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"""

=============

Path Tutorial

=============

Defining paths in your Matplotlib visualization.

The object underlying all of the :mod:`matplotlib.patches` objects is

the :class:`~matplotlib.path.Path`, which supports the standard set of

moveto, lineto, curveto commands to draw simple and compound outlines

consisting of line segments and splines. The ``Path`` is instantiated

with a (N, 2) array of (x, y) vertices, and a N-length array of path

codes. For example to draw the unit rectangle from (0, 0) to (1, 1), we

could use this code:

"""

import matplotlib.pyplot as plt

from matplotlib.path import Path

import matplotlib.patches as patches

verts = [

(0., 0.), # left, bottom

(0., 1.), # left, top

(1., 1.), # right, top

(1., 0.), # right, bottom

(0., 0.), # ignored

]

codes = [

Path.MOVETO,

Path.LINETO,

Path.CLOSEPOLY,

]

path = Path(verts, codes)

fig, ax = plt.subplots()

patch = patches.PathPatch(path, facecolor='orange', lw=2)

ax.add_patch(patch)

ax.set_xlim(-2, 2)

ax.set_ylim(-2, 2)

plt.show()

###############################################################################

# The following path codes are recognized

# ============= ======================== ======================================

# Code Vertices Description

# ============= ======================== ======================================

# ``STOP`` 1 (ignored) A marker for the end of the entire

# path (currently not required and

# ignored).

# ``MOVETO`` 1 Pick up the pen and move to the given

# vertex.

# ``LINETO`` 1 Draw a line from the current position

# to the given vertex.

# ``CURVE3`` 2: Draw a quadratic Bézier curve from the

# 1 control point, current position, with the given

# 1 end point control point, to the given end point.

# ``CURVE4`` 3: Draw a cubic Bézier curve from the

# 2 control points, current position, with the given

# 1 end point control points, to the given end

# point.

# ``CLOSEPOLY`` 1 (the point is ignored) Draw a line segment to the start point

# of the current polyline.

# ============= ======================== ======================================

# .. path-curves:

# Bézier example

# ==============

# Some of the path components require multiple vertices to specify them:

# for example CURVE 3 is a `bézier

# <https://en.wikipedia.org/wiki/B%C3%A9zier_curve>`_ curve with one

# control point and one end point, and CURVE4 has three vertices for the

# two control points and the end point. The example below shows a

# CURVE4 Bézier spline -- the bézier curve will be contained in the

# convex hull of the start point, the two control points, and the end

# point

verts = [

(0., 0.), # P0

(0.2, 1.), # P1

(1., 0.8), # P2

(0.8, 0.), # P3

]

codes = [

Path.MOVETO,

Path.CURVE4,

]

path = Path(verts, codes)

fig, ax = plt.subplots()

patch = patches.PathPatch(path, facecolor='none', lw=2)

ax.add_patch(patch)

xs, ys = zip(*verts)

ax.plot(xs, ys, 'x--', lw=2, color='black', ms=10)

ax.text(-0.05, -0.05, 'P0')

ax.text(0.15, 1.05, 'P1')

ax.text(1.05, 0.85, 'P2')

ax.text(0.85, -0.05, 'P3')

ax.set_xlim(-0.1, 1.1)

ax.set_ylim(-0.1, 1.1)

plt.show()

###############################################################################

# .. compound_paths:

# Compound paths

# ==============

# All of the simple patch primitives in matplotlib, Rectangle, Circle,

# Polygon, etc, are implemented with simple path. Plotting functions

# like :meth:`~matplotlib.axes.Axes.hist` and

# :meth:`~matplotlib.axes.Axes.bar`, which create a number of

# primitives, e.g., a bunch of Rectangles, can usually be implemented more

# efficiently using a compound path. The reason ``bar`` creates a list

# of rectangles and not a compound path is largely historical: the

# :class:`~matplotlib.path.Path` code is comparatively new and ``bar``

# predates it. While we could change it now, it would break old code,

# so here we will cover how to create compound paths, replacing the

# functionality in bar, in case you need to do so in your own code for

# efficiency reasons, e.g., you are creating an animated bar plot.

# We will make the histogram chart by creating a series of rectangles

# for each histogram bar: the rectangle width is the bin width and the

# rectangle height is the number of datapoints in that bin. First we'll

# create some random normally distributed data and compute the

# histogram. Because numpy returns the bin edges and not centers, the

# length of ``bins`` is 1 greater than the length of ``n`` in the

# example below::

# # histogram our data with numpy

# data = np.random.randn(1000)

# n, bins = np.histogram(data, 100)

# We'll now extract the corners of the rectangles. Each of the

# ``left``, ``bottom``, etc, arrays below is ``len(n)``, where ``n`` is

# the array of counts for each histogram bar::

# # get the corners of the rectangles for the histogram

# left = np.array(bins[:-1])

# right = np.array(bins[1:])

# bottom = np.zeros(len(left))

# top = bottom + n

# Now we have to construct our compound path, which will consist of a

# series of ``MOVETO``, ``LINETO`` and ``CLOSEPOLY`` for each rectangle.

# For each rectangle, we need 5 vertices: 1 for the ``MOVETO``, 3 for

# the ``LINETO``, and 1 for the ``CLOSEPOLY``. As indicated in the

# table above, the vertex for the closepoly is ignored but we still need

# it to keep the codes aligned with the vertices::

# nverts = nrects*(1+3+1)

# verts = np.zeros((nverts, 2))

# codes = np.ones(nverts, int) * path.Path.LINETO

# codes[0::5] = path.Path.MOVETO

# codes[4::5] = path.Path.CLOSEPOLY

# verts[0::5, 0] = left

# verts[0::5, 1] = bottom

# verts[1::5, 0] = left

# verts[1::5, 1] = top

# verts[2::5, 0] = right

# verts[2::5, 1] = top

# verts[3::5, 0] = right

# verts[3::5, 1] = bottom

# All that remains is to create the path, attach it to a

# :class:`~matplotlib.patches.PathPatch`, and add it to our axes::

# barpath = path.Path(verts, codes)

# patch = patches.PathPatch(barpath, facecolor='green',

# edgecolor='yellow', alpha=0.5)

# ax.add_patch(patch)

import numpy as np

import matplotlib.patches as patches

import matplotlib.path as path

fig, ax = plt.subplots()

# Fixing random state for reproducibility

np.random.seed(19680801)

# histogram our data with numpy

data = np.random.randn(1000)

n, bins = np.histogram(data, 100)

# get the corners of the rectangles for the histogram

left = np.array(bins[:-1])

right = np.array(bins[1:])

bottom = np.zeros(len(left))

top = bottom + n

nrects = len(left)

nverts = nrects*(1+3+1)

verts = np.zeros((nverts, 2))

codes = np.ones(nverts, int) * path.Path.LINETO

codes[0::5] = path.Path.MOVETO

codes[4::5] = path.Path.CLOSEPOLY

verts[0::5, 0] = left

verts[0::5, 1] = bottom

verts[1::5, 0] = left

verts[1::5, 1] = top

verts[2::5, 0] = right

verts[2::5, 1] = top

verts[3::5, 0] = right

verts[3::5, 1] = bottom

barpath = path.Path(verts, codes)

patch = patches.PathPatch(barpath, facecolor='green',

edgecolor='yellow', alpha=0.5)

ax.add_patch(patch)

ax.set_xlim(left[0], right[-1])

ax.set_ylim(bottom.min(), top.max())

plt.show()

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