sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
36140109c186564fe080e377c72b4e4a9a172725f1c7e63e17611a611890034f | Shell | 216 | 13 | #!/bin/bash
PATTERN=$1
SAVE_DIR=$2
MIN=$3
MAX=$4
for SEG in $PATTERN; do
TMP=$(basename -- "$SEG")
MASK=${TMP%.*}
echo $MASK
sh Meshing/make_stl_surface.sh $SEG $SAVE_DIR 5 $MASK $MIN $MAX
done; |
369c52d18822c0b8dd713977bb88c4f5e7c22be2dd78e36d90a018b6fa9e00d1 | Shell | 217 | 15 | #/bin/bash
for i in $(ls -d [0-9]*)
do
rm -f $i/final*
rm -f $i/log*
rm -f $i/ent*
rm -f $i/output
cp $i/restart.init $i/restart_file
done
echo 1 > lastexchange
cp walker.bkp lastwalker
exit 0
|
8a0baedd45bc12b4bc235c23285859adf14ff8b2106099c6c91d6a436914ba95 | Shell | 218 | 8 | #!/bin/bash
#SBATCH --time=48:00:00
#SBATCH --ntasks=1
#SBATCH --cores 1
#SBATCH --mem-per-cpu 128GB
python pseudobulk_Sestan_2022_DLPFC.py chimpanzee subclass
python pseudobulk_Sestan_2022_DLPFC.py chimpanzee subtype |
f5ced06471e4a9bccb9da472b85230ec4f6ce8a756502fbb18f16c7333b8ccaa | Shell | 218 | 8 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile="config/config.PAQR.yaml" \
--cores 4 \
--use-singularity \
--singularity-args "--bind $PWD/../../../" \
--printshellcmds
|
58e7f49c8f4b62431950ecde6dbcf618aa88e7c05093e8e186a19848244c2bcf | Shell | 219 | 4 | echo "Pulling featurized and split ACNN datasets from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/featurized_datasets/acnn_core.tar.gz
echo "Extracting ACNN datasets"
tar -zxvf acnn_core.tar.gz
|
aede981050a494b3c06eefc100aa874930facc027f5c3ae3f5ee7cf8b86d04a3 | Shell | 219 | 8 | #! /bin/bash
export LABELREPO_CSS_AVAILABLE=1
export LABELREPO_PROJECTS_BASE_URL="./projects/"
export LABELREPO_PROJECTS_HTML_EXTENSION=1
export LABELREPO_PROJECTS_URL_ESCAPE_DOT=1
jupyter-book build -W analysis/book
|
ca5cd23580d6fe3d0fe60de6357ba496dd3a149197f784623917e2ed5001e506 | Shell | 220 | 3 | dca ../data/chu/chu_original.csv ../data/chu/res_dca
dca ../data/francesconi/francesconi_original.csv ../data/francesconi/res_dca
dca --type nb-conddisp ../data/stoeckius/stoeckius_original.csv ../data/stoeckius/res_dca
|
0d0c757c979b3c2cf2ddea61296a63f12f4e282a8566583924aa2c9da48c01b8 | Shell | 221 | 4 | PYTHONLIB="/home/shengq2/python3"
pip3 install --install-option="--prefix=${PYTHONLIB}" biopython
pip3 install --install-option="--prefix=${PYTHONLIB}" pysam
pip3 install --install-option="--prefix=${PYTHONLIB}" cutadapt
|
262747be945b954de541bea5283e0eca43f80fb59f980062393e79b21622b934 | Shell | 222 | 8 | #!/bin/bash
set -ev
SCRIPT_PATH=$(dirname $(realpath -s $0))
cd ${SCRIPT_PATH}/..
wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.8.0%2Bcpu.zip -O ~/libtorch.zip
unzip ~/libtorch.zip
|
af270411ecc7986fffd9e9e016e551146dd2a12325aa30f884ede9d7338e3ae4 | Shell | 222 | 4 | ln -s ../5.fill_Localblanks_phase3_useb/virus.phase3_final.CON ./
rm -rf tmp* *igv.bed log* debug.txt *ct
perl 1.fill_blanks.useRNAstructure.v3.pl virus.phase3_final.CON ../z2.split_domains/out1.domains.bedpe > debug.txt
|
152c696205a63b14a3adf84d5625db0cab5fdd8380c419a19a3b307639480b98 | Shell | 223 | 15 | #!/bin/bash
mol2=$1
pdb=${1/mol2/pdb}
smi=${1/mol2/smi}
cat << EOF | tleap -f - &> /dev/null
x = loadmol2 $mol2
savepdb x $pdb
EOF
# obabel makes a lot of noise
obabel -i pdb $pdb -o smi -O $smi &> /dev/null
rm -f $pdb
|
3a06593ab30b398b38e5a91d31bfbb8ff2fea60a764a8b0dfae651be673f1610 | Shell | 223 | 4 | set -ex
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
conda install pytorch torchvision -c pytorch # add cuda90 if CUDA 9
conda install visdom dominate -c conda-forge # install visdom and dominate
|
33fea04ec1b1054922d3b94c9b96b632d32cca908985220a8ae1bc33cb77d6ea | Shell | 224 | 17 | #!/usr/bin/env bash
set -e
cd /build
# add distribution name to file name
rename ".x86_64" ".${1}.x86_64" *.rpm
# show files
rpm -qlp *.rpm
# show dependencies
rpm -qpR *.rpm
# move to Docker volume
mv -v *.rpm /veyon
|
ece96d05a9568e5c1ad913cf2defda5930d6ee9ef709fb65de43569186c16b1c | Shell | 224 | 13 | #!/bin/bash
# Siwei 22 Apr 2024
mkdir -p summit_cleaned
for eachfile in peaks*summits.bed
do
echo $eachfile
cat $eachfile \
| awk -F '\t' '$1 ~ /chr[0-9]|chr[0-9][0-9]/ {print $0}' \
> summit_cleaned/$eachfile
done
|
98ebe2f0970281bea9c03086b1fd7900ffc25bad860f17ca0eab123f343f390a | Shell | 225 | 4 | echo "Pulling featurized and split ACNN datasets from deepchem"
wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/featurized_datasets/acnn_refined.tar.gz
echo "Extracting ACNN datasets"
tar -zxvf acnn_refined.tar.gz
|
50290a2f6a53a2b4863eb7fb1af66b3db15da0edd5927d0b1b19793981e94fd7 | Shell | 227 | 8 | #!/usr/bin/bash
peaks_folder=
input_file="${peaks_folder}/aggregate_peaks.bed"
output_file="${peaks_folder}/aggregate_annotated_peaks.txt"
homer_bin="../homer/bin"
annotatePeaks.pl ${input_file} hg38 -annStats ${output_file}
|
c2daa12f43a505142dc864caaeb8faf6006b0cd1735f23f6da32c5dba6af2794 | Shell | 229 | 11 | #!/bin/bash
dx login --token TOKEN
ancestry=("eur" "sas" "afr" "eas" "amr" "mid" "oth")
for a in "${ancestry[@]}"; do
echo $a
# Call the helper script with arguments
./1_run_regenie_step1_quant_helper.sh "$a"
done
|
151e4b62631f96bdeee1fe54f30001043240fdb21dbf149b29f687afa4f482ce | Shell | 230 | 7 | TEST_NBS=$(find ../nbs_tests -name "*.ipynb")
TUTORIAL_NBS=$(find ../docs/tutorials -name "*.ipynb")
ALL_NBS=$(echo $TEST_NBS$'\n'$TUTORIAL_NBS)
python -m pytest --nbmake $(echo $ALL_NBS) --nbmake-timeout=1800
python -m pytest
|
ba73bf8c613bdb13a9f57bb258b79079a6062df85e901610016d070aa5394896 | Shell | 230 | 12 | #!/bin/bash
# Siwei 21 Jan 2019
# Check the genome version of the BAMS
# by looking at the @PG tag
for eachfile in *.bam
do
echo ${eachfile/\n/\t}
samtools view -H $eachfile | grep "^@PG" | grep "38" | grep "truncated"
done
|
d10c3ec34c8ed0913fa3e8e6423a6439c89ceb23730df3365fb63362b7c69e9c | Shell | 232 | 5 | #!/usr/bin/env bash
REMOTE_PATH="/lvmraid/www/html/expyfun/"
rsync -rltvz --delete --perms --rsh="ssh -p 2222" --chmod=g+w build/html/ lester.ilabs.uw.edu:$REMOTE_PATH
ssh -p 2222 lester.ilabs.uw.edu "chgrp -R apache $REMOTE_PATH"
|
fc83ab6de4ea0ceb41eb6bc422402c72b4fce0ffedbab91436e49853510b9ca2 | Shell | 234 | 5 | #!/bin/zsh
WORK_DIRECTORY=/Volumes/LaCie/
minimap2 -ax splice -uf -k14 -t 12 --secondary=no $WORK_DIRECTORY/DRS_basic/data/referance/dmel-all-chromosome-r6.43.fa $WORK_DIRECTORY/DRS_basic/data/fastq/control_pooled.fastq.gz > aln.sam |
90b6aeae1d88f6bd9417d978c97bd7e5572b1f3b4b82d2edf440b38eff0ab033 | Shell | 236 | 14 | #!/usr/bin/env bash
# exit if any command fails...
set -e
# activate the conda environment
. env/bin/activate
tar_fn=$1
tar_fn_encrypted=$2
pass=$3
openssl enc -e -aes256 -pbkdf2 -in "$tar_fn" -out "$tar_fn_encrypted" -pass "$pass" |
bddef67e20afdddfff6b304d2d4366d3938b7837041f6817f86364c96e362afd | Shell | 237 | 14 | #! /bin/bash
set -eu -o pipefail
dcmsend\
-v \
${PACS_HOST}\
${PACS_PORT}\
-aet kp-${DATASET}\
-aec ${PROJECT_NAME}\
--scan-directories \
--scan-pattern *.dcm\
--no-halt \
+r \
/kaapana/app/dicoms |
0c526d824f11f36380b07360a13ba54ad6d9f8f17858b2d985cdc3bc613dfc24 | Shell | 239 | 9 | #!/bin/bash
ENV_NAME="dockbiotic"
CURRENT_DIR=$(dirname "$(readlink -f "$0")")
export DEEPCHEM_DATA_DIR=$CURRENT_DIR/../../deepchem_data_dir
COMMAND="python $CURRENT_DIR/script.py"
conda run --no-capture-output --name $ENV_NAME $COMMAND |
6af1abc80b6bd7b3dca1afacecec7a0f11e0c46a39c7eea08efd9cdd92800f63 | Shell | 240 | 13 |
export NA=$1
cd ${NA}
# adapted from https://stackoverflow.com/questions/10523415/execute-command-on-all-files-in-a-directory
for file in ./*.wig
do
echo "$file" >>wigcount.txt
perl ../count_wigs.pl <"$file" >>wigcount.txt
done
cd ..
|
f6ab4499cc0cf0750d2118d05b09f5bc3cbaa59e044d9bbac303e58945b27cdb | Shell | 246 | 4 | cat SILVA_123_LSURef_tax_silva.fasta SILVA_123_SSURef_Nr99_tax_silva.fasta > SILVA_123.fasta
perl remove_duplicate_sequence.pl -i SILVA_123.fasta -o SILVA_123.rmdup.fasta
perl build_rrna_category.pl
perl buildindex.pl -f SILVA_123.rmdup.fasta -b
|
a893fa003c93dbff3fd065633be0f79ac6c2219f9ca9a78bab92946058bd0722 | Shell | 248 | 17 | #!/usr/bin/env bash
set -e
cd /build
# add distribution name to file name
rename "s/_amd64/-${1}_amd64/g" *.deb
# show content
dpkg -c *.deb
# show package information and dependencies
dpkg -I *.deb
# move to Docker volume
mv -v *.deb /veyon
|
c5c282159aacbef1282ef4afcbbe19bb5834da160b0d56f4c648d6b7da82e212 | Shell | 248 | 7 | #!/usr/bin/bash
# Example script for generating contact information.
pymol -c -d 'fetch 1crn, async=0; h_add; save 1crn_h.pdb'
rm -f 1crn.cif
python ../../get_static_contacts.py --structure 1crn_h.pdb --itypes all --output 1crn_all-contacts.tsv
|
7e23448ed0b107899b00252e3d63bc3d9c444449cf9b685fc05ccac507ccc1e0 | Shell | 250 | 6 | #!/bin/bash
ENV_NAME=${1:-alphadia}
NBS=$(find ../nbs/tutorial_nbs -name "*.ipynb" | grep -v "finetuning.ipynb") # exclude finetuning notebook for it takes too long
conda run -n $ENV_NAME --no-capture-output python -m pytest --nbmake $(echo $NBS)
|
a30d1abd12ecdfe22e119037c88f657b2015fe54daff3d0aa5c1f60bdba21f52 | Shell | 253 | 10 | #!/bin/bash
# Orftcr.sh
export NA=$1
export TF=$2
export LEN=$3
cd ${NA}
printf "${NA}_dipy_bk_inbetween_motifindiv.txt\n${LEN}\n${TF}\n${NA}_dipy_inbetween_bk_plus.wig\n${NA}_dipy_inbetween_bk_minus.wig\n\n" | perl ../motifplot_yeastbs_inbetween.pl
|
40788f72f8bb647d1685418e32caef650cb8b3f822af87bfd918d80bd49d7347 | Shell | 254 | 11 | #!/bin/sh
export PYTHONPATH="$PWD"
if [ -z "${DEV_FILES}" ]; then
# Production
exec uvicorn main:app --host 0.0.0.0 --port $PORT
else
# Development
exec uvicorn main:app --host 0.0.0.0 --port $PORT --reload --forwarded-allow-ips '*'
fi |
e383303bc2e73680989aac2b5241a62a60be4c3c5123339c8dd8d4583a9c6fd9 | Shell | 254 | 19 | #!/bin/bash
while getopts ":a:d:s:" i; do
case "${i}" in
a)
aws_path=$OPTARG
;;
d)
download_path=$OPTARG
;;
s)
save_path=$OPTARG
;;
esac
done
$aws_path s3 cp --recursive $download_path $save_path
|
26e6069814c246ec03265f22ec5a27844cd30244216a99512ac81697ad5afdec | Shell | 255 | 9 | #!/bin/bash
set -e
# Check if it's an offline environment
if ! ping -c1 -W1 pypi.org >/dev/null 2>&1; then
echo "WARNING: Offline mode detected — functionalities might be limited." > /kaapana/OFFLINE_WARNING.txt
else
echo "Online mode detected"
fi
|
8d76e25a9a5334eaefaf854ccf41eddf6b757243bf5c5ee2d7f6d5636b574034 | Shell | 257 | 10 | #!/bin/bash
flake8 . --count --max-complexity=13 --max-line-length=90 \
--per-file-ignores="__init__.py:F401 " \
--statistics
#!/bin/bash
flake8 . --count --max-complexity=13 --max-line-length=90 \
--per-file-ignores="__init__.py:F401 " \
--statistics |
ec410641d15cc64ed59ebd40300027e5c80ad1d707f3352917d5b7f1db19c153 | Shell | 257 | 7 | #cqs1
cd /scratch/cqs_share/references/smallrna
tar -czvf /data1/backup/references/smallrna/v5/20200305_viral_genomes.tar.gz 20200305_viral_genomes*
tar -czvf /data1/backup/references/smallrna/v5/20200214_AlgaeSpeciesAll.tar.gz 20200214_AlgaeSpeciesAll*
|
1e491689b437bbab801d2cf16271cad206678aecaccf74e2c96cab59ca71246a | Shell | 258 | 10 | #!/bin/bash
snakemake \
--rerun-incomplete \
--snakefile="workflow/Snakefile" \
--configfile="config/config.PAQR.yaml" \
--cores 4 \
--use-singularity \
--singularity-args "--bind $PWD/../../../" \
--printshellcmds \
--dryrun
|
6ab83d41991ae484f3ae46a13e521eb9ed9a444d750b479f50e2616a83f69e72 | Shell | 258 | 13 | #!/bin/bash
set -e -u
# Build the GUI for MacOS.
# This script needs to be run from the root of the repository.
# Cleanup the GUI build
rm -rf gui/dist
rm -rf gui/out
npm install --prefix gui
# Build the GUI using electron forge
npm run make --prefix gui
|
98bcd001b4e2fd4f983e41337b3fbeec4c4414279242720801f873d01c4e3d39 | Shell | 259 | 8 | for file in ../microarray/*/SampleAnnot.csv; do
paste -d"," $file $(basename $(dirname $file))_SampleAnnot_RAS_mni_nonlin.csv > recombine/$(basename $(dirname $file))_SampleAnnot.csv
#paste -d"," $file $(basename $file) > recombine/$(basename $file)
done
|
808c3e11ac1aa2e79e34b32415ea1af34f944a6afedc2c8d5bc688c94cba5ff2 | Shell | 266 | 12 | CONTAINER_NAME="structure_gen"
IMAGE_NAME="structure_gen"
HOST_PORT=8888
CONTAINER_PORT=8888
HOST_DIR=$(pwd)/..
CONTAINER_DIR=/app
docker run -it --gpus all \
--name $CONTAINER_NAME \
-p $HOST_PORT:$CONTAINER_PORT \
-v $HOST_DIR:$CONTAINER_DIR \
$IMAGE_NAME |
55ac3d1a36ce4061742e51b8274ea18409f18f363bf8e1c4fe118c6cec36934f | Shell | 267 | 11 | #! /bin/bash
# find all bams matching pattern and make index
mapfile -d $'\0' BAMs_to_index < <(find . -type f -name "GA*WASPed.bam" -print0)
for ((i=0; i<${#BAMs_to_index[@]}; i++))
do
echo ${BAMs_to_index[$i]}
samtools index -@ 20 \
${BAMs_to_index[$i]}
done
|
21438465dd86a684136cf05186d28417fbdf9bbc851f209bf827a9b3a972111e | Shell | 269 | 2 | rm -rf tmp.* virus.* log.* debug.txt
perl 1.Heuristics_predict.useRNAstructure.v8.pl ../2.Loop_by_VCsqrt/out3.onlyPart.enriched_pixels.resolution5.score0.05.bedpe ../2.Loop_by_VCsqrt/out6.onlyPart.loops_or_enriched_pixels.resolution5.score0.05.sorted.bedpe > debug.txt
|
6ac993da5a43d335cbacf8e04ed5d1a320a7eb2c0f2245d53740594a0c36da4e | Shell | 270 | 12 | #! /bin/bash
REPO="$HOME/repos/hbmep-paper"
PYTHON_ENV="$REPO/.venv/bin/activate"
# Change to the directory where the script is located
cd $REPO/notebooks/simulations/
# Activate the virtual environment
source $PYTHON_ENV
# Run the script
python -m core__power $1 $2
|
ffb686f8f486ed2b2fd145da24689e47fdae554b1f52c48acbd01374247a200b | Shell | 271 | 9 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile="config/config.APAlyzer.yaml" \
--use-singularity \
--singularity-args="--bind ${PWD}/../../tests/test_data" \
--cores 4 \
--printshellcmds
# adjust number of cores as needed
|
1ae452794b474ed2c51663d3a9e665b64060c2789b584f9ec66b33dfb08f982d | Shell | 272 | 9 | #!/bin/bash
#SBATCH --partition=GPU-a100s
#SBATCH --gres=gpu:a100s:1
#SBATCH --nodes=1
#SBATCH --job-name=train_test_error_bar
#SBATCH --output=%x-%j.out
uv run flow_train.py -m seed=1,2,3,4,5,6,7,8 model.num_steps=25 model.num_samples=25 task_name=train_test_error_bar
|
6901acc68691b5a74fe6293652e8e3ab6d6cb6dd5362ccf577d1b19354259561 | Shell | 272 | 20 | #!/bin/bash
set -e
. env/bin/activate
cd ../..
echo ${pwd}
# Read parameters from command line arguments
JOB_MAIN_DIR=$1
# Run the Python script with the specified parameters
python code/process_run.py stats --main_run_dirs notebooks/osg/condor/"$JOB_MAIN_DIR"
|
95c3286b9d5958792b7a532c01c16c14e06a2d51b1d2d259c49ee0c937758c29 | Shell | 273 | 9 | #!/bin/bash
snakemake \
--snakefile="workflow/Snakefile" \
--configfile="config/config.DaPars2.yaml" \
--cores 4 \
--use-singularity \
--singularity-args="--bind ${PWD}/../../../tests/test_data" \
--printshellcmds
# adjust number of cores as needed
|
0180709b25d6b3df0c40c0ae2558d15d0c614bef73f9d615c70d50577b199d6d | Shell | 274 | 11 | #!/bin/bash
DATA_PATH="data/RDB7"
FULL_CSV="$DATA_PATH/raw_data/rdb7_full.csv"
FULL_XYZ="$DATA_PATH/raw_data/rdb7_full.xyz"
SAVE_DIR="$DATA_PATH/processed_data"
uv run preprocessing.py \
--csv_file "$FULL_CSV" \
--xyz_file "$FULL_XYZ" \
--save_dir "$SAVE_DIR"
|
f2f343350c4d956ccf3913c4e6dbd1fb210c242b9df0b55c6920c91da15f496c | Shell | 275 | 12 | #! /bin/bash
REPO="$HOME/repos/hbmep-paper"
PYTHON_ENV="$REPO/.venv/bin/activate"
# Change to the directory where the script is located
cd $REPO/notebooks/simulations/
# Activate the virtual environment
source $PYTHON_ENV
# Run the script
python -m core__saturation $1 $2
|
37a6745995790daf23fbb14307acee811ae71586dc02282b56e4e503e99efc08 | Shell | 276 | 1 | python -m main --config_file='./vnls.yaml' TRAIN.OPTIMIZER_NAME sgd DATA.PROBLEM_TYPE vqls_direct DATA.VECTOR_CHOICE constant TRAIN.APPLY_SR True DATA.NUM_SITES $1 TRAIN.BATCH_SIZE 1024 TRAIN.NUM_EPOCHS 1000 TRAIN.LEARNING_RATE 0.0025 DATA.NUM_CHAINS 8 MODEL.MODEL_NAME rbm_c
|
7fe2c6467fd547d96b99d50a7e73ea1e50e0969519073aced716b1b80cf5c978 | Shell | 277 | 18 | #!/bin/bash
#Submit to the cluster, give it a unique name
#$ -S /bin/bash
#$ -cwd
#$ -V
#$ -l h_vmem=8G,h_rt=24:00:00,tmem=8G
# join stdout and stderr output
#$ -j y
#$ -R y
snakemake -s snakemake -s upload_to_ena.smk \
--nolock \
--rerun-incomplete \
--latency-wait 100
|
2961b56c3f3e1b029d36b7a1ba3aae17b959dc09c6ed4c2bd2e011f96428bfb6 | Shell | 278 | 9 | #!/usr/bin/env sh
OUTDIR=examples/deep-activity-rec/ibrahim16-cvpr/p4-network2
GPU_ID=0
echo "Running Caffe using GPU" $GPU "In Directory " $OUTDIR
./build/tools/caffe train 2> $OUTDIR/z_trainval-test-log.txt \
--solver $OUTDIR/trainval-test-solver.prototxt --gpu $GPU_ID
|
126a7e5ee2c61377625ada593fcaa8b7a4c2c8ffbef686dd57a290f4bab8f0a6 | Shell | 279 | 8 | #!/bin/bash
# For the GO dataset
python go.py --pretrain 'True' --gpu 6 --level 'cc' --batch-size 64 --lr 1e-3 --wd 5e-4 --num-epochs 300 \
--base-width 32 --kernel-channels 24 --lr-milestone 300 400
# For the EC dataset
python ec.py --pretrain 'True' --gpu 6 --batch-size 24
|
c9e5cf812c9ea91e22e916ca29df5e047e1ce42046dbd0e58d63369436c756cf | Shell | 279 | 2 | python count_pileups.py 2020_04_02_Paired-tag_DNA_Active_Merged_sorted.bam_H3K4me1.bam H3K4me1.filtered.pos_strand.tally.txt
python remove_pileups.py 2020_04_02_Paired-tag_DNA_Active_Merged_sorted.bam_H3K4me1.bam H3K4me1.filtered.pos_strand.tally.txt H3K4me1_filtered_10.bam 10
|
c1acaa38912585d71ba0e5721bba64aa5eb138bf7f0914e4ea7bb6e37c2cef35 | Shell | 280 | 12 | for file in *.csv; do
echo """MNI Tag Point File
Volumes = 1;
Points = """ > $(basename $file .csv).tag
tail -n +2 $file | awk -v FPAT="([^,]+)|(\"[^\"]+\")" -v OFS=" " '{print $14,$15,$16,1,$3,1,$3}' >> $(basename $file .csv).tag
echo ";" >> $(basename $file .csv).tag
done
|
9d2b0c819b16aa17caf442072918ccfc6211fa0ea01b8d6b111f728c8c8e2b40 | Shell | 281 | 12 | #! /bin/bash
REPO="$HOME/repos/hbmep-paper"
PYTHON_ENV="$REPO/.venv/bin/activate"
# Change to the directory where the script is located
cd $REPO/notebooks/simulations/
# Activate the virtual environment
source $PYTHON_ENV
# Run the script
python -m core__number_of_pulses $1 $2
|
2500cc105c4afce07a18e9b9d6d52f5f039a2b31e5e8a0a5ce4edae834f80195 | Shell | 282 | 8 | set -e
# Requirements
# module load R/4.0.3 bedtools2 snakemake bedops/2.4.39
# Example run: bash run_snamekake <config> <workdir> -n -j 12
read directory yaml params <<< "$@"
snakemake --snakefile Snakefile --configfile $yaml -d $directory $params --latency-wait 60 --keep-going
|
5329eff8c57d1b86979b742a7ff6356738b4e274eaf6d15a82b80860b654904e | Shell | 282 | 7 | #!/bin/bash
pubget run --pmcids_file pmcids_for_open_fmri_papers.txt \
--fit_neuroquery \
--labelbuddy \
--nimare \
~/data/pubget_data |
41fdf0e7d4d62c8f8fe00277cb2cbe6b78bea4e3d66274b99032fd6f2a851e9b | Shell | 283 | 12 | #! /bin/bash
REPO="$HOME/repos/hbmep-paper"
PYTHON_ENV="$REPO/.venv/bin/activate"
# Change to the directory where the script is located
cd $REPO/notebooks/simulations/
# Activate the virtual environment
source $PYTHON_ENV
# Run the script
python -m core__number_of_subjects $1 $2
|
b44026fadc20abd3db31bf43a6e8cb0546e3f49ae97323bf523b19993edeedcb | Shell | 283 | 13 | #!/usr/bin/bash
# Example script for generating contact information.
get_static_contacts.py \
--structure 6cvo.pdb \
--sele "nucleic" \
--sele2 "protein" \
--ligand "resname AMP" \
--itypes hb \
--output contacts.tsv \
--ps_cutoff_dist 6.5 \
--hbond_cutoff_ang 70
|
c5693aef93a3fbf7f7ff3a6fef258390b23f15be40d4a226b12b1c264fbbc92f | Shell | 283 | 16 | #!/bin/bash
#OUT_NAME=reproduction-test
OUT_NAME=$1
fpath=/scratch/gpfs/eham/247-encoding-updated/minimal_reproduction/results/$OUT_NAME-hs
echo $fpath
for i in $(seq 1 1 48); do
COUNT="$(ls $fpath$i/777/ | wc -l)"
if [[ $COUNT != 161 ]]
then
echo "$i"
echo $COUNT
fi
done
|
cf1b65d9a93c4a90a8e0cc34eeb863a8a7198c08e27d8c6ef119bab2ddd2db76 | Shell | 283 | 14 | #!/bin/bash
#$ -cwd
#$ -l bluejay,mem_free=1G,h_vmem=1G
#$ -o logs/clean_trash.txt
#$ -e logs/clean_trash.txt
#$ -m e
echo "**** Job starts ****"
date
rm -fr /dcs04/lieber/marmaypag/Tran_LIBD001/Matt/MNT_thesis/snRNAseq/10x_pilot_FINAL/twas/trash/*
echo "**** Job ends ****"
date
|
9bf80d9a17ac9c94a17e07a369f51741c51b90c2c95d95c99abe905dcca0a409 | Shell | 285 | 9 | #!/usr/bin/env sh
OUTDIR=examples/deep-activity-rec/ibrahim16-cvpr-simple/p4-network2
GPU_ID=0
echo "Running Caffe using GPU" $GPU "In Directory " $OUTDIR
./build/tools/caffe train 2> $OUTDIR/z_trainval-test-log.txt \
--solver $OUTDIR/trainval-test-solver.prototxt --gpu $GPU_ID
|
13feb6e0a81d6b88965d75d50a5c3f0530375cceed03c4a522fbbda5d7cd0258 | Shell | 286 | 18 | #!/bin/sh
set -e
cd geppetto-meta
app=$(pwd)
cd $app/geppetto.js/geppetto-ui
yarn && yarn build:dev && yarn publish:yalc
cd $app/geppetto.js/geppetto-client
yarn && yarn build:dev && yarn publish:yalc
cd $app/..
yarn
REACT_APP_BACKEND_URL=https://yale.metacell.us yarn run start
|
9d8052d04eea4a626ca08cf102515bb3f74bc854e7a809c2b61f342ae0a4ed49 | Shell | 286 | 17 | #! /bin/bash
# Siwei 14 May 2021
# Run Caper/Cromwell for ATAC-Seq QC
# use conda env encode-atac-seq-pipeline
for eachfile in json_in/*.json
do
echo $eachfile
caper run \
~/Data/Tools/atac-seq-pipeline-2.1.3/atac.wdl \
-i $eachfile \
--conda encode-atac-seq-pipeline
done
|
906ecad429a8506c804e6f2a1e5142f2f7bff9c060dc1f09569136244a469105 | Shell | 289 | 11 | #!/bin/sh
set -e
which nrniv || (echo "Please load Neuron" && exit 1)
cd "$(dirname "${BASH_SOURCE[0]}")" # cd to this dir
pushd glusynapse
nrnivmodl ../../mod/vecevent.mod ../../mod/GluSynapse.mod # Compile mod
nosetests -v test_transmission.py
nosetests -v test_ltpltd.py
popd
|
fc4873b0cbd5942e852456221b6b6c487961f2bbd371c055958e5a0f2b478097 | Shell | 289 | 12 | #! /bin/bash
REPO="$HOME/repos/hbmep-paper"
PYTHON_ENV="$REPO/.venv/bin/activate"
# Change to the directory where the script is located
cd $REPO/notebooks/simulations/
# Activate the virtual environment
source $PYTHON_ENV
# Run the script
python -m core__number_of_reps_per_pulse $1 $2
|
a56d98846fbcb62e38f65e249f64644dd1d5f82c5f4a53c8a86d57f712160441 | Shell | 290 | 9 | #!/bin/sh
set -e
# Read LAMMPS version from version.h
version_line=$(grep LAMMPS_VERSION ../version.h)
# extract version
tmp=${version_line#*\"} # remove prefix ending in "
version=${tmp%\"*} # remove suffix starting with "
# string to int
date --date="$(printf "$version")" +"%Y%m%d"
|
90e08bdbb53a66ba1555bebf400e4e23ed6f6f91aa0a53212a5f8f33874e6aae | Shell | 292 | 12 | #!/bin/bash
echo "Activate <conda_env> conda environment ..."
source ~/miniconda3/bin/activate <conda_env>
echo "Generate experiments ..."
python -c "import exputils
exputils.generate_experiment_files('experiment_configurations.ods', directory='./experiments/')"
echo "Finished."
$SHELL
|
19d13c2198cd276d6d09f09a16a5d759098aa3e95d6084d23c65e3bc39f87fdb | Shell | 294 | 12 | branchname=$1
if ! git checkout $branchname
then
echo >&2 "branch $branchname does not exist"
exit 1
fi
git checkout master
git tag archive/$branchname $branchname
git push --tags
echo "tagged branch $branchname as archive"
git branch -D $branchname
git push origin --delete $branchname |
5f4d0254b0120e06711eab9dcdf2a805e2379b213c32cc4a6bc2ddc7c69d319e | Shell | 294 | 13 | #!/bin/bash
echo "Start experiments via slurm ..."
python -c "import exputils
exputils.start_slurm_experiments(directory='./experiments/experiment_000*',
start_scripts='run_experiment.slurm',
is_parallel=True,
verbose=True,
post_start_wait_time=0.5)"
echo "Finished"
|
9bc55d27c8acf8001148555ab585764b119c0662c01aafa0fe30c45cd7c2886b | Shell | 296 | 13 | #!/bin/bash
echo "Start experiments via slurm ..."
python -c "import exputils
exputils.start_slurm_experiments(directory='./experiments/experiment_00010*',
start_scripts='run_experiment.slurm',
is_parallel=True,
verbose=True,
post_start_wait_time=0.5)"
echo "Finished"
|
c1f3a8f233c738e71311a77118b65a4e0fd480a717693ad16c17c7111f5781fd | Shell | 297 | 7 | set -e
# Requirements
module load samtools/1.12 deeptools/3.5.0 subread/2.0.0 R/4.0.3 bedtools2/2.27.0 slurm
read directory config params <<< "$@"
snakemake $params --configfile $config -d $directory --latency-wait 180 --keep-going # --cluster "SlurmEasy -n {rule} -t {threads} -l cluster_log"
|
5f4810542e8eef680ce6b79cf0c2a7ab10fbccfc9cb425f4de8e92c6650fcb08 | Shell | 298 | 11 | DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
cd $DIR/featureCalculators_multi
# rm *.c *.o
python3 setup.py clean
python3 setup.py build_ext --inplace --force
cd $DIR/delta_functions_multi
# rm *.c *.o
python3 setup.py clean
python3 setup.py build_ext --inplace --force |
cb60206d4dc4cf67c91ab71c5329d74840cdc14406aa1404d94a7b7ae386e582 | Shell | 298 | 9 | #!/usr/bin/env bash
/usr/bin/env
jupytext --output ../docs/examples/execute_water.ipynb CG_water_difftre.md
cd ../docs/examples && papermill execute_water.ipynb CG_water_difftre.ipynb --progress-bar --request-save-on-cell-execute
cp CG_water_difftre.ipynb ../../examples/CG_water_difftre.ipynb
|
16f84d40202fbffed0ede9cd41f25b1904bf7ce70f4f56a0a4d3bc6ef9092a2a | Shell | 299 | 13 | #!/bin/bash
#Script to extract SARS-Cov-2 genome using blast.
#COPYRIGHT 2022
#Created by Mike Mwanga
#mikemwanga6@gmail.com
#motivation from this link
#https://www.biostars.org/p/433926/
while read id start stop; do
blastdbcmd -db sequences.fasta -entry $id -range $start-$stop
done < hits.txt
|
6c23a947d917740c6533cd80c5e8abdafca304a6e3a38cc7a2a6e87c3dcff8fa | Shell | 303 | 9 | #!/bin/bash
#SBATCH --partition=GPU-a100s
#SBATCH --gres=gpu:a100s:1
#SBATCH --nodes=1
#SBATCH --job-name=train_test_all_model_sizes
#SBATCH --output=%x-%j.out
uv run flow_train.py -m model.num_steps=25 model.num_samples=50 task_name=train_test_all_model_sizes experiment=flow1,flow2,flow3,flow3,flow5 |
a8b94e4d12f6d44968bce544a11cea4e50f5f84a1f854b291b9773b03a998266 | Shell | 305 | 13 | # sample scripts for training vanilla teacher models
python train_teacher.py --model wrn_40_2
python train_teacher.py --model resnet56
python train_teacher.py --model resnet110
python train_teacher.py --model resnet32x4
python train_teacher.py --model vgg13
python train_teacher.py --model ResNet50
|
d18d45ad9cc43dc6b260396b696613015ebcd72da55052bf4a4ae8ae2ba9c74a | Shell | 305 | 14 | #!/usr/bin/env bash
PREFIX="BASE_PATH"
echo "Uninstalling CellTracksColab from $PREFIX"
if [ -f "$PREFIX/pre_uninstall.sh" ]; then
bash "$PREFIX/pre_uninstall.sh"
fi
rm -rf "$PREFIX"
echo "CellTracksColab removed."
if [ -t 0 ]; then
echo
read -rp "Press Enter to close the installer..." _
fi |
b82c04ce47a5076edbcef56fe9126d1041f6a83917f9f87f2a79d6589b66e2dc | Shell | 306 | 10 | #!/bin/bash
# replace *...* with respective value
cellranger count --id=*sampleName* \
--transcriptome=*path to reference* \
--fastqs=*path to folder with all fastqs* \
--sample=*sampleName with all variations of flowcell* \
--include-introns true \
--disable-ui |
040fe2d8a171052c4a61dee4ca117b100d6ce0c27aea779930d75086acdc0ef1 | Shell | 307 | 12 | #! /bin/bash
# Siwei 18 May 2021
# subsample all WASPed bam files to 30M reads for MACS2 peak calling
eachfile=$1
echo $eachfile
cat <(samtools view -H $eachfile) <(samtools view -@ 20 $eachfile | shuf -n 12000000) \
| samtools sort -l 9 -@ 20 -m 10G -o subsampled/${eachfile/%.bam/_12M.bam}
|
585f00278ed74f52d8b74846fdbe8eac1a3a7d93ddbbb1a5f94de22cd3d85710 | Shell | 307 | 11 | #! /bin/bash
# find all bams matching pattern and make index
mapfile -d $'\0' BAMs_to_index < <(find . -type f \( -name "Glut_rapid*WASPed.bam" -o -name "R21*WASPed.bam" \) -print0)
for ((i=0; i<${#BAMs_to_index[@]}; i++))
do
echo ${BAMs_to_index[$i]}
samtools index -@ 20 \
${BAMs_to_index[$i]}
done
|
c3bafce32c00ce2fd582c21db717877cca52457f154ce25341bc50eb237382f9 | Shell | 307 | 8 | #!/bin/bash
# For the GO dataset
python go.py --level 'cc' --gpu 6 --seed 0 --lr 1e-3 --batch-size 64 --wd 5e-4 --num-epochs 300 \
--base-width 32 --kernel-channels 24 --lr-milestone 300 400
# For the EC dataset
python ec.py --gpu 4 --seed 2027 --batch-size 24 --num-pretrain-epochs 0 --ckpt-path './ckpt' |
386a01022e8cf5ba15c02600d69f6e830789042e9c5bccac149db0502bb4bd03 | Shell | 308 | 18 | #!/bin/bash
# Siwei 10 May 2022
# use the improved method as in
# https://www.biorxiv.org/content/10.1101/496521v1
macs2 callpeak \
-t *.bam \
-f BAMPE \
-g 2.7e9 \
-q 0.05 \
--keep-dup all \
--nolambda \
--min-length 100 \
--max-gap 50 \
--buffer-size 1000000 \
-n MGlut_new_peaks \
--seed 42
|
598b82d6c3a93fc87296b50a869ec8e8d3c2b5be1e7a4e19147e16145e05bcbe | Shell | 309 | 7 | pyuic5 mainwindow.ui -o mainwindowui.py
pyuic5 window_newmodel.ui -o window_newmodelui.py
pyuic5 window_train.ui -o window_trainui.py
pyuic5 window_pred.ui -o window_predui.py
pyuic5 window_eval.ui -o window_evalui.py
pyuic5 window_eval_res.ui -o window_evalresui.py
pyrcc5 mainwindow.qrc -o mainwindow_rc.py
|
39e9b37702cbaf65765347630301715a1538746a5d07404534ca927738b975f6 | Shell | 310 | 14 | #!/bin/bash
#SBATCH -c 1
#SBATCH -t 0-08:00
#SBATCH -p gpu
#SBATCH --mem=32G
#SBATCH --gres=gpu:1
#SBATCH -o tests/test_%j.out
#SBATCH -e tests/test_%j.err
module load python/3.10.11
module load gcc/9.2.0 cuda/11.7
source "venv/bin/activate"
/n/cluster/bin/job_gpu_monitor.sh & python3 tests/test_package.py
|
9e6f6c0a3c87100f73e02da75cc88a5ea25bdc46bf873f912a44ad870e869268 | Shell | 315 | 13 | #!/bin/bash
# Orftcr.sh
export DIR=$1
export NA=$2
export LEN=$3
cd ${DIR}
#printf "${NA}_dipy_bk_chxmx.txt\n10\n${NA}\n${NA}_CX_sort.wig\n${NA}_CX_sort.wig\n\n" | perl ../motif_yeastbs.pl
printf "${NA}_dipy_bk_chxmxindiv.txt\n${LEN}\n${NA}\n${NA}_CX.wig\n${NA}_CX.wig\n\n" | perl ../motifplot_yeastbs.pl
cd ..
|
48bd82adec29a97788039f7b9cacff45f15c75f8eaa841f6c3037a54b686da04 | Shell | 318 | 5 | #!/bin/zsh
WORK_DIRECTORY=/Volumes/LaCie/
minimap2 -ax splice -uf -k14 -t 12 --secondary=no $WORK_DIRECTORY/Drosophila/drosophila_nanopore/DRS_basic/data/referance/dmel-all-chromosome-r6.43.fa $WORK_DIRECTORY/Drosophila/drosophila_nanopore/DRS_compairison/data/fastq/tau_pooled_nanopore.fastq.gz > tau_pooled_aln.sam |
506db937027fdc63afbcf00262a1e4b5d3e7e5611da3c020e1ac1760e024c82a | Shell | 318 | 10 | #!/usr/bin/env sh
# This script converts the vollyball data into leveldb format.
OUTDIR=examples/deep-activity-rec/ibrahim16-cvpr/p1-network1
echo "Computing image mean for trainval dataset: " $OUTDIR
./build/tools/compute_image_mean -backend=leveldb $OUTDIR/trainval-leveldb $OUTDIR/mean.binaryproto
echo "Done."
|
edd69bf4518ff0cda4a00751f4cefbf22919f1cc5f38301b291c4b4b6510ef95 | Shell | 319 | 4 | #!/usr/bin/bash
date=$(date '+%Y-%m-%d')
deeperwin setup -i config_dpe_large_systems.yml -p experiment_name "reg_${date}_dpe" -p physical.name K -p comment rep1 rep2
deeperwin setup -i config_dpe_small_molecules.yml -p experiment_name "reg_${date}_dpe" -p physical.name NH3 Ethene N2_bond_breaking -p comment rep1 rep2
|
bd608733a4a704a30c0451bb78512c3619da53582f8384d77a9806ffb5b620de | Shell | 322 | 10 | #!/bin/bash
set -eu
IFS=',' read -ra FORMATS_ARRAY <<< "${OUTPUT_FORMAT}"
for format in "${FORMATS_ARRAY[@]}"; do
jupyter nbconvert --to ${format} --execute --no-input /${WORKFLOW_DIR}/${NOTEBOOK_DIR}/${NOTEBOOK_FILENAME} --output-dir /${WORKFLOW_DIR}/${OPERATOR_OUT_DIR} --output ${RUN_ID}-report.${format}
done
... |
c5dda84f59d8dd66bfdaf635327eb5ed2a91bc3760be2f97dd6ec86c77388fd3 | Shell | 322 | 13 | #!/bin/bash
set -ueo pipefail
DIRBASE=$1
DIROUT=$2
SAMPLE=NewFQ_Test_Hsa-51
# Compare with expression outcome file
cmp --silent $DIRBASE/$SAMPLE/expr_out/$SAMPLE.cRPKM $DIROUT/expr_out/$SAMPLE.cRPKM || exit 1
cmp --silent $DIRBASE/$SAMPLE/to_combine/$SAMPLE.MULTI3X $DIROUT/to_combine/$SAMPLE.MULTI3X || exit 1
exit ... |
f43d6a10309ed05422e1dba019a9974ee321869ffb9b2af4fc3e968941cf8cb3 | Shell | 324 | 13 | #! /bin/bash
# Siwei 18 May 2021
# subsample all WASPed bam files to 30M reads for MACS2 peak calling
for eachfile in *.bam
do
echo $eachfile
cat <(samtools view -H $eachfile) <(samtools view -@ 40 $eachfile | shuf -n 30000000) \
| samtools sort -l 9 -@ 40 -m 10G -o subsampled/${eachfile/%.bam/_30M.bam}
don... |
18378ced57b781a40da35b510068d1e681c88bc51e4db22264e06fc1b59b81eb | Shell | 325 | 10 | #!/usr/bin/env sh
# This script converts the vollyball data into leveldb format.
OUTDIR=examples/deep-activity-rec/ibrahim16-cvpr-simple/p1-network1
echo "Computing image mean for trainval dataset: " $OUTDIR
./build/tools/compute_image_mean -backend=leveldb $OUTDIR/trainval-leveldb $OUTDIR/mean.binaryproto
echo "Do... |
a0c9fd03c4afbb1d7d999c92d6cb8f54044b7bf6ad5b01b3a96e12ce4d0036bb | Shell | 325 | 6 | SETUP_REQUIRES="pip build"
# Numpy and scipy upload nightly/weekly/intermittent wheels
NIGHTLY_WHEELS="https://pypi.anaconda.org/scipy-wheels-nightly/simple"
STAGING_WHEELS="https://pypi.anaconda.org/multibuild-wheels-staging/simple"
PRE_PIP_FLAGS="--pre --extra-index-url $NIGHTLY_WHEELS --extra-index-url $STAGING_WHE... |
8372038acac074d02e088641d88ff20384f7a54acf86914872378ca8b2b9272e | Shell | 328 | 9 | #!/bin/bash
echo "=== Step 1: Simulate data ==="
Rscript simulate_data.R --seed 1 --n_genes 30 --n_causal_genes 20 --N_gwas 200e3
echo "=== Step 2: Run FUSION pipeline ==="
Rscript run_twas.R --coloc_susie_P 0.05 --PATH_gcta /Users/alexandergusev/Downloads/gcta-1.95.1-macOS-arm64/bin/gcta64
echo "=== Pipeline comple... |
d7e8a6397057c9954333991f430fae34c095da2aab7de0806b9f97b404df7ec5 | Shell | 328 | 2 | #get list of FBte IDs and names in flybase
echo "select f.uniquename, f.name, o.genus, o.species from feature f, organism o where f.is_obsolete = 'f' AND f.organism_id = o.organism_id AND f.uniquename like 'FBte%';" | psql -h flybase.org -U flybase flybase -P footer=off -P fieldsep=$'\t' -P format=unaligned > flybase_f... |
23112099a39d5d7052781b6b65906a1fd6acda4cdbd4b756bc3117e6ff82f0f6 | Shell | 329 | 15 | #! /bin/bash
#SBATCH -n 8
#SBATCH --mem 32G
module load Cell-Ranger/7.2.0
Scaffolds="./ref/GCF_018143015.1_Lvar_3.0_genomic.fna"
Annotations="./ref/GCF_018143015.1_Lvar_3.0_genomic.gtf"
cellranger mkref --genome=Lvar3 --fasta=$Scaffolds --genes=$Annotations \
--nthreads=8 \
--memgb=32 \
--localcores 8 \
--loca... |
638fd2cf39065f1ab9124060bf8e01c4a5f8b927936a8016cc036b0a7d11d4d0 | Shell | 329 | 22 | #!/bin/sh
# Siwei 5 Jun 2018
# Siwei 31 Oct 2018
for EACHFILE in *.bam
do
echo $EACHFILE
echo ${EACHFILE/%_WASPed.bam/}
macs2 callpeak -t $EACHFILE \
-n ${EACHFILE/%_WASPed.bam/} \
--outdir MACS2_output/ \
-f BAMPE \
-g 3.2e9 \
--nomodel \
--nolambda \
--keep-dup all \
--call-summits \
--verbose... |
c592adc86d729dba988b9ad63ac59306313f1c7f85c5a09b66b7bc934a4b1343 | Shell | 329 | 10 | #!/bin/bash
git clone --branch v3.3.0 https://github.com/soedinglab/hh-suite.git /tmp/hh-suite \
&& mkdir /tmp/hh-suite/build \
&& pushd /tmp/hh-suite/build \
&& cmake -DCMAKE_INSTALL_PREFIX=/opt/hhsuite .. \
&& make -j 4 && make install \
&& ln -sf /opt/hhsuite/bin/* /usr/bin \
&& popd \
&& rm -rf /tmp/... |
e88152c25711fb583d056c7363206995b872edb2a48aa4a0a07c26ae3e0b284a | Shell | 331 | 9 | #!/bin/bash
#SBATCH -p mit_normal -c 1 --mem-per-cpu 4G
export APPTAINER_BINDPATH="/orcd/pool/003/katiegal_shared/data/raw_reads/250425Gal/,/orcd/pool/003/katiegal_shared/projects/RAS_analysis/RNA-seq/"
source /etc/profile
source /orcd/pool/003/katiegal_shared/hpc-infra/modules/activate.sh
module load snakemake
pwd
sn... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.