sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ebdf347934fb6b18523dc0a69d9699b47346c332f1537a676251ec1be07df1a7 | Shell | 159 | 9 | #!/bin/bash
ENVNAME=lab
source activate $ENVNAME
conda install ipykernel
python -m ipykernel install --user --name ppqm --display-name "PPQM (Python 3.9)"
|
6e61765d8c18f4fc606073616fa757189c49155e077320d7722e54593e712408 | Shell | 163 | 13 | #!/bin/bash
## Test some common command line options for all models.
source env.sh
cleanup
./test_seq_rnn.sh
./test_seq_trans.sh
./test_graph_trans.sh
cleanup |
cfeb8f94d4f16c7e778cf5f5782e0ce7e4783b93a9a6495917cafb5306cc7c70 | Shell | 163 | 7 | #!/usr/bin/env bash
input=$1
output=$2
#python3 ../python/umi_filtering.py "$input" "$output"
python "$BLISS_PATH"/../python/umi_filtering.py "$input" "$output"
|
86a85fd5341279eee94b2f2ae46222c2c325642b5a9db6c1044221dc80a0c972 | Shell | 164 | 7 | #!/bin/bash
cat ../../comparison/masif_ppi_search/benchmark_list.txt | while read line
do
./data_prepare_one.sh $line
./compute_descriptors.sh $line
done
|
cdd77871ad9f12f1ff316bb8a8ec821f0d2868141a30af31b48090089906b994 | Shell | 165 | 9 | #!/bin/sh
mkdir -p resources/wordEmbeddings
cd resources/wordEmbeddings
wget http://nlp.stanford.edu/data/glove.6B.zip
unzip glove.6B.zip
rm glove.6B.zip
cd ../..
|
04034985ec6101cf019547996896b9f4412d005e9b3ded2aa30c1b01af2b3682 | Shell | 166 | 5 | #!/bin/bash
python3.7 main.py --config=../data/model/DCGAN/generator_config.json \
--nimages=1 \
--save_path=../data/sdk_result/
|
2d5542383e86132e96ccafc2f1368825d50997fd67834d9ec13ddeaea8f8fdd2 | Shell | 167 | 7 | #!/bin/bash
cat ../../comparison/masif_ppi_search_ub/benchmark_list.txt | while read line
do
./data_prepare_one.sh $line
./compute_descriptors.sh $line
done
|
5842d8e1288cb69b0c628ad48b4fe7adafb52e3c4a542b56de76b1e4cb4fa6ae | Shell | 168 | 14 | #!/usr/bin/env bash
# stop if anything goes wrong
set -e
rm -rf _build
export PYTHONPATH=`pwd`/../
# configure
sphinx-apidoc -o ./api/ ../drugex/
# make
make html
|
aa02fafa314b5b2c255ae419306adce11267bd06c8da00d8189ecf6174eb89a5 | Shell | 170 | 13 | #!/bin/bash
## Test some common command line options automatically.
source env.sh
./test_graph.sh
./test_ved.sh
./test_attn.sh
./test_single.sh
./test_gpt.sh
cleanup
|
be68e6dfb23239b535fa04c09c182a1a58d5044b4e82567810c5d1efd54549f4 | Shell | 170 | 5 | python -m torch.distributed.launch --nproc_per_node=4 --master_port=6889 pretrain_stylegan2.py \
--batch 4 \
--num_workers 4 \
--dataset cub \
--size 256
|
c22712c5018257a67a49668ea9f7a54f3996b3edbbba3b53a7f01446262072b7 | Shell | 170 | 4 | gcc -o rigidmc -g rigidMC.c \
-I/home/blairlab/tools/package/fftw-3.3.7/install/include \
-L/home/blairlab/tools/package/fftw-3.3.7/install/lib \
-lfftw3 -lm
|
dd2f77239820d1a0405c14fa9c58c9f4e5b88072bf509ce357060409fa548ea5 | Shell | 170 | 7 | set -e
# Copy over gemfiles needed for the build
cp ../NeuroML-DB.org/Gemfile Gemfile
cp ../NeuroML-DB.org/Gemfile.lock Gemfile.lock
docker build -t neuromldb:latest .
|
777745338773aa5a9749c597693a5f7a496a40547247a5864fabc9953f110b8b | Shell | 172 | 2 | wget https://www.dropbox.com/s/rb8zpvrbxbbutxc/global.pth?dl=0 -O ./weights/global.pth
wget https://www.dropbox.com/s/pby9dhpr6cqziyl/local.pth?dl=0 -O ./weights/local.pth
|
c6da03d86122709547ee2db71131d8b81305588731d6322b3959912a4cf53149 | Shell | 172 | 5 | #!/bin/bash
echo "Bag file: $1"
python3 ./image_extractor.py --bag-file=/gpfs/space/projects/Bolt/bagfiles/$1 --extract-dir=$2 --crop-camera-images --resize-camera-images |
04b8996408e3421223852c28894d5e22660f131318c60064982affb3410e0dd4 | Shell | 174 | 5 | python -m torch.distributed.launch --nproc_per_node=4 --master_port=6889 pretrain_stylegan2.py \
--batch 4 \
--num_workers 4 \
--dataset nabirds \
--size 256
|
1f50aa18b3fe3d25546303f1169d7ffedd7bdeb32fbd7d1db55a3836731d6d33 | Shell | 174 | 5 | #!/bin/sh
DOCKER_IMAGE=quay.io/pypa/manylinux1_x86_64
TOP_DIR=$(pwd)/../..
docker run --rm -v $TOP_DIR:/io $DOCKER_IMAGE /bin/bash /io/tools/manylinux-build/build-wheels.sh
|
56bb176fb14ffc5f81eab2295b8e9224b6642bd938a9da6d42116fa1c9ac9296 | Shell | 175 | 5 | python -m torch.distributed.launch --nproc_per_node=4 --master_port=6889 pretrain_stylegan2.py \
--batch 4 \
--num_workers 4 \
--dataset celebahq \
--size 256
|
210cc3a9f20787bfc97f8d28114f51c52a92dd77e867e4aea95895b3778dfd46 | Shell | 176 | 3 | #!/bin/sh
curl -fsLO https://raw.githubusercontent.com/scijava/scijava-scripts/master/travis-build.sh
sh travis-build.sh $encrypted_cf54a6e15083_key $encrypted_cf54a6e15083_iv
|
1334bccb70bf55df74a544d9d18d61e9196dbe6363c6c5f0387cffae0e022ee3 | Shell | 177 | 9 | #!/bin/bash
## conda environment name
ENV_NAME=aquas_chipseq
ENV_NAME_PY3=aquas_chipseq_py3
conda env remove --name ${ENV_NAME} -y
conda env remove --name ${ENV_NAME_PY3} -y
|
bebea209f742c4c1932dbcf2f1d876d0f905de4d2a35d915cf1c53c0a97c5e80 | Shell | 177 | 6 | #!/bin/bash
docker run --rm -it -v $(pwd):/share ucsdtnel/autolfads:$TAG \
--data /share/data \
--checkpoint /share/container_output \
--config-file /share/data/config.yaml
|
3533ea627c7c364698aaddc5fad0f93590baacdf5402cf7227ee7ec0ca445a5f | Shell | 179 | 5 | cerberus create -c boot-crb.json
cerberus attach -c boot-crb.json
cerberus inspect -c boot-crb.json
cerberus train -c boot-crb.json
cerberus predict boot-crb-latest.h5 sample.tif
|
7c888093b07391a994cb2fccc5df868a722d80357ab231ce9c58ca53d89260cf | Shell | 179 | 6 | #!/bin/bash
docker run --rm -it -v $(pwd):/share ucsdtnel/autolfads:latest \
--data /share/data \
--checkpoint /share/container_output \
--config-file /share/data/config.yaml
|
27128a8ecdfd91ac0eaf84998ad4a173f5e0979a40cbe5ce728aab533935b1ce | Shell | 184 | 6 | #!/bin/bash
echo "this script requires minc-tools";
echo "input dir: $1";
echo "output dir: $1";
for i in $1/*.mnc; do mnc2nii -short -nii $i; done
for i in $1/*.nii; do gzip $i; done
|
e28fb0757687b14109fb67e4454a0c7e1071a0d678e37860aa9c28e514f4148a | Shell | 185 | 9 | #!/bin/bash
#SBATCH --time=1-00:00:00
#SBATCH --partition=gpu
#SBATCH -o eval_%j.out
#SBATCH -e eval_%j.err
source activate selene-env
python -u ../../../selene_cli.py ./evaluate.yml
|
ab8a22b0b544beeec988c6a55f9474c6e2f63286d29af549d40e794df18aa409 | Shell | 187 | 12 | #!/bin/bash
NUM_WORKERS=$1
docker rm -f spark-master-fishubu &> /dev/null
i=1
while [ $i -le $NUM_WORKERS ]
do
docker rm -f spark-worker-fishubu-$i &> /dev/null
i=$(( $i + 1 ))
done
|
03b276d8f9c8434ced11684761cbcaba22b5e7b3532b728f5a3afb7bde45a5d3 | Shell | 189 | 11 | {
PYTHONPATH=.:$PYTHONPATH python ft_clip_text.py \
--dataset cub \
--train_split train \
--batch 256 \
--num_workers 26 \
--epoch 100 \
exit
} |
a10bbc8695e8c100785a566194a056fbd3ae612ad7736f88c047bf9b5a660913 | Shell | 189 | 7 | # command to start container
export PATH=$PATH:/usr/local/nsp-server/src/sh/
writeOutput.sh 'NSP container started.'
configAWSCLI.sh
exec node ./src/js/index.js &
runSimManagerS3.sh |
dca9d69be3fcd27b8d7b1ac39fed242cc81dd34e5f8b4f9a51b2860a3d94c323 | Shell | 189 | 10 | {
PYTHONPATH=.:$PYTHONPATH python ft_clip_text.py \
--dataset celebahq \
--batch 192 \
--num_workers 14 \
--epoch 1000 \
--train_split train
}
|
dd0206ee74696ec91d365f16da2d2d35e6266001454b6ff62be943d366743601 | Shell | 192 | 5 | mkdir -p ../data/neuron_meshes_v185
pushd ../data/neuron_meshes_v185 \
&& gdown --id 1lPWicWMBsIiXur68Sqf_FMS5ZzHoSqLj -O neuron_meshes_v185.tgz \
&& tar -xzf neuron_meshes_v185.tgz \
&& popd
|
a4153b7d5e36d62248b366a9d247c4ec0f0935a1710253bf2211ee0b0691d230 | Shell | 196 | 7 | #!/bin/bash
create_model -c boot-unet.json
attach_data_sources -c boot-unet.json
inspect_data_sources -c boot-unet.json
train_model -c boot-unet.json
predict_image boot-unet-latest.h5 sample.tif
|
cb76054678175a922f5e3825ba3a763195d0e44a152848aacca57f4eb9356e9d | Shell | 196 | 8 |
echo "start liftover"
/groups/umcg-biogen/tmp03/tools/liftover/liftOver \
$1 \
/groups/umcg-biogen/tmp03/tools/liftover/hg19ToHg38.over.chain.gz \
$2 \
$3
echo "Done"
|
58f83e242d697853345e86017ffb644ef68aaa70a9049151004fca96f6afb6c5 | Shell | 199 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file configs/e2e_faster_rcnn_DETNAS_COCO_FPN_300M.yaml OUTPUT_DIR models/DETNAS_COCO_FPN_300M_1x
|
5869e0d8a66935ab531c330ad03fe02943b62874bdbb22691b0e43202f886c7c | Shell | 200 | 12 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-08:00
#SBATCH -p gpu
#SBATCH --gres=gpu:1
#SBATCH --mem=10G
#SBATCH -o PGEX.out
#SBATCH -e PGEX.err
#SBATCH -J PGEX_exps
source activate GXAI
python3 PG_exps.py
|
d5e724b63782b824cd15f139285bdb9a76f2654c650fc957e532e99c6106dcdc | Shell | 200 | 8 | #!/bin/bash
# write NSP echo with prompt
if [ -n $nsp_debug ]; then
# Check if the VARNAME variable is greater than zero
if [[ "$nsp_debug" -gt 0 ]]; then
echo "NSP DEBUG> $@"
fi
fi
|
607e18dc0654a95d8dad7393b4c4bf80f0aab2727d2b3a8f5aafefcd295fd785 | Shell | 201 | 9 | unzip SG_HF_HF=-1.pickle.zip
mv SG_house_HF=-1.pickle SG_HF_HF=-1.pickle
unzip SG_HF_HF=1.pickle.zip
mv SG_house_HF=1.pickle SG_HF_HF=1.pickle
unzip SG_fair.pickle.zip
unzip SG_triangles.pickle.zip
|
61e4a2822d7246968da84a8a2dbe8113fe3246f214f3b43ad468b6c8982a20b5 | Shell | 201 | 8 | #!/usr/bin/env bash
set -euo pipefail
HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
CONTROL_PYTHON="${ALPAMAYO_CONTROL_PYTHON:-python3}"
exec "$CONTROL_PYTHON" "$HERE/alpamayo_control.py" "$@"
|
ca68c3fe9fa262491eb90d1ebbd8ae8daa031ceed5660b1571d3ecd9dbc629e9 | Shell | 202 | 8 | #!/bin/bash
cat lists/testing_transient.txt | while read line
do
./data_prepare_one.sh $line\_
done
./predict_site.sh -l lists/testing_transient.txt
./color_site.sh -l lists/testing_transient.txt
|
b43b89806511561bacd92d849722ef3013fdf3ba66818e5e8a7de893f2a309cd | Shell | 203 | 8 | #!/usr/bin/env bash
# Installs dscore
git clone https://github.com/nryant/dscore.git
pip3 install intervaltree --user
pip3 install tabulate --user
pip3 install munkres --user
pip3 install pytest --user
|
cde7bcf3d641921d2ccd8e9dee619e3746fb01958a72df91106d408138235877 | Shell | 204 | 12 | for i in {42..44}
do
cp SUBX_SG.sh SUBX_SG_tmp_${i}.sh
echo "Running SUBX $i"
sed -i "s/NUM/$i/" SUBX_SG_tmp_${i}.sh
sbatch SUBX_SG_tmp_${i}.sh
rm SUBX_SG_tmp_${i}.sh
done
|
61aa2ce58228889b7ae039094323dfd05f71c695ae85d3af66a0f6fc986e2227 | Shell | 205 | 8 | #!/bin/bash
# show list of mod from s3
#!/bin/bash
# show list of experiments from s3
for f in `aws s3 ls s3://nsp-project/models/genesis/| awk '{print $2}'| sed 's/\///g' `; do echo "$f"; done
|
08756bf838cf418be59a6e71260c0399bc9d8dc39f471574443d882af15fe5f5 | Shell | 207 | 8 | #!/usr/bin/env bash
in=$1
out=$2
patfile=$3
echo "Filterig reads based on patterns ..."
cat $in/r1.fa | parallel --block 100M -k --pipe -L 2 "scan_for_matches $patfile - " > $out/filtered.r1.fa
echo "Done"
|
c013b4c036ca1c5c32cf5b7323bb1efd5512ea972b5c99911ded7de7e2504fe7 | Shell | 207 | 3 | #!/bin/bash
exec java -Djava.util.logging.config.file=/etc/jitsi/jibri/logging.properties -Dconfig.file="/etc/jitsi/jibri/jibri.conf" -jar /opt/jitsi/jibri/jibri.jar --config "/etc/jitsi/jibri/config.json"
|
27390b82ed91b63bbe401633cc90772d36189319911eb3504d68a1061f76b7d9 | Shell | 208 | 11 | #!/bin/sh
date=`date +"%Y%m%d"`
pkg=decoder-$date.tar.gz
if [ ! -d ../../decoder ]; then
echo "ERROR: can not find ../../decoder/"
exit 1
fi
tar -C ../../ -czvf $pkg --exclude-from excludes decoder |
da91f55496d4a0f88bf09cc4f5a69e93af01cb25dd767876ba7403c4ae172db7 | Shell | 208 | 5 | rm -rf ./nsp-model
git clone --recursive https://karolchlasta@github.com/KarolChlasta/nsp-model.git
cd nsp-model
aws s3 cp . s3://nsp-project/models/genesis --recursive --exclude "*.md" --exclude ".*"
|
63dc0136c676bf7edc3703107fba16783800db6bdf28bcdddc149e5ebc9080d7 | Shell | 209 | 10 | #!/usr/bin/env bash
# CUDA_PATH=/usr/local/cuda/
cd src
echo "Compiling stnm kernels by nvcc..."
nvcc -c -o nms_cuda_kernel.cu.o nms_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52
cd ../
python build.py
|
e14bfa2fef88e4628ab2312297287e4fce1d164d67b565f801bb961d35d4d3b7 | Shell | 209 | 9 | #!/bin/bash
for file in `ls r_*gz | grep -v div | sed 's/_C.__optfixed.nii.gz//g' | uniq`
do
echo ${file}
fslmaths ${file}_C0__optfixed.nii.gz -div ${file}_C1__optfixed.nii.gz ${file}_C0divC1.nii.gz
done
|
13ac12320d74129bba5055635ef5bf099bb0e5214100558602395e52fb65efe7 | Shell | 211 | 12 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-12:00
#SBATCH -p gpu
#SBATCH --gres=gpu:1
#SBATCH --mem=10G
#SBATCH -o SUBX-3.out
#SBATCH -e SUBX-3.err
#SBATCH -J SUBX-3
source activate GXAI
python3 SX.py --start_ind -3
|
6b2f21a7e7625c9e2e5f881949cfb5fb2038cc90b10abc37556a0904a5bb3231 | Shell | 211 | 10 | #!/usr/bin/env bash
CUDA_PATH=/usr/local/cuda/
cd src
echo "Compiling my_lib kernels by nvcc..."
nvcc -c -o roi_align_kernel.cu.o roi_align_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52
cd ../
python build.py
|
6d67fd303b9e7b34b5c5b9875217901e9cc0f40193c4c2e4a6b8a051df286ae7 | Shell | 211 | 5 | #!/bin/bash
echo "Bag file: $1"
python3 ./image_extractor.py --bag-file=/gpfs/space/projects/Bolt/bagfiles/$1 --extract-dir=$2 --crop-camera-images --resize-camera-images --extract-side-cameras --extract-lidar |
0e65472ea7d3042c56ebc91fff98d5fccde27bc01203d286552745cf4f9d25c7 | Shell | 215 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/e2e_faster_rcnn_DETNAS_COCO_FPN_300M_search.yaml OUTPUT_DIR models/DETNAS_COCO_FPN_300M_1x_search
|
2cd34436adb7530530f2b2ee5610933f1fa880491911a38b2b296943d001d249 | Shell | 215 | 13 | #!/bin/bash
#SBATCH -J tan_projection
#SBATCH -N 1
#SBATCH -n 1
#SBATCH --mem 256G
#SBATCH -t 01:00:00
#SBATCH --mail-type END
module load python
source activate NiLearn
python ./tan_projection.py
conda deactivate |
c94e6ca6ae5306e14d0671ae4756d0c57bb1d83f2bdfc8761f837d524f5e09aa | Shell | 215 | 10 | #!/bin/bash
#SBATCH --time=00:02:00
#SBATCH --gres=gpu:1
#SBATCH --partition=gpu
#SBATCH -o ism_%j.out
#SBATCH -e ism_%j.err
source activate selene-env
python -u ../../../selene_cli.py ./in_silico_mutagenesis.yml
|
ceee4768c07e45419d69f2f8c2fdf65590930b2557a5864b955cd18001dcf088 | Shell | 217 | 11 | gpu=$1
data=$2
start=0
end=`cat config.json | jq '.data_loader.args.num_folds'`
end=$((end-1))
for i in $(eval echo {$start..$end})
do
python train_Kfold_CV.py --fold_id=$i --device $gpu --np_data_dir $data
done
|
04dd8dd137abf3ad07657fe1f25d36b1577c2680b32b70eb33a20d8d60256736 | Shell | 219 | 10 | #!/usr/bin/env bash
CUDA_PATH=/usr/local/cuda/
cd src
echo "Compiling my_lib kernels by nvcc..."
nvcc -c -o roi_crop_cuda_kernel.cu.o roi_crop_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52
cd ../
python build.py
|
1714b27ca27d45fbbd78cde1f23ac0346e490e735e23d4a5578766153db80db2 | Shell | 219 | 10 | #!/bin/bash
python simulations.py learn
python simulations.py reversal &
python simulations.py reversal_prat &
python simulations.py punish &
python simulations.py punish_prat &
python simulations.py devalue &
wait
|
10ce413351f5e30cbfdace91cbe5a1d99d51d0e0e9676c7cdda79aef4e00cb19 | Shell | 223 | 10 | #!/bin/bash
python simulations.py reversal02 &
python simulations.py reversal05 &
python simulations.py reversal20 &
python simulations.py punish02 &
python simulations.py punish10 &
python simulations.py punish20 &
wait
|
09c24e2a9793b45430efb4ee5119e2eafe95f679fb7d973dfc0cd03e120e3fb8 | Shell | 224 | 12 | for i in {36..49}
do
cp SUBX_SG_ggf.sh SUBX_SG_ggf_tmp_${i}.sh
echo "Running SUBX $i"
sed -i "s/NUM/$i/" SUBX_SG_ggf_tmp_${i}.sh
sbatch SUBX_SG_ggf_tmp_${i}.sh
rm SUBX_SG_ggf_tmp_${i}.sh
done
|
894f655c7ea879ca46c3e8ec23a4313ff5ae526919bb9cde1f33d4d24cd236ba | Shell | 226 | 12 | for i in {0..9}
do
cp SUBX_SG_gcf.sh SUBX_SG_gcf_tmp_${i}.sh
echo "Running SUBX GCF $i"
sed -i "s/NUM/$i/" SUBX_SG_gcf_tmp_${i}.sh
sbatch SUBX_SG_gcf_tmp_${i}.sh
rm SUBX_SG_gcf_tmp_${i}.sh
done
|
81a1ab42b04d7d359e589a665a78e1a4afc85b0336c3cf50c5886e879e3cd2f9 | Shell | 227 | 9 | #!/bin/sh
#
# Executes the command at given line of given a file. First argument is the file
# name and the second argument is the line number starting from 0.
#
file="$1"
line=$(expr $2 + 1)
eval $(sed -n ${line}p "${file}")
|
b9ec65312c5157093d9216e393a84fac38210bdbd5da6b85cc7dc16fd991ef77 | Shell | 227 | 14 | cd batch
rm x*.sh
dos2unix simrun.sh
split -d --additional-suffix=.sh -l $(expr `wc -l simrun.sh | cut -d ' ' -f1` / $1) simrun.sh
chmod -R +x .
cd ..
find . -type f -name "x*.sh" -exec gnome-terminal -e {} \& disown \;
|
e908282bc8730ede72ee98b1548fcfdb9ce00f860270bead3e4719cf52374447 | Shell | 228 | 13 | #!/usr/bin/env bash
# This shows what you can potentially run; you'd probably want to pick and choose.
# The ones with _gpu in their name are tuned for GPUs.
use_gpu=true
if $use_gpu; then
local/nnet2/run_6c_gpu.sh
fi
|
a1861f1b6b72063ecebb04c0948ebb8ea5cf09565f81fee1c42b0039fb6bd516 | Shell | 229 | 8 | #!/bin/bash
# Create a kafka topic with 3 partitions
if [[ ! -z $1 ]]
then
docker exec kafka_kafka_1 kafka-topics --create --topic $1 --partitions 3 --replication-factor 1 --if-not-exists --zookeeper kafka_zookeeper_1:2181
fi
|
134864892f507a74cc22a0275894ae4da5d10f8406031e5d784b3c37b6e4d7eb | Shell | 230 | 10 | #!/bin/sh
REPO=$1
TARGET=$2
if [ -z $TARGET ] ; then
echo $(curl -s https://api.github.com/repos/$REPO/releases/latest | grep tarball_url | cut -d\" -f4)
else
echo "https://api.github.com/repos/$REPO/tarball/$TARGET"
fi
|
dc70f28b8182f6022e166c1ef74c369d98fcf76db92a56981317bb50085e4265 | Shell | 230 | 5 | masif_root=$(git rev-parse --show-toplevel)
masif_source=$masif_root/source/
masif_data=$masif_root/data/
export PYTHONPATH=$PYTHONPATH:$masif_source:$masif_data/masif_site/
python3 $masif_source/masif_site/masif_site_train.py $1
|
fa7e424ad23e6aca7a5a6f51914ca8a95b8fd661b56aa399340921568abc0d64 | Shell | 230 | 8 | #!/bin/bash
CONF="/etc/jitsi/jibri/jibri.conf"
PORT=$(hocon -f $CONF get jibri.api.http.internal-api-port 2>/dev/null || true)
[[ -z "$PORT" ]] && PORT=3333
curl -sX POST http://127.0.0.1:$PORT/jibri/api/internal/v1.0/shutdown
|
3c1af5ff7c0c9dae566e2d64fd681ad56a72d0f5efe8068c6c8f40b82976b57d | Shell | 231 | 10 | #!/bin/bash
#SBATCH --time=1-00:00:00
#SBATCH --gres=gpu:1
#SBATCH --partition=gpu
#SBATCH -o variants_%j.out
#SBATCH -e variants_%j.err
source activate selene-env
python -u ../../../selene_cli.py ./variant_effect_prediction.yml
|
836d4b051064d78c14f05e454c58f5cccbd41f6611b0bf128e474bf031f4bf0d | Shell | 235 | 9 | # !/usr/bin/env sh
python ./tools/test.py \
./configs/fna_ssdlite_retrain.py \
--checkpoint ./ssdlite/ssdlite.pth \
--net_config ./ssdlite/net_config \
--data_path ./coco/ \
--out ./results.pkl \
--eval bbox \
|
4dd28731241fc8ca3e3d700ae2bbadae79eea335d301f2b6f9ff85a28b6ef410 | Shell | 236 | 17 | #!/bin/bash
#$ -cwd
#$ -j y
# Resources requested:
#$ -l h_data=8G,h_rt=04:00:00
#$ -V
function runFastQC(){
../FastQC/fastqc *.fastq.gz --outdir=../fastqcOut
}
tar -xvf trimmed_clean_fastqs.tar.gz
cd trimmed_clean && runFastQC |
d63d97f3790cd2f9cf8bdfd1c69f198ecf2fd6e2c52c657520afe32f9f649747 | Shell | 238 | 8 | #!/bin/bash
CONF="/etc/jitsi/jibri/jibri.conf"
PORT=$(hocon -f $CONF get jibri.api.http.internal-api-port 2>/dev/null || true)
[[ -z "$PORT" ]] && PORT=3333
curl -sX POST http://127.0.0.1:$PORT/jibri/api/internal/v1.0/gracefulShutdown
|
00887337d5b84ca7bd0e216250986058197c4f71c6d7b2996cef6348e623faa6 | Shell | 239 | 14 | java -jar Depict2.jar \
-g demoData/demoGwas \
-ge demoData/demoGenes.txt \
-m RUN \
-o demoResult/demo \
-p 1000 \
-r demoData/demoGenotypes.vcf.gz \
-rs demoData/demoSamples.txt \
-R VCF \
-t 1 \
-v 0.95 \
-w 1000 \
--debug
|
c1767b226ac0830003524cdded431589fb1ac1f482cfbc6f1fedb417913ec72f | Shell | 239 | 12 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-12:00
#SBATCH -p gpu
#SBATCH --gres=gpu:1
#SBATCH --mem=10G
#SBATCH -o PGMEX.out
#SBATCH -e PGMEX.err
#SBATCH -J PGMEX_exps
source activate GXAI
python3 eval_SG_exps.py --exp_method PGMEX --model GIN
|
9dcb3c809ac8c1cfffc5d9e89b06d5aa903ae218a94c0acfd24094a211169583 | Shell | 241 | 6 | #! /bin/bash
for f in *.fastq.gz; do
~/bbmap/bbduk.sh in=$f out=${f}_trimmed_clean ref=ref/polyA.fa.gz,ref/truseq_rna.fa.gz k=13 ktrim=r useshortkmers=t mink=5 qtrim=r trimq=10 minlength=20 int=f -Xmx27g
mv ${f}_trimmed_clean tc/$f
done |
d4192fa560432f16aa8e6ab694bf48d45c8626a1f76b6e9491048e5899466b68 | Shell | 241 | 8 | #!/bin/sh
#for ppi_pair in 3F74_A_B 3HRD_E_H 1ERN_A_B 2B3Z_C_D 4KGG_C_A 2FE8_A_C 1SOT_A_C 2LBU_E_D 3PGA_1_4 3QWN_I_J 3TND_B_D 1XDT_T_R
while read ppi_pair;
do
echo "Starting $ppi_pair"
./eval_one.sh $ppi_pair
done < ../benchmark_list.txt
|
f274a65ed33fae2633e2a7b55fb924576628a14424ac20c4e6a959a12e5d3e28 | Shell | 241 | 6 | #!/bin/bash
echo "this script requires minc-tools";
echo "input dir: $1";
echo "output dir: $1";
for i in $1/*.nii*; do nii2mnc -short $i `echo $i | cut -d "." -f1`.mnc; done
#for i in $1/*.nii*; do echo `echo $i | cut -d "." -f1`.mnc; done
|
f5f6bd2268d9d1b7453e3faa19d26afff2e35ddd1daf18f8cbd376db3bc1a564 | Shell | 241 | 8 | #!/bin/bash
CONF="/etc/jitsi/jibri/jibri.conf"
PORT=$(hocon -f $CONF get jibri.api.http.internal-api-port 2>/dev/null || true)
[[ -z "$PORT" ]] && PORT=3333
curl -sX POST http://127.0.0.1:$PORT/jibri/api/internal/v1.0/notifyConfigChanged
|
f73a0aec8cfd4d036b6f167cdfe795734ed0c99777cb61372a02f45d00e108ec | Shell | 241 | 9 | # Build venv with required packages
VENV=".venv"
PYTHON_VENV="${VENV}/bin/python"
python -m venv $VENV
$PYTHON_VENV -m pip install -U pip
$PYTHON_VENV -m pip install -r requirements-dev.txt
# Run tests
$PYTHON_VENV -m pytest ./test_sync.py
|
da9ad40cce33067d12895081142d485b12e078b94e0d6b2c9fd70a534a6f8e30 | Shell | 243 | 8 | #!/bin/sh
#for ppi_pair in 3F74_A_B 3HRD_E_H 1ERN_A_B 2B3Z_C_D 4KGG_C_A 2FE8_A_C 1SOT_A_C 2LBU_E_D 3PGA_1_4 3QWN_I_J 3TND_B_D 1XDT_T_R
while read ppi_pair;
do
echo "Starting $ppi_pair"
./dock_one.sh $ppi_pair &
done < ../benchmark_list.txt
|
3a34450e1fd6872a16df7d517d79a64f353b1438a1b6e1ee066e3169904ef112 | Shell | 245 | 4 | #!/usr/bin/env bash
# Load recorded CARLA paths while preserving explicit environment overrides.
# Source this before resolving authoring tools; it does not launch a server.
source "$(dirname "${BASH_SOURCE[0]}")/../../../_common/env_common.sh"
|
286f51966036f52784b7daf670c0ed84616a5b2af21ccefc4ef53372c8af4fdf | Shell | 247 | 9 | # !/usr/bin/env sh
python ./tools/test.py \
./configs/fna_retinanet_fpn_retrain.py \
--checkpoint ./retinanet/retinanet.pth \
--net_config ./retinanet/net_config \
--data_path ./coco/ \
--out ./results.pkl \
--eval bbox \
|
d067715bef391e766863bc6040dbf66c51f1a4d40eaa741d5a59456d3a2208db | Shell | 247 | 7 | #!/bin/bash
# Assume parameter passed in is a relative path to a directory.
# For brevity, we won't do argument type or length checking.
ABS_PATH=`cd "$1"; pwd` # double quotes for paths that contain spaces etc...
echo "Absolute path: $ABS_PATH" |
7e5d8a2346115482a959d2228dca1cdd7a6733baf670c5ab531dd377977c075a | Shell | 249 | 7 | #!/bin/bash -l
masif_root=$(git rev-parse --show-toplevel)
masif_source=$masif_root/source/
masif_matlab=$masif_root/source/matlab_libs/
export PYTHONPATH=$PYTHONPATH:$masif_source
python -u $masif_source/masif_ligand/masif_ligand_evaluate_test.py
|
0efde5a065ef12facfcd503af0635b09618da946b89b15ef5eeed42c8c197e34 | Shell | 250 | 9 | #!/bin/bash
PRE_COMMIT_HOOK_FILE=".git/hooks/pre-commit"
echo "#!/bin/bash" >> $PRE_COMMIT_HOOK_FILE
echo "echo \"Running linter\"" >> $PRE_COMMIT_HOOK_FILE
echo "mvn exec:exec@ktlint-check" >> $PRE_COMMIT_HOOK_FILE
chmod +x $PRE_COMMIT_HOOK_FILE
|
b7b4e3e8e5433b23939de5a81857fc00fa86c3bc988ddeac0974e94f03b71d5c | Shell | 251 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/retinanet/retinanet_DETNAS_COCO_FPN_300M_1x.yaml OUTPUT_DIR models/DETNAS_COCO_Retinanet_300M_1x_nosubmean_SyncBatchNorm_tong_FPN256
|
bd8e183a59e7688a2e79954ceb8856bbc2c1544a4c6ffd7a9b2e14e96104802c | Shell | 251 | 12 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-24:00
#SBATCH -p gpu_quad
#SBATCH --gres=gpu:1
#SBATCH --mem=10G
#SBATCH -o eval_CAM.out
#SBATCH -e eval_CAM.err
#SBATCH -J CAM_stab
source activate GXAI
python3 eval_SG_stability.py --exp_method CAM --model GIN
|
661b8310b3aaadd8eeae5670aee22d87c9e637c749fee721cbc4406ac01bb651 | Shell | 252 | 9 | # all_test_to_all_train.sh: Align each of the testing set cofactors to the training set cofactors.
# Pablo Gainza - LPDI STI EPFL 2019
# Released under an Apache License 2.0
for i in {1..489}
do
python3 ./pocket_to_pocket_align.py $i &
sleep 1
done
|
aff07c750c3ad05c626d805c54b4a86f08ab495a528d0a6ee9501cfe61487f28 | Shell | 256 | 8 | # !/usr/bin/env sh
python -m torch.distributed.launch --nproc_per_node=8 ./tools/search.py \
./configs/fna_ssdlite_search.py \
--launcher pytorch \
--seed 1 \
--work_dir ./ \
--data_path ./coco/
|
eac34eb2e1129f173c306cea43374e2eff512ad1b6b12b542789c4bd076aa6ca | Shell | 256 | 5 | #!/bin/bash
echo "Bag file: $1"
python3 ./image_extractor.py --bag-file=/gpfs/space/projects/Bolt/bagfiles/$1 --extract-dir=$2 --crop-camera-images --resize-camera-images --extract-side-cameras --extract-lidar --camera-crop-xmin 0 --camera-crop-xmax 1920 |
acbef6cfaaa5a027bd4919ea3c1d021fdb3e8ced63061d4e5b1bc641b98853ee | Shell | 257 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/e2e_faster_rcnn_DETNAS_COCO_FPN_1d3G_2x.yaml OUTPUT_DIR models/DETNAS_COCO_FPN_1d3G_2x_nosubmean_SyncBatchNorm_tong_FPNSYNCBN_BOXHEADSYNCBN
|
af74f52efc23b20795c4ca7848114e12f83a57fa67e821c60cec4f16499934e9 | Shell | 257 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/e2e_faster_rcnn_DETNAS_COCO_FPN_300M_1x.yaml OUTPUT_DIR models/DETNAS_COCO_FPN_300M_1x_nosubmean_SyncBatchNorm_tong_FPNSYNCBN_BOXHEADSYNCBN
|
a06d6d75af4d4be260af489ccaee25fdd4171c02f9f263d1cb9f714814a3a681 | Shell | 258 | 12 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-5:00
#SBATCH -p gpu_quad
#SBATCH --gres=gpu:1
#SBATCH --mem=10G
#SBATCH -o GBP2.out
#SBATCH -e GBP2.err
#SBATCH -J GBP2
source activate GXAI
python3 stab_mult.py --exp_method GBP --model GIN --num_splits 3 --my_split 2
|
df92bc36718c6cfa5026e1d7bfeced4453dbd447fad279884e058e82afec1632 | Shell | 258 | 31 | #!/bin/bash
# upload models to s3 from the current folder
# go to the nsp-model repository
# execute the command
aws s3 cp . s3://nsp-project/models/genesis --recursive --exclude "*.md" --exclude ".*"
|
b0d42c9c45d26ac32192e2ba1a942218b8ef9d30fb9d5f050923766ade83bb13 | Shell | 260 | 17 | #! /bin/bash
# Copyright Johns Hopkins University
# 2019 Fei Wu
printf "\t File Check in folder: %s.\n" "$1"
WavScp="$1/wav.scp"
Text="$1/text"
Utt2Spk="$1/utt2spk"
Gend="$1/utt2gender"
Spk2Utt="$1/spk2utt"
rm -f $WavScp $Text $Utt2Spk $Gend $Spk2Utt
|
81a48f2efc63e11b8a8957f0f2bb304940c8e7f88afb607de54a97a37e8e4add | Shell | 262 | 8 | # !/usr/bin/env sh
python -m torch.distributed.launch --nproc_per_node=8 ./tools/search.py \
./configs/fna_retinanet_fpn_search.py \
--launcher pytorch \
--seed 1 \
--work_dir ./ \
--data_path ./coco/
|
d36c582b27fdd7470c115d6778f044e085317c9fdf849a77262cc67a1a71759b | Shell | 266 | 9 | #!/bin/bash
# show status of simulation
# input:
# simulation folder path
cd /usr/local/nsp-server/simulationFolder/
find . | grep -i .dat
echo "Progress of simulation execution:"
for f in `ls -1 * | grep ".dat"`; do echo ""$f":"`tail -n 1 $f;`; done |
a293351f8e5d37d64f2f0f94ae67712b5208a3cbc3c3795b666ea2a2a5abeb38 | Shell | 270 | 11 | #!/bin/bash
#SBATCH --time=7-00:00:00
#SBATCH --gres=gpu:4
#SBATCH --partition=gpu
#SBATCH --constraint=v100
#SBATCH -o train_online_%j.out
#SBATCH -e train_online_%j.err
source activate selene-env
python -u ../../../selene_cli.py ./train_online_sampler.yml --lr=0.08
|
ae3eed10fed130505466573045247090e17e200ced5f99d7142963c82ad3179a | Shell | 270 | 2 | export NGPUS=8
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/e2e_faster_rcnn_DETNAS_COCO_FPN_3d8G_1x.yaml OUTPUT_DIR models/DETNAS_COCO_FPN_3d8G_1x_nosubmean_SyncBatchNorm_tong_FPNSYNCBN_BOXHEADSYNCBN_retrain_1020
|
bdbc1c4a1a260c537472696a710613db77935fbf876f5c35ee12bfa0a6c32c04 | Shell | 271 | 6 | #!/usr/bin/env bash
BASEDIR=$(dirname "$0")
# echo "$BASEDIR"
(python $BASEDIR/graph_match/graph_match_hemispheres.py) && \
(python $BASEDIR/matched_subgraph_omni_cluster/matched_subgraph_omni_cluster.py) && \
(python $BASEDIR/evaluate_clustering/evaluate_clustering.py)
|
4df78369bb2b0704fb114fb1a961c7902c7189f840e9a632e1e1aaf42919eeaf | Shell | 273 | 8 | #!/bin/bash
python get_probabilities.py learn batch
python get_probabilities.py reversal batch
python get_probabilities.py reversal_prat batch
python get_probabilities.py punish batch
python get_probabilities.py punish_prat batch
python get_probabilities.py devalue batch
|
b32db0751961b46b0f2176c36a785177c7aecd2de681c83e6373476a79bd0eb5 | Shell | 274 | 10 | #!/usr/bin/env bash
# Fixes the CALLHOME stm files
# Copyright 2014 Gaurav Kumar. Apache 2.0
data_dir=$1
cat $data_dir/stm | awk '{$1=substr(tolower($1),0,length($1)-4);print;}' > $data_dir/stm_new
mv $data_dir/stm $data_dir/stm.bak
mv $data_dir/stm_new $data_dir/stm
|
fb229137bee40dea30f3159b3fb84ca2cfcdb60c22bf82c94641bfde41c5efe0 | Shell | 276 | 12 | #!/bin/bash
#SBATCH --time=5-00:00:00
#SBATCH --gres=gpu:2
#SBATCH --partition=gpu
#SBATCH --constraint=v100
#SBATCH -n 1
#SBATCH -o train_mats_%j.out
#SBATCH -e train_mats_%j.err
source activate selene-env
python -u ../../../selene_cli.py ./train_deepsea_mat.yml --lr=0.08
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.