""" Created on Thurs Dec 10 2021 @author: Gerd Duscher """ import unittest import sys import os import ipywidgets import matplotlib as mpl if os.environ.get('DISPLAY', '') == '': print('no display found. Using non-interactive Agg backend') mpl.use('Agg') import numpy as np sys.path.insert(0, "../../sidpy/") import sidpy def get_spectrum(dtype=float): x = np.array(np.random.normal(3, 2.5, size=1024), dtype=dtype) dset = sidpy.Dataset.from_array(x) # dataset metadata dset.data_type = 'spectrum' dset.title = 'random' dset.quantity = 'intensity' dset.units = 'a.u.' scale = .5 offset = 390 dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]) * scale + offset, 'energy')) dset.dim_0.dimension_type = 'spectral' dset.energy.units = 'eV' dset.energy.quantity = 'energy' return dset, x def get_image(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image' dset.units = 'counts' dset.quantity = 'intensity' dset.title = 'random' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_image_stack(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(25, 512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image_stack' dset.units = 'counts' dset.quantity = 'intensity' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]), 'frame')) dset.frame.dimension_type = 'temporal' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_spectral_image(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(25, 512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'spectral_image' dset.units = 'counts' dset.quantity = 'intensity' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]), 'energy')) dset.energy.dimension_type = 'spectral' dset.energy.units = 'eV' dset.energy.quantity = 'energy' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_point_cloud(dtype=float): data = np.array(np.random.normal(3, 2.5, size=(20, 10)), dtype=dtype) data_var = np.array(np.random.normal(10, 2.5, size=(20, 10)), dtype=dtype) coordinates = np.array(np.random.rand(20, 2) + 10, dtype=dtype) dset = sidpy.Dataset.from_array(data, coordinates=coordinates) dset.data_type = 'point_cloud' dset.variance = data_var dset.point_cloud['spacial_units'] = 'um' dset.point_cloud['quantity'] = 'Distance' dset.set_dimension(0, sidpy.Dimension(np.arange(data.shape[0]), name='point number', quantity='Point number', dimension_type='point_cloud')) dset.set_dimension(1, sidpy.Dimension(np.arange(data.shape[1]), name='X', units='a.u.', quantity='X', dimension_type='spectral')) dset.units = 'a.u.' dset.quantity = 'Intensity' return dset, data def get_4d_image(dtype=float): data = np.array(np.random.random([5, 5, 10, 10]), dtype=dtype) for i in range(5): for j in range(5): data[i, j] += (i+j) dataset = sidpy.Dataset.from_array(data) dataset.data_type = 'Image_4d' dataset.title = 'random' dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'u')) dataset.u.dimension_type = 'reciprocal' dataset.u.units = '1/nm' dataset.u.quantity = 'frequency' dataset.set_dimension(1, sidpy.Dimension(np.arange(dataset.shape[1]) * .02, 'v')) dataset.v.dimension_type = 'reciprocal' dataset.v.units = '1/nm' dataset.v.quantity = 'frequency' dataset.set_dimension(2, sidpy.Dimension(np.arange(dataset.shape[2]) * .02, 'x')) dataset.x.dimension_type = 'spatial' dataset.x.units = 'nm' dataset.x.quantity = 'distance' dataset.set_dimension(3, sidpy.Dimension(np.arange(dataset.shape[3]) * .02, 'y')) dataset.y.dimension_type = 'spatial' dataset.y.units = 'nm' dataset.y.quantity = 'distance' return dataset, data class TestSpectrumPlot(unittest.TestCase): def test_spectrum(self): # dimension with metadata dset, x = get_spectrum() view = dset.plot(verbose=True) x_y = view.axes[0].lines[0].get_xydata() self.assertTrue(np.allclose(x_y[:, 1], x)) self.assertTrue(np.allclose(x_y[:, 0], dset.energy)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.energy.quantity} ({dset.energy.units})", view.axes[0].get_xlabel()) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.CurveVisualizer(x) def test_false_dim(self): dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.CurveVisualizer(dset) def test_generic(self): x = np.random.normal(3, 2.5, size=(4, 1024)) dset = sidpy.Dataset.from_array(x) dset.data_type = 'spectrum' dset.plot() def test_complex(self): dset, x = get_spectrum(dtype=complex) view = dset.plot(verbose=True) x_y = view.axes[0].lines[0].get_xydata() self.assertEqual(len(view.axes), 2) self.assertTrue(np.allclose(x_y[:, 1], np.abs(x))) self.assertTrue(np.allclose(x_y[:, 0], dset.energy)) # self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.energy.quantity} ({dset.energy.units})", view.axes[0].get_xlabel()) class TestImagePlot(unittest.TestCase): def test_image(self): dset, x = get_image() view = dset.plot() data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageVisualizer(dset) def test_generic(self): x = np.random.normal(3, 2.5, size=(6, 1024)) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image' dset.dim_0.dimension_type = 'spatial' dset.dim_1.dimension_type = 'spatial' dset.data_type = 'image' dset.plot() def test_complex(self): dset, x = get_image(dtype=complex) view = dset.plot(verbose=True) x_y = view.axes[0].images[0].get_array().data self.assertEqual(len(view.axes), 4) self.assertEqual(x_y.shape, x.shape) # self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_image_scale(self): dset, x = get_image() kwargs = {'scale_bar': True, 'cmap': 'hot'} # or 'cmap': 'gray' view = dset.plot(verbose=True, **kwargs) data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_image_stack(self): dset, x = get_image_stack() view = sidpy.viz.dataset_viz.ImageVisualizer(dset) data = view.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) self.assertEqual('generic_image 0', view.fig.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.fig.axes[0].get_xlabel()) class TestImageStackPlot(unittest.TestCase): def test_plot(self): dset, x = get_image_stack() view = dset.plot() data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) self.assertEqual(view.axes[0].get_title(), 'Image stack: generic\n use scroll wheel to navigate images') self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_scalebar(self): dset, x = get_image_stack() kwargs = {'scale_bar': True, 'cmap': 'hot'} # or 'cmap': 'gray' view = dset.plot(verbose=True, **kwargs) data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageStackVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageStackVisualizer(dset) def test_button_up(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.slider.value = 2 data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_button_average(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.button.value = True data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_button_average2(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.button.value = True viz.button.value = False data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) class TestSpectralImagePlot(unittest.TestCase): def test_plot(self): dset, x = get_spectral_image() view = dset.plot() self.assertEqual(len(view.axes), 2) def test_bin(self): dset, x = get_spectral_image() view = dset.plot() dset.view.set_bin([20, 20]) self.assertEqual(len(view.axes), 2) dset.view.set_bin(10) self.assertEqual(len(view.axes), 2) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) dset, x = get_4d_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) class TestPointCloudPlot(unittest.TestCase): def test_plot_basic(self): dset, x = get_point_cloud() view = dset.plot() self.assertEqual(len(view.axes), 2) self.assertEqual(view.axes[0].get_xlabel(), 'Distance [px]') self.assertEqual(view.axes[0].get_ylabel(), 'Distance [px]') self.assertEqual(view.axes[1].get_xlabel(), 'X (a.u.)') self.assertEqual(view.axes[1].get_ylabel(), 'Intensity (a.u.)') x_y = view.axes[1].lines[0].get_xydata() self.assertEqual(x_y.shape, (10, 2)) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_4d_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_spectral_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) def test_units_button(self): dset, x = get_point_cloud() view = dset.plot() self.assertIsInstance(dset.view.button, ipywidgets.Dropdown) self.assertEqual(dset.view.button.value, 1) dset.view.button.value = 2 self.assertEqual(view.axes[0].get_xlabel(), 'Distance [um]') self.assertEqual(view.axes[0].get_ylabel(), 'Distance [um]') def test_point_selection(self): dset, x = get_point_cloud() view = dset.plot() event = mpl.backend_bases.MouseEvent( name='button_press_event', canvas=dset.view.fig.canvas, x=0, # x-coordinate of the click (adjust as needed) y=0, # y-coordinate of the click (adjust as needed) button=1, # button number (1 for left button) ) xpos, ypos = 25, 25 event.inaxes = dset.view.axes[0] event.xdata = xpos event.ydata = ypos dset.view._onclick(event) selected_point = dset.view.tree.query(np.array([xpos, ypos]))[1] spectrum_title = dset.view.axes[1].get_title() self.assertEqual(spectrum_title, 'point {}'.format(selected_point)) actual = dset.view.axes[1].lines[0].get_ydata() expected = dset[selected_point].compute() self.assertTrue(np.allclose(actual, expected, equal_nan=True, rtol=1e-05, atol=1e-08)) class Test4DImageStackPlot(unittest.TestCase): def test_plot(self): dataset, data = get_4d_image() view = dataset.plot() self.assertEqual(len(view.axes), 2) def test_bin(self): dset, x = get_4d_image() view = dset.plot() dset.view.set_bin([20, 20]) self.assertEqual(len(view.axes), 2) dset.view.set_bin(10) self.assertEqual(len(view.axes), 2) def test_scan_directions(self): dataset, data = get_4d_image() view = dataset.plot(scan_x=3,scan_y=2, image_4d_x=1, image_4d_y=0) self.assertEqual(len(view.axes), 2) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(x) def test_false_dim(self): dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(dset) dset, x = get_spectral_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(dset) def test_plot_complex(self): dataset, data = get_4d_image(dtype=complex) view = dataset.plot() self.assertEqual(len(view.axes), 3) if __name__ == '__main__': unittest.main()