"""utility functions for sidpy; will move to sidpy"""
import numpy as np
import sidpy
import h5py
import pyNSID
import os
import ipywidgets as widgets
from IPython.display import display

import sidpy

class ChooseDataset(object):
    """Widget to select dataset object """

    def __init__(self, input_object, show_dialog=True):
        if isinstance(input_object, sidpy.Dataset):
            if isinstance(input_object.h5_dataset, h5py.Dataset):
                self.current_channel = input_object.h5_dataset.parent
        elif isinstance(input_object, h5py.Group):
            self.current_channel = input_object
        elif isinstance(input_object, h5py.Dataset):
            self.current_channel = input_object.parent
        else:
            raise ValueError('Need hdf5 group or sidpy Dataset to determine image choices')
        self.dataset_names = []
        self.dataset_list = []
        self.dataset_type = None
        self.dataset = None
        self.reader = pyNSID.NSIDReader(self.current_channel.file.filename)

        self.get_dataset_list()
        self.select_image = widgets.Dropdown(options=self.dataset_names,
                                             value=self.dataset_names[0],
                                             description='select dataset:',
                                             disabled=False,
                                             button_style='')
        if show_dialog:
            display(self.select_image)

        self.select_image.observe(self.set_dataset, names='value')
        self.set_dataset(0)
        self.select_image.index = (len(self.dataset_names) - 1)

    def get_dataset_list(self):
        """ Get by Log number sorted list of datasets"""
        datasets = self.reader.read()
        order = []
        for dset in datasets:
            if self.dataset_type is None or dset.data_type == self.data_type:
                if 'Log' in dset.title:
                    position = dset.title.find('Log_') + 4
                    order.append(int(dset.title[position:position + 3])+1)
                else:
                    order.append(0)
        for index in np.argsort(order):
            dset = datasets[index]
            self.dataset_names.append('/'.join(dset.title.replace('-', '_').split('/')[-1:]))
            self.dataset_list.append(dset)

    def set_dataset(self, b):
        index = self.select_image.index
        self.dataset = self.dataset_list[index]
        ## Find
        self.dataset.title = self.dataset.title.split('/')[-1]



def get_dimensions_by_order(dims_in, dataset):
    """get dimension

    Parameters
    ----------
    dims_in: int or list of int
        the dimensions by numerical order
    dataset: sidpy.Dataset

    Returns
    -------
    dims_out: list of dimensions
    """

    if isinstance(dims_in, int):
        dims_in = [dims_in]
    dims_out = []
    for item in dims_in:
        if isinstance(item, int):
            if item in dataset._axes:
                dims_out.append([item, dataset._axes[item]])
    return dims_out


def get_dimensions_by_type(dims_in, dataset):
    """ get dimension by dimension_type name

    Parameters
    ----------
    dims_in: dimension_type or list of dimension_types
        the dimensions by numerical order
    dataset: sidpy.Dataset

    Returns
    -------
    dims_out: list of dimensions
    """

    if isinstance(dims_in, (str, sidpy.DimensionType)):
        dims_in = [dims_in]
    for i in range(len(dims_in)):
        if isinstance(dims_in[i], str):
            dims_in[i] = sidpy.DimensionType[dims_in[i].upper()]
    dims_out = []
    for dim, axis in dataset._axes.items():
        if axis.dimension_type in dims_in:
            dims_out.append([dim, dataset._axes[dim]])
    return dims_out


def make_dummy_dataset(value_type):
    """Make a dummy sidpy.Dataset """

    assert isinstance(value_type, sidpy.DataType)
    if type == sidpy.DataType.SPECTRUM:
        dataset = sidpy.Dataset.from_array(np.arange(100))
        dataset.data_type = 'spectrum'
        dataset.units = 'counts'
        dataset.quantity = 'intensity'

        dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) + 70, name='energy_scale'))
        dataset.dim_0.dimension_type = 'spectral'
        dataset.dim_0.units = 'eV'
        dataset.dim_0.quantity = 'energy loss'
    else:
        raise NotImplementedError('not implemented')
    return dataset


def plot(dataset):
    dataset.plot()


def get_image_dims(dataset):
    """Get all spatial dimensions"""

    image_dims = []
    for dim, axis in dataset._axes.items():
        if axis.dimension_type == sidpy.DimensionType.SPATIAL:
            image_dims.append(dim)
    return image_dims


def get_extent(dataset):
    """get extent to plot with matplotlib"""
    image_dims = get_image_dims(dataset)
    return dataset.get_extent(image_dims)

