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Edit workflow cookbooks (#8)
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cyschneck committed Sep 20, 2024
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15 changes: 9 additions & 6 deletions README.md
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This Project Pythia Cookbook covers working with wavelets in Python

## Motivation
Wavelets are a tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms
Wavelets are a powerful tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms

## Authors

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## Structure

(State one or more sections that will comprise the notebook. E.g., _This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."_ Then, describe each section below.)
This cookbook is broken into two main sections:

- Foundations
- Example Workflow

### Foundations

- Wavelet Basics
"Wavelet Basics" covers the motivation and background for wavelet analysis by review time-series data and the strengths and weaknesses of Fourier transform

### Example Workflows

- PyWavelets and Jingle Bells
- Spy Keypad
- Atmospheric Data: nino3
- "PyWavelets and Jingle Bells": Learn how to use `PyWavelets`, a Python implementation of wavelet analysis, to determine the order of notes in a simple musical piece
- "Spy Keypad": Learn how to use wavelets to undercover the frequency and order of notes in an unkonwn piece of audio data
- "Atmospheric Data: nino3": Replicate the power wavelet scalogram from the original 1999 Torrence and Compo paper in Python

## Running the Notebooks

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