Add implementation of standard deviation on data

inflammation-analysis.py

@@ -27,7 +27,8 @@ def main(args):
     for filename in InFiles:
         inflammation_data = models.load_csv(filename)
 
-        view_data = {'average': models.daily_mean(inflammation_data), 'max': models.daily_max(inflammation_data), 'min': models.daily_min(inflammation_data)}
+        view_data = {'average': models.daily_mean(inflammation_data), 'max': models.daily_max(inflammation_data), 'min': models.daily_min(inflammation_data), **(models.s_dev(inflammation_data))}
+
 
         views.visualize(view_data)
 

inflammation/models.py

@@ -54,3 +54,13 @@ def daily_min(data):
     """Calculate the daily min of a 2d inflammation data array."""
     return np.min(data, axis=0)
 
+
+def s_dev(data):
+    """Computes and returns standard deviation for data."""
+    mmm = np.mean(data, axis=0)
+    devs = []
+    for entry in data:
+        devs.append((entry - mmm) * (entry - mmm))
+
+    s_dev2 = sum(devs) / len(data)
+    return {'standard deviation': s_dev2}

tests/test_models.py

@@ -3,7 +3,7 @@
 import numpy as np
 import numpy.testing as npt
 import os
-
+import pytest
 
 def test_daily_mean_zeros():
     """Test that mean function works for an array of zeros."""
@@ -37,3 +37,14 @@ def test_load_from_json(tmpdir):
         temp_json_file.write('[{"observations":[1, 2, 3]},{"observations":[4, 5, 6]}]')
     result = load_json(example_path)
     npt.assert_array_equal(result, [[1, 2, 3], [4, 5, 6]])
+
+
+@pytest.mark.parametrize('data, expected_standard_deviation', [
+    ([0, 0, 0], 0.0),
+    ([1.0, 1.0, 1.0], 0),
+    ([0.0, 2.0], 1.0)
+])
+def test_daily_standard_deviation(data, expected_standard_deviation):
+    from inflammation.models import s_dev
+    result_data = s_dev(data)['standard deviation']
+    npt.assert_approx_equal(result_data, expected_standard_deviation)