sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
865b907bd953aa75c8ab0a92d90b2fad597174de18e1d33bad76e22b40e53b17 | R | 200,000 | 3,559 | ---
title: "SB R2 SIMMAP ACE - Strepsirrhines - 062522 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
Strepsirrhine Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
... |
a3e9af4060faf46aafd67f55b940f59a130c2a013c07ce20b39fad285654a178 | R | 200,000 | 3,881 |
setwd("E:/project_data_ssd/hpk-1_dataset")
library(Biobase)
library(openxlsx)
library(gProfileR)
library(ggplot2)
library(VennDiagram)
library(gplots)
DE_hpk1.all <- read.xlsx("DE summary hpk1_US_vs_N2_US.xlsx", sheet = 1)
# replace NA p-values with 1
DE_hpk1.all$DESeq2_fdrpval[is.na(DE_hpk1.all$DESeq2_fdrpval)] <-... |
b30222aa5badb3e986aa0048404bb3499dc84613dd14d6515b5256a2ac9f7547 | R | 200,000 | 3,382 | ---
title: "SBR2 - BDS ms Strepsirrhine Analysis for SIMMAPs Plot Code - 062722"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorhine Analysis
Run: SIMMAPS ACE 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is f... |
babb9507d2c92e7fa3f1a54cff6e8f7fb3a9bbc4327cca0a54a115dde218952f | R | 200,000 | 3,564 | ---
title: "SB R2 SIMMAP ACE - Strepsirrhines - 062522 v1 Females"
output: html_notebook
editor_options:
chunk_output_type: console
---
Strepsirrhine Analysis
Run: SIMMAP ACE 062522 v1 Females
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
F... |
c8a567d9e42b841ec1cd82ac5929a398ba8fe12f70a5498276fb662e5d06ea22 | R | 200,000 | 5,601 | ```{r}
source("~/data2/rstudio/birds/utils/seq_analysis.R")
source("~/data2/rstudio/birds/utils/colors.R")
source("~/data2/rstudio/birds/utils/network.R")
source("~/data2/rstudio/birds/utils/db.R")
source("~/data2/rstudio/birds/utils/stats.R")
#source("~/data2/rstudio/birds/utils/scde.R")
source("~/data2/rstudio/birds... |
f35e8ef4e77beb415134c12c1d4ce014afc97bcd9475f3bb1f351e996a1ed70d | R | 200,000 | 3,566 | ---
title: "SB R2 SIMMAP ACE - Strepsirrhines - 062522 v1 Males"
output: html_notebook
editor_options:
chunk_output_type: console
---
Strepsirrhine Analysis
Run: SIMMAP ACE 062522 v1 Males
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.N... |
3b957016a293f62114e8d5df700aa5d66d3e0df24d62acb5caa748b0cc1dcd53 | SPSS | 2,004 | 72 | * Encoding: UTF-8.
* Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis. ... |
59474ddeb37a2f78f3fe314c693976e9968780a5db7ba0f2962420979d4c1a31 | SPSS | 2,291 | 81 | * Encoding: UTF-8.
* Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis. ... |
0d3c9d2a13d3cf14fa7b13b22cc970db67da6addb318c5ab9b0e926f38603f38 | SPSS | 2,474 | 79 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis.
* Change your di... |
5ca78deae134ea65eaecfd1fd79d4ac9d3ea276853fff813274661a39693c3cd | SPSS | 2,828 | 91 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis.
* Change your di... |
e73b7dcacc5125893b6b1a7dab85e64d77baefd47f6100119fc8b625d4551f1a | SPSS | 2,956 | 96 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis.
* Change your di... |
c67c1b7e7256fb76ab2156c9bf168f65a638dee82c53a1e586cc769f3aa10843 | SPSS | 3,407 | 123 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision Distance traveled, time & entries in the interaction zone are ready for... |
fc21bdd9b606708096a68204f7a1f27d820cfecb422f7065c84c447e558b4de7 | SPSS | 4,678 | 201 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
*** This syntax will combine the Ethovision Subjects (since it swtiches them between the mice during tracking) so that the Ethovision 1 sec bin frequency in zone is ready for analysis.
* Change your di... |
28858916cb4176c931547ee088242bcab0c3f96cbe6fcd4116a446162d3a7e90 | SPSS | 59,392 | 3,731 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
* Change your directory.
cd 'Z:\Behavior\CD_x_Ird1-HA Project\020420 CD_x_Ird1-HA Cohort1\6. Social Operant\Batch 1Med Ass txt files'.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEF... |
b26f4db017b59e8594006f8ff7f7fd9697d0ee909bd4c534dd29043948b614a4 | SPSS | 59,956 | 3,770 | * Encoding: UTF-8.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SYNTAX BEFORE YOU START WORKING!.
* Change your directory.
cd 'Z:\Behavior\CD_x_Ird1-HA Project\020420 CD_x_Ird1-HA Cohort1\6. Social Operant\Batch 1\Batch 1Med Ass txt files'.
******DO NOT OVERRIDE THIS SYNTAX FILE! SAVE AS A NEW SY... |
86911e44eeb81898adc3bf4158999b58e0e1d6de80ea2c5af999e2e239086e02 | Shell | 20 | 2 | rm slurm*
rm tasks*
|
e2f7b4d66d9c3c1b07d2ba9e63c0eea765019d3cdf308f36a05485b1ba95062d | Shell | 24 | 1 | python impl.py $1 $2 $3
|
c7e652e99ffa65627e8baeed50a2152c8379110466039ff50fb2e61960ae269e | Shell | 28 | 1 | sudo docker build -t test .
|
33b830ed095902a25ced04726e960b729906ca8f9ba66da326f6a950d46a8319 | Shell | 30 | 2 | mvn install
bash ./auretim.sh
|
5d1b0cf79a2d1d3ed41cd80d04a9a864b82877df8d0c1e6b6a551684fd01af94 | Shell | 32 | 3 | #!/bin/bash
python -m cma.test
|
4d8ac846cda8de41ab7e181c640fa0946be6d4e58e0228c8081df49f71692e0f | Shell | 48 | 1 | python -u ../../main.py -input_file ga_input.in
|
85707b5e64d9b735f8b27f3ae655ece465fdd6b9a7635ab17d07b1c967c6e698 | Shell | 51 | 1 | python -u ../../../main.py -input_file ga_input.in
|
b271d85e1cc3d42286e50c409ebb62731eaf31f04e0add95517a95e13c4ee866 | Shell | 53 | 4 | git add *
git stash
git pull
bash recompileAndRun.sh
|
c1c30d30e6f860df3c843fdff4b7db47c749138e094d33f46899a74ec0402999 | Shell | 57 | 7 | #!/bin/sh
python crispr_ont.py
python crispr_offt.py
|
ce079342c153d350bf2c0f576e2b388507dff1c1a13e7cf1bd6e92149eb5aba7 | Shell | 65 | 4 | cd asreview/webapp/
rm -rf build
npm install
npm run-script build |
d1d33ead841f31f71b1cb379c6264554146986cdbfed1c2fc01b3589d81cf6d4 | Shell | 67 | 3 | #!/bin/bash
$(dirname "$0")/DevelopmentSetupScripts/SetupHooks.sh
|
6cb09b1c6e9f9f7827521145cc1e1f72f972a0b04f0abcc4a811c0bf1f00e3f0 | Shell | 71 | 7 | #!/bin/bash
while read bam; do
sbatch bam2fastq.sh ${bam}
done<${1}
|
bf2c33600cf4d077b4e71ddec727776d67c75ce300e821fbf1da9b063b37d137 | Shell | 72 | 4 | #!/bin/bash
runmitos.py -i ../$1/$3* -o $2/$3 -R . -r refseq81m/ -c 2
|
8f8657df8a62f54382619e11e5b6e699f3b9348a50d655135c1cc64d8944b10e | Shell | 73 | 4 | #!/bin/bash
runmitos.py -i ../$1/*$3* -o $2/$3 -R . -r refseq81m/ -c 2
|
f0ee26eafe1185422d49bac1bfb03fe0a6c4a456f9aeab15565cba20b4f94285 | Shell | 84 | 5 | #!/usr/bin/env bash
set -eou pipefail
pip install --no-deps "${TORCHSERVE_WHEEL}"
|
f6c0ffaa69e1596d23415718122b96e2e2e8822a111d617530722e7590da158b | Shell | 85 | 4 | #!/bin/bash
dag_file=${1}
condor_submit_dag -no_submit -update_submit $dag_file
|
aec66f69527c8b1fdeb9948a3d6e5f371bc81738218ad74aa92dff3aa4199194 | Shell | 86 | 3 | # Path to NEURON installation folder in Linux style
export NEURON_HOME_LIN="/C/nrn" |
2a2b7b634247c3d2c77ec3e5de9a60417e02a93154762e17e17cc1a03a2e828c | Shell | 88 | 6 | #!/bin/bash
# add path
rpath=$(realpath ./scripts)
echo $rpath
export PATH=$PATH:$rpath |
337039c9359dbefd3fe3a862e2816a230b6ca4e9dd678c1478e05f6dfbb041a2 | Shell | 94 | 5 | #!/usr/bin/env bash
set -eou pipefail
pip install --no-deps "${TORCH_MODEL_ARCHIVER_WHEEL}"
|
e029540b56430f481744ef76aaeae5f64e2e11b53fff6a8e7fae1c7aa9432908 | Shell | 95 | 7 | #!/bin/bash
while read -r VGP_ID GEN_ID; do
sh ./repeats.sh $VGP_ID $GEN_ID
done < list.txt
|
0f371ccebe3182a8fcce2d978ef69a79f550f7ab0b38994a5fd3fd2320aa9cb4 | Shell | 96 | 2 | cd GTEx_Analysis_v8_eQTL; find -name "*signif_variant_gene_pairs.txt" | sort > ../LIST_ALL.txt
|
39235745d73e7e19db1a37451f9b9d438cab2ae9c05cc4dee01a00ad7e563b24 | Shell | 96 | 3 | #!/bin/bash
cd ~/github/leifer-Behavior-Triggered-Averaging-Tracker/
echo $(git rev-parse HEAD)
|
fea8de005f63ac5bb51f384880afcddf796069410af10348145ec2df523907cf | Shell | 97 | 8 | #!/bin/bash
while IFS=$'\t' read -r species id
do
sbatch download.sh ${species} ${id}
done<$1
|
9f62b4d8b092cff08215c8eb4aa24df87018e224a093a8ed71ea0af05e7eb72d | Shell | 98 | 4 | BED=$1
cut -f 1,2,3 $BED > $BED\.tmp.bed
bgzip -c $BED\.tmp.bed > $BED\.gz
tabix -p bed $BED\.gz
|
c63f856bd5fff35df2fd9b64217f2ab50a6606cc3cd0578f048c6c3fc3c1309e | Shell | 103 | 7 | #!/bin/bash
javac *.java
jar cf telomere.jar *.class
rm *.class
g++ find_telomere.c -o find_telomere
|
8a639adc6770f13477647032b97721de76531f351d44f5f309a13dadcf2d151a | Shell | 104 | 8 | #!/bin/bash
while IFS=$'\t' read -r species id
do
sbatch extract.sh ${species} ${id}
done<species.ls
|
363971271179abc7a5737da337961a2283833cba85d2a2891da479f2890f3f77 | Shell | 108 | 8 | #!/bin/bash
while read id
do
sh trnF.sh ../*Circular*${id}* ../../mitos2/NOVOPlasty/*${id}*/*bed
done<$1
|
bb9161adc5a0876664218c7792d32a4e1bdf52d9a1b1a40928e7870a1d147c0d | Shell | 109 | 1 | hocrender ../VCN-SBEM-Data/VCN_Cells/VCN_c$1/Morphology/VCN_c$1_Full.hoc --mode cylinders --display sec-type
|
228715e67f56b4d07340e9921a99d23f472f76f94d37c3701917f6aef47fe22b | Shell | 110 | 7 | #!/bin/bash
module load samtools
cpus=$SLURM_CPUS_PER_TASK
samtools view -hb -@$cpus align.sam > align.bam
|
7d47cac3472cc9988e5440eb93a2d5fcf99ff410d5ae8746cb5e40facdc4a921 | Shell | 113 | 10 | #!/bin/bash
# get outfile path from argument
OUTFILE=$1
if [ -f "$OUTFILE" ]
then
exit 0
else
exit 1
fi |
4b94258335fc89a9c81f2fc1ee427b7c8915eb73fc6897f0feeabed23c26af05 | Shell | 116 | 9 | #!/bin/bash
while read -r ID; do
mkdir -p $3/$ID
sbatch --output $3/$ID/log.out runmitos.sh $1 $3 $ID
done < $2
|
ba1a982a6734beaba7b289e9e042554006d60b6db27f5b740cfc377365178a0c | Shell | 116 | 9 | #!/bin/bash
while IFS=$'\t' read -r species id ref
do
sbatch NOVOplasty.sh $species $id refs/*${ref}*
done < $1
|
d5db4167162bfa892d296ad0c6c3c6613b4e152b73eced65f03a637ec6fac5d1 | Shell | 118 | 7 | #not really that useful
x=0
for i in $(ls topol*itp);
do
perl ~/md/top2psf.pl -p $i -o out$x.psf
x=$((x + 1))
done
|
030278b4547ab9955d5a7cb257cd8ce2010d4cc21a582d48fcfc0c5dff31f8d7 | Shell | 122 | 9 | #!/bin/bash
while IFS=$'\t' read -r species id
do
sbatch mito_qv.sh ${species} ${id} VGP_dataset/${id}*.fasta
done<$1
|
f6b1fa5344b9a30327725ec9d172c33815dc182a9f44f379f038de30a9415c5c | Shell | 122 | 5 | #!/bin/bash
n=256
python eam.py -r --domain=$n --runpath=runs-$n && \
python eam.py -d --domain=$n --runpath=runs-$n
done
|
882c5875aa17f4759170c50a359ef3fa3c59d082622a3c3f53213926bbc756b4 | Shell | 123 | 3 | #!/bin/bash
minimap2 -x asm5 -t 24 ${1} ${2} -a --secondary=no | samtools view -S -b -F 4 -F 0x800 | samtools sort > ${3}
|
2d53d29dbe2aa0c570c8a6eea165eee554cae2fb48785e45cd7a4a7ad555d172 | Shell | 127 | 6 |
sort -g -rk 1,1 output_BA_n.txt > all_out_BA.sort
awk -F ',' ' $1 >= 8.6 ' all_out_BA.sort > all_out_select_BA8.6.sort
|
7cb67f0418457f8923f1ff958c88df3303309b980cdd289bfd344d6f99d6e088 | Shell | 127 | 9 | #!/bin/bash
while read -r ID; do
mkdir -p $3/$ID
sbatch --output $3/$ID/log.out NOVOPlasty_runmitos.sh $1 $3 $ID
done < $2
|
bc6d2e30da6572573bc54136b14f04b62c142e0a162624e6c28edabac08e55a0 | Shell | 127 | 8 | #!/bin/bash
while IFS=$'\t' read -r species id
do
awk -v ID=${id} '{sum+=$2}END{print ID"\t"sum}' ${id}/base.counts
done<$1
|
cb97f833e312e5b60ccb151ef065fb94314cbe0c994fe34e31b98d4734377823 | Shell | 130 | 12 |
for name in ??_n.smi
do
base=${name%.smi}
nohup python read_smi_protein_nnn.py $base > $base_log.log 2>&1&
sleep 4s
done
|
aa2eceb8bb400710e732a605898eed224eea102e72b0b76fc36b6ad10ca0f2d8 | Shell | 135 | 3 | #!/bin/bash
for f in $(ls */summary.txt); do printf "%s\t%s\t%s\t%s\t%s\n" $(head -1 $f | awk '{print $1"\t"$2}') $(tail -1 $f); done
|
f411ad2e65b47e53d0aa366f737f869d15815be281763fbb82efcdcc2ffa0369 | Shell | 135 | 4 | #!/usr/bin/env bash
# (1) Send prediction and get request
curl -X POST http://127.0.0.1:8080/predictions/maskrcnn -T oraquick_10.jpg
|
46bea068427a63759c419933427f5c068c0867c90353d020ed2e7edc4faddfdc | Shell | 136 | 4 | #!/bin/bash
jupytext --to notebook --execute --set-kernel python3 de.py
jupytext --to notebook --execute --set-kernel python3 de_ma.py
|
4ac44c99006f7163069e5ca5a3ec806ceead47ac03e527efaf86eee16f2a4dbb | Shell | 138 | 4 | #!/bin/bash
# This script is used to run the odp_basic test case
snakemake --cores 9 -p --snakefile ../../scripts/odp --rerun-incomplete
|
23cf1641d41c6240f881cb8383a9013d736cbb2c4c31684c1ade814535ff0993 | Shell | 139 | 5 | if [ ! -d ".data" ]; then
mkdir .data
fi
python train.py AG_NEWS --device cpu --save-model-path model.pt --dictionary source_vocab.pt |
9fea27224059c90348c07bfb70943ac94d27ef7e66e897d7e8a0793f71de3503 | Shell | 140 | 5 | #!sh
# The next paths must be adjusted before the first launch
NRNDIR="/C/Programs/nrn"
HPCDIR="/D/Work/UCL Phase 14/nano/clusterCaSim/hpc" |
00da744de1f983b74e9760cf9112d05bdd6e0121a39807c12a04ed7405c09475 | Shell | 141 | 12 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./index.sh <asm.fasta>"
exit -1
fi
module load bwa/0.7.17
echo "
bwa index $1"
bwa index $1
|
8bf685debdebe1424bd6a9f63be4c3f11948f847db15be79e587ec2c9b65ccb3 | Shell | 142 | 5 | #!/bin/sh
set -e
docker run -v $HOME/.gitconfig:/root/.gitconfig -v $PWD:/work -w /work --rm -ti obolibrary/odkfull /tools/odk.py seed "$@"
|
b366ac17b984f6f7ab242fa89cfcd46b45e0d8843c1eb605f21bc9fe813be718 | Shell | 142 | 2 | echo ttf-mscorefonts-installer msttcorefonts/accepted-mscorefonts-eula select true | debconf-set-selections
apt-get install msttcorefonts -qq
|
0cb45ef42babb91555f619eedf3b4ec1ab6be7440fa071af7984278298ffd2c5 | Shell | 143 | 3 | echo ttf-mscorefonts-installer msttcorefonts/accepted-mscorefonts-eula select true | debconf-set-selections
apt-get install msttcorefonts -qq
|
5289d875012c9914bf513dc721e31418ef0bc5dc497031211f6fa0ae103120f2 | Shell | 144 | 8 | #!/bin/bash
while IFS=$'\t' read -r id
do
sh extract.sh ${id} NOVOplasty/${id}/Contigs_1*.fasta ../../paper/VGP_dataset/${id}*.fasta
done<$1
|
ed3a70eed48e9a41aa1b395b9ed0089d2966cd0262a8a38c0ddd755a60b3f582 | Shell | 144 | 2 | #conda activate martinize
martinize2 -f snap_martini_ready.pdb -x thyroglobulin_cg.pdb -o topol.top -ff martini3001 -ss $(cat ss.txt) -elastic
|
001cc7d3e561be020fb0c86b10fae716e63d13ebe8c1a386f4eb288995005160 | Shell | 145 | 4 | #!/bin/bash
# This script is used to run the odp_basic test case
snakemake --cores 9 -p --snakefile ../../scripts/odp_onlyDB --rerun-incomplete
|
fc70ff8727218f268e398b7eb3cbb3bd40916d667ec8a7d754b7bc55566ea387 | Shell | 148 | 8 | #!/bin/bash
while IFS=$'\t' read -r id
do
sh extract.sh ${id} NOVOplasty/${id}/Circularized_*.fasta ../../paper/VGP_dataset/${id}*.fasta
done<$1
|
e42020c01852f2ecf8c53d19a7716fdf53e0468a521fa8afe69cc75c825fdbb4 | Shell | 150 | 4 | onmt_build_vocab -config train-from-scratch/PtoR/PtoR-MIT-aug5-build_vocab.yml
onmt_train -config train-from-scratch/PtoR/PtoR-MIT-aug5-config.yml
|
5d36a186bd4e9da7530b5d8073b9cf66ad2e4426e0cae1965fba8b310f9ed60b | Shell | 151 | 3 | onmt_build_vocab -config train-from-scratch/PtoR/PtoR-Full-aug5-build_vocab.yml
onmt_train -config train-from-scratch/PtoR/PtoR-Full-aug5-config.yml
|
e4705ded1ebe1b5ba19331704717487edbf8b3ba93555b0293a817757cb87a0c | Shell | 151 | 4 | #!/usr/bin/env bash
# (1) Send prediction and get request
curl -X POST http://127.0.0.1:8080/predictions/quidelag_classifier -T ios-146074-0-0.08.jpg
|
ec81a8c0ec45f835e30a4fdbaea6e35d512ae2ff1bcade56456c1d81f3fbd397 | Shell | 151 | 4 | onmt_build_vocab -config train-from-scratch/StoR/StoR-50K-aug20-build_vocab.yml
onmt_train -config train-from-scratch/StoR/StoR-50K-aug20-config.yml
|
1087b1743325f3513b7d0cf50602dd93a00bc11a30542ac26dd5a32065845eaf | Shell | 152 | 4 | onmt_build_vocab -config train-from-scratch/PtoR/PtoR-50K-aug20-build_vocab.yml
onmt_train -config train-from-scratch/PtoR/PtoR-50K-aug20-config.yml
|
39e03e21c4498d398fd6e636df697b38f24b929f9e0ff216276d69ac05f7d6a3 | Shell | 152 | 1 | rsync -zvhr --max-size=100m bpedigo@synaptomes1.neurodata.io:/data/bpedigo/bilateral/bilateral-connectome/results/outputs ./bilateral-connectome/results |
8b6fac808af8a40123d0e1dfb6983c94f67d05b855041896cdb569b991156b66 | Shell | 152 | 4 | onmt_build_vocab -config train-from-scratch/PtoS/PtoS-50K-aug20-build_vocab.yml
onmt_train -config train-from-scratch/PtoS/PtoS-50K-aug20-config.yml
|
18bcb701af22b8426d43def5a021702ca517b4d9c95b12fc34867fe342a8d02a | Shell | 155 | 4 | onmt_build_vocab -config pretrain_finetune/pretrain/Pretrain-Full-buildvocab.yml
onmt_train -config pretrain_finetune/pretrain/Pretrain-Full-config.yml
|
a869a1d10959c1ebcb175131a571491c269be0c9c0ab3454b2bd29f3bce05a26 | Shell | 158 | 6 | #for i in `cat sync_files.txt`; do
# rsync -pva ../pipeline_local/$i#
#
#done
rsync -av --files-from=sync_files.txt ../pipeline_local/ ./pipeline_scripts/ |
479e498106167458e2919545600de9661e22dec28e9ffc11242a61042c7624d2 | Shell | 159 | 15 | #!/bin/sh
module load bwa/0.7.17
module load samtools/1.9
fa=$1
echo "bwa index $fa"
bwa index $fa
echo
echo "samtools faidx $fa"
samtools faidx $fa
echo
|
19f158dda4809664399b657624bda23c922f6d3d77f0fc370e11608fd8cc6de2 | Shell | 160 | 6 | #!/usr/bin/env bash
./build.sh
docker save heviai/picai_prostate_segmentation_processor:latest | gzip -c > picai_prostate_segmentation_processor-v1.0.tar.gz
|
3ceed05bd59f0a21fa21b9c0c4c6c2a7065f3773f802e409b4b3036c734ae33b | Shell | 161 | 8 | #!/bin/bash
while IFS=$'\t' read -r id
do
sh extract_multiple_contigs.sh ${id} NOVOplasty/${id}/Contigs_1*.fasta ../../paper/VGP_dataset/${id}*.fasta
done<$1
|
4603733b4eeb36a659c7e7842b05006d0b2b2adf4939afe6631392a5bec64239 | Shell | 161 | 11 | BED=$1
python3 bed2longrange.py $BED
bedtools sort -i $BED\.longrange > $BED\.longrange.bed
bgzip $BED\.longrange.bed
tabix -p bed $BED\.longrange.bed.gz
|
d5f6dbaf5fac9dbd97c2ad98a26cfa873256fb207cdfe320fb23f767b88d8192 | Shell | 161 | 7 |
cat output_??_n.txt > all_out.list
sort -g -rk 1,1 output_BA_n.txt > all_out_BA.sort
awk -F ',' ' $1 >= 0.99 ' all_out_BA.sort > all_out_select_BA.sort
|
4a90d06440ad64fc83b6131efc10528b5d177fa39b6d1c54050da7eb1ed2c4e5 | Shell | 162 | 4 | onmt_build_vocab -config train-from-scratch/RtoP/RtoP-MIT-mixed-aug5-build_vocab.yml
onmt_train -config train-from-scratch/RtoP/RtoP-MIT-mixed-aug5-config.yml
|
4df4b56a452d23a48510878239f963e5354d42b9a7764e8db12efccccbd8ddaf | Shell | 165 | 4 | onmt_build_vocab -config pretrain_finetune/pretrain/PtoR-pretrain-Full-buildvocab.yml
onmt_train -config pretrain_finetune/pretrain/PtoR-pretrain-Full-config.yml
|
5ee269966249cd168b73ade63f1cc9f734e1a3b8b36e64f997109b30489d6f56 | Shell | 165 | 4 | gcc -g -o debuglink debuglink.c
objcopy --only-keep-debug debuglink debuglink.debug
strip --strip-all debuglink
objcopy --add-gnu-debuglink=debuglink.debug debuglink |
752c25aba60b90527bab98dcb2589819990ce1cd3b9a5924e3c446dbfea83ce1 | Shell | 165 | 4 | onmt_build_vocab -config pretrain_finetune/pretrain/RtoP-pretrain-Full-buildvocab.yml
onmt_train -config pretrain_finetune/pretrain/RtoP-pretrain-Full-config.yml
|
d5e838c34532666142d26695a8d501def813438ea73fd24e2b53cdc3a7e4c5d8 | Shell | 170 | 4 | onmt_build_vocab -config train-from-scratch/RtoP/RtoP-MIT-separated-aug5-build_vocab.yml
onmt_train -config train-from-scratch/RtoP/RtoP-MIT-separated-aug5-config.yml
|
775e4c92b17b6079abf2fcaf64c0640984221849d88086f7913add5a98aedafc | Shell | 172 | 12 | for name in data1/processed/L_P_train_BA.pt
do
base=${name:16 }
base_n=${base%.pt}
echo $base
python training_nn3_load_name.py $base_n > $base_n'_predict.log'
done
|
02af2b2f243643e85391bb6694c50dfe020eaa794d55b4d64fccfea31589e18a | Shell | 173 | 6 | #!/bin/zsh
for task in 'beta_gamma' 'beta_sharpness' 'phase' 'pac' 'cross_pac' 'phase_shift' 'burst_length'
do
python main_synthetic.py --name $task --n_jobs 8 &&
done
|
cf240a0a7b02937938923463d737168ae09472ef57c73262d6057e802f3d5401 | Shell | 173 | 5 | #!/usr/bin/env bash
./build.sh
docker save picai_baseline_nnunet_semi_supervised_processor:latest | gzip -c > picai_baseline_nnunet_semi_supervised_processor_2.1.1.tar.gz
|
2a5649a60fbd4e7a4f26754fea3eef817ab17835dcaa68de1113d1fd669fcddf | Shell | 175 | 7 | #!/bin/bash
arg_input=$0
arg_output=$1
# remove illlegal characters and convert the code if necessary
cat $arg_input | tr -c '[:print:]\n' ' ' | sed 's/\*//g' > $arg_output
|
910f3b4fba2b7d437d0534f0ed145fe1a5bb7823c77cf0cb8def11832c03ec20 | Shell | 177 | 6 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
dnn_fe -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -meth pca 3 -axis chn -out $TMP_DIR/dnn_fe.act.h5
|
99fc77fdb2a1e4d6d681affbd3d9a13880f095ef3b1412893edd0801f608f018 | Shell | 177 | 3 | #!/bin/bash
gmx grompp -f ions.mdp -c solvate.gro -p topol.top -o ions.tpr -maxwarn 1
gmx genion -s ions.tpr -p topol.top -o ionized.gro -pname NA -nname CL -neutral -conc 0.15
|
3da1954ba5e77a1d1d2f2224c47ffabedb284b37d798b0c9695953c4461ef874 | Shell | 178 | 5 | #!/usr/bin/env bash
# Create conda environment
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
conda env create -f ${SCRIPT_DIR}/${HOSTNAME}.yml
|
c4540ed94278ede98a1663916fc5f742617ef73d45e60ee8e1bcf3db393aa6fb | Shell | 178 | 6 | #! /bin/bash
export CHROMIUM_VERSION=80.0.3987.106
export PPTR_VERSION=2.1.0
npm run wd -- update --versions.chrome=${CHROMIUM_VERSION}
npm i --no-save puppeteer@${PPTR_VERSION} |
6243bcf5cd069a4a070256eed36baf5070f2c9187abb7f28a257bba909d336a6 | Shell | 179 | 6 | #!/bin/bash
nice python3 optimize_dynamics.py
# [Legacy]
# File required for leaving the optimisation process running
# as a background job after logging out of a remote server.
|
44b9ef2dd6ecca14c6d40a45ee3e516dfb9d7769396c65c63ca3b5785b7e9a36 | Shell | 180 | 7 | # For testing/verification purpose, run the following command
# a prediction.xlsx file will be generated
for kit in ACON_Ab DeepBlue_Ag
do
python main.py --kit-id=${kit}
done
|
4073107fe80498be0c540b775f540beb81b12435583d434b7df9cc5e0227a5df | Shell | 182 | 6 | #! /bin/bash
TMP_DIR=~/.dnnbrain_tmp
mkdir -p $TMP_DIR
dnn_pool -act $DNNBRAIN_DATA/test/image/sub-CSI1_ses-01_imagenet.act.h5 -layer conv5 -meth max -out $TMP_DIR/dnn_pool.act.h5
|
78f12bb65e32b2621890ffb5f5868e12ad8de944a6a3127e3b6f2bbe3baef69b | Shell | 189 | 14 | #!/bin/bash
if [ -z $1 ]; then
echo "Usage: ./ast.sh <gaps.bed>"
exit -1
fi
ls *.bam
gaps=$1
echo "
$asset/bin/ast_10x $gaps *.bam > 10x.bed"
$asset/bin/ast_10x $gaps *.bam > 10x.bed
|
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