By contouring, we can represent earth’s 3D landscape as a 2D map. It only takes 3 variables which are X, Y, and Z. Not only elevation data, we can create a 2D map of geospatial datasets from those 3 variables.
Most earth scientists and geographers rely on GIS to generate any kind of contouring-based map (GIS software is expensive, mostly). Contouring in Python usually requires extensive programming experience and It was an unpleasant experience for me (@_@). However I found a simple way to solve this unpleasant experience. Let’s start!
IMPORT
#libraryimport numpy as np
import pandas as pd
from numpy import linspace, meshgrid
from scipy.interpolate import griddata
import plotly.graph_objects as go
After importing the required library , we should create a set of dummy data.
#Dummy Datax = [2,3,4,6,7,8,9,10]
y = [2,4,2,5,3,2,5,4]
z = [100,80,90,70,70,60,40,40]
Let’s take a look at the data
fig = go.Figure()
fig.update_layout(autosize=False)
fig.add_Trace(go.Scatter(x=x, y=y, text=z, mode='markers+text'))
From these data, we can directly create a 2D contouring map in Plotly
fig = go.Figure()
fig.add_trace(go.Contour(x=x,y=y,z=z,line_smoothing=1.3))
fig.update_layout(autosize=False)
fig.add_trace(go.Scatter(x=y,=y=y,text=z,mode='markers+text'))
It was a nice map. The next step we will try to use gridded value of Z by using scipy.interpolate to interpolate the z data.
xi = linspace(min(x),max(x),200)
yi = linspace(min(y),max(y),200)
X,Y = meshgrid(xi,yi)
Z = griddata((x,y),z,(X,Y), method='cubic')fig = go.Figure()
fig.add_trace(go.Contour(z=Z, x=xi, y=yi, line_smoothing=1.3))
fig.add_trace(go.Scatter(x=x,y=y, mode='markers+text', text=z))
fig.update_layout(autosize=False)
the advantage of gridded data is that we can get the interpolated XY coordinate when hovering the mouse pointer.
BONUS
fig = go.Figure()
fig.add_trace(go.Surface(z=Z, x=xi, y=yi))
fig.update_layout(autosize=False)
Cheers!