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https://github.com/PiBrewing/craftbeerpi4.git
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558 lines
18 KiB
Python
558 lines
18 KiB
Python
import os
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import warnings
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import numpy as np
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from numpy import random
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from pandas.util._decorators import cache_readonly
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.api import is_list_like
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import pandas as pd
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from pandas import DataFrame, Series
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import pandas._testing as tm
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"""
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This is a common base class used for various plotting tests
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"""
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@td.skip_if_no_mpl
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class TestPlotBase:
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def setup_method(self, method):
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import matplotlib as mpl
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from pandas.plotting._matplotlib import compat
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mpl.rcdefaults()
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self.mpl_ge_2_2_3 = compat._mpl_ge_2_2_3()
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self.mpl_ge_3_0_0 = compat._mpl_ge_3_0_0()
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self.mpl_ge_3_1_0 = compat._mpl_ge_3_1_0()
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self.mpl_ge_3_2_0 = compat._mpl_ge_3_2_0()
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self.bp_n_objects = 7
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self.polycollection_factor = 2
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self.default_figsize = (6.4, 4.8)
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self.default_tick_position = "left"
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n = 100
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with tm.RNGContext(42):
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gender = np.random.choice(["Male", "Female"], size=n)
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classroom = np.random.choice(["A", "B", "C"], size=n)
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self.hist_df = DataFrame(
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{
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"gender": gender,
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"classroom": classroom,
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"height": random.normal(66, 4, size=n),
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"weight": random.normal(161, 32, size=n),
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"category": random.randint(4, size=n),
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}
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)
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self.tdf = tm.makeTimeDataFrame()
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self.hexbin_df = DataFrame(
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{
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"A": np.random.uniform(size=20),
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"B": np.random.uniform(size=20),
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"C": np.arange(20) + np.random.uniform(size=20),
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}
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)
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def teardown_method(self, method):
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tm.close()
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@cache_readonly
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def plt(self):
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import matplotlib.pyplot as plt
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return plt
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@cache_readonly
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def colorconverter(self):
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import matplotlib.colors as colors
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return colors.colorConverter
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def _check_legend_labels(self, axes, labels=None, visible=True):
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"""
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Check each axes has expected legend labels
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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labels : list-like
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expected legend labels
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visible : bool
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expected legend visibility. labels are checked only when visible is
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True
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"""
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if visible and (labels is None):
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raise ValueError("labels must be specified when visible is True")
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axes = self._flatten_visible(axes)
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for ax in axes:
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if visible:
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assert ax.get_legend() is not None
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self._check_text_labels(ax.get_legend().get_texts(), labels)
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else:
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assert ax.get_legend() is None
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def _check_legend_marker(self, ax, expected_markers=None, visible=True):
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"""
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Check ax has expected legend markers
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Parameters
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----------
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ax : matplotlib Axes object
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expected_markers : list-like
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expected legend markers
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visible : bool
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expected legend visibility. labels are checked only when visible is
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True
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"""
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if visible and (expected_markers is None):
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raise ValueError("Markers must be specified when visible is True")
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if visible:
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handles, _ = ax.get_legend_handles_labels()
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markers = [handle.get_marker() for handle in handles]
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assert markers == expected_markers
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else:
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assert ax.get_legend() is None
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def _check_data(self, xp, rs):
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"""
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Check each axes has identical lines
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Parameters
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----------
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xp : matplotlib Axes object
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rs : matplotlib Axes object
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"""
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xp_lines = xp.get_lines()
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rs_lines = rs.get_lines()
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def check_line(xpl, rsl):
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xpdata = xpl.get_xydata()
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rsdata = rsl.get_xydata()
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tm.assert_almost_equal(xpdata, rsdata)
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assert len(xp_lines) == len(rs_lines)
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[check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)]
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tm.close()
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def _check_visible(self, collections, visible=True):
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"""
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Check each artist is visible or not
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Parameters
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----------
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collections : matplotlib Artist or its list-like
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target Artist or its list or collection
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visible : bool
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expected visibility
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"""
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from matplotlib.collections import Collection
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if not isinstance(collections, Collection) and not is_list_like(collections):
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collections = [collections]
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for patch in collections:
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assert patch.get_visible() == visible
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def _get_colors_mapped(self, series, colors):
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unique = series.unique()
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# unique and colors length can be differed
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# depending on slice value
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mapped = dict(zip(unique, colors))
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return [mapped[v] for v in series.values]
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def _check_colors(
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self, collections, linecolors=None, facecolors=None, mapping=None
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):
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"""
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Check each artist has expected line colors and face colors
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Parameters
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----------
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collections : list-like
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list or collection of target artist
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linecolors : list-like which has the same length as collections
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list of expected line colors
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facecolors : list-like which has the same length as collections
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list of expected face colors
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mapping : Series
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Series used for color grouping key
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used for andrew_curves, parallel_coordinates, radviz test
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"""
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from matplotlib.collections import Collection, LineCollection, PolyCollection
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from matplotlib.lines import Line2D
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conv = self.colorconverter
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if linecolors is not None:
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if mapping is not None:
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linecolors = self._get_colors_mapped(mapping, linecolors)
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linecolors = linecolors[: len(collections)]
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assert len(collections) == len(linecolors)
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for patch, color in zip(collections, linecolors):
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if isinstance(patch, Line2D):
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result = patch.get_color()
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# Line2D may contains string color expression
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result = conv.to_rgba(result)
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elif isinstance(patch, (PolyCollection, LineCollection)):
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result = tuple(patch.get_edgecolor()[0])
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else:
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result = patch.get_edgecolor()
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expected = conv.to_rgba(color)
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assert result == expected
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if facecolors is not None:
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if mapping is not None:
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facecolors = self._get_colors_mapped(mapping, facecolors)
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facecolors = facecolors[: len(collections)]
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assert len(collections) == len(facecolors)
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for patch, color in zip(collections, facecolors):
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if isinstance(patch, Collection):
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# returned as list of np.array
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result = patch.get_facecolor()[0]
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else:
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result = patch.get_facecolor()
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if isinstance(result, np.ndarray):
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result = tuple(result)
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expected = conv.to_rgba(color)
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assert result == expected
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def _check_text_labels(self, texts, expected):
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"""
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Check each text has expected labels
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Parameters
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----------
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texts : matplotlib Text object, or its list-like
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target text, or its list
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expected : str or list-like which has the same length as texts
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expected text label, or its list
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"""
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if not is_list_like(texts):
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assert texts.get_text() == expected
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else:
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labels = [t.get_text() for t in texts]
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assert len(labels) == len(expected)
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for label, e in zip(labels, expected):
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assert label == e
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def _check_ticks_props(
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self, axes, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None
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):
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"""
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Check each axes has expected tick properties
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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xlabelsize : number
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expected xticks font size
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xrot : number
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expected xticks rotation
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ylabelsize : number
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expected yticks font size
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yrot : number
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expected yticks rotation
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"""
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from matplotlib.ticker import NullFormatter
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axes = self._flatten_visible(axes)
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for ax in axes:
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if xlabelsize is not None or xrot is not None:
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if isinstance(ax.xaxis.get_minor_formatter(), NullFormatter):
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# If minor ticks has NullFormatter, rot / fontsize are not
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# retained
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labels = ax.get_xticklabels()
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else:
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labels = ax.get_xticklabels() + ax.get_xticklabels(minor=True)
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for label in labels:
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if xlabelsize is not None:
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tm.assert_almost_equal(label.get_fontsize(), xlabelsize)
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if xrot is not None:
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tm.assert_almost_equal(label.get_rotation(), xrot)
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if ylabelsize is not None or yrot is not None:
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if isinstance(ax.yaxis.get_minor_formatter(), NullFormatter):
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labels = ax.get_yticklabels()
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else:
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labels = ax.get_yticklabels() + ax.get_yticklabels(minor=True)
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for label in labels:
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if ylabelsize is not None:
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tm.assert_almost_equal(label.get_fontsize(), ylabelsize)
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if yrot is not None:
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tm.assert_almost_equal(label.get_rotation(), yrot)
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def _check_ax_scales(self, axes, xaxis="linear", yaxis="linear"):
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"""
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Check each axes has expected scales
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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xaxis : {'linear', 'log'}
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expected xaxis scale
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yaxis : {'linear', 'log'}
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expected yaxis scale
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"""
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axes = self._flatten_visible(axes)
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for ax in axes:
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assert ax.xaxis.get_scale() == xaxis
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assert ax.yaxis.get_scale() == yaxis
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def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=None):
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"""
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Check expected number of axes is drawn in expected layout
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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axes_num : number
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expected number of axes. Unnecessary axes should be set to
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invisible.
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layout : tuple
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expected layout, (expected number of rows , columns)
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figsize : tuple
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expected figsize. default is matplotlib default
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"""
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from pandas.plotting._matplotlib.tools import _flatten
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if figsize is None:
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figsize = self.default_figsize
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visible_axes = self._flatten_visible(axes)
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if axes_num is not None:
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assert len(visible_axes) == axes_num
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for ax in visible_axes:
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# check something drawn on visible axes
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assert len(ax.get_children()) > 0
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if layout is not None:
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result = self._get_axes_layout(_flatten(axes))
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assert result == layout
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tm.assert_numpy_array_equal(
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visible_axes[0].figure.get_size_inches(),
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np.array(figsize, dtype=np.float64),
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)
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def _get_axes_layout(self, axes):
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x_set = set()
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y_set = set()
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for ax in axes:
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# check axes coordinates to estimate layout
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points = ax.get_position().get_points()
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x_set.add(points[0][0])
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y_set.add(points[0][1])
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return (len(y_set), len(x_set))
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def _flatten_visible(self, axes):
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"""
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Flatten axes, and filter only visible
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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"""
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from pandas.plotting._matplotlib.tools import _flatten
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axes = _flatten(axes)
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axes = [ax for ax in axes if ax.get_visible()]
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return axes
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def _check_has_errorbars(self, axes, xerr=0, yerr=0):
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"""
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Check axes has expected number of errorbars
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Parameters
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----------
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axes : matplotlib Axes object, or its list-like
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xerr : number
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expected number of x errorbar
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yerr : number
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expected number of y errorbar
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"""
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axes = self._flatten_visible(axes)
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for ax in axes:
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containers = ax.containers
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xerr_count = 0
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yerr_count = 0
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for c in containers:
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has_xerr = getattr(c, "has_xerr", False)
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has_yerr = getattr(c, "has_yerr", False)
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if has_xerr:
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xerr_count += 1
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if has_yerr:
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yerr_count += 1
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assert xerr == xerr_count
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assert yerr == yerr_count
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def _check_box_return_type(
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self, returned, return_type, expected_keys=None, check_ax_title=True
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):
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"""
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Check box returned type is correct
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Parameters
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----------
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returned : object to be tested, returned from boxplot
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return_type : str
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return_type passed to boxplot
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expected_keys : list-like, optional
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group labels in subplot case. If not passed,
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the function checks assuming boxplot uses single ax
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check_ax_title : bool
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Whether to check the ax.title is the same as expected_key
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Intended to be checked by calling from ``boxplot``.
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Normal ``plot`` doesn't attach ``ax.title``, it must be disabled.
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"""
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from matplotlib.axes import Axes
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types = {"dict": dict, "axes": Axes, "both": tuple}
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if expected_keys is None:
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# should be fixed when the returning default is changed
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if return_type is None:
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return_type = "dict"
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assert isinstance(returned, types[return_type])
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if return_type == "both":
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assert isinstance(returned.ax, Axes)
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assert isinstance(returned.lines, dict)
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else:
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# should be fixed when the returning default is changed
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if return_type is None:
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for r in self._flatten_visible(returned):
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assert isinstance(r, Axes)
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return
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assert isinstance(returned, Series)
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assert sorted(returned.keys()) == sorted(expected_keys)
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for key, value in returned.items():
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assert isinstance(value, types[return_type])
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# check returned dict has correct mapping
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if return_type == "axes":
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if check_ax_title:
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assert value.get_title() == key
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elif return_type == "both":
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if check_ax_title:
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assert value.ax.get_title() == key
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assert isinstance(value.ax, Axes)
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assert isinstance(value.lines, dict)
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elif return_type == "dict":
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line = value["medians"][0]
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axes = line.axes
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if check_ax_title:
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assert axes.get_title() == key
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else:
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raise AssertionError
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def _check_grid_settings(self, obj, kinds, kws={}):
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# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
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import matplotlib as mpl
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def is_grid_on():
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xticks = self.plt.gca().xaxis.get_major_ticks()
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yticks = self.plt.gca().yaxis.get_major_ticks()
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# for mpl 2.2.2, gridOn and gridline.get_visible disagree.
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# for new MPL, they are the same.
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if self.mpl_ge_3_1_0:
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xoff = all(not g.gridline.get_visible() for g in xticks)
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yoff = all(not g.gridline.get_visible() for g in yticks)
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else:
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xoff = all(not g.gridOn for g in xticks)
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yoff = all(not g.gridOn for g in yticks)
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return not (xoff and yoff)
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spndx = 1
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for kind in kinds:
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self.plt.subplot(1, 4 * len(kinds), spndx)
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spndx += 1
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mpl.rc("axes", grid=False)
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obj.plot(kind=kind, **kws)
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assert not is_grid_on()
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self.plt.subplot(1, 4 * len(kinds), spndx)
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spndx += 1
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mpl.rc("axes", grid=True)
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obj.plot(kind=kind, grid=False, **kws)
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assert not is_grid_on()
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if kind != "pie":
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self.plt.subplot(1, 4 * len(kinds), spndx)
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spndx += 1
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mpl.rc("axes", grid=True)
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obj.plot(kind=kind, **kws)
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assert is_grid_on()
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self.plt.subplot(1, 4 * len(kinds), spndx)
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spndx += 1
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mpl.rc("axes", grid=False)
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obj.plot(kind=kind, grid=True, **kws)
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assert is_grid_on()
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def _unpack_cycler(self, rcParams, field="color"):
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"""
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Auxiliary function for correctly unpacking cycler after MPL >= 1.5
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"""
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return [v[field] for v in rcParams["axes.prop_cycle"]]
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def _check_plot_works(f, filterwarnings="always", **kwargs):
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import matplotlib.pyplot as plt
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ret = None
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with warnings.catch_warnings():
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warnings.simplefilter(filterwarnings)
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try:
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try:
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fig = kwargs["figure"]
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except KeyError:
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fig = plt.gcf()
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plt.clf()
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kwargs.get("ax", fig.add_subplot(211))
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ret = f(**kwargs)
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tm.assert_is_valid_plot_return_object(ret)
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if f is pd.plotting.bootstrap_plot:
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assert "ax" not in kwargs
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else:
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kwargs["ax"] = fig.add_subplot(212)
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ret = f(**kwargs)
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tm.assert_is_valid_plot_return_object(ret)
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with tm.ensure_clean(return_filelike=True) as path:
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plt.savefig(path)
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|
finally:
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tm.close(fig)
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|
|
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return ret
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|
|
|
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def curpath():
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pth, _ = os.path.split(os.path.abspath(__file__))
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return pth
|