November 13th, 2020

There is no discussion whatsoever: Matplotlib is WAY more capable and flexible than Matlab plotting. If you mainly use these functions, then it should be very easy to port the Matplotlib code to native MATLAB. Matplotlib is a python library for visualizations and plotting. I'll also mention that SciPy is in use at some of the biggest companies in the world, and because of its stronger programming language base, it can be used for much larger problem sets than Matlab. Für more cool scientific stuff that is impossible in Matlab, have a look at pandas, scikit-learn, ipython and dask. Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB. 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, … While matplotlib is free, matlab costs money. And you don't even have to use Seaborn. It might not be a good general-purpose language, but it was never intended to be that. 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. It has different stateful APIs for plotting. I saw the argument, agreed, but disagreed in the logic presented that those contributing to Python+numpy+scipy+matplotlib didn't have a vested interest in those tools also being completely correct. But if you want to develop your field (like blending NN and DIP) you will fall in trouble .... The plotting library is also very easy to use and there are many helpful built in functions for matrix, linear algebra manipulations. Matplotlib: Matplotlib has multiple figures can be opened, but need to be closed explicitly. But, it is slow (execution time). Related to above, 3D plots are effectively useless. I already knew Python so I naturally tried numpy+scipy+matplotlib and was literally blown away. 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. Think about how people happily pay for Photoshop when there's GIMP. It is mainly designed to be easy to read and very simple to implement. Seaborn: Seaborn automates the creation of multiple figures. What are some alternatives to MATLAB and NumPy? Which do you prefer and why? I use pyqtgraph for my 3D plotting now and it's awesome but it's even less user friendly than matplotlib. Seaborn, and plotly come to mind but there are many others. Beginners Guide to SQLALchemy In Python For Database Operations. What was going on? It is most probably the first Python visualisation library Data Scientists will learn. In this case, I would default to Matplotlib or other visualisation libraries with better 3D visualisation support. The sage project (sagemath.org) has explicitly stated that its goal is to become a viable open source alternative to Mathematica, Matlab, and Maple. Matplotlib works with data frames and arrays. Both the popular visualization tools used in Python have differences in use cases, scalability and many other things. 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. not just matrix math) much much nicer than trying to work with MATLAB. I found a Java open source matrix library that, while not nearly as pretty as Python, got the job done. I don’t think this is a legitimate comparison (i.e. It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Oh, and great call on Clojure. Python is a high-level programming language. I had the same problem with python when I decided to develop a face detection software by useing Neural Networks ! What is MATLAB? Matplotlib. It turns out, when I ran the simulation from the command line, Matlab graciously accepted my 5-second simulation time. (For example, I later figured out a method I was calling was running all Python code, and if I had called a different method, it would have dropped directly into the fast C code.) You can get a cheap version of MATLAB when you're a student (or know a student) it used to have a limited matrix size (256*256 IIRC) but I think now it doesn't. I'm learning datamining, machine learning, image processing etc by myself now but will start uni next year probably doing the same. It's free, has a great community of incredibly smart people (it's the community with the highest ratio of PhDs to others that I've ever been a part of), and is fun to play with. Fundamental package for scientific computing with Python. With Altair’s declarative and high-level approach to plotting, it makes plotting complex machine learning models and much more difficult. I find matplotlib to very easy to use and creates beautiful plots but I haven't tried Matlab so can't compare obv. I hope you learned something new about both Matplotlib and Altair. The language is almost as cobbled together and inconsistent as PHP with the added bonus of being worked on almost exclusively by engineers instead of people who actually want to write programs (this coming from an engineering student here). Here's a link to NumPy's open source repository on GitHub. Now while Matlab gets a lot of flack, it does have some appeal. I port matlab code over to python pretty frequently. PDL is some inline C code so you get the benefit of both languages. You also get prettier graphics with MATLAB than gnuplot can do. Yes, libs are my biggest concern but Python has neural net libs, don't know the quality though. Did you profile your code? Not being a programmer by birth, I couldn't care less about the fact that it's not a general purpose language ; it is very adequate for my own purpose. 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. While it's a very impressive piece of software, it's on a different level than the plotting functionality of Matlab. 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. The controls lab in the aerospace department at the U of W had a wonderful inverted cart-pendulum device that a grad student was doing some seriously freaky non-linear control research on. Seaborn: Seaborn avoids a ton of boilerplate by providing default themes which are commonly used. I don't think MATLAB is in any danger tho', it does too much too well and has enormous mindshare and legacy (no-one doing civil or mechanical or aeronautical engineering cares what Google uses to show people ads - SRSLY). I've been wondering, are there Java libs as good as numpy/scipy? Numpy/Scipy syntax is very close to matlab, but python is a lot more powerful. Tastes vary (as the mix of comments here will attest), but having sampled a variety of development and analysis tools, I have settled on MATLAB as my tool of choice. MATLAB can be classified as a tool in the "Languages" category, while Matplotlib is grouped under "Charting Libraries". 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. Then there's the upcoming version 2.0, which focuses on improving default styles. And, Matlab is really really, annoyingly powerful. It may not be the same as MATLAB, but the functionality is there. Seaborn, on the other hand, provides a variety of visualization patterns. In my experience, it takes a proficient Matlab user two to three weeks to become productive in Python. 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. One thing for sure matlab plotting has more tutorials. color_map = cm.RdYlGn # reverse the colormap: cm.RdYlGn_r scalarMap = cm.ScalarMappable(norm=Normalize(vmin=min, vmax=max), … Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

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