from logging import getLogger

from cam_server.pipeline.data_processing import functions
from cam_server.pipeline.data_processing.functions import chunk_copy

import json

import numpy
import scipy.signal
import scipy.optimize
import numba
numba.set_num_threads(4)

import threading
import epics


_logger = getLogger(__name__)

output_pv, center_pv, fwhm_pv, ymin_pv, ymax_pv, axis_pv  = None, None, None, None, None, None
roi = [0, 0]
initialized = False
epics_lock=threading.RLock()
_logger.info("Import: "  +str(threading.get_ident()))


@numba.njit(parallel=True)
def get_spectrum (image, background):
    y = image.shape[0]
    x = image.shape[1]

    profile = numpy.zeros(x, dtype=numpy.uint32)
    #for i in range(y):
    for i in numba.prange(y):
        for j in range(x):
            v = image[i,j]
            b = background[i,j]
            if v > b:
                v -= b
            else:
                v = 0
            profile[j] += v
    return profile


def initialize(parameters):
    global ymin_pv, ymax_pv, axis_pv, output_pv, center_pv, fwhm_pv
 
    _logger.info("Initialize: "  + str(threading.get_ident()))

    epics_pv_name_prefix = parameters["camera_name"]
    output_pv_name = epics_pv_name_prefix + ":SPECTRUM_Y"
    center_pv_name = epics_pv_name_prefix + ":SPECTRUM_CENTER"
    fwhm_pv_name = epics_pv_name_prefix + ":SPECTRUM_FWHM"
    ymin_pv_name = epics_pv_name_prefix + ":SPC_ROI_YMIN"
    ymax_pv_name = epics_pv_name_prefix + ":SPC_ROI_YMAX"
    axis_pv_name = epics_pv_name_prefix + ":SPECTRUM_X"
    epics.ca.clear_cache()

    output_pv = epics.PV(output_pv_name)
    center_pv = epics.PV(center_pv_name)
    fwhm_pv = epics.PV(fwhm_pv_name)

    ymin_pv = epics.PV(ymin_pv_name)
    ymax_pv = epics.PV(ymax_pv_name)
    axis_pv = epics.PV(axis_pv_name)
    ymin_pv.wait_for_connection()
    ymax_pv.wait_for_connection()
    axis_pv.wait_for_connection()


def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata=None, background=None):
    global roi, initialized, epics_lock
    global ymin_pv, ymax_pv, axis_pv, output_pv, center_pv, fwhm_pv 

    processed_data = dict()
    epics_pv_name_prefix = parameters["camera_name"]

    with epics_lock:
        if not initialized:
            initialize(parameters)
            initialized = True

        if ymin_pv and ymin_pv.connected:
            roi[0] = ymin_pv.value
        if ymax_pv and ymax_pv.connected:
            roi[1] = ymax_pv.value
        if axis_pv and axis_pv.connected:
            axis = axis_pv.value
        else:
            axis = None

    if axis is None:
        _logger.warning("Energy axis not connected");
        return None

    if len(axis) < image.shape[1]:
        _logger.warning("Energy axis length %d < image width %d", len(axis), image.shape[1])
        return None

    # match the energy axis to image width
    axis = axis[:image.shape[1]]

    processing_image = image
    nrows, ncols = processing_image.shape
    
    # validate background data
    background_image = parameters.pop('background_data', None)

    if isinstance(background_image, numpy.ndarray):
        #background_image = chunk_copy(background_image)
        if background_image.shape != processing_image.shape:
            _logger.info("Invalid background shape: %s instead of %s" % (str(background_image.shape), str(processing_image.shape)))
            background_image = None
    else:
        background_image = None

    processed_data[epics_pv_name_prefix + ":processing_parameters"] = json.dumps(
        {"roi": roi, "background": None if (background_image is None) else parameters.get('image_background')})
    
    # crop the image in y direction
    ymin, ymax = int(roi[0]), int(roi[1])
    if nrows >= ymax > ymin >= 0:
        if (nrows != ymax) or (ymin != 0):
            processing_image = processing_image[ymin: ymax, :]
            if background_image is not None:
                background_image = background_image[ymin:ymax, :]
    
    # remove the background and collapse in y direction to get the spectrum
    if background_image is not None:
        spectrum = get_spectrum(processing_image, background_image)
    else:
    	spectrum = processing_image.sum(0, 'uint32')

    # smooth the spectrum with savgol filter with 51 window size and 3rd order polynomial
    smoothed_spectrum = scipy.signal.savgol_filter(spectrum, 51, 3)

    # check wether spectrum has only noise. the average counts per pixel at the peak
    # should be larger than 1.5 to be considered as having real signals.
    minimum, maximum = smoothed_spectrum.min(), smoothed_spectrum.max()
    amplitude = maximum - minimum
    skip = True
    if amplitude > nrows * 1.5:
        skip = False
    # gaussian fitting
    offset, amplitude, center, sigma = functions.gauss_fit_psss(smoothed_spectrum[::2], axis[::2],
            offset=minimum, amplitude=amplitude, skip=skip)
    
    # outputs
    processed_data[epics_pv_name_prefix + ":SPECTRUM_Y"] = spectrum
    processed_data[epics_pv_name_prefix + ":SPECTRUM_X"] = axis
    processed_data[epics_pv_name_prefix + ":SPECTRUM_CENTER"] = center
    processed_data[epics_pv_name_prefix + ":SPECTRUM_FWHM"] = 2.355 * sigma

    with epics_lock:
        if output_pv and output_pv.connected:
            output_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_Y"])
            _logger.debug("caput on %s for pulse_id %s", output_pv, pulse_id)

        if center_pv and center_pv.connected:
            center_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_CENTER"])

        if fwhm_pv and fwhm_pv.connected:
            fwhm_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_FWHM"])
    
    return processed_data