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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 xarray with MetPy Tutorial
xarray &lt;http
Step1:  Getting Data
While xarray can handle a wide variety of n-dimensional data (essentially anything that can
be stored in a netCDF file), a common use case is working with model output. Such model
data can be obtained from a THREDDS Data Server using the siphon package, but for this
tutorial, we will use an example subset of GFS data from Hurrican Irma (September 5th,
2017).
Step2:  Preparing Data
To make use of the data within MetPy, we need to parse the dataset for projection and
coordinate information following the CF conventions. For this, we use the
data.metpy.parse_cf() method, which will return a new, parsed DataArray or
Dataset.
Additionally, we rename our data variables for easier reference.
Step3:  Units
MetPy's DataArray accessor has a unit_array property to obtain a pint.Quantity array
of just the data from the DataArray (metadata is removed) and a convert_units method to
convert the the data from one unit to another (keeping it as a DataArray). For now, we'll
just use convert_units to convert our pressure coordinates to hPa.
Step4:  Coordinates
You may have noticed how we directly accessed the vertical coordinates above using their
names. However, in general, if we are working with a particular DataArray, we don't have to
worry about that since MetPy is able to parse the coordinates and so obtain a particular
coordinate type directly. There are two ways to do this
Step5:  Projections
Getting the cartopy coordinate reference system (CRS) of the projection of a DataArray is as
straightforward as using the data_var.metpy.cartopy_crs property
Step6:  The cartopy Globe can similarly be accessed via the data_var.metpy.cartopy_globe
property
Step7:  Calculations
Most of the calculations in metpy.calc will accept DataArrays by converting them
into their corresponding unit arrays. While this may often work without any issues, we must
keep in mind that because the calculations are working with unit arrays and not DataArrays
Step8:  Also, a limited number of calculations directly support xarray DataArrays or Datasets (they
can accept and return xarray objects). Right now, this includes
Derivative functions
first_derivative
second_derivative
gradient
laplacian
Cross-section functions
cross_section_components
normal_component
tangential_component
absolute_momentum
More details can be found by looking at the documentation for the specific function of
interest.
There is also the special case of the helper function, grid_deltas_from_dataarray, which
takes a DataArray input, but returns unit arrays for use in other calculations. We could
rewrite the above geostrophic wind example using this helper function as follows
Step9:  Plotting
Like most meteorological data, we want to be able to plot these data. DataArrays can be used
like normal numpy arrays in plotting code, which is the recommended process at the current
point in time, or we can use some of xarray's plotting functionality for quick inspection of
the data.
(More detail beyond the following can be found at xarray's plotting reference
&lt;http
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Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
# Any import of metpy will activate the accessors
import metpy.calc as mpcalc
from metpy.testing import get_test_data
Explanation: xarray with MetPy Tutorial
xarray &lt;http://xarray.pydata.org/&gt;_ is a powerful Python package that provides N-dimensional
labeled arrays and datasets following the Common Data Model. While the process of integrating
xarray features into MetPy is ongoing, this tutorial demonstrates how xarray can be used
within the current version of MetPy. MetPy's integration primarily works through accessors
which allow simplified projection handling and coordinate identification. Unit and calculation
support is currently available in a limited fashion, but should be improved in future
versions.
End of explanation
# Open the netCDF file as a xarray Dataset
data = xr.open_dataset(get_test_data('irma_gfs_example.nc', False))
# View a summary of the Dataset
print(data)
Explanation: Getting Data
While xarray can handle a wide variety of n-dimensional data (essentially anything that can
be stored in a netCDF file), a common use case is working with model output. Such model
data can be obtained from a THREDDS Data Server using the siphon package, but for this
tutorial, we will use an example subset of GFS data from Hurrican Irma (September 5th,
2017).
End of explanation
# To parse the full dataset, we can call parse_cf without an argument, and assign the returned
# Dataset.
data = data.metpy.parse_cf()
# If we instead want just a single variable, we can pass that variable name to parse_cf and
# it will return just that data variable as a DataArray.
data_var = data.metpy.parse_cf('Temperature_isobaric')
# To rename variables, supply a dictionary between old and new names to the rename method
data.rename({
    'Vertical_velocity_pressure_isobaric': 'omega',
    'Relative_humidity_isobaric': 'relative_humidity',
    'Temperature_isobaric': 'temperature',
    'u-component_of_wind_isobaric': 'u',
    'v-component_of_wind_isobaric': 'v',
    'Geopotential_height_isobaric': 'height'
}, inplace=True)
Explanation: Preparing Data
To make use of the data within MetPy, we need to parse the dataset for projection and
coordinate information following the CF conventions. For this, we use the
data.metpy.parse_cf() method, which will return a new, parsed DataArray or
Dataset.
Additionally, we rename our data variables for easier reference.
End of explanation
data['isobaric1'].metpy.convert_units('hPa')
data['isobaric3'].metpy.convert_units('hPa')
Explanation: Units
MetPy's DataArray accessor has a unit_array property to obtain a pint.Quantity array
of just the data from the DataArray (metadata is removed) and a convert_units method to
convert the the data from one unit to another (keeping it as a DataArray). For now, we'll
just use convert_units to convert our pressure coordinates to hPa.
End of explanation
# Get multiple coordinates (for example, in just the x and y direction)
x, y = data['temperature'].metpy.coordinates('x', 'y')
# If we want to get just a single coordinate from the coordinates method, we have to use
# tuple unpacking because the coordinates method returns a generator
vertical, = data['temperature'].metpy.coordinates('vertical')
# Or, we can just get a coordinate from the property
time = data['temperature'].metpy.time
# To verify, we can inspect all their names
print([coord.name for coord in (x, y, vertical, time)])
Explanation: Coordinates
You may have noticed how we directly accessed the vertical coordinates above using their
names. However, in general, if we are working with a particular DataArray, we don't have to
worry about that since MetPy is able to parse the coordinates and so obtain a particular
coordinate type directly. There are two ways to do this:
Use the data_var.metpy.coordinates method
Use the data_var.metpy.x, data_var.metpy.y, data_var.metpy.vertical,
   data_var.metpy.time properties
The valid coordinate types are:
x
y
vertical
time
(Both approaches and all four types are shown below)
End of explanation
data_crs = data['temperature'].metpy.cartopy_crs
print(data_crs)
Explanation: Projections
Getting the cartopy coordinate reference system (CRS) of the projection of a DataArray is as
straightforward as using the data_var.metpy.cartopy_crs property:
End of explanation
data_globe = data['temperature'].metpy.cartopy_globe
print(data_globe)
Explanation: The cartopy Globe can similarly be accessed via the data_var.metpy.cartopy_globe
property:
End of explanation
lat, lon = xr.broadcast(y, x)
f = mpcalc.coriolis_parameter(lat)
dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat, initstring=data_crs.proj4_init)
heights = data['height'].loc[time[0]].loc[{vertical.name: 500.}]
u_geo, v_geo = mpcalc.geostrophic_wind(heights, f, dx, dy)
print(u_geo)
print(v_geo)
Explanation: Calculations
Most of the calculations in metpy.calc will accept DataArrays by converting them
into their corresponding unit arrays. While this may often work without any issues, we must
keep in mind that because the calculations are working with unit arrays and not DataArrays:
The calculations will return unit arrays rather than DataArrays
Broadcasting must be taken care of outside of the calculation, as it would only recognize
  dimensions by order, not name
As an example, we calculate geostropic wind at 500 hPa below:
End of explanation
heights = data['height'].loc[time[0]].loc[{vertical.name: 500.}]
lat, lon = xr.broadcast(y, x)
f = mpcalc.coriolis_parameter(lat)
dx, dy = mpcalc.grid_deltas_from_dataarray(heights)
u_geo, v_geo = mpcalc.geostrophic_wind(heights, f, dx, dy)
print(u_geo)
print(v_geo)
Explanation: Also, a limited number of calculations directly support xarray DataArrays or Datasets (they
can accept and return xarray objects). Right now, this includes
Derivative functions
first_derivative
second_derivative
gradient
laplacian
Cross-section functions
cross_section_components
normal_component
tangential_component
absolute_momentum
More details can be found by looking at the documentation for the specific function of
interest.
There is also the special case of the helper function, grid_deltas_from_dataarray, which
takes a DataArray input, but returns unit arrays for use in other calculations. We could
rewrite the above geostrophic wind example using this helper function as follows:
End of explanation
# A very simple example example of a plot of 500 hPa heights
data['height'].loc[time[0]].loc[{vertical.name: 500.}].plot()
plt.show()
# Let's add a projection and coastlines to it
ax = plt.axes(projection=ccrs.LambertConformal())
ax._hold = True  # Work-around for CartoPy 0.16/Matplotlib 3.0.0 incompatibility
data['height'].loc[time[0]].loc[{vertical.name: 500.}].plot(ax=ax, transform=data_crs)
ax.coastlines()
plt.show()
# Or, let's make a full 500 hPa map with heights, temperature, winds, and humidity
# Select the data for this time and level
data_level = data.loc[{vertical.name: 500., time.name: time[0]}]
# Create the matplotlib figure and axis
fig, ax = plt.subplots(1, 1, figsize=(12, 8), subplot_kw={'projection': data_crs})
# Plot RH as filled contours
rh = ax.contourf(x, y, data_level['relative_humidity'], levels=[70, 80, 90, 100],
                 colors=['#99ff00', '#00ff00', '#00cc00'])
# Plot wind barbs, but not all of them
wind_slice = slice(5, -5, 5)
ax.barbs(x[wind_slice], y[wind_slice],
         data_level['u'].metpy.unit_array[wind_slice, wind_slice].to('knots'),
         data_level['v'].metpy.unit_array[wind_slice, wind_slice].to('knots'),
         length=6)
# Plot heights and temperature as contours
h_contour = ax.contour(x, y, data_level['height'], colors='k', levels=range(5400, 6000, 60))
h_contour.clabel(fontsize=8, colors='k', inline=1, inline_spacing=8,
                 fmt='%i', rightside_up=True, use_clabeltext=True)
t_contour = ax.contour(x, y, data_level['temperature'], colors='xkcd:deep blue',
                       levels=range(248, 276, 2), alpha=0.8, linestyles='--')
t_contour.clabel(fontsize=8, colors='xkcd:deep blue', inline=1, inline_spacing=8,
                 fmt='%i', rightside_up=True, use_clabeltext=True)
# Add geographic features
ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor=cfeature.COLORS['land'])
ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor=cfeature.COLORS['water'])
ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='#c7c783', zorder=0)
ax.add_feature(cfeature.LAKES.with_scale('50m'), facecolor=cfeature.COLORS['water'],
               edgecolor='#c7c783', zorder=0)
# Set a title and show the plot
ax.set_title(('500 hPa Heights (m), Temperature (K), Humidity (%) at ' +
              time[0].dt.strftime('%Y-%m-%d %H:%MZ')))
plt.show()
Explanation: Plotting
Like most meteorological data, we want to be able to plot these data. DataArrays can be used
like normal numpy arrays in plotting code, which is the recommended process at the current
point in time, or we can use some of xarray's plotting functionality for quick inspection of
the data.
(More detail beyond the following can be found at xarray's plotting reference
&lt;http://xarray.pydata.org/en/stable/plotting.html&gt;_.)
End of explanation
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