# How to use the cartopy.crs.PlateCarree function in Cartopy

## To help you get started, weâ€™ve selected a few Cartopy examples, based on popular ways it is used in public projects.

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silburt / DeepMoon / tests / test_input_data_gen.py View on Github
``````# Crater catalogue.
filelroc="../catalogues/LROCCraters.csv",
sortlat=True)

# Long/lat limits
self.cdim = [-180., 180., -60., 60.]

# Coordinate systems.
self.iglobe = ccrs.Globe(semimajor_axis=1737400,
semiminor_axis=1737400,
ellipse=None)
self.geoproj = ccrs.Geodetic(globe=self.iglobe)
self.iproj = ccrs.PlateCarree(globe=self.iglobe)``````
fatiando / verde / dev / _downloads / 1a2cc52b71250d08e9b53208420b66a8 / chain.py View on Github
``````region = vd.get_region((data.longitude, data.latitude))
# The desired grid spacing in degrees (converted to meters using 1 degree approx. 111km)
spacing = 10 / 60
# Use Mercator projection because Spline is a Cartesian gridder
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
proj_coords = projection(data.longitude.values, data.latitude.values)

plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Bathymetry from Baja California")
plt.scatter(
data.longitude,
data.latitude,
c=data.bathymetry_m,
s=0.1,
transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("meters")
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()

########################################################################################
# We'll create a chain that applies a blocked median to the data, fits a polynomial
# trend, and then fits a standard gridder to the trend residuals.

chain = vd.Chain(
[
("reduce", vd.BlockReduce(np.median, spacing * 111e3)),
("trend", vd.Trend(degree=1)),
("spline", vd.Spline()),
]``````
SciTools / cartopy / lib / cartopy / examples / limited_area.py View on Github
``````def main():
rp = ccrs.RotatedPole(pole_latitude=45, pole_longitude=180)
pc = ccrs.PlateCarree()

#    ax = plt.subplot(211, projection=rp)
#    ax.bluemarble()
#    ax.coastlines()
# XXX The change in projection is a result of plt.plot making a poly or a line depending on some things...
x, y = [-44, -44, 45, 45], [-45, 45, 45, -45]
#    x, y = [-44, -44, 45, 45, -44], [-45, 45, 45, -45, -45]
#    # XXX why is the horizontal line on the native projection not straight? BUG!
#    ax.plot(x, y, marker='o', transform=rp)
#    ax.gridlines()

#    ax = plt.subplot(212, projection=pc)
ax = plt.axes(projection=pc)
ax = plt.axes(projection=ccrs.InterruptedGoodeHomolosine())
#    ax = plt.axes(projection=rp)``````
geoschem / gcpy / gcpy / plot.py View on Github
``````vmin = np.min(plot_vals)
vmax = np.max(plot_vals)
norm = normalize_colors(
vmin, vmax, is_difference=use_cmap_RdBu, log_color_scale=log_color_scale
)
if xticklabels == []:
xticklabels = ["{}\$\degree\$".format(x) for x in xtick_positions]

if unit == "" and data_is_xr:
unit = plot_vals.units.strip()

if ax == None:
if plot_type == "zonal_mean":
ax = plt.axes()
if plot_type == "single_level":
ax = plt.axes(projection = ccrs.PlateCarree())

if title == "fill" and data_is_xr:
title = plot_vals.name

# Create plot
ax.set_title(title)
if plot_type == "zonal_mean":
if pedge.all() == -1:
pedge = GEOS_72L_grid.p_edge()
if pedge_ind.all() == -1:
pedge_ind = np.where((pedge &lt;= np.max(pres_range)) &amp; (pedge &gt;= np.min(pres_range)))
pedge_ind = pedge_ind[0]
# Pad edges if subset does not include surface or TOA so data spans entire subrange
if min(pedge_ind) != 0:
pedge_ind = np.append(min(pedge_ind) - 1, pedge_ind)
if max(pedge_ind) != 72:``````
xoolive / traffic / traffic / core / mixins.py View on Github
``````if shift is None:
# flake B006
shift = dict(units="dots", x=15)

if text_kw is None:
text_kw = {}
else:
# since we may modify it, let's make a copy
text_kw = {**text_kw}

if "projection" in ax.__dict__ and "transform" not in kwargs:
from cartopy.crs import PlateCarree
from matplotlib.transforms import offset_copy

kwargs["transform"] = PlateCarree()
geodetic_transform = PlateCarree()._as_mpl_transform(ax)
text_kw["transform"] = offset_copy(geodetic_transform, **shift)

if "color" not in kwargs:
kwargs["color"] = "black"

if "s" not in text_kw:
if hasattr(self, "callsign"):
text_kw["s"] = getattr(self, "callsign")  # noqa: B009
if hasattr(self, "name"):
text_kw["s"] = getattr(self, "name")  # noqa: B009

cumul: List[Artist] = []
cumul.append(ax.scatter(self.longitude, self.latitude, **kwargs))

west, east, south, north = ax.get_extent(PlateCarree())
if west &lt;= self.longitude &lt;= east and south &lt;= self.latitude &lt;= north:``````
``````#######################################
# **Plotting the Isentropic Analysis**

# Set up our projection
crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)

# Coordinates to limit map area
bounds = [(-122., -75., 25., 50.)]
# Choose a level to plot, in this case 296 K
level = 0

fig = plt.figure(figsize=(17., 12.))
ax = fig.add_subplot(1, 1, 1, projection=crs)
ax.set_extent(*bounds, crs=ccrs.PlateCarree())

# Plot the surface
clevisent = np.arange(0, 1000, 25)
cs = ax.contour(lon, lat, isentprs[level, :, :], clevisent,
colors='k', linewidths=1.0, linestyles='solid', transform=ccrs.PlateCarree())
ax.clabel(cs, fontsize=10, inline=1, inline_spacing=7,
fmt='%i', rightside_up=True, use_clabeltext=True)

# Plot RH
cf = ax.contourf(lon, lat, isentrh[level, :, :], range(10, 106, 5),
cmap=plt.cm.gist_earth_r, transform=ccrs.PlateCarree())
cb = fig.colorbar(cf, orientation='horizontal', extend='max', aspect=65, shrink=0.5, pad=0.05,
extendrect='True')
cb.set_label('Relative Humidity', size='x-large')``````
MPAS-Dev / geometric_features / plot_features.py View on Github
``````# markers = ['o', 's', 'v', '^', '&gt;', '&lt;', '*', 'p', 'D', 'h']

feature_num = 0

bounds = None

for feature in featuredat['features']:
geomtype = feature['geometry']['type']
shape = shapely.geometry.shape(feature['geometry'])
if(max_length &gt; 0.0):
shape = subdivide_geom(shape, geomtype, max_length)

featurename = feature['properties']['name']
print '  feature: {}'.format(featurename)

refProjection = cartopy.crs.PlateCarree()

color = colors[feature_num % len(colors)]
# marker = markers[feature_num % len(markers)]

if geomtype in ['Polygon', 'MultiPolygon']:
props = {'linewidth': 2.0, 'edgecolor': color, 'alpha': 0.4,
'facecolor': color}
elif geomtype in ['LineString', 'MultiLineString']:
props = {'linewidth': 4.0, 'edgecolor': color, 'alpha': 1.,
'facecolor': 'none'}

if bounds is None:
bounds = list(shape.bounds)
else:
# expand the bounding box
bounds[:2] = np.minimum(bounds[:2], shape.bounds[:2])``````
Unidata / python-gallery / examples / Wind_Shear_Vectors_Example.py View on Github
``````time = num2date(time_var[:].squeeze(), time_var.units)

# Combine 1D latitude and longitudes into a 2D grid of locations
lon_2d, lat_2d = np.meshgrid(lon, lat)

# Smooth mslp data
mslp = ndimage.gaussian_filter(mslp, sigma=3, order=0)

#####################################
# Begin making figure

# Create new figure
fig = plt.figure(figsize=(15, 12), facecolor='black')

# Add the map and set the extent
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-108., -91., 33., 45.])
ax.background_patch.set_fill(False)

# Add state boundaries to plot

# Contour the MSLP
c = ax.contour(lon_2d, lat_2d, mslp, colors='lime', linewidths=6)
ax.clabel(c, fontsize=12, inline=1, inline_spacing=4, fmt='%i')

wslice = slice(1, None, 4)
# Plot 850-hPa wind vectors
vectors850 = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],
u_wind850[wslice, wslice], v_wind850[wslice, wslice],
scale=12, color='gold', label='850mb wind')``````
SciTools / iris / docs / iris / example_code / General / rotated_pole_mapping.py View on Github
``````# Plot #3: Contourf overlayed by coloured point data
plt.figure()
qplt.contourf(air_pressure)
iplt.points(air_pressure, c=air_pressure.data)
plt.gca().coastlines()
iplt.show()

# For the purposes of this example, add some bounds to the latitude
# and longitude
air_pressure.coord("grid_latitude").guess_bounds()
air_pressure.coord("grid_longitude").guess_bounds()

# Plot #4: Block plot
plt.figure()
plt.axes(projection=ccrs.PlateCarree())
iplt.pcolormesh(air_pressure)
plt.gca().stock_img()
plt.gca().coastlines()
iplt.show()``````

## Cartopy

A Python library for cartographic visualizations with Matplotlib

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