Sentences = splitter.splitSentences(report)Ĭontext.markItems(modifiers, mode="modifier")Ĭontext.markItems(targets, mode="target")Īg = nx.to_pydot(context.getDocumentGraph(), strict=True)Īg.write("case%03d.pdf"%self.currentCase,format="pdf") leteExceptionTerms("A.B.","B.S.",deleteCaseVariants=True) # splitter = ntenceSpliter(useDefaults = False) # alternatively you can skip the default exceptions and add your own Report: a text string containing the radiology reports Given an individual radiology report, creates a p圜onTextGraph Print( "\n".join( ("".join(r) for r in chars) ) ) Img = np.sum( np.asarray( img.resize(S) ), axis=2)Ĭhars = np.asarray(list(' = (1.0 - img / img.max()) ** GCF * (chars.size - 1) S = (int(round(img.size * SC * WCF * 3)), int(round(img.size * SC))) # img_ansii_str = img2txt.generate_ANSI_from_pixels(pixel, width, height, bgcolor) # fill_string += "\x1b[K" # does not move the cursor # img2txt.getANSIbgstring_for_ANSIcolor(img2txt.getANSIcolor_for_rgb(bgcolor)) # img2txt = ut.import_module_from_fpath('/home/joncrall/venv/bin/img2txt.py') Print('pil_img.size = %r' % (pil_img.size,)) Png_str = pydot_graph.create_png(prog='dot')
"""Change labels and colors to be presented with graphviz""" # if the legend attribute is set, create a legend node N.set_width( min_node_size + (float(n.get_width()) - nodewidths.min()) / node_range )Įdge_widths = array( )Įdge_width_range = (edge_widths.max() - edge_widths.min())/(max_edge_width - min_edge_width)Į.set_penwidth( min_edge_width + (float(e.get_penwidth()) - edge_widths.min() )/edge_width_range ) Node_range = (nodewidths.max() - nodewidths.min())/(max_node_size - min_node_size) # done this way in case input has all the same size to avoid divide by zero
Def neato_from_networkx( g, min_node_size = 0.5, max_node_size = 2.0, min_edge_width = 1.0, max_edge_width = 5.0, legend_attribute=None, label_nodes_directly = False ) :
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