sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8374cfcee1152ad80184391af4d60d6ea08d5f3b3ab58e700b2eed409f8ef903 | R | 83,640 | 1,515 | #Written by Cameron Wehrfritz
#and Natan Basisty, PhD
#Schilling Lab, Buck Institute for Research on Aging
#Novato, California, USA
#March, 2020
#updated: January 28, 2021
# PROTEIN TURNOVER ANALYSIS
# STEP 1
# PROCESS DATA FROM SKYLINE
# CORRECT FOR THE NATURALLY OCCURING HEAVY ISOTOPES OF COMMON ELEMENTS
# CALCULAT... |
3477797eba0b73f0883607bdda310d028a9e94c267465c040a7d9a60d07ad13d | R | 85,193 | 1,790 | # $Id: QuaSAR.R 124 2013-10-09 14:27:44Z manidr $
print ("$Id: QuaSAR.R 124 2013-10-09 14:27:44Z manidr $", quote=FALSE)
# The Broad Institute of MIT and Harvard
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# Authors: Rushdy Ahmad, Deepak Mani and D.R. Mani
# This software and its documentation are copyright (2009) by the
# ... |
d68128ebaccb584761f4f4aa0b151e40b40123b985b1c99bce9638e9b2a74b4a | R | 101,519 | 2,836 | # Project: Model for an economic evaluation of increasing use of kidney donors with primary brain malignancy
# Created by: James Hedley
# Date created: 21st January 2021
# Last updated: 22nd June 2022
# Load libraries
library('tidyverse')
library('dplyr')
library('ggplot2')
library('MASS')
library('stringr... |
357ad6f2af4a62bb35ad32abafe908ee45cb92707484532514544a9d49a10163 | R | 120,277 | 2,838 | ---
title: "Analytical pipeline for testing the performance of sleep-tracking technology v1.0.0"
author: Luca Menghini,$^{1,2}$ \and
Nicola Cellini,$^{2,3,4,5}$ \and
Aimee Goldstone,$^1$ \and
Fiona C. Baker,$^{1,6}$ \and
Massimiliano de Zambotti$^1$
date: $^1$Center for Health Sc... |
de709bd6d5cf38a68d573d3ca59b50ce0598a59473c764ae08a1cf0208340dfb | R | 141,532 | 2,972 | ---
title: "ukb_bp_mood_prereg_overview"
author: "lina schaare"
date: "11/21/2019"
output:
html_document:
toc: true
toc_float: true
df_print: kable
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir = "/data/pt_02087/ukbiobank/")
.libPaths(c("/da... |
867e3d07dba843211cd8e61a05cd3d169baeb0ec1f68aed53dfc84d1237357ee | R | 163,765 | 3,388 | # Project: Statistical analyses of ANZDATA data for use as inputs into the economic evaluation
# of increased use of primary brain malignancy donors for kidney transplantation
# Created by: James Hedley
# Date created: 29th July 2021
# Last updated: 22nd June 2022
# Load libraries
library('tidyverse'... |
35eba6f7e877ddbbb57262ebc86d4c9c9d898dcb635a15324d5ecc3606bdf54e | R | 166,928 | 2,624 | #----------------------------------------------------SETUP--------------------------------------------------
# Load libraries (install them first if necessary using install.packages("packagename") )
library(mosaic)
library(dplyr)
library(ggpubr)
library(broom)
library(tidyverse)
library(rstatix)
library(emmeans... |
606afde843da89b05584204ced2cbf0bb5288ae14da6e03ff3caacdfd3071fd2 | SAS | 2,141 | 66 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults
with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman;
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create supplemental table 3 for overlaps between GVLM and AHEI tertiles;
*... |
3f64c77b566b3824fb90af931e9daebb2a11dbe907d0e5bdb4834f7cfeafb3d7 | SAS | 2,817 | 85 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults;
*with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman
*Statisticians: Daniel Alschuler, Melanie Wall;
*Import data from csv file, apply formats, and save to library;
*Last upda... |
98e2861b59fe4d85328c8c662b1a9ff8eb805ca560512c70d5eee22012d02abd | SAS | 7,418 | 229 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults;
*with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create table 2 for longitudinal mixed effect models of test scores by inte... |
910cb1eb9c0b72364c77980ccace238257384aa050509e4b1d266538e54a5671 | SAS | 7,852 | 242 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults;
*with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create supplemental table 2 for longitudinal mixed effect models of test s... |
e1f56361bcf42c9202ec1e0b5c7c41b6736e81244b3d7f0d98f03da9ab08de65 | SAS | 9,374 | 222 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults;
*with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create table 1 for descriptives by intervention;
*Last updated 2023-05;
... |
d38ba77323fb0b78abbebb4dce7a001cf7d854a1ffbdc9f36e05cfc6a7328827 | SAS | 9,524 | 223 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults;
*with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create supplemental table 1 for descriptives by intervention
In partici... |
e2b23f22f4fd190266a92477c34440dca6dfd612c5073247e1abd991fa2a092c | SAS | 9,565 | 279 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults
with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman;
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create table 5 for longitudinal mixed effect model of ModRey Immediate
... |
8f692bb49216f1c9d0fe32580ac1062b15f20ee52d590a20a479b77774caa589 | SAS | 9,593 | 276 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults
with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman;
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create table 4 for longitudinal mixed effect model of ModRey Immediate
... |
6cee37802436645349081dca3a1142722c12b8425ab208391f7d3ea5eae14be5 | SAS | 9,963 | 374 | *Manuscript code;
*Dietary flavanols restore hippocampal-dependent memory in older adults
with lower diet quality and habitual flavanol consumption;
*Principal investigator: Adam Brickman;
*Statisticians: Daniel Alschuler, Melanie Wall;
*Create table 3 for association of baseline AHEI and GVLM with baseline
... |
9090cda2cc5d3f95ce1eb6719baf9fecb98532207b2b050b9f14b27e48ad8794 | Shell | 19 | 1 | pip3 install mxnet
|
aa4f520e050d6e00396faa1caefaf1b7f08046b41105323547576ee65b4171c4 | Shell | 24 | 1 | pip3 install mxnet-cu90
|
05b5380425c59d124f712e3ba34bfbc127a4894b9afb39804c633bdba76e2699 | Shell | 32 | 3 | #!/bin/bash
./acc sample.fasta
|
fe13e8942e818a36c000d8ee3de9d7452894f4472a54065148cfeea640802f88 | Shell | 41 | 1 | DGLBACKEND=numpy $PYTHON -c 'import dgl'
|
4976233c2845512f1149d21ecbfb79763263f3dfb3c34ed1a012dc2d3cccd5f2 | Shell | 47 | 2 | #!/usr/bin/bash
jupyter-lab --notebook-dir='.'
|
2dd20166329e9a463b30b5c930645388e7382989a27e8ecdec58fc6471213e21 | Shell | 48 | 1 | python src/main.py --input data/example --gpu 0
|
03a4c8c2b226fd10e475dc51cb6e60d1cdf31ff97cef54739a0137940a6e0e20 | Shell | 50 | 3 | #!/bin/sh
python manage.py shell_plus --notebook
|
cee7a59627fad272e0cee9dfe1c2965105137635a807fc6c8dd2e081ff83b467 | Shell | 51 | 3 | #!/bin/sh
python manage.py runserver 0.0.0.0:8000
|
bcad8fc69e83c210a10229f776c30fc78ae0d9fe835bff63ff880733c5934047 | Shell | 52 | 3 | #!/bin/sh
python manage.py runserver 0.0.0.0:8000
|
111fe739cd77b809e36fb9e856111e356b35060525b40bfca04b8f336f4e8cf7 | Shell | 55 | 1 | python src/main_sparse.py --input data/example --gpu 0
|
1d8e339c6952507b04a5c8b4b8527d78b7f0feb91c95cce9e7c44297db0be1ec | Shell | 58 | 1 | python src/main_sparse_multi_gpus.py --input data/example
|
a6952e4c261d29009e9e37382776430de75370bff0b3ee30a5c53d8697b80611 | Shell | 61 | 3 | #!/bin/sh
mvn clean package -Dslf4j.internal.verbosity=WARN
|
117b9cee40373d77336c4dfd33060576c9498503ae95b3c2d5a26eb15c5df5c9 | Shell | 62 | 6 | ct=0
let ct=${1}-1
for i in `seq 0 ${ct}`
do
echo $i
done
|
7d7ae38243087e3f4a3786c1850f1e5ec7b815c6e12b29ae08e383081ccf741e | Shell | 67 | 3 | #!/bin/sh
mvn clean package -DskipTests -Dmaven.javadoc.skip=true
|
84dc52cb3a3b0be9188fdaac1119733ac13258239d7c67fb9ab4199cd1b8b794 | Shell | 74 | 6 |
ln -s /home/ajl2/Projects/PD/Data/ChIP/VariantCall/freebayes/*.vcf .
|
f9747f44d6679722543487fc78f691dba335d4f7a6eced6be0dddd9dc8bf22bf | Shell | 74 | 1 | python -m pytest --pdb -vv --capture=tee-sys test_pipeline.py::test_recipe |
2e7163105a9633202726e2cb3fde84664c87e08bfad80510cc596f49ddc0f8f3 | Shell | 76 | 5 | #!/bin/bash
../uTR -f realdata.fasta -o realdata_result.fasta -sda
exit 0
|
4adde493dd0506918f14fa923c0ba0edf0bad34e150b227f0182b5656f12e663 | Shell | 77 | 5 | #!/bin/sh
# merges subword units that were split by BPE
sed -r 's/\@\@ //g' |
488b5737cc608f1e72f02a91dada6a3cd9b0101a57a976c4187e868dbde56bea | Shell | 80 | 6 | #!/bin/bash
#
echo "Starting IGV visualization tool ...";
igv &
echo "Done!";
#
|
49aac4900f59922cd697438dfd12a9c91840a6c0d23248fa28a913fdca88d386 | Shell | 83 | 6 | #!/bin/sh
make clean
rm -rf build
rm -rf source/tutorials
rm -rf source/generated
|
2168bfaa6e561ed9deda60f4749b9e5ecc2a9f6481b955b47a6b336152de0080 | Shell | 86 | 2 | wget https://s3.us-west-2.amazonaws.com/dgl-data/dataset/gowalla.zip
unzip gowalla.zip |
5be856b309f9f780aebcd6e374ab17b6f7af2b710aedf53db600f498d4f392f6 | Shell | 89 | 5 | #!/bin/bash
conda env create -f environment.yml
git submodule init
git submodule update
|
8213e0a1b8d9e032103f6473a8203dc40333c5d9dcbfb4c1ab840c517ded8817 | Shell | 89 | 2 | wget https://dgl-data.s3.cn-north-1.amazonaws.com.cn/dataset/wn18rr.zip
unzip wn18rr.zip
|
7b56033dce07d633a6b9217ddc58cb0a13de3c06991c186976c83d77885bc5e8 | Shell | 91 | 3 | wget https://data.dgl.ai/dataset/AGENDA.tar.gz
mkdir data
tar -C data/ -xvzf AGENDA.tar.gz
|
db096e0044509b356bb4a7515cc5c7bc754ebf9223badbca8a85b665bc70955a | Shell | 94 | 2 | wget https://s3.us-west-2.amazonaws.com/dgl-data/dataset/amazon-book.zip
unzip amazon-book.zip |
ba3b540a6fbd08db5e729d54f8bc1a7037287e48a34e0c59f76a4d8bca19b7b8 | Shell | 95 | 2 | wget https://dgl-data.s3.cn-north-1.amazonaws.com.cn/dataset/FB15k-237.zip
unzip FB15k-237.zip
|
0ccdab46178d4af9fb4d8bc6a98923de39244dbd00933e1bed7593f681c387e5 | Shell | 100 | 6 | #!/usr/bin/bash
rm -R *.egg-info build dist
python setup.py sdist bdist_wheel
twine upload dist/*
|
984889a959e056dd3d72a695a3dd53f080be3b1f76e9e8f8d2293194f0d995af | Shell | 107 | 2 | #!/bin/bash
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/V78_* .
|
cc1d84d91806ae4213ee62b9a555d9c7df8adbb7ce064033aa2abafacca04177 | Shell | 107 | 2 | #!/bin/bash
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/V75_* .
|
9a92aaa10389b64fbf92ef16217d64f97e414951d23e511db7a7bd2641240490 | Shell | 108 | 5 | #!/bin/bash
sudo pip install h5py
sudo pip install --upgrade pandas pip
sudo pip install tensorflow==1.5.0
|
af681184bcfb2f2a3b8ab8baaf5efed077b2dbff8bb1a7651c77f61e67c1d72b | Shell | 109 | 3 | #!/bin/bash
#
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/V72_* .
|
a7b888b0cfe0d0039e962c96e78dcc25c94a502f7a932d923baacc0c43b4a27f | Shell | 111 | 3 | #!/usr/bin/bash
source /home/yeison/Development/BCI-Framework/venv/bin/activate
jupyter-lab --notebook-dir='.'
|
1791aaa0774935a53046f70519ae0f68efe0630887350ad31d525353057e347b | Shell | 112 | 4 | #!/usr/bin/bash
source /home/yeison/Development/BCI-Framework/venv/bin/activate
jupyter-lab --notebook-dir='.'
|
604890f11e502e7a0c27d0fcad8444d9be06c6b29ce219b879a772a1af102e34 | Shell | 117 | 2 | #!/bin/bash
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/\{V69_*,V70_*\} .
|
2c1f0a4035d54c4862708d7bab339982c30c1ae01b5d62d02c77f582fdf99081 | Shell | 118 | 4 | #!/bin/bash
# install torch
pip2 install torch==1.0.1 torchvision==0.2.2
pip3 install torch==1.0.1 torchvision==0.2.2
|
66b5e1ca0b8a6fd84a9022fb6de0177e902d505893cebb33f1a6fbb6b2c724c5 | Shell | 119 | 3 | #!/bin/bash
#
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/\{V55_*,V56_*\} .
|
f044a5d140b5e7509fdcaaceb50884b044fd8b1766331d0d30f0e11e085fb50c | Shell | 119 | 3 | #!/bin/bash
#
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/\{V63_*,V64_*\} .
|
f57da49f2fda4cc046f78e71c00a57f27fc2366290fcf27db2b7c1490d02b126 | Shell | 119 | 3 | #!/bin/bash
#
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/\{V65_*,V66_*\} .
|
f9cbc3073783225fff01f3c81f3ade6e9250b239c1c4a25c34095736a164b41d | Shell | 119 | 3 | #!/bin/bash
#
scp dpratas@atlas.fimm.fi:/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1/\{V59_*,V60_*\} .
|
c30bd1eea11e7934b91dd64ca68dfbcef998c01d3fe9ba384dc49da5710beca9 | Shell | 124 | 6 | mkdir layer${1}
lk=layer${1}/lock${3}
if [[ ! -e $lk ]]; then
mkdir layer${1}/lock${3}
python kmeans.py $1 $3 $2
fi
|
ecce36c0d3bff86032d6b14858c9af16160df045c6ffae05d7d67820b9e4d3ca | Shell | 124 | 10 | #!/bin/bash
set -e
# install
pushd python
rm -rf build *.egg-info dist
pip uninstall -y dgl
python3 setup.py install
popd
|
994a48e81f87cb0e9f2ca9befbafb9803edee6dd6d1b60b45bb47efddb5b3458 | Shell | 131 | 9 | #!/bin/bash
set -e
set -u
set -o pipefail
cd /usr/local/lib
wget -q https://www.antlr.org/download/antlr-4.7.1-complete.jar
cd -
|
77c46452fce5cb00921498b903bd4feeab0929aa97c2203a11943eda68e5a52e | Shell | 133 | 7 | #!/bin/bash
#$ -S /bin/bash
#$ -cwd
#$ -j y
#$ -V
snakemake --use-conda -j -s cellect-ldsc.snakefile --configfile config/config.yml
|
7a0adad0045d3d292730e0f653b1987a38e45774540f41004d5ac94f295b8097 | Shell | 134 | 7 | #!/bin/bash
#$ -S /bin/bash
#$ -cwd
#$ -j y
#$ -V
snakemake --use-conda -j -s cellect-magma.snakefile --configfile config/config.yml
|
f911002d8ccdafc6a4d4e730bed9c91841488dd184a90d0589968f7409b97bfe | Shell | 136 | 7 | #!/bin/bash
#$ -S /bin/bash
#$ -cwd
#$ -j y
#$ -V
snakemake --use-conda -j -s cellect-h-magma.snakefile --configfile config/config.yml
|
223c5b71f6c473e6ee37682e72c7f58d52790adb8d12e7d8d03e89b36d3072a7 | Shell | 137 | 3 | #!/bin/bash
# install torch
pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
|
91540fe20d61010e24cfb3dc17deed088604d3e112933e3c538b114aa824e532 | Shell | 138 | 8 | #!/bin/bash
#
echo "Running hiSeq ..."
cd hiseq/tracespipe/src/
./TRACESPipe.sh --run-meta --run-all-v-alig
cd ../../../
echo "Done!";
#
|
ae7fc25d91029abc677a8edf6bdc50ec5725660cd73362e08597355f5f125813 | Shell | 142 | 2 | # qsub -pe threaded 20 -binding linear:20 -l mem_free=4G run_neurocluster.sh
python /home/walker/rvallat/yasa_classifier/05_SHAP_importance.py |
7374fbdcc76361eab16cf541c6877b5463d8a9275f0e310f0283d5ceafa21a50 | Shell | 149 | 5 | #!/bin/bash
gcc -O3 -ffast-math op_3body_cc.c interaction_map.c cell_list.c events.c log.c secure_search.c utilities.c -o bin/op_comp_3bcc -lm
|
6f63c85ae2425ebd2f6b1512e28f98b06f959a62e0ac83fcfcaceb5b0114d250 | Shell | 152 | 7 | #!/bin/bash
#
# NOTICE: use this at your own risk!
# TRACESPipe does not give any warranty
#
echo ":)" | mail -s "Attach sent by TRACESPipe" -A $1 $2
#
|
71b150bfd6057a6beee6df985b362dbccb1f0e2a855d6ce5779f953b48c95956 | Shell | 153 | 6 | #!/bin/sh
python manage.py makemigrations
python manage.py migrate --run-syncdb
python manage.py createsuperuser
python manage.py shell_plus --notebook
|
9fe1c64d2c511a958b5a286b525f0fd26a04decb0a994d5e68897f19b46295c1 | Shell | 153 | 5 | #!/bin/bash
sudo pip install h5py
sudo pip install --upgrade pip
sudo pip install pandas==1.1.2 numpy=1.16.4 scipy=1.0.0 matplotlib=3.3.2 seaborn=0.11.0 |
29ca1619e57f7d454132ebd7215c236bc27c7fb0af8b405dadf2a1a17c65b970 | Shell | 155 | 7 | #!/bin/bash
ORGAN=$1;
#
cat top-$ORGAN.csv \
| grep -e "nello" -e "orque" \
| awk '{ if($3 > 1 && $2 > 1500 && $2 < 5000) print $1"\t"$2"\t"$3"\t"$4; }'
#
|
2e7beae5bfc2ea1353521e1dd6b82850c46d1b0ff326ac81f7eb828ff458c328 | Shell | 155 | 7 | #!/bin/bash
#
echo "Extracting AV genome from VDB.fa into AV.fa ...";
#
gto_fasta_extract_read_by_pattern -p "MH648911.1" < VDB.fa > AV.fa
#
echo "Done!";
|
6bc28438a28dc022847b316c6010b7023aa81a2a5b97214bb83edc0efaf8dd97 | Shell | 156 | 7 | #!/bin/bash
#
echo "Extracting HV3 genome from VDB.fa into HV3.fa ...";
#
gto_fasta_extract_read_by_pattern -p "X04370.1" < VDB.fa > HV3.fa
#
echo "Done!";
|
e534289e090c87d1d194bb4b30e86ef9f41e977b0f3aa5ae1903901aa854b058 | Shell | 156 | 7 | #!/bin/bash
#
echo "Extracting HV7 genome from VDB.fa into HV7.fa ...";
#
gto_fasta_extract_read_by_pattern -p "AF037218" < VDB.fa > HV7.fa
#
echo "Done!";
|
64e16143d41c80feec3bc0ae45fe543e839be53dfb251f8334aa72e8c9601e2c | Shell | 158 | 7 | #!/bin/bash
#
echo "Extracting B19 genome from VDB.fa into B19.fa ...";
#
gto_fasta_extract_read_by_pattern -p "AY386330.1" < VDB.fa > B19.fa
#
echo "Done!";
|
95fd3266cb886b24174153611fdb717893a18e233306c803b9ff04802683c6c2 | Shell | 158 | 7 | #!/bin/bash
#
echo "Extracting HV4 genome from VDB.fa into HV4.fa ...";
#
gto_fasta_extract_read_by_pattern -p "DQ279927.1" < VDB.fa > HV4.fa
#
echo "Done!";
|
ddcb1084ac22506988997d489b45b1b27705a94c62badca9426b3010db9f13c5 | Shell | 158 | 7 | #!/bin/bash
#
echo "Extracting JCV genome from VDB.fa into JCV.fa ...";
#
gto_fasta_extract_read_by_pattern -p "AB127350.2" < VDB.fa > JCV.fa
#
echo "Done!";
|
08996f4dda6228dd69b0a3fff43d4214a1a093c1ed60d33f143b3ee97541a643 | Shell | 162 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_boxplot.py --configfile $CONFIGFILE --verbose True
fi |
993d7c098dd85f84c5dcaf97a89c1c7d30b7fe66ff157b70bc16a0b3c26c8c78 | Shell | 163 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_training.py --configfile $CONFIGFILE --verbose True
fi |
d5baf4d9300017eba4bf898bae0555151129c6df27b745783c13b7c3b343145b | Shell | 164 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_tsne_plot.py --configfile $CONFIGFILE --verbose True
fi |
989d69e3ca9c63c55adc34a1ddcca702bc6bf9040bc52d2ff3cac1e3d106d1fb | Shell | 166 | 3 | #!/bin/sh
. /work/behnam/testgithub/fromgpu02/packages/cobel/bin/activate
. /work/behnam/testgithub/fromgpu02/packages/nest-simulator-2.20.0_install/bin/nest_vars.sh
|
ac6bafbf8eb9f064f00b4bfacc43fd4f5359b0da88c7b9a96754dc458111fde9 | Shell | 166 | 8 | #!/bin/bash
#
echo "Running hiSeq ..."
cd hiseq_no_duplications/tracespipe/src/
./TRACESPipe.sh --run-meta --run-all-v-alig --remove-dup
cd ../../../
echo "Done!";
#
|
4fedab661eb29f2fda4375178aa04e41ecc46f4eeaf42bb92093f02ee55b6e67 | Shell | 168 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_final_ranking.py --configfile $CONFIGFILE --verbose True
fi |
b157a37372f3ed9eb988fbe492342e8489d31077db4274f18c45c0fc2eff3896 | Shell | 169 | 6 | #!/bin/bash
#
MAX_THREADS=`cat /proc/cpuinfo | awk '/^processor/{print $3}' | wc -l`;
#
echo -e "\e[32mMaximum number of threads of this machine: $MAX_THREADS \e[0m";
#
|
6e807f97fc55ece7077a99a84a27e65538669322eade5a5a7c2ed1958639ef5c | Shell | 170 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_beam_generation.py --configfile $CONFIGFILE --verbose True
fi |
9cf87c3c16757974c4773e3ee6f9cb6d1e5b19c58e77966e2e6ded7f5c22a979 | Shell | 170 | 11 | #!/bin/bash
CONFIGFILE=$1
if [ $# -eq 0 ] ; then
echo "Configfile path not supplied."
else
python do_data_processing.py --configfile $CONFIGFILE --verbose True
fi |
b1933fce1c0a3d34a8de98bc56d7b8a2251a2eb3b3e28e442b05eea58786d5e5 | Shell | 170 | 7 | #!/bin/sh
##source activate django3
python manage.py makemigrations
python manage.py migrate
python manage.py shell < init_db.py
#python manage.py shell_plus --notebook
|
21bdc9e714e7db1c5f97f9276e1dd53d50dc1ea7a2301ae514d5e81ccd12ca94 | Shell | 175 | 7 | #!/bin/bash
#
echo "Extracting B19 coding sequence from VDB.fa into B19_cds.fa ...";
#
gto_fasta_extract_read_by_pattern -p "AY044266.2" < VDB.fa > B19_cds.fa
#
echo "Done!";
|
7aaf1ab2bd94d60deed3e931e34bf36d19279289ad46df253d1f0c326b889142 | Shell | 175 | 2 | MXNET_CUDNN_AUTOTUNE_DEFAULT=0 python train_reldn.py \
--pretrained-faster-rcnn-params faster_rcnn_resnet101_v1d_visualgenome/faster_rcnn_resnet101_v1d_custom_best.params
|
cf351b16f9a839fd75926518597342a078c0b75d0a71accef4b2003233aca45c | Shell | 178 | 11 | #!/bin/bash
function fail {
echo FAIL: $@
exit -1
}
echo $PWD
pushd build
ls -lh
export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH
./runUnitTests || fail "CPP unit test"
popd
|
cf29c19474a0e4feee05d96dd70033ef44ce805f3fe37ebaf3297965121663c4 | Shell | 180 | 9 | #!/bin/bash
#
echo "Changing Reference Human mithocondrial genome (mtDNA.fa) to $1 ..."
#
rm -f mtDNA.fa;
efetch -db nucleotide -format fasta -id "$1" > mtDNA.fa
#
echo "Done!";
#
|
a7fdf24915cd44ec2ad758a8abe684758f8ad1faa63b9b9ff131df8829c71410 | Shell | 181 | 6 | #!/bin/bash
#
lzma -k -d VDB.mfa.lzma;
#
./TRACESPipeLite.sh --reads1 reads_forward.fq.gz --reads2 reads_reverse.fq.gz --database VDB.mfa --output lite_viral_analysis --threads 4
#
|
8ff0d9da6806c4d0dd95d25ccdfec6409f2f276924f6b99338382868c5538142 | Shell | 186 | 9 | #!/bin/bash
#
ls -la ../output_data/TRACES_results/top-* | awk ' { print $9;}' > all_tops.txt
#
while read p; do
echo "$p";
./TRACESQuick_extra_vir_view.sh "$p"
done <all_tops.txt
#
|
26c0c164eaf97fccfaa9310127569a845f813f64c39aa63914e486b4e9b13bdc | Shell | 190 | 9 |
# bash run.layer.sh <layer> <the number of kernels> <threads> <optional: the number of flexible motifs>
bash run.layer.sh 1 300 20
bash run.layer.sh 2 200 20
bash run.layer.sh 3 200 15
|
637b66d127267fe834c45d63b8db1896c9e9b0344843077074f26d7b7160dffe | Shell | 190 | 9 |
# bash run.layer.sh <layer> <the number of kernels> <threads> <optional: the number of flexible motifs>
bash run.layer.sh 1 320 20
bash run.layer.sh 2 480 20
bash run.layer.sh 3 960 10
|
49657e7f899e24bdb81169cfc9a93f587bf7391290aadc8525dd6d7202d53632 | Shell | 196 | 12 | for year in {2000..2020}
do
cd $year
for file in *.nc
do
echo $file
cdo sellonlatbox,261,264,29,32 $file ${file::-3}_austin.nc
mv ${file::-3}_austin.nc ../austin/.
done
cd ../
done
|
e56c6a082d25e99410d731a3c372fc2294af9dbfafa895cbd69651c585d65e20 | Shell | 201 | 7 | #!/bin/bash
rm -f en_seqs.fa;
while read line; do
efetch -db nucleotide -format fasta -id $line >> en_seqs.fa
done < ../system_files/ids_enrichment.txt
cat $1 en_seqs.fa > TMP_VDB.fa
mv TMP_VDB.fa $1
|
c9ac6a79bb1f9d7354402ff77f1b6ef6136656f92395ca0b2f89afe8781f32a0 | Shell | 205 | 9 | mkdir layer${1}
lk=layer${1}/samplinglock${2}
if [[ ! -e $lk ]]; then
mkdir layer${1}/samplinglock${2}
sp=layer${1}/kernel-${2}.h5
if [[ ! -e $sp ]]; then
python vis.py $1 $2
fi
fi
|
4537107228ece20b98bd8598a4727f5bd9b2c552881fd520f85a23b032280165 | Shell | 207 | 7 | #!/bin/bash
USER="dpratas";
SERVER="atlas.fimm.fi";
PATH="/fas/NGS/pipes/fastq/fimm_ngs_mtoppinen/autopsy_NGS_novaseq1";
#
scp $USER@$SERVER:$PATH/\{V1_*,V2_*,V3_*,V4_*,V5_*,V6_*,V7_*,V8_*,V47_*,V48_*\} .
#
|
0bb69e34410a0dd355b025a5322d5bfc0f906f8ea1f51213d671444281c72d31 | Shell | 209 | 8 |
# bash run.layer.sh <layer> <the number of kernels> <threads> <optional: the number of flexible motifs> &> .layer<layer>.log
bash run.layer.sh 1 3 3 &> .layer1.log
bash run.layer.sh 2 1 1 &> .layer2.log
|
287f1817edb3795e9c7ca6cb87ac753e878d1731b32c01f718eac5e9bb689fe3 | Shell | 210 | 13 | #!/bin/bash
#
# accessions.txt with GIDs (line by line)
#
mapfile -t CODES < accessions.txt
#
for xcode in "${CODES[@]}" #
do
echo "Searching $xcode on VDB.fa..."
grep $xcode VDB.fa
echo " ";
done
#
|
2cd03d8c695183b7cb30671c0f4c39768556d3f943f7840e4c5da7e6159cef5a | Shell | 214 | 10 | #!/bin/bash
cd check_uTR; make clean; make;
cd ../gendata; make clean; make;
cd ../parse_RepeatMasker; make clean; make;
cd ../check_RepeatMasker; make clean; make;
cd ../checkTRF; make clean; make;
cd ..
exit 0
|
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