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<a href="https://github.com/Unidata/awips2/edit/unidata_17.1.1/docs/python/surface-obs-plot-metpy.md" title="Edit this page" class="md-icon md-content__icon">edit</a>
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<h1>Surface Obs Plot with MetPy</h1>
|
|
|
|
<p>Based on the MetPy example <a href="http://metpy.readthedocs.org/en/latest/examples/generated/Station_Plot_with_Layout.html">"Station Plot with Layout"</a></p>
|
|
<pre><code class="python">import datetime
|
|
import pandas
|
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import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pprint
|
|
from awips.dataaccess import DataAccessLayer
|
|
from metpy.calc import get_wind_components
|
|
from metpy.cbook import get_test_data
|
|
from metpy.plots.wx_symbols import sky_cover, current_weather
|
|
from metpy.plots import StationPlot, StationPlotLayout, simple_layout
|
|
from metpy.units import units
|
|
|
|
def get_cloud_cover(code):
|
|
if 'OVC' in code:
|
|
return 1.0
|
|
elif 'BKN' in code:
|
|
return 6.0/8.0
|
|
elif 'SCT' in code:
|
|
return 4.0/8.0
|
|
elif 'FEW' in code:
|
|
return 2.0/8.0
|
|
else:
|
|
return 0
|
|
|
|
state_capital_wx_stations = {'Washington':'KOLM', 'Oregon':'KSLE', 'California':'KSAC',
|
|
'Nevada':'KCXP', 'Idaho':'KBOI', 'Montana':'KHLN',
|
|
'Utah':'KSLC', 'Arizona':'KDVT', 'New Mexico':'KSAF',
|
|
'Colorado':'KBKF', 'Wyoming':'KCYS', 'North Dakota':'KBIS',
|
|
'South Dakota':'KPIR', 'Nebraska':'KLNK', 'Kansas':'KTOP',
|
|
'Oklahoma':'KPWA', 'Texas':'KATT', 'Louisiana':'KBTR',
|
|
'Arkansas':'KLIT', 'Missouri':'KJEF', 'Iowa':'KDSM',
|
|
'Minnesota':'KSTP', 'Wisconsin':'KMSN', 'Illinois':'KSPI',
|
|
'Mississippi':'KHKS', 'Alabama':'KMGM', 'Nashville':'KBNA',
|
|
'Kentucky':'KFFT', 'Indiana':'KIND', 'Michigan':'KLAN',
|
|
'Ohio':'KCMH', 'Georgia':'KFTY', 'Florida':'KTLH',
|
|
'South Carolina':'KCUB', 'North Carolina':'KRDU',
|
|
'Virginia':'KRIC', 'West Virginia':'KCRW',
|
|
'Pennsylvania':'KCXY', 'New York':'KALB', 'Vermont':'KMPV',
|
|
'New Hampshire':'KCON', 'Maine':'KAUG', 'Massachusetts':'KBOS',
|
|
'Rhode Island':'KPVD', 'Connecticut':'KHFD', 'New Jersey':'KTTN',
|
|
'Delaware':'KDOV', 'Maryland':'KNAK'}
|
|
single_value_params = ["timeObs", "stationName", "longitude", "latitude",
|
|
"temperature", "dewpoint", "windDir",
|
|
"windSpeed", "seaLevelPress"]
|
|
multi_value_params = ["presWeather", "skyCover", "skyLayerBase"]
|
|
all_params = single_value_params + multi_value_params
|
|
obs_dict = dict({all_params: [] for all_params in all_params})
|
|
pres_weather = []
|
|
sky_cov = []
|
|
sky_layer_base = []
|
|
</code></pre>
|
|
|
|
<pre><code class="python">from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange
|
|
from datetime import datetime, timedelta
|
|
lastHourDateTime = datetime.utcnow() - timedelta(hours = 1)
|
|
start = lastHourDateTime.strftime('%Y-%m-%d %H')
|
|
beginRange = datetime.strptime( start + ":00:00", "%Y-%m-%d %H:%M:%S")
|
|
endRange = datetime.strptime( start + ":59:59", "%Y-%m-%d %H:%M:%S")
|
|
timerange = TimeRange(beginRange, endRange)
|
|
</code></pre>
|
|
|
|
<pre><code class="python">DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
|
|
request = DataAccessLayer.newDataRequest()
|
|
request.setDatatype("obs")
|
|
request.setParameters(*(all_params))
|
|
request.setLocationNames(*(state_capital_wx_stations.values()))
|
|
</code></pre>
|
|
|
|
<pre><code class="python">response = DataAccessLayer.getGeometryData(request,timerange)
|
|
for ob in response:
|
|
avail_params = ob.getParameters()
|
|
if "presWeather" in avail_params:
|
|
pres_weather.append(ob.getString("presWeather"))
|
|
elif "skyCover" in avail_params and "skyLayerBase" in avail_params:
|
|
sky_cov.append(ob.getString("skyCover"))
|
|
sky_layer_base.append(ob.getNumber("skyLayerBase"))
|
|
else:
|
|
for param in single_value_params:
|
|
if param in avail_params:
|
|
if param == 'timeObs':
|
|
obs_dict[param].append(datetime.fromtimestamp(ob.getNumber(param)/1000.0))
|
|
else:
|
|
try:
|
|
obs_dict[param].append(ob.getNumber(param))
|
|
except TypeError:
|
|
obs_dict[param].append(ob.getString(param))
|
|
else:
|
|
obs_dict[param].append(None)
|
|
|
|
obs_dict['presWeather'].append(pres_weather);
|
|
obs_dict['skyCover'].append(sky_cov);
|
|
obs_dict['skyLayerBase'].append(sky_layer_base);
|
|
pres_weather = []
|
|
sky_cov = []
|
|
sky_layer_base = []
|
|
</code></pre>
|
|
|
|
<p>We can now use pandas to retrieve desired subsets of our observations.</p>
|
|
<p>In this case, return the most recent observation for each station.</p>
|
|
<pre><code class="python">df = pandas.DataFrame(data=obs_dict, columns=all_params)
|
|
#sort rows with the newest first
|
|
df = df.sort_values(by='timeObs', ascending=False)
|
|
#group rows by station
|
|
groups = df.groupby('stationName')
|
|
#create a new DataFrame for the most recent values
|
|
df_recent = pandas.DataFrame(columns=all_params)
|
|
#retrieve the first entry for each group, which will
|
|
#be the most recent observation
|
|
for rid, station in groups:
|
|
row = station.head(1)
|
|
df_recent = pandas.concat([df_recent, row])
|
|
</code></pre>
|
|
|
|
<p>Convert DataFrame to something metpy-readable by
|
|
attaching units and calculating derived values</p>
|
|
<pre><code class="python">data = dict()
|
|
data['stid'] = np.array(df_recent["stationName"])
|
|
data['latitude'] = np.array(df_recent['latitude'])
|
|
data['longitude'] = np.array(df_recent['longitude'])
|
|
data['air_temperature'] = np.array(df_recent['temperature'], dtype=float)* units.degC
|
|
data['dew_point'] = np.array(df_recent['dewpoint'], dtype=float)* units.degC
|
|
data['slp'] = np.array(df_recent['seaLevelPress'])* units('mbar')
|
|
u, v = get_wind_components(np.array(df_recent['windSpeed']) * units('knots'),
|
|
np.array(df_recent['windDir']) * units.degree)
|
|
data['eastward_wind'], data['northward_wind'] = u, v
|
|
data['cloud_frac'] = [int(get_cloud_cover(x)*8) for x in df_recent['skyCover']]
|
|
</code></pre>
|
|
|
|
<pre><code class="python">%matplotlib inline
|
|
import cartopy.crs as ccrs
|
|
import cartopy.feature as feat
|
|
from matplotlib import rcParams
|
|
rcParams['savefig.dpi'] = 100
|
|
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
|
|
standard_parallels=[35])
|
|
state_boundaries = feat.NaturalEarthFeature(category='cultural',
|
|
name='admin_1_states_provinces_lines',
|
|
scale='110m', facecolor='none')
|
|
# Create the figure
|
|
fig = plt.figure(figsize=(20, 15))
|
|
ax = fig.add_subplot(1, 1, 1, projection=proj)
|
|
|
|
# Add map elements
|
|
ax.add_feature(feat.LAND, zorder=-1)
|
|
ax.add_feature(feat.OCEAN, zorder=-1)
|
|
ax.add_feature(feat.LAKES, zorder=-1)
|
|
ax.coastlines(resolution='110m', zorder=2, color='black')
|
|
ax.add_feature(state_boundaries)
|
|
ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
|
|
ax.set_extent((-120, -70, 20, 50))
|
|
|
|
# Start the station plot by specifying the axes to draw on, as well as the
|
|
# lon/lat of the stations (with transform). We also set the fontsize to 12 pt.
|
|
stationplot = StationPlot(ax, data['longitude'], data['latitude'],
|
|
transform=ccrs.PlateCarree(), fontsize=12)
|
|
|
|
# The layout knows where everything should go, and things are standardized using
|
|
# the names of variables. So the layout pulls arrays out of `data` and plots them
|
|
# using `stationplot`.
|
|
simple_layout.plot(stationplot, data)
|
|
|
|
# Plot the temperature and dew point to the upper and lower left, respectively, of
|
|
# the center point. Each one uses a different color.
|
|
stationplot.plot_parameter('NW', np.array(data['air_temperature']), color='red')
|
|
stationplot.plot_parameter('SW', np.array(data['dew_point']), color='darkgreen')
|
|
|
|
# A more complex example uses a custom formatter to control how the sea-level pressure
|
|
# values are plotted. This uses the standard trailing 3-digits of the pressure value
|
|
# in tenths of millibars.
|
|
stationplot.plot_parameter('NE', np.array(data['slp']),
|
|
formatter=lambda v: format(10 * v, '.0f')[-3:])
|
|
|
|
# Plot the cloud cover symbols in the center location. This uses the codes made above and
|
|
# uses the `sky_cover` mapper to convert these values to font codes for the
|
|
# weather symbol font.
|
|
stationplot.plot_symbol('C', data['cloud_frac'], sky_cover)
|
|
|
|
# Also plot the actual text of the station id. Instead of cardinal directions,
|
|
# plot further out by specifying a location of 2 increments in x and 0 in y.
|
|
stationplot.plot_text((2, 0), np.array(obs_dict["stationName"]))
|
|
|
|
plt.title("Most Recent Observations for State Capitals")
|
|
</code></pre>
|
|
|
|
<p><img alt="png" src="../../images/output_9_1.png" /></p>
|
|
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