sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c4b90199af35e45c5c138b2f33a666e13407cacbed55d74d165a9edc7cff7a37 | Shell | 572 | 6 | cd ..
#gmx pdb2gmx -vsite hydrogens -f ionized.pdb -o ionized.gro -ter -ff /usr/local/gromacs/share/gromacs/top/charmm36-mar2019.ff -water tip3p -p topol.top
#gmx pdb2gmx -vsite hydrogens -f ATP.pdb -o ATP.gro -ff charmm36-mar2019 -ter -water tip3p -p topol.top
#gmx pdb2gmx -vsite hydrogens -f ionized.pdb -o ionized... |
c862e6ae79208b79cc204b502c8d7ef3fae96d80f43ab611e85560b10789f413 | Shell | 577 | 23 | #!/bin/bash
module load purge_haplotigs/0~20180710.f4fd019
genome=$1
if ! [ -e asm.fasta ]; then
ln -s ../../${genome}_c1.fasta asm.fasta
fi
if ! [ -e asm.fasta.faidx ]; then
module load samtools
samtools faidx asm.fasta
fi
threads=$SLURM_CPUS_PER_TASK
echo "STEP 3. Purge!"
echo "\
purge_haplotigs purge -g a... |
3551630bf61d44d24d05e8cd707abc4271fa6fe2aa684dc880a20487a815d6b0 | Shell | 580 | 3 | cd "$(dirname "$0")"
java --module-path ./jfx/lib/ --add-modules javafx.base,javafx.graphics,javafx.fxml,javafx.controls,javafx.media,javafx.web,javafx.swing -Dcom.sun.javafx.isEmbedded=true -Dcom.sun.javafx.touch=true -Dcom.sun.javafx.virtualKeyboard=javafx --add-exports javafx.base/com.sun.javafx.event=ALL-UNNAMED --... |
4de821575406fbae41ec1e5145da28948177eeec7f321343dfad49a2154a3620 | Shell | 581 | 14 | #!/bin/bash
module load bwa/0.7.17
module load gcc/7.4.0
name=$1
# -nodes: Reserve 2 cores for computational overheads
echo "
$tools/scaff10x/Scaff10X-4.1/src/scaff10x -nodes $((SLURM_CPUS_PER_TASK-2)) -longread 1 -gap 100 -matrix 2000 -read-s1 12 -read-s2 8 -link 10 -score 20 -edge 50000 -block 50000 -data input.d... |
147087c4377aa5cd1c2056236547b2d1ff2db723123b6a8cc20773465fb5f5ec | Shell | 583 | 21 | #!/bin/bash
folder_name=${1%/}
UpdateLog.sh $folder_name zip_data $job_ID START Zipping_Data_And_Deleting_Raw_Images
#read the last line of the log, get job IX
log_name=$folder_name/status.csv
last_line=$(tac $log_name |egrep -m 1 .)
IFS=',' read -ra ADDR <<< "$last_line"
job_ID=${ADDR[2]}
#get how many jpg files ar... |
1623b2055b29ca97aa2ab223e1922400ca51904989fa08eb7cf4cbb4d6123df0 | Shell | 584 | 13 | #!/bin/bash
#SBATCH -p all # partition (queue)
#SBATCH -N 1
#SBATCH --gres=gpu:2
#SBATCH --contiguous
#SBATCH --mem=20000 #20gbs
#SBATCH -t 25 # time (minutes)
#SBATCH -o /tigress/ahoag/cnn/exp2/slurm_logs/cnn_train_%j.out
#SBATCH -e /tigress/ahoag/cnn/exp2/slurm_logs/cnn_train_%j.err
mo... |
c6a8aed149af0d5f3a41f120c1da5c02707acfc1f152cab8e10873bff2d3d88f | Shell | 587 | 15 | #!/bin/bash
#SBATCH -p all # partition (queue)
#SBATCH -N 1
#SBATCH --ntasks-per-node=4
#SBATCH --ntasks-per-socket=2
#SBATCH --gres=gpu:4
#SBATCH --contiguous
#SBATCH --mem=14000 #14gbs
#SBATCH -t 8500 # time (minutes)
#SBATCH -o cnn_train_20190502.out
#SBATCH -e cnn_train_20190502.err
... |
915cb1e37997a2731e2ca825168fc3655df55845f93f21f11f28de509b879029 | Shell | 588 | 48 | #!/usr/bin/env bash
END=7;
filestr="";
searchstr="";
acid="E";
for i in $(seq 1 $END);
do
added=$(( $i + $END ))
if [ $added -lt 10 ]
then
filestr=$acid"0";
else
filestr="$acid";
fi
if [ $i -lt 10 ]
then
searchstr=$acid"0";
else
searchstr="$acid";
fi
echo "$searchstr";
echo "$filestr";
val="$i";
regexSearch="... |
2b342cddc1657df70c131746cd59140043b2f2535a0141ade5da0cc6114dc73a | Shell | 598 | 21 | #!/bin/bash
#SBATCH -p all # partition (queue)
#SBATCH -N 1
#SBATCH --gres=gpu:1
#SBATCH --contiguous
#SBATCH --mem=5000 #5 gbs
#SBATCH -t 10 # time (minutes)
#SBATCH -o demo_%j.out
#SBATCH -e demo_%j.err
module load cudatoolkit/10.0 cudnn/cuda-10.0/7.3.1 anacondapy/2020.11
. activate bra... |
d8cfe1f49de00c66b93ea01342cb1485e2ba1fb6d826d574dd1c79b8056a5790 | Shell | 602 | 25 | #Installations needed:
#pip install sphinx
#pip install sphinx_rtd_theme
#pip install mock
# Remove old docs
rm -r source/* _build
# Auto-generate API docs:
sphinx-apidoc -M -o ./source ../atom3d ../atom3d/datasets/???/*.py
# Format the auto-generated API docs
sed -i 's/ package//g' source/*.rst
sed -i 's/ module//g... |
d58cbd0c12ac204baaeab472c9eed3b1e8c6e9fcce7f854754fe17d5f385c0ca | Shell | 607 | 35 | #!/bin/bash
echo "Usage: ./merge.sh <out>"
echo "Merge bams in bam.list to <out>.bam"
cpus=$SLURM_CPUS_PER_TASK
out=$1
if [ -z $out ]; then
exit -1
fi
module load samtools
bams=`cat bam.list | tr '\n' ' '`
bais=`echo $bams | sed 's/.bam/.bam.bai/g'`
num_bams=`wc -l bam.list | awk '{print $1}'`
if [[ "$num_bams" ... |
0c2fa6a1ceae2103b0453db1a129c15c17583bdc0fb59698be89a7f1c311abaf | Shell | 611 | 18 | #!/bin/bash
#BSUB -M 128000
#BSUB -W 12:00
#BSUB -n 12
#BSUB -R "rusage[mem=128000] span[hosts=1]"
#BSUB -J majiq_builder
#BSUB -o logging/majiq_builder.out
#BSUB -e logging/majiq_builder.err
module load anaconda3
source activate /data/aronow/Kang/py_envir/splice2/
# build
n_jobs=8
gff_file='/data/aronow/Kang/induce... |
db718a0e72112bb8438fab58b62a5e9d0867ca3409a52dc0d25a1b5b03365b6d | Shell | 616 | 31 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./break10x.sh <out_prefix>"
echo -e "\tSymlink asm.fasta, read-BC1.fastq and read-BC2.fastq"
exit -1
fi
asm=asm.fasta
if [ ! -e asm.fasta ]; then
echo "No asm.fasta detected. Symlink asm.fasta"
exit -1
fi
if [ ! -e read-BC1.fastq ]; then
echo "No r... |
356d06a5b8c8cf82d517bab777d660fd12c3665123d41f22c2b34c95a2fd131c | Shell | 618 | 21 |
NET=./eQTL/SNP_TSS_500k.bed.loop.txt
MAT=./eQTL/scAtlas_normalizedData.txt
INDEX=./ics_out/this.index
python3 ../src00_ics/step1_BuildIndex.py $NET $MAT $INDEX
nohup python3 ../src00_ics/step2_CalICS_mean.py $INDEX ics_out/this.out.mean &
nohup python3 ../src00_ics/step2_CalICS_hmean.py $INDEX ics_out/this.out... |
3e0adde5e4ada41495d80b38972b73808a03f79e7fb051c8ad93442c2777bcfb | Shell | 618 | 54 | #!/bin/bash
script_name=$1
case "$script_name" in
delete_tracks)
echo 0
;;
unzip_data)
echo 1
;;
track_image_directory)
echo 2
;;
find_centerlines)
echo 3
;;
auto_resolve_problems)
echo 4
;;
calculate_spectra)
... |
33b9965fa70ba173b88c62303b462a23233c24162e77d068915d8c03020ac340 | Shell | 620 | 54 | #!/bin/bash
ordering=$1
case $ordering in
0)
echo delete_tracks
;;
1)
echo unzip_data
;;
2)
echo track_image_directory
;;
3)
echo find_centerlines
;;
4)
echo auto_resolve_problems
;;
5)
echo calculate_spectra... |
ea18d87f4ce943da4bfb41838a48cd35b93c6568487c42602d3bcb78564749c9 | Shell | 622 | 18 | #!/usr/bin/env bash
# (1) Archive the model into a .mar
torch-model-archiver --model-name btnx_outlier \
--version 1.0 \
--model-file outlier_model.py \
--serialized-file btnx_outlier.pt \
--handler outlier_handler.py \
... |
9ffe068bb15873f05a2785ba1f5ea177dd6a9a29e6313dae5c59e9d6fafd256e | Shell | 624 | 15 | # dataset=USPTO50K-aug-untyped
seed=$1
dataset=USPTO50K-aug-untyped_${seed}
dataset_prefix=USPTO50K-aug-untyped
suffix=test-prediction
source /cm/shared/engaging/anaconda/2018.12/etc/profile.d/conda.sh
conda activate retroxpert_
# propose 200, with seed
python3 ../../onmt/bin/translate.py --model ../../experiments/ch... |
1c893110dc82bc5bd3d149e25184b9bf492d90c6481948354ba814a8c4dbfc11 | Shell | 625 | 9 | docker run --cpus=16 --memory=32g --shm-size=32g --rm -it \
-v /media/hdd2/works/HeviAI_picai/picai_baseline/src/picai_baseline/nnunet_semi_supervised/training_sagemaker/code:/code:ro \
-v /media/hdd2/works/HeviAI_picai/debug_workdir:/workdir \
-v /media/hdd2/works/HeviAI_picai/input_images:/input/images:ro... |
54f6a4cbfee7085f2ef88a41a0db79369e12bee0c8c9765f70a4419fdfc0f0b3 | Shell | 625 | 11 | python3 ../../../tools/average_models.py -output average_model.pt \
-models USPTO50K-aug-untyped_model_step_210000.pt \
USPTO50K-aug-untyped_model_step_220000.pt \
USPTO50K-aug-untyped_model_step_230000.pt \
USPTO50K-aug-untyped_model_step_240000.pt \
USPTO50K-aug-unt... |
77fc45db8e7756fbb69108631b99c737c70891cac1bb35a699b41f86be8f42af | Shell | 625 | 11 | # BIDS Data structure transformation
python data2bids.py /nfs/z1/zhenlab/BrainImageNet/action scaninfo.xlsx -q ok --overwrite --skip-feature-validation
# fMRIprep preprocessing
export prjdir=/nfs/z1/zhenlab/BrainImageNet/action/data/bold
export bids_fold=$prjdir/nifti
export out_dir=$prjdir/derivatives
export work_... |
00211a9b9cfaf6d0c173a833c75018cd91a24ed0e9b1a9ea05d963a5ad747954 | Shell | 626 | 20 | #!/usr/bin/env bash
# Get ReMap 2018 peaks
REMAP="remap2018_all_macs2_hg38_v1_2.bed.gz"
if [ ! -f ${REMAP} ]; then
wget http://remap.univ-amu.fr/storage/remap2018/hg38/MACS/${REMAP}
fi
# Extract peak summits and sort
SUMMITS="summits.bed"
if [ ! -f ${SUMMITS} ]; then
zless ${REMAP} | cut -f 1,4,7,8 | awk '{p... |
5ea7195f304162dc6a1e5979225043de3ab258eb17613bc1f10d9e33e20919ac | Shell | 626 | 17 | #!/bin/bash
set -e
SPECIES=$1
ABBR=$2
CONTIG=$3
cd ${SPECIES}/${ABBR}_10x2
bowtie2-build ${CONTIG} ${ABBR}
bowtie2 -x ${ABBR} -1 ../${ABBR}_10x1/fq/aligned_${ABBR}_all_1.fq -2 ../${ABBR}_10x1/fq/aligned_${ABBR}_all_2.fq -p 16 --no-mixed | samtools view -bSF4 - > "aligned_${ABBR}_all_trimmed.bam"
samtools sort alig... |
cfd1d3cf00e678131b5e18aeb10f793217920a9fba1554328feeab36c52ff108 | Shell | 627 | 9 | docker run --cpus=16 --memory=32g --shm-size=32g --rm -it \
-v /media/hdd2/works/HeviAI_picai/picai_baseline/src/picai_baseline/nnunet_semi_supervised/training_sagemaker/code:/code:ro \
-v /media/hdd2/works/HeviAI_picai/debug_workdir:/workdir \
-v /media/hdd2/works/HeviAI_picai/debug_images:/input/images:ro... |
5511014c10a67b999b29f0e97302d7813ae9ac77b468a3d0dd8ec5f0731ef32c | Shell | 631 | 17 | for REP in $(seq 1 3); do
for CUTOFF in 08; do
for BATCHSIZE in 4; do
FORMAT=LMDB-noH
SB_FILE=run_cutoff${CUTOFF}_bs${BATCHSIZE}_${FORMAT}_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/FORMATDIR/LMDBDIR/g" ${SB_FILE}
sed -i "s/FORMAT/${FORMAT}/g... |
3e76355c8560593e59cde780e796cf7ceac2e864989e516aacbd18cdb317cddb | Shell | 633 | 27 | #!/bin/bash
# concat_two_files 0.0.1
main() {
echo "Value of input1: '$input1'"
echo "Value of input2: '$input2'"
echo "Value of output_extension: '$output_extension'"
dx download "$input1" -o input1
dx download "$input2" -o input2
cat input1 input2 > temp_output.fasta.gz
basename="$in... |
8dcba38e2d755a81950744ad429a72d1e3d5a75bb3d99af0ebe8b548a00c46dc | Shell | 635 | 18 | #!/usr/bin/env bash
# (1) Archive the model into a .mar
torch-model-archiver --model-name maskrcnn \
--version 1.0 \
--model-file maskrcnn_model.py \
--serialized-file ../saved_models/maskrcnn_weights.pth \
--handler maskrcnn_handler.p... |
d3a9d8e4cd104f83ce1e3079d7ccd067693b56c72eec604c425c1ca0de73c714 | Shell | 635 | 15 | # ensure conda is properly initialised, such as by using the line below
# source /cm/shared/engaging/anaconda/2018.12/etc/profile.d/conda.sh
conda activate rxnebm
# GLN + retrosim, 50+50, 200+200
gln_seed=$1
python gen_proposals/gen_fps_from_proposals.py \
--log_file GLN_${gln_seed}_50top200max_retrosim_50top200ma... |
0765067632b700beb1a309a7e7d60ef420d5081ca50cba997d57c07efd393c80 | Shell | 636 | 30 | #!/bin/bash
# path to data
DATA_DIR=/home/goschv/CSB_NeuroRad2/goschv/DATA/Repro
cd $DATA_DIR
for s in CSB*
do
echo $s
# brain extract b0
bet $s/*b0.nii.gz $s/processed/"${s:9:11}"_b0_bet.nii.gz -f 0.2 -R
done
# references
'https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL
32 Woolrich MW, Jbabdi S, Patenau... |
aff2e6eae481cc847140b5aa0dc0ad232779ab006c6a1a8d34492584801532c0 | Shell | 641 | 11 | INFO_CSV="/oak/stanford/groups/rondror/projects/atom3d/supporting_files/ligand_efficacy_prediction/info.csv"
INPUT_FILES="/oak/stanford/groups/rondror/projects/atom3d/supporting_files/ligand_efficacy_prediction/pdb"
SPLIT_PATH="/oak/stanford/groups/rondror/projects/atom3d/ligand_efficacy_prediction/split/"
TEST=$SPLIT_... |
aaed266f85aefaa0d3a53ac73ecbbbd4e807f4a373444a289cd34251a3ef276f | Shell | 644 | 28 | #! /bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_placements.sh <genome>"
exit -1
fi
genome=$1
ln -s ../../${genome}_v1.p.fasta
ln -s ../../${genome}_v1.h.fasta
cpus=24
mem=48g
name=$genome.placements
script=$VGP_PIPELINE/purge_haplotigs/placements.sh
args=$genome
partition=norm
walltime=2-0
path=`pwd`
mkdi... |
68cc0fcda35370c7f8fe5300f1e64bbb13c2d2dc3502c779b81732ab12c0b56b | Shell | 660 | 23 | #!/bin/bash -l
#SBATCH --job-name=bootstrap_reconcile
#SBATCH --output=reconcile.out
#SBATCH --error=reconcile.err
#SBATCH --time=07-00:00:00
#SBATCH --nodes=35
#SBATCH --ntasks-per-node=20
#SBATCH --partition=standard
#Activate the topiary conda environment
source ~/miniconda3/bin/activate
export PATH=/home/users/k/k... |
6f55cd878b76a38ae151935d646b64d9a700417de6e6107a4d85107ada0a1bb0 | Shell | 660 | 12 | wget -cq https://ftp.ncbi.nih.gov/snp/pre_build152/organisms/human_9606_b151_GRCh38p7/VCF/00-All.vcf.gz
wget -cq https://ftp.ncbi.nih.gov/snp/pre_build152/organisms/human_9606_b151_GRCh38p7/VCF/00-All.vcf.gz.tbi
./scripts/topmed 00-All.vcf.gz dbsnp.pre.151.bcf
bcftools index --threads 3 dbsnp.pre.151.bcf
fasta=/data/... |
815a65e9c0198e842e954f2b896cf6af0e2e05768b125168a98e833a485386f8 | Shell | 661 | 12 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
dnn_rsa -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -dmask $DNNBRAIN_DATA/test/alexnet.dmask.csv -iteraxis channel -metric correlation -out $TMP_DIR/dnn_rsa_dmask.rdm.h5
dnn_rsa -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -l... |
03d695961c36d71398c403d4a629614414cb97f3c832a76377b6bfd84732f6dd | Shell | 663 | 26 | #
# Original solution via StackOverflow:
# http://stackoverflow.com/questions/35802939/install-only-available-packages-using-conda-install-yes-file-requirements-t
#
#
# Install via `conda` directly.
# This will fail to install all
# dependencies. If one fails,
# all dependencies will fail to install.
#
conda c... |
02db9d61052adb713a47a1deffc0a9133284cfb357702da6782df6d8c69a759d | Shell | 664 | 24 | #!/bin/bash
#PBS -P r16
#PBS -q gpuvolta
#PBS -l ngpus=1
#PBS -l ncpus=12
#PBS -l storage=scratch/f91
#PBS -l walltime=1:00:00
#PBS -o out.out
#PBS -e error.out
#PBS -l mem=16GB
#PBS -l wd
#PBS -m abe
#PBS -M yearlyboozefest@gmail.com
module load intel-mkl/2020.2.254
module load openmpi/4.0.3
module load cuda/11.0.3
m... |
ac8257f0cc2880ec3534c56f5ac70cba92976dd9aa609d8e1768e948bd7dc75b | Shell | 664 | 18 | #!/usr/bin/env bash
# (1) Archive the model into a .mar
torch-model-archiver --model-name quidelag_classifier \
--version 1.0 \
--model-file classifier_model.py \
--serialized-file quidelag_classifier.pth \
--handler classifier_handler... |
0a62b62e1c84482f8030f38c542963e2aa8b0cfa518e9f6e785df5b7d4c339ac | Shell | 667 | 19 | #!/bin/bash
#SBATCH -p all # partition (queue)
#SBATCH -N 1
#SBATCH --ntasks-per-node=1
#SBATCH --ntasks-per-socket=1
#SBATCH --gres=gpu:1
#SBATCH --contiguous
#SBATCH --mem=30000 #30gbs
#SBATCH -t 360 # time (minutes)
#SBATCH -o /tigress/ahoag/cnn/exp2/slurm_logs/array_jobs/chnk_%a_%j.out... |
4f7b8903ff85858639d271ad9fdf66f8012be2c0b9f68fc58752d900b843ccb2 | Shell | 667 | 31 | #!/bin/bash
# user input
if [ "$#" -lt 2 ]; then
echo "snakemake_sge.sh config.yaml jobs ..."
echo " config.yaml : snakemake config"
echo " jobs : number of parallel qsub jobs"
echo " ... : additional arguments passed to snakemake"
exit
fi
# check for snakemake
command -v snakemake >/dev/null 2>&1... |
c88f2c214d876d4e9da24ec3f3853b87b7a5ab5e1cfedeb53b2b99df7b654778 | Shell | 667 | 22 | #!/bin/bash
export FREESURFER_HOME=/Applications/freesurfer
source $FREESURFER_HOME/SetUpFreeSurfer.sh
export SUBJECTS_DIR=/Users/nagehan/subjects
input="/Users/nagehan/subjects/subjects_yale.txt"
while IFS= read -r line
do
echo "$line"
printf '%s\n' "$line"
mris_convert $SUBJECTS_DIR/Yale/$line/surf/lh.white $... |
be3ae0eaf5d1cf71ca71071ee88cbcb2ae8bbb48bbe51dd3f6fed6708afa37ac | Shell | 671 | 13 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# extract DNN activation from video with -layer
dnn_act -net AlexNet -layer conv5 fc3 -stim $DNNBRAIN_DATA/test/video/sub-CSI1_ses-01_imagenet.stim.csv -out $TMP_DIR/dnn_act_video_layer.act.h5
# extract DNN activation from image with dmask
dnn_act -net AlexNet -... |
80b9b291f8cc39cd6a101b4ead17fafa894c30818956599c580f7cf8586a0d41 | Shell | 673 | 31 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_busco.sh <assembly.fasta>"
exit -1
fi
assembly=$1
if ! [ -e `basename $assembly` ]; then
ln -s $assembly
fi
assembly=`basename $assembly`
name=${assembly/.fasta/}
cpus=24
mem=42g
name=$name.busco
script=$VGP_PIPELINE/busco/busco.sh
args=$assembly
partition=no... |
0b59ec33c634c12bbaa8b14227ae3a8e4e96415c227f2d4c2a75333cbf161439 | Shell | 674 | 24 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_hybrid_scaffold_dle1_bsssi.sh <name>"
echo -e "\t<name>: job name and output dir name"
exit -1
fi
cpus=54
mem=240g
partition=norm
name=$1
script=$VGP_PIPELINE/bionano/hybrid_scaffold_two.sh
args="$1 DLE1 BSSSI $VGP_PIPELINE/bionano/hybridScaffold_two_enzymes_D... |
475d0677b9f15349d1215c020e68344d1c7d8c11bd65ee542671aeb0ad636ae1 | Shell | 674 | 22 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 1 # number of cores
#SBATCH -t 20 # time (minutes)
#SBATCH -o logs/cnn_step4_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/cnn_step4_%j.err # STDERR #add _%a to see each array ... |
5d89410dbf8248a842cf82b7a887343a44f41a2dba2b4e1514347a8daeb90b90 | Shell | 675 | 23 | #!/bin/bash
genome=$1
if [ -z $genome ]; then
echo "Usage: ./to_purge_haplotigs_bed.sh <genome_id>"
exit -1
fi
map1=mashmap_${genome}_curated_to_${genome}_v1.p/out.map
map2=mashmap_${genome}_curated.haplotigs_to_${genome}_v1.p/out.map
# Transform to bedpe
name="PRIM"
cat $map1 | awk -F " " -v name=$name '{print ... |
a7768b25632d682cc7435c1533d94fb81460e8f5cffca74b1760c9714661148d | Shell | 676 | 37 | #!/bin/bash
mkdir -p ${2}
cd ${2}
aws s3 cp --recursive --include="*.fastq.gz" --exclude="*I1*" s3://genomeark/species/${1}/${2}/genomic_data/10x/ genomic_data/10x/
cd genomic_data/10x/
ls *R1*fastq.gz > R1.fofn
ls *R2*fastq.gz > R2.fofn
sbatch /merqury/_submit_build_10x.sh 31 R1.fofn R2.fofn ${2} mem=F
wait_fil... |
0c302db3c815557b63ccd4d9ba1a96687f33b1b84876e5be1357073a3747180d | Shell | 678 | 31 | #!/bin/bash
# user input
if [ "$#" -lt 2 ]; then
echo "snakemake_sge.sh config.yaml jobs ..."
echo " config.yaml : snakemake config"
echo " jobs : number of parallel qsub jobs"
echo " ... : additional arguments passed to snakemake"
exit
fi
# check for snakemake
command -v snakemake >/dev/null 2>&1... |
0ec6b96bcfa7f188b9d5fb3aa1f74359c6cce383fa5f3a65f391127ee9693c92 | Shell | 679 | 23 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 8 # number of cores
#SBATCH -t 20 # time (minutes)
#SBATCH -o logs/cnn_step0_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/cnn_step0_%j.err # STDERR #add _%a to see each arra... |
455cb7f6dc36c3bb0ecba05f44be9eaf8bed58190f91bf274e2d88be0a172310 | Shell | 680 | 21 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 14 # number of cores
#SBATCH -t 700 # time (minutes)
#SBATCH -o logs/step3_%a.out # STDOUT
#SBATCH -e logs/step3_%a.err # STDERR
#SBATCH --contiguous #used to try and get cpu mem to be contigous
... |
6b4d4cab6aa72934dfb6217a3509d9acd4943b6a5d6a927389962a440ac1a014 | Shell | 682 | 22 | #!/bin/bash
mkdir -p $1
cd $1
cat ../../../../VGP_dataset/$1* ../NOVOplasty/$1/Circular* > kmers.fasta
/bin/windowmasker -in kmers.fasta -mk_counts -out kmer.counts
/bin/windowmasker -in ../../../../VGP_dataset/$1* -ustat kmer.counts -out $1.report
/bin/windowmasker -in ../NOVOplasty/$1/Circular* -ustat kmer.coun... |
d803e33b2121b0b5f63fc9d2c2a7db4caeba0b9d539335af83fab00743c04677 | Shell | 682 | 14 | #!/bin/bash
set -xe;
python anomaly_detection.py \
--batch-size 64 \
--bkg-dir "dev/exp/LGNAutoencoder_QCDJet_min&max_tauLS1_tauLV8_encoder3344_decoder4433/test_QCD_jets/anomaly_detection" \
--bkg-type "QCD" \
--sig-dirs \
"dev/exp/LGNAutoencoder_QCDJet_min&max_tauLS1_tauLV8_encoder3344_decoder4433/test_QCD_jets/anom... |
0650f8d99c22b903eb5402c0d43f5fa9d8911a5d669e2270c938cb5ae695aed7 | Shell | 684 | 24 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_hybrid_scaffold_bspqi_bsssi.sh <name>"
echo -e "\t<name>: job name and output dir name"
exit -1
fi
cpus=64
mem=560g
partition=largemem
name=$1
script=$VGP_PIPELINE/bionano/hybrid_scaffold_two.sh
args="$1 BSPQI BSSSI $VGP_PIPELINE/bionano/hybridScaffold_two_enz... |
a90c2f74bc74642b3f583b2d4ee9509b8e8c151dff64992f946454c824227ebb | Shell | 686 | 32 | #!/bin/bash
# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# export ONNX_ML=0, # if you need to disable ONNX_ML
python_exist=$(command -v python)
if [[ -z $python_exist ]]; then
echo "No python is found, please set it in your environment."
exit 1
fi
if [[ ! -f "onnx/defs/gen_d... |
3e6ebd631622e51da1b304b43a9504ae0e415ae8a88d44f29acfa8515cdc1df2 | Shell | 687 | 34 | #!/bin/sh
name=$1
out=$2
bam=$name.bam
bed=$name.bed
if [ -z $name ]; then
echo "Usage: ./salsa2.sh <name>"
echo -e "\tSymlink re_sites.txt"
exit -1
fi
if ! [ -e $bed ]; then
module load bedtools
echo "bedtools bamtobed -i $bam > $bed"
bedtools bamtobed -i $bam > $bed
fi
source $tools/conda/etc/profile.d/con... |
79b9b6a280b07967559e5ce9bdcc1103b9d45454522ccd8c1f00a31eeb06d6b3 | Shell | 689 | 25 | #!/bin/env bash
#set up dictionary and save
OUT0=$(sbatch slurm_files/step0.sh)
echo $OUT0
#process zplns
OUT1=$(sbatch --dependency=afterany:${OUT0##* } --array=0-14 slurm_files/step1.sh)
echo $OUT1
#combine stacks into single downsized tifffiles
OUT2=$(sbatch --dependency=afterany:${OUT1##* } --array=0-1 slurm_... |
54209d2d3016782c029777153df81dfec3c52c9be2fe44be67eadac598959f9f | Shell | 690 | 22 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 1 # number of cores
#SBATCH -t 10 # time (minutes)
#SBATCH -o logs/cnn_step1_check_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/cnn_step1_check_%j.err # STDERR #add _%a to s... |
863b58b0779939d1ebfe8be4c7d3aeb40d9020f0debd6f7ba065dd1d5c3b5e7c | Shell | 691 | 34 | #!/bin/sh
name=$1
out=$2
bam=$name.bam
bed=$name.bed
if [ -z $name ]; then
echo "Usage: ./salsa2.sh <name> <out>"
echo -e "\tSymlink re_sites.txt"
exit -1
fi
if ! [ -s $bed ]; then
module load bedtools
echo "bedtools bamtobed -i $bam > $bed"
bedtools bamtobed -i $bam > $bed
fi
source $tools/conda/etc/profile... |
3d484c3fbf33569860ce20beabcd7066f8e95aa4ed20d2701c9ca7e9908e1699 | Shell | 694 | 27 | #!/bin/bash
ref=$1.fasta
qry=$2.fasta
out=mashmap_${2}_to_${1}
mkdir -p $out
out=$out/out.map
if [ ! -e $out ]; then
echo "\
$tools/mashmap/mashmap -r $ref -q $qry -t $SLURM_CPUS_PER_TASK -o $out --filter_mode one-to-one --pi 90"
$tools/mashmap/mashmap -r $ref -q $qry -t $SLURM_CPUS_PER_TASK -o $out --filter_mod... |
63a218fed93ceb5a90865f75da01b4193ceb0b9a204db25f5ae3cdd77d7e803b | Shell | 697 | 25 |
NET=./scATAC_loop.txt
python3 ../src00_ics/step0_uniqNet.py $NET $NET\.uniq.txt
NET=./scATAC_loop.txt.uniq.txt
MAT=./scATAC_normalized_exp_clustered.txt
INDEX=./ics_out/this.index
python3 ../src00_ics/step1_BuildIndex.py $NET $MAT $INDEX
nohup python3 ../src00_ics/step2_CalICS_mean.py $INDEX ics_out/this.out.m... |
6692fe3a5beed43394546d404a035d6f747a4067a6d28fce3bc5d50318cb5e80 | Shell | 697 | 10 | docker run --cpus=16 --memory=61g --shm-size=61g --gpus='"device=1"' --rm -it \
-v /media/hdd2/works/HeviAI_picai/picai_baseline/src/picai_baseline/nnunet_semi_supervised/training_sagemaker/code:/code:ro \
-v /media/hdd2/works/HeviAI_picai/debug_workdir:/workdir \
-v /media/hdd2/works/HeviAI_picai/input_ima... |
4da52ca168093d654eaee25d9ad90ce540060566eb736d18759343a8dce14956 | Shell | 698 | 34 | #!/bin/sh
name=$1
out=$2
bam=$name.bam
bed=$name.bed
if [ -z $name ]; then
echo "Usage: ./salsa2.sh <name> <out>"
echo -e "\tSymlink re_sites.txt"
exit -1
fi
if ! [ -s $bed ]; then
module load bedtools
echo "bedtools bamtobed -i $bam > $bed"
bedtools bamtobed -i $bam > $bed
fi
source $tools/conda/etc/profile... |
53dbc8455361ceb26bacbb3e306e1b1034a437cb0a8b8bb28cfcc27b9307ebe7 | Shell | 703 | 29 | #!/usr/bin/env sh
set -evx
. ci/get-nprocessors.sh
# Tell make to use the processors. No preceding '-' required.
MAKEFLAGS="j${NPROCESSORS}"
export MAKEFLAGS
env | sort
# Set default values to OFF for these variables if not specified.
: "${NO_EXCEPTION:=OFF}"
: "${NO_RTTI:=OFF}"
: "${COMPILER_IS_GNUCXX:=OFF}"
mkdi... |
26b39538a73169743c4a001c32a3187262f095b158b39dc215d018d090d0306e | Shell | 704 | 32 | set -xe;
mkdir -p dev/exp;
python -u main.py \
--data-paths "./data/g_jets_30p_p4.pt" "./data/q_jets_30p_p4.pt" \
--test-data-paths "./data/g_jets_30p_p4.pt" "./data/q_jets_30p_p4.pt" \
-j QCD \
-e 10 \
-bs 500 \
--train-fraction 0.75 \
--lr 0.0005 \
--loss-choice chamfer \
--get-real-method sum \
--tau-latent-vector... |
9cce53c8e340d2d44f76a3f879087f81026b6481d9e172b38e5b96654515fe30 | Shell | 705 | 27 | #! /bin/bash
if [[ -z $1 ]] ; then
echo "Usage: ./_submit_indexing.sh <genome> <ref.fasta>"
echo "<ref.fasta> will be linked to asm.fasta."
echo "This script is doing indexing only, in case it failed for large genomes."
exit -1
fi
if ! [ -e asm.fasta ]; then
ln -s $2 asm.fasta
fi
cpus=8
mem=32g
name=$1.longrgr
... |
a40d9282a66e716822d89881298c752ca5904aa238f68391c92b80cf08b445b6 | Shell | 709 | 21 | #!/bin/bash
# iterate over all files in the current directory
for file in *
do
# check if file size is greater than 6 KB
if [[ -f "$file" && $(stat -f%z "$file") -gt 6144 ]]; then
echo "Splitting file $file"
# split the file into smaller chunks of 6 KB or less
split -b 6k -d -a 4 "$file" "$file.part"
... |
433df29d6019bc1455c7b502ea1586260e4f57f3f0583ff61e526bcb889ea933 | Shell | 710 | 35 | #!/bin/bash
ref=$1
fastq_map=$2
out=$3
if [[ -z $ref || -z $fastq_map || -z $out ]]; then
echo "Usage: ./bwa.sh <ref.fasta> <fastq_map> <out>"
echo "No <ref.fasta> found. Exit."
exit -1
fi
module load bwa
module load samtools
cpus=$SLURM_CPUS_PER_TASK
idx=$SLURM_ARRAY_TASK_ID
out=$out.$idx
line=`sed -n ${idx}p ... |
a5ce70b1980170a7a200468bdcc15255527883c3c4ebeaa4b9d8874e9108832a | Shell | 710 | 29 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_genomecov.sh <pos_sorted.bam> [jobid_to_set_dependency]"
exit -1
fi
bam=$1
sample=${bam/.bam/}
mkdir -p logs
cpus=4
mem=4g
name=$sample.genomecov
script=$VGP_PIPELINE/qv/genomecov.sh
args="$sample $sample.bam"
walltime=2-0
log=logs/$name.%A_%a.log
if ... |
4e17c462330ef6814494819e8cff3caed4710e15d8b329922c2415cf233c74f0 | Shell | 713 | 23 | #!/bin/bash
id=$1
seq1=$2
seq2=$3
mkdir -p $id
makeblastdb -in $seq1 -parse_seqids -dbtype nucl -out $id/blast.db
blastn -outfmt "1" -query $seq2 -db $id/blast.db > $id/default_${id}.out
blastn -outfmt "6 qseqid sseqid qlen slen length pident" -query $seq2 -db $id/blast.db | sort -k3 -n > $id/output_${id}.out
... |
b378f0ac5c61dbdb354f05bb55af88b112838ff630dd191d2131fbf72a358861 | Shell | 713 | 32 | #! /bin/bash
if [[ -z $1 ]] ; then
echo "Usage: ./_submit_repeatmasker.sh <asm.fasta>"
exit -1
fi
if ! [ -e asm.fasta ]; then
ln -s $1 asm.fasta
fi
asm=`basename $1`
asm=${asm/.fasta/}
echo "Run repeatmasker on $asm using species=vertebrates"
cpus=54
mem=140g
partition=unlimited
name=$asm.rm
script=$VGP_PIPELIN... |
2d982fa0ff4a06edf4803f021e0d2e0bf578cbe33fcbdec46d57bb4356e4812f | Shell | 716 | 27 | #! /bin/bash
if [[ -z $1 ]] ; then
echo "Usage: ./_submit_longranger.sh <genome> <ref.fasta>"
echo "<ref.fasta> will be linked to asm.fasta."
echo "Assumes we have 10x reads located under /data/rhiea/<genome>/genomic_data/10x/."
exit -1
fi
if ! [ -e asm.fasta ]; then
ln -s $2 asm.fasta
fi
cpus=2
mem=12g
name=$1... |
3e18c8e35e1eb0a70c6080fc69e9625ca36fa1d70cf19d5c498efcb4a14d68d9 | Shell | 718 | 28 | #!/bin/bash
tmp_env=$1
# If environment is given, activate it, otherwise print info message.
if [ ! -z "$tmp_env" ]
then
source activate $tmp_env
else
echo "INFO: A source-able environement can be provided as an argument to this script."
fi
# Display the target environment
echo "Installing the relevant packages to... |
3887fc5070d5d806d804853ef986863a2c980aa7a6cde980b2c16922d26cfbd2 | Shell | 723 | 28 | #!/bin/bash
HEADER=$(head -1 $1)
SEQ=$(tail -1 $1)
LN=$(tail -1 $1 | tr -d "\n" | wc -c)
CENTER=$(echo "scale = 0; (${4} + ${5}) / 2" | bc -l)
DIFF=$(echo "scale = 0; ${CENTER} - (${LN} / 2)" | bc -l)
echo $CENTER
echo $DIFF
END=$(tail -1 $1 | tr -d "\n" | cut -c1-${DIFF})
START=$(tail -1 $1 | tr -d "\n" | cut -c$(... |
9de971bf50b5b25cf76584f952bff19dd5c40eb0da5da264b2078326bfce04b7 | Shell | 723 | 25 | #!/bin/bash
if [[ -z $1 ]]; then
echo "Usage: ./merge_bnx.sh <prefix>"
echo " <prefix>: ex. mLynCan4_Saphyr_BspQI for mLynCan4_Saphyr_BspQI_748455.bnx and mLynCan4_Saphyr_BspQI_792415.bnx"
echo " <output>: <prefix>.bnx"
exit 0
fi
prefix=$1
ls ${prefix}*.bnx > $prefix.list
module load perl/5.16.3
module lo... |
60e537b91888e1c9f86956559bb69471429c689d0e43b5e2acf07ff5bffccbf1 | Shell | 727 | 28 | #!/bin/sh
# Type of the network to use
NETWORK_TYPE="Attention_Mean"
#NETWORK_TYPE="Arith_Mean"
#NETWORK_TYPE="RNN"
#NETWORK_TYPE="Annotations"
# Root path for the experiment
MODEL_PATH=experiments/
# Path to the sqlite db with diassembled functions
DB_PATH=../data/OpenSSL_dataset.db
# Path to embedding matrix
EMB... |
663d0a746bbb88a134a04b97a725fc4c63310b51001918bbab9d39fa362f7108 | Shell | 727 | 29 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_hybrid_scaffold_bspqi.sh <name>"
echo -e "\t<name>: job name and output dir name"
exit -1
fi
if [ -e $1 ]; then
echo "$1 already exists. Remove."
exit -1
fi
cpus=32
mem=180g
partition=largemem
name=$1
script=$VGP_PIPELINE/bionano/hybrid_scaffold.sh
args="$1... |
477122cb818348afd8dc19f5df0ea1089c2eb6f21fc4075885d6972573b45f43 | Shell | 730 | 33 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./_submit_mash_screen.sh <genome>"
echo "Requires: input.fofn"
exit -1
fi
if [ ! -e input.fofn ]; then
echo "Requires: input.fofn"
exit -1
fi
LEN=`wc -l input.fofn | awk '{print $1}'`
genome=$1
mkdir -p logs
partition=quick
cpus=16
mem=24g
name=$genome.screen
scrip... |
09b7fd1ee8a46f4518b09eea25fd5df93a9c8d0c8845de01e3354837a2027c20 | Shell | 731 | 2 | #!bin/bash
java -Djavafx.platform=monocle -Dglass.platform=monocle -Dmonocle.platform=Headless -Djavafx.verbose=true -Djavafx.debug=true -Dprism.verbose=true -Dprism.debug=true -Dprism.order=sw -Dmonocle.screen.fb=/dev/fb1 -Dmonocle.platform=Linux -Dmonocle.input.19/0/0/0.flipXY=true -Dmonocle.input.19/0/0/0.maxX=480 -... |
56da3d67b0b1dee87c622c2f5dcfe05ac5bf807c2074de3c3177695d1bba2cbe | Shell | 731 | 26 | #!/bin/bash
set -xe
python test.py \
-tbs 500 \
--num-test-batch 200 \
-j QCD \
--maxdim 2 \
--tau-latent-vectors 8 \
--tau-latent-scalars 1 \
--loss-choice chamfer \
--get-real-method sum \
--map-to-latent "min&max" \
--mlp-width 6 \
--mlp-depth 6 \
--encoder-num-channels 3 3 4 4 \
--decoder-num-channels 4 4 3 3 \
-... |
c0bde6e161826f8a2119e3c23c10a5ff51a88394a24c0d414576e9a44b7ab399 | Shell | 731 | 23 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 1 # number of cores
#SBATCH -t 10 # time (minutes)
#SBATCH -o logs/cnn_step1_check_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/cnn_step1_check_%j.err # STDERR #add _%a to s... |
3a2777a3d99d8c9325f853e294431e99f0a1a42c1bd30074a4559dcf1b328f70 | Shell | 734 | 28 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 10 # number of cores
#SBATCH -t 20 # time (minutes)
#SBATCH -o logs/step2_%a.out # STDOUT
#SBATCH -e logs/step2_%a.err # STDERR
#SBATCH --contiguous
echo "In the directory: `pwd` "
echo "A... |
4136c9138cd533e4f59f380fa66431d0466c759687761e4d865d425ebd41b7bb | Shell | 737 | 22 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 1 # number of cores
#SBATCH -t 300 # time (minutes)
#SBATCH -o logs/array_jobs/cnn_step2_chnk%a_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/array_jobs/cnn_step2_chnk%a_%j.err ... |
0b712a65b4f5542819d0dae09d1b6e5298f690c347ab526f4fffe3ea373b8249 | Shell | 740 | 42 | #!/bin/bash
# path to data
for DATA_DIR in /home/goschv/CSB_NeuroRad2/goschv/DATA/Repro
#/home/goschv/CSB_NeuroRad2/goschv/DATA/11_wakeupstroke.unknown /home/goschv/CSB_NeuroRad2/goschv/DATA/08_deltatinaccurate.unknown /home/goschv/CSB_NeuroRad2/goschv/DATA/11_deltatover9.exact 24_deltatover9.exact
do
cd $DATA_... |
12eb98b00c453adee87cdbb665fb8432e11cd66db0054ab1308bcc71e37e1a24 | Shell | 742 | 29 | #!/bin/bash
mod_params="-r 1 --stimulus tonepip"
stim_params="-f 16000. --dB 30."
AN_params=" --sgcmodel cochlea --SR MS"
targetcell="VCN_c09"
FILES="VCN_c02 VCN_c05 VCN_c10 VCN_c13"
for f in $FILES
do
echo "Cell: $f"
python vcnmodel/model_run.py $targetcell -i $f --protocol initAN -H \
${mod_params... |
8ea8960be237d71482c80a5fec278021e5d94dbdd907a1769bfb330f087c6ea2 | Shell | 742 | 24 | #!/bin/bash
# Define an associative array to store packages and versions
declare -A packages=(
["h5py"]="2.7.1"
["keras"]="2.1.6"
["tensorflow"]="1.8.0"
# Add more packages as needed
)
# Iterate over the array and install each package with the specified version
for package in "${!packages[@]}"; do
... |
9c85686a51afc0636a2ffce254a2aa9f2bbdcc433ff9850c22aa4fb81b5a4df6 | Shell | 743 | 24 | #!/bin/bash
# Define an associative array to store packages and versions
declare -A packages=(
["h5py"]="2.10.0"
["keras"]="2.4.3"
["tensorflow"]="2.4.1"
# Add more packages as needed
)
# Iterate over the array and install each package with the specified version
for package in "${!packages[@]}"; do
... |
cca163d867e9bd8187917f0cb9c3aed1f57db6aaa1b4479793d9a32136bb083b | Shell | 747 | 17 | #!/bin/bash
species=$1
id=$2
mkdir -p $id
aws s3 cp --exclude "*" --include "mtDNA_*.fasta" --recursive s3://genomeark/species/${species}/${id}/assembly_MT_rockefeller/intermediates/reference/ $id
GSIZE=$(awk '$0 ~ ">" {if (NR > 1) {print c;} c=0;printf substr($0,2,100) "\t"; } $0 !~ ">" {c+=length($0);} END { pri... |
bb10707e23d7267662a1ea66a9709bccbe6afdcb25b5af2b7c6896d30d2eeebf | Shell | 749 | 26 | #!/bin/bash
HEADER=$(head -1 $1)
SEQ=$(tail -1 $1)
LN=$(tail -1 $1 | tr -d "\n" | wc -c)
CENTER=$(echo "scale = 0; (${4} + ${5}) / 2" | bc -l)
DIFF=$(echo "scale = 0; ${CENTER} - (${LN} / 2)" | bc -l)
echo $CENTER
echo $DIFF
END=$(tail -1 $1 | tr -d "\n" | cut -c1-${DIFF})
START=$(tail -1 $1 | tr -d "\n" | cut -c$(... |
b34b22760094673ead1ad7c92aa048186caf27ea65c8944bf52ae8ee9169958f | Shell | 750 | 36 | #!/bin/bash
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
source ${SCRIPTPATH}/pipeline_paths.sh
echo $PATH
echo $LD_LIBRARY_PATH
__conda_setup="$(${HOME}'/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "${HOME}/miniconda3/etc/profile.d... |
79c7729419e492b5a57d335c827cd1f974f6abc1763baa69368a9cb24ab89100 | Shell | 753 | 25 | #!/bin/bash
# Downloads all pgn files which are used for supervised training (train, validation, test)
# See configs/main_config_sample.py which dataset-type used wich pgns
file="./pgn_list_lichess_variants.txt"
while IFS= read -r line
do
filename=$(echo $line| cut -d'/' -f 5)
filepgn=${filename::${#filename}-4}
if [[... |
bf784e1a049e87950257e974f02a633d9549700a00b083d5676b3709e09a0dfc | Shell | 755 | 18 | #!/usr/bin/env bash
# Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
1d050352776d6c3d1a609619a6916964f1a913e94194b68bb1f11b1bc1121574 | Shell | 762 | 12 | #!/bin/bash
python model_run.py VCN_c08 -M mGBC -S MS -r 20 -P runANIO --noparallel &
python model_run.py VCN_c09 -M mGBC -S MS -r 20 -P runANIO --noparallel &
python model_run.py VCN_c09h -M mGBC -S MS -r 20 -P runANIO --noparallel &
python model_run.py VCN_c09nd -M mGBC -S MS -r 20 -P runANIO --noparallel &
python ... |
a58c5181af1aedf443823a69396d738426581503018f69eb1f0ead79b2f914b6 | Shell | 767 | 25 | #!/bin/bash -e
# This script is used to build the system dependencies for MacOS
# It is called by test workflow in .github/workflows/
# To build necessary system dependencies for MacOS, run:
# check if os is MacOS using uname
if [ "$(uname)" = "Darwin" ]; then
# install necessary dependencies
echo "installing... |
00cd74b93add57ea5704ccc597766cc5f4dae1b02fc5ba493709d7327d49da84 | Shell | 769 | 34 | #!/bin/bash
assembly=asm.fasta
R1=read-BC1.fastq
R2=read-BC2.fastq
name=$1
module load bwa
mkdir -p round1
mkdir -p round2
# Round 1
cd round1
if [ -e "round1.fasta" ]; then
echo "round1.fasta already exists. Skip the first step..."
else
ln -s ../$assembly
ln -s ../$R1
ln -s ../$R2
$tools/scaff10x/Scaff10X/sr... |
0472d96aae6dfa19071cfd336202f1f12871a6ce886847ec2af9c1357d8a058c | Shell | 769 | 17 | #!/bin/bash
HEADER=$(head -1 $1)
SEQ=$(tail -1 $1)
LN=$(tail -1 $1 | tr -d "\n" | wc -c)
printf "%s\n%s%s" $HEADER $SEQ $SEQ > dup_$1
picard RevertSam I=$2 O=unmapped_$2 MAX_DISCARD_FRACTION=0.000 ATTRIBUTE_TO_CLEAR=XT ATTRIBUTE_TO_CLEAR=XN ATTRIBUTE_TO_CLEAR=AS ATTRIBUTE_TO_CLEAR=OC ATTRIBUTE_TO_CLEAR=OP SORT_ORDER... |
4aabf1bea3f7fded0ce73c770def23cceda1315df033cf8080420169806ba151 | Shell | 771 | 21 | conda activate openretro
python3 preprocess.py \
--model_name="gln" \
--data_name="schneider50k" \
--log_file="gln_preprocess" \
--train_file="./data/gln_schneider50k/clean_train.csv" \
--val_file="./data/gln_schneider50k/clean_valid.csv" \
--test_file="./data/gln_schneider50k/clean_test.csv" \
--process... |
df1336e93397a8f7f245ed07b48cffaaaf7f19ceaae04979ce9610ebde16f08a | Shell | 773 | 23 | #!/bin/bash
# concat_two_files 0.0.1
main() {
echo "Value of s3_fastagz: '$s3_fastagz'"
echo "Value of c2_fastagz: '$c2_fastagz'"
echo "Value of p2_fastagz: '$p2_fastagz'"
echo "Value of outputprefix: '$outputprefix'"
dx download "$s3_fastagz" -o s3_fasta.gz
dx download "$c2_fastagz" -o c2_f... |
477357591ba8f49fa0de4eb0292aa0b5f8cab4f51fddde306878cf4e6bcb9139 | Shell | 774 | 24 | #!/bin/bash
#
#SBATCH --job-name=retatlas-%a
#SBATCH --output=retatlas-%A-%a.txt
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --partition=amd-hdr100
#SBATCH --time=30:00
#SBATCH --mem-per-cpu=8G
# set the path to the dataset directory
export SUBJECTS_DIR=/data/user/mdefende/datasets/MDP/subs
# set the path to... |
7c25a714adeeef862bdf4437f6fc19d1976e49a70fba81d48306afd011875377 | Shell | 778 | 23 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 8 # number of cores
#SBATCH -t 200 # time (minutes)
#SBATCH -o logs/array_jobs/cnn_step1_chnk%a_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/array_jobs/cnn_step1_chnk%a_%j.err ... |
a779541a36e3fdd48c648f660865e6b564ed70631a91d40489eed7a0911f96de | Shell | 778 | 23 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 10 # number of cores
#SBATCH -t 100 # time (minutes)
#SBATCH -o logs/array_jobs/cnn_step3_job%a_%j.out # STDOUT #add _%a to see each array job
#SBATCH -e logs/array_jobs/cnn_step3_job%a_%j.err ... |
55e7641c957a94dc7cbd23fb2d300619131886fbc1347162938a8606e4313f07 | Shell | 780 | 12 | # Ciftify: from the volume space to surface space
# ciftify_recon_all
# Remember to change ciftify_recon_all code before running!!!!
# Line 495: if 'v6.' in fs_version:
# Line 594: add '-nc' between T1w_nii and freesurfer_mgz
export SUBJECTS_DIR=/nfs/z1/zhenlab/BrainImageNet/action/data/bold/derivatives/freesurfer
exp... |
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