"""
Kolmogorov-Smirnov : get the statistics distribution
====================================================
"""
# %%

# %%
# In this example, we draw the Kolmogorov-Smirnov distribution for a sample size 10.
# We want to test the hypothesis that this sample has the `Uniform(0, 1)`
# distribution.
# The K.S. distribution is first plotted in the case where the
# parameters of the uniform distribution are known.
# Then we plot the distribution when the parameters of the uniform
# distribution are estimated from the sample.
#
# *Reference* : Hovhannes Keutelian, "The Kolmogorov-Smirnov test when
# parameters are estimated from data", 30 April 1991, Fermilab
#
# Note: There is a sign error in the paper; the equation:
# `D[i]=max(abs(S+step),D[i])` must be replaced with `D[i]=max(abs(S-step),D[i])`.

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

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

# %%
x = [0.9374, 0.7629, 0.4771, 0.5111, 0.8701, 0.0684, 0.7375, 0.5615, 0.2835, 0.2508]
sample = ot.Sample([[xi] for xi in x])

# %%
samplesize = sample.getSize()
samplesize

# %%
# Plot the empirical distribution function.

# %%
graph = ot.UserDefined(sample).drawCDF()
graph.setLegends(["Sample"])
curve = ot.Curve([0, 1], [0, 1])
curve.setLegend("Uniform")
graph.add(curve)
graph.setXTitle("X")
graph.setTitle("Cumulated distribution function")
view = viewer.View(graph)


# %%
# The computeKSStatisticsIndex function computes the Kolmogorov-Smirnov
# distance between the sample and the distribution.
# The following function is for teaching purposes only: use `FittingTest`
# for real applications.


# %%
def computeKSStatistics(sample, distribution):
    sample = sample.sort()
    n = sample.getSize()
    D = 0.0
    D_previous = 0.0
    for i in range(n):
        F = distribution.computeCDF(sample[i])
        Fminus = F - float(i) / n
        Fplus = float(i + 1) / n - F
        D = max(Fminus, Fplus, D)
        if D > D_previous:
            D_previous = D
    return D


# %%
dist = ot.Uniform(0, 1)
dist

# %%
computeKSStatistics(sample, dist)


# %%
# The following function generates a sample of K.S. distances when the tested distribution is the `Uniform(0,1)` distribution.


# %%
def generateKSSampleKnownParameters(nrepeat, samplesize):
    """
    nrepeat : Number of repetitions, size of the table
    samplesize : the size of each sample to generate from the Uniform distribution
    """
    dist = ot.Uniform(0, 1)
    D = ot.Sample(nrepeat, 1)
    for i in range(nrepeat):
        sample = dist.getSample(samplesize)
        D[i, 0] = computeKSStatistics(sample, dist)
    return D


# %%
# Generate a sample of KS distances.

# %%
nrepeat = 10000  # Size of the KS distances sample
sampleD = generateKSSampleKnownParameters(nrepeat, samplesize)


# %%
# Compute exact Kolmogorov CDF.


# %%
def pKolmogorovPy(x):
    y = ot.DistFunc.pKolmogorov(samplesize, x[0])
    return [y]


# %%
pKolmogorov = ot.PythonFunction(1, 1, pKolmogorovPy)


# %%
def dKolmogorov(x, samplesize):
    """
    Compute Kolmogorov PDF for given x.
    x : an array, the points where the PDF must be evaluated
    samplesize : the size of the sample
    Reference
    Numerical Derivatives in Scilab, Michael Baudin, May 2009
    """
    n = x.getSize()
    y = ot.Sample(n, 1)
    for i in range(n):
        y[i, 0] = pKolmogorov.gradient(x[i])[0, 0]
    return y


# %%
def linearSample(xmin, xmax, npoints):
    """Returns a sample created from a regular grid
    from xmin to xmax with npoints points."""
    step = (xmax - xmin) / (npoints - 1)
    rg = ot.RegularGrid(xmin, step, npoints)
    vertices = rg.getVertices()
    return vertices


# %%
n = 1000  # Number of points in the plot
s = linearSample(0.001, 0.999, n)
y = dKolmogorov(s, samplesize)

# %%
curve = ot.Curve(s, y)
curve.setLegend("Exact distribution")
graph = ot.HistogramFactory().build(sampleD).drawPDF()
graph.setLegends(["Empirical distribution"])
graph.add(curve)
graph.setTitle("Kolmogorov-Smirnov distribution (known parameters)")
graph.setXTitle("KS-Statistics")
view = viewer.View(graph)


# %%
# Known parameters versus estimated parameters
# --------------------------------------------

# %%
# The following function generates a sample of K.S. distances when the tested
# distribution is the `Uniform(a,b)` distribution, where the `a` and `b`
# parameters are estimated from the sample.


# %%
def generateKSSampleEstimatedParameters(nrepeat, samplesize):
    """
    nrepeat : Number of repetitions, size of the table
    samplesize : the size of each sample to generate from the Uniform distribution
    """
    distfactory = ot.UniformFactory()
    refdist = ot.Uniform(0, 1)
    D = ot.Sample(nrepeat, 1)
    for i in range(nrepeat):
        sample = refdist.getSample(samplesize)
        trialdist = distfactory.build(sample)
        D[i, 0] = computeKSStatistics(sample, trialdist)
    return D


# %%
# Generate a sample of KS distances.

# %%
sampleDP = generateKSSampleEstimatedParameters(nrepeat, samplesize)

# %%
graph = ot.KernelSmoothing().build(sampleD).drawPDF()
graph.setLegends(["Known parameters"])
graphP = ot.KernelSmoothing().build(sampleDP).drawPDF()
graphP.setLegends(["Estimated parameters"])
graphP.setColors(["blue"])
graph.add(graphP)
graph.setTitle("Kolmogorov-Smirnov distribution")
graph.setXTitle("KS-Statistics")
view = viewer.View(graph)
plt.show()

# %%
# We see that the distribution of the KS distances when the parameters are
# estimated is shifted towards the left: smaller distances occur more often.
# This is a consequence of the fact that the estimated parameters tend to make
# the estimated distribution closer to the empirical sample.
