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import os |
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import numpy as np |
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import numpy.testing as npt |
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from scipy.spatial import Voronoi |
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import matplotlib as mpl |
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mpl.use('Agg') |
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import diff_classifier.msd as msd |
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import diff_classifier.features as ft |
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import diff_classifier.heatmaps as hm |
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def test_voronoi_finite_polygons_2d(): |
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prefix = 'test' |
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msd_file = 'msd_{}.csv'.format(prefix) |
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ft_file = 'features_{}.csv'.format(prefix) |
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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xs = feat['X'].astype(int) |
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ys = feat['Y'].astype(int) |
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points = np.zeros((xs.shape[0], 2)) |
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points[:, 0] = xs |
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points[:, 1] = ys |
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vor = Voronoi(points) |
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regions, vertices = hm.voronoi_finite_polygons_2d(vor) |
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npt.assert_equal(243.8, np.round(np.mean(vertices), 1)) |
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34
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35
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def test_plot_heatmap(): |
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prefix = 'test' |
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msd_file = 'msd_{}.csv'.format(prefix) |
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ft_file = 'features_{}.csv'.format(prefix) |
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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hm.plot_heatmap(prefix, resolution=520, rows=1, cols=1, figsize=(6,5), upload=False) |
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assert os.path.isfile('hm_asymmetry1_{}.png'.format(prefix)) |
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48
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49
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50
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def test_plot_scatterplot(): |
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prefix = 'test' |
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msd_file = 'msd_{}.csv'.format(prefix) |
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ft_file = 'features_{}.csv'.format(prefix) |
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54
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55
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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61
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hm.plot_scatterplot(prefix, resolution=400, rows=1, cols=1, dotsize=120, upload=False) |
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assert os.path.isfile('scatter_asymmetry1_{}.png'.format(prefix)) |
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63
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64
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65
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def test_plot_trajectories(): |
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prefix = 'test' |
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msd_file = 'msd_{}.csv'.format(prefix) |
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ft_file = 'features_{}.csv'.format(prefix) |
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69
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70
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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76
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hm.plot_trajectories(prefix, resolution=520, rows=1, cols=1, upload=False) |
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assert os.path.isfile('traj_{}.png'.format(prefix)) |
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79
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80
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def test_plot_histogram(): |
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prefix = 'test' |
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msd_file = 'msd_{}.csv'.format(prefix) |
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ft_file = 'features_{}.csv'.format(prefix) |
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84
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85
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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86
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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90
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91
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hm.plot_histogram(prefix, fps=1, umppx=1, frames=100, frame_interval=5, frame_range=5, y_range=10, upload=False) |
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92
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assert os.path.isfile('hist_{}.png'.format(prefix)) |
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93
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94
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95
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def test_plot_individual_msds(): |
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96
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prefix = 'test' |
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97
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msd_file = 'msd_{}.csv'.format(prefix) |
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98
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ft_file = 'features_{}.csv'.format(prefix) |
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99
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100
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dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3) |
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101
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msds = msd.all_msds2(dataf, frames=100) |
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102
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msds.to_csv(msd_file) |
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103
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feat = ft.calculate_features(msds) |
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feat.to_csv(ft_file) |
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105
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106
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geomean, gSEM = hm.plot_individual_msds(prefix, umppx=1, fps=1, y_range=400, alpha=0.3, upload=False) |
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npt.assert_almost_equal(339.9, np.round(np.sum(geomean), 1)) |
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108
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npt.assert_almost_equal(35.3, np.round(np.sum(gSEM), 1)) |
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109
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110
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111
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def test_plot_particles_in_frame(): |
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112
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prefix = 'test' |
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113
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msd_file = 'msd_{}.csv'.format(prefix) |
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114
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ft_file = 'features_{}.csv'.format(prefix) |
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115
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116
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dataf = msd.random_traj_dataset(nparts=10, ndist=(1, 1), seed=3) |
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117
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msds = msd.all_msds2(dataf, frames=100) |
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msds.to_csv(msd_file) |
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feat = ft.calculate_features(msds) |
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120
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feat.to_csv(ft_file) |
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122
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hm.plot_particles_in_frame(prefix, x_range=100, y_range=20, upload=False) |
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assert os.path.isfile('in_frame_{}.png'.format(prefix)) |
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ccurtis7 /
diff_classifier
