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e631019f65a0d35617e18036598c01dc42d00fda4ebebf0394b37ca92a4660d7
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#!/bin/sh cat ORI/build5_locus.gff3 > genes.gff cat ORI/build5_genes.gff3 | ./mRnaAddParent.pl >> genes.gff echo "###" >> genes.gff echo "##FASTA" >> genes.gff cat ORI/IRGSPb5.fa >> genes.gff
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cd toy_data/tomograms/Tomo1/ unzip membranes.zip wget https://ftp.ebi.ac.uk/pub/databases/emdb/structures/EMD-10780/map/emd_10780.map.gz gzip -d emd_10780.map.gz mv emd_10780.map Tomo1_bin4.map
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python launcher_scripts/run_from_config.py configs/glacier/joint_train_nist20.yaml # contrastive finetune python launcher_scripts/run_from_config.py configs/glacier/joint_contr_finetune_nist20.yaml
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. data_scripts/dag/run_magma.sh . run_scripts/GLACIER/add_inten.sh . run_scripts/GLACIER/01_train_joint.sh python run_scripts/GLACIER/02_predict_inten.py python run_scripts/GLACIER/03_run_retrieval.py
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#!/bin/bash -eu set -o pipefail rm -rvf site/ mkdocs build cp -vf src/docs/index.html site/ cd ~/workspace/SnpEff rm -rvf docs/* cp -rvf ~/snpEff/site/* docs/ ./git/commit 'Documentation updated'
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set -e ./get_gm12878.sh 10000 0 ./get_gm12878_inter.sh 1000000 python ../minimds_inter.py --partitioned -l 10 -o hic_data/GM12878_combined hic_data/GM12878_combined 1000000 10000 python3.6 fig10.py
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#!/bin/bash DIR=$1 if test "x$CMD" = "x" then CMD="/usr/bin/python3 ./muller.py" fi valgrind --tool=verrou --exclude=exclude.ex --rounding-mode=random --vr-instr-scalar=yes ${CMD}> $DIR/res.dat
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#!/bin/bash # DATADIR="/home/mcarilli/Desktop/pt18data/apex_stale/examples/imagenet/bare_metal_train_val/" # DATADIR="/opt/home/apex/examples/imagenet/" cp ../common/* . bash run_test.sh single_gpu $1
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#!/bin/bash lang=$1 model_path=$2 tasks=arc_${lang},hellaswag_${lang},mmlu_${lang} device=cuda python main.py \ --tasks=${tasks} \ --model_args pretrained=${model_path} \ --device=${device}
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#!/bin/bash module load bedtools l="../../../loops" # neither anchors overlap, bedtools pairtopair -type neither -a ../w4_fx_nx_linked.bedpe -b $l/w0_mustache_10kb.bedpe > w4_fx_nx_no_w0_loops.bedpe
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printf "# date: $(date)\n" > AUTHORS printf "# this file is auto-generated by scripts/gen-authors.sh\n\n" >> AUTHORS git log --format='%an <%ae>' --reverse --date=short v2 | awk '!seen[$0]++' | sort >> AUTHORS
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#!/bin/bash #MSUB -l nodes=1:ppn=16:gpu,walltime=2:00:00:00 #MSUB -N test #MSUB -v PYTHON=devel/python_intel/3.6 module load $PYTHON source activate tf2-gpu cd hao/DE_HD_test/ python 02_keras_models_single.py
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#!/bin/bash set -xe # define the paths DEST=$1 path_output=${DEST}/hg38.phyloP100way.bw rsync -avz --progress rsync://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP100way/hg38.phyloP100way.bw ${path_output}
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#!/bin/bash rm -rf out_* *_release-* morphology-workflows Initialize --source-database NeuroMorpho morphology-workflows Fetch morphology-workflows Curate morphology-workflows Annotate morphology-workflows Repair
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#!/bin/bash MAXNUM=$1 F_NAME=$2 F_TYPE=$3 SCRIPT_DIR=$(dirname $0) exec > missing.txt for ((i=0 ; i<=${MAXNUM} ; i++)) ; do if [ ! -f "${SCRIPT_DIR%/}/${F_NAME}_$i.${F_TYPE}" ] ; then echo $i fi done
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if [ ! -e ChromSDE ] then wget http://biogpu.ddns.comp.nus.edu.sg/~chipseq/ChromSDE/ChromSDE_program2.2.zip unzip ChromSDE_program2.2.zip rm ChromSDE_program2.2.zip mv program ChromSDE mv *.m ChromSDE fi
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python launcher_scripts/run_from_config.py configs/iceberg/msg_all/dag_inten_train_msg_all_iceberg.yaml python launcher_scripts/run_from_config.py configs/iceberg/msg_all/dag_inten_contr_finetune_msg_all_iceberg.yaml
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PATHTO=/data1/czhao/xiaodong/FOXA2/ROSE PYTHONPATH=$PATHTO/lib export PYTHONPATH export PATH=$PATH:$PATHTO/bin ROSE_main.py -g HG19 -i m1407_m1390asCtrl_peaks.narrowPeak.gff -r m1407_sorted.bam -o m1407_m1390asCtrl_SE
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#!/bin/bash ./path/to/EVE_release/EVEmodel -S -n 6496 -t ../data/8_trees/hypot_ALVAREZ_TreePruned_organized.nh -i ../data/9_indivs/hypot_indivs_ALV -d ../data/7_orthotpms/adult_nopap_hypothALV -f adulthypot_DIVALV -v 10
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oat decorate raw dec -p pos -tsSRh & sleep 1 oat posigen rand2D pos -n 1000 & time oat frameserve test raw -f $1 -c test.toml test oat decorate raw dec -tsS & sleep 1 time oat frameserve test raw -f $1 -c test.toml test
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python launcher_scripts/run_from_config.py configs/iceberg/nist23/dag_inten_train_nist23.yaml # contrastive finetune python launcher_scripts/run_from_config.py configs/iceberg/nist23/dag_inten_contr_finetune_nist23.yaml
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#python launcher_scripts/run_from_config.py configs/iceberg/nist20/dag_inten_train_nist20.yaml # contrastive finetune python launcher_scripts/run_from_config.py configs/iceberg/nist20/dag_inten_contr_finetune_nist20.yaml
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#!/bin/bash set -e ANALYSIS=$1 MEM=$2 FILES=${*:3} mkdir -p log for F in $FILES do qsub -l h_vmem=$MEM -V -j y -N `basename $ANALYSIS`.`basename $F` -o log -cwd -b y $ANALYSIS $F done echo success.`basename $ANALYSIS`
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#!/usr/bin/env bash set -e FILE=$1 name=$(basename ${FILE}) ref=${name:0:4} wget http://hgdownload.cse.ucsc.edu/goldenPath/${ref}/liftOver/${name} \ -O ${FILE} # Ensure that the timestamp is updated for make touch ${FILE}
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Shell
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#!/bin/bash DIR=$1 valgrind --tool=verrou --exclude=exclude.ex --rounding-mode=random /usr/bin/python3 ./muller.py> $DIR/res.dat valgrind --tool=verrou --exclude=exclude.ex --rounding-mode=random ./muller > $DIR/res2.dat
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for bp in 00000500K 00001000K 00002000K 00005000K 00010000K 00020000K 00050000K 00100000K 00200000K do mkdir -p $bp cd $bp ln -s $PWD/../../../varKoder/fam_gen_sp_multi/intermediate_files/split_fastqs/*${bp}.fq.gz ./ cd .. done
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#!/bin/sh STAR --runMode genomeGenerate \ --genomeDir "Index-folder" \ --sjdbGTFfile "GTF-file" \ --genomeFastaFiles "DNA-toplevel-FASTA" \ --runThreadN 28 \ --outFileNamePrefix "Index-folder/Log-Folder"
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#!/bin/sh DIR=$HOME/snpEff/ java -Xmx3G \ -classpath "$DIR/lib/charts4j-1.2.jar:$DIR/lib/flanagan.jar:$DIR/lib/freemarker.jar:$DIR/lib/junit.jar:$DIR/lib/trove-2.1.0.jar:$DIR" \ ca.mcgill.mcb.pcingola.PromoterSequences \ $*
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# Download CID-SMILES wget https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/Extras/CID-SMILES.gz # Unzip gunzip CID-SMILES.gz mkdir data/retrieval mkdir data/retrieval/pubchem mv CID-SMILES data/retrieval/pubchem/pubchem_full.txt
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#!/bin/bash !wget -O t2d_bbj.txt.gz http://jenger.riken.jp/14/ !wget -O bmi_male_bbj.txt.gz http://jenger.riken.jp/2analysisresult_qtl_download/ !wget -O bmi_female_bbj.txt.gz http://jenger.riken.jp/4analysisresult_qtl_download/
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python antifold/main.py \ --pdb_file data/antibody_antigen/3hfm.pdb \ --heavy_chain H \ --light_chain L \ --antigen_chain Y \ --num_seq_per_target 10 \ --sampling_temp "0.2" \ --regions "CDR1 CDR2 CDR3"
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wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/tiny_nerf_data.npz mkdir -p data cd data wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/nerf_example_data.zip unzip nerf_example_data.zip cd ..
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#!/bin/bash set -e verificarlo-c test.c -o test --verbose &> compile.log if ! grep "Instrumenting" compile.log; then echo "New pass manager args not recognized. Test failed." exit 1 fi VFC_BACKENDS="libinterflop_mca.so" ./test
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python antifold/main.py \ --pdb_file data/nanobody/nanobody_antigen_9hzj_imgt.pdb \ --heavy_chain A \ --antigen_chain B \ --nanobody_mode \ --num_seq_per_target 10 \ --sampling_temp "0.2" \ --regions "CDRH"
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#!/bin/sh set -e export VFC_BACKENDS_SILENT_LOAD="True" export VFC_BACKENDS_LOGGER="False" vfc_ci test if ls *.vfcrun.h5; then echo "Run file found, SUCCESS" exit 0 else echo "Run file not found, FAILURE" exit 1 fi
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#!/usr/bin/env bash set -e input=$1 field=$2 C=1 header=`zcat ${input} | head -n 1` for i in ${header} do if [[ $i == "${field}" ]] then break else C=$(( $C + 1 )) fi done zcat ${input} | sed -n '2p' | cut -f$C | xargs printf
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#!/bin/bash dropbox=../../dropbox data=schneider50k tpl=default save_dir=$dropbox/cooked_$data/tpl-$tpl python get_canonical_smarts.py \ -dropbox $dropbox \ -data_name $data \ -save_dir $save_dir \ -tpl_name $tpl \
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#!/bin/bash ./path/to/EVE_release/EVEmodel -S -n 6496 -t ../data/8_trees/hypot_ALVAREZ_TreePruned_organized_DO.nh -i ../data/9_indivs/hypot_indivs_ALV_DO -d ../data/7_orthotpms/adult_nopap_hypothALV_DO -f adulthypot_DIVALVdropout -v 10
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#!/bin/sh zcat Araly1_GeneModels_FilteredModels6.gff.gz \ | sed "s/name/gene_id/" \ | sed "s/transcriptId \(.*\)/transcript_id \"\1\";/" \ | sed "s/exonNumber \(.*\)/exon_number \"\1\";/" \ > g1 ./convert.pl g1 > genes.gtf rm -vf g1
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#!/bin/bash dropbox=../../dropbox data_name=schneider50k tpl_name=default_tpl python graph_feat.py \ -dropbox $dropbox \ -data_name $data_name \ -tpl_name $tpl_name \ -f_atoms $dropbox/cooked_$data_name/atom_list.txt \ $@
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#!/bin/bash set -e export VFC_BACKENDS="libinterflop_mca.so --precision-binary64=53" export VFC_BACKENDS_LOGGER="False" verificarlo-c --function solve -O0 linear.c -o test echo "x y" >output for i in $(seq 1 100); do ./test >>output done
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#!/bin/sh # clean /tmp dir only if there is no rsync job running at the moment # run from crontab RUNNING=`ps --no-headers -Crsync | wc -l` if [ ${RUNNING} -gt 2 ]; then echo rsync is running, not cleaning tmp exit 0 fi rm -f /tmp/*
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#!/bin/bash script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" PATH_TO_JULIA=$script_dir/venv/julia-1.6.7/bin/julia source $script_dir/venv/bin/activate python $script_dir/onedcellsim/simulators/multistability/viewer.py ${PATH_TO_JULIA}
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#!/bin/bash set -e set -o pipefail # prepare cohort-specific histologies file Rscript -e "rmarkdown::render('01-add_histologies.Rmd')" # Get summary stats Rscript --vanilla 02-summary_stats.R # Generate alluvial plot Rscript 03-alluvial_plot.R
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#!/usr/bin/env bash CONFIG=$1 GPUS=$2 PORT=${PORT:-29500} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
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#!/bin/bash source ~/.bashrc echo "running docker-entrypoint.sh" conda activate container echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS echo "printed TPU info" export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" exec "$@"#!/bin/bash
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#!/bin/sh OS_TYPE=`uname` mkdir -p bin mkdir -p bin/$OS_TYPE mkdir -p obj mkdir -p obj/$OS_TYPE echo cp makefile.linux_mac makefile cp makefile.linux_mac makefile echo cp src/makefile.$OS_TYPE src/makefile cp src/makefile.$OS_TYPE src/makefile
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#!/bin/bash module load homer/4.10 mkdir results for i in *bedpe do j=${i%%_*} cp ${i} results/${j}.bedpe done cd results merge2Dbed.pl 93.bedpe 1451.bedpe 1452.bedpe nci.bedpe \ -res 10000 -loop -prefix nepc_gain > output_nepc.bedpe 2>&1
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#!/bin/bash rm -Rf *~ input.txt tmp.* log.error mpfr.txt test.log vprec.txt run_parallel compute_vprec_rounding test_vprec_rounding test_vprec_rounding.log .vfcwrapper* .test* *.o *.ll test compute_mpfr_rounding compute_vprec_rounding_{float,double}
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#!/bin/bash module load homer/4.10 mkdir results for i in *bedpe do j=${i%%_*} cp ${i} results/${j}.bedpe done cd results merge2Dbed.pl 35CR.bedpe 70CR.bedpe 77CR.bedpe 147.bedpe \ -res 10000 -loop -prefix crpc_gain > output_crpc.bedpe 2>&1
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set -e source .venv/bin/activate for f in $(find examples/nnx_toy_examples -name "*.py" -maxdepth 1); do echo -e "\n---------------------------------" echo "$f" echo "---------------------------------" MPLBACKEND=Agg python "$f" done
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#!/bin/sh export MACOSX_DEPLOYMENT_TARGET=10.14 python -m pip install --upgrade pip pip install setuptools wheel twine auditwheel python3 setup.py build bdist_wheel --plat-name macosx_10_14_x86_64 --dist-dir wheel twine upload --skip-existing wheel/*
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#!/bin/sh # Download data wget http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/cpgIslandExt.txt.gz # Annotate Shores & Shelves gunzip -c cpgIslandExt.txt | cut -f 2- | ./cgShore.pl > cpgIslands_Shores_Shelf.bed gzip cpgIslands_Shores_Shelf.bed
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#!/usr/bin/env bash CONFIG=$1 GPUS=$2 PORT=${PORT:-29500} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/mot_param_search.py $CONFIG --launcher pytorch ${@:3}
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#!/bin/bash ./path/to/EVE_release/EVEmodel -O -o 4 -n 6496 -t ../data/8_trees/hypot_ALVAREZ_TreePruned_organized.nh -i ../data/9_indivs/hypot_indivs_ALV -d ../data/7_orthotpms/adult_nopap_hypothALV -f adult_hypothal_BS2ALV -v 10 > adult_hypoth_full2ALV.txt
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FOLDER=$1 tmpdir=`mktemp -d` wget -c https://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam35.0/Pfam-A.clans.tsv.gz \ -O ${tmpdir}/pfam_info.name.tsv.gz gzip -d ${tmpdir}/pfam_info.name.tsv.gz mv ${tmpdir}/pfam_info.name.tsv ${FOLDER}/pfam_info.name.tsv
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#!/bin/sh cd feature_decoding python featdec_eval.py config/vggsound_fmriprep_rep4_500voxel_vggishish_allunits_fastl2lir_alpha100.yaml python featdec_eval_attention.py config/vggsound_attention_fmriprep_rep4_500voxel_vggishish_allunits_fastl2lir_alpha100.yaml
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#!/bin/bash # This script is just a convenience, it activates the virtual # environment and then invokes the `callicog` command, # passing through any arguments supplied. source $HOME/callicog/.env && \ source $CALLICOG_VENV/bin/activate && \ callicog "$@"
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#!/bin/bash cd $(dirname $0) for parc in "aparc" "schaefer-100" "schaefer-200" "schaefer-400"; do for measure in "strength" "length"; do echo "Processing $parc $measure..." uv run prep_sc_sub.py --parc $parc --measure $measure done done
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#!/bin/bash # verify umask for local hgwdev users thisMachine=`uname -n` if [ "${thisMachine}" = "hgwdev" ]; then uMask=`umask` if [ "${uMask}" != "0002" ]; then echo "WARNING: local hgwdev users should run umask 002, yours is '${uMask}'" 1>&2 fi fi
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#!/bin/bash conda create --force -n idelucs python=3.11 pip setuptools conda-forge::parallel conda-forge::exiftool conda activate idelucs git clone https://github.com/millanp95/iDeLUCS.git iDeLUCS cd iDeLUCS python setup.py build_ext --inplace pip install -e .
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#!/bin/bash #SBATCH --account=p32032 #SBATCH --partition=long #SBATCH --nodes=1 #SBATCH --ntasks-per-node=64 #SBATCH --time=168:00:00 #SBATCH --mem=243g #SBATCH --job-name=diya_recalculate module load matlab/r2022b matlab -singleCompThread -batch "ccm_qscript"
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#!/bin/sh # If you're me (sourceforge: ctrapnell), use this to push the # contents of this directory to the appropriate place on sourceforge. # Changes will be instantaneous on http://tophat-bio.sf.net. scp -r * ctrapnell@shell.sourceforge.net:~/tophat-bio/htdocs
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#!/usr/bin/env bash FOLDER=$1 tmpdir=`mktemp -d` wget https://datasets.genepattern.org/data/module_support_files/MutPanning/Hg19.zip \ -O ${tmpdir}/mutpanning.zip unzip -d ${tmpdir} ${tmpdir}/mutpanning.zip mv ${tmpdir}/Hg19 ${FOLDER}/ chmod -R g+rx ${FOLDER}/Hg19
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#!/bin/sh # cp Crubella_183.fa.gz ../genomes/crubella_183.fa.gz # gunzip -c ORI/Crubella_183_gene.gff3.gz > genes.gff gunzip -c ORI/Crubella_183_cds.fa | sed "s/>.*PACid:/>PAC:/" > cds.fa gunzip -c ORI/Crubella_183_protein.fa | sed "s/>.*PACid:/>PAC:/" > protein.fa
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#!/bin/sh TEST_CONFIG_LOCATION=$PWD/conf/transcode_job_configs.yml # The job script traverses parent directories to find the scripts/ dir, which # is why I'm not running as a module here python $PWD/src/aind_data_transfer/jobs/transcode_job.py -c $TEST_CONFIG_LOCATION
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export MODULEPATH=/opt/software/easybuild-AMD/modules/all:/etc/modulefiles:/usr/share/modulefiles:/opt/software/modulefiles:/usr/share/modulefiles/Linux:/usr/share/modulefiles/Core:/usr/share/lmod/lmod/modulefiles/Core module purge module load Python/3.11.3-GCCcore-12.3.0
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#!/usr/bin/env bash CONFIG=$1 CHECKPOINT=$2 GPUS=$3 PORT=${PORT:-29500} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
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#!/bin/sh PYTHON_BIN=`readlink -f $1` OUT_FILE=$2 echo "$PYTHON_BIN" echo "∗" `ldd ${PYTHON_BIN} | grep libm | cut -f 3 -d " " | xargs readlink -f ` > ${OUT_FILE} echo "_Py_HashDouble $PYTHON_BIN" >> ${OUT_FILE} echo "_Py_dg_strtod $PYTHON_BIN" >> ${OUT_FILE}
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cd "$(dirname "$0")" cd ../features python mol_tree.py # takes about 30 min cd ../src . ./cross_run.sh 0 1 # training on the whole dataset will take about 10 days cd ../outputs rsync -av * ../ensemble/model_pred/new_run cd ../ensemble python ensemble.py
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#!/bin/bash # run from crontab to update the browser # only update browser if the "noAutoUpdate" flagfile does not exist if [ -f /root/noAutoUpdate ]; then exit; fi # sleep a little bit, so we don't hammer the rsync server sleep $[RANDOM%30] /root/updateBrowser.sh
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#!/usr/bin/env bash FOLDER=$1 tmpdir=`mktemp -d` wget -c http://genetics.bwh.harvard.edu/cbase/CBaSE_v1.1.zip \ -O ${tmpdir}/cbase.zip unzip -d ${tmpdir} ${tmpdir}/cbase.zip mv ${tmpdir}/CBaSE_v1.1/Auxiliary/*.gz ${FOLDER}/ mv ${tmpdir}/CBaSE_v1.1/Auxiliary/*.txt ${FOLDER}/
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#!/bin/sh # Download files #wget ftp://ftp.plantgdb.org/download/Genomes/PeGDB/Ppersica_139.fa.gz #wget ftp://ftp.plantgdb.org/download/Genomes/PeGDB/Ppersica_139_gene.gff3.gz ( zcat Ppersica_139_gene.gff3.gz ; echo "###" ; echo "##FASTA" ; zcat Ppersica_139.fa.gz ) > genes.gff
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#!sh rm -rf ~/Desktop/Homer3* rm -rf ~/Desktop/SubjDataSample perl ~/Downloads/homer3_install/makefinalapp.pl ~/homer3/run_Homer3.sh ~/Desktop/Homer3.$1 ln -s ~/homer3/SubjDataSample ~/Desktop/SubjDataSample chmod 755 ~/Desktop/Homer3.$1 chmod 755 ~/Desktop/SubjDataSample exit
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#!/bin/bash Rscript 1_preprocess_HiC.R $1 $2 $5 Rscript 2_generate_fragment_data.R $2 $5 Rscript 3_generate_fragment_DNase.R $2 $5 Rscript 4_generate_DNase_profile.R $2/Genomic_fragment $3 Rscript 5_impute_DNase_dist.R $2/Genomic_fragment Rscript 6_generate_backbone_data.R $4 $2 $5
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#!/usr/bin/env bash DOWNLOAD_DIR=$1 DATA_ROOT=$2 tar -xvf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2012/raw/VOCtrainval_11-May-2012.tar -C $DATA_ROOT tar -xvf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2012/raw/VOC2012test.tar -C $DATA_ROOT rm -rf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2012
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#!/bin/sh cd feature_decoding python featdec_fastl2lir_predict.py config/vggsound_fmriprep_rep4_500voxel_vggishish_allunits_fastl2lir_alpha100.yaml python featdec_fastl2lir_predict_attention.py config/vggsound_attention_fmriprep_rep4_500voxel_vggishish_allunits_fastl2lir_alpha100.yaml
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#!/bin/bash git pull cp ./docs/index.md ./README.md git add ./README.md git commit -m "updated README" git push pip install zensical zensical build --clean # Note: For GitHub Pages deployment, use GitHub Actions workflow instead # or manually push the site/ directory to gh-pages branch
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#!/bin/bash # Installs pre-commit hook that ensures attempts to commit files that are # larger than $limit to your _local_ repo fail, with a helpful error message. cd "$(dirname "$0")" # location of this script cd .git/hooks/ ln -sf ../../pre-commit-prevent-large-files.sh pre-commit
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#!sh rm -rf ~/Desktop/AtlasViewerGUI* rm -rf ~/Desktop/Test perl ~/Downloads/atlasviewer_install/makefinalapp.pl ~/atlasviewer/run_AtlasViewerGUI.sh ~/Desktop/AtlasViewerGUI.$1 ln -s ~/atlasviewer/Test ~/Desktop/Test chmod 755 ~/Desktop/AtlasViewerGUI.$1 chmod 755 ~/Desktop/Test exit
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#!/bin/bash dropbox=../../dropbox data=schneider50k num_cores=4 save_dir=$dropbox/cooked_${data} if [ ! -e $save_dir ]; then mkdir -p $save_dir fi python build_raw_template.py \ -dropbox $dropbox \ -data_name $data \ -save_dir $save_dir \ -num_cores $num_cores \
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#!/bin/sh #cp ORI/Lusitatissimum_200.fa ../genomes/lusitatissimum_90.fa #cp ORI/Lusitatissimum_200_gene_exons.gff3 ../genes.gff cat ORI/Lusitatissimum_200_protein.fa \ | sed "s/>.*PACid:/>PAC:/" \ > protein.fa cat ORI/Lusitatissimum_200_cds.fa \ | sed "s/>.*PACid:/>PAC:/" \ > cds.fa
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292
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#!/bin/bash DIR=$1 if [ "x" = "x$PARAM" ] ; then echo "sans PARAM" valgrind --tool=verrou --exclude=exclude.ex --rounding-mode=random ./sqrt > $DIR/res.dat else echo "avec PARAM" valgrind --tool=verrou --exclude=exclude.ex --rounding-mode=random ./sqrt $DIR/$PARAM fi
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#!/bin/bash for FILE in converted/*; do model_name=`basename $FILE` transformers-cli repo create $model_name -y git clone https://huggingface.co/Helsinki-NLP/$model_name mv $FILE/* $model_name/ cd $model_name git add . && git commit -m "initial commit" git push cd .. done
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#!/bin/sh # If you're me (sourceforge: ctrapnell), use this to push the # contents of this directory to the appropriate place on sourceforge. # Changes will be instantaneous on http://tophat-fusion.sf.net. scp fusion_index_redirect.html forephilo@shell.sourceforge.net:~/tophat-fusion/htdocs
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#!/bin/bash set -e pwd; date source ~/.bash_profile source /etc/profile.d/modules.sh module purge module load mpi/mpich-3.2-x86_64 mpiexec -np $(( SLURM_NTASKS + 2 )) singularity exec {sif_path} python -m aind_data_transfer.jobs.zarr_upload_job --json-args '{json_args}' echo "Done" date
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conda install -c anaconda jupyter numpy pandas scikit-learn scipy seaborn pymongo -y conda install -c conda-forge matplotlib einops tslearn imageio hyperopt scikit-posthocs tsfresh -y conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -y conda install -c pytorch captum -y
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#!/bin/bash # Record video to test two color tracking on maze. # JPN and AC # 2015-06 # MWL@MIT # Path to configuration files CONFIG_PATH="./config.toml" oat view raw & sleep 0.1 oat record -i raw -f ./ -n two_color_test -d -F 30 & sleep 0.1 oat frameserve gige raw -c "$CONFIG_PATH" -k gige
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294
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#!/bin/bash awk '{print $4}' ../analysis/results_dropout/indivBetaMLsadulthypot_DIVALVdropout.res | sed '/^$/d' | awk '{printf "%8.6f\n", $1}' > ../analysis/results_dropout/p paste ../analysis/results_dropout/results ../analysis/results_dropout/p > ../analysis/results_dropout/resultsDIV_beta
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295
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#!/bin/sh # # Test, only first columns # #zcat GTEx_Analysis_RNA-seq_RNA-SeQCv1.1.8_gene_rpkm__Pilot_2013_01_31.gct.gz \ # # | cut -f 1-20 \ # # | grep -e ^Name -e ^ENS \ # # > gtex.txt cat GTEx_Analysis_2014-01-17_RNA-seq_RNA-SeQCv1.1.8_gene_rpkm.gct \ | grep -e ^Name -e ^ENS \ > gtex.txt
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#!/usr/bin/env bash DOWNLOAD_DIR=$1 DATA_ROOT=$2 tar -xvf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2007/raw/VOCtrainval_06-Nov-2007.tar -C $DATA_ROOT tar -xvf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2007/raw/VOCtestnoimgs_06-Nov-2007.tar -C $DATA_ROOT rm -rf $DOWNLOAD_DIR/OpenDataLab___PASCAL_VOC2007
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300
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#!/bin/bash set -eu python3.8 regions.py -pd /media/athena-admin/FastSSD1/Athena/francesca_202203/out_filter647_01/*_P_*/align -ld /media/athena-admin/FastSSD1/Athena/francesca_202203/out_filter647_01/*LD*/align -hd /media/athena-admin/FastSSD1/Athena/francesca_202203/out_filter647_01/*_HD*/align
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#!/bin/bash dropbox=../../dropbox data=schneider50k version=default save_dir=$dropbox/cooked_${data}/tpl-$version if [ ! -e $save_dir ]; then mkdir -p $save_dir fi python filter_template.py \ -dropbox $dropbox \ -data_name $data \ -tpl_name $version \ -save_dir $save_dir \
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#!/bin/bash # Unzip source code unzip src/tetgen/source/tetgen1.5.1-beta1.zip; # Build source code cd tetgen1.5.1-beta1; mkdir build cd build cmake .. make # Move executable to the src file. mv tetgen1.5.1-beta1/build/tetgen src/tetgen/lin64/tetgen; # Remove build files. rm -rf tetgen1.5.1-beta1;
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BIN=stencil FMA="-FMA" BINO3DOUBLE=$BIN-O3-DOUBLE${FMA} BINO0DOUBLE=$BIN-O0-DOUBLE${FMA} BINO3FLOAT=$BIN-O3-FLOAT${FMA} BINO0FLOAT=$BIN-O0-FLOAT${FMA} BINS="./$BINO3DOUBLE ./$BINO0DOUBLE ./$BINO3FLOAT ./$BINO0FLOAT" OPT="--scale=1 1" for i in $BINS do echo $i $PREFIX $i $OPT done;
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export CUDA_ENABLE_COREDUMP_ON_EXCEPTION=1 export CUDA_COREDUMP_SHOW_PROGRESS=1 export CUDA_COREDUMP_GENERATION_FLAGS='skip_nonrelocated_elf_images,skip_global_memory,skip_shared_memory,skip_local_memory' # Optional(For containers) # export CUDA_COREDUMP_FILE="/persistent_dir/cuda_coredump_%h.%p.%t"
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#!/bin/bash awk '{print $4}' ../analysis/results_diversity/indivBetaMLparamsadulthypot_DIVALV.res | sed '/^$/d' | awk '{printf "%8.6f\n", $1}' > ../analysis/results_diversity/p paste ../analysis/results_diversity/results ../analysis/results_diversity/p > ../analysis/results_diversity/resultsDIV_beta
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302
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#clean up old dists rm dist/* # python setup.py sdist python2 setup.py bdist_wheel python3 setup.py bdist_wheel python3 setup.py bdist_wheel sdist # python setup.py register -r pypitest # twine upload -r pypitest dist/gputools* #python setup.py register # twine upload dist/*whl twine upload dist/*
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#!/bin/bash # Obviously, put whatever IP and Port in there that you want to use case "$1" in run) oat posisock udp pos -h 10.121.43.222 -p 5555 & sleep 1 oat positest rand2D pos -r 100 ;; clean) oat clean pos ;; *) echo $"Usage: $0 {run|clean}" exit 1 esac
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max_epochs=50 lora_type=trunk batch_size=128 for i in $(seq 27 -1 1); do python -u train_twitter_tlora_withhead.py \ --lora_checkpoint_dir .checkpoints/lora/twitter/with_head/$lora_type/e$max_epochs/$i \ --lora_type $lora_type \ --max_epochs $max_epochs \ --batch_size $batch_size done