# Geographic and polar axes¶

This section documents several useful features for working with polar plots and geographic projections. The geographic features are powered by cartopy (or, optionally, basemap). Note that these features are optional – installation of cartopy or basemap are not required to use proplot.

## Polar axes¶

To create polar axes, pass proj='polar' to an axes-creation command like proplot.figure.Figure.add_subplot. Polar axes are represented with the PolarAxes subclass, which has its own format command. proplot.axes.PolarAxes.format facilitates polar-specific modifications like changing the central radius r0, the zero azimuth location theta0, and the positive azimuthal direction thetadir. It also supports toggling and configuring the “major” and “minor” gridline locations with grid, rlocator, thetalocator, gridminor, rminorlocator, and thetaminorlocator and formatting the gridline labels with rformatter and thetaformatter (analogous to xlocator, xformatter, and xminorlocator used by proplot.axes.CartesianAxes.format), and creating “annular” or “sector” plots by changing the radial or azimuthal bounds rlim and thetalim. Finally, since proplot.axes.PolarAxes.format calls proplot.axes.Axes.format, it can be used to add axes titles, a-b-c labels, and figure titles.

For details, see proplot.axes.PolarAxes.format.

[1]:

import proplot as pplt
import numpy as np
N = 200
state = np.random.RandomState(51423)
x = np.linspace(0, 2 * np.pi, N)[:, None] + np.arange(5) * 2 * np.pi / 5
y = 100 * (state.rand(N, 5) - 0.3).cumsum(axis=0) / N
fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], proj='polar')
axs.format(
ticklabelsize=9, rlines=0.5, rlim=(0, 19),
)
for ax in axs:
ax.plot(x, y, cycle='default', zorder=0, lw=3)

# Standard polar plot
axs[0].format(
title='Normal plot', thetaformatter='tau',
rlabelpos=225, rlines=pplt.arange(5, 30, 5),
)

# Sector plot
axs[1].format(
title='Sector plot', thetadir=-1, thetalines=90, thetalim=(0, 270), theta0='N',
rlim=(0, 22), rlines=pplt.arange(5, 30, 5),
)

# Annular plot
axs[2].format(
r0=-20, rlim=(0, 22), rformatter='null', rlocator=2
)


## Geographic axes¶

To create geographic axes, pass proj='name' to an axes-creation command like proplot.figure.Figure.add_subplot, where name is any valid PROJ projection name. Alternatively, you can pass a cartopy.crs.Projection or Basemap instance returned by the Proj constructor function to proj (see below for details). If you want to create your subplots all-at-once with e.g. subplots but need different projections for each subplot, you can pass a list or dictionary to the proj keyword (e.g., proj=('cartesian', 'pcarree') or proj={2: 'pcarree'} – see subplots for details). Geographic axes are represented with the GeoAxes subclass, which has its own format command. proplot.axes.GeoAxes.format facilitates geographic-specific modifications like meridional and parallel gridlines and land mass outlines. The syntax is very similar to proplot.axes.CartesianAxes.format. Note that the proj keyword and several of the format keywords are inspired by the basemap API. In the below example, we create and format a very simple geographic plot.

[2]:

# Use an on-the-fly projection
import proplot as pplt
fig = pplt.figure(refwidth=3)
axs = fig.subplots(nrows=2, proj='robin', proj_kw={'lon0': 150})
# proj = pplt.Proj('robin', lon0=180)
# axs = pplt.subplots(nrows=2, proj=proj)  # equivalent to above
axs.format(
suptitle='Figure with single projection',
land=True, latlines=30, lonlines=60,
)

/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_land.zip


## Geographic backends¶

The proplot.axes.GeoAxes class uses either cartopy or basemap as “backends” to format the axes and plot stuff in the axes. A few details:

Together, these features let you work with geophysical data without invoking verbose cartopy classes like LambertAzimuthalEqualArea or keeping track of separate Basemap instances. This considerably reduces the amount of code needed to make complex geographic plots. In the below examples, we create a variety of plots using both cartopy and basemap as backends.

Important

• By default, proplot bounds polar cartopy projections like NorthPolarStereo at the equator and gives non-polar cartopy projections global extent by calling set_global. This is a deviation from cartopy, which determines map boundaries automatically based on the coordinates of the plotted content. To revert to cartopy’s default behavior, set rc['geo.extent'] to 'auto or pass extent='auto' to format.

• By default, proplot gives circular boundaries to polar cartopy and basemap projections like NorthPolarStereo (see this example from the cartopy website). To disable this feature, set rc['geo.round'] to False or pass round=False to format. Please note that older versions of cartopy cannot add gridlines to maps bounded by circles.

• To make things more consistent, the Proj constructor function lets you supply native PROJ keyword names for the cartopy Projection classes (e.g., lon0 instead of central_longitude) and instantiates Basemap projections with sensible default PROJ parameters rather than raising an error when they are omitted (e.g., lon0=0 as the default for most projections).

Warning

The basemap package is no longer actively maintained and will not work with matplotlib versions more recent than 3.2.2. We originally included basemap support because its gridline labeling was more powerful than cartopy gridline labeling. However, as cartopy gridline labeling has significantly improved since version 0.18, proplot may deprecate basemap support in a future release and fully remove basemap support by version 1.0.0.

[3]:

import proplot as pplt
fig = pplt.figure()

gs = pplt.GridSpec(ncols=2, nrows=3, hratios=(1, 1, 1.4))
for i, proj in enumerate(('cyl', 'hammer', 'npstere')):
ax1 = fig.subplot(gs[i, 0], proj=proj)  # default cartopy backend
ax2 = fig.subplot(gs[i, 1], proj=proj, backend='basemap')  # basemap backend

# Format projections
axs = fig.subplotgrid
axs.format(
land=True,
suptitle='Figure with several projections',
toplabels=('Cartopy examples', 'Basemap examples'),
toplabelweight='normal',
latlines=30, lonlines=60,
)
axs[:2].format(lonlabels='b', latlabels='r')  # or lonlabels=True, lonlabels='bottom',
axs[2:4].format(lonlabels=False, latlabels='both')
axs[4:].format(lonlabels='all', lonlines=30)
pplt.rc.reset()


## Plotting in projections¶

In proplot, plotting with GeoAxes is just like plotting with CartesianAxes. Proplot makes longitude-latitude (i.e., Plate Carrée) coordinates the default coordinate system for all plotting commands by internally passing transform=ccrs.PlateCarree() to cartopy commands and latlon=True to basemap commands. And again, when basemap is the backend, plotting is done “cartopy-style” by calling methods from the proplot.axes.GeoAxes instance rather than the Basemap instance.

To ensure that a 2D PlotAxes command like contour or pcolor fills the entire globe, simply pass globe=True to the command. This interpolates the data to the North and South poles and across the longitude seam before plotting. This is a convenient and succinct alternative to cartopy’s add_cyclic_point and basemap’s addcyclic.

To draw content above or underneath a given geographic feature, simply change the zorder property for that feature. For example, to draw land patches on top of all plotted content as a “land mask” you can use ax.format(land=True, landzorder=4) or set pplt.rc['land.zorder'] = 4 (see the next section for details).

[4]:

import proplot as pplt
import numpy as np

# Fake data with unusual longitude seam location and without coverage over poles
offset = -40
lon = pplt.arange(offset, 360 + offset - 1, 60)
lat = pplt.arange(-60, 60 + 1, 30)
state = np.random.RandomState(51423)
data = state.rand(len(lat), len(lon))

# Plot data both without and with globe=True
for globe in (False, True):
string = 'with' if globe else 'without'
gs = pplt.GridSpec(nrows=2, ncols=2)
fig = pplt.figure(refwidth=2.5)
for i, ss in enumerate(gs):
cmap = ('sunset', 'sunrise')[i % 2]
backend = ('cartopy', 'basemap')[i % 2]
ax = fig.subplot(ss, proj='kav7', backend=backend)
if i > 1:
ax.pcolor(lon, lat, data, cmap=cmap, globe=globe, extend='both')
else:
m = ax.contourf(lon, lat, data, cmap=cmap, globe=globe, extend='both')
fig.colorbar(m, loc='b', span=i + 1, label='values', extendsize='1.7em')
fig.format(
suptitle=f'Geophysical data {string} global coverage',
toplabels=('Cartopy example', 'Basemap example'),
leftlabels=('Filled contours', 'Grid boxes'),
toplabelweight='normal', leftlabelweight='normal',
coast=True, lonlines=90,
abc='A.', abcloc='ul', abcborder=False,
)

/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip


## Formatting projections¶

The proplot.axes.GeoAxes.format command facilitates geographic-specific axes modifications. It can toggle and configure the “major” and “minor” longitude and latitude gridline locations using the grid, lonlocator, latlocator, gridminor, lonminorlocator, and latminorlocator keys, and configure gridline label formatting with lonformatter and latformatter (analogous to xlocator, xminorlocator, and xformatter used by proplot.axes.CartesianAxes.format). By default, inline cartopy labels and cartopy label rotation are turned off, but inline labels can be turned on using loninline=True, latinline=True, or inlinelabels=True or by setting rc['grid.inlinelabels'] to True, and label rotation can be turned on using rotatelabels=True or by setting rc['grid.rotatelabels'] to True. The padding between the map edge and the labels can be changed using labelpad or by changing rc['grid.labelpad'].

proplot.axes.GeoAxes.format can also set the cartopy projection bounding longitudes and latitudes with lonlim and latlim (analogous to xlim and ylim), set the latitude bound for circular polar projections using boundinglat, and toggle and configure geographic features like land masses, coastlines, and administrative borders using settings like land, landcolor, coast, coastcolor, and coastlinewidth. Finally, since proplot.axes.GeoAxes.format calls proplot.axes.Axes.format, it can be used to add axes titles, a-b-c labels, and figure titles, just like proplot.axes.CartesianAxes.format.

For details, see the proplot.axes.GeoAxes.format documentation.

[5]:

import proplot as pplt
gs = pplt.GridSpec(ncols=3, nrows=2, wratios=(1, 1, 1.2), hratios=(1, 1.2))
fig = pplt.figure(refwidth=4)

# Styling projections in different ways
ax = fig.subplot(gs[0, :2], proj='eqearth')
ax.format(
title='Equal earth', land=True, landcolor='navy', facecolor='pale blue',
coastcolor='gray5', borderscolor='gray5', innerborderscolor='gray5',
gridlinewidth=1.5, gridcolor='gray5', gridalpha=0.5,
gridminor=True, gridminorlinewidth=0.5,
coast=True, borders=True, borderslinewidth=0.8,
)
ax = fig.subplot(gs[0, 2], proj='ortho')
ax.format(
title='Orthographic', reso='med', land=True, coast=True, latlines=10, lonlines=15,
landcolor='mushroom', suptitle='Projection axes formatting demo',
facecolor='petrol', coastcolor='charcoal', coastlinewidth=0.8, gridlinewidth=1
)
ax = fig.subplot(gs[1, :], proj='wintri')
ax.format(
land=True, facecolor='ocean blue', landcolor='bisque', title='Winkel tripel',
lonlines=60, latlines=15,
gridlinewidth=0.8, gridminor=True, gridminorlinestyle=':',
lonlabels=True, latlabels='r', loninline=True,
gridlabelcolor='gray8', gridlabelsize='med-large',
)
fig.format(
suptitle='Projection axes formatting demo',
toplabels=('Column 1', 'Column 2'),
abc='A.', abcloc='ul', abcborder=False, linewidth=1.5
)

/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_boundary_lines_land.zip


## Zooming into projections¶

To zoom into cartopy projections, use set_extent or pass lonlim, latlim, or boundinglat to format. The boundinglat keyword controls the circular latitude boundary for North Polar and South Polar Stereographic, Azimuthal Equidistant, Lambert Azimuthal Equal-Area, and Gnomonic projections. By default, proplot tries to use the degree-minute-second cartopy locators and formatters made available in cartopy 0.18. You can switch from minute-second subintervals to traditional decimal subintervals by passing dms=False to format or by setting rc['grid.dmslabels'] to False.

To zoom into basemap projections, pass any of the boundinglat, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat, llcrnrx, llcrnry, urcrnrx, urcrnry, width, or height keyword arguments to the Proj constructor function either directly or via the proj_kw subplots keyword argument. You can also pass lonlim and latlim to Proj and these arguments will be used for llcrnrlon, llcrnrlat, etc. You cannot zoom into basemap projections with format after they have already been created.

[6]:

import proplot as pplt

# Plate Carrée map projection
pplt.rc.reso = 'med'  # use higher res for zoomed in geographic features
basemap = pplt.Proj('cyl', lonlim=(-20, 180), latlim=(-10, 50), backend='basemap')
fig, axs = pplt.subplots(nrows=2, refwidth=5, proj=('cyl', basemap))
axs.format(
land=True, labels=True, lonlines=20, latlines=20,
gridminor=True, suptitle='Zooming into projections'
)
axs[0].format(lonlim=(-140, 60), latlim=(-10, 50), labels=True)
axs[0].format(title='Cartopy example')
axs[1].format(title='Basemap example')

[7]:

import proplot as pplt

# Pole-centered map projections
basemap = pplt.Proj('npaeqd', boundinglat=60, backend='basemap')
fig, axs = pplt.subplots(ncols=2, refwidth=2.7, proj=('splaea', basemap))
fig.format(suptitle='Zooming into polar projections')
axs.format(land=True, latmax=80)  # no gridlines poleward of 80 degrees
axs[0].format(boundinglat=-60, title='Cartopy example')
axs[1].format(title='Basemap example')

[8]:

import proplot as pplt

# Zooming in on continents
fig = pplt.figure(refwidth=3)
ax = fig.subplot(121, proj='lcc', proj_kw={'lon0': 0})
ax.format(lonlim=(-20, 50), latlim=(30, 70), title='Cartopy example')
proj = pplt.Proj('lcc', lon0=-100, lat0=45, width=8e6, height=8e6, backend='basemap')
ax = fig.subplot(122, proj=proj)
ax.format(lonlines=20, title='Basemap example')
fig.format(suptitle='Zooming into specific regions', land=True)

[9]:

import proplot as pplt

# Zooming in with cartopy degree-minute-second labels
pplt.rc.reso = 'hi'
fig = pplt.figure(refwidth=2.5)
ax = fig.subplot(121, proj='cyl')
ax.format(lonlim=(-7.5, 2), latlim=(49.5, 59))
ax = fig.subplot(122, proj='cyl')
ax.format(lonlim=(-6, -2), latlim=(54.5, 58.5))
fig.format(
land=True, labels=True,
borders=True, borderscolor='white',
suptitle='Cartopy degree-minute-second labels',
)
pplt.rc.reset()

/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_land.zip


## Included projections¶

The available cartopy and basemap projections are plotted below. The full table of projection names with links to the relevant PROJ documentation is found here.

Proplot uses the cartopy API to add the Aitoff, Hammer, Winkel Tripel, and Kavrayskiy VII projections (i.e., 'aitoff', 'hammer', 'wintri', and 'kav7'), as well as North and South polar versions of the Azimuthal Equidistant, Lambert Azimuthal Equal-Area, and Gnomonic projections (i.e., 'npaeqd', 'spaeqd', 'nplaea', 'splaea', 'npgnom', and 'spgnom'), modeled after cartopy’s existing NorthPolarStereo and SouthPolarStereo projections.

[10]:

import proplot as pplt

# Table of cartopy projections
projs = [
'cyl', 'merc', 'mill', 'lcyl', 'tmerc',
'robin', 'hammer', 'moll', 'kav7', 'aitoff', 'wintri', 'sinu',
'geos', 'ortho', 'nsper', 'aea', 'eqdc', 'lcc', 'gnom',
'npstere', 'nplaea', 'npaeqd', 'npgnom', 'igh',
'eck1', 'eck2', 'eck3', 'eck4', 'eck5', 'eck6'
]
fig, axs = pplt.subplots(ncols=3, nrows=10, figwidth=7, proj=projs)
axs.format(
land=True, reso='lo', labels=False,
suptitle='Table of cartopy projections'
)
for proj, ax in zip(projs, axs):
ax.format(title=proj, titleweight='bold', labels=False)

/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/proplot/constructor.py:1499: UserWarning: The default value for the *approx* keyword argument to TransverseMercator will change from True to False after 0.18.
proj = crs(**kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/proplot/conda/latest/lib/python3.8/site-packages/cartopy/mpl/feature_artist.py:149: UserWarning: Unable to determine extent. Defaulting to global.
warnings.warn('Unable to determine extent. Defaulting to global.')

[11]:

import proplot as pplt

# Table of basemap projections
projs = [
'cyl', 'merc', 'mill', 'cea', 'gall', 'sinu',
'eck4', 'robin', 'moll', 'kav7', 'hammer', 'mbtfpq',
'geos', 'ortho', 'nsper',
'vandg', 'aea', 'eqdc', 'gnom', 'cass', 'lcc',
'npstere', 'npaeqd', 'nplaea'
]
fig, axs = pplt.subplots(ncols=3, nrows=8, figwidth=7, proj=projs, backend='basemap')
axs.format(
land=True, labels=False,
suptitle='Table of basemap projections'
)
for proj, ax in zip(projs, axs):
ax.format(title=proj, titleweight='bold', labels=False)
`