In [1]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os.path

# from pydygraphs import pydygraphs
from pandas.tseries.holiday import USFederalHolidayCalendar
from datetime import datetime
from pandas.tseries.holiday import Holiday, USPresidentsDay, USMemorialDay, USLaborDay, USThanksgivingDay, AbstractHolidayCalendar, sunday_to_monday

import tou

from bokeh._legacy_charts import TimeSeries
from bokeh.io import output_notebook, show
output_notebook()


    

    





    


    

    

        
        BokehJS successfully loaded.
    

In [2]:

    

dirName = os.path.normpath("./rooftop.csv")  # path to PVsyst hourly output normpath converts to correct backslashes for OS
df = pd.read_csv(dirName)  # path to PVsyst hourly output
year = 2015
rng = pd.date_range('1/1/' + str(year), periods=8760, freq='h')
df.index = rng
class SCE_Holidays(AbstractHolidayCalendar):
    """
    Southern California Edison Holidays from the tariff- TOU-GS-2
    """
    rules = [
        Holiday('New Years Day', month=1,  day=1,  observance=sunday_to_monday),
        USPresidentsDay,
        USMemorialDay,
        Holiday('July 4th', month=7,  day=4,  observance=sunday_to_monday),
        USLaborDay,
        Holiday('Veterans Day', month=11, day=11, observance=sunday_to_monday),
        USThanksgivingDay,
        Holiday('Christmas', month=12, day=25, observance=sunday_to_monday)
        ]
    
cal = SCE_Holidays()

SCE_TOU_GS_2 = {'offPeak1': {'dates': ('06/01', '9/30'),
                             'times': ('00:00', '08:00'),
                             'days': {},
                             'price': 0.03855},
                'midPeak1': {'dates': ('06/01', '9/30'),
                             'times': ('09:00', '12:00'),
                             'days': {},
                             'price': 0.06143},
                'onPeak': {'dates': ('06/01', '9/30'),
                           'times': ('13:00', '18:00'),
                           'days': {},
                           'price': 0.31971},
                'midPeak2': {'dates': ('06/01', '9/30'),
                             'times': ('19:00', '23:00'),
                             'days': {},
                             'price': 0.06143},
                'midPeak3': {'dates': ('10/01', '5/31'),
                             'times': ('09:00', '21:00'),
                             'days': {},
                             'price': 0.06073},
                'offPeak2': {'dates': ('10/01', '5/31'),
                             'times': ('22:00', '08:00'),
                             'days': {},
                             'price': 0.04064},
                'holWkndsSummer': {'dates': ('06/01', '09/30'),
                                   'times': ('00:00', '23:00'),
                                   'days': {'dropHol': True, 'dropWknd': True, 'inverse': True},
                                   'price': 0.03584},
                'holWkndsWinter': {'dates': ('10/01', '5/31'),
                                   'times': ('00:00', '23:00'),
                                   'days': {'dropHol': True, 'dropWknd': True, 'inverse': True},
                                   'price': 0.04064}
                }
gs2 = tou.TouRate(year, cal, 0.04482, SCE_TOU_GS_2)


    

In [3]:

    

gs2.get_summary(df)


    

    
Out[3]:






  

    

      

      
Energy kWh
      
Prices $/kWh
      
Value $
    
  
  

    

      
holWkndsSummer
      
57577.8022
      
0.08066
      
4644.225525
    
    

      
holWkndsWinter
      
95985.0122
      
0.08546
      
8202.879143
    
    

      
midPeak1
      
68128.4210
      
0.10625
      
7238.644731
    
    

      
midPeak2
      
0.0000
      
0.10625
      
0.000000
    
    

      
midPeak3
      
205467.9377
      
0.10555
      
21687.140824
    
    

      
offPeak1
      
16630.1356
      
0.08337
      
1386.454405
    
    

      
offPeak2
      
18707.1063
      
0.08546
      
1598.709304
    
    

      
onPeak
      
60338.5698
      
0.36453
      
21995.218849
    
  

In [4]:

    

gs2.get_summary(df).sum()


    

    
Out[4]:





Energy kWh      522834.984800
Prices $/kWh         1.017530
Value $          66753.272782
dtype: float64

In [5]:

    

gs2_periods = gs2.get_all_periods(df)


    

In [6]:

    

#Set first row of data to zero to avoid issue with NaNs
#Bokeh github issue #2780
gs2_periods.loc['2015-01-01 00:00:00'] = 0
gs2_periods['date'] = gs2_periods.index


    

In [7]:

    

ts = TimeSeries(gs2_periods, index='date', title="TOU Periods", legend='top_left', ylabel='Energy (kWh)')
show(ts)


    

    




/Users/Ben/anaconda/lib/python2.7/site-packages/bokeh/_legacy_charts/_chart.py:92: UserWarning: Instantiating a Legacy Chart from bokeh._legacy_charts
  warn("Instantiating a Legacy Chart from bokeh._legacy_charts")






    

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