https://github.com/zonca/pandas-astro-example
https://github.com/zonca/pandas-astro-example
astrophysics binder binder-ready numpy pandas
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/zonca/pandas-astro-example
- Owner: zonca
- Created: 2018-10-25T19:16:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-27T00:28:08.000Z (over 6 years ago)
- Last Synced: 2025-01-13T09:23:49.591Z (5 months ago)
- Topics: astrophysics, binder, binder-ready, numpy, pandas
- Language: Jupyter Notebook
- Size: 329 KB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Advanced Pandas topics with an example in Astrophysics
======================================================
[](https://mybinder.org/v2/gh/zonca/pandas-astro-example/master?filepath=astro_example.ipynb)
**Click on the `Launch Binder` icon** to execute the Notebook interactively in a session provided by [Binder](https://mybinder.org/).
In this example we will work with observations of the Cosmic Microwave Background from the [Planck satellite](https://en.wikipedia.org/wiki/Planck_(spacecraft)) using advanced functionalities of `pandas` and `numpy` and we will create plots with `matplotlib`.
In section 1 we will read a map created by the Planck satellite and we will familiarize with HDF5 format and working with hierarchical indexing in `pandas`. We will also visualize sky maps in `matplotlib` using a curvilinear projection.
In section 2 we will use `numpy` to create a simulation of how Planck scans the sky thoughout a year of observations and we will plot scanning rings in different reference frames. We will also create a interactive widget with `ipywidgets`.
In section 3 we will use the scanning coordinates created in section 2 to simulate an observation of the map used in setion 1. We will add white noise and then learn how to use `pandas` groupby with hierarchical index to aggregate the data into a sky map. We will finally compare the simulated noisy map with the original map and study its features.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.