sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
62e5a08120b3c3d40645ac9377d8b86677cf7fec0cca09a472065e76ba5b0600 | Shell | 397 | 15 | echo 'Starting Test for Intel NUC'
echo ''
echo 'Nonspiking Dense:'
python3 test_backend_speed_nonspiking_dense.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_backend_speed_nonspiking_sparse.py
echo ''
echo 'Spiking Dense:'
python3 test_backend_speed_spiking_dense.py
echo ''
echo 'Spiking Sparse:'
python3 test_backe... |
7b5aebc0dc3c88c3ec8a45a8b53db978b1e29ce1b3fa347cc6ce8dda53dc73d7 | Shell | 399 | 10 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# saliency image via Vanilla
dnn_saliency -net AlexNet -layer fc3 -chn 294 23 -stim $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.stim.csv -show -out $TMP_DIR -meth vanilla
# saliency image via Guided
dnn_saliency -net AlexNet -layer fc3 -chn 294 23 -stim $... |
2ea00e9db69819a2c99a6a604c9c26edaadf070c0b80642ec37dfcba8546f8d8 | Shell | 405 | 12 |
N=10000
python3 ../src00_ics/step1_BuildIndex.py ./testN/net$N\.txt ./testN/mat$N\.txt ./testN/this.index.$N
echo $[$(date +%s%N)/1000000]
TAG=r100
python3 ../src00_ics/step2_CalICS_$TAG\.py ./testN/this.index.$N ./testN/this.out.$TAG\.$N
echo $[$(date +%s%N)/1000000]
TAG=ori
python3 ../src00_ics/step2_CalICS_$TAG... |
bda423dff68f784ed49560db3f8441c4b9de810c76177e8caa923ba4d7a4dfe0 | Shell | 407 | 16 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./plot.sh <genomeid>"
exit -1
fi
module load mash
mash paste combined.msh *.msh
mash dist -t combined.msh combined.msh > combined.tbl
head -n 1 combined.tbl |awk '{for (i=2; i <=NF; i++) print $i}' |awk -F "/" '{print $NF}' |sed s/.subreads.fast[aq].gz//g |sed s/.fast[a... |
b762c284d9c9bb774b3ea555d54220b202c8ba9685528dd890e748e2208a012b | Shell | 411 | 14 | #!/bin/bash
mkdir data
mkdir data/seed_sequences
# Mouse
wget -P ./data/seed_sequences/ https://rest.uniprot.org/uniprotkb/Q8C8T7.fasta
# Human
wget -P ./data/seed_sequences/ https://rest.uniprot.org/uniprotkb/P0C7U0.fasta
# Turtle
wget -P ./data/seed_sequences/ https://rest.uniprot.org/uniprotkb/K7FQE3.fasta
# Zebra... |
fabdb0ad9a5af8c88a52cab250439ced13f548be6455be46c9ec7c1b864c149d | Shell | 411 | 4 | wget -O - ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/HG002-HG003-HG004.jointVC.filter.vcf | grep -v "##GATKCommandLine" | grep -v "GVCF" | head -10000 | perl -pe 's/(Sample_Diag-excap51-|-EEogPU)//g' | bgzip -l 9 -c > tests/ashk-trio.vcf.gz
... |
f4275c56074a88a69f240cf8a17c6663ed6ef957a27df804c38a6cf5ef58c63e | Shell | 414 | 18 | #!/bin/bash
#BSUB -M 128000
#BSUB -W 12:00
#BSUB -n 12
#BSUB -R "rusage[mem=128000] span[hosts=1]"
#BSUB -J run_voila_tsv
#BSUB -o logging/run_voila_tsv.out
#BSUB -e logging/run_voila_tsv.err
module load anaconda3
source activate /data/aronow/Kang/py_envir/splice/
voila tsv \
output/spliceGraph/splicegraph.sql \... |
cdca093f3dfea0b86a78834a68731de73d123de4330125d7ae99cfbb0e255c78 | Shell | 417 | 10 | SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
BASE_DIR=$SCRIPT_DIR/..
(cd $BASE_DIR/overleaf && git pull)
rsync -r --max-size=49m $BASE_DIR/results/figs $BASE_DIR/overleaf
rsync $BASE_DIR/results/glued_variables.txt $BASE_DIR/overleaf
rsync $BASE_DIR/results/glued_variables.csv $BASE... |
ac71bf2adbc9cc70a7775636da6137deabf44bc6ec9471fd8f84b2a812ee4c72 | Shell | 419 | 23 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: pbstats.sh <pri_asm.fasta>"
exit -1
fi
pri_asm=$1
export PATH=$tools/purge_dups/bin:$PATH
echo "
pbcstat *.read.paf.gz "
pbcstat *.read.paf.gz #&&
#rm *.paf.gz || echo "ERROR"
calcuts PB.stat > cutoffs 2>calcults.log
purge_dups -2 -T cutoffs -c PB.base.cov $pri_asm.s... |
1d0bd10ce78a3ac34efbebfb46bb86a93bab69536849359ac1b7f6447d80066e | Shell | 421 | 11 | TSS=/home/database/annotation/hg19/Homo_sapiens.GRCh37.75.chr.pc.gene.SYM.bed.TSS.bed
LOOP=/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_RefLoop.bed
GENOME=/home/database/reference/hg19/hg19.fa.size
grep PDGFRA $TSS > PDGFRA_TSS_hg19.bed
bedtools slop -i PDGFRA_TSS_hg19.bed -g $GENOME -b 1000 > PDGFRA_TSS_... |
ea7dfca521866c1ae19dec58101686c4053ec39d8bcb0079c805cd3e88636d06 | Shell | 421 | 9 | cat /home/disk/database/data/ICS_scHIC/f03_atlas/eQTL/GTEx_Analysis_v8_eQTL/*format/pair.bed | cut -f 1,2,3 | bedtools sort -i - | uniq - > ALL_EQTL.bed
GENOME=/home/database/reference/hg38/hg38.fa.size
SLOP=1000
bedtools slop -i ALL_EQTL.bed -g $GENOME -b $SLOP | bedtools merge -i - > ALL_EQTL_1k_merged.bed
bedtoo... |
12a816cc570e91e1f657b23bcfd62b3868605fd331552ca6f89b1f44f8717e32 | Shell | 422 | 20 | #!/bin/bash -l
#SBATCH --job-name="no_spillover"
#SBATCH --time=16:00:00
#SBATCH --nodes=25
#SBATCH --ntasks-per-core=1
#SBATCH --ntasks-per-node=36
#SBATCH --cpus-per-task=1
#SBATCH --partition=normal
#SBATCH --constraint=mc
#SBATCH --hint=nomultithread
#SBATCH -e eta_error_file.e
#SBATCH -o eta_output_file.o
export... |
8ed35433dcda617a496702e2a3f0c57be9c354bddb1ebcc1da24a05cf4efde8d | Shell | 422 | 20 | #!/bin/bash -l
#SBATCH --job-name="alpha"
#SBATCH --time=16:00:00
#SBATCH --nodes=25
#SBATCH --ntasks-per-core=1
#SBATCH --ntasks-per-node=36
#SBATCH --cpus-per-task=1
#SBATCH --partition=normal
#SBATCH --constraint=mc
#SBATCH --hint=nomultithread
#SBATCH -e alpha_error_file.e
#SBATCH -o alpha_output_file.o
export OM... |
ce2ab884ceb5b4ae58eec8328da116cef0b2bba642b701527bb711dc30fe2655 | Shell | 425 | 14 | #!/bin/sh
## This is the final script for removing terminal gaps and contaminant scaffolds.
scaffs=$1
outfile=$2
contam_scaffs=$3
mito_scaffs=$4
newdir=$5
cat $mito_scaffs $contam_scaffs | tr [A-Z] [a-z] > $newdir/full_contam_list.txt
echo "gfastats $scaffs --exclude-bed $contam_scaffs -o $outfile --remove-termin... |
a229f3fa9ff311cf35b9229ccf8c4c6b52275af4d523f745e64df77f41141b8c | Shell | 433 | 20 | #!/bin/sh
name=AlienTrimmer
javac $(pwd)/$name.java ;
if [ ! -e $(pwd)/$name.class ]; then exit; fi;
mkdir $(pwd)/JARBUILDER/
cp $(pwd)/$name.class $(pwd)/JARBUILDER/ ;
mkdir $(pwd)/JARBUILDER/META-INF/ ;
echo "Main-Class: $name" > $(pwd)/JARBUILDER/META-INF/MANIFEST.MF ;
cd ./JARBUILDER/ ;
jar cvfm $name.jar ./MET... |
c38f53a34020d8125445a598af96165565e5cade8dd803a05a0f18ac5d9ea72b | Shell | 434 | 14 | #! /bin/bash
source ~/.profile
[ $# -lt 2 ] && { echo 'Usage :
$1 = the image to register
$2 = reg image output folder
$3 = template (for instance MNI152 1mm)
$4 = the mask to register
$5 = reg mask output folder'; exit 1; }
flirt -in $1 -ref $3 -out $2/reg_$(basename $1) -omat $2/reg_$(basename $1).mat
fl... |
0ed0fd3ce0c915a7c19093b91c02a4634344e1c635cbfbe926dac2204c759939 | Shell | 435 | 15 | :<<!
tabix -p bed GSM6043255_ME11_20um_fragments.tsv.gz
samtools faidx /home/database/reference/mm10/mm10.fa
cut -f 1,2 /home/database/reference/mm10/mm10.fa.fai > /home/database/reference/mm10/mm10.fa.size
!
CHROM_SIZE=/home/database/reference/mm10/mm10.fa.size
BINSIZE=10000
cooler makebins $CHROM_SIZE $BINSIZE >... |
66e8b96ea60fc5bf331cc6479abcbf686e94d773d07bb58898ebd6aca2dd566a | Shell | 435 | 17 | #!/bin/bash
#BSUB -M 128000
#BSUB -W 12:00
#BSUB -n 12
#BSUB -R "rusage[mem=128000] span[hosts=1]"
#BSUB -J run_voila_modulizer
#BSUB -o logging/run_voila_modulizer.out
#BSUB -e logging/run_voila_modulizer.err
module load anaconda3
source activate /data/aronow/Kang/py_envir/splice/
voila modulize \
'output/splic... |
b84fd5fafadae651808829738628db12f10b45b5bf84976c5def8ca8fef56fe8 | Shell | 435 | 8 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
brain_rsa -nif $DNNBRAIN_DATA/test/sub-CSI1_ses-01_imagenet_beta_L.nii.gz -bmask $DNNBRAIN_DATA/test/PHA1_L.nii.gz -roi 1 -out $TMP_DIR/brain_rsa.rdm.h5
brain_rsa -nif $DNNBRAIN_DATA/test/sub-CSI1_ses-01_imagenet_beta_L.nii.gz -bmask $DNNBRAIN_DATA/test/PHA1_L.n... |
a3a345468500a5bf4486bcf29a89ce64e7d51e784ab1f4e52786e9f4ac94e36b | Shell | 438 | 14 | # Build MOD files with "mknrndll" bash script
source neuron_home_lin.sh
# Export path to "mknrndll" script and "sh.exe" used by the script
export PATH=$NEURON_HOME_LIN/bin:$NEURON_HOME_LIN/mingw/bin:$PATH
# Export variable used by "mknrndll" script
export N=$NEURON_HOME_LIN
# Go to the folder containing mechanisms
c... |
c08a863d4cdd9a0b0c3ff82faddb33535bba68dcf51bfdd1d1080465fbfd7b8e | Shell | 439 | 13 | for REP in $(seq 1 3 ); do
for MAXNUM in 400; do
for SAMPLES in 100; do
SB_FILE=run_maxnum-${MAXNUM}_samples-${SAMPLES}_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/MAXNUM/${MAXNUM}/g" ${SB_FILE}
sed -i "s/SAMPLES/${SAMPLES}/g" ${SB_FILE}
sed -i "s/ --load/_maxnum-$... |
015dcf134258235baaf9c78992241567d41d027e658eacdd44864179019fc458 | Shell | 442 | 20 | #!/bin/bash
source /data/Phillippy/tools/conda/etc/profile.d/conda.sh
conda activate kat
assembly=$1
threads=$SLURM_CPUS_PER_TASK
out=`echo $assembly | awk -F "/" '{print $NF}'`
out=${out/.fasta/}
mkdir -p $out
out=$out/kat_comp
options="-H 10000000000 -I 10000000000 -m 21 -h"
echo "\
kat comp -o $out -t $threads... |
216dd8e767588a84eb223811d568f053390bb189c3d71f0e38d333aec3791712 | Shell | 442 | 11 | # 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
python gen_proposals/gen_union_or_clean_proposals.py \
--log_file "clean_retrosim" \
--proposed_smi_file_prefixes "retrosim_200maxtest_200maxprec" \... |
ecb608b482218a51dcb0d5a1eb12ea5d50edf315f1b72298897a34ef5203223e | Shell | 443 | 9 | #!/bin/bash
# Run the entire pipeline. Assumes you're in the repo directory
conda activate elfn1_env
./scripts/preprocess.sh # Puts data into Seurat and AnnData files
python ./scripts/visualize_expression.py # UMAPs and violin plots
Rscript ./scripts/correlate.R # correlate gene expression of birds and mice
Rscript ./... |
3389ddcf6a22fe08394ca660f05080b6b3e9ee2ab5940bf7dfdc79df4f00183d | Shell | 446 | 27 | #!/usr/bin/env bash
set -ex
echo "uploading result files to s3"
hw_type=cpu
if [ "$1" = "True" ]
then
hw_type=gpu
fi
echo `pwd`
cd /tmp/TSBenchmark/out
echo `pwd`
today=`date +"%m-%d-%y"`
echo "Saving on S3 bucket on s3://benchmarkai-metrics-prod/daily/ts/$hw_type/$today"
for dir in $(ls `pwd`/)
do
echo ... |
e7e7696b840e20d7f5a89d1ee710d05d58532b618e507990786afeb8088ed6df | Shell | 446 | 9 | #!/bin/sh
OUT_DIR=$1
LR=$2
MAX_TOKENS=$3
../../fairseq/fairseq_cli/train.py ../data-bin --arch fconv --dropout 0.1 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --optimizer nag --clip-norm 0.1 --lr ${LR} --lr-scheduler fixed --force-anneal 50 --max-tokens ${MAX_TOKENS} --no-epoch-checkpoints --no-las... |
3d93e66e88e8ca93e0264877ebbe4739ed0c9742063563c8d92847ba8295f978 | Shell | 447 | 14 |
SOFT_PATH="/disk/soft//CUDA/CUDA10.1/"
export PATH=${SOFT_PATH}/bin:${PATH}
export LD_LIBRARY_PATH=${SOFT_PATH}/lib64:$LD_LIBRARY_PATH
export LD_RUN_PATH=${SOFT_PATH}/lib64:$LD_RUN_PATH
export LDFLAGS=-L${SOFT_PATH}/lib64
export C_INCLUDE_PATH=${SOFT_PATH}/include:${C_INCLUDE_PATH}
export CPATH=${SOFT_PATH}/include... |
57c21264863857e90b4f405154807a4ddf85cceff4bfa9f1c34837834fbf2162 | Shell | 449 | 6 | onmt_average_models -output ./exp/USPTO_50K_PtoR_aug20/average_model_56-60.pt \
-m exp/USPTO_50K_PtoR_aug20/model.product-reactants_step_560000.pt \
exp/USPTO_50K_PtoR_aug20/model.product-reactants_step_570000.pt \
exp/USPTO_50K_PtoR_aug20/model.product-reactants_step_580000.pt \
exp/USPTO... |
63858e0e0daf53b754017372bc4e78dc6bfad2cd057f53ef933129e0f997847e | Shell | 450 | 20 | #!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks=1
SAMPLE_LIST=($(<$1))
SAMPLE=${SAMPLE_LIST[${SLURM_ARRAY_TASK_ID}-1]}
echo "processing $SAMPLE"
echo "tmp dir ${TMPDIR}"
if [[ ${4} == "10x" ]] && [[ "$SAMPLE" == *"R1"* ]]; then
arg="-bc23"
fi
echo "\
time FastK -v -T${3} -N${2}/$(basename ${SAMPLE} .fastq.gz) -k$... |
e9d7f57759bc3af24dfe0310157edac86282a1af8dbe1b27228fd2609bf774bc | Shell | 451 | 15 | #!/bin/bash
# (Creates python callable GRPC logic from the .proto file)
# You can run this script from anywhere,
# and it will generate the new files in the protobuf folder in this script's directory
THIS_SCRIPTS_DIR="`dirname \"$0\"`"
cd "$THIS_SCRIPTS_DIR" || (echo "Couldn't cd into $THIS_SCRIPTS_DIR" && exit)
pyt... |
3e9a7a61e2ffd55bd8fcd450fac7dc2c0a84fdeb5a02b844bc27a6d79cca006c | Shell | 455 | 19 | #!/bin/env bash
#
#SBATCH -p all # partition (queue)
#SBATCH -c 1 # number of cores
#SBATCH -t 20 # time (minutes)
#SBATCH -o logs/step0.out # STDOUT
#SBATCH -e logs/step0.err # STDERR
module load anacondapy/2020.11
module load elastix/4.8
. activate br... |
d72953814ee0918daddf67e0672ba2796291f445fac2601aa1482879decb6678 | Shell | 455 | 18 | #!/bin/bash
SPECIES=${1}
ID=${2}
rm -fr ${ID}
mkdir -p ${ID}
aws s3 --no-sign-request cp --recursive --exclude="*scrap*" --exclude="*xml*" --exclude="*bai*" --exclude="*pbi*" --include="*.subreads.bam" s3://genomeark/species/${SPECIES}/${ID}/genomic_data/pacbio/ ${ID}
cd ${ID}
ls *bam > bam.ls
awk '{print "samto... |
e9bb7c77ba96b18bf5ae646d97a63a866c202e5120961beedc7d8375f4384046 | Shell | 455 | 6 | onmt_average_models -output ./exp/USPTO_full_PtoR_aug5/average_model_156-160.pt \
-m exp/USPTO_full_PtoR_aug5/model.product-reactants_step_1560000.pt \
exp/USPTO_full_PtoR_aug5/model.product-reactants_step_1570000.pt \
exp/USPTO_full_PtoR_aug5/model.product-reactants_step_1580000.pt \
exp/... |
0fba0fc10f31de8d95447c3dd6ad0c40d254ff1d76490825d1ec436595d339c4 | Shell | 457 | 15 | #!/bin/bash
# (Creates python callable GRPC logic from the .proto file)
# You can run this script from anywhere,
# and it will generate the new files in the protobuf folder in this script's directory
THIS_SCRIPTS_DIR="`dirname \"$0\"`"
cd "$THIS_SCRIPTS_DIR" || (echo "Couldn't cd into $THIS_SCRIPTS_DIR" && exit)
pyt... |
45f7e26d70c922d302257cd41e261872f3fb3b2852d9ef84b5b4c5a9d9bf0487 | Shell | 458 | 20 | #!/bin/bash
date_folder=$1
if [ -f "$date_folder/parameters.txt" ]; then
# this is a experiment folder
ZipExperimentDirectory.sh $date_folder &
else
# this is not an experiment folder, repeat iteratively
# loop through all the directories in a date folder
for folder_name in $date_folder*; do
if [ -d ${fol... |
db471a0c44d6cfc4bc9f12725ed5f3471ae5c6270904d3af6c48a7eedcf5c678 | Shell | 461 | 14 | TORCH_VER=1.6.0
CUDA_VER=10.1
CUDA_CODE=cu101
# ensure conda is initialised properly
# source /cm/shared/engaging/anaconda/2018.12/etc/profile.d/conda.sh
conda create -n neuralsym python=3.6 tqdm pathlib typing scipy pandas joblib -y
conda activate neuralsym
conda install -y pytorch=${TORCH_VER} torchvision cudatool... |
e4b89efb3db0b1c9146e26167f4c8145c468ef357f12b44a3e2c2abd5a560a5f | Shell | 461 | 17 | #!/bin/bash
#SBATCH --job-name=eqm
#SBATCH --partition=medium
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=28
#SBATCH --time=24:00:00
#SBATCH --export=ALL
echo $SLURM_JOB_NODELIST > nodelist.out
module purge
LMP=/home/ppanwar/lammps-21Jul20/src/lmp_mpi
module load openmpi/4.1.1-gcc-8.4.1
export OMP_NUM_THREADS=1
NSLO... |
56edbc5a021488efb1dbf836d6f6ec66873ab9001e68fc19aa45d161979ba332 | Shell | 462 | 20 | #!/bin/bash
date_folder=$1
if [ -f "$date_folder/parameters.txt" ]; then
# this is a experiment folder
UnZipExperimentDirectory.sh $date_folder &
else
# this is not an experiment folder, repeat iteratively
# loop through all the directories in a date folder
for folder_name in $date_folder*; do
if [ -d ${f... |
7dd255d4b3be3c61c564ca7ad5d84274c37d19080aaaeb76de26da70bc14c3c1 | Shell | 463 | 14 | for REP in $(seq 1 3 ); do
for CUTOFF in 06; do
for MAXNUM in 400; do
SB_FILE=run_cutoff${CUTOFF}_maxnum-${MAXNUM}_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/CUTOFF/${CUTOFF}/g" ${SB_FILE}
sed -i "s/MAXNUM/${MAXNUM}/g" ${SB_FILE}
sed -i "s/ --load/-rep${REP} --... |
559c79d09590c588e116ec1f9d9601f8303345f44f2f850c0072fe3fb4bd9c61 | Shell | 464 | 15 | #!/bin/bash
# (Creates python callable GRPC logic from the .proto file)
# You can run this script from anywhere,
# and it will generate the new files in the protobuf folder in this script's directory
THIS_SCRIPTS_DIR="`dirname \"$0\"`"
cd "$THIS_SCRIPTS_DIR" || (echo "Couldn't cd into $THIS_SCRIPTS_DIR" && exit)
pyt... |
d9f598db7eab40e61df6c45fbe4c85808ab28af6eadae8b359df562f3e9af9d0 | Shell | 476 | 6 | onmt_average_models -output ./exp/USPTO_50K_P2S2R_aug20/P2S/average_model_96-100.pt \
-m exp/USPTO_50K_P2S2R_aug20/P2S/model.product-synthons_step_960000.pt \
exp/USPTO_50K_P2S2R_aug20/P2S/model.product-synthons_step_970000.pt \
exp/USPTO_50K_P2S2R_aug20/P2S/model.product-synthons_step_980000.pt \... |
ea10d059e0663c01ae9359dcd56343c67f0ca128f16cf5ddc6abecbfee02ff71 | Shell | 479 | 23 | #!/bin/sh
run() {
LR=$1
MAX_TOKENS=$2
OUT_DIR=result.LR_${LR}.MAX_TOKENS_${MAX_TOKENS}
mkdir -p ${OUT_DIR}
cp akkadian_fairseq.template ${OUT_DIR}/akkadian_fairseq.slurm
sed -i "s/LR_PLACEHOLDER/${LR}/g" ${OUT_DIR}/akkadian_fairseq.slurm
sed -i "s/MAX_TOKENS_PLACEHOLDER/${MAX_TOKENS}/g" ${OUT_DIR}/akkadian_fair... |
13280410ba09fcf970058c2b935c3de47c5acd94521560792c4cb35d9d735b54 | Shell | 481 | 22 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./minimap2_self.sh <ref.fasta>"
exit -1
fi
pri_asm=$1
cpus=$((SLURM_CPUS_PER_TASK-1))
PATH=$tools/purge_dups/bin:$PATH
module load minimap2
echo "\
split_fa $pri_asm > $pri_asm.split"
split_fa $pri_asm > $pri_asm.split
echo "\
minimap2 -xasm5 -DP -t $cpus $pri_asm.spli... |
42adad1bfddafe789d137ecb9a5c26171db647f38ab361b5d3bb323a3512dc79 | Shell | 482 | 14 | for REP in $(seq 1 3 ); do
for CUTOFF in 08; do
for MAXNUM in 400; do
SB_FILE=run_cutoff${CUTOFF}_maxnum-${MAXNUM}_noh_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/CUTOFF/${CUTOFF}/g" ${SB_FILE}
sed -i "s/MAXNUM/${MAXNUM}/g" ${SB_FILE}
sed -i "s/ --load/-noh-rep$... |
4b84de0603834fd30a1b030e24300a33492bea3e40d94d02dbee2694a2aabc69 | Shell | 482 | 12 | # 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
python trainEBM.py \
--log_file retrosim_50top200max_gengraphfeat.log \
--expt_name retrosim_50top200max_gengraphfeat \
--proposals_csv_file_pre... |
4dd3fa7eda940274d314541cab3dd717749e1a4b11136e337fbce48b0bb4f253 | Shell | 487 | 6 | onmt_average_models -output ./exp/USPTO_50K_P2S2R_aug20/S2R/average_model_96-100.pt \
-m exp/USPTO_50K_P2S2R_aug20/S2R/model.synthons-reactants_step_960000.pt \
exp/USPTO_50K_P2S2R_aug20/S2R/model.synthons-reactants_step_970000.pt \
exp/USPTO_50K_P2S2R_aug20/S2R/model.synthons-reactants_step_98000... |
cd9b0ab75bbb9125f9c4013285f328e00146aa791faffcfe61e8859bdc7b27dc | Shell | 488 | 32 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./minimap2_idx.sh <ref>"
echo "Assumes <ref is a fasta file"
exit -1
fi
preset="$2"
if [ -z $2 ]; then
preset="map-pb"
fi
ref=$1
ref=`echo $ref | sed 's/.fasta$//g' | sed 's/.fa$//g'`
cpus=$SLURM_CPUS_PER_TASK
if [ -f $ref.idx ]; then
echo "$ref.idx found Exit."
... |
bd95cf09b8f71f5024289b8f84a3b172810258cbdce5c732a5acaedb925526d4 | Shell | 489 | 32 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./minimap2_idx.sh <ref>"
echo "Assumes <ref> is a fasta file"
exit -1
fi
preset="$2"
if [ -z $2 ]; then
preset="map-pb"
fi
ref=$1
ref=`echo $ref | sed 's/.fasta$//g' | sed 's/.fa$//g'`
cpus=$SLURM_CPUS_PER_TASK
if [ -f $ref.idx ]; then
echo "$ref.idx found Exit."... |
965252480ee1744b8dc73e76f9cb2261402fff4f847af531f1e94e55eaca39d1 | Shell | 492 | 6 | onmt_average_models -output ./exp/USPTO-MIT_RtoP_aug5_mixed/average_model_196-200.pt \
-m exp/USPTO-MIT_RtoP_aug5_mixed/model.reactants-products_step_1960000.pt \
exp/USPTO-MIT_RtoP_aug5_mixed/model.reactants-products_step_1970000.pt \
exp/USPTO-MIT_RtoP_aug5_mixed/model.reactants-products_step_... |
6f2a90e6b622daf1bc853edad6962e8f387905f2a56a92aa42d785510f3657d2 | Shell | 495 | 31 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./minimap2_idx.sh <ref> [num_cpus]"
echo "Assumes <ref is a fasta file"
exit -1
fi
ref=$1
ref=`echo $ref | sed 's/.fasta$//g' | sed 's/.fa$//g'`
if [ ! -z $2 ]; then
cpus=$2
else
cpus=$SLURM_CPUS_PER_TASK
fi
if [ -f $ref.idx ]; then
echo "$ref.idx foun... |
bd2aaf344e40a81c7f0184e394d40a1850f4246628f6d73e5c7493edc80c0be3 | Shell | 496 | 11 | sample=$1
peaks=$2
bins=$3
folder=$4
bedtools subtract -A -a ${folder}/${sample}.bed -b ${peaks}.bed > ${folder}/${sample}_nopeaks.bed
bedtools intersect -a ${bins} -b ${folder}/${sample}_nopeaks.bed -c > ${folder}/${sample}_nopeaks_counts.txt
if [[ ! -f "${folder}/${peaks}_bins.bed" ]]; then
bedtools intersect -a ${... |
b0c53c10e5d3a8a52fa339732b3ccc8377510e21e58c73dfa5bfa651be9d8aa5 | Shell | 501 | 13 | # 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
python gen_proposals/gen_fps_from_proposals.py \
--log_file retrosim_50top200max_makefp.log \
--proposals_file_prefix "retrosim_200topk_200maxk_noGT... |
85c56d05238422aca3dd9fda70e872d4ff484a079ad40f7d73590e8aad3c0ae0 | Shell | 502 | 6 | onmt_average_models -output ./exp/USPTO-MIT_PtoR_aug5/finetuned_average_model_46-50.pt \
-m exp/USPTO-MIT_PtoR_aug5/finetuned_model.product-reactants_step_460000.pt \
exp/USPTO-MIT_PtoR_aug5/finetuned_model.product-reactants_step_470000.pt \
exp/USPTO-MIT_PtoR_aug5/finetuned_model.product-reactant... |
f565b00aecc8bc2422d689462e030c2319b9fc20bcbcdd4c02008dc6580c12a3 | Shell | 502 | 6 | onmt_average_models -output ./exp/USPTO_full_PtoR_aug5/finetune_average_model_46-50.pt \
-m exp/USPTO_full_PtoR_aug5/finetune_model.product-reactants_step_460000.pt \
exp/USPTO_full_PtoR_aug5/finetune_model.product-reactants_step_470000.pt \
exp/USPTO_full_PtoR_aug5/finetune_model.product-reactant... |
73364447b93d9ffaf5cd3a8b1803f90dd0eda23a91be0ba4aa36b4da3bd382f4 | Shell | 503 | 6 | onmt_average_models -output ./exp/USPTO_50K_PtoR_aug20/finetune_average_model_26-30.pt \
-m exp/USPTO_50K_PtoR_aug20/finetune_model.product-reactants_step_260000.pt \
exp/USPTO_50K_PtoR_aug20/finetune_model.product-reactants_step_270000.pt \
exp/USPTO_50K_PtoR_aug20/finetune_model.product-reactant... |
59ae331cc096cd942d4799080c2af4a5ecb2c961be33a585cf9d78e42f2bbc1e | Shell | 506 | 10 | dataset=USPTO50K-aug-typed
python $PWD/../../onmt/bin/preprocess.py \
-train_src $PWD/../../data/${dataset}/src-train-aug-err.txt \
-train_tgt $PWD/../../data/${dataset}/tgt-train-aug-err.txt \
--valid_src $PWD/../../data/${dataset}/src-valid.txt \
--valid_tgt $PWD/../../data/${dataset}/tgt-valid.t... |
6468454f40b42d2af3fe432dc15263528c797d029528b3b33d10c29cc06f2c8a | Shell | 506 | 12 | #!/bin/sh
newdir=$1
scaffs=$2
scaffList=$3
kraken2 --db $decontam/standard_plus_db --conf 0.30 --threads 32 \
--classified-out $newdir/class_$scaffs --unclassified-out $newdir/unclass_$scaffs --use-names \
--output $newdir/classification_$scaffs $newdir/N_sub_masked_$scaffs
echo "grep -i 'scaffold' $newdir/class... |
d590afa03a088bce72a5de56b483fe794acc82c09f2b30fc02a6a9930d2f0c5d | Shell | 507 | 19 | #!/bin/bash
set -xe
mkdir -p "dev";
python train.py \
--batch-size 16 \
--jet-type qcd \
--data-paths "data/g_jets_30p_small.pt" "data/q_jets_30p_small.pt" \
--test-data-paths "data/g_jets_30p_small.pt" "data/q_jets_30p_small.pt" \
--encoder-edge-sizes '16,16,8,8;' \
--decoder-edge-sizes '16,16,8,8;' \
--encoder-node-... |
6848724d558beee70de7919ad393e974fe0a0cff4395bdb5c7e60777c1d24fea | Shell | 515 | 13 | # 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_seed=$1
python trainEBM.py \
--log_file=GLN_${gln_seed}_50top200max_gengraphfeat.log \
--expt_name=GLN_${gln_seed}_50top200max_gengraphfeat \
... |
9749367a10c037b1dd7f3777f86ad8c08d7e5d280ee68ab45cd6b95fdc38901f | Shell | 515 | 11 | #!/usr/bin/env bash
for GENOME in hg38 mm10; do
./get_encode.py --genome ${GENOME} --feature accessibility --out-dir ./${GENOME}/
./get_encode.py --genome ${GENOME} --feature tf --out-dir ./${GENOME}/
# # Extract uniform DNase-seq regions of length 150 bp
# if [ ! -f ./${G}/DNase-seq.150bp.bed ]; then
# ... |
f9867e1233d7bbc614942fb2455923f1607163bcfa060dd5b81995872411b026 | Shell | 515 | 18 | #!/bin/bash
folder_name=${1%/}
UpdateLog.sh $folder_name unzip_data $job_ID START Unzipping_Data
#read the last line of the log, get job IX
log_name=$folder_name/status.csv
last_line=$(tac $log_name |egrep -m 1 .)
IFS=',' read -ra ADDR <<< "$last_line"
job_ID=${ADDR[2]}
if test -f $folder_name/raw_images.zip; then
... |
5f37dcecca6442662af634e0ce865b8a4992480764113aab3feb4b64533b80dd | Shell | 516 | 6 | onmt_average_models -output ./exp/USPTO-MIT_RtoP_aug5_separated/average_model_196-200.pt \
-m exp/USPTO-MIT_RtoP_aug5_separated/model.reactants-products_step_1960000.pt \
exp/USPTO-MIT_RtoP_aug5_separated/model.reactants-products_step_1970000.pt \
exp/USPTO-MIT_RtoP_aug5_separated/model.reactant... |
41489ef408ee5aee67482314505639a8000fee132fc63bf2fd6f79994303c70e | Shell | 517 | 22 | #!/bin/bash
TNAME=$1
GRADSTEP=$2
shift 2
FLIST2=$*
ImageMath 2 $TNAME Normalize $TNAME
for x in $(seq 1 5 ) ; do
for y in $FLIST2 ; do
ImageMath 2 turdx${y} Normalize $y
ImageMath 2 turdx${y} CorrelationUpdate $TNAME turdx${y} 4
# MeasureImageSimilarity 2 1 $y $TNAME
SmoothImage 2 turdx${y} 0.5 t... |
90d224467afc41be5addd1ce1fa59f854cf6a90ff9aac0c902596a5811ec246a | Shell | 517 | 15 | conda activate openretro_cu101
module load cuda/10.1
python3 test.py \
--test_all_ckpts \
--model_name="gln" \
--data_name="schneider50k" \
--log_file="gln_test_vanilla" \
--train_file="./data/gln_schneider50k/clean_train.csv" \
--val_file="./data/gln_schneider50k/clean_valid.csv" \
--test_file="./data/... |
5a48595a1551f68f8790026e685b1cef28a13d98145dca24165ef7a639d716fa | Shell | 518 | 19 | #!sh
# Build MOD files with "mknrndll" bash script,
# i.e. translate MOD-s to C-s, compile C-s to O-s, link O-s to "nrnmech.dll"
# Initialize variables
. ./Core/scripts/win-win/params.sh
# Export path to "mknrndll" script and "sh.exe" used by the script
export PATH=$NRNDIR/bin:$NRNDIR/mingw/bin:$PATH
# Export varia... |
180ed5126581e141f58a2923a180c8b390ff0544116e203123d63095997eb917 | Shell | 519 | 20 | #!/bin/bash
date_folder=$1
repress_completed=${2:-1}
if [ -f "$date_folder/parameters.txt" ]; then
# this is a experiment folder
ViewExperimentDirectory.sh $date_folder $repress_completed
else
# this is not an experiment folder, repeat iteratively
# loop through all the directories in a date folder
for folder_n... |
e78d5ef11264753300aea4981722e5322807a1c3e082cb6db1b6d8400b328355 | Shell | 520 | 13 | #!/usr/bin/env bash
# Builds the static PACNet web app: a shinylive export of webApp/ plus the
# reference data in data/. Usage (from repo root): tools/build_site.sh [outdir]
set -euo pipefail
out="${1:-site}"
rm -rf "$out"
Rscript -e "options(timeout = 900)
shinylive::export('webApp', '$out', template_params = list(
... |
fcc3be6fef54241e94430ce41540dbf5ec261642968d544ed8ef9c617ce966f9 | Shell | 521 | 13 | # 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
sym_seed=$1
python trainEBM.py \
--log_file SYM_${sym_seed}_50top200max_gengraphfeat.log \
--expt_name SYM_${sym_seed}_50top200max_gengraphfeat \
... |
09beb429307167d1a3f0f94d75ec7f90aef9ae36373ea5bb552f19ad4cd35cc1 | Shell | 523 | 11 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# probe fc3 to predict label (uv)
dnn_probe -anal uv -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -layer fc3 -stim $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.stim.csv -beh label -model svc -cv 2 -out $TMP_DIR
# probe fc3 to predict RT (... |
a4b0c047c2d9bb81c2cb67811e8029f867c15ec4c977887ed1751006a9887404 | Shell | 524 | 19 | #!/bin/bash
apt-get -y update
apt-get -y upgrade
apt-get -y install default-jdk
apt-get -y install openjfx
apt-get -y install espeak-ng
apt-get -y install maven
cp -r ./raspberrypi/boot/* /boot
mkdir /home/pi/.config/autostart
cp ./raspberrypi/auretim.desktop /home/pi/.config/autostart/
mkdir /usr/share/mbrola
cp -r ./... |
4935017b63f48b8fc2c9d37e0fd71da4a4303d5f80192161648e626ae1cdb298 | Shell | 525 | 9 | #!/bin/bash
GSIZE=$(awk '$0 ~ ">" {if (NR > 1) {print c;} c=0;printf substr($0,2,100) "\t"; } $0 !~ ">" {c+=length($0);} END { print c; }' $1 | awk -F'\t' '{print $2}')
seqtk seq -a $2 > ${2%.*}.fasta
makeblastdb -in ${2%.*}.fasta -parse_seqids -dbtype nucl -out blast.db
blastn -dust no -outfmt "6 sseqid slen qcovs... |
154b5b362be4a1360105af67e45e2abbd01bb70adfb26c4a92522e92be4d2bad | Shell | 526 | 16 |
python3 ../src00_ics/step1_BuildIndex.py signal_net.txt signal_mat.txt signal_ics_out/this.index
python3 ../src00_ics/step2_CalICS_ori.py signal_ics_out/this.index signal_ics_out/this.out.ori
TAG=r0
python3 ../src00_ics/step2_CalICS_$TAG\.py signal_ics_out/this.index signal_ics_out/this.out.$TAG
TAG=r50
pyth... |
172b405a682af923fb236b4d2b1d48e9fedb7b335480c26d177722f9c3047fcd | Shell | 527 | 19 | #!/bin/bash
HEADER=$(head -1 $1)
SEQ=$(tail -1 $1)
LN=$(tail -1 $1 | tr -d "\n" | wc -c)
printf "sequence length ${LN}"
printf "%s\n%s%s\n" $HEADER $SEQ $SEQ > dup_$1
seqtk subseq $2 reads.ls > filtered.fq
minimap2 -x map-ont -t 32 dup_$1 filtered.fq -a --secondary=no | samtools view -S -b -F 4 -F 0x800 | samtools... |
dd65f3dff1935a04341fa44d5fa4ab1d8ec24ed8c96a04a977f7f0f6596a1b96 | Shell | 527 | 10 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# .nii
db_corr -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -dmask $DNNBRAIN_DATA/test/alexnet.dmask.csv -iteraxis row_col -resp $DNNBRAIN_DATA/test/sub-CSI1_ses-01_imagenet_beta_L.nii.gz -bmask $DNNBRAIN_DATA/test/PHA1_L.nii.gz -out $TMP_DIR
#... |
d0af705a163fc7e5f40b6f1b975ae22ae5151ae533a559a6c009435ff02480b5 | Shell | 528 | 26 | #!/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"
$tools/mashmap/mashmap -r $ref -q $qry -t $SLURM_CPUS_PER_TASK -o $out --filter_mode one-to... |
3620ecae276b8443f3aefde844bcd58f11c0aaf2e35428de5a5cf8ca30251528 | Shell | 531 | 13 | # dataset=USPTO50K-aug-untyped
seed=$1
dataset=USPTO50K-aug-untyped_${seed}
dataset_prefix=USPTO50K-aug-untyped
source /cm/shared/engaging/anaconda/2018.12/etc/profile.d/conda.sh
conda activate retroxpert_
# just score valid, added seed
python3 ../../score_predictions.py --beam_size 200 --invalid_smiles \
--pre... |
4107e685fb48536ed6c6eebe05f30615ef42e030c1694a10276d15805b642912 | Shell | 531 | 26 | #!/bin/bash
ref=$1.fasta
qry=$2.fasta
out=mashmap_${2}_to_${1}_2k
mkdir -p $out
out=$out
if [ ! -e $out/out.map ]; then
echo "\
$tools/mashmap/mashmap -r $ref -q $qry -t $SLURM_CPUS_PER_TASK -o $out/out.map -s 2000 --filter_mode one-to-one"
$tools/mashmap/mashmap -r $ref -q $qry -t $SLURM_CPUS_PER_TASK -o $out/o... |
c5c939a6f32ab906882657d9f9054c52aaa54a2ce4099dd70db6fd6970ff2db2 | Shell | 531 | 12 | #!/bin/sh
# Wrapper script for docker.
#
# This is used primarily for wrapping the GNU Make workflow.
# Instead of typing "make TARGET", type "./run.sh make TARGET".
# This will run the make workflow within a docker container.
#
# The assumption is that you are working in the src/ontology folder;
# we therefore map the... |
1618f5866f92fa25e73a4754d439d2acd0356ccb43e8ec70e3eebbd732eb85d0 | Shell | 534 | 13 | # 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
xpert_seed=$1
python trainEBM.py \
--log_file XPERT_${xpert_seed}_50top200max_gengraphfeat.log \
--expt_name XPERT_${xpert_seed}_50top200max_gengrap... |
8dd552401d44a72dd663e0faa219aca55cb5e30fc37991ca89516d6a82339edf | Shell | 534 | 14 | # 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_seed=$1
python gen_proposals/gen_fps_from_proposals.py \
--log_file GLN_${gln_seed}_50top200max_makefp.log \
--proposals_file_prefix "GLN_200top... |
624dea84910b29ee10da02473288daf71b42737e163d1c0e338ada7ac075b1cc | Shell | 535 | 19 | SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
BASE_DIR=$SCRIPT_DIR/..
build=false
while getopts 'b' opt; do
case $opt in
b) build=true ;;
esac
done
shift $((OPTIND -1))
jupytext --to notebook --output $BASE_DIR/docs/$1.ipynb $BASE_DIR/scripts/$1.py
jupyter nbconvert... |
492782357d08417a52f7efa95d80b66ca88d41c6cff076bf557f90cbdd107ad7 | Shell | 539 | 16 | #!/bin/bash
module load juicer/1.5.6
module load samtools
module load bwa
if [ ! -e reference/chr.sizes ]; then
cd reference
bwa index asm.fasta
python /usr/local/apps/juicer/juicer-1.5.6/misc/generate_site_positions.py MboI asm asm.fasta
samtools faidx asm.fasta
awk '{print $1"\t"$2}' asm.fasta.fai > ... |
7cc9309e0a2de9470590684e13ec1e300317ba5facad909a652a1855392c2a37 | Shell | 539 | 9 | #!/bin/bash
GSIZE=$(awk '$0 ~ ">" {if (NR > 1) {print c;} c=0;printf substr($0,2,100) "\t"; } $0 !~ ">" {c+=length($0);} END { print c; }' $1 | awk -F'\t' '{print $2}')
seqtk seq -a $2 > ${2%.fastq.gz}.fasta
makeblastdb -in ${2%.fastq.gz}.fasta -parse_seqids -dbtype nucl -out blast.db
blastn -dust no -outfmt "6 sse... |
3d97b4e48a12e2132120398120703df9a52b36cf70d0ae3e451e76b7ef6535da | Shell | 540 | 18 | #!/bin/bash
set -xe;
python covariance_test.py \
-j QCD \
--maxdim 2 \
--tau-latent-vectors 8 \
--tau-latent-scalars 1 \
--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 \
--model-path "dev/exp/LGNAutoencoder_QCDJet_min&max_tauLS1_tauLV8_encod... |
fbea880bc6ed158c58411ccf9a940f5134bc95e50aed61b1670a365336f5b8d3 | Shell | 541 | 23 | #!/bin/bash
CONFIG="autoscale_multisite_parallel.toml"
RUNTEXT="running the individual initialization and running AN PSTH protocols"
CELLNAMES="02 05 06 09 10 11 13 17 30"
for f in $CELLNAMES
do
# python vcnmodel/model_run2.py $f -H -P initAN --configfile autoscale.toml
python vcnmodel/model_run2.py VCN_c$f -H... |
57da1462a53bff678c0bfc844519e1a80a6450d9816434e47dea7ffa7641c712 | Shell | 544 | 23 | #!/bin/bash
CONFIG="autoscale_multisite_parallel.toml"
RUNTEXT="running the individual initialization and running AN PSTH protocols"
CELLNAMES="02 05 06 09 10 11 13 17 30"
for f in $CELLNAMES
do
# python vcnmodel/model_run2.py $f -H -P initAN --configfile autoscale.toml
python vcnmodel/model_run2.py VCN_c$f -H... |
c44c63d3196afbb31e0e67e668dc9d007e2fa846cde7847fadfcd5999286022f | Shell | 545 | 23 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./ast.sh <gaps.bed>"
exit -1
fi
ls -a *.bam
gaps=$1
if [[ ! -s hic.bed ]]; then
echo "
$asset/bin/ast_hic $gaps *.bam > hic.bed"
$asset/bin/ast_hic $gaps *.bam > hic.bed
fi
module load bedtools
platform=hic
bedtools subtract -a ../asm.bed -b $platform.bed | bedtool... |
220527e8e62cb5144dd88cbb2e12664ca90539f82f13ba0903c61b788f976cb3 | Shell | 546 | 16 | #!/bin/bash
cd "$(dirname "$0")"/..
pwd
echo -n "Running dos2unix "
find . -name "*\.h" -o -name "*\.cpp"|grep -v bundled|xargs -I {} sh -c "dos2unix '{}' 2>/dev/null; echo -n '.'"
echo
echo -n "Running clang-format "
find . -name "*\.h" -o -name "*\.cpp"|grep -v bundled|xargs -I {} sh -c "clang-format -i {}; echo... |
8d26a275fd2e34ead42aaeb7c54d91adcd8de8a7d35dda23a071a4e7e6b986d3 | Shell | 546 | 23 | #!/bin/bash
CONFIG="autoscale_multisite_parallel.toml"
RUNTEXT="running the individual initialization and running AN PSTH protocols"
CELLNAMES="02 05 06 09 10 11 13 17 30"
for f in $CELLNAMES
do
# python vcnmodel/model_run2.py $f -H -P initAN --configfile autoscale.toml
python vcnmodel/model_run2.py VCN_c$f -H... |
cc543a185bbeb782ed62c5028ce9634eb2df0b7bdf36850aae05ae04f827abf5 | Shell | 546 | 14 | # 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
sym_seed=$1
python gen_proposals/gen_fps_from_proposals.py \
--log_file SYM_${sym_seed}_50top200max_makefp.log \
--proposals_file_prefix "neuralsym_... |
5137c15ebf1cf4ebc914cec92d111b8c643e3166b07b5d92b066f88c06dd5d21 | Shell | 547 | 6 | onmt_average_models -output ./exp/USPTO-MIT_RtoP_aug5_mixed/finetuned_average_model_96-100.pt \
-m exp/USPTO-MIT_RtoP_aug5_mixed/finetuned_model.reactants-products_step_960000.pt \
exp/USPTO-MIT_RtoP_aug5_mixed/finetuned_model.reactants-products_step_970000.pt \
exp/USPTO-MIT_RtoP_aug5_mixed/fin... |
0d4f1052010d620e2367d33afe44ebd15d8608493b84e9eff9b99bfaae6232e7 | Shell | 550 | 16 | for REP in $(seq 1 3); do
for CUTOFF in 06; do
for BATCHSIZE in 4; do
FORMAT=LMDB
SB_FILE=run_cutoff${CUTOFF}_bs${BATCHSIZE}_${FORMAT}_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/FORMAT/${FORMAT}/g" ${SB_FILE}
sed -i "s/BATCHSIZE/${BATCHSIZE}/g" ${SB_FILE}
... |
b24a141d00fc0de66ce1f4dec8fe22b844f6dfd37225beb3b56a31ff11b43b90 | Shell | 555 | 16 | #!/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=10000 #10gbs
#SBATCH -t 900 # time (minutes)
#SBATCH -o cnn_train_cfos.out
#SBATCH -e cnn_train_cfos.err
module lo... |
9070a57b1997057dc7c040e947e0b78763bc215dc17b7979a22e1c5abbb25e5c | Shell | 560 | 16 | #!/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=5000 #5 gbs
#SBATCH -t 10 # time (minutes)
#SBATCH -o /tigress/ahoag/cnn/exp2/slurm_logs/cnn_inf_%j.out
#SBATCH -e /... |
44e51f8fa5b5d6329b776017ec0d279d9a46749729eff68add7625c06792e6da | Shell | 563 | 14 | # 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
xpert_seed=$1
python gen_proposals/gen_fps_from_proposals.py \
--log_file retroxpert_${xpert_seed}_50top200max_makefp.log \
--proposals_file_prefix ... |
ac5df3a21738a953d0c885d303429b194305d240b29a350a680c56b4765f3dfe | Shell | 564 | 21 | #!/bin/bash
cd /opt/auretim
JAVA_HOME=/opt/auretim/jre \
PATH=$JAVA_HOME/bin:$PATH \
java -Djavafx.platform=monocle \
-Dmonocle.screen.fb=/dev/fb1 \
-Dmonocle.platform=Linux \
-Dmonocle.input.0/0/0/0.flipXY=false \
-Dmonocle.input.0/0/0/0.maxX=3900 \
-Dmonocle.input.0/0/0/0.minX=210 \
-Dmonocle.input.0/0/0/0... |
db2ddd05ce7c4f3592f1a383da672625688dedf0d59fc784dac6731b8c6f539f | Shell | 569 | 16 | SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
BASE_DIR=$SCRIPT_DIR/..
SLIDE_DIR=$BASE_DIR/docs/slides/$1
FILE=$SLIDE_DIR/$1.md
if test -f "$FILE"; then
echo "$FILE exists."
marp --theme $SLIDE_DIR/../themes/slides.css --preview --html --allow-local-files $FILE
# REF: htt... |
a08f75cc8238f7a7d11c9c1468ea49968ce20bd1572e3bf4abcd17acc77fdd82 | Shell | 570 | 17 | CH="klt kht ihvcn nacncoop"
CELLNAMES="02 05 06 09 10 11 13 17 24 29 30"
DM="normal active passive"
for f in $CELLNAMES
do
for C in $CH
do
for D in $DM
do
echo "Plotting VCN_c$f $C $D"
python vcnmodel/model_run2.py VCN_c$f -F --configfile autoscale_multisite_parallel.toml --... |
d8922c508e4969e7a02e43aa98b18c723ba7c982ec1f9167d2541a540b000001 | Shell | 570 | 14 | # 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_seed=$1
python trainEBM.py \
--log_file GLN_${gln_seed}_50top200max_SIM_50top200max_gengraphfeat.log \
--expt_name GLN_${gln_seed}_50top200max_SI... |
cc0959386add036eef79da2019eb8e0a1bd16e00f92d7491fd9be6865558113c | Shell | 571 | 6 | onmt_average_models -output ./exp/USPTO-MIT_RtoP_aug5_separated/finetuned_average_model_96-100.pt \
-m exp/USPTO-MIT_RtoP_aug5_separated/finetuned_model.reactants-products_step_960000.pt \
exp/USPTO-MIT_RtoP_aug5_separated/finetuned_model.reactants-products_step_970000.pt \
exp/USPTO-MIT_RtoP_au... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.