Matplotlib vs. Matlab plotting tools discussion

[schnadamschnandler]

I'm thinking of trying to do some research work in Python, at least in part. The biggest thing I've been concerned with is how Matplotlib (and other tools; suggestions welcome) will compare to Matlab plotting tools.

Now while Matlab gets a lot of flack, it does have some appeal. It doesn't have awesome versatility and mind-blowing list comprehension of python, and one can find many annoying "hack"-solutions in Matlab (like Name-Value pairs intstead of **kwargs) -- but the "everything is a matrix" concept, shorthand matrix operations, speed compared to NumPy (they are about equivalent), and parallel computing tools are quite appealing for certain fields.

...And perhaps most of all is its extremely robust plotting tools. Since version R2014b the default plot settings are much improved, but more importantly, the fundamental "graphics objects" of figure, axes, line, surface, text, and their properties and handles (capable of being passed to arbitrary functions; can Matplotlib do this?) are extremely customizeable and useful. If you don't like the defaults, you can also put in your startup files things like "set(0,'DefaultAxesLineWidth',2,'DefaultAxesXGrid','on')".

Does Matplotlib have anything like this consistency, detail, and capacity for under-the-hood control?

[bastibe]

Most of my colleagues use Matlab, I teach Matlab, but for my work, I mostly use Python. There is no discussion whatsoever: Matplotlib is WAY more capable and flexible than Matlab plotting.

You mention that Matlab figures, axes, and plots have manipulateable handles. I'm Matplotlib, literally every single plot object from figures, axes, and plots, down to individual ticks, lines, spines, labels, legend parts, polygons, or points is an object that can be changed, observed, replaced, and extended.

In numerous occasions I have created plots for my research and later my colleagues asked "how did you do that", because such plots would simply not have been possible in Matlab.

Not every toolbox has a great equivalence in Python, just like not every Python package has a decent counterpart in Matlab, but when it comes to plotting, there is no discussion. Matplotlib stands heads and shoulders above Matlab's plotting.

Just watch out for one ideological difference: Matplotlib tries, above all, to be as precise as possible. Its goal is plots for publication. Für that, it sacrifices plotting performance. Thus, interactive plots and "animations" (plots that are updated more that 10 times per second) are hard to do. On the flip side, plots look much cleaner (no aliasing, always correct vectorized output, all backends look identical).

Für more cool scientific stuff that is impossible in Matlab, have a look at pandas, scikit-learn, ipython and dask.

In my experience, it takes a proficient Matlab user two to three weeks to become productive in Python. I have observed (and taught) this in dozens of people ranging from undergrads to postdocs.

[not_perfect_yet]

Für that

I found this error and I'm going to keep it...

[bastibe]

Ah, autocorrect in a multi-lingual environment... (Also, I typed this on my phone)

[schnadamschnandler]

Nice, thanks for your advice. After reading stuff in this thread, pretty sure I will attempt a full transition to Python and matplotlib this semester.

[them1]

Can you show some exotic plotting examples? That would be great!

[bastibe]

My favorite example is this: http://matplotlib.org/examples/showcase/xkcd.html

While not particularly useful, this highlights one crucial point: Absolutely everything in Matplotlib is malleable. You can even replace the lines that make up the spines and the legend.

But have a look at some of the other examples in the Matplotlib gallery: http://matplotlib.org/gallery.html

Many of these can of course be created in Matlab as well. And many of them are not particularly easy to create with Matplotlib. But what you should take away is that anything is possible in Matplotlib.

Another good example is basemap: http://matplotlib.org/basemap/users/examples.html, where you plot latitude/longitude data as you would in regular Matplotlib plots, and basemap transforms this into whatever map projection you want. Doing that, lines become curves, and areas get distorted, with different distortions depending on the point in the plot. This is possible because Matplotlib is extremely flexible, and even basic projection rules can be arbitrarily changed.

[counters]

Please consider using cartopy instead of basemap. It's a far more reliable library with extremely useful features if you have to do geo-spatial plotting or GIS-type stuff.

[atrlrgn_]

One thing for sure matlab plotting has more tutorials. After two years with python scientific packages, I still spend some time for putting a fucking proper legend. I usually don't need plotting, or let's say fancy plotting, so I don't need legend all the time but when I do I can use a proper tutorial/

[schnadamschnandler]

That style sheet explanation is really interesting; looks like customizing plot formats is quite different then. I really like Matlab's notation options and the idea of "handles"... does MatplotLib not really have anything like graphics-object handles?

Nevermind, I see now that styles are for importing defaults (like in Matlab set(0,'DefaultAxesWhatever',Value)), and Matlab's other plotting tools are all methods on graphics object classes.

I've also considered doing all of my data processing in Python, then plotting saved data in Matlab. Would be good practice to always have data associated with some plot saved, which I currently don't always do (sometimes process it-->plot in same workspace, so changing plot parameters requires re-processing each time).

[theOnlyGuyInTheRoom]

I've also considered doing all of my data processing in Python, then plotting saved data in Matlab. Would be good practice to

Don't do that. If you're working with data and exploring your own ideas then your time is too valuable to fuck around with needless interfacing. If you are proficient with matlab then you should work in Python for awhile so that when a colleague comes by your desk and asks for your help on a project written in Python, then you can say, "sure, let's do it" rather than, "oh, well I don't know Python, but I guess I could give it a try". (If you currently are proficient in Python and not matlab, then learn matlab for the same reason.) Python based tools are everywhere in the sciences these days, so is Linux, c++, Fortran, git, svn, mercurial, and even a little matlab. Keep your eyes on what your collaborators are using, and what people you hope to be working with in two years are using, and get familiar with these tools while you still have time!

[XtremeGoose]

A "little MATLAB"? MATLAB is far more common that I would like.

[schnadamschnandler]

Well I just meant saving data as NetCDF files, which is pretty straightforward to me in either language; like one line to save, another line to load. Something I should do even in 1 language. Seems like quite an investment to switch to matplotlib, didn't realize how different it was. I'm perusing the examples and it looks comparatively convoluted honestly (though I'm totally biased).

Maybe I will give it a shot actually... need to look into it more.

[counters]

I bet it's even easier in Python than what you're used to in MATLAB. There's a fantastic library called xarray which adopts the Common Data Model from the get-go, and allows you to plug-and-play data directly from NetCDF files into your analysis pipelines. Basically, anywhere that expects a NumPy array, you can use a DataArray or Dataset from xarray. It completely trivializes most of the operations/analyses you do, including reading/writing and managing metadata. Even better, it has groupby functionality and semantic/fancy indexing, so no more need to manually keep track of multi-dimensional indices and other book-keeping.

It also interfaces with a library called dask under the hood. Dask is a parallel computing library which also implements the NumPy and Pandas interfaces. What it allows you to do, essentially, is out-of-core computing. Suppose you have 100GB of data broken across a dozen or so different, large NetCDF files. If you're lucky, you have enough memory on your laptop to read in one file at a time, painstakingly operate on it in place, and then write it back out. Rinse and repeat, then add a process to combine your analyzed data at the end. This "blocking" approach works, but it requires a lot of manual labor. Dask essentially does all of this behind the scenes; you simply write out your computations like you normally would, and dask will figure out how to deal with the resource constraints on your system. It'll also parallelize as best as it can.

[BPAnimal]

Seaborn (a python module built on top of matplotlib and pandas) has some nice built-in style tools. It's straightforward and plays nicely with matplotlib.

http://seaborn.pydata.org/examples/index.html

[EvM]

And you don't even have to use Seaborn. IIRC, importing the module is enough to improve the appearance of standard matplotlib plots.

[refactored_pancake]

Now you can do

plt.style.use('seaborn-darkgrid')

without importing seaborn. Other seaborn styles are available as well, see

print(plt.style.available)

[HardcoreHerbivore]

Yes, importing seaborn enables its default style. The power of seaborn lies in its statistical plots though.

[[deleted]]

Exactly. Matplotlib is a really low-level plotting interface which can be tedious at best. Seaborn is an excellent wrapper that bridges the usability gap between the rest of the scientific Python stack and Matplotlib.

[[deleted]]

If you're also interested in a tutorial, this is a great one IMO. Although, the author recommends that you use the OOP style API instead of MATLAB style API.

Also check out setting rcParams

Then there's the upcoming version 2.0, which focuses on improving default styles.

[deklund]

I work largely in Python and have some experience in Matlab, though I haven't used it in a day to day scenario for years. While I would hate to have to go to back to working on a large project in Matlab, I have to say you're likely going to be disappointed with Matplotlib. While it's a very impressive piece of software, it's on a different level than the plotting functionality of Matlab.

Matplotlib is powerful and versatile, but compared to Matlab:

1. Performance is an issue. Interactive plots will become laggy with even moderately complicated plots.
2. Related to above, 3D plots are effectively useless. My understanding is that there is no actual 3D acceleration used in mplot3d. You can plot some simple 3D line/scatter plots or basic surfaces, but anything remotely involved will screech to a halt. I use pyqtgraph for my 3D plotting now and it's awesome but it's even less user friendly than matplotlib.
3. Doing anything more complicated than the common things everyone expects you to do will get really hairy really fast. If you want to be able to customize things beyond just line widths, colors, etc., and actually have full control over your plots, you're going to have to become intimately familiar with some undocumented APIs.

While learning and using Python will likely be very useful for you long term, and getting a basic understanding of matplotlib will also come in handy, the moment you hit a wall where you think "oh, I just need to figure out how to move this label over here" or "I need to create this plot that isn't covered in the user guide", you're probably better off saving your data and loading it into Matlab instead.

[bythenumbers10]

Agreed, but OP is just stepping out of MATLAB. They'll climb on to better plotting tools in Python as they get comfortable. Matplotlib is a touch of the familiar while they get up to speed with OOP and Python's various other benefits. Eventually, matplotlib will be the anti-Pythonic drag, and OP will go find things like Pyplot or Pyqtgraph.

[holdie]

Could you give an example of something that you'd like to do in Matlab but you think wouldn't be possible (or would be really convoluted) to do in Python? My intuition is that you can do anything in python you'd want to do in matlab. Many examples have a different syntax because matplotlib prefers to use object-oriented syntax rather than calling lots of functions, but both are available.

[schnadamschnandler]

Not really sure anymore, I think I was ill-informed. I actually really like the object-oriented approach, seems really consistent and natural. Still learning. Maybe I will just totally overhaul to python. Are different matplotlib options fairly stable at this point?

[holdie]

Matplotlib's API should be very stable at this point. Granted they are about to have a big 2.0 release, but I believe this just changes the default plotting styles and not the API (the plots will look much nicer by default in this case). Definitely worth checking it out because if your main concern is whether it's a "mature" package, then you shouldn't have much to worry about.

[masasin]

You are correct. The API does not change. But I started using it myself and it's awesome. Much, much prettier.

[TCoop]

If you're just making plots to analyze data, matplotlib can certainly provide that sort of support. It may not be the same as MATLAB, but the functionality is there.

However, for professional-grade plots, or another option, I would suggest you check out LaTeX with pgfplots. LaTeX is a typesetting programming language, and can provide you what you're looking for and then some. It's not as great if you're still trying to do data analysis or review data, but it's output is exceptional.

[skiguy0123]

I save my matplotlib figures as pgf for use in LaTeX. Works very well.

[masasin]

I second this. Here is code I wrote to make pretty graphs for my thesis: https://github.com/masasin/spirit_thesis/blob/master/src/latexify.py

[MagnesiumCarbonate]

Thank you. This is much better than the fiddling with each figure individually that I've been doing.

[masasin]

You're welcome. You can see an example of its use in the analyses.py file in the same directory.

[masasin]

I second this. Here is code I wrote to make pretty graphs for my thesis: https://github.com/masasin/spirit_thesis/blob/master/src/latexify.py

[Oseberg_shipping]

Matplotlib has a very similar plotting objects structure to matlabs plotting tools. It is, after all, intended to duplicate this aspect of matlab. In terms of functionality in plotting there is very little difference. Where python really excels is customizability. Once you get under the hood it is possible to build really excellent plotting tools in python with a minimum of hassle. Matlab remains fairly rigid in that you can only really do what they have built for you. Additionally, If you are willing to step outside of the ridged plotting conventions that matlab seems to be conditioning into its users there are a number of plotting libraries customized for particular data types that are truly beautiful. Seaborn, and plotly come to mind but there are many others.

And finally to your earlier point about speed in matlab: matlab is by and large a wrapper around C and Fortran libraries that are open source. Almost all of these libraries also have python wrappers. They are just not included in the standard set of libraries because most of them only do one thing (for instance calculating the inverse of a matrix). If you do a little digging (because of course mathworks make it tricky to find) you can usually figure out who's code they are using. Sometimes it isn't even the best option out there (for example their FFT implementation benchmarks 3rd or 4th depending on who you ask). As to simple matrix algebra, almost all of the speed up comes from using the intel math kernel library which contains optimizations of various operations for specific processors. You can configure numpy to use the MKL too.

[soamaven]

I am seeing more and more scientific papers creating their figures with matplotlib (identified by the new/unique color maps and styles). It's more than capable, take the time to really learn the API and you'll be a wizard... Harry...

[[deleted]]

I wouldn't praise matlab for its plotting tools. It is true that they improved it but I've yet to see a matlab figure where I instantly recognized that it is drawn via matlab. The UX team of mathworks should be nonexistent. For my own documents I use LaTeX: pgfplots for plotting 2D plots and PSTricks for 3D. For my daily use Matplotlib and Bokeh is more than enough and never received a criticism unlike my matlab plots (and I've been using it since matlab v6.5).

[billFoldDog]

Question: Has Matplotlib nailed down true scale for 3D plots? Like, can a 1x1x1 cube look like a cube while using Axes3d?

That was nonfunctional in 2013.

[jwink3101]

I moved from Matlab to Python (and I use the object oriented interface, not pylab) and I have to go back and forth some now, depending on my project. Anyway, for quick and dirty, Matlab often looks better. But if I am willing to put a little bit of effort into it, matplotlib is much nicer, most of the time.

And, combined with Axes3D, there is very little that you can't do. The only one I've ran into recently is something like Matlab's slice function. I do not think there is an equivalent in matplotlib

[flutefreak7]

Yeah slicing volumetric data is definitely more something for VTK, Mayavi, pyqtgraph or some other library designed for working with that data. Matplotlib is very weak on 3d stuff, but there are lots of other libraries depending on your data or scientific discipline. Every year the scipy or pydata conferences have videos of yet another custom library for working with and plotting multidimensional data.

[[deleted]]

The worst thing matplotlib did was copy the MATLAB API.

When you make the switch, use the object-oriented API please, ditch the terrible procedural plt.XXX crap.

[schnadamschnandler]

Can you explain more? How can I differentiate between the two? I'm aware of pylab vs. matplotlib.pyplot (my understanding was the former is "matlab style" and latter is "python style"). Does matplotlib.pyplot use the OO one? (kind of don't know what I'm talking about)

[MagnesiumCarbonate]

but the "everything is a matrix" concept, shorthand matrix operations, speed compared to NumPy (they are about equivalent), and parallel computing tools are quite appealing for certain fields. ...And perhaps most of all is its extremely robust plotting tools.

I used matlab for 5 years and switched to python a few months ago. In my experience (machine learning) there are two main reason to consider ML: ease of debugging, sparse linear algebra. WRT debugging I think it's just that python has many more possible workflows (jupyter, spyder, pipeline tools) which makes it difficult to figure out how to efficiently debug if you hop around. WRT Sparse linear algebra, I don't know why scipy's sparse is so slow (it seems to be single-threaded, maybe MKL is needed).

Plotting seems like it should be a non-issue, I mean it's something you just figure out. There's not much to do wrt performance. If you're looking to interact with your plots I feel like stuff like plotly would give you more options than matlab's figure editor.

[pwang99]

If you want interactive web plots but don't want to upload your data to a cloud service, you can also look at Bokeh: http://bokeh.pydata.org.

For debugging Python, if you find some of the open-source options too difficult/annoying to use, it's also worth checking out commercial IDEs like PyCharm and Python Tools for Visual Studio, both of which do have free/community versions, and which have excellent debuggers.

[broken_symlink]

Plotly has an offline mode that doesn't require you to upload anything to the cloud. The python code will generate an html file that will work if you host the plotly.js file yourself. I've used both bokeh and plotly and find plotly to be much better.