import numpy as np

beta = 1.41 #Yahoo finance
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.mlab as mlab
from scipy.stats import multivariate_normal

def convert(a, b):
    current_stock_price = 27.96*beta*(1+a)
    #convert that market into a enterprise value
    eps = current_stock_price / b
    return eps

total = []
for i in range(10000):
    # draw a random chance market
    a = np.random.normal(loc=.05, scale=.21)
    b = np.random.normal(loc=17.51, scale = 2.44)
    total.append(convert(a,b))


# fig = plt.figure()

# n, bins, patches = plt.hist(total, bins=20, normed=1, facecolor='green', alpha=0.5)

# mu, sigma = np.mean(total), np.std(total)
# y = mlab.normpdf(bins, mu, sigma)

# plt.plot(bins, y, 'r--')
# plt.ylabel('Probability')
# plt.xlabel('Earnings Per Share')
# plt.title('Monte Carlo Prediction of Earnings Per Share')
# info = "Mean: " + "%.2f"%mu +" | Standard Deviaiton: " +  "%.2f"% sigma
# plt.text(4, .65, info)

# #  Tweak spacing to prevent clipping of ylabel
# plt.subplots_adjust(left=0.15)
# plt.show()

x = np.linspace(-1,1,500)
y = np.linspace(0,32,500)
X, Y = np.meshgrid(x,y)
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X; pos[:, :, 1] = Y

mu = np.array([.05, 17.51])
sigma = np.array([.21, 2.44])
covariance = np.diag(sigma**2)
z = multivariate_normal(mean=mu, cov=covariance).pdf(pos) * 17.51

#Make a 3D plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, z,cmap='viridis',linewidth=0)
ax.set_xlabel('Projected Market Increase')
ax.set_ylabel('Projected Price/Earning Ratio')
ax.set_zlabel('Projected Earnings Per Share')
plt.show()