Skip to main content

Why I love matplotlib + python


Note this graph. The upper trace just goes from 0 to 1.6ms. The lower trace was a series of such waveforms ocurring from 0 to 10 s. I zoomed in to see one wave. When you do that in Matlab the tick labels would take the form 8.168, 8.169, 8.170 which is legitimate, but gets hard to read when you zoom in a lot and you get lost in the digits. Matplotlib, on the other hand, produces the zoom plot pictured here. It shows you the offset on the right (8.168) and then just shows you the vernier values. Now THAT is what I call service. The matplotlib guys are really good. Its like they actually use their product for data analysis. They must be scientists or something :).
Labels:

Comments

  1. DarrenMarch 21, 2009 at 5:15 PM

    I created that particular feature. Thank you for the compliment!

    ReplyDelete

Post a Comment

Popular posts from this blog

Remove field code from Word document

e.g. before submitting a MS, or hand manipulating some formatting because Word does things (like cross-references) so half-assed [from here ] Select all the text (CTRL-A) Press Ctrl+Shift+F9 Editing to remove anonymous comments that only contain thanks. I really appreciate the thanks, but it makes it harder to find comments that carry pertinent information. I'm also going to try and paste informative comments in the body of the post to make them easier to find.
24 comments
Read more

h5py and multiprocessing

The HDF5 format has been working awesome for me, but I ran into danger when I started to mix it with multiprocessing. It was the worst kind of danger: the intermittent error. Here are the dangers/issues in order of escalation (TL;DR is use a generator to feed data from your file into the child processes as they spawn. It's the easiest way. Read on for harder ways.) An h5py file handle can't be pickled and therefore can't be passed as an argument using pool.map() If you set the handle as a global and access it from the child processes you run the risk of racing which leads to corrupted reads. My personal runin was that my code sometimes ran fine but sometimes would complain that there are NaNs or Infinity in the data. This wasted some time tracking down. Other people have had this kind of problem [ 1 ]. Same problem if you pass the filename and have the different processes open individual instances of the file separately. The hard way to solve this problem is to sw...
Post a Comment
Read more

Reading spreadsheet data in Python: The lack of a good ODS reader

I try and keep long term data in as simple a format as possible, which means text where ever possible. In earlier times I would enter data in excel spreadsheets and then read them from my Python programs using the xlrd package which is excellent. This works well, but in the back of my mind is the thought that someday Microsoft might do something funny with their business model making office software more janky to use and all my fears about keeping data in proprietary formats would come true. Oh, look, that day is today . So, I'm completely abandoning the MS Office suite and going back to basic text files. However, there is a tension between keeping tabulated data in a simple form, such as csv, and entering it in a convenient manner. Excel, of course, nags you everytime you edit a csv file and save it. Libreoffice is excellent: it handles loading and saving in a very streamlined fashion. However, every time you open up the csv file you need to tell Calc what widths you want...
Post a Comment
Read more