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=================
Grids and Cartopy
=================
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Grids_and_Cartopy.ipynb>`_
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Python-AWIPS Tutorial Notebook
--------------
Objectives
==========
- Create a colorized plot for the continental US of model data (grib).
- Access the model data from an EDEX server and limit the data returned
by using model specific parameters.
- Use both *pcolormesh* and *contourf* to create colorized plots, and
compare the differences between the two.
--------------
Table of Contents
-----------------
| `1
Imports <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#imports>`__\
| `2 Define Data
Request <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#define-data-request>`__\
| `3 Limit Results Based on
Time <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#limit-results-based-on-time>`__\
| `4 Function:
make_map() <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#function-make-map>`__\
| `5 Use the Grid
Data! <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#use-the-grid-data>`__\
|
pcolormesh <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#plot-using-pcolormesh>`__\
|
contourf <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#plot-using-contourf>`__\
| `6 See
Also <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#see-also>`__\
|
Notebooks <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#related-notebooks>`__\
|
Documentation <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html#additional-documentation>`__\
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1 Imports
---------
Start by importing both the python-awips specific library, as well as
the libraries needed for plotting and manipulating the data
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.. code:: ipython3
from awips.dataaccess import DataAccessLayer
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
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from scipy.constants import convert_temperature
`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
--------------
2 Define Data Request
---------------------
If you read through the `python-awips: How to Access
Data <https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/dataAccess/python-awips-HowToAccessData.ipynb>`__
training, you will know that we need to set an EDEX url to access our
server, and then we create a data request. In this example we use *grid*
as the data type to define our request. In addition to setting the data
type, the location, parameters and levels are also set as **RAP13**,
**T** (for temperature), and **2.0FHAG** (for Fixed Height Above Ground
in meters), respectively.
.. code:: ipython3
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
request = DataAccessLayer.newDataRequest()
request.setDatatype("grid")
request.setLocationNames("RAP13")
request.setParameters("T")
request.setLevels("2.0FHAG")
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# Take a look at our request
print(request)
.. parsed-literal::
DefaultDataRequest(datatype=grid, identifiers={}, parameters=['T'], levels=[<dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.level.Level.Level object at 0x11127bfd0>], locationNames=['RAP13'], envelope=None)
`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
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3 Limit Results Based on Time
-----------------------------
Models produce many different time variants during their runs, so let’
limit the data to the most recent time and forecast run.
.. container:: alert-info
Note: You can play around with different times and forecast runs to
see the differences.
.. code:: ipython3
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cycles = DataAccessLayer.getAvailableTimes(request, True)
times = DataAccessLayer.getAvailableTimes(request)
fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)
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# Get the most recent grid data
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response = DataAccessLayer.getGridData(request, [fcstRun[0]])
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print('Number of available times:', len(times))
print('Number of available forecast runs:', len(fcstRun))
.. parsed-literal::
Number of available times: 74
Number of available forecast runs: 8
`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
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4 Function: make_map()
----------------------
In order to plot more than one image, it’
logic in a function. Here, a new function called **make_map** is
defined. This function uses the `matplotlib.pyplot package
(plt) <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html>`__
to create a figure and axis. The coastlines (continental boundaries) are
added, along with lat/lon grids.
.. code:: ipython3
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def make_map(bbox, projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(16, 9),
subplot_kw=dict(projection=projection))
ax.set_extent(bbox)
ax.coastlines(resolution='50m')
gl = ax.gridlines(draw_labels=True)
gl.top_labels = gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return fig, ax
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`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
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5 Use the Grid Data!
--------------------
Here we get our grid data object from our previous response, and then
get the raw data array off that object. We also get the latitude and
longitude arrays, and create a bounding box that we’
our plots (by calling **make_map** defined above). Finally, we’
convert our data from degrees Kelvin to Farenheit to make the plot more
understandable.
.. code:: ipython3
grid = response[0]
data = grid.getRawData()
lons, lats = grid.getLatLonCoords()
bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
# Convert temp from Kelvin to F
destUnit = 'F'
data = convert_temperature(data, 'K', destUnit)
5.1 Plot Using pcolormesh
~~~~~~~~~~~~~~~~~~~~~~~~~
This example shows how to use
`matplotlib.pyplot.pcolormesh <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.pcolormesh.html>`__
to create a colorized plot. We use our **make_map** function to create a
subplot and then we create a color scale (cs) and colorbar (cbar) with a
label for our plot.
.. container:: alert-info
Note: You may see a warning appear with a red background, this is
okay, and will go away with subsequent runs of the cell.
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.. code:: ipython3
cmap = plt.get_cmap('rainbow')
fig, ax = make_map(bbox=bbox)
cs = ax.pcolormesh(lons, lats, data, cmap=cmap)
cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
cbar.set_label(grid.getLocationName() +" "+ grid.getLevel() + " " \
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+ grid.getParameter() + " (" + destUnit + ") " \
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+ "valid " + str(grid.getDataTime().getRefTime()))
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.. parsed-literal::
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/Users/scarter/opt/miniconda3/envs/python3-awips/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1598: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
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.. image:: Grids_and_Cartopy_files/Grids_and_Cartopy_21_1.png
5.2 Plot Using contourf
~~~~~~~~~~~~~~~~~~~~~~~
This example shows how to use
`matplotlib.pyplot.contourf <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.contourf.html>`__
to create a colorized plot. We use our **make_map** function to create a
subplot and then we create a color scale (cs2) and colorbar (cbar2) with
a label for our plot.
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.. code:: ipython3
fig2, ax2 = make_map(bbox=bbox)
cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,
vmin=data.min(), vmax=data.max())
cbar2 = fig2.colorbar(cs2, shrink=0.7, orientation='horizontal')
cbar2.set_label(grid.getLocationName() +" "+ grid.getLevel() + " " \
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+ grid.getParameter() + " (" + destUnit + ") " \
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+ "valid " + str(grid.getDataTime().getRefTime()))
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.. image:: Grids_and_Cartopy_files/Grids_and_Cartopy_23_0.png
`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
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6 See Also
----------
6.1 Related Notebooks
~~~~~~~~~~~~~~~~~~~~~
- `Grid Levels and
Parameters <https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html>`__
6.2 Additional Documention
~~~~~~~~~~~~~~~~~~~~~~~~~~
**python-awips:** \*
`awips.DataAccessLayer <http://unidata.github.io/python-awips/api/DataAccessLayer.html>`__
\*
`awips.PyGridData <http://unidata.github.io/python-awips/api/PyGridData.html>`__
**matplotlib:** \*
`matplotlib.pyplot <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html>`__
\*
`matplotlib.pyplot.subplot <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.subplot.html>`__
\*
`matplotlib.pyplot.pcolormesh <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.pcolormesh.html>`__
\*
`matplotlib.pyplot.contourf <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.contourf.html>`__
`Top <https://unidata.github.io/python-awips/examples/generated/Grids_and_Cartopy.html>`__
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