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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:
 Using Python to Access NCEI Archived NEXRAD Level 2 Data
This notebook shows how to access the THREDDS Data Server (TDS) instance that is serving up archived NEXRAD Level 2 data hosted on Amazon S3. The TDS provides a mechanism to query for available data files, as well as provides access to the data as native volume files, through OPeNDAP, and using its own CDMRemote protocol. Since we're using Python, we can take advantage of Unidata's Siphon package, which provides an easy API for talking to THREDDS servers.
NOTE
Step1:  First we'll create an instance of RadarServer to point to the appropriate radar server access URL.
Step2:  Next, we'll create a new query object to help request the data. Using the chaining methods, let's ask for the latest data at the radar KLVX (Louisville, KY). We see that when the query is represented as a string, it shows the encoded URL.
Step3:  We can use the RadarServer instance to check our query, to make sure we have required parameters and that we have chosen valid station(s) and variable(s)
Step4:  Make the request, which returns an instance of TDSCatalog; this handles parsing the returned XML information.
Step5:  We can look at the datasets on the catalog to see what data we found by the query. We find one volume in the return, since we asked for the volume nearest to a single time.
Step6:  We can pull that dataset out of the dictionary and look at the available access URLs. We see URLs for OPeNDAP, CDMRemote, and HTTPServer (direct download).
Step7:  We'll use the CDMRemote reader in Siphon and pass it the appropriate access URL.
Step8:  We define some helper functions to make working with the data easier. One takes the raw data and converts it to floating point values with the missing data points appropriately marked. The other helps with converting the polar coordinates (azimuth and range) to Cartesian (x and y).
Step9:  The CDMRemote reader provides an interface that is almost identical to the usual python NetCDF interface. We pull out the variables we need for azimuth and range, as well as the data itself.
Step10:  Then convert the raw data to floating point values and the polar coordinates to Cartesian.
Step11:  MetPy is a Python package for meteorology (Documentation
Step12:  Finally, we plot them up using matplotlib and cartopy. We create a helper function for making a map to keep things simpler later.
Step13:  Download a collection of historical data
This time we'll make a query based on a longitude, latitude point and using a time range.
Step14:  The specified longitude, latitude are in NY and the TDS helpfully finds the closest station to that point.  We can see that for this time range we obtained multiple datasets.
Step15:  Grab the first dataset so that we can get the longitude and latitude of the station and make a map for plotting. We'll go ahead and specify some longitude and latitude bounds for the map.
Step16:  Use the function to make a new map and plot a colormapped view of the data
Step17:  Now we can loop over the collection of returned datasets and plot them. As we plot, we collect the returned plot objects so that we can use them to make an animated plot. We also add a timestamp for each plot.
Step18:  Using matplotlib, we can take a collection of Artists that have been plotted and turn them into an animation. With matplotlib 1.5 (1.5-rc2 is available now!), this animation can be converted to HTML5 video viewable in the notebook.
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Python Code:
import matplotlib
import warnings
warnings.filterwarnings("ignore", category=matplotlib.cbook.MatplotlibDeprecationWarning)
%matplotlib inline
Explanation: Using Python to Access NCEI Archived NEXRAD Level 2 Data
This notebook shows how to access the THREDDS Data Server (TDS) instance that is serving up archived NEXRAD Level 2 data hosted on Amazon S3. The TDS provides a mechanism to query for available data files, as well as provides access to the data as native volume files, through OPeNDAP, and using its own CDMRemote protocol. Since we're using Python, we can take advantage of Unidata's Siphon package, which provides an easy API for talking to THREDDS servers.
NOTE: Due to data charges, the TDS instance in AWS only allows access to .edu domains. For other users interested in using Siphon to access radar data, you can access recent (2 weeks') data by changing the server URL below to: http://thredds.ucar.edu/thredds/radarServer/nexrad/level2/IDD/
But first!
Bookmark these resources for when you want to use Siphon later!
+ latest Siphon documentation
+ Siphon github repo
+ TDS documentation
Downloading the single latest volume
Just a bit of initial set-up to use inline figures and quiet some warnings.
End of explanation
# The S3 URL did not work for me, despite .edu domain
#url = 'http://thredds-aws.unidata.ucar.edu/thredds/radarServer/nexrad/level2/S3/'
#Trying motherlode URL
url = 'http://thredds.ucar.edu/thredds/radarServer/nexrad/level2/IDD/'
from siphon.radarserver import RadarServer
rs = RadarServer(url)
Explanation: First we'll create an instance of RadarServer to point to the appropriate radar server access URL.
End of explanation
from datetime import datetime, timedelta
query = rs.query()
query.stations('KLVX').time(datetime.utcnow())
Explanation: Next, we'll create a new query object to help request the data. Using the chaining methods, let's ask for the latest data at the radar KLVX (Louisville, KY). We see that when the query is represented as a string, it shows the encoded URL.
End of explanation
rs.validate_query(query)
Explanation: We can use the RadarServer instance to check our query, to make sure we have required parameters and that we have chosen valid station(s) and variable(s)
End of explanation
catalog = rs.get_catalog(query)
Explanation: Make the request, which returns an instance of TDSCatalog; this handles parsing the returned XML information.
End of explanation
catalog.datasets
Explanation: We can look at the datasets on the catalog to see what data we found by the query. We find one volume in the return, since we asked for the volume nearest to a single time.
End of explanation
ds = list(catalog.datasets.values())[0]
ds.access_urls
Explanation: We can pull that dataset out of the dictionary and look at the available access URLs. We see URLs for OPeNDAP, CDMRemote, and HTTPServer (direct download).
End of explanation
from siphon.cdmr import Dataset
data = Dataset(ds.access_urls['CdmRemote'])
Explanation: We'll use the CDMRemote reader in Siphon and pass it the appropriate access URL.
End of explanation
import numpy as np
def raw_to_masked_float(var, data):
    # Values come back signed. If the _Unsigned attribute is set, we need to convert
    # from the range [-127, 128] to [0, 255].
    if var._Unsigned:
        data = data & 255
    # Mask missing points
    data = np.ma.array(data, mask=data==0)
    # Convert to float using the scale and offset
    return data * var.scale_factor + var.add_offset
def polar_to_cartesian(az, rng):
    az_rad = np.deg2rad(az)[:, None]
    x = rng * np.sin(az_rad)
    y = rng * np.cos(az_rad)
    return x, y
Explanation: We define some helper functions to make working with the data easier. One takes the raw data and converts it to floating point values with the missing data points appropriately marked. The other helps with converting the polar coordinates (azimuth and range) to Cartesian (x and y).
End of explanation
sweep = 0
ref_var = data.variables['Reflectivity_HI']
ref_data = ref_var[sweep]
rng = data.variables['distanceR_HI'][:]
az = data.variables['azimuthR_HI'][sweep]
Explanation: The CDMRemote reader provides an interface that is almost identical to the usual python NetCDF interface. We pull out the variables we need for azimuth and range, as well as the data itself.
End of explanation
ref = raw_to_masked_float(ref_var, ref_data)
x, y = polar_to_cartesian(az, rng)
Explanation: Then convert the raw data to floating point values and the polar coordinates to Cartesian.
End of explanation
from metpy.plots import ctables  # For NWS colortable
ref_norm, ref_cmap = ctables.registry.get_with_steps('NWSReflectivity', 5, 5)
Explanation: MetPy is a Python package for meteorology (Documentation: http://metpy.readthedocs.org and GitHub: http://github.com/MetPy/MetPy). We import MetPy and use it to get the colortable and value mapping information for the NWS Reflectivity data.
End of explanation
import matplotlib.pyplot as plt
import cartopy
def new_map(fig, lon, lat):
    # Create projection centered on the radar. This allows us to use x
    # and y relative to the radar.
    proj = cartopy.crs.LambertConformal(central_longitude=lon, central_latitude=lat)
    # New axes with the specified projection
    ax = fig.add_subplot(1, 1, 1, projection=proj)
    # Add coastlines
    ax.coastlines('50m', 'black', linewidth=2, zorder=2)
    # Grab state borders
    state_borders = cartopy.feature.NaturalEarthFeature(
        category='cultural', name='admin_1_states_provinces_lines',
        scale='50m', facecolor='none')
    ax.add_feature(state_borders, edgecolor='black', linewidth=1, zorder=3)
    
    return ax
Explanation: Finally, we plot them up using matplotlib and cartopy. We create a helper function for making a map to keep things simpler later.
End of explanation
query = rs.query()
#dt = datetime(2012, 10, 29, 15) # Our specified time
dt = datetime(2016, 6, 8, 18) # Our specified time
query.lonlat_point(-73.687, 41.175).time_range(dt, dt + timedelta(hours=1))
Explanation: Download a collection of historical data
This time we'll make a query based on a longitude, latitude point and using a time range.
End of explanation
cat = rs.get_catalog(query)
cat.datasets
Explanation: The specified longitude, latitude are in NY and the TDS helpfully finds the closest station to that point.  We can see that for this time range we obtained multiple datasets.
End of explanation
ds = list(cat.datasets.values())[0]
data = Dataset(ds.access_urls['CdmRemote'])
# Pull out the data of interest
sweep = 0
rng = data.variables['distanceR_HI'][:]
az = data.variables['azimuthR_HI'][sweep]
ref_var = data.variables['Reflectivity_HI']
# Convert data to float and coordinates to Cartesian
ref = raw_to_masked_float(ref_var, ref_var[sweep])
x, y = polar_to_cartesian(az, rng)
Explanation: Grab the first dataset so that we can get the longitude and latitude of the station and make a map for plotting. We'll go ahead and specify some longitude and latitude bounds for the map.
End of explanation
fig = plt.figure(figsize=(10, 10))
ax = new_map(fig, data.StationLongitude, data.StationLatitude)
# Set limits in lat/lon space
ax.set_extent([-77, -70, 38, 42])
# Add ocean and land background
ocean = cartopy.feature.NaturalEarthFeature('physical', 'ocean', scale='50m',
                                            edgecolor='face',
                                            facecolor=cartopy.feature.COLORS['water'])
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='50m',
                                           edgecolor='face',
                                           facecolor=cartopy.feature.COLORS['land'])
ax.add_feature(ocean, zorder=-1)
ax.add_feature(land, zorder=-1)
#ax = new_map(fig, data.StationLongitude, data.StationLatitude)
ax.pcolormesh(x, y, ref, cmap=ref_cmap, norm=ref_norm, zorder=0);
Explanation: Use the function to make a new map and plot a colormapped view of the data
End of explanation
meshes = []
for item in sorted(cat.datasets.items()):
    # After looping over the list of sorted datasets, pull the actual Dataset object out
    # of our list of items and access over CDMRemote
    ds = item[1]
    data = Dataset(ds.access_urls['CdmRemote'])
    # Pull out the data of interest
    sweep = 0
    rng = data.variables['distanceR_HI'][:]
    az = data.variables['azimuthR_HI'][sweep]
    ref_var = data.variables['Reflectivity_HI']
    # Convert data to float and coordinates to Cartesian
    ref = raw_to_masked_float(ref_var, ref_var[sweep])
    x, y = polar_to_cartesian(az, rng)
    # Plot the data and the timestamp
    mesh = ax.pcolormesh(x, y, ref, cmap=ref_cmap, norm=ref_norm, zorder=0)
    text = ax.text(0.65, 0.03, data.time_coverage_start, transform=ax.transAxes,
                   fontdict={'size':16})
    
    # Collect the things we've plotted so we can animate
    meshes.append((mesh, text))
Explanation: Now we can loop over the collection of returned datasets and plot them. As we plot, we collect the returned plot objects so that we can use them to make an animated plot. We also add a timestamp for each plot.
End of explanation
# Set up matplotlib to do the conversion to HTML5 video
import matplotlib
matplotlib.rcParams['animation.html'] = 'html5'
# Create an animation
from matplotlib.animation import ArtistAnimation
ArtistAnimation(fig, meshes)
Explanation: Using matplotlib, we can take a collection of Artists that have been plotted and turn them into an animation. With matplotlib 1.5 (1.5-rc2 is available now!), this animation can be converted to HTML5 video viewable in the notebook.
End of explanation
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