sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
915b0951b63e064fe1d36debdb2b0f9d9c74f323b978245658f545558e057c50 | Shell | 127 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc.py ACB
|
9a3991d2c8ba0cfeb6001e8e47a51c2dca32c2b22406c33460b8e0ab7de73a63 | Shell | 127 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc.py ASW
|
f5ca0d75fd5bfe599ee619b70f6eb90b336e7d6e77d4ac9dd9c146d11f71c334 | Shell | 127 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc.py CLM
|
0c14bcc6247a737fcac1d9f97272b7aa80dbc39b1ab67b480890c728c7e99b3b | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py ACB
|
3cd0cf6933b0b03c5a01c6a5e5d1f78cb43a5ebb2fce882d0a8d872d55dd974e | Shell | 128 | 5 | #!/usr/bin/env bash
docker build -t minimap2 .
docker tag minimap2 "$DOCKER"/minimap2:2.17
docker push "$DOCKER"/minimap2:2.17
|
49b721526784ce545fecda36a8fdfe459bc327c4158e38add9389a7804fc7344 | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py CLM
|
4c813dd92963fc2189fd4b236efa9b3e99f1644bdcfeff0cae3ecd5f69a6cdba | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py PEL
|
6146a2c1302e0b7a1d28ec5b296480f0865e761d8723ef13a5c06d8de0be9614 | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py PUR
|
787711663f1e94f17d7e9b26948e54a9c5e90dd0ea584db24bde503679ed2abb | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py MXL
|
9db18b3e45344c21e21136883d5eff3035b58d86eb53c6a8da2035ef46e769cf | Shell | 128 | 5 | python ./inference.py \
--num_designs 10 \
--out examples/out/design \
--contigs 100 \
--T 25 --save_best_plddt
|
dc446d29c9313d60eb1611e6fa7ce66dcc3ea4d45e146a4026cb4de978b6f720 | Shell | 128 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc.py ASW
|
63f328f197c7fa68bfe863ba1f88e2f70b127ec180905a2968609f26db951120 | Shell | 132 | 2 | export GSETTINGS_SCHEMA_DIR_CONDA_BACKUP=${GSETTINGS_SCHEMA_DIR:-}
export GSETTINGS_SCHEMA_DIR=$CONDA_PREFIX/share/glib-2.0/schemas
|
6dd4c2f4f4f2ccc73d200b7986f498be97df99c881f213b337fbf22fa0e98cc1 | Shell | 132 | 6 | #!/bin/bash
for seed in $( cat seeds_20.txt)
do
R CMD BATCH '--args s='$seed'' t10_get_counts.R t10_get_counts_$seed.Rout
done
|
dfc4ac919202bd7247be70fd3f1a3abed9cccb983b415eb1f953d94d0e99993e | Shell | 133 | 7 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
cd ${PYENV_ROOT}/ && $TAR -czf $TASKCLUSTER_ARTIFACTS/pyenv.tar.gz .
|
e14eaa3cee06f929252284e845cf37a37add3de5a0c84062418d3effbb093bab | Shell | 133 | 4 | for dataset in IMDB-MULTI # PROTEINS IMDB-BINARY
do
python main_graph.py --use_cfg --dataset=$dataset --activation='relu'
done |
1e1074def872e349c48f5cb83a32ed37db44ef8e81291c38560cc50b703c33df | Shell | 134 | 3 | apt-get update
apt-get install -y --no-install-recommends python3.7 python3.7-dev python3-pip \
python3-setuptools zip rsync unzip |
3f1aced544341785fced1e017fd8c15aaedf0b587841e83c06e96d0b92da46a3 | Shell | 134 | 3 | #!/bin/bash
source {{ additional variables }}
exec celery --app={{ application_name }}.celery:app worker --loglevel=INFO -n worker.%%h |
5689879b74750793d09fb7fd6ee3016d2515156346ed2e4bf57a13bf1b2e6a0d | Shell | 136 | 10 | #!/bin/bash
for i in {1..85}
do
python sample_neg_tcrs_quantile_match.py \
--hla_class HLA_I \
--hla_i $i
done
|
e6fd3dff2ff18f86287c066c73e59ebc4d0409e553721e873afeeefa7544a5b6 | Shell | 136 | 8 | #!/bin/bash
for i in {1..125}
do
python sample_neg_tcrs_quantile_match.py \
--hla_class HLA_II \
--hla_i $i
done
|
b89c4d6b779b913590a960f6bdb1ad79b237db7ca7aa9b34c6c3fac4418d7916 | Shell | 137 | 5 | DATA_DIR=~/data/datasets
mkdir -p $DATA_DIR/shapes3d
cd $DATA_DIR/shapes3d
curl -O https://storage.googleapis.com/3d-shapes/3dshapes.h5
|
8da942f0025351f80ef2a98bebafc01c109cb49b74f7c1656ac627f62c27575f | Shell | 138 | 6 | #!/bin/bash
singularity exec \
--writable-tmpfs \
--bind $PWD:/dtox_robustness \
dtox-container_1.0.0.sif /app/entrypoint.sh $@ $@
|
0e0b4951485c1d7a255a7252d9e57082813a939a0f2118b9d30918c1bd0d442c | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py CLM
|
184984c91e2e4978f1393d2795a6c8dc6096bd822b3ff1018bc44bd8554e92bd | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py ACB
|
752aa392c7736a5a1dd0118d3faa8a6ef45ecf1e53a2743212829ac54cb00742 | Shell | 139 | 6 | #!/usr/bin/env bash
lib=NAM.EDTA1.8.0.MTEC02052020.TElib.fa
genome=($1)
RepeatMasker -pa 36 -q -div 40 -lib $lib -cutoff 225 -gff $genome |
770c48705344f64412fbc060ed1518b7efe295b898c6b4eaf76dcf4783113c3b | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py PUR
|
78be71e858acf0aa644892c94bd329456eba1464693aad65cb3ddc8a21151698 | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py ASW
|
9090a2e374b486f68daa7476ac4187971255be827c8ec7d669ddd5353c6b2239 | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py PEL
|
c83fc2334c5e77af7e1cd615eef11579441e25124b33defffbd74ea80d6bec4b | Shell | 139 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_rfmix_maxanc_onlyfemales.py MXL
|
2bc67c112a187b5a06e18620c5c2284eef00c54992b416fc1ec9942b73cb59c1 | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py ACB
|
4067f79d63535300595c97697d6262e1d3e2ce6d390c5df418eca1a0249afa17 | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py CLM
|
4c2d327e23f6fc3204c95d0748356b41587c7c5db046d123d06d9e29aba3ed91 | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py PEL
|
5a7f6d42c0a8410b11b0c08d384f0ef0d1fb92730a72886c413b8a0f66297526 | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py ASW
|
90614ebc6e5eead3e67ec2397d8837147664c8f2d2e57d4d60b59a03ef5abf5b | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py PUR
|
976e84e50123eb5e33a8859cee1267873a246ac53a4ef006a1eeced599e0c8db | Shell | 140 | 4 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python bootstrap_gnomix_maxanc_onlyfemales.py MXL
|
ae47610e61e57a024d2fd961e6d0db38d69ab06ab4c46dc922533cb77e49db24 | Shell | 140 | 3 | gmx_mpi_d grompp -f mtd.mdp -c npt.gro -p topol_ion.top -t npt.cpt -o mtd1.tpr
gmx_mpi_d mdrun -ntomp 2 -v -deffnm mtd1 -plumed plumed.dat
|
5fac6d5beea4f26d6838daa964c9505decc54517e5a60916f760254211a8d9ac | Shell | 141 | 7 | #!/bin/bash
VERSION=`cat VERSION.txt`
docker push trinityrnaseq/trinityrnaseq:${VERSION}
docker push trinityrnaseq/trinityrnaseq:latest
|
824577f89ce1923379822bb54c16a942f029f094b202e9c31de1ecc390ca9436 | Shell | 141 | 2 | #!/bin/sh
../../../../Trinity.pl --seqType fq --left clean.left.fq --right clean.right.fq --seqType fq --JM 1G --bfly_opts "-V 15 --stderr "
|
a6cfed90f725f296c1084cbf52d6961f5098aa36eb230917171fad0aa0a5adee | Shell | 141 | 10 | #!/usr/bin/env bash
#
# called from shunit2 test just for debugging
#
# expecting a MY_FLAG varaible to be set
echo "MY_FLAG=${MY_FLAG}"
|
d54eda5c733f5a0598a06fc1df90f4ab15bfe5753448467f4018d9491b9fd843 | Shell | 141 | 4 | #!/bin/bash
# Download matterport mask_rcnn_coco model
wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 |
967934ddfa013e4d76dcc0167c1b0ef04c12b8435f1135bea3cf97d6c05861de | Shell | 142 | 3 | gmx_mpi_d grompp -f mtd.mdp -c npt.gro -p topol_ion.top -t npt.cpt -o amtd1.tpr
gmx_mpi_d mdrun -ntomp 2 -v -deffnm amtd1 -plumed plumed.dat
|
0b1b8eb35db7c3b5668e428a9ce02d0c9f58c72b77949f4232bd50e7bacc663f | Shell | 144 | 1 | CUDA_VISIBLE_DEVICES=6 python main.py --task STR_01 --mode test --beta 0.4 --data_path ./data/processed_inference/ --split_mode mix --model STR
|
0253326ca53f50e35b3dc5c06bc9572bcba9464a8de6add45b69ca0d5fab0666 | Shell | 146 | 11 | #!/bin/bash
set -xe
arm_flavor=$1
source $(dirname "$0")/tc-tests-utils.sh
source $(dirname "$0")/tf_tc-vars.sh
do_deepspeech_java_apk_build
|
913f664829903998e7989f01f5f68176c53178f9400d0945aab175e4ca97782b | Shell | 148 | 2 | #!/bin/sh
../../../../Trinity.pl --seqType fq --left ex5.reads.left.fq --right ex5.reads.right.fq --seqType fq --JM 1G --bfly_opts "-V 15 --stderr"
|
125fb91617d9a6dabe9ab6f9d00e8a294321bb30037d3a757fd368abd91da615 | Shell | 150 | 6 | #!/bin/bash
source /opt/conda/etc/profile.d/conda.sh
conda activate dtox_env
export PYTHONPATH=/app/DTox/code:$PYTHONPATH
python /app/run_dtox.py $@
|
64bea22dcb488472f8f715aaf7dd9580d67a69e0f1a69329bc2f838c4bed94a1 | Shell | 150 | 7 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
cd $HOME/ && tar -czf $TASKCLUSTER_ARTIFACTS/android_cache.tar.gz DeepSpeech/Android/
|
df6d6ee962e9d78be846d202e891a7181cc94f02e6c531cd7b15ab4c65b3208c | Shell | 150 | 8 | #!/bin/sh
# I am not using the network tests here until travis has fixed its openmpi
# ./run_network_tests.sh
./run_unit_tests.sh && exit 0
exit 1
|
a5f7968426915bd7658935dff77ff39e12a002b9bcc28455587c4a6477ff1cff | Shell | 151 | 6 | #calculate p value
species=$1
cd {file_pathway}/$species/Rscript/
python pvalue1_GC.py {file_pathway}/$species.count.merge.GC.more one_line
cd ../../
|
b87a7e42ef1afb75d6aa1b2f0f63a117fff3599388b527966f37886ce70503ce | Shell | 151 | 7 | #!/bin/bash
VERSION=`cat VERSION.txt`
docker build -t trinityrnaseq/trinityrnaseq:${VERSION} .
docker build -t trinityrnaseq/trinityrnaseq:latest .
|
bc4872366d809012b9347b342d166f20f339bb697566e4e138a2d0538a000a1f | Shell | 152 | 6 | #!/bin/bash
for seed in $( cat seeds_20.txt)
do
R CMD BATCH '--args s='$seed'' t9_get_p_values_permuted.R t9_get_p_values_permuted_$seed.Rout
done
|
d76bb5a30b0ae36c9467e8c79496ba2bfd0a8b931a778ec3a7e315c9067d3367 | Shell | 152 | 8 | #!/bin/sh
#$1 is eg `test.avi`
fps=5
/opt/vc/bin/raspivid -o $1 -w 720 -h 540 -fps $fps -e
ffmpeg -r 5 -i $1 -vcodec copy tmp_$1 && mv tmp_$1 $1
|
f2e46d074bc49a4c004e06a1fbb216d05a969314af7409335febd10411c64679 | Shell | 153 | 10 | #!/bin/bash
for i in {1..85}
do
python sample_neg_tcrs_quantile_match_nolessthan2_flag.py \
--hla_class HLA_I \
--hla_i $i
done
|
384f0d9a80b3895566a0b74503edb03fafacd651a4f44957f3ba4bb0e4ce8ea3 | Shell | 154 | 2 | #!/bin/bash
python -m main_vv select_samples_for_vec_ref.fasta select_samples_for_vec_search.fasta 1 --select_num=10 --n_neighbors=15 --metric="euclidean" |
61e86f4226d68d0ff537540fa6f3fcc2fc805c16623b39fbb2a9ac7ad7a3f79e | Shell | 154 | 7 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
cd ${GRADLE_USER_HOME}/../ && tar -czf $TASKCLUSTER_ARTIFACTS/gradle.tar.gz gradle-cache/
|
722ad2acaf7c3736393be5928d9edd5af9580f139238c43b2e2a1e91a6be94e5 | Shell | 155 | 6 | #!/usr/bin/env bash
FTP_HOST=access763254689.webspace-data.io
FTP_USER=u95689219-uschmidt
lftp sftp://$FTP_USER@$FTP_HOST -e "set sftp:auto-confirm yes"
|
a15d0a123a0ae9de3ae7374ea98a47bb0b7c0f0f1676c220ffadb18a88020ba8 | Shell | 155 | 9 | #! /bin/sh
SIMPLEITK="${HOME}/src/SimpleITK"
ITK_XML="${HOME}/temp/xml"
SITK_BUILD_DIR="/dev/shm/SimpleITK/SimpleITK-build"
PYTHON_EXECUTABLE="python"
|
86a9e652e6f81d7ae4b7e1619dccd66ec562d205e6072da74811e579f5e9d327 | Shell | 157 | 8 | #!/bin/bash
for i in {1..125}
do
python sample_neg_tcrs_quantile_match_nolessthan2_flag.py \
--hla_class HLA_II \
--hla_i $i
done
|
f625fe7265f9053f6a65e9f58fe4bf22893a95df34c803b7c9337c01071a652c | Shell | 157 | 5 | #!/bin/bash
## Normative modeling
sbatch --array=1-360 /ncf/hcp/data/analyses/myelin/Adversity_Project/code/batch_container_regional_normative_modeling.sh
|
91bdd171d08f7805d68c10e6794b1ff8b0f408f9b985d7529dde2cec0ee52c59 | Shell | 158 | 5 | bedfile=$1
bedtools coverage \
-a {file_pathway}/mUCE.2186.chr.bed \
-b {file_pathway}/$bedfile \
-counts > mUCES.2186.snp.`basename $bedfile .bed`.counts
|
9c23314aa285ae84f65cd8b4add2a6081ea76f257bf05118bd079b97a10a278a | Shell | 158 | 12 | #!/bin/sh
cd "$(dirname "$0")"
. ./scripts/common_startup_functions.sh
setup_python
python ./scripts/check_model.py "$@"
[ $? -ne 0 ] && exit 1;
exit 0;
|
cb4fad9f5c421acf8642594076712a47d0e8e548adf5abd614342b91e55078d5 | Shell | 158 | 1 | CUDA_VISIBLE_DEVICES=2 python main.py --batch_size 256 --mode train --task STR_01 --beta 0.4 --data_path ./data/processed_data/ --split_mode drug --model STR
|
cfb988a66aece4374fc495687b902d02b6a3895f0e9cbe4e78ce758d7de992d6 | Shell | 159 | 2 | # Evaluate the vulnerability of the GM network
perl get_efficiency.pl 1M_input/AD1M_shortest_path.csv ../Stability evaluation/AD1M_MIS.xls AD1M_efficiency.xls
|
dd1cde25cd6db25644851bce5f9f882a9999cfa7c658111b03f62b8f14427b3e | Shell | 159 | 5 | bedfile=$1
bedtools coverage \
-a {file_pathway}/mUCE.2186.chr.bed \
-b {file_pathway}/$bedfile \
-mean > mUCES.2186.snp.`basename $bedfile .bed`.corverage
|
2f0b3258b0e63d2c9a193f610854a09855d3c75ce1ad4e38dc0bf32d830b9f30 | Shell | 160 | 13 | #!/bin/sh
DOCDIR=doc/
APIDOCDIR=$DOCDIR/api
SRCDIR=libmushu/
rm -rf $APIDOCDIR
mkdir -p $APIDOCDIR
sphinx-apidoc -o $APIDOCDIR $SRCDIR
cd $DOCDIR
make html
|
08b969713ba40fc44943f5fe33ba1d1009cbbcda5d0f2899f4d6c78052f566ff | Shell | 161 | 10 | #!/bin/bash -l
#$ -l h_rt=12:00:00
#$ -pe omp 16
#$ -N HumanBrain
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "OCT_Dat2mat; exit"
|
92abd756088cb915d2dcbd40fc38e2393da70cc82dd03d1fa1a42601bb01c779 | Shell | 161 | 7 | #!/bin/bash
python predict.py \
--compound_path './data/compounds/toy_compounds.csv' \
--baselearner_dir "./results/base-learner/" \
--device 0 \
--batch_size 5 |
c38c9a23c4e9296a32101ca41abcd313e2924ec9d0378745adf793e40d33aa35 | Shell | 162 | 10 | #!/bin/bash -l
#$ -l h_rt=12:00:00
#$ -pe omp 8
#$ -N aip_stitch
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "AIPFijistitch; exit"
|
008c9946e42a630c5e7b30af0ffab37493b2bbe2e857db14eae8566b1e63105e | Shell | 164 | 5 | python ./inference.py \
--num_designs 10 \
--out examples/out/design \
--sequence XXXXXXXXXXXXXXXXPEPSEQXXXXXXXXXXXXXXXX \
--T 25 --save_best_plddt
|
7c50f965f21acc08645ebb2c711a901f78ca273923c8c0ee4ace0c92e4403227 | Shell | 165 | 10 | #!/bin/bash -l
#$ -l h_rt=12:00:00
#$ -pe omp 8
#$ -N volume_stitch
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "stitch_volume; exit"
|
b863a4cb17ddda2276b26ca97949e480a04fdd89000d8b3a292a95907cd0dafa | Shell | 165 | 7 | #!/bin/bash
set -e
python3 -m venv --upgrade-deps .venv
. .venv/bin/activate
pip install -r requirements/dev.txt
pip install -e .
pre-commit install --install-hooks
|
a41a65d8e83e94f230b647e60be1b83716c67d8b03e0f64da63ccb4b504e2dfc | Shell | 166 | 10 | #!/bin/bash -l
#$ -l h_rt=12:00:00
#$ -pe omp 8
#$ -N HumanBrain
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "fittingFijistitch; exit"
|
b073aa0f198db3eef2fdbd26246f092f984522b3f70e316bd4b473e50a87eda4 | Shell | 166 | 7 | #!/bin/bash
eval "$(conda shell.bash hook)"
source ~/anaconda3/etc/profile.d/conda.sh
conda activate UniEnv
unires /home/attilasimko/Documents/out/amri/slice.nii.gz |
d7321da4d4048f50925af7430ab2f4fcfcf753450e45883bc050ea884cc94e12 | Shell | 166 | 7 | #!/bin/bash
for i in {1..125}
do
R CMD BATCH "--args hla_i=${i}" tcr_freq_for_each_HLA_II.R tcr_freq_for_each_HLA_II_Rout/tcr_freq_for_each_HLA_II_${i}.Rout
done
|
0818bf6ea9847c675274c40f48481093f520b46f5e50e75aa39a7bd33c005227 | Shell | 167 | 11 | #!/bin/bash
set -xe
package_option=$1
source $(dirname "$0")/tc-tests-utils.sh
source $(dirname "$0")/tf_tc-vars.sh
do_deepspeech_npm_package "${package_option}"
|
3a92d42d296a5ed3150e4f471e36e6f435b25514beeb16bcb14058c95a7b9f73 | Shell | 167 | 1 | python3 ../Compare_imputation_to_WGS.py --ga input_genotype_array_100samples.vcf.gz --wgs whole_genome_file_100samples.vcf.gz --imputed imputed_file_100samples.vcf.gz
|
4d6f5d509b6078af603b6dadf1f74c61d24920bb2cb6c1941da22b9b2e1333aa | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_PUR_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
c7e54cd4965dfe97c988d904c5d54e05b31668a67f6aaf7e4e9cd4cb6e1a38cf | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_ASW_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
c93cf1acd1e97a253720391a6545add6b7ccadca6c1ae7345c820b703ad13a43 | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_CLM_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
ca9bacfc045df1ecddeb88aa989a06d9b42a3919f418bfb191022a879bf5bcb0 | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_PEL_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
dc4a47e6271d194c076b987222ed14ff4878272a5b300c9d1070875690cc4a9b | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_ACB_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
fba8a0cb8f34a83e90158129227f29bc1161aa27cb93cd260aa493418de6afc8 | Shell | 167 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=8:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_MXL_rfmix_0.1cM_maxanc_X_onlyfemales.py
|
01c344d5ec56a22214637c47c3017388ff04cf926fe04a7e8a3acf3e6a3b0c8b | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_PEL_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
04d6147fa779625404d2776be46ad8f9ad2eb5752857abebd5718dc8fb826fdf | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_PUR_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
0f2d5ac6cb192fb063b74411b4cba98b386002820fe2ce482bca33acdfba8e7a | Shell | 168 | 7 | #!/bin/bash
for i in {1..85}
do
R CMD BATCH "--args hla_i=${i}" tcr_freq_for_each_HLA_I.R tcr_freq_for_each_HLA_I_Rout/tcr_freq_for_each_HLA_I_${i}.Rout
done
|
183a53aa0101f6f4f34c67ce4a095fbae995e91172d2e09c7bf9bb4b0102fb15 | Shell | 168 | 5 | #!/usr/bin/env bash
docker build -t trinityrnaseq-wdl .
docker tag trinityrnaseq-wdl "$DOCKER"/trinityrnaseq-wdl:2.11.0
docker push "$DOCKER"/trinityrnaseq-wdl:2.11.0
|
551697c5aa5ff5c690eeab7aa300aa745683d0207f3ab441bf6bb885f708c49f | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_ACB_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
afd554f037b37e5ba26ba17f5adfa929a13ef61ffebde8be03ebda754c64d26f | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_MXL_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
dbcd4d7f6db71429f1a3b741ea3d977cc85dc329d1ca61032f7e3ff5d1bbdfe5 | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=16gb
module load anaconda3/personal
python get_fragments_and_bootstrap_ASW_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
dd05867857ce4e61e586e5fb66914b44f381b913178680c3ae8423065ebc1ae2 | Shell | 168 | 5 | #PBS -l walltime=7:59:00
#PBS -l select=1:ncpus=1:mem=32gb
module load anaconda3/personal
python get_fragments_and_bootstrap_CLM_gnomix_0.1cM_maxanc_X_onlyfemales.py
|
7b0132c7e1355f24b223b60aafb65fee426ae50d289897b9354bfce2b81d1c71 | Shell | 170 | 10 | #!/bin/bash -l
#$ -l h_rt=24:00:00
#$ -pe omp 8
#$ -N TDE
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "id='$SGE_TASK_ID'; OCT_recon; exit"
|
8acd21d77e329853f516a999fd2f46ea5a2bec84d1ad0f08368aba089ed51238 | Shell | 170 | 10 | #!/bin/bash -l
#$ -l h_rt=24:00:00
#$ -pe omp 8
#$ -N LargeBlock
#$ -j y
module load matlab/2018b
matlab -nodisplay -singleCompThread -r "OCT_recon_mosaic_only; exit"
|
1e396335ed673a08b18c91c2c3d9600b501f16d6f3d73d39464e158d016c06e1 | Shell | 171 | 9 | #!/bin/bash
set -eu
NSEQS=$1
OUTFILE=$2
# To avoid olga confirming over-write.
rm -f $OUTFILE
conda run -n olga olga-generate_sequences --humanTRB -o $OUTFILE -n $NSEQS
|
11439a131d8c86486ca3c14f7cd590283d4e49573543d2cf51efad6f600d6427 | Shell | 173 | 9 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
mkdir -p ${TASKCLUSTER_ARTIFACTS} || true
cp ${DS_DSDIR}/doc/html.zip ${TASKCLUSTER_ARTIFACTS}/doc-html.zip
|
e1e15558ed2c72420b15c95303c03b4f981ce51861ab180f3a515f10006f3344 | Shell | 173 | 9 | #!/bin/bash -l
#$ -l h_rt=24:00:00
#$ -pe omp 2
#$ -N Recon_2P
#$ -j y
module load matlab/2020b
matlab -nodisplay -singleCompThread -r "id='$SGE_TASK_ID'; Recon_2P; exit"
|
1408ee2c90be3add2bedaee7c690bb3c3d48533c4b7e7946977a7788fbdf78ab | Shell | 174 | 7 | python ./inference.py \
--num_designs 10 \
--out examples/out/symmetric_design \
--contigs 25,0 25,0 25,0 \
--T 50 \
--save_best_plddt \
--symmetry 3
|
51841378f44ba4d05b3b6d61b8a3eb78ba4a9e8a270387a29fa34f7af5506a58 | Shell | 174 | 10 | #!/bin/bash -l
#$ -l h_rt=72:00:00
#$ -pe omp 4
#$ -N fit_NC
#$ -j y
module load matlab/2019b
matlab -nodisplay -singleCompThread -r "id='$SGE_TASK_ID'; Fitting_NC; exit"
|
57abe6ff9062010807c30dbe13f62510b08e85a739f0c41d233691eac9fd1a15 | Shell | 174 | 16 | #!/bin/bash
#source /broad/software/scripts/useuse
#reuse Perl-5.8
#reuse .samtools-0.1.19
#reuse GCC-4.9
CMD="`dirname $0`/run_Trinity_eval.pl $*"
eval $CMD
exit $?
|
7d3a580d6d58a9be03a5eb34b1a5761edbc693abae6380fa9450ec58eb4e4e2e | Shell | 174 | 6 | #!/bin/bash
set -e
MAX_MAKO_COUNT=330
project_dir=`dirname $0`/..
cd $project_dir
bash -c "[ `find templates -iname '*.mako' | wc -l | cut -f1 -d' '` -lt $MAX_MAKO_COUNT ]"
|
78b6cb650ac711e0dad23783389e7769ba2d79007a3118f43d4b150bef7949dd | Shell | 175 | 2 | docker run --rm -v`pwd`:`pwd` trinityrnaseq/trinityrnaseq Trinity --seqType fq --single `pwd`/reads.left.fq.gz --max_memory 1G --CPU 2 --output `pwd`/trinity_out_dir_docker
|
ad3628d12b13b088b5b572aa3698f421094932e31766e2fc4ecd9e694ebfe80c | Shell | 175 | 6 | #!/usr/bin/env bash
src="../examples"
for f in `find ${src} -name "*.ipynb"`; do
jupyter nbconvert ${f} --to notebook --ClearOutputPreprocessor.enabled=True --inplace;
done
|
42d32cb2d596423f04308edce27d9c54c9190678fd8ccf6b901e77309f38a927 | Shell | 176 | 6 | export GSETTINGS_SCHEMA_DIR=$GSETTINGS_SCHEMA_DIR_CONDA_BACKUP
unset GSETTINGS_SCHEMA_DIR_CONDA_BACKUP
if [ -z $GSETTINGS_SCHEMA_DIR ]; then
unset GSETTINGS_SCHEMA_DIR
fi
|
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