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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import itertools
import pickle
from weakref import ref
import warnings
import six
from datetime import datetime
import numpy as np
from numpy.testing.utils 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 delete_masked_points as dmp
def test_is_hashable():
s = 'string'
assert cbook.is_hashable(s)
lst = ['list', 'of', 'stings']
assert not cbook.is_hashable(lst)
def test_restrict_dict():
d = {'foo': 'bar', 1: 2}
d1 = cbook.restrict_dict(d, ['foo', 1])
assert d1 == d
d2 = cbook.restrict_dict(d, ['bar', 2])
assert d2 == {}
d3 = cbook.restrict_dict(d, {'foo': 1})
assert d3 == {'foo': 'bar'}
d4 = cbook.restrict_dict(d, {})
assert d4 == {}
d5 = cbook.restrict_dict(d, {'foo', 2})
assert d5 == {'foo': 'bar'}
# check that d was not modified
assert d == {'foo': 'bar', 1: 2}
class Test_delete_masked_points(object):
def setup_method(self):
self.mask1 = [False, False, True, True, False, False]
self.arr0 = np.arange(1.0, 7.0)
self.arr1 = [1, 2, 3, np.nan, np.nan, 6]
self.arr2 = np.array(self.arr1)
self.arr3 = np.ma.array(self.arr2, mask=self.mask1)
self.arr_s = ['a', 'b', 'c', 'd', 'e', 'f']
self.arr_s2 = np.array(self.arr_s)
self.arr_dt = [datetime(2008, 1, 1), datetime(2008, 1, 2),
datetime(2008, 1, 3), datetime(2008, 1, 4),
datetime(2008, 1, 5), datetime(2008, 1, 6)]
self.arr_dt2 = np.array(self.arr_dt)
self.arr_colors = ['r', 'g', 'b', 'c', 'm', 'y']
self.arr_rgba = mcolors.to_rgba_array(self.arr_colors)
def test_bad_first_arg(self):
with pytest.raises(ValueError):
dmp('a string', self.arr0)
def test_string_seq(self):
actual = dmp(self.arr_s, self.arr1)
ind = [0, 1, 2, 5]
expected = (self.arr_s2.take(ind), self.arr2.take(ind))
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
def test_datetime(self):
actual = dmp(self.arr_dt, self.arr3)
ind = [0, 1, 5]
expected = (self.arr_dt2.take(ind),
self.arr3.take(ind).compressed())
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
def test_rgba(self):
actual = dmp(self.arr3, self.arr_rgba)
ind = [0, 1, 5]
expected = (self.arr3.take(ind).compressed(),
self.arr_rgba.take(ind, axis=0))
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
class Test_boxplot_stats(object):
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='range')
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):
results = cbook.boxplot_stats(self.data, labels=labels)
def test_bad_dims(self):
data = np.random.normal(size=(34, 34, 34))
with pytest.raises(ValueError):
results = 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(object):
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_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': ()})
)
warn_passing_mapping = (
({'a': 1, 'b': 2}, {'a': 1}, {'alias_mapping': {'a': ['b']}}, 1),
({'a': 1, 'b': 2}, {'a': 1},
{'alias_mapping': {'a': ['b']}, 'allowed': ('a',)}, 1),
({'a': 1, 'b': 2}, {'a': 2}, {'alias_mapping': {'a': ['a', 'b']}}, 1),
({'a': 1, 'b': 2, 'c': 3}, {'a': 1, 'c': 3},
{'alias_mapping': {'a': ['b']}, 'required': ('a', )}, 1),
)
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.normalize_kwargs(inp, **kwargs_to_norm)
@pytest.mark.parametrize('inp, expected, kwargs_to_norm, warn_count',
warn_passing_mapping)
def test_normalize_kwargs_warn(inp, expected, kwargs_to_norm, warn_count):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
assert expected == cbook.normalize_kwargs(inp, **kwargs_to_norm)
assert len(w) == warn_count
@pytest.mark.parametrize('inp, expected, kwargs_to_norm',
pass_mapping)
def test_normalize_kwargs_pass(inp, expected, kwargs_to_norm):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
assert expected == cbook.normalize_kwargs(inp, **kwargs_to_norm)
assert len(w) == 0
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_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_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)
@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)
class TestFuncParser(object):
x_test = np.linspace(0.01, 0.5, 3)
validstrings = ['linear', 'quadratic', 'cubic', 'sqrt', 'cbrt',
'log', 'log10', 'log2', 'x**{1.5}', 'root{2.5}(x)',
'log{2}(x)',
'log(x+{0.5})', 'log10(x+{0.1})', 'log{2}(x+{0.1})',
'log{2}(x+{0})']
results = [(lambda x: x),
np.square,
(lambda x: x**3),
np.sqrt,
(lambda x: x**(1. / 3)),
np.log,
np.log10,
np.log2,
(lambda x: x**1.5),
(lambda x: x**(1 / 2.5)),
(lambda x: np.log2(x)),
(lambda x: np.log(x + 0.5)),
(lambda x: np.log10(x + 0.1)),
(lambda x: np.log2(x + 0.1)),
(lambda x: np.log2(x))]
bounded_list = [True, True, True, True, True,
False, False, False, True, True,
False,
True, True, True,
False]
@pytest.mark.parametrize("string, func",
zip(validstrings, results),
ids=validstrings)
def test_values(self, string, func):
func_parser = cbook._StringFuncParser(string)
f = func_parser.function
assert_array_almost_equal(f(self.x_test), func(self.x_test))
@pytest.mark.parametrize("string", validstrings, ids=validstrings)
def test_inverse(self, string):
func_parser = cbook._StringFuncParser(string)
f = func_parser.func_info
fdir = f.function
finv = f.inverse
assert_array_almost_equal(finv(fdir(self.x_test)), self.x_test)
@pytest.mark.parametrize("string", validstrings, ids=validstrings)
def test_get_inverse(self, string):
func_parser = cbook._StringFuncParser(string)
finv1 = func_parser.inverse
finv2 = func_parser.func_info.inverse
assert_array_almost_equal(finv1(self.x_test), finv2(self.x_test))
@pytest.mark.parametrize("string, bounded",
zip(validstrings, bounded_list),
ids=validstrings)
def test_bounded(self, string, bounded):
func_parser = cbook._StringFuncParser(string)
b = func_parser.is_bounded_0_1
assert_array_equal(b, bounded)