I am trying to create a surface plot of a mountain in python, of which I have some xyz data. The end result should look something like that. The file is formatted as follows:
616000.0 90500.0 3096.712
616000.0 90525.0 3123.415
616000.0 90550.0 3158.902
616000.0 90575.0 3182.109
616000.0 90600.0 3192.991
616025.0 90500.0 3082.684
616025.0 90525.0 3116.597
616025.0 90550.0 3149.812
616025.0 90575.0 3177.607
616025.0 90600.0 3191.986
and so on. The first column represents the x
coordinate, the middle one the y
coordinate, and z
the altitude that belongs to the xy coordinate.
I read in the data using pandas
and then convert the columns to individual x
, y
, z
NumPy
1D arrays. So far I managed to create a simple 3D scatter plot with a for
loop iterating over each index of each 1D array, but that takes ages and makes the appearance of being quite inefficient.
I've tried to work with scipy.interpolate.griddata
and plt.plot_surface
, but for z
data I always get the error that data should be in a 2D array, but I cannot figure out why or how it should be 2D data. I assume that given I have xyz data, there should be a way to simply create a surface from it. Is there a simple way?
We could plot 3D surfaces in Python too, the function to plot the 3D surfaces is plot_surface(X,Y,Z), where X and Y are the output arrays from meshgrid, and Z=f(X,Y) or Z(i,j)=f(X(i,j),Y(i,j)). The most common surface plotting functions are surf and contour. TRY IT!
Using subplots() method, create a figure and a set of subplots. Plot [1, 2, 3, 4, 5] data points on the left Y-axis scales. Using twinx() method, create a twin of Axes with a shared X-axis but independent Y-axis, ax2. Plot [11, 12, 31, 41, 15] data points on the right Y-axis scale, with blue color.
Using functions plot_trisurf
and scatter
from matplotlib
, given X Y Z
data can be plotted similar to given plot.
import sys
import csv
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
# Read CSV
csvFileName = sys.argv[1]
csvData = []
with open(csvFileName, 'r') as csvFile:
csvReader = csv.reader(csvFile, delimiter=' ')
for csvRow in csvReader:
csvData.append(csvRow)
# Get X, Y, Z
csvData = np.array(csvData)
csvData = csvData.astype(np.float)
X, Y, Z = csvData[:,0], csvData[:,1], csvData[:,2]
# Plot X,Y,Z
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(X, Y, Z, color='white', edgecolors='grey', alpha=0.5)
ax.scatter(X, Y, Z, c='red')
plt.show()
Here,
X Y Z
data provided as argument to above scriptplot_trisurf
, parameters used to control appearance. e.g. alpha
used to control opacity of surfacescatter
, c
parameter specifies color of points plotted on surfaceFor given data file, following plot is generated
Note: Here, the terrain is formed by triangulation of given set of 3D points. Hence, contours along surface in plot are not aligned to X- and Y- axes
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import pandas as pd
df = pd.read_csv("/content/1.csv")
X = df.iloc[:, 0]
Y = df.iloc[:, 1]
Z = df.iloc[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(X, Y, Z, color='white', edgecolors='grey', alpha=0.5)
ax.scatter(X, Y, Z, c='red')
plt.show()
My output image below - I had a lot of data points: enter image description here
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