Difference between revisions of "Visualization and analysis examples"

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== Plotting examples ==
 
== Plotting examples ==
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=== Comparing diagnostics from different simulations ===
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{| border=1 cellspacing=0 cellpadding=5
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|-bgcolor="#CCCCCC"
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!width="200px"|Script or notebook
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!width="200px"|Author
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!width="600px"|Description
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|-valign="top"
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|[https://github.com/geoschem/gcpy/blob/master/examples/diagnostics/compare_diagnostics.ipynb compare_diagnostics.ipynb]
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|Lizzie Lundgren (Harvard)
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|Jupyter notebook that illustrates the steps involved in comparing output from two different GEOS-Chem simulations.
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|}
  
 
=== Timeseries data ===
 
=== Timeseries data ===
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{| border=1 cellspacing=0 cellpadding=5
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|-bgcolor="#CCCCCC"
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!width="200px"|Script or notebook
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!width="200px"|Author
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!width="600px"|Description
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|-valign="top"
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|[https://github.com/geoschem/gcpy/blob/master/examples/timeseries/plot_timeseries.py plot_timeseries.py]
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|Bob Yantosca (Harvard)
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|Example script that plots timeseries from two different [[Guide to GEOS-Chem History diagnostics|GEOS-Chem diagnostic collections]] (ConcAboveSfc, SpeciesConc) at a specified location.  This is a good example of the using Python xarray and matplotlib packages to create line plots.
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|}
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=== Plotting 2D data ===
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{| border=1 cellspacing=0 cellpadding=5
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|-bgcolor="#CCCCCC"
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!width="200px"|Script or notebook
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!width="200px"|Author
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!width="600px"|Description
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|-valign="top"
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|[https://github.com/geoschem/gcpy/blob/master/examples/plotting/plot_global.py plot_global.py]
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|Daniel Rothenberg
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|Creates a global average plot.  Using cartopy_ we can easily visualize gridded model output on maps with different cartographic projections, and then configure them with any aesthetics or features such as continents, geopolitical borders, gridlines, and more.
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|-valign="top"
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|[https://github.com/geoschem/gcpy/blob/master/examples/plotting/plot_seasonsl.py plot_seasons.py]
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|Daniel Rothenberg
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|For a given field timeseries, compute seasonal averages over all data and plot each average on a four-panel figure.
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|-valign="top"
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|[https://github.com/geoschem/gcpy/blob/master/examples/plotting/quickplot.py quickplot.py]
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|Bob Yantosca (Harvard)
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|Creates a quick-and-dirty plot of a variable in a netCDF (HDF5, GRIB, etc. file).  Uses xarray.
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|}
  
  
  
 
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'''''[[Python tools for use with GEOS-Chem|Previous]] | [[Guide to visualization and analysis tools for GEOS-Chem|Next]] | [[Guide to visualization and analysis tools for GEOS-Chem|Guide to Visualization and Analysis Tools]]'''''
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'''''[[Other tools for visualizing and analyzing GEOS-Chem output|Previous]] | [[Guide to visualization and analysis tools for GEOS-Chem|Next]] | [[Guide to visualization and analysis tools for GEOS-Chem|Guide to Visualization and Analysis Tools]]'''''

Revision as of 19:25, 23 August 2019

Previous | Next | Guide to Visualization and Analysis Tools

  1. Python tools for visualizing and analyzing GEOS-Chem output
  2. Other tools for visualizing and analyzing GEOS-Chem output
  3. Visualization and analysis examples


Plotting examples

Comparing diagnostics from different simulations

Script or notebook Author Description
compare_diagnostics.ipynb Lizzie Lundgren (Harvard) Jupyter notebook that illustrates the steps involved in comparing output from two different GEOS-Chem simulations.

Timeseries data

Script or notebook Author Description
plot_timeseries.py Bob Yantosca (Harvard) Example script that plots timeseries from two different GEOS-Chem diagnostic collections (ConcAboveSfc, SpeciesConc) at a specified location. This is a good example of the using Python xarray and matplotlib packages to create line plots.

Plotting 2D data

Script or notebook Author Description
plot_global.py Daniel Rothenberg Creates a global average plot. Using cartopy_ we can easily visualize gridded model output on maps with different cartographic projections, and then configure them with any aesthetics or features such as continents, geopolitical borders, gridlines, and more.
plot_seasons.py Daniel Rothenberg For a given field timeseries, compute seasonal averages over all data and plot each average on a four-panel figure.
quickplot.py Bob Yantosca (Harvard) Creates a quick-and-dirty plot of a variable in a netCDF (HDF5, GRIB, etc. file). Uses xarray.



Previous | Next | Guide to Visualization and Analysis Tools