/jupyter_data_languages

Primary LanguageJupyter NotebookMIT LicenseMIT

Jupyter notebooks for PhD class on data languages.

Installing python

The easiest way to install python on any OS is to use anaconda python. This will install a local version of python on your system so you don't need to worry about needing admin to install new packages. Most of the packages listed above are installed by default with anaconda. For this class we will be using python 3, and I recommend you use this version for you research (unless you have a very good reason to use python 2).

Packages to install (in addition to anaconda's defaults)

  • emcee
  • reproject
  • wcsaxes
pip install emcee reproject wcsaxes

Class 1

  • Git.ipynb: Git and Git-Hub
  • General_Python.ipynb: General python information

Class 2

  • General_plotting.ipynb: How to make publication ready plots
  • mpl_style.py: How to make a matplotlib style
  • Uncertainty_plotting.ipynb: Making plots with errorbars
  • Fits_images.ipynb: Plotting image contained in FITS files

Class 3

  • Stats_with_Scipy.ipynb: using scipy for stats distributions
  • Astropy_fitting.ipynb: using astropy to model and fit data
  • mcmc_fit_with_outliers.ipynb: fitting a line to data while rejecting outliers using MCMC
  • Gaussian_process_regression: fitting data without defining a functional form