sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
cf74a784fa59f5e9e0375fda3dc2c657cea15d107d17654f15177d0b742bc5bc | Shell | 190 | 13 | #!/bin/bash
SEDMAGIC='s;[^/]*/;|-- ;g;s;-- |; |;g'
if [ "$#" -gt 0 ] ; then
dirlist="$@"
else
dirlist="."
fi
for x in $dirlist; do
find "$x" -print | sed -e "$SEDMAGIC"
done
|
6bf2e605e35d062f9a0ca6d67979b52adae22c02ab87af84c1e441ed2c34039b | Shell | 196 | 6 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
brain_roi -nif $DNNBRAIN_DATA/test/sub-CSI1_ses-01_imagenet_beta_L.nii.gz -mask $DNNBRAIN_DATA/test/MMP_L.nii.gz -out $TMP_DIR/test.roi.h5
|
b6bcd358965d93eb7bf00e9d91883bdbed609aa32bd21a767fc1bc934e071c6c | Shell | 196 | 9 | #!/usr/bin/env bash
# (1) Kill torchserve process
torchserve --stop
# (2) Remove model and logging data
# NOTE: You might want to keep these instead in the future...
rm -r model_store
rm -r logs |
4a253eceb9f3cc1a2a7aea34bd22354d975f5baa7649004d97ed4b15fcf12310 | Shell | 198 | 9 | #!/bin/sh
#Build MITK
git clone --branch v2018.04.2 https://phabricator.mitk.org/source/mitk.git MITK
mkdir Build; cd Build
cmake ../MITK
make -j$(nproc)
#cmake -DMITK_EXTENSION_DIRS:PATH=??? ../
|
3d61320e18f61999ff790a774a939f84546d12bdf7ae5cef237a87921aed4c11 | Shell | 199 | 8 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# test
dnn_minstim -net AlexNet -stim $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.stim.csv -layer conv5 -chn 1 -out $TMP_DIR/min_stim
|
41a0509d4a75818b63d0e8cb388efbf673aa599a326f7a16364530852a5b7a47 | Shell | 200 | 6 | #!/bin/sh
set -e
apk add -q bash curl-dev
nimble install -y https://github.com/brentp/duktape-nim@#dev
nimble install -y && nimble test
nim c -d:danger -o:/test/slivar_static -d:nsb_static src/slivar
|
b2fa95fd0ee2d4ad23998994abb6867c56acdd1d7e171bf5c2b578aca483ba59 | Shell | 200 | 9 | #!/usr/bin/env bash
# (1) Kill torchserve process
torchserve --stop
# (2) Remove model and logging data
# NOTE: You might want to keep these instead in the future...
# rm -r model_store
# rm -r logs |
f74a1c3e05227c527c88174c36986a475a6f088f5bebd5b62e93c439ebd9e634 | Shell | 202 | 9 | ARG_XONLY=0
ARG_INIT=1
set -x
for ARG_PRELR in 0.0005
do
CUDA_VISIBLE_DEVICES=3 python main.py --phase=self_train --setname=BTNx --lr=${ARG_PRELR} --xonly=${ARG_XONLY} --self_init=${ARG_INIT}
done |
e7e09aac1ee23bf93ac30384f19f47b5ec5757b5c4d257667e11093c1eeabd31 | Shell | 203 | 8 | DATASETS=`ls -1d ../datasets/*/`
for d in $DATASETS
do
dt=`basename $d`
echo "Processing dataset $dt\n\n"
papermill utils/tf_cnn_classifier.ipynb tf_cnn_experiments/$dt.ipynb -p DATASET $dt
done
|
2451dfe8d6d5557cfa15796c5a4c58b92448a13bf6334cb389dc4a8af4750df3 | Shell | 204 | 7 | #!/bin/bash
for n in 32 64 128 512; do
# python eam.py -n --domain=$n --runpath=runs-$n &&
# python eam.py -f --domain=$n --runpath=runs-$n &&
python eam.py -e 1 --domain=$n --runpath=runs-$n
done
|
c57efbca4c964f14d0dc53040ac0f44c6f001b3df295e98aa47506f741704819 | Shell | 214 | 8 | #!/bin/bash
../scripts/SingleCellFusion \
-i "./datasets/10x_cells_v2.h5ad" "./datasets/snatac.h5ad" \
-im "rna" "atac" \
-f "./datasets/10x_cells_v2.h5ad" \
-op "test_april27" \
-o "./results" |
0310d3e7f6ac366ee248e656b57a885e12fbd7807e551c99cb075c0b3d6efac8 | Shell | 216 | 6 | SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
BASE_DIR=$SCRIPT_DIR/..
cat $BASE_DIR/scripts/manifest.txt | xargs -I % sh $SCRIPT_DIR/make_notebook.sh %
$SCRIPT_DIR/copy_results.sh
|
1cd4e92483cc91c2f960d3695f20323437c864d805a031cf258c826f35c081f3 | Shell | 216 | 6 | #!/usr/bin/env bash
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
docker build "$SCRIPTPATH" \
-t heviai/picai_prostate_segmentation_processor:latest \
-t heviai/picai_prostate_segmentation_processor:v1.0
|
648692b20c48ba3e38f87ee0f90405ee5efffe6203e81cdf836bd1b6a46fbc28 | Shell | 219 | 6 | #!/bin/bash
cd ~/github/leifer-Behavior-Triggered-Averaging-Tracker
echo 'converting tracks to analysis folders'
/usr/licensed/bin/matlab-R2016a -nodisplay -nosplash -nojvm -r "convert_tracks_to_analysis('$1'); exit;" |
f05c30824923f614868193d3bd57ac2eb5fe38347bc68fc757c3927c89d3695a | Shell | 221 | 14 | #!/bin/bash
set -e
if [[ "$1" = "serve" ]]; then
shift 1
printenv
ls /opt
torchserve --start --ts-config /home/model-server/config.properties
else
eval "$@"
fi
# prevent docker exit
tail -f /dev/null |
a27f90fd0baeae4a21dec96c10d071d5e8b818f558cef986b914d01e4e6fbef6 | Shell | 222 | 8 | DATASETS=`ls -1d ../datasets/*/`
for d in $DATASETS
do
dt=`basename $d`
echo "Processing dataset $dt\n\n"
papermill utils/torch_cnn_classifier.ipynb torch_cnn_experiments/$dt.ipynb -p DATASET $dt -p EPOCHS 10
done
|
f3803b43f5bf51e7f771869147dfdd31ed6c28e30fa9796b458d8d1712fbfbef | Shell | 224 | 10 | #!/usr/bin/env bash
# (1) Send prediction and get request
counter=1
while [ $counter -le 77 ]
do
echo $counter
curl -X POST http://127.0.0.1:8080/predictions/btnx_outlier -T test_images/{$counter}.jpg
((counter++))
done
|
fb1c6fc0af39e239cd483d50ba1d63ac48d29307869c0e397755d4d336aa4eac | Shell | 224 | 6 | #!/usr/bin/env bash
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
docker build "$SCRIPTPATH" \
-t picai_baseline_nnunet_semi_supervised_processor:v2.1.1 \
-t picai_baseline_nnunet_semi_supervised_processor:latest
|
251d18ebc3f7caeb3807296a111939746d6627f1d54ec43c01bf55efa59c2ee1 | Shell | 229 | 13 | #!/bin/bash
prefix=$1
if [ -z $SLURM_ARRAY_TASK_ID ]; then
R=$2
else
R=$SLURM_ARRAY_TASK_ID
fi
echo "\
cat $prefix.r$R.*.fastq | gzip > $prefix.read-BC$R.fastq.gz"
cat $prefix.r$R.*.fastq | gzip > $prefix.read-BC$R.fastq.gz
|
47244489e17ad9007eb87dd33f4b2436904437f518275f4296672b2d7988931a | Shell | 229 | 8 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# test
dnn_rf_us -net AlexNet -stim /nfs/s2/userhome/zhouming/workingdir/stim/test_rf.stim.csv -layer conv5 -chn 125 -ip_metric bicubic -up_thres 0.95 -out $TMP_DIR/rf_us
|
f04121048b6beb4d96eaf1c5106de5d3f6133fd7498cf32bd69857ec6aa5522f | Shell | 230 | 10 | #!/bin/bash
LIST_TARGETS=`cat data/list_targets_350.txt`;
echo ${LIST_TARGETS};
for target in ${LIST_TARGETS}; do
echo "Now training and attributing on target ${target}"
python molucn/main_rf.py --target ${target}
done
|
0eb1c1cc80000e55348ef8a96524df3813417fe0d19b5132150b76e2f79dcbe4 | Shell | 232 | 8 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# test
dnn_rf_sw -net AlexNet -layer conv5 -chn 125 -stim /nfs/s2/userhome/zhouming/workingdir/stim/test_rf.stim.csv -wsize 11 11 -stride 2 2 -metric max -out $TMP_DIR/rf_sw
|
defe6733efff08011bfaf01d845a749dae77e1465c595d5521107fe0b54e8b3c | Shell | 236 | 10 | #!/bin/bash
export PATH=$PATH:~/github/leifer-Behavior-Triggered-Averaging-Tracker/Bash
output_file_name=~/logs/viewoutput.csv
cd ~/outputs/
rm $output_file_name
ViewDateDirectory.sh /tigress/LEIFER/Mochi/Data/ $1
ViewSortedStatus.sh |
91379528d261ab78c8677c5412ad94dcabbafe2a1c22711a493970ea1d68563c | Shell | 239 | 7 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# extract DNN activation from video with -layer
dnn_view -net AlexNet -layer conv5 -chn 60 125 -stim $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.stim.csv -show -out $TMP_DIR/
|
7b670344701a91208df8d0d6f86d9c05481d7c3182a0999d44e6bf33f32c0d0b | Shell | 243 | 9 | #!/bin/bash
folder_name=$1
script_name=$2
cd ~/github/leifer-Behavior-Triggered-Averaging-Tracker
echo 'running '$script_name
/usr/licensed/bin/matlab-R2016a -nodisplay -nosplash -nojvm -r "call_function('$script_name;$folder_name'); exit;" |
d0e4b880c1109b7f9d62c3b240a7360bfca6a5e289226d272e700508fba78dda | Shell | 245 | 18 | #!/bin/bash
# Fix python/3.6 installation to prepend PATH
module load python/3.5
python --version
which python
module load python/2.7
python --version
which python
module load perl/5.18.2
perl -v
which perl
module load R
R --version
which R |
518b490c9810d5531bbf18efce3b9c6638d09a89a9528fffc279328d59548845 | Shell | 247 | 9 | echo 'Starting Test for Jetson Nano'
echo ''
echo 'Nonspiking Dense:'
python3 test_jetson_nonspiking_dense_large.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_jetson_nonspiking_sparse_large.py
echo ''
echo 'Testing finished, all data recorded' |
8145c74a25cc2e54f58274b3b21eae294da554730b10dae2eefe989c2ec74522 | Shell | 248 | 6 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
db_hrf -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 -tr 2 -n_vol 10 -out $TMP_DIR/db_hrf.act.h5
|
a3d3dcc7cd4815130a3bb2cb8ac861fb39fe1cdd6ca5ad4ce4f22fddca14a529 | Shell | 250 | 7 | module load gcc openmpi hwloc fftw cuda
mkdir build
cmake .. -DGMX_MPI=ON -DGMX_OPENMP=ON -DGMX_FFT_LIBRARY=fftw3 -DGMX_SIMD=AVX2_256 -DGMX_HWLOC=on -DGMX_GPU=ON -DCMAKE_INSTALL_PREFIX=/mnt/home/YOUR_INSTALL_LOCATION
make
make check
make install
|
1a249364d79e970f75f81cd3a1783eda01bb702313933709f78925a3d3b5adb7 | Shell | 254 | 11 | #!/bin/bash
# Script to run all example notebooks.
set -euxo pipefail
CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.timeout=600"
for notebook in examples/*.ipynb; do
echo "Running: $notebook"
$CMD "$notebook"
done
|
94dd66b1c5b381d59644e03e8430a0c21a531aeebaf8811999243179e4438c01 | Shell | 256 | 9 | #! /bin/bash
[ $# -lt 2 ] && { echo 'Usage :
$1 = the image to be converted
$2 = template (for instance MNI152 1mm)
$3 = matrix without tranformation
$4 = resultFolder'; exit 1; }
flirt -in $1 -ref $2 -applyxfm -init $3 \
-out $4/$(basename $1)
|
b9aa0e62dda6c89e9a794b1024f5580d3753e4a5f69d8f3c1c972793b8a04728 | Shell | 259 | 21 | #!/bin/bash
ref=$1
if [ -z $ref ]; then
echo "Usage: ./bwa_index.sh <ref.fasta>"
echo "No <ref.fasta> given. Exit."
exit -1
fi
module load bwa
module load samtools
echo "
bwa index $ref"
bwa index $ref
echo "
samtools faidx $ref"
samtools faidx $ref
|
7b4fb799e62ce02d76a596d9289807558acb5ba01921425f21bc890d16c14294 | Shell | 260 | 6 | #!/bin/bash
gcc -Wall -pedantic -Wextra -std=c99 -msse2 -O3 -ftree-vectorize -ffast-math -funroll-loops -fomit-frame-pointer -m64 tvbii_multicore.c -o tvb -lgsl -lgslcblas -lm -lpthread 2> /output/build_output.txt
cp tvb /output/tvb
rm tvb tvbii_multicore.c
|
b10dda9fd56fabadf32f40c7f217e582007ed54a4277b31a4d867a4295d54e8f | Shell | 261 | 9 | #!/bin/bash
while read -r SPECIES VGP_ID START END TYPE; do
log=$(echo log_${VGP_ID}_${TYPE}.out)
sbatch --partition=vgl,hpc --cpus-per-task=32 --output=${log} het.sh VGP_dataset/${VGP_ID}* ${VGP_ID} ${SPECIES} ${TYPE} 70 10000000 ${START} ${END}
done < $1
|
ff7b91f4c0482500eaeb84151df058f38f3e2607b0a323589dd07f129d652db9 | Shell | 265 | 15 | #!/bin/bash
FILE=/input
if [ ! -d "$FILE" ]; then
echo "Error ($FILE): input directory does not exist. Terminating."
exit
fi
FILE=/output
if [ ! -d "$FILE" ]; then
echo "Error ($FILE): output directory does not exist. Terminating."
exit
fi
/tvb $1 $2 $3
|
05d308c763e6e8c6f48082cfad717b4a7331919fb4775c6073a83c49d450d405 | Shell | 267 | 6 | # For testing/verification purpose, run the following command
# a prediction.xlsx file will be generated
python main.py --resolution low
# An example of how to adjust code for testing different models, you can use
python main.py --resolution high --thredhold 0.0147 |
96e9fcd80aa07005dbe295786bc77897dd5e756763ca06f9880f43d8bb394ae9 | Shell | 267 | 15 | #!/bin/bash
if [ -z $1 ] ; then
echo "Assumes we have asm.fasta"
exit -1
fi
ref=asm.fasta
echo "=== start indexing reference ==="
echo "\
$tools/longranger/longranger-2.2.2/longranger mkref $ref"
$tools/longranger/longranger-2.2.2/longranger mkref $ref
echo ""
|
e92a34629bba8601c777f28501901f60aea1aa508c131b4f88853890e948cd12 | Shell | 267 | 11 | #!/bin/bash
# Script to run all blog post notebooks.
set -euxo pipefail
CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.timeout=600"
for notebook in examples/blog_posts/*.ipynb; do
echo "Running: $notebook"
$CMD "$notebook"
done
|
23930976c4aba5851bed5aa6a495f8e069cb0c5b0a9cce5848057d1473ff0d31 | Shell | 269 | 12 | source activate my-rdkit-env
for name in ??.smi
do
base=${name%.smi}
grep -v 'StrEd' $name > $base'_n.smi'
nohup mol2vec featurize -i $base'_n.smi' -o $base'_n.csv' -m /home/zhanghaiping/model_300dim.pkl --uncommon UNK -r 1 &
sleep 2s
done
|
e0ff5fbb8b427433f037c903a6f2e09a23523702693cc801aadd40bbedcc8c59 | Shell | 269 | 2 | rsync -zvhr ./bilateral-connectome/data/2021-05-24-v2 bpedigo@synaptomes1.neurodata.io:/data/bpedigo/bilateral/bilateral-connectome/data
rsync -zvhr ./bilateral-connectome/data/processed bpedigo@synaptomes1.neurodata.io:/data/bpedigo/bilateral/bilateral-connectome/data |
cb34ed86c3f12b453b032a888d4310aff5d674214a6dea885d298581906fc431 | Shell | 275 | 10 | #!/bin/bash
PATH_TABULAR_DATA=/home/junrau/projects/def-mlepage/UKBB/current_civet.csv
PATH_DOWNLOAD_DIR=/Users/joshua/Developer/CognitiveSubtypes/data
if [ ! -d $PATH_DOWNLOAD_DIR ]; then
mkdir $PATH_DOWNLOAD_DIR
fi
autossh cc -d $PATH_TABULAR_DATA $PATH_DOWNLOAD_DIR |
f1f2ad1c1d8b6f88dce5c542cda35dc9d55801d26b20b4bbc02fd189df33e6f9 | Shell | 275 | 10 | #!/bin/bash
LIST_TARGETS=`cat data/list_targets_350.txt`;
echo "Using attribution technique: ${1}";
for target in ${LIST_TARGETS}; do
echo "Now training and attributing on target w. PDB Id: ${target}"
python molucn/main.py --target ${target} --explainer ${1}
done
|
1e906d1f58b4680b1152c1571327c72aff4add09bec9e1bb6dd4e660230356c9 | Shell | 277 | 16 | #!/bin/bash
# helper script to create symbolic links to
# notebooks in example folder
# remove all
rm -rf examples/*
# cd into website folder
cd examples/ || return
# create symbolic links in website folder
ln -s ../../examples/* .
# return to initial folder
cd - || exit
|
2da112fc4f360a811c988a826f7eb3d47e67197a99f1b81e9f096a01a7be8840 | Shell | 280 | 8 | SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
BASE_DIR=$SCRIPT_DIR/..
rm $BASE_DIR/results/glued_variables.json
echo {}> $BASE_DIR/results/glued_variables.json
rm $BASE_DIR/results/glued_variables.txt
touch $BASE_DIR/results/glued_variables.txt |
a33c325826dd92ef2e2d4d17c91936dc3707552d633cd8cd9b0851e30cf8124f | Shell | 283 | 10 | #!/bin/bash -l
#SBATCH --time=5:00:00
#SBATCH --mem=35G
#SBATCH --array=0-35
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
module load anaconda
python main.py --param_set_search_number $SLURM_ARRAY_TASK_ID --batch_size 32 --run_name param_search --train_cs_predictor --add_lg_info |
24d3cf9caa4aa7067287e6f6356acb711067edb53c1ed7a2a61f60dc79caf280 | Shell | 284 | 10 | #!/bin/bash
FILES="VCN_c08 VCN_c09 VCN_c09nd VCN_c11 VCN_c14 VCN_c17 VCN_c18 VCN_c19 VCN_c20 VCN_c21 VCN_c22"
for f in $FILES
do
echo "Cell: <$f>"
# rm VCN_Cells/$f/Initialization/IVneuronState.dat
ls -lat VCN_Cells/$f/Initialization/IVneuronState_mGBC.dat
echo " "
done |
5b110cef11d6dd9d0b109cfd5eb2e23a8e1753573ec28d23af15a23e4aac898c | Shell | 287 | 10 | #!/bin/bash
SingleCellFusion \
-i "./datasets/10x_cells_v2.h5ad" \
"./datasets/smarter_cells.h5ad" \
"./datasets/smarter_nuclei.h5ad" \
"./datasets/snmcseq_gene.h5ad" \
-im "rna" "rna" "rna" "mc" \
-f "./datasets/10x_cells_v2.h5ad" \
-o "./results"
|
857f371ed049c247077f579f5739adbfc3e416ee58ede31ca4c97f034634f316 | Shell | 287 | 13 | #!/bin/bash
map=$1
name=$2
if [ -z $map ]; then
echo "Usage: ./to_bed.sh <map> <name: PRIM or HAPLOTIG>"
exit -1
fi
# Transform to bedpe
cat $map | awk -F " " -v name=$name '{print $6"\t"$8"\t"$9+1"\t"$1"\t"$3"\t"$4+1"\t"$1":"$3"-"$4+1":"name"\t"$NF"\t"$5"\t"$7"\t"$2}' > $map.bed
|
9d4f8a0ef45e64ab2d562907147bd5b2f4170d98a9810da678fc8a08778fb905 | Shell | 289 | 9 | #!/bin/bash
runpath=runs
echo "Storing results in $runpath"
python eam.py -n --runpath="$runpath" && \
python eam.py -f --runpath="$runpath" && \
python eam.py -e 1 --runpath="$runpath" && \
python eam.py -r --runpath="$runpath" && \
python eam.py -d --runpath="$runpath"
|
9fc647dd2275487e70e0eac9ceb763d6b219c4f7d396f84e80f4f55812d078fe | Shell | 305 | 15 | #!/bin/bash
# Kill process "nrniv" running on one of the specified nodes
# This script takes names of nodes as arguments
for var in "$@";
do
ssh $var "pkill nrniv" > /dev/null 2>&1
if [ $? -eq 0 ]
then
exit 0 # process is found and killed
fi
done
exit 1 # process is not running |
9cd0ea30888ee38912b59e6db7984a435f192fa0fff4e698af2d65ee7baf496e | Shell | 308 | 14 | #!/bin/bash
# Bash script to run GA
# Load required modules to run GA
# module purge
source /software/ENV/modules
module load anaconda/4.3.1
module load openbabel/2.3.2/gcc-4.9.2
module load amber/16/gcc-6.3.0
module load g16
# Execute GA
cd ../../
python main.py -input_file tests/amber_gpgg/ga_input.in
|
64bcf34cc00739fc3d23120571b93ad3b6fde51374555cb3835593b79c79636d | Shell | 309 | 9 | #!/bin/bash
wget https://github.com/jgm/pandoc/releases/download/3.1.5/pandoc-3.1.5-linux-amd64.tar.gz
mkdir /home/docs/pandoc
tar -xvzf pandoc-3.1.5-linux-amd64.tar.gz --strip-components 1 -C /home/docs/pandoc
export PATH="/home/docs/pandoc/bin:$PATH"
echo $PATH
echo $(whereis pandoc)
echo $(which pandoc)
|
370d101228c8a7dd916fd9b29eb738de70bbd10bd121f9c54dd6498c13657062 | Shell | 314 | 6 | dataset=USPTO50K-aug-typed
suffix=test-prediction
python ../../score_predictions.py --beam_size 50 --invalid_smiles \
--predictions ../../experiments/results/predictions_on_${dataset}_${suffix}.txt \
--targets ../../data/${dataset}/tgt-test.txt \
--sources ../../data/${dataset}/src-${suffix}.txt
|
8c0aa1eeda233b882e9c80223b3178954f5e5ca0fa101c91bd6d572932d9b983 | Shell | 314 | 9 | #!/usr/bin/env bash
if [ ! -f hg38.fa ]; then
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chromFa.tar.gz
tar xvfz hg38.chromFa.tar.gz
find chroms -type f | grep -v "_" | perl -e 'while(<>){chomp;system("mv $_ .");}'
rm -r chroms/ hg38.chromFa.tar.gz
cat *.fa > hg38.fa
fi |
d1b0691b633c474e61051e4139ab615f406877037d866edb5772713e6f82d048 | Shell | 314 | 16 | #!/bin/bash
echo " sh $0 outputprefix "
if [ $# -lt 1 ] ; then
exit
fi
SUB=$1
TH=${SUB}thicknorm.nii.gz
JA=${SUB}logjacobian.nii
MK=${SUB}mask.nii.gz
OUTT=${SUB}smooththick.nii.gz
OUTJ=${SUB}smoothjac.nii.gz
REPS=5
SurfaceBasedSmoothing $TH 1.5 $MK $OUTT $REPS
SurfaceBasedSmoothing $JA 1.5 $MK $OUTJ $REPS
|
39e1b8a2cecc8a82e92300c911db05491ae5f586d534dc6dd5b246150e5bb7ac | Shell | 319 | 13 | for THE_CT in 2.0
do
for THE_INIT in 1
do
for THE_LR in 0.001
do
CUDA_VISIBLE_DEVICES=3 python main.py --phase=adapt_train --setname=ACON_Ag --ct_wgt=${THE_CT} --adapt_init=${THE_INIT} --shot=10 --lr=${THE_LR}
done
done
done
# THE_CT: 0.5 1.0 2.0
# THE_LR: 0.0001 0.001 |
13969aac36902cfba848fd164e88eb0f57285832d0152b5c119693ab3ae27ff9 | Shell | 321 | 14 |
GENOME=/home/database/reference/mm10/mm10.fa.size
TSS=/home/database/annotation/mm10/Mus_musculus.GRCm38.87.chr.gtf.pc.bed.TSS.sorted.bed
LOOP=./scATAC_RefLoop.bed
SLOP=1000
bedtools slop -i $TSS -g $GENOME -b $SLOP > $TSS\.slop$SLOP\.bed
bedtools pairtobed -a $LOOP -b $TSS\.slop$SLOP\.bed > RNA/LoopAndTSS.bed
... |
d113ed461f3d4560d725e328fd8e279a79839a327912107cc09251ccc0719370 | Shell | 321 | 15 | #!/usr/bin/env bash
set -eou pipefail
PYTHON_VERSIONS="3.6 3.7 3.8"
PKGS="torchserve torch-model-archiver"
for pkg in ${PKGS}; do
for python_version in ${PYTHON_VERSIONS}; do
(
set -x
conda build --output-folder output/ --python="${python_version}" "${pkg}"
)
done
done... |
def30a5f293afe6cf4e1057ecf8bbad16efe5ab07e890cb3761f83d4f43316f4 | Shell | 321 | 15 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: merge.sh <out_prefix>"
exit -1
fi
module load samtools
prefix=$1
echo "\
samtools merge -O bam -@$SLURM_CPUS_PER_TASK $prefix.bam *.sort.bam"
samtools merge -O bam -@$SLURM_CPUS_PER_TASK $prefix.bam *.sort.bam
echo "\
samtools index $prefix.bam"
samtools index $prefix.bam... |
e983366a147586ce0b2548d6a107a162b9883aed29da323fc8dab8f6ffcc7e36 | Shell | 324 | 15 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: _mrg.sh <out_prefix>"
exit -1
fi
module load samtools
prefix=$1
echo "\
samtools merge -O bam -@$SLURM_CPUS_PER_TASK $prefix.bam *.sorted.bam"
samtools merge -O bam -@$SLURM_CPUS_PER_TASK $prefix.bam *.sorted.bam
echo "\
samtools index $prefix.bam"
samtools index $prefix.... |
f3a52178bb1783739c947965e997ffd165c52696163dfd69a9e9e2a2899926ad | Shell | 325 | 14 |
GENOME=/home/database/annotation/mm10/mm10.chrom.sizes
TSS=/home/database/annotation/mm10/Mus_musculus.GRCm38.87.chr.gtf.pc.bed.TSS.bed
LOOP=./scATAC_loop.txt.uniq.bed
SLOP=1000
bedtools slop -i $TSS -g $GENOME -b $SLOP > $TSS\.slop$SLOP\.bed
bedtools pairtobed -a $LOOP -b $TSS\.slop$SLOP\.bed > RNA/LoopAndTSS.be... |
8f5318315823e66c7e8d88e4c06c133a53359af70a8b31059b3eac8ce4d61948 | Shell | 326 | 16 | #!/bin/bash
rm -f filenames.ls
for file in ${2}/[^.]*.ktab
do
basename $file >> filenames.ls
done
printf "merging:"
cat filenames.ls
printf "\
Fastmerge -T${3} ${2}/${2} $(awk -F. -v path=${2} '{printf path"/"$1" "}' filenames.ls)"
Fastmerge -T${3} ${2}/${2} $(awk -F. -v path=${2} '{printf path"/"$1" "}' filenames... |
656eb4676ac16c24b7349867ba4d5647697f9659f68c2a20eea0c37d3ed8ddfe | Shell | 333 | 4 | # download and unzip the main bluebert model used in this study
wget https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/NCBI_BERT_pubmed_mimic_uncased_L-12_H-768_A-12.zip
unzip NCBI_BERT_pubmed_mimic_uncased_L-12_H-768_A-12.zip -d NCBI_BERT_pubmed_mimic_uncased_L-12_H-768_A-12/
rm NCBI_BERT_pubmed_mimic_uncased_L-12_H... |
4b6aefae46f138df8f5dcaf8d1d057a4e4c1e09bcf2f80f8b320b846f5258009 | Shell | 334 | 16 | #!/bin/bash
# Check if process "nrniv" is running on one of the specified nodes
# This script takes names of nodes as arguments
for var in "$@";
do
echo " Checking" $var "..."
ssh $var "pidof nrniv" > /dev/null 2>&1
if [ $? -eq 0 ]
then
exit 0 # process is found
fi
done
exit 1 # proces... |
5173f43100c106dc50c7498fa1da92d8de0759341afc6da440ba23025ca0889d | Shell | 335 | 17 | #!/bin/bash
module load singularity/3.7.0
singularity run -W $PWD -B $PWD:$PWD \
ggsashimi_latest.sif -b input_bam_eng.tsv \
-c chr2:50052271-50092494 \
-O 3 \
-C 3 \
-M 5 \
-A median \
--width 10 \
--shrink \
--fix-y-scale \
-g gencode.v41.annotation.gtf \
--ann-height=4 \... |
82503bb5042e2971d6bf703925f42913e8f1ab1bbcef928f5f8b61cfed49378e | Shell | 338 | 16 | #!/bin/bash
# Preprocess all the datasets: create Seurat and AnnData files
# Assumes we're in the repo directory and raw data is in data/raw
echo "Mouse"
Rscript scripts/preprocess_mouse.R
echo "Human"
Rscript scripts/preprocess_human.R
echo "Bird"
Rscript scripts/preprocess_bird.R
echo "Turtle"
Rscript scripts/pre... |
b2facf64039cb214b854be7740eed9547f81df3a2e30cc92d3c8d0ed9bbf2cff | Shell | 339 | 13 | #!/bin/bash
# request Bourne shell as shell for job
ctest -D ExperimentalStart
ctest -D ExperimentalUpdate
ctest -D ExperimentalConfigure
ctest -D ExperimentalBuild
ctest -D ExperimentalSubmit
ctest -D ExperimentalTest
#ctest -D ExperimentalCoverage
ctest -D ExperimentalSubmit
#ctest -D ExperimentalMemCheck
#ctest -D ... |
0a090ae5aabdebcb54f1aae081d6ed3a337484c7b4de865192b59075f42bbf56 | Shell | 345 | 18 |
name='all_file'
for code in BA
#for code in JD JE JF JG JH JI JJ
do
base=$code
#mkdir -p 'mp_data_'$base
#head -n1 all_file/BAAA_n.csv > $name'/XXXXX_n.txt'
##python read_smi_protein_nnn_usage.py $name 'mp_data_'$base
nohup python read_smi_protein_nnn_usage.py $name 'mp_data_'$base > $base'.log' 2>&1&
s... |
a2e250d924fc5f490ccf5d116a3e6b2bd9fd2a59d8fabf2f4aff6670310bb612 | Shell | 346 | 15 | #!/bin/sh
source ~/.bashrc
conda activate qiime2-2021.4
export TBB_CXX_TYPE=gcc
case_control_parallel.py \
--biom-table table.biom \
--metadata-file sample_metadata.txt \
--matching-ids reps \
--groups diff \
--treatment-group 0 \
--monte-carlo-samples 100 \
--processes 10 \
--output-i... |
ba9afaec80832f00de7ad339c697b602ccc2b2b2006e03bf2c672ace80206318 | Shell | 346 | 10 | #!/bin/bash
ga="/cndd/Public_Datasets/BICCN/BICCN2.0_whole_mouse_brain/references/refdata-gex-mm10-2020-A/genes/genes_promoter_2kb_biccn2.0.bed"
../scripts/normalize_and_select_features.py \
-i "./datasets_pre/CEMBA171206_3C_genes_promo2kb.h5ad" \
-inorm "tpm" \
-ga $ga \
-op "test_preproc_may3" \
... |
22bc576f2c4c112e561f424394847502811498fa1107bc70f92a8d013a8f8818 | Shell | 347 | 15 | #!/bin/bash
export PATH=$PATH:~/github/leifer-Behavior-Triggered-Averaging-Tracker/Bash
#start_point=-1 #redo everything
start_point=$(ScriptToOrdering.sh max) #continue analysis
#start_point=$(($start_point + 1))
#start_point=3
echo $start_point
cd ~/outputs/
InitalizeLogs.sh
ProcessDateDirectory.sh /tigress/LEIF... |
b317129497577914ec4fb9f80da4c958c842030a3d18cb1191079f3666ebee20 | Shell | 349 | 12 | TORCH_VER=1.2.0
CUDA_VER=10.1
CUDA_CODE=cu101
source /cm/shared/engaging/anaconda/2018.12/etc/profile.d/conda.sh
conda activate retroxpert_
conda install -y pytorch=${TORCH_VER} torchvision cudatoolkit=${CUDA_VER} torchtext -c pytorch
conda install -y rdkit=2019.03.4.0 -c rdkit
pip install dgl==0.4.2
pip install Ope... |
4eda4c81b65d5a63cf82d806612f9811373928cfedcc4e65a69c2d52949e98af | Shell | 352 | 8 | # This is an installation script to automatically download and compile cmdstan
# wget https://github.com/stan-dev/cmdstan/releases/download/v2.26.0/cmdstan-2.26.0.tar.gz
# tar -zxvf cmdstan-2.26.0.tar.gz
# cd cmdstan-2.26.0 && make build -j4
# see https://github.com/stan-dev/cmdstanpy/issues/374
export CXX=g++
export T... |
939eef719a0f1236484c8d760a6636a4eacdb949e3fb6fc884e6967e6a54a57e | Shell | 352 | 15 | #!/bin/bash
#SBATCH --job-name=PyL3dMD
#SBATCH --partition=desktop
#SBATCH --nodes=1
#SBATCH --cpus-per-task=72
#SBATCH --time=168:00:00
#SBATCH --export=ALL
#SBATCH --mail-type=ALL
#SBATCH --mail-user=panwarp@msoe.edu
echo $SLURM_JOB_NODELIST > nodelist.out
echo "Slurm gave us $SLURM_CPUS_ON_NODE CPU(S) on this node.... |
6e6314699ea4e311d06f8d17b91879ffefb522d7247e33b05c70139d6eefad54 | Shell | 354 | 14 | #!/bin/sh
source ~/.bashrc
conda activate qiime2-2021.4
module load disBatch/2.0-beta
export TBB_CXX_TYPE=gcc
case_control_disbatch.py \
--biom-table table.biom \
--metadata-file sample_metadata.txt \
--matching-ids reps \
--groups diff \
--treatment-group 0 \
--monte-carlo-samples 100 \
--... |
4b08b07494a5c36d73e58fee40b56aa112ef0bfd7545cce7b45fe15796d64754 | Shell | 355 | 19 | #!/bin/bash
echo "Usage: ./pretext.sh <in.bam>"
echo " This will generate <in>.pretext"
module load samtools
bam=$1
if [ -z $bam ]; then
echo "No bam provided. Exit."
exit -1
fi
out=${bam/.bam/.pretext}
echo "\
samtools view -h $bam | $tools/Pretext/PretextMap --mapq 0 -o $out"
samtools view -h $bam | $tools/P... |
aeaf54f680d2c9c785293d2861cb960daaf3f71de6a4be3ac43ca3d1173a11f6 | Shell | 355 | 24 | #!/bin/bash
module load repeatmasker
### repeatmasker/4.0.7
species=vertebrates
par=$(($SLURM_CPUS_PER_TASK / 2))
if [ $par -lt 2 ]
then
par=2
fi
fa=asm.fasta
if ! [ -e $fa ]; then
echo "No $fa. Exit."
exit -1
fi
echo "\
RepeatMasker -pa $par -q -species=$species -xm -dir=out $fa"
RepeatMasker -pa $par -q ... |
9a0379f8755008edf96dbec7cf1070bdcfdb11c575e479e31688be6605efa4c6 | Shell | 357 | 15 | echo 'Starting Test for Intel NUC'
echo ''
echo 'Nonspiking Dense:'
python3 test_nuc_nonspiking_dense.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_nuc_nonspiking_sparse.py
echo ''
echo 'Spiking Dense:'
python3 test_nuc_spiking_dense.py
echo ''
echo 'Spiking Sparse:'
python3 test_nuc_spiking_sparse.py
echo ''
echo ... |
9fb4814e0d39cdf0351019efd5a00d21a5b0e49d36c33511d266e8303e6a41b0 | Shell | 357 | 11 | #!/bin/bash
BAM_NAME=$(sed -n "${SLURM_ARRAY_TASK_ID}p" bam.uniq)
BAM_FILE=$(grep ${BAM_NAME} bam.ls)
if ! [[ -e "filtered/${BAM_NAME}.subreads.bam" ]]; then
java -jar ${PICARD_PATH}/picard.jar FilterSamReads I=${BAM_FILE} O=filtered/${BAM_NAME}.subreads.bam READ_LIST_FILE=reads/${BAM_NAME}.ls VALIDATION_STRINGENC... |
27d491651d62e5adc0207aff5fcdbac7f45831c4956a8dfceeac296da36f1684 | Shell | 359 | 12 | #!/bin/bash
module load purge_haplotigs
module load mummer
genome=$1
threads=$SLURM_CPUS_PER_TASK
echo "\
purge_haplotigs ncbiplace -p ${genome}_v1.p.fasta -h ${genome}_v1.h.fasta -o ${genome}_v1.placements.tsv -t $threads -c 40"
purge_haplotigs ncbiplace -p ${genome}_v1.p.fasta -h ${genome}_v1.h.fasta -o ${genom... |
04ceddcdfd5f4ca8405754eb37ba447437861bedba07705eaa2e1d49a2132cce | Shell | 361 | 14 |
GENOME=/home/database/reference/hg38/hg38.fa.size
TSS=/home/database/annotation/hg38/gencode.v39.annotation.gtf.pc.sorted.bed.TSS.bed
LOOP=/home/disk/database/data/scHIC/Blood/analysis/scATAC_RefLoop.bed
SLOP=1000
bedtools slop -i $TSS -g $GENOME -b $SLOP > $TSS\.slop$SLOP\.bed
bedtools pairtobed -a $LOOP -b $TSS... |
fc6033432838e311e9d3dc0726c2342e972d2b156e15ee2c2b9a128bd51152c4 | Shell | 362 | 12 | #!/bin/bash
folder_name=$1
script_name=$2
cd ~/github/leifer-Behavior-Triggered-Averaging-Tracker
# rm -rf /home/mochil/.matlab
#sleep for an random amount of time in case parallel pool starts on the same node at the same time
sleep $((RANDOM % 100))
/usr/licensed/bin/matlab-R2016a -nodisplay -nosplash -r "call_fun... |
573bbed186dededfdff25bd3556931aae249a52750f901b23179110da09d6d2c | Shell | 363 | 12 | for LABEL in $( cat labels.txt); do
echo "Submitting ${LABEL}"
for REP in $(seq 1 5); do
SB_FILE=run-${LABEL}_rep${REP}.sb
cp template-run.sb ${SB_FILE}
sed -i "s/TARGET/${LABEL}/g" ${SB_FILE}
sed -i "s/ --load/-rep${REP} --seed ${REP}${REP} --load/g" ${SB_FILE}
sed -i "s/-cormorant/-cormorant-rep${REP}/g" ... |
e99ff3c3f013f464ae8318f877364d10a569f8f73c6bc92969f04cb23c2f909d | Shell | 363 | 12 | #!/bin/bash
mkdir -p ${2}
cd ${2}
aws s3 cp --recursive --include="*" --exclude="*_s.fq" s3://genomeark/species/${1}/${2}/assembly_MT_rockefeller/intermediates/bowtie2_round1/fq/ .
fq1=$(wc -l aligned_${2}_all_1.fq | awk '{print $1}')
fq2=$(wc -l aligned_${2}_all_2.fq | awk '{print $1}')
printf "%s\t%s\t%s\n" ${2}... |
153021b6ff6ca3958f4f7cad2e51eace9156b30b1c04af884a3d50a709f00b2d | Shell | 370 | 29 |
OLD_NET=./scATAC_loop.txt
NET=./ics_out/UNIQ_LOOP.txt
python ../src00_ics/step0_uniqNet.py $OLD_NET $NET
MAT=./scATAC_normalized_exp_clustered.txt
INDEX=./ics_out/this.index
python3 ../src00_ics/step1_BuildIndex.py $NET $MAT $INDEX
###########################
TAG=r0
nohup python3 ../src00_ics/step2_CalICS_$TAG\... |
651b616547a9d4470e5eb59da594f3f51888d066482cbb0b98bf0bdff38439fe | Shell | 371 | 13 | #!/bin/bash
# Script to search for missing init FILES.
set -euxo pipefail
FILES=$( find ./sktime -type d '!' -exec test -e "{}/__init__.py" ";" -not -path "**/__pycache__" -not -path "**/datasets/data*" -not -path "**/contrib/*" -print )
if [[ -n "$FILES" ]]
then
echo "Missing __init__.py files detected in the f... |
b4db096fbff7b75b8956f1400b79ae1bd84b20365052ce8ca87046122a6f4cdb | Shell | 371 | 15 | echo 'Starting Test for Jetson Nano'
echo ''
echo 'Nonspiking Dense:'
python3 test_jetson_nonspiking_dense.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_jetson_nonspiking_sparse.py
echo ''
echo 'Spiking Dense:'
python3 test_jetson_spiking_dense.py
echo ''
echo 'Spiking Sparse:'
python3 test_jetson_spiking_sparse.py... |
d3cb10e2331be2bcefdfbc0f82ddb6c958125cca8afbf2a9db81b4dcede0eb4a | Shell | 371 | 10 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# without .dmask.csv
dnn_mask -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -layer conv5 -chn 1 2 3 -out $TMP_DIR/dnn_mask1.act.h5
# use .dmask.csv
dnn_mask -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -dmask $DNNBRAIN_DATA/tes... |
a40f21515b5a43035218ea8ed8eb94288d5afb317f9d9450d3c882416098a882 | Shell | 372 | 10 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
# one layer
dnn_topstim -net AlexNet -top 3 -layer fc3 -chn 1 2 -stim $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.stim.csv -out $TMP_DIR/top_stim1
# two layer
dnn_topstim -net AlexNet -top 3 -layer conv5 fc3 -chn 1 2 -stim $DNNBRAIN_DATA/test/image/sub-CS... |
52561af9f8e93b135ce46f6e693b5638e37b1c9ad854da59f9484950044f88a2 | Shell | 374 | 14 | #!/bin/bash
arg_working_dir=$1
arg_query_id=$2
arg_output_file=$3
MYOWN_LOC=`dirname $0`; # XXX: some hacking:)
c_fetch_blast_report="$MYOWN_LOC/fetch_blast_report_by_index.pl"
blastx_report=$arg_working_dir/blastx.bls
blastx_index=$arg_working_dir/blastx.index
echo $arg_query_id | perl -w $c_fetch_blast_re... |
87cbcef77421f4e985b6bd2699841b2f1c57be690b5d28401e9a2e04f3c702ec | Shell | 376 | 10 | #!/bin/bash
while read -r VGP_ID GEN_ID; do
VGP=$(seqkit fx2tab --length --name --header-line --gc ../VGP_dataset/$VGP_ID* | tail -1)
GEN=$(seqkit fx2tab --length --name --header-line --gc ../Genbank_dataset/$GEN_ID* | tail -1)
printf "%s\t%s\t%s\t%s\t%s\t%s\n" $VGP_ID $GEN_ID $(echo $VGP | awk '{print $2"\t"$3}')... |
17cb39194cebf361785017b8403ee77ec26681a2427ea4c0f3301ac66f0cc296 | Shell | 377 | 16 |
source /software/ENV/modules
module load amber/18/intel-17.0.4
module load openbabel/2.4.1/gcc-6.3.0
module unload anaconda/5.0.1/python-3.6
module load cuda/10.1
source /home/duerr/miniconda3/bin/activate openmm
export PYTHONPATH=/home/duerr/phd/02_Projects/EVOLVE/Development/evolve/:$PYTHONPATH
python test_open... |
8cdfa14c3910496bf2c71dcdcb72c518ab75f1031742c27387e6defa1d27c72c | Shell | 378 | 15 | echo 'Starting Test for Raspberry Pi 3 Model B'
echo ''
echo 'Nonspiking Dense:'
python3 test_rpi3b_nonspiking_dense.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_rpi3b_nonspiking_sparse.py
echo ''
echo 'Spiking Dense:'
python3 test_rpi3b_spiking_dense.py
echo ''
echo 'Spiking Sparse:'
python3 test_rpi3b_spiking_sp... |
c747a3159e3fc70560a805947b1c6f2a833869a26f5bd3c6e340283abce318e6 | Shell | 378 | 7 | dataset=USPTO50K-aug-typed
suffix=test-prediction
python ../../onmt/bin/translate.py --model ../../experiments/checkpoints/${dataset}/average_model.pt \
--gpu 0 --src ../../data/${dataset}/src-${suffix}.txt \
--output ../../experiments/results/predictions_on_${dataset}_${suffix}.txt \
--beam_size 50 --n_b... |
43085fa451a18b918296f7978422a11e9604ae73f31b2a1c1a9cfbdb35bb58a0 | Shell | 382 | 21 | #!/bin/sh
submodule_init ( ) {
local SUBMODULES=$(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
submodule_init
cd ..... |
8bdb55308fbf0d98bae8678fb7e5cc32321c745082ae2e6a9a8fd117c96e3bb3 | Shell | 386 | 15 | echo 'Starting Test for Brian2CUDA'
echo ''
echo 'Nonspiking Dense:'
python3 test_brian2cuda_nonspiking_dense.py
echo ''
echo 'Nonspiking Sparse:'
python3 test_brian2cuda_nonspiking_sparse.py
echo ''
echo 'Spiking Sparse:'
python3 test_brian2cuda_spiking_sparse.py
echo ''
echo 'Spiking Dense:'
python3 test_brian2cuda_s... |
c9837f4efe6adfb1e5b473c68a3238d80b888d6acb3dae954f0cffcc68d548f9 | Shell | 386 | 16 | 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 -v mechanism --configfile autoscale_multisite_par... |
dea40195475a5e3bc20f0189ed0b10bb35608b464231c7edc5c71b9fbc4cb0c9 | Shell | 388 | 15 | #!/bin/bash
HEADER=$(head -1 $1)
SEQ=$(tail -1 $1)
LN=$(tail -1 $1 | tr -d "\n" | wc -c)
pbmm2 align -j 24 --best-n 1 $1 filtered.fq --sort | samtools view -S -b > $2.bam
samtools index $2.bam
samtools depth -a $2.bam | awk '{print $3}' | head -${LN} > $2.head
samtools depth -a $2.bam | awk '{print $3}' | tail -${... |
191b1e58b3dc1be4cedc26b9e2d5ca8b2361b3da4d9b7f168e8a3991815e7066 | Shell | 393 | 8 | python run_infer.py \
--gpu=0 \
--model_path="/root/lsf_workspace/iguana_data/weights/iguana_fold1.tar" \
--model_name="pna" \
--data_dir="/root/lsf_workspace/proc_slides/cobi/colchester/graphs/data" \
--data_info="/root/lsf_workspace/proc_slides/cobi/colchester/graphs/colchester_info.csv" \
--stats_dir="/root/lsf_work... |
3d230018f103f29348c6a9fddb487028918e41c9723edd4f80b24e39384bd6d6 | Shell | 393 | 19 | #!/bin/bash
# concat_two_files 0.0.1
main() {
echo "Value of input1: '$input1'"
echo "Value of input2: '$input2'"
echo "Value of output_name: '$output_name'"
dx download "$input1" -o input1
dx download "$input2" -o input2
cat input1 input2 > "$output_name"
output=$(dx upload "$output_n... |
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