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Commit e4607a2e authored by Yan Zhaojun's avatar Yan Zhaojun
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Delete CTI.py

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csst_mci_sim/CTI/CTI.py deleted 100644 → 0
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"""
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)
#################################################################################
###modify
#sys.path.append('../so')
# 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'],
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)
#################################################################################################################
#################################################################################################################
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