sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
babeccb5c41beaba695a10c96c47f7fbdd838b2ce3ff02ad7161419435f443b3 | Rust | 6,144 | 186 | use anyhow::{bail, Context, Result};
use std::collections::HashMap;
use std::fmt;
use std::fmt::{Display, Formatter};
use crate::tabular::Data;
// module with some helper structs and functions to represent split reads
mod split_read_te {
use anyhow::{bail, Result};
use super::super::split_read::{MSAlignment... |
e17724d70454b69f9a78b745717ab4daeef2d54cbd9d8ab8e12640fad5eaed39 | Rust | 6,451 | 182 | use serde::{Deserialize, Serialize};
use std::fmt::{Display, Formatter, Result};
use super::genome_alignment::SplitReadRanges;
// I could store orientation in a bool
// but this is more readable
#[derive(Eq, PartialEq, Debug, Serialize, Deserialize, Clone)]
pub enum Orientation {
PlusPlus,
PlusMinus,
}
// s... |
c01a210a7fff161363bd8130fc671eaa0651bd7fec567a322eb1fc4103072364 | Rust | 6,562 | 178 | use anyhow::Result;
use lazy_static::lazy_static;
use std::collections::{BinaryHeap, HashMap};
use std::fs::File;
use std::io::BufRead;
use std::io::BufReader;
use super::genome_alignment::{GenomeAlignment, SplitReadGenome};
use crate::tabular::Metadata;
lazy_static! {
static ref SECOND_SAM_FILE_GENOME_ALIGNMENT... |
5083bace2cc4d697b70431fd3b61e759a9fc263e5dd08347dd268feb6f202816 | Rust | 7,974 | 188 | //! Minimal newick tree: parsing, midpoint rooting (same rules as ete3) and the
//! leaf-to-root walk used by the species-overlap analyses of steps 3 and 4.
const NONE: usize = usize::MAX;
pub struct Tree {
parent: Vec<usize>,
children: Vec<Vec<usize>>,
name: Vec<Option<u32>>, // leaf protein id
len: ... |
f0e7eea70515a1809d4f9feb3358738b54ec7f9ea2d9351f2b5a3248256c859e | Rust | 8,090 | 204 | //! Built-in neighbor-joining (`-phylogenies nj`), adapted from R. Derelle's phylo.rs.
//!
//! Distances: amino-acid p-distances ignoring gaps and unknown residues, corrected for
//! multiple substitutions (LG frequencies). The alignment is bit-sliced (64 columns per word) and
//! constant / all-gap columns are folded ... |
c84c2450337c995392172d1463597bae49e0838b6b529413c0389e2c48530ef4 | Rust | 8,390 | 214 | // the CLI app and sub commands
use clap::{App, AppSettings, Arg, SubCommand};
// the download subcommand
fn download_sc() -> App<'static, 'static> {
SubCommand::with_name("download")
.about("Download any file (e.g. reference sequence)")
.arg(
Arg::with_name("URL")
.sho... |
19d0ab5f1f62037c72f083da3fc6bb5db29c6f1b73f7a65a3316eac398879d5b | Rust | 8,611 | 189 | //! Step 1: per-proteome k-mer simplification. Proteins sharing a k-mer are grouped and
//! only the longest one of each group is kept for the next steps.
use crate::{create, fresh_dir, Opts, R};
use flate2::read::MultiGzDecoder;
use std::fs::File;
use std::io::{BufRead, BufReader};
use rayon::prelude::*;
use rustc_ha... |
a54eb4acd4e533e648c53a9467ae8e4742caad7961326b8ff612ee2575ed91ab | Rust | 8,694 | 174 | use anyhow::Result;
use lazy_static::lazy_static;
use std::collections::HashMap;
use std::fs::File;
use std::io::BufRead;
use std::io::BufReader;
use super::te_alignment::TeAlignment;
use crate::tabular::Metadata;
lazy_static! {
static ref FIRST_SAM_FILE_TE_METADATA: Metadata = {
let mut headings = HashM... |
dc80b09792d7c217ca8d6ee37456b5db36ce7a10475c69133308c3c2f13a4089 | Rust | 10,499 | 245 | //! Broccoli: orthology inference combining phylogenies and network analysis.
//! Rust rewrite of https://github.com/rderelle/Broccoli (Derelle et al. 2020, MBE).
mod nj;
mod step1;
mod step2;
mod step3;
mod step4;
mod tree;
use std::collections::HashMap;
use std::fs::{self, File};
use std::io::{BufRead, BufReader, B... |
32520371e8c8cab5218c92137f1bea17e5ba37f6c40ede12976dc23982f766b1 | Rust | 15,354 | 299 | //! <p align="center">
//! <img width="300" src="https://raw.githubusercontent.com/rderelle/Broccoli/master/images/logo_broccoli.png" alt="Broccoli">
//! </p>
//!
//! Broccoli is designed to infer orthologous groups and orthologous pairs using a mixed phylogeny-network approach. It also detects
//! chimeric proteins... |
9be4133b2e2ea5fc7c26c8f155d1f0ef513a15668bb24ef791a5a58c949b7657 | Rust | 16,972 | 346 | //! Step 2: phylomes. DIAMOND searches of every reduced proteome against all of them
//! (N best targets per species); then, for every query with duplicated species among
//! its hits, a trimmed alignment built from the pairwise HSPs is turned into a
//! FastTree BioNJ tree, midpoint-rooted. Queries without duplication... |
dbf830494192f5a4209192eeddcac3534f13660410af4604748a456f5336266e | Rust | 17,515 | 557 | //! # TE finding algorithm:
//! step 0: get a list of reads that are aligned to TE's and also to the same chromosome
//! step 1: split by TE name; now there should be several lists, each of which is for a single TE. For each list:
//! step 2: split this list by whether it is at the start or end of the TE, and whether t... |
b6a3750851102531fca0904a101c781dbb7ccd3d1bd68ae617613bfb1bbcd25e | Rust | 18,888 | 383 | //! Step 3: orthology network and orthologous groups. Orthologous/paralogous pairs
//! are extracted from the trees (species overlap), turned into a weighted network,
//! and communities are found with an asynchronous weighted label propagation,
//! followed by gene-fusion (chimeric protein) detection and spurious-hit ... |
b5a7be91000300db4e737a084a19ef9d6898e57e59392783e4dd2442e84a6059 | Rust | 28,850 | 619 | use anyhow::{bail, Result};
use serde::{Deserialize, Serialize};
use std::cmp::{Ordering, Reverse};
use std::collections::{BinaryHeap, HashMap};
use super::output_data_types::{NonRefTE, Orientation, RefTE};
use crate::tabular::Data;
// module with some helper structs and functions to represent split reads
mod split_... |
966d315424489eb138410c8049c882008741d673ed551680226660791481d757 | Scala | 1,560 | 45 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.tools.walkers.haplotypecaller.ReferenceConfidenceMode
import org.broadinstitute.gatk.utils.variant.GATKVCFIndexType
class VariantCaller ext... |
ea0310cb3bf01f34496629263509079ce537e2e5463b7b2bdd3b535d1d788bee | Scala | 2,258 | 69 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.queue.extensions.cancer._
class SomaticCaller extends QScript {
// Create an alias 'qscript' to be able to access variables in the Somat... |
ebc4b14bb2649942983a545f8c3d4e52f3d37fdd7034645572ae3245da67b97b | Scala | 3,068 | 87 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.queue.function.ListWriterFunction
class Realigner extends QScript {
// Create an alias 'qscript' to be able to access variables in the R... |
3073a6becd65a039c48c5c5e644f837ed6c5c2590c2b496bad82ca503e033683 | Scala | 3,270 | 84 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
class Realigner extends QScript {
// Create an alias 'qscript' to be able to access variables in the Realigner.
// 'qscript' is now the same as 'Realigner.this'
... |
41b9efe8b08695e296974ca3456969ebfd100f0385054ae6568fa61da2d0307b | Scala | 3,466 | 97 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.tools.walkers.genotyper.GenotypeLikelihoodsCalculationModel
import org.broadinstitute.gatk.tools.walkers.genotyper.OutputMode
class VariantC... |
31ea11f336cdf46a8ea93124b4d400ed15552e24d8c0d7908422f5a5ef799454 | Scala | 4,550 | 121 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import htsjdk.variant.variantcontext.VariantContext.Type
import org.broadinstitute.gatk.utils.variant.GATKVariantContextUtils
class HardFilter extends QScript {
// Cre... |
f725e4070b6070a61ddc50acefb3839beab3f50ecef8c1f59cf47e201f518d33 | Scala | 7,115 | 222 | package org.broadinstitute.gatk.queue.qscripts
import org.broadinstitute.gatk.queue.QScript
import org.broadinstitute.gatk.queue.extensions.gatk._
import org.broadinstitute.gatk.tools.walkers.haplotypecaller.ReferenceConfidenceMode
import org.broadinstitute.gatk.utils.commandline.ClassType
class VariantCaller extend... |
226b29283618d4ffc19019c2d89933970958b18eb2deed4a5eec19c0cbc95517 | Shell | 21 | 1 | rm d.nex simTree.nex
|
8cf14ec57cccae3500022ea88ea7d53dea9920700be3a2ea49b96075a44b879a | Shell | 25 | 1 | export PYTHONPATH=$(pwd)
|
90714ed19a6b33737ca11a90996f31995930bc5deb692e32c81a38eea4aa0bf2 | Shell | 36 | 2 | p4 sSim.py
p4 sOptAndDrawRootSeq.py
|
8157fb6238de75da5085c86f41f66fb3ee0a3700b542154b00fa3d248cd9dc76 | Shell | 37 | 1 | rm cons0.nex d.nex mcmc* simTree.nex
|
a721317fc0aa1220edcaf8754c422bbf9eb9acb36537145ae3a7a9cb8c3e15c4 | Shell | 43 | 2 | export SERVER_URL="localhost:6541"
nose2 -v |
8c34d32fd0de26b3c753c0abef4514183c2d4b6bf10f46e8a323e4cef60ba7eb | Shell | 47 | 1 | rsync -rP ../test131_psi4_rest/./outs/ ./outs/
|
a3e0718a45c8bd2b931e011ddb79279c1678da3ff58b3bdc9b508f2587eb286f | Shell | 49 | 3 | rm -f mcmc*
rm -f mbout*
rm -f simTree.nex d.nex
|
ee53a502fef4c64104e5f9806dfb628c4a3a486086fc0aa695737c7ef4e676f7 | Shell | 55 | 1 | gnuplot --persist -e "plot 'mcmc_likes_0' with lines"
|
558b584aa8c114fbd53d58050b3f4912466dd7178a8ee919c8d47aed629bb4ab | Shell | 56 | 1 | bsub -n 2 -W 120:00 -J "mas_q" "python master_slurm.py"
|
a9da9add71327cfcfabb9795684889a0398527e8ca015468bb9b9a145737413b | Shell | 58 | 1 | cp -f setup.py pyquartet_dist.cpp ../tqDist-1.0.2/tqDist/
|
510d07ace08a648e7aa8b6ebb733dfa99de08cd47e6e5d157830d212fdb24331 | Shell | 60 | 4 | #!/bin/sh
echo TestRunCommand Params: 1=$1 2=$2 3=$3 4=$4
|
836c8dcc9d11ca1ea8354df6e34d97ff4988102c3d66505e290035886df034c3 | Shell | 60 | 1 | wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz |
181d8f4d2d00f672b06c75fa7fd62ac008f80d2c6df9b6cb87adf0ee7ba522d3 | Shell | 61 | 1 | wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz |
6cf89be3ee4170576ee911bc1c00d4b5167440160ff018b78991e524a41402ca | Shell | 66 | 2 | export PYTHONPATH=$PYTHONPATH:`pwd`
conda activate acousticswarms
|
2da15611a9914d0db28db57533b9c5d705839d1a92478e4004319f1e1963f308 | Shell | 70 | 4 | NAME="MNIST"
SUBDIR=$1
python3 stats.py --name $NAME --subdir $SUBDIR |
efe4d3bf6d79799703d6019543bfeb0bbea080350212cbd454a1cd1251b7ca95 | Shell | 71 | 1 | bsub -n 2 -W 24:00 -R "rusage[mem=2048]" -J "comb" "python combine.py"
|
1e335c0069fdb8e4b8c1383172fba8a4d5aeb4d04b85ed3008d03a2bbd7c489c | Shell | 79 | 3 | #!/bin/bash
../tetFIM/build/tetFIM -i box.vtu -p seeds.txt -o result.bin -a 3
|
bc63f8eddd096c271081c4a7fe95341f60463bb7714b65d673e34620afce6705 | Shell | 95 | 3 | for i in {1..10}
do python 01S1_simulate_with_noise.py -grid_seed $i -noise_scale 0.05 &
done
|
a286882d6e4f100713283a0d0d4060e9b88207a2eb5a8b2b8f8227ce62b05435 | Shell | 96 | 2 | #!/bin/sh
./loreta.sh loreta-epoc.cfg AF3 F7 F3 FC5 T7/T3 P7/T5 O1 O2 P8/T6 T8/T4 FC6 F4 F8 AF4
|
d6e6fd8f67104204276558c894ddf8c7107a062a30af4f6c49a03b8d1236fc13 | Shell | 98 | 3 | for i in {1..30}
do python tempotron_script_gc_merge.py -grid_seed $i -shuffling shuffled &
done
|
ea9bd48103fedf5e7cf8317ef9e3e6e704a7857fb50c8eca1b05f87e85dc326f | Shell | 98 | 7 | #!/usr/bin/env bash
NAME="shakespeare"
SUBDIR=$1
python3 stats.py --name $NAME --subdir $SUBDIR |
fc740dc109a46e84b80b8fb1358847e458a81323103655e555939d1bda3ff5ca | Shell | 100 | 7 | p4 sSim.py
p4 sNdch2.py
p4 sCons.py
p4 sReadMcmcCheckPoints.py
p4 sPlot.py
p4 sPosteriorSamples.py
|
c0500c27b3983eee8d44870172c28db6abf235c228c2b251d7d01467b4ffc447 | Shell | 107 | 1 | wget --output-document=DB_010621_SM3.json https://www.dropbox.com/s/u7k5ztyzigs4sma/DB_010621_SM3.json?dl=1 |
da45996c99afc638fafe81ea56c27ac48caab7698fa13279e1b338304ef531d4 | Shell | 110 | 2 | wget http://pages.cs.wisc.edu/~hongyiwang/edge_case_attack/edge_case_examples.zip
unzip edge_case_examples.zip |
e9db3d247a40cf211f5edecf66fd6946e62395ea9acffe1f894a3466de0a60a8 | Shell | 118 | 3 | #!/usr/bin/env bash
wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz
gunzip SUSY.csv.gz |
5c3b61ec777fa2eeb71f745b4f4e753937ed687c2f236d8f8fd15c8f4804e47b | Shell | 120 | 1 | sbatch -n 1 --cpus-per-task=48 --time=120:00:00 --job-name="train" --mem-per-cpu=2048 --wrap="python test_058_train.py"
|
dfd4a3027197bee35e72613b84da072e6513ebda3bb624070ea4ea41e69e98e2 | Shell | 124 | 4 | #!/bin/bash
INPUT_FILE=$1
java -jar ambit-tautomers-2.0.0-SNAPSHOT.jar -f $INPUT_FILE -o ${INPUT_FILE}_tautomer.csv -t best |
1a0cbb5afac3efeddb9af57a93760828beac600ded59fe5f585ba283f83ba2ce | Shell | 126 | 2 | wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz
tar -xvf benchmark.tgz
|
4873e4c49910f19233f0032b73b8de642e7aff3b8ca4cb36a495a66a5e549dc4 | Shell | 130 | 5 | #!/bin/sh
script_path=$(readlink -f "$0")
script_dir=$(dirname "${script_path}")
"${script_dir}/../imhex" "$@" > /dev/null 2>&1 & |
caf43fda45488c4abec0057698a94d1baad9e00b43486c3202afa531c01fa60e | Shell | 132 | 4 | #!/bin/sh
wget https://www.dropbox.com/s/gzsozfftc4p7a7s/leafviz_example.zip?dl=0 -O leafviz_example.zip
unzip leafviz_example.zip
|
d38db054b3f2264998c3f1fccbd9421989743fea5fe4bacdcc0beb36723b84fe | Shell | 138 | 4 | #!/bin/sh
wget https://www.dropbox.com/s/mcvdc3isxau2oy6/annotation_codes.zip?dl=0 -O annotation_codes.zip
unzip -f annotation_codes.zip
|
f341f2f1e7baf84584924fecbb156fd03a626200b7745fa65f0908791c682ea2 | Shell | 147 | 4 | ../../bin/lumpy -P \
-msw 4 \
-tt 0 \
-sr bam_file:test.filtered_split.bam,back_distance:10,min_mapping_threshold:20,weight:1,id:test,min_clip:20
|
042ec22448cfa7e075a6a70c6adf6c4d1f3b5fdea831e86122f8c595485a19e0 | Shell | 151 | 7 | #!/usr/bin/env bash
hostname > mpi_host_file
mpirun -np 11 -hostfile ./mpi_host_file python3 ./main_base.py \
--client_number 10 \
--comm_round 3 |
fdfe7a331751cb3f3e239d4077367cb84e97208bc0b11e25c183bae7c9de7745 | Shell | 156 | 2 | #sbatch -n 1 --cpus-per-task=4 --time=4:00:00 --mem-per-cpu=4096 --wrap="python extractor.py"
bsub -n 4 -W 4:00 -R "rusage[mem=4096]" 'python extractor.py'
|
2a68cb21f4ac62a4ef2351f5f141841636868f4eb09c94c7111a3b81e05173f0 | Shell | 157 | 2 | python test.py --checkpoint checkpoints/SMILES/atom/lightning_logs/version_0/checkpoints/epoch\=???.ckpt \
--sample 1000 --decode sample --output sample.txt
|
f26ba6d56e03c9d457f72a2685760f88e5ab81db7a7a33534f7df9fe00a557f3 | Shell | 157 | 3 | #instal deptools
plotHeatmap -m Fig3a_DAR_signals_METH_vs_Sal_in_4region.gz -out matrix_Rat_test1.pdf --colorMap RdBu_r --whatToShow 'heatmap and colorbar'
|
05976f1e1782138209040b1f80502e2a953bcfd2c15ec302ba3091ab260fa817 | Shell | 158 | 7 | #!/usr/bin/env bash
hostname > mpi_host_file
mpirun -np 7 -hostfile ./mpi_host_file python3 ./main_decentralized.py \
--client_number 6 \
--comm_round 3 |
4b6268d2219c25e08095235178ed4774e78e9cf944c841d5d3e7e0b2636cdcbf | Shell | 161 | 1 | bsub -n 4 -W 24:00 -R "rusage[mem=4096,scratch=2000]" -J "q500[1-2000]" -e "./outerr/sub_init.err" -o "./outerr/sub_init.out" "./run_worker.sh \${LSB_JOBINDEX}"
|
66eae892e272d7d7d814b2d4389a75cb2db6906cdc8a938e7c845a20ef8e9bd1 | Shell | 167 | 1 | curl -s -i https://api.github.com/repos/percolator/percolator/releases -H "Accept: application/vnd.github.manifold-preview+json" | grep "\"name\"\|\"download_count\""
|
a0d4c10f42f63d8fd52733f5e604e8a267bc38e90307f4c261c0b962612aac85 | Shell | 171 | 10 | #!/usr/bin/env bash
PROCESS_NUM=$1
CFG_FILE=$2
hostname > mpi_host_file
mpirun -np $PROCESS_NUM -hostfile ./mpi_host_file python3 ./main_fedseg.py \
--cfg $CFG_FILE
|
00e4cacc2604363d54f2df61f2f30bc3fb21f6cade249601f33640ed049557c8 | Shell | 175 | 6 | #!/usr/bin/env bash
mkdir datasets
cd datasets
wget --no-check-certificate --no-proxy https://fedml.s3-us-west-1.amazonaws.com/fed_emnist.tar.bz2
tar -xvf fed_emnist.tar.bz2
|
692f710a8657f3ecbbb08935a6e9bc1a2368ece6bf628fa8a425cbbe2297f5f7 | Shell | 175 | 11 | #!/usr/bin/env bash
PROCESS_NUM=$1
CFG_FILE=$2
hostname > mpi_host_file
mpirun -np $PROCESS_NUM -hostfile ./mpi_host_file python3 ./main_fedmedgan.py \
--cfg $CFG_FILE
|
ba59ee40904e0a439731b9219e0ab080f10171389f8464b413f8f932949c00a1 | Shell | 175 | 2 | #!/bin/bash
python -c "from serenityff.charge.gnn.attention_extraction.extractor import Extractor; Extractor._clean_up(num_files=${1}, batch_size=int(${2}), sdf_file='${3}')"
|
89a8122b8e4be1e4b13f28851eb13b2a132748bc115f110d7ce7646d5d4aa65f | Shell | 177 | 6 | #!/usr/bin/env bash
mkdir datasets
cd datasets
wget --no-check-certificate --no-proxy https://fedml.s3-us-west-1.amazonaws.com/shakespeare.tar.bz2
tar -xvf shakespeare.tar.bz2
|
3765fdc130d1d3554984ca6817e4e40c5a7b1a874f5262988b2387f23487a11e | Shell | 179 | 6 | #!/usr/bin/env bash
mkdir datasets
cd datasets
wget --no-check-certificate --no-proxy https://fedml.s3-us-west-1.amazonaws.com/fed_cifar100.tar.bz2
tar -xvf fed_cifar100.tar.bz2
|
25d197e3e5d6693737d8c4d0581af7ba9957042118da47ef520b4eb5e00fc4d0 | Shell | 181 | 3 | wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip |
0dd57d463cd9d02653a106bba2892eb9092fdd5f934575bc884f5b4a05f99e19 | Shell | 182 | 9 | #!/usr/bin/env bash
DATASET=$1
hostname > mpi_host_file
mpirun -np 3 -hostfile ./mpi_host_file python3 ./main_vfl.py \
--dataset $DATASET \
--client_number 2 \
--comm_round 3 |
dd665e523ed85cf829cba7ce8f80db852e3f190040dff85ea42b2393f388d505 | Shell | 184 | 3 | # 36112
# 100171 // 100 = 1002
sbatch -n 1 --cpus-per-task=4 --time=120:00:00 --job-name="am1bcc" --array=1-1012 --mem-per-cpu=6000 --wrap="python run_am1bcc.py \$SLURM_ARRAY_TASK_ID"
|
49b618e344b0371a0ecad60dc38389a86b022410dc65d9c6850f6c949b58b65a | Shell | 190 | 6 | #!/bin/bash
source activate rdkit
#python ./app.py
gunicorn -w 4 --worker-class=gthread -b 0.0.0.0:5000 --timeout 3600 app:server --access-logfile /app/logs/access.log --max-requests 1000
|
05b8e9bd07c106f60565b46e59e6e96834367169ca67cb766de29ec2161e6e36 | Shell | 191 | 3 | #!/bin/bash
bsub -n 1 -J 'clean_up' -o logfiles/cleanup.out -e logfiles/cleanup.err -w 'done(256439411)' './cleaner.sh 9966 108 /cluster/work/igc/mlehner/test146_combine/combined_multi.sdf'
|
1142b3d86efe245ef6a662d3a990e5b7b5d3281b594c83fde85686c770b6f368 | Shell | 192 | 10 | #!/bin/bash
for f in *.png; do
is_dest=`echo $f | grep offscreen`
if [ "$is_dest" = "" ]; then
echo working on $f...
convert "$f" -sepia-tone 70% -modulate 25% "$f-offscreen"
fi
done
|
73e8016a1ef362930830a9de476cf88456f7ee98b406849766b6eac18d320230 | Shell | 192 | 1 | CUDA_VISIBLE_DEVICES=0 python -m fedml_api.model.cv.deeplab_train --backbone xception --lr 0.01 --workers 4 --epochs 2 --batch-size 2 --gpu-ids 0 --checkname deeplab-xception --eval-interval 1 |
9d416e10a6717d82225c56078c222fd0515c7ddaae15739e58b496b6e8d15836 | Shell | 195 | 3 | # 36112
# 100171 // 100 = 1002
sbatch -n 1 --cpus-per-task=4 --time=24:00:00 --job-name="4chg" --array=1-1002 --mem-per-cpu=16384 --wrap="python test_068_charge4_worker.py \$SLURM_ARRAY_TASK_ID"
|
bbef1f0ccd7a70a1c9055478bad3abe9b07fec5cae4dfc1f269e7467908eafa1 | Shell | 199 | 6 | #!/bin/bash
source activate rdkit
#python ./app.py
gunicorn -w 4 --threads=6 --worker-class=gthread -b 0.0.0.0:5000 --timeout 20 app:server --access-logfile /app/logs/access.log --max-requests 250
|
38c28839bd0867d193ff99aeea2d4babec3cfa668cfc878406357ebd471dd090 | Shell | 203 | 3 | #instal deptools
plotHeatmap -m Fig1e.ATAC_region_specific_OCR_signals_5k_200bin.gz -out region_specific_ATAC_heatmap.pdf --colorMap Blues --whatToShow 'heatmap and colorbar' --missingDataColor 'white'
|
7f18fbe19770dd9a2886e89f8e1fecb3951f8c0997b39e10ef690fba3e95d096 | Shell | 203 | 5 | # Centralized place for urls and files for all linux builders ...
# please do not change compression type in urls, since decompression is
# hardcoded in the respective buiding scripts
# Not used anymore |
d741e637486d42b4ba39e3d029d46a6001ae693e3bdbf9029559c212950bd696 | Shell | 207 | 2 | wget --no-check-certificate --no-proxy https://fedcv.s3-us-west-1.amazonaws.com/landmark/data_user_dict.zip
wget --no-check-certificate --no-proxy https://fedcv.s3-us-west-1.amazonaws.com/landmark/images.zip |
43112918523234cc857ba6cc17aee4e8123c14101e1f2be4bdd6a286ce72339f | Shell | 209 | 10 | #!/bin/bash
## run fastqc
## activate the `master` conda environment before running this script
# conda activate master
echo "Handling " $1
i=$(basename $1)
fastqc -noextract $1 -o . > ${i}_fastqc.log 2>&1
|
06cd8a45cddaedba3285f7fd313a343faf1b48212857ef3a17c9f67ab90424ff | Shell | 214 | 6 | #!/bin/bash
# Run snakemake on an SGE cluster (that uses qsub command for submission)
# Run at most 30 jobs at a time
# To run on an LSF cluster, change "qsub -V" to "bsub"
snakemake --cluster "qsub -V" --jobs 30
|
f00529b143e39e78dada5eec6fb5875d4c9f496080b9fa3f0ea93d0a794521a9 | Shell | 215 | 11 | #!/bin/bash
#SBATCH --ntasks=1
#SBATCH --time=01:00:00
#SBATCH --mem=32000
#SBATCH --gres=gpu:1
module load compiler/clang
module load devel/cuda
../tetFIM/build/tetFIM -i box.vtu -p seeds.txt -o result.bin -a 3
|
016b15e82758857ad077ae6f88a3f22b65d7edc0ff1e4bd03441fc6c8db56d31 | Shell | 217 | 9 | echo "Started worker"
jobIDX=$(($1 + 0))
echo "bash setup: ${jobIDX}"
cd ${TMPDIR}
mkdir scratch
export PSI_SCRATCH=${TMPDIR}/scratch
python ${SLURM_SUBMIT_DIR}/run_psi4_dual.py ${jobIDX}
echo "all done dual worker"
|
b41719b2273e3e7f794dddcf3eb71edbe5f2ba37818a106d2b08eb1477c615db | Shell | 222 | 5 | #!/usr/bin/env bash
wget https://archive.ics.uci.edu/ml/machine-learning-databases/00357/occupancy_data.zip
unzip occupancy_data.zip
# data format: "date","Temperature","Humidity","Light","CO2","HumidityRatio","Occupancy" |
903ab859eb5ac2b1f3652181775f4b7dcdf55f77d7ebac4518b2eb33baf694e0 | Shell | 227 | 3 | #!/bin/bash
bsub -n 1 -o logfiles/extraction.out -e logfiles/extraction.err -W 120:00 -J "ext[1-9966]" "./worker.sh ./GNN_lr_0.00010000_batch_64_seed_1_model_sd.pt /cluster/work/igc/mlehner/test156_explain/sdf_data" > id.txt
|
8cff074828f612c9053c5a51d83ef8152fcd62b3b49a40c7c737885a05f47b2b | Shell | 230 | 3 | #!/bin/bash
python -c "from serenityff.charge.gnn.attention_extraction.extractor import Extractor; Extractor._extract_hpc(model='${1}', sdf_index=int(${LSB_JOBINDEX}), scratch='${TMPDIR}')"
mv ${TMPDIR}/${LSB_JOBINDEX}.csv ${2}/.
|
7a45380f6f81853f29d36606e93e3a326a67dd1b29d08c29a1b7969c35e79bfe | Shell | 231 | 11 | #!/bin/bash
SCRIPT="$(pwd)/script/"
NSIMS=3
# Processes data
python3 ${SCRIPT}simulation/decoding_plasticity.py $NSIMS
python3 ${SCRIPT}simulation/decoding_ideal.py $NSIMS
# Plots
python3 ${SCRIPT}visualization/plot_decoding.py
|
414a053b228498a0228e50f0a5bc15e4331b29ffd28167218f4a709f09210437 | Shell | 233 | 6 | #!/bin/bash
# Run snakemake on an SGE cluster (that uses qsub command for submission)
# Run at most 20 jobs at a time
# To run on an LSF cluster, change "qsub -V" to "bsub"
snakemake --cluster "qsub -V" --jobs 20 --rerun-incomplete
|
16cff4a2dbc4acb1f691dc943bd27783f3012bc3818470813cb8b4ecef2e2de2 | Shell | 235 | 2 | python train.py --train GMTransformer/SMILE_data/SMILES_atom_train.txt --valid GMTransformer/SMILE_data/SMILES_atom_valid.txt --root_dir checkpoints/SMILES/atom/ \
--vocab_size 100 --max_len 200 --model_type blm --share_emb_prj_weight
|
6a7c1faf71d57d3d00678ae7383ecee0b6ca356088c89d1707193bb2646960bf | Shell | 244 | 11 | #!/bin/bash
set -e -x
pushd ./leafcutter
# R refuses to build packages that mark themselves as Priority: Recommended
mv DESCRIPTION DESCRIPTION.old
grep -v '^Priority: ' DESCRIPTION.old > DESCRIPTION
$R CMD INSTALL --preclean --build .
popd
|
e1d085a46598c4910355279342c34a92bf7a173a5ebb207860af01c215c3176f | Shell | 245 | 10 | #!/bin/bash
# Download common models
python -c "
from utils.google_utils import *;
attempt_download('weights/yolov5s.pt');
attempt_download('weights/yolov5m.pt');
attempt_download('weights/yolov5l.pt');
attempt_download('weights/yolov5x.pt')
"
|
0302f7973eca73bb1706b5e8553944e4a365de21eb6d91f1b9ea5463e3cb217e | Shell | 249 | 7 | set -ex
python save_syn_nuclei.py --dataroot /share_hd1/db/Nuclei/for_seg --name nuclei_asyndgan_withoutL1 --model pix2pix --netG resnet_9blocks --direction AtoB --dataset_mode nuclei --epoch 300 --results_dir results/nuclei_asyndgan_withoutL1
|
0d6f19fd53b4e24817e4a19373b06ee83548327e50f86c96b5cfa935c853bba2 | Shell | 260 | 8 | #!/usr/bin/env bash
# Configure Anaconda Client To Upload Successful Builds
anaconda login
conda config --set anaconda_upload yes
# Run Builds Across Matrix Specified In Config
conda-build . -c defaults -c intel --variant-config-file conda_build_config.yaml
|
d500172d2a76a699001fc39d44e812f6e4d1ba4ec4c1b5a4c601b0acb3ee9dc2 | Shell | 263 | 7 | #!/bin/bash
source /home/dell/anaconda3/etc/profile.d/conda.sh
conda activate py36
python train.py -type ACP -use_PSSM True -train_fasta ./data/ACPmain.txt -train_pssm ./data/pssm_acpmain/ -test_fasta ./data/ACPmaintest.txt -test_pssm ./data/pssm_acpmaintest/
|
3ac33356debbb28e5a7568c03a9e303997dd0714893adeb5cf10c4be66cc58a1 | Shell | 266 | 5 | #python train_nuclei_seg.py -c config_files/config_nuclei_seg_asdgan_server1.json -d 0
python train_nuclei_seg.py -c config_files/config_nuclei_seg_asdgan_server1_x5.json -d 3
python train_nuclei_seg.py -c config_files/config_nuclei_seg_fedmedgan_server2.json -d 0 |
5d31d68b12f91ff78270a0a8cf70438420cc15ca1486fa8514f4b227947a4fa9 | Shell | 268 | 19 | #!/usr/bin/env bash
WORKER_NUM=$1
MPI_HOST_FILE=$2
PYTHON=$3
ARGS=$4
PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM
echo $MPI_HOST_FILE
mpirun -np $PROCESS_NUM -hostfile ./$MPI_HOST_FILE $PYTHON ./main.py \
--client_num_per_round $WORKER_NUM \
$ARGS
|
c266fdde4f496b3e33aae89205dbe66bb4de1674541a91082263bd2d87c5eaeb | Shell | 277 | 7 | set -ex
python train.py --dataroot /share_hd1/db/BRATS/2018/tumor_size_split_10 --name brats_asyndgan_verticle_flip --model asyndgan --netG resnet_9blocks --direction AtoB --lambda_L1 100 --dataset_mode brats_split --pool_size 0 --gpu_ids 1 --batch_size 1 --num_threads 0
|
65e5b09e63a287923a1d04fa740ad6abd396ff9d14fe8aefc2ea71bb02f76bf0 | Shell | 285 | 6 |
for i in {10..200..10}
do
python save_syn.py --cfg exp_2.yml --batch_size 1 --save_dir ./run/path/dadgan/experiment_0 --epoch ${i} --GPUid 2 --save_data
mv ./results/dadgan/test_${i}/path_resnet_9blocks_epoch${i}_experiment_0.h5 ../../../../datasets/fedgan_syn/path_h5_all/
done
|
1a3cd228dd03561e659968f620ccbb50edd2706a86784b80ad9174c85ed19c9e | Shell | 287 | 6 |
for i in {10..200..10}
do
python save_syn.py --cfg n_exp_2.yml --batch_size 1 --save_dir ./run/path/asdgan/experiment_0 --epoch ${i} --GPUid 2 --save_data
mv ./results/asdgan/test_${i}/path_resnet_9blocks_epoch${i}_experiment_0.h5 ../../../../datasets/asdgan_syn/path_h5_all/
done
|
fc21d87425caca9d53708f98cd127733b3ff022128890b665d9cf746e3fdb5a7 | Shell | 287 | 5 | for i in {10..200..10}
do
python save_syn.py --cfg exp_1.yml --batch_size 1 --save_dir ./run/heart/dadgan/experiment_0 --epoch ${i} --GPUid 3 --save_data
mv ./results/dadgan/test_${i}/heart_resnet_9blocks_epoch${i}_experiment_0.h5 ../../../../datasets/fedgan_syn/heart_h5_all/
done
|
779bf66e2cdd313c7d19b9373cf9cacad4f32db6179b17b86be0bdec7d2c1565 | Shell | 290 | 6 |
for i in {10..200..10}
do
python save_syn.py --cfg n_exp_1.yml --batch_size 1 --save_dir ./run/heart/asdgan/experiment_0 --epoch ${i} --GPUid 2 --save_data
mv ./results/asdgan/test_${i}/heart_resnet_9blocks_epoch${i}_experiment_0.h5 ../../../../datasets/asdgan_syn/heart_h5_all/
done
|
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