How to plot a map of a semi-sphere (eg northern hemisphere) using cartopy.
I'm trying to plot a map of the northern hemisphere using cartopy. But I don't understand how should I define the extent of the map so that only this region of interest is plotted. I would like the map to be cut off at 0° latitude. I would like to have code where I could easily define any subset of the glob using the ccrs.NearsidePerspective projection, or the ccrs.Orthographic projection.
Below I leave a code for reproduction.
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# Creating fake data
x = np.linspace(-180, 180, 361)
y = np.linspace(-90, 90, 181)
lon, lat = np.meshgrid(x, y)
values = np.random.random(lon.shape)*20
fig = plt.figure(figsize=(15, 10))
proj = ccrs.NearsidePerspective(central_longitude=-45, central_latitude=21)
ax = fig.add_subplot(121, projection=proj)
ax.set_extent([-120, 40, 0, 60])
ax.pcolormesh(lon, lat, values, transform=ccrs.PlateCarree())
ax.coastlines(linewidth=2)
gl = ax.gridlines(draw_labels=True, linestyle='--')
The code generates the following figure:
Thank you very much in advance.
Robson
To plot only the upper hemisphere part of the map projection, a polygon of that part is needed to use as the projection boundary.
That polygon is created as a matplotlib-path object. It vertices' coordinates are data coordinates in my code, so that, no transformation is required when applied to the final plot.
This is a complete code:-
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import matplotlib.path as mpath
import numpy as np
from geographiclib.geodesic import Geodesic
fig = plt.figure(figsize=[12, 12])
proj = ccrs.NearsidePerspective(central_longitude=-45, central_latitude=21, satellite_height=35785831)
ax = plt.subplot(projection=proj)
# The value of r is obtained by previous run of this code ...
# with the line .. #print(ax.get_xlim()) uncommented
r = 5476336.098
ax.set_xlim(-r, r)
ax.set_ylim(-r, r)
ax.stock_img()
ax.coastlines(lw=1, color="darkblue")
# Find the locations of points along the equatorial arc
# start location
lon_fr, lat_fr = 30, 0
# end location
lon_to, lat_to = -120, 0
# This gets geodesic between the two points, WGS84 ellipsoid is used
geodl = Geodesic.WGS84.InverseLine(lat_fr, lon_fr, lat_to, lon_to)
lonlist, latlist = [], []
num_points = 32 #for series of points on geodesic/equator
for ea in np.linspace(0, geodl.s13, num_points):
g = geodl.Position(ea, Geodesic.STANDARD | Geodesic.LONG_UNROLL)
#print("{:.0f} {:.5f} {:.5f} {:.5f}".format(g['s12'], g['lat2'], g['lon2'], g['azi2']))
lon2, lat2 = g['lon2'], g['lat2']
lonlist.append( g['lon2'] )
latlist.append( g['lat2'] )
# Get data-coords from (lonlist, latlist)
# .. as points along equatorial arc
dataxy = proj.transform_points(ccrs.PlateCarree(), np.array(lonlist), np.array(latlist))
# (Uncomment to) Plot equator line
#ax.plot(dataxy[:, 0:1], dataxy[:, 1:2], "go-", linewidth=2, markersize=5, zorder=10)
# Top semi-circle arc for map extent
theta = np.linspace(-0.5*np.pi, 0.5*np.pi, 64)
center, radius = [0, 0], r
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
# Combine vertices of the semi-circle and equatorial arcs
# These points are in data coordinates, ready to plot on the axes.
verts = np.vstack([verts*r, dataxy[:, 0:2]])
polygon = mpath.Path(verts + center)
ax.set_boundary(polygon) #This masks-out unwanted part of the plot
gl = ax.gridlines(draw_labels=True, xlocs=range(-150,180,30), ylocs=range(0, 90, 15),
y_inline=True, linestyle='--', lw= 5, color= "w", )
# Get limits, the values are the radius of the circular map extent
# The values is then used as r = 5476336.09797 on top of the code
#print(ax.get_xlim())
#print(ax.get_ylim())
plt.show()
Related
I am using the following code to make a map for Sweden, Norway and Finland together as one area. however, I am struggling with it. I'm following this example, Python Mapping in Matplotlib Cartopy Color One Country.
from shapely.geometry import Polygon
from cartopy.io import shapereader
import cartopy.io.img_tiles as cimgt
import cartopy.crs as ccrs
import geopandas
import matplotlib.pyplot as plt
def rect_from_bound(xmin, xmax, ymin, ymax):
"""Returns list of (x,y)'s for a rectangle"""
xs = [xmax, xmin, xmin, xmax, xmax]
ys = [ymax, ymax, ymin, ymin, ymax]
return [(x, y) for x, y in zip(xs, ys)]
# request data for use by geopandas
resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'
countries = ['Norway', 'Sweden', 'Finland']
shpfilename = shapereader.natural_earth(resolution, category, name)
df = geopandas.read_file(shpfilename)
extent = [2, 32, 55, 72]
# get geometry of a country
for country in (countries):
poly = [df.loc[df['ADMIN'] == country]['geometry'].values[0]]
stamen_terrain = cimgt.StamenTerrain()
# projections that involved
st_proj = stamen_terrain.crs #projection used by Stamen images
ll_proj = ccrs.PlateCarree() #CRS for raw long/lat
# create fig and axes using intended projection
fig = plt.figure(figsize=(8,9))
ax = fig.add_subplot(122, projection=st_proj)
ax.add_geometries(poly, crs=ll_proj, facecolor='none', edgecolor='black')
pad1 = 0.5 #padding, degrees unit
exts = [poly[0].bounds[0] - pad1, poly[0].bounds[2] + pad1, poly[0].bounds[1] - pad1, poly[0].bounds[3] + pad1];
ax.set_extent(exts, crs=ll_proj)
# make a mask polygon by polygon's difference operation
# base polygon is a rectangle, another polygon is simplified switzerland
msk = Polygon(rect_from_bound(*exts)).difference( poly[0].simplify(0.01) )
msk_stm = st_proj.project_geometry (msk, ll_proj) # project geometry to the projection used by stamen
# get and plot Stamen images
ax.add_image(stamen_terrain, 8) # this requests image, and plot
# plot the mask using semi-transparency (alpha=0.65) on the masked-out portion
ax.add_geometries( msk_stm, st_proj, zorder=12, facecolor='white', edgecolor='none', alpha=0.65)
ax.gridlines(draw_labels=True)
plt.show()
What I have is separated maps. THoguh I need only one map of them.
Can you please help?
Thank you.
The code here that you adapted to your work is good for a single country. If multiple contiguous countries are new target, one need to select all of them and dissolve into a single geometry. Only a few lines of code need to be modified.
Example: new target countries: ['Norway','Sweden', 'Finland']
The line of code that need to be replaced:
poly = [df.loc[df['ADMIN'] == 'Switzerland']['geometry'].values[0]]
Replace it with these lines of code:
scan3 = df[ df['ADMIN'].isin(['Norway','Sweden', 'Finland']) ]
scan3_dissolved = scan3.dissolve(by='LEVEL')
poly = [scan3_dissolved['geometry'].values[0]]
And you should get a plot similar to this:
I want to convert lon/lat (in degrees) to x/y map projection coordinates (in meters) but using cartopy + pyplot rather than basemap.
say this is the basemap code:
>>> from mpl_toolkits.basemap import Basemap
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> # read in topo data (on a regular lat/lon grid)
>>> etopo = np.loadtxt('etopo20data.gz')
>>> lons = np.loadtxt('etopo20lons.gz')
>>> lats = np.loadtxt('etopo20lats.gz')
>>> # create Basemap instance for Robinson projection.
>>> m = Basemap(projection='robin',lon_0=0.5*(lons[0]+lons[-1]))
>>> # compute map projection coordinates for lat/lon grid.
>>> x, y = m(*np.meshgrid(lons,lats))
I want to emulate similar functionality in cartopy, how can I do that?
The steps to achieve the meshgrid points appropriate to plot with Cartopy is different and more difficult, as far as I know.
Here is the working code using Cartopy:
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import numpy as np
# create arrays of values for long and lat
lons = np.linspace(0,160,10)
lats = np.linspace(0,70,5)
# create meshgrid of points
x, y = np.meshgrid(lons, lats)
# select a CRS/projection to tranform/plot points for demo
use_proj = ccrs.Robinson();
# transform all the meshgrid points arrays ..
# .. from geodetic long/lat to Robinson x/y/z
out_xyz = use_proj.transform_points(ccrs.Geodetic(), x, y)
# out_xyz.shape -> (5, 10, 3)
# separate x_array, y_array from the result(x,y,z) above
x_array = out_xyz[:,:,0]
y_array = out_xyz[:,:,1]
# setup fig/axis and plot the meshgrid of points
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=use_proj)
ax.add_feature(cartopy.feature.LAND, facecolor='lightgray')
ax.scatter(x_array, y_array, s=25, c="r", zorder=10)
ax.set_global()
plt.show()
The output plot will be:
I have a Nx2 matrix of lat lon coordinate pairs, spatial_data, and I have an array of measurements at these coordinates.
I would like to plot this data on a globe, and I understand that Basemap can do this. I found this link which shows how to plot data if you have cartesian coordinates. Does there exist functionality to convert lat,lon to cartesian coordinates? Alternatively, is there a way to plot this data with only the lat,lon information?
You can use cartopy:
import numpy as np
import matplotlib.pyplot as plt
from cartopy import crs
# a grid for the longitudes and latitudes
lats = np.linspace(-90, 90, 50)
longs = np.linspace(-180, 180, 50)
lats, longs = np.meshgrid(lats, longs)
# some data
data = lats[1:] ** 2 + longs[1:] ** 2
fig = plt.figure()
# create a new axes with a cartopy.crs projection instance
ax = fig.add_subplot(1, 1, 1, projection=crs.Mollweide())
# plot the date
ax.pcolormesh(
longs, lats, data,
cmap='hot',
transform=crs.PlateCarree(), # this means that x, y are given as longitude and latitude in degrees
)
fig.tight_layout()
fig.savefig('cartopy.png', dpi=300)
Result:
For plotting skymaps I just switched from Basemap to cartopy, I like it a lot more
.
(The main reason was segfaulting of Basemap on some computers, which I could not fix).
The only thing I struggle with, is getting a tissot circle (used to show the view cone of our telescope.)
This is some example code plotting random stars (I use a catalogue for the real thing):
import matplotlib.pyplot as plt
from cartopy import crs
import numpy as np
# create some random stars:
n_stars = 100
azimuth = np.random.uniform(0, 360, n_stars)
altitude = np.random.uniform(75, 90, n_stars)
brightness = np.random.normal(8, 2, n_stars)
fig = plt.figure()
ax = fig.add_subplot(1,1,1, projection=crs.NorthPolarStereo())
ax.background_patch.set_facecolor('black')
ax.set_extent([-180, 180, 75, 90], crs.PlateCarree())
plot = ax.scatter(
azimuth,
altitude,
c=brightness,
s=0.5*(-brightness + brightness.max())**2,
transform=crs.PlateCarree(),
cmap='gray_r',
)
plt.show()
How would I add a tissot circle with a certain radius in degrees to that image?
https://en.wikipedia.org/wiki/Tissot%27s_indicatrix
I keep meaning to go back and add the two functions from GeographicLib which provide the forward and inverse geodesic calculations, with this it is simply a matter of computing a geodetic circle by sampling at appropriate azimuths for a given lat/lon/radius. Alas, I haven't yet done that, but there is a fairly primitive (but effective) wrapper in pyproj for the functionality.
To implement a tissot indicatrix then, the code might look something like:
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from pyproj import Geod
import shapely.geometry as sgeom
def circle(geod, lon, lat, radius, n_samples=360):
"""
Return the coordinates of a geodetic circle of a given
radius about a lon/lat point.
Radius is in meters in the geodetic's coordinate system.
"""
lons, lats, back_azim = geod.fwd(np.repeat(lon, n_samples),
np.repeat(lat, n_samples),
np.linspace(360, 0, n_samples),
np.repeat(radius, n_samples),
radians=False,
)
return lons, lats
def main():
ax = plt.axes(projection=ccrs.Robinson())
ax.coastlines()
geod = Geod(ellps='WGS84')
radius_km = 500
n_samples = 80
geoms = []
for lat in np.linspace(-80, 80, 10):
for lon in np.linspace(-180, 180, 7, endpoint=False):
lons, lats = circle(geod, lon, lat, radius_km * 1e3, n_samples)
geoms.append(sgeom.Polygon(zip(lons, lats)))
ax.add_geometries(geoms, ccrs.Geodetic(), facecolor='blue', alpha=0.7)
plt.show()
if __name__ == '__main__':
main()
I am trying to plot some histogrammed data on a polar axis but it wont seem to work properly. An example is below, I use the custom projection found How to make the angles in a matplotlib polar plot go clockwise with 0° at the top? it works for a scatter plot so I think my problem is with the histogram function. This has been driving me nuts all day, does anyone know what I am doing wrong...........
import random
import numpy as np
import matplotlib.pyplot as plt
baz = np.zeros((20))
freq = np.zeros((20))
pwr = np.zeros((20))
for x in range(20):
baz[x] = random.randint(20,25)*10
freq[x] = random.randint(1,10)*10
pwr[x] = random.randint(-10,-1)*10
baz = baz*np.pi/180.
abins = np.linspace(0,2*np.pi,360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
plt.figure(figsize=(14,14))
plt.subplot(1, 1, 1, projection='northpolar')
#plt.scatter(baz, freq)
plt.pcolormesh(H)
plt.show()
Your code works if you explicitly pass a mgrid (with similar characteristics than your a bins and sbins) to the pcolormesh command.
Below is an example inspired by your code:
import matplotlib.pyplot as plt
import numpy as np
#Generate the data
size = 200
baz = 10*np.random.randint(20, 25, size)*np.pi/180.
freq = 10*np.random.randint(1, 10, size)
pwr = 10*np.random.randint(-10, -1, size)
abins = np.linspace(0, 2*np.pi, 360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100, 50)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
#Grid to plot your data on using pcolormesh
theta, r = np.mgrid[0:2*np.pi:360j, 1:100:50j]
fig, ax = plt.subplots(figsize=(14,14), subplot_kw=dict(projection='northpolar'))
ax.pcolormesh(theta, r, H)
ax.set_yticklabels([]) #remove yticklabels
plt.show()