Kay Quagraine's repositories
probability_cheatsheet
A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.
hands-on-examples
A series of Jupyter notebooks that walk you through the fundamentals of Python, Scientific Computing and Visualization, Machine Learning in Python, etc.
xskillscore
Metrics for verifying forecasts
rainfall_onset
rainfall onset-cessation and extreme indices
Calculate-Precipitation-based-Agricultural-Drought-Indices-with-Python
Precipitation-based indices are generally considered as the simplest indices because they are calculated solely based on long-term rainfall records that are often available. The mostly used precipitation-based indices consist of Decile Index (DI) Hutchinson Drought Severity Index (HDSI) Percen of Normal Index (PNI) Z-Score Index (ZSI) China-Z Index (CZI) Modified China-Z Index (MCZI) Rainfall Anomaly Index (RAI) Effective Drought Index (EDI) Standardized Precipitation Index (SPI).
ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Machine-Learning-The-future
Creation of Machine Learning Models using Python and datasets.
Python-Practical-Application-on-Climate-Variability-Studies
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
ams-ml-python-course
Machine Learning in Python for Environmental Science Problems AMS Short Course Material
machine-learning-notebooks
Stanford Machine Learning course exercises implemented with scikit-learn
Neural-Networks
A basic tutorial on Neural Networks
Deep-Learning-with-Keras
Code repository for Deep Learning with Keras published by Packt
Introduction-to-Time-Series-forecasting-Python
Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
rainfall-teleconnections
Python code to compute global teleconnections from rainfall data
scikit-learn
scikit-learn: machine learning in Python
pycon-ua-2018
Talk at PyCon UA 2018 (Kharkov, Ukraine)
red_river
Python and R code for bias correction and empirical-statistical downscaling of GCM projections for the Red River basin in Vietnam.
Applied-Deep-Learning-with-Keras
Deep Learning examples with Keras.
quantile_mapping
Tools to derive and apply corrections based on quantile mapping
thesemicolon
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
atmos-tools
Package for analyzing and visualizing atmospheric data
AllenDowney-ThinkStats2
Git repository for Think Stats, 2nd Ed.
A-Beginner-Guide-to-Carry-out-Extreme-Value-Analysis-with-Codes-in-Python
A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.
climate_data_science
Python for Climate Data Science