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