jsignell: @dask/maintenance for a review. 
diff --git a/dask/array/core.py b/dask/array/core.py
index ef8970091..a5c51ca87 100644
--- a/dask/array/core.py
+++ b/dask/array/core.py
@@ -4269,13 +4269,11 @@ def stack(seq, axis=0, allow_unknown_chunksizes=False):
     if not seq:
         raise ValueError("Need array(s) to stack")
     if not allow_unknown_chunksizes and not all(x.shape == seq[0].shape for x in seq):
-        idx = np.where(np.asanyarray([x.shape for x in seq]) != seq[0].shape)[0]
+        idx = first(i for i in enumerate(seq) if i[1].shape != seq[0].shape)
         raise ValueError(
             "Stacked arrays must have the same shape. "
-            "The first {0} had shape {1}, while array "
-            "{2} has shape {3}".format(
-                idx[0], seq[0].shape, idx[0] + 1, seq[idx[0]].shape
-            )
+            "The first array had shape {0}, while array "
+            "{1} has shape {2}.".format(seq[0].shape, idx[0] + 1, idx[1].shape)
         )
 
     meta = np.stack([meta_from_array(a) for a in seq], axis=axis)
diff --git a/dask/array/creation.py b/dask/array/creation.py
index 01668df6c..6e7bd92e5 100644
--- a/dask/array/creation.py
+++ b/dask/array/creation.py
@@ -27,7 +27,7 @@ from .wrap import empty, ones, zeros, full
 from .utils import AxisError, meta_from_array, zeros_like_safe
 
 
-def empty_like(a, dtype=None, chunks=None):
+def empty_like(a, dtype=None, order="C", chunks=None, name=None):
     """
     Return a new array with the same shape and type as a given array.
 
@@ -38,9 +38,15 @@ def empty_like(a, dtype=None, chunks=None):
         returned array.
     dtype : data-type, optional
         Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
     chunks : sequence of ints
         The number of samples on each block. Note that the last block will have
         fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
 
     Returns
     -------
@@ -67,11 +73,13 @@ def empty_like(a, dtype=None, chunks=None):
     return empty(
         a.shape,
         dtype=(dtype or a.dtype),
+        order=order,
         chunks=(chunks if chunks is not None else a.chunks),
+        name=name,
     )
 
 
-def ones_like(a, dtype=None, chunks=None):
+def ones_like(a, dtype=None, order="C", chunks=None, name=None):
     """
     Return an array of ones with the same shape and type as a given array.
 
@@ -82,9 +90,15 @@ def ones_like(a, dtype=None, chunks=None):
         the returned array.
     dtype : data-type, optional
         Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
     chunks : sequence of ints
         The number of samples on each block. Note that the last block will have
         fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
 
     Returns
     -------
@@ -104,11 +118,13 @@ def ones_like(a, dtype=None, chunks=None):
     return ones(
         a.shape,
         dtype=(dtype or a.dtype),
+        order=order,
         chunks=(chunks if chunks is not None else a.chunks),
+        name=name,
     )
 
 
-def zeros_like(a, dtype=None, chunks=None):
+def zeros_like(a, dtype=None, order="C", chunks=None, name=None):
     """
     Return an array of zeros with the same shape and type as a given array.
 
@@ -119,9 +135,15 @@ def zeros_like(a, dtype=None, chunks=None):
         the returned array.
     dtype : data-type, optional
         Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
     chunks : sequence of ints
         The number of samples on each block. Note that the last block will have
         fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
 
     Returns
     -------
@@ -141,11 +163,13 @@ def zeros_like(a, dtype=None, chunks=None):
     return zeros(
         a.shape,
         dtype=(dtype or a.dtype),
+        order=order,
         chunks=(chunks if chunks is not None else a.chunks),
+        name=name,
     )
 
 
-def full_like(a, fill_value, dtype=None, chunks=None):
+def full_like(a, fill_value, order="C", dtype=None, chunks=None, name=None):
     """
     Return a full array with the same shape and type as a given array.
 
@@ -158,9 +182,15 @@ def full_like(a, fill_value, dtype=None, chunks=None):
         Fill value.
     dtype : data-type, optional
         Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
     chunks : sequence of ints
         The number of samples on each block. Note that the last block will have
         fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
 
     Returns
     -------
@@ -182,8 +212,10 @@ def full_like(a, fill_value, dtype=None, chunks=None):
     return full(
         a.shape,
         fill_value,
+        order=order,
         dtype=(dtype or a.dtype),
         chunks=(chunks if chunks is not None else a.chunks),
+        name=name,
     )
 
 
diff --git a/dask/array/slicing.py b/dask/array/slicing.py
index ffd5bc7c6..25c7ee448 100644
--- a/dask/array/slicing.py
+++ b/dask/array/slicing.py
@@ -769,9 +769,10 @@ def normalize_index(idx, shape):
     1.  Replaces ellipses with many full slices
     2.  Adds full slices to end of index
     3.  Checks bounding conditions
-    4.  Replaces numpy arrays with lists
-    5.  Posify's integers and lists
-    6.  Normalizes slices to canonical form
+    4.  Replace multidimensional numpy arrays with dask arrays
+    5.  Replaces numpy arrays with lists
+    6.  Posify's integers and lists
+    7.  Normalizes slices to canonical form
 
     Examples
     --------
@@ -785,9 +786,20 @@ def normalize_index(idx, shape):
     (slice(7, None, None),)
     >>> normalize_index((Ellipsis, None), (10,))
     (slice(None, None, None), None)
+    >>> normalize_index(np.array([[True, False], [False, True], [True, True]]), (3, 2))
+    (dask.array<array, shape=(3, 2), dtype=bool, chunksize=(3, 2), chunktype=numpy.ndarray>,)
     """
+    from .core import from_array
+
     if not isinstance(idx, tuple):
         idx = (idx,)
+
+    # if a > 1D numpy.array is provided, cast it to a dask array
+    if len(idx) > 0 and len(shape) > 1:
+        i = idx[0]
+        if isinstance(i, np.ndarray) and i.shape == shape:
+            idx = (from_array(i), *idx[1:])
+
     idx = replace_ellipsis(len(shape), idx)
     n_sliced_dims = 0
     for i in idx:
diff --git a/dask/array/wrap.py b/dask/array/wrap.py
index 82b8e8005..af3d1487f 100644
--- a/dask/array/wrap.py
+++ b/dask/array/wrap.py
@@ -18,6 +18,7 @@ def _parse_wrap_args(func, args, kwargs, shape):
     if not isinstance(shape, (tuple, list)):
         shape = (shape,)
 
+    name = kwargs.pop("name", None)
     chunks = kwargs.pop("chunks", "auto")
 
     dtype = kwargs.pop("dtype", None)
@@ -26,7 +27,6 @@ def _parse_wrap_args(func, args, kwargs, shape):
     dtype = np.dtype(dtype)
 
     chunks = normalize_chunks(chunks, shape, dtype=dtype)
-    name = kwargs.pop("name", None)
 
     name = name or funcname(func) + "-" + tokenize(
         func, shape, chunks, dtype, args, kwargs
@@ -121,8 +121,8 @@ def wrap(wrap_func, func, **kwargs):
     template = """
     Blocked variant of %(name)s
 
-    Follows the signature of %(name)s exactly except that it also requires a
-    keyword argument chunks=(...)
+    Follows the signature of %(name)s exactly except that it also features
+    optional keyword arguments ``chunks: int, tuple, or dict`` and ``name: str``.
 
     Original signature follows below.
     """
@@ -137,13 +137,39 @@ w = wrap(wrap_func_shape_as_first_arg)
 ones = w(np.ones, dtype="f8")
 zeros = w(np.zeros, dtype="f8")
 empty = w(np.empty, dtype="f8")
-full = w(np.full)
 
 
 w_like = wrap(wrap_func_like_safe)
 
 
-ones_like = w_like(np.ones, func_like=np.ones_like)
-zeros_like = w_like(np.zeros, func_like=np.zeros_like)
 empty_like = w_like(np.empty, func_like=np.empty_like)
-full_like = w_like(np.full, func_like=np.full_like)
+
+
+# full and full_like require special casing due to argument check on fill_value
+# Generate wrapped functions only once
+_full = w(np.full)
+_full_like = w_like(np.full, func_like=np.full_like)
+
+
+def full(shape, fill_value, *args, **kwargs):
+    # np.isscalar has somewhat strange behavior:
+    # https://docs.scipy.org/doc/numpy/reference/generated/numpy.isscalar.html
+    if np.ndim(fill_value) != 0:
+        raise ValueError(
+            f"fill_value must be scalar. Received {type(fill_value).__name__} instead."
+        )
+    return _full(shape=shape, fill_value=fill_value, *args, **kwargs)
+
+
+def full_like(a, fill_value, *args, **kwargs):
+    if np.ndim(fill_value) != 0:
+        raise ValueError(
+            f"fill_value must be scalar. Received {type(fill_value).__name__} instead."
+        )
+    return _full_like(a=a, fill_value=fill_value, *args, **kwargs,)
+
+
+full.__doc__ = _full.__doc__
+full.__name__ = _full.__name__
+full_like.__doc__ = _full_like.__doc__
+full_like.__name__ = _full_like.__name__
diff --git a/dask/dataframe/tseries/resample.py b/dask/dataframe/tseries/resample.py
index 851ff2a92..502126020 100644
--- a/dask/dataframe/tseries/resample.py
+++ b/dask/dataframe/tseries/resample.py
@@ -124,16 +124,7 @@ class Resampler(object):
             )
             raise ValueError(msg)
         self.obj = obj
-        rule = pd.tseries.frequencies.to_offset(rule)
-        day_nanos = pd.tseries.frequencies.Day().nanos
-
-        if getnanos(rule) and day_nanos % rule.nanos:
-            raise NotImplementedError(
-                "Resampling frequency %s that does"
-                " not evenly divide a day is not "
-                "implemented" % rule
-            )
-        self._rule = rule
+        self._rule = pd.tseries.frequencies.to_offset(rule)
         self._kwargs = kwargs
 
     def _agg(self, how, meta=None, fill_value=np.nan, how_args=(), how_kwargs={}):

Error when using a Numpy boolean mask on a Dask array
I am trying to extract values from a Dask array using a boolean Numpy array and am running into the following error:

```python
In [8]: import numpy as np                                                                                                                                                                                                                                                                                                                                                                                                                                                                              

In [9]: from dask.array import random                                                                                                                                                                                                                                                                                                                                                                                                                                                                   

In [10]: array = random.random((4, 3, 2))                                                                                                                                                                                                                                                                                                                                                                                                                                                               

In [11]: mask = np.random.random((4, 3, 2)) > 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                       

In [12]: array[mask]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-12-38297b4b7b0f> in <module>
----> 1 array[mask]

~/python/dev/lib/python3.8/site-packages/dask/array/core.py in __getitem__(self, index)
   1537         )
   1538 
-> 1539         index2 = normalize_index(index, self.shape)
   1540 
   1541         dependencies = {self.name}

~/python/dev/lib/python3.8/site-packages/dask/array/slicing.py in normalize_index(idx, shape)
    813     for i, d in zip(idx, none_shape):
    814         if d is not None:
--> 815             check_index(i, d)
    816     idx = tuple(map(sanitize_index, idx))
    817     idx = tuple(map(normalize_slice, idx, none_shape))

~/python/dev/lib/python3.8/site-packages/dask/array/slicing.py in check_index(ind, dimension)
    867         if x.dtype == bool:
    868             if x.size != dimension:
--> 869                 raise IndexError(
    870                     "Boolean array length %s doesn't equal dimension %s"
    871                     % (x.size, dimension)

IndexError: Boolean array length 24 doesn't equal dimension 4
```

Note that converting the mask to a dask array fixes this:

```python
In [14]: from dask.array import asarray                                                                                                                                                                                                                                                                                                                                                                                                                                                                 

In [15]: array[asarray(mask)]                                                                                                                                                                                                                                                                                                                                                                                                                                                                           
Out[15]: dask.array<getitem, shape=(nan,), dtype=float64, chunksize=(nan,), chunktype=numpy.ndarray>

In [16]: array[asarray(mask)].compute()                                                                                                                                                                                                                                                                                                                                                                                                                                                                 
Out[16]: 
array([0.87541677, 0.72561386, 0.80415762, 0.90781457, 0.41807167,
       0.88501242, 0.33921153, 0.5392456 , 0.31133289, 0.86372707,
       0.32839977, 0.77310795])
```

but I thought I should report this as a bug.
diff --git a/dask/array/tests/test_array_core.py b/dask/array/tests/test_array_core.py
index 765e14ebb..d8241de0f 100644
--- a/dask/array/tests/test_array_core.py
+++ b/dask/array/tests/test_array_core.py
@@ -2314,8 +2314,7 @@ def test_from_array_tasks_always_call_getter(x, chunks):
 
 
 def test_from_array_ndarray_onechunk():
-    """ndarray with a single chunk produces a minimal single key dict
-    """
+    """ndarray with a single chunk produces a minimal single key dict"""
     x = np.array([[1, 2], [3, 4]])
     dx = da.from_array(x, chunks=-1)
     assert_eq(x, dx)
@@ -2334,8 +2333,7 @@ def test_from_array_ndarray_getitem():
 
 @pytest.mark.parametrize("x", [[1, 2], (1, 2), memoryview(b"abc")])
 def test_from_array_list(x):
-    """Lists, tuples, and memoryviews are automatically converted to ndarray
-    """
+    """Lists, tuples, and memoryviews are automatically converted to ndarray"""
     dx = da.from_array(x, chunks=-1)
     assert_eq(np.array(x), dx)
     assert isinstance(dx.dask[dx.name, 0], np.ndarray)
@@ -2348,8 +2346,7 @@ def test_from_array_list(x):
 
 @pytest.mark.parametrize("type_", [t for t in np.ScalarType if t is not memoryview])
 def test_from_array_scalar(type_):
-    """Python and numpy scalars are automatically converted to ndarray
-    """
+    """Python and numpy scalars are automatically converted to ndarray"""
     if type_ == np.datetime64:
         x = np.datetime64("2000-01-01")
     else:
@@ -3621,8 +3618,10 @@ def test_stack_errs():
     with pytest.raises(ValueError) as e:
         da.stack([da.zeros((2,), chunks=2)] * 10 + [da.zeros((3,), chunks=3)] * 10)
 
-    assert "shape" in str(e.value).lower()
-    assert "(2,)" in str(e.value)
+    assert (
+        str(e.value)
+        == "Stacked arrays must have the same shape. The first array had shape (2,), while array 11 has shape (3,)."
+    )
     assert len(str(e.value)) < 105
 
 
diff --git a/dask/array/tests/test_creation.py b/dask/array/tests/test_creation.py
index b63b34d8c..1928c5fe8 100644
--- a/dask/array/tests/test_creation.py
+++ b/dask/array/tests/test_creation.py
@@ -29,8 +29,10 @@ from dask.array.numpy_compat import _numpy_117, _numpy_118
 @pytest.mark.parametrize("cast_shape", [tuple, list, np.asarray])
 @pytest.mark.parametrize("cast_chunks", [tuple, list, np.asarray])
 @pytest.mark.parametrize("shape, chunks", [((10, 10), (4, 4))])
+@pytest.mark.parametrize("name", [None, "my-name"])
+@pytest.mark.parametrize("order", ["C", "F"])
 @pytest.mark.parametrize("dtype", ["i4"])
-def test_arr_like(funcname, shape, cast_shape, dtype, cast_chunks, chunks):
+def test_arr_like(funcname, shape, cast_shape, dtype, cast_chunks, chunks, name, order):
     np_func = getattr(np, funcname)
     da_func = getattr(da, funcname)
     shape = cast_shape(shape)
@@ -48,11 +50,11 @@ def test_arr_like(funcname, shape, cast_shape, dtype, cast_chunks, chunks):
     if "like" in funcname:
         a = np.random.randint(0, 10, shape).astype(dtype)
 
-        np_r = np_func(a)
-        da_r = da_func(a, chunks=chunks)
+        np_r = np_func(a, order=order)
+        da_r = da_func(a, order=order, chunks=chunks, name=name)
     else:
-        np_r = np_func(shape, dtype=dtype)
-        da_r = da_func(shape, dtype=dtype, chunks=chunks)
+        np_r = np_func(shape, order=order, dtype=dtype)
+        da_r = da_func(shape, order=order, dtype=dtype, chunks=chunks, name=name)
 
     assert np_r.shape == da_r.shape
     assert np_r.dtype == da_r.dtype
@@ -60,6 +62,16 @@ def test_arr_like(funcname, shape, cast_shape, dtype, cast_chunks, chunks):
     if "empty" not in funcname:
         assert (np_r == np.asarray(da_r)).all()
 
+    if name is None:
+        assert funcname.split("_")[0] in da_r.name
+    else:
+        assert da_r.name == name
+
+    if "order" == "F":
+        assert np.isfortran(da_r.compute())
+    else:
+        assert not np.isfortran(da_r.compute())
+
 
 @pytest.mark.parametrize("endpoint", [True, False])
 def test_linspace(endpoint):
diff --git a/dask/array/tests/test_slicing.py b/dask/array/tests/test_slicing.py
index d457035a8..53429b8f4 100644
--- a/dask/array/tests/test_slicing.py
+++ b/dask/array/tests/test_slicing.py
@@ -948,3 +948,13 @@ def test_gh4043(lock, asarray, fancy):
     a2 = da.from_array(np.ones(3), chunks=1, asarray=asarray, lock=lock, fancy=fancy)
     al = da.stack([a1, a2])
     assert_eq(al, al)
+
+
+def test_slice_array_3d_with_bool_numpy_array():
+    # https://github.com/dask/dask/issues/6089
+    array = da.arange(0, 24).reshape((4, 3, 2))
+    mask = np.arange(0, 24).reshape((4, 3, 2)) > 12
+
+    actual = array[mask].compute()
+    expected = np.arange(13, 24)
+    assert_eq(actual, expected)
diff --git a/dask/array/tests/test_wrap.py b/dask/array/tests/test_wrap.py
index f3d9345c9..6de3943bf 100644
--- a/dask/array/tests/test_wrap.py
+++ b/dask/array/tests/test_wrap.py
@@ -42,6 +42,17 @@ def test_full():
     assert a.name.startswith("full-")
 
 
+def test_full_error_nonscalar_fill_value():
+    with pytest.raises(ValueError, match="fill_value must be scalar"):
+        da.full((3, 3), [100, 100], chunks=(2, 2), dtype="i8")
+
+
+def test_full_like_error_nonscalar_fill_value():
+    x = np.full((3, 3), 1, dtype="i8")
+    with pytest.raises(ValueError, match="fill_value must be scalar"):
+        da.full_like(x, [100, 100], chunks=(2, 2), dtype="i8")
+
+
 def test_can_make_really_big_array_of_ones():
     ones((1000000, 1000000), chunks=(100000, 100000))
     ones(shape=(1000000, 1000000), chunks=(100000, 100000))
diff --git a/dask/dataframe/tseries/tests/test_resample.py b/dask/dataframe/tseries/tests/test_resample.py
index c8a657fe4..327b43927 100644
--- a/dask/dataframe/tseries/tests/test_resample.py
+++ b/dask/dataframe/tseries/tests/test_resample.py
@@ -128,12 +128,32 @@ def test_resample_pads_last_division_to_avoid_off_by_one():
     assert_eq(actual, expected)
 
 
-def test_series_resample_not_implemented():
-    index = pd.date_range(start="2012-01-02", periods=100, freq="T")
+def test_resample_does_not_evenly_divide_day():
+    import numpy as np
+
+    index = pd.date_range("2012-01-02", "2012-02-02", freq="H")
+    index = index.union(pd.date_range("2012-03-02", "2012-04-02", freq="H"))
+    df = pd.DataFrame({"p": np.random.random(len(index))}, index=index)
+    ddf = dd.from_pandas(df, npartitions=5)
+    # Frequency doesn't evenly divide day
+    expected = df.resample("2D").count()
+    result = ddf.resample("2D").count().compute()
+
+    assert_eq(result, expected)
+
+
+def test_series_resample_does_not_evenly_divide_day():
+    index = pd.date_range("2012-01-02 00:00:00", "2012-01-02 01:00:00", freq="T")
+    index = index.union(
+        pd.date_range("2012-01-02 06:00:00", "2012-01-02 08:00:00", freq="T")
+    )
     s = pd.Series(range(len(index)), index=index)
     ds = dd.from_pandas(s, npartitions=5)
     # Frequency doesn't evenly divide day
-    pytest.raises(NotImplementedError, lambda: resample(ds, "57T"))
+    expected = s.resample("57T").mean()
+    result = ds.resample("57T").mean().compute()
+
+    assert_eq(result, expected)
 
 
 def test_unknown_divisions_error():

continuous_integration/environment-3.8.yaml
pip install -e .[complete]
parse_log_pytest
environment.yml
pytest
3.8
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
aiobotocore @ file:///home/conda/feedstock_root/build_artifacts/aiobotocore_1731276079927/work
aiohappyeyeballs @ file:///home/conda/feedstock_root/build_artifacts/aiohappyeyeballs_1727779797566/work
aiohttp @ file:///croot/aiohttp_1725527756643/work
aioitertools @ file:///home/conda/feedstock_root/build_artifacts/aioitertools_1727030270694/work
aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1667935791922/work
asciitree==0.3.3
asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733175639022/work
async-timeout @ file:///home/conda/feedstock_root/build_artifacts/async-timeout_1691763562544/work
attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1722977137225/work
aws-xray-sdk @ file:///home/conda/feedstock_root/build_artifacts/aws-xray-sdk_1717620777976/work
backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
bcolz==1.2.1
blinker @ file:///home/conda/feedstock_root/build_artifacts/blinker_1715091184126/work
bokeh @ file:///home/conda/feedstock_root/build_artifacts/bokeh_1660586590972/work
boto3 @ file:///home/conda/feedstock_root/build_artifacts/boto3_1728498203129/work
botocore @ file:///home/conda/feedstock_root/build_artifacts/botocore_1728459686950/work
Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1648883617327/work
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
cachey @ file:///home/conda/feedstock_root/build_artifacts/cachey_1594854602465/work
certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1725278078093/work/certifi
cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1636046063618/work
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1728479282467/work
chest==0.2.3
click @ file:///home/conda/feedstock_root/build_artifacts/click_1692311806742/work
cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1729059237860/work
colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
coverage @ file:///home/conda/feedstock_root/build_artifacts/coverage_1652409050186/work
cramjam @ file:///home/conda/feedstock_root/build_artifacts/cramjam_1639034986428/work
crick==0.0.3
cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography_1652967113783/work
cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1696677705766/work
cytoolz==0.11.2
-e git+https://github.com/dask/dask.git@9e994bad3be012c4ecb03678a88934c4e66cdebb#egg=dask
decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
distributed==2.30.1
entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1720869315914/work
execnet @ file:///home/conda/feedstock_root/build_artifacts/execnet_1712591832280/work
executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1725214404607/work
fasteners @ file:///home/conda/feedstock_root/build_artifacts/fasteners_1643971550063/work
fastparquet @ file:///home/conda/feedstock_root/build_artifacts/fastparquet_1626708773298/work
Flask @ file:///home/conda/feedstock_root/build_artifacts/flask_1712667726126/work
Flask-Cors @ file:///home/conda/feedstock_root/build_artifacts/flask-cors_1725086079197/work
frozenlist @ file:///home/conda/feedstock_root/build_artifacts/frozenlist_1648771696385/work
fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1729608855534/work
graphviz @ file:///home/conda/feedstock_root/build_artifacts/python-graphviz_1727718607478/work
greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1648882382141/work
h5py @ file:///home/conda/feedstock_root/build_artifacts/h5py_1624405622079/work
HeapDict @ file:///home/conda/feedstock_root/build_artifacts/heapdict_1722613793866/work
httpretty @ file:///home/conda/feedstock_root/build_artifacts/httpretty_1629146758059/work
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1726459485162/work
imagecodecs @ file:///home/conda/feedstock_root/build_artifacts/imagecodecs_1623996795751/work
imageio @ file:///home/conda/feedstock_root/build_artifacts/imageio_1729190692267/work
importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1726082825846/work
importlib_resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1725921340658/work
iniconfig @ file:///home/conda/feedstock_root/build_artifacts/iniconfig_1673103042956/work
ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1683289033986/work
itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1713372668944/work
jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1715127149914/work
jmespath @ file:///home/conda/feedstock_root/build_artifacts/jmespath_1655568249366/work
joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1714665484399/work
joserfc @ file:///home/conda/feedstock_root/build_artifacts/joserfc_1733241231644/work
jsondiff @ file:///home/conda/feedstock_root/build_artifacts/jsondiff_1735926337541/work
jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema_1720529478715/work
jsonschema-path @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-path_1719375856083/work
jsonschema-specifications @ file:///tmp/tmpkv1z7p57/src
kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1648854389294/work
lazy-object-proxy @ file:///home/conda/feedstock_root/build_artifacts/lazy-object-proxy_1649033177169/work
llvmlite==0.36.0
locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work
lxml @ file:///home/conda/feedstock_root/build_artifacts/lxml_1649697664536/work
lz4 @ file:///home/conda/feedstock_root/build_artifacts/lz4_1652795538777/work
MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1648737563195/work
matplotlib @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-suite_1620506037462/work
matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1713250518406/work
mmh3 @ file:///home/conda/feedstock_root/build_artifacts/mmh3_1649666013977/work
mock @ file:///home/conda/feedstock_root/build_artifacts/mock_1689092066756/work
moto @ file:///home/conda/feedstock_root/build_artifacts/moto_1733091015156/work
msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1648745990887/work
multidict @ file:///home/conda/feedstock_root/build_artifacts/multidict_1648882420627/work
networkx @ file:///home/conda/feedstock_root/build_artifacts/networkx_1680692919326/work
numba @ file:///home/conda/feedstock_root/build_artifacts/numba_1623568544775/work
numcodecs @ file:///croot/numcodecs_1674851371595/work
numexpr @ file:///home/conda/feedstock_root/build_artifacts/numexpr_1614971340455/work
numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1607200853443/work
olefile @ file:///home/conda/feedstock_root/build_artifacts/olefile_1701735466804/work
openapi-schema-validator @ file:///home/conda/feedstock_root/build_artifacts/openapi-schema-validator_1696515973645/work
openapi-spec-validator @ file:///home/conda/feedstock_root/build_artifacts/openapi-spec-validator_1697293440056/work
packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work
pandas @ file:///tmp/build/80754af9/pandas_1592841659173/work
pandas-datareader @ file:///home/conda/feedstock_root/build_artifacts/pandas-datareader_1626254169655/work
parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1712320355065/work
partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1695667515973/work
pathable @ file:///home/conda/feedstock_root/build_artifacts/pathable_1662125131941/work/dist
pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1706113125309/work
pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1630696607296/work
pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1694617248815/work
platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1726613481435/work
pluggy @ file:///home/conda/feedstock_root/build_artifacts/pluggy_1713667077545/work
pooch @ file:///home/conda/feedstock_root/build_artifacts/pooch_1717777836653/work
prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1727341649933/work
psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1653089172347/work
ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1721585709575/work
pyarrow==5.0.0
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1711811537435/work
Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work
pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1724616129934/work
PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
pytest @ file:///home/conda/feedstock_root/build_artifacts/pytest_1733087655016/work
pytest-xdist @ file:///home/conda/feedstock_root/build_artifacts/pytest-xdist_1718138413436/work
python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
python-snappy @ file:///home/conda/feedstock_root/build_artifacts/python-snappy_1724981346042/work
pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1726055524169/work
PyWavelets @ file:///home/conda/feedstock_root/build_artifacts/pywavelets_1607290812047/work
PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1648757091578/work
referencing @ file:///home/conda/feedstock_root/build_artifacts/referencing_1714619483868/work
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1717057054362/work
responses @ file:///home/conda/feedstock_root/build_artifacts/responses_1718399541342/work
rfc3339-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1638811747357/work
rpds-py @ file:///croot/rpds-py_1698945930462/work
s3fs @ file:///home/conda/feedstock_root/build_artifacts/s3fs_1729650300071/work
s3transfer @ file:///home/conda/feedstock_root/build_artifacts/s3transfer_1732154317807/work
scikit-image==0.18.2
scikit-learn @ file:///home/conda/feedstock_root/build_artifacts/scikit-learn_1619628586492/work
scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy_1626684344458/work
six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
sortedcontainers @ file:///home/conda/feedstock_root/build_artifacts/sortedcontainers_1621217038088/work
sparse @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_sparse_1736951003/work
SQLAlchemy @ file:///home/conda/feedstock_root/build_artifacts/sqlalchemy_1651017965248/work
stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
tables @ file:///home/conda/feedstock_root/build_artifacts/pytables_1610156075390/work
tblib @ file:///home/conda/feedstock_root/build_artifacts/tblib_1702066284995/work
threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1714400101435/work
thrift @ file:///home/conda/feedstock_root/build_artifacts/thrift_1649442765282/work/lib/py
tifffile @ file:///home/conda/feedstock_root/build_artifacts/tifffile_1625322421387/work
tiledb==0.9.2
tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1727974628237/work
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1728059506884/work
tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1648827257044/work
traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1713535121073/work
types-PyYAML @ file:///home/conda/feedstock_root/build_artifacts/types-pyyaml_1726556272458/work
typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1717802530399/work
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1718728347128/work
wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
Werkzeug @ file:///home/conda/feedstock_root/build_artifacts/werkzeug_1729884156362/work
wrapt @ file:///home/conda/feedstock_root/build_artifacts/wrapt_1651495238964/work
xmltodict @ file:///home/conda/feedstock_root/build_artifacts/xmltodict_1729070904456/work
xxhash @ file:///home/conda/feedstock_root/build_artifacts/python-xxhash_1649442452023/work
yarl @ file:///home/conda/feedstock_root/build_artifacts/yarl_1648966513179/work
zarr @ file:///home/conda/feedstock_root/build_artifacts/zarr_1686774193581/work
zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1681770155528/work
zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1731262100163/work

name: dask
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
  - abseil-cpp=20210324.2=h9c3ff4c_0
  - aiobotocore=2.15.2=pyhd8ed1ab_0
  - aiohappyeyeballs=2.4.3=pyhd8ed1ab_0
  - aiohttp=3.10.5=py38h5eee18b_0
  - aioitertools=0.12.0=pyhd8ed1ab_0
  - aiosignal=1.3.1=pyhd8ed1ab_0
  - arrow-cpp=5.0.0=py38h94c96a7_8_cpu
  - asciitree=0.3.3=py_2
  - asttokens=3.0.0=pyhd8ed1ab_0
  - async-timeout=4.0.3=pyhd8ed1ab_0
  - atk-1.0=2.36.0=h3371d22_4
  - attrs=24.2.0=pyh71513ae_0
  - aws-c-cal=0.5.11=h95a6274_0
  - aws-c-common=0.6.2=h7f98852_0
  - aws-c-event-stream=0.2.7=h3541f99_13
  - aws-c-io=0.10.5=hfb6a706_0
  - aws-checksums=0.1.11=ha31a3da_7
  - aws-sdk-cpp=1.8.186=hb4091e7_3
  - aws-xray-sdk=2.14.0=pyhd8ed1ab_0
  - backcall=0.2.0=pyh9f0ad1d_0
  - bcolz=1.2.1=py38hb3f55d8_1001
  - blinker=1.8.2=pyhd8ed1ab_0
  - blosc=1.21.1=hd32f23e_0
  - bokeh=2.4.3=pyhd8ed1ab_3
  - boto3=1.35.36=pyhd8ed1ab_0
  - botocore=1.35.36=pyge38_1234567_0
  - brotli=1.0.9=h166bdaf_7
  - brotli-bin=1.0.9=h166bdaf_7
  - brotli-python=1.0.9=py38hfa26641_7
  - brunsli=0.1=h9c3ff4c_0
  - bzip2=1.0.8=h7f98852_4
  - c-ares=1.18.1=h7f98852_0
  - ca-certificates=2025.2.25=h06a4308_0
  - cached-property=1.5.2=hd8ed1ab_1
  - cached_property=1.5.2=pyha770c72_1
  - cachey=0.2.1=pyh9f0ad1d_0
  - cairo=1.16.0=h18b612c_1001
  - certifi=2024.8.30=pyhd8ed1ab_0
  - cffi=1.15.0=py38h3931269_0
  - cfitsio=3.470=hb418390_7
  - charls=2.2.0=h9c3ff4c_0
  - charset-normalizer=3.4.0=pyhd8ed1ab_0
  - chest=0.2.3=py_2
  - click=8.1.7=unix_pyh707e725_0
  - cloudpickle=3.1.0=pyhd8ed1ab_1
  - colorama=0.4.6=pyhd8ed1ab_0
  - coverage=6.3.3=py38h0a891b7_0
  - cramjam=2.5.0=py38ha8db356_0
  - crick=0.0.3=py38hab2c0dc_1001
  - cryptography=37.0.2=py38h2b5fc30_0
  - cycler=0.12.1=pyhd8ed1ab_0
  - cytoolz=0.11.2=py38h0a891b7_2
  - decorator=5.1.1=pyhd8ed1ab_0
  - entrypoints=0.4=pyhd8ed1ab_0
  - exceptiongroup=1.2.2=pyhd8ed1ab_0
  - execnet=2.1.1=pyhd8ed1ab_0
  - executing=2.1.0=pyhd8ed1ab_0
  - expat=2.4.8=h27087fc_0
  - fasteners=0.17.3=pyhd8ed1ab_0
  - fastparquet=0.7.0=py38hb5d20a5_1
  - flask=3.0.3=pyhd8ed1ab_0
  - flask-cors=5.0.0=pyhd8ed1ab_0
  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
  - font-ttf-inconsolata=3.000=h77eed37_0
  - font-ttf-source-code-pro=2.038=h77eed37_0
  - font-ttf-ubuntu=0.83=h77eed37_3
  - fontconfig=2.14.0=h8e229c2_0
  - fonts-conda-ecosystem=1=0
  - fonts-conda-forge=1=0
  - freetype=2.10.4=h0708190_1
  - fribidi=1.0.10=h36c2ea0_0
  - frozenlist=1.3.0=py38h0a891b7_1
  - fsspec=2024.10.0=pyhff2d567_0
  - gdk-pixbuf=2.42.6=h04a7f16_0
  - gettext=0.19.8.1=h73d1719_1008
  - gflags=2.2.2=he1b5a44_1004
  - giflib=5.2.1=h36c2ea0_2
  - glib=2.78.4=h6a678d5_0
  - glib-tools=2.78.4=h6a678d5_0
  - glog=0.5.0=h48cff8f_0
  - graphite2=1.3.14=h295c915_1
  - graphviz=2.49.0=h85b4f2f_0
  - greenlet=1.1.2=py38hfa26641_2
  - grpc-cpp=1.40.0=h05f19cf_1
  - gtk2=2.24.33=h90689f9_2
  - gts=0.7.6=h64030ff_2
  - h5py=3.3.0=nompi_py38h9915d05_100
  - harfbuzz=4.3.0=hf52aaf7_1
  - hdf5=1.10.6=nompi_h6a2412b_1114
  - heapdict=1.0.1=pyhd8ed1ab_1
  - httpretty=1.1.4=pyhd8ed1ab_0
  - icu=58.2=hf484d3e_1000
  - idna=3.10=pyhd8ed1ab_0
  - imagecodecs=2021.6.8=py38hf154af1_0
  - imageio=2.36.0=pyh12aca89_1
  - importlib-metadata=8.5.0=pyha770c72_0
  - importlib_resources=6.4.5=pyhd8ed1ab_0
  - iniconfig=2.0.0=pyhd8ed1ab_0
  - ipython=8.12.2=pyh41d4057_0
  - itsdangerous=2.2.0=pyhd8ed1ab_0
  - jbig=2.1=h7f98852_2003
  - jedi=0.19.1=pyhd8ed1ab_0
  - jinja2=3.1.4=pyhd8ed1ab_0
  - jmespath=1.0.1=pyhd8ed1ab_0
  - joblib=1.4.2=pyhd8ed1ab_0
  - joserfc=1.0.1=pyhd8ed1ab_0
  - jpeg=9e=h166bdaf_1
  - jsondiff=2.2.1=pyhd8ed1ab_0
  - jsonschema=4.23.0=pyhd8ed1ab_0
  - jsonschema-path=0.3.3=pyhd8ed1ab_0
  - jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
  - jxrlib=1.1=h7f98852_2
  - keyutils=1.6.1=h166bdaf_0
  - kiwisolver=1.4.2=py38h43d8883_1
  - krb5=1.19.3=h3790be6_0
  - lazy-object-proxy=1.7.1=py38h0a891b7_1
  - lcms2=2.12=hddcbb42_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - lerc=2.2.1=h9c3ff4c_0
  - libaec=1.0.6=h9c3ff4c_0
  - libblas=3.9.0=16_linux64_openblas
  - libbrotlicommon=1.0.9=h166bdaf_7
  - libbrotlidec=1.0.9=h166bdaf_7
  - libbrotlienc=1.0.9=h166bdaf_7
  - libcblas=3.9.0=16_linux64_openblas
  - libcurl=7.79.1=h2574ce0_1
  - libdeflate=1.7=h7f98852_5
  - libedit=3.1.20191231=he28a2e2_2
  - libev=4.33=h516909a_1
  - libevent=2.1.10=h9b69904_4
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgd=2.3.3=h695aa2c_1
  - libgfortran-ng=13.2.0=h69a702a_0
  - libgfortran5=13.2.0=ha4646dd_0
  - libglib=2.78.4=hdc74915_0
  - libgomp=11.2.0=h1234567_1
  - libiconv=1.17=h166bdaf_0
  - liblapack=3.9.0=16_linux64_openblas
  - libllvm10=10.0.1=he513fc3_3
  - libnghttp2=1.43.0=h812cca2_1
  - libopenblas=0.3.21=h043d6bf_0
  - libpng=1.6.37=h21135ba_2
  - libprotobuf=3.18.0=h780b84a_1
  - librsvg=2.52.5=h0a9e6e8_2
  - libssh2=1.10.0=ha56f1ee_2
  - libstdcxx-ng=11.2.0=h1234567_1
  - libthrift=0.15.0=he6d91bd_0
  - libtiff=4.3.0=hf544144_1
  - libtool=2.4.6=h9c3ff4c_1008
  - libutf8proc=2.7.0=h7f98852_0
  - libuuid=2.32.1=h7f98852_1000
  - libwebp-base=1.2.2=h7f98852_1
  - libxcb=1.13=h7f98852_1004
  - libxml2=2.10.4=hcbfbd50_0
  - libxslt=1.1.37=h2085143_0
  - libzopfli=1.0.3=h9c3ff4c_0
  - llvmlite=0.36.0=py38h4630a5e_0
  - locket=1.0.0=pyhd8ed1ab_0
  - lxml=4.8.0=py38h0a891b7_3
  - lz4=4.0.0=py38h1bf946c_2
  - lz4-c=1.9.3=h9c3ff4c_1
  - lzo=2.10=h516909a_1000
  - markupsafe=2.1.1=py38h0a891b7_1
  - matplotlib-base=3.4.2=py38hcc49a3a_0
  - matplotlib-inline=0.1.7=pyhd8ed1ab_0
  - mmh3=3.0.0=py38hfa26641_2
  - mock=5.1.0=pyhd8ed1ab_0
  - moto=5.0.22=pyhd8ed1ab_0
  - msgpack-python=1.0.3=py38h43d8883_1
  - multidict=6.0.2=py38h0a891b7_1
  - ncurses=6.4=h6a678d5_0
  - networkx=3.1=pyhd8ed1ab_0
  - numba=0.53.1=py38h8b71fd7_1
  - numcodecs=0.11.0=py38h6a678d5_0
  - numexpr=2.7.3=py38h51da96c_0
  - numpy=1.17.5=py38h18fd61f_1
  - olefile=0.47=pyhd8ed1ab_0
  - openapi-schema-validator=0.6.2=pyhd8ed1ab_0
  - openapi-spec-validator=0.7.1=pyhd8ed1ab_0
  - openjpeg=2.4.0=hb52868f_1
  - openssl=1.1.1o=h166bdaf_0
  - orc=1.7.0=h68e2c4e_0
  - packaging=24.2=pyhd8ed1ab_2
  - pandas=1.0.5=py38h0573a6f_0
  - pandas-datareader=0.10.0=pyh6c4a22f_0
  - pango=1.50.7=hbd2fdc8_0
  - parquet-cpp=1.5.1=2
  - parso=0.8.4=pyhd8ed1ab_0
  - partd=1.4.1=pyhd8ed1ab_0
  - pathable=0.4.3=pyhd8ed1ab_0
  - pcre2=10.42=hebb0a14_1
  - pexpect=4.9.0=pyhd8ed1ab_0
  - pickleshare=0.7.5=py_1003
  - pillow=8.3.2=py38h8e6f84c_0
  - pip=24.3.1=pyh8b19718_0
  - pixman=0.38.0=h516909a_1003
  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
  - platformdirs=4.3.6=pyhd8ed1ab_0
  - pluggy=1.5.0=pyhd8ed1ab_0
  - pooch=1.8.2=pyhd8ed1ab_0
  - prompt-toolkit=3.0.48=pyha770c72_0
  - prompt_toolkit=3.0.48=hd8ed1ab_1
  - psutil=5.9.1=py38h0a891b7_0
  - pthread-stubs=0.4=h36c2ea0_1001
  - ptyprocess=0.7.0=pyhd3deb0d_0
  - pure_eval=0.2.3=pyhd8ed1ab_0
  - pyarrow=5.0.0=py38h1bc9799_8_cpu
  - pycparser=2.22=pyhd8ed1ab_0
  - pygments=2.18.0=pyhd8ed1ab_0
  - pyparsing=3.1.4=pyhd8ed1ab_0
  - pysocks=1.7.1=pyha2e5f31_6
  - pytables=3.6.1=py38hc386592_3
  - pytest=8.3.4=pyhd8ed1ab_0
  - pytest-xdist=3.6.1=pyhd8ed1ab_0
  - python=3.8.12=hb7a2778_1_cpython
  - python-dateutil=2.9.0=pyhd8ed1ab_0
  - python-graphviz=0.20.3=pyhe28f650_1
  - python-snappy=0.7.3=pyh7a18afe_0
  - python-xxhash=3.0.0=py38h0a891b7_1
  - python_abi=3.8=5_cp38
  - pytz=2024.2=pyhd8ed1ab_0
  - pywavelets=1.1.1=py38h5c078b8_3
  - pyyaml=6.0=py38h0a891b7_4
  - re2=2021.09.01=h9c3ff4c_0
  - readline=8.2=h5eee18b_0
  - referencing=0.35.1=pyhd8ed1ab_0
  - requests=2.32.3=pyhd8ed1ab_0
  - responses=0.25.3=pyhd8ed1ab_0
  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
  - rpds-py=0.10.6=py38hb02cf49_0
  - s2n=1.0.10=h9b69904_0
  - s3fs=2024.10.0=pyhd8ed1ab_0
  - s3transfer=0.10.4=pyhd8ed1ab_0
  - scikit-image=0.18.2=py38h1abd341_0
  - scikit-learn=0.24.2=py38hdc147b9_0
  - scipy=1.7.0=py38h7b17777_1
  - setuptools=75.1.0=py38h06a4308_0
  - six=1.16.0=pyh6c4a22f_0
  - snappy=1.1.9=hbd366e4_1
  - sortedcontainers=2.4.0=pyhd8ed1ab_0
  - sparse=0.15.5=pyh72ffeb9_0
  - sqlalchemy=1.4.36=py38h0a891b7_0
  - sqlite=3.45.3=h5eee18b_0
  - stack_data=0.6.2=pyhd8ed1ab_0
  - tblib=3.0.0=pyhd8ed1ab_0
  - threadpoolctl=3.5.0=pyhc1e730c_0
  - thrift=0.16.0=py38hfa26641_1
  - tifffile=2021.7.2=pyhd8ed1ab_0
  - tiledb=2.3.4=he87e0bf_0
  - tiledb-py=0.9.2=py38h3290b37_0
  - tk=8.6.14=h39e8969_0
  - tomli=2.0.2=pyhd8ed1ab_0
  - toolz=1.0.0=pyhd8ed1ab_0
  - tornado=6.1=py38h0a891b7_3
  - traitlets=5.14.3=pyhd8ed1ab_0
  - types-pyyaml=6.0.12.20240917=pyhd8ed1ab_0
  - typing-extensions=4.12.2=hd8ed1ab_0
  - typing_extensions=4.12.2=pyha770c72_0
  - urllib3=1.26.19=pyhd8ed1ab_0
  - wcwidth=0.2.13=pyhd8ed1ab_0
  - werkzeug=3.0.6=pyhd8ed1ab_0
  - wheel=0.44.0=py38h06a4308_0
  - wrapt=1.14.1=py38h0a891b7_0
  - xmltodict=0.14.2=pyhd8ed1ab_0
  - xorg-kbproto=1.0.7=h7f98852_1002
  - xorg-libice=1.0.10=h7f98852_0
  - xorg-libsm=1.2.3=hd9c2040_1000
  - xorg-libx11=1.7.2=h7f98852_0
  - xorg-libxau=1.0.9=h7f98852_0
  - xorg-libxdmcp=1.1.3=h7f98852_0
  - xorg-libxext=1.3.4=h7f98852_1
  - xorg-libxrender=0.9.10=h7f98852_1003
  - xorg-renderproto=0.11.1=h7f98852_1002
  - xorg-xextproto=7.3.0=h7f98852_1002
  - xorg-xproto=7.0.31=h7f98852_1007
  - xxhash=0.8.0=h7f98852_3
  - xz=5.6.4=h5eee18b_1
  - yaml=0.2.5=h7f98852_2
  - yarl=1.7.2=py38h0a891b7_2
  - zarr=2.15.0=pyhd8ed1ab_0
  - zfp=0.5.5=h9c3ff4c_8
  - zict=3.0.0=pyhd8ed1ab_0
  - zipp=3.21.0=pyhd8ed1ab_0
  - zlib=1.2.13=h5eee18b_1
  - zstd=1.5.0=ha95c52a_0
  - pip:
      - distributed==2.30.1
prefix: /opt/conda/envs/dask

dask/array/tests/test_array_core.py::test_stack_errs dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty_like] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones_like] 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dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros] dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full] dask/array/tests/test_creation.py::test_linspace[True] dask/array/tests/test_creation.py::test_linspace[False] dask/array/tests/test_creation.py::test_arange dask/array/tests/test_creation.py::test_arange_dtypes[0-1-1-None] dask/array/tests/test_creation.py::test_arange_dtypes[1.5-2-1-None] dask/array/tests/test_creation.py::test_arange_dtypes[1-2.5-1-None] dask/array/tests/test_creation.py::test_arange_dtypes[1-2-0.5-None] dask/array/tests/test_creation.py::test_arange_dtypes[start4-stop4-step4-None] dask/array/tests/test_creation.py::test_arange_dtypes[start5-stop5-step5-None] dask/array/tests/test_creation.py::test_arange_dtypes[start6-stop6-step6-None] 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dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape5] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape0] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape1] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape2] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape3] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape4] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape5] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape0] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape1] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape2] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape3] dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape4] 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dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-float32] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-bool] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-uint8] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-int16] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-float32] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-bool] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-uint8] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-int16] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-float32] dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-bool] dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-uint8] dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-int16] dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-float32] 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dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-d-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-d-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-d-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-d-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-w-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-w-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-w-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-w-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-M-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-M-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-M-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-M-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30T-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30T-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30T-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30T-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-d-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-d-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-d-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-d-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-w-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-w-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-w-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-w-left-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-M-right-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-M-right-left] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-M-left-right] dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-M-left-left] dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[count] dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[nunique] dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[size] dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[sum] dask/dataframe/tseries/tests/test_resample.py::test_resample_agg dask/dataframe/tseries/tests/test_resample.py::test_resample_agg_passes_kwargs dask/dataframe/tseries/tests/test_resample.py::test_resample_throws_error_when_parition_index_does_not_match_index dask/dataframe/tseries/tests/test_resample.py::test_unknown_divisions_error dask/dataframe/tseries/tests/test_resample.py::test_resample_index_name dask/dataframe/tseries/tests/test_resample.py::test_series_resample_non_existent_datetime dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[nunique] dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[mean] dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[count] dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[size] dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[quantile]
dask/array/slicing.py dask/array/wrap.py dask/array/core.py dask/dataframe/tseries/resample.py dask/array/creation.py
dask/array/slicing.py dask/array/wrap.py dask/array/core.py dask/dataframe/tseries/resample.py dask/array/creation.py