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())

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

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())),

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")

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],

monkeypatch.setattr(

cbook, "_get_running_interactive_framework", lambda: None)

with pytest.raises(excp):

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)

({'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', )}),

({'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')}),

with patch.object(cbook, "sys") as mock_sys:

mock_sys._getframe = Mock(return_value=None)

with pytest.warns(UserWarning, match=r"\Adummy\Z"):

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)

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)

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)

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:]):

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:]:

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)

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)

# 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

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

# 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)

@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):

@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):

# 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)):

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):