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) # resolution #grid = physics.speedGridCreator(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.distributionSorter(speedVector, grid))] avgDistribution = [i / len(patient_data.X) for i in totalDistribution] return avgDistribution
def mainFunction(patient_data, step, grid): totalDistribution = [0] * len(grid) # resolution #grid = physics.speedGridCreator(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.distributionSorter(speedVector, 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