All Projects → KMFleischer → PyEarthScience

KMFleischer / PyEarthScience

Licence: MIT license
The PyEarthScience repository created by DKRZ (German Climate Computing Center) provides Python scripts and Jupyter notebooks in particular for scientific data processing and visualization used in climate science. It contains scripts for visualization, I/O, and analysis using PyNGL, PyNIO, xarray, cfgrib, xesmf, cartopy, and others.

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PyEarthScience

The PyEarthScience repository created by DKRZ (German Climate Computing Centre) provides various Python modules, scripts and iPython notebooks, in particular for Earth System data processing and visualization used in climate science.

For this, different Python modules are used, like PyNIO, PyNGL, xarray, matplotlib, cartopy, and psyplot.

Those who have decided to write their programs for the visualization of scientific data in Python, will encounter problems and questions such as - which modules are there, which ones are needed, which are well documented and, above all, which are still maintained today.

We added the NCL Transition Examples - NCL to Python from DKRZ to this repository too because most of our users are familiar with NCL but need to pivot to Python.

Content

  • Visualization

    • Cartopy
    • NCL notebooks
    • PyNGL
    • matplotlib
    • psyplot
  • Transition_examples_NCL_to_PyNGL

    • annotations
    • basics
    • contours
    • maps
    • masking
    • overlays
    • panel
    • polylines_polygons_polymarker
    • read_data
    • regrid
    • scatter
    • shapefiles
    • slices
    • streamlines
    • vectors
    • write_data
    • xy
  • Tutorial (notebooks)

    • Python basics
    • numpy basics
    • xarray and PyNIO basics
    • PyNGL basics
      • xy-plots
      • maps
      • contours on maps
  • I/O

    • read GRIB files with PyNIO
    • read GRIB files with xarray/cfgrib
    • read netCDF files with PyNio
    • read netCDF files with xarray
  • Data analysis

    • Introduction to python-cdo
    • CDO - climatology, anomalies, standardized anomalies
    • compute NINOs with CDO
    • convert CSV file to netCDF
    • convert ASCII file to netCDF
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