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
