# Currently, all imports from `forest` must be explicit.  For the below example you need to import the following
# In future, it would be great to have all functions import automatically
import datetime

from forest.bonsai.simulate_log_data import sim_log_data
from forest.bonsai.simulate_gps_data import sim_gps_data, gps_to_csv
from forest.jasmine.traj2stats import Frequency, gps_stats_main
from forest.willow.log_stats import log_stats_main

# 1. If you don't have any smartphone data (yet) you can generate fake data
path_to_synthetic_gps_data = "ENTER/PATH1/HERE"
path_to_synthetic_log_data = "ENTER/PATH2/HERE"
path_to_gps_summary = "ENTER/PATH/TO/DESIRED/OUTPUT/FOLDER1/HERE"
path_to_log_summary = "ENTER/PATH/TO/DESIRED/OUTPUT/FOLDER2/HERE"

# Generate fake call and text logs 
# Because of the explicit imports, you don't have to precede the functions with forest.subpackage.
sim_log_data(path_to_synthetic_log_data)

# Generate synthetic gps data and communication logs data as csv files
# Define parameters for generating the data
# To save smartphone battery power, we typically collect location data intermittently: e.g. during an on-cycle of 3 minutes, followed by an off-cycle of 12 minutes. We'll generate data in this way
# number of persons to generate
n_persons = 1
# location of person to generate format: Country_2_letter_ISO_code/City_Name
location = "GB/Bristol"
# start date of generated trajectories
start_date = datetime.date(2021, 10, 1)
# end date of trajectories
end_date = datetime.date(2021, 10, 5)
# api key for openroute service, generated from https://openrouteservice.org/
api_key = "mock_api_key"
# Length of off-cycle + length of on-cycle in minutes
cycle = 15
# Length off-cycle / (length off-cycle + length on-cycle)
percentage = 0.8
# dictionary of personal attributes for each user, set to None if random, check Attributes class for usage in simulate_gps_data module.
personal_attributes = {
    "User 1":
    {
        "main_employment": "none", 
        "vehicle" : "car",
        "travelling_status": 10,
        "active_status": 7
    },

    "Users 2-4":
    {
        "main_employment": "university",
        "vehicle" : "bicycle",
        "travelling_status": 8,
        "active_status": 8,
        "active_status-16": 2 
    },

    "User 5":
    {
        "main_employment": "office",
        "vehicle" : "foot",
        "travelling_status": 9,
        "travelling_status-20": 1,
        "preferred_exits": ["cafe", "bar", "cinema"] 
    }
}
sample_gps_data = sim_gps_data(n_persons, location, start_date, end_date, cycle, percentage, api_key, personal_attributes)
# save data in format of csv files
gps_to_csv(sample_gps_data, path_to_synthetic_gps_data, start_date, end_date)

# 2. Specify parameters for imputation 
# See https://github.com/onnela-lab/forest/wiki/Jasmine-documentation#input for details
# time zone where the study took place (assumes that all participants were always in this time zone)
tz_str = "Etc/GMT-1"
# Generate summary metrics e.g. Frequency.HOURLY, Frequency.DAILY or Frequency.HOURLY_AND_DAILY (see Frequency class in traj2stats.py)
frequency = Frequency.DAILY
# Save imputed trajectories?
save_traj = False
# Hyperparameters class for imputation (default leave None), from forest.jasmine.traj2stats import Hyperparameters
parameters = None
# list of locations to track if visited, leave None if don't want these summary statistics
places_of_interest = ['cafe', 'bar', 'hospital']
# True if want to save a log of all locations and attributes of those locations visited
save_osm_log = True
# threshold of time spent in a location to count as being in that location, in minutes
threshold = 15

# 3. Impute location data and generate mobility summary metrics using the simulated data above
gps_stats_main(path_to_synthetic_gps_data, path_to_gps_summary, tz_str, frequency, save_traj, parameters, places_of_interest, save_osm_log, threshold)

# 4. Generate daily summary metrics for call/text logs
option = Frequency.DAILY
time_start = None 
time_end = None
participant_ids = None

log_stats_main(path_to_synthetic_log_data, path_to_log_summary, tz_str, option, time_start, time_end, participant_ids)