"""
Truncate a  distribution
========================
"""
# %%
# In this example we are going to define truncated distributions.
#
# It is possible to truncate a distribution in its lower area, or its upper area or in both lower and upper areas.
#
# In 1-d, assuming a and b bounds, its probability density function is defined as:
#
# .. math::
#    \forall y \in \mathbb{R}, p_Y(y) =
#          \begin{array}{|ll}
#            0 &  \mbox{for } y \geq b  \mbox{ or }  y \leq a\\
#            \displaystyle \frac{1}{F_X(b) - F_X(a)}\, p_X(y) & \mbox{for } y\in[a,b]
#          \end{array}
#
# Is is also possible to truncate a multivariate distribution.

# %%
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

ot.Log.Show(ot.Log.NONE)

# the original distribution
distribution = ot.Gumbel(0.45, 0.6)
graph = distribution.drawPDF()
view = viewer.View(graph)

# %%
# truncate on the left
truncated = ot.TruncatedDistribution(distribution, 0.2, ot.TruncatedDistribution.LOWER)
graph = truncated.drawPDF()
view = viewer.View(graph)

# %%
# truncate on the right
truncated = ot.TruncatedDistribution(distribution, 1.5, ot.TruncatedDistribution.UPPER)
graph = truncated.drawPDF()
view = viewer.View(graph)

# %%
# truncated on both bounds
truncated = ot.TruncatedDistribution(distribution, 0.2, 1.5)
graph = truncated.drawPDF()
view = viewer.View(graph)

# %%
# Define a multivariate distribution
dimension = 2
size = 70
sample = ot.Normal(dimension).getSample(size)
ks = ot.KernelSmoothing().build(sample)

# %%
# Truncate it between (-2;2)^n
bounds = ot.Interval([-2.0] * dimension, [2.0] * dimension)
truncatedKS = ot.Distribution(ot.TruncatedDistribution(ks, bounds))

# %%
# Draw its PDF
graph = truncatedKS.drawPDF([-2.5] * dimension, [2.5] * dimension, [256] * dimension)
graph.add(ot.Cloud(truncatedKS.getSample(200)))
graph.setColors(["blue", "red"])
view = viewer.View(graph)
plt.show()
