 500 -n index.ndx
gmx grompp -f ../../inputs/em.mdp -c ../Full-min.gro -p gmx_GMX.top -o em.tpr -maxwarn 1
gmx mdrun -ntmpi 1 -v -deffnm em

sleep 1s

gmx grompp -f ../../inputs/nvt.mdp -c em.gro -r em.gro -p gmx_GMX.top -o nvt.tpr -maxwarn 1

sleep 1s

gmx mdrun -s nvt.tpr -o nvt.trr -x nvt.xtc -cpo nvt.cpt -c nvtout.gro -e nvt.edr -g nvt.log -nb gpu -bonded gpu -pme gpu -pin on -v

sleep 1s

gmx grompp -f ../../inputs/npt.mdp -c nvtout.gro -r nvtout.gro -t nvt.cpt -p gmx_GMX.top -o npt.tpr -maxwarn 1

sleep 1s

gmx mdrun -s npt.tpr -o npt.trr -x npt.xtc -cpo npt.cpt -c nptout.gro -e npt.edr -g npt.log -nb gpu -bonded gpu -pme gpu -pin on -v

sleep 1s

gmx grompp -f ../../inputs/md.mdp -c nptout.gro -t npt.cpt -p gmx_GMX.top -o md.tpr -maxwarn 1

sleep 1s

gmx mdrun -s md.tpr -v -nb gpu -bonded gpu -pme gpu -nstlist 400 -update gpu -plumed plumed.dat
cd ../..
done
","Shell"
"AlphaFold","sbhakat/AF-cryptic-pocket","Metadynamics/reweight.sh",".sh","67","2","plumed driver --plumed plumed-reweight.dat --noatoms > plumed.out 
","Shell"
"AlphaFold","sbhakat/AF-cryptic-pocket","Trp-msm-crypticpocket/bayesian-msm.ipynb",".ipynb","2110252","1356","{
 ""cells"": [
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""import matplotlib.pylab as plt\n"",
    ""from IPython.display import set_matplotlib_formats\n"",
    ""set_matplotlib_formats('retina', quality=100)\n"",
    ""plt.rcParams[\""figure.figsize\""] = (10,8)\n"",
    ""\n"",
    ""import matplotlib as mpl\n"",
    ""mpl.rcParams.update(mpl.rcParamsDefault)\n"",
    ""plt.style.use(['seaborn-poster'])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""import pyemma\n"",
    ""import pickle\n"",
    ""import numpy as np\n"",
    ""from msmbuilder.utils import load,dump\n"",
    ""\n"",
    ""%matplotlib inline\n"",
    ""import matplotlib.pyplot as plt""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""with open('raw-features.pkl', 'rb') as f:\n"",
    ""     features = pickle.load(f)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""5001""
      ]
     },
     ""execution_count"": 4,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""features[0].shape[0] ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Trp Angle distributions""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[5].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[5].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[6].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[6].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[7].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[7].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[8].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[8].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[9].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[9].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<Figure size 864x360 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 352,
       ""width"": 839
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True)\n"",
    ""x = 0.1 * np.arange(features[10].shape[0])\n"",
    ""for i, (ax, tic) in enumerate(zip(axes.flat, features[10].T)):\n"",
    ""    ax.plot(x*20, tic)\n"",
    ""    ax.set_ylabel('$\\chi$ {}'.format(i + 1))\n"",
    ""axes[-1].set_xlabel('time / ns')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""with open('features.pkl', 'rb') as a:\n"",
    ""     transfeatures = pickle.load(a)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""with open('scaled-transformed-features.pkl', 'rb') as b:\n"",
    ""     scaletransfeatures = pickle.load(b)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#cocatenate features\n"",
    ""features_concatenated = np.concatenate(features)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(1600320, 2)""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""features_concatenatedtenated.shape""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#cocatenate transformed features\n"",
    ""transfeatures_concatenated = np.concatenate(transfeatures)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#cocatenate scaled features\n"",
    ""scaletransfeatures_concatenated = np.concatenate(scaletransfeatures)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Clustering""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 54,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""initialize kmeans++ centers:   0%|          | 0/200 [00:00<?, ?it/s]\n"",
      ""initialize kmeans++ centers: 100%|██████████| 200/200 [00:16<00:00, 11.49it/s]\n"",
      ""kmeans iterations:   1%|▏         | 1/75 [00:17<21:41, 17.59s/it]\u001b[A\n"",
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      ""initialize kmeans++ centers: 100%|██████████| 200/200 [00:36<00:00, 11.49it/s]\n"",
      ""kmeans iterations:  27%|██▋       | 20/75 [00:36<00:58,  1.06s/it]\u001b[A\n"",
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      ""kmeans iterations:  89%|████████▉ | 67/75 [01:28<00:09,  1.20s/it]\u001b[A\n"",
      ""kmeans iterations:  91%|█████████ | 68/75 [01:29<00:08,  1.18s/it]\u001b[A\n"",
      ""kmeans iterations:  92%|█████████▏| 69/75 [01:30<00:06,  1.15s/it]\u001b[A\n"",
      ""kmeans iterations:  93%|█████████▎| 70/75 [01:31<00:05,  1.14s/it]\u001b[A\n"",
      ""kmeans iterations:  95%|█████████▍| 71/75 [01:32<00:04,  1.14s/it]\u001b[A\n"",
      ""kmeans iterations:  96%|█████████▌| 72/75 [01:33<00:03,  1.14s/it]\u001b[A\n"",
      ""kmeans iterations:  97%|█████████▋| 73/75 [01:35<00:02,  1.15s/it]\u001b[A\n"",
      ""kmeans iterations:  99%|█████████▊| 74/75 [01:36<00:01,  1.15s/it]\u001b[A\n"",
      ""kmeans iterations: 100%|██████████| 75/75 [01:37<00:00,  1.17s/it]\u001b[A/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/deeptime/clustering/_kmeans.py:466: UserWarning: Algorithm did not reach convergence criterion of 1e-05 in 75 iterations. Consider increasing max_iter.\n"",
      ""  warnings.warn(f\""Algorithm did not reach convergence criterion\""\n"",
      ""100%|██████████| 200/200 [01:37<00:00,  2.05it/s]                             \n"",
      ""\n"",
      ""100%|██████████| 75/75 [01:37<00:00,  1.30s/it]                   \u001b[A""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""25-09-22 21:29:14 pyemma.coordinates.clustering.kmeans.KmeansClustering[11] WARNING  Algorithm did not reach convergence criterion of 1e-05 in 75 iterations. Consider increasing max_iter.\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""\n"",
      ""100%|██████████| 320/320 [00:02<00:00, 148.63it/s]                                    \n""
     ]
    }
   ],
   ""source"": [
    ""cluster = pyemma.coordinates.cluster_kmeans(transfeatures, k=200, max_iter=75, fixed_seed=1)\n"",
    ""dtrajs_concatenated = np.concatenate(cluster.dtrajs)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 55,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 566,
       ""width"": 710
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""pyemma.plots.plot_density(\n"",
    ""    *features_concatenated[:, :2].T, ax=ax, cbar=False, alpha=0.3)\n"",
    ""ax.scatter(*cluster.clustercenters[:, :2].T, s=5, c='C1')\n"",
    ""ax.set_xlabel('Trp $\\chi1$')\n"",
    ""ax.set_ylabel('Trp $\\chi2$')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## ITS""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""100%|██████████| 8/8 [06:16<00:00, 47.03s/it]                         \n""
     ]
    },
    {
     ""data"": {
      ""text/plain"": [
       ""Text(0, 0.5, 'timescale /ns')""
      ]
     },
     ""execution_count"": 12,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 507,
       ""width"": 653
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""its = pyemma.msm.its(cluster.dtrajs, lags=[10, 100, 200, 300, 400, 1000, 2000, 3000], nits=3, errors='bayes')\n"",
    ""pyemma.plots.plot_implied_timescales(its, units='ns', dt=1/50);\n"",
    ""ax.set_xlabel('lag time (ns)', fontsize=25)\n"",
    ""ax.set_ylabel('timescale /ns', fontsize=25)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""100%|██████████| 8/8 [07:30<00:00, 56.25s/it]                         \n""
     ]
    },
    {
     ""data"": {
      ""text/plain"": [
       ""Text(0, 0.5, 'timescale /ns')""
      ]
     },
     ""execution_count"": 30,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 507,
       ""width"": 660
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""its = pyemma.msm.its(cluster.dtrajs, lags=[10, 100, 200, 300, 400, 500, 800, 1000], nits=3, errors='bayes')\n"",
    ""pyemma.plots.plot_implied_timescales(its, units='ns', dt=1/50);\n"",
    ""ax.set_xlabel('lag time (ns)', fontsize=25)\n"",
    ""ax.set_ylabel('timescale /ns', fontsize=25)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## MSM""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 68,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""18224it [00:54, 335.16it/s]                                                                                                             \n"",
      ""100%|██████████| 100/100 [00:04<00:00, 20.63it/s]                                                                  ""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""fraction of states used = 1.00\n"",
      ""fraction of counts used = 1.00\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""\n""
     ]
    }
   ],
   ""source"": [
    ""lag = 6*50\n"",
    ""msm = pyemma.msm.bayesian_markov_model(cluster.dtrajs, lag=lag)\n"",
    ""print('fraction of states used = {:.2f}'.format(msm.active_state_fraction))\n"",
    ""print('fraction of counts used = {:.2f}'.format(msm.active_count_fraction))""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Free Energy Surface""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 69,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x288 with 4 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 277,
       ""width"": 707
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)\n"",
    ""pyemma.plots.plot_contour(\n"",
    ""    *features_concatenated[:, :2].T,\n"",
    ""    msm.pi[np.concatenate(msm.dtrajs_active)],\n"",
    ""    ax=axes[0],\n"",
    ""    mask=True,\n"",
    ""    cbar_label='stationary distribution')\n"",
    ""pyemma.plots.plot_free_energy(\n"",
    ""    *features_concatenated[:, :2].T,\n"",
    ""    weights=np.concatenate(msm.trajectory_weights()),\n"",
    ""    ax=axes[1],\n"",
    ""    legacy=False)\n"",
    ""for ax in axes.flat:\n"",
    ""    ax.set_xlabel('Trp $\\chi1$')\n"",
    ""axes[0].set_ylabel('Trp $\\chi2$')\n"",
    ""axes[0].set_title('Stationary distribution', fontweight='bold')\n"",
    ""axes[1].set_title('Reweighted free energy surface', fontweight='bold')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 70,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 567,
       ""width"": 703
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""pyemma.plots.plot_free_energy(*features_concatenated[:, :2].T, weights=np.concatenate(msm.trajectory_weights()), ax=ax, legacy=False, alpha=0.8)\n"",
    ""ax.set_xlabel('Trp $\\chi1$ (radian)', fontsize=30)\n"",
    ""ax.set_ylabel('Trp $\\chi2$ (radian)', fontsize=30)\n"",
    ""ax.set_xlim(-np.pi, np.pi)\n"",
    ""ax.set_ylim(-np.pi, np.pi)\n"",
    ""ax.set_aspect('equal')\n"",
    ""\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## PCCA""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 71,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""PCCA(P=array([[2.33586e-02, 0.00000e+00, ..., 1.90558e-02, 3.46157e-03],\n"",
       ""       [0.00000e+00, 3.10314e-02, ..., 0.00000e+00, 7.69427e-08],\n"",
       ""       ...,\n"",
       ""       [2.36801e-02, 0.00000e+00, ..., 2.10649e-02, 2.63510e-03],\n"",
       ""       [9.03877e-03, 3.13857e-04, ..., 5.53701e-03, 1.76045e-02]]),\n"",
       ""   m=6)""
      ]
     },
     ""execution_count"": 71,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""nstates = 6\n"",
    ""msm.pcca(nstates)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 72,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 1080x216 with 12 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 246,
       ""width"": 1074
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, axes = plt.subplots(1, 6, figsize=(15, 3), sharex=True, sharey=True)\n"",
    ""for i, ax in enumerate(axes.flat):\n"",
    ""    pyemma.plots.plot_contour(\n"",
    ""        *features_concatenated[:, :2].T,\n"",
    ""        msm.metastable_distributions[i][np.concatenate(msm.dtrajs_active)],\n"",
    ""        ax=ax,\n"",
    ""        cmap='afmhot_r',\n"",
    ""        mask=True,\n"",
    ""        cbar_label='metastable distribution {}'.format(i + 1))\n"",
    ""    ax.set_xlabel('Trp $\\chi1$')\n"",
    ""axes[0].set_ylabel('Trp $\\chi2$')\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 73,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 567,
       ""width"": 695
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""metastable_traj = msm.metastable_assignments[np.concatenate(msm.dtrajs_active)]\n"",
    ""\n"",
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""_, _, misc = pyemma.plots.plot_state_map(\n"",
    ""    *features_concatenated[:, :2].T, metastable_traj, ax=ax)\n"",
    ""ax.set_xlabel('Trp $\\chi1$ (radian)', fontsize=30)\n"",
    ""ax.set_ylabel('Trp $\\chi2$ (radian)', fontsize=30)\n"",
    ""misc['cbar'].set_ticklabels([r'$\\mathcal{S}_%d$' % (i + 1)\n"",
    ""                             for i in range(nstates)])\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## MFPT""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 74,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""MFPT (ns)\n""
     ]
    },
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>1</th>\n"",
       ""      <th>2</th>\n"",
       ""      <th>3</th>\n"",
       ""      <th>4</th>\n"",
       ""      <th>5</th>\n"",
       ""      <th>6</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>0.00</td>\n"",
       ""      <td>332190.50</td>\n"",
       ""      <td>170897.79</td>\n"",
       ""      <td>28902.07</td>\n"",
       ""      <td>36332.93</td>\n"",
       ""      <td>2322.17</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>642000.76</td>\n"",
       ""      <td>0.00</td>\n"",
       ""      <td>157260.18</td>\n"",
       ""      <td>25455.83</td>\n"",
       ""      <td>29406.40</td>\n"",
       ""      <td>3612.48</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>1186529.58</td>\n"",
       ""      <td>782175.46</td>\n"",
       ""      <td>0.00</td>\n"",
       ""      <td>1040.20</td>\n"",
       ""      <td>31087.81</td>\n"",
       ""      <td>2432.98</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>1201932.70</td>\n"",
       ""      <td>811507.50</td>\n"",
       ""      <td>156325.61</td>\n"",
       ""      <td>0.00</td>\n"",
       ""      <td>30915.47</td>\n"",
       ""      <td>2230.61</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td>1201301.09</td>\n"",
       ""      <td>806590.60</td>\n"",
       ""      <td>179586.05</td>\n"",
       ""      <td>22517.64</td>\n"",
       ""      <td>0.00</td>\n"",
       ""      <td>6558.18</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td>1206631.75</td>\n"",
       ""      <td>822453.60</td>\n"",
       ""      <td>190349.37</td>\n"",
       ""      <td>33472.71</td>\n"",
       ""      <td>45957.24</td>\n"",
       ""      <td>0.00</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""            1          2          3         4         5        6\n"",
       ""1        0.00  332190.50  170897.79  28902.07  36332.93  2322.17\n"",
       ""2   642000.76       0.00  157260.18  25455.83  29406.40  3612.48\n"",
       ""3  1186529.58  782175.46       0.00   1040.20  31087.81  2432.98\n"",
       ""4  1201932.70  811507.50  156325.61      0.00  30915.47  2230.61\n"",
       ""5  1201301.09  806590.60  179586.05  22517.64      0.00  6558.18\n"",
       ""6  1206631.75  822453.60  190349.37  33472.71  45957.24     0.00""
      ]
     },
     ""execution_count"": 74,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""from itertools import product\n"",
    ""\n"",
    ""mfpt = np.zeros((nstates, nstates))\n"",
    ""for i, j in product(range(nstates), repeat=2):\n"",
    ""    mfpt[i, j] = msm.mfpt(\n"",
    ""        msm.metastable_sets[i],\n"",
    ""        msm.metastable_sets[j])\n"",
    ""\n"",
    ""from pandas import DataFrame\n"",
    ""print('MFPT (ns)')\n"",
    ""DataFrame(np.round(mfpt /50, decimals=2), index=range(1, nstates + 1), columns=range(1, nstates + 1))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 75,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""normal = msm.metastable_sets[5]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 76,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""flip = msm.metastable_sets[4]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 77,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""MFPT N1 -> F1: (45997.2 ± 3574.7) ns\n"",
      ""MFPT F1 -> N1: (6508.2 ± 516.0) ns\n""
     ]
    }
   ],
   ""source"": [
    ""print('MFPT N1 -> F1: ({:6.1f} ± {:5.1f}) ns'.format(\n"",
    ""    msm.sample_mean('mfpt', normal, flip) /50, msm.sample_std('mfpt', normal, flip) /50 ))\n"",
    ""print('MFPT F1 -> N1: ({:.1f} ± {:5.1f}) ns'.format(\n"",
    ""    msm.sample_mean('mfpt', flip, normal) /50, msm.sample_std('mfpt', flip, normal) /50))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 78,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""state\tπ\t\tG/kT\n"",
      ""1\t0.000199\t8.522936\n"",
      ""2\t0.000999\t6.908295\n"",
      ""3\t0.001035\t6.873310\n"",
      ""4\t0.001206\t6.720110\n"",
      ""5\t0.123649\t2.090304\n"",
      ""6\t0.872911\t0.135922\n""
     ]
    }
   ],
   ""source"": [
    ""print('state\\tπ\\t\\tG/kT')\n"",
    ""for i, s in enumerate(msm.metastable_sets):\n"",
    ""    p = msm.pi[s].sum()\n"",
    ""    print('{}\\t{:f}\\t{:f}'.format(i + 1, p, -np.log(p)))""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## TPT""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""flux = pyemma.msm.tpt(msm, normal, flip)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<bound method ReactiveFlux.pathways of ReactiveFlux(A=array([ 1,  8, 12, 19, 20, 29, 31, 33, 34, 35, 38, 39, 56, 57, 62, 66, 67,\n"",
       ""       68, 74, 83, 85, 94, 95, 99]),\n"",
       ""       B=array([ 3,  9, 18, 36, 42, 47, 78, 89, 92, 97]),\n"",
       ""       dt_model=[TimeUnit 300.0 step],\n"",
       ""       flux=array([[0.00000e+00, 0.00000e+00, ..., 3.48093e-12, 0.00000e+00],\n"",
       ""       [0.00000e+00, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00],\n"",
       ""       ...,\n"",
       ""       [0.00000e+00, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00],\n"",
       ""       [0.00000e+00, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00]]),\n"",
       ""       gross_flux=array([[0.00000e+00, 0.00000e+00, ..., 8.63913e-10, 0.00000e+00],\n"",
       ""       [0.00000e+00, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00],\n"",
       ""       ...,\n"",
       ""       [8.60432e-10, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00],\n"",
       ""       [0.00000e+00, 0.00000e+00, ..., 0.00000e+00, 0.00000e+00]]),\n"",
       ""       mu=array([3.30370e-05, 2.95548e-02, ..., 1.80496e-05, 4.28261e-02]),\n"",
       ""       qminus=array([0.60827, 1.     , ..., 0.60731, 1.     ]),\n"",
       ""       qplus=array([0.39173, 0.     , ..., 0.39269, 0.     ]))>""
      ]
     },
     ""execution_count"": 25,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""flux.pathways""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 32,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:132: MatplotlibDeprecationWarning: print_figure() got unexpected keyword argument \""quality\"" which is no longer supported as of 3.3 and will become an error two minor releases later\n"",
      ""  fig.canvas.print_figure(bytes_io, **kw)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x576 with 2 Axes>""
      ]
     },
     ""metadata"": {
      ""image/png"": {
       ""height"": 567,
       ""width"": 703
      },
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(10, 8))\n"",
    ""\n"",
    ""pyemma.plots.plot_contour(\n"",
    ""    *features_concatenated[:, :2].T,\n"",
    ""    flux.committor[dtrajs_concatenated],\n"",
    ""    cmap='BrBG',\n"",
    ""    ax=ax,\n"",
    ""    mask=True,\n"",
    ""    cbar_label=r'committor $\\mathcal{Closed}_%d \\to \\mathcal{Open}_%d$' % (\n"",
    ""        5 + 1, 4 + 1))\n"",
    ""ax.set_xlabel('Trp $\\chi1$ (radian)', fontsize=30)\n"",
    ""ax.set_ylabel('Trp $\\chi2$ (radian)', fontsize=30)\n"",
    ""fig.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([0.        , 0.        , 0.        , ..., 0.56783079, 0.54586445,\n"",
       ""       0.54586445])""
      ]
     },
     ""execution_count"": 28,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""flux.committor[dtrajs_concatenated]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""cg, cgflux = flux.coarse_grain(msm.metastable_sets)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {},
   ""outputs"": [
    {
     ""ename"": ""NameError"",
     ""evalue"": ""name 'path_fluxes' is not defined"",
     ""output_type"": ""error"",
     ""traceback"": [
      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
      ""\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)"",
      ""\u001b[0;32m<ipython-input-28-e4c5aa91e98b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpath_fluxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m"",
      ""\u001b[0;31mNameError\u001b[0m: name 'path_fluxes' is not defined""
     ]
    }
   ],
   ""source"": [
    ""path_fluxes""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""percentage       \tpath\n"",
      ""-------------------------------------\n"",
      ""0.769  \t [1 5 6]\n"",
      ""0.218  \t [1 6]\n"",
      ""0.007  \t [1 2 3 6]\n"",
      ""0.006  \t [1 4 5 6]\n""
     ]
    }
   ],
   ""source"": [
    ""paths, path_fluxes = cgflux.pathways(fraction=0.99)\n"",
    ""print('percentage       \\tpath')\n"",
    ""print('-------------------------------------')\n"",
    ""for i in range(len(paths)):\n"",
    ""    print(np.round(path_fluxes[i] / np.sum(path_fluxes), 3),' \\t', paths[i] + 1)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## CK Test""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""100%|██████████| 4/4 [01:57<00:00, 29.39s/it]                         \n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 720x720 with 36 Axes>""
      ]
     },
     ""metadata"": {
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""nstates = 6\n"",
    ""cktest = msm.cktest(nstates, mlags=5)\n"",
    ""pyemma.plots.plot_cktest(cktest, dt=1/50, units='ns');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 3"",
   ""language"": ""python"",
   ""name"": ""python3""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 3
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython3"",
   ""version"": ""3.6.15""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 4
}
","Unknown"
"AlphaFold","sbhakat/AF-cryptic-pocket","Trp-msm-crypticpocket/feature-gen.py",".py","1716","57","#msmbuilder imports 
from msmbuilder.dataset import dataset
from msmbuilder.featurizer import ContactFeaturizer
from msmbuilder.featurizer import DihedralFeaturizer
from msmbuilder.decomposition import tICA
from msmbuilder.cluster import MiniBatchKMeans
from msmbuilder.msm import ContinuousTimeMSM
from msmbuilder.utils import verbosedump,verboseload
from msmbuilder.cluster import KCenters
from msmbuilder.utils import load,dump

#other imports
import os,glob,shutil
import numpy as np
import mdtraj as md
import pandas as pd 
import pickle
#prettier plots

#Loading the trajectory
ref = md.load('prot.pdb')
#a = ref.top.select(""resid 73 to 87"")
b = ref.top.select(""resid 42"")
#c = np.concatenate((b, a), axis=None)

## Path to .xtc files
ds = dataset(""../*.xtc"", topology=""prot.pdb"", atom_indices=b, stride=20)


#Featurization
featurizer = DihedralFeaturizer(types=['chi1', 'chi2'])
dump(featurizer,""transformed_raw_featurizer.pkl"")

#from msmbuilder.utils import load,dump
f=DihedralFeaturizer(types=['chi1', 'chi2'], sincos=False)
dump(f,""raw_featurizer.pkl"")

#featurizer = DihedralFeaturizer(types=['chi1', 'chi2'], resids= 73,74,75,76,77,78,79,80,81,82,83)
diheds = featurizer.fit_transform(ds)
dump(diheds, ""features.pkl"")
                            
dihedsraw = f.fit_transform(ds)
dump(dihedsraw, ""raw-features.pkl"")     
#print(ds[0].shape)
print(diheds[0].shape)

# this basically maps every feature to atom indices. 
#df1 = pd.DataFrame(featurizer.describe_features(ds))
#dump(df1, ""feature_descriptor.pkl"")

#Robust scaling
from msmbuilder.preprocessing import RobustScaler
scaler = RobustScaler()
scaled_diheds = scaler.fit_transform(diheds)

dump(scaled_diheds, ""scaled-transformed-features.pkl"")
","Python"
"AlphaFold","benjaminlozanow/pdb_data_download","unzip_flatten.sh",".sh","1547","53","#!/bin/bash

##################################################################
# Adapted script from: 
# https://github.com/deepmind/alphafold/blob/main/scripts/download_pdb_mmcif.sh
# Description: Unzips and flattens files from the PDB (mmCIFs for AlphaFold).
# Example: ./unzip_flatten.sh absolute/path/to/the/downloaded/directory/mmCIF
####################################################################
# 
#
# Usage: bash download_pdb_mmcif.sh /path/to/download/directory
set -e

if [[ $# -eq 0 ]]; then
    echo ""Error: download directory must be provided as an input argument.""
    exit 1
fi

if ! command -v aria2c &> /dev/null ; then
    echo ""Error: aria2c could not be found. Please install aria2c (sudo apt install aria2).""
    exit 1
fi

if ! command -v rsync &> /dev/null ; then
    echo ""Error: rsync could not be found. Please install rsync.""
    exit 1
fi

DOWNLOAD_DIR=""$1""
ROOT_DIR=""${DOWNLOAD_DIR%/*}/data""
RAW_DIR=""${ROOT_DIR}/raw""
FILES_DIR=""${ROOT_DIR}/files""

mkdir -p ""${RAW_DIR}""
mv ""${DOWNLOAD_DIR}""/* ${RAW_DIR}

echo ""Unzipping files...""
find ""${RAW_DIR}/"" -type f -iname ""*.gz"" -exec gunzip {} +

echo ""Flattening files...""
mkdir -p ""${FILES_DIR}""
find ""${RAW_DIR}"" -type d -empty -delete  # Delete empty directories.
for subdir in ""${RAW_DIR}""/*; do
  mv ""${subdir}/""*.* ""${FILES_DIR}""
done

echo ""Deleting empty folders...""
rm -r $RAW_DIR
rm -r $DOWNLOAD_DIR

# Uncomment if you are trying to download de mmCIF data for alphafold
# aria2c ""ftp://ftp.wwpdb.org/pub/pdb/data/status/obsolete.dat"" --dir=""${ROOT_DIR}""
","Shell"
"AlphaFold","benjaminlozanow/pdb_data_download","download_pdb.sh",".sh","3239","92","#!/usr/bin/env bash

##################################################################
# Adapted script from: 
# https://github.com/kajeagentspi/Datahoarder/blob/master/theripper.sh
# Description: Alternative method (to rsync) to download files from 
# https://files.wwpdb.org/pub/pdb/data/structures/divided/ using wget spider and aria2
# Example: ./download_pdb.sh ""https://files.wwpdb.org/pub/pdb/data/structures/divided/mmCIF"" 
####################################################################

set -e

URI=$1
URL=$URI""/""
echo ""This URL should end with a /:""
echo $URL
ROOT_PATH=${URI%/*}
echo ""This URL should NOT end with a /:""
echo $ROOT_PATH
LIST=./list.txt
MAX_CONNECTIONS_PER_SERVER=16
USER_AGENT=""Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36""

usage() {
	cat <<EOF
Uses wget's spider with aria2c's parallel for downloading open
directories.
Usage: $SCRIPT_NAME [options] URL PATH
EOF
}

spider() {
	local logfile=./opendir.log
	wget -o $logfile -e robots=off -r --no-parent --spider -U ""$USER_AGENT"" ""$URL"" || true
	# Grabs all lines with the pattern --2017-07-12 15:40:31-- then from the results removes everthing that ends in / (meaning it's a directory
	# then removes pattern from every line
	grep -B 2 -E '... 404 Not Found|... 403 Forbidden|... 301 Moved Permanently' $logfile | \
	grep -i '^--[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]--' | \
	grep '[^'/']$'  | sed -e 's/^--[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]--  //g' > $logfile.tmp
	while read line; do
		sed -i ""\|$line|d"" $logfile
	done < $logfile.tmp
	cat $logfile | grep -i '^--[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]--' | \
	grep '[^'/']$'  | sed -e 's/^--[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]--  //g' | \
	# Filter URLs that are compressed UNIX files
	grep '.*\.gz$' > $LIST
	#Delete the folder made by wget (deletes all empty directories in the directory this script is run)
	find . -type d -empty -delete
}

download() {
	while read link; do
		#urldecode the links
		DECODED_LINK=$(echo $link | printf ""%b\n"" ""$(sed 's/+/ /g; s/%\([0-9a-f][0-9a-f]\)/\\x\1/g;')"";)
		DECODED_ROOT_PATH=$(echo $ROOT_PATH | printf ""%b\n"" ""$(sed 's/+/ /g; s/%\([0-9a-f][0-9a-f]\)/\\x\1/g;')"";)
		# Remove text after last /
		FULL_PATH=$(echo $DECODED_LINK | sed 's%/[^/]*$%/%')
		FILE_PATH=${FULL_PATH#${DECODED_ROOT_PATH}/}
		echo ""${link}"" >> link.down
		echo "" dir=$FILE_PATH"" >> link.down
		echo "" continue=true"" >> link.down
		echo "" max-connection-per-server=$MAX_CONNECTIONS_PER_SERVER"" >> link.down
		echo "" split=16"" >> link.down
		echo "" user-agent=$USER_AGENT"" >> link.down
		echo "" header=Accept: text/html"" >> link.down
		echo -e "" min-split-size=1M\n"" >> link.down
	done  < $LIST
	#Download links
	aria2c -i link.down -j 16

}

if [[ -z $1 || $# -ge 2 ]]; then
	usage
	exit 1
fi

# Comment spider or download if you want to perform analysis separetly
echo ""Creating list of urls...""
spider
echo ""Index created!""

echo ""Downloading...""
download

# Cleanup removes temporal files
# rm opendir.log
# rm opendir.log.tmp
# rm list.txt
# rm link.down

","Shell"
"AlphaFold","piercelab/tcrmodel2","run_tcrmodel2_ub_tcr.py",".py","7832","207","# Load required packages
import os
import sys
import pandas as pd
import json
from absl import flags
from absl import app
from glob import glob
import subprocess 
from anarci import anarci

from scripts import seq_utils,pdb_utils,tcr_utils,parse_tcr_seq

# input
flags.DEFINE_string('output_dir', ""experiments/"", 'Path to output directory.')
flags.DEFINE_string('tcra_seq', None, 'TCR alpha sequence')
flags.DEFINE_string('tcrb_seq', None, 'TCR beta sequence')
flags.DEFINE_string('job_id', ""test001"", 'Job id')
flags.DEFINE_string('ignore_pdbs_string', None, ""Currently not supported"")
flags.DEFINE_string('max_template_date', ""2100-01-01"", ""Max template date, ""
                    ""format yyyy-mm-dd. Default to 2100-01-01."")
flags.DEFINE_bool('relax_structures', False, ""Run amber minimization ""
                  ""on the structures"")
flags.DEFINE_string(""tp_db"", ""data/databases"" , 
                    ""Customized TCR pMHC database path"")
flags.DEFINE_string(""ori_db"", None,
                    ""Path to the database with pdb_mmcif database"")

FLAGS = flags.FLAGS

def main(_argv):
    output_dir=FLAGS.output_dir
    tcra_seq=FLAGS.tcra_seq
    tcrb_seq=FLAGS.tcrb_seq
    job_id=FLAGS.job_id
    max_template_date=FLAGS.max_template_date
    relax_structures=FLAGS.relax_structures
    tp_db=FLAGS.tp_db
    ori_db=FLAGS.ori_db

    if len(max_template_date)==0:
        max_template_date=""2100-01-01""

    models_to_relax=""none""
    if relax_structures==True:
        models_to_relax=""all""

    # create output directory
    out_dir=os.path.join(output_dir,job_id)

    # make output directory
    os.makedirs(out_dir, exist_ok=True)

    # trim tcr sequence to variable domain only
    anarci_tcra=anarci([('tcra', tcra_seq)], scheme=""aho"", output=False)
    anarci_tcrb=anarci([('tcrb', tcrb_seq)], scheme=""aho"", output=False)
    tcra_seq="""".join([item[-1] for item in anarci_tcra[0][0][0][0] if item[-1] != '-'])
    tcrb_seq="""".join([item[-1] for item in anarci_tcrb[0][0][0][0] if item[-1] != '-'])

    # create fasta files 
    fasta_fn=os.path.join(out_dir, ""%s.fasta"" % job_id)
    fasta="">TCRa\n%s\n"" % tcra_seq
    fasta+="">TCRb\n%s\n"" % tcrb_seq
    with open(fasta_fn,'w+') as fh:
        fh.write(fasta)

    # create status file and update it
    status_file=os.path.join(out_dir,""modeling_status.txt"")

    ###################
    # build structure #
    ###################
    databases=(f""--uniref90_database_path={tp_db}/uniref90.tcrmhc.fasta "" 
            f""--mgnify_database_path={tp_db}/mgnify.fasta ""
            f""--template_mmcif_dir={ori_db}/pdb_mmcif/mmcif_files/ ""
            f""--obsolete_pdbs_path={ori_db}/pdb_mmcif/obsolete.dat ""
            f""--small_bfd_database_path={tp_db}/small_bfd.tcrmhc.fasta ""
            f""--pdb_seqres_database_path={tp_db}/pdb_seqres.txt ""
            f""--uniprot_database_path={tp_db}/uniprot.tcrmhc.fasta ""
            f""--data_dir={ori_db}"")
    cmd=(f""python run_alphafold_tcrmodel2.3.py --db_preset=reduced_dbs ""
         f""--fasta_paths={out_dir}/{job_id}.fasta "" 
         f""--model_preset=multimer --output_dir={out_dir} {databases} ""
         f""--max_template_date={max_template_date} ""
         f""--use_gpu_relax={relax_structures} --models_to_relax={models_to_relax} ""
         ""--use_precomputed_msas=True --num_multimer_predictions_per_model=1 "" 
         f""--save_template_names --status_file={status_file}"")
    subprocess.run(cmd, shell=True)

    # renumber chains to start with A if not relax_structures
    if not relax_structures:
        models_list = [i for i in glob(f""{out_dir}/{job_id}/*.pdb"") if os.path.basename(i).startswith('ranked')]
        for pdb_fn in models_list:
            pdb=[]
            with open(pdb_fn) as fh:
                for line in fh:
                    if line[0:4] == 'ATOM':
                        pdb.append(line.rstrip())
            pdb_renum=pdb_utils.rename_chains_start_A_and_1(pdb)
            pdb_renum_fn = pdb_fn.replace('.pdb', '_renum.pdb')
            with open(pdb_renum_fn,'w+') as fh:
                fh.write(""\n"".join(pdb_renum))
            subprocess.run(""mv %s %s"" % (pdb_renum_fn, pdb_fn), shell=True)


    ####################
    # Parse statistics #
    ####################
    out_json={}
    
    #get scores
    items=['model_confidence','plddt','ptm','iptm']

    with open(""%s/%s/model_scores.txt"" % (out_dir, job_id)) as fh:
        for idx, line in enumerate(fh):
            scores=line.rstrip().split(""\t"")
            out_json[""ranked_%d"" % (idx)]={
                items[0]:float(scores[0]),
                items[1]:float(scores[1]),
                items[2]:float(scores[2]),
                items[3]:float(scores[3])
            }

    #get templates
    def get_template(tmplt_path):
        tmplts=[]
        N=0
        with open(tmplt_path) as fh:
            for line in fh:
                if N==4:
                    break
                tmplts.append(line.rstrip())
                N+=1
        return tmplts

    tmplt_path_prefix=""%s/%s/msas"" % (out_dir, job_id)
    out_json[""tcra_tmplts""]=get_template(""%s/A/template_names.txt"" % tmplt_path_prefix)
    out_json[""tcrb_tmplts""]=get_template(""%s/B/template_names.txt"" % tmplt_path_prefix)


    # clean up unwanted files
    subprocess.run(""mv %s/%s/ranked*pdb %s/; "" % (out_dir, job_id, out_dir), shell=True)
    subprocess.run(""rm -rf %s/%s*; "" % (out_dir, job_id), shell=True)


    ####################
    # # Renumber output  #
    ####################
    
    models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
    for model in models_list:
        tcr_utils.renumber_tcr_pdb(model, '%s/%s' % (out_dir, os.path.basename(model)))

    # align all to ranked_0's pMHC
    try:
        models_list = [i for i in glob(f""{out_dir}/*.pdb"") if os.path.basename(i).startswith('ranked')]
        ref=""%s/ranked_0.pdb"" % out_dir
        for pdb in models_list:
            pdb_aln = pdb.replace('.pdb', '_aln.pdb')
            pdb_utils.align_pdbs(ref, pdb, pdb_aln)
            subprocess.run(""mv %s %s"" % (pdb_aln, pdb), shell=True)
    except:
        print(""unable to align pdbs"")

    #parse tcr template sequences
    tcr_out_json={}
    for chain in ""ab"":
        tcr_key = f""tcr{chain}_seqs""
        tcr_out_json[tcr_key] = {}
        for pdb_chain in out_json[f""tcr{chain}_tmplts""]:
            in_seq = parse_tcr_seq.get_seq(pdb_chain)
            tcr_out_json[tcr_key][pdb_chain] = parse_tcr_seq.parse_tcr(in_seq)

    tcr_out_json[""tcra_user""] = parse_tcr_seq.parse_tcr(tcra_seq)
    tcr_out_json[""tcrb_user""] = parse_tcr_seq.parse_tcr(tcrb_seq)


    tcr_json_output_path = os.path.join(out_dir, 'tcr_seqs.json')
    with open(tcr_json_output_path, 'w') as f:
        f.write(json.dumps(tcr_out_json, indent=4))


    #get CDR3s confidence scores 
    def get_cdr3_conf(pdb_path):
       import MDAnalysis as mda
       pdb_u = mda.Universe(pdb_path)
       cdr3a_bfactors_avg = pdb_u.select_atoms('chainID D and resid 106:139').atoms.bfactors.mean()
       cdr3b_bfactors_avg = pdb_u.select_atoms('chainID E and resid 106:139').atoms.bfactors.mean()
       return cdr3a_bfactors_avg, cdr3b_bfactors_avg

    models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
    for model in models_list:
       cdr3a_bfactors_avg, cdr3b_bfactors_avg = get_cdr3_conf(model)
       out_json[os.path.basename(model).split('.pdb')[0]]['cdr3a_plddt'] = cdr3a_bfactors_avg
       out_json[os.path.basename(model).split('.pdb')[0]]['cdr3b_plddt'] = cdr3b_bfactors_avg

    json_output_path = os.path.join(out_dir, 'statistics.json')
    with open(json_output_path, 'w') as f:
        f.write(json.dumps(out_json, indent=4))


if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass
","Python"
"AlphaFold","piercelab/tcrmodel2","run_tcrmodel2.py",".py","17009","408","# Load required packages
# import pandas as pd
import json
import os
import subprocess
import sys
from glob import glob

from absl import app, flags
from anarci import anarci

from scripts import parse_tcr_seq, pdb_utils, pmhc_templates, seq_utils, tcr_utils

# import shutil



# input
flags.DEFINE_string('output_dir', ""experiments/"", 
                    'Path to output directory.')
flags.DEFINE_string('tcra_seq', None, 'TCR alpha sequence')
flags.DEFINE_string('tcrb_seq', None, 'TCR beta sequence')
flags.DEFINE_string('pep_seq', None, 'Peptide sequence')
flags.DEFINE_string('mhca_seq', None, 'MHC alpha sequence. If your target is a class I '
                    'TCR-pMHC complex, then this input should contain the alpha 1 and '
                     'alpha 2 domain sequence. If your target is a class II TCR-pMHC '
                     'complex, then this input should contain alpha 1 domain sequence. '
                     'If your input has more than the above-mentioned domain(s), the function '
                     'seq_utils.trim_mhc will trim the input sequence down to the desired domains.')
flags.DEFINE_string('mhcb_seq', None, 'MHC beta sequence. Leave this argument blank, or '
                    'leave it out completely if your target is a class I TCR-pMHC complex. '
                    'If your target is a class II TCR-pMHC complex, this input should '
                    'contain beta 1 domain sequence. If your input has more than the '
                    'above-mentioned domain(s), the function seq_utils.trim_mhc will '
                    'trim the input sequence down to the desired domains.')
flags.DEFINE_string('job_id', ""test001"", 'Job id')
flags.DEFINE_string('ignore_pdbs_string', None, ""Do not use these pdbs as pmhc ""
                    ""templates, comma seperated pdb string, no space in between. ""
                    ""Can be upper or lower case. "")
flags.DEFINE_string('max_template_date', ""2100-01-01"", ""Max template date, ""
                    ""format yyyy-mm-dd. Default to 2100-01-01."")
flags.DEFINE_bool('relax_structures', False, ""Run amber minimization ""
                  ""on the structures"")
flags.DEFINE_string(""tp_db"", ""data/databases"" , 
                    ""Customized TCR pMHC database path"")
flags.DEFINE_string(""ori_db"", None,
                    ""Path to AlphaFold database with pdb_mmcif and params"")

FLAGS = flags.FLAGS

def main(_argv):
    output_dir=FLAGS.output_dir
    tcra_seq=FLAGS.tcra_seq
    tcrb_seq=FLAGS.tcrb_seq
    pep_seq=FLAGS.pep_seq
    mhca_seq=FLAGS.mhca_seq
    mhcb_seq=FLAGS.mhcb_seq
    job_id=FLAGS.job_id
    ignore_pdbs_string=FLAGS.ignore_pdbs_string
    max_template_date=FLAGS.max_template_date
    relax_structures=FLAGS.relax_structures
    tp_db=FLAGS.tp_db
    ori_db=FLAGS.ori_db

    if len(max_template_date)==0:
        max_template_date=""2100-01-01""
        
    models_to_relax=""none""
    if relax_structures==True:
        models_to_relax=""all""
    # process ignore_pdb list
    ignore_pdbs=[]
    if ignore_pdbs_string:
        try:
            ignore_pdbs=[pdb.lower() for pdb in ignore_pdbs_string.split("","")]
        except:
            ignore_pdbs=[]

    # create output directory
    out_dir=os.path.join(output_dir,job_id)
    os.makedirs(out_dir, exist_ok=True)

    # check MHC class of the complex
    mhc_cls=1
    if mhcb_seq:
        mhc_cls=2

    # check peptide length of the user input
    pep_len = len(pep_seq)
    if mhc_cls==1:
        if pep_len < 8 or pep_len > 15:
            print(f""It looks like your input peptide is {pep_len} amino acids long. For class I TCR-pMHC complexes, kindly ensure the peptide length is between 8-15."")
            sys.exit()
    else:
        if pep_len != 11:
            print(f""It looks like your input peptide is {pep_len} amino acids (aa) long. For class II TCR-pMHC complexes, kindly ensure that the peptide input is 11 aa in length. Specifically, it should consist of a 9 aa core with an additional 1 aa at both the N-terminal and C-terminal of the core peptide."")
            sys.exit()
    
    # trim tcr sequence to variable domain only
    anarci_tcra=anarci([('tcra', tcra_seq)], scheme=""aho"", output=False)
    anarci_tcrb=anarci([('tcrb', tcrb_seq)], scheme=""aho"", output=False)
    tcra_seq="""".join([item[-1] for item in anarci_tcra[0][0][0][0] if item[-1] != '-'])
    tcrb_seq="""".join([item[-1] for item in anarci_tcrb[0][0][0][0] if item[-1] != '-'])

    # trim mhc sequence to relevant domains only
    if mhc_cls==1:
        try:
            mhca_seq=seq_utils.trim_mhc(mhca_seq, ""1"", ""."", out_dir)
        except:
            print(""Fail to identify alpha 1 and alpha 2 domain sequence in the 'mhca_seq' ""
                  ""input of your class I MHC target."")
            sys.exit()
    else:
        try:
            mhca_seq=seq_utils.trim_mhc(mhca_seq, ""2"", ""."", out_dir)
        except:
            print(""Fail to identify alpha 1 domain sequence in the 'mhca_seq' ""
                  ""input of your class II MHC target. If your input target is a class I ""
                  ""TCR-pMHC complex, then mhcb_seq variable should be left empty or left ""
                  ""out completely."")
            sys.exit()
        try:
            mhcb_seq=seq_utils.trim_mhc(mhcb_seq, ""3"", ""."", out_dir)
        except:
            print(""Fail to identify beta 1 domain sequence in the 'mhcb_seq' ""
                  ""input of your class II MHC target. If your input target is a class I ""
                  ""TCR-pMHC complex, then mhcb_seq variable should be left empty or left ""
                  ""out completely."")
            sys.exit()

    # build pmhc templates
    if mhc_cls==1:
        pmhc_templates.gen_align_file_cls1(pep_seq, mhca_seq, out_dir, ignore_pdbs, max_template_date)
    else:
        pmhc_templates.gen_align_file_corep1_cls2(pep_seq, mhca_seq, mhcb_seq, out_dir, ignore_pdbs, max_template_date)

    # create status file and update it
    status_file=os.path.join(out_dir,""modeling_status.txt"")
    with open(status_file, 'w') as fh:
        fh.write(""Template features for pMHC created! Now generating MSA features and TCR template features...\n"")

    # create fasta files 
    fasta_fn=os.path.join(out_dir, ""%s.fasta"" % job_id)
    pmhc_oc_fasta_fn=os.path.join(out_dir, ""%s_pmhc_oc.fasta"" % job_id)

    fasta="">TCRa\n%s\n"" % tcra_seq
    fasta+="">TCRb\n%s\n"" % tcrb_seq
    fasta+="">Peptide\n%s\n"" % pep_seq
    fasta+="">MHCa\n%s\n"" % mhca_seq
    if mhc_cls==2:
        fasta+="">MHCb\n%s\n"" % mhcb_seq

    pmhc_oc_fasta="">TCRa\n%s\n"" % tcra_seq
    pmhc_oc_fasta+="">TCRb\n%s\n"" % tcrb_seq
    pmhc_oc_fasta+="">pMHC\n%s:%s"" % (pep_seq, mhca_seq)
    if mhc_cls==2:
        pmhc_oc_fasta+="":%s\n"" % mhcb_seq

    with open(fasta_fn,'w+') as fh:
        fh.write(fasta)
    with open(pmhc_oc_fasta_fn,'w+') as fh:
        fh.write(pmhc_oc_fasta)

    ###############
    # build MSA #
    ###############

    template_string="",,,""
    if mhc_cls==2:
        template_string="",,,,""
    databases=(f""--uniref90_database_path={tp_db}/uniref90.tcrmhc.fasta "" 
            f""--mgnify_database_path={tp_db}/mgnify.fasta ""
            f""--template_mmcif_dir={ori_db}/pdb_mmcif/mmcif_files/ ""
            f""--obsolete_pdbs_path={ori_db}/pdb_mmcif/obsolete.dat ""
            f""--small_bfd_database_path={tp_db}/small_bfd.tcrmhc.fasta ""
            f""--pdb_seqres_database_path={tp_db}/pdb_seqres.txt ""
            f""--uniprot_database_path={tp_db}/uniprot.tcrmhc.fasta ""
            f""--data_dir={ori_db}"")
    cmd=(f""python run_alphafold_tcrmodel2.3.py --db_preset=reduced_dbs ""
         f""--fasta_paths={out_dir}/{job_id}.fasta ""
         f""--model_preset=multimer --output_dir={out_dir} {databases} ""
         f""--max_template_date={max_template_date} --use_gpu_relax=False ""
         f""--save_msa_features_only --gen_feats_only ""
         f""--models_to_relax=none --feature_prefix=msa ""
         f""--save_template_names --use_custom_templates ""
         f""--template_alignfile={template_string}"")
    subprocess.run(cmd, shell=True)

    # remove unwanted files to save space
    subprocess.run(""rm -rf %s/%s/msas/"" % (out_dir, job_id), shell=True)

    ###################
    # build structure #
    ###################
    # model_log_output=os.path.join(out_dir, ""modeling_log.txt"")
    cmd=(f""python run_alphafold_tcrmodel2.3.py --db_preset=reduced_dbs ""
         f""--fasta_paths={out_dir}/{job_id}_pmhc_oc.fasta ""
         f""--model_preset=multimer --output_dir={out_dir} {databases} ""
         ""--use_custom_templates --template_alignfile=UseDefaultTemplate,""
         f""UseDefaultTemplate,{out_dir}/pmhc_alignment.tsv ""
         f""--max_template_date={max_template_date} ""
         f""--use_gpu_relax={relax_structures} ""
         f""--models_to_relax={models_to_relax} --use_precomputed_msas=True ""
         ""--num_multimer_predictions_per_model=1  --save_template_names ""
         ""--has_gap_chn_brk --msa_mode=single_sequence --iptm_interface=1:1:2 ""
         f""--substitute_msa={out_dir}/{job_id}/msa_features.pkl ""
         f""--status_file={status_file}"" )
    subprocess.run(cmd, shell=True)

    # renumber chains to start with A if not relax_structures
    if not relax_structures:
        models_list = [i for i in glob(f""{out_dir}/{job_id}_pmhc_oc/*.pdb"") if os.path.basename(i).startswith('ranked')]
        for pdb_fn in models_list:
            pdb=[]
            with open(pdb_fn) as fh:
                for line in fh:
                    if line[0:4] == 'ATOM':
                        pdb.append(line.rstrip())
            pdb_renum=pdb_utils.rename_chains_start_A_and_1(pdb)
            pdb_renum_fn = pdb_fn.replace('.pdb', '_renum.pdb')
            with open(pdb_renum_fn,'w+') as fh:
                fh.write(""\n"".join(pdb_renum))
            subprocess.run(""mv %s %s"" % (pdb_renum_fn, pdb_fn), shell=True)


    ####################
    # Parse statistics #
    ####################
    out_json={}
    
    #get scores
    items=['ranking_confidence','plddt','ptm','iptm','tcr-pmhc_iptm']

    with open(""%s/%s_pmhc_oc/model_scores.txt"" % (out_dir, job_id)) as fh:
        for idx, line in enumerate(fh):
            scores=line.rstrip().split(""\t"")
            out_json[""ranked_%d"" % (idx)]={
                items[0]:float(scores[0]),
                items[1]:float(scores[1]),
                items[2]:float(scores[2]),
                items[3]:float(scores[3]),
                items[4]:float(scores[4])
                }

    #get templates
    def get_template(tmplt_path):
        tmplts=[]
        N=0
        with open(tmplt_path) as fh:
            for line in fh:
                if N==4:
                    break
                tmplts.append(line.rstrip())
                N+=1
        return tmplts

    tmplt_path_prefix=""%s/%s_pmhc_oc/msas"" % (out_dir, job_id)
    out_json[""tcra_tmplts""]=get_template(""%s/A/template_names.txt"" % tmplt_path_prefix)
    out_json[""tcrb_tmplts""]=get_template(""%s/B/template_names.txt"" % tmplt_path_prefix)
    out_json[""pmhc_tmplts""]=get_template(""%s/C/template_names.txt"" % tmplt_path_prefix)

    json_output_path = os.path.join(out_dir, 'statistics.json')
    with open(json_output_path, 'w') as f:
        f.write(json.dumps(out_json, indent=4))

    # clean up unwanted files
    subprocess.run(""mv %s/%s_pmhc_oc/ranked*pdb %s/; "" % (out_dir, job_id, out_dir), shell=True)
    subprocess.run(""rm -rf %s/%s*; "" % (out_dir, job_id), shell=True)
    subprocess.run(""rm %s/pmhc_alignment.tsv; "" % (out_dir), shell=True)
    
        
    ####################
    # Renumber output  #
    ####################
    
    models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
    for model in models_list:
        tcr_utils.renumber_pdb(model, '%s/%s' % (out_dir, os.path.basename(model)), mhc_cls)   

    # align all to ranked_0's pMHC
    try:
        models_list = [i for i in glob(f""{out_dir}/*.pdb"") if os.path.basename(i).startswith('ranked')]
        ref=""%s/ranked_0.pdb"" % out_dir
        for pdb in models_list:
            pdb_aln = pdb.replace('.pdb', '_aln.pdb')
            pdb_utils.align_pdbs_by_pmhc(ref, pdb, pdb_aln, mhc_cls)
            subprocess.run(""mv %s %s"" % (pdb_aln, pdb), shell=True)
    except:
        print(""unable to align pdbs"")


    #get CDR3s confidence scores 
    def get_cdr3_conf(pdb_path):
       import MDAnalysis as mda
       pdb_u = mda.Universe(pdb_path)
       cdr3a_bfactors_avg = pdb_u.select_atoms('chainID D and resid 106:139').atoms.bfactors.mean()
       cdr3b_bfactors_avg = pdb_u.select_atoms('chainID E and resid 106:139').atoms.bfactors.mean()
       return cdr3a_bfactors_avg, cdr3b_bfactors_avg

    models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
    for model in models_list:
       cdr3a_bfactors_avg, cdr3b_bfactors_avg = get_cdr3_conf(model)
       out_json[os.path.basename(model).split('.pdb')[0]]['cdr3a_plddt'] = cdr3a_bfactors_avg
       out_json[os.path.basename(model).split('.pdb')[0]]['cdr3b_plddt'] = cdr3b_bfactors_avg

    ## Calculating iplddt score
     
    def calc_iplddt(pdb_file):
         
         chn1= ""ABC""
         chn2= ""DE""
         dis_cut=4
         lowest=-1.00
         
         try:
             with open(pdb_file, 'r') as file:
                 pdb_lines = file.readlines()
         except IOError:
             sys.exit(f""unable to open file: {pdb_file}"")
         
         chn1int = {char: 1 for char in chn1}
         chn2int = {char: 1 for char in chn2}
         
         dis_cutoff = float(dis_cut) ** 2 
         
         chn1_int_plddt = {}
         chn2_int_plddt = {}
             
         for line in pdb_lines:
                 if not line.startswith(""ATOM""):
                     continue
                 if line[12] == ""H"" or line[13] == ""H"":
                     continue
                 res_num = line[22:27].strip()
                 chn_id = line[21].strip()
                 res_id = line[17:20].strip()
                 atm_id = line[12:16].strip()
                 plddt1 = float(line[60:66].strip())
                 
                 if chn1int.get(chn_id) == 1:
                     x1 = float(line[30:38].strip())
                     y1 = float(line[38:46].strip())
                     z1 = float(line[46:54].strip())
                     
                     for line2 in pdb_lines:
                         if not line2.startswith(""ATOM""):
                             continue
                         if line2[12] == ""H"" or line2[13] == ""H"":
                             continue
                         
                         chn2 = line2[21].strip()
                         res_num2 = line2[22:27].strip()
                         res_id2 = line2[17:20].strip()
                         atm_id = line2[12:16].strip()
                         plddt2 = float(line2[60:66].strip())
                         
                         if chn2int.get(chn2) == 1:
                             x2 = float(line2[30:38].strip())
                             y2 = float(line2[38:46].strip())
                             z2 = float(line2[46:54].strip())
                             dist = ((x1 - x2)**2) + ((y1 - y2)**2) + ((z1 - z2)**2)
                             if dist < dis_cutoff:
                                 chn1_int_plddt[f""{res_num}\t{chn_id}\t{res_id}""] = plddt1
                                 chn2_int_plddt[f""{res_num2}\t{chn2}\t{res_id2}""] = plddt2
                             
         weighted_sum = sum(chn1_int_plddt.values()) + sum(chn2_int_plddt.values())
         counts = len(chn1_int_plddt) + len(chn2_int_plddt)
         
         final_score = lowest if counts == 0 else weighted_sum / counts
         final_score = ""{:.2f}"".format(final_score)
         
         return final_score
    
    models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
    for model in models_list:
       iplddt_score = calc_iplddt(model)
       out_json[os.path.basename(model).split('.pdb')[0]]['IpLDDT'] = iplddt_score
    
    #write statistics
    json_output_path = os.path.join(out_dir, 'statistics.json')
    with open(json_output_path, 'w') as f:
        f.write(json.dumps(out_json, indent=4))

    tcr_out_json = {}
    #parse tcr template sequences
    for chain in ""ab"":
        tcr_key = f""tcr{chain}_seqs""
        tcr_out_json[tcr_key] = {}
        for pdb_chain in out_json[f""tcr{chain}_tmplts""]:
            in_seq = parse_tcr_seq.get_seq(pdb_chain)
            tcr_out_json[tcr_key][pdb_chain] = parse_tcr_seq.parse_tcr(in_seq)

    tcr_out_json[""tcra_user""] = parse_tcr_seq.parse_tcr(tcra_seq)
    tcr_out_json[""tcrb_user""] = parse_tcr_seq.parse_tcr(tcrb_seq)

    tcr_json_output_path = os.path.join(out_dir, 'tcr_seqs.json')
    with open(tcr_json_output_path, 'w') as f:
        f.write(json.dumps(tcr_out_json, indent=4))



if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass
    
","Python"
"AlphaFold","piercelab/tcrmodel2","run_alphafold_tcrmodel2.3.py",".py","30340","713","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Full AlphaFold protein structure prediction script.""""""
import enum
import json
import os
import pathlib
import pickle
import random
import shutil
import sys
import time
from typing import Any, Dict, Mapping, Union

from absl import app
from absl import flags
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import pipeline
from alphafold.data import pipeline_multimer
from alphafold.data import pipeline_custom_templates
from alphafold.data import pipeline_multimer_custom_templates
from alphafold.data import templates
from alphafold.data.tools import hhsearch
from alphafold.data.tools import hmmsearch
from alphafold.model import config
from alphafold.model import data
from alphafold.model import model
from alphafold.relax import relax

import jax.numpy as jnp
import numpy as np
import pickle as pkl

# Internal import (7716).

logging.set_verbosity(logging.INFO)


@enum.unique
class ModelsToRelax(enum.Enum):
  ALL = 0
  BEST = 1
  NONE = 2

flags.DEFINE_list(
    'fasta_paths', None, 'Paths to FASTA files, each containing a prediction '
    'target that will be folded one after another. If a FASTA file contains '
    'multiple sequences, then it will be folded as a multimer. Paths should be '
    'separated by commas. All FASTA paths must have a unique basename as the '
    'basename is used to name the output directories for each prediction.')

flags.DEFINE_string('data_dir', None, 
                    'Path to directory of supporting data.')
flags.DEFINE_string('output_dir', None, 'Path to a directory that will '
                    'store the results.')
flags.DEFINE_string('jackhmmer_binary_path', shutil.which('jackhmmer'),
                    'Path to the JackHMMER executable.')
flags.DEFINE_string('hhblits_binary_path', shutil.which('hhblits'),
                    'Path to the HHblits executable.')
flags.DEFINE_string('hhsearch_binary_path', shutil.which('hhsearch'),
                    'Path to the HHsearch executable.')
flags.DEFINE_string('hmmsearch_binary_path', shutil.which('hmmsearch'),
                    'Path to the hmmsearch executable.')
flags.DEFINE_string('hmmbuild_binary_path', shutil.which('hmmbuild'),
                    'Path to the hmmbuild executable.')
flags.DEFINE_string('kalign_binary_path', shutil.which('kalign'),
                    'Path to the Kalign executable.')
flags.DEFINE_string('uniref90_database_path',None, 
                    'Path to the Uniref90 database for use by JackHMMER.')
flags.DEFINE_string('mgnify_database_path', None,
                    'Path to the MGnify database for use by JackHMMER.')
flags.DEFINE_string('bfd_database_path', None, 'Path to the BFD '
                    'database for use by HHblits.')
flags.DEFINE_string('small_bfd_database_path', None, 'Path to the small '
                    'version of BFD used with the ""reduced_dbs"" preset.')
flags.DEFINE_string('uniref30_database_path', None, 'Path to the UniRef30 '
                    'database for use by HHblits.')
flags.DEFINE_string('uniprot_database_path', None, 'Path to the Uniprot '
                    'database for use by JackHMMer.')
flags.DEFINE_string('pdb70_database_path', None, 'Path to the PDB70 '
                    'database for use by HHsearch.')
flags.DEFINE_string('pdb_seqres_database_path',None, 'Path to the PDB '
                    'seqres database for use by hmmsearch.')
flags.DEFINE_string('template_mmcif_dir', None, 'Path to a directory with '
                    'template mmCIF structures, each named <pdb_id>.cif')
flags.DEFINE_string('max_template_date', None, 'Maximum template release date '
                    'to consider. Important if folding historical test sets.')
flags.DEFINE_string('obsolete_pdbs_path', None, 'Path to file containing a '
                    'mapping from obsolete PDB IDs to the PDB IDs of their '
                    'replacements.')
flags.DEFINE_enum('db_preset', 'full_dbs',
                  ['full_dbs', 'reduced_dbs'],
                  'Choose preset MSA database configuration - '
                  'smaller genetic database config (reduced_dbs) or '
                  'full genetic database config  (full_dbs)')
flags.DEFINE_enum('model_preset', 'monomer',
                  ['monomer', 'monomer_casp14', 'monomer_ptm', 'multimer'],
                  'Choose preset model configuration - the monomer model, '
                  'the monomer model with extra ensembling, monomer model with '
                  'pTM head, or multimer model')
flags.DEFINE_boolean('benchmark', False, 'Run multiple JAX model evaluations '
                     'to obtain a timing that excludes the compilation time, '
                     'which should be more indicative of the time required for '
                     'inferencing many proteins.')
flags.DEFINE_integer('random_seed', None, 'The random seed for the data '
                     'pipeline. By default, this is randomly generated. Note '
                     'that even if this is set, Alphafold may still not be '
                     'deterministic, because processes like GPU inference are '
                     'nondeterministic.')
flags.DEFINE_integer('num_multimer_predictions_per_model', 5, 'How many '
                     'predictions (each with a different random seed) will be '
                     'generated per model. E.g. if this is 2 and there are 5 '
                     'models then there will be 10 predictions per input. '
                     'Note: this FLAG only applies if model_preset=multimer')
flags.DEFINE_boolean('use_precomputed_msas', True, 'Whether to read MSAs that '
                     'have been written to disk instead of running the MSA '
                     'tools. The MSA files are looked up in the output '
                     'directory, so it must stay the same between multiple '
                     'runs that are to reuse the MSAs. WARNING: This will not '
                     'check if the sequence, database or configuration have '
                     'changed.')
flags.DEFINE_enum_class('models_to_relax', ModelsToRelax.BEST, ModelsToRelax,
                        'The models to run the final relaxation step on. '
                        'If `all`, all models are relaxed, which may be time '
                        'consuming. If `best`, only the most confident model '
                        'is relaxed. If `none`, relaxation is not run. Turning '
                        'off relaxation might result in predictions with '
                        'distracting stereochemical violations but might help '
                        'in case you are having issues with the relaxation '
                        'stage.')
flags.DEFINE_boolean('use_gpu_relax', None, 'Whether to relax on GPU. '
                     'Relax on GPU can be much faster than CPU, so it is '
                     'recommended to enable if possible. GPUs must be available'
                     ' if this setting is enabled.')
flags.DEFINE_boolean('use_custom_templates', False, 'Whether to use custom '
                    'templates or not.')
flags.DEFINE_string('template_alignfile', None, 'The path to the custom template'
                    'files. If the target is a monomer, provide the template path '
                    'as-is. If a multimer, provide all template alignment files '
                    'the order they appear in the target, comma seperated. Leave '
                    'the path blank if no template should be used for a chain. '
                    'Write ""UseDefaultTemplate"" to use default alphafold pipeline '
                    'for generating the template for that chain.')
flags.DEFINE_string('msa_mode', None, 'Type ""single_sequence"" to not use any MSA')
flags.DEFINE_integer('num_recycle', 3, 'How many recycles')
flags.DEFINE_integer('num_ensemble', 1, 'How many ensembling iteractions')
flags.DEFINE_enum('use_custom_MSA_database', ""none"", [""none"", ""add"", ""only""], 'Whether to use custom '
                    'MSA database or not.')
flags.DEFINE_string('MSA_database', None, 'The path to the custom MSA database'
                    'files. If the target is a monomer, provide the template path '
                    'as-is. If a multimer, provide all template alignment files '
                    'the order they appear in the target, comma seperated.')
flags.DEFINE_string('run_model_names', None, 'Specify parameter name to run. This'
                    'is comma seperated alphafold parameter name. Only specified'
                    'model names will be run.')
flags.DEFINE_boolean('save_msa_fasta', False, 'Save msa features or not.')
flags.DEFINE_boolean('gen_feats_only', False, 'Only generate features and do not'
                    ' produce structure predictions.')
flags.DEFINE_boolean('save_template_names', False, 'Save template id to txt file.')
flags.DEFINE_boolean('has_gap_chn_brk', False, 'Have chain breaks introduced by "":"".')
flags.DEFINE_string('substitute_msa', None, 'Path to feature.pkl whose MSA will '
                    'be used to substitute whatever MSA that will be generated by '
                    'this prediction round.')
flags.DEFINE_boolean('msa_for_template_query_seq_only', True, 'msa_for_template_query_seq_only')
flags.DEFINE_string('iptm_interface', None, 'iptm_interface')
flags.DEFINE_string('feature_prefix', None, 'Feature prefix')
flags.DEFINE_boolean('save_ranked_pdb_only', False, 'Do not save result pkl files '
                     'or unrelaxed pdbs, or relaxed pdbs that are not ranked.')
flags.DEFINE_boolean('save_msa_features_only', False, 'Only save msa related features.')
flags.DEFINE_string(""status_file"", None, 'Status report file path.')


FLAGS = flags.FLAGS

MAX_TEMPLATE_HITS = 20
RELAX_MAX_ITERATIONS = 0
RELAX_ENERGY_TOLERANCE = 2.39
RELAX_STIFFNESS = 10.0
RELAX_EXCLUDE_RESIDUES = []
RELAX_MAX_OUTER_ITERATIONS = 3


def _check_flag(flag_name: str,
                other_flag_name: str,
                should_be_set: bool):
  if should_be_set != bool(FLAGS[flag_name].value):
    verb = 'be' if should_be_set else 'not be'
    raise ValueError(f'{flag_name} must {verb} set when running with '
                     f'""--{other_flag_name}={FLAGS[other_flag_name].value}"".')


def _jnp_to_np(output: Dict[str, Any]) -> Dict[str, Any]:
  """"""Recursively changes jax arrays to numpy arrays.""""""
  for k, v in output.items():
    if isinstance(v, dict):
      output[k] = _jnp_to_np(v)
    elif isinstance(v, jnp.ndarray):
      output[k] = np.array(v)
  return output


def interface_parser(interfaces_string):
  interfaces=[]
  for interface in interfaces_string.split("",""):
    interfaces.append([int(i) for i in interface.split("":"")])
  return interfaces

def gen_res_str(res):
    res_len = len(str(res))
    res_str = """"
    for i in range(4-res_len):
        res_str += "" ""
    res_str += str(res) + "" ""
    return res_str

def predict_structure(
    fasta_path: str,
    fasta_name: str,
    output_dir_base: str,
    data_pipeline: Union[pipeline.DataPipeline, pipeline_multimer.DataPipeline, pipeline_custom_templates.DataPipeline],
    model_runners: Dict[str, model.RunModel],
    amber_relaxer: None,
    benchmark: bool,
    random_seed: int,
    models_to_relax: ModelsToRelax,
    use_custom_templates: bool,
    template_alignfile: str,
    msa_mode: str,
    use_custom_MSA_database: str,
    MSA_database: str,
    save_msa_fasta: bool,
    gen_feats_only: bool,
    save_template_names: bool,
    has_gap_chn_brk: bool,
    substitute_msa: str,
    msa_for_template_query_seq_only: bool,
    iptm_interface: str,
    feature_prefix: str,
    save_ranked_pdb_only: bool,
    save_msa_features_only: bool,
    status_file: str):
  """"""Predicts structure using AlphaFold for the given sequence.""""""
  logging.info('Predicting %s', fasta_name)
  timings = {}
  output_dir = os.path.join(output_dir_base, fasta_name)
  if not os.path.exists(output_dir):
    os.makedirs(output_dir)
  msa_output_dir = os.path.join(output_dir, 'msas')
  if not os.path.exists(msa_output_dir):
    os.makedirs(msa_output_dir)

  # Get features.
  t_0 = time.time()
  if use_custom_templates or use_custom_MSA_database!=""none"":
    feature_dict = data_pipeline.process(
        input_fasta_path=fasta_path,
        msa_output_dir=msa_output_dir,
        use_custom_templates=use_custom_templates,
        template_alignfile=template_alignfile,
        msa_mode=msa_mode,
        use_custom_MSA_database=use_custom_MSA_database,
        MSA_database=MSA_database,
        save_msa_fasta=save_msa_fasta,
        save_template_names=save_template_names,
        msa_for_template_query_seq_only=msa_for_template_query_seq_only)
  else:
    feature_dict = data_pipeline.process(
      input_fasta_path=fasta_path,
      msa_output_dir=msa_output_dir,
      save_msa_fasta=save_msa_fasta,
      save_template_names=save_template_names,
      msa_for_template_query_seq_only=msa_for_template_query_seq_only)
  timings['features'] = time.time() - t_0

  # # Write out features as a pickled dictionary.
  if not save_ranked_pdb_only:
    features_output_path = os.path.join(output_dir, 'features.pkl')
    if feature_prefix:
      features_output_path = os.path.join(output_dir, '%s_features.pkl' % feature_prefix)
    if save_msa_features_only:
      key_save=['msa','deletion_matrix','cluster_bias_mask','bert_mask','msa_mask']
      feature_dict_new={}
      for key in key_save:
        feature_dict_new[key]=feature_dict[key]
      feature_dict=feature_dict_new
    with open(features_output_path, 'wb') as f:
      pickle.dump(feature_dict, f, protocol=4)


  
  if save_msa_fasta:
    with open(os.path.join(output_dir, ""all_msa_feat_gaptoU.fasta""), 'w+') as fh:
    # fh.write("">query""+""\n""+input_sequence+""\n"")
      counter=1
      for seq in feature_dict['msa']:
          seq=[residue_constants.ID_TO_HHBLITS_AA[num] for num in seq]
          counter+=1
          fh.write("">seq_""+str(counter)+""\n"")
          out="""".join(seq).replace(""-"",""U"")
          fh.write(out+""\n"")

  if gen_feats_only:
    return 
  if status_file:
    with open(status_file,""a"") as fh:
      fh.write(""All MSA and template features generated! Working on models now...\n"")

  if substitute_msa:
    keys_substitute=['msa','deletion_matrix','cluster_bias_mask','bert_mask','msa_mask']
    with open(substitute_msa, 'rb') as fh:
      substitute_feature_dict = pkl.load(fh)
    for key in keys_substitute:
      feature_dict[key]=substitute_feature_dict[key]
      
  unrelaxed_pdbs = {}
  unrelaxed_proteins = {}
  relaxed_pdbs = {}
  ranking_confidences = {}
  model_scores = {}
  relax_metrics = {}

  # Run the models.
  num_models = len(model_runners)
  for model_index, (model_name, model_runner) in enumerate(
      model_runners.items()):
    logging.info('Running model %s on %s', model_name, fasta_name)
    t_0 = time.time()
    model_random_seed = model_index + random_seed * num_models
    processed_feature_dict = model_runner.process_features(
        feature_dict, random_seed=model_random_seed)
    timings[f'process_features_{model_name}'] = time.time() - t_0

    interfaces=[]
    if iptm_interface:
      interfaces=interface_parser(iptm_interface)

    t_0 = time.time()
    prediction_result = model_runner.predict(processed_feature_dict,
                                             random_seed=model_random_seed,
                                             interfaces=interfaces)

    t_diff = time.time() - t_0
    timings[f'predict_and_compile_{model_name}'] = t_diff
    logging.info(
        'Total JAX model %s on %s predict time (includes compilation time, see --benchmark): %.1fs',
        model_name, fasta_name, t_diff)

    if benchmark:
      t_0 = time.time()
      model_runner.predict(processed_feature_dict,
                           random_seed=model_random_seed)
      t_diff = time.time() - t_0
      timings[f'predict_benchmark_{model_name}'] = t_diff
      logging.info(
          'Total JAX model %s on %s predict time (excludes compilation time): %.1fs',
          model_name, fasta_name, t_diff)

    plddt = prediction_result['plddt']
    ranking_confidences[model_name] = prediction_result['ranking_confidence']
    model_scores[model_name] = [prediction_result['ranking_confidence'], np.mean(prediction_result['plddt'])]
    if 'iptm' in prediction_result:
      model_scores[model_name].append(prediction_result['ptm'])
      model_scores[model_name].append(prediction_result['iptm'])
    if ""custom_iptm"" in prediction_result:
      for score in prediction_result['custom_iptm']:
        model_scores[model_name].append(score)



    if not save_ranked_pdb_only:
      # Remove jax dependency from results.
      np_prediction_result = _jnp_to_np(dict(prediction_result))
      result_output_path = os.path.join(output_dir, f'result_{model_name}.pkl')
      with open(result_output_path, 'wb') as f:
        pickle.dump(np_prediction_result, f, protocol=4)
    else:
      text_output_path = os.path.join(output_dir, 'model_%d_done' % model_index)
      with open(text_output_path,'w') as fh:
        fh.write("""")
        
    if status_file:
      if model_index < 4:
        with open(status_file,""a"") as fh:
          fh.write(""Model %d generated! Currently working on model %d...\n"" % (model_index+1, model_index+2))
      else:
        with open(status_file,""a"") as fh:
          fh.write(""Model %d generated! Wrapping things up now!\n"" % (model_index+1))

    # Add the predicted LDDT in the b-factor column.
    # Note that higher predicted LDDT value means higher model confidence.
    plddt_b_factors = np.repeat(
        plddt[:, None], residue_constants.atom_type_num, axis=-1)
    
    unrelaxed_protein = protein.from_prediction(
        features=processed_feature_dict,
        result=prediction_result,
        b_factors=plddt_b_factors,
        remove_leading_feature_dimension=not model_runner.multimer_mode)


    if has_gap_chn_brk:
      #break chain
      prev_res=0
      new_res=0
      curr_chn=0
      chn_idx_adj=[]
      new_res_index=[]
      for res in unrelaxed_protein.residue_index:
          if res-prev_res>199:
              prev_res=res
              curr_chn+=1
              chn_idx_adj.append(curr_chn)
              new_res=1
          else:
              prev_res=res
              chn_idx_adj.append(curr_chn)
              new_res+=1
          new_res_index.append(new_res)
      chain_index=np.add(unrelaxed_protein.chain_index, np.array(chn_idx_adj))
      unrelaxed_protein= protein.Protein(
        aatype=unrelaxed_protein.aatype,
        atom_positions=unrelaxed_protein.atom_positions,
        atom_mask=unrelaxed_protein.atom_mask,
        residue_index=np.array(new_res_index,dtype=np.int32),
        chain_index=np.array(chain_index,dtype=np.int32),
        b_factors=unrelaxed_protein.b_factors) 

    unrelaxed_proteins[model_name] = unrelaxed_protein
    unrelaxed_pdbs[model_name] = protein.to_pdb(unrelaxed_protein)
    
    if not save_ranked_pdb_only:
      unrelaxed_pdb_path = os.path.join(output_dir, f'unrelaxed_{model_name}.pdb')
      with open(unrelaxed_pdb_path, 'w') as f:
        f.write(unrelaxed_pdbs[model_name])



  # Rank by model confidence.
  ranked_order = [
      model_name for model_name, confidence in
      sorted(ranking_confidences.items(), key=lambda x: x[1], reverse=True)]

  # Relax predictions.
  if models_to_relax == ModelsToRelax.BEST:
    to_relax = [ranked_order[0]]
  elif models_to_relax == ModelsToRelax.ALL:
    to_relax = ranked_order
  elif models_to_relax == ModelsToRelax.NONE:
    to_relax = []

  for model_name in to_relax:
    t_0 = time.time()
    relaxed_pdb_str, _, violations = amber_relaxer.process(
        prot=unrelaxed_proteins[model_name])
    relax_metrics[model_name] = {
        'remaining_violations': violations,
        'remaining_violations_count': sum(violations)
    }
    timings[f'relax_{model_name}'] = time.time() - t_0

    relaxed_pdbs[model_name] = relaxed_pdb_str

    if not save_ranked_pdb_only:
      relaxed_output_path = os.path.join(
          output_dir, f'relaxed_{model_name}.pdb')
      with open(relaxed_output_path, 'w') as f:
        f.write(relaxed_pdb_str)

  # Write out relaxed PDBs in rank order.
  for idx, model_name in enumerate(ranked_order):
    ranked_output_path = os.path.join(output_dir, f'ranked_{idx}.pdb')
    with open(ranked_output_path, 'w') as f:
      if model_name in relaxed_pdbs:
        f.write(relaxed_pdbs[model_name])
      else:
        f.write(unrelaxed_pdbs[model_name])

  model_scores_output=""""
  for model in ranked_order:
    model_scores_output+=""%s\n"" % ""\t"".join(map(str,model_scores[model]))
  model_scores_output_path = os.path.join(output_dir, f'model_scores.txt')
  with open(model_scores_output_path, 'w') as f:
    f.write(model_scores_output)

  ranking_output_path = os.path.join(output_dir, 'ranking_debug.json')
  with open(ranking_output_path, 'w') as f:
    label = 'iptm+ptm' if 'iptm' in prediction_result else 'plddts'
    f.write(json.dumps(
        {label: ranking_confidences, 'order': ranked_order}, indent=4))

  logging.info('Final timings for %s: %s', fasta_name, timings)

  timings_output_path = os.path.join(output_dir, 'timings.json')
  with open(timings_output_path, 'w') as f:
    f.write(json.dumps(timings, indent=4))
  if models_to_relax != ModelsToRelax.NONE:
    relax_metrics_path = os.path.join(output_dir, 'relax_metrics.json')
    with open(relax_metrics_path, 'w') as f:
      f.write(json.dumps(relax_metrics, indent=4))


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  for tool_name in (
      'jackhmmer', 'hhblits', 'hhsearch', 'hmmsearch', 'hmmbuild', 'kalign'):
    if not FLAGS[f'{tool_name}_binary_path'].value:
      raise ValueError(f'Could not find path to the ""{tool_name}"" binary. Make '
                       'sure it is installed on your system.')
    
  if FLAGS.gen_feats_only:
    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

  use_small_bfd = FLAGS.db_preset == 'reduced_dbs'
  # _check_flag('small_bfd_database_path', 'db_preset',
  #             should_be_set=use_small_bfd)
  # _check_flag('bfd_database_path', 'db_preset',
  #             should_be_set=not use_small_bfd)
  # _check_flag('uniref30_database_path', 'db_preset',
  #             should_be_set=not use_small_bfd)

  run_multimer_system = 'multimer' in FLAGS.model_preset
  # _check_flag('pdb70_database_path', 'model_preset',
  #             should_be_set=not run_multimer_system)
  # _check_flag('pdb_seqres_database_path', 'model_preset',
  #             should_be_set=run_multimer_system)
  # _check_flag('uniprot_database_path', 'model_preset',
  #             should_be_set=run_multimer_system)

  if FLAGS.model_preset == 'monomer_casp14':
    num_ensemble = 8
  else:
    num_ensemble = 1

  # Check for duplicate FASTA file names.
  fasta_names = [pathlib.Path(p).stem for p in FLAGS.fasta_paths]
  if len(fasta_names) != len(set(fasta_names)):
    raise ValueError('All FASTA paths must have a unique basename.')

  if run_multimer_system:
    template_searcher = hmmsearch.Hmmsearch(
        binary_path=FLAGS.hmmsearch_binary_path,
        hmmbuild_binary_path=FLAGS.hmmbuild_binary_path,
        database_path=FLAGS.pdb_seqres_database_path)
    template_featurizer = templates.HmmsearchHitFeaturizer(
        mmcif_dir=FLAGS.template_mmcif_dir,
        max_template_date=FLAGS.max_template_date,
        max_hits=MAX_TEMPLATE_HITS,
        kalign_binary_path=FLAGS.kalign_binary_path,
        release_dates_path=None,
        obsolete_pdbs_path=FLAGS.obsolete_pdbs_path)
  else:
    template_searcher = hhsearch.HHSearch(
        binary_path=FLAGS.hhsearch_binary_path,
        databases=[FLAGS.pdb70_database_path])
    template_featurizer = templates.HhsearchHitFeaturizer(
        mmcif_dir=FLAGS.template_mmcif_dir,
        max_template_date=FLAGS.max_template_date,
        max_hits=MAX_TEMPLATE_HITS,
        kalign_binary_path=FLAGS.kalign_binary_path,
        release_dates_path=None,
        obsolete_pdbs_path=FLAGS.obsolete_pdbs_path)

  if FLAGS.use_custom_templates or FLAGS.use_custom_MSA_database!=""none"":
    monomer_data_pipeline = pipeline_custom_templates.DataPipeline(
        jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
        hhblits_binary_path=FLAGS.hhblits_binary_path,
        uniref90_database_path=FLAGS.uniref90_database_path,
        mgnify_database_path=FLAGS.mgnify_database_path,
        bfd_database_path=FLAGS.bfd_database_path,
        uniref30_database_path=FLAGS.uniref30_database_path,
        small_bfd_database_path=FLAGS.small_bfd_database_path,
        template_searcher=template_searcher,
        template_featurizer=template_featurizer,
        use_small_bfd=use_small_bfd,
        use_precomputed_msas=FLAGS.use_precomputed_msas)
  else:
    monomer_data_pipeline = pipeline.DataPipeline(
        jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
        hhblits_binary_path=FLAGS.hhblits_binary_path,
        uniref90_database_path=FLAGS.uniref90_database_path,
        mgnify_database_path=FLAGS.mgnify_database_path,
        bfd_database_path=FLAGS.bfd_database_path,
        uniref30_database_path=FLAGS.uniref30_database_path,
        small_bfd_database_path=FLAGS.small_bfd_database_path,
        template_searcher=template_searcher,
        template_featurizer=template_featurizer,
        use_small_bfd=use_small_bfd,
        use_precomputed_msas=FLAGS.use_precomputed_msas)

  if run_multimer_system:
    num_predictions_per_model = FLAGS.num_multimer_predictions_per_model
    if FLAGS.use_custom_templates or FLAGS.use_custom_MSA_database!=""none"":
      data_pipeline = pipeline_multimer_custom_templates.DataPipeline(
          monomer_data_pipeline=monomer_data_pipeline,
          jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
          uniprot_database_path=FLAGS.uniprot_database_path,
          use_precomputed_msas=FLAGS.use_precomputed_msas)
    else:
      data_pipeline = pipeline_multimer.DataPipeline(
          monomer_data_pipeline=monomer_data_pipeline,
          jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
          uniprot_database_path=FLAGS.uniprot_database_path,
          use_precomputed_msas=FLAGS.use_precomputed_msas)
  else:
    num_predictions_per_model = 1
    data_pipeline = monomer_data_pipeline

  num_recycle=FLAGS.num_recycle
  num_ensemble=FLAGS.num_ensemble

  model_runners = {}
  model_names = config.MODEL_PRESETS[FLAGS.model_preset]
  for model_name in model_names:
    if FLAGS.run_model_names and model_name not in FLAGS.run_model_names.split("",""):
        continue
    model_config = config.model_config(model_name)
    if run_multimer_system:
      # model_config.model.num_ensemble_eval = num_ensemble
      model_config.model.num_recycle = num_recycle
      model_config.model.num_ensemble_train = num_ensemble
      model_config.model.num_ensemble_eval = num_ensemble
    else:
      model_config.data.common.num_recycle = num_recycle
      model_config.model.num_recycle = num_recycle
      model_config.data.eval.num_ensemble = num_ensemble
    
    model_params = data.get_model_haiku_params(
        model_name=model_name, data_dir=FLAGS.data_dir)
    model_runner = model.RunModel(model_config, model_params)
    for i in range(num_predictions_per_model):
      model_runners[f'{model_name}_pred_{i}'] = model_runner

  logging.info('Have %d models: %s', len(model_runners),
               list(model_runners.keys()))

  if FLAGS.models_to_relax!=""none"":
    amber_relaxer = relax.AmberRelaxation(
        max_iterations=RELAX_MAX_ITERATIONS,
        tolerance=RELAX_ENERGY_TOLERANCE,
        stiffness=RELAX_STIFFNESS,
        exclude_residues=RELAX_EXCLUDE_RESIDUES,
        max_outer_iterations=RELAX_MAX_OUTER_ITERATIONS,
        use_gpu=FLAGS.use_gpu_relax)
  else:
    amber_relaxer = None

  random_seed = FLAGS.random_seed
  if random_seed is None:
    random_seed = random.randrange(sys.maxsize // len(model_runners))
  logging.info('Using random seed %d for the data pipeline', random_seed)

  # Predict structure for each of the sequences.
  for i, fasta_path in enumerate(FLAGS.fasta_paths):
    fasta_name = fasta_names[i]
    predict_structure(
        fasta_path=fasta_path,
        fasta_name=fasta_name,
        output_dir_base=FLAGS.output_dir,
        data_pipeline=data_pipeline,
        model_runners=model_runners,
        amber_relaxer=amber_relaxer,
        benchmark=FLAGS.benchmark,
        random_seed=random_seed,
        models_to_relax=FLAGS.models_to_relax,
        use_custom_templates=FLAGS.use_custom_templates,
        template_alignfile=FLAGS.template_alignfile,
        msa_mode=FLAGS.msa_mode,
        use_custom_MSA_database=FLAGS.use_custom_MSA_database,
        MSA_database=FLAGS.MSA_database,
        save_msa_fasta=FLAGS.save_msa_fasta,
        gen_feats_only=FLAGS.gen_feats_only,
        save_template_names=FLAGS.save_template_names,
        has_gap_chn_brk=FLAGS.has_gap_chn_brk,
        substitute_msa=FLAGS.substitute_msa,
        msa_for_template_query_seq_only=FLAGS.msa_for_template_query_seq_only,
        iptm_interface=FLAGS.iptm_interface,
        feature_prefix=FLAGS.feature_prefix,
        save_ranked_pdb_only=FLAGS.save_ranked_pdb_only,
        save_msa_features_only=FLAGS.save_msa_features_only,
        status_file=FLAGS.status_file)


if __name__ == '__main__':
  flags.mark_flags_as_required([
      'fasta_paths',
      'output_dir',
      'data_dir',
      'uniref90_database_path',
      'mgnify_database_path',
      'template_mmcif_dir',
      'max_template_date',
      'obsolete_pdbs_path',
      'use_gpu_relax',
  ])

  app.run(main)
","Python"
"AlphaFold","piercelab/tcrmodel2","singularity/run_tcrmodel2_singularity.sh",".sh","893","23","
#!/bin/bash

#databases path
ALPHAFOLD_DB=/path/to/alphafold_db

#singularity sif
ALPHAFOLD_SIF=/path/to/tcrmodel2.sif

#output directory
OUTPUT_DIR=/path/to/output_directory

# run tcrmodel singularity
singularity run --nv -B $ALPHAFOLD_DB  $ALPHAFOLD_SIF \
    --job_id=test_clsI_6kzw \
    --output_dir=$OUTPUT_DIR \
    --tcra_seq=AQEVTQIPAALSVPEGENLVLNCSFTDSAIYNLQWFRQDPGKGLTSLLLIQSSQREQTSGRLNASLDKSSGRSTLYIAASQPGDSATYLCAVTNQAGTALIFGKGTTLSVSS \
    --tcrb_seq=NAGVTQTPKFQVLKTGQSMTLQCSQDMNHEYMSWYRQDPGMGLRLIHYSVGAGITDQGEVPNGYNVSRSTTEDFPLRLLSAAPSQTSVYFCASSYSIRGSRGEQFFGPGTRLTVL \
    --pep_seq=RLPAKAPLL \
    --mhca_seq=SHSLKYFHTSVSRPGRGEPRFISVGYVDDTQFVRFDNDAASPRMVPRAPWMEQEGSEYWDRETRSARDTAQIFRVNLRTLRGYYNQSEAGSHTLQWMHGCELGPDGRFLRGYEQFAYDGKDYLTLNEDLRSWTAVDTAAQISEQKSNDASEAEHQRAYLEDTCVE \
    --ori_db=$ALPHAFOLD_DB \
    --tp_db=/opt/tcrmodel2/data/databases \
    --relax_structures=True","Shell"
"AlphaFold","piercelab/tcrmodel2","alphafold/__init__.py",".py","663","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""An implementation of the inference pipeline of AlphaFold v2.0.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/features.py",".py","3692","105","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Code to generate processed features.""""""
import copy
from typing import List, Mapping, Tuple

from alphafold.model.tf import input_pipeline
from alphafold.model.tf import proteins_dataset

import ml_collections
import numpy as np
import tensorflow.compat.v1 as tf

FeatureDict = Mapping[str, np.ndarray]


def make_data_config(
    config: ml_collections.ConfigDict,
    num_res: int,
    ) -> Tuple[ml_collections.ConfigDict, List[str]]:
  """"""Makes a data config for the input pipeline.""""""
  cfg = copy.deepcopy(config.data)

  feature_names = cfg.common.unsupervised_features
  if cfg.common.use_templates:
    feature_names += cfg.common.template_features

  with cfg.unlocked():
    cfg.eval.crop_size = num_res

  return cfg, feature_names


def tf_example_to_features(tf_example: tf.train.Example,
                           config: ml_collections.ConfigDict,
                           random_seed: int = 0) -> FeatureDict:
  """"""Converts tf_example to numpy feature dictionary.""""""
  num_res = int(tf_example.features.feature['seq_length'].int64_list.value[0])
  cfg, feature_names = make_data_config(config, num_res=num_res)

  if 'deletion_matrix_int' in set(tf_example.features.feature):
    deletion_matrix_int = (
        tf_example.features.feature['deletion_matrix_int'].int64_list.value)
    feat = tf.train.Feature(float_list=tf.train.FloatList(
        value=map(float, deletion_matrix_int)))
    tf_example.features.feature['deletion_matrix'].CopyFrom(feat)
    del tf_example.features.feature['deletion_matrix_int']

  tf_graph = tf.Graph()
  with tf_graph.as_default(), tf.device('/device:CPU:0'):
    tf.compat.v1.set_random_seed(random_seed)
    tensor_dict = proteins_dataset.create_tensor_dict(
        raw_data=tf_example.SerializeToString(),
        features=feature_names)
    processed_batch = input_pipeline.process_tensors_from_config(
        tensor_dict, cfg)

  tf_graph.finalize()

  with tf.Session(graph=tf_graph) as sess:
    features = sess.run(processed_batch)

  return {k: v for k, v in features.items() if v.dtype != 'O'}


def np_example_to_features(np_example: FeatureDict,
                           config: ml_collections.ConfigDict,
                           random_seed: int = 0) -> FeatureDict:
  """"""Preprocesses NumPy feature dict using TF pipeline.""""""
  np_example = dict(np_example)
  num_res = int(np_example['seq_length'][0])
  cfg, feature_names = make_data_config(config, num_res=num_res)

  if 'deletion_matrix_int' in np_example:
    np_example['deletion_matrix'] = (
        np_example.pop('deletion_matrix_int').astype(np.float32))

  tf_graph = tf.Graph()
  with tf_graph.as_default(), tf.device('/device:CPU:0'):
    tf.compat.v1.set_random_seed(random_seed)
    tensor_dict = proteins_dataset.np_to_tensor_dict(
        np_example=np_example, features=feature_names)

    processed_batch = input_pipeline.process_tensors_from_config(
        tensor_dict, cfg)

  tf_graph.finalize()

  with tf.Session(graph=tf_graph) as sess:
    features = sess.run(processed_batch)

  return {k: v for k, v in features.items() if v.dtype != 'O'}
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/r3.py",".py","10935","321","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Transformations for 3D coordinates.

This Module contains objects for representing Vectors (Vecs), Rotation Matrices
(Rots) and proper Rigid transformation (Rigids). These are represented as
named tuples with arrays for each entry, for example a set of
[N, M] points would be represented as a Vecs object with arrays of shape [N, M]
for x, y and z.

This is being done to improve readability by making it very clear what objects
are geometric objects rather than relying on comments and array shapes.
Another reason for this is to avoid using matrix
multiplication primitives like matmul or einsum, on modern accelerator hardware
these can end up on specialized cores such as tensor cores on GPU or the MXU on
cloud TPUs, this often involves lower computational precision which can be
problematic for coordinate geometry. Also these cores are typically optimized
for larger matrices than 3 dimensional, this code is written to avoid any
unintended use of these cores on both GPUs and TPUs.
""""""

import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree

# Array of 3-component vectors, stored as individual array for
# each component.
Vecs = collections.namedtuple('Vecs', ['x', 'y', 'z'])

# Array of 3x3 rotation matrices, stored as individual array for
# each component.
Rots = collections.namedtuple('Rots', ['xx', 'xy', 'xz',
                                       'yx', 'yy', 'yz',
                                       'zx', 'zy', 'zz'])
# Array of rigid 3D transformations, stored as array of rotations and
# array of translations.
Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])


def squared_difference(x, y):
  return jnp.square(x - y)


def invert_rigids(r: Rigids) -> Rigids:
  """"""Computes group inverse of rigid transformations 'r'.""""""
  inv_rots = invert_rots(r.rot)
  t = rots_mul_vecs(inv_rots, r.trans)
  inv_trans = Vecs(-t.x, -t.y, -t.z)
  return Rigids(inv_rots, inv_trans)


def invert_rots(m: Rots) -> Rots:
  """"""Computes inverse of rotations 'm'.""""""
  return Rots(m.xx, m.yx, m.zx,
              m.xy, m.yy, m.zy,
              m.xz, m.yz, m.zz)


def rigids_from_3_points(
    point_on_neg_x_axis: Vecs,  # shape (...)
    origin: Vecs,  # shape (...)
    point_on_xy_plane: Vecs,  # shape (...)
) -> Rigids:  # shape (...)
  """"""Create Rigids from 3 points.

  Jumper et al. (2021) Suppl. Alg. 21 ""rigidFrom3Points""
  This creates a set of rigid transformations from 3 points by Gram Schmidt
  orthogonalization.

  Args:
    point_on_neg_x_axis: Vecs corresponding to points on the negative x axis
    origin: Origin of resulting rigid transformations
    point_on_xy_plane: Vecs corresponding to points in the xy plane
  Returns:
    Rigid transformations from global frame to local frames derived from
    the input points.
  """"""
  m = rots_from_two_vecs(
      e0_unnormalized=vecs_sub(origin, point_on_neg_x_axis),
      e1_unnormalized=vecs_sub(point_on_xy_plane, origin))

  return Rigids(rot=m, trans=origin)


def rigids_from_list(l: List[jnp.ndarray]) -> Rigids:
  """"""Converts flat list of arrays to rigid transformations.""""""
  assert len(l) == 12
  return Rigids(Rots(*(l[:9])), Vecs(*(l[9:])))


def rigids_from_quataffine(a: quat_affine.QuatAffine) -> Rigids:
  """"""Converts QuatAffine object to the corresponding Rigids object.""""""
  return Rigids(Rots(*tree.flatten(a.rotation)),
                Vecs(*a.translation))


def rigids_from_tensor4x4(
    m: jnp.ndarray  # shape (..., 4, 4)
) -> Rigids:  # shape (...)
  """"""Construct Rigids object from an 4x4 array.

  Here the 4x4 is representing the transformation in homogeneous coordinates.

  Args:
    m: Array representing transformations in homogeneous coordinates.
  Returns:
    Rigids object corresponding to transformations m
  """"""
  assert m.shape[-1] == 4
  assert m.shape[-2] == 4
  return Rigids(
      Rots(m[..., 0, 0], m[..., 0, 1], m[..., 0, 2],
           m[..., 1, 0], m[..., 1, 1], m[..., 1, 2],
           m[..., 2, 0], m[..., 2, 1], m[..., 2, 2]),
      Vecs(m[..., 0, 3], m[..., 1, 3], m[..., 2, 3]))


def rigids_from_tensor_flat9(
    m: jnp.ndarray  # shape (..., 9)
) -> Rigids:  # shape (...)
  """"""Flat9 encoding: first two columns of rotation matrix + translation.""""""
  assert m.shape[-1] == 9
  e0 = Vecs(m[..., 0], m[..., 1], m[..., 2])
  e1 = Vecs(m[..., 3], m[..., 4], m[..., 5])
  trans = Vecs(m[..., 6], m[..., 7], m[..., 8])
  return Rigids(rot=rots_from_two_vecs(e0, e1),
                trans=trans)


def rigids_from_tensor_flat12(
    m: jnp.ndarray  # shape (..., 12)
) -> Rigids:  # shape (...)
  """"""Flat12 encoding: rotation matrix (9 floats) + translation (3 floats).""""""
  assert m.shape[-1] == 12
  x = jnp.moveaxis(m, -1, 0)  # Unstack
  return Rigids(Rots(*x[:9]), Vecs(*x[9:]))


def rigids_mul_rigids(a: Rigids, b: Rigids) -> Rigids:
  """"""Group composition of Rigids 'a' and 'b'.""""""
  return Rigids(
      rots_mul_rots(a.rot, b.rot),
      vecs_add(a.trans, rots_mul_vecs(a.rot, b.trans)))


def rigids_mul_rots(r: Rigids, m: Rots) -> Rigids:
  """"""Compose rigid transformations 'r' with rotations 'm'.""""""
  return Rigids(rots_mul_rots(r.rot, m), r.trans)


def rigids_mul_vecs(r: Rigids, v: Vecs) -> Vecs:
  """"""Apply rigid transforms 'r' to points 'v'.""""""
  return vecs_add(rots_mul_vecs(r.rot, v), r.trans)


def rigids_to_list(r: Rigids) -> List[jnp.ndarray]:
  """"""Turn Rigids into flat list, inverse of 'rigids_from_list'.""""""
  return list(r.rot) + list(r.trans)


def rigids_to_quataffine(r: Rigids) -> quat_affine.QuatAffine:
  """"""Convert Rigids r into QuatAffine, inverse of 'rigids_from_quataffine'.""""""
  return quat_affine.QuatAffine(
      quaternion=None,
      rotation=[[r.rot.xx, r.rot.xy, r.rot.xz],
                [r.rot.yx, r.rot.yy, r.rot.yz],
                [r.rot.zx, r.rot.zy, r.rot.zz]],
      translation=[r.trans.x, r.trans.y, r.trans.z])


def rigids_to_tensor_flat9(
    r: Rigids  # shape (...)
) -> jnp.ndarray:  # shape (..., 9)
  """"""Flat9 encoding: first two columns of rotation matrix + translation.""""""
  return jnp.stack(
      [r.rot.xx, r.rot.yx, r.rot.zx, r.rot.xy, r.rot.yy, r.rot.zy]
      + list(r.trans), axis=-1)


def rigids_to_tensor_flat12(
    r: Rigids  # shape (...)
) -> jnp.ndarray:  # shape (..., 12)
  """"""Flat12 encoding: rotation matrix (9 floats) + translation (3 floats).""""""
  return jnp.stack(list(r.rot) + list(r.trans), axis=-1)


def rots_from_tensor3x3(
    m: jnp.ndarray,  # shape (..., 3, 3)
) -> Rots:  # shape (...)
  """"""Convert rotations represented as (3, 3) array to Rots.""""""
  assert m.shape[-1] == 3
  assert m.shape[-2] == 3
  return Rots(m[..., 0, 0], m[..., 0, 1], m[..., 0, 2],
              m[..., 1, 0], m[..., 1, 1], m[..., 1, 2],
              m[..., 2, 0], m[..., 2, 1], m[..., 2, 2])


def rots_from_two_vecs(e0_unnormalized: Vecs, e1_unnormalized: Vecs) -> Rots:
  """"""Create rotation matrices from unnormalized vectors for the x and y-axes.

  This creates a rotation matrix from two vectors using Gram-Schmidt
  orthogonalization.

  Args:
    e0_unnormalized: vectors lying along x-axis of resulting rotation
    e1_unnormalized: vectors lying in xy-plane of resulting rotation
  Returns:
    Rotations resulting from Gram-Schmidt procedure.
  """"""
  # Normalize the unit vector for the x-axis, e0.
  e0 = vecs_robust_normalize(e0_unnormalized)

  # make e1 perpendicular to e0.
  c = vecs_dot_vecs(e1_unnormalized, e0)
  e1 = Vecs(e1_unnormalized.x - c * e0.x,
            e1_unnormalized.y - c * e0.y,
            e1_unnormalized.z - c * e0.z)
  e1 = vecs_robust_normalize(e1)

  # Compute e2 as cross product of e0 and e1.
  e2 = vecs_cross_vecs(e0, e1)

  return Rots(e0.x, e1.x, e2.x, e0.y, e1.y, e2.y, e0.z, e1.z, e2.z)


def rots_mul_rots(a: Rots, b: Rots) -> Rots:
  """"""Composition of rotations 'a' and 'b'.""""""
  c0 = rots_mul_vecs(a, Vecs(b.xx, b.yx, b.zx))
  c1 = rots_mul_vecs(a, Vecs(b.xy, b.yy, b.zy))
  c2 = rots_mul_vecs(a, Vecs(b.xz, b.yz, b.zz))
  return Rots(c0.x, c1.x, c2.x, c0.y, c1.y, c2.y, c0.z, c1.z, c2.z)


def rots_mul_vecs(m: Rots, v: Vecs) -> Vecs:
  """"""Apply rotations 'm' to vectors 'v'.""""""
  return Vecs(m.xx * v.x + m.xy * v.y + m.xz * v.z,
              m.yx * v.x + m.yy * v.y + m.yz * v.z,
              m.zx * v.x + m.zy * v.y + m.zz * v.z)


def vecs_add(v1: Vecs, v2: Vecs) -> Vecs:
  """"""Add two vectors 'v1' and 'v2'.""""""
  return Vecs(v1.x + v2.x, v1.y + v2.y, v1.z + v2.z)


def vecs_dot_vecs(v1: Vecs, v2: Vecs) -> jnp.ndarray:
  """"""Dot product of vectors 'v1' and 'v2'.""""""
  return v1.x * v2.x + v1.y * v2.y + v1.z * v2.z


def vecs_cross_vecs(v1: Vecs, v2: Vecs) -> Vecs:
  """"""Cross product of vectors 'v1' and 'v2'.""""""
  return Vecs(v1.y * v2.z - v1.z * v2.y,
              v1.z * v2.x - v1.x * v2.z,
              v1.x * v2.y - v1.y * v2.x)


def vecs_from_tensor(x: jnp.ndarray  # shape (..., 3)
                    ) -> Vecs:  # shape (...)
  """"""Converts from tensor of shape (3,) to Vecs.""""""
  num_components = x.shape[-1]
  assert num_components == 3
  return Vecs(x[..., 0], x[..., 1], x[..., 2])


def vecs_robust_normalize(v: Vecs, epsilon: float = 1e-8) -> Vecs:
  """"""Normalizes vectors 'v'.

  Args:
    v: vectors to be normalized.
    epsilon: small regularizer added to squared norm before taking square root.
  Returns:
    normalized vectors
  """"""
  norms = vecs_robust_norm(v, epsilon)
  return Vecs(v.x / norms, v.y / norms, v.z / norms)


def vecs_robust_norm(v: Vecs, epsilon: float = 1e-8) -> jnp.ndarray:
  """"""Computes norm of vectors 'v'.

  Args:
    v: vectors to be normalized.
    epsilon: small regularizer added to squared norm before taking square root.
  Returns:
    norm of 'v'
  """"""
  return jnp.sqrt(jnp.square(v.x) + jnp.square(v.y) + jnp.square(v.z) + epsilon)


def vecs_sub(v1: Vecs, v2: Vecs) -> Vecs:
  """"""Computes v1 - v2.""""""
  return Vecs(v1.x - v2.x, v1.y - v2.y, v1.z - v2.z)


def vecs_squared_distance(v1: Vecs, v2: Vecs) -> jnp.ndarray:
  """"""Computes squared euclidean difference between 'v1' and 'v2'.""""""
  return (squared_difference(v1.x, v2.x) +
          squared_difference(v1.y, v2.y) +
          squared_difference(v1.z, v2.z))


def vecs_to_tensor(v: Vecs  # shape (...)
                  ) -> jnp.ndarray:  # shape(..., 3)
  """"""Converts 'v' to tensor with shape 3, inverse of 'vecs_from_tensor'.""""""
  return jnp.stack([v.x, v.y, v.z], axis=-1)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/model.py",".py","7290","193","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Code for constructing the model.""""""
from typing import Any, Mapping, Optional, Union

from absl import logging
from alphafold.common import confidence
from alphafold.model import features
from alphafold.model import modules
from alphafold.model import modules_multimer
import haiku as hk
import jax
import ml_collections
import numpy as np
import tensorflow.compat.v1 as tf
import tree


def get_confidence_metrics(
    prediction_result: Mapping[str, Any],
    multimer_mode: bool,
    interfaces: list) -> Mapping[str, Any]:
  """"""Post processes prediction_result to get confidence metrics.""""""
  confidence_metrics = {}
  confidence_metrics['plddt'] = confidence.compute_plddt(
      prediction_result['predicted_lddt']['logits'])
  if 'predicted_aligned_error' in prediction_result:
    confidence_metrics.update(confidence.compute_predicted_aligned_error(
        logits=prediction_result['predicted_aligned_error']['logits'],
        breaks=prediction_result['predicted_aligned_error']['breaks']))
    confidence_metrics['ptm'] = confidence.predicted_tm_score(
        logits=prediction_result['predicted_aligned_error']['logits'],
        breaks=prediction_result['predicted_aligned_error']['breaks'],
        asym_id=None)
    
    if len(interfaces)!=0:
      iptm=[]
      for interface in interfaces:
        new_asym_id=prediction_result['predicted_aligned_error']['asym_id']
        for old_index, new_index in enumerate(interface):
          new_asym_id=np.where(new_asym_id==old_index+1, new_index, new_asym_id)
        iptm.append(confidence.predicted_tm_score(
              logits=prediction_result['predicted_aligned_error']['logits'],
              breaks=prediction_result['predicted_aligned_error']['breaks'],
              asym_id=new_asym_id,
              interface=True))
      confidence_metrics['custom_iptm'] = iptm

    if multimer_mode:
      # Compute the ipTM only for the multimer model.
      confidence_metrics['iptm'] = confidence.predicted_tm_score(
          logits=prediction_result['predicted_aligned_error']['logits'],
          breaks=prediction_result['predicted_aligned_error']['breaks'],
          asym_id=prediction_result['predicted_aligned_error']['asym_id'],
          interface=True)
      confidence_metrics['ranking_confidence'] = (
          0.8 * confidence_metrics['iptm'] + 0.2 * confidence_metrics['ptm'])

  if not multimer_mode:
    # Monomer models use mean pLDDT for model ranking.
    confidence_metrics['ranking_confidence'] = np.mean(
        confidence_metrics['plddt'])

  return confidence_metrics


class RunModel:
  """"""Container for JAX model.""""""

  def __init__(self,
               config: ml_collections.ConfigDict,
               params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None):
    self.config = config
    self.params = params
    self.multimer_mode = config.model.global_config.multimer_mode

    if self.multimer_mode:
      def _forward_fn(batch):
        model = modules_multimer.AlphaFold(self.config.model)
        return model(
            batch,
            is_training=False)
    else:
      def _forward_fn(batch):
        model = modules.AlphaFold(self.config.model)
        return model(
            batch,
            is_training=False,
            compute_loss=False,
            ensemble_representations=True)

    self.apply = jax.jit(hk.transform(_forward_fn).apply)
    self.init = jax.jit(hk.transform(_forward_fn).init)

  def init_params(self, feat: features.FeatureDict, random_seed: int = 0):
    """"""Initializes the model parameters.

    If none were provided when this class was instantiated then the parameters
    are randomly initialized.

    Args:
      feat: A dictionary of NumPy feature arrays as output by
        RunModel.process_features.
      random_seed: A random seed to use to initialize the parameters if none
        were set when this class was initialized.
    """"""
    if not self.params:
      # Init params randomly.
      rng = jax.random.PRNGKey(random_seed)
      self.params = hk.data_structures.to_mutable_dict(
          self.init(rng, feat))
      logging.warning('Initialized parameters randomly')

  def process_features(
      self,
      raw_features: Union[tf.train.Example, features.FeatureDict],
      random_seed: int) -> features.FeatureDict:
    """"""Processes features to prepare for feeding them into the model.

    Args:
      raw_features: The output of the data pipeline either as a dict of NumPy
        arrays or as a tf.train.Example.
      random_seed: The random seed to use when processing the features.

    Returns:
      A dict of NumPy feature arrays suitable for feeding into the model.
    """"""

    if self.multimer_mode:
      return raw_features

    # Single-chain mode.
    if isinstance(raw_features, dict):
      return features.np_example_to_features(
          np_example=raw_features,
          config=self.config,
          random_seed=random_seed)
    else:
      return features.tf_example_to_features(
          tf_example=raw_features,
          config=self.config,
          random_seed=random_seed)

  def eval_shape(self, feat: features.FeatureDict) -> jax.ShapeDtypeStruct:
    self.init_params(feat)
    logging.info('Running eval_shape with shape(feat) = %s',
                 tree.map_structure(lambda x: x.shape, feat))
    shape = jax.eval_shape(self.apply, self.params, jax.random.PRNGKey(0), feat)
    logging.info('Output shape was %s', shape)
    return shape

  def predict(self,
              feat: features.FeatureDict,
              random_seed: int,
              interfaces: list) -> Mapping[str, Any]:
    """"""Makes a prediction by inferencing the model on the provided features.

    Args:
      feat: A dictionary of NumPy feature arrays as output by
        RunModel.process_features.
      random_seed: The random seed to use when running the model. In the
        multimer model this controls the MSA sampling.

    Returns:
      A dictionary of model outputs.
    """"""
    self.init_params(feat)
    logging.info('Running predict with shape(feat) = %s',
                 tree.map_structure(lambda x: x.shape, feat))
    result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)

    # This block is to ensure benchmark timings are accurate. Some blocking is
    # already happening when computing get_confidence_metrics, and this ensures
    # all outputs are blocked on.
    jax.tree_map(lambda x: x.block_until_ready(), result)
    result.update(
        get_confidence_metrics(result, multimer_mode=self.multimer_mode, interfaces=interfaces))
    logging.info('Output shape was %s',
                 tree.map_structure(lambda x: x.shape, result))
    return result
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/lddt_test.py",".py","2384","80","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for lddt.""""""

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.model import lddt
import numpy as np


class LddtTest(parameterized.TestCase, absltest.TestCase):

  @parameterized.named_parameters(
      ('same',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [1, 1, 1]),
      ('all_shifted',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[-1, 0, 0], [4, 0, 0], [9, 0, 0]],
       [1, 1, 1]),
      ('all_rotated',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[0, 0, 0], [0, 5, 0], [0, 10, 0]],
       [1, 1, 1]),
      ('half_a_dist',
       [[0, 0, 0], [5, 0, 0]],
       [[0, 0, 0], [5.5-1e-5, 0, 0]],
       [1, 1]),
      ('one_a_dist',
       [[0, 0, 0], [5, 0, 0]],
       [[0, 0, 0], [6-1e-5, 0, 0]],
       [0.75, 0.75]),
      ('two_a_dist',
       [[0, 0, 0], [5, 0, 0]],
       [[0, 0, 0], [7-1e-5, 0, 0]],
       [0.5, 0.5]),
      ('four_a_dist',
       [[0, 0, 0], [5, 0, 0]],
       [[0, 0, 0], [9-1e-5, 0, 0]],
       [0.25, 0.25],),
      ('five_a_dist',
       [[0, 0, 0], [16-1e-5, 0, 0]],
       [[0, 0, 0], [11, 0, 0]],
       [0, 0]),
      ('no_pairs',
       [[0, 0, 0], [20, 0, 0]],
       [[0, 0, 0], [25-1e-5, 0, 0]],
       [1, 1]),
  )
  def test_lddt(
      self, predicted_pos, true_pos, exp_lddt):
    predicted_pos = np.array([predicted_pos], dtype=np.float32)
    true_points_mask = np.array([[[1]] * len(true_pos)], dtype=np.float32)
    true_pos = np.array([true_pos], dtype=np.float32)
    cutoff = 15.0
    per_residue = True

    result = lddt.lddt(
        predicted_pos, true_pos, true_points_mask, cutoff,
        per_residue)

    np.testing.assert_almost_equal(result, [exp_lddt], decimal=4)


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/all_atom_multimer.py",".py","40135","969","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Ops for all atom representations.""""""

from typing import Dict, Text

from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np


def squared_difference(x, y):
  return jnp.square(x - y)


def _make_chi_atom_indices():
  """"""Returns atom indices needed to compute chi angles for all residue types.

  Returns:
    A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are
    in the order specified in residue_constants.restypes + unknown residue type
    at the end. For chi angles which are not defined on the residue, the
    positions indices are by default set to 0.
  """"""
  chi_atom_indices = []
  for residue_name in residue_constants.restypes:
    residue_name = residue_constants.restype_1to3[residue_name]
    residue_chi_angles = residue_constants.chi_angles_atoms[residue_name]
    atom_indices = []
    for chi_angle in residue_chi_angles:
      atom_indices.append(
          [residue_constants.atom_order[atom] for atom in chi_angle])
    for _ in range(4 - len(atom_indices)):
      atom_indices.append([0, 0, 0, 0])  # For chi angles not defined on the AA.
    chi_atom_indices.append(atom_indices)

  chi_atom_indices.append([[0, 0, 0, 0]] * 4)  # For UNKNOWN residue.

  return np.array(chi_atom_indices)


def _make_renaming_matrices():
  """"""Matrices to map atoms to symmetry partners in ambiguous case.""""""
  # As the atom naming is ambiguous for 7 of the 20 amino acids, provide
  # alternative groundtruth coordinates where the naming is swapped
  restype_3 = [
      residue_constants.restype_1to3[res] for res in residue_constants.restypes
  ]
  restype_3 += ['UNK']
  # Matrices for renaming ambiguous atoms.
  all_matrices = {res: np.eye(14, dtype=np.float32) for res in restype_3}
  for resname, swap in residue_constants.residue_atom_renaming_swaps.items():
    correspondences = np.arange(14)
    for source_atom_swap, target_atom_swap in swap.items():
      source_index = residue_constants.restype_name_to_atom14_names[
          resname].index(source_atom_swap)
      target_index = residue_constants.restype_name_to_atom14_names[
          resname].index(target_atom_swap)
      correspondences[source_index] = target_index
      correspondences[target_index] = source_index
      renaming_matrix = np.zeros((14, 14), dtype=np.float32)
      for index, correspondence in enumerate(correspondences):
        renaming_matrix[index, correspondence] = 1.
    all_matrices[resname] = renaming_matrix.astype(np.float32)
  renaming_matrices = np.stack([all_matrices[restype] for restype in restype_3])
  return renaming_matrices


def _make_restype_atom37_mask():
  """"""Mask of which atoms are present for which residue type in atom37.""""""
  # create the corresponding mask
  restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
  for restype, restype_letter in enumerate(residue_constants.restypes):
    restype_name = residue_constants.restype_1to3[restype_letter]
    atom_names = residue_constants.residue_atoms[restype_name]
    for atom_name in atom_names:
      atom_type = residue_constants.atom_order[atom_name]
      restype_atom37_mask[restype, atom_type] = 1
  return restype_atom37_mask


def _make_restype_atom14_mask():
  """"""Mask of which atoms are present for which residue type in atom14.""""""
  restype_atom14_mask = []

  for rt in residue_constants.restypes:
    atom_names = residue_constants.restype_name_to_atom14_names[
        residue_constants.restype_1to3[rt]]
    restype_atom14_mask.append([(1. if name else 0.) for name in atom_names])

  restype_atom14_mask.append([0.] * 14)
  restype_atom14_mask = np.array(restype_atom14_mask, dtype=np.float32)
  return restype_atom14_mask


def _make_restype_atom37_to_atom14():
  """"""Map from atom37 to atom14 per residue type.""""""
  restype_atom37_to_atom14 = []  # mapping (restype, atom37) --> atom14
  for rt in residue_constants.restypes:
    atom_names = residue_constants.restype_name_to_atom14_names[
        residue_constants.restype_1to3[rt]]
    atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
    restype_atom37_to_atom14.append([
        (atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
        for name in residue_constants.atom_types
    ])

  restype_atom37_to_atom14.append([0] * 37)
  restype_atom37_to_atom14 = np.array(restype_atom37_to_atom14, dtype=np.int32)
  return restype_atom37_to_atom14


def _make_restype_atom14_to_atom37():
  """"""Map from atom14 to atom37 per residue type.""""""
  restype_atom14_to_atom37 = []  # mapping (restype, atom14) --> atom37
  for rt in residue_constants.restypes:
    atom_names = residue_constants.restype_name_to_atom14_names[
        residue_constants.restype_1to3[rt]]
    restype_atom14_to_atom37.append([
        (residue_constants.atom_order[name] if name else 0)
        for name in atom_names
    ])
  # Add dummy mapping for restype 'UNK'
  restype_atom14_to_atom37.append([0] * 14)
  restype_atom14_to_atom37 = np.array(restype_atom14_to_atom37, dtype=np.int32)
  return restype_atom14_to_atom37


def _make_restype_atom14_is_ambiguous():
  """"""Mask which atoms are ambiguous in atom14.""""""
  # create an ambiguous atoms mask.  shape: (21, 14)
  restype_atom14_is_ambiguous = np.zeros((21, 14), dtype=np.float32)
  for resname, swap in residue_constants.residue_atom_renaming_swaps.items():
    for atom_name1, atom_name2 in swap.items():
      restype = residue_constants.restype_order[
          residue_constants.restype_3to1[resname]]
      atom_idx1 = residue_constants.restype_name_to_atom14_names[resname].index(
          atom_name1)
      atom_idx2 = residue_constants.restype_name_to_atom14_names[resname].index(
          atom_name2)
      restype_atom14_is_ambiguous[restype, atom_idx1] = 1
      restype_atom14_is_ambiguous[restype, atom_idx2] = 1

  return restype_atom14_is_ambiguous


def _make_restype_rigidgroup_base_atom37_idx():
  """"""Create Map from rigidgroups to atom37 indices.""""""
  # Create an array with the atom names.
  # shape (num_restypes, num_rigidgroups, 3_atoms): (21, 8, 3)
  base_atom_names = np.full([21, 8, 3], '', dtype=object)

  # 0: backbone frame
  base_atom_names[:, 0, :] = ['C', 'CA', 'N']

  # 3: 'psi-group'
  base_atom_names[:, 3, :] = ['CA', 'C', 'O']

  # 4,5,6,7: 'chi1,2,3,4-group'
  for restype, restype_letter in enumerate(residue_constants.restypes):
    resname = residue_constants.restype_1to3[restype_letter]
    for chi_idx in range(4):
      if residue_constants.chi_angles_mask[restype][chi_idx]:
        atom_names = residue_constants.chi_angles_atoms[resname][chi_idx]
        base_atom_names[restype, chi_idx + 4, :] = atom_names[1:]

  # Translate atom names into atom37 indices.
  lookuptable = residue_constants.atom_order.copy()
  lookuptable[''] = 0
  restype_rigidgroup_base_atom37_idx = np.vectorize(lambda x: lookuptable[x])(
      base_atom_names)
  return restype_rigidgroup_base_atom37_idx


CHI_ATOM_INDICES = _make_chi_atom_indices()
RENAMING_MATRICES = _make_renaming_matrices()
RESTYPE_ATOM14_TO_ATOM37 = _make_restype_atom14_to_atom37()
RESTYPE_ATOM37_TO_ATOM14 = _make_restype_atom37_to_atom14()
RESTYPE_ATOM37_MASK = _make_restype_atom37_mask()
RESTYPE_ATOM14_MASK = _make_restype_atom14_mask()
RESTYPE_ATOM14_IS_AMBIGUOUS = _make_restype_atom14_is_ambiguous()
RESTYPE_RIGIDGROUP_BASE_ATOM37_IDX = _make_restype_rigidgroup_base_atom37_idx()

# Create mask for existing rigid groups.
RESTYPE_RIGIDGROUP_MASK = np.zeros([21, 8], dtype=np.float32)
RESTYPE_RIGIDGROUP_MASK[:, 0] = 1
RESTYPE_RIGIDGROUP_MASK[:, 3] = 1
RESTYPE_RIGIDGROUP_MASK[:20, 4:] = residue_constants.chi_angles_mask


def get_atom37_mask(aatype):
  return utils.batched_gather(jnp.asarray(RESTYPE_ATOM37_MASK), aatype)


def get_atom14_mask(aatype):
  return utils.batched_gather(jnp.asarray(RESTYPE_ATOM14_MASK), aatype)


def get_atom14_is_ambiguous(aatype):
  return utils.batched_gather(jnp.asarray(RESTYPE_ATOM14_IS_AMBIGUOUS), aatype)


def get_atom14_to_atom37_map(aatype):
  return utils.batched_gather(jnp.asarray(RESTYPE_ATOM14_TO_ATOM37), aatype)


def get_atom37_to_atom14_map(aatype):
  return utils.batched_gather(jnp.asarray(RESTYPE_ATOM37_TO_ATOM14), aatype)


def atom14_to_atom37(atom14_data: jnp.ndarray,  # (N, 14, ...)
                     aatype: jnp.ndarray
                    ) -> jnp.ndarray:  # (N, 37, ...)
  """"""Convert atom14 to atom37 representation.""""""
  assert len(atom14_data.shape) in [2, 3]
  idx_atom37_to_atom14 = get_atom37_to_atom14_map(aatype)
  atom37_data = utils.batched_gather(
      atom14_data, idx_atom37_to_atom14, batch_dims=1)
  atom37_mask = get_atom37_mask(aatype)
  if len(atom14_data.shape) == 2:
    atom37_data *= atom37_mask
  elif len(atom14_data.shape) == 3:
    atom37_data *= atom37_mask[:, :, None].astype(atom37_data.dtype)
  return atom37_data


def atom37_to_atom14(aatype, all_atom_pos, all_atom_mask):
  """"""Convert Atom37 positions to Atom14 positions.""""""
  residx_atom14_to_atom37 = utils.batched_gather(
      jnp.asarray(RESTYPE_ATOM14_TO_ATOM37), aatype)
  atom14_mask = utils.batched_gather(
      all_atom_mask, residx_atom14_to_atom37, batch_dims=1).astype(jnp.float32)
  # create a mask for known groundtruth positions
  atom14_mask *= utils.batched_gather(jnp.asarray(RESTYPE_ATOM14_MASK), aatype)
  # gather the groundtruth positions
  atom14_positions = jax.tree_map(
      lambda x: utils.batched_gather(x, residx_atom14_to_atom37, batch_dims=1),
      all_atom_pos)
  atom14_positions = atom14_mask * atom14_positions
  return atom14_positions, atom14_mask


def get_alt_atom14(aatype, positions: geometry.Vec3Array, mask):
  """"""Get alternative atom14 positions.""""""
  # pick the transformation matrices for the given residue sequence
  # shape (num_res, 14, 14)
  renaming_transform = utils.batched_gather(
      jnp.asarray(RENAMING_MATRICES), aatype)

  alternative_positions = jax.tree_map(
      lambda x: jnp.sum(x, axis=1), positions[:, :, None] * renaming_transform)

  # Create the mask for the alternative ground truth (differs from the
  # ground truth mask, if only one of the atoms in an ambiguous pair has a
  # ground truth position)
  alternative_mask = jnp.sum(mask[..., None] * renaming_transform, axis=1)

  return alternative_positions, alternative_mask


def atom37_to_frames(
    aatype: jnp.ndarray,  # (...)
    all_atom_positions: geometry.Vec3Array,  # (..., 37)
    all_atom_mask: jnp.ndarray,  # (..., 37)
) -> Dict[Text, jnp.ndarray]:
  """"""Computes the frames for the up to 8 rigid groups for each residue.""""""
  # 0: 'backbone group',
  # 1: 'pre-omega-group', (empty)
  # 2: 'phi-group', (currently empty, because it defines only hydrogens)
  # 3: 'psi-group',
  # 4,5,6,7: 'chi1,2,3,4-group'
  aatype_in_shape = aatype.shape

  # If there is a batch axis, just flatten it away, and reshape everything
  # back at the end of the function.
  aatype = jnp.reshape(aatype, [-1])
  all_atom_positions = jax.tree_map(lambda x: jnp.reshape(x, [-1, 37]),
                                    all_atom_positions)
  all_atom_mask = jnp.reshape(all_atom_mask, [-1, 37])

  # Compute the gather indices for all residues in the chain.
  # shape (N, 8, 3)
  residx_rigidgroup_base_atom37_idx = utils.batched_gather(
      RESTYPE_RIGIDGROUP_BASE_ATOM37_IDX, aatype)

  # Gather the base atom positions for each rigid group.
  base_atom_pos = jax.tree_map(
      lambda x: utils.batched_gather(  # pylint: disable=g-long-lambda
          x, residx_rigidgroup_base_atom37_idx, batch_dims=1),
      all_atom_positions)

  # Compute the Rigids.
  point_on_neg_x_axis = base_atom_pos[:, :, 0]
  origin = base_atom_pos[:, :, 1]
  point_on_xy_plane = base_atom_pos[:, :, 2]
  gt_rotation = geometry.Rot3Array.from_two_vectors(
      origin - point_on_neg_x_axis, point_on_xy_plane - origin)

  gt_frames = geometry.Rigid3Array(gt_rotation, origin)

  # Compute a mask whether the group exists.
  # (N, 8)
  group_exists = utils.batched_gather(RESTYPE_RIGIDGROUP_MASK, aatype)

  # Compute a mask whether ground truth exists for the group
  gt_atoms_exist = utils.batched_gather(  # shape (N, 8, 3)
      all_atom_mask.astype(jnp.float32),
      residx_rigidgroup_base_atom37_idx,
      batch_dims=1)
  gt_exists = jnp.min(gt_atoms_exist, axis=-1) * group_exists  # (N, 8)

  # Adapt backbone frame to old convention (mirror x-axis and z-axis).
  rots = np.tile(np.eye(3, dtype=np.float32), [8, 1, 1])
  rots[0, 0, 0] = -1
  rots[0, 2, 2] = -1
  gt_frames = gt_frames.compose_rotation(
      geometry.Rot3Array.from_array(rots))

  # The frames for ambiguous rigid groups are just rotated by 180 degree around
  # the x-axis. The ambiguous group is always the last chi-group.
  restype_rigidgroup_is_ambiguous = np.zeros([21, 8], dtype=np.float32)
  restype_rigidgroup_rots = np.tile(np.eye(3, dtype=np.float32), [21, 8, 1, 1])

  for resname, _ in residue_constants.residue_atom_renaming_swaps.items():
    restype = residue_constants.restype_order[
        residue_constants.restype_3to1[resname]]
    chi_idx = int(sum(residue_constants.chi_angles_mask[restype]) - 1)
    restype_rigidgroup_is_ambiguous[restype, chi_idx + 4] = 1
    restype_rigidgroup_rots[restype, chi_idx + 4, 1, 1] = -1
    restype_rigidgroup_rots[restype, chi_idx + 4, 2, 2] = -1

  # Gather the ambiguity information for each residue.
  residx_rigidgroup_is_ambiguous = utils.batched_gather(
      restype_rigidgroup_is_ambiguous, aatype)
  ambiguity_rot = utils.batched_gather(restype_rigidgroup_rots, aatype)
  ambiguity_rot = geometry.Rot3Array.from_array(ambiguity_rot)

  # Create the alternative ground truth frames.
  alt_gt_frames = gt_frames.compose_rotation(ambiguity_rot)

  fix_shape = lambda x: jnp.reshape(x, aatype_in_shape + (8,))

  # reshape back to original residue layout
  gt_frames = jax.tree_map(fix_shape, gt_frames)
  gt_exists = fix_shape(gt_exists)
  group_exists = fix_shape(group_exists)
  residx_rigidgroup_is_ambiguous = fix_shape(residx_rigidgroup_is_ambiguous)
  alt_gt_frames = jax.tree_map(fix_shape, alt_gt_frames)

  return {
      'rigidgroups_gt_frames': gt_frames,  # Rigid (..., 8)
      'rigidgroups_gt_exists': gt_exists,  # (..., 8)
      'rigidgroups_group_exists': group_exists,  # (..., 8)
      'rigidgroups_group_is_ambiguous':
          residx_rigidgroup_is_ambiguous,  # (..., 8)
      'rigidgroups_alt_gt_frames': alt_gt_frames,  # Rigid (..., 8)
  }


def torsion_angles_to_frames(
    aatype: jnp.ndarray,  # (N)
    backb_to_global: geometry.Rigid3Array,  # (N)
    torsion_angles_sin_cos: jnp.ndarray  # (N, 7, 2)
) -> geometry.Rigid3Array:  # (N, 8)
  """"""Compute rigid group frames from torsion angles.""""""
  assert len(aatype.shape) == 1, (
      f'Expected array of rank 1, got array with shape: {aatype.shape}.')
  assert len(backb_to_global.rotation.shape) == 1, (
      f'Expected array of rank 1, got array with shape: '
      f'{backb_to_global.rotation.shape}')
  assert len(torsion_angles_sin_cos.shape) == 3, (
      f'Expected array of rank 3, got array with shape: '
      f'{torsion_angles_sin_cos.shape}')
  assert torsion_angles_sin_cos.shape[1] == 7, (
      f'wrong shape {torsion_angles_sin_cos.shape}')
  assert torsion_angles_sin_cos.shape[2] == 2, (
      f'wrong shape {torsion_angles_sin_cos.shape}')

  # Gather the default frames for all rigid groups.
  # geometry.Rigid3Array with shape (N, 8)
  m = utils.batched_gather(residue_constants.restype_rigid_group_default_frame,
                           aatype)
  default_frames = geometry.Rigid3Array.from_array4x4(m)

  # Create the rotation matrices according to the given angles (each frame is
  # defined such that its rotation is around the x-axis).
  sin_angles = torsion_angles_sin_cos[..., 0]
  cos_angles = torsion_angles_sin_cos[..., 1]

  # insert zero rotation for backbone group.
  num_residues, = aatype.shape
  sin_angles = jnp.concatenate([jnp.zeros([num_residues, 1]), sin_angles],
                               axis=-1)
  cos_angles = jnp.concatenate([jnp.ones([num_residues, 1]), cos_angles],
                               axis=-1)
  zeros = jnp.zeros_like(sin_angles)
  ones = jnp.ones_like(sin_angles)

  # all_rots are geometry.Rot3Array with shape (N, 8)
  all_rots = geometry.Rot3Array(ones, zeros, zeros,
                                zeros, cos_angles, -sin_angles,
                                zeros, sin_angles, cos_angles)

  # Apply rotations to the frames.
  all_frames = default_frames.compose_rotation(all_rots)

  # chi2, chi3, and chi4 frames do not transform to the backbone frame but to
  # the previous frame. So chain them up accordingly.

  chi1_frame_to_backb = all_frames[:, 4]
  chi2_frame_to_backb = chi1_frame_to_backb @ all_frames[:, 5]
  chi3_frame_to_backb = chi2_frame_to_backb @ all_frames[:, 6]
  chi4_frame_to_backb = chi3_frame_to_backb @ all_frames[:, 7]

  all_frames_to_backb = jax.tree_map(
      lambda *x: jnp.concatenate(x, axis=-1), all_frames[:, 0:5],
      chi2_frame_to_backb[:, None], chi3_frame_to_backb[:, None],
      chi4_frame_to_backb[:, None])

  # Create the global frames.
  # shape (N, 8)
  all_frames_to_global = backb_to_global[:, None] @ all_frames_to_backb

  return all_frames_to_global


def frames_and_literature_positions_to_atom14_pos(
    aatype: jnp.ndarray,  # (N)
    all_frames_to_global: geometry.Rigid3Array  # (N, 8)
) -> geometry.Vec3Array:  # (N, 14)
  """"""Put atom literature positions (atom14 encoding) in each rigid group.""""""

  # Pick the appropriate transform for every atom.
  residx_to_group_idx = utils.batched_gather(
      residue_constants.restype_atom14_to_rigid_group, aatype)
  group_mask = jax.nn.one_hot(
      residx_to_group_idx, num_classes=8)  # shape (N, 14, 8)

  # geometry.Rigid3Array with shape (N, 14)
  map_atoms_to_global = jax.tree_map(
      lambda x: jnp.sum(x[:, None, :] * group_mask, axis=-1),
      all_frames_to_global)

  # Gather the literature atom positions for each residue.
  # geometry.Vec3Array with shape (N, 14)
  lit_positions = geometry.Vec3Array.from_array(
      utils.batched_gather(
          residue_constants.restype_atom14_rigid_group_positions, aatype))

  # Transform each atom from its local frame to the global frame.
  # geometry.Vec3Array with shape (N, 14)
  pred_positions = map_atoms_to_global.apply_to_point(lit_positions)

  # Mask out non-existing atoms.
  mask = utils.batched_gather(residue_constants.restype_atom14_mask, aatype)
  pred_positions = pred_positions * mask

  return pred_positions


def extreme_ca_ca_distance_violations(
    positions: geometry.Vec3Array,  # (N, 37(14))
    mask: jnp.ndarray,  # (N, 37(14))
    residue_index: jnp.ndarray,  # (N)
    max_angstrom_tolerance=1.5
    ) -> jnp.ndarray:
  """"""Counts residues whose Ca is a large distance from its neighbor.""""""
  this_ca_pos = positions[:-1, 1]  # (N - 1,)
  this_ca_mask = mask[:-1, 1]         # (N - 1)
  next_ca_pos = positions[1:, 1]  # (N - 1,)
  next_ca_mask = mask[1:, 1]  # (N - 1)
  has_no_gap_mask = ((residue_index[1:] - residue_index[:-1]) == 1.0).astype(
      jnp.float32)
  ca_ca_distance = geometry.euclidean_distance(this_ca_pos, next_ca_pos, 1e-6)
  violations = (ca_ca_distance -
                residue_constants.ca_ca) > max_angstrom_tolerance
  mask = this_ca_mask * next_ca_mask * has_no_gap_mask
  return utils.mask_mean(mask=mask, value=violations)


def between_residue_bond_loss(
    pred_atom_positions: geometry.Vec3Array,  # (N, 37(14))
    pred_atom_mask: jnp.ndarray,  # (N, 37(14))
    residue_index: jnp.ndarray,  # (N)
    aatype: jnp.ndarray,  # (N)
    tolerance_factor_soft=12.0,
    tolerance_factor_hard=12.0) -> Dict[Text, jnp.ndarray]:
  """"""Flat-bottom loss to penalize structural violations between residues.""""""
  assert len(pred_atom_positions.shape) == 2
  assert len(pred_atom_mask.shape) == 2
  assert len(residue_index.shape) == 1
  assert len(aatype.shape) == 1

  # Get the positions of the relevant backbone atoms.
  this_ca_pos = pred_atom_positions[:-1, 1]  # (N - 1)
  this_ca_mask = pred_atom_mask[:-1, 1]         # (N - 1)
  this_c_pos = pred_atom_positions[:-1, 2]  # (N - 1)
  this_c_mask = pred_atom_mask[:-1, 2]          # (N - 1)
  next_n_pos = pred_atom_positions[1:, 0]  # (N - 1)
  next_n_mask = pred_atom_mask[1:, 0]           # (N - 1)
  next_ca_pos = pred_atom_positions[1:, 1]  # (N - 1)
  next_ca_mask = pred_atom_mask[1:, 1]          # (N - 1)
  has_no_gap_mask = ((residue_index[1:] - residue_index[:-1]) == 1.0).astype(
      jnp.float32)

  # Compute loss for the C--N bond.
  c_n_bond_length = geometry.euclidean_distance(this_c_pos, next_n_pos, 1e-6)

  # The C-N bond to proline has slightly different length because of the ring.
  next_is_proline = (
      aatype[1:] == residue_constants.restype_order['P']).astype(jnp.float32)
  gt_length = (
      (1. - next_is_proline) * residue_constants.between_res_bond_length_c_n[0]
      + next_is_proline * residue_constants.between_res_bond_length_c_n[1])
  gt_stddev = (
      (1. - next_is_proline) *
      residue_constants.between_res_bond_length_stddev_c_n[0] +
      next_is_proline * residue_constants.between_res_bond_length_stddev_c_n[1])
  c_n_bond_length_error = jnp.sqrt(1e-6 +
                                   jnp.square(c_n_bond_length - gt_length))
  c_n_loss_per_residue = jax.nn.relu(
      c_n_bond_length_error - tolerance_factor_soft * gt_stddev)
  mask = this_c_mask * next_n_mask * has_no_gap_mask
  c_n_loss = jnp.sum(mask * c_n_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  c_n_violation_mask = mask * (
      c_n_bond_length_error > (tolerance_factor_hard * gt_stddev))

  # Compute loss for the angles.
  c_ca_unit_vec = (this_ca_pos - this_c_pos).normalized(1e-6)
  c_n_unit_vec = (next_n_pos - this_c_pos) / c_n_bond_length
  n_ca_unit_vec = (next_ca_pos - next_n_pos).normalized(1e-6)

  ca_c_n_cos_angle = c_ca_unit_vec.dot(c_n_unit_vec)
  gt_angle = residue_constants.between_res_cos_angles_ca_c_n[0]
  gt_stddev = residue_constants.between_res_bond_length_stddev_c_n[0]
  ca_c_n_cos_angle_error = jnp.sqrt(
      1e-6 + jnp.square(ca_c_n_cos_angle - gt_angle))
  ca_c_n_loss_per_residue = jax.nn.relu(
      ca_c_n_cos_angle_error - tolerance_factor_soft * gt_stddev)
  mask = this_ca_mask * this_c_mask * next_n_mask * has_no_gap_mask
  ca_c_n_loss = jnp.sum(mask * ca_c_n_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  ca_c_n_violation_mask = mask * (ca_c_n_cos_angle_error >
                                  (tolerance_factor_hard * gt_stddev))

  c_n_ca_cos_angle = (-c_n_unit_vec).dot(n_ca_unit_vec)
  gt_angle = residue_constants.between_res_cos_angles_c_n_ca[0]
  gt_stddev = residue_constants.between_res_cos_angles_c_n_ca[1]
  c_n_ca_cos_angle_error = jnp.sqrt(
      1e-6 + jnp.square(c_n_ca_cos_angle - gt_angle))
  c_n_ca_loss_per_residue = jax.nn.relu(
      c_n_ca_cos_angle_error - tolerance_factor_soft * gt_stddev)
  mask = this_c_mask * next_n_mask * next_ca_mask * has_no_gap_mask
  c_n_ca_loss = jnp.sum(mask * c_n_ca_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  c_n_ca_violation_mask = mask * (
      c_n_ca_cos_angle_error > (tolerance_factor_hard * gt_stddev))

  # Compute a per residue loss (equally distribute the loss to both
  # neighbouring residues).
  per_residue_loss_sum = (c_n_loss_per_residue +
                          ca_c_n_loss_per_residue +
                          c_n_ca_loss_per_residue)
  per_residue_loss_sum = 0.5 * (jnp.pad(per_residue_loss_sum, [[0, 1]]) +
                                jnp.pad(per_residue_loss_sum, [[1, 0]]))

  # Compute hard violations.
  violation_mask = jnp.max(
      jnp.stack([c_n_violation_mask,
                 ca_c_n_violation_mask,
                 c_n_ca_violation_mask]), axis=0)
  violation_mask = jnp.maximum(
      jnp.pad(violation_mask, [[0, 1]]),
      jnp.pad(violation_mask, [[1, 0]]))

  return {'c_n_loss_mean': c_n_loss,  # shape ()
          'ca_c_n_loss_mean': ca_c_n_loss,  # shape ()
          'c_n_ca_loss_mean': c_n_ca_loss,  # shape ()
          'per_residue_loss_sum': per_residue_loss_sum,  # shape (N)
          'per_residue_violation_mask': violation_mask  # shape (N)
         }


def between_residue_clash_loss(
    pred_positions: geometry.Vec3Array,  # (N, 14)
    atom_exists: jnp.ndarray,  # (N, 14)
    atom_radius: jnp.ndarray,  # (N, 14)
    residue_index: jnp.ndarray,  # (N)
    asym_id: jnp.ndarray,  # (N)
    overlap_tolerance_soft=1.5,
    overlap_tolerance_hard=1.5) -> Dict[Text, jnp.ndarray]:
  """"""Loss to penalize steric clashes between residues.""""""
  assert len(pred_positions.shape) == 2
  assert len(atom_exists.shape) == 2
  assert len(atom_radius.shape) == 2
  assert len(residue_index.shape) == 1

  # Create the distance matrix.
  # (N, N, 14, 14)
  dists = geometry.euclidean_distance(pred_positions[:, None, :, None],
                                      pred_positions[None, :, None, :], 1e-10)

  # Create the mask for valid distances.
  # shape (N, N, 14, 14)
  dists_mask = (atom_exists[:, None, :, None] * atom_exists[None, :, None, :])

  # Mask out all the duplicate entries in the lower triangular matrix.
  # Also mask out the diagonal (atom-pairs from the same residue) -- these atoms
  # are handled separately.
  dists_mask *= (
      residue_index[:, None, None, None] < residue_index[None, :, None, None])

  # Backbone C--N bond between subsequent residues is no clash.
  c_one_hot = jax.nn.one_hot(2, num_classes=14)
  n_one_hot = jax.nn.one_hot(0, num_classes=14)
  neighbour_mask = ((residue_index[:, None] + 1) == residue_index[None, :])
  neighbour_mask &= (asym_id[:, None] == asym_id[None, :])
  neighbour_mask = neighbour_mask[..., None, None]
  c_n_bonds = neighbour_mask * c_one_hot[None, None, :,
                                         None] * n_one_hot[None, None, None, :]
  dists_mask *= (1. - c_n_bonds)

  # Disulfide bridge between two cysteines is no clash.
  cys_sg_idx = residue_constants.restype_name_to_atom14_names['CYS'].index('SG')
  cys_sg_one_hot = jax.nn.one_hot(cys_sg_idx, num_classes=14)
  disulfide_bonds = (cys_sg_one_hot[None, None, :, None] *
                     cys_sg_one_hot[None, None, None, :])
  dists_mask *= (1. - disulfide_bonds)

  # Compute the lower bound for the allowed distances.
  # shape (N, N, 14, 14)
  dists_lower_bound = dists_mask * (
      atom_radius[:, None, :, None] + atom_radius[None, :, None, :])

  # Compute the error.
  # shape (N, N, 14, 14)
  dists_to_low_error = dists_mask * jax.nn.relu(
      dists_lower_bound - overlap_tolerance_soft - dists)

  # Compute the mean loss.
  # shape ()
  mean_loss = (jnp.sum(dists_to_low_error)
               / (1e-6 + jnp.sum(dists_mask)))

  # Compute the per atom loss sum.
  # shape (N, 14)
  per_atom_loss_sum = (jnp.sum(dists_to_low_error, axis=[0, 2]) +
                       jnp.sum(dists_to_low_error, axis=[1, 3]))

  # Compute the hard clash mask.
  # shape (N, N, 14, 14)
  clash_mask = dists_mask * (
      dists < (dists_lower_bound - overlap_tolerance_hard))

  # Compute the per atom clash.
  # shape (N, 14)
  per_atom_clash_mask = jnp.maximum(
      jnp.max(clash_mask, axis=[0, 2]),
      jnp.max(clash_mask, axis=[1, 3]))

  return {'mean_loss': mean_loss,  # shape ()
          'per_atom_loss_sum': per_atom_loss_sum,  # shape (N, 14)
          'per_atom_clash_mask': per_atom_clash_mask  # shape (N, 14)
         }


def within_residue_violations(
    pred_positions: geometry.Vec3Array,  # (N, 14)
    atom_exists: jnp.ndarray,  # (N, 14)
    dists_lower_bound: jnp.ndarray,  # (N, 14, 14)
    dists_upper_bound: jnp.ndarray,  # (N, 14, 14)
    tighten_bounds_for_loss=0.0,
) -> Dict[Text, jnp.ndarray]:
  """"""Find within-residue violations.""""""
  assert len(pred_positions.shape) == 2
  assert len(atom_exists.shape) == 2
  assert len(dists_lower_bound.shape) == 3
  assert len(dists_upper_bound.shape) == 3

  # Compute the mask for each residue.
  # shape (N, 14, 14)
  dists_masks = (1. - jnp.eye(14, 14)[None])
  dists_masks *= (atom_exists[:, :, None] * atom_exists[:, None, :])

  # Distance matrix
  # shape (N, 14, 14)
  dists = geometry.euclidean_distance(pred_positions[:, :, None],
                                      pred_positions[:, None, :], 1e-10)

  # Compute the loss.
  # shape (N, 14, 14)
  dists_to_low_error = jax.nn.relu(
      dists_lower_bound + tighten_bounds_for_loss - dists)
  dists_to_high_error = jax.nn.relu(
      dists + tighten_bounds_for_loss - dists_upper_bound)
  loss = dists_masks * (dists_to_low_error + dists_to_high_error)

  # Compute the per atom loss sum.
  # shape (N, 14)
  per_atom_loss_sum = (jnp.sum(loss, axis=1) +
                       jnp.sum(loss, axis=2))

  # Compute the violations mask.
  # shape (N, 14, 14)
  violations = dists_masks * ((dists < dists_lower_bound) |
                              (dists > dists_upper_bound))

  # Compute the per atom violations.
  # shape (N, 14)
  per_atom_violations = jnp.maximum(
      jnp.max(violations, axis=1), jnp.max(violations, axis=2))

  return {'per_atom_loss_sum': per_atom_loss_sum,  # shape (N, 14)
          'per_atom_violations': per_atom_violations  # shape (N, 14)
         }


def find_optimal_renaming(
    gt_positions: geometry.Vec3Array,  # (N, 14)
    alt_gt_positions: geometry.Vec3Array,  # (N, 14)
    atom_is_ambiguous: jnp.ndarray,  # (N, 14)
    gt_exists: jnp.ndarray,  # (N, 14)
    pred_positions: geometry.Vec3Array,  # (N, 14)
) -> jnp.ndarray:  # (N):
  """"""Find optimal renaming for ground truth that maximizes LDDT.""""""
  assert len(gt_positions.shape) == 2
  assert len(alt_gt_positions.shape) == 2
  assert len(atom_is_ambiguous.shape) == 2
  assert len(gt_exists.shape) == 2
  assert len(pred_positions.shape) == 2

  # Create the pred distance matrix.
  # shape (N, N, 14, 14)
  pred_dists = geometry.euclidean_distance(pred_positions[:, None, :, None],
                                           pred_positions[None, :, None, :],
                                           1e-10)

  # Compute distances for ground truth with original and alternative names.
  # shape (N, N, 14, 14)
  gt_dists = geometry.euclidean_distance(gt_positions[:, None, :, None],
                                         gt_positions[None, :, None, :], 1e-10)

  alt_gt_dists = geometry.euclidean_distance(alt_gt_positions[:, None, :, None],
                                             alt_gt_positions[None, :, None, :],
                                             1e-10)

  # Compute LDDT's.
  # shape (N, N, 14, 14)
  lddt = jnp.sqrt(1e-10 + squared_difference(pred_dists, gt_dists))
  alt_lddt = jnp.sqrt(1e-10 + squared_difference(pred_dists, alt_gt_dists))

  # Create a mask for ambiguous atoms in rows vs. non-ambiguous atoms
  # in cols.
  # shape (N ,N, 14, 14)
  mask = (
      gt_exists[:, None, :, None] *  # rows
      atom_is_ambiguous[:, None, :, None] *  # rows
      gt_exists[None, :, None, :] *  # cols
      (1. - atom_is_ambiguous[None, :, None, :]))  # cols

  # Aggregate distances for each residue to the non-amibuguous atoms.
  # shape (N)
  per_res_lddt = jnp.sum(mask * lddt, axis=[1, 2, 3])
  alt_per_res_lddt = jnp.sum(mask * alt_lddt, axis=[1, 2, 3])

  # Decide for each residue, whether alternative naming is better.
  # shape (N)
  alt_naming_is_better = (alt_per_res_lddt < per_res_lddt).astype(jnp.float32)

  return alt_naming_is_better  # shape (N)


def frame_aligned_point_error(
    pred_frames: geometry.Rigid3Array,  # shape (num_frames)
    target_frames: geometry.Rigid3Array,  # shape (num_frames)
    frames_mask: jnp.ndarray,  # shape (num_frames)
    pred_positions: geometry.Vec3Array,  # shape (num_positions)
    target_positions: geometry.Vec3Array,  # shape (num_positions)
    positions_mask: jnp.ndarray,  # shape (num_positions)
    pair_mask: jnp.ndarray,  # shape (num_frames, num_posiitons)
    l1_clamp_distance: float,
    length_scale=20.,
    epsilon=1e-4) -> jnp.ndarray:  # shape ()
  """"""Measure point error under different alignements.

  Computes error between two structures with B points
  under A alignments derived form the given pairs of frames.
  Args:
    pred_frames: num_frames reference frames for 'pred_positions'.
    target_frames: num_frames reference frames for 'target_positions'.
    frames_mask: Mask for frame pairs to use.
    pred_positions: num_positions predicted positions of the structure.
    target_positions: num_positions target positions of the structure.
    positions_mask: Mask on which positions to score.
    pair_mask: A (num_frames, num_positions) mask to use in the loss, useful
      for separating intra from inter chain losses.
    l1_clamp_distance: Distance cutoff on error beyond which gradients will
      be zero.
    length_scale: length scale to divide loss by.
    epsilon: small value used to regularize denominator for masked average.
  Returns:
    Masked Frame aligned point error.
  """"""
  # For now we do not allow any batch dimensions.
  assert len(pred_frames.rotation.shape) == 1
  assert len(target_frames.rotation.shape) == 1
  assert frames_mask.ndim == 1
  assert pred_positions.x.ndim == 1
  assert target_positions.x.ndim == 1
  assert positions_mask.ndim == 1

  # Compute array of predicted positions in the predicted frames.
  # geometry.Vec3Array (num_frames, num_positions)
  local_pred_pos = pred_frames[:, None].inverse().apply_to_point(
      pred_positions[None, :])

  # Compute array of target positions in the target frames.
  # geometry.Vec3Array (num_frames, num_positions)
  local_target_pos = target_frames[:, None].inverse().apply_to_point(
      target_positions[None, :])

  # Compute errors between the structures.
  # jnp.ndarray (num_frames, num_positions)
  error_dist = geometry.euclidean_distance(local_pred_pos, local_target_pos,
                                           epsilon)

  clipped_error_dist = jnp.clip(error_dist, 0, l1_clamp_distance)

  normed_error = clipped_error_dist / length_scale
  normed_error *= jnp.expand_dims(frames_mask, axis=-1)
  normed_error *= jnp.expand_dims(positions_mask, axis=-2)
  if pair_mask is not None:
    normed_error *= pair_mask

  mask = (jnp.expand_dims(frames_mask, axis=-1) *
          jnp.expand_dims(positions_mask, axis=-2))
  if pair_mask is not None:
    mask *= pair_mask
  normalization_factor = jnp.sum(mask, axis=(-1, -2))
  return (jnp.sum(normed_error, axis=(-2, -1)) /
          (epsilon + normalization_factor))


def get_chi_atom_indices():
  """"""Returns atom indices needed to compute chi angles for all residue types.

  Returns:
    A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are
    in the order specified in residue_constants.restypes + unknown residue type
    at the end. For chi angles which are not defined on the residue, the
    positions indices are by default set to 0.
  """"""
  chi_atom_indices = []
  for residue_name in residue_constants.restypes:
    residue_name = residue_constants.restype_1to3[residue_name]
    residue_chi_angles = residue_constants.chi_angles_atoms[residue_name]
    atom_indices = []
    for chi_angle in residue_chi_angles:
      atom_indices.append(
          [residue_constants.atom_order[atom] for atom in chi_angle])
    for _ in range(4 - len(atom_indices)):
      atom_indices.append([0, 0, 0, 0])  # For chi angles not defined on the AA.
    chi_atom_indices.append(atom_indices)

  chi_atom_indices.append([[0, 0, 0, 0]] * 4)  # For UNKNOWN residue.

  return jnp.asarray(chi_atom_indices)


def compute_chi_angles(positions: geometry.Vec3Array,
                       mask: geometry.Vec3Array,
                       aatype: geometry.Vec3Array):
  """"""Computes the chi angles given all atom positions and the amino acid type.

  Args:
    positions: A Vec3Array of shape
      [num_res, residue_constants.atom_type_num], with positions of
      atoms needed to calculate chi angles. Supports up to 1 batch dimension.
    mask: An optional tensor of shape
      [num_res, residue_constants.atom_type_num] that masks which atom
      positions are set for each residue. If given, then the chi mask will be
      set to 1 for a chi angle only if the amino acid has that chi angle and all
      the chi atoms needed to calculate that chi angle are set. If not given
      (set to None), the chi mask will be set to 1 for a chi angle if the amino
      acid has that chi angle and whether the actual atoms needed to calculate
      it were set will be ignored.
    aatype: A tensor of shape [num_res] with amino acid type integer
      code (0 to 21). Supports up to 1 batch dimension.

  Returns:
    A tuple of tensors (chi_angles, mask), where both have shape
    [num_res, 4]. The mask masks out unused chi angles for amino acid
    types that have less than 4 chi angles. If atom_positions_mask is set, the
    chi mask will also mask out uncomputable chi angles.
  """"""

  # Don't assert on the num_res and batch dimensions as they might be unknown.
  assert positions.shape[-1] == residue_constants.atom_type_num
  assert mask.shape[-1] == residue_constants.atom_type_num

  # Compute the table of chi angle indices. Shape: [restypes, chis=4, atoms=4].
  chi_atom_indices = get_chi_atom_indices()
  # Select atoms to compute chis. Shape: [num_res, chis=4, atoms=4].
  atom_indices = utils.batched_gather(
      params=chi_atom_indices, indices=aatype, axis=0)
  # Gather atom positions. Shape: [num_res, chis=4, atoms=4, xyz=3].
  chi_angle_atoms = jax.tree_map(
      lambda x: utils.batched_gather(  # pylint: disable=g-long-lambda
          params=x, indices=atom_indices, axis=-1, batch_dims=1), positions)
  a, b, c, d = [chi_angle_atoms[..., i] for i in range(4)]

  chi_angles = geometry.dihedral_angle(a, b, c, d)

  # Copy the chi angle mask, add the UNKNOWN residue. Shape: [restypes, 4].
  chi_angles_mask = list(residue_constants.chi_angles_mask)
  chi_angles_mask.append([0.0, 0.0, 0.0, 0.0])
  chi_angles_mask = jnp.asarray(chi_angles_mask)
  # Compute the chi angle mask. Shape [num_res, chis=4].
  chi_mask = utils.batched_gather(params=chi_angles_mask, indices=aatype,
                                  axis=0)

  # The chi_mask is set to 1 only when all necessary chi angle atoms were set.
  # Gather the chi angle atoms mask. Shape: [num_res, chis=4, atoms=4].
  chi_angle_atoms_mask = utils.batched_gather(
      params=mask, indices=atom_indices, axis=-1, batch_dims=1)
  # Check if all 4 chi angle atoms were set. Shape: [num_res, chis=4].
  chi_angle_atoms_mask = jnp.prod(chi_angle_atoms_mask, axis=[-1])
  chi_mask = chi_mask * chi_angle_atoms_mask.astype(jnp.float32)

  return chi_angles, chi_mask


def make_transform_from_reference(
    a_xyz: geometry.Vec3Array,
    b_xyz: geometry.Vec3Array,
    c_xyz: geometry.Vec3Array) -> geometry.Rigid3Array:
  """"""Returns rotation and translation matrices to convert from reference.

  Note that this method does not take care of symmetries. If you provide the
  coordinates in the non-standard way, the A atom will end up in the negative
  y-axis rather than in the positive y-axis. You need to take care of such
  cases in your code.

  Args:
    a_xyz: A Vec3Array.
    b_xyz: A Vec3Array.
    c_xyz: A Vec3Array.

  Returns:
    A Rigid3Array which, when applied to coordinates in a canonicalized
    reference frame, will give coordinates approximately equal
    the original coordinates (in the global frame).
  """"""
  rotation = geometry.Rot3Array.from_two_vectors(c_xyz - b_xyz,
                                                 a_xyz - b_xyz)
  return geometry.Rigid3Array(rotation, b_xyz)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/__init__.py",".py","617","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Alphafold model.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/modules_multimer.py",".py","42228","1179","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Core modules, which have been refactored in AlphaFold-Multimer.

The main difference is that MSA sampling pipeline is moved inside the JAX model
for easier implementation of recycling and ensembling.

Lower-level modules up to EvoformerIteration are reused from modules.py.
""""""

import functools
from typing import Sequence

from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.model import modules
from alphafold.model import prng
from alphafold.model import utils

import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np


def reduce_fn(x, mode):
  if mode == 'none' or mode is None:
    return jnp.asarray(x)
  elif mode == 'sum':
    return jnp.asarray(x).sum()
  elif mode == 'mean':
    return jnp.mean(jnp.asarray(x))
  else:
    raise ValueError('Unsupported reduction option.')


def gumbel_noise(key: jnp.ndarray, shape: Sequence[int]) -> jnp.ndarray:
  """"""Generate Gumbel Noise of given Shape.

  This generates samples from Gumbel(0, 1).

  Args:
    key: Jax random number key.
    shape: Shape of noise to return.

  Returns:
    Gumbel noise of given shape.
  """"""
  epsilon = 1e-6
  uniform = utils.padding_consistent_rng(jax.random.uniform)
  uniform_noise = uniform(
      key, shape=shape, dtype=jnp.float32, minval=0., maxval=1.)
  gumbel = -jnp.log(-jnp.log(uniform_noise + epsilon) + epsilon)
  return gumbel


def gumbel_max_sample(key: jnp.ndarray, logits: jnp.ndarray) -> jnp.ndarray:
  """"""Samples from a probability distribution given by 'logits'.

  This uses Gumbel-max trick to implement the sampling in an efficient manner.

  Args:
    key: prng key.
    logits: Logarithm of probabilities to sample from, probabilities can be
      unnormalized.

  Returns:
    Sample from logprobs in one-hot form.
  """"""
  z = gumbel_noise(key, logits.shape)
  return jax.nn.one_hot(
      jnp.argmax(logits + z, axis=-1),
      logits.shape[-1],
      dtype=logits.dtype)


def gumbel_argsort_sample_idx(key: jnp.ndarray,
                              logits: jnp.ndarray) -> jnp.ndarray:
  """"""Samples with replacement from a distribution given by 'logits'.

  This uses Gumbel trick to implement the sampling an efficient manner. For a
  distribution over k items this samples k times without replacement, so this
  is effectively sampling a random permutation with probabilities over the
  permutations derived from the logprobs.

  Args:
    key: prng key.
    logits: Logarithm of probabilities to sample from, probabilities can be
      unnormalized.

  Returns:
    Sample from logprobs in one-hot form.
  """"""
  z = gumbel_noise(key, logits.shape)
  # This construction is equivalent to jnp.argsort, but using a non stable sort,
  # since stable sort's aren't supported by jax2tf.
  axis = len(logits.shape) - 1
  iota = jax.lax.broadcasted_iota(jnp.int64, logits.shape, axis)
  _, perm = jax.lax.sort_key_val(
      logits + z, iota, dimension=-1, is_stable=False)
  return perm[::-1]


def make_masked_msa(batch, key, config, epsilon=1e-6):
  """"""Create data for BERT on raw MSA.""""""
  # Add a random amino acid uniformly.
  random_aa = jnp.array([0.05] * 20 + [0., 0.], dtype=jnp.float32)

  categorical_probs = (
      config.uniform_prob * random_aa +
      config.profile_prob * batch['msa_profile'] +
      config.same_prob * jax.nn.one_hot(batch['msa'], 22))

  # Put all remaining probability on [MASK] which is a new column.
  pad_shapes = [[0, 0] for _ in range(len(categorical_probs.shape))]
  pad_shapes[-1][1] = 1
  mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
  assert mask_prob >= 0.
  categorical_probs = jnp.pad(
      categorical_probs, pad_shapes, constant_values=mask_prob)
  sh = batch['msa'].shape
  key, mask_subkey, gumbel_subkey = key.split(3)
  uniform = utils.padding_consistent_rng(jax.random.uniform)
  mask_position = uniform(mask_subkey.get(), sh) < config.replace_fraction
  mask_position *= batch['msa_mask']

  logits = jnp.log(categorical_probs + epsilon)
  bert_msa = gumbel_max_sample(gumbel_subkey.get(), logits)
  bert_msa = jnp.where(mask_position,
                       jnp.argmax(bert_msa, axis=-1), batch['msa'])
  bert_msa *= batch['msa_mask']

  # Mix real and masked MSA.
  if 'bert_mask' in batch:
    batch['bert_mask'] *= mask_position.astype(jnp.float32)
  else:
    batch['bert_mask'] = mask_position.astype(jnp.float32)
  batch['true_msa'] = batch['msa']
  batch['msa'] = bert_msa

  return batch


def nearest_neighbor_clusters(batch, gap_agreement_weight=0.):
  """"""Assign each extra MSA sequence to its nearest neighbor in sampled MSA.""""""

  # Determine how much weight we assign to each agreement.  In theory, we could
  # use a full blosum matrix here, but right now let's just down-weight gap
  # agreement because it could be spurious.
  # Never put weight on agreeing on BERT mask.

  weights = jnp.array(
      [1.] * 21 + [gap_agreement_weight] + [0.], dtype=jnp.float32)

  msa_mask = batch['msa_mask']
  msa_one_hot = jax.nn.one_hot(batch['msa'], 23)

  extra_mask = batch['extra_msa_mask']
  extra_one_hot = jax.nn.one_hot(batch['extra_msa'], 23)

  msa_one_hot_masked = msa_mask[:, :, None] * msa_one_hot
  extra_one_hot_masked = extra_mask[:, :, None] * extra_one_hot

  agreement = jnp.einsum('mrc, nrc->nm', extra_one_hot_masked,
                         weights * msa_one_hot_masked)

  cluster_assignment = jax.nn.softmax(1e3 * agreement, axis=0)
  cluster_assignment *= jnp.einsum('mr, nr->mn', msa_mask, extra_mask)

  cluster_count = jnp.sum(cluster_assignment, axis=-1)
  cluster_count += 1.  # We always include the sequence itself.

  msa_sum = jnp.einsum('nm, mrc->nrc', cluster_assignment, extra_one_hot_masked)
  msa_sum += msa_one_hot_masked

  cluster_profile = msa_sum / cluster_count[:, None, None]

  extra_deletion_matrix = batch['extra_deletion_matrix']
  deletion_matrix = batch['deletion_matrix']

  del_sum = jnp.einsum('nm, mc->nc', cluster_assignment,
                       extra_mask * extra_deletion_matrix)
  del_sum += deletion_matrix  # Original sequence.
  cluster_deletion_mean = del_sum / cluster_count[:, None]

  return cluster_profile, cluster_deletion_mean


def create_msa_feat(batch):
  """"""Create and concatenate MSA features.""""""
  msa_1hot = jax.nn.one_hot(batch['msa'], 23)
  deletion_matrix = batch['deletion_matrix']
  has_deletion = jnp.clip(deletion_matrix, 0., 1.)[..., None]
  deletion_value = (jnp.arctan(deletion_matrix / 3.) * (2. / jnp.pi))[..., None]

  deletion_mean_value = (jnp.arctan(batch['cluster_deletion_mean'] / 3.) *
                         (2. / jnp.pi))[..., None]

  msa_feat = [
      msa_1hot,
      has_deletion,
      deletion_value,
      batch['cluster_profile'],
      deletion_mean_value
  ]

  return jnp.concatenate(msa_feat, axis=-1)


def create_extra_msa_feature(batch, num_extra_msa):
  """"""Expand extra_msa into 1hot and concat with other extra msa features.

  We do this as late as possible as the one_hot extra msa can be very large.

  Args:
    batch: a dictionary with the following keys:
     * 'extra_msa': [num_seq, num_res] MSA that wasn't selected as a cluster
       centre. Note - This isn't one-hotted.
     * 'extra_deletion_matrix': [num_seq, num_res] Number of deletions at given
        position.
    num_extra_msa: Number of extra msa to use.

  Returns:
    Concatenated tensor of extra MSA features.
  """"""
  # 23 = 20 amino acids + 'X' for unknown + gap + bert mask
  extra_msa = batch['extra_msa'][:num_extra_msa]
  deletion_matrix = batch['extra_deletion_matrix'][:num_extra_msa]
  msa_1hot = jax.nn.one_hot(extra_msa, 23)
  has_deletion = jnp.clip(deletion_matrix, 0., 1.)[..., None]
  deletion_value = (jnp.arctan(deletion_matrix / 3.) * (2. / jnp.pi))[..., None]
  extra_msa_mask = batch['extra_msa_mask'][:num_extra_msa]
  return jnp.concatenate([msa_1hot, has_deletion, deletion_value],
                         axis=-1), extra_msa_mask


def sample_msa(key, batch, max_seq):
  """"""Sample MSA randomly, remaining sequences are stored as `extra_*`.

  Args:
    key: safe key for random number generation.
    batch: batch to sample msa from.
    max_seq: number of sequences to sample.
  Returns:
    Protein with sampled msa.
  """"""
  # Sample uniformly among sequences with at least one non-masked position.
  logits = (jnp.clip(jnp.sum(batch['msa_mask'], axis=-1), 0., 1.) - 1.) * 1e6
  # The cluster_bias_mask can be used to preserve the first row (target
  # sequence) for each chain, for example.
  if 'cluster_bias_mask' not in batch:
    cluster_bias_mask = jnp.pad(
        jnp.zeros(batch['msa'].shape[0] - 1), (1, 0), constant_values=1.)
  else:
    cluster_bias_mask = batch['cluster_bias_mask']

  logits += cluster_bias_mask * 1e6
  index_order = gumbel_argsort_sample_idx(key.get(), logits)
  sel_idx = index_order[:max_seq]
  extra_idx = index_order[max_seq:]

  for k in ['msa', 'deletion_matrix', 'msa_mask', 'bert_mask']:
    if k in batch:
      batch['extra_' + k] = batch[k][extra_idx]
      batch[k] = batch[k][sel_idx]

  return batch


def make_msa_profile(batch):
  """"""Compute the MSA profile.""""""

  # Compute the profile for every residue (over all MSA sequences).
  return utils.mask_mean(
      batch['msa_mask'][:, :, None], jax.nn.one_hot(batch['msa'], 22), axis=0)


class AlphaFoldIteration(hk.Module):
  """"""A single recycling iteration of AlphaFold architecture.

  Computes ensembled (averaged) representations from the provided features.
  These representations are then passed to the various heads
  that have been requested by the configuration file.
  """"""

  def __init__(self, config, global_config, name='alphafold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               batch,
               is_training,
               return_representations=False,
               safe_key=None):

    if is_training:
      num_ensemble = np.asarray(self.config.num_ensemble_train)
    else:
      num_ensemble = np.asarray(self.config.num_ensemble_eval)

    # Compute representations for each MSA sample and average.
    embedding_module = EmbeddingsAndEvoformer(
        self.config.embeddings_and_evoformer, self.global_config)
    repr_shape = hk.eval_shape(
        lambda: embedding_module(batch, is_training))
    representations = {
        k: jnp.zeros(v.shape, v.dtype) for (k, v) in repr_shape.items()
    }

    def ensemble_body(x, unused_y):
      """"""Add into representations ensemble.""""""
      del unused_y
      representations, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      representations_update = embedding_module(
          batch, is_training, safe_key=safe_subkey)

      for k in representations:
        if k not in {'msa', 'true_msa', 'bert_mask'}:
          representations[k] += representations_update[k] * (
              1. / num_ensemble).astype(representations[k].dtype)
        else:
          representations[k] = representations_update[k]

      return (representations, safe_key), None

    (representations, _), _ = hk.scan(
        ensemble_body, (representations, safe_key), None, length=num_ensemble)

    self.representations = representations
    self.batch = batch
    self.heads = {}
    for head_name, head_config in sorted(self.config.heads.items()):
      if not head_config.weight:
        continue  # Do not instantiate zero-weight heads.

      head_factory = {
          'masked_msa':
              modules.MaskedMsaHead,
          'distogram':
              modules.DistogramHead,
          'structure_module':
              folding_multimer.StructureModule,
          'predicted_aligned_error':
              modules.PredictedAlignedErrorHead,
          'predicted_lddt':
              modules.PredictedLDDTHead,
          'experimentally_resolved':
              modules.ExperimentallyResolvedHead,
      }[head_name]
      self.heads[head_name] = (head_config,
                               head_factory(head_config, self.global_config))

    structure_module_output = None
    if 'entity_id' in batch and 'all_atom_positions' in batch:
      _, fold_module = self.heads['structure_module']
      structure_module_output = fold_module(representations, batch, is_training)

    ret = {}
    ret['representations'] = representations

    for name, (head_config, module) in self.heads.items():
      if name == 'structure_module' and structure_module_output is not None:
        ret[name] = structure_module_output
        representations['structure_module'] = structure_module_output.pop('act')
      # Skip confidence heads until StructureModule is executed.
      elif name in {'predicted_lddt', 'predicted_aligned_error',
                    'experimentally_resolved'}:
        continue
      else:
        ret[name] = module(representations, batch, is_training)

    # Add confidence heads after StructureModule is executed.
    if self.config.heads.get('predicted_lddt.weight', 0.0):
      name = 'predicted_lddt'
      head_config, module = self.heads[name]
      ret[name] = module(representations, batch, is_training)

    if self.config.heads.experimentally_resolved.weight:
      name = 'experimentally_resolved'
      head_config, module = self.heads[name]
      ret[name] = module(representations, batch, is_training)

    if self.config.heads.get('predicted_aligned_error.weight', 0.0):
      name = 'predicted_aligned_error'
      head_config, module = self.heads[name]
      ret[name] = module(representations, batch, is_training)
      # Will be used for ipTM computation.
      ret[name]['asym_id'] = batch['asym_id']

    return ret


class AlphaFold(hk.Module):
  """"""AlphaFold-Multimer model with recycling.
  """"""

  def __init__(self, config, name='alphafold'):
    super().__init__(name=name)
    self.config = config
    self.global_config = config.global_config

  def __call__(
      self,
      batch,
      is_training,
      return_representations=False,
      safe_key=None):

    c = self.config
    impl = AlphaFoldIteration(c, self.global_config)

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())
    elif isinstance(safe_key, jnp.ndarray):
      safe_key = prng.SafeKey(safe_key)

    assert isinstance(batch, dict)
    num_res = batch['aatype'].shape[0]

    def get_prev(ret):
      new_prev = {
          'prev_pos':
              ret['structure_module']['final_atom_positions'],
          'prev_msa_first_row': ret['representations']['msa_first_row'],
          'prev_pair': ret['representations']['pair'],
      }
      return jax.tree_map(jax.lax.stop_gradient, new_prev)

    def apply_network(prev, safe_key):
      recycled_batch = {**batch, **prev}
      return impl(
          batch=recycled_batch,
          is_training=is_training,
          safe_key=safe_key)

    prev = {}
    emb_config = self.config.embeddings_and_evoformer
    if emb_config.recycle_pos:
      prev['prev_pos'] = jnp.zeros(
          [num_res, residue_constants.atom_type_num, 3])
    if emb_config.recycle_features:
      prev['prev_msa_first_row'] = jnp.zeros(
          [num_res, emb_config.msa_channel])
      prev['prev_pair'] = jnp.zeros(
          [num_res, num_res, emb_config.pair_channel])

    if self.config.num_recycle:
      if 'num_iter_recycling' in batch:
        # Training time: num_iter_recycling is in batch.
        # Value for each ensemble batch is the same, so arbitrarily taking 0-th.
        num_iter = batch['num_iter_recycling'][0]

        # Add insurance that even when ensembling, we will not run more
        # recyclings than the model is configured to run.
        num_iter = jnp.minimum(num_iter, c.num_recycle)
      else:
        # Eval mode or tests: use the maximum number of iterations.
        num_iter = c.num_recycle

      def distances(points):
        """"""Compute all pairwise distances for a set of points.""""""
        return jnp.sqrt(jnp.sum((points[:, None] - points[None, :])**2,
                                axis=-1))

      def recycle_body(x):
        i, _, prev, safe_key = x
        safe_key1, safe_key2 = safe_key.split() if c.resample_msa_in_recycling else safe_key.duplicate()  # pylint: disable=line-too-long
        ret = apply_network(prev=prev, safe_key=safe_key2)
        return i+1, prev, get_prev(ret), safe_key1

      def recycle_cond(x):
        i, prev, next_in, _ = x
        ca_idx = residue_constants.atom_order['CA']
        sq_diff = jnp.square(distances(prev['prev_pos'][:, ca_idx, :]) -
                             distances(next_in['prev_pos'][:, ca_idx, :]))
        mask = batch['seq_mask'][:, None] * batch['seq_mask'][None, :]
        sq_diff = utils.mask_mean(mask, sq_diff)
        # Early stopping criteria based on criteria used in
        # AF2Complex: https://www.nature.com/articles/s41467-022-29394-2
        diff = jnp.sqrt(sq_diff + 1e-8)  # avoid bad numerics giving negatives
        less_than_max_recycles = (i < num_iter)
        has_exceeded_tolerance = (
            (i == 0) | (diff > c.recycle_early_stop_tolerance))
        return less_than_max_recycles & has_exceeded_tolerance

      if hk.running_init():
        num_recycles, _, prev, safe_key = recycle_body(
            (0, prev, prev, safe_key))
      else:
        num_recycles, _, prev, safe_key = hk.while_loop(
            recycle_cond,
            recycle_body,
            (0, prev, prev, safe_key))
    else:
      # No recycling.
      num_recycles = 0

    # Run extra iteration.
    ret = apply_network(prev=prev, safe_key=safe_key)

    if not return_representations:
      del ret['representations']
    ret['num_recycles'] = num_recycles

    return ret


class EmbeddingsAndEvoformer(hk.Module):
  """"""Embeds the input data and runs Evoformer.

  Produces the MSA, single and pair representations.
  """"""

  def __init__(self, config, global_config, name='evoformer'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def _relative_encoding(self, batch):
    """"""Add relative position encodings.

    For position (i, j), the value is (i-j) clipped to [-k, k] and one-hotted.

    When not using 'use_chain_relative' the residue indices are used as is, e.g.
    for heteromers relative positions will be computed using the positions in
    the corresponding chains.

    When using 'use_chain_relative' we add an extra bin that denotes
    'different chain'. Furthermore we also provide the relative chain index
    (i.e. sym_id) clipped and one-hotted to the network. And an extra feature
    which denotes whether they belong to the same chain type, i.e. it's 0 if
    they are in different heteromer chains and 1 otherwise.

    Args:
      batch: batch.
    Returns:
      Feature embedding using the features as described before.
    """"""
    c = self.config
    gc = self.global_config
    rel_feats = []
    pos = batch['residue_index']
    asym_id = batch['asym_id']
    asym_id_same = jnp.equal(asym_id[:, None], asym_id[None, :])
    offset = pos[:, None] - pos[None, :]
    dtype = jnp.bfloat16 if gc.bfloat16 else jnp.float32

    clipped_offset = jnp.clip(
        offset + c.max_relative_idx, a_min=0, a_max=2 * c.max_relative_idx)

    if c.use_chain_relative:

      final_offset = jnp.where(asym_id_same, clipped_offset,
                               (2 * c.max_relative_idx + 1) *
                               jnp.ones_like(clipped_offset))

      rel_pos = jax.nn.one_hot(final_offset, 2 * c.max_relative_idx + 2)

      rel_feats.append(rel_pos)

      entity_id = batch['entity_id']
      entity_id_same = jnp.equal(entity_id[:, None], entity_id[None, :])
      rel_feats.append(entity_id_same.astype(rel_pos.dtype)[..., None])

      sym_id = batch['sym_id']
      rel_sym_id = sym_id[:, None] - sym_id[None, :]

      max_rel_chain = c.max_relative_chain

      clipped_rel_chain = jnp.clip(
          rel_sym_id + max_rel_chain, a_min=0, a_max=2 * max_rel_chain)

      final_rel_chain = jnp.where(entity_id_same, clipped_rel_chain,
                                  (2 * max_rel_chain + 1) *
                                  jnp.ones_like(clipped_rel_chain))
      rel_chain = jax.nn.one_hot(final_rel_chain, 2 * c.max_relative_chain + 2)

      rel_feats.append(rel_chain)

    else:
      rel_pos = jax.nn.one_hot(clipped_offset, 2 * c.max_relative_idx + 1)
      rel_feats.append(rel_pos)

    rel_feat = jnp.concatenate(rel_feats, axis=-1)

    rel_feat = rel_feat.astype(dtype)
    return common_modules.Linear(
        c.pair_channel,
        name='position_activations')(
            rel_feat)

  def __call__(self, batch, is_training, safe_key=None):

    c = self.config
    gc = self.global_config

    batch = dict(batch)
    dtype = jnp.bfloat16 if gc.bfloat16 else jnp.float32

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    output = {}

    batch['msa_profile'] = make_msa_profile(batch)

    with utils.bfloat16_context():
      target_feat = jax.nn.one_hot(batch['aatype'], 21).astype(dtype)

      preprocess_1d = common_modules.Linear(
          c.msa_channel, name='preprocess_1d')(
              target_feat)

      safe_key, sample_key, mask_key = safe_key.split(3)
      batch = sample_msa(sample_key, batch, c.num_msa)
      batch = make_masked_msa(batch, mask_key, c.masked_msa)

      (batch['cluster_profile'],
       batch['cluster_deletion_mean']) = nearest_neighbor_clusters(batch)

      msa_feat = create_msa_feat(batch).astype(dtype)

      preprocess_msa = common_modules.Linear(
          c.msa_channel, name='preprocess_msa')(
              msa_feat)
      msa_activations = jnp.expand_dims(preprocess_1d, axis=0) + preprocess_msa

      left_single = common_modules.Linear(
          c.pair_channel, name='left_single')(
              target_feat)
      right_single = common_modules.Linear(
          c.pair_channel, name='right_single')(
              target_feat)
      pair_activations = left_single[:, None] + right_single[None]
      mask_2d = batch['seq_mask'][:, None] * batch['seq_mask'][None, :]
      mask_2d = mask_2d.astype(dtype)

      if c.recycle_pos:
        prev_pseudo_beta = modules.pseudo_beta_fn(
            batch['aatype'], batch['prev_pos'], None)

        dgram = modules.dgram_from_positions(
            prev_pseudo_beta, **self.config.prev_pos)
        dgram = dgram.astype(dtype)
        pair_activations += common_modules.Linear(
            c.pair_channel, name='prev_pos_linear')(
                dgram)
      if c.recycle_features:
        prev_msa_first_row = common_modules.LayerNorm(
            axis=[-1],
            create_scale=True,
            create_offset=True,
            name='prev_msa_first_row_norm')(
                batch['prev_msa_first_row']).astype(dtype)
        msa_activations = msa_activations.at[0].add(prev_msa_first_row)

        pair_activations += common_modules.LayerNorm(
            axis=[-1],
            create_scale=True,
            create_offset=True,
            name='prev_pair_norm')(
                batch['prev_pair']).astype(dtype)

      if c.max_relative_idx:
        pair_activations += self._relative_encoding(batch)

      if c.template.enabled:
        template_module = TemplateEmbedding(c.template, gc)
        template_batch = {
            'template_aatype': batch['template_aatype'],
            'template_all_atom_positions': batch['template_all_atom_positions'],
            'template_all_atom_mask': batch['template_all_atom_mask']
        }
        # Construct a mask such that only intra-chain template features are
        # computed, since all templates are for each chain individually.
        multichain_mask = batch['asym_id'][:, None] == batch['asym_id'][None, :]
        safe_key, safe_subkey = safe_key.split()
        template_act = template_module(
            query_embedding=pair_activations,
            template_batch=template_batch,
            padding_mask_2d=mask_2d,
            multichain_mask_2d=multichain_mask,
            is_training=is_training,
            safe_key=safe_subkey)
        pair_activations += template_act

      # Extra MSA stack.
      (extra_msa_feat,
       extra_msa_mask) = create_extra_msa_feature(batch, c.num_extra_msa)
      extra_msa_activations = common_modules.Linear(
          c.extra_msa_channel,
          name='extra_msa_activations')(
              extra_msa_feat).astype(dtype)
      extra_msa_mask = extra_msa_mask.astype(dtype)

      extra_evoformer_input = {
          'msa': extra_msa_activations,
          'pair': pair_activations,
      }
      extra_masks = {'msa': extra_msa_mask, 'pair': mask_2d}

      extra_evoformer_iteration = modules.EvoformerIteration(
          c.evoformer, gc, is_extra_msa=True, name='extra_msa_stack')

      def extra_evoformer_fn(x):
        act, safe_key = x
        safe_key, safe_subkey = safe_key.split()
        extra_evoformer_output = extra_evoformer_iteration(
            activations=act,
            masks=extra_masks,
            is_training=is_training,
            safe_key=safe_subkey)
        return (extra_evoformer_output, safe_key)

      if gc.use_remat:
        extra_evoformer_fn = hk.remat(extra_evoformer_fn)

      safe_key, safe_subkey = safe_key.split()
      extra_evoformer_stack = layer_stack.layer_stack(
          c.extra_msa_stack_num_block)(
              extra_evoformer_fn)
      extra_evoformer_output, safe_key = extra_evoformer_stack(
          (extra_evoformer_input, safe_subkey))

      pair_activations = extra_evoformer_output['pair']

      # Get the size of the MSA before potentially adding templates, so we
      # can crop out the templates later.
      num_msa_sequences = msa_activations.shape[0]
      evoformer_input = {
          'msa': msa_activations,
          'pair': pair_activations,
      }
      evoformer_masks = {
          'msa': batch['msa_mask'].astype(dtype),
          'pair': mask_2d
      }
      if c.template.enabled:
        template_features, template_masks = (
            template_embedding_1d(
                batch=batch, num_channel=c.msa_channel, global_config=gc))

        evoformer_input['msa'] = jnp.concatenate(
            [evoformer_input['msa'], template_features], axis=0)
        evoformer_masks['msa'] = jnp.concatenate(
            [evoformer_masks['msa'], template_masks], axis=0)
      evoformer_iteration = modules.EvoformerIteration(
          c.evoformer, gc, is_extra_msa=False, name='evoformer_iteration')

      def evoformer_fn(x):
        act, safe_key = x
        safe_key, safe_subkey = safe_key.split()
        evoformer_output = evoformer_iteration(
            activations=act,
            masks=evoformer_masks,
            is_training=is_training,
            safe_key=safe_subkey)
        return (evoformer_output, safe_key)

      if gc.use_remat:
        evoformer_fn = hk.remat(evoformer_fn)

      safe_key, safe_subkey = safe_key.split()
      evoformer_stack = layer_stack.layer_stack(c.evoformer_num_block)(
          evoformer_fn)

      def run_evoformer(evoformer_input):
        evoformer_output, _ = evoformer_stack((evoformer_input, safe_subkey))
        return evoformer_output

      evoformer_output = run_evoformer(evoformer_input)

      msa_activations = evoformer_output['msa']
      pair_activations = evoformer_output['pair']

      single_activations = common_modules.Linear(
          c.seq_channel, name='single_activations')(
              msa_activations[0])

    output.update({
        'single':
            single_activations,
        'pair':
            pair_activations,
        # Crop away template rows such that they are not used in MaskedMsaHead.
        'msa':
            msa_activations[:num_msa_sequences, :, :],
        'msa_first_row':
            msa_activations[0],
    })

    # Convert back to float32 if we're not saving memory.
    if not gc.bfloat16_output:
      for k, v in output.items():
        if v.dtype == jnp.bfloat16:
          output[k] = v.astype(jnp.float32)

    return output


class TemplateEmbedding(hk.Module):
  """"""Embed a set of templates.""""""

  def __init__(self, config, global_config, name='template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_batch, padding_mask_2d,
               multichain_mask_2d, is_training,
               safe_key=None):
    """"""Generate an embedding for a set of templates.

    Args:
      query_embedding: [num_res, num_res, num_channel] a query tensor that will
        be used to attend over the templates to remove the num_templates
        dimension.
      template_batch: A dictionary containing:
        `template_aatype`: [num_templates, num_res] aatype for each template.
        `template_all_atom_positions`: [num_templates, num_res, 37, 3] atom
          positions for all templates.
        `template_all_atom_mask`: [num_templates, num_res, 37] mask for each
          template.
      padding_mask_2d: [num_res, num_res] Pair mask for attention operations.
      multichain_mask_2d: [num_res, num_res] Mask indicating which residue pairs
        are intra-chain, used to mask out residue distance based features
        between chains.
      is_training: bool indicating where we are running in training mode.
      safe_key: random key generator.

    Returns:
      An embedding of size [num_res, num_res, num_channels]
    """"""
    c = self.config
    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    num_templates = template_batch['template_aatype'].shape[0]
    num_res, _, query_num_channels = query_embedding.shape

    # Embed each template separately.
    template_embedder = SingleTemplateEmbedding(self.config, self.global_config)
    def partial_template_embedder(template_aatype,
                                  template_all_atom_positions,
                                  template_all_atom_mask,
                                  unsafe_key):
      safe_key = prng.SafeKey(unsafe_key)
      return template_embedder(query_embedding,
                               template_aatype,
                               template_all_atom_positions,
                               template_all_atom_mask,
                               padding_mask_2d,
                               multichain_mask_2d,
                               is_training,
                               safe_key)

    safe_key, unsafe_key = safe_key.split()
    unsafe_keys = jax.random.split(unsafe_key._key, num_templates)

    def scan_fn(carry, x):
      return carry + partial_template_embedder(*x), None

    scan_init = jnp.zeros((num_res, num_res, c.num_channels),
                          dtype=query_embedding.dtype)
    summed_template_embeddings, _ = hk.scan(
        scan_fn, scan_init,
        (template_batch['template_aatype'],
         template_batch['template_all_atom_positions'],
         template_batch['template_all_atom_mask'], unsafe_keys))

    embedding = summed_template_embeddings / num_templates
    embedding = jax.nn.relu(embedding)
    embedding = common_modules.Linear(
        query_num_channels,
        initializer='relu',
        name='output_linear')(embedding)

    return embedding


class SingleTemplateEmbedding(hk.Module):
  """"""Embed a single template.""""""

  def __init__(self, config, global_config, name='single_template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_aatype,
               template_all_atom_positions, template_all_atom_mask,
               padding_mask_2d, multichain_mask_2d, is_training,
               safe_key):
    """"""Build the single template embedding graph.

    Args:
      query_embedding: (num_res, num_res, num_channels) - embedding of the
        query sequence/msa.
      template_aatype: [num_res] aatype for each template.
      template_all_atom_positions: [num_res, 37, 3] atom positions for all
        templates.
      template_all_atom_mask: [num_res, 37] mask for each template.
      padding_mask_2d: Padding mask (Note: this doesn't care if a template
        exists, unlike the template_pseudo_beta_mask).
      multichain_mask_2d: A mask indicating intra-chain residue pairs, used
        to mask out between chain distances/features when templates are for
        single chains.
      is_training: Are we in training mode.
      safe_key: Random key generator.

    Returns:
      A template embedding (num_res, num_res, num_channels).
    """"""
    gc = self.global_config
    c = self.config
    assert padding_mask_2d.dtype == query_embedding.dtype
    dtype = query_embedding.dtype
    num_channels = self.config.num_channels

    def construct_input(query_embedding, template_aatype,
                        template_all_atom_positions, template_all_atom_mask,
                        multichain_mask_2d):

      # Compute distogram feature for the template.
      template_positions, pseudo_beta_mask = modules.pseudo_beta_fn(
          template_aatype, template_all_atom_positions, template_all_atom_mask)
      pseudo_beta_mask_2d = (pseudo_beta_mask[:, None] *
                             pseudo_beta_mask[None, :])
      pseudo_beta_mask_2d *= multichain_mask_2d
      template_dgram = modules.dgram_from_positions(
          template_positions, **self.config.dgram_features)
      template_dgram *= pseudo_beta_mask_2d[..., None]
      template_dgram = template_dgram.astype(dtype)
      pseudo_beta_mask_2d = pseudo_beta_mask_2d.astype(dtype)
      to_concat = [(template_dgram, 1), (pseudo_beta_mask_2d, 0)]

      aatype = jax.nn.one_hot(template_aatype, 22, axis=-1, dtype=dtype)
      to_concat.append((aatype[None, :, :], 1))
      to_concat.append((aatype[:, None, :], 1))

      # Compute a feature representing the normalized vector between each
      # backbone affine - i.e. in each residues local frame, what direction are
      # each of the other residues.
      raw_atom_pos = template_all_atom_positions
      if gc.bfloat16:
        # Vec3Arrays are required to be float32
        raw_atom_pos = raw_atom_pos.astype(jnp.float32)

      atom_pos = geometry.Vec3Array.from_array(raw_atom_pos)
      rigid, backbone_mask = folding_multimer.make_backbone_affine(
          atom_pos,
          template_all_atom_mask,
          template_aatype)
      points = rigid.translation
      rigid_vec = rigid[:, None].inverse().apply_to_point(points)
      unit_vector = rigid_vec.normalized()
      unit_vector = [unit_vector.x, unit_vector.y, unit_vector.z]

      if gc.bfloat16:
        unit_vector = [x.astype(jnp.bfloat16) for x in unit_vector]
        backbone_mask = backbone_mask.astype(jnp.bfloat16)

      backbone_mask_2d = backbone_mask[:, None] * backbone_mask[None, :]
      backbone_mask_2d *= multichain_mask_2d
      unit_vector = [x*backbone_mask_2d for x in unit_vector]

      # Note that the backbone_mask takes into account C, CA and N (unlike
      # pseudo beta mask which just needs CB) so we add both masks as features.
      to_concat.extend([(x, 0) for x in unit_vector])
      to_concat.append((backbone_mask_2d, 0))

      query_embedding = common_modules.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='query_embedding_norm')(
              query_embedding)
      # Allow the template embedder to see the query embedding.  Note this
      # contains the position relative feature, so this is how the network knows
      # which residues are next to each other.
      to_concat.append((query_embedding, 1))

      act = 0

      for i, (x, n_input_dims) in enumerate(to_concat):

        act += common_modules.Linear(
            num_channels,
            num_input_dims=n_input_dims,
            initializer='relu',
            name=f'template_pair_embedding_{i}')(x)
      return act

    act = construct_input(query_embedding, template_aatype,
                          template_all_atom_positions, template_all_atom_mask,
                          multichain_mask_2d)

    template_iteration = TemplateEmbeddingIteration(
        c.template_pair_stack, gc, name='template_embedding_iteration')

    def template_iteration_fn(x):
      act, safe_key = x

      safe_key, safe_subkey = safe_key.split()
      act = template_iteration(
          act=act,
          pair_mask=padding_mask_2d,
          is_training=is_training,
          safe_key=safe_subkey)
      return (act, safe_key)

    if gc.use_remat:
      template_iteration_fn = hk.remat(template_iteration_fn)

    safe_key, safe_subkey = safe_key.split()
    template_stack = layer_stack.layer_stack(
        c.template_pair_stack.num_block)(
            template_iteration_fn)
    act, safe_key = template_stack((act, safe_subkey))

    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='output_layer_norm')(
            act)

    return act


class TemplateEmbeddingIteration(hk.Module):
  """"""Single Iteration of Template Embedding.""""""

  def __init__(self, config, global_config,
               name='template_embedding_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, act, pair_mask, is_training=True,
               safe_key=None):
    """"""Build a single iteration of the template embedder.

    Args:
      act: [num_res, num_res, num_channel] Input pairwise activations.
      pair_mask: [num_res, num_res] padding mask.
      is_training: Whether to run in training mode.
      safe_key: Safe pseudo-random generator key.

    Returns:
      [num_res, num_res, num_channel] tensor of activations.
    """"""
    c = self.config
    gc = self.global_config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    dropout_wrapper_fn = functools.partial(
        modules.dropout_wrapper,
        is_training=is_training,
        global_config=gc)

    safe_key, *sub_keys = safe_key.split(20)
    sub_keys = iter(sub_keys)

    act = dropout_wrapper_fn(
        modules.TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                                       name='triangle_multiplication_outgoing'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    act = dropout_wrapper_fn(
        modules.TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                                       name='triangle_multiplication_incoming'),
        act,
        pair_mask,
        safe_key=next(sub_keys))
    act = dropout_wrapper_fn(
        modules.TriangleAttention(c.triangle_attention_starting_node, gc,
                                  name='triangle_attention_starting_node'),
        act,
        pair_mask,
        safe_key=next(sub_keys))
    act = dropout_wrapper_fn(
        modules.TriangleAttention(c.triangle_attention_ending_node, gc,
                                  name='triangle_attention_ending_node'),
        act,
        pair_mask,
        safe_key=next(sub_keys))
    act = dropout_wrapper_fn(
        modules.Transition(c.pair_transition, gc,
                           name='pair_transition'),
        act,
        pair_mask,
        safe_key=next(sub_keys))

    return act


def template_embedding_1d(batch, num_channel, global_config):
  """"""Embed templates into an (num_res, num_templates, num_channels) embedding.

  Args:
    batch: A batch containing:
      template_aatype, (num_templates, num_res) aatype for the templates.
      template_all_atom_positions, (num_templates, num_residues, 37, 3) atom
        positions for the templates.
      template_all_atom_mask, (num_templates, num_residues, 37) atom mask for
        each template.
    num_channel: The number of channels in the output.
    global_config: The global_config.

  Returns:
    An embedding of shape (num_templates, num_res, num_channels) and a mask of
    shape (num_templates, num_res).
  """"""

  # Embed the templates aatypes.
  aatype_one_hot = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1)

  num_templates = batch['template_aatype'].shape[0]
  all_chi_angles = []
  all_chi_masks = []
  for i in range(num_templates):
    atom_pos = geometry.Vec3Array.from_array(
        batch['template_all_atom_positions'][i, :, :, :])
    template_chi_angles, template_chi_mask = all_atom_multimer.compute_chi_angles(
        atom_pos,
        batch['template_all_atom_mask'][i, :, :],
        batch['template_aatype'][i, :])
    all_chi_angles.append(template_chi_angles)
    all_chi_masks.append(template_chi_mask)
  chi_angles = jnp.stack(all_chi_angles, axis=0)
  chi_mask = jnp.stack(all_chi_masks, axis=0)

  template_features = jnp.concatenate([
      aatype_one_hot,
      jnp.sin(chi_angles) * chi_mask,
      jnp.cos(chi_angles) * chi_mask,
      chi_mask], axis=-1)

  template_mask = chi_mask[:, :, 0]

  if global_config.bfloat16:
    template_features = template_features.astype(jnp.bfloat16)
    template_mask = template_mask.astype(jnp.bfloat16)

  template_activations = common_modules.Linear(
      num_channel,
      initializer='relu',
      name='template_single_embedding')(
          template_features)
  template_activations = jax.nn.relu(template_activations)
  template_activations = common_modules.Linear(
      num_channel,
      initializer='relu',
      name='template_projection')(
          template_activations)
  return template_activations, template_mask
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/quat_affine_test.py",".py","5038","151","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for quat_affine.""""""

from absl import logging
from absl.testing import absltest
from alphafold.model import quat_affine
import jax
import jax.numpy as jnp
import numpy as np

VERBOSE = False
np.set_printoptions(precision=3, suppress=True)

r2t = quat_affine.rot_list_to_tensor
v2t = quat_affine.vec_list_to_tensor

q2r = lambda q: r2t(quat_affine.quat_to_rot(q))


class QuatAffineTest(absltest.TestCase):

  def _assert_check(self, to_check, tol=1e-5):
    for k, (correct, generated) in to_check.items():
      if VERBOSE:
        logging.info(k)
        logging.info('Correct %s', correct)
        logging.info('Predicted %s', generated)
      self.assertLess(np.max(np.abs(correct - generated)), tol)

  def test_conversion(self):
    quat = jnp.array([-2., 5., -1., 4.])

    rotation = jnp.array([
        [0.26087, 0.130435, 0.956522],
        [-0.565217, -0.782609, 0.26087],
        [0.782609, -0.608696, -0.130435]])

    translation = jnp.array([1., -3., 4.])
    point = jnp.array([0.7, 3.2, -2.9])

    a = quat_affine.QuatAffine(quat, translation, unstack_inputs=True)
    true_new_point = jnp.matmul(rotation, point[:, None])[:, 0] + translation

    self._assert_check({
        'rot': (rotation, r2t(a.rotation)),
        'trans': (translation, v2t(a.translation)),
        'point': (true_new_point,
                  v2t(a.apply_to_point(jnp.moveaxis(point, -1, 0)))),
        # Because of the double cover, we must be careful and compare rotations
        'quat': (q2r(a.quaternion),
                 q2r(quat_affine.rot_to_quat(a.rotation))),

    })

  def test_double_cover(self):
    """"""Test that -q is the same rotation as q.""""""
    rng = jax.random.PRNGKey(42)
    keys = jax.random.split(rng)
    q = jax.random.normal(keys[0], (2, 4))
    trans = jax.random.normal(keys[1], (2, 3))
    a1 = quat_affine.QuatAffine(q, trans, unstack_inputs=True)
    a2 = quat_affine.QuatAffine(-q, trans, unstack_inputs=True)

    self._assert_check({
        'rot': (r2t(a1.rotation),
                r2t(a2.rotation)),
        'trans': (v2t(a1.translation),
                  v2t(a2.translation)),
    })

  def test_homomorphism(self):
    rng = jax.random.PRNGKey(42)
    keys = jax.random.split(rng, 4)
    vec_q1 = jax.random.normal(keys[0], (2, 3))

    q1 = jnp.concatenate([
        jnp.ones_like(vec_q1)[:, :1],
        vec_q1], axis=-1)

    q2 = jax.random.normal(keys[1], (2, 4))
    t1 = jax.random.normal(keys[2], (2, 3))
    t2 = jax.random.normal(keys[3], (2, 3))

    a1 = quat_affine.QuatAffine(q1, t1, unstack_inputs=True)
    a2 = quat_affine.QuatAffine(q2, t2, unstack_inputs=True)
    a21 = a2.pre_compose(jnp.concatenate([vec_q1, t1], axis=-1))

    rng, key = jax.random.split(rng)
    x = jax.random.normal(key, (2, 3))
    new_x = a21.apply_to_point(jnp.moveaxis(x, -1, 0))
    new_x_apply2 = a2.apply_to_point(a1.apply_to_point(jnp.moveaxis(x, -1, 0)))

    self._assert_check({
        'quat': (q2r(quat_affine.quat_multiply(a2.quaternion, a1.quaternion)),
                 q2r(a21.quaternion)),
        'rot': (jnp.matmul(r2t(a2.rotation), r2t(a1.rotation)),
                r2t(a21.rotation)),
        'point': (v2t(new_x_apply2),
                  v2t(new_x)),
        'inverse': (x, v2t(a21.invert_point(new_x))),
    })

  def test_batching(self):
    """"""Test that affine applies batchwise.""""""
    rng = jax.random.PRNGKey(42)
    keys = jax.random.split(rng, 3)
    q = jax.random.uniform(keys[0], (5, 2, 4))
    t = jax.random.uniform(keys[1], (2, 3))
    x = jax.random.uniform(keys[2], (5, 1, 3))

    a = quat_affine.QuatAffine(q, t, unstack_inputs=True)
    y = v2t(a.apply_to_point(jnp.moveaxis(x, -1, 0)))

    y_list = []
    for i in range(5):
      for j in range(2):
        a_local = quat_affine.QuatAffine(q[i, j], t[j],
                                         unstack_inputs=True)
        y_local = v2t(a_local.apply_to_point(jnp.moveaxis(x[i, 0], -1, 0)))
        y_list.append(y_local)
    y_combine = jnp.reshape(jnp.stack(y_list, axis=0), (5, 2, 3))

    self._assert_check({
        'batch': (y_combine, y),
        'quat': (q2r(a.quaternion),
                 q2r(quat_affine.rot_to_quat(a.rotation))),
    })

  def assertAllClose(self, a, b, rtol=1e-06, atol=1e-06):
    self.assertTrue(np.allclose(a, b, rtol=rtol, atol=atol))

  def assertAllEqual(self, a, b):
    self.assertTrue(np.all(np.array(a) == np.array(b)))


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/quat_affine.py",".py","17388","460","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Quaternion geometry modules.

This introduces a representation of coordinate frames that is based around a
‘QuatAffine’ object. This object describes an array of coordinate frames.
It consists of vectors corresponding to the
origin of the frames as well as orientations which are stored in two
ways, as unit quaternions as well as a rotation matrices.
The rotation matrices are derived from the unit quaternions and the two are kept
in sync.
For an explanation of the relation between unit quaternions and rotations see
https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation

This representation is used in the model for the backbone frames.

One important thing to note here, is that while we update both representations
the jit compiler is going to ensure that only the parts that are
actually used are executed.
""""""


import functools
from typing import Tuple

import jax
import jax.numpy as jnp
import numpy as np

# pylint: disable=bad-whitespace
QUAT_TO_ROT = np.zeros((4, 4, 3, 3), dtype=np.float32)

QUAT_TO_ROT[0, 0] = [[ 1, 0, 0], [ 0, 1, 0], [ 0, 0, 1]]  # rr
QUAT_TO_ROT[1, 1] = [[ 1, 0, 0], [ 0,-1, 0], [ 0, 0,-1]]  # ii
QUAT_TO_ROT[2, 2] = [[-1, 0, 0], [ 0, 1, 0], [ 0, 0,-1]]  # jj
QUAT_TO_ROT[3, 3] = [[-1, 0, 0], [ 0,-1, 0], [ 0, 0, 1]]  # kk

QUAT_TO_ROT[1, 2] = [[ 0, 2, 0], [ 2, 0, 0], [ 0, 0, 0]]  # ij
QUAT_TO_ROT[1, 3] = [[ 0, 0, 2], [ 0, 0, 0], [ 2, 0, 0]]  # ik
QUAT_TO_ROT[2, 3] = [[ 0, 0, 0], [ 0, 0, 2], [ 0, 2, 0]]  # jk

QUAT_TO_ROT[0, 1] = [[ 0, 0, 0], [ 0, 0,-2], [ 0, 2, 0]]  # ir
QUAT_TO_ROT[0, 2] = [[ 0, 0, 2], [ 0, 0, 0], [-2, 0, 0]]  # jr
QUAT_TO_ROT[0, 3] = [[ 0,-2, 0], [ 2, 0, 0], [ 0, 0, 0]]  # kr

QUAT_MULTIPLY = np.zeros((4, 4, 4), dtype=np.float32)
QUAT_MULTIPLY[:, :, 0] = [[ 1, 0, 0, 0],
                          [ 0,-1, 0, 0],
                          [ 0, 0,-1, 0],
                          [ 0, 0, 0,-1]]

QUAT_MULTIPLY[:, :, 1] = [[ 0, 1, 0, 0],
                          [ 1, 0, 0, 0],
                          [ 0, 0, 0, 1],
                          [ 0, 0,-1, 0]]

QUAT_MULTIPLY[:, :, 2] = [[ 0, 0, 1, 0],
                          [ 0, 0, 0,-1],
                          [ 1, 0, 0, 0],
                          [ 0, 1, 0, 0]]

QUAT_MULTIPLY[:, :, 3] = [[ 0, 0, 0, 1],
                          [ 0, 0, 1, 0],
                          [ 0,-1, 0, 0],
                          [ 1, 0, 0, 0]]

QUAT_MULTIPLY_BY_VEC = QUAT_MULTIPLY[:, 1:, :]
# pylint: enable=bad-whitespace


def rot_to_quat(rot, unstack_inputs=False):
  """"""Convert rotation matrix to quaternion.

  Note that this function calls self_adjoint_eig which is extremely expensive on
  the GPU. If at all possible, this function should run on the CPU.

  Args:
     rot: rotation matrix (see below for format).
     unstack_inputs:  If true, rotation matrix should be shape (..., 3, 3)
       otherwise the rotation matrix should be a list of lists of tensors.

  Returns:
    Quaternion as (..., 4) tensor.
  """"""
  if unstack_inputs:
    rot = [jnp.moveaxis(x, -1, 0) for x in jnp.moveaxis(rot, -2, 0)]

  [[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = rot

  # pylint: disable=bad-whitespace
  k = [[ xx + yy + zz,      zy - yz,      xz - zx,      yx - xy,],
       [      zy - yz, xx - yy - zz,      xy + yx,      xz + zx,],
       [      xz - zx,      xy + yx, yy - xx - zz,      yz + zy,],
       [      yx - xy,      xz + zx,      yz + zy, zz - xx - yy,]]
  # pylint: enable=bad-whitespace

  k = (1./3.) * jnp.stack([jnp.stack(x, axis=-1) for x in k],
                          axis=-2)

  # Get eigenvalues in non-decreasing order and associated.
  _, qs = jnp.linalg.eigh(k)
  return qs[..., -1]


def rot_list_to_tensor(rot_list):
  """"""Convert list of lists to rotation tensor.""""""
  return jnp.stack(
      [jnp.stack(rot_list[0], axis=-1),
       jnp.stack(rot_list[1], axis=-1),
       jnp.stack(rot_list[2], axis=-1)],
      axis=-2)


def vec_list_to_tensor(vec_list):
  """"""Convert list to vector tensor.""""""
  return jnp.stack(vec_list, axis=-1)


def quat_to_rot(normalized_quat):
  """"""Convert a normalized quaternion to a rotation matrix.""""""
  rot_tensor = jnp.sum(
      np.reshape(QUAT_TO_ROT, (4, 4, 9)) *
      normalized_quat[..., :, None, None] *
      normalized_quat[..., None, :, None],
      axis=(-3, -2))
  rot = jnp.moveaxis(rot_tensor, -1, 0)  # Unstack.
  return [[rot[0], rot[1], rot[2]],
          [rot[3], rot[4], rot[5]],
          [rot[6], rot[7], rot[8]]]


def quat_multiply_by_vec(quat, vec):
  """"""Multiply a quaternion by a pure-vector quaternion.""""""
  return jnp.sum(
      QUAT_MULTIPLY_BY_VEC *
      quat[..., :, None, None] *
      vec[..., None, :, None],
      axis=(-3, -2))


def quat_multiply(quat1, quat2):
  """"""Multiply a quaternion by another quaternion.""""""
  return jnp.sum(
      QUAT_MULTIPLY *
      quat1[..., :, None, None] *
      quat2[..., None, :, None],
      axis=(-3, -2))


def apply_rot_to_vec(rot, vec, unstack=False):
  """"""Multiply rotation matrix by a vector.""""""
  if unstack:
    x, y, z = [vec[:, i] for i in range(3)]
  else:
    x, y, z = vec
  return [rot[0][0] * x + rot[0][1] * y + rot[0][2] * z,
          rot[1][0] * x + rot[1][1] * y + rot[1][2] * z,
          rot[2][0] * x + rot[2][1] * y + rot[2][2] * z]


def apply_inverse_rot_to_vec(rot, vec):
  """"""Multiply the inverse of a rotation matrix by a vector.""""""
  # Inverse rotation is just transpose
  return [rot[0][0] * vec[0] + rot[1][0] * vec[1] + rot[2][0] * vec[2],
          rot[0][1] * vec[0] + rot[1][1] * vec[1] + rot[2][1] * vec[2],
          rot[0][2] * vec[0] + rot[1][2] * vec[1] + rot[2][2] * vec[2]]


class QuatAffine(object):
  """"""Affine transformation represented by quaternion and vector.""""""

  def __init__(self, quaternion, translation, rotation=None, normalize=True,
               unstack_inputs=False):
    """"""Initialize from quaternion and translation.

    Args:
      quaternion: Rotation represented by a quaternion, to be applied
        before translation.  Must be a unit quaternion unless normalize==True.
      translation: Translation represented as a vector.
      rotation: Same rotation as the quaternion, represented as a (..., 3, 3)
        tensor.  If None, rotation will be calculated from the quaternion.
      normalize: If True, l2 normalize the quaternion on input.
      unstack_inputs: If True, translation is a vector with last component 3
    """"""

    if quaternion is not None:
      assert quaternion.shape[-1] == 4

    if unstack_inputs:
      if rotation is not None:
        rotation = [jnp.moveaxis(x, -1, 0)   # Unstack.
                    for x in jnp.moveaxis(rotation, -2, 0)]  # Unstack.
      translation = jnp.moveaxis(translation, -1, 0)  # Unstack.

    if normalize and quaternion is not None:
      quaternion = quaternion / jnp.linalg.norm(quaternion, axis=-1,
                                                keepdims=True)

    if rotation is None:
      rotation = quat_to_rot(quaternion)

    self.quaternion = quaternion
    self.rotation = [list(row) for row in rotation]
    self.translation = list(translation)

    assert all(len(row) == 3 for row in self.rotation)
    assert len(self.translation) == 3

  def to_tensor(self):
    return jnp.concatenate(
        [self.quaternion] +
        [jnp.expand_dims(x, axis=-1) for x in self.translation],
        axis=-1)

  def apply_tensor_fn(self, tensor_fn):
    """"""Return a new QuatAffine with tensor_fn applied (e.g. stop_gradient).""""""
    return QuatAffine(
        tensor_fn(self.quaternion),
        [tensor_fn(x) for x in self.translation],
        rotation=[[tensor_fn(x) for x in row] for row in self.rotation],
        normalize=False)

  def apply_rotation_tensor_fn(self, tensor_fn):
    """"""Return a new QuatAffine with tensor_fn applied to the rotation part.""""""
    return QuatAffine(
        tensor_fn(self.quaternion),
        [x for x in self.translation],
        rotation=[[tensor_fn(x) for x in row] for row in self.rotation],
        normalize=False)

  def scale_translation(self, position_scale):
    """"""Return a new quat affine with a different scale for translation.""""""

    return QuatAffine(
        self.quaternion,
        [x * position_scale for x in self.translation],
        rotation=[[x for x in row] for row in self.rotation],
        normalize=False)

  @classmethod
  def from_tensor(cls, tensor, normalize=False):
    quaternion, tx, ty, tz = jnp.split(tensor, [4, 5, 6], axis=-1)
    return cls(quaternion,
               [tx[..., 0], ty[..., 0], tz[..., 0]],
               normalize=normalize)

  def pre_compose(self, update):
    """"""Return a new QuatAffine which applies the transformation update first.

    Args:
      update: Length-6 vector. 3-vector of x, y, and z such that the quaternion
        update is (1, x, y, z) and zero for the 3-vector is the identity
        quaternion. 3-vector for translation concatenated.

    Returns:
      New QuatAffine object.
    """"""
    vector_quaternion_update, x, y, z = jnp.split(update, [3, 4, 5], axis=-1)
    trans_update = [jnp.squeeze(x, axis=-1),
                    jnp.squeeze(y, axis=-1),
                    jnp.squeeze(z, axis=-1)]

    new_quaternion = (self.quaternion +
                      quat_multiply_by_vec(self.quaternion,
                                           vector_quaternion_update))

    trans_update = apply_rot_to_vec(self.rotation, trans_update)
    new_translation = [
        self.translation[0] + trans_update[0],
        self.translation[1] + trans_update[1],
        self.translation[2] + trans_update[2]]

    return QuatAffine(new_quaternion, new_translation)

  def apply_to_point(self, point, extra_dims=0):
    """"""Apply affine to a point.

    Args:
      point: List of 3 tensors to apply affine.
      extra_dims:  Number of dimensions at the end of the transformed_point
        shape that are not present in the rotation and translation.  The most
        common use is rotation N points at once with extra_dims=1 for use in a
        network.

    Returns:
      Transformed point after applying affine.
    """"""
    rotation = self.rotation
    translation = self.translation
    for _ in range(extra_dims):
      expand_fn = functools.partial(jnp.expand_dims, axis=-1)
      rotation = jax.tree_map(expand_fn, rotation)
      translation = jax.tree_map(expand_fn, translation)

    rot_point = apply_rot_to_vec(rotation, point)
    return [
        rot_point[0] + translation[0],
        rot_point[1] + translation[1],
        rot_point[2] + translation[2]]

  def invert_point(self, transformed_point, extra_dims=0):
    """"""Apply inverse of transformation to a point.

    Args:
      transformed_point: List of 3 tensors to apply affine
      extra_dims:  Number of dimensions at the end of the transformed_point
        shape that are not present in the rotation and translation.  The most
        common use is rotation N points at once with extra_dims=1 for use in a
        network.

    Returns:
      Transformed point after applying affine.
    """"""
    rotation = self.rotation
    translation = self.translation
    for _ in range(extra_dims):
      expand_fn = functools.partial(jnp.expand_dims, axis=-1)
      rotation = jax.tree_map(expand_fn, rotation)
      translation = jax.tree_map(expand_fn, translation)

    rot_point = [
        transformed_point[0] - translation[0],
        transformed_point[1] - translation[1],
        transformed_point[2] - translation[2]]

    return apply_inverse_rot_to_vec(rotation, rot_point)

  def __repr__(self):
    return 'QuatAffine(%r, %r)' % (self.quaternion, self.translation)


def _multiply(a, b):
  return jnp.stack([
      jnp.array([a[0][0]*b[0][0] + a[0][1]*b[1][0] + a[0][2]*b[2][0],
                 a[0][0]*b[0][1] + a[0][1]*b[1][1] + a[0][2]*b[2][1],
                 a[0][0]*b[0][2] + a[0][1]*b[1][2] + a[0][2]*b[2][2]]),

      jnp.array([a[1][0]*b[0][0] + a[1][1]*b[1][0] + a[1][2]*b[2][0],
                 a[1][0]*b[0][1] + a[1][1]*b[1][1] + a[1][2]*b[2][1],
                 a[1][0]*b[0][2] + a[1][1]*b[1][2] + a[1][2]*b[2][2]]),

      jnp.array([a[2][0]*b[0][0] + a[2][1]*b[1][0] + a[2][2]*b[2][0],
                 a[2][0]*b[0][1] + a[2][1]*b[1][1] + a[2][2]*b[2][1],
                 a[2][0]*b[0][2] + a[2][1]*b[1][2] + a[2][2]*b[2][2]])])


def make_canonical_transform(
    n_xyz: jnp.ndarray,
    ca_xyz: jnp.ndarray,
    c_xyz: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
  """"""Returns translation and rotation matrices to canonicalize residue atoms.

  Note that this method does not take care of symmetries. If you provide the
  atom positions in the non-standard way, the N atom will end up not at
  [-0.527250, 1.359329, 0.0] but instead at [-0.527250, -1.359329, 0.0]. You
  need to take care of such cases in your code.

  Args:
    n_xyz: An array of shape [batch, 3] of nitrogen xyz coordinates.
    ca_xyz: An array of shape [batch, 3] of carbon alpha xyz coordinates.
    c_xyz: An array of shape [batch, 3] of carbon xyz coordinates.

  Returns:
    A tuple (translation, rotation) where:
      translation is an array of shape [batch, 3] defining the translation.
      rotation is an array of shape [batch, 3, 3] defining the rotation.
    After applying the translation and rotation to all atoms in a residue:
      * All atoms will be shifted so that CA is at the origin,
      * All atoms will be rotated so that C is at the x-axis,
      * All atoms will be shifted so that N is in the xy plane.
  """"""
  assert len(n_xyz.shape) == 2, n_xyz.shape
  assert n_xyz.shape[-1] == 3, n_xyz.shape
  assert n_xyz.shape == ca_xyz.shape == c_xyz.shape, (
      n_xyz.shape, ca_xyz.shape, c_xyz.shape)

  # Place CA at the origin.
  translation = -ca_xyz
  n_xyz = n_xyz + translation
  c_xyz = c_xyz + translation

  # Place C on the x-axis.
  c_x, c_y, c_z = [c_xyz[:, i] for i in range(3)]
  # Rotate by angle c1 in the x-y plane (around the z-axis).
  sin_c1 = -c_y / jnp.sqrt(1e-20 + c_x**2 + c_y**2)
  cos_c1 = c_x / jnp.sqrt(1e-20 + c_x**2 + c_y**2)
  zeros = jnp.zeros_like(sin_c1)
  ones = jnp.ones_like(sin_c1)
  # pylint: disable=bad-whitespace
  c1_rot_matrix = jnp.stack([jnp.array([cos_c1, -sin_c1, zeros]),
                             jnp.array([sin_c1,  cos_c1, zeros]),
                             jnp.array([zeros,    zeros,  ones])])

  # Rotate by angle c2 in the x-z plane (around the y-axis).
  sin_c2 = c_z / jnp.sqrt(1e-20 + c_x**2 + c_y**2 + c_z**2)
  cos_c2 = jnp.sqrt(c_x**2 + c_y**2) / jnp.sqrt(
      1e-20 + c_x**2 + c_y**2 + c_z**2)
  c2_rot_matrix = jnp.stack([jnp.array([cos_c2,  zeros, sin_c2]),
                             jnp.array([zeros,    ones,  zeros]),
                             jnp.array([-sin_c2, zeros, cos_c2])])

  c_rot_matrix = _multiply(c2_rot_matrix, c1_rot_matrix)
  n_xyz = jnp.stack(apply_rot_to_vec(c_rot_matrix, n_xyz, unstack=True)).T

  # Place N in the x-y plane.
  _, n_y, n_z = [n_xyz[:, i] for i in range(3)]
  # Rotate by angle alpha in the y-z plane (around the x-axis).
  sin_n = -n_z / jnp.sqrt(1e-20 + n_y**2 + n_z**2)
  cos_n = n_y / jnp.sqrt(1e-20 + n_y**2 + n_z**2)
  n_rot_matrix = jnp.stack([jnp.array([ones,  zeros,  zeros]),
                            jnp.array([zeros, cos_n, -sin_n]),
                            jnp.array([zeros, sin_n,  cos_n])])
  # pylint: enable=bad-whitespace

  return (translation,
          jnp.transpose(_multiply(n_rot_matrix, c_rot_matrix), [2, 0, 1]))


def make_transform_from_reference(
    n_xyz: jnp.ndarray,
    ca_xyz: jnp.ndarray,
    c_xyz: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
  """"""Returns rotation and translation matrices to convert from reference.

  Note that this method does not take care of symmetries. If you provide the
  atom positions in the non-standard way, the N atom will end up not at
  [-0.527250, 1.359329, 0.0] but instead at [-0.527250, -1.359329, 0.0]. You
  need to take care of such cases in your code.

  Args:
    n_xyz: An array of shape [batch, 3] of nitrogen xyz coordinates.
    ca_xyz: An array of shape [batch, 3] of carbon alpha xyz coordinates.
    c_xyz: An array of shape [batch, 3] of carbon xyz coordinates.

  Returns:
    A tuple (rotation, translation) where:
      rotation is an array of shape [batch, 3, 3] defining the rotation.
      translation is an array of shape [batch, 3] defining the translation.
    After applying the translation and rotation to the reference backbone,
    the coordinates will approximately equal to the input coordinates.

    The order of translation and rotation differs from make_canonical_transform
    because the rotation from this function should be applied before the
    translation, unlike make_canonical_transform.
  """"""
  translation, rotation = make_canonical_transform(n_xyz, ca_xyz, c_xyz)
  return np.transpose(rotation, (0, 2, 1)), -translation
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/all_atom_test.py",".py","4706","136","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for all_atom.""""""

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.model import all_atom
from alphafold.model import r3
import numpy as np

L1_CLAMP_DISTANCE = 10


def get_identity_rigid(shape):
  """"""Returns identity rigid transform.""""""

  ones = np.ones(shape)
  zeros = np.zeros(shape)
  rot = r3.Rots(ones, zeros, zeros,
                zeros, ones, zeros,
                zeros, zeros, ones)
  trans = r3.Vecs(zeros, zeros, zeros)
  return r3.Rigids(rot, trans)


def get_global_rigid_transform(rot_angle, translation, bcast_dims):
  """"""Returns rigid transform that globally rotates/translates by same amount.""""""

  rot_angle = np.asarray(rot_angle)
  translation = np.asarray(translation)
  if bcast_dims:
    for _ in range(bcast_dims):
      rot_angle = np.expand_dims(rot_angle, 0)
      translation = np.expand_dims(translation, 0)
  sin_angle = np.sin(np.deg2rad(rot_angle))
  cos_angle = np.cos(np.deg2rad(rot_angle))
  ones = np.ones_like(sin_angle)
  zeros = np.zeros_like(sin_angle)
  rot = r3.Rots(ones, zeros, zeros,
                zeros, cos_angle, -sin_angle,
                zeros, sin_angle, cos_angle)
  trans = r3.Vecs(translation[..., 0], translation[..., 1], translation[..., 2])
  return r3.Rigids(rot, trans)


class AllAtomTest(parameterized.TestCase, absltest.TestCase):

  @parameterized.named_parameters(
      ('identity', 0, [0, 0, 0]),
      ('rot_90', 90, [0, 0, 0]),
      ('trans_10', 0, [0, 0, 10]),
      ('rot_174_trans_1', 174, [1, 1, 1]))
  def test_frame_aligned_point_error_perfect_on_global_transform(
      self, rot_angle, translation):
    """"""Tests global transform between target and preds gives perfect score.""""""

    # pylint: disable=bad-whitespace
    target_positions = np.array(
        [[ 21.182,  23.095,  19.731],
         [ 22.055,  20.919,  17.294],
         [ 24.599,  20.005,  15.041],
         [ 25.567,  18.214,  12.166],
         [ 28.063,  17.082,  10.043],
         [ 28.779,  15.569,   6.985],
         [ 30.581,  13.815,   4.612],
         [ 29.258,  12.193,   2.296]])
    # pylint: enable=bad-whitespace
    global_rigid_transform = get_global_rigid_transform(
        rot_angle, translation, 1)

    target_positions = r3.vecs_from_tensor(target_positions)
    pred_positions = r3.rigids_mul_vecs(
        global_rigid_transform, target_positions)
    positions_mask = np.ones(target_positions.x.shape[0])

    target_frames = get_identity_rigid(10)
    pred_frames = r3.rigids_mul_rigids(global_rigid_transform, target_frames)
    frames_mask = np.ones(10)

    fape = all_atom.frame_aligned_point_error(
        pred_frames, target_frames, frames_mask, pred_positions,
        target_positions, positions_mask, L1_CLAMP_DISTANCE,
        L1_CLAMP_DISTANCE, epsilon=0)
    self.assertAlmostEqual(fape, 0.)

  @parameterized.named_parameters(
      ('identity',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       0.),
      ('shift_2.5',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[2.5, 0, 0], [7.5, 0, 0], [7.5, 0, 0]],
       0.25),
      ('shift_5',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[5, 0, 0], [10, 0, 0], [15, 0, 0]],
       0.5),
      ('shift_10',
       [[0, 0, 0], [5, 0, 0], [10, 0, 0]],
       [[10, 0, 0], [15, 0, 0], [0, 0, 0]],
       1.))
  def test_frame_aligned_point_error_matches_expected(
      self, target_positions, pred_positions, expected_alddt):
    """"""Tests score matches expected.""""""

    target_frames = get_identity_rigid(2)
    pred_frames = target_frames
    frames_mask = np.ones(2)

    target_positions = r3.vecs_from_tensor(np.array(target_positions))
    pred_positions = r3.vecs_from_tensor(np.array(pred_positions))
    positions_mask = np.ones(target_positions.x.shape[0])

    alddt = all_atom.frame_aligned_point_error(
        pred_frames, target_frames, frames_mask, pred_positions,
        target_positions, positions_mask, L1_CLAMP_DISTANCE,
        L1_CLAMP_DISTANCE, epsilon=0)
    self.assertAlmostEqual(alddt, expected_alddt)


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/common_modules.py",".py","5957","191","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A collection of common Haiku modules for use in protein folding.""""""
import numbers
from typing import Union, Sequence

import haiku as hk
import jax.numpy as jnp
import numpy as np


# Constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
TRUNCATED_NORMAL_STDDEV_FACTOR = np.asarray(.87962566103423978,
                                            dtype=np.float32)


def get_initializer_scale(initializer_name, input_shape):
  """"""Get Initializer for weights and scale to multiply activations by.""""""

  if initializer_name == 'zeros':
    w_init = hk.initializers.Constant(0.0)
  else:
    # fan-in scaling
    scale = 1.
    for channel_dim in input_shape:
      scale /= channel_dim
    if initializer_name == 'relu':
      scale *= 2

    noise_scale = scale

    stddev = np.sqrt(noise_scale)
    # Adjust stddev for truncation.
    stddev = stddev / TRUNCATED_NORMAL_STDDEV_FACTOR
    w_init = hk.initializers.TruncatedNormal(mean=0.0, stddev=stddev)

  return w_init


class Linear(hk.Module):
  """"""Protein folding specific Linear module.

  This differs from the standard Haiku Linear in a few ways:
    * It supports inputs and outputs of arbitrary rank
    * Initializers are specified by strings
  """"""

  def __init__(self,
               num_output: Union[int, Sequence[int]],
               initializer: str = 'linear',
               num_input_dims: int = 1,
               use_bias: bool = True,
               bias_init: float = 0.,
               precision = None,
               name: str = 'linear'):
    """"""Constructs Linear Module.

    Args:
      num_output: Number of output channels. Can be tuple when outputting
          multiple dimensions.
      initializer: What initializer to use, should be one of {'linear', 'relu',
        'zeros'}
      num_input_dims: Number of dimensions from the end to project.
      use_bias: Whether to include trainable bias
      bias_init: Value used to initialize bias.
      precision: What precision to use for matrix multiplication, defaults
        to None.
      name: Name of module, used for name scopes.
    """"""
    super().__init__(name=name)
    if isinstance(num_output, numbers.Integral):
      self.output_shape = (num_output,)
    else:
      self.output_shape = tuple(num_output)
    self.initializer = initializer
    self.use_bias = use_bias
    self.bias_init = bias_init
    self.num_input_dims = num_input_dims
    self.num_output_dims = len(self.output_shape)
    self.precision = precision

  def __call__(self, inputs):
    """"""Connects Module.

    Args:
      inputs: Tensor with at least num_input_dims dimensions.

    Returns:
      output of shape [...] + num_output.
    """"""

    num_input_dims = self.num_input_dims

    if self.num_input_dims > 0:
      in_shape = inputs.shape[-self.num_input_dims:]
    else:
      in_shape = ()

    weight_init = get_initializer_scale(self.initializer, in_shape)

    in_letters = 'abcde'[:self.num_input_dims]
    out_letters = 'hijkl'[:self.num_output_dims]

    weight_shape = in_shape + self.output_shape
    weights = hk.get_parameter('weights', weight_shape, inputs.dtype,
                               weight_init)

    equation = f'...{in_letters}, {in_letters}{out_letters}->...{out_letters}'

    output = jnp.einsum(equation, inputs, weights, precision=self.precision)

    if self.use_bias:
      bias = hk.get_parameter('bias', self.output_shape, inputs.dtype,
                              hk.initializers.Constant(self.bias_init))
      output += bias

    return output


class LayerNorm(hk.LayerNorm):
  """"""LayerNorm module.

  Equivalent to hk.LayerNorm but with different parameter shapes: they are
  always vectors rather than possibly higher-rank tensors. This makes it easier
  to change the layout whilst keep the model weight-compatible.
  """"""

  def __init__(self,
               axis,
               create_scale: bool,
               create_offset: bool,
               eps: float = 1e-5,
               scale_init=None,
               offset_init=None,
               use_fast_variance: bool = False,
               name=None,
               param_axis=None):
    super().__init__(
        axis=axis,
        create_scale=False,
        create_offset=False,
        eps=eps,
        scale_init=None,
        offset_init=None,
        use_fast_variance=use_fast_variance,
        name=name,
        param_axis=param_axis)
    self._temp_create_scale = create_scale
    self._temp_create_offset = create_offset

  def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
    is_bf16 = (x.dtype == jnp.bfloat16)
    if is_bf16:
      x = x.astype(jnp.float32)

    param_axis = self.param_axis[0] if self.param_axis else -1
    param_shape = (x.shape[param_axis],)

    param_broadcast_shape = [1] * x.ndim
    param_broadcast_shape[param_axis] = x.shape[param_axis]
    scale = None
    offset = None
    if self._temp_create_scale:
      scale = hk.get_parameter(
          'scale', param_shape, x.dtype, init=self.scale_init)
      scale = scale.reshape(param_broadcast_shape)

    if self._temp_create_offset:
      offset = hk.get_parameter(
          'offset', param_shape, x.dtype, init=self.offset_init)
      offset = offset.reshape(param_broadcast_shape)

    out = super().__call__(x, scale=scale, offset=offset)

    if is_bf16:
      out = out.astype(jnp.bfloat16)

    return out
  ","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/data.py",".py","1097","34","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Convenience functions for reading data.""""""

import io
import os
from alphafold.model import utils
import haiku as hk
import numpy as np
# Internal import (7716).


def get_model_haiku_params(model_name: str, data_dir: str) -> hk.Params:
  """"""Get the Haiku parameters from a model name.""""""

  path = os.path.join(data_dir, 'params', f'params_{model_name}.npz')

  with open(path, 'rb') as f:
    params = np.load(io.BytesIO(f.read()), allow_pickle=False)

  return utils.flat_params_to_haiku(params)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/mapping.py",".py","7963","224","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Specialized mapping functions.""""""

import functools
import inspect

from typing import Any, Callable, Optional, Sequence, Union

import haiku as hk
import jax
import jax.numpy as jnp


PYTREE = Any
PYTREE_JAX_ARRAY = Any

partial = functools.partial
PROXY = object()


def _maybe_slice(array, i, slice_size, axis):
  if axis is PROXY:
    return array
  else:
    return jax.lax.dynamic_slice_in_dim(
        array, i, slice_size=slice_size, axis=axis)


def _maybe_get_size(array, axis):
  if axis == PROXY:
    return -1
  else:
    return array.shape[axis]


def _expand_axes(axes, values, name='sharded_apply'):
  values_tree_def = jax.tree_util.tree_flatten(values)[1]
  flat_axes = jax.api_util.flatten_axes(name, values_tree_def, axes)
  # Replace None's with PROXY
  flat_axes = [PROXY if x is None else x for x in flat_axes]
  return jax.tree_util.tree_unflatten(values_tree_def, flat_axes)


def sharded_map(
    fun: Callable[..., PYTREE_JAX_ARRAY],
    shard_size: Union[int, None] = 1,
    in_axes: Union[int, PYTREE] = 0,
    out_axes: Union[int, PYTREE] = 0) -> Callable[..., PYTREE_JAX_ARRAY]:
  """"""Sharded vmap.

  Maps `fun` over axes, in a way similar to vmap, but does so in shards of
  `shard_size`. This allows a smooth trade-off between memory usage
  (as in a plain map) vs higher throughput (as in a vmap).

  Args:
    fun: Function to apply smap transform to.
    shard_size: Integer denoting shard size.
    in_axes: Either integer or pytree describing which axis to map over for each
      input to `fun`, None denotes broadcasting.
    out_axes: integer or pytree denoting to what axis in the output the mapped
      over axis maps.

  Returns:
    function with smap applied.
  """"""
  if 'split_rng' in inspect.signature(hk.vmap).parameters:
    vmapped_fun = hk.vmap(fun, in_axes, out_axes, split_rng=False)
  else:
    # TODO(tomhennigan): Remove this when older versions of Haiku aren't used.
    vmapped_fun = hk.vmap(fun, in_axes, out_axes)
  return sharded_apply(vmapped_fun, shard_size, in_axes, out_axes)


def sharded_apply(
    fun: Callable[..., PYTREE_JAX_ARRAY],  # pylint: disable=g-bare-generic
    shard_size: Union[int, None] = 1,
    in_axes: Union[int, PYTREE] = 0,
    out_axes: Union[int, PYTREE] = 0,
    new_out_axes: bool = False) -> Callable[..., PYTREE_JAX_ARRAY]:
  """"""Sharded apply.

  Applies `fun` over shards to axes, in a way similar to vmap,
  but does so in shards of `shard_size`. Shards are stacked after.
  This allows a smooth trade-off between
  memory usage (as in a plain map) vs higher throughput (as in a vmap).

  Args:
    fun: Function to apply smap transform to.
    shard_size: Integer denoting shard size.
    in_axes: Either integer or pytree describing which axis to map over for each
      input to `fun`, None denotes broadcasting.
    out_axes: integer or pytree denoting to what axis in the output the mapped
      over axis maps.
    new_out_axes: whether to stack outputs on new axes. This assumes that the
      output sizes for each shard (including the possible remainder shard) are
      the same.

  Returns:
    function with smap applied.
  """"""
  docstr = ('Mapped version of {fun}. Takes similar arguments to {fun} '
            'but with additional array axes over which {fun} is mapped.')
  if new_out_axes:
    raise NotImplementedError('New output axes not yet implemented.')

  # shard size None denotes no sharding
  if shard_size is None:
    return fun

  @jax.util.wraps(fun, docstr=docstr)
  def mapped_fn(*args):
    # Expand in axes and Determine Loop range
    in_axes_ = _expand_axes(in_axes, args)

    in_sizes = jax.tree_map(_maybe_get_size, args, in_axes_)
    flat_sizes = jax.tree_util.tree_flatten(in_sizes)[0]
    in_size = max(flat_sizes)
    assert all(i in {in_size, -1} for i in flat_sizes)

    num_extra_shards = (in_size - 1) // shard_size

    # Fix Up if necessary
    last_shard_size = in_size % shard_size
    last_shard_size = shard_size if last_shard_size == 0 else last_shard_size

    def apply_fun_to_slice(slice_start, slice_size):
      input_slice = jax.tree_map(
          lambda array, axis: _maybe_slice(array, slice_start, slice_size, axis
                                          ), args, in_axes_)
      return fun(*input_slice)

    remainder_shape_dtype = hk.eval_shape(
        partial(apply_fun_to_slice, 0, last_shard_size))
    out_dtypes = jax.tree_map(lambda x: x.dtype, remainder_shape_dtype)
    out_shapes = jax.tree_map(lambda x: x.shape, remainder_shape_dtype)
    out_axes_ = _expand_axes(out_axes, remainder_shape_dtype)

    if num_extra_shards > 0:
      regular_shard_shape_dtype = hk.eval_shape(
          partial(apply_fun_to_slice, 0, shard_size))
      shard_shapes = jax.tree_map(lambda x: x.shape, regular_shard_shape_dtype)

      def make_output_shape(axis, shard_shape, remainder_shape):
        return shard_shape[:axis] + (
            shard_shape[axis] * num_extra_shards +
            remainder_shape[axis],) + shard_shape[axis + 1:]

      out_shapes = jax.tree_map(make_output_shape, out_axes_, shard_shapes,
                                out_shapes)

    # Calls dynamic Update slice with different argument order
    # This is here since tree_map only works with positional arguments
    def dynamic_update_slice_in_dim(full_array, update, axis, i):
      return jax.lax.dynamic_update_slice_in_dim(full_array, update, i, axis)

    def compute_shard(outputs, slice_start, slice_size):
      slice_out = apply_fun_to_slice(slice_start, slice_size)
      update_slice = partial(
          dynamic_update_slice_in_dim, i=slice_start)
      return jax.tree_map(update_slice, outputs, slice_out, out_axes_)

    def scan_iteration(outputs, i):
      new_outputs = compute_shard(outputs, i, shard_size)
      return new_outputs, ()

    slice_starts = jnp.arange(0, in_size - shard_size + 1, shard_size)

    def allocate_buffer(dtype, shape):
      return jnp.zeros(shape, dtype=dtype)

    outputs = jax.tree_map(allocate_buffer, out_dtypes, out_shapes)

    if slice_starts.shape[0] > 0:
      outputs, _ = hk.scan(scan_iteration, outputs, slice_starts)

    if last_shard_size != shard_size:
      remainder_start = in_size - last_shard_size
      outputs = compute_shard(outputs, remainder_start, last_shard_size)

    return outputs

  return mapped_fn


def inference_subbatch(
    module: Callable[..., PYTREE_JAX_ARRAY],
    subbatch_size: int,
    batched_args: Sequence[PYTREE_JAX_ARRAY],
    nonbatched_args: Sequence[PYTREE_JAX_ARRAY],
    low_memory: bool = True,
    input_subbatch_dim: int = 0,
    output_subbatch_dim: Optional[int] = None) -> PYTREE_JAX_ARRAY:
  """"""Run through subbatches (like batch apply but with split and concat).""""""
  assert len(batched_args) > 0  # pylint: disable=g-explicit-length-test

  if not low_memory:
    args = list(batched_args) + list(nonbatched_args)
    return module(*args)

  if output_subbatch_dim is None:
    output_subbatch_dim = input_subbatch_dim

  def run_module(*batched_args):
    args = list(batched_args) + list(nonbatched_args)
    return module(*args)
  sharded_module = sharded_apply(run_module,
                                 shard_size=subbatch_size,
                                 in_axes=input_subbatch_dim,
                                 out_axes=output_subbatch_dim)
  return sharded_module(*batched_args)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/prng.py",".py","1978","70","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A collection of utilities surrounding PRNG usage in protein folding.""""""

import haiku as hk
import jax


def safe_dropout(*, tensor, safe_key, rate, is_deterministic, is_training):
  if is_training and rate != 0.0 and not is_deterministic:
    return hk.dropout(safe_key.get(), rate, tensor)
  else:
    return tensor


class SafeKey:
  """"""Safety wrapper for PRNG keys.""""""

  def __init__(self, key):
    self._key = key
    self._used = False

  def _assert_not_used(self):
    if self._used:
      raise RuntimeError('Random key has been used previously.')

  def get(self):
    self._assert_not_used()
    self._used = True
    return self._key

  def split(self, num_keys=2):
    self._assert_not_used()
    self._used = True
    new_keys = jax.random.split(self._key, num_keys)
    return jax.tree_map(SafeKey, tuple(new_keys))

  def duplicate(self, num_keys=2):
    self._assert_not_used()
    self._used = True
    return tuple(SafeKey(self._key) for _ in range(num_keys))


def _safe_key_flatten(safe_key):
  # Flatten transfers ""ownership"" to the tree
  return (safe_key._key,), safe_key._used  # pylint: disable=protected-access


def _safe_key_unflatten(aux_data, children):
  ret = SafeKey(children[0])
  ret._used = aux_data  # pylint: disable=protected-access
  return ret


jax.tree_util.register_pytree_node(
    SafeKey, _safe_key_flatten, _safe_key_unflatten)

","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/utils.py",".py","4723","154","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A collection of JAX utility functions for use in protein folding.""""""

import collections
import contextlib
import functools
import numbers
from typing import Mapping

import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np


def bfloat16_creator(next_creator, shape, dtype, init, context):
  """"""Creates float32 variables when bfloat16 is requested.""""""
  if context.original_dtype == jnp.bfloat16:
    dtype = jnp.float32
  return next_creator(shape, dtype, init)


def bfloat16_getter(next_getter, value, context):
  """"""Casts float32 to bfloat16 when bfloat16 was originally requested.""""""
  if context.original_dtype == jnp.bfloat16:
    assert value.dtype == jnp.float32
    value = value.astype(jnp.bfloat16)
  return next_getter(value)


@contextlib.contextmanager
def bfloat16_context():
  with hk.custom_creator(bfloat16_creator), hk.custom_getter(bfloat16_getter):
    yield


def final_init(config):
  if config.zero_init:
    return 'zeros'
  else:
    return 'linear'


def batched_gather(params, indices, axis=0, batch_dims=0):
  """"""Implements a JAX equivalent of `tf.gather` with `axis` and `batch_dims`.""""""
  take_fn = lambda p, i: jnp.take(p, i, axis=axis, mode='clip')
  for _ in range(batch_dims):
    take_fn = jax.vmap(take_fn)
  return take_fn(params, indices)


def mask_mean(mask, value, axis=None, drop_mask_channel=False, eps=1e-10):
  """"""Masked mean.""""""
  if drop_mask_channel:
    mask = mask[..., 0]

  mask_shape = mask.shape
  value_shape = value.shape

  assert len(mask_shape) == len(value_shape)

  if isinstance(axis, numbers.Integral):
    axis = [axis]
  elif axis is None:
    axis = list(range(len(mask_shape)))
  assert isinstance(axis, collections.abc.Iterable), (
      'axis needs to be either an iterable, integer or ""None""')

  broadcast_factor = 1.
  for axis_ in axis:
    value_size = value_shape[axis_]
    mask_size = mask_shape[axis_]
    if mask_size == 1:
      broadcast_factor *= value_size
    else:
      assert mask_size == value_size

  return (jnp.sum(mask * value, axis=axis) /
          (jnp.sum(mask, axis=axis) * broadcast_factor + eps))


def flat_params_to_haiku(params: Mapping[str, np.ndarray]) -> hk.Params:
  """"""Convert a dictionary of NumPy arrays to Haiku parameters.""""""
  hk_params = {}
  for path, array in params.items():
    scope, name = path.split('//')
    if scope not in hk_params:
      hk_params[scope] = {}
    hk_params[scope][name] = jnp.array(array)

  return hk_params


def padding_consistent_rng(f):
  """"""Modify any element-wise random function to be consistent with padding.

  Normally if you take a function like jax.random.normal and generate an array,
  say of size (10,10), you will get a different set of random numbers to if you
  add padding and take the first (10,10) sub-array.

  This function makes a random function that is consistent regardless of the
  amount of padding added.

  Note: The padding-consistent function is likely to be slower to compile and
  run than the function it is wrapping, but these slowdowns are likely to be
  negligible in a large network.

  Args:
    f: Any element-wise function that takes (PRNG key, shape) as the first 2
      arguments.

  Returns:
    An equivalent function to f, that is now consistent for different amounts of
    padding.
  """"""
  def grid_keys(key, shape):
    """"""Generate a grid of rng keys that is consistent with different padding.

    Generate random keys such that the keys will be identical, regardless of
    how much padding is added to any dimension.

    Args:
      key: A PRNG key.
      shape: The shape of the output array of keys that will be generated.

    Returns:
      An array of shape `shape` consisting of random keys.
    """"""
    if not shape:
      return key
    new_keys = jax.vmap(functools.partial(jax.random.fold_in, key))(
        jnp.arange(shape[0]))
    return jax.vmap(functools.partial(grid_keys, shape=shape[1:]))(new_keys)

  def inner(key, shape, **kwargs):
    return jnp.vectorize(
        lambda key: f(key, shape=(), **kwargs),
        signature='(2)->()')(
            grid_keys(key, shape))
  return inner
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/config.py",".py","26814","700","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Model config.""""""

import copy
from alphafold.model.tf import shape_placeholders
import ml_collections

NUM_RES = shape_placeholders.NUM_RES
NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES


def model_config(name: str) -> ml_collections.ConfigDict:
  """"""Get the ConfigDict of a CASP14 model.""""""

  if name not in CONFIG_DIFFS:
    raise ValueError(f'Invalid model name {name}.')
  if 'multimer' in name:
    cfg = copy.deepcopy(CONFIG_MULTIMER)
  else:
    cfg = copy.deepcopy(CONFIG)
  cfg.update_from_flattened_dict(CONFIG_DIFFS[name])
  return cfg


MODEL_PRESETS = {
    'monomer': (
        'model_1',
        'model_2',
        'model_3',
        'model_4',
        'model_5',
    ),
    'monomer_ptm': (
        'model_1_ptm',
        'model_2_ptm',
        'model_3_ptm',
        'model_4_ptm',
        'model_5_ptm',
    ),
    'multimer': (
        'model_1_multimer_v3',
        'model_2_multimer_v3',
        'model_3_multimer_v3',
        'model_4_multimer_v3',
        'model_5_multimer_v3',
    ),
}
MODEL_PRESETS['monomer_casp14'] = MODEL_PRESETS['monomer']


CONFIG_DIFFS = {
    'model_1': {
        # Jumper et al. (2021) Suppl. Table 5, Model 1.1.1
        'data.common.max_extra_msa': 5120,
        'data.common.reduce_msa_clusters_by_max_templates': True,
        'data.common.use_templates': True,
        'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
        'model.embeddings_and_evoformer.template.enabled': True
    },
    'model_2': {
        # Jumper et al. (2021) Suppl. Table 5, Model 1.1.2
        'data.common.reduce_msa_clusters_by_max_templates': True,
        'data.common.use_templates': True,
        'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
        'model.embeddings_and_evoformer.template.enabled': True
    },
    'model_3': {
        # Jumper et al. (2021) Suppl. Table 5, Model 1.2.1
        'data.common.max_extra_msa': 5120,
    },
    'model_4': {
        # Jumper et al. (2021) Suppl. Table 5, Model 1.2.2
        'data.common.max_extra_msa': 5120,
    },
    'model_5': {
        # Jumper et al. (2021) Suppl. Table 5, Model 1.2.3
    },

    # The following models are fine-tuned from the corresponding models above
    # with an additional predicted_aligned_error head that can produce
    # predicted TM-score (pTM) and predicted aligned errors.
    'model_1_ptm': {
        'data.common.max_extra_msa': 5120,
        'data.common.reduce_msa_clusters_by_max_templates': True,
        'data.common.use_templates': True,
        'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
        'model.embeddings_and_evoformer.template.enabled': True,
        'model.heads.predicted_aligned_error.weight': 0.1
    },
    'model_2_ptm': {
        'data.common.reduce_msa_clusters_by_max_templates': True,
        'data.common.use_templates': True,
        'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
        'model.embeddings_and_evoformer.template.enabled': True,
        'model.heads.predicted_aligned_error.weight': 0.1
    },
    'model_3_ptm': {
        'data.common.max_extra_msa': 5120,
        'model.heads.predicted_aligned_error.weight': 0.1
    },
    'model_4_ptm': {
        'data.common.max_extra_msa': 5120,
        'model.heads.predicted_aligned_error.weight': 0.1
    },
    'model_5_ptm': {
        'model.heads.predicted_aligned_error.weight': 0.1
    },
    'model_1_multimer_v3': {},
    'model_2_multimer_v3': {},
    'model_3_multimer_v3': {},
    'model_4_multimer_v3': {
        'model.embeddings_and_evoformer.num_extra_msa': 1152
    },
    'model_5_multimer_v3': {
        'model.embeddings_and_evoformer.num_extra_msa': 1152
    },
}
# Key differences between multimer v1/v2 and v3, mostly due to numerical
# optimisations in the TriangleMultiplication module.
common_updates = {
    'model.embeddings_and_evoformer.num_msa': 252,
    'model.embeddings_and_evoformer.num_extra_msa': 1152,
    'model.embeddings_and_evoformer.evoformer.triangle_multiplication_incoming.fuse_projection_weights': False,
    'model.embeddings_and_evoformer.evoformer.triangle_multiplication_outgoing.fuse_projection_weights': False,
    'model.embeddings_and_evoformer.template.template_pair_stack.triangle_multiplication_incoming.fuse_projection_weights': False,
    'model.embeddings_and_evoformer.template.template_pair_stack.triangle_multiplication_outgoing.fuse_projection_weights': False,
}
CONFIG_DIFFS.update(
    {f'model_{i}_multimer': common_updates for i in range(1, 6)})
CONFIG_DIFFS.update(
    {f'model_{i}_multimer_v2': common_updates for i in range(1, 6)})


CONFIG = ml_collections.ConfigDict({
    'data': {
        'common': {
            'masked_msa': {
                'profile_prob': 0.1,
                'same_prob': 0.1,
                'uniform_prob': 0.1
            },
            'max_extra_msa': 1024,
            'msa_cluster_features': True,
            'num_recycle': 3,
            'reduce_msa_clusters_by_max_templates': False,
            'resample_msa_in_recycling': True,
            'template_features': [
                'template_all_atom_positions', 'template_sum_probs',
                'template_aatype', 'template_all_atom_masks',
                'template_domain_names'
            ],
            'unsupervised_features': [
                'aatype', 'residue_index', 'sequence', 'msa', 'domain_name',
                'num_alignments', 'seq_length', 'between_segment_residues',
                'deletion_matrix'
            ],
            'use_templates': False,
        },
        'eval': {
            'feat': {
                'aatype': [NUM_RES],
                'all_atom_mask': [NUM_RES, None],
                'all_atom_positions': [NUM_RES, None, None],
                'alt_chi_angles': [NUM_RES, None],
                'atom14_alt_gt_exists': [NUM_RES, None],
                'atom14_alt_gt_positions': [NUM_RES, None, None],
                'atom14_atom_exists': [NUM_RES, None],
                'atom14_atom_is_ambiguous': [NUM_RES, None],
                'atom14_gt_exists': [NUM_RES, None],
                'atom14_gt_positions': [NUM_RES, None, None],
                'atom37_atom_exists': [NUM_RES, None],
                'backbone_affine_mask': [NUM_RES],
                'backbone_affine_tensor': [NUM_RES, None],
                'bert_mask': [NUM_MSA_SEQ, NUM_RES],
                'chi_angles': [NUM_RES, None],
                'chi_mask': [NUM_RES, None],
                'extra_deletion_value': [NUM_EXTRA_SEQ, NUM_RES],
                'extra_has_deletion': [NUM_EXTRA_SEQ, NUM_RES],
                'extra_msa': [NUM_EXTRA_SEQ, NUM_RES],
                'extra_msa_mask': [NUM_EXTRA_SEQ, NUM_RES],
                'extra_msa_row_mask': [NUM_EXTRA_SEQ],
                'is_distillation': [],
                'msa_feat': [NUM_MSA_SEQ, NUM_RES, None],
                'msa_mask': [NUM_MSA_SEQ, NUM_RES],
                'msa_row_mask': [NUM_MSA_SEQ],
                'pseudo_beta': [NUM_RES, None],
                'pseudo_beta_mask': [NUM_RES],
                'random_crop_to_size_seed': [None],
                'residue_index': [NUM_RES],
                'residx_atom14_to_atom37': [NUM_RES, None],
                'residx_atom37_to_atom14': [NUM_RES, None],
                'resolution': [],
                'rigidgroups_alt_gt_frames': [NUM_RES, None, None],
                'rigidgroups_group_exists': [NUM_RES, None],
                'rigidgroups_group_is_ambiguous': [NUM_RES, None],
                'rigidgroups_gt_exists': [NUM_RES, None],
                'rigidgroups_gt_frames': [NUM_RES, None, None],
                'seq_length': [],
                'seq_mask': [NUM_RES],
                'target_feat': [NUM_RES, None],
                'template_aatype': [NUM_TEMPLATES, NUM_RES],
                'template_all_atom_masks': [NUM_TEMPLATES, NUM_RES, None],
                'template_all_atom_positions': [
                    NUM_TEMPLATES, NUM_RES, None, None],
                'template_backbone_affine_mask': [NUM_TEMPLATES, NUM_RES],
                'template_backbone_affine_tensor': [
                    NUM_TEMPLATES, NUM_RES, None],
                'template_mask': [NUM_TEMPLATES],
                'template_pseudo_beta': [NUM_TEMPLATES, NUM_RES, None],
                'template_pseudo_beta_mask': [NUM_TEMPLATES, NUM_RES],
                'template_sum_probs': [NUM_TEMPLATES, None],
                'true_msa': [NUM_MSA_SEQ, NUM_RES]
            },
            'fixed_size': True,
            'subsample_templates': False,  # We want top templates.
            'masked_msa_replace_fraction': 0.15,
            'max_msa_clusters': 512,
            'max_templates': 4,
            'num_ensemble': 1,
        },
    },
    'model': {
        'embeddings_and_evoformer': {
            'evoformer_num_block': 48,
            'evoformer': {
                'msa_row_attention_with_pair_bias': {
                    'dropout_rate': 0.15,
                    'gating': True,
                    'num_head': 8,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'msa_column_attention': {
                    'dropout_rate': 0.0,
                    'gating': True,
                    'num_head': 8,
                    'orientation': 'per_column',
                    'shared_dropout': True
                },
                'msa_transition': {
                    'dropout_rate': 0.0,
                    'num_intermediate_factor': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'outer_product_mean': {
                    'first': False,
                    'chunk_size': 128,
                    'dropout_rate': 0.0,
                    'num_outer_channel': 32,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'triangle_attention_starting_node': {
                    'dropout_rate': 0.25,
                    'gating': True,
                    'num_head': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'triangle_attention_ending_node': {
                    'dropout_rate': 0.25,
                    'gating': True,
                    'num_head': 4,
                    'orientation': 'per_column',
                    'shared_dropout': True
                },
                'triangle_multiplication_outgoing': {
                    'dropout_rate': 0.25,
                    'equation': 'ikc,jkc->ijc',
                    'num_intermediate_channel': 128,
                    'orientation': 'per_row',
                    'shared_dropout': True,
                    'fuse_projection_weights': False,
                },
                'triangle_multiplication_incoming': {
                    'dropout_rate': 0.25,
                    'equation': 'kjc,kic->ijc',
                    'num_intermediate_channel': 128,
                    'orientation': 'per_row',
                    'shared_dropout': True,
                    'fuse_projection_weights': False,
                },
                'pair_transition': {
                    'dropout_rate': 0.0,
                    'num_intermediate_factor': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True
                }
            },
            'extra_msa_channel': 64,
            'extra_msa_stack_num_block': 4,
            'max_relative_feature': 32,
            'msa_channel': 256,
            'pair_channel': 128,
            'prev_pos': {
                'min_bin': 3.25,
                'max_bin': 20.75,
                'num_bins': 15
            },
            'recycle_features': True,
            'recycle_pos': True,
            'seq_channel': 384,
            'template': {
                'attention': {
                    'gating': False,
                    'key_dim': 64,
                    'num_head': 4,
                    'value_dim': 64
                },
                'dgram_features': {
                    'min_bin': 3.25,
                    'max_bin': 50.75,
                    'num_bins': 39
                },
                'embed_torsion_angles': False,
                'enabled': False,
                'template_pair_stack': {
                    'num_block': 2,
                    'triangle_attention_starting_node': {
                        'dropout_rate': 0.25,
                        'gating': True,
                        'key_dim': 64,
                        'num_head': 4,
                        'orientation': 'per_row',
                        'shared_dropout': True,
                        'value_dim': 64
                    },
                    'triangle_attention_ending_node': {
                        'dropout_rate': 0.25,
                        'gating': True,
                        'key_dim': 64,
                        'num_head': 4,
                        'orientation': 'per_column',
                        'shared_dropout': True,
                        'value_dim': 64
                    },
                    'triangle_multiplication_outgoing': {
                        'dropout_rate': 0.25,
                        'equation': 'ikc,jkc->ijc',
                        'num_intermediate_channel': 64,
                        'orientation': 'per_row',
                        'shared_dropout': True,
                        'fuse_projection_weights': False,
                    },
                    'triangle_multiplication_incoming': {
                        'dropout_rate': 0.25,
                        'equation': 'kjc,kic->ijc',
                        'num_intermediate_channel': 64,
                        'orientation': 'per_row',
                        'shared_dropout': True,
                        'fuse_projection_weights': False,
                    },
                    'pair_transition': {
                        'dropout_rate': 0.0,
                        'num_intermediate_factor': 2,
                        'orientation': 'per_row',
                        'shared_dropout': True
                    }
                },
                'max_templates': 4,
                'subbatch_size': 128,
                'use_template_unit_vector': False,
            }
        },
        'global_config': {
            'deterministic': False,
            'multimer_mode': False,
            'subbatch_size': 4,
            'use_remat': False,
            'zero_init': True,
            'eval_dropout': False,
        },
        'heads': {
            'distogram': {
                'first_break': 2.3125,
                'last_break': 21.6875,
                'num_bins': 64,
                'weight': 0.3
            },
            'predicted_aligned_error': {
                # `num_bins - 1` bins uniformly space the
                # [0, max_error_bin A] range.
                # The final bin covers [max_error_bin A, +infty]
                # 31A gives bins with 0.5A width.
                'max_error_bin': 31.,
                'num_bins': 64,
                'num_channels': 128,
                'filter_by_resolution': True,
                'min_resolution': 0.1,
                'max_resolution': 3.0,
                'weight': 0.0,
            },
            'experimentally_resolved': {
                'filter_by_resolution': True,
                'max_resolution': 3.0,
                'min_resolution': 0.1,
                'weight': 0.01
            },
            'structure_module': {
                'num_layer': 8,
                'fape': {
                    'clamp_distance': 10.0,
                    'clamp_type': 'relu',
                    'loss_unit_distance': 10.0
                },
                'angle_norm_weight': 0.01,
                'chi_weight': 0.5,
                'clash_overlap_tolerance': 1.5,
                'compute_in_graph_metrics': True,
                'dropout': 0.1,
                'num_channel': 384,
                'num_head': 12,
                'num_layer_in_transition': 3,
                'num_point_qk': 4,
                'num_point_v': 8,
                'num_scalar_qk': 16,
                'num_scalar_v': 16,
                'position_scale': 10.0,
                'sidechain': {
                    'atom_clamp_distance': 10.0,
                    'num_channel': 128,
                    'num_residual_block': 2,
                    'weight_frac': 0.5,
                    'length_scale': 10.,
                },
                'structural_violation_loss_weight': 1.0,
                'violation_tolerance_factor': 12.0,
                'weight': 1.0
            },
            'predicted_lddt': {
                'filter_by_resolution': True,
                'max_resolution': 3.0,
                'min_resolution': 0.1,
                'num_bins': 50,
                'num_channels': 128,
                'weight': 0.01
            },
            'masked_msa': {
                'num_output': 23,
                'weight': 2.0
            },
        },
        'num_recycle': 3,
        'resample_msa_in_recycling': True
    },
})


CONFIG_MULTIMER = ml_collections.ConfigDict({
    'model': {
        'embeddings_and_evoformer': {
            'evoformer_num_block': 48,
            'evoformer': {
                'msa_column_attention': {
                    'dropout_rate': 0.0,
                    'gating': True,
                    'num_head': 8,
                    'orientation': 'per_column',
                    'shared_dropout': True
                },
                'msa_row_attention_with_pair_bias': {
                    'dropout_rate': 0.15,
                    'gating': True,
                    'num_head': 8,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'msa_transition': {
                    'dropout_rate': 0.0,
                    'num_intermediate_factor': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'outer_product_mean': {
                    'chunk_size': 128,
                    'dropout_rate': 0.0,
                    'first': True,
                    'num_outer_channel': 32,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'pair_transition': {
                    'dropout_rate': 0.0,
                    'num_intermediate_factor': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True
                },
                'triangle_attention_ending_node': {
                    'dropout_rate': 0.25,
                    'gating': True,
                    'num_head': 4,
                    'orientation': 'per_column',
                    'shared_dropout': True
                },
                'triangle_attention_starting_node': {
                    'dropout_rate': 0.25,
                    'gating': True,
                    'num_head': 4,
                    'orientation': 'per_row',
                    'shared_dropout': True,
                },
                'triangle_multiplication_incoming': {
                    'dropout_rate': 0.25,
                    'equation': 'kjc,kic->ijc',
                    'num_intermediate_channel': 128,
                    'orientation': 'per_row',
                    'shared_dropout': True,
                    'fuse_projection_weights': True,
                },
                'triangle_multiplication_outgoing': {
                    'dropout_rate': 0.25,
                    'equation': 'ikc,jkc->ijc',
                    'num_intermediate_channel': 128,
                    'orientation': 'per_row',
                    'shared_dropout': True,
                    'fuse_projection_weights': True,
                }
            },
            'extra_msa_channel': 64,
            'extra_msa_stack_num_block': 4,
            'num_msa': 508,
            'num_extra_msa': 2048,
            'masked_msa': {
                'profile_prob': 0.1,
                'replace_fraction': 0.15,
                'same_prob': 0.1,
                'uniform_prob': 0.1
            },
            'use_chain_relative': True,
            'max_relative_chain': 2,
            'max_relative_idx': 32,
            'seq_channel': 384,
            'msa_channel': 256,
            'pair_channel': 128,
            'prev_pos': {
                'max_bin': 20.75,
                'min_bin': 3.25,
                'num_bins': 15
            },
            'recycle_features': True,
            'recycle_pos': True,
            'template': {
                'attention': {
                    'gating': False,
                    'num_head': 4
                },
                'dgram_features': {
                    'max_bin': 50.75,
                    'min_bin': 3.25,
                    'num_bins': 39
                },
                'enabled': True,
                'max_templates': 4,
                'num_channels': 64,
                'subbatch_size': 128,
                'template_pair_stack': {
                    'num_block': 2,
                    'pair_transition': {
                        'dropout_rate': 0.0,
                        'num_intermediate_factor': 2,
                        'orientation': 'per_row',
                        'shared_dropout': True
                    },
                    'triangle_attention_ending_node': {
                        'dropout_rate': 0.25,
                        'gating': True,
                        'num_head': 4,
                        'orientation': 'per_column',
                        'shared_dropout': True
                    },
                    'triangle_attention_starting_node': {
                        'dropout_rate': 0.25,
                        'gating': True,
                        'num_head': 4,
                        'orientation': 'per_row',
                        'shared_dropout': True
                    },
                    'triangle_multiplication_incoming': {
                        'dropout_rate': 0.25,
                        'equation': 'kjc,kic->ijc',
                        'num_intermediate_channel': 64,
                        'orientation': 'per_row',
                        'shared_dropout': True,
                        'fuse_projection_weights': True,
                    },
                    'triangle_multiplication_outgoing': {
                        'dropout_rate': 0.25,
                        'equation': 'ikc,jkc->ijc',
                        'num_intermediate_channel': 64,
                        'orientation': 'per_row',
                        'shared_dropout': True,
                        'fuse_projection_weights': True,
                    }
                }
            },
        },
        'global_config': {
            'bfloat16': True,
            'bfloat16_output': False,
            'deterministic': False,
            'multimer_mode': True,
            'subbatch_size': 4,
            'use_remat': False,
            'zero_init': True,
            'eval_dropout': False,
        },
        'heads': {
            'distogram': {
                'first_break': 2.3125,
                'last_break': 21.6875,
                'num_bins': 64,
                'weight': 0.3
            },
            'experimentally_resolved': {
                'filter_by_resolution': True,
                'max_resolution': 3.0,
                'min_resolution': 0.1,
                'weight': 0.01
            },
            'masked_msa': {
                'weight': 2.0
            },
            'predicted_aligned_error': {
                'filter_by_resolution': True,
                'max_error_bin': 31.0,
                'max_resolution': 3.0,
                'min_resolution': 0.1,
                'num_bins': 64,
                'num_channels': 128,
                'weight': 0.1
            },
            'predicted_lddt': {
                'filter_by_resolution': True,
                'max_resolution': 3.0,
                'min_resolution': 0.1,
                'num_bins': 50,
                'num_channels': 128,
                'weight': 0.01
            },
            'structure_module': {
                'angle_norm_weight': 0.01,
                'chi_weight': 0.5,
                'clash_overlap_tolerance': 1.5,
                'dropout': 0.1,
                'interface_fape': {
                    'atom_clamp_distance': 1000.0,
                    'loss_unit_distance': 20.0
                },
                'intra_chain_fape': {
                    'atom_clamp_distance': 10.0,
                    'loss_unit_distance': 10.0
                },
                'num_channel': 384,
                'num_head': 12,
                'num_layer': 8,
                'num_layer_in_transition': 3,
                'num_point_qk': 4,
                'num_point_v': 8,
                'num_scalar_qk': 16,
                'num_scalar_v': 16,
                'position_scale': 20.0,
                'sidechain': {
                    'atom_clamp_distance': 10.0,
                    'loss_unit_distance': 10.0,
                    'num_channel': 128,
                    'num_residual_block': 2,
                    'weight_frac': 0.5
                },
                'structural_violation_loss_weight': 1.0,
                'violation_tolerance_factor': 12.0,
                'weight': 1.0
            }
        },
        'num_ensemble_eval': 1,
        'num_recycle': 20,
        # A negative value indicates that no early stopping will occur, i.e.
        # the model will always run `num_recycle` number of recycling
        # iterations.  A positive value will enable early stopping if the
        # difference in pairwise distances is less than the tolerance between
        # recycling steps.
        'recycle_early_stop_tolerance': 0.5,
        'resample_msa_in_recycling': True
    }
})
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/layer_stack_test.py",".py","10315","336","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for layer_stack.""""""

import functools
from absl.testing import absltest
from absl.testing import parameterized
from alphafold.model import layer_stack
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import scipy.stats


# Suffixes applied by Haiku for repeated module names.
suffixes = [''] + [f'_{i}' for i in range(1, 100)]


def _slice_layers_params(layers_params):
  sliced_layers_params = {}
  for k, v in layers_params.items():
    for inner_k in v:
      for var_slice, suffix in zip(v[inner_k], suffixes):
        k_new = k.split('/')[-1] + suffix
        if k_new not in sliced_layers_params:
          sliced_layers_params[k_new] = {}
        sliced_layers_params[k_new][inner_k] = var_slice
  return sliced_layers_params


class LayerStackTest(parameterized.TestCase):

  @parameterized.parameters([1, 2, 4])
  def test_layer_stack(self, unroll):
    """"""Compare layer_stack to the equivalent unrolled stack.

    Tests that the layer_stack application of a Haiku layer function is
    equivalent to repeatedly applying the layer function in an unrolled loop.

    Args:
      unroll: Number of unrolled layers.
    """"""
    num_layers = 20

    def inner_fn(x):
      x += hk.Linear(100, name='linear1')(x)
      x += hk.Linear(100, name='linear2')(x)
      return x

    def outer_fn_unrolled(x):
      for _ in range(num_layers):
        x = inner_fn(x)
      return x

    def outer_fn_layer_stack(x):
      stack = layer_stack.layer_stack(num_layers, unroll=unroll)(inner_fn)
      return stack(x)

    unrolled_fn = hk.transform(outer_fn_unrolled)
    layer_stack_fn = hk.transform(outer_fn_layer_stack)

    x = jax.random.uniform(jax.random.PRNGKey(0), [10, 256, 100])

    rng_init = jax.random.PRNGKey(42)

    params = layer_stack_fn.init(rng_init, x)

    sliced_params = _slice_layers_params(params)

    unrolled_pred = unrolled_fn.apply(sliced_params, None, x)
    layer_stack_pred = layer_stack_fn.apply(params, None, x)

    np.testing.assert_allclose(unrolled_pred, layer_stack_pred)

  def test_layer_stack_multi_args(self):
    """"""Compare layer_stack to the equivalent unrolled stack.

    Similar to `test_layer_stack`, but use a function that takes more than one
    argument.
    """"""
    num_layers = 20

    def inner_fn(x, y):
      x_out = x + hk.Linear(100, name='linear1')(y)
      y_out = y + hk.Linear(100, name='linear2')(x)
      return x_out, y_out

    def outer_fn_unrolled(x, y):
      for _ in range(num_layers):
        x, y = inner_fn(x, y)
      return x, y

    def outer_fn_layer_stack(x, y):
      stack = layer_stack.layer_stack(num_layers)(inner_fn)
      return stack(x, y)

    unrolled_fn = hk.transform(outer_fn_unrolled)
    layer_stack_fn = hk.transform(outer_fn_layer_stack)

    x = jax.random.uniform(jax.random.PRNGKey(0), [10, 256, 100])
    y = jax.random.uniform(jax.random.PRNGKey(1), [10, 256, 100])

    rng_init = jax.random.PRNGKey(42)

    params = layer_stack_fn.init(rng_init, x, y)

    sliced_params = _slice_layers_params(params)

    unrolled_x, unrolled_y = unrolled_fn.apply(sliced_params, None, x, y)
    layer_stack_x, layer_stack_y = layer_stack_fn.apply(params, None, x, y)

    np.testing.assert_allclose(unrolled_x, layer_stack_x)
    np.testing.assert_allclose(unrolled_y, layer_stack_y)

  def test_layer_stack_no_varargs(self):
    """"""Test an error is raised when using a function with varargs.""""""

    class VarArgsModule(hk.Module):
      """"""When used, this module should cause layer_stack to raise an Error.""""""

      def __call__(self, *args):
        return args

    class NoVarArgsModule(hk.Module):
      """"""This module should be fine to use with layer_stack.""""""

      def __call__(self, x):
        return x

    def build_and_init_stack(module_class):
      def stack_fn(x):
        module = module_class()
        return layer_stack.layer_stack(1)(module)(x)

      stack = hk.without_apply_rng(hk.transform(stack_fn))
      stack.init(jax.random.PRNGKey(1729), jnp.ones([5]))

    build_and_init_stack(NoVarArgsModule)
    with self.assertRaisesRegex(
        ValueError, 'The function `f` should not have any `varargs`'):
      build_and_init_stack(VarArgsModule)

  @parameterized.parameters([1, 2, 4])
  def test_layer_stack_grads(self, unroll):
    """"""Compare layer_stack gradients to the equivalent unrolled stack.

    Tests that the layer_stack application of a Haiku layer function is
    equivalent to repeatedly applying the layer function in an unrolled loop.

    Args:
      unroll: Number of unrolled layers.
    """"""
    num_layers = 20

    def inner_fn(x):
      x += hk.Linear(100, name='linear1')(x)
      x += hk.Linear(100, name='linear2')(x)
      return x

    def outer_fn_unrolled(x):
      for _ in range(num_layers):
        x = inner_fn(x)
      return x

    def outer_fn_layer_stack(x):
      stack = layer_stack.layer_stack(num_layers, unroll=unroll)(inner_fn)
      return stack(x)

    unrolled_fn = hk.transform(outer_fn_unrolled)
    layer_stack_fn = hk.transform(outer_fn_layer_stack)

    x = jax.random.uniform(jax.random.PRNGKey(0), [10, 256, 100])

    rng_init = jax.random.PRNGKey(42)

    params = layer_stack_fn.init(rng_init, x)

    sliced_params = _slice_layers_params(params)

    unrolled_grad = jax.grad(
        lambda p, x: jnp.mean(unrolled_fn.apply(p, None, x)))(sliced_params, x)
    layer_stack_grad = jax.grad(
        lambda p, x: jnp.mean(layer_stack_fn.apply(p, None, x)))(params, x)

    assert_fn = functools.partial(
        np.testing.assert_allclose, atol=1e-4, rtol=1e-4)

    jax.tree_map(assert_fn, unrolled_grad,
                 _slice_layers_params(layer_stack_grad))

  def test_random(self):
    """"""Random numbers should be handled correctly.""""""
    n = 100

    @hk.transform
    @layer_stack.layer_stack(n)
    def add_random(x):
      x = x + jax.random.normal(hk.next_rng_key())
      return x

    # Evaluate a bunch of times
    key, *keys = jax.random.split(jax.random.PRNGKey(7), 1024 + 1)
    params = add_random.init(key, 0.)
    apply_fn = jax.jit(add_random.apply)
    values = [apply_fn(params, key, 0.) for key in keys]

    # Should be roughly N(0, sqrt(n))
    cdf = scipy.stats.norm(scale=np.sqrt(n)).cdf
    _, p = scipy.stats.kstest(values, cdf)
    self.assertLess(0.3, p)

  def test_threading(self):
    """"""Test @layer_stack when the function gets per-layer state.""""""
    n = 5

    @layer_stack.layer_stack(n, with_state=True)
    def f(x, y):
      x = x + y * jax.nn.one_hot(y, len(x)) / 10
      return x, 2 * y

    @hk.without_apply_rng
    @hk.transform
    def g(x, ys):
      x, zs = f(x, ys)
      # Check here to catch issues at init time
      self.assertEqual(zs.shape, (n,))
      return x, zs

    rng = jax.random.PRNGKey(7)
    x = np.zeros(n)
    ys = np.arange(n).astype(np.float32)
    params = g.init(rng, x, ys)
    x, zs = g.apply(params, x, ys)
    self.assertTrue(np.allclose(x, [0, .1, .2, .3, .4]))
    self.assertTrue(np.all(zs == 2 * ys))

  def test_nested_stacks(self):
    def stack_fn(x):
      def layer_fn(x):
        return hk.Linear(100)(x)

      outer_fn = layer_stack.layer_stack(10)(layer_fn)

      layer_outer = layer_stack.layer_stack(20)(outer_fn)
      return layer_outer(x)

    hk_mod = hk.transform(stack_fn)
    apply_rng, init_rng = jax.random.split(jax.random.PRNGKey(0))

    params = hk_mod.init(init_rng, jnp.zeros([10, 100]))

    hk_mod.apply(params, apply_rng, jnp.zeros([10, 100]))

    p, = params.values()

    assert p['w'].shape == (10, 20, 100, 100)
    assert p['b'].shape == (10, 20, 100)

  def test_with_state_multi_args(self):
    """"""Test layer_stack with state with multiple arguments.""""""
    width = 4
    batch_size = 5
    stack_height = 3

    def f_with_multi_args(x, a, b):
      return hk.Linear(
          width, w_init=hk.initializers.Constant(
              jnp.eye(width)))(x) * a + b, None

    @hk.without_apply_rng
    @hk.transform
    def hk_fn(x):
      return layer_stack.layer_stack(
          stack_height,
          with_state=True)(f_with_multi_args)(x, jnp.full([stack_height], 2.),
                                              jnp.ones([stack_height]))

    x = jnp.zeros([batch_size, width])
    key_seq = hk.PRNGSequence(19)
    params = hk_fn.init(next(key_seq), x)
    output, z = hk_fn.apply(params, x)
    self.assertIsNone(z)
    self.assertEqual(output.shape, (batch_size, width))
    np.testing.assert_equal(output, np.full([batch_size, width], 7.))

  def test_with_container_state(self):
    width = 2
    batch_size = 2
    stack_height = 3

    def f_with_container_state(x):
      hk_layer = hk.Linear(
          width, w_init=hk.initializers.Constant(jnp.eye(width)))
      layer_output = hk_layer(x)
      layer_state = {
          'raw_output': layer_output,
          'output_projection': jnp.sum(layer_output)
      }
      return layer_output + jnp.ones_like(layer_output), layer_state

    @hk.without_apply_rng
    @hk.transform
    def hk_fn(x):
      return layer_stack.layer_stack(
          stack_height,
          with_state=True)(f_with_container_state)(x)

    x = jnp.zeros([batch_size, width])
    key_seq = hk.PRNGSequence(19)
    params = hk_fn.init(next(key_seq), x)
    output, z = hk_fn.apply(params, x)
    self.assertEqual(z['raw_output'].shape, (stack_height, batch_size, width))
    self.assertEqual(output.shape, (batch_size, width))
    self.assertEqual(z['output_projection'].shape, (stack_height,))
    np.testing.assert_equal(np.sum(z['output_projection']), np.array(12.))
    np.testing.assert_equal(
        np.all(z['raw_output'] == np.array([0., 1., 2.])[..., None, None]),
        np.array(True))


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/prng_test.py",".py","1250","47","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for prng.""""""

from absl.testing import absltest
from alphafold.model import prng
import jax


class PrngTest(absltest.TestCase):

  def test_key_reuse(self):

    init_key = jax.random.PRNGKey(42)
    safe_key = prng.SafeKey(init_key)
    _, safe_key = safe_key.split()

    raw_key = safe_key.get()

    self.assertNotEqual(raw_key[0], init_key[0])
    self.assertNotEqual(raw_key[1], init_key[1])

    with self.assertRaises(RuntimeError):
      safe_key.get()

    with self.assertRaises(RuntimeError):
      safe_key.split()

    with self.assertRaises(RuntimeError):
      safe_key.duplicate()


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/modules.py",".py","74156","2194","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Modules and code used in the core part of AlphaFold.

The structure generation code is in 'folding.py'.
""""""
import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import utils
import haiku as hk
import jax
import jax.numpy as jnp


def softmax_cross_entropy(logits, labels):
  """"""Computes softmax cross entropy given logits and one-hot class labels.""""""
  loss = -jnp.sum(labels * jax.nn.log_softmax(logits), axis=-1)
  return jnp.asarray(loss)


def sigmoid_cross_entropy(logits, labels):
  """"""Computes sigmoid cross entropy given logits and multiple class labels.""""""
  log_p = jax.nn.log_sigmoid(logits)
  # log(1 - sigmoid(x)) = log_sigmoid(-x), the latter is more numerically stable
  log_not_p = jax.nn.log_sigmoid(-logits)
  loss = -labels * log_p - (1. - labels) * log_not_p
  return jnp.asarray(loss)


def apply_dropout(*, tensor, safe_key, rate, is_training, broadcast_dim=None):
  """"""Applies dropout to a tensor.""""""
  if is_training and rate != 0.0:
    shape = list(tensor.shape)
    if broadcast_dim is not None:
      shape[broadcast_dim] = 1
    keep_rate = 1.0 - rate
    keep = jax.random.bernoulli(safe_key.get(), keep_rate, shape=shape)
    return keep * tensor / keep_rate
  else:
    return tensor


def dropout_wrapper(module,
                    input_act,
                    mask,
                    safe_key,
                    global_config,
                    output_act=None,
                    is_training=True,
                    **kwargs):
  """"""Applies module + dropout + residual update.""""""
  if output_act is None:
    output_act = input_act

  gc = global_config
  residual = module(input_act, mask, is_training=is_training, **kwargs)
  dropout_rate = 0.0 if gc.deterministic else module.config.dropout_rate

  # Will override `is_training` to True if want to use dropout.
  should_apply_dropout = True if gc.eval_dropout else is_training

  if module.config.shared_dropout:
    if module.config.orientation == 'per_row':
      broadcast_dim = 0
    else:
      broadcast_dim = 1
  else:
    broadcast_dim = None

  residual = apply_dropout(tensor=residual,
                           safe_key=safe_key,
                           rate=dropout_rate,
                           is_training=should_apply_dropout,
                           broadcast_dim=broadcast_dim)

  new_act = output_act + residual

  return new_act


def create_extra_msa_feature(batch):
  """"""Expand extra_msa into 1hot and concat with other extra msa features.

  We do this as late as possible as the one_hot extra msa can be very large.

  Arguments:
    batch: a dictionary with the following keys:
     * 'extra_msa': [N_extra_seq, N_res] MSA that wasn't selected as a cluster
       centre. Note, that this is not one-hot encoded.
     * 'extra_has_deletion': [N_extra_seq, N_res] Whether there is a deletion to
       the left of each position in the extra MSA.
     * 'extra_deletion_value': [N_extra_seq, N_res] The number of deletions to
       the left of each position in the extra MSA.

  Returns:
    Concatenated tensor of extra MSA features.
  """"""
  # 23 = 20 amino acids + 'X' for unknown + gap + bert mask
  msa_1hot = jax.nn.one_hot(batch['extra_msa'], 23)
  msa_feat = [msa_1hot,
              jnp.expand_dims(batch['extra_has_deletion'], axis=-1),
              jnp.expand_dims(batch['extra_deletion_value'], axis=-1)]
  return jnp.concatenate(msa_feat, axis=-1)


class AlphaFoldIteration(hk.Module):
  """"""A single recycling iteration of AlphaFold architecture.

  Computes ensembled (averaged) representations from the provided features.
  These representations are then passed to the various heads
  that have been requested by the configuration file. Each head also returns a
  loss which is combined as a weighted sum to produce the total loss.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 3-22
  """"""

  def __init__(self, config, global_config, name='alphafold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               ensembled_batch,
               non_ensembled_batch,
               is_training,
               compute_loss=False,
               ensemble_representations=False,
               return_representations=False):

    num_ensemble = jnp.asarray(ensembled_batch['seq_length'].shape[0])

    if not ensemble_representations:
      assert ensembled_batch['seq_length'].shape[0] == 1

    def slice_batch(i):
      b = {k: v[i] for k, v in ensembled_batch.items()}
      b.update(non_ensembled_batch)
      return b

    # Compute representations for each batch element and average.
    evoformer_module = EmbeddingsAndEvoformer(
        self.config.embeddings_and_evoformer, self.global_config)
    batch0 = slice_batch(0)
    representations = evoformer_module(batch0, is_training)

    # MSA representations are not ensembled so
    # we don't pass tensor into the loop.
    msa_representation = representations['msa']
    del representations['msa']

    # Average the representations (except MSA) over the batch dimension.
    if ensemble_representations:
      def body(x):
        """"""Add one element to the representations ensemble.""""""
        i, current_representations = x
        feats = slice_batch(i)
        representations_update = evoformer_module(
            feats, is_training)

        new_representations = {}
        for k in current_representations:
          new_representations[k] = (
              current_representations[k] + representations_update[k])
        return i+1, new_representations

      if hk.running_init():
        # When initializing the Haiku module, run one iteration of the
        # while_loop to initialize the Haiku modules used in `body`.
        _, representations = body((1, representations))
      else:
        _, representations = hk.while_loop(
            lambda x: x[0] < num_ensemble,
            body,
            (1, representations))

      for k in representations:
        if k != 'msa':
          representations[k] /= num_ensemble.astype(representations[k].dtype)

    representations['msa'] = msa_representation
    batch = batch0  # We are not ensembled from here on.

    heads = {}
    for head_name, head_config in sorted(self.config.heads.items()):
      if not head_config.weight:
        continue  # Do not instantiate zero-weight heads.

      head_factory = {
          'masked_msa': MaskedMsaHead,
          'distogram': DistogramHead,
          'structure_module': functools.partial(
              folding.StructureModule, compute_loss=compute_loss),
          'predicted_lddt': PredictedLDDTHead,
          'predicted_aligned_error': PredictedAlignedErrorHead,
          'experimentally_resolved': ExperimentallyResolvedHead,
      }[head_name]
      heads[head_name] = (head_config,
                          head_factory(head_config, self.global_config))

    total_loss = 0.
    ret = {}
    ret['representations'] = representations

    def loss(module, head_config, ret, name, filter_ret=True):
      if filter_ret:
        value = ret[name]
      else:
        value = ret
      loss_output = module.loss(value, batch)
      ret[name].update(loss_output)
      loss = head_config.weight * ret[name]['loss']
      return loss

    for name, (head_config, module) in heads.items():
      # Skip PredictedLDDTHead and PredictedAlignedErrorHead until
      # StructureModule is executed.
      if name in ('predicted_lddt', 'predicted_aligned_error'):
        continue
      else:
        ret[name] = module(representations, batch, is_training)
        if 'representations' in ret[name]:
          # Extra representations from the head. Used by the structure module
          # to provide activations for the PredictedLDDTHead.
          representations.update(ret[name].pop('representations'))
      if compute_loss:
        total_loss += loss(module, head_config, ret, name)

    if self.config.heads.get('predicted_lddt.weight', 0.0):
      # Add PredictedLDDTHead after StructureModule executes.
      name = 'predicted_lddt'
      # Feed all previous results to give access to structure_module result.
      head_config, module = heads[name]
      ret[name] = module(representations, batch, is_training)
      if compute_loss:
        total_loss += loss(module, head_config, ret, name, filter_ret=False)

    if ('predicted_aligned_error' in self.config.heads
        and self.config.heads.get('predicted_aligned_error.weight', 0.0)):
      # Add PredictedAlignedErrorHead after StructureModule executes.
      name = 'predicted_aligned_error'
      # Feed all previous results to give access to structure_module result.
      head_config, module = heads[name]
      ret[name] = module(representations, batch, is_training)
      if compute_loss:
        total_loss += loss(module, head_config, ret, name, filter_ret=False)

    if compute_loss:
      return ret, total_loss
    else:
      return ret


class AlphaFold(hk.Module):
  """"""AlphaFold model with recycling.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference""
  """"""

  def __init__(self, config, name='alphafold'):
    super().__init__(name=name)
    self.config = config
    self.global_config = config.global_config

  def __call__(
      self,
      batch,
      is_training,
      compute_loss=False,
      ensemble_representations=False,
      return_representations=False):
    """"""Run the AlphaFold model.

    Arguments:
      batch: Dictionary with inputs to the AlphaFold model.
      is_training: Whether the system is in training or inference mode.
      compute_loss: Whether to compute losses (requires extra features
        to be present in the batch and knowing the true structure).
      ensemble_representations: Whether to use ensembling of representations.
      return_representations: Whether to also return the intermediate
        representations.

    Returns:
      When compute_loss is True:
        a tuple of loss and output of AlphaFoldIteration.
      When compute_loss is False:
        just output of AlphaFoldIteration.

      The output of AlphaFoldIteration is a nested dictionary containing
      predictions from the various heads.
    """"""

    impl = AlphaFoldIteration(self.config, self.global_config)
    batch_size, num_residues = batch['aatype'].shape

    def get_prev(ret):
      new_prev = {
          'prev_pos':
              ret['structure_module']['final_atom_positions'],
          'prev_msa_first_row': ret['representations']['msa_first_row'],
          'prev_pair': ret['representations']['pair'],
      }
      return jax.tree_map(jax.lax.stop_gradient, new_prev)

    def do_call(prev,
                recycle_idx,
                compute_loss=compute_loss):
      if self.config.resample_msa_in_recycling:
        num_ensemble = batch_size // (self.config.num_recycle + 1)
        def slice_recycle_idx(x):
          start = recycle_idx * num_ensemble
          size = num_ensemble
          return jax.lax.dynamic_slice_in_dim(x, start, size, axis=0)
        ensembled_batch = jax.tree_map(slice_recycle_idx, batch)
      else:
        num_ensemble = batch_size
        ensembled_batch = batch

      non_ensembled_batch = jax.tree_map(lambda x: x, prev)

      return impl(
          ensembled_batch=ensembled_batch,
          non_ensembled_batch=non_ensembled_batch,
          is_training=is_training,
          compute_loss=compute_loss,
          ensemble_representations=ensemble_representations)

    prev = {}
    emb_config = self.config.embeddings_and_evoformer
    if emb_config.recycle_pos:
      prev['prev_pos'] = jnp.zeros(
          [num_residues, residue_constants.atom_type_num, 3])
    if emb_config.recycle_features:
      prev['prev_msa_first_row'] = jnp.zeros(
          [num_residues, emb_config.msa_channel])
      prev['prev_pair'] = jnp.zeros(
          [num_residues, num_residues, emb_config.pair_channel])

    if self.config.num_recycle:
      if 'num_iter_recycling' in batch:
        # Training time: num_iter_recycling is in batch.
        # The value for each ensemble batch is the same, so arbitrarily taking
        # 0-th.
        num_iter = batch['num_iter_recycling'][0]

        # Add insurance that we will not run more
        # recyclings than the model is configured to run.
        num_iter = jnp.minimum(num_iter, self.config.num_recycle)
      else:
        # Eval mode or tests: use the maximum number of iterations.
        num_iter = self.config.num_recycle

      body = lambda x: (x[0] + 1,  # pylint: disable=g-long-lambda
                        get_prev(do_call(x[1], recycle_idx=x[0],
                                         compute_loss=False)))
      if hk.running_init():
        # When initializing the Haiku module, run one iteration of the
        # while_loop to initialize the Haiku modules used in `body`.
        _, prev = body((0, prev))
      else:
        _, prev = hk.while_loop(
            lambda x: x[0] < num_iter,
            body,
            (0, prev))
    else:
      num_iter = 0

    ret = do_call(prev=prev, recycle_idx=num_iter)
    if compute_loss:
      ret = ret[0], [ret[1]]

    if not return_representations:
      del (ret[0] if compute_loss else ret)['representations']  # pytype: disable=unsupported-operands
    return ret


class TemplatePairStack(hk.Module):
  """"""Pair stack for the templates.

  Jumper et al. (2021) Suppl. Alg. 16 ""TemplatePairStack""
  """"""

  def __init__(self, config, global_config, name='template_pair_stack'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, pair_act, pair_mask, is_training, safe_key=None):
    """"""Builds TemplatePairStack module.

    Arguments:
      pair_act: Pair activations for single template, shape [N_res, N_res, c_t].
      pair_mask: Pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.
      safe_key: Safe key object encapsulating the random number generation key.

    Returns:
      Updated pair_act, shape [N_res, N_res, c_t].
    """"""

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    gc = self.global_config
    c = self.config

    if not c.num_block:
      return pair_act

    def block(x):
      """"""One block of the template pair stack.""""""
      pair_act, safe_key = x

      dropout_wrapper_fn = functools.partial(
          dropout_wrapper, is_training=is_training, global_config=gc)

      safe_key, *sub_keys = safe_key.split(6)
      sub_keys = iter(sub_keys)

      pair_act = dropout_wrapper_fn(
          TriangleAttention(c.triangle_attention_starting_node, gc,
                            name='triangle_attention_starting_node'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleAttention(c.triangle_attention_ending_node, gc,
                            name='triangle_attention_ending_node'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                                 name='triangle_multiplication_outgoing'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                                 name='triangle_multiplication_incoming'),
          pair_act,
          pair_mask,
          next(sub_keys))
      pair_act = dropout_wrapper_fn(
          Transition(c.pair_transition, gc, name='pair_transition'),
          pair_act,
          pair_mask,
          next(sub_keys))

      return pair_act, safe_key

    if gc.use_remat:
      block = hk.remat(block)

    res_stack = layer_stack.layer_stack(c.num_block)(block)
    pair_act, safe_key = res_stack((pair_act, safe_key))
    return pair_act


class Transition(hk.Module):
  """"""Transition layer.

  Jumper et al. (2021) Suppl. Alg. 9 ""MSATransition""
  Jumper et al. (2021) Suppl. Alg. 15 ""PairTransition""
  """"""

  def __init__(self, config, global_config, name='transition_block'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, act, mask, is_training=True):
    """"""Builds Transition module.

    Arguments:
      act: A tensor of queries of size [batch_size, N_res, N_channel].
      mask: A tensor denoting the mask of size [batch_size, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      A float32 tensor of size [batch_size, N_res, N_channel].
    """"""
    _, _, nc = act.shape

    num_intermediate = int(nc * self.config.num_intermediate_factor)
    mask = jnp.expand_dims(mask, axis=-1)

    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='input_layer_norm')(
            act)

    transition_module = hk.Sequential([
        common_modules.Linear(
            num_intermediate,
            initializer='relu',
            name='transition1'), jax.nn.relu,
        common_modules.Linear(
            nc,
            initializer=utils.final_init(self.global_config),
            name='transition2')
    ])

    act = mapping.inference_subbatch(
        transition_module,
        self.global_config.subbatch_size,
        batched_args=[act],
        nonbatched_args=[],
        low_memory=not is_training)

    return act


def glorot_uniform():
  return hk.initializers.VarianceScaling(scale=1.0,
                                         mode='fan_avg',
                                         distribution='uniform')


class Attention(hk.Module):
  """"""Multihead attention.""""""

  def __init__(self, config, global_config, output_dim, name='attention'):
    super().__init__(name=name)

    self.config = config
    self.global_config = global_config
    self.output_dim = output_dim

  def __call__(self, q_data, m_data, bias, nonbatched_bias=None):
    """"""Builds Attention module.

    Arguments:
      q_data: A tensor of queries, shape [batch_size, N_queries, q_channels].
      m_data: A tensor of memories from which the keys and values are
        projected, shape [batch_size, N_keys, m_channels].
      bias: A bias for the attention, shape [batch_size, N_queries, N_keys].
      nonbatched_bias: Shared bias, shape [N_queries, N_keys].

    Returns:
      A float32 tensor of shape [batch_size, N_queries, output_dim].
    """"""
    # Sensible default for when the config keys are missing
    key_dim = self.config.get('key_dim', int(q_data.shape[-1]))
    value_dim = self.config.get('value_dim', int(m_data.shape[-1]))
    num_head = self.config.num_head
    assert key_dim % num_head == 0
    assert value_dim % num_head == 0
    key_dim = key_dim // num_head
    value_dim = value_dim // num_head

    q_weights = hk.get_parameter(
        'query_w', shape=(q_data.shape[-1], num_head, key_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())
    k_weights = hk.get_parameter(
        'key_w', shape=(m_data.shape[-1], num_head, key_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())
    v_weights = hk.get_parameter(
        'value_w', shape=(m_data.shape[-1], num_head, value_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())

    q = jnp.einsum('bqa,ahc->bqhc', q_data, q_weights) * key_dim**(-0.5)
    k = jnp.einsum('bka,ahc->bkhc', m_data, k_weights)
    v = jnp.einsum('bka,ahc->bkhc', m_data, v_weights)
    logits = jnp.einsum('bqhc,bkhc->bhqk', q, k) + bias
    if nonbatched_bias is not None:
      logits += jnp.expand_dims(nonbatched_bias, axis=0)
    weights = jax.nn.softmax(logits)
    weighted_avg = jnp.einsum('bhqk,bkhc->bqhc', weights, v)

    if self.global_config.zero_init:
      init = hk.initializers.Constant(0.0)
    else:
      init = glorot_uniform()

    if self.config.gating:
      gating_weights = hk.get_parameter(
          'gating_w',
          shape=(q_data.shape[-1], num_head, value_dim),
          dtype=q_data.dtype,
          init=hk.initializers.Constant(0.0))
      gating_bias = hk.get_parameter(
          'gating_b',
          shape=(num_head, value_dim),
          dtype=q_data.dtype,
          init=hk.initializers.Constant(1.0))

      gate_values = jnp.einsum('bqc, chv->bqhv', q_data,
                               gating_weights) + gating_bias

      gate_values = jax.nn.sigmoid(gate_values)

      weighted_avg *= gate_values

    o_weights = hk.get_parameter(
        'output_w', shape=(num_head, value_dim, self.output_dim),
        dtype=q_data.dtype,
        init=init)
    o_bias = hk.get_parameter(
        'output_b', shape=(self.output_dim,),
        dtype=q_data.dtype,
        init=hk.initializers.Constant(0.0))

    output = jnp.einsum('bqhc,hco->bqo', weighted_avg, o_weights) + o_bias

    return output


class GlobalAttention(hk.Module):
  """"""Global attention.

  Jumper et al. (2021) Suppl. Alg. 19 ""MSAColumnGlobalAttention"" lines 2-7
  """"""

  def __init__(self, config, global_config, output_dim, name='attention'):
    super().__init__(name=name)

    self.config = config
    self.global_config = global_config
    self.output_dim = output_dim

  def __call__(self, q_data, m_data, q_mask):
    """"""Builds GlobalAttention module.

    Arguments:
      q_data: A tensor of queries with size [batch_size, N_queries,
        q_channels]
      m_data: A tensor of memories from which the keys and values
        projected. Size [batch_size, N_keys, m_channels]
      q_mask: A binary mask for q_data with zeros in the padded sequence
        elements and ones otherwise. Size [batch_size, N_queries, q_channels]
        (or broadcastable to this shape).

    Returns:
      A float32 tensor of size [batch_size, N_queries, output_dim].
    """"""
    # Sensible default for when the config keys are missing
    key_dim = self.config.get('key_dim', int(q_data.shape[-1]))
    value_dim = self.config.get('value_dim', int(m_data.shape[-1]))
    num_head = self.config.num_head
    assert key_dim % num_head == 0
    assert value_dim % num_head == 0
    key_dim = key_dim // num_head
    value_dim = value_dim // num_head

    q_weights = hk.get_parameter(
        'query_w', shape=(q_data.shape[-1], num_head, key_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())
    k_weights = hk.get_parameter(
        'key_w', shape=(m_data.shape[-1], key_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())
    v_weights = hk.get_parameter(
        'value_w', shape=(m_data.shape[-1], value_dim),
        dtype=q_data.dtype,
        init=glorot_uniform())

    v = jnp.einsum('bka,ac->bkc', m_data, v_weights)

    q_avg = utils.mask_mean(q_mask, q_data, axis=1)

    q = jnp.einsum('ba,ahc->bhc', q_avg, q_weights) * key_dim**(-0.5)
    k = jnp.einsum('bka,ac->bkc', m_data, k_weights)
    bias = (1e9 * (q_mask[:, None, :, 0] - 1.))
    logits = jnp.einsum('bhc,bkc->bhk', q, k) + bias
    weights = jax.nn.softmax(logits)
    weighted_avg = jnp.einsum('bhk,bkc->bhc', weights, v)

    if self.global_config.zero_init:
      init = hk.initializers.Constant(0.0)
    else:
      init = glorot_uniform()

    o_weights = hk.get_parameter(
        'output_w', shape=(num_head, value_dim, self.output_dim),
        dtype=q_data.dtype,
        init=init)
    o_bias = hk.get_parameter(
        'output_b', shape=(self.output_dim,),
        dtype=q_data.dtype,
        init=hk.initializers.Constant(0.0))

    if self.config.gating:
      gating_weights = hk.get_parameter(
          'gating_w',
          shape=(q_data.shape[-1], num_head, value_dim),
          dtype=q_data.dtype,
          init=hk.initializers.Constant(0.0))
      gating_bias = hk.get_parameter(
          'gating_b',
          shape=(num_head, value_dim),
          dtype=q_data.dtype,
          init=hk.initializers.Constant(1.0))

      gate_values = jnp.einsum('bqc, chv->bqhv', q_data, gating_weights)
      gate_values = jax.nn.sigmoid(gate_values + gating_bias)
      weighted_avg = weighted_avg[:, None] * gate_values
      output = jnp.einsum('bqhc,hco->bqo', weighted_avg, o_weights) + o_bias
    else:
      output = jnp.einsum('bhc,hco->bo', weighted_avg, o_weights) + o_bias
      output = output[:, None]
    return output


class MSARowAttentionWithPairBias(hk.Module):
  """"""MSA per-row attention biased by the pair representation.

  Jumper et al. (2021) Suppl. Alg. 7 ""MSARowAttentionWithPairBias""
  """"""

  def __init__(self, config, global_config,
               name='msa_row_attention_with_pair_bias'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               pair_act,
               is_training=False):
    """"""Builds MSARowAttentionWithPairBias module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      pair_act: [N_res, N_res, c_z] pair representation.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m].
    """"""
    c = self.config

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_row'

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = common_modules.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)

    pair_act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='feat_2d_norm')(
            pair_act)

    init_factor = 1. / jnp.sqrt(int(pair_act.shape[-1]))
    weights = hk.get_parameter(
        'feat_2d_weights',
        shape=(pair_act.shape[-1], c.num_head),
        dtype=msa_act.dtype,
        init=hk.initializers.RandomNormal(stddev=init_factor))
    nonbatched_bias = jnp.einsum('qkc,ch->hqk', pair_act, weights)

    attn_mod = Attention(
        c, self.global_config, msa_act.shape[-1])
    msa_act = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, bias],
        nonbatched_args=[nonbatched_bias],
        low_memory=not is_training)

    return msa_act


class MSAColumnAttention(hk.Module):
  """"""MSA per-column attention.

  Jumper et al. (2021) Suppl. Alg. 8 ""MSAColumnAttention""
  """"""

  def __init__(self, config, global_config, name='msa_column_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               is_training=False):
    """"""Builds MSAColumnAttention module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m]
    """"""
    c = self.config

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_column'

    msa_act = jnp.swapaxes(msa_act, -2, -3)
    msa_mask = jnp.swapaxes(msa_mask, -1, -2)

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = common_modules.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)

    attn_mod = Attention(
        c, self.global_config, msa_act.shape[-1])
    msa_act = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, bias],
        nonbatched_args=[],
        low_memory=not is_training)

    msa_act = jnp.swapaxes(msa_act, -2, -3)

    return msa_act


class MSAColumnGlobalAttention(hk.Module):
  """"""MSA per-column global attention.

  Jumper et al. (2021) Suppl. Alg. 19 ""MSAColumnGlobalAttention""
  """"""

  def __init__(self, config, global_config, name='msa_column_global_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               msa_act,
               msa_mask,
               is_training=False):
    """"""Builds MSAColumnGlobalAttention module.

    Arguments:
      msa_act: [N_seq, N_res, c_m] MSA representation.
      msa_mask: [N_seq, N_res] mask of non-padded regions.
      is_training: Whether the module is in training mode.

    Returns:
      Update to msa_act, shape [N_seq, N_res, c_m].
    """"""
    c = self.config

    assert len(msa_act.shape) == 3
    assert len(msa_mask.shape) == 2
    assert c.orientation == 'per_column'

    msa_act = jnp.swapaxes(msa_act, -2, -3)
    msa_mask = jnp.swapaxes(msa_mask, -1, -2)

    bias = (1e9 * (msa_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    msa_act = common_modules.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            msa_act)

    attn_mod = GlobalAttention(
        c, self.global_config, msa_act.shape[-1],
        name='attention')
    # [N_seq, N_res, 1]
    msa_mask = jnp.expand_dims(msa_mask, axis=-1)
    msa_act = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[msa_act, msa_act, msa_mask],
        nonbatched_args=[],
        low_memory=not is_training)

    msa_act = jnp.swapaxes(msa_act, -2, -3)

    return msa_act


class TriangleAttention(hk.Module):
  """"""Triangle Attention.

  Jumper et al. (2021) Suppl. Alg. 13 ""TriangleAttentionStartingNode""
  Jumper et al. (2021) Suppl. Alg. 14 ""TriangleAttentionEndingNode""
  """"""

  def __init__(self, config, global_config, name='triangle_attention'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, pair_act, pair_mask, is_training=False):
    """"""Builds TriangleAttention module.

    Arguments:
      pair_act: [N_res, N_res, c_z] pair activations tensor
      pair_mask: [N_res, N_res] mask of non-padded regions in the tensor.
      is_training: Whether the module is in training mode.

    Returns:
      Update to pair_act, shape [N_res, N_res, c_z].
    """"""
    c = self.config

    assert len(pair_act.shape) == 3
    assert len(pair_mask.shape) == 2
    assert c.orientation in ['per_row', 'per_column']

    if c.orientation == 'per_column':
      pair_act = jnp.swapaxes(pair_act, -2, -3)
      pair_mask = jnp.swapaxes(pair_mask, -1, -2)

    bias = (1e9 * (pair_mask - 1.))[:, None, None, :]
    assert len(bias.shape) == 4

    pair_act = common_modules.LayerNorm(
        axis=[-1], create_scale=True, create_offset=True, name='query_norm')(
            pair_act)

    init_factor = 1. / jnp.sqrt(int(pair_act.shape[-1]))
    weights = hk.get_parameter(
        'feat_2d_weights',
        shape=(pair_act.shape[-1], c.num_head),
        dtype=pair_act.dtype,
        init=hk.initializers.RandomNormal(stddev=init_factor))
    nonbatched_bias = jnp.einsum('qkc,ch->hqk', pair_act, weights)

    attn_mod = Attention(
        c, self.global_config, pair_act.shape[-1])
    pair_act = mapping.inference_subbatch(
        attn_mod,
        self.global_config.subbatch_size,
        batched_args=[pair_act, pair_act, bias],
        nonbatched_args=[nonbatched_bias],
        low_memory=not is_training)

    if c.orientation == 'per_column':
      pair_act = jnp.swapaxes(pair_act, -2, -3)

    return pair_act


class MaskedMsaHead(hk.Module):
  """"""Head to predict MSA at the masked locations.

  The MaskedMsaHead employs a BERT-style objective to reconstruct a masked
  version of the full MSA, based on a linear projection of
  the MSA representation.
  Jumper et al. (2021) Suppl. Sec. 1.9.9 ""Masked MSA prediction""
  """"""

  def __init__(self, config, global_config, name='masked_msa_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

    if global_config.multimer_mode:
      self.num_output = len(residue_constants.restypes_with_x_and_gap)
    else:
      self.num_output = config.num_output

  def __call__(self, representations, batch, is_training):
    """"""Builds MaskedMsaHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'msa': MSA representation, shape [N_seq, N_res, c_m].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * 'logits': logits of shape [N_seq, N_res, N_aatype] with
            (unnormalized) log probabilies of predicted aatype at position.
    """"""
    del batch
    logits = common_modules.Linear(
        self.num_output,
        initializer=utils.final_init(self.global_config),
        name='logits')(
            representations['msa'])
    return dict(logits=logits)

  def loss(self, value, batch):
    errors = softmax_cross_entropy(
        labels=jax.nn.one_hot(batch['true_msa'], num_classes=self.num_output),
        logits=value['logits'])
    loss = (jnp.sum(errors * batch['bert_mask'], axis=(-2, -1)) /
            (1e-8 + jnp.sum(batch['bert_mask'], axis=(-2, -1))))
    return {'loss': loss}


class PredictedLDDTHead(hk.Module):
  """"""Head to predict the per-residue LDDT to be used as a confidence measure.

  Jumper et al. (2021) Suppl. Sec. 1.9.6 ""Model confidence prediction (pLDDT)""
  Jumper et al. (2021) Suppl. Alg. 29 ""predictPerResidueLDDT_Ca""
  """"""

  def __init__(self, config, global_config, name='predicted_lddt_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds PredictedLDDTHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'structure_module': Single representation from the structure module,
             shape [N_res, c_s].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing :
        * 'logits': logits of shape [N_res, N_bins] with
            (unnormalized) log probabilies of binned predicted lDDT.
    """"""
    act = representations['structure_module']

    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='input_layer_norm')(
            act)

    act = common_modules.Linear(
        self.config.num_channels,
        initializer='relu',
        name='act_0')(
            act)
    act = jax.nn.relu(act)

    act = common_modules.Linear(
        self.config.num_channels,
        initializer='relu',
        name='act_1')(
            act)
    act = jax.nn.relu(act)

    logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='logits')(
            act)
    # Shape (batch_size, num_res, num_bins)
    return dict(logits=logits)

  def loss(self, value, batch):
    # Shape (num_res, 37, 3)
    pred_all_atom_pos = value['structure_module']['final_atom_positions']
    # Shape (num_res, 37, 3)
    true_all_atom_pos = batch['all_atom_positions']
    # Shape (num_res, 37)
    all_atom_mask = batch['all_atom_mask']

    # Shape (num_res,)
    lddt_ca = lddt.lddt(
        # Shape (batch_size, num_res, 3)
        predicted_points=pred_all_atom_pos[None, :, 1, :],
        # Shape (batch_size, num_res, 3)
        true_points=true_all_atom_pos[None, :, 1, :],
        # Shape (batch_size, num_res, 1)
        true_points_mask=all_atom_mask[None, :, 1:2].astype(jnp.float32),
        cutoff=15.,
        per_residue=True)
    lddt_ca = jax.lax.stop_gradient(lddt_ca)

    num_bins = self.config.num_bins
    bin_index = jnp.floor(lddt_ca * num_bins).astype(jnp.int32)

    # protect against out of range for lddt_ca == 1
    bin_index = jnp.minimum(bin_index, num_bins - 1)
    lddt_ca_one_hot = jax.nn.one_hot(bin_index, num_classes=num_bins)

    # Shape (num_res, num_channel)
    logits = value['predicted_lddt']['logits']
    errors = softmax_cross_entropy(labels=lddt_ca_one_hot, logits=logits)

    # Shape (num_res,)
    mask_ca = all_atom_mask[:, residue_constants.atom_order['CA']]
    mask_ca = mask_ca.astype(jnp.float32)
    loss = jnp.sum(errors * mask_ca) / (jnp.sum(mask_ca) + 1e-8)

    if self.config.filter_by_resolution:
      # NMR & distillation have resolution = 0
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


class PredictedAlignedErrorHead(hk.Module):
  """"""Head to predict the distance errors in the backbone alignment frames.

  Can be used to compute predicted TM-Score.
  Jumper et al. (2021) Suppl. Sec. 1.9.7 ""TM-score prediction""
  """"""

  def __init__(self, config, global_config,
               name='predicted_aligned_error_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds PredictedAlignedErrorHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'pair': pair representation, shape [N_res, N_res, c_z].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * logits: logits for aligned error, shape [N_res, N_res, N_bins].
        * bin_breaks: array containing bin breaks, shape [N_bins - 1].
    """"""

    act = representations['pair']

    # Shape (num_res, num_res, num_bins)
    logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='logits')(act)
    # Shape (num_bins,)
    breaks = jnp.linspace(
        0., self.config.max_error_bin, self.config.num_bins - 1)
    return dict(logits=logits, breaks=breaks)

  def loss(self, value, batch):
    # Shape (num_res, 7)
    predicted_affine = quat_affine.QuatAffine.from_tensor(
        value['structure_module']['final_affines'])
    # Shape (num_res, 7)
    true_affine = quat_affine.QuatAffine.from_tensor(
        batch['backbone_affine_tensor'])
    # Shape (num_res)
    mask = batch['backbone_affine_mask']
    # Shape (num_res, num_res)
    square_mask = mask[:, None] * mask[None, :]
    num_bins = self.config.num_bins
    # (1, num_bins - 1)
    breaks = value['predicted_aligned_error']['breaks']
    # (1, num_bins)
    logits = value['predicted_aligned_error']['logits']

    # Compute the squared error for each alignment.
    def _local_frame_points(affine):
      points = [jnp.expand_dims(x, axis=-2) for x in affine.translation]
      return affine.invert_point(points, extra_dims=1)
    error_dist2_xyz = [
        jnp.square(a - b)
        for a, b in zip(_local_frame_points(predicted_affine),
                        _local_frame_points(true_affine))]
    error_dist2 = sum(error_dist2_xyz)
    # Shape (num_res, num_res)
    # First num_res are alignment frames, second num_res are the residues.
    error_dist2 = jax.lax.stop_gradient(error_dist2)

    sq_breaks = jnp.square(breaks)
    true_bins = jnp.sum((
        error_dist2[..., None] > sq_breaks).astype(jnp.int32), axis=-1)

    errors = softmax_cross_entropy(
        labels=jax.nn.one_hot(true_bins, num_bins, axis=-1), logits=logits)

    loss = (jnp.sum(errors * square_mask, axis=(-2, -1)) /
            (1e-8 + jnp.sum(square_mask, axis=(-2, -1))))

    if self.config.filter_by_resolution:
      # NMR & distillation have resolution = 0
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


class ExperimentallyResolvedHead(hk.Module):
  """"""Predicts if an atom is experimentally resolved in a high-res structure.

  Only trained on high-resolution X-ray crystals & cryo-EM.
  Jumper et al. (2021) Suppl. Sec. 1.9.10 '""Experimentally resolved"" prediction'
  """"""

  def __init__(self, config, global_config,
               name='experimentally_resolved_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds ExperimentallyResolvedHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'single': Single representation, shape [N_res, c_s].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * 'logits': logits of shape [N_res, 37],
            log probability that an atom is resolved in atom37 representation,
            can be converted to probability by applying sigmoid.
    """"""
    logits = common_modules.Linear(
        37,  # atom_exists.shape[-1]
        initializer=utils.final_init(self.global_config),
        name='logits')(representations['single'])
    return dict(logits=logits)

  def loss(self, value, batch):
    logits = value['logits']
    assert len(logits.shape) == 2

    # Does the atom appear in the amino acid?
    atom_exists = batch['atom37_atom_exists']
    # Is the atom resolved in the experiment? Subset of atom_exists,
    # *except for OXT*
    all_atom_mask = batch['all_atom_mask'].astype(jnp.float32)

    xent = sigmoid_cross_entropy(labels=all_atom_mask, logits=logits)
    loss = jnp.sum(xent * atom_exists) / (1e-8 + jnp.sum(atom_exists))

    if self.config.filter_by_resolution:
      # NMR & distillation examples have resolution = 0.
      loss *= ((batch['resolution'] >= self.config.min_resolution)
               & (batch['resolution'] <= self.config.max_resolution)).astype(
                   jnp.float32)

    output = {'loss': loss}
    return output


def _layer_norm(axis=-1, name='layer_norm'):
  return common_modules.LayerNorm(
      axis=axis,
      create_scale=True,
      create_offset=True,
      eps=1e-5,
      use_fast_variance=True,
      scale_init=hk.initializers.Constant(1.),
      offset_init=hk.initializers.Constant(0.),
      param_axis=axis,
      name=name)


class TriangleMultiplication(hk.Module):
  """"""Triangle multiplication layer (""outgoing"" or ""incoming"").

  Jumper et al. (2021) Suppl. Alg. 11 ""TriangleMultiplicationOutgoing""
  Jumper et al. (2021) Suppl. Alg. 12 ""TriangleMultiplicationIncoming""
  """"""

  def __init__(self, config, global_config, name='triangle_multiplication'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, left_act, left_mask, is_training=True):
    """"""Builds TriangleMultiplication module.

    Arguments:
      left_act: Pair activations, shape [N_res, N_res, c_z]
      left_mask: Pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      Outputs, same shape/type as left_act.
    """"""
    del is_training

    if self.config.fuse_projection_weights:
      return self._fused_triangle_multiplication(left_act, left_mask)
    else:
      return self._triangle_multiplication(left_act, left_mask)

  @hk.transparent
  def _triangle_multiplication(self, left_act, left_mask):
    """"""Implementation of TriangleMultiplication used in AF2 and AF-M<2.3.""""""
    c = self.config
    gc = self.global_config

    mask = left_mask[..., None]

    act = common_modules.LayerNorm(axis=[-1], create_scale=True, create_offset=True,
                       name='layer_norm_input')(left_act)
    input_act = act

    left_projection = common_modules.Linear(
        c.num_intermediate_channel,
        name='left_projection')
    left_proj_act = mask * left_projection(act)

    right_projection = common_modules.Linear(
        c.num_intermediate_channel,
        name='right_projection')
    right_proj_act = mask * right_projection(act)

    left_gate_values = jax.nn.sigmoid(common_modules.Linear(
        c.num_intermediate_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='left_gate')(act))

    right_gate_values = jax.nn.sigmoid(common_modules.Linear(
        c.num_intermediate_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='right_gate')(act))

    left_proj_act *= left_gate_values
    right_proj_act *= right_gate_values

    # ""Outgoing"" edges equation: 'ikc,jkc->ijc'
    # ""Incoming"" edges equation: 'kjc,kic->ijc'
    # Note on the Suppl. Alg. 11 & 12 notation:
    # For the ""outgoing"" edges, a = left_proj_act and b = right_proj_act
    # For the ""incoming"" edges, it's swapped:
    #   b = left_proj_act and a = right_proj_act
    act = jnp.einsum(c.equation, left_proj_act, right_proj_act)

    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='center_layer_norm')(
            act)

    output_channel = int(input_act.shape[-1])

    act = common_modules.Linear(
        output_channel,
        initializer=utils.final_init(gc),
        name='output_projection')(act)

    gate_values = jax.nn.sigmoid(common_modules.Linear(
        output_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='gating_linear')(input_act))
    act *= gate_values

    return act

  @hk.transparent
  def _fused_triangle_multiplication(self, left_act, left_mask):
    """"""TriangleMultiplication with fused projection weights.""""""
    mask = left_mask[..., None]
    c = self.config
    gc = self.global_config

    left_act = _layer_norm(axis=-1, name='left_norm_input')(left_act)

    # Both left and right projections are fused into projection.
    projection = common_modules.Linear(
        2*c.num_intermediate_channel, name='projection')
    proj_act = mask * projection(left_act)

    # Both left + right gate are fused into gate_values.
    gate_values = common_modules.Linear(
        2 * c.num_intermediate_channel,
        name='gate',
        bias_init=1.,
        initializer=utils.final_init(gc))(left_act)
    proj_act *= jax.nn.sigmoid(gate_values)

    left_proj_act = proj_act[:, :, :c.num_intermediate_channel]
    right_proj_act = proj_act[:, :, c.num_intermediate_channel:]
    act = jnp.einsum(c.equation, left_proj_act, right_proj_act)

    act = _layer_norm(axis=-1, name='center_norm')(act)

    output_channel = int(left_act.shape[-1])

    act = common_modules.Linear(
        output_channel,
        initializer=utils.final_init(gc),
        name='output_projection')(act)

    gate_values = common_modules.Linear(
        output_channel,
        bias_init=1.,
        initializer=utils.final_init(gc),
        name='gating_linear')(left_act)
    act *= jax.nn.sigmoid(gate_values)

    return act


class DistogramHead(hk.Module):
  """"""Head to predict a distogram.

  Jumper et al. (2021) Suppl. Sec. 1.9.8 ""Distogram prediction""
  """"""

  def __init__(self, config, global_config, name='distogram_head'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, representations, batch, is_training):
    """"""Builds DistogramHead module.

    Arguments:
      representations: Dictionary of representations, must contain:
        * 'pair': pair representation, shape [N_res, N_res, c_z].
      batch: Batch, unused.
      is_training: Whether the module is in training mode.

    Returns:
      Dictionary containing:
        * logits: logits for distogram, shape [N_res, N_res, N_bins].
        * bin_breaks: array containing bin breaks, shape [N_bins - 1,].
    """"""
    half_logits = common_modules.Linear(
        self.config.num_bins,
        initializer=utils.final_init(self.global_config),
        name='half_logits')(
            representations['pair'])

    logits = half_logits + jnp.swapaxes(half_logits, -2, -3)
    breaks = jnp.linspace(self.config.first_break, self.config.last_break,
                          self.config.num_bins - 1)

    return dict(logits=logits, bin_edges=breaks)

  def loss(self, value, batch):
    return _distogram_log_loss(value['logits'], value['bin_edges'],
                               batch, self.config.num_bins)


def _distogram_log_loss(logits, bin_edges, batch, num_bins):
  """"""Log loss of a distogram.""""""

  assert len(logits.shape) == 3
  positions = batch['pseudo_beta']
  mask = batch['pseudo_beta_mask']

  assert positions.shape[-1] == 3

  sq_breaks = jnp.square(bin_edges)

  dist2 = jnp.sum(
      jnp.square(
          jnp.expand_dims(positions, axis=-2) -
          jnp.expand_dims(positions, axis=-3)),
      axis=-1,
      keepdims=True)

  true_bins = jnp.sum(dist2 > sq_breaks, axis=-1)

  errors = softmax_cross_entropy(
      labels=jax.nn.one_hot(true_bins, num_bins), logits=logits)

  square_mask = jnp.expand_dims(mask, axis=-2) * jnp.expand_dims(mask, axis=-1)

  avg_error = (
      jnp.sum(errors * square_mask, axis=(-2, -1)) /
      (1e-6 + jnp.sum(square_mask, axis=(-2, -1))))
  dist2 = dist2[..., 0]
  return dict(loss=avg_error, true_dist=jnp.sqrt(1e-6 + dist2))


class OuterProductMean(hk.Module):
  """"""Computes mean outer product.

  Jumper et al. (2021) Suppl. Alg. 10 ""OuterProductMean""
  """"""

  def __init__(self,
               config,
               global_config,
               num_output_channel,
               name='outer_product_mean'):
    super().__init__(name=name)
    self.global_config = global_config
    self.config = config
    self.num_output_channel = num_output_channel

  def __call__(self, act, mask, is_training=True):
    """"""Builds OuterProductMean module.

    Arguments:
      act: MSA representation, shape [N_seq, N_res, c_m].
      mask: MSA mask, shape [N_seq, N_res].
      is_training: Whether the module is in training mode.

    Returns:
      Update to pair representation, shape [N_res, N_res, c_z].
    """"""
    gc = self.global_config
    c = self.config

    mask = mask[..., None]
    act = common_modules.LayerNorm([-1], True, True, name='layer_norm_input')(act)

    left_act = mask * common_modules.Linear(
        c.num_outer_channel,
        initializer='linear',
        name='left_projection')(
            act)

    right_act = mask * common_modules.Linear(
        c.num_outer_channel,
        initializer='linear',
        name='right_projection')(
            act)

    if gc.zero_init:
      init_w = hk.initializers.Constant(0.0)
    else:
      init_w = hk.initializers.VarianceScaling(scale=2., mode='fan_in')

    output_w = hk.get_parameter(
        'output_w',
        shape=(c.num_outer_channel, c.num_outer_channel,
               self.num_output_channel),
        dtype=act.dtype,
        init=init_w)
    output_b = hk.get_parameter(
        'output_b', shape=(self.num_output_channel,),
        dtype=act.dtype,
        init=hk.initializers.Constant(0.0))

    def compute_chunk(left_act):
      # This is equivalent to
      #
      # act = jnp.einsum('abc,ade->dceb', left_act, right_act)
      # act = jnp.einsum('dceb,cef->bdf', act, output_w) + output_b
      #
      # but faster.
      left_act = jnp.transpose(left_act, [0, 2, 1])
      act = jnp.einsum('acb,ade->dceb', left_act, right_act)
      act = jnp.einsum('dceb,cef->dbf', act, output_w) + output_b
      return jnp.transpose(act, [1, 0, 2])

    act = mapping.inference_subbatch(
        compute_chunk,
        c.chunk_size,
        batched_args=[left_act],
        nonbatched_args=[],
        low_memory=True,
        input_subbatch_dim=1,
        output_subbatch_dim=0)

    epsilon = 1e-3
    norm = jnp.einsum('abc,adc->bdc', mask, mask)
    act /= epsilon + norm

    return act


def dgram_from_positions(positions, num_bins, min_bin, max_bin):
  """"""Compute distogram from amino acid positions.

  Arguments:
    positions: [N_res, 3] Position coordinates.
    num_bins: The number of bins in the distogram.
    min_bin: The left edge of the first bin.
    max_bin: The left edge of the final bin. The final bin catches
        everything larger than `max_bin`.

  Returns:
    Distogram with the specified number of bins.
  """"""

  def squared_difference(x, y):
    return jnp.square(x - y)

  lower_breaks = jnp.linspace(min_bin, max_bin, num_bins)
  lower_breaks = jnp.square(lower_breaks)
  upper_breaks = jnp.concatenate([lower_breaks[1:],
                                  jnp.array([1e8], dtype=jnp.float32)], axis=-1)
  dist2 = jnp.sum(
      squared_difference(
          jnp.expand_dims(positions, axis=-2),
          jnp.expand_dims(positions, axis=-3)),
      axis=-1, keepdims=True)

  dgram = ((dist2 > lower_breaks).astype(jnp.float32) *
           (dist2 < upper_breaks).astype(jnp.float32))
  return dgram


def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
  """"""Create pseudo beta features.""""""

  is_gly = jnp.equal(aatype, residue_constants.restype_order['G'])
  ca_idx = residue_constants.atom_order['CA']
  cb_idx = residue_constants.atom_order['CB']
  pseudo_beta = jnp.where(
      jnp.tile(is_gly[..., None], [1] * len(is_gly.shape) + [3]),
      all_atom_positions[..., ca_idx, :],
      all_atom_positions[..., cb_idx, :])

  if all_atom_masks is not None:
    pseudo_beta_mask = jnp.where(
        is_gly, all_atom_masks[..., ca_idx], all_atom_masks[..., cb_idx])
    pseudo_beta_mask = pseudo_beta_mask.astype(jnp.float32)
    return pseudo_beta, pseudo_beta_mask
  else:
    return pseudo_beta


class EvoformerIteration(hk.Module):
  """"""Single iteration (block) of Evoformer stack.

  Jumper et al. (2021) Suppl. Alg. 6 ""EvoformerStack"" lines 2-10
  """"""

  def __init__(self, config, global_config, is_extra_msa,
               name='evoformer_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config
    self.is_extra_msa = is_extra_msa

  def __call__(self, activations, masks, is_training=True, safe_key=None):
    """"""Builds EvoformerIteration module.

    Arguments:
      activations: Dictionary containing activations:
        * 'msa': MSA activations, shape [N_seq, N_res, c_m].
        * 'pair': pair activations, shape [N_res, N_res, c_z].
      masks: Dictionary of masks:
        * 'msa': MSA mask, shape [N_seq, N_res].
        * 'pair': pair mask, shape [N_res, N_res].
      is_training: Whether the module is in training mode.
      safe_key: prng.SafeKey encapsulating rng key.

    Returns:
      Outputs, same shape/type as act.
    """"""
    c = self.config
    gc = self.global_config

    msa_act, pair_act = activations['msa'], activations['pair']

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    msa_mask, pair_mask = masks['msa'], masks['pair']

    dropout_wrapper_fn = functools.partial(
        dropout_wrapper,
        is_training=is_training,
        global_config=gc)

    safe_key, *sub_keys = safe_key.split(10)
    sub_keys = iter(sub_keys)

    outer_module = OuterProductMean(
        config=c.outer_product_mean,
        global_config=self.global_config,
        num_output_channel=int(pair_act.shape[-1]),
        name='outer_product_mean')
    if c.outer_product_mean.first:
      pair_act = dropout_wrapper_fn(
          outer_module,
          msa_act,
          msa_mask,
          safe_key=next(sub_keys),
          output_act=pair_act)

    msa_act = dropout_wrapper_fn(
        MSARowAttentionWithPairBias(
            c.msa_row_attention_with_pair_bias, gc,
            name='msa_row_attention_with_pair_bias'),
        msa_act,
        msa_mask,
        safe_key=next(sub_keys),
        pair_act=pair_act)

    if not self.is_extra_msa:
      attn_mod = MSAColumnAttention(
          c.msa_column_attention, gc, name='msa_column_attention')
    else:
      attn_mod = MSAColumnGlobalAttention(
          c.msa_column_attention, gc, name='msa_column_global_attention')
    msa_act = dropout_wrapper_fn(
        attn_mod,
        msa_act,
        msa_mask,
        safe_key=next(sub_keys))

    msa_act = dropout_wrapper_fn(
        Transition(c.msa_transition, gc, name='msa_transition'),
        msa_act,
        msa_mask,
        safe_key=next(sub_keys))

    if not c.outer_product_mean.first:
      pair_act = dropout_wrapper_fn(
          outer_module,
          msa_act,
          msa_mask,
          safe_key=next(sub_keys),
          output_act=pair_act)

    pair_act = dropout_wrapper_fn(
        TriangleMultiplication(c.triangle_multiplication_outgoing, gc,
                               name='triangle_multiplication_outgoing'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))
    pair_act = dropout_wrapper_fn(
        TriangleMultiplication(c.triangle_multiplication_incoming, gc,
                               name='triangle_multiplication_incoming'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    pair_act = dropout_wrapper_fn(
        TriangleAttention(c.triangle_attention_starting_node, gc,
                          name='triangle_attention_starting_node'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))
    pair_act = dropout_wrapper_fn(
        TriangleAttention(c.triangle_attention_ending_node, gc,
                          name='triangle_attention_ending_node'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    pair_act = dropout_wrapper_fn(
        Transition(c.pair_transition, gc, name='pair_transition'),
        pair_act,
        pair_mask,
        safe_key=next(sub_keys))

    return {'msa': msa_act, 'pair': pair_act}


class EmbeddingsAndEvoformer(hk.Module):
  """"""Embeds the input data and runs Evoformer.

  Produces the MSA, single and pair representations.
  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 5-18
  """"""

  def __init__(self, config, global_config, name='evoformer'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, batch, is_training, safe_key=None):

    c = self.config
    gc = self.global_config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    # Embed clustered MSA.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 5
    # Jumper et al. (2021) Suppl. Alg. 3 ""InputEmbedder""
    preprocess_1d = common_modules.Linear(
        c.msa_channel, name='preprocess_1d')(
            batch['target_feat'])

    preprocess_msa = common_modules.Linear(
        c.msa_channel, name='preprocess_msa')(
            batch['msa_feat'])

    msa_activations = jnp.expand_dims(preprocess_1d, axis=0) + preprocess_msa

    left_single = common_modules.Linear(
        c.pair_channel, name='left_single')(
            batch['target_feat'])
    right_single = common_modules.Linear(
        c.pair_channel, name='right_single')(
            batch['target_feat'])
    pair_activations = left_single[:, None] + right_single[None]
    mask_2d = batch['seq_mask'][:, None] * batch['seq_mask'][None, :]

    # Inject previous outputs for recycling.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 6
    # Jumper et al. (2021) Suppl. Alg. 32 ""RecyclingEmbedder""
    if c.recycle_pos:
      prev_pseudo_beta = pseudo_beta_fn(
          batch['aatype'], batch['prev_pos'], None)
      dgram = dgram_from_positions(prev_pseudo_beta, **self.config.prev_pos)
      pair_activations += common_modules.Linear(
          c.pair_channel, name='prev_pos_linear')(
              dgram)

    if c.recycle_features:
      prev_msa_first_row = common_modules.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_msa_first_row_norm')(
              batch['prev_msa_first_row'])
      msa_activations = msa_activations.at[0].add(prev_msa_first_row)

      pair_activations += common_modules.LayerNorm(
          axis=[-1],
          create_scale=True,
          create_offset=True,
          name='prev_pair_norm')(
              batch['prev_pair'])

    # Relative position encoding.
    # Jumper et al. (2021) Suppl. Alg. 4 ""relpos""
    # Jumper et al. (2021) Suppl. Alg. 5 ""one_hot""
    if c.max_relative_feature:
      # Add one-hot-encoded clipped residue distances to the pair activations.
      pos = batch['residue_index']
      offset = pos[:, None] - pos[None, :]
      rel_pos = jax.nn.one_hot(
          jnp.clip(
              offset + c.max_relative_feature,
              a_min=0,
              a_max=2 * c.max_relative_feature),
          2 * c.max_relative_feature + 1)
      pair_activations += common_modules.Linear(
          c.pair_channel, name='pair_activiations')(
              rel_pos)

    # Embed templates into the pair activations.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-13
    if c.template.enabled:
      template_batch = {k: batch[k] for k in batch if k.startswith('template_')}
      template_pair_representation = TemplateEmbedding(c.template, gc)(
          pair_activations,
          template_batch,
          mask_2d,
          is_training=is_training)

      pair_activations += template_pair_representation

    # Embed extra MSA features.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 14-16
    extra_msa_feat = create_extra_msa_feature(batch)
    extra_msa_activations = common_modules.Linear(
        c.extra_msa_channel,
        name='extra_msa_activations')(
            extra_msa_feat)

    # Extra MSA Stack.
    # Jumper et al. (2021) Suppl. Alg. 18 ""ExtraMsaStack""
    extra_msa_stack_input = {
        'msa': extra_msa_activations,
        'pair': pair_activations,
    }

    extra_msa_stack_iteration = EvoformerIteration(
        c.evoformer, gc, is_extra_msa=True, name='extra_msa_stack')

    def extra_msa_stack_fn(x):
      act, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      extra_evoformer_output = extra_msa_stack_iteration(
          activations=act,
          masks={
              'msa': batch['extra_msa_mask'],
              'pair': mask_2d
          },
          is_training=is_training,
          safe_key=safe_subkey)
      return (extra_evoformer_output, safe_key)

    if gc.use_remat:
      extra_msa_stack_fn = hk.remat(extra_msa_stack_fn)

    extra_msa_stack = layer_stack.layer_stack(
        c.extra_msa_stack_num_block)(
            extra_msa_stack_fn)
    extra_msa_output, safe_key = extra_msa_stack(
        (extra_msa_stack_input, safe_key))

    pair_activations = extra_msa_output['pair']

    evoformer_input = {
        'msa': msa_activations,
        'pair': pair_activations,
    }

    evoformer_masks = {'msa': batch['msa_mask'], 'pair': mask_2d}

    # Append num_templ rows to msa_activations with template embeddings.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 7-8
    if c.template.enabled and c.template.embed_torsion_angles:
      num_templ, num_res = batch['template_aatype'].shape

      # Embed the templates aatypes.
      aatype_one_hot = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1)

      # Embed the templates aatype, torsion angles and masks.
      # Shape (templates, residues, msa_channels)
      ret = all_atom.atom37_to_torsion_angles(
          aatype=batch['template_aatype'],
          all_atom_pos=batch['template_all_atom_positions'],
          all_atom_mask=batch['template_all_atom_masks'],
          # Ensure consistent behaviour during testing:
          placeholder_for_undefined=not gc.zero_init)

      template_features = jnp.concatenate([
          aatype_one_hot,
          jnp.reshape(
              ret['torsion_angles_sin_cos'], [num_templ, num_res, 14]),
          jnp.reshape(
              ret['alt_torsion_angles_sin_cos'], [num_templ, num_res, 14]),
          ret['torsion_angles_mask']], axis=-1)

      template_activations = common_modules.Linear(
          c.msa_channel,
          initializer='relu',
          name='template_single_embedding')(
              template_features)
      template_activations = jax.nn.relu(template_activations)
      template_activations = common_modules.Linear(
          c.msa_channel,
          initializer='relu',
          name='template_projection')(
              template_activations)

      # Concatenate the templates to the msa.
      evoformer_input['msa'] = jnp.concatenate(
          [evoformer_input['msa'], template_activations], axis=0)
      # Concatenate templates masks to the msa masks.
      # Use mask from the psi angle, as it only depends on the backbone atoms
      # from a single residue.
      torsion_angle_mask = ret['torsion_angles_mask'][:, :, 2]
      torsion_angle_mask = torsion_angle_mask.astype(
          evoformer_masks['msa'].dtype)
      evoformer_masks['msa'] = jnp.concatenate(
          [evoformer_masks['msa'], torsion_angle_mask], axis=0)

    # Main trunk of the network
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 17-18
    evoformer_iteration = EvoformerIteration(
        c.evoformer, gc, is_extra_msa=False, name='evoformer_iteration')

    def evoformer_fn(x):
      act, safe_key = x
      safe_key, safe_subkey = safe_key.split()
      evoformer_output = evoformer_iteration(
          activations=act,
          masks=evoformer_masks,
          is_training=is_training,
          safe_key=safe_subkey)
      return (evoformer_output, safe_key)

    if gc.use_remat:
      evoformer_fn = hk.remat(evoformer_fn)

    evoformer_stack = layer_stack.layer_stack(c.evoformer_num_block)(
        evoformer_fn)
    evoformer_output, safe_key = evoformer_stack(
        (evoformer_input, safe_key))

    msa_activations = evoformer_output['msa']
    pair_activations = evoformer_output['pair']

    single_activations = common_modules.Linear(
        c.seq_channel, name='single_activations')(
            msa_activations[0])

    num_sequences = batch['msa_feat'].shape[0]
    output = {
        'single': single_activations,
        'pair': pair_activations,
        # Crop away template rows such that they are not used in MaskedMsaHead.
        'msa': msa_activations[:num_sequences, :, :],
        'msa_first_row': msa_activations[0],
    }

    return output


class SingleTemplateEmbedding(hk.Module):
  """"""Embeds a single template.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9+11
  """"""

  def __init__(self, config, global_config, name='single_template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, batch, mask_2d, is_training):
    """"""Build the single template embedding.

    Arguments:
      query_embedding: Query pair representation, shape [N_res, N_res, c_z].
      batch: A batch of template features (note the template dimension has been
        stripped out as this module only runs over a single template).
      mask_2d: Padding mask (Note: this doesn't care if a template exists,
        unlike the template_pseudo_beta_mask).
      is_training: Whether the module is in training mode.

    Returns:
      A template embedding [N_res, N_res, c_z].
    """"""
    assert mask_2d.dtype == query_embedding.dtype
    dtype = query_embedding.dtype
    num_res = batch['template_aatype'].shape[0]
    num_channels = (self.config.template_pair_stack
                    .triangle_attention_ending_node.value_dim)
    template_mask = batch['template_pseudo_beta_mask']
    template_mask_2d = template_mask[:, None] * template_mask[None, :]
    template_mask_2d = template_mask_2d.astype(dtype)

    template_dgram = dgram_from_positions(batch['template_pseudo_beta'],
                                          **self.config.dgram_features)
    template_dgram = template_dgram.astype(dtype)

    to_concat = [template_dgram, template_mask_2d[:, :, None]]

    aatype = jax.nn.one_hot(batch['template_aatype'], 22, axis=-1, dtype=dtype)

    to_concat.append(jnp.tile(aatype[None, :, :], [num_res, 1, 1]))
    to_concat.append(jnp.tile(aatype[:, None, :], [1, num_res, 1]))

    n, ca, c = [residue_constants.atom_order[a] for a in ('N', 'CA', 'C')]
    rot, trans = quat_affine.make_transform_from_reference(
        n_xyz=batch['template_all_atom_positions'][:, n],
        ca_xyz=batch['template_all_atom_positions'][:, ca],
        c_xyz=batch['template_all_atom_positions'][:, c])
    affines = quat_affine.QuatAffine(
        quaternion=quat_affine.rot_to_quat(rot, unstack_inputs=True),
        translation=trans,
        rotation=rot,
        unstack_inputs=True)
    points = [jnp.expand_dims(x, axis=-2) for x in affines.translation]
    affine_vec = affines.invert_point(points, extra_dims=1)
    inv_distance_scalar = jax.lax.rsqrt(
        1e-6 + sum([jnp.square(x) for x in affine_vec]))

    # Backbone affine mask: whether the residue has C, CA, N
    # (the template mask defined above only considers pseudo CB).
    template_mask = (
        batch['template_all_atom_masks'][..., n] *
        batch['template_all_atom_masks'][..., ca] *
        batch['template_all_atom_masks'][..., c])
    template_mask_2d = template_mask[:, None] * template_mask[None, :]

    inv_distance_scalar *= template_mask_2d.astype(inv_distance_scalar.dtype)

    unit_vector = [(x * inv_distance_scalar)[..., None] for x in affine_vec]

    unit_vector = [x.astype(dtype) for x in unit_vector]
    template_mask_2d = template_mask_2d.astype(dtype)
    if not self.config.use_template_unit_vector:
      unit_vector = [jnp.zeros_like(x) for x in unit_vector]
    to_concat.extend(unit_vector)

    to_concat.append(template_mask_2d[..., None])

    act = jnp.concatenate(to_concat, axis=-1)

    # Mask out non-template regions so we don't get arbitrary values in the
    # distogram for these regions.
    act *= template_mask_2d[..., None]

    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 9
    act = common_modules.Linear(
        num_channels,
        initializer='relu',
        name='embedding2d')(
            act)

    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" line 11
    act = TemplatePairStack(
        self.config.template_pair_stack, self.global_config)(
            act, mask_2d, is_training)

    act = common_modules.LayerNorm([-1], True, True, name='output_layer_norm')(act)
    return act


class TemplateEmbedding(hk.Module):
  """"""Embeds a set of templates.

  Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-12
  Jumper et al. (2021) Suppl. Alg. 17 ""TemplatePointwiseAttention""
  """"""

  def __init__(self, config, global_config, name='template_embedding'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, query_embedding, template_batch, mask_2d, is_training):
    """"""Build TemplateEmbedding module.

    Arguments:
      query_embedding: Query pair representation, shape [N_res, N_res, c_z].
      template_batch: A batch of template features.
      mask_2d: Padding mask (Note: this doesn't care if a template exists,
        unlike the template_pseudo_beta_mask).
      is_training: Whether the module is in training mode.

    Returns:
      A template embedding [N_res, N_res, c_z].
    """"""

    num_templates = template_batch['template_mask'].shape[0]
    num_channels = (self.config.template_pair_stack
                    .triangle_attention_ending_node.value_dim)
    num_res = query_embedding.shape[0]

    dtype = query_embedding.dtype
    template_mask = template_batch['template_mask']
    template_mask = template_mask.astype(dtype)

    query_num_channels = query_embedding.shape[-1]

    # Make sure the weights are shared across templates by constructing the
    # embedder here.
    # Jumper et al. (2021) Suppl. Alg. 2 ""Inference"" lines 9-12
    template_embedder = SingleTemplateEmbedding(self.config, self.global_config)

    def map_fn(batch):
      return template_embedder(query_embedding, batch, mask_2d, is_training)

    template_pair_representation = mapping.sharded_map(map_fn, in_axes=0)(
        template_batch)

    # Cross attend from the query to the templates along the residue
    # dimension by flattening everything else into the batch dimension.
    # Jumper et al. (2021) Suppl. Alg. 17 ""TemplatePointwiseAttention""
    flat_query = jnp.reshape(query_embedding,
                             [num_res * num_res, 1, query_num_channels])

    flat_templates = jnp.reshape(
        jnp.transpose(template_pair_representation, [1, 2, 0, 3]),
        [num_res * num_res, num_templates, num_channels])

    bias = (1e9 * (template_mask[None, None, None, :] - 1.))

    template_pointwise_attention_module = Attention(
        self.config.attention, self.global_config, query_num_channels)
    nonbatched_args = [bias]
    batched_args = [flat_query, flat_templates]

    embedding = mapping.inference_subbatch(
        template_pointwise_attention_module,
        self.config.subbatch_size,
        batched_args=batched_args,
        nonbatched_args=nonbatched_args,
        low_memory=not is_training)
    embedding = jnp.reshape(embedding,
                            [num_res, num_res, query_num_channels])

    # No gradients if no templates.
    embedding *= (jnp.sum(template_mask) > 0.).astype(embedding.dtype)

    return embedding
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/folding.py",".py","37264","1010","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Modules and utilities for the structure module.""""""

import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku as hk
import jax
import jax.numpy as jnp
import ml_collections
import numpy as np


def squared_difference(x, y):
  return jnp.square(x - y)


class InvariantPointAttention(hk.Module):
  """"""Invariant Point attention module.

  The high-level idea is that this attention module works over a set of points
  and associated orientations in 3D space (e.g. protein residues).

  Each residue outputs a set of queries and keys as points in their local
  reference frame.  The attention is then defined as the euclidean distance
  between the queries and keys in the global frame.

  Jumper et al. (2021) Suppl. Alg. 22 ""InvariantPointAttention""
  """"""

  def __init__(self,
               config,
               global_config,
               dist_epsilon=1e-8,
               name='invariant_point_attention'):
    """"""Initialize.

    Args:
      config: Structure Module Config
      global_config: Global Config of Model.
      dist_epsilon: Small value to avoid NaN in distance calculation.
      name: Haiku Module name.
    """"""
    super().__init__(name=name)

    self._dist_epsilon = dist_epsilon
    self._zero_initialize_last = global_config.zero_init

    self.config = config

    self.global_config = global_config

  def __call__(self, inputs_1d, inputs_2d, mask, affine):
    """"""Compute geometry-aware attention.

    Given a set of query residues (defined by affines and associated scalar
    features), this function computes geometry-aware attention between the
    query residues and target residues.

    The residues produce points in their local reference frame, which
    are converted into the global frame in order to compute attention via
    euclidean distance.

    Equivalently, the target residues produce points in their local frame to be
    used as attention values, which are converted into the query residues'
    local frames.

    Args:
      inputs_1d: (N, C) 1D input embedding that is the basis for the
        scalar queries.
      inputs_2d: (N, M, C') 2D input embedding, used for biases and values.
      mask: (N, 1) mask to indicate which elements of inputs_1d participate
        in the attention.
      affine: QuatAffine object describing the position and orientation of
        every element in inputs_1d.

    Returns:
      Transformation of the input embedding.
    """"""
    num_residues, _ = inputs_1d.shape

    # Improve readability by removing a large number of 'self's.
    num_head = self.config.num_head
    num_scalar_qk = self.config.num_scalar_qk
    num_point_qk = self.config.num_point_qk
    num_scalar_v = self.config.num_scalar_v
    num_point_v = self.config.num_point_v
    num_output = self.config.num_channel

    assert num_scalar_qk > 0
    assert num_point_qk > 0
    assert num_point_v > 0

    # Construct scalar queries of shape:
    # [num_query_residues, num_head, num_points]
    q_scalar = common_modules.Linear(
        num_head * num_scalar_qk, name='q_scalar')(
            inputs_1d)
    q_scalar = jnp.reshape(
        q_scalar, [num_residues, num_head, num_scalar_qk])

    # Construct scalar keys/values of shape:
    # [num_target_residues, num_head, num_points]
    kv_scalar = common_modules.Linear(
        num_head * (num_scalar_v + num_scalar_qk), name='kv_scalar')(
            inputs_1d)
    kv_scalar = jnp.reshape(kv_scalar,
                            [num_residues, num_head,
                             num_scalar_v + num_scalar_qk])
    k_scalar, v_scalar = jnp.split(kv_scalar, [num_scalar_qk], axis=-1)

    # Construct query points of shape:
    # [num_residues, num_head, num_point_qk]

    # First construct query points in local frame.
    q_point_local = common_modules.Linear(
        num_head * 3 * num_point_qk, name='q_point_local')(
            inputs_1d)
    q_point_local = jnp.split(q_point_local, 3, axis=-1)
    # Project query points into global frame.
    q_point_global = affine.apply_to_point(q_point_local, extra_dims=1)
    # Reshape query point for later use.
    q_point = [
        jnp.reshape(x, [num_residues, num_head, num_point_qk])
        for x in q_point_global]

    # Construct key and value points.
    # Key points have shape [num_residues, num_head, num_point_qk]
    # Value points have shape [num_residues, num_head, num_point_v]

    # Construct key and value points in local frame.
    kv_point_local = common_modules.Linear(
        num_head * 3 * (num_point_qk + num_point_v), name='kv_point_local')(
            inputs_1d)
    kv_point_local = jnp.split(kv_point_local, 3, axis=-1)
    # Project key and value points into global frame.
    kv_point_global = affine.apply_to_point(kv_point_local, extra_dims=1)
    kv_point_global = [
        jnp.reshape(x, [num_residues,
                        num_head, (num_point_qk + num_point_v)])
        for x in kv_point_global]
    # Split key and value points.
    k_point, v_point = list(
        zip(*[
            jnp.split(x, [num_point_qk,], axis=-1)
            for x in kv_point_global
        ]))

    # We assume that all queries and keys come iid from N(0, 1) distribution
    # and compute the variances of the attention logits.
    # Each scalar pair (q, k) contributes Var q*k = 1
    scalar_variance = max(num_scalar_qk, 1) * 1.
    # Each point pair (q, k) contributes Var [0.5 ||q||^2 - <q, k>] = 9 / 2
    point_variance = max(num_point_qk, 1) * 9. / 2

    # Allocate equal variance to scalar, point and attention 2d parts so that
    # the sum is 1.

    num_logit_terms = 3

    scalar_weights = np.sqrt(1.0 / (num_logit_terms * scalar_variance))
    point_weights = np.sqrt(1.0 / (num_logit_terms * point_variance))
    attention_2d_weights = np.sqrt(1.0 / (num_logit_terms))

    # Trainable per-head weights for points.
    trainable_point_weights = jax.nn.softplus(hk.get_parameter(
        'trainable_point_weights', shape=[num_head],
        # softplus^{-1} (1)
        init=hk.initializers.Constant(np.log(np.exp(1.) - 1.))))
    point_weights *= jnp.expand_dims(trainable_point_weights, axis=1)

    v_point = [jnp.swapaxes(x, -2, -3) for x in v_point]

    q_point = [jnp.swapaxes(x, -2, -3) for x in q_point]
    k_point = [jnp.swapaxes(x, -2, -3) for x in k_point]
    dist2 = [
        squared_difference(qx[:, :, None, :], kx[:, None, :, :])
        for qx, kx in zip(q_point, k_point)
    ]
    dist2 = sum(dist2)
    attn_qk_point = -0.5 * jnp.sum(
        point_weights[:, None, None, :] * dist2, axis=-1)

    v = jnp.swapaxes(v_scalar, -2, -3)
    q = jnp.swapaxes(scalar_weights * q_scalar, -2, -3)
    k = jnp.swapaxes(k_scalar, -2, -3)
    attn_qk_scalar = jnp.matmul(q, jnp.swapaxes(k, -2, -1))
    attn_logits = attn_qk_scalar + attn_qk_point

    attention_2d = common_modules.Linear(
        num_head, name='attention_2d')(
            inputs_2d)

    attention_2d = jnp.transpose(attention_2d, [2, 0, 1])
    attention_2d = attention_2d_weights * attention_2d
    attn_logits += attention_2d

    mask_2d = mask * jnp.swapaxes(mask, -1, -2)
    attn_logits -= 1e5 * (1. - mask_2d)

    # [num_head, num_query_residues, num_target_residues]
    attn = jax.nn.softmax(attn_logits)

    # [num_head, num_query_residues, num_head * num_scalar_v]
    result_scalar = jnp.matmul(attn, v)

    # For point result, implement matmul manually so that it will be a float32
    # on TPU.  This is equivalent to
    # result_point_global = [jnp.einsum('bhqk,bhkc->bhqc', attn, vx)
    #                        for vx in v_point]
    # but on the TPU, doing the multiply and reduce_sum ensures the
    # computation happens in float32 instead of bfloat16.
    result_point_global = [jnp.sum(
        attn[:, :, :, None] * vx[:, None, :, :],
        axis=-2) for vx in v_point]

    # [num_query_residues, num_head, num_head * num_(scalar|point)_v]
    result_scalar = jnp.swapaxes(result_scalar, -2, -3)
    result_point_global = [
        jnp.swapaxes(x, -2, -3)
        for x in result_point_global]

    # Features used in the linear output projection. Should have the size
    # [num_query_residues, ?]
    output_features = []

    result_scalar = jnp.reshape(
        result_scalar, [num_residues, num_head * num_scalar_v])
    output_features.append(result_scalar)

    result_point_global = [
        jnp.reshape(r, [num_residues, num_head * num_point_v])
        for r in result_point_global]
    result_point_local = affine.invert_point(result_point_global, extra_dims=1)
    output_features.extend(result_point_local)

    output_features.append(jnp.sqrt(self._dist_epsilon +
                                    jnp.square(result_point_local[0]) +
                                    jnp.square(result_point_local[1]) +
                                    jnp.square(result_point_local[2])))

    # Dimensions: h = heads, i and j = residues,
    # c = inputs_2d channels
    # Contraction happens over the second residue dimension, similarly to how
    # the usual attention is performed.
    result_attention_over_2d = jnp.einsum('hij, ijc->ihc', attn, inputs_2d)
    num_out = num_head * result_attention_over_2d.shape[-1]
    output_features.append(
        jnp.reshape(result_attention_over_2d,
                    [num_residues, num_out]))

    final_init = 'zeros' if self._zero_initialize_last else 'linear'

    final_act = jnp.concatenate(output_features, axis=-1)

    return common_modules.Linear(
        num_output,
        initializer=final_init,
        name='output_projection')(final_act)


class FoldIteration(hk.Module):
  """"""A single iteration of the main structure module loop.

  Jumper et al. (2021) Suppl. Alg. 20 ""StructureModule"" lines 6-21

  First, each residue attends to all residues using InvariantPointAttention.
  Then, we apply transition layers to update the hidden representations.
  Finally, we use the hidden representations to produce an update to the
  affine of each residue.
  """"""

  def __init__(self, config, global_config,
               name='fold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               activations,
               sequence_mask,
               update_affine,
               is_training,
               initial_act,
               safe_key=None,
               static_feat_2d=None,
               aatype=None):
    c = self.config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    def safe_dropout_fn(tensor, safe_key):
      return prng.safe_dropout(
          tensor=tensor,
          safe_key=safe_key,
          rate=c.dropout,
          is_deterministic=self.global_config.deterministic,
          is_training=is_training)

    affine = quat_affine.QuatAffine.from_tensor(activations['affine'])

    act = activations['act']
    attention_module = InvariantPointAttention(self.config, self.global_config)
    # Attention
    attn = attention_module(
        inputs_1d=act,
        inputs_2d=static_feat_2d,
        mask=sequence_mask,
        affine=affine)
    act += attn
    safe_key, *sub_keys = safe_key.split(3)
    sub_keys = iter(sub_keys)
    act = safe_dropout_fn(act, next(sub_keys))
    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='attention_layer_norm')(
            act)

    final_init = 'zeros' if self.global_config.zero_init else 'linear'

    # Transition
    input_act = act
    for i in range(c.num_layer_in_transition):
      init = 'relu' if i < c.num_layer_in_transition - 1 else final_init
      act = common_modules.Linear(
          c.num_channel,
          initializer=init,
          name='transition')(
              act)
      if i < c.num_layer_in_transition - 1:
        act = jax.nn.relu(act)
    act += input_act
    act = safe_dropout_fn(act, next(sub_keys))
    act = common_modules.LayerNorm(
        axis=[-1],
        create_scale=True,
        create_offset=True,
        name='transition_layer_norm')(act)

    if update_affine:
      # This block corresponds to
      # Jumper et al. (2021) Alg. 23 ""Backbone update""
      affine_update_size = 6

      # Affine update
      affine_update = common_modules.Linear(
          affine_update_size,
          initializer=final_init,
          name='affine_update')(
              act)

      affine = affine.pre_compose(affine_update)

    sc = MultiRigidSidechain(c.sidechain, self.global_config)(
        affine.scale_translation(c.position_scale), [act, initial_act], aatype)

    outputs = {'affine': affine.to_tensor(), 'sc': sc}

    affine = affine.apply_rotation_tensor_fn(jax.lax.stop_gradient)

    new_activations = {
        'act': act,
        'affine': affine.to_tensor()
    }
    return new_activations, outputs


def generate_affines(representations, batch, config, global_config,
                     is_training, safe_key):
  """"""Generate predicted affines for a single chain.

  Jumper et al. (2021) Suppl. Alg. 20 ""StructureModule""

  This is the main part of the structure module - it iteratively applies
  folding to produce a set of predicted residue positions.

  Args:
    representations: Representations dictionary.
    batch: Batch dictionary.
    config: Config for the structure module.
    global_config: Global config.
    is_training: Whether the model is being trained.
    safe_key: A prng.SafeKey object that wraps a PRNG key.

  Returns:
    A dictionary containing residue affines and sidechain positions.
  """"""
  c = config
  sequence_mask = batch['seq_mask'][:, None]

  act = common_modules.LayerNorm(
      axis=[-1],
      create_scale=True,
      create_offset=True,
      name='single_layer_norm')(
          representations['single'])

  initial_act = act
  act = common_modules.Linear(
      c.num_channel, name='initial_projection')(
          act)

  affine = generate_new_affine(sequence_mask)

  fold_iteration = FoldIteration(
      c, global_config, name='fold_iteration')

  assert len(batch['seq_mask'].shape) == 1

  activations = {'act': act,
                 'affine': affine.to_tensor(),
                 }

  act_2d = common_modules.LayerNorm(
      axis=[-1],
      create_scale=True,
      create_offset=True,
      name='pair_layer_norm')(
          representations['pair'])

  outputs = []
  safe_keys = safe_key.split(c.num_layer)
  for sub_key in safe_keys:
    activations, output = fold_iteration(
        activations,
        initial_act=initial_act,
        static_feat_2d=act_2d,
        safe_key=sub_key,
        sequence_mask=sequence_mask,
        update_affine=True,
        is_training=is_training,
        aatype=batch['aatype'])
    outputs.append(output)

  output = jax.tree_map(lambda *x: jnp.stack(x), *outputs)
  # Include the activations in the output dict for use by the LDDT-Head.
  output['act'] = activations['act']

  return output


class StructureModule(hk.Module):
  """"""StructureModule as a network head.

  Jumper et al. (2021) Suppl. Alg. 20 ""StructureModule""
  """"""

  def __init__(self, config, global_config, compute_loss=True,
               name='structure_module'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config
    self.compute_loss = compute_loss

  def __call__(self, representations, batch, is_training,
               safe_key=None):
    c = self.config
    ret = {}

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    output = generate_affines(
        representations=representations,
        batch=batch,
        config=self.config,
        global_config=self.global_config,
        is_training=is_training,
        safe_key=safe_key)

    ret['representations'] = {'structure_module': output['act']}

    ret['traj'] = output['affine'] * jnp.array([1.] * 4 +
                                               [c.position_scale] * 3)

    ret['sidechains'] = output['sc']

    atom14_pred_positions = r3.vecs_to_tensor(output['sc']['atom_pos'])[-1]
    ret['final_atom14_positions'] = atom14_pred_positions  # (N, 14, 3)
    ret['final_atom14_mask'] = batch['atom14_atom_exists']  # (N, 14)

    atom37_pred_positions = all_atom.atom14_to_atom37(atom14_pred_positions,
                                                      batch)
    atom37_pred_positions *= batch['atom37_atom_exists'][:, :, None]
    ret['final_atom_positions'] = atom37_pred_positions  # (N, 37, 3)

    ret['final_atom_mask'] = batch['atom37_atom_exists']  # (N, 37)
    ret['final_affines'] = ret['traj'][-1]

    if self.compute_loss:
      return ret
    else:
      no_loss_features = ['final_atom_positions', 'final_atom_mask',
                          'representations']
      no_loss_ret = {k: ret[k] for k in no_loss_features}
      return no_loss_ret

  def loss(self, value, batch):
    ret = {'loss': 0.}

    ret['metrics'] = {}
    # If requested, compute in-graph metrics.
    if self.config.compute_in_graph_metrics:
      atom14_pred_positions = value['final_atom14_positions']
      # Compute renaming and violations.
      value.update(compute_renamed_ground_truth(batch, atom14_pred_positions))
      value['violations'] = find_structural_violations(
          batch, atom14_pred_positions, self.config)

      # Several violation metrics:
      violation_metrics = compute_violation_metrics(
          batch=batch,
          atom14_pred_positions=atom14_pred_positions,
          violations=value['violations'])
      ret['metrics'].update(violation_metrics)

    backbone_loss(ret, batch, value, self.config)

    if 'renamed_atom14_gt_positions' not in value:
      value.update(compute_renamed_ground_truth(
          batch, value['final_atom14_positions']))
    sc_loss = sidechain_loss(batch, value, self.config)

    ret['loss'] = ((1 - self.config.sidechain.weight_frac) * ret['loss'] +
                   self.config.sidechain.weight_frac * sc_loss['loss'])
    ret['sidechain_fape'] = sc_loss['fape']

    supervised_chi_loss(ret, batch, value, self.config)

    if self.config.structural_violation_loss_weight:
      if 'violations' not in value:
        value['violations'] = find_structural_violations(
            batch, value['final_atom14_positions'], self.config)
      structural_violation_loss(ret, batch, value, self.config)

    return ret


def compute_renamed_ground_truth(
    batch: Dict[str, jnp.ndarray],
    atom14_pred_positions: jnp.ndarray,
    ) -> Dict[str, jnp.ndarray]:
  """"""Find optimal renaming of ground truth based on the predicted positions.

  Jumper et al. (2021) Suppl. Alg. 26 ""renameSymmetricGroundTruthAtoms""

  This renamed ground truth is then used for all losses,
  such that each loss moves the atoms in the same direction.
  Shape (N).

  Args:
    batch: Dictionary containing:
      * atom14_gt_positions: Ground truth positions.
      * atom14_alt_gt_positions: Ground truth positions with renaming swaps.
      * atom14_atom_is_ambiguous: 1.0 for atoms that are affected by
          renaming swaps.
      * atom14_gt_exists: Mask for which atoms exist in ground truth.
      * atom14_alt_gt_exists: Mask for which atoms exist in ground truth
          after renaming.
      * atom14_atom_exists: Mask for whether each atom is part of the given
          amino acid type.
    atom14_pred_positions: Array of atom positions in global frame with shape
      (N, 14, 3).
  Returns:
    Dictionary containing:
      alt_naming_is_better: Array with 1.0 where alternative swap is better.
      renamed_atom14_gt_positions: Array of optimal ground truth positions
        after renaming swaps are performed.
      renamed_atom14_gt_exists: Mask after renaming swap is performed.
  """"""
  alt_naming_is_better = all_atom.find_optimal_renaming(
      atom14_gt_positions=batch['atom14_gt_positions'],
      atom14_alt_gt_positions=batch['atom14_alt_gt_positions'],
      atom14_atom_is_ambiguous=batch['atom14_atom_is_ambiguous'],
      atom14_gt_exists=batch['atom14_gt_exists'],
      atom14_pred_positions=atom14_pred_positions,
      atom14_atom_exists=batch['atom14_atom_exists'])

  renamed_atom14_gt_positions = (
      (1. - alt_naming_is_better[:, None, None])
      * batch['atom14_gt_positions']
      + alt_naming_is_better[:, None, None]
      * batch['atom14_alt_gt_positions'])

  renamed_atom14_gt_mask = (
      (1. - alt_naming_is_better[:, None]) * batch['atom14_gt_exists']
      + alt_naming_is_better[:, None] * batch['atom14_alt_gt_exists'])

  return {
      'alt_naming_is_better': alt_naming_is_better,  # (N)
      'renamed_atom14_gt_positions': renamed_atom14_gt_positions,  # (N, 14, 3)
      'renamed_atom14_gt_exists': renamed_atom14_gt_mask,  # (N, 14)
  }


def backbone_loss(ret, batch, value, config):
  """"""Backbone FAPE Loss.

  Jumper et al. (2021) Suppl. Alg. 20 ""StructureModule"" line 17

  Args:
    ret: Dictionary to write outputs into, needs to contain 'loss'.
    batch: Batch, needs to contain 'backbone_affine_tensor',
      'backbone_affine_mask'.
    value: Dictionary containing structure module output, needs to contain
      'traj', a trajectory of rigids.
    config: Configuration of loss, should contain 'fape.clamp_distance' and
      'fape.loss_unit_distance'.
  """"""
  affine_trajectory = quat_affine.QuatAffine.from_tensor(value['traj'])
  rigid_trajectory = r3.rigids_from_quataffine(affine_trajectory)

  gt_affine = quat_affine.QuatAffine.from_tensor(
      batch['backbone_affine_tensor'])
  gt_rigid = r3.rigids_from_quataffine(gt_affine)
  backbone_mask = batch['backbone_affine_mask']

  fape_loss_fn = functools.partial(
      all_atom.frame_aligned_point_error,
      l1_clamp_distance=config.fape.clamp_distance,
      length_scale=config.fape.loss_unit_distance)

  fape_loss_fn = jax.vmap(fape_loss_fn, (0, None, None, 0, None, None))
  fape_loss = fape_loss_fn(rigid_trajectory, gt_rigid, backbone_mask,
                           rigid_trajectory.trans, gt_rigid.trans,
                           backbone_mask)

  if 'use_clamped_fape' in batch:
    # Jumper et al. (2021) Suppl. Sec. 1.11.5 ""Loss clamping details""
    use_clamped_fape = jnp.asarray(batch['use_clamped_fape'], jnp.float32)
    unclamped_fape_loss_fn = functools.partial(
        all_atom.frame_aligned_point_error,
        l1_clamp_distance=None,
        length_scale=config.fape.loss_unit_distance)
    unclamped_fape_loss_fn = jax.vmap(unclamped_fape_loss_fn,
                                      (0, None, None, 0, None, None))
    fape_loss_unclamped = unclamped_fape_loss_fn(rigid_trajectory, gt_rigid,
                                                 backbone_mask,
                                                 rigid_trajectory.trans,
                                                 gt_rigid.trans,
                                                 backbone_mask)

    fape_loss = (fape_loss * use_clamped_fape +
                 fape_loss_unclamped * (1 - use_clamped_fape))

  ret['fape'] = fape_loss[-1]
  ret['loss'] += jnp.mean(fape_loss)


def sidechain_loss(batch, value, config):
  """"""All Atom FAPE Loss using renamed rigids.""""""
  # Rename Frames
  # Jumper et al. (2021) Suppl. Alg. 26 ""renameSymmetricGroundTruthAtoms"" line 7
  alt_naming_is_better = value['alt_naming_is_better']
  renamed_gt_frames = (
      (1. - alt_naming_is_better[:, None, None])
      * batch['rigidgroups_gt_frames']
      + alt_naming_is_better[:, None, None]
      * batch['rigidgroups_alt_gt_frames'])

  flat_gt_frames = r3.rigids_from_tensor_flat12(
      jnp.reshape(renamed_gt_frames, [-1, 12]))
  flat_frames_mask = jnp.reshape(batch['rigidgroups_gt_exists'], [-1])

  flat_gt_positions = r3.vecs_from_tensor(
      jnp.reshape(value['renamed_atom14_gt_positions'], [-1, 3]))
  flat_positions_mask = jnp.reshape(value['renamed_atom14_gt_exists'], [-1])

  # Compute frame_aligned_point_error score for the final layer.
  pred_frames = value['sidechains']['frames']
  pred_positions = value['sidechains']['atom_pos']

  def _slice_last_layer_and_flatten(x):
    return jnp.reshape(x[-1], [-1])
  flat_pred_frames = jax.tree_map(
      _slice_last_layer_and_flatten, pred_frames)
  flat_pred_positions = jax.tree_map(
      _slice_last_layer_and_flatten, pred_positions)
  # FAPE Loss on sidechains
  fape = all_atom.frame_aligned_point_error(
      pred_frames=flat_pred_frames,
      target_frames=flat_gt_frames,
      frames_mask=flat_frames_mask,
      pred_positions=flat_pred_positions,
      target_positions=flat_gt_positions,
      positions_mask=flat_positions_mask,
      l1_clamp_distance=config.sidechain.atom_clamp_distance,
      length_scale=config.sidechain.length_scale)

  return {
      'fape': fape,
      'loss': fape}


def structural_violation_loss(ret, batch, value, config):
  """"""Computes loss for structural violations.""""""
  assert config.sidechain.weight_frac

  # Put all violation losses together to one large loss.
  violations = value['violations']
  num_atoms = jnp.sum(batch['atom14_atom_exists']).astype(jnp.float32)
  ret['loss'] += (config.structural_violation_loss_weight * (
      violations['between_residues']['bonds_c_n_loss_mean'] +
      violations['between_residues']['angles_ca_c_n_loss_mean'] +
      violations['between_residues']['angles_c_n_ca_loss_mean'] +
      jnp.sum(
          violations['between_residues']['clashes_per_atom_loss_sum'] +
          violations['within_residues']['per_atom_loss_sum']) /
      (1e-6 + num_atoms)))


def find_structural_violations(
    batch: Dict[str, jnp.ndarray],
    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
    config: ml_collections.ConfigDict
    ):
  """"""Computes several checks for structural violations.""""""

  # Compute between residue backbone violations of bonds and angles.
  connection_violations = all_atom.between_residue_bond_loss(
      pred_atom_positions=atom14_pred_positions,
      pred_atom_mask=batch['atom14_atom_exists'].astype(jnp.float32),
      residue_index=batch['residue_index'].astype(jnp.float32),
      aatype=batch['aatype'],
      tolerance_factor_soft=config.violation_tolerance_factor,
      tolerance_factor_hard=config.violation_tolerance_factor)

  # Compute the Van der Waals radius for every atom
  # (the first letter of the atom name is the element type).
  # Shape: (N, 14).
  atomtype_radius = jnp.array([
      residue_constants.van_der_waals_radius[name[0]]
      for name in residue_constants.atom_types
  ])
  atom14_atom_radius = batch['atom14_atom_exists'] * utils.batched_gather(
      atomtype_radius, batch['residx_atom14_to_atom37'])

  # Compute the between residue clash loss.
  between_residue_clashes = all_atom.between_residue_clash_loss(
      atom14_pred_positions=atom14_pred_positions,
      atom14_atom_exists=batch['atom14_atom_exists'],
      atom14_atom_radius=atom14_atom_radius,
      residue_index=batch['residue_index'],
      overlap_tolerance_soft=config.clash_overlap_tolerance,
      overlap_tolerance_hard=config.clash_overlap_tolerance)

  # Compute all within-residue violations (clashes,
  # bond length and angle violations).
  restype_atom14_bounds = residue_constants.make_atom14_dists_bounds(
      overlap_tolerance=config.clash_overlap_tolerance,
      bond_length_tolerance_factor=config.violation_tolerance_factor)
  atom14_dists_lower_bound = utils.batched_gather(
      restype_atom14_bounds['lower_bound'], batch['aatype'])
  atom14_dists_upper_bound = utils.batched_gather(
      restype_atom14_bounds['upper_bound'], batch['aatype'])
  within_residue_violations = all_atom.within_residue_violations(
      atom14_pred_positions=atom14_pred_positions,
      atom14_atom_exists=batch['atom14_atom_exists'],
      atom14_dists_lower_bound=atom14_dists_lower_bound,
      atom14_dists_upper_bound=atom14_dists_upper_bound,
      tighten_bounds_for_loss=0.0)

  # Combine them to a single per-residue violation mask (used later for LDDT).
  per_residue_violations_mask = jnp.max(jnp.stack([
      connection_violations['per_residue_violation_mask'],
      jnp.max(between_residue_clashes['per_atom_clash_mask'], axis=-1),
      jnp.max(within_residue_violations['per_atom_violations'],
              axis=-1)]), axis=0)

  return {
      'between_residues': {
          'bonds_c_n_loss_mean':
              connection_violations['c_n_loss_mean'],  # ()
          'angles_ca_c_n_loss_mean':
              connection_violations['ca_c_n_loss_mean'],  # ()
          'angles_c_n_ca_loss_mean':
              connection_violations['c_n_ca_loss_mean'],  # ()
          'connections_per_residue_loss_sum':
              connection_violations['per_residue_loss_sum'],  # (N)
          'connections_per_residue_violation_mask':
              connection_violations['per_residue_violation_mask'],  # (N)
          'clashes_mean_loss':
              between_residue_clashes['mean_loss'],  # ()
          'clashes_per_atom_loss_sum':
              between_residue_clashes['per_atom_loss_sum'],  # (N, 14)
          'clashes_per_atom_clash_mask':
              between_residue_clashes['per_atom_clash_mask'],  # (N, 14)
      },
      'within_residues': {
          'per_atom_loss_sum':
              within_residue_violations['per_atom_loss_sum'],  # (N, 14)
          'per_atom_violations':
              within_residue_violations['per_atom_violations'],  # (N, 14),
      },
      'total_per_residue_violations_mask':
          per_residue_violations_mask,  # (N)
  }


def compute_violation_metrics(
    batch: Dict[str, jnp.ndarray],
    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
    violations: Dict[str, jnp.ndarray],
    ) -> Dict[str, jnp.ndarray]:
  """"""Compute several metrics to assess the structural violations.""""""

  ret = {}
  extreme_ca_ca_violations = all_atom.extreme_ca_ca_distance_violations(
      pred_atom_positions=atom14_pred_positions,
      pred_atom_mask=batch['atom14_atom_exists'].astype(jnp.float32),
      residue_index=batch['residue_index'].astype(jnp.float32))
  ret['violations_extreme_ca_ca_distance'] = extreme_ca_ca_violations
  ret['violations_between_residue_bond'] = utils.mask_mean(
      mask=batch['seq_mask'],
      value=violations['between_residues'][
          'connections_per_residue_violation_mask'])
  ret['violations_between_residue_clash'] = utils.mask_mean(
      mask=batch['seq_mask'],
      value=jnp.max(
          violations['between_residues']['clashes_per_atom_clash_mask'],
          axis=-1))
  ret['violations_within_residue'] = utils.mask_mean(
      mask=batch['seq_mask'],
      value=jnp.max(
          violations['within_residues']['per_atom_violations'], axis=-1))
  ret['violations_per_residue'] = utils.mask_mean(
      mask=batch['seq_mask'],
      value=violations['total_per_residue_violations_mask'])
  return ret


def supervised_chi_loss(ret, batch, value, config):
  """"""Computes loss for direct chi angle supervision.

  Jumper et al. (2021) Suppl. Alg. 27 ""torsionAngleLoss""

  Args:
    ret: Dictionary to write outputs into, needs to contain 'loss'.
    batch: Batch, needs to contain 'seq_mask', 'chi_mask', 'chi_angles'.
    value: Dictionary containing structure module output, needs to contain
      value['sidechains']['angles_sin_cos'] for angles and
      value['sidechains']['unnormalized_angles_sin_cos'] for unnormalized
      angles.
    config: Configuration of loss, should contain 'chi_weight' and
      'angle_norm_weight', 'angle_norm_weight' scales angle norm term,
      'chi_weight' scales torsion term.
  """"""
  eps = 1e-6

  sequence_mask = batch['seq_mask']
  num_res = sequence_mask.shape[0]
  chi_mask = batch['chi_mask'].astype(jnp.float32)
  pred_angles = jnp.reshape(
      value['sidechains']['angles_sin_cos'], [-1, num_res, 7, 2])
  pred_angles = pred_angles[:, :, 3:]

  residue_type_one_hot = jax.nn.one_hot(
      batch['aatype'], residue_constants.restype_num + 1,
      dtype=jnp.float32)[None]
  chi_pi_periodic = jnp.einsum('ijk, kl->ijl', residue_type_one_hot,
                               jnp.asarray(residue_constants.chi_pi_periodic))

  true_chi = batch['chi_angles'][None]
  sin_true_chi = jnp.sin(true_chi)
  cos_true_chi = jnp.cos(true_chi)
  sin_cos_true_chi = jnp.stack([sin_true_chi, cos_true_chi], axis=-1)

  # This is -1 if chi is pi-periodic and +1 if it's 2pi-periodic
  shifted_mask = (1 - 2 * chi_pi_periodic)[..., None]
  sin_cos_true_chi_shifted = shifted_mask * sin_cos_true_chi

  sq_chi_error = jnp.sum(
      squared_difference(sin_cos_true_chi, pred_angles), -1)
  sq_chi_error_shifted = jnp.sum(
      squared_difference(sin_cos_true_chi_shifted, pred_angles), -1)
  sq_chi_error = jnp.minimum(sq_chi_error, sq_chi_error_shifted)

  sq_chi_loss = utils.mask_mean(mask=chi_mask[None], value=sq_chi_error)
  ret['chi_loss'] = sq_chi_loss
  ret['loss'] += config.chi_weight * sq_chi_loss
  unnormed_angles = jnp.reshape(
      value['sidechains']['unnormalized_angles_sin_cos'], [-1, num_res, 7, 2])
  angle_norm = jnp.sqrt(jnp.sum(jnp.square(unnormed_angles), axis=-1) + eps)
  norm_error = jnp.abs(angle_norm - 1.)
  angle_norm_loss = utils.mask_mean(mask=sequence_mask[None, :, None],
                                    value=norm_error)

  ret['angle_norm_loss'] = angle_norm_loss
  ret['loss'] += config.angle_norm_weight * angle_norm_loss


def generate_new_affine(sequence_mask):
  num_residues, _ = sequence_mask.shape
  quaternion = jnp.tile(
      jnp.reshape(jnp.asarray([1., 0., 0., 0.]), [1, 4]),
      [num_residues, 1])

  translation = jnp.zeros([num_residues, 3])
  return quat_affine.QuatAffine(quaternion, translation, unstack_inputs=True)


def l2_normalize(x, axis=-1, epsilon=1e-12):
  return x / jnp.sqrt(
      jnp.maximum(jnp.sum(x**2, axis=axis, keepdims=True), epsilon))


class MultiRigidSidechain(hk.Module):
  """"""Class to make side chain atoms.""""""

  def __init__(self, config, global_config, name='rigid_sidechain'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self, affine, representations_list, aatype):
    """"""Predict side chains using multi-rigid representations.

    Args:
      affine: The affines for each residue (translations in angstroms).
      representations_list: A list of activations to predict side chains from.
      aatype: Amino acid types.

    Returns:
      Dict containing atom positions and frames (in angstroms).
    """"""
    act = [
        common_modules.Linear(  # pylint: disable=g-complex-comprehension
            self.config.num_channel,
            name='input_projection')(jax.nn.relu(x))
        for x in representations_list
    ]
    # Sum the activation list (equivalent to concat then Linear).
    act = sum(act)

    final_init = 'zeros' if self.global_config.zero_init else 'linear'

    # Mapping with some residual blocks.
    for _ in range(self.config.num_residual_block):
      old_act = act
      act = common_modules.Linear(
          self.config.num_channel,
          initializer='relu',
          name='resblock1')(
              jax.nn.relu(act))
      act = common_modules.Linear(
          self.config.num_channel,
          initializer=final_init,
          name='resblock2')(
              jax.nn.relu(act))
      act += old_act

    # Map activations to torsion angles. Shape: (num_res, 14).
    num_res = act.shape[0]
    unnormalized_angles = common_modules.Linear(
        14, name='unnormalized_angles')(
            jax.nn.relu(act))
    unnormalized_angles = jnp.reshape(
        unnormalized_angles, [num_res, 7, 2])
    angles = l2_normalize(unnormalized_angles, axis=-1)

    outputs = {
        'angles_sin_cos': angles,  # jnp.ndarray (N, 7, 2)
        'unnormalized_angles_sin_cos':
            unnormalized_angles,  # jnp.ndarray (N, 7, 2)
    }

    # Map torsion angles to frames.
    backb_to_global = r3.rigids_from_quataffine(affine)

    # Jumper et al. (2021) Suppl. Alg. 24 ""computeAllAtomCoordinates""

    # r3.Rigids with shape (N, 8).
    all_frames_to_global = all_atom.torsion_angles_to_frames(
        aatype,
        backb_to_global,
        angles)

    # Use frames and literature positions to create the final atom coordinates.
    # r3.Vecs with shape (N, 14).
    pred_positions = all_atom.frames_and_literature_positions_to_atom14_pos(
        aatype, all_frames_to_global)

    outputs.update({
        'atom_pos': pred_positions,  # r3.Vecs (N, 14)
        'frames': all_frames_to_global,  # r3.Rigids (N, 8)
    })
    return outputs
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/layer_stack.py",".py","9134","275","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Function to stack repeats of a layer function without shared parameters.""""""

import collections
import contextlib
import functools
import inspect
from typing import Any, Callable, Optional, Tuple, Union

import haiku as hk
import jax
import jax.numpy as jnp

LayerStackCarry = collections.namedtuple('LayerStackCarry', ['x', 'rng'])
LayerStackScanned = collections.namedtuple('LayerStackScanned',
                                           ['i', 'args_ys'])

# WrappedFn should take in arbitrarily nested `jnp.ndarray`, and return the
# exact same type. We cannot express this with `typing`. So we just use it
# to inform the user. In reality, the typing below will accept anything.
NestedArray = Any
WrappedFn = Callable[..., Union[NestedArray, Tuple[NestedArray]]]


def _check_no_varargs(f):
  if list(inspect.signature(
      f).parameters.values())[0].kind == inspect.Parameter.VAR_POSITIONAL:
    raise ValueError(
        'The function `f` should not have any `varargs` (that is *args) '
        'argument. Instead, it should only use explicit positional'
        'arguments.')


@contextlib.contextmanager
def nullcontext():
  yield


def maybe_with_rng(key):
  if key is not None:
    return hk.with_rng(key)
  else:
    return nullcontext()


def maybe_fold_in(key, data):
  if key is not None:
    return jax.random.fold_in(key, data)
  else:
    return None


class _LayerStack(hk.Module):
  """"""Module to compose parameterized functions, implemented as a scan.""""""

  def __init__(self,
               count: int,
               unroll: int,
               name: Optional[str] = None):
    """"""Iterate a function `f` `count` times, with non-shared parameters.""""""
    super().__init__(name=name)
    self._count = count
    self._unroll = unroll

  def __call__(self, x, *args_ys):
    count = self._count
    if hk.running_init():
      # At initialization time, we run just one layer but add an extra first
      # dimension to every initialized tensor, making sure to use different
      # random keys for different slices.
      def creator(next_creator, shape, dtype, init, context):
        del context

        def multi_init(shape, dtype):
          assert shape[0] == count
          key = hk.maybe_next_rng_key()

          def rng_context_init(slice_idx):
            slice_key = maybe_fold_in(key, slice_idx)
            with maybe_with_rng(slice_key):
              return init(shape[1:], dtype)

          return jax.vmap(rng_context_init)(jnp.arange(count))

        return next_creator((count,) + tuple(shape), dtype, multi_init)

      def getter(next_getter, value, context):
        trailing_dims = len(context.original_shape) + 1
        sliced_value = jax.lax.index_in_dim(
            value, index=0, axis=value.ndim - trailing_dims, keepdims=False)
        return next_getter(sliced_value)

      with hk.experimental.custom_creator(
          creator), hk.experimental.custom_getter(getter):
        if len(args_ys) == 1 and args_ys[0] is None:
          args0 = (None,)
        else:
          args0 = [
              jax.lax.dynamic_index_in_dim(ys, 0, keepdims=False)
              for ys in args_ys
          ]
        x, z = self._call_wrapped(x, *args0)
        if z is None:
          return x, z

        # Broadcast state to hold each layer state.
        def broadcast_state(layer_state):
          return jnp.broadcast_to(
              layer_state, [count,] + list(layer_state.shape))
        zs = jax.tree_util.tree_map(broadcast_state, z)
        return x, zs
    else:
      # Use scan during apply, threading through random seed so that it's
      # unique for each layer.
      def layer(carry: LayerStackCarry, scanned: LayerStackScanned):
        rng = carry.rng

        def getter(next_getter, value, context):
          # Getter slices the full param at the current loop index.
          trailing_dims = len(context.original_shape) + 1
          assert value.shape[value.ndim - trailing_dims] == count, (
              f'Attempting to use a parameter stack of size '
              f'{value.shape[value.ndim - trailing_dims]} for a LayerStack of '
              f'size {count}.')

          sliced_value = jax.lax.dynamic_index_in_dim(
              value, scanned.i, axis=value.ndim - trailing_dims, keepdims=False)
          return next_getter(sliced_value)

        with hk.experimental.custom_getter(getter):
          if rng is None:
            out_x, z = self._call_wrapped(carry.x, *scanned.args_ys)
          else:
            rng, rng_ = jax.random.split(rng)
            with hk.with_rng(rng_):
              out_x, z = self._call_wrapped(carry.x, *scanned.args_ys)
        return LayerStackCarry(x=out_x, rng=rng), z

      carry = LayerStackCarry(x=x, rng=hk.maybe_next_rng_key())
      scanned = LayerStackScanned(i=jnp.arange(count, dtype=jnp.int32),
                                  args_ys=args_ys)

      carry, zs = hk.scan(
          layer, carry, scanned, length=count, unroll=self._unroll)
      return carry.x, zs

  def _call_wrapped(self,
                    x: jnp.ndarray,
                    *args,
                    ) -> Tuple[jnp.ndarray, Optional[jnp.ndarray]]:
    raise NotImplementedError()


class _LayerStackNoState(_LayerStack):
  """"""_LayerStack impl with no per-layer state provided to the function.""""""

  def __init__(self,
               f: WrappedFn,
               count: int,
               unroll: int,
               name: Optional[str] = None):
    super().__init__(count=count, unroll=unroll, name=name)
    _check_no_varargs(f)
    self._f = f

  @hk.transparent
  def _call_wrapped(self, args, y):
    del y
    ret = self._f(*args)
    if len(args) == 1:
      # If the function takes a single argument, the wrapped function receives
      # a tuple of length 1, and therefore it must return a tuple of length 1.
      ret = (ret,)
    return ret, None


class _LayerStackWithState(_LayerStack):
  """"""_LayerStack impl with per-layer state provided to the function.""""""

  def __init__(self,
               f: WrappedFn,
               count: int,
               unroll: int,
               name: Optional[str] = None):
    super().__init__(count=count, unroll=unroll, name=name)
    self._f = f

  @hk.transparent
  def _call_wrapped(self, x, *args):
    return self._f(x, *args)


def layer_stack(num_layers: int,
                with_state=False,
                unroll: int = 1,
                name: Optional[str] = None):
  """"""Utility to wrap a Haiku function and recursively apply it to an input.

  A function is valid if it uses only explicit position parameters, and
  its return type matches its input type. The position parameters can be
  arbitrarily nested structures with `jnp.ndarray` at the leaf nodes. Note
  that kwargs are not supported, neither are functions with variable number
  of parameters (specified by `*args`).

  If `with_state=False` then the new, wrapped function can be understood as
  performing the following:
  ```
  for i in range(num_layers):
    x = f(x)
  return x
  ```

  And if `with_state=True`, assuming `f` takes two arguments on top of `x`:
  ```
  for i in range(num_layers):
    x, zs[i] = f(x, ys_0[i], ys_1[i])
  return x, zs
  ```
  The code using `layer_stack` for the above function would be:
  ```
  def f(x, y_0, y_1):
    ...
    return new_x, z
  x, zs = layer_stack.layer_stack(num_layers,
                                  with_state=True)(f)(x, ys_0, ys_1)
  ```

  Crucially, any parameters created inside `f` will not be shared across
  iterations.

  Args:
    num_layers: The number of times to iterate the wrapped function.
    with_state: Whether or not to pass per-layer state to the wrapped function.
    unroll: the unroll used by `scan`.
    name: Name of the Haiku context.

  Returns:
    Callable that will produce a layer stack when called with a valid function.
  """"""
  def iterate(f):
    if with_state:
      @functools.wraps(f)
      def wrapped(x, *args):
        for ys in args:
          assert ys.shape[0] == num_layers
        return _LayerStackWithState(
            f, num_layers, unroll=unroll, name=name)(x, *args)
    else:
      _check_no_varargs(f)
      @functools.wraps(f)
      def wrapped(*args):
        ret = _LayerStackNoState(
            f, num_layers, unroll=unroll, name=name)(args, None)[0]
        if len(args) == 1:
          # If the function takes a single argument, we must also return a
          # single value, and not a tuple of length 1.
          ret = ret[0]
        return ret

    return wrapped
  return iterate
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/folding_multimer.py",".py","42498","1163","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Modules and utilities for the structure module in the multimer system.""""""

import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union

from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from alphafold.model import prng
from alphafold.model import utils
from alphafold.model.geometry import utils as geometry_utils
import haiku as hk
import jax
import jax.numpy as jnp
import ml_collections
import numpy as np


EPSILON = 1e-8
Float = Union[float, jnp.ndarray]


def squared_difference(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
  """"""Computes Squared difference between two arrays.""""""
  return jnp.square(x - y)


def make_backbone_affine(
    positions: geometry.Vec3Array,
    mask: jnp.ndarray,
    aatype: jnp.ndarray,
    ) -> Tuple[geometry.Rigid3Array, jnp.ndarray]:
  """"""Make backbone Rigid3Array and mask.""""""
  del aatype
  a = residue_constants.atom_order['N']
  b = residue_constants.atom_order['CA']
  c = residue_constants.atom_order['C']

  rigid_mask = (mask[:, a] * mask[:, b] * mask[:, c]).astype(
      jnp.float32)

  rigid = all_atom_multimer.make_transform_from_reference(
      a_xyz=positions[:, a], b_xyz=positions[:, b], c_xyz=positions[:, c])

  return rigid, rigid_mask


class QuatRigid(hk.Module):
  """"""Module for projecting Rigids via a quaternion.""""""

  def __init__(self,
               global_config: ml_collections.ConfigDict,
               rigid_shape: Union[int, Iterable[int]] = tuple(),
               full_quat: bool = False,
               init: str = 'zeros',
               name: str = 'quat_rigid'):
    """"""Module projecting a Rigid Object.

    For this Module the Rotation is parametrized as a quaternion,
    If 'full_quat' is True a 4 vector is produced for the rotation which is
    normalized and treated as a quaternion.
    When 'full_quat' is False a 3 vector is produced and the 1st component of
    the quaternion is set to 1.

    Args:
      global_config: Global Config, used to set certain properties of underlying
        Linear module, see common_modules.Linear for details.
      rigid_shape: Shape of Rigids relative to shape of activations, e.g. when
        activations have shape (n,) and this is (m,) output will be (n, m)
      full_quat: Whether to parametrize rotation using full quaternion.
      init: initializer to use, see common_modules.Linear for details
      name: Name to use for module.
    """"""
    self.init = init
    self.global_config = global_config
    if isinstance(rigid_shape, int):
      self.rigid_shape = (rigid_shape,)
    else:
      self.rigid_shape = tuple(rigid_shape)
    self.full_quat = full_quat
    super(QuatRigid, self).__init__(name=name)

  def __call__(self, activations: jnp.ndarray) -> geometry.Rigid3Array:
    """"""Executes Module.

    This returns a set of rigid with the same shape as activations, projecting
    the channel dimension, rigid_shape controls the trailing dimensions.
    For example when activations is shape (12, 5) and rigid_shape is (3, 2)
    then the shape of the output rigids will be (12, 3, 2).
    This also supports passing in an empty tuple for rigid shape, in that case
    the example would produce a rigid of shape (12,).

    Args:
      activations: Activations to use for projection, shape [..., num_channel]
    Returns:
      Rigid transformations with shape [...] + rigid_shape
    """"""
    if self.full_quat:
      rigid_dim = 7
    else:
      rigid_dim = 6
    linear_dims = self.rigid_shape + (rigid_dim,)
    rigid_flat = common_modules.Linear(
        linear_dims,
        initializer=self.init,
        precision=jax.lax.Precision.HIGHEST,
        name='rigid')(
            activations)
    rigid_flat = geometry_utils.unstack(rigid_flat)
    if self.full_quat:
      qw, qx, qy, qz = rigid_flat[:4]
      translation = rigid_flat[4:]
    else:
      qx, qy, qz = rigid_flat[:3]
      qw = jnp.ones_like(qx)
      translation = rigid_flat[3:]
    rotation = geometry.Rot3Array.from_quaternion(
        qw, qx, qy, qz, normalize=True)
    translation = geometry.Vec3Array(*translation)
    return geometry.Rigid3Array(rotation, translation)


class PointProjection(hk.Module):
  """"""Given input reprensentation and frame produces points in global frame.""""""

  def __init__(self,
               num_points: Union[Iterable[int], int],
               global_config: ml_collections.ConfigDict,
               return_local_points: bool = False,
               name: str = 'point_projection'):
    """"""Constructs Linear Module.

    Args:
      num_points: number of points to project. Can be tuple when outputting
          multiple dimensions
      global_config: Global Config, passed through to underlying Linear
      return_local_points: Whether to return points in local frame as well.
      name: name of module, used for name scopes.
    """"""
    if isinstance(num_points, numbers.Integral):
      self.num_points = (num_points,)
    else:
      self.num_points = tuple(num_points)

    self.return_local_points = return_local_points

    self.global_config = global_config

    super().__init__(name=name)

  def __call__(
      self, activations: jnp.ndarray, rigids: geometry.Rigid3Array
  ) -> Union[geometry.Vec3Array, Tuple[geometry.Vec3Array, geometry.Vec3Array]]:
    output_shape = self.num_points
    output_shape = output_shape[:-1] + (3 * output_shape[-1],)
    points_local = common_modules.Linear(
        output_shape,
        precision=jax.lax.Precision.HIGHEST,
        name='point_projection')(
            activations)
    points_local = jnp.split(points_local, 3, axis=-1)
    points_local = geometry.Vec3Array(*points_local)
    rigids = rigids[(...,) + (None,) * len(output_shape)]
    points_global = rigids.apply_to_point(points_local)
    if self.return_local_points:
      return points_global, points_local
    else:
      return points_global


class InvariantPointAttention(hk.Module):
  """"""Invariant point attention module.

  The high-level idea is that this attention module works over a set of points
  and associated orientations in 3D space (e.g. protein residues).

  Each residue outputs a set of queries and keys as points in their local
  reference frame.  The attention is then defined as the euclidean distance
  between the queries and keys in the global frame.
  """"""

  def __init__(self,
               config: ml_collections.ConfigDict,
               global_config: ml_collections.ConfigDict,
               dist_epsilon: float = 1e-8,
               name: str = 'invariant_point_attention'):
    """"""Initialize.

    Args:
      config: iterative Fold Head Config
      global_config: Global Config of Model.
      dist_epsilon: Small value to avoid NaN in distance calculation.
      name: Sonnet name.
    """"""
    super().__init__(name=name)

    self._dist_epsilon = dist_epsilon
    self._zero_initialize_last = global_config.zero_init

    self.config = config

    self.global_config = global_config

  def __call__(
      self,
      inputs_1d: jnp.ndarray,
      inputs_2d: jnp.ndarray,
      mask: jnp.ndarray,
      rigid: geometry.Rigid3Array,
      ) -> jnp.ndarray:
    """"""Compute geometric aware attention.

    Given a set of query residues (defined by affines and associated scalar
    features), this function computes geometric aware attention between the
    query residues and target residues.

    The residues produce points in their local reference frame, which
    are converted into the global frame to get attention via euclidean distance.

    Equivalently the target residues produce points in their local frame to be
    used as attention values, which are converted into the query residues local
    frames.

    Args:
      inputs_1d: (N, C) 1D input embedding that is the basis for the
        scalar queries.
      inputs_2d: (N, M, C') 2D input embedding, used for biases values in the
        attention between query_inputs_1d and target_inputs_1d.
      mask: (N, 1) mask to indicate query_inputs_1d that participate in
        the attention.
      rigid: Rigid object describing the position and orientation of
        every element in query_inputs_1d.

    Returns:
      Transformation of the input embedding.
    """"""

    num_head = self.config.num_head

    attn_logits = 0.

    num_point_qk = self.config.num_point_qk
    # Each point pair (q, k) contributes Var [0.5 ||q||^2 - <q, k>] = 9 / 2
    point_variance = max(num_point_qk, 1) * 9. / 2
    point_weights = np.sqrt(1.0 / point_variance)

    # This is equivalent to jax.nn.softplus, but avoids a bug in the test...
    softplus = lambda x: jnp.logaddexp(x, jnp.zeros_like(x))
    raw_point_weights = hk.get_parameter(
        'trainable_point_weights',
        shape=[num_head],
        # softplus^{-1} (1)
        init=hk.initializers.Constant(np.log(np.exp(1.) - 1.)))

    # Trainable per-head weights for points.
    trainable_point_weights = softplus(raw_point_weights)
    point_weights *= trainable_point_weights
    q_point = PointProjection([num_head, num_point_qk],
                              self.global_config,
                              name='q_point_projection')(inputs_1d,
                                                         rigid)

    k_point = PointProjection([num_head, num_point_qk],
                              self.global_config,
                              name='k_point_projection')(inputs_1d,
                                                         rigid)

    dist2 = geometry.square_euclidean_distance(
        q_point[:, None, :, :], k_point[None, :, :, :], epsilon=0.)
    attn_qk_point = -0.5 * jnp.sum(point_weights[:, None] * dist2, axis=-1)
    attn_logits += attn_qk_point

    num_scalar_qk = self.config.num_scalar_qk
    # We assume that all queries and keys come iid from N(0, 1) distribution
    # and compute the variances of the attention logits.
    # Each scalar pair (q, k) contributes Var q*k = 1
    scalar_variance = max(num_scalar_qk, 1) * 1.
    scalar_weights = np.sqrt(1.0 / scalar_variance)
    q_scalar = common_modules.Linear([num_head, num_scalar_qk],
                                     use_bias=False,
                                     name='q_scalar_projection')(
                                         inputs_1d)

    k_scalar = common_modules.Linear([num_head, num_scalar_qk],
                                     use_bias=False,
                                     name='k_scalar_projection')(
                                         inputs_1d)
    q_scalar *= scalar_weights
    attn_logits += jnp.einsum('qhc,khc->qkh', q_scalar, k_scalar)

    attention_2d = common_modules.Linear(
        num_head, name='attention_2d')(inputs_2d)
    attn_logits += attention_2d

    mask_2d = mask * jnp.swapaxes(mask, -1, -2)
    attn_logits -= 1e5 * (1. - mask_2d[..., None])

    attn_logits *= np.sqrt(1. / 3)     # Normalize by number of logit terms (3)
    attn = jax.nn.softmax(attn_logits, axis=-2)

    num_scalar_v = self.config.num_scalar_v

    v_scalar = common_modules.Linear([num_head, num_scalar_v],
                                     use_bias=False,
                                     name='v_scalar_projection')(
                                         inputs_1d)

    # [num_query_residues, num_head, num_scalar_v]
    result_scalar = jnp.einsum('qkh, khc->qhc', attn, v_scalar)

    num_point_v = self.config.num_point_v
    v_point = PointProjection([num_head, num_point_v],
                              self.global_config,
                              name='v_point_projection')(inputs_1d,
                                                         rigid)

    result_point_global = jax.tree_map(
        lambda x: jnp.sum(attn[..., None] * x, axis=-3), v_point[None])

    # Features used in the linear output projection. Should have the size
    # [num_query_residues, ?]
    output_features = []
    num_query_residues, _ = inputs_1d.shape

    flat_shape = [num_query_residues, -1]

    result_scalar = jnp.reshape(result_scalar, flat_shape)
    output_features.append(result_scalar)

    result_point_global = jax.tree_map(lambda r: jnp.reshape(r, flat_shape),
                                       result_point_global)
    result_point_local = rigid[..., None].apply_inverse_to_point(
        result_point_global)
    output_features.extend(
        [result_point_local.x, result_point_local.y, result_point_local.z])

    point_norms = result_point_local.norm(self._dist_epsilon)
    output_features.append(point_norms)

    # Dimensions: h = heads, i and j = residues,
    # c = inputs_2d channels
    # Contraction happens over the second residue dimension, similarly to how
    # the usual attention is performed.
    result_attention_over_2d = jnp.einsum('ijh, ijc->ihc', attn, inputs_2d)
    output_features.append(jnp.reshape(result_attention_over_2d, flat_shape))

    final_init = 'zeros' if self._zero_initialize_last else 'linear'

    final_act = jnp.concatenate(output_features, axis=-1)

    return common_modules.Linear(
        self.config.num_channel,
        initializer=final_init,
        name='output_projection')(final_act)


class FoldIteration(hk.Module):
  """"""A single iteration of iterative folding.

  First, each residue attends to all residues using InvariantPointAttention.
  Then, we apply transition layers to update the hidden representations.
  Finally, we use the hidden representations to produce an update to the
  affine of each residue.
  """"""

  def __init__(self,
               config: ml_collections.ConfigDict,
               global_config: ml_collections.ConfigDict,
               name: str = 'fold_iteration'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(
      self,
      activations: Mapping[str, Any],
      aatype: jnp.ndarray,
      sequence_mask: jnp.ndarray,
      update_rigid: bool,
      is_training: bool,
      initial_act: jnp.ndarray,
      safe_key: Optional[prng.SafeKey] = None,
      static_feat_2d: Optional[jnp.ndarray] = None,
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:

    c = self.config

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    def safe_dropout_fn(tensor, safe_key):
      return modules.apply_dropout(
          tensor=tensor,
          safe_key=safe_key,
          rate=0.0 if self.global_config.deterministic else c.dropout,
          is_training=is_training)

    rigid = activations['rigid']

    act = activations['act']
    attention_module = InvariantPointAttention(
        self.config, self.global_config)
    # Attention
    act += attention_module(
        inputs_1d=act,
        inputs_2d=static_feat_2d,
        mask=sequence_mask,
        rigid=rigid)

    safe_key, *sub_keys = safe_key.split(3)
    sub_keys = iter(sub_keys)
    act = safe_dropout_fn(act, next(sub_keys))
    act = common_modules.LayerNorm(
        axis=-1,
        create_scale=True,
        create_offset=True,
        name='attention_layer_norm')(
            act)
    final_init = 'zeros' if self.global_config.zero_init else 'linear'

    # Transition
    input_act = act
    for i in range(c.num_layer_in_transition):
      init = 'relu' if i < c.num_layer_in_transition - 1 else final_init
      act = common_modules.Linear(
          c.num_channel,
          initializer=init,
          name='transition')(
              act)
      if i < c.num_layer_in_transition - 1:
        act = jax.nn.relu(act)
    act += input_act
    act = safe_dropout_fn(act, next(sub_keys))
    act = common_modules.LayerNorm(
        axis=-1,
        create_scale=True,
        create_offset=True,
        name='transition_layer_norm')(act)
    if update_rigid:
      # Rigid update
      rigid_update = QuatRigid(
          self.global_config, init=final_init)(
              act)
      rigid = rigid @ rigid_update

    sc = MultiRigidSidechain(c.sidechain, self.global_config)(
        rigid.scale_translation(c.position_scale), [act, initial_act], aatype)

    outputs = {'rigid': rigid, 'sc': sc}

    rotation = jax.tree_map(jax.lax.stop_gradient, rigid.rotation)
    rigid = geometry.Rigid3Array(rotation, rigid.translation)

    new_activations = {
        'act': act,
        'rigid': rigid
    }
    return new_activations, outputs


def generate_monomer_rigids(representations: Mapping[str, jnp.ndarray],
                            batch: Mapping[str, jnp.ndarray],
                            config: ml_collections.ConfigDict,
                            global_config: ml_collections.ConfigDict,
                            is_training: bool,
                            safe_key: prng.SafeKey
                            ) -> Dict[str, Any]:
  """"""Generate predicted Rigid's for a single chain.

  This is the main part of the iterative fold head - it iteratively applies
  folding to produce a set of predicted residue positions.

  Args:
    representations: Embeddings dictionary.
    batch: Batch dictionary.
    config: config for the iterative fold head.
    global_config: global config.
    is_training: is training.
    safe_key: A prng.SafeKey object that wraps a PRNG key.

  Returns:
    A dictionary containing residue Rigid's and sidechain positions.
  """"""
  c = config
  sequence_mask = batch['seq_mask'][:, None]
  act = common_modules.LayerNorm(
      axis=-1, create_scale=True, create_offset=True, name='single_layer_norm')(
          representations['single'])

  initial_act = act
  act = common_modules.Linear(
      c.num_channel, name='initial_projection')(act)

  # Sequence Mask has extra 1 at the end.
  rigid = geometry.Rigid3Array.identity(sequence_mask.shape[:-1])

  fold_iteration = FoldIteration(
      c, global_config, name='fold_iteration')

  assert len(batch['seq_mask'].shape) == 1

  activations = {
      'act':
          act,
      'rigid':
          rigid
  }

  act_2d = common_modules.LayerNorm(
      axis=-1,
      create_scale=True,
      create_offset=True,
      name='pair_layer_norm')(
          representations['pair'])

  safe_keys = safe_key.split(c.num_layer)
  outputs = []
  for key in safe_keys:

    activations, output = fold_iteration(
        activations,
        initial_act=initial_act,
        static_feat_2d=act_2d,
        aatype=batch['aatype'],
        safe_key=key,
        sequence_mask=sequence_mask,
        update_rigid=True,
        is_training=is_training,
        )
    outputs.append(output)

  output = jax.tree_map(lambda *x: jnp.stack(x), *outputs)
  # Pass along for LDDT-Head.
  output['act'] = activations['act']

  return output


class StructureModule(hk.Module):
  """"""StructureModule as a network head.

  Jumper et al. (2021) Suppl. Alg. 20 ""StructureModule""
  """"""

  def __init__(self,
               config: ml_collections.ConfigDict,
               global_config: ml_collections.ConfigDict,
               name: str = 'structure_module'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               representations: Mapping[str, jnp.ndarray],
               batch: Mapping[str, Any],
               is_training: bool,
               safe_key: Optional[prng.SafeKey] = None,
               compute_loss: bool = False
               ) -> Dict[str, Any]:
    c = self.config
    ret = {}

    if safe_key is None:
      safe_key = prng.SafeKey(hk.next_rng_key())

    output = generate_monomer_rigids(
        representations=representations,
        batch=batch,
        config=self.config,
        global_config=self.global_config,
        is_training=is_training,
        safe_key=safe_key)

    ret['traj'] = output['rigid'].scale_translation(c.position_scale).to_array()
    ret['sidechains'] = output['sc']
    ret['sidechains']['atom_pos'] = ret['sidechains']['atom_pos'].to_array()
    ret['sidechains']['frames'] = ret['sidechains']['frames'].to_array()
    if 'local_atom_pos' in ret['sidechains']:
      ret['sidechains']['local_atom_pos'] = ret['sidechains'][
          'local_atom_pos'].to_array()
      ret['sidechains']['local_frames'] = ret['sidechains'][
          'local_frames'].to_array()

    aatype = batch['aatype']
    seq_mask = batch['seq_mask']

    atom14_pred_mask = all_atom_multimer.get_atom14_mask(
        aatype) * seq_mask[:, None]
    atom14_pred_positions = output['sc']['atom_pos'][-1]
    ret['final_atom14_positions'] = atom14_pred_positions  # (N, 14, 3)
    ret['final_atom14_mask'] = atom14_pred_mask  # (N, 14)

    atom37_mask = all_atom_multimer.get_atom37_mask(aatype) * seq_mask[:, None]
    atom37_pred_positions = all_atom_multimer.atom14_to_atom37(
        atom14_pred_positions, aatype)
    atom37_pred_positions *= atom37_mask[:, :, None]
    ret['final_atom_positions'] = atom37_pred_positions  # (N, 37, 3)
    ret['final_atom_mask'] = atom37_mask  # (N, 37)
    ret['final_rigids'] = ret['traj'][-1]

    ret['act'] = output['act']

    if compute_loss:
      return ret
    else:
      no_loss_features = ['final_atom_positions', 'final_atom_mask', 'act']
      no_loss_ret = {k: ret[k] for k in no_loss_features}
      return no_loss_ret

  def loss(self,
           value: Mapping[str, Any],
           batch: Mapping[str, Any]
           ) -> Dict[str, Any]:

    raise NotImplementedError(
        'This function should be called on a batch with reordered chains (see '
        'Evans et al (2021) Section 7.3. Multi-Chain Permutation Alignment.')

    ret = {'loss': 0.}

    ret['metrics'] = {}

    aatype = batch['aatype']
    all_atom_positions = batch['all_atom_positions']
    all_atom_positions = geometry.Vec3Array.from_array(all_atom_positions)
    all_atom_mask = batch['all_atom_mask']
    seq_mask = batch['seq_mask']
    residue_index = batch['residue_index']

    gt_rigid, gt_affine_mask = make_backbone_affine(all_atom_positions,
                                                    all_atom_mask,
                                                    aatype)

    chi_angles, chi_mask = all_atom_multimer.compute_chi_angles(
        all_atom_positions, all_atom_mask, aatype)

    pred_mask = all_atom_multimer.get_atom14_mask(aatype)
    pred_mask *= seq_mask[:, None]
    pred_positions = value['final_atom14_positions']
    pred_positions = geometry.Vec3Array.from_array(pred_positions)

    gt_positions, gt_mask, alt_naming_is_better = compute_atom14_gt(
        aatype, all_atom_positions, all_atom_mask, pred_positions)

    violations = find_structural_violations(
        aatype=aatype,
        residue_index=residue_index,
        mask=pred_mask,
        pred_positions=pred_positions,
        config=self.config,
        asym_id=batch['asym_id'])

    sidechains = value['sidechains']

    gt_chi_angles = get_renamed_chi_angles(aatype, chi_angles,
                                           alt_naming_is_better)

    # Several violation metrics:
    violation_metrics = compute_violation_metrics(
        residue_index=residue_index,
        mask=pred_mask,
        seq_mask=seq_mask,
        pred_positions=pred_positions,
        violations=violations)
    ret['metrics'].update(violation_metrics)

    target_rigid = geometry.Rigid3Array.from_array(value['traj'])
    gt_frames_mask = gt_affine_mask

    # Split the loss into within-chain and between-chain components.
    intra_chain_mask = batch['asym_id'][:, None] == batch['asym_id'][None, :]
    intra_chain_bb_loss, intra_chain_fape = backbone_loss(
        gt_rigid=gt_rigid,
        gt_frames_mask=gt_frames_mask,
        gt_positions_mask=gt_affine_mask,
        target_rigid=target_rigid,
        config=self.config.intra_chain_fape,
        pair_mask=intra_chain_mask)
    interface_bb_loss, interface_fape = backbone_loss(
        gt_rigid=gt_rigid,
        gt_frames_mask=gt_frames_mask,
        gt_positions_mask=gt_affine_mask,
        target_rigid=target_rigid,
        config=self.config.interface_fape,
        pair_mask=1. - intra_chain_mask)

    bb_loss = intra_chain_bb_loss + interface_bb_loss
    ret['fape'] = intra_chain_fape + interface_fape
    ret['bb_loss'] = bb_loss
    ret['loss'] += bb_loss

    pred_frames = geometry.Rigid3Array.from_array(sidechains['frames'])
    pred_positions = geometry.Vec3Array.from_array(sidechains['atom_pos'])
    gt_sc_frames, gt_sc_frames_mask = compute_frames(
        aatype=aatype,
        all_atom_positions=all_atom_positions,
        all_atom_mask=all_atom_mask,
        use_alt=alt_naming_is_better)

    sc_loss = sidechain_loss(
        gt_frames=gt_sc_frames,
        gt_frames_mask=gt_sc_frames_mask,
        gt_positions=gt_positions,
        gt_mask=gt_mask,
        pred_frames=pred_frames,
        pred_positions=pred_positions,
        config=self.config)

    ret['loss'] = ((1 - self.config.sidechain.weight_frac) * ret['loss'] +
                   self.config.sidechain.weight_frac * sc_loss['loss'])
    ret['sidechain_fape'] = sc_loss['fape']

    unnormed_angles = sidechains['unnormalized_angles_sin_cos']
    pred_angles = sidechains['angles_sin_cos']

    sup_chi_loss, ret['chi_loss'], ret[
        'angle_norm_loss'] = supervised_chi_loss(
            sequence_mask=seq_mask,
            target_chi_mask=chi_mask,
            target_chi_angles=gt_chi_angles,
            aatype=aatype,
            pred_angles=pred_angles,
            unnormed_angles=unnormed_angles,
            config=self.config)
    ret['loss'] += sup_chi_loss

    if self.config.structural_violation_loss_weight:

      ret['loss'] += structural_violation_loss(
          mask=pred_mask, violations=violations, config=self.config)

    return ret


def compute_atom14_gt(
    aatype: jnp.ndarray,
    all_atom_positions: geometry.Vec3Array,
    all_atom_mask: jnp.ndarray,
    pred_pos: geometry.Vec3Array
) -> Tuple[geometry.Vec3Array, jnp.ndarray, jnp.ndarray]:
  """"""Find atom14 positions, this includes finding the correct renaming.""""""
  gt_positions, gt_mask = all_atom_multimer.atom37_to_atom14(
      aatype, all_atom_positions,
      all_atom_mask)
  alt_gt_positions, alt_gt_mask = all_atom_multimer.get_alt_atom14(
      aatype, gt_positions, gt_mask)
  atom_is_ambiguous = all_atom_multimer.get_atom14_is_ambiguous(aatype)

  alt_naming_is_better = all_atom_multimer.find_optimal_renaming(
      gt_positions=gt_positions,
      alt_gt_positions=alt_gt_positions,
      atom_is_ambiguous=atom_is_ambiguous,
      gt_exists=gt_mask,
      pred_positions=pred_pos)

  use_alt = alt_naming_is_better[:, None]

  gt_mask = (1. - use_alt) * gt_mask + use_alt * alt_gt_mask
  gt_positions = (1. - use_alt) * gt_positions + use_alt * alt_gt_positions

  return gt_positions, alt_gt_mask, alt_naming_is_better


def backbone_loss(gt_rigid: geometry.Rigid3Array,
                  gt_frames_mask: jnp.ndarray,
                  gt_positions_mask: jnp.ndarray,
                  target_rigid: geometry.Rigid3Array,
                  config: ml_collections.ConfigDict,
                  pair_mask: jnp.ndarray
                  ) -> Tuple[Float, jnp.ndarray]:
  """"""Backbone FAPE Loss.""""""
  loss_fn = functools.partial(
      all_atom_multimer.frame_aligned_point_error,
      l1_clamp_distance=config.atom_clamp_distance,
      length_scale=config.loss_unit_distance)

  loss_fn = jax.vmap(loss_fn, (0, None, None, 0, None, None, None))
  fape = loss_fn(target_rigid, gt_rigid, gt_frames_mask,
                 target_rigid.translation, gt_rigid.translation,
                 gt_positions_mask, pair_mask)

  return jnp.mean(fape), fape[-1]


def compute_frames(
    aatype: jnp.ndarray,
    all_atom_positions: geometry.Vec3Array,
    all_atom_mask: jnp.ndarray,
    use_alt: jnp.ndarray
    ) -> Tuple[geometry.Rigid3Array, jnp.ndarray]:
  """"""Compute Frames from all atom positions.

  Args:
    aatype: array of aatypes, int of [N]
    all_atom_positions: Vector of all atom positions, shape [N, 37]
    all_atom_mask: mask, shape [N]
    use_alt: whether to use alternative orientation for ambiguous aatypes
             shape [N]
  Returns:
    Rigid corresponding to Frames w shape [N, 8],
    mask which Rigids are present w shape [N, 8]
  """"""
  frames_batch = all_atom_multimer.atom37_to_frames(aatype, all_atom_positions,
                                                    all_atom_mask)
  gt_frames = frames_batch['rigidgroups_gt_frames']
  alt_gt_frames = frames_batch['rigidgroups_alt_gt_frames']
  use_alt = use_alt[:, None]

  renamed_gt_frames = jax.tree_map(
      lambda x, y: (1. - use_alt) * x + use_alt * y, gt_frames, alt_gt_frames)

  return renamed_gt_frames, frames_batch['rigidgroups_gt_exists']


def sidechain_loss(gt_frames: geometry.Rigid3Array,
                   gt_frames_mask: jnp.ndarray,
                   gt_positions: geometry.Vec3Array,
                   gt_mask: jnp.ndarray,
                   pred_frames: geometry.Rigid3Array,
                   pred_positions: geometry.Vec3Array,
                   config: ml_collections.ConfigDict
                   ) -> Dict[str, jnp.ndarray]:
  """"""Sidechain Loss using cleaned up rigids.""""""

  flat_gt_frames = jax.tree_map(jnp.ravel, gt_frames)
  flat_frames_mask = jnp.ravel(gt_frames_mask)

  flat_gt_positions = jax.tree_map(jnp.ravel, gt_positions)
  flat_positions_mask = jnp.ravel(gt_mask)

  # Compute frame_aligned_point_error score for the final layer.
  def _slice_last_layer_and_flatten(x):
    return jnp.ravel(x[-1])

  flat_pred_frames = jax.tree_map(_slice_last_layer_and_flatten, pred_frames)
  flat_pred_positions = jax.tree_map(_slice_last_layer_and_flatten,
                                     pred_positions)
  fape = all_atom_multimer.frame_aligned_point_error(
      pred_frames=flat_pred_frames,
      target_frames=flat_gt_frames,
      frames_mask=flat_frames_mask,
      pred_positions=flat_pred_positions,
      target_positions=flat_gt_positions,
      positions_mask=flat_positions_mask,
      pair_mask=None,
      length_scale=config.sidechain.loss_unit_distance,
      l1_clamp_distance=config.sidechain.atom_clamp_distance)

  return {
      'fape': fape,
      'loss': fape}


def structural_violation_loss(mask: jnp.ndarray,
                              violations: Mapping[str, Float],
                              config: ml_collections.ConfigDict
                              ) -> Float:
  """"""Computes Loss for structural Violations.""""""
  # Put all violation losses together to one large loss.
  num_atoms = jnp.sum(mask).astype(jnp.float32) + 1e-6
  between_residues = violations['between_residues']
  within_residues = violations['within_residues']
  return (config.structural_violation_loss_weight *
          (between_residues['bonds_c_n_loss_mean'] +
           between_residues['angles_ca_c_n_loss_mean']  +
           between_residues['angles_c_n_ca_loss_mean'] +
           jnp.sum(between_residues['clashes_per_atom_loss_sum'] +
                   within_residues['per_atom_loss_sum']) / num_atoms
           ))


def find_structural_violations(
    aatype: jnp.ndarray,
    residue_index: jnp.ndarray,
    mask: jnp.ndarray,
    pred_positions: geometry.Vec3Array,  # (N, 14)
    config: ml_collections.ConfigDict,
    asym_id: jnp.ndarray,
    ) -> Dict[str, Any]:
  """"""Computes several checks for structural Violations.""""""

  # Compute between residue backbone violations of bonds and angles.
  connection_violations = all_atom_multimer.between_residue_bond_loss(
      pred_atom_positions=pred_positions,
      pred_atom_mask=mask.astype(jnp.float32),
      residue_index=residue_index.astype(jnp.float32),
      aatype=aatype,
      tolerance_factor_soft=config.violation_tolerance_factor,
      tolerance_factor_hard=config.violation_tolerance_factor)

  # Compute the van der Waals radius for every atom
  # (the first letter of the atom name is the element type).
  # shape (N, 14)
  atomtype_radius = jnp.array([
      residue_constants.van_der_waals_radius[name[0]]
      for name in residue_constants.atom_types
  ])
  residx_atom14_to_atom37 = all_atom_multimer.get_atom14_to_atom37_map(aatype)
  atom_radius = mask * utils.batched_gather(atomtype_radius,
                                            residx_atom14_to_atom37)

  # Compute the between residue clash loss.
  between_residue_clashes = all_atom_multimer.between_residue_clash_loss(
      pred_positions=pred_positions,
      atom_exists=mask,
      atom_radius=atom_radius,
      residue_index=residue_index,
      overlap_tolerance_soft=config.clash_overlap_tolerance,
      overlap_tolerance_hard=config.clash_overlap_tolerance,
      asym_id=asym_id)

  # Compute all within-residue violations (clashes,
  # bond length and angle violations).
  restype_atom14_bounds = residue_constants.make_atom14_dists_bounds(
      overlap_tolerance=config.clash_overlap_tolerance,
      bond_length_tolerance_factor=config.violation_tolerance_factor)
  dists_lower_bound = utils.batched_gather(restype_atom14_bounds['lower_bound'],
                                           aatype)
  dists_upper_bound = utils.batched_gather(restype_atom14_bounds['upper_bound'],
                                           aatype)
  within_residue_violations = all_atom_multimer.within_residue_violations(
      pred_positions=pred_positions,
      atom_exists=mask,
      dists_lower_bound=dists_lower_bound,
      dists_upper_bound=dists_upper_bound,
      tighten_bounds_for_loss=0.0)

  # Combine them to a single per-residue violation mask (used later for LDDT).
  per_residue_violations_mask = jnp.max(jnp.stack([
      connection_violations['per_residue_violation_mask'],
      jnp.max(between_residue_clashes['per_atom_clash_mask'], axis=-1),
      jnp.max(within_residue_violations['per_atom_violations'],
              axis=-1)]), axis=0)

  return {
      'between_residues': {
          'bonds_c_n_loss_mean':
              connection_violations['c_n_loss_mean'],  # ()
          'angles_ca_c_n_loss_mean':
              connection_violations['ca_c_n_loss_mean'],  # ()
          'angles_c_n_ca_loss_mean':
              connection_violations['c_n_ca_loss_mean'],  # ()
          'connections_per_residue_loss_sum':
              connection_violations['per_residue_loss_sum'],  # (N)
          'connections_per_residue_violation_mask':
              connection_violations['per_residue_violation_mask'],  # (N)
          'clashes_mean_loss':
              between_residue_clashes['mean_loss'],  # ()
          'clashes_per_atom_loss_sum':
              between_residue_clashes['per_atom_loss_sum'],  # (N, 14)
          'clashes_per_atom_clash_mask':
              between_residue_clashes['per_atom_clash_mask'],  # (N, 14)
      },
      'within_residues': {
          'per_atom_loss_sum':
              within_residue_violations['per_atom_loss_sum'],  # (N, 14)
          'per_atom_violations':
              within_residue_violations['per_atom_violations'],  # (N, 14),
      },
      'total_per_residue_violations_mask':
          per_residue_violations_mask,  # (N)
  }


def compute_violation_metrics(
    residue_index: jnp.ndarray,
    mask: jnp.ndarray,
    seq_mask: jnp.ndarray,
    pred_positions: geometry.Vec3Array,  # (N, 14)
    violations: Mapping[str, jnp.ndarray],
) -> Dict[str, jnp.ndarray]:
  """"""Compute several metrics to assess the structural violations.""""""
  ret = {}
  between_residues = violations['between_residues']
  within_residues = violations['within_residues']
  extreme_ca_ca_violations = all_atom_multimer.extreme_ca_ca_distance_violations(
      positions=pred_positions,
      mask=mask.astype(jnp.float32),
      residue_index=residue_index.astype(jnp.float32))
  ret['violations_extreme_ca_ca_distance'] = extreme_ca_ca_violations
  ret['violations_between_residue_bond'] = utils.mask_mean(
      mask=seq_mask,
      value=between_residues['connections_per_residue_violation_mask'])
  ret['violations_between_residue_clash'] = utils.mask_mean(
      mask=seq_mask,
      value=jnp.max(between_residues['clashes_per_atom_clash_mask'], axis=-1))
  ret['violations_within_residue'] = utils.mask_mean(
      mask=seq_mask,
      value=jnp.max(within_residues['per_atom_violations'], axis=-1))
  ret['violations_per_residue'] = utils.mask_mean(
      mask=seq_mask, value=violations['total_per_residue_violations_mask'])
  return ret


def supervised_chi_loss(
    sequence_mask: jnp.ndarray,
    target_chi_mask: jnp.ndarray,
    aatype: jnp.ndarray,
    target_chi_angles: jnp.ndarray,
    pred_angles: jnp.ndarray,
    unnormed_angles: jnp.ndarray,
    config: ml_collections.ConfigDict) -> Tuple[Float, Float, Float]:
  """"""Computes loss for direct chi angle supervision.""""""
  eps = 1e-6
  chi_mask = target_chi_mask.astype(jnp.float32)

  pred_angles = pred_angles[:, :, 3:]

  residue_type_one_hot = jax.nn.one_hot(
      aatype, residue_constants.restype_num + 1, dtype=jnp.float32)[None]
  chi_pi_periodic = jnp.einsum('ijk, kl->ijl', residue_type_one_hot,
                               jnp.asarray(residue_constants.chi_pi_periodic))

  true_chi = target_chi_angles[None]
  sin_true_chi = jnp.sin(true_chi)
  cos_true_chi = jnp.cos(true_chi)
  sin_cos_true_chi = jnp.stack([sin_true_chi, cos_true_chi], axis=-1)

  # This is -1 if chi is pi periodic and +1 if it's 2 pi periodic
  shifted_mask = (1 - 2 * chi_pi_periodic)[..., None]
  sin_cos_true_chi_shifted = shifted_mask * sin_cos_true_chi

  sq_chi_error = jnp.sum(
      squared_difference(sin_cos_true_chi, pred_angles), -1)
  sq_chi_error_shifted = jnp.sum(
      squared_difference(sin_cos_true_chi_shifted, pred_angles), -1)
  sq_chi_error = jnp.minimum(sq_chi_error, sq_chi_error_shifted)

  sq_chi_loss = utils.mask_mean(mask=chi_mask[None], value=sq_chi_error)
  angle_norm = jnp.sqrt(jnp.sum(jnp.square(unnormed_angles), axis=-1) + eps)
  norm_error = jnp.abs(angle_norm - 1.)
  angle_norm_loss = utils.mask_mean(mask=sequence_mask[None, :, None],
                                    value=norm_error)
  loss = (config.chi_weight * sq_chi_loss
          + config.angle_norm_weight * angle_norm_loss)
  return loss, sq_chi_loss, angle_norm_loss


def l2_normalize(x: jnp.ndarray,
                 axis: int = -1,
                 epsilon: float = 1e-12
                 ) -> jnp.ndarray:
  return x / jnp.sqrt(
      jnp.maximum(jnp.sum(x**2, axis=axis, keepdims=True), epsilon))


def get_renamed_chi_angles(aatype: jnp.ndarray,
                           chi_angles: jnp.ndarray,
                           alt_is_better: jnp.ndarray
                           ) -> jnp.ndarray:
  """"""Return renamed chi angles.""""""
  chi_angle_is_ambiguous = utils.batched_gather(
      jnp.array(residue_constants.chi_pi_periodic, dtype=jnp.float32), aatype)
  alt_chi_angles = chi_angles + np.pi * chi_angle_is_ambiguous
  # Map back to [-pi, pi].
  alt_chi_angles = alt_chi_angles - 2 * np.pi * (alt_chi_angles > np.pi).astype(
      jnp.float32)
  alt_is_better = alt_is_better[:, None]
  return (1. - alt_is_better) * chi_angles + alt_is_better * alt_chi_angles


class MultiRigidSidechain(hk.Module):
  """"""Class to make side chain atoms.""""""

  def __init__(self,
               config: ml_collections.ConfigDict,
               global_config: ml_collections.ConfigDict,
               name: str = 'rigid_sidechain'):
    super().__init__(name=name)
    self.config = config
    self.global_config = global_config

  def __call__(self,
               rigid: geometry.Rigid3Array,
               representations_list: Iterable[jnp.ndarray],
               aatype: jnp.ndarray
               ) -> Dict[str, Any]:
    """"""Predict sidechains using multi-rigid representations.

    Args:
      rigid: The Rigid's for each residue (translations in angstoms)
      representations_list: A list of activations to predict sidechains from.
      aatype: amino acid types.

    Returns:
      dict containing atom positions and frames (in angstrom)
    """"""
    act = [
        common_modules.Linear(  # pylint: disable=g-complex-comprehension
            self.config.num_channel,
            name='input_projection')(jax.nn.relu(x))
        for x in representations_list]
    # Sum the activation list (equivalent to concat then Conv1D)
    act = sum(act)

    final_init = 'zeros' if self.global_config.zero_init else 'linear'

    # Mapping with some residual blocks.
    for _ in range(self.config.num_residual_block):
      old_act = act
      act = common_modules.Linear(
          self.config.num_channel,
          initializer='relu',
          name='resblock1')(
              jax.nn.relu(act))
      act = common_modules.Linear(
          self.config.num_channel,
          initializer=final_init,
          name='resblock2')(
              jax.nn.relu(act))
      act += old_act

    # Map activations to torsion angles.
    # [batch_size, num_res, 14]
    num_res = act.shape[0]
    unnormalized_angles = common_modules.Linear(
        14, name='unnormalized_angles')(
            jax.nn.relu(act))
    unnormalized_angles = jnp.reshape(
        unnormalized_angles, [num_res, 7, 2])
    angles = l2_normalize(unnormalized_angles, axis=-1)

    outputs = {
        'angles_sin_cos': angles,  # jnp.ndarray (N, 7, 2)
        'unnormalized_angles_sin_cos':
            unnormalized_angles,  # jnp.ndarray (N, 7, 2)
    }

    # Map torsion angles to frames.
    # geometry.Rigid3Array with shape (N, 8)
    all_frames_to_global = all_atom_multimer.torsion_angles_to_frames(
        aatype,
        rigid,
        angles)

    # Use frames and literature positions to create the final atom coordinates.
    # geometry.Vec3Array with shape (N, 14)
    pred_positions = all_atom_multimer.frames_and_literature_positions_to_atom14_pos(
        aatype, all_frames_to_global)

    outputs.update({
        'atom_pos': pred_positions,  # geometry.Vec3Array (N, 14)
        'frames': all_frames_to_global,  # geometry.Rigid3Array (N, 8)
    })
    return outputs
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/all_atom.py",".py","47028","1142","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Ops for all atom representations.

Generally we employ two different representations for all atom coordinates,
one is atom37 where each heavy atom corresponds to a given position in a 37
dimensional array, This mapping is non amino acid specific, but each slot
corresponds to an atom of a given name, for example slot 12 always corresponds
to 'C delta 1', positions that are not present for a given amino acid are
zeroed out and denoted by a mask.
The other representation we employ is called atom14, this is a more dense way
of representing atoms with 14 slots. Here a given slot will correspond to a
different kind of atom depending on amino acid type, for example slot 5
corresponds to 'N delta 2' for Aspargine, but to 'C delta 1' for Isoleucine.
14 is chosen because it is the maximum number of heavy atoms for any standard
amino acid.
The order of slots can be found in 'residue_constants.residue_atoms'.
Internally the model uses the atom14 representation because it is
computationally more efficient.
The internal atom14 representation is turned into the atom37 at the output of
the network to facilitate easier conversion to existing protein datastructures.
""""""

from typing import Dict, Optional
from alphafold.common import residue_constants

from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np


def squared_difference(x, y):
  return jnp.square(x - y)


def get_chi_atom_indices():
  """"""Returns atom indices needed to compute chi angles for all residue types.

  Returns:
    A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are
    in the order specified in residue_constants.restypes + unknown residue type
    at the end. For chi angles which are not defined on the residue, the
    positions indices are by default set to 0.
  """"""
  chi_atom_indices = []
  for residue_name in residue_constants.restypes:
    residue_name = residue_constants.restype_1to3[residue_name]
    residue_chi_angles = residue_constants.chi_angles_atoms[residue_name]
    atom_indices = []
    for chi_angle in residue_chi_angles:
      atom_indices.append(
          [residue_constants.atom_order[atom] for atom in chi_angle])
    for _ in range(4 - len(atom_indices)):
      atom_indices.append([0, 0, 0, 0])  # For chi angles not defined on the AA.
    chi_atom_indices.append(atom_indices)

  chi_atom_indices.append([[0, 0, 0, 0]] * 4)  # For UNKNOWN residue.

  return jnp.asarray(chi_atom_indices)


def atom14_to_atom37(atom14_data: jnp.ndarray,  # (N, 14, ...)
                     batch: Dict[str, jnp.ndarray]
                    ) -> jnp.ndarray:  # (N, 37, ...)
  """"""Convert atom14 to atom37 representation.""""""
  assert len(atom14_data.shape) in [2, 3]
  assert 'residx_atom37_to_atom14' in batch
  assert 'atom37_atom_exists' in batch

  atom37_data = utils.batched_gather(atom14_data,
                                     batch['residx_atom37_to_atom14'],
                                     batch_dims=1)
  if len(atom14_data.shape) == 2:
    atom37_data *= batch['atom37_atom_exists']
  elif len(atom14_data.shape) == 3:
    atom37_data *= batch['atom37_atom_exists'][:, :,
                                               None].astype(atom37_data.dtype)
  return atom37_data


def atom37_to_atom14(
    atom37_data: jnp.ndarray,  # (N, 37, ...)
    batch: Dict[str, jnp.ndarray]) -> jnp.ndarray:  # (N, 14, ...)
  """"""Convert atom14 to atom37 representation.""""""
  assert len(atom37_data.shape) in [2, 3]
  assert 'residx_atom14_to_atom37' in batch
  assert 'atom14_atom_exists' in batch

  atom14_data = utils.batched_gather(atom37_data,
                                     batch['residx_atom14_to_atom37'],
                                     batch_dims=1)
  if len(atom37_data.shape) == 2:
    atom14_data *= batch['atom14_atom_exists'].astype(atom14_data.dtype)
  elif len(atom37_data.shape) == 3:
    atom14_data *= batch['atom14_atom_exists'][:, :,
                                               None].astype(atom14_data.dtype)
  return atom14_data


def atom37_to_frames(
    aatype: jnp.ndarray,  # (...)
    all_atom_positions: jnp.ndarray,  # (..., 37, 3)
    all_atom_mask: jnp.ndarray,  # (..., 37)
) -> Dict[str, jnp.ndarray]:
  """"""Computes the frames for the up to 8 rigid groups for each residue.

  The rigid groups are defined by the possible torsions in a given amino acid.
  We group the atoms according to their dependence on the torsion angles into
  ""rigid groups"".  E.g., the position of atoms in the chi2-group depend on
  chi1 and chi2, but do not depend on chi3 or chi4.
  Jumper et al. (2021) Suppl. Table 2 and corresponding text.

  Args:
    aatype: Amino acid type, given as array with integers.
    all_atom_positions: atom37 representation of all atom coordinates.
    all_atom_mask: atom37 representation of mask on all atom coordinates.
  Returns:
    Dictionary containing:
      * 'rigidgroups_gt_frames': 8 Frames corresponding to 'all_atom_positions'
           represented as flat 12 dimensional array.
      * 'rigidgroups_gt_exists': Mask denoting whether the atom positions for
          the given frame are available in the ground truth, e.g. if they were
          resolved in the experiment.
      * 'rigidgroups_group_exists': Mask denoting whether given group is in
          principle present for given amino acid type.
      * 'rigidgroups_group_is_ambiguous': Mask denoting whether frame is
          affected by naming ambiguity.
      * 'rigidgroups_alt_gt_frames': 8 Frames with alternative atom renaming
          corresponding to 'all_atom_positions' represented as flat
          12 dimensional array.
  """"""
  # 0: 'backbone group',
  # 1: 'pre-omega-group', (empty)
  # 2: 'phi-group', (currently empty, because it defines only hydrogens)
  # 3: 'psi-group',
  # 4,5,6,7: 'chi1,2,3,4-group'
  aatype_in_shape = aatype.shape

  # If there is a batch axis, just flatten it away, and reshape everything
  # back at the end of the function.
  aatype = jnp.reshape(aatype, [-1])
  all_atom_positions = jnp.reshape(all_atom_positions, [-1, 37, 3])
  all_atom_mask = jnp.reshape(all_atom_mask, [-1, 37])

  # Create an array with the atom names.
  # shape (num_restypes, num_rigidgroups, 3_atoms): (21, 8, 3)
  restype_rigidgroup_base_atom_names = np.full([21, 8, 3], '', dtype=object)

  # 0: backbone frame
  restype_rigidgroup_base_atom_names[:, 0, :] = ['C', 'CA', 'N']

  # 3: 'psi-group'
  restype_rigidgroup_base_atom_names[:, 3, :] = ['CA', 'C', 'O']

  # 4,5,6,7: 'chi1,2,3,4-group'
  for restype, restype_letter in enumerate(residue_constants.restypes):
    resname = residue_constants.restype_1to3[restype_letter]
    for chi_idx in range(4):
      if residue_constants.chi_angles_mask[restype][chi_idx]:
        atom_names = residue_constants.chi_angles_atoms[resname][chi_idx]
        restype_rigidgroup_base_atom_names[
            restype, chi_idx + 4, :] = atom_names[1:]

  # Create mask for existing rigid groups.
  restype_rigidgroup_mask = np.zeros([21, 8], dtype=np.float32)
  restype_rigidgroup_mask[:, 0] = 1
  restype_rigidgroup_mask[:, 3] = 1
  restype_rigidgroup_mask[:20, 4:] = residue_constants.chi_angles_mask

  # Translate atom names into atom37 indices.
  lookuptable = residue_constants.atom_order.copy()
  lookuptable[''] = 0
  restype_rigidgroup_base_atom37_idx = np.vectorize(lambda x: lookuptable[x])(
      restype_rigidgroup_base_atom_names)

  # Compute the gather indices for all residues in the chain.
  # shape (N, 8, 3)
  residx_rigidgroup_base_atom37_idx = utils.batched_gather(
      restype_rigidgroup_base_atom37_idx, aatype)

  # Gather the base atom positions for each rigid group.
  base_atom_pos = utils.batched_gather(
      all_atom_positions,
      residx_rigidgroup_base_atom37_idx,
      batch_dims=1)

  # Compute the Rigids.
  gt_frames = r3.rigids_from_3_points(
      point_on_neg_x_axis=r3.vecs_from_tensor(base_atom_pos[:, :, 0, :]),
      origin=r3.vecs_from_tensor(base_atom_pos[:, :, 1, :]),
      point_on_xy_plane=r3.vecs_from_tensor(base_atom_pos[:, :, 2, :])
  )

  # Compute a mask whether the group exists.
  # (N, 8)
  group_exists = utils.batched_gather(restype_rigidgroup_mask, aatype)

  # Compute a mask whether ground truth exists for the group
  gt_atoms_exist = utils.batched_gather(  # shape (N, 8, 3)
      all_atom_mask.astype(jnp.float32),
      residx_rigidgroup_base_atom37_idx,
      batch_dims=1)
  gt_exists = jnp.min(gt_atoms_exist, axis=-1) * group_exists  # (N, 8)

  # Adapt backbone frame to old convention (mirror x-axis and z-axis).
  rots = np.tile(np.eye(3, dtype=np.float32), [8, 1, 1])
  rots[0, 0, 0] = -1
  rots[0, 2, 2] = -1
  gt_frames = r3.rigids_mul_rots(gt_frames, r3.rots_from_tensor3x3(rots))

  # The frames for ambiguous rigid groups are just rotated by 180 degree around
  # the x-axis. The ambiguous group is always the last chi-group.
  restype_rigidgroup_is_ambiguous = np.zeros([21, 8], dtype=np.float32)
  restype_rigidgroup_rots = np.tile(np.eye(3, dtype=np.float32), [21, 8, 1, 1])

  for resname, _ in residue_constants.residue_atom_renaming_swaps.items():
    restype = residue_constants.restype_order[
        residue_constants.restype_3to1[resname]]
    chi_idx = int(sum(residue_constants.chi_angles_mask[restype]) - 1)
    restype_rigidgroup_is_ambiguous[restype, chi_idx + 4] = 1
    restype_rigidgroup_rots[restype, chi_idx + 4, 1, 1] = -1
    restype_rigidgroup_rots[restype, chi_idx + 4, 2, 2] = -1

  # Gather the ambiguity information for each residue.
  residx_rigidgroup_is_ambiguous = utils.batched_gather(
      restype_rigidgroup_is_ambiguous, aatype)
  residx_rigidgroup_ambiguity_rot = utils.batched_gather(
      restype_rigidgroup_rots, aatype)

  # Create the alternative ground truth frames.
  alt_gt_frames = r3.rigids_mul_rots(
      gt_frames, r3.rots_from_tensor3x3(residx_rigidgroup_ambiguity_rot))

  gt_frames_flat12 = r3.rigids_to_tensor_flat12(gt_frames)
  alt_gt_frames_flat12 = r3.rigids_to_tensor_flat12(alt_gt_frames)

  # reshape back to original residue layout
  gt_frames_flat12 = jnp.reshape(gt_frames_flat12, aatype_in_shape + (8, 12))
  gt_exists = jnp.reshape(gt_exists, aatype_in_shape + (8,))
  group_exists = jnp.reshape(group_exists, aatype_in_shape + (8,))
  gt_frames_flat12 = jnp.reshape(gt_frames_flat12, aatype_in_shape + (8, 12))
  residx_rigidgroup_is_ambiguous = jnp.reshape(residx_rigidgroup_is_ambiguous,
                                               aatype_in_shape + (8,))
  alt_gt_frames_flat12 = jnp.reshape(alt_gt_frames_flat12,
                                     aatype_in_shape + (8, 12,))

  return {
      'rigidgroups_gt_frames': gt_frames_flat12,  # (..., 8, 12)
      'rigidgroups_gt_exists': gt_exists,  # (..., 8)
      'rigidgroups_group_exists': group_exists,  # (..., 8)
      'rigidgroups_group_is_ambiguous':
          residx_rigidgroup_is_ambiguous,  # (..., 8)
      'rigidgroups_alt_gt_frames': alt_gt_frames_flat12,  # (..., 8, 12)
  }


def atom37_to_torsion_angles(
    aatype: jnp.ndarray,  # (B, N)
    all_atom_pos: jnp.ndarray,  # (B, N, 37, 3)
    all_atom_mask: jnp.ndarray,  # (B, N, 37)
    placeholder_for_undefined=False,
) -> Dict[str, jnp.ndarray]:
  """"""Computes the 7 torsion angles (in sin, cos encoding) for each residue.

  The 7 torsion angles are in the order
  '[pre_omega, phi, psi, chi_1, chi_2, chi_3, chi_4]',
  here pre_omega denotes the omega torsion angle between the given amino acid
  and the previous amino acid.

  Args:
    aatype: Amino acid type, given as array with integers.
    all_atom_pos: atom37 representation of all atom coordinates.
    all_atom_mask: atom37 representation of mask on all atom coordinates.
    placeholder_for_undefined: flag denoting whether to set masked torsion
      angles to zero.
  Returns:
    Dict containing:
      * 'torsion_angles_sin_cos': Array with shape (B, N, 7, 2) where the final
        2 dimensions denote sin and cos respectively
      * 'alt_torsion_angles_sin_cos': same as 'torsion_angles_sin_cos', but
        with the angle shifted by pi for all chi angles affected by the naming
        ambiguities.
      * 'torsion_angles_mask': Mask for which chi angles are present.
  """"""

  # Map aatype > 20 to 'Unknown' (20).
  aatype = jnp.minimum(aatype, 20)

  # Compute the backbone angles.
  num_batch, num_res = aatype.shape

  pad = jnp.zeros([num_batch, 1, 37, 3], jnp.float32)
  prev_all_atom_pos = jnp.concatenate([pad, all_atom_pos[:, :-1, :, :]], axis=1)

  pad = jnp.zeros([num_batch, 1, 37], jnp.float32)
  prev_all_atom_mask = jnp.concatenate([pad, all_atom_mask[:, :-1, :]], axis=1)

  # For each torsion angle collect the 4 atom positions that define this angle.
  # shape (B, N, atoms=4, xyz=3)
  pre_omega_atom_pos = jnp.concatenate(
      [prev_all_atom_pos[:, :, 1:3, :],  # prev CA, C
       all_atom_pos[:, :, 0:2, :]  # this N, CA
      ], axis=-2)
  phi_atom_pos = jnp.concatenate(
      [prev_all_atom_pos[:, :, 2:3, :],  # prev C
       all_atom_pos[:, :, 0:3, :]  # this N, CA, C
      ], axis=-2)
  psi_atom_pos = jnp.concatenate(
      [all_atom_pos[:, :, 0:3, :],  # this N, CA, C
       all_atom_pos[:, :, 4:5, :]  # this O
      ], axis=-2)

  # Collect the masks from these atoms.
  # Shape [batch, num_res]
  pre_omega_mask = (
      jnp.prod(prev_all_atom_mask[:, :, 1:3], axis=-1)  # prev CA, C
      * jnp.prod(all_atom_mask[:, :, 0:2], axis=-1))  # this N, CA
  phi_mask = (
      prev_all_atom_mask[:, :, 2]  # prev C
      * jnp.prod(all_atom_mask[:, :, 0:3], axis=-1))  # this N, CA, C
  psi_mask = (
      jnp.prod(all_atom_mask[:, :, 0:3], axis=-1) *  # this N, CA, C
      all_atom_mask[:, :, 4])  # this O

  # Collect the atoms for the chi-angles.
  # Compute the table of chi angle indices. Shape: [restypes, chis=4, atoms=4].
  chi_atom_indices = get_chi_atom_indices()
  # Select atoms to compute chis. Shape: [batch, num_res, chis=4, atoms=4].
  atom_indices = utils.batched_gather(
      params=chi_atom_indices, indices=aatype, axis=0, batch_dims=0)
  # Gather atom positions. Shape: [batch, num_res, chis=4, atoms=4, xyz=3].
  chis_atom_pos = utils.batched_gather(
      params=all_atom_pos, indices=atom_indices, axis=-2,
      batch_dims=2)

  # Copy the chi angle mask, add the UNKNOWN residue. Shape: [restypes, 4].
  chi_angles_mask = list(residue_constants.chi_angles_mask)
  chi_angles_mask.append([0.0, 0.0, 0.0, 0.0])
  chi_angles_mask = jnp.asarray(chi_angles_mask)

  # Compute the chi angle mask. I.e. which chis angles exist according to the
  # aatype. Shape [batch, num_res, chis=4].
  chis_mask = utils.batched_gather(params=chi_angles_mask, indices=aatype,
                                   axis=0, batch_dims=0)

  # Constrain the chis_mask to those chis, where the ground truth coordinates of
  # all defining four atoms are available.
  # Gather the chi angle atoms mask. Shape: [batch, num_res, chis=4, atoms=4].
  chi_angle_atoms_mask = utils.batched_gather(
      params=all_atom_mask, indices=atom_indices, axis=-1,
      batch_dims=2)
  # Check if all 4 chi angle atoms were set. Shape: [batch, num_res, chis=4].
  chi_angle_atoms_mask = jnp.prod(chi_angle_atoms_mask, axis=[-1])
  chis_mask = chis_mask * (chi_angle_atoms_mask).astype(jnp.float32)

  # Stack all torsion angle atom positions.
  # Shape (B, N, torsions=7, atoms=4, xyz=3)
  torsions_atom_pos = jnp.concatenate(
      [pre_omega_atom_pos[:, :, None, :, :],
       phi_atom_pos[:, :, None, :, :],
       psi_atom_pos[:, :, None, :, :],
       chis_atom_pos
      ], axis=2)

  # Stack up masks for all torsion angles.
  # shape (B, N, torsions=7)
  torsion_angles_mask = jnp.concatenate(
      [pre_omega_mask[:, :, None],
       phi_mask[:, :, None],
       psi_mask[:, :, None],
       chis_mask
      ], axis=2)

  # Create a frame from the first three atoms:
  # First atom: point on x-y-plane
  # Second atom: point on negative x-axis
  # Third atom: origin
  # r3.Rigids (B, N, torsions=7)
  torsion_frames = r3.rigids_from_3_points(
      point_on_neg_x_axis=r3.vecs_from_tensor(torsions_atom_pos[:, :, :, 1, :]),
      origin=r3.vecs_from_tensor(torsions_atom_pos[:, :, :, 2, :]),
      point_on_xy_plane=r3.vecs_from_tensor(torsions_atom_pos[:, :, :, 0, :]))

  # Compute the position of the forth atom in this frame (y and z coordinate
  # define the chi angle)
  # r3.Vecs (B, N, torsions=7)
  forth_atom_rel_pos = r3.rigids_mul_vecs(
      r3.invert_rigids(torsion_frames),
      r3.vecs_from_tensor(torsions_atom_pos[:, :, :, 3, :]))

  # Normalize to have the sin and cos of the torsion angle.
  # jnp.ndarray (B, N, torsions=7, sincos=2)
  torsion_angles_sin_cos = jnp.stack(
      [forth_atom_rel_pos.z, forth_atom_rel_pos.y], axis=-1)
  torsion_angles_sin_cos /= jnp.sqrt(
      jnp.sum(jnp.square(torsion_angles_sin_cos), axis=-1, keepdims=True)
      + 1e-8)

  # Mirror psi, because we computed it from the Oxygen-atom.
  torsion_angles_sin_cos *= jnp.asarray(
      [1., 1., -1., 1., 1., 1., 1.])[None, None, :, None]

  # Create alternative angles for ambiguous atom names.
  chi_is_ambiguous = utils.batched_gather(
      jnp.asarray(residue_constants.chi_pi_periodic), aatype)
  mirror_torsion_angles = jnp.concatenate(
      [jnp.ones([num_batch, num_res, 3]),
       1.0 - 2.0 * chi_is_ambiguous], axis=-1)
  alt_torsion_angles_sin_cos = (
      torsion_angles_sin_cos * mirror_torsion_angles[:, :, :, None])

  if placeholder_for_undefined:
    # Add placeholder torsions in place of undefined torsion angles
    # (e.g. N-terminus pre-omega)
    placeholder_torsions = jnp.stack([
        jnp.ones(torsion_angles_sin_cos.shape[:-1]),
        jnp.zeros(torsion_angles_sin_cos.shape[:-1])
    ], axis=-1)
    torsion_angles_sin_cos = torsion_angles_sin_cos * torsion_angles_mask[
        ..., None] + placeholder_torsions * (1 - torsion_angles_mask[..., None])
    alt_torsion_angles_sin_cos = alt_torsion_angles_sin_cos * torsion_angles_mask[
        ..., None] + placeholder_torsions * (1 - torsion_angles_mask[..., None])

  return {
      'torsion_angles_sin_cos': torsion_angles_sin_cos,  # (B, N, 7, 2)
      'alt_torsion_angles_sin_cos': alt_torsion_angles_sin_cos,  # (B, N, 7, 2)
      'torsion_angles_mask': torsion_angles_mask  # (B, N, 7)
  }


def torsion_angles_to_frames(
    aatype: jnp.ndarray,  # (N)
    backb_to_global: r3.Rigids,  # (N)
    torsion_angles_sin_cos: jnp.ndarray  # (N, 7, 2)
) -> r3.Rigids:  # (N, 8)
  """"""Compute rigid group frames from torsion angles.

  Jumper et al. (2021) Suppl. Alg. 24 ""computeAllAtomCoordinates"" lines 2-10
  Jumper et al. (2021) Suppl. Alg. 25 ""makeRotX""

  Args:
    aatype: aatype for each residue
    backb_to_global: Rigid transformations describing transformation from
      backbone frame to global frame.
    torsion_angles_sin_cos: sin and cosine of the 7 torsion angles
  Returns:
    Frames corresponding to all the Sidechain Rigid Transforms
  """"""
  assert len(aatype.shape) == 1
  assert len(backb_to_global.rot.xx.shape) == 1
  assert len(torsion_angles_sin_cos.shape) == 3
  assert torsion_angles_sin_cos.shape[1] == 7
  assert torsion_angles_sin_cos.shape[2] == 2

  # Gather the default frames for all rigid groups.
  # r3.Rigids with shape (N, 8)
  m = utils.batched_gather(residue_constants.restype_rigid_group_default_frame,
                           aatype)
  default_frames = r3.rigids_from_tensor4x4(m)

  # Create the rotation matrices according to the given angles (each frame is
  # defined such that its rotation is around the x-axis).
  sin_angles = torsion_angles_sin_cos[..., 0]
  cos_angles = torsion_angles_sin_cos[..., 1]

  # insert zero rotation for backbone group.
  num_residues, = aatype.shape
  sin_angles = jnp.concatenate([jnp.zeros([num_residues, 1]), sin_angles],
                               axis=-1)
  cos_angles = jnp.concatenate([jnp.ones([num_residues, 1]), cos_angles],
                               axis=-1)
  zeros = jnp.zeros_like(sin_angles)
  ones = jnp.ones_like(sin_angles)

  # all_rots are r3.Rots with shape (N, 8)
  all_rots = r3.Rots(ones, zeros, zeros,
                     zeros, cos_angles, -sin_angles,
                     zeros, sin_angles, cos_angles)

  # Apply rotations to the frames.
  all_frames = r3.rigids_mul_rots(default_frames, all_rots)

  # chi2, chi3, and chi4 frames do not transform to the backbone frame but to
  # the previous frame. So chain them up accordingly.
  chi2_frame_to_frame = jax.tree_map(lambda x: x[:, 5], all_frames)
  chi3_frame_to_frame = jax.tree_map(lambda x: x[:, 6], all_frames)
  chi4_frame_to_frame = jax.tree_map(lambda x: x[:, 7], all_frames)

  chi1_frame_to_backb = jax.tree_map(lambda x: x[:, 4], all_frames)
  chi2_frame_to_backb = r3.rigids_mul_rigids(chi1_frame_to_backb,
                                             chi2_frame_to_frame)
  chi3_frame_to_backb = r3.rigids_mul_rigids(chi2_frame_to_backb,
                                             chi3_frame_to_frame)
  chi4_frame_to_backb = r3.rigids_mul_rigids(chi3_frame_to_backb,
                                             chi4_frame_to_frame)

  # Recombine them to a r3.Rigids with shape (N, 8).
  def _concat_frames(xall, x5, x6, x7):
    return jnp.concatenate(
        [xall[:, 0:5], x5[:, None], x6[:, None], x7[:, None]], axis=-1)

  all_frames_to_backb = jax.tree_map(
      _concat_frames,
      all_frames,
      chi2_frame_to_backb,
      chi3_frame_to_backb,
      chi4_frame_to_backb)

  # Create the global frames.
  # shape (N, 8)
  all_frames_to_global = r3.rigids_mul_rigids(
      jax.tree_map(lambda x: x[:, None], backb_to_global),
      all_frames_to_backb)

  return all_frames_to_global


def frames_and_literature_positions_to_atom14_pos(
    aatype: jnp.ndarray,  # (N)
    all_frames_to_global: r3.Rigids  # (N, 8)
) -> r3.Vecs:  # (N, 14)
  """"""Put atom literature positions (atom14 encoding) in each rigid group.

  Jumper et al. (2021) Suppl. Alg. 24 ""computeAllAtomCoordinates"" line 11

  Args:
    aatype: aatype for each residue.
    all_frames_to_global: All per residue coordinate frames.
  Returns:
    Positions of all atom coordinates in global frame.
  """"""

  # Pick the appropriate transform for every atom.
  residx_to_group_idx = utils.batched_gather(
      residue_constants.restype_atom14_to_rigid_group, aatype)
  group_mask = jax.nn.one_hot(
      residx_to_group_idx, num_classes=8)  # shape (N, 14, 8)

  # r3.Rigids with shape (N, 14)
  map_atoms_to_global = jax.tree_map(
      lambda x: jnp.sum(x[:, None, :] * group_mask, axis=-1),
      all_frames_to_global)

  # Gather the literature atom positions for each residue.
  # r3.Vecs with shape (N, 14)
  lit_positions = r3.vecs_from_tensor(
      utils.batched_gather(
          residue_constants.restype_atom14_rigid_group_positions, aatype))

  # Transform each atom from its local frame to the global frame.
  # r3.Vecs with shape (N, 14)
  pred_positions = r3.rigids_mul_vecs(map_atoms_to_global, lit_positions)

  # Mask out non-existing atoms.
  mask = utils.batched_gather(residue_constants.restype_atom14_mask, aatype)
  pred_positions = jax.tree_map(lambda x: x * mask, pred_positions)

  return pred_positions


def extreme_ca_ca_distance_violations(
    pred_atom_positions: jnp.ndarray,  # (N, 37(14), 3)
    pred_atom_mask: jnp.ndarray,  # (N, 37(14))
    residue_index: jnp.ndarray,  # (N)
    max_angstrom_tolerance=1.5
    ) -> jnp.ndarray:
  """"""Counts residues whose Ca is a large distance from its neighbour.

  Measures the fraction of CA-CA pairs between consecutive amino acids that are
  more than 'max_angstrom_tolerance' apart.

  Args:
    pred_atom_positions: Atom positions in atom37/14 representation
    pred_atom_mask: Atom mask in atom37/14 representation
    residue_index: Residue index for given amino acid, this is assumed to be
      monotonically increasing.
    max_angstrom_tolerance: Maximum distance allowed to not count as violation.
  Returns:
    Fraction of consecutive CA-CA pairs with violation.
  """"""
  this_ca_pos = pred_atom_positions[:-1, 1, :]  # (N - 1, 3)
  this_ca_mask = pred_atom_mask[:-1, 1]         # (N - 1)
  next_ca_pos = pred_atom_positions[1:, 1, :]  # (N - 1, 3)
  next_ca_mask = pred_atom_mask[1:, 1]  # (N - 1)
  has_no_gap_mask = ((residue_index[1:] - residue_index[:-1]) == 1.0).astype(
      jnp.float32)
  ca_ca_distance = jnp.sqrt(
      1e-6 + jnp.sum(squared_difference(this_ca_pos, next_ca_pos), axis=-1))
  violations = (ca_ca_distance -
                residue_constants.ca_ca) > max_angstrom_tolerance
  mask = this_ca_mask * next_ca_mask * has_no_gap_mask
  return utils.mask_mean(mask=mask, value=violations)


def between_residue_bond_loss(
    pred_atom_positions: jnp.ndarray,  # (N, 37(14), 3)
    pred_atom_mask: jnp.ndarray,  # (N, 37(14))
    residue_index: jnp.ndarray,  # (N)
    aatype: jnp.ndarray,  # (N)
    tolerance_factor_soft=12.0,
    tolerance_factor_hard=12.0
) -> Dict[str, jnp.ndarray]:
  """"""Flat-bottom loss to penalize structural violations between residues.

  This is a loss penalizing any violation of the geometry around the peptide
  bond between consecutive amino acids. This loss corresponds to
  Jumper et al. (2021) Suppl. Sec. 1.9.11, eq 44, 45.

  Args:
    pred_atom_positions: Atom positions in atom37/14 representation
    pred_atom_mask: Atom mask in atom37/14 representation
    residue_index: Residue index for given amino acid, this is assumed to be
      monotonically increasing.
    aatype: Amino acid type of given residue
    tolerance_factor_soft: soft tolerance factor measured in standard deviations
      of pdb distributions
    tolerance_factor_hard: hard tolerance factor measured in standard deviations
      of pdb distributions

  Returns:
    Dict containing:
      * 'c_n_loss_mean': Loss for peptide bond length violations
      * 'ca_c_n_loss_mean': Loss for violations of bond angle around C spanned
          by CA, C, N
      * 'c_n_ca_loss_mean': Loss for violations of bond angle around N spanned
          by C, N, CA
      * 'per_residue_loss_sum': sum of all losses for each residue
      * 'per_residue_violation_mask': mask denoting all residues with violation
          present.
  """"""
  assert len(pred_atom_positions.shape) == 3
  assert len(pred_atom_mask.shape) == 2
  assert len(residue_index.shape) == 1
  assert len(aatype.shape) == 1

  # Get the positions of the relevant backbone atoms.
  this_ca_pos = pred_atom_positions[:-1, 1, :]  # (N - 1, 3)
  this_ca_mask = pred_atom_mask[:-1, 1]         # (N - 1)
  this_c_pos = pred_atom_positions[:-1, 2, :]   # (N - 1, 3)
  this_c_mask = pred_atom_mask[:-1, 2]          # (N - 1)
  next_n_pos = pred_atom_positions[1:, 0, :]    # (N - 1, 3)
  next_n_mask = pred_atom_mask[1:, 0]           # (N - 1)
  next_ca_pos = pred_atom_positions[1:, 1, :]   # (N - 1, 3)
  next_ca_mask = pred_atom_mask[1:, 1]          # (N - 1)
  has_no_gap_mask = ((residue_index[1:] - residue_index[:-1]) == 1.0).astype(
      jnp.float32)

  # Compute loss for the C--N bond.
  c_n_bond_length = jnp.sqrt(
      1e-6 + jnp.sum(squared_difference(this_c_pos, next_n_pos), axis=-1))

  # The C-N bond to proline has slightly different length because of the ring.
  next_is_proline = (
      aatype[1:] == residue_constants.resname_to_idx['PRO']).astype(jnp.float32)
  gt_length = (
      (1. - next_is_proline) * residue_constants.between_res_bond_length_c_n[0]
      + next_is_proline * residue_constants.between_res_bond_length_c_n[1])
  gt_stddev = (
      (1. - next_is_proline) *
      residue_constants.between_res_bond_length_stddev_c_n[0] +
      next_is_proline * residue_constants.between_res_bond_length_stddev_c_n[1])
  c_n_bond_length_error = jnp.sqrt(1e-6 +
                                   jnp.square(c_n_bond_length - gt_length))
  c_n_loss_per_residue = jax.nn.relu(
      c_n_bond_length_error - tolerance_factor_soft * gt_stddev)
  mask = this_c_mask * next_n_mask * has_no_gap_mask
  c_n_loss = jnp.sum(mask * c_n_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  c_n_violation_mask = mask * (
      c_n_bond_length_error > (tolerance_factor_hard * gt_stddev))

  # Compute loss for the angles.
  ca_c_bond_length = jnp.sqrt(1e-6 + jnp.sum(
      squared_difference(this_ca_pos, this_c_pos), axis=-1))
  n_ca_bond_length = jnp.sqrt(1e-6 + jnp.sum(
      squared_difference(next_n_pos, next_ca_pos), axis=-1))

  c_ca_unit_vec = (this_ca_pos - this_c_pos) / ca_c_bond_length[:, None]
  c_n_unit_vec = (next_n_pos - this_c_pos) / c_n_bond_length[:, None]
  n_ca_unit_vec = (next_ca_pos - next_n_pos) / n_ca_bond_length[:, None]

  ca_c_n_cos_angle = jnp.sum(c_ca_unit_vec * c_n_unit_vec, axis=-1)
  gt_angle = residue_constants.between_res_cos_angles_ca_c_n[0]
  gt_stddev = residue_constants.between_res_bond_length_stddev_c_n[0]
  ca_c_n_cos_angle_error = jnp.sqrt(
      1e-6 + jnp.square(ca_c_n_cos_angle - gt_angle))
  ca_c_n_loss_per_residue = jax.nn.relu(
      ca_c_n_cos_angle_error - tolerance_factor_soft * gt_stddev)
  mask = this_ca_mask * this_c_mask * next_n_mask * has_no_gap_mask
  ca_c_n_loss = jnp.sum(mask * ca_c_n_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  ca_c_n_violation_mask = mask * (ca_c_n_cos_angle_error >
                                  (tolerance_factor_hard * gt_stddev))

  c_n_ca_cos_angle = jnp.sum((-c_n_unit_vec) * n_ca_unit_vec, axis=-1)
  gt_angle = residue_constants.between_res_cos_angles_c_n_ca[0]
  gt_stddev = residue_constants.between_res_cos_angles_c_n_ca[1]
  c_n_ca_cos_angle_error = jnp.sqrt(
      1e-6 + jnp.square(c_n_ca_cos_angle - gt_angle))
  c_n_ca_loss_per_residue = jax.nn.relu(
      c_n_ca_cos_angle_error - tolerance_factor_soft * gt_stddev)
  mask = this_c_mask * next_n_mask * next_ca_mask * has_no_gap_mask
  c_n_ca_loss = jnp.sum(mask * c_n_ca_loss_per_residue) / (jnp.sum(mask) + 1e-6)
  c_n_ca_violation_mask = mask * (
      c_n_ca_cos_angle_error > (tolerance_factor_hard * gt_stddev))

  # Compute a per residue loss (equally distribute the loss to both
  # neighbouring residues).
  per_residue_loss_sum = (c_n_loss_per_residue +
                          ca_c_n_loss_per_residue +
                          c_n_ca_loss_per_residue)
  per_residue_loss_sum = 0.5 * (jnp.pad(per_residue_loss_sum, [[0, 1]]) +
                                jnp.pad(per_residue_loss_sum, [[1, 0]]))

  # Compute hard violations.
  violation_mask = jnp.max(
      jnp.stack([c_n_violation_mask,
                 ca_c_n_violation_mask,
                 c_n_ca_violation_mask]), axis=0)
  violation_mask = jnp.maximum(
      jnp.pad(violation_mask, [[0, 1]]),
      jnp.pad(violation_mask, [[1, 0]]))

  return {'c_n_loss_mean': c_n_loss,  # shape ()
          'ca_c_n_loss_mean': ca_c_n_loss,  # shape ()
          'c_n_ca_loss_mean': c_n_ca_loss,  # shape ()
          'per_residue_loss_sum': per_residue_loss_sum,  # shape (N)
          'per_residue_violation_mask': violation_mask  # shape (N)
         }


def between_residue_clash_loss(
    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
    atom14_atom_exists: jnp.ndarray,  # (N, 14)
    atom14_atom_radius: jnp.ndarray,  # (N, 14)
    residue_index: jnp.ndarray,  # (N)
    overlap_tolerance_soft=1.5,
    overlap_tolerance_hard=1.5
) -> Dict[str, jnp.ndarray]:
  """"""Loss to penalize steric clashes between residues.

  This is a loss penalizing any steric clashes due to non bonded atoms in
  different peptides coming too close. This loss corresponds to the part with
  different residues of
  Jumper et al. (2021) Suppl. Sec. 1.9.11, eq 46.

  Args:
    atom14_pred_positions: Predicted positions of atoms in
      global prediction frame
    atom14_atom_exists: Mask denoting whether atom at positions exists for given
      amino acid type
    atom14_atom_radius: Van der Waals radius for each atom.
    residue_index: Residue index for given amino acid.
    overlap_tolerance_soft: Soft tolerance factor.
    overlap_tolerance_hard: Hard tolerance factor.

  Returns:
    Dict containing:
      * 'mean_loss': average clash loss
      * 'per_atom_loss_sum': sum of all clash losses per atom, shape (N, 14)
      * 'per_atom_clash_mask': mask whether atom clashes with any other atom
          shape (N, 14)
  """"""
  assert len(atom14_pred_positions.shape) == 3
  assert len(atom14_atom_exists.shape) == 2
  assert len(atom14_atom_radius.shape) == 2
  assert len(residue_index.shape) == 1

  # Create the distance matrix.
  # (N, N, 14, 14)
  dists = jnp.sqrt(1e-10 + jnp.sum(
      squared_difference(
          atom14_pred_positions[:, None, :, None, :],
          atom14_pred_positions[None, :, None, :, :]),
      axis=-1))

  # Create the mask for valid distances.
  # shape (N, N, 14, 14)
  dists_mask = (atom14_atom_exists[:, None, :, None] *
                atom14_atom_exists[None, :, None, :])

  # Mask out all the duplicate entries in the lower triangular matrix.
  # Also mask out the diagonal (atom-pairs from the same residue) -- these atoms
  # are handled separately.
  dists_mask *= (
      residue_index[:, None, None, None] < residue_index[None, :, None, None])

  # Backbone C--N bond between subsequent residues is no clash.
  c_one_hot = jax.nn.one_hot(2, num_classes=14)
  n_one_hot = jax.nn.one_hot(0, num_classes=14)
  neighbour_mask = ((residue_index[:, None, None, None] +
                     1) == residue_index[None, :, None, None])
  c_n_bonds = neighbour_mask * c_one_hot[None, None, :,
                                         None] * n_one_hot[None, None, None, :]
  dists_mask *= (1. - c_n_bonds)

  # Disulfide bridge between two cysteines is no clash.
  cys_sg_idx = residue_constants.restype_name_to_atom14_names['CYS'].index('SG')
  cys_sg_one_hot = jax.nn.one_hot(cys_sg_idx, num_classes=14)
  disulfide_bonds = (cys_sg_one_hot[None, None, :, None] *
                     cys_sg_one_hot[None, None, None, :])
  dists_mask *= (1. - disulfide_bonds)

  # Compute the lower bound for the allowed distances.
  # shape (N, N, 14, 14)
  dists_lower_bound = dists_mask * (atom14_atom_radius[:, None, :, None] +
                                    atom14_atom_radius[None, :, None, :])

  # Compute the error.
  # shape (N, N, 14, 14)
  dists_to_low_error = dists_mask * jax.nn.relu(
      dists_lower_bound - overlap_tolerance_soft - dists)

  # Compute the mean loss.
  # shape ()
  mean_loss = (jnp.sum(dists_to_low_error)
               / (1e-6 + jnp.sum(dists_mask)))

  # Compute the per atom loss sum.
  # shape (N, 14)
  per_atom_loss_sum = (jnp.sum(dists_to_low_error, axis=[0, 2]) +
                       jnp.sum(dists_to_low_error, axis=[1, 3]))

  # Compute the hard clash mask.
  # shape (N, N, 14, 14)
  clash_mask = dists_mask * (
      dists < (dists_lower_bound - overlap_tolerance_hard))

  # Compute the per atom clash.
  # shape (N, 14)
  per_atom_clash_mask = jnp.maximum(
      jnp.max(clash_mask, axis=[0, 2]),
      jnp.max(clash_mask, axis=[1, 3]))

  return {'mean_loss': mean_loss,  # shape ()
          'per_atom_loss_sum': per_atom_loss_sum,  # shape (N, 14)
          'per_atom_clash_mask': per_atom_clash_mask  # shape (N, 14)
         }


def within_residue_violations(
    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
    atom14_atom_exists: jnp.ndarray,  # (N, 14)
    atom14_dists_lower_bound: jnp.ndarray,  # (N, 14, 14)
    atom14_dists_upper_bound: jnp.ndarray,  # (N, 14, 14)
    tighten_bounds_for_loss=0.0,
) -> Dict[str, jnp.ndarray]:
  """"""Loss to penalize steric clashes within residues.

  This is a loss penalizing any steric violations or clashes of non-bonded atoms
  in a given peptide. This loss corresponds to the part with
  the same residues of
  Jumper et al. (2021) Suppl. Sec. 1.9.11, eq 46.

  Args:
    atom14_pred_positions: Predicted positions of atoms in
      global prediction frame
    atom14_atom_exists: Mask denoting whether atom at positions exists for given
      amino acid type
    atom14_dists_lower_bound: Lower bound on allowed distances.
    atom14_dists_upper_bound: Upper bound on allowed distances
    tighten_bounds_for_loss: Extra factor to tighten loss

  Returns:
    Dict containing:
      * 'per_atom_loss_sum': sum of all clash losses per atom, shape (N, 14)
      * 'per_atom_clash_mask': mask whether atom clashes with any other atom
          shape (N, 14)
  """"""
  assert len(atom14_pred_positions.shape) == 3
  assert len(atom14_atom_exists.shape) == 2
  assert len(atom14_dists_lower_bound.shape) == 3
  assert len(atom14_dists_upper_bound.shape) == 3

  # Compute the mask for each residue.
  # shape (N, 14, 14)
  dists_masks = (1. - jnp.eye(14, 14)[None])
  dists_masks *= (atom14_atom_exists[:, :, None] *
                  atom14_atom_exists[:, None, :])

  # Distance matrix
  # shape (N, 14, 14)
  dists = jnp.sqrt(1e-10 + jnp.sum(
      squared_difference(
          atom14_pred_positions[:, :, None, :],
          atom14_pred_positions[:, None, :, :]),
      axis=-1))

  # Compute the loss.
  # shape (N, 14, 14)
  dists_to_low_error = jax.nn.relu(
      atom14_dists_lower_bound + tighten_bounds_for_loss - dists)
  dists_to_high_error = jax.nn.relu(
      dists - (atom14_dists_upper_bound - tighten_bounds_for_loss))
  loss = dists_masks * (dists_to_low_error + dists_to_high_error)

  # Compute the per atom loss sum.
  # shape (N, 14)
  per_atom_loss_sum = (jnp.sum(loss, axis=1) +
                       jnp.sum(loss, axis=2))

  # Compute the violations mask.
  # shape (N, 14, 14)
  violations = dists_masks * ((dists < atom14_dists_lower_bound) |
                              (dists > atom14_dists_upper_bound))

  # Compute the per atom violations.
  # shape (N, 14)
  per_atom_violations = jnp.maximum(
      jnp.max(violations, axis=1), jnp.max(violations, axis=2))

  return {'per_atom_loss_sum': per_atom_loss_sum,  # shape (N, 14)
          'per_atom_violations': per_atom_violations  # shape (N, 14)
         }


def find_optimal_renaming(
    atom14_gt_positions: jnp.ndarray,  # (N, 14, 3)
    atom14_alt_gt_positions: jnp.ndarray,  # (N, 14, 3)
    atom14_atom_is_ambiguous: jnp.ndarray,  # (N, 14)
    atom14_gt_exists: jnp.ndarray,  # (N, 14)
    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
    atom14_atom_exists: jnp.ndarray,  # (N, 14)
) -> jnp.ndarray:  # (N):
  """"""Find optimal renaming for ground truth that maximizes LDDT.

  Jumper et al. (2021) Suppl. Alg. 26
  ""renameSymmetricGroundTruthAtoms"" lines 1-5

  Args:
    atom14_gt_positions: Ground truth positions in global frame of ground truth.
    atom14_alt_gt_positions: Alternate ground truth positions in global frame of
      ground truth with coordinates of ambiguous atoms swapped relative to
      'atom14_gt_positions'.
    atom14_atom_is_ambiguous: Mask denoting whether atom is among ambiguous
      atoms, see Jumper et al. (2021) Suppl. Table 3
    atom14_gt_exists: Mask denoting whether atom at positions exists in ground
      truth.
    atom14_pred_positions: Predicted positions of atoms in
      global prediction frame
    atom14_atom_exists: Mask denoting whether atom at positions exists for given
      amino acid type

  Returns:
    Float array of shape [N] with 1. where atom14_alt_gt_positions is closer to
    prediction and 0. otherwise
  """"""
  assert len(atom14_gt_positions.shape) == 3
  assert len(atom14_alt_gt_positions.shape) == 3
  assert len(atom14_atom_is_ambiguous.shape) == 2
  assert len(atom14_gt_exists.shape) == 2
  assert len(atom14_pred_positions.shape) == 3
  assert len(atom14_atom_exists.shape) == 2

  # Create the pred distance matrix.
  # shape (N, N, 14, 14)
  pred_dists = jnp.sqrt(1e-10 + jnp.sum(
      squared_difference(
          atom14_pred_positions[:, None, :, None, :],
          atom14_pred_positions[None, :, None, :, :]),
      axis=-1))

  # Compute distances for ground truth with original and alternative names.
  # shape (N, N, 14, 14)
  gt_dists = jnp.sqrt(1e-10 + jnp.sum(
      squared_difference(
          atom14_gt_positions[:, None, :, None, :],
          atom14_gt_positions[None, :, None, :, :]),
      axis=-1))
  alt_gt_dists = jnp.sqrt(1e-10 + jnp.sum(
      squared_difference(
          atom14_alt_gt_positions[:, None, :, None, :],
          atom14_alt_gt_positions[None, :, None, :, :]),
      axis=-1))

  # Compute LDDT's.
  # shape (N, N, 14, 14)
  lddt = jnp.sqrt(1e-10 + squared_difference(pred_dists, gt_dists))
  alt_lddt = jnp.sqrt(1e-10 + squared_difference(pred_dists, alt_gt_dists))

  # Create a mask for ambiguous atoms in rows vs. non-ambiguous atoms
  # in cols.
  # shape (N ,N, 14, 14)
  mask = (atom14_gt_exists[:, None, :, None] *  # rows
          atom14_atom_is_ambiguous[:, None, :, None] *  # rows
          atom14_gt_exists[None, :, None, :] *  # cols
          (1. - atom14_atom_is_ambiguous[None, :, None, :]))  # cols

  # Aggregate distances for each residue to the non-amibuguous atoms.
  # shape (N)
  per_res_lddt = jnp.sum(mask * lddt, axis=[1, 2, 3])
  alt_per_res_lddt = jnp.sum(mask * alt_lddt, axis=[1, 2, 3])

  # Decide for each residue, whether alternative naming is better.
  # shape (N)
  alt_naming_is_better = (alt_per_res_lddt < per_res_lddt).astype(jnp.float32)

  return alt_naming_is_better  # shape (N)


def frame_aligned_point_error(
    pred_frames: r3.Rigids,  # shape (num_frames)
    target_frames: r3.Rigids,  # shape (num_frames)
    frames_mask: jnp.ndarray,  # shape (num_frames)
    pred_positions: r3.Vecs,  # shape (num_positions)
    target_positions: r3.Vecs,  # shape (num_positions)
    positions_mask: jnp.ndarray,  # shape (num_positions)
    length_scale: float,
    l1_clamp_distance: Optional[float] = None,
    epsilon=1e-4) -> jnp.ndarray:  # shape ()
  """"""Measure point error under different alignments.

  Jumper et al. (2021) Suppl. Alg. 28 ""computeFAPE""

  Computes error between two structures with B points under A alignments derived
  from the given pairs of frames.
  Args:
    pred_frames: num_frames reference frames for 'pred_positions'.
    target_frames: num_frames reference frames for 'target_positions'.
    frames_mask: Mask for frame pairs to use.
    pred_positions: num_positions predicted positions of the structure.
    target_positions: num_positions target positions of the structure.
    positions_mask: Mask on which positions to score.
    length_scale: length scale to divide loss by.
    l1_clamp_distance: Distance cutoff on error beyond which gradients will
      be zero.
    epsilon: small value used to regularize denominator for masked average.
  Returns:
    Masked Frame Aligned Point Error.
  """"""
  assert pred_frames.rot.xx.ndim == 1
  assert target_frames.rot.xx.ndim == 1
  assert frames_mask.ndim == 1, frames_mask.ndim
  assert pred_positions.x.ndim == 1
  assert target_positions.x.ndim == 1
  assert positions_mask.ndim == 1

  # Compute array of predicted positions in the predicted frames.
  # r3.Vecs (num_frames, num_positions)
  local_pred_pos = r3.rigids_mul_vecs(
      jax.tree_map(lambda r: r[:, None], r3.invert_rigids(pred_frames)),
      jax.tree_map(lambda x: x[None, :], pred_positions))

  # Compute array of target positions in the target frames.
  # r3.Vecs (num_frames, num_positions)
  local_target_pos = r3.rigids_mul_vecs(
      jax.tree_map(lambda r: r[:, None], r3.invert_rigids(target_frames)),
      jax.tree_map(lambda x: x[None, :], target_positions))

  # Compute errors between the structures.
  # jnp.ndarray (num_frames, num_positions)
  error_dist = jnp.sqrt(
      r3.vecs_squared_distance(local_pred_pos, local_target_pos)
      + epsilon)

  if l1_clamp_distance:
    error_dist = jnp.clip(error_dist, 0, l1_clamp_distance)

  normed_error = error_dist / length_scale
  normed_error *= jnp.expand_dims(frames_mask, axis=-1)
  normed_error *= jnp.expand_dims(positions_mask, axis=-2)

  normalization_factor = (
      jnp.sum(frames_mask, axis=-1) *
      jnp.sum(positions_mask, axis=-1))
  return (jnp.sum(normed_error, axis=(-2, -1)) /
          (epsilon + normalization_factor))


def _make_renaming_matrices():
  """"""Matrices to map atoms to symmetry partners in ambiguous case.""""""
  # As the atom naming is ambiguous for 7 of the 20 amino acids, provide
  # alternative groundtruth coordinates where the naming is swapped
  restype_3 = [
      residue_constants.restype_1to3[res] for res in residue_constants.restypes
  ]
  restype_3 += ['UNK']
  # Matrices for renaming ambiguous atoms.
  all_matrices = {res: np.eye(14, dtype=np.float32) for res in restype_3}
  for resname, swap in residue_constants.residue_atom_renaming_swaps.items():
    correspondences = np.arange(14)
    for source_atom_swap, target_atom_swap in swap.items():
      source_index = residue_constants.restype_name_to_atom14_names[
          resname].index(source_atom_swap)
      target_index = residue_constants.restype_name_to_atom14_names[
          resname].index(target_atom_swap)
      correspondences[source_index] = target_index
      correspondences[target_index] = source_index
      renaming_matrix = np.zeros((14, 14), dtype=np.float32)
      for index, correspondence in enumerate(correspondences):
        renaming_matrix[index, correspondence] = 1.
    all_matrices[resname] = renaming_matrix.astype(np.float32)
  renaming_matrices = np.stack([all_matrices[restype] for restype in restype_3])
  return renaming_matrices


RENAMING_MATRICES = _make_renaming_matrices()


def get_alt_atom14(aatype, positions, mask):
  """"""Get alternative atom14 positions.

  Constructs renamed atom positions for ambiguous residues.

  Jumper et al. (2021) Suppl. Table 3 ""Ambiguous atom names due to 180 degree-
  rotation-symmetry""

  Args:
    aatype: Amino acid at given position
    positions: Atom positions as r3.Vecs in atom14 representation, (N, 14)
    mask: Atom masks in atom14 representation, (N, 14)
  Returns:
    renamed atom positions, renamed atom mask
  """"""
  # pick the transformation matrices for the given residue sequence
  # shape (num_res, 14, 14)
  renaming_transform = utils.batched_gather(
      jnp.asarray(RENAMING_MATRICES), aatype)

  positions = jax.tree_map(lambda x: x[:, :, None], positions)
  alternative_positions = jax.tree_map(
      lambda x: jnp.sum(x, axis=1), positions * renaming_transform)

  # Create the mask for the alternative ground truth (differs from the
  # ground truth mask, if only one of the atoms in an ambiguous pair has a
  # ground truth position)
  alternative_mask = jnp.sum(mask[..., None] * renaming_transform, axis=1)

  return alternative_positions, alternative_mask
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/lddt.py",".py","3505","89","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""lDDT protein distance score.""""""
import jax.numpy as jnp


def lddt(predicted_points,
         true_points,
         true_points_mask,
         cutoff=15.,
         per_residue=False):
  """"""Measure (approximate) lDDT for a batch of coordinates.

  lDDT reference:
  Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: A local
  superposition-free score for comparing protein structures and models using
  distance difference tests. Bioinformatics 29, 2722–2728 (2013).

  lDDT is a measure of the difference between the true distance matrix and the
  distance matrix of the predicted points.  The difference is computed only on
  points closer than cutoff *in the true structure*.

  This function does not compute the exact lDDT value that the original paper
  describes because it does not include terms for physical feasibility
  (e.g. bond length violations). Therefore this is only an approximate
  lDDT score.

  Args:
    predicted_points: (batch, length, 3) array of predicted 3D points
    true_points: (batch, length, 3) array of true 3D points
    true_points_mask: (batch, length, 1) binary-valued float array.  This mask
      should be 1 for points that exist in the true points.
    cutoff: Maximum distance for a pair of points to be included
    per_residue: If true, return score for each residue.  Note that the overall
      lDDT is not exactly the mean of the per_residue lDDT's because some
      residues have more contacts than others.

  Returns:
    An (approximate, see above) lDDT score in the range 0-1.
  """"""

  assert len(predicted_points.shape) == 3
  assert predicted_points.shape[-1] == 3
  assert true_points_mask.shape[-1] == 1
  assert len(true_points_mask.shape) == 3

  # Compute true and predicted distance matrices.
  dmat_true = jnp.sqrt(1e-10 + jnp.sum(
      (true_points[:, :, None] - true_points[:, None, :])**2, axis=-1))

  dmat_predicted = jnp.sqrt(1e-10 + jnp.sum(
      (predicted_points[:, :, None] -
       predicted_points[:, None, :])**2, axis=-1))

  dists_to_score = (
      (dmat_true < cutoff).astype(jnp.float32) * true_points_mask *
      jnp.transpose(true_points_mask, [0, 2, 1]) *
      (1. - jnp.eye(dmat_true.shape[1]))  # Exclude self-interaction.
  )

  # Shift unscored distances to be far away.
  dist_l1 = jnp.abs(dmat_true - dmat_predicted)

  # True lDDT uses a number of fixed bins.
  # We ignore the physical plausibility correction to lDDT, though.
  score = 0.25 * ((dist_l1 < 0.5).astype(jnp.float32) +
                  (dist_l1 < 1.0).astype(jnp.float32) +
                  (dist_l1 < 2.0).astype(jnp.float32) +
                  (dist_l1 < 4.0).astype(jnp.float32))

  # Normalize over the appropriate axes.
  reduce_axes = (-1,) if per_residue else (-2, -1)
  norm = 1. / (1e-10 + jnp.sum(dists_to_score, axis=reduce_axes))
  score = norm * (1e-10 + jnp.sum(dists_to_score * score, axis=reduce_axes))

  return score
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/vector.py",".py","6896","218","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Vec3Array Class.""""""

from __future__ import annotations
import dataclasses
from typing import Union

from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
import jax
import jax.numpy as jnp
import numpy as np

Float = Union[float, jnp.ndarray]

VERSION = '0.1'


@struct_of_array.StructOfArray(same_dtype=True)
class Vec3Array:
  """"""Vec3Array in 3 dimensional Space implemented as struct of arrays.

  This is done in order to improve performance and precision.
  On TPU small matrix multiplications are very suboptimal and will waste large
  compute ressources, furthermore any matrix multiplication on tpu happen in
  mixed bfloat16/float32 precision, which is often undesirable when handling
  physical coordinates.
  In most cases this will also be faster on cpu's/gpu's since it allows for
  easier use of vector instructions.
  """"""

  x: jnp.ndarray = dataclasses.field(metadata={'dtype': jnp.float32})
  y: jnp.ndarray
  z: jnp.ndarray

  def __post_init__(self):
    if hasattr(self.x, 'dtype'):
      assert self.x.dtype == self.y.dtype
      assert self.x.dtype == self.z.dtype
      assert all([x == y for x, y in zip(self.x.shape, self.y.shape)])
      assert all([x == z for x, z in zip(self.x.shape, self.z.shape)])

  def __add__(self, other: Vec3Array) -> Vec3Array:
    return jax.tree_map(lambda x, y: x + y, self, other)

  def __sub__(self, other: Vec3Array) -> Vec3Array:
    return jax.tree_map(lambda x, y: x - y, self, other)

  def __mul__(self, other: Float) -> Vec3Array:
    return jax.tree_map(lambda x: x * other, self)

  def __rmul__(self, other: Float) -> Vec3Array:
    return self * other

  def __truediv__(self, other: Float) -> Vec3Array:
    return jax.tree_map(lambda x: x / other, self)

  def __neg__(self) -> Vec3Array:
    return jax.tree_map(lambda x: -x, self)

  def __pos__(self) -> Vec3Array:
    return jax.tree_map(lambda x: x, self)

  def cross(self, other: Vec3Array) -> Vec3Array:
    """"""Compute cross product between 'self' and 'other'.""""""
    new_x = self.y * other.z - self.z * other.y
    new_y = self.z * other.x - self.x * other.z
    new_z = self.x * other.y - self.y * other.x
    return Vec3Array(new_x, new_y, new_z)

  def dot(self, other: Vec3Array) -> Float:
    """"""Compute dot product between 'self' and 'other'.""""""
    return self.x * other.x + self.y * other.y + self.z * other.z

  def norm(self, epsilon: float = 1e-6) -> Float:
    """"""Compute Norm of Vec3Array, clipped to epsilon.""""""
    # To avoid NaN on the backward pass, we must use maximum before the sqrt
    norm2 = self.dot(self)
    if epsilon:
      norm2 = jnp.maximum(norm2, epsilon**2)
    return jnp.sqrt(norm2)

  def norm2(self):
    return self.dot(self)

  def normalized(self, epsilon: float = 1e-6) -> Vec3Array:
    """"""Return unit vector with optional clipping.""""""
    return self / self.norm(epsilon)

  @classmethod
  def zeros(cls, shape, dtype=jnp.float32):
    """"""Return Vec3Array corresponding to zeros of given shape.""""""
    return cls(
        jnp.zeros(shape, dtype), jnp.zeros(shape, dtype),
        jnp.zeros(shape, dtype))  # pytype: disable=wrong-arg-count  # trace-all-classes

  def to_array(self) -> jnp.ndarray:
    return jnp.stack([self.x, self.y, self.z], axis=-1)

  @classmethod
  def from_array(cls, array):
    return cls(*utils.unstack(array))

  def __getstate__(self):
    return (VERSION,
            [np.asarray(self.x),
             np.asarray(self.y),
             np.asarray(self.z)])

  def __setstate__(self, state):
    version, state = state
    del version
    for i, letter in enumerate('xyz'):
      object.__setattr__(self, letter, state[i])


def square_euclidean_distance(vec1: Vec3Array,
                              vec2: Vec3Array,
                              epsilon: float = 1e-6) -> Float:
  """"""Computes square of euclidean distance between 'vec1' and 'vec2'.

  Args:
    vec1: Vec3Array to compute  distance to
    vec2: Vec3Array to compute  distance from, should be
          broadcast compatible with 'vec1'
    epsilon: distance is clipped from below to be at least epsilon

  Returns:
    Array of square euclidean distances;
    shape will be result of broadcasting 'vec1' and 'vec2'
  """"""
  difference = vec1 - vec2
  distance = difference.dot(difference)
  if epsilon:
    distance = jnp.maximum(distance, epsilon)
  return distance


def dot(vector1: Vec3Array, vector2: Vec3Array) -> Float:
  return vector1.dot(vector2)


def cross(vector1: Vec3Array, vector2: Vec3Array) -> Float:
  return vector1.cross(vector2)


def norm(vector: Vec3Array, epsilon: float = 1e-6) -> Float:
  return vector.norm(epsilon)


def normalized(vector: Vec3Array, epsilon: float = 1e-6) -> Vec3Array:
  return vector.normalized(epsilon)


def euclidean_distance(vec1: Vec3Array,
                       vec2: Vec3Array,
                       epsilon: float = 1e-6) -> Float:
  """"""Computes euclidean distance between 'vec1' and 'vec2'.

  Args:
    vec1: Vec3Array to compute euclidean distance to
    vec2: Vec3Array to compute euclidean distance from, should be
          broadcast compatible with 'vec1'
    epsilon: distance is clipped from below to be at least epsilon

  Returns:
    Array of euclidean distances;
    shape will be result of broadcasting 'vec1' and 'vec2'
  """"""
  distance_sq = square_euclidean_distance(vec1, vec2, epsilon**2)
  distance = jnp.sqrt(distance_sq)
  return distance


def dihedral_angle(a: Vec3Array, b: Vec3Array, c: Vec3Array,
                   d: Vec3Array) -> Float:
  """"""Computes torsion angle for a quadruple of points.

  For points (a, b, c, d), this is the angle between the planes defined by
  points (a, b, c) and (b, c, d). It is also known as the dihedral angle.

  Arguments:
    a: A Vec3Array of coordinates.
    b: A Vec3Array of coordinates.
    c: A Vec3Array of coordinates.
    d: A Vec3Array of coordinates.

  Returns:
    A tensor of angles in radians: [-pi, pi].
  """"""
  v1 = a - b
  v2 = b - c
  v3 = d - c

  c1 = v1.cross(v2)
  c2 = v3.cross(v2)
  c3 = c2.cross(c1)

  v2_mag = v2.norm()
  return jnp.arctan2(c3.dot(v2), v2_mag * c1.dot(c2))


def random_gaussian_vector(shape, key, dtype=jnp.float32):
  vec_array = jax.random.normal(key, shape + (3,), dtype)
  return Vec3Array.from_array(vec_array)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/__init__.py",".py","1172","32","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Geometry Module.""""""

from alphafold.model.geometry import rigid_matrix_vector
from alphafold.model.geometry import rotation_matrix
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import vector

Rot3Array = rotation_matrix.Rot3Array
Rigid3Array = rigid_matrix_vector.Rigid3Array

StructOfArray = struct_of_array.StructOfArray

Vec3Array = vector.Vec3Array
square_euclidean_distance = vector.square_euclidean_distance
euclidean_distance = vector.euclidean_distance
dihedral_angle = vector.dihedral_angle
dot = vector.dot
cross = vector.cross
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/rigid_matrix_vector.py",".py","4148","107","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Rigid3Array Transformations represented by a Matrix and a Vector.""""""

from __future__ import annotations
from typing import Union

from alphafold.model.geometry import rotation_matrix
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import vector
import jax
import jax.numpy as jnp

Float = Union[float, jnp.ndarray]

VERSION = '0.1'


@struct_of_array.StructOfArray(same_dtype=True)
class Rigid3Array:
  """"""Rigid Transformation, i.e. element of special euclidean group.""""""

  rotation: rotation_matrix.Rot3Array
  translation: vector.Vec3Array

  def __matmul__(self, other: Rigid3Array) -> Rigid3Array:
    new_rotation = self.rotation @ other.rotation
    new_translation = self.apply_to_point(other.translation)
    return Rigid3Array(new_rotation, new_translation)

  def inverse(self) -> Rigid3Array:
    """"""Return Rigid3Array corresponding to inverse transform.""""""
    inv_rotation = self.rotation.inverse()
    inv_translation = inv_rotation.apply_to_point(-self.translation)
    return Rigid3Array(inv_rotation, inv_translation)

  def apply_to_point(self, point: vector.Vec3Array) -> vector.Vec3Array:
    """"""Apply Rigid3Array transform to point.""""""
    return self.rotation.apply_to_point(point) + self.translation

  def apply_inverse_to_point(self, point: vector.Vec3Array) -> vector.Vec3Array:
    """"""Apply inverse Rigid3Array transform to point.""""""
    new_point = point - self.translation
    return self.rotation.apply_inverse_to_point(new_point)

  def compose_rotation(self, other_rotation):
    rot = self.rotation @ other_rotation
    trans = jax.tree_map(lambda x: jnp.broadcast_to(x, rot.shape),
                         self.translation)
    return Rigid3Array(rot, trans)

  @classmethod
  def identity(cls, shape, dtype=jnp.float32) -> Rigid3Array:
    """"""Return identity Rigid3Array of given shape.""""""
    return cls(
        rotation_matrix.Rot3Array.identity(shape, dtype=dtype),
        vector.Vec3Array.zeros(shape, dtype=dtype))  # pytype: disable=wrong-arg-count  # trace-all-classes

  def scale_translation(self, factor: Float) -> Rigid3Array:
    """"""Scale translation in Rigid3Array by 'factor'.""""""
    return Rigid3Array(self.rotation, self.translation * factor)

  def to_array(self):
    rot_array = self.rotation.to_array()
    vec_array = self.translation.to_array()
    return jnp.concatenate([rot_array, vec_array[..., None]], axis=-1)

  @classmethod
  def from_array(cls, array):
    rot = rotation_matrix.Rot3Array.from_array(array[..., :3])
    vec = vector.Vec3Array.from_array(array[..., -1])
    return cls(rot, vec)  # pytype: disable=wrong-arg-count  # trace-all-classes

  @classmethod
  def from_array4x4(cls, array: jnp.ndarray) -> Rigid3Array:
    """"""Construct Rigid3Array from homogeneous 4x4 array.""""""
    assert array.shape[-1] == 4
    assert array.shape[-2] == 4
    rotation = rotation_matrix.Rot3Array(
        array[..., 0, 0], array[..., 0, 1], array[..., 0, 2],
        array[..., 1, 0], array[..., 1, 1], array[..., 1, 2],
        array[..., 2, 0], array[..., 2, 1], array[..., 2, 2]
        )
    translation = vector.Vec3Array(
        array[..., 0, 3], array[..., 1, 3], array[..., 2, 3])
    return cls(rotation, translation)  # pytype: disable=wrong-arg-count  # trace-all-classes

  def __getstate__(self):
    return (VERSION, (self.rotation, self.translation))

  def __setstate__(self, state):
    version, (rot, trans) = state
    del version
    object.__setattr__(self, 'rotation', rot)
    object.__setattr__(self, 'translation', trans)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/utils.py",".py","853","24","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Utils for geometry library.""""""

from typing import List

import jax.numpy as jnp


def unstack(value: jnp.ndarray, axis: int = -1) -> List[jnp.ndarray]:
  return [jnp.squeeze(v, axis=axis)
          for v in jnp.split(value, value.shape[axis], axis=axis)]
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/test_utils.py",".py","4166","99","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Shared utils for tests.""""""

import dataclasses

from alphafold.model.geometry import rigid_matrix_vector
from alphafold.model.geometry import rotation_matrix
from alphafold.model.geometry import vector
import jax.numpy as jnp
import numpy as np


def assert_rotation_matrix_equal(matrix1: rotation_matrix.Rot3Array,
                                 matrix2: rotation_matrix.Rot3Array):
  for field in dataclasses.fields(rotation_matrix.Rot3Array):
    field = field.name
    np.testing.assert_array_equal(
        getattr(matrix1, field), getattr(matrix2, field))


def assert_rotation_matrix_close(mat1: rotation_matrix.Rot3Array,
                                 mat2: rotation_matrix.Rot3Array):
  np.testing.assert_array_almost_equal(mat1.to_array(), mat2.to_array(), 6)


def assert_array_equal_to_rotation_matrix(array: jnp.ndarray,
                                          matrix: rotation_matrix.Rot3Array):
  """"""Check that array and Matrix match.""""""
  np.testing.assert_array_equal(matrix.xx, array[..., 0, 0])
  np.testing.assert_array_equal(matrix.xy, array[..., 0, 1])
  np.testing.assert_array_equal(matrix.xz, array[..., 0, 2])
  np.testing.assert_array_equal(matrix.yx, array[..., 1, 0])
  np.testing.assert_array_equal(matrix.yy, array[..., 1, 1])
  np.testing.assert_array_equal(matrix.yz, array[..., 1, 2])
  np.testing.assert_array_equal(matrix.zx, array[..., 2, 0])
  np.testing.assert_array_equal(matrix.zy, array[..., 2, 1])
  np.testing.assert_array_equal(matrix.zz, array[..., 2, 2])


def assert_array_close_to_rotation_matrix(array: jnp.ndarray,
                                          matrix: rotation_matrix.Rot3Array):
  np.testing.assert_array_almost_equal(matrix.to_array(), array, 6)


def assert_vectors_equal(vec1: vector.Vec3Array, vec2: vector.Vec3Array):
  np.testing.assert_array_equal(vec1.x, vec2.x)
  np.testing.assert_array_equal(vec1.y, vec2.y)
  np.testing.assert_array_equal(vec1.z, vec2.z)


def assert_vectors_close(vec1: vector.Vec3Array, vec2: vector.Vec3Array):
  np.testing.assert_allclose(vec1.x, vec2.x, atol=1e-6, rtol=0.)
  np.testing.assert_allclose(vec1.y, vec2.y, atol=1e-6, rtol=0.)
  np.testing.assert_allclose(vec1.z, vec2.z, atol=1e-6, rtol=0.)


def assert_array_close_to_vector(array: jnp.ndarray, vec: vector.Vec3Array):
  np.testing.assert_allclose(vec.to_array(), array, atol=1e-6, rtol=0.)


def assert_array_equal_to_vector(array: jnp.ndarray, vec: vector.Vec3Array):
  np.testing.assert_array_equal(vec.to_array(), array)


def assert_rigid_equal_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array,
                                rigid2: rigid_matrix_vector.Rigid3Array):
  assert_rot_trans_equal_to_rigid(rigid1.rotation, rigid1.translation, rigid2)


def assert_rigid_close_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array,
                                rigid2: rigid_matrix_vector.Rigid3Array):
  assert_rot_trans_close_to_rigid(rigid1.rotation, rigid1.translation, rigid2)


def assert_rot_trans_equal_to_rigid(rot: rotation_matrix.Rot3Array,
                                    trans: vector.Vec3Array,
                                    rigid: rigid_matrix_vector.Rigid3Array):
  assert_rotation_matrix_equal(rot, rigid.rotation)
  assert_vectors_equal(trans, rigid.translation)


def assert_rot_trans_close_to_rigid(rot: rotation_matrix.Rot3Array,
                                    trans: vector.Vec3Array,
                                    rigid: rigid_matrix_vector.Rigid3Array):
  assert_rotation_matrix_close(rot, rigid.rotation)
  assert_vectors_close(trans, rigid.translation)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/rotation_matrix.py",".py","5751","158","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Rot3Array Matrix Class.""""""

from __future__ import annotations
import dataclasses

from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
from alphafold.model.geometry import vector
import jax
import jax.numpy as jnp
import numpy as np

COMPONENTS = ['xx', 'xy', 'xz', 'yx', 'yy', 'yz', 'zx', 'zy', 'zz']

VERSION = '0.1'


@struct_of_array.StructOfArray(same_dtype=True)
class Rot3Array:
  """"""Rot3Array Matrix in 3 dimensional Space implemented as struct of arrays.""""""

  xx: jnp.ndarray = dataclasses.field(metadata={'dtype': jnp.float32})
  xy: jnp.ndarray
  xz: jnp.ndarray
  yx: jnp.ndarray
  yy: jnp.ndarray
  yz: jnp.ndarray
  zx: jnp.ndarray
  zy: jnp.ndarray
  zz: jnp.ndarray

  __array_ufunc__ = None

  def inverse(self) -> Rot3Array:
    """"""Returns inverse of Rot3Array.""""""
    return Rot3Array(self.xx, self.yx, self.zx,
                     self.xy, self.yy, self.zy,
                     self.xz, self.yz, self.zz)

  def apply_to_point(self, point: vector.Vec3Array) -> vector.Vec3Array:
    """"""Applies Rot3Array to point.""""""
    return vector.Vec3Array(
        self.xx * point.x + self.xy * point.y + self.xz * point.z,
        self.yx * point.x + self.yy * point.y + self.yz * point.z,
        self.zx * point.x + self.zy * point.y + self.zz * point.z)

  def apply_inverse_to_point(self, point: vector.Vec3Array) -> vector.Vec3Array:
    """"""Applies inverse Rot3Array to point.""""""
    return self.inverse().apply_to_point(point)

  def __matmul__(self, other: Rot3Array) -> Rot3Array:
    """"""Composes two Rot3Arrays.""""""
    c0 = self.apply_to_point(vector.Vec3Array(other.xx, other.yx, other.zx))
    c1 = self.apply_to_point(vector.Vec3Array(other.xy, other.yy, other.zy))
    c2 = self.apply_to_point(vector.Vec3Array(other.xz, other.yz, other.zz))
    return Rot3Array(c0.x, c1.x, c2.x, c0.y, c1.y, c2.y, c0.z, c1.z, c2.z)

  @classmethod
  def identity(cls, shape, dtype=jnp.float32) -> Rot3Array:
    """"""Returns identity of given shape.""""""
    ones = jnp.ones(shape, dtype=dtype)
    zeros = jnp.zeros(shape, dtype=dtype)
    return cls(ones, zeros, zeros, zeros, ones, zeros, zeros, zeros, ones)  # pytype: disable=wrong-arg-count  # trace-all-classes

  @classmethod
  def from_two_vectors(cls, e0: vector.Vec3Array,
                       e1: vector.Vec3Array) -> Rot3Array:
    """"""Construct Rot3Array from two Vectors.

    Rot3Array is constructed such that in the corresponding frame 'e0' lies on
    the positive x-Axis and 'e1' lies in the xy plane with positive sign of y.

    Args:
      e0: Vector
      e1: Vector
    Returns:
      Rot3Array
    """"""
    # Normalize the unit vector for the x-axis, e0.
    e0 = e0.normalized()
    # make e1 perpendicular to e0.
    c = e1.dot(e0)
    e1 = (e1 - c * e0).normalized()
    # Compute e2 as cross product of e0 and e1.
    e2 = e0.cross(e1)
    return cls(e0.x, e1.x, e2.x, e0.y, e1.y, e2.y, e0.z, e1.z, e2.z)  # pytype: disable=wrong-arg-count  # trace-all-classes

  @classmethod
  def from_array(cls, array: jnp.ndarray) -> Rot3Array:
    """"""Construct Rot3Array Matrix from array of shape. [..., 3, 3].""""""
    unstacked = utils.unstack(array, axis=-2)
    unstacked = sum([utils.unstack(x, axis=-1) for x in unstacked], [])
    return cls(*unstacked)

  def to_array(self) -> jnp.ndarray:
    """"""Convert Rot3Array to array of shape [..., 3, 3].""""""
    return jnp.stack(
        [jnp.stack([self.xx, self.xy, self.xz], axis=-1),
         jnp.stack([self.yx, self.yy, self.yz], axis=-1),
         jnp.stack([self.zx, self.zy, self.zz], axis=-1)],
        axis=-2)

  @classmethod
  def from_quaternion(cls,
                      w: jnp.ndarray,
                      x: jnp.ndarray,
                      y: jnp.ndarray,
                      z: jnp.ndarray,
                      normalize: bool = True,
                      epsilon: float = 1e-6) -> Rot3Array:
    """"""Construct Rot3Array from components of quaternion.""""""
    if normalize:
      inv_norm = jax.lax.rsqrt(jnp.maximum(epsilon, w**2 + x**2 + y**2 + z**2))
      w *= inv_norm
      x *= inv_norm
      y *= inv_norm
      z *= inv_norm
    xx = 1 - 2 * (jnp.square(y) + jnp.square(z))
    xy = 2 * (x * y - w * z)
    xz = 2 * (x * z + w * y)
    yx = 2 * (x * y + w * z)
    yy = 1 - 2 * (jnp.square(x) + jnp.square(z))
    yz = 2 * (y * z - w * x)
    zx = 2 * (x * z - w * y)
    zy = 2 * (y * z + w * x)
    zz = 1 - 2 * (jnp.square(x) + jnp.square(y))
    return cls(xx, xy, xz, yx, yy, yz, zx, zy, zz)  # pytype: disable=wrong-arg-count  # trace-all-classes

  @classmethod
  def random_uniform(cls, key, shape, dtype=jnp.float32) -> Rot3Array:
    """"""Samples uniform random Rot3Array according to Haar Measure.""""""
    quat_array = jax.random.normal(key, tuple(shape) + (4,), dtype=dtype)
    quats = utils.unstack(quat_array)
    return cls.from_quaternion(*quats)

  def __getstate__(self):
    return (VERSION,
            [np.asarray(getattr(self, field)) for field in COMPONENTS])

  def __setstate__(self, state):
    version, state = state
    del version
    for i, field in enumerate(COMPONENTS):
      object.__setattr__(self, field, state[i])
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/geometry/struct_of_array.py",".py","7745","221","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Class decorator to represent (nested) struct of arrays.""""""

import dataclasses

import jax


def get_item(instance, key):
  sliced = {}
  for field in get_array_fields(instance):
    num_trailing_dims = field.metadata.get('num_trailing_dims', 0)
    this_key = key
    if isinstance(key, tuple) and Ellipsis in this_key:
      this_key += (slice(None),) * num_trailing_dims
    sliced[field.name] = getattr(instance, field.name)[this_key]
  return dataclasses.replace(instance, **sliced)


@property
def get_shape(instance):
  """"""Returns Shape for given instance of dataclass.""""""
  first_field = dataclasses.fields(instance)[0]
  num_trailing_dims = first_field.metadata.get('num_trailing_dims', None)
  value = getattr(instance, first_field.name)
  if num_trailing_dims:
    return value.shape[:-num_trailing_dims]
  else:
    return value.shape


def get_len(instance):
  """"""Returns length for given instance of dataclass.""""""
  shape = instance.shape
  if shape:
    return shape[0]
  else:
    raise TypeError('len() of unsized object')  # Match jax.numpy behavior.


@property
def get_dtype(instance):
  """"""Returns Dtype for given instance of dataclass.""""""
  fields = dataclasses.fields(instance)
  sets_dtype = [
      field.name for field in fields if field.metadata.get('sets_dtype', False)
  ]
  if sets_dtype:
    assert len(sets_dtype) == 1, 'at most field can set dtype'
    field_value = getattr(instance, sets_dtype[0])
  elif instance.same_dtype:
    field_value = getattr(instance, fields[0].name)
  else:
    # Should this be Value Error?
    raise AttributeError('Trying to access Dtype on Struct of Array without'
                         'either ""same_dtype"" or field setting dtype')

  if hasattr(field_value, 'dtype'):
    return field_value.dtype
  else:
    # Should this be Value Error?
    raise AttributeError(f'field_value {field_value} does not have dtype')


def replace(instance, **kwargs):
  return dataclasses.replace(instance, **kwargs)


def post_init(instance):
  """"""Validate instance has same shapes & dtypes.""""""
  array_fields = get_array_fields(instance)
  arrays = list(get_array_fields(instance, return_values=True).values())
  first_field = array_fields[0]
  # These slightly weird constructions about checking whether the leaves are
  # actual arrays is since e.g. vmap internally relies on being able to
  # construct pytree's with object() as leaves, this would break the checking
  # as such we are only validating the object when the entries in the dataclass
  # Are arrays or other dataclasses of arrays.
  try:
    dtype = instance.dtype
  except AttributeError:
    dtype = None
  if dtype is not None:
    first_shape = instance.shape
    for array, field in zip(arrays, array_fields):
      field_shape = array.shape
      num_trailing_dims = field.metadata.get('num_trailing_dims', None)
      if num_trailing_dims:
        array_shape = array.shape
        field_shape = array_shape[:-num_trailing_dims]
        msg = (f'field {field} should have number of trailing dims'
               ' {num_trailing_dims}')
        assert len(array_shape) == len(first_shape) + num_trailing_dims, msg
      else:
        field_shape = array.shape

      shape_msg = (f""Stripped Shape {field_shape} of field {field} doesn't ""
                   f""match shape {first_shape} of field {first_field}"")
      assert field_shape == first_shape, shape_msg

      field_dtype = array.dtype

      allowed_metadata_dtypes = field.metadata.get('allowed_dtypes', [])
      if allowed_metadata_dtypes:
        msg = f'Dtype is {field_dtype} but must be in {allowed_metadata_dtypes}'
        assert field_dtype in allowed_metadata_dtypes, msg

      if 'dtype' in field.metadata:
        target_dtype = field.metadata['dtype']
      else:
        target_dtype = dtype

      msg = f'Dtype is {field_dtype} but must be {target_dtype}'
      assert field_dtype == target_dtype, msg


def flatten(instance):
  """"""Flatten Struct of Array instance.""""""
  array_likes = list(get_array_fields(instance, return_values=True).values())
  flat_array_likes = []
  inner_treedefs = []
  num_arrays = []
  for array_like in array_likes:
    flat_array_like, inner_treedef = jax.tree_util.tree_flatten(array_like)
    inner_treedefs.append(inner_treedef)
    flat_array_likes += flat_array_like
    num_arrays.append(len(flat_array_like))
  metadata = get_metadata_fields(instance, return_values=True)
  metadata = type(instance).metadata_cls(**metadata)
  return flat_array_likes, (inner_treedefs, metadata, num_arrays)


def make_metadata_class(cls):
  metadata_fields = get_fields(cls,
                               lambda x: x.metadata.get('is_metadata', False))
  metadata_cls = dataclasses.make_dataclass(
      cls_name='Meta' + cls.__name__,
      fields=[(field.name, field.type, field) for field in metadata_fields],
      frozen=True,
      eq=True)
  return metadata_cls


def get_fields(cls_or_instance, filterfn, return_values=False):
  fields = dataclasses.fields(cls_or_instance)
  fields = [field for field in fields if filterfn(field)]
  if return_values:
    return {
        field.name: getattr(cls_or_instance, field.name) for field in fields
    }
  else:
    return fields


def get_array_fields(cls, return_values=False):
  return get_fields(
      cls,
      lambda x: not x.metadata.get('is_metadata', False),
      return_values=return_values)


def get_metadata_fields(cls, return_values=False):
  return get_fields(
      cls,
      lambda x: x.metadata.get('is_metadata', False),
      return_values=return_values)


class StructOfArray:
  """"""Class Decorator for Struct Of Arrays.""""""

  def __init__(self, same_dtype=True):
    self.same_dtype = same_dtype

  def __call__(self, cls):
    cls.__array_ufunc__ = None
    cls.replace = replace
    cls.same_dtype = self.same_dtype
    cls.dtype = get_dtype
    cls.shape = get_shape
    cls.__len__ = get_len
    cls.__getitem__ = get_item
    cls.__post_init__ = post_init
    new_cls = dataclasses.dataclass(cls, frozen=True, eq=False)  # pytype: disable=wrong-keyword-args
    # pytree claims to require metadata to be hashable, not sure why,
    # But making derived dataclass that can just hold metadata
    new_cls.metadata_cls = make_metadata_class(new_cls)

    def unflatten(aux, data):
      inner_treedefs, metadata, num_arrays = aux
      array_fields = [field.name for field in get_array_fields(new_cls)]
      value_dict = {}
      array_start = 0
      for num_array, inner_treedef, array_field in zip(num_arrays,
                                                       inner_treedefs,
                                                       array_fields):
        value_dict[array_field] = jax.tree_util.tree_unflatten(
            inner_treedef, data[array_start:array_start + num_array])
        array_start += num_array
      metadata_fields = get_metadata_fields(new_cls)
      for field in metadata_fields:
        value_dict[field.name] = getattr(metadata, field.name)

      return new_cls(**value_dict)

    jax.tree_util.register_pytree_node(
        nodetype=new_cls, flatten_func=flatten, unflatten_func=unflatten)
    return new_cls
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/protein_features_test.py",".py","1822","55","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for protein_features.""""""
import uuid

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.model.tf import protein_features
import tensorflow.compat.v1 as tf


def _random_bytes():
  return str(uuid.uuid4()).encode('utf-8')


class FeaturesTest(parameterized.TestCase, tf.test.TestCase):

  def setUp(self):
    super().setUp()
    tf.disable_v2_behavior()

  def testFeatureNames(self):
    self.assertEqual(len(protein_features.FEATURE_SIZES),
                     len(protein_features.FEATURE_TYPES))
    sorted_size_names = sorted(protein_features.FEATURE_SIZES.keys())
    sorted_type_names = sorted(protein_features.FEATURE_TYPES.keys())
    for i, size_name in enumerate(sorted_size_names):
      self.assertEqual(size_name, sorted_type_names[i])

  def testReplacement(self):
    for name in protein_features.FEATURE_SIZES.keys():
      sizes = protein_features.shape(name,
                                     num_residues=12,
                                     msa_length=24,
                                     num_templates=3)
      for x in sizes:
        self.assertEqual(type(x), int)
        self.assertGreater(x, 0)


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/shape_helpers.py",".py","1415","48","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Utilities for dealing with shapes of TensorFlow tensors.""""""
import tensorflow.compat.v1 as tf


def shape_list(x):
  """"""Return list of dimensions of a tensor, statically where possible.

  Like `x.shape.as_list()` but with tensors instead of `None`s.

  Args:
    x: A tensor.
  Returns:
    A list with length equal to the rank of the tensor. The n-th element of the
    list is an integer when that dimension is statically known otherwise it is
    the n-th element of `tf.shape(x)`.
  """"""
  x = tf.convert_to_tensor(x)

  # If unknown rank, return dynamic shape
  if x.get_shape().dims is None:
    return tf.shape(x)

  static = x.get_shape().as_list()
  shape = tf.shape(x)

  ret = []
  for i in range(len(static)):
    dim = static[i]
    if dim is None:
      dim = shape[i]
    ret.append(dim)
  return ret

","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/shape_helpers_test.py",".py","1318","43","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for shape_helpers.""""""

from alphafold.model.tf import shape_helpers
import numpy as np
import tensorflow.compat.v1 as tf


class ShapeTest(tf.test.TestCase):

  def setUp(self):
    super().setUp()
    tf.disable_v2_behavior()

  def test_shape_list(self):
    """"""Test that shape_list can allow for reshaping to dynamic shapes.""""""
    a = tf.zeros([10, 4, 4, 2])
    p = tf.placeholder(tf.float32, shape=[None, None, 1, 4, 4])
    shape_dyn = shape_helpers.shape_list(p)[:2] + [4, 4]

    b = tf.reshape(a, shape_dyn)
    with self.session() as sess:
      out = sess.run(b, feed_dict={p: np.ones((20, 1, 1, 4, 4))})

    self.assertAllEqual(out.shape, (20, 1, 4, 4))


if __name__ == '__main__':
  tf.test.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/protein_features.py",".py","5051","130","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Contains descriptions of various protein features.""""""
import enum
from typing import Dict, Optional, Sequence, Tuple, Union
from alphafold.common import residue_constants
import tensorflow.compat.v1 as tf

# Type aliases.
FeaturesMetadata = Dict[str, Tuple[tf.dtypes.DType, Sequence[Union[str, int]]]]


class FeatureType(enum.Enum):
  ZERO_DIM = 0  # Shape [x]
  ONE_DIM = 1  # Shape [num_res, x]
  TWO_DIM = 2  # Shape [num_res, num_res, x]
  MSA = 3  # Shape [msa_length, num_res, x]


# Placeholder values that will be replaced with their true value at runtime.
NUM_RES = ""num residues placeholder""
NUM_SEQ = ""length msa placeholder""
NUM_TEMPLATES = ""num templates placeholder""
# Sizes of the protein features, NUM_RES and NUM_SEQ are allowed as placeholders
# to be replaced with the number of residues and the number of sequences in the
# multiple sequence alignment, respectively.


FEATURES = {
    #### Static features of a protein sequence ####
    ""aatype"": (tf.float32, [NUM_RES, 21]),
    ""between_segment_residues"": (tf.int64, [NUM_RES, 1]),
    ""deletion_matrix"": (tf.float32, [NUM_SEQ, NUM_RES, 1]),
    ""domain_name"": (tf.string, [1]),
    ""msa"": (tf.int64, [NUM_SEQ, NUM_RES, 1]),
    ""num_alignments"": (tf.int64, [NUM_RES, 1]),
    ""residue_index"": (tf.int64, [NUM_RES, 1]),
    ""seq_length"": (tf.int64, [NUM_RES, 1]),
    ""sequence"": (tf.string, [1]),
    ""all_atom_positions"": (tf.float32,
                           [NUM_RES, residue_constants.atom_type_num, 3]),
    ""all_atom_mask"": (tf.int64, [NUM_RES, residue_constants.atom_type_num]),
    ""resolution"": (tf.float32, [1]),
    ""template_domain_names"": (tf.string, [NUM_TEMPLATES]),
    ""template_sum_probs"": (tf.float32, [NUM_TEMPLATES, 1]),
    ""template_aatype"": (tf.float32, [NUM_TEMPLATES, NUM_RES, 22]),
    ""template_all_atom_positions"": (tf.float32, [
        NUM_TEMPLATES, NUM_RES, residue_constants.atom_type_num, 3
    ]),
    ""template_all_atom_masks"": (tf.float32, [
        NUM_TEMPLATES, NUM_RES, residue_constants.atom_type_num, 1
    ]),
}

FEATURE_TYPES = {k: v[0] for k, v in FEATURES.items()}
FEATURE_SIZES = {k: v[1] for k, v in FEATURES.items()}


def register_feature(name: str,
                     type_: tf.dtypes.DType,
                     shape_: Tuple[Union[str, int]]):
  """"""Register extra features used in custom datasets.""""""
  FEATURES[name] = (type_, shape_)
  FEATURE_TYPES[name] = type_
  FEATURE_SIZES[name] = shape_


def shape(feature_name: str,
          num_residues: int,
          msa_length: int,
          num_templates: Optional[int] = None,
          features: Optional[FeaturesMetadata] = None):
  """"""Get the shape for the given feature name.

  This is near identical to _get_tf_shape_no_placeholders() but with 2
  differences:
  * This method does not calculate a single placeholder from the total number of
    elements (eg given <NUM_RES, 3> and size := 12, this won't deduce NUM_RES
    must be 4)
  * This method will work with tensors

  Args:
    feature_name: String identifier for the feature. If the feature name ends
      with ""_unnormalized"", this suffix is stripped off.
    num_residues: The number of residues in the current domain - some elements
      of the shape can be dynamic and will be replaced by this value.
    msa_length: The number of sequences in the multiple sequence alignment, some
      elements of the shape can be dynamic and will be replaced by this value.
      If the number of alignments is unknown / not read, please pass None for
      msa_length.
    num_templates (optional): The number of templates in this tfexample.
    features: A feature_name to (tf_dtype, shape) lookup; defaults to FEATURES.

  Returns:
    List of ints representation the tensor size.

  Raises:
    ValueError: If a feature is requested but no concrete placeholder value is
        given.
  """"""
  features = features or FEATURES
  if feature_name.endswith(""_unnormalized""):
    feature_name = feature_name[:-13]

  unused_dtype, raw_sizes = features[feature_name]
  replacements = {NUM_RES: num_residues,
                  NUM_SEQ: msa_length}

  if num_templates is not None:
    replacements[NUM_TEMPLATES] = num_templates

  sizes = [replacements.get(dimension, dimension) for dimension in raw_sizes]
  for dimension in sizes:
    if isinstance(dimension, str):
      raise ValueError(""Could not parse %s (shape: %s) with values: %s"" % (
          feature_name, raw_sizes, replacements))
  return sizes
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/proteins_dataset.py",".py","6344","167","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Datasets consisting of proteins.""""""
from typing import Dict, Mapping, Optional, Sequence
from alphafold.model.tf import protein_features
import numpy as np
import tensorflow.compat.v1 as tf

TensorDict = Dict[str, tf.Tensor]


def parse_tfexample(
    raw_data: bytes,
    features: protein_features.FeaturesMetadata,
    key: Optional[str] = None) -> Dict[str, tf.train.Feature]:
  """"""Read a single TF Example proto and return a subset of its features.

  Args:
    raw_data: A serialized tf.Example proto.
    features: A dictionary of features, mapping string feature names to a tuple
      (dtype, shape). This dictionary should be a subset of
      protein_features.FEATURES (or the dictionary itself for all features).
    key: Optional string with the SSTable key of that tf.Example. This will be
      added into features as a 'key' but only if requested in features.

  Returns:
    A dictionary of features mapping feature names to features. Only the given
    features are returned, all other ones are filtered out.
  """"""
  feature_map = {
      k: tf.io.FixedLenSequenceFeature(shape=(), dtype=v[0], allow_missing=True)
      for k, v in features.items()
  }
  parsed_features = tf.io.parse_single_example(raw_data, feature_map)
  reshaped_features = parse_reshape_logic(parsed_features, features, key=key)

  return reshaped_features


def _first(tensor: tf.Tensor) -> tf.Tensor:
  """"""Returns the 1st element - the input can be a tensor or a scalar.""""""
  return tf.reshape(tensor, shape=(-1,))[0]


def parse_reshape_logic(
    parsed_features: TensorDict,
    features: protein_features.FeaturesMetadata,
    key: Optional[str] = None) -> TensorDict:
  """"""Transforms parsed serial features to the correct shape.""""""
  # Find out what is the number of sequences and the number of alignments.
  num_residues = tf.cast(_first(parsed_features[""seq_length""]), dtype=tf.int32)

  if ""num_alignments"" in parsed_features:
    num_msa = tf.cast(_first(parsed_features[""num_alignments""]), dtype=tf.int32)
  else:
    num_msa = 0

  if ""template_domain_names"" in parsed_features:
    num_templates = tf.cast(
        tf.shape(parsed_features[""template_domain_names""])[0], dtype=tf.int32)
  else:
    num_templates = 0

  if key is not None and ""key"" in features:
    parsed_features[""key""] = [key]  # Expand dims from () to (1,).

  # Reshape the tensors according to the sequence length and num alignments.
  for k, v in parsed_features.items():
    new_shape = protein_features.shape(
        feature_name=k,
        num_residues=num_residues,
        msa_length=num_msa,
        num_templates=num_templates,
        features=features)
    new_shape_size = tf.constant(1, dtype=tf.int32)
    for dim in new_shape:
      new_shape_size *= tf.cast(dim, tf.int32)

    assert_equal = tf.assert_equal(
        tf.size(v), new_shape_size,
        name=""assert_%s_shape_correct"" % k,
        message=""The size of feature %s (%s) could not be reshaped ""
        ""into %s"" % (k, tf.size(v), new_shape))
    if ""template"" not in k:
      # Make sure the feature we are reshaping is not empty.
      assert_non_empty = tf.assert_greater(
          tf.size(v), 0, name=""assert_%s_non_empty"" % k,
          message=""The feature %s is not set in the tf.Example. Either do not ""
          ""request the feature or use a tf.Example that has the ""
          ""feature set."" % k)
      with tf.control_dependencies([assert_non_empty, assert_equal]):
        parsed_features[k] = tf.reshape(v, new_shape, name=""reshape_%s"" % k)
    else:
      with tf.control_dependencies([assert_equal]):
        parsed_features[k] = tf.reshape(v, new_shape, name=""reshape_%s"" % k)

  return parsed_features


def _make_features_metadata(
    feature_names: Sequence[str]) -> protein_features.FeaturesMetadata:
  """"""Makes a feature name to type and shape mapping from a list of names.""""""
  # Make sure these features are always read.
  required_features = [""aatype"", ""sequence"", ""seq_length""]
  feature_names = list(set(feature_names) | set(required_features))

  features_metadata = {name: protein_features.FEATURES[name]
                       for name in feature_names}
  return features_metadata


def create_tensor_dict(
    raw_data: bytes,
    features: Sequence[str],
    key: Optional[str] = None,
    ) -> TensorDict:
  """"""Creates a dictionary of tensor features.

  Args:
    raw_data: A serialized tf.Example proto.
    features: A list of strings of feature names to be returned in the dataset.
    key: Optional string with the SSTable key of that tf.Example. This will be
      added into features as a 'key' but only if requested in features.

  Returns:
    A dictionary of features mapping feature names to features. Only the given
    features are returned, all other ones are filtered out.
  """"""
  features_metadata = _make_features_metadata(features)
  return parse_tfexample(raw_data, features_metadata, key)


def np_to_tensor_dict(
    np_example: Mapping[str, np.ndarray],
    features: Sequence[str],
    ) -> TensorDict:
  """"""Creates dict of tensors from a dict of NumPy arrays.

  Args:
    np_example: A dict of NumPy feature arrays.
    features: A list of strings of feature names to be returned in the dataset.

  Returns:
    A dictionary of features mapping feature names to features. Only the given
    features are returned, all other ones are filtered out.
  """"""
  features_metadata = _make_features_metadata(features)
  tensor_dict = {k: tf.constant(v) for k, v in np_example.items()
                 if k in features_metadata}

  # Ensures shapes are as expected. Needed for setting size of empty features
  # e.g. when no template hits were found.
  tensor_dict = parse_reshape_logic(tensor_dict, features_metadata)
  return tensor_dict
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/shape_placeholders.py",".py","812","21","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Placeholder values for run-time varying dimension sizes.""""""

NUM_RES = 'num residues placeholder'
NUM_MSA_SEQ = 'msa placeholder'
NUM_EXTRA_SEQ = 'extra msa placeholder'
NUM_TEMPLATES = 'num templates placeholder'
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/__init__.py",".py","633","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Alphafold model TensorFlow code.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/input_pipeline.py",".py","5357","167","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Feature pre-processing input pipeline for AlphaFold.""""""

from alphafold.model.tf import data_transforms
from alphafold.model.tf import shape_placeholders
import tensorflow.compat.v1 as tf
import tree

# Pylint gets confused by the curry1 decorator because it changes the number
#   of arguments to the function.
# pylint:disable=no-value-for-parameter


NUM_RES = shape_placeholders.NUM_RES
NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES


def nonensembled_map_fns(data_config):
  """"""Input pipeline functions which are not ensembled.""""""
  common_cfg = data_config.common

  map_fns = [
      data_transforms.correct_msa_restypes,
      data_transforms.add_distillation_flag(False),
      data_transforms.cast_64bit_ints,
      data_transforms.squeeze_features,
      # Keep to not disrupt RNG.
      data_transforms.randomly_replace_msa_with_unknown(0.0),
      data_transforms.make_seq_mask,
      data_transforms.make_msa_mask,
      # Compute the HHblits profile if it's not set. This has to be run before
      # sampling the MSA.
      data_transforms.make_hhblits_profile,
      data_transforms.make_random_crop_to_size_seed,
  ]
  if common_cfg.use_templates:
    map_fns.extend([
        data_transforms.fix_templates_aatype,
        data_transforms.make_template_mask,
        data_transforms.make_pseudo_beta('template_')
    ])
  map_fns.extend([
      data_transforms.make_atom14_masks,
  ])

  return map_fns


def ensembled_map_fns(data_config):
  """"""Input pipeline functions that can be ensembled and averaged.""""""
  common_cfg = data_config.common
  eval_cfg = data_config.eval

  map_fns = []

  if common_cfg.reduce_msa_clusters_by_max_templates:
    pad_msa_clusters = eval_cfg.max_msa_clusters - eval_cfg.max_templates
  else:
    pad_msa_clusters = eval_cfg.max_msa_clusters

  max_msa_clusters = pad_msa_clusters
  max_extra_msa = common_cfg.max_extra_msa

  map_fns.append(
      data_transforms.sample_msa(
          max_msa_clusters,
          keep_extra=True))

  if 'masked_msa' in common_cfg:
    # Masked MSA should come *before* MSA clustering so that
    # the clustering and full MSA profile do not leak information about
    # the masked locations and secret corrupted locations.
    map_fns.append(
        data_transforms.make_masked_msa(common_cfg.masked_msa,
                                        eval_cfg.masked_msa_replace_fraction))

  if common_cfg.msa_cluster_features:
    map_fns.append(data_transforms.nearest_neighbor_clusters())
    map_fns.append(data_transforms.summarize_clusters())

  # Crop after creating the cluster profiles.
  if max_extra_msa:
    map_fns.append(data_transforms.crop_extra_msa(max_extra_msa))
  else:
    map_fns.append(data_transforms.delete_extra_msa)

  map_fns.append(data_transforms.make_msa_feat())

  crop_feats = dict(eval_cfg.feat)

  if eval_cfg.fixed_size:
    map_fns.append(data_transforms.select_feat(list(crop_feats)))
    map_fns.append(data_transforms.random_crop_to_size(
        eval_cfg.crop_size,
        eval_cfg.max_templates,
        crop_feats,
        eval_cfg.subsample_templates))
    map_fns.append(data_transforms.make_fixed_size(
        crop_feats,
        pad_msa_clusters,
        common_cfg.max_extra_msa,
        eval_cfg.crop_size,
        eval_cfg.max_templates))
  else:
    map_fns.append(data_transforms.crop_templates(eval_cfg.max_templates))

  return map_fns


def process_tensors_from_config(tensors, data_config):
  """"""Apply filters and maps to an existing dataset, based on the config.""""""

  def wrap_ensemble_fn(data, i):
    """"""Function to be mapped over the ensemble dimension.""""""
    d = data.copy()
    fns = ensembled_map_fns(data_config)
    fn = compose(fns)
    d['ensemble_index'] = i
    return fn(d)

  eval_cfg = data_config.eval
  tensors = compose(
      nonensembled_map_fns(
          data_config))(
              tensors)

  tensors_0 = wrap_ensemble_fn(tensors, tf.constant(0))
  num_ensemble = eval_cfg.num_ensemble
  if data_config.common.resample_msa_in_recycling:
    # Separate batch per ensembling & recycling step.
    num_ensemble *= data_config.common.num_recycle + 1

  if isinstance(num_ensemble, tf.Tensor) or num_ensemble > 1:
    fn_output_signature = tree.map_structure(
        tf.TensorSpec.from_tensor, tensors_0)
    tensors = tf.map_fn(
        lambda x: wrap_ensemble_fn(tensors, x),
        tf.range(num_ensemble),
        parallel_iterations=1,
        fn_output_signature=fn_output_signature)
  else:
    tensors = tree.map_structure(lambda x: x[None],
                                 tensors_0)
  return tensors


@data_transforms.curry1
def compose(x, fs):
  for f in fs:
    x = f(x)
  return x
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/data_transforms.py",".py","21428","626","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Data for AlphaFold.""""""

from alphafold.common import residue_constants
from alphafold.model.tf import shape_helpers
from alphafold.model.tf import shape_placeholders
from alphafold.model.tf import utils
import numpy as np
import tensorflow.compat.v1 as tf

# Pylint gets confused by the curry1 decorator because it changes the number
#   of arguments to the function.
# pylint:disable=no-value-for-parameter


NUM_RES = shape_placeholders.NUM_RES
NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES


def cast_64bit_ints(protein):

  for k, v in protein.items():
    if v.dtype == tf.int64:
      protein[k] = tf.cast(v, tf.int32)
  return protein


_MSA_FEATURE_NAMES = [
    'msa', 'deletion_matrix', 'msa_mask', 'msa_row_mask', 'bert_mask',
    'true_msa'
]


def make_seq_mask(protein):
  protein['seq_mask'] = tf.ones(
      shape_helpers.shape_list(protein['aatype']), dtype=tf.float32)
  return protein


def make_template_mask(protein):
  protein['template_mask'] = tf.ones(
      shape_helpers.shape_list(protein['template_domain_names']),
      dtype=tf.float32)
  return protein


def curry1(f):
  """"""Supply all arguments but the first.""""""

  def fc(*args, **kwargs):
    return lambda x: f(x, *args, **kwargs)

  return fc


@curry1
def add_distillation_flag(protein, distillation):
  protein['is_distillation'] = tf.constant(float(distillation),
                                           shape=[],
                                           dtype=tf.float32)
  return protein


def make_all_atom_aatype(protein):
  protein['all_atom_aatype'] = protein['aatype']
  return protein


def fix_templates_aatype(protein):
  """"""Fixes aatype encoding of templates.""""""
  # Map one-hot to indices.
  protein['template_aatype'] = tf.argmax(
      protein['template_aatype'], output_type=tf.int32, axis=-1)
  # Map hhsearch-aatype to our aatype.
  new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
  new_order = tf.constant(new_order_list, dtype=tf.int32)
  protein['template_aatype'] = tf.gather(params=new_order,
                                         indices=protein['template_aatype'])
  return protein


def correct_msa_restypes(protein):
  """"""Correct MSA restype to have the same order as residue_constants.""""""
  new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
  new_order = tf.constant(new_order_list, dtype=protein['msa'].dtype)
  protein['msa'] = tf.gather(new_order, protein['msa'], axis=0)

  perm_matrix = np.zeros((22, 22), dtype=np.float32)
  perm_matrix[range(len(new_order_list)), new_order_list] = 1.

  for k in protein:
    if 'profile' in k:  # Include both hhblits and psiblast profiles
      num_dim = protein[k].shape.as_list()[-1]
      assert num_dim in [20, 21, 22], (
          'num_dim for %s out of expected range: %s' % (k, num_dim))
      protein[k] = tf.tensordot(protein[k], perm_matrix[:num_dim, :num_dim], 1)
  return protein


def squeeze_features(protein):
  """"""Remove singleton and repeated dimensions in protein features.""""""
  protein['aatype'] = tf.argmax(
      protein['aatype'], axis=-1, output_type=tf.int32)
  for k in [
      'domain_name', 'msa', 'num_alignments', 'seq_length', 'sequence',
      'superfamily', 'deletion_matrix', 'resolution',
      'between_segment_residues', 'residue_index', 'template_all_atom_masks']:
    if k in protein:
      final_dim = shape_helpers.shape_list(protein[k])[-1]
      if isinstance(final_dim, int) and final_dim == 1:
        protein[k] = tf.squeeze(protein[k], axis=-1)

  for k in ['seq_length', 'num_alignments']:
    if k in protein:
      protein[k] = protein[k][0]  # Remove fake sequence dimension
  return protein


def make_random_crop_to_size_seed(protein):
  """"""Random seed for cropping residues and templates.""""""
  protein['random_crop_to_size_seed'] = utils.make_random_seed()
  return protein


@curry1
def randomly_replace_msa_with_unknown(protein, replace_proportion):
  """"""Replace a proportion of the MSA with 'X'.""""""
  msa_mask = (tf.random.uniform(shape_helpers.shape_list(protein['msa'])) <
              replace_proportion)
  x_idx = 20
  gap_idx = 21
  msa_mask = tf.logical_and(msa_mask, protein['msa'] != gap_idx)
  protein['msa'] = tf.where(msa_mask,
                            tf.ones_like(protein['msa']) * x_idx,
                            protein['msa'])
  aatype_mask = (
      tf.random.uniform(shape_helpers.shape_list(protein['aatype'])) <
      replace_proportion)

  protein['aatype'] = tf.where(aatype_mask,
                               tf.ones_like(protein['aatype']) * x_idx,
                               protein['aatype'])
  return protein


@curry1
def sample_msa(protein, max_seq, keep_extra):
  """"""Sample MSA randomly, remaining sequences are stored as `extra_*`.

  Args:
    protein: batch to sample msa from.
    max_seq: number of sequences to sample.
    keep_extra: When True sequences not sampled are put into fields starting
      with 'extra_*'.

  Returns:
    Protein with sampled msa.
  """"""
  num_seq = tf.shape(protein['msa'])[0]
  shuffled = tf.random_shuffle(tf.range(1, num_seq))
  index_order = tf.concat([[0], shuffled], axis=0)
  num_sel = tf.minimum(max_seq, num_seq)

  sel_seq, not_sel_seq = tf.split(index_order, [num_sel, num_seq - num_sel])

  for k in _MSA_FEATURE_NAMES:
    if k in protein:
      if keep_extra:
        protein['extra_' + k] = tf.gather(protein[k], not_sel_seq)
      protein[k] = tf.gather(protein[k], sel_seq)

  return protein


@curry1
def crop_extra_msa(protein, max_extra_msa):
  """"""MSA features are cropped so only `max_extra_msa` sequences are kept.""""""
  num_seq = tf.shape(protein['extra_msa'])[0]
  num_sel = tf.minimum(max_extra_msa, num_seq)
  select_indices = tf.random_shuffle(tf.range(0, num_seq))[:num_sel]
  for k in _MSA_FEATURE_NAMES:
    if 'extra_' + k in protein:
      protein['extra_' + k] = tf.gather(protein['extra_' + k], select_indices)

  return protein


def delete_extra_msa(protein):
  for k in _MSA_FEATURE_NAMES:
    if 'extra_' + k in protein:
      del protein['extra_' + k]
  return protein


@curry1
def block_delete_msa(protein, config):
  """"""Sample MSA by deleting contiguous blocks.

  Jumper et al. (2021) Suppl. Alg. 1 ""MSABlockDeletion""

  Arguments:
    protein: batch dict containing the msa
    config: ConfigDict with parameters

  Returns:
    updated protein
  """"""
  num_seq = shape_helpers.shape_list(protein['msa'])[0]
  block_num_seq = tf.cast(
      tf.floor(tf.cast(num_seq, tf.float32) * config.msa_fraction_per_block),
      tf.int32)

  if config.randomize_num_blocks:
    nb = tf.random.uniform([], 0, config.num_blocks + 1, dtype=tf.int32)
  else:
    nb = config.num_blocks

  del_block_starts = tf.random.uniform([nb], 0, num_seq, dtype=tf.int32)
  del_blocks = del_block_starts[:, None] + tf.range(block_num_seq)
  del_blocks = tf.clip_by_value(del_blocks, 0, num_seq - 1)
  del_indices = tf.unique(tf.sort(tf.reshape(del_blocks, [-1])))[0]

  # Make sure we keep the original sequence
  sparse_diff = tf.sets.difference(tf.range(1, num_seq)[None],
                                   del_indices[None])
  keep_indices = tf.squeeze(tf.sparse.to_dense(sparse_diff), 0)
  keep_indices = tf.concat([[0], keep_indices], axis=0)

  for k in _MSA_FEATURE_NAMES:
    if k in protein:
      protein[k] = tf.gather(protein[k], keep_indices)

  return protein


@curry1
def nearest_neighbor_clusters(protein, gap_agreement_weight=0.):
  """"""Assign each extra MSA sequence to its nearest neighbor in sampled MSA.""""""

  # Determine how much weight we assign to each agreement.  In theory, we could
  # use a full blosum matrix here, but right now let's just down-weight gap
  # agreement because it could be spurious.
  # Never put weight on agreeing on BERT mask
  weights = tf.concat([
      tf.ones(21),
      gap_agreement_weight * tf.ones(1),
      np.zeros(1)], 0)

  # Make agreement score as weighted Hamming distance
  sample_one_hot = (protein['msa_mask'][:, :, None] *
                    tf.one_hot(protein['msa'], 23))
  extra_one_hot = (protein['extra_msa_mask'][:, :, None] *
                   tf.one_hot(protein['extra_msa'], 23))

  num_seq, num_res, _ = shape_helpers.shape_list(sample_one_hot)
  extra_num_seq, _, _ = shape_helpers.shape_list(extra_one_hot)

  # Compute tf.einsum('mrc,nrc,c->mn', sample_one_hot, extra_one_hot, weights)
  # in an optimized fashion to avoid possible memory or computation blowup.
  agreement = tf.matmul(
      tf.reshape(extra_one_hot, [extra_num_seq, num_res * 23]),
      tf.reshape(sample_one_hot * weights, [num_seq, num_res * 23]),
      transpose_b=True)

  # Assign each sequence in the extra sequences to the closest MSA sample
  protein['extra_cluster_assignment'] = tf.argmax(
      agreement, axis=1, output_type=tf.int32)

  return protein


@curry1
def summarize_clusters(protein):
  """"""Produce profile and deletion_matrix_mean within each cluster.""""""
  num_seq = shape_helpers.shape_list(protein['msa'])[0]
  def csum(x):
    return tf.math.unsorted_segment_sum(
        x, protein['extra_cluster_assignment'], num_seq)

  mask = protein['extra_msa_mask']
  mask_counts = 1e-6 + protein['msa_mask'] + csum(mask)  # Include center

  msa_sum = csum(mask[:, :, None] * tf.one_hot(protein['extra_msa'], 23))
  msa_sum += tf.one_hot(protein['msa'], 23)  # Original sequence
  protein['cluster_profile'] = msa_sum / mask_counts[:, :, None]

  del msa_sum

  del_sum = csum(mask * protein['extra_deletion_matrix'])
  del_sum += protein['deletion_matrix']  # Original sequence
  protein['cluster_deletion_mean'] = del_sum / mask_counts
  del del_sum

  return protein


def make_msa_mask(protein):
  """"""Mask features are all ones, but will later be zero-padded.""""""
  protein['msa_mask'] = tf.ones(
      shape_helpers.shape_list(protein['msa']), dtype=tf.float32)
  protein['msa_row_mask'] = tf.ones(
      shape_helpers.shape_list(protein['msa'])[0], dtype=tf.float32)
  return protein


def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
  """"""Create pseudo beta features.""""""
  is_gly = tf.equal(aatype, residue_constants.restype_order['G'])
  ca_idx = residue_constants.atom_order['CA']
  cb_idx = residue_constants.atom_order['CB']
  pseudo_beta = tf.where(
      tf.tile(is_gly[..., None], [1] * len(is_gly.shape) + [3]),
      all_atom_positions[..., ca_idx, :],
      all_atom_positions[..., cb_idx, :])

  if all_atom_masks is not None:
    pseudo_beta_mask = tf.where(
        is_gly, all_atom_masks[..., ca_idx], all_atom_masks[..., cb_idx])
    pseudo_beta_mask = tf.cast(pseudo_beta_mask, tf.float32)
    return pseudo_beta, pseudo_beta_mask
  else:
    return pseudo_beta


@curry1
def make_pseudo_beta(protein, prefix=''):
  """"""Create pseudo-beta (alpha for glycine) position and mask.""""""
  assert prefix in ['', 'template_']
  protein[prefix + 'pseudo_beta'], protein[prefix + 'pseudo_beta_mask'] = (
      pseudo_beta_fn(
          protein['template_aatype' if prefix else 'all_atom_aatype'],
          protein[prefix + 'all_atom_positions'],
          protein['template_all_atom_masks' if prefix else 'all_atom_mask']))
  return protein


@curry1
def add_constant_field(protein, key, value):
  protein[key] = tf.convert_to_tensor(value)
  return protein


def shaped_categorical(probs, epsilon=1e-10):
  ds = shape_helpers.shape_list(probs)
  num_classes = ds[-1]
  counts = tf.random.categorical(
      tf.reshape(tf.log(probs + epsilon), [-1, num_classes]),
      1,
      dtype=tf.int32)
  return tf.reshape(counts, ds[:-1])


def make_hhblits_profile(protein):
  """"""Compute the HHblits MSA profile if not already present.""""""
  if 'hhblits_profile' in protein:
    return protein

  # Compute the profile for every residue (over all MSA sequences).
  protein['hhblits_profile'] = tf.reduce_mean(
      tf.one_hot(protein['msa'], 22), axis=0)
  return protein


@curry1
def make_masked_msa(protein, config, replace_fraction):
  """"""Create data for BERT on raw MSA.""""""
  # Add a random amino acid uniformly
  random_aa = tf.constant([0.05] * 20 + [0., 0.], dtype=tf.float32)

  categorical_probs = (
      config.uniform_prob * random_aa +
      config.profile_prob * protein['hhblits_profile'] +
      config.same_prob * tf.one_hot(protein['msa'], 22))

  # Put all remaining probability on [MASK] which is a new column
  pad_shapes = [[0, 0] for _ in range(len(categorical_probs.shape))]
  pad_shapes[-1][1] = 1
  mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
  assert mask_prob >= 0.
  categorical_probs = tf.pad(
      categorical_probs, pad_shapes, constant_values=mask_prob)

  sh = shape_helpers.shape_list(protein['msa'])
  mask_position = tf.random.uniform(sh) < replace_fraction

  bert_msa = shaped_categorical(categorical_probs)
  bert_msa = tf.where(mask_position, bert_msa, protein['msa'])

  # Mix real and masked MSA
  protein['bert_mask'] = tf.cast(mask_position, tf.float32)
  protein['true_msa'] = protein['msa']
  protein['msa'] = bert_msa

  return protein


@curry1
def make_fixed_size(protein, shape_schema, msa_cluster_size, extra_msa_size,
                    num_res, num_templates=0):
  """"""Guess at the MSA and sequence dimensions to make fixed size.""""""

  pad_size_map = {
      NUM_RES: num_res,
      NUM_MSA_SEQ: msa_cluster_size,
      NUM_EXTRA_SEQ: extra_msa_size,
      NUM_TEMPLATES: num_templates,
  }

  for k, v in protein.items():
    # Don't transfer this to the accelerator.
    if k == 'extra_cluster_assignment':
      continue
    shape = v.shape.as_list()
    schema = shape_schema[k]
    assert len(shape) == len(schema), (
        f'Rank mismatch between shape and shape schema for {k}: '
        f'{shape} vs {schema}')
    pad_size = [
        pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)
    ]
    padding = [(0, p - tf.shape(v)[i]) for i, p in enumerate(pad_size)]
    if padding:
      protein[k] = tf.pad(
          v, padding, name=f'pad_to_fixed_{k}')
      protein[k].set_shape(pad_size)

  return protein


@curry1
def make_msa_feat(protein):
  """"""Create and concatenate MSA features.""""""
  # Whether there is a domain break. Always zero for chains, but keeping
  # for compatibility with domain datasets.
  has_break = tf.clip_by_value(
      tf.cast(protein['between_segment_residues'], tf.float32),
      0, 1)
  aatype_1hot = tf.one_hot(protein['aatype'], 21, axis=-1)

  target_feat = [
      tf.expand_dims(has_break, axis=-1),
      aatype_1hot,  # Everyone gets the original sequence.
  ]

  msa_1hot = tf.one_hot(protein['msa'], 23, axis=-1)
  has_deletion = tf.clip_by_value(protein['deletion_matrix'], 0., 1.)
  deletion_value = tf.atan(protein['deletion_matrix'] / 3.) * (2. / np.pi)

  msa_feat = [
      msa_1hot,
      tf.expand_dims(has_deletion, axis=-1),
      tf.expand_dims(deletion_value, axis=-1),
  ]

  if 'cluster_profile' in protein:
    deletion_mean_value = (
        tf.atan(protein['cluster_deletion_mean'] / 3.) * (2. / np.pi))
    msa_feat.extend([
        protein['cluster_profile'],
        tf.expand_dims(deletion_mean_value, axis=-1),
    ])

  if 'extra_deletion_matrix' in protein:
    protein['extra_has_deletion'] = tf.clip_by_value(
        protein['extra_deletion_matrix'], 0., 1.)
    protein['extra_deletion_value'] = tf.atan(
        protein['extra_deletion_matrix'] / 3.) * (2. / np.pi)

  protein['msa_feat'] = tf.concat(msa_feat, axis=-1)
  protein['target_feat'] = tf.concat(target_feat, axis=-1)
  return protein


@curry1
def select_feat(protein, feature_list):
  return {k: v for k, v in protein.items() if k in feature_list}


@curry1
def crop_templates(protein, max_templates):
  for k, v in protein.items():
    if k.startswith('template_'):
      protein[k] = v[:max_templates]
  return protein


@curry1
def random_crop_to_size(protein, crop_size, max_templates, shape_schema,
                        subsample_templates=False):
  """"""Crop randomly to `crop_size`, or keep as is if shorter than that.""""""
  seq_length = protein['seq_length']
  if 'template_mask' in protein:
    num_templates = tf.cast(
        shape_helpers.shape_list(protein['template_mask'])[0], tf.int32)
  else:
    num_templates = tf.constant(0, dtype=tf.int32)
  num_res_crop_size = tf.math.minimum(seq_length, crop_size)

  # Ensures that the cropping of residues and templates happens in the same way
  # across ensembling iterations.
  # Do not use for randomness that should vary in ensembling.
  seed_maker = utils.SeedMaker(initial_seed=protein['random_crop_to_size_seed'])

  if subsample_templates:
    templates_crop_start = tf.random.stateless_uniform(
        shape=(), minval=0, maxval=num_templates + 1, dtype=tf.int32,
        seed=seed_maker())
  else:
    templates_crop_start = 0

  num_templates_crop_size = tf.math.minimum(
      num_templates - templates_crop_start, max_templates)

  num_res_crop_start = tf.random.stateless_uniform(
      shape=(), minval=0, maxval=seq_length - num_res_crop_size + 1,
      dtype=tf.int32, seed=seed_maker())

  templates_select_indices = tf.argsort(tf.random.stateless_uniform(
      [num_templates], seed=seed_maker()))

  for k, v in protein.items():
    if k not in shape_schema or (
        'template' not in k and NUM_RES not in shape_schema[k]):
      continue

    # randomly permute the templates before cropping them.
    if k.startswith('template') and subsample_templates:
      v = tf.gather(v, templates_select_indices)

    crop_sizes = []
    crop_starts = []
    for i, (dim_size, dim) in enumerate(zip(shape_schema[k],
                                            shape_helpers.shape_list(v))):
      is_num_res = (dim_size == NUM_RES)
      if i == 0 and k.startswith('template'):
        crop_size = num_templates_crop_size
        crop_start = templates_crop_start
      else:
        crop_start = num_res_crop_start if is_num_res else 0
        crop_size = (num_res_crop_size if is_num_res else
                     (-1 if dim is None else dim))
      crop_sizes.append(crop_size)
      crop_starts.append(crop_start)
    protein[k] = tf.slice(v, crop_starts, crop_sizes)

  protein['seq_length'] = num_res_crop_size
  return protein


def make_atom14_masks(protein):
  """"""Construct denser atom positions (14 dimensions instead of 37).""""""
  restype_atom14_to_atom37 = []  # mapping (restype, atom14) --> atom37
  restype_atom37_to_atom14 = []  # mapping (restype, atom37) --> atom14
  restype_atom14_mask = []

  for rt in residue_constants.restypes:
    atom_names = residue_constants.restype_name_to_atom14_names[
        residue_constants.restype_1to3[rt]]

    restype_atom14_to_atom37.append([
        (residue_constants.atom_order[name] if name else 0)
        for name in atom_names
    ])

    atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
    restype_atom37_to_atom14.append([
        (atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
        for name in residue_constants.atom_types
    ])

    restype_atom14_mask.append([(1. if name else 0.) for name in atom_names])

  # Add dummy mapping for restype 'UNK'
  restype_atom14_to_atom37.append([0] * 14)
  restype_atom37_to_atom14.append([0] * 37)
  restype_atom14_mask.append([0.] * 14)

  restype_atom14_to_atom37 = np.array(restype_atom14_to_atom37, dtype=np.int32)
  restype_atom37_to_atom14 = np.array(restype_atom37_to_atom14, dtype=np.int32)
  restype_atom14_mask = np.array(restype_atom14_mask, dtype=np.float32)

  # create the mapping for (residx, atom14) --> atom37, i.e. an array
  # with shape (num_res, 14) containing the atom37 indices for this protein
  residx_atom14_to_atom37 = tf.gather(restype_atom14_to_atom37,
                                      protein['aatype'])
  residx_atom14_mask = tf.gather(restype_atom14_mask,
                                 protein['aatype'])

  protein['atom14_atom_exists'] = residx_atom14_mask
  protein['residx_atom14_to_atom37'] = residx_atom14_to_atom37

  # create the gather indices for mapping back
  residx_atom37_to_atom14 = tf.gather(restype_atom37_to_atom14,
                                      protein['aatype'])
  protein['residx_atom37_to_atom14'] = residx_atom37_to_atom14

  # create the corresponding mask
  restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
  for restype, restype_letter in enumerate(residue_constants.restypes):
    restype_name = residue_constants.restype_1to3[restype_letter]
    atom_names = residue_constants.residue_atoms[restype_name]
    for atom_name in atom_names:
      atom_type = residue_constants.atom_order[atom_name]
      restype_atom37_mask[restype, atom_type] = 1

  residx_atom37_mask = tf.gather(restype_atom37_mask,
                                 protein['aatype'])
  protein['atom37_atom_exists'] = residx_atom37_mask

  return protein
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/model/tf/utils.py",".py","1276","48","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Shared utilities for various components.""""""
import tensorflow.compat.v1 as tf


def tf_combine_mask(*masks):
  """"""Take the intersection of float-valued masks.""""""
  ret = 1
  for m in masks:
    ret *= m
  return ret


class SeedMaker(object):
  """"""Return unique seeds.""""""

  def __init__(self, initial_seed=0):
    self.next_seed = initial_seed

  def __call__(self):
    i = self.next_seed
    self.next_seed += 1
    return i

seed_maker = SeedMaker()


def make_random_seed():
  return tf.random.uniform([2],
                           tf.int32.min,
                           tf.int32.max,
                           tf.int32,
                           seed=seed_maker())

","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/relax_test.py",".py","3827","90","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for relax.""""""
import os

from absl.testing import absltest
from alphafold.common import protein
from alphafold.relax import relax
import numpy as np
# Internal import (7716).


class RunAmberRelaxTest(absltest.TestCase):

  def setUp(self):
    super().setUp()
    self.test_dir = os.path.join(
        absltest.get_default_test_srcdir(),
        'alphafold/relax/testdata/')
    self.test_config = {
        'max_iterations': 1,
        'tolerance': 2.39,
        'stiffness': 10.0,
        'exclude_residues': [],
        'max_outer_iterations': 1,
        'use_gpu': False}

  def test_process(self):
    amber_relax = relax.AmberRelaxation(**self.test_config)

    with open(os.path.join(self.test_dir, 'model_output.pdb')) as f:
      test_prot = protein.from_pdb_string(f.read())
    pdb_min, debug_info, num_violations = amber_relax.process(prot=test_prot)

    self.assertCountEqual(debug_info.keys(),
                          set({'initial_energy', 'final_energy',
                               'attempts', 'rmsd'}))
    self.assertLess(debug_info['final_energy'], debug_info['initial_energy'])
    self.assertGreater(debug_info['rmsd'], 0)

    prot_min = protein.from_pdb_string(pdb_min)
    # Most protein properties should be unchanged.
    np.testing.assert_almost_equal(test_prot.aatype, prot_min.aatype)
    np.testing.assert_almost_equal(test_prot.residue_index,
                                   prot_min.residue_index)
    # Atom mask and bfactors identical except for terminal OXT of last residue.
    np.testing.assert_almost_equal(test_prot.atom_mask[:-1, :],
                                   prot_min.atom_mask[:-1, :])
    np.testing.assert_almost_equal(test_prot.b_factors[:-1, :],
                                   prot_min.b_factors[:-1, :])
    np.testing.assert_almost_equal(test_prot.atom_mask[:, :-1],
                                   prot_min.atom_mask[:, :-1])
    np.testing.assert_almost_equal(test_prot.b_factors[:, :-1],
                                   prot_min.b_factors[:, :-1])
    # There are no residues with violations.
    np.testing.assert_equal(num_violations, np.zeros_like(num_violations))

  def test_unresolved_violations(self):
    amber_relax = relax.AmberRelaxation(**self.test_config)
    with open(os.path.join(self.test_dir,
                                 'with_violations_casp14.pdb')) as f:
      test_prot = protein.from_pdb_string(f.read())
    _, _, num_violations = amber_relax.process(prot=test_prot)
    exp_num_violations = np.array(
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
         0, 0, 0, 0])
    # Check no violations were added. Can't check exactly due to stochasticity.
    self.assertTrue(np.all(np.array(num_violations) <= exp_num_violations))


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/amber_minimize.py",".py","19061","506","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Restrained Amber Minimization of a structure.""""""

import io
import time
from typing import Collection, Optional, Sequence

from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.model import folding
from alphafold.relax import cleanup
from alphafold.relax import utils
import ml_collections
import numpy as np
import jax
from simtk import openmm
from simtk import unit
from simtk.openmm import app as openmm_app
from simtk.openmm.app.internal.pdbstructure import PdbStructure


ENERGY = unit.kilocalories_per_mole
LENGTH = unit.angstroms


def will_restrain(atom: openmm_app.Atom, rset: str) -> bool:
  """"""Returns True if the atom will be restrained by the given restraint set.""""""

  if rset == ""non_hydrogen"":
    return atom.element.name != ""hydrogen""
  elif rset == ""c_alpha"":
    return atom.name == ""CA""


def _add_restraints(
    system: openmm.System,
    reference_pdb: openmm_app.PDBFile,
    stiffness: unit.Unit,
    rset: str,
    exclude_residues: Sequence[int]):
  """"""Adds a harmonic potential that restrains the system to a structure.""""""
  assert rset in [""non_hydrogen"", ""c_alpha""]

  force = openmm.CustomExternalForce(
      ""0.5 * k * ((x-x0)^2 + (y-y0)^2 + (z-z0)^2)"")
  force.addGlobalParameter(""k"", stiffness)
  for p in [""x0"", ""y0"", ""z0""]:
    force.addPerParticleParameter(p)

  for i, atom in enumerate(reference_pdb.topology.atoms()):
    if atom.residue.index in exclude_residues:
      continue
    if will_restrain(atom, rset):
      force.addParticle(i, reference_pdb.positions[i])
  logging.info(""Restraining %d / %d particles."",
               force.getNumParticles(), system.getNumParticles())
  system.addForce(force)


def _openmm_minimize(
    pdb_str: str,
    max_iterations: int,
    tolerance: unit.Unit,
    stiffness: unit.Unit,
    restraint_set: str,
    exclude_residues: Sequence[int],
    use_gpu: bool):
  """"""Minimize energy via openmm.""""""

  pdb_file = io.StringIO(pdb_str)
  pdb = openmm_app.PDBFile(pdb_file)

  force_field = openmm_app.ForceField(""amber99sb.xml"")
  constraints = openmm_app.HBonds
  system = force_field.createSystem(
      pdb.topology, constraints=constraints)
  if stiffness > 0 * ENERGY / (LENGTH**2):
    _add_restraints(system, pdb, stiffness, restraint_set, exclude_residues)

  integrator = openmm.LangevinIntegrator(0, 0.01, 0.0)
  platform = openmm.Platform.getPlatformByName(""CUDA"" if use_gpu else ""CPU"")
  simulation = openmm_app.Simulation(
      pdb.topology, system, integrator, platform)
  simulation.context.setPositions(pdb.positions)

  ret = {}
  state = simulation.context.getState(getEnergy=True, getPositions=True)
  ret[""einit""] = state.getPotentialEnergy().value_in_unit(ENERGY)
  ret[""posinit""] = state.getPositions(asNumpy=True).value_in_unit(LENGTH)
  simulation.minimizeEnergy(maxIterations=max_iterations,
                            tolerance=tolerance)
  state = simulation.context.getState(getEnergy=True, getPositions=True)
  ret[""efinal""] = state.getPotentialEnergy().value_in_unit(ENERGY)
  ret[""pos""] = state.getPositions(asNumpy=True).value_in_unit(LENGTH)
  ret[""min_pdb""] = _get_pdb_string(simulation.topology, state.getPositions())
  return ret


def _get_pdb_string(topology: openmm_app.Topology, positions: unit.Quantity):
  """"""Returns a pdb string provided OpenMM topology and positions.""""""
  with io.StringIO() as f:
    openmm_app.PDBFile.writeFile(topology, positions, f)
    return f.getvalue()


def _check_cleaned_atoms(pdb_cleaned_string: str, pdb_ref_string: str):
  """"""Checks that no atom positions have been altered by cleaning.""""""
  cleaned = openmm_app.PDBFile(io.StringIO(pdb_cleaned_string))
  reference = openmm_app.PDBFile(io.StringIO(pdb_ref_string))

  cl_xyz = np.array(cleaned.getPositions().value_in_unit(LENGTH))
  ref_xyz = np.array(reference.getPositions().value_in_unit(LENGTH))

  for ref_res, cl_res in zip(reference.topology.residues(),
                             cleaned.topology.residues()):
    assert ref_res.name == cl_res.name
    for rat in ref_res.atoms():
      for cat in cl_res.atoms():
        if cat.name == rat.name:
          if not np.array_equal(cl_xyz[cat.index], ref_xyz[rat.index]):
            raise ValueError(f""Coordinates of cleaned atom {cat} do not match ""
                             f""coordinates of reference atom {rat}."")


def _check_residues_are_well_defined(prot: protein.Protein):
  """"""Checks that all residues contain non-empty atom sets.""""""
  if (prot.atom_mask.sum(axis=-1) == 0).any():
    raise ValueError(""Amber minimization can only be performed on proteins with""
                     "" well-defined residues. This protein contains at least""
                     "" one residue with no atoms."")


def _check_atom_mask_is_ideal(prot):
  """"""Sanity-check the atom mask is ideal, up to a possible OXT.""""""
  atom_mask = prot.atom_mask
  ideal_atom_mask = protein.ideal_atom_mask(prot)
  utils.assert_equal_nonterminal_atom_types(atom_mask, ideal_atom_mask)


def clean_protein(
    prot: protein.Protein,
    checks: bool = True):
  """"""Adds missing atoms to Protein instance.

  Args:
    prot: A `protein.Protein` instance.
    checks: A `bool` specifying whether to add additional checks to the cleaning
      process.

  Returns:
    pdb_string: A string of the cleaned protein.
  """"""
  _check_atom_mask_is_ideal(prot)

  # Clean pdb.
  prot_pdb_string = protein.to_pdb(prot)
  pdb_file = io.StringIO(prot_pdb_string)
  alterations_info = {}
  fixed_pdb = cleanup.fix_pdb(pdb_file, alterations_info)
  fixed_pdb_file = io.StringIO(fixed_pdb)
  pdb_structure = PdbStructure(fixed_pdb_file)
  cleanup.clean_structure(pdb_structure, alterations_info)

  logging.info(""alterations info: %s"", alterations_info)

  # Write pdb file of cleaned structure.
  as_file = openmm_app.PDBFile(pdb_structure)
  pdb_string = _get_pdb_string(as_file.getTopology(), as_file.getPositions())
  if checks:
    _check_cleaned_atoms(pdb_string, prot_pdb_string)
  return pdb_string


def make_atom14_positions(prot):
  """"""Constructs denser atom positions (14 dimensions instead of 37).""""""
  restype_atom14_to_atom37 = []  # mapping (restype, atom14) --> atom37
  restype_atom37_to_atom14 = []  # mapping (restype, atom37) --> atom14
  restype_atom14_mask = []

  for rt in residue_constants.restypes:
    atom_names = residue_constants.restype_name_to_atom14_names[
        residue_constants.restype_1to3[rt]]

    restype_atom14_to_atom37.append([
        (residue_constants.atom_order[name] if name else 0)
        for name in atom_names
    ])

    atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
    restype_atom37_to_atom14.append([
        (atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
        for name in residue_constants.atom_types
    ])

    restype_atom14_mask.append([(1. if name else 0.) for name in atom_names])

  # Add dummy mapping for restype 'UNK'.
  restype_atom14_to_atom37.append([0] * 14)
  restype_atom37_to_atom14.append([0] * 37)
  restype_atom14_mask.append([0.] * 14)

  restype_atom14_to_atom37 = np.array(restype_atom14_to_atom37, dtype=np.int32)
  restype_atom37_to_atom14 = np.array(restype_atom37_to_atom14, dtype=np.int32)
  restype_atom14_mask = np.array(restype_atom14_mask, dtype=np.float32)

  # Create the mapping for (residx, atom14) --> atom37, i.e. an array
  # with shape (num_res, 14) containing the atom37 indices for this protein.
  residx_atom14_to_atom37 = restype_atom14_to_atom37[prot[""aatype""]]
  residx_atom14_mask = restype_atom14_mask[prot[""aatype""]]

  # Create a mask for known ground truth positions.
  residx_atom14_gt_mask = residx_atom14_mask * np.take_along_axis(
      prot[""all_atom_mask""], residx_atom14_to_atom37, axis=1).astype(np.float32)

  # Gather the ground truth positions.
  residx_atom14_gt_positions = residx_atom14_gt_mask[:, :, None] * (
      np.take_along_axis(prot[""all_atom_positions""],
                         residx_atom14_to_atom37[..., None],
                         axis=1))

  prot[""atom14_atom_exists""] = residx_atom14_mask
  prot[""atom14_gt_exists""] = residx_atom14_gt_mask
  prot[""atom14_gt_positions""] = residx_atom14_gt_positions

  prot[""residx_atom14_to_atom37""] = residx_atom14_to_atom37

  # Create the gather indices for mapping back.
  residx_atom37_to_atom14 = restype_atom37_to_atom14[prot[""aatype""]]
  prot[""residx_atom37_to_atom14""] = residx_atom37_to_atom14

  # Create the corresponding mask.
  restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
  for restype, restype_letter in enumerate(residue_constants.restypes):
    restype_name = residue_constants.restype_1to3[restype_letter]
    atom_names = residue_constants.residue_atoms[restype_name]
    for atom_name in atom_names:
      atom_type = residue_constants.atom_order[atom_name]
      restype_atom37_mask[restype, atom_type] = 1

  residx_atom37_mask = restype_atom37_mask[prot[""aatype""]]
  prot[""atom37_atom_exists""] = residx_atom37_mask

  # As the atom naming is ambiguous for 7 of the 20 amino acids, provide
  # alternative ground truth coordinates where the naming is swapped
  restype_3 = [
      residue_constants.restype_1to3[res] for res in residue_constants.restypes
  ]
  restype_3 += [""UNK""]

  # Matrices for renaming ambiguous atoms.
  all_matrices = {res: np.eye(14, dtype=np.float32) for res in restype_3}
  for resname, swap in residue_constants.residue_atom_renaming_swaps.items():
    correspondences = np.arange(14)
    for source_atom_swap, target_atom_swap in swap.items():
      source_index = residue_constants.restype_name_to_atom14_names[
          resname].index(source_atom_swap)
      target_index = residue_constants.restype_name_to_atom14_names[
          resname].index(target_atom_swap)
      correspondences[source_index] = target_index
      correspondences[target_index] = source_index
      renaming_matrix = np.zeros((14, 14), dtype=np.float32)
      for index, correspondence in enumerate(correspondences):
        renaming_matrix[index, correspondence] = 1.
    all_matrices[resname] = renaming_matrix.astype(np.float32)
  renaming_matrices = np.stack([all_matrices[restype] for restype in restype_3])

  # Pick the transformation matrices for the given residue sequence
  # shape (num_res, 14, 14).
  renaming_transform = renaming_matrices[prot[""aatype""]]

  # Apply it to the ground truth positions. shape (num_res, 14, 3).
  alternative_gt_positions = np.einsum(""rac,rab->rbc"",
                                       residx_atom14_gt_positions,
                                       renaming_transform)
  prot[""atom14_alt_gt_positions""] = alternative_gt_positions

  # Create the mask for the alternative ground truth (differs from the
  # ground truth mask, if only one of the atoms in an ambiguous pair has a
  # ground truth position).
  alternative_gt_mask = np.einsum(""ra,rab->rb"",
                                  residx_atom14_gt_mask,
                                  renaming_transform)

  prot[""atom14_alt_gt_exists""] = alternative_gt_mask

  # Create an ambiguous atoms mask.  shape: (21, 14).
  restype_atom14_is_ambiguous = np.zeros((21, 14), dtype=np.float32)
  for resname, swap in residue_constants.residue_atom_renaming_swaps.items():
    for atom_name1, atom_name2 in swap.items():
      restype = residue_constants.restype_order[
          residue_constants.restype_3to1[resname]]
      atom_idx1 = residue_constants.restype_name_to_atom14_names[resname].index(
          atom_name1)
      atom_idx2 = residue_constants.restype_name_to_atom14_names[resname].index(
          atom_name2)
      restype_atom14_is_ambiguous[restype, atom_idx1] = 1
      restype_atom14_is_ambiguous[restype, atom_idx2] = 1

  # From this create an ambiguous_mask for the given sequence.
  prot[""atom14_atom_is_ambiguous""] = (
      restype_atom14_is_ambiguous[prot[""aatype""]])

  return prot


def find_violations(prot_np: protein.Protein):
  """"""Analyzes a protein and returns structural violation information.

  Args:
    prot_np: A protein.

  Returns:
    violations: A `dict` of structure components with structural violations.
    violation_metrics: A `dict` of violation metrics.
  """"""
  batch = {
      ""aatype"": prot_np.aatype,
      ""all_atom_positions"": prot_np.atom_positions.astype(np.float32),
      ""all_atom_mask"": prot_np.atom_mask.astype(np.float32),
      ""residue_index"": prot_np.residue_index,
  }

  batch[""seq_mask""] = np.ones_like(batch[""aatype""], np.float32)
  batch = make_atom14_positions(batch)

  violations = folding.find_structural_violations(
      batch=batch,
      atom14_pred_positions=batch[""atom14_gt_positions""],
      config=ml_collections.ConfigDict(
          {""violation_tolerance_factor"": 12,  # Taken from model config.
           ""clash_overlap_tolerance"": 1.5,  # Taken from model config.
          }))
  violation_metrics = folding.compute_violation_metrics(
      batch=batch,
      atom14_pred_positions=batch[""atom14_gt_positions""],
      violations=violations,
  )

  return violations, violation_metrics


def get_violation_metrics(prot: protein.Protein):
  """"""Computes violation and alignment metrics.""""""
  structural_violations, struct_metrics = find_violations(prot)
  violation_idx = np.flatnonzero(
      structural_violations[""total_per_residue_violations_mask""])

  struct_metrics[""residue_violations""] = violation_idx
  struct_metrics[""num_residue_violations""] = len(violation_idx)
  struct_metrics[""structural_violations""] = structural_violations
  return struct_metrics


def _run_one_iteration(
    *,
    pdb_string: str,
    max_iterations: int,
    tolerance: float,
    stiffness: float,
    restraint_set: str,
    max_attempts: int,
    use_gpu: bool,
    exclude_residues: Optional[Collection[int]] = None):
  """"""Runs the minimization pipeline.

  Args:
    pdb_string: A pdb string.
    max_iterations: An `int` specifying the maximum number of L-BFGS iterations.
    A value of 0 specifies no limit.
    tolerance: kcal/mol, the energy tolerance of L-BFGS.
    stiffness: kcal/mol A**2, spring constant of heavy atom restraining
      potential.
    restraint_set: The set of atoms to restrain.
    max_attempts: The maximum number of minimization attempts.
    use_gpu: Whether to run on GPU.
    exclude_residues: An optional list of zero-indexed residues to exclude from
        restraints.

  Returns:
    A `dict` of minimization info.
  """"""
  exclude_residues = exclude_residues or []

  # Assign physical dimensions.
  tolerance = tolerance * ENERGY
  stiffness = stiffness * ENERGY / (LENGTH**2)

  start = time.time()
  minimized = False
  attempts = 0
  while not minimized and attempts < max_attempts:
    attempts += 1
    try:
      logging.info(""Minimizing protein, attempt %d of %d."",
                   attempts, max_attempts)
      ret = _openmm_minimize(
          pdb_string, max_iterations=max_iterations,
          tolerance=tolerance, stiffness=stiffness,
          restraint_set=restraint_set,
          exclude_residues=exclude_residues,
          use_gpu=use_gpu)
      minimized = True
    except Exception as e:  # pylint: disable=broad-except
      logging.info(e)
  if not minimized:
    raise ValueError(f""Minimization failed after {max_attempts} attempts."")
  ret[""opt_time""] = time.time() - start
  ret[""min_attempts""] = attempts
  return ret


def run_pipeline(
    prot: protein.Protein,
    stiffness: float,
    use_gpu: bool,
    max_outer_iterations: int = 1,
    place_hydrogens_every_iteration: bool = True,
    max_iterations: int = 0,
    tolerance: float = 2.39,
    restraint_set: str = ""non_hydrogen"",
    max_attempts: int = 100,
    checks: bool = True,
    exclude_residues: Optional[Sequence[int]] = None):
  """"""Run iterative amber relax.

  Successive relax iterations are performed until all violations have been
  resolved. Each iteration involves a restrained Amber minimization, with
  restraint exclusions determined by violation-participating residues.

  Args:
    prot: A protein to be relaxed.
    stiffness: kcal/mol A**2, the restraint stiffness.
    use_gpu: Whether to run on GPU.
    max_outer_iterations: The maximum number of iterative minimization.
    place_hydrogens_every_iteration: Whether hydrogens are re-initialized
        prior to every minimization.
    max_iterations: An `int` specifying the maximum number of L-BFGS steps
        per relax iteration. A value of 0 specifies no limit.
    tolerance: kcal/mol, the energy tolerance of L-BFGS.
        The default value is the OpenMM default.
    restraint_set: The set of atoms to restrain.
    max_attempts: The maximum number of minimization attempts per iteration.
    checks: Whether to perform cleaning checks.
    exclude_residues: An optional list of zero-indexed residues to exclude from
        restraints.

  Returns:
    out: A dictionary of output values.
  """"""

  # `protein.to_pdb` will strip any poorly-defined residues so we need to
  # perform this check before `clean_protein`.
  _check_residues_are_well_defined(prot)
  pdb_string = clean_protein(prot, checks=checks)

  exclude_residues = exclude_residues or []
  exclude_residues = set(exclude_residues)
  violations = np.inf
  iteration = 0

  while violations > 0 and iteration < max_outer_iterations:
    ret = _run_one_iteration(
        pdb_string=pdb_string,
        exclude_residues=exclude_residues,
        max_iterations=max_iterations,
        tolerance=tolerance,
        stiffness=stiffness,
        restraint_set=restraint_set,
        max_attempts=max_attempts,
        use_gpu=use_gpu)
    prot = protein.from_pdb_string(ret[""min_pdb""])
    if place_hydrogens_every_iteration:
      pdb_string = clean_protein(prot, checks=True)
    else:
      pdb_string = ret[""min_pdb""]
    # Calculation of violations can cause CUDA errors for some JAX versions.
    with jax.default_device(jax.devices(""cpu"")[0]):
      ret.update(get_violation_metrics(prot))
    ret.update({
        ""num_exclusions"": len(exclude_residues),
        ""iteration"": iteration,
    })
    violations = ret[""violations_per_residue""]
    exclude_residues = exclude_residues.union(ret[""residue_violations""])

    logging.info(""Iteration completed: Einit %.2f Efinal %.2f Time %.2f s ""
                 ""num residue violations %d num residue exclusions %d "",
                 ret[""einit""], ret[""efinal""], ret[""opt_time""],
                 ret[""num_residue_violations""], ret[""num_exclusions""])
    iteration += 1
  return ret
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/cleanup.py",".py","4832","128","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Cleans up a PDB file using pdbfixer in preparation for OpenMM simulations.

fix_pdb uses a third-party tool. We also support fixing some additional edge
cases like removing chains of length one (see clean_structure).
""""""
import io

import pdbfixer
from simtk.openmm import app
from simtk.openmm.app import element


def fix_pdb(pdbfile, alterations_info):
  """"""Apply pdbfixer to the contents of a PDB file; return a PDB string result.

  1) Replaces nonstandard residues.
  2) Removes heterogens (non protein residues) including water.
  3) Adds missing residues and missing atoms within existing residues.
  4) Adds hydrogens assuming pH=7.0.
  5) KeepIds is currently true, so the fixer must keep the existing chain and
     residue identifiers. This will fail for some files in wider PDB that have
     invalid IDs.

  Args:
    pdbfile: Input PDB file handle.
    alterations_info: A dict that will store details of changes made.

  Returns:
    A PDB string representing the fixed structure.
  """"""
  fixer = pdbfixer.PDBFixer(pdbfile=pdbfile)
  fixer.findNonstandardResidues()
  alterations_info['nonstandard_residues'] = fixer.nonstandardResidues
  fixer.replaceNonstandardResidues()
  _remove_heterogens(fixer, alterations_info, keep_water=False)
  fixer.findMissingResidues()
  alterations_info['missing_residues'] = fixer.missingResidues
  fixer.findMissingAtoms()
  alterations_info['missing_heavy_atoms'] = fixer.missingAtoms
  alterations_info['missing_terminals'] = fixer.missingTerminals
  fixer.addMissingAtoms(seed=0)
  fixer.addMissingHydrogens()
  out_handle = io.StringIO()
  app.PDBFile.writeFile(fixer.topology, fixer.positions, out_handle,
                        keepIds=True)
  return out_handle.getvalue()


def clean_structure(pdb_structure, alterations_info):
  """"""Applies additional fixes to an OpenMM structure, to handle edge cases.

  Args:
    pdb_structure: An OpenMM structure to modify and fix.
    alterations_info: A dict that will store details of changes made.
  """"""
  _replace_met_se(pdb_structure, alterations_info)
  _remove_chains_of_length_one(pdb_structure, alterations_info)


def _remove_heterogens(fixer, alterations_info, keep_water):
  """"""Removes the residues that Pdbfixer considers to be heterogens.

  Args:
    fixer: A Pdbfixer instance.
    alterations_info: A dict that will store details of changes made.
    keep_water: If True, water (HOH) is not considered to be a heterogen.
  """"""
  initial_resnames = set()
  for chain in fixer.topology.chains():
    for residue in chain.residues():
      initial_resnames.add(residue.name)
  fixer.removeHeterogens(keepWater=keep_water)
  final_resnames = set()
  for chain in fixer.topology.chains():
    for residue in chain.residues():
      final_resnames.add(residue.name)
  alterations_info['removed_heterogens'] = (
      initial_resnames.difference(final_resnames))


def _replace_met_se(pdb_structure, alterations_info):
  """"""Replace the Se in any MET residues that were not marked as modified.""""""
  modified_met_residues = []
  for res in pdb_structure.iter_residues():
    name = res.get_name_with_spaces().strip()
    if name == 'MET':
      s_atom = res.get_atom('SD')
      if s_atom.element_symbol == 'Se':
        s_atom.element_symbol = 'S'
        s_atom.element = element.get_by_symbol('S')
        modified_met_residues.append(s_atom.residue_number)
  alterations_info['Se_in_MET'] = modified_met_residues


def _remove_chains_of_length_one(pdb_structure, alterations_info):
  """"""Removes chains that correspond to a single amino acid.

  A single amino acid in a chain is both N and C terminus. There is no force
  template for this case.

  Args:
    pdb_structure: An OpenMM pdb_structure to modify and fix.
    alterations_info: A dict that will store details of changes made.
  """"""
  removed_chains = {}
  for model in pdb_structure.iter_models():
    valid_chains = [c for c in model.iter_chains() if len(c) > 1]
    invalid_chain_ids = [c.chain_id for c in model.iter_chains() if len(c) <= 1]
    model.chains = valid_chains
    for chain_id in invalid_chain_ids:
      model.chains_by_id.pop(chain_id)
    removed_chains[model.number] = invalid_chain_ids
  alterations_info['removed_chains'] = removed_chains
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/utils_test.py",".py","1996","56","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for utils.""""""

import os

from absl.testing import absltest
from alphafold.common import protein
from alphafold.relax import utils
import numpy as np
# Internal import (7716).


class UtilsTest(absltest.TestCase):

  def test_overwrite_b_factors(self):
    testdir = os.path.join(
        absltest.get_default_test_srcdir(),
        'alphafold/relax/testdata/'
        'multiple_disulfides_target.pdb')
    with open(testdir) as f:
      test_pdb = f.read()
    n_residues = 191
    bfactors = np.stack([np.arange(0, n_residues)] * 37, axis=-1)

    output_pdb = utils.overwrite_b_factors(test_pdb, bfactors)

    # Check that the atom lines are unchanged apart from the B-factors.
    atom_lines_original = [l for l in test_pdb.split('\n') if l[:4] == ('ATOM')]
    atom_lines_new = [l for l in output_pdb.split('\n') if l[:4] == ('ATOM')]
    for line_original, line_new in zip(atom_lines_original, atom_lines_new):
      self.assertEqual(line_original[:60].strip(), line_new[:60].strip())
      self.assertEqual(line_original[66:].strip(), line_new[66:].strip())

    # Check B-factors are correctly set for all atoms present.
    as_protein = protein.from_pdb_string(output_pdb)
    np.testing.assert_almost_equal(
        np.where(as_protein.atom_mask > 0, as_protein.b_factors, 0),
        np.where(as_protein.atom_mask > 0, bfactors, 0))


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/__init__.py",".py","618","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Amber relaxation.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/cleanup_test.py",".py","6170","138","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for relax.cleanup.""""""
import io

from absl.testing import absltest
from alphafold.relax import cleanup
from simtk.openmm.app.internal import pdbstructure


def _pdb_to_structure(pdb_str):
  handle = io.StringIO(pdb_str)
  return pdbstructure.PdbStructure(handle)


def _lines_to_structure(pdb_lines):
  return _pdb_to_structure('\n'.join(pdb_lines))


class CleanupTest(absltest.TestCase):

  def test_missing_residues(self):
    pdb_lines = ['SEQRES   1 C    3  CYS GLY LEU',
                 'ATOM      1  N   CYS C   1     -12.262  20.115  60.959  1.00 '
                 '19.08           N',
                 'ATOM      2  CA  CYS C   1     -11.065  20.934  60.773  1.00 '
                 '17.23           C',
                 'ATOM      3  C   CYS C   1     -10.002  20.742  61.844  1.00 '
                 '15.38           C',
                 'ATOM      4  O   CYS C   1     -10.284  20.225  62.929  1.00 '
                 '16.04           O',
                 'ATOM      5  N   LEU C   3      -7.688  18.700  62.045  1.00 '
                 '14.75           N',
                 'ATOM      6  CA  LEU C   3      -7.256  17.320  62.234  1.00 '
                 '16.81           C',
                 'ATOM      7  C   LEU C   3      -6.380  16.864  61.070  1.00 '
                 '16.95           C',
                 'ATOM      8  O   LEU C   3      -6.551  17.332  59.947  1.00 '
                 '16.97           O']
    input_handle = io.StringIO('\n'.join(pdb_lines))
    alterations = {}
    result = cleanup.fix_pdb(input_handle, alterations)
    structure = _pdb_to_structure(result)
    residue_names = [r.get_name() for r in structure.iter_residues()]
    self.assertCountEqual(residue_names, ['CYS', 'GLY', 'LEU'])
    self.assertCountEqual(alterations['missing_residues'].values(), [['GLY']])

  def test_missing_atoms(self):
    pdb_lines = ['SEQRES   1 A    1  PRO',
                 'ATOM      1  CA  PRO A   1       1.000   1.000   1.000  1.00 '
                 ' 0.00           C']
    input_handle = io.StringIO('\n'.join(pdb_lines))
    alterations = {}
    result = cleanup.fix_pdb(input_handle, alterations)
    structure = _pdb_to_structure(result)
    atom_names = [a.get_name() for a in structure.iter_atoms()]
    self.assertCountEqual(atom_names, ['N', 'CD', 'HD2', 'HD3', 'CG', 'HG2',
                                       'HG3', 'CB', 'HB2', 'HB3', 'CA', 'HA',
                                       'C', 'O', 'H2', 'H3', 'OXT'])
    missing_atoms_by_residue = list(alterations['missing_heavy_atoms'].values())
    self.assertLen(missing_atoms_by_residue, 1)
    atoms_added = [a.name for a in missing_atoms_by_residue[0]]
    self.assertCountEqual(atoms_added, ['N', 'CD', 'CG', 'CB', 'C', 'O'])
    missing_terminals_by_residue = alterations['missing_terminals']
    self.assertLen(missing_terminals_by_residue, 1)
    has_missing_terminal = [r.name for r in missing_terminals_by_residue.keys()]
    self.assertCountEqual(has_missing_terminal, ['PRO'])
    self.assertCountEqual([t for t in missing_terminals_by_residue.values()],
                          [['OXT']])

  def test_remove_heterogens(self):
    pdb_lines = ['SEQRES   1 A    1  GLY',
                 'ATOM      1  CA  GLY A   1       0.000   0.000   0.000  1.00 '
                 ' 0.00           C',
                 'ATOM      2   O  HOH A   2       0.000   0.000   0.000  1.00 '
                 ' 0.00           O']
    input_handle = io.StringIO('\n'.join(pdb_lines))
    alterations = {}
    result = cleanup.fix_pdb(input_handle, alterations)
    structure = _pdb_to_structure(result)
    self.assertCountEqual([res.get_name() for res in structure.iter_residues()],
                          ['GLY'])
    self.assertEqual(alterations['removed_heterogens'], set(['HOH']))

  def test_fix_nonstandard_residues(self):
    pdb_lines = ['SEQRES   1 A    1  DAL',
                 'ATOM      1  CA  DAL A   1       0.000   0.000   0.000  1.00 '
                 ' 0.00           C']
    input_handle = io.StringIO('\n'.join(pdb_lines))
    alterations = {}
    result = cleanup.fix_pdb(input_handle, alterations)
    structure = _pdb_to_structure(result)
    residue_names = [res.get_name() for res in structure.iter_residues()]
    self.assertCountEqual(residue_names, ['ALA'])
    self.assertLen(alterations['nonstandard_residues'], 1)
    original_res, new_name = alterations['nonstandard_residues'][0]
    self.assertEqual(original_res.id, '1')
    self.assertEqual(new_name, 'ALA')

  def test_replace_met_se(self):
    pdb_lines = ['SEQRES   1 A    1  MET',
                 'ATOM      1  SD  MET A   1       0.000   0.000   0.000  1.00 '
                 ' 0.00          Se']
    structure = _lines_to_structure(pdb_lines)
    alterations = {}
    cleanup._replace_met_se(structure, alterations)
    sd = [a for a in structure.iter_atoms() if a.get_name() == 'SD']
    self.assertLen(sd, 1)
    self.assertEqual(sd[0].element_symbol, 'S')
    self.assertCountEqual(alterations['Se_in_MET'], [sd[0].residue_number])

  def test_remove_chains_of_length_one(self):
    pdb_lines = ['SEQRES   1 A    1  GLY',
                 'ATOM      1  CA  GLY A   1       0.000   0.000   0.000  1.00 '
                 ' 0.00           C']
    structure = _lines_to_structure(pdb_lines)
    alterations = {}
    cleanup._remove_chains_of_length_one(structure, alterations)
    chains = list(structure.iter_chains())
    self.assertEmpty(chains)
    self.assertCountEqual(alterations['removed_chains'].values(), [['A']])


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/utils.py",".py","2501","70","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Utils for minimization.""""""
import io
from alphafold.common import residue_constants
from Bio import PDB
import numpy as np


def overwrite_b_factors(pdb_str: str, bfactors: np.ndarray) -> str:
  """"""Overwrites the B-factors in pdb_str with contents of bfactors array.

  Args:
    pdb_str: An input PDB string.
    bfactors: A numpy array with shape [1, n_residues, 37]. We assume that the
      B-factors are per residue; i.e. that the nonzero entries are identical in
      [0, i, :].

  Returns:
    A new PDB string with the B-factors replaced.
  """"""
  if bfactors.shape[-1] != residue_constants.atom_type_num:
    raise ValueError(
        f'Invalid final dimension size for bfactors: {bfactors.shape[-1]}.')

  parser = PDB.PDBParser(QUIET=True)
  handle = io.StringIO(pdb_str)
  structure = parser.get_structure('', handle)

  curr_resid = ('', '', '')
  idx = -1
  for atom in structure.get_atoms():
    atom_resid = atom.parent.get_id()
    if atom_resid != curr_resid:
      idx += 1
      if idx >= bfactors.shape[0]:
        raise ValueError('Index into bfactors exceeds number of residues. '
                         'B-factors shape: {shape}, idx: {idx}.')
    curr_resid = atom_resid
    atom.bfactor = bfactors[idx, residue_constants.atom_order['CA']]

  new_pdb = io.StringIO()
  pdb_io = PDB.PDBIO()
  pdb_io.set_structure(structure)
  pdb_io.save(new_pdb)
  return new_pdb.getvalue()


def assert_equal_nonterminal_atom_types(
    atom_mask: np.ndarray, ref_atom_mask: np.ndarray):
  """"""Checks that pre- and post-minimized proteins have same atom set.""""""
  # Ignore any terminal OXT atoms which may have been added by minimization.
  oxt = residue_constants.atom_order['OXT']
  no_oxt_mask = np.ones(shape=atom_mask.shape, dtype=bool)
  no_oxt_mask[..., oxt] = False
  np.testing.assert_almost_equal(ref_atom_mask[no_oxt_mask],
                                 atom_mask[no_oxt_mask])
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/amber_minimize_test.py",".py","4348","134","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for amber_minimize.""""""
import os

from absl.testing import absltest
from alphafold.common import protein
from alphafold.relax import amber_minimize
import numpy as np
# Internal import (7716).

_USE_GPU = False


def _load_test_protein(data_path):
  pdb_path = os.path.join(absltest.get_default_test_srcdir(), data_path)
  with open(pdb_path, 'r') as f:
    return protein.from_pdb_string(f.read())


class AmberMinimizeTest(absltest.TestCase):

  def test_multiple_disulfides_target(self):
    prot = _load_test_protein(
        'alphafold/relax/testdata/multiple_disulfides_target.pdb'
        )
    ret = amber_minimize.run_pipeline(prot, max_iterations=10, max_attempts=1,
                                      stiffness=10., use_gpu=_USE_GPU)
    self.assertIn('opt_time', ret)
    self.assertIn('min_attempts', ret)

  def test_raises_invalid_protein_assertion(self):
    prot = _load_test_protein(
        'alphafold/relax/testdata/multiple_disulfides_target.pdb'
        )
    prot.atom_mask[4, :] = 0
    with self.assertRaisesRegex(
        ValueError,
        'Amber minimization can only be performed on proteins with well-defined'
        ' residues. This protein contains at least one residue with no atoms.'):
      amber_minimize.run_pipeline(prot, max_iterations=10,
                                  stiffness=1.,
                                  max_attempts=1,
                                  use_gpu=_USE_GPU)

  def test_iterative_relax(self):
    prot = _load_test_protein(
        'alphafold/relax/testdata/with_violations.pdb'
        )
    violations = amber_minimize.get_violation_metrics(prot)
    self.assertGreater(violations['num_residue_violations'], 0)
    out = amber_minimize.run_pipeline(
        prot=prot, max_outer_iterations=10, stiffness=10., use_gpu=_USE_GPU)
    self.assertLess(out['efinal'], out['einit'])
    self.assertEqual(0, out['num_residue_violations'])

  def test_find_violations(self):
    prot = _load_test_protein(
        'alphafold/relax/testdata/multiple_disulfides_target.pdb'
        )
    viols, _ = amber_minimize.find_violations(prot)

    expected_between_residues_connection_mask = np.zeros((191,), np.float32)
    for residue in (42, 43, 59, 60, 135, 136):
      expected_between_residues_connection_mask[residue] = 1.0

    expected_clash_indices = np.array([
        [8, 4],
        [8, 5],
        [13, 3],
        [14, 1],
        [14, 4],
        [26, 4],
        [26, 5],
        [31, 8],
        [31, 10],
        [39, 0],
        [39, 1],
        [39, 2],
        [39, 3],
        [39, 4],
        [42, 5],
        [42, 6],
        [42, 7],
        [42, 8],
        [47, 7],
        [47, 8],
        [47, 9],
        [47, 10],
        [64, 4],
        [85, 5],
        [102, 4],
        [102, 5],
        [109, 13],
        [111, 5],
        [118, 6],
        [118, 7],
        [118, 8],
        [124, 4],
        [124, 5],
        [131, 5],
        [139, 7],
        [147, 4],
        [152, 7]], dtype=np.int32)
    expected_between_residues_clash_mask = np.zeros([191, 14])
    expected_between_residues_clash_mask[expected_clash_indices[:, 0],
                                         expected_clash_indices[:, 1]] += 1
    expected_per_atom_violations = np.zeros([191, 14])
    np.testing.assert_array_equal(
        viols['between_residues']['connections_per_residue_violation_mask'],
        expected_between_residues_connection_mask)
    np.testing.assert_array_equal(
        viols['between_residues']['clashes_per_atom_clash_mask'],
        expected_between_residues_clash_mask)
    np.testing.assert_array_equal(
        viols['within_residues']['per_atom_violations'],
        expected_per_atom_violations)


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/relax/relax.py",".py","3288","85","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Amber relaxation.""""""
from typing import Any, Dict, Sequence, Tuple
from alphafold.common import protein
from alphafold.relax import amber_minimize
from alphafold.relax import utils
import numpy as np


class AmberRelaxation(object):
  """"""Amber relaxation.""""""

  def __init__(self,
               *,
               max_iterations: int,
               tolerance: float,
               stiffness: float,
               exclude_residues: Sequence[int],
               max_outer_iterations: int,
               use_gpu: bool):
    """"""Initialize Amber Relaxer.

    Args:
      max_iterations: Maximum number of L-BFGS iterations. 0 means no max.
      tolerance: kcal/mol, the energy tolerance of L-BFGS.
      stiffness: kcal/mol A**2, spring constant of heavy atom restraining
        potential.
      exclude_residues: Residues to exclude from per-atom restraining.
        Zero-indexed.
      max_outer_iterations: Maximum number of violation-informed relax
       iterations. A value of 1 will run the non-iterative procedure used in
       CASP14. Use 20 so that >95% of the bad cases are relaxed. Relax finishes
       as soon as there are no violations, hence in most cases this causes no
       slowdown. In the worst case we do 20 outer iterations.
      use_gpu: Whether to run on GPU.
    """"""

    self._max_iterations = max_iterations
    self._tolerance = tolerance
    self._stiffness = stiffness
    self._exclude_residues = exclude_residues
    self._max_outer_iterations = max_outer_iterations
    self._use_gpu = use_gpu

  def process(self, *,
              prot: protein.Protein
              ) -> Tuple[str, Dict[str, Any], Sequence[float]]:
    """"""Runs Amber relax on a prediction, adds hydrogens, returns PDB string.""""""
    out = amber_minimize.run_pipeline(
        prot=prot, max_iterations=self._max_iterations,
        tolerance=self._tolerance, stiffness=self._stiffness,
        exclude_residues=self._exclude_residues,
        max_outer_iterations=self._max_outer_iterations,
        use_gpu=self._use_gpu)
    min_pos = out['pos']
    start_pos = out['posinit']
    rmsd = np.sqrt(np.sum((start_pos - min_pos)**2) / start_pos.shape[0])
    debug_data = {
        'initial_energy': out['einit'],
        'final_energy': out['efinal'],
        'attempts': out['min_attempts'],
        'rmsd': rmsd
    }
    min_pdb = out['min_pdb']
    min_pdb = utils.overwrite_b_factors(min_pdb, prot.b_factors)
    utils.assert_equal_nonterminal_atom_types(
        protein.from_pdb_string(min_pdb).atom_mask,
        prot.atom_mask)
    violations = out['structural_violations'][
        'total_per_residue_violations_mask'].tolist()
    return min_pdb, debug_data, violations
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/residue_constants_test.py",".py","9256","191","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Test that residue_constants generates correct values.""""""

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.common import residue_constants
import numpy as np


class ResidueConstantsTest(parameterized.TestCase):

  @parameterized.parameters(
      ('ALA', 0),
      ('CYS', 1),
      ('HIS', 2),
      ('MET', 3),
      ('LYS', 4),
      ('ARG', 4),
  )
  def testChiAnglesAtoms(self, residue_name, chi_num):
    chi_angles_atoms = residue_constants.chi_angles_atoms[residue_name]
    self.assertLen(chi_angles_atoms, chi_num)
    for chi_angle_atoms in chi_angles_atoms:
      self.assertLen(chi_angle_atoms, 4)

  def testChiGroupsForAtom(self):
    for k, chi_groups in residue_constants.chi_groups_for_atom.items():
      res_name, atom_name = k
      for chi_group_i, atom_i in chi_groups:
        self.assertEqual(
            atom_name,
            residue_constants.chi_angles_atoms[res_name][chi_group_i][atom_i])

  @parameterized.parameters(
      ('ALA', 5), ('ARG', 11), ('ASN', 8), ('ASP', 8), ('CYS', 6), ('GLN', 9),
      ('GLU', 9), ('GLY', 4), ('HIS', 10), ('ILE', 8), ('LEU', 8), ('LYS', 9),
      ('MET', 8), ('PHE', 11), ('PRO', 7), ('SER', 6), ('THR', 7), ('TRP', 14),
      ('TYR', 12), ('VAL', 7)
  )
  def testResidueAtoms(self, atom_name, num_residue_atoms):
    residue_atoms = residue_constants.residue_atoms[atom_name]
    self.assertLen(residue_atoms, num_residue_atoms)

  def testStandardAtomMask(self):
    with self.subTest('Check shape'):
      self.assertEqual(residue_constants.STANDARD_ATOM_MASK.shape, (21, 37,))

    with self.subTest('Check values'):
      str_to_row = lambda s: [c == '1' for c in s]  # More clear/concise.
      np.testing.assert_array_equal(
          residue_constants.STANDARD_ATOM_MASK,
          np.array([
              # NB This was defined by c+p but looks sane.
              str_to_row('11111                                '),  # ALA
              str_to_row('111111     1           1     11 1    '),  # ARG
              str_to_row('111111         11                    '),  # ASP
              str_to_row('111111          11                   '),  # ASN
              str_to_row('11111     1                          '),  # CYS
              str_to_row('111111     1             11          '),  # GLU
              str_to_row('111111     1              11         '),  # GLN
              str_to_row('111 1                                '),  # GLY
              str_to_row('111111       11     1    1           '),  # HIS
              str_to_row('11111 11    1                        '),  # ILE
              str_to_row('111111      11                       '),  # LEU
              str_to_row('111111     1       1               1 '),  # LYS
              str_to_row('111111            11                 '),  # MET
              str_to_row('111111      11      11          1    '),  # PHE
              str_to_row('111111     1                         '),  # PRO
              str_to_row('11111   1                            '),  # SER
              str_to_row('11111  1 1                           '),  # THR
              str_to_row('111111      11       11 1   1    11  '),  # TRP
              str_to_row('111111      11      11         11    '),  # TYR
              str_to_row('11111 11                             '),  # VAL
              str_to_row('                                     '),  # UNK
          ]))

    with self.subTest('Check row totals'):
      # Check each row has the right number of atoms.
      for row, restype in enumerate(residue_constants.restypes):  # A, R, ...
        long_restype = residue_constants.restype_1to3[restype]  # ALA, ARG, ...
        atoms_names = residue_constants.residue_atoms[
            long_restype]  # ['C', 'CA', 'CB', 'N', 'O'], ...
        self.assertLen(atoms_names,
                       residue_constants.STANDARD_ATOM_MASK[row, :].sum(),
                       long_restype)

  def testAtomTypes(self):
    self.assertEqual(residue_constants.atom_type_num, 37)

    self.assertEqual(residue_constants.atom_types[0], 'N')
    self.assertEqual(residue_constants.atom_types[1], 'CA')
    self.assertEqual(residue_constants.atom_types[2], 'C')
    self.assertEqual(residue_constants.atom_types[3], 'CB')
    self.assertEqual(residue_constants.atom_types[4], 'O')

    self.assertEqual(residue_constants.atom_order['N'], 0)
    self.assertEqual(residue_constants.atom_order['CA'], 1)
    self.assertEqual(residue_constants.atom_order['C'], 2)
    self.assertEqual(residue_constants.atom_order['CB'], 3)
    self.assertEqual(residue_constants.atom_order['O'], 4)
    self.assertEqual(residue_constants.atom_type_num, 37)

  def testRestypes(self):
    three_letter_restypes = [
        residue_constants.restype_1to3[r] for r  in residue_constants.restypes]
    for restype, exp_restype in zip(
        three_letter_restypes, sorted(residue_constants.restype_1to3.values())):
      self.assertEqual(restype, exp_restype)
    self.assertEqual(residue_constants.restype_num, 20)

  def testSequenceToOneHotHHBlits(self):
    one_hot = residue_constants.sequence_to_onehot(
        'ABCDEFGHIJKLMNOPQRSTUVWXYZ-', residue_constants.HHBLITS_AA_TO_ID)
    exp_one_hot = np.array(
        [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
         [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])
    np.testing.assert_array_equal(one_hot, exp_one_hot)

  def testSequenceToOneHotStandard(self):
    one_hot = residue_constants.sequence_to_onehot(
        'ARNDCQEGHILKMFPSTWYV', residue_constants.restype_order)
    np.testing.assert_array_equal(one_hot, np.eye(20))

  def testSequenceToOneHotUnknownMapping(self):
    seq = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
    expected_out = np.zeros([26, 21])
    for row, position in enumerate(
        [0, 20, 4, 3, 6, 13, 7, 8, 9, 20, 11, 10, 12, 2, 20, 14, 5, 1, 15, 16,
         20, 19, 17, 20, 18, 20]):
      expected_out[row, position] = 1
    aa_types = residue_constants.sequence_to_onehot(
        sequence=seq,
        mapping=residue_constants.restype_order_with_x,
        map_unknown_to_x=True)
    self.assertTrue((aa_types == expected_out).all())

  @parameterized.named_parameters(
      ('lowercase', 'aaa'),  # Insertions in A3M.
      ('gaps', '---'),  # Gaps in A3M.
      ('dots', '...'),  # Gaps in A3M.
      ('metadata', '>TEST'),  # FASTA metadata line.
  )
  def testSequenceToOneHotUnknownMappingError(self, seq):
    with self.assertRaises(ValueError):
      residue_constants.sequence_to_onehot(
          sequence=seq,
          mapping=residue_constants.restype_order_with_x,
          map_unknown_to_x=True)


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/confidence.py",".py","6066","169","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for processing confidence metrics.""""""

from typing import Dict, Optional, Tuple
import numpy as np
import scipy.special


def compute_plddt(logits: np.ndarray) -> np.ndarray:
  """"""Computes per-residue pLDDT from logits.

  Args:
    logits: [num_res, num_bins] output from the PredictedLDDTHead.

  Returns:
    plddt: [num_res] per-residue pLDDT.
  """"""
  num_bins = logits.shape[-1]
  bin_width = 1.0 / num_bins
  bin_centers = np.arange(start=0.5 * bin_width, stop=1.0, step=bin_width)
  probs = scipy.special.softmax(logits, axis=-1)
  predicted_lddt_ca = np.sum(probs * bin_centers[None, :], axis=-1)
  return predicted_lddt_ca * 100


def _calculate_bin_centers(breaks: np.ndarray):
  """"""Gets the bin centers from the bin edges.

  Args:
    breaks: [num_bins - 1] the error bin edges.

  Returns:
    bin_centers: [num_bins] the error bin centers.
  """"""
  step = (breaks[1] - breaks[0])

  # Add half-step to get the center
  bin_centers = breaks + step / 2
  # Add a catch-all bin at the end.
  bin_centers = np.concatenate([bin_centers, [bin_centers[-1] + step]],
                               axis=0)
  return bin_centers


def _calculate_expected_aligned_error(
    alignment_confidence_breaks: np.ndarray,
    aligned_distance_error_probs: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
  """"""Calculates expected aligned distance errors for every pair of residues.

  Args:
    alignment_confidence_breaks: [num_bins - 1] the error bin edges.
    aligned_distance_error_probs: [num_res, num_res, num_bins] the predicted
      probs for each error bin, for each pair of residues.

  Returns:
    predicted_aligned_error: [num_res, num_res] the expected aligned distance
      error for each pair of residues.
    max_predicted_aligned_error: The maximum predicted error possible.
  """"""
  bin_centers = _calculate_bin_centers(alignment_confidence_breaks)

  # Tuple of expected aligned distance error and max possible error.
  return (np.sum(aligned_distance_error_probs * bin_centers, axis=-1),
          np.asarray(bin_centers[-1]))


def compute_predicted_aligned_error(
    logits: np.ndarray,
    breaks: np.ndarray) -> Dict[str, np.ndarray]:
  """"""Computes aligned confidence metrics from logits.

  Args:
    logits: [num_res, num_res, num_bins] the logits output from
      PredictedAlignedErrorHead.
    breaks: [num_bins - 1] the error bin edges.

  Returns:
    aligned_confidence_probs: [num_res, num_res, num_bins] the predicted
      aligned error probabilities over bins for each residue pair.
    predicted_aligned_error: [num_res, num_res] the expected aligned distance
      error for each pair of residues.
    max_predicted_aligned_error: The maximum predicted error possible.
  """"""
  aligned_confidence_probs = scipy.special.softmax(
      logits,
      axis=-1)
  predicted_aligned_error, max_predicted_aligned_error = (
      _calculate_expected_aligned_error(
          alignment_confidence_breaks=breaks,
          aligned_distance_error_probs=aligned_confidence_probs))
  return {
      'aligned_confidence_probs': aligned_confidence_probs,
      'predicted_aligned_error': predicted_aligned_error,
      'max_predicted_aligned_error': max_predicted_aligned_error,
  }


def predicted_tm_score(
    logits: np.ndarray,
    breaks: np.ndarray,
    residue_weights: Optional[np.ndarray] = None,
    asym_id: Optional[np.ndarray] = None,
    interface: bool = False) -> np.ndarray:
  """"""Computes predicted TM alignment or predicted interface TM alignment score.

  Args:
    logits: [num_res, num_res, num_bins] the logits output from
      PredictedAlignedErrorHead.
    breaks: [num_bins] the error bins.
    residue_weights: [num_res] the per residue weights to use for the
      expectation.
    asym_id: [num_res] the asymmetric unit ID - the chain ID. Only needed for
      ipTM calculation, i.e. when interface=True.
    interface: If True, interface predicted TM score is computed.

  Returns:
    ptm_score: The predicted TM alignment or the predicted iTM score.
  """"""

  # residue_weights has to be in [0, 1], but can be floating-point, i.e. the
  # exp. resolved head's probability.
  if residue_weights is None:
    residue_weights = np.ones(logits.shape[0])

  bin_centers = _calculate_bin_centers(breaks)

  num_res = int(np.sum(residue_weights))
  # Clip num_res to avoid negative/undefined d0.
  clipped_num_res = max(num_res, 19)

  # Compute d_0(num_res) as defined by TM-score, eqn. (5) in Yang & Skolnick
  # ""Scoring function for automated assessment of protein structure template
  # quality"", 2004: http://zhanglab.ccmb.med.umich.edu/papers/2004_3.pdf
  d0 = 1.24 * (clipped_num_res - 15) ** (1./3) - 1.8

  # Convert logits to probs.
  probs = scipy.special.softmax(logits, axis=-1)

  # TM-Score term for every bin.
  tm_per_bin = 1. / (1 + np.square(bin_centers) / np.square(d0))
  # E_distances tm(distance).
  predicted_tm_term = np.sum(probs * tm_per_bin, axis=-1)

  pair_mask = np.ones(shape=(num_res, num_res), dtype=bool)
  if interface:
    pair_mask *= asym_id[:, None] != asym_id[None, :]

  predicted_tm_term *= pair_mask

  pair_residue_weights = pair_mask * (
      residue_weights[None, :] * residue_weights[:, None])
  normed_residue_mask = pair_residue_weights / (1e-8 + np.sum(
      pair_residue_weights, axis=-1, keepdims=True))
  per_alignment = np.sum(predicted_tm_term * normed_residue_mask, axis=-1)
  return np.asarray(per_alignment[(per_alignment * residue_weights).argmax()])
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/__init__.py",".py","655","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Common data types and constants used within Alphafold.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/residue_constants.py",".py","34974","898","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Constants used in AlphaFold.""""""

import collections
import functools
import os
from typing import List, Mapping, Tuple

import numpy as np
import tree

# Internal import (35fd).


# Distance from one CA to next CA [trans configuration: omega = 180].
ca_ca = 3.80209737096

# Format: The list for each AA type contains chi1, chi2, chi3, chi4 in
# this order (or a relevant subset from chi1 onwards). ALA and GLY don't have
# chi angles so their chi angle lists are empty.
chi_angles_atoms = {
    'ALA': [],
    # Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
    'ARG': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
            ['CB', 'CG', 'CD', 'NE'], ['CG', 'CD', 'NE', 'CZ']],
    'ASN': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'OD1']],
    'ASP': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'OD1']],
    'CYS': [['N', 'CA', 'CB', 'SG']],
    'GLN': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
            ['CB', 'CG', 'CD', 'OE1']],
    'GLU': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
            ['CB', 'CG', 'CD', 'OE1']],
    'GLY': [],
    'HIS': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'ND1']],
    'ILE': [['N', 'CA', 'CB', 'CG1'], ['CA', 'CB', 'CG1', 'CD1']],
    'LEU': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
    'LYS': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
            ['CB', 'CG', 'CD', 'CE'], ['CG', 'CD', 'CE', 'NZ']],
    'MET': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'SD'],
            ['CB', 'CG', 'SD', 'CE']],
    'PHE': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
    'PRO': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD']],
    'SER': [['N', 'CA', 'CB', 'OG']],
    'THR': [['N', 'CA', 'CB', 'OG1']],
    'TRP': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
    'TYR': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
    'VAL': [['N', 'CA', 'CB', 'CG1']],
}

# If chi angles given in fixed-length array, this matrix determines how to mask
# them for each AA type. The order is as per restype_order (see below).
chi_angles_mask = [
    [0.0, 0.0, 0.0, 0.0],  # ALA
    [1.0, 1.0, 1.0, 1.0],  # ARG
    [1.0, 1.0, 0.0, 0.0],  # ASN
    [1.0, 1.0, 0.0, 0.0],  # ASP
    [1.0, 0.0, 0.0, 0.0],  # CYS
    [1.0, 1.0, 1.0, 0.0],  # GLN
    [1.0, 1.0, 1.0, 0.0],  # GLU
    [0.0, 0.0, 0.0, 0.0],  # GLY
    [1.0, 1.0, 0.0, 0.0],  # HIS
    [1.0, 1.0, 0.0, 0.0],  # ILE
    [1.0, 1.0, 0.0, 0.0],  # LEU
    [1.0, 1.0, 1.0, 1.0],  # LYS
    [1.0, 1.0, 1.0, 0.0],  # MET
    [1.0, 1.0, 0.0, 0.0],  # PHE
    [1.0, 1.0, 0.0, 0.0],  # PRO
    [1.0, 0.0, 0.0, 0.0],  # SER
    [1.0, 0.0, 0.0, 0.0],  # THR
    [1.0, 1.0, 0.0, 0.0],  # TRP
    [1.0, 1.0, 0.0, 0.0],  # TYR
    [1.0, 0.0, 0.0, 0.0],  # VAL
]

# The following chi angles are pi periodic: they can be rotated by a multiple
# of pi without affecting the structure.
chi_pi_periodic = [
    [0.0, 0.0, 0.0, 0.0],  # ALA
    [0.0, 0.0, 0.0, 0.0],  # ARG
    [0.0, 0.0, 0.0, 0.0],  # ASN
    [0.0, 1.0, 0.0, 0.0],  # ASP
    [0.0, 0.0, 0.0, 0.0],  # CYS
    [0.0, 0.0, 0.0, 0.0],  # GLN
    [0.0, 0.0, 1.0, 0.0],  # GLU
    [0.0, 0.0, 0.0, 0.0],  # GLY
    [0.0, 0.0, 0.0, 0.0],  # HIS
    [0.0, 0.0, 0.0, 0.0],  # ILE
    [0.0, 0.0, 0.0, 0.0],  # LEU
    [0.0, 0.0, 0.0, 0.0],  # LYS
    [0.0, 0.0, 0.0, 0.0],  # MET
    [0.0, 1.0, 0.0, 0.0],  # PHE
    [0.0, 0.0, 0.0, 0.0],  # PRO
    [0.0, 0.0, 0.0, 0.0],  # SER
    [0.0, 0.0, 0.0, 0.0],  # THR
    [0.0, 0.0, 0.0, 0.0],  # TRP
    [0.0, 1.0, 0.0, 0.0],  # TYR
    [0.0, 0.0, 0.0, 0.0],  # VAL
    [0.0, 0.0, 0.0, 0.0],  # UNK
]

# Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi,
# psi and chi angles:
# 0: 'backbone group',
# 1: 'pre-omega-group', (empty)
# 2: 'phi-group', (currently empty, because it defines only hydrogens)
# 3: 'psi-group',
# 4,5,6,7: 'chi1,2,3,4-group'
# The atom positions are relative to the axis-end-atom of the corresponding
# rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
# is defined such that the dihedral-angle-defining atom (the last entry in
# chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
# format: [atomname, group_idx, rel_position]
rigid_group_atom_positions = {
    'ALA': [
        ['N', 0, (-0.525, 1.363, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, -0.000, -0.000)],
        ['CB', 0, (-0.529, -0.774, -1.205)],
        ['O', 3, (0.627, 1.062, 0.000)],
    ],
    'ARG': [
        ['N', 0, (-0.524, 1.362, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, -0.000, -0.000)],
        ['CB', 0, (-0.524, -0.778, -1.209)],
        ['O', 3, (0.626, 1.062, 0.000)],
        ['CG', 4, (0.616, 1.390, -0.000)],
        ['CD', 5, (0.564, 1.414, 0.000)],
        ['NE', 6, (0.539, 1.357, -0.000)],
        ['NH1', 7, (0.206, 2.301, 0.000)],
        ['NH2', 7, (2.078, 0.978, -0.000)],
        ['CZ', 7, (0.758, 1.093, -0.000)],
    ],
    'ASN': [
        ['N', 0, (-0.536, 1.357, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, -0.000, -0.000)],
        ['CB', 0, (-0.531, -0.787, -1.200)],
        ['O', 3, (0.625, 1.062, 0.000)],
        ['CG', 4, (0.584, 1.399, 0.000)],
        ['ND2', 5, (0.593, -1.188, 0.001)],
        ['OD1', 5, (0.633, 1.059, 0.000)],
    ],
    'ASP': [
        ['N', 0, (-0.525, 1.362, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.527, 0.000, -0.000)],
        ['CB', 0, (-0.526, -0.778, -1.208)],
        ['O', 3, (0.626, 1.062, -0.000)],
        ['CG', 4, (0.593, 1.398, -0.000)],
        ['OD1', 5, (0.610, 1.091, 0.000)],
        ['OD2', 5, (0.592, -1.101, -0.003)],
    ],
    'CYS': [
        ['N', 0, (-0.522, 1.362, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.524, 0.000, 0.000)],
        ['CB', 0, (-0.519, -0.773, -1.212)],
        ['O', 3, (0.625, 1.062, -0.000)],
        ['SG', 4, (0.728, 1.653, 0.000)],
    ],
    'GLN': [
        ['N', 0, (-0.526, 1.361, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, 0.000, 0.000)],
        ['CB', 0, (-0.525, -0.779, -1.207)],
        ['O', 3, (0.626, 1.062, -0.000)],
        ['CG', 4, (0.615, 1.393, 0.000)],
        ['CD', 5, (0.587, 1.399, -0.000)],
        ['NE2', 6, (0.593, -1.189, -0.001)],
        ['OE1', 6, (0.634, 1.060, 0.000)],
    ],
    'GLU': [
        ['N', 0, (-0.528, 1.361, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, -0.000, -0.000)],
        ['CB', 0, (-0.526, -0.781, -1.207)],
        ['O', 3, (0.626, 1.062, 0.000)],
        ['CG', 4, (0.615, 1.392, 0.000)],
        ['CD', 5, (0.600, 1.397, 0.000)],
        ['OE1', 6, (0.607, 1.095, -0.000)],
        ['OE2', 6, (0.589, -1.104, -0.001)],
    ],
    'GLY': [
        ['N', 0, (-0.572, 1.337, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.517, -0.000, -0.000)],
        ['O', 3, (0.626, 1.062, -0.000)],
    ],
    'HIS': [
        ['N', 0, (-0.527, 1.360, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, 0.000, 0.000)],
        ['CB', 0, (-0.525, -0.778, -1.208)],
        ['O', 3, (0.625, 1.063, 0.000)],
        ['CG', 4, (0.600, 1.370, -0.000)],
        ['CD2', 5, (0.889, -1.021, 0.003)],
        ['ND1', 5, (0.744, 1.160, -0.000)],
        ['CE1', 5, (2.030, 0.851, 0.002)],
        ['NE2', 5, (2.145, -0.466, 0.004)],
    ],
    'ILE': [
        ['N', 0, (-0.493, 1.373, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.527, -0.000, -0.000)],
        ['CB', 0, (-0.536, -0.793, -1.213)],
        ['O', 3, (0.627, 1.062, -0.000)],
        ['CG1', 4, (0.534, 1.437, -0.000)],
        ['CG2', 4, (0.540, -0.785, -1.199)],
        ['CD1', 5, (0.619, 1.391, 0.000)],
    ],
    'LEU': [
        ['N', 0, (-0.520, 1.363, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, -0.000, -0.000)],
        ['CB', 0, (-0.522, -0.773, -1.214)],
        ['O', 3, (0.625, 1.063, -0.000)],
        ['CG', 4, (0.678, 1.371, 0.000)],
        ['CD1', 5, (0.530, 1.430, -0.000)],
        ['CD2', 5, (0.535, -0.774, 1.200)],
    ],
    'LYS': [
        ['N', 0, (-0.526, 1.362, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, 0.000, 0.000)],
        ['CB', 0, (-0.524, -0.778, -1.208)],
        ['O', 3, (0.626, 1.062, -0.000)],
        ['CG', 4, (0.619, 1.390, 0.000)],
        ['CD', 5, (0.559, 1.417, 0.000)],
        ['CE', 6, (0.560, 1.416, 0.000)],
        ['NZ', 7, (0.554, 1.387, 0.000)],
    ],
    'MET': [
        ['N', 0, (-0.521, 1.364, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, 0.000, 0.000)],
        ['CB', 0, (-0.523, -0.776, -1.210)],
        ['O', 3, (0.625, 1.062, -0.000)],
        ['CG', 4, (0.613, 1.391, -0.000)],
        ['SD', 5, (0.703, 1.695, 0.000)],
        ['CE', 6, (0.320, 1.786, -0.000)],
    ],
    'PHE': [
        ['N', 0, (-0.518, 1.363, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.524, 0.000, -0.000)],
        ['CB', 0, (-0.525, -0.776, -1.212)],
        ['O', 3, (0.626, 1.062, -0.000)],
        ['CG', 4, (0.607, 1.377, 0.000)],
        ['CD1', 5, (0.709, 1.195, -0.000)],
        ['CD2', 5, (0.706, -1.196, 0.000)],
        ['CE1', 5, (2.102, 1.198, -0.000)],
        ['CE2', 5, (2.098, -1.201, -0.000)],
        ['CZ', 5, (2.794, -0.003, -0.001)],
    ],
    'PRO': [
        ['N', 0, (-0.566, 1.351, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.527, -0.000, 0.000)],
        ['CB', 0, (-0.546, -0.611, -1.293)],
        ['O', 3, (0.621, 1.066, 0.000)],
        ['CG', 4, (0.382, 1.445, 0.0)],
        # ['CD', 5, (0.427, 1.440, 0.0)],
        ['CD', 5, (0.477, 1.424, 0.0)],  # manually made angle 2 degrees larger
    ],
    'SER': [
        ['N', 0, (-0.529, 1.360, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, -0.000, -0.000)],
        ['CB', 0, (-0.518, -0.777, -1.211)],
        ['O', 3, (0.626, 1.062, -0.000)],
        ['OG', 4, (0.503, 1.325, 0.000)],
    ],
    'THR': [
        ['N', 0, (-0.517, 1.364, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.526, 0.000, -0.000)],
        ['CB', 0, (-0.516, -0.793, -1.215)],
        ['O', 3, (0.626, 1.062, 0.000)],
        ['CG2', 4, (0.550, -0.718, -1.228)],
        ['OG1', 4, (0.472, 1.353, 0.000)],
    ],
    'TRP': [
        ['N', 0, (-0.521, 1.363, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.525, -0.000, 0.000)],
        ['CB', 0, (-0.523, -0.776, -1.212)],
        ['O', 3, (0.627, 1.062, 0.000)],
        ['CG', 4, (0.609, 1.370, -0.000)],
        ['CD1', 5, (0.824, 1.091, 0.000)],
        ['CD2', 5, (0.854, -1.148, -0.005)],
        ['CE2', 5, (2.186, -0.678, -0.007)],
        ['CE3', 5, (0.622, -2.530, -0.007)],
        ['NE1', 5, (2.140, 0.690, -0.004)],
        ['CH2', 5, (3.028, -2.890, -0.013)],
        ['CZ2', 5, (3.283, -1.543, -0.011)],
        ['CZ3', 5, (1.715, -3.389, -0.011)],
    ],
    'TYR': [
        ['N', 0, (-0.522, 1.362, 0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.524, -0.000, -0.000)],
        ['CB', 0, (-0.522, -0.776, -1.213)],
        ['O', 3, (0.627, 1.062, -0.000)],
        ['CG', 4, (0.607, 1.382, -0.000)],
        ['CD1', 5, (0.716, 1.195, -0.000)],
        ['CD2', 5, (0.713, -1.194, -0.001)],
        ['CE1', 5, (2.107, 1.200, -0.002)],
        ['CE2', 5, (2.104, -1.201, -0.003)],
        ['OH', 5, (4.168, -0.002, -0.005)],
        ['CZ', 5, (2.791, -0.001, -0.003)],
    ],
    'VAL': [
        ['N', 0, (-0.494, 1.373, -0.000)],
        ['CA', 0, (0.000, 0.000, 0.000)],
        ['C', 0, (1.527, -0.000, -0.000)],
        ['CB', 0, (-0.533, -0.795, -1.213)],
        ['O', 3, (0.627, 1.062, -0.000)],
        ['CG1', 4, (0.540, 1.429, -0.000)],
        ['CG2', 4, (0.533, -0.776, 1.203)],
    ],
}

# A list of atoms (excluding hydrogen) for each AA type. PDB naming convention.
residue_atoms = {
    'ALA': ['C', 'CA', 'CB', 'N', 'O'],
    'ARG': ['C', 'CA', 'CB', 'CG', 'CD', 'CZ', 'N', 'NE', 'O', 'NH1', 'NH2'],
    'ASP': ['C', 'CA', 'CB', 'CG', 'N', 'O', 'OD1', 'OD2'],
    'ASN': ['C', 'CA', 'CB', 'CG', 'N', 'ND2', 'O', 'OD1'],
    'CYS': ['C', 'CA', 'CB', 'N', 'O', 'SG'],
    'GLU': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'O', 'OE1', 'OE2'],
    'GLN': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'NE2', 'O', 'OE1'],
    'GLY': ['C', 'CA', 'N', 'O'],
    'HIS': ['C', 'CA', 'CB', 'CG', 'CD2', 'CE1', 'N', 'ND1', 'NE2', 'O'],
    'ILE': ['C', 'CA', 'CB', 'CG1', 'CG2', 'CD1', 'N', 'O'],
    'LEU': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'N', 'O'],
    'LYS': ['C', 'CA', 'CB', 'CG', 'CD', 'CE', 'N', 'NZ', 'O'],
    'MET': ['C', 'CA', 'CB', 'CG', 'CE', 'N', 'O', 'SD'],
    'PHE': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', 'N', 'O'],
    'PRO': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'O'],
    'SER': ['C', 'CA', 'CB', 'N', 'O', 'OG'],
    'THR': ['C', 'CA', 'CB', 'CG2', 'N', 'O', 'OG1'],
    'TRP': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE2', 'CE3', 'CZ2', 'CZ3',
            'CH2', 'N', 'NE1', 'O'],
    'TYR': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', 'N', 'O',
            'OH'],
    'VAL': ['C', 'CA', 'CB', 'CG1', 'CG2', 'N', 'O']
}

# Naming swaps for ambiguous atom names.
# Due to symmetries in the amino acids the naming of atoms is ambiguous in
# 4 of the 20 amino acids.
# (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities
# in LEU, VAL and ARG can be resolved by using the 3d constellations of
# the 'ambiguous' atoms and their neighbours)
residue_atom_renaming_swaps = {
    'ASP': {'OD1': 'OD2'},
    'GLU': {'OE1': 'OE2'},
    'PHE': {'CD1': 'CD2', 'CE1': 'CE2'},
    'TYR': {'CD1': 'CD2', 'CE1': 'CE2'},
}

# Van der Waals radii [Angstroem] of the atoms (from Wikipedia)
van_der_waals_radius = {
    'C': 1.7,
    'N': 1.55,
    'O': 1.52,
    'S': 1.8,
}

Bond = collections.namedtuple(
    'Bond', ['atom1_name', 'atom2_name', 'length', 'stddev'])
BondAngle = collections.namedtuple(
    'BondAngle',
    ['atom1_name', 'atom2_name', 'atom3name', 'angle_rad', 'stddev'])


@functools.lru_cache(maxsize=None)
def load_stereo_chemical_props() -> Tuple[Mapping[str, List[Bond]],
                                          Mapping[str, List[Bond]],
                                          Mapping[str, List[BondAngle]]]:
  """"""Load stereo_chemical_props.txt into a nice structure.

  Load literature values for bond lengths and bond angles and translate
  bond angles into the length of the opposite edge of the triangle
  (""residue_virtual_bonds"").

  Returns:
    residue_bonds: Dict that maps resname -> list of Bond tuples.
    residue_virtual_bonds: Dict that maps resname -> list of Bond tuples.
    residue_bond_angles: Dict that maps resname -> list of BondAngle tuples.
  """"""
  stereo_chemical_props_path = os.path.join(
      os.path.dirname(os.path.abspath(__file__)), 'stereo_chemical_props.txt'
  )
  with open(stereo_chemical_props_path, 'rt') as f:
    stereo_chemical_props = f.read()
  lines_iter = iter(stereo_chemical_props.splitlines())
  # Load bond lengths.
  residue_bonds = {}
  next(lines_iter)  # Skip header line.
  for line in lines_iter:
    if line.strip() == '-':
      break
    bond, resname, length, stddev = line.split()
    atom1, atom2 = bond.split('-')
    if resname not in residue_bonds:
      residue_bonds[resname] = []
    residue_bonds[resname].append(
        Bond(atom1, atom2, float(length), float(stddev)))
  residue_bonds['UNK'] = []

  # Load bond angles.
  residue_bond_angles = {}
  next(lines_iter)  # Skip empty line.
  next(lines_iter)  # Skip header line.
  for line in lines_iter:
    if line.strip() == '-':
      break
    bond, resname, angle_degree, stddev_degree = line.split()
    atom1, atom2, atom3 = bond.split('-')
    if resname not in residue_bond_angles:
      residue_bond_angles[resname] = []
    residue_bond_angles[resname].append(
        BondAngle(atom1, atom2, atom3,
                  float(angle_degree) / 180. * np.pi,
                  float(stddev_degree) / 180. * np.pi))
  residue_bond_angles['UNK'] = []

  def make_bond_key(atom1_name, atom2_name):
    """"""Unique key to lookup bonds.""""""
    return '-'.join(sorted([atom1_name, atom2_name]))

  # Translate bond angles into distances (""virtual bonds"").
  residue_virtual_bonds = {}
  for resname, bond_angles in residue_bond_angles.items():
    # Create a fast lookup dict for bond lengths.
    bond_cache = {}
    for b in residue_bonds[resname]:
      bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b
    residue_virtual_bonds[resname] = []
    for ba in bond_angles:
      bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)]
      bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)]

      # Compute distance between atom1 and atom3 using the law of cosines
      # c^2 = a^2 + b^2 - 2ab*cos(gamma).
      gamma = ba.angle_rad
      length = np.sqrt(bond1.length**2 + bond2.length**2
                       - 2 * bond1.length * bond2.length * np.cos(gamma))

      # Propagation of uncertainty assuming uncorrelated errors.
      dl_outer = 0.5 / length
      dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer
      dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer
      dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer
      stddev = np.sqrt((dl_dgamma * ba.stddev)**2 +
                       (dl_db1 * bond1.stddev)**2 +
                       (dl_db2 * bond2.stddev)**2)
      residue_virtual_bonds[resname].append(
          Bond(ba.atom1_name, ba.atom3name, length, stddev))

  return (residue_bonds,
          residue_virtual_bonds,
          residue_bond_angles)


# Between-residue bond lengths for general bonds (first element) and for Proline
# (second element).
between_res_bond_length_c_n = [1.329, 1.341]
between_res_bond_length_stddev_c_n = [0.014, 0.016]

# Between-residue cos_angles.
between_res_cos_angles_c_n_ca = [-0.5203, 0.0353]  # degrees: 121.352 +- 2.315
between_res_cos_angles_ca_c_n = [-0.4473, 0.0311]  # degrees: 116.568 +- 1.995

# This mapping is used when we need to store atom data in a format that requires
# fixed atom data size for every residue (e.g. a numpy array).
atom_types = [
    'N', 'CA', 'C', 'CB', 'O', 'CG', 'CG1', 'CG2', 'OG', 'OG1', 'SG', 'CD',
    'CD1', 'CD2', 'ND1', 'ND2', 'OD1', 'OD2', 'SD', 'CE', 'CE1', 'CE2', 'CE3',
    'NE', 'NE1', 'NE2', 'OE1', 'OE2', 'CH2', 'NH1', 'NH2', 'OH', 'CZ', 'CZ2',
    'CZ3', 'NZ', 'OXT'
]
atom_order = {atom_type: i for i, atom_type in enumerate(atom_types)}
atom_type_num = len(atom_types)  # := 37.

# A compact atom encoding with 14 columns
# pylint: disable=line-too-long
# pylint: disable=bad-whitespace
restype_name_to_atom14_names = {
    'ALA': ['N', 'CA', 'C', 'O', 'CB', '',    '',    '',    '',    '',    '',    '',    '',    ''],
    'ARG': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'NE',  'CZ',  'NH1', 'NH2', '',    '',    ''],
    'ASN': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'OD1', 'ND2', '',    '',    '',    '',    '',    ''],
    'ASP': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'OD1', 'OD2', '',    '',    '',    '',    '',    ''],
    'CYS': ['N', 'CA', 'C', 'O', 'CB', 'SG',  '',    '',    '',    '',    '',    '',    '',    ''],
    'GLN': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'OE1', 'NE2', '',    '',    '',    '',    ''],
    'GLU': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'OE1', 'OE2', '',    '',    '',    '',    ''],
    'GLY': ['N', 'CA', 'C', 'O', '',   '',    '',    '',    '',    '',    '',    '',    '',    ''],
    'HIS': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'ND1', 'CD2', 'CE1', 'NE2', '',    '',    '',    ''],
    'ILE': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', 'CD1', '',    '',    '',    '',    '',    ''],
    'LEU': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', '',    '',    '',    '',    '',    ''],
    'LYS': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'CE',  'NZ',  '',    '',    '',    '',    ''],
    'MET': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'SD',  'CE',  '',    '',    '',    '',    '',    ''],
    'PHE': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'CE1', 'CE2', 'CZ',  '',    '',    ''],
    'PRO': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  '',    '',    '',    '',    '',    '',    ''],
    'SER': ['N', 'CA', 'C', 'O', 'CB', 'OG',  '',    '',    '',    '',    '',    '',    '',    ''],
    'THR': ['N', 'CA', 'C', 'O', 'CB', 'OG1', 'CG2', '',    '',    '',    '',    '',    '',    ''],
    'TRP': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'NE1', 'CE2', 'CE3', 'CZ2', 'CZ3', 'CH2'],
    'TYR': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'CE1', 'CE2', 'CZ',  'OH',  '',    ''],
    'VAL': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', '',    '',    '',    '',    '',    '',    ''],
    'UNK': ['',  '',   '',  '',  '',   '',    '',    '',    '',    '',    '',    '',    '',    ''],

}
# pylint: enable=line-too-long
# pylint: enable=bad-whitespace


# This is the standard residue order when coding AA type as a number.
# Reproduce it by taking 3-letter AA codes and sorting them alphabetically.
restypes = [
    'A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P',
    'S', 'T', 'W', 'Y', 'V'
]
restype_order = {restype: i for i, restype in enumerate(restypes)}
restype_num = len(restypes)  # := 20.
unk_restype_index = restype_num  # Catch-all index for unknown restypes.

restypes_with_x = restypes + ['X']
restype_order_with_x = {restype: i for i, restype in enumerate(restypes_with_x)}


def sequence_to_onehot(
    sequence: str,
    mapping: Mapping[str, int],
    map_unknown_to_x: bool = False) -> np.ndarray:
  """"""Maps the given sequence into a one-hot encoded matrix.

  Args:
    sequence: An amino acid sequence.
    mapping: A dictionary mapping amino acids to integers.
    map_unknown_to_x: If True, any amino acid that is not in the mapping will be
      mapped to the unknown amino acid 'X'. If the mapping doesn't contain
      amino acid 'X', an error will be thrown. If False, any amino acid not in
      the mapping will throw an error.

  Returns:
    A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of
    the sequence.

  Raises:
    ValueError: If the mapping doesn't contain values from 0 to
      num_unique_aas - 1 without any gaps.
  """"""
  num_entries = max(mapping.values()) + 1

  if sorted(set(mapping.values())) != list(range(num_entries)):
    raise ValueError('The mapping must have values from 0 to num_unique_aas-1 '
                     'without any gaps. Got: %s' % sorted(mapping.values()))

  one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32)

  for aa_index, aa_type in enumerate(sequence):
    if map_unknown_to_x:
      if aa_type.isalpha() and aa_type.isupper():
        aa_id = mapping.get(aa_type, mapping['X'])
      else:
        raise ValueError(f'Invalid character in the sequence: {aa_type}')
    else:
      aa_id = mapping[aa_type]
    one_hot_arr[aa_index, aa_id] = 1

  return one_hot_arr


restype_1to3 = {
    'A': 'ALA',
    'R': 'ARG',
    'N': 'ASN',
    'D': 'ASP',
    'C': 'CYS',
    'Q': 'GLN',
    'E': 'GLU',
    'G': 'GLY',
    'H': 'HIS',
    'I': 'ILE',
    'L': 'LEU',
    'K': 'LYS',
    'M': 'MET',
    'F': 'PHE',
    'P': 'PRO',
    'S': 'SER',
    'T': 'THR',
    'W': 'TRP',
    'Y': 'TYR',
    'V': 'VAL',
}


# NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple
# 1-to-1 mapping of 3 letter names to one letter names. The latter contains
# many more, and less common, three letter names as keys and maps many of these
# to the same one letter name (including 'X' and 'U' which we don't use here).
restype_3to1 = {v: k for k, v in restype_1to3.items()}

# Define a restype name for all unknown residues.
unk_restype = 'UNK'

resnames = [restype_1to3[r] for r in restypes] + [unk_restype]
resname_to_idx = {resname: i for i, resname in enumerate(resnames)}


# The mapping here uses hhblits convention, so that B is mapped to D, J and O
# are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the
# remaining 20 amino acids are kept in alphabetical order.
# There are 2 non-amino acid codes, X (representing any amino acid) and
# ""-"" representing a missing amino acid in an alignment.  The id for these
# codes is put at the end (20 and 21) so that they can easily be ignored if
# desired.
HHBLITS_AA_TO_ID = {
    'A': 0,
    'B': 2,
    'C': 1,
    'D': 2,
    'E': 3,
    'F': 4,
    'G': 5,
    'H': 6,
    'I': 7,
    'J': 20,
    'K': 8,
    'L': 9,
    'M': 10,
    'N': 11,
    'O': 20,
    'P': 12,
    'Q': 13,
    'R': 14,
    'S': 15,
    'T': 16,
    'U': 1,
    'V': 17,
    'W': 18,
    'X': 20,
    'Y': 19,
    'Z': 3,
    '-': 21,
}

# Partial inversion of HHBLITS_AA_TO_ID.
ID_TO_HHBLITS_AA = {
    0: 'A',
    1: 'C',  # Also U.
    2: 'D',  # Also B.
    3: 'E',  # Also Z.
    4: 'F',
    5: 'G',
    6: 'H',
    7: 'I',
    8: 'K',
    9: 'L',
    10: 'M',
    11: 'N',
    12: 'P',
    13: 'Q',
    14: 'R',
    15: 'S',
    16: 'T',
    17: 'V',
    18: 'W',
    19: 'Y',
    20: 'X',  # Includes J and O.
    21: '-',
}

restypes_with_x_and_gap = restypes + ['X', '-']
MAP_HHBLITS_AATYPE_TO_OUR_AATYPE = tuple(
    restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i])
    for i in range(len(restypes_with_x_and_gap)))


def _make_standard_atom_mask() -> np.ndarray:
  """"""Returns [num_res_types, num_atom_types] mask array.""""""
  # +1 to account for unknown (all 0s).
  mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32)
  for restype, restype_letter in enumerate(restypes):
    restype_name = restype_1to3[restype_letter]
    atom_names = residue_atoms[restype_name]
    for atom_name in atom_names:
      atom_type = atom_order[atom_name]
      mask[restype, atom_type] = 1
  return mask


STANDARD_ATOM_MASK = _make_standard_atom_mask()


# A one hot representation for the first and second atoms defining the axis
# of rotation for each chi-angle in each residue.
def chi_angle_atom(atom_index: int) -> np.ndarray:
  """"""Define chi-angle rigid groups via one-hot representations.""""""
  chi_angles_index = {}
  one_hots = []

  for k, v in chi_angles_atoms.items():
    indices = [atom_types.index(s[atom_index]) for s in v]
    indices.extend([-1]*(4-len(indices)))
    chi_angles_index[k] = indices

  for r in restypes:
    res3 = restype_1to3[r]
    one_hot = np.eye(atom_type_num)[chi_angles_index[res3]]
    one_hots.append(one_hot)

  one_hots.append(np.zeros([4, atom_type_num]))  # Add zeros for residue `X`.
  one_hot = np.stack(one_hots, axis=0)
  one_hot = np.transpose(one_hot, [0, 2, 1])

  return one_hot

chi_atom_1_one_hot = chi_angle_atom(1)
chi_atom_2_one_hot = chi_angle_atom(2)

# An array like chi_angles_atoms but using indices rather than names.
chi_angles_atom_indices = [chi_angles_atoms[restype_1to3[r]] for r in restypes]
chi_angles_atom_indices = tree.map_structure(
    lambda atom_name: atom_order[atom_name], chi_angles_atom_indices)
chi_angles_atom_indices = np.array([
    chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms)))
    for chi_atoms in chi_angles_atom_indices])

# Mapping from (res_name, atom_name) pairs to the atom's chi group index
# and atom index within that group.
chi_groups_for_atom = collections.defaultdict(list)
for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items():
  for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res):
    for atom_i, atom in enumerate(chi_group):
      chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i))
chi_groups_for_atom = dict(chi_groups_for_atom)


def _make_rigid_transformation_4x4(ex, ey, translation):
  """"""Create a rigid 4x4 transformation matrix from two axes and transl.""""""
  # Normalize ex.
  ex_normalized = ex / np.linalg.norm(ex)

  # make ey perpendicular to ex
  ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized
  ey_normalized /= np.linalg.norm(ey_normalized)

  # compute ez as cross product
  eznorm = np.cross(ex_normalized, ey_normalized)
  m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose()
  m = np.concatenate([m, [[0., 0., 0., 1.]]], axis=0)
  return m


# create an array with (restype, atomtype) --> rigid_group_idx
# and an array with (restype, atomtype, coord) for the atom positions
# and compute affine transformation matrices (4,4) from one rigid group to the
# previous group
restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int)
restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int)
restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)


def _make_rigid_group_constants():
  """"""Fill the arrays above.""""""
  for restype, restype_letter in enumerate(restypes):
    resname = restype_1to3[restype_letter]
    for atomname, group_idx, atom_position in rigid_group_atom_positions[
        resname]:
      atomtype = atom_order[atomname]
      restype_atom37_to_rigid_group[restype, atomtype] = group_idx
      restype_atom37_mask[restype, atomtype] = 1
      restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position

      atom14idx = restype_name_to_atom14_names[resname].index(atomname)
      restype_atom14_to_rigid_group[restype, atom14idx] = group_idx
      restype_atom14_mask[restype, atom14idx] = 1
      restype_atom14_rigid_group_positions[restype,
                                           atom14idx, :] = atom_position

  for restype, restype_letter in enumerate(restypes):
    resname = restype_1to3[restype_letter]
    atom_positions = {name: np.array(pos) for name, _, pos
                      in rigid_group_atom_positions[resname]}

    # backbone to backbone is the identity transform
    restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4)

    # pre-omega-frame to backbone (currently dummy identity matrix)
    restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4)

    # phi-frame to backbone
    mat = _make_rigid_transformation_4x4(
        ex=atom_positions['N'] - atom_positions['CA'],
        ey=np.array([1., 0., 0.]),
        translation=atom_positions['N'])
    restype_rigid_group_default_frame[restype, 2, :, :] = mat

    # psi-frame to backbone
    mat = _make_rigid_transformation_4x4(
        ex=atom_positions['C'] - atom_positions['CA'],
        ey=atom_positions['CA'] - atom_positions['N'],
        translation=atom_positions['C'])
    restype_rigid_group_default_frame[restype, 3, :, :] = mat

    # chi1-frame to backbone
    if chi_angles_mask[restype][0]:
      base_atom_names = chi_angles_atoms[resname][0]
      base_atom_positions = [atom_positions[name] for name in base_atom_names]
      mat = _make_rigid_transformation_4x4(
          ex=base_atom_positions[2] - base_atom_positions[1],
          ey=base_atom_positions[0] - base_atom_positions[1],
          translation=base_atom_positions[2])
      restype_rigid_group_default_frame[restype, 4, :, :] = mat

    # chi2-frame to chi1-frame
    # chi3-frame to chi2-frame
    # chi4-frame to chi3-frame
    # luckily all rotation axes for the next frame start at (0,0,0) of the
    # previous frame
    for chi_idx in range(1, 4):
      if chi_angles_mask[restype][chi_idx]:
        axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2]
        axis_end_atom_position = atom_positions[axis_end_atom_name]
        mat = _make_rigid_transformation_4x4(
            ex=axis_end_atom_position,
            ey=np.array([-1., 0., 0.]),
            translation=axis_end_atom_position)
        restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat


_make_rigid_group_constants()


def make_atom14_dists_bounds(overlap_tolerance=1.5,
                             bond_length_tolerance_factor=15):
  """"""compute upper and lower bounds for bonds to assess violations.""""""
  restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32)
  restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32)
  restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32)
  residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props()
  for restype, restype_letter in enumerate(restypes):
    resname = restype_1to3[restype_letter]
    atom_list = restype_name_to_atom14_names[resname]

    # create lower and upper bounds for clashes
    for atom1_idx, atom1_name in enumerate(atom_list):
      if not atom1_name:
        continue
      atom1_radius = van_der_waals_radius[atom1_name[0]]
      for atom2_idx, atom2_name in enumerate(atom_list):
        if (not atom2_name) or atom1_idx == atom2_idx:
          continue
        atom2_radius = van_der_waals_radius[atom2_name[0]]
        lower = atom1_radius + atom2_radius - overlap_tolerance
        upper = 1e10
        restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
        restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
        restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
        restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper

    # overwrite lower and upper bounds for bonds and angles
    for b in residue_bonds[resname] + residue_virtual_bonds[resname]:
      atom1_idx = atom_list.index(b.atom1_name)
      atom2_idx = atom_list.index(b.atom2_name)
      lower = b.length - bond_length_tolerance_factor * b.stddev
      upper = b.length + bond_length_tolerance_factor * b.stddev
      restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
      restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
      restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
      restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
      restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev
      restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev
  return {'lower_bound': restype_atom14_bond_lower_bound,  # shape (21,14,14)
          'upper_bound': restype_atom14_bond_upper_bound,  # shape (21,14,14)
          'stddev': restype_atom14_bond_stddev,  # shape (21,14,14)
         }
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/protein.py",".py","9959","279","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Protein data type.""""""
import dataclasses
import io
from typing import Any, Mapping, Optional
from alphafold.common import residue_constants
from Bio.PDB import PDBParser
import numpy as np

FeatureDict = Mapping[str, np.ndarray]
ModelOutput = Mapping[str, Any]  # Is a nested dict.

# Complete sequence of chain IDs supported by the PDB format.
PDB_CHAIN_IDS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
PDB_MAX_CHAINS = len(PDB_CHAIN_IDS)  # := 62.


@dataclasses.dataclass(frozen=True)
class Protein:
  """"""Protein structure representation.""""""

  # Cartesian coordinates of atoms in angstroms. The atom types correspond to
  # residue_constants.atom_types, i.e. the first three are N, CA, CB.
  atom_positions: np.ndarray  # [num_res, num_atom_type, 3]

  # Amino-acid type for each residue represented as an integer between 0 and
  # 20, where 20 is 'X'.
  aatype: np.ndarray  # [num_res]

  # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
  # is present and 0.0 if not. This should be used for loss masking.
  atom_mask: np.ndarray  # [num_res, num_atom_type]

  # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
  residue_index: np.ndarray  # [num_res]

  # 0-indexed number corresponding to the chain in the protein that this residue
  # belongs to.
  chain_index: np.ndarray  # [num_res]

  # B-factors, or temperature factors, of each residue (in sq. angstroms units),
  # representing the displacement of the residue from its ground truth mean
  # value.
  b_factors: np.ndarray  # [num_res, num_atom_type]

  def __post_init__(self):
    if len(np.unique(self.chain_index)) > PDB_MAX_CHAINS:
      raise ValueError(
          f'Cannot build an instance with more than {PDB_MAX_CHAINS} chains '
          'because these cannot be written to PDB format.')


def from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:
  """"""Takes a PDB string and constructs a Protein object.

  WARNING: All non-standard residue types will be converted into UNK. All
    non-standard atoms will be ignored.

  Args:
    pdb_str: The contents of the pdb file
    chain_id: If chain_id is specified (e.g. A), then only that chain
      is parsed. Otherwise all chains are parsed.

  Returns:
    A new `Protein` parsed from the pdb contents.
  """"""
  pdb_fh = io.StringIO(pdb_str)
  parser = PDBParser(QUIET=True)
  structure = parser.get_structure('none', pdb_fh)
  models = list(structure.get_models())
  if len(models) != 1:
    raise ValueError(
        f'Only single model PDBs are supported. Found {len(models)} models.')
  model = models[0]

  atom_positions = []
  aatype = []
  atom_mask = []
  residue_index = []
  chain_ids = []
  b_factors = []

  for chain in model:
    if chain_id is not None and chain.id != chain_id:
      continue
    for res in chain:
      if res.id[2] != ' ':
        raise ValueError(
            f'PDB contains an insertion code at chain {chain.id} and residue '
            f'index {res.id[1]}. These are not supported.')
      res_shortname = residue_constants.restype_3to1.get(res.resname, 'X')
      restype_idx = residue_constants.restype_order.get(
          res_shortname, residue_constants.restype_num)
      pos = np.zeros((residue_constants.atom_type_num, 3))
      mask = np.zeros((residue_constants.atom_type_num,))
      res_b_factors = np.zeros((residue_constants.atom_type_num,))
      for atom in res:
        if atom.name not in residue_constants.atom_types:
          continue
        pos[residue_constants.atom_order[atom.name]] = atom.coord
        mask[residue_constants.atom_order[atom.name]] = 1.
        res_b_factors[residue_constants.atom_order[atom.name]] = atom.bfactor
      if np.sum(mask) < 0.5:
        # If no known atom positions are reported for the residue then skip it.
        continue
      aatype.append(restype_idx)
      atom_positions.append(pos)
      atom_mask.append(mask)
      residue_index.append(res.id[1])
      chain_ids.append(chain.id)
      b_factors.append(res_b_factors)

  # Chain IDs are usually characters so map these to ints.
  unique_chain_ids = np.unique(chain_ids)
  chain_id_mapping = {cid: n for n, cid in enumerate(unique_chain_ids)}
  chain_index = np.array([chain_id_mapping[cid] for cid in chain_ids])

  return Protein(
      atom_positions=np.array(atom_positions),
      atom_mask=np.array(atom_mask),
      aatype=np.array(aatype),
      residue_index=np.array(residue_index),
      chain_index=chain_index,
      b_factors=np.array(b_factors))


def _chain_end(atom_index, end_resname, chain_name, residue_index) -> str:
  chain_end = 'TER'
  return (f'{chain_end:<6}{atom_index:>5}      {end_resname:>3} '
          f'{chain_name:>1}{residue_index:>4}')


def to_pdb(prot: Protein) -> str:
  """"""Converts a `Protein` instance to a PDB string.

  Args:
    prot: The protein to convert to PDB.

  Returns:
    PDB string.
  """"""
  restypes = residue_constants.restypes + ['X']
  res_1to3 = lambda r: residue_constants.restype_1to3.get(restypes[r], 'UNK')
  atom_types = residue_constants.atom_types

  pdb_lines = []

  atom_mask = prot.atom_mask
  aatype = prot.aatype
  atom_positions = prot.atom_positions
  residue_index = prot.residue_index.astype(np.int32)
  chain_index = prot.chain_index.astype(np.int32)
  b_factors = prot.b_factors

  if np.any(aatype > residue_constants.restype_num):
    raise ValueError('Invalid aatypes.')

  # Construct a mapping from chain integer indices to chain ID strings.
  chain_ids = {}
  for i in np.unique(chain_index):  # np.unique gives sorted output.
    if i >= PDB_MAX_CHAINS:
      raise ValueError(
          f'The PDB format supports at most {PDB_MAX_CHAINS} chains.')
    chain_ids[i] = PDB_CHAIN_IDS[i]

  pdb_lines.append('MODEL     1')
  atom_index = 1
  last_chain_index = chain_index[0]
  # Add all atom sites.
  for i in range(aatype.shape[0]):
    # Close the previous chain if in a multichain PDB.
    if last_chain_index != chain_index[i]:
      pdb_lines.append(_chain_end(
          atom_index, res_1to3(aatype[i - 1]), chain_ids[chain_index[i - 1]],
          residue_index[i - 1]))
      last_chain_index = chain_index[i]
      atom_index += 1  # Atom index increases at the TER symbol.

    res_name_3 = res_1to3(aatype[i])
    for atom_name, pos, mask, b_factor in zip(
        atom_types, atom_positions[i], atom_mask[i], b_factors[i]):
      if mask < 0.5:
        continue

      record_type = 'ATOM'
      name = atom_name if len(atom_name) == 4 else f' {atom_name}'
      alt_loc = ''
      insertion_code = ''
      occupancy = 1.00
      element = atom_name[0]  # Protein supports only C, N, O, S, this works.
      charge = ''
      # PDB is a columnar format, every space matters here!
      atom_line = (f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
                   f'{res_name_3:>3} {chain_ids[chain_index[i]]:>1}'
                   f'{residue_index[i]:>4}{insertion_code:>1}   '
                   f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
                   f'{occupancy:>6.2f}{b_factor:>6.2f}          '
                   f'{element:>2}{charge:>2}')
      pdb_lines.append(atom_line)
      atom_index += 1

  # Close the final chain.
  pdb_lines.append(_chain_end(atom_index, res_1to3(aatype[-1]),
                              chain_ids[chain_index[-1]], residue_index[-1]))
  pdb_lines.append('ENDMDL')
  pdb_lines.append('END')

  # Pad all lines to 80 characters.
  pdb_lines = [line.ljust(80) for line in pdb_lines]
  return '\n'.join(pdb_lines) + '\n'  # Add terminating newline.


def ideal_atom_mask(prot: Protein) -> np.ndarray:
  """"""Computes an ideal atom mask.

  `Protein.atom_mask` typically is defined according to the atoms that are
  reported in the PDB. This function computes a mask according to heavy atoms
  that should be present in the given sequence of amino acids.

  Args:
    prot: `Protein` whose fields are `numpy.ndarray` objects.

  Returns:
    An ideal atom mask.
  """"""
  return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def from_prediction(
    features: FeatureDict,
    result: ModelOutput,
    b_factors: Optional[np.ndarray] = None,
    remove_leading_feature_dimension: bool = True) -> Protein:
  """"""Assembles a protein from a prediction.

  Args:
    features: Dictionary holding model inputs.
    result: Dictionary holding model outputs.
    b_factors: (Optional) B-factors to use for the protein.
    remove_leading_feature_dimension: Whether to remove the leading dimension
      of the `features` values.

  Returns:
    A protein instance.
  """"""
  fold_output = result['structure_module']

  def _maybe_remove_leading_dim(arr: np.ndarray) -> np.ndarray:
    return arr[0] if remove_leading_feature_dimension else arr

  if 'asym_id' in features:
    chain_index = _maybe_remove_leading_dim(features['asym_id'])
  else:
    chain_index = np.zeros_like(_maybe_remove_leading_dim(features['aatype']))

  if b_factors is None:
    b_factors = np.zeros_like(fold_output['final_atom_mask'])

  return Protein(
      aatype=_maybe_remove_leading_dim(features['aatype']),
      atom_positions=fold_output['final_atom_positions'],
      atom_mask=fold_output['final_atom_mask'],
      residue_index=_maybe_remove_leading_dim(features['residue_index']) + 1,
      chain_index=chain_index,
      b_factors=b_factors)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/common/protein_test.py",".py","4594","115","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for protein.""""""

import os

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.common import protein
from alphafold.common import residue_constants
import numpy as np
# Internal import (7716).

TEST_DATA_DIR = 'alphafold/common/testdata/'


class ProteinTest(parameterized.TestCase):

  def _check_shapes(self, prot, num_res):
    """"""Check that the processed shapes are correct.""""""
    num_atoms = residue_constants.atom_type_num
    self.assertEqual((num_res, num_atoms, 3), prot.atom_positions.shape)
    self.assertEqual((num_res,), prot.aatype.shape)
    self.assertEqual((num_res, num_atoms), prot.atom_mask.shape)
    self.assertEqual((num_res,), prot.residue_index.shape)
    self.assertEqual((num_res,), prot.chain_index.shape)
    self.assertEqual((num_res, num_atoms), prot.b_factors.shape)

  @parameterized.named_parameters(
      dict(testcase_name='chain_A',
           pdb_file='2rbg.pdb', chain_id='A', num_res=282, num_chains=1),
      dict(testcase_name='chain_B',
           pdb_file='2rbg.pdb', chain_id='B', num_res=282, num_chains=1),
      dict(testcase_name='multichain',
           pdb_file='2rbg.pdb', chain_id=None, num_res=564, num_chains=2))
  def test_from_pdb_str(self, pdb_file, chain_id, num_res, num_chains):
    pdb_file = os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,
                            pdb_file)
    with open(pdb_file) as f:
      pdb_string = f.read()
    prot = protein.from_pdb_string(pdb_string, chain_id)
    self._check_shapes(prot, num_res)
    self.assertGreaterEqual(prot.aatype.min(), 0)
    # Allow equal since unknown restypes have index equal to restype_num.
    self.assertLessEqual(prot.aatype.max(), residue_constants.restype_num)
    self.assertLen(np.unique(prot.chain_index), num_chains)

  def test_to_pdb(self):
    with open(
        os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,
                     '2rbg.pdb')) as f:
      pdb_string = f.read()
    prot = protein.from_pdb_string(pdb_string)
    pdb_string_reconstr = protein.to_pdb(prot)

    for line in pdb_string_reconstr.splitlines():
      self.assertLen(line, 80)

    prot_reconstr = protein.from_pdb_string(pdb_string_reconstr)

    np.testing.assert_array_equal(prot_reconstr.aatype, prot.aatype)
    np.testing.assert_array_almost_equal(
        prot_reconstr.atom_positions, prot.atom_positions)
    np.testing.assert_array_almost_equal(
        prot_reconstr.atom_mask, prot.atom_mask)
    np.testing.assert_array_equal(
        prot_reconstr.residue_index, prot.residue_index)
    np.testing.assert_array_equal(
        prot_reconstr.chain_index, prot.chain_index)
    np.testing.assert_array_almost_equal(
        prot_reconstr.b_factors, prot.b_factors)

  def test_ideal_atom_mask(self):
    with open(
        os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,
                     '2rbg.pdb')) as f:
      pdb_string = f.read()
    prot = protein.from_pdb_string(pdb_string)
    ideal_mask = protein.ideal_atom_mask(prot)
    non_ideal_residues = set([102] + list(range(127, 286)))
    for i, (res, atom_mask) in enumerate(
        zip(prot.residue_index, prot.atom_mask)):
      if res in non_ideal_residues:
        self.assertFalse(np.all(atom_mask == ideal_mask[i]), msg=f'{res}')
      else:
        self.assertTrue(np.all(atom_mask == ideal_mask[i]), msg=f'{res}')

  def test_too_many_chains(self):
    num_res = protein.PDB_MAX_CHAINS + 1
    num_atom_type = residue_constants.atom_type_num
    with self.assertRaises(ValueError):
      _ = protein.Protein(
          atom_positions=np.random.random([num_res, num_atom_type, 3]),
          aatype=np.random.randint(0, 21, [num_res]),
          atom_mask=np.random.randint(0, 2, [num_res]).astype(np.float32),
          residue_index=np.arange(1, num_res+1),
          chain_index=np.arange(num_res),
          b_factors=np.random.uniform(1, 100, [num_res]))


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/notebooks/notebook_utils.py",".py","6736","169","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Helper methods for the AlphaFold Colab notebook.""""""
import json
from typing import Any, Mapping, Optional, Sequence, Tuple

from alphafold.common import residue_constants
from alphafold.data import parsers
from matplotlib import pyplot as plt
import numpy as np


def clean_and_validate_single_sequence(
    input_sequence: str, min_length: int, max_length: int) -> str:
  """"""Checks that the input sequence is ok and returns a clean version of it.""""""
  # Remove all whitespaces, tabs and end lines; upper-case.
  clean_sequence = input_sequence.translate(
      str.maketrans('', '', ' \n\t')).upper()
  aatypes = set(residue_constants.restypes)  # 20 standard aatypes.
  if not set(clean_sequence).issubset(aatypes):
    raise ValueError(
        f'Input sequence contains non-amino acid letters: '
        f'{set(clean_sequence) - aatypes}. AlphaFold only supports 20 standard '
        'amino acids as inputs.')
  if len(clean_sequence) < min_length:
    raise ValueError(
        f'Input sequence is too short: {len(clean_sequence)} amino acids, '
        f'while the minimum is {min_length}')
  if len(clean_sequence) > max_length:
    raise ValueError(
        f'Input sequence is too long: {len(clean_sequence)} amino acids, while '
        f'the maximum is {max_length}. You may be able to run it with the full '
        f'AlphaFold system depending on your resources (system memory, '
        f'GPU memory).')
  return clean_sequence


def clean_and_validate_input_sequences(
    input_sequences: Sequence[str],
    min_sequence_length: int,
    max_sequence_length: int) -> Sequence[str]:
  """"""Validates and cleans input sequences.""""""
  sequences = []

  for input_sequence in input_sequences:
    if input_sequence.strip():
      input_sequence = clean_and_validate_single_sequence(
          input_sequence=input_sequence,
          min_length=min_sequence_length,
          max_length=max_sequence_length)
      sequences.append(input_sequence)

  if sequences:
    return sequences
  else:
    raise ValueError('No input amino acid sequence provided, please provide at '
                     'least one sequence.')


def merge_chunked_msa(
    results: Sequence[Mapping[str, Any]],
    max_hits: Optional[int] = None
    ) -> parsers.Msa:
  """"""Merges chunked database hits together into hits for the full database.""""""
  unsorted_results = []
  for chunk_index, chunk in enumerate(results):
    msa = parsers.parse_stockholm(chunk['sto'])
    e_values_dict = parsers.parse_e_values_from_tblout(chunk['tbl'])
    # Jackhmmer lists sequences as <sequence name>/<residue from>-<residue to>.
    e_values = [e_values_dict[t.partition('/')[0]] for t in msa.descriptions]
    chunk_results = zip(
        msa.sequences, msa.deletion_matrix, msa.descriptions, e_values)
    if chunk_index != 0:
      next(chunk_results)  # Only take query (first hit) from the first chunk.
    unsorted_results.extend(chunk_results)

  sorted_by_evalue = sorted(unsorted_results, key=lambda x: x[-1])
  merged_sequences, merged_deletion_matrix, merged_descriptions, _ = zip(
      *sorted_by_evalue)
  merged_msa = parsers.Msa(sequences=merged_sequences,
                           deletion_matrix=merged_deletion_matrix,
                           descriptions=merged_descriptions)
  if max_hits is not None:
    merged_msa = merged_msa.truncate(max_seqs=max_hits)

  return merged_msa


def show_msa_info(
    single_chain_msas: Sequence[parsers.Msa],
    sequence_index: int):
  """"""Prints info and shows a plot of the deduplicated single chain MSA.""""""
  full_single_chain_msa = []
  for single_chain_msa in single_chain_msas:
    full_single_chain_msa.extend(single_chain_msa.sequences)

  # Deduplicate but preserve order (hence can't use set).
  deduped_full_single_chain_msa = list(dict.fromkeys(full_single_chain_msa))
  total_msa_size = len(deduped_full_single_chain_msa)
  print(f'\n{total_msa_size} unique sequences found in total for sequence '
        f'{sequence_index}\n')

  aa_map = {res: i for i, res in enumerate('ABCDEFGHIJKLMNOPQRSTUVWXYZ-')}
  msa_arr = np.array(
      [[aa_map[aa] for aa in seq] for seq in deduped_full_single_chain_msa])

  plt.figure(figsize=(12, 3))
  plt.title(f'Per-Residue Count of Non-Gap Amino Acids in the MSA for Sequence '
            f'{sequence_index}')
  plt.plot(np.sum(msa_arr != aa_map['-'], axis=0), color='black')
  plt.ylabel('Non-Gap Count')
  plt.yticks(range(0, total_msa_size + 1, max(1, int(total_msa_size / 3))))
  plt.show()


def empty_placeholder_template_features(
    num_templates: int, num_res: int) -> Mapping[str, np.ndarray]:
  return {
      'template_aatype': np.zeros(
          (num_templates, num_res,
           len(residue_constants.restypes_with_x_and_gap)), dtype=np.float32),
      'template_all_atom_masks': np.zeros(
          (num_templates, num_res, residue_constants.atom_type_num),
          dtype=np.float32),
      'template_all_atom_positions': np.zeros(
          (num_templates, num_res, residue_constants.atom_type_num, 3),
          dtype=np.float32),
      'template_domain_names': np.zeros([num_templates], dtype=object),
      'template_sequence': np.zeros([num_templates], dtype=object),
      'template_sum_probs': np.zeros([num_templates], dtype=np.float32),
  }


def get_pae_json(pae: np.ndarray, max_pae: float) -> str:
  """"""Returns the PAE in the same format as is used in the AFDB.

  Note that the values are presented as floats to 1 decimal place,
  whereas AFDB returns integer values.

  Args:
    pae: The n_res x n_res PAE array.
    max_pae: The maximum possible PAE value.
  Returns:
    PAE output format as a JSON string.
  """"""
  # Check the PAE array is the correct shape.
  if (pae.ndim != 2 or pae.shape[0] != pae.shape[1]):
    raise ValueError(f'PAE must be a square matrix, got {pae.shape}')

  # Round the predicted aligned errors to 1 decimal place.
  rounded_errors = np.round(pae.astype(np.float64), decimals=1)
  formatted_output = [{
      'predicted_aligned_error': rounded_errors.tolist(),
      'max_predicted_aligned_error': max_pae
  }]
  return json.dumps(formatted_output, indent=None, separators=(',', ':'))
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/notebooks/__init__.py",".py","626","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""AlphaFold Colab notebook.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/notebooks/notebook_utils_test.py",".py","9107","197","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Tests for notebook_utils.""""""
import io

from absl.testing import absltest
from absl.testing import parameterized
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.notebooks import notebook_utils

import mock
import numpy as np


ONLY_QUERY_HIT = {
    'sto': (
        '# STOCKHOLM 1.0\n'
        '#=GF ID query-i1\n'
        'query   MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEH\n'
        '//\n'),
    'tbl': '',
    'stderr': b'',
    'n_iter': 1,
    'e_value': 0.0001}

# pylint: disable=line-too-long
MULTI_SEQUENCE_HIT_1 = {
    'sto': (
        '# STOCKHOLM 1.0\n'
        '#=GF ID query-i1\n'
        '#=GS ERR1700680_4602609/41-109  DE [subseq from] ERR1700680_4602609\n'
        '#=GS ERR1019366_5760491/40-105  DE [subseq from] ERR1019366_5760491\n'
        '#=GS SRR5580704_12853319/61-125 DE [subseq from] SRR5580704_12853319\n'
        'query                              MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH\n'
        'ERR1700680_4602609/41-109          --INKGAEYHKKAAEHHELAAKHHREAAKHHEAGSHEKAAHHSEIAAGHGLTAVHHTEEATK-HHPEEHTEK--\n'
        'ERR1019366_5760491/40-105          ---RSGAQHHDAAAQHYEEAARHHRMAAKQYQASHHEKAAHYAQLAYAHHMYAEQHAAEAAK-AHAKNHG----\n'
        'SRR5580704_12853319/61-125         ----PAADHHMKAAEHHEEAAKHHRAAAEHHTAGDHQKAGHHAHVANGHHVNAVHHAEEASK-HHATDHS----\n'
        '//\n'),
    'tbl': (
        'ERR1700680_4602609   -          query                -            7.7e-09   47.7  33.8   1.1e-08   47.2  33.8   1.2   1   0   0   1   1   1   1 -\n'
        'ERR1019366_5760491   -          query                -            1.7e-08   46.6  33.1   2.5e-08   46.1  33.1   1.3   1   0   0   1   1   1   1 -\n'
        'SRR5580704_12853319  -          query                -            1.1e-07   44.0  41.6     2e-07   43.1  41.6   1.4   1   0   0   1   1   1   1 -\n'),
        'stderr': b'',
        'n_iter': 1,
        'e_value': 0.0001}

MULTI_SEQUENCE_HIT_2 = {
    'sto': (
        '# STOCKHOLM 1.0\n'
        '#=GF ID query-i1\n'
        '#=GS ERR1700719_3476944/70-137  DE [subseq from] ERR1700719_3476944\n'
        '#=GS ERR1700761_4254522/72-138  DE [subseq from] ERR1700761_4254522\n'
        '#=GS SRR5438477_9761204/64-132  DE [subseq from] SRR5438477_9761204\n'
        'query                              MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH\n'
        'ERR1700719_3476944/70-137          ---KQAAEHHHQAAEHHEHAARHHREAAKHHEAGDHESAAHHAHTAQGHLHQATHHASEAAKLHVEHHGQK--\n'
        'ERR1700761_4254522/72-138          ----QASEHHNLAAEHHEHAARHHRDAAKHHKAGDHEKAAHHAHVAHGHHLHATHHATEAAKHHVEAHGEK--\n'
        'SRR5438477_9761204/64-132          MPKHEGAEHHKKAAEHNEHAARHHKEAARHHEEGSHEKVGHHAHIAHGHHLHATHHAEEAAKTHSNQHE----\n'
        '//\n'),
    'tbl': (
        'ERR1700719_3476944   -          query                -              2e-07   43.2  47.5   3.5e-07   42.4  47.5   1.4   1   0   0   1   1   1   1 -\n'
        'ERR1700761_4254522   -          query                -            6.1e-07   41.6  48.1   8.1e-07   41.3  48.1   1.2   1   0   0   1   1   1   1 -\n'
        'SRR5438477_9761204   -          query                -            1.8e-06   40.2  46.9   2.3e-06   39.8  46.9   1.2   1   0   0   1   1   1   1 -\n'),
        'stderr': b'',
        'n_iter': 1,
        'e_value': 0.0001}
# pylint: enable=line-too-long


class NotebookUtilsTest(parameterized.TestCase):

  @parameterized.parameters(
      ('DeepMind', 'DEEPMIND'), ('A ', 'A'), ('\tA', 'A'), (' A\t\n', 'A'),
      ('ACDEFGHIKLMNPQRSTVWY', 'ACDEFGHIKLMNPQRSTVWY'))
  def test_clean_and_validate_sequence_ok(self, sequence, exp_clean):
    clean = notebook_utils.clean_and_validate_single_sequence(
        sequence, min_length=1, max_length=100)
    self.assertEqual(clean, exp_clean)

  @parameterized.named_parameters(
      ('too_short', 'AA', 'too short'),
      ('too_long', 'AAAAAAAAAA', 'too long'),
      ('bad_amino_acids_B', 'BBBB', 'non-amino acid'),
      ('bad_amino_acids_J', 'JJJJ', 'non-amino acid'),
      ('bad_amino_acids_O', 'OOOO', 'non-amino acid'),
      ('bad_amino_acids_U', 'UUUU', 'non-amino acid'),
      ('bad_amino_acids_X', 'XXXX', 'non-amino acid'),
      ('bad_amino_acids_Z', 'ZZZZ', 'non-amino acid'))
  def test_clean_and_validate_sequence_bad(self, sequence, exp_error):
    with self.assertRaisesRegex(ValueError, f'.*{exp_error}.*'):
      notebook_utils.clean_and_validate_single_sequence(
          sequence, min_length=4, max_length=8)

  @parameterized.parameters(
      (['A', '', '', ' ', '\t', ' \t\n', '', ''], ['A']),
      (['', 'A'], ['A']),
      (['A', 'C ', ''], ['A', 'C']),
      (['', 'A', '', 'C '], ['A', 'C']))
  def test_validate_input_ok(self, input_sequences, exp_sequences):
    sequences = notebook_utils.clean_and_validate_input_sequences(
        input_sequences=input_sequences,
        min_sequence_length=1, max_sequence_length=100)
    self.assertSequenceEqual(sequences, exp_sequences)

  @parameterized.named_parameters(
      ('no_input_sequence', ['', '\t', '\n'], 'No input amino acid sequence'),
      ('too_long_single', ['AAAAAAAAA', 'AAAA'], 'Input sequence is too long'),
      ('too_short_single', ['AAA', 'AAAA'], 'Input sequence is too short'))
  def test_validate_input_bad(self, input_sequences, exp_error):
    with self.assertRaisesRegex(ValueError, f'.*{exp_error}.*'):
      notebook_utils.clean_and_validate_input_sequences(
          input_sequences=input_sequences, min_sequence_length=4,
          max_sequence_length=8)

  def test_merge_chunked_msa_no_hits(self):
    results = [ONLY_QUERY_HIT, ONLY_QUERY_HIT]
    merged_msa = notebook_utils.merge_chunked_msa(
        results=results)
    self.assertSequenceEqual(
        merged_msa.sequences,
        ('MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEH',))
    self.assertSequenceEqual(merged_msa.deletion_matrix, ([0] * 56,))

  def test_merge_chunked_msa(self):
    results = [MULTI_SEQUENCE_HIT_1, MULTI_SEQUENCE_HIT_2]
    merged_msa = notebook_utils.merge_chunked_msa(
        results=results)
    self.assertLen(merged_msa.sequences, 7)
    # The 1st one is the query.
    self.assertEqual(
        merged_msa.sequences[0],
        'MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAP'
        'KPH')
    # The 2nd one is the one with the lowest e-value: ERR1700680_4602609.
    self.assertEqual(
        merged_msa.sequences[1],
        '--INKGAEYHKKAAEHHELAAKHHREAAKHHEAGSHEKAAHHSEIAAGHGLTAVHHTEEATK-HHPEEHT'
        'EK-')
    # The last one is the one with the largest e-value: SRR5438477_9761204.
    self.assertEqual(
        merged_msa.sequences[-1],
        'MPKHEGAEHHKKAAEHNEHAARHHKEAARHHEEGSHEKVGHHAHIAHGHHLHATHHAEEAAKTHSNQHE-'
        '---')
    self.assertLen(merged_msa.deletion_matrix, 7)

  @mock.patch('sys.stdout', new_callable=io.StringIO)
  def test_show_msa_info(self, mocked_stdout):
    single_chain_msas = [
        parsers.Msa(sequences=['A', 'B', 'C', 'C'],
                    deletion_matrix=[None] * 4,
                    descriptions=[''] * 4),
        parsers.Msa(sequences=['A', 'A', 'A', 'D'],
                    deletion_matrix=[None] * 4,
                    descriptions=[''] * 4)
    ]
    notebook_utils.show_msa_info(
        single_chain_msas=single_chain_msas, sequence_index=1)
    self.assertEqual(mocked_stdout.getvalue(),
                     '\n4 unique sequences found in total for sequence 1\n\n')

  @parameterized.named_parameters(
      ('some_templates', 4), ('no_templates', 0))
  def test_empty_placeholder_template_features(self, num_templates):
    template_features = notebook_utils.empty_placeholder_template_features(
        num_templates=num_templates, num_res=16)
    self.assertCountEqual(template_features.keys(),
                          templates.TEMPLATE_FEATURES.keys())
    self.assertSameElements(
        [v.shape[0] for v in template_features.values()], [num_templates])
    self.assertSequenceEqual(
        [t.dtype for t in template_features.values()],
        [np.array([], dtype=templates.TEMPLATE_FEATURES[feat_name]).dtype
         for feat_name in template_features])

  def test_get_pae_json(self):
    pae = np.array([[0.01, 13.12345], [20.0987, 0.0]])
    pae_json = notebook_utils.get_pae_json(pae=pae, max_pae=31.75)
    self.assertEqual(
        pae_json, '[{""predicted_aligned_error"":[[0.0,13.1],[20.1,0.0]],'
        '""max_predicted_aligned_error"":31.75}]')


if __name__ == '__main__':
  absltest.main()
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/pipeline_custom_templates.py",".py","19376","444","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for building the input features for the AlphaFold model.""""""

import os
import pandas as pd
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Union
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import msa_identifiers
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.data.tools import hhblits
from alphafold.data.tools import hhsearch
from alphafold.data.tools import hmmsearch
from alphafold.data.tools import jackhmmer
import numpy as np
from alphafold.data.custom_templates import create_single_template_features, compile_template_features, mk_mock_template
import tree

# Internal import (7716).

FeatureDict = MutableMapping[str, np.ndarray]
TemplateSearcher = Union[hhsearch.HHSearch, hmmsearch.Hmmsearch]


def make_sequence_features(
    sequence: str, description: str, num_res: int) -> FeatureDict:
  """"""Constructs a feature dict of sequence features.""""""
  features = {}
  features['aatype'] = residue_constants.sequence_to_onehot(
      sequence=sequence,
      mapping=residue_constants.restype_order_with_x,
      map_unknown_to_x=True)
  features['between_segment_residues'] = np.zeros((num_res,), dtype=np.int32)
  features['domain_name'] = np.array([description.encode('utf-8')],
                                     dtype=np.object_)
  features['residue_index'] = np.array(range(num_res), dtype=np.int32)
  features['seq_length'] = np.array([num_res] * num_res, dtype=np.int32)
  features['sequence'] = np.array([sequence.encode('utf-8')], dtype=np.object_)
  return features

def make_mock_msa_features(msas) -> FeatureDict:
  """"""Constructs a empty feature dict of MSA features.""""""
  num_res = len(msas[0])
  deletion_matrices = [[0] * len(msas[0])]
  deletion_matrix = []
  int_msa = []
  species_ids = []
  seen_sequences = set()
  for sequence_index, sequence in enumerate(msas):
    # for sequence_index, sequence in enumerate(msa):
      # if sequence in seen_sequences:
      #   continue
      # seen_sequences.add(sequence)
    int_msa.append(
        [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence])
    deletion_matrix.append(deletion_matrices[sequence_index])
    species_ids.append("""".encode('utf-8'))

  num_alignments = len(int_msa)
  features = {}
  features['deletion_matrix_int'] = np.array(deletion_matrix, dtype=np.int32)
  features['msa'] = np.array(int_msa, dtype=np.int32)
  features['num_alignments'] = np.array(
      [num_alignments] * num_res, dtype=np.int32)
  features['msa_species_identifiers'] = np.array(species_ids, dtype=np.object_)
  return features

def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
  """"""Constructs a feature dict of MSA features.""""""
  if not msas:
    raise ValueError('At least one MSA must be provided.')

  int_msa = []
  deletion_matrix = []
  species_ids = []
  seen_sequences = set()
  try:
    for msa_index, msa in enumerate(msas):
      if not msa:
        raise ValueError(f'MSA {msa_index} must contain at least one sequence.')
      for sequence_index, sequence in enumerate(msa.sequences):
        if sequence in seen_sequences:
          continue
        seen_sequences.add(sequence)
        int_msa.append(
            [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence])
        deletion_matrix.append(msa.deletion_matrix[sequence_index])
        identifiers = msa_identifiers.get_identifiers(
            msa.descriptions[sequence_index])
        species_ids.append(identifiers.species_id.encode('utf-8'))
    num_res = len(msas[0].sequences[0])
  except:
    msa=msas
    for sequence_index, sequence in enumerate(msa.sequences):
      if sequence in seen_sequences:
        continue
      seen_sequences.add(sequence)
      int_msa.append(
          [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence])
      deletion_matrix.append(msa.deletion_matrix[sequence_index])
      identifiers = msa_identifiers.get_identifiers(
          msa.descriptions[sequence_index])
      species_ids.append(identifiers.species_id.encode('utf-8'))
    num_res = len(msas.sequences[0])

  num_alignments = len(int_msa)
  features = {}
  features['deletion_matrix_int'] = np.array(deletion_matrix, dtype=np.int32)
  features['msa'] = np.array(int_msa, dtype=np.int32)
  features['num_alignments'] = np.array(
      [num_alignments] * num_res, dtype=np.int32)
  features['msa_species_identifiers'] = np.array(species_ids, dtype=np.object_)
  return features


def run_msa_tool(msa_runner, input_fasta_path: str, msa_out_path: str,
                 msa_format: str, use_precomputed_msas: bool,
                 max_sto_sequences: Optional[int] = None
                 ) -> Mapping[str, Any]:
  """"""Runs an MSA tool, checking if output already exists first.""""""
  if not use_precomputed_msas or not os.path.exists(msa_out_path):
    if msa_format == 'sto' and max_sto_sequences is not None:
      result = msa_runner.query(input_fasta_path, max_sto_sequences)[0]  # pytype: disable=wrong-arg-count
    else:
      result = msa_runner.query(input_fasta_path)[0]
    with open(msa_out_path, 'w') as f:
      f.write(result[msa_format])
  else:
    logging.warning('Reading MSA from file %s', msa_out_path)
    if msa_format == 'sto' and max_sto_sequences is not None:
      precomputed_msa = parsers.truncate_stockholm_msa(
          msa_out_path, max_sto_sequences)
      result = {'sto': precomputed_msa}
    else:
      with open(msa_out_path, 'r') as f:
        result = {msa_format: f.read()}
  return result


class DataPipeline:
  """"""Runs the alignment tools and assembles the input features.""""""

  def __init__(self,
               jackhmmer_binary_path: str,
               hhblits_binary_path: str,
               uniref90_database_path: str,
               mgnify_database_path: str,
               bfd_database_path: Optional[str],
               uniref30_database_path: Optional[str],
               small_bfd_database_path: Optional[str],
               template_searcher: TemplateSearcher,
               template_featurizer: templates.TemplateHitFeaturizer,
               use_small_bfd: bool,
               mgnify_max_hits: int = 501,
               uniref_max_hits: int = 10000,
               use_precomputed_msas: bool = False):
    """"""Initializes the data pipeline.""""""
    self._use_small_bfd = use_small_bfd
    self.jackhmmer_binary_path=jackhmmer_binary_path
    self.jackhmmer_uniref90_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=uniref90_database_path)
    if use_small_bfd:
      self.jackhmmer_small_bfd_runner = jackhmmer.Jackhmmer(
          binary_path=jackhmmer_binary_path,
          database_path=small_bfd_database_path)
    else:
      self.hhblits_bfd_uniref_runner = hhblits.HHBlits(
          binary_path=hhblits_binary_path,
          databases=[bfd_database_path, uniref30_database_path])
    self.jackhmmer_mgnify_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=mgnify_database_path)
    self.template_searcher = template_searcher
    self.template_featurizer = template_featurizer
    self.mgnify_max_hits = mgnify_max_hits
    self.uniref_max_hits = uniref_max_hits
    self.use_precomputed_msas = use_precomputed_msas

  def custom_MSA_features(self,
                      MSA_database: str,
                      input_fasta_path: str,
                      msa_output_dir: str):
    custom_MSA_runner = jackhmmer.Jackhmmer(
      binary_path=self.jackhmmer_binary_path,
      database_path=MSA_database)
    custom_MSA_out_path = os.path.join(msa_output_dir, 'custom_MSA.sto')
    custom_MSA_result = run_msa_tool(
        msa_runner=custom_MSA_runner,
        input_fasta_path=input_fasta_path,
        msa_out_path=custom_MSA_out_path,
        msa_format='sto',
        use_precomputed_msas=self.use_precomputed_msas,
        max_sto_sequences=self.uniref_max_hits)
    custom_msa = parsers.parse_stockholm(custom_MSA_result['sto'])
    logging.info('Custom MSA size: %d sequences.', len(custom_msa))

    return custom_msa
  
  def msa_for_default_template(self,
                                input_fasta_path: str,
                                msa_output_dir: str):
    uniref90_out_path = os.path.join(msa_output_dir, 'uniref90_hits.sto')
    jackhmmer_uniref90_result = run_msa_tool(
        msa_runner=self.jackhmmer_uniref90_runner,
        input_fasta_path=input_fasta_path,
        msa_out_path=uniref90_out_path,
        msa_format='sto',
        use_precomputed_msas=self.use_precomputed_msas,
        max_sto_sequences=self.uniref_max_hits)
    uniref90_msa = parsers.parse_stockholm(jackhmmer_uniref90_result['sto'])      
    logging.info('Uniref90 MSA size: %d sequences. This is for templates, '
                'not MSA construction)', len(uniref90_msa))
    return jackhmmer_uniref90_result,uniref90_msa

  def process(self, 
              input_fasta_path: str,
              msa_output_dir: str, 
              use_custom_templates: bool,
              template_alignfile: str, 
              msa_mode: str,
              use_custom_MSA_database: str,
              MSA_database: str,
              save_msa_fasta: bool,
              save_template_names: bool,
              msa_for_template_query_seq_only: bool) -> FeatureDict:
    """"""Runs alignment tools on the input sequence and creates features.""""""
    with open(input_fasta_path) as f:
      input_fasta_str = f.read()
    # print(input_fasta_str)
    input_seqs, input_descs = parsers.parse_fasta(input_fasta_str)
    # print(""length of input_seqs is %d:"" % len(input_seqs[0]))
    # if len(input_seqs) != 1:
    #   raise ValueError(
    #       f'More than one input sequence found in {input_fasta_path}.')
    input_sequence = """".join(input_seqs[0])
    logging.info(""input_sequence is %s"" % input_sequence)
    input_description = input_descs[0]
    num_res = len(input_sequence)

    ###########################################################
    ## MSA
    ###########################################################

    # if MSA used:
    if msa_mode!=""single_sequence"":
      if use_custom_MSA_database==""only"":
        # custom MSA 
        custom_msa=DataPipeline.custom_MSA_features(self,MSA_database,input_fasta_path,msa_output_dir)
        msa_features = make_msa_features((custom_msa))
        if template_alignfile==""UseDefaultTemplate"": 
          jackhmmer_uniref90_result,uniref90_msa=DataPipeline.msa_for_default_template(self,input_fasta_path,msa_output_dir)
      else:
        # default MSA
        uniref90_out_path = os.path.join(msa_output_dir, 'uniref90_hits.sto')
        jackhmmer_uniref90_result = run_msa_tool(
            msa_runner=self.jackhmmer_uniref90_runner,
            input_fasta_path=input_fasta_path,
            msa_out_path=uniref90_out_path,
            msa_format='sto',
            use_precomputed_msas=self.use_precomputed_msas,
            max_sto_sequences=self.uniref_max_hits)
        mgnify_out_path = os.path.join(msa_output_dir, 'mgnify_hits.sto')
        jackhmmer_mgnify_result = run_msa_tool(
            msa_runner=self.jackhmmer_mgnify_runner,
            input_fasta_path=input_fasta_path,
            msa_out_path=mgnify_out_path,
            msa_format='sto',
            use_precomputed_msas=self.use_precomputed_msas,
            max_sto_sequences=self.mgnify_max_hits)

        uniref90_msa = parsers.parse_stockholm(jackhmmer_uniref90_result['sto'])
        mgnify_msa = parsers.parse_stockholm(jackhmmer_mgnify_result['sto'])


        if self._use_small_bfd:
          bfd_out_path = os.path.join(msa_output_dir, 'small_bfd_hits.sto')
          jackhmmer_small_bfd_result = run_msa_tool(
              msa_runner=self.jackhmmer_small_bfd_runner,
              input_fasta_path=input_fasta_path,
              msa_out_path=bfd_out_path,
              msa_format='sto',
              use_precomputed_msas=self.use_precomputed_msas)
          bfd_msa = parsers.parse_stockholm(jackhmmer_small_bfd_result['sto'])
        else:
          bfd_out_path = os.path.join(msa_output_dir, 'bfd_uniclust_hits.a3m')
          hhblits_bfd_uniclust_result = run_msa_tool(
              msa_runner=self.hhblits_bfd_uniref_runner,
              input_fasta_path=input_fasta_path,
              msa_out_path=bfd_out_path,
              msa_format='a3m',
              use_precomputed_msas=self.use_precomputed_msas)
          bfd_msa = parsers.parse_a3m(hhblits_bfd_uniclust_result['a3m'])

        if use_custom_MSA_database==""add"":
          custom_msa=DataPipeline.custom_MSA_features(self,MSA_database,input_fasta_path,msa_output_dir)
          msa_features = make_msa_features((uniref90_msa, bfd_msa, mgnify_msa, custom_msa))
        else:
          msa_features = make_msa_features((uniref90_msa, bfd_msa, mgnify_msa))

        logging.info('Uniref90 MSA size: %d sequences.', len(uniref90_msa))
        logging.info('BFD MSA size: %d sequences.', len(bfd_msa))
        logging.info('MGnify MSA size: %d sequences.', len(mgnify_msa))

    # if single sequence yet use default template
    elif template_alignfile==""UseDefaultTemplate"": 
      jackhmmer_uniref90_result,uniref90_msa=DataPipeline.msa_for_default_template(self,input_fasta_path,msa_output_dir)
      msas=[input_sequence]
      msa_features = make_mock_msa_features(msas)
      
    # if single sequence and not use default template 
    else:
      msas=[input_sequence]
      msa_features = make_mock_msa_features(msas)

    if save_msa_fasta:
      msa_outpath=os.path.join(msa_output_dir, 'msa_feat_gaptoU.fasta')
      with open(msa_outpath, 'w+') as fh:
        fh.write("">query""+""\n""+input_sequence+""\n"")
        counter=1
        for seq in msa_features['msa']:
            seq=[residue_constants.ID_TO_HHBLITS_AA[num] for num in seq]
            # for x in range(len(seq)):
            counter+=1
            fh.write("">seq_""+str(counter)+""\n"")
            out="""".join(seq).replace(""-"",""U"")
            fh.write(out+""\n"")
                
    ###########################################################
    ## Template
    ###########################################################
    # if defaullt template should be used 
    if (not use_custom_templates) or (use_custom_templates and template_alignfile==""UseDefaultTemplate""):
      msa_for_templates = jackhmmer_uniref90_result['sto']
      msa_for_templates = parsers.deduplicate_stockholm_msa(msa_for_templates)
      msa_for_templates = parsers.remove_empty_columns_from_stockholm_msa(
          msa_for_templates)
      if msa_for_template_query_seq_only:
        '''BP 12/15/22 let's just keep the target (query) sequence'''
        msa_for_templates = parsers.truncate_stockholm_msa2(msa_for_templates, 1)

      if self.template_searcher.input_format == 'sto':
        pdb_templates_result = self.template_searcher.query(msa_for_templates)
      elif self.template_searcher.input_format == 'a3m':
        uniref90_msa_as_a3m = parsers.convert_stockholm_to_a3m(msa_for_templates)
        pdb_templates_result = self.template_searcher.query(uniref90_msa_as_a3m)
      else:
        raise ValueError('Unrecognized template input format: '
                        f'{self.template_searcher.input_format}')

      pdb_hits_out_path = os.path.join(
          msa_output_dir, f'pdb_hits.{self.template_searcher.output_format}')
      with open(pdb_hits_out_path, 'w') as f:
        f.write(pdb_templates_result)

      pdb_template_hits = self.template_searcher.get_template_hits(
          output_string=pdb_templates_result, input_sequence=input_sequence)
          
      templates_result = self.template_featurizer.get_templates(
      query_sequence=input_sequence,
      hits=pdb_template_hits)

    #if user defined template used
    else:
      #############################################
      #update the templates
      if len(template_alignfile)==0:
        templates_result=mk_mock_template(input_sequence)
        # print(templates_result.features['template_domain_names'])
      else:
        data = pd.read_table(template_alignfile)
        # cols = ('template_pdbfile target_to_template_alignstring identities '
        #         'target_len template_len'.split())
        template_features_list = []
        for tnum, row in data.iterrows():
            target_to_template_alignment = {
                int(x.split(':')[0]) : int(x.split(':')[1]) # 0-indexed
                for x in row.target_to_template_alignstring.split(';')
            }

            template_name = os.path.basename(row.template_pdbfile).split(""."")[0]
            template_features = create_single_template_features(
                input_sequence, row.template_pdbfile, target_to_template_alignment,
                template_name, allow_chainbreaks=True, allow_skipped_lines=True,
                expected_identities = None,
                expected_template_len = row.template_len,
            )
            template_features_list.append(template_features)

        templates_result = compile_template_features(
            template_features_list)

      #############################################


    sequence_features = make_sequence_features(
        sequence=input_sequence,
        description=input_description,
        num_res=num_res)

    # if len(input_seqs[0]) > 1:
    if isinstance(input_seqs[0], list):
      # print(""There are %d semi-colon seperated chains in this chain. Therefore updating the residue_index now"" % len(input_seqs[0]))
      # Minkyung's code
      # add big enough number to residue index to indicate chain breaks
      idx_res = sequence_features['residue_index']
      Ls=[len(seq) for seq in input_seqs[0]]
      L_prev = 0
      # Ls: number of residues in each chain
      for L_i in Ls[:-1]:
          idx_res[L_prev + L_i:] += 200
          L_prev += L_i
      # chains = list("""".join([ascii_uppercase[n] * L for n, L in enumerate(Ls)]))
      sequence_features['residue_index'] = idx_res

    logging.info('Final (deduplicated) MSA size: %d sequences.',
                 msa_features['num_alignments'][0])
    logging.info('Total number of templates (NB: this can include bad '
                 'templates and is later filtered to top 4, and mock/no '
                 'templates would also show number of templates as 1): %d.',
                 templates_result.features['template_domain_names'].shape[0])

    if save_template_names:
      temp_name_fn=os.path.join(msa_output_dir, 'template_names.txt')
      template_names=[name.decode('utf-8') for name in templates_result.features['template_domain_names']]
      with open(temp_name_fn, 'w+') as fh:
          fh.write(""\n"".join(template_names))
          
    return {**sequence_features, **msa_features, **templates_result.features}
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/pipeline_multimer.py",".py","11696","297","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for building the features for the AlphaFold multimer model.""""""

import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence

from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from alphafold.data import msa_pairing
from alphafold.data import parsers
from alphafold.data import pipeline
from alphafold.data.tools import jackhmmer
import numpy as np

# Internal import (7716).


@dataclasses.dataclass(frozen=True)
class _FastaChain:
  sequence: str
  description: str


def _make_chain_id_map(*,
                       sequences: Sequence[str],
                       descriptions: Sequence[str],
                       ) -> Mapping[str, _FastaChain]:
  """"""Makes a mapping from PDB-format chain ID to sequence and description.""""""
  if len(sequences) != len(descriptions):
    raise ValueError('sequences and descriptions must have equal length. '
                     f'Got {len(sequences)} != {len(descriptions)}.')
  if len(sequences) > protein.PDB_MAX_CHAINS:
    raise ValueError('Cannot process more chains than the PDB format supports. '
                     f'Got {len(sequences)} chains.')
  chain_id_map = {}
  for chain_id, sequence, description in zip(
      protein.PDB_CHAIN_IDS, sequences, descriptions):
    chain_id_map[chain_id] = _FastaChain(
        sequence=sequence, description=description)
  return chain_id_map


@contextlib.contextmanager
def temp_fasta_file(fasta_str: str):
  with tempfile.NamedTemporaryFile('w', suffix='.fasta') as fasta_file:
    fasta_file.write(fasta_str)
    fasta_file.seek(0)
    yield fasta_file.name


def convert_monomer_features(
    monomer_features: pipeline.FeatureDict,
    chain_id: str) -> pipeline.FeatureDict:
  """"""Reshapes and modifies monomer features for multimer models.""""""
  converted = {}
  converted['auth_chain_id'] = np.asarray(chain_id, dtype=np.object_)
  unnecessary_leading_dim_feats = {
      'sequence', 'domain_name', 'num_alignments', 'seq_length'}
  for feature_name, feature in monomer_features.items():
    if feature_name in unnecessary_leading_dim_feats:
      # asarray ensures it's a np.ndarray.
      feature = np.asarray(feature[0], dtype=feature.dtype)
    elif feature_name == 'aatype':
      # The multimer model performs the one-hot operation itself.
      feature = np.argmax(feature, axis=-1).astype(np.int32)
    elif feature_name == 'template_aatype':
      feature = np.argmax(feature, axis=-1).astype(np.int32)
      new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
      feature = np.take(new_order_list, feature.astype(np.int32), axis=0)
    elif feature_name == 'template_all_atom_masks':
      feature_name = 'template_all_atom_mask'
    converted[feature_name] = feature
  return converted


def int_id_to_str_id(num: int) -> str:
  """"""Encodes a number as a string, using reverse spreadsheet style naming.

  Args:
    num: A positive integer.

  Returns:
    A string that encodes the positive integer using reverse spreadsheet style,
    naming e.g. 1 = A, 2 = B, ..., 27 = AA, 28 = BA, 29 = CA, ... This is the
    usual way to encode chain IDs in mmCIF files.
  """"""
  if num <= 0:
    raise ValueError(f'Only positive integers allowed, got {num}.')

  num = num - 1  # 1-based indexing.
  output = []
  while num >= 0:
    output.append(chr(num % 26 + ord('A')))
    num = num // 26 - 1
  return ''.join(output)


def add_assembly_features(
    all_chain_features: MutableMapping[str, pipeline.FeatureDict],
    ) -> MutableMapping[str, pipeline.FeatureDict]:
  """"""Add features to distinguish between chains.

  Args:
    all_chain_features: A dictionary which maps chain_id to a dictionary of
      features for each chain.

  Returns:
    all_chain_features: A dictionary which maps strings of the form
      `<seq_id>_<sym_id>` to the corresponding chain features. E.g. two
      chains from a homodimer would have keys A_1 and A_2. Two chains from a
      heterodimer would have keys A_1 and B_1.
  """"""
  # Group the chains by sequence
  seq_to_entity_id = {}
  grouped_chains = collections.defaultdict(list)
  for chain_id, chain_features in all_chain_features.items():
    seq = str(chain_features['sequence'])
    if seq not in seq_to_entity_id:
      seq_to_entity_id[seq] = len(seq_to_entity_id) + 1
    grouped_chains[seq_to_entity_id[seq]].append(chain_features)

  new_all_chain_features = {}
  chain_id = 1
  for entity_id, group_chain_features in grouped_chains.items():
    for sym_id, chain_features in enumerate(group_chain_features, start=1):
      new_all_chain_features[
          f'{int_id_to_str_id(entity_id)}_{sym_id}'] = chain_features
      seq_length = chain_features['seq_length']
      chain_features['asym_id'] = chain_id * np.ones(seq_length)
      chain_features['sym_id'] = sym_id * np.ones(seq_length)
      chain_features['entity_id'] = entity_id * np.ones(seq_length)
      chain_id += 1

  return new_all_chain_features


def pad_msa(np_example, min_num_seq):
  np_example = dict(np_example)
  num_seq = np_example['msa'].shape[0]
  if num_seq < min_num_seq:
    for feat in ('msa', 'deletion_matrix', 'bert_mask', 'msa_mask'):
      np_example[feat] = np.pad(
          np_example[feat], ((0, min_num_seq - num_seq), (0, 0)))
    np_example['cluster_bias_mask'] = np.pad(
        np_example['cluster_bias_mask'], ((0, min_num_seq - num_seq),))
  return np_example


class DataPipeline:
  """"""Runs the alignment tools and assembles the input features.""""""

  def __init__(self,
               monomer_data_pipeline: pipeline.DataPipeline,
               jackhmmer_binary_path: str,
               uniprot_database_path: str,
               max_uniprot_hits: int = 50000,
               use_precomputed_msas: bool = False):
    """"""Initializes the data pipeline.

    Args:
      monomer_data_pipeline: An instance of pipeline.DataPipeline - that runs
        the data pipeline for the monomer AlphaFold system.
      jackhmmer_binary_path: Location of the jackhmmer binary.
      uniprot_database_path: Location of the unclustered uniprot sequences, that
        will be searched with jackhmmer and used for MSA pairing.
      max_uniprot_hits: The maximum number of hits to return from uniprot.
      use_precomputed_msas: Whether to use pre-existing MSAs; see run_alphafold.
    """"""
    self._monomer_data_pipeline = monomer_data_pipeline
    self._uniprot_msa_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=uniprot_database_path)
    self._max_uniprot_hits = max_uniprot_hits
    self.use_precomputed_msas = use_precomputed_msas

  def _process_single_chain(
      self,
      chain_id: str,
      sequence: str,
      description: str,
      msa_output_dir: str,
      is_homomer_or_monomer: bool,
      save_msa_fasta: bool,
      save_template_names: bool,
      msa_for_template_query_seq_only: bool) -> pipeline.FeatureDict:
    """"""Runs the monomer pipeline on a single chain.""""""
    chain_fasta_str = f'>chain_{chain_id}\n{sequence}\n'
    chain_msa_output_dir = os.path.join(msa_output_dir, chain_id)
    if not os.path.exists(chain_msa_output_dir):
      os.makedirs(chain_msa_output_dir)
    with temp_fasta_file(chain_fasta_str) as chain_fasta_path:
      logging.info('Running monomer pipeline on chain %s: %s',
                   chain_id, description)
      chain_features = self._monomer_data_pipeline.process(
          input_fasta_path=chain_fasta_path,
          msa_output_dir=chain_msa_output_dir,
          save_msa_fasta=save_msa_fasta,
          save_template_names=save_template_names,
          msa_for_template_query_seq_only=msa_for_template_query_seq_only)

      # We only construct the pairing features if there are 2 or more unique
      # sequences.
      if not is_homomer_or_monomer:
        all_seq_msa_features = self._all_seq_msa_features(chain_fasta_path,
                                                          chain_msa_output_dir)
        chain_features.update(all_seq_msa_features)
    return chain_features

  def _all_seq_msa_features(self, input_fasta_path, msa_output_dir):
    """"""Get MSA features for unclustered uniprot, for pairing.""""""
    out_path = os.path.join(msa_output_dir, 'uniprot_hits.sto')
    result = pipeline.run_msa_tool(
        self._uniprot_msa_runner, input_fasta_path, out_path, 'sto',
        self.use_precomputed_msas)
    msa = parsers.parse_stockholm(result['sto'])
    msa = msa.truncate(max_seqs=self._max_uniprot_hits)
    all_seq_features = pipeline.make_msa_features([msa])
    valid_feats = msa_pairing.MSA_FEATURES + (
        'msa_species_identifiers',
    )
    feats = {f'{k}_all_seq': v for k, v in all_seq_features.items()
             if k in valid_feats}
    return feats

  def process(self,
              input_fasta_path: str,
              msa_output_dir: str,
              save_msa_fasta: bool,
              save_template_names: bool,
              msa_for_template_query_seq_only: bool) -> pipeline.FeatureDict:
    """"""Runs alignment tools on the input sequences and creates features.""""""
    with open(input_fasta_path) as f:
      input_fasta_str = f.read()
    input_seqs, input_descs = parsers.parse_fasta(input_fasta_str)

    chain_id_map = _make_chain_id_map(sequences=input_seqs,
                                      descriptions=input_descs)
    chain_id_map_path = os.path.join(msa_output_dir, 'chain_id_map.json')
    with open(chain_id_map_path, 'w') as f:
      chain_id_map_dict = {chain_id: dataclasses.asdict(fasta_chain)
                           for chain_id, fasta_chain in chain_id_map.items()}
      json.dump(chain_id_map_dict, f, indent=4, sort_keys=True)

    all_chain_features = {}
    sequence_features = {}
    is_homomer_or_monomer = len(set(input_seqs)) == 1
    for chain_id, fasta_chain in chain_id_map.items():
      if fasta_chain.sequence in sequence_features:
        all_chain_features[chain_id] = copy.deepcopy(
            sequence_features[fasta_chain.sequence])
        continue
      chain_features = self._process_single_chain(
          chain_id=chain_id,
          sequence=fasta_chain.sequence,
          description=fasta_chain.description,
          msa_output_dir=msa_output_dir,
          is_homomer_or_monomer=is_homomer_or_monomer,
          save_msa_fasta=save_msa_fasta,
          save_template_names=save_template_names,
          msa_for_template_query_seq_only=msa_for_template_query_seq_only)

      chain_features = convert_monomer_features(chain_features,
                                                chain_id=chain_id)
      all_chain_features[chain_id] = chain_features
      sequence_features[fasta_chain.sequence] = chain_features

    all_chain_features = add_assembly_features(all_chain_features)

    np_example = feature_processing.pair_and_merge(
        all_chain_features=all_chain_features)

    # Pad MSA to avoid zero-sized extra_msa.
    np_example = pad_msa(np_example, 512)

    return np_example
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/mmcif_parsing.py",".py","14174","387","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Parses the mmCIF file format.""""""
import collections
import dataclasses
import functools
import io
from typing import Any, Mapping, Optional, Sequence, Tuple

from absl import logging
from Bio import PDB
from Bio.Data import SCOPData

# Type aliases:
ChainId = str
PdbHeader = Mapping[str, Any]
PdbStructure = PDB.Structure.Structure
SeqRes = str
MmCIFDict = Mapping[str, Sequence[str]]


@dataclasses.dataclass(frozen=True)
class Monomer:
  id: str
  num: int


# Note - mmCIF format provides no guarantees on the type of author-assigned
# sequence numbers. They need not be integers.
@dataclasses.dataclass(frozen=True)
class AtomSite:
  residue_name: str
  author_chain_id: str
  mmcif_chain_id: str
  author_seq_num: str
  mmcif_seq_num: int
  insertion_code: str
  hetatm_atom: str
  model_num: int


# Used to map SEQRES index to a residue in the structure.
@dataclasses.dataclass(frozen=True)
class ResiduePosition:
  chain_id: str
  residue_number: int
  insertion_code: str


@dataclasses.dataclass(frozen=True)
class ResidueAtPosition:
  position: Optional[ResiduePosition]
  name: str
  is_missing: bool
  hetflag: str


@dataclasses.dataclass(frozen=True)
class MmcifObject:
  """"""Representation of a parsed mmCIF file.

  Contains:
    file_id: A meaningful name, e.g. a pdb_id. Should be unique amongst all
      files being processed.
    header: Biopython header.
    structure: Biopython structure.
    chain_to_seqres: Dict mapping chain_id to 1 letter amino acid sequence. E.g.
      {'A': 'ABCDEFG'}
    seqres_to_structure: Dict; for each chain_id contains a mapping between
      SEQRES index and a ResidueAtPosition. e.g. {'A': {0: ResidueAtPosition,
                                                        1: ResidueAtPosition,
                                                        ...}}
    raw_string: The raw string used to construct the MmcifObject.
  """"""
  file_id: str
  header: PdbHeader
  structure: PdbStructure
  chain_to_seqres: Mapping[ChainId, SeqRes]
  seqres_to_structure: Mapping[ChainId, Mapping[int, ResidueAtPosition]]
  raw_string: Any


@dataclasses.dataclass(frozen=True)
class ParsingResult:
  """"""Returned by the parse function.

  Contains:
    mmcif_object: A MmcifObject, may be None if no chain could be successfully
      parsed.
    errors: A dict mapping (file_id, chain_id) to any exception generated.
  """"""
  mmcif_object: Optional[MmcifObject]
  errors: Mapping[Tuple[str, str], Any]


class ParseError(Exception):
  """"""An error indicating that an mmCIF file could not be parsed.""""""


def mmcif_loop_to_list(prefix: str,
                       parsed_info: MmCIFDict) -> Sequence[Mapping[str, str]]:
  """"""Extracts loop associated with a prefix from mmCIF data as a list.

  Reference for loop_ in mmCIF:
    http://mmcif.wwpdb.org/docs/tutorials/mechanics/pdbx-mmcif-syntax.html

  Args:
    prefix: Prefix shared by each of the data items in the loop.
      e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
      _entity_poly_seq.mon_id. Should include the trailing period.
    parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
      parser.

  Returns:
    Returns a list of dicts; each dict represents 1 entry from an mmCIF loop.
  """"""
  cols = []
  data = []
  for key, value in parsed_info.items():
    if key.startswith(prefix):
      cols.append(key)
      data.append(value)

  assert all([len(xs) == len(data[0]) for xs in data]), (
      'mmCIF error: Not all loops are the same length: %s' % cols)

  return [dict(zip(cols, xs)) for xs in zip(*data)]


def mmcif_loop_to_dict(prefix: str,
                       index: str,
                       parsed_info: MmCIFDict,
                       ) -> Mapping[str, Mapping[str, str]]:
  """"""Extracts loop associated with a prefix from mmCIF data as a dictionary.

  Args:
    prefix: Prefix shared by each of the data items in the loop.
      e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
      _entity_poly_seq.mon_id. Should include the trailing period.
    index: Which item of loop data should serve as the key.
    parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
      parser.

  Returns:
    Returns a dict of dicts; each dict represents 1 entry from an mmCIF loop,
    indexed by the index column.
  """"""
  entries = mmcif_loop_to_list(prefix, parsed_info)
  return {entry[index]: entry for entry in entries}


@functools.lru_cache(16, typed=False)
def parse(*,
          file_id: str,
          mmcif_string: str,
          catch_all_errors: bool = True) -> ParsingResult:
  """"""Entry point, parses an mmcif_string.

  Args:
    file_id: A string identifier for this file. Should be unique within the
      collection of files being processed.
    mmcif_string: Contents of an mmCIF file.
    catch_all_errors: If True, all exceptions are caught and error messages are
      returned as part of the ParsingResult. If False exceptions will be allowed
      to propagate.

  Returns:
    A ParsingResult.
  """"""
  errors = {}
  try:
    parser = PDB.MMCIFParser(QUIET=True)
    handle = io.StringIO(mmcif_string)
    full_structure = parser.get_structure('', handle)
    first_model_structure = _get_first_model(full_structure)
    # Extract the _mmcif_dict from the parser, which contains useful fields not
    # reflected in the Biopython structure.
    parsed_info = parser._mmcif_dict  # pylint:disable=protected-access

    # Ensure all values are lists, even if singletons.
    for key, value in parsed_info.items():
      if not isinstance(value, list):
        parsed_info[key] = [value]

    header = _get_header(parsed_info)

    # Determine the protein chains, and their start numbers according to the
    # internal mmCIF numbering scheme (likely but not guaranteed to be 1).
    valid_chains = _get_protein_chains(parsed_info=parsed_info)
    if not valid_chains:
      return ParsingResult(
          None, {(file_id, ''): 'No protein chains found in this file.'})
    seq_start_num = {chain_id: min([monomer.num for monomer in seq])
                     for chain_id, seq in valid_chains.items()}

    # Loop over the atoms for which we have coordinates. Populate two mappings:
    # -mmcif_to_author_chain_id (maps internal mmCIF chain ids to chain ids used
    # the authors / Biopython).
    # -seq_to_structure_mappings (maps idx into sequence to ResidueAtPosition).
    mmcif_to_author_chain_id = {}
    seq_to_structure_mappings = {}
    for atom in _get_atom_site_list(parsed_info):
      if atom.model_num != '1':
        # We only process the first model at the moment.
        continue

      mmcif_to_author_chain_id[atom.mmcif_chain_id] = atom.author_chain_id

      if atom.mmcif_chain_id in valid_chains:
        hetflag = ' '
        if atom.hetatm_atom == 'HETATM':
          # Water atoms are assigned a special hetflag of W in Biopython. We
          # need to do the same, so that this hetflag can be used to fetch
          # a residue from the Biopython structure by id.
          if atom.residue_name in ('HOH', 'WAT'):
            hetflag = 'W'
          else:
            hetflag = 'H_' + atom.residue_name
        insertion_code = atom.insertion_code
        if not _is_set(atom.insertion_code):
          insertion_code = ' '
        position = ResiduePosition(chain_id=atom.author_chain_id,
                                   residue_number=int(atom.author_seq_num),
                                   insertion_code=insertion_code)
        seq_idx = int(atom.mmcif_seq_num) - seq_start_num[atom.mmcif_chain_id]
        current = seq_to_structure_mappings.get(atom.author_chain_id, {})
        current[seq_idx] = ResidueAtPosition(position=position,
                                             name=atom.residue_name,
                                             is_missing=False,
                                             hetflag=hetflag)
        seq_to_structure_mappings[atom.author_chain_id] = current

    # Add missing residue information to seq_to_structure_mappings.
    for chain_id, seq_info in valid_chains.items():
      author_chain = mmcif_to_author_chain_id[chain_id]
      current_mapping = seq_to_structure_mappings[author_chain]
      for idx, monomer in enumerate(seq_info):
        if idx not in current_mapping:
          current_mapping[idx] = ResidueAtPosition(position=None,
                                                   name=monomer.id,
                                                   is_missing=True,
                                                   hetflag=' ')

    author_chain_to_sequence = {}
    for chain_id, seq_info in valid_chains.items():
      author_chain = mmcif_to_author_chain_id[chain_id]
      seq = []
      for monomer in seq_info:
        code = SCOPData.protein_letters_3to1.get(monomer.id, 'X')
        seq.append(code if len(code) == 1 else 'X')
      seq = ''.join(seq)
      author_chain_to_sequence[author_chain] = seq

    mmcif_object = MmcifObject(
        file_id=file_id,
        header=header,
        structure=first_model_structure,
        chain_to_seqres=author_chain_to_sequence,
        seqres_to_structure=seq_to_structure_mappings,
        raw_string=parsed_info)

    return ParsingResult(mmcif_object=mmcif_object, errors=errors)
  except Exception as e:  # pylint:disable=broad-except
    errors[(file_id, '')] = e
    if not catch_all_errors:
      raise
    return ParsingResult(mmcif_object=None, errors=errors)


def _get_first_model(structure: PdbStructure) -> PdbStructure:
  """"""Returns the first model in a Biopython structure.""""""
  return next(structure.get_models())

_MIN_LENGTH_OF_CHAIN_TO_BE_COUNTED_AS_PEPTIDE = 21


def get_release_date(parsed_info: MmCIFDict) -> str:
  """"""Returns the oldest revision date.""""""
  revision_dates = parsed_info['_pdbx_audit_revision_history.revision_date']
  return min(revision_dates)


def _get_header(parsed_info: MmCIFDict) -> PdbHeader:
  """"""Returns a basic header containing method, release date and resolution.""""""
  header = {}

  experiments = mmcif_loop_to_list('_exptl.', parsed_info)
  header['structure_method'] = ','.join([
      experiment['_exptl.method'].lower() for experiment in experiments])

  # Note: The release_date here corresponds to the oldest revision. We prefer to
  # use this for dataset filtering over the deposition_date.
  if '_pdbx_audit_revision_history.revision_date' in parsed_info:
    header['release_date'] = get_release_date(parsed_info)
  else:
    logging.warning('Could not determine release_date: %s',
                    parsed_info['_entry.id'])

  header['resolution'] = 0.00
  for res_key in ('_refine.ls_d_res_high', '_em_3d_reconstruction.resolution',
                  '_reflns.d_resolution_high'):
    if res_key in parsed_info:
      try:
        raw_resolution = parsed_info[res_key][0]
        header['resolution'] = float(raw_resolution)
      except ValueError:
        logging.debug('Invalid resolution format: %s', parsed_info[res_key])

  return header


def _get_atom_site_list(parsed_info: MmCIFDict) -> Sequence[AtomSite]:
  """"""Returns list of atom sites; contains data not present in the structure.""""""
  return [AtomSite(*site) for site in zip(  # pylint:disable=g-complex-comprehension
      parsed_info['_atom_site.label_comp_id'],
      parsed_info['_atom_site.auth_asym_id'],
      parsed_info['_atom_site.label_asym_id'],
      parsed_info['_atom_site.auth_seq_id'],
      parsed_info['_atom_site.label_seq_id'],
      parsed_info['_atom_site.pdbx_PDB_ins_code'],
      parsed_info['_atom_site.group_PDB'],
      parsed_info['_atom_site.pdbx_PDB_model_num'],
      )]


def _get_protein_chains(
    *, parsed_info: Mapping[str, Any]) -> Mapping[ChainId, Sequence[Monomer]]:
  """"""Extracts polymer information for protein chains only.

  Args:
    parsed_info: _mmcif_dict produced by the Biopython parser.

  Returns:
    A dict mapping mmcif chain id to a list of Monomers.
  """"""
  # Get polymer information for each entity in the structure.
  entity_poly_seqs = mmcif_loop_to_list('_entity_poly_seq.', parsed_info)

  polymers = collections.defaultdict(list)
  for entity_poly_seq in entity_poly_seqs:
    polymers[entity_poly_seq['_entity_poly_seq.entity_id']].append(
        Monomer(id=entity_poly_seq['_entity_poly_seq.mon_id'],
                num=int(entity_poly_seq['_entity_poly_seq.num'])))

  # Get chemical compositions. Will allow us to identify which of these polymers
  # are proteins.
  chem_comps = mmcif_loop_to_dict('_chem_comp.', '_chem_comp.id', parsed_info)

  # Get chains information for each entity. Necessary so that we can return a
  # dict keyed on chain id rather than entity.
  struct_asyms = mmcif_loop_to_list('_struct_asym.', parsed_info)

  entity_to_mmcif_chains = collections.defaultdict(list)
  for struct_asym in struct_asyms:
    chain_id = struct_asym['_struct_asym.id']
    entity_id = struct_asym['_struct_asym.entity_id']
    entity_to_mmcif_chains[entity_id].append(chain_id)

  # Identify and return the valid protein chains.
  valid_chains = {}
  for entity_id, seq_info in polymers.items():
    chain_ids = entity_to_mmcif_chains[entity_id]

    # Reject polymers without any peptide-like components, such as DNA/RNA.
    if any(['peptide' in chem_comps[monomer.id]['_chem_comp.type']
            for monomer in seq_info]):
      for chain_id in chain_ids:
        valid_chains[chain_id] = seq_info
  return valid_chains


def _is_set(data: str) -> bool:
  """"""Returns False if data is a special mmCIF character indicating 'unset'.""""""
  return data not in ('.', '?')
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/__init__.py",".py","634","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Data pipeline for model features.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/pipeline_multimer_custom_templates.py",".py","15215","379","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for building the features for the AlphaFold multimer model.""""""

import collections
import contextlib
import copy
import dataclasses
import json
import os
import tempfile
from typing import Mapping, MutableMapping, Sequence

from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import feature_processing
from alphafold.data import msa_pairing
from alphafold.data import parsers
from alphafold.data import pipeline
from alphafold.data.tools import jackhmmer
import numpy as np

# Internal import (7716).


@dataclasses.dataclass(frozen=True)
class _FastaChain:
  sequence: str
  description: str


def _make_chain_id_map(*,
                       sequences: Sequence[str],
                       descriptions: Sequence[str],
                       ) -> Mapping[str, _FastaChain]:
  """"""Makes a mapping from PDB-format chain ID to sequence and description.""""""
  if len(sequences) != len(descriptions):
    raise ValueError('sequences and descriptions must have equal length. '
                     f'Got {len(sequences)} != {len(descriptions)}.')
  if len(sequences) > protein.PDB_MAX_CHAINS:
    raise ValueError('Cannot process more chains than the PDB format supports. '
                     f'Got {len(sequences)} chains.')
  chain_id_map = {}
  for chain_id, sequence, description in zip(
      protein.PDB_CHAIN_IDS, sequences, descriptions):
    chain_id_map[chain_id] = _FastaChain(
        sequence=sequence, description=description)
  return chain_id_map


@contextlib.contextmanager
def temp_fasta_file(fasta_str: str):
  with tempfile.NamedTemporaryFile('w', suffix='.fasta') as fasta_file:
    fasta_file.write(fasta_str)
    fasta_file.seek(0)
    yield fasta_file.name


def convert_monomer_features(
    monomer_features: pipeline.FeatureDict,
    chain_id: str) -> pipeline.FeatureDict:
  """"""Reshapes and modifies monomer features for multimer models.""""""
  converted = {}
  converted['auth_chain_id'] = np.asarray(chain_id, dtype=np.object_)
  unnecessary_leading_dim_feats = {
      'sequence', 'domain_name', 'num_alignments', 'seq_length'}
  for feature_name, feature in monomer_features.items():
    if feature_name in unnecessary_leading_dim_feats:
      # asarray ensures it's a np.ndarray.
      feature = np.asarray(feature[0], dtype=feature.dtype)
    elif feature_name == 'aatype':
      # The multimer model performs the one-hot operation itself.
      feature = np.argmax(feature, axis=-1).astype(np.int32)
    elif feature_name == 'template_aatype':
      feature = np.argmax(feature, axis=-1).astype(np.int32)
      new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
      feature = np.take(new_order_list, feature.astype(np.int32), axis=0)
    elif feature_name == 'template_all_atom_masks':
      feature_name = 'template_all_atom_mask'
    converted[feature_name] = feature
  return converted


def int_id_to_str_id(num: int) -> str:
  """"""Encodes a number as a string, using reverse spreadsheet style naming.

  Args:
    num: A positive integer.

  Returns:
    A string that encodes the positive integer using reverse spreadsheet style,
    naming e.g. 1 = A, 2 = B, ..., 27 = AA, 28 = BA, 29 = CA, ... This is the
    usual way to encode chain IDs in mmCIF files.
  """"""
  if num <= 0:
    raise ValueError(f'Only positive integers allowed, got {num}.')

  num = num - 1  # 1-based indexing.
  output = []
  while num >= 0:
    output.append(chr(num % 26 + ord('A')))
    num = num // 26 - 1
  return ''.join(output)


def add_assembly_features(
    all_chain_features: MutableMapping[str, pipeline.FeatureDict],
    ) -> MutableMapping[str, pipeline.FeatureDict]:
  """"""Add features to distinguish between chains.

  Args:
    all_chain_features: A dictionary which maps chain_id to a dictionary of
      features for each chain.

  Returns:
    all_chain_features: A dictionary which maps strings of the form
      `<seq_id>_<sym_id>` to the corresponding chain features. E.g. two
      chains from a homodimer would have keys A_1 and A_2. Two chains from a
      heterodimer would have keys A_1 and B_1.
  """"""
  # Group the chains by sequence
  seq_to_entity_id = {}
  grouped_chains = collections.defaultdict(list)
  for chain_id, chain_features in all_chain_features.items():
    seq = str(chain_features['sequence'])
    if seq not in seq_to_entity_id:
      seq_to_entity_id[seq] = len(seq_to_entity_id) + 1
    grouped_chains[seq_to_entity_id[seq]].append(chain_features)

  new_all_chain_features = {}
  chain_id = 1
  for entity_id, group_chain_features in grouped_chains.items():
    for sym_id, chain_features in enumerate(group_chain_features, start=1):
      new_all_chain_features[
          f'{int_id_to_str_id(entity_id)}_{sym_id}'] = chain_features
      seq_length = chain_features['seq_length']
      chain_features['asym_id'] = chain_id * np.ones(seq_length)
      chain_features['sym_id'] = sym_id * np.ones(seq_length)
      chain_features['entity_id'] = entity_id * np.ones(seq_length)
      chain_id += 1

  return new_all_chain_features


def pad_msa(np_example, min_num_seq):
  np_example = dict(np_example)
  num_seq = np_example['msa'].shape[0]
  if num_seq < min_num_seq:
    for feat in ('msa', 'deletion_matrix', 'bert_mask', 'msa_mask'):
      np_example[feat] = np.pad(
          np_example[feat], ((0, min_num_seq - num_seq), (0, 0)))
    np_example['cluster_bias_mask'] = np.pad(
        np_example['cluster_bias_mask'], ((0, min_num_seq - num_seq),))
  return np_example


class DataPipeline:
  """"""Runs the alignment tools and assembles the input features.""""""

  def __init__(self,
               monomer_data_pipeline: pipeline.DataPipeline,
               jackhmmer_binary_path: str,
               uniprot_database_path: str,
               max_uniprot_hits: int = 50000,
               use_precomputed_msas: bool = False):
    """"""Initializes the data pipeline.

    Args:
      monomer_data_pipeline: An instance of pipeline.DataPipeline - that runs
        the data pipeline for the monomer AlphaFold system.
      jackhmmer_binary_path: Location of the jackhmmer binary.
      uniprot_database_path: Location of the unclustered uniprot sequences, that
        will be searched with jackhmmer and used for MSA pairing.
      max_uniprot_hits: The maximum number of hits to return from uniprot.
      use_precomputed_msas: Whether to use pre-existing MSAs; see run_alphafold.
    """"""
    self._monomer_data_pipeline = monomer_data_pipeline
    self._uniprot_msa_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=uniprot_database_path)
    self._max_uniprot_hits = max_uniprot_hits
    self.use_precomputed_msas = use_precomputed_msas

  def _process_single_chain(
      self,
      chain_id: str,
      sequence: str,
      description: str,
      msa_output_dir: str,
      is_homomer_or_monomer: bool, 
      use_custom_templates: bool,
      template_alignfile: str,
      msa_mode: str,
      use_custom_MSA_database: str,
      MSA_database: str,
      save_msa_fasta: bool,
      save_template_names: bool,
      msa_for_template_query_seq_only: bool) -> pipeline.FeatureDict:
    """"""Runs the monomer pipeline on a single chain.""""""
    if isinstance(sequence, list):
      chain_fasta_str = f'>chain_{chain_id}\n{"":"".join(sequence)}\n'
    else:
      chain_fasta_str = f'>chain_{chain_id}\n{sequence}\n'
    chain_msa_output_dir = os.path.join(msa_output_dir, chain_id)
    if not os.path.exists(chain_msa_output_dir):
      os.makedirs(chain_msa_output_dir)
    with temp_fasta_file(chain_fasta_str) as chain_fasta_path:
      logging.info('Running monomer pipeline on chain %s: %s',
                   chain_id, description)
      if use_custom_templates or use_custom_MSA_database!=""none"":
        chain_features = self._monomer_data_pipeline.process(
            input_fasta_path=chain_fasta_path,
            msa_output_dir=chain_msa_output_dir, 
            use_custom_templates=use_custom_templates,
            template_alignfile=template_alignfile,
            msa_mode=msa_mode,
            use_custom_MSA_database=use_custom_MSA_database,
            MSA_database=MSA_database,
            save_msa_fasta=save_msa_fasta,
            save_template_names=save_template_names,
            msa_for_template_query_seq_only=msa_for_template_query_seq_only)
      else:
        chain_features = self._monomer_data_pipeline.process(
            input_fasta_path=chain_fasta_path,
            msa_output_dir=chain_msa_output_dir,
            save_msa_fasta=save_msa_fasta,
            save_template_names=save_template_names,
            msa_for_template_query_seq_only=msa_for_template_query_seq_only)

      sequence="""".join(sequence)
      # We only construct the pairing features if there are 2 or more unique
      # sequences.
      if not is_homomer_or_monomer:
        if msa_mode==""single_sequence"" or use_custom_MSA_database==""only"":
          all_seq_msa_features = self._mock_all_seq_msa_features(sequence)
        else:
          all_seq_msa_features = self._all_seq_msa_features(chain_fasta_path,
                                                          chain_msa_output_dir)
        chain_features.update(all_seq_msa_features)
    return chain_features

  def _mock_all_seq_msa_features(self, sequence):
    
    """"""Get MSA features for unclustered uniprot, for pairing.""""""
    # out_path = os.path.join(msa_output_dir, 'uniprot_hits.sto')
    # result = pipeline.run_msa_tool(
    #     self._uniprot_msa_runner, input_fasta_path, out_path, 'sto',
    #     self.use_precomputed_msas)
    # msa = parsers.parse_stockholm(result['sto'])
    # msa = msa.truncate(max_seqs=self._max_uniprot_hits)
    from alphafold.data import  pipeline_custom_templates
    all_seq_features = pipeline_custom_templates.make_mock_msa_features([sequence])
    # valid_feats = msa_pairing.MSA_FEATURES + (
    #     'msa_species_identifiers',
    # )
    feats = {f'{k}_all_seq': v for k, v in all_seq_features.items()}
            #  if k in valid_feats}
    return feats

  # def _all_seq_msa_features_customized(self, input_fasta_path, msa_output_dir):
  #   """"""Get MSA features for unclustered uniprot, for pairing.""""""
  #   out_path = os.path.join(msa_output_dir, 'uniprot_hits.sto')
  #   result = pipeline.run_msa_tool(
  #       self._uniprot_msa_runner, input_fasta_path, out_path, 'sto',
  #       self.use_precomputed_msas)
  #   msa = parsers.parse_stockholm(result['sto'])
  #   msa = msa.truncate(max_seqs=self._max_uniprot_hits)
  #   all_seq_features = pipeline.make_msa_features([msa])
  #   valid_feats = msa_pairing.MSA_FEATURES + (
  #       'msa_species_identifiers',
  #   )
  #   feats = {f'{k}_all_seq': v for k, v in all_seq_features.items()
  #            if k in valid_feats}
  #   return feats

  def _all_seq_msa_features(self, input_fasta_path, msa_output_dir):
    """"""Get MSA features for unclustered uniprot, for pairing.""""""
    out_path = os.path.join(msa_output_dir, 'uniprot_hits.sto')
    result = pipeline.run_msa_tool(
        self._uniprot_msa_runner, input_fasta_path, out_path, 'sto',
        self.use_precomputed_msas)
    msa = parsers.parse_stockholm(result['sto'])
    msa = msa.truncate(max_seqs=self._max_uniprot_hits)
    all_seq_features = pipeline.make_msa_features([msa])
    valid_feats = msa_pairing.MSA_FEATURES + (
        'msa_species_identifiers',
    )
    feats = {f'{k}_all_seq': v for k, v in all_seq_features.items()
             if k in valid_feats}
    return feats

  def process(self,
              input_fasta_path: str,
              msa_output_dir: str, 
              use_custom_templates: bool,
              template_alignfile: str,
              msa_mode: str,
              use_custom_MSA_database: str,
              MSA_database: str,
              save_msa_fasta: bool,
              save_template_names: bool,
              msa_for_template_query_seq_only: bool) -> pipeline.FeatureDict:
    """"""Runs alignment tools on the input sequences and creates features.""""""
    with open(input_fasta_path) as f:
      input_fasta_str = f.read()
    input_seqs_raw, input_descs = parsers.parse_fasta(input_fasta_str)
    input_seqs=[]
    for index,seq in enumerate(input_seqs_raw):
      input_seqs.append("""".join(seq))
    chain_id_map = _make_chain_id_map(sequences=input_seqs,
                                      descriptions=input_descs)
    chain_id_map_path = os.path.join(msa_output_dir, 'chain_id_map.json')
    with open(chain_id_map_path, 'w') as f:
      chain_id_map_dict = {chain_id: dataclasses.asdict(fasta_chain)
                           for chain_id, fasta_chain in chain_id_map.items()}
      json.dump(chain_id_map_dict, f, indent=4, sort_keys=True)

    all_chain_features = {}
    sequence_features = {}
    is_homomer_or_monomer = len(set(input_seqs)) == 1

    if use_custom_templates:
      template_alignfiles=template_alignfile.split("","")
    else:
      template_alignfiles=[""""] * len(chain_id_map)
    if use_custom_MSA_database!=""none"": 
      MSA_databases=MSA_database.split("","")
    else:
      MSA_databases=[""""] * len(chain_id_map)


    for index, (chain_id, fasta_chain) in enumerate(chain_id_map.items()):
      if fasta_chain.sequence in sequence_features:
        all_chain_features[chain_id] = copy.deepcopy(
            sequence_features[fasta_chain.sequence])
        continue
      chain_features = self._process_single_chain(
          chain_id=chain_id,
          sequence=input_seqs_raw[index],
          description=fasta_chain.description,
          msa_output_dir=msa_output_dir,
          is_homomer_or_monomer=is_homomer_or_monomer,
          use_custom_templates=use_custom_templates,
          template_alignfile=template_alignfiles[index],
          msa_mode=msa_mode,
          use_custom_MSA_database=use_custom_MSA_database,
          MSA_database=MSA_databases[index],
          save_msa_fasta=save_msa_fasta,
          save_template_names=save_template_names,
          msa_for_template_query_seq_only=msa_for_template_query_seq_only)

      chain_features = convert_monomer_features(chain_features,
                                                chain_id=chain_id)
      all_chain_features[chain_id] = chain_features
      sequence_features[fasta_chain.sequence] = chain_features

    all_chain_features = add_assembly_features(all_chain_features)

    np_example = feature_processing.pair_and_merge(
        all_chain_features=all_chain_features)

    # Pad MSA to avoid zero-sized extra_msa.
    np_example = pad_msa(np_example, 512)

    return np_example
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/custom_templates.py",".py","9389","229","#Line 1-190 is from https://github.com/phbradley/alphafold_finetune/blob/main/predict_utils.py
from alphafold.common import residue_constants
from alphafold.data import templates
import numpy as np

#this is from alphafold/data.templates.py
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union, List
import dataclasses
@dataclasses.dataclass(frozen=True)
class TemplateSearchResult:
  features: Mapping[str, Any]
#   errors: Sequence[str]
#   warnings: Sequence[str]

def load_pdb_coords(
        pdbfile,
        allow_chainbreaks=False,
        allow_skipped_lines=False,
        verbose=False,
):
    ''' returns: chains, all_resids, all_coords, all_name1s
    '''

    chains = []
    all_resids = {}
    all_coords = {}
    all_name1s = {}

    if verbose:
        print('reading:', pdbfile)
    skipped_lines = False
    with open(pdbfile,'r') as data:
        for line in data:
            if (line[:6] in ['ATOM  ','HETATM'] and line[17:20] != 'HOH' and
                line[16] in ' A1'):
                if ( line[17:20] in residue_constants.restype_3to1
                     or line[17:20] == 'MSE'): # 2022-03-31 change to include MSE
                    name1 = ('M' if line[17:20] == 'MSE' else
                             residue_constants.restype_3to1[line[17:20]])
                    resid = line[22:27]
                    chain = line[21]
                    if chain not in all_resids:
                        all_resids[chain] = []
                        all_coords[chain] = {}
                        all_name1s[chain] = {}
                        chains.append(chain)
                    if line.startswith('HETATM'):
                        print('WARNING: HETATM', pdbfile, line[:-1])
                    atom = line[12:16].split()[0]
                    if resid not in all_resids[chain]:
                        all_resids[chain].append(resid)
                        all_coords[chain][resid] = {}
                        all_name1s[chain][resid] = name1

                    all_coords[chain][resid][atom] = np.array(
                        [float(line[30:38]), float(line[38:46]), float(line[46:54])])
                else:
                    print('skip ATOM line:', line[:-1], pdbfile)
                    skipped_lines = True

    # check for chainbreaks
    maxdis = 1.75
    for chain in chains:
        for res1, res2 in zip(all_resids[chain][:-1], all_resids[chain][1:]):
            coords1 = all_coords[chain][res1]
            coords2 = all_coords[chain][res2]
            if 'C' in coords1 and 'N' in coords2:
                dis = np.sqrt(np.sum(np.square(coords1['C']-coords2['N'])))
                if dis>maxdis:
                    print('WARNING chainbreak:', chain, res1, res2, dis, pdbfile)
                    if not allow_chainbreaks:
                        print('STOP: chainbreaks', pdbfile)
                        print('DONE')
                        exit()

    if skipped_lines and not allow_skipped_lines:
        print('STOP: skipped lines:', pdbfile)
        print('DONE')
        exit()

    return chains, all_resids, all_coords, all_name1s

def fill_afold_coords(
        chain_order,
        all_resids,
        all_coords,
):
    ''' returns: all_positions, all_positions_mask

    these are 'atom37' coords (not 'atom14' coords)

    '''
    assert residue_constants.atom_type_num == 37 #HACK/SANITY
    crs = [(chain,resid) for chain in chain_order for resid in all_resids[chain]]
    num_res = len(crs)
    all_positions = np.zeros([num_res, residue_constants.atom_type_num, 3])
    all_positions_mask = np.zeros([num_res, residue_constants.atom_type_num],
                                  dtype=np.int64)
    for res_index, (chain,resid) in enumerate(crs):
        pos = np.zeros([residue_constants.atom_type_num, 3], dtype=np.float32)
        mask = np.zeros([residue_constants.atom_type_num], dtype=np.float32)
        for atom_name, xyz in all_coords[chain][resid].items():
            x,y,z = xyz
            if atom_name in residue_constants.atom_order.keys():
                pos[residue_constants.atom_order[atom_name]] = [x, y, z]
                mask[residue_constants.atom_order[atom_name]] = 1.0
            elif atom_name != 'NV': # PRO NV OK to skip
                # this is just debugging/verbose output:
                name = atom_name[:]
                while name[0] in '123':
                    name = name[1:]
                if name[0] != 'H':
                    print('unrecognized atom:', atom_name, chain, resid)
            # elif atom_name.upper() == 'SE' and res.get_resname() == 'MSE':
            #     # Put the coordinates of the selenium atom in the sulphur column.
            #     pos[residue_constants.atom_order['SD']] = [x, y, z]
            #     mask[residue_constants.atom_order['SD']] = 1.0

        all_positions[res_index] = pos
        all_positions_mask[res_index] = mask
    return all_positions, all_positions_mask


def create_single_template_features(
        target_sequence,
        template_pdbfile,
        target_to_template_alignment,
        template_name, # goes into template_domain_names, .encode()'ed
        allow_chainbreaks=True,
        allow_skipped_lines=True,
        expected_identities=None,
        expected_template_len=None,
):
    num_res = len(target_sequence)
    chains_tmp, all_resids_tmp, all_coords_tmp, all_name1s_tmp = load_pdb_coords(
        template_pdbfile, allow_chainbreaks=allow_chainbreaks,
        allow_skipped_lines=allow_skipped_lines,
    )

    crs_tmp = [(c,r) for c in chains_tmp for r in all_resids_tmp[c]]
    num_res_tmp = len(crs_tmp)
    template_full_sequence = ''.join(all_name1s_tmp[c][r] for c,r in crs_tmp)

    # if expected_template_len:
    #     assert len(template_full_sequence) == expected_template_len

    all_positions_tmp, all_positions_mask_tmp = fill_afold_coords(
        chains_tmp, all_resids_tmp, all_coords_tmp)

    identities = sum(target_sequence[i] == template_full_sequence[j]
                     for i,j in target_to_template_alignment.items())
    if expected_identities:
        assert identities == expected_identities

    all_positions = np.zeros([num_res, residue_constants.atom_type_num, 3])
    all_positions_mask = np.zeros([num_res, residue_constants.atom_type_num],
                                  dtype=np.int64)

    template_alseq = ['-']*num_res
    for i,j in target_to_template_alignment.items(): # i=target, j=template
        template_alseq[i] = template_full_sequence[j]
        all_positions[i] = all_positions_tmp[j]
        all_positions_mask[i] = all_positions_mask_tmp[j]

    template_sequence = ''.join(template_alseq)
    assert len(template_sequence) == len(target_sequence)
    assert identities == sum(a==b for a,b in zip(template_sequence, target_sequence))

    template_aatype = residue_constants.sequence_to_onehot(
        template_sequence, residue_constants.HHBLITS_AA_TO_ID)

    template_features = {
        'template_all_atom_positions': all_positions,
        'template_all_atom_masks': all_positions_mask,
        'template_sequence': template_sequence.encode(),
        'template_aatype': template_aatype,
        'template_domain_names': template_name.encode(),
        'template_sum_probs': [identities],
        }
    return template_features


def compile_template_features(template_features_list):
    all_template_features = {}
    for name, dtype in templates.TEMPLATE_FEATURES.items():
        all_template_features[name] = np.stack(
            [f[name] for f in template_features_list], axis=0).astype(dtype)
    return TemplateSearchResult(features=all_template_features)


#This function is modified from https://github.com/sokrypton/ColabFold/blob/aa7284b56c7c6ce44e252787011a6fd8d2817f85/colabfold/batch.py
def mk_mock_template(query_sequence: Union[List[str], str], num_temp: int = 1):
    ln = (
        len(query_sequence)
        if isinstance(query_sequence, str)
        else sum(len(s) for s in query_sequence)
    )
    output_templates_sequence = ""A"" * ln
    output_confidence_scores = np.full(ln, 1.0)

    templates_all_atom_positions = np.zeros(
        (ln, templates.residue_constants.atom_type_num, 3)
    )
    templates_all_atom_masks = np.zeros((ln, templates.residue_constants.atom_type_num))
    templates_aatype = templates.residue_constants.sequence_to_onehot(
        output_templates_sequence, templates.residue_constants.HHBLITS_AA_TO_ID
    )
    template_features = {
        ""template_all_atom_positions"": np.tile(
            templates_all_atom_positions[None], [num_temp, 1, 1, 1]
        ),
        ""template_all_atom_masks"": np.tile(
            templates_all_atom_masks[None], [num_temp, 1, 1]
        ),
        ""template_sequence"": [f""none"".encode()] * num_temp,
        ""template_aatype"": np.tile(np.array(templates_aatype)[None], [num_temp, 1, 1]),
        # ""template_confidence_scores"": np.tile(
        #     output_confidence_scores[None], [num_temp, 1]
        # ),
        ""template_domain_names"": np.array([f""none"".encode()] * num_temp),
        # ""template_release_date"": [f""none"".encode()] * num_temp,
        ""template_sum_probs"": np.zeros([num_temp], dtype=np.float32),
    }
    # all_template_features=compile_template_features([template_features])
    return TemplateSearchResult(features=template_features)


    # return all_template_features
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/pipeline.py",".py","11720","272","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for building the input features for the AlphaFold model.""""""

import os
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Union
from absl import logging
from alphafold.common import residue_constants
from alphafold.data import msa_identifiers
from alphafold.data import parsers
from alphafold.data import templates
from alphafold.data.tools import hhblits
from alphafold.data.tools import hhsearch
from alphafold.data.tools import hmmsearch
from alphafold.data.tools import jackhmmer
import numpy as np

# Internal import (7716).

FeatureDict = MutableMapping[str, np.ndarray]
TemplateSearcher = Union[hhsearch.HHSearch, hmmsearch.Hmmsearch]


def make_sequence_features(
    sequence: str, description: str, num_res: int) -> FeatureDict:
  """"""Constructs a feature dict of sequence features.""""""
  features = {}
  features['aatype'] = residue_constants.sequence_to_onehot(
      sequence=sequence,
      mapping=residue_constants.restype_order_with_x,
      map_unknown_to_x=True)
  features['between_segment_residues'] = np.zeros((num_res,), dtype=np.int32)
  features['domain_name'] = np.array([description.encode('utf-8')],
                                     dtype=np.object_)
  features['residue_index'] = np.array(range(num_res), dtype=np.int32)
  features['seq_length'] = np.array([num_res] * num_res, dtype=np.int32)
  features['sequence'] = np.array([sequence.encode('utf-8')], dtype=np.object_)
  return features


def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
  """"""Constructs a feature dict of MSA features.""""""
  if not msas:
    raise ValueError('At least one MSA must be provided.')

  int_msa = []
  deletion_matrix = []
  species_ids = []
  seen_sequences = set()
  for msa_index, msa in enumerate(msas):
    if not msa:
      raise ValueError(f'MSA {msa_index} must contain at least one sequence.')
    for sequence_index, sequence in enumerate(msa.sequences):
      if sequence in seen_sequences:
        continue
      seen_sequences.add(sequence)
      int_msa.append(
          [residue_constants.HHBLITS_AA_TO_ID[res] for res in sequence])
      deletion_matrix.append(msa.deletion_matrix[sequence_index])
      identifiers = msa_identifiers.get_identifiers(
          msa.descriptions[sequence_index])
      species_ids.append(identifiers.species_id.encode('utf-8'))

  num_res = len(msas[0].sequences[0])
  num_alignments = len(int_msa)
  features = {}
  features['deletion_matrix_int'] = np.array(deletion_matrix, dtype=np.int32)
  features['msa'] = np.array(int_msa, dtype=np.int32)
  features['num_alignments'] = np.array(
      [num_alignments] * num_res, dtype=np.int32)
  features['msa_species_identifiers'] = np.array(species_ids, dtype=np.object_)
  return features


def run_msa_tool(msa_runner, input_fasta_path: str, msa_out_path: str,
                 msa_format: str, use_precomputed_msas: bool,
                 max_sto_sequences: Optional[int] = None
                 ) -> Mapping[str, Any]:
  """"""Runs an MSA tool, checking if output already exists first.""""""
  if not use_precomputed_msas or not os.path.exists(msa_out_path):
    if msa_format == 'sto' and max_sto_sequences is not None:
      result = msa_runner.query(input_fasta_path, max_sto_sequences)[0]  # pytype: disable=wrong-arg-count
    else:
      result = msa_runner.query(input_fasta_path)[0]
    with open(msa_out_path, 'w') as f:
      f.write(result[msa_format])
  else:
    logging.warning('Reading MSA from file %s', msa_out_path)
    if msa_format == 'sto' and max_sto_sequences is not None:
      precomputed_msa = parsers.truncate_stockholm_msa(
          msa_out_path, max_sto_sequences)
      result = {'sto': precomputed_msa}
    else:
      with open(msa_out_path, 'r') as f:
        result = {msa_format: f.read()}
  return result


class DataPipeline:
  """"""Runs the alignment tools and assembles the input features.""""""

  def __init__(self,
               jackhmmer_binary_path: str,
               hhblits_binary_path: str,
               uniref90_database_path: str,
               mgnify_database_path: str,
               bfd_database_path: Optional[str],
               uniref30_database_path: Optional[str],
               small_bfd_database_path: Optional[str],
               template_searcher: TemplateSearcher,
               template_featurizer: templates.TemplateHitFeaturizer,
               use_small_bfd: bool,
               mgnify_max_hits: int = 501,
               uniref_max_hits: int = 10000,
               use_precomputed_msas: bool = False):
    """"""Initializes the data pipeline.""""""
    self._use_small_bfd = use_small_bfd
    self.jackhmmer_uniref90_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=uniref90_database_path)
    if use_small_bfd:
      self.jackhmmer_small_bfd_runner = jackhmmer.Jackhmmer(
          binary_path=jackhmmer_binary_path,
          database_path=small_bfd_database_path)
    else:
      self.hhblits_bfd_uniref_runner = hhblits.HHBlits(
          binary_path=hhblits_binary_path,
          databases=[bfd_database_path, uniref30_database_path])
    self.jackhmmer_mgnify_runner = jackhmmer.Jackhmmer(
        binary_path=jackhmmer_binary_path,
        database_path=mgnify_database_path)
    self.template_searcher = template_searcher
    self.template_featurizer = template_featurizer
    self.mgnify_max_hits = mgnify_max_hits
    self.uniref_max_hits = uniref_max_hits
    self.use_precomputed_msas = use_precomputed_msas

  def process(self, input_fasta_path: str, 
              msa_output_dir: str, 
              save_msa_fasta: bool, 
              save_template_names: bool,
              msa_for_template_query_seq_only: bool) -> FeatureDict:
    """"""Runs alignment tools on the input sequence and creates features.""""""
    with open(input_fasta_path) as f:
      input_fasta_str = f.read()
    input_seqs, input_descs = parsers.parse_fasta(input_fasta_str)
    if len(input_seqs) != 1:
      raise ValueError(
          f'More than one input sequence found in {input_fasta_path}.')
    input_sequence = input_seqs[0]
    input_description = input_descs[0]
    num_res = len(input_sequence)

    uniref90_out_path = os.path.join(msa_output_dir, 'uniref90_hits.sto')
    jackhmmer_uniref90_result = run_msa_tool(
        msa_runner=self.jackhmmer_uniref90_runner,
        input_fasta_path=input_fasta_path,
        msa_out_path=uniref90_out_path,
        msa_format='sto',
        use_precomputed_msas=self.use_precomputed_msas,
        max_sto_sequences=self.uniref_max_hits)
    mgnify_out_path = os.path.join(msa_output_dir, 'mgnify_hits.sto')
    jackhmmer_mgnify_result = run_msa_tool(
        msa_runner=self.jackhmmer_mgnify_runner,
        input_fasta_path=input_fasta_path,
        msa_out_path=mgnify_out_path,
        msa_format='sto',
        use_precomputed_msas=self.use_precomputed_msas,
        max_sto_sequences=self.mgnify_max_hits)

    msa_for_templates = jackhmmer_uniref90_result['sto']
    msa_for_templates = parsers.deduplicate_stockholm_msa(msa_for_templates)
    msa_for_templates = parsers.remove_empty_columns_from_stockholm_msa(
        msa_for_templates)
    if msa_for_template_query_seq_only:
      '''BP 12/15/22 let's just keep the target (query) sequence'''
      msa_for_templates = parsers.truncate_stockholm_msa2(msa_for_templates, 1)

    if self.template_searcher.input_format == 'sto':
      pdb_templates_result = self.template_searcher.query(msa_for_templates)
    elif self.template_searcher.input_format == 'a3m':
      uniref90_msa_as_a3m = parsers.convert_stockholm_to_a3m(msa_for_templates)
      pdb_templates_result = self.template_searcher.query(uniref90_msa_as_a3m)
    else:
      raise ValueError('Unrecognized template input format: '
                       f'{self.template_searcher.input_format}')

    pdb_hits_out_path = os.path.join(
        msa_output_dir, f'pdb_hits.{self.template_searcher.output_format}')
    with open(pdb_hits_out_path, 'w') as f:
      f.write(pdb_templates_result)

    uniref90_msa = parsers.parse_stockholm(jackhmmer_uniref90_result['sto'])
    mgnify_msa = parsers.parse_stockholm(jackhmmer_mgnify_result['sto'])

    pdb_template_hits = self.template_searcher.get_template_hits(
        output_string=pdb_templates_result, input_sequence=input_sequence)

    if self._use_small_bfd:
      bfd_out_path = os.path.join(msa_output_dir, 'small_bfd_hits.sto')
      jackhmmer_small_bfd_result = run_msa_tool(
          msa_runner=self.jackhmmer_small_bfd_runner,
          input_fasta_path=input_fasta_path,
          msa_out_path=bfd_out_path,
          msa_format='sto',
          use_precomputed_msas=self.use_precomputed_msas)
      bfd_msa = parsers.parse_stockholm(jackhmmer_small_bfd_result['sto'])
    else:
      bfd_out_path = os.path.join(msa_output_dir, 'bfd_uniref_hits.a3m')
      hhblits_bfd_uniref_result = run_msa_tool(
          msa_runner=self.hhblits_bfd_uniref_runner,
          input_fasta_path=input_fasta_path,
          msa_out_path=bfd_out_path,
          msa_format='a3m',
          use_precomputed_msas=self.use_precomputed_msas)
      bfd_msa = parsers.parse_a3m(hhblits_bfd_uniref_result['a3m'])

    templates_result = self.template_featurizer.get_templates(
        query_sequence=input_sequence,
        hits=pdb_template_hits)

    sequence_features = make_sequence_features(
        sequence=input_sequence,
        description=input_description,
        num_res=num_res)

    msa_features = make_msa_features((uniref90_msa, bfd_msa, mgnify_msa))

    if save_msa_fasta:
      msa_outpath=os.path.join(msa_output_dir, 'msa_feat_gaptoU.fasta')
      with open(msa_outpath, 'w+') as fh:
        fh.write("">query""+""\n""+input_sequence+""\n"")
        counter=1
        for seq in msa_features['msa']:
            seq=[residue_constants.ID_TO_HHBLITS_AA[num] for num in seq]
            # for x in range(len(seq)):
            counter+=1
            fh.write("">seq_""+str(counter)+""\n"")
            out="""".join(seq).replace(""-"",""U"")
            fh.write(out+""\n"")
            
    logging.info('Uniref90 MSA size: %d sequences.', len(uniref90_msa))
    logging.info('BFD MSA size: %d sequences.', len(bfd_msa))
    logging.info('MGnify MSA size: %d sequences.', len(mgnify_msa))
    logging.info('Final (deduplicated) MSA size: %d sequences.',
                 msa_features['num_alignments'][0])
    logging.info('Total number of templates (NB: this can include bad '
                 'templates and is later filtered to top 4): %d.',
                 templates_result.features['template_domain_names'].shape[0])

    if save_template_names:
      temp_name_fn=os.path.join(msa_output_dir, 'template_names.txt')
      # with open(temp_name_fn, 'w+') as fh:
      #     fh.write(""\n"".join((map(str,templates_result.features['template_domain_names']))))
      template_names=[name.decode('utf-8') for name in templates_result.features['template_domain_names']]
      with open(temp_name_fn, 'w+') as fh:
          fh.write(""\n"".join(template_names))
          
    return {**sequence_features, **msa_features, **templates_result.features}
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/msa_pairing.py",".py","17220","462","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Pairing logic for multimer data pipeline.""""""

import collections
import functools
import string
from typing import Any, Dict, Iterable, List, Sequence

from alphafold.common import residue_constants
from alphafold.data import pipeline
import numpy as np
import pandas as pd
import scipy.linalg

MSA_GAP_IDX = residue_constants.restypes_with_x_and_gap.index('-')
SEQUENCE_GAP_CUTOFF = 0.5
SEQUENCE_SIMILARITY_CUTOFF = 0.9

MSA_PAD_VALUES = {'msa_all_seq': MSA_GAP_IDX,
                  'msa_mask_all_seq': 1,
                  'deletion_matrix_all_seq': 0,
                  'deletion_matrix_int_all_seq': 0,
                  'msa': MSA_GAP_IDX,
                  'msa_mask': 1,
                  'deletion_matrix': 0,
                  'deletion_matrix_int': 0}

MSA_FEATURES = ('msa', 'msa_mask', 'deletion_matrix', 'deletion_matrix_int')
SEQ_FEATURES = ('residue_index', 'aatype', 'all_atom_positions',
                'all_atom_mask', 'seq_mask', 'between_segment_residues',
                'has_alt_locations', 'has_hetatoms', 'asym_id', 'entity_id',
                'sym_id', 'entity_mask', 'deletion_mean',
                'prediction_atom_mask',
                'literature_positions', 'atom_indices_to_group_indices',
                'rigid_group_default_frame')
TEMPLATE_FEATURES = ('template_aatype', 'template_all_atom_positions',
                     'template_all_atom_mask')
CHAIN_FEATURES = ('num_alignments', 'seq_length')


def create_paired_features(
    chains: Iterable[pipeline.FeatureDict]) ->  List[pipeline.FeatureDict]:
  """"""Returns the original chains with paired NUM_SEQ features.

  Args:
    chains:  A list of feature dictionaries for each chain.

  Returns:
    A list of feature dictionaries with sequence features including only
    rows to be paired.
  """"""
  chains = list(chains)
  chain_keys = chains[0].keys()

  if len(chains) < 2:
    return chains
  else:
    updated_chains = []
    paired_chains_to_paired_row_indices = pair_sequences(chains)
    paired_rows = reorder_paired_rows(
        paired_chains_to_paired_row_indices)

    for chain_num, chain in enumerate(chains):
      new_chain = {k: v for k, v in chain.items() if '_all_seq' not in k}
      for feature_name in chain_keys:
        if feature_name.endswith('_all_seq'):
          feats_padded = pad_features(chain[feature_name], feature_name)
          new_chain[feature_name] = feats_padded[paired_rows[:, chain_num]]
      new_chain['num_alignments_all_seq'] = np.asarray(
          len(paired_rows[:, chain_num]))
      updated_chains.append(new_chain)
    return updated_chains


def pad_features(feature: np.ndarray, feature_name: str) -> np.ndarray:
  """"""Add a 'padding' row at the end of the features list.

  The padding row will be selected as a 'paired' row in the case of partial
  alignment - for the chain that doesn't have paired alignment.

  Args:
    feature: The feature to be padded.
    feature_name: The name of the feature to be padded.

  Returns:
    The feature with an additional padding row.
  """"""
  assert feature.dtype != np.dtype(np.string_)
  if feature_name in ('msa_all_seq', 'msa_mask_all_seq',
                      'deletion_matrix_all_seq', 'deletion_matrix_int_all_seq'):
    num_res = feature.shape[1]
    padding = MSA_PAD_VALUES[feature_name] * np.ones([1, num_res],
                                                     feature.dtype)
  elif feature_name == 'msa_species_identifiers_all_seq':
    padding = [b'']
  else:
    return feature
  feats_padded = np.concatenate([feature, padding], axis=0)
  return feats_padded


def _make_msa_df(chain_features: pipeline.FeatureDict) -> pd.DataFrame:
  """"""Makes dataframe with msa features needed for msa pairing.""""""
  chain_msa = chain_features['msa_all_seq']
  query_seq = chain_msa[0]
  per_seq_similarity = np.sum(
      query_seq[None] == chain_msa, axis=-1) / float(len(query_seq))
  per_seq_gap = np.sum(chain_msa == 21, axis=-1) / float(len(query_seq))
  msa_df = pd.DataFrame({
      'msa_species_identifiers':
          chain_features['msa_species_identifiers_all_seq'],
      'msa_row':
          np.arange(len(
              chain_features['msa_species_identifiers_all_seq'])),
      'msa_similarity': per_seq_similarity,
      'gap': per_seq_gap
  })
  return msa_df


def _create_species_dict(msa_df: pd.DataFrame) -> Dict[bytes, pd.DataFrame]:
  """"""Creates mapping from species to msa dataframe of that species.""""""
  species_lookup = {}
  for species, species_df in msa_df.groupby('msa_species_identifiers'):
    species_lookup[species] = species_df
  return species_lookup


def _match_rows_by_sequence_similarity(this_species_msa_dfs: List[pd.DataFrame]
                                       ) -> List[List[int]]:
  """"""Finds MSA sequence pairings across chains based on sequence similarity.

  Each chain's MSA sequences are first sorted by their sequence similarity to
  their respective target sequence. The sequences are then paired, starting
  from the sequences most similar to their target sequence.

  Args:
    this_species_msa_dfs: a list of dataframes containing MSA features for
      sequences for a specific species.

  Returns:
   A list of lists, each containing M indices corresponding to paired MSA rows,
   where M is the number of chains.
  """"""
  all_paired_msa_rows = []

  num_seqs = [len(species_df) for species_df in this_species_msa_dfs
              if species_df is not None]
  take_num_seqs = np.min(num_seqs)

  sort_by_similarity = (
      lambda x: x.sort_values('msa_similarity', axis=0, ascending=False))

  for species_df in this_species_msa_dfs:
    if species_df is not None:
      species_df_sorted = sort_by_similarity(species_df)
      msa_rows = species_df_sorted.msa_row.iloc[:take_num_seqs].values
    else:
      msa_rows = [-1] * take_num_seqs  # take the last 'padding' row
    all_paired_msa_rows.append(msa_rows)
  all_paired_msa_rows = list(np.array(all_paired_msa_rows).transpose())
  return all_paired_msa_rows


def pair_sequences(examples: List[pipeline.FeatureDict]
                   ) -> Dict[int, np.ndarray]:
  """"""Returns indices for paired MSA sequences across chains.""""""

  num_examples = len(examples)

  all_chain_species_dict = []
  common_species = set()
  for chain_features in examples:
    msa_df = _make_msa_df(chain_features)
    species_dict = _create_species_dict(msa_df)
    all_chain_species_dict.append(species_dict)
    common_species.update(set(species_dict))

  common_species = sorted(common_species)
  common_species.remove(b'')  # Remove target sequence species.

  all_paired_msa_rows = [np.zeros(len(examples), int)]
  all_paired_msa_rows_dict = {k: [] for k in range(num_examples)}
  all_paired_msa_rows_dict[num_examples] = [np.zeros(len(examples), int)]

  for species in common_species:
    if not species:
      continue
    this_species_msa_dfs = []
    species_dfs_present = 0
    for species_dict in all_chain_species_dict:
      if species in species_dict:
        this_species_msa_dfs.append(species_dict[species])
        species_dfs_present += 1
      else:
        this_species_msa_dfs.append(None)

    # Skip species that are present in only one chain.
    if species_dfs_present <= 1:
      continue

    if np.any(
        np.array([len(species_df) for species_df in
                  this_species_msa_dfs if
                  isinstance(species_df, pd.DataFrame)]) > 600):
      continue

    paired_msa_rows = _match_rows_by_sequence_similarity(this_species_msa_dfs)
    all_paired_msa_rows.extend(paired_msa_rows)
    all_paired_msa_rows_dict[species_dfs_present].extend(paired_msa_rows)
  all_paired_msa_rows_dict = {
      num_examples: np.array(paired_msa_rows) for
      num_examples, paired_msa_rows in all_paired_msa_rows_dict.items()
  }
  return all_paired_msa_rows_dict


def reorder_paired_rows(all_paired_msa_rows_dict: Dict[int, np.ndarray]
                        ) -> np.ndarray:
  """"""Creates a list of indices of paired MSA rows across chains.

  Args:
    all_paired_msa_rows_dict: a mapping from the number of paired chains to the
      paired indices.

  Returns:
    a list of lists, each containing indices of paired MSA rows across chains.
    The paired-index lists are ordered by:
      1) the number of chains in the paired alignment, i.e, all-chain pairings
         will come first.
      2) e-values
  """"""
  all_paired_msa_rows = []

  for num_pairings in sorted(all_paired_msa_rows_dict, reverse=True):
    paired_rows = all_paired_msa_rows_dict[num_pairings]
    paired_rows_product = abs(np.array([np.prod(rows) for rows in paired_rows]))
    paired_rows_sort_index = np.argsort(paired_rows_product)
    all_paired_msa_rows.extend(paired_rows[paired_rows_sort_index])

  return np.array(all_paired_msa_rows)


def block_diag(*arrs: np.ndarray, pad_value: float = 0.0) -> np.ndarray:
  """"""Like scipy.linalg.block_diag but with an optional padding value.""""""
  ones_arrs = [np.ones_like(x) for x in arrs]
  off_diag_mask = 1.0 - scipy.linalg.block_diag(*ones_arrs)
  diag = scipy.linalg.block_diag(*arrs)
  diag += (off_diag_mask * pad_value).astype(diag.dtype)
  return diag


def _correct_post_merged_feats(
    np_example: pipeline.FeatureDict,
    np_chains_list: Sequence[pipeline.FeatureDict],
    pair_msa_sequences: bool) -> pipeline.FeatureDict:
  """"""Adds features that need to be computed/recomputed post merging.""""""

  np_example['seq_length'] = np.asarray(np_example['aatype'].shape[0],
                                        dtype=np.int32)
  np_example['num_alignments'] = np.asarray(np_example['msa'].shape[0],
                                            dtype=np.int32)

  if not pair_msa_sequences:
    # Generate a bias that is 1 for the first row of every block in the
    # block diagonal MSA - i.e. make sure the cluster stack always includes
    # the query sequences for each chain (since the first row is the query
    # sequence).
    cluster_bias_masks = []
    for chain in np_chains_list:
      mask = np.zeros(chain['msa'].shape[0])
      mask[0] = 1
      cluster_bias_masks.append(mask)
    np_example['cluster_bias_mask'] = np.concatenate(cluster_bias_masks)

    # Initialize Bert mask with masked out off diagonals.
    msa_masks = [np.ones(x['msa'].shape, dtype=np.float32)
                 for x in np_chains_list]

    np_example['bert_mask'] = block_diag(
        *msa_masks, pad_value=0)
  else:
    np_example['cluster_bias_mask'] = np.zeros(np_example['msa'].shape[0])
    np_example['cluster_bias_mask'][0] = 1

    # Initialize Bert mask with masked out off diagonals.
    msa_masks = [np.ones(x['msa'].shape, dtype=np.float32) for
                 x in np_chains_list]
    msa_masks_all_seq = [np.ones(x['msa_all_seq'].shape, dtype=np.float32) for
                         x in np_chains_list]

    msa_mask_block_diag = block_diag(
        *msa_masks, pad_value=0)
    msa_mask_all_seq = np.concatenate(msa_masks_all_seq, axis=1)
    np_example['bert_mask'] = np.concatenate(
        [msa_mask_all_seq, msa_mask_block_diag], axis=0)
  return np_example


def _pad_templates(chains: Sequence[pipeline.FeatureDict],
                   max_templates: int) -> Sequence[pipeline.FeatureDict]:
  """"""For each chain pad the number of templates to a fixed size.

  Args:
    chains: A list of protein chains.
    max_templates: Each chain will be padded to have this many templates.

  Returns:
    The list of chains, updated to have template features padded to
    max_templates.
  """"""
  for chain in chains:
    for k, v in chain.items():
      if k in TEMPLATE_FEATURES:
        padding = np.zeros_like(v.shape)
        padding[0] = max_templates - v.shape[0]
        padding = [(0, p) for p in padding]
        chain[k] = np.pad(v, padding, mode='constant')
  return chains


def _merge_features_from_multiple_chains(
    chains: Sequence[pipeline.FeatureDict],
    pair_msa_sequences: bool) -> pipeline.FeatureDict:
  """"""Merge features from multiple chains.

  Args:
    chains: A list of feature dictionaries that we want to merge.
    pair_msa_sequences: Whether to concatenate MSA features along the
      num_res dimension (if True), or to block diagonalize them (if False).

  Returns:
    A feature dictionary for the merged example.
  """"""
  merged_example = {}
  for feature_name in chains[0]:
    feats = [x[feature_name] for x in chains]
    feature_name_split = feature_name.split('_all_seq')[0]
    if feature_name_split in MSA_FEATURES:
      if pair_msa_sequences or '_all_seq' in feature_name:
        merged_example[feature_name] = np.concatenate(feats, axis=1)
      else:
        merged_example[feature_name] = block_diag(
            *feats, pad_value=MSA_PAD_VALUES[feature_name])
    elif feature_name_split in SEQ_FEATURES:
      merged_example[feature_name] = np.concatenate(feats, axis=0)
    elif feature_name_split in TEMPLATE_FEATURES:
      merged_example[feature_name] = np.concatenate(feats, axis=1)
    elif feature_name_split in CHAIN_FEATURES:
      merged_example[feature_name] = np.sum(x for x in feats).astype(np.int32)
    else:
      merged_example[feature_name] = feats[0]
  return merged_example


def _merge_homomers_dense_msa(
    chains: Iterable[pipeline.FeatureDict]) -> Sequence[pipeline.FeatureDict]:
  """"""Merge all identical chains, making the resulting MSA dense.

  Args:
    chains: An iterable of features for each chain.

  Returns:
    A list of feature dictionaries.  All features with the same entity_id
    will be merged - MSA features will be concatenated along the num_res
    dimension - making them dense.
  """"""
  entity_chains = collections.defaultdict(list)
  for chain in chains:
    entity_id = chain['entity_id'][0]
    entity_chains[entity_id].append(chain)

  grouped_chains = []
  for entity_id in sorted(entity_chains):
    chains = entity_chains[entity_id]
    grouped_chains.append(chains)
  chains = [
      _merge_features_from_multiple_chains(chains, pair_msa_sequences=True)
      for chains in grouped_chains]
  return chains


def _concatenate_paired_and_unpaired_features(
    example: pipeline.FeatureDict) -> pipeline.FeatureDict:
  """"""Merges paired and block-diagonalised features.""""""
  features = MSA_FEATURES
  for feature_name in features:
    if feature_name in example:
      feat = example[feature_name]
      feat_all_seq = example[feature_name + '_all_seq']
      merged_feat = np.concatenate([feat_all_seq, feat], axis=0)
      example[feature_name] = merged_feat
  example['num_alignments'] = np.array(example['msa'].shape[0],
                                       dtype=np.int32)
  return example


def merge_chain_features(np_chains_list: List[pipeline.FeatureDict],
                         pair_msa_sequences: bool,
                         max_templates: int) -> pipeline.FeatureDict:
  """"""Merges features for multiple chains to single FeatureDict.

  Args:
    np_chains_list: List of FeatureDicts for each chain.
    pair_msa_sequences: Whether to merge paired MSAs.
    max_templates: The maximum number of templates to include.

  Returns:
    Single FeatureDict for entire complex.
  """"""
  np_chains_list = _pad_templates(
      np_chains_list, max_templates=max_templates)
  np_chains_list = _merge_homomers_dense_msa(np_chains_list)
  # Unpaired MSA features will be always block-diagonalised; paired MSA
  # features will be concatenated.
  np_example = _merge_features_from_multiple_chains(
      np_chains_list, pair_msa_sequences=False)
  if pair_msa_sequences:
    np_example = _concatenate_paired_and_unpaired_features(np_example)
  np_example = _correct_post_merged_feats(
      np_example=np_example,
      np_chains_list=np_chains_list,
      pair_msa_sequences=pair_msa_sequences)

  return np_example


def deduplicate_unpaired_sequences(
    np_chains: List[pipeline.FeatureDict]) -> List[pipeline.FeatureDict]:
  """"""Removes unpaired sequences which duplicate a paired sequence.""""""

  feature_names = np_chains[0].keys()
  msa_features = MSA_FEATURES

  for chain in np_chains:
    # Convert the msa_all_seq numpy array to a tuple for hashing.
    sequence_set = set(tuple(s) for s in chain['msa_all_seq'])
    keep_rows = []
    # Go through unpaired MSA seqs and remove any rows that correspond to the
    # sequences that are already present in the paired MSA.
    for row_num, seq in enumerate(chain['msa']):
      if tuple(seq) not in sequence_set:
        keep_rows.append(row_num)
    for feature_name in feature_names:
      if feature_name in msa_features:
        chain[feature_name] = chain[feature_name][keep_rows]
    chain['num_alignments'] = np.array(chain['msa'].shape[0], dtype=np.int32)
  return np_chains
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/feature_processing.py",".py","8574","229","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Feature processing logic for multimer data pipeline.""""""

from typing import Iterable, MutableMapping, List

from alphafold.common import residue_constants
from alphafold.data import msa_pairing
from alphafold.data import pipeline
import numpy as np

REQUIRED_FEATURES = frozenset({
    'aatype', 'all_atom_mask', 'all_atom_positions', 'all_chains_entity_ids',
    'all_crops_all_chains_mask', 'all_crops_all_chains_positions',
    'all_crops_all_chains_residue_ids', 'assembly_num_chains', 'asym_id',
    'bert_mask', 'cluster_bias_mask', 'deletion_matrix', 'deletion_mean',
    'entity_id', 'entity_mask', 'mem_peak', 'msa', 'msa_mask', 'num_alignments',
    'num_templates', 'queue_size', 'residue_index', 'resolution',
    'seq_length', 'seq_mask', 'sym_id', 'template_aatype',
    'template_all_atom_mask', 'template_all_atom_positions'
})

MAX_TEMPLATES = 4
MSA_CROP_SIZE = 2048


def _is_homomer_or_monomer(chains: Iterable[pipeline.FeatureDict]) -> bool:
  """"""Checks if a list of chains represents a homomer/monomer example.""""""
  # Note that an entity_id of 0 indicates padding.
  num_unique_chains = len(np.unique(np.concatenate(
      [np.unique(chain['entity_id'][chain['entity_id'] > 0]) for
       chain in chains])))
  return num_unique_chains == 1


def pair_and_merge(
    all_chain_features: MutableMapping[str, pipeline.FeatureDict]
    ) -> pipeline.FeatureDict:
  """"""Runs processing on features to augment, pair and merge.

  Args:
    all_chain_features: A MutableMap of dictionaries of features for each chain.

  Returns:
    A dictionary of features.
  """"""

  process_unmerged_features(all_chain_features)

  np_chains_list = list(all_chain_features.values())
  pair_msa_sequences = not _is_homomer_or_monomer(np_chains_list)

  if pair_msa_sequences:
    np_chains_list = msa_pairing.create_paired_features(
        chains=np_chains_list)
    np_chains_list = msa_pairing.deduplicate_unpaired_sequences(np_chains_list)
  np_chains_list = crop_chains(
      np_chains_list,
      msa_crop_size=MSA_CROP_SIZE,
      pair_msa_sequences=pair_msa_sequences,
      max_templates=MAX_TEMPLATES)
  np_example = msa_pairing.merge_chain_features(
      np_chains_list=np_chains_list, pair_msa_sequences=pair_msa_sequences,
      max_templates=MAX_TEMPLATES)
  np_example = process_final(np_example)
  return np_example


def crop_chains(
    chains_list: List[pipeline.FeatureDict],
    msa_crop_size: int,
    pair_msa_sequences: bool,
    max_templates: int) -> List[pipeline.FeatureDict]:
  """"""Crops the MSAs for a set of chains.

  Args:
    chains_list: A list of chains to be cropped.
    msa_crop_size: The total number of sequences to crop from the MSA.
    pair_msa_sequences: Whether we are operating in sequence-pairing mode.
    max_templates: The maximum templates to use per chain.

  Returns:
    The chains cropped.
  """"""

  # Apply the cropping.
  cropped_chains = []
  for chain in chains_list:
    cropped_chain = _crop_single_chain(
        chain,
        msa_crop_size=msa_crop_size,
        pair_msa_sequences=pair_msa_sequences,
        max_templates=max_templates)
    cropped_chains.append(cropped_chain)

  return cropped_chains


def _crop_single_chain(chain: pipeline.FeatureDict,
                       msa_crop_size: int,
                       pair_msa_sequences: bool,
                       max_templates: int) -> pipeline.FeatureDict:
  """"""Crops msa sequences to `msa_crop_size`.""""""
  msa_size = chain['num_alignments']

  if pair_msa_sequences:
    msa_size_all_seq = chain['num_alignments_all_seq']
    msa_crop_size_all_seq = np.minimum(msa_size_all_seq, msa_crop_size // 2)

    # We reduce the number of un-paired sequences, by the number of times a
    # sequence from this chain's MSA is included in the paired MSA.  This keeps
    # the MSA size for each chain roughly constant.
    msa_all_seq = chain['msa_all_seq'][:msa_crop_size_all_seq, :]
    num_non_gapped_pairs = np.sum(
        np.any(msa_all_seq != msa_pairing.MSA_GAP_IDX, axis=1))
    num_non_gapped_pairs = np.minimum(num_non_gapped_pairs,
                                      msa_crop_size_all_seq)

    # Restrict the unpaired crop size so that paired+unpaired sequences do not
    # exceed msa_seqs_per_chain for each chain.
    max_msa_crop_size = np.maximum(msa_crop_size - num_non_gapped_pairs, 0)
    msa_crop_size = np.minimum(msa_size, max_msa_crop_size)
  else:
    msa_crop_size = np.minimum(msa_size, msa_crop_size)

  include_templates = 'template_aatype' in chain and max_templates
  if include_templates:
    num_templates = chain['template_aatype'].shape[0]
    templates_crop_size = np.minimum(num_templates, max_templates)

  for k in chain:
    k_split = k.split('_all_seq')[0]
    if k_split in msa_pairing.TEMPLATE_FEATURES:
      chain[k] = chain[k][:templates_crop_size, :]
    elif k_split in msa_pairing.MSA_FEATURES:
      if '_all_seq' in k and pair_msa_sequences:
        chain[k] = chain[k][:msa_crop_size_all_seq, :]
      else:
        chain[k] = chain[k][:msa_crop_size, :]

  chain['num_alignments'] = np.asarray(msa_crop_size, dtype=np.int32)
  if include_templates:
    chain['num_templates'] = np.asarray(templates_crop_size, dtype=np.int32)
  if pair_msa_sequences:
    chain['num_alignments_all_seq'] = np.asarray(
        msa_crop_size_all_seq, dtype=np.int32)
  return chain


def process_final(np_example: pipeline.FeatureDict) -> pipeline.FeatureDict:
  """"""Final processing steps in data pipeline, after merging and pairing.""""""
  np_example = _correct_msa_restypes(np_example)
  np_example = _make_seq_mask(np_example)
  np_example = _make_msa_mask(np_example)
  np_example = _filter_features(np_example)
  return np_example


def _correct_msa_restypes(np_example):
  """"""Correct MSA restype to have the same order as residue_constants.""""""
  new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
  np_example['msa'] = np.take(new_order_list, np_example['msa'], axis=0)
  np_example['msa'] = np_example['msa'].astype(np.int32)
  return np_example


def _make_seq_mask(np_example):
  np_example['seq_mask'] = (np_example['entity_id'] > 0).astype(np.float32)
  return np_example


def _make_msa_mask(np_example):
  """"""Mask features are all ones, but will later be zero-padded.""""""

  np_example['msa_mask'] = np.ones_like(np_example['msa'], dtype=np.float32)

  seq_mask = (np_example['entity_id'] > 0).astype(np.float32)
  np_example['msa_mask'] *= seq_mask[None]

  return np_example


def _filter_features(np_example: pipeline.FeatureDict) -> pipeline.FeatureDict:
  """"""Filters features of example to only those requested.""""""
  return {k: v for (k, v) in np_example.items() if k in REQUIRED_FEATURES}


def process_unmerged_features(
    all_chain_features: MutableMapping[str, pipeline.FeatureDict]):
  """"""Postprocessing stage for per-chain features before merging.""""""
  num_chains = len(all_chain_features)
  for chain_features in all_chain_features.values():
    # Convert deletion matrices to float.
    chain_features['deletion_matrix'] = np.asarray(
        chain_features.pop('deletion_matrix_int'), dtype=np.float32)
    if 'deletion_matrix_int_all_seq' in chain_features:
      chain_features['deletion_matrix_all_seq'] = np.asarray(
          chain_features.pop('deletion_matrix_int_all_seq'), dtype=np.float32)

    chain_features['deletion_mean'] = np.mean(
        chain_features['deletion_matrix'], axis=0)

    # Add all_atom_mask and dummy all_atom_positions based on aatype.
    all_atom_mask = residue_constants.STANDARD_ATOM_MASK[
        chain_features['aatype']]
    chain_features['all_atom_mask'] = all_atom_mask
    chain_features['all_atom_positions'] = np.zeros(
        list(all_atom_mask.shape) + [3])

    # Add assembly_num_chains.
    chain_features['assembly_num_chains'] = np.asarray(num_chains)

  # Add entity_mask.
  for chain_features in all_chain_features.values():
    chain_features['entity_mask'] = (
        chain_features['entity_id'] != 0).astype(np.int32)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/templates.py",".py","40677","1011","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for getting templates and calculating template features.""""""
import abc
import dataclasses
import datetime
import functools
import glob
import os
import re
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple

from absl import logging
from alphafold.common import residue_constants
from alphafold.data import mmcif_parsing
from alphafold.data import parsers
from alphafold.data.tools import kalign
import numpy as np

# Internal import (7716).


class Error(Exception):
  """"""Base class for exceptions.""""""


class NoChainsError(Error):
  """"""An error indicating that template mmCIF didn't have any chains.""""""


class SequenceNotInTemplateError(Error):
  """"""An error indicating that template mmCIF didn't contain the sequence.""""""


class NoAtomDataInTemplateError(Error):
  """"""An error indicating that template mmCIF didn't contain atom positions.""""""


class TemplateAtomMaskAllZerosError(Error):
  """"""An error indicating that template mmCIF had all atom positions masked.""""""


class QueryToTemplateAlignError(Error):
  """"""An error indicating that the query can't be aligned to the template.""""""


class CaDistanceError(Error):
  """"""An error indicating that a CA atom distance exceeds a threshold.""""""


class MultipleChainsError(Error):
  """"""An error indicating that multiple chains were found for a given ID.""""""


# Prefilter exceptions.
class PrefilterError(Exception):
  """"""A base class for template prefilter exceptions.""""""


class DateError(PrefilterError):
  """"""An error indicating that the hit date was after the max allowed date.""""""


class AlignRatioError(PrefilterError):
  """"""An error indicating that the hit align ratio to the query was too small.""""""


class DuplicateError(PrefilterError):
  """"""An error indicating that the hit was an exact subsequence of the query.""""""


class LengthError(PrefilterError):
  """"""An error indicating that the hit was too short.""""""


TEMPLATE_FEATURES = {
    'template_aatype': np.float32,
    'template_all_atom_masks': np.float32,
    'template_all_atom_positions': np.float32,
    'template_domain_names': object,
    'template_sequence': object,
    'template_sum_probs': np.float32,
}


def _get_pdb_id_and_chain(hit: parsers.TemplateHit) -> Tuple[str, str]:
  """"""Returns PDB id and chain id for an HHSearch Hit.""""""
  # PDB ID: 4 letters. Chain ID: 1+ alphanumeric letters or ""."" if unknown.
  id_match = re.match(r'[a-zA-Z\d]{4}_[a-zA-Z0-9.]+', hit.name)
  if not id_match:
    raise ValueError(f'hit.name did not start with PDBID_chain: {hit.name}')
  pdb_id, chain_id = id_match.group(0).split('_')
  return pdb_id.lower(), chain_id


def _is_after_cutoff(
    pdb_id: str,
    release_dates: Mapping[str, datetime.datetime],
    release_date_cutoff: Optional[datetime.datetime]) -> bool:
  """"""Checks if the template date is after the release date cutoff.

  Args:
    pdb_id: 4 letter pdb code.
    release_dates: Dictionary mapping PDB ids to their structure release dates.
    release_date_cutoff: Max release date that is valid for this query.

  Returns:
    True if the template release date is after the cutoff, False otherwise.
  """"""
  if release_date_cutoff is None:
    raise ValueError('The release_date_cutoff must not be None.')
  if pdb_id in release_dates:
    return release_dates[pdb_id] > release_date_cutoff
  else:
    # Since this is just a quick prefilter to reduce the number of mmCIF files
    # we need to parse, we don't have to worry about returning True here.
    return False


def _parse_obsolete(obsolete_file_path: str) -> Mapping[str, Optional[str]]:
  """"""Parses the data file from PDB that lists which pdb_ids are obsolete.""""""
  with open(obsolete_file_path) as f:
    result = {}
    for line in f:
      line = line.strip()
      # Format:    Date      From     To
      # 'OBSLTE    06-NOV-19 6G9Y'                - Removed, rare
      # 'OBSLTE    31-JUL-94 116L     216L'       - Replaced, common
      # 'OBSLTE    26-SEP-06 2H33     2JM5 2OWI'  - Replaced by multiple, rare
      if line.startswith('OBSLTE'):
        if len(line) > 30:
          # Replaced by at least one structure.
          from_id = line[20:24].lower()
          to_id = line[29:33].lower()
          result[from_id] = to_id
        elif len(line) == 24:
          # Removed.
          from_id = line[20:24].lower()
          result[from_id] = None
    return result


def _parse_release_dates(path: str) -> Mapping[str, datetime.datetime]:
  """"""Parses release dates file, returns a mapping from PDBs to release dates.""""""
  if path.endswith('txt'):
    release_dates = {}
    with open(path, 'r') as f:
      for line in f:
        pdb_id, date = line.split(':')
        date = date.strip()
        # Python 3.6 doesn't have datetime.date.fromisoformat() which is about
        # 90x faster than strptime. However, splitting the string manually is
        # about 10x faster than strptime.
        release_dates[pdb_id.strip()] = datetime.datetime(
            year=int(date[:4]), month=int(date[5:7]), day=int(date[8:10]))
    return release_dates
  else:
    raise ValueError('Invalid format of the release date file %s.' % path)


def _assess_hhsearch_hit(
    hit: parsers.TemplateHit,
    hit_pdb_code: str,
    query_sequence: str,
    release_dates: Mapping[str, datetime.datetime],
    release_date_cutoff: datetime.datetime,
    max_subsequence_ratio: float = 0.95,
    min_align_ratio: float = 0.1) -> bool:
  """"""Determines if template is valid (without parsing the template mmcif file).

  Args:
    hit: HhrHit for the template.
    hit_pdb_code: The 4 letter pdb code of the template hit. This might be
      different from the value in the actual hit since the original pdb might
      have become obsolete.
    query_sequence: Amino acid sequence of the query.
    release_dates: Dictionary mapping pdb codes to their structure release
      dates.
    release_date_cutoff: Max release date that is valid for this query.
    max_subsequence_ratio: Exclude any exact matches with this much overlap.
    min_align_ratio: Minimum overlap between the template and query.

  Returns:
    True if the hit passed the prefilter. Raises an exception otherwise.

  Raises:
    DateError: If the hit date was after the max allowed date.
    AlignRatioError: If the hit align ratio to the query was too small.
    DuplicateError: If the hit was an exact subsequence of the query.
    LengthError: If the hit was too short.
  """"""
  aligned_cols = hit.aligned_cols
  align_ratio = aligned_cols / len(query_sequence)

  template_sequence = hit.hit_sequence.replace('-', '')
  length_ratio = float(len(template_sequence)) / len(query_sequence)

  # Check whether the template is a large subsequence or duplicate of original
  # query. This can happen due to duplicate entries in the PDB database.
  duplicate = (template_sequence in query_sequence and
               length_ratio > max_subsequence_ratio)

  if _is_after_cutoff(hit_pdb_code, release_dates, release_date_cutoff):
    raise DateError(f'Date ({release_dates[hit_pdb_code]}) > max template date '
                    f'({release_date_cutoff}).')

  if align_ratio <= min_align_ratio:
    raise AlignRatioError('Proportion of residues aligned to query too small. '
                          f'Align ratio: {align_ratio}.')

  if duplicate:
    raise DuplicateError('Template is an exact subsequence of query with large '
                         f'coverage. Length ratio: {length_ratio}.')

  if len(template_sequence) < 10:
    raise LengthError(f'Template too short. Length: {len(template_sequence)}.')

  return True


def _find_template_in_pdb(
    template_chain_id: str,
    template_sequence: str,
    mmcif_object: mmcif_parsing.MmcifObject) -> Tuple[str, str, int]:
  """"""Tries to find the template chain in the given pdb file.

  This method tries the three following things in order:
    1. Tries if there is an exact match in both the chain ID and the sequence.
       If yes, the chain sequence is returned. Otherwise:
    2. Tries if there is an exact match only in the sequence.
       If yes, the chain sequence is returned. Otherwise:
    3. Tries if there is a fuzzy match (X = wildcard) in the sequence.
       If yes, the chain sequence is returned.
  If none of these succeed, a SequenceNotInTemplateError is thrown.

  Args:
    template_chain_id: The template chain ID.
    template_sequence: The template chain sequence.
    mmcif_object: The PDB object to search for the template in.

  Returns:
    A tuple with:
    * The chain sequence that was found to match the template in the PDB object.
    * The ID of the chain that is being returned.
    * The offset where the template sequence starts in the chain sequence.

  Raises:
    SequenceNotInTemplateError: If no match is found after the steps described
      above.
  """"""
  # Try if there is an exact match in both the chain ID and the (sub)sequence.
  pdb_id = mmcif_object.file_id
  chain_sequence = mmcif_object.chain_to_seqres.get(template_chain_id)
  if chain_sequence and (template_sequence in chain_sequence):
    logging.info(
        'Found an exact template match %s_%s.', pdb_id, template_chain_id)
    mapping_offset = chain_sequence.find(template_sequence)
    return chain_sequence, template_chain_id, mapping_offset

  # Try if there is an exact match in the (sub)sequence only.
  for chain_id, chain_sequence in mmcif_object.chain_to_seqres.items():
    if chain_sequence and (template_sequence in chain_sequence):
      logging.info('Found a sequence-only match %s_%s.', pdb_id, chain_id)
      mapping_offset = chain_sequence.find(template_sequence)
      return chain_sequence, chain_id, mapping_offset

  # Return a chain sequence that fuzzy matches (X = wildcard) the template.
  # Make parentheses unnamed groups (?:_) to avoid the 100 named groups limit.
  regex = ['.' if aa == 'X' else '(?:%s|X)' % aa for aa in template_sequence]
  regex = re.compile(''.join(regex))
  for chain_id, chain_sequence in mmcif_object.chain_to_seqres.items():
    match = re.search(regex, chain_sequence)
    if match:
      logging.info('Found a fuzzy sequence-only match %s_%s.', pdb_id, chain_id)
      mapping_offset = match.start()
      return chain_sequence, chain_id, mapping_offset

  # No hits, raise an error.
  raise SequenceNotInTemplateError(
      'Could not find the template sequence in %s_%s. Template sequence: %s, '
      'chain_to_seqres: %s' % (pdb_id, template_chain_id, template_sequence,
                               mmcif_object.chain_to_seqres))


def _realign_pdb_template_to_query(
    old_template_sequence: str,
    template_chain_id: str,
    mmcif_object: mmcif_parsing.MmcifObject,
    old_mapping: Mapping[int, int],
    kalign_binary_path: str) -> Tuple[str, Mapping[int, int]]:
  """"""Aligns template from the mmcif_object to the query.

  In case PDB70 contains a different version of the template sequence, we need
  to perform a realignment to the actual sequence that is in the mmCIF file.
  This method performs such realignment, but returns the new sequence and
  mapping only if the sequence in the mmCIF file is 90% identical to the old
  sequence.

  Note that the old_template_sequence comes from the hit, and contains only that
  part of the chain that matches with the query while the new_template_sequence
  is the full chain.

  Args:
    old_template_sequence: The template sequence that was returned by the PDB
      template search (typically done using HHSearch).
    template_chain_id: The template chain id was returned by the PDB template
      search (typically done using HHSearch). This is used to find the right
      chain in the mmcif_object chain_to_seqres mapping.
    mmcif_object: A mmcif_object which holds the actual template data.
    old_mapping: A mapping from the query sequence to the template sequence.
      This mapping will be used to compute the new mapping from the query
      sequence to the actual mmcif_object template sequence by aligning the
      old_template_sequence and the actual template sequence.
    kalign_binary_path: The path to a kalign executable.

  Returns:
    A tuple (new_template_sequence, new_query_to_template_mapping) where:
    * new_template_sequence is the actual template sequence that was found in
      the mmcif_object.
    * new_query_to_template_mapping is the new mapping from the query to the
      actual template found in the mmcif_object.

  Raises:
    QueryToTemplateAlignError:
    * If there was an error thrown by the alignment tool.
    * Or if the actual template sequence differs by more than 10% from the
      old_template_sequence.
  """"""
  aligner = kalign.Kalign(binary_path=kalign_binary_path)
  new_template_sequence = mmcif_object.chain_to_seqres.get(
      template_chain_id, '')

  # Sometimes the template chain id is unknown. But if there is only a single
  # sequence within the mmcif_object, it is safe to assume it is that one.
  if not new_template_sequence:
    if len(mmcif_object.chain_to_seqres) == 1:
      logging.info('Could not find %s in %s, but there is only 1 sequence, so '
                   'using that one.',
                   template_chain_id,
                   mmcif_object.file_id)
      new_template_sequence = list(mmcif_object.chain_to_seqres.values())[0]
    else:
      raise QueryToTemplateAlignError(
          f'Could not find chain {template_chain_id} in {mmcif_object.file_id}. '
          'If there are no mmCIF parsing errors, it is possible it was not a '
          'protein chain.')

  try:
    parsed_a3m = parsers.parse_a3m(
        aligner.align([old_template_sequence, new_template_sequence]))
    old_aligned_template, new_aligned_template = parsed_a3m.sequences
  except Exception as e:
    raise QueryToTemplateAlignError(
        'Could not align old template %s to template %s (%s_%s). Error: %s' %
        (old_template_sequence, new_template_sequence, mmcif_object.file_id,
         template_chain_id, str(e)))

  logging.info('Old aligned template: %s\nNew aligned template: %s',
               old_aligned_template, new_aligned_template)

  old_to_new_template_mapping = {}
  old_template_index = -1
  new_template_index = -1
  num_same = 0
  for old_template_aa, new_template_aa in zip(
      old_aligned_template, new_aligned_template):
    if old_template_aa != '-':
      old_template_index += 1
    if new_template_aa != '-':
      new_template_index += 1
    if old_template_aa != '-' and new_template_aa != '-':
      old_to_new_template_mapping[old_template_index] = new_template_index
      if old_template_aa == new_template_aa:
        num_same += 1

  # Require at least 90 % sequence identity wrt to the shorter of the sequences.
  if float(num_same) / min(
      len(old_template_sequence), len(new_template_sequence)) < 0.9:
    raise QueryToTemplateAlignError(
        'Insufficient similarity of the sequence in the database: %s to the '
        'actual sequence in the mmCIF file %s_%s: %s. We require at least '
        '90 %% similarity wrt to the shorter of the sequences. This is not a '
        'problem unless you think this is a template that should be included.' %
        (old_template_sequence, mmcif_object.file_id, template_chain_id,
         new_template_sequence))

  new_query_to_template_mapping = {}
  for query_index, old_template_index in old_mapping.items():
    new_query_to_template_mapping[query_index] = (
        old_to_new_template_mapping.get(old_template_index, -1))

  new_template_sequence = new_template_sequence.replace('-', '')

  return new_template_sequence, new_query_to_template_mapping


def _check_residue_distances(all_positions: np.ndarray,
                             all_positions_mask: np.ndarray,
                             max_ca_ca_distance: float):
  """"""Checks if the distance between unmasked neighbor residues is ok.""""""
  ca_position = residue_constants.atom_order['CA']
  prev_is_unmasked = False
  prev_calpha = None
  for i, (coords, mask) in enumerate(zip(all_positions, all_positions_mask)):
    this_is_unmasked = bool(mask[ca_position])
    if this_is_unmasked:
      this_calpha = coords[ca_position]
      if prev_is_unmasked:
        distance = np.linalg.norm(this_calpha - prev_calpha)
        if distance > max_ca_ca_distance:
          raise CaDistanceError(
              'The distance between residues %d and %d is %f > limit %f.' % (
                  i, i + 1, distance, max_ca_ca_distance))
      prev_calpha = this_calpha
    prev_is_unmasked = this_is_unmasked


def _get_atom_positions(
    mmcif_object: mmcif_parsing.MmcifObject,
    auth_chain_id: str,
    max_ca_ca_distance: float) -> Tuple[np.ndarray, np.ndarray]:
  """"""Gets atom positions and mask from a list of Biopython Residues.""""""
  num_res = len(mmcif_object.chain_to_seqres[auth_chain_id])

  relevant_chains = [c for c in mmcif_object.structure.get_chains()
                     if c.id == auth_chain_id]
  if len(relevant_chains) != 1:
    raise MultipleChainsError(
        f'Expected exactly one chain in structure with id {auth_chain_id}.')
  chain = relevant_chains[0]

  all_positions = np.zeros([num_res, residue_constants.atom_type_num, 3])
  all_positions_mask = np.zeros([num_res, residue_constants.atom_type_num],
                                dtype=np.int64)
  for res_index in range(num_res):
    pos = np.zeros([residue_constants.atom_type_num, 3], dtype=np.float32)
    mask = np.zeros([residue_constants.atom_type_num], dtype=np.float32)
    res_at_position = mmcif_object.seqres_to_structure[auth_chain_id][res_index]
    if not res_at_position.is_missing:
      res = chain[(res_at_position.hetflag,
                   res_at_position.position.residue_number,
                   res_at_position.position.insertion_code)]
      for atom in res.get_atoms():
        atom_name = atom.get_name()
        x, y, z = atom.get_coord()
        if atom_name in residue_constants.atom_order.keys():
          pos[residue_constants.atom_order[atom_name]] = [x, y, z]
          mask[residue_constants.atom_order[atom_name]] = 1.0
        elif atom_name.upper() == 'SE' and res.get_resname() == 'MSE':
          # Put the coordinates of the selenium atom in the sulphur column.
          pos[residue_constants.atom_order['SD']] = [x, y, z]
          mask[residue_constants.atom_order['SD']] = 1.0

      # Fix naming errors in arginine residues where NH2 is incorrectly
      # assigned to be closer to CD than NH1.
      cd = residue_constants.atom_order['CD']
      nh1 = residue_constants.atom_order['NH1']
      nh2 = residue_constants.atom_order['NH2']
      if (res.get_resname() == 'ARG' and
          all(mask[atom_index] for atom_index in (cd, nh1, nh2)) and
          (np.linalg.norm(pos[nh1] - pos[cd]) >
           np.linalg.norm(pos[nh2] - pos[cd]))):
        pos[nh1], pos[nh2] = pos[nh2].copy(), pos[nh1].copy()
        mask[nh1], mask[nh2] = mask[nh2].copy(), mask[nh1].copy()

    all_positions[res_index] = pos
    all_positions_mask[res_index] = mask
  _check_residue_distances(
      all_positions, all_positions_mask, max_ca_ca_distance)
  return all_positions, all_positions_mask


def _extract_template_features(
    mmcif_object: mmcif_parsing.MmcifObject,
    pdb_id: str,
    mapping: Mapping[int, int],
    template_sequence: str,
    query_sequence: str,
    template_chain_id: str,
    kalign_binary_path: str) -> Tuple[Dict[str, Any], Optional[str]]:
  """"""Parses atom positions in the target structure and aligns with the query.

  Atoms for each residue in the template structure are indexed to coincide
  with their corresponding residue in the query sequence, according to the
  alignment mapping provided.

  Args:
    mmcif_object: mmcif_parsing.MmcifObject representing the template.
    pdb_id: PDB code for the template.
    mapping: Dictionary mapping indices in the query sequence to indices in
      the template sequence.
    template_sequence: String describing the amino acid sequence for the
      template protein.
    query_sequence: String describing the amino acid sequence for the query
      protein.
    template_chain_id: String ID describing which chain in the structure proto
      should be used.
    kalign_binary_path: The path to a kalign executable used for template
        realignment.

  Returns:
    A tuple with:
    * A dictionary containing the extra features derived from the template
      protein structure.
    * A warning message if the hit was realigned to the actual mmCIF sequence.
      Otherwise None.

  Raises:
    NoChainsError: If the mmcif object doesn't contain any chains.
    SequenceNotInTemplateError: If the given chain id / sequence can't
      be found in the mmcif object.
    QueryToTemplateAlignError: If the actual template in the mmCIF file
      can't be aligned to the query.
    NoAtomDataInTemplateError: If the mmcif object doesn't contain
      atom positions.
    TemplateAtomMaskAllZerosError: If the mmcif object doesn't have any
      unmasked residues.
  """"""
  if mmcif_object is None or not mmcif_object.chain_to_seqres:
    raise NoChainsError('No chains in PDB: %s_%s' % (pdb_id, template_chain_id))

  warning = None
  try:
    seqres, chain_id, mapping_offset = _find_template_in_pdb(
        template_chain_id=template_chain_id,
        template_sequence=template_sequence,
        mmcif_object=mmcif_object)
  except SequenceNotInTemplateError:
    # If PDB70 contains a different version of the template, we use the sequence
    # from the mmcif_object.
    chain_id = template_chain_id
    warning = (
        f'The exact sequence {template_sequence} was not found in '
        f'{pdb_id}_{chain_id}. Realigning the template to the actual sequence.')
    logging.warning(warning)
    # This throws an exception if it fails to realign the hit.
    seqres, mapping = _realign_pdb_template_to_query(
        old_template_sequence=template_sequence,
        template_chain_id=template_chain_id,
        mmcif_object=mmcif_object,
        old_mapping=mapping,
        kalign_binary_path=kalign_binary_path)
    logging.info('Sequence in %s_%s: %s successfully realigned to %s',
                 pdb_id, chain_id, template_sequence, seqres)
    # The template sequence changed.
    template_sequence = seqres
    # No mapping offset, the query is aligned to the actual sequence.
    mapping_offset = 0

  try:
    # Essentially set to infinity - we don't want to reject templates unless
    # they're really really bad.
    all_atom_positions, all_atom_mask = _get_atom_positions(
        mmcif_object, chain_id, max_ca_ca_distance=150.0)
  except (CaDistanceError, KeyError) as ex:
    raise NoAtomDataInTemplateError(
        'Could not get atom data (%s_%s): %s' % (pdb_id, chain_id, str(ex))
        ) from ex

  all_atom_positions = np.split(all_atom_positions, all_atom_positions.shape[0])
  all_atom_masks = np.split(all_atom_mask, all_atom_mask.shape[0])

  output_templates_sequence = []
  templates_all_atom_positions = []
  templates_all_atom_masks = []

  for _ in query_sequence:
    # Residues in the query_sequence that are not in the template_sequence:
    templates_all_atom_positions.append(
        np.zeros((residue_constants.atom_type_num, 3)))
    templates_all_atom_masks.append(np.zeros(residue_constants.atom_type_num))
    output_templates_sequence.append('-')

  for k, v in mapping.items():
    template_index = v + mapping_offset
    templates_all_atom_positions[k] = all_atom_positions[template_index][0]
    templates_all_atom_masks[k] = all_atom_masks[template_index][0]
    output_templates_sequence[k] = template_sequence[v]

  # Alanine (AA with the lowest number of atoms) has 5 atoms (C, CA, CB, N, O).
  if np.sum(templates_all_atom_masks) < 5:
    raise TemplateAtomMaskAllZerosError(
        'Template all atom mask was all zeros: %s_%s. Residue range: %d-%d' %
        (pdb_id, chain_id, min(mapping.values()) + mapping_offset,
         max(mapping.values()) + mapping_offset))

  output_templates_sequence = ''.join(output_templates_sequence)

  templates_aatype = residue_constants.sequence_to_onehot(
      output_templates_sequence, residue_constants.HHBLITS_AA_TO_ID)

  return (
      {
          'template_all_atom_positions': np.array(templates_all_atom_positions),
          'template_all_atom_masks': np.array(templates_all_atom_masks),
          'template_sequence': output_templates_sequence.encode(),
          'template_aatype': np.array(templates_aatype),
          'template_domain_names': f'{pdb_id.lower()}_{chain_id}'.encode(),
      },
      warning)


def _build_query_to_hit_index_mapping(
    hit_query_sequence: str,
    hit_sequence: str,
    indices_hit: Sequence[int],
    indices_query: Sequence[int],
    original_query_sequence: str) -> Mapping[int, int]:
  """"""Gets mapping from indices in original query sequence to indices in the hit.

  hit_query_sequence and hit_sequence are two aligned sequences containing gap
  characters. hit_query_sequence contains only the part of the original query
  sequence that matched the hit. When interpreting the indices from the .hhr, we
  need to correct for this to recover a mapping from original query sequence to
  the hit sequence.

  Args:
    hit_query_sequence: The portion of the query sequence that is in the .hhr
      hit
    hit_sequence: The portion of the hit sequence that is in the .hhr
    indices_hit: The indices for each aminoacid relative to the hit sequence
    indices_query: The indices for each aminoacid relative to the original query
      sequence
    original_query_sequence: String describing the original query sequence.

  Returns:
    Dictionary with indices in the original query sequence as keys and indices
    in the hit sequence as values.
  """"""
  # If the hit is empty (no aligned residues), return empty mapping
  if not hit_query_sequence:
    return {}

  # Remove gaps and find the offset of hit.query relative to original query.
  hhsearch_query_sequence = hit_query_sequence.replace('-', '')
  hit_sequence = hit_sequence.replace('-', '')
  hhsearch_query_offset = original_query_sequence.find(hhsearch_query_sequence)

  # Index of -1 used for gap characters. Subtract the min index ignoring gaps.
  min_idx = min(x for x in indices_hit if x > -1)
  fixed_indices_hit = [
      x - min_idx if x > -1 else -1 for x in indices_hit
  ]

  min_idx = min(x for x in indices_query if x > -1)
  fixed_indices_query = [x - min_idx if x > -1 else -1 for x in indices_query]

  # Zip the corrected indices, ignore case where both seqs have gap characters.
  mapping = {}
  for q_i, q_t in zip(fixed_indices_query, fixed_indices_hit):
    if q_t != -1 and q_i != -1:
      if (q_t >= len(hit_sequence) or
          q_i + hhsearch_query_offset >= len(original_query_sequence)):
        continue
      mapping[q_i + hhsearch_query_offset] = q_t

  return mapping


@dataclasses.dataclass(frozen=True)
class SingleHitResult:
  features: Optional[Mapping[str, Any]]
  error: Optional[str]
  warning: Optional[str]


@functools.lru_cache(16, typed=False)
def _read_file(path):
  with open(path, 'r') as f:
    file_data = f.read()
  return file_data


def _process_single_hit(
    query_sequence: str,
    hit: parsers.TemplateHit,
    mmcif_dir: str,
    max_template_date: datetime.datetime,
    release_dates: Mapping[str, datetime.datetime],
    obsolete_pdbs: Mapping[str, Optional[str]],
    kalign_binary_path: str,
    strict_error_check: bool = False) -> SingleHitResult:
  """"""Tries to extract template features from a single HHSearch hit.""""""
  # Fail hard if we can't get the PDB ID and chain name from the hit.
  hit_pdb_code, hit_chain_id = _get_pdb_id_and_chain(hit)

  # This hit has been removed (obsoleted) from PDB, skip it.
  if hit_pdb_code in obsolete_pdbs and obsolete_pdbs[hit_pdb_code] is None:
    return SingleHitResult(
        features=None, error=None, warning=f'Hit {hit_pdb_code} is obsolete.')

  if hit_pdb_code not in release_dates:
    if hit_pdb_code in obsolete_pdbs:
      hit_pdb_code = obsolete_pdbs[hit_pdb_code]

  # Pass hit_pdb_code since it might have changed due to the pdb being obsolete.
  try:
    _assess_hhsearch_hit(
        hit=hit,
        hit_pdb_code=hit_pdb_code,
        query_sequence=query_sequence,
        release_dates=release_dates,
        release_date_cutoff=max_template_date)
  except PrefilterError as e:
    msg = f'hit {hit_pdb_code}_{hit_chain_id} did not pass prefilter: {str(e)}'
    logging.info(msg)
    if strict_error_check and isinstance(e, (DateError, DuplicateError)):
      # In strict mode we treat some prefilter cases as errors.
      return SingleHitResult(features=None, error=msg, warning=None)

    return SingleHitResult(features=None, error=None, warning=None)

  mapping = _build_query_to_hit_index_mapping(
      hit.query, hit.hit_sequence, hit.indices_hit, hit.indices_query,
      query_sequence)

  # The mapping is from the query to the actual hit sequence, so we need to
  # remove gaps (which regardless have a missing confidence score).
  template_sequence = hit.hit_sequence.replace('-', '')

  cif_path = os.path.join(mmcif_dir, hit_pdb_code + '.cif')
  logging.debug('Reading PDB entry from %s. Query: %s, template: %s', cif_path,
                query_sequence, template_sequence)
  # Fail if we can't find the mmCIF file.
  cif_string = _read_file(cif_path)

  parsing_result = mmcif_parsing.parse(
      file_id=hit_pdb_code, mmcif_string=cif_string)

  if parsing_result.mmcif_object is not None:
    hit_release_date = datetime.datetime.strptime(
        parsing_result.mmcif_object.header['release_date'], '%Y-%m-%d')
    if hit_release_date > max_template_date:
      error = ('Template %s date (%s) > max template date (%s).' %
               (hit_pdb_code, hit_release_date, max_template_date))
      if strict_error_check:
        return SingleHitResult(features=None, error=error, warning=None)
      else:
        logging.debug(error)
        return SingleHitResult(features=None, error=None, warning=None)

  try:
    features, realign_warning = _extract_template_features(
        mmcif_object=parsing_result.mmcif_object,
        pdb_id=hit_pdb_code,
        mapping=mapping,
        template_sequence=template_sequence,
        query_sequence=query_sequence,
        template_chain_id=hit_chain_id,
        kalign_binary_path=kalign_binary_path)
    if hit.sum_probs is None:
      features['template_sum_probs'] = [0]
    else:
      features['template_sum_probs'] = [hit.sum_probs]

    # It is possible there were some errors when parsing the other chains in the
    # mmCIF file, but the template features for the chain we want were still
    # computed. In such case the mmCIF parsing errors are not relevant.
    return SingleHitResult(
        features=features, error=None, warning=realign_warning)
  except (NoChainsError, NoAtomDataInTemplateError,
          TemplateAtomMaskAllZerosError) as e:
    # These 3 errors indicate missing mmCIF experimental data rather than a
    # problem with the template search, so turn them into warnings.
    warning = ('%s_%s (sum_probs: %s, rank: %s): feature extracting errors: '
               '%s, mmCIF parsing errors: %s'
               % (hit_pdb_code, hit_chain_id, hit.sum_probs, hit.index,
                  str(e), parsing_result.errors))
    if strict_error_check:
      return SingleHitResult(features=None, error=warning, warning=None)
    else:
      return SingleHitResult(features=None, error=None, warning=warning)
  except Error as e:
    error = ('%s_%s (sum_probs: %.2f, rank: %d): feature extracting errors: '
             '%s, mmCIF parsing errors: %s'
             % (hit_pdb_code, hit_chain_id, hit.sum_probs, hit.index,
                str(e), parsing_result.errors))
    return SingleHitResult(features=None, error=error, warning=None)


@dataclasses.dataclass(frozen=True)
class TemplateSearchResult:
  features: Mapping[str, Any]
  errors: Sequence[str]
  warnings: Sequence[str]


class TemplateHitFeaturizer(abc.ABC):
  """"""An abstract base class for turning template hits to template features.""""""

  def __init__(
      self,
      mmcif_dir: str,
      max_template_date: str,
      max_hits: int,
      kalign_binary_path: str,
      release_dates_path: Optional[str],
      obsolete_pdbs_path: Optional[str],
      strict_error_check: bool = False):
    """"""Initializes the Template Search.

    Args:
      mmcif_dir: Path to a directory with mmCIF structures. Once a template ID
        is found by HHSearch, this directory is used to retrieve the template
        data.
      max_template_date: The maximum date permitted for template structures. No
        template with date higher than this date will be returned. In ISO8601
        date format, YYYY-MM-DD.
      max_hits: The maximum number of templates that will be returned.
      kalign_binary_path: The path to a kalign executable used for template
        realignment.
      release_dates_path: An optional path to a file with a mapping from PDB IDs
        to their release dates. Thanks to this we don't have to redundantly
        parse mmCIF files to get that information.
      obsolete_pdbs_path: An optional path to a file containing a mapping from
        obsolete PDB IDs to the PDB IDs of their replacements.
      strict_error_check: If True, then the following will be treated as errors:
        * If any template date is after the max_template_date.
        * If any template has identical PDB ID to the query.
        * If any template is a duplicate of the query.
        * Any feature computation errors.
    """"""
    self._mmcif_dir = mmcif_dir
    if not glob.glob(os.path.join(self._mmcif_dir, '*.cif')):
      logging.error('Could not find CIFs in %s', self._mmcif_dir)
      raise ValueError(f'Could not find CIFs in {self._mmcif_dir}')

    try:
      self._max_template_date = datetime.datetime.strptime(
          max_template_date, '%Y-%m-%d')
    except ValueError:
      raise ValueError(
          'max_template_date must be set and have format YYYY-MM-DD.')
    self._max_hits = max_hits
    self._kalign_binary_path = kalign_binary_path
    self._strict_error_check = strict_error_check

    if release_dates_path:
      logging.info('Using precomputed release dates %s.', release_dates_path)
      self._release_dates = _parse_release_dates(release_dates_path)
    else:
      self._release_dates = {}

    if obsolete_pdbs_path:
      logging.info('Using precomputed obsolete pdbs %s.', obsolete_pdbs_path)
      self._obsolete_pdbs = _parse_obsolete(obsolete_pdbs_path)
    else:
      self._obsolete_pdbs = {}

  @abc.abstractmethod
  def get_templates(
      self,
      query_sequence: str,
      hits: Sequence[parsers.TemplateHit]) -> TemplateSearchResult:
    """"""Computes the templates for given query sequence.""""""


class HhsearchHitFeaturizer(TemplateHitFeaturizer):
  """"""A class for turning a3m hits from hhsearch to template features.""""""

  def get_templates(
      self,
      query_sequence: str,
      hits: Sequence[parsers.TemplateHit]) -> TemplateSearchResult:
    """"""Computes the templates for given query sequence (more details above).""""""
    logging.info('Searching for template for: %s', query_sequence)

    template_features = {}
    for template_feature_name in TEMPLATE_FEATURES:
      template_features[template_feature_name] = []

    num_hits = 0
    errors = []
    warnings = []

    for hit in sorted(hits, key=lambda x: x.sum_probs, reverse=True):
      # We got all the templates we wanted, stop processing hits.
      if num_hits >= self._max_hits:
        break

      result = _process_single_hit(
          query_sequence=query_sequence,
          hit=hit,
          mmcif_dir=self._mmcif_dir,
          max_template_date=self._max_template_date,
          release_dates=self._release_dates,
          obsolete_pdbs=self._obsolete_pdbs,
          strict_error_check=self._strict_error_check,
          kalign_binary_path=self._kalign_binary_path)

      if result.error:
        errors.append(result.error)

      # There could be an error even if there are some results, e.g. thrown by
      # other unparsable chains in the same mmCIF file.
      if result.warning:
        warnings.append(result.warning)

      if result.features is None:
        logging.info('Skipped invalid hit %s, error: %s, warning: %s',
                     hit.name, result.error, result.warning)
      else:
        # Increment the hit counter, since we got features out of this hit.
        num_hits += 1
        for k in template_features:
          template_features[k].append(result.features[k])

    for name in template_features:
      if num_hits > 0:
        template_features[name] = np.stack(
            template_features[name], axis=0).astype(TEMPLATE_FEATURES[name])
      else:
        # Make sure the feature has correct dtype even if empty.
        template_features[name] = np.array([], dtype=TEMPLATE_FEATURES[name])

    return TemplateSearchResult(
        features=template_features, errors=errors, warnings=warnings)


class HmmsearchHitFeaturizer(TemplateHitFeaturizer):
  """"""A class for turning a3m hits from hmmsearch to template features.""""""

  def get_templates(
      self,
      query_sequence: str,
      hits: Sequence[parsers.TemplateHit]) -> TemplateSearchResult:
    """"""Computes the templates for given query sequence (more details above).""""""
    logging.info('Searching for template for: %s', query_sequence)

    template_features = {}
    for template_feature_name in TEMPLATE_FEATURES:
      template_features[template_feature_name] = []

    already_seen = set()
    errors = []
    warnings = []

    if not hits or hits[0].sum_probs is None:
      sorted_hits = hits
    else:
      sorted_hits = sorted(hits, key=lambda x: x.sum_probs, reverse=True)

    for hit in sorted_hits:
      # We got all the templates we wanted, stop processing hits.
      if len(already_seen) >= self._max_hits:
        break

      result = _process_single_hit(
          query_sequence=query_sequence,
          hit=hit,
          mmcif_dir=self._mmcif_dir,
          max_template_date=self._max_template_date,
          release_dates=self._release_dates,
          obsolete_pdbs=self._obsolete_pdbs,
          strict_error_check=self._strict_error_check,
          kalign_binary_path=self._kalign_binary_path)

      if result.error:
        errors.append(result.error)

      # There could be an error even if there are some results, e.g. thrown by
      # other unparsable chains in the same mmCIF file.
      if result.warning:
        warnings.append(result.warning)

      if result.features is None:
        logging.debug('Skipped invalid hit %s, error: %s, warning: %s',
                      hit.name, result.error, result.warning)
      else:
        already_seen_key = result.features['template_sequence']
        if already_seen_key in already_seen:
          continue
        # Increment the hit counter, since we got features out of this hit.
        already_seen.add(already_seen_key)
        for k in template_features:
          template_features[k].append(result.features[k])

    if already_seen:
      for name in template_features:
        template_features[name] = np.stack(
            template_features[name], axis=0).astype(TEMPLATE_FEATURES[name])
    else:
      num_res = len(query_sequence)
      # Construct a default template with all zeros.
      template_features = {
          'template_aatype': np.zeros(
              (1, num_res, len(residue_constants.restypes_with_x_and_gap)),
              np.float32),
          'template_all_atom_masks': np.zeros(
              (1, num_res, residue_constants.atom_type_num), np.float32),
          'template_all_atom_positions': np.zeros(
              (1, num_res, residue_constants.atom_type_num, 3), np.float32),
          'template_domain_names': np.array([''.encode()], dtype=object),
          'template_sequence': np.array([''.encode()], dtype=object),
          'template_sum_probs': np.array([0], dtype=np.float32)
      }
    return TemplateSearchResult(
        features=template_features, errors=errors, warnings=warnings)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/parsers.py",".py","22226","638","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Functions for parsing various file formats.""""""
import collections
import dataclasses
import itertools
import re
import string
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set

# Internal import (7716).


DeletionMatrix = Sequence[Sequence[int]]


@dataclasses.dataclass(frozen=True)
class Msa:
  """"""Class representing a parsed MSA file.""""""
  sequences: Sequence[str]
  deletion_matrix: DeletionMatrix
  descriptions: Sequence[str]

  def __post_init__(self):
    if not (len(self.sequences) ==
            len(self.deletion_matrix) ==
            len(self.descriptions)):
      raise ValueError(
          'All fields for an MSA must have the same length. '
          f'Got {len(self.sequences)} sequences, '
          f'{len(self.deletion_matrix)} rows in the deletion matrix and '
          f'{len(self.descriptions)} descriptions.')

  def __len__(self):
    return len(self.sequences)

  def truncate(self, max_seqs: int):
    return Msa(sequences=self.sequences[:max_seqs],
               deletion_matrix=self.deletion_matrix[:max_seqs],
               descriptions=self.descriptions[:max_seqs])


@dataclasses.dataclass(frozen=True)
class TemplateHit:
  """"""Class representing a template hit.""""""
  index: int
  name: str
  aligned_cols: int
  sum_probs: Optional[float]
  query: str
  hit_sequence: str
  indices_query: List[int]
  indices_hit: List[int]


def parse_fasta(fasta_string: str) -> Tuple[Sequence[str], Sequence[str]]:
  """"""Parses FASTA string and returns list of strings with amino-acid sequences.

  Arguments:
    fasta_string: The string contents of a FASTA file.

  Returns:
    A tuple of two lists:
    * A list of sequences.
    * A list of sequence descriptions taken from the comment lines. In the
      same order as the sequences.
  """"""
  sequences = []
  descriptions = []
  index = -1
  for line in fasta_string.splitlines():
    line = line.strip()
    if line.startswith('>'):
      index += 1
      descriptions.append(line[1:])  # Remove the '>' at the beginning.
      sequences.append('')
      continue
    elif not line:
      continue  # Skip blank lines.
    sequences[index] += line

  # Use gap to break a single chain
  for index,sequence in enumerate(sequences):
    if "":"" in sequence:
      sequences[index]=sequence.upper().split("":"")
      
  return sequences, descriptions


def parse_stockholm(stockholm_string: str) -> Msa:
  """"""Parses sequences and deletion matrix from stockholm format alignment.

  Args:
    stockholm_string: The string contents of a stockholm file. The first
      sequence in the file should be the query sequence.

  Returns:
    A tuple of:
      * A list of sequences that have been aligned to the query. These
        might contain duplicates.
      * The deletion matrix for the alignment as a list of lists. The element
        at `deletion_matrix[i][j]` is the number of residues deleted from
        the aligned sequence i at residue position j.
      * The names of the targets matched, including the jackhmmer subsequence
        suffix.
  """"""
  name_to_sequence = collections.OrderedDict()
  for line in stockholm_string.splitlines():
    line = line.strip()
    if not line or line.startswith(('#', '//')):
      continue
    name, sequence = line.split()
    if name not in name_to_sequence:
      name_to_sequence[name] = ''
    name_to_sequence[name] += sequence

  msa = []
  deletion_matrix = []

  query = ''
  keep_columns = []
  for seq_index, sequence in enumerate(name_to_sequence.values()):
    if seq_index == 0:
      # Gather the columns with gaps from the query
      query = sequence
      keep_columns = [i for i, res in enumerate(query) if res != '-']

    # Remove the columns with gaps in the query from all sequences.
    aligned_sequence = ''.join([sequence[c] for c in keep_columns])

    msa.append(aligned_sequence)

    # Count the number of deletions w.r.t. query.
    deletion_vec = []
    deletion_count = 0
    for seq_res, query_res in zip(sequence, query):
      if seq_res != '-' or query_res != '-':
        if query_res == '-':
          deletion_count += 1
        else:
          deletion_vec.append(deletion_count)
          deletion_count = 0
    deletion_matrix.append(deletion_vec)

  return Msa(sequences=msa,
             deletion_matrix=deletion_matrix,
             descriptions=list(name_to_sequence.keys()))


def parse_a3m(a3m_string: str) -> Msa:
  """"""Parses sequences and deletion matrix from a3m format alignment.

  Args:
    a3m_string: The string contents of a a3m file. The first sequence in the
      file should be the query sequence.

  Returns:
    A tuple of:
      * A list of sequences that have been aligned to the query. These
        might contain duplicates.
      * The deletion matrix for the alignment as a list of lists. The element
        at `deletion_matrix[i][j]` is the number of residues deleted from
        the aligned sequence i at residue position j.
      * A list of descriptions, one per sequence, from the a3m file.
  """"""
  sequences, descriptions = parse_fasta(a3m_string)
  deletion_matrix = []
  for msa_sequence in sequences:
    deletion_vec = []
    deletion_count = 0
    for j in msa_sequence:
      if j.islower():
        deletion_count += 1
      else:
        deletion_vec.append(deletion_count)
        deletion_count = 0
    deletion_matrix.append(deletion_vec)

  # Make the MSA matrix out of aligned (deletion-free) sequences.
  deletion_table = str.maketrans('', '', string.ascii_lowercase)
  aligned_sequences = [s.translate(deletion_table) for s in sequences]
  return Msa(sequences=aligned_sequences,
             deletion_matrix=deletion_matrix,
             descriptions=descriptions)


def _convert_sto_seq_to_a3m(
    query_non_gaps: Sequence[bool], sto_seq: str) -> Iterable[str]:
  for is_query_res_non_gap, sequence_res in zip(query_non_gaps, sto_seq):
    if is_query_res_non_gap:
      yield sequence_res
    elif sequence_res != '-':
      yield sequence_res.lower()


def convert_stockholm_to_a3m(stockholm_format: str,
                             max_sequences: Optional[int] = None,
                             remove_first_row_gaps: bool = True) -> str:
  """"""Converts MSA in Stockholm format to the A3M format.""""""
  descriptions = {}
  sequences = {}
  reached_max_sequences = False

  for line in stockholm_format.splitlines():
    reached_max_sequences = max_sequences and len(sequences) >= max_sequences
    if line.strip() and not line.startswith(('#', '//')):
      # Ignore blank lines, markup and end symbols - remainder are alignment
      # sequence parts.
      seqname, aligned_seq = line.split(maxsplit=1)
      if seqname not in sequences:
        if reached_max_sequences:
          continue
        sequences[seqname] = ''
      sequences[seqname] += aligned_seq

  for line in stockholm_format.splitlines():
    if line[:4] == '#=GS':
      # Description row - example format is:
      # #=GS UniRef90_Q9H5Z4/4-78            DE [subseq from] cDNA: FLJ22755 ...
      columns = line.split(maxsplit=3)
      seqname, feature = columns[1:3]
      value = columns[3] if len(columns) == 4 else ''
      if feature != 'DE':
        continue
      if reached_max_sequences and seqname not in sequences:
        continue
      descriptions[seqname] = value
      if len(descriptions) == len(sequences):
        break

  # Convert sto format to a3m line by line
  a3m_sequences = {}
  if remove_first_row_gaps:
    # query_sequence is assumed to be the first sequence
    query_sequence = next(iter(sequences.values()))
    query_non_gaps = [res != '-' for res in query_sequence]
  for seqname, sto_sequence in sequences.items():
    # Dots are optional in a3m format and are commonly removed.
    out_sequence = sto_sequence.replace('.', '')
    if remove_first_row_gaps:
      out_sequence = ''.join(
          _convert_sto_seq_to_a3m(query_non_gaps, out_sequence))
    a3m_sequences[seqname] = out_sequence

  fasta_chunks = (f"">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}""
                  for k in a3m_sequences)
  return '\n'.join(fasta_chunks) + '\n'  # Include terminating newline.


def _keep_line(line: str, seqnames: Set[str]) -> bool:
  """"""Function to decide which lines to keep.""""""
  if not line.strip():
    return True
  if line.strip() == '//':  # End tag
    return True
  if line.startswith('# STOCKHOLM'):  # Start tag
    return True
  if line.startswith('#=GC RF'):  # Reference Annotation Line
    return True
  if line[:4] == '#=GS':  # Description lines - keep if sequence in list.
    _, seqname, _ = line.split(maxsplit=2)
    return seqname in seqnames
  elif line.startswith('#'):  # Other markup - filter out
    return False
  else:  # Alignment data - keep if sequence in list.
    seqname = line.partition(' ')[0]
    return seqname in seqnames


def truncate_stockholm_msa(stockholm_msa_path: str, max_sequences: int) -> str:
  """"""Reads + truncates a Stockholm file while preventing excessive RAM usage.""""""
  seqnames = set()
  filtered_lines = []

  with open(stockholm_msa_path) as f:
    for line in f:
      if line.strip() and not line.startswith(('#', '//')):
        # Ignore blank lines, markup and end symbols - remainder are alignment
        # sequence parts.
        seqname = line.partition(' ')[0]
        seqnames.add(seqname)
        if len(seqnames) >= max_sequences:
          break

    f.seek(0)
    for line in f:
      if _keep_line(line, seqnames):
        filtered_lines.append(line)

  return ''.join(filtered_lines)

def truncate_stockholm_msa2(stockholm_msa: str, max_sequences: int) -> str:
  """"""Reads + truncates a Stockholm alignment - BP 12/15/22.""""""
  seqnames = set()
  filtered_lines = []
  
  for line in stockholm_msa.splitlines():    
    if line.strip() and not line.startswith(('#', '//')):
      # Ignore blank lines, markup and end symbols - remainder are alignment
      # sequence parts.
      seqname = line.partition(' ')[0]
      seqnames.add(seqname)
      if len(seqnames) >= max_sequences:
        break

  for line in stockholm_msa.splitlines():
    if _keep_line(line, seqnames):
      filtered_lines.append(line)

  return '\n'.join(filtered_lines) + '\n'

def remove_empty_columns_from_stockholm_msa(stockholm_msa: str) -> str:
  """"""Removes empty columns (dashes-only) from a Stockholm MSA.""""""
  processed_lines = {}
  unprocessed_lines = {}
  for i, line in enumerate(stockholm_msa.splitlines()):
    if line.startswith('#=GC RF'):
      reference_annotation_i = i
      reference_annotation_line = line
      # Reached the end of this chunk of the alignment. Process chunk.
      _, _, first_alignment = line.rpartition(' ')
      mask = []
      for j in range(len(first_alignment)):
        for _, unprocessed_line in unprocessed_lines.items():
          prefix, _, alignment = unprocessed_line.rpartition(' ')
          if alignment[j] != '-':
            mask.append(True)
            break
        else:  # Every row contained a hyphen - empty column.
          mask.append(False)
      # Add reference annotation for processing with mask.
      unprocessed_lines[reference_annotation_i] = reference_annotation_line

      if not any(mask):  # All columns were empty. Output empty lines for chunk.
        for line_index in unprocessed_lines:
          processed_lines[line_index] = ''
      else:
        for line_index, unprocessed_line in unprocessed_lines.items():
          prefix, _, alignment = unprocessed_line.rpartition(' ')
          masked_alignment = ''.join(itertools.compress(alignment, mask))
          processed_lines[line_index] = f'{prefix} {masked_alignment}'

      # Clear raw_alignments.
      unprocessed_lines = {}
    elif line.strip() and not line.startswith(('#', '//')):
      unprocessed_lines[i] = line
    else:
      processed_lines[i] = line
  return '\n'.join((processed_lines[i] for i in range(len(processed_lines))))


def deduplicate_stockholm_msa(stockholm_msa: str) -> str:
  """"""Remove duplicate sequences (ignoring insertions wrt query).""""""
  sequence_dict = collections.defaultdict(str)

  # First we must extract all sequences from the MSA.
  for line in stockholm_msa.splitlines():
    # Only consider the alignments - ignore reference annotation, empty lines,
    # descriptions or markup.
    if line.strip() and not line.startswith(('#', '//')):
      line = line.strip()
      seqname, alignment = line.split()
      sequence_dict[seqname] += alignment

  seen_sequences = set()
  seqnames = set()
  # First alignment is the query.
  query_align = next(iter(sequence_dict.values()))
  mask = [c != '-' for c in query_align]  # Mask is False for insertions.
  for seqname, alignment in sequence_dict.items():
    # Apply mask to remove all insertions from the string.
    masked_alignment = ''.join(itertools.compress(alignment, mask))
    if masked_alignment in seen_sequences:
      continue
    else:
      seen_sequences.add(masked_alignment)
      seqnames.add(seqname)

  filtered_lines = []
  for line in stockholm_msa.splitlines():
    if _keep_line(line, seqnames):
      filtered_lines.append(line)

  return '\n'.join(filtered_lines) + '\n'


def _get_hhr_line_regex_groups(
    regex_pattern: str, line: str) -> Sequence[Optional[str]]:
  match = re.match(regex_pattern, line)
  if match is None:
    raise RuntimeError(f'Could not parse query line {line}')
  return match.groups()


def _update_hhr_residue_indices_list(
    sequence: str, start_index: int, indices_list: List[int]):
  """"""Computes the relative indices for each residue with respect to the original sequence.""""""
  counter = start_index
  for symbol in sequence:
    if symbol == '-':
      indices_list.append(-1)
    else:
      indices_list.append(counter)
      counter += 1


def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
  """"""Parses the detailed HMM HMM comparison section for a single Hit.

  This works on .hhr files generated from both HHBlits and HHSearch.

  Args:
    detailed_lines: A list of lines from a single comparison section between 2
      sequences (which each have their own HMM's)

  Returns:
    A dictionary with the information from that detailed comparison section

  Raises:
    RuntimeError: If a certain line cannot be processed
  """"""
  # Parse first 2 lines.
  number_of_hit = int(detailed_lines[0].split()[-1])
  name_hit = detailed_lines[1][1:]

  # Parse the summary line.
  pattern = (
      'Probab=(.*)[\t ]*E-value=(.*)[\t ]*Score=(.*)[\t ]*Aligned_cols=(.*)[\t'
      ' ]*Identities=(.*)%[\t ]*Similarity=(.*)[\t ]*Sum_probs=(.*)[\t '
      ']*Template_Neff=(.*)')
  match = re.match(pattern, detailed_lines[2])
  if match is None:
    raise RuntimeError(
        'Could not parse section: %s. Expected this: \n%s to contain summary.' %
        (detailed_lines, detailed_lines[2]))
  (_, _, _, aligned_cols, _, _, sum_probs, _) = [float(x)
                                                 for x in match.groups()]

  # The next section reads the detailed comparisons. These are in a 'human
  # readable' format which has a fixed length. The strategy employed is to
  # assume that each block starts with the query sequence line, and to parse
  # that with a regexp in order to deduce the fixed length used for that block.
  query = ''
  hit_sequence = ''
  indices_query = []
  indices_hit = []
  length_block = None

  for line in detailed_lines[3:]:
    # Parse the query sequence line
    if (line.startswith('Q ') and not line.startswith('Q ss_dssp') and
        not line.startswith('Q ss_pred') and
        not line.startswith('Q Consensus')):
      # Thus the first 17 characters must be 'Q <query_name> ', and we can parse
      # everything after that.
      #              start    sequence       end       total_sequence_length
      patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*([0-9]*) \([0-9]*\)'
      groups = _get_hhr_line_regex_groups(patt, line[17:])

      # Get the length of the parsed block using the start and finish indices,
      # and ensure it is the same as the actual block length.
      start = int(groups[0]) - 1  # Make index zero based.
      delta_query = groups[1]
      end = int(groups[2])
      num_insertions = len([x for x in delta_query if x == '-'])
      length_block = end - start + num_insertions
      assert length_block == len(delta_query)

      # Update the query sequence and indices list.
      query += delta_query
      _update_hhr_residue_indices_list(delta_query, start, indices_query)

    elif line.startswith('T '):
      # Parse the hit sequence.
      if (not line.startswith('T ss_dssp') and
          not line.startswith('T ss_pred') and
          not line.startswith('T Consensus')):
        # Thus the first 17 characters must be 'T <hit_name> ', and we can
        # parse everything after that.
        #              start    sequence       end     total_sequence_length
        patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*[0-9]* \([0-9]*\)'
        groups = _get_hhr_line_regex_groups(patt, line[17:])
        start = int(groups[0]) - 1  # Make index zero based.
        delta_hit_sequence = groups[1]
        assert length_block == len(delta_hit_sequence)

        # Update the hit sequence and indices list.
        hit_sequence += delta_hit_sequence
        _update_hhr_residue_indices_list(delta_hit_sequence, start, indices_hit)

  return TemplateHit(
      index=number_of_hit,
      name=name_hit,
      aligned_cols=int(aligned_cols),
      sum_probs=sum_probs,
      query=query,
      hit_sequence=hit_sequence,
      indices_query=indices_query,
      indices_hit=indices_hit,
  )


def parse_hhr(hhr_string: str) -> Sequence[TemplateHit]:
  """"""Parses the content of an entire HHR file.""""""
  lines = hhr_string.splitlines()

  # Each .hhr file starts with a results table, then has a sequence of hit
  # ""paragraphs"", each paragraph starting with a line 'No <hit number>'. We
  # iterate through each paragraph to parse each hit.

  block_starts = [i for i, line in enumerate(lines) if line.startswith('No ')]

  hits = []
  if block_starts:
    block_starts.append(len(lines))  # Add the end of the final block.
    for i in range(len(block_starts) - 1):
      hits.append(_parse_hhr_hit(lines[block_starts[i]:block_starts[i + 1]]))
  return hits


def parse_e_values_from_tblout(tblout: str) -> Dict[str, float]:
  """"""Parse target to e-value mapping parsed from Jackhmmer tblout string.""""""
  e_values = {'query': 0}
  lines = [line for line in tblout.splitlines() if line[0] != '#']
  # As per http://eddylab.org/software/hmmer/Userguide.pdf fields are
  # space-delimited. Relevant fields are (1) target name:  and
  # (5) E-value (full sequence) (numbering from 1).
  for line in lines:
    fields = line.split()
    e_value = fields[4]
    target_name = fields[0]
    e_values[target_name] = float(e_value)
  return e_values


def _get_indices(sequence: str, start: int) -> List[int]:
  """"""Returns indices for non-gap/insert residues starting at the given index.""""""
  indices = []
  counter = start
  for symbol in sequence:
    # Skip gaps but add a placeholder so that the alignment is preserved.
    if symbol == '-':
      indices.append(-1)
    # Skip deleted residues, but increase the counter.
    elif symbol.islower():
      counter += 1
    # Normal aligned residue. Increase the counter and append to indices.
    else:
      indices.append(counter)
      counter += 1
  return indices


@dataclasses.dataclass(frozen=True)
class HitMetadata:
  pdb_id: str
  chain: str
  start: int
  end: int
  length: int
  text: str


def _parse_hmmsearch_description(description: str) -> HitMetadata:
  """"""Parses the hmmsearch A3M sequence description line.""""""
  # Example 1: >4pqx_A/2-217 [subseq from] mol:protein length:217  Free text
  # Example 2: >5g3r_A/1-55 [subseq from] mol:protein length:352
  match = re.match(
      r'^>?([a-z0-9]+)_(\w+)/([0-9]+)-([0-9]+).*protein length:([0-9]+) *(.*)$',
      description.strip())

  if not match:
    raise ValueError(f'Could not parse description: ""{description}"".')

  return HitMetadata(
      pdb_id=match[1],
      chain=match[2],
      start=int(match[3]),
      end=int(match[4]),
      length=int(match[5]),
      text=match[6])


def parse_hmmsearch_a3m(query_sequence: str,
                        a3m_string: str,
                        skip_first: bool = True) -> Sequence[TemplateHit]:
  """"""Parses an a3m string produced by hmmsearch.

  Args:
    query_sequence: The query sequence.
    a3m_string: The a3m string produced by hmmsearch.
    skip_first: Whether to skip the first sequence in the a3m string.

  Returns:
    A sequence of `TemplateHit` results.
  """"""
  # Zip the descriptions and MSAs together, skip the first query sequence.
  parsed_a3m = list(zip(*parse_fasta(a3m_string)))
  if skip_first:
    parsed_a3m = parsed_a3m[1:]

  indices_query = _get_indices(query_sequence, start=0)

  hits = []
  for i, (hit_sequence, hit_description) in enumerate(parsed_a3m, start=1):
    if 'mol:protein' not in hit_description:
      continue  # Skip non-protein chains.
    metadata = _parse_hmmsearch_description(hit_description)
    # Aligned columns are only the match states.
    aligned_cols = sum([r.isupper() and r != '-' for r in hit_sequence])
    indices_hit = _get_indices(hit_sequence, start=metadata.start - 1)

    hit = TemplateHit(
        index=i,
        name=f'{metadata.pdb_id}_{metadata.chain}',
        aligned_cols=aligned_cols,
        sum_probs=None,
        query=query_sequence,
        hit_sequence=hit_sequence.upper(),
        indices_query=indices_query,
        indices_hit=indices_hit,
    )
    hits.append(hit)

  return hits
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/msa_identifiers.py",".py","2966","91","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Utilities for extracting identifiers from MSA sequence descriptions.""""""

import dataclasses
import re
from typing import Optional


# Sequences coming from UniProtKB database come in the
# `db|UniqueIdentifier|EntryName` format, e.g. `tr|A0A146SKV9|A0A146SKV9_FUNHE`
# or `sp|P0C2L1|A3X1_LOXLA` (for TREMBL/Swiss-Prot respectively).
_UNIPROT_PATTERN = re.compile(
    r""""""
    ^
    # UniProtKB/TrEMBL or UniProtKB/Swiss-Prot
    (?:tr|sp)
    \|
    # A primary accession number of the UniProtKB entry.
    (?P<AccessionIdentifier>[A-Za-z0-9]{6,10})
    # Occasionally there is a _0 or _1 isoform suffix, which we ignore.
    (?:_\d)?
    \|
    # TREMBL repeats the accession ID here. Swiss-Prot has a mnemonic
    # protein ID code.
    (?:[A-Za-z0-9]+)
    _
    # A mnemonic species identification code.
    (?P<SpeciesIdentifier>([A-Za-z0-9]){1,5})
    # Small BFD uses a final value after an underscore, which we ignore.
    (?:_\d+)?
    $
    """""",
    re.VERBOSE)


@dataclasses.dataclass(frozen=True)
class Identifiers:
  species_id: str = ''


def _parse_sequence_identifier(msa_sequence_identifier: str) -> Identifiers:
  """"""Gets species from an msa sequence identifier.

  The sequence identifier has the format specified by
  _UNIPROT_TREMBL_ENTRY_NAME_PATTERN or _UNIPROT_SWISSPROT_ENTRY_NAME_PATTERN.
  An example of a sequence identifier: `tr|A0A146SKV9|A0A146SKV9_FUNHE`

  Args:
    msa_sequence_identifier: a sequence identifier.

  Returns:
    An `Identifiers` instance with species_id. These
    can be empty in the case where no identifier was found.
  """"""
  matches = re.search(_UNIPROT_PATTERN, msa_sequence_identifier.strip())
  if matches:
    return Identifiers(
        species_id=matches.group('SpeciesIdentifier'))
  return Identifiers()


def _extract_sequence_identifier(description: str) -> Optional[str]:
  """"""Extracts sequence identifier from description. Returns None if no match.""""""
  split_description = description.split()
  if split_description:
    return split_description[0].partition('/')[0]
  else:
    return None


def get_identifiers(description: str) -> Identifiers:
  """"""Computes extra MSA features from the description.""""""
  sequence_identifier = _extract_sequence_identifier(description)
  if sequence_identifier is None:
    return Identifiers()
  else:
    return _parse_sequence_identifier(sequence_identifier)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/jackhmmer.py",".py","8386","222","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Library to run Jackhmmer from Python.""""""

from concurrent import futures
import glob
import os
import subprocess
from typing import Any, Callable, Mapping, Optional, Sequence
from urllib import request

from absl import logging

from alphafold.data import parsers
from alphafold.data.tools import utils
# Internal import (7716).


class Jackhmmer:
  """"""Python wrapper of the Jackhmmer binary.""""""

  def __init__(self,
               *,
               binary_path: str,
               database_path: str,
               n_cpu: int = 8,
               n_iter: int = 1,
               e_value: float = 0.0001,
               z_value: Optional[int] = None,
               get_tblout: bool = False,
               filter_f1: float = 0.0005,
               filter_f2: float = 0.00005,
               filter_f3: float = 0.0000005,
               incdom_e: Optional[float] = None,
               dom_e: Optional[float] = None,
               num_streamed_chunks: Optional[int] = None,
               streaming_callback: Optional[Callable[[int], None]] = None):
    """"""Initializes the Python Jackhmmer wrapper.

    Args:
      binary_path: The path to the jackhmmer executable.
      database_path: The path to the jackhmmer database (FASTA format).
      n_cpu: The number of CPUs to give Jackhmmer.
      n_iter: The number of Jackhmmer iterations.
      e_value: The E-value, see Jackhmmer docs for more details.
      z_value: The Z-value, see Jackhmmer docs for more details.
      get_tblout: Whether to save tblout string.
      filter_f1: MSV and biased composition pre-filter, set to >1.0 to turn off.
      filter_f2: Viterbi pre-filter, set to >1.0 to turn off.
      filter_f3: Forward pre-filter, set to >1.0 to turn off.
      incdom_e: Domain e-value criteria for inclusion of domains in MSA/next
        round.
      dom_e: Domain e-value criteria for inclusion in tblout.
      num_streamed_chunks: Number of database chunks to stream over.
      streaming_callback: Callback function run after each chunk iteration with
        the iteration number as argument.
    """"""
    self.binary_path = binary_path
    self.database_path = database_path
    self.num_streamed_chunks = num_streamed_chunks

    if not os.path.exists(self.database_path) and num_streamed_chunks is None:
      logging.error('Could not find Jackhmmer database %s', database_path)
      raise ValueError(f'Could not find Jackhmmer database {database_path}')

    self.n_cpu = n_cpu
    self.n_iter = n_iter
    self.e_value = e_value
    self.z_value = z_value
    self.filter_f1 = filter_f1
    self.filter_f2 = filter_f2
    self.filter_f3 = filter_f3
    self.incdom_e = incdom_e
    self.dom_e = dom_e
    self.get_tblout = get_tblout
    self.streaming_callback = streaming_callback

  def _query_chunk(self,
                   input_fasta_path: str,
                   database_path: str,
                   max_sequences: Optional[int] = None) -> Mapping[str, Any]:
    """"""Queries the database chunk using Jackhmmer.""""""
    with utils.tmpdir_manager() as query_tmp_dir:
      sto_path = os.path.join(query_tmp_dir, 'output.sto')

      # The F1/F2/F3 are the expected proportion to pass each of the filtering
      # stages (which get progressively more expensive), reducing these
      # speeds up the pipeline at the expensive of sensitivity.  They are
      # currently set very low to make querying Mgnify run in a reasonable
      # amount of time.
      cmd_flags = [
          # Don't pollute stdout with Jackhmmer output.
          '-o', '/dev/null',
          '-A', sto_path,
          '--noali',
          '--F1', str(self.filter_f1),
          '--F2', str(self.filter_f2),
          '--F3', str(self.filter_f3),
          '--incE', str(self.e_value),
          # Report only sequences with E-values <= x in per-sequence output.
          '-E', str(self.e_value),
          '--cpu', str(self.n_cpu),
          '-N', str(self.n_iter)
      ]
      if self.get_tblout:
        tblout_path = os.path.join(query_tmp_dir, 'tblout.txt')
        cmd_flags.extend(['--tblout', tblout_path])

      if self.z_value:
        cmd_flags.extend(['-Z', str(self.z_value)])

      if self.dom_e is not None:
        cmd_flags.extend(['--domE', str(self.dom_e)])

      if self.incdom_e is not None:
        cmd_flags.extend(['--incdomE', str(self.incdom_e)])

      cmd = [self.binary_path] + cmd_flags + [input_fasta_path,
                                              database_path]

      logging.info('Launching subprocess ""%s""', ' '.join(cmd))
      process = subprocess.Popen(
          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
      with utils.timing(
          f'Jackhmmer ({os.path.basename(database_path)}) query'):
        _, stderr = process.communicate()
        retcode = process.wait()

      if retcode:
        raise RuntimeError(
            'Jackhmmer failed\nstderr:\n%s\n' % stderr.decode('utf-8'))

      # Get e-values for each target name
      tbl = ''
      if self.get_tblout:
        with open(tblout_path) as f:
          tbl = f.read()

      if max_sequences is None:
        with open(sto_path) as f:
          sto = f.read()
      else:
        sto = parsers.truncate_stockholm_msa(sto_path, max_sequences)

    raw_output = dict(
        sto=sto,
        tbl=tbl,
        stderr=stderr,
        n_iter=self.n_iter,
        e_value=self.e_value)

    return raw_output

  def query(self,
            input_fasta_path: str,
            max_sequences: Optional[int] = None) -> Sequence[Mapping[str, Any]]:
    """"""Queries the database using Jackhmmer.""""""
    return self.query_multiple([input_fasta_path], max_sequences)[0]

  def query_multiple(
      self,
      input_fasta_paths: Sequence[str],
      max_sequences: Optional[int] = None,
    ) -> Sequence[Sequence[Mapping[str, Any]]]:
    """"""Queries the database for multiple queries using Jackhmmer.""""""
    if self.num_streamed_chunks is None:
      single_chunk_results = []
      for input_fasta_path in input_fasta_paths:
        single_chunk_results.append([self._query_chunk(
            input_fasta_path, self.database_path, max_sequences)])
      return single_chunk_results

    db_basename = os.path.basename(self.database_path)
    db_remote_chunk = lambda db_idx: f'{self.database_path}.{db_idx}'
    db_local_chunk = lambda db_idx: f'/tmp/ramdisk/{db_basename}.{db_idx}'

    # Remove existing files to prevent OOM
    for f in glob.glob(db_local_chunk('[0-9]*')):
      try:
        os.remove(f)
      except OSError:
        print(f'OSError while deleting {f}')

    # Download the (i+1)-th chunk while Jackhmmer is running on the i-th chunk
    with futures.ThreadPoolExecutor(max_workers=2) as executor:
      chunked_outputs = [[] for _ in range(len(input_fasta_paths))]
      for i in range(1, self.num_streamed_chunks + 1):
        # Copy the chunk locally
        if i == 1:
          future = executor.submit(
              request.urlretrieve, db_remote_chunk(i), db_local_chunk(i))
        if i < self.num_streamed_chunks:
          next_future = executor.submit(
              request.urlretrieve, db_remote_chunk(i+1), db_local_chunk(i+1))

        # Run Jackhmmer with the chunk
        future.result()
        for fasta_index, input_fasta_path in enumerate(input_fasta_paths):
          chunked_outputs[fasta_index].append(self._query_chunk(
              input_fasta_path, db_local_chunk(i), max_sequences))
        # Remove the local copy of the chunk
        os.remove(db_local_chunk(i))
        # Do not set next_future for the last chunk so that this works even for
        # databases with only 1 chunk.
        if i < self.num_streamed_chunks:
          future = next_future
        if self.streaming_callback:
          self.streaming_callback(i)
    return chunked_outputs
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/__init__.py",".py","639","15","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Python wrappers for third party tools.""""""
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/hmmbuild.py",".py","4576","139","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A Python wrapper for hmmbuild - construct HMM profiles from MSA.""""""

import os
import re
import subprocess

from absl import logging
from alphafold.data.tools import utils
# Internal import (7716).


class Hmmbuild(object):
  """"""Python wrapper of the hmmbuild binary.""""""

  def __init__(self,
               *,
               binary_path: str,
               singlemx: bool = False):
    """"""Initializes the Python hmmbuild wrapper.

    Args:
      binary_path: The path to the hmmbuild executable.
      singlemx: Whether to use --singlemx flag. If True, it forces HMMBuild to
        just use a common substitution score matrix.

    Raises:
      RuntimeError: If hmmbuild binary not found within the path.
    """"""
    self.binary_path = binary_path
    self.singlemx = singlemx

  def build_profile_from_sto(self, sto: str, model_construction='fast') -> str:
    """"""Builds a HHM for the aligned sequences given as an A3M string.

    Args:
      sto: A string with the aligned sequences in the Stockholm format.
      model_construction: Whether to use reference annotation in the msa to
        determine consensus columns ('hand') or default ('fast').

    Returns:
      A string with the profile in the HMM format.

    Raises:
      RuntimeError: If hmmbuild fails.
    """"""
    return self._build_profile(sto, model_construction=model_construction)

  def build_profile_from_a3m(self, a3m: str) -> str:
    """"""Builds a HHM for the aligned sequences given as an A3M string.

    Args:
      a3m: A string with the aligned sequences in the A3M format.

    Returns:
      A string with the profile in the HMM format.

    Raises:
      RuntimeError: If hmmbuild fails.
    """"""
    lines = []
    for line in a3m.splitlines():
      if not line.startswith('>'):
        line = re.sub('[a-z]+', '', line)  # Remove inserted residues.
      lines.append(line + '\n')
    msa = ''.join(lines)
    return self._build_profile(msa, model_construction='fast')

  def _build_profile(self, msa: str, model_construction: str = 'fast') -> str:
    """"""Builds a HMM for the aligned sequences given as an MSA string.

    Args:
      msa: A string with the aligned sequences, in A3M or STO format.
      model_construction: Whether to use reference annotation in the msa to
        determine consensus columns ('hand') or default ('fast').

    Returns:
      A string with the profile in the HMM format.

    Raises:
      RuntimeError: If hmmbuild fails.
      ValueError: If unspecified arguments are provided.
    """"""
    if model_construction not in {'hand', 'fast'}:
      raise ValueError(f'Invalid model_construction {model_construction} - only'
                       'hand and fast supported.')

    with utils.tmpdir_manager() as query_tmp_dir:
      input_query = os.path.join(query_tmp_dir, 'query.msa')
      output_hmm_path = os.path.join(query_tmp_dir, 'output.hmm')

      with open(input_query, 'w') as f:
        f.write(msa)

      cmd = [self.binary_path]
      # If adding flags, we have to do so before the output and input:

      if model_construction == 'hand':
        cmd.append(f'--{model_construction}')
      if self.singlemx:
        cmd.append('--singlemx')
      cmd.extend([
          '--amino',
          output_hmm_path,
          input_query,
      ])

      logging.info('Launching subprocess %s', cmd)
      process = subprocess.Popen(cmd, stdout=subprocess.PIPE,
                                 stderr=subprocess.PIPE)

      with utils.timing('hmmbuild query'):
        stdout, stderr = process.communicate()
        retcode = process.wait()
        logging.info('hmmbuild stdout:\n%s\n\nstderr:\n%s\n',
                     stdout.decode('utf-8'), stderr.decode('utf-8'))

      if retcode:
        raise RuntimeError('hmmbuild failed\nstdout:\n%s\n\nstderr:\n%s\n'
                           % (stdout.decode('utf-8'), stderr.decode('utf-8')))

      with open(output_hmm_path, encoding='utf-8') as f:
        hmm = f.read()

    return hmm
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/hhblits.py",".py","5504","156","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Library to run HHblits from Python.""""""

import glob
import os
import subprocess
from typing import Any, List, Mapping, Optional, Sequence

from absl import logging
from alphafold.data.tools import utils
# Internal import (7716).


_HHBLITS_DEFAULT_P = 20
_HHBLITS_DEFAULT_Z = 500


class HHBlits:
  """"""Python wrapper of the HHblits binary.""""""

  def __init__(self,
               *,
               binary_path: str,
               databases: Sequence[str],
               n_cpu: int = 4,
               n_iter: int = 3,
               e_value: float = 0.001,
               maxseq: int = 1_000_000,
               realign_max: int = 100_000,
               maxfilt: int = 100_000,
               min_prefilter_hits: int = 1000,
               all_seqs: bool = False,
               alt: Optional[int] = None,
               p: int = _HHBLITS_DEFAULT_P,
               z: int = _HHBLITS_DEFAULT_Z):
    """"""Initializes the Python HHblits wrapper.

    Args:
      binary_path: The path to the HHblits executable.
      databases: A sequence of HHblits database paths. This should be the
        common prefix for the database files (i.e. up to but not including
        _hhm.ffindex etc.)
      n_cpu: The number of CPUs to give HHblits.
      n_iter: The number of HHblits iterations.
      e_value: The E-value, see HHblits docs for more details.
      maxseq: The maximum number of rows in an input alignment. Note that this
        parameter is only supported in HHBlits version 3.1 and higher.
      realign_max: Max number of HMM-HMM hits to realign. HHblits default: 500.
      maxfilt: Max number of hits allowed to pass the 2nd prefilter.
        HHblits default: 20000.
      min_prefilter_hits: Min number of hits to pass prefilter.
        HHblits default: 100.
      all_seqs: Return all sequences in the MSA / Do not filter the result MSA.
        HHblits default: False.
      alt: Show up to this many alternative alignments.
      p: Minimum Prob for a hit to be included in the output hhr file.
        HHblits default: 20.
      z: Hard cap on number of hits reported in the hhr file.
        HHblits default: 500. NB: The relevant HHblits flag is -Z not -z.

    Raises:
      RuntimeError: If HHblits binary not found within the path.
    """"""
    self.binary_path = binary_path
    self.databases = databases

    for database_path in self.databases:
      if not glob.glob(database_path + '_*'):
        logging.error('Could not find HHBlits database %s', database_path)
        raise ValueError(f'Could not find HHBlits database {database_path}')

    self.n_cpu = n_cpu
    self.n_iter = n_iter
    self.e_value = e_value
    self.maxseq = maxseq
    self.realign_max = realign_max
    self.maxfilt = maxfilt
    self.min_prefilter_hits = min_prefilter_hits
    self.all_seqs = all_seqs
    self.alt = alt
    self.p = p
    self.z = z

  def query(self, input_fasta_path: str) -> List[Mapping[str, Any]]:
    """"""Queries the database using HHblits.""""""
    with utils.tmpdir_manager() as query_tmp_dir:
      a3m_path = os.path.join(query_tmp_dir, 'output.a3m')

      db_cmd = []
      for db_path in self.databases:
        db_cmd.append('-d')
        db_cmd.append(db_path)
      cmd = [
          self.binary_path,
          '-i', input_fasta_path,
          '-cpu', str(self.n_cpu),
          '-oa3m', a3m_path,
          '-o', '/dev/null',
          '-n', str(self.n_iter),
          '-e', str(self.e_value),
          '-maxseq', str(self.maxseq),
          '-realign_max', str(self.realign_max),
          '-maxfilt', str(self.maxfilt),
          '-min_prefilter_hits', str(self.min_prefilter_hits)]
      if self.all_seqs:
        cmd += ['-all']
      if self.alt:
        cmd += ['-alt', str(self.alt)]
      if self.p != _HHBLITS_DEFAULT_P:
        cmd += ['-p', str(self.p)]
      if self.z != _HHBLITS_DEFAULT_Z:
        cmd += ['-Z', str(self.z)]
      cmd += db_cmd

      logging.info('Launching subprocess ""%s""', ' '.join(cmd))
      process = subprocess.Popen(
          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

      with utils.timing('HHblits query'):
        stdout, stderr = process.communicate()
        retcode = process.wait()

      if retcode:
        # Logs have a 15k character limit, so log HHblits error line by line.
        logging.error('HHblits failed. HHblits stderr begin:')
        for error_line in stderr.decode('utf-8').splitlines():
          if error_line.strip():
            logging.error(error_line.strip())
        logging.error('HHblits stderr end')
        raise RuntimeError('HHblits failed\nstdout:\n%s\n\nstderr:\n%s\n' % (
            stdout.decode('utf-8'), stderr[:500_000].decode('utf-8')))

      with open(a3m_path) as f:
        a3m = f.read()

    raw_output = dict(
        a3m=a3m,
        output=stdout,
        stderr=stderr,
        n_iter=self.n_iter,
        e_value=self.e_value)
    return [raw_output]
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/kalign.py",".py","3387","105","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A Python wrapper for Kalign.""""""
import os
import subprocess
from typing import Sequence

from absl import logging

from alphafold.data.tools import utils
# Internal import (7716).


def _to_a3m(sequences: Sequence[str]) -> str:
  """"""Converts sequences to an a3m file.""""""
  names = ['sequence %d' % i for i in range(1, len(sequences) + 1)]
  a3m = []
  for sequence, name in zip(sequences, names):
    a3m.append(u'>' + name + u'\n')
    a3m.append(sequence + u'\n')
  return ''.join(a3m)


class Kalign:
  """"""Python wrapper of the Kalign binary.""""""

  def __init__(self, *, binary_path: str):
    """"""Initializes the Python Kalign wrapper.

    Args:
      binary_path: The path to the Kalign binary.

    Raises:
      RuntimeError: If Kalign binary not found within the path.
    """"""
    self.binary_path = binary_path

  def align(self, sequences: Sequence[str]) -> str:
    """"""Aligns the sequences and returns the alignment in A3M string.

    Args:
      sequences: A list of query sequence strings. The sequences have to be at
        least 6 residues long (Kalign requires this). Note that the order in
        which you give the sequences might alter the output slightly as
        different alignment tree might get constructed.

    Returns:
      A string with the alignment in a3m format.

    Raises:
      RuntimeError: If Kalign fails.
      ValueError: If any of the sequences is less than 6 residues long.
    """"""
    logging.info('Aligning %d sequences', len(sequences))

    for s in sequences:
      if len(s) < 6:
        raise ValueError('Kalign requires all sequences to be at least 6 '
                         'residues long. Got %s (%d residues).' % (s, len(s)))

    with utils.tmpdir_manager() as query_tmp_dir:
      input_fasta_path = os.path.join(query_tmp_dir, 'input.fasta')
      output_a3m_path = os.path.join(query_tmp_dir, 'output.a3m')

      with open(input_fasta_path, 'w') as f:
        f.write(_to_a3m(sequences))

      cmd = [
          self.binary_path,
          '-i', input_fasta_path,
          '-o', output_a3m_path,
          '-format', 'fasta',
      ]

      logging.info('Launching subprocess ""%s""', ' '.join(cmd))
      process = subprocess.Popen(cmd, stdout=subprocess.PIPE,
                                 stderr=subprocess.PIPE)

      with utils.timing('Kalign query'):
        stdout, stderr = process.communicate()
        retcode = process.wait()
        logging.info('Kalign stdout:\n%s\n\nstderr:\n%s\n',
                     stdout.decode('utf-8'), stderr.decode('utf-8'))

      if retcode:
        raise RuntimeError('Kalign failed\nstdout:\n%s\n\nstderr:\n%s\n'
                           % (stdout.decode('utf-8'), stderr.decode('utf-8')))

      with open(output_a3m_path) as f:
        a3m = f.read()

      return a3m
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/hhsearch.py",".py","3601","108","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""Library to run HHsearch from Python.""""""

import glob
import os
import subprocess
from typing import Sequence

from absl import logging

from alphafold.data import parsers
from alphafold.data.tools import utils
# Internal import (7716).


class HHSearch:
  """"""Python wrapper of the HHsearch binary.""""""

  def __init__(self,
               *,
               binary_path: str,
               databases: Sequence[str],
               maxseq: int = 1_000_000):
    """"""Initializes the Python HHsearch wrapper.

    Args:
      binary_path: The path to the HHsearch executable.
      databases: A sequence of HHsearch database paths. This should be the
        common prefix for the database files (i.e. up to but not including
        _hhm.ffindex etc.)
      maxseq: The maximum number of rows in an input alignment. Note that this
        parameter is only supported in HHBlits version 3.1 and higher.

    Raises:
      RuntimeError: If HHsearch binary not found within the path.
    """"""
    self.binary_path = binary_path
    self.databases = databases
    self.maxseq = maxseq

    for database_path in self.databases:
      if not glob.glob(database_path + '_*'):
        logging.error('Could not find HHsearch database %s', database_path)
        raise ValueError(f'Could not find HHsearch database {database_path}')

  @property
  def output_format(self) -> str:
    return 'hhr'

  @property
  def input_format(self) -> str:
    return 'a3m'

  def query(self, a3m: str) -> str:
    """"""Queries the database using HHsearch using a given a3m.""""""
    with utils.tmpdir_manager() as query_tmp_dir:
      input_path = os.path.join(query_tmp_dir, 'query.a3m')
      hhr_path = os.path.join(query_tmp_dir, 'output.hhr')
      with open(input_path, 'w') as f:
        f.write(a3m)

      db_cmd = []
      for db_path in self.databases:
        db_cmd.append('-d')
        db_cmd.append(db_path)
      cmd = [self.binary_path,
             '-i', input_path,
             '-o', hhr_path,
             '-maxseq', str(self.maxseq)
             ] + db_cmd

      logging.info('Launching subprocess ""%s""', ' '.join(cmd))
      process = subprocess.Popen(
          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
      with utils.timing('HHsearch query'):
        stdout, stderr = process.communicate()
        retcode = process.wait()

      if retcode:
        # Stderr is truncated to prevent proto size errors in Beam.
        raise RuntimeError(
            'HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
                stdout.decode('utf-8'), stderr[:100_000].decode('utf-8')))

      with open(hhr_path) as f:
        hhr = f.read()
    return hhr

  def get_template_hits(self,
                        output_string: str,
                        input_sequence: str) -> Sequence[parsers.TemplateHit]:
    """"""Gets parsed template hits from the raw string output by the tool.""""""
    del input_sequence  # Used by hmmseach but not needed for hhsearch.
    return parsers.parse_hhr(output_string)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/utils.py",".py","1223","41","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""Common utilities for data pipeline tools.""""""
import contextlib
import shutil
import tempfile
import time
from typing import Optional

from absl import logging


@contextlib.contextmanager
def tmpdir_manager(base_dir: Optional[str] = None):
  """"""Context manager that deletes a temporary directory on exit.""""""
  tmpdir = tempfile.mkdtemp(dir=base_dir)
  try:
    yield tmpdir
  finally:
    shutil.rmtree(tmpdir, ignore_errors=True)


@contextlib.contextmanager
def timing(msg: str):
  logging.info('Started %s', msg)
  tic = time.time()
  yield
  toc = time.time()
  logging.info('Finished %s in %.3f seconds', msg, toc - tic)
","Python"
"AlphaFold","piercelab/tcrmodel2","alphafold/data/tools/hmmsearch.py",".py","4556","132","# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the ""License"");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an ""AS IS"" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""A Python wrapper for hmmsearch - search profile against a sequence db.""""""

import os
import subprocess
from typing import Optional, Sequence

from absl import logging
from alphafold.data import parsers
from alphafold.data.tools import hmmbuild
from alphafold.data.tools import utils
# Internal import (7716).


class Hmmsearch(object):
  """"""Python wrapper of the hmmsearch binary.""""""

  def __init__(self,
               *,
               binary_path: str,
               hmmbuild_binary_path: str,
               database_path: str,
               flags: Optional[Sequence[str]] = None):
    """"""Initializes the Python hmmsearch wrapper.

    Args:
      binary_path: The path to the hmmsearch executable.
      hmmbuild_binary_path: The path to the hmmbuild executable. Used to build
        an hmm from an input a3m.
      database_path: The path to the hmmsearch database (FASTA format).
      flags: List of flags to be used by hmmsearch.

    Raises:
      RuntimeError: If hmmsearch binary not found within the path.
    """"""
    self.binary_path = binary_path
    self.hmmbuild_runner = hmmbuild.Hmmbuild(binary_path=hmmbuild_binary_path)
    self.database_path = database_path
    if flags is None:
      # Default hmmsearch run settings.
      flags = ['--F1', '0.1',
               '--F2', '0.1',
               '--F3', '0.1',
               '--incE', '100',
               '-E', '100',
               '--domE', '100',
               '--incdomE', '100']
    self.flags = flags

    if not os.path.exists(self.database_path):
      logging.error('Could not find hmmsearch database %s', database_path)
      raise ValueError(f'Could not find hmmsearch database {database_path}')

  @property
  def output_format(self) -> str:
    return 'sto'

  @property
  def input_format(self) -> str:
    return 'sto'

  def query(self, msa_sto: str) -> str:
    """"""Queries the database using hmmsearch using a given stockholm msa.""""""
    hmm = self.hmmbuild_runner.build_profile_from_sto(msa_sto,
                                                      model_construction='hand')
    return self.query_with_hmm(hmm)

  def query_with_hmm(self, hmm: str) -> str:
    """"""Queries the database using hmmsearch using a given hmm.""""""
    with utils.tmpdir_manager() as query_tmp_dir:
      hmm_input_path = os.path.join(query_tmp_dir, 'query.hmm')
      out_path = os.path.join(query_tmp_dir, 'output.sto')
      with open(hmm_input_path, 'w') as f:
        f.write(hmm)

      cmd = [
          self.binary_path,
          '--noali',  # Don't include the alignment in stdout.
          '--cpu', '8'
      ]
      # If adding flags, we have to do so before the output and input:
      if self.flags:
        cmd.extend(self.flags)
      cmd.extend([
          '-A', out_path,
          hmm_input_path,
          self.database_path,
      ])

      logging.info('Launching sub-process %s', cmd)
      process = subprocess.Popen(
          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
      with utils.timing(
          f'hmmsearch ({os.path.basename(self.database_path)}) query'):
        stdout, stderr = process.communicate()
        retcode = process.wait()

      if retcode:
        raise RuntimeError(
            'hmmsearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
                stdout.decode('utf-8'), stderr.decode('utf-8')))

      with open(out_path) as f:
        out_msa = f.read()

    return out_msa

  def get_template_hits(self,
                        output_string: str,
                        input_sequence: str) -> Sequence[parsers.TemplateHit]:
    """"""Gets parsed template hits from the raw string output by the tool.""""""
    a3m_string = parsers.convert_stockholm_to_a3m(output_string,
                                                  remove_first_row_gaps=False)
    template_hits = parsers.parse_hmmsearch_a3m(
        query_sequence=input_sequence,
        a3m_string=a3m_string,
        skip_first=False)
    return template_hits
","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/tcr_utils.py",".py","3883","84","from anarci import anarci
import numpy as np

def renumber_pdb(pdb_path, output_path, mhc_class=1):
        import MDAnalysis as mda 

        '''
        This function takes a pdb from tcrmodel output and renumber the residues according to the AHO numering scheme
        '''
        #load pdb 
        pdb_u = mda.Universe(pdb_path)
        #create list of chains per residue
        list_chains = [np.unique(i)[0] for i in pdb_u.residues.chainIDs]
        #create lsit of residues 
        list_res = [mda.lib.util.convert_aa_code(i) for i  in pdb_u.atoms.residues.resnames]
        #get tcra and tcrb sequence
        tcra_seq = ''.join(list(map(list_res.__getitem__, [i for i,a in enumerate(list_chains) if a == 'A']))) #considering that tcra is chain A in tcrmodel output
        tcrb_seq = ''.join(list(map(list_res.__getitem__, [i for i,a in enumerate(list_chains) if a == 'B']))) #considering that tcra is chain B in tcrmodel output
        #run anarci to get new residue numbers for tcra
        anarci_tcra = anarci([('tcra', tcra_seq)], scheme=""aho"", output=False)
        new_res_tcra = [i[0][0] for i in anarci_tcra[0][0][0][0] if i[1] != '-']
        #run anarci to get new residue numbers for tcrb
        anarci_tcrb = anarci([('tcrb', tcrb_seq)], scheme=""aho"", output=False)
        new_res_tcrb = [i[0][0] for i in anarci_tcrb[0][0][0][0] if i[1] != '-']
        #create copy
        tmp_numbering = pdb_u.residues.resids
        #change the residue number in original PDB for the new one
        tmp_numbering[np.array(list_chains) == 'A'] = new_res_tcra
        tmp_numbering[np.array(list_chains) == 'B'] = new_res_tcrb

        pdb_u.residues.resids = tmp_numbering

        chns=pdb_u.atoms.chainIDs
        chns[chns==""A""]=""F""
        chns[chns==""B""]=""G""
        chns[chns==""D""]=""A""
        if mhc_class==2:
            chns[chns==""E""]=""B""
        chns[chns==""F""]=""D""
        chns[chns==""G""]=""E""
        pdb_u.atoms.chainIDs=chns

        #write PDB
        pdb_u.select_atoms(""protein"").write(output_path)
    
def renumber_tcr_pdb(pdb_path, output_path):
        import MDAnalysis as mda 

        '''
        This function takes a pdb from tcrmodel output and renumber the residues according to the AHO numering scheme
        '''
        #load pdb 
        pdb_u = mda.Universe(pdb_path)
        #create list of chains per residue
        list_chains = [np.unique(i)[0] for i in pdb_u.residues.chainIDs]
        #create lsit of residues 
        list_res = [mda.lib.util.convert_aa_code(i) for i  in pdb_u.atoms.residues.resnames]
        #get tcra and tcrb sequence
        tcra_seq = ''.join(list(map(list_res.__getitem__, [i for i,a in enumerate(list_chains) if a == 'A']))) #considering that tcra is chain A in tcrmodel output
        tcrb_seq = ''.join(list(map(list_res.__getitem__, [i for i,a in enumerate(list_chains) if a == 'B']))) #considering that tcra is chain B in tcrmodel output
        #run anarci to get new residue numbers for tcra
        anarci_tcra = anarci([('tcra', tcra_seq)], scheme=""aho"", output=False)
        new_res_tcra = [i[0][0] for i in anarci_tcra[0][0][0][0] if i[1] != '-']
        #run anarci to get new residue numbers for tcrb
        anarci_tcrb = anarci([('tcrb', tcrb_seq)], scheme=""aho"", output=False)
        new_res_tcrb = [i[0][0] for i in anarci_tcrb[0][0][0][0] if i[1] != '-']
        #create copy
        tmp_numbering = pdb_u.residues.resids
        #change the residue number in original PDB for the new one
        tmp_numbering[np.array(list_chains) == 'A'] = new_res_tcra
        tmp_numbering[np.array(list_chains) == 'B'] = new_res_tcrb

        pdb_u.residues.resids = tmp_numbering

        chns=pdb_u.atoms.chainIDs
        chns[chns==""A""]=""D""
        chns[chns==""B""]=""E""

        pdb_u.atoms.chainIDs=chns

        #write PDB
        pdb_u.select_atoms(""protein"").write(output_path)
    
    ","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/parse_tcr_seq_from_json.py",".py","2368","91","import json
import subprocess
import anarci
import sys
from anarci import anarci

fn=sys.argv[1]
user_json=sys.argv[2]
json_output_path=sys.argv[3]

with open(fn) as fh:
    f=json.load(fh)

def get_seq(pdb_chain):
    command=""grep -A1 %s data/databases/pdb_seqres.txt"" % (pdb_chain)
    output = subprocess.getoutput(command)
    return output.split(""\n"")[1]

def anarci_custom(seq):
    command=""ANARCI --scheme aho -i %s"" % seq
    output = subprocess.getoutput(command)
    return output

def parse_anarci(output):
    cdr3, seq="""",""""
    for i in output.split(""\n""):
        if i[0]!=""#"" and i[0]!=""/"":
            fields=i.rstrip().split()
            num=int(fields[1])
            res=fields[-1]
            if res!=""-"":
                seq+=res
            if res!=""-"" and num >= 106 and num <= 139:
                cdr3+=res
    return cdr3, seq


def get_germlines(seq:str):
    '''
    Get the VJ germlines from TCRa or TCRb sequences
    '''
    input_seq = [('seq',seq)]
    try:
        results = anarci(input_seq, scheme=""aho"", output=False, assign_germline=True)
        v_gene = results[1][0][0]['germlines']['v_gene'][0][1]
        j_gene = results[1][0][0]['germlines']['j_gene'][0][1]
    except:
        v_gene = 'NA'
        j_gene = 'NA'
    return v_gene, j_gene

tcra=f['tcra_tmplts']
tcra_seqs={}
for pdb_chain in tcra:
    in_seq=get_seq(pdb_chain)
    anarci_out=anarci_custom(in_seq)
    cdr3, seq=parse_anarci(anarci_out)
    v_gene, j_gene=get_germlines(in_seq)
    tcra_seqs[pdb_chain]=[cdr3, seq, v_gene, j_gene]

tcrb=f['tcrb_tmplts']
tcrb_seqs={}
for pdb_chain in tcrb:
    in_seq=get_seq(pdb_chain)
    anarci_out=anarci_custom(in_seq)
    cdr3, seq=parse_anarci(anarci_out)
    v_gene, j_gene=get_germlines(in_seq)
    tcrb_seqs[pdb_chain]=[cdr3, seq, v_gene, j_gene]

out_json={}
out_json[""tcra_seqs""]=tcra_seqs
out_json[""tcrb_seqs""]=tcrb_seqs

with open(user_json) as fh:
    uf=json.load(fh)

aseq=uf['aseq_user']
anarci_out=anarci_custom(aseq)
cdr3, seq=parse_anarci(anarci_out)
v_gene, j_gene=get_germlines(in_seq)
out_json[""tcra_user""]=[cdr3, seq, v_gene, j_gene]

bseq=uf['bseq_user']
anarci_out=anarci_custom(bseq)
cdr3, seq=parse_anarci(anarci_out)
v_gene, j_gene=get_germlines(in_seq)
out_json[""tcrb_user""]=[cdr3, seq, v_gene, j_gene]

with open(json_output_path, 'w') as f:
    f.write(json.dumps(out_json, indent=4))
","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/pdb_utils.py",".py","3511","117","import subprocess


three_to_one = {'CYS': 'C', 'ASP': 'D', 'SER': 'S', 'GLN': 'Q', 'LYS': 'K',
     'ILE': 'I', 'PRO': 'P', 'THR': 'T', 'PHE': 'F', 'ASN': 'N', 
     'GLY': 'G', 'HIS': 'H', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W', 
     'ALA': 'A', 'VAL':'V', 'GLU': 'E', 'TYR': 'Y', 'MET': 'M'}
one_to_three = {value : key for (key, value) in three_to_one.items()}

def extract_sequence(pdb_fn):
    # get sequence from pdb
    # return a list of sequences for each chain
    seqs=[]
    pdb = []
    with open(pdb_fn) as fh:
        for line in fh:
            if line[0:4] == 'ATOM':
                pdb.append(line.rstrip())

    chains = []        
    for line in pdb:
        if line[0:4] == 'ATOM':
            chain_name = line[21]
            if (chain_name not in chains):
                chains.append(chain_name)
    res_count = 0
    start_res = 1
    for chain in chains:
        line_count = 0
        prev_res = """"
        seq=""""
        for line in pdb:
            if line[21] == chain:
                curr_res = line[22:27]
                if line_count == 0:
                    prev_res = curr_res
                    seq+=three_to_one[line[17:20]]
                if curr_res != prev_res:
                    res_count += 1
                    prev_res = curr_res
                    seq+=three_to_one[line[17:20]]
                line_count += 1
        seqs.append(seq)

    return seqs


def anarci(seq):
    command=""ANARCI --scheme aho -i %s"" % seq
    output = subprocess.getoutput(command)
    return output

def gen_res_str(res):
    res_len = len(str(res))
    res_str = """"
    for i in range(4-res_len):
        res_str += "" ""
    res_str += str(res) + "" ""
    return res_str

def rename_chains_start_A_and_1(pdb):
    #get unique chain name and the residue start

    chains = []        
    for line in pdb:
        if line[0:4] == 'ATOM':
            chain_name = line[21]
            if (chain_name not in chains):
                chains.append(chain_name)
    chain_dict={}
    for i, j in enumerate(chains):
        chain_dict[j]=chr(ord('@')+i+1)

    pdb_renum = []
    for chain in chains:
        line_count = 0
        res_count = 0
        prev_res = """"
        start_res = 1
        for line in pdb:
            if line[21] == chain:
                curr_res = line[22:27]
                if line_count == 0:
                    prev_res = curr_res
                if curr_res != prev_res:
                    res_count += 1
                out_res = res_count + start_res 
                outline = line[:21] +chain_dict[chain]+ gen_res_str(out_res) + line[27:]
                pdb_renum.append(outline)
                prev_res = curr_res
                line_count += 1
    return pdb_renum

def align_pdbs_by_pmhc(ref_path, pdb_path, output_path, mhc_class):
    import MDAnalysis as mda 
    from MDAnalysis.analysis import align
    pdb_u = mda.Universe(pdb_path)
    ref = mda.Universe(ref_path)
    if mhc_class==1:
        align.alignto(pdb_u, ref, select=""chainID C or chainID A"", tol_mass=1000)
    if mhc_class==2:
        align.alignto(pdb_u, ref, select=""chainID C or chainID A or chainID B"", tol_mass=1000)

    pdb_u.select_atoms(""protein"").write(output_path)


def align_pdbs(ref_path, pdb_path, output_path):
    import MDAnalysis as mda 
    from MDAnalysis.analysis import align
    pdb_u = mda.Universe(pdb_path)
    ref = mda.Universe(ref_path)
    align.alignto(pdb_u, ref, tol_mass=1000)

    pdb_u.select_atoms(""protein"").write(output_path)


","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/pmhc_templates.py",".py","10398","273","import os
from scripts import pdb_utils
import pandas as pd
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
from Bio import Align
from Bio.Align import substitution_matrices
from datetime import datetime


aligner = Align.PairwiseAligner()
aligner.substitution_matrix = substitution_matrices.load(""BLOSUM62"")
tmplt_header=""template_pdbfile\ttarget_to_template_alignstring\ttarget_len\ttemplate_len\tidentities\n""

def gen_aln(biopython_align_out,query_res_cnt,tmplt_res_cnt):
    # return a query-template alignment map
    alignment=""""
    for index, ref_ab_res in enumerate(biopython_align_out[0]):
        if ref_ab_res==""-"":  #query sequence residue not matched to template
            tmplt_res_cnt+=1
            continue
        md_res=biopython_align_out[1][index]
        if md_res==""-"": # template residue not aligned to anything 
            query_res_cnt+=1
            continue
        else:
            alignment+=""%d:%d;"" % (query_res_cnt, tmplt_res_cnt)
            tmplt_res_cnt+=1
            query_res_cnt+=1
    return alignment[:-1]

def gen_pep_aln(pep_len):
    # return a query-template alignment map for peptide
    out=""""
    for i in range(pep_len):
        out+=""%d:%d;"" % (i,i)
    return out

def get_best_hit_cls1(pep, mhc, df1, ignore_pdbs, cutoff):
    # get top ranking templates, ranked by MHC then peptide similarity
    # similarity is measured in BLOSUM62 alignment score 
    # for class I pMHCs, peptide of template must match query length
    df1['release_dates'] =  pd.to_datetime(df1['release_dates'], format='%Y-%m-%d') 

    tmplt_dict={} # keep track of alignment scores
    for idx, row in df1.iterrows():
        if row.release_dates > cutoff:
            # ignore pdbs released after a certain date
            continue
        if row.PDB.lower() in ignore_pdbs:
            # ignore pdbs blacklisted by user
            continue
        if len(row.pep)!=len(pep):
            # ignore templates with peptide of different length than query
            continue
        else:
            # obtain MHC alignment score
            #change penalize gaps back to 0:
            aligner.open_gap_score=0
            aligner.extend_gap_score=0
            alignments = aligner.align(mhc, row.MHC)
            alignment = alignments[0]
            mhc_score = alignment.score


            # obtain peptide alignment score

            #test penalize gaps, default is 0:
            aligner.open_gap_score=-100
            aligner.extend_gap_score=-100
            alignments = aligner.align(pep, row.pep)
            alignment = alignments[0]
            pep_score = alignment.score

            # update template dictionary with scores
            tmplt_dict[row.PDB]= (mhc_score,pep_score)

    # rank templates first by MHC then by peptide similarity 
    tmplt_dict=dict(sorted(tmplt_dict.items(), key=lambda item: item[1],reverse=True))

    # return top 4 templates 
    tmplt_dict=list(tmplt_dict.items())[:4]
    return tmplt_dict

def get_best_hit_cls2(pep, mhc1, mhc2, df2,ignore_pdbs, cutoff):
    # get top ranking templates, ranked by MHC then peptide similarity
    # similarity is measured in BLOSUM62 alignment score 
    # for class II MHCs, the MHC similarity is a sum of MHCa and MHCb similarity 
    df2['release_dates'] =  pd.to_datetime(df2['release_dates'], format='%Y-%m-%d') 

    tmplt_dict={} # keep track of alignment scores
    for idx, row in df2.iterrows():
        if row.release_dates > cutoff:
            # ignore pdbs released after a certain date
            continue
        if row.PDB.lower() in ignore_pdbs:
            # ignore pdbs blacklisted by user
            continue
        # obtain MHC alignment score
        mhc1_tmp, mhc2_tmp=row.MHC.split("" "")
        alignments = aligner.align(mhc1, mhc1_tmp)
        alignment = alignments[0]
        mhc1_score=alignment.score
        alignments = aligner.align(mhc2, mhc2_tmp)
        alignment = alignments[0]
        mhc2_score=alignment.score

        # obtain peptide alignment score
        pep_tmp=row.peptide_core
        alignments = aligner.align(pep_tmp, pep)
        alignment = alignments[0]
        pep_score=alignment.score

        # update template dictionary with scores
        tmplt_dict[row.PDB]=(mhc1_score+mhc2_score,pep_score)

    # rank templates first by MHC then by peptide similarity 
    tmplt_dict=dict(sorted(tmplt_dict.items(), key=lambda item: item[1],reverse=True))

    # return top 4 templates 
    tmplt_dict=list(tmplt_dict.items())[:4]
    return tmplt_dict

def get_best_hit_corep1_cls2(pep, mhc1, mhc2, df2,ignore_pdbs, cutoff):
    # get top ranking templates, ranked by MHC then peptide similarity
    # similarity is measured in BLOSUM62 alignment score 
    # for class II MHCs, the MHC similarity is a sum of MHCa and MHCb similarity 
    df2['release_dates'] =  pd.to_datetime(df2['release_dates'], format='%Y-%m-%d') 

    tmplt_dict={} # keep track of alignment scores
    for idx, row in df2.iterrows():
        if row.release_dates > cutoff:
            # ignore pdbs released after a certain date
            continue
        if row.PDB.lower() in ignore_pdbs:
            # ignore pdbs blacklisted by user
            continue
        # obtain MHC alignment score
        mhc1_tmp, mhc2_tmp=row.MHC.split("" "")

        alignments = aligner.align(mhc1, mhc1_tmp)
        alignment = alignments[0]
        mhc1_score=alignment.score
        alignments = aligner.align(mhc2, mhc2_tmp)
        alignment = alignments[0]
        mhc2_score=alignment.score

        # obtain peptide alignment score
        pep_tmp=row.peptide_core_p1
        alignments = aligner.align(pep_tmp, pep)
        alignment = alignments[0]
        pep_score=alignment.score

        # update template dictionary with scores
        tmplt_dict[row.PDB]=(mhc1_score+mhc2_score,pep_score)

    # rank templates first by MHC then by peptide similarity 
    tmplt_dict=dict(sorted(tmplt_dict.items(), key=lambda item: item[1],reverse=True))

    # return top 4 templates 
    tmplt_dict=list(tmplt_dict.items())[:4]
    return tmplt_dict

def gen_align_for_given_tmplt_cls1(tmplt_pdb, pep_len, mhc_seq):
    # generate alignment between query and template for a given template

    # extract template sequence
    tmplt_seq = pdb_utils.extract_sequence(tmplt_pdb)
    mhc_tmplt_seq=tmplt_seq[1]

    # query and template peptide aligned position-wise
    alignment=gen_pep_aln(pep_len)

    # align MHC by mapping template to query residues after pairwise alignment
    alignments=pairwise2.align.globalxx(mhc_seq, mhc_tmplt_seq, penalize_extend_when_opening=True)
    alignment+=gen_aln(alignments[0],pep_len,pep_len)

    tmplt_out=""%s\t%s\t%d\t%d\t0\n"" % (tmplt_pdb, alignment, pep_len+len(mhc_seq), len("""".join(tmplt_seq)))
    return tmplt_out

def gen_align_for_given_tmplt_cls2(tmplt_pdb, pep_len, mhc1_seq, mhc2_seq):
    # generate alignment between query and template for a given template
    
    # extract template sequence
    tmplt_seq = pdb_utils.extract_sequence(tmplt_pdb)
    mhc1_tmplt_seq=tmplt_seq[1]
    mhc2_tmplt_seq=tmplt_seq[2]

    # align peptide position-wise
    alignment=gen_pep_aln(pep_len)

    # align MHC by mapping template to query residues after pairwise alignment
    alignments=pairwise2.align.globalxx(mhc1_seq, mhc1_tmplt_seq, penalize_extend_when_opening=True)
    alignment+=gen_aln(alignments[0],pep_len, pep_len)+"";""
    alignments=pairwise2.align.globalxx(mhc2_seq, mhc2_tmplt_seq, penalize_extend_when_opening=True)
    alignment+=gen_aln(alignments[0],pep_len+len(mhc1_seq),pep_len+len(mhc1_tmplt_seq))

    tmplt_out=""%s\t%s\t%d\t%d\t0\n"" % (tmplt_pdb, alignment, pep_len+len(mhc1_seq)+len(mhc2_seq), len("""".join(tmplt_seq)))
    return tmplt_out

def gen_align_file_cls1(
        pep_seq, 
        mhc_seq, 
        out_dir, 
        ignore_pdbs, 
        # data_dir,
        cutoff=""2100-01-01""
        ):
    # generate full alignment file for a given query in class I

    # read all nr class I complexes as dataframe
    fn=""data/templates/nr_cls_I_complexes.txt""
    df1=pd.read_csv(fn,sep=""\t"")
    cutoff = datetime.strptime(cutoff, '%Y-%m-%d')

    tmplt_dict=get_best_hit_cls1(pep_seq, mhc_seq, df1, ignore_pdbs, cutoff)
    tmplt_out=""""
    for (tmplt_name,score) in tmplt_dict:
        tmplt_pdb=""data/templates/pdb/%s.pmhc.pdb"" % tmplt_name
        tmplt_out+=gen_align_for_given_tmplt_cls1(tmplt_pdb, len(pep_seq),mhc_seq)

    tmplt_aln_file=os.path.join(out_dir, ""pmhc_alignment.tsv"")    
    with open(""%s"" % tmplt_aln_file,'w+') as fh:
        fh.write(""%s%s"" % (tmplt_header, tmplt_out)) 

def gen_align_file_cls2(
        pep_seq, 
        mhc1_seq, 
        mhc2_seq, 
        out_dir, 
        ignore_pdbs,
        cutoff=""2100-01-01""):
    # generate full alignment file for a given query in class II

    # read all nr class II complexes as dataframe
    fn=""data/templates/nr_cls_II_complexes.txt""
    df2=pd.read_csv(fn,sep=""\t"")
    cutoff = datetime.strptime(cutoff, '%Y-%m-%d')

    tmplt_dict=get_best_hit_cls2(pep_seq, mhc1_seq, mhc2_seq, df2, ignore_pdbs, cutoff)
    tmplt_out=""""
    for (tmplt_name,score) in tmplt_dict:
        tmplt_pdb=""data/templates/pdb/%s.pmhc.pdb"" % tmplt_name
        tmplt_out+=gen_align_for_given_tmplt_cls2(tmplt_pdb, len(pep_seq), mhc1_seq, mhc2_seq)

    tmplt_aln_file=os.path.join(out_dir, ""pmhc_alignment.tsv"")
    with open(""%s"" % tmplt_aln_file,'w+') as fh:
        fh.write(""%s%s"" % (tmplt_header, tmplt_out)) 

def gen_align_file_corep1_cls2(
        pep_seq, 
        mhc1_seq, 
        mhc2_seq, 
        out_dir, 
        ignore_pdbs,
        cutoff=""2100-01-01""):
    # generate full alignment file for a given query in class II

    # read all nr class II complexes as dataframe
    fn=""data/templates/nr_cls_II_complexes.txt""
    df2=pd.read_csv(fn,sep=""\t"")
    cutoff = datetime.strptime(cutoff, '%Y-%m-%d')

    tmplt_dict=get_best_hit_corep1_cls2(pep_seq, mhc1_seq, mhc2_seq, df2, ignore_pdbs, cutoff)
    tmplt_out=""""
    for (tmplt_name,score) in tmplt_dict:
        tmplt_pdb=""data/templates/pdb/cp1/%s.pmhc.pdb"" % tmplt_name
        tmplt_out+=gen_align_for_given_tmplt_cls2(tmplt_pdb, len(pep_seq), mhc1_seq, mhc2_seq)

    tmplt_aln_file=os.path.join(out_dir, ""pmhc_alignment.tsv"")
    with open(""%s"" % tmplt_aln_file,'w+') as fh:
        fh.write(""%s%s"" % (tmplt_header, tmplt_out)) 
","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/parse_tcr_seq.py",".py","1797","58","import subprocess

from anarci import anarci


def get_seq(pdb_chain):
    command=""grep -A1 %s data/databases/pdb_seqres.txt"" % (pdb_chain)
    output = subprocess.getoutput(command)
    return output.split(""\n"")[1]


def parse_anarci(in_seq):
    try:
        command=""ANARCI --scheme aho -i %s"" % in_seq
        output = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
        output = output.decode(""utf-8"")  # decode bytes to string
    except subprocess.CalledProcessError as e:
        print(f""ANARCI failed for {in_seq} with error: {e.output.decode('utf-8')}"")
        return ""NA"", ""NA""

    cdr3, seq="""",""""
    for line in output.split(""\n""):
        if line and line[0] != ""#"" and line[0] != ""/"":
            try:
                fields = line.rstrip().split()
                num = int(fields[1])    # Second field for residue_number
                res = fields[-1]        # Last field for aa
                if res != ""-"":
                    seq += res
                    if num >= 106 and num <= 139:
                        cdr3 += res
            except (ValueError, IndexError) as e:
                print(f""Error parsing line '{line.strip()}': {str(e)}"")
    return cdr3, seq


def get_germlines(seq:str):
    '''
    Get the VJ germlines from TCRa or TCRb sequences
    '''
    input_seq = [('seq',seq)]
    try:
        results = anarci(input_seq, scheme=""aho"", output=False, assign_germline=True)
        v_gene = results[1][0][0]['germlines']['v_gene'][0][1]
        j_gene = results[1][0][0]['germlines']['j_gene'][0][1]
    except:
        v_gene = 'NA'
        j_gene = 'NA'
    return v_gene, j_gene


def parse_tcr(in_seq):
    cdr3, seq = parse_anarci(in_seq)
    v_gene, j_gene = get_germlines(in_seq)
    return [cdr3, seq, v_gene, j_gene]


","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/parse_cdr_confidence_from_json.py",".py","1077","33","# Load required packages
import os
import sys
import pandas as pd
import json
from absl import flags
from absl import app
from glob import glob
import MDAnalysis as mda

fn=sys.argv[1]
out_dir=sys.argv[2]
json_output_path=sys.argv[3]

with open(fn) as fh:
    out_json=json.load(fh)

#get CDR3s confidence scores 
def get_cdr3_conf(pdb_path):
    pdb_u = mda.Universe(pdb_path)
    cdr3a_bfactors_avg = pdb_u.select_atoms('chainID D and resid 106:139').atoms.bfactors.mean()
    cdr3b_bfactors_avg = pdb_u.select_atoms('chainID E and resid 106:139').atoms.bfactors.mean()
    return cdr3a_bfactors_avg, cdr3b_bfactors_avg

models_list = [i for i in glob('%s/*' % (out_dir)) if os.path.basename(i).startswith('ranked')]
for model in models_list:
    cdr3a_bfactors_avg, cdr3b_bfactors_avg = get_cdr3_conf(model)
    out_json[os.path.basename(model).split('.pdb')[0]]['cdr3a_plddt'] = cdr3a_bfactors_avg
    out_json[os.path.basename(model).split('.pdb')[0]]['cdr3b_plddt'] = cdr3b_bfactors_avg

with open(json_output_path, 'w') as f:
    f.write(json.dumps(out_json, indent=4))
","Python"
"AlphaFold","piercelab/tcrmodel2","scripts/seq_utils.py",".py","1711","64","
import subprocess
import os 

def anarci(seq):
    command=""ANARCI --scheme aho -i %s"" % seq
    output = subprocess.getoutput(command)
    return output


def parse_anarci(output):
    #24-42 (CDR1), 57-76 (CDR2), and 107-138 (CDR3)
    seq=""""
    for i in output.split(""\n""):
        if i[0]!=""#"" and i[0]!=""/"":
            fields=i.rstrip().split()
            num=int(fields[1])
            res=fields[-1]
            if res!=""-"":
                seq+=res
    return seq

def trim_tcr(seq):
    # trim TCR sequence to variable domain sequence
    anarci_out=anarci(seq)
    return parse_anarci(anarci_out)

def trim_mhc(seq, type, root_dir, out_dir):
    # trim MHC sequence to alpha1 and alpha2 if class I (type 1)
    # trim MHC to alpha1 if class II alpha (type 2)
    # trim MHC to beta1 if class II beta (type 3)

    if type==""1"":
        hmmfile = ""%s/scripts/mhc_hmm/classI.hmm"" % root_dir 
    elif type==""2"":
        hmmfile = ""%s/scripts/mhc_hmm/classII_alpha.hmm"" % root_dir 
    elif type==""3"":
        hmmfile = ""%s/scripts/mhc_hmm/classII_beta.hmm"" % root_dir 
    else:
        return ""error""

    tmpfn=""%s/tmp.fa"" % out_dir
    with open(tmpfn, 'w') as fh:
        fh.write("">tmp\n%s\n"" % seq)
    
    hmmcmd=""hmmsearch --noali %s %s"" % (hmmfile,tmpfn)
    hmmout=subprocess.getoutput(hmmcmd)

    os.remove(tmpfn)
    start=0
    end=0
    hmmout=hmmout.split(""\n"")
    for i,line in enumerate(hmmout):
        if ""Domain annotation for each sequence"" in line:
            line=hmmout[i+4]
            start=int(line[59:66])
            end=int(line[67:74])
            break
    if start == 0 or end == 0:
        return ""none""
    else:
        start-=1
        return seq[start:end]
","Python"