GUI_main.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script is the main script used to produce a GUI which should help for
cell segmentation. This script can only read .nd2 files containing the
images of cells, especially it displays for each recorded positions (field
of view) the pictures in the time axis.

The script opens first a window which allows you to load an nd2 file and
to load or create an hdf file. The hdf file contains all the masks, so if
it is the first the user segments an nd2 file, a new one should be created.
And it can be then loaded for later use.  Along with new hdf file, created
by the name entered by the user (say filename), it creates three other hdf
files (filename_predicted.h5, filename_thresholded.h5 and
filename_segmented.h5) these contain all the steps of the NN to get to the
segmented picture.

After the first window is finished a second one opens, where at each time
index, three pictures
are displayed the t-1 picture, the t picture (current frame which can be
edited) and the t+1 picture. Using the arrows one can navigate through time.
On top of the picture, there is always a mask which is displayed, if no cells
are present in the mask then the mask is blank and the user does not see it.
If one wants to hand anmotate the pictures, one can just start to draw on the
picture using the different functions (New Cell, Add Region, Brush, Eraser,
Save Mask,...) and the informations will be saved in the mask overlayed on
top of the pictures.

If one wants to segment using a neural network, one can press the
corresponding button (Launch CNN) and select the time range and
the field of views on which the neural network is applied.

Once the neural network has finished predicting, there are still no visible
masks, but on the field of views and time indices where the NN has been
applied, the threshold and segment buttons are enabled. By checking these
two buttons one can either display the thresholded image of the prediction or
display the segmentation of the thresholded prediction.

At this stage, one can correct the segmentation of the prediction using
the functions (New Cell, Add Region, etc..) by selecting the Segment
checkbox and then save them using the Save Seg button.
If the user is happy with the segmentation, the Cell Correspondance button
can be clicked. Until then, the cells get random numbers attributed by
the segmentation algorithm. In order to keep track of the cell through time,
the same cells should have the same number between two different time pictures.
This can be (with some errors) achieved by the Cell Correspondance button,
which tries to attribute the same number to corresponding cells in time.
After that, the final mask is saved and it is always visible when you go on
the corresponding picture. This mask can also be corrected using the
usual buttons (because the Cell Correspondance makes also mistakes).

"""

import sys
#append all the paths where the modules are stored. Such that this script
#looks into all of these folders when importing modules.
sys.path.append("./unet")
sys.path.append("./disk")
sys.path.append("./icons")
sys.path.append("./init")
sys.path.append("./misc")
import time
import os
import numpy as np


# Import everything for the Graphical User Interface from the PyQt5 library.
from PyQt5.QtWidgets import QApplication, QMainWindow, QMenu, QVBoxLayout, QSizePolicy, QMessageBox, QWidget, QPushButton, QShortcut, QComboBox, QCheckBox, QLineEdit, QMenu, QAction, QStatusBar
from PyQt5 import QtGui
from PyQt5.QtCore import pyqtSignal, QObject, Qt

#Import from matplotlib to use it to display the pictures and masks.
from matplotlib.backends.qt_compat import QtCore, QtWidgets, is_pyqt5
from matplotlib.backends.backend_qt5agg import (FigureCanvasQTAgg as FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
#import the colormaps function to create a customed colormap scale with 10
#colors only
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
#import Path functions to handle the regions drawn by the user. ("add region
#and new cell")
from matplotlib.path import Path


#Import all the other python files
#this file handles the interaction with the disk, so loading/saving images
#and masks and it also runs the neural network.
import InteractionDisk_temp as nd
#this file contains a dialog window that takes two integers as entry to swap
#two cell values
import ExchangeCellValues as ecv
#this file contains a dialog window which is opened before the main program
#and allows to load the nd2 and hdf files by browsing through the computer.
import DialogFileBrowser as dfb
#this file contains a window that opens to change the value of one cell. It
#is opened as soon as the user presses with the left click on a specific cell.
import ChangeOneCellValue as cocv
#this file contains a dialog window to browse for the excel file where
#all the extracted information on the fluoerscence is written. Or to create a
#new excel file by typing a name in the text box. It is thought to have one
#excel file per field of view.
import DialogDataBrowser as ddb
#this file contains a dialog window where a time range and the field of views
#can be selected to then launch a prediction of the neural network on
#a specific range of pictures.
import LaunchBatchPrediction as lbp

#this file initializes all the buttons present in the gui, sets the shortcuts
#to these buttons and also connect the buttons to the function that are
#triggered when the buttons are pressed.
import InitButtons

#this file contains the layout of the main window so it justs puts the buttons
#and the pictures at the desired position in the main window.
import InitLayout


import random

#import everything needed to write and read excel files.
from openpyxl import load_workbook
from openpyxl import Workbook




class NavigationToolbar(NavigationToolbar):
    """This is the standard matplotlib toolbar but only the buttons
    that are of interest for this gui are loaded. These buttons allow
    to zoom into the pictures/masks and to navigate in the zoomed picture.
    A Home button can be used to set the view back to the original view.
    """
    toolitems = [t for t in NavigationToolbar.toolitems if
                 t[0] in ('Home', 'Pan', 'Zoom','Back', 'Forward')]

class App(QMainWindow):
    """This class creates the main window.

    """

    def __init__(self, nd2pathstr, hdfpathstr, newhdfstr):
        super().__init__()
#        initializes the window


#        id is an integer that gives the id of the connection between the mouseclick method
#        and the activation of the button.

#       all these ids are integers which are used to set a connection between
#       the button and the function that this button calls.
#       There are three of them because it happens that one can trigger three
#       different functions with one button.
        self.id = 0
        self.id2 = 0
        self.id3 = 0



#       it calls an object of the class Load Image from the InteractionDisk
#       file which is used to load images and masks from the nd2 file. To
#       initialize this object it needs the path of the nd2 file, of an
#       existing hdf file and the name of a new hdf file. If the user has no
#       hdf file yet the hdfpathstr will be empty and vice versa if the user
#       selects an already existing hdf file.
#       It takes all the strings given by the first window
#       (called before the main window opens) from the DialogFileBrowser.py
#       file. (see at the end of this code)

        self.reader = nd.Reader(hdfpathstr, newhdfstr, nd2pathstr)


#       these variables are used to create/read/load the excel file used
#       to write the fluorescence values extracted. For each field of view,
#       the user will be asked each time to create a new xls file for the
#       field of view or to load an existing field of view (this is the role
#       of the boolean variable)
        self.xlsfilename = ''
        self.nd2path = nd2pathstr
        self.FlagFluoExtraction = False




#        Set the indices for the time axis and the field of view index. These
#        indices represent everywhere the current picture (the one that can be
#        edited, i.e. the time t frame)
        self.Tindex = 0
        self.FOVindex = 0

#        loading the first images of the cells from the nd2 file
        self.currentframe = self.reader.LoadOneImage(self.Tindex,self.FOVindex)
        self.nextframe = self.reader.LoadOneImage(self.Tindex+1, self.FOVindex)
        self.previousframe = np.zeros([self.reader.sizey, self.reader.sizex])



#        loading the first masks from the hdf5 file
        self.mask_curr = self.reader.LoadMask(self.Tindex, self.FOVindex)
        self.mask_previous = np.zeros([self.reader.sizey, self.reader.sizex])
        self.mask_next = self.reader.LoadMask(self.Tindex+1, self.FOVindex)


#        creates a list of all the buttons, which will then be used in order
#        to disable all the other buttons at once when one button/function
#        is pressed/used in the gui.
        self.buttonlist = []


#        setting buttons as attributes
#        the shortcuts for the buttons, the functions to which they are
#        connected to,... are all set up in the ButtonInit file which is called
#        in the self.initUI() method below.

        self.button_newcell = QPushButton("New cell")
        self.buttonlist.append(self.button_newcell)

        self.button_add_region = QPushButton("Add region")
        self.buttonlist.append(self.button_add_region)

        self.button_savemask = QPushButton("Save Mask")
        self.buttonlist.append(self.button_savemask)

        self.button_drawmouse = QPushButton('Brush')
        self.buttonlist.append(self.button_drawmouse)

        self.button_eraser = QPushButton('Eraser')
        self.buttonlist.append(self.button_eraser)

        self.button_exval = QPushButton('Exchange Cell Values')
        self.buttonlist.append(self.button_exval)

        self.button_showval = QCheckBox('Show Cell Values')
        self.buttonlist.append(self.button_showval)

        self.button_hidemask = QCheckBox('Hide Mask')
        self.buttonlist.append(self.button_hidemask)

        self.button_nextframe = QPushButton("Next Time Frame")
        self.buttonlist.append(self.button_nextframe)

        self.button_previousframe = QPushButton("Previous Time Frame")
        self.buttonlist.append(self.button_previousframe)

        self.button_cnn = QPushButton('Launch CNN')
        self.buttonlist.append(self.button_cnn)

        self.button_threshold = QCheckBox('Threshold prediction')
        self.buttonlist.append(self.button_threshold)

        self.button_segment = QCheckBox('Segment')
        self.buttonlist.append(self.button_segment)

        self.button_cellcorespondance = QPushButton('Cell Correspondance')
        self.buttonlist.append(self.button_cellcorespondance)

        self.button_changecellvalue = QPushButton('Change cell value')
        self.buttonlist.append(self.button_changecellvalue)

        self.button_extractfluorescence = QPushButton('Extract Fluorescence')
        self.buttonlist.append(self.button_extractfluorescence)


        self.initUI()




    def initUI(self):
        """Initializing the widgets contained in the window.
        Especially, it creates the widget to plot the
        pictures/masks by creating an object of the PlotCanvas class self.m.
        Every interaction with the masks or the pictures (loading new
        frames/editing the frames/masks) occurs through this class.

        This method initializes all the buttons with the InitButtons file.
        It connects the buttons to the functions that they should trigger,
        it sets the shortcuts to the buttons, a tool tip,
        eventually a message on the status bar when the user hovers
        over the button, etc..

        This function also sets all the layout in the InitLayout file. It
        takes and places the widgets (buttons, canvas, toolbar).

        The function initializes a Menu Bar to have a menu which can be
        improved later on.
        It sets a toolbar of the matplotlib library and hides it. But it allows
        to connect to the functions of this toolbar through "homemade"
        QPushButtons instead of the ones provided by matplotlib.
        Finally, it sets a StatusBar which displays some text to describe
        the use of some buttons, or to show that the program is working on
        something (running the neural network, loading frames, etc...)

        After all this has been initialized, the program is ready to be used.

        """
        self._main = QtWidgets.QWidget()
        self.setCentralWidget(self._main)

#       Here our canvas is created where using matplotlib,
#       one can plot data to display the pictures and masks.
        self.m = PlotCanvas(self)

#       Initialize all the buttons that are needed and the functions that are
#       connected when the buttons are triggered.
        InitButtons.Init(self)
        InitLayout.Init(self)

#        MENU, TOOLBAR AND STATUS BAR
#       creates a menu just in case, some other functions can be added later
#        in this menu.
        menubar = self.menuBar()
        self.fileMenu = menubar.addMenu('File')
        self.saveactionmenu = QAction('Save')
        self.fileMenu.addAction(self.saveactionmenu)
        self.saveactionmenu.triggered.connect(self.ButtonSaveMask)


#       hide the toolbar and instead of the original buttons of matplotlib,
#       QPushbuttons are used and are connected to the functions of the toolbar
#       it is than easier to interact with these buttons (for example to
#       to disable them and so on..)
        self.Nvgtlbar = NavigationToolbar(self.m, self)
        self.addToolBar(self.Nvgtlbar)
        self.Nvgtlbar.hide()

#        creates a status bar, which displays (or should display) some text
#        whenever a function is used.
        self.statusBar = QStatusBar()
        self.setStatusBar(self.statusBar)

        self.show()


    def mousePressEvent(self, QMouseEvent):
        """this function is implemented just to have the QLineButtons of the
        change time index button, setthreshold button and the setsegmentation
        button out of focus when the user clicks somewhere
        on the gui. (to unfocus the buttons)
        """
        self.button_timeindex.clearFocus()
        if self.button_SetThreshold.isEnabled():
            self.button_SetThreshold.clearFocus()

        if self.button_SetSegmentation.isEnabled():
            self.button_SetSegmentation.clearFocus()



#        CONNECT the functions of the toolbar to our custom QPushbuttons.
    def ZoomTlbar(self):
        """The button_zoom is connected to the zoom function of the toolbar
        already present in the matplotlib library.

        Depending on the buttons that are active or checked, when the zoom
        function is used, it does not disable all the buttons.

        If the segment and threshold button are not checked or used
        when the zoom button is clicked, it disables all the button
        using self.Disable which disables everything except the button passed
        in argument (in this case button_zoom).


        If the zoom button is used while the segment button is checked,
        it disables all the buttons (1st elif) except the segment button
        but once it is finished (so the zoom button becomes unchecked)
        then it enables only the editing buttons (as long as the segment
        button is still checked) such as New Cell, Add Region, Eraser,
        Brush,etc.. and the other toolbar buttons (3rd elif)


        If the zoom button is clicked while the threshold button is checked,
        it disables all the button except the threshold button (2nd elif).
        Once the zoom button is unchecked, it enables the toolbar buttons
        (4th elif)
        In any other case, it just enables all the buttons again.


        """
        self.Nvgtlbar.zoom()

        if self.button_zoom.isChecked() and not(self.button_segment.isChecked() or self.button_threshold.isChecked()):
            self.Disable(self.button_zoom)

        elif self.button_zoom.isChecked() and self.button_segment.isChecked():
            self.Disable(self.button_zoom)
            self.button_segment.setEnabled(True)

        elif self.button_zoom.isChecked() and self.button_threshold.isChecked():
            self.Disable(self.button_zoom)
            self.button_threshold.setEnabled(True)

        elif self.button_zoom.isChecked() == False and self.button_segment.isChecked():

            self.button_pan.setEnabled(True)
            self.button_home.setEnabled(True)
            self.button_back.setEnabled(True)
            self.button_forward.setEnabled(True)

            self.EnableCorrectionsButtons()

        elif self.button_zoom.isChecked() == False and self.button_threshold.isChecked():

            self.button_pan.setEnabled(True)
            self.button_home.setEnabled(True)
            self.button_back.setEnabled(True)
            self.button_forward.setEnabled(True)

        else:
            self.Enable(self.button_zoom)

    def HomeTlbar(self):
#        connects the home button to the home function of the matplotlib
#        toolbar. It sets the view to the original view (no zoom)
        self.Nvgtlbar.home()


    def BackTlbar(self):
#        It calls the back function of the matplotlib toolbar which sets the
#        view to the previous one (if the user does several zooms/pans,
#        this button allows to go back in the "history of views")
        self.Nvgtlbar.back()



    def ForwardTlbar(self):
#        It calls the forward function of the matplotlib toolbar which sets the
#        view to the next one (if the user does several zooms/pans,
#        this button allows to go forward in the "history of views"

        self.Nvgtlbar.forward()



    def PanTlbar(self):
        """The button_pan is connected to the pan function of the toolbar
        already present in the matplotlib library.

        Depending on the buttons that are active or checked, when the pan
        function is used, it does not disable all the buttons.

        If the segment and threshold button are not checked or used
        when the pan button is clicked, it disables all the button
        using self.Disable which disables everything except the button passed
        in argument (in this case button_pan).


        If the pan button is used while the segment button is checked,
        it disables all the buttons (1st elif) except the segment button
        but once it is finished (so the zoom button becomes unchecked)
        then it enables only the editing buttons (as long as the segment
        button is still checked) such as New Cell, Add Region, Eraser,
        Brush,etc.. and the other toolbar buttons (3rd elif)


        If the pan button is clicked while the threshold button is checked,
        it disables all the button except the threshold button (2nd elif).
        Once the pan button is unchecked, it enables the toolbar buttons
        (4th elif)
        In any other case, it just enables all the buttons again.
        """

        self.Nvgtlbar.pan()

        if self.button_pan.isChecked() and not(self.button_segment.isChecked() or self.button_threshold.isChecked()):
            self.Disable(self.button_pan)

        elif self.button_pan.isChecked() and self.button_segment.isChecked():
            self.Disable(self.button_pan)
            self.button_segment.setEnabled(True)

        elif self.button_pan.isChecked() and self.button_threshold.isChecked():
            self.Disable(self.button_pan)
            self.button_threshold.setEnabled(True)

        elif not(self.button_pan.isChecked()) and self.button_segment.isChecked():

            self.button_zoom.setEnabled(True)
            self.button_home.setEnabled(True)
            self.button_back.setEnabled(True)
            self.button_forward.setEnabled(True)

            self.EnableCorrectionsButtons()

        elif not(self.button_pan.isChecked()) and self.button_threshold.isChecked():
            self.button_zoom.setEnabled(True)
            self.button_home.setEnabled(True)
            self.button_back.setEnabled(True)
            self.button_forward.setEnabled(True)


        else:
            self.Enable(self.button_pan)


    def ButtonFluo(self):
        """This function is called everytime the Extract Fluorescence button is
        clicked (self.button_extractfluorescence).

        self.FlagFluoExtraction is boolean which is True when the path to the
        excel file has already been loaded into self.xlsfilename.
        This pathname changes for each field of view as it is thought to have
        one xls file per field of view.
        So at the beginning and each time the user changes field of view,
        self.FlagFluoExtraction is set to False.

        When it is set to False, this function calls a dialog window where
        the user is asked to load an already existing xls file for the current
        field of view or to give a name to create a new xls file for
        the current field of view. (self.Dialogxls)

        If it set to true, it means that self.xlsfilename contains the path
        to the xls file for the current field of view and it is directly given
        to the function that writes the fluorescence into the xls file.
        (self.ExtractFluo)
        """

        if self.FlagFluoExtraction:
            self.ExtractFluo(self.xlsfilename)
        else:
            self.DialogXls()


    def DialogXls(self):
        """This function creates a dialog window which gives two options to the
        user either to load an existing xls file or to give a new name in order
        to create a new xls file.
        """
#        creates the window
        dwind = ddb.FileBrowser()

#        this test is True if the user presses ok in the dialog window, if the
#        user presses cancels it returns and nothing happens.

        if dwind.exec_():

#            read the entry given by the file browser
            xlsname = dwind.xlsname

#            reads the entry given by the new filename text field.
            newxlsname = dwind.newxlsentry.text()

#            if the string containing the filepath to an existing xls file
#            is not empty then it calls directly the function to write the
#            data into this existing xls file and sets self.xlsfilename
            if xlsname:

                self.xlsfilename = xlsname
                self.ExtractFluo(xlsname)

#           if xlsname is empty then it creates a new pathfilename and puts
#           the new created xls file into the folder where nd2 is located.
#           the string containing the nd2 namepath is split
            else:
                xlsname = ''
                templist = self.nd2path.split('/')

                for k in range(0, len(templist)-1):

                    xlsname = xlsname + templist[k] + '/'
#               this is the new path/filename
                xlsname = xlsname + newxlsname + '.xlsx'
                self.xlsfilename = xlsname

#               here as a new name has been given, it means that a new xls file
#                should be created, this is done with CreateXls
                self.CreateXls(xlsname)
#                once there is an existing xls file, it writes in this file
#                using self.ExtractFluo.
                self.ExtractFluo(xlsname)
#           this flag is set to true, for the current field of view each
#           time extract fluorescence is clicked it writes in the file located
#           at self.xlsfilename.
            self.FlagFluoExtraction = True
        else:

            return


    def CreateXls(self, xlsfilename):
        """In case there is no xls file existing, here a new one is created
        and prepared. For each channel a new sheet is created.
        In the first row for each sheet, the time indices are written t = 0,
        t = 1, etc... but only every third column. Because in the row below,
        three values are extracted 'Total intensity', 'Area' and 'Variance'
        at each time index. So three columns for each time index are needed,
        for the three data points.
        The first column is left empty (starting from third row) because
        the cell numbers will be written in there.
        """

#        creates a new xls file using xlwt library.
        book = Workbook()
        nbrchannels = self.reader.sizec

        for i in range(0,nbrchannels):
            sheetname = self.reader.channel_names[i]
#            creates a sheet with the name of the corresponding channel.
            if i == 0:
                sheet = book.active
                sheet.title = sheetname
            else:
                sheet = book.create_sheet(sheetname)
            sheet.cell(1,1, 'Cell Number / Time axis')
            sheet.cell(2,1, 'labels')
            timeaxissize = self.reader.sizet
#           start writing the time index at column 1, column 0 is reserved for
#           cell numbers.
            timecolindex = 2

            for t in range(1,timeaxissize+1):
#                in row 0 the time index is written
                sheet.cell(1,timecolindex).value = 't = {}'.format(t-1)
#                in row 1, the label of the three data points are written
                sheet.cell(2,timecolindex).value = 'Total Intensity'
                sheet.cell(2,timecolindex+1).value = 'Total Area'
                sheet.cell(2,timecolindex+2).value = 'Mean Intensity'
                sheet.cell(2,timecolindex+3).value =  'Variance'
#                updates the index, where the next time index should be written
                timecolindex = timecolindex + 4

#       saves the xls file.
        book.save(xlsfilename)




    def ExtractFluo(self, xlsfilename):
        """This is the function that takes as argument the filepath to the xls
        file and writes in the file.
        It iterates over the different channels (or the sheets of the file,
        each channel has one sheet.), and reads the image corresponding
        to the time, field of view and channel index. It reads the already
        existing file and makes a copy in which the data will be written in it.

        The first step of calculating the data is to iterate through each
        cell/segment of the mask (so each cell is a submatrix of one value
        in the matrix of the mask).
        For each of these value /cell, the area is extracted as being
        the number of pixels corresponding to this cell/value.
        (it is known from the microscope settings how to convert
        the pixel in area).
        The total intensity is just the value of the pixel and it is added over
        all the pixels corresonding to the cell/value.
        The mean is then calculated as being the total intensity divided by
        the number of pixels (which here is equal to the area also).
        With the mean it is then possible to calculate the variance of the
        signal for one cell/value.

        Then, it is checked if the value of the cell (cell number) already
        exists in the first column, if it already exists it continues to
        find the column corresponding to the time index where the values
        should be written. It sets the flag to True such that it does not
        write the cell as new one and adds it at the end of the column

        If the value is not found in the cell number column (new cell or
        first time writing in the file), the flag is False, thus it adds the
        cell number at the end of the column.
        It then saves the xls file.

        """

#        disables all the buttons except the one passed in argument.
        self.Disable(self.button_extractfluorescence)
#        shows a message on the status bar to show that the program is working
        self.statusBar.showMessage('Extracting the fluorescence...')

#        opens the file to read it.
        book = load_workbook(self.xlsfilename)

#       makes a copy of the reading file to write in it the new values.
#        wb = xlscopy(readbook) # a writable copy (can't read values out of this, only write to it)

#        iterate over all the channels, so over all the sheets in the file
        for channel in range(0, self.reader.sizec):
#           loads the picture corresponding to the channel, time index and fov
            image = self.reader.LoadImageChannel(self.Tindex, self.FOVindex, channel)

#            loads the sheet to read out corresponding to the current channel
            sheet = book.worksheets[channel]
#            sheet = readbook.sheet_by_index(channel)

#            this line is here to prevent some errors of streaming into
#            the file due to read file which is open (I am not sure about this
#            but it is a more or less working solution found on stackoverflow)
#            os.remove(xlsfilename)

#           load the sheet corresponding to the current channel to write in it
#            writingsheet = wb.get_sheet(channel)

#           this index contains the value of the maximum number of rows in the
#           file, it is used to append at the end the cell number column a new
#           cell/value, and it is updated each time a new cell is added.
            tempidx = sheet.max_row

#           np.unique(array) returns an array which contains all the value
#           that appear in self.m.plotmask, so it returns every cell value
#           including the background (value 0) present in self.m.plotmask
            for val in np.unique(self.m.plotmask):

#                exclude the background, as it is not a cell
                if val != 0:

#                    this (self.m.plotmask==val).sum() adds one at every pixel
#                    where self.m.plotmask has the value val
                    area = (self.m.plotmask == val).sum()
#                    it sums the value of the pixel in image at the coordinates
#                    where self.m.plotmask equals val.
                    tot_intensity = image[self.m.plotmask == val].sum()

#                    calculate the mean to use it in the calc. of the variance.
                    mean = tot_intensity/area

#                    create a copy of plotmask because I had weird experiences
#                    with np.where where it modified sometimes the given array
#                    (not sure)
                    temparr = self.m.plotmask.copy()

#                    extract the coordinates of the mask matrix where it equals
#                    the current cell value val
                    coord = np.where(temparr == val)

#                    variable to save the variance.
                    var = 0

#                    we loop over the coord of the pixel where mask == val
                    for i in range(0,len(coord[0])):
#                        extract the intensity at the coordinate
                        val_intensity = image[coord[0][i], coord[1][i]]
#                        substract the value of the intensity with the mean,
#                        and square it. It is then add to the var.
                        var = var + (val_intensity-mean)*(val_intensity-mean)

#                   var is then divided by the number of the pixel
#                   corresponding to this value (also equal to the area)
#                   to get the variance.
                    var = var/area

#                    if flag is false it means that the cell number
#                    corresponding to val is not present in the xls file, first
#                    column.
                    flag = False


#                    iterate over all the rows
                    for row in range(sheet.max_row+1):

#                        test if in the first column 0, the number of the cell
#                        is already present
#                         if sheet.cell_value(row,0) == str(val):
                         if sheet.cell(row = row+1, column = 1).value == str(val):

#                             if is present, the column corresponding to the
#                             current time index is by iterating over the cols.
                             for col in range(sheet.max_column+1):
#                                 test if it is the right column
#                                 if sheet.cell_value(0, col) == 't = {}'.format(self.Tindex):
                                 if sheet.cell(row = 1, column = col+1).value == 't = {}'.format(self.Tindex):
#                                   write in the xls file at the row, col coord
                                     sheet.cell(row+1, col+1, str(tot_intensity))
                                     sheet.cell(row+1, col+2, str(area))
                                     sheet.cell(row+1,col+3, str(mean))
                                     sheet.cell(row+1, col+4, str(var))
                                     book.save(xlsfilename)


#                                     the flag is set to True so that it does
#                                     not execute the code where the cell is
#                                     added in the xls file in a new row.
                                     flag = True
                    if not flag:
#                    this lines are executed if a new cell is detected or if
#                    if it is the first time to write in the file.
                        for col in range(sheet.max_column+1):
                            if sheet.cell(row = 1, column =  col+1).value == 't = {}'.format(self.Tindex):
#                                it write the cell value/cell number in the
#                                column
                                sheet.cell(tempidx+1,1, str(val))

#                                writes the data extracted before
                                sheet.cell(tempidx+1,col+1,str(tot_intensity))
                                sheet.cell(tempidx+1, col+2, str(area))
                                sheet.cell(tempidx+1, col+3, str(mean))
                                sheet.cell(tempidx+1, col+4, str(var))
#                                it updates the number of rows as a new cell
#                                has been added, so there is one more row.
                                tempidx = tempidx + 1
#                               save in the file
                                book.save(xlsfilename)


#       Enable again all the buttons
        self.Enable(self.button_extractfluorescence)

#        clear the message shown in the status bar
        self.statusBar.clearMessage()



    def LaunchBatchPrediction(self):
        """This function is called whenever the button Launch CNN is pressed.
        It allows to run the neural network over a time range and selected
        field of views.

        It creates a dialog window with two entries, that define the time range
        and a list where the user can select the desired fields of view.

        Once it reads all the value, it calls the neural network function
        inside of self.PredThreshSeg and it does the prediction of the neural
        network, thresholds this prediction and then segments it.
        """
#        creates a dialog window from the LaunchBatchPrediction.py file
        dlg = lbp.CustomDialog(self)

#        this if tests if the user pressed 'ok' in the dialog window
        if dlg.exec_():

#           it tests if the user has entered some values
#           if not it ignores and returns.
            if dlg.entry1.text()!= '' and dlg.entry2.text() != '':

#                reads out the entry given by the user and converts the index
#                to integers
                time_value1 = int(dlg.entry1.text())
                time_value2 = int(dlg.entry2.text())


#                it tests if the first value is smaller or equal such that
#                time_value1 is the lower range of the time range
#                and time_value2 the upper boundary of the range.
                if time_value1 <= time_value2 :

#                    displays that the neural network is running
                    self.statusBar.showMessage('Running the neural network...')

#                    it iterates in the list of the user-selected fields
#                    of view, to return the corresponding index, the function
#                    dlg.listfov.row(item) is used which gives an integer
                    for item in dlg.listfov.selectedItems():

#                        iterates over the time indices in the range
                        for t in range(time_value1, time_value2+1):

#                            calls the neural network for time t and selected
#                            fov
                            if dlg.entry_threshold.text() !=  '':
                                thr_val = float(dlg.entry_threshold.text())
                            else:
                                thr_val = None
                            if dlg.entry_segmentation.text() != '':
                                seg_val = int(dlg.entry_segmentation.text())
                            else:
                                seg_val = 10

                            self.PredThreshSeg(t, dlg.listfov.row(item), thr_val, seg_val)


#                   once it has iterated over all the fov, the message in
#                   the status bar is cleared and the buttons are enabled.
                    self.statusBar.clearMessage()
                    self.EnableCNNButtons()

                else:
                    return
            else:
                return
        else:
            return

    def PredThreshSeg(self, timeindex, fovindex, thr_val, seg_val):
          """
          This function is called in the LaunchBatchPrediction function.
          This function calls the neural network function in the
          InteractionDisk.py file and then thresholds the result
          of the prediction, saves this thresholded prediction.
          Then it segments the thresholded prediction and saves the
          segmentation.
          """
#          launches the neural network
          self.reader.LaunchPrediction(timeindex, fovindex)
#          thresholds the prediction
          self.m.ThresholdMask = self.reader.ThresholdPred(thr_val, timeindex,fovindex)
#          saves the thresholded pred.
          self.reader.SaveThresholdMask(timeindex, fovindex, self.m.ThresholdMask)
#          segments the thresholded pred.
          self.m.SegmentedMask = self.reader.Segment(seg_val, timeindex,fovindex)
#          saves the segmentation
          self.reader.SaveSegMask(timeindex, fovindex, self.m.SegmentedMask)


    def LaunchPrediction(self):
        """This function is not used in the gui, but it can be used to launch
        the prediction of one picture, with no thresholding and no segmentation
        """
        if not(self.reader.TestPredExisting(self.Tindex, self.FOVindex)):
            self.statusBar.showMessage('Running the neural network...')
            self.Disable(self.button_cnn)
            self.reader.LaunchPrediction(self.Tindex, self.FOVindex)

            self.Enable(self.button_cnn)

            self.button_cnn.setEnabled(False)
            self.button_threshold.setEnabled(True)
            self.button_segment.setEnabled(True)
            self.button_cellcorespondance.setEnabled(True)
            self.statusBar.clearMessage()

    def ChangeOneValue(self):
        """This function is called when the button Change cell value is
        clicked. It displays the instructions on the status bar.
        And if the user clicks in the graph where the current mask is displayed
        it connects the event of the click (meaning that user has clicked on
        one cell) to the function self.DialogBoxChangeOneValue.
        This function will then replaces the cell selected by the user with
        the click with a new value entered by the user.
        """

#        displaying the instructions on the statusbar
        self.statusBar.showMessage('Select one cell using the left click and then enter the desired value in the dialog box')

#       disables all the buttons
        self.Disable(self.button_changecellvalue)

#        connects the event "press mouse button" in the matplotlib plot
#       (picture) to the function self.DialogBoxChangeOneValue
        self.id = self.m.mpl_connect('button_press_event', self.DialogBoxChangeOneValue)



    def DialogBoxChangeOneValue(self, event):
        """This function is called when the user after the user has selected
        the button Change cell value and clicked in the picture to select
        the desired cell to change.

        It first deconnects the mouse click event in matplotlib with this
        function to not generate any other dialog window.

        It then tests if the click is inside the matplotlib plot (if it is
        outside it equals to None) and if it is the current and editable plot
        (the one in the middle of the gui, self.m.ax)

        If is true, then it sets the coordinates to int. and creates a dialog
        window where the user is asked to type a value to set it to the cell.

        If the user presses ok, it tests if the entry is valid (>0 and not
        empty) and looks for the old cell value and replaces it. And then
        it updates the plot such that the result of the change can be seen.
        """

#        the function is disconnected from the matplotlib event.
        self.m.mpl_disconnect(self.id)

#        test if the button is a left click and if the coordinates
#        chosen by the user click is inside of the current matplotlib plot
#        which is given by self.m.ax
        if event.button == 1 and (event.xdata != None and event.ydata != None) and self.m.ax == event.inaxes:
            newx = int(event.xdata)
            newy = int(event.ydata)

#            creates a dialog window
            dlg = cocv.CustomDialog(self)

#            if the user presses 'ok' in the dialog window it executes the code
#           else it does nothing
            if dlg.exec():
#                it tests that the user has entered some value, that it is not
#                empty and that it is equal or bigger to 0.
                if dlg.entry1.text() != '' and int(dlg.entry1.text()) >= 0:
#                    reads the new value to set and converts it from str to int
                    value = int(dlg.entry1.text())

#                    self.m.plotmask[newy, newx] the value selected by the user
#                    self.m.plotmask == self.m.plotmask[newy, newx]
#                    gives the coordinates where it is equal to the value
#                    selected by the user. And it replaces it with the new
#                    value.
                    self.m.plotmask[self.m.plotmask == self.m.plotmask[newy,newx]] = value
#                    updates the plot to see the modification.
                    self.m.updatedata()

#                   if the button to show cell values is checked, then it
#                   replots the cell values

                    if self.button_showval.isChecked():
                        self.m.ShowCellNumbersCurr()
                        self.m.ShowCellNumbersNext()
                        self.m.ShowCellNumbersPrev()

#       enables the button again
        self.Enable(self.button_changecellvalue)

#        clears the message in the status bar
        self.statusBar.clearMessage()



#        the button is a checkable and it has to be unchecked else it seems
#        that the button is still in use, because it gets a blue color.
        self.button_changecellvalue.setChecked(False)