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There are a few Python examples in the folder for you to study. Open each script, run them and run these scripts and try to change some of control the parameters at the top of their code.

plot_map.py

This is the Python code to generate the same plot as we did interactively above. The title and the symbol plotting value range are automatically computed from the actual data values. In the script we:

  • used odb_visualiser() to generate the plot
  • called odb_filter() to extract additional data for the title
  • used the values() function to access a given ODB column as a numpy array.
plot_diff.py

This script computes the the difference between the forecast fields stored in 'fc.grib' and our ODB observations. This is achieved by using the following steps:

  1. the ODB query is performed and the resulting data is converted into Geopoints
  2. the matching GRIB field is read and interpolated to the observation points
  3. the difference is computed between forecast and observation
to_pandas.py
This script shows how to convert an ODB into a Pandas dataframe with the to_dataframe() function.
plot_wind.py
This script shows how to plot wind data from ODB.

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SCATT.odb' contains scatterometer data. Macro The script 'scatt.mv,py' extracts and plots scatterometer wind (ambiguous wind components) for a limited area and time period. Visualise the macro Python script and drop the provided 'mslp.grib' icon into the plot. This GRIB contains a mean sea level forecast valid at the same time as the observations. 

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Visualise 'scatter_plot' and customise it with the provided provided Symbol Plotting icon. The plot you see is a scatterplot for the first guess departures (x axis) and analysis departures (y axis) for a given channel. 

Visualise 'bin_plot' to get the binned version of the same data (as a heat map). Drop the provided provided  Contouring, Cartesian View and  and  Text Plotting icons into the plot to fully customise it.

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