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Commit 6326829d authored by Yan Zhaojun's avatar Yan Zhaojun
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update setup.py

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csst_ifs_sim/csst_ifs_sim.py
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......@@ -14,46 +14,48 @@ This file contains an image simulator for the CSST IFS.
The approximate sequence of events in the simulator is as follows:
#. Read in a configuration file, which defines for example,
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 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:
* 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].
#. Apply calibration unit flux to mimic flat field exposures [optional].
#. Apply a multiplicative flat-field map to emulate pixel-to-pixel non-uniformity [optional].
#. Add a charge injection line (horizontal and/or vertical) [optional].
#. Add cosmic ray tracks onto the CCD with random positions but known distribution [optional].
#. Apply detector charge bleeding in column direction [optional].
#. Add constant dark current and background light from Zodiacal light [optional].
#. Include spatially uniform scattered light to the pixel grid [optional].
#. Add photon (Poisson) noise [optional]
#. Add cosmetic defects from an input file [optional].
#. Add pre- and overscan regions in the serial direction [optional].
#. Apply the CDM03 radiation damage model [optional].
#. Apply CCD273 non-linearity model to the pixel data [optional].
#. Add readout noise selected from a Gaussian distribution [optional].
#. Convert from electrons to ADUs using a given gain factor.
#. Add a given bias level and discretise the counts (the output is going to be in 16bit unsigned integers).
#. Finally the simulated image is converted to a FITS file, a WCS is assigned
and the output is saved to the current working directory.
#. Read in a configuration file, which defines for example,
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 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:
* 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.
#. Apply calibration unit flux to mimic flat field exposures [optional].
#. Apply a multiplicative flat-field map to emulate pixel-to-pixel non-uniformity [optional].
#. Add a charge injection line (horizontal and/or vertical) [optional].
#. Add cosmic ray tracks onto the CCD with random positions but known distribution [optional].
#. Apply detector charge bleeding in column direction [optional].
#. Add constant dark current and background light from Zodiacal light [optional].
#. Include spatially uniform scattered light to the pixel grid [optional].
#. Add photon (Poisson) noise [optional]
#. Add cosmetic defects from an input file [optional].
#. Add pre- and overscan regions in the serial direction [optional].
#. Apply the CDM03 radiation damage model [optional].
#. Apply CCD273 non-linearity model to the pixel data [optional].
#. Add readout noise selected from a Gaussian distribution [optional].
#. Convert from electrons to ADUs using a given gain factor.
#. Add a given bias level and discretise the counts (the output is going to be in 16bit unsigned integers).
#. Finally the simulated image is converted to a FITS file, a WCS is assigned
and the output is saved to the current working directory.
.. Warning:: The code is still work in progress and new features are being added.
The code has been tested, but nevertheless bugs may be lurking in corners, so
......@@ -1389,14 +1391,20 @@ class IFSsimulator():
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:
# self.result_path='../IFS_simData_'+self.source+ss+result_day
home_path = os.environ['HOME']
# else:
# self.result_path='/data/ifspip/CCD_ima/IFS_simData_'+self.source+ss+result_day
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 = 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)
......@@ -5018,18 +5026,12 @@ class IFSsimulator():
self.log.info('%s = %s' % (key, value))
self.log.info('Finished the ith_Exposure = %i' % (simnumber))
#print('The iLoop= % d simlaiton finished. ' %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)
......@@ -5040,12 +5042,6 @@ def runIFSsim(sourcein, configfile, iLoop, applyhole='no'):
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
......
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