from IPython.display import Image
Image(url = 'https://i.imgur.com/4DrMgLI.png')

%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")

import plotly
plotly.__version__

fig1 = 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(fig1, strip_style = True)

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

library(WDI)
library(ggplot2)
 
#Grab GNI per capita data for Chile, Hungary and Uruguay
 
dat = WDI(indicator='NY.GNP.PCAP.CD', country=c('CL','HU','UY'), start=1960, end=2012)
 
#a quick plot with legend, title and label
 
wb <- ggplot(dat, aes(year, NY.GNP.PCAP.CD, color=country)) + geom_line() 
+     xlab('Year') + ylab('GDI per capita (Atlas Method USD)') 
+     labs(title <- "GNI Per Capita ($USD Atlas Method)")

py$ggplotly(wb)

tls.embed('RgraphingAPI', '1457')

fig = py.get_figure('RgraphingAPI', '1457')
fig.strip_style()
py.iplot(fig)

my_data = py.get_figure('PythonAPI', '455').get_data()

%matplotlib inline
import matplotlib.pyplot as plt

fig1 = plt.figure()

plt.subplot(311)
plt.plot(my_data[0]['x'], my_data[0]['y'])
plt.subplot(312)
plt.plot(my_data[1]['x'], my_data[1]['y'])
plt.subplot(313)
plt.plot(my_data[2]['x'], my_data[2]['y'])

py.iplot_mpl(fig1, strip_style = True)

my_data[1]['yaxis'] = 'y2'
my_data[2]['yaxis'] = 'y3'

layout = Layout(
    yaxis=YAxis(
        domain=[0, 0.33]
    ),
    legend=Legend(
        traceorder='reversed'
    ),
    yaxis2=YAxis(
        domain=[0.33, 0.66]
    ),
    yaxis3=YAxis(
        domain=[0.66, 1]
    )
)

fig = Figure(data=my_data, layout=layout)

py.iplot(fig)

Image(url = 'http://i.imgur.com/rHP53Oz.png')

tls.embed('MattSundquist', '1404')

tls.embed('MattSundquist', '1339')

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

ggplot = py.get_figure('MattSundquist', '1339') 

ggplot #print it

ggplot_data = ggplot.get_data()

ggplot_data

import pandas as pd

my_data = py.get_figure('MattSundquist', '1339').get_data()
frames = {data['name']: {'x': data['x'], 'y': data['y']} for data in my_data['data']}
df = pd.DataFrame(frames)
df

from plotly.graph_objs import Data, Layout, Figure

help(Figure)

Image(url = 'http://i.imgur.com/bGj8EzI.png?1')

%%matlab

close all

% Create a set of values for the damping factor
zeta = [0.01 .02 0.05 0.1 .2 .5 1 ];

% Define a color for each damping factor
colors = ['r' 'g' 'b' 'c' 'm' 'y' 'k'];

% Create a range of frequency values equally spaced logarithmically
w = logspace(-1, 1, 1000);

% Plot the gain vs. frequency for each of the seven damping factors
figure;
for i = 1:7
    a = w.^2 - 1;
    b = 2*w*zeta(i);
    gain = sqrt(1./(a.^2 + b.^2));
    loglog(w, gain, 'color', colors(i), 'linewidth', 2);
    hold on;
end

% Set the axis limits
axis([0.1 10 0.01 100]);

% Add a title and axis labels
title('Gain vs Frequency');
xlabel('Frequency');
ylabel('Gain');

% Turn the grid on
grid on;

% ----------------------------------------
% Let's convert the figure to plotly structures, and set stripping to false
[data, layout] = convertFigure(get(gcf), false);

% But, before we publish, let's modify and add some features:
% Naming the traces
for i=1:numel(data)
    data{i}.name =  ['$\\zeta = ' num2str(zeta(i)) '$']; %LATEX FORMATTING
    data{i}.showlegend = true;
end
% Adding a nice the legend
legendstyle = struct( ...
    'x' , 0.15, ...
    'y' , 0.9, ...
    'bgcolor' , '#E2E2E2', ...
    'bordercolor' , '#FFFFFF', ...
    'borderwidth' , 2, ...
    'traceorder' , 'normal' ...
    );
layout.legend = legendstyle;
layout.showlegend = true;

% Setting the hover mode
layout.hovermode = 'closest';

% Giving the plot a custom name
plot_name = 'My_improved_plot';

% Sending to Plotly
response = plotly(data, struct('layout', layout, ...
    'filename',plot_name, ...
	'fileopt', 'overwrite'));

display(response.url)

tls.embed('MATLAB-Demos', '4')

Image(url = 'https://i.imgur.com/sAHsjk3.png')

Image (url = 'https://i.imgur.com/y4t5hdj.png')

Image (url = 'http://i.imgur.com/BUOe85E.png')

tls.embed('MattSundquist', '1337')

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

Image (url = 'http://i.imgur.com/ATn7vE4.png')

Image(url = 'http://i.imgur.com/QaIw9p4.png?1')

Image(url = 'http://i.imgur.com/OqXKs0r.png')

from IPython.display import HTML

i = """<pre style="background:#f1f1f1;color:#000">&lt;iframe src=<span style="color:#c03030">"https://plot.ly/~MattSundquist/1334/650/550"</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(i); h

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/gUC4ajR.png')

HTML('<center><iframe class="vine-embed" src="https://vine.co/v/M6JBhdiqPqA/embed/simple" width="600" height="600" frameborder="0"></iframe><script async src="//platform.vine.co/static/scripts/embed.js" charset="utf-8"></script></center>')

tls.embed('flann321', '9')

tls.embed('streaming-demos','4')

HTML('<center><iframe src="//instagram.com/p/nJkMMQRyvS/embed/" width="612" height="710" frameborder="0" scrolling="no" allowtransparency="true"></iframe></center>')

# 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()