#%%

%matplotlib inline

import matplotlib.pyplot as plt

import matplotlib as mpl

import numpy as np

x = np.linspace(0, 20, 100)

plt.plot(x, np.sin(x))

plt.show()

# In[]

import sys

print(sys.version)

print(sys.executable)

print(2)

import time

for x in range(0, 3):

print "We're on time %d" % (x)

print(x)

print('1')

# In[]

print('start')

for x in range(0, 3):

print(x)

print('end')

#%%

plt.plot([1, 2, 3, 2, 3, 2, 2, 1])

plt.show()

#%%

import matplotlib.pyplot as plt

import numpy as np

import mpld3

mpld3.enable_notebook()

fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))

ax.grid(color='white', linestyle='solid')

N = 50

scatter = ax.scatter(np.random.normal(size=N),

np.random.normal(size=N),

c=np.random.random(size=N),

s=1000 * np.random.random(size=N),

alpha=0.3,

cmap=plt.cm.jet)

ax.set_title("D3 Scatter Plot", size=18)

#%%

from IPython.core.display import HTML

HTML("<iframe src='http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/cyclones/pfctstorm91/pfctstorm.html' width='750' height='600'></iframe>")

#%%

%matplotlib inline

from bokeh.io import push_notebook, show, output_notebook

from bokeh.layouts import row, gridplot

from bokeh.plotting import figure, show, output_file

output_notebook()

import numpy as np

x = np.linspace(0, 4 * np.pi, 100)

y = np.sin(x)

TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select"

p1 = figure(title="Legend Example", tools=TOOLS)

p1.circle(x, y, legend="sin(x)")

p1.circle(x, 2 * y, legend="2*sin(x)", color="orange")

p1.circle(x, 3 * y, legend="3*sin(x)", color="green")

show(p1)

# In[]

import pandas as pd

import numpy as np

import matplotlib

from matplotlib import pyplot as plt

ts = pd.Series(np.random.randn(1000),

index=pd.date_range('1/1/2000', periods=1000))

ts = ts.cumsum()

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,

columns=['A', 'B', 'C', 'D'])

df = df.cumsum()

df.plot()

plt.legend(loc='best')

#%%

%matplotlib inline

import matplotlib.pyplot as plt

import matplotlib as mpl

from IPython.core.pylabtools import print_figure

from IPython.display import Image, SVG, Math

import numpy as np

class Gaussian(object):

"""A simple object holding data sampled from a Gaussian distribution.

"""

def __init__(self, mean=0.0, std=1, size=1000):

self.data = np.random.normal(mean, std, size)

self.mean = mean

self.std = std

self.size = size

# For caching plots that may be expensive to compute

self._png_data = None

def _figure_data(self, format):

fig, ax = plt.subplots()

ax.hist(self.data, bins=50)

ax.set_title(self._repr_latex_())

ax.set_xlim(-10.0, 10.0)

data = print_figure(fig, format)

# We MUST close the figure, otherwise IPython's display machinery

# will pick it up and send it as output, resulting in a double display

plt.close(fig)

return data

def _repr_png_(self):

if self._png_data is None:

self._png_data = self._figure_data('png')

return self._png_data

def _repr_latex_(self):

return r'$\mathcal{N}(\mu=%.2g, \sigma=%.2g),\ N=%d$' % (self.mean,

self.std, self.size)

x = Gaussian(2.0, 1.0)

x

#%%

from IPython.display import Latex

Latex('''The mass-energy equivalence is described by the famous equation

$$E=mc^2$$

discovered in 1905 by Albert Einstein.

In natural units ($c$ = 1), the formula expresses the identity

\\begin{equation}

E=m

\\end{equation}''')