def _updateWei(self):
###########
## Stats ##
###########
fr = self.statsFrame
# lambda
lamb = self.currentParameters["lambda"]
lambdaStr = helper_functions.roundToSF(lamb, NUMBER_OF_SF)
fr.weiLambdaE.insertNew(lamb)
# k
k = self.currentParameters["k"]
kStr = helper_functions.roundToSF(k, NUMBER_OF_SF)
fr.weiKE.insertNew(k)
# theta
theta = self.currentParameters["theta"]
thetaStr = helper_functions.roundToSF(theta, NUMBER_OF_SF)
fr.weiThetaE.insertNew(theta)
# Volume
volume = calculateWeibullVolume(lamb, k, theta)
volumeStr = helper_functions.roundToSF(volume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(volumeStr)
# Error
thicknessFunction = lambda x: theta * (
(x / lamb)**(k - 2)) * math.exp(-((x / lamb)**k))
error = regression_methods.meanRelativeSquaredError(
self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
invLambdaStr = helper_functions.roundToSF(1 / lamb, NUMBER_OF_SF)
kminus2Str = helper_functions.roundToSF(k - 2, NUMBER_OF_SF)
equationStr = "T = " + thetaStr + "((" + invLambdaStr + "x)^" + kminus2Str + ")exp(-(" + invLambdaStr + "x)^" + kStr + ")"
fr.equation_E.insertNew(equationStr)
############
## Graphs ##
############
# Model
startX = 0
endX = (self.isopachs[-1].distanceFromVentKM() + 50) * SQRT_PI
xs = helper_functions.getStaggeredPoints(startX, endX,
MODEL_PLOTTING_PRECISION)[1:]
ys = [
theta * ((x / lamb)**(k - 2)) * math.exp(-((x / lamb)**k))
for x in xs
]
self.modelGraphFrame.plotFilledLine(xs, ys, colours[0])
def _updateWei(self):
###########
## Stats ##
###########
fr = self.statsFrame
# lambda
lamb = self.currentParameters["lambda"]
lambdaStr = helper_functions.roundToSF(lamb, NUMBER_OF_SF)
fr.weiLambdaE.insertNew(lamb)
# k
k = self.currentParameters["k"]
kStr = helper_functions.roundToSF(k, NUMBER_OF_SF)
fr.weiKE.insertNew(k)
# theta
theta = self.currentParameters["theta"]
thetaStr = helper_functions.roundToSF(theta, NUMBER_OF_SF)
fr.weiThetaE.insertNew(theta)
# Volume
volume = calculateWeibullVolume(lamb, k, theta)
volumeStr = helper_functions.roundToSF(volume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(volumeStr)
# Error
thicknessFunction = lambda x : theta*((x/lamb)**(k-2))*math.exp(-((x/lamb)**k))
error = regression_methods.meanRelativeSquaredError(self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
invLambdaStr = helper_functions.roundToSF(1/lamb, NUMBER_OF_SF)
kminus2Str = helper_functions.roundToSF(k-2, NUMBER_OF_SF)
equationStr = "T = " + thetaStr + "((" + invLambdaStr + "x)^" +kminus2Str + ")exp(-(" + invLambdaStr + "x)^" + kStr + ")"
fr.equation_E.insertNew(equationStr)
############
## Graphs ##
############
# Model
startX = 0
endX = (self.isopachs[-1].distanceFromVentKM()+50)*SQRT_PI
xs = helper_functions.getStaggeredPoints(startX,endX,MODEL_PLOTTING_PRECISION)[1:]
ys = [theta*((x/lamb)**(k-2))*math.exp(-((x/lamb)**k)) for x in xs]
self.modelGraphFrame.plotFilledLine(xs, ys, colours[0])
def _updatePow(self):
###########
## Stats ##
###########
fr = self.statsFrame
# Coefficient
c = self.currentParameters["coefficient"]
coefficientStr = helper_functions.roundToSF(c, NUMBER_OF_SF)
fr.powCoefficient_E.insertNew(c)
# Exponent
m = self.currentParameters["exponent"]
exponentStr = helper_functions.roundToSF(m, NUMBER_OF_SF)
fr.powExponent_E.insertNew(m)
# Proximal limit
proximalLimitKM = self.currentParameters["proximalLimitKM"]
proximalLimitStr = helper_functions.roundToSF(proximalLimitKM, NUMBER_OF_SF)
fr.powProximalLimit_E.insertNew(proximalLimitKM)
# Distal limit
distalLimitKM = self.currentParameters["distalLimitKM"]
distalLimitStr = helper_functions.roundToSF(distalLimitKM, NUMBER_OF_SF)
fr.powDistalLimit_E.insertNew(distalLimitKM)
# Volume
volume = calculatePowerLawVolume(c, m, proximalLimitKM, distalLimitKM)
volumeStr = helper_functions.roundToSF(volume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(volumeStr)
# Error
thicknessFunction = lambda x : c*(x**(-m))
error = regression_methods.meanRelativeSquaredError(self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
equationStr = "T = " + coefficientStr
if m > 0:
equationStr += "x^-" + exponentStr
elif m < 0:
equationStr += "x^" + exponentStr[1:]
fr.equation_E.insertNew(equationStr)
# Suggested proximal limit
suggestedProximalLimit = self.currentParameters["suggestedProximalLimit"]
suggestedProximalLimitStr = helper_functions.roundToSF(suggestedProximalLimit, NUMBER_OF_SF)
fr.powSuggestedProximalLimit_E.insertNew(suggestedProximalLimitStr)
############
## Graphs ##
############
startX = proximalLimitKM*SQRT_PI
endX = distalLimitKM*SQRT_PI
# Model
xs = helper_functions.getStaggeredPoints(startX, endX, MODEL_PLOTTING_PRECISION)
ys = [thicknessFunction(x) for x in xs]
self.modelGraphFrame.plotFilledLine(xs, ys, color=colours[0])
# Regression
logXs = [np.log(a) for a in self.sqrtAreaKM]
logYs = [np.log(t) for t in self.thicknessM]
self.regressionGraphFrame.plotScatter(logXs, logYs, False)
self.regressionGraphFrame.axes.set_xlabel(r"$\log{\sqrt{Area}}$")
lineXs = [np.sqrt(startX), np.sqrt(endX)]
lineYs = [np.log(c) - m*x for x in lineXs]
self.regressionGraphFrame.plotLine(lineXs, lineYs, colours[0])
def _updateExp(self, comboboxUpdate):
n = self.currentParameters["numberOfSegments"]
coefficients = self.currentParameters["segmentCoefficients"]
exponents = self.currentParameters["segmentExponents"]
limits = self.currentParameters["segmentLimits"]
###########
## Stats ##
###########
fr = self.statsFrame
# Segment start
start = limits[self.currentSegment]
startStr = helper_functions.roundToSF(start, NUMBER_OF_SF)
fr.expSegStartLimit_E.insertNew(start)
fr.expSegStartLimit_E.setUserEditable(self.currentSegment != 0)
# Segment end
end = limits[self.currentSegment+1]
endStr = helper_functions.roundToSF(end, NUMBER_OF_SF)
fr.expSegEndLimit_E.insertNew(end)
fr.expSegEndLimit_E.setUserEditable(self.currentSegment != n-1)
# Segment coefficient
coefficient = coefficients [self.currentSegment]
coefficientStr = helper_functions.roundToSF(coefficient, NUMBER_OF_SF)
fr.expSegCoefficent_E.insertNew(coefficient)
# Segment exponent
exponent = exponents[self.currentSegment]
exponentStr = helper_functions.roundToSF(exponent, NUMBER_OF_SF)
fr.expSegExponent_E.insertNew(exponent)
# Segment volume
segmentVolumes = [calculateExponentialSegmentVolume(coefficients[i], exponents[i], limits[i], limits[i+1]) for i in range(n)]
segmentVolumeStr = helper_functions.roundToSF(segmentVolumes[self.currentSegment], NUMBER_OF_SF)
fr.expSegVolume_E.insertNew(segmentVolumeStr)
# Total volume
totalVolume = sum(segmentVolumes)
estimatedTotalVolumeStr = helper_functions.roundToSF(totalVolume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(estimatedTotalVolumeStr)
# Error
def thicknessFunction(x):
for i in range(n):
if limits[i] <= x < limits[i+1]:
return coefficients[i]*math.exp(-exponents[i]*x)
error = regression_methods.meanRelativeSquaredError(self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
equationStr = "T = " + coefficientStr
if exponent > 0:
equationStr += "exp(-" + exponentStr + "x)"
elif exponent < 0:
equationStr += "exp(" + exponentStr[1:] + "x)"
fr.equation_E.insertNew(equationStr)
############
## Graphs ##
############
if not comboboxUpdate:
# Model
endXs = limits[1:-1] + [1.5*max(self.sqrtAreaKM)-0.5*min(self.sqrtAreaKM)]
for i in range(n):
xs = helper_functions.getStaggeredPoints(limits[i], endXs[i], MODEL_PLOTTING_PRECISION)
ys = [coefficients[i]*math.exp(-exponents[i]*x) for x in xs]
self.modelGraphFrame.plotFilledLine(xs, ys, color=colours[i])
# Regression
logThicknessM = [np.log(t) for t in self.thicknessM]
self.regressionGraphFrame.plotScatter(self.sqrtAreaKM, logThicknessM, False)
self.regressionGraphFrame.axes.set_xlabel(r"$\sqrt{Area}$")
for i in range(n):
xs = [limits[i], endXs[i]]
ys = [np.log(thicknessFunction(x)) for x in xs]
self.regressionGraphFrame.plotLine(xs,ys, color=colours[i])
def _updatePow(self):
###########
## Stats ##
###########
fr = self.statsFrame
# Coefficient
c = self.currentParameters["coefficient"]
coefficientStr = helper_functions.roundToSF(c, NUMBER_OF_SF)
fr.powCoefficient_E.insertNew(c)
# Exponent
m = self.currentParameters["exponent"]
exponentStr = helper_functions.roundToSF(m, NUMBER_OF_SF)
fr.powExponent_E.insertNew(m)
# Proximal limit
proximalLimitKM = self.currentParameters["proximalLimitKM"]
proximalLimitStr = helper_functions.roundToSF(proximalLimitKM, NUMBER_OF_SF)
fr.powProximalLimit_E.insertNew(proximalLimitKM)
# Distal limit
distalLimitKM = self.currentParameters["distalLimitKM"]
distalLimitStr = helper_functions.roundToSF(distalLimitKM, NUMBER_OF_SF)
fr.powDistalLimit_E.insertNew(distalLimitKM)
# Volume
volume = calculatePowerLawVolume(c, m, proximalLimitKM, distalLimitKM)
volumeStr = helper_functions.roundToSF(volume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(volumeStr)
# Error
thicknessFunction = lambda x : c*(x**(-m))
error = regression_methods.meanRelativeSquaredError(self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
equationStr = "T = " + coefficientStr
if m > 0:
equationStr += "x^-" + exponentStr
elif m < 0:
equationStr += "x^" + exponentStr[1:]
fr.equation_E.insertNew(equationStr)
# Suggested proximal limit
suggestedProximalLimit = self.currentParameters["suggestedProximalLimit"]
suggestedProximalLimitStr = helper_functions.roundToSF(suggestedProximalLimit, NUMBER_OF_SF)
fr.powSuggestedProximalLimit_E.insertNew(suggestedProximalLimitStr)
############
## Graphs ##
############
startX = proximalLimitKM*SQRT_PI
endX = distalLimitKM*SQRT_PI
# Model
xs = helper_functions.getStaggeredPoints(startX, endX, MODEL_PLOTTING_PRECISION)
ys = [thicknessFunction(x) for x in xs]
self.modelGraphFrame.plotFilledLine(xs, ys, color=colours[0])
# Regression
logXs = [np.log(a) for a in self.sqrtAreaKM]
logYs = [np.log(t) for t in self.thicknessM]
self.regressionGraphFrame.plotScatter(logXs, logYs, False)
self.regressionGraphFrame.axes.set_xlabel(r"$\log{\sqrt{Area}}$")
lineXs = [np.sqrt(startX), np.sqrt(endX)]
lineYs = [np.log(c) - m*x for x in lineXs]
self.regressionGraphFrame.plotLine(lineXs, lineYs, colours[0])
def _updateExp(self, comboboxUpdate):
n = self.currentParameters["numberOfSegments"]
coefficients = self.currentParameters["segmentCoefficients"]
exponents = self.currentParameters["segmentExponents"]
limits = self.currentParameters["segmentLimits"]
###########
## Stats ##
###########
fr = self.statsFrame
# Segment start
start = limits[self.currentSegment]
startStr = helper_functions.roundToSF(start, NUMBER_OF_SF)
fr.expSegStartLimit_E.insertNew(start)
fr.expSegStartLimit_E.setUserEditable(self.currentSegment != 0)
# Segment end
end = limits[self.currentSegment+1]
endStr = helper_functions.roundToSF(end, NUMBER_OF_SF)
fr.expSegEndLimit_E.insertNew(end)
fr.expSegEndLimit_E.setUserEditable(self.currentSegment != n-1)
# Segment coefficient
coefficient = coefficients [self.currentSegment]
coefficientStr = helper_functions.roundToSF(coefficient, NUMBER_OF_SF)
fr.expSegCoefficent_E.insertNew(coefficient)
# Segment exponent
exponent = exponents[self.currentSegment]
exponentStr = helper_functions.roundToSF(exponent, NUMBER_OF_SF)
fr.expSegExponent_E.insertNew(exponent)
# Segment volume
segmentVolumes = [calculateExponentialSegmentVolume(coefficients[i], exponents[i], limits[i], limits[i+1]) for i in range(n)]
segmentVolumeStr = helper_functions.roundToSF(segmentVolumes[self.currentSegment], NUMBER_OF_SF)
fr.expSegVolume_E.insertNew(segmentVolumeStr)
# Total volume
totalVolume = sum(segmentVolumes)
estimatedTotalVolumeStr = helper_functions.roundToSF(totalVolume, NUMBER_OF_SF)
fr.totalEstimatedVolume_E.insertNew(estimatedTotalVolumeStr)
# Error
def thicknessFunction(x):
for i in range(n):
if limits[i] <= x < limits[i+1]:
return coefficients[i]*math.exp(-exponents[i]*x)
error = regression_methods.meanRelativeSquaredError(self.sqrtAreaKM, self.thicknessM, thicknessFunction)
errorStr = helper_functions.roundToSF(error, NUMBER_OF_SF)
fr.relativeSquaredError_E.insertNew(errorStr)
# Equation
equationStr = "T = " + coefficientStr
if exponent > 0:
equationStr += "exp(-" + exponentStr + "x)"
elif exponent < 0:
equationStr += "exp(" + exponentStr[1:] + "x)"
fr.equation_E.insertNew(equationStr)
############
## Graphs ##
############
if not comboboxUpdate:
# Model
endXs = limits[1:-1] + [1.5*max(self.sqrtAreaKM)-0.5*min(self.sqrtAreaKM)]
for i in range(n):
xs = helper_functions.getStaggeredPoints(limits[i], endXs[i], MODEL_PLOTTING_PRECISION)
ys = [coefficients[i]*math.exp(-exponents[i]*x) for x in xs]
self.modelGraphFrame.plotFilledLine(xs, ys, color=colours[i])
# Regression
logThicknessM = [np.log(t) for t in self.thicknessM]
self.regressionGraphFrame.plotScatter(self.sqrtAreaKM, logThicknessM, False)
self.regressionGraphFrame.axes.set_xlabel(r"$\sqrt{Area}$")
for i in range(n):
xs = [limits[i], endXs[i]]
ys = [np.log(thicknessFunction(x)) for x in xs]
self.regressionGraphFrame.plotLine(xs,ys, color=colours[i])