Set of Jupyter Notebooks, along with datafiles, to get started with Scientific Computing in Astronomy

1. Windows

2. MacOS

3. Linux distribution (includes Ubuntu)

   1. Installing Jupyter - Using Conda or Miniconda

   2. Installing Jupyter - Using python-pip (Try this only if you are comfortable with terminal)

1. Opening the Notebooks

2. Jupyter Notebook Dashboard

3. Creating a new notebook document

4. Overview of the Notebook UI

5. Cell types

6. Modal Editor

   1. Edit Mode

   2. Command Mode

   3. Running Code

   4. Keyboard Navigation

   5. Mouse Navigation

   6. Cheat Sheet

• Basic syntax, and syntax for loops, conditional statements, and opening simple files, (lists, tuples and dictionaries)
• Parse a file and make a list containing the number of moons for each planet.

• Numpy arrays, indexing, slicing.
• Parse file as above using numpy (np.where, for example).
• Beehive Cluster.

• Defining functions.
• Hubble Law, Number density of galaxies.

• Simple Plotting. Labels, colors, title, grid
• GW astronomy

• Using Astropy.io to import datafiles, astropy.table.
• Take the data from a harder to parse data file.
• Introduction to fits files.

• Scrape web for some data (try to do this for constellations)
• Make an image of the constellation using RA, Dec or Mars in retrograde (simple scatter plot)

• Curve fitting
• Scrape data from Cepheids, and plot Period-Luminosity relation
• Frequency vs time inspiral

• Using Bias and Flats.
• Reducing an image of a popular target. (And further processing)

• Subplots, and Using Object Oriented approach
• Make several HR diagrams, say, with different filters.
• Compare features, and observe that UV filters are good at filtering multiple stellar populations.

• Using astropy quantities, make a black body spectra.
• Using filters (SDSS), find the instrumental magnitude of a star in different bands.
• Have a function which returns these values, given the temperature of a star.

• Make a class for a star and a filter.
• Rewrite the previous codes to make it more adaptable to changes.

• Time Series analysis.
• Find a peak/Periodicity search.

• Coordinate Systems and Time.
• Convert coordinates. Plot an analemma of the Sun. See how it changes with latitude.

• Differential Equation solving. Scipy.integrate.solve_ivp.
• Solve differential equations for some system numerically.