hameerabbasi: Oh. Apparently you're using an old NumPy version... The docstrings need to be produced with newer versions to pass the tests, unfortunately. (1.14.0 or newer I think).
diff --git a/docs/generated/sparse.COO.all.rst b/docs/generated/sparse.COO.all.rst
new file mode 100644
index 0000000..f79e99c
--- /dev/null
+++ b/docs/generated/sparse.COO.all.rst
@@ -0,0 +1,6 @@
+COO.all
+=======
+
+.. currentmodule:: sparse
+
+.. automethod:: COO.all
\ No newline at end of file
diff --git a/docs/generated/sparse.COO.any.rst b/docs/generated/sparse.COO.any.rst
new file mode 100644
index 0000000..1bdad68
--- /dev/null
+++ b/docs/generated/sparse.COO.any.rst
@@ -0,0 +1,6 @@
+COO.any
+=======
+
+.. currentmodule:: sparse
+
+.. automethod:: COO.any
\ No newline at end of file
diff --git a/docs/generated/sparse.COO.rst b/docs/generated/sparse.COO.rst
index d0a1c9f..cdd16d4 100644
--- a/docs/generated/sparse.COO.rst
+++ b/docs/generated/sparse.COO.rst
@@ -40,10 +40,13 @@ COO
       :toctree:
 
       COO.reduce
+
       COO.sum
-      COO.max
-      COO.min
       COO.prod
+      COO.min
+      COO.max
+      COO.any
+      COO.all
 
    .. rubric:: :ref:`Converting to other formats <converting>`
    .. autosummary::
diff --git a/docs/operations.rst b/docs/operations.rst
index 9ca3183..524f2af 100644
--- a/docs/operations.rst
+++ b/docs/operations.rst
@@ -217,7 +217,21 @@ All of the following will raise an :obj:`IndexError`, like in Numpy 1.13 and lat
    z[1, 4, 8]
    z[-6]
 
-.. note:: Numpy advanced indexing is currently not supported.
+
+Advanced Indexing
+~~~~~~~~~~~~~~~~~
+
+Advanced indexing (indexing arrays with other arrays) is supported, but only for indexing
+with a *single array*. Indexing a single array with multiple arrays is not supported at
+this time. As above, if  :code:`z.shape` is :code:`(5, 6, 7)`, all of the following will
+work like NumPy:
+
+.. code-block:: python
+
+   z[[0, 1, 2]]
+   z[1, [3]]
+   z[1, 4, [3, 6]]
+   z[:3, :2, [1, 5]]
 
 .. _operations-other:
 
diff --git a/requirements.txt b/requirements.txt
index e0f833c..bc7f77c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,3 @@
 numpy >= 1.13
 scipy >= 0.19
-numba
+numba >= 0.39
diff --git a/sparse/coo/core.py b/sparse/coo/core.py
index 1ec395b..881a721 100644
--- a/sparse/coo/core.py
+++ b/sparse/coo/core.py
@@ -811,6 +811,114 @@ class COO(SparseArray, NDArrayOperatorsMixin):
         """
         return np.maximum.reduce(self, out=out, axis=axis, keepdims=keepdims)
 
+    def any(self, axis=None, keepdims=False, out=None):
+        """
+        See if any values along array are ``True``. Uses all axes by default.
+
+        Parameters
+        ----------
+        axis : Union[int, Iterable[int]], optional
+            The axes along which to minimize. Uses all axes by default.
+        keepdims : bool, optional
+            Whether or not to keep the dimensions of the original array.
+
+        Returns
+        -------
+        COO
+            The reduced output sparse array.
+
+        See Also
+        --------
+        :obj:`numpy.all` : Equivalent numpy function.
+
+        Notes
+        -----
+        * This function internally calls :obj:`COO.sum_duplicates` to bring the array into
+          canonical form.
+        * The :code:`out` parameter is provided just for compatibility with Numpy and
+          isn't actually supported.
+
+        Examples
+        --------
+        You can use :obj:`COO.min` to minimize an array across any dimension.
+
+        >>> x = np.array([[False, False],
+        ...               [False, True ],
+        ...               [True,  False],
+        ...               [True,  True ]])
+        >>> s = COO.from_numpy(x)
+        >>> s2 = s.any(axis=1)
+        >>> s2.todense()  # doctest: +SKIP
+        array([False,  True,  True,  True])
+
+        You can also use the :code:`keepdims` argument to keep the dimensions after the
+        minimization.
+
+        >>> s3 = s.any(axis=1, keepdims=True)
+        >>> s3.shape
+        (4, 1)
+
+        By default, this reduces the array down to one number, minimizing along all axes.
+
+        >>> s.any()
+        True
+        """
+        return np.logical_or.reduce(self, out=out, axis=axis, keepdims=keepdims)
+
+    def all(self, axis=None, keepdims=False, out=None):
+        """
+        See if all values in an array are ``True``. Uses all axes by default.
+
+        Parameters
+        ----------
+        axis : Union[int, Iterable[int]], optional
+            The axes along which to minimize. Uses all axes by default.
+        keepdims : bool, optional
+            Whether or not to keep the dimensions of the original array.
+
+        Returns
+        -------
+        COO
+            The reduced output sparse array.
+
+        See Also
+        --------
+        :obj:`numpy.all` : Equivalent numpy function.
+
+        Notes
+        -----
+        * This function internally calls :obj:`COO.sum_duplicates` to bring the array into
+          canonical form.
+        * The :code:`out` parameter is provided just for compatibility with Numpy and
+          isn't actually supported.
+
+        Examples
+        --------
+        You can use :obj:`COO.min` to minimize an array across any dimension.
+
+        >>> x = np.array([[False, False],
+        ...               [False, True ],
+        ...               [True,  False],
+        ...               [True,  True ]])
+        >>> s = COO.from_numpy(x)
+        >>> s2 = s.all(axis=1)
+        >>> s2.todense()  # doctest: +SKIP
+        array([False, False, False,  True])
+
+        You can also use the :code:`keepdims` argument to keep the dimensions after the
+        minimization.
+
+        >>> s3 = s.all(axis=1, keepdims=True)
+        >>> s3.shape
+        (4, 1)
+
+        By default, this reduces the array down to one boolean, minimizing along all axes.
+
+        >>> s.all()
+        False
+        """
+        return np.logical_and.reduce(self, out=out, axis=axis, keepdims=keepdims)
+
     def min(self, axis=None, keepdims=False, out=None):
         """
         Minimize along the given axes. Uses all axes by default.
@@ -865,7 +973,7 @@ class COO(SparseArray, NDArrayOperatorsMixin):
         >>> s3.shape
         (5, 1)
 
-        By default, this reduces the array down to one number, minimizing along all axes.
+        By default, this reduces the array down to one boolean, minimizing along all axes.
 
         >>> s.min()
         0
diff --git a/sparse/coo/indexing.py b/sparse/coo/indexing.py
index a3eaf0f..7ee9688 100644
--- a/sparse/coo/indexing.py
+++ b/sparse/coo/indexing.py
@@ -1,4 +1,3 @@
-from collections import Iterable
 from numbers import Integral
 
 import numba
@@ -60,11 +59,12 @@ def getitem(x, index):
     index = normalize_index(index, x.shape)
 
     # zip_longest so things like x[..., None] are picked up.
-    if len(index) != 0 and all(ind == slice(0, dim, 1) for ind, dim in zip_longest(index, x.shape)):
+    if len(index) != 0 and all(isinstance(ind, slice) and ind == slice(0, dim, 1)
+                               for ind, dim in zip_longest(index, x.shape)):
         return x
 
     # Get the mask
-    mask = _mask(x.coords, index, x.shape)
+    mask, adv_idx = _mask(x.coords, index, x.shape)
 
     # Get the length of the mask
     if isinstance(mask, slice):
@@ -76,8 +76,7 @@ def getitem(x, index):
     shape = []
     i = 0
 
-    sorted = True
-
+    sorted = adv_idx is None or adv_idx.pos == 0
     for ind in index:
         # Nothing is added to shape or coords if the index is an integer.
         if isinstance(ind, Integral):
@@ -88,11 +87,13 @@ def getitem(x, index):
             shape.append(len(range(ind.start, ind.stop, ind.step)))
             coords.append((x.coords[i, mask] - ind.start) // ind.step)
             i += 1
-
             if ind.step < 0:
                 sorted = False
-        elif isinstance(ind, Iterable):
-            raise NotImplementedError('Advanced indexing is not yet supported.')
+        # Add the index and shape for the advanced index.
+        elif isinstance(ind, np.ndarray):
+            shape.append(adv_idx.length)
+            coords.append(adv_idx.idx)
+            i += 1
         # Add a dimension for None.
         elif ind is None:
             coords.append(np.zeros(n, dtype=np.intp))
@@ -122,21 +123,61 @@ def getitem(x, index):
 
 def _mask(coords, indices, shape):
     indices = _prune_indices(indices, shape)
+    indices, adv_idx, adv_idx_pos = _separate_adv_indices(indices)
+
+    if len(adv_idx) != 0:
+        if len(adv_idx) != 1:
+            raise IndexError('Only indices with at most one iterable index are supported.')
+
+        adv_idx = adv_idx[0]
+        adv_idx_pos = adv_idx_pos[0]
+
+        if adv_idx.ndim != 1:
+            raise IndexError('Only one-dimensional iterable indices supported.')
+
+        mask, aidxs = _compute_multi_mask(coords, _ind_ar_from_indices(indices), adv_idx, adv_idx_pos)
+        return mask, _AdvIdxInfo(aidxs, adv_idx_pos, len(adv_idx))
+
+    mask, is_slice = _compute_mask(coords, _ind_ar_from_indices(indices))
 
+    if is_slice:
+        return slice(mask[0], mask[1], 1), None
+    else:
+        return mask, None
+
+
+def _ind_ar_from_indices(indices):
+    """
+    Computes an index "array" from indices, such that ``indices[i]`` is
+    transformed to ``ind_ar[i]`` and ``ind_ar[i].shape == (3,)``. It has the
+    format ``[start, stop, step]``. Integers are converted into steps as well.
+
+    Parameters
+    ----------
+    indices : Iterable
+        Input indices (slices and integers)
+
+    Returns
+    -------
+    ind_ar : np.ndarray
+        The output array.
+
+    Examples
+    --------
+    >>> _ind_ar_from_indices([1])
+    array([[1, 2, 1]])
+    >>> _ind_ar_from_indices([slice(5, 7, 2)])
+    array([[5, 7, 2]])
+    """
     ind_ar = np.empty((len(indices), 3), dtype=np.intp)
 
     for i, idx in enumerate(indices):
         if isinstance(idx, slice):
             ind_ar[i] = [idx.start, idx.stop, idx.step]
-        else:  # idx is an integer
+        elif isinstance(idx, Integral):
             ind_ar[i] = [idx, idx + 1, 1]
 
-    mask, is_slice = _compute_mask(coords, ind_ar)
-
-    if is_slice:
-        return slice(mask[0], mask[1], 1)
-    else:
-        return mask
+    return ind_ar
 
 
 def _prune_indices(indices, shape, prune_none=True):
@@ -185,6 +226,85 @@ def _prune_indices(indices, shape, prune_none=True):
     return indices
 
 
+def _separate_adv_indices(indices):
+    """
+    Separates advanced from normal indices.
+
+    Parameters
+    ----------
+    indices : list
+        The input indices
+
+    Returns
+    -------
+    new_idx : list
+        The normal indices.
+    adv_idx : list
+        The advanced indices.
+    adv_idx_pos : list
+        The positions of the advanced indices.
+    """
+    adv_idx_pos = []
+    new_idx = []
+    adv_idx = []
+
+    for i, idx in enumerate(indices):
+        if isinstance(idx, np.ndarray):
+            adv_idx.append(idx)
+            adv_idx_pos.append(i)
+        else:
+            new_idx.append(idx)
+
+    return new_idx, adv_idx, adv_idx_pos
+
+
+@numba.jit(nopython=True, nogil=True)
+def _compute_multi_mask(coords, indices, adv_idx, adv_idx_pos):  # pragma: no cover
+    """
+    Computes a mask with the advanced index, and also returns the advanced index
+    dimension.
+
+    Parameters
+    ----------
+    coords : np.ndarray
+        Coordinates of the input array.
+    indices : np.ndarray
+        The indices in slice format.
+    adv_idx : int
+        The advanced index.
+    adv_idx_pos : int
+        The position of the advanced index.
+
+    Returns
+    -------
+    mask : np.ndarray
+        The mask.
+    aidxs : np.ndarray
+        The advanced array index.
+    """
+    mask = []
+    a_indices = []
+    full_idx = np.empty((len(indices) + 1, 3), dtype=np.intp)
+
+    full_idx[:adv_idx_pos] = indices[:adv_idx_pos]
+    full_idx[adv_idx_pos + 1:] = indices[adv_idx_pos:]
+
+    for i, aidx in enumerate(adv_idx):
+        full_idx[adv_idx_pos] = [aidx, aidx + 1, 1]
+        partial_mask, is_slice = _compute_mask(coords, full_idx)
+        if is_slice:
+            slice_mask = []
+            for j in range(partial_mask[0], partial_mask[1]):
+                slice_mask.append(j)
+            partial_mask = np.array(slice_mask)
+
+        mask.extend(partial_mask)
+        for _ in range(len(partial_mask)):
+            a_indices.append(i)
+
+    return np.array(mask), np.array(a_indices)
+
+
 @numba.jit(nopython=True, nogil=True)
 def _compute_mask(coords, indices):  # pragma: no cover
     """
@@ -327,8 +447,8 @@ def _get_mask_pairs(starts_old, stops_old, c, idx):  # pragma: no cover
         # For each matching "integer" in the slice, search within the "sub-coords"
         # Using binary search.
         for p_match in range(idx[0], idx[1], idx[2]):
-            start = np.searchsorted(c[starts_old[j]:stops_old[j]], p_match) + starts_old[j]
-            stop = np.searchsorted(c[starts_old[j]:stops_old[j]], p_match + 1) + starts_old[j]
+            start = np.searchsorted(c[starts_old[j]:stops_old[j]], p_match, side='left') + starts_old[j]
+            stop = np.searchsorted(c[starts_old[j]:stops_old[j]], p_match, side='right') + starts_old[j]
 
             if start != stop:
                 starts.append(start)
@@ -413,7 +533,7 @@ def _filter_pairs(starts, stops, coords, indices):  # pragma: no cover
 
                 match &= ((elem - idx[0]) % idx[2] == 0 and
                           ((idx[2] > 0 and idx[0] <= elem < idx[1]) or
-                          (idx[2] < 0 and idx[0] >= elem > idx[1])))
+                           (idx[2] < 0 and idx[0] >= elem > idx[1])))
 
             # and append to the mask if so.
             if match:
@@ -460,3 +580,10 @@ def _join_adjacent_pairs(starts_old, stops_old):  # pragma: no cover
     stops.append(stops_old[-1])
 
     return starts, stops
+
+
+class _AdvIdxInfo(object):
+    def __init__(self, idx, pos, length):
+        self.idx = idx
+        self.pos = pos
+        self.length = length
diff --git a/sparse/slicing.py b/sparse/slicing.py
index 56e5b25..087bd52 100644
--- a/sparse/slicing.py
+++ b/sparse/slicing.py
@@ -3,6 +3,7 @@
 
 import math
 from numbers import Integral, Number
+from collections import Iterable
 
 import numpy as np
 
@@ -103,18 +104,18 @@ def check_index(ind, dimension):
     >>> check_index([6, 3], 5)
     Traceback (most recent call last):
     ...
-    IndexError: Index out of bounds 5
+    IndexError: Index out of bounds for dimension 5
     >>> check_index(slice(0, 3), 5)
     """
     # unknown dimension, assumed to be in bounds
-    if isinstance(ind, (list, np.ndarray)):
+    if isinstance(ind, Iterable):
         x = np.asanyarray(ind)
         if np.issubdtype(x.dtype, np.integer) and \
-                ((x >= dimension).any() or (x < -dimension).any()):
-            raise IndexError("Index out of bounds %s" % dimension)
+                ((x >= dimension) | (x < -dimension)).any():
+            raise IndexError("Index out of bounds for dimension {:d}".format(dimension))
         elif x.dtype == bool and len(x) != dimension:
-            raise IndexError("boolean index did not match indexed array; dimension is %s "
-                             "but corresponding boolean dimension is %s", (dimension, len(x)))
+            raise IndexError("boolean index did not match indexed array; dimension is {:d} "
+                             "but corresponding boolean dimension is {:d}".format(dimension, len(x)))
     elif isinstance(ind, slice):
         return
     elif not isinstance(ind, Integral):
@@ -122,12 +123,11 @@ def check_index(ind, dimension):
                          "integer or boolean arrays are valid indices")
 
     elif ind >= dimension:
-        raise IndexError("Index is not smaller than dimension %d >= %d" %
-                         (ind, dimension))
+        raise IndexError("Index is not smaller than dimension {:d} >= {:d}".format(ind, dimension))
 
     elif ind < -dimension:
-        msg = "Negative index is not greater than negative dimension %d <= -%d"
-        raise IndexError(msg % (ind, dimension))
+        msg = "Negative index is not greater than negative dimension {:d} <= -{:d}"
+        raise IndexError(msg.format(ind, dimension))
 
 
 def sanitize_index(ind):
@@ -157,7 +157,7 @@ def sanitize_index(ind):
     elif isinstance(ind, Number):
         return _sanitize_index_element(ind)
     index_array = np.asanyarray(ind)
-    if index_array.dtype == bool:
+    if index_array.dtype == np.bool_:
         nonzero = np.nonzero(index_array)
         if len(nonzero) == 1:
             # If a 1-element tuple, unwrap the element
diff --git a/sparse/utils.py b/sparse/utils.py
index db16256..f7602ea 100644
--- a/sparse/utils.py
+++ b/sparse/utils.py
@@ -12,7 +12,7 @@ def assert_eq(x, y, check_nnz=True, compare_dtype=True, **kwargs):
     if compare_dtype:
         assert x.dtype == y.dtype
 
-    checking_method = np.array_equal \
+    check_equal = np.array_equal \
         if np.issubdtype(x.dtype, np.integer) and np.issubdtype(y.dtype, np.integer) \
         else functools.partial(np.allclose, equal_nan=True)
 
@@ -22,7 +22,7 @@ def assert_eq(x, y, check_nnz=True, compare_dtype=True, **kwargs):
         assert is_canonical(y)
 
     if isinstance(x, COO) and isinstance(y, COO) and check_nnz:
-        assert np.array_equal(x.coords, y.coords) and checking_method(x.data, y.data, **kwargs)
+        assert np.array_equal(x.coords, y.coords) and check_equal(x.data, y.data, **kwargs)
         return
 
     if hasattr(x, 'todense'):
@@ -37,7 +37,7 @@ def assert_eq(x, y, check_nnz=True, compare_dtype=True, **kwargs):
             assert_nnz(y, yy)
     else:
         yy = y
-    assert checking_method(xx, yy, **kwargs)
+    assert check_equal(xx, yy, **kwargs)
 
 
 def assert_nnz(s, x):

Add any and all methods.
@stsievert Willing to take a crack at this? It's mostly docs/tests/copy-paste with a one-line implementation and can be easily copied from `min` or `max`:

https://github.com/pydata/sparse/blob/6210da31b9fc63c01b083c121421f335d73a580d/sparse/coo/core.py#L692-L939
diff --git a/sparse/tests/test_coo.py b/sparse/tests/test_coo.py
index abca280..3940dea 100644
--- a/sparse/tests/test_coo.py
+++ b/sparse/tests/test_coo.py
@@ -27,6 +27,20 @@ def test_reductions(reduction, axis, keepdims, kwargs, eqkwargs):
     assert_eq(xx, yy, **eqkwargs)
 
 
+@pytest.mark.parametrize('reduction,kwargs,eqkwargs', [
+    ('any', {}, {}),
+    ('all', {}, {}),
+])
+@pytest.mark.parametrize('axis', [None, 0, 1, 2, (0, 2), -3, (1, -1)])
+@pytest.mark.parametrize('keepdims', [True, False])
+def test_reductions_bool(reduction, axis, keepdims, kwargs, eqkwargs):
+    x = sparse.random((2, 3, 4), density=.25).astype(bool)
+    y = x.todense()
+    xx = getattr(x, reduction)(axis=axis, keepdims=keepdims, **kwargs)
+    yy = getattr(y, reduction)(axis=axis, keepdims=keepdims, **kwargs)
+    assert_eq(xx, yy, **eqkwargs)
+
+
 @pytest.mark.parametrize('reduction,kwargs,eqkwargs', [
     (np.max, {}, {}),
     (np.sum, {}, {}),
@@ -433,25 +447,25 @@ def test_trinary_broadcasting(shapes, func):
 
 @pytest.mark.parametrize('shapes, func', [
     ([
-        (2,),
-        (3, 2),
-        (4, 3, 2),
-    ], lambda x, y, z: (x + y) * z),
+         (2,),
+         (3, 2),
+         (4, 3, 2),
+     ], lambda x, y, z: (x + y) * z),
     ([
-        (3,),
-        (2, 3),
-        (2, 2, 3),
-    ], lambda x, y, z: x * (y + z)),
+         (3,),
+         (2, 3),
+         (2, 2, 3),
+     ], lambda x, y, z: x * (y + z)),
     ([
-        (2,),
-        (2, 2),
-        (2, 2, 2),
-    ], lambda x, y, z: x * y * z),
+         (2,),
+         (2, 2),
+         (2, 2, 2),
+     ], lambda x, y, z: x * y * z),
     ([
-        (4,),
-        (4, 4),
-        (4, 4, 4),
-    ], lambda x, y, z: x + y + z),
+         (4,),
+         (4, 4),
+         (4, 4, 4),
+     ], lambda x, y, z: x + y + z),
 ])
 @pytest.mark.parametrize('value', [
     np.nan,
@@ -963,8 +977,11 @@ def test_slicing(index):
     (1, [2, 0], 0),
     ([True, False], slice(1, None), slice(-2, None)),
     (slice(1, None), slice(-2, None), [True, False, True, False]),
+    ([1, 0],),
+    (Ellipsis, [2, 1, 3],),
+    (slice(None), [2, 1, 2],),
+    (1, [2, 0, 1],),
 ])
-@pytest.mark.xfail(reason='Advanced indexing is temporarily broken.')
 def test_advanced_indexing(index):
     s = sparse.random((2, 3, 4), density=0.5)
     x = s.todense()
@@ -1001,6 +1018,8 @@ def test_custom_dtype_slicing():
     0.5,
     [0.5],
     {'potato': 'kartoffel'},
+    ([0, 1],) * 2,
+    ([[0, 1]],),
 ])
 def test_slicing_errors(index):
     s = sparse.random((2, 3, 4), density=0.5)

pip install -e .[all]
parse_log_pytest
requirements.txt
pytest pytest-flake8 pytest-cov
3.6
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
alabaster==0.7.13
asv==0.5.1
attrs==22.2.0
Babel==2.11.0
certifi==2021.5.30
charset-normalizer==2.0.12
coverage==6.2
distlib==0.3.9
docutils==0.17.1
filelock==3.4.1
flake8==5.0.4
idna==3.10
imagesize==1.4.1
importlib-metadata==4.2.0
importlib-resources==5.4.0
iniconfig==1.1.1
Jinja2==3.0.3
llvmlite==0.36.0
MarkupSafe==2.0.1
mccabe==0.7.0
numba==0.53.1
numpy==1.19.5
packaging==21.3
platformdirs==2.4.0
pluggy==1.0.0
py==1.11.0
pycodestyle==2.9.1
pyflakes==2.5.0
Pygments==2.14.0
pyparsing==3.1.4
pytest==7.0.1
pytest-cov==4.0.0
pytest-flake8==1.1.1
pytz==2025.2
requests==2.27.1
scipy==1.5.4
six==1.17.0
snowballstemmer==2.2.0
-e git+https://github.com/pydata/sparse.git@6cb64a5246cf07a59aca21f942602655557f5111#egg=sparse
Sphinx==4.3.2
sphinx-rtd-theme==1.3.0
sphinxcontrib-applehelp==1.0.2
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.0
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
toml==0.10.2
tomli==1.2.3
tox==3.28.0
typing_extensions==4.1.1
urllib3==1.26.20
virtualenv==20.16.2
zipp==3.6.0

name: sparse
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=21.2.2=py36h06a4308_0
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - alabaster==0.7.13
      - asv==0.5.1
      - attrs==22.2.0
      - babel==2.11.0
      - charset-normalizer==2.0.12
      - coverage==6.2
      - distlib==0.3.9
      - docutils==0.17.1
      - filelock==3.4.1
      - flake8==5.0.4
      - idna==3.10
      - imagesize==1.4.1
      - importlib-metadata==4.2.0
      - importlib-resources==5.4.0
      - iniconfig==1.1.1
      - jinja2==3.0.3
      - llvmlite==0.36.0
      - markupsafe==2.0.1
      - mccabe==0.7.0
      - numba==0.53.1
      - numpy==1.19.5
      - packaging==21.3
      - platformdirs==2.4.0
      - pluggy==1.0.0
      - py==1.11.0
      - pycodestyle==2.9.1
      - pyflakes==2.5.0
      - pygments==2.14.0
      - pyparsing==3.1.4
      - pytest==7.0.1
      - pytest-cov==4.0.0
      - pytest-flake8==1.1.1
      - pytz==2025.2
      - requests==2.27.1
      - scipy==1.5.4
      - six==1.17.0
      - snowballstemmer==2.2.0
      - sphinx==4.3.2
      - sphinx-rtd-theme==1.3.0
      - sphinxcontrib-applehelp==1.0.2
      - sphinxcontrib-devhelp==1.0.2
      - sphinxcontrib-htmlhelp==2.0.0
      - sphinxcontrib-jquery==4.1
      - sphinxcontrib-jsmath==1.0.1
      - sphinxcontrib-qthelp==1.0.3
      - sphinxcontrib-serializinghtml==1.1.5
      - toml==0.10.2
      - tomli==1.2.3
      - tox==3.28.0
      - typing-extensions==4.1.1
      - urllib3==1.26.20
      - virtualenv==20.16.2
      - zipp==3.6.0
prefix: /opt/conda/envs/sparse

sparse/coo/core.py::sparse.coo.core.COO.all sparse/coo/core.py::sparse.coo.core.COO.any sparse/coo/indexing.py::sparse.coo.indexing._ind_ar_from_indices sparse/tests/test_coo.py::test_reductions_bool[True-None-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-None-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True-0-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-0-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True-1-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-1-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True-2-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-2-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True-axis4-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-axis4-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True--3-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True--3-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[True-axis6-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[True-axis6-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-None-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-None-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-0-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-0-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-1-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-1-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-2-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-2-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-axis4-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-axis4-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False--3-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False--3-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions_bool[False-axis6-any-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions_bool[False-axis6-all-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_advanced_indexing[index0] sparse/tests/test_coo.py::test_advanced_indexing[index1] sparse/tests/test_coo.py::test_advanced_indexing[index2] sparse/tests/test_coo.py::test_advanced_indexing[index3] sparse/tests/test_coo.py::test_advanced_indexing[index4] sparse/tests/test_coo.py::test_advanced_indexing[index5] sparse/tests/test_coo.py::test_advanced_indexing[index6] sparse/tests/test_coo.py::test_advanced_indexing[index7] sparse/tests/test_coo.py::test_advanced_indexing[index8] sparse/tests/test_coo.py::test_advanced_indexing[index9] sparse/tests/test_coo.py::test_slicing_errors[index10] sparse/tests/test_coo.py::test_slicing_errors[index11] sparse/tests/test_dok.py::test_setitem[shape0-index0-0.5249336378806507] sparse/tests/test_dok.py::test_setitem[shape1-index1-0.8734346827674154] sparse/tests/test_dok.py::test_setitem[shape3-1-0.09148192805554167] sparse/tests/test_dok.py::test_setitem[shape4-index4-0.895175738228745] sparse/tests/test_dok.py::test_setitem[shape5-index5-0.7039365189808376] sparse/tests/test_dok.py::test_setitem[shape9-index9-0.9812604441232181] sparse/tests/test_dok.py::test_setitem[shape11-index11-0.45712861544745875] sparse/tests/test_dok.py::test_setitem[shape13-index13-0.7306825883567905]
sparse/__init__.py::flake-8::FLAKE8 sparse/_version.py::flake-8::FLAKE8 sparse/compatibility.py::flake-8::FLAKE8 sparse/dok.py::flake-8::FLAKE8 sparse/io.py::flake-8::FLAKE8 sparse/slicing.py::flake-8::FLAKE8 sparse/sparse_array.py::flake-8::FLAKE8 sparse/utils.py::flake-8::FLAKE8 sparse/coo/__init__.py::flake-8::FLAKE8 sparse/coo/common.py::flake-8::FLAKE8 sparse/coo/core.py::flake-8::FLAKE8 sparse/coo/indexing.py::flake-8::FLAKE8 sparse/coo/umath.py::flake-8::FLAKE8 sparse/tests/test_coo.py::flake-8::FLAKE8 sparse/tests/test_coo.py::test_op_scipy_sparse_left[func2] sparse/tests/test_coo.py::test_op_scipy_sparse_left[func3] sparse/tests/test_coo.py::test_op_scipy_sparse_left[func4] sparse/tests/test_coo.py::test_op_scipy_sparse_left[func5] sparse/tests/test_dok.py::flake-8::FLAKE8 sparse/tests/test_io.py::flake-8::FLAKE8
sparse/dok.py::sparse.dok.DOK sparse/dok.py::sparse.dok.DOK.from_coo sparse/dok.py::sparse.dok.DOK.from_numpy sparse/dok.py::sparse.dok.DOK.nnz sparse/dok.py::sparse.dok.DOK.to_coo sparse/dok.py::sparse.dok.DOK.todense sparse/io.py::sparse.io.save_npz sparse/slicing.py::sparse.slicing.check_index sparse/slicing.py::sparse.slicing.clip_slice sparse/slicing.py::sparse.slicing.normalize_index sparse/slicing.py::sparse.slicing.posify_index sparse/slicing.py::sparse.slicing.replace_ellipsis sparse/slicing.py::sparse.slicing.replace_none sparse/slicing.py::sparse.slicing.sanitize_index sparse/sparse_array.py::sparse.sparse_array.SparseArray.density sparse/sparse_array.py::sparse.sparse_array.SparseArray.ndim sparse/sparse_array.py::sparse.sparse_array.SparseArray.nnz sparse/sparse_array.py::sparse.sparse_array.SparseArray.size sparse/utils.py::sparse.utils.check_consistent_fill_value sparse/utils.py::sparse.utils.check_zero_fill_value sparse/utils.py::sparse.utils.equivalent sparse/utils.py::sparse.utils.random sparse/coo/core.py::sparse.coo.core.COO sparse/coo/core.py::sparse.coo.core.COO.T sparse/coo/core.py::sparse.coo.core.COO.__len__ sparse/coo/core.py::sparse.coo.core.COO._sort_indices sparse/coo/core.py::sparse.coo.core.COO._sum_duplicates sparse/coo/core.py::sparse.coo.core.COO.dot sparse/coo/core.py::sparse.coo.core.COO.dtype sparse/coo/core.py::sparse.coo.core.COO.from_iter sparse/coo/core.py::sparse.coo.core.COO.from_numpy sparse/coo/core.py::sparse.coo.core.COO.from_scipy_sparse sparse/coo/core.py::sparse.coo.core.COO.linear_loc sparse/coo/core.py::sparse.coo.core.COO.max sparse/coo/core.py::sparse.coo.core.COO.maybe_densify sparse/coo/core.py::sparse.coo.core.COO.min sparse/coo/core.py::sparse.coo.core.COO.nbytes sparse/coo/core.py::sparse.coo.core.COO.nnz sparse/coo/core.py::sparse.coo.core.COO.nonzero sparse/coo/core.py::sparse.coo.core.COO.prod sparse/coo/core.py::sparse.coo.core.COO.reduce sparse/coo/core.py::sparse.coo.core.COO.reshape sparse/coo/core.py::sparse.coo.core.COO.sum sparse/coo/core.py::sparse.coo.core.COO.todense sparse/coo/core.py::sparse.coo.core.COO.transpose sparse/coo/indexing.py::sparse.coo.indexing._compute_mask sparse/coo/indexing.py::sparse.coo.indexing._filter_pairs sparse/coo/indexing.py::sparse.coo.indexing._get_mask_pairs sparse/coo/indexing.py::sparse.coo.indexing._get_slice_len sparse/coo/indexing.py::sparse.coo.indexing._join_adjacent_pairs sparse/coo/indexing.py::sparse.coo.indexing._prune_indices sparse/tests/test_coo.py::test_reductions[True-None-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-None-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-None-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-None-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-None-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True-0-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-0-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-0-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-0-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-0-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True-1-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-1-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-1-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-1-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-1-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True-2-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-2-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-2-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-2-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-2-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True-axis4-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-axis4-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-axis4-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-axis4-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-axis4-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True--3-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True--3-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True--3-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True--3-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True--3-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[True-axis6-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[True-axis6-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[True-axis6-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[True-axis6-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[True-axis6-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-None-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-None-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-None-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-None-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-None-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-0-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-0-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-0-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-0-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-0-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-1-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-1-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-1-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-1-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-1-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-2-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-2-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-2-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-2-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-2-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-axis4-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-axis4-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-axis4-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-axis4-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-axis4-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False--3-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False--3-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False--3-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False--3-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False--3-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_reductions[False-axis6-max-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_reductions[False-axis6-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_reductions[False-axis6-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_reductions[False-axis6-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_reductions[False-axis6-min-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-None-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-None-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-None-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-None-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-None-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-0-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-0-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-0-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-0-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-0-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-1-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-1-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-1-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-1-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-1-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-2-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-2-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-2-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-2-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-2-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis4-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis4-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis4-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis4-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis4-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True--1-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True--1-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True--1-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True--1-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True--1-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis6-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis6-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis6-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis6-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[True-axis6-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False-None-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[False-None-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[False-None-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[False-None-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[False-None-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False-0-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[False-0-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[False-0-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[False-0-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[False-0-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False-1-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[False-1-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[False-1-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[False-1-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[False-1-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False-2-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[False-2-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[False-2-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[False-2-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[False-2-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False-axis4-amax-kwargs0-eqkwargs0] sparse/tests/test_coo.py::test_ufunc_reductions[False-axis4-sum-kwargs1-eqkwargs1] sparse/tests/test_coo.py::test_ufunc_reductions[False-axis4-sum-kwargs2-eqkwargs2] sparse/tests/test_coo.py::test_ufunc_reductions[False-axis4-prod-kwargs3-eqkwargs3] sparse/tests/test_coo.py::test_ufunc_reductions[False-axis4-amin-kwargs4-eqkwargs4] sparse/tests/test_coo.py::test_ufunc_reductions[False--1-amax-kwargs0-eqkwargs0] 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sparse/tests/test_coo.py::test_ufunc_reductions_kwargs[reduce-kwargs3] sparse/tests/test_coo.py::test_ufunc_reductions_kwargs[reduce-kwargs4] sparse/tests/test_coo.py::test_ufunc_reductions_kwargs[reduce-kwargs5] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-None-nansum] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-None-nanprod] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-None-nanmax] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-None-nanmin] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-0-nansum] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-0-nanprod] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-0-nanmax] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-0-nanmin] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-1-nansum] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-1-nanprod] sparse/tests/test_coo.py::test_nan_reductions[0.25-False-1-nanmax] 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sparse/tests/test_coo.py::test_binary_broadcasting[shape14-shape24-add] sparse/tests/test_coo.py::test_binary_broadcasting[shape14-shape24-mul] sparse/tests/test_coo.py::test_binary_broadcasting[shape15-shape25-add] sparse/tests/test_coo.py::test_binary_broadcasting[shape15-shape25-mul] sparse/tests/test_coo.py::test_binary_broadcasting[shape16-shape26-add] sparse/tests/test_coo.py::test_binary_broadcasting[shape16-shape26-mul] sparse/tests/test_coo.py::test_binary_broadcasting[shape17-shape27-add] sparse/tests/test_coo.py::test_binary_broadcasting[shape17-shape27-mul] sparse/tests/test_coo.py::test_binary_broadcasting[shape18-shape28-add] sparse/tests/test_coo.py::test_binary_broadcasting[shape18-shape28-mul] sparse/tests/test_coo.py::test_broadcast_to[shape10-shape20] sparse/tests/test_coo.py::test_broadcast_to[shape11-shape21] sparse/tests/test_coo.py::test_broadcast_to[shape12-shape22] sparse/tests/test_coo.py::test_trinary_broadcasting[<lambda>0-shapes0] 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sparse/tests/test_coo.py::test_slicing[index18] sparse/tests/test_coo.py::test_slicing[index19] sparse/tests/test_coo.py::test_slicing[index20] sparse/tests/test_coo.py::test_slicing[index21] sparse/tests/test_coo.py::test_slicing[index22] sparse/tests/test_coo.py::test_slicing[index23] sparse/tests/test_coo.py::test_slicing[index24] sparse/tests/test_coo.py::test_slicing[index25] sparse/tests/test_coo.py::test_slicing[index26] sparse/tests/test_coo.py::test_slicing[index27] sparse/tests/test_coo.py::test_slicing[index28] sparse/tests/test_coo.py::test_slicing[index29] sparse/tests/test_coo.py::test_slicing[index30] sparse/tests/test_coo.py::test_slicing[index31] sparse/tests/test_coo.py::test_slicing[index32] sparse/tests/test_coo.py::test_slicing[index33] sparse/tests/test_coo.py::test_slicing[index34] sparse/tests/test_coo.py::test_slicing[index35] sparse/tests/test_coo.py::test_slicing[index36] sparse/tests/test_coo.py::test_slicing[index37] sparse/tests/test_coo.py::test_slicing[index38] sparse/tests/test_coo.py::test_slicing[index39] sparse/tests/test_coo.py::test_slicing[index40] sparse/tests/test_coo.py::test_slicing[index41] sparse/tests/test_coo.py::test_slicing[index42] sparse/tests/test_coo.py::test_slicing[index43] sparse/tests/test_coo.py::test_slicing[index44] sparse/tests/test_coo.py::test_slicing[index45] sparse/tests/test_coo.py::test_custom_dtype_slicing sparse/tests/test_coo.py::test_slicing_errors[index0] sparse/tests/test_coo.py::test_slicing_errors[index1] sparse/tests/test_coo.py::test_slicing_errors[index2] sparse/tests/test_coo.py::test_slicing_errors[5] sparse/tests/test_coo.py::test_slicing_errors[-5] sparse/tests/test_coo.py::test_slicing_errors[foo] sparse/tests/test_coo.py::test_slicing_errors[index6] sparse/tests/test_coo.py::test_slicing_errors[0.5] sparse/tests/test_coo.py::test_slicing_errors[index8] sparse/tests/test_coo.py::test_slicing_errors[index9] 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sparse/tests/test_coo.py::test_cache_csr sparse/tests/test_coo.py::test_empty_shape sparse/tests/test_coo.py::test_single_dimension sparse/tests/test_coo.py::test_large_sum sparse/tests/test_coo.py::test_add_many_sparse_arrays sparse/tests/test_coo.py::test_caching sparse/tests/test_coo.py::test_scalar_slicing sparse/tests/test_coo.py::test_triul[shape0-0] sparse/tests/test_coo.py::test_triul[shape1-1] sparse/tests/test_coo.py::test_triul[shape2--1] sparse/tests/test_coo.py::test_triul[shape3--2] sparse/tests/test_coo.py::test_triul[shape4-1000] sparse/tests/test_coo.py::test_empty_reduction sparse/tests/test_coo.py::test_random_shape[0.1-shape0] sparse/tests/test_coo.py::test_random_shape[0.1-shape1] sparse/tests/test_coo.py::test_random_shape[0.1-shape2] sparse/tests/test_coo.py::test_random_shape[0.3-shape0] sparse/tests/test_coo.py::test_random_shape[0.3-shape1] sparse/tests/test_coo.py::test_random_shape[0.3-shape2] sparse/tests/test_coo.py::test_random_shape[0.5-shape0] sparse/tests/test_coo.py::test_random_shape[0.5-shape1] sparse/tests/test_coo.py::test_random_shape[0.5-shape2] sparse/tests/test_coo.py::test_random_shape[0.7-shape0] sparse/tests/test_coo.py::test_random_shape[0.7-shape1] sparse/tests/test_coo.py::test_random_shape[0.7-shape2] sparse/tests/test_coo.py::test_two_random_unequal sparse/tests/test_coo.py::test_two_random_same_seed sparse/tests/test_coo.py::test_random_rvs[0.0-shape0-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.0-shape0-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.0-shape0-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.0-shape1-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.0-shape1-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.0-shape1-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.01-shape0-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.01-shape0-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.01-shape0-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.01-shape1-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.01-shape1-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.01-shape1-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.1-shape0-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.1-shape0-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.1-shape0-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.1-shape1-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.1-shape1-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.1-shape1-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.2-shape0-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.2-shape0-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.2-shape0-<lambda>-bool] sparse/tests/test_coo.py::test_random_rvs[0.2-shape1-None-float64] sparse/tests/test_coo.py::test_random_rvs[0.2-shape1-rvs-int] sparse/tests/test_coo.py::test_random_rvs[0.2-shape1-<lambda>-bool] sparse/tests/test_coo.py::test_random_fv[coo] sparse/tests/test_coo.py::test_random_fv[dok] sparse/tests/test_coo.py::test_scalar_shape_construction sparse/tests/test_coo.py::test_len sparse/tests/test_coo.py::test_density sparse/tests/test_coo.py::test_size sparse/tests/test_coo.py::test_np_array sparse/tests/test_coo.py::test_three_arg_where[shapes0] sparse/tests/test_coo.py::test_three_arg_where[shapes1] sparse/tests/test_coo.py::test_three_arg_where[shapes2] sparse/tests/test_coo.py::test_three_arg_where[shapes3] sparse/tests/test_coo.py::test_three_arg_where[shapes4] sparse/tests/test_coo.py::test_three_arg_where[shapes5] sparse/tests/test_coo.py::test_three_arg_where[shapes6] sparse/tests/test_coo.py::test_three_arg_where[shapes7] sparse/tests/test_coo.py::test_one_arg_where sparse/tests/test_coo.py::test_one_arg_where_dense sparse/tests/test_coo.py::test_two_arg_where sparse/tests/test_coo.py::test_inplace_invalid_shape[imul] sparse/tests/test_coo.py::test_inplace_invalid_shape[iadd] sparse/tests/test_coo.py::test_inplace_invalid_shape[isub] sparse/tests/test_coo.py::test_nonzero sparse/tests/test_coo.py::test_argwhere sparse/tests/test_coo.py::test_asformat[coo] sparse/tests/test_coo.py::test_asformat[dok] sparse/tests/test_coo.py::test_as_coo[COO] sparse/tests/test_coo.py::test_as_coo[DOK] sparse/tests/test_coo.py::test_as_coo[csr_matrix] sparse/tests/test_coo.py::test_as_coo[asarray] sparse/tests/test_coo.py::test_invalid_shape_error sparse/tests/test_coo.py::test_invalid_iterable_error sparse/tests/test_coo.py::TestRoll::test_1d[0] sparse/tests/test_coo.py::TestRoll::test_1d[2] sparse/tests/test_coo.py::TestRoll::test_1d[-2] sparse/tests/test_coo.py::TestRoll::test_1d[20] sparse/tests/test_coo.py::TestRoll::test_1d[-20] sparse/tests/test_coo.py::TestRoll::test_2d[None-0] sparse/tests/test_coo.py::TestRoll::test_2d[None-2] sparse/tests/test_coo.py::TestRoll::test_2d[None--2] sparse/tests/test_coo.py::TestRoll::test_2d[None-20] sparse/tests/test_coo.py::TestRoll::test_2d[None--20] sparse/tests/test_coo.py::TestRoll::test_2d[0-0] sparse/tests/test_coo.py::TestRoll::test_2d[0-2] sparse/tests/test_coo.py::TestRoll::test_2d[0--2] sparse/tests/test_coo.py::TestRoll::test_2d[0-20] sparse/tests/test_coo.py::TestRoll::test_2d[0--20] sparse/tests/test_coo.py::TestRoll::test_2d[1-0] sparse/tests/test_coo.py::TestRoll::test_2d[1-2] sparse/tests/test_coo.py::TestRoll::test_2d[1--2] sparse/tests/test_coo.py::TestRoll::test_2d[1-20] sparse/tests/test_coo.py::TestRoll::test_2d[1--20] sparse/tests/test_coo.py::TestRoll::test_2d[ax3-0] sparse/tests/test_coo.py::TestRoll::test_2d[ax3-2] sparse/tests/test_coo.py::TestRoll::test_2d[ax3--2] sparse/tests/test_coo.py::TestRoll::test_2d[ax3-20] sparse/tests/test_coo.py::TestRoll::test_2d[ax3--20] sparse/tests/test_coo.py::TestRoll::test_multiaxis[ax0-shift0] sparse/tests/test_coo.py::TestRoll::test_multiaxis[ax0-shift1] 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sparse/tests/test_coo.py::TestRoll::test_original_is_copied[ax3-2] sparse/tests/test_coo.py::TestRoll::test_original_is_copied[ax3--2] sparse/tests/test_coo.py::TestRoll::test_original_is_copied[ax3-20] sparse/tests/test_coo.py::TestRoll::test_original_is_copied[ax3--20] sparse/tests/test_coo.py::TestRoll::test_empty sparse/tests/test_coo.py::TestRoll::test_valerr[args0] sparse/tests/test_coo.py::TestRoll::test_valerr[args1] sparse/tests/test_coo.py::TestRoll::test_valerr[args2] sparse/tests/test_coo.py::TestRoll::test_valerr[args3] sparse/tests/test_coo.py::TestFailFillValue::test_nonzero_fv sparse/tests/test_coo.py::TestFailFillValue::test_inconsistent_fv sparse/tests/test_coo.py::test_initialization[2] sparse/tests/test_coo.py::test_initialization[3] sparse/tests/test_coo.py::test_initialization[4] sparse/tests/test_coo.py::test_initialization[5] sparse/tests/test_dok.py::test_random_shape_nnz[0.1-shape0] sparse/tests/test_dok.py::test_random_shape_nnz[0.1-shape1] sparse/tests/test_dok.py::test_random_shape_nnz[0.1-shape2] sparse/tests/test_dok.py::test_random_shape_nnz[0.3-shape0] sparse/tests/test_dok.py::test_random_shape_nnz[0.3-shape1] sparse/tests/test_dok.py::test_random_shape_nnz[0.3-shape2] sparse/tests/test_dok.py::test_random_shape_nnz[0.5-shape0] sparse/tests/test_dok.py::test_random_shape_nnz[0.5-shape1] sparse/tests/test_dok.py::test_random_shape_nnz[0.5-shape2] sparse/tests/test_dok.py::test_random_shape_nnz[0.7-shape0] sparse/tests/test_dok.py::test_random_shape_nnz[0.7-shape1] sparse/tests/test_dok.py::test_random_shape_nnz[0.7-shape2] sparse/tests/test_dok.py::test_convert_to_coo sparse/tests/test_dok.py::test_convert_from_coo sparse/tests/test_dok.py::test_convert_from_numpy sparse/tests/test_dok.py::test_convert_to_numpy sparse/tests/test_dok.py::test_construct[2-data0] sparse/tests/test_dok.py::test_construct[shape1-data1] sparse/tests/test_dok.py::test_construct[shape2-data2] sparse/tests/test_dok.py::test_getitem[0.1-shape0] sparse/tests/test_dok.py::test_getitem[0.1-shape1] sparse/tests/test_dok.py::test_getitem[0.1-shape2] sparse/tests/test_dok.py::test_getitem[0.3-shape0] sparse/tests/test_dok.py::test_getitem[0.3-shape1] sparse/tests/test_dok.py::test_getitem[0.3-shape2] sparse/tests/test_dok.py::test_getitem[0.5-shape0] sparse/tests/test_dok.py::test_getitem[0.5-shape1] sparse/tests/test_dok.py::test_getitem[0.5-shape2] sparse/tests/test_dok.py::test_getitem[0.7-shape0] sparse/tests/test_dok.py::test_getitem[0.7-shape1] sparse/tests/test_dok.py::test_getitem[0.7-shape2] sparse/tests/test_dok.py::test_setitem[shape2-index2-value2] sparse/tests/test_dok.py::test_setitem[shape6-index6-value6] sparse/tests/test_dok.py::test_setitem[shape7-index7-value7] sparse/tests/test_dok.py::test_setitem[shape8-index8-value8] sparse/tests/test_dok.py::test_setitem[shape10-index10-value10] sparse/tests/test_dok.py::test_setitem[shape12-index12-value12] sparse/tests/test_dok.py::test_default_dtype sparse/tests/test_dok.py::test_int_dtype sparse/tests/test_dok.py::test_float_dtype sparse/tests/test_dok.py::test_set_zero sparse/tests/test_dok.py::test_asformat[coo] sparse/tests/test_dok.py::test_asformat[dok] sparse/tests/test_io.py::test_save_load_npz_file[True] sparse/tests/test_io.py::test_save_load_npz_file[False] sparse/tests/test_io.py::test_load_wrong_format_exception
sparse/utils.py docs/generated/sparse.COO.rst sparse/coo/indexing.py sparse/slicing.py docs/generated/sparse.COO.all.rst docs/generated/sparse.COO.any.rst docs/operations.rst sparse/coo/core.py requirements.txt
sparse/utils.py docs/generated/sparse.COO.rst sparse/coo/indexing.py sparse/slicing.py docs/generated/sparse.COO.all.rst docs/generated/sparse.COO.any.rst docs/operations.rst sparse/coo/core.py requirements.txt