Merge pull request #28 from bashtage/sync-upstream

MAINT: Sync with recent upstream changes

README.rst

@@ -1,10 +1,7 @@
 RandomGen
 =========
 
-`Travis Build Status <https://travis-ci.org/bashtage/randomgen>`__
-`Appveyor Build
-Status <https://ci.appveyor.com/project/bashtage/randomgen/branch/master>`__
-`PyPI version <https://pypi.org/project/randomgen/>`__
+|Travis Build Status| |Appveyor Build Status| |PyPI version|
 
 Random Number Generator using settable Basic RNG interface for future
 NumPy RandomState evolution.
@@ -292,3 +289,10 @@ NumPy’s mt19937.
    ThreeFry32       97.0%
    Xoroshiro128    477.4%
    Xorshift1024    360.7%
+
+.. |Travis Build Status| image:: https://travis-ci.org/bashtage/randomgen.svg?branch=master
+   :target: https://travis-ci.org/bashtage/randomgen
+.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/odc5c4ukhru5xicl/branch/master?svg=true
+   :target: https://ci.appveyor.com/project/bashtage/randomgen/branch/master
+.. |PyPI version| image:: https://badge.fury.io/py/randomgen.svg
+   :target: https://pypi.org/project/randomgen/

doc/source/change-log.rst

@@ -1,9 +1,10 @@
 Change Log
 ----------
 
-Changes since v1.14
-===================
-
+v1.15
+=====
+- Synced empty choice changes
+- Synced upstream docstring changes
 - Synced upstream changes in permutation
 - Synced upstream doc fixes
 - Added absolute_import to avoid import noise on Python 2.7
@@ -12,5 +13,3 @@ Changes since v1.14
 - Switch to array-fillers for 0 parameter distribution to improve performance
 - Small changes to build on manylinux
 - Build wheels using multibuild
-
-

randomgen/examples/numba/extending.py

@@ -68,10 +68,10 @@ def normals(n, state):
 start = dt.datetime.now()
 normalsj(1000000, state_addr)
 ms = 1000 * (dt.datetime.now() - start).total_seconds()
-print('1,000,000 Box-Muller (numba/Xoroshiro128) randoms in '
+print('1,000,000 Polar-transform (numba/Xoroshiro128) randoms in '
       '{ms:0.1f}ms'.format(ms=ms))
 
 start = dt.datetime.now()
 np.random.standard_normal(1000000)
 ms = 1000 * (dt.datetime.now() - start).total_seconds()
-print('1,000,000 Box-Muller (NumPy) randoms in {ms:0.1f}ms'.format(ms=ms))
+print('1,000,000 Polar-transform (NumPy) randoms in {ms:0.1f}ms'.format(ms=ms))

randomgen/generator.pyx

@@ -798,7 +798,7 @@ cdef class RandomGenerator:
         else:
             pop_size = a.shape[0]
             if pop_size is 0 and np.prod(size) != 0:
-                raise ValueError("a cannot be empty unless no samples are taken")
+                raise ValueError("'a' cannot be empty unless no samples are taken")
 
         if p is not None:
             d = len(p)
@@ -2102,7 +2102,7 @@ cdef class RandomGenerator:
         Examples
         --------
         From Dalgaard page 83 [1]_, suppose the daily energy intake for 11
-        women in Kj is:
+        women in kilojoules (kJ) is:
 
         >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\
         ...                    7515, 8230, 8770])
@@ -3606,12 +3606,12 @@ cdef class RandomGenerator:
         -----
         The probability density for the Hypergeometric distribution is
 
-        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},
+        .. math:: P(x) = \\frac{\\binom{g}{x}\\binom{b}{n-x}}{\\binom{g+b}{n}},
 
-        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`
+        where :math:`0 \\le x \\le n` and :math:`n-b \\le x \\le g`
 
-        for P(x) the probability of x successes, n = ngood, m = nbad, and
-        N = number of samples.
+        for P(x) the probability of x successes, g = ngood, b = nbad, and
+        n = number of samples.
 
         Consider an urn with black and white marbles in it, ngood of them
         black and nbad are white. If you draw nsample balls without

randomgen/legacy/_legacy.pyx

@@ -171,14 +171,15 @@ cdef class _LegacyGenerator:
         Get or set the augmented state
 
         Returns the basic RNGs state as well as two values added to track
-        normal generation using the Box-Muller method. 
+        normal generation using the Polar (Box-Muller-like) method.
 
         Returns
         -------
         state : dict
             Dictionary containing the information required to describe the
             state of the Basic RNG with two additional fields, gauss and
-            has_gauss, required to store generated Box-Muller normals.
+            has_gauss, required to store generated Polar transformation
+            (Box-Muller-like) normals.
         """
         st = self._basicrng.state
         st['has_gauss'] = self._aug_state.has_gauss

randomgen/src/distributions/distributions.c

@@ -49,9 +49,9 @@ double random_gauss(brng_t *brng_state) {
       r2 = x1 * x1 + x2 * x2;
     } while (r2 >= 1.0 || r2 == 0.0);
 
-    // Box-Muller transform
+    /* Polar method, a more efficient version of the Box-Muller approach.
     f = sqrt(-2.0 * log(r2) / r2);
-    // Keep for next call
+    /* Keep for next call
     brng_state->gauss = f * x1;
     brng_state->has_gauss = true;
     return f * x2;
@@ -73,9 +73,9 @@ float random_gauss_f(brng_t *brng_state) {
       r2 = x1 * x1 + x2 * x2;
     } while (r2 >= 1.0 || r2 == 0.0);
 
-    // Box-Muller transform
+    /* Polar method, a more efficient version of the Box-Muller approach.
     f = sqrtf(-2.0f * logf(r2) / r2);
-    // Keep for next call
+    /* Keep for next call
     brng_state->gauss_f = f * x1;
     brng_state->has_gauss_f = true;
     return f * x2;
@@ -108,7 +108,7 @@ static NPY_INLINE double standard_exponential_zig(brng_t *brng_state) {
   ri >>= 8;
   x = ri * we_double[idx];
   if (ri < ke_double[idx]) {
-    return x; // 98.9% of the time we return here 1st try
+    return x; /* 98.9% of the time we return here 1st try */
   }
   return standard_exponential_zig_unlikely(brng_state, idx, x);
 }
@@ -150,7 +150,7 @@ static NPY_INLINE float standard_exponential_zig_f(brng_t *brng_state) {
   ri >>= 8;
   x = ri * we_float[idx];
   if (ri < ke_float[idx]) {
-    return x; // 98.9% of the time we return here 1st try
+    return x; /* 98.9% of the time we return here 1st try */
   }
   return standard_exponential_zig_unlikely_f(brng_state, idx, x);
 }
@@ -176,7 +176,7 @@ static NPY_INLINE double next_gauss_zig(brng_t *brng_state) {
     if (sign & 0x1)
       x = -x;
     if (rabs < ki_double[idx])
-      return x; // # 99.3% of the time return here
+      return x; /* 99.3% of the time return here */
     if (idx == 0) {
       for (;;) {
         xx = -ziggurat_nor_inv_r * log(next_double(brng_state));
@@ -220,7 +220,7 @@ float random_gauss_zig_f(brng_t *brng_state) {
     if (sign & 0x1)
       x = -x;
     if (rabs < ki_float[idx])
-      return x; // # 99.3% of the time return here
+      return x; /* # 99.3% of the time return here */
     if (idx == 0) {
       for (;;) {
         xx = -ziggurat_nor_inv_r_f * logf(next_float(brng_state));

randomgen/src/legacy/distributions-boxmuller.c

@@ -19,7 +19,7 @@ double legacy_gauss(aug_brng_t *aug_state) {
       r2 = x1 * x1 + x2 * x2;
     } while (r2 >= 1.0 || r2 == 0.0);
 
-    /* Box-Muller transform */
+    /* Polar method, a more efficient version of the Box-Muller approach. */
     f = sqrt(-2.0 * log(r2) / r2);
     /* Keep for next call */
     aug_state->gauss = f * x1;

randomgen/src/mt19937/randomkit.c

@@ -568,7 +568,7 @@ double rk_gauss(rk_state *state) {
       r2 = x1 * x1 + x2 * x2;
     } while (r2 >= 1.0 || r2 == 0.0);
 
-    /* Box-Muller transform */
+    /* Polar method, a more efficient version of the Box-Muller approach. */
     f = sqrt(-2.0 * log(r2) / r2);
     /* Keep for next call */
     state->gauss = f * x1;

randomgen/tests/test_against_numpy.py

@@ -4,6 +4,7 @@
 
 import randomgen
 from randomgen import RandomGenerator, MT19937
+from randomgen._testing import suppress_warnings
 from randomgen.legacy import LegacyGenerator
 
 
@@ -263,9 +264,11 @@ def test_vonmises(self):
     def test_random_integers(self):
         self._set_common_state()
         self._is_state_common()
-        compare_2_input(self.nprs.random_integers,
-                        self.rg.random_integers,
-                        is_scalar=True)
+        with suppress_warnings() as sup:
+            sup.record(DeprecationWarning)
+            compare_2_input(self.nprs.random_integers,
+                            self.rg.random_integers,
+                            is_scalar=True)
         self._is_state_common()
 
     def test_binomial(self):

randomgen/tests/test_direct.py

@@ -146,7 +146,7 @@ def test_raw(self):
         uints = rs.random_raw(1000)
         assert_equal(uints, self.data2['data'])
 
-    @pytest.mark.skip(reason='Box-Muller no longer supported')
+    @pytest.mark.skip(reason='Polar transform no longer supported')
     def test_gauss_inv(self):
         n = 25
         rs = RandomGenerator(self.brng(*self.data1['seed']))
@@ -352,7 +352,7 @@ def test_uniform_double(self):
         assert_equal(uniform_from_dsfmt(self.data2['data']),
                      rs.random_sample(1000))
 
-    @pytest.mark.skip(reason='Box-Muller no longer supported')
+    @pytest.mark.skip(reason='Polar transform no longer supported')
     def test_gauss_inv(self):
         n = 25
         rs = RandomGenerator(self.brng(*self.data1['seed']))

randomgen/tests/test_numpy_mt19937.py

@@ -567,12 +567,13 @@ def test_choice_return_shape(self):
         assert_(mt19937.choice(np.arange(6), s, replace=True).shape, s)
 
         # Check zero-size
-        assert_equal(mt19937.randint(0,0,(3,0,4)).shape, (3,0,4))
-        assert_equal(mt19937.randint(0,-10,0).shape, (0,))
-        assert_equal(mt19937.choice(0,0).shape, (0,))
-        assert_equal(mt19937.choice([],(0,)).shape, (0,))
+        assert_equal(mt19937.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(mt19937.randint(0, -10, size=0).shape, (0,))
+        assert_equal(mt19937.randint(10, 10, size=0).shape, (0,))
+        assert_equal(mt19937.choice(0, size=0).shape, (0,))
+        assert_equal(mt19937.choice([], size=(0,)).shape, (0,))
         assert_equal(mt19937.choice(['a', 'b'], size=(3, 0, 4)).shape, (3, 0, 4))
-        assert_raises(ValueError, mt19937.choice, [], 10)    
+        assert_raises(ValueError, mt19937.choice, [], 10)
 
     def test_bytes(self):
         mt19937.seed(self.seed)

randomgen/tests/test_smoke.py

@@ -8,6 +8,7 @@
 from numpy.testing import assert_almost_equal, assert_equal, assert_, \
     assert_array_equal
 
+from randomgen._testing import suppress_warnings
 from randomgen import RandomGenerator, MT19937, DSFMT, ThreeFry32, ThreeFry, \
     PCG32, PCG64, Philox, Xoroshiro128, Xorshift1024
 from randomgen import entropy
@@ -467,7 +468,9 @@ def test_randint(self):
         assert_(len(vals) == 10)
 
     def test_random_integers(self):
-        vals = self.rg.random_integers(10, 20, 10)
+        with suppress_warnings() as sup:
+            sup.record(DeprecationWarning)
+            vals = self.rg.random_integers(10, 20, 10)
         assert_(len(vals) == 10)
 
     def test_rayleigh(self):