Error in length of number of occupants

[c0nb4]

Please describe the bug

Currently the flats are assigned like this:

def generate_number_occupants(self,area):
"""
Generate number of occupants for different building types.

    Parameters
    ----------
    random_nb : random number in [0,1).

    Returns
    -------
    None.
    """

    if self.building == "SFH":
        # choose random number of occupants (1-5) for single family houses  (assumption)
        probabilities = (0.245114, 0.402323, 0.154148, 0.148869, 0.049623) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a single-family house, assuming a maximum of 5 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
        # loop over all flats of current single family house
        for k in range(self.nb_flats):
            random_nb = rd.random()  # picking random number in [0,1)
            j = 1  # staring with one occupant
            # the random number decides how many occupants are chosen (1-5)
            while j <= 5 :
                if random_nb < sum(probabilities[:j]) :
                    self.nb_occ.append(j)  # minimum is 1 occupant
                    break
                j += 1

    elif self.building == "TH":
        # choose random number of occupants (1-5) for terraced houses  (assumption)
        probabilities = (0.236817, 0.400092, 0.157261, 0.154371, 0.051457) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a terraced house, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
        # loop over all flats of current terraced house
        for k in range(self.nb_flats):
            random_nb = rd.random()  # picking random number in [0,1)
            j = 1  # staring with one occupant
            # the random number decides how many occupants are chosen (1-5)
            while j <= 5 :
                if random_nb < sum(probabilities[:j]) :
                    self.nb_occ.append(j)  # minimum is 1 occupant
                    break
                j += 1

    elif self.building == "MFH":
        # choose random number of occupants (1-5) for each flat in the multi family house  (assumption)
        probabilities = (0.490622, 0.307419, 0.101949, 0.074417, 0.024805) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a flat, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
        # loop over all flats of current multi family house
        for k in range(self.nb_flats):
            random_nb = rd.random()  # picking random number in [0,1)
            j = 1  # staring with one occupant
            # the random number decides how many occupants are chosen (1-5)
            while j <= 5 :
                if random_nb < sum(probabilities[:j]) :
                    self.nb_occ.append(j)  # minimum is 1 occupant
                    break
                j += 1

    elif self.building == "AB":
        # choose random number of occupants (1-5) for each flat in the apartment block  (assumption)
        probabilities = (0.490622, 0.307419, 0.101949, 0.074417, 0.024805) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a flat, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
        # loop over all flats of current apartment block
        for k in range(self.nb_flats):
            random_nb = rd.random()  # picking random number in [0,1)
            j = 1  # staring with one occupant
            # the random number decides how many occupants are chosen (1-5)
            while j <= 5 :
                if random_nb < sum(probabilities[:j]) :
                    self.nb_occ.append(j)  # minimum is 1 occupant
                    break
                j += 1

In large houses, with more than eight flats, this leads to an error.

[c0nb4]

def generate_number_occupants(self, area):
self.nb_occ = []

building_probs = {
    "SFH": (0.245114, 0.402323, 0.154148, 0.148869, 0.049623),
    "TH":  (0.236817, 0.400092, 0.157261, 0.154371, 0.051457),
    "MFH": (0.490622, 0.307419, 0.101949, 0.074417, 0.024805),
    "AB":  (0.490622, 0.307419, 0.101949, 0.074417, 0.024805),
}
probabilities = building_probs.get(self.building, building_probs["MFH"])

# Build cumulative distribution
import random
cdf = []
cumul_sum = 0.0
for p in probabilities:
    cumul_sum += p
    cdf.append(cumul_sum)

# Single pass for all flats
for _ in range(self.nb_flats):
    r = random.random()
    occupant = 1
    # Find the index where r fits into the cumulative distribution
    for idx, threshold in enumerate(cdf):
        if r < threshold:
            occupant = idx + 1  # occupant is 1-based
            break
    self.nb_occ.append(occupant)

[c0nb4]

Confirmed on simulation pc.