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ads_nasa

ADS_NASA

ads_papers_info_access_by_titles

ads_test

This code queries the number of articles per year that give the limit through the API, and the citations of all articles are accumulated.

astronomical-object-detection

Classifying Astronomical objects using SDSS - DR16 Data

astrostatistics2022

Python samples for Astrostatistics 2022 Spring

classification-of-astronomical-objects

This project's goal is to classify sky objects such as star, galaxy, and quasar via sdss data.

cv-arxiv-daily

🎓Automatically Update CV Papers Daily using Github Actions (Update Every 12th hours)

document-layout-analysis

Tools for extract figure, table, text, .. from a pdf document.

get_publications

Retrieves, formats and prints publication library from NASA ADS in a LaTeX-friendly format

hanlaomao

Config files for my GitHub profile.

lamost-

LAMOST光谱分类的一些程序

lamostpapersaccess

lamostpy

matlabfunc

Matlab codes for feature learning

ml-note

:orange_book:慢慢整理所学的机器学习算法，并根据自己所理解的样子叙述出来。(注重数学推导)

poe-api-wrapper

👾 A Python API wrapper for Poe.com, using Httpx. With this, you will have free access to ChatGPT, Claude, Llama, Google-PaLM and more! 🚀

publaynet

pylamost

Python interface for LAMOST Data

python

All Algorithms implemented in Python

random-forest-outlier

Outlier detection algorithm for MaNGA data built upon the Unsupervised Random Fores (URF) in Dalya Baron, Dovi Poznanski, The weirdest SDSS galaxies: results from an outlier detection algorithm, Monthly Notices of the Royal Astronomical Society, Volume 465, Issue 4, March 2017, Pages 4530–4555, https://doi.org/10.1093/mnras/stw3021

sdss-analysis-and-classification

Sloan Digital Sky Survey exploratory analysis, classification model selection and parameter optimization using genetic algorithms.

sdss-ml

These scripts explore galaxy/quasar/star classification from optical and infrared magnitudes using supervised machine learning.

sdss-ml-classification

Analyse the catalogue data of DR-14, 15 & 16 to understand the differences in the spectra of the three classes of celestial objects, namely stars, galaxies, and quasars, using Machine learning techniques

sdss-vae

Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

stellar-classification-sdss17

A stellar dataset from Kaggle analysed, pre-processed and four models (SVM, Random Forest, KNN & Logistic Regression) are made with accuracies over 95%.

tablenet

Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"

waldoinsky

Code Repository for `Where is Waldo (and his friends)? A comparison of anomaly detection algorithms for time-domain astronomy`

weirdestgalaxies

This repository contains the basic outlier detection algorithm that we use to find the weirdest SDSS galaxies.