sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1de0a2c8d73d61ff7e326c887a61c0fee0ad87e8c8562a560e008414986a9e5f | Shell | 838 | 34 | #!/bin/bash
#$ -cwd -V
#$ -l h_fsize=50G
#$ -N hh_dapg_prep
#$ -e processing.log
#$ -o processing.log
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: ${HOSTNAME}"
## List current modules for reproducibility
module... |
5cc2feccb562e2ef7096e33738455c8893ff66bb2883d1e6ae64e9dd16d38c8c | Shell | 843 | 22 | #!/bin/sh
#################### activate conda env #######################
eval "$(conda shell.bash hook)"
# conda activate pyg
#################### prcessing all fna files #######################
counter=0
sourceDir=`ls /home/bli/GNN/Graph_bin/data/homology/alphafold_test/fold_completed_pdb_2697`
num=`ls /home/bli/G... |
2c72af13800105f1699660cf96dbd8232592ac5ad7fe306f697530a5c3d3c21c | Shell | 844 | 42 | #!/bin/bash
# Load Local Variables
source ./SET_VARIABLES.sh
# Generate Diffusion Folders
mkdir -p \
${DIFF_DIR} \
${DIFF_DATA_DIR} \
${DIFF_DATA_N4_DIR} \
${DIFF_DATA_NORM_RELEASE_DIR} \
${DIFF_DATA_RELEASE_DIR} \
${DIFF_DATA_BEDPOSTX_DIR} \
${DTI_DIR} \
${EDDY_DIR} \
${EDDY_FIELD... |
359822f501625875f7f09a897c031c37069e87f803826b216c821f23213176a2 | Shell | 844 | 19 | #!/usr/bin/env bash
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agr... |
72b35d31f67dbb7e9dd9e266824e7c042f37af864a6c6685e8f40d1b762d3ca8 | Shell | 844 | 17 | wfdbcheck -r sample_mat2wfdb_digital
wfdbcheck -r sample_mat2wfdb
wfdbcheck -r sample_wfdbpython
wfdbcheck -r sample_wrsamp
wfdbcheck -r sample_wrsampNG # -v
rdsamp -r sample_mat2wfdb_digital -c -H > sample_mat2wfdb_digital_rec.csv
rdsamp -r sample_mat2wfdb -c -H > sample_mat2wfdb_rec.csv
rdsamp -r sample_wfdbpython -... |
a4fac2bc3c10df23a5b29020985b91323a525bde08902f4799fca319c111ebff | Shell | 844 | 26 | #!/bin/bash
mkdir -p software
wget -O software/configureHomer.pl http://homer.ucsd.edu/homer/configureHomer.pl
cd software
chmod +x configureHomer.pl
perl software/configureHomer.pl -install
# Download genome
wget -O genomes/Mmul_10.fa.gz http://ftp.ensembl.org/pub/release-103/fasta/macaca_mulatta/dna/Macaca_mula... |
c7fcef4739e15145feaa56f519bd35a2dc13497ce52d26c3f29ba1aac3f45e00 | Shell | 845 | 19 | #!/bin/sh
## This script download the dataset and pre-trained network,
## and generates style transferred images.
# Download the dataset. The downloaded dataset is stored in ./datasets/${DATASET_NAME}
DATASET_NAME='ae_photos'
bash ./datasets/download_dataset.sh $DATASET_NAME
# Download the pre-trained model. The dow... |
ae0b3c7826f206059756ef90ca66989265827da975e46f732a84d6c220be2cc7 | Shell | 847 | 38 | #!/bin/bash
#$ -cwd -V
#$ -N dd_dapg
#$ -l mem_free=5G,h_vmem=5G,h_fsize=50G
#$ -e ./logs/dapg_$TASK_ID.out
#$ -o ./logs/dapg_$TASK_ID.out
#$ -t 1-23573:1
#$ -tc 50
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: $... |
3082dceab13e22e0f3795f8514efcc69039d5ea90a704007a1fb8e4d1a6d2c04 | Shell | 850 | 38 | #!/bin/bash
#$ -cwd -V
#$ -N run_dapg
#$ -l mem_free=5G,h_vmem=5G,h_fsize=50G
#$ -e ./logs/dapg_$TASK_ID.out
#$ -o ./logs/dapg_$TASK_ID.out
#$ -t 1-25869:1
#$ -tc 50
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: ... |
13cb057bf4a181c021696cca04f2cb793147c02365bd53516307ab36a64be0f9 | Shell | 851 | 38 | #!/bin/bash
#$ -cwd -V
#$ -N hh_dapg
#$ -l mem_free=5G,h_vmem=5G,h_fsize=50G
#$ -e ./logs/dapg_$TASK_ID.out
#$ -o ./logs/dapg_$TASK_ID.out
#$ -t 1-22307:1
#$ -tc 50
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: $... |
f5dd76aa2c84f65bad568af2e7e141466720b07071566f638654ddba003c3b74 | Shell | 853 | 29 | module load bio/PLINK2
#PLINK/1.9b_6.15-x86_64
module load bio/METAL
for i in `seq 2 34`; do
cp $SCRATCH/biobank/genetics/EURnonWB/WB/sbatch_gwas.sh $SCRATCH/biobank/genetics/EURnonWB/WB/sbatch_gwas${i}.sh
sed -i -e "s/X_SUBJECT_X/${i}/g" $SCRATCH/biobank/genetics/EURnonWB/WB/sbatch_gwas${i}.sh
done
for i in `seq 2... |
a47d65887c9230b31f9f653ab0055239c16d2c65d7df5e5825d58177f68d364c | Shell | 857 | 41 | #!/bin/bash
#BSUB -P "asap"
#BSUB -J "mainseries[1-33]"
#BSUB -n 1
#BSUB -R rusage[mem=8]
#BSUB -R span[hosts=1]
#BSUB -q gpuqueue
#BSUB -sp 12 # low priority. default is 12, max is 25
#BSUB -gpu num=1:j_exclusive=yes:mode=shared
#BSUB -W 24:00
#BSUB -o out_%J_%I.stdout
#BSUB -eo out_%J_%I.stderr
#BSUB -L /bin/bash
so... |
701b077116a08f5680e8084cc91be85965975e5a42eaa1f4dd8f40cfce414630 | Shell | 859 | 33 | #!/bin/bash
# Usage
# from base dir run: bash bash/wes/01_run_sarek_wes.sh
ref_dir=igenomes_ref
NXF_SINGULARITY_CACHEDIR=sarek-2.7.2/singularity-images/
export NXF_SINGULARITY_CACHEDIR=$NXF_SINGULARITY_CACHEDIR
export NXF_OPTS='-Xms1g -Xmx10g'
target_bed=metadata/Ensembl_e99_Twist_RefSeq_exome_targets_hg38.pad150.m... |
1bf4535aa7fe8ce77d7ada83912898c60724fa44129c49677cf94141ea9505ec | Shell | 866 | 27 | #!bash
# -*- coding: utf-8 -*-
set -e
echo "Set up conda package manager"
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh --quiet
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r
conda config --set always_yes yes --set changeps1 no --se... |
caf7fe341ed1dd57292c2c3e9c06e2696eac2466f614200e38d555ca0bcf694a | Shell | 873 | 23 | #!/usr/bin/env bash
# Usage:
# $Bfolder/AddCellExpression.sh <CELL_NAME> <FEATURE_COUNTS_FILE>
[[ "$2" == "" ]] && { echo "Two arguments required: <CELL_NAME> <FEATURE_COUNTS_FILE> "; exit 0 ; }
[[ "$3" != "" ]] && { echo "Two arguments required: <CELL_NAME> <FEATURE_COUNTS_FILE> "; exit 0 ; }
SCRIPT_DIR=$( cd -- "$(... |
08f81e43ba8f6251ab37ea4f6e06345568fd7335c834e1fb124264e2a79b0dd9 | Shell | 877 | 22 | #!/usr/bin/env bash
# ensure paths are correct irrespective from where user runs the script
scriptdir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
maindir="$(dirname "$scriptdir")"
outputdir=${maindir}/derivatives/imaging_plots
# base paths
for TASK in doors socialdoors; do
for TYPE in act ppi... |
a8275b1bacc78b3b4188c3f969fd0a2ff8cea0989e167a11059b9a0c477d963b | Shell | 878 | 24 |
raw_data_file="sample_raw.csv"
channel_gains_file="sample_gain.csv"
# Read CSV file and generate the string with truncated numbers
gain_list_string=$(awk 'BEGIN{FS=","; OFS=" "; ORS="";} {for(i=1;i<=NF;i++){ printf "%.4f", $i; if(i<NF) printf " " } printf "\n" }' "$channel_gains_file")
# Add double quotes around the... |
9deb259d47b4985ecbdbd1e5610fa59162fa39a21abbadd08dbc737a40ef2b1b | Shell | 879 | 14 | #this bash config file allows you to set the paths to all the dependency file you need to run the qtl colocalizer pipeline
#this config file is required for running the colocalizer pipeline on multiple loci at a time
#path to the directory where the ColocQuiaL code is saved locally
colocquial_dir="/project/voight_GWAS... |
fa04ae555cdb75b39705c7cf0f7483efae8949ce5a85e6d87d3b5e752fbd43ae | Shell | 884 | 31 | # Used to run mark duplicates section of rrbs pipeline pipeline
# Replace values with those for your own machine
LOCAL_PIPELINE_DIR=/Users/akre96/Documents/github/rrbs_bismark/Mark_Duplicates
CROMWELL_PATH=/Users/akre96/cromwell/cromwell-36.jar
cd $LOCAL_PIPELINE_DIR
if [[ $# -eq 0 ]] ; then
echo 'ERROR: REQUIRE... |
20aa27a1cd61cb642273b84a2d637c78357eb0cae71fe881f879b84b087c25b8 | Shell | 887 | 28 | #!/bin/bash
#SBATCH -p gpu
#SBATCH --mem=32g
#SBATCH --gres=gpu:rtx2080:1
#SBATCH -c 2
#SBATCH --output=example_1.out
# source activate mlfold
folder_with_pdbs="/data/rsg/chemistry/jyim/projects/protein_diffusion/samples/sweep_0/24D_10M_2022Y_16h_25m_04s/rsv_site_5_16-34/sample_4/mpnn/"
output_dir='/data/rsg/chemist... |
9e866cbba6dfaa91bc38f5639cb771f0646b99f4b8efea7120a745f7d7475785 | Shell | 891 | 17 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=batch # Partition (queue) name
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --mem=120gb # Job memory request
#SBATCH --time=150:00:00 # Time limit hrs:min:sec
#SBATCH --o... |
d002c09654a1caeb9ad4b64dcf471924c0196904c1e0f4b5ef10ffba5fa46d68 | Shell | 891 | 16 | ## Download GO ontology
wget http://current.geneontology.org/ontology/go-basic.obo -P data/GO_terms/
wget https://raw.githubusercontent.com/sgrote/OboToTerm/master/obo_to_term_tables.py -P data/GO_terms/
wget https://raw.githubusercontent.com/sgrote/OboToTerm/master/obo_to_term_functions.py -P data/GO_terms/
... |
c60ac7c4ab847939253727372d2bac3ae5a6d6baab20c02a37553211c97013b6 | Shell | 892 | 9 | #!/bin/bash
# This example is a copy of design_partialdiffusion_withseq.sh
# The only difference is we specify multiple disjoint sequences to hold constant.
# In this case, we provide the residues of the beginning and end of the peptide, instead of the whole sequence.
# We can provide comma-separated ranges to specify ... |
41b9030c3e5d7601154e0a9cc1433883367dac4a69805651a2ea3a223660bf44 | Shell | 896 | 17 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=batch # Partition (queue) name
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --mem=120gb # Job memory request
#SBATCH --time=150:00:00 # Time limit hrs:min:sec
#SBATCH --o... |
57b3fc985594a4148839f94e3ba704a978df9db26990e4cc8da7dfb0a223f024 | Shell | 896 | 19 | #!/bin/bash
# Global variables
SWISSPROT_HITS=04_blast_results/analyzed_genes.hits
ANNOTATION_FOLDER=05_annotations
FISHER_FOLDER=06_fisher_tests
# Get info from uniprot for each hit in parallel
echo "Prepare uniprot sequences for retrieval"
cat $SWISSPROT_HITS | while read i; do feature=$(echo $i | cut -d " " -f 1) ... |
5700a3a597ac83d7e87d6f1fa841a90f1adfabc763abf38e7f513db666d93e9d | Shell | 898 | 17 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=batch # Partition (queue) name
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --mem=120gb # Job memory request
#SBATCH --time=150:00:00 # Time limit hrs:min:sec
#SBATCH --o... |
e10877039f8a5bf22d5059be6e9e0674da06fa0e30d22e0f03787c9f02905e72 | Shell | 898 | 61 | #!/bin/bash
#Copyright 2023. TU Graz. Institute of Biomedical Imaging.
#Author: Moritz Blumenthal
set -eu
SCRIPT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )
usage="Usage: $0 <drawing.svg> <output> <images>"
WIDTH=""
while getopts "hw:" opt; do
case $opt in
w)
WIDTH=" --export-width=$OP... |
c6ad5aec04d2d2c1d7946551e407a0051cb2fbe45b5cb2b8b4113edbb8746c95 | Shell | 899 | 17 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=batch # Partition (queue) name
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --mem=120gb # Job memory request
#SBATCH --time=150:00:00 # Time limit hrs:min:sec
#SBATCH --o... |
0eec686ef963fd80294fb9108357d2ce3071c474871b2707fd659e1ef9230eb9 | Shell | 904 | 42 | #!/bin/bash
#$ -cwd
#$ -R y
#$ -l mem_free=18.0G,h_vmem=20G,h_fsize=50G
#$ -N 'subset_genotypes'
#$ -o ./summary.out
#$ -e ./summary.out
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: ${HOSTNAME}"
## List current... |
a80ee4fa4f2efe4efa7b5f8d8fc6818a822ce6e7787fac8e35084bde213b7a97 | Shell | 907 | 46 | #!/bin/bash
start_time=$(date +%s) # start timer
# CP CLF
cd cp_classification
./run_IT_tests.sh
if [ $? -ne 0 ]; then
echo "Error: IT Test for CP Classification server failed."
exit 1
fi
# CP CLF for TCP model
./run_IT_tests_TCP.sh
if [ $? -ne 0 ]; then
echo "Error: IT Test for - TCP - CP Classification... |
e21138a3b3171fe8f68d484466d09b1edc314b0681bbeff5751b3cde5bce0cf1 | Shell | 907 | 18 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=gpu_p # Partition (queue) name
#SBATCH --gres=gpu:P100:1 # Requests one GPU device
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --cpus-per-task=4 # Number of CPU cores per task... |
f549427af070167c5f49bffb5551eacae3a6910c5360802cf58bfb46cee765ad | Shell | 909 | 18 | #! /bin/bash
#SBATCH --job-name=img_seg_am # Job name
#SBATCH --partition=gpu_p # Partition (queue) name
#SBATCH --gres=gpu:P100:1 # Requests one GPU device
#SBATCH --ntasks=1 # Run on a single CPU
#SBATCH --cpus-per-task=4 # Number of CPU cores per task... |
483b64d2d3157f76fa198c2d269bc6658731bfbcc76ed38ce0b04afeed124fe5 | Shell | 911 | 31 | # Used to run collect qc metrics section of rrbs pipeline pipeline
# Replace values with those for your own machine
LOCAL_PIPELINE_DIR=/Users/akre96/Documents/github/rrbs_bismark/Collect_QC_Metrics
CROMWELL_PATH=/Users/akre96/cromwell/cromwell-36.jar
cd $LOCAL_PIPELINE_DIR
if [[ $# -eq 0 ]] ; then
echo 'ERROR: R... |
2aa6deaf0d2c9388bae798be21c9a85b72c525b0125842b02df94b5eff0f6b49 | Shell | 916 | 20 | #!/bin/bash
#$ -cwd
#$ -o elzar-logs
#$ -e elzar-logs
#$ -j y
#$ -N biohsic
#$ -l m_mem_free=36G
#$ -pe threads 8
#$ -l gpu=1
# Array job. Each job will run a different parameter set.
#$ -t 1-1:1
#$ -tc 10
echo "Running job $JOB_ID, task $SGE_TASK_ID"
echo | which python
echo "Python script: $@"
# python process-check... |
4f0847b563b4b375f6e4dcd6e4eb5b10218a3167a4c660702d3f7613d5c5f9df | Shell | 916 | 37 | #!/bin/bash
set -x
# Activate conda environment
if [ -z ${OAR_JOB_ID+x} ]
then
export PATH="/scratch/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/gvignoud'
elif [ -z ${SLURM_JOB_ID+x} ]
then
export PATH="/home/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/rioc/gvignoud'
fi
source activate py37
... |
57845ecd7120a06f69ad57d8436b3f79be3974276abbd0db6d1dbb4f752da3a3 | Shell | 916 | 25 | #!/bin/bash
# Usage:
# from base dir run: bash bash/wes/02_singularity_norm_and_annot_sarek-2.7.2_tn.sh sarek_run/results/ <n_cores> > sarek_run/vep_hgvs_tn.log 2>&1
# This script expects the nf-core/sarek release 2.7.2 singularity images.
# You can download the sarek-2.7.2 workflow including the image with the downl... |
2fc5238a2f8ce8220b3376e9d98de004b3a3b011596494eccd887158bf9cea4a | Shell | 918 | 37 | #!/bin/bash
set -x
# Activate conda environment
if [ -z ${OAR_JOB_ID+x} ]
then
export PATH="/scratch/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/gvignoud'
elif [ -z ${SLURM_JOB_ID+x} ]
then
export PATH="/home/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/rioc/gvignoud'
fi
source activate py37
... |
cd85ec26cf59cf2ae100fdbb135c34b07a1f3b40f669d8da21153ea4a5f8bf2f | Shell | 918 | 31 | #!/bin/bash
set -e
module load unstable git python-dev
# This script launches a couple of STEPS4 models
# It assumes that STEPS Python module is available in PYTHONPATH
echo "*******************************************************************************"
echo "SPACK_INSTALLED_HASH (steps) : " ${SPACK_INSTALLED_HAS... |
90c2e6db5222f67237485ddf0ac19f4aff77ec3a11238f12000231eb2cdd356e | Shell | 922 | 37 | #!/bin/bash
set -x
# Activate conda environment
if [ -z ${OAR_JOB_ID+x} ]
then
export PATH="/scratch/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/gvignoud'
elif [ -z ${SLURM_JOB_ID+x} ]
then
export PATH="/home/gvignoud/miniconda3/bin:$PATH"
SCRIPT_DIR='/home/rioc/gvignoud'
fi
source activate py37
... |
757fc2814dedf71923eb9a6f29389de3f1ec10e51ff8ec17e7a6bab015f324f9 | Shell | 925 | 27 | #!/bin/bash
if [ $# -eq 0 ]
then
echo "Argument 1 should be a port number, argument 2 a string to use as password."
exit 1
fi
# parse config parameters:
source bash/parse_yaml.sh
eval $(parse_yaml config.yaml CONF_)
docker run -d --rm \
--name ${CONF_project_name}_${USER} \
-p ${1}:8787 \
-e PASSWORD... |
1bf906afc21fbeb928cb2237c0a91580d4fd15b4dd0c5b7bf59e41c1d965190f | Shell | 932 | 33 | #!/bin/bash
#Copyright 2023. TU Graz. Institute of Biomedical Imaging.
#Author: Moritz Blumenthal
./01_scripts/61_download_recodata.sh
./01_scripts/62_reco_rt_data_example1.sh
./01_scripts/63_reco_rt_data_example2.sh
./01_scripts/64_reco_t1_apex.sh
./01_scripts/65_reco_t1_mid.sh
./01_scripts/66_reco_t1_base.sh
./01_s... |
1e6574a3d3cdf1f2301fd5abfe663856503769743e06e929075270e05c33d89b | Shell | 935 | 41 | #!/usr/bin/env bash
# definitions
workDir=/vols/Scratch/alazari/comparative_scripts/mouse_MPM_results
outDir=/vols/Scratch/alazari/comparative_scripts/mouse_MPM_results/final_files
subjScan="20210517_175914_AL2_MPM_1_1@AL2
20210604_181641_AL3_AL3_MPM_3_1_3@AL3
20210609_200948_AL5_MPM_1_3@AL5
20210607_192507_AL6_MPM_4... |
3e87483527a5a063ab19604c9050b4fe462947aec597c2f9d5127a7ffe3dee97 | Shell | 938 | 15 | #### Clinical fine-tuning
MODELPATH="./runs/results_clinical"
TRAINED_MODELPATH='./models_MGM_training_resnet34_image10'
MODELNAME="model_gnn_85.pth"
mkdir $MODELPATH
cp src_clinical/clinical_finetune.py $MODELPATH
cp $0 $MODELPATH
split=defined
batch_size=64
CUDA_VISIBLE_DEVICES=$1 python src_clinical/clinical_fine... |
c5ceb4518ce85c1f98eab906aa0547906b26feb73ab7e963eaa8d1caca50056a | Shell | 938 | 28 | #!/usr/bin/env bash
# One-shot setup for a fresh checkout on macOS/Linux: creates .venv, installs
# the pinned Python dependencies, and builds the frontend (frontend/dist
# isn't in git, and both the web and desktop app serve the built output).
#
# bash setup.sh
#
# Needs Python 3.11+ (tested on 3.14) and Node.js 20+... |
e5317281e411f9f88fddb8ddccbac55349a32a0f1d3b5ce1d5044ffb021fe443 | Shell | 945 | 26 | #!/usr/bin/env bash
# Copyright (c) MONAI Consortium
# 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
# Unless required by applicable law or agr... |
c41a2a4150db09c28aeaea26b84dfc8858c54888bde0ffbe3c2c4dd2beec2692 | Shell | 946 | 31 | #!/usr/bin/bash
vcf_dir=$1
seg_bed=$2
out_dir=$3
# module load tabix/0.2.6
echo Segment VCF using genome block information BED file: $seg_bed
######### Segment VCFs
## Write results to temp directory
## You may also copy your input data files to temp directory if needed
cat $seg_bed | while read $line ; do
chr=$(... |
2aad6baefb5f1faadb22a7f15c7a6ccc84582034c29b960b5a8adcc0a62c8a08 | Shell | 956 | 34 | echo "############################# initial cleanup ############################# "
# cleanup wrapper
rm -r build
rm -r lib
echo "############################# build starspace ############################# "
# build starspace lib
cd ..
make clean
make -f makefile_py
cd -
echo "############################# build wr... |
090ef7710bedc9b8b8fde2c6ae2c20b34a3a54d18cdce546f45d8c318a85ebec | Shell | 965 | 35 | #!/bin/bash
chr=$(echo {1..20} | cut -d ' ' -f $SLURM_ARRAY_TASK_ID)
genome=genomes/Mmul_10.dna.fa
gatk --java-options '-Xmx16g' VariantFiltration \
--reference ${genome} \
--output vcf/cayo_brain_chr${chr}_filtered.vcf \
--variant vcf/cayo_brain_chr${chr}_raw.vcf \
--window 35 \
--cluster 3 \
--filter-name 'F... |
8ddde938d218d5f63c3fd825fa4da5e40d304fa53b51ced3dd2832a28b168e47 | Shell | 965 | 39 | #ieqtl
#phenotype
cd rosmap_ieqtl/pheno
awk 'NR==1;NR>1{print $0 | "LC_ALL=C sort -k1,1n -k2,2n"}' pheno.txt |bgzip > pheno.bed.gz # convert to bed
tabix -p bed pheno.bed.gz
parallel -j 11 tabix pheno.bed.gz {} -h \|bgzip \> pheno/chr{}.bed.gz ::: {1..22}
#genotype
cd rosmap_ieqtl/geno/
for i in {1..22}
do
vcftools... |
07d3725d7403aa436b663c28af59b1233480d29fe7ce7303745e6dcb0f2598f9 | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128608_S3_AD.err.txt
#SBATCH --ou... |
2ccc4def49c9551282526f228c19d4374dd826cf282b97fd078287463d853b1e | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128607_S4_AD.err.txt
#SBATCH --ou... |
90accd825167e293ba0b639115d7aac5627a48060c11df4f36c53e0702499509 | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128606_S5_AD.err.txt
#SBATCH --ou... |
a7494e299249a69f1b5014aa7e044175e3230f75c7faf890a637ec052b6ac606 | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128604_S8_AD.err.txt
#SBATCH --ou... |
ce17232cc1e9bec1b36b789b9fc4aa159ee9ead993c4a7e115c2762593bb1ebe | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128605_S6_AD.err.txt
#SBATCH --ou... |
da6aa7fabccd76097eb9a64c18300696a0f9d87f2a245e675c41233b5dee445a | Shell | 974 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128603_S9_AD.err.txt
#SBATCH --ou... |
dd6e6df2d2b572b0c4798c34fe08bee5fc681b3e0b4c6e054e135a1d1d66136f | Shell | 975 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SSAMN19128611_S7_AD.err.txt
#SBATCH --o... |
82f8c407d4cb0cb0e4b0b864559880fe7cedafcbacbe916b86757741f9919abe | Shell | 977 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128600_S12_AD.err.txt
#SBATCH --o... |
9e96ab4fc34ee12b316477670daaf953fb55e0c6a05675b28e3883b2495737e5 | Shell | 977 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128601_S11_AD.err.txt
#SBATCH --o... |
abf17408003dccb67229bccb2d3202547cc21c119436d547eeea89ab5f6167b8 | Shell | 977 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128602_S10_AD.err.txt
#SBATCH --o... |
e78656d9f05aaa5a10cb278870eaa0dc74c42e9f52c01a8a2c55772010bcffa6 | Shell | 977 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128599_S13_AD.err.txt
#SBATCH --o... |
4d559d00d13d417f98eab88c5eff9a3cb19f1f472724e2511a782fcfde33e0a6 | Shell | 978 | 26 |
echo "juliacon unrealistic simulations (sim 1) - 30 nodes total"
echo "case 1: mean = 0.8, pi = 0.1, samptaxa=8"
julia simulation/unrealistic_sim.jl -s 734 -k 8 -t 30 -j
echo "case 2: mean = 0.8, pi = 0.8, samptaxa=8"
julia simulation/unrealistic_sim.jl -s 734 -p 0.8 -k 8 -t 30 -j
echo "case 3: mean = 1.6, pi = 0.1... |
7ac3975d814cee5b9c0010cf0a8a5b52e1f842535927c1209aec7a246304432f | Shell | 978 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/a3f557dc7e12435cb0ba7a11d934176a/artifacts/final_model"
clinical_dir="/proj... |
eb3c543f3deb726f73200a6c80b9c8476ea3e8a6a772dda15e34620cace6f291 | Shell | 978 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=MFC-B1-S1-Cdx1-pAD0.err.txt
#SBATCH --o... |
058f3be932c86b6c167bb9d4164106e44f2c196b774a3a58a9176717f1fab5d6 | Shell | 979 | 30 | #!/bin/bash
# Pushes the docker image created in build.sh to GCR. Takes 1 arg:
# arg1: docker image tag to push.
# Example usage: scripts/push-image.sh v1.0.0
set -e
# cd to project root directory
cd "$(dirname "$(dirname "$0")")"
# Exit if one arg not given
if [[ $# -ne 1 ]]; then
echo "One argument required.";... |
ab79bb37ad897c2b1cc9a6568e6d326052c443e6a20612929cd25976de940509 | Shell | 980 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128609_S2_CTRL.err.txt
#SBATCH --... |
03839a39f9e2098fccd4cd590428a9664dfda3edd0b2c075943f258c365ad43a | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128596_S16_CTRL.err.txt
#SBATCH -... |
082a3333250403c0fc9ec7433baa451da1130e784a875a74ec4a6896a4eff7dc | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128595_S17_CTRL.err.txt
#SBATCH -... |
2c18dfe9a830b55809a5ca195318124036e1b2a628df2eb7eaa105c4f4fd971f | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128593_S19_CTRL.err.txt
#SBATCH -... |
3d6e3179868aac90e9667b28f483f1fb7d76698ceb0b9d43f55f3a979f154d55 | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128597_S15_CTRL.err.txt
#SBATCH -... |
63c128a4fd8c8a81a3774af461d777244546ea9ef1aeaab36ea24dbf6ee15f37 | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128598_S14_CTRL.err.txt
#SBATCH -... |
af3590763459b0b47f3546bf75986bf7eac40067edb222da3b73daecf21d29fb | Shell | 983 | 27 | #!/bin/bash
#
#SBATCH --mail-type=ALL
#SBATCH --mail-user=tsoelter@uab.edu
#SBATCH --job-name=CellRanger
#SBATCH --ntasks=4
#SBATCH --mem-per-cpu=64000
#SBATCH --nodes=1
#SABTCH --cpus-per-task=4
#SBATCH --time=2-02:00:00
#SBATCH --share
#SBATCH --partition=medium
#SBATCH --error=SAMN19128594_S18_CTRL.err.txt
#SBATCH -... |
07bdd14fe7700f4fc47663fb1084d3100c94f77d3029619a5583c4015abde508 | Shell | 986 | 20 | #!/usr/bin/env bash
# Copyright (c) MONAI Consortium
# 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
# Unless required by applicable law or agr... |
699bdf827d0cab15fed5421c7541e1e9d87f2930645c1b726bf59efdc29a5f38 | Shell | 987 | 34 | #!/bin/bash
#SBATCH -p gpu
#SBATCH --mem=32g
#SBATCH --gres=gpu:rtx2080:1
#SBATCH -c 2
#SBATCH --output=example_2.out
source activate mlfold
folder_with_pdbs="../inputs/PDB_complexes/pdbs/"
output_dir="../outputs/example_2_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$o... |
c6fefd95a5a328f58afe485415f429923c3abcec57dbe629c15437d809e172d5 | Shell | 992 | 40 | #!/bin/bash
NAME_PROJECT=$1
if [ $HOSTNAME == 'cleps' ]
then
SCRIPT_DIR='/scratch/gvignoud'
DIRECTORY_PROJECT=/scratch/gvignoud/pattern
elif [ $HOSTNAME == 'rioc' ]
then
SCRIPT_DIR='/home/rioc/gvignoud'
DIRECTORY_PROJECT=/scratch/gvignoud/pattern
elif [[ $HOSTNAME =~ ^jean-zay[0-9]$ ]]
then
SCRIPT... |
ec6c41e766bcabe962efecdc6806dd86753800b05e1b497584798ab50a5837d6 | Shell | 992 | 31 | #!/usr/bin/env bash
LATEST_TAG="latest"
GIT_COMMIT_HASH=$(git rev-parse --short HEAD)
TAG=$LATEST_TAG
REGISTRY="us-docker.pkg.dev/motrpac-portal/rnaseq"
SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)"
RNA_SEQ_DIR="$(cd "$SCRIPT_DIR" && cd ../../ && pwd)"
cd "$RNA_SEQ_DIR" || exit
t... |
633f4262a7f3a710bf83bac83f260d6bfd0d9c5ea769e59ec5589b5ffdc5f7b9 | Shell | 994 | 36 | #!/bin/bash -uxe
# Quick & dirty installer. This is not a replacement for install-CGAT-tools.sh.
#
# This script will create a cgat-devel environment in your conda
# installation. It assumes that:
#
# a. you have installed conda and activated it
# b. you have downloaded the three CGAT repositories cgat-core, cgat-apps... |
66f164fa0a50833e469464b9a10320c457813b4d58061bfc86f48fa57e7b4ccd | Shell | 998 | 32 | #!/usr/bin/env bash
# definitions
origDir=/vols/Scratch/MPM_rodent_data
workDir=/vols/Scratch/comparative_scripts/mouse_MPM_results
subjList="20210517_175914_AL2_MPM_1_1
20210604_181641_AL3_AL3_MPM_3_1_3
20210609_200948_AL5_MPM_1_3
20210607_192507_AL6_MPM_4_1_6
20210611_205425_AL7_MPM_2_1_3
20210402_231711_AL8_AL8_MP... |
002b5c5945013563d002ebe46ea0e2074b6a73038adbab8f0578531dc13dea2d | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_7"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/0eec80ccae54424cab868254b5b16863/artifacts/final_model"
clinical_dir="/proje... |
0b29da177be877f796be90f6b4a73eea6fe9ff81a8d4920a742eef1c1caf2a40 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_1"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/8a31ee99a94844bda45029a2ad8d9fd6/artifacts/final_model"
clinical_dir="/proje... |
19add3b04f84214c5a256c96e1c42d95cad1d7e39ee1e6081018a71188839332 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_4"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/cb7a0db6883141d7b8c35bc8e9f7ecfd/artifacts/final_model"
clinical_dir="/pr... |
265f3bc27567564f1a22ba39cd8c6a73eb92bcb48b43f560ed4927bc04ef9b87 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_0"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/21345d0794cc4f96a0bae2f9b9863772/artifacts/final_model"
clinical_dir="/proje... |
3d269c3f3486fa9a38e937e95056681a31eeb2a9d1415cde708dc997a624c4dc | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_0"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/21345d0794cc4f96a0bae2f9b9863772/artifacts/final_model"
clinical_dir="/pr... |
53571114e6b4200f8f3e1329f546c0d6777457b2bf11d09f97e8854227429892 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_4"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/cb7a0db6883141d7b8c35bc8e9f7ecfd/artifacts/final_model"
clinical_dir="/proje... |
5a6a6f43b29b6d4ed59a732409768bc2f7c16123f7ecdcbd3cb7a9330e2ef151 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_3"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/35b6574d72bc4ba6ab0aeffd872375c4/artifacts/final_model"
clinical_dir="/pr... |
5c10ab6103ecf6cc0e51b2e95aa6767b6aa27d1b6a451c2b174cd536582ad4e2 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_2"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/2808de27b94245098bfee8b318b98289/artifacts/final_model"
clinical_dir="/proje... |
6c7e58568d0196939478bc64743f87b46848b38f253527fd716b27aa1f49f009 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_3"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/35b6574d72bc4ba6ab0aeffd872375c4/artifacts/final_model"
clinical_dir="/proje... |
ba6c8a43b8b9f8726ee2b4e7296d4f023316f4f044378e982cc3b45062766112 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_8"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/281c9ece0d8f44d6b05f4ce1836fda35/artifacts/final_model"
clinical_dir="/proje... |
c3fae751968bae83e92e2eecf1c27f7299961eb2e1a2b2d16b817ffd98119bb3 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_6"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/9235ae0b3134441db12eeab8ed71c9c3/artifacts/final_model"
clinical_dir="/proje... |
c69d6aef55c1b19171611019c59bd7f7b88fc7351670d9efd3d393943bdb6bd6 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_1"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/8a31ee99a94844bda45029a2ad8d9fd6/artifacts/final_model"
clinical_dir="/pr... |
e7aab8051af3ab455539e842470b7d1a18eacb0d0a6018015b27ff85ce78daa5 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_2"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/2808de27b94245098bfee8b318b98289/artifacts/final_model"
clinical_dir="/pr... |
f2145ec019746b7d1e049e353a58be3d21a534186791123a8edd6b9071c9efc2 | Shell | 1,000 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_BHRCS_5"
val_list="/project/data/participants_BHRCS.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/53af3fb7d6564949bb2cb8129ddadb94/artifacts/final_model"
clinical_dir="/proje... |
ac77b6a593d91c102545984f88598db03efac59f9d2909c6309bd33d7acef2dd | Shell | 1,001 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_8"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/281c9ece0d8f44d6b05f4ce1836fda35/artifacts/final_model"
clinical_dir="/pr... |
b5c652550e16c425e7a9d389824f1e54aa57e6136f0860062c0939434495fa46 | Shell | 1,001 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_6"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/9235ae0b3134441db12eeab8ed71c9c3/artifacts/final_model"
clinical_dir="/pr... |
bc86aaf79463fd73bad0504138d27701490776a72c9745e5abf64a71585f6fe8 | Shell | 1,001 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_7"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/0eec80ccae54424cab868254b5b16863/artifacts/final_model"
clinical_dir="/pr... |
d1aa9aee8e798037179f202512d93da8af694934adabe119f209142387d8f5b6 | Shell | 1,001 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABCD_5"
val_list="/project/data/test_participants_ABCD.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/53af3fb7d6564949bb2cb8129ddadb94/artifacts/final_model"
clinical_dir="/pr... |
37cf2419b686475b4ac8bae8c04c112ff2e381c11946bc2072a22272697a8a94 | Shell | 1,003 | 12 | java -Dconfig.file=google_prod_PAPI.conf -jar ../../tools/cromwell-40.jar run rrbs_pipeline.wdl -i rrbs_pipeline_inputs_GCloud.json
java -Dconfig.file=google_prod_PAPI.conf -jar ../../tools/cromwell-40.jar run rrbs_pipeline.wdl -i rrbs_pipeline_inputs_pass1A_GCloud.json
###Compute checksums
for i in `ls checksum/sinai... |
09fcef872c7f331bc70032110485f91f83604c4069dff32df9658b48dc1a9f18 | Shell | 1,004 | 40 | #!/bin/bash
NAME_PROJECT=$1
if [ $HOSTNAME == 'cleps' ]
then
SCRIPT_DIR='/scratch/gvignoud'
DIRECTORY_PROJECT=/scratch/gvignoud/results_DMS
elif [ $HOSTNAME == 'rioc' ]
then
SCRIPT_DIR='/home/rioc/gvignoud'
DIRECTORY_PROJECT=/scratch/gvignoud/results_DMS
elif [[ $HOSTNAME =~ ^jean-zay[0-9]$ ]]
then
... |
11c5c48d35678adca14e7b7f34778a8ed6a01bfbf9194a04dd760b20d3a5fa5b | Shell | 1,004 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABIDE2_1"
val_list="/project/data/participants_ABIDE2.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/8a31ee99a94844bda45029a2ad8d9fd6/artifacts/final_model"
clinical_dir="/pro... |
29c7cc6474e258614d73dc3030a21adf0ea028e8967beb54a6d34a1f7951cff3 | Shell | 1,004 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABIDE2_7"
val_list="/project/data/participants_ABIDE2.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/0eec80ccae54424cab868254b5b16863/artifacts/final_model"
clinical_dir="/pro... |
9708a18e786cf41c238ec97fcda55d47c66684b0a7fc1719faba443cd5a95519 | Shell | 1,004 | 30 | #!/bin/bash
seed=2
run_dir="get_likelihood_ABIDE2_8"
val_list="/project/data/participants_ABIDE2.tsv"
latent_resolution="low"
vqvae_uri="/project/mlruns/0/f89cb9ee1a824324b04df9f4b7a8e430/artifacts/final_model"
transformer_uri="/project/mlruns/0/281c9ece0d8f44d6b05f4ce1836fda35/artifacts/final_model"
clinical_dir="/pro... |
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