elif overall_min >= 0:
colormap = cm.Reds
norm = mpl.colors.SymLogNorm(linthresh=0.001,
vmin=overall_min,
vmax=overall_max,
clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=colormap)
for data in datasets:
# We can only use a single row at the moment, so process through fold change etc. first
values = data.iloc[0]
for n, v in enumerate(values):
color = rgb2hex(mapper.to_rgba(v))
if luminahex(color) < 0.5:
contrast = "#FFFFFF"
else:
contrast = "#000000"
node_colors[('PATHOMX%d' % id(data), n)] = (color, contrast)
xref_syns = dict(xref_syns.items() + build_xref_list(data).items())
else:
node_colors = None
#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:
colormap = cm.Blues_r
elif overall_min >= 0:
colormap = cm.Reds
norm = mpl.colors.SymLogNorm(linthresh=0.001, vmin=overall_min, vmax=overall_max, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=colormap)
for data in datasets:
ids = [e[ data.columns.names.index('BioCyc') ] for e in data.columns.values]
# We can only use a single row at the moment, so process through fold change etc. first
values = data.iloc[0]
for e,v in zip(ids, values):
if type(e) in [Gene, Compound, Protein]:
hexcol = rgb2hex( mapper.to_rgba(v) )
l = luminahex(hexcol)
if l < 0.5:
contrasthexcol = '#ffffff'
else:
contrasthexcol = '#000000'
analysis[e] = (hexcol, contrasthexcol)
print "Range %.2f..%.2f" % (overall_min, overall_max)
else:
analysis = None
if suggested_pathways is not None:
# Pathways should come in as a column set named 'BioCyc' but containing Pathway objects
norm = mpl.colors.SymLogNorm(linthresh=0.001,
vmin=overall_min,
vmax=overall_max,
clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=colormap)
for data in datasets:
ids = [
e[data.columns.names.index('BioCyc')] for e in data.columns.values
]
# We can only use a single row at the moment, so process through fold change etc. first
values = data.iloc[0]
for e, v in zip(ids, values):
if type(e) in [Gene, Compound, Protein]:
hexcol = rgb2hex(mapper.to_rgba(v))
l = luminahex(hexcol)
if l < 0.5:
contrasthexcol = '#ffffff'
else:
contrasthexcol = '#000000'
analysis[e] = (hexcol, contrasthexcol)
print "Range %.2f..%.2f" % (overall_min, overall_max)
else:
analysis = None
if suggested_pathways is not None:
# Pathways should come in as a column set named 'BioCyc' but containing Pathway objects
if 'BioCyc' in suggested_pathways.columns.names:
elif overall_min < 0:
colormap = cm.Blues_r
elif overall_min >= 0:
colormap = cm.Reds
norm = mpl.colors.SymLogNorm(linthresh=0.001, vmin=overall_min, vmax=overall_max, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=colormap)
for data in datasets:
# We can only use a single row at the moment, so process through fold change etc. first
values = data.iloc[0]
for n, v in enumerate(values):
color = rgb2hex( mapper.to_rgba(v) )
if luminahex(color) < 0.5:
contrast = "#FFFFFF"
else:
contrast = "#000000"
node_colors[('PATHOMX%d' % id(data) , n)] = (color, contrast)
xref_syns = dict( xref_syns.items() + build_xref_list(data).items() )
else:
node_colors = None
#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
