Small changes

  *  turn print statements in eFELFeature into logger.info
  *  change docstring description of exp_mean and exp_std in extraFELFeature
  *  remove MEA dependency in setup
  *  use custom lfpy from fork in setup
  *  use lfpy from extras in tox
  *  add test function for masked_cosine_distance

Author: Jaquier Aurélien Tristan

bluepyopt/ephys/efeatures.py

@@ -316,14 +316,14 @@ def __init__(
                 of channels
             stim_start (float): stimulation start time (ms)
             stim_end (float): stimulation end time (ms)
-            exp_mean (float): experimental mean of this eFeature
-            exp_std(float): experimental standard deviation of this eFeature
-            threshold(float): spike detection threshold (mV)
+            exp_mean (list of floats): experimental mean of this eFeature
+            exp_std (list of floats): experimental standard deviation of this eFeature
+            threshold (float): spike detection threshold (mV)
             comment (str): comment
-            interp_step(float): interpolation step (ms)
-            double_settings(dict): dictionary with efel double settings that
+            interp_step (float): interpolation step (ms)
+            double_settings (dict): dictionary with efel double settings that
                 should be set before extracting the features
-            int_settings(dict): dictionary with efel int settings that
+            int_settings (dict): dictionary with efel int settings that
                 should be set before extracting the features
         """
 
@@ -421,7 +421,6 @@ def calculate_feature(
             responses,
             raise_warnings=False,
             return_waveforms=False,
-            verbose=False,
     ):
         from .extra_features_utils import calculate_features
 
@@ -445,21 +444,18 @@ def calculate_feature(
                 np.diff(response["time"])
         ):
             assert self.fs is not None
-            if verbose:
-                print("interpolate")
+            logger.info("extraFELFeature.calculate_feature: interpolate")
             response_interp = _interpolate_response(response, fs=self.fs)
         else:
             response_interp = response
 
         if self.fcut is not None:
-            if verbose:
-                print("filter enabled")
+            logger.info("extraFELFeature.calculate_feature: enabled")
             response_filter = _filter_response(response_interp,
                                                fcut=self.fcut,
                                                filt_type=self.filt_type)
         else:
-            if verbose:
-                print("filter disabled")
+            logger.info("extraFELFeature.calculate_feature: filter disabled")
             response_filter = response_interp
 
         ewf = _get_waveforms(response_filter, peak_times, self.ms_cut)

bluepyopt/tests/test_ephys/test_features.py

@@ -326,3 +326,36 @@ def test_extraFELFeature():
 
     assert efeature.name == name
     assert extrafel_feature_name in str(efeature)
+
+
+
+
+@pytest.mark.unit
+def test_masked_cosine_distance():
+    """ephys.efeatures: Testing masked_cosine_distance"""
+    from scipy.spatial import distance
+
+    exp = numpy.array([0.5, 0.5, 0.5])
+    model = numpy.array([0.7, 0.8, 0.4])
+
+    score = efeatures.masked_cosine_distance(exp, model)
+    assert score == distance.cosine(exp, model)
+
+    # test nan in model feature values
+    model = numpy.array([0.7, numpy.nan, 0.4])
+    score = efeatures.masked_cosine_distance(exp, model)
+    assert score == distance.cosine([0.5, 0.5], [0.7, 0.4])
+
+    # test nan in experimental feature values
+    exp = numpy.array([0.5, 0.5, numpy.nan])
+    model = numpy.array([0.7, 0.8, 0.4])
+
+    score = efeatures.masked_cosine_distance(exp, model)
+    assert score == distance.cosine([0.5, 0.5], [0.7, 0.8]) * 2. / 3.
+
+    # test na in both exp and model feature values
+    exp = numpy.array([0.5, 0.5, numpy.nan])
+    model = numpy.array([0.7, numpy.nan, 0.4])
+
+    score = efeatures.masked_cosine_distance(exp, model)
+    assert score == distance.cosine([0.5], [0.7]) * 2. / 3.

setup.py

@@ -29,8 +29,7 @@
 ]
 
 EXTRA_LFP = [
-    'MEAutility',
-    'LFPy>=2.2.0',
+    'LFPy @ git+https://github.com/LFPy/LFPy.git@mpi_optional',
 ]
 
 setuptools.setup(

tox.ini

@@ -15,12 +15,12 @@ envdir =
     py27{-unit,-functional,-style,-syntax}: {toxworkdir}/py27
     py3{5,6,7,8,9,10,}{-unit,-functional,-style,-syntax}: {toxworkdir}/py3
     docs: {toxworkdir}/docs
+extras = lfpy
 deps =
     coverage
     flake8
     mock
     neuron-nightly
-    git+https://github.com/LFPy/LFPy.git@mpi_optional
     sh
     pytest-cov
 download = true