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import datetime as dt
from funcy import walk_values, join, flatten, first, rest, rpartial

import pandas as pd
import numpy as np

import plotly.plotly as py
import plotly.graph_objs as go
import cufflinks as cf


    

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# helpers
from toolz import keyfilter

def keep(d, whitelist):
    return keyfilter(lambda k: k in whitelist, d)

def omit(d, blacklist):
    return keyfilter(lambda k: k not in blacklist, d)


    

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import requests

def historic_data(urls=None):
    return list(map(lambda x: requests.get(x).json(), urls))

def merge_historic_data(historic_data):
    """ Simplify and flatten all historic data into a single list of events."""
    data = []
    for hist in historic_data:
        data.append([simplify_fragment(x) for x in hist['history'].values()])
    return list(flatten(data))

def simplify_fragment(obj):
    """ Simplify and flatten individual fragment."""
    # clean up the mess
    def replace_values(val):
        if type(val) == dict:
            return walk_values(replace_values, val)
        if val == "?" or val == 'None':
            return 0
        return val
    obj = walk_values(replace_values, obj)
    
    return {
        'symbol': obj['symbol'],
        'category': obj['category'],
        'supply': obj['availableSupply'],
        'position': int(obj['position']),
        'cap_usd': round(float(obj['marketCap']['usd'])),
        'cap_btc': round(float(obj['marketCap']['btc'])),
        'volume_btc': round(float(obj['volume24']['btc'])),
        'price_usd': float(obj['price']['usd']),
        'price_btc': float(obj['price']['btc']),
        'timestamp': dt.datetime.fromtimestamp(obj['timestamp'])

    }


chart_filter = rpartial(
    keep,
    ['symbol', 'timestamp', 'price_usd', 'price_btc', 'cap_usd', 'volume_btc', 'supply'],
)

def simplify_hist_data(historic_data):
    return [keep(x, ['symbol', 'timestamp', 'price_usd']) for x in historic_data]


    

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steem_urls = [
    'http://coinmarketcap.northpole.ro/api/v6/history/STEEM_2016.json',
    'http://coinmarketcap.northpole.ro/api/v6/history/STEEM_2017.json',
]
from toolz import thread_last

steem_chart = thread_last(
    historic_data(steem_urls),
    merge_historic_data,
    (map, chart_filter),
    list,
)


    

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df = pd.DataFrame(steem_chart)


    

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df.sort_values('timestamp').tail()


    

    
Out[6]:







  

    

      

      
cap_usd
      
price_btc
      
price_usd
      
supply
      
symbol
      
timestamp
      
volume_btc
    
  
  

    

      
416
      
530551374
      
0.000768
      
2.26676
      
234057145
      
STEEM
      
2017-06-12 00:00:02
      
2742
    
    

      
417
      
492335702
      
0.000796
      
2.10289
      
234123374
      
STEEM
      
2017-06-13 00:00:02
      
3129
    
    

      
418
      
546869965
      
0.000860
      
2.33504
      
234201540
      
STEEM
      
2017-06-14 00:00:02
      
3155
    
    

      
419
      
467210043
      
0.000817
      
1.99433
      
234269175
      
STEEM
      
2017-06-15 00:00:02
      
2025
    
    

      
420
      
464083335
      
0.000826
      
1.98022
      
234359483
      
STEEM
      
2017-06-16 00:00:02
      
2504
    
  

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# cf.go_offline()


    

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df.sort_values('timestamp', inplace=True)
df.set_index('timestamp', inplace=True)


    

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df[['cap_usd', 'supply']].iplot(title='USD Market Cap and STEEM Supply',
                 colors=['blue', 'orange'],
                 theme='white',
                 fill=True,
                 filename='steem-supply-cap')


    

    




/home/user/anaconda3/lib/python3.6/site-packages/cufflinks/plotlytools.py:156: FutureWarning:

pandas.tslib is deprecated and will be removed in a future version.
You can access Timestamp as pandas.Timestamp







    
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