sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
980313d16c61bf7dc26785cceecde660ef862e0fd7c8d5d94040bfb79626fbd3 | Shell | 310 | 13 | # Command to download dataset:
# bash script_download_cycles.sh
DIR=cycles/
cd $DIR
FILE=CYCLES_6_56.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://www.dropbox.com/s/9fs9aqfp10q9wue/CYCLES_6_56.pkl?dl=1 -o CYCLES_6_56.pkl -J -L -k
fi
|
af760163830b0e26106120ab6cd10c29731a8523a6d034bafa04678a5528d539 | Shell | 311 | 11 | declare -a arr=("curvature" "depth" "edge" "edge2d" "keypoint" "keypoint2d" "mist" "normal" "reshade" "segment25d" "segment2d")
for i in "${arr[@]}"
do
img_info=$(identify -verbose ~/s3/2fycrku4FjW/$i/point_0_view_0_domain_$i.png )
dest=./infos/$i.txt
echo -e "$img_info" > "./info/$i.txt"
done
|
ddc3ee9a9898a9b7afd445d29c58ea0fa07db457decdf4f6fae412720ec09a64 | Shell | 311 | 8 | #!/bin/bash -e
#SBATCH slurm/HPC parameters
#SBATCH --mem=240G
module add R/4.3.1
cd /path/to/folder
Rscript /path/to/folder/get_dufs_from_pfam.R "/path/to/folder/prok_pfam_comb.pfam" "/path/to/folder/prok_duf3494.csv" #provide paths to concatenated pfam file & output name
# need to run for uc and euk as well |
814d6248e7a18c87c02dd654e1d841026b9e910ea5c8f77448a47d0351a730b4 | Shell | 313 | 25 | #!/bin/bash -l
# Set SCC project
#$ -P ivc-ml
# Request 4 CPUs
#$ -pe omp 3
#$ -m ea
# Request 1 GPU
#$ -l gpus=1
#$ -l gpu_memory=48G
#$ -l h_rt=48:00:00
# run this script from adrd_tool/
conda activate py3.11
# conda activate adrd
pip install -e .
# CUDA_VISIBLE_DEVICES=1
python dev/backbone_shap.py |
8c6b544076f242336c28af01bd4c06d53db78837f15895424062b839242ceeff | Shell | 314 | 8 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/sage
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-sage-inverse-loss \
--pert.perturbation-num 10 \
--pert.sage.n-permutations 256000\
--trainer.pert-num-steps 100 \
--trainer.save-and-sample-every 20 \
--slurm.mode slurm
|
5aab85dd9d72a1ad5ed14b9d54de0b3e9aa911fe4386b80c85ff6d7a40b6eaa6 | Shell | 319 | 15 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=50G
#$ -l h_vmem=50G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -o ./logs
module load bedtools
#This is to create a version of chm13 with all of the repeat masked sections masked
bedtools maskfasta -fi chrm13.mod.fna -bed ucsc-t2t-repeat-masker.bed -fo masked.fasta
|
1b687576709b8a03d944977e51f7f0fb1de2bd2d562fe7a164a028278af5a205 | Shell | 320 | 8 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking run_cuml_dask_pytests.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuml/tests/dask || exit 1
python -m pytest --cache-clear "$@" .
|
a8a6df744ac2c32e1ecb0b24ba8240805af0a089f70bfaeef3d70de23c5d76d4 | Shell | 320 | 11 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
CONFIG=$1
CHECKPOINT=$2
GPUS=$3
PORT=${PORT:-29500}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
$(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
|
0a5fda16d8dd170bb0b3e881640822b43160d9c6ca536a2f71506dddf79689e8 | Shell | 325 | 22 |
# Command to download dataset:
# bash script_download_CSL.sh
DIR=CSL/
cd $DIR
FILE=CSL.zip
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://www.dropbox.com/s/rnbkp5ubgk82ocu/CSL.zip?dl=1 -o CSL.zip -J -L -k
unzip CSL.zip -d ./
rm -r __MACOSX/
fi... |
0305085b6a9a1c8d6cade8fae9eb216b656a65700d7fd531da9f951e58655e2e | Shell | 326 | 21 |
# Command to download dataset:
# bash script_download_WikiCS.sh
DIR=WikiCS/
mkdir $DIR
cd $DIR
FILE=data.json
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset/data.json -o data.json -J -L -k
f... |
f8e561bae028e2e02ab119029579a7ffe5cd6cb771871655d88fecd22495a161 | Shell | 327 | 4 | #!/bin/bash
echo "downloading and filtering uniprot go annotations..."
wget https://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz -O - | gunzip -c | awk 'BEGIN {OFS="\t";FS="\t"} ($1 == "UniProtKB") {print $2,$4,$5,$7,$9,$14}' | sort -u | gzip > data/raw/goa/goa_uniprot_all_ebi_filtered.tsv.gz
echo ... |
8e1e338532fe86db88bf4b257bfae1f4bb40409da1f988eeacb707a67d3dbd67 | Shell | 331 | 8 | #!/bin/bash
# Select read counts per umi for OR genes only
for i in *.umi.distributions.txt; do grep Olfr $i > ORs_$i; done
# Correct umi assignments when the same umi or a umi that is one hamming distance away maps to more than one OR
for i in ORs_*; do python cleanLowEndUmis_ORdeconvolution.py -i $i -o clean_$i -n 2... |
108a8df002c58b379625969f663609ae5918563b3b7433a53cec1e15905da53a | Shell | 333 | 21 | ###
# Write genotype data to BED format for tensorQTL
###
module load plink
genotypes="$1"
inds="$2"
prefix="$3"
plink --make-bed \
--chr 1-22 \
--maf 0.1 \
--geno 0 \
--bp-space 1 \
--aec \
--keep-fam ${inds} \
--keep-allele-order \
--vcf ${genotypes} \
--output-chr chrM \
--... |
c44ca06d478d13bca3ce320788a9af84c030c53898cdb8fba85c4d9b802d3640 | Shell | 334 | 8 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking run_cuml_singlegpu_pytests.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuml/tests || exit 1
python -m pytest --cache-clear --ignor... |
b4619e442bb252e652d1fe213c6e1dd2d3eced0e5131dd1bfdc164b82585f7b9 | Shell | 336 | 12 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=100G
#$ -l mem_free=5G
#$ -l h_vmem=5G
#$ -l h_rt=96:00:00
# Log output
#$ -o /dcs04/scharpf/data/annapragada/DELFI_pipeline_updates/logs
module load conda_R/4.0.x
Rscript 06-FeatureMatrix.r LUCAS_Hiseq_55/zscores_hiseq55.csv LUCAS_Hiseq_55/coverage_hiseq55.csv LUCAS_Hiseq_55/... |
e46b4f1f5e6eacdd5f7ca41b99a644686eba0ada78f348ae98f23cd07c67d76e | Shell | 337 | 8 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking run_cuml_singlegpu_accel_pytests.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuml/cuml_accel_tests || exit 1
python -m pytest --ca... |
91d3b2fa9bbc567ce8c66fd41c1cee31e56359273a7762eadb52c5c0b2d2d3db | Shell | 340 | 13 | # Command to download dataset:
# bash script_download_graphtheoryprop.sh
DIR=graphtheoryprop/
cd $DIR
FILE=GraphTheoryProp.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://www.dropbox.com/s/sat1tj9lzvljtpe/GraphTheoryProp.pkl?dl=1 -o GraphTheoryP... |
814f3a77d90254205acf1f2228e301ab397177aa30ba95724f40ed1b8eca6f6c | Shell | 343 | 18 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=65G
#$ -l h_vmem=65G
#$ -l h_rt=96:00:00
#$ -o ./logs
#$ -pe local 4
jellyfish-linux count --mer-len 24 --size 10G --threads 4 --canonical --quality-start=32 --lower-count=1 --output masked.jellyfish masked.fasta
jellyfish-linux dump --column masked.jel... |
385837f8d6304d903e664a5bdd8ba8ff65613618b536952c6eab15c0fbd5ff4e | Shell | 347 | 11 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=100G
#$ -l mem_free=5G
#$ -l h_vmem=5G
#$ -l h_rt=96:00:00
# Log output
#$ -o /dcs04/scharpf/data/annapragada/DELFI_pipeline_updates/logs
module load conda_R/4.0.x
Rscript 04-getZscores.r hi LUCAS_Hiseq_55/zscores_hiseq55.csv /dcs04/scharpf/data/annapragada/DELFI_pipeline_updat... |
f0f17db21c64f28826d3e5319b80aa0ca6b68642d005793bc262fea28f9ed81a | Shell | 348 | 13 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=100G
#$ -l mem_free=5G
#$ -l h_vmem=5G
#$ -l h_rt=96:00:00
# Log output
#$ -o /dcs04/scharpf/data/annapragada/DELFI_pipeline_updates/logs
module load conda_R/4.0.x
Rscript 05-getCoverage.r LUCAS_Hiseq_55/coverage_hiseq55.csv /dcs04/scharpf/data/annapragada/DELFI_pipeline_updat... |
029210116a7b0b1dfc3d03f4d26675868489711412fb4900c7ae84ee8c1071bf | Shell | 354 | 15 | #!/bin/bash
# custom config
DATA=/path/to/datasets
TRAINER=ZeroshotCLIP
DATASET=$1
CFG=$2 # rn50, rn101, vit_b32 or vit_b16
python train.py \
--root ${DATA} \
--trainer ${TRAINER} \
--dataset-config-file configs/datasets/${DATASET}.yaml \
--config-file configs/trainers/CoOp/${CFG}.yaml \
--output-dir output/${TRAINE... |
458462ae919474ebf28bb49234bfdd08f7a74e57dba8c23c795509e158e3e614 | Shell | 359 | 9 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/sage
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-sage-binary-head \
--head-trainer.train-num-steps 1000 \
--pert.perturbation-num 10 \
--pert.sage.n-permutations 256000\
--trainer.pert-num-steps 100 \
--trainer.save-and-sa... |
9b836c1efcb398a60a1cd1c12ac00c94dc582c119141e755c81d9fa11b14de53 | Shell | 361 | 8 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking run_cuml_singlegpu_pytests.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuml/tests || exit 1
python -m pytest -p cudf.pandas --cach... |
c7e1e71b11f073f813e2bbc2446408673f4480567800aa9c97bf7f64f1ad32f7 | Shell | 361 | 19 | #!/bin/bash
if [ -v $1 ]
then
echo 'to run this script you need to have plink installed'
echo 'this script should be run like this:'
echo './CreateVcf.sh <path folder> <path installation plink> <path genome studio .map .ped files>'
else
path=$1
path_plink=$2
genomestudio_output=$3
cd $path
$path_plink'plink' --fil... |
6e3774f82a6af4f100956f854b08bc9444474d6985e146118e93dbe789fe6d03 | Shell | 362 | 9 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/sage
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-sage-binary-head \
--head-trainer.train-num-steps 500 \
--pert.perturbation-num 10 \
--pert.sage.n-permutations 256000\
--trainer.pert-num-steps 100 \
--trainer.save-and... |
32e32017e98f5ae88a2192859d359c7f16a890f65d18b46d74dd9175885b50a8 | Shell | 363 | 9 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Images/val2017.zip -d $DATA_ROOT
unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Images/train2017.zip -d $DATA_ROOT
unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Annotations/annotations_trainval2017.zip -d $DATA_ROOT
rm... |
61f45012f22250560acd951aa7a3abd31391be7dbb6f2b4618cf1318b7ce97db | Shell | 363 | 12 | #!/usr/bin/env bash
CONFIG=$1
CHECKPOINT=$2
GPUS=$3
PORT=${PORT:-29500}
PYTHONPATH="$(dirname $0)/../..":$PYTHONPATH \
# Arguments starting from the forth one are captured by ${@:4}
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
$(dirname "$0")/clip_feature_extraction.py $CONFIG $... |
665f19ad9a5d04ef73ebd1e986e1d48d39366fe11d7694f2dcb0711803c6433b | Shell | 371 | 12 | #!/bin/sh
SCRIPTSDIR=$(cd "$(dirname "$0")"; pwd)
BASEDIR="$(dirname "$SCRIPTSDIR")"
mialsuperresolutiontoolkit_docker \
"$BASEDIR/data" \
"$BASEDIR/data/derivatives" \
participant --participant_label 01 \
--param_file "$BASEDIR/data/code/participants_params.json" \
--nipype_nb_of_cores 1 \
--o... |
829674c8f3942d093b6baf48db5238407b96fccf22990e9dc4e14b122bff2618 | Shell | 372 | 11 | #!/bin/bash
if [ "$#" -lt 2 ];
then echo $# argument\(s\) are NOT enough!!
echo "Need 2 args: $0 input_csv_file model_file"
echo " For example:"
echo " $0 ~/Documents/run_umap/examples/sampleBalbc12k.csv ~/temp/balbc55"
echo " "
exit 9
fi
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null... |
b9293ae699e4d955f366c42d8252f25e8e038b283a193bdd8f089b6e7d624967 | Shell | 383 | 26 | nohup sh ./Treemix.sh \
./93sawfly_no_miss \
10 \
25 \
2000 \
Mon \
plink \
/home/AHSZ/bin/BITEV2/inst/scripts/treemix_scripts \
true \
true \
3 \
91inds_mig &
for m in {1..10}
do
for i in {1..5}
do
treemix \
-i test.treemix.gz \
-o test.${i}.${m} \
... |
f0db336b97249c8edfadebcf741941af73ded0dbcbda3810c80279d68027a134 | Shell | 392 | 11 | #!/bin/bash
echo "User: $(id -un "$USER")" && echo "Group: $(id -gn "$USER")" && \
export && \
echo "SHELL: $SHELL" && \
echo "PATH: $PATH" && \
xvfb-run -a coverage run --source=pymialsrtk \
/app/run.py "$@" \
|& tee /bids_dir/code/log.txt && \
coverage html -d /bids_dir/code/coverage_html && \
coverage xml -o /bids_d... |
a4434448c9306536aa1db282235ef0eba0b6060f669b7d10f2670fe0bbbca68e | Shell | 394 | 9 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-subset-sampling \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
--pert.subset-sampling.tau-start 2 \
--pert.s... |
862ee4bfa52d05a74e117d92cd901a4060ab5cbb8882168467db64e885c127fd | Shell | 395 | 29 | #!/bin/bash
if [ ! -e ~/anima-build ]; then
break
fi
cd ~/anima-build
make doc
nbFiles=`ls -l ~/anima-build/doc/html/* | wc -l`
if [ $nbFiles -le 2 ]; then
break
fi
cd ~/dox-repo
rm -fr *
cp -r ~/anima-build/doc/html/* .
numChanges=`git diff | wc -l`
if [ $numChanges -eq 0 ]; then
break
fi
echo git add --all... |
1cb48f20da5d04574e22c05cd50d686758460334ebf13c0deeeda3840adc8f86 | Shell | 400 | 8 | export CUDA_VISIBLE_DEVICES=$1
python pretrajectory.py --ann_prc_data outputs/pretrain/pancreas/data/pancreas.h5ad \
--ann_raw_data outputs/pretrain/pancreas/data/pancreas.h5ad.raw \
--npy_prc_data outputs/pretrain/pancreas/data/deepvelo_dataset.pth \
--model_checkpoint outputs/pretrain/pancreas/model/auto... |
2563f78cec9dafbae5f0ab6c077b889d06c0bc5116fb5e9d586511d177ca53e4 | Shell | 404 | 11 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/fimap
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-fimap \
--pert.perturbation-num 10 \
--pert.fimap.tau 0.5 \
--pert.fimap.regularization-weight 1e-1 \
--pert.fimap.optimizer-name Adam \
--pert.fimap.lr 1e-3 \
--trainer.pe... |
b69da7e2945f5a40fbb5084dfb673701cf3c4f0a483913e56bea0ac97c96f25c | Shell | 405 | 8 | export CUDA_VISIBLE_DEVICES=$1
python pretrajectory.py --ann_prc_data datasets/pretrain/forebrain/data/forebrain.h5ad \
--ann_raw_data datasets/pretrain/forebrain/data/forebrain.h5ad.raw \
--npy_prc_data datasets/pretrain/forebrain/data/forebrain.pth \
--model_checkpoint datasets/pretrain/forebrain/model/a... |
8a829a28b6ea9fd88cf82b11add7a04bd5fbc5c0e4a38e55e681983a4a681ea8 | Shell | 408 | 11 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/fimap
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-fimap \
--pert.perturbation-num 10 \
--pert.fimap.tau 0.5 \
--pert.fimap.regularization-weight 1e-1 \
--pert.fimap.optimizer-name Adam \
--pert.fimap.lr 1e-3 \
--traine... |
7d590ec4d63273ee6a7bff658d3c11dc1d4f7989e571ee8c637b9b729eb415ab | Shell | 409 | 8 | #!/bin/bash
sbatch /project2/gilad/umans/oxygen_eqtl/data/snakemake_cellranger.batch \
"/scratch/midway2/umans/miniconda3/envs/chromium" \
"-s /project2/gilad/umans/oxygen_eqtl/data/Snakefile_cellranger2" \
"--configfile /project2/gilad/umans/oxygen_eqtl/data/config.yaml" \
"--config proj_dir=/project2/gilad/umans/oxy... |
c2751bcb65afa96b390f887c5df8ea878af4be41917cc979e07d753546aa379a | Shell | 412 | 11 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/fimap
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-fimap \
--pert.perturbation-num 10 \
--pert.fimap.tau 0.5 \
--pert.fimap.regularization-weight 1e-1 \
--pert.fimap.optimizer-name Adam \
--pert.fimap.lr 1e-3 \
--tr... |
116d81bbb64b4619752a00eba814349505305a6d70cbff56dcf2ae527a41fb86 | Shell | 415 | 23 | #!/bin/sh
#
# Usage:
# sh create_dataset_description_json output.json v1.1.0
#
# Author: Sebastien Tourbier
#
###################################################################
OUTPUT_JSON=$1
(
cat <<EOF
{
"PipelineDescription": {
"Name": "MIAL Super-Resolution ToolKit",
"Version": "$2",
"CodeURL": ... |
f8137e797561eb90201bf7965205bf17da4b888d10538825107841107d48a80a | Shell | 420 | 11 | #!/bin/bash -e
#SBATCH # SLURM/HPC commands here
#SBATCH --mem=495G
module add sra # this is the SRAtools
module add python/anaconda/2020.11/3.8
module add biopython
cd /path/to/files/sra_files
line=$(sed "${SLURM_ARRAY_TASK_ID}q;d" /path/to/metadata/files/fasterq_md.txt) # if running as an array job (recommended for ... |
f66cba7316a368f29266841aa7166cbcc4114599d2fdc866a141c895196e2e22 | Shell | 424 | 13 | #!/bin/bash
#SBATCH --account=rrg-bourqueg-ad
#SBATCH --output=%x.o%j
#SBATCH --error=%x.e%j
#SBATCH --mem=8192
#SBATCH --ntasks-per-node=2
module purge 2>/dev/null
module load mugqic/homer
cd $SLURM_SUBMIT_DIR
annotatePeaks.pl inputs/WT_Merged_musc_REST_H3K4me3_CutTag.narrowPeak_5col_nochrM_filtered_input_homer.txt... |
90c8ac5423a8646768aa45ad82611238ec96adcdb096466eedc63b2b42a25963 | Shell | 426 | 13 | #!/bin/bash
#SBATCH --account=rrg-bourqueg-ad
#SBATCH --output=%x.o%j
#SBATCH --error=%x.e%j
#SBATCH --mem=8192
#SBATCH --ntasks-per-node=2
module purge 2>/dev/null
module load mugqic/homer
cd $SLURM_SUBMIT_DIR
annotatePeaks.pl inputs/peak_call_files_H3K27me3_broad_peaks.broadPeak_5col_nochrM_filtered_homer_input.tx... |
599ea7761abcf5e449f13f76e67231653bc21282034acc0982e78298a36c9d57 | Shell | 429 | 8 | export CUDA_VISIBLE_DEVICES=$1
python pretrajectory.py --ann_prc_data datasets/pretrain/dentategyrus/data/dentategyrus.h5ad \
--ann_raw_data datasets/pretrain/dentategyrus/data/dentategyrus.h5ad.raw \
--npy_prc_data datasets/pretrain/dentategyrus/data/dentategyrus.pth \
--model_checkpoint datasets/pretrain... |
5a260fb15b349528858a199e8686e49d15911cefeadb5147bf0be5c2dbb4e778 | Shell | 432 | 10 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-subset-sampling \
--head-trainer.train-num-steps 5000 \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
--pert.subs... |
e1554e1d35da3378bbbd5b4e740d26b16e7cb13a5f49ae69fcf82fc6df8fb207 | Shell | 439 | 16 | #!/bin/bash
instance_id=$(/usr/bin/ec2metadata --instance-id)
cat /home/ubuntu/task-taxonomy-331b/tools/task_list.txt | shuf > order.txt
IFS=$'\r\n' GLOBIGNORE='*' command eval 'TASKS=($(cat order.txt))'
#printf '%s\n' "${TASKS[@]}"
for i in "${TASKS[@]}"
do
IFS='\*' read -ra ADDR <<< "$i"
new_name="${ADDR[... |
85e0665f30c54f8e7b255f0862e8b7bd7e7e9efaddcaf4db7c5e54c67ddd67a5 | Shell | 440 | 2 | #!/bin/bash
python3 preprocessing/uniprot_downloader.py "https://rest.uniprot.org/uniprotkb/search?compressed=false&fields=accession%2Cgene_names%2Cprotein_name%2Creviewed%2Cprotein_existence%2Csequence%2Corganism_id%2Cgo_id%2Ckeywordid%2Ckeyword%2Cxref_tcdb%2Cxref_interpro&format=tsv&query=%28%28fragment%3Afalse%29%20... |
b6b93f0494195372efbde3f020d1053a48bd963ea87b6c470708fc87c12a0fe9 | Shell | 441 | 13 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l mem_free=1G
#$ -l h_vmem=1G
# Job resource option: max runtime
#$ -l h_rt=96:00:00
qsub fastp.sh
qsub -hold_jid_ad fastp.sh align.sh
qsub -hold_jid_ad align.sh post_alignment.sh
qsub -hold_jid_ad post_alignment.sh,align.sh bed_to_granges.sh
qsub -hold_jid_ad post_alignment.sh,bed_to_gr... |
29d5f96cd8fd51ef2b3a177fc271e69f10b768fb88c2bfb9a9f68da69f963fbf | Shell | 442 | 28 | #!/bin/bash
#SBATCH --partition=gpu4_dev
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=1
#SBATCH --mem=30G
#SBATCH --gres=gpu:1
#SBATCH --job-name=TCGA_03
#SBATCH --output=log_TCGA_03_%A_%a.out
#SBATCH --error=log_TCGA_03_%A_%a.err
module load pathganplus/3.6
##### Combb 5
python3 ./utilities/h5_handling/combine_c... |
0e97890535830ba5f75347d735d8a9a3667526c49a23d25b50ccb07960c035e8 | Shell | 445 | 8 | #!/bin/bash
echo "Extracting Uniref50..."
gunzip -c data/raw/uniref/uniref50/uniref50.fasta.gz > data/raw/blastdb/uniref50/uniref50.fasta
cd data/raw/blastdb/uniref50 && makeblastdb -in uniref50.fasta -parse_seqids -dbtype prot
echo "Extracting Uniref90..."
gunzip -c data/raw/uniref/uniref90/uniref90.fasta.gz > data/r... |
ca0db1cdb667b6d868bda17b01c2c78923c38b15ef68e0452f217283f555c763 | Shell | 446 | 20 | #!/bin/bash
#SBATCH --partition=gpu8_long,gpu4_long
#SBATCH --job-name=01_train
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=5
#SBATCH --output=rq_01_train_%A_%a.out
#SBATCH --error=rq_01_train_%A_%a.err
#SBATCH --mem=20G
#SBATCH --gres=gpu:1
module load pathganplus/3.8.11
python3 run_representationspathology.py --im... |
4c679dc64b8645c2946844dea26e0cacdb68066beeaa0ead472d9d9f357c0d25 | Shell | 451 | 22 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=50G
#$ -l h_vmem=50G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -t 1-1287
#$ -o ./logs
module load bedtools
name=$(find ./bed_type -maxdepth 1 -name "*.bed" | \
sort -u | \
head -n $SGE_TASK_ID | \
tail -n 1)
mkdir -p ./fasta_type
name=$(basename $nam... |
d68751d96ae1e11c885b99e7df0509eec61a95632d2b4b8d49320bc1aba9af60 | Shell | 458 | 18 | #!/usr/bin/sh
mialsrtk_dir="/home/hkebiri/mialsuperresolutiontoolkit"
data_dir="/home/hkebiri/mialsuperresolutiontoolkit/data"
port=8888
version=v2.0.0
cmd="docker run --rm"
cmd="$cmd -v "${mialsrtk_dir}/notebooks":/app/notebooks"
cmd="$cmd -v "${mialsrtk_dir}":/app/mialsuperresolutiontoolkit"
cmd="$cmd -v "${data_... |
e72a22d7ab5d22e9b17025dc8595b0c299b462ed944120be85227c4007bb8958 | Shell | 458 | 13 | #!/usr/bin/env bash
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
CONFIG=$1
GPUS=$2
PORT=${PORT:-29500}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nnodes=$NNODES --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR \
--nproc_per_node=$GPUS... |
0e86452653608d08c10aa7e008cfbbf86977c11afef68f8e579eeb6e4fb65478 | Shell | 462 | 12 | #!/bin/bash
# Copyright (c) OpenMMLab. All rights reserved.
INPUT_IMAGE=$1
python openpose_visualization.py \
../rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py \
models/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth \
../rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x1... |
cdaae2952febb9eda7d944d78cf19c949238ac3c996f7c4de63e0ebe3bc7340e | Shell | 463 | 25 | #!/bin/sh
#
# Usage:
# sh create_scan_preproc_json output.json source.nii.gz
#
# Author: Sebastien Tourbier
#
###################################################################
OUTPUT_JSON=$1
(
cat <<EOF
{
"Description": "Preprocessed image used as input to the Super-Resolution algorithm",
"Sources": "$2",... |
486a2722713b1a49c78c6b1365c4e0f3da37e101f3e62c6be93a2cd40963c2ca | Shell | 466 | 24 | #! /bin/bash
## RUN SNIPE using MCCV64 with Dorothee's original labels
# Directories
HERE="${SNIPE_HVR_DIR:?path of the NLPB (snipe) working directory}"
SCRATCH=${HERE}/proc_4
for img in ${HERE}/original_labels/t1w/*
do
id=$(basename $img _m00_t1w.mnc)
id=${id##*_}
outdir=${SCRATCH}/${id}
[[ -d $outdir ]] || mk... |
aebecb5a4adbfafd52bdcf7c7b5f981b25902e8dccc437f41542f3192940ace3 | Shell | 466 | 14 | ONSIDES_DIR=/data1/home/zietzm/projects/onsides
uv run python \
${ONSIDES_DIR}/src/construct_training_data.py \
--method 14 \
--nwords 125 \
--section ALL \
--prop-before 0.125
uv run python \
${ONSIDES_DIR}/src/analyze_results.py \
--model ${ONSIDES_DIR}/models/bestepoch-bydrug-PMB_14-ALL... |
e50e5cea729e9155aee0777af7aa9eec19fa1c8cca6e989beb246db20133ca0e | Shell | 472 | 14 | # This script will be run in the root directory.
### 1. MLP Baseline ######################
for SEED in {0..3}; do
python main.py --cfg configs/MLP/peptides-func-MLP.yaml device cuda:$SEED seed $SEED wandb.project mlpbaseline-peptides name_tag MLP-peptides-func &
done
wait
for SEED in {0..3}; do
python main.... |
79a997121799cfc03fadb9757358a82879d1ff4570e01112e4825b5922114783 | Shell | 474 | 8 | python pretrain.py --model.raw_dataset_name forebrain \
--model.raw_dataset_path datasets/pretrain/forebrain/data/forebrain.h5ad \
--model.raw_dataset_use_cache \
--model.deepvelo_dataset_path datasets/pretrain/forebrain/data/forebrain.pth \
--model.model_ckpt_path datasets/pretrain/forebrain/model/auto... |
644603477aa963737647d02ace28ba650082e5410e6868de7eda12672f7a0ccf | Shell | 476 | 26 | #!/bin/bash
#SBATCH --partition=gpu4_short,gpu8_short,gpu4_medium,gpu8_medium,gpu8_long,gpu4_long
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=1
#SBATCH --mem=30G
#SBATCH --gres=gpu:1
#SBATCH --job-name=03_combine
#SBATCH --output=rq_03_combine_%A_%a.out
#SBATCH --error=rq_03_combine_%A_%a.err
module load pathganplu... |
faeae25ce4182bcbe3ef29f42a074c49e8652de3bab9b2d0acf99fde4f275e64 | Shell | 476 | 11 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-subset-sampling-sampler-subset \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
--pert.subset-sampling.tau-start 2 \
-... |
dbb394b86a51889fcff31453126b4230de5b2a365ae868d055e7343782506483 | Shell | 482 | 11 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-subset-sampling-sampler-subset \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
--pert.subset-sampling.tau-start 2... |
f34e444c977c8ac1a8f853e13af1bea4617b63e42894b0533d4ccb4b14c2b28c | Shell | 488 | 14 | #!/usr/bin/env bash
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
CONFIG=$1
CHECKPOINT=$2
GPUS=$3
PORT=${PORT:-29500}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
# Arguments starting from the forth one are captured by ${@:4}
python -m torch.distributed.launch --nnodes=$NNODES -... |
b9114c6bb202535ea0a083fa7c3ed191652edb7fc4b8420b8ddc802ef7b9bf1e | Shell | 490 | 20 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
CONFIG=$1
GPUS=$2
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
PORT=${PORT:-29500}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch \
--nnodes=$NNODES \
--node_rank=$NODE_... |
0d98437c6be96a98dbc196a298c2cb4df3689720f36880f164afd8a5c618013b | Shell | 493 | 22 | #!/bin/bash
#SBATCH --account=def-lpenacas
#SBATCH --time=00:10:00
#SBATCH --mem=64G
#SBATCH --cpus-per-task=4
module load python
source ../4_data_process/envr/bin/activate
# -------------TEST----------------
python roc.py txid298386
python roc.py txid6239
# -------------TRAIN----------------
python roc.py txid2243... |
7a1672ee9a68dc7ef97e6f932e0d6a1c142d65e4b6387ab7fb9debd0f3e7fb5a | Shell | 493 | 19 | #! /usr/bin/env bash
## Relabel CNN HVR-AG labels to simplified HC & VC
set -xue
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
OUTDIR=${HERE}/data/derivatives/adni-bl_cnn_hcvc_simplified
[[ -d $OUTDIR ]] || mkdir $OUTDIR
IN=$1
OUT=${OUTDIR}/$(basename $IN .mnc)_simplified.mnc
... |
b5044062c3f1804e9248d92ccac0cc9dc37c54d5573db375c7b9e749a0f8bfa9 | Shell | 496 | 24 | #$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=40G
#$ -l h_vmem=40G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -M aannapr1@jhmi.edu
#$ -t 1-41
module load conda_R/4.0.x
fragdir="../granges"
outdir="../gc-counts"
mkdir -p $outdir
samplepath=$(find $fragdir -maxdepth 1 -name "*.rds" | sort -u | head -n $SGE_TASK_ID | tail -... |
647d1d7789f3290f9ca4bdd49f6b2181fcdaa4ecd87c82b3e62240a9da899876 | Shell | 497 | 20 | #!/bin/bash
# Copyright (c) OpenMMLab. All rights reserved.
# Create models folder
mkdir models
# Go to models folder
cd models
# Download det model
wget https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth
# Download pose model
wget https://download.openm... |
660fa73c6f10acede666e8d790b17c65f3fac5ed106847660d087eb34bf0bddb | Shell | 506 | 24 | #!/bin/bash
cat task_list.txt | shuf > order.txt
IFS=$'\r\n' GLOBIGNORE='*' command eval 'TASKS=($(cat order.txt))'
prefix="cfg.'num.epochs'..=.3"
sub_tring="cfg['num_epochs'] = 12"
echo $prefix
root="/home/ubuntu/task-taxonomy-331b/experiments/aws_batch"
for i in "${TASKS[@]}"
do
for j in 0 1 2 3
do
... |
b982a597d18343e418eaac21d67a0c7663789243f98bd5b0144e94ec10db95e6 | Shell | 506 | 20 | ##!/usr/bin/env bash
CURRDIR=$(pwd)
BASEDIR=$(dirname "$0")
TASKS="ego_motion \
fix_pose \
non_fixated_pose \
point_match"
mkdir -p "$CURRDIR/$BASEDIR/../temp"
SUBFIX="data-00000-of-00001 meta index"
for t in $TASKS; do
mkdir -p "$CURRDIR/$BASEDIR/../temp/${t}"
for s in $SUBFIX; do
echo "Downloading... |
6b29cc47006017361ce020166e79defc0d143c3e5915df35c3adaedb6217ccfa | Shell | 507 | 22 | #!/usr/bin/env bash
if [ $# -lt 2 ]; then
echo "This script is meant to run a command within a Python virtual environment."
echo "It needs at least 2 parameters."
echo "The first one must be the virtualenv path."
echo "The rest will be the command."
exit 255
fi
env_path=$1
echo "Activating ${env_p... |
ef93a257e3c17b88da06fd0fd3b93718fa64846902e09ff9e16186276ebfe19d | Shell | 514 | 13 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/lime
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-lime-inverse-loss \
--pert.perturbation-num 10 \
--pert.lime.mask-type random \
--pert.lime.masked-batch-size 1024 \
--pert.lime.neighbor-size 32768 \
--pert.lime.neighbor-feat-... |
bfb2a6e0a5e07c37a97fa8f1cdd35e85293124e7b36a66d53ff5d558fb0f29b6 | Shell | 516 | 13 | #!/bin/bash -e
#SBATCH #HPC/slurm parameters here
#SBATCH --mem=450G
module load MMSeqs2/14
mmseqs databases UniRef90 /path/to/uniref90 tmp
mmseqs createtaxdb UniRef90 tmp
mmseqs createdb /path/to/all_dufgenes.fasta queryDB
mmseqs taxonomy queryDB uniref90 taxonomyResult tmp
# Additionally annotate marine sequences ... |
0319b4c53b0fe9da145745f493a9d43ff509affd86cc0f023efba5419d35527a | Shell | 517 | 23 | #!/usr/bin/env bash
if [ $# -lt 2 ]; then
echo "This script is meant to run a command within a conda environment."
echo "It needs at least 2 parameters."
echo "The first one must be the environment name."
echo "The rest will be the command."
exit 255
fi
eval "$(conda shell.bash hook)"
env_name=$1
... |
4fbddfcdd8a3a762487a708d5e98bc9830e0aefe7c70b1039e52c28528620ba5 | Shell | 518 | 13 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/lime
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-lime-inverse-loss \
--pert.perturbation-num 10 \
--pert.lime.mask-type random \
--pert.lime.masked-batch-size 1024 \
--pert.lime.neighbor-size 32768 \
--pert.lime.neighbor-f... |
4f5773ec865d36ce6b3fc6617bae9bd8336c46605ebd0f340df356a09a2a7183 | Shell | 520 | 12 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-subset-sampling-sampler-subset \
--head-trainer.train-num-steps 5000 \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
... |
70b9b0caf01135525a8670e50bfcbe7fa45f3740426fc200da9b55c525342731 | Shell | 520 | 6 | #!/bin/bash
for file in data/raw/alphafolddb/tarfiles/*.tar; do tar xf "$file" --directory=data/raw/alphafolddb/pdbs --wildcards "*.pdb.gz"; done
foldseek createdb data/raw/alphafolddb/pdbs data/raw/alphafolddb/foldseekdb/queryDB
# see https://github.com/steineggerlab/foldseek/issues/15#issuecomment-1065876787
foldseek... |
50e35544940156a9cad0569e086023ba362178f10b9957c37f880150e957011a | Shell | 522 | 32 |
# Command to download dataset:
# bash script_download_superpixels.sh
DIR=superpixels/
cd $DIR
FILE=MNIST.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://data.dgl.ai/dataset/benchmarking-gnns/MNIST.pkl -o MNIST.pkl -J -L -k
fi
FILE=CIFAR10.... |
64ee9caa570a5ed4a3d15177158bbf52d77fcdc44061eb027f7ac38c74a21487 | Shell | 522 | 13 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/lime
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-lime-inverse-loss \
--pert.perturbation-num 10 \
--pert.lime.mask-type random \
--pert.lime.masked-batch-size 1024 \
--pert.lime.neighbor-size 32768 \
--pert.lime.neighb... |
bb02f30ba7f2283dd18dd0f0fb715cd995f9a67d454567430292a4dd8c8cf2e6 | Shell | 522 | 17 | #!/bin/bash
# Copyright (c) OpenMMLab. All rights reserved.
WORKSPACE=mmdeploy-1.0.0-linux-x86_64-cxx11abi
export LD_LIBRARY_PATH=${WORKSPACE}/lib:${WORKSPACE}/thirdparty/onnxruntime/lib:$LD_LIBRARY_PATH
INPUT_IMAGE=$1
${WORKSPACE}/bin/pose_tracker \
${WORKSPACE}/rtmpose-ort/rtmdet-nano \
${WORKSPACE}/rtmpos... |
a4f9791236ab1eb29be55282930ca0a140ca74954ce63d43ce6967cb8d1b234e | Shell | 527 | 23 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
CONFIG=$1
CHECKPOINT=$2
GPUS=$3
NNODES=${NNODES:-1}
NODE_RANK=${NODE_RANK:-0}
PORT=${PORT:-29500}
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch \
--nnodes=$NNODES \
--no... |
0f28a84c8384c674f80c028a58775ab9643a2178f2acb60744834a854a7c22c8 | Shell | 531 | 16 | #!/bin/bash
#SBATCH --account=def-lpenacas
#SBATCH --time=20:00:00
#SBATCH --mem=128G
module load python/3.10.13
# virtualenv --no-download envr
# source envr/bin/activate
# pip install --no-index --upgrade pip
# pip install --no-index -r requirements.txt
source envr/bin/activate
# python integrate.py txid224308,txi... |
8f7ca8c9b3ebe9a7617cfcb437645cb38f07dcbac823306c086a8419372864d4 | Shell | 532 | 12 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-subset-sampling-sampler-subset-binary-head \
--head-trainer.train-num-steps 1000 \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-samplin... |
ec5528c43704ed4541cee3a6e92c25f47a4307f58dc7f0440ab4a3e5c33dd202 | Shell | 534 | 28 |
# Command to download dataset:
# bash script_download_SBMs.sh
DIR=SBMs/
cd $DIR
FILE=SBM_CLUSTER.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://data.dgl.ai/dataset/benchmarking-gnns/SBM_CLUSTER.pkl -o SBM_CLUSTER.pkl -J -L -k
fi
FILE=SBM_... |
32ced99ee288d4b016291bc7e60fff422a980a4e7dbf4053f0817ed5bcdf2901 | Shell | 535 | 15 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
for i in {5..9}
do
python perturb.py pancreas-subset-sampling-sampler-subset \
--pert.perturbation-num $i \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
--... |
c5216d99025f708a4af8c42098f8472452aa3cea4043d713bf37e004be2b14ee | Shell | 537 | 12 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-subset-sampling-sampler-subset-binary-head \
--head-trainer.train-num-steps 500 \
--pert.perturbation-num 10 \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-samp... |
8fb7376e4bcea7db3577d62f98fae7080805fed5832ad704b5f8fdc093474048 | Shell | 539 | 9 | python pretrain.py --model.raw_dataset_name dentategyrus \
--model.raw_dataset_path datasets/pretrain/dentategyrus/data/dentategyrus.h5ad \
--model.raw_dataset_use_cache \
--model.deepvelo_dataset_path datasets/pretrain/dentategyrus/data/dentategyrus.pth \
--model.deepvelo_dataset_use_cache \
--mode... |
4f476cb7e88caaf8824e0051d8548871552da76763bdecbde6af2cfdaf47ceca | Shell | 543 | 11 | export CUDA_VISIBLE_DEVICES=$1
python pretrain.py --model.raw_dataset_name pancreas \
--model.raw_dataset_path outputs/pretrain/pancreas/data/pancreas.h5ad \
--model.raw_dataset_use_cache \
--model.deepvelo_dataset_path outputs/pretrain/pancreas/data/deepvelo_dataset.pth \
--model.deepvelo_dataset_use_... |
cf86630ab731565c7f41720d0d7ceeb8a25f2743af880b14030cb7bd1d5f0142 | Shell | 543 | 15 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
for i in {1..9}
do
python perturb.py dentategyrus-subset-sampling-sampler-subset \
--pert.perturbation-num $i \
--pert.subset-sampling.lr 1e-3 \
--pert.subset-sampling.tau 0.1 \
... |
e022c9731bfc86f0d04e3d98116cb9a3e3792603a9dfc82c2139f6a6578a7d32 | Shell | 547 | 18 | #!/bin/bash -e
#SBATCH slurm/HPC parameters
#SBATCH --mem=88G
module add metaeuk/3-8dc7e0b
cd /path/to/folder
line=$(sed "${SLURM_ARRAY_TASK_ID}q;d" /path/to/metadata/files/run_kraken_md.txt) # contains accessions
sample_id=$(echo "$line")
if [ -d "$sample_id" ]; then
if [ -f "$sample_id/eukaryotic.fasta" ]; then
... |
53e22cd8071ec3fca104605ea39896dfb9c98eb30b0e1aac7e7b875c2ea292da | Shell | 550 | 11 | #!/bin/bash
declare -a ARCHS=("dilated_l1l2" "dilated_l1l4""dilated_l1l8" "dilated_l3l8" "dilated_l6l2" "dilated_l6l4" "dilated_l6l8" )
for t in "${ARCHS[@]}"
do
python /home/ubuntu/task-taxonomy-331b/tools/run_viz_notebooks_single.py --idx -1 --hs 8 --arch $t 2>&1 | tee $t.txt
done
# python /home/ubuntu/task-taxo... |
0bf40ae32c12951c4f349177de35655e64dc3cbe54924af36603a3c0b41ef074 | Shell | 556 | 20 | #!/bin/bash
conda activate adrd
ps=16
vs=128
bs=8
heads=6
embed_dim=384
n_samples=1000
dataset="NACC_raw_${n_samples}"
outdim=8192
# arch="vit_tiny"
# export LD_PRELOAD=tcmalloc.so:$LD_PRELOAD
data_path="SET/YOUR/DATA/PATH"
#CUDA_VISIBLE_DEVICES=0
OMP_NUM_THREADS=1 NCCL_DEBUG=INFO python -m torch.distributed.run --n... |
b522f9a26dd8cc379bd875439759f2159bd520fae4d3511c9003b1295d9a94db | Shell | 563 | 21 | #!/bin/bash
#SBATCH --partition=a100_short
#SBATCH --job-name=05b_assessHPC
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=20
#SBATCH --output=rq_05b_AssessHPC_%A_%a.out
#SBATCH --error=rq_05b_AssessHPC_%A_%a.err
#SBATCH --mem=50G
#SBATCH --gres=gpu:1
module load pathganplus/3.8.11
python3 run_representationsleiden_ev... |
73aafac7e44ca57895d0183406378488e483ab8ed33f321dc7c4d675be2a9fd0 | Shell | 564 | 19 | #!/bin/bash
TORCH=$1
CUDA=$2
# 10.2 -> cu102
MMCV_CUDA="cu`echo ${CUDA} | tr -d '.'`"
# MMCV only provides pre-compiled packages for torch 1.x.0
# which works for any subversions of torch 1.x.
# We force the torch version to be 1.x.0 to ease package searching
# and avoid unnecessary rebuild during MMCV's installatio... |
3b4c1d9b635cd4903d76a1656e65a8310234d6d22ab38b6a12748802c279c358 | Shell | 565 | 31 | #!/bin/bash
Help()
{
# Display Help
echo "Syntax: scriptTemplate [-r|t|o]"
echo "options:"
echo "r RNA-seq access codes separated by ','."
echo "t Taxonomy access code of organism(just number)."
echo "o Output directory."
}
while getopts t:r:o flag
do
case "${flag}" in
t) TAX... |
2d4a8a446486a050c125cb94a875baa53d884d91b32aaee0e0c9bb1c8ac954dc | Shell | 566 | 20 | # sh feat_extractor.sh
DATA=/path/to/datasets
OUTPUT='./clip_feat/'
SEED=1
# oxford_pets oxford_flowers fgvc_aircraft dtd eurosat stanford_cars food101 sun397 caltech101 ucf101 imagenet
for DATASET in oxford_pets
do
for SPLIT in train val test
do
python feat_extractor.py \
--split ${SPLIT} \
... |
3197147542cec3f5dce899aedaba3168e4a8ab09d717f94d0bd9725c1314627a | Shell | 569 | 25 | #!/bin/bash
##################################### Run CNV detection for a single line #####################
if [ -v $1 ]
then
echo 'to run this script you need bcftools (and R ?) globally installed'
echo 'this script should be run like this:'
echo './Cnv_Analysis_Single.sh <path folder> <path .txt file with SentrixB... |
ff319ea19814c8e93181ad593e9a6d275cacd26bb60a98cfa8749bd0183a0ce7 | Shell | 575 | 22 | #!/bin/bash
# reorient 2 mni standard space
# Assign options
while getopts ":i:o:" opt; do
case $opt in
i)
inpath=$OPTARG # input directory # /SeaExp_1/MRI_PET/GAAIN/processed/T1_nii or /SeaExp_1/MRI_PET/GAAIN/processed/Amyloid_nii
;;
o)
outpath=$OPTARG # output folder # /SeaExp_1/MRI_PET/... |
a87ee361215ffca0ba859bd3667a046a55eb8ed7610d0909c69d88b6e772dd04 | Shell | 576 | 15 | #!/bin/sh
SCRIPTSDIR=$(cd "$(dirname "$0")"; pwd)
BASEDIR="$(dirname "$SCRIPTSDIR")"
# DOCKER_IMAGE="docker.io/sebastientourbier/mialsuperresolutiontoolkit-bidsapp:v2.0.3"
DOCKER_IMAGE="sebastientourbier/mialsuperresolutiontoolkit-bidsapp:v2.0.3"
docker run -it --rm -u $(id -u):$(id -g) \
-v "$BASEDIR/data":/bids... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.