sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
8069735dd3e4ed83b8ed31d634767a883b5c7f988e9b596c1f1b07a0cfbe81cc | Shell | 416 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage3
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run stage3
python3 ../../../dcibioinfo... |
884a26cbcd1d31bf117ff998020fec6ab74b2c1dc572aec184df4d4e61345cff | Shell | 416 | 16 | pip install -r requirements.txt
pip install pre-commit
pre-commit install
pip install -r docs/requirements.txt
# Check GPU
nvidia-smi
git submodule update --init --recursive
git submodule update --remote --merge ./mllm/backends/cuda/vendors/cccl
git submodule update --remote --merge ./mllm/backends/cuda/vendors/cutla... |
90c61518358436f22e93ba48227bed7f694e0d65f4fc5cd2cf6358355f9eec27 | Shell | 417 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage4_2
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --nodelist=x3-05-1
#SBATCH --mem=300G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run:
python3 ../../../dcibioinformatics-pip... |
ec19c5fe1e89675a33ca6e21cc4b90f29f0e7a55e1ff9e641be0dd4aa551be61 | Shell | 417 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage4_3
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run:
python3 ../../../dcibioinformati... |
3a7d753d8c3d281c22033f94056ee1dadd7c8270814d3bfa32e33deecf679897 | Shell | 418 | 23 | #!/bin/sh
tr="ENST00000268124"
base="testENST00000268124.Mnp.ORI.06"
out="testENST00000268124.Mnp.06"
# Sort by position
cat $base.vcf | sort -k 1n,1n -k 2n,2n | cut -f 1-8 > tmp.vcf
cat $base.vcf.txt \
| grep $tr \
| cut -f 2- \
| sed "s/:/\t/" \
| sort -k 1n,1n -k 2n,2n \
| tr ";" "\t" \
| cut -f 7,11,12,14... |
561cf6f323979965333526b57807ae8d878a079766fda96e5e0ce9e2ddf8c92d | Shell | 418 | 21 | #!/bin/sh
# submodule_init ( ) {
# local SUBMODULES=$(git submodule | awk '{print $2}')
# for submodule in $SUBMODULES; do
# echo "Initializing submodule $submodule"
# git submodule init $submodule
# git submodule update $submodule
# cd $submodule
# if test -f version.sh
# then
# ./version.sh
# fi
... |
6ecc0e8f9f5cfd890112319052129cb9f4b093086798b6ab1a467f72fe1e5e92 | Shell | 420 | 21 | #!/bin/bash
module load homer/4.10
sample="147"
res="0.001"
mkdir ${sample}
for i in 93 1451 1452 NCI
do
cp ../${i}v${sample}_${res}/${i}v${sample}.diffloop2.bedpe ${sample}/
done
cd ${sample}
merge2Dbed.pl 93v${sample}.diffloop2.bedpe 1451v${sample}.diffloop2.bedpe 1452v${sample}.diffloop2.bedpe NCIv${sample}.... |
75635c64238faa7bad3ee6f8a030352b1942803973e99ab67a8680d18d2ca442 | Shell | 421 | 21 | #!/bin/bash
module load homer/4.10
sample="70CR"
res="0.001"
mkdir ${sample}
for i in 93 1451 1452 NCI
do
cp ../${i}v${sample}_${res}/${i}v${sample}.diffloop2.bedpe ${sample}/
done
cd ${sample}
merge2Dbed.pl 93v${sample}.diffloop2.bedpe 1451v${sample}.diffloop2.bedpe 1452v${sample}.diffloop2.bedpe NCIv${sample}... |
75fbb43679143b8a31ca3f418a8d7ea1453b391bea6248a2f4bfb95b7a54983a | Shell | 421 | 21 | #!/bin/bash
module load homer/4.10
sample="35CR"
res="0.001"
mkdir ${sample}
for i in 93 1451 1452 NCI
do
cp ../${i}v${sample}_${res}/${i}v${sample}.diffloop2.bedpe ${sample}/
done
cd ${sample}
merge2Dbed.pl 93v${sample}.diffloop2.bedpe 1451v${sample}.diffloop2.bedpe 1452v${sample}.diffloop2.bedpe NCIv${sample}... |
9a941187501f97628ddf48478a9b836eee3503a4072d56d8464977f185b06077 | Shell | 421 | 21 | #!/bin/bash
module load homer/4.10
sample="77CR"
res="0.001"
mkdir ${sample}
for i in 93 1451 1452 NCI
do
cp ../${i}v${sample}_${res}/${i}v${sample}.diffloop2.bedpe ${sample}/
done
cd ${sample}
merge2Dbed.pl 93v${sample}.diffloop2.bedpe 1451v${sample}.diffloop2.bedpe 1452v${sample}.diffloop2.bedpe NCIv${sample}... |
471c61bc905209105770ac9f0c10f7a933813f7f432e9ed074d16dc1ead24b40 | Shell | 422 | 14 | #!/bin/bash
#SBATCH --job-name=Duke-stage4_2
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=25G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run:
python3 ../../../dcibioinformati... |
625f2e9d732c3bdf65aa1154fdce355ca3edaf467c53752193bcb96e06338c29 | Shell | 422 | 19 | RES=$1
CHROM=$2
TAR=$3
DATA_DIR=$4
PREFIX=$5
RES_KB=$(($RES/1000))
if [ $RES_KB -lt 1000 ]
then
RES_STRING=$RES_KB"kb"
else
RES_STRING=$(($RES_KB/1000))"mb"
fi
OUT_DIR=$PREFIX/$RES_STRING"_resolution_intrachromosomal"/chr$CHROM
test ! -d $DATA_DIR/$OUT_DIR && (tar -C $DATA_DIR -xzf $TAR $OUT_DIR)
test ! -s $DAT... |
62e0f495f7ec9763699fca2d08eaf777f7ab5bcd84edf015eabf019ffed1cb1d | Shell | 423 | 20 | #!/bin/bash
cd $(dirname $0)
cd ..
source ~/.bashrc
root_path="$(pwd)/../../.."
export PYTHONPATH="$root_path/":$PYTHONPATH
CUDA_VISIBLE_DEVICES=0 python -m vae_train.py \
--train zinc_processed \
--vocab data/zinc/vocab.txt \
--config configs/config.json \
--save_dir vae_models \
... |
a9228676517475fe2ea2964ada0683cb457fb19d3342511f57f5e521caa8d9a2 | Shell | 423 | 24 | #!/bin/bash
dropbox=../../dropbox
data=schneider50k
tpl=default
fp_degree=2
num_parts=1
save_dir=$dropbox/cooked_$data/tpl-$tpl
for r in False True; do
python dump_graphs.py \
-dropbox $dropbox \
-data_name $data \
-tpl_name $tpl \
-save_dir $save_dir \
-f_atoms $dropbox/cooked_$data/atom_list.t... |
f6b146effc89c0e20e7350ede9dedd39b6df477cfb802b1a43901f1ac97cdea8 | Shell | 425 | 14 | #!/bin/bash
#SBATCH --job-name=Duke-stage4_1
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run stage4_1:
python3 ../../../dcibio... |
301a1d1cdf6f6cdb8ec64207a2d79db79ffaaaf57504d9c5169a87a1e43f2710 | Shell | 426 | 14 | #!/bin/sh
LISTOFIMAGE_ORG="ubuntu:20.04 ubuntu:latest debian:buster debian:bullseye debian:testing"
# fail : debian:stretch problem with matplotlib
VERROU_BRANCH=master
for IMAGE in $LISTOFIMAGE_ORG
do
echo "Test build image:" $IMAGE
VERROU_IMAGE=$(echo $IMAGE |sed -s "s/:/-/")-verrou
docker build . -... |
8497e310a49751ef807d0a85cb41973924a44164acd62b321a8cbb7f8739ea10 | Shell | 426 | 6 | SUBJECT_ID="subj0${1}"
HEMI="$2h"
FS_DIR=../natural-scenes-dataset/nsddata/freesurfer/$SUBJECT_ID
freeview -f $FS_DIR/surf/$HEMI.inflated:overlay=$FS_DIR/label/$HEMI.prfeccentricity.mgz:overlay_color='colorwheel':overlay=$FS_DIR/label/$HEMI.prfangle.mgz:overlay_custom=./${HEMI}_pol:overlay=$FS_DIR/label/$HEMI.prfsize... |
862889a9b90ccbeb1974fcd3539d2f8b2632c26ba3c5eb52e30406c5a1bcf36c | Shell | 427 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage4_1
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run stage4_1:
python3 ../../../dcibi... |
2c65ec1c0b4178b373c593189cc735e02d23c0f6ced40b4c339374b8e774f150 | Shell | 429 | 18 | #!/bin/sh -e
zcat c_remanei.WS234.annotations.gff3.gz \
| grep -v "^#" \
| grep -v SNP \
| grep -v translated_nucleotide_match \
| grep -v repeat_region \
| grep -v inverted_repeat \
| grep -v tandem_repeat \
| grep -v nucleotide_match \
| grep -v intron \
> genes.gff
# Add fasta s... |
c1349cf424bed8086ebb5ebfac3d6d48ae1cb7d99e24bdeb789dc0454f1604fb | Shell | 431 | 18 | #!/bin/bash
cd eval
echo "installing evaluation dependencies"
echo "downloading e2e-metrics..."
git clone https://github.com/tuetschek/e2e-metrics e2e
pip install -r e2e/requirements.txt
echo "downloading GenerationEval for webnlg and dart..."
git clone https://github.com/WebNLG/GenerationEval.git
cd GenerationEval
.... |
f8d07747a6ea519a91372dc592b76eb573438f20b182ed0839ad75210e79a502 | Shell | 431 | 3 | #!/bin/sh
hgsql $1 -e "select a.obj, a.val Attic_Type, b.val ExpId, c.val Lab,d.val Datatype, e.val Cell, f.val Rep, g.val View from metaDb a,metaDb b, metaDb c, metaDb d, metaDb e, metaDb f, metaDb g where a.obj=b.obj and b.obj=c.obj and c.obj=d.obj and d.obj=e.obj and e.obj=f.obj and f.obj=g.obj and a.var='attic' an... |
a2fd1bc189c8fdf54fb7d1329f25905c5ef13503f7be6fc61d7f22d2427600ef | Shell | 432 | 30 | #!/bin/bash
set -e
SCRIPT=`realpath $0`
SCRIPTPATH=`dirname $SCRIPT`
PYPROF="$SCRIPTPATH/../.."
parse="python $PYPROF/parse/parse.py"
prof="python $PYPROF/prof/prof.py"
for f in *.py
do
base=`basename $f .py`
sql=$base.sql
dict=$base.dict
#NVprof
echo "nvprof -fo $sql python $f"
nvprof -fo $sql python $f
#... |
1e41f5df1ffde64b29da0b60fe9214e159ebb7aa9c0fd073b9978f14fcd0d100 | Shell | 433 | 9 | #!/bin/bash -l
export SIM_FOLDER=$(pwd); \
export LOG_LOC=${SIM_FOLDER}/complete_circuit_$(date "+%Y%m%d_%Hh%Mm%Ss").out; \
rm -rvf ${SIM_FOLDER}/connectome && \
rm -rvf ${SIM_FOLDER}/sonata/networks/edges && \
module load unstable circuit-build && \
# finally complete the circuit-build workflow
nohup circuit-build ru... |
bb520ba7fbda2df918e2eea13bae4db806d1fbcae7c72c6c74148cd0e7b9dee4 | Shell | 433 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage2
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-MDACC-stage2-%j
module load python/3.7.4-gcb01
# run stage2
python3 ../../... |
664f61cfd82caa354c85ffe271a100a3675ca6210d1ba118bd12eb57331c2214 | Shell | 434 | 17 | #!/usr/bin/env bash
# Source from Linux build scripts. Conda's target name differs from the .NET RID.
case "${target_platform:-$(uname -m)}" in
linux-64|x86_64)
WARP_RUNTIME_ID=linux-x64
WARP_PLATFORM_TARGET=x64
;;
linux-aarch64|aarch64|arm64)
WARP_RUNTIME_ID=linux-arm64
WARP_PLATFORM_TARGET=AR... |
6be5244f62e027617171c8c2699b22836087c7c9c2227fa716b7e0bf9defd3fd | Shell | 434 | 13 | #!/bin/sh
echo "random python "
VERROU_LIBM_ROUNDING_MODE=random LD_PRELOAD="./interlibmath.so" ./testCos.py 1.1
echo "average python"
VERROU_ROUNDING_MODE=average LD_PRELOAD="./interlibmath.so" ./testCos.py 1.1
echo "random testCos binary"
VERROU_LIBM_ROUNDING_MODE=random LD_PRELOAD="./interlibmath.so" ./testCos ... |
353b693e72f53531afcff42e5e83ee63442b338919160d9c0cee5d370fd54e2e | Shell | 435 | 17 | #!/bin/bash
# Clean cache directories
find . -type d -name "__pycache__" -exec rm -rf {} +
find . -type d -name ".mypy_cache" -exec rm -rf {} +
find . -type d -name ".pytest_cache" -exec rm -rf {} +
find . -type d -name "venv" -exec rm -rf {} +
find . -type d -name "build" -exec rm -rf {} +
# Clean cache files
find .... |
99df488b9d2b2c0b545323d16da0c084aa692be9011375fffab95e4d686b51c4 | Shell | 435 | 14 | #!/bin/bash
#SBATCH --job-name=Duke-stage5_paired
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=18G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run:
python3 ../../../dcibioinfor... |
ad20e8c81b8449780d361fa48e2f14d15b530524753dffcc58d1ee571f492854 | Shell | 435 | 20 | #!/bin/sh
set -e
verificarlo-c --save-temps -O0 test.c -o test
# Check that all the operations have been instrumented
for op in fadd fsub fmul fdiv; do
if grep ' $op ' test*.2.ll; then
echo "Some $op have not been instrumented"
exit 1
fi
done
VFC_BACKENDS="libinterflop_ieee.so" ./test
#Test ... |
506e043b5d7a5e1b712e4e0d09c7d0cf04a1c2b56d37879c885845b90481e350 | Shell | 436 | 19 | # copyright John Maddock 2005
# Use, modification and distribution are subject to the
# Boost Software License, Version 1.0. (See accompanying file
# LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#
# locate all the header dependencies:
header=""
#
# locate all the source files:
src=link_test.cpp
b... |
a3449ee9c6900c41e61cb9926b3cd091f691b2fbbcdfaaad5571884ead481258 | Shell | 437 | 14 | #!/bin/bash
#SBATCH --job-name=MDACC-stage5_paired
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=18G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run:
python3 ../../../dcibioinfo... |
6c7c1c67db23b6dccced5cb9a556686999f7018e196e2b98556d38e8c91f2fdb | Shell | 439 | 24 | #!/bin/bash
set -e
FILE=$1
BASE=`basename ${FILE%.bam}`
EXONS=genome/exons.saf
OUTDIR=counts
mkdir -p $OUTDIR
if [ ! -s $FILE ]
then
echo "File is missing or empty: $FILE"
exit 65
fi
if [ -s $OUTDIR/$BASE.genecounts.txt ]
then
echo "Output file already exists: $OUTDIR/$BASE.genecounts.txt"
exit 64
fi
echo ... |
9e92e60a8730fe6f16617a7f4db6cf1b29bb8ac914663a9dc06a4dd35da12e5a | Shell | 439 | 22 | #!/usr/bin/env bash
set -e
gpu=${1:--1}
echo Using gpu ${gpu}
# Tox21
echo --- Testing Tox21 ---
cd tox21 && bash -x test_tox21.sh ${gpu} && cd ..
# QM9
echo --- Testing QM9 ---
cd qm9 && bash -x test_qm9.sh ${gpu} && cd ..
# Own dataset
echo --- Testing on own dataset ---
cd own_dataset && bash -x test_own_datase... |
f874f3930fb772c8d051a44cd84bd1e6364e251c9aefd7c61297d93335aec1ac | Shell | 439 | 14 | #!/bin/bash
#SBATCH --job-name=gatk-stage1
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16G
#SBATCH --output=./logs/log-%j
module load python/3.7.4-gcb01
# run stage1
python3 ../../dcibioinformat... |
84adad879681a2f7eb18622ee12dc5df1a7f752ad0e10658b9608b59b62c558d | Shell | 441 | 26 | #!/bin/bash
set -e
export XLA_FLAGS=--xla_force_host_platform_device_count=8
TARGETS=(
mnist
vae
sst2
gemma
imagenet
seq2seq
lm1b
nlp_seq
ogbg_molpcba
wmt
ppo
)
for target in "${TARGETS[@]}"; do
echo "============================================"
echo "Running benchmark: ${target}"
echo "... |
398535e01eb42257388f3e9dc559fb03f3c5476e23ee256ee468465a0a1489a7 | Shell | 442 | 19 | #!/usr/bin/env bash
CONFIG=$1
GPUS=$2
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
PORT=${PORT:-29500}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch \
--nnodes=$NNODES \
--node_rank=$NODE_RANK \
--master_addr=$MASTER_ADDR \
--np... |
418751ee36c7495ed9b6d6c518b7335d3e2c33f8e72e513ed076257f44822781 | Shell | 443 | 23 | #!/bin/bash
dropbox=../../dropbox
data_name=schneider50k
tpl_name=default
num_cores=4
num_parts=1
save_dir=$dropbox/cooked_$data_name/tpl-$tpl_name
python build_all_reactions.py \
-dropbox $dropbox \
-phase cooking \
-data_name $data_name \
-save_dir $save_dir \
-tpl_name $tpl_name \
-f_atom... |
d3f32b012c6db2b90a4ad0fb8e6671be22c07059b1a1fb012797a9417221f1b8 | Shell | 443 | 24 | #!/bin/bash
BASEDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
CLASS=S
export VFC_BACKENDS="libinterflop_ieee.so"
check_success() {
if [[ $? != 0 ]]; then
echo "Benchmark failed"
exit 1
fi
}
make clean
for bench in CG; do
echo "Benchmarking $bench"
sbench=$( echo $bench | tr '[A-Z]' '[a-z... |
07b54836d34a2293cae78b586b863f051630469346d07517b791f45836acb324 | Shell | 445 | 9 | #!/bin/bash
awk '{print $1}' ../analysis/results_branchshift/ADULTresultsSOR_pval_matrx > ../analysis/results_branchshift/adaptTOT
while read j; do
p=$(cat ../data/6_keys/bos_key | grep $j| awk '{print $1'})
cat ../data/6_keys/final_sor_key | grep $p >> ../analysis/results_branchshift/shrew_TOTShrewBS
cat ../data... |
572158590c8a968ff5f43a8d83b07e03defd466c22794b002e829849faf77227 | Shell | 445 | 15 | CUDA_VISIBLE_DEVICES=0
NPROC=1
ROOT=/path/to/root/
MODEL_PATH=network-snapshot.pkl
SAMPLE_DIM=384,320
NUM_SAMPLES=100
BATCH_SIZE=40
ANATOMY=brain
DATA=noisy32dB
torchrun --standalone --nproc_per_node=$NPROC generate.py \
--outdir=$ROOT/results/priors/$ANATOMY/$DATA --seeds=1-$NUM_SAMPLES \
--batch=$BA... |
278eac8f3384375a98390245a45f3a0ec48a46433b3aadf384e2e7106795a7d3 | Shell | 446 | 16 | #!/bin/sh
# Run at FMNH server
set +e
source /home/bdemedeiros/software/miniconda3/etc/profile.d/conda.sh
conda activate varKoder
export CUDA_VISIBLE_DEVICES=0,1
#the folder with query images was converted from varKodes
#varKoder convert --overwrite -n 24 cgr varkoder_query_images vkfCGR_query_images
time varKoder ... |
2ab02eaf5090c70e662c80448fdc92d83326fc59c04b062a3f30b068b8d79b26 | Shell | 446 | 18 | #!/bin/bash
FOLD=${1:-0}
GPU=${2:-0}
echo "Training Fold $FOLD on gpu $GPU"
# Setup nnunet env vars
export nnUNet_raw_data_base="./nnunet_data/nnUNet_raw_data_base"
export nnUNet_preprocessed="./nnunet_data/nnUNet_preprocessed"
export RESULTS_FOLDER="./nnunet_data/nnUNet_trained_models"
# select gpu
export CUDA_DE... |
c6295a330f8bdd3e6845a6bcf22041b36aaddc2811a58e429bb264637585532c | Shell | 447 | 6 | #!/bin/bash
rm datasets/summer2winter_yosemite256 -p
mkdir datasets/summer2winter_yosemite256 -p
axel -n 1 https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/summer2winter_yosemite.zip --output=datasets/summer2winter_yosemite256/summer2winter_yosemite.zip
unzip datasets/summer2winter_yosemite256/summer2wi... |
43af56983577ec7869d4f860070ae4fb302e954493b2d214f12e21992b148a75 | Shell | 448 | 8 | #!/bin/sh
for chr in chr10.fa chr11.fa chr12.fa chr13.fa chr14.fa chr15.fa chr16.fa chr17.fa chr18.fa chr19.fa chr1.fa chr20.fa chr21.fa chr22.fa chr2.fa chr3.fa chr4.fa chr5.fa chr6.fa chr7.fa chr8.fa chr9.fa chrMT.fa chrX.fa chrY.fa
do
MDGR=`cat GRCh37.65/$chr | grep -v "^>" | tr -d "\n" | tr "[a-z]" "[A-Z]" | md5s... |
044b4f14c74f38a5cdfdd35855e42c1b915d9717e973f39e1a39cf78bcd03851 | Shell | 452 | 17 | conda create --name "deconvo_lute-paper" -c
bioconda conda-forge
r-base=4.3.1
bioconductor-scuttle=1.12.0
bioconductor-scran=1.30.0
dplyr=1.1.3
bioconductor-limma=3.58.1
r-ggplot2=3.4.4
ggforce=0.4.1
r-gridextra=2.3
glmGamPoi=1.14.0
sva=3.50.0
bioconductor-singlecellexperiment=1.24.0
bioconductor-summarize... |
3279fa90820398cc02725c56e988cc72a4fd5a2315b29229121c2e38dd7510ce | Shell | 452 | 29 | #!/bin/sh
usage() {
cat <<EOF
wrap_spm_brain_age_preprocess.sh
Usage: wrap_spm_brain_age_preprocess.sh <t1image>
e.g. wrap_spm_brain_age_preprocess.sh t1.nii
<t1image> T1 structural image
EOF
exit 1
}
############################################################################
[ "$#" -lt 1 ] && us... |
e2758b4750382ff5a778b2a2d95e5344c82b58393acda34c7bada351ef7b8c08 | Shell | 453 | 19 | mkdir results/gnn_hyperopt
CUDA_VISIBLE_DEVICES=1,2,3 python src/ms_pred/gnn_pred/gnn_hyperopt.py \
--seed 1 \
--num-workers 8 \
--max-epochs 200 \
--dataset-name nist20 \
--dataset-labels labels.tsv \
--split-name hyperopt.tsv \
--gpu \
--num-bins 15000 \
--save-dir results/gnn_hyperopt_v2 \
--cpus-per-trial 8 \
--... |
8cbd7501f60c980f0c11e2d0baa120be4306f9a6aa4902705fec55da97e01a93 | Shell | 456 | 20 | #!/usr/bin/env bash
CONFIG=$1
GPUS=$2
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
PORT=${PORT:-29500}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch \
--nnodes=$NNODES \
--node_rank=$NODE_RANK \
--master_addr=$MASTER_ADDR \
--np... |
ff0942b5fadd89e113435d229dcaaaba2da29e6c538cb15a0a7b74e252c48f6d | Shell | 456 | 16 | #!/bin/sh
# Run at FMNH server
set +e
source /home/bdemedeiros/software/miniconda3/etc/profile.d/conda.sh
conda activate varKoder
export CUDA_VISIBLE_DEVICES=0,1
# convert not_included
varKoder convert --overwrite -n 24 cgr varkoder_images_SRA_notincluded vkfCGR_images_SRA_notincluded
varKoder query --overwrite -I -... |
d84127379cf5f5060fdec1789869c178bee8240e0f7d5924ae4239619f8917ff | Shell | 457 | 19 | #!/bin/bash
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=4G
#SBATCH --time=0-01:00
#SBATCH --account=def-barrett
#######################################################
### Goal: Merge coverage on Bismark CpG report files
### Author: Janay Fox
#######################################################
# $1 = Bismark... |
5c4ae90ffbe89287dae2d48a4647774e2ba1f0ac405d03ffa58f007b5f9532c4 | Shell | 458 | 9 | #!/bin/bash
awk '{print $1}' ../analysis/results_diversity/ADULTresultsDIV_sigLowBetaSelection > ../analysis/results_diversity/plast
while read j; do
p=$(cat ../data/6_keys/bos_key | grep $j| awk '{print $1'})
cat ../data/6_keys/final_sor_key | grep $p >> ../analysis/results_diversity/shrew_sigLowBetaSelection2
c... |
a5711a199f7cebc8f1db00ac3c87954955bea9fe7c25790325e737a9ef2e4ebb | Shell | 459 | 16 | module load BEDTools/2.30.0-GCCcore-10.2.0
multiome_peaksdir=/gpfs/gibbs/pi/gerstein/jz435/ShareZhangLab/PTSD/Multiome/25_samples_archr_class_peaks/PeakBeds
celltypes=('EXC' 'INH' 'OLI' 'OPC' 'END' 'AST' 'MIC')
for i in ${celltypes[*]}
do
for j in ${celltypes[*]}
do
echo $i $j
bedtools jacc... |
bdcad823bd4ea9360ce785289d39bb7d7cd198d9949c828ae69852f8b7e3eed2 | Shell | 459 | 26 | #!/bin/bash
dropbox=../../dropbox
data_name=$1
tpl_name=default
save_dir=$data_name-results
if [ ! -e $save_dir ];
then
mkdir -p $save_dir
fi
export CUDA_VISIBLE_DEVICES=0
python main_test.py \
-dropbox $dropbox \
-data_name $data_name \
-save_dir $save_dir \
-model_for_test $dropbox/$data_name... |
cadc09c9c9cfaedf449fc5758c8cbd031d8cd4d07525d219a2892c4a0fd7312b | Shell | 459 | 27 | #!/bin/bash
FILES=*
cd ./bin/
if [ $? -ne 0 ]
then
printf "\nError: ./bin folder not found\n" ; exit 1 ;
fi
if [ -d ../ver ]
then
printf "\nver folder exists\n";
else
mkdir ../ver
fi
for f in $FILES
do
if [[ "$f" != lib* ]]
then
# ls -al $f
cp -f $f ../ver/
./$f --version > ../ver/"${f%.*}".txt
iftr... |
4fc3888049183c6c4731ca0fd43c0862fa7dfdb231560d13fbc62d58f4a4f80f | Shell | 460 | 19 | #!/bin/bash
set -euo pipefail
RED='\033[0;31m'
GREEN='\033[0;32m'
NC='\033[0m'
die() { echo -e "${RED}$1${NC}"; exit 1; }
ok() { echo -e "${GREEN}$1${NC}"; }
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
cd "$PROJECT_ROOT"
[ -d frontend/dist ] && rm -rf frontend... |
99a9695e8940b66632c2cb26a097d6533815296d56c4ae2a375f429a514fcf59 | Shell | 461 | 18 | mkdir results/molnetms_hyperopt
CUDA_VISIBLE_DEVICES=1,2,3 python src/ms_pred/molnetms/molnetms_hyperopt.py \
--seed 1 \
--num-workers 8 \
--max-epochs 200 \
--dataset-name nist20 \
--dataset-labels labels.tsv \
--split-name hyperopt.tsv \
--gpu \
--num-bins 15000 \
--save-dir results/molnetms_hyperopt \
--cpus-per-... |
79e6242521a53bd78584c6298bb623f8779360abc5f6cce2b657ef3d1e734f40 | Shell | 464 | 9 | #!/bin/bash
awk '{print $1}' ../analysis/results_diversity/ADULTresultsDIV_sigHighBetaPlasticity > ../analysis/results_diversity/plast2
while read j; do
p=$(cat ../data/6_keys/bos_key | grep $j| awk '{print $1'})
cat ../data/6_keys/final_sor_key | grep $p >> ../analysis/results_diversity/shrew_sigHighBetaPlasticit... |
79255765dd98171b3ef84f5d66f4e1d449bb773b6c3310493e650013be93f3f7 | Shell | 467 | 7 | . data_scripts/dag/run_magma.sh
. run_scripts/marason/01_run_marason_gen_train.sh
python run_scripts/marason/02_sweep_gen_thresh.py # comment this if you want speedup and don't care about evaluations
. run_scripts/marason/03_run_marason_gen_predict.sh
. run_scripts/marason/04_train_marason_inten.sh
python run_scripts/... |
7bfdbf817dead28a2ae5c0cf9041c5a3a507d36c44e64fbc7c23f74e6ca1ccf1 | Shell | 471 | 15 | #!/bin/sh
gunzip -c ORI/parastrongyloides_trichosuri.PRJEB515.WBPS3.annotations.gff3.gz \
| sed "s/gene://" \
| sed "s/transcript://" \
| sed "s/exon://" \
| sed "s/cds://" \
> genes.gff
echo "##FASTA" >> genes.gff
gunzip -c ORI/parastrongyloides_trichosuri.PRJEB515.WBPS3.genomic.fa.gz >> genes.gff
cp ORI/para... |
682097f750d216defa28fb659900fbac2bf9281dc75a1afde08ed21093b9a15a | Shell | 472 | 19 | CUDA_VISIBLE_DEVICES=0
NPROC=1
EPOCHS=10
ANATOMY=brain
DATA=noisy
ROOT=/path/to/root/
METHOD=modl
SNR=32dB
KSP_PATH=/path/to/data/$ANATOMY/train/$SNR/ksp/
DATA_PATH=/path/to/data/$ANATOMY/train/$SNR/$DATA.pt
R=4
torchrun --standalone --nproc_per_node=$NPROC train.py \
--gpu=$CUDA_VISIBLE_DEVICES --data_R=$R --ep... |
6c5f9cc2fa5586bd5e07c408f1aa5d93b95f7e4635d0470dda4d33a33ebdae74 | Shell | 475 | 11 | #! /bin/bash
# quick and dirty way to make sure the CLI help is up to date
echo "Generating CLI help for Neurodocker and its subcommands..."
neurodocker --help > user_guide/cli_help.txt
neurodocker generate --help > user_guide/generate_cli_help.txt
neurodocker generate docker --help > user_guide/generate_docker_cli_... |
92ebf1addbceda7c9875916a47766f244692205a1cb1551db9cc73927949c1f4 | Shell | 475 | 10 | #! /bin/bash
matlab -nodisplay -nosplash -nodesktop -r "run('runExperiments_MOTOR'); exit"
matlab -nosplash -nodesktop -r "run('step3_trainAndTest_SEED(4,5)'); exit"
matlab -nosplash -nodesktop -r "run('step3_trainAndTest_SEED(1,5)'); exit"
matlab -nosplash -nodesktop -r "run('step3_trainAndTest_SEED(2,5)'); exit... |
947355eef36a4aaa8e049d3e464f8b6a291cc40278374511ea37ca22f0a2eb19 | Shell | 475 | 14 | # Utility bash script to move PhysiCell sample project executables for the Studio
# replace this path with wherever you have your PhysiCell pgms
PGM_PATH=/Users/heiland/dev/PhysiCell_v1.10
echo $PGM_PATH
mv $PGM_PATH/biorobots .
mv $PGM_PATH/cancer_biorobots .
mv $PGM_PATH/cancer_immune_3D .
mv $PGM_PATH/celltypes3 .
m... |
0cd4e75d118c8e20db70c263b485785e109c2af71e72356272f649b859ba28d8 | Shell | 476 | 18 | mkdir results/massformer_hyperopt
CUDA_VISIBLE_DEVICES=0,1,2,3 python src/ms_pred/massformer_pred/massformer_hyperopt.py \
--seed 1 \
--num-workers 8 \
--dataset-name nist20 \
--dataset-labels labels.tsv \
--split-name hyperopt.tsv \
--gpu \
--num-bins 15000 \
--save-dir results/massformer_hyperopt \
--cpus-per-tria... |
48ae43293de43c028d95d6f5fbbed089b0fb2c5cd44da9686641f2d6f621195f | Shell | 476 | 28 | #!/bin/bash
#SBATCH -A b1042
#SBATCH -p genomics
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -t 12:00:00
#SBATCH --mem=30G
#SBATCH --mail-user=viriyakeo2023@u.northwestern.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --job-name=z_motif_atac_anchor
#SBATCH -o "%x.o%j"
module purge
# load modules
module load python/anaconda3.6
mo... |
88d59d0cbcc21f068a7c202ff73a383c1f199fc37fb14983a34b6dc8b070ea31 | Shell | 478 | 5 | mkdir -p data/hmp
curl -L -o data/hmp/hmp2_metadata_2018-08-20.csv https://g-227ca.190ebd.75bc.data.globus.org/ibdmdb/metadata/hmp2_metadata_2018-08-20.csv
curl -L -o data/hmp/dysbiosis_scores.tsv https://forum.biobakery.org/uploads/short-url/umwfR0kDJ6s5RXHwtIMgLaOEoOI.tsv
curl -L -o data/hmp/taxonomic_profiles_3.tsv.... |
ba6634414f0ac6e05f0ea54b72573d6849f03da1fbcfad218eff8f904b16d05f | Shell | 479 | 10 | #!/bin/bash
#SBATCH --cpus-per-task=30
#SBATCH --mem-per-cpu=4G
#SBATCH --time=3-10:00:00
#SBATCH --account=def-barrett
module load singularity
module load nextflow
nextflow run nf-core/methylseq --input "Q08723/guppy/*/*/*_R{1,2}_001.fastq.gz" -profile singularity -with-singularity /home/janayfox/scratch/guppyWGBS/n... |
fd562ee6a29c255f5f9e2b71189a2f36b381a85c718adc142a3d6fc31514858b | Shell | 480 | 18 | mkdir results/graff_ms_hyperopt
CUDA_VISIBLE_DEVICES=0,1,2,3 python src/ms_pred/graff_ms/graff_ms_hyperopt.py \
--seed 1 \
--num-workers 5 \
--dataset-name nist20 \
--dataset-labels labels.tsv \
--split-name hyperopt.tsv \
--gpu \
--num-bins 15000 \
--save-dir results/graff_ms_hyperopt \
--cpus-per-trial 5 \
--gpus-... |
263cf51f2a041892234648d2342d26894b66126a4b6f1fd25d1ef32cfe1a6df4 | Shell | 484 | 8 | python launcher_scripts/run_from_config.py configs/iceberg/msg_all/dag_gen_predict_train_msg_known_ce.yaml
# Assign intensities to prediction for next training run
python data_scripts/dag/add_dag_intens.py \
--pred-dag-path results/iceberg_msg_known_ce/split_rnd1/preds_train_100/tree_preds.hdf5 \
--true-dag-path da... |
9434e12dada8f0c886c90c5007b905186c5b82886fdfcc01d4f0b43fa6b12d12 | Shell | 484 | 20 | #!/bin/sh
rm genes.gtf* protein.fa* cds.fa*
# GTF
zcat ORI/Anopheles-gambiae-PEST_BASEFEATURES_AgamP4.2.gtf.gz \
| sed "s/Parent=\(.*\);/transcript_id \"\1\";/" \
> genes.gtf
# Chromosomes FASTA
cp ORI/Anopheles-gambiae-PEST_CHROMOSOMES_AgamP4.fa.gz ../genomes/agam4.2.fa.gz
# CDS
cp ORI/Anopheles-gambiae-PEST_TR... |
147de924a7f22eb9c08a6602c84ecbce1c22e8a48a5e62c3e65ff944263b8edc | Shell | 486 | 10 | set -e
if [ ! -e mESC_chr6.tsv ]
then
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35156/suppl/GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary.txt.gz
gunzip GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary.txt.gz
cat GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary... |
d8f055b86fedf1292fc2ccefbace17f9481c7e335073a2e5eb6757e4debf53f6 | Shell | 486 | 12 | #!/usr/bin/env bash
outdir=tophat_out_fusion
for file in test_*.fasta
do
rm -rf $outdir ; ~/work/tophat/tophat_trunk/src/tophat -o $outdir --fusion-do-not-resolve-conflicts --max-intron-length 500 --fusion-min-dist 500 --keep-tmp --fusion-search --bowtie1 --keep-fasta-order testcases/test $file >& /dev/null
ech... |
e7ecd4409149a86008e2b722365117c0c2436eb2edbe7dc79054a693825c5f4f | Shell | 486 | 10 | set -e
if [ ! -e mESC_chr6.tsv ]
then
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35156/suppl/GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary.txt.gz
gunzip GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary.txt.gz
cat GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary... |
f7da86fe709dd10365e6e1a6ee60c0be1c91dafe000a9e6d7a2c917b34189dc7 | Shell | 486 | 14 | # 通过不同的lora ckpt来获得image的coarse grained embedding
python run_get_embeddings_coco.py
# ------------------------------------------------
# 运行单次imagebind推理 (baseline)
python run_infer_coco_lora.py
# ------------------------------------------------
# 运行端到端实验
# 1. 运行生成所有需要运行的脚本名
python run_e2e_coco_command_lora.py
# 2. 运... |
5a9f70f85b0643784eeac0289646ff86a621509b525e2455929f074a0cb94895 | Shell | 489 | 22 | #!/bin/bash
set -e
export VFC_BACKENDS="libinterflop_mca.so"
# Compile program with -O2 (test_1) and -O2 -ffastmath -freciprocalmath (test_2)
parallel --header : "make --silent options={options}" ::: options "" "-ffast-math -freciprocal-math"
echo "z y" >output1
for i in $(seq 1 30); do
./test_1 1.0 >>output1
do... |
008c06529c9a4fa1babac254eb6a7770214762bd6faa012178a4477f58b35d40 | Shell | 491 | 22 | #!/bin/bash
pkexec apt install python3-venv python3-tk
python3 -m venv ~/IJOQ-venv
~/IJOQ-venv/bin/pip install "pillow" "numpy" "requests"
echo "[Desktop Entry]
Comment=
Exec=${HOME}/IJOQ-venv/bin/python '${PWD}/IJOQ.py'
GenericName=Junction Quantification Script
Icon=${PWD}/IJOQ_icon.png
Name=IJOQ
NoDisplay=false
Pa... |
e99c5a1cde3d3d5915cca97845d37bd3855603bd21fa3458325ed33734ed6236 | Shell | 492 | 8 | for ds in '10X_PBMC' 'Adam' 'mouse_bladder_cell' 'mouse_ES_cell' 'Muraro' 'Quake_10x_Bladder' 'Quake_10x_Limb_Muscle' 'Quake_10x_Spleen' 'Quake_Smart-seq2_Diaphragm' 'Quake_Smart-seq2_Limb_Muscle' 'Quake_Smart-seq2_Lung' 'Quake_Smart-seq2_Trachea' 'Romanov' 'worm_neuron_cell' 'Young';
do
python calculate_dist.py ... |
76753f122e4a1b8a59f46570fd75e150bcf356357e7013c10e113b92c075eb20 | Shell | 495 | 6 | #!/bin/bash
mkdir log
nohup python -u 2.0.clean_up_rxn_condition.py --gpu 0 --split_group 4 --group 0 > log/get_rxn_condition_uspto_0.log &
nohup python -u 2.0.clean_up_rxn_condition.py --gpu 0 --split_group 4 --group 1 > log/get_rxn_condition_uspto_1.log &
nohup python -u 2.0.clean_up_rxn_condition.py --gpu 1 --split... |
b0c205675e8f75a93d1819d855f4d35854dff3161c95bd40668e34def5d54ac0 | Shell | 495 | 17 | #!/bin/sh
my_sample_dir="Raw-data-folder" \
R1=$my_sample_dir"Sample-file_1.fq.gz" \ #Forward
R2=$my_sample_dir"Sample-file_2.fq.gz" \ #Reverse
Output_dir="Output-folder/" \
mysample=$Output_dir"Sample-Folder/"
STAR --genomeDir "Index-Folder" \
--sjdbGTFfile "GTF-File" \
--runThreadN 28 \
--readFilesIn <(z... |
3f385d552e14e04c8c9bc1a52b4963a17ec343dea4288cc4c995ca486628e8d3 | Shell | 496 | 8 | #!/bin/bash
#SBATCH --job-name=task3_aggregate
#SBATCH --time=3-00:00:00
#SBATCH --nodes=20
#SBATCH --ntasks-per-node=3
module load mamba
source activate /hpc/mydata/upt/ns833749/sbdd_analysis_env
python run_analysis.py --model_name "Pocket2Mol" --analysis_output_dir "/hpc/scratch/hdd1/ns833749/SBDD_analysis_out_final/... |
36c1c88e990b1aacd30fedff2f0d999adcf24dff6779b9f3efde4800fbdb7d45 | Shell | 498 | 18 | #!/bin/sh
#SBATCH -J CCST
#SBATCH -p gpu
#SBATCH -o filename_%j.txt
#SBATCH -e filename_%j.err
#SBATCH --mail-type=ALL
#SBATCH --mail-user=trstans@iu.edu
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-node=2
#SBATCH --time=02:00:00
#SBATCH --mem=100G
#SBATCH -A c01064
module load miniconda
conda acti... |
6b15a8ffd04c171d157d82bbce73f8eff99224d89411a5a45750b29e71fbafc8 | Shell | 500 | 14 | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
# From appendix C of paper https://arxiv.org/abs/1912.08777
# Set --gradient_accumulation_steps so that effective batch size is 256 (2*128, 4*64, 8*32, 16*16)
python finetune.py \
--learning_rate=1e-4 \
--do_train \
--do_predict \
--n_val 100... |
6042fb6de6c3238c2320a0397a1b9fa68e2e93b562443b911fc71bd426b70a3e | Shell | 505 | 18 | #!/bin/bash
#SBATCH --cpus-per-task=2
#SBATCH --mem-per-cpu=20G
#SBATCH --time=0-03:00
#SBATCH --account=def-barrett
#SBATCH --mail-type=ALL
#SBATCH --mail-user=janay.fox@mail.mcgill.ca
################################################################################
### Goal: Run gene annotation scripts
### Author: Ja... |
6fb9f1ab9c956cd5111760e1bb708cdd88df8de19bd0af94bbe59d07624588d5 | Shell | 505 | 18 | #!/bin/bash
#SBATCH --ntasks-per-node=1
#SBATCH -p day
#SBATCH --job-name=sumstat
#SBATCH --time=1:00:00
#SBATCH --mem-per-cpu 8G
#SBATCH --cpus-per-task=1
module load miniconda
conda activate ldsc
folder="/gpfs/gibbs/pi/gerstein/jz435/ShareZhangLab/PTSD/LDSC/GWAS_PGC2"
cd $folder
for snp in $folder/*.txt
do
n=$(... |
ff3ea7cb9a83ef33ba7409f31cb3ccbd9bd964ee143e6074bff474a9cc9094ad | Shell | 507 | 2 | for i in ^R* ; do for j in prostate urinary_bladder; do flirt -in $i/$j.nii.gz -ref ../../data/nifti/${i}_pet.nii.gz -usesqform -out $i/${j}-pet.nii.gz -applyxfm; done; done
(echo "pid min max rmin rmax vol_pix vol_mm3 mean sd cx_px cy_px cz_px cx cy cz"; for i in data/nifti_suv/*.nii.gz; do pid=$(basename $i _pet.nii.... |
ecc8c4114c83a686bdbdcab7fd758bc62285575f583e31fa445efdfc8aa16ae6 | Shell | 513 | 25 | #!/bin/bash
set -e
FILE=$1
BASE=`basename ${FILE%.fastq.gz}`
OUTDIR=sickle
mkdir -p $OUTDIR
if [ ! -s $FILE ]
then
echo "File is missing or empty: $FILE"
exit 65
fi
if [ -s $OUTDIR/$BASE.sickle.fastq.gz ]
then
echo "Output file already exists: $OUTDIR/$BASE.sickle.fastq.gz"
exit 64
fi
# Run sickle only cut... |
dfcb10672f67635af8c4fe7a7a5b4ffb335178a35c314fd5fe0cd3d408335226 | Shell | 514 | 8 | python python_single_gpu_unit_test.py
python single_gpu_unit_test.py
python test_batchnorm1d.py
python -m torch.distributed.launch --nproc_per_node=2 two_gpu_unit_test.py
python -m torch.distributed.launch --nproc_per_node=2 two_gpu_unit_test.py --fp16
python -m torch.distributed.launch --nproc_per_node=2 two_gpu_test_... |
278b858a1417e371a3e32c7c8bcdc8e79f8173b3ae600684012ab74ccec42bec | Shell | 515 | 11 | #!/bin/bash
awk '{print $1}' ../analysis/results_branchshift/ADULTresultsSOR_sig > ../analysis/results_branchshift/adapt
while read j; do
p=$(cat ../data/6_keys/bos_key | grep $j| awk '{print $1'})
#in terms of shrew genes for comparing to DESeq2 results
cat ../data/6_keys/final_sor_key | grep $p >> ../analysis/r... |
84f5573c6b42b7617cded6549541a4fdcf9b60ce7e7da9c3e37834c89106ab92 | Shell | 515 | 18 | CUDA_VISIBLE_DEVICES=0,1,3 python src/ms_pred/autoregr_gen/hyperopt.py \
--seed 5 \
--num-workers 10 \
--max-epochs 200 \
--dataset-name nist20 \
--dataset-labels labels.tsv \
--formula-folder magma_subform_50 \
--split-name hyperopt.tsv \
--gpu \
--save-dir results/autoregr_hyperopt \
--cpus-per-trial 10 \
--gp... |
3321773dea326ace0e83aedc6cc16ff2d053cf5b1d738fbb2449dec67373dc99 | Shell | 518 | 18 | #!/bin/sh
# Create a file with genomes databases list
# Remove the first two lines (titles)
mkdir -p galaxy/tool-data
(
echo "# SnpEff databases"
echo "# List created using command: java -jar snpEff.jar databases"
echo "# Version Description"
java -jar snpEff.jar databases \
| tail -n +3 \
| cut -f 1,2 \
... |
46f6bb130cf9e69983d75e771de242cc9b2cf4686572a9ed589b0cb9b7b1cf43 | Shell | 519 | 16 | module load BEDTools/2.30.0-GCCcore-10.2.0
multiome_peaksdir=/gpfs/gibbs/pi/gerstein/jz435/ShareZhangLab/PTSD/Multiome/25_samples_archr_subclass_peaks/PeakBeds
celltypes=('CUX2' 'RORB' 'FEZF2' 'OPRK1' 'LAMP5' 'KCNG1' 'VIP' 'SST' 'PVALB' 'OLI' 'OPC' 'END' 'AST' 'MIC')
for i in ${celltypes[*]}
do
for j in ${cell... |
4987bf9c1f6435c8213182e7515e91686ee80e0089e26b159152c7e1b81d7b2c | Shell | 519 | 6 | cd ..
git clone https://github.com/llvm/llvm-project.git
mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja ../llvm -DMLIR_ENABLE_BINDINGS_PYTHON=On -DLLVM_ENABLE_PROJECTS=mlir -DLLVM_BUILD_EXAMPLES=ON -DLLVM_TARGETS_TO_BUILD="Native;NVPTX" -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_C_COM... |
e43fdcaf0ddc28deb172c6ca1660f225704fb9f6052202f46ac6a5345036bc15 | Shell | 523 | 20 | expe_name="rename"
main_script=/srv/tempdd/egermani/hcp_pipelines/results/run_renaming.py
echo "Create dir for log"
CURRENTDATE=`date +"%Y-%m-%d"`
echo "currentDate :"
echo $CURRENTDATE
PATHLOG="/srv/tempdd/egermani/Logs/${CURRENTDATE}_OARID_${OAR_JOB_ID}/"
echo "path log :"
echo $PATHLOG
mkdir $PATHLOG
chmod 777 $PA... |
1f61e9c99c45512e6c6dcf5c32a55c78fdfe2b557303e51e0cd7b6ee6b9c0988 | Shell | 529 | 18 | #!/bin/sh
#SBATCH -p sapphire
#SBATCH -t 0-71:00
#SBATCH --mem 60G
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=20
#SBATCH -o /n/holyscratch01/davis_lab/pflynn/soil/Ma_images_varKode.%A.out
#SBATCH -e /n/holyscratch01/davis_lab/pflynn/soil/Ma_images_varKode.%A.err # File to which standard err will ... |
d9c8083927a43d256a84ea004938a87353a3a1d2003e110793bfdfbc2034a1b6 | Shell | 530 | 16 | #!/bin/bash
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=$CONDA_PREFIX/lib:$LIBRARY_PATH
export FASTA_DIR=./fasta_dir
export OUTPUT_DIR=./
export PRECOMPUTED_ALIGNMENT_DIR=./alignments
export MMCIF_DIR=/mmcifs # UPDATE with path to your mmcifs directory
python3 run_pretrained_open... |
cae579d2186cdef5256934f87f61a7b58748e8180f8325aeb94055cdfac71d13 | Shell | 531 | 9 | SUBJECT_ID="sub-0${1}"
HEMI="$2h"
PRF_MODEL=prf_Iso_fit_hrf_False
FS_DIR=../relational_neural_control/in_vivo_validation/in_vivo_fmri_dataset/data_FS/$SUBJECT_ID
PRFPY_DIR=../relational_neural_control/in_vivo_validation/in_vivo_fmri_dataset/prf_output/${PRF_MODEL}
freeview -f $FS_DIR/surf/$HEMI.inflated:overlay=$PRF... |
513171f63d88f093c65d013162470e6484b3e677d2a5c9a6c2cbab23c1364606 | Shell | 532 | 16 | #!/bin/bash
model_type="resnet" # candidate model_types: transformer, lstm, resnet
task="secondary_structure" # candidate tasks: pfam, secondary_structure, remote_homology, fluorescence, stability
model_config="./configs/${model_type}_${task}_config.json"
train_data="./secondary_structure_toy_data/"
valid_data="./sec... |
7def5f3cc601b6ace556bfea832a66d118cd53c4d5be2577fca57ae6f7173bff | Shell | 537 | 16 | #!/bin/bash
model_type="transformer" # candidate model_types: transformer, lstm, resnet
task="secondary_structure" # candidate tasks: pfam, secondary_structure, remote_homology, fluorescence, stability
model_config="./configs/${model_type}_${task}_config.json"
train_data="./secondary_structure_toy_data/"
valid_data="... |
b7acb379ef2022a1d91780f1aa8e47b1cb9986f51ac4b9d4cb9a3337ead338aa | Shell | 538 | 21 | #!/bin/sh
#SBATCH -p test
#SBATCH -t 0-08:00
#SBATCH --mem 50G
#SBATCH -n 8
#SBATCH -c 2
#SBATCH -o 1_generate_varkodes.%A.out
sleep 30
scontrol show jobid=$SLURM_JOBID
module load Anaconda3/2020.11 cuda
source activate varKoder
VARKODER_PATH=/n/home08/souzademedeiros/programs/varKoder/varKoder.py
time python $... |
3d1c4d405619188a1633839a80c6f3be3f7f61226f554f241ce4cd71b9b7b001 | Shell | 539 | 12 | #!/bin/bash
# This script will run hap.py (with the given parameters) in its own environment
# (with matching python, libs, etc.)
# It will find the real (environment) directory hap.py is in by following softlinks
# It will change the PATH to include the <environment dir>/bin
# It will change the LD_LIBRARY_PATH to inc... |
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