def test_correct_data(self, filename):
file1 = tf.get_abs_path('chimera_1event_2levels.log')
file2 = tf.get_abs_path('chimera_1event.log')
concat_files([file1, file2], output_filename=filename)
reader1 = get_reader_from_filename(file1)
reader2 = get_reader_from_filename(file2)
reader_out = get_reader_from_filename(filename)
sample_rate1 = reader1.get_sample_rate()
sample_rate2 = reader2.get_sample_rate()
sample_rate_out = reader_out.get_sample_rate()
self.assertEqual(sample_rate_out, sample_rate1,
"Unexpected sample rate. Should be {0}, was {1}.".format(sample_rate1, sample_rate_out))
self.assertEqual(sample_rate_out, sample_rate2,
"Unexpected sample rate. Should be {0}, was {1}.".format(sample_rate2, sample_rate_out))
data1 = reader1.get_all_data()[0]
data2 = reader2.get_all_data()[0]
data_out_should_be = np.zeros(data1.size + data2.size)
data_out_should_be[:data1.size] = data1[:]
data_out_should_be[data1.size:] = data2[:]
data_out = reader_out.get_all_data()[0]
np.testing.assert_array_equal(data_out, data_out_should_be)
reader1.close()
reader2.close()
reader_out.close()
def test_set_output_filename(self, filename):
file_names = [tf.get_abs_path('chimera_1event_2levels.log'), tf.get_abs_path('chimera_1event.log')]
output_filename = concat_files(file_names, output_filename=filename)
self.assertEqual(output_filename, filename)
self.assertTrue(os.path.exists(output_filename))
class TestDataFileReader(unittest.TestCase, ReaderTests):
reader_class = DataFileReader
default_test_data_files = [
tf.get_abs_path('chimera_small.h5'),
tf.get_abs_path('spheres_20140114_154938_beginning.h5')
]
def _test_small_chimera_file_help(self, data_all):
self.assertEqual(len(data_all), 1, 'Too many data channels returned.')
data = data_all[0]
self.assertEqual(data.size, 10, 'Wrong data size returned.')
self.assertAlmostEqual(data[0], 17.45518, 3)
self.assertAlmostEqual(data[9], 18.0743, 3)
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('chimera_small.h5')
reader = self.reader_class(filename)
data = reader.get_all_data(False)
self._test_small_chimera_file_help(data)
reader.close()
def help_scaling(self):
filename = tf.get_abs_path('spheres_20140114_154938_beginning.h5')
mean_should_be = 7.57604 # Value gotten from original MATLAB script
std_should_be = 1.15445 # Value gotten from original MATLAB script
return [filename], [mean_should_be], [std_should_be]
def help_scaling_decimated(self):
filename = tf.get_abs_path('spheres_20140114_154938_beginning.h5')
return [filename]
def test_different_sample_rate_no_resample(self, filename):
"""
Tests that an error is thrown if the files have different sample rates and we do not want to resample.
"""
file_names = [tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd'), tf.get_abs_path('chimera_1event.log')]
self.assertRaises(SamplingRatesMismatchError, concat_files, file_names, output_filename=filename)
def test_open_file_from_extension(self):
"""
Tests that the file extension is parsed correctly and the correct Reader is opened.
"""
# Chimera file
filename = tf.get_abs_path('spheres_20140114_154938_beginning.log')
f = pypore.open_file(filename)
self.assertTrue(isinstance(f, ChimeraReader))
f.close()
# Heka file
filename = tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
f = pypore.open_file(filename)
self.assertTrue(isinstance(f, HekaReader))
f.close()
def test_heka_format_error_raises_binary_file(self):
"""
Tests that trying to open a completely binary file that doesn't fit the Heka specs raises an IOError.
"""
# Test with a complete binary file that has no text header.
filename = tf.get_abs_path('chimera_small.log')
self.assertRaises(IOError, self.SEGMENT_CLASS, filename)
def test_good_thresholds(self, filename):
"""
Tests that we find the correct number of events when the starting and ending thresholds are appropriate.
"""
data_file = tf.get_abs_path('chimera_1event.log')
parameters = Parameters(
threshold_strategy=AbsoluteChangeThresholdStrategy(2., 1.))
event_databases = find_events([data_file],
parameters=parameters,
save_file_names=[filename],
debug=True)
h5file = ed.open_file(filename, mode='r')
#Check the number of events
event_count = h5file.get_event_count()
self.assertEqual(
event_count, 1,
"Unexpected event count. Should be {0}, was {1}.".format(
event_count, 0))
#Check the event length
sample_rate = h5file.get_sample_rate()
event_length = h5file.get_event_row(0)['event_length'] / sample_rate
event_length_should_be = 0.00024
percent_diff = abs(event_length -
event_length_should_be) / event_length_should_be
self.assertLessEqual(
percent_diff, 0.05,
"Unexpected event length. Should be {0}, was {1}.".format(
event_length_should_be, event_length))
h5file.close()
def test_constructor_no_mat_spec(self):
"""
Tests that an IOError is raised when no .cfg config file is next to the .hex file.
"""
test_no_cfg_file = tf.get_abs_path('cnp_empty.hex')
for filename in test_no_cfg_file:
self.assertRaises(IOError, CNP2Reader, filename)
def test_filtered_baseline(self, filename):
"""
Tests that the filtered baseline is the same as the unfiltered.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
reader = get_reader_from_filename(data_filename)
data_all = reader.get_all_data()
data = data_all[0]
reader.close()
baseline = np.mean(data[:150])
# Check at different filter frequencies and re-sample rates
for rates in ([1.e4, 1.e6], [1.e4, 0], [5.e5, 4.e6], [7.7e4, 1.e6]):
filter_freq = rates[0]
re_sample_rate = rates[1]
out_filename = filter_file(data_filename, filter_frequency=filter_freq, out_sample_rate=re_sample_rate,
output_filename=filename)
reader = get_reader_from_filename(out_filename)
data2 = reader.get_all_data()[0]
reader.close()
# Note we re-sampled, which is why only take 30 data points.
baseline2 = np.mean(data2[:20])
ratio = abs((baseline - baseline2) / baseline)
print "ratio:", ratio
self.assertLessEqual(ratio, 0.05, "Filtered baseline different from original. "
"Should be {0}, got {1}.".format(baseline, baseline2))
os.remove(out_filename)
def test_constructor_no_mat_spec(self):
"""
Tests that an IOError is raised when no .cfg config file is next to the .hex file.
"""
test_no_cfg_file = tf.get_abs_path('cnp_empty.hex')
for filename in test_no_cfg_file:
self.assertRaises(IOError, CNP2Reader, filename)
def test_incomplete_heka_file_raises(self):
"""
Tests that opening a Heka file with incomplete blocks raises an IOError.
"""
filename = tf.get_abs_path('heka_incomplete.hkd')
self.assertRaises(IOError, self.SEGMENT_CLASS, filename)
def test_get_all_data(self):
# Make sure path to chimera file is correct.
filename = tf.get_abs_path('chimera_small.log')
chimera_reader = ChimeraReader(filename)
data = chimera_reader.get_all_data(False)
self._test_small_chimera_file_help(data)
chimera_reader.close()
def test_convert_chimera_file_equality(self, filename):
"""
Test that the original/converted matrices and sample rates are the same for one-channel data.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
output_filename = convert_file(data_filename, output_filename=filename)
orig_reader = get_reader_from_filename(data_filename)
orig_data_all = orig_reader.get_all_data()
orig_sample_rate = orig_reader.get_sample_rate()
self.assertEqual(len(orig_data_all), 1)
out_reader = DataFileReader(output_filename)
out_data_all = out_reader.get_all_data()
out_sample_rate = out_reader.get_sample_rate()
self.assertEqual(len(out_data_all), 1)
orig_data = orig_data_all[0]
out_data = out_data_all[0]
# assert sample rates are equal
self.assertAlmostEqual(1.0 * orig_sample_rate / out_sample_rate, 1, 4)
# assert the two arrays are equal
np.testing.assert_array_equal(orig_data, out_data)
orig_reader.close()
out_reader.close()
def test_good_thresholds(self, filename):
"""
Tests that we find the correct number of events when the starting and ending thresholds are appropriate.
"""
data_file = tf.get_abs_path('chimera_1event.log')
parameters = Parameters(threshold_strategy=AbsoluteChangeThresholdStrategy(2., 1.))
event_databases = find_events([data_file], parameters=parameters,
save_file_names=[filename], debug=True)
h5file = ed.open_file(filename, mode='r')
#Check the number of events
event_count = h5file.get_event_count()
self.assertEqual(event_count, 1, "Unexpected event count. Should be {0}, was {1}.".format(event_count, 0))
#Check the event length
sample_rate = h5file.get_sample_rate()
event_length = h5file.get_event_row(0)['event_length']/sample_rate
event_length_should_be = 0.00024
percent_diff = abs(event_length - event_length_should_be) / event_length_should_be
self.assertLessEqual(percent_diff, 0.05,
"Unexpected event length. Should be {0}, was {1}.".format(event_length_should_be,
event_length))
h5file.close()
def test_debug_option(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
output_filename = '_test_debug_option.h5'
reader = get_reader_from_filename(filename)
data = [reader]
event_databases = find_events(data,
save_file_names=[output_filename],
debug=True)
data = reader.get_all_data()[0]
reader.close()
self.assertEqual(len(event_databases), 1)
h5file = ed.open_file(event_databases[0], mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
# check that the file has the correct groups
groups = [x._v_name for x in h5file.walk_groups()]
self.assertIn('debug', groups, "No debug group in debug file.")
print "data:", h5file.root.debug.data[:]
np.testing.assert_array_equal(data, h5file.root.debug.data[0][:])
h5file.close()
os.remove(event_databases[0])
def test_constructor_no_mat_spec(self):
"""
Tests that an IOError is raised when no .mat spec file is next to the .log file.
"""
test_no_mat_chimera_files = tf.get_abs_path('chimera_empty.log')
for filename in test_no_mat_chimera_files:
self.assertRaises(IOError, ChimeraReader, filename)
def test_debug_option(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
output_filename = '_test_debug_option.h5'
reader = get_reader_from_filename(filename)
data = [reader]
event_databases = find_events(data,
save_file_names=[output_filename], debug=True)
data = reader.get_all_data()[0]
reader.close()
self.assertEqual(len(event_databases), 1)
h5file = ed.open_file(event_databases[0], mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
# check that the file has the correct groups
groups = [x._v_name for x in h5file.walk_groups()]
self.assertIn('debug', groups, "No debug group in debug file.")
print "data:", h5file.root.debug.data[:]
np.testing.assert_array_equal(data, h5file.root.debug.data[0][:])
h5file.close()
os.remove(event_databases[0])
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('chimera_small.h5')
reader = self.reader_class(filename)
data = reader.get_all_data(False)
self._test_small_chimera_file_help(data)
reader.close()
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('chimera_small.h5')
reader = self.reader_class(filename)
data = reader.get_all_data(False)
self._test_small_chimera_file_help(data)
reader.close()
def test_multiple_files(self):
filename1 = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
filename2 = tf.get_abs_path('chimera_nonoise_1event_2levels.log')
file_names = [filename1, filename2]
event_databases = find_events(file_names,
save_file_names=['_testMultipleFiles_1_9238.h5', '_testMultipleFiles_2_9238.h5'])
self.assertEqual(len(event_databases), 2)
h5file = ed.open_file(event_databases[0], mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
h5file.close()
os.remove(event_databases[0])
h5file = ed.open_file(event_databases[1], mode='r')
self._test_chimera_no_noise_1event_2levels_helper(h5file)
h5file.close()
os.remove(event_databases[1])
def test_chimera_no_noise_1event_2levels(self):
filename = tf.get_abs_path('chimera_nonoise_1event_2levels.log')
event_database = find_events([filename], save_file_names=['_testChimera_nonoise_1Event_2Levels_9238.h5'])[0]
h5file = ed.open_file(event_database, mode='r')
self._test_chimera_no_noise_1event_2levels_helper(h5file)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_convert_file_set_output_filename(self, filename):
"""
Tests that the output filename can be set correctly.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
output_filename = convert_file(data_filename, output_filename=filename)
# Test that we can set the output filename
self.assertEqual(output_filename, filename, "output_filename not correct")
def test_default_output_filename(self):
"""
Tests that the default output filename is correctly generated from the input file names.
"""
file_names = [tf.get_abs_path('chimera_1event_2levels.log'), tf.get_abs_path('chimera_1event.log')]
output_filename = concat_files(file_names)
self.assertTrue(os.path.exists(output_filename))
# Check that it's saved in the current directory
self.assertEqual(output_filename[0:len('chimera_1event')], 'chimera_1event')
# Check the correct file extension
self.assertEqual(output_filename[-len('.h5'):], '.h5')
self.assertIn('_concatenated_', output_filename,
"Default output filename ''{0}'' should contain ''_concatenated_''")
os.remove(output_filename)
def test_chimera_no_noise_1event_2levels(self):
filename = tf.get_abs_path('chimera_nonoise_1event_2levels.log')
event_database = find_events(
[filename],
save_file_names=['_testChimera_nonoise_1Event_2Levels_9238.h5'])[0]
h5file = ed.open_file(event_database, mode='r')
self._test_chimera_no_noise_1event_2levels_helper(h5file)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_correct_paths(self):
"""
Tests that the returned files are correct.
"""
full_names = tf.get_all_file_names(with_path=True)
short_names = tf.get_all_file_names(with_path=False)
for i, short_name in enumerate(short_names):
abs_path = tf.get_abs_path(short_name)
self.assertEqual(full_names[i], abs_path,
msg="Absolute path not correct.\nShould be '{0}'.\nWas '{1}'.".format(full_names[i],
abs_path))
def test_multiple_files(self):
filename1 = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
filename2 = tf.get_abs_path('chimera_nonoise_1event_2levels.log')
file_names = [filename1, filename2]
event_databases = find_events(file_names,
save_file_names=[
'_testMultipleFiles_1_9238.h5',
'_testMultipleFiles_2_9238.h5'
])
self.assertEqual(len(event_databases), 2)
h5file = ed.open_file(event_databases[0], mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
h5file.close()
os.remove(event_databases[0])
h5file = ed.open_file(event_databases[1], mode='r')
self._test_chimera_no_noise_1event_2levels_helper(h5file)
h5file.close()
os.remove(event_databases[1])
def test_open_file_with_reader_class(self):
"""
Tests that we can pass in a Reader class to open the file with.
"""
# Chimera file
filename = tf.get_abs_path('spheres_20140114_154938_beginning.log')
f = pypore.open_file(filename, ChimeraReader)
f.close()
self.assertTrue(isinstance(f, ChimeraReader))
# Heka file
filename = tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
f = pypore.open_file(filename)
f.close()
self.assertTrue(isinstance(f, HekaReader))
# Test that passing the wrong Reader results in an error
# Aka that the reader_class we pass in is actually used.
filename = tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
# Heka files should produce an error when being ready by ChimeraReader.
self.assertRaises(IOError, pypore.open_file, filename, ChimeraReader)
def test_original_files_unmodified(self, filename):
file1 = tf.get_abs_path('chimera_1event_2levels.log')
file2 = tf.get_abs_path('chimera_1event.log')
reader1 = get_reader_from_filename(file1)
reader2 = get_reader_from_filename(file2)
sample_rate1_orig = reader1.get_sample_rate()
sample_rate2_orig = reader2.get_sample_rate()
data1_orig = reader1.get_all_data()[0]
data2_orig = reader2.get_all_data()[0]
reader1.close()
reader2.close()
concat_files([file1, file2], output_filename=filename)
reader1 = get_reader_from_filename(file1)
reader2 = get_reader_from_filename(file2)
reader_out = get_reader_from_filename(filename)
sample_rate1_final = reader1.get_sample_rate()
sample_rate2_final = reader2.get_sample_rate()
self.assertEqual(sample_rate1_final, sample_rate1_orig,
"Sample rate changed. Was {0}, now {1}.".format(sample_rate1_orig, sample_rate1_final))
self.assertEqual(sample_rate2_final, sample_rate2_orig,
"Sample rate changed. Was {0}, now {1}.".format(sample_rate2_orig, sample_rate2_final))
data1 = reader1.get_all_data()[0]
data2 = reader2.get_all_data()[0]
np.testing.assert_array_equal(data1, data1_orig)
np.testing.assert_array_equal(data2, data2_orig)
reader1.close()
reader2.close()
reader_out.close()
def test_chunk_size(self):
"""
Tests that we cannot change the chunk size
"""
filename = tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
reader = self.SEGMENT_CLASS(filename)
self.assertEqual(reader.chunk_size, reader._chunk_size)
def set_chunk(chunk):
reader.chunk_size = chunk
self.assertRaises(AttributeError, set_chunk, 100)
class TestChimeraReader(unittest.TestCase, ReaderTests):
reader_class = ChimeraReader
default_test_data_files = [tf.get_abs_path('chimera_small.log'),
tf.get_abs_path('spheres_20140114_154938_beginning.log')]
def test_constructor_no_mat_spec(self):
"""
Tests that an IOError is raised when no .mat spec file is next to the .log file.
"""
test_no_mat_chimera_files = tf.get_abs_path('chimera_empty.log')
for filename in test_no_mat_chimera_files:
self.assertRaises(IOError, ChimeraReader, filename)
def test_get_all_data(self):
# Make sure path to chimera file is correct.
filename = tf.get_abs_path('chimera_small.log')
chimera_reader = ChimeraReader(filename)
data = chimera_reader.get_all_data(False)
self._test_small_chimera_file_help(data)
chimera_reader.close()
def _test_small_chimera_file_help(self, data_all):
self.assertEqual(len(data_all), 1, 'Too many data channels returned.')
data = data_all[0]
self.assertEqual(data.size, 10, 'Wrong data size returned.')
self.assertAlmostEqual(data[0], 17.45518, 3)
self.assertAlmostEqual(data[9], 18.0743, 3)
def help_scaling(self):
filename = tf.get_abs_path('spheres_20140114_154938_beginning.log')
mean_should_be = 7.57604 # Value gotten from original MATLAB script
std_should_be = 1.15445 # Value gotten from original MATLAB script
return [filename], [mean_should_be], [std_should_be]
def help_scaling_decimated(self):
filename = tf.get_abs_path('spheres_20140114_154938_beginning.log')
return [filename]
def test_set_output_sample_rate(self, filename):
"""
Tests that we can successfully set the output sample rate, and the number of data points changes correctly.
"""
data_file_names = [tf.get_abs_path('chimera_1event.log'),
tf.get_abs_path('chimera_1event_2levels.log')]
for data_filename in data_file_names:
# Open a reader and read the original sample rate
orig_reader = get_reader_from_filename(data_filename)
orig_sample_rate = orig_reader.get_sample_rate()
orig_data = orig_reader.get_all_data()
n_orig = orig_data[0].size
orig_reader.close()
for out_sample_rate in (100.e4, 1.e6, 5.e5):
out_filename = filter_file(data_filename, 10.e4, out_sample_rate, output_filename=filename)
# The output number of data points should be int(np.ceil(n_orig * out_sample_rate / orig_sample_rate))
n_out = int(np.ceil(n_orig * out_sample_rate / orig_sample_rate))
# The output sample rate should be set by n_out
out_sample_rate = orig_sample_rate * (1.0 * n_out) / n_orig
# Get the params from the output file
out_reader = get_reader_from_filename(out_filename)
o_s_r = out_reader.get_sample_rate()
out_data = out_reader.get_all_data()
n_o = out_data[0].size
out_reader.close()
self.assertEqual(n_out, n_o,
"Number of re-sample points not correct. "
"Original data {0}, output {1}, should be {2}.".format(n_orig, n_o, n_out))
self.assertAlmostEqual(o_s_r, out_sample_rate, 2,
"Sample rate not set correctly. Was {0}, should be {1}".format(o_s_r,
out_sample_rate))
os.remove(out_filename)
def test_correct_paths(self):
"""
Tests that the returned files are correct.
"""
full_names = tf.get_all_file_names(with_path=True)
short_names = tf.get_all_file_names(with_path=False)
for i, short_name in enumerate(short_names):
abs_path = tf.get_abs_path(short_name)
self.assertEqual(
full_names[i],
abs_path,
msg="Absolute path not correct.\nShould be '{0}'.\nWas '{1}'.".
format(full_names[i], abs_path))
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('cnp_test.hex')
cnp_reader = CNP2Reader(filename)
data = cnp_reader.get_all_data(False)[0]
csv_filename = filename[:-4] + '.csv'
csv_data = self._get_test_csv_data(csv_filename)
ratio = np.abs((csv_data - data) / csv_data)
np.testing.assert_array_almost_equal(ratio, np.zeros_like(data))
cnp_reader.close()
def test_one_file_with_events(self, filename):
"""
Tests that events are found and event databases are saved.
"""
test_data_file_names = tf.get_all_file_names()
test_file = tf.get_abs_path('chimera_1event.log')
analyze_thread = AnalyzeDataThread([test_file], Parameters(), debug=True, save_file_names=[filename])
analyze_thread.start()
# wait until the thread has finished
analyze_thread.wait()
self.assertTrue(os.path.exists(filename))
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('cnp_test.hex')
cnp_reader = CNP2Reader(filename)
data = cnp_reader.get_all_data(False)[0]
csv_filename = filename[:-4] + '.csv'
csv_data = self._get_test_csv_data(csv_filename)
ratio = np.abs((csv_data-data) / csv_data)
np.testing.assert_array_almost_equal(ratio, np.zeros_like(data))
cnp_reader.close()
def test_chimera_no_noise_2events_1levels(self):
filename = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
event_databases = find_events([filename], save_file_names=['_testChimera_nonoise_2events_1levels_9238.h5'])
self.assertEqual(len(event_databases), 1)
event_database = event_databases[0]
h5file = ed.open_file(event_database, mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_set_output_name(self, filename):
"""
Tests that setting the output filename of :func:`filter_file <pypore.file_converter.filter_file>` is correct.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
out_filename = filter_file(data_filename, 10.e4, 100.e4, output_filename=filename)
self.assertEqual(filename, out_filename,
"Output filename not set correctly. Was {0}, should be {1}".format(filename,
out_filename))
# Make sure the file actually exists. Try opening it.
f = open(out_filename)
f.close()
def test_two_channel_channel_number(self):
"""
"""
# TODO finish
f = tf.get_abs_path('heka_2channel_1.3s_ch0_mean-24.84fA_rms2.09pA_ch1_mean.hkd')
segment = self.SEGMENT_CLASS(f)
# make sure there are 2 channels
self.assertEqual(len(segment), 2)
# make sure each channel has data
self.assertGreater(len(segment[0]), 1)
self.assertGreater(len(segment[1]), 1)
def test_saving_files(self):
filename = tf.get_abs_path('chimera_1event.log')
event_database = find_events([filename], save_file_names=['_testSavingFiles_9238.h5'])[0]
self.assertTrue(os.path.isfile(event_database))
h5file = ed.open_file(event_database, mode='r')
self.assertTrue(h5file.isopen)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_saving_files(self):
filename = tf.get_abs_path('chimera_1event.log')
event_database = find_events(
[filename], save_file_names=['_testSavingFiles_9238.h5'])[0]
self.assertTrue(os.path.isfile(event_database))
h5file = ed.open_file(event_database, mode='r')
self.assertTrue(h5file.isopen)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_chimera_no_noise_2events_1levels(self):
filename = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
event_databases = find_events(
[filename],
save_file_names=['_testChimera_nonoise_2events_1levels_9238.h5'])
self.assertEqual(len(event_databases), 1)
event_database = event_databases[0]
h5file = ed.open_file(event_database, mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
h5file.close()
# delete the newly created event file
os.remove(event_database)
class TestChimeraReader(unittest.TestCase, ReaderTests):
reader_class = CNP2Reader
default_test_data_files = [tf.get_abs_path('cnp_test.hex')]
def test_constructor_no_mat_spec(self):
"""
Tests that an IOError is raised when no .cfg config file is next to the .hex file.
"""
test_no_cfg_file = tf.get_abs_path('cnp_empty.hex')
for filename in test_no_cfg_file:
self.assertRaises(IOError, CNP2Reader, filename)
def test_get_all_data(self):
# Make sure path to file is correct.
filename = tf.get_abs_path('cnp_test.hex')
cnp_reader = CNP2Reader(filename)
data = cnp_reader.get_all_data(False)[0]
csv_filename = filename[:-4] + '.csv'
csv_data = self._get_test_csv_data(csv_filename)
ratio = np.abs((csv_data - data) / csv_data)
np.testing.assert_array_almost_equal(ratio, np.zeros_like(data))
cnp_reader.close()
def _get_test_csv_data(self, csv_filename):
data = []
with open(csv_filename, 'rb') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
# skip first row
csvreader.next()
for row in csvreader:
data.append(float(row[1]))
return np.array(data) * 1.e9 # scale the data to nA
def help_scaling(self):
filename = tf.get_abs_path('cnp_test.hex')
mean_should_be = 10.363 # Value gotten from the exported CSV file
std_should_be = 4.6866 # Value gotten from the exported CSV file
return [filename], [mean_should_be], [std_should_be]
def help_scaling_decimated(self):
filename = tf.get_abs_path('cnp_test.hex')
return [filename]
def test_one_file_with_events(self, filename):
"""
Tests that events are found and event databases are saved.
"""
test_data_file_names = tf.get_all_file_names()
test_file = tf.get_abs_path('chimera_1event.log')
analyze_thread = AnalyzeDataThread([test_file],
Parameters(),
debug=True,
save_file_names=[filename])
analyze_thread.start()
# wait until the thread has finished
analyze_thread.wait()
self.assertTrue(os.path.exists(filename))
def test_too_large_end_threshold(self, filename):
"""
Tests that we don't find events when the ending threshold is too large.
"""
data_file = tf.get_abs_path('chimera_1event.log')
parameters = Parameters(threshold_strategy=AbsoluteChangeThresholdStrategy(2., 1000.))
event_databases = find_events([data_file], parameters=parameters,
save_file_names=[filename], debug=True)
h5file = ed.open_file(filename, mode='r')
event_count = h5file.get_event_count()
self.assertEqual(event_count, 0, "Unexpected event count. Should be {0}, was {1}.".format(event_count, 0))
h5file.close()
class TestHekaReader(unittest.TestCase, ReaderTests):
reader_class = HekaReader
default_test_data_files = [
tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
]
def help_scaling(self):
filename = tf.get_abs_path('heka_1.5s_mean5.32p_std2.76p.hkd')
mean = 5.32e-12
std_dev = 2.76e-12
return [filename], [mean], [std_dev]
@unittest.skip(
"Test file is too short for decimated and un-decimated means to be equal enough."
)
def test_scaling_decimated(self):
super(TestHekaReader, self).test_scaling_decimated()
def test_passing_reader(self):
"""
Tests that passing an open subtype of :py:class:`pypore.i_o.abstract_reader.AbstractReader` works.
"""
filename = os.path.dirname(os.path.realpath(__file__))
filename = tf.get_abs_path('chimera_nonoise_2events_1levels.log')
reader = get_reader_from_filename(filename)
data = [reader]
event_databases = find_events(
data, save_file_names=['_test_passing_reader.h5'])
self.assertEqual(len(event_databases), 1)
h5file = ed.open_file(event_databases[0], mode='r')
self._test_chimera_no_noise_2events_1levels_wrapper(h5file)
h5file.close()
os.remove(event_databases[0])
def test_same_sample_rate_no_change(self, filename):
"""
Tests that if we set the output sample rate to < 0, the sampling doesn't change, but the file is filtered.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
# Open a reader and read the original sample rate
orig_reader = get_reader_from_filename(data_filename)
orig_sample_rate = orig_reader.get_sample_rate()
orig_data = orig_reader.get_all_data()
orig_reader.close()
for sample_rate in (-1, 0., orig_sample_rate, orig_sample_rate + 100.):
# filter the file, with negative sample rate
out_filename = filter_file(data_filename, 10.e4, sample_rate, output_filename=filename)
self._test_out_sample_rate_data_len_equality(orig_data, orig_sample_rate, out_filename, sample_rate)
os.remove(out_filename)
def test_default_output_name(self):
"""
Tests that the default output filename of :func:`filter_file <pypore.file_converter.filter_file>` is correct.
"""
data_filename = tf.get_abs_path('chimera_1event.log')
output_filename_should_be = os.path.join(tf.TEST_DATA_FOLDER_PATH, 'chimera_1event_filtered.h5')
if os.path.exists(output_filename_should_be):
os.remove(output_filename_should_be)
out_filename = filter_file(data_filename, 10.e4, 100.e4)
self.assertEqual(out_filename, output_filename_should_be,
msg="Default filename incorrect. Was {0}, should be {1}.".format(out_filename,
output_filename_should_be))
# Make sure the file actually exists. Try opening it.
f = open(out_filename)
f.close()
os.remove(out_filename)
