CliMetLab is a Python package which is intended to be used in Jupyter notebooks. Its main goal is to greatly reduce boilerplate code by providing high-level unified access to meteorological and climate datasets, allowing scientists to focus on their research instead of solving technical issues. Datasets are automatically downloaded, cached and transform into standard Python data structures such as NumPy, Pandas or Xarray, that can then be fed into scientific packages like SciPy and TensorFlow. CliMetLab also aims at simplifying plotting of 2D maps, by automatically selecting the most appropriate styles and projections for any given data.
The goal of CliMetLab is to simplify access to climate and meteorological datasets, by hiding the access methods and data formats. The snippet of code below would download the dataset dataset-name, cache it locally and decodes its content as a NumPy array:
import climetlab as clm data = clm.load_dataset("dataset-name") a = data.to_numpy()
To achieve this, CliMetLab introduces two concepts: Data source and Dataset. Data sources represent various access methods, such as reading files, downloading from a web site or using APIs.
CliMetLab provides the interface between the left side and the right side of the figure below:
CliMetLab also provides very high-level map plotting facilities. By default CliMetLab will automatically select the most appropriate way to plot a dataset, choosing the best projection, colours and other graphical attributes. Users can then control how maps are drawn by overriding the automatic choices with their own.
import climetlab as clm data = clm.load_dataset("some-dataset") cml.plot_map(data)