--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/clustergrammerIPG.py	Fri Jul 31 19:06:45 2020 -0400
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+
+######################################################################
+#                  Copyright (c) 2016 Northrop Grumman.
+#                          All rights reserved.
+######################################################################
+
+# version 1.1 -- August 2017
+# added checks for consistency between input files
+# and upper limit on nb of cluster to look at
+# version 1.2 -- June 2018
+# added clustergrammer clustering parameters and normalization options
+
+
+from __future__ import print_function
+import sys
+import os
+
+from jinja2 import Environment, FileSystemLoader
+import pandas as pd
+from clustergrammer import Network
+
+
+def is_integer(s):
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+
+def get_indices(input_text):
+    output_list = []
+    default_value = ['i.e.:1,2,5', 'i.e.:3,6,7']
+    if input_text in default_value:
+        sys.exit(2)
+    else:
+        tmp_col = input_text.split(",")
+        if len(tmp_col) == 1:
+            if not tmp_col[0].strip():
+                sys.exit(2)
+            elif not is_integer(tmp_col[0].strip()):
+                sys.exit(3)
+            else:
+                output_list.append(int(tmp_col[0].strip()) - 1)
+        else:
+            for c in range(0, len(tmp_col)):
+                if not is_integer(tmp_col[c].strip()):
+                    sys.exit(3)
+                else:
+                    output_list.append(int(tmp_col[c].strip()) - 1)
+    return(output_list)
+
+
+def prepare_heatmap(matrix_input, html_file, html_dir, tools_dir, categories, distance, linkage):
+    # prepare directory and html
+    os.mkdir(html_dir)
+
+    env = Environment(loader=FileSystemLoader(tools_dir + "/templates"))
+    template = env.get_template("clustergrammer.template")
+    overview = template.render()
+    with open(html_file, "w") as outf:
+        outf.write(overview)
+
+    json_output = html_dir + "/mult_view.json"
+
+    net = Network()
+    net.load_file(matrix_input)
+    if (categories['row']):
+        net.add_cats('row', categories['row'])
+    if (categories['col']):
+        net.add_cats('col', categories['col'])
+    net.cluster(dist_type=distance, linkage_type=linkage)
+    net.write_json_to_file('viz', json_output)
+
+
+if __name__ == "__main__":
+
+    args = sys.argv
+    categories = {
+        'row': [],
+        'col': []
+    }
+    norm = {}
+
+    if (len(args) > 7):
+        df = pd.read_table(args[1])
+        names = {
+            'row': df.iloc[:, 0],
+            'col': df.columns[1:]
+        }
+
+        tmp_string = "-=-".join(args[8:])
+        print (tmp_string + "\n")
+        # get categories
+        cats = tmp_string.split("-=-new_cat-=-")
+        for cat in cats:
+            tmp_cat = cat.split("-=-")
+            group = {
+                "title": tmp_cat[1],
+                "cats": {}
+            }
+            stg_groups = "--".join(tmp_cat[2:])
+            groups = stg_groups.split("--new_label--")
+            cat_indices = []
+            for g in groups:
+                print(g + "\n")
+                elem = g.split("--")
+                index_list = get_indices(elem[1])
+                index_names = []
+                for i in index_list:
+                    if i in cat_indices:
+                        sys.exit(4)
+                    index_names.append(str(names[tmp_cat[0]][i]))
+                cat_indices = cat_indices + index_list
+                print(index_names, elem[0], sep="\t")
+                group["cats"][elem[0]] = index_names
+            categories[tmp_cat[0]].append(group)
+            print(categories)
+
+
+    prepare_heatmap(args[1], args[2], args[3], args[4], categories, args[5], args[6])