from collections import deque
from logging import getLogger

import numpy as np
from scipy import signal

_logger = getLogger(__name__)

buffer_dark = deque()
buffer_savgol = deque()
initialized = False


def initialize(params):
    global buffer_dark, buffer_savgol, initialized

    buffer_dark = deque(maxlen=params["buffer_length"])
    buffer_savgol = deque(maxlen=params["buffer_length"])

    initialized = True


def find_edge(data, step_length=50, edge_type="falling"):
    # prepare a step function and refine it
    step_waveform = np.ones(shape=(step_length,))
    if edge_type == "rising":
        step_waveform[: int(step_length / 2)] = -1
    elif edge_type == "falling":
        step_waveform[int(step_length / 2) :] = -1

    # find edges
    xcorr = signal.correlate(data, step_waveform, mode="valid")
    edge_position = np.argmax(xcorr)
    xcorr_amplitude = np.amax(xcorr)

    # correct edge_position for step_length
    edge_position += np.floor(step_length / 2)

    return {
        "edge_pos": edge_position,
        "xcorr": xcorr,
        "xcorr_ampl": xcorr_amplitude,
        "signal": data,
    }


def process(data, pulse_id, timestamp, params):
    device = params["device"]
    step_length = params["step_length"]
    edge_type = params["edge_type"]
    dark_event = params["dark_event"]
    fel_on_event = params["fel_on_event"]
    calib = params["calib"]
    filter_window = params["filter_window"]

#    prof_sig = data[params["prof_sig"]]
    events = data[params["events"]]

    if not initialized:
        initialize(params)
    output = {}

    output[f"{device}:raw_wf"] = events
   
    return output