Astronomy Research Data Analysis with Python

Apply Python basics from module one to analyze tabular star data, create visualizations such as bar graphs, box plots, line plots, and HR diagrams, and save results to the system.

Introduce tabular astronomical data in CSV, using Python in Google Colab to create visualizations of star properties, absolute magnitude, surface temperature, spectral type, luminosity, and export HR diagrams for research.

Import and explore a csv dataset using os, pandas, numpy, seaborn, and matplotlib to build visualizations from data stored in Google Drive.

Convert a star dataset from csv to a pandas data frame, inspect with info and head, sample data, and explore properties like temperature, luminosity, radius, and spectral class for visualization.

Create a dedicated directory in python to save visualizations, using os.makedirs with exist_ok, define a base path in Google Colab, and build dynamic paths for saving plots.

Learn to create your first bar chart from a tabular star-type column using pandas and matplotlib, counting categories with value_counts and displaying the basic plot for saving later.

Customize a matplotlib bar chart by adjusting figure size, adding bar labels, applying a dark background, coloring bars, styling the title and axes, rotating x-ticks, and saving the plot.

Visualize star color data with seaborn bar plots, showing color counts (red, blue, white) and how seaborn simplifies bar charts with one line of code compared to matplotlib.

Visualize outliers in astronomy data using box plots and seaborn, compare numeric features like temperature, luminosity, radius, and absolute magnitude across star types with subplots.

Create line plots to visualize trends across numeric features such as temperature, luminosity, radius, and absolute magnitude using matplotlib, employing subplots and loops for clear observations of patterns.

Explore how to create a Seaborn pairplot to visualize relationships across multiple numeric columns, defaulting to scatter plots and enabling hue by spectral class for grouped patterns.

Plot an HR diagram from star data by mapping temperature to absolute magnitude, color- and size-coding by star type, with legend and dynamic 240-star handling.

Learn to download visualizations from Google Colab by zipping the star plots folder and saving it to your system, and explore exporting outputs from matplotlib and seaborn.

Explore the concept of astronomical images and fits files, fetch Andromeda data via SkyView form, apply pixel scaling methods, and understand z scale intervals in Astropy.

Explore how digital images form from pixels, grayscale and color channels, and the sensor's rgb filters that produce 0-255 intensity values in 8-bit and 16-bit images.

The lecture explains fits files, their header units and metadata, and how they store astronomical image data from surveys like Andromeda Galaxy for image processing workflows.

Install and import astroquery and matplotlib in Google Colab to fetch and visualize Andromeda Galaxy images from astronomical surveys.

Explore the Sky View form to fetch Andromeda Galaxy images from DSS surveys and prepare the data for Python analysis with the Astro Query library.

Fetch and visualize the Andromeda galaxy (M31) with the Astro Query sky view in Python, then extract and display the fits header and data as a 300 by 300 image.

Fetches and visualizes the full Andromeda galaxy from the SDSS optical survey using DSS2 blue, adjusting pixels and radius to show M31 and its satellites.

Create a dummy fits file from header and image data using astropy.io.fits, building a primary hdu, adding pixel data and metadata, and validating with fits.open.

Explore the distribution of Andromeda galaxy pixels by computing summary statistics for the m31 pixel array, then visualize with a matplotlib histogram, using a flattened view and 50–300 bins.

Apply min max scaling to the M31 pixel array, create helper functions for visualization, and compare the original and scaled images to assess intensity normalization.

Compare standard scaling, log normalization, and square root normalization on M31 pixel data, visualize results, and find that log normalization best reveals core details of the Andromeda galaxy.

Use the Astropy z scale interval to clip image data between a computed minimum and maximum, producing a visualization that highlights features by capping values beyond the interval.

Explore convolution operations, apply denoising with a gaussian kernel, and enhance features using majoring and pseudo filters, then extract corner förstner and multi basic features with the src image library.

Explore how convolution operates on images using a Gaussian kernel to denoise and extract features for astronomy data analysis with Python.

Explore gaussian denoising by building gaussian kernels and applying 2d convolution with SciPy, demonstrating noise reduction while preserving details on M31 grayscale images.

Enhance the convolved denoised image from Gaussian blurring by applying majoring and set of filters in src image, visualizing with spectral color maps to reveal linear features in M31 data.

Apply the corner fastener feature from the image features documentation to a grayscale input using Python, producing two outputs: the error ellipse sizes and the roundness of the error ellipse.

Explore multiscale basic features in astronomy image analysis by extracting 24 local features across scales with gaussian blurring, visualizing multi-channel outputs from grayscale inputs using skimage.

Conclude by reflecting on mastering python basics for visualizations, creating astronomical data visualizations such as hr diagrams, and applying feature extraction to enhance images for research or theses.