PDF Download Think DSP: Digital Signal Processing in Python
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Think DSP: Digital Signal Processing in Python
PDF Download Think DSP: Digital Signal Processing in Python
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Book Description
Digital Signal Processing in Python
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About the Author
Allen Downey is a Professor of Computer Science at Olin College of Engineering. He has taught at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master's and Bachelor's degrees from MIT.
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Product details
Paperback: 168 pages
Publisher: O'Reilly Media; 1 edition (August 4, 2016)
Language: English
ISBN-10: 1491938455
ISBN-13: 978-1491938454
Product Dimensions:
7 x 0.4 x 9.2 inches
Shipping Weight: 11.2 ounces (View shipping rates and policies)
Average Customer Review:
3.4 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#433,110 in Books (See Top 100 in Books)
Good intro to dsp for beginners
Simple and good book that explains signal processing basics with python samples. Very good approach for learning DSP and python.
Excellent book to explore digital signal processing. Follow the author's advice to install "Anaconda" and use "Spyder" to open the programs he provides for download. This is a clear and concise way to play with advanced concepts for processing signals. Recommend "Practical Signal Processing" by Mark Owen as an adjunct which takes a deeper dive into the underlying math.
Echoing another reviewer, the custom code requirement means you learn their custom code rather than, you know, the standard modules numpy and scipy. For example, at least four separate classes are required, representing hundreds of lines of code, are required just to execute the first six lines of code in the book. All those lines do is define two signals, a cosine and a sine, sums them, then plots them. This, infuriatingly, hides some basic steps. Here's how you can create a cosine wave with frequency 440Hz:duration = 0.5framerate = 11025n = round(duration*framerate)ts = np.arange(n)/framerateamp = 1.0freq = 440offset = 0.0cos_sig = amp * numpy.cos( 2*numpy.pi*ts*freq + offset)freq = 880sin_sig = amp * numpy.sin( 2*numpy.pi*ts*freq + offset)Instead, these clowns havecos_sig = thinkdsp.CosSignal(freq=440,amp=1.0,offset=0)sin_sig = thinkdsp.SinSignal(freq=440,amp=1.0,offset=0)mix = cos_sig + sin_sigwhere CosSignal and SinSignal are custom classes, not functions, which inherits four separate classes, NONE of which are necessary, and all of which serve to make things more complex than necessary, on the pretense this makes things easier. The classes these class inherit are a generic Sinusoid and SumSignal classes, which inherits a Signal class, which depends on a Wave class, which performs plotting using pyplot in matplotlib. None of which make anything really any easier, but does serve to hide a lot of basic functionality, like hiding how to use numpy, matplotlib, and pyplot.In short, just to get through the first two pages, you have to have access to github to import their ridiculous thinkdsp, thinkplot, and thinkstats, totalling around 5500 lines of code, or you are just screwed and can't use this book. All decent teaching books develops code you need as necessary and do NOT require half a dozen files with thousands of lines of custom code just to get to page 2. What kind of clown does this when trying to write a book to show how to do basic signal processing? Someone not interested in teaching you DSP, but trying to show off their subpar programming skills by adding unnecessary complexity (a sure sign of a basic programmer, not a good).The authors openly admit their custom code is nothing more than wrappers in numpy and scipy, so the authors KNEW they were writing a crappy book and filling it with a LOT of unnecessary complexity. Bad code is bad code. Using bad code to teach makes bad teaching. It's obvious Allen B. Downey has spent his career in academia, where writing quality code doesn't matter.
O'Reilly needs to stop having their authors write custom code for their books. Teach with the standard toolboxes.
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