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csst_ifs_sim/support/IFSinstrumentModel.py deleted 100644 → 0
View file @ 25bdb62f
"""
VIS Instrument Model
====================
The file provides a function that returns VIS related information such as pixel
size, dark current, gain, zeropoint, and sky background.
:requires: NumPy
:requires: numexpr
:version: 0.7
"""
# import datetime, math
# import numpy as np
# import numexpr as ne
def IFSinformation():
"""
Returns a dictionary describing VIS. The following information is provided (id: value - reference)::
:return: instrument model parameters
:rtype: dict
"""
#########################################################################################################
#out = dict(readnoise=4, pixel_size=0.1, dark=0.0008333, fullwellcapacity=90000, bluesize=4000, redsize=6000, readtime=300.)
out=dict()
out.update({'dob' : 0, 'rdose' : 8.0e9,
'parallelTrapfile' : 'cdm_euclid_parallel.dat', 'serialTrapfile' : 'cdm_euclid_serial.dat',
'beta_s' : 0.6, 'beta_p': 0.6, 'fwc' : 90000, 'vth' : 1.168e7, 't' : 20.48e-3, 'vg' : 6.e-11,
'st' : 5.0e-6, 'sfwc' : 730000., 'svg' : 1.0e-10})
return out
def CCDnonLinearityModel(data, beta=6e-7):
"""
The non-linearity is modelled based on the results presented.
:param data: data to which the non-linearity model is being applied to
:type data: ndarray
:return: input data after conversion with the non-linearity model
:rtype: float or ndarray
"""
out = data-beta*data**2
return out
# if __name__ == '__main__':
# print('IFSinstrument')
csst_ifs_sim/support/cosmicrays.py deleted 100644 → 0
View file @ 25bdb62f
"""
Cosmic Rays
===========
This simple class can be used to include cosmic ray events to an image.
By default the cosmic ray events are drawn from distributions describing
the length and energy of the events. Such distributions can be generated
for example using Stardust code (http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04636917).
The energy of the cosmic ray events can also be set to constant for
testing purposes. The class can be used to draw a single cosmic ray
event or up to a covering fraction.
:requires: NumPy
:requires: SciPy
:version: 0.2
"""
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
class cosmicrays():
"""
Cosmic ray generation class. Can either draw events from distributions or
set the energy of the events to a constant.
:param log: logger instance
:param image: image to which cosmic rays are added to (a copy is made not to change the original numpy array)
:param crInfo: column information (cosmic ray file)
:param information: cosmic ray track information (file containing track length and energy information) and
exposure time.
"""
def __init__(self, log, image, crInfo=None, information=None):
"""
Cosmic ray generation class. Can either draw events from distributions or
set the energy of the events to a constant.
:param log: logger instance
:param image: image to which cosmic rays are added to (a copy is made not to change the original numpy array)
:param crInfo: column information (cosmic ray file)
:param information: cosmic ray track information (file containing track length and energy information) and
exposure time.
"""
#setup logger
self.log = log
#image and size
self.image = image.copy()
self.ysize, self.xsize = self.image.shape
#set up the information dictionary, first with defaults and then overwrite with inputs if given
self.information = (dict(cosmicraylengths='/home/yan/csst-master/data/cdf_cr_length.dat',
cosmicraydistance='/home/yan/csst-master/data/cdf_cr_total.dat',
exptime=565))
if information is not None:
self.information.update(information)
if crInfo is not None:
self.cr = crInfo
else:
self._readCosmicrayInformation()
# def _readCosmicrayInformation(self):
# self.log.info('Reading in cosmic ray information from %s and %s' % (self.information['cosmicraylengths'],
# self.information['cosmicraydistance']))
# #read in the information from the files
# crLengths = np.loadtxt(self.information['cosmicraylengths'])
# crDists = np.loadtxt(self.information['cosmicraydistance'])
# #set up the cosmic ray information dictionary
# self.cr = dict(cr_u=crLengths[:, 0], cr_cdf=crLengths[:, 1], cr_cdfn=np.shape(crLengths)[0],
# cr_v=crDists[:, 0], cr_cde=crDists[:, 1], cr_cden=np.shape(crDists)[0])
# return self.cr
def _cosmicRayIntercepts(self, lum, x0, y0, l, phi):
"""
Derive cosmic ray streak intercept points.
:param lum: luminosities of the cosmic ray tracks
:param x0: central positions of the cosmic ray tracks in x-direction
:param y0: central positions of the cosmic ray tracks in y-direction
:param l: lengths of the cosmic ray tracks
:param phi: orientation angles of the cosmic ray tracks
:return: cosmic ray map (image)
:rtype: nd-array
"""
#create empty array
crImage = np.zeros((self.ysize, self.xsize), dtype=np.float64)
#x and y shifts
dx = l * np.cos(phi) / 2.
dy = l * np.sin(phi) / 2.
mskdx = np.abs(dx) < 1e-8
mskdy = np.abs(dy) < 1e-8
dx[mskdx] = 0.
dy[mskdy] = 0.
#pixels in x-direction
ilo = np.round(x0.copy() - dx)
msk = ilo < 0.
ilo[msk] = 0
ilo = ilo.astype(int)
ihi = 1 + np.round(x0.copy() + dx)
msk = ihi > self.xsize
ihi[msk] = self.xsize
ihi = ihi.astype(int)
#pixels in y-directions
jlo = np.round(y0.copy() - dy)
msk = jlo < 0.
jlo[msk] = 0
jlo = jlo.astype(int)
jhi = 1 + np.round(y0.copy() + dy)
msk = jhi > self.ysize
jhi[msk] = self.ysize
jhi = jhi.astype(int)
#loop over the individual events
for i, luminosity in enumerate(lum):
n = 0 # count the intercepts
u = []
x = []
y = []
#Compute X intercepts on the pixel grid
if ilo[i] < ihi[i]:
for xcoord in range(ilo[i], ihi[i]):
ok = (xcoord - x0[i]) / dx[i]
if np.abs(ok) <= 0.5:
n += 1
u.append(ok)
x.append(xcoord)
y.append(y0[i] + ok * dy[i])
else:
for xcoord in range(ihi[i], ilo[i]):
ok = (xcoord - x0[i]) / dx[i]
if np.abs(ok) <= 0.5:
n += 1
u.append(ok)
x.append(xcoord)
y.append(y0[i] + ok * dy[i])
#Compute Y intercepts on the pixel grid
if jlo[i] < jhi[i]:
for ycoord in range(jlo[i], jhi[i]):
ok = (ycoord - y0[i]) / dy[i]
if np.abs(ok) <= 0.5:
n += 1
u.append(ok)
x.append(x0[i] + ok * dx[i])
y.append(ycoord)
else:
for ycoord in range(jhi[i], jlo[i]):
ok = (ycoord - y0[i]) / dy[i]
if np.abs(ok) <= 0.5:
n += 1
u.append(ok)
x.append(x0[i] + ok * dx[i])
y.append(ycoord)
#check if no intercepts were found
if n < 1:
xc = int(np.floor(x0[i]))
yc = int(np.floor(y0[i]))
crImage[yc, xc] += luminosity
#Find the arguments that sort the intersections along the track
u = np.asarray(u)
x = np.asarray(x)
y = np.asarray(y)
args = np.argsort(u)
u = u[args]
x = x[args]
y = y[args]
#Decide which cell each interval traverses, and the path length
for i in range(1, n - 1):
w = u[i + 1] - u[i]
cx = int(1 + np.floor((x[i + 1] + x[i]) / 2.))
cy = int(1 + np.floor((y[i + 1] + y[i]) / 2.))
if 0 <= cx < self.xsize and 0 <= cy < self.ysize:
crImage[cy, cx] += (w * luminosity)
return crImage
# def _drawCosmicRays(self, limit=None):
# """
# Add cosmic rays to the arrays based on a power-law intensity distribution for tracks.
# Cosmic ray properties (such as location and angle) are chosen from random Uniform distribution.
# """
# #estimate the number of cosmics
# cr_n = self.xsize * self.ysize * 0.014 / 43.263316 * 2.
# #scale with exposure time, the above numbers are for the nominal 565s exposure
# cr_n *= (self.information['exptime'] / 565.0)
# #assume a power-law intensity distribution for tracks
# fit = dict(cr_lo=1.0e3, cr_hi=1.0e5, cr_q=2.0e0)
# fit['q1'] = 1.0e0 - fit['cr_q']
# fit['en1'] = fit['cr_lo'] ** fit['q1']
# fit['en2'] = fit['cr_hi'] ** fit['q1']
# #pseudo-random numbers taken from a uniform distribution between 0 and 1
# np.random.seed()
# luck = np.random.rand(int(np.floor(cr_n)))
# #draw the length of the tracks
# if self.cr['cr_cdfn'] > 1:
# ius = InterpolatedUnivariateSpline(self.cr['cr_cdf'], self.cr['cr_u'])
# self.cr['cr_l'] = ius(luck)
# else:
# self.cr['cr_l'] = np.sqrt(1.0 - luck ** 2) / luck
# #draw the energy of the tracks
# if self.cr['cr_cden'] > 1:
# ius = InterpolatedUnivariateSpline(self.cr['cr_cde'], self.cr['cr_v'])
# self.cr['cr_e'] = ius(luck)
# else:
# np.random.seed()
# self.cr['cr_e'] = (fit['en1'] + (fit['en2'] - fit['en1']) *
# np.random.rand(int(np.floor(cr_n)))) ** (1.0 / fit['q1'])
# #Choose the properties such as positions and an angle from a random Uniform dist
# np.random.seed()
# cr_x = self.xsize * np.random.rand(int(np.floor(cr_n)))
# np.random.seed()
# cr_y = self.ysize * np.random.rand(int(np.floor(cr_n)))
# np.random.seed()
# cr_phi = np.pi * np.random.rand(int(np.floor(cr_n)))
# #find the intercepts
# if limit is None:
# self.cosmicrayMap = self._cosmicRayIntercepts(self.cr['cr_e'], cr_x, cr_y, self.cr['cr_l'], cr_phi)
# print ('Number of cosmic ray events:', len(self.cr['cr_e']))
# else:
# #limit to electron levels < limit
# msk = self.cr['cr_e'] < limit
# print ('Number of cosmic ray events: %i / %i' % (len(self.cr['cr_e'][msk]), int(np.floor(cr_n))))
# self.cosmicrayMap = self._cosmicRayIntercepts(self.cr['cr_e'][msk], cr_x[msk], cr_y[msk],
# self.cr['cr_l'][msk], cr_phi[msk])
# #count the covering factor
# area_cr = np.count_nonzero(self.cosmicrayMap)
# text = 'The cosmic ray covering factor is %i pixels i.e. %.3f per cent' \
# % (area_cr, 100.*area_cr / (self.xsize*self.ysize))
# self.log.info(text)
# print (text)
# def _drawSingleEvent(self, limit=1000, cr_n=1):
# """
# Generate a single cosmic ray event and include it to a cosmic ray map (self.cosmicrayMap).
# :param limit: limiting energy for the cosmic ray event
# :type limit: float
# :param cr_n: number of cosmic ray events to include
# :type cr_n: int
# :return: None
# """
# #pseudo-random numbers taken from a uniform distribution between 0 and 1
# np.random.seed()
# luck = np.random.rand(cr_n)
# #draw the length of the tracks
# ius = InterpolatedUnivariateSpline(self.cr['cr_cdf'], self.cr['cr_u'])
# self.cr['cr_l'] = ius(luck)
# #set the energy directly to the limit
# self.cr['cr_e'] = np.asarray([limit, ]*cr_n)
# #Choose the properties such as positions and an angle from a random Uniform dist
# np.random.seed()
# cr_x = self.xsize * np.random.rand(int(np.floor(cr_n)))
# np.random.seed()
# cr_y = self.ysize * np.random.rand(int(np.floor(cr_n)))
# np.random.seed()
# cr_phi = np.pi * np.random.rand(int(np.floor(cr_n)))
# #find the intercepts
# self.cosmicrayMap = self._cosmicRayIntercepts(self.cr['cr_e'], cr_x, cr_y, self.cr['cr_l'], cr_phi)
# #count the covering factor
# area_cr = np.count_nonzero(self.cosmicrayMap)
# text = 'The cosmic ray covering factor is %i pixels i.e. %.3f per cent' \
# % (area_cr, 100.*area_cr / (self.xsize*self.ysize))
# self.log.info(text)
# print( text)
def _drawEventsToCoveringFactor(self, coveringFraction=3.0, limit=1000, verbose=False):
"""
Generate cosmic ray events up to a covering fraction and include it to a cosmic ray map (self.cosmicrayMap).
:param coveringFraction: covering fraction of cosmic rya events in per cent of total number of pixels
:type coveringFraction: float
:param limit: limiting energy for the cosmic ray event [None = draw from distribution]
:type limit: None or float
:param verbose: print out information to stdout
:type verbose: bool
:return: None
"""
self.cosmicrayMap = np.zeros((self.ysize, self.xsize))
#how many events to draw at once, too large number leads to exceeding the covering fraction
cr_n = int(295 * self.information['exptime'] / 565. * coveringFraction / 1.4)
covering = 0.0
while covering < coveringFraction:
#pseudo-random numbers taken from a uniform distribution between 0 and 1
np.random.seed()
luck = np.random.rand(cr_n)
#draw the length of the tracks
ius = InterpolatedUnivariateSpline(self.cr['cr_cdf'], self.cr['cr_u'])
self.cr['cr_l'] = ius(luck)
if limit is None:
ius = InterpolatedUnivariateSpline(self.cr['cr_cde'], self.cr['cr_v'])
self.cr['cr_e'] = ius(luck)
else:
#set the energy directly to the limit
self.cr['cr_e'] = np.asarray([limit,])
#Choose the properties such as positions and an angle from a random Uniform dist
np.random.seed()
cr_x = self.xsize * np.random.rand(int(np.floor(cr_n)))
np.random.seed()
cr_y = self.ysize * np.random.rand(int(np.floor(cr_n)))
np.random.seed()
cr_phi = np.pi * np.random.rand(int(np.floor(cr_n)))
#find the intercepts
self.cosmicrayMap += self._cosmicRayIntercepts(self.cr['cr_e'], cr_x, cr_y, self.cr['cr_l'], cr_phi)
#count the covering factor
area_cr = np.count_nonzero(self.cosmicrayMap)
covering = 100.*area_cr / (self.xsize*self.ysize)
# text = 'The cosmic ray covering factor is %i pixels i.e. %.3f per cent' % (area_cr, covering)
# self.log.info(text)
# if verbose:
# print( text)
# def addCosmicRays(self, limit=None):
# """
# Include cosmic rays to the image given.
# :return: image with cosmic rays
# :rtype: ndarray
# """
# self._drawCosmicRays(limit=limit)
# #paste cosmic rays
# self.image += self.cosmicrayMap
# return self.image
# def addSingleEvent(self, limit=None):
# """
# Include a single cosmic ray event to the image given.
# :return: image with cosmic rays
# :rtype: ndarray
# """
# self._drawSingleEvent(limit=limit)
# #paste cosmic rays
# self.image += self.cosmicrayMap
# return self.image
def addUpToFraction(self, coveringFraction, limit=None, verbose=False):
"""
Add cosmic ray events up to the covering Fraction.
:param coveringFraction: covering fraction of cosmic rya events in per cent of total number of pixels
:type coveringFraction: float
:param limit: limiting energy for the cosmic ray event [None = draw from distribution]
:type limit: None or float
:param verbose: print out information to stdout
:type verbose: bool
:return: image with cosmic rays
:rtype: ndarray
"""
self._drawEventsToCoveringFactor(coveringFraction, limit=limit, verbose=verbose)
#paste cosmic rays
self.image += self.cosmicrayMap
return self.image
# if __name__ == "__main__":
# print()
csst_ifs_sim/support/logger.py deleted 100644 → 0
View file @ 25bdb62f
"""
These functions can be used for logging information.
.. Warning:: logger is not multiprocessing safe.
:version: 0.3
"""
import logging
import logging.handlers
def setUpLogger(log_filename, loggername='logger'):
"""
Sets up a logger.
:param: log_filename: name of the file to save the log.
:param: loggername: name of the logger
:return: logger instance
"""
# create logger
logger = logging.getLogger(loggername)
logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(log_filename)
#maxBytes=20, backupCount=5)
# create formatter
formatter = logging.Formatter('%(asctime)s - %(module)s - %(funcName)s - %(levelname)s - %(message)s')
# add formatter to ch
handler.setFormatter(formatter)
# add handler to logger
if (logger.hasHandlers()):
logger.handlers.clear()
logger.addHandler(handler)
return logger
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