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| Name | Astropy Python Full course On Mastering Astronomical Data analysis & Visualization | DESI ASTRO |
| Video Uploader | Video From DESI ASTRO |
| Upload Date | This Video Uploaded At 15-07-2024 09:35:43 |
| Video Discription | Astropy is a powerful and widely-used library in Python, tailored specifically for astronomy and astrophysics. It provides a robust framework and tools for handling and analyzing astronomical data, making it an essential resource for astronomers and astrophysicists. Here is a more detailed look at Astropy and its applications: Chapters Timestamp: All are independents 00:00:00 Introduction to Astropy 00:02:18 Astropy Library Installation 00:04:17 Read & Visualizing JWST FITS Image File 00:35:50 Read & Visualizing FITS Image Colormaps 00:44:14 Read & Visualizing SDSS Telescope FITS BOSS Spectra 01:09:00 Read & Visualizing SDSS Telescope FITS Image 01:32:38 Read & Visualizing SPITZER Telescope Optical FITS Image 01:52:45 Read & Visualizing ASTROSAT Telescope FUV FITS Image 02:24:08 Read & Visualizing HST Telescope FITS Image 02:48:25 Read & Visualizing HST Telescope FITS Spectra 03:07:27 Create RGB Colorful Image with FITS File 03:22:17 Read & Visualize FITS File Data Cube of SDSS-MANGA 03:46:30 Extracting FITS Data Cube Spectra & Import Into Files 04:12:56 2D Cutout of FITS image 04:35:55 Save Cutout Image Into FITS Image File 04:46:55 Constructing White Light Image from Data Cube 04:54:50 Contours Construction on FITS Image 05:04:42 Kernel Convolution With FITS image 05:22:36 Creating FITS 2D image with Header Information 05:36:02 Creating Multi-Extension FITS File with Image. Spectra & Data Cube 06:12:11 Creating FITS Data Table & Saving Into File 06:21:03 Creating FITS Spectra& Data table with Header Information 06:42:21 Astropy 1D Modeling: Linear Modeling 06:56:30 Astropy 1D Modeling: Gaussian Modeling 07:07:29 Zoom Plot of FITS Image File 07:23:13 Convert FITS Image Pixel(x,y) Into RA, DEC & Vice-Versa 07:29:51 Astropy 1D Convolution 07:42:26 Create Sub-Cube Fits Data Cube and Save Into FITS Data Cube 07:52:28 Astropy 1D Sigma Clipping Key Components of Astropy Core Package: Units and Quantities: Handles physical units and quantities, allowing for seamless unit conversions and ensuring consistency in calculations. Constants: Provides access to fundamental physical constants. Time: Deals with time and date calculations, and conversions between different time systems ( Coordinates: Facilitates celestial coordinate transformations and representations (e.g., equatorial, galactic coordinates). Data Handling: Table: Provides flexible and powerful tools for handling tabular data, similar to pandas DataFrames but with features tailored for astronomy. FITS (Flexible Image Transport System): Tools for reading, writing, and manipulating FITS files, the standard data format in astronomy. Modeling: Tools for creating mathematical models and fitting them to data, including non-linear least squares fitting. Visualization: Plotting: Integrates with Matplotlib for creating visualizations, including specific tools for plotting astronomical data. WCS (World Coordinate System): Handles the conversion between pixel coordinates in images and real-world celestial coordinates. Applications of Astropy Data Analysis: Astronomers use Astropy to analyze observational data, perform statistical analyses, and visualize results. The Table and FITS modules are handy for manipulating and examining large datasets. Coordinate Transformations: Converting between different celestial coordinate systems is a common task in astronomy, and Astropy simplifies this process with its Coordinates module. Cosmological Calculations: Researchers use the Cosmology module to study the universe's expansion, calculate distances to celestial objects, and model cosmological phenomena. Simulation and Modeling: Astropy's modeling tools allow for creating and fitting complex models to observational data, which is crucial for understanding underlying astrophysical processes. Time Series Analysis: The Time module handles and converts between various time formats, essential for time-series analysis of variable stars, exoplanet transits, and other phenomena. Data Integration and Interoperability: #Python#PythonProgramming#PythonCourse#LearnPython#PythonTutorial#PythonDeveloper #Programming #AstroPython #Astronomy #PythonAstronomy #AstroCoding #AstroData #AstroScience #AstroResearch #PythonInAstronomy #Astrophysics #DataScience #SpaceScience #AstroTools #AstroAnalysis #ScienceCoding #AstronomyData #AstroSoftware #AstroDev #PythonForScience #PythonResearch #SpaceTech #PythonForScience #PythonResearch #SpaceTech #FITSFiles #PythonFITS #AstroFITS #AstroDataAnalysis #AstroImaging #AstroDataProcessing Storing and processing images captured by telescopes, such as sky surveys, galaxy images, and star fields.Spectroscopy: Handling spectral data from instruments that analyze the light from astronomical objects. Time-series Data: Managing data that varies over time, such as light curves of variable stars. Simulation Data: Storing the results of simulations in astrophysics and cosmology. |
| Category | Education |
| Tags | DESI ASTRO | python | animation | matplotlib | jupyternotebook | plotting in python | python for beginners | numpy | scatter plot | line plot | line fitting in python | gauusian fit in python | IUCAA PUNE | IUCAA PUNE PHD | PYTHON IN HINDI | PYTHON PLOTTING | PYTHON FOR ASTRONOMY | ASTRONOMY | ASTROPHYSICS | DATA SCIENCE | DATA ANALYSIS | PYTHON PTOJECTS | MACHINE LEARNINGS | MATPLOTLIB PLOTTING | DATA VISULAIZATION | ASTROPY | ASTRONOMICAL DATA ANALYSIS | IMAGE PROCESSING | DS9 | PYTHON FOR DATA SCIENCE |
