%matplotlib inline
import matplotlib.pyplot as plt # side-stepping mpl backend
import matplotlib.gridspec as gridspec # subplots
import numpy as np

import plotly.plotly as py
import plotly.tools as tls
from plotly.graph_objs import *
py.sign_in("IPython.Demo", "1fw3zw2o13")

# tls.set_credentials_file("IPython.Demo", "1fw3zw2o13")
# tls.get_credentials_file()

import plotly
plotly.__version__

fig1 = plt.figure()
# Make a legend for specific lines.
import matplotlib.pyplot as plt
import numpy as np


t1 = np.arange(0.0, 2.0, 0.1)
t2 = np.arange(0.0, 2.0, 0.01)

# note that plot returns a list of lines.  The "l1, = plot" usage
# extracts the first element of the list into l1 using tuple
# unpacking.  So l1 is a Line2D instance, not a sequence of lines
l1, = plt.plot(t2, np.exp(-t2))
l2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')
l4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')

plt.xlabel('time')
plt.ylabel('volts')
plt.title('Damped oscillation')

plt.show()

py.iplot_mpl(fig1)

fig = tls.mpl_to_plotly(fig1)
fig['layout'].update(showlegend=True)
fig.strip_style()
py.iplot(fig)

fig2 = plt.figure()

from pylab import *

def f(t):
    'a damped exponential'
    s1 = cos(2*pi*t)
    e1 = exp(-t)
    return multiply(s1,e1)

t1 = arange(0.0, 5.0, .2)


l = plot(t1, f(t1), 'ro')
setp(l, 'markersize', 30)
setp(l, 'markerfacecolor', 'b')

py.iplot_mpl(fig2)

tls.mpl_to_plotly(fig2).get_data()

pylab = py.get_figure('IPython.Demo', '3357')

#print figure
print pylab.to_string()

from IPython.display import Image
Image(url='https://i.imgur.com/WG0gb9J.png')

Image(url='https://i.imgur.com/Mq490fb.png')

tls.embed('MattSundquist', '1307')

from IPython.display import HTML

s = """<pre style="background:#f1f1f1;color:#000">&lt;iframe src=<span style="color:#c03030">"https://plot.ly/~etpinard/176/650/550"</span> width=<span style="color:#c03030">"650"</span> height=550<span style="color:#c03030">" frameBorder="</span>0<span style="color:#c03030">" seamless="</span>seamless<span style="color:#c03030">" scrolling="</span>no<span style="color:#c03030">">&lt;/iframe></span></pre>"""

h = HTML(s); h

Image(url='http://i.imgur.com/XjvtYMr.png')

HTML('<br><center><iframe class="vine-embed" src="https://vine.co/v/Mvzin6HZzLB/embed/simple" width="600" height="600" frameborder="0"></iframe><script async src="//platform.vine.co/static/scripts/embed.js" charset="utf-8"></script></center><br>')

Image(url='https://i.imgur.com/CxIYtzG.png')

Image(url='https://i.imgur.com/gUC4ajR.png')

fig3 = plt.figure()

import numpy as np
import matplotlib.pyplot as plt

# make a little extra space between the subplots
plt.subplots_adjust(wspace=0.5)

dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t))                 # white noise 1
nse2 = np.random.randn(len(t))                 # white noise 2
r = np.exp(-t/0.05)

cnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1
cnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2

# two signals with a coherent part and a random part
s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2

plt.subplot(211)
plt.plot(t, s1, 'b-', t, s2, 'g-')
plt.xlim(0,5)
plt.xlabel('time')
plt.ylabel('s1 and s2')
plt.grid(True)

plt.subplot(212)
cxy, f = plt.csd(s1, s2, 256, 1./dt)
plt.ylabel('CSD (db)')

py.iplot_mpl(fig3)

fig4 = plt.figure()

from pylab import figure, show
from numpy import arange, sin, pi

t = arange(0.0, 1.0, 0.01)

fig = figure(1)

ax1 = fig.add_subplot(211)
ax1.plot(t, sin(2*pi*t))
ax1.grid(True)
ax1.set_ylim( (-2,2) )
ax1.set_ylabel('1 Hz')
ax1.set_title('A sine wave or two')

for label in ax1.get_xticklabels():
    label.set_color('r')


ax2 = fig.add_subplot(212)
ax2.plot(t, sin(2*2*pi*t))
ax2.grid(True)
ax2.set_ylim( (-2,2) )
l = ax2.set_xlabel('Hi mom')
l.set_color('g')
l.set_fontsize('large')

py.iplot_mpl(fig4, strip_style = True)

fig5 = plt.figure()

from __future__ import print_function
"""
Edward Tufte uses this example from Anscombe to show 4 datasets of x
and y that have the same mean, standard deviation, and regression
line, but which are qualitatively different.

matplotlib fun for a rainy day
"""

from pylab import *

x =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
y1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
y2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
y3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
x4 = array([8,8,8,8,8,8,8,19,8,8,8])
y4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])

def fit(x):
    return 3+0.5*x



xfit = array( [amin(x), amax(x) ] )

subplot(221)
plot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)
axis([2,20,2,14])
setp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
text(3,12, 'I', fontsize=20)

subplot(222)
plot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)
axis([2,20,2,14])
setp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))
text(3,12, 'II', fontsize=20)

subplot(223)
plot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)
axis([2,20,2,14])
text(3,12, 'III', fontsize=20)
setp(gca(), yticks=(4,8,12), xticks=(0,10,20))

subplot(224)

xfit = array([amin(x4),amax(x4)])
plot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)
axis([2,20,2,14])
setp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
text(3,12, 'IV', fontsize=20)

#verify the stats
pairs = (x,y1), (x,y2), (x,y3), (x4,y4)
for x,y in pairs:
    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))

py.iplot_mpl(fig5, strip_style = True)

fig6 = plt.figure()

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


# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(10000)

num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
# add a 'best fit' line
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--')
plt.xlabel('Smarts')
plt.ylabel('Probability')

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

py.iplot_mpl(fig6, strip_style = True)

Image(url='https://i.imgur.com/HEJEnjQ.png')

from ggplot import *

a = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + facet_wrap("cut") 

fig = a.draw() 
py.iplot_mpl(fig, strip_style=True)

b = ggplot(aes(x='date', y='beef'), data=meat) + \
    geom_line() 

fig = b.draw() 
py.iplot_mpl(fig)

c = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + ggtitle('My Diamond Histogram')

fig = c.draw() 
py.iplot_mpl(fig, strip_style=True)

d = ggplot(aes(x='x', y='y', color='z'), data=diamonds.head(1000)) +\
    geom_point() 

fig = d.draw() 
py.iplot_mpl(fig, strip_style=True)

import pandas as pd

random_walk1 = pd.DataFrame({
  "x": np.arange(100),
  "y": np.cumsum(np.random.choice([-1, 1], 100))
})
random_walk2 = pd.DataFrame({
  "x": np.arange(100),
  "y": np.cumsum(np.random.choice([-1, 1], 100))
})
e = ggplot(aes(x='x', y='y'), data=random_walk1) + \
    geom_step() + \
    geom_step(aes(x='x', y='y'), data=random_walk2)

fig = e.draw() 
py.iplot_mpl(fig, strip_style=True)

fig12 = plt.figure()

import prettyplotlib as ppl

# Set the random seed for consistency
np.random.seed(12)

# Show the whole color range
for i in range(8):
    x = np.random.normal(loc=i, size=800)
    y = np.random.normal(loc=i, size=800)
    ax = ppl.scatter(x, y, label=str(i))
    
ppl.legend(ax)
ax.set_title('prettyplotlib `scatter`')
ax.legend().set_visible(False)

py.iplot_mpl(fig12)

fig13 = plt.figure()

import prettyplotlib as ppl

# Set the random seed for consistency
np.random.seed(12)

# Show the whole color range
for i in range(8):
    y = np.random.normal(size=1000).cumsum()
    x = np.arange(1000)

    # Specify both x and y
    ppl.plot(x, y, label=str(i), linewidth=0.75)
        
py.iplot_mpl(fig13)

import seaborn as sns
from matplotlylib import fig_to_plotly

def sinplot(flip=1):
    x = np.linspace(0, 14, 100)
    for i in range(1, 7):
        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)

fig14 = plt.figure()

sns.set_style("dark")
sinplot()

py.iplot_mpl(fig14, strip_style = True)

fig15 = plt.figure()

with sns.axes_style("darkgrid"):
    plt.subplot(211)
    sinplot()
plt.subplot(212)
sinplot(-1)

py.iplot_mpl(fig15, strip_style = True)

import numpy as np
from numpy.random import randn
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

fig16 = plt.figure()

sns.set_palette("hls")
mpl.rc("figure", figsize=(8, 4))
data = randn(200)
sns.distplot(data);

py.iplot_mpl(fig16, strip_style = True)

fig17 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
hist, bins = np.histogram(x, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)

py.iplot_mpl(fig17, strip_style = True)

fig18 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
density = gaussian_kde(data)
xs = np.linspace(0,8,200)
density.covariance_factor = lambda : .25
density._compute_covariance()
plt.plot(xs,density(xs))

py.iplot_mpl(fig18, strip_style = True)

fig19 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(10)

plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)

py.iplot_mpl(fig19, strip_style = True)

fig20 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np

num_plots = 10

# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.gist_ncar
plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])

# Plot several different functions...
x = np.arange(10)
labels = []
for i in range(1, num_plots + 1):
    plt.plot(x, i * x + 5 * i)
    labels.append(r'$y = %ix + %i$' % (i, 5*i))

py.iplot_mpl(fig20, strip_style = True)

fig21 = plt.figure()

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

mean = [10,12,16,22,25]
variance = [3,6,8,10,12]

x = np.linspace(0,40,1000)

for i in range(4):
    sigma = np.sqrt(variance[i])
    y = mlab.normpdf(x,mean[i],sigma)
    plt.plot(x,y, label=r'$v_{}$'.format(i+1))

plt.xlabel("X")
plt.ylabel("P(X)")        

py.iplot_mpl(fig21, strip_style = True)

# CSS styling within IPython notebook
from IPython.core.display import HTML
import urllib2
def css_styling():
    url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'
    styles = urllib2.urlopen(url).read()
    return HTML(styles)

css_styling()