Code style cleanup

.gen_ci_support/ff_ci_pr_build.py

@@ -13,15 +13,6 @@
 normal branch), then the build proceeds without issues.
 """
 
-
-try:
-    from future_builtins import (
-        map,
-        filter,
-    )
-except ImportError:
-    pass
-
 import argparse
 import codecs
 import contextlib

dask_image/__init__.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
 from ._version import get_versions
+
 __version__ = get_versions()['version']
 del get_versions

dask_image/dispatch/_dispatch_ndfilters.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-import scipy.ndimage.filters
+import scipy.ndimage
 
 from ._dispatcher import Dispatcher
 

dask_image/dispatch/_dispatch_ndinterp.py

@@ -10,9 +10,9 @@
     "dispatch_asarray",
 ]
 
-
 dispatch_affine_transform = Dispatcher(name="dispatch_affine_transform")
 
+
 # ================== affine_transform ==================
 @dispatch_affine_transform.register(np.ndarray)
 def numpy_affine_transform(*args, **kwargs):
@@ -32,6 +32,7 @@ def cupy_affine_transform(*args, **kwargs):
 
 dispatch_asarray = Dispatcher(name="dispatch_asarray")
 
+
 # ===================== asarray ========================
 @dispatch_asarray.register(np.ndarray)
 def numpy_asarray(*args, **kwargs):

dask_image/imread/__init__.py

@@ -3,10 +3,8 @@
 import numbers
 import warnings
 
-import dask
-import dask.array
-import dask.delayed
-import numpy
+import dask.array as da
+import numpy as np
 import pims
 
 from . import _utils
@@ -41,14 +39,14 @@ def imread(fname, nframes=1, *, arraytype="numpy"):
         raise ValueError("`nframes` must be greater than zero.")
 
     if arraytype == "numpy":
-        arrayfunc = numpy.asanyarray
+        arrayfunc = np.asanyarray
     elif arraytype == "cupy":   # pragma: no cover
         import cupy
         arrayfunc = cupy.asanyarray
 
     with pims.open(sfname) as imgs:
         shape = (len(imgs),) + imgs.frame_shape
-        dtype = numpy.dtype(imgs.pixel_type)
+        dtype = np.dtype(imgs.pixel_type)
 
     if nframes == -1:
         nframes = shape[0]
@@ -69,16 +67,16 @@ def imread(fname, nframes=1, *, arraytype="numpy"):
     # place source filenames into dask array
     filenames = sorted(glob.glob(sfname))  # pims also does this
     if len(filenames) > 1:
-        ar = dask.array.from_array(filenames, chunks=(nframes,))
+        ar = da.from_array(filenames, chunks=(nframes,))
         multiple_files = True
     else:
-        ar = dask.array.from_array(filenames * shape[0], chunks=(nframes,))
+        ar = da.from_array(filenames * shape[0], chunks=(nframes,))
         multiple_files = False
 
     # read in data using encoded filenames
     a = ar.map_blocks(
         _map_read_frame,
-        chunks=dask.array.core.normalize_chunks(
+        chunks=da.core.normalize_chunks(
             (nframes,) + shape[1:], shape),
         multiple_files=multiple_files,
         new_axis=list(range(1, len(shape))),

dask_image/imread/_utils.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
-import numpy
+import numpy as np
 import pims
 
 
-def _read_frame(fn, i, *, arrayfunc=numpy.asanyarray):
+def _read_frame(fn, i, *, arrayfunc=np.asanyarray):
     with pims.open(fn) as imgs:
         return arrayfunc(imgs[i])

dask_image/ndfilters/__init__.py

@@ -19,46 +19,16 @@
     "threshold_local",
 ]
 
-from ._conv import (
-    convolve,
-    correlate,
-)
-
-from ._diff import (
-    laplace,
-)
-
-from ._edge import (
-    prewitt,
-    sobel,
-)
-
-from ._gaussian import (
-    gaussian_filter,
-    gaussian_gradient_magnitude,
-    gaussian_laplace,
-)
-
-from ._generic import (
-    generic_filter,
-)
-
-from ._order import (
-    minimum_filter,
-    median_filter,
-    maximum_filter,
-    rank_filter,
-    percentile_filter,
-)
-
-from ._smooth import (
-    uniform_filter,
-)
-
-from ._threshold import (
-    threshold_local,
-)
-
+from ._conv import convolve, correlate
+from ._diff import laplace
+from ._edge import prewitt, sobel
+from ._gaussian import (gaussian_filter, gaussian_gradient_magnitude,
+                        gaussian_laplace)
+from ._generic import generic_filter
+from ._order import (maximum_filter, median_filter, minimum_filter,
+                     percentile_filter, rank_filter)
+from ._smooth import uniform_filter
+from ._threshold import threshold_local
 
 convolve.__module__ = __name__
 correlate.__module__ = __name__

dask_image/ndfilters/_conv.py

@@ -1,11 +1,10 @@
 # -*- coding: utf-8 -*-
-import scipy.ndimage.filters
+import scipy.ndimage
 
-from . import _utils
+from ..dispatch._dispatch_ndfilters import (dispatch_convolve,
+                                            dispatch_correlate)
 from ..dispatch._utils import check_arraytypes_compatible
-from ..dispatch._dispatch_ndfilters import (
-    dispatch_convolve,
-    dispatch_correlate)
+from . import _utils
 
 __all__ = [
     "convolve",

dask_image/ndfilters/_diff.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 
 
-import scipy.ndimage.filters
+import scipy.ndimage
 
-from . import _utils
 from ..dispatch._dispatch_ndfilters import dispatch_laplace
+from . import _utils
 
 __all__ = [
     "laplace",

dask_image/ndfilters/_edge.py

@@ -3,10 +3,10 @@
 
 import numbers
 
-import scipy.ndimage.filters
+import scipy.ndimage
 
-from . import _utils
 from ..dispatch._dispatch_ndfilters import dispatch_prewitt, dispatch_sobel
+from . import _utils
 
 __all__ = [
     "prewitt",

dask_image/ndfilters/_gaussian.py

@@ -3,14 +3,13 @@
 
 import numbers
 
-import numpy
-import scipy.ndimage.filters
+import numpy as np
+import scipy.ndimage
 
-from . import _utils
 from ..dispatch._dispatch_ndfilters import (
-    dispatch_gaussian_filter,
-    dispatch_gaussian_gradient_magnitude,
+    dispatch_gaussian_filter, dispatch_gaussian_gradient_magnitude,
     dispatch_gaussian_laplace)
+from . import _utils
 
 __all__ = [
     "gaussian_filter",
@@ -23,9 +22,9 @@
 def _get_sigmas(image, sigma):
     ndim = image.ndim
 
-    nsigmas = numpy.array(sigma)
+    nsigmas = np.array(sigma)
     if nsigmas.ndim == 0:
-        nsigmas = numpy.array(ndim * [nsigmas[()]])
+        nsigmas = np.array(ndim * [nsigmas[()]])
 
     if nsigmas.ndim != 1:
         raise RuntimeError(
@@ -46,12 +45,12 @@ def _get_sigmas(image, sigma):
 
 
 def _get_border(image, sigma, truncate):
-    sigma = numpy.array(_get_sigmas(image, sigma))
+    sigma = np.array(_get_sigmas(image, sigma))
 
     if not isinstance(truncate, numbers.Real):
         raise TypeError("Must have a real truncate value.")
 
-    half_shape = tuple(numpy.ceil(sigma * truncate).astype(int))
+    half_shape = tuple(np.ceil(sigma * truncate).astype(int))
 
     return half_shape
 

dask_image/ndfilters/_generic.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-import scipy.ndimage.filters
+import scipy.ndimage
 
-from . import _utils
 from ..dispatch._dispatch_ndfilters import dispatch_generic_filter
+from . import _utils
 
 __all__ = [
     "generic_filter",

dask_image/ndfilters/_order.py

@@ -1,14 +1,13 @@
 # -*- coding: utf-8 -*-
 
-import scipy.ndimage.filters
+import scipy.ndimage
 
+from ..dispatch._dispatch_ndfilters import (dispatch_maximum_filter,
+                                            dispatch_median_filter,
+                                            dispatch_minimum_filter,
+                                            dispatch_percentile_filter,
+                                            dispatch_rank_filter)
 from . import _utils
-from ..dispatch._dispatch_ndfilters import (
-    dispatch_minimum_filter,
-    dispatch_median_filter,
-    dispatch_maximum_filter,
-    dispatch_rank_filter,
-    dispatch_percentile_filter)
 
 __all__ = [
     "minimum_filter",

dask_image/ndfilters/_smooth.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 
 
-import scipy.ndimage.filters
+import scipy.ndimage
 
+from ..dispatch._dispatch_ndfilters import dispatch_uniform_filter
 from . import _utils
 from ._gaussian import gaussian_filter
-from ..dispatch._dispatch_ndfilters import dispatch_uniform_filter
 
 __all__ = [
     "uniform_filter",

dask_image/ndfilters/_threshold.py

@@ -1,8 +1,8 @@
 import dask.array as da
 import numpy as np
 
-from . import _gaussian, _generic, _order
 from ..dispatch._dispatch_ndfilters import dispatch_threshold_local_mean
+from . import _gaussian, _generic, _order
 
 __all__ = [
     "threshold_local",

dask_image/ndfilters/_utils.py

@@ -1,13 +1,10 @@
 # -*- coding: utf-8 -*-
-
-from __future__ import division
-
 import collections
 import inspect
 import numbers
 import re
 
-import numpy
+import numpy as np
 
 
 def _get_docstring(func):
@@ -103,7 +100,7 @@ def _get_size(ndim, size):
 
     if isinstance(size, numbers.Number):
         size = ndim * (size,)
-    size = numpy.array(size)
+    size = np.array(size)
 
     if size.ndim != 1:
         raise RuntimeError("The size must have only one dimension.")
@@ -120,13 +117,13 @@ def _get_size(ndim, size):
 
 
 def _get_origin(size, origin=0):
-    size = numpy.array(size)
+    size = np.array(size)
     ndim = len(size)
 
     if isinstance(origin, numbers.Number):
         origin = ndim * (origin,)
 
-    origin = numpy.array(origin)
+    origin = np.array(origin)
 
     if not issubclass(origin.dtype.type, numbers.Integral):
         raise TypeError("The origin must be of integral type.")
@@ -150,8 +147,8 @@ def _get_origin(size, origin=0):
 
 
 def _get_depth(size, origin=0):
-    origin = numpy.array(_get_origin(size, origin))
-    size = numpy.array(size)
+    origin = np.array(_get_origin(size, origin))
+    size = np.array(size)
 
     half_size = size // 2
     depth = half_size + abs(origin)
@@ -171,7 +168,7 @@ def _get_footprint(ndim, size=None, footprint=None):
     # Get a footprint based on the size.
     if size is not None:
         size = _get_size(ndim, size)
-        footprint = numpy.ones(size, dtype=bool)
+        footprint = np.ones(size, dtype=bool)
 
     # Validate the footprint.
     if footprint.ndim != ndim: