Source code for drizzlepac.createMedian

"""
Create a median image from the singly drizzled images.

:Authors: Warren Hack

:License: `<http://www.stsci.edu/resources/software_hardware/pyraf/LICENSE>`_

"""

# Import external packages
from __future__ import absolute_import, division, print_function # confidence medium

import sys
import numpy as np
from astropy.io import fits
import os, math

from stsci.imagestats import ImageStats
from stsci.image import numcombine
from stsci.tools import iterfile, nimageiter, teal, logutil

from . import imageObject
from . import util
from .minmed import minmed
from . import processInput
from .adrizzle import _single_step_num_


from .version import *

__taskname__= "drizzlepac.createMedian" #looks in drizzlepac for createMedian.cfg
_step_num_ = 4  #this relates directly to the syntax in the cfg file

log = logutil.create_logger(__name__)


#this is the user access function
[docs]def median(input=None, configObj=None, editpars=False, **inputDict): """ Create a median image from the seperately drizzled images. """ if input is not None: inputDict["input"] = input else: raise ValueError("Please supply an input image") configObj = util.getDefaultConfigObj(__taskname__, configObj, inputDict, loadOnly=(not editpars)) if configObj is None: return if not editpars: run(configObj)
#this is the function that will be called from TEAL
[docs]def run(configObj): imgObjList,outwcs = processInput.setCommonInput(configObj,createOutwcs=False) #outwcs is not needed here createMedian(imgObjList,configObj)
# #### Top-level interface from inside MultiDrizzle #
[docs]def createMedian(imgObjList,configObj,procSteps=None): """ Top-level interface to createMedian step called from top-level MultiDrizzle. This function parses the input parameters then calls the `_median()` function to median-combine the input images into a single image. """ if imgObjList is None: msg = "Please provide a list of imageObjects to the median step" print(msg, file=sys.stderr) raise ValueError(msg) if procSteps is not None: procSteps.addStep('Create Median') step_name = util.getSectionName(configObj,_step_num_) if not configObj[step_name]['median']: log.info('Median combination step not performed.') return paramDict=configObj[step_name] paramDict['proc_unit'] = configObj['proc_unit'] # include whether or not compression was performed driz_sep_name = util.getSectionName(configObj,_single_step_num_) driz_sep_paramDict = configObj[driz_sep_name] paramDict['compress'] = driz_sep_paramDict['driz_sep_compress'] log.info('USER INPUT PARAMETERS for Create Median Step:') util.printParams(paramDict, log=log) _median(imgObjList, paramDict) if procSteps is not None: procSteps.endStep('Create Median')
# this is the internal function, the user called function is below def _median(imageObjectList, paramDict): """Create a median image from the list of image Objects that has been given. """ newmasks = paramDict['median_newmasks'] comb_type = paramDict['combine_type'] nlow = paramDict['combine_nlow'] nhigh = paramDict['combine_nhigh'] grow = paramDict['combine_grow'] maskpt = paramDict['combine_maskpt'] proc_units = paramDict['proc_unit'] compress = paramDict['compress'] bufsizeMb = paramDict['combine_bufsize'] sigma=paramDict["combine_nsigma"] sigmaSplit=sigma.split() nsigma1 = float(sigmaSplit[0]) nsigma2 = float(sigmaSplit[1]) #print "Checking parameters:" #print comb_type,nlow,nhigh,grow,maskpt,nsigma1,nsigma2 if paramDict['combine_lthresh'] is None: lthresh = None else: lthresh = float(paramDict['combine_lthresh']) if paramDict['combine_hthresh'] is None: hthresh = None else: hthresh = float(paramDict['combine_hthresh']) #the name of the output median file isdefined in the output wcs object #and stuck in the image.outputValues["outMedian"] dict of every imageObject medianfile=imageObjectList[0].outputNames["outMedian"] """ Builds combined array from single drizzled images.""" # Start by removing any previous products... if(os.access(medianfile,os.F_OK)): os.remove(medianfile) # Define lists for instrument specific parameters, these should be in the image objects # need to be passed to the minmed routine readnoiseList = [] exposureTimeList = [] backgroundValueList = [] #list of MDRIZSKY *platescale values singleDrizList=[] #these are the input images singleWeightList=[] #pointers to the data arrays #skylist=[] #the list of platescale values for the images _wht_mean = [] # Compute the mean value of each wht image _single_hdr = None virtual = None #for each image object for image in imageObjectList: if virtual is None: virtual = image.inmemory det_gain = image.getGain(1) img_exptime = image._image['sci',1]._exptime native_units = image.native_units if lthresh is not None: if proc_units.lower() == 'native': if native_units.lower() == "counts": lthresh = lthresh * det_gain if native_units.lower() == "counts/s": lthresh = lthresh * img_exptime if hthresh is not None: if proc_units.lower() == 'native': if native_units.lower() == "counts": hthresh = hthresh * det_gain if native_units.lower() == "counts/s": hthresh = hthresh * img_exptime singleDriz = image.getOutputName("outSingle") singleDriz_name = image.outputNames['outSingle'] singleWeight = image.getOutputName("outSWeight") singleWeight_name = image.outputNames['outSWeight'] #singleDriz=image.outputNames["outSingle"] #all chips are drizzled to a single output image #singleWeight=image.outputNames["outSWeight"] # If compression was used, reference ext=1 as CompImageHDU only writes # out MEF files, not simple FITS. if compress: wcs_ext = '[1]' wcs_extnum = 1 else: wcs_ext = '[0]' wcs_extnum = 0 if not virtual: if isinstance(singleDriz,str): iter_singleDriz = singleDriz + wcs_ext iter_singleWeight = singleWeight + wcs_ext else: iter_singleDriz = singleDriz[wcs_extnum] iter_singleWeight = singleWeight[wcs_extnum] else: iter_singleDriz = singleDriz_name + wcs_ext iter_singleWeight = singleWeight_name + wcs_ext # read in WCS from first single drizzle image to use as WCS for median image if _single_hdr is None: if virtual: _single_hdr = singleDriz[wcs_extnum].header else: _single_hdr = fits.getheader(singleDriz_name, ext=wcs_extnum, memmap=False) _singleImage=iterfile.IterFitsFile(iter_singleDriz) if virtual: _singleImage.handle = singleDriz _singleImage.inmemory = True singleDrizList.append(_singleImage) #add to an array for bookkeeping # If it exists, extract the corresponding weight images if (not virtual and os.access(singleWeight,os.F_OK)) or ( virtual and singleWeight): _weight_file=iterfile.IterFitsFile(iter_singleWeight) if virtual: _weight_file.handle = singleWeight _weight_file.inmemory = True singleWeightList.append(_weight_file) try: tmp_mean_value = ImageStats(_weight_file.data, lower=1e-8, fields="mean", nclip=0).mean except ValueError: tmp_mean_value = 0.0 _wht_mean.append(tmp_mean_value * maskpt) # Extract instrument specific parameters and place in lists # If an image has zero exposure time we will # redefine that value as '1'. Although this will cause inaccurate scaling # of the data to occur in the 'minmed' combination algorith, this is a # necessary evil since it avoids divide by zero exceptions. It is more # important that the divide by zero exceptions not cause Multidrizzle to # crash in the pipeline than it is to raise an exception for this obviously # bad data even though this is not the type of data you would wish to process # with Multidrizzle. # # Get the exposure time from the InputImage object # # MRD 19-May-2011 # Changed exposureTimeList to take exposure time from img_exptime # variable instead of hte image._exptime attribute, since # image._exptime was just giving 1. # exposureTimeList.append(img_exptime) # Use only "commanded" chips to extract subtractedSky and rdnoise: rdnoise = 0.0 nchips = 0 bsky = None # minimum sky across **used** chips for chip in image.returnAllChips(extname=image.scienceExt): # compute sky value as sky/pixel using the single_drz pixel scale if bsky is None or bsky > chip.subtractedSky: bsky = chip.subtractedSky * chip._conversionFactor # Extract the readnoise value for the chip rdnoise += (chip._rdnoise)**2 nchips += 1 if bsky is None: bsky = 0.0 if nchips > 0: rdnoise = math.sqrt(rdnoise/nchips) backgroundValueList.append(bsky) readnoiseList.append(rdnoise) ## compute sky value as sky/pixel using the single_drz pixel scale #bsky = image._image[image.scienceExt,1].subtractedSky# * (image.outputValues['scale']**2) #backgroundValueList.append(bsky) ## Extract the readnoise value for the chip #sci_chip = image._image[image.scienceExt,1] #readnoiseList.append(sci_chip._rdnoise) #verify this is calculated correctly in the image object print("reference sky value for image ",image._filename," is ", backgroundValueList[-1]) # # END Loop over input image list # # create an array for the median output image, use the size of the first image in the list medianImageArray = np.zeros(singleDrizList[0].shape,dtype=singleDrizList[0].type()) if ( comb_type.lower() == "minmed") and not newmasks: # Issue a warning if minmed is being run with newmasks turned off. print('\nWARNING: Creating median image without the application of bad pixel masks!\n') # create the master list to be used by the image iterator masterList = [] masterList.extend(singleDrizList) masterList.extend(singleWeightList) print('\n') # Specify the location of the drz image sections startDrz = 0 endDrz = len(singleDrizList)+startDrz # Specify the location of the wht image sections startWht = len(singleDrizList)+startDrz endWht = startWht + len(singleWeightList) _weight_mask_list = None # Fire up the image iterator # # The overlap value needs to be set to 2*grow in order to # avoid edge effects when scrolling down the image, and to # insure that the last section returned from the iterator # has enough row to span the kernel used in the boxcar method # within minmed. _overlap = 2*int(grow) #Start by computing the buffer size for the iterator _imgarr = masterList[0].data _bufsize = nimageiter.BUFSIZE if bufsizeMb is not None: _bufsize *= bufsizeMb _imgrows = _imgarr.shape[0] _nrows = nimageiter.computeBuffRows(_imgarr) # _overlaprows = _nrows - (_overlap+1) # _niter = int(_imgrows/_nrows) # _niter = 1 + int( (_imgrows - _overlaprows)/_nrows) niter = nimageiter.computeNumberBuff(_imgrows,_nrows,_overlap) #computeNumberBuff actually returns (niter,buffrows) _niter= niter[0] _nrows = niter[1] _lastrows = _imgrows - (_niter*(_nrows-_overlap)) # check to see if this buffer size will leave enough rows for # the section returned on the last iteration if _lastrows < _overlap+1: _delta_rows = (_overlap+1 - _lastrows)//_niter if _delta_rows < 1 and _delta_rows >= 0: _delta_rows = 1 _bufsize += (_imgarr.shape[1]*_imgarr.itemsize) * _delta_rows if not virtual: masterList[0].close() del _imgarr for imageSectionsList,prange in nimageiter.FileIter(masterList,overlap=_overlap,bufsize=_bufsize): if newmasks: """ Build new masks from single drizzled images. """ _weight_mask_list = [] listIndex = 0 for _weight_arr in imageSectionsList[startWht:endWht]: # Initialize an output mask array to ones # This array will be reused for every output weight image _weight_mask = np.zeros(_weight_arr.shape,dtype=np.uint8) """ Generate new pixel mask file for median step. This mask will be created from the single-drizzled weight image for this image. The mean of the weight array will be computed and all pixels with values less than 0.7 of the mean will be flagged as bad in this mask. This mask will then be used when creating the median image. """ # Compute image statistics _mean = _wht_mean[listIndex] # 0 means good, 1 means bad here... np.putmask(_weight_mask, np.less(_weight_arr,_mean), 1) #_weight_mask.info() _weight_mask_list.append(_weight_mask) listIndex += 1 # Do MINMED if ( "minmed" in comb_type.lower()): if comb_type.lower()[0] == 'i': # set up use of 'imedian'/'imean' in minmed algorithm fillval = True else: fillval = False if (_weight_mask_list in [None,[]]): _weight_mask_list = None # Create the combined array object using the minmed algorithm result = minmed(imageSectionsList[startDrz:endDrz], # list of input data to be combined. imageSectionsList[startWht:endWht],# list of input data weight images to be combined. readnoiseList, # list of readnoise values to use for the input images. exposureTimeList, # list of exposure times to use for the input images. backgroundValueList, # list of image background values to use for the input images weightMaskList = _weight_mask_list, # list of imput data weight masks to use for pixel rejection. combine_grow = grow, # Radius (pixels) for neighbor rejection combine_nsigma1 = nsigma1, # Significance for accepting minimum instead of median combine_nsigma2 = nsigma2, # Significance for accepting minimum instead of median fillval=fillval # turn on use of imedian/imean ) # medianOutput[prange[0]:prange[1],:] = result.out_file1 # minOutput[prange[0]:prange[1],:] = result.out_file2 # DO NUMCOMBINE else: # Create the combined array object using the numcombine task result = numcombine.numCombine(imageSectionsList[startDrz:endDrz], numarrayMaskList=_weight_mask_list, combinationType=comb_type.lower(), nlow=nlow, nhigh=nhigh, upper=hthresh, lower=lthresh ) # We need to account for any specified overlap when writing out # the processed image sections to the final output array. if prange[1] != _imgrows: medianImageArray[prange[0]:prange[1]-_overlap,:] = result.combArrObj[:-_overlap,:] else: medianImageArray[prange[0]:prange[1],:] = result.combArrObj del result del _weight_mask_list _weight_mask_list = None # Write out the combined image # use the header from the first single drizzled image in the list #header=fits.getheader(imageObjectList[0].outputNames["outSingle"]) _pf = _writeImage(medianImageArray, inputHeader=_single_hdr) if virtual: mediandict = {} mediandict[medianfile] = _pf for img in imageObjectList: img.saveVirtualOutputs(mediandict) else: try: print("Saving output median image to: ",medianfile) _pf.writeto(medianfile) except IOError: msg = "Problem writing file: "+medianfile print(msg) raise IOError(msg) del _pf # Always close any files opened to produce median image; namely, # single drizzle images and singly-drizzled weight images # for img in singleDrizList: if not virtual: img.close() singeDrizList = [] # Close all singly drizzled weight images used to create median image. for img in singleWeightList: if not virtual: img.close() singleWeightList = [] # If new median masks was turned on, close those files if _weight_mask_list: for arr in _weight_mask_list: del arr _weight_mask_list = None del masterList del medianImageArray def _writeImage( dataArray=None, inputHeader=None): """ Writes out the result of the combination step. The header of the first 'outsingle' file in the association parlist is used as the header of the new image. Parameters ---------- dataArray : arr Array of data to be written to a fits.PrimaryHDU object inputHeader : obj fits.header.Header object to use as basis for the PrimaryHDU header """ #_fname =inputFilename #_file = fits.open(_fname, mode='readonly') #_prihdu = fits.PrimaryHDU(header=_file[0].header,data=dataArray) _prihdu = fits.PrimaryHDU(data=dataArray, header=inputHeader) """ if inputHeader is None: #use a general primary HDU _prihdu = fits.PrimaryHDU(data=dataArray) else: _prihdu = inputHeader _prihdu.data=dataArray """ _pf = fits.HDUList() _pf.append(_prihdu) return _pf
[docs]def help(file=None): """ Print out syntax help for running astrodrizzle Parameters ---------- file : str (Default = None) If given, write out help to the filename specified by this parameter Any previously existing file with this name will be deleted before writing out the help. """ helpstr = getHelpAsString(docstring=True, show_ver = True) if file is None: print(helpstr) else: if os.path.exists(file): os.remove(file) f = open(file, mode = 'w') f.write(helpstr) f.close()
[docs]def getHelpAsString(docstring = False, show_ver = True): """ return useful help from a file in the script directory called __taskname__.help """ install_dir = os.path.dirname(__file__) taskname = util.base_taskname(__taskname__, __package__) htmlfile = os.path.join(install_dir, 'htmlhelp', taskname + '.html') helpfile = os.path.join(install_dir, taskname + '.help') if docstring or (not docstring and not os.path.exists(htmlfile)): if show_ver: helpString = os.linesep + \ ' '.join([__taskname__, 'Version', __version__, ' updated on ', __vdate__]) + 2*os.linesep else: helpString = '' if os.path.exists(helpfile): helpString += teal.getHelpFileAsString(taskname, __file__) else: if __doc__ is not None: helpString += __doc__ + os.linesep else: helpString = 'file://' + htmlfile return helpString
median.__doc__ = getHelpAsString(docstring = True, show_ver = False)