Pratiman Patel, 26 March 2023
3 min read.Some of the intersting Python libraries that may be useful in future.
xclim: xclim is an operational Python library for climate services, providing numerous climate-related indicator tools with an extensible framework for constructing custom climate indicators, statistical downscaling and bias adjustment of climate model simulations, as well as climate model ensemble analysis tools.
elm: Ensemble Learning Models (elm) is a set of tools for creating multiple unsupervised and supervised machine learning models and training them in parallel on datasets too large to fit into the RAM of a single machine, with a focus on applications in climate science, GIS, and satellite imagery.
xbatcher: Xbatcher is a small library for iterating Xarray DataArrays and Datasets in batches. The goal is to make it easy to feed Xarray objects to machine learning libraries such as Keras.
pyvista-xarray: xarray DataArray accessors for PyVista to visualize datasets in 3D
Awesome Spectral Indices: pectral Indices are widely used in the Remote Sensing community. This repository keeps track of classical as well as novel spectral indices for different Remote Sensing applications. All spectral indices in the repository are curated and can be used in different environments and programming languages.
CarbonPlan-CMIP6-downscaling: This repository includes our tools/scripts/models/etc for climate downscaling. This work is described in more detail in a web article with a companion map tool to explore the data.
CLIMADA: Using state-of-the-art probabilistic modelling, CLIMADA allows to estimate the expected economic damage as a measure of risk today, the incremental increase from economic growth and the further incremental increase due to climate change. The economics of climate adaptation methodology as implemented in CLIMADA provides decision makers with a fact base to understand the impact of weather and climate on their economies, including cost/benefit perspectives on specific risk reduction measures. The model is well suited to provide an open and independent view on physical risk, in line with e.g. the TCFD (Task Force for Climate-related Financial Disclosure), and underpins the Economics of Climate Adaptation (ECA) approach.
CliMetLab: CliMetLab is a Python package aiming at simplifying access to climate and meteorological datasets, allowing users to focus on science instead of technical issues such as data access and data formats. It is mostly intended to be used in Jupyter notebooks, and be interoperable with all popular data analytic packages, such as NumPy, Pandas, Xarray, SciPy, Matplotlib, etc. as well as machine learning frameworks, such as TensorFlow, Keras or PyTorch.
kepler.gl: kepler.gl is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets.
ClimateLearn: ClimateLearn is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods.
ClimaX: ClimaX is the first foundation model for weather and climate science.▶️ Simple, flexible, and easy to use.▶️ Ample examples for the workflow to apply to various downstream tasks ranging from weather forecasting to climate downscaling.
pytest tips and tricks : This is a set of tips/tricks for learning and using pytest.