maps.py

# import PyQt5 elements
from PyQt5.QtWidgets import QApplication
# general imports
from os import path
import numpy
import pickle
# datatype imports
from datatypes.maps1d.qtlab import QtLab1D
from datatypes.maps1d.qcodes import QCoDeS1D
from datatypes.maps2d.qtlab import QtLab2D
from datatypes.maps2d.qcodes import QCoDeS2D
from datatypes.maps2d.horiba import Horiba2D
from datatypes.maps2d.vuckovic import Vuckovic2D

import time

class Map:

    """
    Map
    This class defines the basic functions of all maps.
    The Map1D and Map2D classes inherit from this class.
    """

    def __init__(self):

        # create all general variables
        self._app = None
        self._data_names = {}       # dictionary for all data names
        self._dimension = 0         # the dimension of the map (1D, 2D)
        self._focus = []            # the currently focused pixel
        self._id = 0                # the map id
        self._interval = [0, 0]     # integration interval for energy
        self._map_name = ''         # the map name
        self._resolution = 0        # the pixels on the CCD
        self._selected_data = 0     # a flag for the currently selected data

    def get_data_names(self):

        # return data names
        return self._data_names

    def get_dimension(self):

        # return map dimension
        return self._dimension

    def get_focus(self):

        # return focus
        return self._focus

    def get_id(self):

        # return the map ID
        return self._id

    def get_interval(self):

        # return interval
        return self._interval

    def get_map_name(self):

        # return map name
        return self._map_name

    def get_resolution(self):

        # return pixels on CCD
        return self._resolution

    def get_selected_data(self):

        # return the currently selected data
        return self._selected_data

    def set_app(self, app):

        # set map list
        self._app = app

    def set_id(self, map_id):

        # set map id
        self._id = map_id

    def set_selected_data(self, selected_data):

        # update data selection if the new data is different from the old one
        if selected_data != self._selected_data:

            self._selected_data = selected_data

            # emit signal
            self._app.selected_data_changed.emit(self._id)


class Map1D(Map):

    """
    Map1D
    Class for one-dimensional maps such as gate-dependent measurements.
    """

    def __init__(self, map_id, file_name):

        # call super init
        super(Map1D, self).__init__()

        # set dimension of the map
        self._dimension = 1

        # set the map id
        self._id = map_id

        # get the directory from path
        dir_name = path.dirname(file_name)

        # check for the file type of the map
        # .dat files are acquired in the PGI9 (FZJ) lab using QTLab
        if file_name[-4:] == '.dat':

            # define map loader
            map_loader = QtLab1D(file_name)

        elif file_name[-4:] == '.pck':

            # define map loader
            map_loader = QCoDeS1D(file_name)

        # load data
        self._map_name, self._spectra, self._data_names, self._data = map_loader.load_data()

        # set map size
        self._nx = self._spectra.shape[0]

        # set spectral resolution
        self._resolution = self._spectra.shape[1]

        # set initial interval for the integration of the spectra
        self._interval = [0, self._resolution-1]

        # get integrated counts, average energy and maximum energy
        self._int_counts = numpy.sum(self._spectra[:, :, 1], axis=1)
        self._mean_energies = numpy.sum(self._spectra[:, :, 0]*self._spectra[:, :, 1], axis=1)/self._int_counts
        max_pixels = numpy.argmax(self._spectra[:, :, 1], axis=1)
        self._max_energies = numpy.zeros((self._nx))
        for ix in range(self._nx):
            self._max_energies[ix] = self._spectra[ix, max_pixels[ix], 0]

        # create variables for the fit data
        self._fit_functions = numpy.zeros((self._nx, 6))
        self._fit_initial_parameters = numpy.zeros((self._nx, 6, 4))
        self._fit_initial_parameters[:, :, :] = numpy.NAN
        self._fit_optimized_parameters = numpy.zeros((self._nx, 6, 4))
        self._fit_optimized_parameters[:, :, :] = numpy.NAN

        # set focus to the center of the map
        self._focus = [int(self._nx / 2)]

    def clear_fit(self, **kwargs):

        # if no pixel was provided the current pixel is updated
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
        else:
            px = kwargs['pixel'][0]

        # clear fit
        self._fit_functions[px, :] = numpy.zeros(6)
        self._fit_initial_parameters[px, :, :] = numpy.NAN
        self._fit_optimized_parameters[px, :, :] = numpy.NAN

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.fit_changed.emit(self._id)

    # TODO: Flip for 1D

    def get_data(self, **kwargs):

        # if no data index is given, return the currently selected data
        if 'data_index' in kwargs.keys():
            data_index = kwargs['data_index']
        else:
            data_index = self._selected_data

        # return a data
        if data_index == 0:

            return self._spectra[:, :, 1]

        elif data_index == 1:

            # check if the whole map data (pixel = -1) or the focussed pixel (pixel = -2)
            # or a specific pixel (pixel = [x,y]) are requested
            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                # return whole map data
                return self._int_counts

            elif kwargs['pixel'] == -2:

                # return data at focused pixel
                return self._int_counts[self._focus[0]]

            else:

                # return data at requested pixel
                return self._int_counts[kwargs['pixel'][0]]


        elif data_index == 2:

            # check if the whole map data (pixel = -1) or the focussed pixel (pixel = -2)
            # or a specific pixel (pixel = [x,y]) are requested
            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                # return whole map data
                return self._mean_energies

            elif kwargs['pixel'] == -2:

                # return data at focused pixel
                return self._mean_energies[self._focus[0]]

            else:

                # return data at requested pixel
                return self._mean_energies[kwargs['pixel'][0]]

        elif data_index == 3:

            # check if the whole map data (pixel = -1) or the focussed pixel (pixel = -2)
            # or a specific pixel (pixel = [x,y]) are requested
            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                # return whole map data
                return self._max_energies

            elif kwargs['pixel'] == -2:

                # return data at focused pixel
                return self._max_energies[self._focus[0]]

            else:

                # return data at requested pixel
                return self._max_energies[kwargs['pixel'][0]]

        elif 3 < data_index < 4 + len(self._data):

            data_index -= 4

            # check if the whole map data (pixel = -1) or the focussed pixel (pixel = -2)
            # or a specific pixel (pixel = [x,y]) are requested
            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                # return whole map data
                return self._data[data_index]

            elif kwargs['pixel'] == -2:

                # return data at focused pixel
                return self._data[data_index][self._focus[0]]

            else:

                # return data at requested pixel
                return self._data[data_index][kwargs['pixel'][0]]

        # return a fit data
        else:

            # check which fit parameters are there
            data_index -= 4 + len(self._data)

            # get fit functions and fit parameters once
            fit_functions = self._fit_functions
            fit_optimized_parameters = self._fit_optimized_parameters

            parameters = []
            for i_peak in range(6):
                if numpy.sum(numpy.int_(fit_functions[:, i_peak] > 0)) > 0:
                    parameters.append([i_peak, 0])
                    parameters.append([i_peak, 1])
                if numpy.sum(numpy.int_(fit_functions[:, i_peak] == 2)) > 0 or \
                        numpy.sum(numpy.int_(fit_functions[:, i_peak] == 3)) > 0:
                    parameters.append([i_peak, 2])
                if numpy.sum(numpy.int_(fit_functions[:, i_peak] == 1)) > 0 or \
                        numpy.sum(numpy.int_(fit_functions[:, i_peak] == 3)) > 0:
                    parameters.append([i_peak, 3])
                if numpy.sum(numpy.int_(fit_functions[:, i_peak] > 0)) > 0:
                    parameters.append([i_peak, 4])

            # return intensities
            if parameters[data_index][1] == 0:
                return fit_optimized_parameters[:, parameters[data_index][0], 0]

            # return central energies
            elif parameters[data_index][1] == 1:
                return fit_optimized_parameters[:, parameters[data_index][0], 1]

            # return sigma
            elif parameters[data_index][1] == 2:
                sigma = numpy.zeros((self._nx))
                sigma[:] = numpy.NAN
                sigma_from_gaussian = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 2]
                sigma_from_voigt = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 3]
                sigma[fit_functions[:, parameters[data_index][0]] == 2] = sigma_from_gaussian
                sigma[fit_functions[:, parameters[data_index][0]] == 3] = sigma_from_voigt
                return 1000*sigma

            # return gamma
            elif parameters[data_index][1] == 3:
                gamma = numpy.zeros((self._nx))
                gamma[:] = numpy.NAN
                gamma_from_lorentzian = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 1]
                gamma_from_voigt = fit_optimized_parameters[:, parameters[data_index][0], 3][fit_functions[:, parameters[data_index][0]] == 3]
                gamma[fit_functions[:, parameters[data_index][0]] == 1] = gamma_from_lorentzian
                gamma[fit_functions[:, parameters[data_index][0]] == 3] = gamma_from_voigt
                return 1000*gamma

            # return FWHM
            elif parameters[data_index][1] == 4:
                fwhm = numpy.zeros((self._nx))
                fwhm[:] = numpy.NAN
                sigma_from_gaussian = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 2]
                gamma_from_lorentzian = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 1]
                gamma_from_voigt = fit_optimized_parameters[:, parameters[data_index][0], 3][fit_functions[:, parameters[data_index][0]] == 3]
                sigma_from_voigt = fit_optimized_parameters[:, parameters[data_index][0], 2][fit_functions[:, parameters[data_index][0]] == 3]
                fwhm[fit_functions[:, parameters[data_index][0]] == 1] = 2*gamma_from_lorentzian
                fwhm[fit_functions[:, parameters[data_index][0]] == 2] = 2.35482*sigma_from_gaussian
                fwhm[fit_functions[:, parameters[data_index][0]] == 3] = 0.5346*2.*gamma_from_voigt+numpy.sqrt(0.2166*4.*gamma_from_voigt**2.+2.35482**2.*sigma_from_voigt**2.)
                return 1000*fwhm

    def get_data_name(self, **kwargs):

        # if no data index is given, return the currently selected data name
        if 'data_index' in kwargs.keys():
            data_index = kwargs['data_index']
        else:
            data_index = self._selected_data

        # check whether a data or a micrograph or a fit parameter is selected
        if data_index == 0:

            return 'spectra'

        elif data_index == 1:

            return 'spectra --integral'

        elif data_index == 2:

            return 'spectra --mean'

        elif data_index == 3:

            return 'spectra --maximum'

        elif 3 < data_index < 4 + len(self._data):

            data_index -= 4
            # return data name
            return self._data_names[data_index]

        else:

            data_index -= 4 + len(self._data)

            # return parameter name
            parameters = []
            subscripts = [u'\u2081', u'\u2082', u'\u2083', u'\u2084', u'\u2085', u'\u2086']
            for i_peak in range(6):
                if numpy.sum(numpy.int_(self._fit_functions[:, i_peak] > 0)) > 0:
                    parameters.append('I'+subscripts[i_peak])
                    parameters.append('ε'+subscripts[i_peak])
                if numpy.sum(numpy.int_(self._fit_functions[:, i_peak] == 1)) > 0 or \
                        numpy.sum(numpy.int_(self._fit_functions[:, i_peak] == 3)) > 0:
                    parameters.append('σ'+subscripts[i_peak])
                if numpy.sum(numpy.int_(self._fit_functions[:, i_peak] == 2)) > 0 or \
                        numpy.sum(numpy.int_(self._fit_functions[:, i_peak] == 3)) > 0:
                    parameters.append('γ'+subscripts[i_peak])
                parameters.append('FWHM'+subscripts[i_peak])
            return parameters[data_index]

    def get_fit(self, **kwargs):

        # if no pixel was provided the current pixel is returned
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
        else:
            px = kwargs['pixel'][0]

        return self._fit_functions[px, :], self._fit_initial_parameters[px, :, :], self._fit_optimized_parameters[px, :, :]

    def get_fit_functions(self, **kwargs):

        # if no pixel was provided return the whole fit functions array
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._fit_functions[:, :]

        # if pixel is set to -2 return the fit functions for the focused pixel
        elif kwargs['pixel'] == -2:
            return self._fit_functions[self._focus[0], :]

        # return the fit functions for the desired pixel
        else:
            return self._fit_functions[kwargs['pixel'][0], :]

    def get_fit_parameters(self, **kwargs):

        # if no pixel was provided return the whole fit parameter array
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._fit_optimized_parameters[:, :, :]

        # if pixel is set to -2 return the fit parameters for the focused pixel
        elif kwargs['pixel'] == -2:
            return self._fit_optimized_parameters[self._focus[0], :, :]

        # return the fit parameters for the desired pixel
        else:
            return self._fit_optimized_parameters[kwargs['pixel'][0], :, :]

    def get_size(self):

        # return map size
        return [self._nx]

    def get_spectrum(self, **kwargs):

        # if no pixel is given, return the focused pixel's spectrum
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._spectra[self._focus[0]]
        else:
            return self._spectra[kwargs['pixel'][0], :, :]

    def set_fit(self, fit_functions, fit_initial_parameters, fit_optimized_parameters, **kwargs):

        # if no pixel was provided the current pixel is updated
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
        else:
            px = kwargs['pixel'][0]

        # clear the old fit data
        self._fit_functions[px, :] = numpy.zeros(6)
        self._fit_initial_parameters[px, :, :] = numpy.NAN
        self._fit_optimized_parameters[px, :, :] = numpy.NAN

        # set new fit data
        i_parameter = 0
        for i_peak in range(len(fit_functions)):
            if fit_functions[i_peak] == 1:
                self._fit_functions[px, i_peak] = 1
                self._fit_initial_parameters[px, i_peak, :3] = fit_initial_parameters[i_parameter:i_parameter+3]
                self._fit_initial_parameters[px, i_peak, 3] = 0
                self._fit_optimized_parameters[px, i_peak, :3] = fit_optimized_parameters[i_parameter:i_parameter+3]
                self._fit_optimized_parameters[px, i_peak, 3] = 0
                i_parameter += 3
            elif fit_functions[i_peak] == 2:
                self._fit_functions[px, i_peak] = 2
                self._fit_initial_parameters[px, i_peak, :3] = fit_initial_parameters[i_parameter:i_parameter+3]
                self._fit_initial_parameters[px, i_peak, 3] = 0
                self._fit_optimized_parameters[px, i_peak, :3] = fit_optimized_parameters[i_parameter:i_parameter+3]
                self._fit_optimized_parameters[px, i_peak, 3] = 0
                i_parameter += 3
            elif fit_functions[i_peak] == 3:
                self._fit_functions[px, i_peak] = 3
                self._fit_initial_parameters[px, i_peak, :] = fit_initial_parameters[i_parameter:i_parameter+4]
                self._fit_optimized_parameters[px, i_peak, :] = fit_optimized_parameters[i_parameter:i_parameter+4]
                i_parameter += 4

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.fit_changed.emit(self._id)

    def set_focus(self, focus):

        # set focus if it is within the map
        if 0 <= numpy.round(focus[0]) < self._nx:
                self._focus = [int(numpy.round(focus[0]))]

                # emit signal
                self._app.focus_changed.emit(self._id)

    def set_interval(self, side, value):

        # check if a good interval has been given
        if side == 'left' and value < 0:
            return
        if side == 'right' and value >= self._resolution:
            return
        if side == 'left' and self._interval[1] <= value:
            return
        if side == 'right' and self._interval[0] >= value:
            return

        # set interval
        if side == 'left':
            self._interval[0] = value
        elif side == 'right':
            self._interval[1] = value

        # recalculate intensities
        self._int_counts = numpy.sum(self._spectra[:, self._interval[0]:self._interval[1], 1], axis=1)
        self._mean_energies = numpy.sum(self._spectra[:, self._interval[0]:self._interval[1], 0]*self._spectra[:, self._interval[0]:self._interval[1], 1], axis=1)/self._int_counts
        max_pixels = numpy.argmax(self._spectra[:, self._interval[0]:self._interval[1], 1], axis=1)
        for ix in range(self._nx):
            self._max_energies[ix] = self._spectra[ix, max_pixels[ix], 0]

        # emit signal
        self._app.interval_changed.emit(self._id)

    def set_spectrum(self, spectrum, **kwargs):

        # if no pixel was provided update the focused pixel
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
        else:
            px = kwargs['pixel'][0]

        # update spectrum
        self._spectra[px, :, :] = spectrum

        self._int_counts[px] = numpy.sum(self._spectra[px, self._interval[0]:self._interval[1], 1])
        self._mean_energies[px] = numpy.sum(self._spectra[px, self._interval[0]:self._interval[1], 0]*self._spectra[px, self._interval[0]:self._interval[1], 1])/self._int_counts[px]
        max_pixel = numpy.argmax(self._spectra[px, self._interval[0]:self._interval[1], 1])
        self._max_energies[px] = self._spectra[px, self._interval[0]+max_pixel, 0]

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.spectrum_changed.emit(self._id)


class Map2D(Map):

    """
    Map2D
    Class for two-dimensional maps which are mainly spatial maps.
    """

    def __init__(self, map_id, file_name):

        # call super init
        super(Map2D, self).__init__()

        # set dimension of the map
        self._dimension = 2

        # set the map id
        self._id = map_id

        # check for the file type of the map
        # .dat files are acquired in the PGI9 (FZJ) lab using QTLab
        if file_name[-4:] == '.dat':

            # define map loader
            map_loader = QtLab2D(file_name)

        # .txt files are acquired by the Horiba machine in the Heinz Group at Stanford
        elif file_name[-4:] == '.pck':

            # define map loader
            map_loader = QCoDeS2D(file_name)

        # .txt files are acquired by the Horiba machine in the Heinz Group at Stanford
        elif file_name[-4:] == '.txt':

            # define map loader
            map_loader = Horiba2D(file_name)

        # .dat2 files are acquired in the J. Vuckovic group at Stanford
        elif file_name[-5:] == '.dat2':

            # define map loader
            map_loader = Vuckovic2D(file_name)

        # load data
        self._map_name, self._spectra, self._data_names, self._data = map_loader.load_data()

        # set map size
        self._nx = self._spectra.shape[0]
        self._ny = self._spectra.shape[1]

        # set spectral resolution
        self._resolution = self._spectra.shape[2]

        # set initial interval for the integration of the spectra
        self._interval = [0, self._resolution-1]

        # get integrated counts, average energy and maximum energy
        self._int_counts = numpy.sum(self._spectra[:, :, :, 1], axis=2)
        self._mean_energies = numpy.sum(self._spectra[:, :, :, 0]*self._spectra[:, :, :, 1], axis=2)/self._int_counts
        max_pixels = numpy.argmax(self._spectra[:, :, :, 1], axis=2)
        self._max_energies = numpy.zeros((self._nx, self._ny))
        for ix in range(self._nx):
            for iy in range(self._ny):
                self._max_energies[ix, iy] = self._spectra[ix, iy, max_pixels[ix, iy], 0]

        # create variables for the fit data
        self._fit_functions = numpy.zeros((self._nx, self._ny, 6))
        self._fit_initial_parameters = numpy.zeros((self._nx, self._ny, 6, 4))
        self._fit_initial_parameters[:, :, :, :] = numpy.NAN
        self._fit_optimized_parameters = numpy.zeros((self._nx, self._ny, 6, 4))
        self._fit_optimized_parameters[:, :, :, :] = numpy.NAN

        # set focus to the center of the map
        self._focus = [int(self._nx / 2), int(self._ny / 2)]

        # dictionary for micrographs
        self._micrographs = {}
        self._micrograph_names = {}

        # set selected data to integral
        self._selected_data = 1

    def add_micrograph(self, file_name, micrograph):

        # obtain maximum key so far
        if len(self._micrographs) == 0:
            max_key = -1
        else:
            max_key = max(self._micrographs.keys())

        # add micrograph and micrograph name
        self._micrographs[int(max_key) + 1] = micrograph
        self._micrograph_names[int(max_key) + 1] = file_name

        # return the data id of the new micrograph
        return 5 + len(self._data_names) + int(max_key)

    def clear_fit(self, **kwargs):

        # if no pixel was provided the current pixel is updated
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
            py = self._focus[1]
        else:
            px = kwargs['pixel'][0]
            py = kwargs['pixel'][1]

        # clear fit
        self._fit_functions[px, py, :] = numpy.zeros(6)
        self._fit_initial_parameters[px, py, :, :] = numpy.NAN
        self._fit_optimized_parameters[px, py, :, :] = numpy.NAN

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.fit_changed.emit(self._id)

    def flip(self, direction):

        # flip the map horizontally
        if direction == 'horizontally':

            # flip data
            self._data = numpy.flip(self._data, 1)

            # flip spectra
            self._spectra = numpy.flip(self._spectra, 0)

            # flip data derived from spectra
            self._int_counts = numpy.flip(self._int_counts, 0)
            self._mean_energies = numpy.flip(self._mean_energies, 0)
            self._max_energies = numpy.flip(self._max_energies, 0)

            # flip micrographs
            for i_micrograph in range(len(self._micrographs)):
                self._micrographs[i_micrograph] = numpy.flip(self._micrographs[i_micrograph], 1)

            # flip fit data
            self._fit_functions = numpy.flip(self._fit_functions, 0)
            self._fit_initial_parameters = numpy.flip(self._fit_initial_parameters, 0)
            self._fit_optimized_parameters = numpy.flip(self._fit_optimized_parameters, 0)

            # update focus
            self._focus[0] = self._nx - self._focus[0]

            # emit signal
            self._app.geometry_changed.emit(self._id)

        # flip the map vertically
        elif direction == 'vertically':

            # flip data
            self._data = numpy.flip(self._data, 2)

            # flip spectra
            self._spectra = numpy.flip(self._spectra, 1)

            # flip data derived from spectra
            self._int_counts = numpy.flip(self._int_counts, 1)
            self._mean_energies = numpy.flip(self._mean_energies, 1)
            self._max_energies = numpy.flip(self._max_energies, 1)

            # flip micrographs
            for i_micrograph in range(len(self._micrographs)):
                self._micrographs[i_micrograph] = numpy.flip(self._micrographs[i_micrograph], 0)

            # flip fit data
            self._fit_functions = numpy.flip(self._fit_functions, 1)
            self._fit_initial_parameters = numpy.flip(self._fit_initial_parameters, 1)
            self._fit_optimized_parameters = numpy.flip(self._fit_optimized_parameters, 1)

            # update focus
            self._focus[1] = self._ny - self._focus[1]

            # emit signal
            self._app.geometry_changed.emit(self._id)

    def get_data(self, **kwargs):

        # if no data index is given, return the currently selected data
        if 'data_index' in kwargs.keys():
            data_index = kwargs['data_index']
        else:
            data_index = self._selected_data

        # return a data
        if data_index == 1:

            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                return self._int_counts

            elif kwargs['pixel'] == -2:

                return self._int_counts[self._focus[0], self._focus[1]]

            else:

                return self._int_counts[kwargs['pixel'][0], kwargs['pixel'][1]]

        elif data_index == 2:

            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                return self._mean_energies

            elif kwargs['pixel'] == -2:

                return self._mean_energies[self._focus[0], self._focus[1]]

            else:

                return self._mean_energies[kwargs['pixel'][0], kwargs['pixel'][1]]

        elif data_index == 3:

            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                return self._max_energies

            elif kwargs['pixel'] == -2:

                return self._max_energies[self._focus[0], self._focus[1]]

            else:

                return self._max_energies[kwargs['pixel'][0], kwargs['pixel'][1]]

        elif 3 < data_index < 4 + len(self._data):

            data_index -= 4

            # check if the whole map data (pixel = -1) or the focussed pixel (pixel = -2)
            # or a specific pixel (pixel = [x,y]) are requested
            if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:

                # return whole map data
                return self._data[data_index]

            elif kwargs['pixel'] == -2:

                # return data at focused pixel
                return self._data[data_index][self._focus[0], self._focus[1]]

            else:

                # return data at requested pixel
                return self._data[data_index][kwargs['pixel'][0], kwargs['pixel'][1]]

        # return a micrograph
        elif 3 + len(self._data) < data_index < 4 + len(self._data) + len(self._micrographs):

            # return micrograph
            data_index -= 4 + len(self._data)
            return self._micrographs[data_index]

        # return a fit data
        else:

            # check which fit parameters are there
            data_index -= 4 + len(self._data) + len(self._micrographs)

            # get fit functions and fit parameters once
            fit_functions = self._fit_functions
            fit_optimized_parameters = self._fit_optimized_parameters

            parameters = []
            for i_peak in range(6):
                if numpy.sum(numpy.int_(fit_functions[:, :, i_peak] > 0)) > 0:
                    parameters.append([i_peak, 0])
                    parameters.append([i_peak, 1])
                if numpy.sum(numpy.int_(fit_functions[:, :, i_peak] == 2)) > 0 or \
                        numpy.sum(numpy.int_(fit_functions[:, :, i_peak] == 3)) > 0:
                    parameters.append([i_peak, 2])
                if numpy.sum(numpy.int_(fit_functions[:, :, i_peak] == 1)) > 0 or \
                        numpy.sum(numpy.int_(fit_functions[:, :, i_peak] == 3)) > 0:
                    parameters.append([i_peak, 3])
                if numpy.sum(numpy.int_(fit_functions[:, :, i_peak] > 0)) > 0:
                    parameters.append([i_peak, 4])

            # return intensities
            if parameters[data_index][1] == 0:
                return fit_optimized_parameters[:, :, parameters[data_index][0], 0]

            # return central energies
            elif parameters[data_index][1] == 1:
                return fit_optimized_parameters[:, :, parameters[data_index][0], 1]

            # return sigma
            elif parameters[data_index][1] == 2:
                sigma = numpy.zeros((self._nx, self._ny))
                sigma[:] = numpy.NAN
                sigma_from_gaussian = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 2]
                sigma_from_voigt = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 3]
                sigma[fit_functions[:, :, parameters[data_index][0]] == 2] = sigma_from_gaussian
                sigma[fit_functions[:, :, parameters[data_index][0]] == 3] = sigma_from_voigt
                return 1000*sigma

            # return gamma
            elif parameters[data_index][1] == 3:
                gamma = numpy.zeros((self._nx, self._ny))
                gamma[:] = numpy.NAN
                gamma_from_lorentzian = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 1]
                gamma_from_voigt = fit_optimized_parameters[:, :, parameters[data_index][0], 3][fit_functions[:, :, parameters[data_index][0]] == 3]
                gamma[fit_functions[:, :, parameters[data_index][0]] == 1] = gamma_from_lorentzian
                gamma[fit_functions[:, :, parameters[data_index][0]] == 3] = gamma_from_voigt
                return 1000*gamma

            # return FWHM
            elif parameters[data_index][1] == 4:
                fwhm = numpy.zeros((self._nx, self._ny))
                fwhm[:] = numpy.NAN
                sigma_from_gaussian = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 2]
                gamma_from_lorentzian = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 1]
                gamma_from_voigt = fit_optimized_parameters[:, :, parameters[data_index][0], 3][fit_functions[:, :, parameters[data_index][0]] == 3]
                sigma_from_voigt = fit_optimized_parameters[:, :, parameters[data_index][0], 2][fit_functions[:, :, parameters[data_index][0]] == 3]
                fwhm[fit_functions[:, :, parameters[data_index][0]] == 1] = 2*gamma_from_lorentzian
                fwhm[fit_functions[:, :, parameters[data_index][0]] == 2] = 2.35482*sigma_from_gaussian
                fwhm[fit_functions[:, :, parameters[data_index][0]] == 3] = 0.5346*2.*gamma_from_voigt+numpy.sqrt(0.2166*4.*gamma_from_voigt**2.+2.35482**2.*sigma_from_voigt**2.)
                return 1000*fwhm

    def get_data_name(self, **kwargs):

        # if no data index is given, return the currently selected data name
        if 'data_index' in kwargs.keys():
            data_index = kwargs['data_index']
        else:
            data_index = self._selected_data

        if data_index == 1:

            return 'spectra --integral'

        elif data_index == 2:

            return 'spectra --mean'

        elif data_index == 3:

            return 'spectra --maximum'


        elif 3 < data_index < 4 + len(self._data):

            data_index -= 4
            return self._data_names[data_index]

        elif 3 + len(self._data) < data_index < 4 + len(self._data) + len(self._micrographs):

            data_index -= 4 + len(self._data)
            return self._micrograph_names[data_index]

        else:

            # check which fit parameters are there
            data_index -= 4 + len(self._data) + len(self._micrographs)

            parameters = []
            subscripts = [u'\u2081', u'\u2082', u'\u2083', u'\u2084', u'\u2085', u'\u2086']
            for i_peak in range(6):
                if numpy.sum(numpy.int_(self._fit_functions[:, :, i_peak] > 0)) > 0:
                    parameters.append('I'+subscripts[i_peak])
                    parameters.append('ε'+subscripts[i_peak])
                if numpy.sum(numpy.int_(self._fit_functions[:, :, i_peak] == 1)) > 0 or \
                        numpy.sum(numpy.int_(self._fit_functions[:, :, i_peak] == 3)) > 0:
                    parameters.append('σ'+subscripts[i_peak])
                if numpy.sum(numpy.int_(self._fit_functions[:, :, i_peak] == 2)) > 0 or \
                        numpy.sum(numpy.int_(self._fit_functions[:, :, i_peak] == 3)) > 0:
                    parameters.append('γ'+subscripts[i_peak])
                parameters.append('FWHM'+subscripts[i_peak])
            return parameters[data_index]

    def get_fit(self, **kwargs):

        # if no pixel was provided the current pixel is returned
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
            py = self._focus[1]
        else:
            px = kwargs['pixel'][0]
            py = kwargs['pixel'][1]

        return self._fit_functions[px, py, :], self._fit_initial_parameters[px, py, :, :], self._fit_optimized_parameters[px, py, :, :]

    def get_fit_functions(self, **kwargs):

        # if no pixel was provided return the whole fit functions array
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._fit_functions[:, :, :]

        # if pixel is set to -2 return the fit functions for the focused pixel
        elif kwargs['pixel'] == -2:
            return self._fit_functions[self._focus[0], self._focus[1], :]

        # return the fit functions for the desired pixel
        else:
            return self._fit_functions[kwargs['pixel'][0], kwargs['pixel'][1], :]

    def get_fit_parameters(self, **kwargs):

        # if no pixel was provided return the whole fit parameter array
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._fit_optimized_parameters[:, :, :, :]

        # if pixel is set to -2 return the fit parameters for the focused pixel
        elif kwargs['pixel'] == -2:
            return self._fit_optimized_parameters[self._focus[0], self._focus[1], :, :]

        # return the fit parameters for the desired pixel
        else:
            return self._fit_optimized_parameters[kwargs['pixel'][0], kwargs['pixel'][1], :, :]

    def get_micrographs(self):

        # return micrographs
        return self._micrographs

    def get_micrograph_names(self):

        # return micrograph names
        return self._micrograph_names

    def get_size(self):

        # return map size
        return [self._nx, self._ny]

    def get_spectrum(self, **kwargs):

        # if no pixel is given, return the focused pixel's spectrum
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            return self._spectra[self._focus[0], self._focus[1]]
        else:
            return self._spectra[kwargs['pixel'][0], kwargs['pixel'][1], :, :]

    def rotate(self, direction):

        if direction == 'clockwise':

            # rotate data
            self._data = numpy.swapaxes(self._data, 1, 2)
            self._data = numpy.flip(self._data, 2)

            # rotate spectra
            self._spectra = numpy.swapaxes(self._spectra, 0, 1)
            self._spectra = numpy.flip(self._spectra, 1)

            # rotate data derived from spectra
            self._int_counts = numpy.swapaxes(self._int_counts, 0, 1)
            self._int_counts = numpy.flip(self._int_counts, 1)
            self._mean_energies = numpy.swapaxes(self._mean_energies, 0, 1)
            self._mean_energies = numpy.flip(self._mean_energies, 1)
            self._max_energies = numpy.swapaxes(self._max_energies, 0, 1)
            self._max_energies = numpy.flip(self._max_energies, 1)

            # rotate micrographs
            for i_micrograph in range(len(self._micrographs)):
                self._micrographs[i_micrograph] = numpy.swapaxes(self._micrographs[i_micrograph], 0, 1)
                self._micrographs[i_micrograph] = numpy.flip(self._micrographs[i_micrograph], 0)

            # rotate fit data
            self._fit_functions = numpy.swapaxes(self._fit_functions, 0, 1)
            self._fit_initial_parameters = numpy.swapaxes(self._fit_initial_parameters, 0, 1)
            self._fit_optimized_parameters = numpy.swapaxes(self._fit_optimized_parameters, 0, 1)
            self._fit_functions = numpy.flip(self._fit_functions, 1)
            self._fit_initial_parameters = numpy.flip(self._fit_initial_parameters, 1)
            self._fit_optimized_parameters = numpy.flip(self._fit_optimized_parameters, 1)

            # update map size
            tmp = self._nx
            self._nx = self._ny
            self._ny = tmp

            # update focus
            tmp = [self._focus[0], self._focus[1]]
            self._focus[0] = tmp[1]
            self._focus[1] = tmp[0]
            self._focus[1] = self._ny-tmp[0]

            # emit signal
            self._app.geometry_changed.emit(self._id)

        elif direction == 'anticlockwise':

            # rotate data
            self._data = numpy.swapaxes(self._data, 1, 2)
            self._data = numpy.flip(self._data, 1)

            # rotate spectra
            self._spectra = numpy.swapaxes(self._spectra, 0, 1)
            self._spectra = numpy.flip(self._spectra, 0)

            # rotate data derived from spectra
            self._int_counts = numpy.swapaxes(self._int_counts, 0, 1)
            self._int_counts = numpy.flip(self._int_counts, 0)
            self._mean_energies = numpy.swapaxes(self._mean_energies, 0, 1)
            self._mean_energies = numpy.flip(self._mean_energies, 0)
            self._max_energies = numpy.swapaxes(self._max_energies, 0, 1)
            self._max_energies = numpy.flip(self._max_energies, 0)

            # rotate micrographs
            for i_micrograph in range(len(self._micrographs)):
                self._micrographs[i_micrograph] = numpy.swapaxes(self._micrographs[i_micrograph], 0, 1)
                self._micrographs[i_micrograph] = numpy.flip(self._micrographs[i_micrograph], 1)

            # rotate fit data
            self._fit_functions = numpy.swapaxes(self._fit_functions, 0, 1)
            self._fit_initial_parameters = numpy.swapaxes(self._fit_initial_parameters, 0, 1)
            self._fit_optimized_parameters = numpy.swapaxes(self._fit_optimized_parameters, 0, 1)
            self._fit_functions = numpy.flip(self._fit_functions, 0)
            self._fit_initial_parameters = numpy.flip(self._fit_initial_parameters, 0)
            self._fit_optimized_parameters = numpy.flip(self._fit_optimized_parameters, 0)

            # update map size
            tmp = self._nx
            self._nx = self._ny
            self._ny = tmp

            # update focus
            tmp = [self._focus[0], self._focus[1]]
            self._focus[0] = tmp[1]
            self._focus[1] = tmp[0]
            self._focus[0] = self._nx-tmp[1]

            # emit signal
            self._app.geometry_changed.emit(self._id)

    def set_fit(self, fit_functions, fit_initial_parameters, fit_optimized_parameters, **kwargs):

        # if no pixel was provided the current pixel is updated
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
            py = self._focus[1]
        else:
            px = kwargs['pixel'][0]
            py = kwargs['pixel'][1]

        # clear the old fit data
        self._fit_functions[px, py, :] = numpy.zeros(6)
        self._fit_initial_parameters[px, py, :, :] = numpy.NAN
        self._fit_optimized_parameters[px, py, :, :] = numpy.NAN

        # set new fit data
        i_parameter = 0
        for i_peak in range(len(fit_functions)):
            if fit_functions[i_peak] == 1:
                self._fit_functions[px, py, i_peak] = 1
                self._fit_initial_parameters[px, py, i_peak, :3] = fit_initial_parameters[i_parameter:i_parameter+3]
                self._fit_initial_parameters[px, py, i_peak, 3] = 0
                self._fit_optimized_parameters[px, py, i_peak, :3] = fit_optimized_parameters[i_parameter:i_parameter+3]
                self._fit_optimized_parameters[px, py, i_peak, 3] = 0
                i_parameter += 3
            elif fit_functions[i_peak] == 2:
                self._fit_functions[px, py, i_peak] = 2
                self._fit_initial_parameters[px, py, i_peak, :3] = fit_initial_parameters[i_parameter:i_parameter+3]
                self._fit_initial_parameters[px, py, i_peak, 3] = 0
                self._fit_optimized_parameters[px, py, i_peak, :3] = fit_optimized_parameters[i_parameter:i_parameter+3]
                self._fit_optimized_parameters[px, py, i_peak, 3] = 0
                i_parameter += 3
            elif fit_functions[i_peak] == 3:
                self._fit_functions[px, py, i_peak] = 3
                self._fit_initial_parameters[px, py, i_peak, :] = fit_initial_parameters[i_parameter:i_parameter+4]
                self._fit_optimized_parameters[px, py, i_peak, :] = fit_optimized_parameters[i_parameter:i_parameter+4]
                i_parameter += 4

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.fit_changed.emit(self._id)

    def set_focus(self, focus):

        # set focus if it is within the map
        if 0 <= numpy.round(focus[0]) < self._nx:
            if 0 <= numpy.round(focus[1]) < self._ny:
                self._focus = [int(numpy.round(focus[0])), int(numpy.round(focus[1]))]

                # emit signal
                self._app.focus_changed.emit(self._id)

    def set_interval(self, side, value):

        # check if a good interval has been given
        if side == 'left' and value < 0:
            return
        if side == 'right' and value >= self._resolution:
            return
        if side == 'left' and self._interval[1] <= value:
            return
        if side == 'right' and self._interval[0] >= value:
            return

        # set interval
        if side == 'left':
            self._interval[0] = value
        elif side == 'right':
            self._interval[1] = value

        # recalculate data
        self._int_counts = numpy.sum(self._spectra[:, :, self._interval[0]:self._interval[1], 1], axis=2)
        self._mean_energies = numpy.sum(self._spectra[:, :, self._interval[0]:self._interval[1], 0]*self._spectra[:, :, self._interval[0]:self._interval[1], 1], axis=2)/self._int_counts
        max_pixels = numpy.argmax(self._spectra[:, :, self._interval[0]:self._interval[1], 1], axis=2)
        for ix in range(self._nx):
            for iy in range(self._ny):
                self._max_energies[ix, iy] = self._spectra[ix, iy, self._interval[0]+max_pixels[ix, iy], 0]

        # emit signal
        self._app.interval_changed.emit(self._id)

    def set_spectrum(self, spectrum, **kwargs):

        # if no pixel was provided update the focused pixel
        if 'pixel' not in kwargs.keys() or kwargs['pixel'] == -1:
            px = self._focus[0]
            py = self._focus[1]
        else:
            px = kwargs['pixel'][0]
            py = kwargs['pixel'][1]

        # update spectrum
        self._spectra[px, py, :, :] = spectrum

        # update derived data
        self._int_counts[px, py] = numpy.sum(self._spectra[px, py, self._interval[0]:self._interval[1], 1])
        self._mean_energies[px, py] = numpy.sum(self._spectra[px, py, self._interval[0]:self._interval[1], 0]*self._spectra[px, py, self._interval[0]:self._interval[1], 1])/self._int_counts[px, py]
        max_pixel = numpy.argmax(self._spectra[px, py, self._interval[0]:self._interval[1], 1])
        self._max_energies[px, py] = self._spectra[px, py, self._interval[0]+max_pixel, 0]

        # emit signal
        if 'emit' not in kwargs or kwargs['emit']:
            self._app.spectrum_changed.emit(self._id)


class MapList:

    """
    MapList
    A map list stores all maps loaded into the program.
    """

    def __init__(self):

        # link app
        self._app = QApplication.instance()

        # create dictionary for maps
        self._maps = {}

        # set flags for selected map and map counter
        self._selected = None
        self._counter = 0
        self._id_counter = 0

        # call super init
        super(MapList, self).__init__()

    def append_1d(self, file_name):

        # check if the loaded file is a .py2dl file
        if file_name[-6:] == '.py2ds':

            # pickle map object from .py2dl file
            map_file = open(file_name, 'rb')
            self._maps[self._id_counter] = pickle.load(map_file)
            self._maps[self._id_counter].set_id(self._id_counter)
            self._maps[self._id_counter].set_app(self._app)

        else:

            # create a new map object and select this map
            self._maps[self._id_counter] = Map1D(self._id_counter, file_name)
            self._maps[self._id_counter].set_app(self._app)

        # increase the map and id counter
        self._counter += 1
        self._id_counter += 1

        # emit signal
        self._app.map_added.emit(self._id_counter - 1)

        # return map object
        return self._maps[self._id_counter - 1]

    def append_2d(self, file_name):

        # check if the loaded file is a .py2dl file
        if file_name[-6:] == '.py2ds':

            # pickle map object from .py2dl file
            map_file = open(file_name, 'rb')
            self._maps[self._id_counter] = pickle.load(map_file)
            self._maps[self._id_counter].set_id(self._id_counter)
            self._maps[self._id_counter].set_app(self._app)

        else:

            # create a new map object and select this map
            self._maps[self._id_counter] = Map2D(self._id_counter, file_name)
            self._maps[self._id_counter].set_app(self._app)

        # increase the map and id counter
        self._counter += 1
        self._id_counter += 1

        # emit signal
        self._app.map_added.emit(self._id_counter - 1)

        # return map object
        return self._maps[self._id_counter - 1]

    def get_count(self):

        # return the map counter
        return self._counter

    def get_map(self, map_id):

        # return the requested map
        return self._maps[map_id]

    def get_maps(self):

        # return maps
        return self._maps

    def get_selected_map(self):

        # return currently selected map object
        return self._selected

    def remove_map(self, map_handle):

        # get id of the map that is to be removed
        map_id = map_handle.get_id()

        # remove map from dictionary
        del self._maps[map_id]

        # decrease map counter
        self._counter -= 1

        # emit signal
        self._app.map_removed.emit(map_id)

    def reset(self):

        # create dictionary for maps
        self._maps = {}

        # set flags for selected map and map counter
        self._selected = -1
        self._counter = 0
        self._id_counter = 0

    def save_map(self, file_name):

        # dump map object to the pickle file
        self._selected.set_app(None)
        dump_file = open(file_name, 'wb')
        pickle.dump(self._selected, dump_file, protocol=pickle.HIGHEST_PROTOCOL)
        self._selected.set_app(self._app)

    def set_selected_map(self, map_handle):

        # set the selected map flag
        self._selected = map_handle

        # emit signal
        self._app.selected_map_changed.emit(map_handle.get_id())