plot multivariate function based on max

Question

I would like to create a multivariate function that takes the max value of 2 functions and then to plot it. However by using the max function there is an error when applying the function on the meshgrid. I have tried this on other multivariate function without the max function and it worked.

import numpy as np
import pandas as pd
import plotly.graph_objects as go

def f(x,y):
    return max(np.cos(x),np.sin(y))

x=np.linspace(0,5,20)
y=np.linspace(-3,2,20)
X, Y = np.meshgrid(x, y)
Z=f(X,Y)

fig = go.Figure(data=[go.Surface(x=X, y=Y, z=Z)])
fig.show()

The error I get is : The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(). However, I don’t think that the suggestion is adapted to my case. I also tried by defining the max function with if statement but as I expected I get the same error. Does anyone could help?

Asked By: alice

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Source

Answer 1

The np.sin and np.cos functions work with arrays and the max function would produce an ambiguous answer (do you want the maximum of both functions or a comparison – numpy doesn’t know). I recommend doing the built-in math.sin, math.cos on each of the values in the arrays and compare to get the desired max value .

def f(x,y):
    max_values = []
    for x_value, y_value in zip(x,y): #(iterate both together)
        max_values.append(max(math.cos(x_value), math.sin(y_value)))

This may run slower than before, but does this help?

Answered By: StonedTensor

Answer 2

try using ax.scatter3D with

from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt

x=np.linspace(0,5,20)
y=np.linspace(-3,2,20)

def f(x,y):
    max_values = []
    for x_value, y_value in zip(x,y): #(iterate both together)
        max_values.append(max(math.cos(x_value), math.sin(y_value)))

X, Y = np.meshgrid(x, y)

max_values = []
for x_value, y_value in zip(X,Y):
    Z=zip([math.cos(item) for item in x_value],[math.sin(item) for item in y_value])
    max_values.append([max(x,y) for x,y in Z])
        

fig = plt.figure()
ax = plt.axes(projection="3d")

ax.scatter3D(X, Y,max_values, c=max_values, cmap='Greens');
plt.show()

Answered By: Golden Lion

Answer 3

I have found a very simple alternative without using np.meshgrid, and put it here in case it could help someone later.

from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt

x=np.linspace(0,5,20)
y=np.linspace(-3,2,20)

S=np.zeros([x.shape[0],y.shape[0]])
for i in range(x.shape[0]):
    for j in range(y.shape[0]):
        S[i,j]=f(x[i],y[j])
        
fig = go.Figure(data=[go.Surface(x=x, y=y, z=S, colorscale='Temps')])    
fig.show()

Answered By: alice

plot multivariate function based on max

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