/usr/lib/python2.7/dist-packages/matplotlib/tests/test_cbook.py is in python-matplotlib 1.4.2-3.1.
This file is owned by root:root, with mode 0o644.
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unicode_literals)
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)
from nose.tools import assert_equal, raises, assert_true
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
from matplotlib.cbook import delete_masked_points as dmp
def test_is_string_like():
y = np.arange(10)
assert_equal(cbook.is_string_like(y), False)
y.shape = 10, 1
assert_equal(cbook.is_string_like(y), False)
y.shape = 1, 10
assert_equal(cbook.is_string_like(y), False)
assert cbook.is_string_like("hello world")
assert_equal(cbook.is_string_like(10), False)
def test_restrict_dict():
d = {'foo': 'bar', 1: 2}
d1 = cbook.restrict_dict(d, ['foo', 1])
assert_equal(d1, d)
d2 = cbook.restrict_dict(d, ['bar', 2])
assert_equal(d2, {})
d3 = cbook.restrict_dict(d, {'foo': 1})
assert_equal(d3, {'foo': 'bar'})
d4 = cbook.restrict_dict(d, {})
assert_equal(d4, {})
d5 = cbook.restrict_dict(d, set(['foo', 2]))
assert_equal(d5, {'foo': 'bar'})
# check that d was not modified
assert_equal(d, {'foo': 'bar', 1: 2})
class Test_delete_masked_points:
def setUp(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.colorConverter.to_rgba_array(self.arr_colors)
@raises(ValueError)
def test_bad_first_arg(self):
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])
def test_allequal():
assert(cbook.allequal([1, 1, 1]))
assert(not cbook.allequal([1, 1, 0]))
assert(cbook.allequal([]))
assert(cbook.allequal(('a', 'a')))
assert(not cbook.allequal(('a', 'b')))
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_true(isinstance(self.std_results, list))
def test_form_each_dict(self):
for res in self.std_results:
assert_true(isinstance(res, dict))
def test_form_dict_keys(self):
for res in self.std_results:
keys = sorted(list(res.keys()))
for key in keys:
assert_true(key in self.known_keys)
def test_results_baseline(self):
res = self.std_results[0]
for key in list(self.known_nonbootstrapped_res.keys()):
if key != 'fliers':
assert_statement = assert_approx_equal
else:
assert_statement = assert_array_almost_equal
assert_statement(
res[key],
self.known_nonbootstrapped_res[key]
)
def test_results_bootstrapped(self):
results = cbook.boxplot_stats(self.data, bootstrap=10000)
res = results[0]
for key in list(self.known_bootstrapped_ci.keys()):
assert_approx_equal(
res[key],
self.known_bootstrapped_ci[key]
)
def test_results_whiskers_float(self):
results = cbook.boxplot_stats(self.data, whis=3)
res = results[0]
for key in list(self.known_whis3_res.keys()):
if key != 'fliers':
assert_statement = assert_approx_equal
else:
assert_statement = assert_array_almost_equal
assert_statement(
res[key],
self.known_whis3_res[key]
)
def test_results_whiskers_range(self):
results = cbook.boxplot_stats(self.data, whis='range')
res = results[0]
for key in list(self.known_res_range.keys()):
if key != 'fliers':
assert_statement = assert_approx_equal
else:
assert_statement = assert_array_almost_equal
assert_statement(
res[key],
self.known_res_range[key]
)
def test_results_whiskers_percentiles(self):
results = cbook.boxplot_stats(self.data, whis=[5, 95])
res = results[0]
for key in list(self.known_res_percentiles.keys()):
if key != 'fliers':
assert_statement = assert_approx_equal
else:
assert_statement = assert_array_almost_equal
assert_statement(
res[key],
self.known_res_percentiles[key]
)
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_equal(res['label'], lab)
results = cbook.boxplot_stats(self.data)
for res in results:
assert('label' not in res)
@raises(ValueError)
def test_label_error(self):
labels = [1, 2]
results = cbook.boxplot_stats(self.data, labels=labels)
@raises(ValueError)
def test_bad_dims(self):
data = np.random.normal(size=(34, 34, 34))
results = cbook.boxplot_stats(data)
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