def prerender(self,
compound_data=None,
gene_data=None,
protein_data=None,
gpml=None):
node_colors = {}
for dso in compound_data, gene_data, protein_data:
if dso == None:
continue
mini, maxi = min(abs(np.median(dso.data)),
0), max(abs(np.median(dso.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1],
out=np.around) # rdbu9 scale
for n, m in enumerate(dso.entities[1]):
xref = self.get_xref(m)
ecol = utils.calculate_rdbu9_color(scale, dso.data[0, n])
#print xref, ecol
if xref is not None and ecol is not None:
node_colors[xref] = ecol
print(node_colors)
return {'View': {'gpml': gpml, 'node_colors': node_colors}}
def generate(self, input=None, **kwargs):
dsi = input
opener = register_openers()
url = 'http://www.kegg.jp/kegg-bin/mcolor_pathway'
data = io.StringIO()
node_colors = {}
if dsi:
mini, maxi = min(abs(np.median(dsi.data)), 0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -2.0, +2.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1], out=np.around) # rdbu9 scale
#for n, m in enumerate(dsi.entities[1]):
# xref = self.get_xref( m )
for n, m in enumerate(dsi.entities[1]):
if m:
if 'LIGAND-CPD' in m.databases:
kegg_id = m.databases['LIGAND-CPD']
ecol = utils.calculate_rdbu9_color(scale, dsi.data[0, n])
if kegg_id is not None and ecol is not None:
node_colors[kegg_id] = ecol
elif 'NCBI-GENE' in m.databases:
kegg_id = m.databases['NCBI-GENE']
ecol = utils.calculate_rdbu9_color(scale, dsi.data[0, n])
if kegg_id is not None and ecol is not None:
node_colors[kegg_id] = ecol
tmp = open(os.path.join(QDir.tempPath(), 'kegg-pathway-data.txt'), 'w')
tmp.write('#hsa\tData\n')
for k, c in list(node_colors.items()):
tmp.write('%s\t%s\n' % (k, c[0]))
tmp = open(os.path.join(QDir.tempPath(), 'kegg-pathway-data.txt'), 'r')
values = {
'map': self.kegg_pathway_t.text(),
'mapping_list': tmp,
'mode': 'color',
'submit': 'Exec',
}
self.status.emit('waiting')
datagen, headers = multipart_encode(values)
# Create the Request object
# Actually do the request, and get the response
request = Request(url, datagen, headers)
try:
response = urlopen(request)
except urllib.error.HTTPError as e:
print(e)
return
return {'html': response.read()}
def prerender(self, compound_data=None, gene_data=None, protein_data=None, gpml=None):
node_colors = {}
for dso in compound_data, gene_data, protein_data:
if dso == None:
continue
mini, maxi = min(abs(np.median(dso.data)), 0), max(abs(np.median(dso.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1], out=np.around) # rdbu9 scale
for n, m in enumerate(dso.entities[1]):
xref = self.get_xref(m)
ecol = utils.calculate_rdbu9_color(scale, dso.data[0, n])
#print xref, ecol
if xref is not None and ecol is not None:
node_colors[xref] = ecol
#logging.debug("Calculated node colors: %s" % (','.join(node_colors)) )
return {'View': {'gpml': gpml, 'node_colors': node_colors}}
def generateGraph(self, filename, suggested_pathways=[], compound_data=None, gene_data=None, protein_data=None, format='svg'):
# Build options-like structure for generation of graph
# (compatibility with command line version, we need to fake it)
options = Values()
options._update_loose({
'file': None,
#'pathways': self.config.Read('/Pathways/Show'),
#'not_pathways':'',
'show_all': False, # self.config.ReadBool('/Pathways/ShowAll'),
'search': '',
'cluster_by': self.config.get('/App/ClusterBy'),
'show_enzymes': self.config.get('/App/ShowEnzymes'), # self.config.ReadBool('/App/ShowEnzymes'),
'show_secondary': self.config.get('/App/Show2nd'),
'show_molecular': self.config.get('/App/ShowMolecular'),
'show_network_analysis': self.config.get('/App/ShowAnalysis'),
'show_gibbs': self.config.get('/App/ShowGibbs'),
'highlightpathways': self.config.get('/App/HighlightPathways'),
'highlightregions': self.config.get('/App/HighlightRegions'),
'splines': 'true',
'focus': False,
'show_pathway_links': self.config.get('/Pathways/ShowLinks'),
# Always except when saving the file
'output': format,
})
#pathway_ids = self.config.value('/Pathways/Show').split(',')
if suggested_pathways:
pathway_ids = [p.id for p in suggested_pathways.entities[1]]
else:
pathway_ids = []
# Add the manually Shown pathways
pathway_ids_show = self.config.get('/Pathways/Show')
pathway_ids.extend(pathway_ids_show)
# Now remove the Hide pathways
pathway_ids_hide = self.config.get('/Pathways/Hide')
pathway_ids = [p for p in pathway_ids if p not in pathway_ids_hide]
# Convert pathways_ids to pathways
pathways = [self.m.db.pathway(pid) for pid in pathway_ids if self.m.db.pathway(pid) is not None]
if pathway_ids == []:
return None
if compound_data or gene_data or protein_data:
# Generate independent scales
node_colors = {}
for dsi in compound_data, gene_data, protein_data:
if dsi == None:
continue
#if self.m.data.analysis_timecourse:
# # Generate the multiple views
# tps = sorted( self.m.data.analysis_timecourse.keys(), key=int )
# # Insert counter variable into the filename
# filename = self.get_filename_with_counter(filename)
# print "Generate timecourse..."
# for tp in tps:
# print "%s" % tp
# graph = generator( pathways, options, self.m.db, analysis=self.m.data.analysis_timecourse[ tp ]) #, layout=self.layout)
# graph.write(filename % tp, format=options.output, prog='neato')
# return tps
#else:
print("Generate map for single control:test...")
# Build analysis lookup dict; we want a single color for each metabolite
mini, maxi = min(abs(np.median(dsi.data)), 0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1], out=np.around) # rdbu9 scale
for n, m in enumerate(dsi.entities[1]):
if m is not None:
ecol = utils.calculate_rdbu9_color(scale, dsi.data[0, n])
#print xref, ecol
if ecol is not None:
node_colors[m.id] = ecol
graph = generator(pathways, options, self.m.db, analysis=node_colors) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
else:
graph = generator(pathways, options, self.m.db) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
def generate(self, input=None):
dsi = input
opener = register_openers()
url = 'http://www.kegg.jp/kegg-bin/mcolor_pathway'
data = io.StringIO()
node_colors = {}
if dsi:
mini, maxi = min(abs(np.median(dsi.data)),
0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -2.0, +2.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1],
out=np.around) # rdbu9 scale
#for n, m in enumerate(dsi.entities[1]):
# xref = self.get_xref( m )
for n, m in enumerate(dsi.entities[1]):
if m:
if 'LIGAND-CPD' in m.databases:
kegg_id = m.databases['LIGAND-CPD']
ecol = utils.calculate_rdbu9_color(
scale, dsi.data[0, n])
if kegg_id is not None and ecol is not None:
node_colors[kegg_id] = ecol
elif 'NCBI-GENE' in m.databases:
kegg_id = m.databases['NCBI-GENE']
ecol = utils.calculate_rdbu9_color(
scale, dsi.data[0, n])
if kegg_id is not None and ecol is not None:
node_colors[kegg_id] = ecol
tmp = open(os.path.join(QDir.tempPath(), 'kegg-pathway-data.txt'), 'w')
tmp.write('#hsa\tData\n')
for k, c in list(node_colors.items()):
tmp.write('%s\t%s\n' % (k, c[0]))
tmp = open(os.path.join(QDir.tempPath(), 'kegg-pathway-data.txt'), 'r')
values = {
'map': self.kegg_pathway_t.text(),
'mapping_list': tmp,
'mode': 'color',
'submit': 'Exec',
}
self.status.emit('waiting')
datagen, headers = multipart_encode(values)
# Create the Request object
# Actually do the request, and get the response
request = Request(url, datagen, headers)
try:
response = urlopen(request)
except urllib.error.HTTPError as e:
print(e)
return
return {'html': response.read()}
def generateGraph(self,
filename,
suggested_pathways=[],
compound_data=None,
gene_data=None,
protein_data=None,
format='svg'):
# Build options-like structure for generation of graph
# (compatibility with command line version, we need to fake it)
options = Values()
options._update_loose({
'file':
None,
#'pathways': self.config.Read('/Pathways/Show'),
#'not_pathways':'',
'show_all':
False, # self.config.ReadBool('/Pathways/ShowAll'),
'search':
'',
'cluster_by':
self.config.get('/App/ClusterBy'),
'show_enzymes':
self.config.get('/App/ShowEnzymes'
), # self.config.ReadBool('/App/ShowEnzymes'),
'show_secondary':
self.config.get('/App/Show2nd'),
'show_molecular':
self.config.get('/App/ShowMolecular'),
'show_network_analysis':
self.config.get('/App/ShowAnalysis'),
'highlightpathways':
self.config.get('/App/HighlightPathways'),
'highlightregions':
self.config.get('/App/HighlightRegions'),
'splines':
'true',
'focus':
False,
'show_pathway_links':
self.config.get('/Pathways/ShowLinks'),
# Always except when saving the file
'output':
format,
})
#pathway_ids = self.config.value('/Pathways/Show').split(',')
if suggested_pathways:
pathway_ids = [p.id for p in suggested_pathways.entities[1]]
else:
pathway_ids = []
print(self.config.get('/Pathways/Show'))
# Add the manually Shown pathways
pathway_ids_show = self.config.get('/Pathways/Show')
pathway_ids.extend(pathway_ids_show)
# Now remove the Hide pathways
pathway_ids_hide = self.config.get('/Pathways/Hide')
pathway_ids = [p for p in pathway_ids if p not in pathway_ids_hide]
# Convert pathways_ids to pathways
pathways = [
self.m.db.pathways[pid] for pid in pathway_ids
if pid in list(self.m.db.pathways.keys())
]
if pathway_ids == []:
return None
if compound_data or gene_data or protein_data:
# Generate independent scales
node_colors = {}
for dsi in compound_data, gene_data, protein_data:
if dsi == None:
continue
#if self.m.data.analysis_timecourse:
# # Generate the multiple views
# tps = sorted( self.m.data.analysis_timecourse.keys(), key=int )
# # Insert counter variable into the filename
# filename = self.get_filename_with_counter(filename)
# print "Generate timecourse..."
# for tp in tps:
# print "%s" % tp
# graph = generator( pathways, options, self.m.db, analysis=self.m.data.analysis_timecourse[ tp ]) #, layout=self.layout)
# graph.write(filename % tp, format=options.output, prog='neato')
# return tps
#else:
print("Generate map for single control:test...")
# Build analysis lookup dict; we want a single color for each metabolite
mini, maxi = min(abs(np.median(dsi.data)),
0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1],
out=np.around) # rdbu9 scale
for n, m in enumerate(dsi.entities[1]):
if m is not None:
ecol = utils.calculate_rdbu9_color(
scale, dsi.data[0, n])
#print xref, ecol
if ecol is not None:
node_colors[m.id] = ecol
graph = generator(pathways,
options,
self.m.db,
analysis=node_colors) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
else:
graph = generator(pathways, options,
self.m.db) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None