#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook


# In[7]:


b = 0.8 #　相関項
x = 10.0
y = 3.0
times = 200

output_notebook()
p = figure()

for i in range(1, times):
    x_old = x
    x = np.random.normal(b*y, 1.0)
    p.line([x_old, x], [y, y], color="blue",line_width=2)
    y_old = y
    y = np.random.normal(b*x, 1.0)
    p.line([x, x], [y_old, y], color="blue", line_width=2)

show(p)


# In[7]:


#  ボックスミューラー法による実装．

b = 0.8 # 相関項
x = 10.0
y = 3.0
times = 150

output_notebook()
p = figure()

for i in range(1, times):
    u1 = np.random.rand()
    u2 = np.random.rand()
    x_old = x
    x = np.sqrt(-2*np.log(u1))*np.cos(2*np.pi*u2) + b*y
    p.line([x_old, x], [y, y], color="blue",line_width=2)
    y_old = y
    y = np.sqrt(-2*np.log(u1))*np.sin(2*np.pi*u2)  + b*x
    p.line([x, x], [y_old, y], color="blue", line_width=2)

show(p)


# In[20]:


p = figure()
p.line([5, 4],[4,4])
show(p)


# In[5]:


p = figure()
imax=400
for i in range(1, imax):
    u1 = np.random.rand()
    u2 = np.random.rand()
    
    p.scatter(u1, u2)
show(p)


# In[8]:


np.random.rand()


# In[23]:


y=2
y_old =1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - b*y)**2 / (2 * sigma**2))
y_old


# In[17]:


p = figure()
mu, sigma = 0, 0.1
s = np.random.normal(mu, sigma, 100)
p.scatter(s, s)
show(p)


# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

#  ボックスミューラー法による実装．

b = 0.8 # 相関項
x = 10.0
y = 3.0
times = 200
im = []
ims = []

fig = plt.figure()

for i in range(1, times):
    u1 = np.random.rand()
    u2 = np.random.rand()
    x_old = x
    x = np.sqrt(-2*np.log(u1))*np.cos(2*np.pi*u2) + b*y
    im = im + plt.plot([x_old, x], [y, y], color="blue")
    y_old = y
    y = np.sqrt(-2*np.log(u1))*np.sin(2*np.pi*u2)  + b*x
    im = im + plt.plot([x, x], [y_old, y], color="blue")
    ims.append(im)

ani = animation.ArtistAnimation(fig, ims, interval=400)
ani.save("output.gif", writer="imagemagick")
plt.show()


# In[3]:


import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()
xdata, ydata = [], []
ln, = plt.plot([], [], 'ro', animated=True)

def init():
    ax.set_xlim(0, 2*np.pi)
    ax.set_ylim(-1, 1)
    return ln,

def update(frame):
    xdata.append(frame)
    ydata.append(np.sin(frame))
    ln.set_data(xdata, ydata)
    return ln,

ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
                    init_func=init)
plt.show()


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