check if there are no p values to correct

expan/core/correction.py

@@ -3,15 +3,17 @@
 
 def benjamini_hochberg(false_discovery_rate, original_p_values):
     """ Benjamini-Hochberg procedure.
-    
+
     :param false_discovery_rate: proportion of significant results that are actually false positives
     :type  false_discovery_rate: float
     :param original_p_values: p values from all the tests
     :type  original_p_values: list[float]
-    
+
     :return: new critical value (i.e. the corrected alpha)
     :rtype: float
     """
+    assert original_p_values, "empty array of p values to analyse"
+
     p_values_sorted = np.sort(np.asarray(original_p_values))
     number_tests = len(original_p_values)
     significant_ranks = [i for i, val in enumerate(p_values_sorted, 1) if val <= i * false_discovery_rate / number_tests]
@@ -21,13 +23,15 @@ def benjamini_hochberg(false_discovery_rate, original_p_values):
 
 def bonferroni(false_positive_rate, original_p_values):
     """ Bonferrnoi correction.
-    
+
     :param false_positive_rate: alpha value before correction
     :type  false_positive_rate: float
     :param original_p_values: p values from all the tests
     :type  original_p_values: list[float]
-    
+
     :return: new critical value (i.e. the corrected alpha)
     :rtype: float
     """
+    assert original_p_values, "empty array of p values to analyse"
+
     return false_positive_rate / len(original_p_values)

tests/tests_core/test_correction.py

@@ -1,5 +1,5 @@
 import unittest
-from expan.core.correction import *
+from expan.core.correction import benjamini_hochberg, bonferroni
 
 
 class CorrectionTestCase(unittest.TestCase):
@@ -28,6 +28,10 @@ def test_benjamini_hochberg_one_p_value(self):
         corrected_alpha = benjamini_hochberg(false_discovery_rate, original_p_values)
         self.assertAlmostEqual(corrected_alpha, 0.05)
 
+    def test_benjamini_hochberg_empty(self):
+        with self.assertRaises(AssertionError):
+            _ = benjamini_hochberg(0.5, [])
+
     def test_bonferroni(self):
         false_discovery_rate = 0.05
         original_p_values = [0.1, 0.03, 0.02, 0.03]
@@ -41,3 +45,7 @@ def test_bonferroni_one_p_value(self):
 
         corrected_alpha = bonferroni(false_discovery_rate, original_p_values)
         self.assertAlmostEqual(corrected_alpha, 0.05)
+
+    def test_bonferroni_empty(self):
+        with self.assertRaises(AssertionError):
+            _ = bonferroni(0.5, [])