sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e1756df504097c938bbb7a8afd1a5e69c7211f183fe0e57970ea9b47bd0c781f | Shell | 1,064 | 34 | #!/usr/bin/env bash
aizynth_config="config_synthsense.yml" # Default.
nproc_line="" # Default to empty line, i.e. do not include --nproc.
while [[ "$#" -gt 0 ]]; do
case $1 in
--config) aizynth_config="$2"; shift ;;
--nproc) nproc_line=" --nproc $2 "; shift ;;
*) echo "Unknown parameter ... |
654165eea894d254b90cfd33ae6ce5850085dd6b4eac792a801beeea344ee563 | Shell | 1,065 | 36 | # All rights `PaddleDetection` reserved
#!/bin/bash
model_dir=$1
model_name=$2
export img_dir="demo"
export log_path="output_pipeline"
echo "model_dir : ${model_dir}"
echo "img_dir: ${img_dir}"
# TODO: support batch size>1
for use_mkldnn in "True" "False"; do
for threads in "1" "6"; do
echo "${model... |
b08804450dc90e195ac9a23e72449e4370ff71fdcfe572d889a1044a3d4258d3 | Shell | 1,075 | 11 | #!/bin/bash
#SBATCH --time=120:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
samtools merge rmdup.remap.kept.bam rmdup.chr1.kept.sort.bam rmdup.chr2A.kept.sort.bam rmdup.chr2B.kept.sort.bam rmdup.chr3.kept.sort.bam rmdup.chr4.kept.sort.bam rmdup.chr5.kept.sort.bam rmdup.chr6.kept.sort.ba... |
fd1a2130bac09d40c2c0cccb6d15210a194485557bd6893c59cb34b0dcd9c893 | Shell | 1,079 | 34 | #!/bin/bash
# cd PROJECT_DIR/results/restfmri/dnn/niis/group_niis_for_figures
cd PROJECT_DIR/results/restfmri/dnn/niis/consensus_multiple_cv
# cd PROJECT_DIR/results/restfmri/dnn/niis_DL/consensus_multiple_cv
pnt="80"
## declare an array variable
# declare -a arr=("REST1_LR" "REST2_LR" "REST1_RL" "REST2_RL"
# ... |
cc25817176b3c47c8aa8b44c2f3c74124fdeb10e47739da131d500d0f6f00501 | Shell | 1,083 | 37 | #!/bin/bash
#
#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
#################
#set a job name
#SBATCH --job-name=deeplift
#################
#a file for job output, you can check job progress
#SBATCH --output=Jobs/deeplift-%j.out
#################
# a file for er... |
974ce4c80c21a42e372789ceb345f5da26cdb8c89e1dc0c41766071db799c258 | Shell | 1,085 | 48 | #!/bin/bash
#
# Simple run script for running AiZynthFinder via slurm.
source /home/me/miniconda/bin/activate aizynthfinder
worker="worker.sh"
config="${PWD}/config.yml" # CAZP generated AiZynthFinder configuration
nmols=8
# split the SMILES from stdin into batches
split --numeric-suffixes=1 --additional-suffix=_i... |
bff030b54a754de99df1b96bc7f6da0f8753ba9c0d90843829684196d826a994 | Shell | 1,087 | 37 | #!/bin/bash
#
#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
#################
#set a job name
#SBATCH --job-name=deeplift
#################
#a file for job output, you can check job progress
#SBATCH --output=Jobs/deeplift-%j.out
#################
# a file for er... |
a418723b496f4c28ad31a6548c04618d13d73f26d6c06690747943ebe168ba15 | Shell | 1,089 | 47 | #!/bin/bash
abort(){
echo "Run unittest failed" 1>&2
echo "Please check your code" 1>&2
echo " 1. you can run unit tests by 'bash .travis/unittest.sh' locally" 1>&2
echo " 2. you can add python requirements in .travis/requirements.txt if you use new requirements in unit tests" 1>&2
exit 1
}
unit... |
6984919d94ac7b65fa8691986b9f814755a61e37f7bb95f65f8f7b5c28bbf764 | Shell | 1,096 | 33 | #!/bin/bash
#
#SBATCH --job-name=simul_dfc_job # Optional: Name of your job
#SBATCH --output=logs/simul_out.txt # Standard output log
#SBATCH --error=logs/simul_err.txt # Standard error log
#SBATCH --account=YOUR_ACCOUNT # Account
#SBATCH --time=24:00:00 # Walltime for each task (24 hours)... |
9c36e31063329a4222f06ed7bf52922f8f3295ddd73221d75fde1355096bb614 | Shell | 1,118 | 29 | #!/bin/bash
# Run .fsf file per subject (2nd level GLM in 3-level approach).
#
# Make executable:
# chmod a+x feat_run_GLM_Subject_2ndlevel.sh # make executable
#
# Execute:
# ./feat_run_GLM_Subject_2ndlevel.sh # run
# running GLM with 4 contrasts on each block takes ~ 3 minutes and ~ 7 GB
# running GLM with 6 contras... |
86879b25feada728a8ac882feb693756d1a90574ecef4b884b50a95ad19fa11c | Shell | 1,120 | 53 | #/bin/bash
# Output dir
output_dir="output"
rm -rf ${output_dir}
mkdir -p ${output_dir}
# Layer to optimise
opt_layer=fc6
# Clipping
clip=0
multiplier=3
bound_file=act_range/${multiplier}x/${opt_layer}.txt
# Get label for each unit
path_labels="misc/synset_words.txt"
IFS=$'\n' read -d '' -r -a labels < ${path_labe... |
0fdc981977575857f35b19c831c27f7fe27d9691d415ec50f381da8e51852772 | Shell | 1,123 | 39 | #!/usr/bin/env bash
set -euo pipefail
repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
out_dir="${TMPDIR:-/tmp}/gha-runtime-report-test-$$"
mkdir -p "$out_dir"
"$repo_root/bin/ci/gha-runtime-report.sh" \
--jobs-file "$repo_root/bin/ci/fixtures/gha-runtime-jobs.json" \
--repo chroma-core/chroma \
... |
a2298df5e70445d01488d901b96ab0bbccfe22af55fecc200cac7a3e03c60f0a | Shell | 1,123 | 38 | #!/bin/bash
# Choose dataset to use for training ('light' or 'heavy'). Needs to be generated first using generate_data.py.
DATASET='heavy'
# Number of GPUs and the number of samples per batch per GPU (total batch size = N_GPU x BATCH_SIZE).
N_GPU=1
BATCH_SIZE=30
# Number of parallel workers per GPU for loading data ... |
df308ba468e38b220c06f4b2775c9ded4b8a0e76521d9416d522e534a8bf62af | Shell | 1,125 | 32 | #!/bin/bash
# Automatically run .fsf files for preprocessing based on .fsf created per block per subject (see folder "Create_FEAT")
#
# Make executable:
# chmod a+x feat_run_preAroma.sh
#
# Execute:
# ./feat_run_preAroma.sh
#
# EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
# J. Algermissen, 2018-2023.
rootDir=/projec... |
ef46407d9d5240752320f67b6f5fb8303ee17f602106ff3a5eaac26c26de423e | Shell | 1,141 | 36 | #!/bin/bash
# Thin wrapper that activates the reinvent4 conda environment and launches
# run.sh. Useful on clusters where you submit via sbatch or qsub.
#
# Usage:
# bash local_submitter.sh # run locally
# sbatch local_submitter.sh # submit to SLURM (add #SBATCH headers below)
#
# SLURM exam... |
e73d4a54afe65d6980f96378a8ae44b36e35ca24a32b1fc66c90a13c5cdc6878 | Shell | 1,167 | 34 | #!/bin/bash
# The following script downloads models made available by the Computer Vision group at University of Freiburg
# http://lmb.informatik.uni-freiburg.de/resources/software.php
# Models are from this paper:
# Dosovitskiy A, Brox T. "Generating images with perceptual similarity metrics based on deep networks". ... |
a1c4675bb4384c842a2a11b7d60be0edc42b3264cef035299061204833fcb73d | Shell | 1,174 | 34 | #!/bin/bash
#PBS -N svaba-test
#PBS -l walltime=20:00:00
#PBS -l nodes=1:ppn=15
#PBS -r n
#PBS -M kevin.c.johnson@jax.org
#PBS -m a
#PBS -k oe
#PBS -q batch
#PBS -o /projects/verhaak-lab/sulman_GSCs/GSC-BAM/svaba-test/logs/svaba-${PBS_JOBID}.log
#PBS -e /projects/verhaak-lab/sulman_GSCs/GSC-BAM/svaba-test/logs/svaba-$... |
69ab320454119adf766ea59a8ee6c784a6ca805292fdec9c1109c556cfb4867b | Shell | 1,188 | 35 | #!/bin/bash
#SBATCH --get-user-env
#SBATCH --job-name=bulk-calcDNAme_disorder-%j
#SBATCH --chdir=${WORKDIR}
#SBATCH --error=${LOG_DIR}/calcDNAme_disorder-%j.err
#SBATCH --output=${LOG_DIR}/calcDNAme_disorder-%j.out
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
#SBATCH --gres-flags=enforce-bindi... |
8ed5e261bf9405e684a0666c0be6a5c1ce86715cda8d3ff2dd6b972ece08084f | Shell | 1,199 | 10 | python network.py --nruns 108 --corr 1 --max 1.08 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 1 --max 1.18 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 1 --max 1.27 --group strength02 --streng... |
30a2a4434266487b837500f13ccd4ef3b237bc0950a22540b38dfcbee38bd681 | Shell | 1,219 | 10 | python network.py --nruns 108 --corr 0.5 --max 1.05 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.5 --max 1.17 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.5 --max 1.30 --group strength02 --... |
7cc9498507b7737c599a688e850915f52fe361b8d44b79b69ba135dc6ebdee4d | Shell | 1,219 | 60 | #/bin/bash
# Output dir
output_dir="output"
rm -rf ${output_dir}
mkdir -p ${output_dir}
# Layer to optimise
opt_layer=fc6
# Clipping
clip=0
multiplier=3
bound_file=act_range/${multiplier}x/${opt_layer}.txt
# Get label for each unit
path_labels="misc/synset_words.txt"
IFS=$'\n' read -d '' -r -a labels < ${path_labe... |
d66c44606c575ae099be01d70cb9fae0a9b687d5773b5b24109131792d1871ee | Shell | 1,219 | 10 | python network.py --nruns 108 --corr 0.8 --max 1.08 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.8 --max 1.14 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.8 --max 1.27 --group strength02 --... |
470413920b3e6d8128bf51dbeaabc415dd8f779c4329baa6c21956436e3fc44a | Shell | 1,220 | 10 | python network.py --nruns 108 --corr 0.1 --max 1.08 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.1 --max 1.16 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.1 --max 1.23 --group strength02 --... |
48adb136913e32b872e66b293a10580780ba8d55f3788dfe5e953e1238b5ef31 | Shell | 1,224 | 47 | #/bin/bash
# Generate images for the image selection task
dir=Validations
rm -rf ${dir}
mkdir ${dir}
label=1_veridical
bash image_generation.sh fc8 DGN winner original original 1 validation
mv export ${dir}/${label}
label=2_complex_neuro_wta
bash image_generation.sh fc8 DGN winner original shifted 1 validation
mv e... |
230cbf4e929676e7d01915651a6aab9e478a41b9c8daff7502bb6315c473192a | Shell | 1,229 | 10 | python network.py --nruns 108 --corr 0.05 --max 1.08 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.05 --max 1.17 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.05 --max 1.24 --group strength02 ... |
3d6314f41eaf50c08af7fec23267a0b5861394ba66d22b25ceecba3f83aebb14 | Shell | 1,229 | 10 | python network.py --nruns 108 --corr 0.01 --max 1.06 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.01 --max 1.13 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.01 --max 1.22 --group strength02 ... |
40f51d892d43fe1956d03198003873c4977d0ab0d06652545eb42e783c7abb5f | Shell | 1,229 | 10 | python network.py --nruns 108 --corr 0.03 --max 1.06 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.03 --max 1.17 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.03 --max 1.24 --group strength02 ... |
6816c9978d577e1ad025affec16673022b6829ffe125b8b7a8296e1679f943f7 | Shell | 1,229 | 10 | python network.py --nruns 108 --corr 0.02 --max 1.06 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.02 --max 1.15 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.02 --max 1.22 --group strength02 ... |
9fb10dc92263b6cef387fb9d266255117647bc202cfbe68f9a1d5cae911e7e0f | Shell | 1,229 | 10 | python network.py --nruns 108 --corr 0.08 --max 1.06 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.08 --max 1.18 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10
python network.py --nruns 108 --corr 0.08 --max 1.24 --group strength02 ... |
59d06884fef375594ec381fadec01d416024ea6e52f1d16a6ea53974e93cba53 | Shell | 1,235 | 42 | #!/bin/bash -ex
# install go
export VERSION=1.11 OS=linux ARCH=amd64
wget -q https://dl.google.com/go/go$VERSION.$OS-$ARCH.tar.gz
sudo tar -C /usr/local -xzf go$VERSION.$OS-$ARCH.tar.gz
rm go$VERSION.$OS-$ARCH.tar.gz
echo 'export GOPATH=${HOME}/go' >> ~/.bashrc
echo 'export PATH=/usr/local/go/bin:${PATH}:${GOPATH}/bin... |
32e14bd53e7cae1b5157f009c98b78cbe9f9d46abafc63900484565d154c6416 | Shell | 1,247 | 13 | #export LD_LIBRARY_PATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/lib/:$LD_LIBRARY_PATH
#export PATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/bin:$PATH
#export PYTHONPATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/lib/python/:/home/kusch/Docu... |
d91c22de244c51f1ad7e724f093c7a20517147a454f40c052f7e8f36bb681f89 | Shell | 1,256 | 35 | #!/bin/bash
# Runs first-level GLM per subject based on .fsf file (see folder "Create_GLM")
#
# Make executable:
# chmod a+x feat_run_GLM_Subject_1stlevel.sh
#
# Loop over subjects, submit job per subject:
# ./feat_run_GLM_Subject_1stlevel.sh
#
# EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
# J. Algermissen, 2018-2023... |
d78c374db4a2c16831d5cff2a5009a62928e5b8a0e1481d0efae79ff6be66d1a | Shell | 1,257 | 46 | #!/usr/bin/env bash
# Define the paths to the existing and new toml files
existing_toml="pyproject.toml"
thin_client_toml="clients/python/pyproject.toml"
# Define the path to the thin client flag script
is_thin_client_py="clients/python/is_thin_client.py"
is_thin_client_target="chromadb/is_thin_client.py"
# Define t... |
a34782f2f5bd8eaae0c3a389f38332fbff62f47600517df482d9d5e38e8dda5d | Shell | 1,264 | 27 | # Wall time per pipeline stage, recorded rather than read off the Actions UI.
#
# `src/vdjdb/timing.py` measures the stages *inside* one command. The stages a build is actually
# budgeted in are the commands themselves - proofreading, assembly, motifs, dashboard, the release
# comparison - and those were visible only i... |
18d637a8ea7a7dbeabd3e14d41e91901f6a755bdd914bd1828c84f109200064e | Shell | 1,271 | 36 | #!/bin/bash
## run the Rscript 01_Setbp1_vision_cortex.R and schedule this job to SLURM
#SBATCH --job-name=cor_vis_hall
#SBATCH --mail-type=ALL
#SBATCH --mail-user=jwhitlock@uab.edu #CHANGE THE EMAIL TO YOUR OWN
#
#Number of tasks needed for this job, the partition, and parameters
#SBATCH --ntasks=1
#SBATCH --mem-per... |
3c703da9e0146d7cfcb59d79da93939f18f91a53ebf7e7ec3b9099298c69e068 | Shell | 1,273 | 28 | #!/bin/bash
#SBATCH --time 48:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
for file in *chpreffed*.bam
do
samtools view -b $file chr1 chr2A chr2B chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX chrY > ${file::-4}... |
a2498605ed7e18fec79c69df47d0ab5293b8cb8d0c1d095dd4eb153480e9bb39 | Shell | 1,292 | 36 | #!/bin/bash
## run the Rscript 01_Setbp1_vision_subset_cortex.R and schedule this job to SLURM
#SBATCH --job-name=cor_vis_sub
#SBATCH --mail-type=ALL
#SBATCH --mail-user=jwhitlock@uab.edu #CHANGE THE EMAIL TO YOUR OWN
#
#Number of tasks needed for this job, the partition, and parameters
#SBATCH --ntasks=1
#SBATCH --m... |
99a5c0fda5dc0119317451a112b8e6429508cc4ed52f6c6b9b8eab5869e22d47 | Shell | 1,301 | 36 | #!/bin/bash
## run the Rscript 02_Setbp1_vision_kidney_hallmarks.R and schedule this job to SLURM
#SBATCH --job-name=kid_vis_hall
#SBATCH --mail-type=ALL
#SBATCH --mail-user=jwhitlock@uab.edu #CHANGE THE EMAIL TO YOUR OWN
#
#Number of tasks needed for this job, the partition, and parameters
#SBATCH --ntasks=1
#SBATCH... |
f74d8e6f167a7b0661206cffd14d8d8f16d6cb1ee04347168ae8c99bf76937a8 | Shell | 1,311 | 59 | #!/usr/bin/env bash
set -e
# ----------------------------------------------
# Chroma CLI Installer Script
# Usage:
# curl -sSL https://raw.githubusercontent.com/chroma-core/chroma/main/rust/cli/install/install.sh | bash
# ----------------------------------------------
REPO="chroma-core/chroma"
RELEASE="cli-1.4.4"
O... |
24af9299fe88299688ead1a769fe321a99e8eb4b25c2ca57889533c0770e46f2 | Shell | 1,322 | 60 | #!/bin/bash
function func_parser_key(){
strs=$1
echo ${strs%%:*}
}
function func_parser_value(){
strs=$1
echo ${strs#*:}
}
function func_set_params(){
key=$1
value=$2
if [ ${key}x = "null"x ];then
echo " "
elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le... |
83a8cfa6878c6d11d2fe5a1243feeb7cb55cc141777e9b02c4ef2a5f953bdd92 | Shell | 1,329 | 34 | #!/bin/bash
#SBATCH --job-name=bamcoverage
#SBATCH --time=3-00:00:00
##SBATCH --qos=long
#SBATCH --mem=16GB
#SBATCH -p hbfraser
#SBATCH --mail-type=END,FAIL
ml python/3.6.1
ml biology
ml samtools
ml py-deeptools
cd /scratch/users/banwang3/celllines/ATAC/human/bam/
celltypes="CM HP MN PP SKM"
# bamCoverage bam to bigw... |
6007cbdd7484025cfc50a5d679dfb60afa5605ae839d9476c9f16e420689b3b3 | Shell | 1,331 | 47 | #!/bin/sh
#
#$ -N across_dataset_analysis
#$ -o logs/across_dataset_analysis_out.txt
#$ -e logs/across_dataset_analysis_err.txt
#$ -l h_rt=05:00:00
#$ -l h_vmem=32g
#$ -q YOUR_QUEUE
# ---- Cluster configuration (set these for your system) ----
VENV_PATH="/path/to/your/venv/bin/activate"
PYDFC_CODE_DIR="/path/to/pydfc"... |
27cacb6d796e15c01614b471c6d9412c28a3ddb7c43a2dfe395f896d93f23ee3 | Shell | 1,332 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J dome3D
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends --
#B... |
73cd2efe740150901096f0b7050a519f1f2e8737b68358add2051c912e9b7949 | Shell | 1,333 | 38 | #!/bin/bash
#Set the pairs of modality we want to run with the R script.
#"--modality1 rest --modality2 movie" "--modality1 task --modality2 movie"
declare -a Modalities=("--modality1 rest --modality2 task")
declare -a Type=("--type within" "--type between")
echo "--------------------------"
echo "R Script - FPC/ASC ... |
3bce5109c5a06e2ba8eca7cf30b393b9018336625d0166493cfb71b583fe86b4 | Shell | 1,338 | 33 | #!/bin/bash
# Automatically creates .fsf files for running 1st-level subject-wise GLM based on GLM-specific template.
#
# Make executable:
# chmod a+x feat_create_GLM_Subject_1stlevel.sh # make executable
#
# Execute:
# ./feat_create_GLM_Subject_1stlevel.sh
#
# EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
# J. Alger... |
3ef2d666277c34b2b0908f6a999f32007f765598dd1fcc7a5df321ba76ca6893 | Shell | 1,343 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J deeponet3D_SSD
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends... |
9cd3279506ae50e1aec2e9ddcfb1592bae34092ec2c544cb8e5b8447220e0693 | Shell | 1,343 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J cube3D
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends --
#B... |
0e03bbabc6d9b7d6c65c7788ee489a13947cfcf792fd2ee58f5fd6437565dd8e | Shell | 1,345 | 39 | #!/bin/bash
#SBATCH -p scavenge # partition (queue)
#SBATCH -n 1 # number of cores
#SBATCH --output=setup_%A.txt
#SBATCH --job-name=VocalMat
user_email="antonio.fonseca@yale.edu"
export FOLDER=/gpfs/ysm/project/ahf38/Antonio_VocalMat/USVs/test_vocalmat_analysis
folder_name=$(basenam... |
3a525cc849a6f1318e112a31e71343f68d70734a7cbb56ae5226a375b48fdf9c | Shell | 1,345 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J Lshape3D
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends --
... |
aa80162171e12263049b303ff7d0df4a423af72d69b051132734f787f2302850 | Shell | 1,345 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J deeponet3D_SSD
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends... |
af0c68938050f66367a564162011b7e6e14bb58e0ee32425b9a997d1428569a4 | Shell | 1,348 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J furn3D
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends --
#B... |
6efc059bd65c78a4fbc8ba1c2c1286b89add107ccbf6df4da30e981b6c445d21 | Shell | 1,349 | 10 | python network.py --nruns 540 --corr 0.2 --max 1.09 --group feedforward --strength 0 --seed 0 --eps 0.2 --repeats 10 --currents 1
python network.py --nruns 540 --corr 0.2 --max 1.13 --group strength01 --strength 0.01 --seed 0 --eps 0.2 --repeats 10 --currents 1
python network.py --nruns 540 --corr 0.2 --max 1.2... |
b9e093108627db7b2f7d6e9b931d73ec1f397c8a937ef20356b11e3cf86cd510 | Shell | 1,354 | 39 | #!/bin/bash
#SBATCH -p scavenge # partition (queue)
#SBATCH -n 1 # number of cores
#SBATCH --output=setup_%A.txt
#SBATCH --job-name=VocalMat
user_email="antonio.fonseca@yale.edu"
export FOLDER=/gpfs/ysm/project/ahf38/Antonio_VocalMat/USVs/test_vocalmat_analysis
folder_name=$(basenam... |
a88a9535f7eaab6e8bb4da0cf72a7b3d42d000168856e973dbc1f8ef0ee60c2e | Shell | 1,357 | 35 | #!/bin/bash
# Run .fsf file per block (1st level GLM in 3-level approach).
#
# Make executable:
# chmod a+x feat_run_GLM_Block_1stlevel.sh
#
# Execute:
# ./feat_run_GLM_Block_1stlevel.sh
# running GLM with 4 contrasts on each block takes ~ 20 minutes and ~ 5 GB
# running GLM with 6 contrasts on each block takes ~ 10... |
5eb1f82ddeabd0250e1cf097dd98d470649a02639d387baeb20e65cd11bb0d1c | Shell | 1,372 | 49 | #!/usr/bin/env bash
set -euo pipefail
retry() {
local attempt=1
local max_attempts=5
local delay=5
until "$@"; do
local status=$?
if (( attempt >= max_attempts )); then
echo "::error::Command failed after ${max_attempts} attempts: $*"
return "$status"
fi... |
e757e882031b2719a2c0d3232bbfafe55cc62aa1350cde311f7b81c2bfc588dd | Shell | 1,375 | 14 | export LD_LIBRARY_PATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/lib/:$LD_LIBRARY_PATH
export PATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/bin:$PATH
export PYTHONPATH=/home/kusch/Documents/project/co_simulation/TVB-NEST-proof/lib/soft/lib/python3.8/site-packages/:/h... |
4a19d37d48b8b71f931b804cde869e9e50f996f7c2d8f6fe0f911956655e1ccc | Shell | 1,377 | 23 | set -e
python3 ParseBCIOUpperLevel.py
bash build-owl-files.sh
robot merge\
--catalog catalog-v001.xml\
--input bcio_upper_level.owl\
--input ../Setting/bcio_setting.owl\
--input ../ModeOfDelivery/bcio_mode_of_delivery.owl\
--input ../Source/bcio_source.owl\
--input ../MechanismOfAction/bcio_moa.owl\
--i... |
48fd466678cdcc2e3445490969b26e313b1da39d507f4c0c9f2e483a9728e96f | Shell | 1,380 | 36 | #!/bin/bash
#BSUB -W 24:00
#BSUB -q gpuv100
#BSUB -n 4
#BSUB -gpu "num=1:mode=exclusive_process:gmem=30GB"
#BSUB -R "span[hosts=1]"
#BSUB -R "rusage[mem=64GB]"
#BSUB -R "select[localssd0]"
#BSUB -J dome_quarter3D
### -- Notify me by email when execution begins --
#BSUB -B
### -- Notify me by email when execution ends... |
91916721538e36b5a8e95602d166cdfae4cfd6658c17de984fb056673fd99e90 | Shell | 1,380 | 10 | python network.py --nruns 108 --corr 0.01 --max 1.09 --group feedforwardfix --strength 0 --seed 0 --eps 0.2 --repeats 10 --fixext 1
python network.py --nruns 108 --corr 0.01 --max 1.13 --group strength01fix --strength 0.01 --seed 0 --eps 0.2 --repeats 10 --fixext 1
python network.py --nruns 108 --corr 0.01 --... |
ca477df201e150b7388f48f65bef15f8105a0381e7dba0a4f540c374bed5c210 | Shell | 1,394 | 36 | #!/usr/bin/env bash
set -e
export CHROMA_PORT=8000
# Define the path to the thin client flag script
is_thin_client_py="clients/python/is_thin_client.py"
is_thin_client_target="chromadb/is_thin_client.py"
function cleanup {
rm "$is_thin_client_target"
docker compose -f docker-compose.test.yml down --rmi local --... |
79ab83db5efb6852779550cbf48f67e19b7b83624d01811b43f3813c93162d01 | Shell | 1,396 | 47 | #!/bin/bash
# Automatically creates .fsf files for running 3rd-level GLM on group-level based on template; vary cluster-forming threshold.
#
# Make executable:
# chmod a+x feat_create_GLM_Sample_threshold.sh # make executable
#
# Execute:
# ./feat_create_GLM_Sample_threshold.sh # run
#
# EEG/fMRI STUDY, DONDERS INSTIT... |
af642da211606beac465ff1e2900ad6b86e12797bcaa1e1ffe1b205704a3e9e6 | Shell | 1,444 | 34 | #!/bin/bash
# Automatically creates .fsf files per subject per block for preprocessing based on template feat_template_preAroma.fsf.
#
# Make executable:
# chmod a+x feat_create_preAroma.sh
#
# Submit as job to cluster:
# qsub -N "feat_create_preAroma" -l walltime=0:02:00,mem=1mb feat_create_preAroma.sh
#
# EEG/fMRI ... |
6e9817b56524a1802dec75bb315eab069ef7ade8616d23f8883b6338c6ced420 | Shell | 1,447 | 56 | #!/usr/bin/env bash
# -*- coding: utf-8 -*-
### Get the current date and start making the results directory
CURRDATE=$(date +"%FT%H%M")
RESBASE="./results_opt"
RESDIR="$RESBASE"_"$CURRDATE"
### Check and make directory for saving results
if [ -d $RESDIR ]; then
echo "Directory $RESDIR exists. Cleaning..."
rm $RESDI... |
dec35d92164010e216f6234940ae0e1e05d5e420026ef8e34f1b4dce04d47f93 | Shell | 1,450 | 50 | #!/bin/bash
# Check the number of input arguments
if [ $# -ne 5 ]; then
echo "Usage: $0 <vcf_file> <hg_version> <output_dir> [--cuda] <feature_file>"
exit 1
fi
# Get input files and parameters from command line arguments
vcf_file=$1
hg_version=$2
output_dir=$3
feature_file=$5
cuda=""
# Check if CUDA is enab... |
1c0e549dd971f251c87a6fd5336f9207ece316c031e4628fb65e491ab1fbd064 | Shell | 1,455 | 48 | #!/bin/sh
#
#SBATCH --job-name=across_dataset_analysis
#SBATCH --output=logs/%x_out.txt
#SBATCH --error=logs/%x_err.txt
#SBATCH --time=05:00:00
#SBATCH --mem=32G
# Note: run sbatch from your multi_dataset_analysis/codes directory, or uncomment and set --chdir:
# #SBATCH --chdir=/path/to/multi_dataset_analysis/codes
# ... |
469975bbc9ad3d326c31f4c92ccc3945ca49e6e29b9f9264cc02091063020381 | Shell | 1,490 | 41 | #!/bin/bash
#Set the pairs of modality we want to run with the R script.
#"--modality1 rest --modality2 movie" "--modality1 task --modality2 movie" "--modality1 task --modality2 rest" "--modality1 movie --modality2 rest" "--modality1 movie --modality2 task"
declare -a Modalities=("--modality1 rest --modality2 task")
d... |
66cd6afe760c454c4f82c356b5511b36922a257132d3cb91b9b196277b97368f | Shell | 1,497 | 53 | #!/bin/bash
# Launch the NaviDiv Streamlit app pre-configured with the bundled sample data.
#
# Usage:
# conda activate NaviDiv
# bash run_app.sh
#
# Or let the script activate the environment itself:
# bash run_app.sh --activate
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
SAMPL... |
7b42cdf522b4712fab3e4ee8a672a65be5be17a2c1ea1d11969a5240d6a69efc | Shell | 1,520 | 70 | #!/bin/zsh
# set variable
local -A opthash
zparseopts -D -A opthash -- h m: l:
model=0
LF=$(printf '\\\012_')
LF=${LF%_}
if [[ -n "${opthash[(i)-h]}" ]]; then
echo "Usage : ./mtsim [option]"
echo " -m # : specify model (ex. -m 1 )"
echo " 0: MT angle fixed"
echo " 1: MT angle variable"
ret... |
36b18c760a829b5facccb53084d4173c64af9582602de42a5c31cff1d528a4b5 | Shell | 1,535 | 70 | #!/bin/bash
trap 'kill 0' EXIT
if [ $# -ge 1 ]; then
WAIT_PID=$1
else
WAIT_PID=
fi
INTERVAL=5
COUNT=12
LOGDIR=logs
if [ ! -d $LOGDIR ]; then
mkdir $LOGDIR
fi
if [ -z "$(type lsblk 2>&1 | grep 'not found')" ]; then
lsblk > $LOGDIR/lsblk.txt
fi
TOPLOG=$LOGDIR/top-$(date '+%Y%m%d').log
VMLOG=$LOGDIR/vm... |
05990fce43b247ab1ab679d865d02ce8007839f3d6edb65e3e905f8d309d9c17 | Shell | 1,540 | 41 | #!/usr/bin/env bash
# Determines which test suites to run based on changed paths.
# Uses a whitelist approach: run a suite only when its paths change.
# Reads filter outputs from env vars (set by the calling workflow).
# Writes tests-to-run to GITHUB_OUTPUT.
#
# Env vars (from dorny/paths-filter):
# FILTER_JS_CLIENT,... |
7f8ad170f747b5d323cb188a938d546abae7fc48270dfbb80a2706439a83422e | Shell | 1,545 | 49 | #!/bin/bash
INSTALLATION_FOLDER=/home/kusch/Documents/project/co_simulation/Demonstration/venv/
# install MPI
mkdir $INSTALLATION_FOLDER/tmp
cd $INSTALLATION_FOLDER/tmp
wget -q http://www.mpich.org/static/downloads/3.1.4/mpich-3.1.4.tar.gz
tar xf mpich-3.1.4.tar.gz
cd mpich-3.1.4
./configure --disable-fortran --enabl... |
0ac6cbf8919aa8034309951f6ab3bf1451462bbf8bb0ed8e10e871cedc216ddb | Shell | 1,549 | 26 | #!/bin/bash
#SBATCH --time 48:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=64G
#ml load system rclone
#rclone copy fraser:backup/astarr/Myriad_Hybrid_ATAC_Final_Bams/ATAC16_HP_Hyb1_humreffed.rmdup.remap.kept.merged.filt.bam ./
#for file in *chpreffed*.bam
#do
#samtools index $file
# ... |
3092e884d2fc27592fcdc6136096cc1cbb7e03343d3828a3924503aa55ecf3b6 | Shell | 1,558 | 67 | usage() { echo "Usage: $0 [-n <num_iterations> -c <corpus file> -i <input vector file> -d <num_dimensions> -k <num_topics> " 1>&2;
exit 1; }
SAMPLE_APHA=0 #by default donot sample for alpha
while getopts ":d:i:n:k:v:c:a:" o; do
case "${o}" in
d)
NUM_DIM=$OPTARG
;;
c)
CORPUS_FILE=$OPTARG
;;
... |
a735c54f97e7d554758045c0bb474e0d3346edcb43dda9178a0541f34bd59a2a | Shell | 1,560 | 55 | #!/usr/bin/env bash
set -e
# Check if the test-name and version-number are provided as arguments
if [ -z "$1" ]; then
echo "Usage: $0 <output-file-path>"
exit 1 # Exit with code 1 indicating an error
fi
OUTPUT_FILE_PATH=$(readlink -m "$1")
TEMP_DIR=$(mktemp -d)
mkdir "$TEMP_DIR/logs"
mkdir "$TEMP_DIR/traces"
f... |
2c55f4fc56a392adb22e7e4ddff6895d79830172fde0e0433e23c82a0d58d9d0 | Shell | 1,568 | 54 | #!/bin/bash
#This script uses subject-level beta coefficient and tstatistic maps to create a group-level general linear model using AFNI.
#Check if the inputs are correct
if [ $# -ne 8 ]
then
echo "Insufficient inputs"
echo "Input 1 should be the input folder for the beta coef and tstat maps in MNI space"
echo "... |
36a155366df9df699fee15229aedcbca19fde36d0bcf565683db52c7bdb26d20 | Shell | 1,585 | 48 | ## run.sh -- run tests
if [ -z $1 ]
then
contam_rate=0.4
else
contam_rate=$1
fi
$btrim=btrim
$cutadapt=../cutadapt-1.0/cutadapt
mkdir -p scythe/results/$contam_rate
## scythe trimming - multiple priors for 40% contamination
cfile=simulated-reads/$contam_rate
for prior in 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0... |
270da4df1effdf6e434387e8a71cd41715558f91333599c41db8749e99b6a150 | Shell | 1,587 | 41 | #!/bin/bash
# Automatically creates .fsf files for running 1st-level block-wise GLM based on template
#
# Make executable:
# chmod a+x feat_create_GLM_Block.sh # make executable
#
# Execute:
# ./feat_create_GLM_Block.sh # run
#
# EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
# J. Algermissen, 2018-2023.
GLMID=1
rootD... |
4d302158ce088c5ed7b4f8441fbb4aa7a822fdcaf713fba562f25e5229999a46 | Shell | 1,588 | 61 | # Assign NEUROD_SCRIPTS
clear
echo ========================================================================================
echo Please type the path to folder which will contain the subjects scripts and press ENTER
echo
read -p 'Path: ' FOLDER
:choice
if test -d "$FOLDER"; then
echo 'export NEUROD_SCRIPTS='$FOLD... |
90da3e7f3a0f72055da6e85904b52722dae06b9c6ce8b2e3d0ce4c1724188046 | Shell | 1,633 | 30 | #!/bin/bash
# Runs second-level GLM one sample level based on .fsf file (see respective "FEAT_GLM${GLMID}_Sample_Scripts" folder)
#
# Make executable:
# chmod a+x feat_run_GLM_Sample_2ndlevel.sh
#
# ./feat_run_GLM_Sample_2ndlevel.sh # run
#use fsl_sub_DCCN.sh (in my home directory), run on -T, minutes, storage space, ... |
5dea09b4f511b93b9a201045be90283bd3a467ae13e49fa9cf9e3409fc894a4f | Shell | 1,636 | 12 | #!/bin/bash
PATH_CONN="/PATH/TO/CONNECTIVITY/MATRICES"
PATH_QC="/PATH/TO/SUBJECT/LISTS"
parallel echo ::: "-m1=rest -m2=task --path_m1=${PATH_CONN}/adj_matrix_Schaefer_rest_n400_pcor --path_m2=${PATH_CONN}/adj_matrix_Schaefer_task_n400_pcor -q1=${PATH_QC}/final_list_subset_qc_rest -q2=${PATH_QC}/final_list_subset_qc_t... |
c0167224cb8bbe21dbb22e688e207c0ded6667de9fd094d411ea03e8a7ac45df | Shell | 1,636 | 47 | #!/bin/bash
HOST_SPACE=${PWD}
echo ${HOST_SPACE}
WORK_SPACE=/data/local/tmp/test
# The first parameter represents the demo name
DEMO_NAME=image_classification_demo
if [ -n "$1" ]; then
DEMO_NAME=$1
fi
# The second parameter represents the model name
MODEL_NAME=mobilenet_v1_fp32_224
if [ -n "$2" ]; then
MODEL_NAME... |
3ae2149d66f20b699cb819267a68f1db72188d8b1175a3fe2300d95495ff58a8 | Shell | 1,637 | 49 | #!/bin/bash
## run the Rscript alpaca_networks.R and schedule this job to SLURM with
## `sbatch aplaca_net_job.sh`
#SBATCH --job-name=alpaca
#SBATCH --mail-type=ALL
#SBATCH --mail-user=jbarham3@uab.edu #CHANGE THE EMAIL TO YOUR OWN
#
#Number of tasks needed for this job, the partition, and parameters
#SBATCH --ntasks=... |
2010e46f106a3ca1afd183e3e871b97aadcae2d541a3e1d91a0fa5ddd572b7ca | Shell | 1,647 | 53 | #!/bin/bash
set -x
# Script to generate Rust operator constants from Go source
# Run this whenever go/pkg/sysdb/metastore/db/dbmodel/constants.go changes
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
WORKSPACE_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
echo "Generating Rust operator constants from Go source..... |
cbe8d4a69d46bbf39cd5ca698b9109442da46c3314318a7d55264bb4566c7fe2 | Shell | 1,650 | 39 | #!/bin/bash
# It can help to centrally invert these matrices for all subjects/ blocks (takes 2 min per matrix), which saves time because they are often used to bring (different) ROIs to native space
# Input: highres2standard_warp in GLM run
# Output: standard2highres_warp in same GLM
#
# Make executable:
# chmod a+x ... |
4686d91633633627eee67e71641f46230ce4b12c674d6af904470f66b6c2f3eb | Shell | 1,661 | 61 | #!/usr/bin/env bash
set -e
server_pid=""
function cleanup {
if [[ -n "$server_pid" ]]; then
kill "$server_pid" 2>/dev/null || true
fi
}
trap cleanup EXIT
if [[ $CHROMA_THIN_CLIENT -eq 1 ]]; then
echo "Using thin client"
is_thin_client_py="clients/python/is_thin_client.py"
is_thin_client... |
173a7977bb0de8b144debe7de0fd9cbeb9760e021d068efbbccf4f8c34510697 | Shell | 1,671 | 44 | #!/bin/bash
set -e
if [ "${TEST_PYPI}" = true ]; then
echo "Using Test PyPI"
old_version=$(curl -s https://test.pypi.org/pypi/openfl-nightly/json | python -c "import sys, json; print(json.load(sys.stdin)['info']['version']);")
else
old_version=$(curl -s https://pypi.org/pypi/openfl-nightly/json | python -c... |
e862be3c65fe28bdbfa13dd1b5354bfb5eb15a82083947fcc9db0ecb0cb7b199 | Shell | 1,676 | 54 | #!/usr/bin/env bash
# List running marimo instances from the server registry.
# Cleans up stale entries (dead PIDs) and outputs live servers as JSON.
# No marimo installation required.
set -euo pipefail
# Locate the servers directory
is_windows=false
if [[ "$OSTYPE" == msys* || "$OSTYPE" == cygwin* ]]; then
is_windo... |
f1d6ec599fee37698129a3a5fcd387766c28d302256c6f8bb4076c65da5aecbc | Shell | 1,677 | 55 | #/bin/bash
dir=Results
rm -rf ${dir}
mkdir ${dir}
# veridical
label=1_veridical
bash image_generation.sh fc8 DGN winner original original 1
bash scripts/make_montage.sh fc8 original original
mv export ${dir}/${label}
cp ${dir}/${label}/result1.jpg ${dir}/${label}.jpg
# complex neuro
label=2_complex_neuro
bash image_g... |
d859c3f286483d5a1690541bdd9ed2b33d5913076fb710d0049a290102acf1ab | Shell | 1,682 | 75 | #!/bin/zsh
# set variable
local -A opthash
zparseopts -D -A opthash -- h m: l: p: a:
model=0
len=0
para=4
ar=1.0
LF=$(printf '\\\012_')
LF=${LF%_}
if [[ -n "${opthash[(i)-h]}" ]]; then
echo "Usage : ./mtsim [option]"
echo " -m # : specify model (ex. -m 1 )"
echo " 0: MT angle fixed"
echo " 1: ... |
d9428f94f878ea6967ccf32cf4f0b8de700c42727959945dbf28db414acf32dc | Shell | 1,690 | 54 | #!/bin/bash
export OMP_NUM_THREADS=1
# Unlike for the PosNet training, the GraphImgNet at the moment only supports single-GPU training, so we don't use torchrun here.
# Slightly different parameters where used with the different datasets for training the models in the paper:
# - AFM-ice-Cu111:
# --dataset AFM-ice-C... |
39b4e0ea5cd3144df751b9ca45c6dbb192645f0723731a12a7ae43ceafc7019d | Shell | 1,708 | 43 | #!/bin/sh
JOB_NAME="${1:-$(echo $RANDOM | md5 | head -c20; echo;)}"
TARGET_CLASS="${2:-1}"
export GOOGLE_APPLICATION_CREDENTIALS=/home/google_application_credentials/${GOOGLE_APPLICATION_CREDENTIALS_FILE}
gcloud init --project ${PROJECT_ID}
gcloud config set project ${PROJECT_ID}
gcloud beta services identity create... |
c78c91411d5572c02533a3aa28c5abcd98a376e22cf672648ed7495c9a789a3c | Shell | 1,714 | 51 | #/usr/bin/env bash
# Example code for heudiconv and pydeface. This will get your data ready for analyses.
# This code will convert DICOMS to BIDS (PART 1). Will also deface (PART 2) and run MRIQC (PART 3).
# usage: bash prepdata.sh sub nruns
# example: bash prepdata.sh 104 3
# Notes:
# 1) containers live under /data... |
0e8f67228f4f008110e2b297f31753eeb5f22fe1fe37210bee794ea71b11479c | Shell | 1,724 | 68 | #!/bin/bash
#set up the environment
export PATH
. /etc/profile.d/modules.sh
#try to solve the shared library problem
#sudo apt-get install libreadline6:i386
#Step 1
#grab one 4-taxon dataset from a randomly chosen file
wc -l sequences4728283-0.5-10.0-0.01.in ## 400000
sed -n "$2,$1 p" sequences4728283-0.5-10.0-0.01.i... |
32713204e898e746880be2b53c1f47fc37f651236d32d4dae7609f5335a21d52 | Shell | 1,725 | 63 | #!/bin/bash
module load star
# Set the number of threads for STAR
nThreads=60
# Set directories
genomeDir="../data/genome/ensembl"
indexDir="../data/genome/index"
# Make directories if they don't exist
mkdir -p $genomeDir
mkdir -p $indexDir
# Set URLs
genomeURL="ftp://ftp.ensembl.org/pub/current_fasta/mus_musculus... |
3a1dee2a11474fb42c0ef7fc3c98f62e52f24071798807cf2b0d71db6f545300 | Shell | 1,726 | 50 | #!/bin/bash
# Concatenate blocks and register to example_func of first block.
#
# Make executable:
# chmod a+x concat_flirt_blocks.sh #
#
# Submit as job to cluster:
# qsub -N "concat_flirt_blocks" -l walltime=1:00:00,mem=17gb concat_flirt_blocks.sh
# 17 GB works, 15 min. per subject
#
# EEG/fMRI STUDY, DONDERS INSTI... |
cf4b4d34e704b8574a8347d3a807fa0a18e6831236f8228248815ef06dece6c9 | Shell | 1,741 | 47 | #!/bin/bash
HOST_SPACE=${PWD}
echo ${HOST_SPACE}
WORK_SPACE=/data/local/tmp/test
# The first parameter represents the demo name
DEMO_NAME=image_classification_demo
if [ -n "$1" ]; then
DEMO_NAME=$1
fi
# The second parameter represents the model name
MODEL_NAME=mobilenet_v1_fp32_224
if [ -n "$2" ]; then
MODEL_NAME... |
d58acb9d14745f7f4ea864813ce9c2b33530117f2554a6c84573bfcf44ea3bcd | Shell | 1,747 | 44 | #!/bin/sh
JOB_NAME="${1:-$(echo $RANDOM | md5 | head -c20; echo;)}"
export GOOGLE_APPLICATION_CREDENTIALS=/home/google_application_credentials/${GOOGLE_APPLICATION_CREDENTIALS_FILE}
gcloud init --project ${PROJECT_ID}
gcloud config set project ${PROJECT_ID}
gcloud beta services identity create --service tpu.googleap... |
c639ec17b9760bf1782cc662b2c0019109587679ada485ad37759e45076f4d0c | Shell | 1,751 | 45 | #!/bin/sh
JOB_NAME="${1:-$(echo $RANDOM | md5 | head -c20; echo;)}"
export GOOGLE_APPLICATION_CREDENTIALS=/home/google_application_credentials/${GOOGLE_APPLICATION_CREDENTIALS_FILE}
gcloud init --project ${PROJECT_ID}
gcloud config set project ${PROJECT_ID}
gcloud beta services identity create --service tpu.googleap... |
e3066ee3493c7c92cf60fb402eb6147b888037862ec5d66b1fbdb116d43bda21 | Shell | 1,758 | 17 | #/bin/bash
# validation initial images
init_dir=images/Validation/
declare -a original_init_files=("01_sewingmachine.jpg" "02_goldfish.jpg" "03_dialphone.jpg" "04_redpepper.jpg" "05_birdhouse.jpg" "06_junco.jpg" "07_bolete.jpg" "08_lightshade.jpg" "09_volcano.jpg" "10_ladybug.jpg" "11_cock.jpg" "12_balloons.jpg" "13... |
62d184cf80d313963cb810330553b545741cb26e44d607fa49d7561eccbed980 | Shell | 1,766 | 48 | #!/bin/bash
# It can help to centrally invert these matrices for all subjects/ blocks (takes 2 min per matrix), which saves time because they are often used to bring (different) ROIs to native space
#
# Make executable:
# chmod a+x invert_highres2standard_warp_block.sh # make executable
#
# Execute:
# ./invert_highre... |
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