Refactor the code and move some functions into class methods

.gitignore

@@ -1 +1,3 @@
-.vscode
+.vscode
+.idea
+*.pyc

ramp.py

@@ -39,7 +39,8 @@
                 num_profiles = num_profiles * len(input_files_to_run)
             else:
                 if len(num_profiles) != len(input_files_to_run):
-                    raise ValueError("The number of profiles parameters  should match the number of input files provided")
+                    raise ValueError(
+                        "The number of profiles parameters  should match the number of input files provided")
         else:
             num_profiles = [None] * len(input_files_to_run)
 

ramp/core/constants.py

@@ -1,3 +1,25 @@
+def switch_on_parameters():
+    """
+    Calibration parameters. These can be changed in case the user has some real data against which the model can be calibrated
+    They regulate the probability of coincident switch-on within the peak window
+
+    mu_peak corresponds to \mu_{%} in [1], p.8
+    s_peak corresponds to \sigma_{%} in [1], p.8
+
+    Notes
+    -----
+    [1] F. Lombardi, S. Balderrama, S. Quoilin, E. Colombo,
+        Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model,
+        Energy, 2019, https://doi.org/10.1016/j.energy.2019.04.097.
+    """
+
+    mu_peak = 0.5  # median value of gaussian distribution [0,1] by which the number of coincident switch_ons is randomly selected
+    s_peak = 0.5  # standard deviation (as percentage of the median value) of the gaussian distribution [0,1] above mentioned
+    op_factor = 0.5  # off-peak coincidence calculation parameter
+
+    return mu_peak, s_peak, op_factor
+
+
 OLD_TO_NEW_MAPPING = {
     "name": "user_name",
     "n_users": "num_users",

ramp/core/initialise.py

@@ -2,65 +2,78 @@
 
 #%% Initialisation of a model instance
 
-import numpy as np 
+import numpy as np
 import importlib
 from ramp.core.core import UseCase
 
 
 def yearly_pattern():
-    '''
-    Definition of a yearly pattern of weekends and weekdays, in case some appliances have specific wd/we behaviour
-    '''
-    #Yearly behaviour pattern
-    Year_behaviour = np.zeros(365)
-    Year_behaviour[5:365:7] = 1
-    Year_behaviour[6:365:7] = 1
-
-    return(Year_behaviour)
+    """Definition of a yearly pattern of weekends and weekdays, in case some appliances have specific wd/we behaviour"""
+
+    year_behaviour = np.zeros(365)
+    year_behaviour[5:365:7] = 1
+    year_behaviour[6:365:7] = 1
+
+    return year_behaviour
 
 
 def user_defined_inputs(j=None, fname=None):
-    '''
-    Imports an input file and returns a processed User_list
-    '''
+    """Imports an input file and returns a processed user_list
+
+    Parameters
+    ----------
+    j: int
+        the index of a .py input file that is in the format input_files/input_file_j.py
+    fname: path
+        path to a .xlsx input file
+        if provided, overrides the loading of input_files/input_file_j.py
+
+    Returns
+    -------
+    A list of User instances
+    """
+
     # Back compatibility with old code
     if j is not None:
-        file_module = importlib.import_module(f'ramp.input_files.input_file_{j}')
-        User_list = file_module.User_list
+        file_module = importlib.import_module(f"ramp.input_files.input_file_{j}")
+        user_list = file_module.User_list
 
     if fname is not None:
         usecase = UseCase()
         usecase.load(fname)
-        User_list = usecase.users
+        user_list = usecase.users
 
-    return(User_list)
+    return user_list
 
 
-def Initialise_model(num_profiles):
-    '''
-    The model is ready to be initialised
-    '''
+def initialise_inputs(j=None, fname=None, num_profiles=None):
+    """Loads the provided input file and prompt the user for number of profiles if not defined
 
-    if num_profiles is None:
-        num_profiles = int(input("please indicate the number of profiles to be generated: ")) #asks the user how many profiles (i.e. code runs) he wants
-    print('Please wait...') 
-    Profile = [] #creates an empty list to store the results of each code run, i.e. each stochastically generated profile
-
-    return (Profile, num_profiles)
-
-def Initialise_inputs(j=None, fname=None):
-    Year_behaviour = yearly_pattern()
+    Parameters
+    ----------
+    j: int
+        the index of a .py input file that is in the format input_files/input_file_j.py
+    fname: path
+        path to a .xlsx input file
+        if provided, overrides the loading of input_files/input_file_j.py
+    num_profiles: int
+        the number of different usecase profiles which need to be generated
+
+    Returns
+    -------
+
+    """
+
+    year_behaviour = yearly_pattern()
     user_list = user_defined_inputs(j, fname)
-
-    # Calibration parameters
-    '''
-    Calibration parameters. These can be changed in case the user has some real data against which the model can be calibrated
-    They regulate the probabilities defining the largeness of the peak window and the probability of coincident switch-on within the peak window
-    '''
-    peak_enlarg = 0.15 #percentage random enlargement or reduction of peak time range length
-    mu_peak = 0.5 #median value of gaussian distribution [0,1] by which the number of coincident switch_ons is randomly selected
-    s_peak = 0.5 #standard deviation (as percentage of the median value) of the gaussian distribution [0,1] above mentioned
-    op_factor = 0.5 #off-peak coincidence calculation parameter
-
-    return (peak_enlarg, mu_peak, s_peak, op_factor, Year_behaviour, user_list)
 
+    peak_enlarge = 0.15  # percentage random enlargement or reduction of peak time range length, corresponds to \delta_{peak} in [1], p.7
+
+    if num_profiles is None:
+        # asks the user how many profiles (i.e. code runs) they want
+        num_profiles = int(
+            input("please indicate the number of profiles to be generated: ")
+        )
+        print("Please wait...")
+
+    return (peak_enlarge, year_behaviour, user_list, num_profiles)