Cosmology with Python
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Cosmology with Python
- Cosmology with Python, IA-UNAM
Temario.
- Why Python ?
Intro.key
- Installation: There are two recommended ways to install it:
1.- With Anaconda which contains a complete data science toolkit.
Anaconda.
2.- Plain python - which is much lighter -
Python.
- An starting guide to Python, and in general to data science:
The Python compendium on github:
GitHub.
- Bibliography:
1.- Mathematical Methods for Physics and Engineering. Riley, Hobson and Bence.
2.- Learning Python: Powerful OO Programming, Mark Lutz.
3.- Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython, Wes McKinney.
4.- Numerical Analysis, Richard L Burden, Faires.
5.- Numerical Methods for Engineers and Scientists, Joe D. Hoffman.
6.- Numerical Methods for Engineers, Steven Chapra.
Lectures
- Lecture 0:
Intro to the Notebook, along with H-W:
Jupyter.
- Lecture 1:
The basics:
Jupyter 1.
The not so basics:
Jupyter 2.
- Lecture 2:
Still analytics, with symbolic python:
Sympy 1,
Sympy 2.
- Lecture 3:
Visualization, with Matplotlib, Seaborn and D3:
Matplotlib,
Matplotlib 2,
Seaborn and D3.
- Lecture 4:
Scientific python:
Scipy 1,
Scipy 2.
- Lecture 5:
Optimizacion:
Notas.
Algoritmos bioinspirados:
Paper.
The Genetic Algorithm:
Paper,
Thesis,
More biblio,
GA in influenza.
Codes:
Code 1,
Code 2,
Code 3a,
Code 3b,
Code 3c,
Cosmo.
Genetic Programming:
GP.
- Lecture 6:
An introduction to Markov Chain Monte Carlo:
Paper.
Cosmological Parameter Inference with Bayesian Statistics:
Paper.
MCMC Nb:
Notebook.
- Lecture 7:
Artificial Neural Networks:
Paper.