ASTR 302, Winter 2022, University of Washington:

Python for Astronomy

Mario Jurić

Location

When: 2:30-3:50, Monday & Wednesday, Winter quarter 2022
Where: PAA 216

Class Materials

Useful Textbooks

We will largely rely on material freely available on the web; however, these two books may be useful for a more in-depth dive:

(note: both are freely available as e-books through UW University Libraries).

Class Description

ASTR 302, “Python for Astronomy”, is a course designed to teach how to effectively use Python for research and astronomical data analysis. We will begin with a gentle introduction to key tools and libraries used in astronomy, use these to analyze data (from kilobytes to tens of gigabytes!), visualize (sometimes large) datasets, automate analyses, and apply what we’ve learned to reproduce results of some key astronomy papers.

This course assumes you know basic Python and related astronomy libraries at the ASTR 300 level. It will give you the broad foundation needed to proceed to “ASTR 324: Introduction to Astrostatistics and Machine Learning in Astronomy”, or ASTR 497 “Big Data in Astronomy: Hands-on with Large Surveys”, or independent research projects.

ASTR 302: Python for Astronomy (Winter '22)

Related tags

Overview

ASTR 302, Winter 2022, University of Washington:

Python for Astronomy

Location

Class Materials

Useful Textbooks

Class Description

Owner

UW ASTR 302: Python for Astronomy

SNV calling pipeline developed explicitly to process individual or trio vcf files obtained from Illumina based pipeline (grch37/grch38).

Tools for working with MARC data in Catalogue Bridge.

ToeholdTools is a Python package and desktop app designed to facilitate analyzing and designing toehold switches, created as part of the 2021 iGEM competition.

Zipline, a Pythonic Algorithmic Trading Library

This python script allows you to manipulate the audience data from Sl.ido surveys

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

Desafio 1 ~ Bantotal

talkbox is a scikit for signal/speech processing, to extend scipy capabilities in that domain.

Integrate bus data from a variety of sources (batch processing and real time processing).

Lale is a Python library for semi-automated data science.

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

DataPrep — The easiest way to prepare data in Python

A python package which can be pip installed to perform statistics and visualize binomial and gaussian distributions of the dataset

Full automated data pipeline using docker images

CubingB is a timer/analyzer for speedsolving Rubik's cubes, with smart cube support

Transform-Invariant Non-Negative Matrix Factorization

In this tutorial, raster models of soil depth and soil water holding capacity for the United States will be sampled at random geographic coordinates within the state of Colorado.

Building house price data pipelines with Apache Beam and Spark on GCP

Statistical Analysis 📈 focused on statistical analysis and exploration used on various data sets for personal and professional projects.

First and foremost, we want dbt documentation to retain a DRY principle. Every time we repeat ourselves, we waste our time. Second, we want to understand column level lineage and automate impact analysis.