Once you find what works, re-implementation in C++ is a breeze. I still really hate the 1 based array indexing after using it for years. They were both designed for engineers (I mean engineers, not computer programmers) to explore matrix models interactively, then save their work as scripts - you were never meant to use m-files for general purpose programming. If one is doing statistics then Seaborn is a good choice because it has a lot of things suitable for statistical tasks, built-in. 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? See Datapane roles →. Matlab was indeed stupid. Because of it’s low-level interface nature, plotting simple data will be easy. For every additional MATLAB toolbox you wish to install and run, you need to incur extra charges. Part of my reasoning can be found in the Nov-08-2006 posting to my Weblog, "Why MATLAB for Data Mining? Unfortunately, in any real project I've ever done with Matlab you cannot survive just inside the matrix DSL and once you start touching the bolted-on bits you lose patience very quickly. I've been having fun with it too. 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/. Python is a general purpose programming language created by Guido Van Rossum. Matlab is a numerical computing environment which includes extensive and excellent visualization and plotting tools. the GIMP isn’t a strictly more powerful but less focused superset of Photoshop’s features; instead it’s a strictly less powerful knock-off with an often unintelligible interface and much less polish). If you're also interested in a tutorial, this is a great one IMO. In this case, I would default to Matplotlib or other visualisation libraries with better 3D visualisation support. I really like Matlab's notation options and the idea of "handles"... does MatplotLib not really have anything like graphics-object handles? It turns out, when I ran the simulation from the command line, Matlab graciously accepted my 5-second simulation time. Also, NumPy and SciPy are being integrated with Sage. Matplotlib: Matplotlib is highly customizable and powerful. What are some alternatives to MATLAB and NumPy? I've been wondering, are there Java libs as good as numpy/scipy? First, lets analyse what Matplotlib is good at. Matplotlib: Figure; Matplotlib: Axes; Pyplot: plt.gcf() Pyplot: plt.gca() Pyplot: plt.cla() VS plt.clf() TL;DR: Matplotlib is the toolkit, PyPlot is an interactive way to use Matplotlib and PyLab is the same thing as PyPlot but with some extra shortcuts. Since I came here looking specifically for differences between Octave and Sage, and haven’t found much, I think it’s worth contributing a bit: I found a blog post from the original creator of Sage: Why isn't Sage just part of Octave? I talked to a classmate who was using Java, and not having any speed problems at all. 1. Matplotlib also offers a package for live animations. I finally had to give up, after investing my time and effort, because the platform was fundamentally and fatally incapable of doing what it was asked. I have worked in Matlab for years and years, and know most of its ugliness. We use python for pretty much everything in my research group -- high-level scripting of simulations written in C; controlling experiments over serial, GPIB, usb; and data analysis and plotting for publications. Access to any Java library with no extra effort on your part. It’s concept is based on MATLAB’s plotting API. And the visualization functions generally suck compared to the handy plot() of Matlab. Matplotlib is powerful and versatile, but compared to Matlab: Performance is an issue. Seaborn: Seaborn works with the dataset as a whole and is much more intuitive than Matplotlib. While it's a very impressive piece of software, it's on a different level than the plotting functionality of Matlab. One of the most unique features of Altair is the interactive plots. people analysing data in Python to publish interactive reports Here is a related, more direct comparison: NumPy vs SciPy, Functions, statements, plots, directory navigation easy, Parameter-value pairs syntax to pass arguments clunky, Does not support named function arguments, Doesn't allow unpacking tuples/arguments lists with *. I have observed (and taught) this in dozens of people ranging from undergrads to postdocs. are extremely customizeable and useful. Matplotlib: Matplotlib is a graphics package for data visualization in Python. It's really nice, definitely worth trying, your university probably already has it. Not sure about interoperability with other tools. I do research in graphics, and I find it extremely productive to test ideas in Matlab. I don't like the looks of the pylab graphs, I wouldn't put them in a paper. Python is an interpreted, interactive and object-oriented programming language similar to PERL or Ruby. Just watch out for one ideological difference: Matplotlib tries, above all, to be as precise as possible. That would have happened regardless of the language you chose. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. Seaborn, and plotly come to mind but there are many others. If you don't like Octave's language, you won't like MATLAB's, they're almost identical. But if you want to develop your field (like blending NN and DIP) you will fall in trouble .... It is an excellent domain-specific language. You've got almost anything to try your ideas, implement an academic paper. Seaborn works with the dataset as a whole and is much more intuitive than Matplotlib. It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. 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. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. By using Datapane, Anything that needs to be fast you can write in C/C++ and wrap with swig or ctypes so that you can still use a high-level language to run all your simulations, and do the data analysis as well. I would consider Matlab to be a DSL grown out of control. It is, after all, intended to duplicate this aspect of matlab. Matplotlib is highly customizable and powerful. But that doesn’t sound like what the question is really about. Here's a link to NumPy's open source repository on GitHub. I think it was the EM (expectation maximization) algorithm, though, which is hard to reduce entirely to matrix operations, at least for me it was. Tableau can help anyone see and understand their data. Sorry, it's just been too long now to remember the details. If you have just started out in Python, you might have heard of Matplotlib. Numpy/Scipy syntax is very close to matlab, but python is a lot more powerful. As a solution, you can mix python and libs like OpenCV for computer vision and image processing . MATLAB vs NumPy: What are the differences? The night before it was due, I rewrote the whole thing in Java and got it to finish running (I handed in a day late, but at least I had something to hand in.). The other big problem with Matlab is that because it's licensed, you can't just do what you like with it. Anyway, it's a strong enough system that I've used it to replace both Mathematica and Matlab in my daily activities. 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. The sage project (sagemath.org) has explicitly stated that its goal is to become a viable open source alternative to Mathematica, Matlab, and Maple. I port matlab code over to python pretty frequently. Better, the built in toolboxes have already solved huge (engineering) problem spaces. We also replace, e.g., 2^3 by 23. Space Telescope Science Institute (they run Hubble under a NASA contract) has contributed to development of numarray and numpy. You can configure numpy to use the MKL too. Constrained, simple and declarative to allow focus on the data rather than trivial issues such as formatting. lib for python. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
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