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Initial Commit. I'm afraid of testing it.

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This commit is contained in:
Lars Bogner
2025-05-01 15:48:11 +02:00
parent e4dae9ec35
commit 51bc491cc8
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.gitignore
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# PyPI configuration file
.pypirc
# csv containing personal information
test-config.csv
.pre-commit-config.yaml
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repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.4.2 # Replace with the latest version
hooks:
- id: ruff
name: ruff check
args: ["check"]
- id: ruff
name: ruff format
args: ["format"]
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.9.0 # Replace with the latest version
hooks:
- id: mypy
args:
- "--allow-redefinition"
- "import-untyped"
- "src/"
.python-version
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3.13
pyproject.toml
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[project]
name = "tatami"
version = "0.1.0"
description = "Tool for Arranging Tasty Appointments, Meetings & Invitations"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"numpy>=2.2.4",
"pandas>=2.2.3",
"pandas-stubs>=2.2.3.250308",
"requests>=2.32.3",
"tqdm>=4.67.1",
"types-requests>=2.32.0.20250328",
"types-tqdm>=4.67.0.20250404",
]
[dependency-groups]
dev = [
"mypy>=1.15.0",
"ruff>=0.11.5",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
running_dinner/running_dinner.py
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import copy
import datetime
import random
import os
import pandas
import requests
import xlsxwriter
from progress.bar import IncrementalBar
EXCEL_DIR = "C:/Daten/RunningDinner/running_dinner/running_dinner.xlsx"
# Set the base url for the Google Maps Distance Matrix API
base_url = "https://maps.googleapis.com/maps/api/distancematrix/json?"
# Google Maps API Key
api_key = os.environ["MAPS_API_KEY"]
# Excel Sheet muss 7 Spalten haben:
# Zeitstempel (Erzeugt von Google Forms, wird in Zeile 30 entfernt) | Name | | Zimmergröße | Handynummer | Adresse | Entfernung zu Afterparty
# Spalte mit Namen muss 'Namen' als Header haben
# Maximale Distanz von Nachspeise zu Afterparty
max_dist_to_ap = 2
rd = pandas.read_excel(EXCEL_DIR)
appendants = rd.set_index("Name").T.to_dict("list")
addresses = []
address_indices = []
for i, app in enumerate(appendants):
appendants[app].pop(0)
if "passen" in appendants[app][1]: # Zimmergröße sehr gut
appendants[app][1] = 2
elif "gehen" in appendants[app][1]: # Zimmergröße gut
appendants[app][1] = 1
else: # Zimmergröße passt nicht
appendants[app][1] = 0
if type(appendants[app][3]) == str:
adr = appendants[app][3]
addresses.append(adr)
address_indices.append(i)
names = list(appendants.keys())
# print(appendants['Fabio Rodrigues'])
ind = 0
distances = []
for _ in range(len(names)):
distances.append([float("inf")] * len(names))
for i, adr in enumerate(addresses):
addresses_wo_adr = addresses.copy()
addresses_wo_adr.pop(i)
address_indices_wo_adr = address_indices.copy()
address_indices_wo_adr.pop(i)
# Build the request parameters
params = {
"origins": [adr],
"destinations": "|".join(addresses_wo_adr),
"key": api_key,
"mode": "bicycling",
}
# Make the request to the Google Maps Distance Matrix API
response = requests.get(base_url, params=params)
# Extract the distance matrix from the response
distance_matrix = response.json()["rows"][0]["elements"]
distances[address_indices[i]][address_indices[i]] = 0.0
for j in range(len(distance_matrix)):
distances[address_indices[i]][address_indices_wo_adr[j]] = distance_matrix[j][
"duration"
]["value"]
top1_max_distance = float("inf")
top1_total_distance = float("inf")
top1_teams = {}
top1_loc = {}
top2_max_distance = float("inf")
top2_total_distance = float("inf")
top2_teams = {}
top2_loc = {}
top3_max_distance = float("inf")
top3_total_distance = float("inf")
top3_teams = {}
top3_loc = {}
top4_max_distance = float("inf")
top4_total_distance = float("inf")
top4_teams = {}
top4_loc = {}
top5_max_distance = float("inf")
top5_total_distance = float("inf")
top5_teams = {}
top5_loc = {}
iterations = 1000
bar = IncrementalBar("Searching optimal Route", max=iterations)
for _ in range(iterations):
searching = True
while searching == True:
random.shuffle(names)
teams = dict()
for i in range(len(names)):
if i % 2 == 0 and i < len(names) - (len(names) % 6):
teams["Team " + str(int(i / 2 + 1))] = {
"Namen": [names[i], names[i + 1]]
}
if i >= len(names) - (len(names) % 6):
teams["Team " + str(i - (len(names) - (len(names) % 6)) + 1)][
"Namen"
].append(names[i])
for t in teams:
teams[t]["Location"] = []
for n in teams[t]["Namen"]:
if type(appendants[n][3]) == str:
teams[t]["Location"].append(appendants[n][3])
num_teams = len(teams)
t = list(range(1, num_teams + 1))
num_groups = int(num_teams / 3)
Hauptspeisen = t[0:num_groups]
Vorspeisen = t[num_groups : num_groups * 2]
Nachspeisen = t[num_groups * 2 : num_groups * 3]
for i in Vorspeisen + Hauptspeisen + Nachspeisen:
if i in Vorspeisen:
index = Vorspeisen.index(i)
teams["Team " + str(i)]["Gang"] = "Vorspeise"
teams["Team " + str(i)]["Gäste"] = [
"Team " + str(Hauptspeisen[index]),
"Team " + str(Nachspeisen[index]),
]
teams["Team " + str(i)]["Route"] = ["Team " + str(i)]
teams["Team " + str(Hauptspeisen[index])]["Route"] = ["Team " + str(i)]
teams["Team " + str(Nachspeisen[index])]["Route"] = ["Team " + str(i)]
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"] = []
for g in teams["Team " + str(i)]["Gäste"]:
for n in teams[g]["Namen"]:
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"].append(
appendants[n][0]
)
elif i in Hauptspeisen:
index = Hauptspeisen.index(i)
teams["Team " + str(i)]["Gang"] = "Hauptspeise"
teams["Team " + str(i)]["Gäste"] = [
"Team " + str(Vorspeisen[(index - 1) % num_groups]),
"Team " + str(Nachspeisen[(index + 1) % num_groups]),
]
teams["Team " + str(i)]["Route"].append("Team " + str(i))
teams["Team " + str(Vorspeisen[(index - 1) % num_groups])][
"Route"
].append("Team " + str(i))
teams["Team " + str(Nachspeisen[(index + 1) % num_groups])][
"Route"
].append("Team " + str(i))
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"] = []
for g in teams["Team " + str(i)]["Gäste"]:
for n in teams[g]["Namen"]:
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"].append(
appendants[n][0]
)
elif i in Nachspeisen:
index = Nachspeisen.index(i)
teams["Team " + str(i)]["Gang"] = "Nachspeise"
teams["Team " + str(i)]["Gäste"] = [
"Team " + str(Vorspeisen[(index - 1) % num_groups]),
"Team " + str(Hauptspeisen[(index + 1) % num_groups]),
]
teams["Team " + str(i)]["Route"].append("Team " + str(i))
teams["Team " + str(Vorspeisen[(index - 1) % num_groups])][
"Route"
].append("Team " + str(i))
teams["Team " + str(Hauptspeisen[(index + 1) % num_groups])][
"Route"
].append("Team " + str(i))
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"] = []
for g in teams["Team " + str(i)]["Gäste"]:
for n in teams[g]["Namen"]:
teams["Team " + str(i)]["Unverträglichkeiten der Gäste"].append(
appendants[n][0]
)
searching = False
for t in teams:
size = 0
for n in teams[t]["Namen"]:
size = size + appendants[n][1]
if size == 0:
# print(str(teams[t]['Namen']) + ' Raum zu klein')
searching = True
break
max_room = 0
for n in teams[t]["Namen"]:
max_room = max(max_room, appendants[n][1])
dist = 6
for n in teams[t]["Namen"]:
if appendants[n][1] == max_room:
dist = min(dist, int(appendants[n][4]))
if dist > max_dist_to_ap and teams[t]["Gang"] == "Nachspeise":
# print(str(teams[t]['Namen']) + ' Nachspeise, aber zu weit von Afterparty entfernt')
searching = True
break
# if 'Fabio Rodrigues' in teams[t]['Namen'] and len(teams[t]['Namen']) == 3:
# print('Fabio in 3er Gruppe')
# searching = True
# break
# if 'Fabio Rodrigues' in teams[t]['Namen'] and teams[t]['Gang'] != 'Nachspeise':
# print('Fabio macht nicht Nachspeise')
# searching = True
# break
# if 'Fabio Rodrigues' in teams[t]['Namen'] and 'Alex Peeters' not in teams[t]['Namen']:
# print('Fabio nicht mit Alex')
# searching = True
# break
possible_location_permutations = 1
number_locations = []
for t in teams:
n_loc = len(teams[t]["Location"])
number_locations.append(n_loc)
possible_location_permutations *= n_loc
perm = [0] * len(number_locations)
for j in range(possible_location_permutations):
if j > 0:
i = 0
adding = True
while adding:
if perm[i] + 1 < number_locations[i]:
perm[i] += 1
adding = False
elif perm[i] + 1 == number_locations[i] and perm[i] > 0:
perm[i] = 0
i += 1
loc = {}
for k, l in enumerate(teams):
loc[l] = perm[k]
max_distance = 0.0
total_distance = 0.0
for t in teams:
way1 = distances[
address_indices[
addresses.index(
teams[teams[t]["Route"][0]]["Location"][
loc[teams[t]["Route"][0]]
]
)
]
][
address_indices[
addresses.index(
teams[teams[t]["Route"][1]]["Location"][
loc[teams[t]["Route"][1]]
]
)
]
]
way2 = distances[
address_indices[
addresses.index(
teams[teams[t]["Route"][1]]["Location"][
loc[teams[t]["Route"][1]]
]
)
]
][
address_indices[
addresses.index(
teams[teams[t]["Route"][2]]["Location"][
loc[teams[t]["Route"][2]]
]
)
]
]
way3 = distances[
address_indices[
addresses.index(
teams[teams[t]["Route"][2]]["Location"][
loc[teams[t]["Route"][2]]
]
)
]
][
address_indices[
addresses.index("Sebastian-Kneipp-Straße 6, 76131 Karlsruhe")
]
]
team_distance = way1 + way2 + way3
total_distance += team_distance
max_distance = max(max_distance, way1, way2)
teams[t]["Zeit"] = str(datetime.timedelta(seconds=team_distance))
if total_distance < top1_total_distance:
top5_max_distance = top4_max_distance
top5_total_distance = top4_total_distance
top5_teams = copy.deepcopy(top4_teams)
top5_loc = copy.deepcopy(top4_loc)
top4_max_distance = top3_max_distance
top4_total_distance = top3_total_distance
top4_teams = copy.deepcopy(top3_teams)
top4_loc = copy.deepcopy(top3_loc)
top3_max_distance = top2_max_distance
top3_total_distance = top2_total_distance
top3_teams = copy.deepcopy(top2_teams)
top3_loc = copy.deepcopy(top2_loc)
top2_max_distance = top1_max_distance
top2_total_distance = top1_total_distance
top2_teams = copy.deepcopy(top1_teams)
top2_loc = copy.deepcopy(top1_loc)
top1_max_distance = max_distance
top1_total_distance = total_distance
top1_teams = copy.deepcopy(teams)
top1_loc = copy.deepcopy(loc)
elif total_distance < top2_total_distance:
top5_max_distance = top4_max_distance
top5_total_distance = top4_total_distance
top5_teams = copy.deepcopy(top4_teams)
top5_loc = copy.deepcopy(top4_loc)
top4_max_distance = top3_max_distance
top4_total_distance = top3_total_distance
top4_teams = copy.deepcopy(top3_teams)
top4_loc = copy.deepcopy(top3_loc)
top3_max_distance = top2_max_distance
top3_total_distance = top2_total_distance
top3_teams = copy.deepcopy(top2_teams)
top3_loc = copy.deepcopy(top2_loc)
top2_max_distance = max_distance
top2_total_distance = total_distance
top2_teams = copy.deepcopy(teams)
top2_loc = copy.deepcopy(loc)
elif total_distance < top3_total_distance:
top5_max_distance = top4_max_distance
top5_total_distance = top4_total_distance
top5_teams = copy.deepcopy(top4_teams)
top5_loc = copy.deepcopy(top4_loc)
top4_max_distance = top3_max_distance
top4_total_distance = top3_total_distance
top4_teams = copy.deepcopy(top3_teams)
top4_loc = copy.deepcopy(top3_loc)
top3_max_distance = max_distance
top3_total_distance = total_distance
top3_teams = copy.deepcopy(teams)
top3_loc = copy.deepcopy(loc)
elif total_distance < top4_total_distance:
top5_max_distance = top4_max_distance
top5_total_distance = top4_total_distance
top5_teams = copy.deepcopy(top4_teams)
top5_loc = copy.deepcopy(top4_loc)
top4_max_distance = max_distance
top4_total_distance = total_distance
top4_teams = copy.deepcopy(teams)
top4_loc = copy.deepcopy(loc)
elif total_distance < top5_total_distance:
top5_max_distance = max_distance
top5_total_distance = total_distance
top5_teams = copy.deepcopy(teams)
top5_loc = copy.deepcopy(loc)
bar.next()
bar.finish()
for t in top5_teams:
top5_teams[t]["Location"] = top5_teams[t]["Location"][top5_loc[t]]
for t in top4_teams:
top4_teams[t]["Location"] = top4_teams[t]["Location"][top4_loc[t]]
for t in top3_teams:
top3_teams[t]["Location"] = top3_teams[t]["Location"][top3_loc[t]]
for t in top2_teams:
top2_teams[t]["Location"] = top2_teams[t]["Location"][top2_loc[t]]
for t in top1_teams:
top1_teams[t]["Location"] = top1_teams[t]["Location"][top1_loc[t]]
print("######################################################")
print("Top 5 Team:")
print(top3_teams)
print(
"The max time of one single route is: {}".format(
str(datetime.timedelta(seconds=top5_max_distance))
)
)
print(
"The total time spent on bike is: {}".format(
str(datetime.timedelta(seconds=top5_total_distance))
)
)
print("Top 4 Team:")
print(top3_teams)
print(
"The max time of one single route is: {}".format(
str(datetime.timedelta(seconds=top4_max_distance))
)
)
print(
"The total time spent on bike is: {}".format(
str(datetime.timedelta(seconds=top4_total_distance))
)
)
print("Top 3 Team:")
print(top3_teams)
print(
"The max time of one single route is: {}".format(
str(datetime.timedelta(seconds=top3_max_distance))
)
)
print(
"The total time spent on bike is: {}".format(
str(datetime.timedelta(seconds=top3_total_distance))
)
)
print("Top 2 Team:")
print(top2_teams)
print(
"The max time of one single route is: {}".format(
str(datetime.timedelta(seconds=top2_max_distance))
)
)
print(
"The total time spent on bike is: {}".format(
str(datetime.timedelta(seconds=top2_total_distance))
)
)
print("Top 1 Team:")
print(top1_teams)
print(
"The max time of one single route is: {}".format(
str(datetime.timedelta(seconds=top1_max_distance))
)
)
print(
"The total time spent on bike is: {}".format(
str(datetime.timedelta(seconds=top1_total_distance))
)
)
print("Type 1, 2, 3, 4 or 5 to choose the teams configuration:")
inp = int(input())
if inp == 1:
print("You chose the 1st option.")
teams = copy.deepcopy(top1_teams)
elif inp == 2:
print("You chose the 2nd option.")
teams = copy.deepcopy(top2_teams)
elif inp == 3:
print("You chose the 3rd option.")
teams = copy.deepcopy(top3_teams)
elif inp == 4:
print("You chose the 4th option.")
teams = copy.deepcopy(top4_teams)
elif inp == 5:
print("You chose the 5th option.")
teams = copy.deepcopy(top5_teams)
else:
print("Input could not be resolved, choosing number 1.")
teams = copy.deepcopy(top1_teams)
for t in teams:
if "Fabio Rodrigues" in teams[t]["Namen"]:
print(t + ": " + str(teams[t]["Namen"]) + " machen " + str(teams[t]["Gang"]))
if "Arnold Resch" in teams[t]["Namen"]:
print(t + ": " + str(teams[t]["Namen"]) + " machen " + str(teams[t]["Gang"]))
print("######################################################")
# Creating xlsx file
workbook = xlsxwriter.Workbook("running_dinner_masterplan.xlsx")
bold = workbook.add_format({"bold": True})
worksheet = workbook.add_worksheet("Übersicht")
worksheet.set_column(0, 0, 20)
worksheet.set_column(1, 1, 30)
worksheet.set_column(2, 2, 20)
worksheet.set_column(3, 3, 40)
worksheet.set_column(4, 4, 80)
row = 0
col = 0
header = workbook.add_format(
{
"bold": 1,
"border": 1,
"align": "center",
"valign": "vcenter",
"fg_color": "yellow",
}
)
worksheet.merge_range("A1:E1", "Der große Running-Dinner Masterplan", header)
row += 2
worksheet.write(row, col, "Teams", bold)
worksheet.write(row, col + 1, "Namen", bold)
worksheet.write(row, col + 2, "Telefonnummer", bold)
worksheet.write(row, col + 3, "Adresse", bold)
worksheet.write(row, col + 4, "Unverträglichkeiten", bold)
row += 1
for t in teams:
worksheet.write(row, col, t, bold)
worksheet.write(row + 1, col, teams[t]["Gang"])
for name in teams[t]["Namen"]:
worksheet.write(row, col + 1, name)
worksheet.write(row, col + 2, appendants[name][2])
if type(appendants[name][3]) == str:
worksheet.write(row, col + 3, appendants[name][3])
worksheet.write(row, col + 4, appendants[name][0])
row += 1
row += 3
worksheet.merge_range("A" + str(row) + ":E" + str(row), "Die Routen", header)
row += 1
worksheet.write(row, col, "18:30 Uhr", bold)
worksheet.write(row + 6, col, "20:00 Uhr", bold)
worksheet.write(row + 12, col, "22:00 Uhr", bold)
row += 1
vor_g = 1
haupt_g = 1
nach_g = 1
for t in teams:
if teams[t]["Gang"] == "Vorspeise":
worksheet.write(row, col + vor_g - 1, "Vorspeise Gruppe " + str(vor_g), bold)
worksheet.write(row + 1, col + vor_g - 1, t, bold)
for i, g in enumerate(teams[t]["Gäste"]):
worksheet.write(row + 2 + i, col + vor_g - 1, g)
vor_g += 1
elif teams[t]["Gang"] == "Hauptspeise":
worksheet.write(
row + 6, col + haupt_g - 1, "Hauptspeise Gruppe " + str(haupt_g), bold
)
worksheet.write(row + 7, col + haupt_g - 1, t, bold)
for i, g in enumerate(teams[t]["Gäste"]):
worksheet.write(row + 8 + i, col + haupt_g - 1, g)
haupt_g += 1
elif teams[t]["Gang"] == "Nachspeise":
worksheet.write(
row + 12, col + nach_g - 1, "Nachspeise Gruppe " + str(nach_g), bold
)
worksheet.write(row + 13, col + nach_g - 1, t, bold)
for i, g in enumerate(teams[t]["Gäste"]):
worksheet.write(row + 14 + i, col + nach_g - 1, g)
nach_g += 1
for t in teams:
worksheet = workbook.add_worksheet(t)
worksheet.set_column(0, 0, 60)
worksheet.set_column(1, 1, 100)
row = 0
col = 0
worksheet.merge_range("A1:B1", t, header)
row += 2
for n in [
"Namen",
"Gang",
"Location",
"Route",
"Gäste",
"Unverträglichkeiten der Gäste",
"Zeit",
]:
if n == "Namen":
for i, name in enumerate(teams[t][n]):
if i == 0:
text = name
else:
text += " und " + name
worksheet.write(row, col, "Namen", bold)
worksheet.write(row, col + 1, text)
row += 1
elif n == "Gang":
worksheet.write(row, col, "Euer Gang", bold)
worksheet.write(row, col + 1, teams[t][n])
row += 1
elif n == "Location":
worksheet.write(row, col, "Location (automatisch optimal gewählt)", bold)
worksheet.write(row, col + 1, teams[t][n])
row += 1
elif n == "Gäste":
for i, gaeste in enumerate(teams[t][n]):
if i == 0:
text = gaeste
else:
text += " und " + gaeste
worksheet.write(row, col, "Eure Gäste", bold)
worksheet.write(row, col + 1, text)
row += 1
for i, gaeste in enumerate(teams[t][n]):
for j, namen in enumerate(teams[gaeste]["Namen"]):
if i == 0 and j == 0:
text = namen
elif (
i == len(teams[t][n]) - 1
and j == len(teams[gaeste]["Namen"]) - 1
):
text += " und " + namen
else:
text += ", " + namen
worksheet.write(row, col + 1, text)
row += 2
elif n == "Route":
for i, route in enumerate(teams[t][n]):
if i == 0:
text = "Zuerst bei " + route
else:
text += ", dann bei " + route
worksheet.write(row, col, "Eure Route", bold)
worksheet.write(row, col + 1, text)
row += 2
elif n == "Unverträglichkeiten der Gäste":
for i, unv in enumerate(teams[t][n]):
if i == 0:
text = unv
else:
text += " und " + unv
worksheet.write(row, col, "Achtet auf diese Eigenarten eurer Gäste", bold)
worksheet.write(row, col + 1, text)
row += 2
elif n == "Zeit":
worksheet.write(
row,
col,
"Gesamtzeit für eure Route von Vorspeise bis Afterparty (per Fahrrad)",
bold,
)
worksheet.write(row, col + 1, teams[t][n])
workbook.close()
src/tatami/__init__.py
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def main() -> None:
print("Hello from tatami-core!")
src/tatami/classes.py
+114
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import datetime as dt
import pandas as pd
from uuid import uuid4
class Participant:
def __init__(
self, name: str, address: str, phone: str, kitchen_size: float, allergies: str
):
self.uuid = str(uuid4())
self.name = name
self.address = address
self.phone = phone
self.kitchen_size = kitchen_size # bigger is better; range 0-10
self.allergies = allergies
def get_penalty(self) -> dt.timedelta:
return dt.timedelta(minutes=3 * (10 - self.kitchen_size))
def get_after_party_time(
self, distance_matrix: pd.DataFrame, after_party_group: "Group"
) -> dt.timedelta:
return (
self.get_penalty()
+ pd.to_timedelta(
distance_matrix.loc[self.uuid, after_party_group.main_member.uuid]
).to_pytimedelta()
)
def __repr__(self) -> str:
return f"Participant(name={self.name}, uuid={self.uuid})[address={self.address}, phone={self.phone}, kitchen_size={self.kitchen_size}, allergies={self.allergies}]"
def __str__(self) -> str:
return self.__repr__()
def dict(self) -> dict:
return {
"uuid": self.uuid,
"name": self.name,
"address": self.address,
"phone": self.phone,
"kitchen_size": self.kitchen_size,
"allergies": self.allergies,
}
class Group:
def __init__(self, members: list[Participant], main_member: int = 0):
self.uuid = "Group_" + str(uuid4())
self.members = members
self.main_member = members[main_member]
self.course: str | None = None # For ordering the groups allowed values: "starter", "main", "dessert"
self.hosts: list[Group] | None = None
def set_course(self, course: str):
self.course = course
def set_hosts(self, hosts: list["Group"]):
self.hosts = hosts
self.sort_hosts()
def sort_hosts(self):
if self.hosts is None:
return
courses = {"starter": 0, "main": 1, "dessert": 2}
self.hosts.sort(key=lambda x: courses[x.course] if x.course in courses else 3)
def add_host(self, host: "Group"):
if self.hosts is None:
self.hosts = []
self.hosts.append(host)
self.sort_hosts()
def add_member(self, member: Participant, main_member: bool = False):
if main_member:
self.main_member = member
self.members.append(member)
def get_total_time(
self, distance_matrix: pd.DataFrame, after_party_group: "Group"
) -> dt.timedelta:
if self.hosts is None:
raise ValueError("Hosts must be set.")
if after_party_group.uuid in [h.uuid for h in self.hosts]:
groups = self.hosts
else:
groups = self.hosts + [after_party_group]
total_time = dt.timedelta()
for group, next_group in zip(groups[:-1], groups[1:]):
total_time += pd.to_timedelta(
distance_matrix.loc[group.main_member.uuid, next_group.main_member.uuid]
).to_pytimedelta()
total_time += group.main_member.get_penalty()
return total_time
def dict(self) -> dict:
return {
"uuid": self.uuid,
"members": [member.uuid for member in self.members],
"main_member": self.main_member.uuid,
"course": self.course,
"hosts": [host.uuid for host in self.hosts] if self.hosts else None,
}
def get_guests(self, groups: list["Group"]) -> list[Participant]:
guests: list[Participant] = []
if self.hosts is None:
return guests
for group in groups:
if group.hosts is not None and self.uuid in [g.uuid for g in group.hosts]:
guests.extend(group.members)
return guests
src/tatami/tatami_masterplan.py
+210
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from tatami.classes import Participant, Group
from tatami.traveltimes import reduce_distance_matrix, get_participant_distance_matrix
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
from itertools import permutations
def get_after_party_group(address: str) -> Group:
"""
Create a group for the after party with a single participant.
"""
participant = Participant(
name="After Party", address=address, phone="", kitchen_size=10, allergies=""
)
return Group(members=[participant], main_member=0)
def get_masterplan(
participants: list[Participant],
distance_matrix: pd.DataFrame,
after_party_group: Group,
) -> tuple[list[dict], list[dict]]:
groups_per_course = np.floor(len(participants) / 6).astype(int)
participants.sort(
key=lambda x: x.get_after_party_time(distance_matrix, after_party_group)
)
hosts = participants[: 3 * groups_per_course]
semi_hosts = participants[3 * groups_per_course :]
courses = ["starter", "main", "dessert"] * groups_per_course
groups = []
for host in hosts:
group = Group(members=[host])
groups.append(group)
random.shuffle(semi_hosts)
for i, member in enumerate(semi_hosts):
groups[i % len(groups)].add_member(member)
distance_matrix = reduce_distance_matrix(
distance_matrix, [*groups, after_party_group]
)
best_order = run_simulated_annealing(
groups,
distance_matrix,
initial_temperature=1000,
cooling_rate=0.99,
max_iterations=10000,
multiprocessing=1,
)
assign_courses(best_order, courses)
group_dicts = [group.dict() for group in best_order]
participant_dicts = [participant.dict() for participant in participants]
return group_dicts, participant_dicts
def assign_courses(groups: list[Group], courses: list[str]) -> None:
"""
Assign courses to groups.
"""
for i, (group, course) in enumerate(zip(groups, courses)):
group.set_course(course)
hosts = [groups[i] for i in get_courses(i, len(groups))]
group.set_hosts(hosts)
def run_simulated_annealing(
groups: list[Group],
reduced_distance_matrix: pd.DataFrame,
initial_temperature: float,
cooling_rate: float,
max_iterations: int,
multiprocessing: int = 1,
) -> list[Group]:
"""
Run simulated annealing to find the optimal order of groups.
"""
group_indices = list(range(len(groups)))
distance_matrix = reduced_distance_matrix.to_numpy()
# Convert group indices to a list of integers
group_indices = [int(i) for i in group_indices]
if multiprocessing > 1:
raise NotImplementedError("Multiprocessing is not implemented yet.")
else:
# Run simulated annealing without multiprocessing
best_order = simulated_annealing(
distance_matrix,
group_indices,
initial_temperature,
cooling_rate,
max_iterations,
)
best_ordererd_groups = [groups[i] for i in best_order]
return best_ordererd_groups
def next_permutation(
group_indices: list[int],
distance_matrix: np.ndarray,
T: float,
) -> list[int]:
"""
Generate the next permutation of group indices that minimizes the total travel time using boltzmann annealing.
"""
all_permutations = list(permutations(group_indices))
times = {
i: fast_total_time(distance_matrix, perm)
for i, perm in enumerate(all_permutations)
}
min_time = min(times.values())
probabilities = {i: np.exp(-(time - min_time) / T) for i, time in times.items()}
key = np.random.choice(list(probabilities.keys()), p=list(probabilities.values()))
return all_permutations[key]
def simulated_annealing(
distance_matrix: np.ndarray,
group_indices: list[int],
initial_temperature: float,
cooling_rate: float,
max_iterations: int,
) -> list[int]:
"""
Simulated annealing algorithm to find the optimal order of groups.
"""
solution = group_indices.copy()
np.random.shuffle(solution)
current_temperature = initial_temperature
for iteration in tqdm(range(max_iterations)):
try:
solution = next_permutation(solution, distance_matrix, current_temperature)
time = fast_total_time(distance_matrix, solution)
if iteration % 100 == 0:
print(f"Iteration {iteration}: Time = {time}")
current_temperature *= cooling_rate
except KeyboardInterrupt:
print("Simulation interrupted. Returning current solution.")
break
return solution
def get_courses(
group_index: int,
total_meetings: int,
):
a = group_index
b = (group_index + 1) % total_meetings
c = (group_index - 4) % total_meetings
order = sorted((a, b, c), key=lambda x: x % 3)
return order
def fast_total_time(distance_matrix: np.ndarray, group_indices: list[int]):
"""Calculates the total travel time for all groups.
Args:
distance_matrix (np.ndarray): 2D array representing the distance matrix, where
distance_matrix[i][j] is the travel time from group i to group j in seconds. Should be of shape (n+1, n+1). The last column/row should be the after party group.
group_indices (list[int]): List of group indices for which to calculate the total travel time. Should be of length n.
"""
n = len(group_indices)
after_party_idx = n # Since distance_matrix is (n+1)x(n+1)
total_time = 0
total_meetings = n
for group_index in group_indices:
a, b, c = get_courses(group_index, total_meetings)
total_time += distance_matrix[a][b]
total_time += distance_matrix[b][c]
total_time += distance_matrix[c][after_party_idx]
return total_time
def load_csv_to_participants(file_path: str) -> list[Participant]:
"""
Load participants from a CSV file.
"""
df = pd.read_csv(file_path, sep="\t")
participants = []
for _, row in df.iterrows():
participant = Participant(
name=row["name"],
address=row["address"],
phone=row["phone"],
kitchen_size=row["kitchen_size"],
allergies=row["allergies"],
)
participants.append(participant)
return participants
if __name__ == "__main__":
# Example usage
participants = load_csv_to_participants("test-config.csv")
after_party_group = get_after_party_group(
"Sebastian-Kneipp-Straße 6, 76131 Karlsruhe"
)
distance_matrix = get_participant_distance_matrix(
[*participants, after_party_group.main_member], mode="BICYCLE"
)
masterplan = get_masterplan(participants, distance_matrix, after_party_group)
print(masterplan)
print("Masterplan generated successfully.")
src/tatami/traveltimes.py
+104
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import pandas as pd
import requests
import os
from tatami.classes import Participant, Group
GOOGLE_MAPS_API_URL = (
"https://routes.googleapis.com/distanceMatrix/v2:computeRouteMatrix"
)
GOOGLE_MAPS_API_KEY = os.getenv("GOOGLE_MAPS_API_KEY")
if not GOOGLE_MAPS_API_KEY:
raise ValueError("GOOGLE_MAPS_API_KEY environment variable is not set.")
def get_distance_matrix(
addresses: list[str], mode: str = "BICYCLE", value: str = "duration"
) -> pd.DataFrame:
if not addresses:
raise ValueError("The list of addresses cannot be empty.")
locations = [{"waypoint": {"address": address}} for address in addresses]
params = {
"origins": locations,
"destinations": locations,
"travelMode": mode,
}
headers = {
"Content-Type": "application/json",
"X-Goog-Api-Key": GOOGLE_MAPS_API_KEY,
"X-Goog-FieldMask": "originIndex,destinationIndex,duration,distanceMeters",
}
response = requests.post(GOOGLE_MAPS_API_URL, json=params, headers=headers)
if response.status_code != 200:
raise Exception(f"Error fetching data from Google Maps API: {response.text}")
data = response.json()
df = pd.DataFrame(
data
) # Keys: originIndex, destinationIndex, duration, distanceMeters
distance_matrix = pd.DataFrame(
index=df["originIndex"].unique(), columns=df["destinationIndex"].unique()
)
for index, row in df.iterrows():
origin = row["originIndex"]
destination = row["destinationIndex"]
if value == "duration":
distance_matrix.at[origin, destination] = pd.to_timedelta(row["duration"])
elif value == "distanceMeters":
distance_matrix.at[origin, destination] = float(row["distanceMeters"])
else:
raise ValueError(
"Invalid value specified. Use 'duration' or 'distanceMeters'."
)
# Sort the DataFrame by index and columns
distance_matrix = distance_matrix.sort_index().sort_index(axis=1)
return distance_matrix
def get_participant_distance_matrix(
participants: list[Participant], mode: str = "BICYCLE"
) -> pd.DataFrame:
addresses = [p.address for p in participants]
distance_matrix = get_distance_matrix(addresses, mode)
distance_matrix.index = pd.Index([p.uuid for p in participants])
distance_matrix.columns = pd.Index([p.uuid for p in participants])
return distance_matrix
def reduce_distance_matrix(
distance_matrix: pd.DataFrame, groups: list[Group]
) -> pd.DataFrame:
uuids = [g.main_member.uuid for g in groups]
reduced_distance_matrix = distance_matrix.loc[uuids, uuids]
reduced_distance_matrix.columns = pd.Index(uuids)
reduced_distance_matrix.index = pd.Index(uuids)
for group in groups:
reduced_distance_matrix.loc[group.main_member.uuid] += (
group.main_member.get_penalty()
)
return reduced_distance_matrix
if __name__ == "__main__":
# Example usage
participants = [
Participant("Alice", "Römerstr. 12 76189 Karlsruhe", "555-1234", 8.0, "None"),
Participant(
"Bob", "Gottesauerstr. 30 76131 Karlsruhe", "555-5678", 7.5, "Peanuts"
),
Participant(
"Charlie", "Hermann-Hesse-Str 50 76189 Karlsruhe", "555-8765", 9.0, "None"
),
]
distance_matrix = get_participant_distance_matrix(participants)
print(distance_matrix)
uv.lock
Generated
+326
View File
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version = 1
requires-python = ">=3.13"
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