SR1.1.1 Add support for showing daily standard deviation on inflammation data

inflammation-analysis.py

@@ -20,10 +20,8 @@ def main(args):
     for filename in in_files:
         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 = {'mean': 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

@@ -44,6 +44,18 @@ def daily_min(data):
     """
     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}
+
 def patient_normalise(data):
     """Normalise patient data from a 2D inflammation data array."""
     if not isinstance(data, np.ndarray):
@@ -57,3 +69,4 @@ def patient_normalise(data):
         normalised = data / max_data[:, np.newaxis]
     normalised[np.isnan(normalised)] = 0
     return normalised
+

tests/test_models.py

@@ -1,5 +1,6 @@
 """Tests for statistics functions within the Model layer."""
 
+import os
 import numpy as np
 import numpy.testing as npt
 import pytest
@@ -97,10 +98,14 @@ def test_patient_normalise(test, expected, expect_raises):
     if isinstance(test, list):
         test = np.array(test)
 
-    if expect_raises is not None:
-        with pytest.raises(expect_raises):
-            result = patient_normalise(test)
-            npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
-    else:
-        result = patient_normalise(test)
-        npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
+
+@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)
+