sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
aae2b3f5310a40ea3d3e4326a061c84cb95725bdd0f91430db6190d0487d62b1 | Shell | 225 | 10 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
mkdir -p ${TASKCLUSTER_ARTIFACTS} || true
# NodeJS package
cp ${DS_ROOT_TASK}/DeepSpeech/ds/native_client/javascript/deepspeech-*.tgz ${TASKCLUSTER_ARTIFACTS}/
|
15a0e0e5a55688c5314732f3ab2efdda6c893451252c36080cdd071264c7fc30 | Shell | 227 | 6 | #!/bin/bash
set -e
MAX_LINE_COUNT=19900
project_dir=`dirname $0`/..
cd $project_dir
bash -c "[ `find lib/galaxy/webapps/galaxy/controllers/ -name '*.py' | xargs wc -l | tail -n 1 | awk '{ printf \$1; }'` -lt $MAX_LINE_COUNT ]"
|
7391b1f239a62537c5edf10d9b4ad04661429d349b68d7a1966ae7fdb348258c | Shell | 232 | 13 | #!/bin/bash
ml purge
module load teflon
ml anaconda3
conda init bash >/dev/null 2>&1
source ~/.bashrc
conda activate mne
COHORTS=$1
GROUP=camcan
python -u /scratch/n/tbi-meg/v/pipeline/averages/get_avg.py "${GROUP}" "${COHORTS}"
|
bf1136f73464becf222ff5744ad209d15666ef57bd89d93a063db791ed5677df | Shell | 232 | 13 | #! /bin/bash
# User: fcruz
# Date: 2022-06-09
# Brief description/notes:
# Pass input via standard input (gap file should preserve original contig names, use fasta-stats-py)
# Dependencies
gawk '{print $1"\t"$2"\t"$3"\t100"}'
|
033c4cbf0c7177d0de0925d34bf0bb4a51024c2ba56039875f1c9097a864769f | Shell | 233 | 14 | #!/bin/sh
set -eu
addgroup --gid 1000 "$CELERY_USER"
adduser \
--system \
--disabled-password \
--uid 1000 \
--gid 1000 \
--home "/home/$CELERY_USER" \
"$CELERY_USER"
chown -R 1000:1000 "/home/$CELERY_USER" |
0692472072e4b4096df85dc7bf31e0d47810c3826c0ba38b045efba5e2b1d46e | Shell | 234 | 6 | pip install tensorflow==2.4.2 keras==2.4.0
pip install tensorflow-probability==0.12.2
pip install --force-reinstall charset-normalizer==3.1.0
pip install tensorflow-models==0.12.1
pip install numpy==1.23.0
pip install protobuf==3.20.0 |
d0e8f59cf727c4f6bbc0fe613376b45d0e765f380c25ffbfdb816dc2055fc5f7 | Shell | 235 | 8 | FILE=$1
URL=http://efrosgans.eecs.berkeley.edu/BicycleGAN/testset/${FILE}.tar.gz
TAR_FILE=./datasets/$FILE.tar.gz
TARGET_DIR=./datasets/$FILE/
wget -N $URL -O $TAR_FILE
mkdir $TARGET_DIR
tar -zxvf $TAR_FILE -C ./datasets/
rm $TAR_FILE
|
bc2d66830a27a0fc62120152c35b848b9df78829db50c194c29cfb1e10b3f6d3 | Shell | 236 | 9 | #!/bin/bash
set -e
DATE=$(date +%y.%m.%d)
LOCAL_TAG="flash-attn-4:flash-attn-cu13.0-${DATE}"
REMOTE_TAG="togethercomputer/training-performance:flash-attn-cu13.0-${DATE}"
docker tag "$LOCAL_TAG" "$REMOTE_TAG"
docker push "$REMOTE_TAG"
|
47d7cf466380eda7c575e4fcce9572e478a03a8864ba0ac7d29d92498361d621 | Shell | 237 | 12 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
mkdir -p ${TASKCLUSTER_ARTIFACTS} || true
if [ -d ${DS_ROOT_TASK}/DeepSpeech/ds/wheels ]; then
cp ${DS_ROOT_TASK}/DeepSpeech/ds/wheels/* ${TASKCLUSTER_ARTIFACTS}/
fi;
|
c527bb352c579c9e0429209969b800c6b11440d81796fe102a0465ee1dd440d8 | Shell | 239 | 11 | #!/bin/bash -ve
${TRINITY_HOME}/Trinity --samples_file samples.PE.txt \
--seqType fq \
--max_memory 1G \
--min_kmer_cov 3 \
--output trinity_test_samples_PE_min_kmer_cov_3
exit 0
|
00727fe255218c6701187e6534c64b08d638d121cbc10862a19d02e54bdb628a | Shell | 242 | 5 | #extract UCEs with GC substitutions more than AT substitutions
species=$1
cd {file_pathway}/${species}
less {file_pathway}/${species}/${species}.AT-GC.GC-AT.count.merge |awk '$5>$6' > {file_pathway}/${species}/${species}.count.merge.GC.more
|
7db9484b665d13b6ab0c460b8fa6b2bc293a6a6df8f3a69432aa2aff39b91a3f | Shell | 244 | 15 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
source $(dirname "$0")/tf_tc-vars.sh
if [ "${OS}" = "${TC_MSYS_VERSION}" ]; then
export SYSTEM_TARGET=host-win
else
export SYSTEM_TARGET=host
fi;
do_deepspeech_decoder_build
|
f455a5d3dd3deed8e4d071d65f97011b9313a635ef27262a36d29ebe6d563dc5 | Shell | 244 | 8 | #!/bin/bash
all_pdc_bedpe=$1
pdc_alignv1=$2
join -1 1 -2 7 <(cut -f 1 ${pdc_alignv1} | sort) <(sort -k7,7 ${all_pdc_bedpe}) -t$'\t' \
| awk 'BEGIN{FS=OFS="\t"}{print $2,$3,$4,$5,$6,$7,$1,$8,$9,$10}' \
| sort -u -k1,1 -k2,2n -k8,8nr
|
e1db0343eaf9e7db1e9d35f2c93585cf6514e53f014043d40d16b7da5b8a7012 | Shell | 246 | 9 | #!/bin/bash
../../../bin/lmplz --discount_fallback -o 3 -S 100M --intermediate toy0 --arpa toy0.arpa <<EOF
a a b a
b a a b
EOF
../../../bin/lmplz --discount_fallback -o 3 -S 100M --intermediate toy1 --arpa toy1.arpa <<EOF
a a b b b b b b b
c
EOF
|
4a15270ead3b4cdd81d48587f0c8bdf57ee2177d945889160c8689e654bc16d0 | Shell | 249 | 7 | #!/bin/bash -ve
${TRINITY_HOME}/Trinity --seqType fq --left reads.left.fq.gz --right reads.right.fq.gz --SS_lib_type RF --CPU 1 --jaccard_clip --max_memory 1G --output __test_trinity_w_jaccard --grid_node_max_memory 2G --grid_node_CPU 2
exit 0
|
ab150efdc369beb62d70d77b8dd24c3453bdf175f33cc39ac8cac13c9c5d69ab | Shell | 250 | 1 | git rev-list --objects --all | git cat-file --batch-check='%(objecttype) %(objectname) %(objectsize) %(rest)' | sed -n 's/^blob //p' | sort --numeric-sort --key=2 | cut -c 1-12,41- | numfmt --field=2 --to=iec-i --suffix=B --padding=7 --round=nearest
|
2314611bb2931a19f8a5d1ffa32aa9b651d1083724f24d35caabed1d9b1093f9 | Shell | 251 | 12 | python ./inference.py \
--num_designs 10 \
--out examples/out/design \
--contigs 110 \
--T 25 --save_best_plddt \
--dssp_pdb examples/pdbs/cd86.pdb
# FOR SECONDARY STRUCTURE:
# X - mask
# H - helix
# E - strand
# L - loop
|
bcb85aef7e46de2ddda777a27d1d4a77132e2ce560d8c91a13670433e63f75bf | Shell | 251 | 11 | #!/bin/bash
#SBATCH --gres=gpu:2
#SBATCH --cpus-per-task=4
#SBATCH --ntasks=2
#SBATCH --mem=12G
#SBATCH --time=0-00:19
#SBATCH --output=logdnn.out
module load cuda cudnn python/3.5.2
source tensorflow/bin/activate
python3 SimBoost/xgboost/DNN_est.py
|
7fb3de25e91c6cbfa7903ef76d917a2b6c83118cc420aa02954b5b2b7ed8f75d | Shell | 252 | 2 | python get_seq_23.py 3pos.site.txt /home/weiqi/project/01.weiqi/20200929_inosine/process/index/GRCm38.p6.fa > 3_frequency.txt
python get_seq_23.py 4pos.site.txt /home/weiqi/project/01.weiqi/20200929_inosine/process/index/GRCm38.p6.fa > 4_frequency.txt
|
a770685e8e097521aaa61c897f22908a2ac2584f32f1c281d9a90243d399e2a6 | Shell | 252 | 6 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset metz
--model tf_regression --prot_desc_path metz_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_metz --filter_threshold 1
--arithmetic_mean --aggregate toxcast --plot '
eval $spec
|
bd3dd986d094602e2c2f40639117c12800c44f111b09fd6ebb2e77b1e263e659 | Shell | 252 | 6 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset kiba
--model tf_regression --prot_desc_path KIBA_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_kiba --filter_threshold 6
--arithmetic_mean --aggregate toxcast --plot '
eval $spec
|
57e5a78d703c7c652c3bf79d9acb584f5e902f4d53565b60a67d0973229f2a78 | Shell | 253 | 6 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset davis
--model graphconvreg --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir_davis --filter_threshold 1
--arithmetic_mean --aggregate toxcast --plot '
eval $spec
|
ed1fed4623866ada8581c771cae9c125d300ffa4c364967ffa52f0a5ebe753c0 | Shell | 253 | 8 | #!/usr/bin/env sh
# Compute the mean image from the imagenet training leveldb
# N.B. this is available in data/ilsvrc12
./build/tools/compute_image_mean examples/imagenet/ilsvrc12_train_leveldb \
data/ilsvrc12/imagenet_mean.binaryproto
echo "Done."
|
3a5f78b04569057e076c6767c3f4c5faa330f561fc622e02336913afedf63040 | Shell | 254 | 11 | #!/bin/bash
set -eu
# INFILE should be a TSV with CDR3 amino acid sequence, V, and J genes.
INFILE=$1
OUTFILE=$2
# To avoid olga confirming over-write.
rm -f $OUTFILE
conda run -n olga olga-compute_pgen --display_off --humanTRB -i $INFILE -o $OUTFILE
|
bca5f7dffc209d6a359aa98941fa51246b0414aa910371023bbadf3c7855c86b | Shell | 254 | 3 | wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/* -P 1000genomes/
wget https://raw.githubusercontent.com/gkichaev/PAINTOR_V3.0/22529435aa3b2e100c92373372714098e8e6c901/PAINTOR_Utilities/CalcLD_1KG_VCF.py
mv CalcLD_1KG_VCF.py 1000genomes/
|
b2ba50b938ad741efc176ac3dabbf8d19f7acbdd43027c902eee04e9cbbdf5b5 | Shell | 255 | 6 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset davis
--model tf_regression --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_davis --filter_threshold 1
--arithmetic_mean --aggregate toxcast --plot '
eval $spec
|
b3fd1c1aeb6214a8f0c888d089f4a94275f739b26ac0b96e102041ad70eaaacb | Shell | 256 | 10 | ## RUN FREESURFER
# Test if Freesurfer (https://freesurfer.net/) can run on warped templates.
export SUBJECTS_DIR='~/mri_warpimg/fs_subjects_dir'
recon-all -all -subjid 0177
recon-all -all -subjid 0177warp
recon-all -all -subjid colin
echo 'done'
#END |
defc75af7b19e88f90581aa570b158a5413c1e25c92b24abab9279829ea58c26 | Shell | 256 | 12 | #!/bin/bash
for test_hla in HLA-B_08_01 HLA-B_07_02 HLA-C_07_01
do
python sample_neg_tcrs_leave1out_match_test.py \
--hla_class HLA_I \
--test_hla $test_hla \
--label leave1out_match \
--n_fold 5
done
|
02ccdb1b173aa6a7a5e9830a459779ac2f333e64a8c71f11c55713b276223612 | Shell | 258 | 8 | #!/usr/bin/env bash
rm -rf build/
rm -rf dist/
rm -rf bioseq2seq.egg-info/
#rm -rf venv/lib/python3.7/site-packages/bioseq2seq/
#rm -rf venv/lib/python3.7/site-packages/bioseq2seq-1.0.0.dist-info/
python setup.py sdist bdist_wheel
python -m pip install -e .
|
17b4ac264afa650e4009b80d32b8df5d3fcfae59a3971a8167e6ae2f725a365c | Shell | 259 | 11 | #$ -pe threaded 8
#$ -l h_vmem=20G
#!/bin/bash
SIF=/cbica/projects/pnc_fixel_cs/scripts/ss3t_csd_beta.sif
#Create the mean response function
singularity exec \
-B /cbica/projects/pnc_fixel_cs:/work \
$SIF bash /work/scripts/MRtrix_recon/response_mean.sh
|
b1dd8403a8c27a413d56db65a768bc77f888b6bfe829b37bd6f3659bbe82dcb1 | Shell | 259 | 9 | FILE=$1
echo "Specified [$FILE]"
URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip
ZIP_FILE=./datasets/$FILE.zip
TARGET_DIR=./datasets/$FILE/
wget -N $URL -O $ZIP_FILE
mkdir $TARGET_DIR
unzip $ZIP_FILE -d ./datasets/
rm $ZIP_FILE
|
ffe68c5cf01d6a60fd3c12295394e35d6caca964322230490936996113b1db42 | Shell | 259 | 10 | #!/bin/bash
export XDG_CACHE_HOME=/tmp/.cache/
export LD_LIBRARY_PATH=/opt/conda/lib
export PATH=/var/DAFi-gating/inst/bin:/var/DAFi-gating/FCSTrans:/var/DAFi-gating/Python:/var/DAFi-gating/inst/bin:/var/DAFi-gating/FCSTrans:/var/DA
echo PATH IS $PATH
$@
|
3fe5931e3bde13372b3de6d8e322263e3cd0593ba13ce4d91b2023681ff630a7 | Shell | 260 | 3 | Rscript find_motifs.R --rds ../data/rds/full_cocaine_dataset_with_naive_rn6/harmony_with_predicted_id.rds \
--celltype ExNeuron --outfh ExNeuron_test.csv \
--dapeaks ../out/full_cocaine_dataset_with_naive_rn6/da_peaks/high_vs_low/negbinom/ExNeuron_with_fdr.csv |
5d6d5b5f776187ee168456648d81aa2d43ae50d3afc39a585af38f16b7eef3ba | Shell | 263 | 4 |
VERSION=`cat VERSION.txt`
docker run --rm -v`pwd`:`pwd` trinityrnaseq/trinityrnaseq:$VERSION Trinity --seqType fq --left `pwd`/test_data/reads.left.fq.gz --right `pwd`/test_data/reads.right.fq.gz --max_memory 1G --CPU 4 --output `pwd`/trinity_out_dir_docker
|
0de7e500f7ec4c63ed83dec9ab965be99d1f02dd2500310f1dc375883e95931c | Shell | 265 | 5 | python ./inference.py \
--num_designs 10 \
--sequence SAKVEELLETAKALGISEEEVREILELLEAGFIVIEVVSLGDAVILILENKKLGKYYILKNGEIERIKKPENARELKRKIAEILNISVEEIEAIIEKLRAK \
--out examples/out/partial_diffusion_design \
--sampling_temp 0.3 --T 50 --save_best_plddt
|
efed990f0c4f561e4e8fb36c16d64246f9f01aa89f824e2ed62bc2966cca5a22 | Shell | 266 | 10 | #!/bin/bash
MYPYPATH=src mypy \
--namespace-packages \
--explicit-package-bases \
--exclude src/rpy2/rinterface/tests \
--exclude src/rpy2/rinterface_lib/_rinterface_cffi_build.py \
--exclude build \
--exclude setup.py \
.
|
b29ff6dbb947fb5802f4e860e6ad54f42b30877e08cb1c6ace872f490f2b7af3 | Shell | 267 | 10 | #extract GC-AT and AT-GC sbustitutions in each UCE
species=$1
cd {file_pathway}/${species}
less $species.pos |awk '$3=="G"||$3=="C"'|awk '$4=="A"||$4=="T"' >${species}_GC-AT
less $species.pos |awk '$3=="A"||$3=="T"'|awk '$4=="G"||$4=="C"' >${species}_AT-GC
cd ..
|
2ddfd26f9ba19a398c9ae269ea2dd7aec7f9a6cda89f4dc0b0af7af86b92f6f9 | Shell | 269 | 13 | #!/bin/bash
set -xe
arm_flavor=$1
source $(dirname "$0")/tc-tests-utils.sh
mkdir -p ${TASKCLUSTER_ARTIFACTS} || true
cp ${DS_ROOT_TASK}/DeepSpeech/ds/tensorflow/bazel*.log ${TASKCLUSTER_ARTIFACTS}/
package_native_client_ndk "native_client.tar.xz" "${arm_flavor}"
|
78a92cd2fb10bdc59f064e5c8e1df3fe300ec05798a615d496b2731279174b6b | Shell | 272 | 18 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
bitrate=$1
set_ldc_sample_filename "${bitrate}"
download_material "${TASKCLUSTER_TMP_DIR}/ds"
export PATH=${TASKCLUSTER_TMP_DIR}/ds/:$PATH
check_versions
ensure_cuda_usage "$2"
run_basic_inference_tests
|
3466bdd4e11f53c1c010191840c2d1011bd2325aa868efb491a13e173c6b4a60 | Shell | 273 | 14 | #!/bin/bash
set -e
set -x
pytest \
--cov-append \
--cov-report=xml \
--cov-report=term \
--cov=rpy2.rinterface_lib \
--cov=rpy2.rinterface \
--cov=rpy2.rlike \
--cov=rpy2.ipython \
--cov=rpy2.robjects \
./rpy2-robjects/src/rpy2/ipython
|
63361e620300768462bffdbc66f549c4307dc0092e1377bf766046bd2d805e7c | Shell | 273 | 6 | #get conserved and variable sites within pairwise aligned UCEs
species=$1
cd {file_pathway}/$species
perl {file_pathway}/0-{Avian_or_Mammal}.getConservedSite.pl {file_pathway}/$species/$species.list {file_pathway}/$species_UCE_pairwise.last.maf $species.inf $species.pos
|
4c4def6d6cefa69531713649518a7f4ebc826174097b97e2c34c0c17f9a70e44 | Shell | 274 | 9 | #!/bin/bash
for file_path in $(ls ../../logs/FirstLvl*.err ) ; do
file_size=$(stat -c%s "$file_path")
name=$(basename $file_path)
if [ "$file_size" -ne 510 ] && [ "$file_size" -ne 1124 ] ; then
echo "${name} is NOT 510 or 1124 bytes. Size: ${file_size}."
fi
done
|
9b01ddc335fea29c805e697dc8653cd047e3a1e30c827597cf3a1112ae249a74 | Shell | 275 | 4 | #Get the elements absolutely aligned to the ancestor genome without any mismatch from each species
species=$1
samtools view {file_pathway}/${species}_50bp_reads.sort.bam |awk '$6=="50M"&&$14=="XM:i:0"'|awk '{print $3"\t"$4"\t"$4+49}' > {file_pathway}/${species}.aligned.bed
|
61fb3d9e3faac1a436c2288b0f8bb34cd4f12bb388453f4f254f26172b500d64 | Shell | 277 | 7 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset toxcast
--model tf_regression --prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir4_tc_w
--arithmetic_mean --aggregate toxcast
--intermediate_file intermediate_cv_warm.csv '
eval $spec
|
849db37d653a95f2f779dbfea93af8da71973cc2a84d1c0ede7c8bbc9014dbc6 | Shell | 277 | 3 | #get a species list containing two names (including Avian_ance/Mammal_ance and a Avian/Mammalian species)
for i in `cat {file_pathway}/species_list`;do cd {file_pathway}/$i;echo $i|awk '{print $1"\t"1}'|sed '1i {Avain_ance_or_Mammal_ance}\t2' > {file_pathway}/$i/$i.list;done
|
9f907254f1aa40a397e8b90cfea2b87692154f68691b706adc3572b61513fb57 | Shell | 277 | 20 | #!/bin/bash
set -xe
source $(dirname "$0")/all-vars.sh
source $(dirname "$0")/all-utils.sh
source $(dirname "$0")/asserts.sh
bitrate=$1
set_ldc_sample_filename "${bitrate}"
download_data
export_py_bin_path
deepspeech --version
run_all_inference_tests
run_hotword_tests
|
94f75880510a08ceccb9c955b971de76089bd2222d2be1fa0869a233444041ab | Shell | 281 | 11 | #!/bin/bash
for file_path in $(ls ../../logs/FirstLvl*.err ) ; do
file_size=$(stat -c%s "$file_path")
name=$(basename $file_path)
if [ "$file_size" -eq 510 ]; then
echo "${name}: exactly 510 bytes."
else
echo "${name} is NOT 510 bytes. Size: ${file_size}."
fi
done
|
8dafc2c6ef25c9d21761ebb92abfe1d5d16b70de93abe43299f4a3c8e21c1347 | Shell | 283 | 10 | path='./datasets/SEN1-2/10%_train/ROIs1158_spring_10%_train'
python train_attn.py \
--netG resnet_9blocks \
--netD posthoc_attn \
--model attn_cycle_gan \
--concat rmult \
--dataroot $path \
--name SEN1-2_10%_spring \
--mask_size 256 |
51b842813544df1869e64a6899681aa813d2ffa1a895f623db8b07e140d797d0 | Shell | 289 | 6 | # for if you ever wish to use proxyservers, some commands that might be helpful.
# I had less success with proxyserver, so just prefer using the Wifi stick.
conda config --set proxy_servers.http http://id:pw@address:port
conda config --set proxy_servers.https https://id:pw@address:port
|
bd49831cbb8eeaa9431c6555cff656ae725bfceadeac0047eb50bfe0186aa755 | Shell | 289 | 12 | #!/bin/bash
source $HOME/.bashrc
#conda create -n multirbp-gpu-2 python==3.7
conda activate multirbp-gpu-2
conda config --add channels r
conda config --add channels bioconda
mamba install pysam
mamba install -c conda-forge pandas numpy
mamba install -c conda-forge tensorflow-gpu=2.0.0
|
0aa3a9bb3129c4fbed22d9a35d41f30e8cf71bb4b83650fca4787251e1a2e6c2 | Shell | 291 | 14 | #!/bin/bash
#train=$1
rm -rf build pyCRF.so
#ln -sf ../lib/pCRF_pair_align_${train}.so pCRF_pair_align.so
ln -sf ../lib/pCRF_pair_align.so pCRF_pair_align.so
python setup.py build
#cp build/*/pyCRF.so .
#cp build/*/pyCRF.so ../lib/pyCRF_${train}.so
cp build/*/pyCRF.*.so ../lib/.
|
f419d6300a39171404e3301c5aeb529edbc4dc67f11697ac097e6d95fb497d49 | Shell | 291 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 sync_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
2548a9607ba07286141d4324f62a3463ff191eccba822561cb8e73418a4aff21 | Shell | 292 | 19 | #!/bin/bash
#SBATCH -J train
#SBATCH -o logs/train.out
#SBATCH --gres-flags=enforce-binding
echo "train run"
log=./logs/train_log
rm -rf $log
touch $log
conda activate unifold
echo "Start `date`"
mpirun --oversubscribe -n $num_gpus python train.py 2>&1 | tee -a $log
echo "End `date`"
|
d697ca637fa657e0c199371fd608eefb2384661d8a672a8cbd56790e50460d3f | Shell | 293 | 11 | #!/bin/bash
set -eux
NSEQS=1e8
OLGA_DEST=_ignore/olga-generated.tsv
mkdir -p _ignore
../../scripts/olga-generate.sh $NSEQS $OLGA_DEST
python ../../thymic_Q.py lvj-frequency --col-name model_P_lvj _ignore/olga-generated.tsv model-lvj-frequency.csv
bzip2 model-lvj-frequency.csv
rm $OLGA_DEST
|
be061aabceb60107fc82b742d93c136d87bc0173e4494168a6bb6b9d8f21c995 | Shell | 295 | 14 | #!/usr/bin/bash
set -e
mkdir -p fixed;
for file in ./umiout/*;
do
prefix="${file%.dot.gz}";
prefix="${prefix/.\/umiout\//}";
gunzip "$file";
sed "s/umiout\/$prefix.dot.gz/$prefix/" "umiout/$prefix.dot" > "fixed/$prefix.dot";
dot -Tpdf "fixed/$prefix.dot" -o "fixed/$prefix.pdf";
done
ls fixed
|
d78d6c1103ddd0b795a8c4108d9ae8ff16c103c688457c48310aa59a3913188f | Shell | 295 | 13 | #!/bin/bash
DIR=${HOME}
USERID=`id -u $USER`
echo "Starting bisweb/devel container with external persistent directory ${DIR} and uid=${USERID}"
docker run --rm -it --mount src=${DIR},target=/hostfiles,type=bind \
-e LOCAL_USER_ID=${USERID} \
--name bisweb \
bisweb/devel-gpu
|
4c35690d526db0cf5764e973f84393c102da0901cc9d171ee83ca330be9cc313 | Shell | 297 | 14 | #!/bin/sh
# Use longer max pre-post interval of 50ms
# different initial conditions
FREQ=10
python mk_spikes.py --interval 50e-3 --freq $FREQ
for MEAN in 0 5 10 12 15 20 30; do
for SIGMA2 in 0.0 0.001 0.005 0.01 0.1 0.2 1.0 100; do
./run_stdp_long.sh $MEAN $SIGMA2 $FREQ &
done
wait
done
|
5fb25021c9918ac9d0a9d58a8b845f9707c24a7d7d0d0cb7c20abe330fa0fc62 | Shell | 298 | 9 | #Filter the elements that are not longer than 100bp
#step1
less -S {file_pathway}/all.sort.merge.bed| awk '$2-$1+1>100' > {file_pathway}/candidate_UCEs.bed
#step2
bedtools getfasta \
-fi {file_pathway}/ancestor.fa \
-bed {file_pathway}/candidate_UCEs.bed \
-fo {file_pathway}/candidate_UCEs.fa
|
dd4b82762efaa94cc34cc5e9765b02d2c162ee579fb31c9d9dfc8f5c4f5c0798 | Shell | 298 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 camera_ros2_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
0f0c863f10dd3b9983bc0444523bcbb65c56d12e35567ac9d2249eb5c478b346 | Shell | 299 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 saccade_ros2_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
18ec3c6b38164031991386f16f6bbdaf35fe72aa6b36b67c00a4ab84ad4c716c | Shell | 300 | 7 | #/bin/bash
apt-get install -y python-pip bedtools libxslt1-dev libxml2-dev
pip install configparser pandas pybedtools requests biopython matplotlib progressbar progressbar2
git clone https://github.com/wikipathways/wikipathways-api-client-py.git
cd wikipathways-api-client-py
pip install -e .
cd ../
|
1ee73a00dfa19c21dec0d826c90b1e83d9371df8af76eaaac0d65ff0ad9e6639 | Shell | 300 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 saliency_ros2_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
7a17a051974a5467c619c45953090a0cb3a59b64e6ea108f9a4daec168a0879f | Shell | 300 | 9 | DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
export PATH=${DIR}/bin:${DIR}/lib:${DIR}/server:${DIR}/python/modules:${PATH}
echo "------------------------------------------------------------------------------------"
echo "BISWEB scripts are now in your path"
echo "PATH=${PATH}"
echo ""
|
4e2201636c6147df0f6dfc90ffcd88a1e147a59a81027df1a75d6aba51f81f4b | Shell | 301 | 8 | CUDA_VISIBLE_DEVICES=4
spec='python driver.py --dataset toxcast --cross_validation
--model graphconvreg --cold_drug
--prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir_tc_cd
--arithmetic_mean --aggregate toxcast
--intermediate_file intermediate_cv_cdrug.csv '
eval $spec |
77f97257e01ffcb5bba12047d591657b4875d49227ed23cd191031d39103e903 | Shell | 301 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 selection_ros2_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
46bf620a35eddb55dac542ef7e7aad74b1482f15c00732e3794b0c30e502b7b5 | Shell | 302 | 16 | #!/bin/bash
# Trap signals to shut down the Docker container
trap "exit" SIGINT SIGTERM
# Start the ROS node
python3 classifier_ros2_node.py &
# Store the PID of the ROS node
pid=$!
# Wait for the ROS node to finish
wait $pid
# Stop the Docker container once the ROS node finishes
kill -s SIGINT 1 |
41549a34d303419ed07edd3b534ba2dd3f83c5a9dd1895bd164ac624a6caff10 | Shell | 305 | 8 | #paste two substitution files together
species=$1
cd {file_pathway}/${species}
paste {file_pathway}/${species}/${species}_AT-GC.count {file_pathway}/${species}/${species}_GC-AT.count |awk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$NF}' > {file_pathway}/${species}/${species}.AT-GC.GC-AT.count.merge
cd ..
|
c4a272912b7c763ba7ba42efeca3170bdfe77c9b17b2444a9707ae3348a6a9ae | Shell | 306 | 10 | #!/usr/bin/env bash
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
OUTPUTDIR="$DIR/build/html"
FTP_HOST=access763254689.webspace-data.io
FTP_USER=u95689219-uschmidt
FTP_TARGET_DIR=/doc
lftp sftp://$FTP_USER@$FTP_HOST -e "set sftp:auto-confirm yes ; mirror -R $OUTPUTDIR $FTP_TARGET_DIR ; quit"
|
3d1052dc77ab4040f28c1534849b9742751182ab459b7f8068982749d287003b | Shell | 307 | 7 | #!/bin/bash
#SBATCH --job-name=MyModel_EC40_classic_r5
#SBATCH --chdir=/home/genouest/dyliss/nbuton/
#SBATCH --output=tfpc/data/outputs_jobs/output_MyModel_EC40_classic_r5.txt
source python_env_these/bin/activate
cd tfpc/
python3 training.py data/models/fine_tune_models/MyModel_EC40_classic_r5/config.json
|
77a7a4199d9cd2fa0d8bd365ba98ceaf951d3a61b6bf53f98ec4067ac5ffd415 | Shell | 307 | 17 | PKG=embgam
cd ../$PKG
pdoc --html . --output-dir ../docs --template-dir ../docs
cp -rf ../docs/$PKG/* ../docs/
rm -rf ../docs/$PKG
cd ../docs
rm -rf tests
# remove unnecessary files
rm data.html
rm embed.html
rm linear.html
rm index.html
mv embgam.html index.html
# style the new file
python style_docs.py |
7446519596ce0da627e1909d8383598661f478dfec7c97a44b160e448d76fa1b | Shell | 308 | 13 | dataroot='Image_translation_codes/contrastive-unpaired-translation/datasets/sar2opt'
name='sar2opt_CUT'
for ((i=1; i<=5; i++));
do
echo "Processing"$i"th epoch..."
python test.py --dataroot $dataroot \
--name $name \
--model cut
done |
834a87d42b4ca3a5ebf1af0fb4c71550ee6104d75312ebc684e95515e881bf27 | Shell | 308 | 11 | #!/usr/bin/env bash
#
# Build Windows x64 opencc.node using Zig and copy it to the prebuilds directory.
# Usage: ./scripts/build-node-windows-prebuild-zig.sh
set -euo pipefail
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
cd "$ROOT_DIR"
exec ./scripts/build-node-prebuild-bazel.sh win32-x64
|
ca572e76b14206dd9c29e2273279bf8d908654b7612d78e35975762fd798e2a9 | Shell | 308 | 10 | #!/bin/bash
BDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
source ${BDIR}/emsdk_portable/emsdk_env.sh
DIR=${BDIR}/..
echo "Source dir=${DIR}"
cmake -DBIS_A_EMSCRIPTEN=OFF -DPYTHON_EXECUTABLE=`which python3` -DEigen3_DIR=${BDIR}/eigen3/share/eigen3/cmake -DCMAKE_VERBOSE_MAKEFILE=ON ${DIR}/cpp
|
47726a090e5ba9e1d2d183101b7cba430ee870c2a030597a412c6622f62583fc | Shell | 309 | 10 | #!/bin/bash
BDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
source ${BDIR}/emsdk_portable/emsdk_env.sh
DIR=${BDIR}/..
echo "Source dir=${DIR}"
ccmake -DBIS_A_EMSCRIPTEN=OFF -DPYTHON_EXECUTABLE=`which python3` -DEigen3_DIR=${BDIR}/eigen3/share/eigen3/cmake -DCMAKE_VERBOSE_MAKEFILE=ON ${DIR}/cpp
|
f9432f070a233d298a1ab427c12154f84764d73d4fc3d3beb9ccaea538159b38 | Shell | 309 | 9 | #!/bin/bash -eu
# Note: need to shift slightly as eigen value is zero.
./arpackmm --A issue215.mtx --mag LM --nbEV 1 --nbCV 4 --shiftReal 0.1
if [ "$?" -ne "0" ]; then exit 1; fi
./arpackmm --A issue215.mtx --mag LM --nbEV 1 --nbCV 4 --shiftReal 0.1 --restart
if [ "$?" -ne "0" ]; then exit 1; fi
echo "OK"
|
1a026ea24c7b97349c1de52c0daf81e84bdfde09572c22e4fade77e8d2617f6d | Shell | 310 | 14 | #!/bin/bash
set -x
pytest \
--cache-clear \
--cov-append \
--cov-report=xml \
--cov-report=term \
--cov=rpy2.rinterface_lib \
--cov=rpy2.rinterface \
--cov=rpy2.rlike \
--cov=rpy2.robjects \
--cov=rpy2.robjects.lib \
rpy2-robjects/src/rpy2/robjects/tests/robjects/lib/
|
38c62cc31707196325c29403ef1f46298fd07523495ffdebd5e9c68cf22de456 | Shell | 310 | 21 | # !/bin/sh
echo "Do you want to install Pattern Completor Modeling"
select yn in "Yes" "No"; do
case $yn in
Yes ) matlab -nodesktop -nosplash -batch "usetup;exit"; break;;
No ) exit
esac
done
echo "Designated MATLAB MEX Directory"
cd thirdparty/QPBO-v1.32.src
make
echo "Installation Complete"
|
437ca4987b8d57cb0c3a29972609ef50cddf5129d39dc2c9cf118b39e5f82848 | Shell | 310 | 14 | #!/bin/bash
for test_hla_i in 1 11 23
do
python generate_data_leave1out.py \
--hla_class HLA_I \
--test_hla_i ${test_hla_i} \
--limit_hla_i 85 \
--label leave1out_match \
--prop_train 0.75 \
--n_fold 5 \
--rseed 1627
done
|
75a186bbf644267a7bbc57bbe75c03f47b78e370dcd4218ee73d0ee99cf3ded8 | Shell | 311 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J pred-Pysster-snakemake
#SBATCH -p cpu_p
#SBATCH -c 4
#SBATCH --mem=8G
#SBATCH -t 06:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate rbpnet
python scripts/predict_Pysster.py $1 --model $2 --output $3
EOF |
aef9433eec47585612c329159b7944b94378be7e4b13821a3ae6de80aec91a65 | Shell | 311 | 13 | #!/bin/bash -l
#$ -P npbssmic
#$ -l h_rt=24:00:00
#$ -pe omp 36
#$ -m ea
#$ -N concat_ref_in_high_res
#$ -j y
cd /projectnb/npbssmic/s/Matlab_code/PSOCT_code
module load matlab/2019b
matlab -nodisplay -singleCompThread -r "Concat_ref_high_res_vol(156,'/projectnb/npbssmic/ns/210104_2x2x2cm_BA44_45/'); exit"
|
2e8eb73ad934fe03a88002680c3afd59c32b4553b6e2602f5d1e4443658750ba | Shell | 312 | 13 | #!/bin/bash
pytest \
--cov-append \
--cov-report=xml \
--cov-report=term \
--cov=rpy2.rinterface_lib \
--cov=rpy2.rinterface \
--cov=rpy2.rlike \
--cov=rpy2.ipython \
--cov=rpy2.robjects \
rpy2-rinterface/src/rpy2/rinterface/tests \
rpy2-robjects/src/rpy2/robjects/tests
|
21fdbfae0e32dee6e7d81f2ffea7756421f976986546186b7d967d709285696e | Shell | 313 | 10 | #count GC-AT and AT-GC sbustitutions in each UCE
species=$1
cd {file_pathway}/${species}
bedtools coverage -counts -a {file_pathway}/200bp_UCE.bed -b ${species}_GC-AT.bed > ${species}_GC-AT.count
bedtools coverage -counts -a {file_pathway}/200bp_UCE.bed -b ${species}_AT-GC.bed > ${species}_AT-GC.count
cd ..
|
855243b6161a9c889b7424e3ba81a3b827d9fd9706113c05327994028ae85135 | Shell | 313 | 8 | #!/bin/bash
# * mm10 lift to hg38
liftOver whole.mouse.brain.cCREs.bed mm10ToHg38.over.chain.gz -minMatch=0.5 peak.conserve0.5.bed peak.unMapped0.5
# * hg38 lift back to mm10
liftOver peak.conserve0.5.bed hg38ToMm10.over.chain.gz -minMatch=0.5 peak.conserve0.5.reciprocal0.5.bed peak.conserver0.5.unMapped0.5
|
953b7acb7db8c3a8b8e9a30c56778c1e3663f037b1909e9b12a955abea3f693e | Shell | 314 | 7 | CUDA_VISIBLE_DEVICES=3
spec='python driver.py --dataset toxcast --cross_validation
--model tf_regression --prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir4_tc_ct --cold_target
--arithmetic_mean --aggregate toxcast
--intermediate_file intermediate_cv_ctarget.csv '
eval $spec
|
0ba9890b4f4621a9a1e88c06ea045b6918d47f2bc0515df936b9ecf009e004d4 | Shell | 315 | 6 | #!/bin/bash
ls *.csv|cut -d. -f1 > finished
ls *.slurm|cut -d. -f1 > all
squeue|grep "single"|awk '{print $1}'|xargs -L1 scontrol show job|grep "JobName="|cut -d "=" -f3|cut -d "." -f1 > running
cat running >> finished
grep -v -f finished all|awk '{print $1}'|xargs -i echo sbatch {}.tif.slurm > resubmit_failed.sh
|
60d21bea37cecfd27198f46488cb953e305292bfacba3eba75c32db823bb0784 | Shell | 317 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J pred-GraphProt-snakemake
#SBATCH -p cpu_p
#SBATCH -c 2
#SBATCH --mem=8G
#SBATCH -t 14:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate graphprot
GraphProt.pl --action predict -fasta $1 -model $2 -prefix $3
EOF |
def45cb41a359febafdda4943282a70839d540f5d84c5b36f86fa61c391099e9 | Shell | 317 | 20 | #!/bin/bash
# Set up script for the project
# Get setup dir
SETUPDIR=$(dirname $0)
PROJECTDIR=$(dirname $SETUPDIR)
# Step into projectdir
cd ${PROJECTDIR}
# Initialize SQLite DB
touch "database/database.sqlite"
# Add migrations table
php artisan migrate:install
# Run migrations
php artisan migrate
echo 'Done'
|
6cee2fe7e4cb74051014995dc9971e4a7952691246f337b407567eba5ea15a42 | Shell | 318 | 20 | #!/bin/bash
set -xe
source $(dirname "$0")/all-vars.sh
source $(dirname "$0")/all-utils.sh
source $(dirname "$0")/asserts.sh
bitrate=$1
set_ldc_sample_filename "${bitrate}"
download_material "${CI_TMP_DIR}/ds"
export PATH=${CI_TMP_DIR}/ds/:$PATH
check_versions
ensure_cuda_usage "$2"
run_basic_inference_tests
|
afa92516aedd37f4e7edc8f7aa2cc5c8a8ebd62dbc55ebab4f4692b99dd087c3 | Shell | 318 | 12 | #!/bin/bash
root=../OsconetInput
outdir=../OsconetOutput
mkdir -p $outdir
log=run_all.log
experiment=YourDataset # change this to reflect the 'case' name set in script 2
for fn in ls $root/*$experiment*; do
echo "file $fn"
python FindOsc.py $fn 128
mv $root/*.out.csv $outdir/
mv $root/*.psi_ng.csv $outdir/
done
|
f52cbea421a3e1806dd95cb05f734c95c389d8b18f0eaa0591da2e5d45e25877 | Shell | 319 | 17 | PKG=imodelsx
cd ../$PKG
pdoc --html . --output-dir ../docs --template-dir ../docs
cp -rf ../docs/$PKG/* ../docs/
rm -rf ../docs/$PKG
cd ../docs
rm -rf tests
# remove unnecessary files
# rm data.html
# rm embed.html
# rm linear.html
# rm index.html
# mv embgam.html index.html
# style the new file
python style_docs.py |
547fdfbcb30edf5c83661dce3cc86ebee6a79edaeec463e805e73608040e4a9d | Shell | 321 | 10 | R --vanilla --slave --args $1 $2 $3 $4 $5 $6 $7 $8 $9 ${10} ${11} < /HOME/galaxy/galaxy-dist/tools/MiClip/MiClip.R > dump
if [ -f ${__tool_data_path__}clusters.csv ]
then
zip temp ${__tool_data_path__}log.txt
zip temp ${__tool_data_path__}*.csv
mv ${__tool_data_path__}temp.zip ${12}
else
cat dump >&2
fi
... |
36e1b17b52bbf76177e469b79bd3d56ba007b05ae53c8875b917af7565e69f6c | Shell | 322 | 22 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
bitrate=$1
set_ldc_sample_filename "${bitrate}"
download_material "${TASKCLUSTER_TMP_DIR}/ds"
export PATH=${TASKCLUSTER_TMP_DIR}/ds/:$PATH
check_versions
run_all_inference_tests
run_multi_inference_tests
run_cpp_only_inference_tests
run_hotword_test... |
4f5dea4312e43eae68370519831f567c086b2b5150cef10d80d80fb39c5b2e7b | Shell | 323 | 9 | epochs=30000;
lr=1e-4;
logfile=out.log
for N in 100 200 350 500 1000 2000 4000 5000 10000 20000 40000 60000; do
for gpu in {0..7}; do i=$(($gpu+1234));echo $i;CUDA_VISIBLE_DEVICES=${gpu} python hvd_hval.py --save --seed ${i} --N $N --lr ${lr} --epochs ${epochs} 2> /dev/null&echo "$N"-"$gpu"> "$logfile";
done
wait;
d... |
7acac5b38802ed98cdb8c20c59934c3f20a8720ee750269bc45416f9b6f73af5 | Shell | 325 | 11 | #!/usr/bin/env sh
TOOLS=./build/tools
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_quick_solver.prototxt
# reduce learning rate by factor of 10 after 8 epochs
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
--snapshot=examples/cifar10/cifar10_quick_iter_4000.solvers... |
09504bd35d8d7dd40d2d19d967d47ad4a6d93656a47a8385afe5d635841cd8df | Shell | 327 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J multirbp-predict
#SBATCH -p cpu_p
#SBATCH -c 48
#SBATCH --mem=32G
#SBATCH -t 04:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate multirbp-cpu
python -u scripts/eval.py --test-input $1 --model-file $2 --output-csv ... |
1a0a1bdf516c33f6dab3490a20d7c1d7962f049b46789f8bbfa5a165979d1a8d | Shell | 327 | 3 | #!/bin/bash
python scripts/main.py -wf data/aug_22/wf-subset.csv -tu data/aug_22/tool_popularity_Aug_22.csv -om data/aug_22/tool_recommendation_model.hdf5 -cd '2017-12-31' -pl 25 -ti 200 -nhd 4 -ed 128 -fd 128 -dt 0.2 -lr 0.001 -ts 0.2 -trbs 512 -tebs 512 -trlg 10 -telg 10 -ud false --model_type transformer --restart_... |
fe0e7286014228c4f59cf027c84011ed13256d82615e582e8705192797c42d84 | Shell | 327 | 19 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J GraphProt-snakemake
#SBATCH -p cpu_p
#SBATCH -c 4
#SBATCH --mem=22G
#SBATCH -t 30:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate graphprot
GraphProt.pl --action train -fasta $1 -negfasta $2
mv ./GraphProt.model ... |
c4159dda460dc06d0625786a0a9285859325802765a0e1abb4a12955d4f159b7 | Shell | 328 | 19 | #!/bin/bash
python train.py \
--meta_batchsz 15 \
--meta_lr 0.001 \
--num_updates 5 \
--random_seed 68 \
--p_dropout 0.2 \
--fingerprint_dim 250 \
--radius 3 \
--T 2 \
--n_way 2 \
--k_shot 3 \
--k_query 1 \
--device 0 \
--train_path 'data/train/decoys/clus_train_tasks_434_decoys.csv' \
--save_dir './results/meta-learn... |
e484f10ddc7e4c087e0dbd77d90673ebb0858de713f867a76f5315ef71d2411d | Shell | 328 | 16 | #!/bin/bash
#--- Switch to the correct path (where the script is in)
backpath=`pwd`
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd $DIR
#--- Download the compressed file
wget -nc http://modelnet.cs.princeton.edu/ModelNet40.zip
#--- Extract it
unzip ModelNet40
#--- Go back to the initial directory
cd $b... |
1f2a1c0d12b0625b26631eca7c65b76c590f167c3157fe903e2b84122d0f815f | Shell | 329 | 6 | #!/bin/bash
export XDG_CACHE_HOME=/tmp/.cache/
export LD_LIBRARY_PATH=/opt/conda/lib
export PATH=/var/DAFi-gating/inst/bin:/var/DAFi-gating/FCSTrans:/var/DAFi-gating/Python:/var/DAFi-gating/inst/bin:/var/DAFi-gating/FCSTrans:/var/DAFi-gating/Python:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:... |
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