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Merge pull request #314 from tonysyu/doctest-fixes

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Doctest fixes
This commit is contained in:
Johannes Schönberger
2012-09-12 10:29:49 -07:00
parent ef06d4e474 538ce329a5
commit a454b63421
10 changed files with 70 additions and 66 deletions
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skimage/filter/lpi_filter.py
+27 -27
View File
@@ -7,7 +7,7 @@ __all__ = ['inverse', 'wiener', 'LPIFilter2D']
__docformat__ = 'restructuredtext en'
import numpy as np
from scipy.fftpack import fftshift, ifftshift
from scipy.fftpack import ifftshift
eps = np.finfo(float).eps
@@ -50,16 +50,19 @@ class LPIFilter2D(object):
Parameters
----------
impulse_response : callable `f(r, c, **filter_params)`
Function that yields the impulse response. `r` and
`c` are 1-dimensional vectors that represent row and
column positions, in other words coordinates are
(r[0],c[0]),(r[0],c[1]) etc. `**filter_params` are
passed through.
Function that yields the impulse response. `r` and `c` are
1-dimensional vectors that represent row and column positions, in
other words coordinates are (r[0],c[0]),(r[0],c[1]) etc.
`**filter_params` are passed through.
In other words, example would be called like this:
In other words, `impulse_response` would be called like this:
>>> def impulse_response(r, c, **filter_params):
... pass
>>>
>>> r = [0,0,0,1,1,1,2,2,2]
>>> c = [0,1,2,0,1,2,0,1,2]
>>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3}
>>> impulse_response(r, c, **filter_params)
Examples
@@ -68,8 +71,7 @@ class LPIFilter2D(object):
Gaussian filter:
>>> def filt_func(r, c):
return np.exp(-np.hypot(r, c)/1)
... return np.exp(-np.hypot(r, c)/1)
>>> filter = LPIFilter2D(filt_func)
"""
@@ -113,8 +115,9 @@ class LPIFilter2D(object):
def __call__(self, data):
"""Apply the filter to the given data.
*Parameters*:
data : (M,N) ndarray
Parameters
----------
data : (M,N) ndarray
"""
F, G = self._prepare(data)
@@ -139,9 +142,8 @@ def forward(data, impulse_response=None, filter_params={},
Other Parameters
----------------
predefined_filter : LPIFilter2D
If you need to apply the same filter multiple times over
different images, construct the LPIFilter2D and specify
it here.
If you need to apply the same filter multiple times over different
images, construct the LPIFilter2D and specify it here.
Examples
--------
@@ -149,9 +151,10 @@ def forward(data, impulse_response=None, filter_params={},
Gaussian filter:
>>> def filt_func(r, c):
return np.exp(-np.hypot(r, c)/1)
>>> forward(data, filt_func)
... return np.exp(-np.hypot(r, c)/1)
>>>
>>> from skimage import data
>>> filtered = forward(data.coins(), filt_func)
"""
if predefined_filter is None:
@@ -172,17 +175,15 @@ def inverse(data, impulse_response=None, filter_params={}, max_gain=2,
filter_params : dict
Additional keyword parameters to the impulse_response function.
max_gain : float
Limit the filter gain. Often, the filter contains
zeros, which would cause the inverse filter to have
infinite gain. High gain causes amplification of
artefacts, so a conservative limit is recommended.
Limit the filter gain. Often, the filter contains zeros, which would
cause the inverse filter to have infinite gain. High gain causes
amplification of artefacts, so a conservative limit is recommended.
Other Parameters
----------------
predefined_filter : LPIFilter2D
If you need to apply the same filter multiple times over
different images, construct the LPIFilter2D and specify
it here.
If you need to apply the same filter multiple times over different
images, construct the LPIFilter2D and specify it here.
"""
if predefined_filter is None:
@@ -219,9 +220,8 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
Other Parameters
----------------
predefined_filter : LPIFilter2D
If you need to apply the same filter multiple times over
different images, construct the LPIFilter2D and specify
it here.
If you need to apply the same filter multiple times over different
images, construct the LPIFilter2D and specify it here.
"""
if predefined_filter is None:
skimage/graph/mcp.py
+2
View File
@@ -39,7 +39,9 @@ def route_through_array(array, start, end, fully_connected=True,
Examples
--------
>>> import numpy as np
>>> from skimage.graph import route_through_array
>>>
>>> image = np.array([[1, 3], [10, 12]])
>>> image
array([[ 1, 3],
skimage/io/collection.py
+4 -3
View File
@@ -56,9 +56,10 @@ def alphanumeric_key(s):
--------
>>> alphanumeric_key('z23a')
['z', 23, 'a']
>>> filenames = ['f9.10.png', 'f9.9.png', 'f10.10.png', 'f10.9.png']
>>> filenames = ['f9.10.png', 'e10.png', 'f9.9.png', 'f10.10.png',
... 'f10.9.png']
>>> sorted(filenames)
['f10.10.png', 'f10.9.png', 'f9.10.png', 'f9.9.png', 'e10.png']
['e10.png', 'f10.10.png', 'f10.9.png', 'f9.10.png', 'f9.9.png']
>>> sorted(filenames, key=alphanumeric_key)
['e10.png', 'f9.9.png', 'f9.10.png', 'f10.9.png', 'f10.10.png']
"""
@@ -284,7 +285,7 @@ class ImageCollection(object):
>>> len(coll)
2
>>> coll[0].shape
(128, 128, 3)
(512, 512, 3)
>>> ic = io.ImageCollection('/tmp/work/*.png:/tmp/other/*.jpg')
skimage/morphology/grey.py
+6 -6
View File
@@ -57,7 +57,7 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False):
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
@@ -109,7 +109,7 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False):
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
@@ -158,7 +158,7 @@ def opening(image, selem, out=None):
[1, 1, 0, 1, 1],
[1, 1, 0, 1, 1],
[1, 1, 0, 1, 1],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
@@ -208,7 +208,7 @@ def closing(image, selem, out=None):
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
@@ -257,7 +257,7 @@ def white_tophat(image, selem, out=None):
[0, 0, 1, 0, 0],
[0, 1, 5, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
if image is out:
@@ -306,7 +306,7 @@ def black_tophat(image, selem, out=None):
[0, 0, 1, 0, 0],
[0, 1, 5, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]], dtype='uint8')
[0, 0, 0, 0, 0]], dtype=uint8)
"""
skimage/morphology/watershed.py
+2 -2
View File
@@ -261,7 +261,7 @@ def is_local_maximum(image, labels=None, footprint=None):
array([[ True, False, False, False],
[ True, False, True, False],
[ True, False, False, False],
[ True, True, False, True]], dtype='bool')
[ True, True, False, True]], dtype=bool)
>>> image = np.arange(16).reshape((4, 4))
>>> labels = np.array([[1, 2], [3, 4]])
>>> labels = np.repeat(np.repeat(labels, 2, axis=0), 2, axis=1)
@@ -279,7 +279,7 @@ def is_local_maximum(image, labels=None, footprint=None):
array([[False, False, False, False],
[False, True, False, True],
[False, False, False, False],
[False, True, False, True]], dtype='bool')
[False, True, False, True]], dtype=bool)
"""
if labels is None:
labels = np.ones(image.shape, dtype=np.uint8)
skimage/segmentation/random_walker_segmentation.py
+10 -10
View File
@@ -324,16 +324,16 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
>>> b[3,3] = 1 #Marker for first phase
>>> b[6,6] = 2 #Marker for second phase
>>> random_walker(a, b)
array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 2., 2., 2., 1., 1.],
[ 1., 1., 1., 1., 1., 2., 2., 2., 1., 1.],
[ 1., 1., 1., 1., 1., 2., 2., 2., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
"""
# Parse input data
skimage/transform/finite_radon_transform.py
+1 -1
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@@ -52,7 +52,7 @@ def frt2(a):
>>> plt.imshow(f, interpolation='nearest', cmap=plt.cm.gray)
>>> plt.xlabel('Angle')
>>> plt.ylabel('Translation')
>>> plt.show()
>>> # plt.show()
References
----------
skimage/transform/hough_transform.py
+2 -2
View File
@@ -124,7 +124,7 @@ def hough(img, theta=None):
>>> img[:, 65] = 1
>>> img[35:45, 35:50] = 1
>>> for i in range(90):
>>> img[i, i] = 1
... img[i, i] = 1
>>> img += np.random.random(img.shape) > 0.95
Apply the Hough transform:
@@ -137,7 +137,7 @@ def hough(img, theta=None):
>>> plt.imshow(out, cmap=plt.cm.bone)
>>> plt.xlabel('Angle (degree)')
>>> plt.ylabel('Distance %d (pixel)' % d[0])
>>> plt.show()
>>> # plt.show()
.. plot:: hough_tf.py
skimage/viewer/plugins/base.py
+13 -13
View File
@@ -54,17 +54,17 @@ class Plugin(QDialog):
Examples
--------
>>> def my_func(image, arg1, arg2, optional_arg=0):
>>> ...
>>> from skimage.viewer import ImageViewer
>>> from skimage.viewer.widgets import Slider
>>> from skimage import data
>>>
>>> plugin = Plugin(image_filter=lambda img, threshold: img > threshold)
>>> plugin += Slider('threshold', 0, 255)
>>>
>>> image = data.coins()
>>> viewer = ImageViewer(image)
>>>
>>> plugin = Plugin(image_filter=my_func)
>>> plugin += Widget('arg1', ..., ptype='arg')
>>> plugin += Widget('arg2', ..., ptype='arg')
>>> plugin += Widget('optional_arg', ..., ptype='kwarg')
>>>
>>> viewer.show()
>>> viewer += plugin
>>> # viewer.show()
The plugin will automatically delegate parameters to `image_filter` based
on its parameter type, i.e., `ptype` (widgets for required arguments must
@@ -108,9 +108,9 @@ class Plugin(QDialog):
"""Attach the plugin to an ImageViewer.
Note that the ImageViewer will automatically call this method when the
plugin is added to the ImageViewer. For example:
plugin is added to the ImageViewer. For example::
>>> viewer += Plugin(...)
viewer += Plugin(...)
Also note that `attach` automatically calls the filter function so that
the image matches the filtered value specified by attached widgets.
@@ -131,9 +131,9 @@ class Plugin(QDialog):
def add_widget(self, widget):
"""Add widget to plugin.
Alternatively, Plugin's `__add__` method is overloaded to add widgets:
Alternatively, Plugin's `__add__` method is overloaded to add widgets::
>>> plugin += Widget(...)
plugin += Widget(...)
Widgets can adjust required or optional arguments of filter function or
parameters for the plugin. This is specified by the Widget's `ptype'.
skimage/viewer/viewers/core.py
+3 -2
View File
@@ -49,9 +49,10 @@ class ImageViewer(QMainWindow):
Examples
--------
>>> from skimage import data
>>> image = data.coins()
>>> viewer = ImageViewer(image)
>>> viewer += SomePlugin()
>>> viewer.show()
>>> # viewer.show()
"""
def __init__(self, image):