Related
My question is similar to this one (R + ggplot) but for Python.
Question
How to generate a (2D) Voronoi diagram where cells have a maximum extend / growth limit ?
it would result in some cells with curved boundary (circle arc), and some space regions not filled by any cell.
An image processing interpretation of the desired output would be to perform some "AND" mask between the voronoi cells and circles centered on each cell with the required radius.
(For my particular problem, I am working with geolocated data, and for now I am willing to accept discrepancies from most voronoi libs that will use lat/lon as cartesian coordinates. That is another matter...)
This question is NOT about clipping or handling of "inifinite" voronoi cells (see scypi.spatial.Voronoi), so the following links are NOT related :
clipping a voronoi diagram python
How to limit Voronoi cells even when infinite with python?
Vaguely related topics:
Is there a way to vary the rate of Voronoi cell growth?
https://gis.stackexchange.com/questions/366471/weighted-voronoi-polygons-in-r
https://gis.stackexchange.com/questions/17282/create-weighted-thiessen-polygons/17284#17284
https://github.com/HichamZouarhi/Weighted-Voronoi-PyQGIS
Example from the R + ggplot answer:
sample code for tests:
from typing import Tuple, List, Union
import geovoronoi
import matplotlib.colors as clr
import matplotlib.pyplot as plt
import numpy as np
from geographiclib.geodesic import Geodesic
from shapely.geometry import Polygon, Point
from simplekml import LineStyle, Color, PolyStyle, Container
WGS84_Tool = Geodesic.WGS84
T_Color_List = List[Union[clr.Colormap, clr.LinearSegmentedColormap, clr.ListedColormap]]
def generate_n_colors(n, cmap_name='tab20') -> T_Color_List:
"""
https://github.com/WZBSocialScienceCenter/geovoronoi/blob/master/geovoronoi/plotting.py
Get a list of `n` numbers from matplotlib color map `cmap_name`. If `n` is larger than the number of colors in the
color map, the colors will be recycled, i.e. they are not unique in this case.
:param n: number of colors to generate
:param cmap_name: matplotlib color map name
:return: list of `n` colors
"""
pt_region_colormap = plt.get_cmap(cmap_name)
max_i = len(pt_region_colormap.colors)
return [pt_region_colormap(i % max_i) for i in range(n)]
def _plot_cell_and_seed(kml: Container,
name: str,
region_polygon: Polygon,
seed_coords: Tuple[float, float],
_kml_color: str):
_p = kml.newpolygon(name=f"{name} zone",
outerboundaryis=[(lon, lat)
for lat, lon
in region_polygon.exterior.coords], )
_p.style.linestyle = LineStyle(color=Color.darkgrey, width=1.)
_p.style.polystyle = PolyStyle(color=_kml_color)
p = kml.newpoint(coords=[seed_coords],
name=name)
p.style.iconstyle.icon.href = "http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png"
p.style.iconstyle.scale = 0.5
p.style.labelstyle.scale = 0.5
def plot_regions(region_polys,
region_pts,
seeds_coords_list: np.ndarray,
seeds_names: List[str],
kml: Container,
colors: T_Color_List,
):
assert (len(seeds_names) == len(seeds_coords_list))
index = 0
for region_id, region_polygon in region_polys.items():
_cell_point_indexes = region_pts[region_id]
_cell_seed_coords = seeds_coords_list[_cell_point_indexes][0]
name = seeds_names[_cell_point_indexes[0]]
_kml_airport_coords = (_cell_seed_coords[-1], _cell_seed_coords[0])
_mpl_color = colors[index]
_mpl_hexa_color = clr.to_hex(_mpl_color, keep_alpha=True)
_hexa_color_no_sharp = _mpl_hexa_color.split("#")[-1]
_kml_color = Color.hexa(_hexa_color_no_sharp)
_kml_color = Color.changealphaint(alpha=7 * 255 // 10, gehex=_kml_color)
_plot_cell_and_seed(kml=kml,
name=name,
region_polygon=region_polygon,
seed_coords=_kml_airport_coords,
_kml_color=_kml_color)
index += 1
# bounding box for geovoronoi
geo_boundaries = {"min": {"lat": +30, "lon": -12},
"max": {"lat": +75, "lon": +35}, }
# a list of [[lat,lon],[lat,lon],[lat,lon],[lat,lon],]
n = 150
seeds_coords_list = np.dstack(
[np.random.uniform(low=geo_boundaries["min"]["lat"], high=geo_boundaries["max"]["lat"], size=n),
np.random.uniform(low=geo_boundaries["min"]["lon"], high=geo_boundaries["max"]["lon"], size=n), ]).reshape((n, 2))
seeds_names = [f"{lat:+_.2f};{lon:+_.2f}" for lat, lon in seeds_coords_list]
boundary_points = geovoronoi.coords_to_points([[geo_boundaries["min"]["lat"], geo_boundaries["min"]["lon"]],
[geo_boundaries["min"]["lat"], geo_boundaries["max"]["lon"]],
[geo_boundaries["max"]["lat"], geo_boundaries["max"]["lon"]],
[geo_boundaries["max"]["lat"], geo_boundaries["min"]["lon"]],
# last necessary ?
[geo_boundaries["min"]["lat"], geo_boundaries["min"]["lon"]]])
boundary_polygon = Polygon(boundary_points)
# beware that geodesics and geovoronoi may not be accurate since it uses planar cartesian formulas...
region_polys, region_pts = geovoronoi.voronoi_regions_from_coords(seeds_coords_list, boundary_polygon)
# DO SOMETHING HERE
#...
#...
#...
kdoc = Kml()
p_kml = Path.cwd() / "voronoi_so.kml"
colors: T_Color_List = generate_n_colors(len(region_polys))
plot_regions(region_polys=region_polys,
region_pts=region_pts,
seeds_coords_list=seeds_coords_list,
seeds_names=seeds_names,
kml=kdoc.newfolder(name="raw regions"),
colors=colors)
print("save KML")
kdoc.save(p_kml.as_posix())
print(p_kml.as_uri())
After some thinking, here is an approach using Shapely and using the intersection between computed circles (360 vertices) and each voronoi cell.
Performance is not convincing since we create 360 points and a polygon + 1 intersection computation for each cell... (at least it is a bounded ...).
However, the underlying voronoi computation data such as cells adjacency is lost, and we can't know if some cells are isolated after this transformation. Maybe some ideas here: Determining and storing Voronoi Cell Adjacency
I do believe better solutions may exist.
So here is a solution, with random geographical data, and corresponding kml rendering.
EDIT: using weighted voronoi diagrams, a possible weight function could be f(d) = d <= radius ? I could not explore this for now...
Raw regions:
Modified raw regions:
from pathlib import Path
from typing import Tuple, List, Union
import geovoronoi
import matplotlib.colors as clr
import matplotlib.pyplot as plt
import numpy as np
from geographiclib.geodesic import Geodesic
from shapely.geometry import Polygon, Point
from simplekml import LineStyle, Kml, Color, PolyStyle, Container
WGS84_Tool = Geodesic.WGS84
T_Color_List = List[Union[clr.Colormap, clr.LinearSegmentedColormap, clr.ListedColormap]]
def generate_n_colors(n, cmap_name='tab20') -> T_Color_List:
"""
https://github.com/WZBSocialScienceCenter/geovoronoi/blob/master/geovoronoi/plotting.py
Get a list of `n` numbers from matplotlib color map `cmap_name`. If `n` is larger than the number of colors in the
color map, the colors will be recycled, i.e. they are not unique in this case.
:param n: number of colors to generate
:param cmap_name: matplotlib color map name
:return: list of `n` colors
"""
pt_region_colormap = plt.get_cmap(cmap_name)
max_i = len(pt_region_colormap.colors)
return [pt_region_colormap(i % max_i) for i in range(n)]
def _create_bounded_regions(region_polys,
region_pts,
distance_criteria_m: float,
cell_seed_coords_list: np.ndarray,
nb_vertices:int=36):
new_polygons = {}
for region_id, region_polygon in region_polys.items():
_cell_point_indexes = region_pts[region_id]
_cell_seed_coords = cell_seed_coords_list[_cell_point_indexes][0]
_arpt_lat = _cell_seed_coords[0]
_arpt_lon = _cell_seed_coords[-1]
cycle_vertices = []
for a in np.linspace(0,359,nb_vertices):
p = WGS84_Tool.Direct(lat1=_arpt_lat, lon1=_arpt_lon, azi1=a, s12=distance_criteria_m)
_point = Point(p["lat2"], p["lon2"])
cycle_vertices.append(_point)
circle = Polygon(cycle_vertices)
new_polygons[region_id] = region_polygon.intersection(circle)
return new_polygons
def _plot_cell_and_seed(kml: Container,
name: str,
region_polygon: Polygon,
seed_coords: Tuple[float, float],
_kml_color: str):
_p = kml.newpolygon(name=f"{name} zone",
outerboundaryis=[(lon, lat)
for lat, lon
in region_polygon.exterior.coords], )
_p.style.linestyle = LineStyle(color=Color.darkgrey, width=1.)
_p.style.polystyle = PolyStyle(color=_kml_color)
p = kml.newpoint(coords=[seed_coords],
name=name)
p.style.iconstyle.icon.href = "http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png"
p.style.iconstyle.scale = 0.5
p.style.labelstyle.scale = 0.5
def plot_regions(region_polys,
region_pts,
seeds_coords_list: np.ndarray,
seeds_names: List[str],
kml: Container,
colors: T_Color_List,
):
assert (len(seeds_names) == len(seeds_coords_list))
index = 0
for region_id, region_polygon in region_polys.items():
_cell_point_indexes = region_pts[region_id]
_cell_seed_coords = seeds_coords_list[_cell_point_indexes][0]
name = seeds_names[_cell_point_indexes[0]]
_kml_airport_coords = (_cell_seed_coords[-1], _cell_seed_coords[0])
_mpl_color = colors[index]
_mpl_hexa_color = clr.to_hex(_mpl_color, keep_alpha=True)
_hexa_color_no_sharp = _mpl_hexa_color.split("#")[-1]
_kml_color = Color.hexa(_hexa_color_no_sharp)
_kml_color = Color.changealphaint(alpha=7 * 255 // 10, gehex=_kml_color)
_plot_cell_and_seed(kml=kml,
name=name,
region_polygon=region_polygon,
seed_coords=_kml_airport_coords,
_kml_color=_kml_color)
index += 1
# bounding box for geovoronoi
geo_boundaries = {"min": {"lat": +30, "lon": -12},
"max": {"lat": +75, "lon": +35}, }
# a list of [[lat,lon],[lat,lon],[lat,lon],[lat,lon],]
n = 150
seeds_coords_list = np.dstack(
[np.random.uniform(low=geo_boundaries["min"]["lat"], high=geo_boundaries["max"]["lat"], size=n),
np.random.uniform(low=geo_boundaries["min"]["lon"], high=geo_boundaries["max"]["lon"], size=n), ]).reshape((n, 2))
seeds_names = [f"{lat:+_.2f};{lon:+_.2f}" for lat, lon in seeds_coords_list]
boundary_points = geovoronoi.coords_to_points([[geo_boundaries["min"]["lat"], geo_boundaries["min"]["lon"]],
[geo_boundaries["min"]["lat"], geo_boundaries["max"]["lon"]],
[geo_boundaries["max"]["lat"], geo_boundaries["max"]["lon"]],
[geo_boundaries["max"]["lat"], geo_boundaries["min"]["lon"]],
# last necessary ?
[geo_boundaries["min"]["lat"], geo_boundaries["min"]["lon"]]])
boundary_polygon = Polygon(boundary_points)
# beware that geodesics and geovoronoi may not be accurate since it uses planar cartesian formulas...
region_polys, region_pts = geovoronoi.voronoi_regions_from_coords(seeds_coords_list, boundary_polygon)
new_region_polys = _create_bounded_regions(region_polys=region_polys,
region_pts=region_pts,
distance_criteria_m=300_000.,
cell_seed_coords_list=seeds_coords_list, )
kdoc = Kml()
p_kml = Path.cwd() / "voronoi_so.kml"
colors: T_Color_List = generate_n_colors(len(region_polys))
plot_regions(region_polys=region_polys,
region_pts=region_pts,
seeds_coords_list=seeds_coords_list,
seeds_names=seeds_names,
kml=kdoc.newfolder(name="raw regions"),
colors=colors)
plot_regions(region_polys=new_region_polys,
region_pts=region_pts,
seeds_coords_list=seeds_coords_list,
seeds_names=seeds_names,
kml=kdoc.newfolder(name="new_regions (range)"),
colors=colors)
print("save KML")
kdoc.save(p_kml.as_posix())
print(p_kml.as_uri())
I have the following script. However, I'm getting an error that points are the data I'm passing to the location parameter. When printing this, I'm getting what location generally receives.
Here is an example of the txt info: VOLCANX020,NUMBER,NAME,LOCATION,STATUS,ELEV,TYPE,TIMEFRAME,LAT,LON
509.000000000000000,1201-01=,Baker,US-Washington,Historical,3285.000000000000000,Stratovolcanoes,D3,48.7767982,-121.8109970
import folium
import pandas as pd
map = folium.Map(location=[38.58, -99.09], zoom_start=4, tiles = "Stamen Terrain")
# Reads the data
df_volcanos = pd.read_csv('Volcanoes.txt')
fg_volcanoes=folium.FeatureGroup(name="Volcanos Map")
fg_population=folium.FeatureGroup(name='Populations')
for index, data in df_volcanos.head(1).iterrows():
fg_volcanoes.add_child(folium.CircleMarker(location=data[['LAT','LON']].values,
radius=6,
popup=f"{data['ELEV']} mts",
fill_color='green' if data['ELEV'] < 1000 else 'orange' if data['ELEV'] < 3000 else 'red',
color="grey",
fill_opacity=1))
map.add_child(folium.LayerControl())
map.save('Map2.html')
My modification is to make the circle marker itself belong to a group and add that group to the map. Also, in the sequential processing of the data frame, I did not use indexes to specify rows, so I used .loc to limit the rows. At the same time, I also modified the row specification in the popup.
import folium
m = folium.Map(location=[38.58, -99.09], zoom_start=4, tiles="Stamen Terrain")
fg_volcanoes=folium.FeatureGroup(name="Volcanos Map")
fg_population=folium.FeatureGroup(name='Populations')
for index, data in df_volcanos.head(1).iterrows():
color = 'green' if df_volcanos.loc[index,'ELEV'] < 1000 else 'orange' if df_volcanos.loc[index, 'ELEV'] > 3000 else 'red'
#fg_volcanoes.add_child(
folium.CircleMarker(
location=df_volcanos[['LAT','LON']].values[0].tolist(),
radius=6,
popup=f"{df_volcanos.loc[index,'ELEV']} mts",
fill_color=color,
color="grey",
fill_opacity=1
).add_to(fg_volcanoes)
#)
fg_volcanoes.add_to(m)
m.add_child(folium.LayerControl())
#m.save('Map2.html')
m
I'm trying to draw some circles to outline koala sightings around Brisbane Aus. When I try and run it in jupyter, it draws the map and if I replace the coordinate[["decimalLatitude"],coordinate["decimalLongitude"]] with actual values, it displays, but otherwise it doesn't. Here's my code and a sample pic of the data.
import folium
map_of_koala_sightings = folium.Map(
location =[-27.470125,153.021072],
zoom_start=13, tiles='Stamen Terrain'
)
for coordinate in koala_df:
try:
folium.Circle(
radius=300,
location = coordinate[["decimalLatitude"],coordinate["decimalLongitude"]],
color='#3388ff',
fill=True).add_to(map_of_koala_sightings)
except:
pass
map_of_koala_sightings
Sample Data
This works for me (with a subset of coordinates).
import folium
import pandas as pd
koala_df = pd.DataFrame(columns = ['decimalLatitude','decimalLongitude'],
data = [[-26.770484,152.838467],
[-28.103458,153.375882],
[-26.4,146.25]])
print(koala_df.head())
map_of_koala_sightings = folium.Map(
location =[-27.470125,153.021072],
zoom_start=5, tiles='Stamen Terrain'
)
for idx,coordinate in koala_df.iterrows():
folium.Circle(radius=300,
location = [coordinate['decimalLatitude'],coordinate['decimalLongitude']],
color='#3388ff',
fill=True).add_to(map_of_koala_sightings)
#map_of_koala_sightings
#For saving to html file
map_of_koala_sightings.save('koalas.html')
Output:
decimalLatitude decimalLongitude
0 -26.770484 152.838467
1 -28.103458 153.375882
2 -26.400000 146.250000
and 'koalas.html' contains:
I am having a pandas DataFrames with latitude longitude of start and end of a route, so these are my columns ('origin_lat, 'origin_lon',destination_lat','destination_lon').
I am able to plot the locations on Folium map but I am looking for a way to plot the routes between each location.
Here is the code I am using:
m = folium.Map([16.7, 81.095], zoom_start=11)
m
# mark each origin as a point
for index, row in df.iterrows():
folium.CircleMarker([row['origin_lat'], row['origin_lng']],
radius=15,
fill_color="#3db7e4", # divvy color
).add_to(m)
for index, row in df.iterrows():
folium.CircleMarker([row['destination_lat'], row['destination_lng']],
radius=15,
fill_color="red", # divvy color
).add_to(m)
#add routes
folium.PolyLine([list(zip(df.origin_lat, df.origin_lng)),list(zip(df.destination_lat, df.destination_lng))], line_opacity = 0.5, color='white',line_weight=5).add_to(m)
The code I am using is connecting all the origin locations together and all the destination locations together but I want to plot routes between origin and destination locations instead. Any way I can fix it?
If I understood your question correctly, I believe I have your solution
Some imports
import pandas as pd
import numpy as np
import folium
Some sample data
centroid_lat = 16.7
centroid_lon = 81.095
x = .1
n = 10
o_lats = np.random.uniform(low=centroid_lat - x, high=centroid_lat + x, size=(n,))
o_lons = np.random.uniform(low=centroid_lon - x, high=centroid_lon + x, size=(n,))
d_lats = np.random.uniform(low=centroid_lat - x, high=centroid_lat + x, size=(n,))
d_lons = np.random.uniform(low=centroid_lon - x, high=centroid_lon + x, size=(n,))
df = pd.DataFrame({'origin_lng' : o_lons, 'origin_lat' : o_lats,
'destination_lng': d_lons, 'destination_lat': d_lats})
print(df.head())
destination_lat destination_lng origin_lat origin_lng
0 16.797057 81.074000 16.660164 81.080038
1 16.615371 81.001004 16.772645 80.997770
2 16.784289 81.117082 16.670008 81.032719
3 16.686201 81.184775 16.787999 81.189585
4 16.757704 81.127280 16.720080 81.178466
Then the map. No need for two for loops and I'm creating a line each iteration
m = folium.Map([centroid_lat, centroid_lon], zoom_start=11)
for _, row in df.iterrows():
folium.CircleMarker([row['origin_lat'], row['origin_lng']],
radius=15,
fill_color="#3db7e4", # divvy color
).add_to(m)
folium.CircleMarker([row['destination_lat'], row['destination_lng']],
radius=15,
fill_color="red", # divvy color
).add_to(m)
folium.PolyLine([[row['origin_lat'], row['origin_lng']],
[row['destination_lat'], row['destination_lng']]]).add_to(m)
m
Would there be a way to plot the borders of the continents with Basemap (or without Basemap, if there is some other way), without those annoying rivers coming along? Especially that piece of Kongo River, not even reaching the ocean, is disturbing.
EDIT: I intend to further plot data over the map, like in the Basemap gallery (and still have the borderlines of the continents drawn as black lines over the data, to give structure for the worldmap) so while the solution by Hooked below is nice, masterful even, it's not applicable for this purpose.
Image produced by:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(projection='robin',lon_0=0,resolution='c')
m.fillcontinents(color='gray',lake_color='white')
m.drawcoastlines()
plt.savefig('world.png',dpi=75)
For reasons like this i often avoid Basemap alltogether and read the shapefile in with OGR and convert them to a Matplotlib artist myself. Which is alot more work but also gives alot more flexibility.
Basemap has some very neat features like converting the coordinates of input data to your 'working projection'.
If you want to stick with Basemap, get a shapefile which doesnt contain the rivers. Natural Earth for example has a nice 'Land' shapefile in the physical section (download 'scale rank' data and uncompress). See http://www.naturalearthdata.com/downloads/10m-physical-vectors/
You can read the shapefile in with the m.readshapefile() method from Basemap. This allows you to get the Matplotlib Path vertices and codes in the projection coordinates which you can then convert into a new Path. Its a bit of a detour but it gives you all styling options from Matplotlib, most of which are not directly available via Basemap. Its a bit hackish, but i dont now another way while sticking to Basemap.
So:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.collections import PathCollection
from matplotlib.path import Path
fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
# MPL searches for ne_10m_land.shp in the directory 'D:\\ne_10m_land'
m = Basemap(projection='robin',lon_0=0,resolution='c')
shp_info = m.readshapefile('D:\\ne_10m_land', 'scalerank', drawbounds=True)
ax = plt.gca()
ax.cla()
paths = []
for line in shp_info[4]._paths:
paths.append(Path(line.vertices, codes=line.codes))
coll = PathCollection(paths, linewidths=0, facecolors='grey', zorder=2)
m = Basemap(projection='robin',lon_0=0,resolution='c')
# drawing something seems necessary to 'initiate' the map properly
m.drawcoastlines(color='white', zorder=0)
ax = plt.gca()
ax.add_collection(coll)
plt.savefig('world.png',dpi=75)
Gives:
How to remove "annoying" rivers:
If you want to post-process the image (instead of working with Basemap directly) you can remove bodies of water that don't connect to the ocean:
import pylab as plt
A = plt.imread("world.png")
import numpy as np
import scipy.ndimage as nd
import collections
# Get a counter of the greyscale colors
a = A[:,:,0]
colors = collections.Counter(a.ravel())
outside_and_water_color, land_color = colors.most_common(2)
# Find the contigous landmass
land_idx = a == land_color[0]
# Index these land masses
L = np.zeros(a.shape,dtype=int)
L[land_idx] = 1
L,mass_count = nd.measurements.label(L)
# Loop over the land masses and fill the "holes"
# (rivers without outlays)
L2 = np.zeros(a.shape,dtype=int)
L2[land_idx] = 1
L2 = nd.morphology.binary_fill_holes(L2)
# Remap onto original image
new_land = L2==1
A2 = A.copy()
c = [land_color[0],]*3 + [1,]
A2[new_land] = land_color[0]
# Plot results
plt.subplot(221)
plt.imshow(A)
plt.axis('off')
plt.subplot(222)
plt.axis('off')
B = A.copy()
B[land_idx] = [1,0,0,1]
plt.imshow(B)
plt.subplot(223)
L = L.astype(float)
L[L==0] = None
plt.axis('off')
plt.imshow(L)
plt.subplot(224)
plt.axis('off')
plt.imshow(A2)
plt.tight_layout() # Only with newer matplotlib
plt.show()
The first image is the original, the second identifies the land mass. The third is not needed but fun as it ID's each separate contiguous landmass. The fourth picture is what you want, the image with the "rivers" removed.
Following user1868739's example, I am able to select only the paths (for some lakes) that I want:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='white',lake_color='white',zorder=2)
coasts = m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths = coasts.get_paths()
ipolygons = range(83) + [84] # want Baikal, but not Tanganyika
# 80 = Superior+Michigan+Huron, 81 = Victoria, 82 = Aral, 83 = Tanganyika,
# 84 = Baikal, 85 = Great Bear, 86 = Great Slave, 87 = Nyasa, 88 = Erie
# 89 = Winnipeg, 90 = Ontario
for ipoly in ipolygons:
r = coasts_paths[ipoly]
# Convert into lon/lat vertices
polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
r.iter_segments(simplify=False)]
px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
py = [polygon_vertices[i][2] for i in xrange(len(polygon_vertices))]
m.plot(px,py,linewidth=0.5,zorder=3,color='black')
plt.savefig('world2.png',dpi=100)
But this only works when using white background for the continents. If I change color to 'gray' in the following line, we see that other rivers and lakes are not filled with the same color as the continents are. (Also playing with area_thresh will not remove those rivers that are connected to ocean.)
m.fillcontinents(color='gray',lake_color='white',zorder=2)
The version with white background is adequate for further color-plotting all kind of land information over the continents, but a more elaborate solution would be needed, if one wants to retain the gray background for continents.
I frequently modify Basemap's drawcoastlines() to avoid those 'broken' rivers. I also modify drawcountries() for the sake of data source consistency.
Here is what I use in order to support the different resolutions available in Natural Earth data:
from mpl_toolkits.basemap import Basemap
class Basemap(Basemap):
""" Modify Basemap to use Natural Earth data instead of GSHHG data """
def drawcoastlines(self):
shapefile = 'data/naturalearth/coastline/ne_%sm_coastline' % \
{'l':110, 'm':50, 'h':10}[self.resolution]
self.readshapefile(shapefile, 'coastline', linewidth=1.)
def drawcountries(self):
shapefile = 'data/naturalearth/countries/ne_%sm_admin_0_countries' % \
{'l':110, 'm':50, 'h':10}[self.resolution]
self.readshapefile(shapefile, 'countries', linewidth=0.5)
m = Basemap(llcrnrlon=-90, llcrnrlat=-40, urcrnrlon=-30, urcrnrlat=+20,
resolution='l') # resolution = (l)ow | (m)edium | (h)igh
m.drawcoastlines()
m.drawcountries()
Here is the output:
Please note that by default Basemap uses resolution='c' (crude), which is not supported in the code shown.
If you're OK with plotting outlines rather than shapefiles, it's pretty easy to plot coastlines that you can get from wherever. I got my coastlines from the NOAA Coastline Extractor in MATLAB format:
http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html
To edit the coastlines, I converted to SVG, then edited them with Inkscape, then converted back to the lat/lon text file ("MATLAB" format).
All Python code is included below.
# ---------------------------------------------------------------
def plot_lines(mymap, lons, lats, **kwargs) :
"""Plots a custom coastline. This plots simple lines, not
ArcInfo-style SHAPE files.
Args:
lons: Longitude coordinates for line segments (degrees E)
lats: Latitude coordinates for line segments (degrees N)
Type Info:
len(lons) == len(lats)
A NaN in lons and lats signifies a new line segment.
See:
giss.noaa.drawcoastline_file()
"""
# Project onto the map
x, y = mymap(lons, lats)
# BUG workaround: Basemap projects our NaN's to 1e30.
x[x==1e30] = np.nan
y[y==1e30] = np.nan
# Plot projected line segments.
mymap.plot(x, y, **kwargs)
# Read "Matlab" format files from NOAA Coastline Extractor.
# See: http://www.ngdc.noaa.gov/mgg/coast/
lineRE=re.compile('(.*?)\s+(.*)')
def read_coastline(fname, take_every=1) :
nlines = 0
xdata = array.array('d')
ydata = array.array('d')
for line in file(fname) :
# if (nlines % 10000 == 0) :
# print 'nlines = %d' % (nlines,)
if (nlines % take_every == 0 or line[0:3] == 'nan') :
match = lineRE.match(line)
lon = float(match.group(1))
lat = float(match.group(2))
xdata.append(lon)
ydata.append(lat)
nlines = nlines + 1
return (np.array(xdata),np.array(ydata))
def drawcoastline_file(mymap, fname, **kwargs) :
"""Reads and plots a coastline file.
See:
giss.basemap.drawcoastline()
giss.basemap.read_coastline()
"""
lons, lats = read_coastline(fname, take_every=1)
return drawcoastline(mymap, lons, lats, **kwargs)
# =========================================================
# coastline2svg.py
#
import giss.io.noaa
import os
import numpy as np
import sys
svg_header = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!-- Created with Inkscape (http://www.inkscape.org/) -->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns="http://www.w3.org/2000/svg"
version="1.1"
width="360"
height="180"
id="svg2">
<defs
id="defs4" />
<metadata
id="metadata7">
<rdf:RDF>
<cc:Work
rdf:about="">
<dc:format>image/svg+xml</dc:format>
<dc:type
rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
<dc:title></dc:title>
</cc:Work>
</rdf:RDF>
</metadata>
<g
id="layer1">
"""
path_tpl = """
<path
d="%PATH%"
id="%PATH_ID%"
style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
"""
svg_footer = "</g></svg>"
# Set up paths
data_root = os.path.join(os.environ['HOME'], 'data')
#modelerc = giss.modele.read_modelerc()
#cmrun = modelerc['CMRUNDIR']
#savedisk = modelerc['SAVEDISK']
ifname = sys.argv[1]
ofname = ifname.replace('.dat', '.svg')
lons, lats = giss.io.noaa.read_coastline(ifname, 1)
out = open(ofname, 'w')
out.write(svg_header)
path_id = 1
points = []
for lon, lat in zip(lons, lats) :
if np.isnan(lon) or np.isnan(lat) :
# Process what we have
if len(points) > 2 :
out.write('\n<path d="')
out.write('m %f,%f L' % (points[0][0], points[0][1]))
for pt in points[1:] :
out.write(' %f,%f' % pt)
out.write('"\n id="path%d"\n' % (path_id))
# out.write('style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"')
out.write(' />\n')
path_id += 1
points = []
else :
lon += 180
lat = 180 - (lat + 90)
points.append((lon, lat))
out.write(svg_footer)
out.close()
# =============================================================
# svg2coastline.py
import os
import sys
import re
# Reads the output of Inkscape's "Plain SVG" format, outputs in NOAA MATLAB coastline format
mainRE = re.compile(r'\s*d=".*"')
lineRE = re.compile(r'\s*d="(m|M)\s*(.*?)"')
fname = sys.argv[1]
lons = []
lats = []
for line in open(fname, 'r') :
# Weed out extraneous lines in the SVG file
match = mainRE.match(line)
if match is None :
continue
match = lineRE.match(line)
# Stop if something is wrong
if match is None :
sys.stderr.write(line)
sys.exit(-1)
type = match.group(1)[0]
spairs = match.group(2).split(' ')
x = 0
y = 0
for spair in spairs :
if spair == 'L' :
type = 'M'
continue
(sdelx, sdely) = spair.split(',')
delx = float(sdelx)
dely = float(sdely)
if type == 'm' :
x += delx
y += dely
else :
x = delx
y = dely
lon = x - 180
lat = 90 - y
print '%f\t%f' % (lon, lat)
print 'nan\tnan'
Okay I think I have a partial solution.
The basic idea is that the paths used by drawcoastlines() are ordered by the size/area. Which means the first N paths are (for most applications) the main land masses and lakes and the later paths the smaller islands and rivers.
The issue is that the first N paths that you want will depend on the projection (e.g., global, polar, regional), if area_thresh has been applied and whether you want lakes or small islands etc. In other words, you will have to tweak this per application.
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
mp = 'cyl'
m = Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)
fill_color = '0.9'
# If you don't want lakes set lake_color to fill_color
m.fillcontinents(color=fill_color,lake_color='white')
# Draw the coastlines, with a thin line and same color as the continent fill.
coasts = m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)
# Exact the paths from coasts
coasts_paths = coasts.get_paths()
# In order to see which paths you want to retain or discard you'll need to plot them one
# at a time noting those that you want etc.
for ipoly in xrange(len(coasts_paths)):
print ipoly
r = coasts_paths[ipoly]
# Convert into lon/lat vertices
polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
r.iter_segments(simplify=False)]
px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
m.plot(px,py,'k-',linewidth=1)
plt.show()
Once you know the relevant ipoly to stop drawing (poly_stop) then you can do something like this...
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
mproj = ['nplaea','cyl']
mp = mproj[0]
if mp == 'nplaea':
m = Basemap(resolution='c',projection=mp,lon_0=0,boundinglat=30,area_thresh=200000,round=1)
poly_stop = 10
else:
m = Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)
poly_stop = 18
fill_color = '0.9'
# If you don't want lakes set lake_color to fill_color
m.fillcontinents(color=fill_color,lake_color='white')
# Draw the coastlines, with a thin line and same color as the continent fill.
coasts = m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)
# Exact the paths from coasts
coasts_paths = coasts.get_paths()
# In order to see which paths you want to retain or discard you'll need to plot them one
# at a time noting those that you want etc.
for ipoly in xrange(len(coasts_paths)):
if ipoly > poly_stop: continue
r = coasts_paths[ipoly]
# Convert into lon/lat vertices
polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
r.iter_segments(simplify=False)]
px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
m.plot(px,py,'k-',linewidth=1)
plt.show()
As per my comment to #sampo-smolander
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='gray',lake_color='white',zorder=2)
coasts = m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths = coasts.get_paths()
ipolygons = range(83) + [84]
for ipoly in xrange(len(coasts_paths)):
r = coasts_paths[ipoly]
# Convert into lon/lat vertices
polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
r.iter_segments(simplify=False)]
px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
if ipoly in ipolygons:
m.plot(px,py,linewidth=0.5,zorder=3,color='black')
else:
m.plot(px,py,linewidth=0.5,zorder=4,color='grey')
plt.savefig('world2.png',dpi=100)