Add in implementation of standard deviation on data

inflammation-analysis.py

@@ -21,7 +21,8 @@ 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

@@ -31,3 +31,14 @@ def daily_max(data):
 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."""
+    number = np.mean(data, axis=0)
+    devs = []
+    for entry in data:
+        devs.append((entry - number) * (entry - number))
+
+    s_dev2 = sum(devs) / len(data)
+    return {'standard deviation': s_dev2}

tests/test_models.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 import numpy.testing as npt
-
+import pytest
 
 def test_daily_mean_zeros():
     """Test that mean function works for an array of zeros."""
@@ -29,3 +29,20 @@ def test_daily_mean_integers():
     # Need to use Numpy testing functions to compare arrays
     npt.assert_array_equal(daily_mean(test_input), test_result)
 
+@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)
+
+
+def test_daily_standard_deviation_on_np_array():
+    from inflammation.models import s_dev
+    data = np.array([[0.0]])
+    expected_std_devs = [[0.0]]
+    out = s_dev(data)['standard deviation']
+    npt.assert_array_almost_equal(data, expected_std_devs)