Documentation for PyImfit
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OVERVIEW AND SAMPLE USAGE:
Installation of PyImfit
Standard Installation: macOS
Installation on Apple Silicon (e.g., M1, M2) Macs
Standard Installation: Linux
Sample Usage
Overview of PyImfit
For Those Already Familiar with Imfit
The Basics
Specify the model (and its parameters)
Fit a model to the data
Generate a model image (without fitting)
Pixel Coordinate Conventions
Image Coordinates
Specifying Image Subsets for Fitting
USER DOCUMENTATION:
Defining Models
Image Functions
More Information
PSF Convolution
Requirements for PSF images
Basic PSF Convolution
Convolving with Oversampled PSFs
Fitting an Image (Choosing Fit Statistics and Solvers)
Finding the best-fit solution by minimizing a fit statistic
Fit statistics (chi^2 and all that)
Minimizers/Solvers
More Information
Bootstrap Resampling for Parameter Uncertainties
Bootstrap Resampling
Using MCMC
Example of using PyImfit with Markov-Chain Monte Carlo code “emcee”
Create image-fitting model using PyImfit
Define log-probability functions for use with emcee
Set up and run Markov-Chain Monte Carlo using emcee
Corner plot of converged MCMC samples
Example of using PyImfit to Estimate B/T Uncertainties
Introduction
Acknowledgments
Imfit-related Acknowledgements
API DOCUMENTATION:
PyImfit API
Description Classes
The Imfit class
Useful Functions
Imfit manual (PDF)
PyImfit
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OVERVIEW AND SAMPLE USAGE:
Installation of PyImfit
Sample Usage
Overview of PyImfit
Pixel Coordinate Conventions
USER DOCUMENTATION:
Defining Models
PSF Convolution
Fitting an Image (Choosing Fit Statistics and Solvers)
Bootstrap Resampling for Parameter Uncertainties
Example of using PyImfit with Markov-Chain Monte Carlo code “emcee”
Example of using PyImfit to Estimate B/T Uncertainties
Acknowledgments
API DOCUMENTATION:
PyImfit API
Related Topics
Documentation overview
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Installation of PyImfit
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