# How to use the cartopy.crs.Robinson 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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bird-house / flyingpigeon / flyingpigeon / visualisation.py View on Github
``````V = D.variables[variable]
lon, lat = map(float, V.target_location.split(','))

except Exception as e:
msg = 'Failed to get data for plotting: {0}\n{1}'.format(ncfile, e)
LOGGER.exception(msg)
raise Exception(msg)

try:
fig = plt.figure(facecolor='w', edgecolor='k')

ax = plt.axes(
projection=ccrs.Robinson(central_longitude=int(np.mean(lons))))

divider = make_axes_locatable(ax)

ax.plot(lon, lat, marker='o', mfc='#292421', ms=13, transform=ccrs.PlateCarree())
ax.plot(lon, lat, marker='o', mfc='#ffffff', ms=7, transform=ccrs.PlateCarree())

cs = ax.contourf(lons, lats, var, 60,
transform=ccrs.PlateCarree(),
cmap=cmap, interpolation='nearest')

ax.coastlines(color='k', linewidth=.8)
ax.set_title(title)

cb = plt.colorbar(cs, cax=cax, orientation='vertical')``````
linsalrob / crAssphage / bin / map_drawing / drawing.py View on Github
``````# there are three different alphas that we are looking at
# the lines between samples (and potentially split those to the lines within and between continents)
# the dots
# the circles to themselves.

# These are the maximum values
#
# Primer:     A       B      C
# Cirlces:   530     485    289
# Lines:      10       9     13
# Dots:     2040    1806    680
# at most out of these we only have 30 different numbers.

# These numbers adjust the size of the things drawn
ax = plt.axes(projection=ccrs.Robinson())

# make the map global rather than have it zoom in to
# the extents of any plotted data
ax.set_global()
# convert to a grayscale image. Uncomment stock_img to get color
ax.background_img(name='greyscale_ocean', resolution='low')

#ax.stock_img()
#ax.coastlines()

# figure out our dots and lines
t = time.time()
dotdata, linedata = calculate_lines_dots(ll, dd, verbose=verbose)
if verbose:
sys.stderr.write(f"Calculating the lines took {time.time() - t} seconds\n")``````
linsalrob / crAssphage / bin / map_drawing / pie_charts.py View on Github
``````# there are three different alphas that we are looking at
# the lines between samples (and potentially split those to the lines within and between continents)
# the dots
# the circles to themselves.

# These are the maximum values
#
# Primer:     A       B      C
# Cirlces:   530     485    289
# Lines:      10       9     13
# Dots:     2040    1806    680
# at most out of these we only have 30 different numbers.

# These numbers adjust the size of the things drawn
ax = plt.axes(projection=ccrs.Robinson())

# make the map global rather than have it zoom in to
# the extents of any plotted data
ax.set_global()
# convert to a grayscale image. Uncomment stock_img to get color
#ax.background_img(name='PC_test', resolution='low') ## &lt;&lt; OUR  FAVORITE
ax.background_img(name='GreyScaleEarth', resolution='low')

#ax.stock_img()
#ax.coastlines()

# figure out our dots and lines
t = time.time()
data = calculate_samediff(ll, dd, samekm=150, verbose=verbose)
if verbose:``````
linsalrob / EdwardsLab / cartopy / crAssphage_distance.py View on Github
``````def plotmap(ll, dd, maxdist=1, maxlinewidth=3):
"""
Plot the map of the dna distances and lat longs
:param ll: The lon-lats
:param dd: The distances to use
:param maxdist: The maximum distance that we will scale to be maxlinewidth
:return:
"""

ax = plt.axes(projection=ccrs.Robinson())

# make the map global rather than have it zoom in to
# the extents of any plotted data
ax.set_global()

ax.stock_img()
ax.coastlines()

## color the lines based on the maximum distance value
jet = cm = plt.get_cmap('jet')
cNorm = colors.Normalize(vmin=0, vmax=maxdist)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)

# Using contourf to provide my colorbar info, then clearing the figure
Z = [[0, 0], [0, 0]]
levels = range(0, int(100 * maxdist) + 10, 10)``````
SuperElastix / SimpleElastix / Utilities / Statistics / download_stats.py View on Github
``````# Cartography plot of downloads by country
#
c_series = cbo_df.sum(axis=1)
c_series = c_series.sort_values(ascending=False)
cmap = mpl.cm.OrRd

max_dl = float(c_series.max())
min_dl = float(c_series.min())

norm = mpl.colors.LogNorm(vmin=min_dl, vmax=max_dl)
total_users = c_series.sum()

plt.figure(figsize=(14,8))
ax = plt.axes(projection=ccrs.Robinson() )

hdl_dict = dict()

name = country.attributes['name_long']
if name in c_series:
num_users = c_series[name]
facecolor=cmap(norm(num_users),alpha=True))
hdl_dict[name] = hdl

SciTools / cartopy / lib / cartopy / examples / logo.py View on Github
``````def main():
fig = plt.figure(figsize=[12, 6])
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())

ax.coastlines()
ax.gridlines()

# generate a matplotlib path representing the word "cartopy"
fp = FontProperties(family='Bitstream Vera Sans', weight='bold')
logo_path = matplotlib.textpath.TextPath((-175, -35), 'cartopy',
size=1, prop=fp)
# scale the letters up to sensible longitude and latitude sizes
logo_path._vertices *= np.array([80, 160])

im = ax.stock_img()
# clip the image according to the logo_path. mpl v1.2.0 does not support
# the transform API that cartopy makes use of, so we have to convert the
# projection into a transform manually``````
valentjn / vscode-ltex / tools / plotStats.py View on Github
``````def plotMap():
mapPath = os.path.join(repoDirPath, "_data", "stats", "map.json")
with open(mapPath, "r") as f: map_ = json.load(f)

fig = plt.figure(figsize=(14, 7))
ax = fig.add_subplot(1, 1, 1, projection=cartopy.crs.Robinson())

ax.set_global()
ax.stock_img()
ax.coastlines(alpha=0.3)
ax.outline_patch.set_edgecolor("#0000004c")

for child in ax.get_children():
if isinstance(child, mpl.image.AxesImage): child.set_alpha(0.5)

for stargazer in map_["stargazers"].values():
if stargazer is None: continue
ax.plot(stargazer["lon"], stargazer["lat"], ".", color="#8888ff", mec="#5555ff", ms=14,
transform=cartopy.crs.PlateCarree())

ax.set_facecolor("none")
plt.savefig(os.path.join(repoDirPath, "images", "stats", "map.png"),``````
bellockk / alphashape / examples / plot_geodataframe.py View on Github
``````# Generate the alpha shape
alpha_shape = alphashape.alphashape(gdf)

# Initialize plot
ax = plt.axes(projection=ccrs.PlateCarree())

# Plot input points
gdf_proj = gdf.to_crs(ccrs.Robinson().proj4_init)
ax.scatter([p.x for p in gdf_proj['geometry']],
[p.y for p in gdf_proj['geometry']],
transform=ccrs.Robinson())

# Plot alpha shape
alpha_shape.to_crs(ccrs.Robinson().proj4_init)['geometry'],
crs=ccrs.Robinson(), alpha=.2)

plt.show()``````
bradyrx / climpred / climpred / vis.py View on Github
``````def make_cartopy(projection=ccrs.Robinson(), land_color='k',
grid_color='#D3D3D3', grid_lines=True, figsize=(12, 8),
frameon=True):
"""
Returns a global cartopy projection with the defined projection style.

Parameters
----------
projection : ccrs projection instance (optional)
Named map projection from Cartopy
land_color : HEX or char string (optional)
Color string to fill in continents with
figsize : tuple (optional)
Size of figure
grid_color : HEX or char string (optional)
Color string for the color of the grid lines
grid_lines : boolean (optional)``````
bird-house / flyingpigeon / flyingpigeon / visualisation.py View on Github
``````:return graphic: graphic in specified format
"""
import matplotlib.patches as mpatches
lats, lons = utils.get_coordinates(resource, unrotate=True)

# box_top = 45
# x, y = [-20, -20, 45, 45, -44], [-45, box_top, box_top, -45, -45]

xy = np.array([[np.min(lons), np.min(lats)],
[np.max(lons), np.min(lats)],
[np.max(lons), np.max(lats)],
[np.min(lons), np.max(lats)]])

fig = plt.figure(figsize=(20, 10), dpi=600, facecolor='w', edgecolor='k')
projection = ccrs.Robinson()
#  ccrs.Orthographic(central_longitude=np.mean(xy[:, 0]),
#  central_latitude=np.mean(xy[:, 1]),
#  globe=None)  # Robinson()

ax = plt.axes(projection=projection)
ax.stock_img()
ax.coastlines()
# ccrs.Geodetic()
ax.gridlines()
plt.show()

if file_extension is None:
map_graphic = fig
else:
map_graphic = fig2plot(fig=fig, file_extension=file_extension)``````

## Cartopy

A Python library for cartographic visualizations with Matplotlib

BSD-3-Clause