sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e3daff97c230c8ddbfd2f5cd794fdbd1baf7e19f74368e88e92f1501a25fa8e3 | Shell | 156 | 5 | DSSPDIR=./PDB/DSSP/
PDBDIR=./PDB/
META=./dockground.csv
OUTDIR=./
python3 ./parse_dssp.py --dsspdir $DSSPDIR --pdbdir $PDBDIR --meta $META --outdir $OUTDIR
|
0228f81141ca4c949c32fd0579cd206d18b827173541afc1f56c17f1686d34a3 | Shell | 157 | 9 | #!/usr/bin/env bash
cd ./databases/
# Download laser dataset from zenodo.
wget https://zenodo.org/records/15035128/files/all_data.zip
unzip all_data.zip
|
815e0983beca022c1e1c72a9bb69c8b397251edce35c1320d0d30fbc96d4c92e | Shell | 158 | 1 | EnsembleTR --vcfs eh_example_smaller.vcf.gz,hipstr_example_smaller.vcf.gz,gangstr_example_smaller.vcf.gz --out ensemble_example.vcf --ref hg38.analysisSet.fa
|
3d5b855b11192d5c0dc604a4d790051269de41c4e19694718916cc0a43e9fafd | Shell | 160 | 5 | export OMP_NUM_THREADS=8
wandb online
cd /your_path/MERL/pretrain
torchrun --nnodes=1 --nproc_per_node=8 --rdzv_id=101 --rdzv_endpoint=localhost:29502 main.py
|
40ede6149a9eddc9b6936047b5ab0c5b146db19ea5b367f0e19c42f3c01cc617 | Shell | 161 | 6 | #!/bin/bash
export FLASK_APP=project/interface.py
export FLASK_ENV=development
echo "FLASK APP"
echo $FLASK_APP
nohup flask run --host=0.0.0.0 >> log.txt 2>&1 &
|
71ca6ba0abd25d9a942bf819214e34a9d34967a544e4d6bc884f5f5d97d34cd1 | Shell | 161 | 8 | export PQANALYSIS_BEARTYPE_LEVEL=DEBUG
python -m pytest "$@"
if [ $? -ne 0 ]; then
exit 1
fi
export PQANALYSIS_BEARTYPE_LEVEL=RELEASE
python -m pytest "$@"
|
3b69731cedd86d490e6fc8b0ce77ebc471598aa3c8347cca23be240abfe5308b | Shell | 162 | 2 | BWA_REF=/exports/igmm/eddie/Glioblastoma-WGS/resources/refgenome38/hg38.fa
BLACKLISTED_PEAKS=/exports/igmm/eddie/Glioblastoma-WGS/resources/hg38-blacklist.v2.bed
|
f9a87636dc2ac110bb07a904ecc0d87a8f9258c23f55f3c6d716c314a6f5012d | Shell | 162 | 11 | python ./load_model/kfold_review.py \
--fold_dir ./train_kfold/lg_transformer_kfold_runs/sheet_EOV/fold_1 \
--device cuda \
--repro_which test
|
4fde9cfc59329f071f480ed215567e55b225c390b5d6ae320eddb52d3687c93b | Shell | 165 | 4 | # command
humann_rename_table -i rename_table-input.txt -c rename_table-custom.txt.gz -o rename_table-custom_output.txt
# output
# 1) rename_table-custom_output.txt
|
67d474ff390ba5d72f0db300cb31ed9a884c86028591940d974fe88a464e1e26 | Shell | 165 | 4 | # command
humann_regroup_table -i regroup_table-builtin_input.txt -g uniref50_rxn -o regroup_table-builtin_output.txt
# output
# 1) regroup_table-builtin_output.txt
|
33ec2cf56e16cddca96cee62ee8b67383635acbd900f3ed535fb82fc1fd4d2cb | Shell | 166 | 3 | # USE AT YOUR OWN RISK
# from: https://forums.docker.com/t/command-to-remove-all-unused-images/20/2
docker rmi -f $(docker images | grep "<none>" | awk "{print \$3}") |
736bfa03c82796ffa0dda65b6f854b7e072383d03a17e314c1ffb94d91b64907 | Shell | 167 | 4 | #!/usr/bin/env bash
set -eu -o pipefail
grep FROM Dockerfile.dev | grep 'builder$\|argoexec-base$' | awk '{print $2}' | while read image; do docker pull $image; done
|
a7b5098a62069a08c93f325e18cf76f688ff0c89d64a3b1b3ee6c562c676ebec | Shell | 169 | 9 | #Run DSSP to determine the 2ndary structure of protein complexes
DSSP=/home/pbryant/dssp
for file in ls PDB/*_bc.pdb
do
echo $file
$DSSP -i $file -o $file.dssp
done
|
acd2503583f794120b4d9303029e7a4cc442bd9ec772271abda725705ea30a43 | Shell | 169 | 7 | #!/usr/bin/env bash
# Run count reads on genome data in GCS.
. utils.sh
time_gatk "CountReadsSpark -I gs://hellbender/q4_spark_eval/WGS-G94982-NA12878.bam" 4 4 4g 4g
|
c9d80d4ce3b37d1a12218dc367d957f170beef615453d4955cc0d3bd621245e2 | Shell | 169 | 4 | sed -i.bak '/torch/d' ./requirements.txt
sed -i '/opencv-python/c\opencv-python-headless' ./requirements.txt
ibmcloud cf push
mv -f requirements.txt.bak requirements.txt |
da6f1e276539a0ee30f4166d5c888ef0fae0b9f45e41470ccf26ff62fdf587bd | Shell | 169 | 4 | #!/usr/bin/env bash
python2 Admin.py 4 generate
python2 ./scripts/generate_SWC_data.py generate generate.db
python2 ./scripts/generate_radii_plot.py generate generate.db |
dcf17353e2abac921f6678a1bdba83f3b318f0f3e45b2e9a973498ad2f5b2a04 | Shell | 171 | 4 | #!/bin/bash
# Use taskset to limit benchmarks to specific cores, ensuring a fair test of limited resources:
taskset -c 0-10 /benchmarks/run_benchmarks.py /input /outputs
|
fdf0c50d7f57ebf2fffec431225413f94cf69ddd95f00d0ed146bf9d41915791 | Shell | 171 | 5 |
jupyter nbconvert __init__.ipynb --to html
jupyter nbconvert *.ipynb --to python
jupyter nbconvert ./plot/*ipynb --to python
jupyter nbconvert ./scanpy/*ipynb --to python |
2d76c1208ece143fcedfe1e2d45cd5902414667d6d966dcd9c2d815490f080e1 | Shell | 172 | 14 | #!/bin/bash
# source run_all.sh
#
set -e
for f in test_*.py
do
echo "Processing $f script.."
python3 "$f"
done
echo '---------'
echo "All good."
echo '---------'
|
de3e09f5c14d1386ded29998be0f413e13dafaad1dbf4cebb116198e9c9d7fb2 | Shell | 172 | 4 | #! /bin/bash
python ../../scripts/generate_wire_plot.py demo_distant_attraction.cfg simul_distant.db &
python ../../scripts/generate_wire_plot.py to_pia.cfg simul_pia.db
|
e95b398079456ecfb189839ea16090d342aa0f8a3dedf7cda9168394dc82962e | Shell | 175 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=3:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
julia script_V0_basin_km.jl $i_I_range |
8d9519786db30d6954a8e8917ae6679c0a32488afcf2a4160fc9a69477371386 | Shell | 176 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=3:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
julia script_V0_basin_kir.jl $i_I_range |
1a67fc046b8ad018fd2312051381611bf93cb5c0e5f7b7845ab03cd2b1ea233a | Shell | 178 | 6 | 3#!/bin/bash
# Run deformetrica
source activate deformetrica
deformetrica estimate model.xml data_set.xml --p optimization_parameters.xml -v DEBUG
conda deactivate deformetrica
|
85472905b5e27b650b628baf6016369cf7f910184f157dce18d74c79a1cc6e19 | Shell | 178 | 8 | #! /bin/sh
#
# pack_lfs_data.sh
tar -czf models.tar.gz models/
tar -czf reactions.tar.gz reactions/
tar -czf binaries.tar.gz -C . binaries/get_params binaries/prep
echo "Done."
|
d063c03d80e4b2e201bbf56326abdc1d958b4eb4a021c35651894e6fc14f5bf8 | Shell | 178 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=2:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_noise_km.jl $i_std $i_batch |
99194673b5f5cab86a6375f2a66374eddc68277bbd63056f227cca97a7a54226 | Shell | 179 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=7:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_fI_curve.jl $i_pCaLs_d $i_Kir_s |
ece1e5a3522f5d42f3afdc8e5f0fa836cbda9b992e5250fee5a0880477ab1e54 | Shell | 179 | 3 | # borrowed from cobrapy package https://github.com/opencobra/cobrapy/blob/devel/cobra/clean.sh
find . -type f -regex '.*pyc' | xargs rm
find . -type f -regex '.*class' | xargs rm
|
525085e29b1e724fa50c9c07a6ea2659559a6f39a0a91fd475ded885f24bf324 | Shell | 181 | 4 | # command
humann_regroup_table -i regroup_table-custom_input.txt -c regroup_table-custom_groups.txt -o regroup_table-custom_output.txt
# output
# 1) regroup_table-custom_output.txt
|
4cbc6155fd1cf14e982c79e1ee293637d9c2250d52df4b05c4f038c0421076e6 | Shell | 182 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=2:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_noise_cyc_km.jl $i_std $i_batch |
82b46ed77fff07fee3b52c4b70092adf710c34ea6c5620dcd516ba6ae38e456a | Shell | 182 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=2:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_grad_km.jl $i_pCaLs $i_batch_KM |
982449a0ce1347c96c5395e806f04f17f0732301930503891b3f72c7eb278e70 | Shell | 182 | 10 | #!/bin/bash
# source run_all.sh
#
echo Press Esc at anytime to skip example, F1 to interrupt
for f in *.py
do
echo "Processing $f script.."
python "$f"
done
|
f576ac4554351cff978890cb80ca44d7b0260fe9e4b0e482e62db3d853ee1d05 | Shell | 183 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=1:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_noise_cyc_kir.jl $i_std $i_batch |
a01aca64444b76e699f63d18ee44114e4b45883cf60aa13db5b4a29a0690bd66 | Shell | 184 | 7 | #!/usr/bin/env bash
. utils.sh
for exec_mem in 1g 500m 256m 128m 64m; do
time_gatk "CountReadsSpark -I hdfs:///user/tom/q4_spark_eval/WGS-G94982-NA12878.bam" 4 4 $exec_mem 1g
done
|
a1251083f6416a9557c426aa2888aca5b31dc25968633b7200f928aa84a33997 | Shell | 184 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=4:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_grad_Kir_s.jl $i_pCaLs_d $i_Kir_s |
ee626137411d8044b503a042e3e9c8552257ca40cb195c5df011b19b29e78aac | Shell | 184 | 12 | #!/bin/bash
cd /data/mat/zhi/BigNeuron/first2000/images/
for foldername in `ls -d *`
do
cd $foldername
for filename in `ls *.qsub`
do
qsub $filename
done
cd ..
done
|
b0c90e7179b1044271a5e2d9b77b420645f387b3bc212fabcc75174303be715f | Shell | 185 | 2 | source ~/catkin_ws/devel/setup.bash
roslaunch midas_cpp midas_cpp.launch model_name:="model-small-traced.pt" input_topic:="image_topic" output_topic:="midas_topic" out_orig_size:="true" |
884cd2045d2f2dd4d4fdd1a19f0d2be10f5c20092042cbccc3ac7736514ba755 | Shell | 186 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=2:30:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_grad.jl $i_pCaLs $i_Kir $i_batch_KM |
2005baf57e19ba9a8a903a3542ba240fd05f12899e43f4b39553c09db10abb76 | Shell | 187 | 11 |
#!/usr/bin/env bash
umask 0000
#for sub in 101 103 104 105; do
for sub in 104; do
for ses in $(seq -w 01 12); do
qsub fmriprep-anat/fmriprep_sub-${sub}_ses-${ses}.sh
done
done
|
9cfa5c9f91d50e1f7065cf25c07cbf4021629b8c751e54691e6892cc1bae79a3 | Shell | 187 | 6 | #!/bin/bash
# Run deformetrica
source activate deformetrica
deformetrica estimate model.xml data_set.xml --p optimization_parameters.xml --output='cpu_output/' -v DEBUG
conda deactivate
|
4ba4e629c705e699fe5b531855406cc5685ba2b24a7a777fff25157ee1ea38e0 | Shell | 188 | 7 | #!/bin/bash
./make_reference.sh \
/storage/resources/dbase/human/hg19/chromFa \
/storage/mgymrek/repeat-expansions/gangstr_references/gangstr_reference_hg19.bed \
trf \
1
|
6d3b0ffff0fb3551db48d75cd3b32c134e37553db059cd16e0d077499b678cb3 | Shell | 188 | 10 | #!/bin/bash
mkdir -p ./logs
export CUDA_VISIBLE_DEVICES=1
model_name=CAA
log_dir="./logs/"
cmd="python -u run.py \
--model $model_name >${log_dir}$.log"
eval $cmd
|
7ed24ef294691558235e1e97e5d7979fd29ee41ebbf6c7f1890608a898a5e04e | Shell | 188 | 10 | #!/bin/bash
mkdir -p ./logs
export CUDA_VISIBLE_DEVICES=0
model_name=CAA
log_dir="./logs/"
cmd="python -u run.py \
--model $model_name >${log_dir}$.log"
eval $cmd
|
8890b181d758a75c036d9dcd24b39d9641f524ddaf62907563babcf237aace01 | Shell | 189 | 6 | IMPORTED_TASKS=50000 \
LOCUST_USERS=3 \
LOCUST_HOST=http://localhost:8000 \
LOCUST_LOCUSTFILE=one_imports_other_annotate.py \
LOCUST_SPAWN_RATE=1 \
locust --headless -t 1m --csv results.csv |
d089afd7fe531e70f677fc88280c0617a1c4b861f4f32f63e866d8e0b8fdb3e1 | Shell | 189 | 5 | # Install hollow from https://github.com/Becksteinlab/hollow in a Python 2.7 environment
# mamba activate bp
hollow -g 1.0 -c constraint.txt -o hollow.pdb ../model_processing/rotated.pdb
|
d5f13588eb2fe5432343b099eb52e647b02e7cafc4164cb1384ff22af4069330 | Shell | 189 | 4 | #!/bin/bash
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd $SCRIPT_DIR/..
wasmtime run --max-wasm-stack=4194304 --env INSTA_WORKSPACE_ROOT=/ --mapdir "/::$(pwd)" -- "$@"
|
3781a167753cfe009d8f2d21b720bfaf3b033d696e1d485bb4f8dd504511091c | Shell | 190 | 8 | #Run DSSP to determine the 2ndary structure of protein complexes
DSSP=/home/pbryant/dssp
for file in /home/pbryant/data/MSAmatch/pdb/*.pdb
do
echo $file
$DSSP -i $file -o $file.dssp
done
|
661f48d40aba3a94be60800b25b52718ae1d744b364ac2f1b6a7c6c70e2195a1 | Shell | 190 | 10 | #!/bin/sh
#
#SBATCH --partition=batch
#
#SBATCH --time=8:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_fI_curve.jl $i_pCaLs $i_Kir $i_KM $i_batch |
fc3befc9ab31057892d4fc469ea5e658dd7cdc34d9a9e8c63771ed7e03a0912f | Shell | 192 | 5 | SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
if bash ${SCRIPT_DIR}/../deploy/prebuild.sh; then
docker build -t heartexlabs/label-studio ${SCRIPT_DIR}/..
fi |
56be5a482ac8966570bf848a6eecfc3a1bc6231cb9e41917fd992a0dc0c2a204 | Shell | 193 | 7 | #Preprocess the new set
#Extract PDB
PDBDIR=./PDB/
META=./newset.csv
ATOM_LIST='N,CA,C'
OUTDIR=./
python3 ./extract_pdb.py --pdbdir $PDBDIR --meta $META --atom_list $ATOM_LIST --outdir $OUTDIR
|
64040b14864987e6d136732fa4531fafe9294515d68b9d06b4b13f7def482716 | Shell | 194 | 5 | DSSPDIR=/home/pbryant/data/FoldDock/dssp/
CBDIR=./PDB/CB_coordinates/
META=./final_ids_lens.csv
OUTDIR=./
python3 ./parse_dssp.py --dsspdir $DSSPDIR --cbdir $CBDIR --meta $META --outdir $OUTDIR
|
0d9463d6300488654a14df503a84832df1b53e6b0acd83cf782e3723c4d80bcf | Shell | 195 | 3 | #!/bin/bash
docker run --rm=false -t -v $WORKDIR:/work -v $HOME/examples:/data/examples:ro -w /work -e CI_SKIP_TEST=1 -e NIPYPE_RESOURCE_MONITOR=1 "${DOCKER_IMAGE}:py38" /usr/bin/run_pytests.sh
|
e93dde48bbe188be37ff51aac1d9b39e5021cc1c91bce8602406b1b5469d732a | Shell | 195 | 3 | #!/bin/bash
docker run --rm=false -t -v $WORKDIR:/work -v $HOME/examples:/data/examples:ro -w /work -e CI_SKIP_TEST=1 -e NIPYPE_RESOURCE_MONITOR=1 "${DOCKER_IMAGE}:py36" /usr/bin/run_pytests.sh
|
2694a2c001553f1eb3f8bf16fc9b08b17ab417136b9293db01c0ae3f982635fc | Shell | 197 | 11 | #!/bin/sh
N="`$1/bin/cygpath -u $1`"
export N
shift
PATH=$N/bin:/usr/bin:$PATH
export PATH
SH_PATH=$N/bin/sh
export SH_PATH
rxvt -fn 16 -fg black -bg white -sl 1000 -e sh $N/lib/mos2nrn1.sh "$1"
|
33edea44e8c9a796174d01ce14e1334bc8594aff7d5bb555d2f892c8c8d38e3d | Shell | 197 | 12 | #!/bin/bash
set -e
if [[ "$1" = "serve" ]]; then
shift 1
torchserve --start --ts-config /home/model-server/config.properties
else
eval "$@"
fi
# prevent docker exit
tail -f /dev/null
|
61c36f5a96b203f35f230ed5d38633256f3d6236d513f67ba453d3d58dbe1650 | Shell | 199 | 5 | #!/bin/bash
# first arg is fs install directory, second arg is subject directory, third arg is subject
export FREESURFER_HOME="$1"
source $FREESURFER_HOME/SetUpFreeSurfer.sh
segmentHA_T1.sh "$3" "$2" |
109dc0ce46718a0dba853308f8a9f7f505c876cae89c6273d4911861df861a8c | Shell | 201 | 6 | source /Users/lorenzo/Dropbox/Research/Decodanda/venv/bin/activate
rm -f source/decodanda.* source/decodanda.rst
sphinx-apidoc -o ./source/ ../decodanda
make clean
make html
open build/html/index.html
|
cfab988c249f2c2950792a54a4258f69289e536036ee233282bbe25cb19d6cdd | Shell | 201 | 7 |
cd ..stages
python elan_video_processing.py data_path/videos data_path/elan_videos
python whisper2elan.py data_path/elan_videos data_path/auto-annotations
python git-sync data_path/auto-annotations
|
ccb7694f4839852f056e739ea230bacd4c920eb6a1992c0944562b612f876382 | Shell | 202 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/find_and_get_fast5.py "${input_dir}/fast5" "${output_dir}" --get-column samples --select-ids "${full_output_dir}/selected_read_ids.csv"
|
eebde0e81c9c066ac84c056d5d7262abf3b04bb84285e6dba1b80e35042ec994 | Shell | 202 | 14 |
#support the system("shell command") under mswin
N="`$1/bin/cygpath -u $1`"
export N
shift
PATH=$N/bin:/usr/bin:$PATH
export PATH
fout="`cygpath -u $1`"
shift
eval "$*" > $fout
echo 1 > tmpdos1.tmp
|
367b5430c7ba717293a55fa145a53d282cc29d86f0840006712a036e04755280 | Shell | 204 | 2 | surf_name=$1 ; surf_ext=$2 ; cd ${surf_name}
montage -mode concatenate -tile 2x2 lh.lateral.${surf_ext} rh.lateral.${surf_ext} lh.medial.${surf_ext} rh.medial.${surf_ext} ${surf_name}_montage.${surf_ext}
|
8e6a6cc24b245883d6f7b12a93df1e46afb04073d5fda3d189efd43484487518 | Shell | 205 | 5 |
R CMD BATCH --no-save --no-restore "--args cell_type='cd8'" _collect_data.R _collect_data_cd8.Rout &
R CMD BATCH --no-save --no-restore "--args cell_type='cd4'" _collect_data.R _collect_data_cd4.Rout &
|
14565fd8e6bf175e5dda01c96ca67a711687745430a3a2f873fc57c56090209b | Shell | 206 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/find_and_get_fast5.py "${input_dir}/fast5" "${output_dir}" --get-column read_number --select-ids "${full_output_dir}/selected_read_ids.csv"
|
2437259bca48793a164562c5a035d076ba9f280016158cc15017bece5ef88eda | Shell | 206 | 14 | #!/bin/sh
# Transform MP3 to WAV for adapting requirememt of OpenSIMLE
echo "Please Enter the MP3Path -> "
read mpath
cd ${mpath}
mkdir 'wav'
for m in *.mp3
do
ffmpeg -i "${m}" "wav/${m%.mp3}.wav"
done
|
600390ef5915d57e37763aa0a517117e54673adf1692c25e44729f4caaff463c | Shell | 207 | 10 | #!/usr/bin/env bash
set -eu -o pipefail
dir=$1
image_tag=$2
find "$dir" -type f -name '*.yaml' | while read -r f ; do
sed "s|argoproj/\(.*\):.*|argoproj/\1:${image_tag}|" "$f" > .tmp
mv .tmp "$f"
done
|
690a2fb3c11a2c6818c98a81314cb8b3b26517d99c5b34f53fe2960c9b57d073 | Shell | 207 | 8 | #!/bin/bash
tag=$(cat cellbender/__init__.py | sed -e 's?__version__ = ??' | sed "s/^'\(.*\)'$/\1/")
docker build \
-t us.gcr.io/broad-dsde-methods/cellbender:${tag} \
-f docker/Dockerfile \
.
|
6b5608cf5b17b1abfd24629df607331e7ca371049779aeb386e473e3abff9f51 | Shell | 207 | 10 | INPUT_DIR=/path/to/the/folder/with/inputs
OUTPUT_DIR=/path/to/the/folder/for/outputs
mkdir -p $OUTPUT_DIR
SCRIPT_DIR=$(dirname $0)
for d in /data/*/ ; do
$SCRIPT_DIR/process_file.sh $d $OUTPUT_DIR
done
|
88621e66288383501b00eebd87b888169aa5abe221b7034092314cb77ff387e9 | Shell | 207 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/find_and_get_fast5.py "${input_dir}/fast5" "${output_dir}" --get-column sample_count --select-ids "${full_output_dir}/selected_read_ids.csv"
|
00cc51a79c30a3ccefa4a902590f3b2b2c8d78c44cb003e48b4e6be4a436511a | Shell | 208 | 10 | #!/bin/bash
tag=$(cat cellbender/VERSION.txt)
release=v${tag}
docker build \
-t us.gcr.io/broad-dsde-methods/cellbender:${tag} \
--build-arg GIT_SHA=${release} \
-f docker/DockerfileGit \
.
|
087586e2bd6e97cb343cda223f5188a8924cc8e18de894af4f071124fc194fcd | Shell | 208 | 12 | #!/bin/bash
PIPELINE_CONDA_ENVS=(
encd-atac
encd-atac-macs2
encd-atac-spp
encd-atac-py2
)
for PIPELINE_CONDA_ENV in "${PIPELINE_CONDA_ENVS[@]}"
do
conda env remove -n ${PIPELINE_CONDA_ENV} -y
done
|
f8adc495dfe83a4bbe588e189ecb17081cb9da0367416275ba346935c992f3c6 | Shell | 208 | 5 | #!/bin/bash
# first arg is fs install directory, second arg is subject directory, third arg is subject
export FREESURFER_HOME="$1"
source $FREESURFER_HOME/SetUpFreeSurfer.sh
segmentThalamicNuclei.sh "$3" "$2" |
38f6154decf41b6c596f303f574d5a6acfd9a17d34b26faf140d9f8b6dddd7d9 | Shell | 209 | 4 | #!/bin/sh
## Get readgroups
BAMDIR="/fastscratch/barthf/GLASS-WG/download"
find "${BAMDIR}" -maxdepth 2 -type f -name "*bam*" | xargs -I% sh -c "samtools view -H % | grep ^@RG | sed 's|^|%\t|' | grep -v '^\['" |
49881426c7f92048d1dcfc637f586ea0898cef8c4a48ce5b5573058c6df1e27c | Shell | 209 | 8 | #!/bin/bash
URLS=$1
URL=`head -$LSB_JOBINDEX $URLS | tail -1`
TAG=`basename $URL`
TAG2=${TAG%%.fastq.gz}
wget --retry-connrefused --read-timeout=20 --timeout=15 --tries=0 --continue $URL &> $TAG2.wget.log
|
264a96b3f1d0e18a99f862a0729226642e6ec4cfcf2d23afea17661b0a6ffa54 | Shell | 211 | 6 | #!/usr/bin/env bash
aws --version
aws s3 ls s3://superconductive-public/static/gallery/ | grep "_full"
aws s3 cp s3://superconductive-public/static/gallery/ . --recursive --exclude "*" --include "*_full.json"
|
3e37a43ac9a1728f35a261d32f98ac79d117975b209d188a56677ccd1b33a2ef | Shell | 212 | 11 | # /bin/bash
[ $# -lt 2 ] && { echo "Usage : $0 LesionFolder result_file(csv)"; exit 1; }
echo -n "" > $2
for f in $1/*nii*;
do
vol=`fslstats $f -V | awk '{print $1}'`;
echo $(basename $f),$vol >> $2
done;
|
4fb89501431f1d88933241d28086bf0251ee11602ca39eda5d5585ea29e6a8e9 | Shell | 212 | 7 | #!/bin/bash
input_dir=$1
type_output_dir=$2
full_output_dir=$3
./tools/find_and_get_pod5.py "${input_dir}/pod5" "${type_output_dir}" --get-column samples --select-ids "${full_output_dir}/selected_read_ids.csv"
|
c61486728b1d157095aa75af21db7b554f75845d460e92dda15d15c30f9f600b | Shell | 212 | 1 | for i in *.vcf; do echo $i; qctool -g "$i" -os "samples.txt" 2>/dev/null; of=$(echo $i | perl -pe 's/\.vcf$/.bgen/'); echo "$of"; qctool -g $i -vcf-genotype-field "GP" -og $of -s "samples.txt" 2>/dev/null; done
|
c60c4ec0bcb444b2dd7c5e6ff926f1b9e93245ba9aa7d416d976b167979ae312 | Shell | 213 | 8 | #!/bin/bash
dir='methane/0000'
xfile="${dir}/data_x.bin"
vfile="${dir}/data_v.bin"
molef='methane.json5'
nnfile="${dir}/nn-parameters-0.pt"
python wf_local_energy_on_nnpsi.py ${xfile} ${vfile} ${molef} ${nnfile}
|
32e9ba856e6cde98ec34c064cf978819cb5de901cf0dd899808dab9059cd49a0 | Shell | 214 | 9 | python train_imagenet.py \
--ngpu 8 \
--workers 20 \
--arch resnet --depth 50 \
--epochs 100 \
--batch-size 256 --lr 0.1 \
--att-type BAM \
--prefix RESNET50_IMAGENET_BAM \
./data/ImageNet/
|
1ba6e2f4ef396169b3f4db1efd9f9dbbc74e48eaae5e7bfa19c5d61823a7e85a | Shell | 215 | 6 | #! /bin/bash
#exec 1> log.txt
PYTHONPATH=.:$PYTHONPATH python ~/work/OIST/git/neuromac/Admin.py 16 demo_distant_attraction.cfg &
PYTHONPATH=.:$PYTHONPATH python ~/work/OIST/git/neuromac/Admin.py 8 to_pia.cfg &
|
1dd81605e104b3c806f080c36aa7318c088ad47fac563745677b8a0473374c7d | Shell | 215 | 12 | #!/usr/bin/env bash
PORT=$1
if [ "$PORT" = "" ]; then
PORT="8091"
fi
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )";
cd ${DIR};
../node_modules//slimerjs/src/slimerjs js/slimer/gallery.js port:${PORT}
|
6ca1714f4f90ebf26f854d9c52be754c76e87a2145242b9fbef8510f11875a98 | Shell | 215 | 12 | #! /bin/bash
[ $# -lt 2 ] && { echo 'Usage :
$1 = fmri folder
$2 = result folder'; exit 1; }
mkdir -p $2
for d in $1/*/;
do
name=$(basename $d)
imcp $d/RS_clean_plusmean_bptf_MNI_2mm.nii.gz $2/$name
done;
|
8a1f4abca35a0dfed0a20558394b42fe43f7ea6f4ecb081942dba3f582f6f6b5 | Shell | 216 | 7 | #!/bin/bash
input_dir=$1
type_output_dir=$2
full_output_dir=$3
./tools/find_and_get_pod5.py "${input_dir}/pod5" "${type_output_dir}" --get-column read_number --select-ids "${full_output_dir}/selected_read_ids.csv"
|
add3ea029f3c9438927dcab00a21c33fcdadea237e7e2b5b641e1e8223aaf73b | Shell | 216 | 9 | python train_imagenet.py \
--ngpu 8 \
--workers 20 \
--arch resnet --depth 50 \
--epochs 100 \
--batch-size 256 --lr 0.1 \
--att-type CBAM \
--prefix RESNET50_IMAGENET_CBAM \
./data/ImageNet/
|
3ed15d643ccbbca6f65411eef78124b778cbf06045432e22af7586113c825007 | Shell | 217 | 7 | #!/bin/bash
input_dir=$1
type_output_dir=$2
full_output_dir=$3
./tools/find_and_get_pod5.py "${input_dir}/pod5" "${type_output_dir}" --get-column sample_count --select-ids "${full_output_dir}/selected_read_ids.csv"
|
b50b4009051765c8b604aece36976d5c59b33dcd0e39ef2a02e0541428180c11 | Shell | 217 | 9 | #!/bin/bash
#SBATCH --account=rrg-pbellec
#SBATCH --time=12:00:00
#SBATCH --job-name=shinobi_script
#SBATCH --mem=32G
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16
/home/hyruuk/python_envs/shinobi_env/bin/python $1
|
65e86218ce6d4db4f42af51131e80b347fd69b234acb0ba6e7df6fd43dd0485f | Shell | 218 | 5 | OUTPUT_DIR = $HOME/phy
wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $OUTPUT_DIR
export PATH="$OUTPUT_DIR/bin:$PATH"
conda env create -n phy --force
|
e3d69b51c515f73f24fa501408142c412f386fc8498238b46ebc828ede878934 | Shell | 218 | 9 | #!/usr/bin/env bash
if [ -z "$1" ]; then
echo "Usage: $(basename $0) <input_filepath>"
exit 1
fi
# Remove empty lines, comments, and timestamp.
sed -e '/\s*#.*$/d' -e '/^\s*$/d' -e '/generation_timestamp/d' "$1"
|
d57226c7b532e9a769de66eac562bdd5e8f8fcf9110f98db94ef32b448e10537 | Shell | 219 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/pyslow5_tests.py "${input_dir}"/blow5/*.blow5 "${output_dir}" sample_values --get-column samples --select-ids "${full_output_dir}/selected_read_ids.csv"
|
e9a9c66483bd4b47373e7475505221498505822b1762bbc741df3563ac149928 | Shell | 220 | 8 | #!/bin/bash
dir='propane/ground_state'
xfile="${dir}/data_x.bin"
vfile="${dir}/data_v.bin"
molef='propane.json5'
nnfile="${dir}/nn-parameters-0.pt"
python wf_local_energy_on_nnpsi.py ${xfile} ${vfile} ${molef} ${nnfile} |
c103d886450d8b2e914e4920b5aa662fad703822b8e1cc95f0f914a968aa3ebf | Shell | 221 | 11 | # Run CellBender
module load python/3.8.x-anaconda
module load gcc/12.2.0
source activate cellbender
for dir in `ls -d $PWD/*`
do
cellbender remove-background --input $dir --output $dir"/CellBender_out.h5" --cuda
done
|
8cbb97ff4d2440e61a2cf461a907a396643a56b507fd0221bd89d61e07734c33 | Shell | 223 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/pyslow5_tests.py "${input_dir}"/blow5/*.blow5 "${output_dir}" sample_values --get-column read_number --select-ids "${full_output_dir}/selected_read_ids.csv"
|
1d9f14d138a7469f2578c2f6c6c7d769186abcbba1512df1a459011db456e384 | Shell | 224 | 11 | #!/bin/sh
#
#SBATCH --error=data/output/kir_noise-%j.err
#SBATCH --partition=batch
#
#SBATCH --time=1:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_dI_noise_kir.jl $i_std $i_batch |
7ae9040ecc85c113c4b8b6f197a716e43ded0f6474f4992822fe553b23911d78 | Shell | 224 | 10 | #!/bin/bash
# change to the dir of the script
cd $( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
# change to the dir to the project
cd ../..
app_config=AI/app.yaml
./run.py app --config ${app_config}
|
fd35bde789cf1cccccda513420d59910894e9fca33ff9be64dc03527e6d2e887 | Shell | 224 | 7 | #!/bin/bash
input_dir=$1
output_dir=$2
full_output_dir=$3
tools/pyslow5_tests.py "${input_dir}"/blow5/*.blow5 "${output_dir}" sample_values --get-column sample_count --select-ids "${full_output_dir}/selected_read_ids.csv"
|
da66fea82b72d32569431d08d4b672efeaf4ad8d8a7453458b46b748066a5899 | Shell | 225 | 7 | #!/bin/bash
set -euxo pipefail
# The PyPI sdist has no .git, so tell setuptools_scm the version explicitly.
export SETUPTOOLS_SCM_PRETEND_VERSION="${PKG_VERSION}"
$PYTHON -m pip install . -vv --no-deps --no-build-isolation
|
44ee7a2132f7b9033560df7242a78b8fc7019ec9aa81bc96665b1dcee5f50dea | Shell | 226 | 7 | #!/usr/bin/env bash
bash ./build.sh
docker tag transmorph_brain_mri_t1 jchen245/transmorph_brain_mri_registration:transmorph_brain_mri_t1_v0
docker push jchen245/transmorph_brain_mri_registration:transmorph_brain_mri_t1_v0
|
4f86a98e011537433de336895b9e22e4b59f94070eacaad134c59d0ca7222708 | Shell | 226 | 12 | #!/bin/bash
cd /global/homes/z/zhizhou/bigneuron/BigNeuron_bench_test_LBNL_v1/testing_data/images/
for foldername in `ls -d *`
do
cd $foldername
for filename in `ls *.qsub`
do
qsub $filename
done
cd ..
done
|
5c425233a4e407e9afd0af00d0f05d0265fae909a488fa08c55437393d0ec7e3 | Shell | 226 | 11 | #!/bin/sh
#
#SBATCH --error=data/output/kir_fI-%j.err
#SBATCH --partition=batch
#
#SBATCH --time=4:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_fI_curve.jl $i_pCaLs $i_Kir $i_batch |
f10204159c80e8bb88b25c2bbe5b5d726dd0f89684d1a16858f662c7f43e44aa | Shell | 226 | 9 | #!/bin/bash
Gene_Results_File="GWAS.genes.raw"
Set_Annot_File="Genesets_for_enrichment.txt"
Output_Prefix="GWAS_enrichment"
magma --gene-results $Gene_Results_File --set-annot $Set_Annot_File col=1,2 --out $Output_Prefix |
16ed493779026bb08b66bf4b98b4492149069ee423b3655797cfab8e58596a6d | Shell | 227 | 11 | #!/bin/sh
#
#SBATCH --error=data/output/km_fI-%j.err
#SBATCH --partition=batch
#
#SBATCH --time=4:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
julia script_fI_curve_km.jl $i_pCaLs $i_KM $i_batch |
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