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I often find myself wondering what the field at large is using. I still use matlab daily in my job (signal processing), and have octave on my personal laptop.

I often find myself wondering what the field at large is using. I still use matlab daily in my job (signal processing), and have octave on my personal laptop. Ive played w/ the anaconda/spyder/scipi/numpi suite on my own a bit, mostly cuz it seems thats gotten some momentum, but i dont have a big use for it outside work. We are pretty deep in having a lot of tooling built up in matlab, and i dont see us leaving it anytime soon, and to be honest, I love it, probably cuz thats what im most proficient at.

jacksonkmarley 6y ago
I'm interested to know how you rate the matlab alternatives on interacting visually with the data? I often want to do things in Matlab like plot a largeish data set, then zoom in to different features, or open an array of data in a window and check values. How is octave for that sort of thing? How about python?
- alexhutcheson 6y ago
  Octave is ok for that, but not as good as Matlab. All the Matlab plotting functions work, but the plots don't look as nice, and require more tweaking to get them to "publication quality". Octave includes a Qt-based GUI, but it is not nearly as polished as the Matlab GUI. Python is also just ok. You can use matplotlib[1] to create plots using an interface explicitly modeled on the Matlab interface, or use higher-level wrappers like Seaborn[2] which reduce the amount of work required to create nice-looking plots of certain types. "Zoom to different features" can be done via Pandas[3], or directly via NumPy, although the syntax can get a little cumbersome. The best Matlab-like GUI is probably "Scientific mode" in PyCharm[4], which requires a paid license. Jupyter notebooks[5] are a free option, but don't include all the features you'd have in a Matlab-style GUI. R is, in my opinion, best-in-class for exploratory data analysis. The combination of ggplot2[6] and dplyr[7] makes plotting, drill-down, aggregation, etc. easier than any other environment I've used. Chapters 2 and 4 of the free "R for Data Science" book[8] provide great examples of this. The RStudio[9] GUI is comparable to Matlab in features and overall polish, and it's available with either an AGPLv3 or commercial license. Jupyter notebooks also work. The major downside of R is that it's very optimized for interactive use, which makes deploying it to any sort of production environment tricky and error-prone. I'd mostly advise doing your ad hoc analysis in R, but rewriting things in another language if you want to deploy them. The other downside is that all the major deep learning libraries use Python, so if you want to do work in that space you should really just use Python. [1] https://matplotlib.org/ https://matplotlib.org/ [2] https://seaborn.pydata.org/ https://seaborn.pydata.org/ [3] https://pandas.pydata.org/docs/user_guide/indexing.html https://pandas.pydata.org/docs/user_guide/indexing.html [4] https://www.jetbrains.com/help/pycharm/matplotlib-support.html#scientific-project https://www.jetbrains.com/help/pycharm/matplotlib-support.ht... [5] https://jupyter-notebook.readthedocs.io/en/stable/notebook.html https://jupyter-notebook.readthedocs.io/en/stable/notebook.h... [6] https://ggplot2.tidyverse.org/ https://ggplot2.tidyverse.org/ [7] https://dplyr.tidyverse.org/ https://dplyr.tidyverse.org/ [8] https://r4ds.had.co.nz/ https://r4ds.had.co.nz/ [9] https://rstudio.com/products/rstudio/ https://rstudio.com/products/rstudio/
  - jacksonkmarley 6y ago
    Hey, thanks for the great detailed answer!

jacksonkmarley 6y ago

I'm interested to know how you rate the matlab alternatives on interacting visually with the data? I often want to do things in Matlab like plot a largeish data set, then zoom in to different features, or open an array of data in a window and check values. How is octave for that sort of thing? How about python?

alexhutcheson 6y ago
Octave is ok for that, but not as good as Matlab. All the Matlab plotting functions work, but the plots don't look as nice, and require more tweaking to get them to "publication quality". Octave includes a Qt-based GUI, but it is not nearly as polished as the Matlab GUI. Python is also just ok. You can use matplotlib[1] to create plots using an interface explicitly modeled on the Matlab interface, or use higher-level wrappers like Seaborn[2] which reduce the amount of work required to create nice-looking plots of certain types. "Zoom to different features" can be done via Pandas[3], or directly via NumPy, although the syntax can get a little cumbersome. The best Matlab-like GUI is probably "Scientific mode" in PyCharm[4], which requires a paid license. Jupyter notebooks[5] are a free option, but don't include all the features you'd have in a Matlab-style GUI. R is, in my opinion, best-in-class for exploratory data analysis. The combination of ggplot2[6] and dplyr[7] makes plotting, drill-down, aggregation, etc. easier than any other environment I've used. Chapters 2 and 4 of the free "R for Data Science" book[8] provide great examples of this. The RStudio[9] GUI is comparable to Matlab in features and overall polish, and it's available with either an AGPLv3 or commercial license. Jupyter notebooks also work. The major downside of R is that it's very optimized for interactive use, which makes deploying it to any sort of production environment tricky and error-prone. I'd mostly advise doing your ad hoc analysis in R, but rewriting things in another language if you want to deploy them. The other downside is that all the major deep learning libraries use Python, so if you want to do work in that space you should really just use Python. [1] https://matplotlib.org/ https://matplotlib.org/ [2] https://seaborn.pydata.org/ https://seaborn.pydata.org/ [3] https://pandas.pydata.org/docs/user_guide/indexing.html https://pandas.pydata.org/docs/user_guide/indexing.html [4] https://www.jetbrains.com/help/pycharm/matplotlib-support.html#scientific-project https://www.jetbrains.com/help/pycharm/matplotlib-support.ht... [5] https://jupyter-notebook.readthedocs.io/en/stable/notebook.html https://jupyter-notebook.readthedocs.io/en/stable/notebook.h... [6] https://ggplot2.tidyverse.org/ https://ggplot2.tidyverse.org/ [7] https://dplyr.tidyverse.org/ https://dplyr.tidyverse.org/ [8] https://r4ds.had.co.nz/ https://r4ds.had.co.nz/ [9] https://rstudio.com/products/rstudio/ https://rstudio.com/products/rstudio/
- jacksonkmarley 6y ago
  Hey, thanks for the great detailed answer!