API Reference

Use Case class

UseCase.__init__([name, users, date_start, ...])	Creates a UseCase instance for gathering a list of User instances which own Appliance instances
UseCase.add_user(user)	adds new user to the user property list
UseCase.save([filename])	Saves/returns the model databas including all the users and their appliances as a single pd.DataFrame or excel file.
UseCase.export_to_dataframe()	exports the model database to a pd.DataFrame containing all the data related to users and their appliances
UseCase.load(filename)	Open an .xlsx file which was produced via the save method and create instances of Users and Appliances
UseCase.collect_appliances_from_users()	Gather all appliances from a UseCase instance users into self.appliances attribute
UseCase.initialize([num_days, peak_enlarge, ...])	Sets the list of days for which to generate profiles and compute peak time range
UseCase.calc_peak_time_range([peak_enlarge])	Calculate the peak time range, which is used to discriminate between off-peak and on-peak coincident switch-on probability Calculate first the overall Peak Window (taking into account all User classes).
UseCase.generate_daily_load_profiles([days, ...])	Iterate over the days and generate a daily profile for each of the days
UseCase.generate_daily_load_profiles_parallel([...])	Generate a daily profile for each of the days in a parallel process
UseCase.date_start	Start date of the daily profiles generated by the UseCase instance
UseCase.date_end	End date of the daily profiles generated by the UseCase instance
UseCase.peak_enlarge
UseCase.num_days	Number of days for which the UseCase instance will be able to generate profiles
UseCase.datetimeindex	Return the datetimeindex of the UseCase and call UseCase.initialize() if it was not set

User class

User.__init__([user_name, num_users, ...])	Creates a User instance (User Category)
User.add_appliance(*args, **kwargs)	adds an appliance to the user category with all the appliance characteristics in a single function
User.save([filename])	Saves/returns the model databas including allappliances as a single pd.DataFrame or excel file.
User.export_to_dataframe()	Saves/returns the model databas including allappliances as a single pd.DataFrame or excel file.
User.Appliance([number, power, num_windows, ...])	Back-compatibility with legacy code
User.generate_single_load_profile([prof_i, ...])	Generates a load profile for a single user taking all its appliances into consideration
User.generate_aggregated_load_profile([...])	Generates an aggregated load profile from single load profile of each user
User.maximum_profile	Aggregate the theoretical maximal profiles of each appliance of the user by switching the appliance always on

Appliance class

Appliance.__init__(user[, number, power, ...])	Creates an appliance for a given user
Appliance.save()	returns a pd.DataFrame containing the appliance data
Appliance.export_to_dataframe()	returns a pd.DataFrame containing the appliance data
Appliance.windows([window_1, window_2, ...])	assings functioning windows to the appliance and adds the appliance to the user class
Appliance.specific_cycle(cycle_num, **kwargs)	assigining specific duty cycle for the appliance (maximum of three cycles can be assigned)
Appliance.specific_cycle_1([p_11, t_11, ...])	assigining the frist specific duty cycle for the appliace (maximum of three cycles can be assigned)
Appliance.specific_cycle_2([p_21, t_21, ...])	assigining the frist specific duty cycle for the appliace (maximum of three cycles can be assigned)
Appliance.specific_cycle_3([p_31, t_31, ...])	assigining the frist specific duty cycle for the appliace (maximum of three cycles can be assigned)
Appliance.cycle_behaviour([cw11, cw12, ...])	_summary_
Appliance.maximum_profile	Virtual maximum load profile of the appliance
Appliance.__eq__(other_appliance)	checks the equality of two appliances

Utilities

load_data(example)
download_example(destination)	Copies the model files from the ramp package to a given path :param destination: The path to copy the model files.

Visualization

Plot.__init__(df[, index])	initializes a Plot object using a pd.DataFrame
Plot.from_file(file[, sheet_name, sep, index])	initializing a Plot object from a file results
Plot.freq	return the frequency of the pd.DatetimeIndex
Plot.columns	returns a list of columns (cases) of the Plot dataframe
Plot.index
Plot.resample(freq, rule[, conversion, inplace])	returns a resampled version of the data
Plot.line([columns, engine])	creating a like plot
Plot.shadow([main_column, columns, average, ...])	creating a shadow plot
Plot.area([columns, engine])	an area plot
Plot.load_duration_curve(column[, engine])	plots the load duration curve
Plot.error(base_column, validated_data)	returns the error
Plot.peak	a dict with all peak hours for each column of the pd.DataFrame
Plot.DataFrame	returns the data of the Plot object
Plot.add_column(var)	adds new column to the data
Plot.loc([index, columns])	loc method to filter the data
Plot.head(var)	returns the top var numbers of data
Plot.plot(**kwargs)	returns a pd.DataFrame.plot object
Plot.to_excel(path)	saves the data into excel
Plot.to_csv(path[, sep])	saves the data into csv
Plot.mean()	returns the mean of the columns
Plot.sum()	returns the sum of the columns