sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a4c01526b0918482185c75595fcd972ac57174b76ab48da7e15bfba4312e3562 | Shell | 663 | 13 | # generate data for fixed operator, used for pretraining classic operator learning
gpu=0
dir=data0604_weno_cubic_fix_0.2_0.2_0.2
traineqns=100
trainnum=100
testeqns=10 # only for visualization during training
testnum=100
CUDA_VISIBLE_DEVICES=$gpu python3 datagen_weno.py --eqn_types weno_cubic --dir $dir --name test -... |
88966eef6edcedfd0ba59829ce29537b6f800d4611497eb63155c330d3af4c44 | Shell | 664 | 23 | #!/bin/sh
# PREFIX=../3d_common
PREFIX=`Rscript -e "cat(MixfMRI:::get.workflow())"`/3d_common
NP=4
Rscript ${PREFIX}/01-plot_pv.r
mpiexec -np ${NP} \
Rscript ${PREFIX}/02-spmd_fclust.r > ./output/log.out
Rscript ${PREFIX}/04-get_ic_lrt.r > ./summary.txt
### new
# mpiexec -np ${NP} \
# Rscript ${PREFI... |
d79def61a95d11a966a79f8fe9acb755a66272105a579a074ec36ba0af2da615 | Shell | 664 | 51 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
SERVER_NUM=$3
GPU_NUM_PER_SERVER=$4
MODEL=$5
DISTRIBUTION=$6
EPOCH=$7
BATCH_SIZE=$8
LR=${9}
DATASET=${10}
DATA_DIR=${11}
CLIENT_OPTIMIZER=${12}
CI=${13}
GPU=${14}
echo $BATCH_SIZE
echo $LR
python ./main.py \
--gpu_server_num $SERVER_NUM \
--gpu_num_per_server $GPU... |
f8e385196e519b09815bbdd3725f3a3a0934fa1de434848b8ade62c4d3feb7eb | Shell | 664 | 23 | #!/bin/sh
# PREFIX=../2d_common
PREFIX=`Rscript -e "cat(MixfMRI:::get.workflow())"`/2d_common
NP=4
Rscript ${PREFIX}/01-plot_pv.r
mpiexec -np ${NP} \
Rscript ${PREFIX}/02-spmd_fclust.r > ./output/log.out
Rscript ${PREFIX}/04-get_ic_lrt.r > ./summary.txt
### new
# mpiexec -np ${NP} \
# Rscript ${PREFI... |
05f00dd3a07d1f9735e537986984d5ef45718cb460de7357eaa80736137328ca | Shell | 665 | 24 | #!/bin/sh
# PREFIX=../3d_common
PREFIX=`Rscript -e "cat(MixfMRI:::get.workflow())"`/3d_common
NP=4
Rscript ${PREFIX}/01-plot_pv.r
mpiexec -np ${NP} \
Rscript ${PREFIX}/02-spmd_fclust.r > ./output/log.out
Rscript ${PREFIX}/04-get_ic_lrt.r > ./summary.txt
### new
# mpiexec -np ${NP} \
# Rscript ${PREF... |
f9c0256bafe15611dea845f721dccfe33fb66f8c943eaff3e4f0c04bccd5bbf4 | Shell | 670 | 23 | #!/bin/sh
# PREFIX=../2d_common
PREFIX=`Rscript -e "cat(MixfMRI:::get.workflow())"`/2d_common
NP=4
# Rscript ${PREFIX}/01-plot_pv.r
mpiexec -np ${NP} \
Rscript ${PREFIX}/02-spmd_fclust.r > ./output/log.out
# Rscript ${PREFIX}/04-get_ic_lrt.r > ./summary.txt
### new
# mpiexec -np ${NP} \
# Rscript ${P... |
30cf1470ebfb090b63dbed6544c33a37b5e68ab95f25f04b390bd5d4da561956 | Shell | 676 | 29 | #!/bin/bash
## convert bam to bigwig
## activate the `master` conda environment before running this script
# conda activate master
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $CONDA_PREFIX"
echo "Starting at $(date)"
echo "samtool... |
d672d509369c081895530f13619d066c991399335e373d7687617067b6d96623 | Shell | 677 | 19 | echo "Started worker"
jobIDX=$1
fileIdx=$((${jobIDX} % 2000))
lineIdx=$((${jobIDX} / 2000 + 1))
smiles="$(sed "${lineIdx}q;d" smiles/sorted_smiles_${fileIdx}.csv)"
echo "bash setup: ${jobIDX}, ${fileIdx}, ${lineIdx}, ${smiles}"
cd ${TMPDIR}
mkdir scratch
export PSI_SCRATCH=${TMPDIR}/scratch
#mkdir sdfs
mkdir outs
#cp ... |
e6a75ac228baf2ef2c491c310e3a67d31afc33bb5ef4e056f8924683870fdf24 | Shell | 690 | 21 | #!/bin/bash
# This script runs analysis on the trained DFTBML model. Only execute this on a cluster
# with adequate memory AFTER the train_step.sh script
# Copy this script into the DFTBML directory (one level above 20000_cc_reproduction)
# before running.
# Activate the virtual environment created during setup
con... |
d4237f4925ec220bc884483da228ed5f1618f49912de066e7f1b090793b5d4f4 | Shell | 699 | 20 | #!/bin/bash -Eeu
# Copyright [1999-2017] EMBL-European Bioinformatics Institute
#
# 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 r... |
3ca6ff2dc9e98cf2314b75fb08339007c5d351580ff99e3fdec4229347f9e1d0 | Shell | 705 | 21 | #!/bin/bash
# This script runs analysis on the trained DFTBML model. Only execute this on a cluster
# with adequate memory AFTER the train_step.sh script
# Copy this script into the DFTBML directory (one level above 20000_cc_reproduction)
# before running.
# Activate the virtual environment created during setup
con... |
fd0e1189117cf6e32f521888a450273f8a5db3f025e6ed7ad8589ed0c65149a8 | Shell | 710 | 14 | stamp='20231209-222440'
analysis_dir='/home/shared/icon/analysis/icon_weno_'$stamp'_light'
restore_dir='/home/shared/icon/save/user/ckpts/icon_weno/'$stamp
test_data_dirs='/home/shared/icon/data/data0904_weno_cubic_test_light' # use the light version for quick analysis
CUDA_VISIBLE_DEVICES=0 python3 analysis.py --mod... |
0f872069cb78f4bb46b2b14cbb8792be49a4c3747a5446ae2f638a0a7797c961 | Shell | 711 | 29 | #!/bin/ksh
#
# Linear Sync Toolkit (linsync)
# Copyright (C) 2023 Joseph T. Lizier
# Distributed under GNU General Public License v3
# Originally distributed as Simple cluster jobs framework under GPL by J.T. Lizier
#
# -----------------------------------------------------------------
#
# delete all of our submitted/ru... |
eff9925358a29c9f1b47b5627d5e41cd7633d8b44bfd5901b580c4a5be4230a3 | Shell | 726 | 34 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
SERVER_NUM=$3
GPU_NUM_PER_SERVER=$4
MODEL=$5
ROUND=$6
EPOCH=$7
BATCH_SIZE=$8
LR=${9}
DATASET=${10}
DATA_DIR=${11}
SERVER_OPTIMIZER=${12}
CI=${13}
PROCESS_NUM=`expr $WORKER_NUM + 1`
echo process num : $PROCESS_NUM
hostname > mpi_host_file
mpirun -np $PROCESS_NUM -hostfi... |
de7f62e12d98697b2e89ca68f465030228b4eeaf53751a47ef9a2c4f4f46b139 | Shell | 730 | 32 | #!/usr/bin/env sh
zypper install \
cmake \
ninja \
gcc14 \
gcc14-c++ \
fontconfig-devel \
freetype2-devel \
libcurl-devel \
dbus-1-devel \
file-devel \
Mesa-libGL-devel \
wayland-devel \
libxkbcommo... |
3f806eec8c6919c254ef873d26812bc12b5686ef5e467240e3a93bc300d3507d | Shell | 742 | 22 | #!/bin/bash
## split cell meta data by sample
## activate the `master` conda environment before running this script
# conda activate master
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $CONDA_PREFIX"
echo "Starting at $(date)"
awk... |
c12c0d5d0b9bb9718e8928b21cd75560a78609429676a9e0525dc9dbe17445a0 | Shell | 743 | 35 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
MODEL=$3
DISTRIBUTION=$4
ROUND=$5
EPOCH=$6
BATCH_SIZE=$7
LR=$8
DATASET=$9
DATA_DIR=${10}
CLIENT_OPTIMIZER=${11}
CI=${12}
PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM
hostname > mpi_host_file
mpirun -np $PROCESS_NUM -hostfile ./mpi_host_file python3 ./main_feda... |
2b5c88b621bee364d58dd3514a56340288d3d8450368c384ee0955a3c220ed97 | Shell | 745 | 30 | #!/usr/bin/env sh
dnf install -y \
cmake \
dbus-devel \
file-devel \
fontconfig-devel \
freetype-devel \
libcurl-devel \
gcc-c++ \
git \
mesa-libGL-devel \
wayland-devel \
libxkbcommon-... |
bb9321ab65b4d1fe103a12a70749d3993d3bd396623dab6db1f7c79556ca0767 | Shell | 745 | 41 | #!/bin/bash
# run_histo.sh
# (c) 2012 - Ryan M. Layer
# Hall Laboratory
# Quinlan Laboratory
# Department of Computer Science
# Department of Biochemistry and Molecular Genetics
# Department of Public Health Sciences and Center for Public Health Genomics,
# University of Virginia
# rl6sf@virginia.edu
#
# Lic... |
d164218b90f3e871635105f7c5aa3b9fc1f0c35ee5419ba62755058d3c4b676e | Shell | 747 | 15 | #!/bin/bash
# Hyperparameter evolution commands (avoids CUDA memory leakage issues)
# Replaces train.py python generations 'for' loop with a bash 'for' loop
# Start on 4-GPU machine
#for i in 0 1 2 3; do
# t=ultralytics/yolov5:evolve && sudo docker pull $t && sudo docker run -d --ipc=host --gpus all -v "$(pwd)"/VOC:/... |
cdb5fead577b582a1e410d809eb1fb5ba3dcae1e290c7e483cfe9492deb16280 | Shell | 756 | 20 | #!/bin/bash
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $CONDA_PREFIX"
echo "Starting at $(date)"
for i in diffBind_H3K27ac_ALS_vs_Contro*Term_res*.txt
d... |
a41c6d7aed033c77dac6d5096ede4068442ca7203a86b93f4a6ac43704fc669f | Shell | 759 | 32 | #!/bin/bash
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
download_dir=$1
# check arguments
if [ $# != 1 ]; then
echo "Usage: $0 <download_dir>"
exit 1
fi
set -euo pipefail
# download dataset
cwd=$(pwd)
if [ ! -e "${download_dir}/CSMSC" ]; then
mkdir -p "${downloa... |
f19a3d910f067faa69e30770344431206695fcd89617a2f7714dcf26f3cb579e | Shell | 783 | 52 | #!/usr/bin/env bash
GPU=$1
DATASET=$2
# homo; hetero
DISTRIBUTION=$3
ROUND=$4
EPOCH_CLIENT=$5
EPOCH_SERVER=$6
OPTM=$7
LR=$8
TRAIN_OR_NOT=$9
DISTILL_ON_SERVER=$10
CLIENT_MODEL=$11
NAME=$12
DATA_DIR=$13
BATCH_SIZE=$14
hostname > mpi_host_file
mpirun -np 9 -hostfile ./mpi_host_file python3 ./main_fedgkt.py... |
2785d2331956bbb03d64131234d099535ddad05e882c2c7c441d37fd721c348e | Shell | 786 | 33 | #!/bin/bash
# Check if the directory is provided
if [ -z "$1" ]; then
echo "Usage: $0 <directory>"
exit 1
fi
# Get the directory path
DIRECTORY=$1
# Check if the provided path is a directory
if [ ! -d "$DIRECTORY" ]; then
echo "Error: $DIRECTORY is not a directory."
exit 1
fi
# Loop through all .xz.part fil... |
8592834a7c762cff4179e61fbdc743a7240af96020cfd0058602b42632662faf | Shell | 812 | 14 | rm -rf build
mkdir build
cd build
# module purge
module load /path/to/your/nvhpc/modulefile/modulefiles
# for older versions of pgi, load the moduleflie as following
## module purge
# module load /path/to/your/pgi/modulefiles/pgi/19.10
# module load /path/to/your/pgi/modulefiles/openmpi/3.1.3/2019
export CC=mpicc
expor... |
2a649b567db02c11aba44e93ad935a36c549adc0a650c6ea2a0de83065289bb4 | Shell | 814 | 9 | #MAPS SUPER-ENHANCERS USING REGIONS DEFINED BY HG18_MM1S_MED1.gff AND RANKING BY MED1 ChIP-SEQ SIGNAL FROM MM1S_MED1.hg18.bwt.sorted.bam CHIP-SEQ SIGNAL WITH BACKGROUND CORRECTION FROM MM1S_WCE.hg18.bwt.sorted.bam
export PATH=$PATH:$HOME/github/rose/
python2 $(which ROSE_main.py) -g HG18 -i ./data/HG18_MM1S_MED1.gff -... |
67cf4228751d4c6033bc95607ed903ecb4ec2a4b2b025f168edc1e59b9bea18c | Shell | 823 | 35 | #!/bin/bash
# Copyright 2022 Jiatong Shi
# MIT License (https://opensource.org/licenses/MIT)
# shellcheck disable=SC1091
. ./path.sh || exit 1;
fs=22050
# shellcheck disable=SC1091
. utils/parse_options.sh || exit 1;
db_root=$1
data_dir=$2
# check arguments
if [ $# != 2 ]; then
echo "Usage: $0 [Options] <db_... |
e5f74e4a2a98ce9504ad79cfb98a9634f6efc02025712a71f6ab6c674397e2c9 | Shell | 843 | 15 | #!/bin/bash
CWD="$(pwd)"
FIGURE_MODELS_SAVE_DIR=$CWD/exsclaim/figures/checkpoints/
mkdir $FIGURE_MODELS_SAVE_DIR
# classifier_model.pt
gdown https://docs.google.com/uc\?export\=download\&id\=16BxXXGJyHfMtzhDEufIwMkcDoBftZosh -O $FIGURE_MODELS_SAVE_DIR
# object_detection_model.pt
gdown https://docs.google.com/uc\?expor... |
008065255ec0235434bd57719611379aba41e92b1102f21928af99ade1734571 | Shell | 857 | 30 | #!/bin/sh
# PREFIX=../2d_common
PREFIX=`Rscript -e "cat(MixfMRI:::get.workflow())"`/2d_common
NP=8
# Rscript ${PREFIX}/01-plot_pv.r
# mpiexec -np ${NP} \
# Rscript ${PREFIX}/02-spmd_fclust.r > ./output/log.out
Rscript ${PREFIX}/04-get_ic_lrt.r > ./summary.txt
### new
# mpiexec -np ${NP} \
# Rscript $... |
8a42c8ad4e639e401f215a6353b099be983b20b8cc5f34c8a64b78a7b2a1a4ea | Shell | 862 | 42 | #!/usr/bin/env bash
PROCESS_NUM=$1
CFG_FILE=$2
#CLIENT_NUM=$1
#WORKER_NUM=$2
#MODEL=$3
#DISTRIBUTION=$4
#ROUND=$5
#EPOCH=$6
#BATCH_SIZE=$7
#CLIENT_OPTIMIZER=$8
#LR=$9
#DATASET=${10}
#DATA_DIR=${11}
#MAP_KEY=${12}
#CHECKNAME=${13}
#echo $MODEL
#echo $DATASET
#PROCESS_NUM=`expr $WORKER_NUM + 1`
#echo $PROCESS_NUM
hos... |
fe3b629ee6b1fab3e16624be50bfc5cf6fcd3bfe60b82adac753c1e4b357badb | Shell | 871 | 41 | #!/bin/bash
set -e
if [ ! `command -v samtools` ]; then
echo "samtools not in path"
exit
fi
if [ -z $5 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <pe bam>"
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
PE_BAM=$5
Z=4
WEIGHT=4
OUT_DIR=`dirname $PE_BAM`
OUT_FILE=`basename $PE... |
c1ff6bb357c5e92c33af58cf44a92b71e6ff5729a99cdb3ae187476fd2debc1f | Shell | 874 | 38 | ##!/bin/bash
if [ -z $6 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <pe bam> <sr bam> "
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
PE_BAM=$5
SR_BAM=$6
Z=4
WEIGHT=4
OUT_FILE=`basename $PE_BAM .bam`
MEAN_STDEV=`samtools view $PE_BAM \
| ../../scripts/pairend_distro.py \
-r $RE... |
be97476782083865981956cce0d90585cfdebc201c4d64ff2e65233059a1d7ce | Shell | 876 | 27 | #!/bin/bash
## combine files run in the two different lanes for each biological sample
## activate the `master` conda environment before running this script
# conda activate master
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $COND... |
b7b8e098e078409d13fa13b2c4f7644bfb202515596da2e22899d8df7368fb91 | Shell | 881 | 26 | #!/bin/bash
## get the TSS from a gene annotation file in BED format and then get flanking regions as the promoter regions
## activate the `master` conda environment before running this script
# conda activate master
## logging which server and which conda environment this scirpt is running in
echo "Running this on ... |
eea5793e9535266fb17e0a829f516b5cbeb3be2705f79bac6c6a154789aa1c16 | Shell | 882 | 45 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
SERVER_NUM=$3
GPU_NUM_PER_SERVER=$4
MODEL=$5
DISTRIBUTION=$6
ROUND=$7
EPOCH=$8
BATCH_SIZE=$9
LR=${10}
DATASET=${11}
DATA_DIR=${12}
CLIENT_OPTIMIZER=${13}
CI=${14}
GPU_UTIL_FILE=${15}
MPI_HOST_FILE=${16}
PYTHON=${17}
echo $GPU_UTIL_FILE
PROCESS_NUM=`expr $WORKER_NUM +... |
fa63d9334e927137180fbcd79a85c9d0cf89aaa2b07c9b78b4ad4b03d78b0a5c | Shell | 887 | 15 | #!/bin/bash
# wget bam files (4Gb!)
wget https://www.dropbox.com/s/pni1zq5y6cr4tx5/example_geuvadis.tar.gz?dl=0 -O example_geuvadis.tar.gz
tar -xvf example_geuvadis.tar.gz
# download gencode annotation
wget https://web.stanford.edu/~dak33/leafcutter/gencode.v19.annotation.gtf.gz
## Differential splicing analysis usi... |
bb67c21d5fad29c38ad8a5d1e26ae34949ca48162e1d47c3285e069613bdffbb | Shell | 891 | 46 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
SERVER_NUM=$3
GPU_NUM_PER_SERVER=$4
MODEL=$5
DISTRIBUTION=$6
ROUND=$7
EPOCH=$8
BATCH_SIZE=$9
LR=${10}
DATASET=${11}
DATA_DIR=${12}
CLIENT_OPTIMIZER=${13}
CI=${14}
PYTHON=${15}
GPU=${16}
GPU_UTIL=${17}
NPROC_PER_NODE=${18}
echo $BATCH_SIZE
echo $LR
$PYTHON -m torch.d... |
6945cc3a902fbc78bb3d7734904402ba9ed62c9d8a4c440ce2a36d2668ad4015 | Shell | 900 | 29 | #!/bin/bash
# This script runs through all the DFTBML model training.
# Only execute this on a cluster with adequate memory AFTER
# the precompute_step.sh script
# Copy this script into the DFTBML directory (one level above 20000_cc_reproduction)
# before running.
# Activate the virtual environment created during ... |
db90a1bd0b7f3f5bd85843d5124037166763a3fe38b54cbaac71d6b083a5e989 | Shell | 904 | 44 | #!/usr/bin/env bash
CLIENT_NUM=$1
WORKER_NUM=$2
SERVER_NUM=$3
GPU_NUM_PER_SERVER=$4
MODEL=$5
DISTRIBUTION=$6
ROUND=$7
EPOCH=$8
BATCH_SIZE=$9
LR=$10
DATASET=$11
DATA_DIR=$12
DEFENSE_TYPE=$11
NORM_BOUND=$12
STDDEV=$13
POISON_TYPE=$14
ATTACK_FREQ=$15
PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM
hostname > mpi_... |
83d20aee543aed7367ff4dfc9eba72d86ef056745118a27177d08e1589411466 | Shell | 905 | 20 | # This sh file focuses on Problem #14 the inverse nonlinear reaction-diffusion PDE problem
# It will restore the trained model from restore_dir, run analysis on the test set of Problem #14, and store results in the analysis directory
stamp='20231005-094726'
analysis_dir='/home/shared/icon/analysis/icon_lm_learn_'$sta... |
bb0ac8b5c6059d195cfa034dbef25365e7a082b38d987c28ea658aefa0673ef3 | Shell | 909 | 29 | #!/bin/bash
# This script runs through all the DFTBML model training.
# Only execute this on a cluster with adequate memory AFTER
# the precompute_step.sh script
# Copy this script into the DFTBML directory (one level above 20000_cc_reproduction)
# before running.
# Activate the virtual environment created during ... |
e0af72829fb83c81cde20f77396210972d49db115d29e52304d02f6dd34f7831 | Shell | 911 | 45 | #!/usr/bin/env bash
PROCESS_NUM=$1
CFG_FILE=$2
#CLIENT_NUM=$1
#WORKER_NUM=$2
#MODEL=$3
#DISTRIBUTION=$4
#EPOCH=$5
#BATCH_SIZE=$6
#CLIENT_OPTIMIZER=$7
#LR=$8
#SAMPLE=$9
#DATASET=${10}
#DATA_DIR=${11}
#MAP_KEY=${12}
#NETG=${13}
#echo $MODEL
#echo $DATASET
#PROCESS_NUM=`expr $WORKER_NUM + 1`
#echo $PROCESS_NUM
hostnam... |
bfac5de62387d3f67bb229392f87b03d49f64a25a1aab02a827eebe03949af05 | Shell | 913 | 29 | #!/bin/bash
#SBATCH -n 1
#SBATCH --cpus-per-task=128
#SBATCH --time=120:00:00
#SBATCH --job-name="treP2"
#SBATCH --mem-per-cpu=8000
#SBATCH --tmp=50000
#SBATCH --output="tree.out"
#SBATCH --error="tree.err"
#SBATCH --open-mode=append
#cp ../test154_explain/combined.csv ${TMPDIR}
cp /cluster/work/igc/mlehner/test159_t... |
30f0fddeee6098f21dc8863b3684243711814541a870eed297bc54992bb3a1ba | Shell | 921 | 30 | #!/bin/bash
################################################################################
### COPYRIGHT ##################################################################
# New York Genome Center
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2023) by the New York
# Geno... |
4bd586225a1c0c2fe8d1972ab566b1718173094fce37cc6e5e9d5b5b140e6f43 | Shell | 922 | 32 | #!/bin/bash
## Create annot files
## activate the `ldsc` conda environment before running this script
# conda activate ldsc
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ##... |
4c33c2fae27b0eb8c18f27ed650431b6b4fa67dda28d02479d7e38ceef368328 | Shell | 925 | 51 | ##!/bin/bash
if [ -z $5 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <rl bam> <e>"
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
SR_BAM=$5
E=$6
Z=4
WEIGHT=4
OUT_DIR=`dirname $SR_BAM`
OUT_FILE=`basename $SR_BAM .bam`
../bin/lumpy \
-b \
-mw $WEIGHT \
-tt $TT \
-sr \
... |
cd8bad45ddf6f29b04b976076217d9c6e21634c4688c6ca8939744644cd7999f | Shell | 925 | 37 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
download_dir=$1
# check arguments
if [ $# != 1 ]; then
echo "Usage: $0 <download_dir>"
exit 1
fi
set -euo pipefail
cwd=$(pwd)
if [ ! -e "${download_dir}/VCTK-Corpus" ]; then
mkdir -p "${download_dir}"
c... |
280f2d7e00f7c64638d5db1d221f2f135368c79a8b27c0f1f13d092b091b62a3 | Shell | 926 | 21 | #!/bin/bash
# COCO 2017 dataset http://cocodataset.org
# Download command: bash data/scripts/get_coco.sh
# Train command: python train.py --data coco.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /coco
# /yolov5
# Download/unzip labels
echo 'Downloading COCO 2017 labels ...'
d='../' ... |
60d5d6f7c90f51ea62c73420b7c51d71036e1bc37a3d17644dbbb9ceeabf31d2 | Shell | 929 | 40 | #!/bin/bash
## convert bam to bigwig, separating plus and minus strands
## activate the `master` conda environment before running this script
# conda activate master
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $CONDA_PREFIX"
echo ... |
2d94a02f25680eb068dd6d426f8328f14d12ae08e85d5bdf83d92b979f0ca096 | Shell | 978 | 40 | #!/bin/bash
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
download_dir=$1
spk=$2
available_spks=(
"slt" "clb" "bdl" "rms" "jmk" "awb" "ksp"
)
# check arguments
if [ $# != 2 ]; then
echo "Usage: $0 <download_dir> <spk>"
echo "Available speakers: ${available_spks[*]}... |
31dae6dbc5c6d7ce3561fea258ec00f443abf9f7786a714d2ea70376a2d68a46 | Shell | 991 | 41 | #!/bin/bash
# Check if the directory is provided
if [ -z "$1" ]; then
echo "Usage: $0 <directory>"
exit 1
fi
# Get the directory path
DIRECTORY=$1
# Check if the provided path is a directory
if [ ! -d "$DIRECTORY" ]; then
echo "Error: $DIRECTORY is not a directory."
exit 1
fi
# Loop through all files in the... |
39e52e9f359331c50cbdd183a5de15b82680517f78ef9975d81b72f011876e82 | Shell | 999 | 39 | #!/bin/bash
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
download_dir=$1
# check arguments
if [ $# != 1 ]; then
echo "Usage: $0 <download_dir>"
exit 1
fi
set -euo pipefail
cwd=$(pwd)
if [ ! -e "${download_dir}/jsut_ver1.1" ]; then
mkdir -p "${download_dir}"
c... |
1e24b19bf588d9f26e28e6b91e1af5b294b3d3c15a1ab393faf22e4d0d86c14f | Shell | 1,004 | 28 | REGION=chr12:10,000,000-12,000,000
T=/Users/jeremiahwala/Desktop/svaba_compare/SG.wgs.UCLA.2025.01.tumor_cleaned.recal.bam
N=/Users/jeremiahwala/Desktop/svaba_compare/blood.recal.bam
# prime the page cache so cold-I/O doesn't dominate
REF=${HOME}/ref_genome/Homo_sapiens_assembly38.fasta
for f in $REF ${REF}.bwt ${REF}... |
e7a9dd4619fdcc4d3c0a9fbccdbc6dafc1503e5a9ea32f604e53170ba058e32b | Shell | 1,004 | 26 | problem="hero"
stamp="20230515-094404"
step=1000000
testdata="data0511a"
savedir="analysis0511a-v4-ind"
# you can comment out "--save_raw --save_prompt --batch_size 1 \" to speed up
# but you will not be able to plot the raw data then
# if you keep it, for each demo_num_begin, it takes about 90 seconds to finish
for ... |
df40a572ce4740805854603dcd82f0243fb56d5bea75668c7499d97cdc9c8b63 | Shell | 1,009 | 35 | # Simulate some data
p4 sSim.py
# Set up the MCMC with simulated tempering. Do a pre-run with
# writeSamples turned off. Then do a long run, collecting samples.
# On-the-fly tuning of temperature pseudo priors continues during the
# run, writing tuning results to mcmc_simTemp_0
p4 sMcmc.py
# We can use gnuplot to p... |
7b2292917dfe8274b03a86133a4460108662e5771d366618722cc58401a10edd | Shell | 1,012 | 38 | #!/usr/bin/env bash
# update repo
cd "$semehr_path"
git pull
datapath=/data
sesetting="$datapath/semehr_settings.json"
if [ ! -f "$sesetting" ]; then
echo "setting file not found at/data/emehr_settings.json, use default settings in SemEHR repo"
sesetting="$semehr_path/docker/docker_doc_based_settings.json"
fi... |
572862b748b18b6591438c58d4039a3e3b75915d447fb7a07b1fdf056bb3e5c2 | Shell | 1,016 | 40 | #!/bin/ksh
#
# Linear Sync Toolkit (linsync)
# Copyright (C) 2023 Joseph T. Lizier
# Distributed under GNU General Public License v3
# Modified from Simple cluster jobs framework distributed under GPL by J.T. Lizier
#
# -----------------------------------------------------------------
#
# Example script to submit sever... |
f3c087348218d6512b1d4d196967ba6aa6a67fbd1a6a724a45c6223a875232a3 | Shell | 1,034 | 41 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
# Download JSSS Corpus
# shellcheck disable=SC1091
. ./path.sh || exit 1
download_dir=$1
# check arguments
if [ $# != 1 ]; then
echo "Usage: $0 <download_dir>"
exit 1
fi
set -euo pipefail
url="https://drive.g... |
721f886165eec6afa1710472bac2228545afc37b07eeef8daf2dbad8191e5756 | Shell | 1,035 | 37 | #!/bin/bash
#
# This script takes VCF files and generates files that can be used
# by the find_intersecting_snps.py script.
# The script takes an INPUT directory and an OUTPUT directory.
#
# The INPUT directory is expected to contain files ending with .vcf.gz
# and to contain the name of the chromosome (like chr22 or ... |
2ec12db32deae463704af56161aecd728a41a097fd443438483c5c9c2c92c5c9 | Shell | 1,037 | 30 | #!/bin/bash
## use wiggletools to compute the mean signal across individuals
## and convert output wiggle files to bigwigs
## activate the `wiggletools` conda environment before running this script
# conda activate wiggletools
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment:... |
d089442fc84aeea4df54d9a8d3a1804df7601f93f9f6a57c0e664c4c05009b1a | Shell | 1,047 | 36 | #!/bin/bash
#SBATCH -n 1
#SBATCH --cpus-per-task=2
#SBATCH --time=24:00:00
#SBATCH --job-name='dual'
#SBATCH --nodes=1
#SBATCH --mem-per-cpu=8192
#SBATCH --tmp=20000
#SBATCH --output="dual/dual.%A.%a.out"
#SBATCH --error="dual/dual.%A.%a.err"
#SBATCH --array=1-10000
max_molJobIndex=180000
min_molJobIndex=1
cd ${TMPDI... |
575677927d14ad69d392393e666cd082920c6d4afd2c91a4b878302f65e5c602 | Shell | 1,048 | 37 | #!/bin/bash
#
# This script takes IMPUTE-formatted files and
# creates files that can be used by the find_intersecting_snps.py script.
# The script takes an INPUT directory and an OUTPUT directory.
#
# The INPUT directory is expected to contain files ending with .impute2.gz
# and to contain the name of the chromosome ... |
3ee5cf8d016d0efa2bc14b4901dc36cae9d0bb185b2808696330f9ac7cdf1c47 | Shell | 1,062 | 37 | #!/bin/bash
## Compute LD scores with annot files
## activate the `ldsc` conda environment before running this script
# conda activate ldsc
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $COND... |
d86a53efb96f9e294aefd370b7298d682c498167e2f36b246095bb59864807a8 | Shell | 1,064 | 29 | source ./utils.sh
# Data path of all the project
echo "Enter the complete data path of the project: "
path=$(get_path ) #E.g. path="/home/brainstimmaps/20xx_Projects/2023_Deep/03_Data"
# Patient subfolder
echo "Enter the patient ID: "
pat=$(get_patient )
pat_exist=$(check_original_data $path/$pat)
echo "${pat_exist%... |
422a629ba11e127c28d657f2fca57b36f9a968dee337d9e7e3b41a9467536770 | Shell | 1,065 | 46 | #!/usr/bin/env bash
# memusg -- Measure memory usage of processes
# Usage: memusg COMMAND [ARGS]...
#
# Author: Jaeho Shin <netj@sparcs.org>
# Created: 2010-08-16
set -um
# check input
[ $# -gt 0 ] || { sed -n '2,/^#$/ s/^# //p' <"$0"; exit 1; }
# TODO support more options: peak, footprint, sampling rate, etc.
pg... |
195733ad370a2378c7ac6556bfab9cc31f0c6ac71aedf6765ea2a9f3010c37b5 | Shell | 1,069 | 33 | # cuda related
export CUDA_HOME=/usr/local/cuda-11.3
export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}"
# path related
export PRJ_ROOT="${PWD}/../../.."
if [ -e "${PRJ_ROOT}/tools/venv/bin/activate" ]; then
# shellcheck disable=SC1090
. "${PRJ_ROOT}/tools/venv/bin/activate"
fi
# python related
exp... |
42c70d48644a3f2823fc79e3dd15750b3891d8c8fb6be3044a3dfaf60ac2a4a4 | Shell | 1,069 | 33 | # cuda related
export CUDA_HOME=/usr/local/cuda-10.0
export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}"
# path related
export PRJ_ROOT="${PWD}/../../.."
if [ -e "${PRJ_ROOT}/tools/venv/bin/activate" ]; then
# shellcheck disable=SC1090
. "${PRJ_ROOT}/tools/venv/bin/activate"
fi
# python related
exp... |
2c19bfd2c373ed875fad1254837a4912a5eb17b3b92d842f4bf532423e4bb311 | Shell | 1,077 | 17 | mkdir train
cd train
#https://drive.google.com/file/d/1mD6_4ju7n2WFAahMKDtozaGxUASaHAPH/view?usp=sharing
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt \
--keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?ex... |
7594e5e942a3db5336e57ff7983d260144b52c8df656e621acbc1a3447a2be04 | Shell | 1,083 | 33 | # cuda related
export CUDA_HOME=/usr/local/cuda-11.3
export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}"
# path related
export PRJ_ROOT="${PWD}/../../../../Muskits/"
if [ -e "${PRJ_ROOT}/tools/activate_python.sh" ]; then
# shellcheck disable=SC1090
. "${PRJ_ROOT}/tools/activate_python.sh"
fi
# pyth... |
f153cf8a23796f6235f53b8dd95446cd7b3c09e9de56eb21eef0a9623311fc90 | Shell | 1,085 | 24 | gpu=0
dir=data0904_weno_cubic
traineqns=1000
trainnum=100
testeqns=10 # only for visualization during training
testnum=100
CUDA_VISIBLE_DEVICES=$gpu python3 datagen_weno.py --eqn_types weno_cubic --dir $dir --name test --eqns $testeqns --num $testnum --dt 0.0005 --file_split 1 --truncate 100 --seed 101 &&
CUDA_VISIB... |
6300cdb896df12a1eef3db730776bed2af3d531ebc5870523f03aa23d2d41103 | Shell | 1,115 | 30 | #!/bin/bash
## Run cellranger-atac reanalyze, only used the valid barcodes
## activate the `master` conda environment before running this script
# conda activate master
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### "`hostname`" ### \(* w *)/"
echo "Currently ... |
6f4f626c0abcf61167ac939ec1f87f5fbb819f7cbd2ca3cd246f799842e0be4b | Shell | 1,117 | 20 |
python test_general_brats.py -c config_files/config_BRATS_seg_exp4.json -r saved/models/brats_seg_AsynDGANv2_exp4/1012_162127/checkpoint-epoch47.pth
python test_general_brats.py -c config_files/config_BRATS_seg_exp6.json -r saved/models/brats_seg_AsynDGANv2_exp6/1012_162214/checkpoint-epoch33.pth
python test_general... |
cfa140889b6693a6ffe0e87ec444cbe511903e19b4be85994598dd7750af1b84 | Shell | 1,117 | 52 | #!/bin/bash
echo activate conda environment
eval "$(conda shell.bash hook)"
conda activate tensorflow2_latest_p37
# check if data are already there, if not download from s3 bucket
DIR=""
DIRCode=""
if [ -d "$DIR" ]; then
### Take action if $DIR exists ###
echo "${DIR} exists"
else
### Control will jump here if... |
b3a2711b85e8a96ccc7a7bba4b812f34dc7801ed0bf709745006bf44d9e36fd3 | Shell | 1,118 | 45 | #!/usr/bin/env sh
# Install pkgconf (adds minimum dependencies) only if the equivalent pkf-config is not already installed.
if ! which pkg-config
then
PKGCONF="pkgconf"
fi
apt install -y \
build-essential \
gcc-16 \
g++-16 \
lld \
${PKGCONF:-} ... |
262d98685d3d9559a15293329b16a6c8697d56c43ad7206302c4aecff8c599d4 | Shell | 1,134 | 29 | #!/bin/bash
## get the TSS from a gene annotation file in BED format and then get flanking regions as the promoter regions
## activate the `master` conda environment before running this script
# conda activate master
## logging which server and which conda environment this scirpt is running in
echo "Running this on ... |
9a842851b4235a63f41fbdfbd5531e9d0a9c7bb9f35d62f8e08f3dfda4c37e29 | Shell | 1,165 | 30 | #!/bin/bash
## Run `clean_barcode_multiplets_1.1.py` provided by 10X genomics support team to remove barcode multiplets
## activate the `master` conda environment before running this script
# conda activate master
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### ... |
3b1b27a4ffecfa2722678d897638e2b5bb758e6d34311849833a22fa58db24d1 | Shell | 1,184 | 33 | #!/bin/bash
# Run STAR and RSEM for each sample
## activate the `master` conda environment before running this script
# conda activate master
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ###, located in: $CONDA_PREFIX"
echo "Starting at $(date)"
ec... |
77662993a8b7f0857528b8e13ecf7106ad42aaa1a41b6c51bd9ec4fd34378df3 | Shell | 1,200 | 37 | #!/bin/sh
mkdir -p new
mkdir -p new/summary
# mkdir -p new/summary_plotpv
# mkdir -p new/summary_plotdensity
# mkdir -p new/summary_plotfclust
mkdir -p new/summary_table
mkdir -p new/summary_alternative
Rscript script_summary/summary.r \
> new/summary/summary.txt
# Rscript script_summary/summary_plotpv.r \... |
25c48ed46fd0a825c3771bc6cc2fb7447badefa781b863f9a45e6ff73c4b4573 | Shell | 1,211 | 56 | #!/bin/bash
set -e -E -u -o pipefail
echo "installing lightgbm and its dependencies"
pip install \
--prefer-binary \
--upgrade \
--constraint ./.ci/pip-envs/requirements-oldest.txt \
"$(echo dist/*.whl)[arrow,pandas,polars,scikit-learn]"
echo "installed package versions:"
pip freeze
echo ""
echo "ch... |
d97ce16093cb04fa3564ae53be610337dc5232f04c29a1bbb1431930d61ae373 | Shell | 1,229 | 36 | #!/bin/bash
## Create annot files
## activate the `ldsc` conda environment before running this script
# conda activate ldsc
## logging which server and which conda environment this scirpt is running in
echo "Running this on ### $(hostname) ### \(* w *)/"
echo "Currently in conda environment: ### $CONDA_DEFAULT_ENV ##... |
c3a69bf8f127c745d476baf670ead39d5e6074ea5361194947622257e1f4c501 | Shell | 1,238 | 48 | #!usr/bin/env/ bash
# Add cojo conditional and stepwise model selection in GJA5 locus
gcta64 cojo--bfile /mnt/rreal/RDS/DATA/AMPPD/AMPPD_w_rsids_rsidsOnly
--chr 1 \
--maf 0.01 \
--cojo-file rsloped_v2_forcojo.ma \
--cojo-slct \
--out COJO_CHR1
gcta64 --bfile /mnt/rreal/RDS/DATA/AMPPD/AMPPD_w_rsids_rsidsOnly \
--chr 1... |
7b01eed14815a42854d0c8024e08cf08ac76173bd4da6a3eefcaf168d40859c7 | Shell | 1,242 | 35 | #!/bin/bash
#
# Linear Sync Toolkit (linsync)
# Copyright (C) 2023 Joseph T. Lizier
# Distributed under GNU General Public License v3
# Modified from Simple cluster jobs framework distributed under GPL by J.T. Lizier
#
# -----------------------------------------------------------------
#
# Main script to submit to the ... |
d33a0f8f9e575ed934811f35943fdf32831efebd1ddca2464804917ed7ccdd9b | Shell | 1,244 | 51 | #!/usr/bin/env bash
# Generates documentation for FiftyOne app & plugins.
# parse the following flags into variables
# --rebuild-sphinx: rebuild the sphinx docs
REBUILD_SHPINX=false
REBUILD_TYPEDOC=true
DOCS_JSON_FILE="docs.json"
while [[ $# -gt 0 ]]; do
key="$1"
case $key in
--skip-typedoc)
... |
bf00c6441cedfb4d105a41cc67879ca035434708fa24a2331653632f1f930574 | Shell | 1,254 | 42 | #!/bin/bash
# copy fMRI already preprocessed from Dimitri's lab to ours (on external drive) :)
echo "Where do you want to save the HCP functional data? "
read HCP_path
#echo $HCP_path
echo ----------------------------------------
# you should cd till you are in the data path, the one that contains the subjects subfol... |
91557371962ac299c262edb2226f2c9cc06d2c6fc8938c284fa4ae8040fe5d56 | Shell | 1,258 | 51 | #!/bin/bash
# managing input arguments
while getopts “s:b:r:t:” OPTION; do
case $OPTION in
s) src_dir=${OPTARG};;
t) branch=${OPTARG};;
r) release_dir=${OPTARG};;
b) build_dir=${OPTARG};;
\?) echo "Invalid option: -${OPTARG}" >&2;;
esac
done
if [[ -z ${build_dir} ]]; then
build_dir="$(mktemp... |
9e7a6461ecd1a55f59afdfeddd371dc4b657b774e30d15542604eb47644709ec | Shell | 1,258 | 44 | #! /bin/bash
# Copyright 2016 Vimal Manohar
# 2018 Xiaohui Zhang
# Apache 2.0.
if [ $# -ne 2 ]; then
echo "This script adds a sox line in wav.scp to resample the audio at a "
echo "different sampling-rate"
echo "Usage: $0 <frequency> <data-dir>"
echo " e.g.: $0 8000 data/dev"
exit 1
fi
freq=$1
... |
4b132092abe29a607c2d3a7448a3bcbf24d87ac7d6f2120c9f8778b2e7d9af15 | Shell | 1,266 | 24 | #!/bin/bash
# Execute this inside a VirtualBox image to turn it ino a vagrant box.
# The execution should be followed by:
# 1. Exportning the VirtualBox using vbox's export facilities into a .ovf
# the export will as well include a vmdk-file with the disc image
# 2. Execute a echo '{"provider":"virtualbox"}' > meta... |
e5f9cb1b6213a67a0f006ca4d846d6deadc4e5e23ac343aa4a661a59d9a2d435 | Shell | 1,276 | 34 | #!/usr/bin/env bash
#py_env for imaris-yuqi
# windows python3.7, and apache-maven
# To use:
# 1. open gitbash
# 2. run this script (bash imaris_py37.bash)
# Alternativly, use "source imaris_py37.bash" to allow starting arbirary python code easily.
# ex (after the source command): python example_ij_classifier.... |
1163d38f9cc97400fcfb8452fd254d947bfb91dfaa3c3f497083498e76bbe836 | Shell | 1,329 | 35 | #!/bin/bash
export LANG=en_US.UTF-8
export LC_ALL="en_US.UTF-8"
trap 'echo "Batch script killed"; exit 1' INT TERM
release_dir=${HOME}/release
platforms=()
platforms+=(osx)
platforms+=(ubuntu)
platforms+=(centos)
platforms+=(fedora)
platforms+=(win64)
platforms+=(win32)
for platform in ${platforms[@]}; do
echo "Bu... |
6d0b79c5dea9c2e8863177b1047f86cdaec4f0d649ddce9b0f4213dd47a3091f | Shell | 1,334 | 60 | ##!/bin/bash
if [ -z $5 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <pe bam> <e>"
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
PE_BAM=$5
E=$6
Z=4
WEIGHT=4
OUT_DIR=`dirname $PE_BAM`
OUT_FILE=`basename $PE_BAM .bam`
MEAN_STDEV=`samtools view $PE_BAM \
| ../scripts/pairend_distro... |
ad25b78072040568555832e11c73ed8641d54e911be6825f7ae1ff3fe3ea7b24 | Shell | 1,338 | 50 | #!/bin/bash
# managing input arguments
while getopts “s:b:r:t:” OPTION; do
case $OPTION in
s) src_dir=${OPTARG};;
t) branch=${OPTARG};;
r) release_dir=${OPTARG};;
b) build_dir=${OPTARG};;
\?) echo "Invalid option: -${OPTARG}" >&2;;
esac
done
if [[ -z ${build_dir} ]]; then
build_dir="$(mktemp... |
a7ea1c2b4b93557e72c369b0eae74afffc2bf08e6210c708b6592b5497b1d171 | Shell | 1,339 | 35 | #!/bin/bash
################################################################################
### COPYRIGHT ##################################################################
# New York Genome Center
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2023) by the New York
# Geno... |
b6dd3e16631a8b3da1e7dee17dee6d8eae8cefcd3c2df117904dd74a01e10fc4 | Shell | 1,342 | 61 | ##!/bin/bash
if [ -z $5 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <pe bam> <e>"
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
PE_BAM=$5
E=$6
Z=4
WEIGHT=4
OUT_DIR=`dirname $PE_BAM`
OUT_FILE=`basename $PE_BAM .bam`
MEAN_STDEV=`samtools view $PE_BAM \
| ../scripts/pairend_distro... |
1e6554a6d397d05a4a30bbd8f4b85a94cb17baa2860bbb03f1d382e99934ee8c | Shell | 1,361 | 51 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
download_dir=$1
# check arguments
if [ $# != 1 ]; then
echo "Usage: $0 <download_dir>"
exit 1
fi
set -euo pipefail
base_url=http://www.openslr.org/resources/60
parts="dev-clean test-clean dev-other test-other t... |
e60cb71d06c586b220a2011ba60f4b64fe61f84e1ce771f0762c2d595acdc503 | Shell | 1,379 | 23 |
# results are saved in experiments/nuclei_seg/.../models/.../test_results/...
## fedgan
python test_nuclei_seg.py -c config_files/config_nuclei_seg_fedgan_server2.json -d 1 -r /data/zhennan/code_seg_cvpr/experiments/nuclei_seg/fedgan_unet/models/path_seg_fedgan/0824_103005/model_best.pth
## asdgan
#python test_nucl... |
07a8fe6397d41b92e1924d27cbc7775dad5c3c2ed9817258d94abb3b163f2e46 | Shell | 1,394 | 53 | #!/bin/bash
# managing input arguments
while getopts “s:b:r:t:” OPTION; do
case $OPTION in
s) src_dir=${OPTARG};;
t) branch=${OPTARG};;
r) release_dir=${OPTARG};;
b) build_dir=${OPTARG};;
\?) echo "Invalid option: -${OPTARG}" >&2;;
esac
done
if [[ -z ${build_dir} ]]; then
build_dir="${HOME}/... |
548d8ed2b62d035bab9653259f43bf60ba6977424a5788135e364629429c13ba | Shell | 1,398 | 18 | gpu=0
icon_stamp=icon_lm_learn_20231005-094726-pde3-inverse
tune_stamp=icon_lm_fno_20240121-203841-pde3-inverse
restore_dir=/home/shared/icon/save/user/ckpts/fno_pretrain/20240121-203841
model_config=model_fno_pde_config.json
CUDA_VISIBLE_DEVICES=$gpu python3 finetune.py --model_name fno --model_config $model_config ... |
8a44aeb3adf225048f9ec850b4cd2926f4931eb20efc95f187be135386f22210 | Shell | 1,414 | 18 | gpu=0
icon_stamp=icon_lm_learn_20231005-094726-pde3-inverse
tune_stamp=icon_lm_deepo_20240121-203825-pde3-inverse
restore_dir=/home/shared/icon/save/user/ckpts/deepo_pretrain/20240121-203825
model_config=model_deepo_pde_config.json
CUDA_VISIBLE_DEVICES=$gpu python3 finetune.py --model_name deepo --model_config $model... |
acb0fe497311ec10d035b2d4407111aedf38d53030d8cd8dbe681e1f6f34dfe0 | Shell | 1,448 | 46 | #!/bin/bash
## split bam files by cluster assigned by cell ids
## activate the `master` conda environment before running this script
# conda activate master
sample=$1
split_by_column=$2
save_to_path_prefix=$3
num_thread=$4
raw_bam_path="/cndd2/junhao/ALS_FTD_singleCell/snRNA_postCellBender/split_bams/raw_bam"
cell_... |
4cb262e2a94ca9789c5e5676fcf0e62d37e4431c30e6c264e355cc777285b10f | Shell | 1,458 | 63 | ##!/bin/bash
if [ -z $7 ]
then
echo "$0 <min_mapping_threshold> <back_distance> <tt> <read len> <pe bam> <sr bam> <e>"
exit
fi
MIN_MAP_T=$1
BACK=$2
TT=$3
READ_LENGTH=$4
PE_BAM=$5
SR_BAM=$6
E=$7
Z=4
WEIGHT=4
OUT_DIR=`dirname $PE_BAM`
OUT_FILE=`basename $PE_BAM .bam`
MEAN_STDEV=`samtools view $PE_BAM \
| ../scr... |
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