import itertools
import pickle
import re
from weakref import ref
from unittest.mock import patch, Mock
from datetime import datetime
import numpy as np
from numpy.testing import (assert_array_equal, assert_approx_equal,
assert_array_almost_equal)
import pytest
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
from matplotlib.cbook import MatplotlibDeprecationWarning, delete_masked_points
class Test_delete_masked_points:
def test_bad_first_arg(self):
with pytest.raises(ValueError):
delete_masked_points('a string', np.arange(1.0, 7.0))
def test_string_seq(self):
a1 = ['a', 'b', 'c', 'd', 'e', 'f']
a2 = [1, 2, 3, np.nan, np.nan, 6]
result1, result2 = delete_masked_points(a1, a2)
ind = [0, 1, 2, 5]
assert_array_equal(result1, np.array(a1)[ind])
assert_array_equal(result2, np.array(a2)[ind])
def test_datetime(self):
dates = [datetime(2008, 1, 1), datetime(2008, 1, 2),
datetime(2008, 1, 3), datetime(2008, 1, 4),
datetime(2008, 1, 5), datetime(2008, 1, 6)]
a_masked = np.ma.array([1, 2, 3, np.nan, np.nan, 6],
mask=[False, False, True, True, False, False])
actual = delete_masked_points(dates, a_masked)
ind = [0, 1, 5]
assert_array_equal(actual[0], np.array(dates)[ind])
assert_array_equal(actual[1], a_masked[ind].compressed())
def test_rgba(self):
a_masked = np.ma.array([1, 2, 3, np.nan, np.nan, 6],
mask=[False, False, True, True, False, False])
a_rgba = mcolors.to_rgba_array(['r', 'g', 'b', 'c', 'm', 'y'])
actual = delete_masked_points(a_masked, a_rgba)
ind = [0, 1, 5]
assert_array_equal(actual[0], a_masked[ind].compressed())
assert_array_equal(actual[1], a_rgba[ind])
class Test_boxplot_stats:
def setup(self):
np.random.seed(937)
self.nrows = 37
self.ncols = 4
self.data = np.random.lognormal(size=(self.nrows, self.ncols),
mean=1.5, sigma=1.75)
self.known_keys = sorted([
'mean', 'med', 'q1', 'q3', 'iqr',
'cilo', 'cihi', 'whislo', 'whishi',
'fliers', 'label'
])
self.std_results = cbook.boxplot_stats(self.data)
self.known_nonbootstrapped_res = {
'cihi': 6.8161283264444847,
'cilo': -0.1489815330368689,
'iqr': 13.492709959447094,
'mean': 13.00447442387868,
'med': 3.3335733967038079,
'fliers': np.array([
92.55467075, 87.03819018, 42.23204914, 39.29390996
]),
'q1': 1.3597529879465153,
'q3': 14.85246294739361,
'whishi': 27.899688243699629,
'whislo': 0.042143774965502923
}
self.known_bootstrapped_ci = {
'cihi': 8.939577523357828,
'cilo': 1.8692703958676578,
}
self.known_whis3_res = {
'whishi': 42.232049135969874,
'whislo': 0.042143774965502923,
'fliers': np.array([92.55467075, 87.03819018]),
}
self.known_res_percentiles = {
'whislo': 0.1933685896907924,
'whishi': 42.232049135969874
}
self.known_res_range = {
'whislo': 0.042143774965502923,
'whishi': 92.554670752188699
}
def test_form_main_list(self):
assert isinstance(self.std_results, list)
def test_form_each_dict(self):
for res in self.std_results:
assert isinstance(res, dict)
def test_form_dict_keys(self):
for res in self.std_results:
assert set(res) <= set(self.known_keys)
def test_results_baseline(self):
res = self.std_results[0]
for key, value in self.known_nonbootstrapped_res.items():
assert_array_almost_equal(res[key], value)
def test_results_bootstrapped(self):
results = cbook.boxplot_stats(self.data, bootstrap=10000)
res = results[0]
for key, value in self.known_bootstrapped_ci.items():
assert_approx_equal(res[key], value)
def test_results_whiskers_float(self):
results = cbook.boxplot_stats(self.data, whis=3)
res = results[0]
for key, value in self.known_whis3_res.items():
assert_array_almost_equal(res[key], value)
def test_results_whiskers_range(self):
results = cbook.boxplot_stats(self.data, whis=[0, 100])
res = results[0]
for key, value in self.known_res_range.items():
assert_array_almost_equal(res[key], value)
def test_results_whiskers_percentiles(self):
results = cbook.boxplot_stats(self.data, whis=[5, 95])
res = results[0]
for key, value in self.known_res_percentiles.items():
assert_array_almost_equal(res[key], value)
def test_results_withlabels(self):
labels = ['Test1', 2, 'ardvark', 4]
results = cbook.boxplot_stats(self.data, labels=labels)
res = results[0]
for lab, res in zip(labels, results):
assert res['label'] == lab
results = cbook.boxplot_stats(self.data)
for res in results:
assert 'label' not in res
def test_label_error(self):
labels = [1, 2]
with pytest.raises(ValueError):
cbook.boxplot_stats(self.data, labels=labels)
def test_bad_dims(self):
data = np.random.normal(size=(34, 34, 34))
with pytest.raises(ValueError):
cbook.boxplot_stats(data)
def test_boxplot_stats_autorange_false(self):
x = np.zeros(shape=140)
x = np.hstack([-25, x, 25])
bstats_false = cbook.boxplot_stats(x, autorange=False)
bstats_true = cbook.boxplot_stats(x, autorange=True)
assert bstats_false[0]['whislo'] == 0
assert bstats_false[0]['whishi'] == 0
assert_array_almost_equal(bstats_false[0]['fliers'], [-25, 25])
assert bstats_true[0]['whislo'] == -25
assert bstats_true[0]['whishi'] == 25
assert_array_almost_equal(bstats_true[0]['fliers'], [])
class Test_callback_registry:
def setup(self):
self.signal = 'test'
self.callbacks = cbook.CallbackRegistry()
def connect(self, s, func):
return self.callbacks.connect(s, func)
def is_empty(self):
assert self.callbacks._func_cid_map == {}
assert self.callbacks.callbacks == {}
def is_not_empty(self):
assert self.callbacks._func_cid_map != {}
assert self.callbacks.callbacks != {}
def test_callback_complete(self):
# ensure we start with an empty registry
self.is_empty()
# create a class for testing
mini_me = Test_callback_registry()
# test that we can add a callback
cid1 = self.connect(self.signal, mini_me.dummy)
assert type(cid1) == int
self.is_not_empty()
# test that we don't add a second callback
cid2 = self.connect(self.signal, mini_me.dummy)
assert cid1 == cid2
self.is_not_empty()
assert len(self.callbacks._func_cid_map) == 1
assert len(self.callbacks.callbacks) == 1
del mini_me
# check we now have no callbacks registered
self.is_empty()
def dummy(self):
pass
def test_pickling(self):
assert hasattr(pickle.loads(pickle.dumps(cbook.CallbackRegistry())),
"callbacks")
def test_callbackregistry_default_exception_handler(monkeypatch):
cb = cbook.CallbackRegistry()
cb.connect("foo", lambda: None)
monkeypatch.setattr(
cbook, "_get_running_interactive_framework", lambda: None)
with pytest.raises(TypeError):
cb.process("foo", "argument mismatch")
monkeypatch.setattr(
cbook, "_get_running_interactive_framework", lambda: "not-none")
cb.process("foo", "argument mismatch") # No error in that case.
def raising_cb_reg(func):
class TestException(Exception):
pass
def raising_function():
raise RuntimeError
def raising_function_VE():
raise ValueError
def transformer(excp):
if isinstance(excp, RuntimeError):
raise TestException
raise excp
# old default
cb_old = cbook.CallbackRegistry(exception_handler=None)
cb_old.connect('foo', raising_function)
# filter
cb_filt = cbook.CallbackRegistry(exception_handler=transformer)
cb_filt.connect('foo', raising_function)
# filter
cb_filt_pass = cbook.CallbackRegistry(exception_handler=transformer)
cb_filt_pass.connect('foo', raising_function_VE)
return pytest.mark.parametrize('cb, excp',
[[cb_old, RuntimeError],
[cb_filt, TestException],
[cb_filt_pass, ValueError]])(func)
@raising_cb_reg
def test_callbackregistry_custom_exception_handler(monkeypatch, cb, excp):
monkeypatch.setattr(
cbook, "_get_running_interactive_framework", lambda: None)
with pytest.raises(excp):
cb.process('foo')
def test_sanitize_sequence():
d = {'a': 1, 'b': 2, 'c': 3}
k = ['a', 'b', 'c']
v = [1, 2, 3]
i = [('a', 1), ('b', 2), ('c', 3)]
assert k == sorted(cbook.sanitize_sequence(d.keys()))
assert v == sorted(cbook.sanitize_sequence(d.values()))
assert i == sorted(cbook.sanitize_sequence(d.items()))
assert i == cbook.sanitize_sequence(i)
assert k == cbook.sanitize_sequence(k)
fail_mapping = (
({'a': 1}, {'forbidden': ('a')}),
({'a': 1}, {'required': ('b')}),
({'a': 1, 'b': 2}, {'required': ('a'), 'allowed': ()}),
({'a': 1, 'b': 2}, {'alias_mapping': {'a': ['b']}}),
({'a': 1, 'b': 2}, {'alias_mapping': {'a': ['b']}, 'allowed': ('a',)}),
({'a': 1, 'b': 2}, {'alias_mapping': {'a': ['a', 'b']}}),
({'a': 1, 'b': 2, 'c': 3},
{'alias_mapping': {'a': ['b']}, 'required': ('a', )}),
)
pass_mapping = (
({'a': 1, 'b': 2}, {'a': 1, 'b': 2}, {}),
({'b': 2}, {'a': 2}, {'alias_mapping': {'a': ['a', 'b']}}),
({'b': 2}, {'a': 2},
{'alias_mapping': {'a': ['b']}, 'forbidden': ('b', )}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3},
{'required': ('a', ), 'allowed': ('c', )}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3},
{'required': ('a', 'c'), 'allowed': ('c', )}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3},
{'required': ('a', 'c'), 'allowed': ('a', 'c')}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3},
{'required': ('a', 'c'), 'allowed': ()}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3}, {'required': ('a', 'c')}),
({'a': 1, 'c': 3}, {'a': 1, 'c': 3}, {'allowed': ('a', 'c')}),
)
@pytest.mark.parametrize('inp, kwargs_to_norm', fail_mapping)
def test_normalize_kwargs_fail(inp, kwargs_to_norm):
with pytest.raises(TypeError), \
cbook._suppress_matplotlib_deprecation_warning():
cbook.normalize_kwargs(inp, **kwargs_to_norm)
@pytest.mark.parametrize('inp, expected, kwargs_to_norm',
pass_mapping)
def test_normalize_kwargs_pass(inp, expected, kwargs_to_norm):
with cbook._suppress_matplotlib_deprecation_warning():
# No other warning should be emitted.
assert expected == cbook.normalize_kwargs(inp, **kwargs_to_norm)
def test_warn_external_frame_embedded_python():
with patch.object(cbook, "sys") as mock_sys:
mock_sys._getframe = Mock(return_value=None)
with pytest.warns(UserWarning, match=r"\Adummy\Z"):
cbook._warn_external("dummy")
def test_to_prestep():
x = np.arange(4)
y1 = np.arange(4)
y2 = np.arange(4)[::-1]
xs, y1s, y2s = cbook.pts_to_prestep(x, y1, y2)
x_target = np.asarray([0, 0, 1, 1, 2, 2, 3], dtype=float)
y1_target = np.asarray([0, 1, 1, 2, 2, 3, 3], dtype=float)
y2_target = np.asarray([3, 2, 2, 1, 1, 0, 0], dtype=float)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
assert_array_equal(y2_target, y2s)
xs, y1s = cbook.pts_to_prestep(x, y1)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
def test_to_prestep_empty():
steps = cbook.pts_to_prestep([], [])
assert steps.shape == (2, 0)
def test_to_poststep():
x = np.arange(4)
y1 = np.arange(4)
y2 = np.arange(4)[::-1]
xs, y1s, y2s = cbook.pts_to_poststep(x, y1, y2)
x_target = np.asarray([0, 1, 1, 2, 2, 3, 3], dtype=float)
y1_target = np.asarray([0, 0, 1, 1, 2, 2, 3], dtype=float)
y2_target = np.asarray([3, 3, 2, 2, 1, 1, 0], dtype=float)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
assert_array_equal(y2_target, y2s)
xs, y1s = cbook.pts_to_poststep(x, y1)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
def test_to_poststep_empty():
steps = cbook.pts_to_poststep([], [])
assert steps.shape == (2, 0)
def test_to_midstep():
x = np.arange(4)
y1 = np.arange(4)
y2 = np.arange(4)[::-1]
xs, y1s, y2s = cbook.pts_to_midstep(x, y1, y2)
x_target = np.asarray([0, .5, .5, 1.5, 1.5, 2.5, 2.5, 3], dtype=float)
y1_target = np.asarray([0, 0, 1, 1, 2, 2, 3, 3], dtype=float)
y2_target = np.asarray([3, 3, 2, 2, 1, 1, 0, 0], dtype=float)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
assert_array_equal(y2_target, y2s)
xs, y1s = cbook.pts_to_midstep(x, y1)
assert_array_equal(x_target, xs)
assert_array_equal(y1_target, y1s)
def test_to_midstep_empty():
steps = cbook.pts_to_midstep([], [])
assert steps.shape == (2, 0)
@pytest.mark.parametrize(
"args",
[(np.arange(12).reshape(3, 4), 'a'),
(np.arange(12), 'a'),
(np.arange(12), np.arange(3))])
def test_step_fails(args):
with pytest.raises(ValueError):
cbook.pts_to_prestep(*args)
def test_grouper():
class dummy:
pass
a, b, c, d, e = objs = [dummy() for j in range(5)]
g = cbook.Grouper()
g.join(*objs)
assert set(list(g)[0]) == set(objs)
assert set(g.get_siblings(a)) == set(objs)
for other in objs[1:]:
assert g.joined(a, other)
g.remove(a)
for other in objs[1:]:
assert not g.joined(a, other)
for A, B in itertools.product(objs[1:], objs[1:]):
assert g.joined(A, B)
def test_grouper_private():
class dummy:
pass
objs = [dummy() for j in range(5)]
g = cbook.Grouper()
g.join(*objs)
# reach in and touch the internals !
mapping = g._mapping
for o in objs:
assert ref(o) in mapping
base_set = mapping[ref(objs[0])]
for o in objs[1:]:
assert mapping[ref(o)] is base_set
def test_flatiter():
x = np.arange(5)
it = x.flat
assert 0 == next(it)
assert 1 == next(it)
ret = cbook.safe_first_element(it)
assert ret == 0
assert 0 == next(it)
assert 1 == next(it)
def test_reshape2d():
class dummy:
pass
xnew = cbook._reshape_2D([], 'x')
assert np.shape(xnew) == (1, 0)
x = [dummy() for j in range(5)]
xnew = cbook._reshape_2D(x, 'x')
assert np.shape(xnew) == (1, 5)
x = np.arange(5)
xnew = cbook._reshape_2D(x, 'x')
assert np.shape(xnew) == (1, 5)
x = [[dummy() for j in range(5)] for i in range(3)]
xnew = cbook._reshape_2D(x, 'x')
assert np.shape(xnew) == (3, 5)
# this is strange behaviour, but...
x = np.random.rand(3, 5)
xnew = cbook._reshape_2D(x, 'x')
assert np.shape(xnew) == (5, 3)
# Test a list of lists which are all of length 1
x = [[1], [2], [3]]
xnew = cbook._reshape_2D(x, 'x')
assert isinstance(xnew, list)
assert isinstance(xnew[0], np.ndarray) and xnew[0].shape == (1,)
assert isinstance(xnew[1], np.ndarray) and xnew[1].shape == (1,)
assert isinstance(xnew[2], np.ndarray) and xnew[2].shape == (1,)
# Now test with a list of lists with different lengths, which means the
# array will internally be converted to a 1D object array of lists
x = [[1, 2, 3], [3, 4], [2]]
xnew = cbook._reshape_2D(x, 'x')
assert isinstance(xnew, list)
assert isinstance(xnew[0], np.ndarray) and xnew[0].shape == (3,)
assert isinstance(xnew[1], np.ndarray) and xnew[1].shape == (2,)
assert isinstance(xnew[2], np.ndarray) and xnew[2].shape == (1,)
# We now need to make sure that this works correctly for Numpy subclasses
# where iterating over items can return subclasses too, which may be
# iterable even if they are scalars. To emulate this, we make a Numpy
# array subclass that returns Numpy 'scalars' when iterating or accessing
# values, and these are technically iterable if checking for example
# isinstance(x, collections.abc.Iterable).
class ArraySubclass(np.ndarray):
def __iter__(self):
for value in super().__iter__():
yield np.array(value)
def __getitem__(self, item):
return np.array(super().__getitem__(item))
v = np.arange(10, dtype=float)
x = ArraySubclass((10,), dtype=float, buffer=v.data)
xnew = cbook._reshape_2D(x, 'x')
# We check here that the array wasn't split up into many individual
# ArraySubclass, which is what used to happen due to a bug in _reshape_2D
assert len(xnew) == 1
assert isinstance(xnew[0], ArraySubclass)
# check list of strings:
x = ['a', 'b', 'c', 'c', 'dd', 'e', 'f', 'ff', 'f']
xnew = cbook._reshape_2D(x, 'x')
assert len(xnew[0]) == len(x)
assert isinstance(xnew[0], np.ndarray)
def test_reshape2d_pandas(pd):
# seperate to allow the rest of the tests to run if no pandas...
X = np.arange(30).reshape(10, 3)
x = pd.DataFrame(X, columns=["a", "b", "c"])
Xnew = cbook._reshape_2D(x, 'x')
# Need to check each row because _reshape_2D returns a list of arrays:
for x, xnew in zip(X.T, Xnew):
np.testing.assert_array_equal(x, xnew)
X = np.arange(30).reshape(10, 3)
x = pd.DataFrame(X, columns=["a", "b", "c"])
Xnew = cbook._reshape_2D(x, 'x')
# Need to check each row because _reshape_2D returns a list of arrays:
for x, xnew in zip(X.T, Xnew):
np.testing.assert_array_equal(x, xnew)
def test_contiguous_regions():
a, b, c = 3, 4, 5
# Starts and ends with True
mask = [True]*a + [False]*b + [True]*c
expected = [(0, a), (a+b, a+b+c)]
assert cbook.contiguous_regions(mask) == expected
d, e = 6, 7
# Starts with True ends with False
mask = mask + [False]*e
assert cbook.contiguous_regions(mask) == expected
# Starts with False ends with True
mask = [False]*d + mask[:-e]
expected = [(d, d+a), (d+a+b, d+a+b+c)]
assert cbook.contiguous_regions(mask) == expected
# Starts and ends with False
mask = mask + [False]*e
assert cbook.contiguous_regions(mask) == expected
# No True in mask
assert cbook.contiguous_regions([False]*5) == []
# Empty mask
assert cbook.contiguous_regions([]) == []
def test_safe_first_element_pandas_series(pd):
# deliberately create a pandas series with index not starting from 0
s = pd.Series(range(5), index=range(10, 15))
actual = cbook.safe_first_element(s)
assert actual == 0
def test_delete_parameter():
@cbook._delete_parameter("3.0", "foo")
def func1(foo=None):
pass
@cbook._delete_parameter("3.0", "foo")
def func2(**kwargs):
pass
for func in [func1, func2]:
func() # No warning.
with pytest.warns(MatplotlibDeprecationWarning):
func(foo="bar")
def pyplot_wrapper(foo=cbook.deprecation._deprecated_parameter):
func1(foo)
pyplot_wrapper() # No warning.
with pytest.warns(MatplotlibDeprecationWarning):
func(foo="bar")
def test_make_keyword_only():
@cbook._make_keyword_only("3.0", "arg")
def func(pre, arg, post=None):
pass
func(1, arg=2) # Check that no warning is emitted.
with pytest.warns(MatplotlibDeprecationWarning):
func(1, 2)
with pytest.warns(MatplotlibDeprecationWarning):
func(1, 2, 3)
def test_warn_external(recwarn):
cbook._warn_external("oops")
assert len(recwarn) == 1
assert recwarn[0].filename == __file__
def test_array_patch_perimeters():
# This compares the old implementation as a reference for the
# vectorized one.
def check(x, rstride, cstride):
rows, cols = x.shape
row_inds = [*range(0, rows-1, rstride), rows-1]
col_inds = [*range(0, cols-1, cstride), cols-1]
polys = []
for rs, rs_next in zip(row_inds[:-1], row_inds[1:]):
for cs, cs_next in zip(col_inds[:-1], col_inds[1:]):
# +1 ensures we share edges between polygons
ps = cbook._array_perimeter(x[rs:rs_next+1, cs:cs_next+1]).T
polys.append(ps)
polys = np.asarray(polys)
assert np.array_equal(polys,
cbook._array_patch_perimeters(
x, rstride=rstride, cstride=cstride))
def divisors(n):
return [i for i in range(1, n + 1) if n % i == 0]
for rows, cols in [(5, 5), (7, 14), (13, 9)]:
x = np.arange(rows * cols).reshape(rows, cols)
for rstride, cstride in itertools.product(divisors(rows - 1),
divisors(cols - 1)):
check(x, rstride=rstride, cstride=cstride)
@pytest.mark.parametrize('target,test_shape',
[((None, ), (1, 3)),
((None, 3), (1,)),
((None, 3), (1, 2)),
((1, 5), (1, 9)),
((None, 2, None), (1, 3, 1))
])
def test_check_shape(target, test_shape):
error_pattern = (f"^'aardvark' must be {len(target)}D.*" +
re.escape(f'has shape {test_shape}'))
data = np.zeros(test_shape)
with pytest.raises(ValueError,
match=error_pattern):
cbook._check_shape(target, aardvark=data)
def test_setattr_cm():
class A:
cls_level = object()
override = object()
def __init__(self):
self.aardvark = 'aardvark'
self.override = 'override'
self._p = 'p'
def meth(self):
...
@classmethod
def classy(klass):
...
@staticmethod
def static():
...
@property
def prop(self):
return self._p
@prop.setter
def prop(self, val):
self._p = val
class B(A):
...
other = A()
def verify_pre_post_state(obj):
# When you access a Python method the function is bound
# to the object at access time so you get a new instance
# of MethodType every time.
#
# https://docs.python.org/3/howto/descriptor.html#functions-and-methods
assert obj.meth is not obj.meth
# normal attribute should give you back the same instance every time
assert obj.aardvark is obj.aardvark
assert a.aardvark == 'aardvark'
# and our property happens to give the same instance every time
assert obj.prop is obj.prop
assert obj.cls_level is A.cls_level
assert obj.override == 'override'
assert not hasattr(obj, 'extra')
assert obj.prop == 'p'
assert obj.monkey == other.meth
assert obj.cls_level is A.cls_level
assert 'cls_level' not in obj.__dict__
assert 'classy' not in obj.__dict__
assert 'static' not in obj.__dict__
a = B()
a.monkey = other.meth
verify_pre_post_state(a)
with cbook._setattr_cm(
a, prop='squirrel',
aardvark='moose', meth=lambda: None,
override='boo', extra='extra',
monkey=lambda: None, cls_level='bob',
classy='classy', static='static'):
# because we have set a lambda, it is normal attribute access
# and the same every time
assert a.meth is a.meth
assert a.aardvark is a.aardvark
assert a.aardvark == 'moose'
assert a.override == 'boo'
assert a.extra == 'extra'
assert a.prop == 'squirrel'
assert a.monkey != other.meth
assert a.cls_level == 'bob'
assert a.classy == 'classy'
assert a.static == 'static'
verify_pre_post_state(a)