from collections import OrderedDict
from cam_server.pipeline.data_processing import functions
from logging import getLogger

_logger = getLogger(__name__)

def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata):
    ret = OrderedDict()
    prefix = parameters["camera_name"]
    (x_profile, y_profile) = functions.get_x_y_profile(image)
    _, _, _, x_fit_mean, x_fit_standard_deviation, _, _ = functions.gauss_fit(x_profile, x_axis)
    _, _, _, y_fit_mean, y_fit_standard_deviation, _, _ = functions.gauss_fit(y_profile, y_axis)
    x_fwhm = functions.get_fw(x_axis,x_profile)
    y_fwhm = functions.get_fw(y_axis,y_profile)
    intensity = x_profile.sum()
    ret[prefix+":intensity"] = intensity
    ret[prefix+":x_fit_mean"] = x_fit_mean
    ret[prefix+":y_fit_mean"] = y_fit_mean
    ret[prefix+":x_fwhm"] = x_fwhm
    ret[prefix+":y_fwhm"] = y_fwhm
    ret[prefix+":x_fit_standard_deviation"] = x_fit_standard_deviation
    ret[prefix+":y_fit_standard_deviation"] = y_fit_standard_deviation
    return ret


"""
from collections import OrderedDict
from cam_server.pipeline.data_processing import functions, processor
from logging import getLogger
import numpy as np

_logger = getLogger(__name__)

def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata):
    r = processor.process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata)
    ret = OrderedDict()
    channels = ["intensity","x_fit_mean","y_fit_mean"]
    prefix = parameters["camera_name"]
    for c in channels:
        value = r[c]
        if value is None:          
          _logger.warning("None:" + str(c))
          return None
        if value == float('nan'):
          _logger.warning("NAN:" + str(c))
          return None
        if isinstance(value,np.floating):
          value=float(value)
        if isinstance(value,np.integer):
          value=int(value)    
        ret[prefix+":"+c] = value
    return ret
"""