update setup.py

   detector characteristics (bias, dark and readout noise, gain,

   plate scale and pixel scale, oversampling factor, exposure time etc.).

#. Read in another file containing charge trap definitions (for CTI modelling).

      #. Read in a file defining the cosmic rays (trail lengths and cumulative distributions).

#. Read in a file defining the cosmic rays (trail lengths and

cumulative distributions).

#. Read in CCD offset information, displace the image, and modify

   the output file name to contain the CCD and quadrant information

#. Load the wavefront aberration data used to calculate PSF with defined

 wavelength and field of view.

#. Loop over the number of exposures to co-add and for each object in the

object catalog:

      #. Load the wavefront aberration data used to calculate PSF with defined wavelength and field of view.

      #. Loop over the number of exposures to co-add and for each object in the object catalog:

            * determine the number of electrons an object should have by scaling the object's magnitude

  * determine the number of electrons an object should have by scaling the

object's magnitude

with the given zeropoint and exposure time.

  * determine whether the object lands on to the detector or not and if it is

a star or an extended source (i.e. a galaxy).

            * if object is extended determine the size (using a size-magnitude relation) and scale counts,

              convolve with the PSF, and finally overlay onto the detector according to its position.

            * if object is a star, scale counts according to the derived

              scaling (first step), and finally overlay onto the detector according to its position.

            * add a ghost of image of the object (scaled to the peak pixel of the object) [optional].

  * if object is extended determine the size (using a size-magnitude relation)

  and scale counts, convolve with the PSF, and finally overlay onto the

  detector according to its position.

  * if object is a star, scale counts according to the derived scaling

 (first step), and finally overlay onto the detector according to its position.

#. Apply calibration unit flux to mimic flat field exposures [optional].

#. Apply a multiplicative flat-field map to emulate pixel-to-pixel non-uniformity [optional].

        else:

            ss = '_'

        # if currentpath =='/home/yan/IFS':

        if self.information['dir_path']=='/nfsdata/share/simulation-unittest/ifs_sim/':                        

            self.result_path = self.information['dir_path']+'ifs_sim_result/'+self.source+ss+result_day

        else:

            home_path = os.environ['HOME']

            if home_path == '/home/yan':

                self.result_path = '../IFS_simData_'+self.source+ss+result_day

            else:

                self.result_path = '/data/ifspip/CCD_ima/IFS_simData_'+self.source+ss+result_day

        #     self.result_path='../IFS_simData_'+self.source+ss+result_day

        # else:

        #     self.result_path='/data/ifspip/CCD_ima/IFS_simData_'+self.source+ss+result_day

        self.result_path = self.information['dir_path']+'ifs_sim_result/'+self.source+ss+result_day

        if os.path.isdir(self.result_path) == False:

            os.mkdir(self.result_path)

        self.log.info('Finished the ith_Exposure = %i' % (simnumber))

# print('The iLoop= % d simlaiton finished. ' %simnumber)

##############################################################################################

##############################################################################################

############################################################################

############################################################################

def runIFSsim(sourcein, configfile, iLoop, applyhole='no'):

    # opts, args = processArgs()

    # opts.configfile = configfile

    simulate = dict()

    simulate[iLoop] = IFSsimulator(configfile)

    dir_path = os.path.join(os.environ['UNIT_TEST_DATA_ROOT'], 'ifs_sim/')

    simulate[iLoop].information['dir_path'] = dir_path

    ###############

    ##############

    simulate[iLoop].simulate(sourcein, iLoop)

    return 1