sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
eb0983e0a5670c71e4db74fcd88c24f40212c351e8e6fb21626190bf2a712b17 | Shell | 134 | 8 | #!/bin/bash
#SBATCH -p RM-shared
#SBATCH -t 24:00:00
#SBATCH -N 1
#SBATCH --ntasks-per-node=32
conda activate tf_gpu
python qtaim.py
|
4b3c2ce76c464a73d19b438d526a89175a8dc09d60ddfeee3d1789391e617870 | Shell | 135 | 3 | set -ex
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --pool_size 50 --no_dropout --use_wandb
```
|
62ee0b3f73d7732e7f4cdd14704ca4d7dedc94ac77051a685b2cc410c2b4468d | Shell | 135 | 1 | python -u run_regnn.py --dataset ACM --model regcn --save_postfix ACM-regcn --feats_type 2 --weight_decay 0.005 --repeat 10 --device $1 |
edce06f55e77303d71c46cc1501151a1d36474e6e8793ac0df97fb096424dfd0 | Shell | 136 | 13 | #!/bin/bash
#fastqc of raw RNA-seq data
module load fastQC/v0.12.1
fileList="*.gz"
for file in ${fileList}
do
fastqc ${file}
done
|
469674883b815306733c4332fb804510aae76ee97314f36ca1ec6d9790491cd7 | Shell | 137 | 16 | #$ -S /bin/bash
#$ -cwd
#$ -V
#$ -l h_vmem=4G,h_rt=6:00:00,tmem=4G
# join stdout and stderr output
#$ -j y
#$ -sync y
{exec_job}
|
95fbd2b2e774d977da4178803b77438070c536de8e17aa86391ca3af666945c7 | Shell | 142 | 4 | # conda activate <environment>
read genomefa rest <<< "$@"
mkdir hisat2_index
hisat2-build -p 24 $genomefa hisat2_index/$(basename $genomefa)
|
166604847f9006407f7a8c8410bdae375b8038fa419904f70701114544c72f72 | Shell | 144 | 5 | #!/bin/bash
sudo veyon-cli authkeys delete tpp/public
sudo veyon-cli authkeys delete tpp/private
sudo veyon-cli config clear
sudo dpkg -r veyon
|
eca8f49be95d5c4da270361159777306935bb7d2e8bea2236af19bb38ff4ac40 | Shell | 144 | 13 | #!/bin/bash
for eachfile in *.bam
do
echo $eachfile
samtools index -@ 20 $eachfile
samtools view \
-@ 10 \
$eachfile \
| wc -l
done
|
4500ada43ac481556d4f11c8b498f60cd5feb78f44dd509d97c3ffebc182f80a | Shell | 145 | 2 | curl -LsSf https://astral.sh/uv/install.sh | sh
curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - && sudo apt install -y nodejs
|
d71fb74676abf809e13bb7e997c7c90788c6e33a5c6ed8ac64b740777317d0b5 | Shell | 146 | 5 | #!/usr/bin/env bash
if g++ -std=c++0x -pthread main.cpp cxstring.cpp readgenome.cpp reachtools.cpp -o ./reachtools
then
echo make_finished
fi
|
827ac64f1c66cc8777cda8479a52177bc131c5071004fc7d67ae076c143a8cb4 | Shell | 147 | 3 | curl -L http://cpanmin.us | perl - File::Basename;
curl -L http://cpanmin.us | perl - Getopt::Long;
curl -L http://cpanmin.us | perl - Bio::SeqIO;
|
34aa98e2fa80d6cd5d9046b5f1f7b7d4b1597c7d11768303c102aac92182275a | Shell | 149 | 8 | dirlist=$1
pwd=$(pwd)
for f in $(less $dirlist); do
cd $f
Rscript ~/bin/MESuSiE_run_from_lmm.R plink.race.list > MESuSiE_run.log
cd $pwd
done
|
703ab462d4f44b60cc648d5b25c7b9ff5e192ddc8a6eb13b382dbdf07bbc960d | Shell | 149 | 6 | #!/bin/bash
CUDA_VISIBLE_DEVICES="0"
python3 -m unittest "$@" || \
echo -e "\nTest(s) failed. Make sure you've installed all Python dependencies."
|
7862f7cb4f861250968aabd2e04f8b645d52bd9b765e61514226ee9b4496cbd1 | Shell | 149 | 9 | #!/bin/bash
# helloworld.sh
set -e
# Sleep for 5 minutes (300 seconds) to ensure the job runs for at least 5 minutes
sleep 300
echo "Hello world!" |
d694779afca70e283a0725a6bd092066a525435340bdef0c64c00a059bc65b6d | Shell | 150 | 4 | #! /bin/bash
sed -n '/include::{exampledir}\(.*\)\[\]/{s%%modules/ROOT/examples/\1%p}' \
modules/ROOT/pages/documentation.adoc | paste -sd " " -
|
01e3ff9330c90477a425d50f23d165ea931a52bf624bea038a6494b7ce4d74a5 | Shell | 151 | 4 | #!/bin/bash
# extract the current version from the code and print, e.g. 1.5.5
grep "__version__" alphadia/__init__.py | cut -f3 -d ' ' | sed 's/"//g'
|
ed674d0da8088b0584c15620bf7ecd0747ab8a2fb695965a9ac8d1cd25ad1d97 | Shell | 151 | 1 | python -u run_regnn.py --dataset IMDB --model regat --feats_type 1 --save_postfix IMDB-regat --weight_decay 0.005 --dropout 0.5 --repeat 10 --device $1 |
4d77d26c1f30ab1f0090b321780afb4777e77d0c06cd0ddd753b08e7df78091d | Shell | 152 | 12 | #!/bin/bash
set -e
. env/bin/activate
cd ../..
INPUT_FN=$1
# Run the Python script with the specified parameters
python code/condor.py @"$INPUT_FN" |
b25ea508f5bf84e98ca43fb70a641d42672f8cc640fdce8b52ac90d76c66f9f5 | Shell | 153 | 11 | #!/bin/bash
#fastqc of 18S sea urchin cell culture data
fileList="*.fastq.gz"
for file in ${fileList}
do
fastqc ${file} -t 5
done
|
b5e67d8e453051697df33c7d547113b3cc5e069f771ae8e11b6a26e5a5c54232 | Shell | 155 | 8 | #!/bin/bash
for f in [^d]*; do (head -n2 < $f; echo '
.. meta::
:robots: noindex
.. warning::
**DEPRECATED**
'; tail -n+3 $f) > deprecated_$f; done
|
c551603b641142852593b2efea3d99e330a5a4661efd82c79c99369e7326acb7 | Shell | 155 | 14 | #!/bin/bash
#fastqc of trimmed RNA-seq data
#K. Castellano
module load fastQC/v0.12.1
fileList="*.gz"
for file in ${fileList}
do
fastqc ${file}
done
|
f38809ee317fed195c542aeb243fdd1823dcce4dd0f2f874f4592c14dabbb749 | Shell | 157 | 7 | #!/bin/sh
flask db init
flask db migrate
flask db upgrade
SCRIPT_NAME=$APPLICATION_ROOT exec gunicorn -b :5000 --access-logfile - --error-logfile - run:app |
37442476baaaf80f1b1a63bcff0d39aa84a19bd8ccf5b9780efb75972e80c1b5 | Shell | 161 | 5 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile "config/config.PAQR.yaml" \
--rulegraph -np | dot -Tpng > rulegraph.PAQR.png
|
66a42440ef99aaa0af0f3b386e9e857fa3659ab2f2a3dfcd6923ba28578adc63 | Shell | 161 | 4 | #! /bin/bash
python mol2vec.py > data.txt
python -m gensim.scripts.word2vec_standalone -train data.txt -output vec.txt -size 200 -sample 1e-4 -binary 0 -iter 3
|
7d9b3768a354f89003a785c0551423f19ada3710087ca89035a00f771d7f7cab | Shell | 161 | 2 | set -ex
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --netG unet_256 --direction BtoA --dataset_mode aligned --norm batch
|
a02493d66011e63a5d5ff5371eb4e3131cf1fa06e391b14627a120ea40c98e55 | Shell | 161 | 8 | #!/usr/bin/env bash
source scl_source enable devtoolset-7
set -e
/veyon/.ci/common/linux-build.sh /veyon /build
/veyon/.ci/common/finalize-rpm.sh "centos-77"
|
a3d09d773ef4ff1fec3c2d87fdd641ec36c8d87bac289e2ba3d0fc86ae5538d7 | Shell | 161 | 1 | python -u run_regnn.py --dataset ACM --model regat --save_postfix ACM-regat --feats_type 2 --hidden 16 --weight_decay 0.005 --dropout 0.2 --repeat 10 --device $1 |
a74a9ca08779ad5b4e61fd8397b29abc250add31f808bbaeddfd1969bda36aa8 | Shell | 163 | 7 | DATAROOT={/path/to/fractal1k}
python pretrain.py \
data.set.root=$DATAROOT \
model=deit_tiny_patch16_224 \
optim.args.lr=3.0e-4 \
scheduler.args.warmup_epochs=10
|
49e1b379f76611ccac1f0cceea75e51639663a5730f3c85973d31340ec35aa1a | Shell | 164 | 5 | set -e
read circRNA_tab circRNA_reference <<< "$@"
head -n 1 $circRNA_reference | grep "^circRNA_ID"
grep -f <(grep -v circRNA_ID $circRNA_tab) $circRNA_reference
|
ebc755cdb63b625580b40312dba2fa44c43be92b6f6260611718c09e560b791f | Shell | 164 | 2 | set -ex
python test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --model test --netG unet_256 --direction BtoA --dataset_mode single --norm batch
|
f860d1cf2c34257602ed41a81088fefbff77a273188c17edc2d792b823e1db9d | Shell | 164 | 3 | gdown "https://drive.google.com/uc?id=1wmcVdSxXMWnlccJnfDrrUVlY3Fr87Ru0" -O training_output.zip
unzip training_output.zip -d training_output
rm training_output.zip
|
1096349aa709f6c98dd365beb248d914eaba2e83a909f3d5e2f8a70c79770527 | Shell | 165 | 4 | #!/bin/bash
. "$FSLDIR/etc/fslconf/fsl.sh" &&
. activate "${CONDA_ENV}" &&
xvfb-run -s "-screen 0 900x900x24 -ac +extension GLX -noreset" -a python /app/run.py "$@"
|
88720638b5d4759df4c08789359da5dbe5581f134e07d5d76b59cf0a8a463c9f | Shell | 165 | 11 | #!/bin/bash
# Siwei 29 Oct 2021
rm MD5_sum.txt
for eachfile in *.bam
do
echo $eachfile
printf "$eachfile\t" >> MD5_sum.txt
md5sum $eachfile >> MD5_sum.txt
done
|
24e55027658a570e54463d65675b66f7eaf8852bbc3fbff3f50a9e6812a7b293 | Shell | 167 | 14 | #!/bin/bash
bash runFSL-3_ostt.sh
bash runFSL-3_ostt_con.sh
bash runFSL-3_tstt.sh
bash runFSL-3_tstt-cov.sh
bash runFSL-3_ostt_con-cov.sh
/sbin/shutdown -h now
|
8108c62a406b5e43638d011d9f56ec593028aa5e8cd12264ab1fce6fc7cf4574 | Shell | 167 | 7 | #!/bin/bash
### need to run in the st environment
cores=45
snakemake -j $cores -s Snakefile --rerun-incomplete --resources --cluster 'sbatch -t 60 --mem=30g -c 45'
|
a0c4040cd1d3bd403d99494702aa018b71a473b154372114a5b1b9d7d76a7de4 | Shell | 167 | 5 | export APPTAINER_BIND="/orcd/pool/003/katiegal_shared/"
source /etc/profile
source /orcd/pool/003/katiegal_shared/hpc-infra/modules/activate.sh
module load snakemake
|
dab0abc71223b07757b1ab6176e1056cf6ee2d0ccc7eb926553df8b34eac2fd8 | Shell | 167 | 5 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile "config/config.DaPars2.yaml" \
--rulegraph -np | dot -Tpng > rulegraph.DaPars2.png
|
a6912aa4715b037a7a14e7a71d457c8b4d4dbc63a4759216c261b61368078f1c | Shell | 168 | 8 | #!/bin/bash
sbatch -n 10 \
-N 1 \
--mem=100G \
--wrap="cp -r /datacommons/wraylab/Alejo_Files/alejo/singlecell/atlas_metamorphosis/ /work/cjm124/scRNAanalysis/"
|
b454801895081c76504e2a1defb88525b11e5c0ae50cb74614ccdd8e044764ff | Shell | 169 | 5 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile "config/config.APAlyzer.yaml" \
--rulegraph -np | dot -Tpng > rulegraph.APAlyzer.png
|
ec63b8be94ab8cd1eda899a297a8b2ec797090e03b38daf4aa8ec7970b6a589e | Shell | 169 | 5 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile "config/config.[METHOD].yaml" \
--rulegraph -np | dot -Tpng > rulegraph.[METHOD].png
|
c5921fa7fcded28c63032933be4f92ba08fc675ee3c8654a571e9401c7c2db6a | Shell | 173 | 6 | #!/bin/bash
# downloads 722M dataset file
wget https://hmgubox.helmholtz-muenchen.de/f/1a014dc377f64b2b964c/?dl=1 -O datasets.zip
mkdir data; cd data
unzip ../datasets.zip
|
5075bd8744cc1f3e65843fa74c19a6920938d8ae8734ee30b9f8df71dc45e8f8 | Shell | 174 | 17 | #$ -S /bin/bash
#$ -cwd
#$ -V
#$ -l h_vmem=4G,h_rt=6:00:00,tmem=4G
#$ -l tscratch=20G
# join stdout and stderr output
#$ -j y
#$ -sync y
#$ -R y
echo $JOB_ID
{exec_job} |
a49df741ff31fbb15de520bc681102948e2274711fcc488696f06d4526a6b6c2 | Shell | 174 | 16 | #! /bin/bash
# Siwei 24 Apr 2024
for eachfile in *.bed
do
echo $eachfile
findMotifsGenome.pl \
$eachfile \
hg38 \
${eachfile/%.bed/} \
-size 100 \
-p 16
done
|
877dad91a879dae5bacf67fe4e2d6bcbfaf299e4f1cfe041be10aef99eb93f3a | Shell | 175 | 4 | echo "Pulling HOPV dataset from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/hopv.tar.gz
echo "Extracting HOPV dataset"
tar -zxvf hopv.tar.gz
|
8f17d60649ad258c1462ec4f358f9bbc0f64451b47682005580a52ad21ebff09 | Shell | 175 | 17 | #$ -S /bin/bash
#$ -cwd
#$ -V
#$ -l h_vmem=4G,h_rt=6:00:00,tmem=4G
#$ -l tscratch=20G
# join stdout and stderr output
#$ -j y
#$ -sync y
#$ -R y
echo $JOB_ID
{exec_job}
|
3cbfdbefe38abcac6fbb92435f3be8ab751c8f497e4feafa137b13d228d09163 | Shell | 176 | 9 | #! /bin/bash
STEP=50
START=0
END=$((4000-$STEP))
for (( COUNTER=START; COUNTER<=END; COUNTER+=STEP )); do
sbatch -c 32 --mem=64GB core.sh $COUNTER $((COUNTER+STEP))
done
|
4990f123b79aa18a1d4a18da755b1e58a085f3fc8b0672b01cf7daab7517a7c2 | Shell | 176 | 4 | echo "Pulling qm8 dataset from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/gdb8.tar.gz
echo "Extracting qm8 structures"
tar -zxvf gdb8.tar.gz
|
628ded1b7a1d686bbffaa1b12eaf0c1afbee10b9fa5eb1ce35f255797e7f8cae | Shell | 176 | 9 | #! /bin/bash
STEP=50
START=0
END=$((2000-$STEP))
for (( COUNTER=START; COUNTER<=END; COUNTER+=STEP )); do
sbatch -c 32 --mem=64GB core.sh $COUNTER $((COUNTER+STEP))
done
|
ef7d24a8fd43ddde368715b184257c6957daffca5064fc6eb71b22f37f54fbe0 | Shell | 176 | 4 | echo "Pulling qm9 dataset from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/gdb9.tar.gz
echo "Extracting qm9 structures"
tar -zxvf gdb9.tar.gz
|
0045778ab9d8d8dcde71b48ecb253757050ece4cc090cd598436c92944bfd4d0 | Shell | 178 | 4 | echo "Pulling GDB7 dataset from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/gdb7.tar.gz
echo "Extracting gdb7 structures"
tar -zxvf gdb7.tar.gz
|
3ce78ffb74f28a354572bd6b99014cbd0e333626c68a92a9ea4a09dfb8182a81 | Shell | 178 | 5 | #!/bin/sh
export PYTHONPATH="$PWD"
alembic upgrade head
exec uvicorn app.main:app --host 0.0.0.0 --port 8080 --workers 1 --root-path $APPLICATION_ROOT --forwarded-allow-ips '*'
|
7c09a622f90c829a13e201103b6d386cf7d2a477bcabe1130e8c2e7e45f79711 | Shell | 178 | 6 | #!/bin/bash
pubget run -q "(elephant[Abstract] AND brain[Abstract]) AND (2021[PubDate] : 2022[PubDate])" \
--labelbuddy \
. |
dc30552897571a269d1edeebe34893e68308844faf79794ffc324dfc65b4f64a | Shell | 178 | 1 | wget https://storage.googleapis.com/public-download-files/hgnc/tsv/tsv/locus_groups/protein-coding_gene.txt -P /Users/deeprobanerjee/Documents/bmi_project/BMI_monogenic/data/hgnc |
dc5c7cbcea675a8f390e92136af3611b97e1c5c4b54840cc5c9231a3c5f02a97 | Shell | 178 | 9 | #! /bin/bash
cd ../docs/algorithms || exit
for file in *.md
do
output=$(sed 's/\.md/\.ipynb/g' <<< $file)
jupytext --output ../../examples/$output $file --execute
done
|
836650ef2988271624be161b7c6d101f17071f873b331cc3df0f6a72d515ba2a | Shell | 180 | 5 | for version in random block culture_10; do
for split in training testing; do
wget -P data/$version/ ftp://m1613658:m1613658@dataserv.ub.tum.de/$version/$split.h5
done
done
|
b6c2499209f271f632410279cbb9e39efd82d626b88c15e06155444da4caeafe | Shell | 180 | 8 | #!/bin/bash
# This command requires tleap (ambertools) and obabel (openbabel)
log=$(basename $0).log
lib/process_PheEthOH_mol2_to_smi.sh |& tee ${log}
sed -i 's/^.*\r//' ${log}
|
e7f23eed7cbe82db608c21b1233233198dbf37f2bb6c5ab975c73095b81cf6ad | Shell | 181 | 7 | #!/bin/bash
#Make the L. variegatus 3.0 genome into a hisat2 index
#K. Castellano
module load histat2/v2.2.1
hisat2-build GCA_018143015.1_Lvar_3.0_genomic.fna Lvar3.0_hisat2index
|
5931afdae5ad285c840d5a9e69cb25b00d316c8e0a35d72fef14972f9d8cdf7d | Shell | 183 | 3 | mpirun -np 4 $LMP -in in.langevin.metal -p 4x1 -log log.langevin.metal -screen screen
mpirun -np 4 $LMP -in in.pimd-langevin.metal -p 4x1 -log log.pimd-langevin.metal -screen screen
|
f64a900dffb1fd1e4e431f93b09d7033d4205ddf6645f510d64fab5728ec7f83 | Shell | 183 | 10 | #! Siwei 09 Dec 2021
for eachfile in *.bed
do
cat $eachfile \
| grep -E '^chr[0-9]{1,2}' \
| grep -v 'random' \
| sed 's/chr//g' \
> ${eachfile/%.bed/_ENSEMBL_chr.bed}
done
|
01ed883ceca60c4ee354b4adc448d12d776f63a5535563c44a927d58a33de79d | Shell | 186 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk.py chimp cross_species_cluster
python pseudobulk.py chimp subclass |
2c7e67e1b37e03fccc09d226cc4e8ebf94d414fff1f5e30bb3b43db9661758c6 | Shell | 186 | 8 | #!/bin/sh
PAPERDIR=$(cd "$(dirname "$0")"; pwd)
docker run --rm \
--volume "${PAPERDIR}":/data \
--user $(id -u):$(id -g) \
--env JOURNAL=joss \
openjournals/paperdraft
|
8a85bfee2fe131555733a3d451e523eb0e444ea331af0471a0ec749ce5169ea8 | Shell | 187 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk.py human cross_species_cluster
python pseudobulk.py human subclass
|
8f84771c1599d1cfa765d50adf9d2d78b5692cb97ff5b99645cce918c10af066 | Shell | 188 | 11 | #! /bin/bash
set -e
apt-get update
apt-get install -y cmake file lsb-release bzr bzr-builddeb dh-make
codename=$(lsb_release -cs)
apt-get install -y g++
apt-get install -y qtbase5-dev
|
9b30fdeae775aa3a3d859785571208e875bfac104037212a4825b56f30b6729b | Shell | 188 | 12 | #!/bin/sh
NPROCS=1
if [ $# -gt 0 ]; then
NPROCS=$1
fi
bash ./clean.sh
python ./double-re-short.py $NPROCS $HOME/compile/lammps-icms/src/lmp_omp in.gREM > total_output.$NPROCS
exit 0
|
0748922d4717af95b0ce1a7d0a963ded66e02bc4a539b5e928153021cb5962f8 | Shell | 189 | 3 | URL=http://efrosgans.eecs.berkeley.edu/pix2pix_extra/fcn-8s-cityscapes.caffemodel
OUTPUT_FILE=./scripts/eval_cityscapes/caffemodel/fcn-8s-cityscapes.caffemodel
wget -N $URL -O $OUTPUT_FILE
|
ed5c815398147b084cb75bc99af701013cc51cb823e261a8d688f694c7fe85f4 | Shell | 189 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk.py rhesus cross_species_cluster
python pseudobulk.py rhesus subclass
|
7ad236bdd5b2e498e8f13cc9cb8cd6e98b115a9d9b65e8b1d83fd884dc616cfd | Shell | 191 | 3 | #!/usr/bin/sh
DIR="$( dirname "${0}" )" # Get the directory where this script is stored
jupyter-notebook --no-browser --ip=0.0.0.0 --port=8889 --NotebookApp.token='cmp' --notebook-dir="$DIR"
|
b0cf5fc5e35c425046afba67045a00655cdbed621cd49c0fd4a35ae258b57e7b | Shell | 191 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk.py gorilla cross_species_cluster
python pseudobulk.py gorilla subclass
|
c8adb23293f9a4cb988a51b122cfd8aec20a9605cd90f6ace125d219e2e3a43c | Shell | 191 | 5 | #!/usr/bin/env bash
set -e
DATASET=${1:-amazon}
python train.py --dataset ${DATASET} --config configs/${DATASET}.yaml
python eval.py --dataset ${DATASET} --ckpt outputs/${DATASET}/best.ckpt
|
de27fb0a34f73c171a16558dd6bbb99c9d21e8974721aa5e0261cc602df45f1e | Shell | 191 | 12 | #!/bin/env -S bash --login
cd /opt/src
echo " -o- Activate kmol environment"
conda activate kmol
echo " -o- Installing kmol package"
pip install --no-build-isolation .
cd /opt
rm -rf src
|
be213e4afd1b55ab7e3d1e11e7b9d77ad14495c9b0095be8939d38b77ae4eac3 | Shell | 192 | 5 | #!/bin/bash -l
# get data from harvard dataverse at https://doi.org/10.7910/DVN/FGWMUF
wget -nc https://dataverse.harvard.edu/api/access/datafile/7239342 -O cadpyr_l5.zip
unzip cadpyr_l5.zip
|
e7f17f7aa33fc771e7761efd9ea0784d4acda6cd6293142bbdc3135069dafd77 | Shell | 193 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk.py marmoset cross_species_cluster
python pseudobulk.py marmoset subclass
|
d4e14e1675eb7be212ef6e9139aa9a70370bdcb37bddfa58a08fa081dfd93891 | Shell | 195 | 14 | #!/bin/bash
# Siwei 24 Jun 2022
rm read_sum.txt
for eachfile in *.bam
do
echo $eachfile
samtools flagstat -@ 4 $eachfile \
| grep '0 mapped' \
| cut -d " " -f 1 \
>> read_sum.txt
done
|
7e8a407d4884dadafca8dd934a97011a2a4ccdfefdf4d64041a30c8e4f1d4ca7 | Shell | 196 | 9 | #!/bin/bash
#SBATCH -p RM-shared
#SBATCH -t 24:00:00
#SBATCH -N 1
#SBATCH --ntasks-per-node=8
module load AI/anaconda3-tf2.2020.11
conda activate base
module load orca/5.0.1
python controller.py
|
a8640fa7bc6e5af4af1457a496545158393230d808a31ff7a8e7573f47895ac8 | Shell | 201 | 8 | #!/bin/bash
set -ev
SCRIPT_PATH=$(dirname $(realpath -s $0))
cd ${SCRIPT_PATH}/..
uv sync --dev --python 3.12 --extra cpu --extra torch --extra jax --extra lmp --extra test --extra docs
prek install
|
bb425754654b8e7c2ef4021a62436c6b5cee5966ba4b41dbf15cbb5f7d66ff78 | Shell | 201 | 4 | #!/bin/bash
set -e
echo "Running pre_uninstall"
"BASE_PATH/bin/python" -c "from menuinst.api import remove; import os; remove(os.path.join(r'BASE_PATH', 'CellTracksColab', 'notebook_launcher.json'))"
|
dc27864bcb952245e1d9c6b85715234a415307ba56b155d7e07bc4021a216171 | Shell | 201 | 8 | #!/bin/bash
# Orftcr.sh
export NA=$1
export TF=$2
cd ${NA}
printf "${NA}_dipy_bk_Pugh.txt\n100\n${TF}\n${NA}_dipy_bk_plus.wig\n${NA}_dipy_bk_minus.wig\n\n" | perl ../uvpp_offset_yeastbs_mutations.pl
|
3de0105738f4dc6df1dd632c7abc3556b4b79bc0fc74fb3a93ed70f5e7024b6d | Shell | 202 | 7 | # Requirements
# module load bwa/0.7.17 seqtk/1.2 cutadapt/2.10
read directory config params <<< "$@"
mkdir -p $directory/cluster_log
snakemake -d $directory --configfile $config $params --keep-going
|
764a0d6e347a6a376bdbc57b08a63f9a327953e279efab1da651b5acfa8a95e1 | Shell | 202 | 5 | #!/usr/bin/bash
# Example script for generating contact information.
get_dynamic_contacts.py --topology 5xnd_topology.pdb --trajectory 5xnd_trajectory.dcd --itypes all --output 5xnd_all-contacts.tsv
|
9c9cf66e0c12a90e662b36243316319e5b30df72d48f527968ece8b0d61d41b5 | Shell | 204 | 8 | #!/bin/bash
source .venv/bin/activate
# Name of the FLASK application
export FLASK_APP=phas
# Path to the instance directory (config files and database will go here)
export FLASK_INSTANCE_PATH=/instance |
4c13c7b9b841a06d9bdcb721268293f0803caec5e8f531213b172bb65e3f169d | Shell | 205 | 4 | echo "Pulling pdbbind dataset from deepchem"
wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/pdbbind_v2015.tar.gz
echo "Extracting pdbbind structures"
tar -zxvf pdbbind_v2015.tar.gz
|
cd8b4627a7a2510e9d9b8fffdb39adf9fd783f26db67a942331e21f798915e9a | Shell | 206 | 7 | # Requirements
# module load slurm snakemake R/4.0.0 sambamba
# conda activate ciriquant w/ STAR/2.7.7a
#
read workdir configyaml params <<< "$@"
snakemake -d ${workdir} ${params} --configfile $configyaml
|
da2f217c2d1d8e6444a53beb88bb5c7e947970f95a72f4df8d02c2257dc58524 | Shell | 207 | 9 | #!/bin/bash
snakemake \
--rerun-incomplete \
--snakefile="workflow/Snakefile" \
--configfile="config/config.[METHOD].yaml" \
--cores 4 \
--use-conda \
--printshellcmds \
--dryrun
|
a611c8a8b2fda2f6110a254675b1f0a8472618917bb4f2596ca14eda499aaf85 | Shell | 208 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk_Sestan_2022_DLPFC.py human subclass
python pseudobulk_Sestan_2022_DLPFC.py human subtype |
a77610f42df0fa1b3f3be0a6a3a5bb9dc62958f137b5d9ebdb00d87994376226 | Shell | 209 | 3 | set -ex
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --netG unet_256 --direction BtoA --lambda_L1 100 --dataset_mode aligned --norm batch --pool_size 0 --use_wandb
```
|
3857957b772442e3de3a14dd3ed8dd916276f1a41a34a93b34b70c887155da42 | Shell | 210 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk_Sestan_2022_DLPFC.py rhesus subclass
python pseudobulk_Sestan_2022_DLPFC.py rhesus subtype |
654e7f5a7a5d299f85cbdcc099623fbf1ebc39cb2b73bb0d8c6846ea8064eec6 | Shell | 211 | 10 | #!/bin/bash
echo "Generate experiments ..."
python -c "import exputils
exputils.generate_experiment_files('experiment_configurations.ods', directory='./experiments/', verbose=True)"
echo "Finished."
$SHELL
|
79b9dae22f982ae5b76e117bd0cdc3ab5f810425b9a9d145fac128dcdd6847e0 | Shell | 212 | 9 | #!/bin/bash
# Orftcr.sh
export NA=$1
cd ${NA}
printf "${NA}_dipy_bk_inbetween_PughAll.txt\n${NA}_dipy_inbetween_bk_plus.wig\n${NA}_dipy_inbetween_bk_minus.wig\n\n" | perl ../indivtfbs_cpdsigs_Pugh_fastall.pl
|
8fc7046a984a7db661e796295779c5ea4af6de40f232e8354cf0fc11db97a764 | Shell | 212 | 9 | #!/bin/bash
snakemake \
--rerun-incomplete \
--snakefile="workflow/Snakefile" \
--configfile="config/config.APAlyzer.yaml" \
--cores 4 \
--use-singularity \
--printshellcmds \
--dryrun |
dd67afc0a3145e4083aa1872b318cf173935309d72b1352b9e41acf47fa9c1f9 | Shell | 212 | 9 | #!/bin/bash
snakemake \
--rerun-incomplete \
--snakefile="workflow/Snakefile" \
--configfile="config/config.DaPars2.yaml" \
--cores 4 \
--use-singularity \
--printshellcmds \
--dryrun
|
50f948c891f1446f47e034f40c02dc566f2bfd7351ceec4b83b875d9c5787d00 | Shell | 213 | 11 | #!/bin/bash
dx login --token TOKEN
ancestry=("eur" "sas" "afr" "eas" "amr" "mid" "oth")
for a in "${ancestry[@]}"; do
echo $a
# Call the helper script with arguments
./0_snp_anc_helper.sh "$a"
done
|
bdef95e5c64ed72baca1f6ec5c8c83ab9a584218c4929167cd872e6983122a47 | Shell | 213 | 4 | wget https://github.com/ulelab/cv_coverage/archive/refs/tags/v1.1.0.tar.gz
tar -xvf v1.1.0.tar.gz cv_coverage-1.1.0/cv_coverage.py
mv cv_coverage-1.1.0/cv_coverage.py scripts/
rm -r cv_coverage-1.1.0 v1.1.0.tar.gz |
f1131f65f14a25db455f4cc5d4ab52babc5f8430da30cb9c6ae36dfdff6175e6 | Shell | 213 | 15 | #!/bin/bash
DATASET=human
# DATASET=celegans
# DATASET=yourdata
# radius=0 # w/o fingerprints (i.e., atoms).
# radius=1
radius=2
# radius=3
# ngram=2
ngram=3
python preprocess_data.py $DATASET $radius $ngram
|
7c09a2886a6f6658ede2216363027faab7a969e38ad501ed108e4c21fcd06c5a | Shell | 214 | 10 | #!/bin/bash
while read requirement;
do
if conda install --yes $requirement; then
echo "Successfully install: ${requirement}"
else
conda install --yes -c conda-forge $requirement
fi
done < requirements.txt
|
ac45f1893c2d277f8c1e7696b5c1180cdb9e2ed41756759b2035351e4cc91bd1 | Shell | 214 | 8 | #!/bin/bash
HOST=127.0.0.1
PORT=38242
NNODES=1
NPROC=3
OMP_NUM_THREADS=8 CUDA_VISIBLE_DEVICES=0,1,3 \
uv run torchrun --nnodes=$NNODES --nproc_per_node=$NPROC --rdzv_endpoint=$HOST:$PORT src/saliency_overlap_v2.py |
fcedba2b24d3aafd78e12ba5fdff36eea3d49e841ac2048d9bc1883fb38a0163 | Shell | 214 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk_Sestan_2022_DLPFC.py marmoset subclass
python pseudobulk_Sestan_2022_DLPFC.py marmoset subtype |
5126c58bd4c2762e61d8f444d92efe993a223d4b44fdc5707904337948ed7e8d | Shell | 215 | 18 | #!/bin/bash
#
this_work_dir=$1
dtn=$2
#
#
this_scripts_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
#
WORK_ROOT_DIR=${this_work_dir}
work=${WORK_ROOT_DIR}/${dtn}
ls -al ${work}/HH_tmt10_human_jump
|
ab7ff94ec6923302b777e425dd1b2a544cca48d159a1fa77e6799583e33c6fb6 | Shell | 215 | 5 | #!/bin/bash
module load snakemake/5.16.0 slurm
snakemake -s /data/hilgers/group2/Shi/2024_circSplice/code/CircSplice_snakefile3.0 --use-conda --cluster "SlurmEasy -t 20" -j 30 --rerun-incomplete --latency-wait 60
|
333b9920e0d26f836a575b0dce9eaad5f3af1c650a09ebc7e2c0688accd45b0e | Shell | 216 | 7 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile="config/config.[METHOD].yaml" \
--cores 4 \ # adjust number as needed
--use-conda \ # or --use-singularity
--printshellcmds
|
35f04b0017ce168cceb179eb156af123c09ed12bf4aff1fee2f6d8d353b45220 | Shell | 216 | 15 | #!/bin/bash
#calculate mapping stats
#K. Castellano
module load samtools/v1.18
fileList="*.bam"
for file in ${fileList}
do
prefix=$(echo ${file} | cut -d "." -f 1)
samtools stats ${file} > ${prefix}.stats
done |
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