sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7291475df448dda3f1093ba3985ac920699d8a1b73674e8a9812072b6025f016 | Shell | 316 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on sider (random split seed=$seed)====="
python ../src/main.py --dataset sider \
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
done |
80e0b40e06da20a7be40a623f66ed16bda9bc4089863997c559c10bba989af28 | Shell | 316 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on tox21 (random split seed=$seed)====="
python ../src/main.py --dataset tox21\
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
done
|
1adf8aded304ae9b7b03fc97315d602c9df1ccdbacd0ff77c046498890cad25c | Shell | 319 | 14 | #!/bin/bash
# must have python loaded first
# $1 is input trajectory file name
# $2 is water-free trajectory output file name
echo "Extracting protein..."
python extract_protein.py $1 $2
# load vmd
module load vmd/1.9.3
# $3 is output CV file
echo "Calculating CVs..."
vmd -dispdev none -e dist-spin.tcl -args $2 $3
|
982667df5806f9bdbc5a963c96f7eee0baf8b610f2fa4e401b5f19f0920b1745 | Shell | 320 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on clintox (random split seed=$seed)====="
python ../src/main.py --dataset clintox \
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
done |
020212196f07a9cb4611297a17c405bf5ec0b96bff56f65bb55c2287b39536bb | Shell | 322 | 13 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN_params_multi_size.py;
# Run SUNS batch
python demo_test_batch_multi_size.py;
# Run SUNS online
python demo_test_online_multi_size.py;
# Run SUNS online with tracking
python demo_test_online_track_multi_size.py... |
570c04666e532ec7df3c673449d4437c5009c351eb7a12baf259f17ec1c4d18c | Shell | 322 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on delaney (random split seed=$seed)====="
python ../src/main.py --dataset delaney \
--metric RMSE --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
don... |
755a67d1e9e4c1def497f33c813888c1de4f1c799630b17d6b38c50e77d42316 | Shell | 324 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on freesolv (random split seed=$seed)====="
python ../src/main.py --dataset freesolv \
--metric RMSE --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kernel/
echo ""
d... |
f053c7b5ca8d7657c6838dde49d45695f7f5175062aa9ca1d4d695938ba53bad | Shell | 324 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on lipo (random split seed=$seed)====="
python ../src/main.py --dataset lipo\
--metric RMSE --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
echo ""
d... |
a0293e1c717d8aab24bc4c954074859293acb64852c250410cb695f5ebb1a39a | Shell | 325 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on sider (random split seed=$seed)====="
python ../src/main.py --dataset sider \
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
echo ""... |
c452cf4b0e457a34a47e4e02f60dfe3593f56e772fffca8aa57d2645c8c1881a | Shell | 325 | 14 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN_1to3.py;
python demo_train_params_1to3.py;
# Run SUNS batch
python demo_test_batch_1to3.py;
# Run SUNS online
python demo_test_online_1to3.py;
# Run SUNS online with tracking
python demo_test_online_track_1to... |
f60d30e153cd9042517efcb410bb0efa497c2c23106459b321eb190e9b1ce3bb | Shell | 325 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on tox21 (random split seed=$seed)====="
python ../src/main.py --dataset tox21\
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
echo ""
... |
857a040f3774714fa077def9b18e01bfe63f1fea11029d0059aeecfd3d430e89 | Shell | 326 | 18 | #!/bin/bash
mkdir $1
cp ./civsd.prmtop $1/
cp ./run.sbatch $1/
cp ./template_proj.dat $1/plumed.dat
cp ./smd.mdin $1/
cp ./down-seed2.pdb $1/civsd.pdb
cd $1/
sed -i -e "s/XX/$2/g" plumed.dat
sed -i -e "s/YY/$3/g" plumed.dat
sed -i -e "s/ZZ/$4/g" plumed.dat
sed -i -e "s/XXX/plumed.dat/g" smd.mdin
sbatch run.sbatch
c... |
a5b19c5189bf6f495c8db2b0d04cf781a7e27411fc07e897c93d4aa79883d46e | Shell | 327 | 13 | #!/bin/bash
#SBATCH --time=02:00:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL
#SBATCH --output=initialization.log
folder=$1
branchNo=$2
module load matlab/2020a
matlab -nodisplay -r "initialize_network('$folder',$branchNo,${SLURM_ARRAY_TASK_... |
3007bee2914d63d30fc285d12ab6e0415ae0de8b9d2c3eea6e4aa966b457d857 | Shell | 328 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=2-1:0:0
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --constraint="ampere|turing"
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v3.py trainoptlstm_emb_40_hid_80_nlays... |
2d25b616631bd93221e11bccd7ab8e035a18de875221feb65fe64db7a80d80d1 | Shell | 329 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on clintox (random split seed=$seed)====="
python ../src/main.py --dataset clintox \
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
ech... |
fabcc05be933660f387ca6eb36e5a0e54b4778efa187bdc071d2d4ce4a671f33 | Shell | 329 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=2-1:0:0
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --constraint="ampere|turing"
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v3.py trainoptlstm_emb_80_hid_120_nlay... |
b21b1777d595c195982c3d73b90a7134d28c89c0aa1c8e518ea56f5df52aed82 | Shell | 331 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on delaney (random split seed=$seed)====="
python ../src/main.py --dataset delaney \
--metric RMSE --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
ec... |
267f19a66edfb83f141bcdf3017eb38dc30a38d598c3054df475b006f215f04d | Shell | 332 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on freesolv (random split seed=$seed)====="
python ../src/main.py --dataset freesolv \
--metric RMSE --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/sequence_kernel/
... |
4bef1a761bfc648c1a8277f67679587fcaaa39f19d66a8e4949cc72939e9d1e3 | Shell | 336 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on hiv (scaffold split seed=$seed)====="
python ../src/main.py --dataset hiv --split_mode scaffold\
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_kerne... |
e25f4f4274037f020bf83c4ca244c14b055c1b9339c8d975761116a4c92e20db | Shell | 336 | 16 | #!/bin/bash
# This script monitors CPU and memory usage
while :
do
# Get the current usage of CPU and memory
cpuUsage=$(top -bn1 | awk '/Cpu/ { print $2}')
memUsage=$(free -m | awk '/Mem/{print $3}')
# Print the usage
echo "CPU Usage: $cpuUsage%"
echo "Memory Usage: $memUsage MB"
# Sleep for 1 second... |
7b8c18f0d0aecaf7e3e06489cbe96bae75ba86f5fc9b5b684e6f0f96140aea38 | Shell | 337 | 9 | # cd into data directory and execute shell script
wget -P cath ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/latest-release/non-redundant-data-sets/cath-dataset-nonredundant-S40.pdb.tgz
# Check md5sums
md5sum -c cath_checksums.md5
# cd into the cath directory and untar the file
cd cath
tar -xzf cath-dataset-nonredu... |
f2478f254f7bc54565b756bf2a9e63c0d5e11e20258f0d2f7527463d951e6e16 | Shell | 338 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on bace (scaffold split seed=$seed)====="
python ../src/main.py --dataset bace --split_mode scaffold\
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_ker... |
f7a57a4ef86dd849b275b8de6192b25b6316e89501ebc54ad4dfd6aaf194d815 | Shell | 338 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on bbbp (scaffold split seed=$seed)====="
python ../src/main.py --dataset bbbp --split_mode scaffold\
--metric ROC --kernel_type graph --seed $seed \
--model_path ../trained_models/graph_kernel/ \
--prediction_path ../predictions/graph_ker... |
71d7b91be8b01a943572a7d0e23f411567dfd432c7e96dca4dab99ef9aadcc71 | Shell | 343 | 13 | #!/usr/bin/env bash
#SBATCH --partition=research
#SBATCH --time=2-1:0:0
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --constraint="ampere|turing"
module load anaconda/full/2021.05
bootstrap_conda
conda activate pytorch
python ../network_zou/network_perm_eq_lstm_5_taxa_v3.py trainoptlstm_emb_40_hid_80_nlays... |
e4c60e90d78decf0dda2ac5fb75abb82d82e30f5ef4a69f91c38f1662ce1609e | Shell | 345 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on hiv (scaffold split seed=$seed)====="
python ../src/main.py --dataset hiv --split_mode scaffold\
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/seque... |
4df8c23479ff847e50395c7e71d49de2c996b3f946cea9fdda04e47f7c8444d0 | Shell | 347 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on bace (scaffold split seed=$seed)====="
python ../src/main.py --dataset bace --split_mode scaffold\
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/seq... |
88bd95fe331487e35a3fb0a778e6fde6c467ce5a98a7be678e485f2919f07c10 | Shell | 347 | 9 | for seed in 122 123 124
do
echo "=====Train and evaluate on bbbp (scaffold split seed=$seed)====="
python ../src/main.py --dataset bbbp --split_mode scaffold\
--metric ROC --kernel_type sequence --seed $seed \
--model_path ../trained_models/sequence_kernel/ \
--prediction_path ../predictions/seq... |
57c9a425b67bdddd81ac806c3d66d1651f949614b18da6add61d90488e1eef4b | Shell | 348 | 27 | #!/usr/bin/env bash
pushd () {
command pushd "$@" > /dev/null
}
popd () {
command popd "$@" > /dev/null
}
if [ "$1" = "clean" ]; then
pushd src/SimulationCode
make clean
popd
exit 0
fi
pushd src/SimulationCode
make &&
./mtsim -m 1 &&
cp out_* ../../Result/Simulation/ &&
popd
pwd
pushd src/AnalysisC... |
d97481d034a334adc594a3430b04bf9c026b4bcf2369b8cf181138638aa0d7e2 | Shell | 351 | 21 | #!/bin/bash
function abort(){
echo "Your commit not fit PaddlePaddle code style" 1>&2
echo "Please use pre-commit scripts to auto-format your code" 1>&2
exit 1
}
trap 'abort' 0
set -e
cd `dirname $0`
cd ..
export PATH=/usr/bin:$PATH
pre-commit install
if ! pre-commit run -a ; then
ls -lh
git diff --e... |
5a24885bd682d91ac1c45926d87439820accafcf18fdffc5c7307ff65649d657 | Shell | 355 | 14 | #!/bin/sh
# Generate sparse GT masks
python generate_sparse_GT.py;
# Training pipeline
python demo_train_CNN_multi_size.py;
python demo_train_params_multi_size.py;
# Run SUNS batch
python demo_test_batch_multi_size.py;
# Run SUNS online
python demo_test_online_multi_size.py;
# Run SUNS online with tracking
python dem... |
dbb8e40c5541029085b3ab6c8620e15867f3e25d2768f0795387bc781b340dc0 | Shell | 358 | 16 | #!/bin/bash
tar -zxvf training_data.tar.gz
tar -zxvf testing_data.tar.gz
# Preprocess Gene expression dataset
python3 preprocessing_gene_expression.py
python3 get_promoter_region.py
./run_bedtools.sh
python3 preprocessing_accessibility.py
python3 preprocessing_step2.py
python3 merge_omics.py
python3 tf_each_attn.... |
1a655a2c6569a32b374ba55283f7b7cfe6867f63287d3b7995f062330111e708 | Shell | 361 | 14 | #!/bin/bash
#SBATCH --time=12:00:00
#SBATCH --account=def-akhadra
#SBATCH --nodes=1
#SBATCH --cpus-per-task=40
#SBATCH --mem=0
# #SBATCH --cpus-per-task=12
# #SBATCH --mem=48G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=synthetic_spikes.log
module load matlab/2020a
sru... |
8dcf7d66662c33622575c45f7de3c23f4b2580698e1e54695276d0b9cac6e632 | Shell | 366 | 10 | ## gpu id
gpus="2,3,4"
## path to your training data file
train='path/to/your/training/data/train.csv'
## path to your validation data file
valid='path/to/your/validation/data/valid.csv'
## path to store output results, i.e. model, validation records
out='out'
python src/train.py --split_ready --trainfile $train --val... |
385b18d7b13fdada3788aefba25ae8a9ba6bad050e827699d114cfbbe60e36c5 | Shell | 386 | 4 | bash scripts/make_montage_interview.sh fc8 original shifted Interview/interview_complex_psyche/
bash scripts/make_montage_interview.sh fc8 original shifted Interview/interview_complex_neuro/
bash scripts/make_montage_interview.sh conv4 original original Interview/interview_simple_psyche/
bash scripts/make_montage_inter... |
4c461baa9a95a6daf7de655b267c5d486fc4c0588cec4b6a928a60656a22cccc | Shell | 388 | 16 | #!/bin/bash
for i in {X,Y,MT}
do
cat Compara.H.sap_P.tro_lastz_net_on_H.sap.$i.*.coords > Compara.H.sap_P.tro_lastz_net_on_H.sap.$i.ALL.maf.coords
echo "Finished processing chromosome $i."
done
for i in {1..22}
do
cat Compara.H.sap_P.tro_lastz_net_on_H.sap.$i.*.coords > Compara.H.sap_P.tro_lastz_net_on_H.sap.$i... |
5049413bc395f7dc809bf442a45895f964ecb6cfed1013c5c2b82ff80cbed993 | Shell | 396 | 16 | #!/bin/bash
if [ "${TRAVIS_OS_NAME}" = "linux" ]; then
wget -nv https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
chmod +x miniconda.sh
./miniconda.sh -b
PATH=/home/travis/miniconda3/bin:${PATH}
fi
conda update --yes conda
conda create --yes -n test python="${PYTHON_VERSIO... |
d69fcd6570bf6398af135ed6bb6a209bed922b4c44866d4a3ae52a718f13e270 | Shell | 397 | 9 | seqkit rmdup -s raw/Cis-reg.fasta > preprocessed/Cis-reg.fasta
seqkit rmdup -s raw/rRNA.fasta > preprocessed/rRNA.fasta
seqkit rmdup -s raw/sRNA.fasta > preprocessed/sRNA.fasta
seqkit rmdup -s raw/tRNA.fasta > preprocessed/tRNA.fasta
seqkit stats -a preprocessed/Cis-reg.fasta
seqkit stats -a preprocessed/rRNA.fasta
se... |
71c920b8e9e66005509a1c89372ac2f204363266e1668e4c6f1e5bafbe7066ab | Shell | 405 | 2 | #./runPipeline.sh -N 20 -b exseqauto-xy10 -c "'ch00','ch01SHIFT','ch02SHIFT','ch03SHIFT'" -R /home/dgoodwin/ExSeqRepo/registration/ -B 4 -s "setup-cluster,color-correction,normalization,calc-descriptors"
./runPipeline.sh -N 6 -b splintr180621xy09deconvHP -c "'ch00','ch01SHIFT','ch02SHIFT','ch03SHIFT'" -R /home/dgoodw... |
ab167691cde495be3cad5e6477d6fd0017c45084b878e4634bd9fec8c9b2d952 | Shell | 406 | 17 | #!/usr/bin/env bash
# Build REINVENT4 documentation with Sphinx
# Run from the repo root: bash doc/build_docs.sh
set -e
DOC_DIR="$(cd "$(dirname "$0")" && pwd)"
BUILD_DIR="$DOC_DIR/_build/html"
echo "==> Installing doc dependencies..."
pip install -r "$DOC_DIR/requirements.txt"
echo "==> Building HTML..."
sphinx-bu... |
2f7e225b891218f8b685098f604bf6d1faaa18d77b8ffcf5be2bd140523fd48d | Shell | 420 | 17 | #!/bin/bash
source /home/me/miniconda/bin/activate aizynthfinder
aizynth_config="config.yml" # CAZP generated AiZynthFinder configuration
nproc=8
# the SMILES are reveived via stdin
cp /dev/stdin input.smi
aizynthcli --smiles input.smi \
--config "$aizynth_config" \
--output output.json.gz \
--nproc "$... |
96f8a78324dc9cdfb0d4eaf5090836e4c192d104647b480808ddb125431f2361 | Shell | 422 | 7 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
/home/wrr239/c3202333/plink2 --pmerge-list /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/To_merge.txt --merge-max-allele-ct 2 \
--pmerge-list-dir /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/0.3_rsq_assoc \
--make-pgen ... |
b8f0713ca41f2c743dc4db469ebece5f8d1e56fcb9205a1c32d8289087172db0 | Shell | 424 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 80:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/res/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/res/runs/%j.err
source activate dd_torch
m... |
fabb4897a7fa392a960c42bdb4cb3294339b08892894fc79bd5a8406b3e4f8e6 | Shell | 424 | 3 | python -W ignore /scGNN-master/PreprocessingscGNN.py --datasetName Adult-Brain_Lake_dge.csv --datasetDir /scGNN/test/brain/ --LTMGDir /scGNN/test/brain/ --filetype CSV --geneSelectnum 27068
python -W ignore /scGNN-master/scGNN.py --datasetName preprocess --datasetDir ./ --outputDir epoch400_lr01/ --EM-iteration 2 -... |
28296e3c06c70abf9e856d34b056b2d067eafce4fa6e3f0390f95d39dd63a19a | Shell | 429 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 80:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/res/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/res/runs/%j.err
source activate dd_torch
m... |
ef89cbfb030ada8c5608dcb8a68ea0e859adea10af069526778ce0772b378201 | Shell | 430 | 15 | #!/bin/bash
# untar your R installation. Make sure you are using the right version!
tar -xzf R402.tar.gz
# (optional) if you have a set of packages (created in Part 1), untar them also
tar -xzf packages.tar.gz
# make sure the script will use your R installation,
# and the working directory as its home location
export... |
2719c244d80fe615653b7a86da1043544f10cb7ed3d94fce390a6d59d58d9820 | Shell | 432 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 10:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/res/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/res/runs/%j.err
source activate dd_torch
m... |
114c06cd6413a500c58f14b9e0b09b13391de44d504022896b8dc8f4c95e2d29 | Shell | 437 | 13 | #!/bin/bash
# Prepare fastq files
mkdir out/fastq
for file in out/Output_bam/*.output.bam; do name=$(basename $file .output.bam); samtools fastq -F 256 -F 1024 $file | pigz -c > out/fastq/$name.fq.gz; done
# Run fastQC
mkdir out/fastQC
fastqc -t 24 --nogroup -o out/fastQC out/fastq/*.fq.gz
# Run multiQC
OUTPUT_NAME=... |
6a63c0683b358aa78bdeb8eec2e89c0ba617cb0a24c9d891e63afbbb0e76928e | Shell | 437 | 19 | #!/bin/bash
p=1
while read -r start stop; do
echo Start: $start Stop: $stop
ffmpeg \
-i Grandbudapesthotel_HI.mkv \
-ss "$start" \
-to "$stop" \
-map 0:v -map 0:a:0 \
-map_metadata -1 \
-af compand="0|0:1|1:-90/-900|-70/-70|-30/-9|0/-3:6:0:0:0" \
-c:a aac -vbr 5 \
-c:v libx264 -pre... |
d23a7700359f3471e965f405c2b90a9601613b4e19de0574fcded7e4a92859d2 | Shell | 444 | 21 | #!/bin/bash
python sims.py
python ptcda.py
python bcb.py
python water.py
python surface_sims_bcb_water.py
pdflatex model_schem.tex
python stats.py
python stats_spring_constants.py
python esmap_sample.py
pdflatex esmap_schem.tex
python afm_stacks.py
python afm_stacks2.py
python sims_hartree.py
python ptcda_surface_sim.... |
53317bbde04841f40e31ab4f57614ee643ae95b46dd53e2d6d44249522ffef0d | Shell | 446 | 22 | #!/bin/bash --login
#SBATCH --nodes=10
#SBATCH --ntasks-per-node=40
#SBATCH --time=0:25:00
#SBATCH --job-name='LFPy Circuit'
#SBATCH --account=rrg-etayhay
#SBATCH --mail-type=ALL
#SBATCH --mail-user=agmccrei@gmail.com
#SBATCH -o output.out
#SBATCH -e error.out
module load NiaEnv/2018a
module load intel/2018.2
module ... |
930d346e7257ef5d0ad6e429c2ad8dbb7565a650be48ec17ea2f8c5e6fbbcdc0 | Shell | 453 | 17 | #!/bin/bash
#
#SBATCH --job-name="synthsense"
#SBATCH --time=48:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=12
#SBATCH --partition=cpu
################################################################################
export CAZP_PROFILES="stocks_and_reactions_profiles.json"
# Set up env... |
dce4d46fa4676c500d62d046cfa3bad988bfbd91c77b55c0615fcd2b7109a84e | Shell | 455 | 13 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## Convert TwinsUK vcf into pgen
for chr in $(seq 1 23);
do
/home/wrr239/c3202333/plink2 \
--vcf /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/1000G_P3V5_PID_chr${chr}.dose.vcf.gz dosage=DS \
--make-pgen --keep-allele-or... |
af355a8dfd467add378781c171a13522a3cbef7e3ceeb2ec0d5db013da1aec87 | Shell | 458 | 11 | #!/bin/bash
#SBATCH --cpus-per-task=2
#SBATCH --time=13:00:00
#SBATCH --output=job-%j.out
#SBATCH --error=job-%j.err
./ldsc.py --h2-cts ~/data/users/william/Meta_retinol_GWAS/Meta_munged/Hm3_munged_Stouffer_ALL_THREE.sumstats.gz \
--ref-ld-chr 1000G_EUR_Phase3_baseline/baseline. \
--out ~/data/users/william/Meta_reti... |
bcc6fc9944ec2c0071ef506f807a13d2a4c965c1b0d637fff26c04619a580e97 | Shell | 458 | 10 |
python figure.py ../../sparse_ct/data/ct1.jpg
python figure.py ../../sparse_ct/data/ct2.jpg
python figure.py ../../sparse_ct/data/benchmark_human/8.png
python figure.py ../../sparse_ct/data/benchmark_human/10.png
python figure.py ../../sparse_ct/data/benchmark_human/36.png
python figure.py ../../sparse_ct/data/benchm... |
d1a2a38ecce07a18702eb454966ebfb683bca4256f70d789be1117fb18d90df1 | Shell | 459 | 21 | #!/bin/sh
#
# convert light-script Python files to Notebook
#
py_files='Reinvent_demo.py Reinvent_TLRL.py'
jupytext="$(which jupytext 2> /dev/null)"
if [ $? -ne 0 ]; then
echo "Please install jupytext into your Python environment" 1>&2
exit 1
fi
for py_file in $py_files; do
ipynb_file="$(echo $py_f... |
03a569ef2d361cd73c925665671a5551be2665adfabc622b2c31f4319e21b02f | Shell | 460 | 15 | #!/bin/bash
#SBATCH --job-name=snakemake
#SBATCH --time=7-00:00:00
##SBATCH --qos=long
#SBATCH -p hbfraser
#SBATCH --mail-type=END,FAIL
#SBATCH --mail-user=astarr97@stanford.edu
snakemake --unlock
echo "---start running ----"
snakemake --keep-going --rerun-incomplete --configfile config.yaml -j 500 --latency-wait 60... |
3e110edb56d2fadb4f687b6554224b90cb95187549a6bfd547139b56db18e50e | Shell | 460 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 5:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/res/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/res/runs/%j.err
source activate /n/groups/m... |
3bf0c34d808a43bd3ab02affa89a4fcbef5effcf57cbfd1e3d2c583f1415cfce | Shell | 464 | 24 | #!/bin/bash
pip_install_from_tarball() {
local tarball=$(readlink -f $1)
if [ -f "$tarball" ]; then
echo "Testing PIP package from tarball: $tarball"
else
echo "Could not find PIP package: $tarball"
return 1
fi
# Create temporary project dir
local dir=$(mktemp -d)
... |
934e412962d62fd3883ab65e65541c49c85e549a42afca8494ebd74a6abf197f | Shell | 468 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 24:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/esm_if/data/seq_to_score/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/esm_if/data/seq_to_scor... |
e354581b2f13c160cf2983ae9a5316234ca298f5ca3662b5c620540b17dbec96 | Shell | 470 | 11 | #!/bin/bash
#SBATCH --cpus-per-task=2
#SBATCH --time=13:00:00
#SBATCH --output=job-%j.out
#SBATCH --error=job-%j.err
./ldsc.py --h2-cts ~/data/users/william/Meta_retinol_GWAS/Meta_munged/Hm3_munged_Stouffer_INTERVAL_METSIM.sumstats.gz \
--ref-ld-chr 1000G_EUR_Phase3_baseline/baseline. \
--out ~/data/users/william/Met... |
16ea4d9422f61f9ea07f1ef661d4f599c9485f36be0b5acf0d49d4c832f50d4c | Shell | 479 | 15 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 5:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/esm_if/data/gen_seqs/runs_gfp/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/esm_if/data/gen_seqs/run... |
ecd2eae6ca175449f8696607b0380c13325fbb04fbfe371ccf53403306e4b077 | Shell | 484 | 15 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 5:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/ex62/proteinMPNN/samples/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/ex62/proteinMPNN/samples... |
8548c4a0045c7c90a3528eea1ade851d95c26e609f707206db3a84641733df5c | Shell | 487 | 8 | folder_name="FABP7"
ml R/4.2.0
cd /scratch/users/banwang3/references/outputs/${folder_name}
Rscript /scratch/users/banwang3/references/scripts/summariseresult.R --generator ${folder_name}
Rscript /scratch/users/banwang3/references/scripts/summariseresult_chp.R --generator ${folder_name}
Rscript /scratch/users/banwang3... |
e17dce0ce326844f531a57188f9bd236152b026a4a9d83101c47f930a1956260 | Shell | 489 | 23 | #!/bin/sh
#
#$ -N ml_job
#$ -o logs/ML_out.txt
#$ -e logs/ML_err.txt
#$ -pe smp 8
#$ -l h_vmem=16g
#$ -q YOUR_QUEUE
# ---- Cluster configuration (set these for your system) ----
VENV_PATH="/path/to/your/venv/bin/activate"
PYDFC_CODE_DIR="/path/to/pydfc"
# -----------------------------------------------------------
DA... |
1f664d9e3de9ec8386ee4b8bc4fef0acadc7094ddb142a37008db621d8002480 | Shell | 491 | 11 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## PCA processing - STAGE 2
for chr in $(seq 1 22);
do
/home/wrr239/c3202333/plink2 --bfile /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/PCA_calculations_in_either_TwinSubset/Chr${chr}_Stage_1_PCA \
--keep-allele-order --ind... |
afd72aa10371fe9eccb78a724d299b236e6d45e0d747285208633d833a71c4de | Shell | 505 | 21 | #!/bin/bash
p=2
#-vf pad=720:540:0:30,setdar=dar=4/3 \
while read -r start stop; do
echo Start: $start Stop: $stop
ffmpeg \
-i Grandbudapesthotel_HI.mkv \
-ss "$start" \
-to "$stop" \
-map 0:v -map 0:a:0 \
-map_metadata -1 \
-af compand="0|0:1|1:-90/-900|-70/-70|-30/-9|0/-3:6:0:0:0" \
... |
f400ed112bc9ec1d4483060e050421edb5ccfe9d65f17acbc287f43c84e2fc2c | Shell | 514 | 10 | #!/bin/sh
### An example script used to train model
parent_path=$( cd "$(dirname "${BASH_SOURCE[0]}")" ; pwd -P )
cd "$parent_path"
GPU=0
echo "=====Train ml2 on qm8 (random split seed=122)====="
CUDA_VISIBLE_DEVICES=${GPU} python ../src/main.py --dataset=qm8 --ori_dataset_path='../../datasets/moleculenet/' --pro_da... |
575616939d7708b1ecf7ef25209fda21c3ff4b1d57876f7517eef03cc7fb0177 | Shell | 522 | 18 | #!/usr/bin/env bash
set -e
# TODO make url configuration consistent.
export CHROMA_CLUSTER_TEST_ONLY=1
export CHROMA_SERVER_HOST="${CHROMA_SERVER_HOST:-localhost:8000}"
export CHROMA_COORDINATOR_HOST="${CHROMA_COORDINATOR_HOST:-localhost}"
echo "Chroma Server is running at port $CHROMA_SERVER_HOST"
echo "Chroma Coord... |
72cf92b56dc8a83cbf9b1ff0d869f056dfcddac820f7cd156607a21aae090a07 | Shell | 525 | 14 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
python evaluate_casf2016.py \
--model_name multistage-hgvp \
--input_type multistage-hetero \
--residue_featurizer_name MolT5-small-grad \
--checkpoint_path /home/ec2-user/SageMaker/efs/model_logs/brandry/PDBBind_MS-HG... |
ffbc6eb4d3682bc2cca1afe09b2a303d8b9ee4c1bbf32905b0948f8c38580e7d | Shell | 526 | 16 | ## gpu id
gpus="2,3,4"
## path to your data file
file='../datasets/moleculenet/qm8.csv'
## path to store output results
out='out'
## split method
mode='random'
## split ratio
train_ratio=0.8 #The ratio for training set
valid_ratio=0.1 #The ratio for validation set
#The data except for training and validation is test se... |
d37c45caea5ac81068f1c580cc1ec6dd8318a78ed55b4e2d5e7b14bcea193c12 | Shell | 530 | 16 | multiqc \
../data/00_sequencing_runs/190219_A00642_0010_AHHCHVDMXX \
../data/00_sequencing_runs/190830_A00642_0037_BHL3LFDMXX \
../data/00_sequencing_runs/190830_A00642_0038_AHCWFLDRXX \
../data/01_fastq/0010 \
../data/01_fastq/0037 \
../data/01_fastq/0038 \
../data/03_qc/fastqc/0010 \
../data/03_qc/fastqc/0037 \
../da... |
abc1eddc1c08672fe4e23399b7d6f280a4552f0e2f8f8f82f72f54513c3cedfe | Shell | 531 | 15 | #!/bin/bash
set -e
export IS_PERSISTENT=1
export CHROMA_SERVER_NOFILE=${CHROMA_SERVER_NOFILE:-65536}
args="$@"
if [[ $args =~ ^uvicorn.* ]]; then
echo "Starting server with args: $(eval echo "$args")"
echo -e "\033[31mWARNING: Please remove 'uvicorn chromadb.app:app' from your command line arguments. This is ... |
4dbb406ef58d08bd2655a22ea7829d28987d37ca0f5b81b99460bcc80b2ac908 | Shell | 533 | 33 | #!/usr/bin/env bash
pushd () {
command pushd "$@" > /dev/null
}
popd () {
command popd "$@" > /dev/null
}
if [ "$1" = "clean" ]; then
pushd src/SimulationCode
make clean
popd
exit 0
fi
pushd src/SimulationCode
make &&
./mtsim -m 0 &&
cp out_* ../../Result/Simulation/led/MTFixed/ &&
./mtsim -m 0 -s&&... |
892bfba4cecc690d28dcf5cce5dc570358e3998a03d60d2da85ca4c59e077125 | Shell | 535 | 12 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## KING relatedness estimates in each subcohort
for i in $(seq 1 2);
do
/home/wrr239/c3202333/plink2 \
--bfile /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/PCA_calculations_in_either_TwinSubset/Merged_stage_4/Subset_${i}_all... |
f06c18e8f33adf91a10d9d81eea9c2af9951caf77dc850588c42c7655117e9ae | Shell | 537 | 17 | #!/bin/bash
module load bioinfo-tools
module load samtools/1.9
module load FastQC/0.11.8
module load MultiQC/1.9
# Prepare fastq files
mkdir out/fastq
for file in out/Output_bam/*.output.bam; do name=$(basename $file .output.bam); samtools fastq -F 256 -F 1024 $file | pigz -c > out/fastq/$name.fq.gz; done
# Run fastQ... |
f7b1ff6c9a7df532c142584856a7b3ae199de2590dac4793a3d324c0ae17e27f | Shell | 539 | 16 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## First tranche of INFO filtering
## MAF, missing, and HWE
for chr in $(seq 1 23);
do
/home/wrr239/c3202333/plink2 \
--pfile /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/Raw_pgen/Raw_chr${chr}_TwinsUK \
--mach-r2-filt... |
e0891a9c34464e421fd45d7f787f3a9824c3e496dd84621b467eeb642f0ffef1 | Shell | 542 | 16 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## First tranche of INFO filtering
## MAF, missing, and HWE
for chr in $(seq 1 23);
do
/home/wrr239/c3202333/plink2 \
--pfile /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/Raw_pgen/Raw_chr${chr}_TwinsUK \
--mach-r2-filt... |
ffd36232a1db111b717422da2a85d919bbaef63554da7cd8dde810213e3d618f | Shell | 543 | 17 | #!/bin/bash
#n installation.
tar -xzf python38.tar.gz
#untar a set of packages
tar -xzf packages.tar.gz
# make sure the script will use your Python installation,
# and the working directory as its home location
export PATH=$PWD/python/bin:$PATH
export PYTHONPATH=$PWD/packages
export HOME=$PWD
#copy the compressed tar ... |
435a6f286e4afeda2abf6facdec782a03a7601fe6be8b8a135d23ad1f8222452 | Shell | 546 | 18 | #!/bin/bash
#n installation.
tar -xzf python38.tar.gz
#untar a set of packages
tar -xzf packages.tar.gz
# make sure the script will use your Python installation,
# and the working directory as its home location
export PATH=$PWD/python/bin:$PATH
export PYTHONPATH=$PWD/packages
export HOME=$PWD
#copy the compressed tar ... |
74946f90cd82ca9b3c389210641bb74e897644f8fe86db436e9f8701e2fe4789 | Shell | 546 | 16 | #!/bin/bash
#n installation.
tar -xzf python38.tar.gz
#untar a set of packages
tar -xzf Cudapackages.tar.gz
# make sure the script will use your Python installation,
# and the working directory as its home location
export PATH=$PWD/python/bin:$PATH
export PYTHONPATH=$PWD/Cudapackages
export HOME=$PWD
#copy the compres... |
48a5ccdb2f2f0cbbd926a54de9791ae55ed76d87c15e6ede4d55016b1103ea9a | Shell | 547 | 19 | #!/bin/bash
#SBATCH --cpus-per-task=3
#SBATCH --time=90:00:00
#SBATCH --output=job-%j.out
#SBATCH --error=job-%j.err
## FIZI Retinol sumstats imputation
eval "$(conda shell.bash hook)"
conda activate fizi_env
cd /home/control/data/users/william/Retinol_GWAS_summ_stats_imputation/FIZI_imputation
for chr in $(seq 1 2... |
983a466becd2f01db676d10ce931cb4ed6c88604b0515d0f6c87b2a69e2a5e65 | Shell | 548 | 16 | #!/bin/bash
#SBATCH --partition=scavenge
#SBATCH -o kernel_alignment.out
#SBATCH --requeue
#SBATCH -e kernel_alignment.err
#SBATCH --ntasks=1 --nodes=1
#SBATCH --cpus-per-task=20
#SBATCH --mem-per-cpu=6000
#SBATCH --time=12:00:00
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
module load MATLAB/2018b
matlab -nodispla... |
77218fa8c403c577a655cce7a3c4ea14fbfa6b4ae1637fe1ef061806205e03f2 | Shell | 553 | 16 | ## gpu id
gpus="2,3,4"
##path to your data file
file='../datasets/moleculenet/qm8.csv'
##path to your saved model file
model='path/to/your/saved/model'
## split method
mode='random'
## split ratio
train_ratio=0.8 #The ratio for training set
valid_ratio=0.1 #The ratio for validation set
#The data except for training and... |
0844595c3c6247b084a237c0841a94ce62ef9597d2ab9f49244f3292522d5052 | Shell | 556 | 23 | # All rights `PaddleDetection` reserved
#!/bin/bash
model_dir=$1
model_name=$2
export img_dir="demo"
export log_path="output_pipeline"
echo "model_dir : ${model_dir}"
echo "img_dir: ${img_dir}"
# TODO: support batch size>1
for run_mode in "trt_int8"; do
echo "${model_name} ${model_dir}, run_mode: ${run_mode}"
... |
fbbea0f4824e5ed9b26e8965fdf2fc82a77366eea35036ad658cf33bc8214817 | Shell | 556 | 17 | #!/bin/bash
#n installation.
tar -xzf python38.tar.gz
#untar a set of packages
tar -xzf Cudapackages.tar.gz
# make sure the script will use your Python installation,
# and the working directory as its home location
export PATH=$PWD/python/bin:$PATH
export PYTHONPATH=$PWD/Cudapackages
export HOME=$PWD
#copy the compres... |
2cd87eb89e5af5f67d224c2aa9b2f072a46a2d71c907139cfc74705a7014bfdd | Shell | 559 | 14 | #!/bin/bash
#SBATCH -c 4
#SBATCH -t 5:00:00
#SBATCH --mem=30G
#SBATCH -p gpu,gpu_marks
#SBATCH --gres=gpu:1
#SBATCH -o /n/groups/marks/users/david/esm_if/data/seq_to_score/runs/%j.out # File to which STDOUT will be written, including job ID
#SBATCH -e /n/groups/marks/users/david/esm_if/data/seq_to_score... |
ed2d07767e803944fc539e19df7611d5088f3f27bdc218c20f2c2b079ca8599e | Shell | 559 | 13 | #!/bin/bash
# Make output & log directories
mkdir output
mkdir logs
for i in $(ls -1 *_1.fastq | sed 's/_1.fastq//')
do
# Make a copy of each SRA fastq file with "/1" at end of each sequence identifier line
awk -v "n=$n" 'NR % 4 == 1 {$0 = $0 "/1"} {print}' $i\_1.fastq > $i.R1.fastq
# Remove SRA fastq file
rm $i\_1.fas... |
1bbc8fc793d13e14e62154dfab73120132276ceb2bc594e294c150445759ab80 | Shell | 560 | 24 | #!/bin/bash
#SBATCH --time=30:00:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=simulation.log
folder=$1
S=$2
module load python/3.8.10
module load mpi4py
module load scipy-stack
module load matlab/2020a
module load neuro... |
f34eb30873ec81b6ed9cf299b08c58c6fa0b6923149c0796d509d3a40b1f23c2 | Shell | 571 | 15 | #! /bin/bash
# Tool for adding revision and date to m-files that were prepared in tfce.
# ______________________________________________________________________
#
# Christian Gaser
# Structural Brain Mapping Group (https://neuro-jena.github.io)
# Departments of Neurology and Psychiatry
# Jena University Hospital
# ____... |
8ad6937c5ec5e384c35644ea4f1a042bf76e73ecb0d5ff4d68bd266d1fe54f6a | Shell | 574 | 10 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## PCA processing - STAGE 4: merging and extracting each half of the cohort - half one
/home/wrr239/c3202333/plink2 \
--pmerge-list /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/Input_for_merge.txt bfile --merge-max-allele-ct 2 \
... |
adbd51c1c3d86939692abd842dffdbc34a3a764919b33a761f48458afc18206b | Shell | 574 | 10 | #!/bin/bash
#PBS -l select=1:ncpus=16:mem=150G,walltime=200:00:00
## PCA processing - STAGE 4: merging and extracting each half of the cohort - half one
/home/wrr239/c3202333/plink2 \
--pmerge-list /home/wrr239/TwinsUK/DTR_Genotypes_1000G_V3P5_Collab_PublicID/Input_for_merge.txt bfile --merge-max-allele-ct 2 \
... |
ff1b9a53985545e473a06047fcc83c72a39e26e5b1b32341fddd74bcfd535e91 | Shell | 574 | 25 | #!/bin/sh
#
#$ -N report_job
#$ -o logs/report_out.txt
#$ -e logs/report_err.txt
#$ -l h_rt=24:00:00
#$ -l h_vmem=64g
#$ -q YOUR_QUEUE
# ---- Cluster configuration (set these for your system) ----
VENV_PATH="/path/to/your/venv/bin/activate"
PYDFC_CODE_DIR="/path/to/pydfc"
# --------------------------------------------... |
1f090631dc200dd9db537c5a4ba6abea2e708d1a75227e3b958061a485929e54 | Shell | 575 | 32 |
if test -f $1/bin/cygpath ; then
N="`$1/bin/cygpath -u $1`"
PATH=$N/bin
else
N=$1
if test -d $N/mingw/bin ; then
PATH=$N/mingw/bin:$PATH
fi
if test -d $N/bin64 ; then
PATH=$N/bin64:$PATH
fi
if test -d $N/mingw/mingw64/bin ; then
PATH=$N/mingw/mingw64/bin:$PATH
else
PATH=$N/bin:$PATH
... |
33e3e073c9bb259a6a86a9989e7bae9f4d83953f32bc7de20a6210697e00098d | Shell | 581 | 22 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### Print current simulation start time [24H format]
CURRTIME=$(date +"%T")
echo "Running simulation at time $CURRTIME";
command time -v python3 simulate_I_F_curves_CA1.py -p configs/LFP_test_sim_DC.json -sd /beegfs/nvardala/results_I_F_CA1_DC > /beegfs/nvardala/results_... |
e22da405e5ed37d6db5bd43d369d0cf77ebde2ac6f625ba1f851b76f34aaa91f | Shell | 587 | 21 | #!/bin/bash
# Define the runs
runs=("0010" "0037" "0038")
# Loop through each run
for run in ${runs[@]}
do
# Define the input and output directories
input_dir="../data/01_fastq/${run}"
output_dir="../data/03_qc/fastqc/${run}"
# Create the output directory if it doesn't already exist
mkdir -p ${ou... |
d981fdcd55d673b44d1c47ce96e63778eb6500278cb291f4e7b283e44f3f804f | Shell | 589 | 22 | #!/bin/bash
set -e
set -u
function create_user_and_database() {
local database=$1
echo " Creating user and database '$database'"
psql -v ON_ERROR_STOP=1 --username "$POSTGRES_USER" <<-EOSQL
CREATE USER $database;
CREATE DATABASE $database;
GRANT ALL PRIVILEGES ON DATABASE $database TO $database;
EO... |
81b0b12383d4f85b441cd295e023962622df0d038facc6cf7cf7b81ce32844a2 | Shell | 594 | 22 | #!/usr/bin/env bash
# -*- encoding: utf-8 -*-
### Print current simulation start time [24H format]
CURRTIME=$(date +"%T")
echo "Running simulation at time $CURRTIME";
command time -v python3 simulate_I_F_curves_CA1.py -p configs/LFP_test_sim_DC.json -sd /beegfs/nvardala/results_I_F_CA1_DC_PR > /beegfs/nvardala/resul... |
eb145c181e323f80d8bb86b8ddc4ab3ee4b470c6b88f84d4351336a34d50406d | Shell | 600 | 22 | #!/bin/bash
#SBATCH --time=10:00:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
module load python/3.8.10
module load mpi4py
module load scipy-stack
module load matlab/2020a
module load neuron
virtualenv --no-download $SLURM_TMPDIR/env
so... |
31dd2ded6d4543f470e76095eac10a9174327d85efbf3dcc15ac1d89630b30b1 | Shell | 603 | 23 | #!/bin/bash
#SBATCH --time=01:00:00
#SBATCH --account=def-akhadra
#SBATCH --mem=4G
#SBATCH --mail-user=niklas.brake@mail.mcgill.ca
#SBATCH --mail-type=FAIL,END
#SBATCH --output=simulation.log
module load python/3.8.10
module load mpi4py
module load scipy-stack
module load matlab/2020a
module load neuron
virtualenv --... |
c0ad4b490c6ce49e82aff7e570806989899c3fda8659824c571196535c158107 | Shell | 604 | 18 | ## gpu id
gpus="2,3,4"
##path to your data file
file='../datasets/moleculenet/qm8.csv'
##path to your saved model file
model='path/to/your/saved/model'
## split method
mode='random'
## split ratio
train_ratio=0.8 #The ratio for training set
valid_ratio=0.1 #The ratio for validation set
#The data except for training and... |
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