def search_for_summary(self, form):
"""
Searches references with form object,
populates has_* exists attributes for reference
summary
"""
references = self.reference_dao.search(
accids=form.accids.data,
primeAuthor=form.primeAuthor.data,
authors=form.authors.data,
journal=form.journal.data,
title=form.title.data,
volume=form.volume.data,
year=form.year.data,
marker_id=form.marker_id.data,
allele_id=form.allele_id.data,
limit=form.reference_limit.data
)
# load any exists attributes for associated data links
batchLoadAttributeExists(references, ['all_markers',
'expression_assays',
'gxdindex_records',
'explicit_alleles',
'antibodies',
'probes',
'specimens',
'gxd_images',
'mapping_experiments'])
return references
def buildDagTrees(adstructures, batchloadOn=True):
"""
Builds a list of DAG tree views.
one for every path of every dag node passed in
(However, there should only ever be one path for an AD term)
"""
dagtrees = []
for adstructure in adstructures:
root = TreeNodeForAD(adstructure)
dagtree = {"name": 'Anatomical Dictionary',
"root":root}
dagtrees.append(dagtree)
# track all the found dag nodes for batch loading
# vocterms later on
foundNodes = set()
# get immediate children
# batch load nodes of each fetched edge
loadFirstChildren(root, foundNodes)
# recurse parents
stack = [dagtree]
while stack:
dtree = stack.pop()
tree_node = dtree['root']
# if node is first parent
if len(tree_node.children) == 1 and \
adstructure._structure_key in [t._term_key for t in tree_node.children]:
original = tree_node.children[0]
# load siblings for the original node
loadSiblings(original, tree_node, foundNodes)
if tree_node.dagnode.parent:
parent_node = tree_node.dagnode.parent
ptree_node = TreeNodeForAD(parent_node)
# move root down a notch
tree_node.parent = ptree_node
ptree_node.children.append(tree_node)
dtree['root'] = ptree_node
stack.append(dtree)
# batch load all the term objects for every found node
if batchloadOn:
batchLoadAttributeExists(list(foundNodes), ["children"])
# sort all term children
for tree in dagtrees:
tree['root'].sort()
return dagtrees
def buildDagTrees(adstructures, batchloadOn=True):
"""
Builds a list of DAG tree views.
one for every path of every dag node passed in
(However, there should only ever be one path for an AD term)
"""
dagtrees = []
for adstructure in adstructures:
root = TreeNodeForAD(adstructure)
dagtree = {"name": 'Anatomical Dictionary', "root": root}
dagtrees.append(dagtree)
# track all the found dag nodes for batch loading
# vocterms later on
foundNodes = set()
# get immediate children
# batch load nodes of each fetched edge
loadFirstChildren(root, foundNodes)
# recurse parents
stack = [dagtree]
while stack:
dtree = stack.pop()
tree_node = dtree['root']
# if node is first parent
if len(tree_node.children) == 1 and \
adstructure._structure_key in [t._term_key for t in tree_node.children]:
original = tree_node.children[0]
# load siblings for the original node
loadSiblings(original, tree_node, foundNodes)
if tree_node.dagnode.parent:
parent_node = tree_node.dagnode.parent
ptree_node = TreeNodeForAD(parent_node)
# move root down a notch
tree_node.parent = ptree_node
ptree_node.children.append(tree_node)
dtree['root'] = ptree_node
stack.append(dtree)
# batch load all the term objects for every found node
if batchloadOn:
batchLoadAttributeExists(list(foundNodes), ["children"])
# sort all term children
for tree in dagtrees:
tree['root'].sort()
return dagtrees
def _prepMarker(marker):
"""
Load any attributes a detail page might need
"""
if marker:
# add the 'has_' existence checks
batchLoadAttributeExists([marker], [
'all_references', 'expression_assays', 'gxdindex_records',
'alleles', 'probes', 'antibodies', 'mapping_experiment_assocs',
'sequences'
])
def _prepMarker(marker):
"""
Load any attributes a detail page might need
"""
if marker:
# add the 'has_' existence checks
batchLoadAttributeExists([marker], ['all_references',
'expression_assays',
'gxdindex_records',
'alleles',
'probes',
'antibodies',
'mapping_experiment_assocs',
'sequences'])
def renderAlleleDetail(allele):
# gather other objects for this allele
molecularimage = None
if allele.molecularimageid:
molecularimage = image_hunter.getImageByMGIID(allele.molecularimageid)
# detect if allele has phenotype data
hasPheno = allele_hunter.doesAlleleHavePheno(allele._allele_key)
hasAssays = allele_hunter.doesAlleleHaveAssays(allele._allele_key)
# load has_explicit_references, and has_assays existence properties for links
batchLoadAttributeExists([allele], ['explicit_references'])
return render_template('detail/allele_detail.html',
allele = allele,
molecularimage = molecularimage,
hasPheno = hasPheno,
hasAssays = hasAssays)
def renderAlleleDetail(allele):
# gather other objects for this allele
molecularimage = None
if allele.molecularimageid:
molecularimage = image_hunter.getImageByMGIID(allele.molecularimageid)
# detect if allele has phenotype data
hasPheno = allele_hunter.doesAlleleHavePheno(allele._allele_key)
hasAssays = allele_hunter.doesAlleleHaveAssays(allele._allele_key)
# load has_explicit_references, and has_assays existence properties for links
batchLoadAttributeExists([allele], ['explicit_references'])
return render_template('detail/allele_detail.html',
allele=allele,
molecularimage=molecularimage,
hasPheno=hasPheno,
hasAssays=hasAssays)
def buildChildNodes(vocTerm,
ignoreObsoletes=True):
"""
returns list of child nodes for given parent vocTerm
"""
children = []
# expand startNode one level down
if vocTerm.dagnode:
if vocTerm.dagnode.child_edges:
# pre-load needed relationships
batchLoadAttribute(vocTerm.dagnode.child_edges, "child_node")
batchLoadAttribute(vocTerm.dagnode.child_edges, "child_node.vocterm")
childNodes = [edge.child_node for edge in vocTerm.dagnode.child_edges]
batchLoadAttributeExists(childNodes, ["child_edges"])
childNodes.sort(key=lambda x: x.vocterm.term)
for childNode in childNodes:
childTerm = childNode.vocterm
# skip if obsolete
if ignoreObsoletes and childTerm.isobsolete:
continue
children.append({
'id': childTerm.primaryid,
'label': childTerm.term
})
# check for future children expansion
if (childNode.has_child_edges):
# set child node as expandable
children[-1]['ex'] = True
return children
