def run(self):
self.time1 = time.time()
if os.name == 'posix':
self._pipe, child_conn = Pipe()
self.p = Process(target=find_events, args=(self.file_names,),
kwargs={'parameters': self.parameters, 'pipe': child_conn, 'debug': self.debug,
'save_file_names': self.save_file_names})
self.p.start()
# child_conn needs to be closed in all processes before EOFError is thrown (on Linux)
# So close it here immediately
child_conn.close()
while True:
time.sleep(0)
try:
data = self._pipe.recv()
if 'status_text' in data:
self.status_text = data['status_text']
if 'Events' in data:
self.events += data['Events']
except:
break
else:
# TODO add ability to listen for info from find_events on Windows.
# If we are on windows, we can only fork a process if __name__ == '__main__'. Which
# is not true here (because AnalyzeDataThread is imported).
# So on Windows, just use this thread, don't use an additional separate process.
find_events(self.file_names, parameters=self.parameters, debug=self.debug,
save_file_names=self.save_file_names)
self.cancelled = True
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_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_number_of_events_help(name):
# Need to seed, otherwise each process will generate the same random data!
np.random.seed()
# Check that setting the event rate gives correct number of events.
seconds = 5.
sample_rate = 1.e6
baseline = 1.
event_rate = 50.
event_duration = stats.norm(loc=100.e-6, scale=5.e-6)
event_depth = stats.norm(loc=-1., scale=.05)
noise = stats.norm(scale=0.02)
# create a list of file_names so we can average the number of events.
if os.path.exists(name):
os.remove(name)
n_e_r = create_random_data(filename=name, seconds=seconds, sample_rate=sample_rate,
baseline=baseline, noise=noise,
event_rate=event_rate, event_durations=event_duration,
event_depths=event_depth)
save_file_name = name[:-len('.h5')] + '_Events.h5'
if os.path.exists(save_file_name):
os.remove(save_file_name)
event_database = find_events([name], save_file_names=[save_file_name])[0]
ed = EventDatabase(event_database)
n_events_found = ed.get_event_count()
os.remove(name)
ed.close()
os.remove(event_database)
return n_e_r, n_events_found, name
def test_multiple_files(self):
filename1 = os.path.dirname(os.path.realpath(__file__))
filename1 = os.path.join(filename1, 'testDataFiles',
'chimera_nonoise_2events_1levels.log')
filename2 = os.path.dirname(os.path.realpath(__file__))
filename2 = os.path.join(filename2, 'testDataFiles',
'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_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_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_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_chimera_no_noise_1event_2levels(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = os.path.join(filename, "testDataFiles", "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_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_event_params(self, filename):
"""
Tests that setting the event depth and duration give correct values.
"""
seconds = 5.
sample_rate = 1.e6
baseline = 10.
event_rate = 100.
event_duration_loc = 1.e-4
event_duration_scale = 5.e-6
event_duration = stats.norm(loc=event_duration_loc, scale=event_duration_scale)
event_depth_loc = -1.
event_depth_scale = .05
event_depth = stats.norm(loc=-1., scale=.05)
noise = stats.norm(scale=0.01)
n_events_returned = create_random_data(filename, seconds=seconds, sample_rate=sample_rate,
baseline=baseline, event_rate=event_rate,
event_durations=event_duration, event_depths=event_depth,
noise=noise)
save_file_name = filename[:-len('.h5')] + 'Events.h5'
if os.path.exists(save_file_name):
os.remove(save_file_name)
event_database = find_events([filename], save_file_names=[save_file_name])[0]
ed = EventDatabase(event_database)
count = ed.get_event_count()
count_should_be = event_rate * seconds
diff = abs(count - count_should_be)
self.assertLessEqual(diff, 100, "Unexpected number of events. "
"Expected {0}, was {1}.".format(count_should_be, count))
table_sample_rate = ed.get_sample_rate()
durations = [x['event_length'] / table_sample_rate for x in ed.get_event_table().iterrows()]
depths = [x['current_blockage'] for x in ed.get_event_table().iterrows()]
mean_duration = np.mean(durations)
self.assertAlmostEqual(event_duration_loc, mean_duration, 5, "Unexpected mean event duration. "
"Wanted {0}, got {1}.".format(event_duration_loc,
mean_duration))
mean_depth = np.mean(depths)
self.assertAlmostEqual(event_depth_loc, mean_depth, 1, "Unexpected mean event depth. "
"Wanted {0}, got {1}.".format(event_depth_loc,
mean_depth))
ed.close()
os.remove(event_database)
def test_chimera_no_noise_1event_2levels(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = os.path.join(filename, 'testDataFiles',
'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_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 run(self):
self.time1 = time.time()
if os.name == 'posix':
self._pipe, child_conn = Pipe()
self.p = Process(target=find_events,
args=(self.file_names, ),
kwargs={
'parameters': self.parameters,
'pipe': child_conn,
'debug': self.debug,
'save_file_names': self.save_file_names
})
self.p.start()
# child_conn needs to be closed in all processes before EOFError is thrown (on Linux)
# So close it here immediately
child_conn.close()
while True:
time.sleep(0)
try:
data = self._pipe.recv()
if 'status_text' in data:
self.status_text = data['status_text']
if 'Events' in data:
self.events += data['Events']
except:
break
else:
# TODO add ability to listen for info from find_events on Windows.
# If we are on windows, we can only fork a process if __name__ == '__main__'. Which
# is not true here (because AnalyzeDataThread is imported).
# So on Windows, just use this thread, don't use an additional separate process.
find_events(self.file_names,
parameters=self.parameters,
debug=self.debug,
save_file_names=self.save_file_names)
self.cancelled = True
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 = os.path.dirname(os.path.realpath(__file__))
filename = os.path.join(filename, "testDataFiles", "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)
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 = os.path.join(filename, "testDataFiles", "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_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()
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_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_multiple_files(self):
filename1 = os.path.dirname(os.path.realpath(__file__))
filename1 = os.path.join(filename1, "testDataFiles", "chimera_nonoise_2events_1levels.log")
filename2 = os.path.dirname(os.path.realpath(__file__))
filename2 = os.path.join(filename2, "testDataFiles", "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(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = os.path.join(filename, 'testDataFiles',
'chimera_nonoise_1event.log')
event_database = find_events(
[filename],
save_file_names=['_testChimera_nonoise_1Event_9238.h5'])[0]
h5file = ed.open_file(event_database, mode='r')
events = h5file.root.events
# check event table correct length
event_table = events.eventTable
self.assertTrue(event_table.nrows, 1)
# check raw data array correct length
raw_data_matrix = events.raw_data
self.assertEqual(raw_data_matrix.nrows, 1)
event_length = event_table[0]['event_length']
self.assertEqual(event_length, 1000)
# Make sure only 1 event with 1 level
levels_matrix = events.levels
self.assertEqual(levels_matrix.nrows, 1)
n_levels = event_table[0]['n_levels']
self.assertEqual(n_levels, 1)
levels = levels_matrix[0]
self.assertAlmostEqual(levels[0], 15.13955, 3)
# Check 1 event with 1 levelLength
lengths_matrix = events.level_lengths
self.assertEqual(lengths_matrix.nrows, 1)
lengths = lengths_matrix[0]
self.assertEqual(lengths[0], 1000)
h5file.close()
# delete the newly created event file
os.remove(event_database)
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()
def test_chimera_no_noise_1event(self):
filename = os.path.dirname(os.path.realpath(__file__))
filename = os.path.join(filename, "testDataFiles", "chimera_nonoise_1event.log")
event_database = find_events([filename], save_file_names=["_testChimera_nonoise_1Event_9238.h5"])[0]
h5file = ed.open_file(event_database, mode="r")
events = h5file.root.events
# check event table correct length
event_table = events.eventTable
self.assertTrue(event_table.nrows, 1)
# check raw data array correct length
raw_data_matrix = events.raw_data
self.assertEqual(raw_data_matrix.nrows, 1)
event_length = event_table[0]["event_length"]
self.assertEqual(event_length, 1000)
# Make sure only 1 event with 1 level
levels_matrix = events.levels
self.assertEqual(levels_matrix.nrows, 1)
n_levels = event_table[0]["n_levels"]
self.assertEqual(n_levels, 1)
levels = levels_matrix[0]
self.assertAlmostEqual(levels[0], 15.13955, 3)
# Check 1 event with 1 levelLength
lengths_matrix = events.level_lengths
self.assertEqual(lengths_matrix.nrows, 1)
lengths = lengths_matrix[0]
self.assertEqual(lengths[0], 1000)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def _test_number_of_events_help(name):
# Need to seed, otherwise each process will generate the same random data!
np.random.seed()
# Check that setting the event rate gives correct number of events.
seconds = 5.
sample_rate = 1.e6
baseline = 1.
event_rate = 50.
event_duration = stats.norm(loc=100.e-6, scale=5.e-6)
event_depth = stats.norm(loc=-1., scale=.05)
noise = stats.norm(scale=0.02)
# create a list of file_names so we can average the number of events.
if os.path.exists(name):
os.remove(name)
n_e_r = create_random_data(filename=name,
seconds=seconds,
sample_rate=sample_rate,
baseline=baseline,
noise=noise,
event_rate=event_rate,
event_durations=event_duration,
event_depths=event_depth)
save_file_name = name[:-len('.h5')] + '_Events.h5'
if os.path.exists(save_file_name):
os.remove(save_file_name)
event_database = find_events([name], save_file_names=[save_file_name])[0]
ed = EventDatabase(event_database)
n_events_found = ed.get_event_count()
os.remove(name)
ed.close()
os.remove(event_database)
return n_e_r, n_events_found, name
def test_chimera_no_noise_1event(self):
filename = tf.get_abs_path('chimera_nonoise_1event.log')
event_database = find_events([filename], save_file_names=['_testChimera_nonoise_1Event_9238.h5'])[0]
h5file = ed.open_file(event_database, mode='r')
events = h5file.root.events
# check event table correct length
event_table = events.eventTable
self.assertTrue(event_table.nrows, 1)
# check raw data array correct length
raw_data_matrix = events.raw_data
self.assertEqual(raw_data_matrix.nrows, 1)
event_length = event_table[0]['event_length']
self.assertEqual(event_length, 1000)
# Make sure only 1 event with 1 level
levels_matrix = events.levels
self.assertEqual(levels_matrix.nrows, 1)
n_levels = event_table[0]['n_levels']
self.assertEqual(n_levels, 1)
levels = levels_matrix[0]
self.assertAlmostEqual(levels[0], 15.13955, 3)
# Check 1 event with 1 levelLength
lengths_matrix = events.level_lengths
self.assertEqual(lengths_matrix.nrows, 1)
lengths = lengths_matrix[0]
self.assertEqual(lengths[0], 1000)
h5file.close()
# delete the newly created event file
os.remove(event_database)
def test_event_params(self, filename):
"""
Tests that setting the event depth and duration give correct values.
"""
seconds = 5.
sample_rate = 1.e6
baseline = 10.
event_rate = 100.
event_duration_loc = 1.e-4
event_duration_scale = 5.e-6
event_duration = stats.norm(loc=event_duration_loc,
scale=event_duration_scale)
event_depth_loc = -1.
event_depth_scale = .05
event_depth = stats.norm(loc=-1., scale=.05)
noise = stats.norm(scale=0.01)
n_events_returned = create_random_data(filename,
seconds=seconds,
sample_rate=sample_rate,
baseline=baseline,
event_rate=event_rate,
event_durations=event_duration,
event_depths=event_depth,
noise=noise)
save_file_name = filename[:-len('.h5')] + 'Events.h5'
if os.path.exists(save_file_name):
os.remove(save_file_name)
event_database = find_events([filename],
save_file_names=[save_file_name])[0]
ed = EventDatabase(event_database)
count = ed.get_event_count()
count_should_be = event_rate * seconds
diff = abs(count - count_should_be)
self.assertLessEqual(
diff, 100, "Unexpected number of events. "
"Expected {0}, was {1}.".format(count_should_be, count))
table_sample_rate = ed.get_sample_rate()
durations = [
x['event_length'] / table_sample_rate
for x in ed.get_event_table().iterrows()
]
depths = [
x['current_blockage'] for x in ed.get_event_table().iterrows()
]
mean_duration = np.mean(durations)
self.assertAlmostEqual(
event_duration_loc, mean_duration, 5,
"Unexpected mean event duration. "
"Wanted {0}, got {1}.".format(event_duration_loc, mean_duration))
mean_depth = np.mean(depths)
self.assertAlmostEqual(
event_depth_loc, mean_depth, 1, "Unexpected mean event depth. "
"Wanted {0}, got {1}.".format(event_depth_loc, mean_depth))
ed.close()
os.remove(event_database)
