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test_histogram.py
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from copy import deepcopy
import numpy as np
import pytest
from napari_matplotlib import FeaturesHistogramWidget, HistogramWidget
from napari_matplotlib.tests.helpers import (
assert_figures_equal,
assert_figures_not_equal,
)
@pytest.mark.mpl_image_compare
def test_histogram_2D_bins(make_napari_viewer, astronaut_data):
viewer = make_napari_viewer()
viewer.theme = "light"
viewer.add_image(astronaut_data[0], **astronaut_data[1])
widget = HistogramWidget(viewer)
viewer.window.add_dock_widget(widget)
widget.num_bins_widget.setValue(25)
fig = widget.figure
# Need to return a copy, as original figure is too eagerley garbage
# collected by the widget
return deepcopy(fig)
@pytest.mark.mpl_image_compare
def test_histogram_2D(make_napari_viewer, astronaut_data):
viewer = make_napari_viewer()
viewer.theme = "light"
viewer.add_image(astronaut_data[0], **astronaut_data[1])
fig = HistogramWidget(viewer).figure
# Need to return a copy, as original figure is too eagerley garbage
# collected by the widget
return deepcopy(fig)
@pytest.mark.mpl_image_compare
def test_histogram_3D(make_napari_viewer, brain_data):
viewer = make_napari_viewer()
viewer.theme = "light"
viewer.add_image(brain_data[0], **brain_data[1])
axis = viewer.dims.last_used
slice_no = brain_data[0].shape[0] - 1
viewer.dims.set_current_step(axis, slice_no)
fig = HistogramWidget(viewer).figure
# Need to return a copy, as original figure is too eagerley garbage
# collected by the widget
return deepcopy(fig)
def test_feature_histogram(make_napari_viewer):
n_points = 1000
random_points = np.random.random((n_points, 3)) * 10
random_directions = np.random.random((n_points, 3)) * 10
random_vectors = np.stack([random_points, random_directions], axis=1)
feature1 = np.random.random(n_points)
feature2 = np.random.normal(size=n_points)
viewer = make_napari_viewer()
viewer.add_points(
random_points,
properties={"feature1": feature1, "feature2": feature2},
name="points1",
)
viewer.add_vectors(
random_vectors,
properties={"feature1": feature1, "feature2": feature2},
name="vectors1",
)
widget = FeaturesHistogramWidget(viewer)
viewer.window.add_dock_widget(widget)
# Check whether changing the selected key changes the plot
widget._set_axis_keys("feature1")
fig1 = deepcopy(widget.figure)
widget._set_axis_keys("feature2")
assert_figures_not_equal(widget.figure, fig1)
# check whether selecting a different layer produces the same plot
viewer.layers.selection.clear()
viewer.layers.selection.add(viewer.layers[1])
assert_figures_equal(widget.figure, fig1)
@pytest.mark.mpl_image_compare
def test_feature_histogram_vectors(make_napari_viewer):
n_points = 1000
np.random.seed(42)
random_points = np.random.random((n_points, 3)) * 10
random_directions = np.random.random((n_points, 3)) * 10
random_vectors = np.stack([random_points, random_directions], axis=1)
feature1 = np.random.random(n_points)
viewer = make_napari_viewer()
viewer.add_vectors(
random_vectors,
properties={"feature1": feature1},
name="vectors1",
)
widget = FeaturesHistogramWidget(viewer)
viewer.window.add_dock_widget(widget)
widget._set_axis_keys("feature1")
fig = FeaturesHistogramWidget(viewer).figure
return deepcopy(fig)
@pytest.mark.mpl_image_compare
def test_feature_histogram_points(make_napari_viewer):
np.random.seed(0)
n_points = 1000
random_points = np.random.random((n_points, 3)) * 10
feature1 = np.random.random(n_points)
viewer = make_napari_viewer()
viewer.add_points(
random_points,
properties={"feature1": feature1},
name="points1",
)
widget = FeaturesHistogramWidget(viewer)
viewer.window.add_dock_widget(widget)
widget._set_axis_keys("feature1")
fig = FeaturesHistogramWidget(viewer).figure
return deepcopy(fig)
def test_change_layer(make_napari_viewer, brain_data, astronaut_data):
viewer = make_napari_viewer()
widget = HistogramWidget(viewer)
viewer.add_image(brain_data[0], **brain_data[1])
viewer.add_image(astronaut_data[0], **astronaut_data[1])
# Select first layer
viewer.layers.selection.clear()
viewer.layers.selection.add(viewer.layers[0])
fig1 = deepcopy(widget.figure)
# Re-selecting first layer should produce identical plot
viewer.layers.selection.clear()
viewer.layers.selection.add(viewer.layers[0])
assert_figures_equal(widget.figure, fig1)
# Plotting the second layer should produce a different plot
viewer.layers.selection.clear()
viewer.layers.selection.add(viewer.layers[1])
assert_figures_not_equal(widget.figure, fig1)
def test_change_contrast(make_napari_viewer, astronaut_data):
viewer = make_napari_viewer()
viewer.add_image(astronaut_data[0], **astronaut_data[1])
widget = HistogramWidget(viewer)
viewer.window.add_dock_widget(widget)
# update contrast limits of image layer, and check no errors are thrown
image_layer = viewer.layers[0]
image_layer.contrast_limits = [2, 50]