Re: [Matplotlib-users] Efficient matplotlib use on iOS and Android apps
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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] question on spherical coordinate plots
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: romain.ma...@cern.ch
Tel. : +33 (0)4 73 40 71 57
Off. : 8204-8205
=
--
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[Matplotlib-users] how to install freetype & png for matplotlib
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. -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] how to install freetype & png for matplotlib
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 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. > > > -- > 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 > Matplotlib-users@lists.sourceforge.net > 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] Efficient matplotlib use on iOS and Android apps
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 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 > Matplotlib-users@lists.sourceforge.net > 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] question on spherical coordinate plots
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 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: romain.ma...@cern.ch
> 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
> Matplotlib-users@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
--
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Re: [Matplotlib-users] How to visualize a, b results of x, y variables
Thanks for all the ideas. On Thu, Jul 9, 2015 at 8:09 PM, Joy merwin monteiro wrote: > Maybe you could plot the ratio? That should give you rainfall per degree > Celsius. > On 9 Jul 2015 20:11, "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? >> >> On Thu, Jul 9, 2015 at 12:28 AM, Sterling Smith >> 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 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 >>> > Matplotlib-users@lists.sourceforge.net >>> > 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 >> Matplotlib-users@lists.sourceforge.net >> 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] New matplotlib book: Mastering matplotlib
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 -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
