sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
67887813261577f7451a09054cd94b5be7a0ef49bc3907f64b40c603b6675a0f | Shell | 456 | 13 | #!/bin/bash
conda create --name pytorch_env --file requirements_pytorch_env.txt -c conda-forge -c pytorch -c nvidia
eval "$(conda shell.bash hook)"
conda activate pytorch_env
pip install 'asteroid==0.5.2' --no-dependencies
pip install 'comet-ml==3.32.8' --no-dependencies
pip install 'tensorboardX==2.6' --no-dependenc... |
7cd0e49f1bafbe0f2071673e38c65f3a0a6b1d65fcd29f1efc2fc57563e5923a | Shell | 466 | 19 | CUDA_VISIBLE_DEVICES=$1 python3 train.py \
--model_dir "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext" \
--train_dir $PATH_TO_TRAIN_FILE \
--output_dir tmp/sapbert\
--use_cuda \
--epoch 1 \
--train_batch_size 256 \
--learning_rate 2e-5 \
--max_length 25 \
--checkpoint_step 999999 \
--parallel ... |
0fe7f1de51c7da555461fcbf200cf811dcee755930127bec63ca355b5eba07f8 | Shell | 473 | 12 | # Get blender
#if operating system is mac
if [[ "$OSTYPE" == "darwin"* ]]
then
curl https://download.blender.org/release/Blender2.79/blender-2.79b-macOS-10.6.zip -o blender-2.79b-macOS-10.6.zip
unzip blender-2.79b-macOS-10.6.zip
rm blender-2.79b-macOS-10.6.zip
else
wget https://download.blender.org/release/Blender2... |
c742893805fb424345e6b7d168a6ee696ad05798f305ff85cd9c2d268464b573 | Shell | 483 | 22 | #!/usr/bin/bash
mkdir ./ext
cd ./ext
git clone https://github.com/Christina-hshi/psirc.git
cd psirc
cd psirc-quant
# you may need to compile htslib under "ext/htslib" by following the README there ("make install" is optional and only possible with admin permissions)
cd ext/htslib/
autoheader
autoconf
./configure
make
m... |
29593fd091230e7cde87a225319c2507a077d7e048e1dfaf701c03db6585f2c1 | Shell | 486 | 17 |
MODEL_DIR="cambridgeltl/SapBERT-from-PubMedBERT-fulltext" # can switch to your /path/to/model
DICT_PATH=data/askapatient/AskAPatient.dict.txt
for VAR in 0 1 2 3 4 5 6 7 8 9
do
DATA_DIR=data/askapatient/AskAPatient.fold-$VAR.test.preprocessed_query.txt
CUDA_VISIBLE_DEVICES=$1 python3 eval.py \
--model_dir $MODEL_D... |
682e744edf3cc7deebe72328602e1743b133a13ee863bc6a5213acd47949aef5 | Shell | 486 | 9 | #!/usr/bin/env bash
python -m ensurepip # ensures pip is installed in the current environment
pip install --upgrade pip==24.0
pip install wheel
pip install openvino-dev==2023.0.1 # [OPTIONAL] to generate optimized models for inference
pip install mlcube_docker # [OPTIONAL] to deploy GaNDLF models as MLCube-co... |
e295e0c0978e201a634e057a276021b6bca714009426616caa62f89322cb767a | Shell | 493 | 29 | #!/bin/bash
if [ -z "$1" ]; then
echo "usage: $0 FILE "
exit 1
fi
cat > "$1" <<EOF
# Travis variables
CI=${CI}
TRAVIS=${TRAVIS}
TRAVIS_BUILD_DIR=/src
TRAVIS_BRANCH=${TRAVIS_BRANCH}
TRAVIS_COMMIT=${TRAVIS_COMMIT}
TRAVIS_JOB_ID=${TRAVIS_JOB_ID}
TRAVIS_JOB_NUMBER=${TRAVIS_JOB_NUMBER}
TRAVIS_PULL_REQUEST=${TRAVIS... |
a522efd8bc0cb864c2ba4830bc199fad65d37b814cffb3c457f37e682786510d | Shell | 522 | 19 | CUDA_VISIBLE_DEVICES=$1 python3 train.py \
--model_dir "cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR" \
--train_dir ../training_data/general_domain_parallel_data/en_zh_muse_pairs.txt \
--output_dir tmp/xlmr_base_sap_xling_tuned \
--use_cuda \
--epoch 5 \
--train_batch_size 256 \
--learning_rate 2e-5 \
--... |
a71bd250486cbda2034d302dc990b9d7ff014721d01b8ddeb0625144db96ff6c | Shell | 540 | 14 | #!/usr/bin/env bash
# if there is a gpu present in the container, install the gpu version of pytorch
# we will check for the presence of the nvidia-smi command as that signifies the presence of a gpu
# if runnning on a GPU machine, install the GPU version of pytorch
if command -v nvidia-smi &> /dev/null
then
pip ins... |
c5224b57f819b05de3527ed023ddfbee32dea69a3dcfe700b5b076bc0b2f821b | Shell | 553 | 16 | #!/usr/bin/env bash
wget https://transfer.sh/3rm5B/data2.tar.xz -O data.tar.xz
tar xvf data.tar.xz
unzip data/M2NIST/combined.npy.zip -d data/M2NIST
unzip data/M2NIST/segmented.npy.zip -d data/M2NIST/
cd ../
git clone https://github.com/rusty1s/pytorch_geometric.git
cd ./pytorch_geometric
pip install --no-cache-dir tor... |
3e867ab63aea6802019e4c7d6ecd12883ead558bb525f009abfb448b9e95cffd | Shell | 557 | 23 | #!/bin/bash
## Build and test oneAPI
set -euox pipefail
mkdir build
pushd build
cmake .. -DGOOGLE_TEST=ON -DPLUGIN_SYCL=ON -DCMAKE_CXX_COMPILER=g++ \
-DCMAKE_C_COMPILER=gcc -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX \
-DCMAKE_PREFIX_PATH=$CONDA_PREFIX -GNinja
ninja
popd
# Both suites use the same SYCL library; build o... |
5c044b4c8163c76074c7675a58768cc218ee549a7cdfe48ed2ec67a3a386a698 | Shell | 568 | 33 | #!/bin/bash
./upool_eval.sh 5ch 1
./upool_eval.sh 5ch 2
./upool_eval.sh 5ch 3
./upool_eval.sh 5ch 4
./depth_pos_eval.sh 5ch 1
./depth_pos_eval.sh 5ch 2
./depth_pos_eval.sh 5ch 3
./depth_pos_eval.sh 5ch 4
./depth_eval.sh 5ch 1
./depth_eval.sh 5ch 2
./depth_eval.sh 5ch 3
./depth_eval.sh 5ch 4
./cmp_eval.sh 5ch 1
./... |
c3293064d4b5695a5cb01374eb7761529258b0ee18bbcfef07cefb47633fb044 | Shell | 569 | 16 | #!/bin/sh
#
# Basic sanity check for tiffcp + tiffsplit + tiffcp
#
# First we use tiffcp to join our test files into a multi-frame TIFF
# then we use tiffsplit to split them out again, and then we use
# tiffcp to recombine again.
. ${srcdir:-.}/common.sh
conjoined=o-tiffcp-split-join-conjoined.tif
reconjoined=o-tiffcp... |
e381faf74539a3469adf063d80768d24bf86493a0c241a85802a6e255816bc47 | Shell | 590 | 24 | #!/bin/bash
prepScript=/home/OBIWAN/ANALYSIS/fsl_scripts/preproc/fsl_ANTS/03-FIX/prepForTraining.sh
# loop over subjects
for subjectID in control100
do
# loop over sessions
for sessionID in second
do
# loop over tasks
for taskID in pavlovianlearning PIT hedonicreactivity
do
# prepare FIX tr... |
3150bfaeb8363532ae80f72b993ec547f0a81c10abad56039cac48773ffac807 | Shell | 637 | 21 | #!/bin/bash
#
#SBATCH --job-name=Monitoring
#SBATCH --error=../outputs/Monitoring.err
#SBATCH --output=../outputs/Monitoring.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xl... |
3026415aedc81b9632e782eed7140f9f4110f883bcc991e9ec726d1f283a88d8 | Shell | 640 | 21 | #!/bin/bash
#SBATCH -J v6_sensitivity_analysis
#SBATCH -A MENON-SL3-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=0-999
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_outputs/v6-0/hpc... |
a703606cbc06704231d7e3cc8dc613d474edc34dc2be9ed0a76f22ca0b71b2cf | Shell | 642 | 21 | #!/bin/bash
#SBATCH -J v6_static_only_preds
#SBATCH -A MENON-SL3-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=0-99
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_outputs/v6-0/hpc_log... |
3dcc826c722b92afcc5a85808f1be733fbc9546f4466691e87c3e2905f2baaba | Shell | 643 | 20 | #!/bin/bash
#SBATCH -J v7_validation_set_bootstrapping
#SBATCH -A MENON-SL2-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=0-999
#SBATCH --mail-type=NONE
#SBATCH --output=/home/sb2406/rds/hpc-work/model_performance/v7-0/hpc_logs/validation_bootstr... |
0f61c96ff2fe6913ce42a4f2f858b4ce2e4a379f593a816a2292ae7e936491cc | Shell | 648 | 21 | #!/bin/bash
#SBATCH -J v7_test_set_bootstrapping
#SBATCH -A MENON-SL2-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=0-999
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_performance/v6... |
3719b751525dec8aefd05e244666dcb43bc747a49a6ecb0860a10f3a174f1287 | Shell | 651 | 21 | #!/bin/bash
#SBATCH -J v6_TimeSHAP_calculation_dynAPM
#SBATCH -A MENON-SL2-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:30:00
#SBATCH --mem=54080
#SBATCH --array=0-9999
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_interpre... |
a5a101c14223d08ee78140e87ff0c95874caed71e6d27e168f4329a63ac726fe | Shell | 652 | 25 | #!/usr/bin/env sh
KEY_CHAIN=build.keychain
CERTIFICATE_P12=certificate.p12
# Recreate the certificate from the secure environment variable
echo $CERTIFICATE_OSX_APPLICATION | base64 --decode > $CERTIFICATE_P12
#create a keychain
security create-keychain -p actions $KEY_CHAIN
# Make the keychain the default so ident... |
fd75d000915c2a69f5a87f875d603f4281a0a9c34526cbdbf4812bd87aae422c | Shell | 667 | 21 | #!/bin/bash
#SBATCH --job-name=v7_calibrated_dynAPM_training
#SBATCH --time=02:00:00
#SBATCH --array=0-1439
#SBATCH --gres=gpu:1
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --account=MENON-SL3-GPU
#SBATCH --partition=ampere
#SBATCH --mail-type=ALL
#SBATCH --output=/home/sb2406/rds/hpc-work/mo... |
45b49b7df68faec05b7c87437fadff3c2535d7ff8f8423ba4e3513b0822594fd | Shell | 676 | 21 | #!/bin/bash
#SBATCH -J static_test_set_bootstrapping
#SBATCH -A MENON-SL2-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=0-999
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_performanc... |
c3ce13a482a5aefacdbdef8697547dde5f55ae6ada708e3fc0b8e943a2fb0c3a | Shell | 676 | 21 | #!/bin/bash
#SBATCH -J v7_robustness_bootstrapping
#SBATCH -A MENON-SL3-CPU
#SBATCH -p icelake
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=54080
#SBATCH --array=12-4999
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sb2406@cam.ac.uk
#SBATCH --output=/home/sb2406/rds/hpc-work/model_interpreta... |
dba6f948d7824b4ca6138c775c41de942d2daba6ec62500741e842cc2b67cdee | Shell | 677 | 20 | # from quarantine
module load matlab/R2014a
module load FSL/5.0.10
module load FIX/1.061
module load R/3.2.4
module load R-extras/3.2.4
module load AFNI/2014.12.16
module load freesurfer/6.0.0
module load connectome-workbench/1.2.3
# load home-spun tools
module load /archive/code/packages.module
export PATH=$PATH:/pro... |
a47a61673732742125065386572cf760e911724d6dc6909b6d2aed1d175e8ef0 | Shell | 704 | 22 | #!/bin/bash
# name of classifier
classifierName=FIX_obiwan_03
# directory containing FIX training data
trainDir=/home/shared/fix/data/OBIWAN/
# locate all hand-classified labels (hand_labels_noise.txt)
OBIWANdata=$(find ${taskDir} -type d -name "melodic_run.ica")
echo "Data used to train the classifier: ${OBIWANda... |
c9f2a6c2dfd2e20adbaaf7710cf84ab5272ca47f52cbf1670fe5622846136d86 | Shell | 712 | 28 | # Getting colin27
#if operating system is mac
if [[ "$OSTYPE" == "darwin"* ]]
then
curl -L https://www.dropbox.com/sh/hjto15nnebrdg27/AABpdynLda9KoRkWaNB0WCDaa?dl=1 -o colin27_dropbox.zip
else
#sudo apt install unzip
wget https://www.dropbox.com/sh/hjto15nnebrdg27/AABpdynLda9KoRkWaNB0WCDaa?dl=1 -O colin27_dropbox... |
78d5eee8308e8a211a5f1f93f1f8bc5508ce97f0dd33bd19724cbc7d3e1f6816 | Shell | 714 | 21 | #!/bin/bash
#
#SBATCH --job-name=Monitoring
#SBATCH --error=../outputs/Monitoring.err
#SBATCH --output=../outputs/Monitoring.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xl... |
80c16dc5844cacac0949496f0462451ab2ddb4679ad5e4ac63a55683385f7235 | Shell | 722 | 28 |
MODEL_DIR="cambridgeltl/SapBERT-from-PubMedBERT-fulltext" # can switch to your /path/to/model
DICT_PATH=data/cometa/COMETA_id_sf_dictionary.txt
# stratified general
DATA_DIR=data/cometa/splits/stratified_general/test.csv
CUDA_VISIBLE_DEVICES=$1 python3 eval.py \
--model_dir $MODEL_DIR \
--dictionary_path $DICT_PAT... |
fb10ae26216c373d521c629c8b477708a016a30ad9e7c49518265bcf26933cb3 | Shell | 722 | 30 | #!/bin/bash
## Build libxgboost4j.dylib for MacOS (Apple Silicon or Intel)
set -euox pipefail
# Display system info
echo "--- Display system information"
system_profiler SPSoftwareDataType
sysctl -n machdep.cpu.brand_string
uname -m
# Use the runner image's Homebrew without auto-updating its taps.
HOMEBREW_NO_AUTO_U... |
2eb7cb32c07200e1a93cb1a05bc6d7bf3cf4319b24a83ae50adaf303323a51e1 | Shell | 730 | 12 | #!/bin/sh
# Training implicit network + NeuralODE for Irregular sampling
########################################################
################ Batch time 2 ##########################
########################################################
mkdir -p ./Results/Linear2D_irregular/
python Linear_IrregularSampling.py... |
137fbde14228379b97c67a8a8402f69095f75e65b843d3c99d25d1fe5a7af85a | Shell | 746 | 16 |
#Create image
cd ~
mkdir cfmid4
cd cfmid4
/public/software/singularity/bin/singularity pull cfmid4.sif docker://wishartlab/cfmid
#Run command in image
/public/software/singularity/bin/singularity exec cfmid4.sif cfm-train --help
/public/software/singularity/bin/singularity run -i cfmid4.sif sh -c "cd /train... |
07ce173c67665e2faca457e112d891fa4ce4c625d89db154e93917c35c898b17 | Shell | 747 | 20 | if [ -z "$1" ]
then
PYTHON_VERSION=3.6
else
PYTHON_VERSION=$1
fi
git clone http://gitlab.com/bu_cnio/sc-test-data data
conda create -y -q -n bollito_snakemake snakemake python=${PYTHON_VERSION}
source activate bollito_snakemake
cd data
snakemake --use-conda
cd -
conda create -y -q -n bollito_prep star=2.7.1a
c... |
f044b14d2b5b0390359d3e386cebb94cc079810a8f38afe21ba445eb2937009b | Shell | 763 | 19 | # FreeSurfer
#if operating system is mac
if [[ "$OSTYPE" == "darwin"* ]]
then
cur https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/7.3.2/freesurfer-darwin-macOS-7.3.2.tar.gz -o freesurfer.tar.gz
else
# todo: check if ubuntu or centos
# wget https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/7.3.2/freesu... |
f7c3665c9979acc0019d98d765f87d2b0350e47eff615fce7ef4c3ee5e5bf956 | Shell | 763 | 27 | #!/usr/bin/bash
#############################################################################
#############################################################################
cw=$(pwd)
echo "Loading Freesurfer!"
module load freesurfer/7.1
echo "FREESURFER_HOME=${FREESURFER_HOME}"
######################################... |
304fa56c2c189dcc7b61ba04a6911c4d14ccf8c5be216175f9b094db8839b1ec | Shell | 773 | 31 | #!/bin/bash
#SBATCH --job-name=R_DXboots
#SBATCH --output=logs/%x_%j.out
#SBATCH --nodes=1
#SBATCH --cpus-per-task=8
#SBATCH --time=2:00:00
#SBATCH --mem=12000
## set the second environment variable to get the base directory
BASEDIR=${SLURM_SUBMIT_DIR}
## set up a trap that will clear the ramdisk if it is not clea... |
248fbcb648ab7d0d7da4d5458df16ca4b2a3bcebdda40b0e3108f7ff3b4ed768 | Shell | 777 | 31 | #!/bin/bash
#SBATCH --job-name=R_DXbootS
#SBATCH --output=logs/%x_%j.out
#SBATCH --nodes=1
#SBATCH --cpus-per-task=8
#SBATCH --time=2:00:00
#SBATCH --mem=12000
## set the second environment variable to get the base directory
BASEDIR=${SLURM_SUBMIT_DIR}
## set up a trap that will clear the ramdisk if it is not clea... |
cecc9b41e03207662f8bf8ae7dd5b178d99e4260126e370a1e41d1c876452ea4 | Shell | 788 | 21 | #!/bin/sh
dir=$1; currdir=`pwd`
cd ${currdir}/${dir}
n=`sort -nbr -k2,2 Cell_Clusters.tsv | head -n1 | cut -f2`
echo "===> Clusters 0 to ${n} were found."
markers="/working/lab_julietF/mainaB/Project_NormalBreast/scRNAseq/LiteratureMarkers/Markers_Revised_Annotation"
echo "Using markers in file: /working/lab_juli... |
6643958d1a80612cf1c43336ca308fa61032e3d02f6713357a2fe85cf2ef2c24 | Shell | 793 | 30 | #!/bin/bash
set -x -e -o pipefail
cd data/
mkdir reads raw de coex go
tar xf meta.tar.xz
#Download GO annotations
#See http://current.geneontology.org/products/pages/downloads.html
#See http://geneontology.org/docs/download-ontology/
cd go
wget -O goa-human.gaf.gz 'http://geneontology.org/gene-associations/goa_human... |
0ba29b9ec4f21b534a1feace7b52af25c3b906eaf39278ff94c40a3eafd88e9f | Shell | 799 | 25 | #!/bin/bash
export PWD=`pwd -P`
cd ./matlab/
git clone https://github.com/gllmflndn/gifti.git
cd gifti
git checkout cd1356fa6023acc6c9577e0e42ccf67aba9f4483
cd ./../
git clone https://github.com/fieldtrip/fieldtrip.git
cd fieldtrip
git checkout 6183b3ef726eff2bcc34aeb4252534bf7f21dbf8
cd ./../
singularity --silent ... |
7de462b53490b4d255b973dd7a8b776e68019e60ff076b907476faf4960b73c9 | Shell | 808 | 30 | #!/bin/bash
PROJECT_ROOT=./data/ATAC-seq
MYPATH=./data/
OUT_DIR=$MYPATH/count_from_sc_my_peaks_AD
ANNOT=./data/peaks.saf
mkdir $OUT_DIR
mkdir $OUT_DIR/AD
for type in $PROJECT_ROOT/AD/; do
for region in $type/; do
type=$(basename $type)
echo $type
region=$(basename $region)
echo ... |
18727e0da7e6e8d1990962046fc4d1e641c67b3fd792454beffbb2022a7272b0 | Shell | 810 | 30 | #!/bin/bash
PROJECT_ROOT=./data/bulk_ATAC-seq
MYPATH=./data/
OUT_DIR=$MYPATH/count_from_sc_my_peaks_AD
ANNOT=$MYPATH/peaks.saf
mkdir $OUT_DIR
mkdir $OUT_DIR/AD
for type in $PROJECT_ROOT/AD/*; do
for region in $type/*; do
type=$(basename $type)
echo $type
region=$(basename $region)
... |
16f07270ff17e1be97fc24dda0d2e297032941e048297b03014db1ffeee2b4a4 | Shell | 815 | 46 | #!/bin/bash
help()
{
echo "Usage: trainModel -p | --model_path MODEL_PATH
[ -h | --help ]"
exit 2
}
SHORT=p:,h
LONG=model_path:,help
OPTS=$(getopt -a -n trainModel --option $SHORT --longoptions $LONG -- "$@")
VALID_ARGUMENTS=$# # Returns the count of arguments that are in shor... |
dfee92ff16dd3486d5c03488a560fa9b388ff0af6826753dccd98441a573df37 | Shell | 817 | 61 | #!/bin/bash
#Project directory
PROJECT_DIR=/home/awijeratne/Projects/capture_seq
#path to fastq files
FASTQ_FILE=$PROJECT_DIR/01_Preproprocessing/01_Preproprocessing/BB_Adapter_Removed
#this will creat a path to a folder called "abudnace_est"
COUNT=$PROJECT_DIR/02_kalisto_alginment
#assign the output to the ... |
00fb5e2dacf214748c4e85b6de2c81b9dbd48a66cd08ec76d22c9974829c45ac | Shell | 829 | 26 | #!/bin/bash
### This is a sample of submission script to run large number of simulations in parallel on using a slurm array job
#SBATCH --clusters=all
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=15
#SBATCH --mem-per-cpu=2G
#SBATCH --time=11:59:00
#SBATCH --job-name=Train
###SBATCH --gres=gpu
#... |
789501e00eba6e3d1bc806dabe13fc5391a334c0b105115aadc857e083499c86 | Shell | 832 | 40 | #!/bin/bash
if [ "$#" -lt 1 ];then
echo "Usage: $0 <install folder (absolute path)> <runtime version (default: v92)> <release (default: R2017a)"
echo "For sudoer recommend: $0 /opt"
echo "For normal user recommend: $0 $HOME/app"
exit 0
fi
INSTALL=$1
mkdir -p $INSTALL
if [ "$#" -lt 3 ]
then
VERSION=v92
RELEASE=... |
77490138c1ec571a19cbcb7c7d963e19869251d484c93500fd2ec55b08a206c9 | Shell | 833 | 12 | #!/usr/bin/env sh
cd ./dist/CellProfiler.app/Contents/Resources || exit 1
sudo rm -r Home/legal/
sudo codesign --timestamp -f -s "Developer ID Application: Beth Cimini (27YQ9U45D9)" Home/lib/server/classes.jsa
find . -type f -print0 | xargs -0 -I file codesign --timestamp -f -s "Developer ID Application: Beth Cimini (... |
65963f0e97cd6bd7e70654ef90112229d075c2786da48db48fa1db325b0a2979 | Shell | 841 | 23 | #!/bin/bash
## Convert an existing audited CUDA 13 wheel using the WheelNext prototype.
set -eo pipefail
set -x
gosu root chown -R $(id -u):$(id -g) /opt/miniforge/envs /opt/miniforge/pkgs/cache
gosu root chown $(id -u):$(id -g) /opt/miniforge/pkgs
set +x
mamba create -y -n wheelnext python=3.13 python-build
source... |
bc36142420c9d126315617a31da60c55a78336b8ee814744d81f639a462d0e07 | Shell | 853 | 18 | #!/bin/bash
set -x -e -o pipefail
cd data/
mkdir reads highmoi
tar xf meta.tar.xz
#Download data GSE120861 highmoi
cd reads
wget -O highmoi_cells.txt.gz 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE120861&format=file&file=GSE120861%5Fpilot%5Fhighmoi%5Fscreen%2Ecells%2Etxt%2Egz'
wget -O highmoi_exprs.mtx 'https... |
39e4dc74d26d045a87f316a78b474bd846ba2ed7d2bbe1435724e956385125a4 | Shell | 868 | 32 | #!/bin/bash
merge_figures () {
# define figures
fig1=../img/lme_${1}_gait.png
fig2=../img/lme_${1}_tug.png
#shave white borders
convert $fig1 -trim +repage tmpfig1.png
convert $fig2 -trim +repage tmpfig2.png
#add labels
#convert tmpfig1.png -gravity North -pointsize 75 -fill "black" -annotate +300+100 "A" tmpfig1.pn... |
bb34e08a381bb295a918845f31aa5f4864734766ff7e533c1b7454c00074b1de | Shell | 879 | 22 | #!/usr/bin/python
head -n 2 ${1}/matrix.mtx > ${1}/mtx_header.txt
head -n 3 ${1}/matrix.mtx | tail -n 1 > ${1}/mtx_summary.txt
tail -n +4 ${1}/matrix.mtx | cut -d " " -f 1-3 > ${1}/ms.txt
tail -n +4 ${1}/matrix.mtx | cut -d " " -f 1-2,4 > ${1}/mu.txt
tail -n +4 ${1}/matrix.mtx | cut -d " " -f 1-2,5 > ${1}/ma.txt
#rm ... |
c764380ec28e7763a1377556eecf9def20f27d6b0e6984140885b71f164717bc | Shell | 889 | 21 | #MODEL_DIR="bert-base-multilingual-uncased" # can switch to your /path/to/model
#MODEL_DIR="xlm-roberta-base"
#MODEL_DIR="xlm-roberta-large"
#MODEL_DIR="cambridgeltl/SapBERT-from-PubMedBERT-fulltext"
# note that this model is trained with UMLS 2020AB, performance will be slightly better than the model trained with UML... |
dc18bd030fc54b06ed5813162bf32973f183d0f89dd255ce0943797f82bc4b3a | Shell | 929 | 19 | # >>Ciftify: from the volume space to surface space
# ciftify_recon_all
# Remember to change a few codes in 'ciftify_recon_all' before excuting!!!!
# Line 495: if 'v6.' in fs_version:
# Line 594: add '-nc' between T1w_nii and freesurfer_mgz
export prjdir=/nfs/z1/userhome/GongZhengXin/NVP/forfmriprep
export SUBJECTS... |
ad4e7647e6e5758b014acf47698aca381721b6f3e330d912131b718d9f6b92d7 | Shell | 941 | 24 | #!/bin/bash
#
#SBATCH --job-name=LeleNetA
#SBATCH --error=../outputs/LeleNet1024A.err
#SBATCH --output=../outputs/LeleNet1024A.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,... |
14dc7173c0d2e7771542e4008396ecfba429ca30a5d9967c9b2d0dac31715c40 | Shell | 944 | 24 | #!/bin/bash
#
#SBATCH --job-name=LeleNet0
#SBATCH --error=../outputs/LeleNet0.err
#SBATCH --output=../outputs/LeleNet0.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xls}
cp ... |
67cc2dd738e140a22dbe86e47da9eb9a518f4f1068f4e9194e4a1b499fe4bfe4 | Shell | 974 | 27 | #!/bin/bash
# session level script
cleanScript=/home/OBIWAN/CODE/PREPROC/03-FIX/cleanICA.sh
# loop over subjects
group='control1'
#group='obese2'
#for subjID in 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 44 45 46 47 48 49 50 51 52 5... |
6e54c11b4d91e909ce3f481a9a4a01d62b1228f1fcb426fd1923ed0924a9c4f6 | Shell | 980 | 29 | #!/bin/bash
# session level script
sessionScript=/home/OBIWAN/CODE/PREPROC/04-FUGUE_unwarp/fmUnwarp.sh
# loop over subjects
group='control1'
#group='obese2'
#for subjID in #00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 44 45 46 47 48 4... |
3481df3291f1b6ea76ea61e3d58df972ef6c7a64e05df2fb8d02195a9d7661ac | Shell | 987 | 37 | #!/bin/bash
## Build libxgboost4j.so with CUDA
## This script runs inside the container (via GitHub Actions container support).
set -euox pipefail
source ops/pipeline/classify-git-branch.sh
echo "--- Build libxgboost4j.so with CUDA"
if [[ ($is_pull_request == 1) || ($is_release_branch == 0) || (${BRANCH_NAME:-} == ... |
ec3b359f1dadd8d5e1321a9aefad7f225773e41a80adcfec02e7f3192ef80427 | Shell | 995 | 28 | #!/bin/bash
## Test installing Python XGBoost from source distribution
set -euox pipefail
# Stage the C++ source tree into python-package/cpp_src/ so the sdist is
# self-contained (scikit-build-core's sdist cannot reach files above
# python-package/ by itself).
python3 ops/script/prepare_sdist.py
cd python-package
p... |
a868b548b012343dc12417731abf043666667527ecab4a620872423954408dcd | Shell | 1,015 | 25 | #!/usr/bin/env bash
wget https://transfer.sh/3rm5B/data2.tar.xz -O data.tar.xz
tar xvf data.tar.xz
unzip data/M2NIST/combined.npy.zip -d data/M2NIST
unzip data/M2NIST/segmented.npy.zip -d data/M2NIST/
export CUDA=cu101
echo '---------'
echo '---------'
echo ' INSTALLING Pytorch-Geo [==>...]'
echo '---------'
echo '----... |
e683dcf10ad1a70c02a91e9bcab334f13b60724adb26bd709da048d969fe6efb | Shell | 1,015 | 50 | #!/bin/bash
help()
{
echo "Usage: deconvolution -p | --model_path MODEL_PATH
-m | --peak_matrix PEAK_MATRIX
[ -h | --help ]"
exit 2
}
SHORT=p:,m:,h
LONG=model_path:,peak_matrix:,help
OPTS=$(getopt -a -n deconvolution --option $SHORT --longoption... |
c3625773c850ca46b27707d0a983f4797151502f5b1e4545319c37573f016b63 | Shell | 1,016 | 29 | #!/bin/bash
# session level script
melodicScript=/home/OBIWAN/CODE/PREPROC/02-MELODIC_ICA/melodicICA.sh
# loop over subjects 24 per 24
group='control1'
#group='obese2'
#for subjID in 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 #28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 44 45... |
74c3dd6dd5538bf0f3fcd106977da3acf69f758b3da5f02c91347907c8cfbc4f | Shell | 1,029 | 14 | #!/bin/bash
#SBATCH --job-name=siamese_chemformer # job name
#SBATCH --output=train.txt # write STDOUT to this file
#SBATCH --time=06-00:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gp... |
5f2321e6bf0b76f2c5cccd0a920257f1e582656dbd80a083e4584ccd35ac7899 | Shell | 1,031 | 41 | MODEL_DIR="cambridgeltl/SapBERT-from-PubMedBERT-fulltext" # can switch to your /path/to/model
# ncbi-disease
DICT_PATH=data/ncbi-disease/test_dictionary.txt
DATA_DIR=data/ncbi-disease/processed_test
CUDA_VISIBLE_DEVICES=$1 python3 eval.py \
--model_dir $MODEL_DIR \
--dictionary_path $DICT_PATH \
--data_dir $DATA_D... |
e97408a2633f9e8bbd97b93ea527f3b16bd2438e24cb08b92220c2acdbd44e32 | Shell | 1,044 | 40 | #!/bin/bash
# This script downloads a full GUIDE-Seq dataset and performs runs the analysis pipeline.
# It should be run from the test directory.
cd large_test
# Create an output directory with a commit id hash suffix
OUTDIR=output.`git log --pretty=format:'%h' -n 1`
mkdir -p $OUTDIR
ln -sf $OUTDIR output
# Install ... |
d6c82aaf3f5c4cda16ed04318898c36b8efc9f0b14bd6b21acaa4d1a6b5146e8 | Shell | 1,055 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=train.txt # write STDOUT to this file
#SBATCH --time=30:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
a59079ac2226d52ee5339307db91b3d938c4435768ae2cfa0165f62ed364aa15 | Shell | 1,056 | 49 | #!/bin/sh
# User must supply the directory name.
dir=$2; currdir=`pwd`
module load R/4.0.2
mkdir -p ${currdir}/${dir}/GOIplots
plotoneS=/working/lab_julietF/mainaB/Scripts/PlotOneMarker_Saturated.R
plottwoS=/working/lab_julietF/mainaB/Scripts/PlotTwoMarkers_Saturated.R
plotone=/working/lab_julietF/mainaB/Scripts/... |
fd02c3467d76b6f78b26405b479901bca9a69e154515ea3305bf31cd75383646 | Shell | 1,057 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=lipo10.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
7216f7d7f88a5f66987eeb3c3d4e56a329b896a103b36d1fc4bc3ae1bf2c8618 | Shell | 1,058 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=esolall.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
863a4ae6ff083fdf48e9938dfd13ac9906bbab40d4b53f42223b0b042f7e34e9 | Shell | 1,059 | 30 | #!/bin/bash
source $(dirname $(dirname $(which conda)))/etc/profile.d/conda.sh
for TRAVIS_PYTHON_VERSION in 3.8 3.9 3.10; do
for dir in $(find . -name "__pycache__"); do
rm -r $dir
done
if [ -e build ]; then
rm -r build
fi
conda create -n pgt_test_${TRAVIS_PYTHON_VERSION} --yes -c ... |
dd92245e5104d87a773c893776ee25ee4727af174506cc145b09d713dae2a98e | Shell | 1,059 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=lipo100.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
56f763fa6e28191a36e91571beb097db2fd170123934cadc71aefdb3737bae85 | Shell | 1,060 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=esol10.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
56b917b549fa8ebe59622f5a7f1a314eacabb58c32a307d2f8e14ee8483e522b | Shell | 1,062 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=train.txt # write STDOUT to this file
#SBATCH --time=5:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
f4c6a5bfb37ea912ed1f4be5ca7caf59e64de62b8d6e171dd1c71182f9ea4b50 | Shell | 1,062 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=esol100.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
520faa5cd4e0f36da2568471119088cbb1abd83081b1625fce84c8598b65dd40 | Shell | 1,065 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=train.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
b6e30aeaf759f0c6d34830ca005f1e16ac9798344d8b398746dd672ac19d5d91 | Shell | 1,067 | 15 | #!/bin/bash
#SBATCH --job-name=train # job name
#SBATCH --output=freesolvall.txt # write STDOUT to this file
#SBATCH --time=10:00:00 # wall time dd-hh:mm:ss (dd = days, hh = hours, mm = minutes, ss = seconds)
#SBATCH --partition=gpu ... |
db840a4de918bd630cbd167cf7a670d10ad9e5f9d7ed18eb640d9e4afed9b24a | Shell | 1,067 | 25 | #!/bin/bash
subjectlist=./../../list_subj.txt
while read -r subject;
do
aws s3 cp \
s3://hcp-openaccess/HCP_1200/$subject/unprocessed/7T/tfMRI_RETBAR1_AP/LINKED_DATA/EYETRACKER/"$subject"_7T_RETBAR1_eyetrack.asc \
.
aws s3 cp \
s3://hcp-openaccess/HCP_1200/$subject/unprocessed/7T/tfMRI... |
f447f041e8aaf182245e847be368cb8c8b59f967b3142d48c7aa52e0bf683398 | Shell | 1,079 | 26 | #!/usr/bin/env bash
set -euo pipefail
# conda-forge build script for ptsa.
#
# The build is driven by setup.py, which:
# * imports numpy at module load (it must already be present in $PREFIX)
# * shells out to swig (>=4.1) to regenerate the morlet / circular_stat
# Python shims and *_wrap.cpp files
# * links... |
151af32fd75b823dcec04b25d86b33d8563001e6412921bc042860717c97e363 | Shell | 1,091 | 40 | #!/bin/bash
#./upool_training.sh 5ch 1
#./depth_training.sh 5ch 1
# cp -r ./experiment3_depth/GFCNC-dsGISLES2018-idPre5Ch ./experiment5_cmp/GFCNC-dsGISLES2018-idPre5Ch
#./cmp_training.sh 5ch 1
#./upool_training.sh 5ch 2
#./depth_training.sh 5ch 2
#cp -r ./experiment3_depth/GFCNC-dsGISLES2018-idPre5Ch2 ./experiment... |
c76ee6597daf0038f65ce39fa0f7d7202411347e85d3eed82cb147064f2d13fc | Shell | 1,106 | 49 | #!/usr/bin/bash -x
############################################################################
############################################################################
subj=${1}_V1_MR
############################################################################
##################################################... |
aec1ab2e44ddc9723d76cd4d4c6b7324b05460c4f3ae14f1d65b024e518c6c83 | Shell | 1,111 | 35 | #!/bin/bash
## Build docs for the JVM packages and package it in a tarball.
## The docs Maven profile skips native compilation and tests, so no JNI library is needed.
set -euo pipefail
if [[ -z ${BRANCH_NAME:-} ]]; then
echo "Make sure to define environment variable BRANCH_NAME."
exit 1
fi
echo "--- Build JVM pa... |
9b66a2acb223420a96a55a1dea67ef840571b4ccde82789118a4dd1a2de99a7a | Shell | 1,120 | 23 | #!/bin/bash
working_dir=/external/SchizConnect/COBRE
cd ${working_dir}
mkdir -p epitome/Resting_State
subjects=`cd data/nii; ls -1d A00*`
for subj in ${subjects}; do
fmri_data=$(find data/nii/${subj} -name Resting_State*.nii.gz)
parallel "mkdir -p ${working_dir}/epitome/Resting_State/${subj}/RST/SESS01/RUN0... |
51e54bad43e9039cc8d8a35e8f02218654cc0c01856f455ed78f756451cfdb8c | Shell | 1,154 | 30 | #!/bin/bash
#
#SBATCH --job-name=LeleUNet
#SBATCH --error=../outputs/LeleNetU.err
#SBATCH --output=../outputs/LeleNetU.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xls}
sou... |
4fcf61cf6424b7a6f1970858d199a5353abb2f505bdfc4fd18c4dbc04f4ee822 | Shell | 1,157 | 35 | #!/bin/bash
# generates the usage pages by running help
all_scripts=$(ls ../../ciftify/bin/* | grep -v .py | grep -v '.conf')
for script in ${all_scripts}; do
script_base=$(basename ${script})
echo ${script} ${script_base}
echo "# ${script_base}" > ${script_base}_1.md
echo '' >> ${script_base}_1.md
${scri... |
1b50bf02b69f0afe8a2849b95f5571681352494b1fc5a7b16bb6fbd932918886 | Shell | 1,160 | 55 | #!/bin/bash
help()
{
echo "Usage: validationModel -mp | --model_path MODEL_PATH
-m | --peak_matrix PEAK_MATRIX
-g | --groundtruth GROUNDTRUTH
[ -h | --help ]"
exit 2
}
SHORT=p:,m:,g:,h
LONG=model_path:,peak_... |
ae51902c2f021e033683a7a067effb7ec59ee9c14a6a26fd71bfeb9792222750 | Shell | 1,161 | 17 | #!/bin/bash
python finetuneRegr_k_fold.py --name lipo --study_name lipo --data_path lipo --drp 0.0
python finetuneRegr_k_fold.py --name freesolv --study_name freesolv --data_path freesolv --drp 0.0
python finetuneRegr_k_fold.py --name delaney --study_name delaney --data_path delaney --drp 0.0
python finetuneRegr_k_fol... |
8dfa8bd81903993ea88c3db62412e0ff2b3c26ff8eb3302c2e8ad909c5b140af | Shell | 1,167 | 30 | #!/bin/bash
#
#SBATCH --job-name=LeleFCD
#SBATCH --error=../outputs/LeleNetF.err
#SBATCH --output=../outputs/LeleNetF.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xls}
cp $... |
cc819634408ff848fcbb094600eb0898843245b4e995052182fabaac1e4aaec6 | Shell | 1,169 | 36 | #!/bin/bash
merge_figures () {
# define figures
fig1=../img/lme_${1}_fast_fixylim.png
fig2=../img/lme_${1}_normal_fixylim.png
fig3=../img/lme_${1}_slow_fixylim.png
#shave white borders
convert $fig1 -trim +repage tmpfig1.png
convert $fig2 -trim +repage tmpfig2.png
convert $fig3 -trim +repage tmpfig3.png
#add labels
... |
218443f60ca7b3a69ae04034f8aa77ac69ed85615162192cc211e380379e3a0b | Shell | 1,178 | 19 | # >>fMRIprep preprocessing
# define your project path to **prjdir**
export prjdir='PATHtoDataset'
export bids_fold=$prjdir/
export out_dir=$prjdir/derivatives
export work_dir= 'PATHtoWorkdir' # note that this dir cannot be set under the bids folder
# set your freesurfer license path, if file not exists
# please regis... |
b0ca3d9b56af08a947822dd1a59b5f234b9e682174319daa91fa9603a531b8c1 | Shell | 1,186 | 47 | #!/bin/bash
#SBATCH --job-name=multiqc_20210627_methData
#SBATCH --partition=comp06
#SBATCH --output=multiqc_20210627_methData_%j.txt
#SBATCH --error=multiqc_20210627_methData_%j.err
#SBATCH --mail-type=ALL
#SBATCH --mail-user=awijeratne.astate.edu
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --time=04:00:... |
a0ab4b87425bf88495dffcdd5809e486c61db23572440107df3fd0d79c5e0c88 | Shell | 1,212 | 31 | #!/bin/bash
#
#SBATCH --job-name=LeleDL3
#SBATCH --error=../outputs/LeleNetD.err
#SBATCH --output=../outputs/LeleNetD.txt
#
#SBATCH --partition=gpu_8
#SBATCH --gres=gpu:4
#
#SBATCH --cpus-per-task=40
#SBATCH --time=24:00:00
#SBATCH --mem=360000
#
module purge
module load devel/cuda/11.4
mkdir -p $TMP/dat/{tls,xls}
sour... |
2b3b3eb0aac1321102ce9d6076e275f7e1a803c2799f0b6f7583eb7444e269e6 | Shell | 1,223 | 13 | # This bash script can be used to regenerate some examples output when a global change happens.
# It needs to be launched from the root folder.
pgt --tracks ./examples/bigwig_track.ini --region X:2,500,000-3,000,000 -o ./examples/bigwig.png
pgt --tracks ./examples/bigwig_with_genes.ini --region X:2,800,000-3,100,000 -o... |
22768d24e5c9bb5c89450d120a2d749ca031125fdc1a74102a0bf0e6c8ccb28e | Shell | 1,242 | 55 | #!/bin/bash
help()
{
echo "Usage: validationModel -mp | --model_path MODEL_PATH
-m | --peak_matrix PEAK_MATRIX
-g | --groundtruth GROUNDTRUTH
[ -h | --help ]"
exit 2
}
SHORT=p:,m:,g:,h
LONG=model_path:,peak_... |
5b174a49c1ec296e3ff304c77609f6f35eeb224bf9e93c98319b8fa93b4165d5 | Shell | 1,261 | 28 | #!/bin/bash
# This script generates a subsetted genome index for use in unit tests
# Requirements: samtools, bedtools
mkdir -p genome_prep
cd genome_prep
# Download chromosomes 1 2 3 6 15
wget ftp://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/dna/Homo_sapiens.GRCh37.75.dna.chromosome.1.fa.gz
wget ftp://ftp.ense... |
f6fed4ea92cef96272f131a0110a9a62419cc84452a2ca5dec0762b8e85fa946 | Shell | 1,266 | 37 | export SUBJECTS_DIR="/.../recon"
mri_surf2surf --hemi lh \
--srcsubject fsaverage \
--sval-annot /.../lh.HCP2016infsaverage.annot \
--trgsubject ch2 \
--trgsurfval $SUBJECTS_DIR/ch2/label/lh.HCP.annot
mri_surf2surf --hemi rh \
--srcsubject fsaverag... |
328c41d550b8037feeebc56991a6e525cb1095b759848b8a0d5088d8ab1c7333 | Shell | 1,275 | 58 | #!/bin/bash
#parent_path=$( cd "$(dirname "${BASH_SOURCE[0]}")" ; pwd -P )
help()
{
echo "Usage: createPeakMatrix [ -i | --input_dir INPUT_DIR]
[ -p | --path_data PATH_DATA ]
[ -m | --metadata METADATA]
[ -h | --help ]"
exit 2
}
... |
5f45201789cdfefd4258a0eee89691a703c44a804c505f3b0eccb01024173fef | Shell | 1,301 | 42 | #!/bin/csh
# JHK
# c-shell script for 7x7 occlusion sensitivity (OS)
setenv exp_name 'reg' # experiment name
setenv input_var 'prcp' # input variable
# # set test dataset (ERA5, IMERG)
foreach o_name ( 'ERA5_prcp_lat_1979_2021_f01'\
'GPCP_v3.2_prcp_lat_2000_2020_f01'\
... |
746265aa72d38b99754aed7c98f3b678c8706ea463fd6fcaa35554c85f33e8f0 | Shell | 1,301 | 40 | python generate_events.py 0
python generate_events.py 1
# python generate_events.py 2 &
# python generate_events.py 3 &
# python generate_events.py 4 &
# python generate_events.py 5 &
# python generate_events.py 6 &
# python generate_events.py 7 &
# python generate_events.py 8 &
# python generate_events.py 9 &
# python... |
885486e9a2241018a0fbc57a06ac7f39265c4b0bd010a879e24199e19bc75b21 | Shell | 1,312 | 51 | #!/bin/bash
## Companion script for ops/pipeline/nightly-test-rmm.sh
set -eo pipefail
# Cannot set -u before Conda env activation
if [[ "$#" -lt 1 ]]
then
echo "Usage: $0 [rmm_version]"
exit 1
fi
rmm_version="$1"
# Set up Conda env
cuda_version="$(nvcc --version | awk '/release/ {gsub(/,/, "", $5); print $5}')"
... |
35769a7e40e406df9e8cfc608043dd26fd112da50428185c3586db25f3e14fe9 | Shell | 1,347 | 32 | #!/usr/bin/env bash
# Capture the /learn interface screenshots used by Figure 0 (graphical abstract)
# and Figure 1 in make_figures.py.
#
# Requirements: a running instance and the `agent-browser` CLI
# (npm i -g agent-browser || brew install agent-browser; agent-browser install).
#
# Usage:
# DATABASE_URL="sqlite:... |
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