sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e3db868684f6c80995912fa309b0ed71341ecfb0e15e9b583dd8965fbcce06b6 | Shell | 199 | 9 | #!/bin/bash
source config.sh
docker run -it --rm \
cromwell-${image_name}:${version} CITE-seq-Count --help
docker run -it --rm \
cromwell-${image_name}:${version} CITE-seq-Count --version
|
23f8de9a84ac4fbab0b78488096759f7e3599727e7da091351b0641d4e3bf28e | Shell | 200 | 8 | #!/bin/bash -e
source config.sh
echo "${registry}/${image_name}:${version}"
scing push --image=${registry}/${image_name}:${version}
scing push --image=${registry}/cromwell-${image_name}:${version}
|
35c98f91a4b4c869a56a4237f0cf915a9b36391aabddf5e42ca60ec9742e0364 | Shell | 200 | 1 | mpicc main.c CaBuffer.c CaRu_SERCA.c conc.c contraction.c currents1.c diff.c euler.c potential.c initial_cond.c open.c stimulus.c write.c my_allocate.c mpi_send_rec.c bound_cond.c -lm -o asakura.exe
|
422299d2a28bb63fe939fa186a52693bf1e57cb8f1ea03fb008f08067f143872 | Shell | 200 | 7 | #! /bin/sh
cd tests
. ./compat.sh
${DIR}/generate_sequence -v -q -o seq10m -m 19 -s 2718281828 10000000
${DIR}/generate_sequence -v -q -o seq1m -s 3246465313 1000000 1000000 1000000 1000000 1000000
|
5033bf805adaf7a946b56d2f7974e5270a27184ca9cf947c84b33ed021f89dcf | Shell | 200 | 3 | topup --imain=data_appa_b0.nii --datain=para.txt --config=b02b0.cnf --out=Topup_Output
applytopup --imain=data.nii,datapa.nii --inindex=1,2 --datain=para.txt --topup=Topup_Output --out=my_hifi_data
|
f908d365e62ea09036ee45b6b34af280c2ddfbf47edd1437087484ba02a53930 | Shell | 201 | 9 | #!/bin/bash -e
source config.sh
echo "${registry}/${image_name}:${version}"
scing push --image=${registry}/${image_name}:${version}
scing push --image=${registry}/cromwell-${image_name}:${version}
|
530796f59866461d7ffae9bf1a08fe7951aa437bf4a0d2b5e45b1280a2ae5607 | Shell | 202 | 11 | #!/bin/bash -e
source config.sh
if [ ! -r ./data/10x-hto-gex-mapper.pickle ]
then
# pre-build 10x-hto-gex-mapper.pickle
python hto_gex_mapper.py
fi
docker build -t ${image_name}:${version} .
|
54419f39c128932e62855a6d69d74a65a1e7fc79f3b8090a888e48f5a5173fe1 | Shell | 202 | 1 | mpicc main.c CaBuffer.c CaRu_SERCA.c conc.c contraction.c currents_2d.c diff.c euler.c potential.c initial_cond.c open.c stimulus.c write.c my_allocate.c mpi_send_rec.c bound_cond.c -lm -o asakura.exe
|
69f0c259072678d3baee26f649826d664ec5866fb384caf46d51617252dcd68a | Shell | 202 | 7 | #!/bin/bash
bash getData.sh
jupyter execute DataExtraction.ipynb
jupyter execute ContourIdentification.ipynb ## requires interaction
jupyter execute TotalVideo.ipynb
jupyter execute ContourVideo.ipynb |
7d60e09c9c570973a6261792b60b6364ae0b382c9cdbcf8916a9d68e522d1ceb | Shell | 203 | 5 | #!/bin/sh
source activate hipsternet
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0rc0-py3-none-any.whl
pip install --ignore-installed --upgrade $TF_BINARY_URL
|
e1d87d84fe3f88edf04089acfc06af6bb3830eac64614a5b284edb504dff7bc1 | Shell | 203 | 5 | LIB_STATIC_LIBS="config_helpers utils tinyexpr sds"
CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_extra_data" "extra_data.c helper_functions.c ${CUSTOM_FILE}" "helper_functions.h" "${LIB_STATIC_LIBS}"
|
0e92f820e1239b03d449628a8029c4e5aeb286eef2bf52b5477bf7111ca72d63 | Shell | 205 | 10 | #!/bin/bash
#
# upsample_for-layer-sampling.sh <statsdir> <rim> [<additional> ...]
#
for filename in ${fBase}_${fBaseExt}_*_cope* ${fBase}_${fBaseExt}_*_tstat*
do
f_upsample $filename NN
done
|
37b8109f33c390bbce643fb7dd31cc0ef5a5fb24354639efb43e6ba11af87bf5 | Shell | 206 | 5 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"english": "", "spanish": "Tengo mucho hambre"}]' |
3a016df9d4b56d3268ddc9189eea3fd9964aec95cfe8db578451a1f501e9e201 | Shell | 206 | 10 | #!/bin/bash
conda remove -n bioinfo --all -y
conda create -n bioinfo python=3.10 -y
conda activate bioinfo
pip install torch==2.0.0
pip install numpy==1.24.2 scipy==1.10.0 pandas==1.5.3
pip install -e .
|
ead6626bece685c98d2bba25b1052f606386730c04bb250f2f5c24137541ab95 | Shell | 207 | 15 | #!/bin/bash
BUILD_DIR="build"
if [[ "$#" -eq 1 ]]; then
BUILD_DIR=$1
fi
if [[ ! -d "${BUILD_DIR}" ]]; then
echo "Directory ${BUILD_DIR} does not exist"
exit
fi
rm -fr ${BUILD_DIR}/*
rm -fr bin/*
|
57014fad37eabecadd0a48e324b7fdabd2d313c589c402343d9440b41461265f | Shell | 208 | 6 | snakemake -s /rhome/naotok/SnakeNgs/snakefile/preprocessing_ChIPseq.smk \
--configfile config_preprocessing.yaml \
--cores 48 \
--use-singularity \
--singularity-args "--bind $HOME:$HOME" \
--rerun-incomplete |
f660bb6d1f242fe1261ea37eba5f4d57c1e90ef5cfd8d0c316694133516b8ace | Shell | 209 | 7 | snakemake \
-s /rhome/naotok/SnakeNgs/snakefile/preprocessing_RNAseq.smk \
--configfile config_preprocessing.yaml \
--cores 32 \
--use-singularity \
--singularity-args "--bind $HOME:$HOME" \
--rerun-incomplete |
6fbde6180794c8fe4ac18056ffccc941d320ed1787c573d3ab6a13379263f984 | Shell | 210 | 14 | #!/bin/bash
set -e -E -u -o pipefail
conda env create \
-q \
--name test-env \
--file ./docs/env.yml \
|| exit 1
# shellcheck disable=SC1091
source activate test-env
make -C docs html || exit 1
|
f948664f7ab474153a6d813429bc28d8667c084d053f4f6429b3ba63536a709e | Shell | 213 | 10 | #!/bin/bash
LIBFUZZER_SRC_DIR=$(dirname $0)
for f in $LIBFUZZER_SRC_DIR/*.cpp; do
clang -g -O2 -fno-omit-frame-pointer -std=c++11 $f -c &
done
wait
rm -f libFuzzer.a
ar ru libFuzzer.a Fuzzer*.o
rm -f Fuzzer*.o
|
f98eda1d6453c0c9ca3486ed1ca2db6043eb33ca84b08f1a07bed2aad8e6a556 | Shell | 213 | 5 | DATASET=proteingym-benchmark
CUDA_VISIBLE_DEVICES=0 python get_embedding.py \
--gnn_model_name k10_h512 k20_h512 \
--mutant_dataset_dir data/mutant_example/$DATASET \
--result_dir result/embed/$DATASET |
27c4fd5ac650af44ca8b6bc134247d501ca7b1f6d5ed0c574d1e7a7da62d7c6f | Shell | 214 | 12 | #!/bin/bash
WORKDIR=""
Data_File="$WORKDIR/path/to/genes.raw"
Annot_File="$WORKDIR/AUX/C5.annot"
Output_Prefix=""
./magma \
--gene-results $Data_File \
--set-annot $Annot_File \
--out $WORKDIR/$Output_Prefix
|
f7c704dee11e6e8e4dbfb903fabaac4133636248979dc611d2c3aa4c5fe26739 | Shell | 214 | 6 | ############## Tworld ##############################
MODEL_FILE_CPU="Tworld.c"
MODEL_FILE_GPU="Tworld.cu"
COMMON_HEADERS="Tworld.h"
COMPILE_MODEL_LIB "Tworld" "$MODEL_FILE_CPU" "$MODEL_FILE_GPU" "$COMMON_HEADERS"
|
b0e3a0a097f3e7815b4524b33a39e29af87fd2d88a3417df8e81ac3407151588 | Shell | 215 | 3 | #!/usr/bin/env bash
mpirun -n 2 python3 co-sim-arbor.py --final-time 20000 --time-step 0.01 --cell-count 1000 --weight 0.5 --k-bath-bad 17.0 --k-bath-ok 9.5 --pathological-fraction 1.0 --beta 0.1 --proxy-region 72
|
8a9add18ba208678508598bfd03e1b8476f5027f90771ff1847fa114e08979ad | Shell | 217 | 9 | #!/bin/bash
cd "$(dirname "${BASH_SOURCE[0]}")" #cd into the directory containing this script
echo | pwd
n_subsets=10
for subset in 0 1 2 3 4 5 6 7 8 9; do
sbatch ./eval_sg_test_genes.sh $subset $n_subsets
done
|
e30d4fb8f0100ef1b28610fc5d9a42d60459fe3b36a046be8ae01ce8f89fb1a2 | Shell | 217 | 7 | H=512
K=20
CUDA_VISIBLE_DEVICES=0 python protssn.py \
--gnn_hidden_dim $H \
--gnn_model_path model/protssn_k"$K"_h"$H".pt \
--c_alpha_max_neighbors $K \
--pdb_file data/sol/esmfold_pdb/protein_1.ef.pdb |
cf39170932d7c4bb9bfd46f6566edf21bba91b29414e7f9f67c7bc4460233a51 | Shell | 218 | 4 | UTILS_SOURCE_FILES="search.c stop_watch.c sort.c file_utils.c batch_utils.c"
UTILS_HEADER_FILES="utils.h stop_watch.h file_utils.h batch_utils.c"
COMPILE_STATIC_LIB "utils" "$UTILS_SOURCE_FILES" "$UTILS_HEADER_FILES"
|
1ea4fc5f5014abdc10fd0bb84eafc9a2096138a8990c91d556f238581a8a8e74 | Shell | 219 | 6 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"imdb_output": "This movie was great! I loved "}]
' |
d43e983edaccc582f283fd36ab500c82903a404348d162c581ce31e293df5628 | Shell | 219 | 13 | #!/bin/bash
#SBATCH --time=10-00:00:00
#SBATCH --gpus=v100-32gb:4
#SBATCH --partition=gpu
#SBATCH --constraint=v100
#SBATCH -n 20
#SBATCH -N 1
#SBATCH --mem 100000
python3 -u ../selene_sdk/cli.py train.yml --lr=0.1
|
d6c03f8b7474296a612fd977b4ce13b1cd9486e32aabb73b740a47a1c139cec9 | Shell | 219 | 9 | #!/bin/sh
set -eu
script_dir="$(dirname "$0")"
DATUMARO_HEADLESS=1 pip-compile-multi -d "$script_dir" \
--header "$script_dir/HEADER.txt" \
--backtracking --allow-unsafe --autoresolve --skip-constraints "$@"
|
2a80cb98b97bf9af653c3f3da169e9c77492530bb502831a3f8d53dc33b92465 | Shell | 223 | 6 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"imdb_reviews": "This movie was great! I loved it!"}]
' |
74c879e84c0ae8ea236b876d077b01ed64936c4d8341feaac8a26f82f8e3a877 | Shell | 223 | 3 | flirt -in dataB -ref ${FSLDIR}/data/standard/MNI152_T1_2mm_brain -omat T12MNIaffine.mat
fnirt --in=dataB --aff=T12MNIaffine.mat --cout=T12MNItransf --config=T1_2_MNI152_2mm
invwarp -w T12MNItransf -o MNI2T1transf -r dataB
|
83dcd433eeb5ab71bf107c18f842006101c0ed8527fd5a35b1b258f44233b332 | Shell | 223 | 14 | #!/bin/bash
limits=$1
limits=`wc $limits | awk '{print $1}'`
limits=`echo "$limits * 1.2" | bc -l`
limits=`printf "%.0f\n" $limits`
if [ $limits -lt 1000000 ]; then
limits=1000000
else
limits=$limits
fi
echo "$limits"
|
860ced0fc9167b531c6820ae9fc7873740fdc24bd66ad373dcf62d1d8ef40b49 | Shell | 223 | 8 | gzip -c geno_part > geno_part.gz
gzip -c geno_part > geno_part.bgz
bzip2 -k geno_part
tar -cvf geno_part.tar geno_part
tar -cvzf geno_part.tar.gz geno_part
tar -cvjf geno_part.tar.bz2 geno_part
zip geno_part.zip geno_part
|
c6a80a7b4ef1ca599c11e004dacd998e1a835f70a23d61b50c6580c81256ab17 | Shell | 226 | 15 | #!/bin/bash
source /net/eichler/vol2/home/mvollger/projects/SDA/env_RM.cfg
fasta=$1
dir=tmpRM
mkdir -p $dir
RepeatMasker \
-species human \
-e wublast \
-dir $dir \
-pa $(nproc) \
$fasta
|
bb6d35915a57c9b711dbed8cd469ee4e4c714870ed55e492ddd586ce8338e3fd | Shell | 228 | 8 |
input_folder=$1
sample=$2
output_folder=$3
echo Filtering sample: $sample
samtools view -bh -q 30 -F 4 $input_folder/$sample*.sam|samtools sort -@ 10 -|samtools view -h ->$output_folder/$sample.sam
echo Filtering $sample done. |
75318828153369d388443e0972b36e513b1854b0d81104ddc470789d9930aee2 | Shell | 230 | 12 | #!/bin/bash
if [[ $# -eq 0 ]] ; then
echo 'Please provide model name and ontology'
exit 1
fi
for i in `seq 0 9`; do
echo $i
echo Training model $1_$i for ontology $2
python train.py -m $1 -mi $i -ont $2
done
|
7f9ef7ac849511eeb1716acd1c67d2f3a6281fbfb5aff207d979589e5594f25a | Shell | 232 | 8 |
${CELLRANGER_ARC} count --id=${NAME} \
--reference=${reference} \
--libraries=${LIBRARIES} \
--localcores=32 \
--localmem=64
|
d7f23e146c6460ce168283406ae43a38b7c068d4c7c5b722ae515422730f8b10 | Shell | 233 | 11 | pcg=$1
function get_data(){
for x in 0hpa1 0hpa2 10dpa1 10dpa2 12hpa1 12hpa2 14dpa1 14dpa2 36hpa1 36hpa2 3dpa1 3dpa2 5dpa1 5dpa2 7dpa1 7dpa2 WT
do
sh run_pcgs_oneindv.sh $x $1 $pcg &
done
wait
}
get_data 10
|
85de16b21c866f834e1432e7bc529bef38a1a14ea6ea259e9e45f7e969cb5ae3 | Shell | 234 | 12 | #!/bin/bash
if [[ $# -eq 0 ]] ; then
echo 'Please provide model name and ontology'
exit 1
fi
for i in `seq 0 9`; do
echo $i
echo Training model $1_$i for ontology $2
python train_gat.py -m $1 -mi $i -ont $2
done
|
b59e4c9989279b2d4c18beb707debe904f8b367268c89e211df4c530fbbca47c | Shell | 235 | 9 | #!/bin/bash
wget https://s3.eu-central-1.amazonaws.com/corupublic/mtlcc/pytorch/data.zip
unzip data.zip
rm data.zip
mkdir -p checkpoints
cd checkpoints
wget https://s3.eu-central-1.amazonaws.com/corupublic/mtlcc/pytorch/model_00.pth
|
19c401673869fe2bc9b6e34ab21e4f25f09484a00ebb7253146a149515d9b865 | Shell | 238 | 5 | echo MSVC
echo QQQQQQ $1 $2 $3 $4 $5
$1 --target-os=win64 --toolchain=msvc --enable-sdl --enable-static --disable-shared --extra-cflags="$3" --extra-ldflags="$5" $4 --prefix=$2 | tee ffmpeg_config.log
sed -i s/-Z7// ffbuild/config.mak
|
0d5e67b8891abccd79251373ccaf3dfb7383a1d829848f9300ffb08f81a9d174 | Shell | 239 | 10 | # config_path=configs/blood_config.yaml
config_path=$1
model_type=MultiGene
seed=0
fold=0
random_weights=1
keep_checkpoint=1
sbatch slurm_train_gtex_random_weights.sh $config_path $fold $seed $model_type $random_weights $keep_checkpoint
|
10c7aaa0087e7d75c4a367032ffbb28e91ae08a0e13e9d54d79413cc844b45f5 | Shell | 239 | 9 | #!/bin/bash
cd "$(dirname "${BASH_SOURCE[0]}")" #cd into the directory containing this script
echo | pwd
n_subsets=10
for subset in 0 1 2 3 4 5 6 7 8 9; do
sbatch ./submit_performer_sg_blood_ism_test_genes.sh $subset $n_subsets
done
|
65fa6b952b0cc24fbc7db1e90e13298dd2018baf195f85a6da6f41e9a952bd0b | Shell | 240 | 8 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{
"power_input": "19 3 10 11 14 3 3 4 4 9 39 27 12 5 20 20 38 39 41 61 52 31 43 31",
"power_output": ""
}]'
|
e312bfa45aee09d9460ed3afe6f073b2d9018a45183cb7d81d959bb2117b9f1a | Shell | 240 | 2 | flirt -in Results/Fiber -ref T1Img/dataB -out Results/Fiber_T1Sp -init T1Img/FA2T1.mat -applyxfm
applywarp --ref=${FSLDIR}/data/standard/MNI152_T1_2mm_brain --in=Results/Fiber_T1Sp --warp=T1Img/T12MNItransf.nii.gz --out=Results/Fiber_MNISp
|
1d32e4ded3d59d52d5e4efcbadb71db76c1e1b00dad4bb74d4fe49739b14bc2b | Shell | 242 | 6 | #!/bin/bash
NUMOFFILE=$(($(ls -1 output_neighbors-00000-*.json | wc -l)-1))
tmpfile="tmplist"
for i in $(seq -f %05.f 0 $NUMOFFILE); do
echo $i: $(cat output_neighbors-*-$i.json | cut -d: -f1 | tr '\n' ',')
done | uniq -s6 > $tmpfile.list
|
35de96ea81668a1b5987d8c43319ae2519c492b5920bf9ffab51954cb81301e4 | Shell | 242 | 6 | ############## LIRUDY 2011 ##############################
MODEL_FILE_CPU="li_rudy_2011.c"
MODEL_FILE_GPU="li_rudy_2011.cu"
COMMON_HEADERS="li_rudy_2011.h"
COMPILE_MODEL_LIB "li_rudy_2011" "$MODEL_FILE_CPU" "$MODEL_FILE_GPU" "$COMMON_HEADERS" |
edcf267d22698be71d95ea7aeb95b165bbfbc995ba0104a9a3d861c0ad38573e | Shell | 243 | 4 | #!/bin/bash
tar -zcf custom_files.tgz ../src/domains_library/custom_* ../src/extra_data_library/custom_* ../src/matrix_assembly_library/custom_*
#tar -zcf local_files.tgz ../local_meshes ../local_networks ../private_configs ../private_models
|
48be2428c091b00aad0e148fe63a2c16c06276532d6b69b94d49ce918606b890 | Shell | 244 | 6 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"proteins_tabular": {"classification": "HYDROLASE"},
"protein_sequence": ""}]' |
acacdb221b537ce6c61f834f17c5bcd4889ce52751ca33cfde5b28eb6f0a0941 | Shell | 247 | 9 | #!/usr/bin/env bash
CONFIG=$1
GPUS=$2
PORT=${PORT:-38423}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
$(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
|
d128e1be03d0aaf7e7989ffd0052a418fd3328f4fddcb7e9aabf96c0fca5f9eb | Shell | 249 | 6 | ############## minimal_model ##############################
MODEL_FILE_CPU="minimal_model.c"
MODEL_FILE_GPU="minimal_model.cu"
COMMON_HEADERS="minimal_model.h"
COMPILE_MODEL_LIB "minimal_model" "$MODEL_FILE_CPU" "$MODEL_FILE_GPU" "$COMMON_HEADERS"
|
2a54980f3cfc6c052c6fd3662dcd8684205635e775552b8cfff45e985b93351d | Shell | 250 | 10 | #!/bin/sh
${CELLRANGER} count --id=${NAME} \
--transcriptome=${ref_gex} \
--fastqs=${FQ_DIR} \
--create-bam=true \
--localcores=16 \
--localmem=64
|
5789ea5141f2241a2f1f7d1f5c0bd2ccafc8dc829a05f35cae0ebdb66de543af | Shell | 252 | 11 | PRINT_INFO "COMPILING STATIC LIB raylib"
if [ "$BUILD_TYPE" == "debug" ]; then
MAKE_FLAGS="RAYLIB_BUILD_MODE=DEBUG"
fi
make ${MAKE_FLAGS}
CUR_DIR=$(pwd)
PARENT_DIR="$(dirname "$CUR_DIR")"
COMPILED_STATIC_LIBS["raylib"]="${PARENT_DIR}/libraylib.a"
|
e62f2490d49495c04c680e37ce9820c984f54667f021989df251f2ec14934d44 | Shell | 253 | 13 | #!/bin/bash
source /opt/conda/etc/profile.d/conda.sh
conda activate morphometrics || {
echo "Error: Failed to activate morphometrics environment"
exit 1
}
# Execute command or start bash
if [ $# -eq 0 ]; then
exec bash
else
exec "$@"
fi |
a625c1b21a7ff1e9d13179cfb7e294833dcb097d892c4fbfdc46b47374807141 | Shell | 254 | 14 | #!/bin/bash
#SBATCH --account=def-rfm
#SBATCH --cpus-per-task=4
#SBATCH --mem=12G
module load python/3.8 blender/3.6 scipy-stack
## initialize variables
IMAGEDIR=$1
rm $IMAGEDIR/train.tar
rm -r $IMAGEDIR/tmp
python stack_taring.py --imgdir $IMAGEDIR
|
0571b487b2901adcce3a10ba68e4a3ccff7e9f743537c97928722a323a2fb350 | Shell | 256 | 8 | #!/bin/bash
# first recalculate gff index
rm -rf gff_data/
index_gff --index ./settings/new_splice_site_events.gff ./gff_data/
# create sashimi plots
sashimi_plot --plot-event "TIMMDC1" gff_data/ settings/plot_timmdc1_new_exon.conf --output-dir plots/
|
5b4c4eb286a6a762227bb80dfc0e4c3e64d89a20118c76cdd218a206a7a9ed5e | Shell | 256 | 9 | #!/bin/bash
#SBATCH -J generate_samples
#SBATCH -o /project/roysam/rwmills/repos/cluster-contrast-reid/examples/QC/run.txt
#SBATCH -t 1-00:00:00
#SBATCH -N 2 -n 48
/project/roysam/rwmills/apps/miniconda3/envs/pytorch_gpu/bin/python generate_samples1.py |
d3b7f9c7fe65af2f98510d7575b8f15923353f70ac945bc6c59a42cd9217e1fc | Shell | 257 | 14 | #!/usr/bin/env bash
set -e -u -x
ROOT_DIR="$PWD/../.."
INPUT_DIR=${ROOT_DIR}/inputs/raw
OUTPUT_DIR=${ROOT_DIR}/outputs/derivatives
CODE_DIR=${ROOT_DIR}/code
# fmriprep version
VERSION="21.0.1"
OUTPUT_SPACES='MNI152NLin2009cAsym T1w'
TASK_ID="visMotion"
|
50777fbb857797ffece6f3629123d3724529fcfc01c91516af8d900876a823eb | Shell | 258 | 15 | #!/usr/bin/env bash
git clone https://github.com/CRG-Barcelona/libbeato.git
cd ./libbeato
git checkout 0c30432
./configure
make
make install
cd ..
git clone https://github.com/CRG-Barcelona/bwtool.git
cd ./bwtool
./configure
make
make check
make install |
636c97b6d88dae5399087a3142df1947784a4e1b6a3d35d6a81c0ecbb5bd944e | Shell | 261 | 11 | #!/bin/bash
# Loop over chromosomes 1 to 22
for chr in {1..22}; do
echo "Running mix3r_int_weights for chromosome $chr..."
apptainer run --nv /path/to/mix3r.sif mix3r_int_weights --config /path/to/config_chr${chr}.json
echo "Chromosome $chr complete."
done
|
0316e8d889251a400c92cb0026fdd2611c6e35344dca984f1ae44bd8878bc93a | Shell | 264 | 10 | #!/bin/bash
set -ev
gunzip -c longest_orfs.cds.top_longest_5000.nr80.gz > test.train_orfs.fa
gunzip -c pasa_assemblies.fasta.gz > test.transcripts.fa
../train_start_PWM.pl --transcripts test.transcripts.fa --selected_orfs test.train_orfs.fa --out_prefix test
|
14738b0332bfcf6f253013ec8add179e17ce71bbbb73052a28f03ff45e7cd091 | Shell | 264 | 14 | #!/bin/bash
#SBATCH --time=0-01:09:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=test_module
#SBATCH --output=logs/test_module_%j.log
#SBATCH --mem=2GB
#SBATCH --partition=regular
ml R
ml GSL
Rscript ../Script/test_module.R |
d7db376078443b87249dc2533905b05c3fac6c708946d0c84c0cca3bc723057b | Shell | 264 | 10 | #!/bin/bash
set -eu
scriptloc="$(dirname "$0")"
cd "$scriptloc"/../templates/
wget -O NHP_NNP_Templates-20260504.zip 'https://balsa.wustl.edu/myelin/download?dirName=public&filepath=NHP_NNP_Templates-20260504.zip&dirPass='
unzip NHP_NNP_Templates-20260504.zip
|
57980db753b46dcce372c67fe6ca4c9f0b832c40e571eb8e4758cdcf7408e069 | Shell | 266 | 6 | #!/usr/bin/env bash
pyuic5 elastix_plugin.ui > elastix_plugin_UI.py
pyrcc5 ./elastix_plugin.qrc > ./elastix_plugin_rc.py
pyuic5 transformix_plugin.ui > transformix_plugin_UI.py
pyuic5 reorder_stack.ui > reorder_stack_UI.py
pyuic5 selectstack.ui > selectstack_UI.py
|
c2ff4f71102050d95535d877240cd05aba014d72ca9294985168698551b1136d | Shell | 268 | 8 | echo $1
echo $2
cd $3
fslmerge -t Merged.nii.gz *
design_ttest2 design $1 $2
randomise -i Merged.nii.gz -o Diff -m mask.nii.gz -d design.mat -t design.con -n 5000 -T
fsl2ascii Diff_tfce_corrp_tstat2.nii Corrp_tstat2
fsl2ascii Diff_tfce_corrp_tstat1.nii Corrp_tstat1
|
22e2787f33584444bfeb928d48aeed69229d9ec2942aefa3511dedaf2c9ee09c | Shell | 269 | 9 | #!/bin/bash
VERSION=`cat VERSION.txt`
singularity build transdecoder.v${VERSION}.simg docker://trinityrnaseq/transdecoder:$VERSION
singularity exec -e transdecoder.v${VERSION}.simg util/TransDecoder.LongOrfs
ln -sf transdecoder.v${VERSION}.simg transdecoder.simg
|
58b0474aa007ac38a82b4f6c15337e33670bf3567d37044d747933f05c2c378b | Shell | 271 | 9 | #!/usr/bin/env bash
CONFIG=$1
CHECKPOINT=$2
GPUS=$3
PORT=${PORT:-29547}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
$(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
|
2dde8552fcf81feac704abbcc74c6ac7797ba568963f697c67887583afcc990e | Shell | 272 | 14 | #!/bin/bash
#SBATCH --time=23:59:59
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=gnn_ddd_pars_free_mle
#SBATCH --mem=3GB
#SBATCH --partition=regular
index=${1}
name=${2}
ml R
Rscript ../Script/ddd_pars_est_free_mle.R ${index} ${name} |
ab87e8f9947ea7067e230bc97a3c32742270fd7165f0b3a46e0ca0de561b4c23 | Shell | 274 | 12 | #!/bin/bash
git rev-parse --short HEAD > ./git.hash
if [ "$1" = "--experimental" ]; then
docker build . -t "deepmi/lit:dev" -f ./containerization/Dockerfile_experimental
exit 0
fi
docker build . -t "deepmi/lit:dev" -f ./containerization/Dockerfile
rm ./git.hash
|
3fe3261a20aff6320a0ed55d9c9de036caea3ce5831d1fb7b281a39ea2202244 | Shell | 275 | 5 | #!/bin/bash
lcov --capture --directory CMakeFiles/ --base-directory . --gcov-tool ../scripts/ci/llvm-gcov.sh -o coverage.info
lcov --remove coverage.info /usr/\* \*/external/\* *evaluator.stdout* *configfile.stdout* -o coverage_clean.info
#Regex: \s*lines\.*:\s*(\d+.\d+\%)
|
68ff7925723ae9d3265d4d0698494eaa8af41dcd1c9d1233458b7866b1fb98a6 | Shell | 275 | 18 | #!/bin/bash
#/mnt/hgfs/zhang_bo/fmri_script/bash
#/Users/boo/Desktop/fmri_script/bash
fsf_list=(/Users/bo/Documents/script_bash/template_osci2025/fsf_*)
for i in "${fsf_list[@]}"; do
(
feat $i
)&
if (( $(wc -w <<<$(jobs -p)) % 2 == 0 )); then wait; fi
done
|
dfbc461673d3516dbd2c82a55ad4eda3c0a27cd87e42f2ad78d39dcd2198436e | Shell | 275 | 7 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"text_output": "### Instruction: Explain three ways to reduce carbon
emissions. ### Response:"}]
' |
644232aca5cada9746b96ceee67327bfd7b8dd25a4b97fc37020b084456836ca | Shell | 278 | 7 | if [ -n "$CUDA_FOUND" ]; then
EXTRA_CUDA_LIBS="cudart cublas cusparse"
fi
CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_calc_ecg" "ecg.c ${CUSTOM_FILE}" "" "config_helpers vtk_utils utils alg sds tinyexpr" "m $EXTRA_CUDA_LIBS" "$CUDA_LIBRARY_PATH $CUDA_MATH_LIBRARY_PATH"
|
cc73efa6680c4a6e00275a59117db86765b8b35c6a98fafb9144af6c8842386a | Shell | 279 | 6 | #!/bin/bash
# Customize multibuild logic that is run before entering Docker. Sourced from travis.yml .
export PS4='+(${BASH_SOURCE}:${LINENO}): ${FUNCNAME[0]:+${FUNCNAME[0]}(): }'
set -x
REPO_DIR=$(dirname "${BASH_SOURCE[0]}")
DOCKER_IMAGE='quay.io/asenyaev/manylinux2014_$plat'
|
1aaaf7d3d0b850934a547c28a8aaaac54df5dbe00cab444840e0fd174fb45081 | Shell | 280 | 7 | ############## FHN 1961 MOD ##############################
MODEL_FILE_CPU="fhn_mod.c"
MODEL_FILE_GPU="fhn_mod.cu"
COMMON_HEADERS="fhn_mod.h"
COMPILE_MODEL_LIB "fhn_mod" "$MODEL_FILE_CPU" "$MODEL_FILE_GPU" "$COMMON_HEADERS"
######################################################## |
bdf2975f2354cabcf54be89b845da8c28de5ac36f8015a9ac7bea6264a526b8b | Shell | 280 | 11 | #!/bin/sh
# Trained Sei model
wget https://zenodo.org/record/4906997/files/sei_model.tar.gz
tar -xzvf sei_model.tar.gz
# Sei framework resources (FASTA files)
wget https://zenodo.org/record/4906962/files/sei_framework_resources.tar.gz
tar -xzvf sei_framework_resources.tar.gz
|
885d7e3e87809d9c79ed466bd0b542121fc3e31424a1237ccf2f3e79ac497645 | Shell | 282 | 15 | #! /bin/sh
cd tests
. ./compat.sh
sort -k2,2 > ${pref}.md5sum <<EOF
EOF
echo "Test double fifo" &&
$DIR/test_double_fifo_input 1 seq1m_0.fa > ${pref}_double_fifo.stats &&
echo "Test read parser" &&
$DIR/test_read_parser seq10m.fa > ${pref}_read_parser_fa.dump
RET=$?
exit $RET
|
9d7dac045f8d38c932bd4de53645e8204ab0f0a0d16d4ecece8d6406e135e8a9 | Shell | 282 | 14 | #!/bin/bash
#SBATCH --time=1-23:59:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --job-name=gnn_bd_poly
#SBATCH --output=logs/gnn_bd_poly-%j.log
#SBATCH --mem=32GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/bd_polymorph_data.R ${name} |
d9fd31fed2f1e5f8fa0c528974bc57a95e10fac68ee889f98152016f18266029 | Shell | 282 | 14 | #!/bin/bash
set -e
set -o pipefail
# set up running directory
cd "$(dirname "${BASH_SOURCE[0]}")"
# Run R script to generate JSON file
Rscript --vanilla 00-ATRT-select-pathology-dx.R
# Run R script to subtype ATRT using methylation data
Rscript --vanilla 01-ATRT_subtyping.R
|
e1e37cc49a83b43fe0cee5a8e9f018359abb43c3137b5c1ffbd4def93958a4bd | Shell | 282 | 14 | #!/bin/bash
#SBATCH --time=2-23:59:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=gnn_emp_mle
#SBATCH --output=logs/gnn_emp_mle-%j.log
#SBATCH --mem=3GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/empirical_tree_mle.R ${name} |
da5772e6ee876994b5cb9fb75871b53494fb0e6d7069808f4bb8dae41c77ca8c | Shell | 283 | 12 | fold=0
seed=0
config_path=configs/blood_config.yaml
model_type=SingleGene
sbatch slurm_train_gtex_tpm.sh $config_path $fold $model_type $seed
config_path=configs/multi_gene_196kb_blood.yaml
model_type=MultiGene
sbatch slurm_train_gtex_tpm.sh $config_path $fold $model_type $seed
|
77d1993a24f26d98106576bc2777b72922f2ae2dab7f8d613734268bfe5034da | Shell | 284 | 14 | #!/bin/bash
#SBATCH --job-name=trainCOM
# Job name
#SBATCH --mem=30000
# Job memory request
#SBATCH -t 3-00:00
# Time limit hrs:min:sec
#SBATCH -N 1
#SBATCH -c 8
#SBATCH -p olveczkygpu,gpu
#SBATCH --gres=gpu:1
module load Anaconda3/5.0.1-fasrc02
source activate dannce
com-train "$@"
|
a1be3280ce3715242b3f868e038dc015dfaa792a221b453b12210fb9dc3e2b36 | Shell | 285 | 14 | #!/bin/bash
#SBATCH --job-name=predCOM
# Job name
#SBATCH --mem=30000
# Job memory request
#SBATCH -t 3-00:00
# Time limit hrs:min:sec
#SBATCH -N 1
#SBATCH -c 8
#SBATCH -p olveczkygpu,gpu
#SBATCH --gres=gpu:1
module load Anaconda3/5.0.1-fasrc02
source activate dannce
com-predict "$@"
|
35ad1e5548c5f3c1dbaf0d2437e8ff7196a011d0bdf1478f0cf4326397b344a4 | Shell | 287 | 5 | #!/usr/bin/env bash
pyuic5 ./designerFiles/lasagna_mainWindow.ui > ./lasagna/lasagna_mainWindow.py
pyrcc5 ./designerFiles/mainWindow.qrc > ./mainWindow_rc.py
pyuic5 ./designerFiles/alert.ui > ./lasagna/alert_UI.py
pyuic5 ./designerFiles/loader_dialog.ui > ./lasagna/loader_dialog_UI.py
|
8b26f6815a492e02565ff4905e69c1f67a5ce63de58313d7019fa4481cc4e97d | Shell | 288 | 14 | #!/bin/bash
#SBATCH --job-name=snakerunner
#SBATCH --time='48:00:00'
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --output=logs/%x-%j.txt
mkdir -p jobs logs
module load singularity-ce
profile=$1
shift
echo "profile: $profile"
snakemake -p $@ --profile $profile
|
4b48c575e26f636b878989291d8f2f59f97c4b533560a69c338b8c63c035b7f1 | Shell | 291 | 14 | #!/bin/bash
#SBATCH --job-name=trainDannce
# Job name
#SBATCH --mem=60000
# Job memory request
#SBATCH -t 2-00:00
# Time limit hrs:min:sec
#SBATCH -N 1
#SBATCH -c 16
#SBATCH -p olveczkygpu,gpu
#SBATCH --gres=gpu:1
module load Anaconda3/5.0.1-fasrc02
source activate dannce
dannce-train "$@"
|
963c3eec24988e07fc62391789feccb5550d808a31faf99756c3e8459e0b6328 | Shell | 291 | 5 | #TODO: check zlib??
VTK_UTILS_SOURCE_FILES="pvd_utils.c data_utils.c vtk_polydata_grid.c vtk_unstructured_grid.c"
VTK_UTILS_HEADER_FILES="pvd_utils.h data_utils.h vtk_polydata_grid.h vtk_unstructured_grid.h"
COMPILE_STATIC_LIB "vtk_utils" "$VTK_UTILS_SOURCE_FILES" "$VTK_UTILS_HEADER_FILES" |
fdd2c0b474b02471ed78af70acef47ee5f6ccefb5e7abbc49e89e5c32fb4abeb | Shell | 291 | 7 | ############## NOBLE 1962 ##############################
MODEL_FILE_CPU="noble_1962.c"
MODEL_FILE_GPU="noble_1962.cu"
COMMON_HEADERS="noble_1962.h"
COMPILE_MODEL_LIB "noble_1962" "$MODEL_FILE_CPU" "$MODEL_FILE_GPU" "$COMMON_HEADERS"
######################################################### |
0187ace350a518d841ba3512f71d3d475d868c5c9a89735f30c175134908f4b3 | Shell | 292 | 11 | #!/bin/bash
PROJECT=/Volumes/Elements_CR/atrialmtk/src/3Processing/UAC_Codes
DATA="/Volumes/Elements_CR/atrialmtk/Examples/Example-Biatrial-CT-shape-model/3Processing"
export PYTHONPATH=$PROJECT
echo $DATA
python $PROJECT/scripts/label_endo_epi_surfaces.py ${DATA}/ LA_1 LA_2 RA_1 RA_2;
|
2148662115613208cbd976e4e03b2bea825b23e3bc446f807e6f7e66a2ef3457 | Shell | 292 | 14 | #!/bin/bash
#SBATCH --time=6:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=gnn_ddd_pars_cap
#SBATCH --output=logs/gnn_ddd_pars_cap-%j.log
#SBATCH --mem=3GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/ddd_pars_est_cap_data.R ${name} |
9f5ad430412ed5dd463e3528e82a25ba3c990e6b6e5ba6afaa6f95e39aa47d10 | Shell | 293 | 8 | python3 gnn_gcn_bash_script_generation.py
python3 gnn_gcn_emb_bash_script_generation.py
python3 gnn_sage_bash_script_generation.py
python3 gnn_sage_emb_bash_script_generation.py
python3 mf_bash_script_generation.py
python3 mlp_bash_script_generation.py
python3 seal_bash_script_generation.py
|
25bfb68cdc5ff04992f925796c5b686d1b398fb285788b8babf0b28af38367b4 | Shell | 295 | 14 | #!/bin/bash
#SBATCH --time=6:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=gnn_ddd_pars_lamu
#SBATCH --output=logs/gnn_ddd_pars_lamu-%j.log
#SBATCH --mem=3GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/ddd_pars_est_lamu_data.R ${name} |
de191b47fa6c34ed7bca35252fe68ec299eff1e762c4468610aab756fdd07931 | Shell | 295 | 7 | #!/bin/bash
# use navigator data to estimate shot phase
python ../../examples/run_zsssl.py --config /figures/motion/config_zsssl_navi.yaml --mode train
# use self-gating data to estimate shot phase
python ../../examples/run_zsssl.py --config /figures/motion/config_zsssl_self.yaml --mode train |
08d8a22a2f0cae18289db6c57a4e319889ec3a4ae83b1a275903e349fd96aaf6 | Shell | 296 | 6 | curl -X POST \
"http://localhost:8000/predict" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '[{"poker_hands": {"S1": "3", "C1": "12", "S2": "3", "C2": "2", "S3": "3",
"C3": "11", "S4": "4", "C4": "5", "S5": "2", "C5": "5"}}]' |
7705c9b1bd9ad9d69d4484516fbf2252b777e9309a091154687bc9e28d0f7846 | Shell | 296 | 14 | #!/bin/bash
#SBATCH --time=1-23:59:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=6
#SBATCH --job-name=gnn_bd_pars_free
#SBATCH --output=logs/gnn_bd_pars_free-%j.log
#SBATCH --mem=32GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/bd_pars_est_free_data.R ${name} |
27d088c23eb35cf00cc8ded32b8121c1a99e267f63c041f13b44c8128c0adcca | Shell | 299 | 14 | #!/bin/bash
#SBATCH --time=0-23:59:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=6
#SBATCH --job-name=gnn_ddd_pars_free
#SBATCH --output=logs/gnn_ddd_pars_free-%j.log
#SBATCH --mem=32GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/ddd_pars_est_free_data.R ${name} |
f41ccf643b96fe3b56a66a1fa5906aa3567383305b1c5619c06100abcb803404 | Shell | 299 | 15 | #!/bin/bash
#SBATCH --time=00:30:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --job-name=gnn_boot_start
#SBATCH --output=logs/gnn_boot_start-%j.log
#SBATCH --mem=4GB
#SBATCH --partition=regular
ml R
name=${1}
Rscript ../Script/empirical_tree_gnn_bootstrap.R "${name}" |
43467291b531fc20f2a7b666ca41d67de95f1d1f6b5a92ec1c4fb01a7a468e42 | Shell | 300 | 14 | #!/bin/bash
#SBATCH --time=3-23:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --job-name=gnn_pbd_pars_free
#SBATCH --output=logs/gnn_pbd_pars_free-%j.log
#SBATCH --mem=64GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/pbd_pars_est_free_data.R ${name} |
75fedcaf8f4a76e07f6b46ce8a229d57375d0d5b9ff6284fd30c9d23910dc39e | Shell | 300 | 6 | pip install cookiecutter dvc
cookiecutter https://github.com/HelmholtzAI-Consultants-Munich/Quicksetup-ai.git --no-input
cd Quicksetup-ai/
pip install -e .
python scripts/train.py trainer.min_epochs=1 trainer.max_epochs=2 log_dir=test_case
python scripts/test.py ckpt_path=logs/checkpoints/last.ckpt
|
b1c3fabbabc61562e82687f8bd6ceaae5bcd759aad407d948656c095c05860c2 | Shell | 300 | 14 | #!/bin/bash
#SBATCH --time=3-23:59:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --job-name=gnn_eve_pars_free
#SBATCH --output=logs/gnn_eve_pars_free-%j.log
#SBATCH --mem=80GB
#SBATCH --partition=regular
name=${1}
ml R
Rscript ../Script/eve_pars_est_free_data.R ${name} |
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