From ef80c72194dd8a0b8868ece15589e0a3cf04516f Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 9 Oct 2021 21:55:39 +0300
Subject: unittest for processing wgcna output

---
 wqflask/tests/unit/wqflask/wgcna/__init__.py   |  0
 wqflask/tests/unit/wqflask/wgcna/test_wgcna.py | 50 ++++++++++++++++++++++++++
 2 files changed, 50 insertions(+)
 create mode 100644 wqflask/tests/unit/wqflask/wgcna/__init__.py
 create mode 100644 wqflask/tests/unit/wqflask/wgcna/test_wgcna.py

diff --git a/wqflask/tests/unit/wqflask/wgcna/__init__.py b/wqflask/tests/unit/wqflask/wgcna/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/wqflask/tests/unit/wqflask/wgcna/test_wgcna.py b/wqflask/tests/unit/wqflask/wgcna/test_wgcna.py
new file mode 100644
index 00000000..8e947e2f
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/wgcna/test_wgcna.py
@@ -0,0 +1,50 @@
+
+"""module contains for processing gn3 wgcna data"""
+from unittest import TestCase
+
+from wqflask.wgcna.gn3_wgcna import process_wgcna_data
+
+
+class DataProcessingTests(TestCase):
+    """class contains data processing tests"""
+
+    def test_data_processing(self):
+        """test for parsing data for datatable"""
+        output = {
+            "input": {
+                "sample_names": ["BXD1", "BXD2", "BXD3", "BXD4", "BXD5", "BXD6"],
+
+            },
+            "output": {
+                "ModEigens": {
+                    "MEturquoise": [
+                        0.0646677768085351,
+                        0.137200224277058,
+                        0.63451113720732,
+                        -0.544002665501479,
+                        -0.489487590361863,
+                        0.197111117570427
+                    ],
+                    "MEgrey": [
+                        0.213,
+                        0.214,
+                        0.3141,
+                        -0.545,
+                        -0.423,
+                        0.156,
+                    ]
+                }}}
+
+        row_data = [['BXD1', 0.065, 0.213],
+                    ['BXD2', 0.137, 0.214],
+                    ['BXD3', 0.635, 0.314],
+                    ['BXD4', -0.544, -0.545],
+                    ['BXD5', -0.489, -0.423],
+                    ['BXD6', 0.197, 0.156]]
+
+        expected_results = {
+            "col_names": ["sample_names", "MEturquoise", "MEgrey"],
+            "mod_dataset": row_data
+        }
+
+        self.assertEqual(process_wgcna_data(output), expected_results)
-- 
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