Module src.test_pipeline
Expand source code
import logging
from encoding_analysis import P3_LinearModel
from erpanalysis import ERPAnalysis
from preprocessing import CleaningData, PrecomputedICA, SimpleMNEFilter
from decoding_analysis import EEGDecoder
from pipeline import MultiPipeline, Pipeline
from mne_bids import BIDSPath
import unittest
import os
class PipelineTestCase(unittest.TestCase):
def setUp(self):
bids_root = os.path.join('data', 'P3')
self.bids_path = BIDSPath(subject='001',
session='P3',
task='P3',
datatype='eeg',
suffix='eeg',
root=bids_root)
self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR)
self.erp = ERPAnalysis(-0.1, 1)
self.pipeline.load_data()
self.pipeline.set_montage()
self.pipeline.set_custom_events_mapping(task='P3')
def test_preprocessing(self):
self.pipeline.make_pipeline([
SimpleMNEFilter(0.1, 50, 'firwin'),
CleaningData(self.bids_path),
PrecomputedICA(self.bids_path)
])
# self.assertEqual(self.widget.size(), (50, 50),
# 'incorrect default size')
def test_erp_epochs(self):
self.pipeline.make_pipeline(
SimpleMNEFilter(0.1, 50, 'firwin'),
[CleaningData(self.bids_path),
PrecomputedICA(self.bids_path)])
erp_clean = ERPAnalysis(-0.1, 0.8)
erp_clean.compute_epochs(self.pipeline.raw,
self.pipeline.events,
self.pipeline.event_ids,
baseline=(0, None),
reject_by_annotation=False)
print(erp_clean.epochs)
def test_erp_peaks(self):
self.pipeline.make_pipeline([
SimpleMNEFilter(0.5, 50, 'firwin'),
CleaningData(self.bids_path),
PrecomputedICA(self.bids_path)
])
erp_clean = ERPAnalysis(-0.1, 0.8)
erp_clean.compute_epochs(self.pipeline.raw,
self.pipeline.events,
self.pipeline.event_ids,
baseline=(0, None),
reject_by_annotation=True)
peak = erp_clean.compute_peak('freq', 0.3, 0.6, ['Pz', 'CPz'])
print(peak)
def test_all_subject(self):
self.pipeline.load_multiple_subjects(40)
print("done")
class MultiPipelineTestCase(unittest.TestCase):
def setUp(self):
bids_root = os.path.join('data', 'P3')
self.multi_pipeline = MultiPipeline(bids_root)
self.erp = ERPAnalysis(-0.1,
0.8,
baseline=(None, 0),
reject_by_annotation=True,
all_subjects=True)
def test_erp_analysis(self):
self.multi_pipeline.start_erp_analysis(self.erp)
def test_encoding_analysis(self):
self.test_erp_analysis()
encoder = P3_LinearModel()
self.multi_pipeline.start_encoding_analysis(self.erp, encoder)
def test_erp_epochs(self):
self.erp.compute_peak('rare', 0.3, 0.6, ['Pz', 'CPz'], 'pos')
self.erp.compute_peak('freq', 0.3, 0.6, ['Cz'], 'pos')
def decoding_across_time(self):
self.multi_pipeline.start_decoding()
def test(self):
pass
class DecodingAnalysisTestCase(unittest.TestCase):
def setUp(self):
bids_root = os.path.join('data', 'P3')
self.bids_path = BIDSPath(subject='030',
session='P3',
task='P3',
datatype='eeg',
suffix='eeg',
root=bids_root)
self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR)
self.pipeline.load_data()
self.pipeline.set_montage()
self.pipeline.set_custom_events_mapping(task='P3')
self.pipeline.make_pipeline([
SimpleMNEFilter(0.1, 50, 'firwin'),
CleaningData(self.bids_path),
PrecomputedICA(self.bids_path)
])
def test_feature_transform(self):
decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8),
self.pipeline.raw)
csp_data, labels = decoder.feature_transform()
def test_get_train_from_channels(self):
decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8),
self.pipeline.raw)
data, labels = decoder.get_train(channels=['Cz', 'CPz'],
equalize_labels_count=True)
def test_sliding_window(self):
decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8),
self.pipeline.raw,
baseline=(None, 0),
reject_by_annotation=True)
times, scores = decoder.run_sliding_()
print(times)
def test_classify_all_stim(self):
self.pipline.load_multiple_subjects(40)
self.pipline.compute_epochs(self.erp)
epoch_train = self.erp.epochs['stimulus']
decoder = EEGDecoder(epoch_train)
stim_data = decoder.get_all_stim()
print("done")
def suite():
suite = unittest.TestSuite()
# suite.addTest(PipelineTestCase('test_preprocessing'))
suite.addTest(PipelineTestCase('test_erp_peaks'))
# suite.addTest(PipelineTestCase('test_all_subject'))
# suite.addTest(DecodingAnalysisTestCase('test_feature_transform'))
# suite.addTest(DecodingAnalysisTestCase('test_get_train_from_channels'))
# suite.addTest(DecodingAnalysisTestCase('test_sliding_window'))
# suite.addTest(DecodingAnalysisTestCase('test_classify_over_time'))
# suite.addTest(DecodingAnalysisTestCase('test_classify_all_stim'))
# suite.addTest(MultiPipelineTestCase('test_erp_analysis'))
# suite.addTest(MultiPipelineTestCase('test_erp_epochs'))
# suite.addTest(MultiPipelineTestCase('decoding_across_time'))
# suite.addTest(MultiPipelineTestCase('test_encoding_analysis'))
return suite
if __name__ == '__main__':
runner = unittest.TextTestRunner()
runner.run(suite())Functions
def suite()-
Expand source code
def suite(): suite = unittest.TestSuite() # suite.addTest(PipelineTestCase('test_preprocessing')) suite.addTest(PipelineTestCase('test_erp_peaks')) # suite.addTest(PipelineTestCase('test_all_subject')) # suite.addTest(DecodingAnalysisTestCase('test_feature_transform')) # suite.addTest(DecodingAnalysisTestCase('test_get_train_from_channels')) # suite.addTest(DecodingAnalysisTestCase('test_sliding_window')) # suite.addTest(DecodingAnalysisTestCase('test_classify_over_time')) # suite.addTest(DecodingAnalysisTestCase('test_classify_all_stim')) # suite.addTest(MultiPipelineTestCase('test_erp_analysis')) # suite.addTest(MultiPipelineTestCase('test_erp_epochs')) # suite.addTest(MultiPipelineTestCase('decoding_across_time')) # suite.addTest(MultiPipelineTestCase('test_encoding_analysis')) return suite
Classes
class DecodingAnalysisTestCase (methodName='runTest')-
A class whose instances are single test cases.
By default, the test code itself should be placed in a method named 'runTest'.
If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.
When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
Expand source code
class DecodingAnalysisTestCase(unittest.TestCase): def setUp(self): bids_root = os.path.join('data', 'P3') self.bids_path = BIDSPath(subject='030', session='P3', task='P3', datatype='eeg', suffix='eeg', root=bids_root) self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR) self.pipeline.load_data() self.pipeline.set_montage() self.pipeline.set_custom_events_mapping(task='P3') self.pipeline.make_pipeline([ SimpleMNEFilter(0.1, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) def test_feature_transform(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw) csp_data, labels = decoder.feature_transform() def test_get_train_from_channels(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw) data, labels = decoder.get_train(channels=['Cz', 'CPz'], equalize_labels_count=True) def test_sliding_window(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw, baseline=(None, 0), reject_by_annotation=True) times, scores = decoder.run_sliding_() print(times) def test_classify_all_stim(self): self.pipline.load_multiple_subjects(40) self.pipline.compute_epochs(self.erp) epoch_train = self.erp.epochs['stimulus'] decoder = EEGDecoder(epoch_train) stim_data = decoder.get_all_stim() print("done")Ancestors
- unittest.case.TestCase
Methods
def setUp(self)-
Hook method for setting up the test fixture before exercising it.
Expand source code
def setUp(self): bids_root = os.path.join('data', 'P3') self.bids_path = BIDSPath(subject='030', session='P3', task='P3', datatype='eeg', suffix='eeg', root=bids_root) self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR) self.pipeline.load_data() self.pipeline.set_montage() self.pipeline.set_custom_events_mapping(task='P3') self.pipeline.make_pipeline([ SimpleMNEFilter(0.1, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) def test_classify_all_stim(self)-
Expand source code
def test_classify_all_stim(self): self.pipline.load_multiple_subjects(40) self.pipline.compute_epochs(self.erp) epoch_train = self.erp.epochs['stimulus'] decoder = EEGDecoder(epoch_train) stim_data = decoder.get_all_stim() print("done") def test_feature_transform(self)-
Expand source code
def test_feature_transform(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw) csp_data, labels = decoder.feature_transform() def test_get_train_from_channels(self)-
Expand source code
def test_get_train_from_channels(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw) data, labels = decoder.get_train(channels=['Cz', 'CPz'], equalize_labels_count=True) def test_sliding_window(self)-
Expand source code
def test_sliding_window(self): decoder = EEGDecoder('stimulus', (-0.2, 1), (0.0, 0.8), self.pipeline.raw, baseline=(None, 0), reject_by_annotation=True) times, scores = decoder.run_sliding_() print(times)
class MultiPipelineTestCase (methodName='runTest')-
A class whose instances are single test cases.
By default, the test code itself should be placed in a method named 'runTest'.
If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.
When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
Expand source code
class MultiPipelineTestCase(unittest.TestCase): def setUp(self): bids_root = os.path.join('data', 'P3') self.multi_pipeline = MultiPipeline(bids_root) self.erp = ERPAnalysis(-0.1, 0.8, baseline=(None, 0), reject_by_annotation=True, all_subjects=True) def test_erp_analysis(self): self.multi_pipeline.start_erp_analysis(self.erp) def test_encoding_analysis(self): self.test_erp_analysis() encoder = P3_LinearModel() self.multi_pipeline.start_encoding_analysis(self.erp, encoder) def test_erp_epochs(self): self.erp.compute_peak('rare', 0.3, 0.6, ['Pz', 'CPz'], 'pos') self.erp.compute_peak('freq', 0.3, 0.6, ['Cz'], 'pos') def decoding_across_time(self): self.multi_pipeline.start_decoding() def test(self): passAncestors
- unittest.case.TestCase
Methods
def decoding_across_time(self)-
Expand source code
def decoding_across_time(self): self.multi_pipeline.start_decoding() def setUp(self)-
Hook method for setting up the test fixture before exercising it.
Expand source code
def setUp(self): bids_root = os.path.join('data', 'P3') self.multi_pipeline = MultiPipeline(bids_root) self.erp = ERPAnalysis(-0.1, 0.8, baseline=(None, 0), reject_by_annotation=True, all_subjects=True) def test(self)-
Expand source code
def test(self): pass def test_encoding_analysis(self)-
Expand source code
def test_encoding_analysis(self): self.test_erp_analysis() encoder = P3_LinearModel() self.multi_pipeline.start_encoding_analysis(self.erp, encoder) def test_erp_analysis(self)-
Expand source code
def test_erp_analysis(self): self.multi_pipeline.start_erp_analysis(self.erp) def test_erp_epochs(self)-
Expand source code
def test_erp_epochs(self): self.erp.compute_peak('rare', 0.3, 0.6, ['Pz', 'CPz'], 'pos') self.erp.compute_peak('freq', 0.3, 0.6, ['Cz'], 'pos')
class PipelineTestCase (methodName='runTest')-
A class whose instances are single test cases.
By default, the test code itself should be placed in a method named 'runTest'.
If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.
When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
Expand source code
class PipelineTestCase(unittest.TestCase): def setUp(self): bids_root = os.path.join('data', 'P3') self.bids_path = BIDSPath(subject='001', session='P3', task='P3', datatype='eeg', suffix='eeg', root=bids_root) self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR) self.erp = ERPAnalysis(-0.1, 1) self.pipeline.load_data() self.pipeline.set_montage() self.pipeline.set_custom_events_mapping(task='P3') def test_preprocessing(self): self.pipeline.make_pipeline([ SimpleMNEFilter(0.1, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) # self.assertEqual(self.widget.size(), (50, 50), # 'incorrect default size') def test_erp_epochs(self): self.pipeline.make_pipeline( SimpleMNEFilter(0.1, 50, 'firwin'), [CleaningData(self.bids_path), PrecomputedICA(self.bids_path)]) erp_clean = ERPAnalysis(-0.1, 0.8) erp_clean.compute_epochs(self.pipeline.raw, self.pipeline.events, self.pipeline.event_ids, baseline=(0, None), reject_by_annotation=False) print(erp_clean.epochs) def test_erp_peaks(self): self.pipeline.make_pipeline([ SimpleMNEFilter(0.5, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) erp_clean = ERPAnalysis(-0.1, 0.8) erp_clean.compute_epochs(self.pipeline.raw, self.pipeline.events, self.pipeline.event_ids, baseline=(0, None), reject_by_annotation=True) peak = erp_clean.compute_peak('freq', 0.3, 0.6, ['Pz', 'CPz']) print(peak) def test_all_subject(self): self.pipeline.load_multiple_subjects(40) print("done")Ancestors
- unittest.case.TestCase
Methods
def setUp(self)-
Hook method for setting up the test fixture before exercising it.
Expand source code
def setUp(self): bids_root = os.path.join('data', 'P3') self.bids_path = BIDSPath(subject='001', session='P3', task='P3', datatype='eeg', suffix='eeg', root=bids_root) self.pipeline = Pipeline(self.bids_path, verbose=logging.ERROR) self.erp = ERPAnalysis(-0.1, 1) self.pipeline.load_data() self.pipeline.set_montage() self.pipeline.set_custom_events_mapping(task='P3') def test_all_subject(self)-
Expand source code
def test_all_subject(self): self.pipeline.load_multiple_subjects(40) print("done") def test_erp_epochs(self)-
Expand source code
def test_erp_epochs(self): self.pipeline.make_pipeline( SimpleMNEFilter(0.1, 50, 'firwin'), [CleaningData(self.bids_path), PrecomputedICA(self.bids_path)]) erp_clean = ERPAnalysis(-0.1, 0.8) erp_clean.compute_epochs(self.pipeline.raw, self.pipeline.events, self.pipeline.event_ids, baseline=(0, None), reject_by_annotation=False) print(erp_clean.epochs) def test_erp_peaks(self)-
Expand source code
def test_erp_peaks(self): self.pipeline.make_pipeline([ SimpleMNEFilter(0.5, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) erp_clean = ERPAnalysis(-0.1, 0.8) erp_clean.compute_epochs(self.pipeline.raw, self.pipeline.events, self.pipeline.event_ids, baseline=(0, None), reject_by_annotation=True) peak = erp_clean.compute_peak('freq', 0.3, 0.6, ['Pz', 'CPz']) print(peak) def test_preprocessing(self)-
Expand source code
def test_preprocessing(self): self.pipeline.make_pipeline([ SimpleMNEFilter(0.1, 50, 'firwin'), CleaningData(self.bids_path), PrecomputedICA(self.bids_path) ]) # self.assertEqual(self.widget.size(), (50, 50), # 'incorrect default size')