For the past few days I have been trying to get weather station data interpolated in a map for my country only. I do this as follows:
Loading the data, I create a grid using interpolation
Based on this grid I draw a contour and contourf image
I then draw shapefiles for Germany, Belgium and France on top of the map in order to cover the irrelevant contour/contourf elements. I used this tutorial for that.
Finally, I use an oceans shapefile (IHO Sea Areas; www.marineregions.org/downloads.php#iho) to plot as well in order to cover the North Sea. Using QGIS, I edited this oceans shapefile and removed everything except for the North Sea - given time constraints :)
You would say that everything goes fine -- but for some reason parts of the country as well as the islands are interpreted as being water. I guess this is because these are distinct parts, all not connected to the main land (due to waters/rivers).
Strangely enough, the edges are drawn, but they are not filled.
I've tried and searched a lot but have no clue about how to fix this. I guess it is somewhere in the LineCollection because in QGIS the shapefile is correct (i.e. it recognizes no shape when clicking the islands etc., which is correct since it should only recognize a shape when clicking on the sea).
I sincerely hope that you could help me point out where I'm wrong and how I can fix this!
Thanks a lot!
This is the map that I'm getting:
https://i.imgur.com/GHISN7n.png
My code is as follows (and you can probably see that I'm very new to this kind of programming, I started yesterday :)):
import numpy as np
import matplotlib
matplotlib.use('Agg')
from scipy.interpolate import griddata
from mpl_toolkits.basemap import Basemap, maskoceans
import matplotlib.pyplot as plt
from numpy.random import seed
import shapefile as shp
from matplotlib.collections import LineCollection
from matplotlib import cm
# Set figure size
plt.figure(figsize=(15,15), dpi=80)
# Define map bounds
xMin, xMax = 2.5, 8.0
yMin, yMax = 50.6, 53.8
# Create map
m = Basemap(projection='merc',llcrnrlon=xMin,llcrnrlat=yMin,urcrnrlon=xMax,urcrnrlat=yMax,resolution='h')
m.drawmapboundary(fill_color='#d4dadc',linewidth=0.25)
# m.drawcoastlines(linewidth=0.5,color='#333333')
# Load data
y = [54.325666666667,52.36,53.269444444444,55.399166666667,54.116666666667,53.614444444444,53.491666666667,53.824130555556,52.918055555556,54.03694,52.139722,52.926865008825,54.853888888889,52.317222,53.240026656696,52.642696895243,53.391265948394,52.505333893732,52.098821802977,52.896643913235,52.457270486008,53.223000488316,52.701902388132,52.0548617826,53.411581103636,52.434561756559,52.749056395511,53.123676213651,52.067534268959,53.194409573306,52.27314817052,51.441334059998,51.224757511326,51.990941918858,51.447744494043,51.960667359998,51.969031121385,51.564889021961,51.857593837453,51.449772459909,51.658528382201,51.196699902606,50.905256257898,51.497306260089,yMin,yMin,yMax,yMax]
x = [2.93575,3.3416666666667,3.6277777777778,3.8102777777778,4.0122222222222,4.9602777777778,5.9416666666667,2.9452777777778,4.1502777777778,6.04167,4.436389,4.7811453228565,4.6961111111111,4.789722,4.9207907082729,4.9787572406902,5.3458010937365,4.6029300588208,5.1797058644882,5.383478899702,5.5196324030324,5.7515738887123,5.8874461671401,5.8723225499118,6.1990994508938,6.2589770334531,6.5729701105864,6.5848470019087,6.6567253619722,7.1493220605216,6.8908745111116,3.5958241584686,3.8609657214986,4.121849767852,4.342014,4.4469005114756,4.9259216999194,4.9352386335384,5.1453989235756,5.3770039280214,5.7065946674719,5.7625447234516,5.7617834850481,6.1961067840608,xMin,xMax,xMin,xMax]
z = [4.8,5.2,5.8,5.4,5,5.3,5.4,4.6,5.8,6.3,4.8,5.4,5.3,4.6,5.4,4.4,4.1,5.5,4.5,4.2,3.9,3.7,4.2,3.2,4,3.8,2.7,2.3,3.4,2.5,3.7,5.2,2.9,5.1,3.8,4.4,4.2,3.9,3.8,3.2,2.6,2.8,2.4,3.1]
avg = np.average(z)
z.extend([avg,avg,avg,avg])
x,y = m(x,y)
# target grid to interpolate to
xis = np.arange(min(x),max(x),2000)
yis = np.arange(min(y),max(y),2000)
xi,yi = np.meshgrid(xis,yis)
# interpolate
zi = griddata((x,y),z,(xi,yi),method='cubic')
# Decide on proper values for colour bar (todo)
vrange = max(z)-min(z)
mult = 2
vmin = min(z)-(mult*vrange)
vmax = max(z)+(mult*vrange)
# Draw contours
cs = m.contour(xi, yi, zi, 5, linewidths=0.25, colors='k')
cs = m.contourf(xi, yi, zi, 5,vmax=vmax,vmin=vmin,cmap=plt.get_cmap('jet'))
# Plot seas from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/northsea')
shapes = sf.shapes()
print shapes[0].parts
records = sf.records()
ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
print len(shape.parts)
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,),zorder=3)
lines.set_facecolors('#abc0d3')
lines.set_edgecolors('red')
lines.set_linewidth(0.5)
ax.add_collection(lines)
# Plot France from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/FRA_adm0')
shapes = sf.shapes()
records = sf.records()
ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors('#fafaf8')
lines.set_edgecolors('#333333')
lines.set_linewidth(0.5)
ax.add_collection(lines)
# Plot Belgium from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/BEL_adm0')
shapes = sf.shapes()
records = sf.records()
ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors('#fafaf8')
lines.set_edgecolors('#333333')
lines.set_linewidth(0.5)
ax.add_collection(lines)
# Plot Germany from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/DEU_adm0')
shapes = sf.shapes()
records = sf.records()
ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors('#fafaf8')
lines.set_edgecolors('#333333')
lines.set_linewidth(0.5)
ax.add_collection(lines)
# Finish plot
plt.axis('off')
plt.savefig("test2.png",bbox_inches='tight',pad_inches=0);
Your problem is that LineCollection does not do what you think it does. What you want is an outer filled shape with 'holes punched in' that have the shapes of the other lines in your LineCollection (i.e. the islands in the north sea). LineCollection, however, fills every line segment in the list, so the filled shapes just overlay each other.
Inspired by this post, I wrote an answer that seems to solve your problem using Patches. However, I'm not entirely sure how robust the solution is: according to the linked (unanswered) post, the vertices of the outer shape should be ordered clockwise while the vertices of the 'punched in' holes should be ordered counter-clock wise (I checked that too and it appears to be correct; this matplotlib example shows the same behaviour). As the shapefiles are binary, it is hard to verify the ordering of the vertices, but the result appears to be correct. In the example below I assume that in each shapefile the longest line segment is the outline of the country (or north sea), while shorter segments are islands or some such. I thus first order the segments of each shapefile by length and create a Path and a PathPatch thereafter. I took the freedom to use a different colour for each shapefile in order to make sure that everything works as it should.
import numpy as np
import matplotlib
matplotlib.use('Agg')
from scipy.interpolate import griddata
from mpl_toolkits.basemap import Basemap, maskoceans
import matplotlib.pyplot as plt
from numpy.random import seed
import shapefile as shp
from matplotlib.collections import LineCollection
from matplotlib.patches import Path, PathPatch
from matplotlib import cm
# Set figure size
fig, ax = plt.subplots(figsize=(15,15), dpi = 80)
# Define map bounds
xMin, xMax = 2.5, 8.0
yMin, yMax = 50.6, 53.8
shapefiles = [
'shapefiles/BEL_adm0',
'shapefiles/FRA_adm0',
'shapefiles/DEU_adm0',
'shapefiles/northsea',
]
colors = ['red', 'green', 'yellow', 'blue']
y = [54.325666666667,52.36,53.269444444444,55.399166666667,54.116666666667,53.614444444444,53.491666666667,53.824130555556,52.918055555556,54.03694,52.139722,52.926865008825,54.853888888889,52.317222,53.240026656696,52.642696895243,53.391265948394,52.505333893732,52.098821802977,52.896643913235,52.457270486008,53.223000488316,52.701902388132,52.0548617826,53.411581103636,52.434561756559,52.749056395511,53.123676213651,52.067534268959,53.194409573306,52.27314817052,51.441334059998,51.224757511326,51.990941918858,51.447744494043,51.960667359998,51.969031121385,51.564889021961,51.857593837453,51.449772459909,51.658528382201,51.196699902606,50.905256257898,51.497306260089,yMin,yMin,yMax,yMax]
x = [2.93575,3.3416666666667,3.6277777777778,3.8102777777778,4.0122222222222,4.9602777777778,5.9416666666667,2.9452777777778,4.1502777777778,6.04167,4.436389,4.7811453228565,4.6961111111111,4.789722,4.9207907082729,4.9787572406902,5.3458010937365,4.6029300588208,5.1797058644882,5.383478899702,5.5196324030324,5.7515738887123,5.8874461671401,5.8723225499118,6.1990994508938,6.2589770334531,6.5729701105864,6.5848470019087,6.6567253619722,7.1493220605216,6.8908745111116,3.5958241584686,3.8609657214986,4.121849767852,4.342014,4.4469005114756,4.9259216999194,4.9352386335384,5.1453989235756,5.3770039280214,5.7065946674719,5.7625447234516,5.7617834850481,6.1961067840608,xMin,xMax,xMin,xMax]
z = [4.8,5.2,5.8,5.4,5,5.3,5.4,4.6,5.8,6.3,4.8,5.4,5.3,4.6,5.4,4.4,4.1,5.5,4.5,4.2,3.9,3.7,4.2,3.2,4,3.8,2.7,2.3,3.4,2.5,3.7,5.2,2.9,5.1,3.8,4.4,4.2,3.9,3.8,3.2,2.6,2.8,2.4,3.1]
avg = np.average(z)
z.extend([avg,avg,avg,avg])
# Create map
m = Basemap(
ax = ax,
projection='merc',
llcrnrlon=xMin,
llcrnrlat=yMin,
urcrnrlon=xMax,
urcrnrlat=yMax,
resolution='h'
)
x,y = m(x,y)
m.drawmapboundary(fill_color='#d4dadc',linewidth=0.25)
# target grid to interpolate to
xis = np.arange(min(x),max(x),2000)
yis = np.arange(min(y),max(y),2000)
xi,yi = np.meshgrid(xis,yis)
# interpolate
zi = griddata((x,y),z,(xi,yi),method='cubic')
# Decide on proper values for colour bar (todo)
vrange = max(z)-min(z)
mult = 2
vmin = min(z)-(mult*vrange)
vmax = max(z)+(mult*vrange)
# Draw contours
cs = m.contour(xi, yi, zi, 5, linewidths=0.25, colors='k')
cs = m.contourf(xi, yi, zi, 5,vmax=vmax,vmin=vmin,cmap=plt.get_cmap('jet'))
for sf_name,color in zip(shapefiles, colors):
print(sf_name)
# Load data
#drawing shapes:
sf = shp.Reader(sf_name)
shapes = sf.shapes()
##print shapes[0].parts
records = sf.records()
##ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
seg = data[index:index2]
segs.append(seg)
segs.append(data[index2:])
##assuming that the longest segment is the enclosing
##line and ordering the segments by length:
lens=np.array([len(s) for s in segs])
order = lens.argsort()[::-1]
segs = [segs[i] for i in order]
##producing the outlines:
lines = LineCollection(segs,antialiaseds=(1,),zorder=4)
##note: leaving the facecolors out:
##lines.set_facecolors('#abc0d3')
lines.set_edgecolors('red')
lines.set_linewidth(0.5)
ax.add_collection(lines)
##producing a path from the line segments:
segs_lin = [v for s in segs for v in s]
codes = [
[Path.MOVETO]+
[Path.LINETO for p in s[1:]]
for s in segs]
codes_lin = [c for s in codes for c in s]
path = Path(segs_lin, codes_lin)
##patch = PathPatch(path, facecolor="#abc0d3", lw=0, zorder = 3)
patch = PathPatch(path, facecolor=color, lw=0, zorder = 3)
ax.add_patch(patch)
plt.axis('off')
fig.savefig("shapefiles.png",bbox_inches='tight',pad_inches=0)
The result looks like this:
Hope this helps.
You haven't plotted the Netherlands.
# Create map
...
# Draw contours
...
# Plot seas from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/northsea')
...
# Plot France from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/FRA_adm0')
...
# Plot Belgium from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/BEL_adm0')
...
# Plot Germany from shapefile
sf = shp.Reader(r'/DIR_TO_SHP/shapefiles/DEU_adm0')
...
# Finish plot
So parts of the Netherlands are filled in with your plotted data, The coastlines are there (from drawing the seas?), but the rest of the country isn't there because you haven't drawn it.
I would steer away from this method of drawing from each shapefile individually, and try to just use basemap for your coastlines, etc. If you must do it from the shapefiles, I would encourage you to define a function that does something like:
def drawContentsOfShapeFile(mymap, path_to_shapefile):
<whatever>
#and use it:
m = Basemap(...)
myshapefiles = []
myshapefiles.append("/DIR_TO_SHP/shapefiles/FRA_adm0")
myshapefiles.append("/DIR_TO_SHP/shapefiles/norway")
for sf in myshapefiles: drawContentOfShapefile(m, sf)
Related
I've started working with Basemap, which seems potentially very useful.
If I plot some global data on a latitude/longitude grid as filled contours, it works great: Iff I leave the lat_0 and lon_0 as zero. Once I change the center location, the map moves but the data doesn't. I would be grateful for advice.
I've created a simple version of the code I'm using, with some simple sample data that illustrates the problem. The values should be (are) large at the equator but small at the poles. If you run the code with lat_0 and lon_0 = 0, it works fine. But if you change the center location to a different coordinate, the same pattern/data is presented even though the map has moved.
from mpl_toolkits.basemap import Basemap, cm
import matplotlib.pyplot as plt
import numpy as np
# create data
lat = np.linspace(-90,90,num=180)
lon = np.linspace(-180,180,num=361)
h2o_north = np.linspace(1,65,num=90)
h2o_south = np.flipud(h2o_north)
h2o = np.append(h2o_north,h2o_south)
data = np.transpose(np.tile(h2o,(len(lon),1)))
# create figure and axes instances
fig = plt.figure(figsize=(10,10))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
# create map
m = Basemap(projection='ortho',lon_0=-50,lat_0=50,resolution='l')
# draw coastlines and country boundaries
m.drawcoastlines()
m.drawcountries()
# draw parallels
parallels = np.arange(-90.,90,10.)
m.drawparallels(parallels)
# draw meridians
meridians = np.arange(180.,360.,10.)
m.drawmeridians(meridians)
ny = data.shape[0]
nx = data.shape[1]
lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly space grid
x, y = m(lons, lats) # compute map projection coordinates
# draw filled contours.
clevs = np.linspace(0,70,num=281)
cs = m.contourf(x,y,data,clevs,cmap=plt.cm.jet)
# colorbar
cbar = m.colorbar(cs,location='bottom',pad="5%",ticks=np.linspace(0,70,15))
cbar.set_label('Scale of the data')
plt.title('Some global data', fontsize=14)
Use np.meshgrid() to create the meshgrid of lon-lat, then, convert it to projection coordinates, and the data are ready to generate contours and plot.
Here is the working code:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# data for z (2D array)
h2o_north = np.linspace(1, 65, num=90)
h2o_south = np.flipud(h2o_north)
h2o = np.append(h2o_north, h2o_south)
data = np.transpose(np.tile(h2o, (len(h2o_north), 1)))
# create figure and axes instances
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot()
# create basemap instance
m = Basemap(projection='ortho', lon_0=-50, lat_0=50, resolution='c', ax=ax)
# create meshgrid covering the whole globe with ...
# conforming dimensions of the `data`
lat = np.linspace(-90, 90, data.shape[0])
lon = np.linspace(-180, 180, data.shape[1])
xs, ys = np.meshgrid(lon, lat) # basic mesh in lon, lat (degrees)
x, y = m(xs, ys) # convert (lon,lat) to map (x,y)
# draw filled contours
clevs = np.linspace(0, np.max(data), 60)
cs = m.contourf(x, y, data, clevs, cmap=plt.cm.jet)
m.drawcoastlines()
m.drawcountries()
m.drawmeridians(range(-180, 180, 30))
m.drawparallels(range(-90, 90, 30))
# draw colorbar
cbar = m.colorbar(cs, location='bottom', pad="5%", ticks=np.linspace(0, np.max(data), 5))
cbar.set_label('Scale of the data')
plt.show()
The resulting plot:
I am reading data from a csv file and plotting contours using python. The code works but when I check shapefile created, I can see only few contour lines are getting plotted whereas when I check image plot there are many more lines. What is going wrong in this code? I think the cs.collections is creating some problem but I am not able to figure it out.
import os
import numpy as np
import pandas as pd
from matplotlib.mlab import griddata
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from shapely.geometry import mapping, Polygon, LineString,MultiLineString
import fiona
# set up plot
plt.clf()
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, axisbg='w', frame_on=False)
# grab data
data = pd.read_csv('Filename.csv', sep=',')
norm = Normalize()
# define map extent
lllon = -180
lllat = -90
urlon = 180
urlat = 90
# Set up Basemap instance
m = Basemap(#projection = 'merc',llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat,resolution='h', epsg=4326)
# transform lon / lat coordinates to map projection
data['projected_lon'], data['projected_lat'] = m(*(data.CLON.values, data.CLAT.values))
# grid data
numcols, numrows = 100, 100
xi = np.linspace(data['projected_lon'].min(), data['projected_lon'].max(), numcols)
yi = np.linspace(data['projected_lat'].min(), data['projected_lat'].max(), numrows)
xi, yi = np.meshgrid(xi, yi)
# interpolate
x, y, z = data['projected_lon'].values, data['projected_lat'].values, data.PRES.values
zi = griddata(x, y, z, xi, yi)
# contour plot
cs = plt.contour(xi, yi, zi,zorder=4)
plt.show()
lenvar = (len(cs.collections)) #number of contours
print(lenvar)
#print(cs.collections)
lines = [] # Empty list for contour sections
for i in range(lenvar):
n = i
p = cs.collections[i].get_paths()[0]
v = p.vertices # getting the individual verticies as a numpy.ndarray
x = v[:,0] #takes first column
y = v[:,1] #takes second column
line = LineString([(i[0], i[1]) for i in zip(x,y)]) #Defines Linestring
lines.append(line) #appends to list
schema = {'geometry': 'LineString','properties': {'id': 'int'}} # sets up parameter for shapefile
with fiona.open('ConShp.shp', 'w', 'ESRI Shapefile', schema) as c: # creates new file to be written to
for j in range(len(lines)):
l = (lines[j]) # creates variable
print(l)
print(type(l))
c.write({'geometry': mapping(l),'properties': {'id': j},})
data Sample is like:
Lat, Lon, Value
18.73, 26.34, 5000
20.00, 60.00, 7000
Shapefile_plot_in_GIS
Image of Plot in python
There are many similar questions to this (How to draw rectangles on a Basemap , and http://matplotlib.1069221.n5.nabble.com/display-a-filled-lat-lon-basemap-rectangle-td11562.html) but I still cannot figure out how to do this.
I want to shade an area covered by two boxes, and the coordinates of each corner (UR = upper right, LL = lower left..) are given by :
box 1:
UR_box1_lat = 72.9
UR_box1_lon = -160
LL_box1_lat = 71.2
LL_box1_lon = -176.5
box 2 :
UL_box2_lat = LL_box1_lat
UL_box2_lon = LL_box1_lon
LR_box2_lat = 69.304
LR_box2_lon = -164.5
This produces my underlying map of the domain where I want to shade my polygons on top of:
# make map with these (i_total, j_total) indices as a box shaded or outlined..
# read in etopo5 topography/bathymetry.
url = 'http://ferret.pmel.noaa.gov/thredds/dodsC/data/PMEL/etopo5.nc'
etopodata = Dataset(url)
topoin = etopodata.variables['ROSE'][:]
lons = etopodata.variables['ETOPO05_X'][:]
lats = etopodata.variables['ETOPO05_Y'][:]
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topoin,lons,start=False)
# plot topography/bathymetry as an image.
# create the figure and axes instances.
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
m = Basemap(llcrnrlon=175.,llcrnrlat=50.,urcrnrlon=-120.,urcrnrlat=75.,\
rsphere=(6378137.00,6356752.3142),\
resolution='l',area_thresh=1000.,projection='lcc',\
lat_1=66.,lon_0=-169.,ax=ax)
# transform to nx x ny regularly spaced 5km native projection grid
nx = int((m.xmax-m.xmin)/5000.)+1; ny = int((m.ymax-m.ymin)/5000.)+1
topodat = m.transform_scalar(topoin,lons,lats,nx,ny)
# plot image over map with imshow.
im = m.imshow(topodat,cm.GMT_haxby)
# draw coastlines and political boundaries.
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw parallels and meridians.
# label on left and bottom of map.
parallels = np.arange(0.,80,15.)
m.drawparallels(parallels,labels=[1,0,0,0])
#m.drawparallels(np.array([50.,70]))
meridians = np.arange(10.,360.,30.)
m.drawmeridians(meridians,labels=[1,0,0,1])
# add colorbar
cb = m.colorbar(im,"right", size="5%", pad='2%')
I have tried (but this is only an attempt at the top box, but I want the entire area shaded of both boxes):
def draw_screen_poly( lats, lons, m):
x, y = m( lons, lats )
xy = zip(x,y)
poly = Polygon( xy, facecolor='red', alpha=0.4 )
plt.gca().add_patch(poly)
lats_box1 = np.linspace(71.2, 72.9, num=25)
lons_box1 = np.linspace(-176.5, -160, num=25)
#lats_box1 = [71.2,72.9,72.9,71.2]
#lons_box1 = [-160,-160,-176.5,-176.5]
#m = Basemap(projection='sinu',lon_0=0)
#m.drawcoastlines()
#m.drawmapboundary()
draw_screen_poly( lats_box1, lons_box1, m )
and also (this is also an attempt at just the top box):
x1,y1 = m(71.2,-176.5)
x2,y2 = m(72.9,-176.5)
x3,y3 = m(72.9,-160)
x4,y4 = m(71.2,-160)
p = Polygon([(x1,y1),(x2,y2),(x3,y3),(x4,y4)],facecolor='red',edgecolor='blue',linewidth=2)
plt.gca().add_patch(p)
plt.show()
Any help is greatly appreciated, I have been stuck on this for quite some time
I'm trying to interpolate data within boundaries and plot contour lines (polygons) based on Latitude, longitude, value data from csv files.
Results I want print as geojson.
I'm stuck on the basic contour plot from csv. I really appreciate help here.
This is what I got in this moment but can't make it work.
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt('temp.csv', delimiter=',')
x = data[:1]
y = data[:2]
[x,y] = meshgrid(x,y);
z = data[:3];
plt.contour(x,y,z)
plt.show()
The CSV looks like this
2015-10-28T09:25:12.800Z,51.56325,17.529043333,4.6484375,19.8046875
2015-10-28T09:25:12.900Z,51.56325,17.529041667,4.4921875,19.6875
2015-10-28T09:25:13.000Z,51.563248333,17.529041667,4.453125,19.9609375
2015-10-28T09:25:13.200Z,51.563245,17.529041667,4.140625,19.765625
2015-10-28T09:25:13.300Z,51.563243333,17.529041667,4.609375,19.6484375
2015-10-28T09:25:13.500Z,51.563241667,17.529041667,4.609375,19.53125
2015-10-28T09:25:13.600Z,51.56324,17.529041667,4.4921875,19.375
2015-10-28T09:25:13.700Z,51.563238333,17.529041667,4.4140625,19.765625
2015-10-28T09:25:13.800Z,51.563236667,17.529041667,4.453125,20.234375
2015-10-28T09:25:13.900Z,51.563235,17.529041667,4.3359375,19.84375
2015-10-28T09:25:14.000Z,51.563233333,17.529041667,4.53125,19.453125
2015-10-28T09:25:14.100Z,51.563231667,17.529041667,4.53125,19.8046875
2015-10-28T09:25:14.200Z,51.563228333,17.529041667,4.1796875,19.4921875
2015-10-28T09:25:14.300Z,51.563226667,17.529041667,4.2578125,19.453125
2015-10-28T09:25:14.400Z,51.563225,17.529041667,4.4921875,19.4921875
2015-10-28T09:25:14.500Z,51.563223333,17.529041667,4.375,19.453125
2015-10-28T09:25:14.600Z,51.563221667,17.529041667,4.609375,18.90625
2015-10-28T09:25:14.700Z,51.563218333,17.529041667,4.53125,19.6875
2015-10-28T09:25:14.900Z,51.563215,17.529041667,4.140625,18.75
2015-10-28T09:25:15.000Z,51.563213333,17.529041667,4.453125,18.828125
Column 1 - Latitude
Column 2 - Longitude
Column 3 - Value
For contour lines I need also limits - for example (4.1,4.3,4.6)
I guess there is some mistake in your code (according to your data you shouldn't do x = data[:1] but more x = data[..., 1]).
With your of data, the basic steps I will follow to interpolate the z value and fetch an output as a geojson would require at least the shapely module (and here geopandas is used for the convenience).
import numpy as np
import matplotlib.pyplot as plt
from geopandas import GeoDataFrame
from matplotlib.mlab import griddata
from shapely.geometry import Polygon, MultiPolygon
def collec_to_gdf(collec_poly):
"""Transform a `matplotlib.contour.QuadContourSet` to a GeoDataFrame"""
polygons, colors = [], []
for i, polygon in enumerate(collec_poly.collections):
mpoly = []
for path in polygon.get_paths():
try:
path.should_simplify = False
poly = path.to_polygons()
# Each polygon should contain an exterior ring + maybe hole(s):
exterior, holes = [], []
if len(poly) > 0 and len(poly[0]) > 3:
# The first of the list is the exterior ring :
exterior = poly[0]
# Other(s) are hole(s):
if len(poly) > 1:
holes = [h for h in poly[1:] if len(h) > 3]
mpoly.append(Polygon(exterior, holes))
except:
print('Warning: Geometry error when making polygon #{}'
.format(i))
if len(mpoly) > 1:
mpoly = MultiPolygon(mpoly)
polygons.append(mpoly)
colors.append(polygon.get_facecolor().tolist()[0])
elif len(mpoly) == 1:
polygons.append(mpoly[0])
colors.append(polygon.get_facecolor().tolist()[0])
return GeoDataFrame(
geometry=polygons,
data={'RGBA': colors},
crs={'init': 'epsg:4326'})
data = np.genfromtxt('temp.csv', delimiter=',')
x = data[..., 1]
y = data[..., 2]
z = data[..., 3]
xi = np.linspace(x.min(), x.max(), 200)
yi = np.linspace(y.min(), y.max(), 200)
zi = griddata(x, y, z, xi, yi, interp='linear') # You could also take a look to scipy.interpolate.griddata
nb_class = 15 # Set the number of class for contour creation
# The class can also be defined by their limits like [0, 122, 333]
collec_poly = plt.contourf(
xi, yi, zi, nb_class, vmax=abs(zi).max(), vmin=-abs(zi).max())
gdf = collec_to_gdf(collec_poly)
gdf.to_json()
# Output your collection of features as a GeoJSON:
# '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[51.563214073747474,
# (...)
Edit:
You can grab the colors values (as an array of 4 values in range 0-1, representing RGBA values) used by matplotplib by fetching them on each item of the collection with the get_facecolor() method (and then use them to populate one (or 4) column of your GeoDataFrame :
colors = [p.get_facecolor().tolist()[0] for p in collec_poly.collections]
gdf['RGBA'] = colors
Once you have you GeoDataFrame you can easily get it intersected with your boundaries. Use these boundaries to make a Polygon with shapely and compute the intersection with your result:
mask = Polygon([(51,17), (51,18), (52,18), (52,17), (51,17)])
gdf.geometry = gdf.geometry.intersection(mask)
Or read your geojson as a GeoDataFrame:
from shapely.ops import unary_union, polygonize
boundary = GeoDataFrame.from_file('your_geojson')
# Assuming you have a boundary as linestring, transform it to a Polygon:
mask_geom = unary_union([i for i in polygonize(boundary.geometry)])
# Then compute the intersection:
gdf.geometry = gdf.geometry.intersection(mask_geom)
Almost I got what I excepted. Based on mgc answer. I change griddata as you suggest to scipy.interpolate.griddata and interpolation method to nearest. Now its works like I want.
Only last thing that I need is to limit this interpolation to polygon (boundaries) also from geoJson.
The other problem is to export to geojson polygons WITH colors as attributes.
import numpy as np
import matplotlib.pyplot as plt
#from matplotlib.mlab import griddata
from scipy.interpolate import griddata
from geopandas import GeoDataFrame
from shapely.geometry import Polygon, MultiPolygon
def collec_to_gdf(collec_poly):
"""Transform a `matplotlib.contour.QuadContourSet` to a GeoDataFrame"""
polygons = []
for i, polygon in enumerate(collec_poly.collections):
mpoly = []
for path in polygon.get_paths():
try:
path.should_simplify = False
poly = path.to_polygons()
# Each polygon should contain an exterior ring + maybe hole(s):
exterior, holes = [], []
if len(poly) > 0 and len(poly[0]) > 3:
# The first of the list is the exterior ring :
exterior = poly[0]
# Other(s) are hole(s):
if len(poly) > 1:
holes = [h for h in poly[1:] if len(h) > 3]
mpoly.append(Polygon(exterior, holes))
except:
print('Warning: Geometry error when making polygon #{}'
.format(i))
if len(mpoly) > 1:
mpoly = MultiPolygon(mpoly)
polygons.append(mpoly)
elif len(mpoly) == 1:
polygons.append(mpoly[0])
return GeoDataFrame(geometry=polygons, crs={'init': 'epsg:4326'})
data = np.genfromtxt('temp2.csv', delimiter=',')
x = data[..., 1]
y = data[..., 2]
z = data[..., 4]
xi = np.linspace(x.min(), x.max(), num=100)
yi = np.linspace(y.min(), y.max(), num=100)
#zi = griddata(x, y, z, xi, yi, interp='nn') # You could also take a look to scipy.interpolate.griddata
zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='nearest')
nb_class = [5,10,15,20,25,30,35,40,45,50] # Set the number of class for contour creation
# The class can also be defined by their limits like [0, 122, 333]
collec_poly = plt.contourf(
xi, yi, zi, nb_class, vmax=abs(zi).max(), vmin=-abs(zi).max())
gdf = collec_to_gdf(collec_poly)
#gdf.to_json()
print gdf.to_json()
plt.plot(x,y)
plt.show()
I am drawing a map using basemap from matplotlib. The data are spreaded all over the world, but I just want to retain all the data on the continent and drop those on the ocean. Is there a way that I can filter the data, or is there a way to draw the ocean again to cover the data?
There's method in matplotlib.basemap: is_land(xpt, ypt)
It returns True if the given x,y point (in projection coordinates) is over land, False otherwise. The definition of land is based upon the GSHHS coastline polygons associated with the class instance. Points over lakes inside land regions are not counted as land points.
For more information, see here.
is_land() will loop all the polygons to check whether it's land or not. For large data size, it's very slow. You can use points_inside_poly() from matplotlib to check an array of points quickly. Here is the code. It doesn't check lakepolygons, if you want remove points in lakes, you can add your self.
It took 2.7 seconds to check 100000 points on my PC. If you want more speed, you can convert the polygons into a bitmap, but it's a little difficult to do this. Please tell me if the following code is not fast enought for your dataset.
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.nxutils as nx
def points_in_polys(points, polys):
result = []
for poly in polys:
mask = nx.points_inside_poly(points, poly)
result.extend(points[mask])
points = points[~mask]
return np.array(result)
points = np.random.randint(0, 90, size=(100000, 2))
m = Basemap(projection='moll',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
x, y = m(points[:,0], points[:,1])
loc = np.c_[x, y]
polys = [p.boundary for p in m.landpolygons]
land_loc = points_in_polys(loc, polys)
m.plot(land_loc[:, 0], land_loc[:, 1],'ro')
plt.show()
The HYRY's answer won't work on new versions of matplotlib (nxutils is deprecated). I've made a new version that works:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.path import Path
import numpy as np
map = Basemap(projection='cyl', resolution='c')
lons = [0., 0., 16., 76.]
lats = [0., 41., 19., 51.]
x, y = map(lons, lats)
locations = np.c_[x, y]
polygons = [Path(p.boundary) for p in map.landpolygons]
result = np.zeros(len(locations), dtype=bool)
for polygon in polygons:
result += np.array(polygon.contains_points(locations))
print result
The simplest way is to use basemap's maskoceans.
If for each lat, lon you have a data and you want to
use contours:
After meshgrid and interpolation:
from scipy.interpolate import griddata as gd
from mpl_toolkits.basemap import Basemap, cm, maskoceans
xi, yi = np.meshgrid(xi, yi)
zi = gd((mlon, mlat),
scores,
(xi, yi),
method=grid_interpolation_method)
#mask points on ocean
data = maskoceans(xi, yi, zi)
con = m.contourf(xi, yi, data, cmap=cm.GMT_red2green)
#note instead of zi we have data now.
Update (much faster than in_land or in_polygon solutions):
If for each lat, lon you don't have any data, and you just want to scatter the points only over land:
x, y = m(lons, lats)
samples = len(lons)
ocean = maskoceans(lons, lats, datain=np.arange(samples),
resolution='i')
ocean_samples = np.ma.count_masked(ocean)
print('{0} of {1} points in ocean'.format(ocean_samples, samples))
m.scatter(x[~ocean.mask], y[~ocean.mask], marker='.', color=colors[~ocean.mask], s=1)
m.drawcountries()
m.drawcoastlines(linewidth=0.7)
plt.savefig('a.png')
I was answering this question, when I was told that it would be better to post my answer over here. Basically, my solution extracts the polygons that are used to draw the coastlines of the Basemap instance and combines these polygons with the outline of the map to produce a matplotlib.PathPatch that overlays the ocean areas of the map.
This especially useful if the data is coarse and interpolation of the data is not wanted. In this case using maskoceans produces a very grainy outline of the coastlines, which does not look very good.
Here is the same example I posted as answer for the other question:
from matplotlib import pyplot as plt
from mpl_toolkits import basemap as bm
from matplotlib import colors
import numpy as np
import numpy.ma as ma
from matplotlib.patches import Path, PathPatch
fig, ax = plt.subplots()
lon_0 = 319
lat_0 = 72
##some fake data
lons = np.linspace(lon_0-60,lon_0+60,10)
lats = np.linspace(lat_0-15,lat_0+15,5)
lon, lat = np.meshgrid(lons,lats)
TOPO = np.sin(np.pi*lon/180)*np.exp(lat/90)
m = bm.Basemap(resolution='i',projection='laea', width=1500000, height=2900000, lat_ts=60, lat_0=lat_0, lon_0=lon_0, ax = ax)
m.drawcoastlines(linewidth=0.5)
x,y = m(lon,lat)
pcol = ax.pcolormesh(x,y,TOPO)
##getting the limits of the map:
x0,x1 = ax.get_xlim()
y0,y1 = ax.get_ylim()
map_edges = np.array([[x0,y0],[x1,y0],[x1,y1],[x0,y1]])
##getting all polygons used to draw the coastlines of the map
polys = [p.boundary for p in m.landpolygons]
##combining with map edges
polys = [map_edges]+polys[:]
##creating a PathPatch
codes = [
[Path.MOVETO] + [Path.LINETO for p in p[1:]]
for p in polys
]
polys_lin = [v for p in polys for v in p]
codes_lin = [c for cs in codes for c in cs]
path = Path(polys_lin, codes_lin)
patch = PathPatch(path,facecolor='white', lw=0)
##masking the data:
ax.add_patch(patch)
plt.show()
This produces the following plot:
Hope this is helpful to someone :)