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[Fix] instantaneous rate with list[neo.SpikeTrain] #649

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[Fix] instantaneous rate with list[neo.SpikeTrain] #649

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1757196
add tests ensuring consistent output for pooling options trial object
Moritz-Alexander-Kern Nov 15, 2024
b21bfe1
refactoring
Moritz-Alexander-Kern Nov 15, 2024
685ebbb
Merge branch 'master' into fix/instantaneous_rate
Moritz-Alexander-Kern Jan 14, 2025
f898526
add test and fix for pool_spiketrains with list of spiketrains
Moritz-Alexander-Kern Jan 17, 2025
fbcbd4a
add function to utils to check for iterable containing spiketrains
Moritz-Alexander-Kern Jan 20, 2025
21ba059
add to __all__
Moritz-Alexander-Kern Jan 20, 2025
4ff1dc7
import utils function
Moritz-Alexander-Kern Jan 20, 2025
024d595
fix docstring
Moritz-Alexander-Kern Jan 20, 2025
6b47e0e
pep8
Moritz-Alexander-Kern Jan 20, 2025
3a96ee3
rename utils function to check is_list_spiketrains, fix tests and rep…
Moritz-Alexander-Kern Jan 24, 2025
b15b77d
add test for single spiketrain
Moritz-Alexander-Kern Jan 24, 2025
0df030d
make return for single spiketrain explicit
Moritz-Alexander-Kern Jan 28, 2025
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4 changes: 2 additions & 2 deletions elephant/statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -613,7 +613,7 @@ def instantaneous_rate(spiketrains, sampling_period, kernel='auto',

Parameters
----------
spiketrains : neo.SpikeTrain, list of neo.SpikeTrain or elephant.trials.Trials # noqa
spiketrains : neo.SpikeTrain, list of neo.SpikeTrain or elephant.trials.Trials
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@kohlerca kohlerca May 20, 2025

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Other PRs explicitly add links to the documentation of the referred classes using Sphinx's :class: statements, which facilitates navigating through the documentation.

Input spike train(s) for which the instantaneous firing rate is
calculated. If a list of spike trains is supplied, the parameter
pool_spike_trains determines the behavior of the function. If a Trials
Expand Down Expand Up @@ -1031,7 +1031,7 @@ def optimal_kernel(st):
sigma=str(kernel.sigma),
invert=kernel.invert)

if isinstance(spiketrains, neo.core.spiketrainlist.SpikeTrainList) and (
if isinstance(spiketrains, (neo.core.spiketrainlist.SpikeTrainList, list, tuple)) and (
pool_spike_trains):
rate = np.mean(rate, axis=1)

Expand Down
58 changes: 53 additions & 5 deletions elephant/test/test_statistics.py
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Expand Up @@ -482,15 +482,15 @@ def test_cv2_raise_error(self):
self.assertRaises(ValueError, statistics.cv2, np.array([seq, seq]))


class InstantaneousRateTest(unittest.TestCase):
class InstantaneousRateTestCase(unittest.TestCase):

@classmethod
def setUpClass(cls) -> None:
"""
Run once before tests:
"""

block = _create_trials_block(n_trials=36)
block = _create_trials_block(n_trials=36, n_spiketrains=5)
cls.block = block
cls.trial_object = TrialsFromBlock(block,
description='trials are segments')
Expand Down Expand Up @@ -988,8 +988,44 @@ def test_instantaneous_rate_trials_pool_trials(self):
pool_spike_trains=False,
pool_trials=True)
self.assertIsInstance(rate, neo.core.AnalogSignal)
self.assertEqual(rate.shape[1], self.trial_object.n_spiketrains_trial_by_trial[0])
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@kohlerca kohlerca May 20, 2025

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This test checks for consistency between the dimensions of the computed rate and the data in the Trials objects. However, an additional test against the integer values would protect against errors in implementing the object attributes. These hard expected values are supposed to be known when designing the test data in line 493.


def test_instantaneous_rate_list_pool_spike_trains(self):
def test_instantaneous_rate_trials_pool_spiketrains(self):
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@kohlerca kohlerca May 20, 2025

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A suggestion to improve understanding of these unit tests, where the dimensions of the outputs change depending on the pooling, is to include a comment block at the beginning to explicitly state the input dimensions --> expected output dimensions. It would be easier to assess the behavior of the object and what is being tested in each test case.

kernel = kernels.GaussianKernel(sigma=500 * pq.ms)

rate = statistics.instantaneous_rate(self.trial_object,
sampling_period=0.1 * pq.ms,
kernel=kernel,
pool_spike_trains=True,
pool_trials=False)
self.assertIsInstance(rate, list)
self.assertEqual(len(rate), self.trial_object.n_trials)
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@kohlerca kohlerca May 20, 2025

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Same comment as above, where the test does not compare directly to the expected values, but retrieves them dynamically from the objects.

self.assertEqual(rate[0].shape[1], 1)

def test_instantaneous_rate_trials_pool_spiketrains_pool_trials(self):
kernel = kernels.GaussianKernel(sigma=500 * pq.ms)

rate = statistics.instantaneous_rate(self.trial_object,
sampling_period=0.1 * pq.ms,
kernel=kernel,
pool_spike_trains=True,
pool_trials=True)
self.assertIsInstance(rate, neo.AnalogSignal)
self.assertEqual(rate.shape[1], 1)

def test_instantaneous_rate_trials_pool_spiketrains_false_pool_trials_false(self):
kernel = kernels.GaussianKernel(sigma=500 * pq.ms)

rate = statistics.instantaneous_rate(self.trial_object,
sampling_period=0.1 * pq.ms,
kernel=kernel,
pool_spike_trains=False,
pool_trials=False)
self.assertIsInstance(rate, list)
self.assertEqual(len(rate), self.trial_object.n_trials)
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@kohlerca kohlerca May 20, 2025

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Same comment as above, where the test does not compare directly to the expected values, but retrieves them dynamically from the objects.

self.assertEqual(rate[0].shape[1], self.trial_object.n_spiketrains_trial_by_trial[0])

def test_instantaneous_rate_spiketrainlist_pool_spike_trains(self):
kernel = kernels.GaussianKernel(sigma=500 * pq.ms)

rate = statistics.instantaneous_rate(
Expand All @@ -999,7 +1035,19 @@ def test_instantaneous_rate_list_pool_spike_trains(self):
pool_spike_trains=True,
pool_trials=False)
self.assertIsInstance(rate, neo.core.AnalogSignal)
self.assertEqual(rate.magnitude.shape[1], 1)
self.assertEqual(rate.shape[1], 1)

def test_instantaneous_rate_list_pool_spike_trains(self):
kernel = kernels.GaussianKernel(sigma=500 * pq.ms)

rate = statistics.instantaneous_rate(
list(self.trial_object.get_spiketrains_from_trial_as_list(0)),
sampling_period=0.1 * pq.ms,
kernel=kernel,
pool_spike_trains=True,
pool_trials=False)
self.assertIsInstance(rate, neo.core.AnalogSignal)
self.assertEqual(rate.shape[1], 1)

def test_instantaneous_rate_list_of_spike_trains(self):
kernel = kernels.GaussianKernel(sigma=500 * pq.ms)
Expand All @@ -1010,7 +1058,7 @@ def test_instantaneous_rate_list_of_spike_trains(self):
pool_spike_trains=False,
pool_trials=False)
self.assertIsInstance(rate, neo.core.AnalogSignal)
self.assertEqual(rate.magnitude.shape[1], 2)
self.assertEqual(rate.magnitude.shape[1], self.trial_object.n_spiketrains_trial_by_trial[0])
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@kohlerca kohlerca May 20, 2025

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Same comment as above, where the test does not compare directly to the expected values, but retrieves them dynamically from the objects. The integer in the old test could be updated, and the new one just added as an additional check.



class TimeHistogramTestCase(unittest.TestCase):
Expand Down
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