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functions.py
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functions.py
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# Helper functions
import os
import gurobipy as gp
import pandas as pd
import glob
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import re
from datetime import datetime
def glob_result_files(folder_name):
""" Glob lp files from specified folder.
Args:
folder_name: an absolute or relative path to a directory
Returns:
list of filenames that match the pattern '*.h5'
"""
glob_pattern = os.path.join(folder_name, '*.h5')
result_files = sorted(glob.glob(glob_pattern))
return result_files
legend_position = ['center left', (1.01, 0.5)]
def axis_thousand_comma(axes, axis):
""" Set a comma after thousand for axis
Args:
axes: axes object
axis: list of strings to specify which axes to take, e.g. ['x', 'y'] or just ['y']
Returns:
Nothing
"""
if 'x' in axis:
axes.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
if 'y' in axis:
axes.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
def legend_right(axes):
""" Set legend to right
Args:
axes: axes object
Returns:
Nothing
"""
axes.legend(loc=legend_position[0], bbox_to_anchor=legend_position[1])
def legend_above(axes, location=3, cols=4, anchorbox=(0., 1.3, 1., .102)):
""" Set legend to top
Args:
axes: axes object
Returns:
Nothing
"""
axes.legend(bbox_to_anchor=anchorbox, loc=location, ncol=cols, mode="expand", borderaxespad=0.)
def stack_plot(element, axes, period):
"""
Args:
element: DataFrame to plot
axes: axes object
period: time period to plot
Returns:
Nothing
"""
plot_element = element[plot_periods[period][0]:plot_periods[period][1]]
(plot_element/1000).plot(kind='area', stacked=True, ax=axes, legend=False, color=color_fuels.values())
def concatination(variable, rc, scenario, realization, iteration, subproblems, rc_master=None, com='Elec'):
"""
Concatinate variable values over iterations
Args:
variable: string to state extracted variable
rc: dictionary of result container
scenario: current scenario
realization: current uncertaint realization
iteration: current iteration
subproblems: list of subproblem numbers
rc_master: dictionary of master result containers
com: string to define to extract commodity (default: Elec)
Return:
Concatenated pandas series
"""
if variable == 'e_pro_out':
concat_list = [rc[scenario, sub, realization,
iteration]._result[variable].xs(com,level='com').unstack().unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario,
iteration]._result[variable].xs(com,
level='com').unstack().unstack()] + concat_list
elif 'sto' in variable:
if 'con' in variable:
concat_list = [rc[scenario, sub, realization,
iteration]._result[variable].xs(com,level='com').xs('Pumped storage',
level='sto').unstack()[1:]
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario,
iteration]._result[variable].xs(com,
level='com').xs('Pumped storage',
level='sto').unstack()[1:]] + concat_list
else:
concat_list = [rc[scenario, sub, realization,
iteration]._result[variable].xs(com,level='com').xs('Pumped storage',
level='sto').unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario,
iteration]._result[variable].xs(com,
level='com').xs('Pumped storage',
level='sto').unstack()] + concat_list
elif 'tra' in variable:
if 'in' in variable:
lvl = 'sit_'
elif 'out' in variable:
lvl = 'sit'
if rc_master:
concat_list = [rc[scenario, sub, realization,
iteration]._result[variable].xs('Elec',
level='com').xs('hvac',
level='tra').unstack(level=lvl).sum(axis=1).unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario,
iteration]._result[variable].xs('Elec',
level='com').xs('hvac',
level='tra').unstack(level=lvl).sum(axis=1).unstack()]+ concat_list
series = pd.concat(concat_list).sort_index()
return series
def concatination_wo_iteration(variable, rc, scenario, realization, subproblems, rc_master=None, com='Elec'):
"""
Concatinate variable values over iterations
Args:
variable: string to state extracted variable
rc: dictionary of result container
scenario: current scenario
realization: current uncertaint realization
subproblems: list of subproblem numbers
rc_master: dictionary of master result containers
com: string to define to extract commodity (default: Elec)
Return:
Concatenated pandas series
"""
if variable == 'e_pro_out':
concat_list = [rc[scenario, sub, realization]._result[variable].xs(com,level='com').unstack().unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario]._result[variable].xs(com,
level='com').unstack().unstack()] + concat_list
elif 'sto' in variable:
if 'con' in variable:
concat_list = [rc[scenario, sub, realization]._result[variable].xs(com,level='com').xs('Pumped storage',
level='sto').unstack()[1:]
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario]._result[variable].xs(com,
level='com').xs('Pumped storage',
level='sto').unstack()[1:]] + concat_list
else:
concat_list = [rc[scenario, sub, realization]._result[variable].xs(com,level='com').xs('Pumped storage',
level='sto').unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario]._result[variable].xs(com,
level='com').xs('Pumped storage',
level='sto').unstack()] + concat_list
elif 'tra' in variable:
if 'in' in variable:
lvl = 'sit_'
elif 'out' in variable:
lvl = 'sit'
if rc_master:
concat_list = [rc[scenario, sub, realization]._result[variable].xs('Elec',
level='com').xs('hvac',
level='tra').unstack(level=lvl).sum(axis=1).unstack()
for sub in subproblems]
if rc_master:
concat_list = [rc_master[scenario]._result[variable].xs('Elec',
level='com').xs('hvac',
level='tra').unstack(level=lvl).sum(axis=1).unstack()]+ concat_list
series = pd.concat(concat_list).sort_index()
return series
def set_date_index(df, origin):
"""
Change index of dataframe to datetime index
Args:
df: Pandas DataFrame with numerical index or Series
origin: DateTime starting point
Return:
DataFrame with DateTime index
"""
df.index = pd.to_datetime(pd.to_numeric(df.index), unit='h', origin=origin)
if type(df) == pd.core.frame.DataFrame:
df.sort_index(axis=1, inplace=True)
df.sort_index(inplace=True)
return df
def date_axis_formatting(ax):
"""
Set xaxis formatting to datetime
Args:
ax: Matplotlib axes object
Return:
Nothing
"""
ax.xaxis.set_minor_locator(dates.WeekdayLocator(byweekday=(0), interval=1))
ax.xaxis.set_minor_formatter(dates.DateFormatter('%d\n%a'))
ax.xaxis.grid(True, which="minor")
ax.xaxis.set_major_locator(dates.MonthLocator())
ax.xaxis.set_major_formatter(dates.DateFormatter('%b\n%Y'))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=0)
def hour_axis_formatting(ax):
"""
Set xaxis formatting to datetime
Args:
ax: Matplotlib axes object
Return:
Nothing
"""
ax.xaxis.set_minor_locator(dates.HourLocator(byhour=range(0,24,4)))
ax.xaxis.set_minor_formatter(dates.DateFormatter('%H'))
ax.xaxis.grid(True, which="minor")
ax.xaxis.set_major_locator(dates.MonthLocator())
ax.xaxis.set_major_formatter(dates.DateFormatter('%b\n%Y'))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=0)
def summarize_plants(help_df):
# Try to store gas plants in one row and delete original rows
#import pdb; pdb.set_trace()
help_df = help_df.stack()
help_df['Gas plant'] = help_df['CC plant'] + help_df['Natural gas plant']
help_df = help_df.unstack()
help_df.drop(['CC plant', 'Natural gas plant'], axis=1, level=0, inplace=True)
# Try to delete curtailment and slack rows, if zero
try:
#if help_df['Slack powerplant'].sum().sum() == 0:
help_df.drop(['Slack powerplant'], axis=1, level=0, inplace=True)
#if help_df['Curtailment'].sum().sum() == 0:
help_df.drop(['Curtailment'], axis=1, level=0, inplace=True)
except:
pass
return help_df
def summarize_caps(help_df):
# Try to store gas plants in one row and delete original rows
#import pdb; pdb.set_trace()
help_df = help_df.T
help_df['Gas plant'] = help_df['CC plant'] + help_df['Natural gas plant']
help_df = help_df
help_df.drop(['CC plant', 'Natural gas plant'], axis=1, inplace=True)
# Try to delete curtailment and slack rows, if zero
try:
#if help_df['Slack powerplant'].sum().sum() == 0:
help_df.drop(['Slack powerplant'], axis=1, inplace=True)
#if help_df['Curtailment'].sum().sum() == 0:
help_df.drop(['Curtailment'], axis=1, inplace=True)
except:
pass
return help_df.T
def extract_season(df, season, year='2015'):
""" Slice dataframe according to season months
Args:
df: DataFrame with datetimeindex
season: string describing season ('spring', 'summer', 'autumn', 'winter')
year: string selected year, default 2015
Returns:
Sliced dataframe with season months
"""
# seasons
seasons = {'spring': [3, 4, 5], 'summer': [6, 7, 8],
'autumn': [9, 10, 11], 'winter': [1, 2, 12]}
if season in ['spring', 'summer', 'autumn']:
help_df = df[year+'-'+str(seasons[season][0]):year+'-'+str(seasons[season][2])]
elif season == 'winter':
mask = ((df.index <= year+'-'+str(seasons[season][1]))
| (df.index >= year+'-'+str(seasons[season][2])))
help_df = df[mask]
else:
print(f'{season} is not defined!')
return help_df