<jupyter_start><jupyter_text>Cell tracking and identificationWe analyze the images starting from the raw images. They are arganized in the following order: - experiment - well no. - channel no. We call them from the trackingparr function.<jupyter_code>from nd2reader import ND2Reader<jupyter_output><empty_output><jupyter_text>Spheroid segmentationInitial step is to retrieve spheroid coords so that we can compare them to the cell displacements and classify them. The first set of functions are workhouse functions to identify the well center and then crop away the left-over data points.This function saves all the files at the destination indicated in the SAVEPATH.<jupyter_code>import DetermineCellState

DATAPATH = r'\\atlas.pasteur.fr\Multicell\Shreyansh\20191017\exp_matrigel_conc_Tcell_B16\50pc\TIFF\DataFrames'
PATH = r'\\atlas.pasteur.fr\Multicell\Shreyansh\20191017\exp_matrigel_conc_Tcell_B16\50pc\TIFF'
SAVEPATH = r'C:\Users\gronteix\Documents\Research\SpheroidPositionAnalysis\20191017\B1650pcMatrigel' 

wellDiameter = 440
marginDistance = 160
aspectRatio = 3
CHANNEL = '2'

DetermineCellState._loopThroughExperiments(PATH, DATAPATH, SAVEPATH, CHANNEL, wellDiameter, marginDistance, aspectRatio)<jupyter_output><empty_output>
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