Create cryodrgn pmodule
Wraps the conda environment
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11
EM/cryodrgn/README.md
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EM/cryodrgn/README.md
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# cryoDRGN
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CryoDRGN is installed in a central conda environment.
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## Adding New Versions
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To add a new version, first create the conda environment, as described in
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../../Programming/anaconda/2019.07/conda-env-defs/cryodrgn/README.md.
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Next, update files/variants with the new version and run `./build <VERSION>` to
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install the modulefile
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20
EM/cryodrgn/build
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EM/cryodrgn/build
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#!/usr/bin/env modbuild
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pbuild::add_to_group 'EM'
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pbuild::prep() {
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:
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}
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pbuild::configure() {
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:
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}
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pbuild::compile() {
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:
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}
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pbuild::install() {
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:
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}
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2
EM/cryodrgn/files/variants
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EM/cryodrgn/files/variants
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cryodrgn/1.1.0 stable anaconda/2019.07
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49
EM/cryodrgn/modulefile
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EM/cryodrgn/modulefile
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#%Module
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module-whatis "cryoDRGN: Deep Reconstructing Generative Networks for cryo-EM heterogeneous reconstruction"
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module-url "https://github.com/zhonge/cryodrgn"
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module-license "GPL v3.0"
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module-maintainer "Spencer Bliven <spencer.bliven@psi.ch>"
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module-help "
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CryoDRGN is a neural network based algorithm for heterogeneous cryo-EM
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reconstruction. In particular, the method models a continuous distribution over
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3D structures by using a neural network based representation for the volume.
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> CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural
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> networks. Ellen D. Zhong, Tristan Bepler, Bonnie Berger, Joseph H. Davis.
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> https://www.nature.com/articles/s41592-020-01049-4
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> Reconstructing continuous distributions of 3D protein structure from cryo-EM
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> images. Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger.
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> ICLR 2020, Spotlight presentation, https://arxiv.org/abs/1909.05215
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"
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# Check for supported shell types
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set shelltype [module-info shelltype]
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switch -- $shelltype {
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"sh" {
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puts stdout "source \"\${ANACONDA_PREFIX}\"/admintools/conda_setup.sh;\n"
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}
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default {
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puts stderr "Shells of type '$shelltype' are NOT supported!"
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}
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}
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# Determine useflags and construct conda env name
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set list [split $V "_"]
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set version [lindex ${list} 0]
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set useflags [lrange ${list} 1 end]
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set env_to_activate [string tolower ${P}]-${version}
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switch [module-info mode] {
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"load" {
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# Relies on the anaconda module being loaded since we need to update the path
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puts stdout "conda activate $env_to_activate;\n"
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}
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"unload" -
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"remove" {
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puts stdout "conda deactivate;\n"
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}
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}
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