[Scipy-svn] r6922 - trunk/scipy/ndimage
scipy-svn at scipy.org
scipy-svn at scipy.org
Sat Nov 20 02:52:46 EST 2010
Author: warren.weckesser
Date: 2010-11-20 01:52:46 -0600 (Sat, 20 Nov 2010)
New Revision: 6922
Modified:
trunk/scipy/ndimage/_ni_support.py
trunk/scipy/ndimage/filters.py
trunk/scipy/ndimage/fourier.py
trunk/scipy/ndimage/interpolation.py
trunk/scipy/ndimage/measurements.py
trunk/scipy/ndimage/morphology.py
Log:
ENH: ndimage: update 'raise' statements
Modified: trunk/scipy/ndimage/_ni_support.py
===================================================================
--- trunk/scipy/ndimage/_ni_support.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/_ni_support.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -45,7 +45,7 @@
elif mode == 'constant':
return 4
else:
- raise RuntimeError, 'boundary mode not supported'
+ raise RuntimeError('boundary mode not supported')
def _normalize_sequence(input, rank, array_type = None):
"""If input is a scalar, create a sequence of length equal to the
@@ -59,7 +59,7 @@
normalized = list(input)
if len(normalized) != rank:
err = "sequence argument must have length equal to input rank"
- raise RuntimeError, err
+ raise RuntimeError(err)
return normalized
import warnings
@@ -67,13 +67,13 @@
if output_type is not None:
msg = "'output_type' argument is deprecated."
msg += " Assign type to 'output' instead."
- raise RuntimeError, msg
+ raise RuntimeError(msg)
warnings.warn(msg, DeprecationWarning)
if output is None:
output = output_type
elif ((type(output) is not type(types.TypeType)) or
output.dtype != output_type):
- raise RuntimeError, "'output' type and 'output_type' not equal"
+ raise RuntimeError("'output' type and 'output_type' not equal")
if shape is None:
shape = input.shape
if output is None:
@@ -88,7 +88,7 @@
return_value = output
else:
if output.shape != shape:
- raise RuntimeError, "output shape not correct"
+ raise RuntimeError("output shape not correct")
return_value = None
return output, return_value
@@ -96,5 +96,5 @@
if axis < 0:
axis += rank
if axis < 0 or axis >= rank:
- raise ValueError, 'invalid axis'
+ raise ValueError('invalid axis')
return axis
Modified: trunk/scipy/ndimage/filters.py
===================================================================
--- trunk/scipy/ndimage/filters.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/filters.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -113,17 +113,17 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
- raise RuntimeError, 'no filter weights given'
+ raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if ((len(weights) // 2 + origin < 0) or
(len(weights) // 2 + origin > len(weights))):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
@@ -473,12 +473,12 @@
convolution):
input = numpy.asarray(input)
if numpy.iscomplexobj(int):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
weights = numpy.asarray(weights, dtype=numpy.float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.ndim:
- raise RuntimeError, 'filter weights array has incorrect shape.'
+ raise RuntimeError('filter weights array has incorrect shape.')
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.ndim)]
for ii in range(len(origins)):
@@ -487,7 +487,7 @@
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if (lenw // 2 + origin < 0) or (lenw // 2 + origin > lenw):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
if not weights.flags.contiguous:
weights = weights.copy()
output, return_value = _ni_support._get_output(output, input)
@@ -667,13 +667,13 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
- raise RuntimeError, 'incorrect filter size'
+ raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.uniform_filter1d(input, size, axis, output, mode, cval,
origin)
@@ -743,13 +743,13 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
- raise RuntimeError, 'incorrect filter size'
+ raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 1)
@@ -777,13 +777,13 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
- raise RuntimeError, 'incorrect filter size'
+ raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 0)
@@ -795,7 +795,7 @@
if structure is None:
if footprint is None:
if size is None:
- raise RuntimeError, "no footprint provided"
+ raise RuntimeError("no footprint provided")
separable= True
else:
footprint = numpy.asarray(footprint)
@@ -816,7 +816,7 @@
footprint = footprint.astype(bool)
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
origins = _ni_support._normalize_sequence(origin, input.ndim)
if separable:
@@ -837,15 +837,15 @@
else:
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
- raise RuntimeError, 'footprint array has incorrect shape.'
+ raise RuntimeError('footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
if structure is not None:
if len(structure.shape) != input.ndim:
- raise RuntimeError, 'structure array has incorrect shape'
+ raise RuntimeError('structure array has incorrect shape')
if not structure.flags.contiguous:
structure = structure.copy()
mode = _ni_support._extend_mode_to_code(mode)
@@ -895,21 +895,21 @@
mode = "reflect", cval = 0.0, origin = 0, operation = 'rank'):
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
- raise RuntimeError, "no footprint or filter size provided"
+ raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint, dtype=bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
- raise RuntimeError, 'filter footprint array has incorrect shape.'
+ raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
filter_size = numpy.where(footprint, 1, 0).sum()
@@ -920,7 +920,7 @@
if percentile < 0.0:
percentile += 100.0
if percentile < 0 or percentile > 100:
- raise RuntimeError, 'invalid percentile'
+ raise RuntimeError('invalid percentile')
if percentile == 100.0:
rank = filter_size - 1
else:
@@ -928,7 +928,7 @@
if rank < 0:
rank += filter_size
if rank < 0 or rank >= filter_size:
- raise RuntimeError, 'rank not within filter footprint size'
+ raise RuntimeError('rank not within filter footprint size')
if rank == 0:
return minimum_filter(input, None, footprint, output, mode, cval,
origin)
@@ -1059,14 +1059,14 @@
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
- raise RuntimeError, 'invalid filter size'
+ raise RuntimeError('invalid filter size')
axis = _ni_support._check_axis(axis, input.ndim)
if ((filter_size // 2 + origin < 0) or
(filter_size // 2 + origin > filter_size)):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments, extra_keywords)
@@ -1100,11 +1100,11 @@
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
- raise RuntimeError, "no footprint or filter size provided"
+ raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
@@ -1112,10 +1112,10 @@
footprint = footprint.astype(bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
- raise RuntimeError, 'filter footprint array has incorrect shape.'
+ raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
- raise ValueError, 'invalid origin'
+ raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
Modified: trunk/scipy/ndimage/fourier.py
===================================================================
--- trunk/scipy/ndimage/fourier.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/fourier.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -44,12 +44,12 @@
elif type(output) is types.TypeType:
if output not in [numpy.complex64, numpy.complex128,
numpy.float32, numpy.float64]:
- raise RuntimeError, "output type not supported"
+ raise RuntimeError("output type not supported")
output = numpy.zeros(input.shape, dtype = output)
return_value = output
else:
if output.shape != input.shape:
- raise RuntimeError, "output shape not correct"
+ raise RuntimeError("output shape not correct")
return_value = None
return output, return_value
@@ -62,12 +62,12 @@
return_value = output
elif type(output) is types.TypeType:
if output not in [numpy.complex64, numpy.complex128]:
- raise RuntimeError, "output type not supported"
+ raise RuntimeError("output type not supported")
output = numpy.zeros(input.shape, dtype = output)
return_value = output
else:
if output.shape != input.shape:
- raise RuntimeError, "output shape not correct"
+ raise RuntimeError("output shape not correct")
return_value = None
return output, return_value
Modified: trunk/scipy/ndimage/interpolation.py
===================================================================
--- trunk/scipy/ndimage/interpolation.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/interpolation.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -68,10 +68,10 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input,
output_type)
if order in [0, 1]:
@@ -103,10 +103,10 @@
"""
if order < 2 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input,
output_type)
if order not in [0, 1] and input.ndim > 0:
@@ -188,14 +188,14 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
- raise RuntimeError, 'input and output rank must be > 0'
+ raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
@@ -285,18 +285,18 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
coordinates = numpy.asarray(coordinates)
if numpy.iscomplexobj(coordinates):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
output_shape = coordinates.shape[1:]
if input.ndim < 1 or len(output_shape) < 1:
- raise RuntimeError, 'input and output rank must be > 0'
+ raise RuntimeError('input and output rank must be > 0')
if coordinates.shape[0] != input.ndim:
- raise RuntimeError, 'invalid shape for coordinate array'
+ raise RuntimeError('invalid shape for coordinate array')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
@@ -366,14 +366,14 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
- raise RuntimeError, 'input and output rank must be > 0'
+ raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
@@ -383,17 +383,17 @@
output_type, shape = output_shape)
matrix = numpy.asarray(matrix, dtype = numpy.float64)
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
- raise RuntimeError, 'no proper affine matrix provided'
+ raise RuntimeError('no proper affine matrix provided')
if matrix.shape[0] != input.ndim:
- raise RuntimeError, 'affine matrix has wrong number of rows'
+ raise RuntimeError('affine matrix has wrong number of rows')
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
- raise RuntimeError, 'affine matrix has wrong number of columns'
+ raise RuntimeError('affine matrix has wrong number of columns')
if not matrix.flags.contiguous:
matrix = matrix.copy()
offset = _ni_support._normalize_sequence(offset, input.ndim)
offset = numpy.asarray(offset, dtype = numpy.float64)
if offset.ndim != 1 or offset.shape[0] < 1:
- raise RuntimeError, 'no proper offset provided'
+ raise RuntimeError('no proper offset provided')
if not offset.flags.contiguous:
offset = offset.copy()
if matrix.ndim == 1:
@@ -450,12 +450,12 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if input.ndim < 1:
- raise RuntimeError, 'input and output rank must be > 0'
+ raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
@@ -515,12 +515,12 @@
"""
if order < 0 or order > 5:
- raise RuntimeError, 'spline order not supported'
+ raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if input.ndim < 1:
- raise RuntimeError, 'input and output rank must be > 0'
+ raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
@@ -604,7 +604,7 @@
if axes[1] < 0:
axes[1] += rank
if axes[0] < 0 or axes[1] < 0 or axes[0] > rank or axes[1] > rank:
- raise RuntimeError, 'invalid rotation plane specified'
+ raise RuntimeError('invalid rotation plane specified')
if axes[0] > axes[1]:
axes = axes[1], axes[0]
angle = numpy.pi / 180 * angle
Modified: trunk/scipy/ndimage/measurements.py
===================================================================
--- trunk/scipy/ndimage/measurements.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/measurements.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -141,20 +141,20 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if structure is None:
structure = morphology.generate_binary_structure(input.ndim, 1)
structure = numpy.asarray(structure, dtype = bool)
if structure.ndim != input.ndim:
- raise RuntimeError, 'structure and input must have equal rank'
+ raise RuntimeError('structure and input must have equal rank')
for ii in structure.shape:
if ii != 3:
- raise RuntimeError, 'structure dimensions must be equal to 3'
+ raise RuntimeError('structure dimensions must be equal to 3')
if not structure.flags.contiguous:
structure = structure.copy()
if isinstance(output, numpy.ndarray):
if output.dtype.type != numpy.int32:
- raise RuntimeError, 'output type must be int32'
+ raise RuntimeError('output type must be int32')
else:
output = numpy.int32
output, return_value = _ni_support._get_output(output, input)
@@ -217,7 +217,7 @@
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
@@ -299,7 +299,7 @@
if labels is None:
if index is not None:
- raise ValueError, "index without defined labels"
+ raise ValueError("index without defined labels")
if not pass_positions:
return func(input.ravel())
else:
@@ -308,7 +308,8 @@
try:
input, labels = numpy.broadcast_arrays(input, labels)
except ValueError:
- raise ValueError, "input and labels must have the same shape (excepting dimensions with width 1)"
+ raise ValueError("input and labels must have the same shape "
+ "(excepting dimensions with width 1)")
if index is None:
if not pass_positions:
@@ -318,7 +319,9 @@
index = numpy.atleast_1d(index)
if np.any(index.astype(labels.dtype).astype(index.dtype) != index):
- raise ValueError, "Cannot convert index values from <%s> to <%s> (labels' type) without loss of precision"%(index.dtype, labels.dtype)
+ raise ValueError("Cannot convert index values from <%s> to <%s> "
+ "(labels' type) without loss of precision" %
+ (index.dtype, labels.dtype))
index = index.astype(labels.dtype)
# optimization: find min/max in index, and select those parts of labels, input, and positions
@@ -1105,20 +1108,20 @@
"""
input = numpy.asarray(input)
if input.dtype.type not in [numpy.uint8, numpy.uint16]:
- raise TypeError, 'only 8 and 16 unsigned inputs are supported'
+ raise TypeError('only 8 and 16 unsigned inputs are supported')
if structure is None:
structure = morphology.generate_binary_structure(input.ndim, 1)
structure = numpy.asarray(structure, dtype = bool)
if structure.ndim != input.ndim:
- raise RuntimeError, 'structure and input must have equal rank'
+ raise RuntimeError('structure and input must have equal rank')
for ii in structure.shape:
if ii != 3:
- raise RuntimeError, 'structure dimensions must be equal to 3'
+ raise RuntimeError('structure dimensions must be equal to 3')
if not structure.flags.contiguous:
structure = structure.copy()
markers = numpy.asarray(markers)
if input.shape != markers.shape:
- raise RuntimeError, 'input and markers must have equal shape'
+ raise RuntimeError('input and markers must have equal shape')
integral_types = [numpy.int0,
numpy.int8,
@@ -1130,10 +1133,10 @@
numpy.intp]
if markers.dtype.type not in integral_types:
- raise RuntimeError, 'marker should be of integer type'
+ raise RuntimeError('marker should be of integer type')
if isinstance(output, numpy.ndarray):
if output.dtype.type not in integral_types:
- raise RuntimeError, 'output should be of integer type'
+ raise RuntimeError('output should be of integer type')
else:
output = markers.dtype
output, return_value = _ni_support._get_output(output, input)
Modified: trunk/scipy/ndimage/morphology.py
===================================================================
--- trunk/scipy/ndimage/morphology.py 2010-11-20 07:33:29 UTC (rev 6921)
+++ trunk/scipy/ndimage/morphology.py 2010-11-20 07:52:46 UTC (rev 6922)
@@ -219,27 +219,27 @@
border_value, origin, invert, brute_force):
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
- raise TypeError, 'Complex type not supported'
+ raise TypeError('Complex type not supported')
if structure is None:
structure = generate_binary_structure(input.ndim, 1)
else:
structure = numpy.asarray(structure)
structure = structure.astype(bool)
if structure.ndim != input.ndim:
- raise RuntimeError, 'structure rank must equal input rank'
+ raise RuntimeError('structure rank must equal input rank')
if not structure.flags.contiguous:
structure = structure.copy()
if numpy.product(structure.shape,axis=0) < 1:
- raise RuntimeError, 'structure must not be empty'
+ raise RuntimeError('structure must not be empty')
if mask is not None:
mask = numpy.asarray(mask)
if mask.shape != input.shape:
- raise RuntimeError, 'mask and input must have equal sizes'
+ raise RuntimeError('mask and input must have equal sizes')
origin = _ni_support._normalize_sequence(origin, input.ndim)
cit = _center_is_true(structure, origin)
if isinstance(output, numpy.ndarray):
if numpy.iscomplexobj(output):
- raise TypeError, 'Complex output type not supported'
+ raise TypeError('Complex output type not supported')
else:
output = bool
output, return_value = _ni_support._get_output(output, input)
@@ -1864,7 +1864,7 @@
"""
if (not return_distances) and (not return_indices):
msg = 'at least one of distances/indices must be specified'
- raise RuntimeError, msg
+ raise RuntimeError(msg)
tmp1 = numpy.asarray(input) != 0
struct = generate_binary_structure(tmp1.ndim, tmp1.ndim)
tmp2 = binary_dilation(tmp1, struct)
@@ -1879,7 +1879,7 @@
elif metric == 'chessboard':
metric = 3
else:
- raise RuntimeError, 'distance metric not supported'
+ raise RuntimeError('distance metric not supported')
if sampling is not None:
sampling = _ni_support._normalize_sequence(sampling, tmp1.ndim)
sampling = numpy.asarray(sampling, dtype = numpy.float64)
@@ -1897,13 +1897,13 @@
dt = numpy.zeros(tmp1.shape, dtype = numpy.uint32)
else:
if distances.shape != tmp1.shape:
- raise RuntimeError, 'distances array has wrong shape'
+ raise RuntimeError('distances array has wrong shape')
if metric == 1:
if distances.dtype.type != numpy.float64:
- raise RuntimeError, 'distances array must be float64'
+ raise RuntimeError('distances array must be float64')
else:
if distances.dtype.type != numpy.uint32:
- raise RuntimeError, 'distances array must be uint32'
+ raise RuntimeError('distances array must be uint32')
dt = distances
else:
dt = None
@@ -1911,9 +1911,9 @@
if return_indices:
if isinstance(indices, numpy.ndarray):
if indices.dtype.type != numpy.int32:
- raise RuntimeError, 'indices must of int32 type'
+ raise RuntimeError('indices must of int32 type')
if indices.shape != (tmp1.ndim,) + tmp1.shape:
- raise RuntimeError, 'indices has wrong shape'
+ raise RuntimeError('indices has wrong shape')
tmp2 = indices
else:
tmp2 = numpy.indices(tmp1.shape, dtype = numpy.int32)
@@ -1963,7 +1963,7 @@
"""
if (not return_distances) and (not return_indices):
msg = 'at least one of distances/indices must be specified'
- raise RuntimeError, msg
+ raise RuntimeError(msg)
ft_inplace = isinstance(indices, numpy.ndarray)
dt_inplace = isinstance(distances, numpy.ndarray)
input = numpy.asarray(input)
@@ -1977,17 +1977,17 @@
try:
metric = numpy.asarray(metric)
except:
- raise RuntimeError, 'invalid metric provided'
+ raise RuntimeError('invalid metric provided')
for s in metric.shape:
if s != 3:
- raise RuntimeError, 'metric sizes must be equal to 3'
+ raise RuntimeError('metric sizes must be equal to 3')
if not metric.flags.contiguous:
metric = metric.copy()
if dt_inplace:
if distances.dtype.type != numpy.int32:
- raise RuntimeError, 'distances must be of int32 type'
+ raise RuntimeError('distances must be of int32 type')
if distances.shape != input.shape:
- raise RuntimeError, 'distances has wrong shape'
+ raise RuntimeError('distances has wrong shape')
dt = distances
dt[...] = numpy.where(input, -1, 0).astype(numpy.int32)
else:
@@ -2010,9 +2010,9 @@
ft = numpy.ravel(ft)
if ft_inplace:
if indices.dtype.type != numpy.int32:
- raise RuntimeError, 'indices must of int32 type'
+ raise RuntimeError('indices must of int32 type')
if indices.shape != (dt.ndim,) + dt.shape:
- raise RuntimeError, 'indices has wrong shape'
+ raise RuntimeError('indices has wrong shape')
tmp = indices
else:
tmp = numpy.indices(dt.shape, dtype = numpy.int32)
@@ -2148,7 +2148,7 @@
"""
if (not return_distances) and (not return_indices):
msg = 'at least one of distances/indices must be specified'
- raise RuntimeError, msg
+ raise RuntimeError(msg)
ft_inplace = isinstance(indices, numpy.ndarray)
dt_inplace = isinstance(distances, numpy.ndarray)
# calculate the feature transform
@@ -2161,9 +2161,9 @@
if ft_inplace:
ft = indices
if ft.shape != (input.ndim,) + input.shape:
- raise RuntimeError, 'indices has wrong shape'
+ raise RuntimeError('indices has wrong shape')
if ft.dtype.type != numpy.int32:
- raise RuntimeError, 'indices must be of int32 type'
+ raise RuntimeError('indices must be of int32 type')
else:
ft = numpy.zeros((input.ndim,) + input.shape,
dtype = numpy.int32)
@@ -2179,9 +2179,9 @@
if dt_inplace:
dt = numpy.add.reduce(dt, axis = 0)
if distances.shape != dt.shape:
- raise RuntimeError, 'indices has wrong shape'
+ raise RuntimeError('indices has wrong shape')
if distances.dtype.type != numpy.float64:
- raise RuntimeError, 'indices must be of float64 type'
+ raise RuntimeError('indices must be of float64 type')
numpy.sqrt(dt, distances)
del dt
else:
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