Namespace all modules under gn2.

We move all modules under a gn2 directory. This is important for
"correct" packaging and deployment as a Guix service.

2 files changed, 188 insertions, 0 deletions

diff --git a/gn2/wqflask/network_graph/__init__.py b/gn2/wqflask/network_graph/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/gn2/wqflask/network_graph/__init__.py
diff --git a/gn2/wqflask/network_graph/network_graph.py b/gn2/wqflask/network_graph/network_graph.py
new file mode 100644
index 00000000..d60d50ff
--- /dev/null
+++ b/gn2/wqflask/network_graph/network_graph.py
@@ -0,0 +1,188 @@
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Dr. Robert W. Williams at rwilliams@uthsc.edu
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+
+import scipy
+import simplejson as json
+
+from gn2.base.trait import create_trait
+from gn2.base import data_set
+from gn2.utility import helper_functions
+from gn2.utility import corr_result_helpers
+from gn2.utility.tools import GN2_BRANCH_URL
+
+
+class NetworkGraph:
+
+    def __init__(self, start_vars):
+        trait_db_list = [trait.strip()
+                         for trait in start_vars['trait_list'].split(',')]
+
+        helper_functions.get_trait_db_obs(self, trait_db_list)
+
+        self.all_sample_list = []
+        self.traits = []
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            self.traits.append(this_trait)
+            this_sample_data = this_trait.data
+
+            for sample in this_sample_data:
+                if sample not in self.all_sample_list:
+                    self.all_sample_list.append(sample)
+
+        self.sample_data = []
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            this_sample_data = this_trait.data
+
+            this_trait_vals = []
+            for sample in self.all_sample_list:
+                if sample in this_sample_data:
+                    this_trait_vals.append(this_sample_data[sample].value)
+                else:
+                    this_trait_vals.append('')
+            self.sample_data.append(this_trait_vals)
+
+        # ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
+        self.lowest_overlap = 8
+
+        self.nodes_list = []
+        self.edges_list = []
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            this_db = trait_db[1]
+
+            this_db_samples = this_db.group.all_samples_ordered()
+            this_sample_data = this_trait.data
+
+            corr_result_row = []
+            is_spearman = False  # ZS: To determine if it's above or below the diagonal
+
+            max_corr = 0  # ZS: Used to determine whether node should be hidden when correlation coefficient slider is used
+
+            for target in self.trait_list:
+                target_trait = target[0]
+                target_db = target[1]
+
+                if str(this_trait) == str(target_trait) and str(this_db) == str(target_db):
+                    continue
+
+                target_samples = target_db.group.all_samples_ordered()
+
+                target_sample_data = target_trait.data
+
+                this_trait_vals = []
+                target_vals = []
+                for index, sample in enumerate(target_samples):
+
+                    if (sample in this_sample_data) and (sample in target_sample_data):
+                        sample_value = this_sample_data[sample].value
+                        target_sample_value = target_sample_data[sample].value
+                        this_trait_vals.append(sample_value)
+                        target_vals.append(target_sample_value)
+
+                this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(
+                    this_trait_vals, target_vals)
+
+                if num_overlap < self.lowest_overlap:
+                    self.lowest_overlap = num_overlap
+                if num_overlap < 2:
+                    continue
+                else:
+                    pearson_r, pearson_p = scipy.stats.pearsonr(
+                        this_trait_vals, target_vals)
+                    if is_spearman == False:
+                        sample_r, sample_p = pearson_r, pearson_p
+                        if sample_r == 1:
+                            continue
+                    else:
+                        sample_r, sample_p = scipy.stats.spearmanr(
+                            this_trait_vals, target_vals)
+
+                    if -1 <= sample_r < -0.7:
+                        color = "#0000ff"
+                        width = 3
+                    elif -0.7 <= sample_r < -0.5:
+                        color = "#00ff00"
+                        width = 2
+                    elif -0.5 <= sample_r < 0:
+                        color = "#000000"
+                        width = 0.5
+                    elif 0 <= sample_r < 0.5:
+                        color = "#ffc0cb"
+                        width = 0.5
+                    elif 0.5 <= sample_r < 0.7:
+                        color = "#ffa500"
+                        width = 2
+                    elif 0.7 <= sample_r <= 1:
+                        color = "#ff0000"
+                        width = 3
+                    else:
+                        color = "#000000"
+                        width = 0
+
+                    if abs(sample_r) > max_corr:
+                        max_corr = abs(sample_r)
+
+                    edge_data = {'id': f"{str(this_trait.name)}:{str(this_trait.dataset.name)}" + '_to_' + f"{str(target_trait.name)}:{str(target_trait.dataset.name)}",
+                                 'source': str(this_trait.name) + ":" + str(this_trait.dataset.name),
+                                 'target': str(target_trait.name) + ":" + str(target_trait.dataset.name),
+                                 'correlation': round(sample_r, 3),
+                                 'abs_corr': abs(round(sample_r, 3)),
+                                 'p_value': round(sample_p, 3),
+                                 'overlap': num_overlap,
+                                 'color': color,
+                                 'width': width}
+
+                    edge_dict = {'data': edge_data}
+
+                    self.edges_list.append(edge_dict)
+
+            if trait_db[1].type == "ProbeSet":
+                node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
+                                      'label': this_trait.symbol,
+                                      'symbol': this_trait.symbol,
+                                      'geneid': this_trait.geneid,
+                                      'omim': this_trait.omim,
+                                      'max_corr': max_corr}}
+            elif trait_db[1].type == "Publish":
+                node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
+                                      'label': this_trait.name,
+                                      'max_corr': max_corr}}
+            else:
+                node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
+                                      'label': this_trait.name,
+                                      'max_corr': max_corr}}
+            self.nodes_list.append(node_dict)
+
+        self.elements = json.dumps(self.nodes_list + self.edges_list)
+        self.gn2_url = GN2_BRANCH_URL
+
+        groups = []
+        for sample in self.all_sample_list:
+            groups.append(1)
+
+        self.js_data = dict(traits=[trait.name for trait in self.traits],
+                            groups=groups,
+                            cols=list(range(len(self.traits))),
+                            rows=list(range(len(self.traits))),
+                            samples=self.all_sample_list,
+                            sample_data=self.sample_data,
+                            elements=self.elements,)