From c0fb156d27120e48824121e1dd8a0e164759a8ae Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 6 Nov 2023 05:24:25 +0000 Subject: [PATCH] Add changes --- _data/containers.yaml | 2710 +- _data/repos.yml | 3425 +- .../container/recipes/Singularity.template | 38 + .../recipes/nas/Singularity.TabularBenchmarks | 30 + .../recipes/nas/Singularity.nasbench_101 | 26 + .../recipes/nas/Singularity.nasbench_1shot1 | 30 + .../recipes/nas/Singularity.nasbench_201 | 24 + .../recipes/od/Singularity.ODBenchmarks | 23 + .../od/Singularity.ODKernelDensityEstimation | 23 + .../container/recipes/rl/Singularity.Cartpole | 25 + .../recipes/rl/Singularity.learnaBenchmark | 41 + .../surrogates/Singularity.ParamnetBenchmark | 24 + .../Singularity.SupportVectorMachine | 26 + .../archived/ai/DeepStream/Singularity | 43 + .../ai/DeepStream_Perf_Lab/Singularity | 79 + .../ai/Distributed_Deep_Learning/Singularity | 32 + .../archived/ai/RAPIDS/Singularity | 24 + .../archived/hpc_ai/PINN/Singularity | 17 + .../hpc_ai/ai_science_cfd/Singularity | 27 + .../hpc_ai/ai_science_climate/Singularity | 20 + assets/js/repos.js | 35706 ++++++++-------- 21 files changed, 21483 insertions(+), 20910 deletions(-) create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/Singularity.template create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/nas/Singularity.TabularBenchmarks create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/nas/Singularity.nasbench_101 create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/nas/Singularity.nasbench_1shot1 create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/nas/Singularity.nasbench_201 create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/od/Singularity.ODBenchmarks create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/rl/Singularity.Cartpole create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/rl/Singularity.learnaBenchmark create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark create mode 100644 _recipes/maopl/TransOpt/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/ai/DeepStream/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/ai/DeepStream_Perf_Lab/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/ai/Distributed_Deep_Learning/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/ai/RAPIDS/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/hpc_ai/PINN/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/hpc_ai/ai_science_cfd/Singularity create mode 100644 _recipes/openhackathons-org/gpubootcamp/archived/hpc_ai/ai_science_climate/Singularity diff --git a/_data/containers.yaml b/_data/containers.yaml index e0dd963c..9729b400 100644 --- a/_data/containers.yaml +++ b/_data/containers.yaml @@ -13,10 +13,10 @@ bases: name: aibasel/downward - count: 204 name: continuumio/miniconda3 +- count: 189 + name: python - count: 182 name: ghcr.io/mkandes/naked-singularity -- count: 178 - name: python - count: 129 name: tensorflow/tensorflow - count: 96 @@ -27,10 +27,10 @@ bases: name: nfcore/base - count: 65 name: neurodebian -- count: 50 - name: cokelaer/damona/conda - count: 50 name: nvcr.io/nvidia/cuda +- count: 50 + name: cokelaer/damona/conda - count: 48 name: fedora - count: 47 @@ -49,12 +49,12 @@ bases: name: godloved/secure/ubuntu-bionic - count: 29 name: bids/validator +- count: 29 + name: nvcr.io/nvidia/tensorflow - count: 28 name: continuumio/miniconda - count: 27 name: modakopt/modak -- count: 26 - name: nvcr.io/nvidia/tensorflow - count: 26 name: pytorch/pytorch - count: 25 @@ -62,35 +62,35 @@ bases: - count: 23 name: vitchyr/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5 - count: 22 - name: khanlab/tar2bids + name: pennbbl/qsiprep - count: 22 name: nipreps/fmriprep - count: 22 - name: pennbbl/qsiprep + name: khanlab/tar2bids - count: 19 name: r-base +- count: 18 + name: bensonyang88/tensorflow-rdma - count: 18 name: glotzerlab/software - count: 18 name: ezlabgva/busco -- count: 18 - name: continuumio/anaconda3 - count: 18 name: nvcr.io/nvidia/nvhpc - count: 18 - name: bensonyang88/tensorflow-rdma + name: continuumio/anaconda3 - count: 18 name: rocker/r-ver - count: 16 name: rocker/tidyverse - count: 15 - name: jtchilders/singularity_image_recipes -- count: 15 - name: khanlab/neuroglia-dwi + name: ResearchIT/spack-singularity - count: 15 name: poldracklab/mriqc - count: 15 - name: ResearchIT/spack-singularity + name: jtchilders/singularity_image_recipes +- count: 15 + name: khanlab/neuroglia-dwi - count: 14 name: shahzebmsiddiqui/default/easybuild - count: 14 @@ -99,96 +99,94 @@ bases: name: nipy/heudiconv - count: 14 name: mambaorg/micromamba -- count: 12 - name: openfluid/openfluid -- count: 12 - name: khanlab/heudiconv - count: 12 name: pawsey/openfoam -- count: 12 - name: nitishnarula/secure/ubuntu-focal - count: 12 name: conda/miniconda2 -- count: 12 - name: rocker/verse - count: 12 name: ufscar/ubuntu_ompi +- count: 12 + name: 'GodloveD/busybox ' +- count: 12 + name: openfluid/openfluid +- count: 12 + name: khanlab/heudiconv - count: 12 name: julia - count: 12 - name: 'GodloveD/busybox ' -- count: 11 - name: deeplearnphysics/larcv2 + name: nitishnarula/secure/ubuntu-focal +- count: 12 + name: rocker/verse - count: 11 name: qiime2/core - count: 11 name: khanlab/hippunfold - count: 11 name: hariszaf/pema -- count: 11 - name: perl -- count: 11 - name: trinityrnaseq/trinityrnaseq - count: 11 name: golang +- count: 11 + name: perl - count: 11 name: sequana.img +- count: 11 + name: trinityrnaseq/trinityrnaseq - count: 11 name: rockylinux +- count: 11 + name: deeplearnphysics/larcv2 - count: 10 - name: khanlab/prepdwi + name: quay.io/vgteam/vg +- count: 10 + name: provarepro/openvino - count: 10 name: images/common.simg - count: 10 name: intel/oneapi-hpckit - count: 10 - name: provarepro/openvino -- count: 10 - name: quay.io/vgteam/vg -- count: 9 - name: vitchyr/railrl-torch4cuda9 + name: khanlab/prepdwi - count: 9 - name: DeepLearnPhysics/larcv3-singularity + name: nipreps/mriqc - count: 9 name: scientificlinux/sl - count: 9 name: dcgc-bfx/dcgc-jupyter-rstudio - count: 9 - name: nipreps/mriqc + name: rust +- count: 9 + name: ros - count: 9 name: google/deepvariant - count: 9 - name: rust + name: vitchyr/railrl-torch4cuda9 - count: 9 - name: ros -- count: 8 - name: quay.io/qiime2/core + name: DeepLearnPhysics/larcv3-singularity - count: 8 name: ewels/multiqc +- count: 8 + name: quay.io/qiime2/core - count: 8 name: bids/rshrf +- count: 8 + name: drneavin/SingularityBaseImages - count: 8 name: arezaii/pf_singularity - count: 8 - name: slaclab/relion + name: dynverse/dynwrap - count: 8 - name: bioconductor/release_core2 + name: bioconductor/bioconductor_docker - count: 8 - name: dynverse/dynwrap + name: slaclab/relion - count: 8 name: rocker/geospatial - count: 8 name: kaitj/mrtpipelines -- count: 8 - name: drneavin/SingularityBaseImages - count: 8 name: tpall/singularity-r - count: 8 - name: bioconductor/bioconductor_docker -- count: 7 - name: khanlab/neuroglia-core -- count: 7 - name: jekriske/r-base + name: bioconductor/release_core2 +- count: 8 + name: nvcr.io/nvidia/deepstream - count: 7 name: mattocci/rstan - count: 7 @@ -196,133 +194,133 @@ bases: - count: 7 name: nipreps/nibabies - count: 7 - name: iarcbioinfo/mutect-nf + name: cpllab/language-models +- count: 7 + name: nickjer/singularity-rstudio +- count: 7 + name: thomasrobinson/centos7-netcdff - count: 7 name: continuumio/miniconda2 - count: 7 - name: cpllab/language-models + name: iarcbioinfo/mutect-nf - count: 7 name: singularityhub/ubuntu - count: 7 - name: archlinux + name: pranithavangala/singularity - count: 7 - name: node + name: khanlab/neuroglia-core - count: 7 - name: bioconductor/bioconductor_full + name: nickjer/singularity-r - count: 7 - name: nickjer/singularity-rstudio + name: archlinux - count: 7 - name: nickjer/singularity-r + name: node - count: 7 name: bids/example - count: 7 - name: thomasrobinson/centos7-netcdff + name: jekriske/r-base - count: 7 - name: pranithavangala/singularity -- count: 6 - name: khanlab/gradcorrect + name: bioconductor/bioconductor_full - count: 6 - name: einar90/fieldopt + name: i386/ubuntu - count: 6 - name: gnuoctave/octave + name: bensonyang88/hpl-cuda - count: 6 - name: metabat/metabat + name: barbagroup/petibm - count: 6 name: nipreps/smriprep - count: 6 name: bids/mrtrix3_connectome - count: 6 - name: i386/ubuntu -- count: 6 - name: openjdk + name: elisadonnard/singularity - count: 6 - name: broadinstitute/gatk + name: gipert/default/arch-base - count: 6 name: broadinstitute/gatk3 - count: 6 - name: bids/freesurfer + name: openjdk - count: 6 - name: centos-7.7.1908.sif + name: condaforge/mambaforge - count: 6 - name: bud42/fs6 + name: ml4ai/UA-hpc-containers - count: 6 - name: conda/miniconda3-centos7 + name: archlinux/base - count: 6 name: continuumio/anaconda - count: 6 - name: openresty/openresty -- count: 6 - name: ml4ai/UA-hpc-containers + name: conda/miniconda3-centos7 - count: 6 - name: nvcr.io/nvidia/deepstream + name: anibali/pytorch - count: 6 name: bids/hcppipelines - count: 6 - name: condaforge/mambaforge + name: gnuoctave/octave - count: 6 - name: barbagroup/petibm + name: bud42/fs6 - count: 6 - name: gipert/default/arch-base + name: khanlab/gradcorrect - count: 6 - name: bensonyang88/hpl-cuda + name: bids/freesurfer - count: 6 - name: anibali/pytorch + name: centos-7.7.1908.sif - count: 6 - name: elisadonnard/singularity + name: metabat/metabat - count: 6 - name: archlinux/base -- count: 5 - name: anair17/railrl_hand_v2 + name: einar90/fieldopt +- count: 6 + name: broadinstitute/gatk +- count: 6 + name: openresty/openresty - count: 5 name: rocker/ml - count: 5 - name: shreyaskamathkm/cluster_images -- count: 5 - name: finalduty/archlinux + name: alexpacheco/rstudio - count: 5 name: neuronets/kwyk - count: 5 - name: quay.io/biocontainers/bowtie2 -- count: 5 - name: rockylinux/rockylinux -- count: 5 - name: ensemblorg/ensembl-vep -- count: 5 - name: combinelab/salmon + name: singularity-hub.org/mikemhenry/cme-lab-images - count: 5 name: sternacht/hpcc_openblas - count: 5 - name: vanessa/salad + name: ufscar/ubuntu_mpich - count: 5 name: vanessa/pokemon - count: 5 - name: ../library/micromamba/micromamba_1.4.3.img + name: vanessa/salad - count: 5 - name: alexpacheco/rstudio + name: rockylinux/rockylinux - count: 5 name: rocker/rstudio +- count: 5 + name: finalduty/archlinux +- count: 5 + name: quay.io/biocontainers/bowtie2 +- count: 5 + name: genomicpariscentre/guppy-gpu - count: 5 name: nextgenusfs/funannotate - count: 5 - name: ufscar/ubuntu_mpich + name: ../library/micromamba/micromamba_1.4.3.img - count: 5 - name: singularity-hub.org/mikemhenry/cme-lab-images + name: shreyaskamathkm/cluster_images - count: 5 - name: genomicpariscentre/guppy-gpu + name: combinelab/salmon +- count: 5 + name: ensemblorg/ensembl-vep +- count: 5 + name: anair17/railrl_hand_v2 - count: 4 - name: mafreitas/singularity-openms + name: godloved/secure/centos8 - count: 4 - name: nvcr.io/hpc/namd + name: quay.io/wtsicgp/dockstore-cgpwgs - count: 4 name: willgpaik/qt5_aci - count: 4 - name: dcgc-bfx/dcgc-base-conda -- count: 4 - name: TomHarrop/singularity-containers + name: intel2021.2_netcdfc4.7.4_ubuntu.sif - count: 4 - name: quay.io/wtsicgp/dockstore-cgpwgs + name: matmu/vep - count: 4 - name: godloved/secure/centos8 + name: fempar/fempar-env - count: 4 name: bids/pymvpa - count: 4 @@ -330,1639 +328,1641 @@ bases: - count: 4 name: repronim/reproin - count: 4 - name: bvlc/caffe -- count: 4 - name: fempar/fempar-env -- count: 4 - name: kiwiroy/singularity-perlbrew -- count: 4 - name: magland/mlsing + name: twongjirad/ssnetcaffeserver - count: 4 - name: rocker/shiny + name: ISU-HPC/ml-base - count: 4 - name: registry.gitlab.com/rode0day/fuzzer-testing/aflpp_runner + name: danabl/glimslib - count: 4 - name: matmu/vep + name: dcgc-bfx/dcgc-base-conda - count: 4 - name: ufscar/hpc/arch_spark + name: mafreitas/singularity-openms - count: 4 - name: ufscar/hpc/cuda_pytorch + name: jupyter/datascience-notebook - 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count: 1 - name: archlinux + name: deeplearnphysics - count: 1 - name: thakk + name: hkmo tags: latest: 664 - other: 5767 + other: 5785 versions: /nrs/funke/singularity/linajea/pylp_base: v1.5.img: 1 @@ -4386,14 +4386,14 @@ versions: nvcr.io/nvidia/cuda-arm64: 11.2.2-devel-ubuntu20.04: 1 nvcr.io/nvidia/deepstream: - 5.0-20.07-triton: 5 + 5.0-20.07-triton: 7 6.1.1-devel: 1 nvcr.io/nvidia/hpc-benchmarks: 21.4-hpl: 1 nvcr.io/nvidia/isaac-sim: 2022.1.0: 1 nvcr.io/nvidia/modulus/modulus: - '22.09': 1 + '22.09': 2 nvcr.io/nvidia/mxnet: 20.03-py3: 2 21.02-py3: 1 @@ -4439,8 +4439,8 @@ versions: 20.08-tf2-py3: 1 20.10-tf2-py3: 1 21.03-tf2-py3: 1 - 21.05-tf2-py3: 3 - 21.07-tf2-py3: 2 + 21.05-tf2-py3: 4 + 21.07-tf2-py3: 3 21.08-tf1-py3: 1 21.08-tf2-py3: 1 21.09-tf2-py3: 1 @@ -4452,7 +4452,7 @@ versions: 22.04-tf2-py3: 2 22.08-tf2-py3: 1 23.04-tf2-py3: 1 - 23.06-tf2-py3: 1 + 23.06-tf2-py3: 2 nvcr.io/nvidia/tlt-streamanalytics: v3.0-dp-py3: 2 nvcr.io/nvidia/tritonserver: @@ -4832,7 +4832,7 @@ versions: 3.6.8-slim-jessie: 1 3.6.8-stretch: 1 '3.7': 2 - 3.7-slim: 46 + 3.7-slim: 57 3.7-stretch: 1 3.7.1-stretch: 2 3.7.16-slim-bullseye: 1 @@ -5030,7 +5030,7 @@ versions: raj76/singularity: biocontainers: 2 rapidsai/rapidsai: - cuda11.0-runtime-ubuntu18.04-py3.7: 2 + cuda11.0-runtime-ubuntu18.04-py3.7: 3 rbartelme/anvio-pangenomics: latest: 1 recount-base: diff --git a/_data/repos.yml b/_data/repos.yml index 73e401f7..a70c39d6 100644 --- a/_data/repos.yml +++ b/_data/repos.yml @@ -293,10 +293,10 @@ \ Harbor Laboratory},\n\tURL = {https://www.biorxiv.org/content/early/2023/05/16/2023.05.16.541002},\n\ \teprint = {https://www.biorxiv.org/content/early/2023/05/16/2023.05.16.541002.full.pdf},\n\ \tjournal = {bioRxiv}\n}\n\n" - stargazers_count: 76 + stargazers_count: 79 subscribers_count: 7 topics: [] - updated_at: 1698188103.0 + updated_at: 1699239722.0 3dimaging/DeepLearningCamelyon: data_format: 2 description: DeepLearning - Camelyon16 dataset @@ -9486,9 +9486,9 @@ Bioconductor/bioconductor_full: all software needed to build all Bioconductor packages filenames: - Singularity - - Singularity.RELEASE_3_10 - Singularity.RELEASE_3_9 - Singularity.RELEASE_3_8 + - Singularity.RELEASE_3_10 full_name: Bioconductor/bioconductor_full latest_release: null readme: "
Alevin-fry is a fast,
accurate, and memory-frugal tool for preprocessing single-cell and single-nucleus
@@ -12365,141 +12365,141 @@ Characterisation-Virtual-Laboratory/CharacterisationVL-Software:
data_format: 2
description: null
filenames:
- - vscode/Singularity.vscode_1.39.2
+ - jupyter-ml/Singularity.jupyter-ml_20201120
+ - jupyter-ml/Singularity.jupyter-ml_20210415
+ - ariba/Singularity.ariba_2.12.1
+ - ariba/Singularity.ariba_2.14.4
+ - ccp-em/Singularity.ccp-em_v1.3.0-cuda-9.0
+ - ccp-em/Singularity.ccp-em_nightly-20210608-cuda-9.0
+ - chimera/Singularity.chimera_v1.10.2-cuda10.1
+ - chimera/Singularity.chimera_v1.11-cuda10.1
+ - chimera/Singularity.chimera_v1.14-cuda10.1
+ - chimera/Singularity.chimera_v1.13-cuda10.1
+ - apex/Singularity.apex_master
+ - cvmfs-client/Singularity.cvmfs-client
+ - dtsa2/Singularity.dtsa2
+ - mydata-python/Singularity.mydata-python_20200603
- imod/Singularity.imod_v4_11_15
- - bids-validator/Singularity.bids-validator-1.3.1
- - bids-validator/Singularity.bids-validator-1.2.2
- - libertem/Singularity.libertem-0.7.0
- - libertem/Singularity.libertem-0.10.0
- - libertem/Singularity.libertem-0.4.0
- - libertem/Singularity.libertem-0.9.0
- - libertem/Singularity.libertem-0.11.0
- - libertem/Singularity.libertem-0.5.1
- - libertem/Singularity.libertem-0.5.0
- - libertem/Singularity.libertem-0.8.0
- - libertem/Singularity.libertem-0.2.2
- - libertem/Singularity.libertem-0.4.1
- - libertem/Singularity.libertem-0.6.0
- - deeplabcut/Singularity.latest
- - ants/Singularity.ants_2.3.4
- - ants/Singularity.ants_2.3.1
- - nanconvert/Singularity.nanconvert
- - mytardisclient/Singularity.mytardisclient_20200603
- - argos/Singularity.argos_3.0.0-beta52
- - argos/Singularity.argos_3.0.0-beta53
- - protomo/Singularity.protomo-2.4.2
- - fsl/Singularity.fsl
- - cistem/Singularity.cisTEM-1.0.0-beta
- gimp/Singularity.gimp_2.8.22
- gimp/Singularity.gimp_2.8
- - imagemagick/Singularity.imagemagick-7.0.8-68
- - pyprismatic/Singularity.pyprismatic_1_2_1-cuda-11.0
- - graphviz/Singularity.graphviz-2.40.1
- - mrtrix3tissue/Singularity.mrtrix3tissue-5.2.8
- - mrtrix3tissue/Singularity.mrtrix3tissue-5.2.9-cuda10.1
- - apex/Singularity.apex_master
- - cvmfs-client/Singularity.cvmfs-client
- - 3dslicer/Singularity.3dslicer_4.10.2
- - 3dslicer/Singularity.3dslicer_4.8.1
- - mydata/Singularity.mydata_0.9.2-1
- - ariba/Singularity.ariba_2.14.4
- - ariba/Singularity.ariba_2.12.1
- - matlab/Singularity.MATLAB_SAMPLE
- - eman/Singularity.eman_2.9
- - eman/Singularity.eman_2.22
+ - scipoptsuite/Singularity.scipoptsuite-8.0.0_sh
+ - scipoptsuite/Singularity.scipoptsuite-7.0.3_sh
+ - eman/Singularity.eman_2.3.1
- eman/Singularity.eman_2.3
- eman/Singularity.eman_2.91
- - eman/Singularity.eman_2.3.1
- - dragondisk/Singularity.dragondisk_v1_0_5
- - dristhi/Singularity.dristhi_2.7
- - meshlab/Singularity.meshlab
- - anaconda3/Singularity.anaconda3_5.3.0
- - anaconda3/Singularity.anaconda3_5.3.0-cuda-11.0.3
- - x2goclient/Singularity.x2goclient_latest
- - jupyter-ml/Singularity.jupyter-ml_20210415
- - jupyter-ml/Singularity.jupyter-ml_20201120
- - digitalmicrograph-free/Singularity.digitalmicrograph-free_2.32.888.0
- - ubuntu-base-image/Singularity.1804-cuda9
- - ubuntu-base-image/Singularity.1804
- - ubuntu-base-image/Singularity.2004
- - ubuntu-base-image/Singularity.2004-cuda11.5
- - ubuntu-base-image/Singularity.1804-cuda10.1
- - ubuntu-base-image/Singularity.2004-cuda11.0
+ - eman/Singularity.eman_2.9
+ - eman/Singularity.eman_2.22
+ - nanconvert/Singularity.nanconvert
+ - mib/Singularity.mib_2.511
- mantid/Singularity.mantid_v_3_13_0
- - ashs/Singularity.ashs_2.0.0
- - datalad/Singularity.datalad_0.13.3
- - quit/Singularity.quit_2.0.2
- - omero.insight/Singularity.omero_5.5.10
- - mydata-python/Singularity.mydata-python_20200603
- - dtsa2/Singularity.dtsa2
- - cellprofiler/Singularity.cellprofiler_3.1.9
- - cellprofiler/Singularity.cellprofiler_3.1.5
- - crisprcas/Singularity.crisprcas
- - jmrui/Singularity.jmrui_5.2
- - darknet/Singularity.darknet_yolo_v3-cuda-9.0
- - paraview/Singularity.paraview_5.6.0-cuda-9.0
- - openrefine/Singularity.openrefine-3.1
- - mango/Singularity.mango_4.0.1
+ - mydata/Singularity.mydata_0.9.2-1
+ - connectome-workbench/Singularity.connectome-workbench_1.4.2
+ - mytardisclient/Singularity.mytardisclient_20200603
- ilastik/Singularity.ilastik_1.3.3post3
- - caffe-unet/Singularity.caffe-unet_1.0
- - imagej/Singularity.imagej_1.50e
- - amide/Singularity.amide-1.0.5
+ - globus-cli/Singularity.globus-cli-v2.0.0
+ - cryolo/Singularity.cryolo_v1_6_1
+ - cryolo/Singularity.cryolo_v1_5_6
+ - cryolo/Singularity.ubuntu-cryyolo
+ - cryolo/Singularity.cryolo_v1_0_0
+ - cryolo/Singularity.cryolo_v1_0_4
- bidscoin/Singularity.bidscoin_3
- - relion/Singularity.relion_4.0
- - relion/Singularity.relion_3.0.7
- - relion/Singularity.relion_3.1.3
- - relion/Singularity.relion_3.1_beta
- - relion/Singularity.relion_3.1.0
- - git-annex/Singularity.git-annex.6.20180227
- - chimerax/Singularity.chimerax-v0.8-cuda9
- - chimerax/Singularity.chimerax-v0.6-cuda9
+ - octave/Singularity.octave
+ - octave/Singularity.octave-4.2.2
+ - cellprofiler/Singularity.cellprofiler_3.1.5
+ - cellprofiler/Singularity.cellprofiler_3.1.9
+ - paraview/Singularity.paraview_5.6.0-cuda-9.0
+ - dke/Singularity.dke
- haystack/Singularity.haystack_bio-0.5.0
- haystack/Singularity.haystack_bio-0.5.5
- - colmap/Singularity.colmap_3.5
- - colmap/Singularity.colmap_3.6-dev.3
- - connectome-workbench/Singularity.connectome-workbench_1.4.2
- - jupyter-fileuploadtool/Singularity.jupyter-fileuploadtool_20201211
- - chimera/Singularity.chimera_v1.10.2-cuda10.1
- - chimera/Singularity.chimera_v1.11-cuda10.1
- - chimera/Singularity.chimera_v1.14-cuda10.1
- - chimera/Singularity.chimera_v1.13-cuda10.1
- - dke/Singularity.dke
- - mrtrix/Singularity.mrtrix-3.0.1
- - mrtrix/Singularity.mrtrix-3.0.2
+ - fsl/Singularity.fsl
+ - argos/Singularity.argos_3.0.0-beta52
+ - argos/Singularity.argos_3.0.0-beta53
+ - R/Singularity.R_4.0.5
+ - bids-validator/Singularity.bids-validator-1.3.1
+ - bids-validator/Singularity.bids-validator-1.2.2
+ - openrefine/Singularity.openrefine-3.1
+ - fiji/Singularity.fiji
- mrtrix/Singularity.mrtrix_3_beta
+ - mrtrix/Singularity.mrtrix-3.0.1
- mrtrix/Singularity.mrtrix-3.0.0
+ - mrtrix/Singularity.mrtrix-3.0.2
- mrtrix/Singularity.mrtrix-3.0.3
- - ccp-em/Singularity.ccp-em_nightly-20210608-cuda-9.0
- - ccp-em/Singularity.ccp-em_v1.3.0-cuda-9.0
+ - caffe/Singularity.caffe_1.0
+ - 3dslicer/Singularity.3dslicer_4.8.1
+ - 3dslicer/Singularity.3dslicer_4.10.2
+ - chimerax/Singularity.chimerax-v0.8-cuda9
+ - chimerax/Singularity.chimerax-v0.6-cuda9
+ - mrtrix3tissue/Singularity.mrtrix3tissue-5.2.8
+ - mrtrix3tissue/Singularity.mrtrix3tissue-5.2.9-cuda10.1
+ - omero.insight/Singularity.omero_5.5.10
+ - darknet/Singularity.darknet_yolo_v3-cuda-9.0
+ - deeplabcut/Singularity.latest
+ - ubuntu-base-image/Singularity.1804
+ - ubuntu-base-image/Singularity.1804-cuda10.1
+ - ubuntu-base-image/Singularity.2004
+ - ubuntu-base-image/Singularity.2004-cuda11.0
+ - ubuntu-base-image/Singularity.1804-cuda9
+ - ubuntu-base-image/Singularity.2004-cuda11.5
+ - volview/Singularity.VolView_3.4-cuda-9.0
+ - volview/Singularity.volview
+ - colmap/Singularity.colmap_3.5
+ - colmap/Singularity.colmap_3.6-dev.3
- atom/Singularity.atom_1.39.1
- atom/Singularity.atom_1.45.0
- - globus-connect-personal/Singularity.globus-connect-personal_latest
- - caffe/Singularity.caffe_1.0
- - openmodelica/Singularity.openmodelica_1.14.2-cuda-10.1
- - imblproc/Singularity.imblproc
- - globus-cli/Singularity.globus-cli-v2.0.0
- - octopus/Singularity.octopus
+ - meshlab/Singularity.meshlab
+ - octopus/Singularity.octopus_11.4_parallel
- octopus/Singularity.octopus_11.4
+ - octopus/Singularity.octopus
- octopus/Singularity.octopus_8.4_parallel
- - octopus/Singularity.octopus_11.4_parallel
- octopus/Singularity.octopus_8.4
- - octave/Singularity.octave
- - octave/Singularity.octave-4.2.2
- - omero-insight/Singularity.1804
- - scipoptsuite/Singularity.scipoptsuite-7.0.3_sh
- - scipoptsuite/Singularity.scipoptsuite-8.0.0_sh
+ - imagemagick/Singularity.imagemagick-7.0.8-68
- cloudstor/Singularity.cloudstor-2.4.1
- - volview/Singularity.VolView_3.4-cuda-9.0
- - volview/Singularity.volview
- - cryolo/Singularity.cryolo_v1_5_6
- - cryolo/Singularity.ubuntu-cryyolo
- - cryolo/Singularity.cryolo_v1_6_1
- - cryolo/Singularity.cryolo_v1_0_0
- - cryolo/Singularity.cryolo_v1_0_4
- - R/Singularity.R_4.0.5
- - mib/Singularity.mib_2.511
- - fiji/Singularity.fiji
+ - amide/Singularity.amide-1.0.5
+ - jmrui/Singularity.jmrui_5.2
+ - relion/Singularity.relion_4.0
+ - relion/Singularity.relion_3.1_beta
+ - relion/Singularity.relion_3.0.7
+ - relion/Singularity.relion_3.1.0
+ - relion/Singularity.relion_3.1.3
+ - imagej/Singularity.imagej_1.50e
+ - pyprismatic/Singularity.pyprismatic_1_2_1-cuda-11.0
+ - ants/Singularity.ants_2.3.4
+ - ants/Singularity.ants_2.3.1
+ - globus-connect-personal/Singularity.globus-connect-personal_latest
+ - dragondisk/Singularity.dragondisk_v1_0_5
+ - omero-insight/Singularity.1804
+ - quit/Singularity.quit_2.0.2
+ - matlab/Singularity.MATLAB_SAMPLE
+ - openmodelica/Singularity.openmodelica_1.14.2-cuda-10.1
+ - crisprcas/Singularity.crisprcas
+ - protomo/Singularity.protomo-2.4.2
+ - libertem/Singularity.libertem-0.8.0
+ - libertem/Singularity.libertem-0.5.0
+ - libertem/Singularity.libertem-0.11.0
+ - libertem/Singularity.libertem-0.4.0
+ - libertem/Singularity.libertem-0.4.1
+ - libertem/Singularity.libertem-0.5.1
+ - libertem/Singularity.libertem-0.2.2
+ - libertem/Singularity.libertem-0.6.0
+ - libertem/Singularity.libertem-0.7.0
+ - libertem/Singularity.libertem-0.10.0
+ - libertem/Singularity.libertem-0.9.0
+ - digitalmicrograph-free/Singularity.digitalmicrograph-free_2.32.888.0
+ - x2goclient/Singularity.x2goclient_latest
+ - datalad/Singularity.datalad_0.13.3
- cytoscape/Singularity.cytoscape_3.8.0
+ - imblproc/Singularity.imblproc
+ - anaconda3/Singularity.anaconda3_5.3.0
+ - anaconda3/Singularity.anaconda3_5.3.0-cuda-11.0.3
+ - cistem/Singularity.cisTEM-1.0.0-beta
+ - graphviz/Singularity.graphviz-2.40.1
+ - jupyter-fileuploadtool/Singularity.jupyter-fileuploadtool_20201211
+ - dristhi/Singularity.dristhi_2.7
+ - caffe-unet/Singularity.caffe-unet_1.0
+ - mango/Singularity.mango_4.0.1
+ - ashs/Singularity.ashs_2.0.0
+ - git-annex/Singularity.git-annex.6.20180227
+ - vscode/Singularity.vscode_1.39.2
full_name: Characterisation-Virtual-Laboratory/CharacterisationVL-Software
latest_release: null
readme: " Summary: Perform integrated preprocessing and quality assurance\
@@ -36488,11 +36488,11 @@ MASILab/PreQual:
\ applicable) Leon Y. Cai, Qi Yang, Colin\
- \ B. Hansen, Vishwesh Nath, Karthik Ramadass, Graham W. Johnson, Benjamin N. Conrad,\
+ \ of MD\n\n\n\n Leon Y. Cai, Qi Yang, Colin B.\
+ \ Hansen, Vishwesh Nath, Karthik Ramadass, Graham W. Johnson, Benjamin N. Conrad,\
\ Brian D. Boyd, John P. Begnoche, Lori L. Beason-Held, Andrea T. Shafer, Susan\
\ M. Resnick, Warren D. Taylor, Gavin R. Price, Victoria L. Morgan, Baxter P.\
\ Rogers, Kurt G. Schilling, Bennett A. Landman. PreQual: An automated pipeline\
@@ -36501,41 +36501,40 @@ MASILab/PreQual:
\ rel=\"nofollow\">Magnetic Resonance in MedicineUsefulaf
is an all-in-one Docker/Singularity image for single-cell processing with Alevin-fry(
-
+ Usefulaf history
Usefulaf
+ history
mutect-nfPreQual (dtiQA v7 Multi) User\
- \ Guide
\nContents
\n\n
\nPreQual (dtiQA v7 Multi) User Guide
\nContents
\n\n\
+
\nOverview
\nOverview
\n\n\n
\nAuthors and Reference
\n\
- Authors and Reference
\n
Medical-image Analysis and Statistical\ \ Interpretation (MASI) Lab, Vanderbilt University, Nashville, TN, USA
\n\ -The PreQual software is designed to run inside\ - \ a Singularity container. The\ - \ container requires an \"inputs\" folder that\ - \ holds all required input diffusion image files (i.e., .nii.gz, .bval, and .bvec\ - \ files) and a configuration file. For those\ - \ running Synb0-DisCo to correct susceptibility distortions without reverse phase-encoded\ - \ images, this folder will also contain the structural\ - \ T1 image. The container also requires an \"outputs\" folder that will hold all the outputs after the pipeline runs.\ - \ We also need to know the image axis\ - \ on which phase encoding was performed for all inputs (i.e., \"i\" for the first\ - \ dimension, \"j\" for the second). To build the configuration file, we need to\ - \ know the direction along said axis\ - \ in which each image was phase encoded (i.e., \"+\" for positive direction and\ - \ \"-\" for the negative direction) and the readout\ - \ time for each input image. Once we have this information, we bind the inputs\ - \ and outputs directories into the container to run the pipeline.
\n\ -Note: The phase encoding axis, direction, and readout time must be known ahead\ - \ of time, as this information is not stored in NIFTI headers. Depending on the\ - \ scanner used, they may be available in JSON sidecars when NIFTIs are converted\ - \ from DICOMs with dcm2niix.
\ngit\
- \ clone https://github.com/MASILab/PreQual.git\ncd /path/to/repo/PreQual\ngit\
- \ checkout v1.1.0\nsudo singularity build /path/to/prequal.simg Singularity\n\
+ Getting Started
\n\
+ The PreQual software is designed to run inside a Singularity container. The container requires an \"inputs\" folder that holds all required input diffusion image files (i.e.,\
+ \ .nii.gz, .bval, and .bvec files) and a configuration\
+ \ file. For those running Synb0-DisCo to correct susceptibility distortions\
+ \ without reverse phase-encoded images, this folder will also contain the structural T1 image. The container also requires an \"\
+ outputs\" folder that will hold all the outputs\
+ \ after the pipeline runs. We also need to know the image axis on which phase encoding was performed for all inputs (i.e., \"\
+ i\" for the first dimension, \"j\" for the second). To build the configuration\
+ \ file, we need to know the direction\
+ \ along said axis in which each image was phase encoded (i.e., \"+\" for positive\
+ \ direction and \"-\" for the negative direction) and the readout time for each input image. Once we have this information, we bind\
+ \ the inputs and outputs directories into the container to run the pipeline.
\nNote: The phase encoding axis, direction, and readout\
+ \ time must be known ahead of time, as this information is not stored in NIFTI\
+ \ headers. Depending on the scanner used, they may be available in JSON sidecars\
+ \ when NIFTIs are converted from DICOMs with dcm2niix.
\nContainerization\
+ \ of Source Code
\n\
+ git clone https://github.com/MASILab/PreQual.git\ncd /path/to/repo/PreQual\n\
+ git checkout v1.1.0\nsudo singularity build /path/to/prequal.simg Singularity\n\
\nWe use Singularity version 3.8 CE with root permissions.
\n\
Alternatively, a pre-built container can be downloaded here.
\nCommand
\nsingularity run \n\
- -e \n--contain\n--home /path/to/inputs/directory/\n-B /path/to/inputs/directory/:/INPUTS\n\
- -B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n\
+ \ rel=\"nofollow\">here
.\nsingularity run \n-e \n--contain\n\
+ --home /path/to/inputs/directory/\n-B /path/to/inputs/directory/:/INPUTS\n-B /path/to/outputs/directory/:/OUTPUTS\n\
+ -B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n\
-B /path/to/cuda:/usr/local/cuda\n--nv\n/path/to/prequal.simg\npe_axis\n[options]\n\
\n--nv
\
\ and -B /path/to/cuda:/usr/local/cuda
are optional. See options\
\ --eddy_cuda
and --eddy_extra_args
. GPU support\
- \ is currently experimental.\nInput Directory: The dtiQA_config.csv configuration\ - \ file and at least one diffusion weighted image must be provided.
\ndtiQA_config.csv (see below for format,\ - \ must be named exactly)
\n<image1>.nii.gz (diffusion\ - \ weighted image)
\n<image1>.bval (units of s/mm2,\ - \ in the \n
Input\ + \ Directory: The dtiQA_config.csv configuration file and at least one\ + \ diffusion weighted image must be provided.
\ndtiQA_config.csv\ + \ (see below for format, must be named exactly)
\n\ +<image1>.nii.gz (diffusion weighted image)
\n<image1>.bval (units of s/mm2, in the FSL format)
\n<image1>.bvec (normalized\ \ unit vectors in the FSL format)
\n:
\n<imageN>.nii.gz\ @@ -36579,49 +36577,48 @@ MASILab/PreQual: \ The phase encoding directions of the input images along this axis are specified\ \ in the dtiQA_config.csv file. See Configuration\ \ File and Examples for more information.
\n\ -The format for the lines\ - \ of the configuration CSV file, dtiQA_config.csv (must be named exactly), are\ - \ as follows:
\n<image1>,pe_dir,readout_time\n:\n<imageN>,pe_dir,readout_time\n\
-
\n<image> is the shared file\ - \ PREFIX between the corresponding NIFTI, BVAL, and BVEC files for that particular\ - \ image in the input directory (i.e., my_dwi.nii.gz/.bval/.bvec -> my_dwi).\ - \ Do NOT include the path to the input directory.
\npe_dir\
- \ is either + or -, corresponding to the direction along the phase encoding axis\
- \ (as defined by the parameter pe_axis
) on which the image is phase\
- \ encoded.
readout_time is a non-negative number, the readout_time\ - \ parameter required by FSL\u2019s eddy. The absolute value of this parameter\ - \ is used to scale the estimated b0 field. Note a value of 0 indicates that the\ - \ images are infinite bandwidth (i.e. no susceptibility distortion). See Examples for more information.
\nHere are some different example combinations of pe_axis, pe_dir, and readout_time\ - \ parameters and the corresponding FSL acquisition parameters lines:
\npe_axis | \npe_dir | \nreadout_time | \nacqparams\ - \ line | \n
---|---|---|---|
i | \n+ | \n0.05 | \n\ -1, 0, 0, 0.05 | \n
j | \n- | \n0.1 | \n0,\ - \ -1, 0, 0.1 | \n
These are examples of common\ - \ use cases. They also all share the same command, as detailed above. The PREPROCESSED\ - \ output folder will contain the final outputs and the PDF folder will contain\ - \ the QA report.
\nPhase Encoding Axis | \n\
- Reverse Phase Encoded (RPE) Image | \nT1 Image | \nContents\
- \ of Input Directory | \nContents of dtiQA_config.csv | \n||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
j | \nYes | \nN/A | \ndti1.nii.gz dti1.bval dti1.bvec dti2.nii.gz dti2.bval dti2.bvec rpe.nii.gz rpe.bval rpe.bvec dtiQA_config.csv | \n\
+ \n\n
pe_axis | \npe_dir | \n\ +readout_time | \nacqparams line | \n
---|---|---|---|
i | \n+ | \n0.05 | \n1, 0, 0, 0.05 | \n
j | \n\ +- | \n0.1 | \n0, -1, 0, 0.1 | \n
These are examples of common use cases. They also all share the same command,\ + \ as detailed above. The PREPROCESSED output folder will contain the final outputs\ + \ and the PDF folder will contain the QA report.
\nPhase Encoding Axis | \nReverse Phase Encoded (RPE) Image | \n\
+ T1 Image | \nContents of Input Directory | \nContents of dtiQA_config.csv | \n\
+
---|---|---|---|---|
j | \nYes | \nN/A | \ndti1.nii.gz dti1.bval dti1.bvec dti2.nii.gz dti2.bval dti2.bvec rpe.nii.gz rpe.bval rpe.bvec dtiQA_config.csv | \n\
dti1,+,0.05 dti2,+,0.05 rpe,-,0.05 | \n
j | \nNo | \n\Yes | \ndti1.nii.gz dti1.bval dti1.bvec dti2.nii.gz dti2.bval dti2.bvec t1.nii.gz dtiQA_config.csv | \n\
dti1,+,0.05 dti2,+,0.05 | \n
j | \nNo | \nNo | \n\dti1.nii.gz dti1.bval dti1.bvec dti2.nii.gz dti2.bval dti2.bvec dtiQA_config.csv | \n\
- dti1,+,0.05 dti2,+,0.05 | \n
While\ +
While\ \ not a BIDS pipeline, data in BIDS format can be run with PreQual without moving\ \ or copying data. The key is that the input directory structure must be as described\ \ relative to inside the container. By creatively binding files/folders\ @@ -36632,9 +36629,9 @@ MASILab/PreQual: \n
The outputs directory and configuration file can be created\ \ wherever makes the most sense for the user. The contents of the configuration\ \ file will look something like this:
\nsub-X_ses-X_acq-1_dwi,pe_dir,readout_time\n\
- :\nsub-X_ses-X_acq-N_dwi,pe_dir,readout_time\n
\n--bval_threshold\
+ :\nsub-X_ses-X_acq-N_dwi,pe_dir,readout_time\n\n --bval_threshold\
\ N A non-negative integer threshold under which to consider\
\ a b-value to be zero. Useful when some MRI machines do not allow for more than\
\ one b0 volume to be acquired so some users acquire scans with extremely low\
@@ -36790,12 +36787,12 @@ MASILab/PreQual:
\ were acquired to label PDF output Default = subj --session\
\ string String describing session in which the input data were\
\ acquired to label PDF output Default = sess --help,\
- \ -h All NIFTI images are consistent with a conversion from a DICOM using\
- \ Options
\nPipeline Assumptions
\n\n\
-
dcm2niix
(at least v1.0.20180622) by Chris Rorden and are raw NIFTIs without distortion\
+ \ -h
All NIFTI images\
+ \ are consistent with a conversion from a DICOM using dcm2niix
(at\
+ \ least v1.0.20180622) by Chris Rorden and are raw NIFTIs without distortion\
\ correction. We require this as dcm2niix exports b-value/b-vector files in FSL\
\ format and removes ADC or trace images auto-generated in some Philips DICOMs.\
\ In addition dcm2niix
correctly moves the gradients from scanner\
@@ -36853,70 +36850,70 @@ MASILab/PreQual:
No b0 drift correction is performed.
\nWe use\ \ the fit tensor model primarily for QA. If b-values less than 500 s/mm2\ \ or greater than 1500 s/mm2 are present, we suggest careful review\ - \ of the fit prior to use for non-QA purposes.
\nThreshold\
- \ all b-values such that values less than the --bval_threshold
parameter\
- \ are 0.
Check that all b-vectors are unit normalized and\ - \ all b-values greater than zero have associated non-zero b-vectors. For any volumes\ - \ where this is not the case, we remove them, flag a warning for the output PDF,\ - \ and continue the pipeline.
\nIf applicable, denoise all diffusion\
- \ scans with dwidenoise
(Marchenko-Pastur PCA) from MrTrix3 and save\
- \ the noise profiles (needed for Rician correction later).
If applicable, perform Gibbs de-ringing on all diffusion scans with mrdegibbs
\
- \ from MRTrix3.
If applicable, perform Rician correction\ - \ on all diffusion scans with the method of moments.
\nIf applicable,\ - \ prenormalize all diffusion scans. To accomplish this, extract all b0 images\ - \ from each diffusion scan and average them. Then find a rough brain-mask with\ - \ FSL\u2019s bet and calculate an intensity scale factor such that the histogram\ - \ intersection between the intra-mask histogram of the different scans\u2019 averaged\ - \ b0s to that of the first scan is maximized. Apply this scale factor to the entire\ - \ diffusion weighted scan. This is done to avoid gain differences between different\ - \ diffusion scans.
\nPrepare data for and run preprocessing with topup and eddy
\n\ -Topup:
\nExtract all b0s from all scans, maintaining\ - \ their relative order.
\n(Optional) If a T1 is supplied and\ - \ no complementary (i.e. reverse) phase encoded images are provided, use Synb0-DisCo\ - \ to convert the first b0 of the first scan to a susceptibility-corrected b0.
\n\ -Build the acquisition parameters file required by both topup and\ - \ eddy
\nFor the number of b0s from each image, add the same\ - \ phase encoding and readout time line to the acquisition parameters file, as\ - \ outlined in \"Example Phase Encoding Schemes\".
\nThreshold all b-values such that values less than the --bval_threshold
\
+ \ parameter are 0.
Check that all b-vectors are unit normalized\ + \ and all b-values greater than zero have associated non-zero b-vectors. For any\ + \ volumes where this is not the case, we remove them, flag a warning for the output\ + \ PDF, and continue the pipeline.
\nIf applicable, denoise\
+ \ all diffusion scans with dwidenoise
(Marchenko-Pastur PCA) from\
+ \ MrTrix3 and save the noise profiles (needed for Rician correction later).
If applicable, perform Gibbs de-ringing on all diffusion scans\
+ \ with mrdegibbs
from MRTrix3.
If applicable,\ + \ perform Rician correction on all diffusion scans with the method of moments.
\n\ +If applicable, prenormalize all diffusion scans. To accomplish\ + \ this, extract all b0 images from each diffusion scan and average them. Then\ + \ find a rough brain-mask with FSL\u2019s bet and calculate an intensity scale\ + \ factor such that the histogram intersection between the intra-mask histogram\ + \ of the different scans\u2019 averaged b0s to that of the first scan is maximized.\ + \ Apply this scale factor to the entire diffusion weighted scan. This is done\ + \ to avoid gain differences between different diffusion scans.
\nPrepare\ + \ data for and run preprocessing with topup and eddy
\nTopup:
\n\ +Extract all b0s from all scans, maintaining their relative order.
\n\ +(Optional) If a T1 is supplied and no complementary (i.e. reverse)\ + \ phase encoded images are provided, use Synb0-DisCo to convert the first b0 of\ + \ the first scan to a susceptibility-corrected b0.
\nBuild\ + \ the acquisition parameters file required by both topup and eddy
\nFor the number of b0s from each image, add the same phase encoding and\ + \ readout time line to the acquisition parameters file, as outlined in \"Example\ + \ Phase Encoding Schemes\".
\n(Optional) If Synb0-DisCo is run because no complementary\ + \ phase encoding directions are supplied and --synb0 is not off, we add an additional\ + \ line to the end of the file. This line is the same as the first line of the\ + \ file except that the readout time is 0 instead.
\n(Optional) If Synb0-DisCo\ - \ is run because no complementary phase encoding directions are supplied and --synb0\ - \ is not off, we add an additional line to the end of the file. This line is the\ - \ same as the first line of the file except that the readout time is 0 instead.
\n\ -We then concatenate\ - \ all the b0s maintaining their order and run topup with the acquisition parameters\ - \ file if images with complementary phase encoding directions are supplied or\ - \ if a T1 was supplied. Otherwise, we move on to the next step, eddy.
\nEddy
\nUsing the acquisition parameters\ - \ file from the topup step, regardless of whether topup was performed, we build\ - \ the eddy index file such that each volume in each image corresponds to the line\ - \ in the acquisition parameters file associated with the first b0 of each scan.\ - \ This is done to tell eddy that each volume in a given scan has the same underlying\ - \ phase encoding scheme as the first b0 of that scan.
\nEddy is then run with either a\ - \ mask generated with bet and the -f 0.25 and -R options or without a mask (aka\ - \ with a mask of all 1\u2019s), depending on user input (see the --eddy_mask option)\ - \ and with the output of topup if topup was run. Eddy also runs with the --repol\ - \ option for outlier slice replacement. We also first run eddy with a check looking\ - \ for shelled data. If the check fails, eddy is then run with the --data_is_shelled\ + \ + direction, and 0.05 readout time and one with 3 b0s, + direction, and 0.02\ + \ readout time, this file will have 11 lines. The first 7 lines are identical\ + \ and equal to [0, 1, 0, 0.05]. The next three lines are also identical and equal\ + \ to [0, 1, 0, 0.02]. Finally, the last line is equal to [0, 1, 0, 0].
We then concatenate all the b0s maintaining\ + \ their order and run topup with the acquisition parameters file if images with\ + \ complementary phase encoding directions are supplied or if a T1 was supplied.\ + \ Otherwise, we move on to the next step, eddy.
\nEddy
\nUsing the acquisition parameters file from the topup\ + \ step, regardless of whether topup was performed, we build the eddy index file\ + \ such that each volume in each image corresponds to the line in the acquisition\ + \ parameters file associated with the first b0 of each scan. This is done to tell\ + \ eddy that each volume in a given scan has the same underlying phase encoding\ + \ scheme as the first b0 of that scan.
\nEddy is then run with either a mask generated\ + \ with bet and the -f 0.25 and -R options or without a mask (aka with a mask of\ + \ all 1\u2019s), depending on user input (see the --eddy_mask option) and with\ + \ the output of topup if topup was run. Eddy also runs with the --repol option\ + \ for outlier slice replacement. We also first run eddy with a check looking for\ + \ shelled data. If the check fails, eddy is then run with the --data_is_shelled\ \ flag to force eddy to run on all scans, DSI included. Note that DSI data is\ \ not officially supported by FSL\u2026 yet?
\nIf eddy detects\ \ data is not shelled, we report this as a warning
\nAs noted\ @@ -36948,16 +36945,16 @@ MASILab/PreQual: \ analysis later. dwi2tensor does this for us.
We\ \ then convert the tensor to FA and MD images (and visualize them later too) as\ \ well as AD, RD, and V1 eigenvector images for the user. The latter 3 are not\ - \ visualized.
\nWe start with the brain mask generated\ - \ above to generate a mask used for the following quantification of tensor fit\ - \ using a chi-squared statistic.
\nFirst, we calculate the mean\ - \ image for each unique b-value (0 not included). Then we run FSL\u2019s FAST\ - \ to isolate the CSF on each meaned image. We then take the average probability\ - \ of a voxel being CSF across all unique b-values and assign >15% probability\ - \ to be a positive CSF voxel.
\nThen we call the final chi-squared\ + \ visualized.
\nWe start with the brain mask generated above\ + \ to generate a mask used for the following quantification of tensor fit using\ + \ a chi-squared statistic.
\nFirst, we calculate the mean image\ + \ for each unique b-value (0 not included). Then we run FSL\u2019s FAST to isolate\ + \ the CSF on each meaned image. We then take the average probability of a voxel\ + \ being CSF across all unique b-values and assign >15% probability to be a\ + \ positive CSF voxel.
\nThen we call the final chi-squared\ \ mask to be the intersection of the inverted CSF mask and a 1-pixel eroded version\ \ of the brain mask.
\nOn the voxels inside the\ \ chi-squared mask, we perform the following quality assurance:
\nLastly, we visualize\ \ some central slices of the MD map clipped from 0 to 0.003 (ADC of water at 37\xB0\ - C).
\n<imageN_%> denotes the\ - \ original prefix of imageN with the preceding preprocessing step descriptors\ - \ tacked on the end. For example, in the case of the PRENORMALIZED directory,\ - \ the prefix for imageJ may or may not include \"_denoised\" depending on whether\ - \ the denoising step was run.
\nFolders and files in bold\ - \ are always included.
\nFolders and files in italics are removed\
- \ if --keep_intermediates
is NOT indicated
THRESHOLDED_BVALS
\n\ + C).\n<imageN_%> denotes the original prefix\ + \ of imageN with the preceding preprocessing step descriptors tacked on the end.\ + \ For example, in the case of the PRENORMALIZED directory, the prefix for imageJ\ + \ may or may not include \"_denoised\" depending on whether the denoising step\ + \ was run.
\nFolders and files in bold are always included.
\n\ +Folders and files in italics are removed if --keep_intermediates
\
+ \ is NOT indicated
THRESHOLDED_BVALS
\n\<image1>.bval
\n:
\n<imageN>.bval
\nCHECKED\ \ (these contain the volumes that have passed the bval/bvec checks)
\ndwigradcheck
and are only used for QA purposes)\ndwmri.bval
\ndwmri.bvec
\n\PreQual was developed at Vanderbilt\ - \ under the project name \"dtiQA v7 Multi\". PreQual v1.0.0 represents dtiQA v7.2.0.\ + \ (final QA document)
PreQual was developed at Vanderbilt under\ + \ the project name \"dtiQA v7 Multi\". PreQual v1.0.0 represents dtiQA v7.2.0.\ \ Thus, on XNAT, dtiQA v7.2.x refers to PreQual v1.0.x.
\n" - stargazers_count: 26 + stargazers_count: 33 subscribers_count: 1 topics: - diffusion @@ -37140,7 +37137,7 @@ MASILab/PreQual: - preprocessing - quality - assurance - updated_at: 1681220660.0 + updated_at: 1696841326.0 MASILab/Synb0-DISCO: data_format: 2 description: Distortion correction of diffusion weighted MRI without reverse phase-encoding @@ -38114,22 +38111,22 @@ MPIB/singularity-fsl: data_format: 2 description: various singularity recipes for FSL filenames: - - Singularity.6.0.6.1 - Singularity.6.0.2-Cuda8-xtract_viewer - - Singularity.5.0.11 - - Singularity.6.0.5 - - Singularity.6.0.2-Cuda8 - - Singularity.6.0.6 - - Singularity.6.0.1 - - Singularity.6.0.3 - - Singularity.5.0.10 - - Singularity.6.0.0 + - Singularity.5.0.9 - Singularity.6.0.2 - - Singularity.6.0.4-Cuda8 - Singularity.6.0.5.1 - Singularity.6.0.4 - - Singularity.5.0.9 + - Singularity.6.0.6 + - Singularity.6.0.4-Cuda8 + - Singularity.5.0.10 + - Singularity.5.0.11 + - Singularity.6.0.0 + - Singularity.6.0.6.1 + - Singularity.6.0.3 - Singularity.5-Cuda8 + - Singularity.6.0.1 + - Singularity.6.0.2-Cuda8 + - Singularity.6.0.5 full_name: MPIB/singularity-fsl latest_release: null readme: 'checkQC\n\ +
\n\n Instrument types supported in checkQC are the\
\ following: CheckQC requires Python 3.6 (or higher\
- \ to run). CheckQC can be installed with pip. Alternatively it can be installed with conda using the bioconda\
- \ channel: After installing CheckQC\
+ CheckQC\
+ \ requires Python 3.10. CheckQC can be installed with pip. Alternatively it can be installed\
+ \ with conda using the bioconda channel: After installing CheckQC\
\ you can run it by specifying the path to the runfolder you want to\nanalyze\
\ like this: This\
\ will use the default configuration file packaged with CheckQC if you want to\
@@ -40923,20 +40919,20 @@ Molmed/checkQC:
: 90,\n \"warning\": \"unknown\"\n },\n {\n\
\ \"handler\": \"UndeterminedPercentageHandler\",\n \
\ \"error\": 10,\n \"warning\": \"unknown\"\n }\n\
- \ ]\n }\n}\n\n In addition to running like a commandline application,\
- \ CheckQC can be run as a simple webservice. To run it you simply need\
- \ to provide the path to a directory where runfolders that you want to\nbe able\
- \ to check are located. This is given as Once the webserver is running you can query the In addition to running like a commandline application, CheckQC can be run as\
+ \ a simple webservice. To run it you simply need to provide the path to\
+ \ a directory where runfolders that you want to\nbe able to check are located.\
+ \ This is given as Once the webserver is running you can query the Test reads can be obtained by running: Software
@@ -50242,8 +50238,8 @@ PetroleumCyberneticsGroup/FieldOpt:
description: New main repository for the FieldOpt project
filenames:
- Docker/Singularity
- - Docker/Develop/Singularity
- Docker/Release/Singularity
+ - Docker/Develop/Singularity
full_name: PetroleumCyberneticsGroup/FieldOpt
latest_release: v1.1-0
readme: ' This repo contains tools and scripts for SHIFT Dataset's downloading, conversion, and more! Homepage\
- \ | Paper\
- \ (CVPR 2022) | SHIFT Dataset DevKit \n This repo contains\
+ \ tools and scripts for SHIFT Dataset's downloading, conversion, and more! Homepage | Paper (CVPR 2022) | Poster | Talk | Demo\n
\nInstall\
- \ instructions
\n
\npip install checkqc\n\
-
\n\
- conda install -c bioconda checkqc\n
Running CheckQC
\nInstall instructions
\n
\npip install checkqc\n
\nconda install -c bioconda\
+ \ checkqc\n
Running CheckQC
\n
\ncheckqc <RUNFOLDER>\n
Running\
- \ CheckQC as a webservice
\nMONITOR_PATH
below. There\
- \ are also a number\nof optional arguments that can be passed to the service.
\n$ checkqc-ws --help\nUsage: checkqc-ws [OPTIONS] MONITOR_PATH\n\nOptions:\n\
- \ --port INTEGER Port which checkqc-ws will listen to (default: 9999).\n\
- \ --config PATH Path to the checkQC configuration file (optional)\n --log_config\
- \ PATH Path to the checkQC logging configuration file (optional)\n --debug \
- \ Enable debug mode.\n --help Show this message and exit.\n\
- \n
/qc/
\
+ \ ]\n }\n}\n\nRunning CheckQC\
+ \ as a webservice
\n\
+ MONITOR_PATH
below. There are also a number\n\
+ of optional arguments that can be passed to the service.
\n$ checkqc-ws\
+ \ --help\nUsage: checkqc-ws [OPTIONS] MONITOR_PATH\n\nOptions:\n --port INTEGER\
+ \ Port which checkqc-ws will listen to (default: 9999).\n --config PATH \
+ \ Path to the checkQC configuration file (optional)\n --log_config PATH \
+ \ Path to the checkQC logging configuration file (optional)\n --debug \
+ \ Enable debug mode.\n --help Show this message and exit.\n\n\
+
/qc/
\
\ endpoint and get any errors and warnings back as json.\nHere is an example how\
\ to query the endpoint, and what type of results it will return:
\n$\
\ curl -s -w'\\n' localhost:9999/qc/170726_D00118_0303_BCB1TVANXX | python -m\
@@ -41722,10 +41718,10 @@ Mykrobe-tools/mykrobe:
\ \\\n --species tb \\\n --output out.json \\\n --format json \\\n --seq reads.fq.gz\n\
\n"
- stargazers_count: 85
+ stargazers_count: 86
subscribers_count: 9
topics: []
- updated_at: 1695828445.0
+ updated_at: 1699099814.0
Mykrobe-tools/mykrobe-lineage-test:
data_format: 2
description: Test species and lineage calls made by mykrobe
@@ -42011,10 +42007,10 @@ NBISweden/K9-WGS-Pipeline:
description: Nextflow pipeline for standardised variant calls on canine genomes
filenames:
- containers/fastqc/Singularity.fastqc-0.11.5
+ - containers/bwa/Singularity.bwa-0.7.12
+ - containers/gatk/Singularity.gatk-3.5
- containers/picard/Singularity.picard-2.10.6
- containers/picard/Singularity.picard-1.97
- - containers/gatk/Singularity.gatk-3.5
- - containers/bwa/Singularity.bwa-0.7.12
- containers/htslib/Singularity.htslib-1.5
full_name: NBISweden/K9-WGS-Pipeline
latest_release: null
@@ -42923,12 +42919,12 @@ NIH-HPC/singularity-examples:
- Singularity.digits
- Singularity.tensorflow
- Singularity.theano
- - keras/Singularity.keras-py2
- - keras/Singularity.keras-py3
- - rnaseq/Singularity.rnaseq
- torch/Singularity.torch
- torch/Singularity.torch-extras
+ - keras/Singularity.keras-py3
+ - keras/Singularity.keras-py2
- rstudio/Singularity.rstudio
+ - rnaseq/Singularity.rnaseq
full_name: NIH-HPC/singularity-examples
latest_release: null
readme: 'wget\
\ -O reads.fq.gz https://ndownloader.figshare.com/files/21059229\n
@@ -50353,8 +50349,8 @@ PetroleumCyberneticsGroup/FieldOpt-Research-Open:
description: FieldOpt C++ Optimization Framework [Open Research Version]
filenames:
- Docker/Singularity
- - Docker/Develop/Singularity
- Docker/Release/Singularity
+ - Docker/Develop/Singularity
full_name: PetroleumCyberneticsGroup/FieldOpt-Research-Open
latest_release: null
readme: '
FieldOpt-Research-Opensingularity_containersInstructionsgh-pages branch of this repository.
uwyo_2019 SHIFT Dataset DevKit
\n\
- \n\n\n\n News
\n\
- \n
)\nNews
\n\n\
+
We recommend\ - \ downloading SHIFT using our Python download script. You can select the subset\ - \ of views, data groups, splits, and framerates of the data to download. A usage\ - \ example is shown below. You can find the abbreviation for views and data groups\ - \ in the following tables.
\npython download.py --view \"\
- [front, left_stereo]\" \\ # list of\
- \ view abbreviation to download\n --group \"[img, semseg]\"\
- \\ # list of data group abbreviation to download \n\
- \ --split \"[train,\
- \ val, test]\" \\\
- \ # list of splits\
- \ to download \n --framerate \"[images, videos]\"\
- \ \\ # chooses the desired frame rate (images=1fps, videos=10fps)\n\
- \ --shift \"discrete\n\nDownloading
\nWe recommend downloading\
+ \ SHIFT using our Python download script. You can select the subset of views,\
+ \ data groups, splits, and framerates of the data to download. A usage example\
+ \ is shown below. You can find the abbreviation for views and data groups in the\
+ \ following tables.
\npython\
+ \ download.py --view \"[front,\
+ \ left_stereo]\" \\\
+ \ # list of view abbreviation\
+ \ to download\n --group \"[img, semseg]\" \\ #\
+ \ list of data group abbreviation to download \n --split\
+ \ \"[train, val, test]\" \\ # list of splits to download \n\
+ \ --framerate \"\
+ [images, videos]\" \\ # chooses\
+ \ the desired frame rate (images=1fps, videos=10fps)\n \
+ \ --shift \"discrete\" \\ \
\ # type of domain shifts.\
\ Options: discrete, continuous/1x, continuous/10x, continuous/100x \n\
@@ -55802,16 +55797,16 @@ SysCV/shift-dev:
>\"all\" --split\
\ \"all\" --framerate \"[images]\" ./data
\nManually download
\nYou\
+ \ class=\"pl-pds\">\"
./data
You\ \ could find the download links on our download page or file server.
\nWe have\ - \ implemented reference dataset classes for SHIFT. They\nshow how to load the\ - \ data via the PyTorch Dataset (file server.
\nWe have implemented\ + \ reference dataset classes for SHIFT. They\nshow how to load the data via the\ + \ PyTorch Dataset (torch_dataset.py) and CustomDataset in mmdet framework (mmdet_dataset.py).
\nBelow is an example for PyTorch Dataset.
\n\ @@ -55842,46 +55837,46 @@ SysCV/shift-dev: >=ZipBackend(), # also\ \ supports HDF5Backend(), FileBackend()\n verbose=True,\n)\n\ -Instead of unzipping the downloaded zip files, you can also convert\ - \ them into corresponding HDF5 files. HDF5 file is designed to store a large dataset\ - \ in a single file and, meanwhile, to support efficient I/O for training purposes.\ - \ Converting to HDF5 is a good practice in an environment where the number of\ - \ files that can be stored is limited.
\nHowever, if you want to preprocess\ - \ the data before using them, we don't recommend converting them\ - \ into HDF5 before the processing, which will complicate the loading.
\nExample\
- \ 1: Packing directly from the downloaded zip files. (You can set the\
- \ number of processes by -j
)
Instead\ + \ of unzipping the downloaded zip files, you can also convert them into corresponding\ + \ HDF5 files. HDF5 file is designed to store a large dataset in a single file\ + \ and, meanwhile, to support efficient I/O for training purposes. Converting to\ + \ HDF5 is a good practice in an environment where the number of files that can\ + \ be stored is limited.
\nHowever, if you want to preprocess the data before\ + \ using them, we don't recommend converting them into HDF5 before\ + \ the processing, which will complicate the loading.
\nExample 1:\
+ \ Packing directly from the downloaded zip files. (You can set the number of processes\
+ \ by -j
)
python\
+ \ -m shift_dev.io.to_hdf5 \"./data/discrete/**/*.zip\" --zip -j 1
Example\ + \ 2: Packing from an unzipped folder.
\npython -m shift_dev.io.to_hdf5 \"./data/discrete/**/*.zip\" --zip\
- \ -j 1
Example 2: Packing from an unzipped folder.
\n\ -python -m shift_dev.io.to_hdf5\
- \ \"./data/discrete/images/val/left_45/img/\"
Note: The converted HDF5 file\
- \ will maintain the same file structure of the zip file/folder, i.e., <seq>/<frame>_<group>_<view>.<ext>
.
Below is a code snippet for reading images from\ - \ an HDF5 file.
\nimport io\nimport h5py\nfrom\
- \ PIL import Image\n\nname = \"0123-abcd/00000000_img_front.jpg\"\nwith h5py.File(\"/path/to/file.hdf5\", \"r\") as hdf5:\
- \ # load the HDF5 file\n bytes = bytearray(hdf5[name]) \
- \ # select the file we want\n img = Image.\"./data/discrete/images/val/left_45/img/\"
Note: The converted HDF5 file will maintain the same file structure of the\
+ \ zip file/folder, i.e., <seq>/<frame>_<group>_<view>.<ext>
.
Below is a code snippet for reading images from an HDF5 file.
\n\ +import\
+ \ io\nimport h5py\nfrom PIL\
+ \ import Image\n\nname = \"0123-abcd/00000000_img_front.jpg\"\nwith\
+ \ h5py.File(\"/path/to/file.hdf5\", \"r\") as hdf5: # load the HDF5 file\n bytes = bytearray(hdf5[name]) \
+ \ # select the file we want\n img = Image.open(io.BytesIO(bytes)) # same as opening an ordinary png file from IO stream.
For easier retrieval of frames during training, we recommend decompressing\ - \ all video sequences into image frames before training. Make sure there is enough\ - \ disk space to store the decompressed frames. The video sequences are used for\ - \ the RGB data group only.
\nThe mode option ( For easier\
+ \ retrieval of frames during training, we recommend decompressing all video sequences\
+ \ into image frames before training. Make sure there is enough disk space to store\
+ \ the decompressed frames. The video sequences are used for the RGB data group\
+ \ only. The mode option (--mode, -m
) controls\
- \ the storage type of the decompressed frames. When the mode is set to folder
\
+ \n
\nDecompress video files
\n--mode, -m
) controls the storage\
+ \ type of the decompressed frames. When the mode is set to folder
\
\ (default option) the frames are extracted to local file systems directly; when\
\ the mode is set to zip
, tar
or hdf5
,\
\ the frames are stored in the corresponding archive file, e.g., img_decompressed.zip
.MODE
and JOBS
.
We provide a visualization tool\
- \ for object-level labels (e.g., bounding box, instance segmentation). The main\
- \ rendering functions are provided in shift_dev/vis/render.py
file.\
- \ We believe you can reuse many of them for other kinds of visualization.
We also provide a tool to make videos with annotations:
\npython -m shift_dev.vis.video <seq_id> \\ # specify the video\ - \ sequence\n -d <path/to/img.zip> \\ \ - \ # path to the img.zip or its\ - \ unzipped folder\n -l <path/to/label.json> \\ # path to the corresponding label\ - \ ({det_2d/det_3d/det_insseg_2d}.json)\n -o <path/for/output> \\ \ - \ # output path\n \ - \ --view front # specify the view, needed to be corresponded with images and label\ - \ file
This command will render an MP4 video with the bounding\ - \ boxes or instance masks plotted over the background images. Check out the example\ - \ here\ - \ (starting from 00:10)!
\nYou can find\ - \ some helpful repos based on the SHIFT dataset below! Please don't hesitate to\ - \ contact us to include your repos.
\nWe provide a visualization tool for object-level\
+ \ labels (e.g., bounding box, instance segmentation). The main rendering functions\
+ \ are provided in shift_dev/vis/render.py
file. We believe you can\
+ \ reuse many of them for other kinds of visualization.
We also provide\ + \ a tool to make videos with annotations:
\npython -m shift_dev.vis.video <seq_id> \\ # specify the video sequence\n -d\
+ \ <path/to/img.zip>\
+ \ \\ # path to the img.zip or its unzipped folder\n\
+ \ -l <path/to/label.json> \\ # path to the corresponding label ({det_2d/det_3d/det_insseg_2d}.json)\n\
+ \ -o <path/for/output>\
+ \ \\ # output path\n --view front \
+ \ # specify the view,\
+ \ needed to be corresponded with images and label file
This\ + \ command will render an MP4 video with the bounding boxes or instance masks plotted\ + \ over the background images. Check out the example here (starting from 00:10)!
\n\nYou\ + \ can find some helpful repos based on the SHIFT dataset below! Please don't hesitate\ + \ to contact us to include your repos.
\nThe SHIFT Dataset is made\ - \ freely available to academic and non-academic entities for research purposes\ - \ such as academic research, teaching, scientific publications, or personal experimentation.\ - \ If you use our dataset, we kindly ask you to cite our paper as:
\n@InProceedings{shift2022,\n\
+ >@Chengxiang Fan
The SHIFT Dataset is made freely available\ + \ to academic and non-academic entities for research purposes such as academic\ + \ research, teaching, scientific publications, or personal experimentation. If\ + \ you use our dataset, we kindly ask you to cite our paper as:
\n@InProceedings{shift2022,\n\
\ author = {Sun, Tao and Segu, Mattia and Postels, Janis and Wang, Yuxuan\
\ and Van Gool, Luc and Schiele, Bernt and Tombari, Federico and Yu, Fisher},\n\
\ title = {{SHIFT:} A Synthetic Driving Dataset for Continuous Multi-Task\
@@ -56000,7 +55994,7 @@ SysCV/shift-dev:
\ \xA9 2022, Tao Sun (@suniique), ETH VIS Group.\n"
- stargazers_count: 77
+ stargazers_count: 84
subscribers_count: 6
topics:
- autonomous-vehicles
@@ -56009,7 +56003,7 @@ SysCV/shift-dev:
- cvpr2022
- computer-vision
- continual-learning
- updated_at: 1690253257.0
+ updated_at: 1698393274.0
SystemsBioinformatics/ecmtool:
data_format: 2
description: Uncover organisms' metabolic blueprints
@@ -56422,16 +56416,16 @@ TACC/WMA-Tapis-Templates:
data_format: 2
description: null
filenames:
- - applications/hello-world/Singularity
- - applications/opensees-mp/opensees-mp-3.5.0/Singularity
- - applications/interactive/Singularity
- - applications/extract/Singularity
- - applications/rstudio/nginx/Singularity.conf
- - applications/jupyter-lab-hpc-openmpi/Singularity
- - applications/designsafe/jupyter-lab-hpc/Singularity
- applications/opensees-sp/opensees-sp-3.5.0/Singularity
+ - applications/designsafe/jupyter-lab-hpc/Singularity
+ - applications/rstudio/nginx/Singularity.conf
- applications/jupyter-lab-hpc/Singularity
+ - applications/interactive/Singularity
+ - applications/jupyter-lab-hpc-openmpi/Singularity
- applications/compress/Singularity
+ - applications/opensees-mp/opensees-mp-3.5.0/Singularity
+ - applications/extract/Singularity
+ - applications/hello-world/Singularity
full_name: TACC/WMA-Tapis-Templates
latest_release: null
readme: 'WMA-Tapis-Templates\n Title
@@ -59403,8 +59397,8 @@ UPennBJPrager/CNT_Research_Template:
description: Research template repository for the University of Pennsylvania CNT
lab
filenames:
- - core_libraries/subtrees/MRtrix3/Singularity
- core_libraries/subtrees/PreQual/Singularity
+ - core_libraries/subtrees/MRtrix3/Singularity
full_name: UPennBJPrager/CNT_Research_Template
latest_release: null
readme: "\n\nVALENCE
\n
A Massively\
- \ Parallel Implementation of Variational Subspace Valence Bond
\n16 November,\
- \ 2018
\nContents:
\n\n- Overview
\n- Introduction\
+ max-width: 100%;\">
\nVALENCE
\nA Massively Parallel Implementation of Variational Subspace\
+ \ Valence Bond
\n16 November, 2018
\nContents:
\n\n- Overview
\n- Introduction\
\ to VSVB
\n- About the code
\n- Input
\n- Output
\n- Examples
\n\
- Automatic input generation with vtools
\n- How to make spin-coupled\
\ orbitals
\n- How to use derived basis functions
\n- Acknowledgements
\n\
- - Contact information
\n
\nSection\
- \ 1. Overview
\nWhen first downloaded, this repository includes
\n\n\
- - .travis.yml
\n- How-to-build-VALENCE.md
\n- LICENSE
\n- Makefile
\n\
- - README.md
\n- doc/
\n- examples/
\n- install-mpich.sh*
\n\
- - install-simint.sh*
\n- nitrogen/
\n- src/
\n- testing/
\n\
- - valence_tutorial.ipynb
\n- vtools/
\n
\nMost of the above\
- \ items are self-explanatory. The directories, /examples/ and /testing/, both\
- \ contain input files that can be used to validate the binary. /testing/ contains\
- \ an established set of inputs primarily for internal use. While there is some\
- \ overlap with /testing/, /examples/ is oriented more toward educating the user\
- \ about the various functions of VALENCE and features of VSVB (see Section 6 for\
- \ more details). /vtools/ contains tools for automatic input generation and is\
- \ described in Section 7. /doc/ contains a write-up of the method and Doxygen-generated\
- \ documentation. /nitrogen/ is concerned with the interface to NITROGEN (https://www.colorado.edu/nitrogen/)\
- \ for optimizing molecular geometries and computing vibrational frequencies. The\
- \ next section contains a brief, practical introduction to VSVB - the method implemented\
- \ by VALENCE.
\nSection\
- \ 2. Introduction to VSVB
\nIn molecular electronic structure theory, variational\
- \ subspace valence bond, or VSVB, is a type of generalized valence bond theory\
- \ using gaussian atomic basis sets. Unlike the majority of methods in the mainstream\
- \ of quantum chemistry such as Hartree-Fock, MP2, coupled-cluster, DFT, and so\
- \ on, VSVB is based purely on orbitals that are allowed to overlap with one another.\
- \ That is, VSVB does not use orthogonal ('molecular') orbitals. The first benefit\
- \ is that VSVB orbitals tend to be highly local, typically involving just one\
- \ or two atoms, in contrast to molecular orbitals which are typically delocalized\
- \ over all the atoms in any given problem. The highly local orbitals have obvious\
- \ advantages for chemical interpretability and computational scalability.
\n\
- The first method paper is:
\nGraham D. Fletcher, \"The variational subspace\
- \ valence bond method\",\nJ. Chem. Phys. 142, 134112 (2015).\nContact information\n\nSection 1. Overview
\nWhen\
+ \ first downloaded, this repository includes
\n\n- .travis.yml
\n\
+ - How-to-build-VALENCE.md
\n- LICENSE
\n- Makefile
\n- README.md
\n\
+ - doc/
\n- examples/
\n- install-mpich.sh*
\n- install-simint.sh*
\n\
+ - nitrogen/
\n- src/
\n- testing/
\n- valence_tutorial.ipynb
\n\
+ - vtools/
\n
\nMost of the above items are self-explanatory. The\
+ \ directories, /examples/ and /testing/, both contain input files that can be\
+ \ used to validate the binary. /testing/ contains an established set of inputs\
+ \ primarily for internal use. While there is some overlap with /testing/, /examples/\
+ \ is oriented more toward educating the user about the various functions of VALENCE\
+ \ and features of VSVB (see Section 6 for more details). /vtools/ contains tools\
+ \ for automatic input generation and is described in Section 7. /doc/ contains\
+ \ a write-up of the method and Doxygen-generated documentation. /nitrogen/ is\
+ \ concerned with the interface to NITROGEN (https://www.colorado.edu/nitrogen/) for optimizing molecular\
+ \ geometries and computing vibrational frequencies. The next section contains\
+ \ a brief, practical introduction to VSVB - the method implemented by VALENCE.
\n\
+ Section 2. Introduction to VSVB
\nIn\
+ \ molecular electronic structure theory, variational subspace valence bond, or\
+ \ VSVB, is a type of generalized valence bond theory using gaussian atomic basis\
+ \ sets. Unlike the majority of methods in the mainstream of quantum chemistry\
+ \ such as Hartree-Fock, MP2, coupled-cluster, DFT, and so on, VSVB is based purely\
+ \ on orbitals that are allowed to overlap with one another. That is, VSVB does\
+ \ not use orthogonal ('molecular') orbitals. The first benefit is that VSVB orbitals\
+ \ tend to be highly local, typically involving just one or two atoms, in contrast\
+ \ to molecular orbitals which are typically delocalized over all the atoms in\
+ \ any given problem. The highly local orbitals have obvious advantages for chemical\
+ \ interpretability and computational scalability.
\nThe first method paper\
+ \ is:
\nGraham D. Fletcher, \"The variational subspace valence bond method\"\
+ ,\nJ. Chem. Phys. 142, 134112 (2015).\nhttp://dx.doi.org/10.1063/1.4916743
\nSee the CITATION\
\ file for more papers.
\nIn addition to a general background in quantum\
\ chemistry, it helps to read the above paper and references therein. The\ndocument\
@@ -59963,13 +59956,13 @@ VALENCE-software/VALENCE:
\ with VSVB is the cost of optimization which typically corresponds to many energy\
\ calculations. However, the overall cost in terms of the time-to-solution is\
\ greatly mitigated by the ability to re-use previously determined orbitals and\
- \ by the high concurrency that is possible.
\nSection\
- \ 3. About the code
\nVALENCE is the 'number-crunching' code in an overall\
- \ system for executing VSVB calculations that includes various tools (the 'vtools')\
- \ for generating input and processing output. Currently, VALENCE can compute energies\
- \ and optimize single-reference wave functions for the following types of situation-
\n\
+ \ by the high concurrency that is possible.
\nSection 3. About\
+ \ the code
\n\
+ VALENCE is the 'number-crunching' code in an overall system for executing VSVB\
+ \ calculations that includes various tools (the 'vtools') for generating input\
+ \ and processing output. Currently, VALENCE can compute energies and optimize\
+ \ single-reference wave functions for the following types of situation-
\n\
\n- Ground states.
\n- Excited states, including multiple excitations\
\ (single-reference).
\n- Closed-shell systems.
\n- Open-shell systems.
\n\
- Bond-breaking/formation, majority or all of the nuclear potential energy surface.
\n\
@@ -60001,232 +59994,231 @@ VALENCE-software/VALENCE:
\ VALENCE takes an input file on the command line. To run VALENCE sequentially\
\ just type,\n./valence [name of input file]
\nSee section\
\ 5 for example input files. To run VALENCE in parallel, please consult the documentation\
- \ for your target platform as to how MPI runs are specified.
\nSection 4. Input
\nThe 'valence' binary makes no choices regarding\
- \ the orbitals, it merely processes the wave function specified by the input to\
- \ compute energies, execute optimization procedures, and so forth. That said,\
- \ a highly versatile system for defining the orbitals is supported, together with\
- \ the N-electron form of the wave function, and this is described in this section.
\n\
- The direct input to VALENCE is a plain-text numerical input file. No attempt\
- \ is made to improve the 'look' of the input because advanced tools are included\
- \ in this package to generate input automatically and offer a more user-friendly\
- \ environment (see 'vtools'). Although the tools continue to be refined and improved\
- \ in our research group, it is important to understand how the direct input is\
- \ structured since it offers the highest degree of generality.
\nIn what\
- \ follows, a helpful concept is that of the 'orbital basis set' (OBS). In contrast\
- \ to the more familiar 'molecular' basis set, an OBS spans the basis sets of just\
- \ those atoms needed to support a given spatial orbital in VSVB. OBS often involve\
- \ just one or two atoms in order to represent, for example, core electrons and\
- \ 'lone-pairs', or chemical bonds, respectively. Unlike a molecular basis set,\
- \ as the molecular system increases in size the component OBS stay approximately\
- \ the same size. Thus, OBS facilitate a concise and efficient definition of an\
- \ orbital that is independent of the target molecule, allowing orbitals to be\
- \ stored and re-combined to make new wave functions.
\nBroadly, the input\
- \ to VALENCE consists of specifications for the geometry, basis set, and a guess\
- \ wave function, structured to facilitate dynamic memory. Thus, all the counts/sizes/dims/lengths,\
- \ etc, are given on the first line so the program can allocate sufficient memory\
- \ to read the data that follow. Lines may be separated by blank lines and items\
- \ on a line by spaces, as permitted by FORTRAN free-format input rules. All data\
- \ items are of INTeger type, unless specified to be FLOAT/REAL. Note also that\
- \ VALENCE checks few input errors, but many errors can be avoided by using the\
- \ 'vtools'.
\nThe input is organized in the order:
\n\n- A) sizes,\
- \ counts, array dims (1 line)
\n- B) Wave function optimization control\
- \ (1 line)
\n- C) Geometry
\n- D) Basis set
\n- E) N-electron\
- \ Wave function information (spin-couplings, optional)
\n- F) Orbitals\
- \ (various kinds, in order: spin-coupled, single-occupied, double-occupied)
\n\
- - G) Derived basis functions
\n
\nParts (A) through (G) are now described\
- \ in more detail.
\nA) sizes, counts, array dims
\nThere are 15 integers\
- \ on a single line, they are-
\n\n- Item 1. The number of atoms/point\
- \ charges in the geometry
\n- Item 2. The number of unique atom types
\n\
- - Item 3. Number of spin-coupled electron/orbital PAIRS
\n- Item 4.\
- \ Number of unpaired electrons/orbitals
\n- Item 5. Number of double-occupied\
- \ (DOCC) orbitals
\n- Item 6. Total length of the orbital weight list (array\
- \ dim)
\n- Item 7. Length of the largest orbital expansion (array dim)
\n\
- - Item 8. Number of spin-couplings
\n- Item 9. Number of unique atomic\
- \ basis set shells
\n- Item 10. Number of unique atomic basis set primitives
\n\
- - Item 11. Highest angular momentum in the basis set
\n- Item 12. Number\
- \ of derived basis functions (DBF)
\n- Item 13. Number of orbital optimization\
- \ groups
\n- Item 14. Number of orbital excitations
\n- Item 15: Largest\
- \ atom count of the orbital basis sets
\n
\nB) Optimization control
\n\
- There are 8 or more items on a single line, they are-
\n\n- Item 1.\
- \ The charge-cloud screening tolerance (integer), e.g. '5' means 0.00001 or 1d-5\
- \ or 10^-5.
\n- Item 2. The density screening tolerance
\n- Item 3.\
- \ The integral screening tolerance (Schwarz inequality)
\n- Item 4: The\
- \ coarsest wave function convergence tolerance, given in kilocalories per mole\
- \ (kCal/Mol).
\n- Item 5: The finest wave function convergence tolerance.\
- \ The optimization will proceed through multiple orbital groups from coarse to\
- \ fine.
\n- Item 6: Maximum number of iterations.
\n- Item 7: Initial\
- \ weight perturbation (DEM only)
\n- Item 8: Weight perturbation scalar\
- \ (DEM only)
\n- Item 9: The orbital optimization groups as begin/end pairs\
- \ of orbital labels (in order), e.g. \" 1 3 5 6 \" shows 2 groups: first group\
- \ optimizes orbitals 1 through 3, second group is orbitals 5 and 6 (skipping 4)
\n\
-
\nC) Geometry
\nA line for each atom/point charge with the layout:
\n\
- [Atom type] X Y Z
\neg. 1 0.0 0.0 0.0
\nThe atom type\
- \ is an integer that addresses the basis set(s) given in the next section. For\
- \ example, type '1' addresses the first basis set listed, type '2' the second,\
- \ and so on. 'X,Y,Z' refers to cartesian coordinates in Angstroms (FLOATs).
\n\
- D) Basis Set
\nVALENCE recognizes basis sets of cartesian atom-centered\
- \ contracted gaussians. The basis set for each atom/etc type is given with the\
- \ layout:
\n\n- Item 1: The nuclear/point charge (FLOAT)
\n- Item\
- \ 2: The number of shells (zero or more), NS
\n
\nThere follow NS datasets\
- \ defining the shells, as follows:
\nNext line:
\n\n- Item 1: The\
- \ shell angular momentum
\n- Item 2: The number of primitive gaussian functions,\
- \ NP, in the shell.
\n
\nThere follow NP lines, as follows:
\nNext\
- \ line:
\n\n- Item 1: The primitive exponent (FLOAT)\nIf NP>1,
\n\
- - Item 2: The primitive coefficient (FLOAT)
\n
\nNote that VALENCE\
- \ skips input of the redundant unit weight when NP=1. The counts, NS and NP, are\
- \ iterated until all shells are input for all the atom types. A point charge can\
- \ be input as a 'nuclear charge' with NS=0. This input is quite general. For example,\
- \ 'floating' basis set shells can be placed anywhere using a nuclear charge of\
- \ zero.
\nE) The 'N-electron' wave function (optional)
\nIf the number\
- \ of spin-couplings, NC (Item 8 of (A), above), is greater than zero, then the\
- \ spin-coupling information will be read next. Currently, the code can make 'Rumer'\
- \ type couplings for the singlet parts of a system. Each spin-coupling is read\
- \ as follows. If NC=1, a list of the orbital (electron) label pairs defining the\
- \ (singlet) couplings is given, e.g. 1 2 3 4, means electrons '1 and 2' are\
- \ singlet coupled, then electrons '3 and 4' are singlet coupled. If NC>1, the\
- \ pair list is preceeded with the spin-coupling weight (FLOAT). This is repeated\
- \ for all NC spin-couplings.
\nExcited states may be entered next, according\
- \ to Item 14 of (A), as a sequence of {NX,NR} pairs, where NX addresses the orbital\
- \ to be promoted and NR labels the root of the secular equation (e.g. '0' for\
- \ lowest/ground, '1' for first excited, etc). It is an error if spin-coupled orbitals\
- \ are desired but no spin-couplings are input (Item 8 = 0). Without spin-coupled\
- \ orbitals and/or excited states, this section is null/empty.
\nF) Spatial\
- \ wave function
\nThe spatial wave function in terms of the orbitals is\
- \ now given. In general, the total number of orbitals is given by-
\n2*[no.\
- \ spin-coupled PAIRs] + [no. unpaired] + [no. DOCC]
\nWhatever orbitals\
- \ are needed, they must be entered in the order: spin-coupled; then unpaired;\
- \ then DOCC, as per the intended use. The layout for each orbital is as follows.\
- \ The first line defines the orbital basis set (OBS):
\n\n- Item 1. \
- \ The number of atoms, NN, whose basis sets make up the OBS.
\n- Item 2.\
- \ List of NN atoms.
\n- Item 3. Total number of AO's, NA.
\n
\n\
- There follow NA {AO address, coefficient (FLOAT)}-pairs. The AO addresses\
- \ lie within the OBS specified by the NN atoms and in the order the atoms are\
- \ listed. The scheme iterates until all orbitals required by the above formula,\
- \ based on items 3-5 of (A), are input.
\n(G) Derived basis functions
\n\
- DBFs are input using the same OBS format as described in (F) for the main orbital\
- \ types. The total number of DBFs is preempted by item 12 of (A). See section\
- \ 7 for more details.
\nSection 5. Output
\n\
- Broadly, the output of VALENCE is the VSVB wave function and its total energy.\
- \ VALENCE first prints the outcome of a 'guess' energy calculation, preceded by\
- \ the nuclear repulsion energy in the case of a molecule, optionally followed\
- \ by an optimization run with a progress update at each iteration. Each optimization\
- \ step reports the cumulative relaxation in kCal/Mol compared to the 'guess energy'.\
- \ Also printed is the relaxation obtained at that step divided by the convergence\
- \ tolerance to indicate how near the optimization is to convergence. Every energy\
- \ calculation or optimization step prints a file called 'orbitals' which contains\
- \ the updated orbitals together with the current total energy at the end. If spin-coupled\
+ \ for your target platform as to how MPI runs are specified.
\nSection 4. Input
\nThe 'valence' binary\
+ \ makes no choices regarding the orbitals, it merely processes the wave function\
+ \ specified by the input to compute energies, execute optimization procedures,\
+ \ and so forth. That said, a highly versatile system for defining the orbitals\
+ \ is supported, together with the N-electron form of the wave function, and this\
+ \ is described in this section.
\nThe direct input to VALENCE is a plain-text\
+ \ numerical input file. No attempt is made to improve the 'look' of the input\
+ \ because advanced tools are included in this package to generate input automatically\
+ \ and offer a more user-friendly environment (see 'vtools'). Although the tools\
+ \ continue to be refined and improved in our research group, it is important to\
+ \ understand how the direct input is structured since it offers the highest degree\
+ \ of generality.
\nIn what follows, a helpful concept is that of the 'orbital\
+ \ basis set' (OBS). In contrast to the more familiar 'molecular' basis set, an\
+ \ OBS spans the basis sets of just those atoms needed to support a given spatial\
+ \ orbital in VSVB. OBS often involve just one or two atoms in order to represent,\
+ \ for example, core electrons and 'lone-pairs', or chemical bonds, respectively.\
+ \ Unlike a molecular basis set, as the molecular system increases in size the\
+ \ component OBS stay approximately the same size. Thus, OBS facilitate a concise\
+ \ and efficient definition of an orbital that is independent of the target molecule,\
+ \ allowing orbitals to be stored and re-combined to make new wave functions.
\n\
+ Broadly, the input to VALENCE consists of specifications for the geometry,\
+ \ basis set, and a guess wave function, structured to facilitate dynamic memory.\
+ \ Thus, all the counts/sizes/dims/lengths, etc, are given on the first line so\
+ \ the program can allocate sufficient memory to read the data that follow. Lines\
+ \ may be separated by blank lines and items on a line by spaces, as permitted\
+ \ by FORTRAN free-format input rules. All data items are of INTeger type, unless\
+ \ specified to be FLOAT/REAL. Note also that VALENCE checks few input errors,\
+ \ but many errors can be avoided by using the 'vtools'.
\nThe input is organized\
+ \ in the order:
\n\n- A) sizes, counts, array dims (1 line)
\n- B)\
+ \ Wave function optimization control (1 line)
\n- C) Geometry
\n- D)\
+ \ Basis set
\n- E) N-electron Wave function information (spin-couplings,\
+ \ optional)
\n- F) Orbitals (various kinds, in order: spin-coupled, single-occupied,\
+ \ double-occupied)
\n- G) Derived basis functions
\n
\nParts\
+ \ (A) through (G) are now described in more detail.
\nA) sizes, counts,\
+ \ array dims
\nThere are 15 integers on a single line, they are-
\n\n\
+ - Item 1. The number of atoms/point charges in the geometry
\n- Item\
+ \ 2. The number of unique atom types
\n- Item 3. Number of spin-coupled\
+ \ electron/orbital PAIRS
\n- Item 4. Number of unpaired electrons/orbitals
\n\
+ - Item 5. Number of double-occupied (DOCC) orbitals
\n- Item 6. Total\
+ \ length of the orbital weight list (array dim)
\n- Item 7. Length of the\
+ \ largest orbital expansion (array dim)
\n- Item 8. Number of spin-couplings
\n\
+ - Item 9. Number of unique atomic basis set shells
\n- Item 10. Number\
+ \ of unique atomic basis set primitives
\n- Item 11. Highest angular momentum\
+ \ in the basis set
\n- Item 12. Number of derived basis functions (DBF)
\n\
+ - Item 13. Number of orbital optimization groups
\n- Item 14. Number of\
+ \ orbital excitations
\n- Item 15: Largest atom count of the orbital basis\
+ \ sets
\n
\nB) Optimization control
\nThere are 8 or more items\
+ \ on a single line, they are-
\n\n- Item 1. The charge-cloud screening\
+ \ tolerance (integer), e.g. '5' means 0.00001 or 1d-5 or 10^-5.
\n- Item\
+ \ 2. The density screening tolerance
\n- Item 3. The integral screening\
+ \ tolerance (Schwarz inequality)
\n- Item 4: The coarsest wave function\
+ \ convergence tolerance, given in kilocalories per mole (kCal/Mol).
\n- Item\
+ \ 5: The finest wave function convergence tolerance. The optimization will proceed\
+ \ through multiple orbital groups from coarse to fine.
\n- Item 6: Maximum\
+ \ number of iterations.
\n- Item 7: Initial weight perturbation (DEM only)
\n\
+ - Item 8: Weight perturbation scalar (DEM only)
\n- Item 9: The orbital\
+ \ optimization groups as begin/end pairs of orbital labels (in order), e.g. \"\
+ \ 1 3 5 6 \" shows 2 groups: first group optimizes orbitals 1 through 3, second\
+ \ group is orbitals 5 and 6 (skipping 4)
\n
\nC) Geometry
\nA\
+ \ line for each atom/point charge with the layout:
\n[Atom type] X Y \
+ \ Z
\neg. 1 0.0 0.0 0.0
\nThe atom type is an integer that\
+ \ addresses the basis set(s) given in the next section. For example, type '1'\
+ \ addresses the first basis set listed, type '2' the second, and so on. 'X,Y,Z'\
+ \ refers to cartesian coordinates in Angstroms (FLOATs).
\nD) Basis Set
\n\
+ VALENCE recognizes basis sets of cartesian atom-centered contracted gaussians.\
+ \ The basis set for each atom/etc type is given with the layout:
\n\n- Item\
+ \ 1: The nuclear/point charge (FLOAT)
\n- Item 2: The number of shells (zero\
+ \ or more), NS
\n
\nThere follow NS datasets defining the shells, as\
+ \ follows:
\nNext line:
\n\n- Item 1: The shell angular momentum
\n\
+ - Item 2: The number of primitive gaussian functions, NP, in the shell.
\n\
+
\nThere follow NP lines, as follows:
\nNext line:
\n\n- Item\
+ \ 1: The primitive exponent (FLOAT)\nIf NP>1,
\n- Item 2: The primitive\
+ \ coefficient (FLOAT)
\n
\nNote that VALENCE skips input of the redundant\
+ \ unit weight when NP=1. The counts, NS and NP, are iterated until all shells\
+ \ are input for all the atom types. A point charge can be input as a 'nuclear\
+ \ charge' with NS=0. This input is quite general. For example, 'floating' basis\
+ \ set shells can be placed anywhere using a nuclear charge of zero.
\nE)\
+ \ The 'N-electron' wave function (optional)
\nIf the number of spin-couplings,\
+ \ NC (Item 8 of (A), above), is greater than zero, then the spin-coupling information\
+ \ will be read next. Currently, the code can make 'Rumer' type couplings for the\
+ \ singlet parts of a system. Each spin-coupling is read as follows. If NC=1, a\
+ \ list of the orbital (electron) label pairs defining the (singlet) couplings\
+ \ is given, e.g. 1 2 3 4, means electrons '1 and 2' are singlet coupled, then\
+ \ electrons '3 and 4' are singlet coupled. If NC>1, the pair list is preceeded\
+ \ with the spin-coupling weight (FLOAT). This is repeated for all NC spin-couplings.
\n\
+ Excited states may be entered next, according to Item 14 of (A), as a sequence\
+ \ of {NX,NR} pairs, where NX addresses the orbital to be promoted and NR labels\
+ \ the root of the secular equation (e.g. '0' for lowest/ground, '1' for first\
+ \ excited, etc). It is an error if spin-coupled orbitals are desired but no spin-couplings\
+ \ are input (Item 8 = 0). Without spin-coupled orbitals and/or excited states,\
+ \ this section is null/empty.
\nF) Spatial wave function
\nThe spatial\
+ \ wave function in terms of the orbitals is now given. In general, the total number\
+ \ of orbitals is given by-
\n2*[no. spin-coupled PAIRs] + [no. unpaired]\
+ \ + [no. DOCC]
\nWhatever orbitals are needed, they must be entered in the\
+ \ order: spin-coupled; then unpaired; then DOCC, as per the intended use. The\
+ \ layout for each orbital is as follows. The first line defines the orbital basis\
+ \ set (OBS):
\n\n- Item 1. The number of atoms, NN, whose basis sets\
+ \ make up the OBS.
\n- Item 2. List of NN atoms.
\n- Item 3. Total\
+ \ number of AO's, NA.
\n
\nThere follow NA {AO address, coefficient\
+ \ (FLOAT)}-pairs. The AO addresses lie within the OBS specified by the NN atoms\
+ \ and in the order the atoms are listed. The scheme iterates until all orbitals\
+ \ required by the above formula, based on items 3-5 of (A), are input.
\n(G)\
+ \ Derived basis functions
\nDBFs are input using the same OBS format as\
+ \ described in (F) for the main orbital types. The total number of DBFs is preempted\
+ \ by item 12 of (A). See section 7 for more details.
\nSection 5. Output
\nBroadly, the output\
+ \ of VALENCE is the VSVB wave function and its total energy. VALENCE first prints\
+ \ the outcome of a 'guess' energy calculation, preceded by the nuclear repulsion\
+ \ energy in the case of a molecule, optionally followed by an optimization run\
+ \ with a progress update at each iteration. Each optimization step reports the\
+ \ cumulative relaxation in kCal/Mol compared to the 'guess energy'. Also printed\
+ \ is the relaxation obtained at that step divided by the convergence tolerance\
+ \ to indicate how near the optimization is to convergence. Every energy calculation\
+ \ or optimization step prints a file called 'orbitals' which contains the updated\
+ \ orbitals together with the current total energy at the end. If spin-coupled\
\ orbitals are used with more than one spin-coupling, an additional file called\
\ 'nelecwfn' is produced, containing the updated spin-coupling weights.
\n\
- Section 6. Examples
\nThe current\
- \ release includes many examples (see directory './examples/') chosen to illustrate\
- \ the main features and types of calculation that can currently be performed with\
- \ VALENCE, while also being of a convenient size for verifying correct execution.\
- \ The examples can be tested using the 'test_all' script and take less than five\
- \ minutes on a typical processor. In /examples/, simply type-
\n./test_all\
- \ valence
\nIn this section, five examples are described in detail.\
- \ Narrative text is contained within brackets where it is helpful to distinguish\
- \ it from the example input text. It is also helpful to note the following: the\
- \ magnitude of a linear weight can occasionally be greater than unity; the overall\
- \ sign of an orbital is arbitrary as the wave function is only determined to within\
- \ a phase; agreement between total energies from the same run on different hardware\
- \ is rarely greater than 10 places, and 6 places is more typical.
\n\n\
- - Beryllium atom
\n
\nThis example uses two DOCC orbitals to model\
- \ the singlet-S ground state of beryllium, with electronic configuration, 1s^2\
- \ 2s^2, expanded over a basis set of three 's'-type functions. The variational\
- \ subspace (VS) of the first orbital consists of functions 1 and 3, with function\
- \ 1 as its unique degree-of-freedom (UDF). The second orbital's VS contains functions\
- \ 2 and 3, with 2 as the UDF. Thus, function 3 is the 'shared' basis. Other choices\
- \ exist (in fact there are two, accounting for symmetry), but this is the most\
- \ efficient choice given the chemical intuition that function 1 resembles a '1s'\
- \ orbital, function 2 a '2s' orbital, and so on, in accordance with the structure\
- \ of a typical atomic basis set. Since there are only DOCC orbitals involved,\
- \ this run optimizes a VSHF wave function.
\n[counts and array dims]\n\
- \ 1 1 0 0 2 4 2 0 3 17 0 0 1 0 1\n\n[run parameters]\n\
- \ 16 16 16 3 3 100 0.0 0.0 1 2\n\n\n[geometry]\n 1 0.0 0.0 0.0\n\n\
- \n[basis set]\n 4.0 3\n0 8\n 2940.0000000 0.0006800 \
- \ \n 441.2000000 0.0052360 \n 100.5000000 \
- \ 0.0266060 \n 28.4300000 0.0999930 \
- \ \n 9.1690000 0.2697020 \n 3.1960000\
- \ 0.4514690 \n 1.1590000 0.2950740 \
- \ \n 0.1811000 0.0125870 \n0 8\n 2940.0000000\
- \ -0.0001230 \n 441.2000000 -0.0009660 \
- \ \n 100.5000000 -0.0048310 \n 28.4300000\
- \ -0.0193140 \n 9.1690000 -0.0532800 \
- \ \n 3.1960000 -0.1207230 \n 1.1590000\
- \ -0.1334350 \n 0.1811000 0.5307670 \
- \ \n0 1\n 0.0589000\n\n[orbitals]\n 1 1 2\n 1 \
- \ 1.0 3 0.0\n 1 1 2\n 2 1.0 3 0.0\n
\n\
- [A guess consisting of ones for the UDF and zeros elsewhere may be termed a\
- \ 'unit guess' by analogy with a unit matrix. The output to the screen is given\
- \ below.]
\n guess energy in atomic units -14.4746666438408660\n\
- \ orbital optimization \n\n\
- \ (full) first-order method \n\n\
- \ cycle orbital relaxation(kCal) (..per orb.)/tol \n \
- \ 1 1 -0.038504 -0.3850E+02\n 1 2 -36.147608\
- \ -0.3611E+05\n 2 1 -58.488652 -0.2234E+05\n 2\
- \ 2 -60.233942 -0.1745E+04\n 3 1 -61.130090\
- \ -0.8961E+03\n 3 2 -61.228910 -0.9882E+02\n 4\
- \ 1 -61.280549 -0.5164E+02\n 4 2 -61.286071\
- \ -0.5522E+01\n 5 1 -61.288965 -0.2894E+01\n 5\
- \ 2 -61.289277 -0.3120E+00\n 6 1 -61.289441\
- \ -0.1637E+00\n 6 2 -61.289458 -0.1761E-01\n\n calculation\
- \ converged \n\n total energy\
- \ in atomic units -14.5723376136726923\n
\n[After six\
- \ cycles through the orbital list, the final energy (which matches the Hartree-Fock\
- \ energy) is printed. The 'orbitals' file looks like this:]
\n \
- \ 1 1 2\n 1 1.00065097 3 -0.00375468\n 1 1 2\n\
- \ 2 0.48912818 3 0.58002975\n\n\n total energy in atomic units \
- \ -14.5723376136726923\n converged to 0.10E-02 kCal/mol\n
\n\
- [There is no 'nelecwfn' file with this run]
\n\n- H2O/VSHF/cc-VDZ
\n\
-
\nThis example highlights chemically intuitive choices for the UDF, so\
- \ just the optimized wave function and energy are given.
\n 3 2\
- \ 0 0 5 30 8 0 7 25 1 0 1 0 3\n\n\n 16 16 16 6 6 100 0.0 0.0\
- \ 1 5\n\n\n 1 0.0000 0.0000 0.1271\n 2 0.0000 0.7580 -0.5085\n\
- \ 2 0.0000 -0.7580 -0.5085 \n\n\n 8.0 5\n0 8\n 11720.0000000 \
- \ 0.0007100 \n 1759.0000000 0.0054700 \n\
- \ 400.8000000 0.0278370 \n 113.7000000 \
- \ 0.1048000 \n 37.0300000 0.2830620 \n \
- \ 13.2700000 0.4487190 \n 5.0250000 \
- \ 0.2709520 \n 1.0130000 0.0154580 \n0 8\n\
- \ 11720.0000000 -0.0001600 \n 1759.0000000 \
- \ -0.0012630 \n 400.8000000 -0.0062670 \n \
- \ 113.7000000 -0.0257160 \n 37.0300000 \
- \ -0.0709240 \n 13.2700000 -0.1654110 \n \
- \ 5.0250000 -0.1169550 \n 1.0130000 \
- \ 0.5573680 \n0 1\n 0.3023000 \n1 3\n \
- \ 17.7000000 0.0430180 \n 3.8540000 \
- \ 0.2289130 \n 1.0460000 0.5087280 \n1 1\n\
- \ 0.2753000 \n\n 1.0 2\n0 3\n 13.0100000\
- \ 0.0196850 \n 1.9620000 0.1379770 \
- \ \n 0.4446000 0.4781480 \n0 1\n 0.1220000\
- \ \n\n\n\n\n 3 1 2 3 6\n 1 1.00085930 2\
- \ 0.00438407 6 0.00072731 9 0.00346894\n 11 0.00045391 13 \
- \ 0.00045392\n 3 1 2 3 8\n 2 0.08137627 5 0.30386789 6\
- \ -0.39873764 8 0.16245941\n 9 -0.27280445 10 0.40165595 11 \
- \ 0.05882459 13 -0.02137198\n 3 1 2 3 8\n 2 0.08137642 5\
- \ -0.30386766 6 -0.39873750 8 -0.16245933\n 9 -0.27280444 11 \
- \ -0.02137131 12 0.40165620 13 0.05882437\n 3 1 2 3 6\n 2\
- \ 0.44922797 3 0.56835690 6 0.25068561 9 0.21482539\n 11 \
- \ -0.03046886 13 -0.03046865\n 1 1 2\n 4 0.63677843 7 \
- \ 0.51530766 \n\n[ The total energy is: -75.97812747 AU ]\n
\n\
- Here, the UDF for the orbitals are as follows:\nOrbital UDF AO label\n\
- oxygen core: O 1s 1\nOH(a) bond: H(a) 1s 10\nOH(b) bond: H(b)\
- \ 1s 12\nSigma lone-pair: O 3s 3\nPi lone-pair: (O 2px 4)
\n\
- Chemically sensible alternatives for the Sigma lone-pair UDF include the O2s\
- \ function. Different orbitals would be obtained with this choice but the same\
- \ energy. The Pi lone-pair has a different symmetry to the other orbitals as it\
- \ is perpendicular to the plane of the atoms. Since it is the only orbital of\
- \ its symmetry, the UDF issue is null (hence the parentheses). The in-plane orbitals\
- \ have Sigma symmetry.
\nNote that the O 2,3py,z functions are not chemically\
- \ sensible UDF for the OH bonds. The atoms are placed in the y,z plane to simplify\
- \ the symmetry definitions, so the 2py,z functions are needed to direct hybridization\
+
Section 6. Examples
\nThe current release includes many examples (see directory\
+ \ './examples/') chosen to illustrate the main features and types of calculation\
+ \ that can currently be performed with VALENCE, while also being of a convenient\
+ \ size for verifying correct execution. The examples can be tested using the 'test_all'\
+ \ script and take less than five minutes on a typical processor. In /examples/,\
+ \ simply type-
\n./test_all valence
\nIn this section,\
+ \ five examples are described in detail. Narrative text is contained within brackets\
+ \ where it is helpful to distinguish it from the example input text. It is also\
+ \ helpful to note the following: the magnitude of a linear weight can occasionally\
+ \ be greater than unity; the overall sign of an orbital is arbitrary as the wave\
+ \ function is only determined to within a phase; agreement between total energies\
+ \ from the same run on different hardware is rarely greater than 10 places, and\
+ \ 6 places is more typical.
\n\n- Beryllium atom
\n
\nThis\
+ \ example uses two DOCC orbitals to model the singlet-S ground state of beryllium,\
+ \ with electronic configuration, 1s^2 2s^2, expanded over a basis set of three\
+ \ 's'-type functions. The variational subspace (VS) of the first orbital consists\
+ \ of functions 1 and 3, with function 1 as its unique degree-of-freedom (UDF).\
+ \ The second orbital's VS contains functions 2 and 3, with 2 as the UDF. Thus,\
+ \ function 3 is the 'shared' basis. Other choices exist (in fact there are two,\
+ \ accounting for symmetry), but this is the most efficient choice given the chemical\
+ \ intuition that function 1 resembles a '1s' orbital, function 2 a '2s' orbital,\
+ \ and so on, in accordance with the structure of a typical atomic basis set. Since\
+ \ there are only DOCC orbitals involved, this run optimizes a VSHF wave function.
\n\
+ [counts and array dims]\n 1 1 0 0 2 4 2 0 3\
+ \ 17 0 0 1 0 1\n\n[run parameters]\n 16 16 16 3 3 100 0.0 0.0\
+ \ 1 2\n\n\n[geometry]\n 1 0.0 0.0 0.0\n\n\n[basis set]\n 4.0 3\n0 \
+ \ 8\n 2940.0000000 0.0006800 \n 441.2000000 \
+ \ 0.0052360 \n 100.5000000 0.0266060 \
+ \ \n 28.4300000 0.0999930 \n 9.1690000 \
+ \ 0.2697020 \n 3.1960000 0.4514690 \
+ \ \n 1.1590000 0.2950740 \n 0.1811000 \
+ \ 0.0125870 \n0 8\n 2940.0000000 -0.0001230\
+ \ \n 441.2000000 -0.0009660 \n 100.5000000\
+ \ -0.0048310 \n 28.4300000 -0.0193140 \
+ \ \n 9.1690000 -0.0532800 \n 3.1960000\
+ \ -0.1207230 \n 1.1590000 -0.1334350 \
+ \ \n 0.1811000 0.5307670 \n0 1\n 0.0589000\n\
+ \n[orbitals]\n 1 1 2\n 1 1.0 3 0.0\n 1 1 \
+ \ 2\n 2 1.0 3 0.0\n
\n[A guess consisting of ones for\
+ \ the UDF and zeros elsewhere may be termed a 'unit guess' by analogy with a unit\
+ \ matrix. The output to the screen is given below.]
\n guess energy\
+ \ in atomic units -14.4746666438408660\n orbital optimization \
+ \ \n\n (full) first-order method \
+ \ \n\n cycle orbital relaxation(kCal)\
+ \ (..per orb.)/tol \n 1 1 -0.038504 \
+ \ -0.3850E+02\n 1 2 -36.147608 -0.3611E+05\n 2 1\
+ \ -58.488652 -0.2234E+05\n 2 2 -60.233942 \
+ \ -0.1745E+04\n 3 1 -61.130090 -0.8961E+03\n 3 2\
+ \ -61.228910 -0.9882E+02\n 4 1 -61.280549 \
+ \ -0.5164E+02\n 4 2 -61.286071 -0.5522E+01\n 5 1\
+ \ -61.288965 -0.2894E+01\n 5 2 -61.289277 \
+ \ -0.3120E+00\n 6 1 -61.289441 -0.1637E+00\n 6 2\
+ \ -61.289458 -0.1761E-01\n\n calculation converged \
+ \ \n\n total energy in atomic units \
+ \ -14.5723376136726923\n
\n[After six cycles through the orbital\
+ \ list, the final energy (which matches the Hartree-Fock energy) is printed. The\
+ \ 'orbitals' file looks like this:]
\n 1 1 2\n 1 \
+ \ 1.00065097 3 -0.00375468\n 1 1 2\n 2 0.48912818 3 \
+ \ 0.58002975\n\n\n total energy in atomic units -14.5723376136726923\n\
+ \ converged to 0.10E-02 kCal/mol\n
\n[There is no 'nelecwfn'\
+ \ file with this run]
\n\n- H2O/VSHF/cc-VDZ
\n
\n\
+ This example highlights chemically intuitive choices for the UDF, so just the\
+ \ optimized wave function and energy are given.
\n 3 2 0 0 5\
+ \ 30 8 0 7 25 1 0 1 0 3\n\n\n 16 16 16 6 6 100 0.0 0.0 1 5\n\
+ \n\n 1 0.0000 0.0000 0.1271\n 2 0.0000 0.7580 -0.5085\n 2 0.0000\
+ \ -0.7580 -0.5085 \n\n\n 8.0 5\n0 8\n 11720.0000000 0.0007100\
+ \ \n 1759.0000000 0.0054700 \n 400.8000000\
+ \ 0.0278370 \n 113.7000000 0.1048000 \
+ \ \n 37.0300000 0.2830620 \n 13.2700000 \
+ \ 0.4487190 \n 5.0250000 0.2709520 \
+ \ \n 1.0130000 0.0154580 \n0 8\n 11720.0000000\
+ \ -0.0001600 \n 1759.0000000 -0.0012630 \
+ \ \n 400.8000000 -0.0062670 \n 113.7000000 \
+ \ -0.0257160 \n 37.0300000 -0.0709240 \
+ \ \n 13.2700000 -0.1654110 \n 5.0250000 \
+ \ -0.1169550 \n 1.0130000 0.5573680 \
+ \ \n0 1\n 0.3023000 \n1 3\n 17.7000000 \
+ \ 0.0430180 \n 3.8540000 0.2289130 \
+ \ \n 1.0460000 0.5087280 \n1 1\n 0.2753000\
+ \ \n\n 1.0 2\n0 3\n 13.0100000 0.0196850\
+ \ \n 1.9620000 0.1379770 \n 0.4446000\
+ \ 0.4781480 \n0 1\n 0.1220000 \
+ \ \n\n\n\n\n 3 1 2 3 6\n 1 1.00085930 2 0.00438407 6 \
+ \ 0.00072731 9 0.00346894\n 11 0.00045391 13 0.00045392\n 3\
+ \ 1 2 3 8\n 2 0.08137627 5 0.30386789 6 -0.39873764 8\
+ \ 0.16245941\n 9 -0.27280445 10 0.40165595 11 0.05882459 13 \
+ \ -0.02137198\n 3 1 2 3 8\n 2 0.08137642 5 -0.30386766 6\
+ \ -0.39873750 8 -0.16245933\n 9 -0.27280444 11 -0.02137131 12 \
+ \ 0.40165620 13 0.05882437\n 3 1 2 3 6\n 2 0.44922797 3\
+ \ 0.56835690 6 0.25068561 9 0.21482539\n 11 -0.03046886 13 \
+ \ -0.03046865\n 1 1 2\n 4 0.63677843 7 0.51530766 \n\n\
+ [ The total energy is: -75.97812747 AU ]\n
\nHere, the UDF\
+ \ for the orbitals are as follows:\nOrbital UDF AO label\noxygen core:\
+ \ O 1s 1\nOH(a) bond: H(a) 1s 10\nOH(b) bond: H(b) 1s 12\n\
+ Sigma lone-pair: O 3s 3\nPi lone-pair: (O 2px 4)
\nChemically\
+ \ sensible alternatives for the Sigma lone-pair UDF include the O2s function.\
+ \ Different orbitals would be obtained with this choice but the same energy. The\
+ \ Pi lone-pair has a different symmetry to the other orbitals as it is perpendicular\
+ \ to the plane of the atoms. Since it is the only orbital of its symmetry, the\
+ \ UDF issue is null (hence the parentheses). The in-plane orbitals have Sigma\
+ \ symmetry.
\nNote that the O 2,3py,z functions are not chemically sensible\
+ \ UDF for the OH bonds. The atoms are placed in the y,z plane to simplify the\
+ \ symmetry definitions, so the 2py,z functions are needed to direct hybridization\
\ of the oxygen valence electrons toward the hydrogens for both bonds. So choosing,\
\ say, 2py for one bond and 2pz for the other would yield unsymmetric bond orbitals.
\n\
Yet another alternative would be to hybridize the O2s,y,z (and/or the O3s,y,z)\
@@ -60322,11 +60314,11 @@ VALENCE-software/VALENCE:
\ impact of multiple spin-couplings can be much greater than this, particularly\
\ in systems with multiple near-degenerate orbitals, such as aromatics. In benzene,\
\ the Kekule and Dewar structures correspond to different spin-couplings of the\
- \ six equivalent Pi electrons.
\nSection\
- \ 7. Automatic input generation with vtools
\nThe previous sections describe\
- \ how the input to VALENCE can be generated 'by hand'. However, it is also possible\
+ \ six equivalent Pi electrons.
\nSection 7. Automatic input generation with vtools
\nThe previous sections describe how\
+ \ the input to VALENCE can be generated 'by hand'. However, it is also possible\
\ to generate this input automatically using the 'vtools' software provided in\
\ this package. A description of the vtools command syntax is provided in the\
\ file, ./vtools/README.md.
\nvtools uses the 'Model Kit' method for building\
@@ -60356,10 +60348,10 @@ VALENCE-software/VALENCE:
\ for the first time), we recommend visualizing them in order to check that their\
\ form is reasonable from the chemical standpoint. To this end, we are working\
\ to incorporate a visualization capability directly into the vtools and expect\
- \ this to be available in the near future.
\nSection\
- \ 8. How to make spin-coupled orbitals
\nThe following procedure is recommended.\
+ \ this to be available in the near future.
\nSection 8. How to make spin-coupled orbitals
\nThe following procedure is recommended.\
\ It is advisable to begin a pair of spin-coupled (SC) orbitals from the corresponding\
\ converged double-occupied (DOCC) orbital. So, first, choose the DOCC orbital\
\ of interest from a previously obtained optimized wave function. In a suitable\
@@ -60375,10 +60367,10 @@ VALENCE-software/VALENCE:
\ is just a suggestion, but this has proved to be a reasonable choice. Execute\
\ an optimization run for the pair of SC orbitals, keeping the others fixed. If\
\ necessary, re-optimize, including any other orbitals that interact significantly\
- \ with the new SC pair.
\nSection\
- \ 9. How to use derived basis functions (DBF)
\nAs mentioned in section\
+ \ with the new SC pair.
\nSection 9. How to use derived basis functions (DBF)
\nAs mentioned in section\
\ 2, VALENCE allows the user to define combinations of the atomic basis functions\
\ to form new functions over which to expand the electronic orbitals. A typical\
\ use of this feature is to symmetry-adapt the basis set to yield more convenient\
@@ -60457,19 +60449,18 @@ VALENCE-software/VALENCE:
\ 0.00106951 16 -0.99531842 17 -0.02391508 24 -0.00095836\n 25 \
\ 0.00374769 27 0.00374779 30 0.00204220 0 0.00159441\n\n 2\
\ 1 2 2\n 6 0.60840187 21 -0.60840187\n 2 1 2\
- \ 2\n 6 0.87759376 21 0.87759376\n\n
\nThis work was supported by Argonne Leadership\ - \ Computing Facility, which is a DOE Office of Science User Facility supported\ - \ under Contract DE-AC02-06CH11357.\nThe name, 'VALENCE', was chosen in honor\ - \ of the famous book by Charles Coulson, an early pioneer of valence bond theory.
\n\ -Please feel free to send questions/comments to any/all members of 'The VALENCE\ - \ Group, at Argonne':
\nGraham D. Fletcher\nComputational Science Division\n\
- Argonne National Laboratory\nLemont, IL, USA\n\n This\
+ \ work was supported by Argonne Leadership Computing Facility, which is a DOE\
+ \ Office of Science User Facility supported under Contract DE-AC02-06CH11357.\n\
+ The name, 'VALENCE', was chosen in honor of the famous book by Charles Coulson,\
+ \ an early pioneer of valence bond theory. Please feel free to send questions/comments to any/all members\
+ \ of 'The VALENCE Group, at Argonne': Graham D. Fletcher\nComputational\
+ \ Science Division\nArgonne National Laboratory\nLemont, IL, USA\ngfletcher@anl.gov Murat Keceli\nComputational Science Division\n\
Argonne National Laboratory\nLemont, IL, USA\nkeceli@anl.gov Colleen Bertoni\nArgonne Leadership Computing Facility\n\
@@ -61409,8 +61400,8 @@ VowpalWabbit/vowpal_wabbit:
of machine learning with techniques such as online, hashing, allreduce, reductions,
learning2search, active, and interactive learning. '
filenames:
- - singularity/Singularity.debian-unstable-i386
- singularity/Singularity.debian-unstable-amd64
+ - singularity/Singularity.debian-unstable-i386
full_name: VowpalWabbit/vowpal_wabbit
latest_release: 9.9.0
readme: '
'
- stargazers_count: 8306
+ stargazers_count: 8310
subscribers_count: 349
topics:
- c-plus-plus
@@ -61515,7 +61506,7 @@ VowpalWabbit/vowpal_wabbit:
- active-learning
- learning-to-search
- cpp
- updated_at: 1698618098.0
+ updated_at: 1699036543.0
WIPACrepo/monitoring-scripts:
data_format: 2
description: Tools for monitoring HTCondor and other things
@@ -61523,15 +61514,14 @@ WIPACrepo/monitoring-scripts:
- Singularity
full_name: WIPACrepo/monitoring-scripts
latest_release: 0.3.0
- readme: ' Some scripts for sending data to ES, or plotting it, or other misc activities. Developers: Sourya Bhattacharyya, Ferhat Ay https://ay-lab.github.io/FitHiChIP/ FitHiChIP is now published at Nature Communications (https://www.nature.com/articles/s41467-019-11950-y) All the results in FitHiChIP, like the significant loops, HiChIP peak calling,
performance analysis is now available in Zenodo https://doi.org/10.5281/zenodo.3255048 For the earlier release notes, please check the file Release_Notes.txt Please use the GitHub issues page for reporting any issues / suggestions (recommended).Section 10. Acknowledgements
\nSection 11.\
+ \ Contact information
\n
+ readme: ' monitoring-scripts
monitoring-scripts
Singularity FitHiChIP
+ readme: '
FitHiChIP
+ Citation
Citation
+ Data repository
Data
+ repository
+ Release notes - Version 11.0 (December
- 2022)
Release notes - Version 11.0
+ (December 2022)
1. FitHiChIP now support HiChIP interactions in .hic and .cool / .mcool
formats, in addition to the earlier formats.
@@ -71219,17 +71208,17 @@ ay-lab/FitHiChIP:
+ Utility scripts for the manuscript
Utility scripts for the manuscript
- Check the folder *UtilScript* and corresponding README file for the
links to various utility scripts used to generate the figures in this manuscript.
+ Contact
Contact
If you just want to install bali-phy, please visit the release page. If you want to compile BAli-phy from source, the quick start instructions are below.
-You will need a C++ compiler that understands C++20.
@@ -78794,9 +78783,8 @@ bredelings/BAli-Phy: -On Ubuntu, you can use apt-get:
@@ -78820,9 +78808,9 @@ bredelings/BAli-Phy: -The meson version needs to be at least 1.0. +
The meson version needs to be at least 1.1. - If your distribution provides a version of meson that is less than 1.0, + If your distribution provides a version of meson that is less than 1.1, then you may need to install meson through the python package manager "pip" or "pip3":
@@ -78835,8 +78823,8 @@ bredelings/BAli-Phy: -This will build the latest unreleased beta version of BAli-Phy, which fixes some memory issues in 3.6. There are some changes to the model language. Check @@ -78846,7 +78834,7 @@ bredelings/BAli-Phy: cd BAli-Phy - meson build --prefix=$HOME/Applications/bali-phy + meson setup build --prefix=$HOME/Applications/bali-phy ninja -C build install @@ -78854,18 +78842,16 @@ bredelings/BAli-Phy: -
$PATH
- In order to run the installed software, you should add bali-phy to your $PATH.
-If you installed in $HOME/Applications/bali-phy/
as recommended
above, then files will be in:
@@ -101407,10 +101392,10 @@ firelab/windninja: alt="Example output" style="max-width: 100%;">
' - stargazers_count: 95 + stargazers_count: 97 subscribers_count: 23 topics: [] - updated_at: 1692291420.0 + updated_at: 1693308693.0 fksato/caffe2_singularity: data_format: 2 description: null @@ -106942,14 +106927,14 @@ github-linguist/linguist: \ licenses.\nvendor/README.md
lists\
\ the repository for each grammar.\nAll other files are covered by the\
\ MIT license, see LICENSE
.
The results from the simulations, which were run on a dedicated HPC
@@ -121329,9 +121314,8 @@ jolars/HessianScreening:
figures/
and tables/
respectively.
The results from our paper were run through a singularity container. @@ -121351,9 +121335,9 @@ jolars/HessianScreening: folder
, such assimulateddata.R
.
- If you want to re-build the singularity container from scratch (or @@ -121410,10 +121394,10 @@ jolars/HessianScreening: to run the experiments.
-Alternatively, you may also reproduce the results by cloning this @@ -121441,8 +121425,8 @@ jolars/HessianScreening: systems, compilers, and BLAS/LAPACK implementations.
-If you want to build and experiment with the package, you can do so by
@@ -121457,8 +121441,8 @@ jolars/HessianScreening:
calling renv::restore()
(see the section above).
The datasets used in these simulations are stored in the data
@@ -121466,9 +121450,8 @@ jolars/HessianScreening:
sources can be found in data-raw/
.
Note that pushing large files using Git-LFS against forks of this repo @@ -121976,16 +121959,15 @@ josephwkania/radio_transients: data_format: 2 description: 'Singularity containers with common radio transient search software. ' filenames: - - Singularity.gpu - - Singularity.arm - Singularity + - Singularity.arm - Singularity.cpu + - Singularity.gpu full_name: josephwkania/radio_transients latest_release: null - readme: "
These are my\ - \ Singularity Recipes for common radio transient software.\nThere are three containers
\n\ -Contains\ - \ everything (CPU+GPU)
\nCUDA 10.2\nFETCH https://github.com/devanshkv/fetch\
+ \ style=\"max-width: 100%;\">\nOverview
\nThese are my Singularity Recipes for\
+ \ common radio transient software.\nThere are three containers
\nradio_transients
\n\
+ Contains everything (CPU+GPU)
\nCUDA 10.2\nFETCH https://github.com/devanshkv/fetch\
\ -- In Conda environment `FE`\nheimdall https://sourceforge.net/p/heimdall-astro/wiki/Use/\n\
- dedisp https://github.com/ajameson/dedisp\nhtop https://htop.dev/\n\
iqrm_apollo https://gitlab.com/kmrajwade/iqrm_apollo\njupyterlab https://jupyter.org/\n\
@@ -122019,37 +122000,36 @@ josephwkania/radio_transients:
\ PRESTO is in RT
, which is loaded by default.\nPRESTO is in PE
,\
\ in the shell you can activate this\nwith conda activate PE
. If\
\ you need access outside the container,\nyou should use radio_transients:cpu,\
- \ which has PRESTO in the default environment.\nradio_transients_cpu
\n\
- Contains CPU based programs
\nhtop\niqrm_apollo\njupyterlab \
- \ \nPRESTO\npsrcat\npysigproc\nriptide\nsigproc\nTempo \nRFIClean\nYAPP \nyour\n\
-
\nGet with\nsingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:cpu
\n\
+ \ which has PRESTO in the default environment.
\nradio_transients_cpu
\nContains\
+ \ CPU based programs
\nhtop\niqrm_apollo\njupyterlab \nPRESTO\n\
+ psrcat\npysigproc\nriptide\nsigproc\nTempo \nRFIClean\nYAPP \nyour\n
\n\
+ Get with\nsingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:cpu
\n\
There is an arm version Singularity.arm
,\nsingularity pull\
\ --arch arm library://josephwkania/radio_transients/radio_transients:arm
\n\
- radio_transients_gpu
\nContains\
- \ gpu based programs
\nCUDA 10.2\nFETCH \njupyterlab\nheimdall\n\
- - dedisp\nhtop \npsrdada \npsrdada-python\nyour\n
\nGet with\n\
- singularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:gpu
\n\
- How to use
\nYour $HOME
automatically gets mounted.\n\
- You can mount a directory with -B /dir/on/host:/mnt
, which will mount\
- \ /dir/on/host
to /mnt
in the container.
\nFor\
- \ the gpu processes, you must pass --nv
when running singularity.
\n\
- singularity shell --nv -B /data:/mnt radio_transients_gpu.sif
\n\
- will mount /data
to /mnt
, give you GPU access, and drop\
- \ you into the interactive shell.
\nsingularity exec --nv -B /data:/mnt\
- \ radio_transients_gpu.sif your_heimdall.py -f /mnt/data.fil
\nwill mount\
- \ /data
to /mnt
, give you GPU access, and run your_heimdall.py\
- \ without entering the container.
\nAll the Python scripts are installed\
- \ in a Conda environment RT
, this environment is automatically loaded.
\n\
- You can see the commits and corresponding dates by running singularity\
- \ inspect radio_transients.sif
\nSylabs Cloud
\n\
+ radio_transients_gpu
\nContains gpu based programs
\n\
+ CUDA 10.2\nFETCH \njupyterlab\nheimdall\n- dedisp\nhtop \npsrdada\
+ \ \npsrdada-python\nyour\n
\nGet with\nsingularity pull --arch\
+ \ amd64 library://josephwkania/radio_transients/radio_transients:gpu
\n\
+ How to use
\n\
+ Your $HOME
automatically gets mounted.\nYou can mount a directory\
+ \ with -B /dir/on/host:/mnt
, which will mount /dir/on/host
\
+ \ to /mnt
in the container.
\nFor the gpu processes, you must\
+ \ pass --nv
when running singularity.
\nsingularity shell\
+ \ --nv -B /data:/mnt radio_transients_gpu.sif
\nwill mount /data
\
+ \ to /mnt
, give you GPU access, and drop you into the interactive\
+ \ shell.
\nsingularity exec --nv -B /data:/mnt radio_transients_gpu.sif\
+ \ your_heimdall.py -f /mnt/data.fil
\nwill mount /data
to /mnt
,\
+ \ give you GPU access, and run your_heimdall.py without entering the container.
\n\
+ All the Python scripts are installed in a Conda environment RT
,\
+ \ this environment is automatically loaded.
\nYou can see the commits and\
+ \ corresponding dates by running singularity inspect radio_transients.sif
\n\
+ Sylabs Cloud
\n\
These are built on a E5 v3 family machine and uploaded to Sylabs Cloud at\n\
https://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\n\
@@ -122062,22 +122042,21 @@ josephwkania/radio_transients:
https://singularity-hub.org/collections/5231\" rel=\"nofollow\">
\nImprovements
\n\
- If you come across bug or have suggestions for improvements, let me know or\
- \ submit a pull request.
\nThanks
\nTo Kshitij Aggarwal\
- \ for bug reports and suggestions.
\n"
- stargazers_count: 5
+ \ style=\"max-width: 100%;\">\nImprovements
\nIf you come across\
+ \ bug or have suggestions for improvements, let me know or submit a pull request.
\n\
+ Thanks
\nTo\
+ \ Kshitij Aggarwal for bug reports and suggestions.
\n"
+ stargazers_count: 6
subscribers_count: 2
topics:
- pulsars
- fast-radio-bursts
- psrdada
- radio-astronomy
- updated_at: 1654716758.0
+ updated_at: 1696925758.0
jrenslo/singularity:
data_format: 2
description: Singularity Container Handle
@@ -123262,9 +123241,9 @@ kalibera/rchk:
data_format: 2
description: null
filenames:
+ - image/Singularity.jammy
- image/Singularity.def
- image/Singularity.bionic
- - image/Singularity.jammy
full_name: kalibera/rchk
latest_release: null
readme: 'This project consists of several bug-finding tools that look for memory
@@ -129491,10 +129470,10 @@ lscsoft/bilby_pipe:
data_format: 2
description: null
filenames:
- - containers/Singularity.0.0.3
- - containers/Singularity.0.0.4
- containers/Singularity.dev
+ - containers/Singularity.0.0.4
- containers/Singularity.0.0.2
+ - containers/Singularity.0.0.3
- containers/Singularity.0.0.1
full_name: lscsoft/bilby_pipe
latest_release: null
@@ -130703,6 +130682,31 @@ manuel-munoz-aguirre/singularity-pytorch-gpu:
- machine-learning
- environment
updated_at: 1678054757.0
+maopl/TransOpt:
+ data_format: 2
+ description: null
+ filenames:
+ - hpobench/container/recipes/Singularity.template
+ - hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark
+ - hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine
+ - hpobench/container/recipes/rl/Singularity.Cartpole
+ - hpobench/container/recipes/rl/Singularity.learnaBenchmark
+ - hpobench/container/recipes/od/Singularity.ODBenchmarks
+ - hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation
+ - hpobench/container/recipes/nas/Singularity.nasbench_1shot1
+ - hpobench/container/recipes/nas/Singularity.nasbench_201
+ - hpobench/container/recipes/nas/Singularity.TabularBenchmarks
+ - hpobench/container/recipes/nas/Singularity.nasbench_101
+ full_name: maopl/TransOpt
+ latest_release: null
+ readme: '
TransOpt
+
+ '
+ stargazers_count: 3
+ subscribers_count: 1
+ topics: []
+ updated_at: 1698837283.0
maplesond/portcullis:
data_format: 2
description: Splice junction analysis and filtering from BAM files
@@ -135143,7 +135147,7 @@ microgenlab/porefile:
\ versions of the porefile dependencies.\nCitation
\nA manuscript is under preparation.
\n"
- stargazers_count: 17
+ stargazers_count: 19
subscribers_count: 5
topics:
- nextflow
@@ -135153,7 +135157,7 @@ microgenlab/porefile:
- docker
- singularity
- nanopore
- updated_at: 1696420824.0
+ updated_at: 1698183958.0
miguelcarcamov/container_docker:
data_format: 2
description: null
@@ -136890,18 +136894,18 @@ ml4ai/UA-hpc-containers:
data_format: 2
description: Container recipes for use with the U of A HPC resources
filenames:
- - Singularity.delphi
+ - Singularity.im2markup
+ - Singularity.nvidia_docker
+ - Singularity.tensorflow
+ - Singularity.dynet
- Singularity.pytorch
- Singularity.torch
- - Singularity.dynet
+ - Singularity.cuda9_py36
+ - Singularity.keras
- Singularity.pytorch_skimage
+ - Singularity.delphi
- Singularity.numba
- - Singularity.im2markup
- Singularity.caffe2
- - Singularity.tensorflow
- - Singularity.nvidia_docker
- - Singularity.cuda9_py36
- - Singularity.keras
- openmnt/Singularity.opennmt
- word2vec/Singularity.w2v
full_name: ml4ai/UA-hpc-containers
@@ -136933,8 +136937,8 @@ ml4ai/automates:
data_format: 2
description: 'AutoMATES: Automated Model Assembly from Text, Equations, and Software'
filenames:
- - automates/equation_reading/equation_extraction/containers/Singularity.pytorch_skimage
- automates/equation_reading/equation_extraction/containers/Singularity.im2markup
+ - automates/equation_reading/equation_extraction/containers/Singularity.pytorch_skimage
full_name: ml4ai/automates
latest_release: v1.4.0
readme: " == =============== === =\n\
@@ -148743,18 +148747,18 @@ openhackathons-org/gpubootcamp:
description: This repository consists for gpu bootcamp material for HPC and AI
filenames:
- misc/Bootcamp_template/appName/Singularity
- - ai/RAPIDS/Singularity
- - ai/Distributed_Deep_Learning/Singularity
- - ai/DeepStream/Singularity
- - ai/DeepStream_Perf_Lab/Singularity
- - archived/hpc/miniprofiler/Singularity
- - archived/hpc/openacc/Singularity
- archived/hpc/nways/Singularity_python
- archived/hpc/nways/Singularity
+ - archived/hpc/miniprofiler/Singularity
+ - archived/hpc/openacc/Singularity
- archived/hpc/multi_gpu_nways/Singularity
- - hpc_ai/ai_science_cfd/Singularity
- - hpc_ai/PINN/Singularity
- - hpc_ai/ai_science_climate/Singularity
+ - archived/ai/RAPIDS/Singularity
+ - archived/ai/DeepStream/Singularity
+ - archived/ai/DeepStream_Perf_Lab/Singularity
+ - archived/ai/Distributed_Deep_Learning/Singularity
+ - archived/hpc_ai/ai_science_cfd/Singularity
+ - archived/hpc_ai/PINN/Singularity
+ - archived/hpc_ai/ai_science_climate/Singularity
- experimental/TAO_Transfer_Learning/Singularity
full_name: openhackathons-org/gpubootcamp
latest_release: null
@@ -148805,7 +148809,7 @@ openhackathons-org/gpubootcamp:
rel="nofollow">OpenACC Slack Channel.
'
- stargazers_count: 477
+ stargazers_count: 479
subscribers_count: 21
topics:
- machine-learning
@@ -148820,30 +148824,27 @@ openhackathons-org/gpubootcamp:
- rapidsai
- openmp
- ai4hpc
- updated_at: 1697699767.0
+ updated_at: 1698990754.0
openhpc/ohpc:
data_format: 2
description: OpenHPC Integration, Packaging, and Test Repo
filenames:
- containers/Singularity.1.3.3.el7
full_name: openhpc/ohpc
- latest_release: v2.6.1.GA
+ latest_release: v3.0.1.GA
readme: '
-
+
- Community building
- blocks for HPC systems
+ Community building blocks for
+ HPC systems
- Introduction
+ Introduction
This stack provides a variety of common, pre-built ingredients required to
@@ -148854,16 +148855,24 @@ openhpc/ohpc:
scientific libraries.
- There are currently two release series: 1.3.x
- and 2.x,
+
There are currently three release series: 1.3.x,
+ 2.x and
- which target different major Linux OS distributions. The 1.3.x series targets
+ 3.x, which target different
+ major Linux OS distributions:
- CentOS7 and SLES12 while the 2.x series targets CentOS8 and Leap15.
+
+
+ - The 1.3.x series targets CentOS7 and SLES12.
- Getting
- started
+ - The 2.x series targets CentOS8 and Leap15.
+
+ - The 3.x series targets EL9, Leap 15 and openEuler 22.03.
+
+
+
+ Getting
+ started
OpenHPC provides pre-built binaries via repositories for use with standard
@@ -148886,13 +148895,14 @@ openhpc/ohpc:
more information is available on the relevant release series pages
- (1.3.x or 2.x).
+ (1.3.x, 2.x
+ or 3.x).
- Questions, Comments, or Bug Reports?
+ Questions, Comments, or Bug Reports?
Subscribe to the users
email list or see the
@@ -148900,24 +148910,21 @@ openhpc/ohpc:
https://openhpc.community/
page for more pointers.
- Additional Software Requests?
+ Additional
+ Software Requests?
Please see the component submission
page for more information
regarding new software inclusion requests.
- Contributing to OpenHPC
+ Contributing
+ to OpenHPC
Please see the steps described in CONTRIBUTING.md.
- Register your system
+ Register
+ your system
If you are using elements of OpenHPC, please consider registering your system(s)
@@ -148925,8 +148932,8 @@ openhpc/ohpc:
rel="nofollow">System Registration Form.
'
- stargazers_count: 763
- subscribers_count: 89
+ stargazers_count: 782
+ subscribers_count: 87
topics:
- hpc
- scientific-computing
@@ -148935,7 +148942,7 @@ openhpc/ohpc:
- devtools
- mpi
- linuxfoundation
- updated_at: 1693516555.0
+ updated_at: 1698842944.0
opensciencegrid/osgvo-blaylockbk:
data_format: 2
description: OSGVO image for blaylockbk
@@ -152275,11 +152282,11 @@ phelelani/nf-rnaSeqMetagen:
data_format: 2
description: Metagenomics pipeline
filenames:
- - containers/Singularity.kraken2
- containers/Singularity.multiQC
- - containers/Singularity.trinity
- - containers/Singularity.star
- containers/Singularity.upset
+ - containers/Singularity.kraken2
+ - containers/Singularity.star
+ - containers/Singularity.trinity
full_name: phelelani/nf-rnaSeqMetagen
latest_release: null
readme: 'nf-rnaSeqMetagen#SBATCH --gpus=2
\nand AlphaFold\
\ Singularity will use only two of the four GPUs. This is\nbecause the cgroup
\
\ for the job only shows 2 GPUs to the job.
\n"
- stargazers_count: 20
+ stargazers_count: 21
subscribers_count: 3
topics:
- alphafold
@@ -157139,7 +157146,7 @@ prehensilecode/alphafold_singularity:
- high-performance-computing
- slurm
- apptainer
- updated_at: 1697835117.0
+ updated_at: 1698923747.0
prete/singularity-recipes:
data_format: 2
description: null
@@ -165582,16 +165589,16 @@ radinformatics/bone-age:
- Singularity
full_name: radinformatics/bone-age
latest_release: null
- readme: "Bone-Age Demo
\nunder development
\n\
- This repository builds a Docker image and a Singularity image, each that will run the bone age demo\
- \ to predict bone age from a radiograph. The user has the option to run the prediction\
- \ algorithm from the command line with an image file input, or to run a web server\
- \ to see an interactive demo.
\n\n\nThe predict_image.py\
- \ script is a light wrapper around the model and includes the functions that are\
- \ needed for such a demo. The user would upload a image which would then be processed\
+ readme: "
Bone-Age Demo
\nunder development
\nThis repository\
+ \ builds a Docker image and a Singularity image, each that will run the bone age demo to predict bone age\
+ \ from a radiograph. The user has the option to run the prediction algorithm from\
+ \ the command line with an image file input, or to run a web server to see an\
+ \ interactive demo.
\n\n\nThe predict_image.py script\
+ \ is a light wrapper around the model and includes the functions that are needed\
+ \ for such a demo. The user would upload a image which would then be processed\
\ with the given model on the back-end. The results would then be displayed for\
\ the user.
\n
\n
\nIf you are working on your local\
\ machine, you can use either Docker or Singularity. If you are running in a shared\
@@ -165599,43 +165606,41 @@ radinformatics/bone-age:
\ is your best option. Instructions are included for both.
\nPackages that\
\ need to be installed are included in requirements.txt\
\ and installed into the container via the Dockerfile.
\n\
- Docker
\nGetting Started
\nYou\
- \ should first Docker\n
Getting Started
\n\
+ You should first install Docker. The container is provided on Docker Hub and can be downloaded from there when you run\
\ it, and this is recommended because building it takes a while to compile OpenCV.
\n\
- I want to build it!
\nIf you\
- \ want to look at or make changes to the code, it's recommended to clone the repo\
- \ and build the container locally:
\ngit clone http://www.github.com/radinformatics/bone-age\n\
- cd bone-age\ndocker build -t vanessa/boneage .\n
\nThe docker daemon\
- \ will first look for an image called vanessa/boneage
locally, and\
- \ if not found, will then try Dockerhub, and download it from there. If for any\
- \ reason you want to remove your image, just do the following:
\ndocker\
- \ rmi vanessa/boneage\n
\nRunning\
- \ commands
\nThe entry to the container is done simply by using it as an\
- \ executable:
\ndocker run vanessa/boneage --help\nusage: cli.py\
- \ [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\t [--width WIDTH]\
- \ [--height HEIGHT] [--debug]\n\nPredict bone age of an image.\n\noptional arguments:\n\
- \ -h, --help show this help message and exit\n --image IMAGE Path to\
- \ single bone image.\n --output OUTPUT Path to output file to write results.\n\
- \ --gender {M,F} the gender of the individual (M or F), default is M (male)\n\
- \ --width WIDTH warped width to resize the image in pixels (default 256)\n\
- \ --height HEIGHT warped height to resize the image in pixels (default 256)\n\
- \ --debug use verbose logging to debug.\n
\nRun Prediction With Example
\n\
- To run the bone-age demo non interactively to get a prediction, you can run\
- \ it without any arguments:
\n docker run vanessa/boneage\n\n***\
- \ Starting Bone Age Prediction ****\nNo image selected, will use provided example...\n\
+ I want to build it!
\nIf you want to look at or make changes to the code, it's\
+ \ recommended to clone the repo and build the container locally:
\ngit\
+ \ clone http://www.github.com/radinformatics/bone-age\ncd bone-age\ndocker build\
+ \ -t vanessa/boneage .\n
\nThe docker daemon will first look for\
+ \ an image called vanessa/boneage
locally, and if not found, will\
+ \ then try Dockerhub, and download it from there. If for any reason you want to\
+ \ remove your image, just do the following:
\ndocker rmi vanessa/boneage\n\
+
\nRunning commands
\nThe entry to the container is done\
+ \ simply by using it as an executable:
\ndocker run vanessa/boneage\
+ \ --help\nusage: cli.py [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\
+ \t [--width WIDTH] [--height HEIGHT] [--debug]\n\nPredict bone age of an\
+ \ image.\n\noptional arguments:\n -h, --help show this help message and\
+ \ exit\n --image IMAGE Path to single bone image.\n --output OUTPUT Path\
+ \ to output file to write results.\n --gender {M,F} the gender of the individual\
+ \ (M or F), default is M (male)\n --width WIDTH warped width to resize the\
+ \ image in pixels (default 256)\n --height HEIGHT warped height to resize the\
+ \ image in pixels (default 256)\n --debug use verbose logging to debug.\n\
+
\nRun Prediction With Example
\nTo\
+ \ run the bone-age demo non interactively to get a prediction, you can run it\
+ \ without any arguments:
\n docker run vanessa/boneage\n\n*** Starting\
+ \ Bone Age Prediction ****\nNo image selected, will use provided example...\n\
Building model, please wait.\nPredicted Age : 14 Months\nWeighted Prediction :\
\ 11.832177 Months\n
\nThe command above is saying \"map the folder\
\ $PWD/example_images
(where my 4.png is located) to the /data
\
@@ -165648,36 +165653,35 @@ radinformatics/bone-age:
DEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM\
\ IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please\
\ wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.610813 Months\n
\n\
- Run Prediction\
- \ With Your Own Image
\nIf you want to provide your own image, you need\
- \ to bind it to the /data directory in the folder, and map a path to it. Don't\
- \ forget to specify the gender - the default is male, and you may want to change\
- \ that:
\n docker run -v $PWD/example_images:/data vanessa/boneage\
- \ --image /data/4.png\n\n*** Starting Bone Age Prediction ****\nBuilding model,\
- \ please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\
-
\nWe can of course add debug to verify that the default is male,\
- \ and we are using our mapped image:
\n docker run -v $PWD/example_images:/data\
- \ vanessa/boneage --image /data/4.png --debug\nEnvironment message level found\
- \ to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male:\
- \ True\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width:\
- \ 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM\
- \ IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted\
- \ Prediction : 8.641131 Months\n
\nWe can specify a different gender,\
- \ and the prediction changes:
\n docker run -v $PWD/example_images:/data\
- \ vanessa/boneage --image /data/4.png --gender F --debug\nEnvironment message\
- \ level found to be DEBUG\n\nEnvironment message level found to be DEBUG\n\n***\
- \ Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: False\nDEBUG:bone-age:image:\
+ Run Prediction With Your Own Image
\nIf\
+ \ you want to provide your own image, you need to bind it to the /data directory\
+ \ in the folder, and map a path to it. Don't forget to specify the gender - the\
+ \ default is male, and you may want to change that:
\n docker\
+ \ run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\n\n***\
+ \ Starting Bone Age Prediction ****\nBuilding model, please wait.\nPredicted Age\
+ \ : 8 Months\nWeighted Prediction : 8.641131 Months\n
\nWe can\
+ \ of course add debug to verify that the default is male, and we are using our\
+ \ mapped image:
\n docker run -v $PWD/example_images:/data vanessa/boneage\
+ \ --image /data/4.png --debug\nEnvironment message level found to be DEBUG\n\n\
+ *** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image:\
\ /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM\
\ IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please\
- \ wait.\nPredicted Age : 16 Months\nWeighted Prediction : 16.000000 Months\n
\n\
- Save output to file
\nIf you\
- \ specify the --output
argument, you can save the result as a json\
- \ to file. Again, we will need to specify a file in a folder mapped to our local\
- \ machine:
\n docker run -v $PWD/example_images:/data vanessa/boneage\
+ \ wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n
\n\
+ We can specify a different gender, and the prediction changes:
\n\
+ \ docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\
+ \ --gender F --debug\nEnvironment message level found to be DEBUG\n\nEnvironment\
+ \ message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male:\
+ \ False\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width:\
+ \ 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM\
+ \ IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 16 Months\nWeighted\
+ \ Prediction : 16.000000 Months\n
\nSave output to file
\nIf\
+ \ you specify the --output
argument, you can save the result as a\
+ \ json to file. Again, we will need to specify a file in a folder mapped to our\
+ \ local machine:
\n docker run -v $PWD/example_images:/data vanessa/boneage\
\ --output /data/demo.json --debug\n\nEnvironment message level found to be DEBUG\n\
\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided\
\ example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/4.png\n\
@@ -165697,47 +165701,44 @@ radinformatics/bone-age:
\ into a single output file. All of these could be implemented, it really depends\
\ on the desired outcome. For the current purpose (plugging the container into\
\ a web server for a demo) the above that produces a single file, or multiple\
- \ single files, is a reasonable approach.\nHow do I\
- \ shell into the container?
\nBy default, running the container uses the\
- \ ENTRYPOINT
, meaning it is used as an executable and you do not\
- \ enter the container. In the case that you want a container-based environment\
- \ that is installed with the dependencies of boneage, or if you want to interactively\
- \ work with the code, you may want to shell into the container.
\n\
- \ docker run -it --entrypoint /bin/bash vanessa/boneage\n
\nKeep\
- \ in mind that once you exit from this run, the container image is not saved,\
- \ including your changes.
\nSingularity
\n1. Install Singularity
\nInstructions can be\
- \ found on the singularity site.
\n2. Bootstrap\
- \ the image
\nBootstrapping means using something to build from, or not\
- \ starting from nothing. In this case, we are going to use a build file that bootstraps\
- \ a Docker image of boneage (yes, the same one discussed above). This build file\
- \ is called Singularity, and for more details about\
- \ this you can read here.
\nsudo singularity create --size 6000 boneage.img\n\
- sudo singularity bootstrap boneage.img Singularity\n
\n3. Run commands
\nThe commands are equivalent as above, except\
- \ we can use the container as an executable:
\n ./boneage.img --help\n\
-
\nand to make a drive, we use --bind
instead
\n\
- singularity run --bind $PWD/example_images:/data boneage.img --debug\n\
-
\nHow do I\
- \ shell into the container?
\nSingularity has an easy, intuitive way to\
- \ shell inside!
\n singularity shell boneage.img\n
\n\
- Interactive Web Interface
\n\
+ \ single files, is a reasonable approach.\nHow do\
+ \ I shell into the container?
\nBy default, running the container uses the ENTRYPOINT
,\
+ \ meaning it is used as an executable and you do not enter the container. In the\
+ \ case that you want a container-based environment that is installed with the\
+ \ dependencies of boneage, or if you want to interactively work with the code,\
+ \ you may want to shell into the container.
\n docker run -it --entrypoint\
+ \ /bin/bash vanessa/boneage\n
\nKeep in mind that once you exit\
+ \ from this run, the container image is not saved, including your changes.
\n\
+ Singularity
\n\
+ 1. Install Singularity
\nInstructions can be found on the singularity site.
\n\
+ 2. Bootstrap the image
\nBootstrapping means using something\
+ \ to build from, or not starting from nothing. In this case, we are going to use\
+ \ a build file that bootstraps a Docker image of boneage (yes, the same one discussed\
+ \ above). This build file is called Singularity, and\
+ \ for more details about this you can read here.
\nsudo singularity create --size\
+ \ 6000 boneage.img\nsudo singularity bootstrap boneage.img Singularity\n
\n\
+ 3. Run commands
\n\
+ The commands are equivalent as above, except we can use the container as an\
+ \ executable:
\n ./boneage.img --help\n
\nand to\
+ \ make a drive, we use --bind
instead
\n singularity\
+ \ run --bind $PWD/example_images:/data boneage.img --debug\n
\nHow do I shell into the container?
\nSingularity\
+ \ has an easy, intuitive way to shell inside!
\n singularity shell\
+ \ boneage.img\n
\nInteractive Web\
+ \ Interface
\n\
todo
\nUltimately, we will build this demo and serve\
\ on singularity hub\
\ and then have an application that takes inputs / outputs for the container,\
@@ -166573,14 +166574,14 @@ researchapps/sherlock:
data_format: 2
description: repository for collaborating with sherlock users to create containers
filenames:
- - pynio/Singularity
- beelaj/Singularity.beelaj
- - pvacseq/Singularity.pvacseq
- - naras/Singularity.naras
- - dwesche/Singularity.dwesche
- - rllab/Singularity.rllab
- - kdwispe/Singularity.kdwispe
- rflynn/Singularity.rflynn
+ - kdwispe/Singularity.kdwispe
+ - rllab/Singularity.rllab
+ - dwesche/Singularity.dwesche
+ - pynio/Singularity
+ - naras/Singularity.naras
+ - pvacseq/Singularity.pvacseq
full_name: researchapps/sherlock
latest_release: null
readme: "
SingularityCE
'
- stargazers_count: 97
+ stargazers_count: 98
subscribers_count: 5
topics: []
- updated_at: 1696345849.0
+ updated_at: 1697462433.0
sylabs/singularity:
data_format: 2
description: SingularityCE is the Community Edition of Singularity, an open source
container platform designed to be simple, fast, and secure.
filenames:
- - examples/opensuse/Singularity
- - examples/debian/Singularity
- - examples/scientific/Singularity
+ - e2e/testdata/Singularity
+ - examples/ubuntu/Singularity
+ - examples/self/Singularity
+ - examples/library/Singularity
+ - examples/scratch/Singularity.busybox
+ - examples/scratch/Singularity.alpine
+ - examples/fedora/Singularity
+ - examples/asciinema/Singularity
+ - examples/sle/Singularity
+ - examples/shub/Singularity
+ - examples/docker/Singularity
+ - examples/busybox/Singularity
- examples/multistage/Singularity
+ - examples/opensuse-arm64/Singularity
+ - examples/centos/Singularity
+ - examples/scientific/Singularity
- examples/instances/Singularity
- - examples/busybox/Singularity
- - examples/scratch/Singularity.alpine
- - examples/scratch/Singularity.busybox
- - examples/fedora-arm64/Singularity
- - examples/almalinux-arm64/Singularity
- - examples/arch/Singularity
- examples/centos-arm64/Singularity
+ - examples/almalinux-arm64/Singularity
+ - examples/opensuse/Singularity
- examples/apps/Singularity
- examples/apps/Singularity.cowsay
- - examples/sle/Singularity
- - examples/ubuntu/Singularity
- examples/almalinux/Singularity
- - examples/shub/Singularity
- - examples/docker/Singularity
- - examples/centos/Singularity
+ - examples/arch/Singularity
+ - examples/fedora-arm64/Singularity
- examples/raspbian/Singularity
- - examples/opensuse-arm64/Singularity
- - examples/asciinema/Singularity
- - examples/library/Singularity
- - examples/fedora/Singularity
- - examples/self/Singularity
- - e2e/testdata/Singularity
+ - examples/debian/Singularity
full_name: sylabs/singularity
latest_release: v4.0.1
readme: 'SingularityCElicense file.
'
- stargazers_count: 564
+ stargazers_count: 566
subscribers_count: 15
topics:
- containers
- hpc
- linux
- updated_at: 1698343006.0
+ updated_at: 1698740579.0
sylvainschmitt/singularity-octopus:
data_format: 2
description: 'octopus Singularity container '
@@ -192477,10 +192478,10 @@ vfonov/minc-toolkit-containers:
description: Making containers with minc-toolkit
filenames:
- singularity/Singularity.1.9.16
- - singularity/Singularity.base
- - singularity/Singularity.1.9.15
- - singularity/Singularity.1.9.16-min
- singularity/Singularity.base-min
+ - singularity/Singularity.1.9.16-min
+ - singularity/Singularity.1.9.15
+ - singularity/Singularity.base
- singularity/Singularity.1.0.09
full_name: vfonov/minc-toolkit-containers
latest_release: null
@@ -195957,8 +195958,8 @@ wheaton5/souporcell:
data_format: 2
description: Clustering scRNAseq by genotypes
filenames:
- - Singularity.2.0
- Singularity
+ - Singularity.2.0
- Singularity.2.5
full_name: wheaton5/souporcell
latest_release: v2.5
@@ -196239,7 +196240,7 @@ wheaton5/souporcell:
\ created\n
\nSo generally
\nconsensus.py -c clusters.tsv\
\ -a alt.mtx -r ref.mtx --soup_out soup.txt -v <freebayes vcf> --vcf_out\
\ cluster_genotypes.vcf --output_dir .\n
\n"
- stargazers_count: 134
+ stargazers_count: 135
subscribers_count: 10
topics:
- scrna-seq
@@ -196248,7 +196249,7 @@ wheaton5/souporcell:
- bioinformatics
- computational-biology
- genomics
- updated_at: 1695854134.0
+ updated_at: 1698840003.0
willgpaik/centos7_aci:
data_format: 2
description: Centos 7 base image for ACI
@@ -196710,62 +196711,58 @@ winni2k/GLPhase:
- Singularity
full_name: winni2k/GLPhase
latest_release: v1.7.1
- readme: "GLPhase
\nThis is a cuda-enabled fork of\n\
- SNPTools impute.cpp. This\ncode should scale linearly with sample size up\
- \ to a small multiple of\nthe number of CUDA cores (shaders) on the GPU being\
- \ used.
\nGLPhase also has an option for incorporating pre-existing haplotypes\n\
- into the phasing and imputation\nprocess. GLPhase\n\
+
This is a cuda-enabled fork of\nSNPTools impute.cpp. This\ncode should scale linearly with\
+ \ sample size up to a small multiple of\nthe number of CUDA cores (shaders) on\
+ \ the GPU being used.
\nGLPhase also has an option for incorporating pre-existing\
+ \ haplotypes\ninto the phasing and imputation\nprocess. Release 1.4.13\nwas used with this option\nto impute genotypes for the first\
\ release of the\nHaplotype Reference Consortium.
\nInstallation
\n\
- If you have Singularity version 3 installed, then you can run the glphase container located\
- \ here.
\nDependencies
\nGLPhase\
- \ depends on libgsl,\n\
- boost, and libz.
\nCompilation
\n#\
- \ Clone this repository recursively\ngit clone --recursive https://github.com/winni2k/GLPhase.git\n\
+ nofollow\">Haplotype Reference Consortium.\nInstallation
\nIf you have Singularity version\
+ \ 3 installed, then you can run the glphase container located here.
\nDependencies
\nGLPhase depends on libgsl,\nboost, and libz.
\n\
+ Compilation
\n\
+ # Clone this repository recursively\ngit clone --recursive https://github.com/winni2k/GLPhase.git\n\
cd GLPhase\n\n# to compile all code (with all optimizations turned on)\nmake\n\
\n# run the glphase executable to get a description of the\n# glphase command\
\ line arguments\nbin/glphase\n\n# run regression tests (turns off optimizations)\n\
make test\n\n# run regression tests + longer integration tests\nmake disttest\n\
\n# compile without CUDA support\n# first clean the work dir\nmake clean\nmake\
\ NCUDA=1\n\n# compile without CUDA or OMP support (on MacOSX for example)\nmake\
- \ NCUDA=1 NOMP=1\n
\nConverting\
- \ a VCF to SNPTools .bin
format
\nA perl script at scripts/vcf2STBin.pl
\
+ \ NCUDA=1 NOMP=1\n
\nConverting\
+ \ a VCF to SNPTools .bin
format
\nA perl script at scripts/vcf2STBin.pl
\
\ can be used to convert a VCF\nwith PL format fields to a SNPTools conformant\
\ .bin
file. For\nexample, this command will convert a gzipped input\
\ VCF at\ninput.vcf.gz
into a SNPTools .bin
file at\
\ input.bin
:
\nscripts/vcf2STbin.pl input.vcf.gz\n\
-
\nRunning GLPhase (v1.4.13)
\n\
- As a drop-in\
- \ replacement for SNPTools/impute.cpp
\nGLPhase can be run as a CUDA-enabled\
+
\nRunning GLPhase (v1.4.13)
\nAs a drop-in replacement for SNPTools/impute.cpp
\nGLPhase can be run as a CUDA-enabled\
\ drop-in replacement for\nSNPTools/impute.cpp
. Assuming a SNPTools\
\ style .bin
file with\ngenotype likelihoods exists:
\nbin/glphase\
- \ input.bin\n
\nUsing pre-existing\
- \ haplotypes
\nGLPhase can use pre-existing haplotypes to restrict the\
- \ set of\npossible haplotypes from which the MH sampler may choose surrogate\n\
- parent haplotypes. This approach is described in:
\n\nThe Haplotype\
- \ Reference Consortium. A reference panel of 64,976\nhaplotypes for genotype imputation.\
- \ Nature Genetics (accepted) --\n
\nGLPhase can use pre-existing haplotypes to restrict the set of\npossible haplotypes\ + \ from which the MH sampler may choose surrogate\nparent haplotypes. This approach\ + \ is described in:
\n\n\nThe Haplotype Reference Consortium. A\ + \ reference panel of 64,976\nhaplotypes for genotype imputation. Nature Genetics\ + \ (accepted) --\nbioRxiv
\n
This command phases and\
\ imputes haplotypes on a SNPTools .bin
file\nusing a genetic map\
\ and pre-existing haplotypes. The output file is\na gzipped VCF file at output_base_name.vcf.gz
.
GLPhase can use a reference panel of haplotypes to\
- \ inform genotype\nimputation of samples for which genotype likelihoods are available.\n\
- In contrast to pre-existing haplotypes, the haplotypes\nin the reference panel\
- \ do not need to be from the same samples that\nare being imputed. In this mode,\
- \ when surrogate parent haplotypes\nare being chosen for a sample, the haplotypes\
- \ may come from the\ncurrent estimate of sample haplotypes or the reference panel.\
- \ -k
\ncan be specified to restrict the choice of surrogate parent\
- \ haplotypes\nto the reference panel in the first iteration of haplotype estimation.
glphase \\\n input.bin\
- \ \\\n -g samples/hapGen/ex.map \\\n -H samples/hapGen/ex.haps.gz \\\n \
- \ -L samples/hapGen/ex.leg \\\n -k \\\n -o output_base_name
The reference haplotypes and legend should be in\nImpute2 website.
\nGLPhase can use a reference panel of haplotypes to inform genotype\nimputation\
+ \ of samples for which genotype likelihoods are available.\nIn contrast to pre-existing\
+ \ haplotypes, the haplotypes\nin the reference panel do not need to be from the\
+ \ same samples that\nare being imputed. In this mode, when surrogate parent haplotypes\n\
+ are being chosen for a sample, the haplotypes may come from the\ncurrent estimate\
+ \ of sample haplotypes or the reference panel. -k
\ncan be specified\
+ \ to restrict the choice of surrogate parent haplotypes\nto the reference panel\
+ \ in the first iteration of haplotype estimation.
glphase \\\n input.bin \\\n -g samples/hapGen/ex.map\
+ \ \\\n -H samples/hapGen/ex.haps.gz \\\n -L samples/hapGen/ex.leg \\\n \
+ \ -k \\\n -o output_base_name
The reference haplotypes and\ + \ legend should be in\nImpute2 format,\nand a genetic map can be obtained from\ \ the Impute2 website.
\nIt is recommended to ligate haplotypes using\nhapfuse. Before\n\ - fusing, the output from GLPhase needs to be converted from gzipped VCF\nto something\ - \ htslib can read. Here an example using bcftools:
\nzcat output_base_name.vcf.gz\
- \ | bcftools -Ob -o \\\n output_base_name.bcf\n
\n"
+ \ rel=\"nofollow\">Impute2 website.\nIt\ + \ is recommended to ligate haplotypes using\nhapfuse. Before\nfusing, the output from GLPhase needs\ + \ to be converted from gzipped VCF\nto something htslib can read. Here an example\ + \ using bcftools:
\nzcat output_base_name.vcf.gz | bcftools -Ob -o\
+ \ \\\n output_base_name.bcf\n
\n"
stargazers_count: 10
subscribers_count: 3
topics: []
@@ -200691,8 +200688,8 @@ zalf-rpm/build-pipeline:
data_format: 2
description: This project contains build scripts, setup and how-to instructions.
filenames:
- - hpc/simplace/Singularityfile_HM.def
- hpc/simplace/Singularityfile.def
+ - hpc/simplace/Singularityfile_HM.def
full_name: zalf-rpm/build-pipeline
latest_release: null
readme: '