BUG: Fix boundschecking for `random.logseries`

Logseries previously did not enforce bounds to be strictly exclusive
for the upper bound, where it leads to incorrect behavior.

The NOT_NAN check is removed, since it was never used: The current bounded
version always excludes NaNs.

6 files changed, 46 insertions, 19 deletions

diff --git a/numpy/random/_common.pxd b/numpy/random/_common.pxd
index 3625634cd..3eaf39ddf 100644
--- a/numpy/random/_common.pxd
+++ b/numpy/random/_common.pxd
@@ -17,8 +17,8 @@ cdef enum ConstraintType:
     CONS_POSITIVE
     CONS_POSITIVE_NOT_NAN
     CONS_BOUNDED_0_1
-    CONS_BOUNDED_0_1_NOTNAN
     CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
     CONS_GT_1
     CONS_GTE_1
     CONS_POISSON
diff --git a/numpy/random/_common.pyx b/numpy/random/_common.pyx
index 607034a38..7b6f69303 100644
--- a/numpy/random/_common.pyx
+++ b/numpy/random/_common.pyx
@@ -392,6 +392,9 @@ cdef int check_array_constraint(np.ndarray val, object name, constraint_type con
     elif cons == CONS_BOUNDED_GT_0_1:
         if not np.all(np.greater(val, 0)) or not np.all(np.less_equal(val, 1)):
             raise ValueError("{0} <= 0, {0} > 1 or {0} contains NaNs".format(name))
+    elif cons == CONS_BOUNDED_LT_0_1:
+        if not np.all(np.greater_equal(val, 0)) or not np.all(np.less(val, 1)):
+            raise ValueError("{0} < 0, {0} >= 1 or {0} contains NaNs".format(name))
     elif cons == CONS_GT_1:
         if not np.all(np.greater(val, 1)):
             raise ValueError("{0} <= 1 or {0} contains NaNs".format(name))
@@ -428,6 +431,9 @@ cdef int check_constraint(double val, object name, constraint_type cons) except
     elif cons == CONS_BOUNDED_GT_0_1:
         if not val >0 or not val <= 1:
             raise ValueError("{0} <= 0, {0} > 1 or {0} contains NaNs".format(name))
+    elif cons == CONS_BOUNDED_LT_0_1:
+        if not (val >= 0) or not (val < 1):
+            raise ValueError("{0} < 0, {0} >= 1 or {0} is NaN".format(name))
     elif cons == CONS_GT_1:
         if not (val > 1):
             raise ValueError("{0} <= 1 or {0} is NaN".format(name))
diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx
index 5218c6d0e..2c25b7191 100644
--- a/numpy/random/_generator.pyx
+++ b/numpy/random/_generator.pyx
@@ -25,7 +25,7 @@ from ._pcg64 import PCG64
 from numpy.random cimport bitgen_t
 from ._common cimport (POISSON_LAM_MAX, CONS_POSITIVE, CONS_NONE,
             CONS_NON_NEGATIVE, CONS_BOUNDED_0_1, CONS_BOUNDED_GT_0_1,
-            CONS_GT_1, CONS_POSITIVE_NOT_NAN, CONS_POISSON,
+            CONS_BOUNDED_LT_0_1, CONS_GT_1, CONS_POSITIVE_NOT_NAN, CONS_POISSON,
             double_fill, cont, kahan_sum, cont_broadcast_3, float_fill, cont_f,
             check_array_constraint, check_constraint, disc, discrete_broadcast_iii,
             validate_output_shape
@@ -3437,12 +3437,12 @@ cdef class Generator:
         Draw samples from a logarithmic series distribution.
 
         Samples are drawn from a log series distribution with specified
-        shape parameter, 0 < ``p`` < 1.
+        shape parameter, 0 <= ``p`` < 1.
 
         Parameters
         ----------
         p : float or array_like of floats
-            Shape parameter for the distribution.  Must be in the range (0, 1).
+            Shape parameter for the distribution.  Must be in the range [0, 1).
         size : int or tuple of ints, optional
             Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
             ``m * n * k`` samples are drawn.  If size is ``None`` (default),
@@ -3506,7 +3506,7 @@ cdef class Generator:
 
         """
         return disc(&random_logseries, &self._bitgen, size, self.lock, 1, 0,
-                 p, 'p', CONS_BOUNDED_0_1,
+                 p, 'p', CONS_BOUNDED_LT_0_1,
                  0.0, '', CONS_NONE,
                  0.0, '', CONS_NONE)
 
diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx
index fcc1f27d2..ae40931d0 100644
--- a/numpy/random/mtrand.pyx
+++ b/numpy/random/mtrand.pyx
@@ -19,8 +19,8 @@ from ._bounded_integers cimport (_rand_bool, _rand_int32, _rand_int64,
 from ._mt19937 import MT19937 as _MT19937
 from numpy.random cimport bitgen_t
 from ._common cimport (POISSON_LAM_MAX, CONS_POSITIVE, CONS_NONE,
-            CONS_NON_NEGATIVE, CONS_BOUNDED_0_1, CONS_BOUNDED_GT_0_1, CONS_GTE_1,
-            CONS_GT_1, LEGACY_CONS_POISSON,
+            CONS_NON_NEGATIVE, CONS_BOUNDED_0_1, CONS_BOUNDED_GT_0_1,
+            CONS_BOUNDED_LT_0_1, CONS_GTE_1, CONS_GT_1, LEGACY_CONS_POISSON,
             double_fill, cont, kahan_sum, cont_broadcast_3,
             check_array_constraint, check_constraint, disc, discrete_broadcast_iii,
             validate_output_shape
@@ -3895,7 +3895,7 @@ cdef class RandomState:
         Draw samples from a logarithmic series distribution.
 
         Samples are drawn from a log series distribution with specified
-        shape parameter, 0 < ``p`` < 1.
+        shape parameter, 0 <= ``p`` < 1.
 
         .. note::
             New code should use the ``logseries`` method of a ``default_rng()``
@@ -3904,7 +3904,7 @@ cdef class RandomState:
         Parameters
         ----------
         p : float or array_like of floats
-            Shape parameter for the distribution.  Must be in the range (0, 1).
+            Shape parameter for the distribution.  Must be in the range [0, 1).
         size : int or tuple of ints, optional
             Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
             ``m * n * k`` samples are drawn.  If size is ``None`` (default),
@@ -3969,7 +3969,7 @@ cdef class RandomState:
 
         """
         out = disc(&legacy_logseries, &self._bitgen, size, self.lock, 1, 0,
-                   p, 'p', CONS_BOUNDED_0_1,
+                   p, 'p', CONS_BOUNDED_LT_0_1,
                    0.0, '', CONS_NONE,
                    0.0, '', CONS_NONE)
         # Match historical output type
diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py
index b550cd508..73d915e02 100644
--- a/numpy/random/tests/test_generator_mt19937.py
+++ b/numpy/random/tests/test_generator_mt19937.py
@@ -1363,10 +1363,22 @@ class TestRandomDist:
                             [5, 1]])
         assert_array_equal(actual, desired)
 
-    def test_logseries_exceptions(self):
-        with np.errstate(invalid='ignore'):
-            assert_raises(ValueError, random.logseries, np.nan)
-            assert_raises(ValueError, random.logseries, [np.nan] * 10)
+    def test_logseries_zero(self):
+        random = Generator(MT19937(self.seed))
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        random = Generator(MT19937(self.seed))
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
 
     def test_multinomial(self):
         random = Generator(MT19937(self.seed))
diff --git a/numpy/random/tests/test_randomstate.py b/numpy/random/tests/test_randomstate.py
index 22b167224..c0e42ec1e 100644
--- a/numpy/random/tests/test_randomstate.py
+++ b/numpy/random/tests/test_randomstate.py
@@ -942,11 +942,20 @@ class TestRandomDist:
                             [3, 6]])
         assert_array_equal(actual, desired)
 
-    def test_logseries_exceptions(self):
-        with suppress_warnings() as sup:
-            sup.record(RuntimeWarning)
-            assert_raises(ValueError, random.logseries, np.nan)
-            assert_raises(ValueError, random.logseries, [np.nan] * 10)
+    def test_logseries_zero(self):
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
 
     def test_multinomial(self):
         random.seed(self.seed)