"""

Tests specific to the lines module.

"""

import itertools

import timeit

from types import SimpleNamespace

from cycler import cycler

import numpy as np

from numpy.testing import assert_array_equal

import pytest

import matplotlib

import matplotlib.lines as mlines

from matplotlib.markers import MarkerStyle

from matplotlib.path import Path

import matplotlib.pyplot as plt

from matplotlib.testing.decorators import image_comparison, check_figures_equal

def test_segment_hits():

"""Test a problematic case."""

cx, cy = 553, 902

x, y = np.array([553., 553.]), np.array([95., 947.])

radius = 6.94

assert_array_equal(mlines.segment_hits(cx, cy, x, y, radius), [0])

# Runtimes on a loaded system are inherently flaky. Not so much that a rerun

# won't help, hopefully.

@pytest.mark.flaky(reruns=3)

def test_invisible_Line_rendering():

"""

GitHub issue #1256 identified a bug in Line.draw method

Despite visibility attribute set to False, the draw method was not

returning early enough and some pre-rendering code was executed

though not necessary.

Consequence was an excessive draw time for invisible Line instances

holding a large number of points (Npts> 10**6)

"""

# Creates big x and y data:

N = 10**7

x = np.linspace(0, 1, N)

y = np.random.normal(size=N)

# Create a plot figure:

fig = plt.figure()

ax = plt.subplot()

# Create a "big" Line instance:

l = mlines.Line2D(x, y)

l.set_visible(False)

# but don't add it to the Axis instance `ax`

# [here Interactive panning and zooming is pretty responsive]

# Time the canvas drawing:

t_no_line = min(timeit.repeat(fig.canvas.draw, number=1, repeat=3))

# (gives about 25 ms)

# Add the big invisible Line:

ax.add_line(l)

# [Now interactive panning and zooming is very slow]

# Time the canvas drawing:

t_invisible_line = min(timeit.repeat(fig.canvas.draw, number=1, repeat=3))

# gives about 290 ms for N = 10**7 pts

slowdown_factor = t_invisible_line / t_no_line

slowdown_threshold = 2 # trying to avoid false positive failures

assert slowdown_factor < slowdown_threshold

def test_set_line_coll_dash():

fig, ax = plt.subplots()

np.random.seed(0)

# Testing setting linestyles for line collections.

# This should not produce an error.

ax.contour(np.random.randn(20, 30), linestyles=[(0, (3, 3))])

@image_comparison(['line_dashes'], remove_text=True)

def test_line_dashes():

fig, ax = plt.subplots()

ax.plot(range(10), linestyle=(0, (3, 3)), lw=5)

def test_line_colors():

fig, ax = plt.subplots()

ax.plot(range(10), color='none')

ax.plot(range(10), color='r')

ax.plot(range(10), color='.3')

ax.plot(range(10), color=(1, 0, 0, 1))

ax.plot(range(10), color=(1, 0, 0))

fig.canvas.draw()

def test_valid_colors():

line = mlines.Line2D([], [])

with pytest.raises(ValueError):

line.set_color("foobar")

def test_linestyle_variants():

fig, ax = plt.subplots()

for ls in ["-", "solid", "--", "dashed",

"-.", "dashdot", ":", "dotted"]:

ax.plot(range(10), linestyle=ls)

fig.canvas.draw()

def test_valid_linestyles():

line = mlines.Line2D([], [])

with pytest.raises(ValueError):

line.set_linestyle('aardvark')

@image_comparison(['drawstyle_variants.png'], remove_text=True)

def test_drawstyle_variants():

fig, axs = plt.subplots(6)

dss = ["default", "steps-mid", "steps-pre", "steps-post", "steps", None]

# We want to check that drawstyles are properly handled even for very long

# lines (for which the subslice optimization is on); however, we need

# to zoom in so that the difference between the drawstyles is actually

# visible.

for ax, ds in zip(axs.flat, dss):

ax.plot(range(2000), drawstyle=ds)

ax.set(xlim=(0, 2), ylim=(0, 2))

def test_valid_drawstyles():

line = mlines.Line2D([], [])

with pytest.raises(ValueError):

line.set_drawstyle('foobar')

def test_set_drawstyle():

x = np.linspace(0, 2*np.pi, 10)

y = np.sin(x)

fig, ax = plt.subplots()

line, = ax.plot(x, y)

line.set_drawstyle("steps-pre")

assert len(line.get_path().vertices) == 2*len(x)-1

line.set_drawstyle("default")

assert len(line.get_path().vertices) == len(x)

@image_comparison(['line_collection_dashes'], remove_text=True, style='mpl20')

def test_set_line_coll_dash_image():

fig, ax = plt.subplots()

np.random.seed(0)

ax.contour(np.random.randn(20, 30), linestyles=[(0, (3, 3))])

@image_comparison(['marker_fill_styles.png'], remove_text=True)

def test_marker_fill_styles():

colors = itertools.cycle([[0, 0, 1], 'g', '#ff0000', 'c', 'm', 'y',

np.array([0, 0, 0])])

altcolor = 'lightgreen'

y = np.array([1, 1])

x = np.array([0, 9])

fig, ax = plt.subplots()

for j, marker in enumerate(mlines.Line2D.filled_markers):

for i, fs in enumerate(mlines.Line2D.fillStyles):

color = next(colors)

ax.plot(j * 10 + x, y + i + .5 * (j % 2),

marker=marker,

markersize=20,

markerfacecoloralt=altcolor,

fillstyle=fs,

label=fs,

linewidth=5,

color=color,

markeredgecolor=color,

markeredgewidth=2)

ax.set_ylim([0, 7.5])

ax.set_xlim([-5, 155])

def test_markerfacecolor_fillstyle():

"""Test that markerfacecolor does not override fillstyle='none'."""

l, = plt.plot([1, 3, 2], marker=MarkerStyle('o', fillstyle='none'),

markerfacecolor='red')

assert l.get_fillstyle() == 'none'

assert l.get_markerfacecolor() == 'none'

@image_comparison(['scaled_lines'], style='default')

def test_lw_scaling():

th = np.linspace(0, 32)

fig, ax = plt.subplots()

lins_styles = ['dashed', 'dotted', 'dashdot']

cy = cycler(matplotlib.rcParams['axes.prop_cycle'])

for j, (ls, sty) in enumerate(zip(lins_styles, cy)):

for lw in np.linspace(.5, 10, 10):

ax.plot(th, j*np.ones(50) + .1 * lw, linestyle=ls, lw=lw, **sty)

def test_nan_is_sorted():

line = mlines.Line2D([], [])

assert line._is_sorted(np.array([1, 2, 3]))

assert line._is_sorted(np.array([1, np.nan, 3]))

assert not line._is_sorted([3, 5] + [np.nan] * 100 + [0, 2])

@check_figures_equal()

def test_step_markers(fig_test, fig_ref):

fig_test.subplots().step([0, 1], "-o")

fig_ref.subplots().plot([0, 0, 1], [0, 1, 1], "-o", markevery=[0, 2])

@check_figures_equal(extensions=('png',))

def test_markevery(fig_test, fig_ref):

np.random.seed(42)

t = np.linspace(0, 3, 14)

y = np.random.rand(len(t))

casesA = [None, 4, (2, 5), [1, 5, 11],

[0, -1], slice(5, 10, 2), 0.3, (0.3, 0.4),

np.arange(len(t))[y > 0.5]]

casesB = ["11111111111111", "10001000100010", "00100001000010",

"01000100000100", "10000000000001", "00000101010000",

"11011011011110", "01010011011101", "01110001110110"]

axsA = fig_ref.subplots(3, 3)

axsB = fig_test.subplots(3, 3)

for ax, case in zip(axsA.flat, casesA):

ax.plot(t, y, "-gD", markevery=case)

for ax, case in zip(axsB.flat, casesB):

me = np.array(list(case)).astype(int).astype(bool)

ax.plot(t, y, "-gD", markevery=me)

def test_marker_as_markerstyle():

fig, ax = plt.subplots()

line, = ax.plot([2, 4, 3], marker=MarkerStyle("D"))

fig.canvas.draw()

assert line.get_marker() == "D"

# continue with smoke tests:

line.set_marker("s")

fig.canvas.draw()

line.set_marker(MarkerStyle("o"))

fig.canvas.draw()

# test Path roundtrip

triangle1 = Path([[-1., -1.], [1., -1.], [0., 2.], [0., 0.]], closed=True)

line2, = ax.plot([1, 3, 2], marker=MarkerStyle(triangle1), ms=22)

line3, = ax.plot([0, 2, 1], marker=triangle1, ms=22)

assert_array_equal(line2.get_marker().vertices, triangle1.vertices)

assert_array_equal(line3.get_marker().vertices, triangle1.vertices)

@check_figures_equal()

def test_odd_dashes(fig_test, fig_ref):

fig_test.add_subplot().plot([1, 2], dashes=[1, 2, 3])

fig_ref.add_subplot().plot([1, 2], dashes=[1, 2, 3, 1, 2, 3])

def test_picking():

fig, ax = plt.subplots()

mouse_event = SimpleNamespace(x=fig.bbox.width // 2,

y=fig.bbox.height // 2 + 15)

# Default pickradius is 5, so event should not pick this line.

l0, = ax.plot([0, 1], [0, 1], picker=True)

found, indices = l0.contains(mouse_event)

assert not found

# But with a larger pickradius, this should be picked.

l1, = ax.plot([0, 1], [0, 1], picker=True, pickradius=20)

found, indices = l1.contains(mouse_event)

assert found

assert_array_equal(indices['ind'], [0])

# And if we modify the pickradius after creation, it should work as well.

l2, = ax.plot([0, 1], [0, 1], picker=True)

found, indices = l2.contains(mouse_event)

assert not found

l2.set_pickradius(20)

found, indices = l2.contains(mouse_event)

assert found

assert_array_equal(indices['ind'], [0])