"""
==============================
Filling the area between lines
==============================

This example shows how to use `~.axes.Axes.fill_between` to color the area
between two lines.
"""

import matplotlib.pyplot as plt
import numpy as np

###############################################################################
#
# Basic usage
# -----------
# The parameters *y1* and *y2* can be a scalar, indicating a horizontal
# boundary a  the given y-values. If only *y1* is given, *y2* defaults to 0.

x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2 * np.pi * x)
y2 = 0.8 * np.sin(4 * np.pi * x)

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(6, 6))

ax1.fill_between(x, y1)
ax1.set_title('fill between y1 and 0')

ax2.fill_between(x, y1, 1)
ax2.set_title('fill between y1 and 1')

ax3.fill_between(x, y1, y2)
ax3.set_title('fill between y1 and y2')
ax3.set_xlabel('x')
fig.tight_layout()

###############################################################################
#
# Example: Confidence bands
# -------------------------
# A common application for `~.axes.Axes.fill_between` is the indication of
# confidence bands.
#
# `~.axes.Axes.fill_between` uses the colors of the color cycle as the fill
# color. These may be a bit strong when applied to fill areas. It is
# therefore often a good practice to lighten the color by making the area
# semi-transparent using *alpha*.

# sphinx_gallery_thumbnail_number = 2

N = 21
x = np.linspace(0, 10, 11)
y = [3.9, 4.4, 10.8, 10.3, 11.2, 13.1, 14.1,  9.9, 13.9, 15.1, 12.5]

# fit a linear curve an estimate its y-values and their error.
a, b = np.polyfit(x, y, deg=1)
y_est = a * x + b
y_err = x.std() * np.sqrt(1/len(x) +
                          (x - x.mean())**2 / np.sum((x - x.mean())**2))

fig, ax = plt.subplots()
ax.plot(x, y_est, '-')
ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2)
ax.plot(x, y, 'o', color='tab:brown')

###############################################################################
#
# Selectively filling horizontal regions
# --------------------------------------
# The parameter *where* allows to specify the x-ranges to fill. It's a boolean
# array with the same size as *x*.
#
# Only x-ranges of contiguous *True* sequences are filled. As a result the
# range between neighboring *True* and *False* values is never filled. This
# often undesired when the data points should represent a contiguous quantity.
# It is therefore recommended to set ``interpolate=True`` unless the
# x-distance of the data points is fine enough so that the above effect is not
# noticeable. Interpolation approximates the actual x position at which the
# *where* condition will change and extends the filling up to there.

x = np.array([0, 1, 2, 3])
y1 = np.array([0.8, 0.8, 0.2, 0.2])
y2 = np.array([0, 0, 1, 1])

fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)

ax1.set_title('interpolation=False')
ax1.plot(x, y1, 'o--')
ax1.plot(x, y2, 'o--')
ax1.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3)
ax1.fill_between(x, y1, y2, where=(y1 < y2), color='C1', alpha=0.3)

ax2.set_title('interpolation=True')
ax2.plot(x, y1, 'o--')
ax2.plot(x, y2, 'o--')
ax2.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3,
                 interpolate=True)
ax2.fill_between(x, y1, y2, where=(y1 <= y2), color='C1', alpha=0.3,
                 interpolate=True)
fig.tight_layout()

###############################################################################
#
# .. note::
#
#    Similar gaps will occur if *y1* or *y2* are masked arrays. Since missing
#    values cannot be approximated, *interpolate* has no effect in this case.
#    The gaps around masked values can only be reduced by adding more data
#    points close to the masked values.

###############################################################################
#
# Selectively marking horizontal regions across the whole Axes
# ------------------------------------------------------------
# The same selection mechanism can be applied to fill the full vertical height
# of the axes. To be independent of y-limits, we add a transform that
# interprets the x-values in data coorindates and the y-values in axes
# coordinates.
#
# The following example marks the regions in which the y-data are above a
# given threshold.

fig, ax = plt.subplots()
x = np.arange(0, 4 * np.pi, 0.01)
y = np.sin(x)
ax.plot(x, y, color='black')

threshold = 0.75
ax.axhline(threshold, color='green', lw=2, alpha=0.7)
ax.fill_between(x, 0, 1, where=y > threshold,
                color='green', alpha=0.5, transform=ax.get_xaxis_transform())

#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions, methods and classes is shown
# in this example:

import matplotlib
matplotlib.axes.Axes.fill_between
matplotlib.pyplot.fill_between
matplotlib.axes.Axes.get_xaxis_transform
