EVOLUTION-MANAGER

Edit File: test_lines.py

"""
Tests specific to the lines module.
"""

import itertools
import timeit

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(111)

# 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 = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    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 = plt.figure()
    ax = fig.add_subplot(1, 1, 1)

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

def test_line_colors():
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    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_linestyle_variants():
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    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 = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    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])