def test_1d_result_attributes(self):
x = self.x
v = self.v
res = binned_statistic(x, v, "count", bins=10)
attributes = ("statistic", "bin_edges", "binnumber")
check_named_results(res, attributes)
def test_1d_result_attributes(self):
x = self.x
v = self.v
res = binned_statistic(x, v, 'count', bins=10)
attributes = ('statistic', 'bin_edges', 'binnumber')
check_named_results(res, attributes)
def test_result_attributes(self):
x = np.array([
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,
2., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1.
])
y = np.array([
1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2., 1., 1.,
1., 2., 1., 1., 1., 1., 1., 2., 1., 1., 3., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,
1., 2., 2., 1., 1., 2., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2., 2.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1.,
1., 1., 1., 2., 2., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 2., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 2., 1.,
1., 1., 1., 1., 1.
])
res = mstats.mannwhitneyu(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
with warnings.catch_warnings(record=True): # Ties preclude use ...
res = stats.ansari(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
x = np.arange(15)
attributes = ('statistic', 'minmax')
res = stats.bayes_mvs(x)
for i in res:
check_named_results(i, attributes)
def test_1d_result_attributes(self):
x = self.x
v = self.v
res = binned_statistic(x, v, 'count', bins=10)
attributes = ('statistic', 'bin_edges', 'binnumber')
check_named_results(res, attributes)
def test_result_attributes(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
res = mstats.ttest_1samp(outcome[:, 0], 1)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
res = mstats.ttest_1samp(outcome[:, 0], 1)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
x = np.arange(15)
attributes = ('statistic', 'minmax')
res = stats.bayes_mvs(x)
for i in res:
check_named_results(i, attributes)
def test_mode(self):
a1 = [0,0,0,1,1,1,2,3,3,3,3,4,5,6,7]
a2 = np.reshape(a1, (3,5))
a3 = np.array([1,2,3,4,5,6])
a4 = np.reshape(a3, (3,2))
ma1 = ma.masked_where(ma.array(a1) > 2, a1)
ma2 = ma.masked_where(a2 > 2, a2)
ma3 = ma.masked_where(a3 < 2, a3)
ma4 = ma.masked_where(ma.array(a4) < 2, a4)
assert_equal(mstats.mode(a1, axis=None), (3,4))
assert_equal(mstats.mode(a1, axis=0), (3,4))
assert_equal(mstats.mode(ma1, axis=None), (0,3))
assert_equal(mstats.mode(a2, axis=None), (3,4))
assert_equal(mstats.mode(ma2, axis=None), (0,3))
assert_equal(mstats.mode(a3, axis=None), (1,1))
assert_equal(mstats.mode(ma3, axis=None), (2,1))
assert_equal(mstats.mode(a2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
assert_equal(mstats.mode(ma2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
assert_equal(mstats.mode(a2, axis=-1), ([[0],[3],[3]], [[3],[3],[1]]))
assert_equal(mstats.mode(ma2, axis=-1), ([[0],[1],[0]], [[3],[1],[0]]))
assert_equal(mstats.mode(ma4, axis=0), ([[3,2]], [[1,1]]))
assert_equal(mstats.mode(ma4, axis=-1), ([[2],[3],[5]], [[1],[1],[1]]))
a1_res = mstats.mode(a1, axis=None)
# test for namedtuple attributes
attributes = ('mode', 'count')
check_named_results(a1_res, attributes, ma=True)
def test_dd_result_attributes(self):
X = self.X
v = self.v
res = binned_statistic_dd(X, v, "count", bins=3)
attributes = ("statistic", "bin_edges", "binnumber")
check_named_results(res, attributes)
def test_mode(self):
a1 = [0, 0, 0, 1, 1, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7]
a2 = np.reshape(a1, (3, 5))
a3 = np.array([1, 2, 3, 4, 5, 6])
a4 = np.reshape(a3, (3, 2))
ma1 = ma.masked_where(ma.array(a1) > 2, a1)
ma2 = ma.masked_where(a2 > 2, a2)
ma3 = ma.masked_where(a3 < 2, a3)
ma4 = ma.masked_where(ma.array(a4) < 2, a4)
assert_equal(mstats.mode(a1, axis=None), (3, 4))
assert_equal(mstats.mode(a1, axis=0), (3, 4))
assert_equal(mstats.mode(ma1, axis=None), (0, 3))
assert_equal(mstats.mode(a2, axis=None), (3, 4))
assert_equal(mstats.mode(ma2, axis=None), (0, 3))
assert_equal(mstats.mode(a3, axis=None), (1, 1))
assert_equal(mstats.mode(ma3, axis=None), (2, 1))
assert_equal(mstats.mode(a2, axis=0),
([[0, 0, 0, 1, 1]], [[1, 1, 1, 1, 1]]))
assert_equal(mstats.mode(ma2, axis=0),
([[0, 0, 0, 1, 1]], [[1, 1, 1, 1, 1]]))
assert_equal(mstats.mode(a2, axis=-1),
([[0], [3], [3]], [[3], [3], [1]]))
assert_equal(mstats.mode(ma2, axis=-1),
([[0], [1], [0]], [[3], [1], [0]]))
assert_equal(mstats.mode(ma4, axis=0), ([[3, 2]], [[1, 1]]))
assert_equal(mstats.mode(ma4, axis=-1),
([[2], [3], [5]], [[1], [1], [1]]))
a1_res = mstats.mode(a1, axis=None)
# test for namedtuple attributes
attributes = ('mode', 'count')
check_named_results(a1_res, attributes, ma=True)
def test_result_attributes(self):
x = [1, 3, 5, 7, 9]
y = [2, 4, 6, 8, 10]
res = mstats.kruskal(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_spearmanr(self):
# Tests some computations of Spearman's rho
(x, y) = ([5.05, 6.75, 3.21, 2.66], [1.65, 2.64, 2.64, 6.95])
assert_almost_equal(mstats.spearmanr(x, y)[0], -0.6324555)
(x, y) = ([5.05, 6.75, 3.21, 2.66,
np.nan], [1.65, 2.64, 2.64, 6.95, np.nan])
(x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
assert_almost_equal(mstats.spearmanr(x, y)[0], -0.6324555)
x = [
2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1, 1.0, 1.4, 7.9, 0.3,
3.9, 0.3, 6.7
]
y = [
22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6, 0.0, 0.6, 6.7, 3.8,
1.0, 1.2, 1.4
]
assert_almost_equal(mstats.spearmanr(x, y)[0], 0.6887299)
x = [
2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1, 1.0, 1.4, 7.9, 0.3,
3.9, 0.3, 6.7, np.nan
]
y = [
22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6, 0.0, 0.6, 6.7, 3.8,
1.0, 1.2, 1.4, np.nan
]
(x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
assert_almost_equal(mstats.spearmanr(x, y)[0], 0.6887299)
# test for namedtuple attributes
res = mstats.spearmanr(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_kendalltau(self):
# Tests some computations of Kendall's tau
x = ma.fix_invalid([5.05, 6.75, 3.21, 2.66, np.nan])
y = ma.fix_invalid([1.65, 26.5, -5.93, 7.96, np.nan])
z = ma.fix_invalid([1.65, 2.64, 2.64, 6.95, np.nan])
assert_almost_equal(np.asarray(mstats.kendalltau(x, y)),
[+0.3333333, 0.4969059])
assert_almost_equal(np.asarray(mstats.kendalltau(x, z)),
[-0.5477226, 0.2785987])
#
x = ma.fix_invalid([
0, 0, 0, 0, 20, 20, 0, 60, 0, 20, 10, 10, 0, 40, 0, 20, 0, 0, 0, 0,
0, np.nan
])
y = ma.fix_invalid([
0, 80, 80, 80, 10, 33, 60, 0, 67, 27, 25, 80, 80, 80, 80, 80, 80,
0, 10, 45, np.nan, 0
])
result = mstats.kendalltau(x, y)
assert_almost_equal(np.asarray(result), [-0.1585188, 0.4128009])
# test for namedtuple attributes
res = mstats.kendalltau(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_dd_result_attributes(self):
X = self.X
v = self.v
res = binned_statistic_dd(X, v, 'count', bins=3)
attributes = ('statistic', 'bin_edges', 'binnumber')
check_named_results(res, attributes)
def test_result_attributes(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
with warnings.catch_warnings(record=True): # Ties preclude use ...
res = stats.ansari(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
x = [1, 3, 5, 7, 9]
y = [2, 4, 6, 8, 10]
res = mstats.kruskal(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_dd_result_attributes(self):
X = self.X
v = self.v
res = binned_statistic_dd(X, v, 'count', bins=3)
attributes = ('statistic', 'bin_edges', 'binnumber')
check_named_results(res, attributes)
def test_2d_result_attributes(self):
x = self.x
y = self.y
v = self.v
res = binned_statistic_2d(x, y, v, 'count', bins=5)
attributes = ('statistic', 'x_edge', 'y_edge', 'binnumber')
check_named_results(res, attributes)
def test_2d_result_attributes(self):
x = self.x
y = self.y
v = self.v
res = binned_statistic_2d(x, y, v, 'count', bins=5)
attributes = ('statistic', 'x_edge', 'y_edge', 'binnumber')
check_named_results(res, attributes)
def test_2d_result_attributes(self):
x = self.x
y = self.y
v = self.v
res = binned_statistic_2d(x, y, v, "count", bins=5)
attributes = ("statistic", "x_edge", "y_edge", "binnumber")
check_named_results(res, attributes)
def test_pointbiserial(self):
x = [1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,
0,0,0,0,1,-1]
y = [14.8,13.8,12.4,10.1,7.1,6.1,5.8,4.6,4.3,3.5,3.3,3.2,3.0,
2.8,2.8,2.5,2.4,2.3,2.1,1.7,1.7,1.5,1.3,1.3,1.2,1.2,1.1,
0.8,0.7,0.6,0.5,0.2,0.2,0.1,np.nan]
assert_almost_equal(mstats.pointbiserialr(x, y)[0], 0.36149, 5)
# test for namedtuple attributes
res = mstats.pointbiserialr(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_pointbiserial(self):
x = [1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,
0,0,0,0,1,-1]
y = [14.8,13.8,12.4,10.1,7.1,6.1,5.8,4.6,4.3,3.5,3.3,3.2,3.0,
2.8,2.8,2.5,2.4,2.3,2.1,1.7,1.7,1.5,1.3,1.3,1.2,1.2,1.1,
0.8,0.7,0.6,0.5,0.2,0.2,0.1,np.nan]
assert_almost_equal(mstats.pointbiserialr(x, y)[0], 0.36149, 5)
# test for namedtuple attributes
res = mstats.pointbiserialr(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_regress_simple():
# Regress a line with sinusoidal noise. Test for #1273.
x = np.linspace(0, 100, 100)
y = 0.2 * np.linspace(0, 100, 100) + 10
y += np.sin(np.linspace(0, 20, 100))
slope, intercept, r_value, p_value, sterr = mstats.linregress(x, y)
assert_almost_equal(slope, 0.19644990055858422)
assert_almost_equal(intercept, 10.211269918932341)
# test for namedtuple attributes
res = mstats.linregress(x, y)
attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
check_named_results(res, attributes, ma=True)
def test_regress_simple():
# Regress a line with sinusoidal noise. Test for #1273.
x = np.linspace(0, 100, 100)
y = 0.2 * np.linspace(0, 100, 100) + 10
y += np.sin(np.linspace(0, 20, 100))
slope, intercept, r_value, p_value, sterr = mstats.linregress(x, y)
assert_almost_equal(slope, 0.19644990055858422)
assert_almost_equal(intercept, 10.211269918932341)
# test for namedtuple attributes
res = mstats.linregress(x, y)
attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
res = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
def test_result_attributes(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
res = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
def test_friedmanchisq(self):
# No missing values
args = ([9.0,9.5,5.0,7.5,9.5,7.5,8.0,7.0,8.5,6.0],
[7.0,6.5,7.0,7.5,5.0,8.0,6.0,6.5,7.0,7.0],
[6.0,8.0,4.0,6.0,7.0,6.5,6.0,4.0,6.5,3.0])
result = mstats.friedmanchisquare(*args)
assert_almost_equal(result[0], 10.4737, 4)
assert_almost_equal(result[1], 0.005317, 6)
# Missing values
x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1],
[4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3],
[3, 2, 5, 6, 18, 4, 9, 1, 1,nan, 1, 1,nan],
[nan, 6, 11, 4, 17,nan, 6, 1, 1, 2, 5, 1, 1]]
x = ma.fix_invalid(x)
result = mstats.friedmanchisquare(*x)
assert_almost_equal(result[0], 2.0156, 4)
assert_almost_equal(result[1], 0.5692, 4)
# test for namedtuple attributes
attributes = ('statistic', 'pvalue')
check_named_results(result, attributes, ma=True)
def test_kendalltau(self):
# Tests some computations of Kendall's tau
x = ma.fix_invalid([5.05, 6.75, 3.21, 2.66,np.nan])
y = ma.fix_invalid([1.65, 26.5, -5.93, 7.96, np.nan])
z = ma.fix_invalid([1.65, 2.64, 2.64, 6.95, np.nan])
assert_almost_equal(np.asarray(mstats.kendalltau(x,y)),
[+0.3333333,0.4969059])
assert_almost_equal(np.asarray(mstats.kendalltau(x,z)),
[-0.5477226,0.2785987])
#
x = ma.fix_invalid([0, 0, 0, 0,20,20, 0,60, 0,20,
10,10, 0,40, 0,20, 0, 0, 0, 0, 0, np.nan])
y = ma.fix_invalid([0,80,80,80,10,33,60, 0,67,27,
25,80,80,80,80,80,80, 0,10,45, np.nan, 0])
result = mstats.kendalltau(x,y)
assert_almost_equal(np.asarray(result), [-0.1585188, 0.4128009])
# test for namedtuple attributes
res = mstats.kendalltau(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_friedmanchisq(self):
# No missing values
args = ([9.0, 9.5, 5.0, 7.5, 9.5, 7.5, 8.0, 7.0, 8.5,
6.0], [7.0, 6.5, 7.0, 7.5, 5.0, 8.0, 6.0, 6.5, 7.0, 7.0],
[6.0, 8.0, 4.0, 6.0, 7.0, 6.5, 6.0, 4.0, 6.5, 3.0])
result = mstats.friedmanchisquare(*args)
assert_almost_equal(result[0], 10.4737, 4)
assert_almost_equal(result[1], 0.005317, 6)
# Missing values
x = [[nan, nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1],
[4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3],
[3, 2, 5, 6, 18, 4, 9, 1, 1, nan, 1, 1, nan],
[nan, 6, 11, 4, 17, nan, 6, 1, 1, 2, 5, 1, 1]]
x = ma.fix_invalid(x)
result = mstats.friedmanchisquare(*x)
assert_almost_equal(result[0], 2.0156, 4)
assert_almost_equal(result[1], 0.5692, 4)
# test for namedtuple attributes
attributes = ('statistic', 'pvalue')
check_named_results(result, attributes, ma=True)
def test_result_attributes(self):
x = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 2.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1.])
y = np.array([1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1.,
2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2., 1., 1., 3.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,
1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,
2., 1., 1., 2., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2.,
2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 2., 1., 1., 1., 1., 1., 2., 2., 2., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
2., 1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 2., 1., 1.,
1., 1., 1., 1.])
res = mstats.mannwhitneyu(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_spearmanr(self):
# Tests some computations of Spearman's rho
(x, y) = ([5.05,6.75,3.21,2.66],[1.65,2.64,2.64,6.95])
assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555)
(x, y) = ([5.05,6.75,3.21,2.66,np.nan],[1.65,2.64,2.64,6.95,np.nan])
(x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555)
x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7]
y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4]
assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299)
x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7, np.nan]
y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4, np.nan]
(x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299)
# test for namedtuple attributes
res = mstats.spearmanr(x, y)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_wilcoxon_result_attributes():
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
res = stats.wilcoxon(x, y, correction=False)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
rs = RandomState(1234567890)
x = rs.standard_exponential(size=50)
res = stats.anderson(x)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
def test_result_attributes(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
res = stats.ansari(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
res = stats.levene(*args)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
res = stats.ansari(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_wilcoxon_result_attributes():
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
res = stats.wilcoxon(x, y, correction=False)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_describe_result_attributes(self):
actual = mstats.describe(np.arange(5))
attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness',
'kurtosis')
check_named_results(actual, attributes, ma=True)
def test_kurtosistest_result_attributes(self):
x = np.array((-2, -1, 0, 1, 2, 3) * 4)**2
res = mstats.kurtosistest(x)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_kurtosistest_result_attributes(self):
x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
res = mstats.kurtosistest(x)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
a = np.array([655, 788], dtype=np.uint16)
b = np.array([789, 772], dtype=np.uint16)
res = mstats.f_oneway(a, b)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
res = stats.levene(*args)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_result_attributes(self):
a = np.array([655, 788], dtype=np.uint16)
b = np.array([789, 772], dtype=np.uint16)
res = mstats.f_oneway(a, b)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
rs = RandomState(1234567890)
x = rs.standard_exponential(size=50)
res = stats.anderson(x)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
def test_describe_result_attributes(self):
actual = mstats.describe(np.arange(5))
attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness',
'kurtosis')
check_named_results(actual, attributes, ma=True)