Warning printed too many times (RuntimeWarning: invalid value encountered in scalar divide ....)

copulas/univariate/truncated_gaussian.py

@@ -1,5 +1,7 @@
 """TruncatedGaussian module."""
 
+import warnings
+
 import numpy as np
 from scipy.optimize import fmin_slsqp
 from scipy.stats import truncnorm
@@ -47,10 +49,12 @@ def nnlf(params):
             return truncnorm.nnlf((a, b, loc, scale), X)
 
         initial_params = X.mean(), X.std()
-        optimal = fmin_slsqp(nnlf, initial_params, iprint=False, bounds=[
-            (self.min, self.max),
-            (0.0, (self.max - self.min)**2)
-        ])
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', category=RuntimeWarning)
+            optimal = fmin_slsqp(nnlf, initial_params, iprint=False, bounds=[
+                (self.min, self.max),
+                (0.0, (self.max - self.min)**2)
+            ])
 
         loc, scale = optimal
         a = (self.min - loc) / scale

tests/unit/univariate/test_truncated_gaussian.py

@@ -1,4 +1,6 @@
+import warnings
 from unittest import TestCase
+from unittest.mock import patch
 
 import numpy as np
 from scipy.stats import truncnorm
@@ -35,6 +37,27 @@ def test__fit(self):
         for key, value in distribution._params.items():
             np.testing.assert_allclose(value, expected[key], atol=0.3)
 
+    @patch('copulas.univariate.truncated_gaussian.fmin_slsqp')
+    def test__fit_silences_warnings(self, mocked_wrapper):
+        """Test the ``_fit`` method does not emit RuntimeWarnings."""
+        # Setup
+        def mock_fmin_sqlsqp(*args, **kwargs):
+            warnings.warn(
+                message='Runtime Warning occured!',
+                category=RuntimeWarning
+            )
+            return 0, 1
+
+        mocked_wrapper.side_effect = mock_fmin_sqlsqp
+        distribution = TruncatedGaussian()
+
+        data = truncnorm.rvs(size=10000, a=0, b=3, loc=3, scale=1)
+
+        # Run and assert
+        with warnings.catch_warnings():
+            warnings.simplefilter('error')
+            distribution._fit(data)
+
     def test__is_constant_true(self):
         distribution = TruncatedGaussian()