Plot Station Maps Combining CROCUS and NOAA Data#
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from PythonMETAR import Metar
import datetime
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io.img_tiles import GoogleTiles, OSM
import metpy.calc as mpcalc
from metpy.calc import reduce_point_density, wind_components
from metpy.calc import dewpoint_from_relative_humidity, wet_bulb_temperature
from metpy.calc import altimeter_to_station_pressure
from metpy.units import units
from metpy.io import parse_metar_to_dataframe
from metpy.io import metar
from metpy.plots import current_weather, sky_cover, StationPlot
import sage_data_client
import matplotlib.pyplot as plt
import pandas as pd
import xarray as xr
Show code cell source
def pressure_to_altimeter(pressure, height):
pressure = pressure.to("hPa").m
height = height.to("m").m
return ((pressure - 0.3) * ((1 +(((1013.25 ** 0.190284) * 0.0065)/288) * (height / ((pressure - 0.3) ** 0.190284))) ** (1/0.190284)) * units.hPa).to("inHg")
Show code cell source
wxt_global_NEIU = {'conventions': "CF 1.10",
'site_ID' : "NEIU",
'CAMS_tag' : "CMS-WXT-002",
'datastream' : "CMS_wxt536_NEIU_a1",
'datalevel' : "a1",
"plugin" : "registry.sagecontinuum.org/jrobrien/waggle-wxt536:0.*",
'WSN' : 'W08D',
'latitude' : 41.9804526,
'longitude' : -87.7196038}
wxt_global_NU = {'conventions': "CF 1.10",
'WSN':'W099',
'site_ID' : "NU",
'CAMS_tag' : "CMS-WXT-005",
'datastream' : "CMS_wxt536_NU_a1",
'plugin' : "registry.sagecontinuum.org/jrobrien/waggle-wxt536:0.*",
'datalevel' : "a1",
'latitude' : 42.051469749,
'longitude' : -87.677667183}
wxt_global_CSU = {'conventions': "CF 1.10",
'WSN':'W08E',
'site_ID' : "CSU",
'CAMS_tag' : "CMS-WXT-003",
'datastream' : "CMS_wxt536_CSU_a1",
'plugin' : "local/waggle-wxt536",
'datalevel' : "a1",
'latitude' : 41.71996846,
'longitude' : -87.612805717}
wxt_global_ATMOS = {'conventions': "CF 1.10",
'WSN':'W0A4',
'site_ID' : "ATMOS",
'CAMS_tag' : "CMS-WXT-001",
'datastream' : "CMS_wxt536_ATMOS_a1",
'plugin' : "registry.sagecontinuum.org/jrobrien/waggle-wxt536:0.*",
'datalevel' : "a1",
'latitude' : 41.7016264,
'longitude' : -87.9956515}
wxt_global_UIC = {'conventions': "CF 1.10",
'WSN':'W096',
'site_ID' : "UIC",
'CAMS_tag' : "CMS-WXT-011",
'datastream' : "CMS_wxt536_UIC_a1",
'plugin' : "registry.sagecontinuum.org/jrobrien/waggle-wxt536:0.*",
'datalevel' : "a1",
'latitude' : 41.869407936,
'longitude' : -87.645806251}
wxt_global_CCIS = {'conventions': "CF 1.10",
'WSN':'W08B',
'site_ID' : "NEIU_CCIS",
'CAMS_tag' : "CMS-WXT-001",
'datastream' : "CMS_wxt536_NEIU_CCIS_a1",
'plugin' : "registry.sagecontinuum.org/jrobrien/waggle-wxt536:0.*",
'datalevel' : "a1",
'latitude' : 41.822966818,
'longitude' : -87.609655739}
var_attrs_wxt = {'temperature': {'standard_name' : 'air_temperature',
'units' : 'celsius'},
'humidity': {'standard_name' : 'relative_humidity',
'units' : 'percent'},
'dewpoint': {'standard_name' : 'dew_point_temperature',
'units' : 'celsius'},
'pressure': {'standard_name' : 'air_pressure',
'units' : 'hPa'},
'wind_mean_10s': {'standard_name' : 'wind_speed',
'units' : 'celsius'},
'wind_max_10s': {'standard_name' : 'wind_speed',
'units' : 'celsius'},
'wind_dir_10s': {'standard_name' : 'wind_from_direction',
'units' : 'degrees'},
'rainfall': {'standard_name' : 'precipitation_amount',
'units' : 'kg m-2'}}
Show code cell source
def ingest_wxt_latest(global_attrs, var_attrs):
# Access the last ten minutes of data
df_temp = sage_data_client.query(start="-10m",
filter={
"name" : 'wxt.env.temp|wxt.env.humidity|wxt.env.pressure|wxt.rain.accumulation',
"vsn" : global_attrs['WSN'],
"sensor" : "vaisala-wxt536"
}
)
winds = sage_data_client.query(start="-10m",
filter={
"name" : 'wxt.wind.speed|wxt.wind.direction',
"vsn" : global_attrs['WSN'],
"sensor" : "vaisala-wxt536"
}
)
hums = df_temp[df_temp['name']=='wxt.env.humidity']
temps = df_temp[df_temp['name']=='wxt.env.temp']
pres = df_temp[df_temp['name']=='wxt.env.pressure']
rain = df_temp[df_temp['name']=='wxt.rain.accumulation']
npres = len(pres)
nhum = len(hums)
ntemps = len(temps)
nrains = len(rain)
minsamps = min([nhum, ntemps, npres, nrains])
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
vals = temps.set_index('time')[0:minsamps]
vals['temperature'] = vals.value.to_numpy()[0:minsamps]
vals['humidity'] = hums.value.to_numpy()[0:minsamps]
vals['pressure'] = pres.value.to_numpy()[0:minsamps]
vals['rainfall'] = rain.value.to_numpy()[0:minsamps]
direction = winds[winds['name']=='wxt.wind.direction']
speed = winds[winds['name']=='wxt.wind.speed']
nspeed = len(speed)
ndir = len(direction)
minsamps = min([nspeed, ndir])
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
windy = speed.set_index('time')[0:minsamps]
windy['speed'] = windy.value.to_numpy()[0:minsamps]
windy['direction'] = direction.value.to_numpy()[0:minsamps]
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
dp = dewpoint_from_relative_humidity( vals.temperature.to_numpy() * units.degC,
vals.humidity.to_numpy() * units.percent)
vals['dewpoint'] = dp
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
wb = wet_bulb_temperature(vals10.pressure.to_numpy() * units.hPa,
vals10.temperature.to_numpy() * units.degC,
vals10.dewpoint.to_numpy() * units.degC)
vals10['wetbulb'] = wb
vals10['wind_dir_10s'] = winds10mean['direction']
vals10['wind_mean_10s'] = winds10mean['speed']
vals10['wind_max_10s'] = winds10max['speed']
_ = vals10.pop('value')
vals10xr = xr.Dataset.from_dataframe(vals10)
vals10xr = vals10xr.sortby('time')
vals10xr = vals10xr.assign_attrs(global_attrs)
for varname in var_attrs.keys():
vals10xr[varname] = vals10xr[varname].assign_attrs(var_attrs[varname])
return vals10xr
Show code cell source
kord = parse_metar_to_dataframe(Metar('KORD').metar)
kmdw = parse_metar_to_dataframe(Metar('KMDW').metar)
klot = parse_metar_to_dataframe(Metar('KLOT').metar)
ds_list = []
station_ids = []
elevations = []
metars = pd.concat([kord, kmdw, klot])
try:
xNU = ingest_wxt_latest(wxt_global_NU, var_attrs_wxt).isel(time=-1)
xNU['latitude'] = xNU.attrs['latitude']
xNU['longitude'] = xNU.attrs['longitude']
xNU['site'] = 'NU'
elevations.append(187)
ds_list.append(xNU)
except:
pass
try:
xNEIU = ingest_wxt_latest(wxt_global_NEIU, var_attrs_wxt).isel(time=-1)
xNEIU['latitude'] = xNEIU.attrs['latitude']
xNEIU['longitude'] = xNEIU.attrs['longitude']
xNEIU['site'] = 'NEIU'
elevations.append(182)
ds_list.append(xNEIU)
except:
pass
try:
xUIC = ingest_wxt_latest(wxt_global_UIC, var_attrs_wxt).isel(time=-1)
xUIC['latitude'] = xUIC.attrs['latitude']
xUIC['longitude'] = xUIC.attrs['longitude']
xUIC['site'] = 'UIC'
elevations.append(181)
ds_list.append(xUIC)
except:
pass
try:
xATMOS = ingest_wxt_latest(wxt_global_ATMOS, var_attrs_wxt).isel(time=-1)
xATMOS['latitude'] = xATMOS.attrs['latitude']
xATMOS['longitude'] = xATMOS.attrs['longitude']
xATMOS['site'] = 'ATMOS'
elevations.append(231)
ds_list.append(xATMOS)
except:
pass
try:
xCSU = ingest_wxt_latest(wxt_global_CSU, var_attrs_wxt).isel(time=-1)
xCSU['latitude'] = xCSU.attrs['latitude']
xCSU['longitude'] = xCSU.attrs['longitude']
xCSU['site'] = 'CSU'
elevations.append(176)
ds_list.append(xCSU)
except:
pass
try:
xCCIS = ingest_wxt_latest(wxt_global_CCIS, var_attrs_wxt).isel(time=-1)
xCCIS['latitude'] = xCCIS.attrs['latitude']
xCCIS['longitude'] = xCCIS.attrs['longitude']
xCCIS['site'] = 'NEIU_CCIS'
elevations.append(176)
ds_list.append(xCCIS)
except:
pass
ds = xr.concat(ds_list, dim='site')
data = ds.to_dataframe()
data['station_id'] = list(ds.site.values)
data["elevation"] = elevations
# Compute altimeter and mslp values
data["altimeter"] = pressure_to_altimeter(data.pressure.values * units.hPa, 176 * units.meter)
data["mslp"] = mpcalc.altimeter_to_sea_level_pressure(data.altimeter.values * units.inHg,
data.elevation.values * units.meter,
data.temperature.values * units.degC).to("hPa")
Downloading file 'sfstns.tbl' from 'https://github.com/Unidata/MetPy/raw/v1.6.3/staticdata/sfstns.tbl' to '/home/runner/.cache/metpy/v1.6.3'.
Downloading file 'master.txt' from 'https://github.com/Unidata/MetPy/raw/v1.6.3/staticdata/master.txt' to '/home/runner/.cache/metpy/v1.6.3'.
Downloading file 'stations.txt' from 'https://github.com/Unidata/MetPy/raw/v1.6.3/staticdata/stations.txt' to '/home/runner/.cache/metpy/v1.6.3'.
Downloading file 'airport-codes.csv' from 'https://github.com/Unidata/MetPy/raw/v1.6.3/staticdata/airport-codes.csv' to '/home/runner/.cache/metpy/v1.6.3'.
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/tmp/ipykernel_3153/812442133.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
temps['time'] = pd.DatetimeIndex(temps['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:46: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
speed['time'] = pd.DatetimeIndex(speed['timestamp'].values)
/tmp/ipykernel_3153/812442133.py:52: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10mean = windy.resample('10S').mean(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:53: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
winds10max = windy.resample('10S').max(numeric_only=True).ffill()
/tmp/ipykernel_3153/812442133.py:58: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
vals10 = vals.resample('10S').mean(numeric_only=True).ffill() #ffil gets rid of nans due to empty resample periods
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/xarray/core/concat.py:540: UserWarning: No index created for dimension site because variable site is not a coordinate. To create an index for site, please first call `.set_coords('site')` on this object.
ds.expand_dims(dim_name, create_index_for_new_dim=create_index_for_new_dim)
Show code cell source
# Set up the map projection
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
standard_parallels=[35])
# Use the Cartopy map projection to transform station locations to the map and
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['longitude'].values,
data['latitude'].values)
fig = plt.figure(figsize=(20, 10))
tiler = OSM()
mercator = tiler.crs
#add_metpy_logo(fig, 1100, 300, size='large')
ax = fig.add_subplot(1, 1, 1, projection=mercator)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
#ax.add_feature(cfeature.NaturalEarthFeature("physical", "land", "10m"),
# ec="red", fc="yellow", lw=2, alpha=0.4)
#ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS)
ax.add_image(tiler, 11)
# Set plot bounds
ax.set_extent((-88.2, -87.4, 41.5, 42.1))
#
# Here's the actual station plot
#
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'].values, data['latitude'].values,
clip_on=True, transform=ccrs.PlateCarree(), fontsize=12)
stationplot.plot_parameter('NW', (data['temperature'].values * units.degC).to("degF"), color='red', formatter = '0.1f')
stationplot.plot_parameter('SW', (data['dewpoint'].values * units.degC).to("degF"),
color='darkgreen', formatter = '0.1f')
stationplot.plot_parameter('NE', data['mslp'].values,
formatter=lambda v: format(10 * v, '.0f')[-3:])
ew, nw = wind_components((data['wind_mean_10s'].values * units.meter_per_second).to("knot") ,
data['wind_dir_10s'].values * units.degrees_north)
stationplot.plot_barb(ew, nw)
stationplot.plot_text('SE', data['station_id'].values, fontsize=10)
stationplot_metars = StationPlot(ax, metars['longitude'].values, metars['latitude'].values,
clip_on=True, transform=ccrs.PlateCarree(), fontsize=12)
stationplot_metars.plot_parameter('NW', (metars['air_temperature'].values * units.degC).to("degF"), color='red', formatter = '0.1f')
stationplot_metars.plot_parameter('SW', (metars['dew_point_temperature'].values * units.degC).to("degF"),
fontsize=11,
color='darkgreen', formatter = '0.1f')
stationplot_metars.plot_parameter('NE', metars['air_pressure_at_sea_level'].values,
fontsize=11,
formatter=lambda v: format(10 * v, ' .0f')[-3:])
ew, nw = wind_components(metars['wind_speed'].values * units.knot ,
metars['wind_direction'].values * units.degrees_north)
stationplot_metars.plot_barb(ew, nw)
stationplot_metars.plot_text('SE', metars['station_id'].values, fontsize=10)
plt.title(pd.to_datetime(data.time.values[0]).strftime("%H:%M UTC \n %d %b %Y \n CROCUS and NOAA Surface Observations"), fontsize=20)
plt.savefig("current-conditions-crocus.png", dpi=300)
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_land.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_ocean.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces_lakes.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/miniconda3/envs/instrument-cookbooks-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_boundary_lines_land.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
