sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
8fbc602b0294cc2f8163fd6f9126324aeff85fe8a08d682dd05001aed85a0dfc | Shell | 210 | 10 | #!/bin/bash
#SBATCH --time=1:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=mriPostprocess
#SBATCH --array=0-25
module load python
python -m src.utils.MRI_PostProcessing |
a782b9ed0b2b505eeda7dd349e8f2326bd2921c903fa27f1deaf6efd375e366e | Shell | 210 | 10 | #!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=petPostprocess
#SBATCH --array=0-25
module load python
python -m src.utils.PET_PostProcessing |
5c892064f3498def6993e74877dcc91a181e05756f19215707061ba6d48fe53b | Shell | 212 | 10 | #!/bin/bash
# set default environment variables
set -e
WITH_CMAKE=${WITH_CMAKE:-false}
WITH_PYTHON3=${WITH_PYTHON3:-false}
WITH_IO=${WITH_IO:-true}
WITH_CUDA=${WITH_CUDA:-false}
WITH_CUDNN=${WITH_CUDNN:-false}
|
a7d82159949fa04fd5a7a55d6a7683c86177ee51101b53f2540e621730c50f33 | Shell | 213 | 10 | #!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=12
#SBATCH --job-name=petPreprocess
#SBATCH --array=1-25
module load python
python -m src.utils.PET_PreProcessingSafe |
382920fbe76908efafc2c41fe9367778e6dcf2fa39029a374142932963511ab8 | Shell | 217 | 8 | #!/bin/bash
OLD_TAG="$1" &&
NEW_TAG=$(echo "$OLD_TAG" | sed 's/v//g') &&
echo "$OLD_TAG" "$NEW_TAG" &&
git tag "$NEW_TAG" "$OLD_TAG" &&
git tag -d "$OLD_TAG" &&
git push $2 "$NEW_TAG" :"$OLD_TAG"
|
77325d3647b2d5dcde886c706aa490bbdd603bcd63c71007a7af0795dbb0b3ac | Shell | 219 | 9 | #!/bin/bash
$PYTHON setup.py install
# Add more build steps here, if they are necessary.
# See
# http://docs.continuum.io/conda/build.html
# for a list of environment variables that are set during the build process.
|
9595274260f61a3ac8b782220980f655beffc4e45cba0064732eef17af35c101 | Shell | 219 | 7 | #!/bin/bash
echo "Extracting the features from the frames..."
python ../../CNN/Activation_extraction_and_prep/activation_extraction_cnn_images.py \
--config_dir ../config.ini \
--config control_10 \
--init |
ead9158375c878e693fe7c2b39311d5a296c3ac5de36604a26b7f85701bedfd6 | Shell | 219 | 9 | #!/bin/bash
# Standard encoding analysis with all features for miniclips
source /home/alexandel91/.bashrc
conda activate encoding
python ./control_analysis_4.py \
--config_dir ../config.ini \
--config default |
f9c39743af228d39e83e2fd912105e3d5478bf8c30d455d3ce78e70920f6e3f5 | Shell | 219 | 9 | #!/bin/bash
# Standard encoding analysis with all features for miniclips
source /home/alexandel91/.bashrc
conda activate encoding
python ./control_analysis_5.py \
--config_dir ../config.ini \
--config default |
3b44b8dd0e753028c141af9e3edc1fb90843059c905db83adb006313729ef884 | Shell | 223 | 7 | #!/bin/bash
# Get PRAD_Behavioral_Dynamics folder
git clone --depth 1 https://github.com/scsnl/2024_Mistry_PRAD.git /tmp/2024_Mistry_PRAD
cp -r /tmp/2024_Mistry_PRAD/PRAD_Behavioral_Dynamics .
rm -r /tmp/2024_Mistry_PRAD
|
2dac28fe8b12a80c7dcd8c4ac6dd7d49da8d2e31c8742cd22fbf1063883bf5d3 | Shell | 227 | 10 | #!/bin/bash
analyzeRepeats.pl rna mm10 -d tags/* -raw -count genes -condenseGenes -strand - > rawMinus.txt
mv rawMinus.txt raw.txt
analyzeRepeats.pl rna mm10 -d tags/* -tpm -count genes -condenseGenes -strand - > tpm.txt
|
a519fd7b30c70c11ff485e9b79cca82e91a78c70d9eb4672a6b9ab661c0d4f4f | Shell | 230 | 13 | #!/bin/bash
# build the project
BASEDIR=$(dirname $0)
source $BASEDIR/defaults.sh
if ! $WITH_CMAKE ; then
make --jobs $NUM_THREADS all test pycaffe warn
else
cd build
make --jobs $NUM_THREADS all test.testbin
fi
make lint
|
e10fa4466f240cd1e6bc04b0eebb99833fa3aea212a36805dab530af6dbae887 | Shell | 231 | 14 | #!/bin/bash
# install extra Python dependencies
# (must come after setup-venv)
BASEDIR=$(dirname $0)
source $BASEDIR/defaults.sh
if ! $WITH_PYTHON3 ; then
# Python2
:
else
# Python3
pip install --pre protobuf==3.0.0b3
fi
|
0ca92bd1c4ed845fc6345e1990a335202f84eaa133b42681b9d0974e760d83d9 | Shell | 236 | 12 | for file in LCL RPE1-WT RPE-BM510 C7; do;
cd "$file"/fastq
for f in *.gz; do;
if [ ! -f "$f".md5 ]
then
# echo "File not found"
echo "$f"
md5sum "$f" > "$f".md5
fi
done;
cd ../..
done; |
ac825ce957f06da4c535304bf860b777f244ac669b6b3b9c246968cfd270ba1e | Shell | 243 | 8 | #!/bin/bash
echo "Extracting the features from the frames..."
python ../../CNN/Activation_extraction_and_prep/activation_extraction_cnn_images.py \
--config_dir ../config.ini \
--config control_11 \
--init \
--transform "vid"
|
64d079e31f2d847681591f8b6d9c696d62fab610e2423e77702cf31a871a4e70 | Shell | 254 | 12 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --nodes=1
#SBATCH --mem=8g
#SBATCH --cpus-per-task=4
#SBATCH --job-name=styleTransfer
#SBATCH --array=1-10
#SBATCH --partition=gpu
#SBATCH --gres=gpu:p100:1
module load python
python -m src.utils.styleTransfer |
497361ad07f14687eef52409ac54bdc17ca9ca5bf245fd5bb357cfa5b8a86bb3 | Shell | 257 | 19 | #!/bin/bash
# test the project
BASEDIR=$(dirname $0)
source $BASEDIR/defaults.sh
if $WITH_CUDA ; then
echo "Skipping tests for CUDA build"
exit 0
fi
if ! $WITH_CMAKE ; then
make runtest
make pytest
else
cd build
make runtest
make pytest
fi
|
0da4964ce9d65bf1d76b0812221d35c72ead4212004fb251f4301c7a8b0dd31d | Shell | 260 | 15 | #!/bin/bash
# Jeff Eilbott, 2017, jeilbott@surveybott.com
# inputs
FILE="${1}*"
FILE=$(echo $FILE | awk '{print $1}')
if [ -e "$FILE" ]; then
ARGS=
if [ -f "$FILE" ]; then
ARGS="$(cat $FILE)"
fi
BASE=$(dirname $0)
$BASE/ABA_bott.sh $ARGS
rm $FILE
fi
|
3ecec26f18e8cd451ad8c7a425a086b1e2e7cb75ea1dd9b65107a2c12b210962 | Shell | 263 | 11 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=structuralSimilarityIndex
#SBATCH --error=evaluateSSIM.err
#SBATCH --output=evaluateSSIM.out
module load python
python -m src.evaluation.calcSSIM |
f4c3df559b2be71a30a2aec26de984117d9a57dea388bc8dfbeaab06f704caf3 | Shell | 264 | 11 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=peak_signal_to_noise_ratio
#SBATCH --error=evaluatePSNR.err
#SBATCH --output=evaluatePSNR.out
module load python
python -m src.evaluation.calcPSNR |
d5e45c2e6ab549288b4710e3c89acfa4f6ecac26c84e5022c7b0005d9c2c3a9d | Shell | 267 | 9 | #!/bin/bash
# @ Stefan Sunaert - UZ/KUL - stefan.sunaert@uzleuven.be
#
# v0.1 - dd 23/09/2020
echo "This script will give back ownership of all fmriprep/mriqc directories. Type your password"
sudo chown -R $(id -u):$(id -g) mriqc* fmriprep* freesurfer*
echo "Done"
|
ded6dc7d979f0a57d185148bf7fe66dec4f96a0153e97169f93b3266f32b7fc2 | Shell | 270 | 9 | #!/bin/bash
# Generate any missing parameters
parmchk2 -i cb7_am1-bcc.mol2 -f mol2 -o cb7_am1-bcc.frcmod
parmchk2 -i b2_am1-bcc.mol2 -f mol2 -o b2_am1-bcc.frcmod
# Create benzene-toluene system.
rm -f leap.log {complex,vacuum}*.{crd,prmtop,pdb}
tleap -f setup.leap.in
|
4430c79b58f37d87ff53a95c615f5b47f2330162485cfd7680cf95c15f245740 | Shell | 274 | 14 | #!/bin/tcsh
# Name of system
setenv SYSTEM alanine-dipeptide
# Clean up old files, if present.
rm -f leap.log ${SYSTEM}.{crd,prmtop,pdb}
# Create prmtop/crd files.
tleap -f setup.leap.in
# Create PDB file.
cat ${SYSTEM}.crd | ambpdb -p ${SYSTEM}.prmtop > ${SYSTEM}.pdb
|
fed4c4db4b941e5798270e10325dca7b7e442da47df3610b6d176e2de9a2a3b3 | Shell | 274 | 5 | wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh &&
sh Mambaforge-Linux-x86_64.sh -u -b &&
/home/gitpod/mambaforge/bin/mamba init bash &&
source ~/.bashrc &&
mamba create -n snakemake -c bioconda snakemake -y
|
92cfeec30dec9d5edf4339b624227d80477550e11d33dc81a51e68f878f4c681 | Shell | 279 | 5 | #!/bin/bash
data="$1"
categ="$2"
bedtools intersect -a stats/${data}.FDRsig_eGenes.snps.bed.gz -b /path/to/hg19.refGene.${categ}_per_gene.bed.gz -wa -wb | awk '{OFS="\t"}{if($6==$10)print $4,$5,$6}' | uniq | gzip -c > stats/${data}.FDRsig_eGenes.snps_in_${categ}.withPIP.txt.gz
|
e13908c0eafc3c84831c397ac096c0ccb51671f815045708526f0458e995ef0d | Shell | 290 | 11 | DIRECTORY="experiments/Fig5_rep_unit_noise"
for i in $(seq 0 50)
do
python runner.py --params $DIRECTORY/runs/lr$i/params.json &
done
DIRECTORY="experiments/Fig5_error_unit_noise"
for i in $(seq 0 40)
do
python runner.py --params $DIRECTORY/runs/lr$i/params.json &
done |
2b54a0a5f07409c5129cf383837e7b81f3883eeb2f8c78f1f7de849e870b9d79 | Shell | 293 | 12 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=evaluateUtility
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateUtility.err
#SBATCH --output=evaluateUtility.out
python -m src.evaluation.calcUtility |
17663441e4e6387e0b864a70fa704b5a797fa4b3c4322b817c2a60889b2ca0ed | Shell | 294 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=baseGAN
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=baseGAN.err
#SBATCH --output=baseGAN.out
module load python
python -m src.train_scripts.train_baseGAN |
73ede7afbe72677b514395174f3a80aea885430972ce4bb09d2f86c098dc5ce2 | Shell | 296 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=inceptionScore
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateIS.err
#SBATCH --output=evaluateIS.out
module load python
python -m src.evaluation.calcIS |
049090f416e8f08f26b0f409b11b4e124cdf21a14ce71daa1c28825bbd1ab9c4 | Shell | 298 | 7 | #!/bin/bash
~/.pixi/bin/pixi run snakemake --cores 1 \
--configfile .tests/config/simple_config_mosaicatcher.yaml \
--sdm conda --conda-frontend mamba --nolock \
--force .tests/data_CHR17/RPE-BM510/plots/sv_clustering/stringent-filterTRUE-chromosome.pdf \
--skip-script-cleanup -p
|
938e742f7f3216a854bc3440af9e2706e284c63a96f7d2663b3719138663d540 | Shell | 298 | 15 | #!/bin/tcsh
# Name of system
setenv SYSTEM alanine-dipeptide
# Clean up old files, if present.
rm -f leap.log ${SYSTEM}.{crd,prmtop,pdb}
# Create prmtop/crd files.
tleap -f setup.leap.in
# Create PDB file.
#cat ${SYSTEM}.crd | ambpdb -p ${SYSTEM}.prmtop > ${SYSTEM}.pdb
python generate-pdb.py
|
26dfcd1ea9feca9d41ad5995e018a838f83141c01ad8bb97659e799790f70f2e | Shell | 300 | 10 | #!/bin/bash
cd "$(dirname "$0")/.."
python ./Model_training/training.py \
--datasetname MassSpecGym \
--path_train ./results/MassSpecGym/input_dataset.dataset \
--checkpoint_path ./weights/Pretrained_Weight_MetGenX.pth \
--batch_size 64 \
--lr 5e-6 \
--accelerator gpu \
--num_workers 4 |
219996b4929d49d0ff8462b513e6d0d229c6c6b0847b267c17f9794aeced07ef | Shell | 304 | 4 | # Prior to this, run makedocumentation.py
rsync -auvz --delete html/ /home/leeping/Dropbox/Public/ForceBalance_Doc/
cp ForceBalance-Manual.pdf /home/leeping/Dropbox/Public/ForceBalance_Doc/ForceBalance-Manual.pdf
cp ForceBalance-API.pdf /home/leeping/Dropbox/Public/ForceBalance_Doc/ForceBalance-API.pdf
|
79e76a61c508438bfb67c7e6c680e5a28ac3511f9d18582fa79f014887dc3a79 | Shell | 308 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=tune_mri2pet
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=tune_mri2pet.err
#SBATCH --output=tune_mri2pet.out
module load python
python -m src.train_scripts.tune_MRI2PET |
e98dafc1167f47a23064362d2c5372142ef13da6e19efc4c5c8cec8d3d730680 | Shell | 308 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=bitsPerDimension
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateBPD.err
#SBATCH --output=evaluateBPD.out
module load python
python -m src.evaluation.calcBitsPerDim |
0da956d434b9cd9e1b91b14df44050abed79bea6fc4a3dee4852e326c43dacaf | Shell | 309 | 12 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=evaluateUtilityFull
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateUtilityFull.err
#SBATCH --output=evaluateUtilityFull.out
python -m src.evaluation.calcUtilityFull |
245f12fdc5ec575f79aa1d0a25e0c8be31dabc0d869302ace2a253c9d27df947 | Shell | 309 | 12 | #!/bin/bash
set -ex
VERSION=`cat VERSION.txt`
singularity build --disable-cache ctat_mutations.v${VERSION}.simg docker://trinityctat/ctat_mutations:$VERSION
singularity exec -e ctat_mutations.v${VERSION}.simg env
ln -sf ctat_mutations.v${VERSION}.simg ctat_mutations.vLATEST.simg #for local testing
|
c47e12abdfc6267917b140ff8aca6a91ad9cf930edb9565213a63e6eeee7a376 | Shell | 309 | 13 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=frechetInceptionDistance
#SBATCH --partition=gpu
#SBATCH --gres=gpu:p100:1
#SBATCH --error=evaluateFID.err
#SBATCH --output=evaluateFID.out
module load python
python -m src.evaluation.calcFID |
a880a4054bf6cd47f4271e775451ebb92d2ae19c8ed7a3d860838d9645d49c06 | Shell | 310 | 12 | #!/bin/bash
#SBATCH --time=120:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=evaluateUtilityMMSE
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateUtilityMMSE.err
#SBATCH --output=evaluateUtilityMMSE.out
python -m src.evaluation.calcUtilityMMSE |
4ef841f9926ed2acf03398e726fdaa9f7f7a409b0ea1ad9ded4f974e485091b7 | Shell | 316 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=generateDataset
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=generateDataset.err
#SBATCH --output=generateDataset.out
module load python
python -m src.generation.generateDataset |
5f8a54d7cf777ceb7b7f09250295ec7488f71e1551b11407343d8f917cc261f0 | Shell | 316 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=generateSamples
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=generateSamples.err
#SBATCH --output=generateSamples.out
module load python
python -m src.generation.generateSamples |
0a7c0d78c7345b289b514da27078bfe1a81e26d9d985269e78a15b23a10d6868 | Shell | 317 | 14 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=baselineFID
#SBATCH --partition=gpu
#SBATCH --gres=gpu:p100:1
#SBATCH --error=baselineFID.err
#SBATCH --output=baselineFID.out
module load python
echo "FID"
python -m src.baselines.evaluation.calcFID |
0e59a6eabe789f8841b882c12e3026293412af6c27ba310d2d4f0981790d3b65 | Shell | 317 | 12 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=evaluateUtilityBinary
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=evaluateUtilityBinary.err
#SBATCH --output=evaluateUtilityBinary.out
python -m src.evaluation.calcUtilityBinary |
4e83133e5efb5b7934813519916492e171d5c41a19bdde734315e84ee9164b24 | Shell | 318 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=baseDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=baseDiffusion.err
#SBATCH --output=baseDiffusion.out
module load python
python -m src.train_scripts.train_baseDiffusion |
7af178576f66edad063192a4a5a61a687acaf0365c66cd438e6c5d63cdf5ed09 | Shell | 319 | 12 | #!/bin/bash
#SBATCH --job-name=copy_results
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --mem=8000
#SBATCH --cpus-per-task=1
#SBATCH --qos=standard
#SBATCH --partition=main
#SBATCH --time=04:00:00
cp -r /scratch/alexandel91/mid_level_features/results/EEG /scratch/alexandel91/mid_level_features/results_mvnn_epochs/
|
fb828f1a1164193384b852b80701013d11df7d62885a861bf6240ad101c1da8a | Shell | 320 | 13 | #!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=frechetInceptionDistance
#SBATCH --partition=gpu
#SBATCH --gres=gpu:p100:1
#SBATCH --error=evaluateRealFID.err
#SBATCH --output=evaluateRealFID.out
module load python
python -m src.evaluation.calcRealFID |
fc8926c37fa6b0053aa93bd43d92e8eeffdf1422ed8cba3732f8bed7b869e9fa | Shell | 324 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=pretrain_mri2pet
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=pretrain_mri2pet.err
#SBATCH --output=pretrain_mri2pet.out
module load python
python -m src.train_scripts.pretrain_MRI... |
7e050ec424b579967e739d8d254db20990c2d519482f9c4cd3b9bf9fecf38cfa | Shell | 327 | 16 | #!/bin/bash
# Standard decoding analysis (images)
source /home/alexandel91/.bashrc
conda activate encoding
sub=$1
export LD_PRELOAD=$CONDA_PREFIX/lib/libstdc++.so.6
# First step: Decoding
python ../EEG/Decoding/decoding.py \
--config_dir ./config.ini \
--config default \
--input_type "images" \
--su... |
246543dd407506d4996365bca38ffc0236e37909f6c01c6648a4906021558301 | Shell | 328 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=generateDownstream
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=generateDownstream.err
#SBATCH --output=generateDownstream.out
module load python
python -m src.generation.generateDo... |
731fd244e0b90fdf691ceb64185d95ad4e694e88c682dfa867841436c14f4da8 | Shell | 332 | 7 | # Sourcing environment variables
# This is because I compiled GROMACS with Intel compilers
. /opt/intel/Compiler/11.1/072/bin/iccvars.sh intel64
. /opt/intel/Compiler/11.1/072/bin/ifortvars.sh intel64
# This is because the modified Gromacs requires us to turn off solvent
# optimization (no longer needed)
export GMX_NO_... |
a985572b25c608ab45b82c42e672fe6b8eb32f45169a806f6995e138c419ece6 | Shell | 332 | 12 | #!/bin/bash
# Standard encoding analysis with all features for miniclips
source /home/alexandel91/.bashrc
conda activate encoding
echo "Extracting the features from the frames..."
python ../CNN/Activation_extraction_and_prep/activation_extraction_cnn_videos.py \
--config_dir ./config.ini \
--config default \
... |
5681c1c3e619cc28abe5c0e99211e1d476a5ff7ef989b67801adbc75657d0958 | Shell | 334 | 17 | #!/bin/bash
# Standard decoding analysis (miniclips)
source /home/alexandel91/.bashrc
conda activate encoding
sub=$1
export LD_PRELOAD=$CONDA_PREFIX/lib/libstdc++.so.6
# First step: Decoding
python ../EEG/Decoding/decoding.py \
--config_dir ./config.ini \
--config default \
--input_type "miniclips" \
... |
d9be67ed228e688a598414b2c13bea9024a41e73b362cc98ec8c4580d7c641ad | Shell | 334 | 14 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=baselineNonFID
#SBATCH --error=baselineNonFID.err
#SBATCH --output=baselineNonFID.out
module load python
echo "PSNR"
python -m src.baselines.evaluation.calcPSNR
echo "SSIM"
python -m src.baselines.evalu... |
daef43a01d8ab3c65dbab1b0ec30144c768a53df2294c27e57da1783cceb978f | Shell | 336 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=tune_mri2pet_noLoss
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=tune_mri2pet_noLoss.err
#SBATCH --output=tune_mri2pet_noLoss.out
module load python
python -m src.train_scripts.tun... |
0d0d4bcc55299f60c1870de41b2cd6c7e41fdf7cfd01305aee287a1e7eb6e61b | Shell | 338 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=mri2pet_noPretrain
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=mri2pet_noPretrain.err
#SBATCH --output=mri2pet_noPretrain.out
module load python
python -m src.train_scripts.train_... |
0d4cfd8711e2ce272a13ddaf5db8200ea78703bdcf7c4b63e2089a5aa86cd73d | Shell | 343 | 16 | #!/bin/bash
# List all SkyPilot clusters
clusters=$(sky status | awk '{print $1}' | grep '^minh-ft-daemon-')
if [ -z "$clusters" ]; then
echo "No matching minh-ft-daemon-* clusters found."
exit 0
fi
# Loop through each cluster and shut it down
for cluster in $clusters; do
echo "Shutting down $cluster..."
sky... |
2281f185fb5c52a25481062e12059c33f2914f18b0b9ade5055f40b118f51124 | Shell | 343 | 15 | #!/bin/bash
# Control analysis 7 with RSA
source /home/alexandel91/.bashrc
conda activate encoding
python ./control_analysis_7_rsa.py \
--config_dir ../config.ini \
--config default \
--input_type "miniclips"
python ./control_analysis_7_rsa.py \
--config_dir ../config.ini \
--config default \
... |
2e43da8a2c859e143c2b592c5e7edbc8df2233dcdd82a96282d01ea795b13f8d | Shell | 343 | 13 | FASTQDirList=$1
outDir=$2
SN=$3
splitCnt=16
mkdir -p ${outDir}/00.mapping/mergeList
for ((i=1;i<=$splitCnt;i++)); do
if [[ $(echo ${#i}) == '1' ]];then a=0$i; else a=$i;fi
while IFS= read -r line
do
ls $line/* | grep _$i.fq.gz >> ${outDir}/00.mapping/mergeList/$a.${SN}.Q4.fq.list
done ... |
a14219f2a913fadf14d57ebebe3230f1e2d5732ce8c22024bce22564033e75b7 | Shell | 343 | 15 | #!/bin/bash
# Control analysis 7 with CKA
source /home/alexandel91/.bashrc
conda activate encoding
python ./control_analysis_7_cka.py \
--config_dir ../config.ini \
--config default \
--input_type "miniclips"
python ./control_analysis_7_cka.py \
--config_dir ../config.ini \
--config default \
... |
9b03e7dd7d32edb4aafcc29cb228680e4212c7a14b1163cafe2b7bb2bf889d6f | Shell | 347 | 20 | #!/bin/bash
for i in `seq 2 14`; do
j=`printf "%02i" $i`
cd cluster-$j
mkdir settings
cp ../shot.mdp .
cat <<EOF > topol.top
#include "water.itp"
[ system ]
Clusters of $i water molecules extracted from liquid, solid, and gas phase
[ molecules ]
SOL $i
EOF
../modify-gro.py all.gro
mv new.... |
5b7ca169abd094cdcfd6dfdd75a6e589ee450262a182a25fb0715733b3ec59dc | Shell | 351 | 14 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=cdcGAN
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=cdcGAN.err
#SBATCH --output=cdcGAN.out
module load python
python -m src.baselines.train_scripts.train_cdcGAN
python -m src.basel... |
8089dca56aab352d44013895a63902133eaeeeecc9a2a1a3fdea0ba2be6066af | Shell | 351 | 14 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=dclGAN
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=dclGAN.err
#SBATCH --output=dclGAN.out
module load python
python -m src.baselines.train_scripts.train_dclGAN
python -m src.basel... |
284071191c3879616049388c5371a94e4f46dd84a96abd8658e8fd9ef08116d8 | Shell | 357 | 16 | #!/bin/bash
set -ex
#!/bin/bash
export CONDA_PREFIX=$PREFIX
mkdir -p build/conda
cd build/conda
cmake -DCMAKE_INSTALL_PREFIX=$PREFIX \
-DCMAKE_BUILD_TYPE=Debug \
-DCMAKE_TOOLCHAIN_FILE=$CONDA_PREFIX/lib/cmake/Qt6/qt.toolchain.cmake \
-G "Unix Makefiles" \
../..
cmake --build . --target install... |
fcd490a116d2d9341a0676c8c338514b64db95f5b3b4fcd4227c30daeff87700 | Shell | 357 | 10 | #!/bin/env bash
module purge
module use /hits/fast/mbm/hartmaec/sw/easybuild/modules/all
module load GROMACS/2022.5-plumed2.9_runtime--cuda-11.5
ml load Python/3.10.4-GCCcore-11.3.0
source /hits/fast/mbm/hartmaec/workdir/collagen_HAT/.venv_kimmdy_full/bin/activate
#source /hits/fast/mbm/hartmaec/workdir/collagen_HAT/... |
9c1f851cf43610e2583d65df89dbd563787b280efb67ad1d2271556f45477079 | Shell | 361 | 15 | #!/bin/bash
# Control analysis 7 with naive correlation
source /home/alexandel91/.bashrc
conda activate encoding
python ./control_analysis_7_naive.py \
--config_dir ../config.ini \
--config default \
--input_type "miniclips"
python ./control_analysis_7_naive.py \
--config_dir ../config.ini \
--c... |
d46a9981fa12bd418612f7dd6288662512f0a71f38a34d96fc9f3ac808b96af3 | Shell | 366 | 14 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=maskedGAN
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=maskedGAN.err
#SBATCH --output=maskedGAN.out
module load python
python -m src.baselines.train_scripts.train_maskedGAN
python ... |
2617685b134021f47ebc9a137bea8af3df014ec9e5cf5e4af40c045e3351ec7b | Shell | 372 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=tune_selfPretrainedDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=tune_selfPretrainedDiffusion.err
#SBATCH --output=tune_selfPretrainedDiffusion.out
module load python
pyth... |
340a3ed08655172b6dc60900c6af18216521ae28c03f7829ce5bbd0957214fbe | Shell | 376 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=tune_noisyPretrainedDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=tune_noisyPretrainedDiffusion.err
#SBATCH --output=tune_noisyPretrainedDiffusion.out
module load python
p... |
a9b7269f347d886bb098f71050be70bc5dd35d6e4104c7c561c626f77a3aebef | Shell | 376 | 14 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=paDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=paDiffusion.err
#SBATCH --output=paDiffusion.out
module load python
python -m src.baselines.train_scripts.train_paDiffusion... |
8f8b12a4d343103bf482e44bbf3b0c31685204296f193b5369b55bd7f0c000a1 | Shell | 379 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=selfPretrainedDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=pretrain_selfPretrainedDiffusion.err
#SBATCH --output=pretrain_selfPretrainedDiffusion.out
module load python
p... |
d7fafeef5e111264dd0150588b1efd84a5d4fd41f1d9619aa242aa99e7d7b794 | Shell | 383 | 13 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=noisyPretrainedDiffusion
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=pretrain_noisyPretrainedDiffusion.err
#SBATCH --output=pretrain_noisyPretrainedDiffusion.out
module load pytho... |
4d5dc219d8ba25ff1f4d9f65ec1cea68ad1f7329bbef71e2a903a9a796c20c35 | Shell | 385 | 13 | ROOT_DIR="${PWD}/..";
for snr in $(seq 0.1 0.1 2.5);
do
for i in $(seq 1 1 10);
do
sbatch run_optim.sh \
"${ROOT_DIR}/Rfiles/fit_data_snsrfit_ode_snr${snr}_sample${i}.R" \
"${ROOT_DIR}/Rfiles/param_init.R" \
"${ROOT_DIR}/samples/samples_snr${snr}_sample${i}.csv" \
"${ROO... |
fdd8a2708a0bbcb3d0df896e37f5fd3de3e0f10a675197a941750bc449795395 | Shell | 385 | 19 | #!/bin/bash
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=4G
#SBATCH --time=24:00:00
#SBATCH -o slurm_logs/slurm-%j.out
DATA_PATH=${1};
INIT_PATH=${2};
RES_PATH=${3};
LOG_PATH=${4};
./vep-snsrfit-ode-rk4 optimize algorithm=lbfgs iter=20000 save_iterations=0 \
data file=${DATA_PATH} \
init=${INIT... |
75605bc21a8cac59a41ad9c29329f4f576746f8630873194779474df7b45f7c3 | Shell | 388 | 13 | #!/bin/bash
#SBATCH --time=96:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=evaluateUtilityFull
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --array=0-7
#SBATCH --error=evaluateUtilityFull_%A_%a.err
#SBATCH --output=evaluateUtilityFull_%A_%a.out
python -m src.evalu... |
867712e47729a8dec706101257c09f376ea40c4cde218f8fd27cc6e6be6f2edf | Shell | 391 | 14 | #!/bin/bash
#SBATCH --time=240:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=diffAugmentGAN
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --error=diffAugmentGAN.err
#SBATCH --output=diffAugmentGAN.out
module load python
python -m src.baselines.train_scripts.train_di... |
339a52e42a5b79f7b63b64dc7450fc2a950cd0395b3de8d2aeddec7305357bf9 | Shell | 392 | 17 | #!/bin/bash
#SBATCH --ntasks=4
#SBATCH -t 24:00:00
#SBATCH -o slurm_logs/slurm-%j.out
STAN_EXEC_FNAME=${1}
DATA_FILE=${2}
OUTPUT_FILE=${3}
LOG_FILE=${4}
for j in `seq 1 4`;
do
./${STAN_EXEC_FNAME} variational iter=1000000 tol_rel_obj=0.01 output_samples=1000 \
data file=${DATA_FILE} output file=${OUTPUT_... |
efb6013775d8a6d9c9fc31cb5285cc42017da89b1473126587758eb161f777cd | Shell | 395 | 20 | #!/bin/bash -l
#
#SBATCH --job-name="errormc"
#SBATCH --time=06:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=32
#SBATCH --cpus-per-task=1
#SBATCH --partition=batch
#SBATCH --wait
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
export CRAY_CUDA_MPS=1
source ~/error-mc/numpy_model/MCenv/activate.sh
#pwd
#module list
wh... |
68536f1841643940eac4d2110b3452bb4e09b94019e760acabcc99ad9fae9252 | Shell | 400 | 18 | #!/bin/bash
# setup a Python virtualenv
# (must come after install-deps)
BASEDIR=$(dirname $0)
source $BASEDIR/defaults.sh
VENV_DIR=${1:-~/venv}
# setup our own virtualenv
if $WITH_PYTHON3; then
PYTHON_EXE='/usr/bin/python3'
else
PYTHON_EXE='/usr/bin/python2'
fi
# use --system-site-packages so that Python w... |
650f312bd4d584123a15fef074c8c7efba9b842794e23dc40332bd5ddc20b1d1 | Shell | 402 | 20 | #!/bin/bash
# Build documentation for display in web browser.
PORT=${1:-4000}
echo "usage: build_docs.sh [port]"
# Find the docs dir, no matter where the script is called
ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )"
cd $ROOT_DIR
# Gather docs.
scripts/gather_examples.sh
# Generate developer docs.
make docs
# ... |
49b34568e40077109816380a650d74fb3fe2e4504f15538e0b52a7054188326a | Shell | 404 | 14 | #!/bin/bash
# Standard encoding analysis with all features for miniclips
source /home/alexandel91/.bashrc
conda activate encoding
echo "Extracting the features from the frames..."
python ../CNN/Activation_extraction_and_prep/activation_extraction_cnn_images.py \
--config_dir ./config.ini \
--config default \
... |
5f57b42263d199cfc392e347fef57b112cde8ed39a30f86cf59ee78197024011 | Shell | 407 | 14 | #!/bin/bash
#SBATCH --time=16:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=generateDownstreamParallel
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --array=0-4
#SBATCH --error=generateDownstreamParallel_%a.err
#SBATCH --output=generateDownstreamParallel_%a.err
modu... |
63558e1263eebb97051bdebd4ba7a5cd1ffd3eae683a48161422c3169f913285 | Shell | 416 | 9 | DIRECTORY="experiments/Fig3_multilayer_comparison"
for net in 2-1 4-2-1 8-4-2-1 16-8-4-2-1 32-16-8-4-2-1
do
for model in ann errormc sacramento2018 dPC
do
python runner.py --params $DIRECTORY/$net/$model/params.json --task fw_only --compare BP &
python runner.py --param... |
be585179896af194390657e755b44852cc67fcc610fb35e5a9b008358eedaef1 | Shell | 419 | 14 | #!/bin/bash
#
# Copyright (c) 2018 German Cancer Research Center (DKFZ).
#
# Distributed under the MIT License (license terms are at https://github.com/DKFZ-ODCF/AlignmentAndQCWorkflows).
#
#PBS -l nodes=1:ppn=2
#PBS -l walltime=2:00:00
#PBS -m a
#PBS -l mem=4g
#PBS -j oe
R -f ${TOOL_ON_TARGET_COVERAGE_PLOTTER_BINARY... |
bd13f176158beee1811614624ccd3b2a214e7300db9c1118f146b6a1d8cf1783 | Shell | 421 | 11 | #
# Copyright (c) 2018 German Cancer Research Center (DKFZ).
#
# Distributed under the MIT License (license terms are at https://github.com/DKFZ-ODCF/AlignmentAndQCWorkflows).
#
# Unstage several files which might confuse git and which don't neccessarily need to be added to the repo everytime.
files="$(basename $PWD).j... |
08235632b4636e68ae9a52872a02826492a922de1801215636bdae68ce9e42e7 | Shell | 422 | 16 | #!/usr/bin/env bash
set -euo pipefail
repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
cd "$repo_root"
matlab_bin="${MATLAB_BIN:-matlab}"
xvfb_screen="${XVFB_SCREEN:-1024x768x24}"
if [[ $# -gt 0 ]]; then
matlab_command="$*"
else
matlab_command="addOptickaToPath; cd(optickaRoot); addpath('tests'); runOp... |
4b83e3b61f930b8aeba7ab6350f6bf15e03e1122f223d09421221ff5944eda64 | Shell | 428 | 19 | #!/usr/bin/env bash
# swap_base_path.sh <old_prefix> <new_prefix>
# Example: ./swap_base_path.sh "/home2/ebrahim" "/home3/ebrahim2"
set -euo pipefail
if [ "$#" -ne 2 ]; then
echo "Usage: $0 <old_prefix> <new_prefix>" >&2
exit 1
fi
OLD=$1
NEW=$2
git grep -IlZ "$OLD" -- . ':(exclude).git' \
| xargs -0 sed -i... |
22e3e7476d82a2896ac29135ae15c7a175bb1f93f78731ebcfad593d3f1e50fe | Shell | 430 | 9 | DIRECTORY="experiments/FigA1_multilayer_comparison_hierarchical"
for net in 2-1 4-2-1 8-4-2-1 16-8-4-2-1 32-16-8-4-2-1
do
for model in ann errormc sacramento2018 dPC
do
python runner.py --params $DIRECTORY/$net/$model/params.json --task fw_only --compare BP &
python run... |
3c387190782f8868870f65c58ad5e9cab374a93d1198d802cb2fa0c45fc5776f | Shell | 431 | 9 | DIRECTORY="experiments/FigA2_multilayer_comparison_ideal_lat_inh"
for net in 2-1 4-2-1 8-4-2-1 16-8-4-2-1 32-16-8-4-2-1
do
for model in ann errormc sacramento2018 dPC
do
python runner.py --params $DIRECTORY/$net/$model/params.json --task fw_only --compare BP &
python ru... |
4996af7884a7901749a09c9d5dcde331695fbb1611e51aaf2c04000449c7413c | Shell | 435 | 14 | #!/bin/bash
#SBATCH --time=16:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=generateDownstreamParallelTweaked
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --array=0-4
#SBATCH --error=generateDownstreamParallelTweaked_%a.err
#SBATCH --output=generateDownstreamParalle... |
2a36aac9e5a1d3a469b33a1408220ad0984e2f99a74a9e005eb4c61c3e4e8a32 | Shell | 439 | 12 | #!/bin/sh
# Thanks to cdiener: https://hub.docker.com/r/cdiener/cobra-docker/~/dockerfile/
# For the solution of simply getting the bins and python hooks
#
echo "Installing and Moving CPLEX files"
## Default Py3.9 install
if [ -d /solvers/ibm ]; then cd /solvers/ibm/ILOG/CPLEX_Studio221/cplex/python/3.9/x86-64_linux/... |
e4391363a54960f2a9247f201079447533587ef04c39ca318da55b2c653fef6c | Shell | 450 | 19 | #first variable is BIDS subject label
# -B /data/MoL_clean/BIDS/:/data \
singularity run --cleanenv \
-B /data/MoL_experts/data/:/data \
-B /data/MoL_clean/fmriprep:/out \
-B /data/MoL_clean/scratch:/work \
fmriprep-23.0.2.simg \
/data /out \
participant \
--ignore=slicetiming \
--use-syn-sdc \
--fs-license... |
5b09bd80f83dc3801149ef8c3d5def6265d4e30ef22fc96f262bd4215e12d77d | Shell | 454 | 13 | ROOT_DIR="$(echo "$(cd ../ && pwd)")"
for sigma_prior in $(seq 0.1 0.1 1.0);
do
for i in $(seq 1 1 10);
do
sbatch run_optim.sh \
"${ROOT_DIR}/Rfiles/fit_data_snsrfit_ode.R" \
"${ROOT_DIR}/Rfiles/param_init_sigmaprior${sigma_prior}_sample${i}.R" \
"${ROOT_DIR}/samples/samples_sigmapr... |
fa5d9bf17662605c5bb9766b0067777c90448aeda5530098b98183c29a11ccfd | Shell | 459 | 13 | #!/bin/bash
# Script to download the Kinetics-400 dataset using torchvision.datasets.Kinetics.
num_workers=$1
echo "Removing old Kinetics-400 dataset directories if they exist..."
rm -rf /scratch/alexandel91/mid_level_features/kinetics_400/train
echo "Downloading Kinetics-400 dataset..."
python ./download_kinetics.p... |
549945c6b6b747c7859d1e6035cfc0e594f86898583e65f844923671c93d2836 | Shell | 468 | 11 | #!/bin/bash
# Run the entire pipeline on a sample dataset
# 2D pose estimation
run_deeplabcut --txt_dir dirs.txt --pose2d
# Filtering and triangulation
run_anipose --txt_dir dirs.txt --filter_2d --calibrate --triangulate
# Check the 3D pose estimation quality
folder_path=$(head -1 dirs.txt)
echo $folder_path
animate_3... |
67a758b3a0d3ce171759bf7be166b5f6f7e7d069a000221328d73950fedee7ef | Shell | 468 | 15 | #!/bin/bash
# Run tests
# run target sass in makefile
R -e "options(authentication='none'); x <- shiny::runTests(assert = FALSE); writeLines(as.character(all(x[[2]])), 'test_result.txt')"
# Read test results from file
res=$(cat test_result.txt)
# # return test result as an output (will be deprecated)
echo ::set-out... |
08773d526bd0a2b73e6d9254cc5adfed1a64d7493a151448a815aa9969198ea9 | Shell | 473 | 18 | #!/bin/bash
# @ Stefan Sunaert & Ahmed Radwan- UZ/KUL - stefan.sunaert@uzleuven.be
what_to_build=$1
if [ "$what_to_build" = "" ]; then
echo "Use KUL_build_singularity what_to_build "
echo " what to build could be e.g. fmriprep:latest or mriqc:0.12.4"
exit 0
fi
cwd=$(pwd)
sudo docker run --privileged -t... |
0ba2b419a6dde7e94ffda7e0f1e3b4ec6f325e4594b13b8b4c1c57d981a789ea | Shell | 473 | 13 | #!/bin/bash
# Script to download the Kinetics-400 dataset using torchvision.datasets.Kinetics.
num_workers=$1
echo "Removing old Kinetics-400 validation dataset directory if it exists..."
rm -rf /scratch/alexandel91/mid_level_features/kinetics_400/val
echo "Downloading Kinetics-400 validation dataset..."
python ./do... |
9fad9e079f23fdc2c6a83a8dbff863d1335b6e1d1027d9a4912570853b33ac34 | Shell | 473 | 18 | #!/bin/bash
# Latest scikits odes distribution needs Sundails 5.1.0
wget https://github.com/LLNL/sundials/releases/download/v5.1.0/sundials-5.1.0.tar.gz
tar -xzf sundials-5.1.0.tar.gz -C $HOME
cd $HOME/sundials-5.1.0
mkdir $HOME/build-sundials-5.1.0
cd $HOME/build-sundials-5.1.0/
cmake -DLAPACK_ENABLE=ON \
-DSU... |
67a6b31cf39301805ea82d5ce678751e84df20cc9a143fff6b948cd0443cc794 | Shell | 484 | 20 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --mem=64g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=baselineEval
#SBATCH --partition=gpu
#SBATCH --gres=gpu:p100:1
#SBATCH --error=baselineEval.err
#SBATCH --output=baselineEval.out
module load python
echo "FID"
python -m src.baselines.evaluation.calcFID... |
a94e819c609c36a052df8ab5ea067e924e0b8fa9bf21d133cc909d3952389574 | Shell | 486 | 24 | #!/bin/bash
# Source and destination
SRC="CLAUDE.md"
DEST="GEMINI.md"
# Check if source exists
if [ ! -f "$SRC" ]; then
echo "Error: $SRC not found!"
exit 1
fi
echo "Syncing $DEST from $SRC..."
# Copy and replace terms
# 1. Claude Code -> Gemini CLI
# 2. Claude -> Gemini
# 3. claude.ai/code -> Gemini CLI
se... |
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