def test_calc_rdm_movie_rdm_movie_poisson(self):
noise = np.random.randn(10, 5)
noise = np.matmul(noise.T, noise)
rdm = rsr.calc_rdm_movie(self.test_data_time_balanced,
method='poisson',
descriptor='conds',
noise=noise)
assert rdm.n_cond == 5
def test_calc_rdm_movie_correlation(self):
rdm = rsr.calc_rdm_movie(self.test_data_time,
descriptor='conds',
method='correlation',
time_descriptor='time')
assert rdm.n_cond == 6
assert len([r for r in rdm]) == 15
assert rdm.rdm_descriptors['time'][0] == 0.0
def test_calc_rdm_movie_crossnobis(self):
rdm = rsr.calc_rdm_movie(self.test_data_time,
descriptor='conds',
method='crossnobis',
time_descriptor='time',
cv_descriptor='fold')
assert rdm.n_cond == 6
assert len([r for r in rdm]) == 15
assert rdm.rdm_descriptors['time'][0] == 0.0
def test_calc_rdm_movie_binned(self):
time = self.test_data_time.time_descriptors['time']
bins = np.reshape(time, [5, 3])
rdm = rsr.calc_rdm_movie(self.test_data_time,
descriptor='conds',
method='mahalanobis',
time_descriptor='time',
bins=bins)
assert rdm.n_cond == 6
assert len([r for r in rdm]) == 5
assert rdm.rdm_descriptors['time'][0] == np.mean(time[:3])
def test_temporal_rsa(self):
import numpy as np
import matplotlib.pyplot as plt
import pyrsa
import pickle
from pyrsa.rdm import calc_rdm_movie
import os
path = os.path.dirname(os.path.abspath(__file__))
dat = pickle.load(
open(
os.path.join(path, '..', 'demos', "TemporalSampleData",
"meg_sample_data.pkl"), "rb"))
measurements = dat['data']
cond_names = [x for x in dat['cond_names'].keys()]
cond_idx = dat['cond_idx']
channel_names = dat['channel_names']
times = dat['times']
print(
'there are %d observations (trials), %d channels, and %d time-points\n'
% (measurements.shape))
print('conditions:')
print(cond_names)
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax = ax.flatten()
for jj, chan in enumerate(channel_names[:2]):
for ii, cond_ii in enumerate(np.unique(cond_idx)):
mn = measurements[cond_ii == cond_idx, jj, :].mean(0).squeeze()
ax[jj].plot(times, mn, label=cond_names[ii])
ax[jj].set_title(chan)
ax[jj].legend()
tim_des = {'time': times}
des = {'session': 0, 'subj': 0}
obs_des = {'conds': cond_idx}
chn_des = {'channels': channel_names}
data = pyrsa.data.TemporalDataset(measurements,
descriptors=des,
obs_descriptors=obs_des,
channel_descriptors=chn_des,
time_descriptors=tim_des)
data.sort_by('conds')
print('shape of original measurements')
print(data.measurements.shape)
data_split_time = data.split_time('time')
print('\nafter splitting')
print(len(data_split_time))
print(data_split_time[0].measurements.shape)
print('shape of original measurements')
print(data.measurements.shape)
data_subset_time = data.subset_time('time', t_from=-.1, t_to=.5)
print('\nafter subsetting')
print(data_subset_time.measurements.shape)
print(data_subset_time.time_descriptors['time'][0])
bins = np.reshape(tim_des['time'], [-1, 2])
print(len(bins))
print(bins[0])
print('shape of original measurements')
print(data.measurements.shape)
data_binned = data.bin_time('time', bins=bins)
print('\nafter binning')
print(data_binned.measurements.shape)
print(data_binned.time_descriptors['time'][0])
print('shape of original measurements')
print(data.measurements.shape)
data_dataset = data.convert_to_dataset('time')
print('\nafter binning')
print(data_dataset.measurements.shape)
print(data_dataset.obs_descriptors['time'][0])
rdms_data = calc_rdm_movie(data,
method='euclidean',
descriptor='conds')
print(rdms_data)
rdms_data_binned = calc_rdm_movie(data,
method='euclidean',
descriptor='conds',
bins=bins)
print(rdms_data_binned)
plt.figure(figsize=(10, 15))
# add formated time as rdm_descriptor
rdms_data_binned.rdm_descriptors['time_formatted'] = [
'%0.0f ms' % (np.round(x * 1000, 2))
for x in rdms_data_binned.rdm_descriptors['time']
]
pyrsa.vis.show_rdm(rdms_data_binned,
do_rank_transform=False,
pattern_descriptor='conds',
rdm_descriptor='time_formatted')
from pyrsa.rdm import get_categorical_rdm
rdms_model_in = get_categorical_rdm(['%d' % x for x in range(4)])
rdms_model_lr = get_categorical_rdm(['l', 'r', 'l', 'r'])
rdms_model_av = get_categorical_rdm(['a', 'a', 'v', 'v'])
model_names = ['independent', 'left/right', 'audio/visual']
# append in one RDMs object
model_rdms = rdms_model_in
model_rdms.append(rdms_model_lr)
model_rdms.append(rdms_model_av)
model_rdms.rdm_descriptors['model_names'] = model_names
model_rdms.pattern_descriptors['cond_names'] = cond_names
plt.figure(figsize=(10, 10))
pyrsa.vis.show_rdm(model_rdms,
rdm_descriptor='model_names',
pattern_descriptor='cond_names')
from pyrsa.rdm import compare
r = []
for mod in model_rdms:
r.append(compare(mod, rdms_data_binned, method='cosine'))
for i, r_ in enumerate(r):
plt.plot(rdms_data_binned.rdm_descriptors['time'],
r_.squeeze(),
label=model_names[i])
plt.xlabel('time')
plt.ylabel('model-data cosine similarity')
plt.legend()