How to obtain the contour plot data for each scatter points? - python-2.7

Question

I have plotted a contour plot as background which represent the altitude of the area.
And 100 scatter points were set represent the real pollutant emission source. Is there a method to obtain the altitude of each point?
This is my code:
%matplotlib inline
fig=plt.figure(figsize=(16,16))
ax=plt.subplot()
xi,yi = np.linspace(195.2260,391.2260,50),
np.linspace(4108.9341,4304.9341,50)
height=np.array(list(csv.reader(open("/Users/HYF/Documents/SJZ_vis/Concentration/work/terr_grd.csv","rb"),delimiter=','))).astype('float')
cmap = cm.get_cmap(name='terrain', lut=None)
terrf = plt.contourf(xi, yi, height,100, cmap=cmap)
terr = plt.contour(xi, yi, height, 100,
colors='k',alpha=0.5
)
plt.clabel(terr, fontsize=7, inline=20)
ax.autoscale(False)
point= plt.scatter(dat_so2["xp"], dat_so2["yp"], marker='o',c="grey",s=40)
ax.autoscale(False)
for i in range(0,len(dat_so2["xp"]),1):
plt.text(dat_so2["xp"][i], dat_so2["yp"][i],
str(i),color="White",fontsize=16)
ax.set_xlim(225,275)
ax.set_ylim(4200,4260)
plt.show()

Answer 1

You can do this with scipy.interpolate.interp2d
For example, you could add to your code:
from scipy import interpolate
hfunc = interpolate.interp2d(xi,yi,height)
pointheights = np.zeros(dat_so2["xp"].shape)
for i,(x,y) in enumerate(zip(dat_so2["xp"],dat_so2["yp"])):
pointheights[i]=hfunc(x,y)
Putting this together with the rest of your script, and some sample data, gives this (I've simplified a couple of things here, but you get the idea):
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
from scipy import interpolate
fig=plt.figure(figsize=(8,8))
ax=plt.subplot()
#xi,yi = np.linspace(195.2260,391.2260,50),np.linspace(4108.9341,4304.9341,50)
xi,yi = np.linspace(225,275,50),np.linspace(4200,4260,50)
# A made up function of height (in place of your data)
XI,YI = np.meshgrid(xi,yi)
height = (XI-230.)**2 + (YI-4220.)**2
#height=np.array(list(csv.reader(open("/Users/HYF/Documents/SJZ_vis/Concentration/work/terr_grd.csv","rb"),delimiter=','))).astype('float')
cmap = cm.get_cmap(name='terrain', lut=None)
terrf = plt.contourf(xi, yi, height,10, cmap=cmap)
terr = plt.contour(xi, yi, height, 10,
colors='k',alpha=0.5
)
plt.clabel(terr, fontsize=7, inline=20)
ax.autoscale(False)
# Some made up sample points
dat_so2 = np.array([(230,4210),(240,4220),(250,4230),(260,4240),(270,4250)],dtype=[("xp","f4"),("yp","f4")])
point= plt.scatter(dat_so2["xp"], dat_so2["yp"], marker='o',c="grey",s=40)
# The interpolation function
hfunc = interpolate.interp2d(xi,yi,height)
# Now, for each point, lets interpolate the height
pointheights = np.zeros(dat_so2["xp"].shape)
for i,(x,y) in enumerate(zip(dat_so2["xp"],dat_so2["yp"])):
pointheights[i]=hfunc(x,y)
print pointheights
ax.autoscale(False)
for i in range(0,len(dat_so2["xp"]),1):
plt.text(dat_so2["xp"][i], dat_so2["yp"][i],
str(i),color="White",fontsize=16)
# We can also add a height label to the plot
plt.text(dat_so2["xp"][i], dat_so2["yp"][i],
"{:4.1f}".format(pointheights[i]),color="black",fontsize=16,ha='right',va='top')
ax.set_xlim(225,275)
ax.set_ylim(4200,4260)
plt.show()