import numpy as np
from Utilities.Time import generate_timestrings, index_peak_times


def get_no_of_peaks(peak_bounds):
    peak_occurences = np.random.randint(peak_bounds["min"], peak_bounds["max"], 1)
    return peak_occurences


def generate_peak_info(c, dt):
    no_of_peaks = get_no_of_peaks(c["site_info"]["no_of_peaks"])
    operating_hours = generate_timestrings(
        c["site_info"]["operating hours"]["start"],
        c["site_info"]["operating hours"]["end"],
        dt,
    )
    peak_times = np.random.choice(operating_hours, no_of_peaks, replace=False)
    peak_durations = np.random.randint(
        c["site_info"]["peak_duration"]["min"],
        c["site_info"]["peak_duration"]["max"],
        no_of_peaks,
    )

    return peak_times, peak_durations


def get_load_profile(c, dt, batch_start_time, batch_process_duration):
    # Generate load profile for each site

    # c is the configuration dictionary
    # dt is the time step in seconds
    # batch_start_time is the start time for the batch process in seconds since the epoch
    # batch_process_duration is the duration of the batch process in seconds

    # start with indexing all the peak occurences
    # generate timeseries from start to end time
    start_time = batch_start_time
    end_time = start_time + batch_process_duration
    timestamps = np.arange(start_time, end_time, dt)

    # Generate peak times and durations
    peak_times, peak_durations = generate_peak_info(c, dt)

    # Generate peak times and durations
    peak_indices = index_peak_times(timestamps, peak_times, peak_durations)