Revision: 7728
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7728&view=rev
Author: mdboom
Date: 2009-09-09 19:55:52 +0000 (Wed, 09 Sep 2009)
Log Message:
-----------
Fix some documentation warnings.
Modified Paths:
--------------
branches/v0_99_maint/doc/api/spine_api.rst
branches/v0_99_maint/doc/mpl_toolkits/mplot3d/tutorial.rst
branches/v0_99_maint/doc/users/image_tutorial.rst
branches/v0_99_maint/doc/users/index.rst
branches/v0_99_maint/doc/users/pyplot_tutorial.rst
branches/v0_99_maint/lib/matplotlib/axes.py
branches/v0_99_maint/lib/matplotlib/mlab.py
Modified: branches/v0_99_maint/doc/api/spine_api.rst
===================================================================
--- branches/v0_99_maint/doc/api/spine_api.rst 2009-09-09 18:41:43 UTC (rev
7727)
+++ branches/v0_99_maint/doc/api/spine_api.rst 2009-09-09 19:55:52 UTC (rev
7728)
@@ -6,7 +6,7 @@
:mod:`matplotlib.spine`
========================
-.. automodule:: matplotlib.spine
+.. automodule:: matplotlib.spines
:members:
:undoc-members:
:show-inheritance:
Modified: branches/v0_99_maint/doc/mpl_toolkits/mplot3d/tutorial.rst
===================================================================
--- branches/v0_99_maint/doc/mpl_toolkits/mplot3d/tutorial.rst 2009-09-09
18:41:43 UTC (rev 7727)
+++ branches/v0_99_maint/doc/mpl_toolkits/mplot3d/tutorial.rst 2009-09-09
19:55:52 UTC (rev 7728)
@@ -55,7 +55,7 @@
Polygon plots
====================
-.. automethod:: add_collection3d
+.. automethod:: Axes3D.add_collection3d
.. plot:: mpl_examples/mplot3d/polys3d_demo.py
Modified: branches/v0_99_maint/doc/users/image_tutorial.rst
===================================================================
--- branches/v0_99_maint/doc/users/image_tutorial.rst 2009-09-09 18:41:43 UTC
(rev 7727)
+++ branches/v0_99_maint/doc/users/image_tutorial.rst 2009-09-09 19:55:52 UTC
(rev 7728)
@@ -10,17 +10,20 @@
Startup commands
===================
-At the very least, you'll need to have access to the
:func:`~matplotlib.pyplot.imshow` function. There are a couple of ways to do
it. The easy way for an interactive environment::
+At the very least, you'll need to have access to the
+:func:`~matplotlib.pyplot.imshow` function. There are a couple of
+ways to do it. The easy way for an interactive environment::
- $ipython -pylab
-
+ $ipython -pylab
+
The imshow function is now directly accessible (it's in your
`namespace <http://bytebaker.com/2008/07/30/python-namespaces/>`_).
See also :ref:`pyplot-tutorial`.
The more expressive, easier to understand later method (use this in
your scripts to make it easier for others (including your future self)
-to read) is to use the matplotlib API (see :ref:`artist-tutorial`) where you
use explicit namespaces and control object creation, etc...
+to read) is to use the matplotlib API (see :ref:`artist-tutorial`)
+where you use explicit namespaces and control object creation, etc...
.. sourcecode:: ipython
@@ -28,16 +31,22 @@
In [2]: import matplotlib.image as mpimg
In [3]: import numpy as np
-Examples below will use the latter method, for clarity. In these examples, if
you use the -pylab method, you can skip the "mpimg." and "plt." prefixes.
+Examples below will use the latter method, for clarity. In these
+examples, if you use the -pylab method, you can skip the "mpimg." and
+"plt." prefixes.
.. _importing_data:
Importing image data into Numpy arrays
===============================================
-Plotting image data is supported by the Python Image Library (`PIL
<http://www.pythonware.com/products/pil/>`_), . Natively, matplotlib only
supports PNG images. The commands shown below fall back on PIL if the native
read fails.
+Plotting image data is supported by the Python Image Library (`PIL
+<http://www.pythonware.com/products/pil/>`_), . Natively, matplotlib
+only supports PNG images. The commands shown below fall back on PIL
+if the native read fails.
-The image used in this example is a PNG file, but keep that PIL requirement in
mind for your own data.
+The image used in this example is a PNG file, but keep that PIL
+requirement in mind for your own data.
Here's the image we're going to play with:
@@ -55,11 +64,11 @@
.. sourcecode:: ipython
In [4]: img=mpimg.imread('stinkbug.png')
- Out[4]:
+ Out[4]:
array([[[ 0.40784314, 0.40784314, 0.40784314],
[ 0.40784314, 0.40784314, 0.40784314],
[ 0.40784314, 0.40784314, 0.40784314],
- ...,
+ ...,
[ 0.42745098, 0.42745098, 0.42745098],
[ 0.42745098, 0.42745098, 0.42745098],
[ 0.42745098, 0.42745098, 0.42745098]],
@@ -67,7 +76,7 @@
[[ 0.41176471, 0.41176471, 0.41176471],
[ 0.41176471, 0.41176471, 0.41176471],
[ 0.41176471, 0.41176471, 0.41176471],
- ...,
+ ...,
[ 0.42745098, 0.42745098, 0.42745098],
[ 0.42745098, 0.42745098, 0.42745098],
[ 0.42745098, 0.42745098, 0.42745098]],
@@ -75,16 +84,16 @@
[[ 0.41960785, 0.41960785, 0.41960785],
[ 0.41568628, 0.41568628, 0.41568628],
[ 0.41568628, 0.41568628, 0.41568628],
- ...,
+ ...,
[ 0.43137255, 0.43137255, 0.43137255],
[ 0.43137255, 0.43137255, 0.43137255],
[ 0.43137255, 0.43137255, 0.43137255]],
- ...,
+ ...,
[[ 0.43921569, 0.43921569, 0.43921569],
[ 0.43529412, 0.43529412, 0.43529412],
[ 0.43137255, 0.43137255, 0.43137255],
- ...,
+ ...,
[ 0.45490196, 0.45490196, 0.45490196],
[ 0.4509804 , 0.4509804 , 0.4509804 ],
[ 0.4509804 , 0.4509804 , 0.4509804 ]],
@@ -92,7 +101,7 @@
[[ 0.44313726, 0.44313726, 0.44313726],
[ 0.44313726, 0.44313726, 0.44313726],
[ 0.43921569, 0.43921569, 0.43921569],
- ...,
+ ...,
[ 0.4509804 , 0.4509804 , 0.4509804 ],
[ 0.44705883, 0.44705883, 0.44705883],
[ 0.44705883, 0.44705883, 0.44705883]],
@@ -100,26 +109,46 @@
[[ 0.44313726, 0.44313726, 0.44313726],
[ 0.4509804 , 0.4509804 , 0.4509804 ],
[ 0.4509804 , 0.4509804 , 0.4509804 ],
- ...,
+ ...,
[ 0.44705883, 0.44705883, 0.44705883],
[ 0.44705883, 0.44705883, 0.44705883],
[ 0.44313726, 0.44313726, 0.44313726]]], dtype=float32)
-Note the dtype there - float32. Matplotlib has rescaled the 8 bit data from
each channel to floating point data between 0.0 and 1.0. As a side note, the
only datatype that PIL can work with is uint8. Matplotlib plotting can handle
float32 and uint8, but image reading/writing for any format other than PNG is
limited to uint8 data. Why 8 bits? Most displays can only render 8 bits per
channel worth of color gradation. Why can they only render 8 bits/channel?
Because that's about all the human eye can see. More here (from a photography
standpoint): `Luminous Landscape bit depth tutorial
<http://www.luminous-landscape.com/tutorials/bit-depth.shtml>`_
+Note the dtype there - float32. Matplotlib has rescaled the 8 bit
+data from each channel to floating point data between 0.0 and 1.0. As
+a side note, the only datatype that PIL can work with is uint8.
+Matplotlib plotting can handle float32 and uint8, but image
+reading/writing for any format other than PNG is limited to uint8
+data. Why 8 bits? Most displays can only render 8 bits per channel
+worth of color gradation. Why can they only render 8 bits/channel?
+Because that's about all the human eye can see. More here (from a
+photography standpoint): `Luminous Landscape bit depth tutorial
+<http://www.luminous-landscape.com/tutorials/bit-depth.shtml>`_.
-Each inner list represents a pixel. Here, with an RGB image, there are 3
values. Since it's a black and white image, R, G, and B are all similar. An
RGBA (where A is alpha, or transparency), has 4 values per inner list, and a
simple luminance image just has one value (and is thus only a 2-D array, not a
3-D array). For RGB and RGBA images, matplotlib supports float32 and uint8
data types. For grayscale, matplotlib supports only float32. If your array
data does not meet one of these descriptions, you need to rescale it.
+Each inner list represents a pixel. Here, with an RGB image, there
+are 3 values. Since it's a black and white image, R, G, and B are all
+similar. An RGBA (where A is alpha, or transparency), has 4 values
+per inner list, and a simple luminance image just has one value (and
+is thus only a 2-D array, not a 3-D array). For RGB and RGBA images,
+matplotlib supports float32 and uint8 data types. For grayscale,
+matplotlib supports only float32. If your array data does not meet
+one of these descriptions, you need to rescale it.
.. _plotting_data:
Plotting numpy arrays as images
===================================
-So, you have your data in a numpy array (either by importing it, or by
generating it). Let's render it. In Matplotlib, this is performed using the
:func:`~matplotlib.pyplot.imshow` function. Here we'll grab the plot object.
This object gives you an easy way to manipulate the plot from the prompt.
+So, you have your data in a numpy array (either by importing it, or by
+generating it). Let's render it. In Matplotlib, this is performed
+using the :func:`~matplotlib.pyplot.imshow` function. Here we'll grab
+the plot object. This object gives you an easy way to manipulate the
+plot from the prompt.
.. sourcecode:: ipython
In [5]: imgplot = plt.imshow(img)
-
+
.. plot::
import matplotlib.pyplot as plt
@@ -128,23 +157,31 @@
img = mpimg.imread('_static/stinkbug.png')
imgplot = plt.imshow(img)
-You can also plot any numpy array - just remember that the datatype must be
float32 (and range from 0.0 to 1.0) or uint8.
+You can also plot any numpy array - just remember that the datatype
+must be float32 (and range from 0.0 to 1.0) or uint8.
.. _Pseudocolor:
-
+
Applying pseudocolor schemes to image plots
-------------------------------------------------
-Pseudocolor can be a useful tool for enhancing contrast and visualizing your
data more easily. This is especially useful when making presentations of your
data using projectors - their contrast is typically quite poor.
+Pseudocolor can be a useful tool for enhancing contrast and
+visualizing your data more easily. This is especially useful when
+making presentations of your data using projectors - their contrast is
+typically quite poor.
-Pseudocolor is only relevant to single-channel, grayscale, luminosity images.
We currently have an RGB image. Since R, G, and B are all similar (see for
yourself above or in your data), we can just pick on channel of our data:
+Pseudocolor is only relevant to single-channel, grayscale, luminosity
+images. We currently have an RGB image. Since R, G, and B are all
+similar (see for yourself above or in your data), we can just pick on
+channel of our data:
.. sourcecode:: ipython
In [6]: lum_img = img[:,:,0]
-
-This is array slicing. You can read more `here
<http://www.scipy.org/Tentative_NumPy_Tutorial>`_
-
+
+This is array slicing. You can read more `here
+<http://www.scipy.org/Tentative_NumPy_Tutorial>`_.
+
.. sourcecode:: ipython
In [7]: imgplot = mpimg.imshow(lum_img)
@@ -158,7 +195,11 @@
lum_img = img[:,:,0]
plt.imshow(lum_img)
-Now, with a luminosity image, the default colormap (aka lookup table, LUT), is
applied. The default is called jet. There are plenty of others to choose
from. Let's set some others using the :meth:`~matplotlib.image.Image.set_cmap`
method on our image plot object:
+Now, with a luminosity image, the default colormap (aka lookup table,
+LUT), is applied. The default is called jet. There are plenty of
+others to choose from. Let's set some others using the
+:meth:`~matplotlib.image.Image.set_cmap` method on our image plot
+object:
.. sourcecode:: ipython
@@ -173,11 +214,11 @@
lum_img = img[:,:,0]
imgplot = plt.imshow(lum_img)
imgplot.set_cmap('hot')
-
+
.. sourcecode:: ipython
In [9]: imgplot.set_cmap('spectral')
-
+
.. plot::
import matplotlib.pyplot as plt
@@ -188,18 +229,22 @@
imgplot = plt.imshow(lum_img)
imgplot.set_cmap('spectral')
-There are many other colormap schemes available. See a list and images of the
colormaps `here
<http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html>`_
-
+There are many other colormap schemes available. See a list and
+images of the colormaps `here
+<http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html>`_.
+
.. _Color Bars
Color scale reference
------------------------
-It's helpful to have an idea of what value a color represents. We can do that
by adding color bars. It's as easy as one line:
+It's helpful to have an idea of what value a color represents. We can
+do that by adding color bars. It's as easy as one line:
.. sourcecode:: ipython
+
In [10]: plt.colorbar()
-
+
.. plot::
import matplotlib.pyplot as plt
@@ -210,15 +255,22 @@
imgplot = plt.imshow(lum_img)
imgplot.set_cmap('spectral')
plt.colorbar()
-
-This adds a colorbar to your existing figure. This won't automatically change
if you change you switch to a different colormap - you have to re-create your
plot, and add in the colorbar again.
+This adds a colorbar to your existing figure. This won't
+automatically change if you change you switch to a different
+colormap - you have to re-create your plot, and add in the colorbar
+again.
+
.. _Data ranges
Examining a specific data range
---------------------------------
-Sometimes you want to enhance the contrast in your image, or expand the
contrast in a particular region while sacrificing the detail in colors that
don't vary much, or don't matter. A good tool to find interesting regions is
the histogram. To create a histogram of our image data, we use the
:func:`~matplotlib.pyplot.hist` function.
+Sometimes you want to enhance the contrast in your image, or expand
+the contrast in a particular region while sacrificing the detail in
+colors that don't vary much, or don't matter. A good tool to find
+interesting regions is the histogram. To create a histogram of our
+image data, we use the :func:`~matplotlib.pyplot.hist` function.
.. sourcecode:: ipython
@@ -233,7 +285,14 @@
lum_img = img[:,:,0]
plt.hist(lum_img, range=(0.0,1.0))
-Most often, the "interesting" part of the image is around the peak, and you
can get extra contrast by clipping the regions above and/or below the peak. In
our histogram, it looks like there's not much useful information in the high
end (not many white things in the image). Let's adjust the upper limit, so
that we effectively "zoom in on" part of the histogram. We do this by calling
the :meth:`~matplotlib.image.Image.set_clim` method of the image plot object.
+Most often, the "interesting" part of the image is around the peak,
+and you can get extra contrast by clipping the regions above and/or
+below the peak. In our histogram, it looks like there's not much
+useful information in the high end (not many white things in the
+image). Let's adjust the upper limit, so that we effectively "zoom in
+on" part of the histogram. We do this by calling the
+:meth:`~matplotlib.image.Image.set_clim` method of the image plot
+object.
.. sourcecode:: ipython
@@ -249,8 +308,8 @@
img = mpimg.imread('_static/stinkbug.png')
lum_img = img[:,:,0]
imgplot = plt.imshow(lum_img)
- a.set_title('Before')
- plt.colorbar(ticks=[0.1,0.3,0.5,0.7], orientation ='horizontal')
+ a.set_title('Before')
+ plt.colorbar(ticks=[0.1,0.3,0.5,0.7], orientation ='horizontal')
a=fig.add_subplot(1,2,2)
imgplot = plt.imshow(lum_img)
imgplot.set_clim(0.0,0.7)
@@ -260,9 +319,21 @@
.. _Interpolation:
Array Interpolation schemes
------------------------------------
-Interpolation calculates what the color or value of a pixel "should" be,
according to different mathematical schemes. One common place that this
happens is when you resize an image. The number of pixels change, but you want
the same information. Since pixels are discrete, there's missing space.
Interpolation is how you fill that space. This is why your images sometimes
come out looking pixelated when you blow them up. The effect is more
pronounced when the difference between the original image and the expanded
image is greater. Let's take our image and shrink it. We're effectively
discarding pixels, only keeping a select few. Now when we plot it, that data
gets blown up to the size on your screen. The old pixels aren't there anymore,
and the computer has to draw in pixels to fill that space.
+---------------------------
+Interpolation calculates what the color or value of a pixel "should"
+be, according to different mathematical schemes. One common place
+that this happens is when you resize an image. The number of pixels
+change, but you want the same information. Since pixels are discrete,
+there's missing space. Interpolation is how you fill that space.
+This is why your images sometimes come out looking pixelated when you
+blow them up. The effect is more pronounced when the difference
+between the original image and the expanded image is greater. Let's
+take our image and shrink it. We're effectively discarding pixels,
+only keeping a select few. Now when we plot it, that data gets blown
+up to the size on your screen. The old pixels aren't there anymore,
+and the computer has to draw in pixels to fill that space.
+
.. sourcecode:: ipython
In [8]: import Image
@@ -272,7 +343,7 @@
In [12]: imgplot = mpimg.imshow(rsizeArr)
.. plot::
-
+
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
@@ -283,7 +354,8 @@
lum_img = rsizeArr[:,:,0]
imgplot = plt.imshow(rsizeArr)
-Here we have the default interpolation, bilinear, since we did not give
:func:`~matplotlib.pyplot.imshow` any interpolation argument.
+Here we have the default interpolation, bilinear, since we did not
+give :func:`~matplotlib.pyplot.imshow` any interpolation argument.
Let's try some others:
@@ -302,10 +374,10 @@
rsizeArr = np.asarray(rsize)
lum_img = rsizeArr[:,:,0]
imgplot = plt.imshow(rsizeArr)
- imgplot.set_interpolation('nearest')
-
+ imgplot.set_interpolation('nearest')
+
.. sourcecode:: ipython
-
+
In [10]: imgplot.set_interpolation('bicubic')
.. plot::
@@ -320,5 +392,6 @@
lum_img = rsizeArr[:,:,0]
imgplot = plt.imshow(rsizeArr)
imgplot.set_interpolation('bicubic')
-
-Bicubic interpolation is often used when blowing up photos - people tend to
prefer blurry over pixelated.
\ No newline at end of file
+
+Bicubic interpolation is often used when blowing up photos - people
+tend to prefer blurry over pixelated.
Modified: branches/v0_99_maint/doc/users/index.rst
===================================================================
--- branches/v0_99_maint/doc/users/index.rst 2009-09-09 18:41:43 UTC (rev
7727)
+++ branches/v0_99_maint/doc/users/index.rst 2009-09-09 19:55:52 UTC (rev
7728)
@@ -23,7 +23,6 @@
artists.rst
legend_guide.rst
event_handling.rst
- legend.rst
transforms_tutorial.rst
path_tutorial.rst
annotations_guide.rst
Modified: branches/v0_99_maint/doc/users/pyplot_tutorial.rst
===================================================================
--- branches/v0_99_maint/doc/users/pyplot_tutorial.rst 2009-09-09 18:41:43 UTC
(rev 7727)
+++ branches/v0_99_maint/doc/users/pyplot_tutorial.rst 2009-09-09 19:55:52 UTC
(rev 7728)
@@ -165,17 +165,17 @@
``figure(1)`` will be created by default, just as a ``subplot(111)``
will be created by default if you don't manually specify an axes. The
:func:`~matplotlib.pyplot.subplot` command specifies ``numrows,
- numcols, fignum`` where ``fignum`` ranges from 1 to
-``numrows*numcols``. The commas in the ``subplot`` command are optional
-if ``numrows*numcols<10``. So ``subplot(211)`` is identical to
-``subplot(2,1,1)``. You can create an arbitrary number of subplots
+numcols, fignum`` where ``fignum`` ranges from 1 to
+``numrows*numcols``. The commas in the ``subplot`` command are
+optional if ``numrows*numcols<10``. So ``subplot(211)`` is identical
+to ``subplot(2,1,1)``. You can create an arbitrary number of subplots
and axes. If you want to place an axes manually, ie, not on a
rectangular grid, use the :func:`~matplotlib.pyplot.axes` command,
which allows you to specify the location as ``axes([left, bottom,
width, height])`` where all values are in fractional (0 to 1)
coordinates. See :ref:`pylab_examples-axes_demo` for an example of
-placing axes manually and :ref:`pylab_examples-line_styles` for an example
-with lots-o-subplots.
+placing axes manually and :ref:`pylab_examples-line_styles` for an
+example with lots-o-subplots.
You can create multiple figures by using multiple
@@ -267,6 +267,6 @@
In this basic example, both the ``xy`` (arrow tip) and ``xytext``
locations (text location) are in data coordinates. There are a
variety of other coordinate systems one can choose -- see
-:ref:`annotations-tutorial` and :ref:`plotting-guide-annotation`
-for details. More examples can be found
-in :ref:`pylab_examples-annotation_demo`.
+:ref:`annotations-tutorial` and :ref:`plotting-guide-annotation` for
+details. More examples can be found in
+:ref:`pylab_examples-annotation_demo`.
Modified: branches/v0_99_maint/lib/matplotlib/axes.py
===================================================================
--- branches/v0_99_maint/lib/matplotlib/axes.py 2009-09-09 18:41:43 UTC (rev
7727)
+++ branches/v0_99_maint/lib/matplotlib/axes.py 2009-09-09 19:55:52 UTC (rev
7728)
@@ -1338,7 +1338,7 @@
def add_artist(self, a):
'''
- Add any :class:`~matplotlib.artist.Artist` to the axes
+ Add any :class:`~matplotlib.artist.Artist` to the axes.
Returns the artist.
'''
@@ -1351,8 +1351,8 @@
def add_collection(self, collection, autolim=True):
'''
- add a :class:`~matplotlib.collections.Collection` instance
- to the axes
+ Add a :class:`~matplotlib.collections.Collection` instance
+ to the axes.
Returns the collection.
'''
@@ -3710,15 +3710,15 @@
*maxlags* is a positive integer detailing the number of lags
to show. The default value of *None* will return all
- :math:`2 \mathrm{len}(x) - 1` lags.
+ :math:`2 \times \mathrm{len}(x) - 1` lags.
The return value is a tuple (*lags*, *c*, *linecol*, *b*)
where
- - *linecol* is the
- :class:`~matplotlib.collections.LineCollection`
+ - *linecol* is the
+ :class:`~matplotlib.collections.LineCollection`
- - *b* is the *x*-axis.
+ - *b* is the *x*-axis.
.. seealso::
Modified: branches/v0_99_maint/lib/matplotlib/mlab.py
===================================================================
--- branches/v0_99_maint/lib/matplotlib/mlab.py 2009-09-09 18:41:43 UTC (rev
7727)
+++ branches/v0_99_maint/lib/matplotlib/mlab.py 2009-09-09 19:55:52 UTC (rev
7728)
@@ -569,7 +569,7 @@
example script that shows that this :func:`cohere_pairs` and
:func:`cohere` give the same results for a given pair.
- .. sealso::
+ .. seealso::
:func:`psd`
For information about the methods used to compute
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