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@@ 269-295 (lines=27) @@
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-0.21501108474766228) |
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def test_efficiency(): |
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frames = 100 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.sin(np.linspace(0, frames, frames)+3), |
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'Y': np.cos(np.linspace(0, frames, frames)+3), |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.efficiency(dframe) ==\ |
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(0.003548421265914009, 0.0059620286331768385) |
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frames = 10 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.linspace(0, frames, frames)+5, |
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'Y': np.linspace(0, frames, frames)+3, |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.efficiency(dframe) == (10.0, 1.0) |
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def test_msd_ratio(): |
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npt.assert_almost_equal(ft.aspectratio(dframe)[2], np.array([1.5, 1.])) |
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def test_boundedness(): |
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frames = 100 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.sin(np.linspace(0, frames, frames)+3), |
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'Y': np.cos(np.linspace(0, frames, frames)+3), |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.boundedness(dframe) == (0.607673328076712, 5.674370543833708, |
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-0.0535555587618044) |
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frames = 10 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.linspace(0, frames, frames)+5, |
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'Y': np.linspace(0, frames, frames)+3, |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.boundedness(dframe) == (0.039999999999999994, 1.0, |
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-0.21501108474766228) |
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def test_efficiency(): |
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npt.assert_almost_equal(o4, d4) |
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def test_kurtosis(): |
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frames = 5 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.linspace(0, frames, frames)+5, |
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'Y': np.linspace(0, frames, frames)+3, |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.kurtosis(dframe) == 4.079999999999999 |
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frames = 10 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.sin(np.linspace(0, frames, frames)+3), |
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'Y': np.cos(np.linspace(0, frames, frames)+3), |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.kurtosis(dframe) == 1.4759027695843443 |
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def test_asymmetry(): |
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pdt.assert_frame_equal(ft.unmask_track(m_df[m_df['Track_ID'] == 2]), dft) |
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def test_alpha_calc(): |
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frames = 5 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.linspace(0, frames, frames)+5, |
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'Y': np.linspace(0, frames, frames)+3, |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.alpha_calc(dframe) == (2.0000000000000004, 0.4999999999999998) |
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frames = 10 |
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data = {'Frame': np.linspace(0, frames, frames), |
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'X': np.sin(np.linspace(0, frames, frames)+5), |
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'Y': np.cos(np.linspace(0, frames, frames)+3), |
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'Track_ID': np.ones(frames), |
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'Quality': 10.0*np.ones(frames), |
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'SN_Ratio': 0.1*np.ones(frames), |
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'Mean_Intensity': 10.0*np.ones(frames)} |
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dframe = pd.DataFrame(data=data) |
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dframe = msd.all_msds2(dframe, frames=frames+1) |
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assert ft.alpha_calc(dframe) == (0.8201034110620524, 0.1494342948594476) |
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def test_gyration_tensor(): |
ccurtis7 /
diff_classifier
