def test_log_likelihood_coupled(self):
"""Tests that log_likelihood_coupled function returns a reasonable estimate"""
test_params = [0.03178564, 0.00310762, 0.00017541, 0.00022762]
path = io.get_data_file_path('simulated_coupled_6x6.pkl')
testdata = celldensity.CellDen(pd.read_pickle(path))
mu_n = -0.15
sigma_n = 0.1
val = model.log_likelihood_coupled(test_params, testdata, mu_n,
sigma_n)
np.testing.assert_almost_equal(val, -10289.069087654105, 4)
def read(data_file,
CellA,
CellB,
BinDiv,
ImgWidth=1024,
time_scale=0.25,
length_scale=1.33):
'''
Parameters:
-----------
data_file: the .csv file containing the data with the following columns
Identity Labeling:
Column 0:'ImageNumber': denoting the time step image
Column 1: 'ObjectNumber': denoting the arbitrary identity of the cell
Intensity classifications:
'Classify_Intensity_UpperQuartileIntensity_CellA_high_Intensity_UpperQuartileIntensity_CellB_high'
'Classify_Intensity_UpperQuartileIntensity_CellA_high_Intensity_UpperQuartileIntensity_CellB_low'
'Classify_Intensity_UpperQuartileIntensity_CellA_low_Intensity_UpperQuartileIntensity_CellB_high'
'Classify_Intensity_UpperQuartileIntensity_CellA_low_Intensity_UpperQuartileIntensity_CellB_low'
where the order of CellA and CellB should be the same as that of the input parameter
Locations:
'Location_Center_X'
'Location_Center_Y'
CellA: Name of the first cell type, e.g.'Sox2'
CellB: Name of the second cell type, e.g. 'Oct4'
BinDiv: An integer telling the function to divide the orginal cell image into BinDiv x BinDiv bins
ImgWidth: the width dimension of the image in pixels (e.g. for an image of 1024x1024, just enter 1024)
time_scale: time scale of each time step between subsequent images, in hours
length_scale: linear size scale of the image, in mm
Returns:
-----------
An object with the following attributes:
CellDen.data: A dataframe with the density of different types of cells in the bins of the
original cell image at different time t.The format of the output dataframe would be like --
Rows-> time index
Main Column-> cell types
Sub Column-> bin index
CellDen.cellname: a tuple of the first and second cell name
CellDen.bin_num: total bin number
CellDen.tot_time: total time step of the experiment
CellDen.time_scale: time scale between subsequent time steps, in hours
CellDen.length_scale: linear size scale of the image, in mm
'''
data = celldensity.cell_density(data_file, CellA, CellB, BinDiv, ImgWidth)
return celldensity.CellDen(data, time_scale, length_scale)