multiple.py

multiple.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.dates import DateFormatter
from datetime import timedelta, date


def datetime(x):
  return np.array(x, dtype = np.datetime64)


if __name__ == '__main__':
  
  # Save the file as a .csv file from your excel.
  file_name = 'RTC_RTD_DNI_LBMP_EXT.csv'
  
  # Read the file into the code.
  df = pd.read_csv(file_name, parse_dates = ['BINDING_TIMESTAMP'])
  
  # This is for xticks..  the format for ticks in the x-axis. Lets not use this for now.
  date_formatter = '%H:%M'
  xfmt = DateFormatter(date_formatter)
  
  # loop through all the unique zone_names
  for zone_name in df['ZONE_NAME'].unique():
    # Read all the data for the zone_name
    print('> Reading all the data for the zone %s', zone_name)
    dfz = df[df.ZONE_NAME == zone_name]
    
    #Goes through dates.
    for i in range(1, 32):
      start = date(2016, 12, i)
      end = start + timedelta(1)
    
      mask = (dfz['BINDING_TIMESTAMP'] >= start) & (dfz['BINDING_TIMESTAMP'] < end)
      _dfz = dfz[mask]
    
      # fig = plt.figure()
      # ax = plt.gca()
      
      # _dfz = dfz
      
      # GRAPH PLOTTING
    
      # Dont change
      # -----------------------------------------------------------------------------------------------------
      fig, ax = plt.subplots(figsize = (15, 5))
      fig.subplots_adjust(right = 0.75)
      
      # Twin the x-axis twice to make independent y-axes.
      axes = [ax, ax.twinx()]
      
      # Move the last y-axis spine over to the right by 20% of the width of the axes
      axes[1].spines['right'].set_position(('axes', 1.0))
      
      # To make the border of the right-most axis visible, we need to turn the frame
      # on. This hides the other plots, however, so we need to turn its fill off.
      axes[-1].set_frame_on(True)
      axes[-1].patch.set_visible(False)
      
      # -----------------------------------------------------------------------------------------------------
      
      # Set the title
      plt.title("Zone %s" % (zone_name))
      
      # Graph 01 -- all the axes[0] variables refer to graph 1
      axes[0].plot(x = datetime(_dfz['BINDING_TIMESTAMP']), y = _dfz['RTD_ZONAL_LBMP'], label = 'RTD_LBMP', c = 'red')
      axes[0].plot(x = datetime(_dfz['BINDING_TIMESTAMP']), y = _dfz['RTC_ZONAL_LBMP'], label = 'RTC_LBMP', c = 'blue')
      axes[0].set_ylabel('Load Fixed MW')
      
      # Graph 02 -- all the axes[1] variables refer to graph 2
      axes[1].plot(datetime(_dfz['BINDING_TIMESTAMP']), _dfz['RTC_RTD_div ($/MWh))'], label = 'RTC_RTD_div ($/MWh))', c = 'green')
      axes[1].set_ylabel('RTC_RTD_div ($/MWh))')
      
      # xaxis set up
      axes[0].xaxis.set_major_locator(MinuteLocator(byminute = [0, 30], interval = 1))
      axes[0].xaxis.set_major_formatter(xfmt)
      plt.xlabel('Binding Timestamp')
      labels = axes[0].get_xticklabels()
      plt.setp(labels, rotation = 90)
      
      plt.grid(linestyle = 'dotted')
      axes[0].legend(loc = 'upper left')
      axes[1].legend(loc = 'upper right')
      
      plt.tight_layout()
      start = df['BINDING_TIMESTAMP'].min()
      end = df['BINDING_TIMESTAMP'].max()
      plt.xlim(start, end)
      plt.show()