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

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from IPython.display import Latex
Latex(r"$\sqrt{x^2+y^2}$")


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from IPython.external.mathjax import install_mathjax
install_mathjax()


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get_ipython().run_line_magic('reload_ext', 'sympyprinting')
#from sympy import *
#import sympy
from sympy import init_printing
init_printing()
x, y = symbols("x,y")
sqrt(x**2+y**2)

from IPython.display import Image
Image(filename="python.png")
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import cv2
import numpy as np
from IPython.display import Image
img = np.random.randint(0,255,(250,250,3))
cv2.blur(img, (11,11), img)
r, dat = cv2.imencode(".png",img)
Image(dat.tostring())


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from IPython.display import Javascript
Javascript("alert('JavaScript call, ok!客户端调用')")


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get_ipython().run_line_magic('timeit', '1 + 1')
get_ipython().run_line_magic('timeit', '1.0 + 1.0')
get_ipython().run_line_magic('timeit', '"1" + "1"')

#10000000 loops, best of 3: 52 ns per loop
#10000000 loops, best of 3: 53.4 ns per loop
#10000000 loops, best of 3: 50.9 ns per loop

#%%timeit
s = 0
for i in range(100):
    s += i

#100000 loops, best of 3: 11 us per loop


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get_ipython().run_line_magic('pylab', 'inline')
plot(random.randn(100));

%load http://matplotlib.org/mpl_examples/pylab_examples/histogram_demo.py

#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma*np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)

# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r--', linewidth=1)

plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)

plt.show()
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get_ipython().run_cell_magic('prun', '', 'for i in range(100):\n    linalg.det(random.rand(10,10))\n')


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