sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
c94c6e13a610c5aacda0ddf98d326acbdcd2080c0e345bf6f6c49aec533287dd | Shell | 107 | 3 | #!/bin/bash
gsutil -m rsync -r -d . gs://encode-pipeline-test-samples/encode-chip-seq-pipeline/ref_output
|
9d673b6f1d618bb2d6b2190d5dc6993b4299087afe2406a01efbf58ad76d62d1 | Shell | 109 | 3 | CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_modify_domain" "modify_domain.c ${CUSTOM_FILE}" "" "tinyexpr"
|
b901fca98aed69a33b76598ba69afd0f7512c55b5d82830bd952bf569712c1ee | Shell | 109 | 3 | #!/bin/bash
../PreFreeSurferPipelineBatch.sh --StudyFolder=${HOME}/data/3T_Size_Checking --Subjlist=100307
|
dbbb7053f69307fad595ecba9a07f5aafc45923d5953f2730957a53672f2aa85 | Shell | 109 | 4 | NO_TAG="ghcr.io/${1}/pyafq"
NO_TAG="$(echo "${NO_TAG}" | tr -d '[:space:]')"
docker push --all-tags $NO_TAG
|
4bf8b59d6c9621e6579afdc4ff0ea4359d68f49484c547814babb183219037da | Shell | 110 | 3 | SOURCE_FILES="sds.c"
HEADER_FILES="sds.h sdsalloc.h"
COMPILE_STATIC_LIB "sds" "$SOURCE_FILES" "$HEADER_FILES"
|
bffbe800e409a6064f24436008128f5fa29a4aeb5a7602fcedae0ca743c12b2d | Shell | 110 | 2 | # Authenticate GitHub CLI
gh auth login -h github.com --with-token < /home/vscode/secrets/gh_token_classic.txt |
3171ccd60c144dbbd6f5b543c1c06ecf15472e4dedc9f7e668683f72450e8bfc | Shell | 111 | 4 | #!/bin/bash
../PostFreeSurferPipelineBatch.sh --StudyFolder=${HOME}/data/3T_Size_Checking --Subjlist=100307
|
590bcfd1737abe0f159a7f5f48d2eb9b5a292d18833da3069f74707735112af9 | Shell | 111 | 4 | #!/bin/bash
../DiffusionPreprocessingBatch.sh --StudyFolder=${HOME}/data/3T_Size_Checking --Subjlist=100307
|
5cda0f53c355842f6290514e544d7271b0d0e8406277a9e25b7b4c1a67136de5 | Shell | 113 | 3 | #!/bin/sh
curl -fsLO https://raw.githubusercontent.com/scijava/scijava-scripts/master/ci-build.sh
sh ci-build.sh
|
b2e208595c5e045b6d71103ec27223c2c35cbab4f99c9816a517db0cd3a6abf2 | Shell | 113 | 7 | #!/bin/bash
../evaluate \
-a ./annotations/ \
-d ./results/ \
-i ./images/ \
-l ./test_list.txt \
-o ./ \
|
680de788979e255e87c4e29a8688bd5dae0407f43724a8fdc2836c94408317c4 | Shell | 114 | 6 | #!/bin/sh
./generate_enhancers.py $1
./generate_promoters.py $1
./generate_pairs.py $1
./generate_training.py $1
|
4521dcc0c6c6df2506c1a416a93072a6d2631327de7233a5e2b6f8369722bcac | Shell | 115 | 4 | SOURCE_FILES="tinyexpr.c"
HEADER_FILES="tinyexpr.h"
COMPILE_STATIC_LIB "tinyexpr" "$SOURCE_FILES" "$HEADER_FILES"
|
f510e7e0ef7bd69083d53c6258793082cdb0551f0ea2974cbb1c361cf3cfd0f6 | Shell | 118 | 6 | #!/bin/bash
# "CondA" "CondB" "CondC"
for VARIABLE in "CondA" "CondB" "CondC"
do
sbatch Predict.sh $VARIABLE
done
|
7697d8c3f4953987a57efd8dee7ca9f97236f027bce223ffaf58d09ea48e1d74 | Shell | 119 | 11 | #!/bin/bash
set -ev
rm -f ./atg.*
rm -f ./pipeliner.*
rm -f ./enhanced.*
rm -rf ./test__checkpoints
rm -f ./test.*
|
78b1e85fa494c7417d1d3cc100d073d00150fd90629e52320e46ab1172dcf3f5 | Shell | 119 | 3 | python bash_script_generation_node2vec.py
cd ..
sh hyper_param_tunning-use_embedd_n2vec/parallelize_mlp_cs_node2vec.sh
|
eba613bc2a24cbf532cb8b7ca798a0ffb470c89fc9963100be7b3764c16bea36 | Shell | 120 | 8 | #!/bin/bash
# properties = {properties}
source ~/.bashrc
conda activate mooney_comp
exp_pypath mooney_comp
{exec_job}
|
3359baa7d1956c44df9e992e5f7b6f559fd9cb8b22ad67189611906ad62d1e28 | Shell | 121 | 7 |
K=30
python src/data.py \
--data_type cath \
--c_alpha_max_neighbors $K \
--cath_dataset data/cath/cath_k$K |
d0d7708b4d7e933afcd5c9ecce15c49d167b8f80b6343328b0b1f6dab55253f9 | Shell | 121 | 2 | #!/bin/bash
genhtml coverage_clean.info -o coverage --num-spaces 2 --legend --demangle-cpp --title "$CI_COMMIT_REF_SLUG"
|
300d0566348e0cd8fb66c5226a61c4d10c7d910cd3dfa08988ce0728a89a4f2e | Shell | 123 | 4 | NO_TAG="ghcr.io/${1}/pyafq_gpu_cuda_${2}"
NO_TAG="$(echo "${NO_TAG}" | tr -d '[:space:]')"
docker push --all-tags $NO_TAG
|
58b4acff2bd11fa22abd4b7277c3a549b951bed2c8d75e71c0354c6ac5b92f15 | Shell | 123 | 3 | #!/bin/bash
../GenericfMRIVolumeProcessingPipelineBatch.sh --StudyFolder=${HOME}/data/3T_Size_Checking --Subjlist=100307
|
02b89c13038010edd04253251514fd04116e895a4ecd3c48fd90391edd0b3fab | Shell | 124 | 6 | num_cv=17
for (( i=0; i<$num_cv; i++ )); do
inp="bemeta.dat.$i"
out="bemeta.dat.$i"
sed -i -e '1iRESTART\' $inp
done
|
ed24611d39fdc47eac72d7ab44b9fa809525cbe2892bf041d9dfc5379106fece | Shell | 124 | 3 | #!/bin/bash
../GenericfMRISurfaceProcessingPipelineBatch.sh --StudyFolder=${HOME}/data/3T_Size_Checking --Subjlist=100307
|
97cb93f634f5b6f3b510ecedd40d1ac4e3165a647ebd940fe9ad4adfe0e15754 | Shell | 126 | 7 | #!/bin/bash
binary=$(which llvm-cov-6.0)
if [ -z "$binary" ]; then
binary=$(which llvm-cov-5.0);
fi
exec $binary gcov "$@"
|
efd7e59feb0c353f4af5ba1578604f73a218b279675ddd245c3f2e049ec07c93 | Shell | 126 | 3 | CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_stimuli" "stimuli.c ${CUSTOM_FILE}" "" "alg config_helpers utils tinyexpr" "m"
|
069ac140428f149336f31501be10807800ffc2b95c87f936dba3538b9fbb13fb | Shell | 128 | 4 | #!/bin/bash -l
convert -delay 150 -loop 0 *.png motion_diff.gif
convert motion_diff.gif -resize 19% motion_diff_compressed.gif
|
e3cadf4459aeb61827413c8005d79dabc6f7ae8463b2e712cdf2c020f7310897 | Shell | 128 | 4 | #!/bin/bash -l
convert -delay 150 -loop 0 *.png motion_diff.gif
convert motion_diff.gif -resize 20% motion_diff_compressed.gif
|
4fa3ea58a4a8082ba1d64af2507a2f7def1344617763606ede4e2718cd47823b | Shell | 129 | 5 | log_dir=YOUR_LOG_DIR
mkdir -p $log_dir
python3 -u main_simulation.py --outf $log_dir 2>&1 | tee $log_dir/out.log
echo "Success"
|
d033b128d5b0dae26db3614d928e6218d240c5a4a83559d98fd2a9ac68561280 | Shell | 131 | 10 | #! /bin/sh
cd tests
. ./compat.sh
if [ -n "$VALGRIND" ]; then
exec valgrind ${DIR}/test_all
else
exec ${DIR}/test_all
fi
|
1158aca5f5a73fc651e09fc9621870e04682c3c6771b250715329b899bf45eb1 | Shell | 133 | 8 | #cd ../analysis/
for i in $(seq 0 31)
do
rm -rf procesor_$i
mkdir procesor_$i
#cp radius5.f90 ../run$i/analysis
done
|
c384ef2370758259b7c5694f3aa0475cd5acd5c65fb7e3981a677b64d66546e4 | Shell | 133 | 6 | #!/bin/bash --login
source $FREESURFER_HOME/SetUpFreeSurfer.sh
. /gonzo/conda/etc/profile.d/conda.sh
conda activate gonzo
exec "$@"
|
18d838130070008f32b96d02eb81616e81d8fcc8d7c3b4bc61af5ccf41930083 | Shell | 134 | 6 | log_dir=YOUR_LOG_DIR
mkdir -p $log_dir
python3 -u main_mocap.py --outf $log_dir 2>&1 | tee $log_dir/out.log
echo "Success"
echo "END" |
24cdc1d53f0b870c99b4a6ed338493e524139143e449afbf7399457af99d7855 | Shell | 134 | 2 | #!/usr/bin/env bash
valgrind --xml=yes --xml-file=val.xml --suppressions=scripts/valgrind.supp --leak-check=full --leak-check=yes $@
|
4934dbc80dfeaaef1f7aa82babeba61ff6a8ef8bb840cf36df01be9062c9a1de | Shell | 135 | 7 | #!/bin/bash -e
source config.sh
echo "${registry}/${image_name}:${version}"
scing push --image=${registry}/${image_name}:${version}
|
4fdd60df7a5ffaf48a0e78e6e1dda20eb53fbfda584259f271647170c101c063 | Shell | 135 | 5 | #!/bin/bash
python ../eval_3D_lane.py \
--dataset_dir=./annotations/ \
--pred_dir=./results/ \
--test_list=./test_list.txt
|
a531e7f22fc389ce476d6604c86c170a38b299c95fb1850d73b2e10c3524e5f8 | Shell | 135 | 7 | ifort -fast -march=core-avx2 -o pmodel DG_mechanism.f90 dranxor.f90
start=1
end=20
for ((i=start; i<=end; i++))
do
run pmodel $i
done
|
63742f8ca4185f711985349b6390a86d55f72b0cd2a91d25bd545e2f0c8ff914 | Shell | 136 | 9 | #!/bin/bash
#
# avgruns.sh <avgname> <run1_file> <run2_file> ...
#
avgName=$1
fileNames=${@:2}
3dMean -prefix ${avgName} ${fileNames}
|
922466b3891eb161f3327222e3d2e7e515423b2d1f6f958c9176ac55c2285781 | Shell | 136 | 4 | SOURCE_FILES="tinyfiledialogs.c"
HEADER_FILES="tinyfiledialogs.h"
COMPILE_STATIC_LIB "tinyfiledialogs" "$SOURCE_FILES" "$HEADER_FILES"
|
2c647eda6cfdef291c62854813c106c4a75630e27bacb355e1a411680ad135de | Shell | 138 | 5 | export protein=Ago
python protein_DIFF/dataset/generate_graph.py \
--pdb_dir dataset/$protein/pdb/ \
--save_dir dataset/$protein/process/ |
9513be76f562db7fa575f58ac9d84f41bd5aeff0369add3cd3fe0848bbcb5f8f | Shell | 138 | 4 | #!/usr/bin/env bash
# profile_file="ut_${2// /_}.profraw"
# profile_file="${profile_file//\//_}"
# LLVM_PROFILE_FILE="$profile_file" "$@"
|
14d9798f274696b071436cb8861b4aba7cd524dd645e96b635914c59f8d6e123 | Shell | 139 | 6 | #!/bin/bash
# Sei training data
wget https://zenodo.org/record/4907038/files/sei_training_data.tar.gz
tar -xzvf sei_training_data.tar.gz
|
8291c8688e15df2f9aabf9e780cfc1ec90d07f82fbe8133a99a1952d9ede0483 | Shell | 139 | 6 | log_dir=YOUR_LOG_DIR
mkdir -p $log_dir
python3 -u main_mdanalysis.py --outf $log_dir 2>&1 | tee $log_dir/out.log
echo "Success"
echo "END" |
b121a6f451dd375e9d50a9ada7a377145f6180a7e5bdc458b96e73dc8e416739 | Shell | 140 | 14 | ## build htslib
cd htslib
autoreconf -i
./configure
make
cd ..
## build qgenlib
cd qgenlib
mkdir -p build
cd build
cmake ..
make
cd ../../
|
f5d9ab817e5a62397dfdbdc69dfd42061a3485a59776a8b9251cdb9f64515895 | Shell | 142 | 4 | LOGGER_SOURCE_FILES="logger.c"
LOGGER_HEADER_FILES="logger.h"
COMPILE_SHARED_LIB "logger" "$LOGGER_SOURCE_FILES" "$LOGGER_HEADER_FILES" "sds" |
6ef47196bb7c863026a1a4966a167c44a576d0034adb5af3323c6373ece98612 | Shell | 143 | 4 | INI_SOURCE_FILES="ini.c"
INI_HEADER_FILES="ini.h ini_file_sections.h"
COMPILE_STATIC_LIB "ini_parser" "$INI_SOURCE_FILES" "$INI_HEADER_FILES"
|
d2afbf35fa596bd305c037a2670655ac5cec88b9ff28ca785129bc878d7ecd92 | Shell | 143 | 3 | #!/bin/sh
curl -fsLO https://raw.githubusercontent.com/scijava/scijava-scripts/master/ci-setup-github-actions.sh
sh ci-setup-github-actions.sh
|
658f57424f8a5c0fb43cb87112c7a6af1f6cd42697f2794a9ee6c2eb6bd3575d | Shell | 144 | 3 | mpicc bound_cond.c currents.c initial_cond.c main.c memory_alloc.c open_write.c potential.c send_recieve.c stimulus.c variable.c -lm -o a.out
|
ca6e0927a9ef2cc352943b7176a37d7f2b3889a77f2c10a263c287394215e2e7 | Shell | 144 | 9 | #!/bin/bash
set -e
VERSION=`cat VERSION.txt`
docker push trinityrnaseq/transdecoder:$VERSION
docker push trinityrnaseq/transdecoder:latest
|
8f206f243adcd7c679a452deb6748f5114b12fe5156e68461c2b6c314a521e42 | Shell | 147 | 3 | GRAPH_SOURCE_FILES="graph.c pqueue.c"
GRAPH_HEADER_FILES="graph.h pqueue.h"
COMPILE_STATIC_LIB "graph" "$GRAPH_SOURCE_FILES" "$GRAPH_HEADER_FILES"
|
8d02217024a41dd3d16b51cb4ca8e1d699a277c2178e4e4be9ae4b3cb38256bd | Shell | 148 | 3 | #!/usr/bin/env bash
# profile_file="../st_$(basename \"$3\" .json)-%p.profraw" # solver call is in tmp_...
# LLVM_PROFILE_FILE="$profile_file" "$@"
|
1eeb2721a02c43bce2513dcc20f461f51351c6f7e98a64efd857cdbf4a98263c | Shell | 150 | 3 | python3 make_master_file.py
cp master.csv ../../ogb/ogb/linkproppred/master.csv
cp make_master_file.py ../../ogb/ogb/linkproppred/make_master_file.py
|
3e3b27c962264f4baeb0046c28127d62a3921a9f58f3455c29be9835ceda0063 | Shell | 150 | 3 | mpicc boundary_cond.c initial_cond.c main.c open_files.c send_recieve.c currents.c Istimulus.c memory_allocation.c potential.c write.c -lm -o a.out
|
865753837c39022648a05e6a92e7d977d7dcfcb85156cb6c7ba583d726434c72 | Shell | 150 | 5 | fpc tpmath.dpr -Fu../units -Mdelphi
fpc tpmath.dpr -Fi../units -Mdelphi
fpc tpgraph.dpr -Fu../units -Mdelphi
fpc tpgraph.dpr -Fi../units -Mdelphi |
ab88f44d7c887ade08cf949478eadf61ac7bcadbeff7410206a4d47e5ec3daeb | Shell | 150 | 3 | python3 make_master_file.py
cp master.csv ../../ogb/ogb/nodeproppred/master.csv
cp make_master_file.py ../../ogb/ogb/nodeproppred/make_master_file.py
|
a8a51d56c0174d9505d5aad59854590513917c9912a5c2be050ca00abfb7714b | Shell | 151 | 7 |
force="$1"
for chn in A B C D E F G H I J K L M N O P Q R S T U V W X Y Z a b c d
do
cp pace_top/posre_$chn-$force.itp pace_top/posre_$chn.itp
done
|
f44daf07dbd3846d6d515a383b2c939139efaecb5b8d6f9618aa26a896d2ced9 | Shell | 152 | 3 | python3 make_master_file.py
cp master.csv ../../ogb/ogb/graphproppred/master.csv
cp make_master_file.py ../../ogb/ogb/graphproppred/make_master_file.py
|
fd696c436b66909cea0fef8db8a09efdc2c34a7f202deef5a9fa339a9fc055d0 | Shell | 152 | 7 | #!/bin/bash
# Loop through case_IDs and run mycode.py with the specified case_ID
for i in {0..999}
do
python3 generate_dataset.py --case_ID $i
done |
0b1ea5d97e720828bbbe7774eac41f55fc3684914202ec1d2a53e60de1072b04 | Shell | 154 | 4 | log_dir=YOUR_LOG_DIR
mkdir -p $log_dir
python3 -u eval_mdanalysis.py --outf $log_dir --model_dir ${MODEL_PATH} 2>&1 | tee $log_dir/out.log
echo "Success"
|
d07f81d9a3c1a9bdd33dc4c9e578eb9a9790ca89fe90cd32179f2b55e41185c9 | Shell | 155 | 3 | #!/bin/bash
apptainer run /path/to/mix3r.sif make_euler /path/to/extract_p/analysis_all_runs.json.parameters.csv my_analysis.euler.png "MDD" "BPD" "ADHD"
|
83f458aed1e4a7a023399e26a1cb0643448ef4a475d24c24f9b432f47266e571 | Shell | 156 | 3 | CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_postprocess" "postprocessing.c ${CUSTOM_FILE}" "" "config_helpers utils tinyexpr vtk_utils yxml miniz" "" ""
|
0a374131fd4f5c867ca102c251367abbd0fa53fb562dfc3c79d4864500586e70 | Shell | 158 | 7 | #!/bin/bash
bet "/Users/bo/Documents/data_dean_lab/data_swati/t1_sub1.nii" "/Users/bo/Documents/data_dean_lab/data_swati/t1_sub1_brain.nii" -S -B -f 0.2
|
6a8c8f55906c2025652fcd643cee82dbcb213bb35da15538350ffd70fca3cac8 | Shell | 158 | 9 | scripts/process_tables.py;
scripts/process_connectivity.py;
scripts/train_model.py;
scripts/predict.py;
scripts/model_diag.py;
scripts/trace_back_model.py
|
de5835d1548e069ad7b723abedf06ba5e2e03ebf189a8b13edd20ba1c296a1c7 | Shell | 158 | 8 | gzip -c test > test.gz
gzip -c test > test.bgz
bzip2 -k test
tar -cvf test.tar test
tar -cvzf test.tar.gz test
tar -cvjf test.tar.bz2 test
zip test.zip test
|
2df7c20e57e60ba52b3282f593c27582ffd809fd8ea55e766b7a3099620559cf | Shell | 159 | 5 | #!/bin/bash -l
pdftoppm -r 300 plot_sms_diff_model/fwd.pdf fwd -png
# pdftoppm -r 300 plot_nn/vae.pdf vae -png
pdftoppm -r 300 plot_zsssl/zsssl.pdf zsssl -png |
92a4789556d2df915572a4a55234efd0998c10ebe3631285e08194ebb7bcbc09 | Shell | 159 | 8 | #!/bin/bash
for file in *.png
do
./png2c.py ${file} > ${file:0:${#file}-4}.h
#rm ${file:0:${#file}-4}
#echo "${file} : ${file:0:${#file}-4} "
done
|
cb04a8fa56328a4121ba7d94ad17882d6afd7fbaa5d915a981c7b2c61b7ae4bd | Shell | 159 | 4 | #!/bin/bash
# use self-gating data to estimate shot phase
python ../../examples/run_zsssl.py --config /figures/motion_self/config_zsssl_self.yaml --mode train |
5b5e39b34a3ab0b0693a19e3c71b1fb2dd0cd6c99cd09e18a80d50a4c6548529 | Shell | 161 | 8 | #/bin/bash
tmp=$(mktemp)
find . \( -name "*.h" -o -name "*.cpp" \) | while read file; do
cat ../scripts/license.template $file > $tmp
mv $tmp $file
done
|
88a2aeb36f65492ff38ce849f9c41ce1b32f95dde5a73aaae114e4e35ebdb2ac | Shell | 161 | 8 | config_path=configs/multi_gene_196kb_blood.yaml
model_type=MultiGene
for fold in 0 1 2; do
sbatch slurm_train_gtex.sh $config_path $fold $model_type
done
|
8d8dc6bec73c86d2d4d31b4b3ee347f7278c303753bf268a9740bd2643f987aa | Shell | 161 | 8 | # run pan-cancer analysis
bash run-pan-cancer-plots.sh
# run tumor vs normal analysis
bash run-tumor-normal-gtex-plots.sh
# run annotator
bash run-annotator.sh |
7add31e5ea38aa04c37b22c69fd687963a552d7b7a87357883fb3fc68e533867 | Shell | 162 | 7 | config_path=configs/PPIF_196kb_blood_config.yaml
model_type=SingleGene
for fold in 0 1 2; do
sbatch slurm_train_gtex.sh $config_path $fold $model_type
done
|
bda949abbdcdb017f88bd94b6b814ee38a219c28ab509018ca5f3c93fbc06cfc | Shell | 163 | 12 | #!/bin/bash
# Downloads data from gro.pub
# Author: Vitali Telezki
# Prep dirstructure
mkdir -p Datasets
mkdir -p ClassifiedContours
# TODO:
# wget .....
|
cd23a1277acc418cb26bfe7774a27c1edd0a7d75009097e3247f9d65f562075c | Shell | 163 | 4 | #!/bin/sh
wget -N https://raw.githubusercontent.com/cpplint/cpplint/master/cpplint.py
wget -N https://raw.githubusercontent.com/cpplint/cpplint/master/README.rst
|
1a23c330b109cfd8616074862c5663d5a9c20dc3facb6915833ebe267e0d00c8 | Shell | 165 | 4 | ODE_SOLVER_SOURCE_FILES="ode_solver.c"
ODE_SOLVER_HEADER_FILES="ode_solver.h"
COMPILE_STATIC_LIB "ode_solver" "$ODE_SOLVER_SOURCE_FILES" "$ODE_SOLVER_HEADER_FILES"
|
1854276a36b10da870ced5d0f24943630649a57d1321a2357e6dc7a3863592c3 | Shell | 166 | 4 | while IFS= read -r sample_id; do
echo "running data processing for sample: $sample_id"
python scanpy_processing.py --sample_id $sample_id
done < ../sample_ids |
ff63748cacd91f042bd7c8dc20719642621ad7d2806c2c4c1be420ce44408518 | Shell | 167 | 10 | #!/bin/bash
FILE=.gitignore
while read CMD; do
if [ "$CMD" != "*.svn" -a "$CMD" != ".git" ]; then
echo "rm -rf $CMD"
rm -rf $CMD
fi
done < "$FILE"
|
c840e26c76fe647363761f506a1638b14d5b6934f25a5b3aedeffc77be72ee12 | Shell | 169 | 5 | #!/bin/bash
let MASTER_PORT=RANDOM%10000+1500
python3 -W ignore -m torch.distributed.launch --master_port=$MASTER_PORT --nproc_per_node=8 train.py \
--config ./cfg.yml
|
57120d3ae56bfdc87cc0f31b6d7620da7863b709f1be22934e9d3f995eb9c6c3 | Shell | 170 | 5 | #!/bin/bash
perf record -F 99 -g -- $@
perf script | ../FlameGraph/stackcollapse-perf.pl > out.perf-folded
../FlameGraph/flamegraph.pl out.perf-folded > perf-kernel.svg
|
e1c669403e55f3b7b35025dce97eb7c4de3a03ed5bf77072e0e8a25a76a1872c | Shell | 170 | 6 | #!/bin/bash
for S in {0..87}; do
echo $S
python ../../examples/run_zsssl.py --config /figures/motion_self/config_zsssl_self.yaml --mode test --slice_idx $S
done
|
9a944cc0448f0394a728ab2ecdfccfd72dc3a839bbcadb1676de675885f2a8d0 | Shell | 171 | 12 | #!/bin/bash
set -e
VERSION=`cat VERSION.txt`
rm -f ./*simg
docker build -t trinityrnaseq/transdecoder:$VERSION .
docker build -t trinityrnaseq/transdecoder:latest .
|
03a2896f71c57d575d983a6d18080a8f761e3d71b26285c1ff790487550022b7 | Shell | 172 | 3 | CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_domains" "domain.c ${CUSTOM_FILE} domain_helpers.c" "domain_helpers.h" "config_helpers vtk_utils utils alg sds tinyexpr" "m"
|
082219d189ece69d850956a400442dd76940f2a4719c2d87fca2a8639da9c450 | Shell | 174 | 7 | snakemake \
-s /rhome/naotok/Shiba/SnakeShiba \
--configfile config_Shiba.yaml \
--cores 32 \
--use-singularity \
--singularity-args "--bind $HOME:$HOME" \
--rerun-incomplete |
5eb191ecada239d179826618c45b1ad8751441b4f8c2aca5f12e7c248e95feb8 | Shell | 175 | 3 | CHECK_CUSTOM_FILE
COMPILE_SHARED_LIB "default_purkinje" "purkinje.c ${CUSTOM_FILE} purkinje_helpers.c" "purkinje_helpers.h" "alg config_helpers utils solvers graph tinyexpr"
|
ce214ef2279c2a40b9c70f3ba6456303b074731f6876347116c1d0b51e5aa78f | Shell | 175 | 12 | chmod -R 750 ./models train.py
rm -r checkpoint1
rm -r results/*
rm -r wandb/*
rm -r train_log
mkdir train_log
rm -r param_infor
mkdir param_infor
rm *.out
rm *.tar
rm *.log
|
37dc83ada53af1963897e644f84698a722c0073ec299473f15c2d3968334e82f | Shell | 179 | 4 | MONODOMAIN_SOURCE_FILES="monodomain_solver.c"
MONODOMAIN_HEADER_FILES="monodomain_solver.h"
COMPILE_STATIC_LIB "monodomain" "$MONODOMAIN_SOURCE_FILES" "$MONODOMAIN_HEADER_FILES"
|
cfe5aac8a4ca47821fd1d67ffce6cf61ad2d9345d5dd47b1271123abe1d36bf2 | Shell | 179 | 3 | #!/usr/bin/env bash
[ ! -e "$FREESURFER_HOME" ] && echo "error: freesurfer has not been properly sourced" && exit 1
exec python3 $FREESURFER_HOME/python/scripts/mri_synthseg "$@"
|
51193a9a844dfb7184b8610d2bafa2c1be01bb368af2adb2dbfa6235fbcdb973 | Shell | 182 | 7 | #! /bin/sh
for i in count stats histo dump merge query qhisto qdump qmerge cite; do
echo "\\subsection{$i}"
jellyfish $i --help | ruby option_to_tex /dev/fd/0
echo
done
|
24e3bb50c931eeb5d2706aec732df99e13d913200f307f92da9134017a460acd | Shell | 184 | 4 | ENSIGTH_UTILS_SOURCE_FILES="ensight_grid.c"
ENSIGTH_UTILS_HEADER_FILES="ensight_grid.h"
COMPILE_STATIC_LIB "ensight_utils" "$ENSIGTH_UTILS_SOURCE_FILES" "$ENSIGTH_UTILS_HEADER_FILES"
|
6b16bb71e87143cd460e52ed1e7580a506e87c62bb16e80834bcfe04d7f564b2 | Shell | 184 | 6 | poetry run black .
poetry run isort . --profile black
poetry run flake8 scpca
poetry run flake8 tests
poetry run coverage run -m --source=scpca pytest tests
poetry run coverage report
|
a87902912c90851481fdb4ee493b70f3d11a5b3a341b6ce991298633ee60d933 | Shell | 186 | 6 | snakemake -s /rhome/naotok/SnakeNgs/snakefile/kb-nac.smk \
--configfile config_kb-nac.yaml \
--cores 48 \
--use-singularity \
--singularity-args "--bind $HOME:$HOME" \
--rerun-incomplete |
dfa2d4e1d65465717e613b63deb872b1802ed36a4418f072962913035fe052bd | Shell | 186 | 5 | #!/bin/bash
let MASTER_PORT=RANDOM%10000+1500
python3 -W ignore -m torch.distributed.launch --master_port=$MASTER_PORT --nproc_per_node=1 train.py \
--clip_grad=1.0 --config ./cfg.yml
|
86e084981019468cfda19921e5a6dd07389aa4dbcbba5b9173a2d3490fcc2b78 | Shell | 188 | 7 | config_path=configs/blood_config.yaml
for model_type in SingleGene MultiGene; do
for fold in 0 1 2; do
sbatch slurm_train_gtex.sh $config_path $fold $model_type
done
done
|
473c2a76ebb07b3501ba928ba935d1d4c87ac2415cd04f8e5b16fb118c8ffaad | Shell | 190 | 7 | config_path=configs/brain_config.yaml
for model_type in SingleGene MultiGene; do
for fold in 0 1 2; do
sbatch slurm_train_rosmap.sh $config_path $fold $model_type
done
done
|
4cc5b50f360ec7784883d43529c4009590e843d5c08b7064372065ecd6e0c9ae | Shell | 190 | 8 | export CUDA_DEVICE_ORDER=PCI_BUS_ID
sh final_gnn_gcn_parallelize.sh &
sh final_gnn_sage_parallelize.sh &
wait
sh final_gnn_gcn_emb_parallelize.sh &
sh final_gnn_sage_emb_parallelize.sh &
|
76b4083493ba9b4105b9d6f6dc974a9c360be75e9670f533d49f5d73d2ae97df | Shell | 192 | 11 | #!/bin/bash
PIPELINE_CONDA_ENVS=(
encd-chip
encd-chip-macs2
encd-chip-spp
)
for PIPELINE_CONDA_ENV in "${PIPELINE_CONDA_ENVS[@]}"
do
conda env remove -n ${PIPELINE_CONDA_ENV} -y
done
|
a1b361257872cab9f3dd7f585962500515ea7dd37bf19df9d4fd48e5dd93283f | Shell | 192 | 7 | #/bin/bash
tmp=$(mktemp)
find . \( -name "*.h" -o -name "*.cpp" -o -name "*.cu" \) | while read file; do
diff ../scripts/license.template <(head -8 $file) > /dev/null || echo $file
done
|
05ee01b0a5103821272aa097ecc7857cdc52e08216b85529d0e06ff7a2bc9b7d | Shell | 193 | 8 | #!/bin/bash
for i in $(ls -d Brain*)
do
cd $i
perl ../EANMD_filterPSI.pl -o $i.PSIfilter.out -i 0.1 -d 20 -m 2 -f 0.05 -c1 6 -c2 2 -mf 0.05 SE.MATS.JCEC.txt
echo "Done $i.PSIfilter"
cd ..
done
|
51f249f318aca55ffd7f7f5ef14d44f83000c87aecf030a804e91d614758fc3b | Shell | 193 | 4 | CONFIG_HELPERS_SOURCE_FILES="config_helpers.c"
CONFIG_HELPERS_HEADER_FILES="config_helpers.h"
COMPILE_STATIC_LIB "config_helpers" "$CONFIG_HELPERS_SOURCE_FILES" "$CONFIG_HELPERS_HEADER_FILES"
|
8800842d7c20e2afc7e3ffa42282d9be4789e5a4d73155973e59b42cba62c8e4 | Shell | 195 | 6 | # setting up the Conda Environment
# to create the `luo_wm_dev` environment, run:
cd /cbica/projects/luo_wm_dev/two_axes/software
conda env create -f luo_wm_dev_env.yml
conda activate luo_wm_dev |
f9fbf22106c6fbe25ed80973ba4c438217c0c4735b1ffb720c5a56dd71e3c440 | Shell | 196 | 10 | #!/bin/bash
source config.sh
docker run -it --rm \
-p 8888:8888 \
-v $(pwd)/notebooks:/home/jovyan/work \
-v $(pwd)/test:/test \
--entrypoint bash \
${image_name}:${version}
|
0cd5e0115d56def824eb1e492e70fb0453c235dab3fa2c84750f3306d014b461 | Shell | 197 | 8 | #!/bin/bash
#
# select_runs.sh <runlist_file> <idx1> <idx2> ...
#
# - returns a selected list of files from runlist_file
# - lines idx1, idx2, ... are selected
select_runs_add-ext.sh $1 "" ${@:2}
|
f75aedfff5f4bb62099e6958205f462b9a980b7983918fcd15a043d713b4c758 | Shell | 197 | 7 | #!/bin/bash
#VCF should only contain SNPs in loci of interest
./plink2 --vcf /path/to/1000G_genotype/vcf \
--r-unphased square zs \
--threads 1 \
--out /path/to/output_dir
|
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