def plot_background(ax):
ax.coastlines(resolution='10m', linewidth=2, color='black', zorder=4)
political_boundaries = NaturalEarthFeature(
category='cultural',
name='admin_0_boundary_lines_land',
scale='10m',
facecolor='none')
states = NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(political_boundaries,
linestyle='-',
edgecolor='black',
zorder=4)
ax.add_feature(states,
linestyle='-',
edgecolor='black',
linewidth=2,
zorder=4)
ax.add_feature(
USCOUNTIES.with_scale('500k'),
edgecolor='black',
linewidth=1,
zorder=1
) #### Using Metpy's county shapefiles due to hi-resolution and they also help with spartial awareness
ax.add_feature(roads,
facecolor='none',
edgecolor='dimgrey',
zorder=1,
linewidth=1)
return ax
def add_geographic_features(self):
"""
"""
self.ax.add_feature(cfeature.STATES.with_scale('50m'),
edgecolor='black',
linewidth=1.0)
self.ax.add_feature(USCOUNTIES.with_scale('5m'),
edgecolor='black',
linewidth=0.1)
def test_us_county_scales():
"""Test US county plotting with all scales."""
proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)
fig = plt.figure(figsize=(12, 9))
ax1 = fig.add_subplot(1, 3, 1, projection=proj)
ax2 = fig.add_subplot(1, 3, 2, projection=proj)
ax3 = fig.add_subplot(1, 3, 3, projection=proj)
for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
axis.add_feature(USCOUNTIES.with_scale(scale), edgecolor='black')
return fig
def test_us_county_scales():
"""Test US county plotting with all scales."""
proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)
fig = plt.figure(figsize=(12, 9))
ax1 = fig.add_subplot(1, 3, 1, projection=proj)
ax2 = fig.add_subplot(1, 3, 2, projection=proj)
ax3 = fig.add_subplot(1, 3, 3, projection=proj)
for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
axis.add_feature(USCOUNTIES.with_scale(scale))
return fig
def drawcounties(self,
linewidths=0.2,
linestyle='solid',
color='k',
res='l',
ax=None,
**kwargs):
"""
Draws county borders similarly to Basemap's m.drawcounties() function. Since cartopy currently
does not offer a county shapefile, this function uses MetPy's available county shapefiles.
Parameters:
----------------------
linewidths
Line width (default is 0.2)
linestyle
Line style to plot (default is solid)
color
Color of line (default is black)
res
Resolution of country borders. Can be a character ('l','m','h') or one of MetPy's available
resolutions ('20m','5m','500k'). If none is specified, then the default resolution specified
when creating an instance of Map is used.
ax
Axes instance, if not None then overrides the default axes instance.
"""
#Get current axes if not specified
ax = ax or self._check_ax()
#Error check resolution
res = self.check_res(res, counties=True)
#Draw counties using metpy
counties = ax.add_feature(USCOUNTIES.with_scale(res),
linewidths=linewidths,
linestyle=linestyle,
edgecolor=color,
**kwargs)
#Return value
return counties
mslp = data['mslp'] / 100.
mslpc = mslp.squeeze()
mslpc = ndimage.gaussian_filter(mslpc, sigma=1, order=0)
sfc_pressure = data['spres'].squeeze() / 100
#This creates a nice-looking datetime label
dtfs = str(time.dt.strftime('%Y-%m-%d_%H%MZ').item())
########## SET UP FIGURE ##################################################
fig = plt.figure(figsize=(15, 15))
ax1 = fig.add_subplot(111, projection=zH5_crs)
ax1.coastlines(resolution='10m')
ax1.add_feature(cfeature.BORDERS.with_scale('10m'), linewidth=1.5)
ax1.add_feature(cfeature.STATES.with_scale('10m'), linewidth=2.0)
ax1.add_feature(USCOUNTIES.with_scale('500k'), edgecolor='silver')
########## PLOTTING #######################################################
#Plot 2m 32F isotherm
tmp_2m32 = ax1.contour(x, y, t2m, colors='b', alpha=0.8, levels=[32])
#Plot labeled MSLP contours
h_contour = ax1.contour(x,
y,
mslpc,
colors='dimgray',
levels=range(940, 1080, 4),
linewidths=1,
alpha=0.7)
#h_contour.clabel(fontsize=14, colors='dimgray', inline=1, inline_spacing=4, fmt='%i mb', rightside_up=True, use_clabeltext=True)
def common_features(
scale="110m",
ax=None,
crs=pc,
*,
figsize=None,
dpi=None,
counties_scale="20m",
dark=False,
verbose=False,
COASTLINES=True,
COASTLINES_kwargs={},
BORDERS=False,
BORDERS_kwargs={},
STATES=False,
STATES_kwargs={},
COUNTIES=False,
COUNTIES_kwargs={},
OCEAN=False,
OCEAN_kwargs={},
LAND=False,
LAND_kwargs={},
RIVERS=False,
RIVERS_kwargs={},
LAKES=False,
LAKES_kwargs={},
ROADS=False,
ROADS_kwargs={},
PLACES=False,
PLACES_kwargs={},
STAMEN=False,
STAMEN_kwargs={},
OSM=False,
OSM_kwargs={},
**kwargs,
):
"""
Add common features to a cartopy axis.
This completes about 95% of my cartopy needs.
.. tip:: This is a great way to initialize a new cartopy axes.
Parameters
----------
scale : {'10m', '50m' 110m'}
The cartopy feature's level of detail.
.. note:: The ``'10m'`` scale for OCEAN and LAND takes a *long* time.
ax : plot axes
The axis to add the feature to.
If None, it will create a new cartopy axes with ``crs``.
crs : cartopy.crs
Coordinate reference system (aka "projection") to create new map
if no cartopy axes is given. Default is ccrs.PlateCarree.
dark : bool
If True, use alternative "dark theme" colors for land and water.
.. figure:: _static/BB_maps/common_features-1.png
.. figure:: _static/BB_maps/common_features-2.png
counties_scale: {'20m', '5m', '500k'}
Counties are plotted via MetPy and have different resolutions
available than other features.
- 20m = 20,000,000 resolution (Ok if you show a large area)
- 5m = 5,000,000 resolution (provides good detail)
- 500k = 500,000 resolution (high resolution, plots very slow)
figsize : tuple
Set the figure size
dpi : int
Set the figure dpi
FEATURES : bool
Toggle on various features. By default, only COASTLINES is
turned on. Each feature has a cooresponding ``FEATURE_kwargs={}``
dictionary to supply additional arguments to cartopy's add_feature
method (e.g., change line color or width by feature type).
========== =========================================================
FEATURE Description
========== =========================================================
COASTLINES Coastlines, boundary between land and ocean.
BORDERS Borders between countries. *Does not includes coast*.
STATES US state borders. Includes coast.
COUNTIES US Counties. Includes coast.
OCEAN Colored ocean area
LAND Colored land area
RIVERS Lines where rivers exist
LAKES Colored lake area
ROADS All major roads. Can break filter by road type.
PLACES Points and labels for major cities (filter by rank).
========== =========================================================
========== =========================================================
MAP TILE Description
========== =========================================================
Stamen Specify type and zoom level. http://maps.stamen.com/
Style: ``terrain-background``, ``terrain``,
``toner-background``, ``toner``, `watercolor``
zoom: int [0-10]
alpha: [0-1]
alpha_color: an overlay color to put on top of map
use 'k' to darken background
use 'w' to lighten background
OSM Open Street Maps
zoom: int
========== =========================================================
.. note::
For ``ROADS_kwargs`` you may provide a key for 'type' to filter
out the types of roads you want. The road type may be a single
string or a list of road types. For example
``ROADS_kwargs=dict(type='Major Highway')`` or
``ROADS_kwargs=dict(type=['Secondary Highway', 'Major Highway'])
Of course, the shapefile has many other road classifiers for each
road, like "level" (Federal, State, Interstate), road "name",
"length_km", etc. Filters for each of these could be added if I
need them later.
.. note::
When adding a tile product to a map, it might be better to add
it to the map first, then set the map extent, then make a separate
call to ``common_features`` to add other features like roads and
counties. The reason is because, if you add a tile map to
Methods
-------
.adjust_extent
.center_extent
.copy_extent
Examples
--------
https://github.com/blaylockbk/Carpenter_Workshop/blob/main/notebooks/demo_cartopy_tools.ipynb
Returns
-------
The cartopy axes (obviously you don't need this if you gave an ax
as an argument, but it is useful if you initialize a new map).
"""
ax = check_cartopy_axes(ax=ax, crs=crs, verbose=verbose)
if (LAND or OCEAN) and scale in ["10m"]:
if verbose:
warnings.warn(
"🕖 OCEAN or LAND features at 10m may take a long time (3+ mins) to display on large maps."
)
kwargs.setdefault("linewidth", 0.75)
COASTLINES_kwargs.setdefault("zorder", 100)
COASTLINES_kwargs.setdefault("facecolor", "none")
COUNTIES_kwargs.setdefault("linewidth", 0.5)
STATES_kwargs.setdefault("alpha", 0.15)
LAND_kwargs.setdefault("edgecolor", "none")
LAND_kwargs.setdefault("linewidth", 0)
OCEAN_kwargs.setdefault("edgecolor", "none")
LAKES_kwargs.setdefault("linewidth", 0)
# NOTE: I don't use the 'setdefault' method here because it doesn't
# work as expect when switching between dark and normal themes.
# The defaults would be set the first time the function is called,
# but the next time it is called and `dark=True` the defaults do not
# reset. I don't know why this is the behavior.
if dark:
land = "#060613"
water = "#0f2b38"
# https://github.com/SciTools/cartopy/issues/880
ax.set_facecolor(land) # requires cartopy >= 0.18
kwargs = {**{"edgecolor": ".5"}, **kwargs}
LAND_kwargs = {**{"facecolor": land}, **LAND_kwargs}
OCEAN_kwargs = {**{"facecolor": water}, **OCEAN_kwargs}
RIVERS_kwargs = {**{"edgecolor": water}, **RIVERS_kwargs}
LAKES_kwargs = {**{"facecolor": water}, **LAKES_kwargs}
else:
kwargs = {**{"edgecolor": ".15"}, **kwargs}
RIVERS_kwargs = {
**{
"edgecolor": feature.COLORS["water"]
},
**RIVERS_kwargs
}
LAKES_kwargs = {
**{
"edgecolor": feature.COLORS["water"]
},
**LAKES_kwargs
}
##------------------------------------------------------------------
## Add each element to the plot
## When combining kwargs,
## - kwargs is the main value
## - FEATURE_kwargs is the overwrite for the feature
## For example:
## {**kwargs, **FEATURE_kwargs}
## the kwargs are overwritten by FEATURE_kwargs
##------------------------------------------------------------------
if COASTLINES:
# ax.coastlines(scale, **kwargs) # Nah, use the crs.feature instead
ax.add_feature(feature.COASTLINE.with_scale(scale), **{
**kwargs,
**COASTLINES_kwargs
})
if verbose == "debug":
print("🐛 COASTLINES:", {**kwargs, **COASTLINES_kwargs})
if BORDERS:
ax.add_feature(feature.BORDERS.with_scale(scale), **{
**kwargs,
**BORDERS_kwargs
})
if verbose == "debug":
print("🐛 BORDERS:", {**kwargs, **BORDERS_kwargs})
if STATES:
ax.add_feature(feature.STATES.with_scale(scale), **{
**kwargs,
**STATES_kwargs
})
if verbose == "debug":
print("🐛 STATES:", {**kwargs, **STATES_kwargs})
if COUNTIES:
_counties_scale = {"20m", "5m", "500k"}
assert (
counties_scale
in _counties_scale), f"counties_scale must be {_counties_scale}"
ax.add_feature(USCOUNTIES.with_scale(counties_scale), **{
**kwargs,
**COUNTIES_kwargs
})
if verbose == "debug":
print("🐛 COUNTIES:", {**kwargs, **COUNTIES_kwargs})
if OCEAN:
ax.add_feature(feature.OCEAN.with_scale(scale), **{
**kwargs,
**OCEAN_kwargs
})
if verbose == "debug":
print("🐛 OCEAN:", {**kwargs, **OCEAN_kwargs})
if LAND and not dark:
# If `dark=True`, the face_color is the land color.
ax.add_feature(feature.LAND.with_scale(scale), **{
**kwargs,
**LAND_kwargs
})
if verbose == "debug":
print("🐛 LAND:", {**kwargs, **LAND_kwargs})
if RIVERS:
ax.add_feature(feature.RIVERS.with_scale(scale), **{
**kwargs,
**RIVERS_kwargs
})
if verbose == "debug":
print("🐛 RIVERS:", {**kwargs, **RIVERS_kwargs})
if LAKES:
ax.add_feature(feature.LAKES.with_scale(scale), **{
**kwargs,
**LAKES_kwargs
})
if verbose == "debug":
print("🐛 LAKES:", {**kwargs, **LAKES_kwargs})
if ROADS:
ROADS_kwargs.setdefault("edgecolor", "#b30000")
ROADS_kwargs.setdefault("facecolor", "none")
ROADS_kwargs.setdefault("linewidth", 0.2)
if "type" not in ROADS_kwargs:
# Plot all roadways
roads = feature.NaturalEarthFeature("cultural", "roads", "10m",
**ROADS_kwargs)
ax.add_feature(roads)
else:
# Specify the type of road to include in plot
road_types = ROADS_kwargs.pop("type")
if isinstance(road_types, str):
road_types = [road_types]
shpfilename = shapereader.natural_earth("10m", "cultural", "roads")
df = geopandas.read_file(shpfilename)
_types = df["type"].unique()
assert np.all([
i in _types for i in road_types
]), f"`ROADS_kwargs['type']` must be a list of these: {_types}"
road_geos = df.loc[df["type"].apply(
lambda x: x in road_types)].geometry.values
ax.add_geometries(road_geos, crs=pc, **ROADS_kwargs)
if verbose == "debug":
print("🐛 ROADS:", ROADS_kwargs)
if PLACES:
PLACES_kwargs.setdefault("country", "United States")
PLACES_kwargs.setdefault("rank", 2)
PLACES_kwargs.setdefault("scatter_kw", {"marker": "."})
PLACES_kwargs.setdefault("label_kw", dict(fontweight="bold",
alpha=0.5))
country = PLACES_kwargs.pop("country")
rank = PLACES_kwargs.pop("rank")
scatter_kw = PLACES_kwargs.pop("scatter_kw")
label_kw = PLACES_kwargs.pop("label_kw")
places = shapereader.natural_earth("10m", "cultural",
"populated_places")
df = geopandas.read_file(places)
df = df[df.SOV0NAME == country]
df = df[df.SCALERANK <= rank]
xs = df.geometry.values.x
ys = df.geometry.values.y
names = df.NAME
if scatter_kw is not None:
ax.scatter(xs, ys, transform=pc, **scatter_kw)
if label_kw is not None:
for x, y, name in zip(xs, ys, names):
plt.text(x, y, name, clip_on=True, **label_kw)
if STAMEN:
if verbose:
print(
"😎 Please use `ax.set_extent` before increasing Zoom level for faster plotting."
)
STAMEN_kwargs.setdefault("style", "terrain-background")
STAMEN_kwargs.setdefault("zoom", 3)
stamen_terrain = cimgt.Stamen(STAMEN_kwargs["style"])
ax.add_image(stamen_terrain, STAMEN_kwargs["zoom"])
if "alpha" in STAMEN_kwargs:
# Need to manually put a white layer over the STAMEN terrain
if dark:
STAMEN_kwargs.setdefault("alpha_color", "k")
else:
STAMEN_kwargs.setdefault("alpha_color", "w")
poly = ax.projection.domain
ax.add_feature(
feature.ShapelyFeature([poly], ax.projection),
color=STAMEN_kwargs["alpha_color"],
alpha=1 - STAMEN_kwargs["alpha"],
zorder=1,
)
if verbose == "debug":
print("🐛 STAMEN:", STAMEN_kwargs)
if OSM:
image = cimgt.OSM()
OSM_kwargs.setdefault("zoom", 1)
ax.add_image(image, OSM_kwargs["zoom"])
if "alpha" in OSM_kwargs:
# Need to manually put a white layer over the STAMEN terrain
if dark:
OSM_kwargs.setdefault("alpha_color", "k")
else:
OSM_kwargs.setdefault("alpha_color", "w")
poly = ax.projection.domain
ax.add_feature(
feature.ShapelyFeature([poly], ax.projection),
color=OSM_kwargs["alpha_color"],
alpha=1 - OSM_kwargs["alpha"],
zorder=1,
)
if verbose == "debug":
print("🐛 OSM:", OSM_kwargs)
if figsize is not None:
plt.gcf().set_figwidth(figsize[0])
plt.gcf().set_figheight(figsize[1])
if dpi is not None:
plt.gcf().set_dpi(dpi)
# Add my custom methods
ax.__class__.adjust_extent = _adjust_extent
ax.__class__.center_extent = _center_extent
ax.__class__.copy_extent = _copy_extent
return ax
basin = sys.argv[1].upper()
season = sys.argv[2]
tc = sys.argv[3].capitalize()
#### Load the NHC HURDAT2 best-track for the desired TC via the 'onetrack' function
track = np.array(onetrack(season, basin, tc, printalert=False)[1])
tcdates = np.array([
datetime.strptime(track[t, 0], '%Y%m%d%H%M') for t in range(track.shape[0])
])
#### Declare fixed variables for mapping purposes
mlon, xlon, mlat, xlat = [-97., -85., 27., 35.
] # fixed region centered on Louisiana/Mississippi
lat = None
pc = ccrs.PlateCarree()
counties = USCOUNTIES.with_scale('5m')
scl = '10m'
borders = cfeature.BORDERS.with_scale(scl)
lakes = cfeature.LAKES.with_scale(scl)
coast = cfeature.COASTLINE.with_scale(scl)
states = cfeature.NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lines',
scale=scl,
facecolor='none')
levs = np.arange(
0., 12.1, 0.5) # will shade rainfall values every 0.5 inches from 0 to 12
cmap = plt.get_cmap('gist_heat_r') # colormap for mapping rainfall values
mapProj = ccrs.LambertConformal(central_longitude=(xlon - mlon) / 2. + mlon,
central_latitude=(xlat - mlat) / 2. + mlat)
#### Iterate through available AHPS netCDF files to create 24-h rainfall maps
def makeStationPlot(plotTitle, plotFileName, maxDataAge, maxLat, minLat,
maxLon, minLon, stationDensity, textSize, figX, figY, dpi,
showCountryBorders, showStateBorders, showCountyBorders):
#
# Data Polling
# Get data from AWC TDS
dataURL = "https://www.aviationweather.gov/adds/dataserver_current/httpparam?dataSource=metars&requestType=retrieve&format=csv&minLat=" + str(
minLat) + "&minLon=" + str(minLon) + "&maxLat=" + str(
maxLat) + "&maxLon=" + str(maxLon) + "&hoursBeforeNow=" + str(
maxDataAge)
# First read in the data. We use pandas because it simplifies a lot of tasks, like dealing
# with strings
data = pd.read_csv(dataURL,
header=5,
usecols=(1, 3, 4, 5, 6, 7, 8, 12, 21, 22),
names=[
'stid', 'lat', 'lon', 'air_temperature',
'dew_point_temperature', 'wind_dir', 'wind_speed',
'slp', 'weather', 'cloud_fraction'
],
na_values=-99999)
#
# Data Handling
# convert T and Td from °C to °F
data['air_temperature'] = (data['air_temperature'] * (9 / 5.0)) + 32
data['dew_point_temperature'] = (data['dew_point_temperature'] *
(9 / 5.0)) + 32
# change sky category to %
data['cloud_fraction'] = data['cloud_fraction'].replace(
'SKC', 0.0).replace('CLR', 0.0).replace('CAVOK', 0.0).replace(
'FEW', 0.1875).replace('SCT',
0.4375).replace('BKN', 0.750).replace(
'OVC', 1.000).replace('OVX', 1.000)
# Drop rows with missing winds
data = data.dropna(how='any', subset=['wind_dir', 'wind_speed'])
# Set up the map projection
proj = ccrs.LambertConformal(
central_longitude=(minLon + (maxLon - minLon) / 2),
central_latitude=(minLat + (maxLat - minLat) / 2))
# Set station density, in x meter radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values,
data['lat'].values)
data = data[reduce_point_density(point_locs, stationDensity * 1000)]
# Get the wind components, converting from m/s to knots as will be appropriate
u, v = wind_components(
(data['wind_speed'].values * units('m/s')).to('knots'),
data['wind_dir'].values * units.degree)
# Convert the fraction value into a code of 0-8 and compensate for NaN values
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# Map weather strings to WMO codes. Only use the first symbol if there are multiple
wx = [
wx_code_map[s.split()[0] if ' ' in s else s]
for s in data['weather'].fillna('')
]
#
# Plot Setup
# Set DPI of the resulting figure
plt.rcParams['savefig.dpi'] = dpi
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(figX, figY))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Set plot bounds
ax.set_extent((minLon, maxLon, minLat, maxLat))
# Add geographic features
if showCountyBorders:
ax.add_feature(USCOUNTIES.with_scale('500k'),
edgecolor='gray',
linewidth=0.25)
if showStateBorders:
state_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
ax.add_feature(state_borders, edgecolor='gray', linewidth=0.5)
if showCountryBorders:
country_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_0_countries',
scale='50m',
facecolor='none')
ax.add_feature(country_borders, edgecolor='black', linewidth=0.7)
#
# Create Station Plots
# Set station location, setup plot
stationplot = StationPlot(ax,
data['lon'].values,
data['lat'].values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=textSize)
# Plot the temperature and dew point
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW',
data['dew_point_temperature'],
color='darkgreen')
# Plot pressure data
stationplot.plot_parameter('NE',
data['slp'],
formatter=lambda v: format(10 * v, '.0f')[-3:])
# Plot cloud cover
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Plot current weather
stationplot.plot_symbol('W', wx, current_weather)
# Add wind barbs
stationplot.plot_barb(u, v)
# Plot station id
stationplot.plot_text((2, 0), data['stid'])
# Set a title and show the plot
ax.set_title(plotTitle)
# Export fig
fig.savefig('/home/CarterHumphreys/bin/send2web/' +
datetime.utcnow().strftime("%Y%m%d-%H00") + '_' +
plotFileName + '.png',
bbox_inches='tight')
fig.savefig('/home/CarterHumphreys/bin/send2web/' + plotFileName + '.png',
bbox_inches='tight')
# Copyright (c) 2018 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
US Counties
===========
Demonstrate how to plot US counties at all three available resolutions.
"""
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from metpy.plots import USCOUNTIES
###########################################
proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)
fig = plt.figure(figsize=(12, 9))
ax1 = fig.add_subplot(1, 3, 1, projection=proj)
ax2 = fig.add_subplot(1, 3, 2, projection=proj)
ax3 = fig.add_subplot(1, 3, 3, projection=proj)
for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
axis.add_feature(USCOUNTIES.with_scale(scale), edgecolor='black')
def radsite_counties(site):
radlon, radlat = nexrad_loc(site)
# read all vertices
uscnt = USCOUNTIES.with_scale('20m').geometries()
cntx = []
cnty = []
for usc in uscnt:
if isinstance(usc, Polygon):
usc = [usc]
for poly in usc:
lon, lat = poly.exterior.xy
# convert to radar-relative x-y coordinates
x, y = lonlat2xy(np.array(lon), np.array(lat), radlon, radlat)
cntx.append(x)
cnty.append(y)
'''
for poly in uscnt:
lon, lat = poly.exterior.xy
# convert to radar-relative x-y coordinates
x, y = lonlat2xy(np.array(lon), np.array(lat), radlon, radlat)
cntx.append(x)
cnty.append(y)
'''
# subset county polygons close enough to radar site
ncnt = len(cntx)
cntx_sub = []
cnty_sub = []
maxdist = 450.
for i in range(ncnt):
dist = np.sqrt(cntx[i]**2. + cnty[i]**2.)
if np.min(dist) < maxdist:
cntx_sub.append(cntx[i])
cnty_sub.append(cnty[i])
# create matplotlib path arrays
npoly_cnt = len(cntx_sub)
verts = np.vstack((cntx_sub[0], cnty_sub[0])).T
codes = np.full(len(cntx_sub[0]), Path.LINETO)
codes[0] = Path.MOVETO
codes[-1] = Path.CLOSEPOLY
for i in range(npoly_cnt - 1):
verts = np.concatenate(
(verts, np.vstack((cntx_sub[i + 1], cnty_sub[i + 1])).T))
codes_new = np.full(len(cntx_sub[i + 1]), Path.LINETO)
codes_new[0] = Path.MOVETO
codes_new[-1] = Path.CLOSEPOLY
codes = np.concatenate((codes, codes_new))
# write to netcdf file
nvert = len(codes)
cfile = Dataset(f'site_geom/{site}_counties.nc', 'w')
vind = cfile.createDimension('vert_ind', nvert)
cdim = cfile.createDimension('coor_dim', 2)
vert = cfile.createVariable('vertex', 'f4', ('vert_ind', 'coor_dim'))
vertc = cfile.createVariable('vertex_code', 'i4', ('vert_ind'))
vert.units = 'km'
vertc.units = ''
vert.description = 'distances of vertex from radar site'
vertc.description = 'matplotlib path code for vertex'
vert[:] = verts
vertc[:] = codes
cfile.close()
import cartopy.feature as cfeature
from metpy.plots import USCOUNTIES
# Plot extent
plotExtent = [-81, -74.5, 42.3, 45.3]
# Create the figure and an axes set to the projection
proj = ccrs.Stereographic(
central_longitude=((plotExtent[1] - plotExtent[0]) / 2 + plotExtent[0]),
central_latitude=((plotExtent[3] - plotExtent[2]) / 2 + plotExtent[2]))
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.set_extent(plotExtent)
# Add geographic features
ax.add_feature(USCOUNTIES.with_scale('500k'), edgecolor='gray', linewidth=0.25)
state_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
ax.add_feature(state_borders, edgecolor='black', linewidth=0.5)
country_borders = cfeature.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='50m',
facecolor='none')
ax.add_feature(country_borders, edgecolor='black', linewidth=1.0)
# Plot Points
markerSymbol = 'o'
markerSize = 100
def mapper(self,zoom=None):
"""
This method will map all the points found in the find_my_station method. It
uses cartopy, so make sure you have that if you want a map.
zoom: int, how far you wish to zoom out (if positive). In degrees
"""
if self.station_df is None:
print('ERROR: Please run find_my_station method first')
else:
###import cartopy stuff
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.ticker as mticker
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.io.shapereader as shpreader
from cartopy.mpl.geoaxes import GeoAxes
from mpl_toolkits.axes_grid1 import AxesGrid
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from metpy.plots import USCOUNTIES
###
#make figure
fig = plt.figure(figsize=(10, 10))
#add the map
ax = fig.add_subplot(1, 1, 1,projection=ccrs.PlateCarree())
#draw map things
ax.add_feature(cfeature.STATES.with_scale('10m'),lw=0.5)
ax.add_feature(cartopy.feature.OCEAN.with_scale('50m'))
ax.add_feature(cartopy.feature.LAND.with_scale('50m'), edgecolor='black',lw=0.5,facecolor=[0.95,0.95,0.95])
ax.add_feature(cartopy.feature.LAKES.with_scale('50m'), edgecolor='black')
ax.add_feature(cartopy.feature.RIVERS.with_scale('50m'))
ax.add_feature(USCOUNTIES.with_scale('5m'))
if zoom is None:
zoom = 1
#set corners of the map.
corners = [CS.reference_point[1] - zoom,CS.reference_point[1] + zoom,CS.reference_point[0]- zoom,CS.reference_point[0]+ zoom]
#this set_extent crashes the python session. Not sure why...
# ax.set_extent((CS.reference_point[1] - 5,CS.reference_point[1] + 5,CS.reference_point[0]- 2,CS.reference_point[0]+ 2),crs=ccrs.PlateCarree())
#draw ticks
ax.set_xticks(np.arange(corners[0], corners[1], 1), crs=ccrs.PlateCarree())
ax.set_yticks(np.linspace(corners[2], corners[3], 5), crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
#force bounds
ax.set_xlim([corners[0],corners[1]])
ax.set_ylim([corners[2],corners[3]])
#plot stations colored by distance
pm = ax.scatter(self.station_df.longitude,self.station_df.latitude,c=self.station_df.distance,s=(self.station_df.length_of_record.values.astype(int)/3.154e+16)/2,zorder=2)
plt.colorbar(pm,ax=ax,label='Distance')
ax.plot(self.reference_point[1],self.reference_point[0],'*k',ms=15,markerfacecolor='w',markeredgewidth=2,zorder=10,label='Reference_point')
ax.plot(self.station.longitude,self.station.latitude,'sk',ms=10,markerfacecolor='w',markeredgewidth=2,label='Closest_Station')
ax.legend()
# Copyright (c) 2018 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
US Counties
===========
Demonstrate how to plot US counties at all three available resolutions.
"""
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from metpy.plots import USCOUNTIES
###########################################
proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)
fig = plt.figure(figsize=(12, 9))
ax1 = fig.add_subplot(1, 3, 1, projection=proj)
ax2 = fig.add_subplot(1, 3, 2, projection=proj)
ax3 = fig.add_subplot(1, 3, 3, projection=proj)
for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
axis.add_feature(USCOUNTIES.with_scale(scale))
def plot_low_sweeps(site,
fpath,
ds_set=150.,
xcen_set=150.,
ycen_set=70.,
sw_ang=0.5):
# open radar file
radar = read_nexrad_archive(fpath)
radlat = radar.latitude['data'][0]
radlon = radar.longitude['data'][0]
nyvel = radar.get_nyquist_vel(1)
# plot stuff
mpl.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
mpl.rc('text', usetex=True)
figcnt = 0
# get lat lon corners of data domain
ds = ds_set
xcen = xcen_set
ycen = ycen_set
minlat, minlon = xy2latlon(xcen - ds, ycen - ds, radlat, radlon)
maxlat, maxlon = xy2latlon(xcen + ds, ycen + ds, radlat, radlon)
xlabel = 'X-distance (km)'
ylabel = 'Y-distance (km)'
# color map stuff
zh_map = createCmap('zh2_map')
zdr_map = createCmap('zdr_map')
vel_map = createCmap('vel2_map')
phv_map = createCmap('phv_map')
kdp_map = createCmap('kdp_map')
numcolors = 33
numlevs = numcolors - 1
cinds = np.linspace(0., 254., numcolors).astype(int)
zh_cols = zh_map.colors[cinds]
zdr_cols = zdr_map.colors[cinds]
vel_cols = vel_map.colors[cinds]
phv_cols = phv_map.colors[cinds]
kdp_cols = kdp_map.colors[cinds]
zh_levs = np.linspace(-10., 70., numlevs)
sw_levs = np.linspace(0., 8., numlevs)
zdr_levs = np.linspace(-2.4, 6.9, numlevs)
phidp_levs = np.linspace(50., 100., numlevs)
phv_levs = np.linspace(0.71, 1.06, numlevs)
kdp_levs = np.linspace(-0.8, 2.3, numlevs)
vel_levs = np.linspace(-35., 35., numlevs)
zh_mapn, zh_norm = cm.from_levels_and_colors(zh_levs,
zh_cols,
extend='both')
zdr_mapn, zdr_norm = cm.from_levels_and_colors(zdr_levs,
zdr_cols,
extend='both')
phidp_mapn, phidp_norm = cm.from_levels_and_colors(phidp_levs,
zh_cols,
extend='both')
phv_mapn, phv_norm = cm.from_levels_and_colors(phv_levs,
phv_cols,
extend='both')
kdp_mapn, kdp_norm = cm.from_levels_and_colors(kdp_levs,
kdp_cols,
extend='both')
vel_mapn, vel_norm = cm.from_levels_and_colors(vel_levs,
vel_cols,
extend='both')
sw_mapn, sw_norm = cm.from_levels_and_colors(sw_levs,
zh_cols,
extend='both')
# set common font sizes
cblb_fsize = 18
cbti_fsize = 16
axtl_fsize = 20
axlb_fsize = 20
axti_fsize = 18
# cartopy features
coast = cfeature.GSHHSFeature(scale='high', levels=[1], edgecolor='gray')
states = cfeature.STATES.with_scale('50m')
shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='50m',
category='cultural',
name=shapename)
# create basemap
#m = Basemap(llcrnrlon=minlon, llcrnrlat=minlat, urcrnrlon=maxlon, urcrnrlat=maxlat,
# projection='merc', lat_1=15., lat_2=35., lon_0=-80.,
# resolution='h', area_thresh=1000.)
# get sweeps
fixed_angles = radar.fixed_angle['data']
nang = len(fixed_angles)
sw05 = np.arange(nang)[np.abs(fixed_angles - sw_ang) < 0.2]
print(fixed_angles)
# seperate out z and mdv sweeps
sw_inds = sw05[::2]
swv_inds = sw05[1::2]
# loop over sweeps
for sw in sw_inds:
azi_p = 90. - radar.get_azimuth(sw)
elev_p = radar.get_elevation(sw)
ran = radar.range['data']
sweep = radar.extract_sweeps([sw])
fixed_angle = fixed_angles[sw]
if fixed_angle < 2.:
sweep_v = radar.extract_sweeps([sw + 1])
else:
sweep_v = sweep
# calculate sweep time
vol_time = sweep.time['units']
sw_toffs = sweep.time['data'][0]
sw_time = datetime.fromtimestamp(
time.mktime(
time.strptime(vol_time, 'seconds since %Y-%m-%dT%H:%M:%SZ')))
sw_time = sw_time + timedelta(seconds=sw_toffs)
# get time strings
yyyy = '{:04d}'.format(sw_time.year)
mm = '{:02d}'.format(sw_time.month)
dd = '{:02d}'.format(sw_time.day)
hh = '{:02d}'.format(sw_time.hour)
mn = '{:02d}'.format(sw_time.minute)
ss = '{:02d}'.format(sw_time.second)
print(yyyy, mm, dd, hh, mn, ss)
ref_p = sweep.fields['reflectivity']['data']
zdr_p = sweep.fields['differential_reflectivity']['data']
rhohv_p = sweep.fields['cross_correlation_ratio']['data']
vel_p = sweep_v.fields['velocity']['data']
phidp_p = sweep.fields['differential_phase']['data']
sw_p = sweep_v.fields['spectrum_width']['data']
azi_v_p = 90. - radar.get_azimuth(sw + 1)
elev_v_p = radar.get_elevation(sw + 1)
dims = ref_p.shape
numradials = dims[0] + 1
numgates = dims[1]
# expand radially to remove no data spike
elev = np.empty([numradials])
elev_v = np.empty([numradials])
azi = np.empty([numradials])
azi_v = np.empty([numradials])
ref = np.empty([numradials, numgates])
zdr = np.empty([numradials, numgates])
phidp = np.empty([numradials, numgates])
rhohv = np.empty([numradials, numgates])
vel = np.empty([numradials, numgates])
sw = np.empty([numradials, numgates])
elev[0:numradials - 1] = elev_p
elev[numradials - 1] = elev_p[0]
elev_v[0:numradials - 1] = elev_v_p
elev_v[numradials - 1] = elev_v_p[0]
azi[0:numradials - 1] = azi_p
azi[numradials - 1] = azi_p[0]
azi_v[0:numradials - 1] = azi_v_p
azi_v[numradials - 1] = azi_v_p[0]
ref[0:numradials - 1, :] = ref_p
ref[numradials - 1, :] = ref_p[0]
zdr[0:numradials - 1, :] = zdr_p
zdr[numradials - 1, :] = zdr_p[0]
phidp[0:numradials - 1, :] = phidp_p
phidp[numradials - 1, :] = phidp_p[0]
rhohv[0:numradials - 1, :] = rhohv_p
rhohv[numradials - 1, :] = rhohv_p[0]
vel[0:numradials - 1, :] = vel_p
vel[numradials - 1, :] = vel_p[0]
sw[0:numradials - 1, :] = sw_p
sw[numradials - 1, :] = sw_p[0]
# get stats on velocity
vcount = Counter(vel.flatten())
angle = np.mean(elev)
angle_v = np.mean(elev_v)
# mask data by rhohv and threshold
#-----------------------------------------------
ref = np.ma.masked_where(rhohv < 0.4, ref)
zdr = np.ma.masked_where(rhohv < 0.4, zdr)
phidp = np.ma.masked_where(rhohv < 0.4, phidp)
vel = np.ma.masked_where(rhohv < 0.4, vel)
sw = np.ma.masked_where(rhohv < 0.4, sw)
rhohv = np.ma.masked_where(rhohv < 0.4, rhohv)
zdr = np.ma.masked_where(ref < -15., zdr)
phidp = np.ma.masked_where(ref < -15., phidp)
rhohv = np.ma.masked_where(ref < -15., rhohv)
vel = np.ma.masked_where(ref < -15., vel)
sw = np.ma.masked_where(ref < -15., sw)
ref = np.ma.masked_where(ref < -15., ref)
# calculate kdp
#-----------------------------------------------
print('Calculating KDP...')
phidp = dealiasPhiDP(phidp)
kdp_alt, delta, phidp_alt = calc_kdp(phidp)
kdp_alt = np.ma.masked_where(ref < -5., kdp_alt)
# calculate x and y coordinates (wrt beampath) for plotting
#-----------------------------------------------------------
ran_2d = np.tile(ran, (numradials, 1))
azi.shape = (azi.shape[0], 1)
azi_v.shape = (azi_v.shape[0], 1)
azi_2d = np.tile(azi, (1, numgates))
azi_v_2d = np.tile(azi_v, (1, numgates))
radz = 10.
erad = np.pi * angle / 180.
erad_v = np.pi * angle_v / 180.
ke = 4. / 3.
a = 6378137.
# beam height and beam distance
zcor = np.sqrt(ran_2d**2. + (ke * a)**2. +
2. * ran_2d * ke * a * np.sin(erad)) - ke * a + radz
scor = ke * a * np.arcsin(ran_2d * np.cos(erad) /
(ke * a + zcor)) / 1000.
xcor = scor * np.cos(np.pi * azi_2d / 180.)
ycor = scor * np.sin(np.pi * azi_2d / 180.)
# for velocity
zcor_v = np.sqrt(ran_2d**2. + (ke * a)**2. +
2. * ran_2d * ke * a * np.sin(erad_v)) - ke * a + radz
scor_v = ke * a * np.arcsin(ran_2d * np.cos(erad_v) /
(ke * a + zcor)) / 1000.
xcor_v = scor_v * np.cos(np.pi * azi_v_2d / 180.)
ycor_v = scor_v * np.sin(np.pi * azi_v_2d / 180.)
# convert to lon,lat for basemap plotting
lat, lon = xy2latlon(xcor, ycor, radlat, radlon)
lat_v, lon_v = xy2latlon(xcor_v, ycor_v, radlat, radlon)
# plot
#---------------------
print('Plotting...')
plt.figure(figcnt)
figcnt = figcnt + 1
#x, y = m(lon, lat)
#x_v, y_v = m(lon_v, lat_v)
#rx, ry = m(radlon, radlat)
# ZH plot
#------------------------------
ax1 = plt.subplot(2, 2, 1, projection=ccrs.PlateCarree())
plt.pcolormesh(lon,
lat,
ref,
cmap=zh_map,
vmin=-10.,
vmax=80.,
transform=ccrs.PlateCarree())
cb1 = plt.colorbar(fraction=0.04)
cb1.set_label('(dBZ)', fontsize=cblb_fsize)
cb1_la = [f'{ti:.0f}' for ti in cb1.get_ticks()]
cb1.ax.set_yticklabels(cb1_la, fontsize=cbti_fsize)
ax1.set_title('Z$\sf{_H}$',
x=0.0,
y=1.02,
horizontalalignment='left',
fontsize=axtl_fsize)
#ax1.add_feature(coast)
#ax1.add_feature(states, edgecolor='k')
ax1.add_geometries(shpreader.Reader(states_shp).geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
linewidth=0.5)
ax1.add_feature(USCOUNTIES.with_scale('20m'),
edgecolor='k',
linewidth=0.2)
ax1.set_extent([minlon, maxlon, minlat, maxlat])
# ZDR plot
#------------------------------
ax2 = plt.subplot(2, 2, 2, projection=ccrs.PlateCarree())
plt.pcolormesh(lon,
lat,
zdr,
cmap=zdr_map,
vmin=-2.4,
vmax=6.9,
transform=ccrs.PlateCarree())
cb2 = plt.colorbar(fraction=0.04)
cb2.set_label('(dB)', fontsize=cblb_fsize)
cb2_la = [f'{ti:.1f}' for ti in cb2.get_ticks()]
cb2.ax.set_yticklabels(cb2_la, fontsize=cbti_fsize)
ax2.set_title('Z$_{\sf{DR}}$',
x=0.0,
y=1.02,
horizontalalignment='left',
fontsize=axtl_fsize)
ax2.add_geometries(shpreader.Reader(states_shp).geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
linewidth=0.5)
ax2.add_feature(USCOUNTIES.with_scale('20m'),
edgecolor='k',
linewidth=0.2)
ax2.set_extent([minlon, maxlon, minlat, maxlat])
# KDP plot
#------------------------------
ax3 = plt.subplot(2, 2, 3, projection=ccrs.PlateCarree())
plt.pcolormesh(lon,
lat,
kdp_alt,
cmap=kdp_map,
vmin=-1.6,
vmax=4.3,
transform=ccrs.PlateCarree())
cb3 = plt.colorbar(fraction=0.04)
cb3.set_label('(deg./km)', fontsize=cblb_fsize)
cb3_la = [f'{ti:.1f}' for ti in cb3.get_ticks()]
cb3.ax.set_yticklabels(cb3_la, fontsize=cbti_fsize)
ax3.set_title('$\sf{K_{DP}}$',
x=0.0,
y=1.02,
horizontalalignment='left',
fontsize=axtl_fsize)
ax3.add_geometries(shpreader.Reader(states_shp).geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
linewidth=0.5)
ax3.add_feature(USCOUNTIES.with_scale('20m'),
edgecolor='k',
linewidth=0.2)
ax3.set_extent([minlon, maxlon, minlat, maxlat])
# RhoHV plot
#------------------------------
ax4 = plt.subplot(2, 2, 4, projection=ccrs.PlateCarree())
plt.pcolormesh(lon,
lat,
rhohv,
cmap=phv_map,
vmin=0.695,
vmax=1.045,
transform=ccrs.PlateCarree())
cb4 = plt.colorbar(fraction=0.04)
cb4.set_label('', fontsize=20)
cb4_la = [f'{ti:.2f}' for ti in cb4.get_ticks()]
cb4.ax.set_yticklabels(cb4_la, fontsize=cbti_fsize)
ax4.set_title('$\\rho\sf{_{HV}}$',
x=0.0,
y=1.02,
horizontalalignment='left',
fontsize=axtl_fsize)
ax4.add_geometries(shpreader.Reader(states_shp).geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
linewidth=0.5)
ax4.add_feature(USCOUNTIES.with_scale('20m'),
edgecolor='k',
linewidth=0.2)
ax4.set_extent([minlon, maxlon, minlat, maxlat])
# save image as .png
#-------------------------------
title = '{} - {}/{}/{} - {}:{} UTC - {:.1f} deg. PPI'.format(
site, yyyy, mm, dd, hh, mn, float(angle))
plt.suptitle(title, fontsize=24)
plt.subplots_adjust(top=0.95, hspace=-0.1, wspace=0.2)
imgname = yyyy + mm + dd + '_' + hh + mn + '_' + site.lower() + '.png'
plt.savefig(imgname, format='png', dpi=120)
plt.close()
# crop out white space from figure
os.system('convert -trim ' + imgname + ' ' + imgname)
geoms = (intersection(geom) for geom in geometries
if bounds.intersects(geom))
print(vtec[phensigString]['hdln'] + " (" + phensigString +
") issued at " + str(ref) + " (" + str(poly.geom_type) +
geom_count + ")")
color = warning_color(phensigString)
shape_feature = ShapelyFeature(geoms,
ccrs.PlateCarree(),
facecolor=color,
edgecolor=color)
ax.add_feature(shape_feature)
# Add geographic features
ax.add_feature(USCOUNTIES.with_scale('500k'),
edgecolor='gray',
linewidth=0.25)
state_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
ax.add_feature(state_borders, edgecolor='gray', linewidth=0.5)
country_borders = cfeature.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='50m',
facecolor='none')
ax.add_feature(country_borders, edgecolor='black', linewidth=0.7)
# Copyright (c) 2018 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
===========
US Counties
===========
Demonstrate how to plot US counties at all three available resolutions.
"""
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from metpy.plots import USCOUNTIES
###########################################
proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)
fig = plt.figure(figsize=(12, 9))
ax1 = fig.add_subplot(1, 3, 1, projection=proj)
ax2 = fig.add_subplot(1, 3, 2, projection=proj)
ax3 = fig.add_subplot(1, 3, 3, projection=proj)
for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
axis.add_feature(USCOUNTIES.with_scale(scale))
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import awips
from awips.dataaccess import DataAccessLayer
from metpy.plots import USCOUNTIES
#Setting the Projection
proj = ccrs.LambertConformal(central_longitude=-72.5, central_latitude=42, standard_parallels=[35])
plt.rcParams['savefig.dpi']
#Creating the Internal Figure
fig = plt.figure(figsize = (20,10))
ax = fig.add_subplot(1,1,1, projection=proj)
#Adding County Boundaries
for scale, axis in zip(['5m'], ['ax']):
ax.add_feature(USCOUNTIES.with_scale(scale), edgecolor="lightgray")
#Adding State Boundaries
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS)
ax.set_extent((-65, -80, 37, 47))
ax.set_title(str("***ADD HEADLINE HERE***"))
plt.show()
def create_map(alert):
''' Create the alert map'''
alert_map_info = (convert_geojson_to_geopandas_df(alert)
if alert['geometry'] else calculate_ugc_geography(alert))
warning_cmap = {
'Flood Watch': '#2E8B57',
'Flash Flood Watch': '#2E8B57',
'Flash Flood Warning': '#8B0000',
'Flood Warning': '#00FF00',
'Coastal Flood Watch': '#66CDAA',
'Coastal Flood Warning': '#228B22',
'Severe Thunderstorm Watch': '#DB7093',
'Severe Thunderstorm Warning': '#FFA500',
'Special Weather Statement': '#FFE4B5',
'Tornado Watch': '#FFFF00',
'Tornado Warning': '#FF0000',
'Storm Surge Watch': '#DB7FF7',
'Storm Surge Warning': '#B524F7',
'Dense Fog Advisory': '#708090',
'Rip Current Statement': '#40E0D0',
'Red Flag Warning': '#FF1493'
}
google_tiles = GoogleTiles()
data_crs = ccrs.PlateCarree()
# Setup matplotlib figure
fig = Figure(figsize=(1280 / 72, 720 / 72))
ax = fig.add_axes([0, 0, 1, 1], projection=data_crs)
ax.add_image(google_tiles, 8)
ax.set_extent([
alert_map_info['west_bound'] - 0.5, alert_map_info['east_bound'] + 0.5,
alert_map_info['south_bound'] - 0.5,
alert_map_info['north_bound'] + 0.6
], data_crs)
ax.set_adjustable('datalim')
# Setup borders (states, countries, coastlines, etc)
ax.add_feature(USCOUNTIES.with_scale('20m'),
edgecolor='gray',
zorder=5,
linewidth=1.2)
ax.add_feature(cfeature.STATES.with_scale('10m'), linewidth=3, zorder=5)
# Add radar
ax.add_wms(
wms='https://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0q-t.cgi?',
layers='nexrad-n0q-wmst',
wms_kwargs={
'transparent': True,
'time': get_radar_timestamp()
},
zorder=4,
alpha=0.4)
# Plot alerts on the map
for key in warning_cmap.keys():
if key == alert['properties']['event'] and alert['geometry']:
ax.add_geometries(alert_map_info['polygon'],
crs=data_crs,
facecolor=warning_cmap[key],
edgecolor='black',
linewidth=4,
zorder=6,
alpha=0.04)
elif key == alert['properties']['event'] and not alert['geometry']:
for polys in alert_map_info['polygon']:
ax.add_geometries(polys,
crs=data_crs,
facecolor=warning_cmap[key],
edgecolor='black',
linewidth=4,
alpha=0.5,
zorder=6)
else:
continue
# Set title
title = ('SIGNIFICANT WEATHER ALERT'
if alert['properties']['event'] == 'Special Weather Statement'
else alert['properties']['event'].upper())
ax.set_title(title,
loc='left',
ha='left',
va='top',
fontsize=48,
color='white',
fontweight='bold',
fontname='Arial',
y=0.96,
x=0.03,
zorder=11,
bbox={
'facecolor': '#0c3245',
'alpha': 1.0,
'edgecolor': 'none',
'boxstyle': 'square,pad=0.2'
})
fig.savefig('alert_visual.png', dpi=72)
import cartopy.io.shapereader as shpreader
from matplotlib.axes import Axes
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh
proj_cart = ccrs.PlateCarree(central_longitude=-95)
# Add a note about plotting counties by default if metpy is available in docs, and how to add your own map data without relying on built-ins.
# reader = shpreader.Reader('/Users/vannac/Documents/UScounties/UScounties.shp')
# reader = shpreader.Reader('/home/vanna/status_plots/UScounties/UScounties.shp')
# counties = list(reader.geometries())
# COUNTIES = cfeature.ShapelyFeature(counties, ccrs.PlateCarree())
try:
from metpy.plots import USCOUNTIES
county_scales = ['20m', '5m', '500k']
COUNTIES = USCOUNTIES.with_scale(county_scales[0])
except ImportError:
COUNTIES = None
M2KM = 1000.0
COORD_TH = [0.1, 0.8, 0.83, 0.1]
COORD_PLAN = [0.1, 0.1, 0.65, 0.5]
COORD_LON = [0.1, 0.65, 0.65, 0.1]
COORD_LAT = [0.8, 0.1, 0.13, 0.5]
COORD_HIST = [0.8, 0.65, 0.13, 0.1]
xdiv = 0.01
ydiv = 0.01
zdiv = 0.1
class FractionalSecondFormatter(Formatter):
def common_features(scale='110m',
counties_scale='20m',
figsize=None,
*,
ax=None,
projection=pc,
verbose=False,
dark_theme=False,
COASTLINES=True,
BORDERS=False,
STATES=False,
COUNTIES=False,
OCEAN=False,
LAND=False,
RIVERS=False,
LAKES=False,
STAMEN=False,
COASTLINES_kwargs={},
BORDERS_kwargs={},
STATES_kwargs={},
COUNTIES_kwargs={},
OCEAN_kwargs={},
LAND_kwargs={},
RIVERS_kwargs={},
LAKES_kwargs={},
STAMEN_kwargs={},
**kwargs):
"""
Add common features to a cartopy axis.
.. Tip:: This is a great way to initialize a new cartopy axes.
Parameters
----------
scale : {'10m', '50m' 110m'}
The cartopy feature's level of detail
.. note::
The ``'10m'`` scale for OCEAN and LAND takes a *long* time.
Consider using ``'50m'`` if you need OCEAN and LAND colored.
counties_scale: {'20m', '5m', '500k'}
Counties are plotted via MetPy and have different resolutions
available than other features.
- 20m = 20,000,000 resolution (Ok if you show a large area)
- 5m = 5,000,000 resolution (provides good detail)
- 500k = 500,000 resolution (plots very slow)
ax : plot axes
The axis to add the feature to.
If None, it will create a new cartopy axes with ``projection``.
projection : cartopy.crs
Projection to create new map if no cartopy axes is given.
Default is PlateCarree.
dark_theme : bool
If True, use alternative "dark theme" colors for land and water.
.. figure:: _static/BB_maps/common_features-1.png
.. figure:: _static/BB_maps/common_features-2.png
FEATURES : bool
Toggle on various features. By default, only COASTLINES is
turned on. Each feature has a cooresponding ``FEATURE_kwargs={}``
dictionary to supply additional arguments to cartopy's add_feature
method (e.g., change line color or width by feature type).
========== =========================================================
FEATURE Description
========== =========================================================
COASTLINES Coastlines, boundary between land and ocean.
BORDERS Borders between countries. *Does not includes coast*.
STATES US state borders. Includes coast.
COUNTIES US Counties. Includes coast.
OCEAN Colored ocean area
LAND Colored land area
RIVERS Lines where rivers exist
LAKES Colored lake area
========== =========================================================
========== =========================================================
MAP TILE Description
========== =========================================================
Stamen Specify type and zoom level. http://maps.stamen.com/
Style: ``terrain-background``, ``terrain``,
``toner-background``, ``toner``, `watercolor``
Zoom: int [0-10]
Examples
--------
https://inversion.nrlmry.navy.mil/confluence/display/~Blaylock/2020/08/07/Cartopy%3A+Add+Common+Features
Each feature can be toggled on by setting the argument to ``True``.
.. figure:: _static/BB_maps/individual_features.png
By default, the COASTLINES=True
.. figure:: _static/BB_maps/features_with_coastlines.png
.. figure:: _static/BB_maps/features_with_coastlines_DARK.png
The next two illustrate the level of detail for ``'50m'`` and ``'10m'``.
Note that the OCEAN and LAND features take 3+ minutes to render for the
10m resolution the first time you plot it.
.. figure:: _static/BB_maps/features_with_coastlines_10m.png
.. figure:: _static/BB_maps/features_with_coastlines_50m.png
Returns
-------
The cartopy axes (obviously you don't need this if you gave an ax
as an argument, but it is useful if you initialize a new map).
"""
ax = check_cartopy_axes(ax, projection)
if (LAND or OCEAN) and scale in ['10m']:
warnings.warn(
'🕖 OCEAN or LAND features at 10m will take a long time (3+ mins) to display.'
)
kwargs.setdefault('linewidth', .75)
COASTLINES_kwargs = {
**dict(zorder=100, facecolor='none'),
**COASTLINES_kwargs
}
COUNTIES_kwargs = {**{'linewidth': .5}, **COUNTIES_kwargs}
LAND_kwargs = {**{'edgecolor': 'none'}, **LAND_kwargs}
OCEAN_kwargs = {**{'edgecolor': 'none'}, **OCEAN_kwargs}
LAKES_kwargs = {**{'linewidth': 0}, **LAKES_kwargs}
if dark_theme:
kwargs = {**kwargs, **{'edgecolor': '.5'}}
land = '#060613'
water = '#0f2b38'
LAND_kwargs = {**{'facecolor': land}, **LAND_kwargs}
OCEAN_kwargs = {**{'facecolor': water}, **OCEAN_kwargs}
RIVERS_kwargs = {**{'edgecolor': water}, **RIVERS_kwargs}
LAKES_kwargs = {**{'facecolor': water}, **LAKES_kwargs}
#https://github.com/SciTools/cartopy/issues/880
ax.background_patch.set_facecolor(land) # depreciated
#ax.set_facecolor(land) # Might work if I update cartopy
else:
kwargs = {**kwargs, **{'edgecolor': '.15'}}
RIVERS_kwargs = {
**{
'edgecolor': feature.COLORS['water']
},
**RIVERS_kwargs
}
##------------------------------------------------------------------
## Add each element to the plot
## When combining kwargs,
## - kwargs is the main value
## - FEATURE_kwargs is the overwrite for the feature
## For example:
## {**kwargs, **FEATURE_kwargs}
## the kwargs are overwritten by FEATURE kwargs
##------------------------------------------------------------------
if COASTLINES:
#ax.coastlines(scale, **kwargs) # Nah, use the crs.feature instead
ax.add_feature(feature.COASTLINE.with_scale(scale), **{
**kwargs,
**COASTLINES_kwargs
})
if BORDERS:
ax.add_feature(feature.BORDERS.with_scale(scale), **{
**kwargs,
**BORDERS_kwargs
})
if STATES:
ax.add_feature(feature.STATES.with_scale(scale), **{
**kwargs,
**STATES_kwargs
})
if COUNTIES:
_counties_scale = {'20m', '5m', '500k'}
assert counties_scale in _counties_scale, f"counties_scale must be {_counties_scale}"
ax.add_feature(USCOUNTIES.with_scale(counties_scale), **{
**kwargs,
**COUNTIES_kwargs
})
if OCEAN:
ax.add_feature(feature.OCEAN.with_scale(scale), **{
**kwargs,
**OCEAN_kwargs
})
if LAND and not dark_theme:
# If `dark_theme=True`, the face_color is the land color.
ax.add_feature(feature.LAND.with_scale(scale), **{
**kwargs,
**LAND_kwargs
})
if RIVERS:
ax.add_feature(feature.RIVERS.with_scale(scale), **{
**kwargs,
**RIVERS_kwargs
})
if LAKES:
ax.add_feature(feature.LAKES.with_scale(scale), **{
**kwargs,
**LAKES_kwargs
})
if STAMEN:
if verbose:
print("Please use `ax.set_extent` before increasing Zoom level.")
STAMEN_kwargs.setdefault('style', 'terrain-background')
STAMEN_kwargs.setdefault('zoom', 3)
style = STAMEN_kwargs['style']
zoom = STAMEN_kwargs['zoom']
stamen_terrain = cimgt.Stamen(style)
ax.add_image(stamen_terrain, zoom)
if 'alpha' in STAMEN_kwargs:
# Need to manually put a white layer over the STAMEN terrain
STAMEN_kwargs.setdefault('alpha_color', 'w')
poly = ax.projection.domain
ax.add_feature(feature.ShapelyFeature([poly], ax.projection),
color=STAMEN_kwargs['alpha_color'],
alpha=1 - STAMEN_kwargs['alpha'],
zorder=1)
if figsize is not None:
plt.gcf().set_figwidth(figsize[0])
plt.gcf().set_figheight(figsize[1])
return ax
