Test point with to / from vector shapefile

Question

Here is my question.

1. Introduction

• shapefile in the polygon represents the study area

http://i8.tietuku.com/08fdccbb7e11c0a9.png

• some point located in the entire map of the rectangle

http://i8.tietuku.com/877f87022bf817b8.png

I want to check if each point was inside / outside the polygon and performed some further operation (for example, sum the sum of the grid point within the study area)

2. My idea

I have two methods, thanks to stack overflow information.

2.1 Idea A

Rasterize the shapefile to a raster file, and then check.

I have not done this yet, but I asked one question here and got an answer.

2.2 Idea B

I tried to use the poly.contain()scatter point to check the location, but the result was not true.

3. My code based on Idea B:

For example:

• The source data is pt (a pandas Dataframe), which contains 1000 X, Y grids.
• shapefile I already showed the area of ​​study, I want to filter the source data, leaving only a point in this area.

3.1 Preparation

# map four boundaries
xc1,xc2,yc1,yc2 = 113.49805889531724,115.5030664238035,37.39995194888143,38.789235929357105
# grid definition
lon_grid  = np.linspace(x_map1,x_map2,38)
lat_grid  = np.linspace(y_map1,y_map2,32)

3.1 Preparation

# generate (lon,lat)   
xx = lon_grid[pt.X.iloc[:].as_matrix()]
yy = lat_grid[pt.Y.iloc[:].as_matrix()]

sh = (len(xx),2)
data = np.zeros(len(xx)*2).reshape(*sh)
for i in range(0,len(xx),1):
    data[i] = np.array([xx[i],yy[i]])

# reading the shapefile

map = Basemap(llcrnrlon=x_map1,llcrnrlat=y_map1,urcrnrlon=x_map2,\
              urcrnrlat=y_map2)
map.readshapefile('/xx,'xx')

3.2 Test

patches=[]
for info, shape in zip(map.xxx_info, map.xxx):
    x,y=zip(*shape)
    patches.append(Polygon(np.array(shape), True) )
for poly in patches:
     mask = np.array([poly.contains_point(xy) for xy in data])

• Then I have a numpy array with a value of 0.1 representing inside / outside.
• Combine the mask in pt ==> pt = pt [[pt.mask == 1]], I can filter the points

But the problem is the use poly,contains_point(xy), I could not get the results consistent with my attempt.

Example of my idea 2

sum the value 0.1:

unique, counts = np.unique(mask, return_counts=True)      
print np.asarray((unique, counts)).T
#result:  
> [[0 7]  
  [1 3]]

http://i4.tietuku.com/7d156db62c564a30.png

3 , .

40

http://i4.tietuku.com/5fc12514265b5a50.png

4.

, .
, :

• polygon ( , , , ).
• poly.contains_point(xy) .

2016-01-16

, , , . ., .

c =    fiona.open("xxx.shp")
pol = c.next()
geom = shape(pol['geometry'])
poly_data = pol["geometry"]["coordinates"][0]
poly = Polygon(poly_data)
ax.add_patch(plt.Polygon(poly_data))

xx = lon_grid[pt_select.X.iloc[:].as_matrix()]
yy = lat_grid[pt_select.Y.iloc[:].as_matrix()]

sh = (len(xx),2)
points = np.zeros(len(xx)*2).reshape(*sh)
for i in range(0,len(xx),1):
    points[i] = np.array([xx[i],yy[i]])
mask = np.array([poly.contains(Point(x, y)) for x, y in points])

ax.plot(points[:, 0], points[:, 1], "rx")
ax.plot(points[mask, 0], points[mask, 1], "ro")    

http://i4.tietuku.com/8d895efd3d9d29ff.png

Answer 1

:

import numpy as np
from shapely.geometry import Polygon, Point

poly_data = [[0, 0], [0, 1], [1, 0], [0.2, 0.5]]
poly = Polygon(poly_data)

points = np.random.rand(100, 2)

mask = np.array([poly.contains(Point(x, y)) for x, y in points])

:

import pylab pl

fig, ax = pl.subplots()
ax.add_patch(pl.Polygon(poly_data))
ax.plot(points[:, 0], points[:, 1], "rx")
ax.plot(points[mask, 0], points[mask, 1], "ro")

:

MultiPoint :

from shapely.geometry import Polygon, MultiPoint

poly_data = [[0, 0], [0, 1], [1, 0], [0.2, 0.5]]
poly = Polygon(poly_data)
points = np.random.rand(100, 2)
inside_points = np.array(MultiPoint(points).intersection(poly))

Polygon.contains_point() matplotlib:

poly = pl.Polygon(poly_data)
mask = [poly.contains_point(p) for p in points]