def test_cubic_ximax(self):
# Test exceeding ximax
xi_ximax, p_vals_ximax, k_ximax, test_aborted = cubic.cubic(
self.data_signal, alpha=1, ximax=self.ximax)
self.assertEqual(test_aborted, True)
self.assertEqual(xi_ximax - 1, self.ximax)
def test_cubic_ximax(self):
# Test exceeding ximax
xi_ximax, p_vals_ximax, k_ximax, test_aborted = cubic.cubic(
self.data_signal, alpha=1, ximax=self.ximax)
self.assertEqual(test_aborted, True)
self.assertEqual(xi_ximax - 1, self.ximax)
def test_cubic_ximax(self):
# Test exceeding ximax
with self.assertWarns(UserWarning):
xi_ximax, p_vals_ximax, k_ximax, test_aborted = cubic.cubic(
self.data_signal, alpha=1, max_iterations=self.ximax)
self.assertEqual(test_aborted, True)
self.assertEqual(xi_ximax - 1, self.ximax)
def cubic_task(h5_file, binsize, alpha):
"""
Task Manifest Version: 1
Full Name: cubic_task
Caption: cubic
Author: Elephant_Developers
Description: |
Analyses the correlation of parallel recorded spike trains
Categories:
- FDAT
Compatible_queues: ['cscs_viz', 'cscs_bgq', 'epfl_viz']
Accepts:
h5_file:
type: application/unknown
description: Input file that contains spiking data from a
HDF5 file.
binsize:
type: double
description: Bin width used to compute the PSTH in ms.
alpha:
type: double
description: The significance level of the test.
Returns:
res: image/png
"""
h5_path = cubic_task.task.uri.get_file(h5_file)
ion = neo.io.NeoHdf5IO(filename=h5_path)
number_of_spike_trains = ion.get_info()['SpikeTrain']
spiketrains = []
for k in range(number_of_spike_trains):
spiketrains.append(ion.get("/" + "SpikeTrain_" + str(k)))
ion.close()
psth_as = elephant.statistics.time_histogram(spiketrains,
binsize=binsize * pq.ms)
result = cubic.cubic(psth_as, alpha=alpha)
# Plot
plt.bar(np.arange(0.75, len(result[1]) + .25, 1), result[1], width=.5)
plt.axhline(alpha, ls='--', color='r')
plt.xlabel('$\\xi$')
plt.ylabel('P value')
plt.title('$\hat\\xi$=' + str(result[0]))
output_filename = os.path.splitext(h5_path)[0] + '.png'
with open(output_filename, 'w') as result_pth:
plt.savefig(result_pth, dpi=100)
dst_name = os.path.basename(output_filename)
return cubic_task.task.uri.save_file(mime_type='image/png',
src_path=output_filename,
dst_path=dst_name)
def cubic_task(h5_file, binsize, alpha):
"""
Task Manifest Version: 1
Full Name: cubic_task
Caption: cubic
Author: Elephant Developers
Description: |
Analyses the correlation of parallel recorded spike trains
Categories:
- FDAT
Compatible_queues: ['cscs_viz', 'cscs_bgq', 'epfl_viz']
Accepts:
h5_file:
type: application/unknown
description: Input file that contains spiking data from a
HDF5 file.
binsize:
type: double
description: Bin width used to compute the PSTH in ms.
alpha:
type: double
description: The significance level of the test.
Returns:
res: image/png
"""
h5_path = cubic_task.task.uri.get_file(h5_file)
ion = neo.io.NeoHdf5IO(filename=h5_path)
number_of_spike_trains = ion.get_info()['SpikeTrain']
spiketrains = []
for k in range(number_of_spike_trains):
spiketrains.append(ion.get("/" + "SpikeTrain_" + str(k)))
ion.close()
psth_as = time_histogram(spiketrains, binsize=binsize * pq.ms)
result = cubic.cubic(psth_as, alpha=alpha)
# Plot
plt.bar(np.arange(0.75, len(result[1]) + .25, 1), result[1], width=.5)
plt.axhline(alpha, ls='--', color='r')
plt.xlabel('$\\xi$')
plt.ylabel('P value')
plt.title('$\hat\\xi$=' + str(result[0]))
output_filename = os.path.splitext(h5_path)[0] + '.png'
with open(output_filename, 'w') as result_pth:
plt.savefig(result_pth, dpi=100)
dst_name = os.path.basename(output_filename)
return cubic_task.task.uri.save_file(mime_type='image/png',
src_path=output_filename,
dst_path=dst_name)
def test_cubic(self):
# Computing the output of CuBIC for the test data AnalogSignal
xi, p_vals, k, test_aborted = cubic.cubic(self.data_signal,
alpha=self.alpha)
# Check the types of the outputs
self.assertIsInstance(xi, int)
self.assertIsInstance(p_vals, list)
self.assertIsInstance(k, list)
# Check that the number of tests is the output order of correlation
self.assertEqual(xi, len(p_vals))
# Check that all the first xi-1 tests have not passed the
# significance level alpha
for p in p_vals[:-1]:
self.assertGreater(self.alpha, p)
# Check that the last p-value has passed the significance level
self.assertGreater(p_vals[-1], self.alpha)
# Check that the number of cumulant of the output is 3
self.assertEqual(3, len(k))
# Check the analytical constrain of the cumulants for which K_1<K_2
self.assertGreater(k[1], k[0])
# Check the computed order of correlation is the expected
# from the test data
self.assertEqual(xi, self.xi)
# Computing the output of CuBIC for the test data Array
xi, p_vals, k, test_aborted = cubic.cubic(self.data_array,
alpha=self.alpha)
# Check the types of the outputs
self.assertIsInstance(xi, int)
self.assertIsInstance(p_vals, list)
self.assertIsInstance(k, list)
# Check that the number of tests is the output order of correlation
self.assertEqual(xi, len(p_vals))
# Check that all the first xi-1 tests have not passed the
# significance level alpha
for p in p_vals[:-1]:
self.assertGreater(self.alpha, p)
# Check that the last p-value has passed the significance level
self.assertGreater(p_vals[-1], self.alpha)
# Check that the number of cumulant of the output is 3
self.assertEqual(3, len(k))
# Check the analytical constrain of the cumulants for which K_1<K_2
self.assertGreater(k[1], k[0])
# Check the computed order of correlation is the expected
# from the test data
self.assertEqual(xi, self.xi)
# Check the output for test_aborted
self.assertEqual(test_aborted, False)
def test_cubic(self):
# Computing the output of CuBIC for the test data AnalogSignal
xi, p_vals, k, test_aborted = cubic.cubic(
self.data_signal, alpha=self.alpha)
# Check the types of the outputs
self.assertIsInstance(xi, int)
self.assertIsInstance(p_vals, list)
self.assertIsInstance(k, list)
# Check that the number of tests is the output order of correlation
self.assertEqual(xi, len(p_vals))
# Check that all the first xi-1 tests have not passed the
# significance level alpha
for p in p_vals[:-1]:
self.assertGreater(self.alpha, p)
# Check that the last p-value has passed the significance level
self.assertGreater(p_vals[-1], self.alpha)
# Check that the number of cumulant of the output is 3
self.assertEqual(3, len(k))
# Check the analytical constrain of the cumulants for which K_1<K_2
self.assertGreater(k[1], k[0])
# Check the computed order of correlation is the expected
# from the test data
self.assertEqual(xi, self.xi)
# Computing the output of CuBIC for the test data Array
xi, p_vals, k, test_aborted = cubic.cubic(
self.data_array, alpha=self.alpha)
# Check the types of the outputs
self.assertIsInstance(xi, int)
self.assertIsInstance(p_vals, list)
self.assertIsInstance(k, list)
# Check that the number of tests is the output order of correlation
self.assertEqual(xi, len(p_vals))
# Check that all the first xi-1 tests have not passed the
# significance level alpha
for p in p_vals[:-1]:
self.assertGreater(self.alpha, p)
# Check that the last p-value has passed the significance level
self.assertGreater(p_vals[-1], self.alpha)
# Check that the number of cumulant of the output is 3
self.assertEqual(3, len(k))
# Check the analytical constrain of the cumulants for which K_1<K_2
self.assertGreater(k[1], k[0])
# Check the computed order of correlation is the expected
# from the test data
self.assertEqual(xi, self.xi)
# Check the output for test_aborted
self.assertEqual(test_aborted, False)