def test_basic(self):
file_contents = np.load(
os.path.join(os.path.dirname(__file__),
'test_data/tiny_spikes.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
self.assertEqual(spikes._spikes.sum(), 9)
self.assertEqual(spikes.rasterize(stop=5).sum(), 7)
spikes.rasterize(save_png_name=os.path.join(self.TMP_PATH, 'spikes'))
self.assertTrue(
os.path.exists(os.path.join(self.TMP_PATH, 'spikes.png')))
file_contents = np.load(
os.path.join(os.path.dirname(__file__),
'test_data/spikes_trials.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
spikes.rasterize(save_png_name=os.path.join(self.TMP_PATH, 'spikes'))
self.assertTrue(
os.path.exists(os.path.join(self.TMP_PATH, 'spikes.png')))
file_contents = np.load(
os.path.join(os.path.dirname(__file__),
'test_data/spikes_trials.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
spikes.restrict_to_most_active_neurons(top_neurons=2)
self.assertEqual(spikes._N, 2)
def test_basic(self):
file_contents = np.load(os.path.join(os.path.dirname(__file__), 'test_data/tiny_spikes.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
self.assertEqual(spikes._spikes.sum(), 9)
self.assertEqual(spikes.rasterize(stop=5).sum(), 7)
spikes.rasterize(save_png_name=os.path.join(self.TMP_PATH, 'spikes'))
self.assertTrue(os.path.exists(os.path.join(self.TMP_PATH, 'spikes.png')))
file_contents = np.load(os.path.join(os.path.dirname(__file__), 'test_data/spikes_trials.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
spikes.rasterize(save_png_name=os.path.join(self.TMP_PATH, 'spikes'))
self.assertTrue(os.path.exists(os.path.join(self.TMP_PATH, 'spikes.png')))
file_contents = np.load(os.path.join(os.path.dirname(__file__), 'test_data/spikes_trials.npz'))
spikes = Spikes(file_contents[file_contents.keys()[0]])
spikes.restrict_to_most_active_neurons(top_neurons=2)
self.assertEqual(spikes._N, 2)
import numpy as np
import matplotlib.pyplot as plt
from hdnet.stimulus import Stimulus
from hdnet.spikes import Spikes
from hdnet.spikes_model import SpikeModel, BernoulliHomogeneous, DichotomizedGaussian
# Let's first make up some simuilated spikes: 2 trials
spikes = (np.random.random((2, 10, 200)) < .05).astype(int)
spikes[0, [1, 5], ::5] = 1 # insert correlations
spikes[1, [2, 3, 6], ::11] = 1 # insert correlations
spikes = Spikes(spikes=spikes)
# let's look at them: quick save as PNG or make PSTH pyplot
plt.matshow(spikes.rasterize(), cmap='gray')
plt.title('Raw spikes')
plt.show()
buff = input('Press a key to continue!')
plt.close()
#spikes.rasterize(save_png_name='raster')
plt.matshow(spikes.covariance().reshape((2 * 10, 10)), cmap='gray')
plt.title('Raw spikes covariance')
plt.show()
buff = input('Press a key to continue!')
plt.close()
#spikes.covariance(save_png_name='simul_cov_matrices')
# let's examine the structure in spikes using a spike modeler
spikes_model = BernoulliHomogeneous(spikes=spikes)
BH_sample_spikes = spikes_model.sample_from_model()
import numpy as np
import matplotlib.pyplot as plt
from hdnet.stimulus import Stimulus
from hdnet.spikes import Spikes
from hdnet.spikes_model import SpikeModel, BernoulliHomogeneous, DichotomizedGaussian
# Let's first make up some simuilated spikes: 2 trials
spikes = (np.random.random((2, 10, 200)) < .05).astype(int)
spikes[0, [1, 5], ::5] = 1 # insert correlations
spikes[1, [2, 3, 6], ::11] = 1 # insert correlations
spikes = Spikes(spikes=spikes)
# let's look at them: quick save as PNG or make PSTH pyplot
plt.matshow(spikes.rasterize(), cmap='gray')
plt.title('Raw spikes')
#spikes.rasterize(save_png_name='raster')
plt.matshow(spikes.covariance().reshape((2 * 10, 10)), cmap='gray')
plt.title('Raw spikes covariance')
#spikes.covariance(save_png_name='simul_cov_matrices')
# let's examine the structure in spikes using a spike modeler
spikes_model = BernoulliHomogeneous(spikes=spikes)
BH_sample_spikes = spikes_model.sample_from_model()
plt.matshow(BH_sample_spikes.rasterize(), cmap='gray')
plt.title('BernoulliHomogeneous sample')
print "%1.4f means" % BH_sample_spikes.spikes.mean()
plt.matshow(BH_sample_spikes.covariance().reshape((2 * 10, 10)), cmap='gray')
plt.title('BernoulliHomogeneous covariance')
