import plotly.plotly as py    # signing in with your credentials file
import plotly.tools as tls

from plotly.graph_objs import Figure, Data, Layout
from plotly.graph_objs import Scatter
from plotly.graph_objs import Marker, Font
from plotly.graph_objs import XAxis, YAxis, Annotation, Annotations

import numpy as np
import pandas as pd
import urllib2

# The datasets' url. Thanks Jennifer Bryan!
url_csv = 'http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/\
gapminder/data/gapminderDataFiveYear.txt'

file_csv = urllib2.urlopen(url_csv)   # import csv file into this session
df = pd.read_csv(file_csv, sep='\t')  # load csv file into a dataframe

df.head()  # print the first 5 lines of the dataframe

# Start year, end year and number of year is dataset
df['year'].min(), df['year'].max(), len(df['year'].unique())

countries = df['country'].unique()            # list of countries
N_country = len(countries)                    # number of countries
N_rowcol = int(np.ceil(np.sqrt(N_country)))   # size of the square subplot grid

N_country, N_rowcol  # print to screen

# Generate Figure object with 144 axes (12 rows x 12 columns),
fig = tls.get_subplots(
    rows= N_rowcol,            # number of rows
    columns= N_rowcol,         # number of columns
    horizontal_spacing= 0.02,  # horiz. spacing (norm. coord)
    vertical_spacing= 0.02,    # vert. spacing (norm. coord)
    print_grid=True)           #  print axis grid ids to screen

# Function to make list of subplot indices
def get_splts(N_rowcol, N_country):

    N_splt = N_rowcol**2         # number of subplots
    N_empty = N_splt-N_country   # number of empty subplots
    
    tmp1d = np.arange(1,N_splt+1)                 # => [1,2,..,N_splt]
    tmp2d = np.resize(tmp1d, (N_rowcol,N_rowcol)) # => [[1,2,..,N_rowcol],..[..,N_splt]]
    tmp2d_flip = tmp2d[::-1,:]                    # => [[..,N_spl],..[1,2,..,N_rowcol]]
    
    splts_left = tmp2d_flip[:,0]     # indices of the left-hand side subplots
    splts_bottom = tmp2d_flip[-1,:]  # indices of the bottom subplots
    
    tmp1d_in_order = tmp2d_flip.flatten().tolist()  # => [..,N_spl,..,1,2,..N_rowcol]
    
    splts_empty = range(N_rowcol-N_empty+1,N_rowcol+1)  # indices of empty subplots
    
    for splt in splts_empty:           
        tmp1d_in_order.remove(splt)  # remove indices of empty subplots  
    splts = tmp1d_in_order           #   and get the complete list of subplots
    
    return splts, splts_empty, splts_left, splts_bottom

# Get lists of subplot indices
splts, splts_empty, splts_left, splts_bottom = get_splts(N_rowcol, N_country)

splts  # print list

# Function to make Scatter graph object
def make_Scatter_gdp(splt, x, y, color, country, text):
    return Scatter(
        x= x,  # x coordinates
        y= y,  # y coordinates
        name= country,   # label name (on hover)
        text = text,     # hover text
        mode='lines+markers', # show marker pts and line beween them  
        fill= 'tozeroy',                # fill area down to y=0
        marker= Marker(color= color),   # marker, line and fill color
        xaxis= 'x{}'.format(splt),   # bind coordinate to given x-axis
        yaxis= 'y{}'.format(splt))   # bind coordinate to given y-axis

# Colors corresponding to contients
colors = dict(
    Asia='#1f77b4', 
    Europe='#ff7f0e',
    Africa='#2ca02c',
    Americas='#d62728',
    Oceania='#9467bd')

# Function to make hover text list (for each data point)
def make_text(X):
    return 'Continent: %s\
    <br>Year: %s\
    <br>GDP per capita: %s $\
    <br>Life Expectancy: %s years\
    <br>Population: %s million'\
    % (X['continent'], X['year'], X['gdpPercap'], X['lifeExp'], X['pop']/1e6)   

df['gdpPercap'].min(), df['gdpPercap'].max()

# For all x axes
axis_style_x = dict(
    range = [1950,2010])  # Set x-axis range

# For all y axes
axis_style_y = dict(
    type='log',                             # N.B. log y-axis
    range = [np.log10(90),np.log10(5e5)])   # N.B. set y-axis range w.r.t. log scale

# For all axes
axis_style_all = dict(
    ticks='outside',      # no ticks
    showline=True,        # show axis bounding line
    showgrid=False,       # remove grid
    zeroline=False,       # no thick line at x=y=0
    showticklabels=False) # remove tick labels

# For y axes on the left hand side of the subplot grid 
axis_style_left = dict(
    showticklabels=True,  # N.B. add back tick labels (overwrite axis_style_all)
    title='GDP per cap.') # title of the y axes

# For x axes on the bottom of the subplot grid
axis_style_bottom = dict(
    showticklabels=True,  # N.B. add back tick labels (overwrite axis_style_all)
    title='year')         # title of the x axes

# Function to make annotation labelling each classifier (at top of each column)
def make_splt_anno(splt_in, country):
    if len(country)>14:
        country = country[0:14]+'.'   # truncate country's name if too long
    return Annotation(
        x= 1955,               # x position
        y= np.log10(2.5e5),    # y position
        text= country,         # text 
        align='center',        # align text in the center
        font= Font(size=14),   # font size
        showarrow=False,       # no arrow 
        xref= 'x{}'.format(splt),  # position in relation to the x
        yref= 'y{}'.format(splt))  #   and y axes

width = 2000   # plot's width 
height = 1800  #  and height in pixels

title = "GDP per Capita from 1952 to 2007 in USD of the year 2000 [GapMinder]"
fig['layout'].update(
    title= title,    #  plot's title
    font= Font( 
        family='Georgia, serif',  # global font,
        color='#635F5D'),         #   same as in 3.1
    titlefont= Font(size=30),     # increase title font size
    showlegend=False,  # remove legend
    autosize=False,    # turn off autosize
    width= width,    # plot's width
    height= height)  # plot's height
    
fig['layout']['annotations'] = Annotations([])  # init. 'annotations' key

i = 0  # init. subplot counter

# Group dataframe by country in alphabetical order and loop
for country, X in df.groupby('country'):

    splt = splts[i]   # N.B. get axes id
    
    x = X['year'].values                              # get years
    y = X['gdpPercap'].values                         # get GDP values
    color = colors[X['continent'].values[0]]          # get fill color
    text = X.apply(make_text,axis=1).tolist()         # get hover text

    # Append data object
    fig['data'] += [make_Scatter_gdp(splt, x, y, color, country, text)]
    
    # Make shortcut to xaxis of splt id, update its style
    xaxis_splt = fig['layout']['xaxis{}'.format(splt)]  
    xaxis_splt.update(axis_style_x)
    xaxis_splt.update(axis_style_all)
    if splt in splts_bottom:
        xaxis_splt.update(axis_style_bottom)
    
    # Make shortcut to yaxis of splt id, update its style
    yaxis_splt = fig['layout']['yaxis{}'.format(splt)]
    yaxis_splt.update(axis_style_y)
    yaxis_splt.update(axis_style_all)  
    if splt in splts_left:
        yaxis_splt.update(axis_style_left)
    
    # Append annotations object, label each subplot
    fig['layout']['annotations'] += [make_splt_anno(splt, country)]        
    
    i += 1   # increment counter

for splt in splts_empty:  # loop through list of empty subplot ids
    
    # Make shortcut to xaxis of splt id, update its style
    xaxis_splt = fig['layout']['xaxis{}'.format(splt)]
    xaxis_splt.update(axis_style_x)
    xaxis_splt.update(axis_style_all)
    xaxis_splt.update(axis_style_bottom)  # empty subplots are on the bottom row
    
    # Make shortcut to yaxis of splt id, update its style
    yaxis_splt = fig['layout']['yaxis{}'.format(splt)]
    yaxis_splt.update(axis_style_y)
    yaxis_splt.update(axis_style_all)

py.plot(fig, filename='small-multiple_gdp-time', auto_open=False)

tls.embed('etpinard','311', 
          width=width, height=height)

py.image.save_as(fig, 'small-multiple_gdp-time')

from IPython.display import Image
Image('small-multiple_gdp-time.png')

# 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()