Think DSP: Digital Signal Processing in Python, by Allen B. Downey.

Related tags

MiscellaneousThinkDSP
Overview

ThinkDSP

LaTeX source and Python code for Think DSP: Digital Signal Processing in Python, by Allen B. Downey.

The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. I am writing this book because I think the conventional approach to digital signal processing is backward: most books (and the classes that use them) present the material bottom-up, starting with mathematical abstractions like phasors.

With a programming-based approach, I can go top-down, which means I can present the most important ideas right away. By the end of the first chapter, you can break down a sound into its harmonics, modify the harmonics, and generate new sounds.

Here's a notebook that previews what you will see in Chapter 1:

And if you want to see where were headed, here's a preview of Chapter 10:

Running the code

Most of the code for this book is in Jupyter notebooks. If you are not familiar with Jupyter, you can run a tutorial by clicking here. Then select "Try Classic Notebook". It will open a notebook with instructions for getting started.

To run the ThinkDSP code, you have several options:

Option 1: Run the notebooks on Google Colab.

Option 2: Run the notebooks on Binder.

Option 3: Use Conda to install the libraries you need and run the notebooks on your computer.

Option 4: Use poetry to install the libraries you need and run the notebooks on your computer.

The following sections explain these options in detail.

Note: I have heard from a few people who tried to run the code in Spyder. Apparently there were problems, so I don't recommend it.

Option 1: Run on Colab

I have recently updated most of the notebooks in this repository so they run on Colab.

You can open any of them by clicking on the links below. If you want to modify and save any of them, you can use Colab to save a copy in a Google Drive or your own GitHub repo, or on your computer.

Option 2: Run on Binder

To run the code for this book on Binder, press this button:

Binder

It takes a minute or so to start up, but then you should see the Jupyter home page with a list of files. Click on code to open the folder with the notebooks, then click on one of the notebooks (with the .ipynb extension).

Option 3: Install Python+Jupyter

First, download the files from this repository. If you are a Git user, you can run

git clone --depth 1 https://github.com/AllenDowney/ThinkDSP.git

Otherwise you can download this Zip file and unzip it. Either way, you should end up with a directory called ThinkDSP.

Now, if you don't already have Jupyter, I highly recommend installing Anaconda, which is a Python distribution that contains everything you need to run the ThinkDSP code. It is easy to install on Windows, Mac, and Linux, and because it does a user-level install, it will not interfere with other Python installations.

Information about installing Anaconda is here.

If you have the choice of Python 2 or 3, choose Python 3.

There are two ways to get the packages you need for ThinkDSP. You can install them by hand or create a Conda environment.

To install them by hand run

conda install jupyter numpy scipy pandas matplotlib seaborn

Or, to create a conda environment, run

cd ThinkDSP
conda env create -f environment.yml
conda activate ThinkDSP

Option 4: Use poetry to manage the project on your computer or notebook locally.

First, download the files from this repository. If you are a Git user, you can run

git clone --depth 1 https://github.com/AllenDowney/ThinkDSP.git

Then, assuming you have poetry installed on your machine, run

cd ThinkDSP
poetry install

to install the libraries you need in a virtual environment. To activate the environment, run

poetry shell

Then you can run Jupyter.

Run Jupyter

To start Jupyter, run:

jupyter notebook

Jupyter should launch your default browser or open a tab in an existing browser window. If not, the Jupyter server should print a URL you can use. For example, when I launch Jupyter, I get

~/ThinkComplexity2$ jupyter notebook
[I 10:03:20.115 NotebookApp] Serving notebooks from local directory: /home/downey/ThinkDSP
[I 10:03:20.115 NotebookApp] 0 active kernels
[I 10:03:20.115 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
[I 10:03:20.115 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

In this case, the URL is http://localhost:8888. When you start your server, you might get a different URL. Whatever it is, if you paste it into a browser, you should see a home page with a list of directories.

Click on code to open the folder with the notebooks, then click on one of the notebooks (with the .ipynb extension).

Select the cell with the import statements and press "Shift-Enter" to run the code in the cell. If it works and you get no error messages, you are all set.

If you get error messages about missing packages, you can install the packages you need using your package manager, or install Anaconda.

If you run into problems with these instructions, let me know and I will make corrections. Good luck!

Freesound

Special thanks to Freesound (http://freesound.org), which is the source of many of the sound samples I use in this book, and to the Freesound users who uploaded those sounds. I include some of their wave files in the GitHub repository for this book, using the original file names, so it should be easy to find their sources.

Unfortunately, most Freesound users don't make their real names available, so I can only thank them using their user names. Samples used in this book were contributed by Freesound users: iluppai, wcfl10, thirsk, docquesting, kleeb, landup, zippi1, themusicalnomad, bcjordan, rockwehrmann, marchascon7, jcveliz. Thank you all!

Here are links to the sources:

http://www.freesound.org/people/iluppai/sounds/100475/

http://www.freesound.org/people/wcfl10/sounds/105977/

http://www.freesound.org/people/Thirsk/sounds/120994/

http://www.freesound.org/people/ciccarelli/sounds/132736/

http://www.freesound.org/people/Kleeb/sounds/180960/

http://www.freesound.org/people/zippi1/sounds/18871/

http://www.freesound.org/people/themusicalnomad/sounds/253887/

http://www.freesound.org/people/bcjordan/sounds/28042/

http://www.freesound.org/people/rockwehrmann/sounds/72475/

http://www.freesound.org/people/marcgascon7/sounds/87778/

http://www.freesound.org/people/jcveliz/sounds/92002/

Owner
Allen Downey
Professor at Olin College, author of Think Python, Think Bayes, Think Stats, and other books. Blog author of Probably Overthinking It.
Allen Downey
A custom advent of code I am completing

advent-of-code-custom A custom advent of code I am doing in python. The link to the problems I am solving is here: https://github.com/seldoncode/Adven

Rocio PV 2 Dec 11, 2021
A middle-to-high level algorithm book designed with coding interview at heart!

Hands-on Algorithmic Problem Solving A one-stop coding interview prep book! About this book In short, this is a middle-to-high level algorithm book de

Li Yin 1.8k Jan 02, 2023
Unofficial package for fetching users information based on National ID Number (Tanzania)

Nida Unofficial package for fetching users information based on National ID Number made by kalebu Installation You can install it directly or using pi

Jordan Kalebu 57 Dec 28, 2022
Paintbot - Forward & Inverse Kinematics

PAINTBOT - FORWARD & INVERSE KINEMATICS: Overview: We built a simulation of a RRR robot shown in the figure below. The robot has 3 links and is connec

Alex Lin 1 Oct 21, 2021
Insights in greek football league 2020-2021 and bookmaker's accuracy

Greek_Football_League_Analysis_2020_2021 Aim of Project: This project aims in deriving useful insights from greek football league 2020-2021 by mean st

2 Jan 16, 2022
A Lite Package focuses on making overwrite and mending functions easier and more flexible.

Overwrite Make Overwrite More flexible In Python A Lite Package focuses on making overwrite and mending functions easier and more flexible. Certain Me

2 Jun 15, 2022
Ingestinator is my personal VFX pipeline tool for ingesting folders containing frame sequences that have been pulled and downloaded to a local folder

Ingestinator Ingestinator is my personal VFX pipeline tool for ingesting folders containing frame sequences that have been pulled and downloaded to a

Henry Wilkinson 2 Nov 18, 2022
Tools to convert SQLAlchemy models to Pydantic models

Pydantic-SQLAlchemy Tools to generate Pydantic models from SQLAlchemy models. Still experimental. How to use Quick example: from typing import List f

Sebastián Ramírez 893 Dec 29, 2022
Web service which feeds Navitia with real-time disruptions

Chaos Chaos is the web service which can feed Navitia with real-time disruptions. It can work together with Kirin which can feed Navitia with real-tim

KISIO Digital 7 Jan 07, 2022
Program to send ROM files to Turbo Everdrive; reverse-engineered and designed to be platform-independent

PCE_TurboEverdrive_USB What is this "TurboEverdrive USB" thing ? For those who have a TurboEverdrive v2.x from krikzz.com, there was originally an opt

David Shadoff 10 Sep 18, 2022
Protocol Buffers for the Rest of Us

Protocol Buffers for the Rest of Us Motivation protoletariat has one goal: fixing the broken imports for the Python code generated by protoc. Usage He

Phillip Cloud 76 Jan 04, 2023
This wishes a mentioned users on their birthdays

BirthdayWisher Requirements: "mysqlserver", "email id and password", "Mysqlconnector" In-Built Modules: "smtplib", "datetime","imghdr" In Mysql: A tab

vellalaharshith 1 Sep 13, 2022
Gunakan Dengan Bijak!!

YMBF Made with ❤️ by ikiwzXD_ menu Results notice me: if you get cp results, save 3/7 days then log in. Install script on Termux $ pkg update && pkg u

Ikiwz 0 Jul 11, 2022
Check if Python package names are available on PyPI.

😻 isavailable Can I haz this Python package on PyPI? Check if Python package names are available on PyPI. Usage $ isavailable checks whether your des

Felipe S. S. Schneider 3 May 18, 2022
💡 Fully automatic light management based on conditions like motion, illuminance, humidity, and other clever features

Fully automatic light management based on motion as AppDaemon app. 🕓 multiple daytimes to define different scenes for morning, noon, ... 💡 supports

Ben 105 Dec 23, 2022
A not exist cat image generator python package

A not exist cat image generator python package

Fayas Noushad 2 Dec 03, 2021
Subcert is an subdomain enumeration tool, that finds all the subdomains from certificate transparency logs.

Subcert Subcert is a subdomain enumeration tool, that finds all the valid subdomains from certificate transparency logs. Table of contents Setup Demo

A3h1nt 59 Dec 16, 2022
Yet another Python Implementation of the Elo rating system.

Python Implementation - Elo Rating System Yet another Python Implementation of the Elo rating system (how innovative am I right?). Only supports 1vs1

Kraktoos 5 Dec 22, 2022
Film-dosimetry - Film dosimetry for DUVS

film-dosimetry Film dosimetry for DUVS Hi David and Joe, here we go this is a te

Christine L Kuryla 3 Jan 20, 2022
Covid-ml-predictors - COVID predictions using AI.

COVID Predictions This repo contains ML models to be trained on COVID-19 data from the UK, sourced off of Kaggle here. This uses many different ML mod

1 Jan 09, 2022