JA-Vazquez

Python Lectures

• Python
  Temario.
• Why Python ?
  Intro.key , python vs C?
• 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.
• 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.

Python

• An starting guide to Python, and in general to data science:
  The Python compendium on github: GitHub.
• Lectures:
  Using the Notebook, along with H-W: Lectures.
  In particular Lecs: 16, 17 & 23, 24

Cosmo

• Comology Lectures, along with H-W: Lectures.

Stats & ML

• ANN, GA, RF: MACSS2021 .
• II Mini-Workshop on HPC: Mini-Workshop 2 .
• III Mini-Workshop on HPC: Mini-Workshop 3.
• V Taller de Métodos Numéricos y Estadísticos en Cosmo: Taller V.
• Main code: SimpleMC,
  Documentation: link.
• Models, Data, Calc, GS, Samplers, Evidence , Toy .

Lectures

• HW1. Repaso de Python, etc.. : Tarea 1b.
  HW 2. Resolver: 2a, 2b, 2c, 2d, 2e, 2f, 2g, 2h.
  Entregar: Martes 4.Feb 9:30am
• Lecture 1:
  Symbolic python: Sympy, Tutorial.
  Sympy 1, Sympy 2.
  FLRW metric: B(r), FRW.
  
  HW3. Analytical Calculations:
  HW_a, HW_b, HW_c, HW_d.
  Entregar: Martes 11.Feb 9:30am
  
  HW4. GR and Einstein: HW_a. HW_b.
  HW4. Distances and World models: HW_c. HW_d.
  Entregar: Jueves 13.Feb 9:30am
  
  Links: Gravipy, Eisteinpy, Sagemath,
  Project 1 (Pert), Project 2 (E-L), Files.
  k=k(a)?, More metrics?.
• Lecture.py:
  Numpy: Numpy 1, Numpy 2, Numpy summary.
  Intro a Scipy: Notebook.
  Stats, Integrals and ODEs: Notebook.
  
  Tarea Numerica, GitHub. .
  Files: file_f, file_g.
  HW5. Numeric: HW_b, HW_d.
  HW5. Entregar: Martes 18.Feb 9:30am
• HW6. Likelihood: HW_a, datos_1, datos_2, datos_3, Hz_all.dat.
  HW6. Entregar: Martes 25.Feb 9:30am
  
  HW7. Observables: HW_b.
  HW7. Entregar: Jueves 27.Feb 9:30am
  Diapositivas 1-3.
• Lecture.plots:
  Files and Matplotlib: Notebook.
  More plotting: Notebook.
  Seaborn, Bokeh and D3: Seaborn and D3.
  Tortuga: 1py, 2py.
  Animation: Codes, Notebook.
  HW8: HW.
  HW8: Entregar Martes 11.Mar 9:30.
• Pandas :
  Just in case: Pandas.
  
• Optimization :
  Algoritmos bioinspirados: Paper.
  
  The Genetic Algorithm: Paper, More biblio, GA in influenza.
  Thesis: (GA) Alfredo (2022_05 Divulgae, White, CNF),
  Cosmological Parameter Estimation with Genetic Algorithms: paper,
  Codes: Code 1, Code 2, Code 3a, Code 3b, Code 3c, Cosmo.
  HW9: HW.
  HW9: Entregar Jueves 13.Mar 9:30.
  
  Particle Swarm Optimization:
  Thesis: (PSO) Daniel.
  PSO.py
  
  Genetic Programming:
  Thesis: Mario, Jimena.
  Code: GP.
  HW10: HW.
  HW10: Entregar Jueves ?.Mar 9:30.
  
• CMB :
  CLASS: Curso Dra. Gabriela, Colab.
  CAMB: Documentation, Notes.
  Some links: Notes.
  HW11: HW.
  HW11: Entregar Jueves ?.Mar 9:30.
  Project 3, Files.
• Lecture MCMC:
  An introduction to Markov Chain Monte Carlo: Paper.
  github: Notebook.
  random.py
  
  Cosmological Parameter Inference with Bayesian Statistics: Paper.
  Paper's Nb: Notebook,
  MCMC Nb: Notebook, datos.
  chains.py
  
  Monte Python: Notas, Notebook, Notas A. Cuesta.
  CosmoSis , Cobaya.
  
  HW12: HW. Correr MPython con datos ligeros y generar posteriors
  * Nested
  * Parallel
  * Tarea MPI, Proyecto de Convergencia
• Reconstructions:
  Thesis: L Escamilla.
  Reconstructing the Universe: Testing the Mutual Consistency Data Sets with Gaussian Processes. Paper.
  Model selection applied to reconstructions of the Dark Energy. Paper.
  Model independent reconstruction of the Interacting Dark Energy Kernel: Binned and Gaussian process. Paper.
  Reconstruction of the Dark Energy equation of state. Paper.
  
  Gaussian Process.
  Thesis: J de Jesus, Paper, Notebook. Gapp.
  Principal Component Analysis Notas 1, Notas 2, Notebook, Scikit-Learn, Link.
• Artificial Neural Networks:
  Una Aplicación de las Redes Neuronales Artificiales en la Cosmología. Paper.
  Observational cosmology with Artificial Neural Networks. Paper.
  Code: code.
  Neural network reconstructions for the Hubble parameter, growth rate and distance modulus. Paper.
  Code: code.
  
  Physically Informed Neural Networks.
  Thesis: Juan.
  Code: code.
• ANN+GA:
  Neural Networks Optimized by Genetic Algorithms in Cosmology. Paper.
  Code: code.
  Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up. Paper.
  Code: code.
• Classification + N-body:
  Algoritmos de clasificacion aplicados a simulaciones de formacion de estructura cosmologica Thesis.
  Classification algorithms applied to structure formation simulations. Paper.
  Code: code.
  Analysis of Dark Matter Halo Structure Formation in N-body Simulations with Machine Learning. Paper.
  Code: code.
  
  Clasificación de modelos cosmológicos en simulaciones de N-cuerpos con aprendizaje profundo Thesis.
  Code: code.
  
  Funciones de correlación de dos puntos con algoritmos de agrupamiento. Thesis.
  Code: code.

HW.py