def main(args):
solar_output_dir = args.solar_output_dir
solar_plot = args.solar_plot
input_solar_file = args.input_solar_file
simulations_num = args.simulations_num
input_start_time = args.input_start_time
input_end_time = args.input_end_time
simulation_start_time = args.simulation_start_time
simulation_end_time = args.simulation_end_time
location_coor = args.location_coordinate
solar_target_scaled_capacity = args.solar_target_scaled_capacity
args.input_xyid_file = 'deviation.csv'
args.output_dir = 'midstep_output'
args.title = None
args.x_legend = None
args.target_scaled_capacity = None
args.scale_by_capacity = 0
# save (then delete) output file from MapeMaker, but do not show on log
args.use_output_as_intermidiate = True
args.plot = False
if args.sid_feature == "actuals":
x_name = "forecasts"
elif args.sid_feature == "forecasts":
x_name = "actuals"
upper, cap = deviation(input_start_time, input_end_time,
location_coor, input_solar_file, x_name)
upper = upper[(upper.index >= simulation_start_time)
& (upper.index < simulation_end_time)]
mapemain(args)
filename = glob.glob(args.output_dir+'/'+'*.csv')
filename = filename[0]
after_mape = pd.read_csv(filename)
after_mape.rename(columns={'Unnamed: 0': 'datetime'}, inplace=True)
after_mape['datetime'] = pd.to_datetime(after_mape['datetime'])
after_mape.set_index("datetime", inplace=True)
after_mape = after_mape[(after_mape.index >= simulation_start_time)
& (after_mape.index < simulation_end_time)]
after_mape = -after_mape
for i in range(simulations_num):
after_mape.iloc[:, i] = (after_mape.iloc[:, i]+upper['upper bound'])
if solar_target_scaled_capacity != None:
after_mape = after_mape * \
(solar_target_scaled_capacity/cap)
if solar_plot == True:
fig = after_mape.plot()
fig.figure.savefig('results')
dir = (solar_output_dir)
if not os.path.exists(dir):
os.mkdir(dir)
after_mape.to_csv(dir + '/simulations.csv')
print('output saved to' + ' ' + solar_output_dir)
else:
raise ValueError('Directory already exists.')
# delete midstep files
shutil.rmtree(args.output_dir)
os.remove('deviation.csv')
def main(args):
args.use_output_as_intermidiate = False
actual, forecast = load_dataset(args.data_source)
sum_or_indv_sites(args, args.geographic_scale, actual, forecast)
if args.geographic_scale == "sum":
data_forecast, data_actual = sum_or_indv_sites(
args, args.geographic_scale, actual, forecast)
if args.output_dir == None:
raise ValueError("No output directory specified")
args.input_xyid_file = 'data_sum.csv'
df = pd.read_csv(args.input_xyid_file)
start_date = df.iloc[0][0]
end_date = df.iloc[0][-1]
start_date = pd.to_datetime(start_date)
end_date = pd.to_datetime(end_date)
if args.simulation_start_time == None:
args.simulation_start_time = start_date
if args.simulation_end_time == None:
args.simulation_end_time = end_date
args.input_sid_file = None
args.verbosity_output = None
args.input_start_time = None
args.input_end_time = None
args.a = 4
args.curvature_target = None
args.mip_gap = 0.3
args.time_limit = 3600
args.plot_start_date = 0
args.verbosity = 2
args.sid_feature = 'actuals'
args.base_process = 'ARMA'
args.load_pickle = False
args.curvature = False
args.show_curv_model = False
args.plot = False
args.solver = 'gurobi'
args.title = None
args.x_legend = None
mapemain(args)
os.remove("data_sum.csv")
name = args.output_dir
if args.t7k_plot == True:
filename = glob.glob(name+'/'+'*.csv')
filename = filename[0]
df = pd.read_csv(filename, index_col=0)
df.index = pd.to_datetime(df.index)
df['actual'] = data_actual
df['forecast'] = data_forecast
act = df.pop("actual")
df.insert(0, act.name, act)
fore = df.pop("forecast")
df.insert(0, fore.name, fore)
df = df.head(50)
fig = df.plot(style=['--', '--'])
fig.figure.savefig(name+'/'+name+'_results')
def main(args):
if args.output_dir == None:
raise ValueError("No output directory specified.")
args.input_xyid_file = file_path
df = pd.read_csv(args.input_xyid_file, header=0) # dataset has a header
start_date = df.iloc[0][0]
end_date = df.iloc[-1][0]
print(start_date, end_date)
start_date = pd.to_datetime(start_date)
end_date = pd.to_datetime(end_date)
if args.simulation_start_time == None:
args.simulation_start_time = start_date
if args.simulation_end_time == None:
args.simulation_end_time = end_date
print(start_date, end_date)
if not (end_date >= pd.to_datetime(args.simulation_start_time) >=
start_date):
raise ValueError(
'Invalid start time. Valid date range: 2012-06-02 00:00:00 to 2014-01-01 23:00:00'
)
if not (end_date >= pd.to_datetime(args.simulation_end_time) >=
start_date):
raise ValueError(
'Invalid end time. Valid date range: 2012-06-02 00:00:00 to 2014-01-01 23:00:00'
)
args.input_xyid_file = file_path
args.target_scaled_capacity = None
args.input_sid_file = None
args.verbosity_output = None
args.input_start_time = None
args.input_end_time = None
args.a = 4
args.curvature_target = None
args.mip_gap = 0.3
args.time_limit = 3600
args.plot_start_date = 0
args.verbosity = 2
args.sid_feature = 'actuals'
args.base_process = 'ARMA'
args.load_pickle = False
args.curvature = False
args.show_curv_model = False
args.solver = 'gurobi'
args.title = None
args.x_legend = None
args.scale_by_capacity = 0
args.use_output_as_intermidiate = False
mapemain(args)
def sum_or_indv_sites(args, sum_or_indv, actual, forecast):
if sum_or_indv == 'sum':
data_forecast = forecast.sum(axis=1)
data_actual = actual.sum(axis=1)
data_sum = pd.DataFrame(data_forecast, columns=[
'forecasts'], index=forecast.index)
data_sum['actuals'] = data_actual
data_sum.to_csv('data_sum.csv')
return data_forecast, data_actual
else:
for i in range(0, len(forecast.columns)):
name = str(forecast.columns[i])
data = pd.DataFrame(index=forecast.index)
data['forecasts'] = forecast.iloc[:, i]
data['actuals'] = actual.iloc[:, i]
data.to_csv('temp_data_for_individual_sites.csv')
args.input_xyid_file = 'temp_data_for_individual_sites.csv'
df = pd.read_csv(args.input_xyid_file)
start_date = df.iloc[0][0]
end_date = df.iloc[0][-1]
start_date = pd.to_datetime(start_date)
end_date = pd.to_datetime(end_date)
if args.simulation_start_time == None:
args.simulation_start_time = start_date
if args.simulation_end_time == None:
args.simulation_end_time = end_date
args.input_sid_file = None
args.verbosity_output = None
args.input_start_time = None
args.input_end_time = None
args.a = 15
args.curvature_target = None
args.mip_gap = 0.3
args.time_limit = 3600
args.plot_start_date = 0
args.verbosity = 2
args.sid_feature = 'actuals'
args.base_process = 'ARMA'
args.load_pickle = False
args.curvature = False
args.show_curv_model = False
args.plot = False
args.solver = 'gurobi'
args.title = None
args.x_legend = None
args.output_dir = name
mapemain(args)
# code for plot goes here
if args.t7k_plot == True:
filename = glob.glob(name+'/'+'*.csv')
filename = filename[0]
df = pd.read_csv(filename, index_col=0)
df.index = pd.to_datetime(df.index)
df['actual'] = actual.iloc[:, i]
df['forecast'] = forecast.iloc[:, i]
act = df.pop("actual")
df.insert(0, act.name, act)
fore = df.pop("forecast")
df.insert(0, fore.name, fore)
df = df.head(50)
fig = df.plot(style=['--', '--'])
fig.figure.savefig(name+'/'+name+'_results')
os.remove('temp_data_for_individual_sites.csv')