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test_utils.py
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# -*- coding: utf-8 -*-
"""
Unit tests for elephant.utils
"""
import unittest
import neo
import numpy as np
import quantities as pq
from elephant import utils
from numpy.testing import assert_array_equal
from elephant.spike_train_generation import StationaryPoissonProcess
from elephant.trials import TrialsFromLists
from neo.core.spiketrainlist import SpikeTrainList
from neo.core import SpikeTrain
class TestUtils(unittest.TestCase):
def test_check_neo_consistency(self):
self.assertRaises(TypeError,
utils.check_neo_consistency,
[], object_type=neo.SpikeTrain)
self.assertRaises(TypeError,
utils.check_neo_consistency,
[neo.SpikeTrain([1]*pq.s, t_stop=2*pq.s),
np.arange(2)], object_type=neo.SpikeTrain)
self.assertRaises(ValueError,
utils.check_neo_consistency,
[neo.SpikeTrain([1]*pq.s,
t_start=1*pq.s,
t_stop=2*pq.s),
neo.SpikeTrain([1]*pq.s,
t_start=0*pq.s,
t_stop=2*pq.s)],
object_type=neo.SpikeTrain)
self.assertRaises(ValueError,
utils.check_neo_consistency,
[neo.SpikeTrain([1]*pq.s, t_stop=2*pq.s),
neo.SpikeTrain([1]*pq.s, t_stop=3*pq.s)],
object_type=neo.SpikeTrain)
self.assertRaises(ValueError,
utils.check_neo_consistency,
[neo.SpikeTrain([1]*pq.ms, t_stop=2000*pq.ms),
neo.SpikeTrain([1]*pq.s, t_stop=2*pq.s)],
object_type=neo.SpikeTrain)
def test_round_binning_errors(self):
n_bins = utils.round_binning_errors(0.999999, tolerance=1e-6)
self.assertEqual(n_bins, 1)
self.assertEqual(utils.round_binning_errors(0.999999, tolerance=None),
0)
array = np.array([0, 0.7, 1 - 1e-8, 1 - 1e-9])
corrected = utils.round_binning_errors(array.copy())
assert_array_equal(corrected, [0, 0, 1, 1])
assert_array_equal(
utils.round_binning_errors(array.copy(), tolerance=None),
[0, 0, 0, 0])
class DecoratorTest:
"""
This class is used as a mock for testing the decorator.
"""
@utils.trials_to_list_of_spiketrainlist
def method_to_decorate(self, trials=None, trials_obj=None):
# This is just a mock implementation for testing purposes
if trials_obj:
return trials_obj
else:
return trials
class TestTrialsToListOfSpiketrainlist(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.n_channels = 10
cls.n_trials = 5
cls.list_of_list_of_spiketrains = [
StationaryPoissonProcess(rate=5 * pq.Hz, t_stop=1000.0 * pq.ms
).generate_n_spiketrains(cls.n_channels)
for _ in range(cls.n_trials)]
cls.trial_object = TrialsFromLists(cls.list_of_list_of_spiketrains)
def test_decorator_applied(self):
# Test that the decorator is applied correctly
self.assertTrue(hasattr(
DecoratorTest.method_to_decorate, '__wrapped__'
))
def test_decorator_return_with_trials_input_as_arg(self):
# Test if decorator takes in trial-object and returns
# list of spiketrainlists
new_class = DecoratorTest()
list_of_spiketrainlists = new_class.method_to_decorate(
self.trial_object)
self.assertEqual(len(list_of_spiketrainlists), self.n_trials)
for spiketrainlist in list_of_spiketrainlists:
self.assertIsInstance(spiketrainlist, SpikeTrainList)
def test_decorator_return_with_list_of_lists_input_as_arg(self):
# Test if decorator takes in list of lists of spiketrains
# and does not change input
new_class = DecoratorTest()
list_of_list_of_spiketrains = new_class.method_to_decorate(
self.list_of_list_of_spiketrains)
self.assertEqual(len(list_of_list_of_spiketrains), self.n_trials)
for list_of_spiketrains in list_of_list_of_spiketrains:
self.assertIsInstance(list_of_spiketrains, list)
for spiketrain in list_of_spiketrains:
self.assertIsInstance(spiketrain, SpikeTrain)
def test_decorator_return_with_trials_input_as_kwarg(self):
# Test if decorator takes in trial-object and returns
# list of spiketrainlists
new_class = DecoratorTest()
list_of_spiketrainlists = new_class.method_to_decorate(
trials_obj=self.trial_object)
self.assertEqual(len(list_of_spiketrainlists), self.n_trials)
for spiketrainlist in list_of_spiketrainlists:
self.assertIsInstance(spiketrainlist, SpikeTrainList)
def test_decorator_return_with_list_of_lists_input_as_kwarg(self):
# Test if decorator takes in list of lists of spiketrains
# and does not change input
new_class = DecoratorTest()
list_of_list_of_spiketrains = new_class.method_to_decorate(
trials_obj=self.list_of_list_of_spiketrains)
self.assertEqual(len(list_of_list_of_spiketrains), self.n_trials)
for list_of_spiketrains in list_of_list_of_spiketrains:
self.assertIsInstance(list_of_spiketrains, list)
for spiketrain in list_of_spiketrains:
self.assertIsInstance(spiketrain, SpikeTrain)
class TestIsListNeoSpiketrains(unittest.TestCase):
def setUp(self):
# Set up common test spiketrains.
self.spiketrain1 = neo.SpikeTrain([1, 2, 3] * pq.s, t_stop=4 * pq.s)
self.spiketrain2 = neo.SpikeTrain([2, 3, 4] * pq.s, t_stop=5 * pq.s)
def test_valid_list_input(self):
valid_list = [self.spiketrain1, self.spiketrain2]
self.assertTrue(utils.is_list_neo_spiketrains(valid_list))
def test_valid_tuple_input(self):
valid_tuple = (self.spiketrain1, self.spiketrain2)
self.assertTrue(utils.is_list_neo_spiketrains(valid_tuple))
def test_valid_spiketrainlist_input(self):
valid_spiketrainlist = neo.core.spiketrainlist.SpikeTrainList(items=(self.spiketrain1, self.spiketrain2))
self.assertTrue(utils.is_list_neo_spiketrains(valid_spiketrainlist))
def test_non_iterable_input(self):
with self.assertRaises(TypeError):
utils.is_list_neo_spiketrains(42)
def test_non_spiketrain_objects(self):
invalid_list = [self.spiketrain1, "not a spiketrain"]
with self.assertRaises(TypeError):
utils.is_list_neo_spiketrains(invalid_list)
def test_mixed_types_input(self):
invalid_mixed = [self.spiketrain1, 42, self.spiketrain2]
with self.assertRaises(TypeError):
utils.is_list_neo_spiketrains(invalid_mixed)
def test_none_input(self):
with self.assertRaises(TypeError):
utils.is_list_neo_spiketrains(None)
if __name__ == '__main__':
unittest.main()