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_method(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, 'Aardvark', 4]

results = cbook.boxplot_stats(self.data, labels=labels)

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_method(self):

self.signal = 'test'

self.callbacks = cbook.CallbackRegistry()

def connect(self, s, func, pickle):

if pickle:

return self.callbacks.connect(s, func)

else:

return self.callbacks._connect_picklable(s, func)

def disconnect(self, cid):

return self.callbacks.disconnect(cid)

def count(self):

count1 = sum(s == self.signal for s, p in self.callbacks._func_cid_map)

count2 = len(self.callbacks.callbacks.get(self.signal))

assert count1 == count2

return count1

def is_empty(self):

np.testing.break_cycles()

assert [*self.callbacks._func_cid_map] == []

assert self.callbacks.callbacks == {}

assert self.callbacks._pickled_cids == set()

def is_not_empty(self):

np.testing.break_cycles()

assert [*self.callbacks._func_cid_map] != []

assert self.callbacks.callbacks != {}

def test_cid_restore(self):

cb = cbook.CallbackRegistry()

cb.connect('a', lambda: None)

cb2 = pickle.loads(pickle.dumps(cb))

cid = cb2.connect('c', lambda: None)

assert cid == 1

@pytest.mark.parametrize('pickle', [True, False])

@pytest.mark.parametrize('cls', [Hashable, Unhashable])

def test_callback_complete(self, pickle, cls):

# ensure we start with an empty registry

self.is_empty()

# create a class for testing

mini_me = cls()

# test that we can add a callback

cid1 = self.connect(self.signal, mini_me.dummy, pickle)

assert type(cid1) is int

self.is_not_empty()

# test that we don't add a second callback

cid2 = self.connect(self.signal, mini_me.dummy, pickle)

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()

@pytest.mark.parametrize('pickle', [True, False])

@pytest.mark.parametrize('cls', [Hashable, Unhashable])

def test_callback_disconnect(self, pickle, cls):

# ensure we start with an empty registry

self.is_empty()

# create a class for testing

mini_me = cls()

# test that we can add a callback

cid1 = self.connect(self.signal, mini_me.dummy, pickle)

assert type(cid1) is int

self.is_not_empty()

self.disconnect(cid1)

# check we now have no callbacks registered

self.is_empty()

@pytest.mark.parametrize('pickle', [True, False])

@pytest.mark.parametrize('cls', [Hashable, Unhashable])

def test_callback_wrong_disconnect(self, pickle, cls):

# ensure we start with an empty registry

self.is_empty()

# create a class for testing

mini_me = cls()

# test that we can add a callback

cid1 = self.connect(self.signal, mini_me.dummy, pickle)

assert type(cid1) is int

self.is_not_empty()

self.disconnect("foo")

# check we still have callbacks registered

self.is_not_empty()

@pytest.mark.parametrize('pickle', [True, False])

@pytest.mark.parametrize('cls', [Hashable, Unhashable])

def test_registration_on_non_empty_registry(self, pickle, cls):

# ensure we start with an empty registry

self.is_empty()

# setup the registry with a callback

mini_me = cls()

self.connect(self.signal, mini_me.dummy, pickle)

# Add another callback

mini_me2 = cls()

self.connect(self.signal, mini_me2.dummy, pickle)

# Remove and add the second callback

mini_me2 = cls()

self.connect(self.signal, mini_me2.dummy, pickle)

# We still have 2 references

self.is_not_empty()

assert self.count() == 2

# Removing the last 2 references

mini_me = None

mini_me2 = None

self.is_empty()

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")

outerr = capsys.readouterr()

assert outerr.out == outerr.err == ""

monkeypatch.setattr(

cbook, "_get_running_interactive_framework", lambda: "not-none")

cb.process("foo", "argument mismatch") # No error in that case.

outerr = capsys.readouterr()

assert outerr.out == ""

class TestException(Exception):

pass

def raise_runtime_error():

raise RuntimeError

def raise_value_error():

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', raise_runtime_error)

# filter

cb_filt = cbook.CallbackRegistry(exception_handler=transformer)

cb_filt.connect('foo', raise_runtime_error)

# filter

cb_filt_pass = cbook.CallbackRegistry(exception_handler=transformer)

cb_filt_pass.connect('foo', raise_value_error)

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):

cr = cbook.CallbackRegistry(signals=["foo"])

results = []

def cb(x): results.append(x)

cr.connect("foo", cb)

with pytest.raises(ValueError):

cr.connect("bar", cb)

cr.process("foo", 1)

with pytest.raises(ValueError):

cr.process("bar", 1)

# Needs an exception handler for interactive testing environments

# that would only print this out instead of raising the exception

def raise_handler(excp):

raise excp

cb = cbook.CallbackRegistry(exception_handler=raise_handler)

def test_func1():

raise ValueError("1 should be blocked")

def test_func2():

raise ValueError("2 should be blocked")

cb.connect("test1", test_func1)

cb.connect("test2", test_func2)

# block all of the callbacks to make sure they aren't processed

with cb.blocked():

cb.process("test1")

cb.process("test2")

# block individual callbacks to make sure the other is still processed

with cb.blocked(signal="test1"):

# Blocked

cb.process("test1")

# Should raise

with pytest.raises(ValueError, match="2 should be blocked"):

cb.process("test2")

# Make sure the original callback functions are there after blocking

with pytest.raises(ValueError, match="1 should be blocked"):

cb.process("test1")

with pytest.raises(ValueError, match="2 should be blocked"):

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)

(None, {}, {}),

({'a': 1, 'b': 2}, {'a': 1, 'b': 2}, {}),

({'b': 2}, {'a': 2}, {'alias_mapping': {'a': ['a', 'b']}}),

_api.warn_external("oops")

assert len(recwarn) == 1

if sys.version_info[:2] >= (3, 12):

# With Python 3.12, we let Python figure out the stacklevel using the

# `skip_file_prefixes` argument, which cannot exempt tests, so just confirm

# the filename is not in the package.

basedir = pathlib.Path(__file__).parents[2]

assert not recwarn[0].filename.startswith((str(basedir / 'matplotlib'),

str(basedir / 'mpl_toolkits')))

else:

# On older Python versions, we manually calculated the stacklevel, and had an

# exception for our own tests.

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 _ 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 _ in range(5)]

g = cbook.Grouper()

g.join(*objs)

# reach in and touch the internals !

mapping = g._mapping

for o in objs:

assert o in mapping

base_set = mapping[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_finite(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 _ 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 _ in range(5)] for _ 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,)

# Test a list of zero-dimensional arrays

x = [np.array(0), np.array(1), np.array(2)]

xnew = cbook._reshape_2D(x, 'x')

assert isinstance(xnew, list)

assert len(xnew) == 1

assert isinstance(xnew[0], np.ndarray) and xnew[0].shape == (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

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)

# separate 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):

# separate to allow the rest of the tests to run if no xarray...

X = np.arange(30).reshape(10, 3)

x = xr.DataArray(X, dims=["x", "y"])

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):

# separate 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"])

Idx, Xnew = cbook.index_of(x)

np.testing.assert_array_equal(X, Xnew)

IdxRef = np.arange(10)

# separate to allow the rest of the tests to run if no xarray...

X = np.arange(30).reshape(10, 3)

x = xr.DataArray(X, dims=["x", "y"])

Idx, Xnew = cbook.index_of(x)

np.testing.assert_array_equal(X, Xnew)

IdxRef = np.arange(10)

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_finite(s)

# 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 itertools.pairwise(row_inds):

for cs, cs_next in itertools.pairwise(col_inds):

# +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)):

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(cls):

...

@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'

f = cbook._format_approx

assert f(0, 1) == '0'

assert f(0, 2) == '0'

assert f(0, 3) == '0'

assert f(-0.0123, 1) == '-0'

assert f(1e-7, 5) == '0'

assert f(0.0012345600001, 5) == '0.00123'

assert f(-0.0012345600001, 5) == '-0.00123'

datetime_lst = [date.today() + timedelta(days=i) for i in range(10)]

datetime_lst[0] = None

actual = cbook._safe_first_finite(datetime_lst)

"""

class Tensor:

def __init__(self, data):

self.data = data

def __array__(self):

return self.data

torch = ModuleType('torch')

torch.Tensor = Tensor

sys.modules['torch'] = torch

data = np.arange(10)

torch_tensor = torch.Tensor(data)

result = cbook._unpack_to_numpy(torch_tensor)

assert isinstance(result, np.ndarray)

# compare results, do not check for identity: the latter would fail

# if not mocked, and the implementation does not guarantee it

# is the same Python object, just the same values.