def test_avg_two_maps_unbiased(self):
maps2 = Maps(self.array2, Ni=self.Ni, Nj=self.Nj, Nk=self.Nk)
sigma = 2.
avg, _ = maps2.iterative_smooth_avg_var(sigma=sigma, bias=False)
maps2.smooth(sigma=sigma, inplace=True)
self.assertTrue(np.array_equal(maps2.avg().to_array(), avg.to_array()))
def test_var_two_maps_unbiased(self):
maps2 = Maps(self.array2, Ni=self.Ni, Nj=self.Nj, Nk=self.Nk)
sigma = 2.
_, var = maps2.iterative_smooth_avg_var(sigma=sigma, bias=False)
maps2.smooth(sigma=sigma, inplace=True)
self.assertTrue(
np.allclose(maps2.var(bias=False).to_array(), var.to_array()))
import pytest
import matplotlib.pyplot as plt
import nilearn
from meta_analysis import Maps, plotting
from globals_test import template, atlas, df
# Parameters
sigma = 2.
# Maps
maps = Maps(df, template=template, groupby_col='pmid')
maps_dense = Maps(df, template=template, groupby_col='pmid', save_memory=False)
maps_atlas = Maps(df, template=template, groupby_col='pmid', atlas=atlas)
avg, var = maps.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_dense, var_dense = maps_dense.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_atlas, var_atlas = maps_atlas.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_biased, var_biased = maps.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
avg_dense_biased, var_dense_biased = maps_dense.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
avg_atlas_biased, var_atlas_biased = maps_atlas.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
@pytest.mark.mpl_image_compare
def test_sum():
"""Test sum of maps."""
sum = maps.summed_map()
return plotting.plot_activity_map(sum.to_img())
@pytest.mark.mpl_image_compare