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