sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
08151696d2f91da31d89e142f3973f5fb4e13a766bdc6aeb14f2f9b489da1845 | Shell | 1,786 | 50 | #!/bin/bash
#This script uses beta coefficient and t-statistic maps to run analysis within an ROI on the group level.
#Check if the inputs are correct
if [ $# -ne 6 ]
then
echo "Insufficient inputs"
echo "Input 1 should be the beta coefficient map (unthresholded)"
echo "Input 2 should be the t-statistic map (unt... |
6b3838f019b9d3e946a7a7f474e0edfa72cc90f4fe4d9defe32179d636188290 | Shell | 1,821 | 47 | #!/bin/bash
# Sets regressors in .fsf scripts to empty if specified as empty in emptyregressors.txt file in specific folder of respective GLM of respective subject.
#
# Make executable:
# chmod a+x feat_set_empty_regressors_subject.sh
#
# Execute:
# ./feat_set_empty_regressors_subject.sh
#
# EEG/fMRI STUDY, DONDERS ... |
ba681d8c11e3e94f35bc6a813eabebaf85948e05bead5d9e680c72cf0f5fb013 | Shell | 1,849 | 59 | #/bin/bash
USAGE="
sh tests/nenufaar_test.sh -v version_number - launch from nenufaar folder
"
if [ "$#" -eq 0 ]; then
echo "${USAGE}"
echo "Error Message : No arguments provided"
echo ""
exit 1
fi
while [[ "$#" -gt 0 ]]
do
KEY="$1"
case "${KEY}" in
-v|--version) #mandatory
VERSION="$2"
shift
;;
-h|--... |
c9fd50f6acecfc5cf79a73690d329c0e8955aece02f0c1fc21658f02251968b7 | Shell | 1,859 | 51 | #!/bin/bash
# Sets regressors in .fsf scripts to empty if specified as empty in emptyregressors.txt file in specific folder of respective GLM of respective block of respective subject.
#
# Make executable:
# chmod a+x feat_set_empty_regressors_block.sh # make executable
#
# Execute:
# ./feat_set_empty_regressors_bloc... |
59f61f867f49e2677d942cbffb55596a0b300653949ca546be7646b67907547f | Shell | 1,887 | 58 | #!/bin/bash
# Number of GPUs and the number of samples per batch per GPU (total batch size = N_GPU x BATCH_SIZE).
# The original training used 32 GPUs divided over 4 nodes with one sample per GPU, but only a single
# node is used here for simplicity.
N_GPU=4
BATCH_SIZE=8
# Number of parallel workers per GPU for loadi... |
f03a5ae4916e6593d335fc1394b3966ba1cd3a65976b59048fd1cd883fe0b851 | Shell | 1,887 | 53 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#Create .coords files from MAF. Relies on callSNPsFromMAF.py
for f in chimpa*
do
python callSNPsFromMAF.py $f z$f.coords
echo "Finished processing file: $f"
done
#Grab only the ones with differences
for f i... |
5c796e4d900563bd8b345123a685f68ad9e6f861403c64f08684c27838f9e154 | Shell | 1,904 | 74 | #!/usr/bin/env bash
# This is a simple script to gather all external images referenced in Kubernetes manifests and then pull them in parallel.
set -euo pipefail
k8s_dir="k8s"
# Collect literal image references and ignore templated values
mapfile -t images < <(
grep -RhoE '^[[:space:]]*image:[[:space:]]*[[:graph:]... |
400c1441fa9b7705e936fb08c69709881b2df84c497a653fad989426c48d173f | Shell | 1,912 | 47 | #!/bin/bash
#Simple bash file to launch the CPM.
# The goal is to be able to launch the script and let it run, without needing anything from my end
# Due to the nature of the time constraints, I am doing this a bit quick and dirty
#The script below assumes that the subject lists are stored and labeled identically.
#... |
e0b7e7c28176049753561385c0e8000db03b066c8a0fff00b30b317e2c232106 | Shell | 1,923 | 48 | #!/usr/bin/env bash
#
# Download dataset from https://osf.io/rymks/ which includes data for the
# experiments shown in `examples/nhp_mapping.json` and `examples/rat_mapping.json`
#
# Usage:
# ./download_nhp_rat_dataset.sh <OUTPUT_PATH>
#
# NHP data will be found under `<OUTPUT_PATH>/nhp` and
# Rat data will be found ... |
8ebe2af7f3f4f6054271044727b2d0153535b9aa45a74d9d132e08774c9d2667 | Shell | 1,936 | 69 | source common_setting.sh
dir="${1:-Results}"
declare -a target_dirs=( "3_complex_CBS" "4_simple_CBS_conv4")
declare -a labels=("Complex VHs in CBS" "Simple VHs in CBS")
montage_iters="000 100 1000"
result_file=result_CBS.jpg
all_files=""
for ((n=0;n<${#target_dirs[@]};++n)); do
target_dir=${target_dirs[n]}
... |
ec8ade98d14e2a079a7fb97f946c6fa6fe60c15cd7c725a32fbc65877f1bb992 | Shell | 1,953 | 78 | #! /bin/bash
#Wrapper for running interpro on Biowulf
##Define defaults and parameters
message_use="
dointerproBW.sh
By John McCulloch
Runs Interproscan on Biowulf (module by W. Resch) using as input a fasta file.
Use: $(basename "$0") [options] -i aminoacids.fasta
-h Help
-v Version
-i File aminoacid sequences in F... |
290b304dfe22dc98626febc342245ae41cb2bce47ea7c8b25a9d9958e54af59e | Shell | 1,960 | 48 | #!/bin/bash
# Multiply two cluster masks to see overlap (like conjunction)
# use fslmaths with option -mul
# uses cluster_mask_zstat1.nii.gz which is automatically created by FSL
# binarize at the end, so everything > 1 becomes 1
# chmod a+x conjunction_clusterMasks.sh # make executable
# ./conjunction_clusterMasks.s... |
8bb46519206fae873588f1f0aa8e9190eee0de99a65c23438f81b1176fb90dfb | Shell | 1,966 | 51 | #! /bin/bash
# Note: This is run as root
cd ~
export enable_auth="${enable_auth}"
export basic_auth_credentials="${basic_auth_credentials}"
export auth_type="${auth_type}"
export token_auth_credentials="${token_auth_credentials}"
apt-get update -y
apt-get install -y ca-certificates curl gnupg lsb-release
mkdir -m 075... |
fb137201fd802dce8d492c7a770e29b28f5112fb6253ee15bd492e532672f774 | Shell | 1,966 | 51 | #! /bin/bash
# Note: This is run as root
cd ~
export enable_auth="${enable_auth}"
export basic_auth_credentials="${basic_auth_credentials}"
export auth_type="${auth_type}"
export token_auth_credentials="${token_auth_credentials}"
apt-get update -y
apt-get install -y ca-certificates curl gnupg lsb-release
mkdir -m 075... |
43128bb4de72f85d85501d8f0bc8111d65f81b18f5b462dd619c02f82c5afcac | Shell | 1,968 | 51 | #! /bin/bash
# Note: This is run as root
cd ~
export enable_auth="${enable_auth}"
export basic_auth_credentials="${basic_auth_credentials}"
export auth_type="${auth_type}"
export token_auth_credentials="${token_auth_credentials}"
apt-get update -y
apt-get install -y ca-certificates curl gnupg lsb-release
mkdir -m 075... |
503623bba50122551a167d5de3c761f02e14b3a90e80e1b47821877696c7dacc | Shell | 1,988 | 47 | # Use docker: paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 python3.7
#
# Usage:
# git clone https://github.com/PaddlePaddle/PaddleDetection.git
# cd PaddleDetection
# bash benchmark/run_all.sh
log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
# run prep... |
df5c6a6816cfd018823f0eeafb63cd327649fe1a21a8fa60edeead647c9d475f | Shell | 1,992 | 72 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
# Make directories
# TODO: results directories per phase
echo "[+] Making directories..."
# Phase 1: Noise opt
# ------------------
OUTDIR="results_opt_phase1"
JVAL="1000.0"
THRESHOLD="40.0"
CNT=0
TARGET=(1 1 1 1 1 1 1 1 1 6)
while [ "$(bc <<< "$JVAL > $THRESHOLD")" == "1... |
06f84fb05e2812061f63f416bc89f4700145bbe2ef6c28b4b530715dbbfc707e | Shell | 2,001 | 76 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### -------------------------------------------------------------- ###
# ARG $1 is the configuration file to be used; ARG $2 is the current count
run_sim() {
FNAME=$(basename $1)
FNAME=$(echo $FNAME | cut -f 1 -d '.')
# python3 -c "from brian2 import *; clear_... |
a9419308aa15231d101728b344f8d79c4bba54b68e2383ec33bc970742eddd24 | Shell | 2,027 | 27 |
cat NC_006468.4.indels.txt NC_006468.4.snps.txt | gzip > NC_006468.4.snps.txt.gz
cat NC_006469.4.indels.txt NC_006469.4.snps.txt | gzip > NC_006469.4.snps.txt.gz
cat NC_006470.4.indels.txt NC_006470.4.snps.txt | gzip > NC_006470.4.snps.txt.gz
cat NC_006471.4.indels.txt NC_006471.4.snps.txt | gzip > NC_006471.4.snps.t... |
da004ae0da668e1b55a941c1a09fd7cb7967d3249808244a864a32899501c7c7 | Shell | 2,043 | 43 | #!/bin/bash
## run the Rscript PANDA.R and schedule this job to SLURM with
## `sbatch 02_PANDA_array.sh`
#SBATCH --job-name=PANDA
#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 ... |
bb862de1b2f25bca7c4a3fd1f88b4bf8be95bec0d4179c7fcf03c297ddc013e4 | Shell | 2,044 | 50 | #!/bin/bash
# Downloads thirdparty licenses.
# Save the list of installed packages to base_packages.txt
dpkg --get-selections | grep -v deinstall | awk '{print $1}' > base_packages.txt
# If INSTALL_SOURCES is set to "yes", perform additional operations
if [ "$INSTALL_SOURCES" = "yes" ]; then
# Save the list of all... |
2c877a3fb2fbae591943d7c7598982c7d5a99f195b5f67ce33c46ed6bd2d3e94 | Shell | 2,075 | 67 | #!/bin/bash
# Gather necessary files for running sliceDisplay for all contrasts of 1 GLM (with 3 levels)
# use thresh_zstat1 to create maps of significant clusters
# use cope1, tstat1, zstat1 as "background"
# chmod a+x gather_unzip_contrasts_3level.sh # make executable
# ./gather_unzip_contrasts_3level.sh # run
GLMI... |
fd8f51219b7525fd4dd4a533e70c61ac70884164caa8927f5c11274adfc6bf3c | Shell | 2,085 | 78 | #!/bin/bash
# compile MetaLDA
cd ..
mvn package
echo 'compiling finished ...'
cd demo
MALLET_LOCATION='../../Mallet-master' #change to your own Mallet location
dataset='WS'
# prepare training/testing documents
$MALLET_LOCATION/bin/mallet import-file --input ../data/$dataset/train_doc.txt \
--output ../data/$d... |
e4f8fc61c0564b6b80a0412270e4201a27fc8c2383e8a86bc5a6cf7106d256ba | Shell | 2,089 | 51 | #!/bin/bash
# Set up FSL (if not already done so in the running environment)
# Uncomment the following 2 lines (remove the leading #) and correct the FSLDIR setting for your setup
export FSLDIR=/opt/fsl
. ${FSLDIR}/etc/fslconf/fsl.sh > /dev/null 2>&1
# Let FreeSurfer know what version of FSL to use
# FreeSurfer uses ... |
0cac8df124b58d50ccceaa361108294599305e2980c6a4d6986ece946815a3a9 | Shell | 2,092 | 27 |
cat NC_000001.11.indels.txt NC_000001.11.snps.txt | gzip > NC_000001.11.snps.txt.gz
cat NC_000002.12.indels.txt NC_000002.12.snps.txt | gzip > NC_000002.12.snps.txt.gz
cat NC_000003.12.indels.txt NC_000003.12.snps.txt | gzip > NC_000003.12.snps.txt.gz
cat NC_000004.12.indels.txt NC_000004.12.snps.txt | gzip > NC_0000... |
93b8d59855c84524402d51d94cdbe38069bada6af7320cae9585c5a44d8739f2 | Shell | 2,100 | 52 | #!/bin/bash
#
#$ -N UKBB_cluster_size #name for submission
#$ -q short.qc # submit to short queues on cluster
#$ -t 1:1000 # number of bootstrap samples
### request maximum of 24 hours of compute time
#$ -l h_rt = 30:00:00
#$ -l s_rt = 30:00:00
#
### -cwd means work in the directory where sub... |
07228f49c8d5ebe330afee9ea280a7924a3c23b01a9419ce094a8601d20caeb9 | Shell | 2,109 | 61 | #!/usr/bin/env bash
pushd () {
command pushd "$@" > /dev/null
}
popd () {
command popd "$@" > /dev/null
}
if [ "$1" = "clean" ]; then
pushd ./src/SimulationCode/celegans/
make clean
popd
pushd ./src/SimulationCode/sea-urchin/
make clean
popd
exit 0
fi
pushd ./src/SimulationCode/celegans/
./bec... |
3d77fa356920798a47ee46dba265d422f3b3399e8ea4a27dd9ed269aeb7ea2eb | Shell | 2,137 | 78 | # 是否使用GPU(即是否使用 CUDA)
WITH_GPU=OFF
# 是否使用MKL or openblas，TX2需要设置为OFF
WITH_MKL=ON
# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
WITH_TENSORRT=OFF
# paddle 预测库lib名称，由于不同平台不同版本预测库lib名称不同，请查看所下载的预测库中`paddle_inference/lib/`文件夹下`lib`的名称
PADDLE_LIB_NAME=libpaddle_inference
# TensorRT 的include路径
TENSORRT_INC_DIR=/path/to/tensorrt/inclu... |
3cb0d5628535c715a7dd64381d70adaacf89e59f3005e6da662e85e0274c92f5 | Shell | 2,151 | 61 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
# multistage-physical
for input_type in physical hetero geometric; do
CUDA_VISIBLE_DEVICES=2 python train.py --accelerator gpu \
--devices 1 \
--max_epochs 500 \
--precision 16 \
--protein_num_layers 3... |
60d6b87fca3f453bf89aab334e59e6040e1ce3d1044a5e97d1c99149d1573355 | Shell | 2,169 | 72 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### Get the current date and start making the results directory
CURRDATE=$(date +"%FT%H%M")
RESBASE="/beegfs/nvardala/results_MS_stim_pacing"
RESDIRTOT="$RESBASE"_"$CURRDATE"
### Check and make directory for saving ALL the results
if [ -d $RESDIRTOT ]; then
echo "Direct... |
c1351d3435cb3f79423ff05f2a9feb5d7a49797be78d4b7e89d48f08010cb79e | Shell | 2,179 | 46 | #!/bin/bash
if [ "$1" != "" ]; then
build_gui="$1"
else
build_gui="true"
fi
ubuntu_version=$(grep 'DISTRIB_RELEASE' /etc/lsb-release | cut -d'=' -f2)
ubuntu_version_no_dots=$(echo ${ubuntu_version} | tr -d ".")
echo "Building ASAP; building GUI = ${build_gui}; on Ubuntu ${ubuntu_version}"
git clone https://git... |
79738cb19ca1b7ded365d9e01f26d9a92b13a975acfa1d310b73f6695c14a19f | Shell | 2,197 | 39 | #!/bin/bash
# Run .fsf file across subjects per GLM (3nd level GLM in 3-level approach).
#
# Make executable:
# chmod a+x feat_run_GLM_Sample_3rdlevel.sh
#
# Execute:
# ./feat_run_GLM_Sample_3rdlevel.sh # run
#use fsl_sub_DCCN.sh, run on -T, minutes, storage space, feat command, file to run (with complete path)
#
# E... |
294781d43c012cea63a735677a6b4c643507c8ca7cf76eebfacb99ac3967c3f1 | Shell | 2,201 | 73 | #!/bin/bash
if (return 0 2>/dev/null); then
MODEL_ANGELO_FINISH=return
else
MODEL_ANGELO_FINISH=exit
fi
ENVNAME=model_angelo
while test $# -gt 0; do
case "$1" in
-h | --help)
echo "Make sure you have conda installed"
echo "Make sure you have set the TORCH_HOME environment variable to a suitable public ... |
5beba500e1e2e5698d3eb57f7fc062466611d27375c85954cbdfe9fae4d244e2 | Shell | 2,207 | 48 | #!/bin/bash
#get header lines of grep file, remove everything after space and store as *.headers.txt
for i in $(ls -1 *.grep15_8471.txt | sed 's/\.grep15_8471.txt//')
do
awk 'NR % 3 == 1' $i\.grep15_8471.txt > $i\.grep15_8471.headers.txt
sed -i 's/\s.*$//' $i\.grep15_8471.headers.txt
done
for j in $(ls -1 *.grep10_633... |
85f155c5618c8cac6548de7669a6dd39318c7611ca907430695a62098d4242ca | Shell | 2,211 | 85 | source common_setting.sh
## Target Layer [fc8 | conv3 | conv4 | conv5]
act_layer="${1:-fc8}"
## initial images [original | blurred]
init_img="${2:-original}"
## target categories [original | shifted]
target_cat="${3:-original}"
## initial images
if [ "${init_img}" = original ]; then
init_files=(${original_init_... |
8651b339f6fe29aa8686587971759b28be5aabf7f792b1a7201a2a03e945e972 | Shell | 2,222 | 69 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
python train.py --model_name hgvp \
--accelerator gpu \
--devices 1 \
--max_epochs 500 \
--precision 32 \
--num_layers 3 \
--node_h_dim 200 32 \
--edge_h_dim 64 2 \
--dataset_name PDBBind \
--input_type... |
5b7a87b2f193a443e0d0a3b2fb6401b0d7c7bdf724e42c5e5f096361bc160c93 | Shell | 2,226 | 85 | source common_setting2.sh
## Target Layer [fc8 | conv3 | conv4 | conv5]
act_layer="${1:-fc8}"
## initial images [original | blurred]
init_img="${2:-original}"
## target categories [original | shifted]
target_cat="${3:-original}"
## initial images
if [ "${init_img}" = original ]; then
init_files=(${original_init... |
d74d21582329899b391fc6100f9bc3b0969725b8e621380757d8f53ec5d737a2 | Shell | 2,239 | 66 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#Create .coords files from MAF. Relies on callSNPsFromMAF.py
for f in *.maf
do
python callSNPsFromMAF.py $f z$f.coords
echo "Finished processing file: $f"
done
#Concatenate all the files by chromosome.
for ... |
f5ce8e42318715b931f20b2368f3c0d345b6b5e7788ff8f4916bed9f1818cb15 | Shell | 2,292 | 72 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### Get the current date and start making the results directory
CURRDATE=$(date +"%FT%H%M%S")
RESBASE="/beegfs/nvardala/results_opt_new/$1"
RESDIRTOT="$RESBASE"_"$CURRDATE"
### Check and make directory for saving ALL the results
if [ -d $RESDIRTOT ]; then
echo "Di... |
024a3add631db7c23c4b475f2fb218e0bab135c6e60c082550e6c45987313bd8 | Shell | 2,314 | 79 | #!/bin/bash
source test_tipc/utils_func.sh
function readlinkf() {
perl -MCwd -e 'print Cwd::abs_path shift' "$1";
}
function func_parser_config() {
strs=$1
IFS=" "
array=(${strs})
tmp=${array[2]}
echo ${tmp}
}
function func_parser_dir() {
strs=$1
IFS="/"
array=(${st... |
a8ce5b68bbd6ab24cd044b36852f5e91cf48662ee8b679c6eef23ce282154c48 | Shell | 2,365 | 53 | #!/bin/bash
## run the Rscript 01_decoupleR_analysis.R and schedule this job to SLURM with
## `sbatch 01_decoupleR_array_job.sh`
#SBATCH --job-name=gtex_decoupleR
#SBATCH --mail-type=ALL
#SBATCH --mail-user=$USER@uab.edu #CHANGE THE EMAIL TO YOUR OWN
#
#Number of tasks needed for this job, the partition, and paramete... |
70c13ca38b04073e6da1d605c72ecfb2529ab247b351b46c398aa854d73ea29b | Shell | 2,398 | 76 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### Get the current date and start making the results directory
CURRDATE=$(date +"%FT%H%M")
RESDATA="/beegfs/nvardala/results_fig4_kN2/$1/data"
RESLOGS="/beegfs/nvardala/results_fig4_kN2/$1/logs/$1_$CURRDATE"
### Check and make directory for saving ALL the results
while :... |
a50d4a1c53a476a80545158ff81c8166340fdf0736dd05907ff119f9c0c2a8b9 | Shell | 2,430 | 70 | #!/bin/bash
#This script uses pre-processed brain fMRI data and and creates a general linear model using AFNI and FSL.
#Check if the inputs are correct
if [ $# -ne 7 ]
then
echo "Insufficient inputs"
echo "Input 1 should be the fMRI data you want to model"
echo "Input 2 should be the demeaned motion parameters"
... |
ca8cc9dc53cfd882bbf55f453d048ea42249602f957bf8c24848735c3153252e | Shell | 2,459 | 45 | #!/bin/bash
# Performs ICA AROMA on pre-processed data looping over blocks of subjects (submits single jobs to the computing cluster).
#
# Make executable:
# chmod a+x ICA_Aroma_run.sh
#
# Submit job like this:
# qsub -N "ICA_Aroma_run" -l walltime=0:00:10,mem=1gb ICA_Aroma_run.sh # run
#
# EEG/fMRI STUDY, DONDERS IN... |
6958f7cb4fd7c61d558944961db84040d47c72e8034ec472910fca7e6816d30b | Shell | 2,460 | 89 | ##nb: copied from ag1000g/jobs/create_training_data.080921.job
##run as an array of ten jobs with 8G memory each
control_dir=/proj/dschridelab/rrlove/ag1000g/data/ms/ms_modified/training/control_files/
base_dir=/proj/dschridelab/rrlove/ag1000g/data/ms/ms_modified/training/
##full model, two-way introgression
cd ${ba... |
ee8c1f5897a16ddfa3e73a8bf7e57bc730f370e3bfd98f6a475f146c09abf9a7 | Shell | 2,538 | 69 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#Call Indels from the .maf files.
for f in chimpa*
do
python callINDELsFromMAF.py $f z$f.indels
echo "Finished processing file: $f"
done
#Concatenate all the indels together
cat *.indels > all_indels_mixed.t... |
654a4e082b3028eab5456751f647e4148aef5e30be6d79b3b99d647cc2229946 | Shell | 2,541 | 40 | #!/usr/bin/env bash
# Download JUnit 5 and required dependencies into ./lib
# Usage: ./download.sh [version]
# Default JUnit version: 5.10.0 (platform 1.10.0)
set -euo pipefail
JUPITER_VERSION="${1:-5.10.0}"
# Map 5.10.x to platform 1.10.x by default
PLATFORM_VERSION="${PLATFORM_VERSION:-1.${JUPITER_VERSION#5.}}"
LIB_... |
8c39bdd04896554961151bbd446a6e0985fce51d327ff78366ae1989a8f1b1f4 | Shell | 2,549 | 51 | #!/bin/bash
## run the Rscript decoupleR_analysis.R and schedule this job to SLURM with
## `sbatch decoupleR_array_job.sh`
#SBATCH --job-name=kid_decoupleR #Note: when running kidney this needs to be changed to 'kid_decoupleR' from 'cor_decoupleR'
#SBATCH --mail-type=ALL
#SBATCH --mail-user=jbarham3@uab.edu #CHANGE TH... |
0e2f7d4814c77352156ca139ea42bc9b8460ee9bd63800a3291eb276e506ee9e | Shell | 2,599 | 65 | #!/bin/bash
#This script uses beta coefficient and t-statistic maps to run analysis within an ROI.
#Check if the inputs are correct
if [ $# -ne 7 ]
then
echo "Insufficient inputs"
echo "Input 1 should be the beta coefficient map"
echo "Input 2 should be the t-statistic map (don't include '.nii.gz' in file name)"... |
8ab33f249ef2ee8720ff0df7c17d2ba96d02d2e6f92f6ce0f2ee7ba5db06a8ad | Shell | 2,619 | 47 | #!/bin/bash
#SBATCH --output=MAF_Download.out
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --time=48:00:00
#Note, I used UCSC mafs when available and ensembl mafs if there was none from UCSC
#The goal was to ensure the greatest degree of compatibility with the UCSC fasta and gtf fil... |
a8d88add249221119d876a8d37d4bde1d0681b44b8b85537a240b3b6078fa694 | Shell | 2,679 | 98 | #!/usr/bin/env bash
# Developer ID sign of the existing unsigned (ad-hoc) SanPy.app.
# Fail fast. No notary, no staple, no rebuild.
set -euo pipefail
cd "$(dirname "$0")"
# shellcheck source=config.sh
source ./config.sh
RUN_NAME="${1:-}"
if [[ -z "${RUN_NAME}" ]]; then
if [[ ! -f "${LATEST_FILE}" ]]; then
echo ... |
36ad579e187b2ae591972a3001c3558a8a870cac84aa8f0401fd54d840a4d0bd | Shell | 2,680 | 44 | peak_id="peak_HP_MN_PP_SKM_295546_promoter_FABP7"
peak_short="peak_HP_MN_PP_SKM_295546_"
folder_name="FABP7"
ml python/3.6.1
ml R/4.2.0
cd /scratch/users/banwang3/references/outputs/
mkdir ${folder_name}
cd /scratch/users/banwang3/references/outputs/${folder_name}
# Get Peak coordinate
grep ${peak_short} /scratch/use... |
f4b3b59343bce670c2234419fa2ac4b84188459185e63e43855487b7ec7dd924 | Shell | 2,688 | 71 | #!/bin/bash
# Gather cope and zstat from first cope, combine
# use thresh_zstat1 to create maps of significant clusters
# use cope1, tstat1, zstat1 as "background"
# chmod a+x gather_conjunction_3level.sh # make executable
# ./gather_conjunction_3level.sh # run
## Define root directory:
rootDir=/project/3017042.02 #... |
d675f926c9359a0030151666b72b32a01428853de8134e00be0610b0ecce5256 | Shell | 2,747 | 81 | #!/bin/bash
# Gather thresh_zstat1, cope1, tstat1, and zstat1 files of positive and negative group-level contrast of given GLM,
# unzip all, combine and unzip thresholded masks
#
# Make executable:
# chmod a+x gather_unzip_contrasts_2level.sh
#
# Execute:
# ./gather_unzip_contrasts_2level.sh # run
#
# EEG/fMRI STUDY... |
59a87d4bc339897bb2bb791ba8138589ba5de48d9232c0b5d4ee6820dc21e35c | Shell | 2,753 | 68 | # -- Conventional --
python main.py --algorithm 0 --train data/train/ --test data/test/ --feat_extraction 1 2 3 4 5 6 --output results/svm
python main.py --algorithm 1 --train data/train/ --test data/test/ --feat_extraction 1 2 3 4 5 6 --features_exist 1 --output results/xgboost
# -- Deep Learning --
## -- CNN --
f... |
6cc25775e9c492acfc54f7be1c05f8c21b72fcaffe3fe8789573dba52af3acae | Shell | 2,771 | 53 | #!/bin/bash
# Extract volume-by-volume out-of-brain (OOB) signal as suggested by Maarten Mennes.
# Saved as OOB_noise.txt in AROMA folder of specific block of specific subject.
#
# Make executable:
# chmod a+x create_regressor_OOB.sh
#
# Submit as job to the cluster:
# qsub -N "create_regressor_OOB" -l walltime=2:00... |
fb4efcfa0e5f8cce861baab3dd39eb33726ff46c61d35867355e485b2fe63647 | Shell | 2,825 | 74 | #!/bin/bash
# Extract mean volume-by-volume signal (time series) within mask for selected ROIs to be used as regressors in fMRI-informed EEG analyses (TAfT).
# Steps involved:
# 1) Run invert_highres2standard_warp_block.sh first to invert FNIRT registration matrix.
# 2) Brings given mask from standard to functional sp... |
c93a6255a9647eb2729690f454a6c2eb166d833c1b160e73302787888f28149f | Shell | 2,837 | 114 | # 是否使用GPU(即是否使用 CUDA)
WITH_GPU=OFF
# 是否使用MKL or openblas，TX2需要设置为OFF
WITH_MKL=ON
# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
WITH_TENSORRT=OFF
# paddle 预测库lib名称，由于不同平台不同版本预测库lib名称不同，请查看所下载的预测库中`paddle_inference/lib/`文件夹下`lib`的名称
PADDLE_LIB_NAME=libpaddle_inference
# TensorRT 的include路径
TENSORRT_INC_DIR=/path/to/tensorrt/inclu... |
5aceec36ae8138994fb87b0fd37cff38c980db388bd81caaf77735a22d1be804 | Shell | 2,886 | 69 | #!/bin/sh
# This script determines if the input methylation bam file is single or paired-end
# and then runs bulk-bam2epiallele.R to identify concordant and discordant reads
# The input is a text file containing the full file pathname of desired bam file(s)
# Format for calling script:
# sbatch bulk-calcEpiallele.s... |
7594caa796fe65952fb6237066898eb17a481443e8f13ec0fe354f3b74d47ed0 | Shell | 2,932 | 83 | #!/bin/bash
#!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#Call Indels from the .maf files.
for f in *.maf
do
python callINDELsFromMAF.py $f $f.indels
echo "Finished processing file: $f"
done
#Concatenate by chromosome.
for i in {1..22}
do
cat Compara... |
4add67731f3c7522c792a4a7f089d11d9cf9f8bec977a3fecca44da92e65fadc | Shell | 2,938 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
79b1423334d4bca516df0fd6714b4a0dddbcf238172e2cad1562ac30a2f1591f | Shell | 2,938 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
cf8319813318f07cbc221e06f8422b3c8d7dec9be856c5c9c76969b3cb6b3597 | Shell | 2,938 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
340beba3b68cdd0d454a035ac215c67ff5cd860e521eb1db3ff93509c0166ece | Shell | 2,940 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
6e0728e54945995797f75bd5ac562ae77f46b333879e56d557d8d9280230c99c | Shell | 2,940 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
c7b18cfcc4f9c465c637b2b832805e8c8e5c77e2097f2efde7350a8abd9d0ab8 | Shell | 2,940 | 99 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
23d4e61ef56e79e24dade0ec805945bc963eb607a7a0a24e35925ac503d2eca7 | Shell | 3,083 | 75 | #/bin/bash
USAGE="
sh tests/nenufaar_annot_test.sh -v version_number - launch from nenufaar folder
"
if [ "$#" -eq 0 ]; then
echo "${USAGE}"
echo "Error Message : No arguments provided"
echo ""
exit 1
fi
while [[ "$#" -gt 0 ]]
do
KEY="$1"
case "${KEY}" in
-v|--version) #mandatory
VERSION="$2"
shift
;;
... |
fbf21dc8d8cbf317efbba1d958a6d5307c7aa0aa92a8b9006e03191348cbf564 | Shell | 3,112 | 41 | #!/bin/bash
#SBATCH --time 48:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#Split the files, requires ASE_SNPs.FILTER.SPLIT_SPECIES_HUMAN.bed
for bamfile in *humreffed*.bam;
do
echo splitting $bamfile
samtools sort -n -o ${bamfile::-4}.sortn.bam $bamfile
python splitSpeciesR... |
03d9ee3323b134668d648296f850796fe2841b11e745e090e9bf6b053bd19621 | Shell | 3,122 | 70 | #使用镜像：
#registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82
#编译Serving Server：
#client和app可以直接使用release版本
#server因为加入了自定义OP，需要重新编译
apt-get update
apt install -y libcurl4-openssl-dev libbz2-dev
wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && tar xf centos_ssl.tar && rm -rf c... |
66127cad39470563b271368d24160b372deaac3304c16d2a01e24166d34d7847 | Shell | 3,134 | 105 | ## Global func to check 'special' files
checksum_vcf() {
printf $(basename "$1")'\t'
bcftools view --no-header "$1" | md5sum
}
export -f checksum_vcf
checksum_bam() {
printf $(basename "$1")'\t'
samtools view "$1" | md5sum
}
export -f checksum_bam
excel_to_tsv() {
local in_xlsx=$1
local sheet_name=$2
/mnt/... |
0d5769c54adb4c82eddd7e52be74f79cc83a2339f2856e83f58de25fdd1ab437 | Shell | 3,157 | 79 | #!/bin/bash
# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved.
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this ... |
7817500a68b545929ed1d87e812524e3ae3c117ae5fe066e1f0b103a1eb550c1 | Shell | 3,161 | 81 | #!/bin/bash
# 此脚本可以逐个删除pass，然后运行测试脚本，检查去掉该pass是否对模型精度有影响
pass_array=("mkldnn_placement_pass" "simplify_with_basic_ops_pass"
"layer_norm_fuse_pass"
"attention_lstm_fuse_pass"
"seqconv_eltadd_relu_fuse_pass"
"seqpool_cvm_concat_fuse_pass"
"mul_lstm_fuse_pass"
"fc_gru_fuse_... |
b4e2544a0ae2bfb20e336880d96847e2e9715893c46b4d34d66edcf33b702864 | Shell | 3,169 | 121 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
9c83b0f9d8c1f53007f45577f848d811529ad36b282a2f43f563acd501934642 | Shell | 3,191 | 94 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="pipeline_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet pipeline_python_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parse... |
cad9db7c537098df34f884b31e220c184bd4aafb17f752c3a2997617b57f26a8 | Shell | 3,191 | 128 | #!/bin/bash
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://github.com/rs... |
2759cf33f262144da1850c93af1463da2430d39ffd5c0c748bbe85414199fff7 | Shell | 3,248 | 83 | #!/bin/bash
#This script uses pre-processed brain fMRI data and and creates a general linear model using AFNI and FSL.
#Check if the inputs are correct
if [ $# -ne 9 ]
then
echo "Insufficient inputs"
echo "Input 1 should be the fMRI data you want to model"
echo "Input 2 should be the demeaned motion parameters"
... |
2f7340b5ded3f57a38dd6248e78ca4cb6d1d9b42dbe24e10f89729d5ebf8dd36 | Shell | 3,314 | 94 | #!/bin/bash
#change to your own Mallet location
MALLET_LOCATION='/home/wbuntine/Code/mallet-2.0.8'
METALDA_LOCATION='/home/wbuntine/GitHub/MetaLDA'
# this is a directory, must contain 'train_doc.txt'
dataset=$1
topics=$2
rm -rf $dataset/save
# prepare training/testing documents
$MALLET_LOCATION/bin/mallet import-... |
8e1ea2b7b06f22a8df99deedee12beba6de6cf327e70d1445a76d44cec746057 | Shell | 3,369 | 81 | #!/bin/bash
# Loop over subjects, extract summary statistics of parameter estimates within given mask using featquery.
# Steps involved:
# 1) Run invert_highres2standard_warp_subject.sh first to invert FNIRT registration matrix.
# 2) Brings given mask from standard to functional space for each subject.
# 3) Uses featq... |
90f5370fb27ff19b89a9b25512d471ada0985f4add7a92f69ca1cdaa2df60f4d | Shell | 3,427 | 92 | #!/bin/bash
source test_tipc/utils_func.sh
function readlinkf() {
perl -MCwd -e 'print Cwd::abs_path shift' "$1";
}
function func_parser_config() {
strs=$1
IFS=" "
array=(${strs})
tmp=${array[2]}
echo ${tmp}
}
function func_parser_dir() {
strs=$1
IFS="/"
array=(${strs})
len=${#ar... |
b9afaf4c6024da127305e22c16d9e9415963611f7489b247f149bfa2c0cad44a | Shell | 3,444 | 119 | #!/bin/bash
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
# Run this on Amazon DLAMI or SageMaker
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
# global variables shared across all tests:
n_gpus=4
pdbbind_data=/home/ec2-user/SageMaker/efs... |
7b7f6e8c13e6e0a727cb1ad7c10d258d1025eb3f9fac1d3119f95a8f13606a73 | Shell | 3,471 | 85 | #!/bin/bash
# Run all NaviDiv diversity strategies against QED optimisation, then
# post-process each run with t-SNE and comprehensive diversity analysis.
#
# Usage (from any directory):
# chmod +x run.sh
# ./run.sh
#
# Prerequisites:
# conda env 'reinvent4' with NaviDiv + REINVENT4 installed
# NAVIDIV_ROOT exp... |
592996630f3610799537ce6f05e6213233cb8d6defdc31845ba45ca4ebc2b0af | Shell | 3,512 | 80 | #!/bin/sh
# This script determines if the input methylation bam files are single or paired-end
# and then runs bam2epiallele.R to identify concordant and discordant reads
# The input is a text file containing the full file pathname of desired bam file(s)
# Format for calling script:
# qsub -v BAMS=<filename> calcEp... |
ed44bf90911eb89f82a7f015d33422de70b1ca0ff7026168077285c3d47845b5 | Shell | 3,704 | 27 | python network.py --nruns 108 --corr 0.01 --max 1.09 --group feedforwardfix --strength 0 --seed 0 --eps 0.2 --repeats 10 --fixext 1
python network.py --nruns 108 --corr 0.01 --max 4.46 --group strength20fix --strength 0.2 --seed 0 --eps 0.2 --repeats 10 --fixext 1
python network.py --nruns 108 --corr 0.01 --... |
b1a7baff35626cd5798af837e78e32f8d10bef884c1ce7c4aa6c2b5c5c519190 | Shell | 3,883 | 138 | #!/usr/bin/env bash
# Sign the latest build, notarize it, staple it, and create the distribution zip.
set -euo pipefail
cd "$(dirname "$0")"
# shellcheck source=config.sh
source ./config.sh
if [[ ! -f "${LATEST_FILE}" ]]; then
echo "error: ${LATEST_FILE} not found. Run ./build_local.sh first." >&2
exit 1
fi
RUN_N... |
8610b81bb99ef18f9cb4ce0237561b1cde95a050d8f057833b09cac814558fbe | Shell | 3,932 | 129 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
# PDBBind dataset
python train.py --accelerator gpu \
--devices -1 \
--max_epochs 1 \
--precision 32 \
--stage1_num_layers 3 \
--stage1_node_h_dim 200 32 \
--stage1_edge_h_dim 64 2 \
--stage2_num_layers 3 \... |
4b45fb2e5343cafb096ff0f1cf18cdcd9f3f40c0ec2b2b6577119049e7ac7179 | Shell | 4,030 | 110 | #!/bin/sh
JOB_NAME="${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... |
d12f07623f7422e108acd56bc78cb35a7001bccb25745fae2612781774c0121c | Shell | 4,037 | 101 | #!/bin/bash -eu
export DEPS_PATH=/src/deps
mkdir $DEPS_PATH
# Build libxml2 without ICU support, https://github.com/igraph/igraph/issues/1992
# Building libxml2 from scratch is also required for MemorySanitizer.
# It may be necessary to leave CMAKE_BUILD_TYPE empty and specify LIBXML2_WITH_MODULES=OFF
# in order for ... |
4f4d313e4e42fa0db6f12c248dfaefac0ea2f04d68705476f770581cb28e9f3a | Shell | 4,128 | 110 | #!/bin/bash
#Step 1
#grab ten 4-taxon data from a randomly chosen file
sed -n "$((399997-$1*40)),$((400000-$1*40)) p" sequences372783-0.1-40.0-1.0.in > test0.in
sed -n "$((399993-$1*40)),$((399996-$1*40)) p" sequences372783-0.1-40.0-1.0.in > test1.in
sed -n "$((399989-$1*40)),$((399992-$1*40)) p" sequences372783-0.... |
1068fb6944ab5b1f192464ea904a15129a3ffba7b289fac3ac50f918ae956a4f | Shell | 4,194 | 126 | #!/bin/bash
#snakemake -k --jobs 999 --latency-wait 120 --max-jobs-per-second 8 --cluster-config cluster.json --cluster "qsub -N {cluster.name} -j {cluster.j} -M {cluster.M} -m {cluster.m} -l nodes={cluster.nodes}:ppn={cluster.ppn},walltime={cluster.walltime} -l mem={cluster.mem} -e {cluster.stderr} -o {cluster.stdout}... |
03aaae4f21c42630d11ec51ae4d4a827281ca5add49b9ab678c550b6f1402077 | Shell | 4,215 | 92 | #!/usr/bin/env bash
set -xe
# Usage：CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${batch_size} ${fp_item} ${max_epoch} ${model_name}
python="python3.7"
# Parameter description
function _set_params(){
run_mode=${1:-"sp"} # sp|mp
batch_size=${2:-"2"}
fp_item=${3:-"fp32"} ... |
b2ebb92c3f25e0dbb34fb9d9bbd8b9938aea8be899f020a9b4fc3122e21e32c3 | Shell | 4,228 | 170 | #/bin/bash
source common_setting.sh
## Target Layer [fc8 | conv3 | conv4 | conv5]
act_layer="${1:-fc8}"
## Generation Type [DGN | DCNN]
gen_type="${2:-DGN}"
## Select Error Function [winner, l2norm, fixed]
act_mode="${3:-winner}"
## initial images [original | blurred]
init_img="${4:-original}"
## target categorie... |
5feb19ec798a5204ab9b426da0313b2a422175fa3ecef0f83d43c344b0cc422c | Shell | 4,336 | 156 | #!/usr/bin/env bash
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... |
8b56d09e995a066c2ee2ee5a53e9cb5722bb04eece31462790846fe13042c52b | Shell | 4,564 | 154 | #!/bin/bash
###########################################################################
######### ###########
######### AutoDL ###########
######### @uthor : D Baux david.baux<at>inserm.fr ###########
######### Date : 27/10/2021 ###########
###... |
12d79d14eb4a652da6ee1fa94b1bdea868b9f72cae8cdce6a72e082c0f39f74e | Shell | 4,565 | 157 | #!/usr/bin/env bash
#
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://git... |
bfad02ac201569a1e33d29b8ec54ad2a7e15e2d247046b211c8bfc82371081ec | Shell | 5,008 | 61 | #!/bin/bash
# 本脚本用于测试PaddleDection系列模型的自动压缩功能
## 运行脚本前，请确保处于以下环境：
## CUDA11.2+TensorRT8.0.3.4+Paddle2.5.2
## rtdetr_hgnetv2_l_6x_coco
## 启动自动化压缩训练
CUDA_VISIBLE_DEVICES=0 python run.py --save_dir ./models/rtdetr_hgnetv2_l_6x_coco_qat/ --act_config_path ./configs/rtdetr_hgnetv2_l_qat_dis.yaml --config_path ./configs/rt... |
aba232f3227e4c465964dab7bd79031550440c1347859f4cce8b9f1520086dd7 | Shell | 5,143 | 97 | #!/bin/bash
# Extract volume-by-volume cerebrospinal fluid (CSF) signal as suggested by from Maarten Mennes.
# Saved as CSF_noise.txt in AROMA folder of specific block of specific subject.
#
# Make executable:
# chmod a+x create_regressor_CSF.sh
#
# Submit as job to the cluster:
# qsub -N "create_regressor_CSF_01_36... |
61afdec1d9698a9b379145e64e02bbb1a1d736272497e287f05b9cab307baf74 | Shell | 5,250 | 148 | #!/bin/bash
# The script requires the openMIINDS repository to be cloned in the same root directory into the directory "openMINDS_documentation".
# This needs to be done externally since we need to push back to it and this can only be achieved when the repo is cloned via the workflow action
if [ ! -d "openMINDS_docume... |
7d00dc91199603ee857ee2e7323b8693f17cd179ca59b9abfdd2bcafd421aa96 | Shell | 5,258 | 130 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="serving_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet serving_python_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_... |
235e9fbe281729087599f93a2e657701ce07a4769d7c1ff6a3c6fda72daac87a | Shell | 5,404 | 115 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="whole_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet ptq: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]}")
... |
4e3355441abd2a71a4c54b8f456acf8584da9a4c6d4383ff95014c62c94bf925 | Shell | 5,426 | 119 | #!/bin/bash
# For FEAT GLM outputs computed at different cluster-forming thresholds:
# Threshold 2 copes at given threshold,
# combine both cluster
# chmod a+x combine_conjunctions_multiple_thresholds.sh # make executable
# ./combine_conjunctions_multiple_thresholds.sh # run
rootdir=/project/3017042.02 # root direct... |
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