feat: rewrite resize

add more nodes

bioxelnodes/scipy/_ni_support.py

@@ -0,0 +1,119 @@
+# Copyright (C) 2003-2005 Peter J. Verveer
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. The name of the author may not be used to endorse or promote
+#    products derived from this software without specific prior
+#    written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from collections.abc import Iterable
+import operator
+import warnings
+import numpy as np
+
+
+def _extend_mode_to_code(mode):
+    """Convert an extension mode to the corresponding integer code.
+    """
+    if mode == 'nearest':
+        return 0
+    elif mode == 'wrap':
+        return 1
+    elif mode in ['reflect', 'grid-mirror']:
+        return 2
+    elif mode == 'mirror':
+        return 3
+    elif mode == 'constant':
+        return 4
+    elif mode == 'grid-wrap':
+        return 5
+    elif mode == 'grid-constant':
+        return 6
+    else:
+        raise RuntimeError('boundary mode not supported')
+
+
+def _normalize_sequence(input, rank):
+    """If input is a scalar, create a sequence of length equal to the
+    rank by duplicating the input. If input is a sequence,
+    check if its length is equal to the length of array.
+    """
+    is_str = isinstance(input, str)
+    if not is_str and isinstance(input, Iterable):
+        normalized = list(input)
+        if len(normalized) != rank:
+            err = "sequence argument must have length equal to input rank"
+            raise RuntimeError(err)
+    else:
+        normalized = [input] * rank
+    return normalized
+
+
+def _get_output(output, input, shape=None, complex_output=False):
+    if shape is None:
+        shape = input.shape
+    if output is None:
+        if not complex_output:
+            output = np.zeros(shape, dtype=input.dtype.name)
+        else:
+            complex_type = np.promote_types(input.dtype, np.complex64)
+            output = np.zeros(shape, dtype=complex_type)
+    elif isinstance(output, (type, np.dtype)):
+        # Classes (like `np.float32`) and dtypes are interpreted as dtype
+        if complex_output and np.dtype(output).kind != 'c':
+            warnings.warn("promoting specified output dtype to complex", stacklevel=3)
+            output = np.promote_types(output, np.complex64)
+        output = np.zeros(shape, dtype=output)
+    elif isinstance(output, str):
+        output = np.dtype(output)
+        if complex_output and output.kind != 'c':
+            raise RuntimeError("output must have complex dtype")
+        elif not issubclass(output.type, np.number):
+            raise RuntimeError("output must have numeric dtype")
+        output = np.zeros(shape, dtype=output)
+    elif output.shape != shape:
+        raise RuntimeError("output shape not correct")
+    elif complex_output and output.dtype.kind != 'c':
+        raise RuntimeError("output must have complex dtype")
+    return output
+
+
+def _check_axes(axes, ndim):
+    if axes is None:
+        return tuple(range(ndim))
+    elif np.isscalar(axes):
+        axes = (operator.index(axes),)
+    elif isinstance(axes, Iterable):
+        for ax in axes:
+            axes = tuple(operator.index(ax) for ax in axes)
+            if ax < -ndim or ax > ndim - 1:
+                raise ValueError(f"specified axis: {ax} is out of range")
+        axes = tuple(ax % ndim if ax < 0 else ax for ax in axes)
+    else:
+        message = "axes must be an integer, iterable of integers, or None"
+        raise ValueError(message)
+    if len(tuple(set(axes))) != len(axes):
+        raise ValueError("axes must be unique")
+    return axes