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import pandas as pd |
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import numpy as np |
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import skimage.io as sio |
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import numpy.ma as ma |
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import pandas.util.testing as pdt |
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import numpy.testing as npt |
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import diff_classifier.msd as msd |
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def test_nth_diff(): |
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data1 = {'col1': [1, 2, 3, 4, 5]} |
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df = pd.DataFrame(data=data1) |
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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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pdt.assert_series_equal(msd.nth_diff(df['col1'], 1), test_df['col1']) |
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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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npt.assert_equal(msd.nth_diff(df, 1, 1), test_df) |
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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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def test_msd_calc(): |
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data1 = {'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=data1) |
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new_track = msd.msd_calc(df, 5) |
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npt.assert_equal(np.array([0, 2, 8, 18, 32] |
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).astype('float64'), new_track['MSDs']) |
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npt.assert_equal(np.array([0, 0.25, 0.25, 0.25, 0.25] |
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).astype('float64'), new_track['Gauss']) |
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data1 = {'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=data1) |
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new_track = msd.msd_calc(df) |
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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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50
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np.nan]).astype('float64'), new_track['MSDs']) |
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npt.assert_equal(np.array([0, 0.25, 0.25, 0.25, 0.25, np.nan, np.nan, |
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np.nan, np.nan, np.nan] |
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).astype('float64'), new_track['Gauss']) |
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def test_all_msds(): |
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data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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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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df = pd.DataFrame(data=data1) |
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64
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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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'Track_ID': [float(i) for i in[1, 1, 1, 1, 1, 2, 2, 2, 2, 2]], |
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'X': [float(i) for i in[5, 6, 7, 8, 9, 1, 2, 3, 4, 5]], |
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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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'Gauss': [0, 0.25, 0.25, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25]} |
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cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss'] |
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dfi = pd.DataFrame(data=di)[cols] |
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pdt.assert_frame_equal(dfi, msd.all_msds(df)[cols]) |
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def test_make_xyarray(): |
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data1 = {'Frame': [0, 1, 2, 3, 4, 0, 1, 2, 3, 4], |
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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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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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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df = pd.DataFrame(data=data1) |
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length = max(df['Frame']) + 1 |
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xyft = msd.make_xyarray(df, length=length) |
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tt_array = np.array([[1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]).astype(float) |
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ft_array = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]).astype(float) |
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xt_array = np.array([[5, 1], [6, 2], [7, 3], [8, 4], [9, 5]]).astype(float) |
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yt_array = np.array([[6, 2], [7, 3], [8, 4], [9, 5], [10, 6]]).astype(float) |
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npt.assert_equal(xyft['tarray'], tt_array) |
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npt.assert_equal(xyft['farray'], ft_array) |
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npt.assert_equal(xyft['xarray'], xt_array) |
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npt.assert_equal(xyft['yarray'], yt_array) |
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# Second test |
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data1 = {'Frame': [0, 1, 2, 3, 4, 2, 3, 4, 5, 6], |
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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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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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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df = pd.DataFrame(data=data1) |
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length = max(df['Frame']) + 1 |
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xyft = msd.make_xyarray(df, length=length) |
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tt_array = np.array([[1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]] |
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).astype(float) |
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ft_array = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6]] |
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).astype(float) |
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xt_array = np.array([[5, np.nan], [6, np.nan], [7, 1], [8, 2], [9, 3], |
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[np.nan, 4], [np.nan, 5]]).astype(float) |
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yt_array = np.array([[6, np.nan], [7, np.nan], [8, 2], [9, 3], [10, 4], |
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[np.nan, 5], [np.nan, 6]]).astype(float) |
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npt.assert_equal(xyft['tarray'], tt_array) |
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npt.assert_equal(xyft['farray'], ft_array) |
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npt.assert_equal(xyft['xarray'], xt_array) |
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npt.assert_equal(xyft['yarray'], yt_array) |
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def test_all_msds2(): |
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data1 = {'Frame': [0, 1, 2, 3, 4, 0, 1, 2, 3, 4], |
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'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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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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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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df = pd.DataFrame(data=data1) |
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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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'Track_ID': [float(i) for i in[1, 1, 1, 1, 1, 2, 2, 2, 2, 2]], |
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'X': [float(i) for i in[5, 6, 7, 8, 9, 1, 2, 3, 4, 5]], |
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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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'Gauss': [0, 0.25, 0.25, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25], |
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'Quality': [float(i) for i in[10, 10, 10, 10, 10, |
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10, 10, 10, 10, 10]], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [float(i) for i in[10, 10, 10, 10, 10, |
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10, 10, 10, 10, 10]]} |
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cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss', 'Quality', |
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'SN_Ratio', 'Mean_Intensity'] |
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dfi = pd.DataFrame(data=di)[cols] |
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156
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length = max(df['Frame']) + 1 |
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pdt.assert_frame_equal(dfi, msd.all_msds2(df, frames=length)[cols]) |
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159
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160
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def test_geomean_msdisp(): |
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data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], |
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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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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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169
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geomean_t = np.array([2., 8., 18., 32.]) |
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geostder_t = np.array([]) |
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df = pd.DataFrame(data=data1) |
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msds = msd.all_msds2(df) |
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msds.to_csv('msd_test.csv') |
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175
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geomean, geostder = msd.geomean_msdisp('test', umppx=1, fps=1, upload=False) |
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npt.assert_equal(np.round(np.exp(geomean[geomean.mask == False].data), 1), |
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geomean_t) |
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npt.assert_equal(np.round(np.exp(geostder[geostder.mask == False].data), 1), |
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geostder_t) |
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# test 2 |
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data1 = {'Frame': [1, 2, 1, 2], |
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'Track_ID': [1, 1, 2, 2], |
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'X': [1, 2, 3, 4], |
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'Y': [1, 2, 3, 4], |
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'Quality': [10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1], |
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'Mean_Intensity': [10, 10, 10, 10]} |
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df = pd.DataFrame(data=data1) |
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msds = msd.all_msds2(df) |
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msds.to_csv('msd_test.csv') |
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geomean, geostder = msd.geomean_msdisp('test', umppx=1, fps=1, upload=False) |
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193
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npt.assert_equal(geomean, np.nan*np.ones(651)) |
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npt.assert_equal(geostder, np.nan*np.ones(651)) |
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196
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# test 3 |
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data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], |
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'X': [5, 6, 7, 8, 9, 2, 4, 6, 8, 10], |
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'Y': [6, 7, 8, 9, 10, 6, 8, 10, 12, 14], |
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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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203
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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204
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df = pd.DataFrame(data=data1) |
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205
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geomean_t = np.array([4., 16., 36., 64.]) |
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206
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geostder_t = np.array([2., 2., 2., 2]) |
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207
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msds = msd.all_msds2(df) |
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208
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msds.to_csv('msd_test.csv') |
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209
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210
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geomean, geostder = msd.geomean_msdisp('test', umppx=1, fps=1, upload=False) |
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211
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npt.assert_equal(np.round(np.exp(geomean[geomean.mask == False].data), 1), |
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212
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geomean_t) |
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213
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npt.assert_equal(np.round(np.exp(geostder[geostder.mask == False].data), 1), |
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214
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geostder_t) |
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215
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216
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217
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def test_binning(): |
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218
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experiments = [] |
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for num in range(8): |
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220
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experiments.append('test_{}'.format(num)) |
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221
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bins_t = {'test_W0': ['test_0', 'test_1'], |
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222
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'test_W1': ['test_2', 'test_3'], |
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223
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'test_W2': ['test_4', 'test_5'], |
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224
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'test_W3': ['test_6', 'test_7']} |
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225
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bin_names_t = ['test_W0', 'test_W1', 'test_W2', 'test_W3'] |
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226
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slices, bins, bin_names = msd.binning(experiments) |
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227
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228
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assert slices == 2 |
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assert bins == bins_t |
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assert bin_names == bin_names_t |
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231
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232
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233
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def test_precision_weight(): |
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234
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experiments = [] |
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235
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geomean = {} |
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236
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geostder = {} |
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237
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for num in range(4): |
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238
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name = 'test_{}'.format(num) |
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239
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experiments.append(name) |
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240
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data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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241
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'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], |
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242
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'X': [x*(num+1) for x in [5, 6, 7, 8, 9, 2, 4, 6, 8, 10]], |
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243
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'Y': [x*(num+1) for x in [6, 7, 8, 9, 10, 6, 8, 10, 12, 14]], |
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244
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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
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245
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
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246
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
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247
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df = pd.DataFrame(data=data1) |
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248
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msds = msd.all_msds2(df) |
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249
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msds.to_csv('msd_test_{}.csv'.format(num)) |
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250
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geomean[name], geostder[name] = msd.geomean_msdisp(name, umppx=1, fps=1, |
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251
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upload=False) |
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252
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253
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slices, bins, bin_names = msd.binning(experiments, wells=1) |
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254
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weights, w_holder = msd.precision_weight(experiments, geostder) |
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255
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weights_t = np.array([8.3, 8.3, 8.3, 8.3]) |
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256
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npt.assert_equal(np.round(weights[weights.mask == False].data, 1), |
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257
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weights_t) |
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258
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259
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260
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def test_precision_averaging(): |
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261
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experiments = [] |
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262
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geomean = {} |
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263
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geostder = {} |
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264
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for num in range(4): |
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|
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265
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name = 'test_{}'.format(num) |
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266
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experiments.append(name) |
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267
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data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
|
268
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'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], |
|
269
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'X': [x*(num+1) for x in [5, 6, 7, 8, 9, 2, 4, 6, 8, 10]], |
|
270
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'Y': [x*(num+1) for x in [6, 7, 8, 9, 10, 6, 8, 10, 12, 14]], |
|
271
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|
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'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
|
272
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|
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'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
|
273
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|
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'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
|
274
|
|
|
df = pd.DataFrame(data=data1) |
|
275
|
|
|
msds = msd.all_msds2(df) |
|
276
|
|
|
msds.to_csv('msd_test_{}.csv'.format(num)) |
|
277
|
|
|
geomean[name], geostder[name] = msd.geomean_msdisp(name, umppx=1, fps=1, |
|
278
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|
|
upload=False) |
|
279
|
|
|
|
|
280
|
|
|
slices, bins, bin_names = msd.binning(experiments, wells=1) |
|
281
|
|
|
weights, w_holder = msd.precision_weight(experiments, geostder) |
|
282
|
|
|
geodata = msd.precision_averaging(experiments, geomean, geostder, weights, |
|
283
|
|
|
save=False) |
|
284
|
|
|
|
|
285
|
|
|
geostd_t = np.array([0.3, 0.3, 0.3, 0.3]) |
|
286
|
|
|
geo_t = np.array([19.6, 78.4, 176.4, 313.5]) |
|
287
|
|
|
npt.assert_equal(np.round(geodata.geostd[geodata.geostd.mask == False].data, |
|
288
|
|
|
1), geostd_t) |
|
289
|
|
|
npt.assert_equal(np.round( |
|
290
|
|
|
np.exp(geodata.geomean[ |
|
291
|
|
|
geodata.geomean.mask == False].data), 1), geo_t) |
|
292
|
|
|
|
|
293
|
|
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|
|
294
|
|
|
def test_random_walk(): |
|
295
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|
|
xi = np.array([0., 1., 2., 2., 1.]) |
|
296
|
|
|
yi = np.array([0., 0., 0., 1., 1.]) |
|
297
|
|
|
x, y = msd.random_walk(nsteps=5) |
|
298
|
|
|
npt.assert_equal(xi, x) |
|
299
|
|
|
npt.assert_equal(yi, y) |
|
300
|
|
|
|
|
301
|
|
|
|
|
302
|
|
|
def test_random_traj_dataset(): |
|
303
|
|
|
di = {'Frame': [float(i) for i in[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]], |
|
304
|
|
|
'Track_ID': [float(i) for i in[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]], |
|
305
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|
|
'X': np.array([1., 1.93045975532, 1.0, 1.0, 1.0, 0.0, 0.288183500979, 0.576367001958, |
|
306
|
|
|
0.864550502937, 0.864550502937]), |
|
307
|
|
|
'Y': np.array([1., 1., 1., 0.06954024468115816, 1.0, 4.0, 4.0, 4.0, 4.0, 4.288183500978857 |
|
308
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|
|
]), |
|
309
|
|
|
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], |
|
310
|
|
|
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], |
|
311
|
|
|
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]} |
|
312
|
|
|
cols = ['Frame', 'Track_ID', 'X', 'Y'] |
|
313
|
|
|
dfi = pd.DataFrame(data=di)[cols] |
|
314
|
|
|
|
|
315
|
|
|
pdt.assert_frame_equal(dfi, msd.random_traj_dataset(nframes=5, nparts=2, |
|
316
|
|
|
fsize=(0, 5))[cols]) |
|
317
|
|
|
|
|
318
|
|
|
|
|
319
|
|
|
def test_plot_all_experiments(): |
|
320
|
|
|
print('To do later.') |
|
321
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|
ccurtis7 /
diff_classifier
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