Create crYOLO module

EM/crYOLO/build

+#!/usr/bin/env modbuild
+
+pbuild::add_to_group 'EM'
+pbuild::prep() {
+	echo "prepping"
+	mkdir -p "$SRC_DIR"
+	curl -fsSLo "$SRC_DIR/miniconda.sh" 'https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-x86_64.sh'
+	curl -fsSLo "$SRC_DIR/cryoloBM.tgz" 'ftp://ftp.gwdg.de/pub/misc/sphire/crYOLO_BM_V1_1_1/cryoloBM-1.1.1.tar.gz'
+	curl -fsSLo "$SRC_DIR/cryolo.tgz" 'ftp://ftp.gwdg.de/pub/misc/sphire/crYOLO_V1_2_3/cryolo-1.2.3.tar.gz'
+	:
+}
+
+pbuild::configure() {
+	:
+}
+
+pbuild::compile() {
+	:
+}
+
+pbuild::install() {
+	mkdir -p $PREFIX
+
+	# Install conda
+	bash "$SRC_DIR/miniconda.sh" -b -p $PREFIX/conda
+
+	# Create environment
+	$PREFIX/conda/bin/conda create -y --name crYOLO anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy
+	
+	# Activate
+	source $PREFIX/conda/bin/activate crYOLO
+	
+	# Install
+	pip install $SRC_DIR/cryolo.tgz
+	pip install $SRC_DIR/cryoloBM.tgz
+
+	# Deactivate
+	source deactivate
+}
+

EM/crYOLO/files/variants

+crYOLO/1.2.3	unstable	cuda/9.0.176
+

EM/crYOLO/modulefile

+#%Module
+
+module-whatis	        "crYOLO is a fast and accurate particle picking procedure for electron microscopy"
+module-url		"http://sphire.mpg.de/wiki/doku.php?id=pipeline:window:cryolo"
+module-license	        "SPHIRE-crYOLO Complimentary Science Software License (http://sphire.mpg.de/wiki/doku.php?id=cryolo_license). Non-commercial academic and research purposes only"
+module-maintainer	"Spencer Bliven <spencer.bliven@psi.ch>"
+
+module-help	"
+CrYOLO is a fast and accurate particle picking procedure. It's based on
+convolutional neural networks and utilizes the popular You Only Look Once
+(YOLO) object detection system.
+
+  * crYOLO makes picking fast – On a modern GPU it will pick your particles at
+    up to 6 micrographs per second.
+  * crYOLO makes picking smart – The network learns the context of particles
+    (e.g. not to pick particles on carbon or within ice contamination )
+  * crYOLO makes training easy – You might use a general network model and skip
+    training completely. However, if the general model doesn't give you
+    satisfactory results or if you would like to improve them, you might want
+    to train a specialized model specific for your data set by selecting
+    particles (no selection of negative examples necessary) on a small number
+    of micrographs.
+"
+
+# Check for supported shell types
+set shelltype [module-info shelltype]
+switch -- $shelltype {
+    "sh" {
+    }
+    default {
+        puts stderr "Shells of type '$shelltype' are NOT supported!"
+    }
+}
+
+#puts stderr "Switching crYOLO mode to [module-info mode]"
+#puts stdout "{ date; echo Switching crYOLO mode to [module-info mode] ; } >> /gpfs/home/bliven_s/cryolo_mod.log;\n"
+
+
+switch [module-info mode] {
+    "load" {
+        #puts stderr "source $PREFIX/conda/bin/activate crYOLO"
+        puts stdout "source $PREFIX/conda/bin/activate crYOLO;\n"
+    }
+    "unload" -
+    "remove" {
+        #puts stderr "source deactivate"
+        puts stdout "source $PREFIX/conda/bin/deactivate;\n"
+        puts stdout {unset $(set|sed -rn 's/^(_?conda[a-z_]*).*$/\1/pI');}
+    }
+}
+