from cam_server.pipeline.data_processing import functions, processor
from cam_server.utils import create_thread_pvs
from collections import OrderedDict
import numpy as np

initialized = False
offset_x = offset_y = None
background = None
x_axis_px = y_axis_px = None


def initialize(parameters, image):
    global background, offset_x, offset_y, x_axis_px, y_axis_px, initialized
    camera_name = parameters["camera_name"]
    offset_x_ch, offset_y_ch = create_thread_pvs([camera_name + ":REGIONX_START", camera_name + ":REGIONY_START"])
    offset_x_ch.wait_for_connection()
    offset_y_ch.wait_for_connection()
    offset_x = offset_x_ch.get()
    offset_y = offset_y_ch.get()
    background = parameters.get("background_data")
    if background is not None:
        background = background.astype(int)
    #Axis in pixel
    ysize, xsize = image.shape     
    x_axis_px = np.array(range(xsize)).astype('float')
    y_axis_px = np.array(range(ysize)).astype('float')
    initialized = True


def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata=None):   
    if not initialized:
        initialize(parameters, image)
        
    r = processor.process_image(image, pulse_id, timestamp, x_axis_px, y_axis_px, parameters, bsdata)
    camera_name = parameters["camera_name"]

    ret = OrderedDict()
    channels = ["intensity", "x_fwhm", "x_fit_amplitude", "x_fit_mean", "x_fit_offset", "x_fit_standard_deviation",
                "y_fwhm", "y_fit_amplitude", "y_fit_mean", "y_fit_offset", "y_fit_standard_deviation"]
    prefix = camera_name
    for c in channels:
        ret[prefix + ":" + c] = r[c]
        
    ret[prefix + ":x_fit_mean_ccd_px"] = r["x_fit_mean"] + offset_x
    ret[prefix + ":y_fit_mean_ccd_px"] = r["y_fit_mean"] + offset_y
    ret[prefix + ":roi_x"] = offset_x
    ret[prefix + ":roi_y"] = offset_y
    return ret