sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2a012fbc502533a2c7160399230173d8a740d5a73fa36163e2b0dbc8cf3a9443 | Shell | 329 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J snakemake
#SBATCH -p gpu_p
#SBATCH --gres=gpu:1
#SBATCH --qos=gpu
#SBATCH -c 4
#SBATCH --mem=15G
#SBATCH -t 06:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate rnaprotenv
rnaprot train --in $1 --out $1 --verbose-t... |
42d144f7ebeb69b63da46320298079445f3b2efad97a6ab0df7f92de8ed25dc8 | Shell | 329 | 14 | #!/bin/bash
DIR=${HOME}
USERID=`id -u $USER`
echo "Starting bisweb/devel container with external persistent directory ${DIR} and uid=${USERID}"
docker run --rm -it -p 8080:8080 -p 24000:24000 \
--mount src=${DIR},target=/hostfiles,type=bind \
-e LOCAL_USER_ID=${USERID} \
--name bisweb \
bisweb/... |
5ac8210b33cf4b18a8e3ba32ca0ef7738e8b70d5a0e1f435d1b39b84516c5245 | Shell | 329 | 7 | #! /usr/bin/env bash
input_exp="./example_data/exp_hESC_hvg1000.csv"
input_prior="./prior_data/network_human.zip"
input_genesDE="./example_data/DE_hESC_hvg1000.csv"
cefcon --input_expData $input_exp --input_priorNet $input_prior --input_genesDE $input_genesDE --cuda 3 --repeats 3 --edge_threshold_param 8 --out_dir ./... |
6bbd1ef26410e5a9f996f87699f38297c147c9487bff9b9f7533c071d6f04f37 | Shell | 331 | 16 | #!/bin/bash
if [[ $# -ne 0 ]]; then
echo "Usage: ./$0"
exit 1
fi
src/dynamics.o \
-fileroot "out/oscillation/baseline" \
-X_dir "out/pathways/N2048-p3-s512-a02-2" \
-p 3 -s 50 \
-gamma 0.05 \
-square -theta_range 0.6 0.2 20 \
-incomp 0. -inacc 0.01 -beta 100 \
-n_cue 30 -T_sim 120 -T_rec -2 \
-sav... |
3bb3f37a493c2a2612ad86f9d9e336801c30f18b2872e66616e731af5e356ab8 | Shell | 333 | 9 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset toxcast --cross_validation
--model graphconvreg
--no_r2 --prot_desc_path full_toxcast/prot_desc.csv
--log_file GPhypersearch_t4_tc_w.log
--model_dir ./model_dir/model_dir_tc_w
--arithmetic_mean --aggregate toxcast
--intermediate_file intermediate_cv_warm.csv... |
5f99f97ea95bb3229bdc0a2087c8e1100e7fb4ffd022d5c2734df6d29dfb8c64 | Shell | 334 | 23 | #!/bin/bash
# echo $1
# echo $2
# echo $3
# echo done
if [ ! -f $1 ]; then
echo "$1 does not exist."
exit 1
fi
if [ ! -f $2 ]; then
echo "$2 does not exist."
exit 1
fi
echo "intersect merge peak for $3 ."
intersectBed -wa -a $1 -b <(sed -e "1d" $2) -nonamecheck \
| sort -k1,1 -k2,2n | uniq > $3
... |
8bac8a459f7ec7ec536b68e84cecb594f53b91c2e074bcc52ded030b197cf030 | Shell | 334 | 8 | PROJDIR=/projects/pfenninggroup/singleCell/Logan_BU_Striatum_snRNA-seq
DATADIR=$PROJDIR/data/tidy_data/Piechota_et_al_mouse_striatum_on_drugs
mkdir -p $DATADIR/tables
cd $DATADIR/tables
wget --no-check-certificate https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2010-11-5-r48/MediaObjects/13059_2010_2341_MOE... |
6fb127142deeedca35cc76efed47bcbe11582670558a12475af4d3ebdd3adb1c | Shell | 335 | 11 | # !bin/bash
python /src/extract_train.py -b $1 -o $2/dsRID_train.tsv
python /src/extract_null.py -b $1 -o $2/dsRID_null.tsv
python /src/concat_train_null.py -t $2/dsRID_train.tsv \
-n $2/dsRID_null.tsv -o $2/dsRID_data.tsv
python /src/extract_whole.py -b $1 -o $2/dsRID_whole.tsv
python /src/model_predict.py $2/dsR... |
1105e0fdb0703f433f0796fd13802d7901db44a3ff6a5470fa11c1be48e609fd | Shell | 336 | 7 | # run this script in the root directory of the project
# ./install2pypi.sh
# This script will remove the build, dist, and *.egg-info directories, create a new distribution, and upload it to PyPI.
# It assumes you have the necessary permissions to upload to PyPI.
rm -rf build dist src/*.egg-info
python -m build .
twin... |
b8839ec755393f26a39a67fde956f6dc8c816604a24a880771eeca6a1bf42cc4 | Shell | 336 | 16 | #!/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
# Bytes output mode with LDC93S1 takes too long to converge so we simply test
# that loading the model won't crash... |
e5412ab4893623e0eb693813130c46246091ac19c41fc9dae6f40ae90d672f2d | Shell | 336 | 11 | #! /bin/bash
ref=/reference/Homo_sapiens/Ensembl/GRCh38.p13/Annotation/Release_105-2021
for f in ../*/raw_data/*/; do
f2=`basename $f | sed 's/pl_//'`;
echo $f2;
/common/salmon-1.7.0/bin/salmon quant -p 10 -i $ref/salmon1.7.0/ \
-l A -1 "$f"*_1.fq.gz -2 "$f"*_2.fq.gz --validateMappings \
-q -g $ref/tx2ge... |
e559084b201710b38fe349ea27fde643e6f8a269c5fcc5e31c006d1e81d8a286 | Shell | 337 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J mrb-predict
#SBATCH -p cpu_p
#SBATCH -c 32
#SBATCH --mem=32G
#SBATCH -t 04:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate multiresbind-2
python -u scripts/eval.py --test-input $1 --model-file $2 --output-csv $3 ... |
cc87f128004b838aac327d52e91345525e1283db9035b7df05ac20ab92457b1d | Shell | 338 | 13 | #!/bin/bash
if [ ! -e trinity_ext_sample_data ]; then
git clone https://github.com/trinityrnaseq/trinity_ext_sample_data.git
fi
VERSION=`cat VERSION.txt`
docker run --rm -v `pwd`/trinity_ext_sample_data:/trinity_ext_sample_data \
trinityrnaseq/trinityrnaseq:$VERSION \
bash -c 'cd /trinity_ext_samp... |
aa4830a63670332ee523c89da86032a76bf6b0441a79c2ce9df08a241d7def82 | Shell | 339 | 7 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset kiba --cross_validation
--model graphconvreg --prot_desc_path KIBA_data/prot_desc.csv
--split_warm --model_dir ./model_dir/model_dir_kiba_w
--filter_threshold 6 --arithmetic_mean --aggregate toxcast
--intermediate_file ./interm_files/intermediate_cv_warm.... |
123d4ca7193bdc76931f4c54ffd2c6fbb6359e916c5940fcc003ab64051c9433 | Shell | 340 | 14 | #!/bin/bash
set -xe
source $(dirname $0)/tc-all-vars.sh
mkdir -p ${TASKCLUSTER_ARTIFACTS} || true
cd ${DS_ROOT_TASK}/DeepSpeech/ds/kenlm/build/bin/ && \
tar \
-czf ${TASKCLUSTER_ARTIFACTS}/kenlm.tar.gz \
build_binary${PLATFORM_EXE_SUFFIX} \
filter${PLATFORM_EXE_SUFFIX} \
lmplz${P... |
191cec582343490f83f19201105fe048838f4432020e3877e67e3aac5ca9aa3c | Shell | 340 | 11 | # !bin/bash
python ./src/extract_train.py -b $1 -o $2/dsRID_train.tsv
python ./src/extract_null.py -b $1 -o $2/dsRID_null.tsv
python ./src/concat_train_null.py -t $2/dsRID_train.tsv \
-n $2/dsRID_null.tsv -o $2/dsRID_data.tsv
python ./src/extract_whole.py -b $1 -o $2/dsRID_whole.tsv
python ./src/model_predict.py $... |
3f3fb987a56f662e69eea8adc8f241383229976266963ef2a5f4465fc7616d01 | Shell | 340 | 7 | CUDA_VISIBLE_DEVICES=4
spec='python driver.py --dataset kiba --cross_validation
--model graphconvreg --prot_desc_path KIBA_data/prot_desc.csv
--model_dir ./model_dir/model_dir_kiba_cd --cold_drug
--arithmetic_mean --aggregate toxcast --filter_threshold 6
--intermediate_file ./interm_files/intermediate_cv_cdrug... |
d29ae3b2fc595a09532ec8a6ee01f56e42c7327fc8f3e1c0f9bd0630d7c460d2 | Shell | 340 | 10 | #!/bin/bash -eu
./arpackmm --A issue401.mtx --mag LA --nbEV 1 --nbCV 5
if [ "$?" -ne "0" ]; then exit 1; fi
./arpackmm --A issue401.mtx --mag LA --nbEV 1 --nbCV 5 --restart
if [ "$?" -ne "0" ]; then exit 1; fi
./arpackmm --A issue401.mtx --mag LA --nbEV 1 --nbCV 5 --restart --shiftReal 0.9
if [ "$?" -ne "0" ]; then ex... |
e0781be55d2b8975de3eaba76ede094c4a9fc67416cd792efa3c364e1369556f | Shell | 340 | 12 | python ./inference.py \
--num_designs 10 \
--out examples/out/design \
--contigs 100 \
--T 25 --save_best_plddt \
--secondary_structure XXXXXHHHHXXXLLLXXXXXXXXXXHHHHXXXLLLXXXXXXXXXXHHHHXXXLLLXXXXXXXXXXHHHHXXXLLLXXXXXXXXXXHHHHXXXLLLXXXXX
# FOR SECONDARY STRUCTURE:
# X - mask
# H - helix
# E - ... |
97d6a7c8cfb579b6fd27fe450fc4d713cf09f566771e2030e97059748c50904e | Shell | 341 | 7 | CUDA_VISIBLE_DEVICES=3
spec='python driver.py --dataset metz --cross_validation
--model graphconvreg --prot_desc_path metz_data/prot_desc.csv
--model_dir ./model_dir/model_dir_metz_w --filter_threshold 1
--arithmetic_mean --aggregate toxcast --split_warm
--intermediate_file ./interm_files/intermediate_cv_warm_... |
50cd5369e9d795d0aaa8c9a94c220ffa602a15a272bd7206fe4a4b513ad183fd | Shell | 342 | 7 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset metz --cross_validation
--model graphconvreg --prot_desc_path metz_data/prot_desc.csv
--model_dir ./model_dir/model_dir_metz_cd --cold_drug
--arithmetic_mean --aggregate toxcast --filter_threshold 1
--intermediate_file ./interm_files/intermediate_cv_cdrug... |
ce3e364bcf6ef2deeb9cbf8eb55a6ecb1a8ae2acc12df2b4307b571cfb68e55a | Shell | 342 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J deepripe-predict
#SBATCH -p cpu_p
#SBATCH -c 16
#SBATCH --mem=32G
#SBATCH -t 04:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate multiresbind-2
python -u scripts/eval.py --test-input $1 --model-file $2 --output-cs... |
dab5b7ae669ea04c954f95669f46e983efeae2c59e893e17fb36c348755a6a1b | Shell | 342 | 9 | # generate fast-evolving UCEs by phyloP
chr=$1
species=$2 # specifying species or clade
phyloP --msa-format MAF \
--features {file_pathway}/mUCEs_info/$chr.bed \
--subtree $species \
--method LRT \
--mode CONACC {file_pathway}/95_mammals.nonconserved-4d.mod {file_pathway}/$chr.project.maf > {file_pathway}/$species... |
16604062ec3297169e7654fa8e91d818ac53b49a963be613543dac421e28d0b9 | Shell | 343 | 12 | #!/bin/bash -ve
#######################################################
## Run Trinity to Generate Transcriptome Assemblies ##
#######################################################
## use jellyfish
../../Trinity.pl --seqType fq --JM 2G --left reads.left.fq.gz --right reads.right.fq.gz --CPU 4
##### Done Running... |
8ca5a7954260d535cf68aecbd376ce628d23506315c02a2b11ac156b889f2aa4 | Shell | 343 | 7 | CUDA_VISIBLE_DEVICES=0
spec='python driver.py --dataset kiba --cross_validation
--model tf_regression --prot_desc_path KIBA_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_kiba_w --filter_threshold 6
--arithmetic_mean --aggregate toxcast --split_warm
--intermediate_file ./interm_files/intermediate_cv_war... |
97e7b6c03774844bdeec595ad5cc3c400b977000980c19914f170d93ec9b889f | Shell | 343 | 14 | #!/bin/bash
DIR=${HOME}
USERID=`id -u $USER`
echo "Starting bisweb/devel container with external persistent directory ${DIR} and uid=${USERID}"
docker run --rm --gpus all -it -p 8080:8080 -p 24000:24000 \
--mount src=${DIR},target=/hostfiles,type=bind \
-e LOCAL_USER_ID=${USERID} \
--name bisweb \
... |
d6f1d233836f53081b91c9bad867d80504a8acdf4320d403368f35d1f7bdbb09 | Shell | 343 | 7 | CUDA_VISIBLE_DEVICES=5
spec='python driver.py --dataset metz --cross_validation
--model tf_regression --prot_desc_path metz_data/prot_desc.csv
--split_warm --model_dir ./model_dir/model_dir4_metz_w
--filter_threshold 1 --arithmetic_mean --aggregate toxcast
--intermediate_file ./interm_files/intermediate_cv_war... |
df5e3ecda0b82d06db03079ac5089708b10a01b7b4643b9ec5542e0481f79778 | Shell | 343 | 25 | #!/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
node --version
npm --version
export_node_bin_path
check_runtime_nodejs
run_all_inference_tests
run_js_streaming_inference_te... |
06672a12b583a6d9f4c1867286760e5fc07f033cd53a20c57a4cd7b27b3c5ec4 | Shell | 344 | 7 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset davis --cross_validation
--model graphconvreg --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir_davis_w --filter_threshold 1
--arithmetic_mean --aggregate toxcast --split_warm
--intermediate_file ./interm_files/intermediate_cv_wa... |
27f9af362b4760bcfbe59056256db302795df88069a01082e7f664bc0963c7d3 | Shell | 344 | 16 | #!/bin/bash -e
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Run this script at project root by ".linter.sh" before you commit.
echo "Running isort..."
isort -y -sp .
echo "Running black..."
black -l 80 .
echo "Running flake..."
flake8 .
command -v arc > /dev/null && {
echo "Running arc... |
2e19ebf41bd1db56ba6bbaedc83d523bda5ac0886e8e53317419fc57aefe5703 | Shell | 345 | 7 | CUDA_VISIBLE_DEVICES=6
spec='python driver.py --dataset davis --cross_validation
--model graphconvreg --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir_davis_cd --cold_drug
--arithmetic_mean --aggregate toxcast --filter_threshold 1
--intermediate_file ./interm_files/intermediate_cv_cd... |
67249c45e5c2d751451bac0f838499742b1f506371f9e5d6663bc73a02418c43 | Shell | 345 | 24 | #!/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 PATH=${CI_TMP_DIR}/ds/:$PATH
check_versions
run_all_inference_tests
run_multi_inference_tests
run_cpp_only_inference_... |
b03ca3196a6fbb4d53c05c46be44b2002d923c69609e51f9edfa2b450b92b6d1 | Shell | 345 | 10 | 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 N in 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"-"$gpu"> "$... |
03095a369b8b75b9273d8fcf1e7868f98a4fe0bff2c2f89481b591908f064695 | Shell | 346 | 7 | CUDA_VISIBLE_DEVICES=5
spec='python driver.py --dataset davis --cross_validation
--model tf_regression --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_davis_w --filter_threshold 1
--arithmetic_mean --aggregate toxcast --split_warm
--intermediate_file ./interm_files/intermediate_cv_... |
165c3e89dfd18164219618f97123f5aa085be99ecd53e950bdc71779ebab0a0d | Shell | 346 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J predict-rnaprotext
#SBATCH -p gpu_p
#SBATCH --gres=gpu:1
#SBATCH --qos=gpu
#SBATCH -c 4
#SBATCH --mem=8G
#SBATCH -t 01:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate rnaprotenv
rnaprot predict --in $1 --train-in... |
1673f26051ea84bcd8fed4a4ee6482c50cd59493da4af5e0a9f17cdc9557f8b1 | Shell | 346 | 5 | #!/bin/bash
python call.rpmf.peak.py -i 3 -c 1 -w 3
python call.rpmf.peak.py -i 4 -c 2 -w 3
python peak_finalize.py -p 31RPMF_out.txt -a ~/project/00.DATABASE/GRmm38/Annotation/mRNA/gencode.vM24.gene.slidingwindows.gtf
python peak_finalize.py -p 42RPMF_out.txt -a ~/project/00.DATABASE/GRmm38/Annotation/mRNA/gencode.vM2... |
d7c7679c298c91c86aa642bbb88c79fef2c82cd37460d62603370fb9b2d98a6c | Shell | 347 | 18 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J pred-DeepCLIP-snakemake
#SBATCH -p cpu_p
#SBATCH -c 4
#SBATCH --mem=10G
#SBATCH -t 14:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate deepclip
python deepclip/DeepCLIP.py --runmode predict -P $1 --sequences $2 --... |
a6b3696dcfc7ca6a7d6b13424e58d71a2433147632668fed062b25aa080dc100 | Shell | 348 | 7 | CUDA_VISIBLE_DEVICES=1
spec='python driver.py --dataset kiba --cross_validation
--model tf_regression --prot_desc_path KIBA_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_kiba_ct --cold_target
--arithmetic_mean --aggregate toxcast --filter_threshold 6
--intermediate_file ./interm_files/intermediate_cv_c... |
a71c47d70ef7be53e7418961e85426a8ba68454f342febd0afc7df0a30328827 | Shell | 348 | 7 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset metz --cross_validation
--model tf_regression --prot_desc_path metz_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_metz_ct --cold_target
--arithmetic_mean --aggregate toxcast --filter_threshold 1
--intermediate_file ./interm_files/intermediate_cv_c... |
41ccbc6dda864f8b233fc0cd0ae7ff9c5546268284979ed0a0f33a80fb4861b6 | Shell | 349 | 16 | #!/bin/bash
#--- Switch to the correct path (where the script is in)
backpath=`pwd`
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd $DIR
#--- Download the compressed file
wget -nc http://vision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip
#--- Extract it
unzip ModelNet10
#--- Go back to the in... |
447ea4f22a37f25d7117bb9ed58fec0c7aa4a4ca900472c9022d9035f5368ffd | Shell | 349 | 9 | #!/bin/bash
set -e
# Using dependencies from client/node_modules, run eslint against args passed
# to this script. Primary use case here is for a pre-commit check. ESLint finds
# client/eslint.config.mjs by looking up from each file, so this can run from
# the repository root.
node client/node_modules/eslint/bin/esli... |
44bce48d3bc00cb1aac4f094af0ebda93896be0c6d8c05b38a9b71f0253bbecd | Shell | 350 | 10 | #!/usr/bin/env bash
# PCA
python3 src/create_pca_images.py --number=10 --output_dir="./demo_out/pca" --input_dir=./dataset28/validation
# SSM
python3 src/create_ssm_images.py --number=10 --output_dir="./demo_out/ssm" --input_dir=./ssm
# GAN
python3 src/create_gan_images.py --number=10 --out_dir="./demo_out/gan" --ge... |
036ced6cfaa66e838e7509fef6ab5a7f350952a5ee498112ba150691f5de5f4f | Shell | 351 | 12 | #!/bin/bash
set -e
DATE=$(date +%y.%m.%d)
IMAGE_NAME="flash-attn-4:flash-attn-cu13.0-${DATE}"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(git -C "$SCRIPT_DIR" rev-parse --show-toplevel)"
echo "Building $IMAGE_NAME ..."
sudo docker build -t "$IMAGE_NAME" -f "$SCRIPT_DIR/Dockerfile" "$REPO_... |
f1010943eef26b45a6c88ffe7f3f9f5a76c4c1080b2155fd1cb746a0a9d18912 | Shell | 351 | 7 | CUDA_VISIBLE_DEVICES=1
spec='python driver.py --dataset davis --cross_validation
--model tf_regression --prot_desc_path davis_data/prot_desc.csv
--model_dir ./model_dir/model_dir4_davis_ct --cold_target
--arithmetic_mean --aggregate toxcast --filter_threshold 1
--intermediate_file ./interm_files/intermediate_c... |
5a1415cc66cd0a86cc12622bd84651c9f9abb8789f1ff1380e6a53840763b898 | Shell | 353 | 8 | # Authors Tuomo Mäki-Marttunen & Saana Seppälä
for myseed in 1
do
cat Model_1d_rxd.xml | sed "s/245/$myseed/" > Model_1d_rxd_seed${myseed}.xml
echo "java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_1d_rxd_seed${myseed}.xml"
java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_... |
f8e6050fd43a3355af963083bdee6de85d3a1c2e13b7fe61df8dbb420c6eebe8 | Shell | 353 | 8 | # Authors Saana Seppälä & Tuomo Mäki-Marttunen
for myseed in 1
do
cat Model_3d_rxd.xml | sed "s/245/$myseed/" > Model_3d_rxd_seed${myseed}.xml
echo "java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_3d_rxd_seed${myseed}.xml"
java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_... |
66607a63c9f8bc2f0dcc7413dbf622d571c36de0383c5530ace184d575bf517f | Shell | 354 | 7 | CUDA_VISIBLE_DEVICES=6
spec='python driver.py --dataset toxcast --cross_validation
--model graphconvreg --prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir_tc_oversp_cv_cd --oversampled
--arithmetic_mean --aggregate toxcast --cold_drug
--intermediate_file ./interm_files/intermediate_c... |
41c1fedfa5d0af1054c7cb43421c9e3e0669fb9828fd6bf7d306bca17a61bafe | Shell | 356 | 12 | #!/bin/bash
IBM_CLOUD_DBSERVER_SPACE="covid-19-data-india-storage"
ibmcloud target -s ${IBM_CLOUD_DBSERVER_SPACE} -o ${IBM_CLOUD_ORG}
cd ${TRAVIS_BUILD_DIR}/serve_db
# Download latest DB from the source folder
curl -L -o "./covid-india.db" "https://www.dropbox.com/s/hbe04q6vtzapdam/covid-india.db?dl=1"
# Push and d... |
8084544137a5a7b1c22a339a2a30985983d2d462a865a22d210c737c840ddecf | Shell | 356 | 7 | CUDA_VISIBLE_DEVICES=6
spec='python driver.py --dataset toxcast --cross_validation
--model tf_regression --prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir4_tc_oversp_cv_cd --oversampled
--arithmetic_mean --aggregate toxcast --cold_drug
--intermediate_file ./interm_files/intermediate... |
e33581d04715e67abd18c5678b0955ee765c7c3ed2a4e81a1869a25c660ffadc | Shell | 356 | 11 | #!/bin/bash -ve
#######################################################
## Run Trinity to Generate Transcriptome Assemblies ##
#######################################################
../../Trinity --seqType fq --JM 2G --left reads.left.fq.gz --right reads.right.fq.gz --SS_lib_type RF --CPU 4 --grid_conf_file ../..... |
0c1653f40972c95e936206f4318909d0bfb336f69d955c4fa52f43f1e85ab282 | Shell | 357 | 8 | # Authors Tuomo Mäki-Marttunen & Saana Seppälä
for myseed in 1
do
cat Model_1d_diff.xml | sed "s/245/$myseed/" > Model_1d_diff_seed${myseed}.xml
echo "java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_1d_diff_seed${myseed}.xml"
java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Mod... |
72f34ec623e02d4a5a83b1cdd5c47403b6e3ef5cf50bc340d98b4bc9b6cd1941 | Shell | 358 | 9 | # generate fast-evolving UCEs by phyloP
chr=$1
species=$2 # specifying species or clade
phyloP --msa-format MAF \
--features {file_pathway}/aUCEs_info/$chr.bed \
--subtree $species \
--method LRT \
--mode CONACC {file_pathway}/94_birds_nonconserved-4d-{our_or_ref}-tree.mod {file_pathway}/$chr.project.maf > {file_p... |
f8e43c53c6079277490f92102a256495c58d152089257c6b332c19144e90de79 | Shell | 358 | 7 | CUDA_VISIBLE_DEVICES=7
spec='python driver.py --dataset toxcast --cross_validation
--model graphconvreg --prot_desc_path full_toxcast/prot_desc.csv
--model_dir ./model_dir/model_dir_tc_oversp_cv_ct --oversampled
--arithmetic_mean --aggregate toxcast --cold_target
--intermediate_file ./interm_files/intermediate... |
3bdddc4ef061654a75d95ad1bb4bd106017b6c3b684d88610295dd759d881d13 | Shell | 360 | 7 | CUDA_VISIBLE_DEVICES=7
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_oversp_cv_ct --oversampled
--arithmetic_mean --aggregate toxcast --cold_target
--intermediate_file ./interm_files/intermedia... |
ef4ccf5c7d482f1d5fc2e636ccf17bd369d591f2494f31add723fc786e9af390 | Shell | 360 | 11 |
# ## download mclean snRNA_seq files
# rclone sync -P dropbox:Monkey_opioid_511GB \
# /projects/pfenninggroup/singleCell/McLean_chronic_opioid_monkey_snRNA-seq/data
## download mclean snRNA_seq files
rclone sync -P /projects/pfenninggroup/singleCell/McLean_chronic_opioid_monkey_snRNA-seq/ \
pfenninglab_gdrive:Data/... |
dbee704d14b612a7f386b16c612876a60ae64c012aea40c3a1ce9ea3e9c74dcf | Shell | 361 | 13 | #!/bin/bash
#SBATCH --job-name=plot20230531
#SBATCH --time=00:15:00
#SBACTH --ntasks=1
#SBATCH --output="out/plot20230531-%j.out"
#SBATCH --mem=1G
#SBATCH -p evlab
source /mindhive/mcdermott/u/gretatu/anaconda/etc/profile.d/conda.sh
conda activate auditory_brain_dnn
cd /mindhive/mcdermott/u/gretatu/auditory_brain_dnn... |
dde774c20960e77aa85ba3f0f285b003137ed8a2a9ec78b9f26a76011e2edb2e | Shell | 361 | 27 | #!/bin/bash
#
#SBATCH --account=p33_norment
#SBATCH --time=1:00:00
#SBATCH --job-name=dcm2nii
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
#
source /cluster/bin/jobsetup
module purge
set -o errexit
echo "We run SBATCH ..."
echo "Start..."
echo "dcm 2 nifti ..."
cd ${1}
/cluster/projects/p33/users/ivanma/bi... |
56eb7f630097188d60e297e1d782746a0ea3b15a8b077826f7945a032f006c71 | Shell | 362 | 22 | #!/bin/sh -e
expected=$EXPECTED_ARCH
arch=$(uname -m)
case $arch in
arm*|aarch*) actual=arm ;;
x86*) actual=intel ;;
*)
echo Unrecognized uname result $arch >&2
exit 2
;;
esac
if test $actual = $expected
then
echo Running on $arch as expected
exit 0
else
echo Platform $arch is not... |
baf290dd0411696aa56386a4665703864c09156854a65452b992403e9104d8e4 | Shell | 364 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J iDeepS-snakemake
#SBATCH -p cpu_p
#SBATCH -c 2
#SBATCH --mem=80G
#SBATCH -t 24:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate ideeps
cp $1 sequences.fa.gz
python iDeepS/ideeps.py --train=True --data_file=sequenc... |
ae95690996fadfc47941c74cf03cc4908e2cfe76872edaae043ca9259d6c0b86 | Shell | 365 | 22 | #!/bin/bash
subj_ids=$1
ses=2YearFollowUpYArm1
if [ -z "$1" ]; then
echo
echo "Error: Missing list. Provide subject list w/o 'sub-' prefix in positon 1."
echo
exit 1
fi
n=0
cat $subj_ids | while read line ; do
sed -e "s|SUBJECT|${line}|g; s|SESSION|${ses}|g" ./templates/firstlvl.txt > ./batch_run/first${n}
n=... |
08313e94bb1f5da81ed685b804fa51dee33c3fc16f7ccf1f80a11aa72247e30d | Shell | 366 | 8 | #!/bin/csh
#cd /groups/apig/tomo/Fiji.app/
#./fiji-linux64 -Xmx24g -Xincgc -XX:MaxPermSize=256m -XX:PermSize=256m -XX:NewRatio=5 #-XX:CMSTriggerRatio=50 -XX:+UseCompressedOops -- -port23
cd /groups/bock/bocklab/people/eric/fiji/
./fiji -Xmx24g -Xincgc -XX:MaxPermSize=256m -XX:PermSize=256m -XX:NewRatio=5 -XX:CMSTrigg... |
ad75034badf46ac25bfdb7e593b8c66922d77bb3f57d17644c229762bd13d5da | Shell | 366 | 13 | #!/bin/bash
python finetune.py \
--kinase_data_path "./data/finetune/toy_finetune_task.csv" \
--meta_model_path './results/meta-learner/2022-08-24-04_18_42_meta-learner_state_dict.pth' \
--save_dir "./results/base-learner/toy_finetune_task/" \
--random_seed 68 \
--num_train 50 \
--device 0
#the other args of models... |
59d55b6d4a3d0c484b7a9e592e84f445a251fe120cc300355ba1939997ffbdba | Shell | 367 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J predict-prismnet
#SBATCH -p cpu_p
#SBATCH -c 4
#SBATCH --mem=10G
#SBATCH -t 02:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate prismnet
python -u ../../methods/PrismNet/main.py --load_best --infer --infer_file $1... |
92dfded66dc488ce36e4668ab1df88c498665ea8c6863383f32843b62fbbc5ca | Shell | 368 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J snakemake
#SBATCH -p gpu_p
#SBATCH --gres=gpu:1
#SBATCH --qos=gpu
#SBATCH -c 4
#SBATCH --mem=15G
#SBATCH -t 06:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate rnaprotenv
rnaprot train --in $1 --out $1 --use-eia -... |
4fbb08468aa033823ddebbb1a2fd4aec486cf5dff21beb95749a4981f2e5a0c5 | Shell | 369 | 16 | #!/bin/bash
#SBATCH --time=0-01:00:00
#SBATCH --mem-per-cpu=8000
#SBATCH --output=./slurm_logs/slurm-%A.out
if [ -z "$MRI_DIR" ]
then
echo "MRI_DIR not set, exiting"
exit 1
fi
module load matlab/r2017b
module load spm/12.2019
srun matlab -nosplash -nodesktop -r "try; cd $PWD; disp(pwd); dicom2nifti(\"$MRI_DIR\"... |
3b61f8082ab57e3aca218483f4e888e8463ad2698b8d7c17207f5b22a363e9ee | Shell | 371 | 18 | #! /usr/bin/env bash
set -u
script_dir="$(dirname "$(realpath "$0")")"
nmodl="$1"
usecase_dir="$2"
references_dir="${script_dir}/references/$(basename "$2")"
output_dir="$(mktemp -d)"
"${script_dir}"/generate_references.sh ${nmodl} "${usecase_dir}" "${output_dir}"
diff -U 8 -r "${references_dir}" "${output_dir}"
ex... |
4191cc2ace785096682bc19205af83af628670c3c6d13a1bbc8dc0266131fd3a | Shell | 374 | 10 | #!/bin/bash -ve
#######################################################
## Run Trinity to Generate Transcriptome Assemblies ##
#######################################################
## use jellyfish
../../Trinity.pl --seqType fq --JM 2G --left reads.left.fq.gz --right reads.right.fq.gz --SS_lib_type RF --CPU 4 --gr... |
997862f7da1e094b659ebd5895e7ab34c2efd9781e187ba7ad0aaee1e048680f | Shell | 374 | 18 | #!/bin/bash
set -ex
datadir="trinity_ext_sample_data_singularity"
if [ ! -e "$datadir" ]; then
git clone https://github.com/trinityrnaseq/trinity_ext_sample_data.git ${datadir}
fi
version=`cat VERSION.txt`
simg="trinityrnaseq.v${version}.simg"
singularity exec -e -H /tmp -B `pwd`/${datadir}:/${datadir} \
... |
aa83ea6d80181cbfb52f787cd0ca901bedecce54af5635a73a2a7e891c70a2bc | Shell | 374 | 23 | #!/bin/bash
module load anaconda3
source activate neuroimaging
OUTPUT_DIR=/scratch/n/tbi-meg/v/processed
echo "OUTPUT_DIR set as $OUTPUT_DIR"
cd "$OUTPUT_DIR" || exit 1
dirnames=(*/)
for d in "${dirnames[@]}"
do
sub=${d%?}
if [ -n "$sub" ]
then
echo "${sub}"
srun python /scratch/n/tbi-meg/v/pipeline... |
5e211e12759791b933ada86f0f91414756382a9d27341eda156e84b12c83548b | Shell | 376 | 24 | #!/bin/bash
#
#SBATCH --account=p33_norment
#SBATCH --time=1:00:00
#SBATCH --job-name=dcm2nii
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
#
source /cluster/projects/p33/users/ivanma/bin/
module purge
set -o errexit
echo "We run SBATCH ..."
echo "Start..."
echo "dcm 2 nifti ..."
cd ${1}
/cluster/projects/p... |
dc065fdbd5ae8abb19451b1e6a595f812405548b051c046d9da29644cfe10067 | Shell | 376 | 9 | #!/bin/bash
31 c -n synth_%s_%te_%tu_%nb -wn synthetic \
-f %s 1,2,3,4,5,6,7,8,9,10 \
-f %te 5,10,20 \
-f %tu 5,20 \
-f %nb 0,0.1,0.25,0.75 \
-w 100 -fw %w $(echo {1..100} | sed 's/ /,/g') \
"echo quiet; taskset -c %w nice -n 19 python -m working.synthetic_data.experiment main --seed %s --tempe... |
9ce6db6d59619727ba4179d9cfea592b4705115dfbba5fa59ee5f43214dfa672 | Shell | 377 | 21 | set -euo pipefail
NIM_VERSION=v0.19.0
base=$(pwd)
git clone -b $NIM_VERSION --depth 1 git://github.com/nim-lang/nim nim-$NIM_VERSION/
cd nim-$NIM_VERSION
sh build_all.sh
export PATH=$PATH:$base/nim-$NIM_VERSION/bin/
cd $base
cd $base
git clone --depth 1 git://github.com/brentp/mosdepth.git
cd mosdepth
nimble ins... |
a5b36090ea3d3cebe3d45bf4ff2c126533eaffacd58b95f475eb486c9d6cde9d | Shell | 381 | 14 | #!/bin/sh
BUILDDIR="../build/release"
echo "Running unit tests..."
$BUILDDIR/test/src/test_AurynVector && \
$BUILDDIR/test/src/test_AurynStateVector && \
$BUILDDIR/test/src/test_AurynDelayVector && \
$BUILDDIR/test/src/test_EulerTrace && \
$BUILDDIR/test/src/test_LinearTrace && \
$BUILDDIR/test/src/test_ComplexMatrix... |
d11f870ec331e53a496f578e470409d7dc6eeb567670a9fcf7772338f59766d6 | Shell | 381 | 22 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
arm_flavor=$1
api_level=$2
api_kind=$3
export ANDROID_HOME=${ANDROID_SDK_HOME}
android_install_ndk
android_install_sdk
# Required for running APK tests later
android_install_sdk_platform "android-27"
if [ "${arm_flavor}" != "sdk" ]; then
android_set... |
47293e4d643712afd04a442b6c3d7e2df8881194b28d39d59cc4777757ae5589 | Shell | 382 | 18 | #!/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
# Bytes output mode with LDC93S1 takes too long to converge so we si... |
bfd1f424c63d3ffc259f265a914af0813ff18242590d36ed4aa582ab90beecf5 | Shell | 384 | 7 | CUDA_VISIBLE_DEVICES=3
spec='python driver.py --dataset toxcast --hyper_param_search
--max_iter 1 --evaluate_freq 1 --patience 1 --model graphconvreg
--early_stopping --prot_desc_path full_toxcast/prot_desc.csv --no_r2
--log_file GPhypersearch_temp_tc_toy.log --model_dir ./model_dir/model_dir_sch_tc_toy
--arit... |
51362bd573f67b1f395ef01c78d281793382c9c6554f2ee930e5ee1204fc62d0 | Shell | 385 | 18 | #/bin/bash
#train=$1
#ln -sf RTrees_hhm/RTrees_hhm_${train}.h RTrees_hhm.h
rm -f pCRF_pair_align.o pCRF_pair_align.so
#g++ -fPIC -g -c pCRF_pair_align.cpp -o pCRF_pair_align.o
#g++ -shared -o pCRF_pair_align.so pCRF_pair_align.o
make pCRF_pair_align.so
#cp pCRF_pair_align.so ../lib/pCRF_pair_align_${train}.so
c... |
cebe785fa873c595979cbd859dd588dbeed0fd71ad9cb211e55db7e2de1e68bc | Shell | 385 | 11 | path='./datasets/SEN1-2/10%_train/ROIs1158_spring_10%_train'
# train_attn_v2.py中修复了使用visdom存储数据时，训练到epoch=20就自动中断的bug
python train_attn_v2.py \
--netG resnet_9blocks \
--netD posthoc_attn \
--model attn_cycle_gan \
--concat rmult \
--dataroot $path \
--name SEN1-2_10%_spring \... |
e051c21a78b93ed3e859bac0f087d288b1a65b6282f699517e3c207de1d737be | Shell | 385 | 20 | sbatch --wait << EOF
#!/bin/bash
#SBATCH -o logs/%j.job
#SBATCH -e logs/%j.job
#SBATCH -J Pysster-snakemake
#SBATCH -p gpu_p
#SBATCH --gres=gpu:1
#SBATCH --qos=gpu
#SBATCH -c 4
#SBATCH --mem=15G
#SBATCH -t 06:00:00
#SBATCH --nice=10000
source $HOME/.bashrc
conda activate pysster-same-padding
python scripts/train_Pys... |
18ba0934e60cf4b311fc694d17bdb1079c75bdc7d24250fc40dbbdc25d990ca2 | Shell | 386 | 8 | CUDA_VISIBLE_DEVICES=2
spec='python driver.py --dataset toxcast --hyper_param_search
--max_iter 1 --evaluate_freq 1 --patience 1 --model tf_regression
--early_stopping --prot_desc_path full_toxcast/prot_desc.csv --no_r2
--log_file GPhypersearch_t4_tc_toy.log
--model_dir ./model_dir/model_dir4_tc_sch_toy
--ar... |
f9418a7da991b47eb586f6c01516545c1ed7d89a967200ecb8c125ba4b3fdb40 | Shell | 388 | 8 | python trans_neg2pos.py 3neg.site.txt > 3neg.site.rev
python trans_neg2pos.py 4neg.site.txt > 4neg.site.rev
cat 3pos.site.rev 3neg.site.rev | sort -k1,1 -k2,2n > final.3.sites
cat 4pos.site.rev 4neg.site.rev | sort -k1,1 -k2,2n > final.4.sites
python normal_RT.py final.3.sites final.4.sites F-2-3.sorted.bam.filter.ba... |
10f1f590c8d81086a660b16e0a1a6f334c8e2bb9bf39b1353469fcb73ebf1a96 | Shell | 389 | 15 |
dataset_root='Image_translation_codes/NICE-GAN-pytorch/dataset/'
dataset_name='SEN1_2/Splited_by_percent/20%_train/ROIs2017_winter_20%_train'
dataset=$dataset_root$dataset_name
for ((i=1; i<=5; i++));
do
echo "Processing"$i"th epoch..."
python Image_translation_codes/NICE-GAN-pytorch/main.py \
-... |
13a7ec2027dfb93d5dea720612b06ddd524d1f1552ef6ba1ef277759142546b0 | Shell | 389 | 8 | # Authors Saana Seppälä & Tuomo Mäki-Marttunen
for myseed in 1
do
cat Model_3d_diff.xml | sed "s/245/$myseed/" > Model_3d_diff_seed${myseed}.xml
echo "java -jar neurord-3.3.0-all-deps.jar -Dneurord.writers=text Model_3d_diff_seed${myseed}.xml"
java -jar /Users/seppal22/PycharmProjects/neurord-3.3.0-all-dep... |
002849b9615e9a853afc303a8d5c00d4be454dfc0d45dacaf38a76d8de86b0fa | Shell | 390 | 7 | CUDA_VISIBLE_DEVICES=4
spec='python driver.py --dataset toxcast --model graphconvreg
--model_dir ./model_dir/model_dir_tc_cdrug_clu_nop --no_input_protein
--arithmetic_mean --aggregate toxcast --cross_validation --cold_drug_cluster
--aggregate_suffix_file ./full_toxcast/Assay_UniprotID_subgroup.csv
--intermediate_f... |
0a59e75254c75d4d6b53a97584aae4f432641d1bfbf153c326e911b2eda71273 | Shell | 390 | 9 | export DATA_FOLDER=./fasta_files
python data_preprocessing/compute_coupling.py --input_folder $DATA_FOLDER
python data_preprocessing/compute_splicing_outcomes.py \
--input_folder $DATA_FOLDER \
--output_folder $DATA_FOLDER \
--plasmid_coupling_file_name $DATA_FOLDER/Sample_BS06911A/coupling.csv
python data... |
85f07d828435e67a9c9ef707a71d1f7ca827f3ee54785e02c53f25722c6e2f0b | Shell | 390 | 20 | # CPAT
pip install CPAT
# CPC2
wget http://cpc2.gao-lab.org/data/CPC2-beta.tar.gz
export DIR="$PWD"
gzip -dc CPC2-beta.tar.gz | tar xf -
cd CPC2-beta
export CPC_HOME="$PWD"
cd libs/libsvm
gzip -dc libsvm-3.18.tar.gz | tar xf -
cd libsvm-3.18
make clean && make
cd $DIR
# rnasamba
conda create -n rnasamba_env
conda act... |
ae68e0e546b5c63292eea28c0bd401623655e60b4f3ebc881329971f15e67813 | Shell | 390 | 8 | CUDA_VISIBLE_DEVICES=6
spec='python driver.py --dataset metz --hyper_param_search
--max_iter 42 --evaluate_freq 3 --patience 3 --model tf_regression
--early_stopping --no_concord
--prot_desc_path metz_data/prot_desc.csv --no_r2
--log_file GPhypersearch_t4_metz.log --model_dir ./model_dir/model_dir4_metz_sch
... |
537307dd5d923ef1c19e98c420e9ddf2cf2a67dd1e801e3323b1bd81e207db1f | Shell | 391 | 20 | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
bitrate=$1
set_ldc_sample_filename "${bitrate}"
model_source=${DEEPSPEECH_TEST_MODEL//.pb/.tflite}
model_name=$(basename "${model_source}")
export DATA_TMP_DIR=${TASKCLUSTER_TMP_DIR}
download_material "${TASKCLUSTER_TMP_DIR}/ds"
export PATH=${TASKCLUSTE... |
13b92e4f76e436451030480530828adf72f76e3e459ac285ea7a65f01288b451 | Shell | 392 | 13 | #!/bin/bash
# Process the data for correct plotting format
python process_tp53_junctions.py
# Extract data for TP53 exon enrichment
jupyter nbconvert --to=html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=100000 \
--execute process-tp53-exons.ipynb
# Plot summa... |
9108e894071ce4da53e9da5ea4bad3202d0c1a4a1d329fa3d71154ee9d937dc6 | Shell | 393 | 5 | #!/bin/bash
# train the model
python -u DeepGamiTrain.py --input_files "./demo/expMat_filtered.csv,./demo/efeature_filtered.csv" --disease_label_file "./demo/label_visual.csv" --num_fc_neurons '50' --latent_dim 100 --n_iter 100 --batch_size 30 --learn_rate 0.001 --out_reg 0.005 --corr_reg 1 --epochs 100 --cross_valida... |
ec90fa62845bdac57f655fe5610d0a86689e112a93445be231deb9496aa51dde | Shell | 394 | 11 | #!/bin/sh
#PBS -V
#PBS -q sugon10
#PBS -N 6.2A
#PBS -l nodes=1:ppn=24
#export CUDA_VISIBLE_DEVICES="0,1"
export OMP_NUM_THREADS=24
source /share/home/bjiangch/group-zyl/.bash_profile
#export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:$LD_LIBRARY_PATH #for specify the cuda path to overcome error: failed to open libnvrtc... |
e9f59a741fcda4e5ed3c31c25d72103cd438e95bfa9b96705973fdb5cb1a67c3 | Shell | 395 | 17 | #!/bin/bash
DIR=${HOME}
USERID=`id -u $USER`
echo "+++++ Starting bisweb/tools container with external directory ${DIR} and user=${USER}:${USERID}"
docker run -it --rm -p 8080:80 -p 24000:24000 \
--mount src=${DIR},target=/data,type=bind \
-e ORIG_DIR=${DIR} \
-e LOCAL_USER_ID=${USERID} \
-e LOC... |
0a16f7768d7e5c7a9cd2b17be415d3737336240d2f53bc18ce39d01cbc543870 | Shell | 396 | 13 | #Merge all of the 100% aligned elements together
#step1
cat {file_pathway}/*aligned.bed > {file_pathway}/all.bed
#step2
bedtools sort \
-faidx {file_pathway}/ancestor.chrinfo \
-i {file_pathway}/all.bed > {file_pathway}/all.sort.bed
#ancestor.chrinfo is a chromosome list of the ancestor genome
#step3
bedtools me... |
1142abe7d462c1a88938f8207e832e7f819a2ae232553acb8c43a563653047a4 | Shell | 396 | 15 | #!/bin/bash
module load singularity/3.7.0
singularity exec \
--env WORKING_DIRECTORY=/home/pymc/src/examples/experiments \
--env PYTHONPATH=/home/pymc/src \
--env AESARA_FLAGS="base_compiledir=/tmp/.aesara" \
--env PYCOX_DATA_DIR=/tmp/.pycox \
--no-home \
--bind $HOME/src/pymc-survival:/home/p... |
4b315895a927d1883a3ef48f18b4f6b51cfe1fcf55d473532d4f69fc8ef60840 | Shell | 397 | 15 | #!/bin/bash -ve
#######################################################
## Run Trinity to Generate Transcriptome Assemblies ##
#######################################################
## use jellyfish
../../Trinity.pl --seqType fq --JM 2G --left reads.left.fq.gz --right reads.right.fq.gz --SS_lib_type RF --CPU 2 --no... |
668e82938c1404fed5f57ff3d4dfdc6ac4e3df148535521b3c4b4c514fdfcbaf | Shell | 397 | 20 | #!/bin/bash
# Build documentation for display in web browser.
PORT=${1:-4000}
echo "usage: build.sh [port]"
# Find the docs dir, no matter where the script is called
ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )"
cd $ROOT_DIR
# Gather docs.
scripts/gather_examples.sh
# Generate developer docs.
make docs
# Displ... |
6bef48ab8becb9e3b3b5bd9efb0f5ce5ff99760ef70e923b0fe8f03af09e6210 | Shell | 397 | 16 | #!/bin/bash
set -u
set -e
echo "#cell rms-rmsd median-rmsd"
for dir in $(ls | grep "^3D")
do
log_file=$(find $dir -name "*20k.align.log")
if [ $log_file ]; then
rms_rmsd=$(grep "RMS RMSD" $log_file | cut -d ' ' -f 4)
median_rmsd=$(grep "median RMSD" $log_file | cut -d ' ' -f 4)
echo "$dir $rms_rmsd $median_rm... |
037b200bafe66df5edcd5655699296ed33a42fe7c525e9452a54c1060a0af96b | Shell | 398 | 8 | CUDA_VISIBLE_DEVICES=1
spec='python driver.py --dataset metz --hyper_param_search
--max_iter 42 --evaluate_freq 3 --patience 3 --model graphconvreg
--early_stopping --prot_desc_path metz_data/prot_desc.csv
--no_concord --no_r2 --log_file GPhypersearch_temp_metz_updated.log
--model_dir ./model_dir/model_dir_sch... |
8a9eb4ac160015f2d1d932f81690ce2872500aaff52e722dafb37bce1cb60407 | Shell | 398 | 19 | #!/bin/sh
for FREQ in 0.1 1 5 10 20 30 40 50; do
python mk_spikes.py --freq $FREQ
./run_stdp.sh
cp ~/data/lpl/stdp/input_con.0.wmat ~/data/lpl/stdp/input_con_${FREQ}hz.0.wmat
done
# different initial conditions
for FREQ in 0.1 1 5 10 20 30 40 50; do
python mk_spikes.py --freq $FREQ
for MEAN in 0 20 50; do
... |
bed02ef9ba2cc108a2d8116fa1e91107546de002efa67cdb32454c06032355f8 | Shell | 398 | 25 | #!/bin/bash
set -xe
arch=$1
source $(dirname "$0")/tc-tests-utils.sh
source $(dirname "$0")/tf_tc-vars.sh
BAZEL_TARGETS="
//native_client:deepspeech_ios
"
if [ "${arch}" = "--arm64" ]; then
BAZEL_BUILD_FLAGS="${BAZEL_IOS_ARM64_FLAGS}"
else
BAZEL_BUILD_FLAGS="${BAZEL_IOS_X86_64_FLAGS}"
fi
BAZEL_ENV_FLAGS=... |
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