"""
Parallel coordinates graph as sensitivity tool
==============================================
"""
# %%
# The parallel coordinates graph enables to visualize all the combinations of the input
# variables which lead to a specific range of the output variable.
#
# Let us consider a model :math:`f: \mathbb{R}^n \longrightarrow \mathbb{R}`, where :math:`f(\underline{X}) = Y`.
#
# The graph requires to have an input sample :math:`X_s` and an output sample :math:`Y_s`.
#
# The first figure draws such a graph: each column represents one component
# :math:`X_i` of the input vector :math:`\underline{X}`.
# The last column represents the scalar output variable :math:`Y`.
# For each point :math:`\underline{X}^j`, each component :math:`X_i^j`
# is noted on its respective axe and the last mark is the one which corresponds to
# the associated :math:`Y^j`. A line joins all the marks. Thus, each point of the sample
# corresponds to a particular line on the graph.
#
# The scale of the axes are quantile based: each axe runs between 0 and 1 and each
# value is represented by its quantile with respect to its marginal empirical distribution.
#
# It is interesting to select, among those lines, the ones which correspond to a specific range of the output variable.
# These particular lines are colored differently.
# This specific range is defined in the quantile based scale of :math:`Y` or in its specific scale.
# In that second case, the range is automatically converted into a quantile based scale range.
# %%
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
# %%
# Create data to visualize
# Create the model Y = x1^2 + x2
model = ot.SymbolicFunction(["x1", "x2"], ["x1^2+x2"])
# Create the input distribution and random vector X
myCorMat = ot.CorrelationMatrix(2)
myCorMat[0, 1] = -0.6
inputDist = ot.Normal([0.0, 0.0], myCorMat)
inputDist.setDescription(["X1", "X2"])
inputVector = ot.RandomVector(inputDist)
# Create the output random vector Y=model(X)
output = ot.CompositeRandomVector(model, inputVector)
# Generate the input sample
N = 500
X = inputVector.getSample(N)
# Evaluate the associated output sample
Y = model(X)
Y.setDescription("Y")
print(Y.getMin(), Y.getMax(), Y.computeQuantilePerComponent(0.9))
# %%
# Example 1: value based scale to describe the Y range
minValue = 3.35
maxValue = 20.0
quantileScale = False
graphCobweb = ot.VisualTest.DrawParallelCoordinates(
X, Y, minValue, maxValue, "red", quantileScale
)
graphCobweb.setLegendPosition("lower right")
view = viewer.View(graphCobweb)
# %%
# Example 2: rank based scale to describe the Y range
minValue = 0.9
maxValue = 1.0
quantileScale = True
graphCobweb = ot.VisualTest.DrawParallelCoordinates(
X, Y, minValue, maxValue, "red", quantileScale
)
graphCobweb.setLegendPosition("lower right")
view = viewer.View(graphCobweb)
plt.show()