sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8f97543c518ccf12d60a38d7611c5410539c7cb526be807206d03003fdf586de | Shell | 274 | 10 | #!/bin/bash
FILE=${1}
MAXPROCS=${2}
if [ "x$MAXPROCS" == "x" ]; then
MAXPROCS=1
fi
#parallel implementation
cat ${FILE} | xargs -n 1 -P ${MAXPROCS} -I sub /net/parasite/SchizConnect/Scripts/AROMA/AROMA.sh /net/parasite/SchizConnect/Sites/COBRE/sub/func/s6w3rtrest.nii
|
aa686ed0c414af3332499ae4b1df1c7b0e04dc3bc702eb0bca5fda4fa369fd58 | Shell | 274 | 13 | #!/bin/bash
set -euo pipefail
SOURCE_IMAGE="$1"
DEST_IMAGE="$2"
ECR="$3"
# publish anyscale image
aws ecr get-login-password --region us-west-2 | \
docker login --username AWS --password-stdin "$ECR"
docker tag "$SOURCE_IMAGE" "$DEST_IMAGE"
docker push "$DEST_IMAGE"
|
40d25698f1d8d53f2ccc1ee24d3c1cd0b32e02d293df94db344cd97223969a6c | Shell | 275 | 12 | #!/bin/bash
log_dir="LOG"
mkdir $log_dir
LO_BND=$(($1))
UP_BND=$(($2-1))
for (( sim=$LO_BND; sim<=$UP_BND; sim++))
do
echo $sim
rm "$log_dir/$sim.out" "$log_dir/$sim.err"
python simulateNetworks_hpc.py $sim $2 $3>"$log_dir/$sim.out" 2>"$log_dir/$sim.err" &
done
|
1fc4dd2580c3d128e31904091d603c9ad3736d6c37866535cee9c3a9fa327c49 | Shell | 277 | 15 | #!/bin/bash
cd ..
git clone --branch py-spinnaker2 git@gitlab.com:tud-hpsn/internal/qpe-software-fixed.git
cd qpe-software-fixed
source quadpe_env.sh
cd host-software/rev2/experiment/app/
cmake CMakeLists.txt
make
cd -
cd host-software/rev2/experiment/
./make_all_apps.py
|
26b002d9444ad0206cc427c866f0d2ffc7ed296fd8024a2854686f1ab07f190c | Shell | 277 | 10 | #!/bin/bash
FILE=`basename $1`
DIR=`dirname $1`
cd ${DIR}
export FSLOUTPUTTYPE=NIFTI
echo ${1}
fsl_regfilt --in=${FILE} --design=ICA_AROMA/melodic.ica/melodic_mix --filter="`cat ICA_AROMA/classified_motion_ICs.txt`" --out=ICA_AROMA/unsmoothed_denoised_func_data_nonaggr.nii
|
28dbaea40c7fce299ca76a5fb64b906ff8044a15e20fdeea4698afee41f25e36 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=4,5 python train.py --exp newnet_resnet18_64_newHead_m3_a1 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 3 --feature_accumumation_start 1
|
3cc71687ddfae12aef9a1167217bd907199e8f2a9b9e95b734d39e0441f1dc36 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=4,5 python train.py --exp newnet_resnet18_64_newHead_m2_a3 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 2 --feature_accumumation_start 3
|
3fb36db5b1880fac1d7e84391605a19434d3206e9c8d434ec413ad577048dbe6 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=6,7 python train.py --exp newnet_resnet18_64_newHead_m2_a4 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 2 --feature_accumumation_start 4
|
403363a9b05b00aa8a03208e578b5852b1d2d26cafb5bf2e1414860281a9bb90 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=0,1 python train.py --exp newnet_resnet18_64_newHead_m2_a1 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 2 --feature_accumumation_start 1
|
80b4f4cc0d941b8ddeab842ec6852e890ee79022442cdefe368baf0d5b0d3b44 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=2,3 python train.py --exp newnet_resnet18_64_newHead_m2_a1 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 2 --feature_accumumation_start 1
|
da550351a29fedb8e174853cd51262999b4cd2091eeee485e045def0b5d7c5f5 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=2,3 python train.py --exp newnet_resnet18_64_newHead_m2_a2 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 2 --feature_accumumation_start 2
|
ecd4784233f6c713909a602774a3ea30c94a71fb561e26a7666be6e9b5149b04 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=6,7 python train.py --exp newnet_resnet18_64_newHead_m4_a1 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 4 --feature_accumumation_start 1
|
fd30f4c62471837b604ced074a5be8fc461964dc9e353edd4dbed70c8319bff2 | Shell | 278 | 3 | cd ..
CUDA_VISIBLE_DEVICES=0,1 python train.py --exp newnet_resnet18_64_newHead_m1_a1 --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN --backbone 'resnet18' --feature_channel 64 --feature_map_size_layer 1 --feature_accumumation_start 1
|
006b165b6f27d55a40a2877c4d8458cd2e2a17bf19678ba71a2c42330f7e1282 | Shell | 279 | 12 | #!/usr/bin/bash
module load tools computerome_utils/2.0
module load anaconda3/2023.03
module unload gcc
module load gcc/11.1.0
module load minimap2/2.17r941 samtools/1.10
source ~/.bashrc
conda init bash
conda activate /home/projects/cpr_10006/people/svekut/.conda/vamb
./$1 $2 |
d09addfe1227bfe5e1f6801669242881fece40f2c20ff797a3405e3a1d6eb22c | Shell | 279 | 11 | #!/bin/bash
source 00_global_variables.sh
OUT=PRS
KoGES_bim=KCHIP.snp.imputed.HRC.rsID.rmdup
for idx in "${!Phenotypes[@]}"
do
outDIR="${OUT}/${Phenotypes[$idx]}"
cd ${outDIR}
plink --bfile ${KoGES_bim} --score ./score_sum.txt 2 4 6 sum --out ./score_sum_indiv
done
|
e6465826f6c336775885902f4464e3e8c4293fde3185c0301277a15437fc302a | Shell | 280 | 9 | #!/bin/sh
#$ -S /bin/sh
#Version1.0 hewm@genomics.org.cn 2020-01-10
echo Start Time :
date
g++ -static -g -O2 LDBlockShow.cpp -lz -L/usr/lib/ -L./include/zlib/ -L /hwfssz4/BC_PUB/Software/01.Usr/lib/ -o ../bin/LDBlockShow_linux -lm -lc
chmod 755 ../bin/*
echo End Time :
date
|
bd353c3decbd9642e4c38725a885f5e2dbd9d9ace88a79288e093373bfd562b3 | Shell | 281 | 7 | mkdir ~/envs
virtualenv --no-download ~/envs/lop
source ~/envs/lop/bin/activate
pip3 install --no-index --upgrade pip
pip3 install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cpu
pip3 install -r requirements.txt
pip3 install -e . |
01075347f4ec59d8ca09b70e653885451c9f7f0727ad63c2c76a0c1b32b7b790 | Shell | 283 | 14 | #!/usr/bin/env bash
set -e
set -o pipefail
if [ -z "$PYTHON_BIN_PATH" ]; then
PYTHON_BIN_PATH=$(which python3 || which python || true)
fi
# Set all env variables
CONFIGURE_DIR=$(dirname "$0")
"$PYTHON_BIN_PATH" "${CONFIGURE_DIR}/configure.py" "$@"
echo "Configuration finished" |
b38e4fdbe13c063d8f34d6e6049863c30ed9a3312181b2b38e409861d561ffe5 | Shell | 283 | 5 | #!/bin/bash
pip install -e packages/fairchem-core[docs,adsorbml,quacc] -e packages/fairchem-data-oc[dev] -e packages/fairchem-applications-cattsunami jupytext
# Convert all .md docs to ipynb for easy viewing in vscode later!
find ./docs -name '*.md' -exec jupytext --to ipynb {} \;
|
da495cdfa1910eb64b3638c73689dc5f4b6015b2cc976204d8a1c1386be6e795 | Shell | 285 | 4 | #!/bin/bash
podman run --rm --security-opt label=disable -v Native:/ref -v Prediction:/mod -v Output:/out -it openstructure:latest compare-structures --reference /ref/6YMC_A_native.pdb --model /mod/6YMC_A_DeepFoldRNA.pdb --output /out/output.json --lddt --lddt-no-stereochecks &
wait
|
08435f890c7496dc00d151a7d2ce3769f5ff0350e0501d850a176371f624d634 | Shell | 286 | 12 | #!/usr/bin/bash
module load tools computerome_utils/2.0
module load anaconda3/2023.03
module unload gcc
module load gcc/11.1.0
module load minimap2/2.17r941 samtools/1.10
source ~/.bashrc
conda init bash
conda activate /home/projects/cpr_10006/people/svekut/.conda/vamb
./$1 $2 $3 $4
|
2702b64f6838535e36041fdf199d88f08f265af50648d7fde6d75c842e0f1ee1 | Shell | 286 | 8 | #!/usr/bin/env bash
set -e
# install all unbounded dependencies in setup.py and any additional test dependencies
# for the min build for ray core
DEPS=(requests protobuf pytest-httpserver==1.1.3 jsonschema==4.23.0)
python -m pip install -U --pre --upgrade-strategy=eager "${DEPS[@]}"
|
cea2c4d366528ddf598209befdd879d10701f9d4659b882ef1a035c08f73f156 | Shell | 286 | 2 | gffread -w transcripts_chr1_chr9.fa -g ../../Dataset/GCF_000001405.40_GRCh38.p14_genomic.fa ../../Dataset/refseq_chr1_chr9_transcripts.gff
gffread -w transcripts_chr1_chr9_rev.fa -g ../../Dataset/GCF_000001405.40_GRCh38.p14_genomic.fa ../../Dataset/refseq_chr1_chr9_rev_transcripts.gff
|
d051c2fc63fceb5a535e84c8a6656b89ffdec76bd49314278b9ce7b9aa3187df | Shell | 290 | 11 | #!/usr/bin/env bash
DISK=${1:-data}
shift 1
PROJECT=${1:-broad-ml4cvd}
shift 1
VMS=$(gcloud compute instances list --project ${PROJECT} | awk '{print $1}')
ZONE=us-central1-a
for VM in $VMS;
do gcloud compute instances detach-disk $VM --project ${PROJECT} --zone $ZONE --disk=$DISK;
done |
6fe2961c839a8cbd2ea0fce9241433cd1eb82dda2db051e05b1bd2bb0559bbac | Shell | 291 | 3 | SOURCE_FILES="gui.c gui_colors.c gui_window_helpers.c gui_mesh_helpers.c gui_draw.c raylib_ext.c"
HEADER_FILES="gui.h gui_colors.h gui_window_helpers.h gui_mesh_helpers.h gui_draw.h raylib_ext.h ../3dparty/raylib/src/extras/raygui.h"
COMPILE_STATIC_LIB "gui" "$SOURCE_FILES" "$HEADER_FILES"
|
a8b407ff5e5b40796c5a77a20c50cd43f921a9b946a28c47d33c198cf7b61da0 | Shell | 292 | 8 | #!/bin/bash
set -e
LOCAL_DIR="your/path/to/BOLDMomentsDataset"
mkdir -p "${LOCAL_DIR}/derivatives/Temporal_Shift_Module_DNN"
aws s3 cp --no-sign-request --recursive \
"s3://openneuro.org/ds005165/derivatives/Temporal_Shift_Module_DNN/" \
"${LOCAL_DIR}/derivatives/Temporal_Shift_Module_DNN/" |
0709a8bfe233421b86eee9a92135c27bf5f4a23704fa9ea2f292b0f2a8669218 | Shell | 293 | 4 | for sample in R2826 R2835 R2839 R2845 R2855 R2857 R2869 R2874 R2894 R2895 ; do
echo samtools sort ../results/polyA/$sample/cleaned.bam -o ../results/polyA/$sample/cleaned.sort.bam &
samtools sort ../results/polyA/$sample/cleaned.bam -o ../results/polyA/$sample/cleaned.sort.bam &
done
|
4c6661a399bd8722d2aed4a7abc7e62901a1ef9c115caeb4f4521499bab6be68 | Shell | 294 | 8 | #!/bin/bash
dwi=`jq -r '.dwi' config.json`
bvals=`jq -r '.bvals' config.json`
bvecs=`jq -r '.bvecs' config.json`
shell=`jq -r '.shell' config.json`
dwiextract ${dwi} dwi.nii.gz -singleshell -shells 0,${shell} -fslgrad ${bvecs} ${bvals} -export_grad_fsl dwi.bvecs dwi.bvals -force -nthreads 8
|
68c3f0d7e2b203a2171e632ba3de83760c472216303d73e38bd909e7c6b5f7c6 | Shell | 297 | 20 | #!/bin/bash
subj_id=99
lr=1e-3
update_freq=100
dim_hidden=16
dim_context=16
ctx_wt=.5
penalty_new_context=.5
stickiness=.5
sbatch train.sh \
${subj_id} \
${lr} \
${update_freq} \
${dim_hidden} \
${dim_context} \
${ctx_wt} \
${penalty_new_context} \
${stickiness}
|
8b07a76ca2efb5e0a4c013654fdae350fd74730acda470f4aec885cc28c831ff | Shell | 301 | 2 | grep -E "^NC_000001.11|^NC_000009.12" ../../Dataset/refseq_GCF_000001405.40_GRCh38.p14_genomic.gff > ../../Dataset/refseq_chr1_chr9_transcripts.gff
grep -Ev "^NC_000001.11|^NC_000009.12" ../../Dataset/refseq_GCF_000001405.40_GRCh38.p14_genomic.gff > ../../Dataset/refseq_chr1_chr9_rev_transcripts.gff
|
8f01243223992dfeffcaf908540a4d5774e628dd6afc003f6ce27282db6416db | Shell | 303 | 8 | for f in geneset/*.gs.batch; do
prefix="$(basename -- $f)"
prefix=${prefix::-9}
echo $prefix
n_batch=$(ls -l geneset/${prefix}.gs.batch | wc -l)
sbatch --array 0-$((n_batch-2)) compute_score.sh ${prefix} vs
sbatch --array 0-$((n_batch-2)) compute_score.sh ${prefix} uniform
done
|
986a667a47c63ae60e92e4358b3cfe15b40c2d1622751606248b49eb1fc8c337 | Shell | 303 | 4 | #!/bin/bash -ue
mkdir demux_dash
cp -R /net/trapnell/vol1/home/readdf/bin/bbi-dmux/bin/skeleton_dash/* demux_dash/
generate_html.R "." --p7_rows "D" --p5_cols "4" --p7_wells "0" --p5_wells "0" --level "2" --project_name "2022_01_11_Dana_and_HM11_nobackup" --sample_sheet "good_sample_sheet.csv"
|
2a4cb523f41b970d595fa32b1ad1fabcdbd3dd925adf70da848418e8c0d7d710 | Shell | 307 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Liver/Liver
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Liver \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
--thread-num 5
|
c573dbb5cae8ef013d340840e5b0b562c79cb0a4585c9451d4fe58abd47e5819 | Shell | 307 | 13 | for tis in Brain Lung Muscle Heart; do
for n in `seq 22`; do
python Pair_phenotypes.py $tis m6A ha $n
python Pair_phenotypes.py $tis ha m6A $n
done
done
for tis in Lung Muscle; do
for n in `seq 22`; do
python Pair_phenotypes.py $tis m6A me $n
python Pair_phenotypes.py $tis me m6A $n
done
done
|
490b971d580c20c837e55feff0960a096bd8a60cf00288d3433b68e58ea35d7a | Shell | 308 | 7 | #!/bin/bash
UKB_bim=ukb_eur_unrel_comb
plink --bfile ${UKB_bim} --score ./score_sum_F1.txt 2 4 6 sum --out ./score_sum_indiv_F1
plink --bfile ${UKB_bim} --score ./score_sum_F2.txt 2 4 6 sum --out ./score_sum_indiv_F2
plink --bfile ${UKB_bim} --score ./score_sum_F3.txt 2 4 6 sum --out ./score_sum_indiv_F3
|
004883b8edf47b7127e279c63de7772f1a81166ede13b0b94b623cf19b7b5d8e | Shell | 310 | 11 | #!/bin/bash
#SBATCH --job-name=mediumjob
#SBATCH --account=theory
#SBATCH -c 8
#SBATCH --time=0-11:59:00
#SBATCH --mem-per-cpu=6gb
#SBATCH --array=0-{n_tot}%50
module load anaconda/3-2020.11
source activate pete
python {file_dir}/run_comparison.py $SLURM_ARRAY_TASK_ID {dset_idx}
# python -c "print('success')" |
1179f4ca12d3b7eabdf02c8898639f965a53c20ef76cd4245cd21abbaf6eea9f | Shell | 310 | 5 | python train.py --exp dm_new_net_addBN_addLeaf --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net DeepLabv3_modified_addBN
python train.py --exp HED_fc64_multiLoss --epochs 5000 --batch_size 10 --base_lr 0.0001 --workers 8 --net HED --backbone 'resnet18' --feature_channel 64 --multiLoss True
|
4e0a4ce3a2035e565e4f05a67f5bb8f4c09ba447b881a5cedb2647274d777e22 | Shell | 310 | 10 |
cd /nfs/turbo/lsa-csmonk/FF_Data/Working_Files/Resting/Subjects/
for sub in *
do
echo $sub
gunzip $sub/func/run_01/ICA_AROMA/denoised_func_data_nonaggr.nii.gz;
mv $sub/func/run_01/ICA_AROMA/denoised_func_data_nonaggr.nii $sub/func/run_01/ICA_AROMA/sdenoised_func_data_nonaggr.nii
done
|
c78fcfebce908bf3f223ad9ec7c2f264308adf197b83f19fbc3b3e689c8e1409 | Shell | 312 | 10 | plink_dir="/path/to/plink/v1.9"
geno_dir="/path/to/genotype/beds"
out_dir="/out_dir" # output directory
for chr in `seq 1 22`; do
${plink_dir}/./plink --bfile ${geno_dir}/chr${chr}.topmed.hg38 \
--score /path/to/polygenic/score/weights/${file_in} \
sum \
--out ${out_dir}/plink_prs_${file_out}_chr${chr}
done |
43a4f87a985ee7ce86bcffef29f3431b2b57eacf5f8b91f3432c9f6d0e94a19a | Shell | 313 | 15 | #!/bin/bash
# Exit if not found
set -e
# Files that were modified in this commit
FILES_CHANGED=$(git diff --name-only HEAD~1)
echo "Files changed in current commit:"
echo ${FILES_CHANGED}
# Exit if files
for file in $@
do
echo ${FILES_CHANGED} | grep -q $file || ( echo "$file not in commit"; exit 1 )
done
|
df59de02e465d6356e8088665ca11c7b9117f1ec24210fa34557e93c72e07195 | Shell | 315 | 11 | #!/bin/bash
#SBATCH --job-name=bigjob
#SBATCH --account=theory
#SBATCH -c 8
#SBATCH --time=0-11:59:00
#SBATCH --mem-per-cpu=6gb
#SBATCH --array=0-{n_tot}%50
module load anaconda/3-2020.11
source activate pete
python {file_dir}/run_experiment.py $SLURM_ARRAY_TASK_ID {n_dat} {dset_idx}
# python -c "print('success')" |
7c05c115d3c010bed50d4d2bd5bec1cbdc34fd4668b7e9550049dc3ea8490b50 | Shell | 317 | 15 | #!/bin/bash
export PATH=$PATH:~/github/ProjectAPI/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/LEIFER/Mochi/APIData/ $start_point |
f0e35124b67c8b124f2e38cd950b435d1f0a6ab611fe831c75eab234b95ae122 | Shell | 317 | 12 | #!/bin/bash
# 'KAS-Analyzer uninstall' was developed by Ruitu Lyu on 1-24-2022.
conda deactivate
CONDA_ENV_PY3=KAS-Analyzer
CONDA_ENV_OLD_PY3=KAS-Analyzer-python3
conda env remove -n ${CONDA_ENV_PY3} -y
conda env remove -n ${CONDA_ENV_OLD_PY3} -y
echo "'KAS-Analyzer' conda environment was successfully removed!"
|
0d25ea173931d92f3b1e3cd7e62a7ffc1d902e210ac038be89a7b477a25d5ba6 | Shell | 318 | 12 | #!/bin/bash
# Install the github repo version
git clone https://github.com/fastai/fastai
cd fastai
# Peg our version to a known-working SHA, since they make
# post-1.0 breaking changes literally every day...
git reset --hard 14868ca69483afbaa8e28d4e281c148d1dad1c89
tools/run-after-git-clone
pip install -e .[dev]
|
7a141c64ec2f0b2d28dc41f977cd591c9511a8a6bb385ffa776ba6609c914529 | Shell | 318 | 4 | #! /bin/bash
Rscript /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_11_08_motifInPeaksRegression_nobackup/runMotifCelltypeRegression.R --cellType $1 --fixedEffects $2 --pvalToUse $3 --matrixPath /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_01_20_Vary_Motif_Pval_nobackup/fileOutputs/
|
04789307f2987d03c56b2b28c483aa70d55dda1609bb416056897cc20ea922fc | Shell | 320 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Brain_Cortex.lite
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Brain_Cortex \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
--thread-num 5
|
cfb1f8dbc5c4c70ba552d5621b3de0a22ff6e45ebcb2e424f176933569ce419e | Shell | 320 | 10 | for split in 'random' 'complex_disease' 'cell_proliferation' 'mental_health' 'cardiovascular' 'anemia' 'adrenal_gland' 'autoimmune' 'metabolic_disorder' 'diabetes' 'neurodigenerative'
do
for seed in 1 2 3 4 5
do
for model in TxGNN
do
python train.py --device $1 --seed $seed --split $split --model $model
done
done
done |
fba556cbb10ea4d40a7528863da47008da423f6942fd87b9a71d85c58b747848 | Shell | 320 | 3 | ##first thing is to combine the ligand and protein structure file before docking
cat pro.pdb LIG_0001.pdb > complex.pdb
rosetta_scripts.mpi.linuxgccrelease -s complex.pdb -parser:protocol dock.xml -extra_res_fa LIG.params -nstruct 200 -ex1 -ex2 -use_input_sc -flip_HNQ -no_optH false -overwrite -ignore_unrecognized_res
|
77b09d89cca15cb3dc1d82d73fa1dbad87ef5682a01fe16f20e351d32f31d491 | Shell | 321 | 10 | #!/bin/bash
FILE=${1}
MAXPROCS=${2}
if [ "x$MAXPROCS" == "x" ]; then
MAXPROCS=1
fi
#parallel implementation
cat ${FILE} | xargs -n 1 -P ${MAXPROCS} -I FILE /nfs/csmonk-lab/FF_Data/Working_Files/Resting/Scripts/AROMA/AROMA.sh /nfs/csmonk-lab/FF_Data/Working_Files/Resting/Subjects/FILE/func/run_01/s6w3rtprun_01.nii... |
102de267eade10f1a1ab52631e517824070ea9b5b49b9d7e7a387f82acb173d8 | Shell | 323 | 4 | for sample in R2826 R2835 R2839 R2845 R2855 R2857 R2869 R2874 R2894 R2895 ; do
echo samtools sort ../results/polyA/$sample/cleaned_2stage.bam -o ../results/polyA/$sample/cleaned_2stage.sort.bam &
samtools sort ../results/polyA/$sample/cleaned_2stage.bam -o ../results/polyA/$sample/cleaned_2stage.sort.bam &
do... |
f837f168d6205c7d5eadc5e7bc502df3208a8cb02a27b7a41fbcad45fe1416fe | Shell | 323 | 4 | for sample in R12258 R12260 R12263 R12265 R12266 R12277 R12278 R12280 R12285 R12287; do
echo samtools sort ../results/ribozero/$sample/cleaned.bam -o ../results/ribozero/$sample/cleaned.sort.bam &
samtools sort ../results/ribozero/$sample/cleaned.bam -o ../results/ribozero/$sample/cleaned.sort.bam &
do... |
8735b378a53e161c03c15f073c8da17fdb69de0f429482a5da172cb76b307b6b | Shell | 325 | 7 | #!/bin/bash
echo ${1}
DIR=`dirname ${1}`
FILE=`basename ${1}`
/usr/bin/python2.7 /home/slab/users/mangstad/repos/ICA-AROMA/ICA_AROMA.py -in ${DIR}/${FILE} -out ${DIR}/ICA_AROMA -mc ${DIR}/rp*.txt -m /net/parasite/SchizConnect/ROIs/rs_spm12_mni_icbm152_nlin_sym_09b_1xero_s8_thrp35_mask.nii -tr 2 -den both > $DIR/aroma... |
4e20dfc88337b735322c77fc14cc37af34b29901ba15262f8bb97089775daf1a | Shell | 326 | 12 | #!/bin/bash
set -Ceu
outdir=$(mktemp -d)
gpu="$1"
# gym/dqn
python examples/gym/train_dqn_gym.py --steps 100 --replay-start-size 50 --outdir $outdir/gym/dqn --gpu $gpu
model=$(find $outdir/gym/dqn -name "*_finish")
python examples/gym/train_dqn_gym.py --demo --load $model --eval-n-runs 1 --outdir $outdir/temp --gpu... |
00b9cfada6bcd69be4c9d15ab2d4fe75b11bf6dd03f9df48330a76e9a15024e2 | Shell | 327 | 14 | #!/bin/bash -e
git checkout master
ver=$(fgrep '#define GCLIB_VERSION ' GBase.h)
ver=${ver#*\"}
ver=${ver%%\"*}
#git fetch --tags
if [[ "$1" == "delete" || "$1" == "del" ]]; then
echo "Deleting tag v$ver .."
git tag -d v$ver
git push origin :refs/tags/v$ver
exit
fi
git tag -a "v$ver" -m "release $ver"
git push ... |
a86b841edc553c5d3b4813e7190a19111623dc0dfb86375b87a4d5ce89d78867 | Shell | 327 | 18 |
export ROOTPATH=$1
export ROOTSYS=$ROOTPATH/bin/root-config
export PATH=$ROOTSYS:$PATH
export THISROOT=$ROOTPATH/bin/thisroot.sh
echo $THISROOT
source $THISROOT
binFolder="$PWD"
testFolder="`dirname \"$0\"`"
cd $testFolder
export TERM="xterm"
$binFolder/Gate mac/carbon.mac
if [ $? -ne 0 ]; then
exit 1
fi
cd $binFo... |
5724b6d2451169db812c9ca308068837a8540ab1079233e155498961e0825cb0 | Shell | 328 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Artery_Aorta/Artery_Aorta
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Artery_Aorta \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
--thread... |
39d3ae12fd3c31b9a6ef71d2f18074ab69664448fcd2756c95fa84d3a5aa93c1 | Shell | 329 | 7 | #! /bin/bash
cp ~/../gtb7/git/sciatac_pipeline/src/sylvia_scripts/scripts/atac_helper_functions.R /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/misc/atac_helper_functions.R
cp ~/../gtb7/genomes/GRCh38/annotations/EncodeBlacklist/ENCFF356LFX.bed /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/misc/ENCFF356... |
647612c9350488c1fec872e789b3161f0c7ab1a66b1d7bc58b5e36eb52c4f83d | Shell | 330 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Artery_Tibial/Artery_Tibial
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Artery_Tibial \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
--thr... |
70c7e27f244a0e9c59b956b4247e9690534b98d4574c35eb53bc4898c16c31e5 | Shell | 330 | 7 | #!/bin/bash
KoGES_bim=KCHIP.snp.imputed.HRC.rsID.rmdup
plink --bfile ${KoGES_bim} --score ./score_sum_F1.txt 2 4 6 sum --out ./score_sum_indiv_F1
plink --bfile ${KoGES_bim} --score ./score_sum_F2.txt 2 4 6 sum --out ./score_sum_indiv_F2
plink --bfile ${KoGES_bim} --score ./score_sum_F3.txt 2 4 6 sum --out ./score_sum... |
826dbad5c814b648f29e6d3c58c5f125a734f72cd653e172484d071260192be8 | Shell | 330 | 12 | #!/bin/bash
folder_name=$1
script_name=$2
cd ~/github/ProjectAPI
# 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 $((RANDOM % 100))
/usr/licensed/bin/matlab-R2016a -nodisplay -nosplash -r "call_function('$script_name;$folder_name'... |
68586fb90531acf0d5c9ace6a0cac5f750551569db790e93fe5c7ce896318837 | Shell | 331 | 10 | #! /bin/bash
for eachType in Adipocytes Endothelium Fibroblast Lymphocyte Neuron Perivascular Ventricular_Cardiomyocytes Mast_Cell Lymphatic_Endothelium Myeloid
do
qsub -l mfree=80G -l centos=7 /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_09_02_DE_Testing_nobackup/runSingleBulkDE.sh eachType
d... |
9b1bed52ec9a46a894dd370f368f7c630588b13a79f738376dae0968ced7e3f6 | Shell | 332 | 16 | #!/usr/bin/env bash
ROOT="$(realpath "$(dirname "$0")")"
function localexec(){
prog="$1"
shift
PORTDIR_OVERLAY="$ROOT" DOTGENTOO_PACKAGE_ROOT="$ROOT/../" $prog "$@"
}
EBUILD="$(find "${ROOT}" -name "*.ebuild" | head -n1)"
echo "Installing ebuild $EBUILD"
localexec ebuild "$EBUILD" manifest
localexec emerge "$@"... |
c80000411a8b3a371f765ac087155c2fcba8d7517a746e66338b227422619812 | Shell | 334 | 13 | #!/bin/bash
SNPlist=w_hm3.snplist
SUMSTAT=MetS_GWAS.csv
EffN=1384348
MungeName=MetS_GWAS_munge
python2 ./ldsc/munge_sumstats.py --sumstats ${SUMSTAT} \
--N ${EffN} \
--out ${MungeName} \
--merge-alleles ${SNPlist} \
--chu... |
d24e42c4a0dc35451393d1a705fe11ae424bd40468834d1b484741393da0aae3 | Shell | 336 | 9 | #!/bin/bash
python3 -m venv /home/$USER/.virtualenvs/marugoto
source /home/$USER/.virtualenvs/marugoto/bin/activate
git pull
python3 -m pip install --upgrade pip
pip3 install .
pip3 install openpyxl
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
python3 marugoto/gui/... |
003fe58271c09b4dd65fa893208e89941b9246ddf0d9f4c640c45c93ef2fa513 | Shell | 337 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Artery_Coronary/Artery_Coronary
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Artery_Coronary \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
... |
499df6ff8d17fafe969ecc0fe491f3eef09f3f0d5f99448051671b602e7ead69 | Shell | 337 | 16 | #!/bin/bash
# Identify binary files in the repository:
list_files() {
git ls-tree --full-tree --name-only -r HEAD \
| xargs -n1 grep -IL . \
| xargs -n1 wc -c \
| grep -v '^0 '
}
NUM_FILES=$(list_files | wc -l)
if [[ ${NUM_FILES} -ne 0 ]]; then
echo "Repository contains binary files:"
list_fil... |
96090615ae790ae47d17d475e7a6c2a86dbff40a833152480ac08e378a3aa034 | Shell | 337 | 12 | #!/bin/bash
set -Ceu
outdir=$(mktemp -d)
gpu="$1"
# atari/a2c
python examples/atari/train_a2c_ale.py --env PongNoFrameskip-v4 --steps 100 --update-steps 50 --outdir $outdir/atari/a2c
model=$(find $outdir/atari/a2c -name "*_finish")
python examples/atari/train_a2c_ale.py --env PongNoFrameskip-v4 --demo --load $model... |
d9841fea6567b2b2a8f9720a554f4fd8593542625da9c4573ef5cad6b05fec46 | Shell | 337 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Muscle_Skeletal/Muscle_Skeletal
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Muscle_Skeletal \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.2 \
... |
2b049750629c041722d525f0e74bdef8657bf9db0a1e6f25f74ef6276bcc7992 | Shell | 338 | 2 | #!/bin/bash -ue
check_sample_sheet.py --sample_sheet /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_01_11_Dana_and_HM11_nobackup/Hm11_dana_test_samplesheet.csv --star_file star_file.txt --level 2 --rt_barcode_file /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/misc/RT_2level_indices_plate_format --max_... |
388423d579d79ccabae8cf7e4d0e1cd7ab2c43d1da5ecd1a1e0a428d22d87e41 | Shell | 338 | 15 | #! /bin/bash
# 8-25-21: Fixed failure to reverse complement sequence
python fitCNN_model.py --featureSet Promoter_Only
python fitCNN_model.py --featureSet Promoter_Only --cnnStruct 21filt_pool100_thenDense
python fitCNN_model.py --featureSet Promoter_and_Distal --cnnStruct 21filt_pool100_thenDense -... |
def313002f1a229d1369ac9740832006879a4afd8927433e9dd95782fa59afdc | Shell | 340 | 10 | #!/bin/bash
source 00_global_variables.sh
MungeName=MetS_GWAS_munge
LDSCName=MetS_GWAS_ldsc
python2 ${src_LDSC} --h2 ${MungeName}.sumstats.gz \
--ref-ld-chr ./1000G_Phase3_ldscores/LDscore. \
--w-ld-chr ./1000G_Phase3_weights_hm3_no_MHC/weights.hm3_noMHC. \
... |
56ec305dd39e5551e7a3267f3ad072e1d053ce20676db36366f32f41831b1f30 | Shell | 342 | 13 | _args() {
_init_completion -n 2> /dev/null
local program comparg
program="${COMP_WORDS[0]}"
comparg="--complete" # replace this with your flag
COMPREPLY=($("$program" "$comparg" bash "$COMP_CWORD" "${COMP_WORDS[@]}" 2> /dev/null))
[[ $COMPREPLY ]] && return
_filedir
}
complete... |
92ce520a21753f43d9dec39e0b85b3c8e7500002048deee215f9bf902fb52878 | Shell | 343 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Brain_Frontal_Cortex_BA9.lite
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Brain_Frontal_Cortex_BA9 \
--smr-multi \
--set-wind 1000 \
--ld-multi-snp 0.... |
693e7b0add0f7863eca01157dfa6210c32284a3574842c7e30b3ccf919c73541 | Shell | 347 | 12 | #!/bin/bash
set -Ceu
outdir=$(mktemp -d)
gpu="$1"
# gym/reinforce
python examples/gym/train_reinforce_gym.py --steps 100 --batchsize 1 --outdir $outdir/gym/reinforce --gpu $gpu
model=$(find $outdir/gym/reinforce -name "*_finish")
python examples/gym/train_reinforce_gym.py --demo --load $model --eval-n-runs 1 --outd... |
776d02def671d5d66b5b3ba365b16bf1aa3737cf3a7d7d7200a5828d1b835090 | Shell | 347 | 14 | #!/usr/bin/env bash
# Source this once
# Exit and reconnect after adding any groups
# Allow this user to run docker images without sudo
sudo usermod -aG docker $(whoami)
# Use the docker-credential-gcr that we installed on bootup
docker-credential-gcr configure-docker
# Install pre-commit
sudo apt install python3-p... |
38c7b45b831fe606d831e31501aa43fec1dbfb36b24415bc7ff7aab6d0e7729a | Shell | 350 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=METSIM_adipose_ciseQTLs_summaryStats_all
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.METSIM.Adipose_Subcutenuous \
--smr-multi \
--set-wind 1000 \
--ld-multi... |
e1dfce813008816cf6351bb264d6bfbeaf7b271f240a81adde4d88deab58dfe4 | Shell | 350 | 12 | #!/bin/bash
set -Ceu
outdir=$(mktemp -d)
gpu="$1"
# grasping/dqn
python examples/grasping/train_dqn_batch_grasping.py --gpu $gpu --steps 100 --outdir $outdir/grasping/dqn
model=$(find $outdir/grasping/dqn -name "*_finish")
python examples/grasping/train_dqn_batch_grasping.py --demo --load $model --eval-n-runs 1 --o... |
c1c758bb962ad92a9fb96eb641164c96e7a9cadfb5685a461d0d1bc0cf9c6492 | Shell | 351 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./FUSION.Skeletal_muscle/qtltools_nominal.self
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.FUSION.Muscle_Skeletal \
--smr-multi \
--set-wind 1000 \
--ld-mult... |
f90c38f1806dc383231e4397322eb1d7e6475f09d18f10f88c7e0a61605ebee0 | Shell | 351 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Adipose_Subcutaneous/Adipose_Subcutaneous
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Adipose_Subcutaneous \
--smr-multi \
--set-wind 1000 \
--ld-mult... |
989400bd7c535010a072ca1c18b49a86940d3099a20697563c463ca6b61ecd89 | Shell | 353 | 4 | for sample in R12258 R12260 R12263 R12265 R12266 R12277 R12278 R12280 R12285 R12287; do
echo samtools sort ../results/ribozero/$sample/cleaned_2stage.bam -o ../results/ribozero/$sample/cleaned_2stage.sort.bam &
samtools sort ../results/ribozero/$sample/cleaned_2stage.bam -o ../results/ribozero/$sampl... |
c91748633cb2edcbedf95fa4e3db7b5dc80de71eab10caa5a9a24773090f8193 | Shell | 357 | 10 | python main_oc20.py \
--distributed \
--num-gpus 8 \
--num-nodes 2 \
--mode train \
--config-yml 'oc20/configs/s2ef/2M/equiformer_v2/equiformer_v2_N@12_L@6_M@2.yml' \
--run-dir 'models/oc20/s2ef/2M/equiformer_v2/N@12_L@6_M@2/bs@64_lr@2e-4_wd@1e-3_epochs@12_warmup-epochs@0.1_g@8x2' \
--print-... |
2cffa431d77a607d714551f39868d772a523a48cf3de749173b25c110a87e7b7 | Shell | 360 | 8 | #! /bin/bash
# Rscript /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_15_Greg_ATAC_Code_nobackup/NB6.5_UMAP_and_IntegrateSamplesSeparately.R --sampleRNAname $1
Rscript /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_15_Greg_ATAC_Code_nobackup/NB6.5_UMAP_and_IntegrateSamplesSepara... |
09226012a067c898779f3c75cbf44f4607d6f2f679234f9f77c04dc108189ef5 | Shell | 362 | 8 | # Step 1: extract introns in the annotation
splam extract mouse_chr19_subset.gff -o tmp_out_generalization
# Step 2: score introns in the annotation
splam score -G mouse_chr19.fa -m ../model/splam_script.pt -o tmp_out_generalization tmp_out_generalization/junction.bed
#Step 3: output statistics of each transcript
spl... |
55a41741af4279c9ddfa1f4eaa998913d273f9f648e26fd5364ff2947bcb9290 | Shell | 363 | 20 | set -ex
USERNAME=dekkerlab
IMAGE=cworld_env
# ensure we're up to date
git pull
version=$(cat ../VERSION | sed -E "s/\s*version\s*=\s*'(.*)'\s*$/\1/")
echo "version: $version"
# run build
bash ./build.sh
# tag it
docker tag $USERNAME/$IMAGE:latest $USERNAME/$IMAGE:$version
# push it
docker push $USERNAME/$IMAGE:lat... |
36a63c1d5b24ea7ac82902b4d8b6736ddde8dfad4dba26e57ecfc10baea64f09 | Shell | 364 | 15 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./Adipose_Visceral_Omentum/Adipose_Visceral_Omentum
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.GTEx.Adipose_Visceral_Omentum \
--smr-multi \
--set-wind 1000 \
... |
e63b53ea1f6ad4f2249ca5eb23b531cb63b85b9c1f977328bc82aaebfbf74a86 | Shell | 367 | 10 | #!/usr/bin/env bash
# This is to run the check_UMIs script over a list of CBCs
# The list has to be provided as a text file with one CBC per line
while read fraction;
do
echo $fraction
java -jar $PICARD DownsampleSam I=gene_function_tagged.bam P=$fraction M=downsample_metrics.txt O=/dev/stdout | python $DRUGSEQ/src... |
d209ce3d1b177fdd583708b38536b06e902876d89b4d51bfb3069ee93cf34893 | Shell | 369 | 2 | python3 ../../RUN_mdgru.py --datapath `pwd`/nifti --locationtraining train --locationvalidation val --locationtesting test --optionname discardme --modelname discardme -w 256 256 --paddingtesting 10 10 --windowsizetesting 256 256 -p 0 0 -f flair.nii.gz t2.nii.gz pd.nii.gz mprage.nii.gz -m mask1.nii.gz --iterations 100 ... |
0b07ce546bad636a0af56daa9f5254c6077019947d3656b90763d8cc6df14511 | Shell | 370 | 8 | python -u -m torch.distributed.launch --nproc_per_node=8 main_oc20.py \
--distributed \
--num-gpus 8 \
--mode train \
--config-yml 'oc20/configs/s2ef/2M/equiformer_v2/equiformer_v2_N@12_L@6_M@2.yml' \
--run-dir 'models/oc20/s2ef/2M/equiformer_v2/N@12_L@6_M@2/bs@32_lr@2e-4_wd@1e-3_epochs@12_warmup-ep... |
4ef89f19118d0d0e27e1ee0924f03ae233c5d3485ce53aec61b6a067dc77a94f | Shell | 371 | 14 | #!/bin/bash
gwas=MetS_GWAS.txt
ref_random_ukb=.test.rand10k
eqtl_besd=./eQTLGen/cis-eQTLs-full_eQTLGen_AF_incl_nr_formatted_20191212.new.txt_besd-dense
smr_Linux --bfile ${ref_random_ukb} \
--gwas-summary ${gwas} \
--beqtl-summary ${eqtl_besd} \
--out ./smr.MetS.eQTLGen \
--smr-multi \
--set-wind ... |
bde504e5934bb0039e939468d3caaca2898400335da51a9b83226524225dd7f4 | Shell | 371 | 8 | # Step 1: extract splice junctions in the alignment file
splam extract -P SRR1352129_chr9_sub.bam -o tmp_out_alignment --fr
# Step 2: score all the extracted splice junctions
splam score -G chr9_subset.fa -m ../model/splam_script.pt -o tmp_out_alignment tmp_out_alignment/junction.bed
#Step 3: output the cleaned align... |
0847be0a37911782f74c9ff2acfb32037d569b0c7ca5321ebb8ed73c58bc295f | Shell | 372 | 11 | #!/bin/bash
set -e
source_256="https://upenn.box.com/shared/static/p0hc12l1bpu5c7fzieotv1d6592btv1l.pth"
source_4k="https://upenn.box.com/shared/static/8qayhxzmdjpcr5loi88xtkfbqomag8a9.pth"
target_256="checkpoints/vit256_small_dino.pth"
target_4k="checkpoints/vit4k_xs_dino.pth"
mkdir -p checkpoints
wget ${source_256}... |
52077f48bc065b405bfb3003ec2d012934824d131ae09102779bc1142be05aee | Shell | 372 | 10 | python main_oc20.py \
--distributed \
--num-gpus 8 \
--num-nodes 8 \
--mode train \
--config-yml 'oc20/configs/s2ef/all_md/equiformer_v2/equiformer_v2_N@8_L@4_M@2_31M.yml' \
--run-dir 'models/oc20/s2ef/all_md/equiformer_v2/N@8_L@4_M@2_31M/bs@512_lr@4e-4_wd@1e-3_epochs@3_warmup-epochs@0.01_g@8x8'... |
270581c14f6cad0ca5b2c251ce41a8b642db6d67d673e3ccb8d826b9f89f46c7 | Shell | 373 | 8 | # Example content
echo "Starting the application..."
exec "$@"
# if you plan to serve deepface over grpc, then activate this line and generate grpc stubs in Dockerfile
# python grpc_server.py &
gunicorn --workers=1 --timeout=7200 --bind=0.0.0.0:5000 --log-level=debug --access-logformat='%(h)s - - [%(t)s] "%(r)s" %(s)... |
465ec397d577ce78184f0a6ee09c50fec3442757edcfd325eb3029b1a568f245 | Shell | 375 | 8 | # Step 1: extract introns in the annotation
splam extract refseq_110_GRCh38_chr_fixed.gff -o tmp_out_annotation -F feature_gene.txt
# Step 2: score introns in the annotation
splam score -G chr9_subset.fa -m ../model/splam_script.pt -o tmp_out_annotation tmp_out_annotation/junction.bed
#Step 3: output statistics of ea... |
c6f6fa322577dcea5d70e52010c493e833637e3eb977c3a83b4447a8742fc300 | Shell | 375 | 14 |
#!/bin/bash
# Get local process ID from OpenMPI or alternatively from SLURM
if [ -z "${CUDA_VISIBLE_DEVICES:-}" ]; then
if [ -n "${OMPI_COMM_WORLD_LOCAL_RANK:-}" ]; then
LOCAL_RANK="${OMPI_COMM_WORLD_LOCAL_RANK}"
elif [ -n "${SLURM_LOCALID:-}" ]; then
LOCAL_RANK="${SLURM_LOCALID}"
fi
e... |
00503014ad446d7e79c683e117c12d5f378789667888d01ca112fbd6b375abb9 | Shell | 378 | 10 | python main_oc20.py \
--distributed \
--num-gpus 8 \
--num-nodes 16 \
--mode train \
--config-yml 'oc20/configs/s2ef/all_md/equiformer_v2/equiformer_v2_N@20_L@6_M@3_153M.yml' \
--run-dir 'models/oc20/s2ef/all_md/equiformer_v2/N@20_L@6_M@3_153M/bs@512_lr@4e-4_wd@1e-3_epochs@1_warmup-epochs@0.01_g... |
5477d0a6a48f9dddc57db5bf2fa64417eb304315c4808ab5913a04987fcb0326 | Shell | 378 | 10 | python3 python/data/generate_mlife.py && \
python3 python/data/train_val_test_split.py && \
pg_ctl -D $HOME/postgres_nils -w start && \
python3 python/data/generate_eicu.py && \
pg_ctl -D $HOME/postgres_nils -w stop && \
Rscript R/execute_internal_test.R && \
Rscript R/execute_eicu.R && \
python3 ml/torch_tune.py && \
... |
8c1bd33b45c7255fa5f603f56413ff6b60e4264f95a59da1e97fb9ebace4ac53 | Shell | 378 | 8 | #!/bin/bash
echo ${1}
DIR=`dirname ${1}`
FILE=`basename ${1}`
/usr/bin/python2.7 /nfs/csmonk-lab/FF_Data/Working_Files/Resting/Scripts/AROMA/ICA_AROMA.py -in ${DIR}/${FILE} -out ${DIR}/ICA_AROMA -mc ${DIR}/rp*.txt -m /nfs/csmonk-lab/FF_Data/Working_Files/Resting/Scripts/AROMA/ROIs/rs_spm12_mni_icbm152_nlin_sym_09b_1... |
30ecd29954b8cb1a5e73c379441c0d268a1a4eb92c55ad8ce342acf74c4686e6 | Shell | 382 | 15 | #!/usr/bin/env bash
DATE=`date +%Y%m%d`
PN="mouse-brain-atlases"
PV="${1}"
./make_archives.sh -v "${PV}" -n "${PN}" -m || exit 1
rsync -avP ${PN}*${PV}.tar.xz dreamhost:chymera.eu/distfiles/
rsync -avP ${PN}*${PV}.sha512 dreamhost:chymera.eu/distfiles/
if [ $? -eq 0 ]; then
rm -rf ${PN}*${PV}*
else
echo "Could not... |
dbe2b269ea812f2af0ad47ad83ba45bd9ed24f70a8ed653f01ada003e6668fc6 | Shell | 382 | 15 | #!/bin/sh
python3 ../ntd/train_diffusion_model.py \
base.experiment=ner_experiment \
base.tag=conv_wn \
dataset=ner \
diffusion=diffusion_linear_200 \
diffusion_kernel=white_noise \
network=ada_conv_ner \
optimizer=base_optimizer \
optimizer.num_epochs=1000 \
optimizer.lr=0.0004 \
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.