sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
db25f10e781cbe30c15c96d83f1ffc82efdc9845daea2e5e3fba12378d56392e | Shell | 767 | 29 | #!/usr/bin/env bash
set -e
# List of available datasets.
# TODO: Investigate why training on `clearance` fails.
datasets=(bace_Class bace_pIC50 bbbp clintox delaney HIV hopv lipo \
muv nci pcba ppb qm7 qm8 qm9 SAMPL sider tox21 toxcast)
# device identifier; set it to -1 to train on the CPU (default).
devic... |
fb97ed0acf05b589581f078822d075a05dded3f4bcfc53f4b0c930c61128acc3 | Shell | 770 | 35 | while getopts "c:d:m:r:" opt; do
case ${opt} in
c)
run_on_cyverse=$OPTARG
echo "Running on cyverse: $run_on_cyverse"
;;
d)
data_path=$OPTARG
echo "Data path: $data_path"
;;
m)
model_path=$OPTARG
echo "Model path: $model_path"
;;
r)
results_pa... |
e385537e9b6bb8e05643d8e224874e6e2e353493d6e46ccdfd082252e5560c0c | Shell | 771 | 21 | #!/bin/sh
# Run at FMNH server
set +e
source /home/bdemedeiros/software/miniconda3/etc/profile.d/conda.sh
conda activate varKoder
export CUDA_VISIBLE_DEVICES=2,3
# Start by pretraining with single labels on the training set only, no validation metrics
#pretrain
#time varKoder train --label-table labels_SL_trainin... |
0bcf919cea6fcff1efeb57306ec6ee1207770f64991f26976207101dcd4177ee | Shell | 772 | 28 | #!/bin/sh
# Run at FMNH server
set +e
source /home/bdemedeiros/software/miniconda3/etc/profile.d/conda.sh
conda activate base
#create folder with query images (i. e. those chosen as validation)
mkdir -p query_varKodes
cat validation_set.txt | tr ',' '\n' | xargs -L 100 -I {} bash -c '
chunk_index=$(printf "%03d"... |
cb60ca5dcc4624340c8f93a43b8783ce5898a4c09ce576eee7a515258d381205 | Shell | 772 | 22 | #!/bin/bash
SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
mkdir -p $SCRIPTPATH/deps
## REQUIREMENTS
echo ">> INSTALLING PYTHON REQUIREMENTS"; pip install -r $SCRIPTPATH/requirements.txt
## NNUNET (was v2.3.1)
echo ">> INSTALLING NNUNET"; git clone https://github.com/MIC-DKFZ/nnUNet.git $SCRIPTP... |
d3203c262e06acb77798f4959fbc90db116c9a9b8bac5500cc57ecef57f50476 | Shell | 775 | 34 | #!/bin/bash
set -e
# Update the package list
apt-get update
# Install necessary packages and build tools
apt-get install -y \
software-properties-common \
python3 \
python3-dev \
python3-pip \
build-essential \
llvm \
clang \
lsb-release
# Add the LLVM repository to get the latest ve... |
3871fe667e7201eaa1ace1eeb85c88ae855dce9fadc41c68f48666da68c94971 | Shell | 777 | 21 | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
python distillation.py \
--teacher facebook/bart-large-xsum --data_dir xsum \
--tokenizer_name facebook/bart-large-xsum \
--student_decoder_layers 6 --student_encoder_layers 12 \
--freeze_encoder --freeze_embeds \
--learning_rate=3e-4 \
--do_train ... |
57bd0fe6e635dff3a3c73a85bf4560779883ef9537b51cf64282e7846a0cdd92 | Shell | 778 | 30 | #!/bin/bash
case "$1" in
run)
#oat record -i FINAL -n mouse -f ./ &
#oat view FINAL &
#oat decorate RAW FINAL -p POS -sS &
#oat posicom mean FGRN FBLU POS -c config.toml combine &
#oat posifilt kalman PBLU FBLU -c config.toml kalman ... |
cabb723574693e733f7bdcc60de96eb569b70f9dd75f195dda0ea232cd3a9e10 | Shell | 779 | 27 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./rte"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path roberta-ba... |
8c55a2f2e7d008c21fd6679fd7215488b37360bf36a24ff3f68986e1fd845060 | Shell | 780 | 35 | #!/bin/sh
#SBATCH -N 1
#SBATCH --cpus-per-task=1
#SBATCH --mem=2GB
#SBATCH --partition=long
#SBATCH -o spm12_preprocessing_-%A_%a.out
#SBATCH -e spm12_preprocessing_-%A_%a.err
#SBATCH -J spm12_pipeline
module load matlab
module load anaconda
module load fsl
nifti_files=()
filename="$1"
while read -r line; do
nif... |
634fee6cdb3939bec7a0651da9cfed696b458c7675612e1b909353a6a941edd9 | Shell | 781 | 27 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./mrpc"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path roberta-b... |
c1ac9f6ce9532d7fa409708e8994e55d3169138e6a64af51ab070d4e489089dc | Shell | 781 | 20 |
#mkdir -p /data/bdemedeiros/skmer_ref
#ln -s /data/bdemedeiros/skmer_ref ./
#create folders
mkdir -p skmer_ref
mkdir -p training_data
mkdir -p query_data
#get reference data: the largest sequence file for each sample used as training in varKoding
grep False ../varkoder_trained_model/input_data.csv | cut -d , -f 3 |... |
f3e257a2e26a8cd6e5d6931d9f256bbe82e01bfa0fff7a13b4d54eb1193c1998 | Shell | 781 | 27 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./stsb"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path roberta-b... |
c45e387c4be3601c3faf79afa21ff85379963960c8a1c0cce40b6b9c43883801 | Shell | 783 | 11 | #!/bin/sh
# commands to update the document pages from homepage
lynx -dont_wrap_pre -dump "http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?keywords=Download&embed=1" > Download_and_License.txt
lynx -dont_wrap_pre -dump "http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?keywords=Doc/Installation&embed=1" > INSTALL.t... |
2fcedd1d2db6d228dd4647ade1c93d031c7529ff9736ee0e5bd0101fa67a210f | Shell | 784 | 28 | #!/usr/bin/env bash
set -e # stop on first error
set -o pipefail
echo "Installing MMAction2 dependencies via mim..."
for pkg in mmcls==0.25.0 mmengine==0.10.4 mmcv==2.1.0 mmpretrain==1.2.0; do
echo "Installing $pkg..."
python -m mim install "$pkg"
done
TAG="v1.2.0"
TMPDIR=$(mktemp -d)
REPO="$TMPDIR/mmaction2... |
c1b522180b109b12958b4bcd7ea1b1fb62a971017b16ebd923b63dd50fd01575 | Shell | 784 | 29 | #!/usr/bin/env bash
BIN=$1
SPARSITY=$2
export VFC_BACKENDS_SILENT_LOAD=True
export VFC_BACKENDS_LOGGER=False
BACKENDS_OPTIONS="libinterflop_mca.so --mode=rr --precision-binary64=1 --precision-binary32=1 --sparsity=${SPARSITY}"
# if bin ends with float print FLOAT else DOUBLE
if [[ $BIN == *"float"* ]]; then
type... |
23011a5a6bd9983050e5f6e276d29e6f8f3c5c069187725fdd21081490bdb043 | Shell | 786 | 18 | #!/bin/sh
# echo Creating reference genome fasta file
# zcat ORI/cfe_ref_CB1_chrMT.fa.gz | sed "s/^>gi|[0-9]*|ref|\(.*\..*\)|.*/>\1/" > ../genomes/camelus_ferus.fa
# zcat ORI/cfe_ref_CB1_chrUn.fa.gz | sed "s/^>gi|[0-9]*|ref|\(.*\..*\)|.*/>\1/" >> ../genomes/camelus_ferus.fa
#
# echo Creating proteins fasta file
# zca... |
7c3a0c1aa7c325954c20dcefe26fb12905f53f28b1b44ab00517805a7260fde1 | Shell | 786 | 33 | #!/bin/bash
# Run human_dnn experiment
jackknife=false
comparison=false
accuracy=false
rsa=false
while getopts "jcras" opt; do
case $opt in
c) comparison=true ;;
j) jackknife=true ;;
r) rsa=true ;;
\?) echo "Invalid option -$OPTARG" >&2 ;;
esac
done
if $jackknife; then
echo "Run jackknife an... |
54f86d040210672005762d187478e99a97f8e79e2142a1d0d39bce5e797c2d0e | Shell | 790 | 30 | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
python... |
8ca6ca6c39748fdce93f1370a5e63fc9c6335b3289b46a76be75a264d8de2af0 | Shell | 792 | 33 | #!/bin/bash
set -e
FILE=$1
BASE=`basename ${FILE%.fastq.gz}`
OUTDIR=trim
# Directory to send file with reads w/o UMIs
INVALID_DIR=invalid
mkdir -p $OUTDIR $INVALID_DIR
if [ ! -s $FILE ]
then
echo "File is missing or empty: $FILE"
exit 65
fi
if [ -s $OUTDIR/$BASE.trim.fastq.gz ]
then
echo "Output file already... |
bc09522cc97bf75ef14294ea6741fb5a3b4413682908cc1e8ff9799dff704437 | Shell | 793 | 30 | #!/bin/bash
set -euo pipefail
RED='\033[0;31m'
GREEN='\033[0;32m'
NC='\033[0m'
die() { echo -e "${RED}$1${NC}"; exit 1; }
ok() { echo -e "${GREEN}$1${NC}"; }
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
cd "$PROJECT_ROOT"
command -v node >/dev/null 2>&1 || die ... |
bc83ab43cfc6827c6cc140b55f43aabc21f339986b3d5efd0bdd13d3f8b7ca61 | Shell | 793 | 26 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./adapter_houlsby_roberta_large_mnli"
python -m torch.distributed.launch --nproc_per_node=num_gpus \
examples/text-classification/run_glue.py \
-... |
29ea2d033a1370d245d997029d30c23b7f6a49f04081c2f993ec42ba185ed33b | Shell | 795 | 26 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./adapter_houlsby_roberta_large_mnli"
python -m torch.distributed.launch --nproc_per_node=num_gpus \
examples/text-classification/run_glue.py \
-... |
1025820edba9f0f593fa713894de9920ea701271022436d9dc1bebd7cb3a92e4 | Shell | 810 | 20 | #!/bin/bash
reference_file=genome_assembly/Gencode_M25_Mus_musculus/GRCm38.p6.genome.fa
variant_file=snps/PRJEB43298/mgp_REL2005_snps_BC_chr_selected.vcf
input_dir=star_output_wasp/
input_file_suffix=_Aligned.sortedByCoord.out.bam
output_dir=gatk_output_ase/
output_file_suffix=_ASEReadCounter.tab
mkdir -p $output_dir
... |
5f135782afba1b5f72d8c3356a26a2e0e444972efad2dddbc54c0cb4a232cf8d | Shell | 812 | 28 | #!/bin/bash
# run as: ./me.sh <list of merged_cells.vtk.out files>
set -eu
# Sorted:
# 1 CB 819496
# 2 HB 452692
# 3 HPF 682678
# 4 HY 117093
# 5 Isocortex 2005659
# 6 Other 2928295
# 7 TH 51202
printf "%10s %10s %10s %10s %10s %10s %10s\n" "Sample" "CB" "HB" "HPF" "HY" "IC" "TH"
for f; do
id=`echo $f | awk -F ... |
6f96b4b28f88905232b50e24c6c51b7dcf3cf9a5bf3b248531cc53575a347862 | Shell | 812 | 16 | #!/bin/bash
# libprism-static.so and libprism-dynamic.so call Highway (hwy::...) without
# listing libhwy.so as a dependency. That works when the program itself links
# libhwy, as programs built by verificarlo-c do. But when an uninstrumented
# program, such as the ieee CPython, dlopens the first instrumented library
#... |
c9960ba7b054d3fe9980d29c733ab61b35ff509ffb1c0c125e504088c6a68f6f | Shell | 813 | 24 | #!/usr/bin/env bash
if [ $1 = '' ]
then
echo "Please supply the dot file name (along with path) to convert. Program ending."
else
echo "converting to a temporary tmp file"
cat $1 | sed 's/L:\([^ ]\+\), R:/\1-/g' | sed 's/label="[0-9][^"]*"/label=""/g' | awk -F'=' '{ if ($1 == "penwidth") {print $1 "=" ($2 ^ 6) ... |
2911ab5fc3ba7b11b57f9bb008f6579c55f212d0025529a0b73f1213ccdc8f09 | Shell | 819 | 33 | #!/bin/bash
set -e
rm -rf logs/ binary32-* binary64-* sr
mkdir logs/
export VFC_BACKENDS_LOGFILE="logs/verificarlo.log"
ITERATIONS=100
parallel --header : "verificarlo-c -DITERATIONS=$ITERATIONS -O0 sr-{type}.c -o sr-{type} -lm" ::: type binary32 binary64
trap 'echo "Failed with backend $BACKEND"; exit 1' ERR
for... |
26a5b9879bd2e1cba21ec49a89a28c64d735ae7c5715726662fc350a545ecf2f | Shell | 820 | 30 | #!/bin/sh -e
#---
# Convert chromosome names for FASTA files
#---
zcat ORI/ref_Zonotrichia_albicollis-1.0.1_top_level.gff3.gz > genes.ORI.gff
grep "Parent=rna" genes.ORI.gff | cut -f 3- | grep ^CDS | cut -f 7 | tr ";" "\t" | cut -f 2,3 | tr "=" "\t" | cut -f 2,4 | uniq > replace_list.txt
cat genes.ORI.gff | ./replac... |
9c86d9f444dbb57d431fefbca7e15cac6cb9669aa138fb685041413d6f7b8743 | Shell | 821 | 34 | #!/bin/bash
# Check if the destination folder is provided as the last argument
if [ $# -lt 2 ]; then
echo "Usage: $0 <source files>... <destination folder>"
exit 1
fi
# Get the destination folder
destination="${@:$#}"
# Check if the destination folder exists
if [ ! -d "$destination" ]; then
echo "Error: Destin... |
9fd0fd2253df905dd6c2e3bf411766b997482251e613b0d3e44c538102181714 | Shell | 827 | 31 | #!/bin/bash
case "$1" in
run)
oat view final &
oat decorate raw final -p pr1 pr2 pr3 pcr -SsR &
oat posifilt region pc pcr -c config.toml region &
oat posifilt region p3 pr3 -c config.toml region &
oat posifilt region p2 pr2 -c config.toml region ... |
cf75a7af90cc14e337fbebf378ff15eaa1e8517ae2c9c94f181e62195c554bde | Shell | 827 | 40 | #!/bin/sh
#---
# Download
#---
mkdir ORI
cd ORI
wget http://www.icrisat.org/gt-bt/ICGGC/genomedata.zip
unzip genomedata.zip
cd -
#---
# Get propper chromosome names in FASTA file
#---
cat ORI/genomedata/assembly/Cicer_arietinum_GA_v1.0.masked.fa \
| sed "s/^>\(\S*\).*/>\1/"
> Cicer_arietinum_v1.fa
gzip Cicer_a... |
c3a6d48b7db9eec416e6fdfee5b2d094a2d374f8104d4b94897f020c9a6c7e45 | Shell | 829 | 44 | #!/bin/bash
#SBATCH --account INSERT
#SBATCH --partition=INSERT
#SBATCH --job-name=xrai
#SBATCH --nodes=1
#SBATCH --gpus=INSERT
#SBATCH --mem=128G
#SBATCH -t 24:00:00
#SBATCH -n 32
#SBATCH --output=logs/%j.log
# USE_CUPY needs to be set for Skopi to use GPUs
# only use CPU for this
export USE_CUPY=0
POSITIONAL=()
whi... |
398d96459edd362651639cea687509070113b4383ff26ac9c35020c9ac8eb65c | Shell | 833 | 31 | #!/bin/sh
#SBATCH -p shared
#SBATCH -t 7-00:00
#SBATCH --mem 50G
#SBATCH -N 1
#SBATCH -n 8
#SBATCH -c 2
#SBATCH -o 4_generate_varkodes.%A.out
#This was initially run in Harvard cluster but later iterations
#were done at the Field Museum, so we comment out Harvard-specific parts
#sleep 30
#scontrol show jobid=$SL... |
a4b8d1208f7816e75c21a6a3c5549d1765f7320f5168591312880a8da3b1dae9 | Shell | 834 | 19 | #!/bin/bash
source /home/bdemedeiros/software/miniconda3/etc/profile.d/conda.sh
conda activate sra
#first, find all metadata about DNA records with fastq files on SRA
#esearch -db sra -query '"filetype fastq"[Properties] AND "biomol dna"[Properties]' | efetch -format runinfo | gzip -9 > SRA_records.csv.gz
#now, retr... |
f7816b868a5b6affe00d4d2da5d0a8e9d241dcc956844a1abb995e1cda55ddd0 | Shell | 835 | 16 | #!/bin/bash
module load cuda/10.2.89-fasrc01 freesurfer/6.0.0-fasrc01 ANTs/2.3.1-fasrc01 Anaconda3/5.0.1-fasrc01 netpbm/10.73.20-fasrc01 ncf dcm2niix/2019_09_04-ncf fsl/6.0.4-ncf
# Anaconda3/2022.05 matlab/R2021a-fasrc01
export tools=/n/gaab_mri_l3/Lab/DMC-Gaab2/tools/tkt_tools/babyBOLD_dmri_proc_pyafq
export FSLDIR=... |
7b14975d920da0ac4568d0b588710fee4ecd8f157233fa2964e3b82f8d27e646 | Shell | 842 | 25 | python launcher_scripts/run_from_config.py configs/iceberg/msg_all/dag_gen_predict_train_msg_all_iceberg_decoy0.yaml
decoy0_status=$?
if [ "$decoy0_status" -ne 0 ]; then
exit "$decoy0_status"
fi
python data_scripts/dag/add_dag_intens.py \
--pred-dag-path results/iceberg_msg_all_iceberg/split_rnd1/preds_train_100/t... |
260ccbca66502d5366ff9fb38ab97f7b0e82fdd4127779344a8c7710ccbc48e1 | Shell | 843 | 25 | #!/bin/bash
CONDA_INSTALLER=Miniconda3-latest-MacOSX-x86_64.sh
CONDA_INSTALL_PATH="$(pwd)/lib/multiaxial/miniconda"
ENV_NAME="$(pwd)/lib/multiaxial/multiaxialEnvMac"
YAML_FILE="$(pwd)/lib/multiaxial/multiaxialEnvMac.yml"
# Download and install Miniconda
curl -O https://repo.anaconda.com/miniconda/${CONDA_INSTALLER}
b... |
c0f63e937019e59ba615c14e5f897d8ea57ebadca54722c2a203430472987b09 | Shell | 843 | 43 | #!/usr/bin/env bash
set -e
OUTPUT=$1
BIOMART_PFAM=$2
PANNO_SCRIPT=$3
TRANSVAR=$4
TRANSVAR_DATA=$5
TRANSCRIPTS=$6
CORES=$7
echo -e "CHROMOSOME\tSTART\tEND\tSTRAND\tELEMENT_ID\tSEGMENT\tSYMBOL" \
> ${OUTPUT}
tmpdir=$(mktemp -d)
if [[ "${CORES}" -eq 1 ]]
then
zcat ${BIOMART_PFAM} > ${tmpdir}/split.0
else
divisor=$... |
efd18e6fbb9ac60a9c654b29684a41a4f83eaf426bff524bdf1e35cc21a32f64 | Shell | 844 | 33 | #!/bin/bash
TARGET_DIR="./"
mkdir -p "$TARGET_DIR"
# Zenodo folder URLs (returning ZIP archives)
FOLDERS=(
"https://zenodo.org/records/14967806/files/data.zip?download=1"
)
# Download folder ZIPs
for url in "${FOLDERS[@]}"; do
echo "Downloading folder ZIP: $url"
pushd "$TARGET_DIR" > /dev/null
# Download as ... |
b5d41b65e4f244694fb8baa4690d876f221e6df7565d050e95d511214716e521 | Shell | 846 | 25 | #!/bin/bash
CONDA_INSTALLER=Miniconda3-latest-Linux-x86_64.sh
CONDA_INSTALL_PATH="$(pwd)/lib/multiaxial/miniconda"
ENV_NAME="$(pwd)/lib/multiaxial/multiaxialEnvLinux"
YAML_FILE="$(pwd)/lib/multiaxial/multiaxialEnvLinux.yml"
# Download and install Miniconda
curl -O https://repo.anaconda.com/miniconda/${CONDA_INSTALLER... |
aac1318f06c7d8c4a6b5b95f0f5aab845a00c1b99aadf856fc07f33ff65703e0 | Shell | 847 | 41 | #!/bin/sh
set -e
set -o pipefail
# Set the working directory to the directory of this file
cd "$(dirname "${BASH_SOURCE[0]}")"
# Get base directory of project
cd ..
BASEDIR="$(pwd)"
cd -
analyses_dir="$BASEDIR/analyses"
# Run add-histologies analysis module
echo "Run add-histologies"
cd ${analyses_dir}/add-histolo... |
9bfbabe031ed3ecef2ad4b9890d2119eabc73a5cc4a7b6e61d403f70dadd9e6a | Shell | 848 | 32 | # Script for verifying that run_bart_sum can be invoked from its directory
# Get tiny dataset with cnn_dm format (4 examples for train, val, test)
wget https://cdn-datasets.huggingface.co/summarization/cnn_tiny.tgz
tar -xzvf cnn_tiny.tgz
rm cnn_tiny.tgz
export OUTPUT_DIR_NAME=bart_utest_output
export CURRENT_DIR=${PW... |
9d8c54561550d993d335e759cfe51f22e02564fab624ef52bbb469dbd8ebac83 | Shell | 850 | 31 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./cola"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
88d6272a4212abd01dd63807e562695066d0e434a5f41cc2befec41e2bc65df0 | Shell | 851 | 25 | python launcher_scripts/run_from_config.py configs/iceberg/msg_simulation/dag_gen_predict_train_msg_simulation_decoy0.yaml
decoy0_status=$?
if [ "$decoy0_status" -ne 0 ]; then
exit "$decoy0_status"
fi
python data_scripts/dag/add_dag_intens.py \
--pred-dag-path results/iceberg_msg_simulation/split_rnd1/preds_train_... |
8f78ee849e1415b5d3586a74a7b5df63df0e126e85d5e72c05a0742d25054314 | Shell | 851 | 32 | #!/bin/bash
export VFC_BACKENDS="libinterflop_mca.so;"
if python3 --version 2>/dev/null; then
if ! python3 -c "import cython" 2>/dev/null; then
echo "this test is not running without Cython installed"
# Exit with 77 to mark the test skipped
# https://www.gnu.org/software/automake/manual/html_node/Scripts... |
62e0e07e5134416578505f776469d3b31b094d8f953b2eb07064a6a0058ea65a | Shell | 852 | 43 | #!/bin/bash
set -e
OPTIONS_LIST=(
"-O0"
"-O3 -ffast-math"
)
check_output() {
if [[ $? != 0 ]]; then
echo "Fail"
exit 1
fi
}
run() {
BIN=$1
ARG=$2
./$BIN $ARG >$BIN.log
check_output
grep "Kahan =" $BIN.log | cut -d'=' -f 2
}
verificarlo-c -lm -DDOUBLE --function su... |
e2cbe3d99610539612709a40d15e881f6321c5e9b776f38c5da7eb593c4ce357 | Shell | 852 | 31 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./mnli"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
8c6f8723c1fb02e283eda7f462f96a32aaa35af1bbeb046991e6323685b7e723 | Shell | 853 | 31 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./qnli"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
cfc9d60eee5860c1e7873b503449c488f9e1ed99b7a858f509f7ab8fb80b32b3 | Shell | 853 | 31 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./sst2"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
134b7c3053f02093417da88620a6ccbd7f1665f53ca319219734274ea608d3bd | Shell | 854 | 31 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./qqp"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft/... |
f09ab5c18f0394f38a6536bcbc9078909532021b230991d71a8557c2b31723d4 | Shell | 858 | 31 | #!/usr/bin/env bash
dataset=${1:-${dataset:-nist23}} # nist20, nist23, msg_all, msg_simulate
max_peaks=${max_peaks:-50}
ppm_diff=${ppm_diff:-20}
workers=${workers:-32}
data_dir="data/spec_datasets/$dataset"
labels="$data_dir/labels.tsv"
spec_files="$data_dir/spec_files.hdf5"
subform_file="$data_dir/subformulae/no_sub... |
48d60566677a5db9ca431168e2d6696f3058815c7b7d69d811b38926c67a9588 | Shell | 862 | 32 | #!/bin/sh -e
VCF="$HOME/snpEff/db/GRCh37/1kg/1kg.vcf"
VCF="$HOME/snpEff/db/GRCh37/1kg/1kg.no_gt.vcf"
REF=GRCh37.75
REF=hg19
VCFOUT_BASE=`dirname $VCF`/`basename $VCF .vcf`
VCFOUT_ANN=$VCFOUT_BASE.ann.vcf
VCFOUT_DBSNP=$VCFOUT_BASE.ann.dbSNp.vcf
VCFOUT_CLINVAR=$VCFOUT_BASE.ann.dbSNp.clinvar.vcf
VCFOUT_DBNSFP=$VCFOUT_B... |
9f471fb4886b0fcdcb59edb4bdbcbcc8dbac4b1c00d30105f2b144d938ec456e | Shell | 863 | 22 | #!/bin/bash
# Copyright 2025 the Aeneas Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
3070600b9f134e2a22d4185e1d481d54549ca056acf04107f1b97e91fc29fc68 | Shell | 864 | 17 | Rscript step2_SPAtests.R \
--vcfFile=./input/seedNumLow_126001_seedNumHigh_127000_nfam_1000_nindep_0.sav \
--vcfFileIndex=./input/seedNumLow_126001_seedNumHigh_127000_nfam_1000_nindep_0.sav.s1r \
--vcfField=DS \
--chrom=chr1 \
--minMAF=0 \
--minMAC=0.5 \
--maxMAFf... |
3cbf509a2316fd7831afa3862364358b724b24fe302fbfb2436b3114b994a089 | Shell | 865 | 25 | #!/bin/sh -e
#---
# Convert chromosome names for FASTA files
#
# Original files downloaded from
# http://ftp.ensembl.org/pub/rapid-release/species/Sciurus_carolinensis/GCA_902686445.2/
#---
# Fixing GFF file. Remove 'gene:' from IDs to make them consistent with CDS and protein fasta files
gunzip -c ORI/Sciurus_caro... |
ed127526a795d852bcf10398366a93aa70651a24072a7bf1bdf449fb4d2f7aab | Shell | 872 | 58 | #!/bin/bash
if [[ $# == 1 ]]; then
usecase=$1
echo $usecase
fi
case $usecase in
fast)
range_min=2
range_step=3
precision_min=3
precision_step=10
abs_err_min=-1
abs_err_step=-20
nb_tests=1000
;;
medium)
range_min=2
range_step=2
precision_min=3
precision_step=5
abs_err_min=-1
abs_err_step=-10
nb_tests=... |
deed4bec850b5b57692537da122cb74501b903005eedb4aa862660a7924676b7 | Shell | 874 | 32 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./mnli"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
4519b0090a4348df56a186eadbab172cbff9889a8c29ea19e35d26a91fdd388d | Shell | 875 | 42 | #!/bin/bash
cd $(dirname $0)
cd ..
############
# config
############
root="data"
train() {
local dataset=$1
local model_dir=$2
local extra_args=${@:3}
python train.py --device gpu \
--train_data "$root/$dataset/processed/train/" \
--test_data "$root/$dataset/processed/test/" \
... |
fafca340b64a8fdf1f9d32535f7261b4884005c4fcf45bfbd9223aefcf58e6fc | Shell | 875 | 31 | #!/bin/bash
case "$1" in
run)
#oat record -i final -f ./ -n result -F 30 &
#oat record -p pix wld -f ./ -n result &
#oat posifilt homography posi wld -c config.toml homography &
oat view final &
oat decorate raw final -p pi... |
edba919107af139ea5e04e11019e47e1b5377c9afff3ea0e2a4ed5f2bcc0379e | Shell | 881 | 44 | #!/bin/bash
# base name of this script (without extension)
scriptname=$(basename $0 .sh)
echo -e "\n*** Running batch script '"$scriptname"' ***\n"
# path to Matlab code directory
codedir=$LOCALREPO/matlab/DI_opt_02
# current directory (the directory this script is run in - log files will go here)
currdir=$(pwd -P)... |
39804cc8fb195fbf5898619471630fea0788e4b9b5580f1d2b64e597bc9c1c0c | Shell | 882 | 46 | #!/bin/bash
CURRENT_REP=$PWD
BUILD_REP=$1
INSTALL_REP=${CURRENT_REP}/$1/install
VALGRIND_ARCHIVE=$2
BRANCH_VERROU=$3
FLAGS=$4
VALGRIND_NAME=$(basename ${VALGRIND_ARCHIVE} |sed "s/.tar.bz2//")
echo "BUILD_REP:" ${BUILD_REP}
echo "VALGRIND_ARCHIVE:" ${VALGRIND_ARCHIVE}
echo "BRANCH_VERROU:" ${BRANCH_VERROU}
echo "VAL... |
e802265f0e0b5421a6cc112925c4c94bcd8d3eaeb253eadf0894f6eddf15c0b2 | Shell | 882 | 36 | #!/bin/bash
numRun=2
repInteger=dd.line.integer.post
rep=dd.line.post
echo "integer loop"
for i in `seq 0 $numRun`;
do
for subrep in FullPerturbation rddmin-cmp ddmin0;
do
cmd="diff ${repInteger}/${subrep}-trace/nearness/dd.run${i}/cover ${repInteger}/NoPerturbation-trace/default/dd.run0/cover"
echo ${cmd}... |
c9620dfff02e7936350338e3de44102cac4e69d49ffd0707372afbaa96401b0c | Shell | 887 | 46 | #/bin/bash
conda activate scnn_env
cd /local/projects/scnn-investigation_haykel
bash scripts/estimate_fod_coefficients.sh
# ==> dwi.nii.gz, dwi.bval, dwi.bvec, brain_mask.nii.gz
# ==> dwi_norm.nii.gz
# ==> wm_response.txt; wmfod.nii.gz
# ==> gm_response.txt; gm.nii.gz
# ==> csf_response.txt; csf.nii.gz
# NO LONGE... |
cc9c5d4ed98bf880e6dcdf512c7de1cef0480aa109a97720a4dd8dda7f265c04 | Shell | 887 | 25 | #!/bin/sh
REF=GRCh37.66
IN_VCF=$HOME/snpEff/1kg/test.Broad_NS_SYN.vcf.gz
OUT_VCF=all.vcf
# Run SnpEff
./scripts/snpEffXL.sh eff -v -noStats -o vcf $REF $IN_VCF > $OUT_VCF
# Calculate number of lines
SILENT=`cat $OUT_VCF | grep "SILENT" | wc -l`
MISSENSE=`cat $OUT_VCF | grep "MISSENSE" | wc -l`
NONSENSE=`cat $OUT_VC... |
572ecd83e4db19e545355c200f543cd0c63f41143448ca9d16636f82dd537098 | Shell | 888 | 26 | #!/bin/bash
# If you get an error like:
# Cloning into 'datasets'...
# fatal: cannot change to 'https://github.com/tensorflow/datasets/': No such file or directory
# error: failed to initialize sparse-checkout
# This mean your git version is outdated. Just update it.
set -e
# Download TFDS metadata to flax/.tf... |
9f35f93f19fdbb772176f35540247dad76028a360c1d0347a9f12a17510b20da | Shell | 888 | 32 | #!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=12:00:00
#SBATCH --job-name=scipy
#SBATCH --mem=90G
#SBATCH --signal=B:SIGUSR1@120
#SBATCH --mail-type=end
#SBATCH --mail-type=fail
#SBATCH --mail-type=begin
#SBATCH -o dasktest.o.%J
#SBATCH -e dasktest.o.%J
# setup python env
# ... |
59fb0a48cab49dc1fdfe4635fb53ba670b821462cb6a6d0a187d86225b138747 | Shell | 890 | 25 | #!/usr/bin/env bash
echo "Alignment script called"
while getopts ':o:n:1:2:r:i:f:' flag; do
case "${flag}" in
r) Read_Count=${OPTARG} ;;
i) Index_Path=${OPTARG} ;;
1) Read_1=${OPTARG} ;;
2) Read_2=${OPTARG} ;;
o) Quant_Destination=${OPTARG} ;;
n) nThreads=${OPTARG} ;; ... |
1558caf864c28b948760deb71c00a2b41e31e69244c81f8e98dd8e2651e8335b | Shell | 893 | 32 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./mrpc"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
2272af10e72151fc27a7fff9ccdfed6f7126b5a3d282fb3db2d52f78077976b6 | Shell | 893 | 32 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./rte"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft/... |
300f93d35e4ea90e98d6161f29599f75a78df5ba8590331a9a5bd0118c0895d6 | Shell | 893 | 32 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./stsb"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
410907ea3f09fe8a0d47a191a43c77318ed631e51400df7684dd76b6212b49ee | Shell | 895 | 33 | export num_gpus=8
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
export output_dir="./mnli"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path microsoft... |
f531210d3db87352a4d288222cf71b80346b60f8f177b873e87f4a5c419362ad | Shell | 895 | 22 | #!/bin/bash
# Create symlinks to fastq files in /rawdata
mkdir -p data/umi
LANE1=/rawdata/Illumina_Runs/140923_SN_0795_0387_AHA9RUADXX/Unaligned4/Undetermined_indices/Sample_lane1
LANE2=/rawdata/Illumina_Runs/140923_SN_0795_0387_AHA9RUADXX/Unaligned4/Undetermined_indices/Sample_lane2
for fq in $LANE1/*fastq.gz $LAN... |
1316a63cde358f58029517c7248c23c0dbc7b32371795e0f8853aefd5f961f35 | Shell | 898 | 36 | #!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=36:00:00
#SBATCH --job-name=dask-boruta
#SBATCH --mem=50GB
#SBATCH --signal=B:SIGUSR1@120
#SBATCH --mail-type=end
#SBATCH --mail-type=fail
#SBATCH --mail-type=begin
#SBATCH --output=job.%j.log
#SBATCH --error=job.%j.err
# set to ... |
a7af4299e4a0dceaebe03ad6b46d6eac41a57b60da7d21992e3032b23a356c36 | Shell | 899 | 22 | #!/bin/bash
#move this to results folder for branch shift then run it
while getopts f:s:g:o: flag
do
case "${flag}" in
f) filename=${OPTARG};;
s) species_num=${OPTARG};;
g) genelist=${OPTARG};;
o) output=${OPTARG};;
esac
done
var=1
header=$(($species_num+$var))
#how to convert ... |
b4fc74e3f12f251c8e42157d969931747825ea87a581aeae2860abda0c834953 | Shell | 899 | 31 | #!/bin/bash
# This script simply starts the callicog task agent (aka 'the listener').
# Read the CalliCog configuration variables into the shell environment:
source $HOME/callicog/.env
# Tell the X server what display mode to use for which output:
model_info=$(hostnamectl | grep "Hardware Model")
if [[ $model_info =... |
278916befbcb362c09bf06dc2ea063519608910c7db02d7950f92b792c605882 | Shell | 903 | 5 | ./software/snape-pooled/snape-pooled -nchr 64 -theta 0.001 -D 0.1 -fold folded -priortype informative < ./pileups/MON_AA_filtered.pileup 1> MON_AA_ng_SAB3.sfs 2> MON_AA_ng_SAB3.err
./software/snape-pooled/snape-pooled -nchr 58 -theta 0.001 -D 0.1 -fold folded -priortype informative < ./pileups/BAG_AA_filtered.pileup 1>... |
6eec0d030fa2d64a39bdd8378e1cbec4f9e74eefd49be44ab3a18db8cfb6ac7d | Shell | 904 | 33 | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
ENTROPI... |
62e1fe9bd3e1713eb0815b1a88734fb4eee5db550f3703b14bc0565c35ef8556 | Shell | 906 | 26 | #!/bin/bash
echo 'DEPRECATION WARNING: Read comments inside this script file for info.'
# NOTE: I believe this has been superceded by the current configuration
# protocol as specified in the Ubuntu task-agent computer setup documentation.
#
# I will leave this script in the repository for now for reference, in case
#... |
4babf344fe8463debe41642a97677d7bd0d55ca4582b8abcdab5c8f4aab1f3e5 | Shell | 907 | 34 | # Install example requirements
pip install -r ../requirements.txt
# Download glue data
python3 ../../utils/download_glue_data.py
export TASK=mrpc
export DATA_DIR=./glue_data/MRPC/
export MAX_LENGTH=128
export LEARNING_RATE=2e-5
export BERT_MODEL=bert-base-cased
export BATCH_SIZE=32
export NUM_EPOCHS=3
export SEED=2
e... |
a8065ee3db3e3286661664a15d669c330f0e501d55d538ddc6c9d689a2dadd0e | Shell | 907 | 27 | #!/bin/sh
#SBATCH -p test
#SBATCH -t 0-08:00
#SBATCH --mem 10G
#SBATCH -n 1
#SBATCH -c 1
#SBATCH -o 1_download_entrez.%A.out
sleep 30
scontrol show jobid=$SLURM_JOBID
module load Anaconda3/2020.11
source activate sra
#esearch -db sra -query '"library selection random"[Properties] AND "wgs"[Strategy] AND txid275... |
7f8b53a534590361c8d954fe1197ec7b04697dc916c9892cc089161af3b6c7bf | Shell | 908 | 41 | #! /bin/bash
# Infer the number of GPUs from the rank size
D=$OMPI_COMM_WORLD_SIZE
# For weak scaling, adjust the number of reps accordingly
if [ "$1" == "strong" ]; then
script="strong_scaling.lmp"
logg="strong"
elif [ "$1" == "weak" ]; then
script="weak_scaling.lmp"
logg="weak"
else
echo "Invalid mode. Pl... |
9241945534f9053910f540f918b2dfb6f81ae0220b86bf46dc8759f0133a656b | Shell | 910 | 38 | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
EPOCHS=10
if [ $MODEL_TYPE = 'bert' ]
then
EPOCHS=3
MODEL_NAME=${MODEL_NAME... |
c22d602c5ba28dca22a7620ec97f2494ee4e463eff4f5c8677ccdaa3a13fe600 | Shell | 910 | 26 | #!/bin/bash
dotenv_path=".env"
if [ -f "$dotenv_path" ]; then
# Export CalliCog environment variables for use in subsequent commands:
# Enable automatic export of all variables
set -a
# Set (and export) CalliCog environment variables
source .env
# Disable automatic export of all variables
... |
1ed681f070274375931685b151577322c6101e2f7bdb2649db6086c6b2d42806 | Shell | 911 | 22 | #!/bin/bash
#move this to results folder for branch shift then run it
while getopts f:s:g:o: flag
do
case "${flag}" in
f) filename=${OPTARG};;
s) species_num=${OPTARG};;
g) genelist=${OPTARG};;
o) output=${OPTARG};;
esac
done
var=1
header=$(($species_num+$var))
#how to convert ... |
60366a17696c354046e5f6909acd3395ee22c7442e68721eb5d98cd44fb0fa4d | Shell | 912 | 28 | #!/bin/bash
# Define input and output directories
input_dir="../../fam_gen_sp_multi/intermediate_files/split_fastqs"
output_dir="./fastas"
# Create output directory if it doesn't exist
mkdir -p "$output_dir"
# Loop through all *.fq.gz files in the input directory
for fq_gz_file in "$input_dir"/*.fq.gz; do
# Extr... |
c0f82564eac1140f2b5bd0048ee3e47018b9025aed898fa681cf9172bfbe5852 | Shell | 912 | 30 | set -e
bash install_mogen.sh
bash get_gm12878.sh 100000 22
bash get_gm12878.sh 10000 22
BEDPATH=hic_data/GM12878_combined_22_10kb.bed
python ../minimds.py -o hic_data/GM12878_combined_22_10kb_mmds_coords.tsv $BEDPATH
python ../minimds.py --classical -o hic_data/GM12878_combined_22_10kb_cmds_coords.tsv $BEDPATH
pyth... |
08f0ff8bdf6c33fd4705ae50f275f0df878b9cc74cc0aa19319f6614b43d1a00 | Shell | 913 | 29 | #!/bin/bash
# Script to run MATLAB tests for GSN functions
# Usage: ./run_tests.sh [test_file_name]
# Set MATLAB path (adjust if your MATLAB is in a different location)
MATLAB_PATH="/Applications/MATLAB_R2022b.app/bin/matlab"
# Check if MATLAB exists
if [ ! -f "$MATLAB_PATH" ]; then
echo "MATLAB not found at $MA... |
eee763e6c3dd2873447582d54532a56172c72757d297b832bef7d244b8afd064 | Shell | 913 | 34 | #!/bin/bash
# Define the paths to the folders
folderA="../fam_gen_sp_multi/images"
folderB="./cgr_imgs"
# Create the CSV file with headers
echo "sample,labels" > "$folderB/labels.csv"
# Loop through all PNG files in Folder B
for fileB in "$folderB"/*.png; do
# Extract the base filename without the path
filen... |
da5fe80aa935a2490be1da62b4469a6ad5ff01104f95c31856f552354b7387b7 | Shell | 917 | 18 | #!/usr/bin/env bash
######################
# MassSpecGym simulation-challenge ICEBERG pipeline.
#
# Assumes data/spec_datasets/msg_simulation already exists with labels.tsv,
# spec_files.hdf5, retrieval candidate tables, magma outputs, and
# subformulae/no_subform.hdf5. Unlike msg_all, this setting uses spectra with
#... |
2d33b49997a3740af64a6d918937bc93717ce73a79bf5af988cefadbc998e8a7 | Shell | 919 | 28 | #!/bin/bash
#SBATCH --job-name=Duke-vcf2maf-paired
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --mem-per-cpu=18G
#SBATCH --output=./logs/log-%j
wd=/data/dcibioinformatics/Anders/MelBrM_rerun/Proc/analysis/vcf2... |
44e5623f47eef3126472c803edbfa341cbf4432398a83630b8aff1ebeb99a88b | Shell | 922 | 28 | #!/bin/bash
#SBATCH --job-name=MDACC-vcf2maf-paired
#SBATCH --account=dcibioinformatics
#SBATCH --mail-user=lwr11@duke.edu
#SBATCH --mail-type=END,FAIL
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --mem-per-cpu=18G
#SBATCH --output=./logs/log-%j
wd=/data/dcibioinformatics/Anders/MelBrM_rerun/Proc/analysis/vcf... |
b96cd22f7aef949c7280006bbdb2013d425da18cd93225d45fdabffa2a6f3951 | Shell | 922 | 27 | #!/bin/sh
zcat="gunzip -c"
# Download FASTA
#wget ftp://ftp.solgenomics.net/tomato_genome/annotation/ITAG3.2_release/S_lycopersicum_chromosomes.3.00.fa.gz
#wget ftp://ftp.solgenomics.net/tomato_genome/annotation/ITAG3.2_release/ITAG3.2_cds.fasta
#wget ftp://ftp.solgenomics.net/tomato_genome/annotation/ITAG3.2_releas... |
91fe1d3090e67aee47addf90b9521e8f6090e9c61a957ebeb61401046ea1630b | Shell | 924 | 41 | #! /bin/bash
# Infer the number of GPUs from the rank size
D=$OMPI_COMM_WORLD_SIZE
# For weak scaling_million_atoms, adjust the number of reps accordingly
if [ "$1" == "strong" ]; then
script="strong_scaling.lmp"
logg="strong"
elif [ "$1" == "weak" ]; then
script="weak_scaling.lmp"
logg="weak"
else
echo "In... |
0ae4f635cd984b16a79d6ec73038634efb945a6b450c2872f0bb5aa83a3c3bbe | Shell | 925 | 26 | #!/bin/bash
#
#
# copies files quantifying subject motion in rsfMRI data
#
# Inputs:
# $1 : source directory
# $2 : target directory
#
#
# 2025 Mark C Nelson, McConnell Brain Imaging Centre, MNI, McGill
#--------------------------------------------------------------------------------------------------------------------... |
f1609dc844476bcce134fdacf968a78a60fd3e540668ee5e31bf9b32810286bc | Shell | 925 | 5 | ./snape-pooled/snape-pooled/snape-pooled -nchr 64 -theta 0.001 -D 0.1 -fold folded -priortype informative -spectrum < ./pileups/MON_AA_SAB3.pileup 1> MON_AA_SAB3.sfs 2> MON_AA_SAB3.err
./snape-pooled/snape-pooled/snape-pooled -nchr 58 -theta 0.001 -D 0.1 -fold folded -priortype informative -spectrum < ./pileups/BAG_AA... |
2e73f21cb4bc1c6220384031f4ce04f1e6892593f20b345d515c36ac922c9fc9 | Shell | 927 | 26 | #!/bin/bash
# Example paths (update these as needed)
dataset="nist20"
PRED_MAGMA_H5="data/spec_datasets/$dataset/magma_outputs/magma_tree.hdf5"
TRUE_DAG_H5="data/spec_datasets/$dataset/subformulae/no_subform.hdf5"
OUT_MAGMA_H5="data/spec_datasets/$dataset/magma_outputs/magma_tree_with_inten.hdf5"
python data_scripts/... |
905009eae2f0493beea0bf98957b612ff3a0d38e415990cc4c61b1a5c48fa899 | Shell | 930 | 30 | #!/usr/bin bash
# Author: Sean Maden
#
# Make HPC conda environment for manuscript.
#
#
#
srun --pty --x11 --mem=10G bash
module load conda
conda create --name "deconvo_lute-paper" -c bioconda conda-forge r-base=4.3.1
conda activate deconvo_lute-paper
conda install conda-forge::r-biocmanager
conda install bioconda:... |
8773314828e98e4c7fe5210b0d99b64111ef914a6ba3a6384ac694d5b5027890 | Shell | 933 | 21 | #!/bin/bash
#
#SBATCH -n 8 # Number of cores
#SBATCH -N 1 # Number of nodes for the cores
#SBATCH -t 2-12:05 # Runtime in D-HH:MM format
#SBATCH -p serial_requeue # Partition to submit to
#SBATCH --mem=80000 # Memory pool for all CPUs
#SBATCH -o lmcai.out # F... |
6aea5e5e40282698400b9af7e4f4f6a0dc68ced05dfa1318ad4bbaa35e6b2b22 | Shell | 937 | 28 | #!/bin/bash
set -e
# required so that seq behaves correctly in all locales
export LC_ALL=C
# Compile tchebychev.c using MCA lib instrumentation
# Tchebychev polynom becomes unstable around 1, when computed with
# single precision
export VFC_BACKENDS="libinterflop_mca.so --precision-binary32 23"
export VFC_BACKENDS_S... |
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