sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a33a6a68121524fddaa298dc550cf848ac4262a93e9598f7ae89036e4ad879eb | Scala | 1,586 | 43 | import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
class performDiagnoseTargets extends QScript {
@Input(doc="Reference file for the bam files", shortName="R")
var referenceFile: File = _
@Input(doc="Input bam file", shortName="I")
var bamFile: File = _
@Input(d... |
e4748d0eb3a31044bf3a51d930ef88696b81fec53a027f5a8c180e2e04dbd240 | Scala | 2,335 | 53 | import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
class performVariantFiltration extends QScript {
@Input(doc="Reference file for the bam files", shortName="R")
var referenceFile: File = _
@Input(doc="Input bam file", shortName="V")
var vcfFile: File = _
@Outpu... |
510ffd3babd3588caefd9179fc4ffdc8484d8230c86e246d4e351ad9b8a8670d | Scala | 2,350 | 52 | import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.tools.walkers.indels.IndelRealigner.ConsensusDeterminationModel
class performIndelRealigner extends QScript {
@Input(doc="Reference file for the bam files", shortName="R")
var reference... |
b9b3cb46df6981123917965ffaa0bce5c73204ddb82a15eafea5c17733ba9629 | Scala | 2,491 | 58 | import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.tools.walkers.genotyper.GenotypingOutputMode
import org.broadinstitute.gatk.utils.downsampling.DownsampleType
class performHaplotypeCaller extends QScript {
@Input(doc="Reference file fo... |
0e3ee87ea81b7d9ae703094c52129f3b6021fff1ca9b38b64355459a9121c211 | Scala | 2,710 | 64 | import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
class performBaseRecalibration extends QScript {
qscript =>
// qscript now equals performBaseRecalibration.this
@Input(doc="Reference file for the bam files", shortName="R")
var referenceFile: File = _
@Input(do... |
c11df20bf04472fe556fcd7194ecdb3e92b6c627a1489f9c4d3adf6aa8b3589f | Shell | 56 | 3 | #!/bin/bash
python -m pytest ./tests/test_functions.py
|
12d6be4c3c47ca184e44b34dea96c45a0947979ce701c8eee87643d99ac4ccd9 | Shell | 62 | 3 | #!/bin/bash
python $NEUROD_ROOT/neurodecode/gui/mainWindow.py |
070295f0825b782df1b7a6a92e90096af22f1d43fb8f93b05f02096e043e86a8 | Shell | 64 | 1 | matlab -nodisplay -r "run('switch_between_experiments.m');exit"
|
c82969b6f7ab88b3a5c35e413003e7328397d97b5ab78f069f83300fd2199590 | Shell | 67 | 3 | #/bin/bash
bash image_generation.sh fc8 DGN fixed blurred shifted
|
559527790c788af7ee32811d066de3edc18d3b17c9ea6acaffe3f37ec4a5705f | Shell | 68 | 3 | #/bin/bash
bash image_generation.sh fc8 DGN fixed original shifted
|
7f28853dae122252aacdad757cc4c72a73808778f031cc776a436130009ca92f | Shell | 70 | 3 | #/bin/bash
bash image_generation.sh fc8 DCNN l2norm original shifted
|
fdb0b1ab7ed2b6340e3215e01329477196c3111f657c51729dcf8f45a172e12e | Shell | 70 | 3 | #/bin/bash
bash image_generation.sh fc8 DGN winner original original
|
d5949746dd2d5f6d877ae6847258c3efff75e48e10fade2abbaeca63e11aa159 | Shell | 75 | 4 | #!/bin/bash
set -e
fx director start --disable-tls -c director_config.yaml |
ba30302b3a822c81e0311c605a476e1e6ce187cb93b8420cde01b4d27b4e3cb9 | Shell | 78 | 3 | #!/bin/bash
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*
|
d95fe22e40ec7f2e3fcfdc65f2ec0e42f5f1258f5add92e0877fc27ea8a48e0d | Shell | 80 | 1 | matlab -nodisplay -r "reporting_registration({'summedNorm_registered'});exit;"
|
a9c28553a8e2dcb3273b823e30a241a39aab43e852d597ea7b9898fa709640a4 | Shell | 81 | 5 | #!/bin/bash
service nginx stop
nginx -g "daemon off;" &
service nginx start
bash
|
4982428c84d2bd72aed1880a14ae33f5309a053ebac85c6c1761a8d469001105 | Shell | 82 | 6 | #!/bin/bash
service nginx stop
nginx -g "daemon off;" &
service nginx start
bash
|
7677e4fee960a3bee5a52efa3bedda774fcd07c4d863ef0bd38f53bf1c15ce9f | Shell | 86 | 5 | #!/bin/bash
service nginx stop
nginx -g "daemon off;" &
service nginx start
bash
|
a6c475dbef32eb16af46637c30ef8647fd07efa708d26c6cbe433af5a215ea09 | Shell | 87 | 6 | while [ true ]; do
printf "\n"
# tail step3_0.out
squeue -u ahf38
sleep 1
done
|
e49543c702c200ae8cdfa8f83163de9dba30b3bf8ee820a873d3b856649279a6 | Shell | 88 | 6 | #!/bin/bash
service nginx stop
nginx -g "daemon off;" &
service nginx start
bash
|
0f238ce0cdac687729277ed36bfeee922555d2b3f6e404a9747bac91461a721d | Shell | 93 | 4 | #/bin/bash
layer="${1:-conv4}"
bash image_generation.sh ${layer} DGN winner blurred shifted
|
b5ce293edc1622452a28ce531111cf24941290869440d2241f9c6271a923ea2d | Shell | 95 | 4 | #! /bin/bash
gdown --id 17nMPwGCs-Op8AV3DEdQlOb2UrTh7Nmbe
unzip -o datasets.zip
rm datasets.zip |
5e04bf777dd94ddd489b3803e5b59e0739c5007afe41744aac2a0ef7dee779d4 | Shell | 97 | 4 | #/bin/bash
layer="${1:-conv4}"
bash image_generation.sh ${layer} DCNN winner original shifted 1
|
563a8a24835a8741aed8db3f5bbf1cd92f895c59bc49d96cbccc2c2aa4d348df | Shell | 102 | 8 | #!/bin/bash
nanoplexer \
-b barcode.fa \
-t 16 \
-p raw_data \
pass.fastq.gz
pigz raw_data/*fastq |
a8ea11d7c29c6775a8c59ed98189fe939da5b192b8f4ca69745f8beae0eff4ae | Shell | 109 | 4 | #! /bin/bash
gdown --id 1GWUdXJvEQdjf6jlxhfj-D-T5oZyv-Sqq
unzip -o preprocessed2h5.zip
rm preprocessed2h5.zip |
b221058b1bf94b204c455136cc6a287d3816ad30c202eafc1b3c507b9456976c | Shell | 110 | 6 | #!/bin/bash
set -e
ENVOY_NAME=$1
ENVOY_CONF=$2
fx envoy start -n "$ENVOY_NAME" --disable-tls -c "$ENVOY_CONF" |
0af1ffbc627260f6cd6ec16cdac26676c9e7df0023754b0c85b33a8d6f1920bc | Shell | 111 | 6 | #!/bin/bash
set -e
ENVOY_NAME=$1
ENVOY_CONF=$2
fx envoy start -n "$ENVOY_NAME" --disable-tls -c "$ENVOY_CONF"
|
251655ad52c71ebb4233e0d473b6fb4a25607169260ad29a8ccf951894782e9b | Shell | 115 | 7 | #!/bin/bash
for f in *.maf
do
python callSNPsFromMAF.py $f $f.coords
echo "Finished processing file: $f"
done
|
2545e7db2e6acaf6f8020547fa6d6b9153ce8e50841e307fb2e9ca96d940cd77 | Shell | 119 | 4 | #!/bin/bash
#merge all outputs from each individual methods and inputs into a output .csv file
cat *.csv > output.csv
|
c7d2df0bec03b5b3082300a3e7736d2939ea1f7743c9dca28a8b419da83e9e37 | Shell | 125 | 5 | atlas schema apply \
-u "$1" \
--to file://pkg/log/store/schema/ \
--dev-url "docker://postgres/15/dev" \
--auto-approve
|
38989798437c967bd3b1acbbbf439b2dc38d0db802fdee1bbe6ea8614892b5f2 | Shell | 128 | 6 | #!/bin/bash
for i in $(ls -1 *.fastq | sed 's/\.fastq//')
do
grep -B 1 CTCCGTAGACCTAACCTGAC $i.fastq > $i.grep10_6335.txt
done
|
134b36251e38bd68dee0ae80140fc11c2ba2284aab9a5462f0b9f76970434119 | Shell | 138 | 1 | matlab -nodisplay -logfile logs/colorCorrectionProcessing.log -r " parpool(5); parfor roundnum=1:20; colorcorrection_3D(roundnum); end;"
|
2d322c4d2deaf9cd3f0be4bfcd4f07434da79250c1df5db9b8e3816c93560069 | Shell | 138 | 6 | #!/bin/bash
for i in $(ls -1 *.fastq | sed 's/\.fastq//')
do
grep -B 1 CAAACTACCACCTACCTCCCTCACCATTGG $i.fastq > $i.grep15_8471.txt
done
|
d7b3faa260a7a1146d520888b5186b2f5109278ab53b6e5078f5a82129157986 | Shell | 146 | 1 | matlab -nodisplay -logfile ../logs/File_renaming_from_solidformat.log -r "run('../experiment_specific_scripts/copy_scope_names_to_reg_names.m')"
|
e780b0929e3b29502434e84aee8b2b7d416f4601499a4f7218381af2b33bfffa | Shell | 146 | 7 | #!/bin/bash
#merge outputs from each individual methods and inputs into a directory
cat *.csv >> output.csv
mkdir intercsv
mv *.csv intercsv/
|
f0d02cd3d77717331c4a4ee04c215516a314a5a7724e562f78bb6ff88a67ac63 | Shell | 148 | 2 | matlab -nodisplay -logfile logs/colorCorrectionProcessing.log -r "parpool(5); parfor roundnum=1:20; interpolateDataVolume(roundnum,.5/.17); end;"
|
fa7e1a30418bb201466097bbce28c0bdd79907e427a9abd549e68026096e8ed1 | Shell | 156 | 10 | #!/bin/bash
f=bvlc_googlenet.caffemodel
if [ ! -f "${f}" ]; then
echo "Downloading..."
wget http://dl.caffe.berkeleyvision.org/${f}
fi
echo "Done."
|
828df9497a1ae189594194295b8fb627b8bed8979539faee0e4ea93e4833551f | Shell | 162 | 5 | for i in {1..100}
do
../raxml-ng --msa ../fasta/test$i.fasta --model Dayhoff --prefix T$i --threads 2 --seed 616
sed -i '' -e $'s/>//g' T$i.raxml.bestTree
done
|
6e32edc3e4808bff6fb8628225c7c9ea7137b38bd39ad53af2e206192c5cb994 | Shell | 164 | 5 | #!/bin/bash
vcftools --gzvcf /mnt/data0/public_data/human_9606_b151_GRCh38p7/00-All.vcf.gz --out Disease_02-filter-SNP --snps Disease_sort_uniq_02_list --recode
|
f4ba8b9b83b7d1fbe0c726e386eb3ecc6fcb9b8c450a844987d09e30c7314659 | Shell | 165 | 6 | #!/bin/bash
vcftools --gzvcf /mnt/data0/public_data/human_9606_b151_GRCh38p7/00-All.vcf.gz --out Disease_00_filter-SNP --snps Disease_sort_uniq_00_list --recode
|
c36cf9bcad6528c3d2c8b6ca7aaeefcc0f245187eef5b3076748e7ab1cddb80f | Shell | 167 | 6 | #!/bin/bash
for i in $(ls -1 *.fastq | sed 's/\.fastq//')
do
grep -B 1 TCATCGACCTCCCCACCC $i.fastq | grep -B1 ATCCTAGT | grep -B1 GAAACTTCGG > $i.grep_7816.txt
done
|
73d5464e803a4d3f34347f22858e97d31d04a9479506106406733883d4a520f4 | Shell | 176 | 8 | #!/bin/bash
# Sanity check to ensure package is installed correctly
source ./install.sh
pip_install_from_tarball $1
python -c "import chromadb; print(chromadb.__version__)"
|
6bbfa2c864ae869ea5ee06078e874cae4824f4d286fd9d0a051ec0f138ef4e1f | Shell | 186 | 7 | #!/bin/bash
# Copyright (C) 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -Eeuo pipefail
python3 setup.py sdist bdist_wheel
echo "Pip wheel created under dist/"
|
0a3cf8c47a559dcd3c149357ec6b413d996f13bb127e9c651849e42438e3cef2 | Shell | 190 | 8 | #!/bin/bash
script_path=$(realpath $0)
script_dir=$(dirname $script_path)
cd $script_dir/mlspm/_c
g++ -fPIC -O3 -c matching.cpp peaks.cpp &&
g++ -shared matching.o peaks.o -o mlspm_lib.so
|
9bb78b43542baed27990edcd44dea429c43d366d7d504d85e0de1e20b82ece92 | Shell | 192 | 3 | ID1=$(sbatch --array=0-5 --parsable run_parameter_combinations.sh)
ID2=$(sbatch --depend=afterok:$ID1 --parsable analyze_simulations.sh)
sbatch --depend=afterok:$ID2 combine_unitary_spectra.sh |
b7eb4496dd2e916172f99b0206d63e5dd1ce56a87e2e0687076cdeaa0e078a83 | Shell | 192 | 10 | #!/usr/bin/env bash
rm lattice_filter.so
rm lattice_filter_op_loader.py
cd permutohedral_lattice
sh build.sh
cp lattice_filter.so ../
cp lattice_filter_op_loader.py ../
rm lattice_filter.so
|
fb146b941915dbbbaf2c5aa15bd671ce3533a5518cd161ff69968e3151ec5533 | Shell | 193 | 3 | ID1=$(sbatch --array=1-25 --parsable run_parameter_combinations.sh)
ID2=$(sbatch --depend=afterok:$ID1 --parsable analyze_simulations.sh)
sbatch --depend=afterok:$ID2 combine_unitary_spectra.sh |
20b5b2b4a1743fb8a06714cf815e116d23b97cef506d41173f820ae5ac26ca21 | Shell | 195 | 5 | #!/bin/bash
source /home/$USER/anaconda3/etc/profile.d/conda.sh # TODO to determined by setup.py
conda activate sssort
INSTALL_DIR = XX # to be inserted by setup.py
python $INSTALL_DIR/run.py $@
|
60765703b6f103c55bbcbce36056beb50e2edd3e99b7f55038c29b93d34760ef | Shell | 195 | 10 | #!/bin/bash
f=bvlc_reference_caffenet.caffemodel
if [ ! -f "${f}" ]; then
echo "Downloading..."
wget http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
fi
echo "Done."
|
8d295866d7b9326bf3e80066c5d55c95cedc11ba459f293387c3ec77b81a9246 | Shell | 195 | 8 | #!/bin/bash
for f in results/*
do
datevar=$(date -r "$f" +"%a_%b_%d_%Y_%H_%M_%S")
datevar2=$(date -r "$f" +"%Y_%m_%d_%H_%M_%S_")
fname=$(basename "$f")
mv "$f" "results/$datevar2$fname"
done
|
8775f2fe6174dbd209422f39f95fa39b89faea22b6eef36ea19610d9824d05db | Shell | 196 | 8 | #!/bin/bash
# Sanity check to ensure package is installed correctly
source ./install.sh
pip_install_from_tarball $1
python -c "import chromadb; api = chromadb.Client(); print(api.heartbeat())"
|
4348d1be3b0f9bfcd9d40766f7fc179e4f2ee5b1f0052fa6e835053b3773badb | Shell | 199 | 8 | # Autodock GPU
mk_prepare_receptor.py \
--read_pdb 1DB5_apo.pdb \
--output_basename 1DB5 \
--write_pdbqt --write_gpf \
--box_enveloping 6IN.sdf --padding 5
autogrid4 -p 1DB5.gpf -l 1DB5.log
|
7a582dc8b5b77f51fe74446c1acd627fb1356b25082fb51fb7d335e98888de32 | Shell | 200 | 3 | export RENDER_CLIENT_JAR=/allen/aibs/pipeline/image_processing/volume_assembly/render-jars/production/render-ws-java-client-standalone.jar
export RENDER_JAVA_HOME=/usr/
export RENDER_HOST='127.0.0.1'
|
46f13d32a5ad16704facbbbcb46a16d3865e1a1d9d47d7b31b148025696ce04b | Shell | 206 | 12 | for dir in ~/DATA/project/scSHAPE_Map/RiboSwitches_scSHAPE/riboswitch_in_h9_transcriptome/done/R*
do
s=${dir#*done/}
echo $s
mkdir $s
cd $s
cp $dir/*.mutrate.txt.gz .
cd ../
done
|
e675e6732b073196793f405aedc85306c7b1a1eb3b3637763570398d979da931 | Shell | 209 | 11 | #!/bin/sh
#
# Remove symlinks
# Run in top level
#
rm -f reinvent_models
rm -f reinvent/models/reinvent_core
rm -f reinvent/models/lib_invent
rm -f reinvent/models/link_invent
rm -f reinvent/models/molformer
|
8f9ae3cd335d57ceeaacd001375d42cf9203b265cf65dd44a6b0dc3ae2c6dfe4 | Shell | 212 | 8 | #!/bin/bash
cat Disease_01_chr_pos_sort_uniq.vcf | while read line
do
tabix /mnt/data0/public_data/human_9606_b151_GRCh38p7/00-All.vcf.gz $line >> Disease_01_chr_pos_sort_uniq_dbsnp_tabix.vcf
done
|
890d7f4497bc5b367eba9b568bb661a42dfb8f6900c9990ccefa06d638732219 | Shell | 221 | 3 | # Downloads the AlphaFold predictions for all of SwissProt as described
# Requires aria2c to be installed, see https://aria2.github.io
aria2c -x10 https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/swissprot_pdb_v4.tar
|
6edfa5cbdbd75a4c059f3f5b81fdb4e5fd2b4734ab83099fd85e5bbd85a8f270 | Shell | 222 | 5 | #!/bin/bash
esl-sfetch --index $1
cmsearch --nohmmonly --rfam --cut_ga -o $3/$2.out --tblout $3/$2.tblout cms/$2.cm $1
grep -v "^#" $3/$2.tblout | awk '{print $1"/"$8"-"$9, $8, $9, $1}' | esl-sfetch -Cf $1 - > $3/$2.fasta |
02c9c853648667d3d1c61adad4200471a700ccbf6664345f1d50750d1fd1addf | Shell | 224 | 12 | #!/bin/bash
#!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=20
#SBATCH --mem-per-cpu=4G
for f in *.maf
do
python callINDELsFromMAF.py $f $f.indels
echo "Finished processing file: $f"
done
|
12d9f03ff0ac5a92aeef4b8b6e716bbcd81ccea9898b21805cfb052a0a2a81c0 | Shell | 232 | 13 | #!/bin/bash
CFG="-c $2"
if [ -z "$1" ]; then
echo "No path to scan provided"
exit 1
fi
if [ -z "$2" ]; then
CFG = ""
fi
bandit -f "$1" ${CFG} -r "$3" -o "$4"
exit 0 #we want to ignore the exit code of bandit (for now)
|
95a0382d75b8de997b271e4300c811fbc102f9b7b2be7bc1e8d2406b2c43353c | Shell | 242 | 8 | ## gpu id
gpus="2,3,4"
##path to your data file
file='path/to/your/test/data/test.csv'
##path to your saved model file
model='path/to/your/saved/model'
python src/evaluate.py --split_ready --testfile $file --modelfile $model --gpu_ids $gpus |
408d8656f34d555ae39926b266c35f9a4c86332012f25942d3d7f10604fafc88 | Shell | 247 | 14 | #!/bin/sh
#
# Create symlinks to support old model files
# Run in top level
#
ln -s reinvent/models reinvent_models
cd reinvent/models
ln -s reinvent reinvent_core
ln -s libinvent lib_invent
ln -s linkinvent link_invent
ln -s mol2mol molformer
|
ef186c20a6f77149602c2fa17af4b520c8c88a0d1b33bdd6c804dd67be9181a6 | Shell | 253 | 11 | #!/bin/bash
cat Disease_sort_uniq_01_cut.vcf | while read line
do
eval $(echo "$line" | awk -F ':' '{printf("CHR=%s\nPOS=%s",$1,$2)}')
COL=":"
SEP="-"
line="$CHR$COL$POS$SEP$POS"
echo "$line" >> Disease_01_chr_pos.vcf
done
|
dcf59bcda1b4c78d0bf3ba6148a325d24d3619f781449bfb639b3a466ea409ae | Shell | 255 | 11 | #!/bin/bash
set -Euo pipefail
base_dir=$(dirname $(dirname $0))
# Run the pre-commit checks
SKIP=bandit pre-commit run --all-files
ruff check --config "${base_dir}/pyproject.toml" --fix openfl/
ruff format --config "${base_dir}/pyproject.toml" openfl/ |
7dbd0fc4a13695677a82959bf312a8c1411380fb6d656569bf88521defedbc8d | Shell | 257 | 20 | #!/bin/bash
rm benchmarks/*/*
rmdir benchmarks/*
rmdir benchmarks
rm logs/*/*
rmdir logs/*
rmdir logs
mkdir -p logs/drmaa
rm results/*/*/*/*
rm results/*/*/*
rm results/*/*
rm results/*
rmdir results/*/*/*
rmdir results/*/*
rmdir results/*
rmdir results |
880fd5ff9d445b12d5847877a67141a8e4681e2693d58f3eef472f9e566ee5ad | Shell | 261 | 12 | #!/bin/bash
#SBATCH --time=00:10:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --cpus-per-task=1
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
module load matlab/2020a
matlab -nodisplay -r "combine_unitary_spectra"
|
2b784c664c7d33f1f350612ec22f2a4587f11f776b53c28bb973b24f489a29d1 | Shell | 262 | 14 | # Run Gnina with local optimization
receptor="$1"
in_sdf="$2"
out_sdf="$3"
gnina.1.3.2.cuda12.8 \
--receptor "$receptor" \
--ligand "$in_sdf" \
--out "$out_sdf" \
--local_only \
--minimize \
--cnn_scoring rescore
#--scoring vinardo
|
01d941dddf43e581f4f6293224f531759c379c5416d0cf271e07c3e653b203fb | Shell | 266 | 13 | #!/bin/bash
cat Disease_sort_uniq_00_cut.vcf | while read line
do
eval $(echo "$line" | awk -F '\t' '{printf("CHR=%s\nPOS=%s",$1,$2)}')
COL=":"
SEP="-"
line="$CHR$COL$POS$SEP$POS"
echo "$line" >> Disease_sort_uniq_00_chr_pos.vcf
done
|
561a7923311336814011931435acb8c8b28aa0098073877e62b29f1b2d961524 | Shell | 267 | 11 | #!/bin/bash
SCRIPT="run-fmriprep-discovery-submit.pbs"
for SUBID in ../data/sub-*; do
SUBID=$(basename "$SUBID")
SUBID="${SUBID##*\-}"
echo "Submitting job for sub-"$SUBID""
qsub -v SUBID="$SUBID" -o logs/fmriprep-"$SUBID".out "$SCRIPT"
echo ""
done
|
81e385c503d545b1a8398192949df79406177fbb6b87732ec9102480a0380fdd | Shell | 267 | 10 | ## gpu id
gpus="2,3,4"
##dataset name
dataset='qm8'
##random split seed, 122, 123 or 124
seed=122
##path to your saved model file
model='moleculenet_model/qm8/bert_qm8_0.pth'
python src/reproduce.py --dataset $dataset --modelfile $model --seed $seed --gpu_ids $gpus |
3420aaf4b3e37e20cf66e5d5d857bdff4b1d58a1ddf28962b5ddd88d30443138 | Shell | 269 | 9 | #!/bin/bash
cat Disease_sort_uniq_00_dbsnp_others.vcf | while read line
do
tabix /mnt/data0/public_data/ALL_WGS_1000Genomes/ALL.wgs.shapeit2_integrated_snvindels_v2a.GRCh38.27022019.sites.vcf.gz $line >> Disease_sort_uniq_00_dbsnp_others_tabix.vcf
done
|
9a0964d41979d8872dbd08e457ed6305d28e4cd965dedb77358f761cedb046f4 | Shell | 269 | 9 | #!/bin/bash
cat Disease_sort_uniq_01_dbsnp_others.vcf | while read line
do
tabix /mnt/data0/public_data/ALL_WGS_1000Genomes/ALL.wgs.shapeit2_integrated_snvindels_v2a.GRCh38.27022019.sites.vcf.gz $line >> Disease_sort_uniq_01_dbsnp_others_tabix.vcf
done
|
3fb7e332d714c9d53f3d0efb5565fc39d2525513a0a11db11fa695e6b686b4f1 | Shell | 270 | 12 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
CONF_DIRS="configs/$1"
for DIR in $CONF_DIRS;
do
echo "/beegfs/nvardala/results_$1"
# mkdir "/beegfs/nvardala/results_$1"
# mkdir "/beegfs/nvardala/results_$1/logs"
# mkdir "/beegfs/nvardala/results_$1/data"
done |
99bf3cc338a1f76bec33aeb1e4676ee086f352d7ae0ff73d4daf4413e3b1c92a | Shell | 270 | 4 | cd /lustre04/scratch/nbrake/code/parameter_sensitivity_analysis
ID1=$(sbatch --array=1-20 --parsable run_parameter_combinations.sh)
ID2=$(sbatch --array=1-20 --depend=afterok:$ID1 --parsable analyze_simulations.sh)
sbatch --depend=afterok:$ID2 combine_unitary_spectra.sh |
83b679bce88daaa6a5270991757e6cd241a2462f93aaa85e610fc101673b4c74 | Shell | 278 | 13 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN_params.py;
# Run SUNS batch
python demo_test_batch.py;
# Run SUNS online
python demo_test_online.py;
# Run SUNS online with tracking
python demo_test_online_track.py;
|
33cd96170e5d1048dcab4d524f7f19be069e316c89c8ecc599ce3adb7a70c452 | Shell | 279 | 7 | gpus="2,3,4"
train='../datasets/pretrain/zinc_2_train.txt'
valid='../datasets/pretrain/zinc_2_valid.txt'
test='../datasets/pretrain/zinc_2_test.txt'
out='pretrain_out'
python src/pretrain.py --trainfile $train --validfile $valid --testfile $test --gpu_ids $gpus --out_path $out |
818a0544931cf36e1b0416c6e418632170006b0883a1e611fcdf28527f36cae8 | Shell | 279 | 8 | #!/bin/bash
# Setting up conda env for this project
# Note: this requires pytorch_p38 in SageMaker or DLAMI
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
pip install dgl-cu111 dglgo -f https://data.dgl.ai/wheels/repo.html
pip install -r requirements.txt
|
dbf08cb500c60d73d6809f1d15b9b7d3b74a716c9658aa8727bdbc9f2a92e132 | Shell | 281 | 12 | #!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --mem=4GB
#SBATCH --account=def-akhadra
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=analysis.log
folder=$1
module load matlab/2020a
matlab -nodisplay -r "combine_simulations('$folder')" |
c43dcbfcd4d79cc3e7a8cfbd16a54eb8b09251e1ab452cc1e4451a576e6b224f | Shell | 287 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../chtc/train_opt_lstm.py trainoptlstm_emb_40_hid_20_nlays_5.json |
3554cc6d511913c3d176c4e21724626b961d71633508652e065a0fa830aa2c35 | Shell | 288 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../chtc/train_opt_lstm.py trainoptlstm_emb_40_hid_20_nlays_3.json
|
5320b5c5a41b3607724e56e1e9eb6dc3c786b474674144be9b439f96ef7a29a1 | Shell | 288 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../chtc/train_opt_lstm.py trainoptlstm_emb_40_hid_80_nlays_3.json
|
60ad393eabb3419fde854d5efc73b8188a811b1a8b62129e277e68473bf34b4c | Shell | 288 | 11 | #!/bin/bash
cat Disease_00_filter-SNP.recode.dbsnp.vcf | while read line
do
eval $(echo "$line" | awk -F '\t' '{printf("CHR=%s\nPOS=%s",$1,$2)}')
COL=":"
SEP="-"
line="$CHR$COL$POS$SEP$POS"
echo "$line" >> Disease_00_filter-SNP.recode.dbsnp_chr_pos.vcf
done
|
b5a6ec7ac9128ae4f22de9a44b34c6ee7402a37df584c29d087066352cc75e74 | Shell | 289 | 13 | #!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=analysis.log
folder=$1
S=$2
module load matlab/2020a
matlab -nodisplay -r "analyze_simulations('$folder',$S)"
|
d57177c1f2b79c603dc2a1cf5189524341f2fccf00ce2fc8ed21e4041c577535 | Shell | 293 | 13 | #!/bin/bash
ffmpeg \
-i Grandbudapesthotel_HI.mkv \
-ss 00:41:50 \
-to 00:46:00 \
-map 0:v -map 0:a:0 \
-map_metadata -1 \
-c:a aac -vbr 5 \
-c:v libx264 -preset slow -crf 22 \
-vf scale=720x406,setsar=1:1 \
-copyts \
-y \
budapest_soundcheck.mp4
|
3e8d627fa4041932faf87b0c87cd8df90e6e0064347383808cb69b37418147fe | Shell | 294 | 16 | #!/bin/bash
f=placesCNN.tar.gz
if [ ! -f ${f} ]; then
echo "Downloading..."
wget http://places.csail.mit.edu/model/${f}
fi
echo "Extracting ${f}..."
tmp="tmp"
mkdir -p ${tmp}
tar xvf placesCNN.tar.gz -C ${tmp}/
mv ${tmp}/places205CNN_iter_300000.caffemodel ./
rm -rf ${tmp}
echo "Done."
|
5b80c71de70dca460f4908ab219a4abd7b70fc6818cbd8eb322275dee18c2715 | Shell | 294 | 8 |
find . -name *.m -exec dos2unix {} \;
find . -iname "*.md" -exec dos2unix "{}" \;
find . -iname *.yaml -exec dos2unix {} \;
find . -iname *.yml -exec dos2unix {} \;
find . -iname *.txt -exec dos2unix {} \;
find . -name *.gitignore -exec dos2unix {} \;
find . -name LICENSE -exec dos2unix {} \; |
5e51a38a25e50014907c89c7726f529dd8024d76465c0d8f5f5609959c311258 | Shell | 295 | 13 | #!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --gpus-per-node=1
#SBATCH --account=def-akhadra
#SBATCH --mem=48G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL
#SBATCH --output=analysis.log
folder=$1
module load matlab/2020a
matlab -nodisplay -r "compute_PSD('$folder')" |
148bb8fa13d394a199368e1dc5a2676cd074179da28ccac6100292785f760cce | Shell | 296 | 10 | #!/bin/sh
### An example script used to transform original data (SMILES) to PytorchGeometric Data
parent_path=$( cd "$(dirname "${BASH_SOURCE[0]}")" ; pwd -P )
cd "$parent_path"
GPU=0
echo "=====Transform data for qm8====="
CUDA_VISIBLE_DEVICES=${GPU} python ../src/tran_data.py --dataset=qm8
|
4ad3098adf9324da499ff3110b651ba8453b056f4e291b4af82b2c65120f29b5 | Shell | 297 | 10 | ## gpu id
gpus="2,3,4"
##path to your data file
file='path/to/your/test/data/test.csv'
##path to your saved model file
model='path/to/your/saved/model'
##path to save prediction.npy
out='out'
python src/predict.py --split_ready --testfile $file --modelfile $model --gpu_ids $gpus --out_path $out |
72c3346c99e3acbf4c8b1ae35abace503b5647fd573e4149344fb7681b806ba2 | Shell | 297 | 12 | #!/bin/bash
cat Disease_01_chr_pos_sort_uniq_dbsnp_tabix.vcf | while read line
do
eval $(echo "$line" | awk -F '\t' '{printf("CHR=%s\nPOS=%s",$1,$2)}')
COL=":"
SEP="-"
line="$CHR$COL$POS$SEP$POS"
echo "$line" >> Disease_01_chr_pos_sort_uniq_dbsnp_tabix_cut.vcf
done
|
21cd8f38b32f343bdfe964f7155560dda8dde30b4ede1ac933477fb6b0620be2 | Shell | 298 | 9 | sudo apt-get update
sudo apt-get install -y \
libfreetype6 fonts-dejavu fonts-dejavu-core fonts-dejavu-extra \
fonts-liberation fonts-liberation2 \
fontconfig \
xvfb libxrender1 libxtst6 libxi6 libxrandr2 libxinerama1 libxfixes3 libxext6
fc-cache -f -v
java -version
ant -version |
a6c6bd0dc2588b15eab34c1ffaa61cc28690dc5f05a3b67e597ddb3d006ef3f5 | Shell | 298 | 13 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN_params_1to3.py;
# Run SUNS batch
python demo_test_batch_1to3.py;
# Run SUNS online
python demo_test_online_1to3.py;
# Run SUNS online with tracking
python demo_test_online_track_1to3.py; |
94aac1774c18380e76a458bb43bfbc1ca57fa112480c394ad028fb574b45fbad | Shell | 300 | 14 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN.py;
python demo_train_params.py;
# Run SUNS batch
python demo_test_batch.py;
# Run SUNS online
python demo_test_online.py;
# Run SUNS online with tracking
python demo_test_online_track.py;
|
e1bbf8934e792d343f519f9a1a5509cc8281d4899611016a6e8fb2c14d36806d | Shell | 305 | 3 | scvae train scVAE/Adult-Brain_Lake_dge_normalized.tsv -m VAE --split-data-set --splitting-fraction 0.95 -r negative_binomial -l 16 -H 256 64 16 -w 200 -e 400 --learning-rate 0.0001
scvae train /scVAE/Adult-Brain_Lake_dge_normalized.tsv -m GMVAE -l 16 -H 256 64 16 -w 200 -e 100 --learning-rate 0.0001
|
505a2e40ac7662740ba706081ea37a8ea84ee4537a5e36dd4c3570b2ea4b5055 | Shell | 309 | 11 | #!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --gpus-per-node=1
#SBATCH --account=def-akhadra
#SBATCH --mem=48G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=analysis.log
module load matlab/2020a
matlab -nodisplay -r "analyze_simulations(${SLURM_ARRAY_TASK_ID})" |
df1ad3ead905ecf1f0a811b9e326efe1ca3c600d3d162ea959ed1f03f6c976a6 | Shell | 311 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v2.py trainoptlstm_emb_40_hid_20_nlays_3.json
|
f203e16c8d03ea57eeeadb7853dbb8a017a99d75a328697af7267a60c542beaf | Shell | 311 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v2.py trainoptlstm_emb_40_hid_80_nlays_3.json
|
0194cb292ed90609f26a7501d88d2081bbceb9090586ffeecedf902aeaf0b21b | Shell | 312 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=1-1:0:0
#SBATCH --mem 16000
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v2.py trainoptlstm_emb_80_hid_120_nlays_5.json
|
506a48ccefb458395b282c2a4aeaddc8692ab50af8299d29655010b544fab5a4 | Shell | 314 | 15 | #!/bin/bash
set -Euo pipefail
base_dir=$(dirname $(dirname $0))
# Run the pre-commit checks
SKIP=bandit pre-commit run --all-files
ruff check --config "${base_dir}/pyproject.toml" openfl/
exitcode=$?
ruff format --check --config "${base_dir}/pyproject.toml" openfl/
exitcode=$(($exitcode + $?))
exit $exitcode |
86b4632dc497a1c9964d00b6bed681e3af25993470da2ab65854aaf7b0ccc8b2 | Shell | 315 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on lipo (random split seed=$seed)====="
python ../src/main.py --dataset lipo\
--metric RMSE --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
done
|
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