It is possible?

[mtorressa]

Good afternoon, I am developing a code to optimize an investment portfolio, but I need to set some limits for the investment of certain assets. I wanted to ask if the above is possible?
For the above I need to create a list with weights.
For example, a portfolio of 3 assets A, B and C, with the following restrictions, asset A cannot represent more than 30% nor less than 10%, asset B cannot represent more than 80% nor less than 50% and asset C a maximum of 20%.
Is there a way to generate a list of weights that meets these restrictions? Thanks in advance.
The code I am using is the following, but it generates weights between 1% and 100%.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
retornos = pd.read_excel(r"C:\Users\marce\Desktop\Magister en Finanzas\Mercado de Capitales\PythonSA.xlsx", sheet_name="Hoja1")
symbols = ["IPSA", "MSCI world", "PIMCO CORP"]
noa = len(symbols)
rets= retornos
#print(rets.corr())
weights = np.random.random(noa)
weights /= np.sum(weights)
def port_ret(weights):
    return np.sum(rets.mean() * weights) * 12
def port_vol(weights):
    return np.sqrt(np.dot(weights.T, np.dot(rets.cov() * 12, weights)))
prets = []
pvols = []
for p in range (5000):
    weights = np.random.random(noa)
    weights /= np.sum(weights)
    prets.append(port_ret(weights))
    pvols.append(port_vol(weights))
prets = np.array(prets)
pvols = np.array(pvols)
plt.figure(figsize=(10, 6))
plt.scatter(pvols, prets, c=prets / pvols,marker='o', cmap='coolwarm')
plt.xlabel('expected volatility')
plt.ylabel('expected return')
plt.colorbar(label='Sharpe ratio')