Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Updated zscore for in-place operations #440

Merged
merged 3 commits into from
Dec 17, 2021
Merged

Updated zscore for in-place operations #440

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0993cc7
Updated zscore to change the units to dimensionless when performing i…
Dec 12, 2021
7a84a94
Changed to raise ValueError instead of a warning. New AnalogSignal ob…
Dec 13, 2021
d454a50
Updated unit tests to check for same/different objects when doing in-…
Dec 13, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
51 changes: 34 additions & 17 deletions elephant/signal_processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,8 @@

from elephant.utils import deprecated_alias, check_same_units

import warnings

__all__ = [
"zscore",
"cross_correlation_function",
Expand Down Expand Up @@ -66,16 +68,22 @@ def zscore(signal, inplace=True):
inplace : bool, optional
If True, the contents of the input `signal` is replaced by the
z-transformed signal, if possible, i.e when the signal type is float.
If the signal type is not float, an error is raised.
If False, a copy of the original `signal` is returned.
Default: True

Returns
-------
signal_ztransofrmed : neo.AnalogSignal or list of neo.AnalogSignal
signal_ztransformed : neo.AnalogSignal or list of neo.AnalogSignal
The output format matches the input format: for each input
`neo.AnalogSignal`, a corresponding `neo.AnalogSignal` is returned,
containing the z-transformed signal with dimensionless unit.

Raises
------
ValueError
If `inplace` is True and the type of `signal` is not float.

Notes
-----
You may supply a list of `neo.AnalogSignal` objects, where each object in
Expand Down Expand Up @@ -153,29 +161,38 @@ def zscore(signal, inplace=True):
mean = signal_stacked.mean(axis=0)
std = signal_stacked.std(axis=0)

signal_ztransofrmed = []
signal_ztransformed = []
for sig in signal:
# Perform inplace operation only if array is of dtype float.
# Otherwise, raise an error.
if inplace and not np.issubdtype(np.float, sig.dtype):
raise ValueError(f"Cannot perform inplace operation as the "
f"signal dtype is not float. Source: {sig.name}")

sig_normalized = sig.magnitude.astype(mean.dtype, copy=not inplace)
sig_normalized -= mean

# items where std is zero are already zero
np.divide(sig_normalized, std, out=sig_normalized, where=std != 0)
sig_dimless = neo.AnalogSignal(signal=sig_normalized,
units=pq.dimensionless,
dtype=sig_normalized.dtype,
copy=False,
t_start=sig.t_start,
sampling_rate=sig.sampling_rate,
name=sig.name,
file_origin=sig.file_origin,
description=sig.description,
array_annotations=sig.array_annotations,
**sig.annotations)
signal_ztransofrmed.append(sig_dimless)

if inplace:
# Replace unit in the original array by dimensionless
sig._dimensionality = pq.dimensionless.dimensionality
sig_dimless = sig
else:
# Create new object
sig_dimless = sig.duplicate_with_new_data(sig_normalized,
units=pq.dimensionless)
# todo use flag once is fixed
# https://github.com/NeuralEnsemble/python-neo/issues/752
sig_dimless.array_annotate(**sig.array_annotations)

signal_ztransformed.append(sig_dimless)

# Return single object, or list of objects
if len(signal_ztransofrmed) == 1:
signal_ztransofrmed = signal_ztransofrmed[0]
return signal_ztransofrmed
if len(signal_ztransformed) == 1:
signal_ztransformed = signal_ztransformed[0]
return signal_ztransformed


@deprecated_alias(ch_pairs='channel_pairs', nlags='n_lags',
Expand Down
45 changes: 32 additions & 13 deletions elephant/test/test_signal_processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -195,6 +195,9 @@ def test_zscore_single_dup(self):
# Assert original signal is untouched
self.assertEqual(signal[0].magnitude, self.test_seq1[0])

# Assert original and returned objects are different
self.assertIsNot(result, signal)

def test_zscore_single_inplace(self):
"""
Test z-score on a single AnalogSignal, asking for an inplace
Expand All @@ -218,6 +221,9 @@ def test_zscore_single_inplace(self):
# Assert original signal is overwritten
self.assertEqual(signal[0].magnitude, target[0])

# Assert original and returned objects are the same
self.assertIs(result, signal)

def test_zscore_single_multidim_dup(self):
"""
Test z-score on a single AnalogSignal with multiple dimensions, asking
Expand All @@ -232,13 +238,15 @@ def test_zscore_single_multidim_dup(self):
s = np.std(signal.magnitude, axis=0, keepdims=True)
target = (signal.magnitude - m) / s

assert_array_almost_equal(
elephant.signal_processing.zscore(
signal, inplace=False).magnitude, target, decimal=9)
result = elephant.signal_processing.zscore(signal, inplace=False)
assert_array_almost_equal(result.magnitude, target, decimal=9)

# Assert original signal is untouched
self.assertEqual(signal[0, 0].magnitude, self.test_seq1[0])

# Assert original and returned objects are different
self.assertIsNot(result, signal)

def test_zscore_array_annotations(self):
signal = neo.AnalogSignal(
self.test_seq1, units='mV',
Expand Down Expand Up @@ -269,6 +277,9 @@ def test_zscore_single_multidim_inplace(self):
# Assert original signal is overwritten
self.assertAlmostEqual(signal[0, 0].magnitude, ground_truth[0, 0])

# Assert original and returned objects are the same
self.assertIs(result, signal)

def test_zscore_single_dup_int(self):
"""
Test if the z-score is correctly calculated even if the input is an
Expand All @@ -283,28 +294,28 @@ def test_zscore_single_dup_int(self):
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s

assert_array_almost_equal(
elephant.signal_processing.zscore(signal, inplace=False).magnitude,
target.reshape(-1, 1), decimal=9)
result = elephant.signal_processing.zscore(signal, inplace=False)
assert_array_almost_equal(result.magnitude, target.reshape(-1, 1),
decimal=9)

# Assert original signal is untouched
self.assertEqual(signal.magnitude[0], self.test_seq1[0])

# Assert original and returned objects are different
self.assertIsNot(result, signal)

def test_zscore_single_inplace_int(self):
"""
Test if the z-score is correctly calculated even if the input is an
AnalogSignal of type int, asking for an inplace operation.
Test if the z-score operation fails if the input is an
AnalogSignal of type int, when asking for an inplace operation.
"""
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s

signal = neo.AnalogSignal(
self.test_seq1, units='mV',
t_start=0. * pq.ms, sampling_rate=1000. * pq.Hz, dtype=int)
zscored = elephant.signal_processing.zscore(signal, inplace=True)

assert_array_almost_equal(zscored.magnitude.squeeze(), target)
with self.assertRaises(ValueError):
elephant.signal_processing.zscore(signal, inplace=True)

def test_zscore_list_dup(self):
"""
Expand Down Expand Up @@ -344,6 +355,10 @@ def test_zscore_list_dup(self):
self.assertEqual(signal1.magnitude[0, 0], self.test_seq1[0])
self.assertEqual(signal2.magnitude[0, 1], self.test_seq2[0])

# Assert original and returned objects are different
self.assertIsNot(result[0], signal_list[0])
self.assertIsNot(result[1], signal_list[1])

def test_zscore_list_inplace(self):
"""
Test zscore on a list of AnalogSignal objects, asking for an
Expand Down Expand Up @@ -382,6 +397,10 @@ def test_zscore_list_inplace(self):
self.assertEqual(signal1[0, 0].magnitude, target11[0])
self.assertEqual(signal2[0, 0].magnitude, target21[0])

# Assert original and returned objects are the same
self.assertIs(result[0], signal_list[0])
self.assertIs(result[1], signal_list[1])

def test_wrong_input(self):
# wrong type
self.assertRaises(TypeError, elephant.signal_processing.zscore,
Expand Down