def generate_data():
defectDist = ot.Uniform(0.11, 0.59)
epsilon = ot.Normal(0, sigmaEps)
def computeSignal(a):
a = np.array(a)
size = a.size
y = beta0 + beta1 * a + np.array(epsilon.getSample(size))
return y
defectSupport = defectDist.getSample(N)
signal = computeSignal(defectSupport)
invBoxCox = ot.InverseBoxCoxTransform(lamb)
signalInvBoxCox = invBoxCox(signal)
return defectSupport, signalInvBoxCox
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
# Create a Box Cox transformation
lambdas = [0.0, 0.1, 1.0, 1.5]
graph = ot.Graph("Inverse Box Cox transformations", 'x', 'y', True)
for i in range(len(lambdas)):
iBoxCoxT = ot.InverseBoxCoxTransform(lambdas[i])
graph.add(iBoxCoxT.draw(0.1, 2.1))
graph.setColors(['red', 'blue', 'black', 'green'])
graph.setLegends(['lambda = ' + str(lam) for lam in lambdas])
graph.setLegendPosition("topleft")
fig = plt.figure(figsize=(8, 4))
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
View(graph, figure=fig, axes=[axis], add_legend=True)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
# Create a Box Cox transformation
myLambda = ot.NumericalPoint([0.0, 0.1, 1.0, 1.5])
graph = ot.Graph()
for i in range(myLambda.getDimension()):
myBoxCox = ot.InverseBoxCoxTransform(myLambda[i])
graph.add(myBoxCox.draw(0.1, 2.1))
graph.setColors(['red', 'blue', 'black', 'green'])
graph.setLegends(['lambda = ' + str(lam) for lam in myLambda])
graph.setLegendPosition("topleft")
fig = plt.figure(figsize=(8, 4))
plt.suptitle("Inverse Box Cox transformations")
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
View(graph, figure=fig, axes=[axis], add_legend=True)