def test_demo_rdm_scatterplot(self):
import os
from scipy.io import loadmat
import numpy as np
from rsatoolbox.rdm import concat, RDMs
from rsatoolbox.vis import rdm_comparison_scatterplot
path = os.path.dirname(os.path.abspath(__file__))
condition_descriptors = {
'type': (["A"] * 23) + (["B"] * 23) + (["C"] * 46)
}
matlab_data = loadmat(
os.path.join(path, '..', 'demos',
'92imageData/92_brainRDMs.mat'))['RDMs']
n_rdms = len(matlab_data[0])
rdms_ = RDMs(
np.array([matlab_data[0][i][0][0] for i in range(n_rdms)]),
pattern_descriptors=condition_descriptors,
rdm_descriptors={'name': np.array([f"RDM{i}" for i in range(4)])})
rdms_a = concat([rdms_[0], rdms_[1]])
rdms_b = concat([rdms_[2], rdms_[3]])
rdm_comparison_scatterplot((rdms_a, rdms_b),
show_marginal_distributions=True,
show_identity_line=True,
show_legend=False,
highlight_selector='type',
highlight_categories=["A", "B", "C"],
colors={
"A": (1, 0, 0),
"B": (0, 1, 0),
"C": (0, 0, 1),
})
def _dummy_rdm():
import PIL
import matplotlib.markers
from collections import defaultdict
markers = list(matplotlib.markers.MarkerStyle().markers.keys())
images = np.meshgrid(np.linspace(0.5, 1.0, 50), np.linspace(0.5, 1.0, 30),
np.linspace(0.5, 1.0, 3))
images = [
this_image * this_ind / 4.0 for this_ind in range(4)
for this_image in images
]
images = [
PIL.Image.fromarray(255 * this_image, "RGB") for this_image in images
]
names = [
this_class + this_ex for this_class in ("a", "b", "c", "d")
for this_ex in ("1", "2", "3")
]
n_con = len(names)
icons = defaultdict(list)
for this_marker, this_image, this_name in zip(markers, images, names):
icons["image"].append(rsv.Icon(image=this_image))
icons["marker"].append(rsv.Icon(marker=this_marker, color=[0, 0, 0]))
icons["string"].append(rsv.Icon(string=this_name))
icons["text"].append(this_name)
ROIs = ["X", "Y", "Z"]
return rsr.concat([
rsr.RDMs(
dissimilarities=np.random.rand(1, int((n_con - 1) * n_con / 2)),
dissimilarity_measure="1-rho",
rdm_descriptors=dict(ROI=this_roi),
pattern_descriptors=icons,
) for this_roi in ROIs
])
def setUp(self):
from rsatoolbox.data import Dataset
from rsatoolbox.rdm import calc_rdm
from rsatoolbox.rdm import concat
rdms = []
for _ in range(5):
data = np.random.rand(6, 20)
data_s = Dataset(data)
rdms.append(calc_rdm(data_s))
self.rdms = concat(rdms)
def test_concat_varargs_multiple_rdms(self):
from rsatoolbox.rdm import concat
dis = np.zeros((8, 10))
dis2 = np.random.rand(8, 10)
mes = "Euclidean"
des = {'subj': 0}
pattern_des = {'type': np.array([0, 1, 2, 2, 4])}
rdm_des = {'session': np.array([0, 1, 2, 2, 4, 5, 6, 7])}
rdms1 = rsr.RDMs(dissimilarities=dis,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des,
rdm_descriptors=rdm_des)
rdms2 = rsr.RDMs(dissimilarities=dis2,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des,
rdm_descriptors=rdm_des)
rdm_c1 = concat((rdms1, rdms2))
rdm_c2 = concat(rdms1, rdms2)
self.assertEqual(rdm_c1.n_rdm, 16)
self.assertEqual(rdm_c2.n_rdm, 16)
assert_array_equal(rdm_c1.dissimilarities, rdm_c2.dissimilarities)
def test_concat_varargs_one_rdm(self):
from rsatoolbox.rdm import concat
dis = np.zeros((8, 10))
mes = "Euclidean"
des = {'subj': 0}
pattern_des = {'type': np.array([0, 1, 2, 2, 4])}
rdm_des = {'session': np.array([0, 1, 2, 2, 4, 5, 6, 7])}
rdms = rsr.RDMs(dissimilarities=dis,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des,
rdm_descriptors=rdm_des)
rdm_c1 = concat(rdms)
self.assertEqual(rdm_c1.n_rdm, 8)
assert_array_equal(rdm_c1.dissimilarities, rdms.dissimilarities)
def test_two_rdms_nan(self):
from rsatoolbox.model import ModelInterpolate, ModelWeighted
from rsatoolbox.model.fitter import fit_regress, fit_optimize_positive
from rsatoolbox.rdm import concat, compare
rdms = self.rdms.subsample_pattern('index', [0, 1, 1, 3, 4, 5])
model_rdms = concat([rdms[0], rdms[1]])
model_weighted = ModelWeighted('m_weighted', model_rdms)
model_interpolate = ModelInterpolate('m_interpolate', model_rdms)
for i_method in ['cosine', 'corr', 'cosine_cov', 'corr_cov']:
theta_m_i = model_interpolate.fit(rdms, method=i_method)
theta_m_w = model_weighted.fit(rdms, method=i_method)
theta_m_w_pos = fit_optimize_positive(model_weighted,
rdms,
method=i_method)
theta_m_w_linear = fit_regress(model_weighted,
rdms,
method=i_method)
eval_m_i = np.mean(
compare(model_weighted.predict_rdm(theta_m_i),
rdms,
method=i_method))
eval_m_w = np.mean(
compare(model_weighted.predict_rdm(theta_m_w),
rdms,
method=i_method))
eval_m_w_pos = np.mean(
compare(model_weighted.predict_rdm(theta_m_w_pos),
rdms,
method=i_method))
eval_m_w_linear = np.mean(
compare(model_weighted.predict_rdm(theta_m_w_linear),
rdms,
method=i_method))
self.assertAlmostEqual(
eval_m_i,
eval_m_w_pos,
places=4,
msg='weighted fit differs from interpolation fit!' +
'\nfor %s' % i_method)
self.assertAlmostEqual(
eval_m_w,
eval_m_w_linear,
places=4,
msg='regression fit differs from optimization fit!' +
'\nfor %s' % i_method)
def test_concat(self):
from rsatoolbox.rdm import concat
dis = np.zeros((8, 10))
dis2 = np.random.rand(8, 10)
mes = "Euclidean"
des = {'subj': 0}
pattern_des = {'type': np.array([0, 1, 2, 2, 4])}
rdm_des = {'session': np.array([0, 1, 2, 2, 4, 5, 6, 7])}
rdms1 = rsr.RDMs(dissimilarities=dis,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des,
rdm_descriptors=rdm_des)
rdms2 = rsr.RDMs(dissimilarities=dis2,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des,
rdm_descriptors=rdm_des)
rdms = concat((rdms1, rdms2))
self.assertEqual(rdms.n_rdm, 16)
assert len(rdms.rdm_descriptors['session']) == 16