Implementation of statistical methods used to describe, analyze, test, and forecast atmospheric data using: Python, Apache Spark, Dask, Xarray, Pandas, GeoPandas.
This repository contains code, notes, exercises and solutions for statistical methods in the atmospheric sciences. These are meant to serve as a learning tool to complement the theoretical materials from:
Python 2.7 and Python 3:
- basemap==1.0.7
- bokeh==0.12.6
- Cartopy==0.14.2
- dask==0.15.1
- distributed==1.18.0
- fastparquet==0.1.0
- Fiona==1.7.8
- folium==0.3.0
- GDAL==1.11.5
- geopandas==0.2.1
- geoviews==1.2.0
- graphviz==0.8
- h5py==2.7.0
- ipython==5.4.1
- Iris==2.0
- Keras==2.0.6
- matplotlib==2.0.2
- matplotlib-venn==0.11.5
- netCDF4==1.2.9
- notebook==5.0.0
- numpy==1.13.1
- pandas==0.20.3
- pandas-datareader==0.4.0
- pyproj==1.9.5.1
- pyshp==1.2.11
- pyspark==2.2.0
- pyspark4climate==0.1.dev0
- rasterio==0.36.0
- scikit-learn==0.18.2
- scipy==0.19.1
- seaborn==0.8
- Shapely==1.5.17.post1
- -e [email protected]:andersy005/spark-xarray.git@1349b44c3c476ed8491ce241af956263eb1c62c9#egg=spark_xarray
- statsmodels==0.8.0
- tables==3.4.2
- xarray==0.9.6
Command Line Operators:
Name | Summary |
---|---|
Climate Data Guide | Community-authored guide for climate datasets |
ISCCP | The International Satellite Cloud Climatology Project |