import plotly
import math
import numpy as np

un='jackp'
k='11m2qbzob9'
plotly.plotly('IPython.Demo', '1fw3zw2o13')
py = plotly.plotly(username=un, key=k)

# RGB color scale from colorbrewer.com
GnBu = [(247, 252, 240),(224, 243, 219),(204, 235, 197),\
        (168, 221, 181),(123, 204, 196),(78, 179, 211),\
        (43, 140, 190),(8, 104, 172),(8, 64, 129)]

def rgbToHsl(rgb):
    ''' Adapted from M Bostock's RGB to HSL converter in d3.js
        https://github.com/mbostock/d3/blob/master/src/color/rgb.js '''
    r,g,b = float(rgb[0])/255.0,\
            float(rgb[1])/255.0,\
            float(rgb[2])/255.0
    mx = max(r, g, b)
    mn = min(r, g, b)
    h = s = l = (mx + mn) / 2
    if mx == mn: # achromatic
        h = 0
        s = 0 if l > 0 and l < 1 else h
    else:
        d = mx - mn;        
        s =  d / (mx + mn) if l < 0.5 else d / (2 - mx - mn)
        if mx == r:
            h = (g - b) / d + ( 6 if g < b else 0 )
        elif mx == g:
            h = (b - r) / d + 2
        else:
            h = r - g / d + 4

    return (round(h*60,4), round(s*100,4), round(l*100,4))

def interp3(fraction, start, end):
    ''' Interpolate between values of 2, 3-member tuples '''
    def intp(f, s, e):
        return s + (e - s)*f 
    return tuple([intp(fraction, start[i], end[i]) for i in range(3)])

def colorscale(scl, r):
    ''' Interpolate a hsl colorscale from "scl" with length "r" '''
    c = []
    SCL_FI = len(scl)-1 # final index of color scale 
    
    for i in r:
        c_i = int(i*math.floor(SCL_FI)/round(r[-1])) # start color index
        hsl_o = rgbToHsl( scl[c_i] ) # convert rgb to hls
        hsl_f = rgbToHsl( scl[c_i+1] ) 
        section_min = c_i*r[-1]/SCL_FI
        section_max = (c_i+1)*(r[-1]/SCL_FI)
        fraction = (i-section_min)/(section_max-section_min)
        hsl = interp3( fraction, hsl_o, hsl_f )
        c.append( 'hsl'+str(hsl) )
    return c

r = np.arange(0,20,0.1)
x = [2*np.cos(i)*i+(i*0.2*np.random.rand()) for i in r]
y = [2*np.sin(i)*i+(i*0.2*np.random.rand()) for i in r]
s = [(i+5)*i/5 for i in r] # diameter of bubble size in pixels
t = [('Area: '+str(round(3.14*math.pow(d/2,2),2))+' (sq. pixels)<br>\
    Radius: '+str(round(d/2,2))+' pixels') for d in s] # Show hover text as bubble area
c = colorscale(GnBu, r)

# set hovermode to 'closest' to turn off showing all points near the same x on hover
layout = { 'hovermode':'closest','title':'click-drag to zoom-in<br>double-click to zoom-out',\
'xaxis':{'showticklabels':False,'ticks':'','linecolor':'white','showgrid':False,'zeroline':False},\
'yaxis':{'showticklabels':False,'ticks':'','linecolor':'white','showgrid':False,'zeroline':False} }

data = [ {'x':x,'y':y,'mode':'markers','opacity':0.7,'text':t,\
          'marker':{'size':s,'color':c,'line':{'width':2}}} ]

py.iplot(data, layout=layout)

# color scales from colorbrewer.com

BuGn = [(247,252,253), (229,245,249), (204,236,230),\
    (153,216,201), (102,194,164), (65,174,118),\
    (35,139,69), (0,109,44), (0,68,27)]
BuPu = [(247,252,253), (224,236,244), (191,211,230),\
    (158,188,218), (140,150,198), (140,107,177),\
    (136,65,157), (129,15,124), (77,0,75)]
GnBu = [(247, 252, 240),(224, 243, 219),(204, 235, 197),\
    (168, 221, 181),(123, 204, 196),(78, 179, 211),\
    (43, 140, 190),(8, 104, 172),(8, 64, 129)]

PuBu = [(255,247,251), (236,231,242), (208,209,230),\
        (166,189,219), (116,169,207), (54,144,192),\
        (5,112,176), (4,90,141), (2,56,88)]
PuBuGn = [(255,247,251), (236,226,240), (208,209,230),\
    (166,189,219), (103,169,207), (54,144,192),\
    (2,129,138), (1,108,89), (1,70,54)]
PuRd = [(247,244,249), (231,225,239), (212,185,218),\
    (201,148,199), (223,101,176), (231,41,138),\
    (206,18,86), (152,0,67), (103,0,31)]

RdPu = [(255,247,243), (253,224,221), (252,197,192),\
    (250,159,181), (247,104,161), (221,52,151),\
    (174,1,126), (122,1,119), (73,0,106)]
YlGn = [(255,255,229), (247,252,185), (217,240,163),\
    (173,221,142), (120,198,121), (65,171,93),\
    (35,132,67), (0,104,55), (0,69,41)]
YlGnBu = [(255,255,217), (237,248,177), (199,233,180),\
    (127,205,187), (65,182,196), (29,145,192),\
    (34,94,168), (37,52,148), (8,29,88)]

data = []
layout = {'showlegend':False,'hovermode':'closest',\
          'title':'drag your mouse along the left and bottom border to pan<br>\
              or hold down shift and drag inside. double-click to re-center'}
padding = 0.0
domains = [[i*(1-3*padding)/3, ((i+1)*(1-3*padding)/3)] for i in range(3)]
cscl = [[BuGn,BuPu,GnBu],[PuBu,PuBuGn,PuRd],[RdPu,YlGn,YlGnBu]] # colorscale
s = [(i+5)*i/10 for i in r] # diameter of bubble size in pixels

for j in range(3):
    for k in range(3):
        c = colorscale(cscl[j][k], r)
        data.append({'name': cscl[j][k]+' Spiral '+str(j)+str(k), 
            'x': [2*np.cos(i*(k/4+1))*i+(i*0.2*np.random.rand()) for i in r],
            'y': [2*np.sin(i)*i+(i*0.2*np.random.rand()) for i in r],
            'type':'scatter','mode':'markers','width': 950,'height': 950,'opacity': 0.7,     
            'marker': {'color':c, 'size':s, 'opacity':0.9, 'line':{'width':2}},            
            'xaxis': 'x' + str(j) if j!=0 else '', 
            'yaxis': 'y' + str(k) if k!=0 else '' })
    xy_i = str(j) if j!=0 else ''
    layout['xaxis'+xy_i] = layout['yaxis'+xy_i] = \
        {'domain':domains[j],'showticklabels':False,'ticks':'',\
         'linecolor':'#E3E3E3','linewidth':8,'showgrid':False,'zeroline':False }
    
py.iplot(data, layout=layout, width=1000, height=1000) # iframe size

import pandas as pd
pd.set_option('display.max_columns', 15)
pd.set_option('display.line_width', 400)
pd.set_option('display.mpl_style', 'default')
crime_data = pd.read_csv('crimeRatesByState2005.tsv',sep='\t')
crime_data[:2]

data = []
for i in range(len(crime_data['state'])):
    # Create 1 data object per point, so every point is a different color
    mx = float(crime_data['population'].max())
    s = [ math.sqrt(float(crime_data['population'][i])/mx)*60.0 ]
    t = [ 'State: %s<br>Population: %s' % (crime_data['state'][i], crime_data['population'][i]) ]
    d = {'x':[crime_data['murder'][i]],\
         'y':[crime_data['burglary'][i]],\
         'marker': {'size':s, 'opacity':0.9, 'line':{'width':1}},\
         'type':'scatter','mode':'markers','text':t}
    data.append(d)

citation = {'showarrow':False, 'font':{'size':10},'xref':'paper','yref':'paper','x':-0.18,'y':-0.18,'align':'left',\
    'text':'Data source and inspiration:<br>http://flowingdata.com/2010/11/23/how-to-make-bubble-charts/'}

layout = {'showlegend':False,'hovermode':'closest', 'title':'','annotations':[citation],\
    'title':'US Crime Rate by State<br>Bubble Size is State Population',\
    'xaxis':{ 'ticks':'','linecolor':'white','showgrid':False,'zeroline':False, 'title': 'Number of Murders', 'nticks':12 },
    'yaxis':{ 'ticks':'','linecolor':'white','showgrid':False,'zeroline':False, 'title': 'Number of Burglaries', 'nticks':12 }}

py.iplot(data, layout=layout)

gdp_data = pd.read_csv('gapMinderDataFiveYear.txt',sep='\t')
gdp_data[20:30]

data = []
years = (1987,2007,1952,1967) # ordering of years is funny for placement in subplot quadrants
sp = [('x','y'), ('x2','y'), ('x','y2'), ('x2','y2')]
# color scale from d3's 'category10' colorscale
cmap = {'Asia':'#1f77b4','Europe':'#ff7f0e','Africa':'#2ca02c',\
        'Americas':'#d62728','Oceania':'#9467bd'}
for i in range(len(years)):
    # grab all rows of this year, turn off Kuwait - its gdp per cap is extremely high
    df = gdp_data[(gdp_data['year']==years[i]) & (gdp_data['country']!='Kuwait')] 
    for name, g in df.groupby('continent'):
        mx = g['pop'].max()
        s = [ math.sqrt(j/mx)*60.0 for j in g['pop'] ]
        t = g.apply(lambda x:'Country: %s<br>Life Expectancy: %s<br>GDP per capita: %s<br>Population: %s<br>Year: %s' \
            % (x['country'], x['lifeExp'],x['gdpPercap'], x['pop'], str(years[i])),axis=1)
        d = {'name':name,'x':g['gdpPercap'],'y':g['lifeExp'],\
            'type':'scatter','mode':'markers','text':t,\
            'marker': {'color':cmap[name],'size':s, 'opacity':0.9, 'line':{'width':1}}}
        d['xaxis'] = sp[i][0]
        d['yaxis'] = sp[i][1]
        data.append(d)    

layout = { 'showlegend':False, 'width':1000, 'height': 700, 'hovermode':'closest',\
          'title':'drag to zoom-in; double-click to zoom-out<br>shift-drag to pan' }

for ax in ('xaxis','yaxis','xaxis2','yaxis2'):
    layout[ax] = { 'ticks':'','linecolor':'white','showgrid':False,'zeroline':False }
    layout[ax]['domain'] = [0.5,1] if '2' in ax else [0,0.5]
    layout[ax]['title'] = 'gdp per capita (usd, 2000)' if 'x' in ax else 'life expectancy (years)'

layout['annotations'] = []
# annotation positions correspond to order of 'years' tuple: 1987,2007,1952,1967
anno_positions = ((0.05,0.4), (0.6,0.4), (0.05,0.95), (0.6,0.95))

for yr in range(len(years)):
    anno_obj = { 'xref': 'paper', 'yref': 'paper', 'showarrow': False,\
        'font': {'family':'','size': 36,'color':'rgb(23, 190, 207)'} }    
    anno_obj['x'] = anno_positions[yr][0]
    anno_obj['y'] = anno_positions[yr][1]
    anno_obj['text'] = str(years[yr])
    layout['annotations'].append( anno_obj )

py.iplot(data,layout=layout,width=1050,height=750)

service = {'name': 'Service, sales, and office',
		  'x': [359, 363, 381, 403, 413, 429, 437, 439, 448, 473, 501, 502, 505, 509, 523, 532, 547, 556, 574, 575, 584, 585, 594,\
                618, 629, 635, 647, 662, 683, 689, 744, 800, 807, 887, 896, 905, 931, 958, 973, 1024, 1239],
		  'y': [333, 351, 341, 348, 363, 385, 379, 357, 442, 389, 483, 418, 522, 503, 468, 486, 406, 568, 521, 489, 550, 426, 525,\
                519, 513, 414, 555, 608, 579, 602, 541, 678, 855, 660, 684, 796, 806, 645, 787, 705, 1030],
		  'text': ['Food preparation workers<br>Women make 8% less than men', 
		  			'Dining room attendants and bartender helpers<br>Women make 6% less than men',
					'Cooks<br>Women make 9% less than men', 'Cashiers<br>Women make 15% less than men',
					'Waiters and waitresses<br>Women make 13% less than men', 'Telemarketers<br>Women make 7% less than men',
					'Personal home and care aides<br>Women make 14% less than men', 'Maids and housekeeping cleaners<br>Women make 18% less than men',
					'Stock clerks<br>Women make 1% less than men', 'Janitors and building cleaners<br>Women make 18% less than men',
					'Receptionists<br>Women make 4% less than men', 'Nursing, psychiatric, and home health aides<br>Women make 16% less than men', 
					'Data entry keyers<br>Women make 1% more than men', 'Shipping and receiving clerks<br>Women make 2% less than men', 
					'Security guards<br>Women make 11% less than men', 'Chefs and head cooks<br>Women make 9% less than men',
					'Bartenders<br>Women make 26% less than men', 'Ticket agents and travel clerks<br>Women make 9% less than men',
					'File clerks<br>Women make 9% less than men', 'Medical assistants<br>Women make 15% less than men',
					'Office clerks<br>Women make 5% less than men', 'Supervisors of food preparation workers<br>Women make 27% less than men',
					'Bill and account collectors<br>Women make 11% less than men', 'Customer service representatives<br>Women make 14% less than men',
					'Recreation and fitness workers<br>Women make 18% less than men', 'Retail sales workers<br>Women make 35% less than men',
					'Dispatchers<br>Women make 15% less than men', 'Bookkeeping, accounting, and auditing clerks<br>Women make 9% less than men',
					'Bailers, correctional officers, and jailers<br>Women make 15% less than men', 'Secretaries and administrative assistants<br>Women make 13% less than men',
					'Supervisors of retail workers<br>Women make 27% less than men', 'Supervisors of office and administrative support<br>Women make 15% less than men',
					'Postal service clerks<br>Women make 4% more than men', 'Production clerks<br>Women make 25% less than men', 
					'Advertising sales agents<br>Women make 24% less than men', 'Police officers<br>Women make 13% less than men', 
					'Postal service mail carriers<br>Women make 13% less than men', 'Insurance sales agents<br>Women make 32% less than men', 
					'Sales representatives<br>Women make 19% less than men', 'Real estate brokers<br>Women make 31% less than men',
					'Financial services sales agents<br>Women make 17% less than men'],
				'type':'scatter',
				'mode':'markers',
				'marker':{'size':9,'color':'#CA4D64'}}

production = {'name': 'Production and transportation',
		  'x': [482, 495, 499, 541, 543, 564, 590, 668, 732, 858],
		  'y': [419, 342, 403, 448, 474, 411, 484, 504, 514, 619],
		  'text': ['Laborers and freight movers<br>Women make 13% less than men', 
		  			'Laundry workers<br>Women make 31% less than men',
					'Bakers<br>Women make 18% less than men',
					'Electronics assemblers<br>Women make 17% less than men', 'Bus drivers<br>Women make 11% less than men',
					'Butchers and other meat processing workers<br>Women make 27% less than men', 'Metal workers and plastic workers<br>Women make 18% less than men',
					'Truck drivers<br>Women make 25% less than men', 'Inspectors and testers<br>Women make 31% less than men',
					'Supervisors of production workers<br>Women make 28% less than men'],
			'type':'scatter',
			'mode':'markers',
			'marker':{'size':9,'color':'#88A4B8'}}

science = {'name': 'Science, computers, and health care',
		  'x': [683, 904, 1050, 1050, 1098, 1159, 1239, 1245, 1266, 1351, 1367, 1508, 1793, 1883],
		  'y': [540, 766, 848, 805, 983, 1039, 1045, 1099, 1078, 985, 861, 1323, 1065, 1609],
		  'text': ['Health diagnosing and treatment technicians<br>Women make 21% less than men', 
		  			'Computer support specialists<br>Women make 15% less than men',
					'Diagnostic related technicians<br>Women make 19% less than men',
					'Clinical laboratory technicians<br>Women make 23% less than men', 'Registered nurses<br>Women make 11% less than men',
					'Market and survey researchers<br>Women make 10% less than men', 'Computer scientists and system analysts<br>Women make 16% less than men',
					'Physical therapists<br>Women make 12% less than men', 'Computer programmers<br>Women make 15% less than men',
					'Chemists and material scientists<br>Women make 27% less than men', 'Medical scientists<br>Women make 37% less than men',
					'Computer software engineers<br>Women make 12% less than men', 'Physicians and surgeons<br>Women make 40% less than men',
					'Pharmacists<br>Women make 15% less than men'],
		   'type':'scatter',
		   'mode':'markers',
           'marker':{'size':9,'color':'#8D9F69'}}

management = {'name': 'Management, business, and financial',
		  'x': [730, 790, 964, 991, 1038, 1064, 1123, 1129, 1183, 1328, 1364, 1376, 1384, 1409, 1450, 1511, 1507, 1597, 1917],
		  'y': [586, 741, 731, 747, 814, 920, 749, 849, 863, 993, 967, 1051, 1088, 1072, 915, 1033, 1077, 1370, 1540],
		  'text': ['Food service managers<br>Women make 20% less than men', 
		  			'Whole and retail buyers<br>Women make 15% less than men',
					'Property managers<br>Women make 24% less than men',
					'Claims adjusters<br>Women make 24% less than men', 'Human resources specialists<br>Women make 21% less than men',
					'Social and community service managers<br>Women make 14% less than men', 'Compliance officers<br>Women make 33% less than men',
					'Loan officers<br>Women make 25% less than men', 'Accountants and auditors<br>Women make 27% less than men',
					'General and operations managers<br>Women make 25% less than men', 'Education administrators<br>Women make 29% less than men',
					'Personal financial advisors<br>Women make 23% less than men', 'Management analysts<br>Women make 21% less than men',
					'Medical and health services managers<br>Women make 24% less than men', 'Financial managers<br>Women make 37% less than men',
					'Marketing and sales managers<br>Women make 31% less than men', 'Human resources managers<br>Women make 32% less than men',
					'Computer and information systems managers<br>Women make 14% less than men', 'Chief executives<br>Women make 19% less than men'],
			'type':'scatter',
			'mode':'markers',
			'marker':{'size':9,'color':'#005082'}}

entertainment = {'name': 'Entertainment, education, and law',
		  'x': [762, 828, 860, 890, 938, 957, 979, 1000, 1236, 1779],
		  'y': [755, 723, 887, 702, 848, 781, 809, 904, 971, 1388],
		  'text': ['Social workers<br>Women make 1% less than men', 
		  			'Counselors<br>Women make 13% less than men',
					'Special education teachers<br>Women make 3% more than men',
					'Designers<br>Women make 22% less than men', 'Elementary and middle school teachers<br>Women make 9% less than men', 
					'Engineering technicians, except drafters<br>Women make 18% less than men', 'Editors<br>Women make 17% less than men', 
					'High school teachers<br>Women make 10% less than men', 'Professors and postsecondary teachers<br>Women make 22% less than men',
					'Lawyers<br>Women make 22% less than men'],
			'type':'scatter',
			'mode':'markers',
			'marker':{'size':9,'color':'#D28628'}}

blank = {'name': '', 'x': [0, 0], 'y': [0, 0], 'line':{'color':'#F0F0F0','width':3},'mode':'lines'}

source = {'name': '<i> Source: Bureau of Labor Statistics:</i><br><i>Census Bureau </i>', 'x': [0, 0], 'y': [0, 0],\
        'line':{'color':'#F0F0F0','width':3},'mode':'lines'}

equal = {'name': 'Equal Wages', 'x': [0, 1650], 'y': [0, 1650],'line':{'color':'black','width':3},'mode':'lines'}

tenpercentless = {'name': 'Women earn 10% less than men','x': [0, 1833.3], 'y': [0, 0.9*1833.3],'line':{'color':'#606060', 'width':2},'mode':'lines'}
twentypercentless = {'name': 'Women earn 20% less than men', 'x': [0, 2062.5], 'y': [0, 0.8*2062.5],'line':{'color':'#909090','width':2},'mode':'lines'}
thirtypercentless = {'name': 'Women earn 30% less than men', 'x': [0, 2357.143], 'y': [0, 0.7*2357.143],'line':{'color':'#D2D2D2','width':2},'mode':'lines'}

layout = {'autosize':False,
			'font':{'color':"rgb(33, 33, 33)",'family':"Arial, sans-serif",'size':12},
			'height':650,
			'width':1100,
			'xaxis':{
				'range':[0,2437],
				'type': 'linear',
				'ticks': 'none',
				'autorange': False,
				'zeroline': False,
				'mirror': False,
				'linecolor':'white',
				'tickcolor':'white',
				'autotick':False,
				'dtick': 250,
				'gridwidth': .7
			},
			'yaxis':{
				'range':[0,1650],
				'type': 'linear',
				'ticks': 'none',
				'autorange': False,
				'zeroline': False,
				'mirror': False,
				'linecolor':'white',
				'tickcolor':'white',
				'autotick':False,
				'dtick': 250,
				'gridwidth': .7
			},
			'legend':{
				 'bgcolor': "#F0F0F0",
				 'bordercolor': "#F0F0F0",
				 'borderwidth': 10,
				 'x': 0.0845912623961403,
				 'y': 0.9811399147727271,
				 'traceorder': 'reversed'
			},
			'margin':{'b':80,'l':100,'pad':2,'r':250,'t':80},
			'annotations':[{
					'text':"<i>Source: Bureau of Labor Statistics - Census Bureau</i>",
					'x':2100,
					'y':30,
					'showarrow':False,
					'ref':'plot',
					'align':'left',
					'font':{'size':'9'}
				},{
					'text':"<b>Men's</b> median weekly earnings",
					'x':1120,
					'y':70,
					'showarrow':False,
					'ref':'plot',
					'align':'left',
					'font':{'size':'13'}
				},{
					'text':"<b>Women's</b><br>median weekly<br>earnings",
					'x':40,
					'y':870,
					'showarrow':False,
					'ref':'plot',
					'align':'left',
					'font':{'size':'13'}
				},{
					'text':"<i>Roll over</i><br><i>dots for</i><br><i>information</i>",
					'x':1870,
					'y':630,
					'showarrow':False,
					'ref':'plot',
					'align':'left',
					'font':{'size':'14'}
				},
			]
		}
py.iplot([equal, tenpercentless, twentypercentless, thirtypercentless,\
          service, production, science, management, entertainment], layout=layout, width=1150, height=700)