matplotlib-users Mailing List for matplotlib

> Hi all,
> 
> I've been making figures for a paper I'm writing (to be submitted to the
> ApJ).  I'm using LaTeX and so need to use encapsulated PostScript for
> the figures.  The problem is that when the paper is translated to pdf
> from PostScript, the mathtext in the figures disappears.  The reason
> that I think this is a matplotlib issue is that it's never happened to
> me with eps figures created in different ways.  It's clear that this
> must be related to fonts, but I'm not sure how to get around it.  Should
> I set text.usetex to True?  I have a feeling this issue must have come
> up before, but I haven't found anything obviously pertinent in the
> mailing list archives.
> 
> Jon

Are you dead set on using eps files? If the journal doesn't mind, you could always save your figures as pdf files using the pdf backend, then you can use pdflatex on everything and you never have to generate a postscript file.

Damon McDougall
d.m...@wa...
http://damon.is-a-geek.com
B2.39
Mathematics Institute
Coventry
West Midlands
CV4 7AL

Hi all,

I've been making figures for a paper I'm writing (to be submitted to the
ApJ).  I'm using LaTeX and so need to use encapsulated PostScript for
the figures.  The problem is that when the paper is translated to pdf
from PostScript, the mathtext in the figures disappears.  The reason
that I think this is a matplotlib issue is that it's never happened to
me with eps figures created in different ways.  It's clear that this
must be related to fonts, but I'm not sure how to get around it.  Should
I set text.usetex to True?  I have a feeling this issue must have come
up before, but I haven't found anything obviously pertinent in the
mailing list archives.

Jon

On Mon, Aug 15, 2011 at 9:34 PM, Benjamin Root <ben...@ou...> wrote:
> The mpl developers are getting very close to the long-awaited v1.1.0 release
> of matplotlib.  Before we do so, we are doing some final checking of the
> documentation to make sure that all critical pieces of information iss
> correct and up to date.
>
> In checking over the instructions for building and installing matplotlib on
> MacOSX, I have found two separate sets of instructions.  On the install
> page, there is a reference to a README.txt file in "release/osx".  This file
> is there, but it seems to refer to other files that no longer exists.
> Meanwhile, there is an un-referenced file in the top directory called
> README.osx that seems a lot more current.
>
> Because I do not have a Mac that I can use for development, I would like to
> ask the community for help in determining the correct set of instructions
> and to eliminate cruft.  I think it would also be useful to point users to
> any relevant instructions for installing/building numpy on Macs.  I would
> also like to make  sure we are current with information on installing on a
> stock Lion install.
>
> Please feel free to respond on this list, or better, make a branch on github
> and submit pull requests to help us improve these documents.

I wrote both of those files originally (make.osx and releases/osx/*).
The original division of labor was the stuff in "releases" was
designed to build the release binaries, and the stuff in make.osx was
primarily used to build from svn or src.  Overtime, most of the effort
has gone into make.osx, and it now includes support for binaries.  I
no longer build the OSX binaries (Russell does) and no longer use OS X
(back to ubuntu) so if Russell is not using the stuff in releases/osx,
we can flush it.

JDH

Hi ,

I wish to add a custom legend to a multicolored line drawn using matplotlib
as shown in the figure below
http://www.scipy.org/Cookbook/Matplotlib/MulticoloredLine
I tried exploring the legend but somehow couldn't figure out its usage and
how to adapt it to multicolored line as in the above link
I need to use say Red line  for label1 and blue line for label2 and green
line for label3. All the three colors are a part of a single sinusoidal wave
as in the above link.
Any thoughts/ideas/suggestions which could provide me some direction on this
would be highly appreciated.

-- 
Thanks and Regards,

RaviKanth

I've been trying the Qt4Agg backend for matplotlib and I've encountered two
problems. The following code illustrates the issues:

import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
plt.ion()
plt.plot([1,2,3])
canvas=plt.gcf().canvas

def on_key(event):
    print 'you pressed', event.key, event.xdata, event.ydata

canvas.mpl_connect('key_press_event', on_key)

The problems:
1) The user has to click within the canvas before key press events are
registered. Activating the window is not sufficient.

2) Keys 'up', 'down', 'left', 'right', 'pageup', and 'pagedown' all return
event.key=None.

The tkagg and gtkagg backends don't have these problems - key press events
are active as soon as the window is active, and event.key reports the
correct key. Does anyone have an idea why these problems are cropping up
with Qt4 or know a workaround?

Thanks,

-Andrew Mark

On Tue, Aug 16, 2011 at 2:15 AM, 4ndre <and...@gm...> wrote:

>
> Hi,
>
> I'm a little bit desperated. I googled for days to find an appropriate way
> to calculate the absolute errors of parameters of non-linear fits. I found
> different sites which suggest different, sometimes more or less similar
> ways. But the results don't make sense. Here is a simple example:
>
> from pylab import *
> from scipy.optimize import curve_fit
> def f(x,a,b):
>        return a*x+b
> def fit(n=1000):
>        x,y=array([0,1,1]),array([1,2,4])
>        xf=[(min(x)+i*(max(x)-min(x))/float(n)) for i in range(0,int(n)+1)]
>        a,acov=curve_fit(f,x,y)
>        yf=[f(*tuple([i]+list(a))) for i in xf]
>        meansq=sum([pow(y[i]-f(x[i],a[0],a[1]),2)/(len(x)) for i in
> range(0,len(x))])
>        redchi2=sum([pow(y[i]-f(x[i],a[0],a[1]),2)/(len(x)-len(a)) for i in
> range(0,len(x))])
>        print 'standard dev:',sqrt(diag(acov))
>        print 'abs. meansq err.:',[acov[i][i]*sqrt(meansq) for i in
> range(0,len(a))]
>        print 'abs. reduced meansq err.:',[acov[i][i]*sqrt(redchi2) for i in
> range(0,len(a))]
> fit()
>
> I included 3 ways I found. The used arrays have 3 points, where for x=1 I
> give 2 different y values. I would say,
> a=2+/-1, b=1+/-0,
> but no way yields to a similar result! How can I trust this function on
> more
> complicated, really non-linear functions? Or is simply this example wrong?
> Or are the methods not right?
> I'm very thankful for any help!
>
> Best regards,
> André
>

Andre,

Typically, I do something like the following:

from scipy.optimize import polyfit, polyval
import numpy as np.

# x and y are arrays of floats

a, b = polyfit(x, y, 1)  # first-order polynomial
y_fit = polyval([a, b], x)
mean_abs_err = np.mean(np.abs(y_fit - y))


I hope that helps!
Ben Root

2011/8/16 Eric Firing <ef...@ha...>:
> On 07/25/2011 08:21 AM, Ben Breslauer wrote:
>> I think that I have found the problem here.  Line2D.draw() (and I
>> presume other Artist subclasses) calls
>>
>> gc.set_foreground(self._color)
>> ...
>> gc.set_alpha(self._alpha)
>>
>> self._color is defined by the color kwarg, whether it be a hex value,
>> 3-tuple, 4-tuple, or something else.  self._alpha is defined by the
>> alpha kwarg, but if the alpha kwarg is None, it is not overwritten with
>> color[3].  Therefore, using color=(R,G,B,A) does not set alpha
>> correctly.  I'm not sure the best (i.e. most matplotlib-like) way to
>> change this so that the A value gets used iff alpha is None, but I've
>> attached a patch that does it one way (diff'ed against the github
>> master, commit 67b1cd650f574f9c53ce).  I know the patch is less than
>> ideal, as it adds kwarg-specific handling into a place where none had
>> previously been done, and it's also in a for loop. Maybe it would be
>> better to do the checking along with the scalex and scaley pops?
>
>
> Alpha handling is a real can of worms; it has gotten better, but as you
> note, there are still problems.  Unfortunately, I don't think your
> proposed solution will work in general, because self._color could be a
> 4-letter string, like "gray", in which case alpha would be set to "y",
> and that would not be good at all.  I suspect the right place to solve
> the problem is down in the GraphicsContext--although I thought I had
> already straightened that out some time ago.
>...
>
> Eric
>
>>
>>
>> Separately, I noticed that
>> backend_bases.GraphicsContextBase.set_foreground claims to only expect
>> an RGB definition, and not an RGBA definition.  As such, I think that it
>> should call
>>
>> self._rgb = colors.colorConverter.to_rgb(fg)
>>
>> instead of
>>
>> self._rgb = colors.colorConverter.to_rgba(fg)
>>
>> since that will make self._rgb a 3-tuple instead of a 4-tuple.
>>
>> Ben
>> ...
>>
Just a remark for this recent proposed patch, as I unfortunately don't
know the implications for the whole codebase.
I believe, that the condition added in the patch
if len(line._color) == 4 and line._alpha == None:
could be adjusted to e.g.:
if isinstance(line._color, tuple) and len(line._color) == 4 and
line._alpha == None

If the alpha value can be set in the color-tuple (for "#RRGGBBAA" some
parsing would be needed, if it is accepted).
Possibly the _alpha should be replace with tha alpha value of the
colour passed this way like
http://matplotlib.sourceforge.net/api/colors_api.html#matplotlib.colors.ColorConverter.to_rgba
"If arg is an RGBA sequence and alpha is not None, alpha will replace
the original A."

But as has been suggested already, it might be better adressed in some
more general way, than in this single function.

 regards,
  vbr

>> Hi Jeff,
>> 
>> I am able to run the tex_demo.py with no problems and I can create
>> 
>> output files using the Agg backend.  When I try to use the PDF
>> 
>> backend, however, I get an error which stems from dviread.py (pasted
>> 
>> below).  Any thoughts on what could be going wrong?
>> 
>> Thanks!
>> 
>> Jeff
>> 
>> Hi Jeff,
>> 
>> How did you install matplotlib? From source yourself?
>> 
>> No, I'm using the Enthought Python Distribution (EPD64).
>> 
>> And when you installed matplotlib, did it see that you had dvipng installed?
>> FYI, you can do
>> which dvipng
>> to return the path where dvipng lives (if it is in your path already) or you
>> can do
>> locate dvipng
>> to do a search for the binary if it isn't already in your path.
>> P.S. You forgot to reply-all so everyone can see your response.
> 
> $ which dvipng
> /usr/texbin/dvipng
> 
> I don't remember any complaints while running the EPD installer.
> Would there be a way to check this?  Is the problem definitely related
> to an inability to find dvipng, or could there be other possibilities?
> 
> Thanks,
> Jeff

I had a similar problem. I don't think it's an inability to find dvipng, I think it's that it couldn't find dvipng when matplotlib was built.

When matplotlib builds there'll be a section of text sent to stdout under the heading of 'OPTIONAL DEPENDENCIES', there it will tell you whether it found dvipng when it was building. Are you able to find this? If not, are there install logs saved somewhere after you installed matplotlib?

Damon McDougall
d.m...@wa...
http://damon.is-a-geek.com
B2.39
Mathematics Institute
Coventry
West Midlands
CV4 7AL

Hello,
for a interactive gui program i want to change the shown data without changing
the rest of the figure. For normal plots, there is a set_xdata/set_ydata
methods. For QuadMesh, there is a set_array method, which i don't get to work.

Thanks,
Till

Hi,

I'm a little bit desperated. I googled for days to find an appropriate way
to calculate the absolute errors of parameters of non-linear fits. I found
different sites which suggest different, sometimes more or less similar
ways. But the results don't make sense. Here is a simple example:

from pylab import *
from scipy.optimize import curve_fit
def f(x,a,b):
	return a*x+b
def fit(n=1000):
	x,y=array([0,1,1]),array([1,2,4])
	xf=[(min(x)+i*(max(x)-min(x))/float(n)) for i in range(0,int(n)+1)]
	a,acov=curve_fit(f,x,y)
	yf=[f(*tuple([i]+list(a))) for i in xf]
	meansq=sum([pow(y[i]-f(x[i],a[0],a[1]),2)/(len(x)) for i in
range(0,len(x))])
	redchi2=sum([pow(y[i]-f(x[i],a[0],a[1]),2)/(len(x)-len(a)) for i in
range(0,len(x))])
	print 'standard dev:',sqrt(diag(acov))
	print 'abs. meansq err.:',[acov[i][i]*sqrt(meansq) for i in
range(0,len(a))]
	print 'abs. reduced meansq err.:',[acov[i][i]*sqrt(redchi2) for i in
range(0,len(a))]	
fit()

I included 3 ways I found. The used arrays have 3 points, where for x=1 I
give 2 different y values. I would say, 
a=2+/-1, b=1+/-0, 
but no way yields to a similar result! How can I trust this function on more
complicated, really non-linear functions? Or is simply this example wrong?
Or are the methods not right?
I'm very thankful for any help!

Best regards,
André
-- 
View this message in context: http://old.nabble.com/absolute-error-of-curve-fit-parameters-tp32269685p32269685.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Hi All,

I am trying to create a subplot - where a specific subcell looks like the attached sample_fig.png

To make this fig, I did something like

	ax1 = plt.axes([0.1,0.1,0.65,0.8])
	ax2 = plt.axes([0.77,0.1,0.13,0.8])

and then I loaded my data and fit curves onto the separate figs.

Now I am trying to create something like this, but where this fig is only one cell of a subplot.  But I can't figure this one out, at least not yet, and haven't found a similar enough example.

Is there a way to manipulate the equivalent of the axes in a subplot to mimic this figure?


Thanks,

Andre

On 07/25/2011 08:21 AM, Ben Breslauer wrote:
> I think that I have found the problem here.  Line2D.draw() (and I
> presume other Artist subclasses) calls
>
> gc.set_foreground(self._color)
> ...
> gc.set_alpha(self._alpha)
>
> self._color is defined by the color kwarg, whether it be a hex value,
> 3-tuple, 4-tuple, or something else.  self._alpha is defined by the
> alpha kwarg, but if the alpha kwarg is None, it is not overwritten with
> color[3].  Therefore, using color=(R,G,B,A) does not set alpha
> correctly.  I'm not sure the best (i.e. most matplotlib-like) way to
> change this so that the A value gets used iff alpha is None, but I've
> attached a patch that does it one way (diff'ed against the github
> master, commit 67b1cd650f574f9c53ce).  I know the patch is less than
> ideal, as it adds kwarg-specific handling into a place where none had
> previously been done, and it's also in a for loop. Maybe it would be
> better to do the checking along with the scalex and scaley pops?


Alpha handling is a real can of worms; it has gotten better, but as you 
note, there are still problems.  Unfortunately, I don't think your 
proposed solution will work in general, because self._color could be a 
4-letter string, like "gray", in which case alpha would be set to "y", 
and that would not be good at all.  I suspect the right place to solve 
the problem is down in the GraphicsContext--although I thought I had 
already straightened that out some time ago.

As Ben notes, you should file a bug report.
Given that the handling of colors and alpha is inherently tricky (and 
backend-dependent--ps doesn't support alpha at all), solving the problem 
well is going to take a bit of time and care, most likely--and testing 
to make sure that a fix in one place doesn't cause a bug in another.  I 
think we should leave it alone until after the next release.

Eric

>
>
> Separately, I noticed that
> backend_bases.GraphicsContextBase.set_foreground claims to only expect
> an RGB definition, and not an RGBA definition.  As such, I think that it
> should call
>
> self._rgb = colors.colorConverter.to_rgb(fg)
>
> instead of
>
> self._rgb = colors.colorConverter.to_rgba(fg)
>
> since that will make self._rgb a 3-tuple instead of a 4-tuple.
>
> Ben
>
> On Sat, Jul 23, 2011 at 3:13 PM, Ben Breslauer <bbr...@gm...
> <mailto:bbr...@gm...>> wrote:
>
>     Hi,
>
>     I'm trying to fade some data, using alpha values, that I am plotting
>     with Axes.plot().  I can recreate this problem with 1 line of
>     pylab.plot.  If I use
>
>     pylab.plot([1,2,3],[1,4,9], color=(1,0,0,.2), linewidth=7)
>
>     then I get the equivalent of
>
>     pylab.plot([1,2,3],[1,4,9], color=(1,0,0), linewidth=7)
>
>     i.e. it does not use the alpha value.  However, if instead I use
>
>     pylab.plot([1,2,3],[1,4,9], color=(1,0,0), linewidth=7, alpha=.2)
>
>     then the line is faded out appropriately.  The plot documentation
>     indicates that RGBA tuples should be valid, so I'm wondering if this
>     is a bug.  Maybe alpha is defaulting to 1 or None and then not being
>     overwritten if color is a 4-tuple?
>
>     I'm using Arch Linux with kernel 2.6.39, matplotlib 1.0.1 from the
>     Arch repo, and the Qt4 backend.  My installed Qt version is 4.7.3,
>     and I am using KDE 4.6.5.  Below is verbose-debug output.  Thanks
>     for any help!
>
>     Ben
>
>
>     $HOME=/home/ben
>     CONFIGDIR=/home/ben/.matplotlib
>     matplotlib data path
>     /usr/lib/python2.7/site-packages/matplotlib/mpl-data
>     loaded rc file
>     /usr/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc
>     matplotlib version 1.0.1
>     verbose.level debug
>     interactive is False
>     units is False
>     platform is linux2
>     loaded modules: ['heapq', 'numpy.core.info
>     <http://numpy.core.info>', 'distutils', 'numpy.lib.format',
>     'functools', 'pylab', '_bisect', 'subprocess', 'sysconfig', 'gc',
>     'matplotlib.tempfile', 'distutils.sysconfig', 'ctypes._endian',
>     'encodings.encodings', 'pyparsing', 'matplotlib.colors',
>     'numpy.core.numerictypes', 'numpy.testing.sys',
>     'numpy.lib.__future__', 'numpy.fft.types', 'numpy.ma.cPickle',
>     'struct', 'numpy.matrixlib.defmatrix', 'numpy.random.info
>     <http://numpy.random.info>', 'tempfile', 'numpy.compat.types',
>     'pprint', 'numpy.linalg', 'matplotlib.threading',
>     'numpy.core.machar', 'numpy.testing.types', 'numpy.testing',
>     'collections', 'numpy.polynomial.sys', 'unittest.sys',
>     'numpy.core.umath', 'unittest.main', 'distutils.types',
>     'numpy.testing.operator', 'numpy.core.scalarmath', 'numpy.ma.sys',
>     'zipimport', 'string', 'matplotlib.subprocess', 'numpy.testing.os',
>     'unittest.functools', 'numpy.lib.arraysetops',
>     'numpy.testing.unittest', 'numpy.lib.math', 'encodings.utf_8',
>     'matplotlib.__future__', 'unittest.types', 'unittest.util',
>     'numpy.testing.re <http://numpy.testing.re>', 'numpy.version',
>     'numpy.lib.re <http://numpy.lib.re>', 'distutils.re
>     <http://distutils.re>', 'numpy.matrixlib.sys', 'ctypes.os',
>     'numpy.core.os', 'numpy.lib.type_check', 'numpy.compat.sys',
>     'unittest.StringIO', 'bisect', 'numpy.core._internal', 'signal',
>     'numpy.lib.types', 'numpy.lib._datasource', 'random',
>     'numpy.lib.__builtin__', 'numpy.fft.fftpack_lite',
>     'matplotlib.cbook', 'textwrap', 'numpy.core.multiarray',
>     'numpy.polynomial.string', 'distutils.version', 'cStringIO',
>     'numpy.polynomial', 'numpy.numpy', 'matplotlib.StringIO',
>     'unittest.time', 'locale', 'numpy.add_newdocs', 'unittest.difflib',
>     'numpy.core.getlimits', 'base64', '_ssl', 'numpy.lib.sys',
>     'encodings', 'numpy.ma.itertools', 'unittest.pprint', 'unittest.re
>     <http://unittest.re>', 'abc', 'numpy.matrixlib', 'numpy.ctypes',
>     'numpy.testing.decorators', 'matplotlib.warnings', 'rfc822',
>     'numpy.lib.npyio', 'numpy.lib.numpy', 'matplotlib.sys', 're',
>     'numpy.lib._compiled_base', 'numpy.polynomial.legendre',
>     'threading', 'new', 'numpy.ma.warnings', 'numpy.random.mtrand',
>     'urllib2', 'matplotlib.cPickle', 'math', 'numpy.fft.helper',
>     'fcntl', 'unittest.case', 'matplotlib.numpy', 'UserDict',
>     'unittest.suite', 'numpy.lib.function_base', 'distutils.os',
>     'matplotlib', 'numpy.fft.numpy', 'exceptions', 'numpy.lib.info
>     <http://numpy.lib.info>', 'ctypes', 'numpy.lib.warnings',
>     'ctypes.struct', 'codecs', 'numpy.core._sort', 'numpy.os',
>     'unittest.loader', '_functools', '_locale', 'numpy.__builtin__',
>     'matplotlib.os', 'thread', 'StringIO', 'numpy.core.memmap',
>     'traceback', 'numpy.testing.warnings', 'weakref', 'itertools',
>     'numpy.fft.fftpack', 'numpy.linalg.lapack_lite', 'numpy.ma
>     <http://numpy.ma>', 'distutils.sys', 'os', 'marshal',
>     'numpy.lib.itertools', '__future__', '_collections', 'urllib',
>     'matplotlib.traceback', '_sre', 'unittest', 'numpy.core.sys',
>     'numpy.random', 'numpy.linalg.numpy', '__builtin__',
>     'numpy.lib.twodim_base', 'numpy.ma.core', 'matplotlib.re
>     <http://matplotlib.re>', 'numpy.core.cPickle', 'unittest.runner',
>     'operator', 'numpy.polynomial.polytemplate',
>     'numpy.core.arrayprint', 'distutils.string',
>     'numpy.lib.arrayterator', 'select', 'ctypes._ctypes', '_heapq',
>     'ctypes.sys', 'matplotlib.errno', 'unittest.collections',
>     'posixpath', 'numpy.lib.financial', 'numpy.polynomial.laguerre',
>     'matplotlib.random', 'errno', '_socket', 'binascii',
>     'sre_constants', 'datetime', 'numpy.core.shape_base', 'os.path',
>     'numpy.core.function_base', 'numpy.compat.py3k',
>     'numpy.lib.stride_tricks', 'numpy.core.numpy', 'numpy', '_warnings',
>     'numpy.polynomial.chebyshev', 'matplotlib.types', 'cPickle',
>     'encodings.__builtin__', 'numpy.polynomial.warnings',
>     'matplotlib.new', '_codecs', 'numpy.lib.operator',
>     'unittest.fnmatch', 'numpy.polynomial.polynomial',
>     'numpy.__config__', 'pwd', 'matplotlib.pyparsing', 'httplib',
>     'numpy.lib.ufunclike', 'copy', 'numpy.core.re
>     <http://numpy.core.re>', '_struct', 'numpy.core.fromnumeric',
>     'hashlib', 'numpy.ctypeslib', 'keyword', 'array',
>     'numpy.lib.scimath', 'numpy.fft', 'numpy.lib', 'numpy.ma.numpy',
>     'numpy.random.numpy', 'matplotlib.urllib2', 'unittest.weakref',
>     'numpy.ma.extras', 'numpy.polynomial.__future__', 'posix',
>     'encodings.aliases', 'matplotlib.fontconfig_pattern', 'fnmatch',
>     'sre_parse', 'pickle', 'numpy.core.ctypes', 'mimetools',
>     'distutils.distutils', 'unittest.signal', 'copy_reg', 'sre_compile',
>     '_hashlib', '_random', '_ctypes', 'site', 'numpy.lib.polynomial',
>     'numpy.compat', 'numpy._import_tools', 'numpy.polynomial.hermite',
>     '__main__', 'numpy.fft.info <http://numpy.fft.info>',
>     'numpy.core.records', 'shutil', 'numpy.lib.cPickle', 'numpy.sys',
>     'numpy.polynomial.hermite_e', 'urlparse', 'unittest.result',
>     'strop', 'grp', 'linecache', 'numpy.polynomial.numpy',
>     'numpy.core.numeric', 'numpy.linalg.info
>     <http://numpy.linalg.info>', 'encodings.codecs', '_abcoll',
>     'numpy.core', 'matplotlib.locale', 'matplotlib.rcsetup',
>     'matplotlib.time', 'matplotlib.weakref', 'genericpath', 'stat',
>     'socket', 'numpy.lib._iotools', 'numpy.lib.index_tricks',
>     'numpy.testing.utils', 'warnings', 'numpy.lib.utils',
>     'numpy.core.defchararray', 'numpy.polynomial.polyutils',
>     'numpy.lib.shape_base', 'numpy.core.types', 'unittest.signals',
>     'sys', 'numpy.core.warnings', 'numpy.compat._inspect',
>     'numpy.core.__builtin__', 'ctypes.ctypes',
>     'numpy.testing.traceback', 'numpy.lib.os',
>     'numpy.testing.nosetester', 'types', 'numpy.lib.shutil',
>     'matplotlib.datetime', 'matplotlib.distutils', '_weakref',
>     'numpy.testing.numpytest', 'difflib', 'distutils.errors',
>     'numpy.matrixlib.numpy', 'matplotlib.matplotlib', '_weakrefset',
>     'matplotlib.shutil', 'numpy.lib.cStringIO', 'unittest.warnings',
>     'time', 'unittest.traceback', 'ssl', 'numpy.linalg.linalg',
>     'unittest.os', 'numpy.testing.numpy']
>     Using fontManager instance from /home/ben/.matplotlib/fontList.cache
>     backend Qt4Agg version 0.9.1
>     findfont: Matching
>     :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=medium
>     to Bitstream Vera Sans
>     (/usr/lib/python2.7/site-packages/matplotlib/mpl-data/fonts/ttf/Vera.ttf)
>     with score of 0.000000
>
>
>
>
> ------------------------------------------------------------------------------
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On Monday, August 15, 2011, Ben Breslauer <bbr...@gm...> wrote:
> Hi,
>
> Has anyone else noticed this behavior?  For the devs, do you prefer a
github bug to the SF list?
>
> Ben

I have not personally observed this, but usually, people don't specify rgba
tuples for plot.  I think the lack of response is due to our focus on larger
bugs right now for the upcoming release.  Therefore, it would probably be
best to file a bug report with your patch so that we have a record of it.

The fuller patch might be more involved (consider scatter and other
functions).  Plus, we would likely need to consider what to do if both an
rgba tuple and alpha were specified,

As a rule of thumb, if we don't respond on list, file a bug report.

Thanks for bringing this to our attention!
Ben Root

Hi,

Has anyone else noticed this behavior?  For the devs, do you prefer a github
bug to the SF list?

Ben

On Mon, Jul 25, 2011 at 2:21 PM, Ben Breslauer <bbr...@gm...> wrote:

> I think that I have found the problem here.  Line2D.draw() (and I presume
> other Artist subclasses) calls
>
> gc.set_foreground(self._color)
> ...
> gc.set_alpha(self._alpha)
>
> self._color is defined by the color kwarg, whether it be a hex value,
> 3-tuple, 4-tuple, or something else.  self._alpha is defined by the alpha
> kwarg, but if the alpha kwarg is None, it is not overwritten with color[3].
> Therefore, using color=(R,G,B,A) does not set alpha correctly.  I'm not sure
> the best (i.e. most matplotlib-like) way to change this so that the A value
> gets used iff alpha is None, but I've attached a patch that does it one way
> (diff'ed against the github master, commit 67b1cd650f574f9c53ce).  I know
> the patch is less than ideal, as it adds kwarg-specific handling into a
> place where none had previously been done, and it's also in a for loop.
> Maybe it would be better to do the checking along with the scalex and scaley
> pops?
>
>
> Separately, I noticed that backend_bases.GraphicsContextBase.set_foreground
> claims to only expect an RGB definition, and not an RGBA definition.  As
> such, I think that it should call
>
> self._rgb = colors.colorConverter.to_rgb(fg)
>
> instead of
>
> self._rgb = colors.colorConverter.to_rgba(fg)
>
> since that will make self._rgb a 3-tuple instead of a 4-tuple.
>
> Ben
>
>
> On Sat, Jul 23, 2011 at 3:13 PM, Ben Breslauer <bbr...@gm...>wrote:
>
>> Hi,
>>
>> I'm trying to fade some data, using alpha values, that I am plotting with
>> Axes.plot().  I can recreate this problem with 1 line of pylab.plot.  If I
>> use
>>
>> pylab.plot([1,2,3],[1,4,9], color=(1,0,0,.2), linewidth=7)
>>
>> then I get the equivalent of
>>
>> pylab.plot([1,2,3],[1,4,9], color=(1,0,0), linewidth=7)
>>
>> i.e. it does not use the alpha value.  However, if instead I use
>>
>> pylab.plot([1,2,3],[1,4,9], color=(1,0,0), linewidth=7, alpha=.2)
>>
>> then the line is faded out appropriately.  The plot documentation
>> indicates that RGBA tuples should be valid, so I'm wondering if this is a
>> bug.  Maybe alpha is defaulting to 1 or None and then not being overwritten
>> if color is a 4-tuple?
>>
>> I'm using Arch Linux with kernel 2.6.39, matplotlib 1.0.1 from the Arch
>> repo, and the Qt4 backend.  My installed Qt version is 4.7.3, and I am using
>> KDE 4.6.5.  Below is verbose-debug output.  Thanks for any help!
>>
>> Ben
>>
>>
>> $HOME=/home/ben
>> CONFIGDIR=/home/ben/.matplotlib
>> matplotlib data path /usr/lib/python2.7/site-packages/matplotlib/mpl-data
>> loaded rc file
>> /usr/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc
>> matplotlib version 1.0.1
>> verbose.level debug
>> interactive is False
>> units is False
>> platform is linux2
>> loaded modules: ['heapq', 'numpy.core.info', 'distutils',
>> 'numpy.lib.format', 'functools', 'pylab', '_bisect', 'subprocess',
>> 'sysconfig', 'gc', 'matplotlib.tempfile', 'distutils.sysconfig',
>> 'ctypes._endian', 'encodings.encodings', 'pyparsing', 'matplotlib.colors',
>> 'numpy.core.numerictypes', 'numpy.testing.sys', 'numpy.lib.__future__',
>> 'numpy.fft.types', 'numpy.ma.cPickle', 'struct',
>> 'numpy.matrixlib.defmatrix', 'numpy.random.info', 'tempfile',
>> 'numpy.compat.types', 'pprint', 'numpy.linalg', 'matplotlib.threading',
>> 'numpy.core.machar', 'numpy.testing.types', 'numpy.testing', 'collections',
>> 'numpy.polynomial.sys', 'unittest.sys', 'numpy.core.umath', 'unittest.main',
>> 'distutils.types', 'numpy.testing.operator', 'numpy.core.scalarmath',
>> 'numpy.ma.sys', 'zipimport', 'string', 'matplotlib.subprocess',
>> 'numpy.testing.os', 'unittest.functools', 'numpy.lib.arraysetops',
>> 'numpy.testing.unittest', 'numpy.lib.math', 'encodings.utf_8',
>> 'matplotlib.__future__', 'unittest.types', 'unittest.util', '
>> numpy.testing.re', 'numpy.version', 'numpy.lib.re', 'distutils.re',
>> 'numpy.matrixlib.sys', 'ctypes.os', 'numpy.core.os', 'numpy.lib.type_check',
>> 'numpy.compat.sys', 'unittest.StringIO', 'bisect', 'numpy.core._internal',
>> 'signal', 'numpy.lib.types', 'numpy.lib._datasource', 'random',
>> 'numpy.lib.__builtin__', 'numpy.fft.fftpack_lite', 'matplotlib.cbook',
>> 'textwrap', 'numpy.core.multiarray', 'numpy.polynomial.string',
>> 'distutils.version', 'cStringIO', 'numpy.polynomial', 'numpy.numpy',
>> 'matplotlib.StringIO', 'unittest.time', 'locale', 'numpy.add_newdocs',
>> 'unittest.difflib', 'numpy.core.getlimits', 'base64', '_ssl',
>> 'numpy.lib.sys', 'encodings', 'numpy.ma.itertools', 'unittest.pprint', '
>> unittest.re', 'abc', 'numpy.matrixlib', 'numpy.ctypes',
>> 'numpy.testing.decorators', 'matplotlib.warnings', 'rfc822',
>> 'numpy.lib.npyio', 'numpy.lib.numpy', 'matplotlib.sys', 're',
>> 'numpy.lib._compiled_base', 'numpy.polynomial.legendre', 'threading', 'new',
>> 'numpy.ma.warnings', 'numpy.random.mtrand', 'urllib2', 'matplotlib.cPickle',
>> 'math', 'numpy.fft.helper', 'fcntl', 'unittest.case', 'matplotlib.numpy',
>> 'UserDict', 'unittest.suite', 'numpy.lib.function_base', 'distutils.os',
>> 'matplotlib', 'numpy.fft.numpy', 'exceptions', 'numpy.lib.info',
>> 'ctypes', 'numpy.lib.warnings', 'ctypes.struct', 'codecs',
>> 'numpy.core._sort', 'numpy.os', 'unittest.loader', '_functools', '_locale',
>> 'numpy.__builtin__', 'matplotlib.os', 'thread', 'StringIO',
>> 'numpy.core.memmap', 'traceback', 'numpy.testing.warnings', 'weakref',
>> 'itertools', 'numpy.fft.fftpack', 'numpy.linalg.lapack_lite', 'numpy.ma',
>> 'distutils.sys', 'os', 'marshal', 'numpy.lib.itertools', '__future__',
>> '_collections', 'urllib', 'matplotlib.traceback', '_sre', 'unittest',
>> 'numpy.core.sys', 'numpy.random', 'numpy.linalg.numpy', '__builtin__',
>> 'numpy.lib.twodim_base', 'numpy.ma.core', 'matplotlib.re',
>> 'numpy.core.cPickle', 'unittest.runner', 'operator',
>> 'numpy.polynomial.polytemplate', 'numpy.core.arrayprint',
>> 'distutils.string', 'numpy.lib.arrayterator', 'select', 'ctypes._ctypes',
>> '_heapq', 'ctypes.sys', 'matplotlib.errno', 'unittest.collections',
>> 'posixpath', 'numpy.lib.financial', 'numpy.polynomial.laguerre',
>> 'matplotlib.random', 'errno', '_socket', 'binascii', 'sre_constants',
>> 'datetime', 'numpy.core.shape_base', 'os.path', 'numpy.core.function_base',
>> 'numpy.compat.py3k', 'numpy.lib.stride_tricks', 'numpy.core.numpy', 'numpy',
>> '_warnings', 'numpy.polynomial.chebyshev', 'matplotlib.types', 'cPickle',
>> 'encodings.__builtin__', 'numpy.polynomial.warnings', 'matplotlib.new',
>> '_codecs', 'numpy.lib.operator', 'unittest.fnmatch',
>> 'numpy.polynomial.polynomial', 'numpy.__config__', 'pwd',
>> 'matplotlib.pyparsing', 'httplib', 'numpy.lib.ufunclike', 'copy', '
>> numpy.core.re', '_struct', 'numpy.core.fromnumeric', 'hashlib',
>> 'numpy.ctypeslib', 'keyword', 'array', 'numpy.lib.scimath', 'numpy.fft',
>> 'numpy.lib', 'numpy.ma.numpy', 'numpy.random.numpy', 'matplotlib.urllib2',
>> 'unittest.weakref', 'numpy.ma.extras', 'numpy.polynomial.__future__',
>> 'posix', 'encodings.aliases', 'matplotlib.fontconfig_pattern', 'fnmatch',
>> 'sre_parse', 'pickle', 'numpy.core.ctypes', 'mimetools',
>> 'distutils.distutils', 'unittest.signal', 'copy_reg', 'sre_compile',
>> '_hashlib', '_random', '_ctypes', 'site', 'numpy.lib.polynomial',
>> 'numpy.compat', 'numpy._import_tools', 'numpy.polynomial.hermite',
>> '__main__', 'numpy.fft.info', 'numpy.core.records', 'shutil',
>> 'numpy.lib.cPickle', 'numpy.sys', 'numpy.polynomial.hermite_e', 'urlparse',
>> 'unittest.result', 'strop', 'grp', 'linecache', 'numpy.polynomial.numpy',
>> 'numpy.core.numeric', 'numpy.linalg.info', 'encodings.codecs', '_abcoll',
>> 'numpy.core', 'matplotlib.locale', 'matplotlib.rcsetup', 'matplotlib.time',
>> 'matplotlib.weakref', 'genericpath', 'stat', 'socket', 'numpy.lib._iotools',
>> 'numpy.lib.index_tricks', 'numpy.testing.utils', 'warnings',
>> 'numpy.lib.utils', 'numpy.core.defchararray', 'numpy.polynomial.polyutils',
>> 'numpy.lib.shape_base', 'numpy.core.types', 'unittest.signals', 'sys',
>> 'numpy.core.warnings', 'numpy.compat._inspect', 'numpy.core.__builtin__',
>> 'ctypes.ctypes', 'numpy.testing.traceback', 'numpy.lib.os',
>> 'numpy.testing.nosetester', 'types', 'numpy.lib.shutil',
>> 'matplotlib.datetime', 'matplotlib.distutils', '_weakref',
>> 'numpy.testing.numpytest', 'difflib', 'distutils.errors',
>> 'numpy.matrixlib.numpy', 'matplotlib.matplotlib', '_weakrefset',
>> 'matplotlib.shutil', 'numpy.lib.cStringIO', 'unittest.warnings', 'time',
>> 'unittest.traceback', 'ssl', 'numpy.linalg.linalg', 'unittest.os',
>> 'numpy.testing.numpy']
>> Using fontManager instance from /home/ben/.matplotlib/fontList.cache
>> backend Qt4Agg version 0.9.1
>> findfont: Matching
>> :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=medium
>> to Bitstream Vera Sans
>> (/usr/lib/python2.7/site-packages/matplotlib/mpl-data/fonts/ttf/Vera.ttf)
>> with score of 0.000000
>>
>>
>

Is there some way to determine the corresponding y coordinate of a shape
(the edge of a circle for example)
given an x coordinate of that shape?
I know that for a circle there would be to 2 coordinates for each x
coordinate, but I'm not actually plotting a whole circle, only a quarter of
a circle.
Which brings me to me 2nd quest of how you plot a quarter of a circle with
radius of length of 1 onto an a canvas.

-- 
View this message in context: http://old.nabble.com/finding-the-y-coordinate-at-a-particular-x-coordinate-tp32268951p32268951.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

The mpl developers are getting very close to the long-awaited v1.1.0 release
of matplotlib.  Before we do so, we are doing some final checking of the
documentation to make sure that all critical pieces of information iss
correct and up to date.

In checking over the instructions for building and installing matplotlib on
MacOSX, I have found two separate sets of instructions.  On the install
page, there is a reference to a README.txt file in "release/osx".  This file
is there, but it seems to refer to other files that no longer exists.
Meanwhile, there is an un-referenced file in the top directory called
README.osx that seems a lot more current.

Because I do not have a Mac that I can use for development, I would like to
ask the community for help in determining the correct set of instructions
and to eliminate cruft.  I think it would also be useful to point users to
any relevant instructions for installing/building numpy on Macs.  I would
also like to make  sure we are current with information on installing on a
stock Lion install.

Please feel free to respond on this list, or better, make a branch on github
and submit pull requests to help us improve these documents.

Thanks!
Ben Root

2011/8/15 Michael Droettboom <md...@st...>:
> On 08/14/2011 11:28 AM, Vlastimil Brom wrote:
>> ...
>> Is there maybe some mechanism available in matplotlib, which would
>> select the appropriate font for the given characters? (Like in wx or,
>> even more powerful in tk?) Or is it the expected way to set the
>> suitable font individually, as only the font list in rcParams are
>> tried sequentially - regardless of the character support?
> That's correct: matplotlib doesn't currently do this.  It renders a
> particular string of text with a single font and does not do font
> substitution for characters that are not in that font.  The best
> practice is to use a font that contains the full set of characters, such
> as Arial or Deja Vu as you've discovered.
>
> There a lot of other i18n shortcomings to the way matplotlib supports
> text (such as a lack of bidi support).  That's a large wheel to
> reinvent, so the solution would involve using a third-party text layout
> library such as pango, but I know there is licensing friction against
> that.  It may be worth revisiting that at some point.
>
> Mike
>
>
Thanks for the confirmation, I'll stick with this apporoach, as I
didn't manage to fix the code for wx backend on the newest version
(cf. above). Given the status of the non-Agg backends it might be more
appropriate.

Regards,
   vbr