from __future__ import absolute_import, division, print_function

import logging
import re
from collections import OrderedDict

import numpy as np
from atom.api import Atom, Bool, Int, List, Str, Typed, observe
from skbeam.core.fitting.xrf_model import K_LINE, L_LINE, M_LINE
from skbeam.fluorescence import XrfElement as Element

from ..core.map_processing import TerminalProgressBar, compute_selected_rois
from .fileio import save_fitdata_to_hdf
from .fit_spectrum import get_energy_bin_range

logger = logging.getLogger(__name__)


class ROISettings(Atom):
    """
    This class defines basic data structure for roi calculation.

    Attributes
    ----------
    prefix : str
        prefix name
    line_val : float
        emission energy of primary line
    left_val : float
        left boundary
    right_val : float
        right boundary
    default_left : float
    default_right : float
    step : float
        min step value to change
    show_plot : bool
        option to plot
    """

    prefix = Str()
    line_val = Int()
    left_val = Int()
    right_val = Int()
    default_left = Int()
    default_right = Int()
    step = Int(1)
    show_plot = Bool(False)

    @observe("left_val")
    def _value_update(self, change):
        if change["type"] == "create":
            return
        logger.debug("left value is changed {}".format(change))

    @observe("show_plot")
    def _plot_opt(self, change):
        if change["type"] == "create":
            return
        logger.debug("show plot is changed {}".format(change))


class ROIModel(Atom):
    """
    Control roi calculation according to given inputs.

    Parameters
    ----------
    parameters : Dict
        parameter values used for fitting
    data_dict : Dict
        dict of 3D data
    element_for_roi : str
        inputs given by users
    element_list_roi : list
        list of elements after parsing
    roi_dict : dict
        dict of ROISettings object
    enable_roi_computation : Bool
        enables/disables GUI element that start ROI computation
        At least one element must be selected and all entry in the element
          list must be valid before ROI may be computed

    hdf_path : Str
        full path to the HDF5 file, in which results are saved

    data_title : str
        The title of the selected dataset (from ``fileio`` module)
    data_title_base : str
        The title changed for internal use (suffix is removed)
    data_title_adjusted : str
        The title changed for internal use (suffix 'sum' is removed if it exists)
    suffix_name_roi : str
        The suffix may have values 'sum', 'det1', 'det2' etc.
    """

    # Reference to ParamModel object
    param_model = Typed(object)
    # Reference to FileIOModel object
    io_model = Typed(object)

    element_for_roi = Str()
    element_list_roi = List()
    roi_dict = OrderedDict()
    enable_roi_computation = Bool(False)

    subtract_background = Bool(False)

    hdf_path = Str()

    data_title = Str()
    data_title_base = Str()
    data_title_adjusted = Str()
    suffix_name_roi = Str()

    def filepath_update(self, change):
        """
        Observer function to be connected to the fileio model
        in the top-level gui.py startup

        Parameters
        ----------
        changed : dict
            This is the dictionary that gets passed to a function
            with the @observe decorator
        """
        self.hdf_path = change["value"]

    def data_title_update(self, change):
        """
        Observer function to be connected to the fileio model
        in the top-level gui.py startup

        Parameters
        ----------
        changed : dict
            This is the dictionary that gets passed to a function
            with the @observe decorator
        """
        self.data_title = change["value"]

        # It is assumed, that ``self.data_title`` was created in the ``fileio`` module
        #   and has dataset label attached to the end of it.
        #   The labels are ``sum``, ``det1``, ``det2`` etc. depending on the number
        #   of detector channels.
        self.suffix_name_roi = self.data_title.split("_")[-1]

        self.data_title_base = "_".join(self.data_title.split("_")[:-1])

        if self.suffix_name_roi == "sum":
            # If suffix is 'sum', then remove the suffix
            self.data_title_adjusted = self.data_title_base
        else:
            # Else keep the original title
            self.data_title_adjusted = self.data_title

    def __init__(self, *, param_model, io_model):
        # Initialize with an empty string (no elements selected)
        self.param_model = param_model
        self.io_model = io_model
        self.element_for_roi = ""
        self.enable_roi_computation = False

    @observe("element_for_roi")
    def _update_element(self, change):
        """
        Get element information as a string and parse it as a list.
        This element information means the ones for roi setup.
        """
        self.element_for_roi = self.element_for_roi.strip(" ")
        # Remove leading and trailing ','
        self.element_for_roi = self.element_for_roi.strip(",")
        # Remove leading and trailing '.'
        self.element_for_roi = self.element_for_roi.strip(".")
        try:
            if len(self.element_for_roi) == 0:
                logger.debug("No elements entered.")
                self.remove_all_roi()
                self.element_list_roi = []
                self.enable_roi_computation = False
                return
            elif "," in self.element_for_roi:
                element_list = [v.strip(" ") for v in self.element_for_roi.split(",")]
            else:
                element_list = [v for v in self.element_for_roi.split(" ")]

            # with self.suppress_notifications():
            #     self.element_list_roi = element_list
            logger.debug("Current elements for ROI sum are: {}".format(element_list))
            self.update_roi(element_list)
            self.element_list_roi = element_list
            self.enable_roi_computation = True
        except Exception as ex:
            logger.warning(f"Incorrect specification of element lines for ROI computation: {ex}")
            self.enable_roi_computation = False

    def select_elements_from_list(self, element_list):
        self.element_for_roi = ", ".join(element_list)

    def use_all_elements(self):
        self.element_for_roi = ", ".join(K_LINE + L_LINE)  # +M_LINE)

    def clear_selected_elements(self):
        self.element_for_roi = ""

    def remove_all_roi(self):
        self.roi_dict.clear()

    def update_roi(self, element_list, std_ratio=4):
        """
        Update elements without touching old ones.

        Parameters
        ----------
        element_list : list
            list of elements for roi
        std_ratio : float, optional
            Define the range of roi for given element.

        Notes
        -----
        The unit of energy is in ev in this function. The reason is
        SpinBox in Enaml can only read integer as input. To be updated.
        """

        eline_list = K_LINE + L_LINE + M_LINE

        for v in element_list:
            if v in self.roi_dict:
                continue

            if v not in eline_list:
                raise ValueError(f"Emission line {v} is unknown")

            if "_K" in v:
                temp = v.split("_")[0]
                e = Element(temp)
                val = int(e.emission_line["ka1"] * 1000)
            elif "_L" in v:
                temp = v.split("_")[0]
                e = Element(temp)
                val = int(e.emission_line["la1"] * 1000)
            elif "_M" in v:
                temp = v.split("_")[0]
                e = Element(temp)
                val = int(e.emission_line["ma1"] * 1000)

            delta_v = int(self.get_sigma(val / 1000) * 1000)

            roi = ROISettings(
                prefix=self.suffix_name_roi,
                line_val=val,
                left_val=val - delta_v * std_ratio,
                right_val=val + delta_v * std_ratio,
                default_left=val - delta_v * std_ratio,
                default_right=val + delta_v * std_ratio,
                step=1,
                show_plot=False,
            )

            self.roi_dict.update({v: roi})

        # remove old items not included in element_list
        for k in self.roi_dict.copy().keys():
            if k not in element_list:
                del self.roi_dict[k]

    def get_sigma(self, energy, epsilon=2.96):
        """
        Calculate the std at given energy.
        """
        temp_val = 2 * np.sqrt(2 * np.log(2))
        return np.sqrt(
            (self.param_model.param_new["fwhm_offset"]["value"] / temp_val) ** 2
            + energy * epsilon * self.param_model.param_new["fwhm_fanoprime"]["value"]
        )

    def get_roi_sum(self):
        """
        Save roi sum into a dict.

        Returns
        -------
        dict
            nested dict as output
        """
        roi_result = {}

        datav = self.io_model.data_sets[self.data_title].raw_data

        logger.info(f"Computing ROIs for dataset {self.data_title} ...")

        snip_param = {
            "e_offset": self.param_model.param_new["e_offset"]["value"],
            "e_linear": self.param_model.param_new["e_linear"]["value"],
            "e_quadratic": self.param_model.param_new["e_quadratic"]["value"],
            "b_width": self.param_model.param_new["non_fitting_values"]["background_width"],
        }

        n_bin_low, n_bin_high = get_energy_bin_range(
            num_energy_bins=datav.shape[2],
            low_e=self.param_model.param_new["non_fitting_values"]["energy_bound_low"]["value"],
            high_e=self.param_model.param_new["non_fitting_values"]["energy_bound_high"]["value"],
            e_offset=self.param_model.param_new["e_offset"]["value"],
            e_linear=self.param_model.param_new["e_linear"]["value"],
        )

        # Prepare the 'roi_dict' parameter for computations
        roi_dict = {
            _: (self.roi_dict[_].left_val / 1000.0, self.roi_dict[_].right_val / 1000.0)
            for _ in self.roi_dict.keys()
        }

        roi_dict_computed = compute_selected_rois(
            data=datav,
            data_sel_indices=(n_bin_low, n_bin_high),
            roi_dict=roi_dict,
            snip_param=snip_param,
            use_snip=self.subtract_background,
            chunk_pixels=5000,
            n_chunks_min=4,
            progress_bar=TerminalProgressBar("Computing ROIs: "),
            client=None,
        )

        # Save ROI data to HDF5 file
        self.saveROImap_to_hdf(roi_dict_computed)

        # Add scalers to the ROI dataset, so that they can be selected from Image Wizard.
        # We don't want to save scalers to the file, since they are already in the file.
        # So we add scalers after data is saved.
        scaler_key = f"{self.data_title_base}_scaler"
        if scaler_key in self.io_model.img_dict:
            roi_dict_computed.update(self.io_model.img_dict[scaler_key])

        roi_result[f"{self.data_title_adjusted}_roi"] = roi_dict_computed

        logger.info("ROI is computed.")
        return roi_result

    def saveROImap_to_hdf(self, data_dict_roi):
        # Generate the path to computed ROIs in the HDF5 file
        det_name = "detsum"  # Assume that ROIs are computed using the sum of channels

        # Search for channel name in the data title. Channels are named
        #   det1, det2, ... , i.e. 'det' followed by integer number.
        # The channel name is always located at the end of the ``data_title``.
        # If the channel name is found, then build the path using this name.
        srch = re.search("det\d+$", self.data_title)  # noqa: W605
        if srch:
            det_name = srch.group(0)
        inner_path = f"xrfmap/{det_name}"

        try:
            save_fitdata_to_hdf(
                self.hdf_path,
                data_dict_roi,
                datapath=inner_path,
                data_saveas="xrf_roi",
                dataname_saveas="xrf_roi_name",
            )
        except Exception as ex:
            logger.error(f"Failed to save ROI data to file '{self.hdf_path}'\n    Exception: {ex}")
        else:
            logger.info(f"ROI data was successfully saved to file '{self.hdf_path}'")
