def test_simulate_model_bos_np_array_R():
bo = se.simulate_model_bos(n_samples=10,
sample_rate=1000,
locs=locs,
sample_locs=n_elecs,
cov=R)
assert isinstance(bo, se.Brain)
iter_val = 5
append_d = pd.DataFrame()
param_grid = [(p, m, n) for p in m_patients for m in m_elecs for n in n_elecs]
for p, m, n in param_grid:
d = []
for i in range(iter_val):
# create brain objects with m_patients and loop over the number of model locations and subset locations to build model
model_bos = [
se.simulate_model_bos(n_samples=1000,
sample_rate=100,
locs=locs,
sample_locs=m,
noise=.3) for x in range(p)
]
# create model from subsampled gray locations
model = se.Model(model_bos, locs=locs)
# brain object locations subsetted entirely from both model and gray locations
sub_locs = locs.sample(n).sort_values(['x', 'y', 'z'])
# simulate brain object
bo = se.simulate_bo(n_samples=1000,
sample_rate=100,
locs=locs,
noise=.3)
our simulated brain objects. Then, we will create a model from these brain
objects and plot it.
"""
# Code source: Lucy Owen & Andrew Heusser
# License: MIT
import supereeg as se
# simulate 100 locations
locs = se.simulate_locations(n_elecs=100)
# simulate correlation matrix
R = se.create_cov(cov='toeplitz', n_elecs=len(locs))
# create list of simulated brain objects
model_bos = [
se.simulate_model_bos(n_samples=1000,
sample_rate=1000,
cov=R,
locs=locs,
sample_locs=10) for x in range(3)
]
# create model from subsampled gray locations
model = se.Model(model_bos, locs=locs)
# plot the model
model.plot_data()
[ 19., -57., -23.],
[ 19., 23., -3.],
[ 39., -57., 17.],
[ 39., 3., 37.],
[ 59., -17., 17.]])
# number of timeseries samples
n_samples = 10
# number of subjects
n_subs = 6
# number of electrodes
n_elecs = 5
# simulate correlation matrix
data = [se.simulate_model_bos(n_samples=10, sample_rate=10, locs=locs, sample_locs = n_elecs,
set_random_seed=123, noise=0) for x in range(n_subs)]
# test model to compare
test_model = se.Model(data=data[0:3], locs=locs, rbf_width=20, n_subs=3)
def test_create_model_1bo():
model = se.Model(data=data[0], locs=locs)
assert isinstance(model, se.Model)
def test_create_model_2bo():
model = se.Model(data=data[0:2], locs=locs)
assert isinstance(model, se.Model)
def test_create_model_superuser():
locs = np.random.multivariate_normal(np.zeros(3), np.eye(3), size=10)
import pytest
bo = se.load('example_data')
bo_s = bo.get_slice(sample_inds=[0, 1, 2])
locs = np.array([[-61., -77., -3.], [-41., -77., -23.], [-21., -97., 17.],
[-21., -37., 77.], [-21., 63., -3.], [-1., -37., 37.],
[-1., 23., 17.], [19., -57., -23.], [19., 23., -3.],
[39., -57., 17.], [39., 3., 37.], [59., -17., 17.]])
n_samples = 10
n_subs = 3
n_elecs = 10
data = [
se.simulate_model_bos(n_samples=10,
sample_rate=10,
locs=locs,
sample_locs=n_elecs) for x in range(n_subs)
]
test_bo = data[0]
test_model = se.Model(data=data, locs=locs)
bo = se.load('example_data')
def test_load_example_data():
bo = se.load('example_data')
assert isinstance(bo, se.Brain)
def test_load_example_filter():
bo = se.load('example_filter')
assert isinstance(bo, se.Brain)
# import libraries
import matplotlib.pyplot as plt
import supereeg as se
# simulate 100 locations
locs = se.simulate_locations(n_elecs=100)
# simulate correlation matrix
R = se.create_cov(cov='toeplitz', n_elecs=len(locs))
# simulate brain objects for the model that subsample n_elecs for each synthetic patient
model_bos = [
se.simulate_model_bos(n_samples=1000,
sample_rate=1000,
locs=locs,
sample_locs=10,
cov='toeplitz') for x in range(3)
]
# create the model object
model = se.Model(data=model_bos, locs=locs, n_subs=3)
model.plot_data()
# brain object locations subsetted
sub_locs = locs.sample(10).sort_values(['x', 'y', 'z'])
# simulate a new brain object using the same covariance matrix
bo = se.simulate_bo(n_samples=1000,
sample_rate=1000,
locs=sub_locs,
n_subs = 3
# number of electrodes
n_elecs = 5
# full brain object to parse and compare
bo_full = se.simulate_bo(n_samples=10, sessions=2, sample_rate=10, locs=locs)
# create brain object from subset of locations
sub_locs = bo_full.locs.iloc[6:]
sub_data = bo_full.data.iloc[:, sub_locs.index]
bo = se.Brain(data=sub_data.as_matrix(),
sessions=bo_full.sessions,
locs=sub_locs,
sample_rate=10,
meta={'brain object locs sampled': 2})
# simulate correlation matrix
data = [
se.simulate_model_bos(n_samples=10, locs=locs, sample_locs=n_elecs)
for x in range(n_subs)
]
# test model to compare
test_model = se.Model(data=data, locs=locs, rbf_width=100)
bo_nii = se.Brain(_gray(20))
nii = _brain_to_nifti(bo_nii, _gray(20))
a = np.array([[1, 2, 3], [4, 5, 6], [
7,
8,
9,
]])
b = np.array([[-1, 2, 2], [-4, 5, 5], [
-7,
8,
import os
import supereeg as se
import numpy as np
import pandas as pd
import nibabel as nib
nii = se.load('example_nifti')
locs = np.array([[-61., -77., -3.], [-41., -77., -23.], [-21., -97., 17.],
[-21., -37., 77.], [-21., 63., -3.], [-1., -37., 37.],
[-1., 23., 17.], [19., -57., -23.], [19., 23., -3.],
[39., -57., 17.], [39., 3., 37.], [59., -17., 17.]])
data = [
se.simulate_model_bos(n_samples=10,
sample_rate=10,
locs=locs,
sample_locs=3) for x in range(2)
]
# test model to compare
mo = se.Model(data=data, locs=locs)
bo = data[0]
nii_bo = se.Nifti(bo)
nii_mo = se.Nifti(mo)
def test_nifti():
assert isinstance(nii, se.Nifti)
assert issubclass(nii.__class__, nib.nifti1.Nifti1Image)
