Merge pull request scipy#11573 from pvanmulbregt/warnings_stats

MAINT: Removed unused imports; fixed unused assignments in scipy/stats.

scipy/stats/stats.py

@@ -164,7 +164,6 @@
 from __future__ import division, print_function, absolute_import
 
 import warnings
-import sys
 import math
 from math import gcd
 from collections import namedtuple
@@ -5874,7 +5873,7 @@ def _compute_prob_inside_method(m, n, g, h):
     for i in range(1, m + 1):
         # Generate the next column.
         # First calculate the sliding window
-        lastminj, lastmaxj, lastlen = minj, maxj, curlen
+        lastminj, lastlen = minj, curlen
         minj = max(int(np.floor((ng * i - h) / mg)) + 1, 0)
         minj = min(minj, n)
         maxj = min(int(np.ceil((ng * i + h) / mg)), n + 1)

scipy/stats/tests/test_contingency.py

@@ -76,33 +76,33 @@ def test_chi2_contingency_trivial():
 def test_chi2_contingency_R():
     # Some test cases that were computed independently, using R.
 
-    Rcode = \
-    """
-    # Data vector.
-    data <- c(
-      12, 34, 23,     4,  47,  11,
-      35, 31, 11,    34,  10,  18,
-      12, 32,  9,    18,  13,  19,
-      12, 12, 14,     9,  33,  25
-      )
-
-    # Create factor tags:r=rows, c=columns, t=tiers
-    r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
-    c <- factor(gl(3, 1,   2*3*4, labels=c("c1", "c2", "c3")))
-    t <- factor(gl(2, 3,   2*3*4, labels=c("t1", "t2")))
-
-    # 3-way Chi squared test of independence
-    s = summary(xtabs(data~r+c+t))
-    print(s)
-    """
-    Routput = \
-    """
-    Call: xtabs(formula = data ~ r + c + t)
-    Number of cases in table: 478
-    Number of factors: 3
-    Test for independence of all factors:
-            Chisq = 102.17, df = 17, p-value = 3.514e-14
-    """
+    # Rcode = \
+    # """
+    # # Data vector.
+    # data <- c(
+    #   12, 34, 23,     4,  47,  11,
+    #   35, 31, 11,    34,  10,  18,
+    #   12, 32,  9,    18,  13,  19,
+    #   12, 12, 14,     9,  33,  25
+    #   )
+    #
+    # # Create factor tags:r=rows, c=columns, t=tiers
+    # r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
+    # c <- factor(gl(3, 1,   2*3*4, labels=c("c1", "c2", "c3")))
+    # t <- factor(gl(2, 3,   2*3*4, labels=c("t1", "t2")))
+    #
+    # # 3-way Chi squared test of independence
+    # s = summary(xtabs(data~r+c+t))
+    # print(s)
+    # """
+    # Routput = \
+    # """
+    # Call: xtabs(formula = data ~ r + c + t)
+    # Number of cases in table: 478
+    # Number of factors: 3
+    # Test for independence of all factors:
+    #         Chisq = 102.17, df = 17, p-value = 3.514e-14
+    # """
     obs = np.array(
         [[[12, 34, 23],
           [35, 31, 11],
@@ -117,42 +117,42 @@ def test_chi2_contingency_R():
     assert_approx_equal(p, 3.514e-14, significant=4)
     assert_equal(dof, 17)
 
-    Rcode = \
-    """
-    # Data vector.
-    data <- c(
-        #
-        12, 17,
-        11, 16,
-        #
-        11, 12,
-        15, 16,
-        #
-        23, 15,
-        30, 22,
-        #
-        14, 17,
-        15, 16
-        )
-
-    # Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
-    r <- factor(gl(2, 2,  2*2*2*2, labels=c("r1", "r2")))
-    c <- factor(gl(2, 1,  2*2*2*2, labels=c("c1", "c2")))
-    d <- factor(gl(2, 4,  2*2*2*2, labels=c("d1", "d2")))
-    t <- factor(gl(2, 8,  2*2*2*2, labels=c("t1", "t2")))
-
-    # 4-way Chi squared test of independence
-    s = summary(xtabs(data~r+c+d+t))
-    print(s)
-    """
-    Routput = \
-    """
-    Call: xtabs(formula = data ~ r + c + d + t)
-    Number of cases in table: 262
-    Number of factors: 4
-    Test for independence of all factors:
-            Chisq = 8.758, df = 11, p-value = 0.6442
-    """
+    # Rcode = \
+    # """
+    # # Data vector.
+    # data <- c(
+    #     #
+    #     12, 17,
+    #     11, 16,
+    #     #
+    #     11, 12,
+    #     15, 16,
+    #     #
+    #     23, 15,
+    #     30, 22,
+    #     #
+    #     14, 17,
+    #     15, 16
+    #     )
+    #
+    # # Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
+    # r <- factor(gl(2, 2,  2*2*2*2, labels=c("r1", "r2")))
+    # c <- factor(gl(2, 1,  2*2*2*2, labels=c("c1", "c2")))
+    # d <- factor(gl(2, 4,  2*2*2*2, labels=c("d1", "d2")))
+    # t <- factor(gl(2, 8,  2*2*2*2, labels=c("t1", "t2")))
+    #
+    # # 4-way Chi squared test of independence
+    # s = summary(xtabs(data~r+c+d+t))
+    # print(s)
+    # """
+    # Routput = \
+    # """
+    # Call: xtabs(formula = data ~ r + c + d + t)
+    # Number of cases in table: 262
+    # Number of factors: 4
+    # Test for independence of all factors:
+    #         Chisq = 8.758, df = 11, p-value = 0.6442
+    # """
     obs = np.array(
         [[[[12, 17],
            [11, 16]],

scipy/stats/tests/test_morestats.py

@@ -10,7 +10,7 @@
 from numpy.random import RandomState
 from numpy.testing import (assert_array_equal,
     assert_almost_equal, assert_array_less, assert_array_almost_equal,
-    assert_, assert_allclose, assert_equal, assert_warns, suppress_warnings)
+    assert_, assert_allclose, assert_equal, suppress_warnings)
 import pytest
 from pytest import raises as assert_raises
 
@@ -1510,7 +1510,6 @@ def test_empty(self):
     def test_array_like(self):
         np.random.seed(54321)
         x = stats.norm.rvs(size=100, loc=0)
-        lmbda = 1.5
         xt1, _ = stats.yeojohnson(x)
         xt2, _ = stats.yeojohnson(list(x))
         assert_allclose(xt1, xt2, rtol=1e-12)

scipy/stats/tests/test_mstats_basic.py

@@ -575,7 +575,7 @@ def test_mode_modifies_input(self):
         im[:50, :] += 1
         im[:, :50] += 1
         cp = im.copy()
-        a = mstats.mode(im, None)
+        mstats.mode(im, None)
         assert_equal(im, cp)
 
 

scipy/stats/tests/test_multivariate.py

@@ -1505,8 +1505,8 @@ class TestRandomCorrelation(object):
     def test_reproducibility(self):
         np.random.seed(514)
         eigs = (.5, .8, 1.2, 1.5)
-        x = random_correlation.rvs((.5, .8, 1.2, 1.5))
-        x2 = random_correlation.rvs((.5, .8, 1.2, 1.5), random_state=514)
+        x = random_correlation.rvs(eigs)
+        x2 = random_correlation.rvs(eigs, random_state=514)
         expected = np.array([[1., -0.20387311, 0.18366501, -0.04953711],
                              [-0.20387311, 1., -0.24351129, 0.06703474],
                              [0.18366501, -0.24351129, 1., 0.38530195],

scipy/stats/tests/test_stats.py

@@ -9,7 +9,6 @@
 from __future__ import division, print_function, absolute_import
 
 import os
-import sys
 import warnings
 from collections import namedtuple
 import multiprocessing
@@ -28,9 +27,7 @@
 import scipy.stats.mstats as mstats
 import scipy.stats.mstats_basic as mstats_basic
 from .common_tests import check_named_results
-from scipy.special import kv
 from scipy.sparse.sputils import matrix
-from scipy.integrate import quad
 
 """ Numbers in docstrings beginning with 'W' refer to the section numbers
     and headings found in the STATISTICS QUIZ of Leland Wilkinson.  These are
@@ -89,7 +86,7 @@ def test_tvar(self):
         assert_approx_equal(y, 4.666666666666667, significant=self.dprec)
 
         with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, "Degrees of freedom <= 0 for slice.")
+            sup.record(RuntimeWarning, "Degrees of freedom <= 0 for slice.")
 
             # Limiting some values along one axis
             y = stats.tvar(x_2d, limits=(1, 5), axis=1, inclusive=(True, True))
@@ -123,7 +120,7 @@ def test_tmin(self):
         x = np.arange(10.)
         x[9] = np.nan
         with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, "invalid value*")
+            sup.record(RuntimeWarning, "invalid value*")
             assert_equal(stats.tmin(x), np.nan)
             assert_equal(stats.tmin(x, nan_policy='omit'), 0.)
             assert_raises(ValueError, stats.tmin, x, nan_policy='raise')
@@ -148,7 +145,7 @@ def test_tmax(self):
         x = np.arange(10.)
         x[6] = np.nan
         with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, "invalid value*")
+            sup.record(RuntimeWarning, "invalid value*")
             assert_equal(stats.tmax(x), np.nan)
             assert_equal(stats.tmax(x, nan_policy='omit'), 9.)
             assert_raises(ValueError, stats.tmax, x, nan_policy='raise')
@@ -2092,13 +2089,13 @@ def test_nanpolicy(self):
 
         # Yes NaNs
         x[1, 2] = np.nan
-        with warnings.catch_warnings(record=True) as w:
+        with warnings.catch_warnings(record=True):
             warnings.simplefilter("always")
             assert_equal(stats.iqr(x, nan_policy='propagate'), np.nan)
             assert_equal(stats.iqr(x, axis=0, nan_policy='propagate'), [5, 5, np.nan, 5, 5])
             assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, np.nan, 2])
 
-        with warnings.catch_warnings(record=True) as w:
+        with warnings.catch_warnings(record=True):
             warnings.simplefilter("always")
             assert_equal(stats.iqr(x, nan_policy='omit'), 7.5)
             assert_equal(stats.iqr(x, axis=0, nan_policy='omit'), np.full(5, 5))
@@ -2121,7 +2118,7 @@ def test_scale(self):
 
         # Yes NaNs
         x[1, 2] = np.nan
-        with warnings.catch_warnings(record=True) as w:
+        with warnings.catch_warnings(record=True):
             warnings.simplefilter("always")
             assert_equal(stats.iqr(x, scale='raw', nan_policy='propagate'), np.nan)
             assert_equal(stats.iqr(x, scale='normal', nan_policy='propagate'), np.nan)
@@ -4711,7 +4708,7 @@ def test_inf_values(self):
             stats.wasserstein_distance([1, -np.inf, np.inf], [1, 1]),
             np.inf)
         with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, "invalid value*")
+            sup.record(RuntimeWarning, "invalid value*")
             assert_equal(
                 stats.wasserstein_distance([1, 2, np.inf], [np.inf, 1]),
                 np.nan)
@@ -4778,7 +4775,7 @@ def test_inf_values(self):
             stats.energy_distance([1, -np.inf, np.inf], [1, 1]),
             np.inf)
         with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, "invalid value*")
+            sup.record(RuntimeWarning, "invalid value*")
             assert_equal(
                 stats.energy_distance([1, 2, np.inf], [np.inf, 1]),
                 np.nan)