Merge branch 'master' into 'main'

build

See merge request shaosim/ifs!1

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

LICENSE

+MIT License
+
+Copyright (c) 2022 CSST-L1
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

-# Ifs
+# csst_ifs_common
 
 
 
@@ -15,14 +15,14 @@ Already a pro? Just edit this README.md and make it your own. Want to make it ea
 
 ```
 cd existing_repo
-git remote add origin https://csst-tb.bao.ac.cn/code/shaosim/ifs.git
+git remote add origin https://csst-tb.bao.ac.cn/code/csst-l1/ifs/csst_ifs_common.git
 git branch -M main
 git push -uf origin main
 ```
 
 ## Integrate with your tools
 
-- [ ] [Set up project integrations](https://csst-tb.bao.ac.cn/code/shaosim/ifs/-/settings/integrations)
+- [ ] [Set up project integrations](http://10.3.10.28/code/csst-l1/ifs/csst_ifs_common/-/settings/integrations)
 
 ## Collaborate with your team
 

csst_ifs_sim/support/IFSinstrumentModel.py

+"""
+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

+"""
+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(np.int)
+
+        ihi = 1 + np.round(x0.copy() + dx)
+        msk = ihi > self.xsize
+        ihi[msk] = self.xsize
+        ihi = ihi.astype(np.int)
+
+        #pixels in y-directions
+        jlo = np.round(y0.copy() - dy)
+        msk = jlo < 0.
+        jlo[msk] = 0
+        jlo = jlo.astype(np.int)
+
+        jhi = 1 + np.round(y0.copy() + dy)
+        msk = jhi > self.ysize
+        jhi[msk] = self.ysize
+        jhi = jhi.astype(np.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

+"""
+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
+
+

setup.py

+import setuptools
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+    name='csst_ifs_sim',
+    version='2.0.0-IFS1.0.0',
+    author='CSST Team',
+    author_email='zhaojunyan@shao.ac.cn',
+    description='The CSST - ifs - sim',  # short description
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url='https://csst-tb.bao.ac.cn/',
+    # project_urls={
+    #     'Source': 'https://csst-tb.bao.ac.cn/code/csst-l1/ifs/csst_ifs_sim',
+    # },
+    packages=setuptools.find_packages(),
+    license='MIT',
+    classifiers=["Development Status :: 5 - Production/Stable",
+                 "Intended Audience :: Science/Research",
+                 "License :: OSI Approved :: MIT License",
+                 "Operating System :: OS Independent",
+                 "Programming Language :: Python :: 3.8",
+                 "Topic :: Scientific/Engineering :: Physics",
+                 "Topic :: Scientific/Engineering :: Astronomy"],
+    package_dir={'csst_ifs_sim': 'csst_ifs_sim'},
+    # include_package_data=True,
+    package_data={"": ["LICENSE", "README.md"],
+                  "csst_ifs_sim": ["ifs_so/*", "ifs_data/*", "ifs_data/refs/*", "ifs_data/refs/orbit20160925/*"]},
+    # install_requires=['sphinx',
+    #                   'numpy',
+    #                   'scipy', 'matplotlib',
+    #                   'astropy', 'healpy', 'ccdproc', 'deepCR', 'photutils'],
+    python_requires='>=3.8',
+)