import numpy as np

from thunderfish.eventdetection import detect_peaks


def detect_spiketimes(time: np.ndarray, v1, threshold=2.0, min_length=5000, split_step=1000):
    all_peak_indicies = detect_spike_indices_automatic_split(v1, threshold, min_length, split_step)

    return time[all_peak_indicies]


def detect_spike_indices_automatic_split(v1, threshold, min_length=5000, split_step=1000):
    split_start = 0
    step_size = split_step
    break_threshold = 0.25
    splits = []

    if len(v1) <= min_length:
        splits = [(0, len(v1))]
    else:
        last_max = max(v1[0:min_length])
        last_min = min(v1[0:min_length])
        idx = min_length

        while idx <= len(v1):
            if idx + step_size > len(v1):
                splits.append((split_start, len(v1)))
                break

            max_similar = abs((max(v1[idx:idx+step_size]) - last_max) / last_max) < break_threshold
            min_similar = abs((min(v1[idx:idx+step_size]) - last_min) / last_min) < break_threshold

            if not max_similar or not min_similar:
                # print("new split")
                end_idx = np.argmin(v1[idx-20:idx+21]) - 20
                splits.append((split_start, idx+end_idx))
                split_start = idx+end_idx
                last_max = max(v1[split_start:split_start + min_length])
                last_min = min(v1[split_start:split_start + min_length])
                idx = split_start + min_length
                continue
            else:
                pass
                # print("elongated!")

            idx += step_size

    if splits[-1][1] != len(v1):
        splits.append((split_start, len(v1)))

    # plt.plot(v1)

    # for s in splits:
    #     plt.plot(s, (max(v1[s[0]:s[1]]), max(v1[s[0]:s[1]])))

    all_peaks = []
    for s in splits:
        first_index = s[0]
        last_index = s[1]
        std = np.std(v1[first_index:last_index])
        peaks, _ = detect_peaks(v1[first_index:last_index], std * threshold)
        peaks = peaks + first_index
        # plt.plot(peaks, [np.max(v1[first_index:last_index]) for _ in peaks], 'o')
        all_peaks.extend(peaks)

    # plt.show()
    # plt.close()
    # all_peaks = np.array(all_peaks)

    return all_peaks