Anyone here using #Python for their DSP work? I know @jms_nh (Aventures in Signal Processing with Python), @tpuolivali, @cfelton (many posts) and @AllenDowney (Think DSP) are using Python. Who else?
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Thank you! #FAQ
I can provide a little feedback on my experience. Personally, I have found Python to be a great tool for signal processing (DSP) design and analysis. It should be noted that we are discussing using Python similar to the Matlab, Scilab, Mathematica, etc. and not for real-time DSP implementation (e.g. programming a DSP processor).
For me, Python is a powerful generic programming language that has become very popular with scientist and engineers. My personal opinion, the popularity in these domains is because it is a high-level programming language and the language designers have done a great job. In many ways, Python allows me to focus on the task at hand and less on the programming. I can use Python for many programming tasks not just multiplying matrices.
Two books that specifically use Python and signal processing:
- Think DSP by Allen Downey (already mentioned in the OP)
- Signal and systems for dummies by Mark Wickert (discloser Dr. Wickert was my advisor)
Most of functions DSP folks are familiar with are available in two Python packages: `numpy` and `scipy.signal`.
- scipy.signal (along with numpy for low-level numerics and matplotlib for plotting) is a must for doing signal processing in Python (lti, lsim, lsim2, step, and step2 are the ones I've used most often.) I used it in my Padé article quite extensively.
- The python-control library https://github.com/python-control/python-control may be of interest also
@stephaneb Thanks for mentioning Think DSP!
Everyone else: You can download the PDF or read it online at http://think-dsp.com
Think DSP is in technical review now and will be published in July. So if you get a chance to look at it in the next few weeks, comments and corrections are particularly welcome. (They will still be welcome after that, but harder to fix).
I still use Octave and Matlab for high-level analysis scripting, but I contributed to a free online course that uses Python for its audio signal processing examples:
https://www.coursera.org/course/audio
I found the Python onboarding to be helpful and effective.
- Julius
I've used Xavier Serra's sms-tools, available at https://github.com/MTG/sms-tools
It's a bit clunky, but interesting. I used it for his Coursera MOOC, Audio Signal Processing for Music Applications, which is a great course.
There's also a fork of sms-tools that has been optimized, at https://github.com/bzamecnik/sms-tools
Here is a list of dsprelated posts that have used Python:
- Pade delay is okay today
- Autocorrelation and the case of the missing fundamental
- Generating pink noise
- Amplitude modulation and the sampling theorem
- Differentiating and integrating discrete signals
- 60 numbers
- Approximating the area of a chirp by fitting a polynomial
- Bayes meets Fourier
- Ten little algorithms, part 2
- Constrained integer behavior
- Adventures in signal processing with Python
- Python scipy.signal IIR filtering: an example
- Polyphase filters and filterbanks
- Python scipy.signal IIR filter
- Python scipy.signal IIR filter cont.
- Python number crunching faster?
The above is not an exhaustive list.
Apart from the other things that have been mentioned before, I also like Python Data Analysis Library (pandas). Reading data in from files is a breeze with pandas. For example, say you have a large CSV file with multiple columns (with first row being labels for your columns). Let us your columns are named column1, column2 etc.,These are a few things you can do:
import pandas as pd df = pd.read_csv(fileName.csv, index_col=False) column1 = df['column1'] column2_unique = df['column2'].unique() # Filtering filter = df['column3'] == 50 column3_50 = df[filter]['column3'] # Check if you have a field if('column4' in df): print "column4 exists"
@macsdev, I agree pandas is a very useful package. I don't know if it is just me or if it is a general trend that data folks seem to use column-data whereas signal folks use row-data.