由于我正在使用 function，因此如何将浮点数转换为字符串

Question

I'm practicing python and I've encountered an error with string to float error. My intention is to give a function as an input, where I can put ^ as an indicative of **. Then, the 3d graph, related to the function, will be shown. I've searched for the string to float error, but since my function need to have special characters, the solutions didn't work for my case.

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import random

funciao = []
func = input("Enter your function: ")
function = func.replace('^','**')

def fun(x,y):
    return function

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-3.0, 3.0, 0.05)
X, Y = np.meshgrid(x, y)
zs = np.array([fun(x,y) for x,y in zip(np.ravel(X), np.ravel(Y))])
Z = zs.reshape(X.shape)

ax.plot_surface(X, Y, Z)

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

Answer 1

This works:

import sympy
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import random

funciao = []
func = input("Enter your function: ")
function = func.replace('^', '**')


def fun(x, y):
    in_dict = {
        "x": x,
        "y": y
    }
    subs = {sympy.symbols(key): item for key, item in in_dict.items()}
    ans = sympy.simplify(function).evalf(subs=subs)

    return str(ans)


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-3.0, 3.0, 0.05)
X, Y = np.meshgrid(x, y)
zs = np.array([fun(x, y) for x, y in zip(np.ravel(X), np.ravel(Y))])
Z = zs.reshape(X.shape)

ax.plot_surface(X, Y, Z)

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

Now fun() will actually return the correct numerical string values, rather than function which is just the string you entered for the function. It does this by subbing in x and y into the formula using sympy .

Note: You will need to install sympy , and the math for this in high definition will be very slow. So be prepared to wait a while for a graph to appear.

Consider changing this line x = y = np.arange(-3.0, 3.0, 0.01) to x = y = np.arange(-3.0, 3.0, 0.5) . Which will generate the data much quicker (resolution from 0.01 to 0.5 ).

---

You can also define zs in a tqdm loop to see the progress bar:

from tqdm import tqdm

# ...

zs = []
for x, y in tqdm(zip(np.ravel(X), np.ravel(Y)), total=len(np.ravel(X)),desc="Calculating data"):
    zs.append(fun(x, y))
zs = np.array(zs)

# ...