Loading csst_mci_sim/CTI/CTI.py +2 −0 Original line number Diff line number Diff line Loading @@ -159,6 +159,8 @@ class CDM03bidir(): # from ifs_so.cdm03.cpython-38-x86_64-linux-gnu import cdm03bidir # import cdm03bidir from .mci_so import cdm03bidir CTIed = cdm03bidir.cdm03(np.asfortranarray(data), jflip, iflip, self.values['dob'], self.values['rdose'], Loading csst_mci_sim/csst_mci_sim.py +178 −5 Original line number Diff line number Diff line Loading @@ -71,7 +71,7 @@ from scipy import ndimage #sys.path.append('./csst_mci_sim') from .CTI import CTI #from .CTI import CTI from .support import logger as lg from .support import cosmicrays from .support import shao Loading @@ -80,7 +80,6 @@ from .support import MCIinstrumentModel from .mci_so import cdm03bidir from joblib import Parallel, delayed from astropy.coordinates import SkyCoord from scipy import interpolate Loading @@ -91,6 +90,180 @@ import astropy.coordinates as coord from scipy.interpolate import interp1d ########################### functions ######################### """ Charge Transfer Inefficiency ============================ This file contains a simple class to run a CDM03 CTI model developed by Alex Short (ESA). This now contains both the official CDM03 and a new version that allows different trap parameters in parallel and serial direction. :requires: NumPy :requires: CDM03 (FORTRAN code, f2py -c -m cdm03bidir cdm03bidir.f90) :version: 0.35 """ import numpy as np #CDM03bidir class CDM03bidir(): """ Class to run CDM03 CTI model, class Fortran routine to perform the actual CDM03 calculations. :param settings: input parameters :type settings: dict :param data: input data to be radiated :type data: ndarray :param log: instance to Python logging :type log: logging instance """ def __init__(self, settings, data, log=None): """ Class constructor. :param settings: input parameters :type settings: dict :param data: input data to be radiated :type data: ndarray :param log: instance to Python logging :type log: logging instance """ self.data = data self.values = dict(quads=(0,1,2,3), xsize=2048, ysize=2066, dob=0.0, rdose=8.0e9) self.values.update(settings) self.log = log self._setupLogger() #default CDM03 settings self.params = dict(beta_p=0.6, beta_s=0.6, fwc=200000., vth=1.168e7, vg=6.e-11, t=20.48e-3, sfwc=730000., svg=1.0e-10, st=5.0e-6, parallel=1., serial=0.0) #update with inputs self.params.update(self.values) #read in trap information trapdata = np.loadtxt(self.values['dir_path']+self.values['paralleltrapfile']) if trapdata.ndim > 1: self.nt_p = trapdata[:, 0] self.sigma_p = trapdata[:, 1] self.taur_p = trapdata[:, 2] else: #only one trap species self.nt_p = [trapdata[0],] self.sigma_p = [trapdata[1],] self.taur_p = [trapdata[2],] trapdata = np.loadtxt(self.values['dir_path']+self.values['serialtrapfile']) if trapdata.ndim > 1: self.nt_s = trapdata[:, 0] self.sigma_s = trapdata[:, 1] self.taur_s = trapdata[:, 2] else: #only one trap species self.nt_s = [trapdata[0],] self.sigma_s = [trapdata[1],] self.taur_s = [trapdata[2],] #scale thibaut's values if 'thibaut' in self.values['parallelTrapfile']: self.nt_p /= 0.576 #thibaut's values traps / pixel self.sigma_p *= 1.e4 #thibaut's values in m**2 if 'thibaut' in self.values['serialTrapfile']: self.nt_s *= 0.576 #thibaut's values traps / pixel #should be division? self.sigma_s *= 1.e4 #thibaut's values in m**2 def _setupLogger(self): """ Set up the logger. """ self.logger = True # if self.log is None: # self.logger = False def applyRadiationDamage(self, data, iquadrant=0): """ Apply radian damage based on FORTRAN CDM03 model. The method assumes that input data covers only a single quadrant defined by the iquadrant integer. :param data: imaging data to which the CDM03 model will be applied to. :type data: ndarray :param iquandrant: number of the quadrant to process :type iquandrant: int cdm03 - Function signature:: sout = cdm03(sinp,iflip,jflip,dob,rdose,in_nt,in_sigma,in_tr,[xdim,ydim,zdim]) Required arguments: sinp : input rank-2 array('d') with bounds (xdim,ydim) iflip : input int jflip : input int dob : input float rdose : input float in_nt : input rank-1 array('d') with bounds (zdim) in_sigma : input rank-1 array('d') with bounds (zdim) in_tr : input rank-1 array('d') with bounds (zdim) Optional arguments: xdim := shape(sinp,0) input int ydim := shape(sinp,1) input int zdim := len(in_nt) input int Return objects: sout : rank-2 array('d') with bounds (xdim,ydim) .. Note:: Because Python/NumPy arrays are different row/column based, one needs to be extra careful here. NumPy.asfortranarray will be called to get an array laid out in Fortran order in memory. Before returning the array will be laid out in memory in C-style (row-major order). :return: image that has been run through the CDM03 model :rtype: ndarray """"" #return data iflip = iquadrant / 2 jflip = iquadrant % 2 params = [self.params['beta_p'], self.params['beta_s'], self.params['fwc'], self.params['vth'], self.params['vg'], self.params['t'], self.params['sfwc'], self.params['svg'], self.params['st'], self.params['parallel'], self.params['serial']] if self.logger: self.log.info('nt_p=' + str(self.nt_p)) self.log.info('nt_s=' + str(self.nt_s)) self.log.info('sigma_p= ' + str(self.sigma_p)) self.log.info('sigma_s= ' + str(self.sigma_s)) self.log.info('taur_p= ' + str(self.taur_p)) self.log.info('taur_s= ' + str(self.taur_s)) self.log.info('dob=%f' % self.values['dob']) self.log.info('rdose=%e' % self.values['rdose']) self.log.info('xsize=%i' % data.shape[1]) self.log.info('ysize=%i' % data.shape[0]) self.log.info('quadrant=%i' % iquadrant) self.log.info('iflip=%i' % iflip) self.log.info('jflip=%i' % jflip) ################################################################################# CTIed = cdm03bidir.cdm03(np.asfortranarray(data), jflip, iflip, self.values['dob'], self.values['rdose'], self.nt_p, self.sigma_p, self.taur_p, self.nt_s, self.sigma_s, self.taur_s, params, [data.shape[0], data.shape[1], len(self.nt_p), len(self.nt_s), len(self.params)]) return np.asanyarray(CTIed) ################################################################################################################# ################################################################################################################# def transRaDec2D(ra, dec): # radec转为竞天程序里的ob, 赤道坐标系下的笛卡尔三维坐标xyz. x1 = np.cos(dec / 57.2957795) * np.cos(ra / 57.2957795) Loading Loading @@ -2599,7 +2772,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_g = cti.applyRadiationDamage(self.image_g.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading @@ -2611,7 +2784,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_r = cti.applyRadiationDamage(self.image_r.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading @@ -2622,7 +2795,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_i = cti.applyRadiationDamage(self.image_i.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading Loading
csst_mci_sim/CTI/CTI.py +2 −0 Original line number Diff line number Diff line Loading @@ -159,6 +159,8 @@ class CDM03bidir(): # from ifs_so.cdm03.cpython-38-x86_64-linux-gnu import cdm03bidir # import cdm03bidir from .mci_so import cdm03bidir CTIed = cdm03bidir.cdm03(np.asfortranarray(data), jflip, iflip, self.values['dob'], self.values['rdose'], Loading
csst_mci_sim/csst_mci_sim.py +178 −5 Original line number Diff line number Diff line Loading @@ -71,7 +71,7 @@ from scipy import ndimage #sys.path.append('./csst_mci_sim') from .CTI import CTI #from .CTI import CTI from .support import logger as lg from .support import cosmicrays from .support import shao Loading @@ -80,7 +80,6 @@ from .support import MCIinstrumentModel from .mci_so import cdm03bidir from joblib import Parallel, delayed from astropy.coordinates import SkyCoord from scipy import interpolate Loading @@ -91,6 +90,180 @@ import astropy.coordinates as coord from scipy.interpolate import interp1d ########################### functions ######################### """ Charge Transfer Inefficiency ============================ This file contains a simple class to run a CDM03 CTI model developed by Alex Short (ESA). This now contains both the official CDM03 and a new version that allows different trap parameters in parallel and serial direction. :requires: NumPy :requires: CDM03 (FORTRAN code, f2py -c -m cdm03bidir cdm03bidir.f90) :version: 0.35 """ import numpy as np #CDM03bidir class CDM03bidir(): """ Class to run CDM03 CTI model, class Fortran routine to perform the actual CDM03 calculations. :param settings: input parameters :type settings: dict :param data: input data to be radiated :type data: ndarray :param log: instance to Python logging :type log: logging instance """ def __init__(self, settings, data, log=None): """ Class constructor. :param settings: input parameters :type settings: dict :param data: input data to be radiated :type data: ndarray :param log: instance to Python logging :type log: logging instance """ self.data = data self.values = dict(quads=(0,1,2,3), xsize=2048, ysize=2066, dob=0.0, rdose=8.0e9) self.values.update(settings) self.log = log self._setupLogger() #default CDM03 settings self.params = dict(beta_p=0.6, beta_s=0.6, fwc=200000., vth=1.168e7, vg=6.e-11, t=20.48e-3, sfwc=730000., svg=1.0e-10, st=5.0e-6, parallel=1., serial=0.0) #update with inputs self.params.update(self.values) #read in trap information trapdata = np.loadtxt(self.values['dir_path']+self.values['paralleltrapfile']) if trapdata.ndim > 1: self.nt_p = trapdata[:, 0] self.sigma_p = trapdata[:, 1] self.taur_p = trapdata[:, 2] else: #only one trap species self.nt_p = [trapdata[0],] self.sigma_p = [trapdata[1],] self.taur_p = [trapdata[2],] trapdata = np.loadtxt(self.values['dir_path']+self.values['serialtrapfile']) if trapdata.ndim > 1: self.nt_s = trapdata[:, 0] self.sigma_s = trapdata[:, 1] self.taur_s = trapdata[:, 2] else: #only one trap species self.nt_s = [trapdata[0],] self.sigma_s = [trapdata[1],] self.taur_s = [trapdata[2],] #scale thibaut's values if 'thibaut' in self.values['parallelTrapfile']: self.nt_p /= 0.576 #thibaut's values traps / pixel self.sigma_p *= 1.e4 #thibaut's values in m**2 if 'thibaut' in self.values['serialTrapfile']: self.nt_s *= 0.576 #thibaut's values traps / pixel #should be division? self.sigma_s *= 1.e4 #thibaut's values in m**2 def _setupLogger(self): """ Set up the logger. """ self.logger = True # if self.log is None: # self.logger = False def applyRadiationDamage(self, data, iquadrant=0): """ Apply radian damage based on FORTRAN CDM03 model. The method assumes that input data covers only a single quadrant defined by the iquadrant integer. :param data: imaging data to which the CDM03 model will be applied to. :type data: ndarray :param iquandrant: number of the quadrant to process :type iquandrant: int cdm03 - Function signature:: sout = cdm03(sinp,iflip,jflip,dob,rdose,in_nt,in_sigma,in_tr,[xdim,ydim,zdim]) Required arguments: sinp : input rank-2 array('d') with bounds (xdim,ydim) iflip : input int jflip : input int dob : input float rdose : input float in_nt : input rank-1 array('d') with bounds (zdim) in_sigma : input rank-1 array('d') with bounds (zdim) in_tr : input rank-1 array('d') with bounds (zdim) Optional arguments: xdim := shape(sinp,0) input int ydim := shape(sinp,1) input int zdim := len(in_nt) input int Return objects: sout : rank-2 array('d') with bounds (xdim,ydim) .. Note:: Because Python/NumPy arrays are different row/column based, one needs to be extra careful here. NumPy.asfortranarray will be called to get an array laid out in Fortran order in memory. Before returning the array will be laid out in memory in C-style (row-major order). :return: image that has been run through the CDM03 model :rtype: ndarray """"" #return data iflip = iquadrant / 2 jflip = iquadrant % 2 params = [self.params['beta_p'], self.params['beta_s'], self.params['fwc'], self.params['vth'], self.params['vg'], self.params['t'], self.params['sfwc'], self.params['svg'], self.params['st'], self.params['parallel'], self.params['serial']] if self.logger: self.log.info('nt_p=' + str(self.nt_p)) self.log.info('nt_s=' + str(self.nt_s)) self.log.info('sigma_p= ' + str(self.sigma_p)) self.log.info('sigma_s= ' + str(self.sigma_s)) self.log.info('taur_p= ' + str(self.taur_p)) self.log.info('taur_s= ' + str(self.taur_s)) self.log.info('dob=%f' % self.values['dob']) self.log.info('rdose=%e' % self.values['rdose']) self.log.info('xsize=%i' % data.shape[1]) self.log.info('ysize=%i' % data.shape[0]) self.log.info('quadrant=%i' % iquadrant) self.log.info('iflip=%i' % iflip) self.log.info('jflip=%i' % jflip) ################################################################################# CTIed = cdm03bidir.cdm03(np.asfortranarray(data), jflip, iflip, self.values['dob'], self.values['rdose'], self.nt_p, self.sigma_p, self.taur_p, self.nt_s, self.sigma_s, self.taur_s, params, [data.shape[0], data.shape[1], len(self.nt_p), len(self.nt_s), len(self.params)]) return np.asanyarray(CTIed) ################################################################################################################# ################################################################################################################# def transRaDec2D(ra, dec): # radec转为竞天程序里的ob, 赤道坐标系下的笛卡尔三维坐标xyz. x1 = np.cos(dec / 57.2957795) * np.cos(ra / 57.2957795) Loading Loading @@ -2599,7 +2772,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_g = cti.applyRadiationDamage(self.image_g.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading @@ -2611,7 +2784,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_r = cti.applyRadiationDamage(self.image_r.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading @@ -2622,7 +2795,7 @@ class MCIsimulator(): self.log.debug('Starting to apply radiation damage model...') #at this point we can give fake data... cti = CTI.CDM03bidir(self.information, [], log=self.log) cti = CDM03bidir(self.information, [], log=self.log) #here we need the right input data self.image_i = cti.applyRadiationDamage(self.image_i.copy().transpose(), iquadrant=self.information['quadrant']).transpose() self.log.info('Radiation damage added.') Loading
