"""
Colormap Normalization
======================

Objects that use colormaps by default linearly map the colors in the
colormap from data values *vmin* to *vmax*.  For example::

    pcm = ax.pcolormesh(x, y, Z, vmin=-1., vmax=1., cmap='RdBu_r')

will map the data in *Z* linearly from -1 to +1, so *Z=0* will
give a color at the center of the colormap *RdBu_r* (white in this
case).

Matplotlib does this mapping in two steps, with a normalization from
the input data to [0, 1] occurring first, and then mapping onto the
indices in the colormap.  Normalizations are classes defined in the
:func:`matplotlib.colors` module.  The default, linear normalization
is :func:`matplotlib.colors.Normalize`.

Artists that map data to color pass the arguments *vmin* and *vmax* to
construct a :func:`matplotlib.colors.Normalize` instance, then call it:

.. ipython::

   In [1]: import matplotlib as mpl

   In [2]: norm = mpl.colors.Normalize(vmin=-1.,vmax=1.)

   In [3]: norm(0.)
   Out[3]: 0.5

However, there are sometimes cases where it is useful to map data to
colormaps in a non-linear fashion.

Logarithmic
-----------

One of the most common transformations is to plot data by taking its logarithm
(to the base-10).  This transformation is useful to display changes across
disparate scales.  Using `.colors.LogNorm` normalizes the data via
:math:`log_{10}`.  In the example below, there are two bumps, one much smaller
than the other. Using `.colors.LogNorm`, the shape and location of each bump
can clearly be seen:

"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cbook as cbook

N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]

# A low hump with a spike coming out of the top right.  Needs to have
# z/colour axis on a log scale so we see both hump and spike.  linear
# scale only shows the spike.
Z1 = np.exp(-(X)**2 - (Y)**2)
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
Z = Z1 + 50 * Z2

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolor(X, Y, Z,
                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),
                   cmap='PuBu_r')
fig.colorbar(pcm, ax=ax[0], extend='max')

pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r')
fig.colorbar(pcm, ax=ax[1], extend='max')
plt.show()

###############################################################################
# Symmetric logarithmic
# ---------------------
#
# Similarly, it sometimes happens that there is data that is positive
# and negative, but we would still like a logarithmic scaling applied to
# both.  In this case, the negative numbers are also scaled
# logarithmically, and mapped to smaller numbers; e.g., if ``vmin=-vmax``,
# then they the negative numbers are mapped from 0 to 0.5 and the
# positive from 0.5 to 1.
#
# Since the logarithm of values close to zero tends toward infinity, a
# small range around zero needs to be mapped linearly.  The parameter
# *linthresh* allows the user to specify the size of this range
# (-*linthresh*, *linthresh*).  The size of this range in the colormap is
# set by *linscale*.  When *linscale* == 1.0 (the default), the space used
# for the positive and negative halves of the linear range will be equal
# to one decade in the logarithmic range.

N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z,
                       norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
                                              vmin=-1.0, vmax=1.0, base=10),
                       cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[0], extend='both')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
fig.colorbar(pcm, ax=ax[1], extend='both')
plt.show()

###############################################################################
# Power-law
# ---------
#
# Sometimes it is useful to remap the colors onto a power-law
# relationship (i.e. :math:`y=x^{\gamma}`, where :math:`\gamma` is the
# power).  For this we use the `.colors.PowerNorm`.  It takes as an
# argument *gamma* (*gamma* == 1.0 will just yield the default linear
# normalization):
#
# .. note::
#
#    There should probably be a good reason for plotting the data using
#    this type of transformation.  Technical viewers are used to linear
#    and logarithmic axes and data transformations.  Power laws are less
#    common, and viewers should explicitly be made aware that they have
#    been used.

N = 100
X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]
Z1 = (1 + np.sin(Y * 10.)) * X**(2.)

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=0.5),
                       cmap='PuBu_r')
fig.colorbar(pcm, ax=ax[0], extend='max')

pcm = ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r')
fig.colorbar(pcm, ax=ax[1], extend='max')
plt.show()

###############################################################################
# Discrete bounds
# ---------------
#
# Another normalization that comes with Matplotlib is `.colors.BoundaryNorm`.
# In addition to *vmin* and *vmax*, this takes as arguments boundaries between
# which data is to be mapped.  The colors are then linearly distributed between
# these "bounds".  For instance:
#
# .. ipython::
#
#   In [2]: import matplotlib.colors as colors
#
#   In [3]: bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
#
#   In [4]: norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)
#
#   In [5]: print(norm([-0.2,-0.15,-0.02, 0.3, 0.8, 0.99]))
#   [0 0 1 2 3 3]
#
# Note: Unlike the other norms, this norm returns values from 0 to *ncolors*-1.

N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

fig, ax = plt.subplots(3, 1, figsize=(8, 8))
ax = ax.flatten()
# even bounds gives a contour-like effect
bounds = np.linspace(-1, 1, 10)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[0].pcolormesh(X, Y, Z,
                       norm=norm,
                       cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')

# uneven bounds changes the colormapping:
bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')

pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
plt.show()


###############################################################################
# TwoSlopeNorm: Different mapping on either side of a center
# ----------------------------------------------------------
#
# Sometimes we want to have a different colormap on either side of a
# conceptual center point, and we want those two colormaps to have
# different linear scales.  An example is a topographic map where the land
# and ocean have a center at zero, but land typically has a greater
# elevation range than the water has depth range, and they are often
# represented by a different colormap.

filename = cbook.get_sample_data('topobathy.npz', asfileobj=False)
with np.load(filename) as dem:
    topo = dem['topo']
    longitude = dem['longitude']
    latitude = dem['latitude']

fig, ax = plt.subplots()
# make a colormap that has land and ocean clearly delineated and of the
# same length (256 + 256)
colors_undersea = plt.cm.terrain(np.linspace(0, 0.17, 256))
colors_land = plt.cm.terrain(np.linspace(0.25, 1, 256))
all_colors = np.vstack((colors_undersea, colors_land))
terrain_map = colors.LinearSegmentedColormap.from_list('terrain_map',
    all_colors)

# make the norm:  Note the center is offset so that the land has more
# dynamic range:
divnorm = colors.TwoSlopeNorm(vmin=-500., vcenter=0, vmax=4000)

pcm = ax.pcolormesh(longitude, latitude, topo, rasterized=True, norm=divnorm,
    cmap=terrain_map,)
# Simple geographic plot, set aspect ratio beecause distance between lines of
# longitude depends on latitude.
ax.set_aspect(1 / np.cos(np.deg2rad(49)))
fig.colorbar(pcm, shrink=0.6)
plt.show()


###############################################################################
# Custom normalization: Manually implement two linear ranges
# ----------------------------------------------------------
#
# The `.TwoSlopeNorm` described above makes a useful example for
# defining your own norm.

class MidpointNormalize(colors.Normalize):
    def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
        self.vcenter = vcenter
        colors.Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))


fig, ax = plt.subplots()
midnorm = MidpointNormalize(vmin=-500., vcenter=0, vmax=4000)

pcm = ax.pcolormesh(longitude, latitude, topo, rasterized=True, norm=midnorm,
    cmap=terrain_map)
ax.set_aspect(1 / np.cos(np.deg2rad(49)))
fig.colorbar(pcm, shrink=0.6, extend='both')
plt.show()
