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import pandas as pd |
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2
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import numpy as np |
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3
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import skimage.io as sio |
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4
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import numpy.ma as ma |
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5
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import pandas.util.testing as pdt |
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6
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import numpy.testing as npt |
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7
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import diff_classifier.msd as msd |
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8
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9
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10
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def test_nth_diff(): |
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11
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12
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d = {'col1': [1, 2, 3, 4, 5]} |
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df = pd.DataFrame(data=d) |
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15
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test_d = {'col1': [1, 1, 1, 1]} |
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test_df = pd.DataFrame(data=test_d) |
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18
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pdt.assert_series_equal(msd.nth_diff(df['col1'], 1), test_df['col1']) |
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20
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#test2 |
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df = np.ones((5, 10)) |
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test_df = np.zeros((5, 9)) |
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23
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npt.assert_equal(msd.nth_diff(df, 1, 1), test_df) |
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25
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df = np.ones((5, 10)) |
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test_df = np.zeros((4, 10)) |
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npt.assert_equal(msd.nth_diff(df, 1, 0), test_df) |
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29
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30
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def test_msd_calc(): |
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31
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32
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d = {'Frame': [1, 2, 3, 4, 5], |
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33
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'X': [5, 6, 7, 8, 9], |
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34
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'Y': [6, 7, 8, 9, 10]} |
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35
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df = pd.DataFrame(data=d) |
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36
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new_track = msd.msd_calc(df, 5) |
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37
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38
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npt.assert_equal(np.array([0, 2, 8, 18, 32]).astype('float64'), new_track['MSDs']) |
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39
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npt.assert_equal(np.array([0, 0.25, 0.25, 0.25, 0.25]).astype('float64'), new_track['Gauss']) |
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40
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41
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d = {'Frame': [1, 2, 3, 4, 5], |
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'X': [5, 6, 7, 8, 9], |
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'Y': [6, 7, 8, 9, 10]} |
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df = pd.DataFrame(data=d) |
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new_track = msd.msd_calc(df) |
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46
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47
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npt.assert_equal(np.array([0, 2, 8, 18, 32, np.nan, np.nan, np.nan, np.nan, |
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48
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np.nan]).astype('float64'), new_track['MSDs']) |
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49
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npt.assert_equal(np.array([0, 0.25, 0.25, 0.25, 0.25, np.nan, np.nan, np.nan, |
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50
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np.nan, np.nan]).astype('float64'), new_track['Gauss']) |
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51
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52
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53
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54
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def test_all_msds(): |
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55
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56
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d = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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57
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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58
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'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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60
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df = pd.DataFrame(data=d) |
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61
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62
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di = {'Frame': [float(i) for i in[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]], |
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63
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'Track_ID': [float(i) for i in[1, 1, 1, 1, 1, 2, 2, 2, 2, 2]], |
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64
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'X': [float(i) for i in[5, 6, 7, 8, 9, 1, 2, 3, 4, 5]], |
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65
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'Y': [float(i) for i in[6, 7, 8, 9, 10, 2, 3, 4, 5, 6]], |
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'MSDs': [float(i) for i in[0, 2, 8, 18, 32, 0, 2, 8, 18, 32]], |
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67
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'Gauss': [0, 0.25, 0.25, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25]} |
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68
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cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss'] |
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69
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70
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dfi = pd.DataFrame(data=di)[cols] |
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71
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72
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pdt.assert_frame_equal(dfi, msd.all_msds(df)[cols]) |
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73
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74
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75
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def test_make_xyarray(): |
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76
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77
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d = {'Frame': [0, 1, 2, 3, 4, 0, 1, 2, 3, 4], |
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78
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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79
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'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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80
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'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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81
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df = pd.DataFrame(data=d) |
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82
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83
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length = max(df['Frame']) + 1 |
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84
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f_array, t_array, x_array, y_array = msd.make_xyarray(df, length=length) |
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85
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86
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tt_array = np.array([[1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]).astype(float) |
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87
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ft_array = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]).astype(float) |
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88
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xt_array = np.array([[5, 1], [6, 2], [7, 3], [8, 4], [9, 5]]).astype(float) |
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89
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yt_array = np.array([[6, 2], [7, 3], [8, 4], [9, 5], [10, 6]]).astype(float) |
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90
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91
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npt.assert_equal(t_array, tt_array) |
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92
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npt.assert_equal(f_array, ft_array) |
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93
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npt.assert_equal(x_array, xt_array) |
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npt.assert_equal(y_array, yt_array) |
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95
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96
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#Second test |
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97
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d = {'Frame': [0, 1, 2, 3, 4, 2, 3, 4, 5, 6], |
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98
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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99
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'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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100
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'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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101
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df = pd.DataFrame(data=d) |
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102
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103
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length = max(df['Frame']) + 1 |
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104
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f_array, t_array, x_array, y_array = msd.make_xyarray(df, length=length) |
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105
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106
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tt_array = np.array([[1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]).astype(float) |
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107
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ft_array = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6]]).astype(float) |
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108
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xt_array = np.array([[5, np.nan], [6, np.nan], [7, 1], [8, 2], [9, 3], [np.nan, 4], [np.nan, 5]]).astype(float) |
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109
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yt_array = np.array([[6, np.nan], [7, np.nan], [8, 2], [9, 3], [10, 4], [np.nan, 5], [np.nan, 6]]).astype(float) |
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110
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111
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npt.assert_equal(t_array, tt_array) |
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112
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npt.assert_equal(f_array, ft_array) |
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113
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npt.assert_equal(x_array, xt_array) |
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114
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npt.assert_equal(y_array, yt_array) |
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115
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116
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def test_all_msds2(): |
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117
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118
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d = {'Frame': [0, 1, 2, 3, 4, 0, 1, 2, 3, 4], |
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119
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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120
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'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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121
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'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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122
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df = pd.DataFrame(data=d) |
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123
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124
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di = {'Frame': [float(i) for i in[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]], |
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125
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'Track_ID': [float(i) for i in[1, 1, 1, 1, 1, 2, 2, 2, 2, 2]], |
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126
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'X': [float(i) for i in[5, 6, 7, 8, 9, 1, 2, 3, 4, 5]], |
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127
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'Y': [float(i) for i in[6, 7, 8, 9, 10, 2, 3, 4, 5, 6]], |
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128
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'MSDs': [float(i) for i in[0, 2, 8, 18, 32, 0, 2, 8, 18, 32]], |
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129
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'Gauss': [0, 0.25, 0.25, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25]} |
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130
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cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss'] |
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131
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132
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dfi = pd.DataFrame(data=di)[cols] |
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133
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134
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length = max(df['Frame']) + 1 |
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135
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pdt.assert_frame_equal(dfi, msd.all_msds2(df, frames=length)[cols]) |
ccurtis7 /
diff_classifier
