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Now edit the 'vis_temp' icon and set the following parameters:

Odb DataDrop the temp.odb icon into this box
Odb Where

varno = 2 and vertco_reference_1=50000

Save the icon and visualise it. Then drag the the provided Symbol Plotting, Coastlines, Legend and Text Plotting icons into the plot for further customisation (either one at a time, or all together). Keep the plot window open.

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Inspecting the Data Values in the Plot

Data values at the cursor position can The data values can  be inspected with the Cursor Data Tool , which can be activated by pressing on the Image Removed icon in the toolbar of the Display Window. The Cursor Data Tool displays the co-ordinates of the current cursor position and the information for the nearest data point to this position.You may find it hard to use the Cursor Data Tool in data dense regions. To overcome this launch the Magnifier with the Image Removed icon in the toolbar and move it to your area of interest in the plot. The magnifying glass can be moved and resized using the mouse, and the magnification scale on its left-hand side can also be adjusted. You can also zoom into areas of the map using the Zoom controls Image Removed in the toolbar(you can combine it with the Magnifier to see the fine details).

Enable the sidebar of the Display Window with the button and select the Data tab (and select the 'vis_temp' layer at the top if it is not yet selected). Now select the Metadata panel inside the tab. Here you will find some statistics about the data plotted and a histogram as well.

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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() (see 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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Enter folder 'scatterometer'.

'SCATT.odb' contains scatterometer data. The script 'scatt,py' extracts and plots scatterometer wind (ambiguous wind components) for a limited area and time period. Visualise the 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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