Redo lost changes

Flaskserver/venv/Lib/site-packages/dask/array/image.py

@@ -15,7 +15,7 @@ def add_leading_dimension(x):
 
 
 def imread(filename, imread=None, preprocess=None):
-    """ Read a stack of images into a dask array
+    """Read a stack of images into a dask array
 
     Parameters
     ----------

Flaskserver/venv/Lib/site-packages/dask/array/linalg.py

@@ -48,7 +48,7 @@ def _wrapped_qr(a):
 
 
 def tsqr(data, compute_svd=False, _max_vchunk_size=None):
-    """ Direct Tall-and-Skinny QR algorithm
+    """Direct Tall-and-Skinny QR algorithm
 
     As presented in:
 
@@ -506,7 +506,7 @@ def tsqr(data, compute_svd=False, _max_vchunk_size=None):
 
 
 def sfqr(data, name=None):
-    """ Direct Short-and-Fat QR
+    """Direct Short-and-Fat QR
 
     Currently, this is a quick hack for non-tall-and-skinny matrices which
     are one chunk tall and (unless they are one chunk wide) have chunks
@@ -611,7 +611,7 @@ def sfqr(data, name=None):
 
 
 def compression_level(n, q, oversampling=10, min_subspace_size=20):
-    """ Compression level to use in svd_compressed
+    """Compression level to use in svd_compressed
 
     Given the size ``n`` of a space, compress that that to one of size
     ``q`` plus oversampling.
@@ -630,7 +630,7 @@ def compression_level(n, q, oversampling=10, min_subspace_size=20):
 
 
 def compression_matrix(data, q, n_power_iter=0, seed=None, compute=False):
-    """ Randomly sample matrix to find most active subspace
+    """Randomly sample matrix to find most active subspace
 
     This compression matrix returned by this algorithm can be used to
     compute both the QR decomposition and the Singular Value
@@ -685,7 +685,7 @@ def compression_matrix(data, q, n_power_iter=0, seed=None, compute=False):
 
 
 def svd_compressed(a, k, n_power_iter=0, seed=None, compute=False):
-    """ Randomly compressed rank-k thin Singular Value Decomposition.
+    """Randomly compressed rank-k thin Singular Value Decomposition.
 
     This computes the approximate singular value decomposition of a large
     array.  This algorithm is generally faster than the normal algorithm

Flaskserver/venv/Lib/site-packages/dask/array/numpy_compat.py

@@ -9,6 +9,7 @@ _numpy_115 = LooseVersion(np.__version__) >= "1.15.0"
 _numpy_116 = LooseVersion(np.__version__) >= "1.16.0"
 _numpy_117 = LooseVersion(np.__version__) >= "1.17.0"
 _numpy_118 = LooseVersion(np.__version__) >= "1.18.0"
+_numpy_120 = LooseVersion(np.__version__) >= "1.20.0"
 
 
 # Taken from scikit-learn:
@@ -18,7 +19,7 @@ try:
         if (
             not np.allclose(
                 np.divide(0.4, 1, casting="unsafe"),
-                np.divide(0.4, 1, casting="unsafe", dtype=np.float),
+                np.divide(0.4, 1, casting="unsafe", dtype=float),
             )
             or not np.allclose(np.divide(1, 0.5, dtype="i8"), 2)
             or not np.allclose(np.divide(0.4, 1), 0.4)

Flaskserver/venv/Lib/site-packages/dask/array/optimization.py

@@ -29,7 +29,7 @@ def optimize(
     rename_fused_keys=True,
     **kwargs
 ):
-    """ Optimize dask for array computation
+    """Optimize dask for array computation
 
     1.  Cull tasks not necessary to evaluate keys
     2.  Remove full slicing, e.g. x[:]
@@ -71,7 +71,7 @@ def optimize(
 
 
 def hold_keys(dsk, dependencies):
-    """ Find keys to avoid fusion
+    """Find keys to avoid fusion
 
     We don't want to fuse data present in the graph because it is easier to
     serialize as a raw value.
@@ -109,7 +109,7 @@ def hold_keys(dsk, dependencies):
 
 
 def optimize_slices(dsk):
-    """ Optimize slices
+    """Optimize slices
 
     1.  Fuse repeated slices, like x[5:][2:6] -> x[7:11]
     2.  Remove full slices, like         x[:] -> x
@@ -196,7 +196,7 @@ def optimize_slices(dsk):
 
 
 def normalize_slice(s):
-    """ Replace Nones in slices with integers
+    """Replace Nones in slices with integers
 
     >>> normalize_slice(slice(None, None, None))
     slice(0, None, 1)
@@ -228,7 +228,7 @@ def check_for_nonfusible_fancy_indexing(fancy, normal):
 
 
 def fuse_slice(a, b):
-    """ Fuse stacked slices together
+    """Fuse stacked slices together
 
     Fuse a pair of repeated slices into a single slice:
 

Flaskserver/venv/Lib/site-packages/dask/array/overlap.py

@@ -61,7 +61,7 @@ def fractional_slice(task, axes):
 
 
 def expand_key(k, dims, name=None, axes=None):
-    """ Get all neighboring keys around center
+    """Get all neighboring keys around center
 
     Parameters
     ----------
@@ -116,7 +116,7 @@ def expand_key(k, dims, name=None, axes=None):
 
 
 def overlap_internal(x, axes):
-    """ Share boundaries between neighboring blocks
+    """Share boundaries between neighboring blocks
 
     Parameters
     ----------
@@ -197,7 +197,7 @@ def trim_overlap(x, depth, boundary=None):
 
 
 def trim_internal(x, axes, boundary=None):
-    """ Trim sides from each block
+    """Trim sides from each block
 
     This couples well with the overlap operation, which may leave excess data on
     each block
@@ -234,7 +234,11 @@ def trim_internal(x, axes, boundary=None):
     chunks = tuple(olist)
 
     return map_blocks(
-        partial(_trim, axes=axes, boundary=boundary), x, chunks=chunks, dtype=x.dtype
+        partial(_trim, axes=axes, boundary=boundary),
+        x,
+        chunks=chunks,
+        dtype=x.dtype,
+        meta=x._meta,
     )
 
 
@@ -275,7 +279,7 @@ def _trim(x, axes, boundary, block_info):
 
 
 def periodic(x, axis, depth):
-    """ Copy a slice of an array around to its other side
+    """Copy a slice of an array around to its other side
 
     Useful to create periodic boundary conditions for overlap
     """
@@ -299,7 +303,7 @@ def periodic(x, axis, depth):
 
 
 def reflect(x, axis, depth):
-    """ Reflect boundaries of array on the same side
+    """Reflect boundaries of array on the same side
 
     This is the converse of ``periodic``
     """
@@ -329,7 +333,7 @@ def reflect(x, axis, depth):
 
 
 def nearest(x, axis, depth):
-    """ Each reflect each boundary value outwards
+    """Each reflect each boundary value outwards
 
     This mimics what the skimage.filters.gaussian_filter(... mode="nearest")
     does.
@@ -386,7 +390,7 @@ def _remove_overlap_boundaries(l, r, axis, depth):
 
 
 def boundaries(x, depth=None, kind=None):
-    """ Add boundary conditions to an array before overlaping
+    """Add boundary conditions to an array before overlaping
 
     See Also
     --------
@@ -419,7 +423,7 @@ def boundaries(x, depth=None, kind=None):
 
 
 def overlap(x, depth, boundary):
-    """ Share boundaries between neighboring blocks
+    """Share boundaries between neighboring blocks
 
     Parameters
     ----------
@@ -537,7 +541,7 @@ def add_dummy_padding(x, depth, boundary):
 def map_overlap(
     func, *args, depth=None, boundary=None, trim=True, align_arrays=True, **kwargs
 ):
-    """ Map a function over blocks of arrays with some overlap
+    """Map a function over blocks of arrays with some overlap
 
     We share neighboring zones between blocks of the array, map a
     function, and then trim away the neighboring strips.

Flaskserver/venv/Lib/site-packages/dask/array/percentile.py

@@ -52,7 +52,7 @@ def _percentiles_from_tdigest(qs, digests):
 
 
 def percentile(a, q, interpolation="linear", method="default"):
-    """ Approximate percentile of 1-D array
+    """Approximate percentile of 1-D array
 
     Parameters
     ----------
@@ -150,7 +150,7 @@ def percentile(a, q, interpolation="linear", method="default"):
 
 
 def merge_percentiles(finalq, qs, vals, interpolation="lower", Ns=None):
-    """ Combine several percentile calculations of different data.
+    """Combine several percentile calculations of different data.
 
     Parameters
     ----------

Flaskserver/venv/Lib/site-packages/dask/array/random.py

@@ -78,7 +78,7 @@ class RandomState(object):
     def _wrap(
         self, funcname, *args, size=None, chunks="auto", extra_chunks=(), **kwargs
     ):
-        """ Wrap numpy random function to produce dask.array random function
+        """Wrap numpy random function to produce dask.array random function
 
         extra_chunks should be a chunks tuple to append to the end of chunks
         """

Flaskserver/venv/Lib/site-packages/dask/array/rechunk.py

@@ -26,7 +26,7 @@ from .. import config
 
 
 def cumdims_label(chunks, const):
-    """ Internal utility for cumulative sum with label.
+    """Internal utility for cumulative sum with label.
 
     >>> cumdims_label(((5, 3, 3), (2, 2, 1)), 'n')  # doctest: +NORMALIZE_WHITESPACE
     [(('n', 0), ('n', 5), ('n', 8), ('n', 11)),
@@ -112,7 +112,7 @@ def _intersect_1d(breaks):
 
 
 def _old_to_new(old_chunks, new_chunks):
-    """ Helper to build old_chunks to new_chunks.
+    """Helper to build old_chunks to new_chunks.
 
     Handles missing values, as long as the missing dimension
     is unchanged.
@@ -263,8 +263,7 @@ def _largest_block_size(chunks):
 
 
 def estimate_graph_size(old_chunks, new_chunks):
-    """ Estimate the graph size during a rechunk computation.
-    """
+    """Estimate the graph size during a rechunk computation."""
     # Estimate the number of intermediate blocks that will be produced
     # (we don't use intersect_chunks() which is much more expensive)
     crossed_size = reduce(
@@ -278,7 +277,7 @@ def estimate_graph_size(old_chunks, new_chunks):
 
 
 def divide_to_width(desired_chunks, max_width):
-    """ Minimally divide the given chunks so as to make the largest chunk
+    """Minimally divide the given chunks so as to make the largest chunk
     width less or equal than *max_width*.
     """
     chunks = []
@@ -293,7 +292,7 @@ def divide_to_width(desired_chunks, max_width):
 
 
 def merge_to_number(desired_chunks, max_number):
-    """ Minimally merge the given chunks so as to drop the number of
+    """Minimally merge the given chunks so as to drop the number of
     chunks below *max_number*, while minimizing the largest width.
     """
     if len(desired_chunks) <= max_number:
@@ -450,7 +449,7 @@ def find_split_rechunk(old_chunks, new_chunks, graph_size_limit):
 def plan_rechunk(
     old_chunks, new_chunks, itemsize, threshold=None, block_size_limit=None
 ):
-    """ Plan an iterative rechunking from *old_chunks* to *new_chunks*.
+    """Plan an iterative rechunking from *old_chunks* to *new_chunks*.
     The plan aims to minimize the rechunk graph size.
 
     Parameters
@@ -530,8 +529,7 @@ def plan_rechunk(
 
 
 def _compute_rechunk(x, chunks):
-    """ Compute the rechunk of *x* to the given *chunks*.
-    """
+    """Compute the rechunk of *x* to the given *chunks*."""
     if x.size == 0:
         # Special case for empty array, as the algorithm below does not behave correctly
         return empty(x.shape, chunks=chunks, dtype=x.dtype)

Flaskserver/venv/Lib/site-packages/dask/array/reductions.py

@@ -53,7 +53,7 @@ def reduction(
     output_size=1,
     meta=None,
 ):
-    """ General version of reductions
+    """General version of reductions
 
     Parameters
     ----------
@@ -206,7 +206,7 @@ def _tree_reduce(
     concatenate=True,
     reduced_meta=None,
 ):
-    """ Perform the tree reduction step of a reduction.
+    """Perform the tree reduction step of a reduction.
 
     Lower level, users should use ``reduction`` or ``arg_reduction`` directly.
     """
@@ -255,7 +255,7 @@ def _tree_reduce(
 def partial_reduce(
     func, x, split_every, keepdims=False, dtype=None, name=None, reduced_meta=None
 ):
-    """ Partial reduction across multiple axes.
+    """Partial reduction across multiple axes.
 
     Parameters
     ----------
@@ -945,7 +945,7 @@ def nanarg_agg(func, argfunc, data, axis=None, **kwargs):
 
 
 def arg_reduction(x, chunk, combine, agg, axis=None, split_every=None, out=None):
-    """ Generic function for argreduction.
+    """Generic function for argreduction.
 
     Parameters
     ----------
@@ -1004,7 +1004,7 @@ def arg_reduction(x, chunk, combine, agg, axis=None, split_every=None, out=None)
 
 
 def make_arg_reduction(func, argfunc, is_nan_func=False):
-    """ Create an argreduction callable
+    """Create an argreduction callable
 
     Parameters
     ----------
@@ -1051,7 +1051,7 @@ nanargmax = make_arg_reduction(chunk.nanmax, _nanargmax, True)
 
 
 def cumreduction(func, binop, ident, x, axis=None, dtype=None, out=None):
-    """ Generic function for cumulative reduction
+    """Generic function for cumulative reduction
 
     Parameters
     ----------
@@ -1144,7 +1144,7 @@ def cumprod(x, axis=None, dtype=None, out=None):
 
 
 def topk(a, k, axis=-1, split_every=None):
-    """ Extract the k largest elements from a on the given axis,
+    """Extract the k largest elements from a on the given axis,
     and return them sorted from largest to smallest.
     If k is negative, extract the -k smallest elements instead,
     and return them sorted from smallest to largest.
@@ -1203,7 +1203,7 @@ def topk(a, k, axis=-1, split_every=None):
 
 
 def argtopk(a, k, axis=-1, split_every=None):
-    """ Extract the indices of the k largest elements from a on the given axis,
+    """Extract the indices of the k largest elements from a on the given axis,
     and return them sorted from largest to smallest. If k is negative, extract
     the indices of the -k smallest elements instead, and return them sorted
     from smallest to largest.

Flaskserver/venv/Lib/site-packages/dask/array/reshape.py

@@ -111,7 +111,7 @@ def expand_tuple(chunks, factor):
 
 
 def contract_tuple(chunks, factor):
-    """ Return simple chunks tuple such that factor divides all elements
+    """Return simple chunks tuple such that factor divides all elements
 
     Examples
     --------
@@ -134,7 +134,7 @@ def contract_tuple(chunks, factor):
 
 
 def reshape(x, shape):
-    """ Reshape array to new shape
+    """Reshape array to new shape
 
     This is a parallelized version of the ``np.reshape`` function with the
     following limitations:

Flaskserver/venv/Lib/site-packages/dask/array/routines.py

@@ -659,20 +659,55 @@ def histogram(a, bins=None, range=None, normed=False, weights=None, density=None
     """
     Blocked variant of :func:`numpy.histogram`.
 
-    Follows the signature of :func:`numpy.histogram` exactly with the following
-    exceptions:
-
-    - Either an iterable specifying the ``bins`` or the number of ``bins``
-      and a ``range`` argument is required as computing ``min`` and ``max``
+    Parameters
+    ----------
+    a : array_like
+        Input data. The histogram is computed over the flattened array.