transport_optimization_plus.py

import os

import pandas as pd
import warnings
from typing import Optional, Dict, Tuple
import logging
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'pyloghub')))
from save_to_platform import save_scenario_check, create_button
from input_data_validation import convert_timestamps, validate_and_convert_data_types, convert_to_float, convert_df_to_dict_excluding_nan
from sending_requests import post_method, create_headers, create_url, get_workspace_entities

def forward_transport_optimization_plus(vehicles: pd.DataFrame, jobs: pd.DataFrame, timeWindowProfiles: pd.DataFrame, breaks: pd.DataFrame, parameters: Dict, api_key: str, save_scenario = {}, show_buttons = False) -> Optional[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]]:
    """
    Perform transport optimization based on vehicles, jobs, time window profiles, and breaks.

    Parameters:
    vehicles (pd.DataFrame): DataFrame containing vehicle information.
        Columns:
        - vehicleTypeId (str): Vehicle Type Id.
        - availableVehicles (int): Available Vehicles.
        - [startId, endId] (str): Start and End Ids.
        - [startCountry, endCountry] (str): Start and End Country.
        - [startState, endState] (str): Start and End State.
        - [startPostalCode, endPostalCode] (str): Start and End Postal Codes.
        - [startCity, endCity] (str): Start and End City.
        - [startStreet, endStreet] (str): Start and End Street.
        - maxWeight (float): Maximum Weight.
        - maxVolume (float): Maximum Volume.
        - maxPallets (int): Maximum Pallets.
        - maxStops (int): Maximum Stops.
        - timeWindowStart, timeWindowEnd (str): Time Window Start and End (ISO 8601 format).
        - profile (str): Profile.
        - speedFactor (float): Speed Factor.
        - fixed, perHour, perKilometer, costPerStop (float): Fixed and Costs Per Hour, Kilometer and Stop.
        - minimumTravelTime, maxTravelTime (float): Minimum and Maximum Travel Time.
        - max_distance (float): Maximum number of kilometers that vehicle can drive per route.
        - maximumDistanceBetweenStops (float): Maximum number of kilometers that vehicle can drive between each stop.
        - breakId (str): Break Id.

    jobs (pd.DataFrame): DataFrame containing job information.
        Columns:
        - shipmentId (str): Shipment Id.
        - [fromName, toName] (str): From and To Names.
        - [fromCountry, toCountry] (str): From and To Countries.
        - [fromState, toState] (str): From and To States.
        - [fromPostalCode, toPostalCode] (str): From and To Postal Codes.
        - [fromCity, toCity] (str): From and To Cities.
        - [fromStreet, toStreet] (str): From and To Streets.
        - [senderStopDuration, recipientStopDuration] (float): Sender and Recipient Stop Durations.
        - [senderTimeWindowProfile, recipientTimeWindowProfile] (str): Sender and Recipient Time Window Profiles.
        - weight, volume (float): Weight and Volume.
        - pallets (int): Pallets.
        - vehicleTypeId (str): Vehicle Type Id.
        - external_costs (number): External costs for order.

    timeWindowProfiles (pd.DataFrame): DataFrame containing time window profile information.
        Columns:
        - timeWindowProfileId (str): Time Window Profile Id.
        - timeWindowProfileStart, timeWindowProfileEnd (str): Time Window Profile Start and End (ISO 8601 format).

    breaks (pd.DataFrame): DataFrame containing break information.
        Columns:
        - breakId (str): Break Id.
        - earliestBreakStart, latestBreakStart (str): Earliest and Latest Break Start (ISO 8601 format).
        - earliestRelativeBreakStart, latestRelativeBreakStart (str): Earliest and Latest Relative Break Start (ISO 8601 format).
        - breakDuration (float): Break Duration.

    parameters (Dict): Dictionary containing parameters like durationUnit and distanceUnit.

    api_key (str): Log-hub API key for accessing the transport optimization service.

    save_scenario (dict): A dictionary containg information about saving scenario, empty by default. Allowed key vales are
                        'saveScenario' (boolean), 'overwriteScenario' (boolean), 'workspaceId' (str) and
                        'scenarioName' (str).
    
    show_buttons (boolean): If this parameter is set to True and the scenario is saved on the platform, the buttons linking to the output results, map, dashboard and the input table 
                           will be created. If the scenario is not saved, a proper message will be shown.

    Returns:
    Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: Three pandas DataFrames containing route overview, route details, 
                                                     and external orders. Returns None if the process fails.
    """
    def create_buttons():
        links = get_workspace_entities(save_scenario, api_key)
        create_button(links = [links['map'], links['dashboard'], links['inputDataset'], links['outputDataset']], texts = ["🌍 Open Map", "📊 Open Dashboard", "📋 Show Input Dataset", "📋 Show Output Dataset"])

    # Define expected columns and data types for each DataFrame
    vehicle_mandatory_columns = {
        'vehicleTypeId': 'str', 'availableVehicles': 'int'
    }
    vehicle_optional_columns = {
        'startId': 'str', 'startCountry': 'str', 'startState': 'str', 'startPostalCode': 'str', 'startCity': 'str', 'startStreet': 'str', 'endId': 'str', 
        'endCountry': 'str', 'endState': 'str', 'endPostalCode': 'str', 'endCity': 'str', 'endStreet': 'str', 
        'timeWindowStart': 'str', 'timeWindowEnd': 'str', 'profile': 'str', 'breakId': 'str'
    }
    vehicle_optional_floats = ['maxWeight', 'maxVolume', 'maxPallets', 'maxStops', 'speedFactor', 'fixed', 'perHour', 'perKilometer', 'costPerStop', 'minimumTravelTime', 'maxTravelTime', 'max_distance', 'maximumDistanceBetweenStops']
    job_mandatory_columns = {
        'shipmentId': 'str', 'fromName': 'str', 'fromCountry': 'str', 'toName': 'str', 'toCountry': 'str', 'vehicleTypeId': 'str'
    }
    job_optional_columns = {
        'fromState': 'str', 'fromPostalCode': 'str', 'fromCity': 'str', 'fromStreet': 'str', 'senderTimeWindowProfile': 'str', 'toState': 'str', 'toPostalCode': 'str', 'toCity': 'str', 
        'toStreet': 'str', 'recipientTimeWindowProfile': 'str',
    }
    job_optional_floats = ['senderStopDuration', 'recipientStopDuration', 'weight', 'volume', 'pallets', 'external_costs']
    timeWindowProfile_optional_columns = {
        'timeWindowProfileId': 'str', 'timeWindowProfileStart': 'str', 'timeWindowProfileEnd': 'str'
    }
    break_optional_columns = {
        'breakId': 'str', 'earliestBreakStart': 'str', 'latestBreakStart': 'str', 'earliestRelativeBreakStart': 'str', 'latestRelativeBreakStart': 'str'
    }
    break_optional_floats = ['breakDuration']

    # Perform validation and conversion for each DataFrame
    vehicles = validate_and_convert_data_types(vehicles, vehicle_mandatory_columns, 'mandatory', 'vehicles')
    if not vehicles is None:
        vehicles = validate_and_convert_data_types(vehicles, vehicle_optional_columns, 'optional', 'vehicles')
        if not vehicles is None:
            vehicles = convert_timestamps(vehicles)
            vehicles = convert_to_float(vehicles, vehicle_optional_floats, 'optional')
            vehicles = convert_df_to_dict_excluding_nan(vehicles, vehicle_optional_floats)
    jobs = validate_and_convert_data_types(jobs, job_mandatory_columns, 'mandatory', 'jobs')
    if not jobs is None:
        jobs = validate_and_convert_data_types(jobs, job_optional_columns, 'optional', 'jobs')
        if not jobs is None:
            jobs = convert_timestamps(jobs)
            jobs = convert_to_float(jobs, job_optional_floats, 'optional')
            jobs = convert_df_to_dict_excluding_nan(jobs, job_optional_floats)
    timeWindowProfiles = validate_and_convert_data_types(timeWindowProfiles, timeWindowProfile_optional_columns, 'optional', 'time window profiles')
    if not timeWindowProfiles is None:
        timeWindowProfiles = convert_timestamps(timeWindowProfiles)
    breaks = validate_and_convert_data_types(breaks, break_optional_columns, 'optional', 'breaks')
    if not breaks is None:
        breaks = convert_timestamps(breaks)
        breaks = convert_to_float(breaks, break_optional_floats, 'optional')
        breaks = convert_df_to_dict_excluding_nan(breaks, break_optional_floats)

    # Exit if any DataFrame validation failed
    if any(df is None for df in [vehicles, jobs, timeWindowProfiles, breaks]):
        return None

    url = create_url("transportoptimizationplus")
    
    headers = create_headers(api_key)

    payload = {
        "vehicles": vehicles,
        "jobs": jobs,
        "timeWindowProfiles": timeWindowProfiles.to_dict(orient='records'),
        "breaks": breaks,
        "parameters": parameters
    }
    payload = save_scenario_check(save_scenario, payload)
    
    response_data = post_method(url, payload, headers, "transport optimization plus")
    if response_data is None:
        return None
    else:
        route_overview_df = pd.DataFrame(response_data['routeOverview'])
        route_details_df = pd.DataFrame(response_data['routeDetails'])
        external_orders_df = pd.DataFrame(response_data['externalOrders'])
        if (show_buttons and payload['saveScenarioParameters']['saveScenario']):
            create_buttons()
        if (not payload['saveScenarioParameters']['saveScenario'] and show_buttons):
            logging.info("Please, save the scenario in order to create the buttons for opening the results on the platform.")
        return route_overview_df, route_details_df, external_orders_df
           
def forward_transport_optimization_plus_sample_data():
    warnings.simplefilter("ignore", category=UserWarning)
    data_path = os.path.join(os.path.dirname(__file__), 'sample_data', 'transportPlusAddresses.xlsx')
    vehicles_df = pd.read_excel(data_path, sheet_name='vehicles', usecols='A:AF', dtype={'startState': str, 'startPostalCode': str, 'endState': str, 'endPostalCode': str, 'maxTravelTime': int}).fillna("")
    shipments_df = pd.read_excel(data_path, sheet_name='shipments', usecols='A:W').fillna("")
    time_window_profiles_df = pd.read_excel(data_path, sheet_name='timeWindowProfile', usecols='A:D').fillna("")
    breaks_df = pd.read_excel(data_path, sheet_name='breaks', usecols='A:G').fillna("")

    parameters = {
        "durationUnit": "min",
        "distanceUnit": "km"
    }
    save_scenario = {
        'saveScenario': False,
        'overwriteScenario': False,
        'workspaceId': 'Your workspace id',
        'scenarioName': 'Your scenario name'
    }
    return {'vehicles': vehicles_df, 'shipments': shipments_df, 'timeWindowProfiles': time_window_profiles_df,
             'breaks': breaks_df, 'parameters': parameters, 'saveScenarioParameters': save_scenario}


def reverse_transport_optimization_plus(vehicles: pd.DataFrame, jobs: pd.DataFrame, timeWindowProfiles: pd.DataFrame, breaks: pd.DataFrame, parameters: Dict, api_key: str, save_scenario = {}, show_buttons = False) -> Optional[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]]:
    """
    Perform reverse transport optimization based on vehicles, jobs, time window profiles, and breaks.

    Parameters:
    vehicles (pd.DataFrame): DataFrame containing vehicle information with latitude and longitude.
        Columns:
        - vehicleTypeId (str): Vehicle Type Id.
        - availableVehicles (int): Number of available vehicles.
        - [startId, endId] (str): Start and End Ids.
        - [startLatitude, endLatitude] (float): Start and End Latitude.
        - [startLongitude, endLongitude] (float): Start and End Longitude.
        - maxWeight (float): Maximum Weight.
        - maxVolume (float): Maximum Volume.
        - maxPallets (int): Maximum Pallets.
        - maxStops (int): Maximum Stops.
        - timeWindowStart, timeWindowEnd (str): Time Window Start and End (ISO 8601 format).
        - profile (str): Profile.
        - speedFactor (float): Speed Factor.
        - fixed, perHour, perKilometer, costPerStop (float): Fixed and Costs Per Hour, Kilometer and Stop.
        - minimumTravelTime, maxTravelTime (float): Minimum and Maximum Travel Time.
        - max_distance (float): Maximum number of kilometers that vehicle can drive per route.
        - maximumDistanceBetweenStops (float): Maximum number of kilometers that vehicle can drive between each stop.
        - breakId (str): Break Id.

    jobs (pd.DataFrame): DataFrame containing job information with latitude and longitude.
        Columns:
        - shipmentId (str): Shipment Id.
        - [fromName, toName] (str): From and To Names.
        - [fromLatitude, toLatitude] (float): From and To Latitude.
        - [fromLongitude, toLongitude] (float): From and To Longitude.
        - [senderStopDuration, recipientStopDuration] (float): Sender and Recipient Stop Durations.
        - [senderTimeWindowProfile, recipientTimeWindowProfile] (str): Sender and Recipient Time Window Profiles.
        - weight, volume (float): Weight and Volume.
        - pallets (int): Pallets.
        - vehicleTypeId (str): Vehicle Type Id.
        - external_costs (number): External costs for order.

    timeWindowProfiles (pd.DataFrame): DataFrame containing time window profile information.
        Columns:
        - timeWindowProfileId (str): Time Window Profile Id.
        - timeWindowProfileStart, timeWindowProfileEnd (str): Time Window Profile Start and End (ISO 8601 format).

    breaks (pd.DataFrame): DataFrame containing break information.
        Columns:
        - breakId (str): Break Id.
        - earliestBreakStart, latestBreakStart (str): Earliest and Latest Break Start (ISO 8601 format).
        - earliestRelativeBreakStart (str): Minimum driving time before the break can start (ISO 8601 format).
        - latestRelativeBreakTime (str): Maximum driving time until the break must start (ISO 8601 format).
        - breakDuration (float): Break Duration.

    parameters (Dict): Dictionary containing parameters like durationUnit and distanceUnit.

    api_key (str): Log-hub API key for accessing the reverse transport optimization service.

    save_scenario (dict): A dictionary containg information about saving scenario, empty by default. Allowed key vales are
                        'saveScenario' (boolean), 'overwriteScenario' (boolean), 'workspaceId' (str) and
                        'scenarioName' (str).
    
    show_buttons (boolean): If this parameter is set to True and the scenario is saved on the platform, the buttons linking to the output results, map, dashboard and the input table 
                           will be created. If the scenario is not saved, a proper message will be shown.

    Returns:
    Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: Three pandas DataFrames containing route overview, route details, 
                                                     and external orders. Returns None if the process fails.
    """
    def create_buttons():
        links = get_workspace_entities(save_scenario, api_key)
        create_button(links = [links['map'], links['dashboard'], links['inputDataset'], links['outputDataset']], texts = ["🌍 Open Map", "📊 Open Dashboard", "📋 Show Input Dataset", "📋 Show Output Dataset"])
    # Define expected columns and data types for each DataFrame
    vehicle_mandatory_columns = {
        'vehicleTypeId': 'str', 'availableVehicles': 'int'
    }
    vehicle_optional_columns = {
        'startId': 'str', 'endId': 'str', 'timeWindowStart': 'str', 'timeWindowEnd': 'str', 'profile': 'str', 'breakId': 'str'
    }
    vehicle_optional_floats = ['startLatitude', 'startLongitude', 'endLatitude', 'endLongitude', 'maxWeight', 'maxVolume', 'maxPallets', 'maxStops', 'speedFactor', 'fixed', 'perHour',  'perKilometer', 'costPerStop', 'minimumTravelTime', 'maxTravelTime', 'max_distance', 'maximumDistanceBetweenStops']
    job_mandatory_columns = {
        'shipmentId': 'str', 'fromName': 'str', 'fromLatitude': 'float', 'fromLongitude': 'float', 'toName': 'str', 'toLatitude': 'float', 'toLongitude': 'float'
    }
    job_optional_columns = {
        'senderTimeWindowProfile': 'str', 'recipientTimeWindowProfile': 'str', 'vehicleTypeId': 'str',
    }
    job_optional_floats = ['senderStopDuration', 'recipientStopDuration', 'weight', 'volume', 'pallets', 'external_costs']
    timeWindowProfile_optional_columns = {
        'timeWindowProfileId': 'str', 'timeWindowProfileStart': 'str', 'timeWindowProfileEnd': 'str'
    }
    break_optional_columns = {
        'breakId': 'str', 'earliestBreakStart': 'str', 'latestBreakStart': 'str', 'earliestRelativeBreakStart': 'str', 'latestRelativeBreakStart': 'str'
    }
    break_optional_floats = ['breakDuration']

    # Perform validation and conversion for each DataFrame
    vehicles = validate_and_convert_data_types(vehicles, vehicle_mandatory_columns, 'mandatory', 'vehicles')
    if not vehicles is None:
        vehicles = validate_and_convert_data_types(vehicles, vehicle_optional_columns, 'optional', 'vehicles')
        if not vehicles is None:
            vehicles = convert_timestamps(vehicles)
            vehicles = convert_to_float(vehicles, vehicle_optional_floats, 'optional')
            vehicles = convert_df_to_dict_excluding_nan(vehicles, vehicle_optional_floats)
    jobs = validate_and_convert_data_types(jobs, job_mandatory_columns, 'mandatory', 'jobs')
    if not jobs is None:
        jobs = validate_and_convert_data_types(jobs, job_optional_columns, 'optional', 'jobs')
        if not jobs is None:
            jobs = convert_timestamps(jobs)
            jobs = convert_to_float(jobs, job_optional_floats, 'optional')
            jobs = convert_df_to_dict_excluding_nan(jobs, job_optional_floats)
    timeWindowProfiles = validate_and_convert_data_types(timeWindowProfiles, timeWindowProfile_optional_columns, 'optional', 'time window profiles')
    if not timeWindowProfiles is None:
        timeWindowProfiles = convert_timestamps(timeWindowProfiles)
    breaks = validate_and_convert_data_types(breaks, break_optional_columns, 'optional', 'breaks')
    if not breaks is None:
        breaks = convert_timestamps(breaks)
        breaks = convert_to_float(breaks, break_optional_floats, 'optional')
        breaks = convert_df_to_dict_excluding_nan(breaks, break_optional_floats)

    # Exit if any DataFrame validation failed
    if any(df is None for df in [vehicles, jobs, timeWindowProfiles, breaks]):
        return None

    url = create_url("reversetransportoptimizationplus")
    
    headers = create_headers(api_key)
    payload = {
        "vehicles": vehicles,
        "jobs": jobs,
        "timeWindowProfiles": timeWindowProfiles.to_dict(orient='records'),
        "breaks": breaks,
        "parameters": parameters
    }
    payload = save_scenario_check(save_scenario, payload)
    response_data = post_method(url, payload, headers, "reverse transport optimization plus")
    if response_data is None:
        return None
    else:
        route_overview_df = pd.DataFrame(response_data['routeOverview'])
        route_details_df = pd.DataFrame(response_data['routeDetails'])
        external_orders_df = pd.DataFrame(response_data['externalOrders'])
        if (show_buttons and payload['saveScenarioParameters']['saveScenario']):
            create_buttons()
        if (not payload['saveScenarioParameters']['saveScenario'] and show_buttons):
            logging.info("Please, save the scenario in order to create the buttons for opening the results on the platform.")
        return route_overview_df, route_details_df, external_orders_df

def reverse_transport_optimization_plus_sample_data():
    warnings.simplefilter("ignore", category=UserWarning)
    data_path = os.path.join(os.path.dirname(__file__), 'sample_data', 'transportPlusReverse.xlsx')
    vehicles_df = pd.read_excel(data_path, sheet_name='vehicles', usecols='A:Z', dtype={'maxTravelTime': int}).fillna("")
    shipments_df = pd.read_excel(data_path, sheet_name='shipments', usecols='A:Q').fillna("")
    time_window_profiles_df = pd.read_excel(data_path, sheet_name='timeWindowProfiles', usecols='A:D').fillna("")
    breaks_df = pd.read_excel(data_path, sheet_name='breaks', usecols='A:G').fillna("")

    parameters = {
        "durationUnit": "min",
        "distanceUnit": "km"
    }
    save_scenario = {
        'saveScenario': False,
        'overwriteScenario': False,
        'workspaceId': 'Your workspace id',
        'scenarioName': 'Your scenario name'
    }
    return {'vehicles': vehicles_df, 'shipments': shipments_df, 'timeWindowProfiles': time_window_profiles_df,
             'breaks': breaks_df, 'parameters': parameters, 'saveScenarioParameters': save_scenario}