matplotlib-users Mailing List for matplotlib (Page 2)

The PR to fix this is still open (
https://github.com/matplotlib/matplotlib/pull/4202).
Tom
On Wed, Jul 15, 2015 at 10:29 AM John Coppens <jo...@jc...> wrote:
> Hello again,
>
> I've posted these two issues in separate mails, as I suspect they're
> actually different problems.
>
> This error is particular to the default version of MacOSX's matplotlib
> version 1.4.3:
>
> When doing a simple plot:
>
> import matplotlib.pyplot as plt
>
> def test_plot():
> x = range(11)
> y = [x0**2 for x0 in x]
>
> plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
> plt.legend()
> plt.show()
>
> def main(args):
> test_plot()
> return 0
>
> if __name__ == '__main__':
> import sys
> sys.exit(main(sys.argv))
>
> Much of the data is available on this thread on stackoverflow:
>
>
> http://stackoverflow.com/questions/31408928/how-can-i-plot-hollowed-symbols-connected-with-dotted-lines-in-one-go/31410105?noredirect=1#comment50794519_31410105
>
> The gist is that a dotted line ('o:') works correctly
> on my system (Linux Slackware/matplotlib 1.3.1 and 1.4.3), on C.C.Yang's
> Linux Mint, but not on his MacOSX (on which the _circle symbols_ are also
> dotted).
>
> It does work if he defines TkAgg or GtkAgg (even though he does not have
> Gtk installed on his Mac)
>
> Any suggestions to solve this?
>
> Is there a problem in the MacOSXAgg backend?
>
> John
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
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> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

We have been recently fixing a bunch of issues in the macosx backend (which
is default on Macs). Having the circle be dotted sounds exactly like the
sort of problem that would be caused by some of the bugs we are addressing.
I think we have some of the fixes committed to the master branch, so if you
could build and install from git, you can see if the problem is fixed yet
or not.
Ben Root
On Wed, Jul 15, 2015 at 10:29 AM, John Coppens <jo...@jc...> wrote:
> Hello again,
>
> I've posted these two issues in separate mails, as I suspect they're
> actually different problems.
>
> This error is particular to the default version of MacOSX's matplotlib
> version 1.4.3:
>
> When doing a simple plot:
>
> import matplotlib.pyplot as plt
>
> def test_plot():
> x = range(11)
> y = [x0**2 for x0 in x]
>
> plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
> plt.legend()
> plt.show()
>
> def main(args):
> test_plot()
> return 0
>
> if __name__ == '__main__':
> import sys
> sys.exit(main(sys.argv))
>
> Much of the data is available on this thread on stackoverflow:
>
>
> http://stackoverflow.com/questions/31408928/how-can-i-plot-hollowed-symbols-connected-with-dotted-lines-in-one-go/31410105?noredirect=1#comment50794519_31410105
>
> The gist is that a dotted line ('o:') works correctly
> on my system (Linux Slackware/matplotlib 1.3.1 and 1.4.3), on C.C.Yang's
> Linux Mint, but not on his MacOSX (on which the _circle symbols_ are also
> dotted).
>
> It does work if he defines TkAgg or GtkAgg (even though he does not have
> Gtk installed on his Mac)
>
> Any suggestions to solve this?
>
> Is there a problem in the MacOSXAgg backend?
>
> John
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
> https://www.gigenetcloud.com/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

The Agg backend is a non-gui backend, it just saves to file. The `TkAgg`
and `GtkAgg` are gui backends (which are more of front ends, but I digress)
which show the output of Agg in a gui window (and provide a layer for
handling user interaction).
I suspect that how ever your 1.3.1 was installed the system level
matplotlibrc file was modified to set the default backend to be one of the
GUI backends.
See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file You
want to set the 'backend' parameter.
Tom
On Wed, Jul 15, 2015 at 10:22 AM John Coppens <jo...@jc...> wrote:
> Hello all.
>
> I had MatPlotLib 1.3.1 installed, and decided to upgrade to 1.4.3. I
> compiled the
> .tar.gz package, which went without a hitch (except for a number of
> warnings
> from gcc). Installation also completed without problems.
>
> But, on running the same simple plot I was working on, no plot was output:
>
> import matplotlib.pyplot as plt
>
> def test_plot():
> x = range(11)
> y = [x0**2 for x0 in x]
>
> plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
> plt.legend()
> plt.show()
>
> def main(args):
> test_plot()
> return 0
>
> if __name__ == '__main__':
> import sys
> sys.exit(main(sys.argv))
>
> which was somewhat annoying, as I was trying to help out someone on
> Stackoverflow. Only after experimenting somewhat, I found that
> setting the Agg to GtkAgg, the plot started working again:
>
> import matplotlib
> matplotlib.use('GtkAgg')
>
> Is this normal? I'm not actually using gtk in this project.
> TkAgg also works.
>
> John
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
> https://www.gigenetcloud.com/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

If your backend is set to Agg, then no interactive window will appear upon
call to show(). Agg is intended for headless servers. What might be
happening is that somewhere, you have Agg set as the default backend.
Ben Root
On Wed, Jul 15, 2015 at 10:16 AM, John Coppens <jo...@jc...> wrote:
> Hello all.
>
> I had MatPlotLib 1.3.1 installed, and decided to upgrade to 1.4.3. I
> compiled the
> .tar.gz package, which went without a hitch (except for a number of
> warnings
> from gcc). Installation also completed without problems.
>
> But, on running the same simple plot I was working on, no plot was output:
>
> import matplotlib.pyplot as plt
>
> def test_plot():
> x = range(11)
> y = [x0**2 for x0 in x]
>
> plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
> plt.legend()
> plt.show()
>
> def main(args):
> test_plot()
> return 0
>
> if __name__ == '__main__':
> import sys
> sys.exit(main(sys.argv))
>
> which was somewhat annoying, as I was trying to help out someone on
> Stackoverflow. Only after experimenting somewhat, I found that
> setting the Agg to GtkAgg, the plot started working again:
>
> import matplotlib
> matplotlib.use('GtkAgg')
>
> Is this normal? I'm not actually using gtk in this project.
> TkAgg also works.
>
> John
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
> https://www.gigenetcloud.com/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Hello again,
I've posted these two issues in separate mails, as I suspect they're 
actually different problems.
This error is particular to the default version of MacOSX's matplotlib
version 1.4.3:
When doing a simple plot:
import matplotlib.pyplot as plt
def test_plot():
 x = range(11)
 y = [x0**2 for x0 in x]
 plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
 plt.legend()
 plt.show()
def main(args):
 test_plot()
 return 0
if __name__ == '__main__':
 import sys
 sys.exit(main(sys.argv))
Much of the data is available on this thread on stackoverflow:
http://stackoverflow.com/questions/31408928/how-can-i-plot-hollowed-symbols-connected-with-dotted-lines-in-one-go/31410105?noredirect=1#comment50794519_31410105
The gist is that a dotted line ('o:') works correctly
on my system (Linux Slackware/matplotlib 1.3.1 and 1.4.3), on C.C.Yang's
Linux Mint, but not on his MacOSX (on which the _circle symbols_ are also
dotted).
It does work if he defines TkAgg or GtkAgg (even though he does not have
Gtk installed on his Mac)
Any suggestions to solve this?
Is there a problem in the MacOSXAgg backend?
John

Hello all.
I had MatPlotLib 1.3.1 installed, and decided to upgrade to 1.4.3. I compiled the
.tar.gz package, which went without a hitch (except for a number of warnings
from gcc). Installation also completed without problems.
But, on running the same simple plot I was working on, no plot was output:
import matplotlib.pyplot as plt
def test_plot():
 x = range(11)
 y = [x0**2 for x0 in x]
 plt.plot(x, y, 'o:', fillstyle='none', label = "1", ms = 10)
 plt.legend()
 plt.show()
def main(args):
 test_plot()
 return 0
if __name__ == '__main__':
 import sys
 sys.exit(main(sys.argv))
which was somewhat annoying, as I was trying to help out someone on
Stackoverflow. Only after experimenting somewhat, I found that 
setting the Agg to GtkAgg, the plot started working again:
import matplotlib
matplotlib.use('GtkAgg')
Is this normal? I'm not actually using gtk in this project.
TkAgg also works. 
John

I cleared the texcache in $HOME/.cache/matplotlib/texcache and everything
seems to be back on track. So no need to go over this guys. The issue has
been resolved 
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/Have-issues-with-tex-rendering-tp45899p45929.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Thomas Caswell <tca...@gm...> schrieb am So., 12. Juli 2015 um
18:21 Uhr:
> The new default color map will be 'viridis' (aka option D).
>
hi,
how did you get to that decision?
from at cursory glance at the opinions thread there didn’t seem to be a
majority for option D as opposed to A, B, and C.
A, B, and C were variations on a theme, so a fair vote would be ABC or D,
and then if applicable a second one to decide which variation of ABC to use.
best, philipp

Le 12/07/2015 18:11, Thomas Caswell a écrit :
> I recommend everyone watch Nathaniel Smith and Stéfan van der Walt's
> talk from SciPy2015 introducing the new color map and providing an
> introduction to the math of color
> perception: https://www.youtube.com/watch?v=xAoljeRJ3lU
Great presentation, thanks for sharing !
-- 
Pierre

Hello all,
Following much discussion, we are changing the default color map and styles
in the upcoming 2.0 release!
The new default color map will be 'viridis' (aka option D). I recommend
everyone watch Nathaniel Smith and Stéfan van der Walt's talk from
SciPy2015 introducing the new color map and providing an introduction to
the math of color perception: https://www.youtube.com/watch?v=xAoljeRJ3lU
We are soliciting proposals to change any and all other visual defaults
(including adding new rcParams as needed).
If you have a proposal please create a PR or issue with the changes to
`rcsetup.py` and `matplotlibrc.template` implementing the changes by August
9, 2015 (1 month from now). Do not worry about updating any failing tests.
At the end, Micheal Droettboom and I will decide on the new defaults.
A 'classic' style will be provided so reverting to the current default
values will be a single line of python (`mpl.style.use('classic')`).
Please distribute this as widely as possible. We only want to do this once
and want to get feedback from as many users as possible.
Thomas Caswell
PS jet is harmful to you and those around you
See https://github.com/matplotlib/matplotlib/pull/4622 for an example
proposal PR.

Hey all,
I wanted to let folks know that there is a new matplotlib book available,
having just been published:
 *
https://www.packtpub.com/big-data-and-business-intelligence/mastering-matplotlib
The IPython notebooks are listed here (with links to NBViewer as well as
the individual chapter repos):
 * https://github.com/masteringmatplotlib/notebooks
The book didn't ship with an Acknowledgements section, so I am attempting
to make up for that here:
 *
http://oubiwann.blogspot.com/2015/07/mastering-matplotlib-acknowledgments.html
The ToC for the book hasn't been updated on the publisher's (or Amazon's)
site, so for your reading pleasure I have included the text from the
section "What this book covers" below:
Chapter 1, Getting Up to Speed, covers some history and background of
matplotlib, goes over some of the latest features of the library, provides
a refresher on Python 3 and IPython Notebooks, and whets the reader's
appetite with some advanced plotting examples.
Chapter 2, matplotlib Architecture, reviews the original design goals of
matplotlib and then proceeds to discuss its current architecture in detail,
providing visualizations of the conceptual structure and relationships
between the Python modules.
Chapter 3, matplotlib APIs and Integrations, walks the reader through the
matplotlib APIs adapting a single example accordingly, examines how the
third-party libraries are integrated with matplotlib, and gives migration
advice to the advanced users of the deprecated pylab API.
Chapter 4, Event Handling and Interactive Plots, provides a review of the
event-based systems, covers event loops in matplotlib and IPython, goes
over a selection of matplotlib events, and shows how to take advantage of
these to create interactive plots.
Chapter 5, High-level Plotting and Data Analysis, combines the interrelated
topics, providing a historical background of plotting, a discussion on the
grammar of graphics, and an overview of high-level plotting libraries. This
is then put to use in a detailed analysis of weather-related data that
spans 120 years.
Chapter 6, Customization and Configuration, covers the custom styles in
matplotlib and the use of grid specs to create a dashboard effect with the
combined plots. The lesser-known configuration options are also discussed
with an eye to optimization.
Chapter 7, Deploying matplotlib in Cloud Environments, explores a use case
for matplotlib in a remote deployment, which is followed by a detailed
programmatic batch-job example using Docker and Amazon AWS.
Chapter 8, matplotlib and Big Data, provides detailed examples of working
with large local data sets as well as the distributed ones, covering
options such as numpy.memmap, HDF5, and Hadoop. Plots with millions of
points will also be demonstrated.
Chapter 9, Clustering for matplotlib, introduces parallel programming and
clusters that are designed for use with matplotlib, demonstrating how to
distribute parts of a problem and then assemble the results for analysis in
matplotlib.
Hope everyone's having a good time at SciPy 2015!
d

Thanks for all the ideas.
On Thu, Jul 9, 2015 at 8:09 PM, Joy merwin monteiro <joy...@gm...>
wrote:
> Maybe you could plot the ratio? That should give you rainfall per degree
> Celsius.
> On 9 Jul 2015 20:11, "Jonno" <jon...@gm...> wrote:
>
>> I was thinking of doing that or having 2 surface plots but I think it
>> would be visually quite confusing.
>> I was trying to think of an example since I'm sure someone has come up
>> with a nice way to display this kind of data.
>> Imagine if the data was average temperature (a) and average rainfall (b)
>> for a region in the world (lat/long = x,y). The goal is to display the data
>> such that it's obvious where the locations are that have closest to the
>> ideal temp/rain combination.
>> How would you go about that?
>>
>> On Thu, Jul 9, 2015 at 12:28 AM, Sterling Smith <sm...@fu...>
>> wrote:
>>
>>> In the x,y plane, could you overlay contours of a with contours of b?
>>> -Sterling
>>>
>>> On Jul 8, 2015, at 8:19PM, Jonno <jon...@gm...> wrote:
>>>
>>> > I have a bunch of experimental data points each of which has 2
>>> variables (x,y) and 2 results (a,b). Each pair or x,y values produces a
>>> pair of a,b resultant values.
>>> > There is a single optimal pair of a,b values and I'd like to figure
>>> out a way to illustrate the data to show the relationship between each x,y
>>> pair and how close each a,b pair is to the ideal.
>>> > I'm thinking about a dual surface/contour plot with 2 different
>>> z-axes. Ideally I would center both z-axes at the ideal values. I don't
>>> know if this is possible. Might be kinda messy.
>>> >
>>> > Any other thoughts? I'm sure there must be other examples where this
>>> is a problem.
>>> >
>>> ------------------------------------------------------------------------------
>>> > Don't Limit Your Business. Reach for the Cloud.
>>> > GigeNET's Cloud Solutions provide you with the tools and support that
>>> > you need to offload your IT needs and focus on growing your business.
>>> > Configured For All Businesses. Start Your Cloud Today.
>>> >
>>> https://www.gigenetcloud.com/_______________________________________________
>>> > Matplotlib-users mailing list
>>> > Mat...@li...
>>> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>
>>>
>>
>>
>> ------------------------------------------------------------------------------
>> Don't Limit Your Business. Reach for the Cloud.
>> GigeNET's Cloud Solutions provide you with the tools and support that
>> you need to offload your IT needs and focus on growing your business.
>> Configured For All Businesses. Start Your Cloud Today.
>> https://www.gigenetcloud.com/
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>

Your theta and phi were essentially 1D rather than 2D, so it didn't allow
for 2 degrees of freedom. And you don't need np.outer() for this:
theta = np.linspace(0, np.pi, 500)[:, None]
phi = np.linspace(0, 2*np.pi, 500)[None, :]
r = f(theta, phi)
x = r**2 * np.cos(phi) * np.sin(theta)
y = r**2 * np.sin(phi) * np.sin(theta)
z = r**2 * np.cos(theta)
The use of np.outer() in the original example acted a bit like a creating a
grid of u/v values in a 2D grid. However, your formulation required
computing a 2D grid of radius values in order to work correctly.
Cheers!
Ben Root
On Fri, Jul 10, 2015 at 7:54 AM, Romain Madar <rom...@ce...> wrote:
> Dear experts,
>
> I am trying to plot spherical harmonics with matplotlib and I have some
> troubles. I am starting from the example
> http://matplotlib.org/examples/mplot3d/surface3d_demo2.html where I
> change the factor 10 in a function of r=f(theta,phi) (or r=f(u,v) as they
> are named in the example). I observe very strange behaviours:
>
> (1) (x,y,z) = (r cos(phi) sin(theta) , r sin(phi) sin(theta) , r
> cos(theta)). But np.outer(a,b) is not commutative while the multiplication
> is. So how to choose the order in the np.outer() product? In fact,
> different order gives very different results.
>
> (2) It's seem impossible to reproduce the well known Ylm(theta,phi) plots.
> Using for example this document
> http://www.cs.dartmouth.edu/~wjarosz/publications/dissertation/appendixB.pdf
> :
>
>
>
>
>
> I don't know if I am doing something wrong or so, but I don't understand
> ... My full code is bellow.
>
> Thanks a lot in advance !
> Cheers,
> Romain
>
>
> PS:
>
> import math
> import numpy as np
> import pylab as p
> from mpl_toolkits.mplot3d import Axes3D
>
> def f(theta,phi):
> return np.sin(phi)*np.cos(phi)*np.sin(theta)**2
>
> fig = p.figure()
> ax = fig.add_subplot(111, projection='3d')
>
> theta = np.linspace(0, np.pi, 500)
> phi = np.linspace(0, 2*np.pi, 500)
>
> r = f(theta,phi)
> x = r**2 * np.outer( np.cos(phi) , np.sin(theta) )
> y = r**2 * np.outer( np.sin(phi) , np.sin(theta) )
> z = r**2 * np.outer(np.ones(phi.shape), np.cos(theta))
>
> #x = r**2 * np.outer( np.sin(theta) , np.cos(phi)
> )
>
> #y = r**2 * np.outer( np.sin(theta) , np.sin(phi) )
> #z = r**2 * np.outer( np.cos(theta), np.ones(theta.shape) )
>
> ax.plot_surface(x,y,z)
> ax.set_xlabel("X")
> ax.set_ylabel("Y")
> ax.set_zlabel("Z")
>
> p.show()
>
>
> --
> =========================================================
> 	Romain Madar
>
> Laboratoire de Physique Corpusculaire de Clermont-Ferrand
> Campus Universitaire des Cézeaux
> 4 avenue Blaise Pascal
> TSA 60026, CS 60026
> 63178 Aubière cedex, FRANCE
>
> Email: rom...@ce...
> Tel. : +33 (0)4 73 40 71 57
> Off. : 8204-8205
> =========================================================
>
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
> https://www.gigenetcloud.com/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

The way matplotlib does its MathText rendering is 1) incomplete (we don't
support all of MathTex), and 2) has *massive* overhead (relatively
speaking). Matplotlib is intended for producing figures with many disparate
components. The amount of code it takes to just generate a simple plot is
fairly significant (along with also firing up a python interpreter).
Meanwhile, MathJax is much lighter in the sense that all it needs to do is
parse a string and render out font characters.
As for matplotlib vs. MathJax, you will likely sending bitmaps to OpenGL
(if possible) anyway because that is pretty much what you will need to do
with matplotlib as well as MathJax. It is technically possible to obtain
the stroke data to send the font lines to OpenGL, but it will not look the
same as it would if you let a font renderer generate the bitmap. There are
a few reasons why matplotlib does not have an OpenGL backend yet, one of
them is because OpenGL does a terrible job in rendering text.
This is not to say that what you are thinking of doing is impossible to do.
It may be quite possible, but given that no one (that I am aware of) have
managed to get matplotlib running on a mobile OS, you have a huge
undertaking ahead of you just to get started. And, once you get there, it
is quite likely that the performance won't be what you need. In addition,
you might not like the resulting render. More power to you if you can get
it working, and I know many people who are interested in getting that stack
working on tablets and such.
On the other hand, there are plenty of documentation on how to build mobile
apps that take advantage of javascript-based technologies. Your startup
cost is very low here. And given that you will likely going to need to use
bitmaps anyway, it might not be all that bad of an option. I have no clue
what the performance penalty of firing up a javascript renderer on a mobile
OS, but in the face of the unknown, I avoid guessing. Don't fall victim to
premature optimization. I have been very surprised at how fast certain
(slow) technologies can be.
A minimalist LaTeX distro is an intriguing idea. I have no clue how much
effort it would take to do that, but that may be quite feasible.
Best of luck to you, and I look forward to finding out what you manage to
get working.
Cheers!
Ben Root
On Fri, Jul 10, 2015 at 4:37 AM, asiga <asi...@ya...> wrote:
> Why do you suggest MathJax? I assume Javascript will be less efficient than
> Python. Moreover, I'm not sure I can get the MathJax output as polygonal
> primitives that I can send to OpenGL. And, to complicate things, you cannot
> use JIT Javascript engines on iOS such as V8, due to sandboxing.
>
> In fact, I'm considering to build myself a minimal LaTeX distro. Maybe that
> would be the best option.
>
>
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/Efficient-matplotlib-use-on-iOS-and-Android-apps-tp45901p45914.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
> Configured For All Businesses. Start Your Cloud Today.
> https://www.gigenetcloud.com/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

See
http://matplotlib.org/1.4.3/faq/installing_faq.html?highlight=install#linux-notes
On Fri, Jul 10, 2015, 9:10 AM Varada Anirudhan <var...@gm...>
wrote:
> Hello there
>
> When I was trying to install matplotlib, the output said that I needed to
> install freetype and png first
>
> How do I install freetype and png on my Ubuntu 14.04 powered-Linux system?
> Please help me with the lines of code for this installation
>
> Thanks in advance
>
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/how-to-install-freetype-png-for-matplotlib-tp45916.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
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Hello there
When I was trying to install matplotlib, the output said that I needed to
install freetype and png first
How do I install freetype and png on my Ubuntu 14.04 powered-Linux system?
Please help me with the lines of code for this installation
Thanks in advance
--
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Dear experts,
I am trying to plot spherical harmonics with matplotlib and I have some 
troubles. I am starting from the example 
http://matplotlib.org/examples/mplot3d/surface3d_demo2.html where I 
change the factor 10 in a function of r=f(theta,phi) (or r=f(u,v) as 
they are named in the example). I observe very strange behaviours:
(1) (x,y,z) = (r cos(phi) sin(theta) , r sin(phi) sin(theta) , r 
cos(theta)). But np.outer(a,b) is not commutative while the 
multiplication is. So how to choose the order in the np.outer() product? 
In fact, different order gives very different results.
(2) It's seem impossible to reproduce the well known Ylm(theta,phi) 
plots. Using for example this document 
http://www.cs.dartmouth.edu/~wjarosz/publications/dissertation/appendixB.pdf 
:
I don't know if I am doing something wrong or so, but I don't understand 
... My full code is bellow.
Thanks a lot in advance !
Cheers,
Romain
PS:
import math
import numpy as np
import pylab as p
from mpl_toolkits.mplot3d import Axes3D
def f(theta,phi):
 return np.sin(phi)*np.cos(phi)*np.sin(theta)**2
fig = p.figure()
ax = fig.add_subplot(111, projection='3d')
theta = np.linspace(0, np.pi, 500)
phi = np.linspace(0, 2*np.pi, 500)
r = f(theta,phi)
x = r**2 * np.outer( np.cos(phi) , np.sin(theta) )
y = r**2 * np.outer( np.sin(phi) , np.sin(theta) )
z = r**2 * np.outer(np.ones(phi.shape), np.cos(theta))
#x = r**2 * np.outer( np.sin(theta) , np.cos(phi) )
#y = r**2 * np.outer( np.sin(theta) , np.sin(phi) )
#z = r**2 * np.outer( np.cos(theta), np.ones(theta.shape) )
ax.plot_surface(x,y,z)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
p.show()
-- 
=========================================================
	Romain Madar
Laboratoire de Physique Corpusculaire de Clermont-Ferrand
 Campus Universitaire des Cézeaux
 4 avenue Blaise Pascal
 TSA 60026, CS 60026
 63178 Aubière cedex, FRANCE
Email: rom...@ce...
Tel. : +33 (0)4 73 40 71 57
Off. : 8204-8205
=========================================================

Why do you suggest MathJax? I assume Javascript will be less efficient than
Python. Moreover, I'm not sure I can get the MathJax output as polygonal
primitives that I can send to OpenGL. And, to complicate things, you cannot
use JIT Javascript engines on iOS such as V8, due to sandboxing.
In fact, I'm considering to build myself a minimal LaTeX distro. Maybe that
would be the best option.
 
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Maybe you could plot the ratio? That should give you rainfall per degree
Celsius.
On 9 Jul 2015 20:11, "Jonno" <jon...@gm...> wrote:
> I was thinking of doing that or having 2 surface plots but I think it
> would be visually quite confusing.
> I was trying to think of an example since I'm sure someone has come up
> with a nice way to display this kind of data.
> Imagine if the data was average temperature (a) and average rainfall (b)
> for a region in the world (lat/long = x,y). The goal is to display the data
> such that it's obvious where the locations are that have closest to the
> ideal temp/rain combination.
> How would you go about that?
>
> On Thu, Jul 9, 2015 at 12:28 AM, Sterling Smith <sm...@fu...>
> wrote:
>
>> In the x,y plane, could you overlay contours of a with contours of b?
>> -Sterling
>>
>> On Jul 8, 2015, at 8:19PM, Jonno <jon...@gm...> wrote:
>>
>> > I have a bunch of experimental data points each of which has 2
>> variables (x,y) and 2 results (a,b). Each pair or x,y values produces a
>> pair of a,b resultant values.
>> > There is a single optimal pair of a,b values and I'd like to figure out
>> a way to illustrate the data to show the relationship between each x,y pair
>> and how close each a,b pair is to the ideal.
>> > I'm thinking about a dual surface/contour plot with 2 different z-axes.
>> Ideally I would center both z-axes at the ideal values. I don't know if
>> this is possible. Might be kinda messy.
>> >
>> > Any other thoughts? I'm sure there must be other examples where this is
>> a problem.
>> >
>> ------------------------------------------------------------------------------
>> > Don't Limit Your Business. Reach for the Cloud.
>> > GigeNET's Cloud Solutions provide you with the tools and support that
>> > you need to offload your IT needs and focus on growing your business.
>> > Configured For All Businesses. Start Your Cloud Today.
>> >
>> https://www.gigenetcloud.com/_______________________________________________
>> > Matplotlib-users mailing list
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>> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
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> GigeNET's Cloud Solutions provide you with the tools and support that
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>
>

Yeah it makes sense. I still can't find the bug, which it most likely is. I will keep looking and see what i find.
Thanks for the help
-----Original Message-----
From: Eric Firing [mailto:ef...@ha...] 
Sent: Tuesday, June 30, 2015 1:11 PM
To: mat...@li...
Subject: Re: [Matplotlib-users] TypeError: Dimensions of C (645, 536) are incompatible with X (538) and/or Y (646); see help(pcolormesh)
On 2015年06月30日 6:41 AM, Benjamin Root wrote:
> It looks like your X data is one element larger than it needs to be. I 
> know pcolor() accepts grids that are (N+1,M+1), and I *think* 
> pcolormesh does the same. It will also accept grids that are (N,M) as 
> well, but will drop the last row and collumn.
Yes, pcolormesh and pcolor use the same argument parsing and checking. 
They actually *want* N+1, M+1; the *acceptance* of N, M is a matlab-ism that is convenient for quick looks, but is also a potential source of error.
The OP has an X dimension of M+2, which indicates an error earlier in the OP's code.
Eric
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GigeNET's Cloud Solutions provide you with the tools and support that you need to offload your IT needs and focus on growing your business.
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Can you be more specific about the problem you are having?
-Sterling
On Jul 9, 2015, at 9:40AM, peter <com...@ya...> wrote:
> hi,
> 
> my code was working fine, but now i cant figure out what went wrong.
> any ideas?
> 
> the code is supposed to plot a timeseries which it does and overlay it with another that is partially defined
> the input file is contructed like this:
> the first line is just for information purposes.
> after that:
> the first row is a growing number (the x value), the second is the timeseries and the third is the partially defined second timeseries
> 
> this is the code, after the code is a example input file.
> the code is also accessible via this paste service: https://dpaste.de/5ZrX it got a nice python code formatter.
> 
> 	• def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
> 	• 
> 	• if verbose:
> 	• print "plotTimeSeriesAndSAX()"
> 	• print "\tinputfile:", inputfile_tmp
> 	• print "\toutputfile: %s.png" % inputfile_tmp
> 	• 
> 	• inputfile = open(inputfile_tmp, "r");
> 	• 
> 	• 
> 	• # this holds my timeseries
> 	• x = []
> 	• y = []
> 	• 
> 	• # this holds my "pattern"
> 	• pattern_x_values = []
> 	• pattern_y_values = []
> 	• 
> 	• # these are for temporary use only, hold the current pattern data
> 	• tmp_x = []
> 	• tmp_y = []
> 	• 
> 	• 
> 	• # remove pattern/sax string, sax_string_with_Z from the datafile, only used as text in the plot
> 	• first_line = inputfile.readline()
> 	• pattern, sax, sax_string_with_Z = first_line.split()
> 	• 
> 	• 
> 	• 
> 	• 
> 	• for line in inputfile.readlines():
> 	• 
> 	• data = line.split()
> 	• x_data = data[0]
> 	• y_data = data[1]
> 	• 
> 	• #if there is a third line (pattern at this position)
> 	• if len(data) == 3:
> 	• y2_data = data[2]
> 	• tmp_y.append(y2_data)
> 	• tmp_x.append(x_data)
> 	• else:
> 	• # if the pattern ends, add it to pattern_x/y_value and clear the tmp list
> 	• if len(tmp_x) != 0:
> 	• pattern_x_values.append(tmp_x)
> 	• pattern_y_values.append(tmp_y)
> 	• tmp_x = []
> 	• tmp_y = []
> 	• 
> 	• 
> 	• x.append(x_data)
> 	• y.append(y_data)
> 	• 
> 	• #if pattern == "ccd":
> 	• # print "pattern x_values:", pattern_x_values
> 	• # print "pattern y_values:", pattern_y_values
> 	• if verbose:
> 	• print "\ttimeseries y value", y
> 	• print "pattern x_values:", pattern_x_values
> 	• print "pattern y_values:", pattern_y_values
> 	• 
> 	• 
> 	• 
> 	• colors = ["red", "magenta", "mediumblue", "darkorchid", "grey"]
> 	• #linestyle = ["-", "--"]
> 	• 
> 	• # without this, the second plot contains the first and the second
> 	• # the third plot contains: the first, second and third
> 	• plot.clf()
> 	• 
> 	• # plot all my patterns into the plot
> 	• for s in range(0,len(pattern_x_values)):
> 	• #if verbose:
> 	• # print "\tpattern x value:", pattern_x_values[s]
> 	• # print "\tpattern y value:", pattern_y_values[s]
> 	• 
> 	• plot.plot(pattern_x_values[s], pattern_y_values[s], colors[1])
> 	• 
> 	• 
> 	• #plot.plot(x_all[0], y_all[0]) 
> 	• 
> 	• 
> 	• import matplotlib.patches as mpatches
> 	• 
> 	• 
> 	• #red_patch = mpatches.Patch(color='red', label='The red data')
> 	• 
> 	• from time import gmtime, strftime
> 	• current_date = strftime("%Y-%m-%d %H:%M:%S", gmtime())
> 	• 
> 	• 
> 	• fig = plot.figure()
> 	• 
> 	• 
> 	• fig.text(0, 0, 'bottom-left corner')
> 	• fig.text(0, 1, current_date, ha='left', va='top')
> 	• mytext = "pattern: %s sax: %s sax with Z: %s" % (pattern, sax, sax_string_with_Z)
> 	• fig.text(1,1, mytext )
> 	• 
> 	• 
> 	• # add the original timeseries to the plot
> 	• plot.plot(x,y, "forestgreen")
> 	• #if pattern == "ccd":
> 	• # plot.show()
> 	• 
> 	• 
> 	• directory, filename = os.path.split(inputfile_tmp)
> 	• 
> 	• plot.savefig(os.path.join(directory, "plots/%s.png" % filename))#, bbox_inches='tight')
> 	• # remove the last figure from memory
> 	• #plot.close()
> 	• 
> 	• 
> 	• 
> 	• 
> 	• 
> 	• 
> 	• 
> 	• 
> 	• #input:
> 	• dee ccccccccccaacddeedcccccccdc ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
> 	• 1 -0.015920084 
> 	• 2 -0.044660769 
> 	• 3 -0.044660769 
> 	• 4 -0.092561907 
> 	• 5 0.012820599 
> 	• 6 -0.015920084 
> 	• 7 0.012820599 
> 	• 8 -0.054240996 
> 	• 9 0.031981054 
> 	• 10 0.031981054 
> 	• 11 -0.025500313 
> 	• 12 -0.044660769 
> 	• 13 0.012820599 
> 	• 14 -0.025500313 
> 	• 15 0.0032403709 
> 	• 16 -0.006339857 
> 	• 17 0.0032403709 
> 	• 18 -0.025500313 
> 	• 19 0.031981054 
> 	• 20 0.031981054 
> 	• 21 0.031981054 
> 	• 22 0.022400826 
> 	• 23 0.031981054 
> 	• 24 0.05114151 
> 	• 25 0.079882193 
> 	• 26 0.05114151 
> 	• 27 0.05114151 
> 	• 28 0.05114151 
> 	• 29 0.099042646 
> 	• 30 0.060721738 
> 	• 31 -0.015920084 
> 	• 32 -0.054240996 
> 	• 33 0.23316584 
> 	• 34 0.26190652 
> 	• 35 0.37686926 
> 	• 36 0.12778333 
> 	• 37 -0.044660769 
> 	• 38 -0.26500601 
> 	• 39 -0.41828965 
> 	• 40 -0.38954897 
> 	• 41 -0.26500601 
> 	• 42 -0.14046305 
> 	• 43 -0.073401452 
> 	• 44 -0.12130259 
> 	• 45 -0.082981679 
> 	• 46 -0.14046305 
> 	• 47 -0.054240996 
> 	• 48 -0.082981679 
> 	• 49 -0.015920084 
> 	• 50 -0.073401452 
> 	• 51 -0.015920084 
> 	• 52 0.10862288 
> 	• 53 1.1816084 
> 	• 54 -1.3379915 
> 	• 55 -4.6335899 
> 	• 56 -6.74124 
> 	• 57 -4.7772933 
> 	• 58 -3.4839626 
> 	• 59 -2.075669 
> 	• 60 -1.0984858 
> 	• 61 -0.37038851 
> 	• 62 -0.063821223 
> 	• 63 0.11820311 
> 	• 64 0.13736356 
> 	• 65 0.15652401 
> 	• 66 0.11820311 
> 	• 67 0.32896812 
> 	• 68 0.27148675 
> 	• 69 0.30022744 
> 	• 70 0.31938789 
> 	• 71 0.3577088 0.5449999999999999 
> 	• 72 0.40560994 0.5449999999999999 
> 	• 73 0.44393085 0.5449999999999999 
> 	• 74 0.49183198 0.5449999999999999 
> 	• 75 0.67385632 0.5449999999999999 
> 	• 76 0.79839928 0.84 
> 	• 77 0.9995841 0.84 
> 	• 78 1.1528677 0.84 
> 	• 79 1.4115338 0.84 
> 	• 80 1.5552373 0.84 
> 	• 81 1.7468418 0.84 
> 	• 82 1.7755825 0.84 
> 	• 83 1.7276813 0.84 
> 	• 84 1.4115338 0.84 
> 	• 85 1.0858061 0.84 
> 	• 86 0.65469586 
> 	• 87 0.43435063 
> 	• 88 0.21400538 
> 	• 89 0.14694379 
> 	• 90 0.089462421 
> 	• 91 0.070301966 
> 	• 92 0.031981054 
> 	• 93 0.05114151 
> 	• 94 0.070301966 
> 	• 95 0.13736356 
> 	• 96 0.079882193 
> 	• 97 0.12778333 
> 	• 98 0.15652401 
> 	• 99 0.16610425 
> 	• 100 0.13736356 
> 	• 101 0.13736356 
> 	• 102 0.089462421 
> 	• 103 0.2523263 
> 	• 104 0.21400538 
> 	• 105 0.22358561 
> 	• 106 0.1852647 
> 	• 107 0.19484493 
> 	• 108 0.1852647 
> 	• 109 0.16610425 
> 	• 110 0.13736356 
> 	• 111 0.15652401 
> 	• 112 0.14694379 
> 	• 113 0.16610425 
> 	• 114 0.099042646 
> 	• 115 0.12778333 
> 	• 116 0.13736356 
> 	• 117 0.089462421 
> 	• 118 0.079882193 
> 	• 119 0.089462421 
> 	• 120 0.041561282 
> 	• 121 0.041561282 
> 	• 122 0.079882193 
> 	• 123 0.11820311 
> 	• 124 0.099042646 
> 	• 125 0.089462421 
> 	• 126 0.05114151 
> 	• 127 0.17568447 
> 	• 128 0.30022744 
> 	• 129 0.32896812 
> 	• 130 0.42477039 
> 	• 131 0.17568447 
> 	• 132 0.022400826 
> 	• 133 -0.20752464 
> 	• 134 -0.24584556 
> 	• 135 -0.24584556 
> 
> 
> 
> ------------------------------------------------------------------------------
> Don't Limit Your Business. Reach for the Cloud.
> GigeNET's Cloud Solutions provide you with the tools and support that
> you need to offload your IT needs and focus on growing your business.
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On 2015年07月09日 07:40, Jonno wrote:
> I was thinking of doing that or having 2 surface plots but I think it
> would be visually quite confusing.
> I was trying to think of an example since I'm sure someone has come up
> with a nice way to display this kind of data.
> Imagine if the data was average temperature (a) and average rainfall (b)
> for a region in the world (lat/long = x,y). The goal is to display the
> data such that it's obvious where the locations are that have closest to
> the ideal temp/rain combination.
> How would you go about that?
	It's not an easy thing to visualize in general. You might want to look 
at approaches to visualizing complex functions (i.e., functions whose 
input and output are both complex variables). These essentially map 
pairs (a, b) to pairs (x, y) as in your situation, and mathematicians 
have come up with various ways to visualize them. Some are described at 
https://www.pacifict.com/ComplexFunctions.html and the wikipedia article 
at https://en.wikipedia.org/wiki/Complex_analysis has some links in the 
references to web pages for graphing such functions.
	If the data are measured at (or can be reasonably reduced to) discrete 
points (as temp/rainfall are likely to be), another possibility is a 
scatterplot using, say, the color and size of the markers as indicators 
of the two variables (e.g., red/blue for hot/cold temp, larger/smaller 
circles for higher/lower rainfall).
	In some cases, like your example with temperature and rainfall, you may 
instead be able to combine the two output dimensions into a single one 
that somehow captures the overall "distance" from the ideal point. That 
is, for a given point, if your goal is to show how close it is to the 
ideal *combination* of temp and rain, you may not need to display how 
close it is on each dimension separately, but just how close it is to 
the ideal overall. Exactly how to compute this would vary based on the 
data (e.g., standardizing the values and taking the euclidean distance 
from the ideal).
	Your temp/rainfall example caught my eye because a few years ago I did 
a blog post on a similar topic, considering temperature and humidity 
(http://iq.brenbarn.net/2011/11/18/good-days-mate/). There I decided to 
graph just a single variable, namely the number of days on which either 
temperature *or* humidity is outside a "comfortable" range. Obviously 
this approach may not make sense for every situation. But what I mean 
is that, in some cases, you can use domain-specific knowledge about what 
the dimensions mean to combine them into one dimension that approximates 
what it is you're trying to illustrate with the graph.
-- 
Brendan Barnwell
"Do not follow where the path may lead. Go, instead, where there is no 
path, and leave a trail."
 --author unknown

On 07/09/2015 06:40 PM, peter wrote:
> hi,
>
> my code was working fine, but now i cant figure out what went wrong.
> any ideas?
>
> the code is supposed to plot a timeseries which it does and overlay it 
> with another that is partially defined
> the input file is contructed like this:
> the first line is just for information purposes.
> after that:
> the first row is a growing number (the x value), the second is the 
> timeseries and the third is the partially defined second timeseries
>
> this is the code, after the code is a example input file.
> the code is also accessible via this paste service: 
> https://dpaste.de/5ZrX it got a nice python code formatter.
>
ups, the last mail had a leading number from dpaste, this is the code 
without:
def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
 if verbose:
 print "plotTimeSeriesAndSAX()"
 print "\tinputfile:", inputfile_tmp
 print "\toutputfile: %s.png" % inputfile_tmp
 inputfile = open(inputfile_tmp, "r");
 # this holds my timeseries
 x = []
 y = []
 # this holds my "pattern"
 pattern_x_values = []
 pattern_y_values = []
 # these are for temporary use only, hold the current pattern data
 tmp_x = []
 tmp_y = []
 # remove pattern/sax string, sax_string_with_Z from the datafile, 
only used as text in the plot
 first_line = inputfile.readline()
 pattern, sax, sax_string_with_Z = first_line.split()
 for line in inputfile.readlines():
 data = line.split()
 x_data = data[0]
 y_data = data[1]
 #if there is a third line (pattern at this position)
 if len(data) == 3:
 y2_data = data[2]
 tmp_y.append(y2_data)
 tmp_x.append(x_data)
 else:
 # if the pattern ends, add it to pattern_x/y_value and 
clear the tmp list
 if len(tmp_x) != 0:
 pattern_x_values.append(tmp_x)
 pattern_y_values.append(tmp_y)
 tmp_x = []
 tmp_y = []
 x.append(x_data)
 y.append(y_data)
 #if pattern == "ccd":
 # print "pattern x_values:", pattern_x_values
 # print "pattern y_values:", pattern_y_values
 if verbose:
 print "\ttimeseries y value", y
 print "pattern x_values:", pattern_x_values
 print "pattern y_values:", pattern_y_values
 colors = ["red", "magenta", "mediumblue", "darkorchid", "grey"]
 #linestyle = ["-", "--"]
 # without this, the second plot contains the first and the second
 # the third plot contains: the first, second and third
 plot.clf()
 # plot all my patterns into the plot
 for s in range(0,len(pattern_x_values)):
 #if verbose:
 # print "\tpattern x value:", pattern_x_values[s]
 # print "\tpattern y value:", pattern_y_values[s]
 plot.plot(pattern_x_values[s], pattern_y_values[s], colors[1])
 #plot.plot(x_all[0], y_all[0])
 import matplotlib.patches as mpatches
 #red_patch = mpatches.Patch(color='red', label='The red data')
 from time import gmtime, strftime
 current_date = strftime("%Y-%m-%d %H:%M:%S", gmtime())
 fig = plot.figure()
 fig.text(0, 0, 'bottom-left corner')
 fig.text(0, 1, current_date, ha='left', va='top')
 mytext = "pattern: %s sax: %s sax with Z: %s" % (pattern, sax, 
sax_string_with_Z)
 fig.text(1,1, mytext )
 # add the original timeseries to the plot
 plot.plot(x,y, "forestgreen")
 #if pattern == "ccd":
 # plot.show()
 directory, filename = os.path.split(inputfile_tmp)
 plot.savefig(os.path.join(directory, "plots/%s.png" % filename))#, 
bbox_inches='tight')
 # remove the last figure from memory
 #plot.close()
dee ccccccccccaacddeedcccccccdc ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
1 -0.015920084
2 -0.044660769
3 -0.044660769
4 -0.092561907
5 0.012820599
6 -0.015920084
7 0.012820599
8 -0.054240996
9 0.031981054
10 0.031981054
11 -0.025500313
12 -0.044660769
13 0.012820599
14 -0.025500313
15 0.0032403709
16 -0.006339857
17 0.0032403709
18 -0.025500313
19 0.031981054
20 0.031981054
21 0.031981054
22 0.022400826
23 0.031981054
24 0.05114151
25 0.079882193
26 0.05114151
27 0.05114151
28 0.05114151
29 0.099042646
30 0.060721738
31 -0.015920084
32 -0.054240996
33 0.23316584
34 0.26190652
35 0.37686926
36 0.12778333
37 -0.044660769
38 -0.26500601
39 -0.41828965
40 -0.38954897
41 -0.26500601
42 -0.14046305
43 -0.073401452
44 -0.12130259
45 -0.082981679
46 -0.14046305
47 -0.054240996
48 -0.082981679
49 -0.015920084
50 -0.073401452
51 -0.015920084
52 0.10862288
53 1.1816084
54 -1.3379915
55 -4.6335899
56 -6.74124
57 -4.7772933
58 -3.4839626
59 -2.075669
60 -1.0984858
61 -0.37038851
62 -0.063821223
63 0.11820311
64 0.13736356
65 0.15652401
66 0.11820311
67 0.32896812
68 0.27148675
69 0.30022744
70 0.31938789
71 0.3577088 0.5449999999999999
72 0.40560994 0.5449999999999999
73 0.44393085 0.5449999999999999
74 0.49183198 0.5449999999999999
75 0.67385632 0.5449999999999999
76 0.79839928 0.84
77 0.9995841 0.84
78 1.1528677 0.84
79 1.4115338 0.84
80 1.5552373 0.84
81 1.7468418 0.84
82 1.7755825 0.84
83 1.7276813 0.84
84 1.4115338 0.84
85 1.0858061 0.84
86 0.65469586
87 0.43435063
88 0.21400538
89 0.14694379
90 0.089462421
91 0.070301966
92 0.031981054
93 0.05114151
94 0.070301966
95 0.13736356
96 0.079882193
97 0.12778333
98 0.15652401
99 0.16610425
100 0.13736356
101 0.13736356
102 0.089462421
103 0.2523263
104 0.21400538
105 0.22358561
106 0.1852647
107 0.19484493
108 0.1852647
109 0.16610425
110 0.13736356
111 0.15652401
112 0.14694379
113 0.16610425
114 0.099042646
115 0.12778333
116 0.13736356
117 0.089462421
118 0.079882193
119 0.089462421
120 0.041561282
121 0.041561282
122 0.079882193
123 0.11820311
124 0.099042646
125 0.089462421
126 0.05114151
127 0.17568447
128 0.30022744
129 0.32896812
130 0.42477039
131 0.17568447
132 0.022400826
133 -0.20752464
134 -0.24584556
135 -0.24584556

Fails on MacOSX backend.
Just tried it, and it works fine with the QT backend.
So I guess a MacOSX bug...
Thanks for your help,
Mark
On Thu, Jul 9, 2015 at 6:18 PM, Sterling Smith <sm...@fu...>
wrote:
> Works for me with TkAgg backend on 1.4.3.
>
> -Sterling
>
> On Jul 9, 2015, at 3:52AM, Mark Bakker <ma...@gm...> wrote:
>
> > Hello list,
> >
> > I am trying to set the backgroundcolor of a textbox:
> >
> > from pylab import *
> > plot([1, 2, 3])
> > text(1, 2, 'Hello', backgroundcolor = 'red')
> >
> > This plots a nice red box but no text. It looks like the backgroundcolor
> is set as the foreground. Am I doing something wrong or is this a bug? mpl
> version 1.4.3
> >
> > Thanks, Mark
> >
> >
> ------------------------------------------------------------------------------
> > Don't Limit Your Business. Reach for the Cloud.
> > GigeNET's Cloud Solutions provide you with the tools and support that
> > you need to offload your IT needs and focus on growing your business.
> > Configured For All Businesses. Start Your Cloud Today.
> >
> https://www.gigenetcloud.com/_______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

hi,
my code was working fine, but now i cant figure out what went wrong.
any ideas?
the code is supposed to plot a timeseries which it does and overlay it 
with another that is partially defined
the input file is contructed like this:
the first line is just for information purposes.
after that:
the first row is a growing number (the x value), the second is the 
timeseries and the third is the partially defined second timeseries
this is the code, after the code is a example input file.
the code is also accessible via this paste service: 
https://dpaste.de/5ZrX it got a nice python code formatter.
 1. def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
 2.
 3. if verbose:
 4. print "plotTimeSeriesAndSAX()"
 5. print "\tinputfile:", inputfile_tmp
 6. print "\toutputfile: %s.png" % inputfile_tmp
 7.
 8. inputfile = open(inputfile_tmp, "r");
 9.
10.
11. # this holds my timeseries
12. x = []
13. y = []
14.
15. # this holds my "pattern"
16. pattern_x_values = []
17. pattern_y_values = []
18.
19. # these are for temporary use only, hold the current pattern data
20. tmp_x = []
21. tmp_y = []
22.
23.
24. # remove pattern/sax string, sax_string_with_Z from the datafile,
 only used as text in the plot
25. first_line = inputfile.readline()
26. pattern, sax, sax_string_with_Z = first_line.split()
27.
28.
29.
30.
31. for line in inputfile.readlines():
32.
33. data = line.split()
34. x_data = data[0]
35. y_data = data[1]
36.
37. #if there is a third line (pattern at this position)
38. if len(data) == 3:
39. y2_data = data[2]
40. tmp_y.append(y2_data)
41. tmp_x.append(x_data)
42. else:
43. # if the pattern ends, add it to pattern_x/y_value and clear the tmp
 list
44. if len(tmp_x) != 0:
45. pattern_x_values.append(tmp_x)
46. pattern_y_values.append(tmp_y)
47. tmp_x = []
48. tmp_y = []
49.
50.
51. x.append(x_data)
52. y.append(y_data)
53.
54. #if pattern == "ccd":
55. # print "pattern x_values:", pattern_x_values
56. # print "pattern y_values:", pattern_y_values
57. if verbose:
58. print "\ttimeseries y value", y
59. print "pattern x_values:", pattern_x_values
60. print "pattern y_values:", pattern_y_values
61.
62.
63.
64. colors = ["red", "magenta", "mediumblue", "darkorchid", "grey"]
65. #linestyle = ["-", "--"]
66.
67. # without this, the second plot contains the first and the second
68. # the third plot contains: the first, second and third
69. plot.clf()
70.
71. # plot all my patterns into the plot
72. for s in range(0,len(pattern_x_values)):
73. #if verbose:
74. # print "\tpattern x value:", pattern_x_values[s]
75. # print "\tpattern y value:", pattern_y_values[s]
76.
77. plot.plot(pattern_x_values[s], pattern_y_values[s], colors[1])
78.
79.
80. #plot.plot(x_all[0], y_all[0])
81.
82.
83. import matplotlib.patches as mpatches
84.
85.
86. #red_patch = mpatches.Patch(color='red', label='The red data')
87.
88. from time import gmtime, strftime
89. current_date = strftime("%Y-%m-%d%H:%M:%S", gmtime())
90.
91.
92. fig = plot.figure()
93.
94.
95. fig.text(0, 0, 'bottom-left corner')
96. fig.text(0, 1, current_date, ha='left', va='top')
97. mytext = "pattern: %ssax: %ssax with Z: %s" % (pattern, sax,
 sax_string_with_Z)
98. fig.text(1,1, mytext )
99.
100.
101. # add the original timeseries to the plot
102. plot.plot(x,y, "forestgreen")
103. #if pattern == "ccd":
104. # plot.show()
105.
106.
107. directory, filename = os.path.split(inputfile_tmp)
108.
109. plot.savefig(os.path.join(directory, "plots/%s.png" % filename))#,
 bbox_inches='tight')
110. # remove the last figure from memory
111. #plot.close()
112.
113.
114.
115.
116.
117.
118.
119.
120. #input:
121. dee ccccccccccaacddeedcccccccdc ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
122. 1 -0.015920084
123. 2 -0.044660769
124. 3 -0.044660769
125. 4 -0.092561907
126. 5 0.012820599
127. 6 -0.015920084
128. 7 0.012820599
129. 8 -0.054240996
130. 9 0.031981054
131. 10 0.031981054
132. 11 -0.025500313
133. 12 -0.044660769
134. 13 0.012820599
135. 14 -0.025500313
136. 15 0.0032403709
137. 16 -0.006339857
138. 17 0.0032403709
139. 18 -0.025500313
140. 19 0.031981054
141. 20 0.031981054
142. 21 0.031981054
143. 22 0.022400826
144. 23 0.031981054
145. 24 0.05114151
146. 25 0.079882193
147. 26 0.05114151
148. 27 0.05114151
149. 28 0.05114151
150. 29 0.099042646
151. 30 0.060721738
152. 31 -0.015920084
153. 32 -0.054240996
154. 33 0.23316584
155. 34 0.26190652
156. 35 0.37686926
157. 36 0.12778333
158. 37 -0.044660769
159. 38 -0.26500601
160. 39 -0.41828965
161. 40 -0.38954897
162. 41 -0.26500601
163. 42 -0.14046305
164. 43 -0.073401452
165. 44 -0.12130259
166. 45 -0.082981679
167. 46 -0.14046305
168. 47 -0.054240996
169. 48 -0.082981679
170. 49 -0.015920084
171. 50 -0.073401452
172. 51 -0.015920084
173. 52 0.10862288
174. 53 1.1816084
175. 54 -1.3379915
176. 55 -4.6335899
177. 56 -6.74124
178. 57 -4.7772933
179. 58 -3.4839626
180. 59 -2.075669
181. 60 -1.0984858
182. 61 -0.37038851
183. 62 -0.063821223
184. 63 0.11820311
185. 64 0.13736356
186. 65 0.15652401
187. 66 0.11820311
188. 67 0.32896812
189. 68 0.27148675
190. 69 0.30022744
191. 70 0.31938789
192. 71 0.3577088 0.5449999999999999
193. 72 0.40560994 0.5449999999999999
194. 73 0.44393085 0.5449999999999999
195. 74 0.49183198 0.5449999999999999
196. 75 0.67385632 0.5449999999999999
197. 76 0.79839928 0.84
198. 77 0.9995841 0.84
199. 78 1.1528677 0.84
200. 79 1.4115338 0.84
201. 80 1.5552373 0.84
202. 81 1.7468418 0.84
203. 82 1.7755825 0.84
204. 83 1.7276813 0.84
205. 84 1.4115338 0.84
206. 85 1.0858061 0.84
207. 86 0.65469586
208. 87 0.43435063
209. 88 0.21400538
210. 89 0.14694379
211. 90 0.089462421
212. 91 0.070301966
213. 92 0.031981054
214. 93 0.05114151
215. 94 0.070301966
216. 95 0.13736356
217. 96 0.079882193
218. 97 0.12778333
219. 98 0.15652401
220. 99 0.16610425
221. 100 0.13736356
222. 101 0.13736356
223. 102 0.089462421
224. 103 0.2523263
225. 104 0.21400538
226. 105 0.22358561
227. 106 0.1852647
228. 107 0.19484493
229. 108 0.1852647
230. 109 0.16610425
231. 110 0.13736356
232. 111 0.15652401
233. 112 0.14694379
234. 113 0.16610425
235. 114 0.099042646
236. 115 0.12778333
237. 116 0.13736356
238. 117 0.089462421
239. 118 0.079882193
240. 119 0.089462421
241. 120 0.041561282
242. 121 0.041561282
243. 122 0.079882193
244. 123 0.11820311
245. 124 0.099042646
246. 125 0.089462421
247. 126 0.05114151
248. 127 0.17568447
249. 128 0.30022744
250. 129 0.32896812
251. 130 0.42477039
252. 131 0.17568447
253. 132 0.022400826
254. 133 -0.20752464
255. 134 -0.24584556
256. 135 -0.24584556