def mainFunction(patient_data, step, grid, metric_scaling): totalDistribution = [0] * len(grid) #resolution #referenceSpace = physics.referenceSpaceCreator(resolution) for i in range(len(patient_data.X)): speedVector = baseMetrics.slidingSpeed(patient_data.X[i][0].tolist(), patient_data.Y[i][0].tolist(), patient_data.T[i][0].tolist(), step) totalDistribution = [ sum(x) for x in zip( totalDistribution, baseStatistics.distanceSorter(speedVector, patient_data.D[i] [0].tolist(), grid)) ] if metric_scaling: avgDistribution = [ i / (len(patient_data.X) * screen_to_cm_ratio) for i in totalDistribution ] else: avgDistribution = [i / len(patient_data.X) for i in totalDistribution] return avgDistribution
def mainFunction(patient_data, step, grid): totalDistribution = [0] * len(grid) for i in range(len(patient_data.X)): #GOAL IS A FAKE GOAL AT THE MOMENT!!!! -- have to do the segmentation before it would make sense!!! speedVector = bmet.slidingGradient(patient_data.X[i][0].tolist(), patient_data.Y[i][0].tolist(), 0,0 , step) totalDistribution = [sum(x) for x in zip(totalDistribution, bstat.distanceSorter(speedVector, patient_data.D[i][0].tolist(),grid))] avgDistribution = [i / len(patient_data.X) for i in totalDistribution] return avgDistribution
def mainFunction(patient_data, step, grid): totalDistribution = [0] * len(grid) for i in range(len(patient_data.X)): #GOAL IS A FAKE GOAL AT THE MOMENT!!!! -- have to do the segmentation before it would make sense!!! speedVector = bmet.slidingGradient(patient_data.X[i][0].tolist(), patient_data.Y[i][0].tolist(), 0, 0, step) totalDistribution = [ sum(x) for x in zip( totalDistribution, bstat.distanceSorter(speedVector, patient_data.D[i] [0].tolist(), grid)) ] avgDistribution = [i / len(patient_data.X) for i in totalDistribution] return avgDistribution
def mainFunction(patient_data, step, grid, metric_scaling): totalDistribution = [0] * len(grid) # resolution # referenceSpace = physics.referenceSpaceCreator(resolution) for i in range(len(patient_data.X)): speedVector = baseMetrics.slidingSpeed( patient_data.X[i][0].tolist(), patient_data.Y[i][0].tolist(), patient_data.T[i][0].tolist(), step ) totalDistribution = [ sum(x) for x in zip( totalDistribution, baseStatistics.distanceSorter(speedVector, patient_data.D[i][0].tolist(), grid) ) ] if metric_scaling: avgDistribution = [i / (len(patient_data.X) * screen_to_cm_ratio) for i in totalDistribution] else: avgDistribution = [i / len(patient_data.X) for i in totalDistribution] return avgDistribution