# Authors: Alexandre Gramfort # # License: Simplified BSD import os.path as op import numpy as np from numpy.testing import assert_allclose import pytest from mne.viz.utils import (compare_fiff, _fake_click, _compute_scalings, _validate_if_list_of_axes, _get_color_list, _setup_vmin_vmax, center_cmap) from mne.viz import ClickableImage, add_background_image, mne_analyze_colormap from mne.utils import run_tests_if_main from mne.io import read_raw_fif from mne.event import read_events from mne.epochs import Epochs # Set our plotters to test mode import matplotlib matplotlib.use('Agg') # for testing don't use X server base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') cov_fname = op.join(base_dir, 'test-cov.fif') ev_fname = op.join(base_dir, 'test_raw-eve.fif') def test_setup_vmin_vmax_warns(): """Test that _setup_vmin_vmax warns properly.""" expected_msg = r'\(min=0.0, max=1\) range.*minimum of data is -1' with pytest.warns(UserWarning, match=expected_msg): _setup_vmin_vmax(data=[-1, 0], vmin=None, vmax=None, norm=True) def test_get_color_list(): """Test getting a colormap from rcParams.""" colors = _get_color_list() assert isinstance(colors, list) colors_no_red = _get_color_list(annotations=True) assert '#ff0000' not in colors_no_red def test_mne_analyze_colormap(): """Test mne_analyze_colormap.""" pytest.raises(ValueError, mne_analyze_colormap, [0]) pytest.raises(ValueError, mne_analyze_colormap, [-1, 1, 2]) pytest.raises(ValueError, mne_analyze_colormap, [0, 2, 1]) def test_compare_fiff(): """Test compare_fiff.""" import matplotlib.pyplot as plt compare_fiff(raw_fname, cov_fname, read_limit=0, show=False) plt.close('all') def test_clickable_image(): """Test the ClickableImage class.""" # Gen data and create clickable image import matplotlib.pyplot as plt im = np.random.RandomState(0).randn(100, 100) clk = ClickableImage(im) clicks = [(12, 8), (46, 48), (10, 24)] # Generate clicks for click in clicks: _fake_click(clk.fig, clk.ax, click, xform='data') assert_allclose(np.array(clicks), np.array(clk.coords)) assert (len(clicks) == len(clk.coords)) # Exporting to layout lt = clk.to_layout() assert (lt.pos.shape[0] == len(clicks)) assert_allclose(lt.pos[1, 0] / lt.pos[2, 0], clicks[1][0] / float(clicks[2][0])) clk.plot_clicks() plt.close('all') def test_add_background_image(): """Test adding background image to a figure.""" import matplotlib.pyplot as plt rng = np.random.RandomState(0) for ii in range(2): f, axs = plt.subplots(1, 2) x, y = rng.randn(2, 10) im = rng.randn(10, 10) axs[0].scatter(x, y) axs[1].scatter(y, x) for ax in axs: ax.set_aspect(1) # Background without changing aspect if ii == 0: ax_im = add_background_image(f, im) return assert (ax_im.get_aspect() == 'auto') for ax in axs: assert (ax.get_aspect() == 1) else: # Background with changing aspect ax_im_asp = add_background_image(f, im, set_ratios='auto') assert (ax_im_asp.get_aspect() == 'auto') for ax in axs: assert (ax.get_aspect() == 'auto') plt.close('all') # Make sure passing None as image returns None f, axs = plt.subplots(1, 2) assert (add_background_image(f, None) is None) plt.close('all') def test_auto_scale(): """Test auto-scaling of channels for quick plotting.""" raw = read_raw_fif(raw_fname) epochs = Epochs(raw, read_events(ev_fname)) rand_data = np.random.randn(10, 100) for inst in [raw, epochs]: scale_grad = 1e10 scalings_def = dict([('eeg', 'auto'), ('grad', scale_grad), ('stim', 'auto')]) # Test for wrong inputs pytest.raises(ValueError, inst.plot, scalings='foo') pytest.raises(ValueError, _compute_scalings, 'foo', inst) # Make sure compute_scalings doesn't change anything not auto scalings_new = _compute_scalings(scalings_def, inst) assert (scale_grad == scalings_new['grad']) assert (scalings_new['eeg'] != 'auto') pytest.raises(ValueError, _compute_scalings, scalings_def, rand_data) epochs = epochs[0].load_data() epochs.pick_types(eeg=True, meg=False) pytest.raises(ValueError, _compute_scalings, dict(grad='auto'), epochs) def test_validate_if_list_of_axes(): """Test validation of axes.""" import matplotlib.pyplot as plt fig, ax = plt.subplots(2, 2) pytest.raises(ValueError, _validate_if_list_of_axes, ax) ax_flat = ax.ravel() ax = ax.ravel().tolist() _validate_if_list_of_axes(ax_flat) _validate_if_list_of_axes(ax_flat, 4) pytest.raises(ValueError, _validate_if_list_of_axes, ax_flat, 5) pytest.raises(ValueError, _validate_if_list_of_axes, ax, 3) pytest.raises(ValueError, _validate_if_list_of_axes, 'error') pytest.raises(ValueError, _validate_if_list_of_axes, ['error'] * 2) pytest.raises(ValueError, _validate_if_list_of_axes, ax[0]) pytest.raises(ValueError, _validate_if_list_of_axes, ax, 3) ax_flat[2] = 23 pytest.raises(ValueError, _validate_if_list_of_axes, ax_flat) _validate_if_list_of_axes(ax, 4) plt.close('all') def test_center_cmap(): """Test centering of colormap.""" import matplotlib.cm as cm from matplotlib.colors import LinearSegmentedColormap from matplotlib.pyplot import Normalize cmap = center_cmap(cm.get_cmap("RdBu"), -5, 10) assert isinstance(cmap, LinearSegmentedColormap) # get new colors for values -5 (red), 0 (white), and 10 (blue) new_colors = cmap(Normalize(-5, 10)([-5, 0, 10])) # get original colors for 0 (red), 0.5 (white), and 1 (blue) reference = cm.RdBu([0., 0.5, 1.]) assert_allclose(new_colors, reference) # new and old colors at 0.5 must be different assert not np.allclose(cmap(0.5), reference[1]) run_tests_if_main()