using for loop to install conda package

.CondaPkg/env/Lib/site-packages/skimage/_shared/_dependency_checks.py

@@ -0,0 +1,3 @@
+from .version_requirements import is_installed
+
+has_mpl = is_installed("matplotlib", ">=3.3")

.CondaPkg/env/Lib/site-packages/skimage/_shared/_geometry.py

@@ -0,0 +1,54 @@
+__all__ = ['polygon_clip', 'polygon_area']
+
+import numpy as np
+
+from .version_requirements import require
+
+
+@require("matplotlib", ">=3.3")
+def polygon_clip(rp, cp, r0, c0, r1, c1):
+    """Clip a polygon to the given bounding box.
+
+    Parameters
+    ----------
+    rp, cp : (N,) ndarray of double
+        Row and column coordinates of the polygon.
+    (r0, c0), (r1, c1) : double
+        Top-left and bottom-right coordinates of the bounding box.
+
+    Returns
+    -------
+    r_clipped, c_clipped : (M,) ndarray of double
+        Coordinates of clipped polygon.
+
+    Notes
+    -----
+    This makes use of Sutherland-Hodgman clipping as implemented in
+    AGG 2.4 and exposed in Matplotlib.
+
+    """
+    from matplotlib import path, transforms
+
+    poly = path.Path(np.vstack((rp, cp)).T, closed=True)
+    clip_rect = transforms.Bbox([[r0, c0], [r1, c1]])
+    poly_clipped = poly.clip_to_bbox(clip_rect).to_polygons()[0]
+
+    return poly_clipped[:, 0], poly_clipped[:, 1]
+
+
+def polygon_area(pr, pc):
+    """Compute the area of a polygon.
+
+    Parameters
+    ----------
+    pr, pc : (N,) array of float
+        Polygon row and column coordinates.
+
+    Returns
+    -------
+    a : float
+        Area of the polygon.
+    """
+    pr = np.asarray(pr)
+    pc = np.asarray(pc)
+    return 0.5 * np.abs(np.sum((pc[:-1] * pr[1:]) - (pc[1:] * pr[:-1])))

.CondaPkg/env/Lib/site-packages/skimage/_shared/_tempfile.py

@@ -0,0 +1,27 @@
+from tempfile import NamedTemporaryFile
+from contextlib import contextmanager
+import os
+
+@contextmanager
+def temporary_file(suffix=''):
+    """Yield a writeable temporary filename that is deleted on context exit.
+
+    Parameters
+    ----------
+    suffix : string, optional
+        The suffix for the file.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skimage import io
+    >>> with temporary_file('.tif') as tempfile:
+    ...     im = np.arange(25, dtype=np.uint8).reshape((5, 5))
+    ...     io.imsave(tempfile, im)
+    ...     assert np.all(io.imread(tempfile) == im)
+    """
+    with NamedTemporaryFile(suffix=suffix, delete=False) as tempfile_stream:
+        tempfile = tempfile_stream.name
+
+    yield tempfile
+    os.remove(tempfile)

.CondaPkg/env/Lib/site-packages/skimage/_shared/_warnings.py

@@ -0,0 +1,147 @@
+from contextlib import contextmanager
+import sys
+import warnings
+import re
+import functools
+import os
+
+__all__ = ['all_warnings', 'expected_warnings', 'warn']
+
+
+# A version of `warnings.warn` with a default stacklevel of 2.
+# functool is used so as not to increase the call stack accidentally
+warn = functools.partial(warnings.warn, stacklevel=2)
+
+
+@contextmanager
+def all_warnings():
+    """
+    Context for use in testing to ensure that all warnings are raised.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def foo():
+    ...     warnings.warn(RuntimeWarning("bar"), stacklevel=2)
+
+    We raise the warning once, while the warning filter is set to "once".
+    Hereafter, the warning is invisible, even with custom filters:
+
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('once')
+    ...     foo()                         # doctest: +SKIP
+
+    We can now run ``foo()`` without a warning being raised:
+
+    >>> from numpy.testing import assert_warns
+    >>> foo()                             # doctest: +SKIP
+
+    To catch the warning, we call in the help of ``all_warnings``:
+
+    >>> with all_warnings():
+    ...     assert_warns(RuntimeWarning, foo)
+    """
+    # _warnings.py is on the critical import path.
+    # Since this is a testing only function, we lazy import inspect.
+    import inspect
+    # Whenever a warning is triggered, Python adds a __warningregistry__
+    # member to the *calling* module.  The exercise here is to find
+    # and eradicate all those breadcrumbs that were left lying around.
+    #
+    # We proceed by first searching all parent calling frames and explicitly
+    # clearing their warning registries (necessary for the doctests above to
+    # pass).  Then, we search for all submodules of skimage and clear theirs
+    # as well (necessary for the skimage test suite to pass).
+
+    frame = inspect.currentframe()
+    if frame:
+        for f in inspect.getouterframes(frame):
+            f[0].f_locals['__warningregistry__'] = {}
+    del frame
+
+    for mod_name, mod in list(sys.modules.items()):
+        try:
+            mod.__warningregistry__.clear()
+        except AttributeError:
+            pass
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        yield w
+
+
+@contextmanager
+def expected_warnings(matching):
+    r"""Context for use in testing to catch known warnings matching regexes
+
+    Parameters
+    ----------
+    matching : None or a list of strings or compiled regexes
+        Regexes for the desired warning to catch
+        If matching is None, this behaves as a no-op.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> image = rng.integers(0, 2**16, size=(100, 100), dtype=np.uint16)
+    >>> # rank filters are slow when bit-depth exceeds 10 bits
+    >>> from skimage import filters
+    >>> with expected_warnings(['Bad rank filter performance']):
+    ...     median_filtered = filters.rank.median(image)
+
+    Notes
+    -----
+    Uses `all_warnings` to ensure all warnings are raised.
+    Upon exiting, it checks the recorded warnings for the desired matching
+    pattern(s).
+    Raises a ValueError if any match was not found or an unexpected
+    warning was raised.
+    Allows for three types of behaviors: `and`, `or`, and `optional` matches.
+    This is done to accommodate different build environments or loop conditions
+    that may produce different warnings.  The behaviors can be combined.
+    If you pass multiple patterns, you get an orderless `and`, where all of the
+    warnings must be raised.
+    If you use the `|` operator in a pattern, you can catch one of several
+    warnings.
+    Finally, you can use `|\A\Z` in a pattern to signify it as optional.
+
+    """
+    if isinstance(matching, str):
+        raise ValueError('``matching`` should be a list of strings and not '
+                         'a string itself.')
+
+    # Special case for disabling the context manager
+    if matching is None:
+        yield None
+        return
+
+    strict_warnings = os.environ.get('SKIMAGE_TEST_STRICT_WARNINGS', '1')
+    if strict_warnings.lower() == 'true':
+        strict_warnings = True
+    elif strict_warnings.lower() == 'false':
+        strict_warnings = False
+    else:
+        strict_warnings = bool(int(strict_warnings))
+
+    with all_warnings() as w:
+        # enter context
+        yield w
+        # exited user context, check the recorded warnings
+        # Allow users to provide None
+        while None in matching:
+            matching.remove(None)
+        remaining = [m for m in matching if r'\A\Z' not in m.split('|')]
+        for warn in w:
+            found = False
+            for match in matching:
+                if re.search(match, str(warn.message)) is not None:
+                    found = True
+                    if match in remaining:
+                        remaining.remove(match)
+            if strict_warnings and not found:
+                raise ValueError(f'Unexpected warning: {str(warn.message)}')
+        if strict_warnings and (len(remaining) > 0):
+            newline = "\n"
+            msg = f"No warning raised matching:{newline}{newline.join(remaining)}"
+            raise ValueError(msg)

.CondaPkg/env/Lib/site-packages/skimage/_shared/coord.py

@@ -0,0 +1,121 @@
+import numpy as np
+from scipy.spatial import cKDTree, distance
+
+
+def _ensure_spacing(coord, spacing, p_norm, max_out):
+    """Returns a subset of coord where a minimum spacing is guaranteed.
+
+    Parameters
+    ----------
+    coord : ndarray
+        The coordinates of the considered points.
+    spacing : float
+        the maximum allowed spacing between the points.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+    max_out: int
+        If not None, at most the first ``max_out`` candidates are
+        returned.
+
+    Returns
+    -------
+    output : ndarray
+        A subset of coord where a minimum spacing is guaranteed.
+
+    """
+
+    # Use KDtree to find the peaks that are too close to each other
+    tree = cKDTree(coord)
+
+    indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
+    rejected_peaks_indices = set()
+    naccepted = 0
+    for idx, candidates in enumerate(indices):
+        if idx not in rejected_peaks_indices:
+            # keep current point and the points at exactly spacing from it
+            candidates.remove(idx)
+            dist = distance.cdist([coord[idx]],
+                                  coord[candidates],
+                                  distance.minkowski,
+                                  p=p_norm).reshape(-1)
+            candidates = [c for c, d in zip(candidates, dist)
+                          if d < spacing]
+
+            # candidates.remove(keep)
+            rejected_peaks_indices.update(candidates)
+            naccepted += 1
+            if max_out is not None and naccepted >= max_out:
+                break
+
+    # Remove the peaks that are too close to each other
+    output = np.delete(coord, tuple(rejected_peaks_indices), axis=0)
+    if max_out is not None:
+        output = output[:max_out]
+
+    return output
+
+
+def ensure_spacing(coords, spacing=1, p_norm=np.inf, min_split_size=50,
+                   max_out=None, *, max_split_size=2000):
+    """Returns a subset of coord where a minimum spacing is guaranteed.
+
+    Parameters
+    ----------
+    coords : array_like
+        The coordinates of the considered points.
+    spacing : float
+        the maximum allowed spacing between the points.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+    min_split_size : int
+        Minimum split size used to process ``coords`` by batch to save
+        memory. If None, the memory saving strategy is not applied.
+    max_out : int
+        If not None, only the first ``max_out`` candidates are returned.
+    max_split_size : int
+        Maximum split size used to process ``coords`` by batch to save
+        memory. This number was decided by profiling with a large number
+        of points. Too small a number results in too much looping in
+        Python instead of C, slowing down the process, while too large
+        a number results in large memory allocations, slowdowns, and,
+        potentially, in the process being killed -- see gh-6010. See
+        benchmark results `here
+        <https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691>`_.
+
+    Returns
+    -------
+    output : array_like
+        A subset of coord where a minimum spacing is guaranteed.
+
+    """
+
+    output = coords
+    if len(coords):
+
+        coords = np.atleast_2d(coords)
+        if min_split_size is None:
+            batch_list = [coords]
+        else:
+            coord_count = len(coords)
+            split_idx = [min_split_size]
+            split_size = min_split_size
+            while coord_count - split_idx[-1] > max_split_size:
+                split_size *= 2
+                split_idx.append(split_idx[-1] + min(split_size,
+                                                     max_split_size))
+            batch_list = np.array_split(coords, split_idx)
+
+        output = np.zeros((0, coords.shape[1]), dtype=coords.dtype)
+        for batch in batch_list:
+            output = _ensure_spacing(np.vstack([output, batch]),
+                                     spacing, p_norm, max_out)
+            if max_out is not None and len(output) >= max_out:
+                break
+
+    return output

.CondaPkg/env/Lib/site-packages/skimage/_shared/fast_exp.h

@@ -0,0 +1,47 @@
+/* A fast approximation of the exponential function.
+ * Reference [1]: https://schraudolph.org/pubs/Schraudolph99.pdf
+ * Reference [2]: https://doi.org/10.1162/089976600300015033
+ * Additional improvements by Leonid Bloch. */
+
+#include <stdint.h>
+
+/* use just EXP_A = 1512775 for integer version, to avoid FP calculations */
+#define EXP_A (1512775.3951951856938)  /* 2^20/ln2 */
+/* For min. RMS error */
+#define EXP_BC 1072632447              /* 1023*2^20 - 60801 */
+/* For min. max. relative error */
+/* #define EXP_BC 1072647449 */        /* 1023*2^20 - 45799 */
+/* For min. mean relative error */
+/* #define EXP_BC 1072625005 */        /* 1023*2^20 - 68243 */
+
+__inline double _fast_exp (double y)
+{
+    union
+    {
+        double d;
+        struct { int32_t i, j; } n;
+        char t[8];
+    } _eco;
+
+    _eco.n.i = 1;
+
+    switch(_eco.t[0]) {
+        case 1:
+            /* Little endian */
+            _eco.n.j = (int32_t)(EXP_A*(y)) + EXP_BC;
+            _eco.n.i = 0;
+            break;
+        case 0:
+            /* Big endian */
+            _eco.n.i = (int32_t)(EXP_A*(y)) + EXP_BC;
+            _eco.n.j = 0;
+            break;
+    }
+
+    return _eco.d;
+}
+
+__inline float _fast_expf (float y)
+{
+  return (float)_fast_exp((double)y);
+}

.CondaPkg/env/Lib/site-packages/skimage/_shared/filters.py

@@ -0,0 +1,166 @@
+"""Filters used across multiple skimage submodules.
+
+These are defined here to avoid circular imports.
+
+The unit tests remain under skimage/filters/tests/
+"""
+from collections.abc import Iterable
+
+import numpy as np
+from scipy import ndimage as ndi
+
+from .._shared.utils import _supported_float_type, convert_to_float, warn
+
+
+class _PatchClassRepr(type):
+    """Control class representations in rendered signatures."""
+    def __repr__(cls):
+        return f"<{cls.__name__}>"
+
+
+class ChannelAxisNotSet(metaclass=_PatchClassRepr):
+    """Signal that the `channel_axis` parameter is not set.
+
+    This is a proxy object, used to signal to `skimage.filters.gaussian` that
+    the `channel_axis` parameter has not been set, in which case the function
+    will determine whether a color channel is present. We cannot use ``None``
+    for this purpose as it has its own meaning which indicates that the given
+    image is grayscale.
+
+    This automatic behavior was broken in v0.19, recovered but deprecated in
+    v0.20 and will be removed in v0.21.
+    """
+
+
+def gaussian(image, sigma=1, output=None, mode='nearest', cval=0,
+             preserve_range=False, truncate=4.0, *,
+             channel_axis=ChannelAxisNotSet):
+    """Multi-dimensional Gaussian filter.
+
+    Parameters
+    ----------
+    image : array-like
+        Input image (grayscale or color) to filter.
+    sigma : scalar or sequence of scalars, optional
+        Standard deviation for Gaussian kernel. The standard
+        deviations of the Gaussian filter are given for each axis as a
+        sequence, or as a single number, in which case it is equal for
+        all axes.
+    output : array, optional
+        The ``output`` parameter passes an array in which to store the
+        filter output.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+        The ``mode`` parameter determines how the array borders are
+        handled, where ``cval`` is the value when mode is equal to
+        'constant'. Default is 'nearest'.
+    cval : scalar, optional
+        Value to fill past edges of input if ``mode`` is 'constant'. Default
+        is 0.0
+    preserve_range : bool, optional
+        If True, keep the original range of values. Otherwise, the input
+        ``image`` is converted according to the conventions of ``img_as_float``
+        (Normalized first to values [-1.0 ; 1.0] or [0 ; 1.0] depending on
+        dtype of input)
+
+        For more information, see:
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+    truncate : float, optional
+        Truncate the filter at this many standard deviations.
+    channel_axis : int or None, optional
+        If None, the image is assumed to be a grayscale (single channel) image.
+        Otherwise, this parameter indicates which axis of the array corresponds
+        to channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+        .. warning::
+
+            Automatic detection of the color channel based on the old deprecated
+            `multichannel=None` was broken in version 0.19. In 0.20 this
+            behavior is fixed. The last axis of an `image` with dimensions
+            (M, N, 3) is interpreted as a color channel if `channel_axis` is not
+            set by the user (signaled by the default proxy value
+            `ChannelAxisNotSet`). Starting with 0.21, `channel_axis=None` will
+            be used as the new default value.
+
+    Returns
+    -------
+    filtered_image : ndarray
+        the filtered array
+
+    Notes
+    -----
+    This function is a wrapper around :func:`scipy.ndi.gaussian_filter`.
+
+    Integer arrays are converted to float.
+
+    The ``output`` should be floating point data type since gaussian converts
+    to float provided ``image``. If ``output`` is not provided, another array
+    will be allocated and returned as the result.
+
+    The multi-dimensional filter is implemented as a sequence of
+    one-dimensional convolution filters. The intermediate arrays are
+    stored in the same data type as the output. Therefore, for output
+    types with a limited precision, the results may be imprecise
+    because intermediate results may be stored with insufficient
+    precision.
+
+    Examples
+    --------
+
+    >>> a = np.zeros((3, 3))
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 0.]])
+    >>> gaussian(a, sigma=0.4)  # mild smoothing
+    array([[0.00163116, 0.03712502, 0.00163116],
+           [0.03712502, 0.84496158, 0.03712502],
+           [0.00163116, 0.03712502, 0.00163116]])
+    >>> gaussian(a, sigma=1)  # more smoothing
+    array([[0.05855018, 0.09653293, 0.05855018],
+           [0.09653293, 0.15915589, 0.09653293],
+           [0.05855018, 0.09653293, 0.05855018]])
+    >>> # Several modes are possible for handling boundaries
+    >>> gaussian(a, sigma=1, mode='reflect')
+    array([[0.08767308, 0.12075024, 0.08767308],
+           [0.12075024, 0.16630671, 0.12075024],
+           [0.08767308, 0.12075024, 0.08767308]])
+    >>> # For RGB images, each is filtered separately
+    >>> from skimage.data import astronaut
+    >>> image = astronaut()
+    >>> filtered_img = gaussian(image, sigma=1, channel_axis=-1)
+
+    """
+    if channel_axis is ChannelAxisNotSet:
+        if image.ndim == 3 and image.shape[-1] == 3:
+            warn(
+                "Automatic detection of the color channel was deprecated in "
+                "v0.19, and `channel_axis=None` will be the new default in "
+                "v0.21. Set `channel_axis=-1` explicitly to silence this "
+                "warning.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            channel_axis = -1
+        else:
+            channel_axis = None
+
+    if np.any(np.asarray(sigma) < 0.0):
+        raise ValueError("Sigma values less than zero are not valid")
+    if channel_axis is not None:
+        # do not filter across channels
+        if not isinstance(sigma, Iterable):
+            sigma = [sigma] * (image.ndim - 1)
+        if len(sigma) == image.ndim - 1:
+            sigma = list(sigma)
+            sigma.insert(channel_axis % image.ndim, 0)
+    image = convert_to_float(image, preserve_range)
+    float_dtype = _supported_float_type(image.dtype)
+    image = image.astype(float_dtype, copy=False)
+    if (output is not None) and (not np.issubdtype(output.dtype, np.floating)):
+        raise ValueError("Provided output data type is not float")
+    return ndi.gaussian_filter(image, sigma, output=output,
+                               mode=mode, cval=cval, truncate=truncate)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tester.py

@@ -0,0 +1,120 @@
+import os
+import sys
+
+
+def _show_skimage_info():
+    import skimage
+    print(f"skimage version {skimage.__version__}")
+
+
+class PytestTester:
+    """
+    Pytest test runner.
+
+    This class is made available in ``skimage._shared.testing``, and a test
+    function is typically added to a package's __init__.py like so::
+
+      from skimage._shared.testing import PytestTester
+      test = PytestTester(__name__)
+      del PytestTester
+
+    Calling this test function finds and runs all tests associated with the
+    module and all its sub-modules.
+
+    Attributes
+    ----------
+    module_name : str
+        Full path to the package to test.
+
+    Parameters
+    ----------
+    module_name : module name
+        The name of the module to test.
+
+    """
+    def __init__(self, module_name):
+        self.module_name = module_name
+
+    def __call__(self, label='fast', verbose=1, extra_argv=None,
+                 doctests=False, coverage=False, durations=-1, tests=None):
+        """
+        Run tests for module using pytest.
+
+        Parameters
+        ----------
+        label : {'fast', 'full'}, optional
+            Identifies the tests to run. When set to 'fast', tests decorated
+            with `pytest.mark.slow` are skipped, when 'full', the slow marker
+            is ignored.
+        verbose : int, optional
+            Verbosity value for test outputs, in the range 1-3. Default is 1.
+        extra_argv : list, optional
+            List with any extra arguments to pass to pytests.
+        doctests : bool, optional
+            .. note:: Not supported
+        coverage : bool, optional
+            If True, report coverage of scikit-image code. Default is False.
+            Requires installation of (pip) pytest-cov.
+        durations : int, optional
+            If < 0, do nothing, If 0, report time of all tests, if > 0,
+            report the time of the slowest `timer` tests. Default is -1.
+        tests : test or list of tests
+            Tests to be executed with pytest '--pyargs'
+
+        Returns
+        -------
+        result : bool
+            Return True on success, false otherwise.
+        """
+        import pytest
+
+        module = sys.modules[self.module_name]
+        module_path = os.path.abspath(module.__path__[0])
+
+        # setup the pytest arguments
+        pytest_args = ["-l"]
+
+        # offset verbosity. The "-q" cancels a "-v".
+        pytest_args += ["-q"]
+
+        # Filter out annoying import messages. Want these in both develop and
+        # release mode.
+        pytest_args += [
+            "-W ignore:Not importing directory",
+            "-W ignore:numpy.dtype size changed",
+            "-W ignore:numpy.ufunc size changed", ]
+
+        if doctests:
+            raise ValueError("Doctests not supported")
+
+        if extra_argv:
+            pytest_args += list(extra_argv)
+
+        if verbose > 1:
+            pytest_args += ["-" + "v" * (verbose - 1)]
+
+        if coverage:
+            pytest_args += ["--cov=" + module_path]
+
+        if label == "fast":
+            pytest_args += ["-m", "not slow"]
+        elif label != "full":
+            pytest_args += ["-m", label]
+
+        if durations >= 0:
+            pytest_args += [f"--durations={durations}"]
+
+        if tests is None:
+            tests = [self.module_name]
+
+        pytest_args += ["--pyargs"] + list(tests)
+
+        # run tests.
+        _show_skimage_info()
+
+        try:
+            code = pytest.main(pytest_args)
+        except SystemExit as exc:
+            code = exc.code
+
+        return code == 0

.CondaPkg/env/Lib/site-packages/skimage/_shared/testing.py

@@ -0,0 +1,331 @@
+"""
+Testing utilities.
+"""
+
+import os
+import re
+import struct
+import threading
+import functools
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+from numpy import testing
+from numpy.testing import (
+    TestCase, assert_, assert_warns, assert_no_warnings,
+    assert_equal, assert_almost_equal,
+    assert_array_equal, assert_allclose,
+    assert_array_almost_equal, assert_array_almost_equal_nulp,
+    assert_array_less
+)
+
+import warnings
+
+from .. import data, io
+from ..data._fetchers import _fetch
+from ..util import img_as_uint, img_as_float, img_as_int, img_as_ubyte
+from ._warnings import expected_warnings
+
+import pytest
+
+
+skipif = pytest.mark.skipif
+xfail = pytest.mark.xfail
+parametrize = pytest.mark.parametrize
+raises = pytest.raises
+fixture = pytest.fixture
+
+SKIP_RE = re.compile(r"(\s*>>>.*?)(\s*)#\s*skip\s+if\s+(.*)$")
+
+
+# true if python is running in 32bit mode
+# Calculate the size of a void * pointer in bits
+# https://docs.python.org/3/library/struct.html
+arch32 = struct.calcsize("P") * 8 == 32
+
+
+_error_on_warnings = os.environ.get('SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL', '0')
+if _error_on_warnings.lower() == 'true':
+    _error_on_warnings = True
+elif _error_on_warnings.lower() == 'false':
+    _error_on_warnings = False
+else:
+    try:
+        _error_on_warnings = bool(int(_error_on_warnings))
+    except ValueError:
+        _error_on_warnings = False
+
+def assert_less(a, b, msg=None):
+    message = f"{a!r} is not lower than {b!r}"
+    if msg is not None:
+        message += ": " + msg
+    assert a < b, message
+
+
+def assert_greater(a, b, msg=None):
+    message = f"{a!r} is not greater than {b!r}"
+    if msg is not None:
+        message += ": " + msg
+    assert a > b, message
+
+
+def doctest_skip_parser(func):
+    """ Decorator replaces custom skip test markup in doctests
+
+    Say a function has a docstring::
+
+        >>> something, HAVE_AMODULE, HAVE_BMODULE = 0, False, False
+        >>> something # skip if not HAVE_AMODULE
+        0
+        >>> something # skip if HAVE_BMODULE
+        0
+
+    This decorator will evaluate the expression after ``skip if``.  If this
+    evaluates to True, then the comment is replaced by ``# doctest: +SKIP``. If
+    False, then the comment is just removed. The expression is evaluated in the
+    ``globals`` scope of `func`.
+
+    For example, if the module global ``HAVE_AMODULE`` is False, and module
+    global ``HAVE_BMODULE`` is False, the returned function will have docstring::
+
+        >>> something # doctest: +SKIP
+        >>> something + else # doctest: +SKIP
+        >>> something # doctest: +SKIP
+
+    """
+    lines = func.__doc__.split('\n')
+    new_lines = []
+    for line in lines:
+        match = SKIP_RE.match(line)
+        if match is None:
+            new_lines.append(line)
+            continue
+        code, space, expr = match.groups()
+
+        try:
+            # Works as a function decorator
+            if eval(expr, func.__globals__):
+                code = code + space + "# doctest: +SKIP"
+        except AttributeError:
+            # Works as a class decorator
+            if eval(expr, func.__init__.__globals__):
+                code = code + space + "# doctest: +SKIP"
+
+        new_lines.append(code)
+    func.__doc__ = "\n".join(new_lines)
+    return func
+
+
+def roundtrip(image, plugin, suffix):
+    """Save and read an image using a specified plugin"""
+    if '.' not in suffix:
+        suffix = '.' + suffix
+    with NamedTemporaryFile(suffix=suffix, delete=False) as temp_file:
+        fname = temp_file.name
+    io.imsave(fname, image, plugin=plugin)
+    new = io.imread(fname, plugin=plugin)
+    try:
+        os.remove(fname)
+    except Exception:
+        pass
+    return new
+
+
+def color_check(plugin, fmt='png'):
+    """Check roundtrip behavior for color images.
+
+    All major input types should be handled as ubytes and read
+    back correctly.
+    """
+    img = img_as_ubyte(data.chelsea())
+    r1 = roundtrip(img, plugin, fmt)
+    testing.assert_allclose(img, r1)
+
+    img2 = img > 128
+    r2 = roundtrip(img2, plugin, fmt)
+    testing.assert_allclose(img2, r2.astype(bool))
+
+    img3 = img_as_float(img)
+    r3 = roundtrip(img3, plugin, fmt)
+    testing.assert_allclose(r3, img)
+
+    img4 = img_as_int(img)
+    if fmt.lower() in (('tif', 'tiff')):
+        img4 -= 100
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img4)
+    else:
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img_as_ubyte(img4))
+
+    img5 = img_as_uint(img)
+    r5 = roundtrip(img5, plugin, fmt)
+    testing.assert_allclose(r5, img)
+
+
+def mono_check(plugin, fmt='png'):
+    """Check the roundtrip behavior for images that support most types.
+
+    All major input types should be handled.
+    """
+
+    img = img_as_ubyte(data.moon())
+    r1 = roundtrip(img, plugin, fmt)
+    testing.assert_allclose(img, r1)
+
+    img2 = img > 128
+    r2 = roundtrip(img2, plugin, fmt)
+    testing.assert_allclose(img2, r2.astype(bool))
+
+    img3 = img_as_float(img)
+    r3 = roundtrip(img3, plugin, fmt)
+    if r3.dtype.kind == 'f':
+        testing.assert_allclose(img3, r3)
+    else:
+        testing.assert_allclose(r3, img_as_uint(img))
+
+    img4 = img_as_int(img)
+    if fmt.lower() in (('tif', 'tiff')):
+        img4 -= 100
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img4)
+    else:
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img_as_uint(img4))
+
+    img5 = img_as_uint(img)
+    r5 = roundtrip(img5, plugin, fmt)
+    testing.assert_allclose(r5, img5)
+
+
+def setup_test():
+    """Default package level setup routine for skimage tests.
+
+    Import packages known to raise warnings, and then
+    force warnings to raise errors.
+
+    Also set the random seed to zero.
+    """
+    warnings.simplefilter('default')
+
+    if _error_on_warnings:
+
+        np.random.seed(0)
+
+        warnings.simplefilter('error')
+
+        warnings.filterwarnings(
+            'default', message='unclosed file', category=ResourceWarning
+        )
+
+        # Ignore other warnings only seen when using older versions of
+        # dependencies.
+        warnings.filterwarnings(
+            'default',
+            message='Conversion of the second argument of issubdtype',
+            category=FutureWarning
+        )
+
+        warnings.filterwarnings(
+            'default',
+            message='the matrix subclass is not the recommended way',
+            category=PendingDeprecationWarning, module='numpy'
+        )
+
+        warnings.filterwarnings(
+            'default',
+            message='Your installed pillow version',
+            category=UserWarning,
+            module='skimage.io'
+        )
+
+        # ignore warning from cycle_spin about Dask not being installed
+        warnings.filterwarnings(
+            'default',
+            message='The optional dask dependency is not installed.',
+            category=UserWarning
+        )
+
+        warnings.filterwarnings(
+            'default',
+            message='numpy.ufunc size changed',
+            category=RuntimeWarning
+        )
+
+        warnings.filterwarnings(
+            'default',
+            message='\n\nThe scipy.sparse array containers',
+            category=DeprecationWarning
+        )
+
+        # ignore dtype deprecation warning from NumPy arising from use of SciPy
+        # as a reference in test_watershed09. Should be fixed in scipy>=1.9.4
+        # https://github.com/scipy/scipy/commit/da3ff893b9ac161938e11f9bcd5380e09cf03150
+        warnings.filterwarnings(
+            'default',
+            message=('`np.int0` is a deprecated alias for `np.intp`'),
+            category=DeprecationWarning
+        )
+
+
+def teardown_test():
+    """Default package level teardown routine for skimage tests.
+
+    Restore warnings to default behavior
+    """
+    if _error_on_warnings:
+        warnings.resetwarnings()
+        warnings.simplefilter('default')
+
+
+def fetch(data_filename):
+    """Attempt to fetch data, but if unavailable, skip the tests."""
+    try:
+        return _fetch(data_filename)
+    except (ConnectionError, ModuleNotFoundError):
+        pytest.skip(f'Unable to download {data_filename}',
+                    allow_module_level=True)
+
+
+@pytest.mark.skip()
+def test_parallel(num_threads=2, warnings_matching=None):
+    """Decorator to run the same function multiple times in parallel.
+
+    This decorator is useful to ensure that separate threads execute
+    concurrently and correctly while releasing the GIL.
+
+    Parameters
+    ----------
+    num_threads : int, optional
+        The number of times the function is run in parallel.
+
+    warnings_matching: list or None
+        This parameter is passed on to `expected_warnings` so as not to have
+        race conditions with the warnings filters. A single
+        `expected_warnings` context manager is used for all threads.
+        If None, then no warnings are checked.
+
+    """
+
+    assert num_threads > 0
+
+    def wrapper(func):
+        @functools.wraps(func)
+        def inner(*args, **kwargs):
+            with expected_warnings(warnings_matching):
+                threads = []
+                for i in range(num_threads - 1):
+                    thread = threading.Thread(target=func, args=args,
+                                              kwargs=kwargs)
+                    threads.append(thread)
+                for thread in threads:
+                    thread.start()
+
+                func(*args, **kwargs)
+
+                for thread in threads:
+                    thread.join()
+
+        return inner
+
+    return wrapper

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_coord.py

@@ -0,0 +1,94 @@
+import time
+
+import numpy as np
+import pytest
+from scipy.spatial.distance import pdist, minkowski
+
+from skimage._shared.coord import ensure_spacing
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_trivial(p, size):
+    # --- Empty input
+    assert ensure_spacing([], p_norm=p) == []
+
+    # --- A unique point
+    coord = np.random.randn(1, 2)
+    assert np.array_equal(coord, ensure_spacing(coord, p_norm=p,
+                                                min_split_size=size))
+
+    # --- Verified spacing
+    coord = np.random.randn(100, 2)
+
+    # --- 0 spacing
+    assert np.array_equal(coord, ensure_spacing(coord, spacing=0, p_norm=p,
+                                                min_split_size=size))
+
+    # Spacing is chosen to be half the minimum distance
+    spacing = pdist(coord, metric=minkowski, p=p).min() * 0.5
+
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p,
+                         min_split_size=size)
+
+    assert np.array_equal(coord, out)
+
+
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("size", [2, 10, None])
+def test_ensure_spacing_nD(ndim, size):
+    coord = np.ones((5, ndim))
+
+    expected = np.ones((1, ndim))
+
+    assert np.array_equal(ensure_spacing(coord, min_split_size=size), expected)
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [50, 100, None])
+def test_ensure_spacing_batch_processing(p, size):
+    coord = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = np.median(pdist(coord, metric=minkowski, p=p))
+
+    expected = ensure_spacing(coord, spacing=spacing, p_norm=p)
+
+    assert np.array_equal(ensure_spacing(coord, spacing=spacing, p_norm=p,
+                                         min_split_size=size),
+                          expected)
+
+
+def test_max_batch_size():
+    """Small batches are slow, large batches -> large allocations -> also slow.
+
+    https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
+    """
+    coords = np.random.randint(low=0, high=1848, size=(40000, 2))
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50,
+                   max_split_size=2000)
+    dur1 = time.time() - tstart
+
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50,
+                   max_split_size=20000)
+    dur2 = time.time() - tstart
+
+    # Originally checked dur1 < dur2 to assert that the default batch size was
+    # faster than a much larger batch size. However, on rare occasion a CI test
+    # case would fail with dur1 ~5% larger than dur2. To be more robust to
+    # variable load or differences across architectures, we relax this here.
+    assert dur1 < 1.33 * dur2
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_p_norm(p, size):
+    coord = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = np.median(pdist(coord, metric=minkowski, p=p))
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert pdist(out, metric=minkowski, p=p).min() > spacing

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_fast_exp.py

@@ -0,0 +1,21 @@
+from ..fast_exp import fast_exp
+import numpy as np
+
+
+def test_fast_exp():
+
+    X = np.linspace(-5, 0, 5000, endpoint=True)
+
+    # Ground truth
+    Y = np.exp(X)
+
+    # Approximation at double precision
+    _y_f64 = np.array([fast_exp['float64_t'](x) for x in X])
+
+    # Approximation at single precision
+    _y_f32 = np.array([fast_exp['float32_t'](x) for x in X.astype('float32')],
+                      dtype='float32')
+
+    for _y in [_y_f64, _y_f32]:
+
+        assert np.abs(Y - _y).mean() < 3e-3

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_geometry.py

@@ -0,0 +1,78 @@
+import pytest
+from skimage._shared._geometry import polygon_clip, polygon_area
+
+import numpy as np
+from numpy.testing import assert_equal, assert_almost_equal
+
+pytest.importorskip("matplotlib")
+
+
+hand = np.array(
+    [[ 1.64516129,  1.16145833 ],
+     [ 1.64516129,  1.59375    ],
+     [ 1.35080645,  1.921875   ],
+     [ 1.375     ,  2.18229167 ],
+     [ 1.68548387,  1.9375     ],
+     [ 1.60887097,  2.55208333 ],
+     [ 1.68548387,  2.69791667 ],
+     [ 1.76209677,  2.56770833 ],
+     [ 1.83064516,  1.97395833 ],
+     [ 1.89516129,  2.75       ],
+     [ 1.9516129 ,  2.84895833 ],
+     [ 2.01209677,  2.76041667 ],
+     [ 1.99193548,  1.99479167 ],
+     [ 2.11290323,  2.63020833 ],
+     [ 2.2016129 ,  2.734375   ],
+     [ 2.25403226,  2.60416667 ],
+     [ 2.14919355,  1.953125   ],
+     [ 2.30645161,  2.36979167 ],
+     [ 2.39112903,  2.36979167 ],
+     [ 2.41532258,  2.1875     ],
+     [ 2.1733871 ,  1.703125   ],
+     [ 2.07782258,  1.16666667 ]])
+
+
+def test_polygon_area():
+    x = [0, 0, 1, 1]
+    y = [0, 1, 1, 0]
+
+    assert_almost_equal(polygon_area(y, x), 1)
+
+    x = [0, 0, 1]
+    y = [0, 1, 1]
+
+    assert_almost_equal(polygon_area(y, x), 0.5)
+
+    x = [0, 0, 0.5, 1, 1, 0.5]
+    y = [0, 1, 0.5, 1, 0, 0.5]
+
+    assert_almost_equal(polygon_area(y, x), 0.5)
+
+
+def test_poly_clip():
+    x = [0,  1, 2, 1]
+    y = [0, -1, 0, 1]
+
+    yc, xc = polygon_clip(y, x, 0, 0, 1, 1)
+    assert_equal(polygon_area(yc, xc), 0.5)
+
+    x = [-1, 1.5, 1.5, -1]
+    y = [.5, 0.5, 1.5, 1.5]
+    yc, xc = polygon_clip(y, x, 0, 0, 1, 1)
+    assert_equal(polygon_area(yc, xc), 0.5)
+
+
+def test_hand_clip():
+    (r0, c0, r1, c1) = (1.0, 1.5, 2.1, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 19)
+    assert_equal(clip_r[0], clip_r[-1])
+    assert_equal(clip_c[0], clip_c[-1])
+
+    (r0, c0, r1, c1) = (1.0, 1.5, 1.7, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 6)
+
+    (r0, c0, r1, c1) = (1.0, 1.5, 1.5, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 5)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_interpolation.py

@@ -0,0 +1,27 @@
+from skimage._shared.interpolation import coord_map_py
+from skimage._shared.testing import assert_array_equal
+
+def test_coord_map():
+    symmetric = [coord_map_py(4, n, 'S') for n in range(-6, 6)]
+    expected_symmetric = [2, 3, 3, 2, 1, 0, 0, 1, 2, 3, 3, 2]
+    assert_array_equal(symmetric, expected_symmetric)
+
+    wrap = [coord_map_py(4, n, 'W') for n in range(-6, 6)]
+    expected_wrap = [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1]
+    assert_array_equal(wrap, expected_wrap)
+
+    edge = [coord_map_py(4, n, 'E') for n in range(-6, 6)]
+    expected_edge = [0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3]
+    assert_array_equal(edge, expected_edge)
+
+    reflect = [coord_map_py(4, n, 'R') for n in range(-6, 6)]
+    expected_reflect = [0, 1, 2, 3, 2, 1, 0, 1, 2, 3, 2, 1]
+    assert_array_equal(reflect, expected_reflect)
+
+    reflect = [coord_map_py(1, n, 'R') for n in range(-6, 6)]
+    expected_reflect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    assert_array_equal(reflect, expected_reflect)
+
+    other = [coord_map_py(4, n, 'undefined') for n in range(-6, 6)]
+    expected_other = list(range(-6, 6))
+    assert_array_equal(other, expected_other)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_safe_as_int.py

@@ -0,0 +1,42 @@
+import numpy as np
+from skimage._shared.utils import safe_as_int
+from skimage._shared import testing
+
+
+def test_int_cast_not_possible():
+    with testing.raises(ValueError):
+        safe_as_int(7.1)
+    with testing.raises(ValueError):
+        safe_as_int([7.1, 0.9])
+    with testing.raises(ValueError):
+        safe_as_int(np.r_[7.1, 0.9])
+    with testing.raises(ValueError):
+        safe_as_int((7.1, 0.9))
+    with testing.raises(ValueError):
+        safe_as_int(((3,   4,   1),
+                     (2, 7.6, 289)))
+    with testing.raises(ValueError):
+        safe_as_int(7.1, 0.09)
+    with testing.raises(ValueError):
+        safe_as_int([7.1, 0.9], 0.09)
+    with testing.raises(ValueError):
+        safe_as_int(np.r_[7.1, 0.9], 0.09)
+    with testing.raises(ValueError):
+        safe_as_int((7.1, 0.9), 0.09)
+    with testing.raises(ValueError):
+        safe_as_int(((3,   4,   1),
+                     (2, 7.6, 289)), 0.25)
+
+
+def test_int_cast_possible():
+    testing.assert_equal(safe_as_int(7.1, atol=0.11), 7)
+    testing.assert_equal(safe_as_int(-7.1, atol=0.11), -7)
+    testing.assert_equal(safe_as_int(41.9, atol=0.11), 42)
+    testing.assert_array_equal(safe_as_int([2, 42, 5789234.0, 87, 4]),
+                               np.r_[2, 42, 5789234, 87, 4])
+    testing.assert_array_equal(safe_as_int(np.r_[[[3, 4,  1.000000001],
+                                                  [7, 2, -8.999999999],
+                                                  [6, 9, -4234918347.]]]),
+                               np.r_[[[3, 4,           1],
+                                      [7, 2,          -9],
+                                      [6, 9, -4234918347]]])

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_testing.py

@@ -0,0 +1,123 @@
+""" Testing decorators module
+"""
+
+from numpy.testing import assert_equal
+from skimage._shared.testing import doctest_skip_parser, test_parallel
+from skimage._shared import testing
+
+from skimage._shared._warnings import expected_warnings
+from warnings import warn
+
+
+def test_skipper():
+    def f():
+        pass
+
+    class c():
+
+        def __init__(self):
+            self.me = "I think, therefore..."
+
+    docstring = \
+        """ Header
+
+            >>> something # skip if not HAVE_AMODULE
+            >>> something + else
+            >>> a = 1 # skip if not HAVE_BMODULE
+            >>> something2   # skip if HAVE_AMODULE
+        """
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+
+    global HAVE_AMODULE, HAVE_BMODULE
+    HAVE_AMODULE = False
+    HAVE_BMODULE = True
+
+    f2 = doctest_skip_parser(f)
+    c2 = doctest_skip_parser(c)
+    assert f is f2
+    assert c is c2
+
+    expected = \
+        """ Header
+
+            >>> something # doctest: +SKIP
+            >>> something + else
+            >>> a = 1
+            >>> something2
+        """
+    assert_equal(f2.__doc__, expected)
+    assert_equal(c2.__doc__, expected)
+
+    HAVE_AMODULE = True
+    HAVE_BMODULE = False
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+    f2 = doctest_skip_parser(f)
+    c2 = doctest_skip_parser(c)
+
+    assert f is f2
+    expected = \
+        """ Header
+
+            >>> something
+            >>> something + else
+            >>> a = 1 # doctest: +SKIP
+            >>> something2   # doctest: +SKIP
+        """
+    assert_equal(f2.__doc__, expected)
+    assert_equal(c2.__doc__, expected)
+
+    del HAVE_AMODULE
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+    with testing.raises(NameError):
+        doctest_skip_parser(f)
+    with testing.raises(NameError):
+        doctest_skip_parser(c)
+
+
+def test_test_parallel():
+    state = []
+
+    @test_parallel()
+    def change_state1():
+        state.append(None)
+    change_state1()
+    assert len(state) == 2
+
+    @test_parallel(num_threads=1)
+    def change_state2():
+        state.append(None)
+    change_state2()
+    assert len(state) == 3
+
+    @test_parallel(num_threads=3)
+    def change_state3():
+        state.append(None)
+    change_state3()
+    assert len(state) == 6
+
+
+def test_parallel_warning():
+    @test_parallel()
+    def change_state_warns_fails():
+        warn("Test warning for test parallel", stacklevel=2)
+
+    with expected_warnings(['Test warning for test parallel']):
+        change_state_warns_fails()
+
+    @test_parallel(warnings_matching=['Test warning for test parallel'])
+    def change_state_warns_passes():
+        warn("Test warning for test parallel", stacklevel=2)
+
+    change_state_warns_passes()
+
+
+def test_expected_warnings_noop():
+    # This will ensure the line beolow it behaves like a no-op
+    with expected_warnings(['Expected warnings test']):
+
+        # This should behave as a no-op
+        with expected_warnings(None):
+            warn('Expected warnings test')

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_utils.py

@@ -0,0 +1,294 @@
+import sys
+import warnings
+import inspect
+
+import numpy as np
+import pytest
+
+from skimage._shared import testing
+from skimage._shared.utils import (check_nD, deprecate_kwarg,
+                                   _validate_interpolation_order,
+                                   change_default_value, remove_arg,
+                                   _supported_float_type,
+                                   channel_as_last_axis)
+
+complex_dtypes = [np.complex64, np.complex128]
+if hasattr(np, 'complex256'):
+    complex_dtypes += [np.complex256]
+
+have_numpydoc = False
+try:
+    import numpydoc  # noqa: F401
+    have_numpydoc = True
+except ImportError:
+    pass
+
+
+def test_remove_argument():
+
+    @remove_arg('arg1', changed_version='0.12')
+    def foo(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    @remove_arg('arg1', changed_version='0.12',
+                help_msg="Some indication on future behavior")
+    def bar(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    # Assert warning messages
+    expected_msg = ("arg1 argument is deprecated and will be removed "
+                    "in version 0.12. To avoid this warning, "
+                    "please do not use the arg1 argument. Please see "
+                    "foo documentation for more details.")
+
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0, 1) == (0, 1, 1)
+
+    assert str(record[0].message) == expected_msg
+
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0, arg1=1) == (0, 1, 1)
+
+    assert str(record[0].message) == expected_msg
+
+    expected_msg = ("arg1 argument is deprecated and will be removed "
+                    "in version 0.12. To avoid this warning, "
+                    "please do not use the arg1 argument. Please see "
+                    "bar documentation for more details."
+                    " Some indication on future behavior")
+
+    with pytest.warns(FutureWarning) as record:
+        assert bar(0, 1) == (0, 1, 1)
+
+    assert str(record[0].message) == expected_msg
+
+    with pytest.warns(FutureWarning) as record:
+        assert bar(0, arg1=1) == (0, 1, 1)
+
+    assert str(record[0].message) == expected_msg
+    with warnings.catch_warnings(record=True) as recorded:
+        # No kwargs
+        assert foo(0) == (0, 0, 1)
+        assert foo(0, arg2=0) == (0, 0, 0)
+
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert foo.__doc__ == 'Expected docstring'
+    # Assert no warnings were raised
+    assert len(recorded) == 0
+
+
+def test_change_default_value():
+
+    @change_default_value('arg1', new_value=-1, changed_version='0.12')
+    def foo(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    @change_default_value('arg1', new_value=-1, changed_version='0.12',
+                          warning_msg="Custom warning message")
+    def bar(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    # Assert warning messages
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0) == (0, 0, 1)
+        assert bar(0) == (0, 0, 1)
+
+    expected_msg = ("The new recommended value for arg1 is -1. Until "
+                    "version 0.12, the default arg1 value is 0. From "
+                    "version 0.12, the arg1 default value will be -1. "
+                    "To avoid this warning, please explicitly set arg1 value.")
+
+    assert str(record[0].message) == expected_msg
+    assert str(record[1].message) == "Custom warning message"
+
+    # Assert that nothing happens if arg1 is set
+    with warnings.catch_warnings(record=True) as recorded:
+        # No kwargs
+        assert foo(0, 2) == (0, 2, 1)
+        assert foo(0, arg1=0) == (0, 0, 1)
+
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert foo.__doc__ == 'Expected docstring'
+    # Assert no warnings were raised
+    assert len(recorded) == 0
+
+
+def test_deprecate_kwarg():
+
+    @deprecate_kwarg({'old_arg1': 'new_arg1'}, '0.19')
+    def foo(arg0, new_arg1=1, arg2=None):
+        """Expected docstring"""
+        return arg0, new_arg1, arg2
+
+    @deprecate_kwarg({'old_arg1': 'new_arg1'},
+                     deprecated_version='0.19',
+                     warning_msg="Custom warning message")
+    def bar(arg0, new_arg1=1, arg2=None):
+        """Expected docstring"""
+        return arg0, new_arg1, arg2
+
+    # Assert that the DeprecationWarning is raised when the deprecated
+    # argument name is used and that the reasult is valid
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0, old_arg1=1) == (0, 1, None)
+        assert bar(0, old_arg1=1) == (0, 1, None)
+
+    msg = ("`old_arg1` is a deprecated argument name "
+           "for `foo`. Please use `new_arg1` instead.")
+    assert str(record[0].message) == msg
+    assert str(record[1].message) == "Custom warning message"
+
+    # Assert that nothing happens when the function is called with the
+    # new API
+    with warnings.catch_warnings(record=True) as recorded:
+        # No kwargs
+        assert foo(0) == (0, 1, None)
+        assert foo(0, 2) == (0, 2, None)
+        assert foo(0, 1, 2) == (0, 1, 2)
+        # Kwargs without deprecated argument
+        assert foo(0, new_arg1=1, arg2=2) == (0, 1, 2)
+        assert foo(0, new_arg1=2) == (0, 2, None)
+        assert foo(0, arg2=2) == (0, 1, 2)
+        assert foo(0, 1, arg2=2) == (0, 1, 2)
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            if not have_numpydoc:
+                assert foo.__doc__ == """Expected docstring"""
+            else:
+                assert foo.__doc__ == """Expected docstring
+
+
+    Other Parameters
+    ----------------
+    old_arg1 : DEPRECATED
+        Deprecated in favor of `new_arg1`.
+
+        .. deprecated:: 0.19
+"""
+
+    assert len(recorded) == 0
+
+
+def test_check_nD():
+    z = np.random.random(200**2).reshape((200, 200))
+    x = z[10:30, 30:10]
+    with testing.raises(ValueError):
+        check_nD(x, 2)
+
+
+@pytest.mark.parametrize('dtype', [bool, int, np.uint8, np.uint16,
+                                   float, np.float32, np.float64])
+@pytest.mark.parametrize('order', [None, -1, 0, 1, 2, 3, 4, 5, 6])
+def test_validate_interpolation_order(dtype, order):
+    if order is None:
+        # Default order
+        assert (_validate_interpolation_order(dtype, None) == 0
+                if dtype == bool else 1)
+    elif order < 0 or order > 5:
+        # Order not in valid range
+        with testing.raises(ValueError):
+            _validate_interpolation_order(dtype, order)
+    elif dtype == bool and order != 0:
+        # Deprecated order for bool array
+        with pytest.raises(ValueError):
+            _validate_interpolation_order(bool, order)
+    else:
+        # Valid use case
+        assert _validate_interpolation_order(dtype, order) == order
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    [bool, np.float16, np.float32, np.float64, np.uint8, np.uint16, np.uint32,
+     np.uint64, np.int8, np.int16, np.int32, np.int64]
+)
+def test_supported_float_dtype_real(dtype):
+    float_dtype = _supported_float_type(dtype)
+    if dtype in [np.float16, np.float32]:
+        assert float_dtype == np.float32
+    else:
+        assert float_dtype == np.float64
+
+
+@pytest.mark.parametrize('dtype', complex_dtypes)
+@pytest.mark.parametrize('allow_complex', [False, True])
+def test_supported_float_dtype_complex(dtype, allow_complex):
+    if allow_complex:
+        float_dtype = _supported_float_type(dtype, allow_complex=allow_complex)
+        if dtype == np.complex64:
+            assert float_dtype == np.complex64
+        else:
+            assert float_dtype == np.complex128
+    else:
+        with testing.raises(ValueError):
+            _supported_float_type(dtype, allow_complex=allow_complex)
+
+
+@pytest.mark.parametrize(
+    'dtype', ['f', 'float32', np.float32, np.dtype(np.float32)]
+)
+def test_supported_float_dtype_input_kinds(dtype):
+    assert _supported_float_type(dtype) == np.float32
+
+
+@pytest.mark.parametrize(
+    'dtypes, expected',
+    [
+        ((np.float16, np.float64), np.float64),
+        ([np.float32, np.uint16, np.int8], np.float64),
+        ({np.float32, np.float16}, np.float32),
+    ]
+)
+def test_supported_float_dtype_sequence(dtypes, expected):
+    float_dtype = _supported_float_type(dtypes)
+    assert float_dtype == expected
+
+
+@channel_as_last_axis(multichannel_output=False)
+def _decorated_channel_axis_size(x, *, channel_axis=None):
+    if channel_axis is None:
+        return None
+    assert channel_axis == -1
+    return x.shape[-1]
+
+
+@testing.parametrize('channel_axis', [None, 0, 1, 2, -1, -2, -3])
+def test_decorated_channel_axis_shape(channel_axis):
+    # Verify that channel_as_last_axis modifies the channel_axis as expected
+
+    # need unique size per axis here
+    x = np.zeros((2, 3, 4))
+
+    size = _decorated_channel_axis_size(x, channel_axis=channel_axis)
+    if channel_axis is None:
+        assert size is None
+    else:
+        assert size == x.shape[channel_axis]
+
+
+@deprecate_kwarg({"old_kwarg": "new_kwarg"}, deprecated_version="x.y.z")
+def _function_with_deprecated_kwarg(*, new_kwarg):
+    pass
+
+
+def test_deprecate_kwarg_location():
+    """Assert that warning message issued by deprecate_kwarg points to
+    file and line number where decorated function is called.
+    """
+    with pytest.warns(FutureWarning) as record:
+        _function_with_deprecated_kwarg(old_kwarg=True)
+        expected_lineno = inspect.currentframe().f_lineno - 1
+    assert record[0].lineno == expected_lineno
+    assert record[0].filename == __file__

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_version_requirements.py

@@ -0,0 +1,41 @@
+"""Tests for the version requirement functions.
+
+"""
+import numpy as np
+from numpy.testing import assert_equal
+from skimage._shared import version_requirements as version_req
+from skimage._shared import testing
+
+
+def test_get_module_version():
+    assert version_req.get_module_version('numpy')
+    assert version_req.get_module_version('scipy')
+    with testing.raises(ImportError):
+        version_req.get_module_version('fakenumpy')
+
+
+def test_is_installed():
+    assert version_req.is_installed('python', '>=2.7')
+    assert not version_req.is_installed('numpy', '<1.0')
+
+
+def test_require():
+    # A function that only runs on Python >2.7 and numpy > 1.5 (should pass)
+    @version_req.require('python', '>2.7')
+    @version_req.require('numpy', '>1.5')
+    def foo():
+        return 1
+
+    assert_equal(foo(), 1)
+
+    # function that requires scipy < 0.1 (should fail)
+    @version_req.require('scipy', '<0.1')
+    def bar():
+        return 0
+
+    with testing.raises(ImportError):
+        bar()
+
+
+def test_get_module():
+    assert version_req.get_module("numpy") is np

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_warnings.py

@@ -0,0 +1,37 @@
+import os
+from skimage._shared._warnings import expected_warnings
+import pytest
+
+
+@pytest.fixture(scope='function')
+def setup():
+    # Remove any environment variable if it exists
+    old_strictness = os.environ.pop('SKIMAGE_TEST_STRICT_WARNINGS', None)
+    yield
+    # Add the user's desired strictness
+    if old_strictness is not None:
+        os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = old_strictness
+
+
+def test_strict_warnigns_default(setup):
+    # By default we should fail on missing expected warnings
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['1', 'true', 'True', 'TRUE'])
+def test_strict_warning_true(setup, strictness):
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['0', 'false', 'False', 'FALSE'])
+def test_strict_warning_false(setup, strictness):
+    # If the user doesn't wish to be strict about warnings
+    # the following shouldn't raise any error
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with expected_warnings(['some warnings']):
+        pass

.CondaPkg/env/Lib/site-packages/skimage/_shared/utils.py

@@ -0,0 +1,759 @@
+import inspect
+import functools
+import sys
+import warnings
+from collections.abc import Iterable
+
+import numpy as np
+
+from ._warnings import all_warnings, warn
+
+
+__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings',
+           'safe_as_int', 'check_shape_equality', 'check_nD', 'warn',
+           'reshape_nd', 'identity', 'slice_at_axis']
+
+
+class skimage_deprecation(Warning):
+    """Create our own deprecation class, since Python >= 2.7
+    silences deprecations by default.
+
+    """
+    pass
+
+
+def _get_stack_rank(func):
+    """Return function rank in the call stack."""
+    if _is_wrapped(func):
+        return 1 + _get_stack_rank(func.__wrapped__)
+    else:
+        return 0
+
+
+def _is_wrapped(func):
+    return "__wrapped__" in dir(func)
+
+
+def _get_stack_length(func):
+    """Return function call stack length."""
+    return _get_stack_rank(func.__globals__.get(func.__name__, func))
+
+
+class _DecoratorBaseClass:
+    """Used to manage decorators' warnings stacklevel.
+
+    The `_stack_length` class variable is used to store the number of
+    times a function is wrapped by a decorator.
+
+    Let `stack_length` be the total number of times a decorated
+    function is wrapped, and `stack_rank` be the rank of the decorator
+    in the decorators stack. The stacklevel of a warning is then
+    `stacklevel = 1 + stack_length - stack_rank`.
+    """
+
+    _stack_length = {}
+
+    def get_stack_length(self, func):
+        return self._stack_length.get(func.__name__,
+                                      _get_stack_length(func))
+
+
+class change_default_value(_DecoratorBaseClass):
+    """Decorator for changing the default value of an argument.
+
+    Parameters
+    ----------
+    arg_name: str
+        The name of the argument to be updated.
+    new_value: any
+        The argument new value.
+    changed_version : str
+        The package version in which the change will be introduced.
+    warning_msg: str
+        Optional warning message. If None, a generic warning message
+        is used.
+
+    """
+
+    def __init__(self, arg_name, *, new_value, changed_version,
+                 warning_msg=None):
+        self.arg_name = arg_name
+        self.new_value = new_value
+        self.warning_msg = warning_msg
+        self.changed_version = changed_version
+
+    def __call__(self, func):
+        parameters = inspect.signature(func).parameters
+        arg_idx = list(parameters.keys()).index(self.arg_name)
+        old_value = parameters[self.arg_name].default
+
+        stack_rank = _get_stack_rank(func)
+
+        if self.warning_msg is None:
+            self.warning_msg = (
+                f'The new recommended value for {self.arg_name} is '
+                f'{self.new_value}. Until version {self.changed_version}, '
+                f'the default {self.arg_name} value is {old_value}. '
+                f'From version {self.changed_version}, the {self.arg_name} '
+                f'default value will be {self.new_value}. To avoid '
+                f'this warning, please explicitly set {self.arg_name} value.')
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            stacklevel = 1 + self.get_stack_length(func) - stack_rank
+            if len(args) < arg_idx + 1 and self.arg_name not in kwargs.keys():
+                # warn that arg_name default value changed:
+                warnings.warn(self.warning_msg, FutureWarning,
+                              stacklevel=stacklevel)
+            return func(*args, **kwargs)
+
+        return fixed_func
+
+
+class remove_arg(_DecoratorBaseClass):
+    """Decorator to remove an argument from function's signature.
+
+    Parameters
+    ----------
+    arg_name: str
+        The name of the argument to be removed.
+    changed_version : str
+        The package version in which the warning will be replaced by
+        an error.
+    help_msg: str
+        Optional message appended to the generic warning message.
+
+    """
+
+    def __init__(self, arg_name, *, changed_version, help_msg=None):
+        self.arg_name = arg_name
+        self.help_msg = help_msg
+        self.changed_version = changed_version
+
+    def __call__(self, func):
+
+        parameters = inspect.signature(func).parameters
+        arg_idx = list(parameters.keys()).index(self.arg_name)
+        warning_msg = (
+            f'{self.arg_name} argument is deprecated and will be removed '
+            f'in version {self.changed_version}. To avoid this warning, '
+            f'please do not use the {self.arg_name} argument. Please '
+            f'see {func.__name__} documentation for more details.')
+
+        if self.help_msg is not None:
+            warning_msg += f' {self.help_msg}'
+
+        stack_rank = _get_stack_rank(func)
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            stacklevel = 1 + self.get_stack_length(func) - stack_rank
+            if len(args) > arg_idx or self.arg_name in kwargs.keys():
+                # warn that arg_name is deprecated
+                warnings.warn(warning_msg, FutureWarning,
+                              stacklevel=stacklevel)
+            return func(*args, **kwargs)
+
+        return fixed_func
+
+
+def docstring_add_deprecated(func, kwarg_mapping, deprecated_version):
+    """Add deprecated kwarg(s) to the "Other Params" section of a docstring.
+
+    Parameters
+    ---------
+    func : function
+        The function whose docstring we wish to update.
+    kwarg_mapping : dict
+        A dict containing {old_arg: new_arg} key/value pairs as used by
+        `deprecate_kwarg`.
+    deprecated_version : str
+        A major.minor version string specifying when old_arg was
+        deprecated.
+
+    Returns
+    -------
+    new_doc : str
+        The updated docstring. Returns the original docstring if numpydoc is
+        not available.
+    """
+    if func.__doc__ is None:
+        return None
+    try:
+        from numpydoc.docscrape import FunctionDoc, Parameter
+    except ImportError:
+        # Return an unmodified docstring if numpydoc is not available.
+        return func.__doc__
+
+    Doc = FunctionDoc(func)
+    for old_arg, new_arg in kwarg_mapping.items():
+        desc = [f'Deprecated in favor of `{new_arg}`.',
+                '',
+                f'.. deprecated:: {deprecated_version}']
+        Doc['Other Parameters'].append(
+            Parameter(name=old_arg,
+                      type='DEPRECATED',
+                      desc=desc)
+        )
+    new_docstring = str(Doc)
+
+    # new_docstring will have a header starting with:
+    #
+    # .. function:: func.__name__
+    #
+    # and some additional blank lines. We strip these off below.
+    split = new_docstring.split('\n')
+    no_header = split[1:]
+    while not no_header[0].strip():
+        no_header.pop(0)
+
+    # Store the initial description before any of the Parameters fields.
+    # Usually this is a single line, but the while loop covers any case
+    # where it is not.
+    descr = no_header.pop(0)
+    while no_header[0].strip():
+        descr += '\n    ' + no_header.pop(0)
+    descr += '\n\n'
+    # '\n    ' rather than '\n' here to restore the original indentation.
+    final_docstring = descr + '\n    '.join(no_header)
+    # strip any extra spaces from ends of lines
+    final_docstring = '\n'.join(
+        [line.rstrip() for line in final_docstring.split('\n')]
+    )
+    return final_docstring
+
+
+class deprecate_kwarg(_DecoratorBaseClass):
+    """Decorator ensuring backward compatibility when argument names are
+    modified in a function definition.
+
+    Parameters
+    ----------
+    kwarg_mapping: dict
+        Mapping between the function's old argument names and the new
+        ones.
+    deprecated_version : str
+        The package version in which the argument was first deprecated.
+    warning_msg: str
+        Optional warning message. If None, a generic warning message
+        is used.
+    removed_version : str
+        The package version in which the deprecated argument will be
+        removed.
+
+    """
+
+    def __init__(self, kwarg_mapping, deprecated_version, warning_msg=None,
+                 removed_version=None):
+        self.kwarg_mapping = kwarg_mapping
+        if warning_msg is None:
+            self.warning_msg = ("`{old_arg}` is a deprecated argument name "
+                                "for `{func_name}`. ")
+            if removed_version is not None:
+                self.warning_msg += (f'It will be removed in '
+                                     f'version {removed_version}. ')
+            self.warning_msg += "Please use `{new_arg}` instead."
+        else:
+            self.warning_msg = warning_msg
+
+        self.deprecated_version = deprecated_version
+
+    def __call__(self, func):
+
+        stack_rank = _get_stack_rank(func)
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            stacklevel = 1 + self.get_stack_length(func) - stack_rank
+
+            for old_arg, new_arg in self.kwarg_mapping.items():
+                if old_arg in kwargs:
+                    #  warn that the function interface has changed:
+                    warnings.warn(self.warning_msg.format(
+                        old_arg=old_arg, func_name=func.__name__,
+                        new_arg=new_arg), FutureWarning,
+                        stacklevel=stacklevel)
+                    # Substitute new_arg to old_arg
+                    kwargs[new_arg] = kwargs.pop(old_arg)
+
+            # Call the function with the fixed arguments
+            return func(*args, **kwargs)
+
+        if func.__doc__ is not None:
+            newdoc = docstring_add_deprecated(func, self.kwarg_mapping,
+                                              self.deprecated_version)
+            fixed_func.__doc__ = newdoc
+        return fixed_func
+
+
+class channel_as_last_axis:
+    """Decorator for automatically making channels axis last for all arrays.
+
+    This decorator reorders axes for compatibility with functions that only
+    support channels along the last axis. After the function call is complete
+    the channels axis is restored back to its original position.
+
+    Parameters
+    ----------
+    channel_arg_positions : tuple of int, optional
+        Positional arguments at the positions specified in this tuple are
+        assumed to be multichannel arrays. The default is to assume only the
+        first argument to the function is a multichannel array.
+    channel_kwarg_names : tuple of str, optional
+        A tuple containing the names of any keyword arguments corresponding to
+        multichannel arrays.
+    multichannel_output : bool, optional
+        A boolean that should be True if the output of the function is not a
+        multichannel array and False otherwise. This decorator does not
+        currently support the general case of functions with multiple outputs
+        where some or all are multichannel.
+
+    """
+    def __init__(self, channel_arg_positions=(0,), channel_kwarg_names=(),
+                 multichannel_output=True):
+        self.arg_positions = set(channel_arg_positions)
+        self.kwarg_names = set(channel_kwarg_names)
+        self.multichannel_output = multichannel_output
+
+    def __call__(self, func):
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+
+            channel_axis = kwargs.get('channel_axis', None)
+
+            if channel_axis is None:
+                return func(*args, **kwargs)
+
+            # TODO: convert scalars to a tuple in anticipation of eventually
+            #       supporting a tuple of channel axes. Right now, only an
+            #       integer or a single-element tuple is supported, though.
+            if np.isscalar(channel_axis):
+                channel_axis = (channel_axis,)
+            if len(channel_axis) > 1:
+                raise ValueError(
+                    "only a single channel axis is currently supported")
+
+            if channel_axis == (-1,) or channel_axis == -1:
+                return func(*args, **kwargs)
+
+            if self.arg_positions:
+                new_args = []
+                for pos, arg in enumerate(args):
+                    if pos in self.arg_positions:
+                        new_args.append(np.moveaxis(arg, channel_axis[0], -1))
+                    else:
+                        new_args.append(arg)
+                new_args = tuple(new_args)
+            else:
+                new_args = args
+
+            for name in self.kwarg_names:
+                kwargs[name] = np.moveaxis(kwargs[name], channel_axis[0], -1)
+
+            # now that we have moved the channels axis to the last position,
+            # change the channel_axis argument to -1
+            kwargs["channel_axis"] = -1
+
+            # Call the function with the fixed arguments
+            out = func(*new_args, **kwargs)
+            if self.multichannel_output:
+                out = np.moveaxis(out, -1, channel_axis[0])
+            return out
+
+        return fixed_func
+
+
+class deprecated:
+    """Decorator to mark deprecated functions with warning.
+
+    Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>.
+
+    Parameters
+    ----------
+    alt_func : str
+        If given, tell user what function to use instead.
+    behavior : {'warn', 'raise'}
+        Behavior during call to deprecated function: 'warn' = warn user that
+        function is deprecated; 'raise' = raise error.
+    removed_version : str
+        The package version in which the deprecated function will be removed.
+    """
+
+    def __init__(self, alt_func=None, behavior='warn', removed_version=None):
+        self.alt_func = alt_func
+        self.behavior = behavior
+        self.removed_version = removed_version
+
+    def __call__(self, func):
+
+        alt_msg = ''
+        if self.alt_func is not None:
+            alt_msg = f' Use ``{self.alt_func}`` instead.'
+        rmv_msg = ''
+        if self.removed_version is not None:
+            rmv_msg = f' and will be removed in version {self.removed_version}'
+
+        msg = f'Function ``{func.__name__}`` is deprecated{rmv_msg}.{alt_msg}'
+
+        @functools.wraps(func)
+        def wrapped(*args, **kwargs):
+            if self.behavior == 'warn':
+                func_code = func.__code__
+                warnings.simplefilter('always', skimage_deprecation)
+                warnings.warn_explicit(msg,
+                                       category=skimage_deprecation,
+                                       filename=func_code.co_filename,
+                                       lineno=func_code.co_firstlineno + 1)
+            elif self.behavior == 'raise':
+                raise skimage_deprecation(msg)
+            return func(*args, **kwargs)
+
+        # modify doc string to display deprecation warning
+        doc = '**Deprecated function**.' + alt_msg
+        if wrapped.__doc__ is None:
+            wrapped.__doc__ = doc
+        else:
+            wrapped.__doc__ = doc + '\n\n    ' + wrapped.__doc__
+
+        return wrapped
+
+
+def get_bound_method_class(m):
+    """Return the class for a bound method.
+
+    """
+    return m.im_class if sys.version < '3' else m.__self__.__class__
+
+
+def safe_as_int(val, atol=1e-3):
+    """
+    Attempt to safely cast values to integer format.
+
+    Parameters
+    ----------
+    val : scalar or iterable of scalars
+        Number or container of numbers which are intended to be interpreted as
+        integers, e.g., for indexing purposes, but which may not carry integer
+        type.
+    atol : float
+        Absolute tolerance away from nearest integer to consider values in
+        ``val`` functionally integers.
+
+    Returns
+    -------
+    val_int : NumPy scalar or ndarray of dtype `np.int64`
+        Returns the input value(s) coerced to dtype `np.int64` assuming all
+        were within ``atol`` of the nearest integer.
+
+    Notes
+    -----
+    This operation calculates ``val`` modulo 1, which returns the mantissa of
+    all values. Then all mantissas greater than 0.5 are subtracted from one.
+    Finally, the absolute tolerance from zero is calculated. If it is less
+    than ``atol`` for all value(s) in ``val``, they are rounded and returned
+    in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is
+    returned.
+
+    If any value(s) are outside the specified tolerance, an informative error
+    is raised.
+
+    Examples
+    --------
+    >>> safe_as_int(7.0)
+    7
+
+    >>> safe_as_int([9, 4, 2.9999999999])
+    array([9, 4, 3])
+
+    >>> safe_as_int(53.1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Integer argument required but received 53.1, check inputs.
+
+    >>> safe_as_int(53.01, atol=0.01)
+    53
+
+    """
+    mod = np.asarray(val) % 1                # Extract mantissa
+
+    # Check for and subtract any mod values > 0.5 from 1
+    if mod.ndim == 0:                        # Scalar input, cannot be indexed
+        if mod > 0.5:
+            mod = 1 - mod
+    else:                                    # Iterable input, now ndarray
+        mod[mod > 0.5] = 1 - mod[mod > 0.5]  # Test on each side of nearest int
+
+    try:
+        np.testing.assert_allclose(mod, 0, atol=atol)
+    except AssertionError:
+        raise ValueError(f'Integer argument required but received '
+                         f'{val}, check inputs.')
+
+    return np.round(val).astype(np.int64)
+
+
+def check_shape_equality(*images):
+    """Check that all images have the same shape"""
+    image0 = images[0]
+    if not all(image0.shape == image.shape for image in images[1:]):
+        raise ValueError('Input images must have the same dimensions.')
+    return
+
+
+def slice_at_axis(sl, axis):
+    """
+    Construct tuple of slices to slice an array in the given dimension.
+
+    Parameters
+    ----------
+    sl : slice
+        The slice for the given dimension.
+    axis : int
+        The axis to which `sl` is applied. All other dimensions are left
+        "unsliced".
+
+    Returns
+    -------
+    sl : tuple of slices
+        A tuple with slices matching `shape` in length.
+
+    Examples
+    --------
+    >>> slice_at_axis(slice(None, 3, -1), 1)
+    (slice(None, None, None), slice(None, 3, -1), Ellipsis)
+    """
+    return (slice(None),) * axis + (sl,) + (...,)
+
+
+def reshape_nd(arr, ndim, dim):
+    """Reshape a 1D array to have n dimensions, all singletons but one.
+
+    Parameters
+    ----------
+    arr : array, shape (N,)
+        Input array
+    ndim : int
+        Number of desired dimensions of reshaped array.
+    dim : int
+        Which dimension/axis will not be singleton-sized.
+
+    Returns
+    -------
+    arr_reshaped : array, shape ([1, ...], N, [1,...])
+        View of `arr` reshaped to the desired shape.
+
+    Examples
+    --------
+    >>> rng = np.random.default_rng()
+    >>> arr = rng.random(7)
+    >>> reshape_nd(arr, 2, 0).shape
+    (7, 1)
+    >>> reshape_nd(arr, 3, 1).shape
+    (1, 7, 1)
+    >>> reshape_nd(arr, 4, -1).shape
+    (1, 1, 1, 7)
+    """
+    if arr.ndim != 1:
+        raise ValueError("arr must be a 1D array")
+    new_shape = [1] * ndim
+    new_shape[dim] = -1
+    return np.reshape(arr, new_shape)
+
+
+def check_nD(array, ndim, arg_name='image'):
+    """
+    Verify an array meets the desired ndims and array isn't empty.
+
+    Parameters
+    ----------
+    array : array-like
+        Input array to be validated
+    ndim : int or iterable of ints
+        Allowable ndim or ndims for the array.
+    arg_name : str, optional
+        The name of the array in the original function.
+
+    """
+    array = np.asanyarray(array)
+    msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array"
+    msg_empty_array = "The parameter `%s` cannot be an empty array"
+    if isinstance(ndim, int):
+        ndim = [ndim]
+    if array.size == 0:
+        raise ValueError(msg_empty_array % (arg_name))
+    if array.ndim not in ndim:
+        raise ValueError(
+            msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))
+        )
+
+
+def convert_to_float(image, preserve_range):
+    """Convert input image to float image with the appropriate range.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    preserve_range : bool
+        Determines if the range of the image should be kept or transformed
+        using img_as_float. Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Notes
+    -----
+    * Input images with `float32` data type are not upcast.
+
+    Returns
+    -------
+    image : ndarray
+        Transformed version of the input.
+
+    """
+    if image.dtype == np.float16:
+        return image.astype(np.float32)
+    if preserve_range:
+        # Convert image to double only if it is not single or double
+        # precision float
+        if image.dtype.char not in 'df':
+            image = image.astype(float)
+    else:
+        from ..util.dtype import img_as_float
+        image = img_as_float(image)
+    return image
+
+
+def _validate_interpolation_order(image_dtype, order):
+    """Validate and return spline interpolation's order.
+
+    Parameters
+    ----------
+    image_dtype : dtype
+        Image dtype.
+    order : int, optional
+        The order of the spline interpolation. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+
+    Returns
+    -------
+    order : int
+        if input order is None, returns 0 if image_dtype is bool and 1
+        otherwise. Otherwise, image_dtype is checked and input order
+        is validated accordingly (order > 0 is not supported for bool
+        image dtype)
+
+    """
+
+    if order is None:
+        return 0 if image_dtype == bool else 1
+
+    if order < 0 or order > 5:
+        raise ValueError("Spline interpolation order has to be in the "
+                         "range 0-5.")
+
+    if image_dtype == bool and order != 0:
+        raise ValueError(
+            "Input image dtype is bool. Interpolation is not defined "
+            "with bool data type. Please set order to 0 or explicitly "
+            "cast input image to another data type.")
+
+    return order
+
+
+def _to_np_mode(mode):
+    """Convert padding modes from `ndi.correlate` to `np.pad`."""
+    mode_translation_dict = dict(nearest='edge', reflect='symmetric',
+                                 mirror='reflect')
+    if mode in mode_translation_dict:
+        mode = mode_translation_dict[mode]
+    return mode
+
+
+def _to_ndimage_mode(mode):
+    """Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
+    mode_translation_dict = dict(constant='constant', edge='nearest',
+                                 symmetric='reflect', reflect='mirror',
+                                 wrap='wrap')
+    if mode not in mode_translation_dict:
+        raise ValueError(
+            f"Unknown mode: '{mode}', or cannot translate mode. The "
+             f"mode should be one of 'constant', 'edge', 'symmetric', "
+             f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
+             f"more info.")
+    return _fix_ndimage_mode(mode_translation_dict[mode])
+
+
+def _fix_ndimage_mode(mode):
+    # SciPy 1.6.0 introduced grid variants of constant and wrap which
+    # have less surprising behavior for images. Use these when available
+    grid_modes = {'constant': 'grid-constant', 'wrap': 'grid-wrap'}
+    return grid_modes.get(mode, mode)
+
+
+new_float_type = {
+    # preserved types
+    np.float32().dtype.char: np.float32,
+    np.float64().dtype.char: np.float64,
+    np.complex64().dtype.char: np.complex64,
+    np.complex128().dtype.char: np.complex128,
+    # altered types
+    np.float16().dtype.char: np.float32,
+    'g': np.float64,      # np.float128 ; doesn't exist on windows
+    'G': np.complex128,   # np.complex256 ; doesn't exist on windows
+}
+
+
+def _supported_float_type(input_dtype, allow_complex=False):
+    """Return an appropriate floating-point dtype for a given dtype.
+
+    float32, float64, complex64, complex128 are preserved.
+    float16 is promoted to float32.
+    complex256 is demoted to complex128.
+    Other types are cast to float64.
+
+    Parameters
+    ----------
+    input_dtype : np.dtype or Iterable of np.dtype
+        The input dtype. If a sequence of multiple dtypes is provided, each
+        dtype is first converted to a supported floating point type and the
+        final dtype is then determined by applying `np.result_type` on the
+        sequence of supported floating point types.
+    allow_complex : bool, optional
+        If False, raise a ValueError on complex-valued inputs.
+
+    Returns
+    -------
+    float_type : dtype
+        Floating-point dtype for the image.
+    """
+    if isinstance(input_dtype, Iterable) and not isinstance(input_dtype, str):
+        return np.result_type(*(_supported_float_type(d) for d in input_dtype))
+    input_dtype = np.dtype(input_dtype)
+    if not allow_complex and input_dtype.kind == 'c':
+        raise ValueError("complex valued input is not supported")
+    return new_float_type.get(input_dtype.char, np.float64)
+
+
+def identity(image, *args, **kwargs):
+    """Returns the first argument unmodified."""
+    return image
+
+
+def as_binary_ndarray(array, *, variable_name):
+    """Return `array` as a numpy.ndarray of dtype bool.
+
+    Raises
+    ------
+    ValueError:
+        An error including the given `variable_name` if `array` can not be
+        safely cast to a boolean array.
+    """
+    array = np.asarray(array)
+    if array.dtype != bool:
+        if np.any((array != 1) & (array != 0)):
+            raise ValueError(
+                f"{variable_name} array is not of dtype boolean or "
+                f"contains values other than 0 and 1 so cannot be "
+                f"safely cast to boolean array."
+            )
+    return np.asarray(array, dtype=bool)

.CondaPkg/env/Lib/site-packages/skimage/_shared/version_requirements.py

@@ -0,0 +1,168 @@
+import sys
+
+from packaging import version as _version
+
+
+def ensure_python_version(min_version):
+    if not isinstance(min_version, tuple):
+        min_version = (min_version, )
+    if sys.version_info < min_version:
+        # since ensure_python_version is in the critical import path,
+        # we lazy import it.
+        from platform import python_version
+
+        raise ImportError("""
+
+You are running scikit-image on an unsupported version of Python.
+
+Unfortunately, scikit-image 0.15 and above no longer work with your installed
+version of Python ({}).  You therefore have two options: either upgrade to
+Python {}, or install an older version of scikit-image.
+
+For Python 2.7 or Python 3.4, use
+
+ $ pip install 'scikit-image<0.15'
+
+Please also consider updating `pip` and `setuptools`:
+
+ $ pip install pip setuptools --upgrade
+
+Newer versions of these tools avoid installing packages incompatible
+with your version of Python.
+""".format(python_version(), '.'.join([str(v) for v in min_version])))
+
+
+def _check_version(actver, version, cmp_op):
+    """
+    Check version string of an active module against a required version.
+
+    If dev/prerelease tags result in TypeError for string-number comparison,
+    it is assumed that the dependency is satisfied.
+    Users on dev branches are responsible for keeping their own packages up to
+    date.
+    """
+    try:
+        if cmp_op == '>':
+            return _version.parse(actver) > _version.parse(version)
+        elif cmp_op == '>=':
+            return _version.parse(actver) >= _version.parse(version)
+        elif cmp_op == '=':
+            return _version.parse(actver) == _version.parse(version)
+        elif cmp_op == '<':
+            return _version.parse(actver) < _version.parse(version)
+        else:
+            return False
+    except TypeError:
+        return True
+
+
+def get_module_version(module_name):
+    """Return module version or None if version can't be retrieved."""
+    mod = __import__(module_name,
+                     fromlist=[module_name.rpartition('.')[-1]])
+    return getattr(mod, '__version__', getattr(mod, 'VERSION', None))
+
+
+def is_installed(name, version=None):
+    """Test if *name* is installed.
+
+    Parameters
+    ----------
+    name : str
+        Name of module or "python"
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    out : bool
+        True if `name` is installed matching the optional version.
+    """
+    if name.lower() == 'python':
+        actver = sys.version[:6]
+    else:
+        try:
+            actver = get_module_version(name)
+        except ImportError:
+            return False
+    if version is None:
+        return True
+    else:
+        # since version_requirements is in the critical import path,
+        # we lazy import re
+        import re
+
+        match = re.search('[0-9]', version)
+        assert match is not None, "Invalid version number"
+        symb = version[:match.start()]
+        if not symb:
+            symb = '='
+        assert symb in ('>=', '>', '=', '<'),\
+            f"Invalid version condition '{symb}'"
+        version = version[match.start():]
+        return _check_version(actver, version, symb)
+
+
+def require(name, version=None):
+    """Return decorator that forces a requirement for a function or class.
+
+    Parameters
+    ----------
+    name : str
+        Name of module or "python".
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    func : function
+        A decorator that raises an ImportError if a function is run
+        in the absence of the input dependency.
+    """
+    # since version_requirements is in the critical import path, we lazy import
+    # functools
+    import functools
+
+    def decorator(obj):
+        @functools.wraps(obj)
+        def func_wrapped(*args, **kwargs):
+            if is_installed(name, version):
+                return obj(*args, **kwargs)
+            else:
+                msg = f'"{obj}" in "{obj.__module__}" requires "{name}'
+                if version is not None:
+                    msg += f" {version}"
+                raise ImportError(msg + '"')
+        return func_wrapped
+    return decorator
+
+
+def get_module(module_name, version=None):
+    """Return a module object of name *module_name* if installed.
+
+    Parameters
+    ----------
+    module_name : str
+        Name of module.
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    mod : module or None
+        Module if *module_name* is installed matching the optional version
+        or None otherwise.
+    """
+    if not is_installed(module_name, version):
+        return None
+    return __import__(module_name,
+                      fromlist=[module_name.rpartition('.')[-1]])