sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
51312adb5fc265d64d2d1127b4ecf8ca5cba59f4d57ab534c535639e14ce608a | Shell | 227 | 7 | #!/usr/bin/env bash
nohup python -m sample_qc.apply_hard_filters \
--exome_cohort 'chd_ukbb' \
--overwrite \
--write_to_file \
> apply_hard_filters.log 2>&1 & |
80499184e9f94a05aaff0f60cfc6ef399fd70bef9f62bb9070fe64132a2162e2 | Shell | 227 | 5 | for train_seed in 41 42 43; do
for split_seed in 41 42; do
python scripts/train.py name=0001_${split_seed}_${train_seed} trainer.seed=$train_seed datamodule.splitter.seed=$split_seed log_dir=logs/norm/
done
done |
e09fb7804aa570d64b00a1fc8a95849adb2cf214796323febf0cbd9ad5e5782c | Shell | 229 | 9 | if [ -e virtenv/bin/activate ]; then
source virtenv/bin/activate
elif [ -e virtenv/Scripts/activate ]; then
source virtenv/Scripts/activate
else
echo Cannot activate virtual environment
ls -R virtenv
false
fi
|
c009cae4166d8eb4a2e1f8168ad9a3d487df594faf1194075014ddd7942af193 | Shell | 231 | 6 | # command
humann_rna_dna_norm -d rna_dna_norm-dna.txt -r rna_dna_norm-rna.txt -o rna_dna_norm -m witten_bell
# output:
# 1) rna_dna_norm-smoothed_dna.txt
# 2) rna_dna_norm-smoothed_rna.txt
# 1) rna_dna_norm-relative_expression.txt
|
2fdef1878b3351e47f10b22e85bbc8828e87e465e3406f300e8ac6007973f2fa | Shell | 232 | 10 | #!/bin/bash
python3 ./standalone_stage2.py -f 0
python3 ./standalone_stage2.py -f 1
python3 ./standalone_stage2.py -f 2
python3 ./standalone_stage2.py -f 3
python3 ./standalone_stage2.py -f 4
python3 ./standalone_stage2_pred.py
#
|
7d048690151db999899e122237b88d5dd72d9ab810637c7dc5fb32e3de9e8367 | Shell | 232 | 10 | #!/bin/bash
# Merge a bunch of freqency files that have been calculated from 1000 genomes data
files=("$@")
for i in "${files[@]}"; do
echo "$i"
pop=$(basename "$i" | sed 's/_.\+//')
echo $pop
zcat "$i" | head
done
|
8ea11346bab60cc9f3c46b555ab67f22259c2b7548aae1791c8271abb76e3d5d | Shell | 233 | 6 | export TF_FORCE_UNIFIED_MEMORY="1"
export XLA_PYTHON_CLIENT_MEM_FRACTION="4.0"
export XLA_PYTHON_CLIENT_ALLOCATOR="platform"
export TF_FORCE_GPU_ALLOW_GROWTH="true"
colabfold_batch ./inputs.csv ./result_nothing --random-seed 55555
|
370453ceb4befdcf09695f93590df9847bf726835aeb7c68bbc949cb988a0a25 | Shell | 234 | 13 | #!/bin/bash
# Run CellBender
module load python/3.8.x-anaconda
module load gcc/12.2.0
source activate cellbender
for dir in `ls -d $PWD/*`
do
cellbender remove-background --input $dir --output $dir"/CellBender_out.h5" --cuda
done
|
ba2c5c7b06aec994988df219a0d56eb0fa9f5bb598e267b9fcfa8ea2e5216657 | Shell | 234 | 15 | #!/usr/bin/env bash
set -eux -o pipefail
file=$1
url=$2
if [ ! -f "$file" ]; then
# loop forever
while ! curl -L --fail -o "$file" -- "$url" ; do
echo "sleeping before trying again"
sleep 10s
done
fi
chmod +x "$file"
|
d5e57b8a94c91af289f24f502440f0d1d93d1866f4510816c32b07c353160766 | Shell | 234 | 13 | export N=$1
shift
export NEURONHOME=$N
export PATH=$N/bin:$PATH
source set_nrnpyenv.sh
export PYTHONPATH=$PYTHONPATH:$N/lib/python
if ! $* ; then
echo '
NEURON exited abnormally. Press the return key to close this window
'
read a
fi
|
d9e2f53a8c104ca6b553b140bdd6fa3d79ff37259286a6b7291473cee9a94abe | Shell | 234 | 5 | #!/bin/bash
mv build_A_to_dummy-data-B.chain buildA-to-buildB.chain
mv build_dummy-data-B_to_A.chain buildB-to-buildA.chain
mv build_dummy-data-B.fasta dummy-data-genome.B.faa
mv build_dummy-data-B.vcf dummy-data-primary-mapper.B.vcf
|
dfaf48c89bc2b2ac6585aa32d41263a3f474374bd2c3c46a56dd908f0c1555f9 | Shell | 235 | 7 | echo "==================== make ======================"
make
rm *.tree
echo "==================Create Tree===================="
./create_tree test.tif 0 100 1
echo "===================View Tree====================="
./view_tree *.tree
|
08aeeb1ea45e2d1a410f8d9fe72d16ad9c5ad56c00e4b2f6b706f2c5ce8cf856 | Shell | 237 | 12 | #!/bin/sh
sed -n '
1i\
static JNINativeMethod '$2'_methods[] = {
/Method/s/.*Method: *\(.*\)$/{"\1",/p
/Signature/s/.*Signature: *\(.*\)$/"\1",/p
/JNICALL/s/.*JNICALL *\(.*\)$/(void*)\1},/p
$a\
{0,0,0}\
};
' < $1 | tr -d '\015' > $2.h
|
8b528b7405e3865a113b77d123dbd9a076d53c3f00d4a5266b8306d7717c17b4 | Shell | 238 | 11 | #!/bin/bash
# source run_all.sh
#
echo Press Esc at anytime to skip example, F1 to interrupt
for f in *.py
do
echo "----------------------------------------"
echo "Processing $f script.."
python "$f"
done
|
9fa301d0c05fcaae89ae567474652800e4a0cef61e8a398dc4d8f8a724983451 | Shell | 241 | 4 | #!/bin/bash
# Run the tracking script
roicat --pipeline tracking --path_params /path/to/params.yaml --dir_data /directory/path/containing/data/ --dir_save /directory/path/to/save/outputs/ --prefix_name_save experimentName_20240131 --verbose |
d8b26f96ea0f6ca0e1be55cc7e4e79513c6bf1d473916a79138a95f2deb4f623 | Shell | 242 | 6 | curl -sSL https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -o /tmp/miniconda2.sh \
&& mkdir /root/.conda \
&& bash /tmp/miniconda2.sh -bfp /usr/local \
&& rm -rf /tmp/miniconda2.sh
export PATH=$PATH:/opt/conda/bin
|
bfb75283c49670065661a42462a64ebf1f144b3c29fb8c2b67beb748096bcd4b | Shell | 243 | 12 | #!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
# set -o xtrace
script_dir=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd "${script_dir}"
cd image/
docker build -t pod5-benchmark-base -f Dockerfile.base .
|
e0d2c17fa58d7388442c40b78a657f378ee807dc8f89dee47c7481a14945c53e | Shell | 246 | 13 | #!/bin/sh
if test "$1" = "" ; then
NVER=`$HOME/neuron/nrn/nrnversion.sh`
NDIR=NEURON-$NVER
else
NDIR="$1"
fi
cd $HOME
rm -r -f tmpdmg
mkdir tmpdmg
cp -R /Applications/$NDIR tmpdmg
hdiutil create -srcfolder tmpdmg -volname $NDIR -ov $NDIR.dmg
|
01a586ca9e8647ed810a82ee6abdb6ea72017aa478bfaf9c300564525c6abab4 | Shell | 247 | 7 | #!/bin/sh
### Launch the code for the times 0-5 and using 5 cores
codepath="../High_order_TS/simplicial_multivariate.py"
filename="./../Kaneko_CLM/trial_N50_T240_r175_eps012_008_03_0068_005.txt_kaneko"
python ${codepath} ${filename} -t 0 5 -p 5
|
db4db5f5972ec24f798e3772b489d04fb365f45cd5542fa13a0fcb638ec44eca | Shell | 247 | 6 | #!/bin/bash
cd /lustre/atlas2/nro101/proj-shared/BigNeuron/Bin_Vaa3D_BigNeuron_version1/
for i in {1..27}
do
/usr/bin/time -v sh /lustre/atlas2/nro101/proj-shared/BigNeuron/zhi/janelia_13k_batch_run/gaussiansmooth_set2/text_jobs/$i/0.txt
done
|
1fdb2154f5623d45f0e8fbd0eedcde6ecd19e47ccf6977d01d81b3b9763c7418 | Shell | 250 | 11 | #Fetch the atos N, CA and C to do the dockq comparisons
INDIR=./PDB
OUTDIR=./PDB/rewritten_complex/
for i in {1..219}
do
ID=$(sed -n $i'p' pdb_ids.txt)
FILE=$INDIR/$ID'_bc.pdb'
echo $i
grep 'N \|CA \|C ' $FILE > $OUTDIR/$ID'_bc.pdb'
done
|
916d9b325cadb9be1430f5264613a2bf773268d079ee69f4f196cfae05dee9cf | Shell | 251 | 7 | # Establish SSH to localhost for test_sftp_artifact_repo.py
ssh-keygen -t rsa -N '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
ssh-keyscan -H localhost >> ~/.ssh/known_hosts
ssh $(whoami)@localhost exit
export LOGNAME=$(whoami)
|
bb81fcbfe78b3b5235e11a05c088b66f2564f4d0bb18b77dbe48d7ddd03e3a38 | Shell | 251 | 13 | #!/bin/bash
set -euxo pipefail
# runs from the root directory of the repo
# find all WDL files
# WDL_FILES=$(find . -type f -name "*.wdl")
WDL_FILES=("./wdl/cellbender_remove_background.wdl")
for WDL in ${WDL_FILES}; do
miniwdl check ${WDL}
done |
a65d969ccada05ff7ba2d0af78f97674c3524eaf4d6ba07fd438fbb90e5bb66f | Shell | 252 | 14 | #!/bin/bash -l
#SBATCH -A [NAME OF PROJECT]
#SBATCH -t 40:00:00
#SBATCH -J train_unet
#SBATCH -C gpu
#SBATCH --gres=gpu:1
module load conda
export CONDA_ENVS_PATH=[path/to/project]
source conda_init.sh
conda activate dl-env
python train_new_unet.py
|
1c1a0e1a3b12d85b019819630a4b5cf5eaf865fa7c693c1243438841ec0d4bc2 | Shell | 253 | 8 | #!/bin/bash
./src/GangSTR \
--bam /storage/nmmsv/plat_genome/NA12878_S1.bam \
--ref /storage/resources/dbase/human/hg19/hg19.fa \
--regions test.bed \
--out test --verbose
#/storage/resources/dbase/human/hg19/pathogenic_loci_hg19.bed \
|
240d9de4b02b70dfb605851b29d1a1bb2a75dcc46f206050a0f8bdf0693ad44a | Shell | 253 | 3 | echo '##### SSDLabeler Artifact Classification #####'
python main_EEGArtifact.py --dataset preprocessing_SSD --data_type SSD --gpus 1 --worker 8 --max_epochs 5000 --batch_size 48 --clamp_value 10 --learning_rate 0.003 --seed 2 --training_log_name temp
|
c45a6e17ce21a0d6dbcf5c82a48d33c92a1c008efb4e59a61ef1f3e0a91407ea | Shell | 254 | 16 | #!/bin/zsh
set -e
# Deletes everything.
./hard_reset.sh
# Runs all the models that don't benefit from GPU
./run_cpu.sh
# Optionally, run the GPU models
if [[ "$1" == "-gpu" || "$1" == "--gpu" ]]; then
./run_lstm.sh
fi
./make_all_plots.sh --reset |
b4796cf99ff5d63efa59416e29366c5625874886f38812c72f5f9d9285306bf8 | Shell | 257 | 6 | #!/bin/bash
cd /lustre/atlas2/nro101/proj-shared/BigNeuron/Bin_Vaa3D_BigNeuron_version1/
for i in {1..23}
do
/usr/bin/time -v sh /lustre/atlas2/nro101/proj-shared/BigNeuron/zhi/bigneuron_annotation_consolidated_batch_run/gold166/text_jobs/1/$i.txt
done
|
be1eb5de278f3bd66bda83768c2ea22aba79d8110a9072dacd8f8d2635f36d07 | Shell | 257 | 5 | #!/bin/bash
echo "******************************************************************* RUN TEST **********************************************************************"
git clone https://gitlab.com/cfinan/bio-misc.git
pytest bio-misc/tests
echo "*** END ***"
|
3d0bef9d5cfea623f0e54252eb777de714346c14db3a96d1ddeacf400a2f4ee9 | Shell | 258 | 16 | #!/bin/bash
rm -f error.txt
if [ -f "${1}" ] ; then
docker run --rm \
-v `pwd`:/data \
flair-brainage-v1.0 \
compute_brainAge.sh \
/data/${1} ${2}
else
docker run --rm \
-it \
-v `pwd`:/data \
flair-brainage-v1.0 \
bash
fi
|
c77b5e2b0d8e12493f22d51c37b91bd7937d7e7b940a9927d6e170954f238612 | Shell | 258 | 13 | cd fsl/data/masks
roi=$1
# Convert 1mm masks to 2mm
flirt \
-in $roi.nii.gz \
-ref $FSLDIR/data/standard/MNI152_T1_2mm_brain.nii.gz \
-out 2mm/$roi.nii.gz \
-applyisoxfm 2
# Binarize 2mm masks
fslmaths 2mm/$roi.nii.gz -bin 2mm/$roi.nii.gz
|
02af12cd4dccfc07539e250294be3f5a79c73d1f27b8940067aa3779335f3af0 | Shell | 261 | 13 | #!/usr/bin/env bash
mkdir -p ./test/results/
python hase.py \
-th 0 \
-o ./test/results/ \
-mode regression \
-g ./test/ExampleStudy/examplestudy/ \
-ph ./test/ExampleStudy/phenotype/ \
-cov ./test/ExampleStudy/covariates/ \
-study_name example_study \
-maf 0 |
247a1797275223ee4b19beb77015e4bc284ad69938797ff4b0fb66d64f26e709 | Shell | 261 | 11 | #! /bin/sh -e
PYTHONPATH=$(dirname -- "$0")${PYTHONPATH+:}$PYTHONPATH
FLASK_APP=hbp_spatial_backend
FLASK_ENV=development
export PYTHONPATH FLASK_APP FLASK_ENV
HBP_SPATIAL_BACKEND_SETTINGS=$PWD/settings.cfg
export HBP_SPATIAL_BACKEND_SETTINGS
flask run "$@"
|
6573d2e7717388d6dc305c22f4617b2f4a4491035f64d2c5e8af485a5ae56b94 | Shell | 262 | 13 | #!/bin/bash
set -Eeuo pipefail
if ! command -v pre-commit 2>&1 >/dev/null
then
echo 'Please `pip install pre-commit` to run format.sh.'
exit 1
fi
base_dir=$(dirname $(dirname $0))
echo "Formatting all files..."
SKIP=api-gen pre-commit run --all-files
|
dcdeb26b25da18857f0e4b92a8d2c562ac4bed8c2749d37bb3f6d74a6bae711b | Shell | 262 | 13 | cd fsl/data/masks
roi=$1
# Convert 1mm masks to 2mm
flirt \
-in 2mm/$roi.nii.gz \
-ref $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz \
-out 1mm/$roi.nii.gz \
-applyisoxfm 1
# Binarize 1mm masks
fslmaths 1mm/$roi.nii.gz -bin 1mm/$roi.nii.gz
|
11921562206da654b6d645b4d579351e38c2d0f1752a99fc2b5ff9a559bdb24a | Shell | 264 | 6 | #!\bin\bash
cd /projects/verhaak-lab/verhaak_ref/gatk-legacy-bundles/b37/scattered_wgs_intervals/scatter-5
for f in `find . -type f -name "scattered.interval_list"`;
do
cat $f | grep -vE "^@" | awk 'OFS="\t" {print $1, $2-1, $3, $5, 0, $4}' > ${f%.*}.bed;
done |
2dfdbae7a344936a463de83782d21b174a7953aee06591b0c21a70607af06656 | Shell | 264 | 6 |
scp -r tfu42@orcus1.cc.gatech.edu:/project/molecular_data/graphnn/GCPN/result.1.done .
scp -r tfu42@orcus1.cc.gatech.edu:/project/molecular_data/graphnn/GCPN/result.2.done .
scp -r tfu42@orcus1.cc.gatech.edu:/project/molecular_data/graphnn/GCPN/result.3.done .
|
b6ceea41955e7e04c03da6d812114152e66cddfc43af5b4e621b6dabb99489de | Shell | 265 | 8 | #!/usr/bin/env bash
SCRIPT_PATH=$(dirname $(readlink -f "$0"))
set -e -o pipefail
# Pass all arguments directly to run_clair3.py which handles
# argument parsing, validation, default values, and pipeline execution
exec python3 "${SCRIPT_PATH}/run_clair3.py" "$@"
|
4be80a44c90f3b0851b442e4d48884774934e0415461eebc4fe9eae56030e93b | Shell | 267 | 16 | # place any test.mp4 file near with this file
# roscore
# rosnode kill -a
source ~/catkin_ws/devel/setup.bash
roscore &
P1=$!
rosrun midas_cpp talker.py &
P2=$!
rosrun midas_cpp listener_original.py &
P3=$!
rosrun midas_cpp listener.py &
P4=$!
wait $P1 $P2 $P3 $P4 |
29fae45039028c115f073c51c90aac65d36d9fb4b3997d48551c8925633a03b8 | Shell | 268 | 6 | zcat consensus.norm.vcf.gz | awk '{OFS="\t"; \
if (!/^#/ && (length($4) > 1 || length($5) > 1))\
{ print $1,$2-sqrt((length($4)-length($5))^2)-1,$2+sqrt((length($4)-length($5))^2)+1,$4"/"$5,"+" } \
else if (!/^#/) \
{ print $1,$2-1,$2,$4"/"$5,"+" } \
}' | less -S |
49e7b26e5019a8ea8f571d8042d15d110808ebc8dafb4db24a7851d0bc6b8a25 | Shell | 269 | 8 | sed -i.bak '/torch/d' ./requirements.txt
sed -i '/opencv-python/c\opencv-python-headless' ./requirements.txt
mv .env .env.bak
mv .env_aws_deploy .env
docker build --tag egg-counting .
mv -f requirements.txt.bak requirements.txt
mv .env .env_aws_deploy
mv .env.bak .env
|
64f55613c5ed60a514984c5e9b8ba9a6e66db8d69f8f1fa4c6205a3401a0ceb0 | Shell | 271 | 6 | #!/bin/bash
TAG=$1
ADAPTERS=/software/cellgen/cellgeni/bbmap/resources/adapters.fa
bbduk.sh in1=${TAG}_1.fastq.gz in2=${TAG}_2.fastq.gz out1=$TAG.bbduk.R1.fastq out2=$TAG.bbduk.R2.fastq ref=$ADAPTERS trimpolya=10 ktrim=r k=23 mink=11 hdist=1 tpe tbo &> $TAG.bbduk.log
|
9b7d8b11f8ad02193b6ed7674980c55377ce8f8583df77b675a40510a435fca4 | Shell | 271 | 11 | #!/bin/bash
arg1="Sleep_Cov"
arg2="Stroop"
# Export the variables so they can be used in the SLURM script
export ARG1=$arg1
export ARG2=$arg2
# Call sbatch
sbatch /p/project/cinm-7/bi1/ENIGMA_Sleep_Cog_Prediction/Code/AutoGluon/test_sbatch_AutoGluon_SHIP_Stroop_NAI.sh |
1081bd909d6ceb16eb3e4178046267129203f5ef68735b52843b446b5d0b03fc | Shell | 272 | 11 | #!/bin/bash
arg1="Sleep_APOE"
arg2="Stroop"
# Export the variables so they can be used in the SLURM script
export ARG1=$arg1
export ARG2=$arg2
# Call sbatch
sbatch /p/project/cinm-7/bi1/ENIGMA_Sleep_Cog_Prediction/Code/AutoGluon/sbatch_AutoGluon_SHIP_APOE_Stroop_NAI.sh |
d4a5017aab3e756e0f185c55465e3b2039f10f52852b87efd7e557bb87d5d7ba | Shell | 272 | 12 | #!/bin/bash
for z in 10 64;
do
for j in $(seq 10 10 100);
do
for i in $(seq 100 100 900);
do
python sample_mols.py --checkpoint checkpoints/model_updates_738999.ckpt --n-molecules $z --n-atoms $j --output-dir raw_mols --device cuda --start_t $i --beta 25
done
done
done
|
f2e3155112bea31745a20441c8b3a42331d318e78c02cfa94a57a7061048579b | Shell | 272 | 6 | #!/bin/bash
cd /media/Synology3/Robert/counting-3-debug
PATH=/usr/local/bin:$PATH
source /home/robert/miniconda3/bin/activate torchEnv
python project/scripts/test_counting_page.py http://10.122.168.56:5000\
watch parallel rca30@duke.edu /media/Synology3/Robert/counting-3 |
55a1f7f0a3a98ed99e91ab050e92936eaab6ad8acbf05eab525d71d1312cea3c | Shell | 273 | 10 | #!/usr/bin/env bash
. utils.sh
for exec_cores in 1 2 4; do
for total_cores in 4 8 16 32 64; do
num_exec=$((total_cores / exec_cores))
time_gatk "CountReadsSpark -I hdfs:///user/$USER/q4_spark_eval/WGS-G94982-NA12878.bam" $num_exec $exec_cores 1g 1g
done
done
|
78516ebbd4e70719b45934b5aec885cb3871ffd8646af975d802e08c9fba21f4 | Shell | 273 | 4 | #!/bin/bash
model_file="./data/trained_models/scAgeClock_GMA_model_state_dict.pth"
h5ad_folder="./data/pytest_data/train_val_test_mode/test/"
poetry run scAgeClock --model_file ${model_file} --testing_h5ad_files_dir ${h5ad_folder} --output_file './tmp/test_predicted.xlsx'
|
ac5a94b99e353267c42365ef1579e64e84bf4f74e0820412a873029f08d2b184 | Shell | 273 | 6 | #!/bin/bash
# first arg is fs install directory, second arg is subject directory, third arg is subject # ID, fourth is the filename of the first dicom file
export FREESURFER_HOME=$1
source $FREESURFER_HOME/SetUpFreeSurfer.sh
export FS_LOAD_DWI=0
mri_convert -i "$2" -o "$3" |
ff8440c76d7be6e2e10ebb5276e32fb750659a5a06dbc6cc825a7d416e24a9da | Shell | 273 | 6 | for name in $(cat `dirname $0`/../splits/atlas.csv | grep -v name | awk -F ',' {'print $1'}); do
wget https://www.dsimb.inserm.fr/ATLAS/database/ATLAS/${name}/${name}_protein.zip
mkdir ${name}
unzip ${name}_protein.zip -d ${name}
rm ${name}_protein.zip
done |
e07be0aad0bb00aea4926454fff2aecf1a0b570a36b41206ea7ec399e05e61f3 | Shell | 275 | 9 | #!/bin/bash
# point to working directory
cd /Path/to/personal/working/directory
apptainer exec --env PATH=/work/CaPTk/bin/install/appdir/usr/bin:$PATH \
/Path/to/Wrappers/captk.sif "$@"
# To use greedy, use:
# /Path/to/Wrappers/greedy_wrapper.sh greedy <parameters>
|
7125124e986fab4d9dad81e8793f49545a95ef821e8a8014ffa3cae1d43725ad | Shell | 276 | 13 | #!/bin/bash
chr=$1
bfile=resources/1000G_EUR_Phase3_plink/1000G.EUR.QC.$chr
outdir=QTL_fsVars/out/mpra_LDblock
snp_file="mpra.$chr.txt"
out_prefix="LDblock_mpra.$chr"
plink --bfile $bfile \
--blocks no-pheno-req \
--extract $outdir/$snp_file \
--out $outdir/$out_prefix
|
06045d0d9d2a10e2158cbde3419c0b6f11af2ecc0ece05de77a0c1a4481736c3 | Shell | 277 | 11 | #!/bin/bash
set -euo pipefail
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd $SCRIPT_DIR/..
cargo publish -p minijinja
cargo publish -p minijinja-autoreload
cargo publish -p minijinja-embed
cargo publish -p minijinja-contrib
cargo publish -p minijinja-cli
|
a50ffa4a4f202920865eb4a6c975857533e07f380e147ced7e258518d43ec3e4 | Shell | 278 | 11 | #!/bin/bash
arg1="Sleep_APOE"
arg2="Stroop"
# Export the variables so they can be used in the SLURM script
export ARG1=$arg1
export ARG2=$arg2
# Call sbatch
sbatch /p/project/cinm-7/bi1/ENIGMA_Sleep_Cog_Prediction/Code/AutoGluon/sbatch_AutoGluon_SHIP_Liege_APOE_Stroop_NAI.sh |
4a0523c9762e1b9a3fd33e2866cb56ebbd42f4bf4d7319ea7bbbe93c273ff44f | Shell | 279 | 13 | #!/bin/bash
# define project directory and working directory
pd=/your/project/directory/
wd=$pd/data/neural_differentiation_dataset/genomes
# create STAR indices for all genomes
for genome in hg38 gorGor6 macFas6
do
minimap2 -d $wd/$genome.mmi $wd/$genome.fa
done
|
cce3be4864b4f9598e9b5526dbe596c893382688c1b9ca1239b905c2c174f850 | Shell | 279 | 14 | #!/bin/bash
# source run_all.sh
#
echo #############################################
echo Press Esc at anytime to skip example
echo #############################################
echo
echo
for f in *.py
do
echo "Processing $f script.."
python3 $f
done
|
7c9c2a511bcc816f66b22f31e714075448444e79dee30dd50197d7343233f433 | Shell | 281 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_mc=1
end_i_batch_mc=300
n_batch_mc=5
for i_batch_mc in $(eval echo "{$sta_i_batch_mc..$end_i_batch_mc..$n_batch_mc}")
do
echo " $i_batch_mc"
sbatch --partition=batch --job-name dI_mc --export=ALL job_kir.sh
done
|
88831358ed3258a9514e84c891d0cc1cd8fa957607c9fedcdaab6d8da6cd4d39 | Shell | 281 | 14 | #!/bin/bash
# source run_all.sh
#
echo #############################################
echo Press Esc at anytime to skip example
echo #############################################
echo
echo
for f in *.py
do
echo "Processing $f script.."
python3 "$f"
done
|
e135e1e7ce045a95f908cb6cc7a0a35ac634215f9bba5a6a822a542fbf11c242 | Shell | 281 | 13 | #!/bin/bash
# Submits slurm batches to do HCP coherency calculations
# Created on 20200804 by Max B Wang
MEG_Dir=/media/mwang/easystore/Processed_Data/
subjList="EP1169 EP1188"
for subjID in ${subjList}; do
python3 Gen_SpatialAutocorrelation.py ${MEG_Dir} ${subjID} 0
done
|
34d953c62e69d94dc2202ffaa5350f2df5c3d5b82aa5b4e3cba17b98f1dac23b | Shell | 282 | 11 | #!/bin/bash
ABSOLUTE_SCRIPT=`readlink -m $0`
SCRIPTS_DIR=`dirname ${ABSOLUTE_SCRIPT}`
. ${SCRIPTS_DIR}/setup_java_env.sh 240G
trakemProjectPath=$1
dataPath=$2
outputJson=$3
runJavaCommandAndExit org.janelia.alignment.trakem2.Converter $trakemProjectPath $dataPath $outputJson $*
|
787ad8ac146b619e5d8255ac7f651b4f7124648ad27635dc7e868b85c65bede3 | Shell | 282 | 7 | # execute the recipe
qunex_container run_recipe \
--recipe_file="/data/qunex_run_recipe/example_2/dtifit_noddi.yaml" \
--recipe="dtifit_noddi" \
--nv \
--scheduler="SLURM,time=01-00,mem=32G,gres=gpus:1,jobname=qx" \
--container="/data/qx_containers/qunex_suite-1.3.1.sif"
|
1d4b4d53729be7ec2bbc7d53eb8b91295d86e9b7461b8861ff1fc60cabf5325b | Shell | 283 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_mc=1
end_i_batch_mc=300
n_batch_mc=5
for i_batch_mc in $(eval echo "{$sta_i_batch_mc..$end_i_batch_mc..$n_batch_mc}")
do
echo " $i_batch_mc"
sbatch --partition=batch --job-name dI_mc_km --export=ALL job_km.sh
done
|
0b7fec72bac524af70c8d280fd21c078157e404a62bb64d8d1c28047a834d7b0 | Shell | 284 | 13 | #!/bin/sh
#
#SBATCH --job-name=dI_mc
#SBATCH --output=data/output/dI_mc-%j.txt
#SBATCH --error=data/output/dI_mc-%j.err
#SBATCH --partition=batch
#
#SBATCH --time=3:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_var_mc_kir.jl $i_batch_mc |
1bc3f86e83fe0e9cca4e4087695114a9e6fd11fe0799dfdbac25631dfe0c5af5 | Shell | 284 | 5 | #!/bin/bash
# first arg is fs install directory, second arg is input volume, third arg is path to talairach_mixed_with_skull.gca, fourth is path to face.gca, and fifth is output volume
export FREESURFER_HOME=$1
source $FREESURFER_HOME/SetUpFreeSurfer.sh
mri_deface "$2" "$3" "$4" "$5" |
6b610916abbe97f6493525deda65d31cfefa9e5ec68151d5b9393a18afb8da66 | Shell | 284 | 8 | #!/bin/sh
#Synchronizes notebooks with script representations
# Copyright (c) Meta Platforms, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
pre-commit run 'jupytext' --files "$@"
|
b0f0479a6d7455e52c6ed254619dd8bc7eb09b6795fe335004e8df757297ad7c | Shell | 285 | 10 | #!/bin/bash
DATASET_DIR="qsar_datasets/datasets"
mkdir -p ${DATASET_DIR}
### QSAR Dataset
# Download dataset as deposited for MolkGNN
wget --content-disposition "https://api.figshare.com/v2/file/download/36760680"
tar -C ${DATASET_DIR} -xzv -f datasets.tar.gz && rm datasets.tar.gz
|
b1860845b1441325b21d236c25d1d7c3856a148724bca0e3ae4486685db54bab | Shell | 285 | 8 | #!/bin/bash
# Create the "data" folder if it doesn't exist
mkdir -p data
# Download the files into the "data" folder
wget -P data https://public-nlp-datasets.s3.us-west-2.amazonaws.com/wikihowAll.csv
wget -P data https://public-nlp-datasets.s3.us-west-2.amazonaws.com/wikihowSep.csv
|
bb568a90ccd1dd27f4c89e8489dfed07e15f37a035395b0e9dd694d93e92ca3c | Shell | 285 | 11 | #!/bin/bash
set -e
set -x
set -u
WORKDIR=${WORK:-/work}
mkdir -p ${WORKDIR}/docs
make html 2>&1 | tee ${WORKDIR}/builddocs.log
cp -r /src/nipype/doc/_build/html/* ${WORKDIR}/docs/
cat ${WORKDIR}/builddocs.log && if grep -q "ERROR" ${WORKDIR}/builddocs.log; then false; else true; fi
|
38e7faf0b3edbf7680e5679d77fbdb241493c5a5a216ba55ff648a10a8e9d3ed | Shell | 286 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_KM=1
end_i_batch_KM=51
n_batch_KM=5
for i_batch_KM in $(eval echo "{$sta_i_batch_KM..$end_i_batch_KM..$n_batch_KM}")
do
echo " $i_batch_KM"
sbatch --partition=batch --job-name dI_KMCaLs_inw --export=ALL job_km.sh
done |
6e400cf4a2681c7de83abf984dc7288f2ae3b7b281d05f02ce5ae1147fff99d2 | Shell | 287 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_KM=1
end_i_batch_KM=51
n_batch_KM=5
for i_batch_KM in $(eval echo "{$sta_i_batch_KM..$end_i_batch_KM..$n_batch_KM}")
do
echo " $i_batch_KM"
sbatch --partition=batch --job-name dI_KMCaLs_outw --export=ALL job_km.sh
done |
a47d4790661f6c9b8902019854ae8e52af13573d0918e05c18db36a64ed03855 | Shell | 287 | 10 | #!/bin/bash
dir='methane'
vibstate='0000'
mcCfg='mc_parameters-methane.json5'
x0file="${dir}/equilibrium_configuration_of_methane.xyz"
nnfile="${dir}/${vibstate}/nn-parameters-0.pt"
savepath="${dir}/${vibstate}/data_x.bin"
python mc_sampling.py ${mcCfg} ${x0file} ${nnfile} ${savepath} |
53ff5522cfbc9b504906a31b2eec6872039f2eeb761e194bbdaa153c3a565316 | Shell | 288 | 8 | set -xe
conda config --set solver libmamba
conda create -n testenv --yes -c conda-forge python=$LATEST_PYTHON_VERSION pip pytest scipy
conda activate testenv
pip install -v .
pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scikit-learn
|
9d24bc290ea6a9e895d00bdfc1ac57f76a039794fd3e6851a668b49270f6eb18 | Shell | 289 | 8 | #!/usr/bin/env bash
nohup python -m sample_qc.sex_chrom_coverage \
--exome_cohort 'chd_ukbb' \
--interval_to_include 'ssv5_idt_intersect' \
--overwrite \
--write_to_file \
> sex_chrom_coverage.log 2>&1 & |
e2b15981e4210b01f06407a3eb4332c1eb56ca5c39a329802fcc090dd16f56a8 | Shell | 289 | 9 | #!/bin/bash
nohup python -u data/scripts/vcf2mt.py \
--vcf_path 'file:///home/ubuntu/data/chd_ukbb_vcfs/all_chromosomes/*.vcf.gz' \
--output_path 'file:///home/ubuntu/data/hail_data/mts/chd_ukbb_08092020.mt' \
--force_bgz \
--overwrite \
> nohup.log 2>&1 &
|
adea4d5a676051079617f3642c6cc828b6cd32dd79963462bf910072c9a2692d | Shell | 290 | 1 | docker run -it --rm -v /home/lfaure/scFates/:/rapids/rapids/ louisfaure/scfates:env-ready /bin/bash -c "pip install -U pip && pip install . pytest coverage mock git+https://github.com/j-bac/elpigraph-python.git && coverage run -m pytest scFates/tests/test_w_plots.py && coverage report -m"
|
76f060500712a5cf7cf58a052dc4cc1a9bdbc5ab307b162924fadb96782d5c1a | Shell | 291 | 18 | #!/bin/bash
#SBATCH --time=00-00:10:00
#SBATCH --mem=20G
#SBATCH --cpus-per-task=22
# load bcftools
module load bcftools
# read the list of vcfs
vcflist=$1
# read the output vcf
outvcf=$2
# run bcftools concat
bcftools concat \
-f ${vcflist} \
-O z -o ${outvcf} \
--threads 22 |
689a26565f34c9b2afe7a552f51f6a3896af2133407537a9ef3365c527c28224 | Shell | 292 | 8 | task_name=$1
backbone=$2
pretrain_path=$3
bash sub_ptbxl_form.sh $task_name $backbone $pretrain_path
bash sub_ptbxl_rhythm.sh $task_name $backbone $pretrain_path
bash sub_ptbxl_super_class.sh $task_name $backbone $pretrain_path
bash sub_ptbxl_sub_class.sh $task_name $backbone $pretrain_path |
99d495a367ce4a0004a264176101238f3d49e30cec038831101d62f5020c5114 | Shell | 292 | 13 | #!/bin/sh
#
#SBATCH --job-name=dI_mc_km
#SBATCH --output=data/output/dI_mc_km-%j.txt
#SBATCH --error=data/output/dI_mc_km-%j.err
#SBATCH --partition=batch
#
#SBATCH --time=3:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_var_mc_km.jl $i_batch_mc |
dd05431c8c4f82ce3d3be73e3c06feffaee3644b87d0d9e9920ecc3be016521f | Shell | 292 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_Kir=1
end_i_batch_Kir=51
n_batch_Kir=5
for i_batch_Kir in $(eval echo "{$sta_i_batch_Kir..$end_i_batch_Kir..$n_batch_Kir}")
do
echo " $i_batch_Kir"
sbatch --partition=batch --job-name dI_KirCaLs_inw --export=ALL job.sh
done |
4446cf5c93a805291c4fb4e540edb2986dcf625e9d47ad440adec844510f708c | Shell | 293 | 7 | #!/bin/sh
cd `dirname $0`
export DISPLAY=:$RANDOM
Xvfb $DISPLAY -auth /dev/null &
export LD_LIBRARY_PATH=/global/homes/z/zhizhou/bigneuron/BigNeuron_bench_test_LBNL_v1/Vaa3D_BigNeuron_version1
/global/homes/z/zhizhou/bigneuron/BigNeuron_bench_test_LBNL_v1/Vaa3D_BigNeuron_version1/vaa3d $*
|
5bc3e5889c64573be5bcfd2e73a7ace61096caa0caee4de64a9fefccc6069248 | Shell | 293 | 5 | #!/bin/bash
echo "Running the script to extract embeddings..."
. ./.venv/bin/activate
# Run the Python script extract_embd.py extracting embeddings
python3 extract_embd.py --path_data "../../test_data/fusionai_test_sim.txt" --output_folder "./output" --output_name "nt_test" --embd_type "mean" |
793fb683175f0e5693c52aafd4e678178f0052ffab244525aac346dcb41a4a4e | Shell | 293 | 13 | #!/bin/bash
python3 ./standalone_stage1.py
python3 ./standalone_stage2.py -f 0
python3 ./standalone_stage2.py -f 1
python3 ./standalone_stage2.py -f 2
python3 ./standalone_stage2.py -f 3
python3 ./standalone_stage2.py -f 4
python3 ./standalone_stage2_pred.py
python3 ./standalone_test.py
#
|
f3175ddef8d8a807af4d4a3319a63fa6685c321f25aa30aca0d1e74939c0a2ad | Shell | 293 | 13 | #!/bin/sh
set -o allexport
sta_i_batch_Kir=1
end_i_batch_Kir=51
n_batch_Kir=3
for i_batch_Kir in $(eval echo "{$sta_i_batch_Kir..$end_i_batch_Kir..$n_batch_Kir}")
do
echo " $i_batch_Kir"
sbatch --partition=batch --job-name dI_KirCaLs_outw --export=ALL job.sh
done |
032b46a1bc122c3749b3590265fa6fc2f1790779b720ea0c1332979024b2a2d5 | Shell | 294 | 14 | taskname='your_taskname'
backbone='resnet18'
pretrain_path='your_pretrained_encoder.pth'
cd icbeb
bash sub_icbeb.sh $taskname $backbone $pretrain_path
cd ..
cd chapman
bash sub_chapman.sh $taskname $backbone $pretrain_path
cd ..
cd ptbxl
bash sub_ptbxl.sh $taskname $backbone $pretrain_path
|
a4902b18deafcf9ca8f2923283656429590184e0d63d87958167e536c02e747b | Shell | 294 | 10 |
IN_BED=/PATH/TO/TRIMMED.bed
OUT_BED=/PATH/TO/TRIMMED_supp.bed
SUPP_BED1=/PATH/TO/DISEASE.bed
SUPP_BED2=/PATH/TO/CODIS.bed
SUPP_BED3=/PATH/TO/ADDITIONAL.bed
echo ">> Supplement with extra loci."
python scripts/supplement_ref.py $IN_BED $OUT_BED $SUPP_BED1,$SUPP_BED2,$SUPP_BED3
echo ">> Step"
|
c12317a93ad2742ce2f618f5893ce76dbd3202a777275524d9da23e3a369efff | Shell | 294 | 10 | #!/bin/bash
dir='propane'
vibstate='ground_state'
mcCfg='mc_parameters-propane.json5'
x0file="${dir}/equilibrium_configuration_of_propane.xyz"
nnfile="${dir}/${vibstate}/nn-parameters-0.pt"
savepath="${dir}/${vibstate}data_x.bin"
python mc_sampling.py ${mcCfg} ${x0file} ${nnfile} ${savepath} |
18fce9814cc14ef790e7ed783c832d0214afeed2db7c8a64cd7cd6dd7fc7aa64 | Shell | 295 | 12 | #!/bin/bash
#SBATCH --job-name=TWAS_bre
#SBATCH --error=%x-%j.error
#SBATCH --out=%x-%j.out
#SBATCH --mem=10G
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=7-0:0:0
#SBATCH --partition=cpu2021,cpu2022,cpu2023,cpu2019,theia
Rscript TWAS_modelbuilding_GTEx.R $1 $2
|
0389a38b98c2bc69da7e3b8ccb250dfafb2a8d2b827f578a41f752ef9f7741a5 | Shell | 296 | 5 | #!/bin/bash
echo "Running the script to extract embeddings..."
. ./.venv/bin/activate
# Run the Python script extract_embd.py extracting embeddings
python3 extract_embd.py --path_data "../../test_data/fusionai_test_sim.txt" --output_folder "./output" --output_name "hyena_test" --embd_type "mean" |
1b2a355a92347491f789a0d2600a89f21a03cc20f4d434bf32fc39daeae6c993 | Shell | 297 | 10 | #! /bin/bash
git -C ./siibra-configuration fetch && git -C ./siibra-configuration merge --ff-only origin/master
docker pull docker-registry.ebrains.eu/siibra/siibra-api:latest
docker pull docker-registry.ebrains.eu/siibra/siibra-explorer:staging
docker-compose down
sleep 5
docker-compose up -d |
d057a099b03335be3f668e947573a49a53f570e280398c64eb81100316d4bcdf | Shell | 299 | 5 | #!/bin/sh -e
conda create -n rosettafold -c conda-forge cudnn cudatoolkit==11.3.1 cudatoolkit-dev==11.3.1 python==3.7
conda activate rosettafold
pip install jupyter papermill matplotlib tensorflow-gpu==1.15
pip install torch==1.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
|
8c134066fae0b099250958bd4b45fc43dd6fa40b13c43eb55cf43068e4f3c777 | Shell | 300 | 19 | #!/bin/bash
#SBATCH --time=01-00:00:00
#SBATCH --mem=20G
#SBATCH --cpus-per-task=1
module load gatk
# read the reference genome
ref=$1
# read the input gvcf
ingvcf=$2
# read the output vcf
outvcf=$3
# run GATK GenotypeGVCFs
gatk GenotypeGVCFs \
-R ${ref} \
-V ${ingvcf} \
-O ${outvcf}
|
579006970310fcaddc2f7efa14cd86c916bc7c7c2510be1ed52110e8d322c108 | Shell | 303 | 13 | #!/bin/sh
#
#SBATCH --job-name=dI_KMCaLs
#SBATCH --output=data/output/dI_KMCaLs-%j.txt
#SBATCH --error=data/output/dI_KMCaLs--%j.err
#SBATCH --partition=batch
#
#SBATCH --time=2:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_grad_km.jl $i_pCaLs $i_batch_KM |
589d1e34a7d70da98a297994529d933f9591f6fd6739ed77737ab7eeb83efb7e | Shell | 303 | 12 | set -e
#if [[ "$COVERAGE" == "true" ]]; then
# black --check $MODULE
#fi
if [[ "$COVERAGE" == "true" ]]; then
export NUMBA_DISABLE_JIT=1
pytest --cov=umap/ --cov-report=xml --cov-report=html --show-capture=no -v --disable-warnings
else
pytest --show-capture=no -v --disable-warnings
fi
|
90d5d851da4c514e68f15381b3b28797748b2996c9dcefbc1300c47641b66702 | Shell | 303 | 6 | #!/bin/sh
cd `dirname $0`
#export LD_LIBRARY_PATH=`pwd`
export LD_LIBRARY_PATH=`pwd`:/usr/local/MATLAB/R2015a/runtime/glnxa64:/usr/local/MATLAB/R2015a/bin/glnxa64:/usr/local/MATLAB/R2015a/sys/os/glnxa64:/usr/local/MATLAB/R2015a/sys/opengl/lib/glnxa64:/home/g/grad/zhihaozh/bigneuron/matlab
./vaa3d $*
|
f07699d6c6064a8e16ad66854538e05ebe3628e8ce6c80a2d3dddd555ae93b34 | Shell | 304 | 13 | #!/bin/sh
#
#SBATCH --job-name=dI_KirCaLs
#SBATCH --output=data/output/dI_KirCaLs-%j.txt
#SBATCH --error=data/output/dI_KirCaLs--%j.err
#SBATCH --partition=batch
#
#SBATCH --time=2:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_grad.jl $i_pCaLs $i_batch_Kir |
ba8dec1fe68ac68237a598a3a0809523262bf41495ba06269c8d25040c03af37 | Shell | 305 | 13 | #!/bin/sh
#
#SBATCH --job-name=dI_KMCaLs_inw
#SBATCH --output=data/output/dI_KMCaLs_inw-%j.txt
#SBATCH --error=data/output/dI_KMCaLs_inw--%j.err
#SBATCH --partition=batch
#
#SBATCH --time=2:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_inw_km.jl $i_batch_KM |
5c0fa6029fcc77e35f9dc4b475dd2a02ea3e690cf1622f06403105bb12ae3c82 | Shell | 307 | 12 | pyinstaller --noconfirm --clean \
--exclude-module PyQt4 \
--exclude-module PyQt4.QtCore \
--exclude-module PyQt4.QtGui \
--hidden-import=h5py.defs \
--hidden-import=h5py.utils \
--hidden-import=h5py.h5ac \
--hidden-import='h5py._proxy' \
--onedir \
test_pyinstaller.py
cp test.avi ./dist/test_pyinstaller |
c33fa880c977dc9efb597aed2ccd3567ac115a64958697da6c1a89b330067dfb | Shell | 307 | 5 | #!/bin/sh -f
# Written by CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
sh ${CBIG_CODE_DIR}/utilities/matlab/fslr_matlab/bash_scripts/CBIG_resample_metric_to_164k.sh $1 $2
sh ${CBIG_CODE_DIR}/utilities/matlab/fslr_matlab/bash_scripts/CBIG_border_to_fsaverage.sh $1 $2
|
51af4800ccf16babaefe36dd3726980dbc63e980a248c774859f1cafa81ffe08 | Shell | 308 | 7 | #!/bin/bash
conda create -n mbbn python=3.10 -y
conda activate mbbn
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu114
pip install nibabel nilearn nitime timm tensorboard numpy pandas wandb weightwatcher tqdm scikit-learn scikit-image matplotlib transformers lmfit |
f03624fd1c7cfb4a9467b35a214508c57be751f555827b3787251e5bf1ddaaae | Shell | 308 | 14 | #!/bin/bash
#SBATCH --job-name=peer
#SBATCH --error=%x-%j.error
#SBATCH --out=%x-%j.out
#SBATCH --mem=40G
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=7-0:0:0
#SBATCH --partition=cpu2021,cpu2022,cpu2019,theia,mtst
#conda activate peer
Rscript bulk_GTEX_PEER.R $1 #prostate
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