Source code for spec

"""
Suite to reduce spectroscopic data.

subfunctions:
   calibrate
      setheaders -- exptime, gain, readnoise, etc.
      makeflat -- make median flat and noisy pixel map
      makedark -- make median dark, and estimate  noise in each pixel.
   clean -- clean and replace bad pixels
   extract
      trace -- trace spectral orders
      makeprofile -- compute mean spectral PSF (a spline) for an order
      fitprofile -- fit given spline-PSF to a spectral cross-section

Utilities:
   pickloc   
   fitPSF
"""
# 2010-07-02 10:56 IJC: Began the great endeavor.



try:
    from astropy.io import fits as pyfits
except:
    import pyfits

import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize, interpolate
import pdb


[docs]class baseObject: """Empty object container. """ def __init__(self): return ######## # Utilities to add in a separate cohesive package at a later date:
from analysis import polyfitr, stdr, binarray, gaussian, egaussian import analysis as an from nsdata import bfixpix ######## # Parameters to put in a GUI: gain = 5 # e-/ADU readnoise = 25 # e- #########
[docs]def message(text): """Display a message; for now, with text.""" from sys import stdout print text stdout.flush()
[docs]def pickloc(ax=None, zoom=10): """ :INPUTS: ax : (axes instance) -- axes in which to pick a location zoom : int -- zoom radius for target confirmation : 2-tuple -- (x,y) radii for zoom confirmation. """ # 2011-04-29 19:26 IJC: # 2011-09-03 20:59 IJMC: Zoom can now be a tuple; x,y not cast as int. pickedloc = False if ax is None: ax = plt.gca() axlimits = ax.axis() if hasattr(zoom, '__iter__') and len(zoom)>1: xzoom, yzoom = zoom else: xzoom = zoom yzoom = zoom while not pickedloc: ax.set_title("click to select location") ax.axis(axlimits) x = None while x is None: selectevent = plt.ginput(n=1,show_clicks=False) if len(selectevent)>0: # Prevent user from cancelling out. x,y = selectevent[0] #x = x.astype(int) #y = y.astype(int) if zoom is not None: ax.axis([x-xzoom,x+xzoom,y-yzoom,y+yzoom]) ax.set_title("you selected xy=(%i,%i)\nclick again to confirm, or press Enter/Return to try again" %(x,y) ) plt.draw() confirmevent = plt.ginput(n=1,show_clicks=False) if len(confirmevent)>0: pickedloc = True loc = confirmevent[0] return loc
[docs]def fitTophat(vec, err=None, verbose=False, guess=None): """Fit a 1D tophat function to an input data vector. Return the fit, and uncertainty estimates on that fit. SEE ALSO: :func:`analysis.gaussian`""" xtemp = np.arange(1.0*len(vec)) if guess is None: # Make some educated guesses as to the parameters: pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1])) area = (vec-pedestal).sum() centroid = (vec*xtemp).sum()/vec.sum() if centroid<0: centroid = 1. elif centroid>len(vec): centroid = len(vec)-2. sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi) if sigma<=0: sigma = 0.01 guess = [area,sigma,centroid,pedestal] if verbose: print 'Gaussian guess parameters>>', guess if err is None: fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec), full_output=True)[0:2] pc.resfunc fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec), full_output=True)[0:2] else: fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2] if fitcov is None: # The fitting was really bad! fiterr = np.abs(fit) else: fiterr = np.sqrt(np.diag(fitcov)) if verbose: print 'Best-fit parameters>>', fit f = plt.figure() ax = plt.axes() plt.plot(xtemp, vec, 'o', \ xtemp, gaussian(fit, xtemp), '-', \ xtemp, gaussian(guess, xtemp), '--') return fit, fiterr
[docs]def fitGaussian(vec, err=None, verbose=False, guess=None): """Fit a Gaussian function to an input data vector. Return the fit, and uncertainty estimates on that fit. SEE ALSO: :func:`analysis.gaussian`""" # 2012-12-20 13:28 IJMC: Make a more robust guess for the centroid. xtemp = np.arange(1.0*len(vec)) if guess is None: # Make some educated guesses as to the parameters: pedestal = (0.8*np.median(vec) + 0.2*(vec[0]+vec[1])) area = (vec-pedestal).sum() centroid = ((vec-pedestal)**2*xtemp).sum()/((vec-pedestal)**2).sum() if centroid<0: centroid = 1. elif centroid>len(vec): centroid = len(vec)-2. #pdb.set_trace() sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi) if sigma<=0: sigma = .01 guess = [area,sigma,centroid,pedestal] if err is None: err = np.ones(vec.shape, dtype=float) badvals = True - (np.isfinite(xtemp) * np.isfinite(err) * np.isfinite(vec)) vec[badvals] = np.median(vec[True - badvals]) err[badvals] = vec[True - badvals].max() * 1e9 if verbose: print 'Gaussian guess parameters>>', guess if not np.isfinite(xtemp).all(): pdb.set_trace() if not np.isfinite(vec).all(): pdb.set_trace() if not np.isfinite(err).all(): pdb.set_trace() try: fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2] except: pdb.set_trace() if fitcov is None: # The fitting was really bad! fiterr = np.abs(fit) else: fiterr = np.sqrt(np.diag(fitcov)) #pdb.set_trace() if verbose: print 'Best-fit parameters>>', fit f = plt.figure() ax = plt.axes() plt.plot(xtemp, vec, 'o', \ xtemp, gaussian(fit, xtemp), '-', \ xtemp, gaussian(guess, xtemp), '--') return fit, fiterr
[docs]def fitGaussiann(vec, err=None, verbose=False, guess=None, holdfixed=None): """Fit a Gaussian function to an input data vector. Return the fit, and uncertainty estimates on that fit. SEE ALSO: :func:`analysis.gaussian`""" from phasecurves import errfunc from analysis import fmin xtemp = np.arange(1.0*len(vec)) if guess is None: # Make some educated guesses as to the parameters: holdfixed = None pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1])) area = (vec-pedestal).sum() centroid = (vec*xtemp).sum()/vec.sum() if centroid<0: centroid = 1. elif centroid>len(vec): centroid = len(vec)-2. sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi) if sigma<=0: sigma = 0.01 guess = [area,sigma,centroid,pedestal] if err is None: err = np.ones(vec.shape, dtype=float) badvals = True - (np.isfinite(xtemp) * np.isfinite(err) * np.isfinite(vec)) vec[badvals] = np.median(vec[True - badvals]) err[badvals] = vec[True - badvals].max() * 1e9 if verbose: print 'Gaussian guess parameters>>', guess if not np.isfinite(xtemp).all(): pdb.set_trace() if not np.isfinite(vec).all(): pdb.set_trace() if not np.isfinite(err).all(): pdb.set_trace() try: #fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2] fitargs = (gaussian, xtemp, vec, 1./err**2) fit = fmin(errfunc, guess, args=fitargs, full_output=True, disp=False, holdfixed=holdfixed)[0] fitcov = None except: pdb.set_trace() if fitcov is None: # The fitting was really bad! fiterr = np.abs(fit) else: fiterr = np.sqrt(np.diag(fitcov)) if verbose: print 'Best-fit parameters>>', fit f = plt.figure() ax = plt.axes() plt.plot(xtemp, vec, 'o', \ xtemp, gaussian(fit, xtemp), '-', \ xtemp, gaussian(guess, xtemp), '--') return fit, fiterr
[docs]def fit2Gaussian(vec, err=None, verbose=False, guess=None, holdfixed=None): """Fit two Gaussians simultaneously to an input data vector. :INPUTS: vec : sequence 1D array or list of values to fit to err : sequence uncertainties on vec guess : sequence guess parameters: [area1, sigma1, cen1, area2, sig2, cen2, constant]. Note that parameters are in pixel units. holdfixed : sequence parameters to hold fixed in analysis, _IF_ guess is passed in. SEE ALSO: :func:`analysis.gaussian`, :func:`fitGaussian`""" # 2012-03-14 14:33 IJMC: Created from tools import sumfunc from phasecurves import errfunc from analysis import fmin # Don't use SciPy, because I want keywords! xtemp = np.arange(1.0*len(vec)) if err is None: err = np.ones(xtemp.shape) else: err = np.array(err, copy=False) if guess is None: # Make some semi-educated guesses as to the parameters: holdfixed = None pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1])) area = (vec-pedestal).sum() centroid = (vec*xtemp).sum()/vec.sum() if centroid<0: centroid = 1. elif centroid>len(vec): centroid = len(vec)-2. sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi) if sigma<=0: sigma = 0.01 guess1 = [area/2.,sigma/1.4,centroid+xtemp.size/5.] guess2 = [area/2.,sigma/1.4,centroid-xtemp.size/5.] guess = guess1 + guess2 + [pedestal] ## Testing: #mod = sumfunc(guess, gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) #fitargs = (sumfunc, gaussian, gaussian, 3, 4, (xtemp,), (xtemp,), None, vec, 1./err**2) #fitkw = dict(useindepvar=False, testfinite=False) #chisq = errfunc(guess, *fitargs, **fitkw) #thisfit = fmin(errfunc, guess, args=fitargs, kw=fitkw, full_output=True) #mod2 = sumfunc(thisfit[0], gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) if verbose: print '2-Gaussian guess parameters>>', guess fitargs = (sumfunc, gaussian, gaussian, 3, 4, (xtemp,), (xtemp,), vec, 1./err**2) fitkw = dict(useindepvar=False, testfinite=False) fit = fmin(errfunc, guess, args=fitargs, kw=fitkw, full_output=True, disp=False, holdfixed=holdfixed) if verbose: model = sumfunc(fit[0], gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) model1 = gaussian(fit[0][0:3], xtemp) model2 = gaussian(fit[0][3:6], xtemp) print 'Best-fit parameters>>', fit[0] f = plt.figure() ax = plt.axes() plt.plot(xtemp, vec, 'o', \ xtemp, model, '--') plt.plot(xtemp, model1+fit[0][6], ':') plt.plot(xtemp, model2+fit[0][6], ':') return fit[0]
[docs]def fitPSF(ec, guessLoc, fitwidth=20, verbose=False, sigma=5, medwidth=6, err_ec=None): """ Helper function to fit 1D PSF near a given region. Assumes spectrum runs horizontally across the frame! ec : 2D numpy array echellogram array, with horizontal dispersion direction guessLoc : 2-tuple A slight misnomer for this (x,y) tuple: y is a guess and will be fit, but x is the coordinate at which the fitting takes place. fitwidth : int width of cross-dispersion direction to use in fitting medwidth : int number of columns to average over when fitting a profile verbose : bool verbosity/debugging printout flag sigma : scalar sigma scale for clipping bad values """ # 2010-08-24 22:00 IJC: Added sigma option # 2010-11-29 20:54 IJC: Added medwidth option # 2011-11-26 23:17 IJMC: Fixed bug in computing "badval" # 2012-04-27 05:15 IJMC: Now allow error estimates to be passed in. # 2012-04-28 08:48 IJMC: Added better guessing for initial case. if verbose<0: verbose = False ny, nx = ec.shape x = guessLoc[0].astype(int) y = guessLoc[1].astype(int) # Fit the PSF profile at the initial, selected location: ymin = max(y-fitwidth/2, 0) ymax = min(y+fitwidth/2, ny) xmin = max(x-medwidth/2, 0) xmax = min(x+medwidth/2, nx) if verbose: message("Sampling: ec[%i:%i,%i:%i]"%(ymin,ymax,xmin,xmax)) firstSeg = np.median(ec[ymin:ymax,xmin:xmax],1) if verbose: print firstSeg if err_ec is None: ey = stdr(firstSeg, sigma) badval = abs((firstSeg-np.median(firstSeg))/ey) > sigma err = np.ones(firstSeg.shape, float) err[badval] = 1e9 else: err = np.sqrt((err_ec[ymin:ymax,xmin:xmax]**2).mean(1)) err[True - np.isfinite(err)] = err[np.isfinite(err)].max() * 1e9 guessAmp = (an.wmean(firstSeg, 1./err**2) - np.median(firstSeg)) * fitwidth if not np.isfinite(guessAmp): pdb.set_trace() #fit, efit = fitGaussian(firstSeg, verbose=verbose, err=err, guess=[guessAmp[0], 5, fitwidth/2., np.median(firstSeg)]) fit, efit = fitGaussian(firstSeg, verbose=verbose, err=err, guess=None) newY = ymin+fit[2] err_newY = efit[2] if verbose: message("Initial position: (%3.2f,%3.2f)"%(x,newY)) return x, newY, err_newY
[docs]def traceorders(filename, pord=5, dispaxis=0, nord=1, verbose=False, ordlocs=None, stepsize=20, fitwidth=20, plotalot=False, medwidth=6, xylims=None, uncertainties=None, g=gain, rn=readnoise, badpixelmask=None, retsnr=False, retfits=False): """ Trace spectral orders for a specified filename. filename : str OR 2D array full path and filename to a 2D echelleogram FITS file, _OR_ a 2D numpy array representing such a file. OPTIONAL INPUTS: pord : int polynomial order of spectral order fit dispaxis : int set dispersion axis: 0 = horizontal and = vertical nord : int number of spectral orders to trace. ordlocs : (nord x 2) numpy array Location to "auto-click" and verbose: int 0,1,2; whether (and how much) to print various verbose debugging text stepsize : int number of pixels to step along the spectrum while tracing fitwidth : int number of pixels to use when fitting a spectrum's cross-section medwidth : int number of columns to average when fitting profiles to echelleograms plotalot : bool Show pretty plot? If running in batch mode (using ordlocs) default is False; if running in interactive mode (ordlocs is None) default is True. xylims : 4-sequence Extract the given subset of the data array: [xmin, xmax, ymin, ymax] uncertainties : str OR 2D array full path and filename to a 2D uncertainties FITS file, _OR_ a 2D numpy array representing such a file. If this is set, 'g' and 'rn' below are ignored. This is useful if, e.g., you are analyzing data which have already been sky-subtracted, nodded on slit, or otherwise altered. But note that this must be the same size as the input data! g : scalar > 0 Detector gain, electrons per ADU (for setting uncertainties) rn : scalar > 0 Detector read noise, electrons (for setting uncertainties) retsnr : bool If true, also return the computed S/N of the position fit at each stepped location. retfits : bool If true, also return the X,Y positions at each stepped location. :RETURNS: (nord, pord) shaped numpy array representing the polynomial coefficients for each order (suitable for use with np.polyval) :NOTES: If tracing fails, a common reason can be that fitwidth is too small. Try increasing it! """ # 2010-08-31 17:00 IJC: If best-fit PSF location goes outside of # the 'fitwidth' region, nan values are returned that don't # cause the routine to bomb out quite so often. # 2010-09-08 13:54 IJC: Updated so that going outside the # 'fitwidth' region is determined only in the local # neighborhood, not in relation to the (possibly distant) # initial guess position. # 2010-11-29 20:56 IJC: Added medwidth option # 2010-12-17 09:32 IJC: Changed color scaling options; added xylims option. # 2012-04-27 04:43 IJMC: Now perform _weighted_ fits to the measured traces! global gain global readnoise if verbose < 0: verbose = 0 if not g==gain: message("Setting gain to " + str(g)) gain = g if not rn==readnoise: message("Setting readnoise to " + str(rn)) readnoise = rn if ordlocs is not None: ordlocs = np.array(ordlocs, copy=False) if ordlocs.ndim==1: ordlocs = ordlocs.reshape(1, ordlocs.size) autopick = True else: autopick = False plotalot = (not autopick) or plotalot if isinstance(filename, np.ndarray): ec = filename.copy() else: try: ec = pyfits.getdata(filename) except: message("Could not open filename %s" % filename) return -1 if isinstance(uncertainties, np.ndarray): err_ec = uncertainties.copy() else: try: err_ec = pyfits.getdata(uncertainties) except: err_ec = np.sqrt(ec * gain + readnoise**2) if dispaxis<>0: ec = ec.transpose() err_ec = err_ec.transpose() if verbose: message("Took transpose of echelleogram to rotate dispersion axis") else: pass if xylims is not None: try: ec = ec[xylims[0]:xylims[1], xylims[2]:xylims[3]] err_ec = err_ec[xylims[0]:xylims[1], xylims[2]:xylims[3]] except: message("Could not extract subset: ", xylims) return -1 if badpixelmask is None: badpixelmask = np.zeros(ec.shape, dtype=bool) else: if not hasattr(badpixelmask, 'shape'): badpixelmask = pyfits.getdata(badpixelmask) if xylims is not None: badpixelmask = badpixelmask[xylims[0]:xylims[1], xylims[2]:xylims[3]] err_ec[badpixelmask.nonzero()] = err_ec[np.isfinite(err_ec)].max() * 1e9 try: ny, nx = ec.shape except: message("Echellogram file %s does not appear to be 2D: exiting." % filename) return -1 if plotalot: f = plt.figure() ax = plt.axes() plt.imshow(ec, interpolation='nearest',aspect='auto') sortedvals = np.sort(ec.ravel()) plt.clim([sortedvals[nx*ny*.01], sortedvals[nx*ny*.99]]) #plt.imshow(np.log10(ec-ec.min()+np.median(ec)),interpolation='nearest',aspect='auto') ax.axis([0, nx, 0, ny]) orderCoefs = np.zeros((nord, pord+1), float) position_SNRs = [] xyfits = [] if not autopick: ordlocs = np.zeros((nord, 2),float) for ordernumber in range(nord): message('Selecting %i orders; please click on order %i now.' % (nord, 1+ordernumber)) plt.figure(f.number) guessLoc = pickloc(ax, zoom=fitwidth) ordlocs[ordernumber,:] = guessLoc ax.plot([guessLoc[0]],[guessLoc[1]], '*k') ax.axis([0, nx, 0, ny]) plt.figure(f.number) plt.draw() if verbose: message("you picked the location: ") message(guessLoc) for ordernumber in range(nord): guessLoc = ordlocs[ordernumber,:] xInit, yInit, err_yInit = fitPSF(ec, guessLoc, fitwidth=fitwidth,verbose=verbose, medwidth=medwidth, err_ec=err_ec) if plotalot: ax.plot([xInit],[yInit], '*k') ax.axis([0, nx, 0, ny]) plt.figure(f.number) plt.draw() if verbose: message("Initial (fit) position: (%3.2f,%3.2f)"%(xInit,yInit)) # Prepare to fit PSFs at multiple wavelengths. # Determine the other positions at which to fit: xAbove = np.arange(1, np.ceil(1.0*(nx-xInit)/stepsize))*stepsize + xInit xBelow = np.arange(-1,-np.ceil((1.+xInit)/stepsize),-1)*stepsize + xInit nAbove = len(xAbove) nBelow = len(xBelow) nToMeasure = nAbove + nBelow + 1 iInit = nBelow if verbose: message("Going to measure PSF at the following %i locations:"%nToMeasure ) message(xAbove) message(xBelow) # Measure all positions "above" the initial selection: yAbove = np.zeros(nAbove,float) err_yAbove = np.zeros(nAbove,float) lastY = yInit for i_meas in range(nAbove): guessLoc = xAbove[i_meas], lastY thisx, thisy, err_thisy = fitPSF(ec, guessLoc, fitwidth=fitwidth, verbose=verbose-1, medwidth=medwidth, err_ec=err_ec) if abs(thisy - yInit)>fitwidth/2: thisy = yInit err_thisy = yInit lastY = yInit else: lastY = thisy.astype(int) yAbove[i_meas] = thisy err_yAbove[i_meas] = err_thisy if verbose: print thisx, thisy if plotalot and not np.isnan(thisy): #ax.plot([thisx], [thisy], 'xk') ax.errorbar([thisx], [thisy], [err_thisy], fmt='xk') # Measure all positions "below" the initial selection: yBelow = np.zeros(nBelow,float) err_yBelow = np.zeros(nBelow,float) lastY = yInit for i_meas in range(nBelow): guessLoc = xBelow[i_meas], lastY thisx, thisy, err_thisy = fitPSF(ec, guessLoc, fitwidth=fitwidth, verbose=verbose-1, medwidth=medwidth, err_ec=err_ec) if abs(thisy-lastY)>fitwidth/2: thisy = np.nan else: lastY = thisy.astype(int) yBelow[i_meas] = thisy err_yBelow[i_meas] = err_thisy if verbose: print thisx, thisy if plotalot and not np.isnan(thisy): ax.errorbar([thisx], [thisy], [err_thisy], fmt='xk') # Stick all the fit positions together: yPositions = np.concatenate((yBelow[::-1], [yInit], yAbove)) err_yPositions = np.concatenate((err_yBelow[::-1], [err_yInit], err_yAbove)) xPositions = np.concatenate((xBelow[::-1], [xInit], xAbove)) if verbose: message("Measured the following y-positions:") message(yPositions) theseTraceCoefs = polyfitr(xPositions, yPositions, pord, 3, \ w=1./err_yPositions**2, verbose=verbose) orderCoefs[ordernumber,:] = theseTraceCoefs # Plot the traces if plotalot: ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx)), '-k') ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx))+fitwidth/2, '--k') ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx))-fitwidth/2, '--k') ax.axis([0, nx, 0, ny]) plt.figure(f.number) plt.draw() if retsnr: position_SNRs.append(yPositions / err_yPositions) if retfits: xyfits.append((xPositions, yPositions)) # Prepare for exit and return: ret = (orderCoefs,) if retsnr: ret = ret + (position_SNRs,) if retfits: ret = ret + (xyfits,) if len(ret)==1: ret = ret[0] return ret
[docs]def makeprofile(filename, trace, **kw): #dispaxis=0, fitwidth=20, verbose=False, oversamp=4, nsigma=20, retall=False, neg=False, xylims=None, rn=readnoise, g=gain, extract_radius=10, bkg_radii=[15, 20], bkg_order=0, badpixelmask=None, interp=False): """ Make a spatial profile from a spectrum, given its traced location. We interpolate the PSF at each pixel to a common reference frame, and then average them. filename : str _OR_ 2D numy array 2D echellogram trace : 1D numpy array set of polynomial coeficients of order (P-1) dispaxis : int set dispersion axis: 0 = horizontal and = vertical fitwidth : int Total width of extracted spectral sub-block. WIll always be increased to at least twice the largest value of bkg_radii. neg : bool scalar set True for a negative spectral trace nsigma : scalar Sigma-clipping cut for bad pixels (beyond read+photon noise). Set it rather high, and feel free to experiment with this parameter! xylims : 4-sequence Extract the given subset of the data array: [xmin, xmax, ymin, ymax] retall : bool Set True to output several additional parameters (see below) rn : scalar Read noise (electrons) g : scalar Detector gain (electrons per data unit) bkg_radii : 2-sequence Inner and outer radius for background computation and removal; measured in pixels from the center of the profile. bkg_order : int > 0 Polynomial order of background trend computed in master spectral profile interp : bool Whether to (bi-linearly) interpolate each slice to produce a precisely-centered spectral profile (according to the input 'trace'). If False, slices will only be aligned to the nearest pixel. OUTPUT: if retall: a spline-function that interpolates pixel locations onto the mean profile a stack of data slices estimates of the uncertainties good pixel flag list of splines else: a spline-function that interpolates pixel locations onto the mean profile """ from scipy import signal #import numpy as np # 2010-12-17 10:22 IJC: Added xylims option # 2012-03-15 06:34 IJMC: Updated documentation. # 2012-04-24 16:43 IJMC: Now properly update gain and readnoise, # as neccessary. Much better flagging of # bad pixels. Fixed interpolation on RHS. # 2012-08-19 17:53 IJMC: Added dispaxis option. # 2012-09-05 10:17 IJMC: Made keyword options into a dict. global gain global readnoise # Parse inputs: names = ['dispaxis', 'fitwidth', 'verbose', 'oversamp', 'nsigma', 'retall', 'neg', 'xylims', 'rn', 'g', 'extract_radius', 'bkg_radii', 'bkg_order', 'badpixelmask', 'interp'] defaults = [0, 20, False, 4, 20, False, False, None, readnoise, gain, 10, [15, 20], 0, None, False] for n,d in zip(names, defaults): #exec('%s = [d, kw["%s"]][kw.has_key("%s")]' % (n, n, n)) exec('%s = kw["%s"] if kw.has_key("%s") else d' % (n, n, n)) if fitwidth < (bkg_radii[1]*2): fitwidth = bkg_radii[1]*2 if not g==gain: message("Setting gain to " + str(g)) gain = g if not rn==readnoise: message("Setting readnoise to " + str(rn)) readnoise = rn if verbose: f = plt.figure() ax = plt.axes() # Check whether we have a filename, or an array: if isinstance(filename, np.ndarray): ec = filename.copy() else: try: ec = pyfits.getdata(filename) except: message("Could not open filename %s" % filename) return -1 if neg: ec *= -1 if xylims is not None: try: ec = ec[xylims[0]:xylims[1], xylims[2]:xylims[3]] if verbose: message("Extracted subset: " + str(xylims)) except: message("Could not extract subset: " + str(xylims)) return -1 if badpixelmask is None: badpixelmask = np.zeros(ec.shape, dtype=bool) else: if not hasattr(badpixelmask, 'shape'): badpixelmask = pyfits.getdata(badpixelmask) if xylims is not None: badpixelmask = badpixelmask[xylims[0]:xylims[1], xylims[2]:xylims[3]] trace = np.array(trace,copy=True) if len(trace.shape)>1: if verbose: message("Multi-order spectrum input...") #rets = [] rets = [makeprofile(filename, thistrace, dispaxis=dispaxis, fitwidth=fitwidth,verbose=verbose,oversamp=oversamp,nsigma=nsigma,retall=retall, neg=neg, xylims=xylims, rn=rn, g=g, extract_radius=extract_radius, bkg_radii=bkg_radii, bkg_order=bkg_order,interp=interp, badpixelmask=badpixelmask) for thistrace in trace] return rets if dispaxis==1: if verbose: message("Transposing spectra and bad pixel mask...") ec = ec.transpose() badpixelmask = badpixelmask.transpose() if verbose: message("Making a spatial profile...") ny, nx = ec.shape pord = len(trace) - 1 if verbose: message("Echellogram of size (%i,%i)" % (nx,ny)) message("Polynomial of order %i (i.e., %i coefficients)" % (pord, pord+1)) xPositions = np.arange(nx) yPositions = np.polyval(trace, xPositions) yPixels = yPositions.astype(int) # Placeholder for a more comprehensive workaround: if (yPixels.max()+fitwidth/2)>=ny: message("Spectrum may be too close to the upper boundary; try decreasing fitwidth") if (yPixels.min()-fitwidth/2)<0: message("Spectrum may be too close to the lower boundary; try decreasing fitwidth") yProfile = np.linspace(-fitwidth/2, fitwidth/2-1,fitwidth*oversamp) #yProfile2 = np.linspace(-fitwidth/2, fitwidth/2-1,(fitwidth+1)*oversamp) profileStack = np.zeros((nx,fitwidth),float) badpixStack = np.zeros((nx,fitwidth),bool) profileSplineStack = np.zeros((nx,(fitwidth)*oversamp),float) xProf = np.arange(fitwidth, dtype=float) - fitwidth/2. xProf2 = np.arange(ny, dtype=float) # Loop through to extract the spectral cross-sections at each pixel: for i_pix in range(nx): xi = xPositions[i_pix] yi = yPixels[i_pix] ymin = max(yi-fitwidth/2, 0) ymax = min(yi+fitwidth/2, ny) # Ensure that profile will always be "fitwidth" wide: if (ymax-ymin)<fitwidth and ymin==0: ymax = fitwidth elif (ymax-ymin)<fitwidth and ymax==ny: ymin = ymax - fitwidth profile = ec[ymin:ymax,xi] if interp: profile = np.interp(xProf + yPositions[i_pix], xProf2[ymin:ymax], profile) if verbose: print 'i_pix, xi, yi, y0, ymin, ymax', i_pix, xi, yi, yPositions[i_pix], ymin, ymax try: profileStack[i_pix] = profile.copy() * gain badpixStack[i_pix] = badpixelmask[ymin:ymax, xi] except: print "Busted!!!!" print "profileStack[i_pix].shape, profile.shape, gain", \ profileStack[i_pix].shape, profile.shape, gain stop # Use the profile stack to flag bad pixels in the interpolation process: medianProfile = np.median(profileStack,0) scaledProfileStack = 0*profileStack myMatrix = np.linalg.pinv(np.vstack((medianProfile, np.ones(profileStack.shape[1]))).transpose()) for ii in range(profileStack.shape[0]): these_coefs = np.dot(myMatrix, profileStack[ii]) scaledProfileStack[ii] = (profileStack[ii] - these_coefs[1]) / these_coefs[0] #import pylab as py #pdb.set_trace() ######### # Flag all the bad pixels: ######### # Hot Pixels should be 1-pixel wide: nFilt = 3 filteredProfileStack = signal.medfilt2d(profileStack, nFilt) errorFiltStack = np.sqrt(filteredProfileStack + readnoise**2) errorFiltStack[badpixStack] = errorFiltStack.max() * 1e9 goodPixels = (True - badpixStack) * np.abs((profileStack - filteredProfileStack) / errorFiltStack) < nsigma badPixels = True - goodPixels #cleaned_profileStack = ns.bfixpix(profStack, badpix, retdat=True) if False: # Another (possibly worse?) way to do it: stdProfile = stdr(scaledProfileStack, nsigma=nsigma, axis=0) #profileStack.std(0) goodPixels = (scaledProfileStack-medianProfile.reshape(1,fitwidth)) / \ stdProfile.reshape(1,fitwidth)<=nsigma goodPixels *= profileStack>=0 goodColumns = np.ones(goodPixels.shape[0],bool) errorStack = np.sqrt(profileStack + readnoise**2) errorStack[badPixels] = profileStack[goodPixels].max() * 1e9 # Loop through and fit weighted splines to each cross-section for i_pix in range(nx): xi = xPositions[i_pix] yi = yPixels[i_pix] ymin = max(yi-fitwidth/2, 0) ymax = min(yi+fitwidth/2, ny) # Ensure that profile will always be "fitwidth" wide: if (ymax-ymin)<fitwidth and ymin==0: ymax = fitwidth elif (ymax-ymin)<fitwidth and ymax==ny: ymin = ymax - fitwidth profile = profileStack[i_pix] index = goodPixels[i_pix] ytemp = np.arange(ymin,ymax)-yPositions[i_pix] if verbose>1: print "ii, index.sum()>>",i_pix,index.sum() print "xi, yi>>",xi,yi if verbose: print "ymin, ymax>>",ymin,ymax print "ytemp.shape, profile.shape, index.shape>>",ytemp.shape, profile.shape, index.shape if index.sum()>fitwidth/2: ydata_temp = np.concatenate((ytemp[index], ytemp[index][-1] + np.arange(1,6))) pdata_temp = np.concatenate((profile[index], [profile[index][-1]]*5)) profileSpline = interpolate.UnivariateSpline(ydata_temp, pdata_temp, k=3.0,s=0.0) profileSplineStack[i_pix,:] = profileSpline(yProfile) else: if verbose: message("not enough good pixels in segment %i" % i_pix) profileSplineStack[i_pix,:] = 0.0 goodColumns[i_pix] = False finalYProfile = np.arange(fitwidth)-fitwidth/2 finalSplineProfile = np.median(profileSplineStack[goodColumns,:],0) ## Normalize the absolute scaling of the final Profile #xProfile = np.arange(-50.*fitwidth, 50*fitwidth)/100. #profileParam = fitGaussian(finalSplineProfile, verbose=True) #profileScaling = profileParam[0]*.01 #print 'profileScaling>>', profileScaling backgroundAperture = (np.abs(yProfile) > bkg_radii[0]) * (np.abs(yProfile) < bkg_radii[1]) #pdb.set_trace() backgroundFit = np.polyfit(yProfile[backgroundAperture], finalSplineProfile[backgroundAperture], bkg_order) extractionAperture = (np.abs(yProfile) < extract_radius) normalizedProfile = finalSplineProfile - np.polyval(backgroundFit, yProfile) normalizedProfile *= oversamp / normalizedProfile[extractionAperture].sum() finalSpline = interpolate.UnivariateSpline(yProfile, normalizedProfile, k=3.0, s=0.0) if verbose: ax.plot(yProfile, finalSplineProfile, '--',linewidth=2) plt.draw() if retall==True: ret = finalSpline, profileStack, errorStack, goodPixels,profileSplineStack else: ret = finalSpline return ret
def makeFitModel(param, spc,profile, xtemp=None): scale, background, shift = param[0:3] npix = len(spc) if xtemp is None: xtemp = np.arange(-npix/2,npix/2,dtype=float) model = scale*profile(xtemp-shift)+background return model
[docs]def profileError(param, spc, profile, w, xaxis=None, retresidual=False): "Compute the chi-squared error on a spectrum vs. a profile " # 2012-04-25 12:59 IJMC: Slightly optimised (if retresidual is False) #scale, background, shift = param[0:3] #npix = len(spc) #xtemp = np.arange(-npix/2,npix/2,dtype=float) interpolatedProfile = makeFitModel(param,spc,profile, xtemp=xaxis) #scale*profile(xtemp-shift)+background #wresiduals = #wresiduals = (w*((spc-interpolatedProfile))**2).sum() if retresidual: ret = (w**0.5) * (spc - interpolatedProfile) else: ret = (w * (spc - interpolatedProfile)**2).sum() return ret
[docs]def fitprofile(spc, profile, w=None,verbose=False, guess=None, retall=False): """Fit a spline-class spatial profile to a spectrum cross-section """ # 2010-11-29 14:14 IJC: Added poor attempt at catching bad pixels import analysis as an import numpy as np spc = np.array(spc) try: npix = len(spc) except: npix = 0 if w is None: w = np.ones(spc.shape) else: w[True - np.isfinite(w)] = 0. if guess is None: guessParam = [1., 0., 0.] else: guessParam = guess if verbose>1: #message("profileError>>"+str(profileError)) #message("guessParam>>"+str(guessParam)) #message("spc>>"+str(spc)) message("w>>"+str(w)) good_index = w<>0.0 xaxis = np.arange(-npix/2,npix/2,dtype=float) # take outside of fitting loop bestFit = optimize.fmin_powell(profileError, guessParam, args=(spc,profile,w * good_index, xaxis),disp=verbose, full_output=True) best_bic = bestFit[1] + len(bestFit[0]) * np.log(good_index.sum()) keepCleaning = True if False: while keepCleaning is True: if verbose: print "best bic is: ", best_bic #w_residuals = profileError(bestFit[0], spc, profile, w, retresidual=True) #good_values = (abs(w_residuals)<>abs(w_residuals).max()) diffs = np.hstack(([0], np.diff(spc))) d2 = diffs * (-np.ones(len(spc)))**np.arange(len(spc)) d3 = np.hstack((np.vstack((d2[1::], d2[0:len(spc)-1])).mean(0), [0])) good_values = abs(d3)<>abs(d3).max() good_index *= good_values xaxis = np.arange(-npix/2,npix/2,dtype=float) # take outside of fitting loop latestFit = optimize.fmin_powell(profileError, guessParam, args=(spc,profile,w * good_index, xaxis),disp=verbose, full_output=True) latest_bic = latestFit[1] + len(latestFit[0]) * np.log(good_index.sum()) if latest_bic < best_bic: best_bic = latest_bic bestFit = latestFit keepCleaning = True else: keepCleaning = False if good_index.any() is False: keepCleaning = False #good_index = good_index * an.removeoutliers(w_residuals, 5, retind=True)[1] if verbose: message("initial guess chisq>>%3.2f" % profileError(guessParam, spc, profile,w,xaxis)) message("final fit chisq>>%3.2f" % profileError(bestFit[0], spc, profile,w, xaxis)) if retall: ret = bestFit else: ret = bestFit[0] return ret
[docs]def fitProfileSlices(splineProfile, profileStack, stdProfile, goodPixels,verbose=False, bkg_radii=None, extract_radius=None): """Fit a given spatial profile to a spectrum Helper function for :func:`extractSpectralProfiles` """ npix, fitwidth = profileStack.shape stdProfile = stdProfile.copy() goodPixels[np.isnan(stdProfile) + stdProfile==0] = False stdProfile[True - goodPixels] = 1e9 #varPData = stdProfile**2 if extract_radius is None: extract_radius = fitwidth x2 = np.arange(npix) x = np.arange(-fitwidth/2, fitwidth/2) extractionAperture = np.abs(x) < extract_radius nextract = extractionAperture.sum() fitparam = np.zeros((npix, 3),float) fitprofiles = np.zeros((npix,nextract),float) tempx = np.arange(-fitwidth/2,fitwidth/2,dtype=float) # Start out by getting a rough estimate of any additional bad # pixels (to flag): #backgroundAperture = (np.abs(x) > bkg_radii[0]) * (np.abs(x) < bkg_radii[1]) #background = an.wmean(profileStack[:, backgroundAperture], (goodPixels/varPData)[:, backgroundAperture], axis=1) #badBackground = True - np.isfinite(background) #background[badBackground] = 0. #subPData = profileStack - background #standardSpectrum = an.wmean(subPData[:, extractionAperture], (goodPixels/varPData)[:,extractionAperture], axis=1) * extractionAperture.sum() if False: varStandardSpectrum = an.wmean(varPData[:, extractionAperture], goodPixels[:, extractionAperture], axis=1) * extractionAperture.sum() badSpectrum = True - np.isfinite(standardSpectrum) standardSpectrum[badSpectrum] = 1. varStandardSpectrum[badSpectrum] = varStandardSpectrum[True - badSpectrum].max() * 1e9 mod = background + standardSpectrum * splineProfile(x) * (splineProfile(x).sum() / splineProfile(x[extractionAperture]).sum()) anyNewBadPixels = True nbp0 = goodPixels.size - goodPixels.sum() gp0 = goodPixels.copy() while anyNewBadPixels is True: for ii in range(npix): if verbose>1: print "*****In fitProfileSlices, pixel %i/%i" % (ii+1, npix) if ii>0: # take the median of the last several guesses gindex = [max(0,ii-2), min(npix, ii+2)] guess = np.median(fitparam[gindex[0]:gindex[1]], 0) else: guess = None if verbose>1: message("ii>>%i"%ii) message("goodPixels>>"+str(goodPixels.astype(float)[ii,:])) message("stdProfile>>"+str(stdProfile[ii,:])) thisfit = fitprofile(profileStack[ii,extractionAperture], splineProfile, (goodPixels.astype(float)/stdProfile**2)[ii,extractionAperture],verbose=verbose, guess=guess, retall=True) bestfit = thisfit[0] bestchi = thisfit[1] if verbose>1: print "this fit: ",bestfit fitparam[ii,:] = bestfit fitprofiles[ii,:] = makeFitModel(bestfit, profileStack[ii,extractionAperture],splineProfile) if verbose>1: print "finished pixel %i/%i" % (ii+1,npix) deviations = (fitprofiles - profileStack[:, extractionAperture])/stdProfile[:, extractionAperture] for kk in range(nextract): kk0 = extractionAperture.nonzero()[0][kk] thisdevfit = an.polyfitr(x2, deviations[:, kk], 1, 3) theseoutliers = np.abs((deviations[:, kk] - np.polyval(thisdevfit, x2))/an.stdr(deviations, 3)) > 5 goodPixels[theseoutliers, kk0] = False nbp = goodPixels.size - goodPixels.sum() if nbp <= nbp0: anyNewBadPixels = False else: nbp0 = nbp for ii in range(fitparam.shape[1]): fitparam[:,ii] = bfixpix(fitparam[:,ii], goodPixels[:,extractionAperture].sum(1) <= (nextract/2.), retdat=True) return fitparam, fitprofiles
[docs]def extractSpectralProfiles(args, **kw): """ Extract spectrum :INPUTS: args : tuple or list either a tuple of (splineProfile, profileStack, errorStack, profileMask), or a list of such tuples (from makeprofile). :OPTIONS: bkg_radii : 2-sequence inner and outer radii to use in computing background extract_radius : int radius to use for both flux normalization and extraction :RETURNS: 3-tuple: [0] -- spectrum flux (in electrons) [1] -- background flux [2] -- best-fit pixel shift :EXAMPLE: :: out = spec.traceorders('aug16s0399.fits',nord=7) out2 = spec.makeprofile('aug16s0399.fits',out,retall=True) out3 = spec.extractSpectralProfiles(out2) :SEE_ALSO: :func:`optimalExtract` :NOTES: Note that this is non-optimal with highly tilted or curved spectra, for the reasons described by Marsh (1989) and Mukai (1990). """ # 2010-08-24 21:24 IJC: Updated comments if kw.has_key('verbose'): verbose = kw['verbose'] else: verbose = False if kw.has_key('bkg_radii'): bkg_radii = kw['bkg_radii'] else: bkg_radii = [15, 20] if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii)) if kw.has_key('extract_radius'): extract_radius = kw['extract_radius'] else: extract_radius = 10 #print 'starting...', args[0].__class__, isinstance(args[0],tuple)#, len(*arg) fitprofiles = [] if isinstance(args,list): # recurse with each individual set of arguments #nord = len(arg[0]) spectrum = [] background = [] pixshift = [] for ii, thesearg in enumerate(args): if verbose: print "----------- Iteration %i/%i" % (ii+1, len(args)) #print 'recursion loop', thesearg.__class__, verbose, kw tempout = extractSpectralProfiles(thesearg, **kw) spectrum.append(tempout[0]) background.append(tempout[1]) pixshift.append(tempout[2]) fitprofiles.append(tempout[3]) spectrum = np.array(spectrum) background = np.array(background) pixshift = np.array(pixshift) else: #print 'actual computation', args.__class__, verbose, kw splineProfile = args[0] profileStack = args[1] errorStack = args[2] profileMask = args[3] #print splineProfile.__class__, profileStack.__class__, errorStack.__class__, profileMask.__class__ #print profileStack.shape, errorStack.shape, profileMask.shape fps = dict() if kw.has_key('verbose'): fps['verbose'] = kw['verbose'] if kw.has_key('bkg_radii'): fps['bkg_radii'] = kw['bkg_radii'] if kw.has_key('extract_radius'): fps['extract_radius'] = kw['extract_radius'] fitparam, fitprofile = fitProfileSlices(splineProfile, profileStack, errorStack, profileMask, **fps) spectrum = fitparam[:,0] background = fitparam[:,1] pixshift = fitparam[:,2] fitprofiles.append(fitprofile) ret = (spectrum, background, pixshift, fitprofiles) return ret
[docs]def gaussint(x): """ :PURPOSE: Compute the integral from -inf to x of the normalized Gaussian :INPUTS: x : scalar upper limit of integration :NOTES: Designed to copy the IDL function of the same name. """ # 2011-10-07 15:41 IJMC: Created from scipy.special import erf scalefactor = 1./np.sqrt(2) return 0.5 + 0.5 * erf(x * scalefactor)
[docs]def slittrans(*varargin): """+ :NAME: slittrans :PURPOSE: Compute flux passing through a slit assuming a gaussian PSF. :CATEGORY: Spectroscopy :CALLING SEQUENCE: result = slittrans(width,height,fwhm,xoffset,yoffset,CANCEL=cancel) :INPUTS: width - Width of slit. height - Height of slit. fwhm - Full-width at half-maximum of the gaussian image. xoffset - Offset in x of the image from the center of the slit. yoffset - Offset in y of the image from the center of the slit. Note: the units are arbitrary but they must be the same across all of the input quantities. KEYWORD PARAMETERS: CANCEL - Set on return if there is a problem OUTPUTS: Returned is the fraction of the total gaussian included in the slit. EXAMPLE: result = slittrans(0.3,15,0.6,0,0) Computes the fraction of the flux transmitted through a slit 0.3x15 arcseconds with a PSF of 0.6 arcseconds FWHM. The PSF is centered on the slit. MODIFICATION HISTORY: Based on M Buie program, 1991 Mar., Marc W. Buie, Lowell Observatory Modified 2000 Apr., M. Cushing to include y offsets. 2011-10-07 15:45 IJMC: Converted to Python 2011-11-14 16:29 IJMC: Rewrote to use :func:`erf` rather than :func:`gaussint` """ #function slittrans,width,height,fwhm,xoffset,yoffset,CANCEL=cancel from scipy.special import erf cancel = 0 n_params = len(varargin) if n_params <> 5: print 'Syntax - result = slittrans(width,height,fwhm,xoffset,yoffset,', print ' CANCEL=cancel)' cancel = 1 return cancel width, height, fwhm, xoffset, yoffset = varargin[0:5] #cancel = cpar('slittrans',width,1,'Width',[2,3,4,5],0) #if cancel then return,-1 #cancel = cpar('slittrans',height,2,'Height',[2,3,4,5],0) #if cancel then return,-1 #cancel = cpar('slittrans',fwhm,3,'FWHM',[2,3,4,5],0) #if cancel then return,-1 #cancel = cpar('slittrans',xoffset,4,'Xoffset',[2,3,4,5],0) #if cancel then return,-1 #cancel = cpar('slittrans',yoffset,5,'Yoffset',[2,3,4,5],0) #if cancel then return,-1 # Go ahead a = 0.5 * width b = 0.5 * height #s = fwhm/(np.sqrt(8.0*np.log(2.0))) #slit = ( 1.0 - gaussint( -(a+xoffset)/s ) - gaussint( -(a-xoffset)/s ) ) * \ # ( 1.0 - gaussint( -(b+yoffset)/s ) - gaussint( -(b-yoffset)/s ) ) invs2 = (np.sqrt(4.0*np.log(2.0)))/fwhm # sigma * sqrt(2) slit4 = 0.25 * ( (erf( -(a+xoffset)*invs2) + erf( -(a-xoffset)*invs2) ) ) * \ ( (erf( -(b+yoffset)*invs2) + erf( -(b-yoffset)*invs2) ) ) #print 'smin/max,',slit.min(), slit5.max() #print 'smin/max,',slit4.min(), slit4.max() return slit4
[docs]def atmosdisp(wave, wave_0, za, pressure, temp, water=2., fco2=0.0004, obsalt=0.): """:NAME: atmosdisp PURPOSE: Compute the atmosperic dispersion relative to lambda_0. CATEGORY: Spectroscopy CALLING SEQUENCE: result = atmosdisp(wave,wave_0,za,pressure,temp,[water],[obsalt],$ CANCEL=cancel) INPUTS: wave - wavelength in microns wave_0 - reference wavelength in microns za - zenith angle of object [in degrees] pressure - atmospheric pressure in mm of Hg temp - atmospheric temperature in degrees C OPTIONAL INPUTS: water - water vapor pressure in mm of Hg. fco2 - relative concentration of CO2 (by pressure) obsalt - The observatory altitude in km. KEYWORD PARAMETERS: CANCEL - Set on return if there is a problem OUTPUTS: Returns the atmospheric disperion in arcseconds. PROCEDURE: Computes the difference between the dispersion at two wavelengths. The dispersion for each wavelength is derived from Section 4.3 of Green's "Spherical Astronomy" (1985). EXAMPLE: MODIFICATION HISTORY: 2000-04-05 - written by M. Cushing, Institute for Astronomy, UH 2002-07-26 - cleaned up a bit. 2003-10-20 - modified formula - WDV 2011-10-07 15:51 IJMC: Converted to Python, with some unit conversions -""" #function atmosdisp,wave,wave_0,za,pressure,temp,water,obsalt,CANCEL=cancel from nsdata import nAir # Constants mmHg2pa = 101325./760. # Pascals per Torr (i.e., per mm Hg) rearth = 6378.136e6 #6371.03 # mean radius of earth in km [Allen's] hconst = 2.926554e-2 # R/(mu*g) in km/deg K, R=gas const=8.3143e7 # mu=mean mol wght. of atm=28.970, g=980.665 tempk = temp + 273.15 pressure_pa = pressure * mmHg2pa water_pp = water/pressure # Partial pressure hratio = (hconst * tempk)/(rearth + obsalt) # Compute index of refraction nindx = nAir(wave,P=pressure_pa,T=tempk,pph2o=water_pp, fco2=fco2) nindx0 = nAir(wave_0,P=pressure_pa,T=tempk,pph2o=water_pp, fco2=fco2) # Compute dispersion acoef = (1. - hratio)*(nindx - nindx0) bcoef = 0.5*(nindx*nindx - nindx0*nindx0) - (1. + hratio)*(nindx - nindx0) tanz = np.tan(np.deg2rad(za)) disp = 206265.*tanz*(acoef + bcoef*tanz*tanz) #print nindx #print nindx0 #print acoef #print bcoef #print tanz #print disp return disp
[docs]def parangle(HA, DEC, lat): """ + NAME: parangle PURPOSE: To compute the parallactic angle at a given position on the sky. CATEGORY: Spectroscopy CALLING SEQUENCE: eta, za = parangle(HA, DEC, lat) INPUTS: HA - Hour angle of the object, in decimal hours (0,24) DEC - Declination of the object, in degrees lat - The latitude of the observer, in degrees KEYWORD PARAMETERS: CANCEL - Set on return if there is a problem OUTPUTS: eta - The parallactic angle za - The zenith angle PROCEDURE: Given an objects HA and DEC and the observers latitude, the zenith angle and azimuth are computed. The law of cosines then gives the parallactic angle. EXAMPLE: NA MODIFICATION HISTORY: 2000-04-05 - written by M. Cushing, Institute for Astronomy,UH 2002-08-15 - cleaned up a bit. 2003-10-21 - changed to pro; outputs zenith angle as well - WDV 2011-10-07 17:58 IJMC: Converted to Python -""" #pro parangle, HA, DEC, lat, eta, za, CANCEL=cancel cancel = 0 d2r = np.deg2rad(1.) r2d = np.rad2deg(1.) # If HA equals zero then it is easy. HA = HA % 24 # Check to see if HA is greater than 12. if hasattr(HA, '__iter__'): HA = np.array(HA, copy=False) HAind = HA > 12 if HAind.any(): HA[HAind] = 24. - HA[HAind] else: if HA>12.: HA = 24. - HA HA = HA*15. # Determine Zenith angle and Azimuth cos_za = np.sin(lat*d2r) * np.sin(DEC*d2r) + \ np.cos(lat*d2r) * np.cos(DEC*d2r) * np.cos(HA*d2r) za = np.arccos(cos_za) * r2d cos_az = (np.sin(DEC*d2r) - np.sin(lat*d2r)*np.cos(za*d2r)) / \ (np.cos(lat*d2r) * np.sin(za*d2r)) az = np.arccos(cos_az)*r2d if hasattr(az, '__iter__'): azind = az==0 if azind.any() and DEC<lat: az[azind] = 180. else: if az==0. and DEC<lat: az = 180. tan_eta = np.sin(HA*d2r)*np.cos(lat*d2r) / \ (np.cos(DEC*d2r)*np.sin(lat*d2r) - \ np.sin(DEC*d2r)*np.cos(lat*d2r)*np.cos(HA*d2r)) eta = np.arctan(tan_eta)*r2d if hasattr(eta, '__iter__'): etaind = eta < 0 ezind = (eta==0) * (az==0) zaind = za > 90 if etaind.any(): eta[etaind] += 180. elif ezind.any(): eta[ezind] = 180. if zaind.any(): eta[zaind] = np.nan else: if eta < 0: eta += 180. elif eta==0 and az==0: eta = 180. if za>90: eta = np.nan HA = HA/15.0 return eta, za
[docs]def lightloss(objfile, wguide, seeing, press=None, water=None, temp=None, fco2=None, wobj=None, dx=0, dy=0, retall=False): """ + NAME: lightloss PURPOSE: To determine the slit losses from a spectrum. CATEGORY: Spectroscopy CALLING SEQUENCE: ### TBD lightloss, obj, std, wguide, seeing, out, CANCEL=cancel INPUTS: obj - FITS file of the object spectrum wguide - wavelength at which guiding was done seeing - seeing FWHM at the guiding wavelength OPTIONAL INPUTS: press - mm Hg typical value (615 for IRTF, unless set) water - mm Hg typical value (2 for IRTF, unless set) temp - deg C typical value (0 for IRTF, unless set) fco2 - relative concentration of CO2 (0.004, unless set) wobj - wavelength scale for data dx - horizontal offset of star from slit center dy - vertical offset of star from slit center retall- whether to return much diagnostic info, or just lightloss. NOTES: 'seeing', 'dx', and 'dy' should all be in the same units, and also the same units used to define the slit dimensions in the obj FITS file header KEYWORD PARAMETERS: CANCEL - Set on return if there is a problem OUTPUTS: array : fractional slit loss at each wavelength value OR: tuple of arrays: (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj) PROCEDURE: Reads a Spextool FITS file. EXAMPLE: None REQUIREMENTS: :doc:`phot` :doc:`pyfits` MODIFICATION HISTORY: 2003-10-21 - Written by W D Vacca 2011-10-07 20:19 IJMC: Converted to Python, adapted for single objects 2011-10-14 14:01 IJMC: Added check for Prism mode (has different slit dimension keywords) and different pyfits header read mode. 2011-11-07 15:53 IJMC: Added 'retall' keyword - """ #pro lightloss, objfile, stdfile, wguide, seeing, outfile,CANCEL=cancel try: from astropy.io import fits as pyfits except: import pyfits from phot import hms, dms r2d = np.rad2deg(1) # --- Open input files obj = pyfits.getdata(objfile) objhdu = pyfits.open(objfile) #objhdr = pyfits.getheader(objfile) objhdu.verify('silentfix') objhdr = objhdu[0].header if wobj is None: try: wobj = obj[:,0,:] #fobj = obj[:,1,:] #eobj = obj[:,2,:] except: wobj = obj[0,:] #fobj = obj[1,:] #eobj = obj[2,:] # --- Read header keywords tele = objhdr['TELESCOP'] try: slitwd = objhdr['SLTW_ARC'] slitht = objhdr['SLTH_ARC'] except: # for SpeX prism mode: slitwd, slitht = map(float, objhdr['SLIT'].split('x')) #xaxis = objhdr['XAXIS'] #xunits = objhdr['XUNITS'] #yaxis = objhdr['YAXIS'] #yunits = objhdr['YUNITS'] posang_obj = objhdr['POSANGLE'] HA_objstr = objhdr['HA'] DEC_objstr = objhdr['DEC'] # --- Process keywords #coord_str = HA_objstr + ' ' + DEC_objstr #get_coords, coords, InString=coord_str HA_obj, DEC_obj = dms(HA_objstr), dms(DEC_objstr) if posang_obj<0.0: posang_obj += 180. if posang_obj >= 180.0: posang_obj -= 180.0 if tele=='NASA IRTF': obsdeg = 19.0 obsmin = 49.0 obssec = 34.39 obsalt = 4.16807 # observatory altitude in km teldiam = 3.0 # diameter of primary in meters if press is None: press = 615.0 # mm Hg typical value if water is None: water = 2.0 # mm Hg typical value if temp is None: temp = 0.0 # deg C typical value else: print 'Unknown Telescope - stopping!' return if fco2 is None: fco2 = 0.0004 obslat = dms('%+02i:%02i:%02i' % (obsdeg, obsmin, obssec)) # --- Compute Parallactic Angle pa_obj, za_obj = parangle(HA_obj, DEC_obj, obslat) dtheta_obj = posang_obj - pa_obj #print posang_obj, pa_obj, dtheta_obj, za_obj #print posang_obj, pa_obj, dtheta_obj, HA_obj # --- Compute Differential Atmospheric Dispersion disp_obj = atmosdisp(wobj, wguide, za_obj, press, temp, \ water=water, obsalt=obsalt, fco2=fco2) # --- Compute FWHM at each input wavelength diff = 2.0*1.22e-6*3600.0*r2d*(wobj/teldiam) # arcsec fwhm = (seeing*(wobj/wguide)**(-0.2)) fwhm[fwhm < diff] = diff[fwhm < diff] # --- Compute Relative Fraction of Light contained within the slit dx_obj = (disp_obj*np.sin(dtheta_obj/r2d)) + dx dy_obj = (disp_obj*np.cos(dtheta_obj/r2d)) + dy slitloss = slittrans(slitwd,slitht,fwhm,dx_obj,dy_obj) #debug_check = lightloss2(wobj, slitwd, slitht, posang_obj/57.3, pa_obj/57.3, za_obj/57.3, wguide, seeing, retall=retall) if retall: return (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj) else: return slitloss
[docs]def lightloss2(wobj, slitwd, slitht, slitPA, targetPA, zenith_angle, wguide, seeing, press=615., water=2., temp=0., fco2=0.004, obsalt=4.16807, teldiam=3., dx=0, dy=0, retall=False, ydisp=None, xdisp=None, fwhm=None): """ + NAME: lightloss2 PURPOSE: To determine the slit losses from an observation (no FITS file involved) CATEGORY: Spectroscopy CALLING SEQUENCE: ### TBD lightloss, obj, std, wguide, seeing, out, CANCEL=cancel INPUTS: wobj - wavelength scale for data slitwd - width of slit, in arcsec slitht - height of slit, in arcsec slitPA - slit Position Angle, in radians targetPA - Parallactic Angle at target, in radians zenith_angle - Zenith Angle, in radians wguide - wavelength at which guiding was done seeing - seeing FWHM at the guiding wavelength OPTIONAL INPUTS: press - mm Hg typical value (615, unless set) water - mm Hg typical value (2 , unless set) temp - deg C typical value (0 , unless set) fco2 - relative concentration of CO2 (0.004, unless set) obsalt- observatory altitude, in km teldiam- observatory limiting aperture diameter, in m dx - horizontal offset of star from slit center dy - vertical offset of star from slit center retall- whether to return much diagnostic info, or just lightloss. ydisp - The position of the spectrum in the slit at all values of wobj. This should be an array of the same size as wobj, with zero corresponding to the vertical middle of the slit and positive values tending toward zenith. In this case xdisp will be computed as XXXX rather than from the calculated atmospheric dispersion; dx and dy will also be ignored. fwhm - Full-width at half-maximum of the spectral trace, at all values of wobj. This should be an array of the same size as wobj, measured in arc seconds. NOTES: 'slitwidth', 'slitheight', 'seeing', 'dx', 'dy', and 'fwhm' (if used) should all be in the same units: arc seconds. OUTPUTS: array : fractional slit loss at each wavelength value OR: tuple of arrays: (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj) PROCEDURE: All input-driven. For the SpeXTool-version analogue, see :func:`lightloss` EXAMPLE: import numpy as np import spec w = np.linspace(.5, 2.5, 100) # Wavelength, in microns d2r = np.deg2rad(1.) #targetPA, za = spec.parangle(1.827, 29.67*d2r, lat=20.*d2r) targetPA, za = 105.3, 27.4 slitPA = 90. * d2r spec.lightloss2(w, 3., 15., slitPA, targetPA*d2r, za*d2r, 2.2, 1.0) REQUIREMENTS: :doc:`phot` MODIFICATION HISTORY: 2003-10-21 - Written by W D Vacca 2011-10-07 20:19 IJMC: Converted to Python, adapted for single objects 2011-10-14 14:01 IJMC: Added check for Prism mode (has different slit dimension keywords) and different pyfits header read mode. 2011-11-07 15:53 IJMC: Added 'retall' keyword 2011-11-07 21:17 IJMC: Cannibalized from SpeXTool version 2011-11-25 15:06 IJMC: Added ydisp and fwhm options. - """ #pro lightloss, objfile, stdfile, wguide, seeing, outfile,CANCEL=cancel try: from astropy.io import fits as pyfits except: import pyfits from phot import hms, dms r2d = np.rad2deg(1) d2r = np.deg2rad(1.) if slitPA<0.0: slitPA += np.pi if slitPA >= np.pi: slitPA -= np.pi # --- Compute Parallactic Angle dtheta_obj = slitPA - targetPA #print slitPA, targetPA, dtheta_obj, zenith_angle # --- Compute FWHM at each input wavelength diff = 2.0*1.22e-6*3600.0*r2d*(wobj/teldiam) # arcsec if fwhm is None: fwhm = (seeing*(wobj/wguide)**(-0.2)) fwhm[fwhm < diff] = diff[fwhm < diff] if ydisp is None or xdisp is None: # --- Compute Differential Atmospheric Dispersion disp_obj = atmosdisp(wobj, wguide, zenith_angle*r2d, press, temp, \ water=water, obsalt=obsalt, fco2=fco2) if ydisp is None: dy_obj = (disp_obj*np.cos(dtheta_obj)) + dy else: dy_obj = ydisp if xdisp is None: dx_obj = (disp_obj*np.sin(dtheta_obj)) + dx else: dx_obj = xdisp else: dx_obj = np.array(xdisp, copy=False) dy_obj = np.array(ydisp, copy=False) if retall: disp_obj = (dy_obj - dy) / np.cos(dtheta_obj) # if xdisp is None and ydisp is None: # guide_index = np.abs(wobj - wguide).min() == np.abs(wobj-wguide) # dy = ydisp[guide_index].mean() # dy_obj = ydisp # dx_obj = (dy_obj - dy) * np.tan(dtheta_obj) # --- Compute Relative Fraction of Light contained within the slit slitloss = slittrans(slitwd, slitht, fwhm, dx_obj, dy_obj) if retall: return slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj else: return slitloss
[docs]def humidpressure(RH, T): """ + NAME: humidpressure PURPOSE: To convert relative humidity into a H2O vapor partial pressure CATEGORY: Spectroscopy CALLING SEQUENCE: humidpressure(RH, 273.15) INPUTS: RH - relative humidity, in percent T - temperature, in Kelvin OUTPUTS: h2o_pp : water vapor partial pressure, in Pascals PROCEDURE: As outlined in Butler (1998): "Precipitable Water at KP", MMA Memo 238 (which refers in turn to Liebe 1989, "MPM - An Atmospheric Millimeter-Wave Propagation Model"). Liebe claims that this relation has an error of <0.2% from -40 C to +40 C. EXAMPLE: None MODIFICATION HISTORY: 2011-10-08 17:08 IJMC: Created. - """ # units of Pa #theta = 300./T #return 2.408e11 * RH * np.exp(-22.64*theta) * (theta*theta*theta*theta) theta_mod = 6792./T # theta * 22.64 return 9.1638 * RH * np.exp(-theta_mod) * \ (theta_mod*theta_mod*theta_mod*theta_mod)
[docs]def runlblrtm(lamrange, pwv=2., zang=0., alt=4.2, co2=390, res=200, dotrans=True, dorad=False, pwv_offset=4., verbose=False, _save='/Users/ianc/temp/testatmo.mat', _wd='/Users/ianc/proj/atmo/aerlbl_v12.0_package/caltech_wrapper_v02/', scriptname='runtelluric.m', command = '/Applications/Octave.app/Contents/Resources/bin/octave'): """ Run LBLRTM to compute atmospheric transmittance and/or radiance. :INPUTS: lamrange : 2-sequence approximate minimum and maximum wavelengths :OPTIONS: pwv : float mm of Precipitable Water Vapor above observation site. If negative, then the value abs(pwv_offset-pwv) will be used instead. pwv_offset : float Only used if (pwv < 0); see above for description. zang : float observation angle from zenith, in degrees alt : float Observation elevation, in km. co2 : float CO2 concentration in ppm by volume. Concentration is assumed to be uniform throughout the atmosphere. res : float approximate spectral resolution desired dotrans : bool compute atmospheric transmittance dorad : bool compute atmospheric radiance. NOT CURRENTLY WORKING _save : str path where temporary MAT save file will be stored _wd : str path where MATLAB wrapper scripts for LBLRTM are located scriptname : str filename for temporary MATLAB/OCTAVE script (saved after exit) command : str path to MATLAB/OCTAVE executable :OUTPUTS: A 2- or 3-tuple: First element is wavelength in microns, second element is transmittance (if requested). Radiance will (if requested) always be the last element, and in f_nu units: W/cm2/sr/(cm^-1) :REQUIREMENTS: SciPy OCTAVE or MATLAB `LBLRTM <http://rtweb.aer.com/lblrtm_code.html>`_ D. Feldman's set of MATLAB `wrapper scripts <http://www.mathworks.com/matlabcentral/fileexchange/6461-lblrtm-wrapper-version-0-2/>`_ """ # 2011-10-13 13:59 IJMC: Created. # 2011-10-19 23:35 IJMC: Added scipy.__version__ check for MAT IO # 2011-10-25 17:00 IJMC: Added pwv_offset option. # 2011-11-07 08:47 IJMC: Now path(path..) uses _wd input option; call --wd option. # 2012-07-20 21:25 IJMC: Added 'alt' option for altitude. # 2012-09-16 15:13 IJMC: Fixed for 'dotrans' and 'dorad' # 2014-02-17 18:48 IJMC: Specified approximate units of radiance output. import os from scipy.io import loadmat from scipy import __version__ # Define variables: def beforev8(ver): v1, v2, v3 = ver.split('.') if v1==0 and v2<8: before = True else: before = False return before if pwv<0: pwv = np.abs(pwv_offset - pwv) #lamrange = [1.12, 2.55] # microns #res = 200; # lambda/dlambda # Try to delete old files, if possible. if os.path.isfile(_save): #try: # os.remove(_save) #except: while os.path.isfile(_save): _save = _save.replace('.mat', '0.mat') if os.path.isfile(scriptname): #try: # os.remove(scriptname) #except: while os.path.isfile(scriptname): scriptname = scriptname.replace('.m', '0.m') aerlbl_dir = '/'.join(_wd.split('/')[0:-2]) + '/' # Make the matlab script: matlines = [] matlines.append("_path = '%s';\n" % _wd ) matlines.append("_save = '%s';\n" % _save ) matlines.append("_dir0 = pwd;\n") matlines.append("path(path, '%s')\n" % _wd ) matlines.append("lamrange = [%1.5f, %1.5f];\n" % (lamrange[0], lamrange[1])) if verbose: matlines.append("pwd\n") matlines.append("wt = which('telluric_simulator')\n") matlines.append("strfind(path, 'caltech')\n") matlines.append("tran_out = telluric_simulator(lamrange, '--dotran %i', '--dorad %i', '--R %s', '--alt %1.4f', '--pwv %1.4f', '--co2 %1.1f', '--zang %1.3f', '--verbose %i', '--wd %s');\n" % (int(dotrans), int(dorad), res, alt, pwv, co2, zang, verbose, aerlbl_dir) ) matlines.append("save('-v6', _save, 'tran_out');\n") # Write script to disk, and execute it: f = open(scriptname, 'w') f.writelines(matlines) f.close() os.system('%s %s' % (command, scriptname)) # Open the MAT file and extract the desired output: # try: if os.path.isfile(_save): mat = loadmat(_save) else: trycount = 1 print "Saved file '%s' could not be loaded..." % _save while trycount < 5: os.system('%s %s' % (command, scriptname)) try: mat = loadmat(_save) trycount = 10 except: trycount += 1 if trycount < 10: # never successfully loaded the file. pdb.set_trace() if beforev8(__version__): w = mat['tran_out'][0][0].wavelength.ravel() else: w = mat['tran_out']['wavelength'][0][0].ravel() if dotrans: if beforev8(__version__): t = mat['tran_out'][0][0].transmittance.ravel() else: t = mat['tran_out']['transmittance'][0][0].ravel() if dorad: if beforev8(__version__): r = mat['tran_out'][0][0].radiance.ravel() else: r = mat['tran_out']['radiance'][0][0].ravel() os.remove(scriptname) os.remove(_save) if dotrans: ret = (w, t) else: ret = (w,) if dorad: ret = ret + (r,) return ret
[docs]def zenith(ra, dec, ha, obs): """ Compute zenith angle (in degrees) for an observation. :INPUTS: ra : str Right Ascension of target, in format: HH:MM:SS.SS dec : str Declination of target, in format: +ddd:mm:ss ha : str Hour Angle of target, in format: +HH:MM:SS.SS obs : str Name of observatory site (keck, irtf, lick, lapalma, ctio, andersonmesa, mtgraham, kpno) or a 3-tuple containing (longitude_string, latitude_string, elevation_in_meters) :OUTPUTS: Zenith angle, in degrees, for the specified observation :REQUIREMENTS: :doc:`phot` Numpy """ # 2011-10-14 09:43 IJMC: Created import phot #observer = ephem.Observer() if obs=='lick': obs_long, obs_lat = '-121:38.2','37:20.6' obs_elev = 1290 elif obs=='keck': obs_long, obs_lat = '-155:28.7','19:49.7' obs_elev = 4160 elif obs=='irtf': obs_long, obs_lat = '-155:28:21.3', '19:49:34.8' obs_elev = 4205 elif obs=='lapalma': obs_long, obs_lat = '17:53.6','28:45.5' obs_elev = 4160 elif obs=='ctio': obs_long, obs_lat = '-70:48:54','-30:9.92' obs_elev = 2215 elif obs=='andersonmesa': # obs_long, obs_lat = '-111:32:09', '30:05:49' obs_elev = 2163 elif obs=='mtgraham': obs_long, obs_lat = '-109:53:23', '32:42:05' obs_elev = 3221 elif obs=='kpno': obs_long, obs_lat = '-111:25:48', '31:57:30' obs_elev = 2096 elif len(obs)==3: obs_long, obs_lat, obs_elev = obs else: print "Unknown or imparseable observatory site." return -1 lat = phot.dms(obs_lat) * np.pi/180. long = phot.dms(obs_long) * np.pi/180. ra = (phot.hms(ra)) * np.pi/180. dec= (phot.dms(dec)) * np.pi/180. # Compute terms for coordinate conversion if hasattr(ha, '__iter__'): zang = [] for ha0 in ha: har = (phot.hms(ha0)) * np.pi/180. term1 = np.sin(lat)*np.sin(dec)+np.cos(lat)*np.cos(dec)*np.cos(har) term2 = np.cos(lat)*np.sin(dec)-np.sin(lat)*np.cos(dec)*np.cos(har) term3 = -np.cos(dec)*np.sin(har) rad = np.abs(term3 +1j*term2) az = np.arctan2(term3,term2) alt = np.arctan2(term1, rad) zang.append(90. - (alt*180./np.pi)) else: har = (phot.hms(ha0)) * np.pi/180. term1 = np.sin(lat)*np.sin(dec)+np.cos(lat)*np.cos(dec)*np.cos(har) term2 = np.cos(lat)*np.sin(dec)-np.sin(lat)*np.cos(dec)*np.cos(har) term3 = -np.cos(dec)*np.sin(har) rad = np.abs(term3 +1j*term2) az = np.arctan2(term3,term2) alt = np.arctan2(term1, rad) zang = 90. - (alt*180./np.pi) ## Compute airmass #z = pi/2. - alt #airmass = 1./(np.cos(z) + 0.50572*(96.07995-z*180./pi)**(-1.6364)) return zang
[docs]def spexsxd_scatter_model(dat, halfwid=48, xlims=[470, 1024], ylims=[800, 1024], full_output=False, itime=None): """Model the scattered light seen in SpeX/SXD K-band frames. :INPUTS: dat : str or numpy array filename of raw SXD frame to be corrected, or a Numpy array containing its data. :OPTIONS: halfwid : int half-width of the spectral orders. Experience shows this is approximately 48 pixels. This value is not fit! xlims : list of length 2 minimum and maximum x-pixel values to use in the fitting ylims : list of length 2 minimum and maximum y-pixel values to use in the fitting full_output : bool whether to output only model, or the tuple (model, fits, chisq, nbad) itime : float integration time, in seconds, with which to scale the initial guesses :OUTPUT: scatter_model : numpy array Model of the scattered light component, for subtraction or saving. OR: scatter_model, fits, chis, nbad :REQUIREMENTS: :doc:`pyfits`, :doc:`numpy`, :doc:`fit_atmo`, :doc:`analysis`, :doc:`phasecurves` :TO_DO_LIST: I could stand to be more clever in modeling the scattered light components -- perhaps fitting for the width, or at least allowing the width to be non-integer. """ # 2011-11-10 11:10 IJMC: Created import analysis as an import phasecurves as pc try: from astropy.io import fits as pyfits except: import pyfits ############################################################ # Define some helper functions: ############################################################ def tophat(param, x): """Grey-pixel tophat function with set width param: [cen_pix, amplitude, background] x : must be array of ints, arange(0, size-1) returns the model.""" # 2011-11-09 21:37 IJMC: Created intpix, fracpix = int(param[0]), param[0] % 1 th = param[1] * ((-halfwid <= (x - intpix)) * ((x - intpix) < halfwid)) # th = * th.astype(float) if (intpix >= halfwid) and ((intpix - halfwid) < x.size): th[intpix - halfwid] = param[1]*(1. - fracpix) if (intpix < (x.size - halfwid)) and ((intpix + halfwid) >= 0): th[intpix + halfwid] = param[1]*fracpix return th + param[2] def tophat2g(param, x, p0prior=None): """Grey-pixel double-tophat plus gaussian param: [cen_pix1, amplitude1, cen_pix2, amplitude2, g_area, g_sigma, g_center, background] x : must be ints, arange(0, size-1) returns the model.""" # 2011-11-09 21:37 IJMC: Created #th12 = #th2 = #gauss = # if p0prior is not None: # penalty = return tophat([param[0], param[1], 0], x) + \ tophat([param[2], param[3], 0], x) + \ gaussian(param[4:7], x) + param[7] ############################################################ # Parse inputs ############################################################ halfwid = int(halfwid) if isinstance(dat, np.ndarray): if itime is None: itime = 1. else: if itime is None: try: itime = pyfits.getval(dat, 'ITIME') except: itime = 1. dat = pyfits.getdata(dat) nx, ny = dat.shape scatter_model = np.zeros((nx, ny), dtype=float) chis, fits, nbad = [], [], [] iivals = np.arange(xlims[1]-1, xlims[0], -1, dtype=int) position_offset = 850 - ylims[0] est_coefs = np.array([ -5.02509772e-05, 2.97212397e-01, -7.65702234e+01]) estimated_position = np.polyval(est_coefs, iivals) + position_offset estimated_error = 0.5 # to hold scattered light position fixed, rather than fitting for # that position, uncomment the following line: #holdfixed = [0] holdfixed = None ############################################################ # Start fitting ############################################################ for jj, ii in enumerate(iivals): col = dat[ylims[0]:ylims[1], ii] ecol = np.ones(col.size, dtype=float) x = np.arange(col.size, dtype=float) if len(fits)==0: all_guess = [175 + position_offset, 7*itime, \ 70 + position_offset, 7*itime, \ 250*itime, 5, 89 + position_offset, 50] else: all_guess = fits[-1] all_guess[0] = estimated_position[jj] model_all = tophat2g(all_guess, x) res = (model_all - col) badpix = np.abs(res) > (4*an.stdr(res, nsigma=4)) ecol[badpix] += 1e9 fit = an.fmin(pc.errfunc, all_guess, args=(tophat2g, x, col, 1./ecol**2), full_output=True, maxiter=1e4, maxfun=1e4, disp=False, kw=dict(testfinite=False), holdfixed=holdfixed) best_params = fit[0].copy() res = tophat2g(best_params, x) - col badpix = np.abs(res) > (4*an.stdr(res, nsigma=4)) badpix[((np.abs(np.abs(x - best_params[0]) - 48.)) < 2) + \ ((np.abs(np.abs(x - best_params[2]) - 48.)) < 2)] = False badpix += (np.abs(res) > (20*an.stdr(res, nsigma=4))) ecol = np.ones(col.size, dtype=float) ecol[badpix] += 1e9 best_chisq = pc.errfunc(best_params, tophat2g, x, col, 1./ecol**2) # Make sure you didn't converge on the wrong model: for this_offset in ([-2, 0, 2]): this_guess = fit[0].copy() this_guess[2] += this_offset this_guess[0] = estimated_position[jj] #pc.errfunc(this_guess, tophat2g, x, col, 1./ecol**2) this_fit = an.fmin(pc.errfunc, this_guess, args=(tophat2g, x, col, 1./ecol**2), full_output=True, maxiter=1e4, maxfun=1e4, disp=False, kw=dict(testfinite=False), holdfixed=holdfixed) #print this_offset1, this_offset2, this_fit[1] if this_fit[1] < best_chisq: best_chisq = this_fit[1] best_params = this_fit[0].copy() fits.append(best_params) chis.append(best_chisq) nbad.append(badpix.sum()) mod2 = tophat2g(best_params, x) scatter_model[ylims[0]:ylims[1], ii] = tophat(list(best_params[0:2])+[0], x) if full_output: return scatter_model, fits, chis, nbad else: return scatter_model
[docs]def spexsxd_scatter_fix(fn1, fn2, **kw): """ Fix scattered light in SpeX/SXD K-band and write a new file. :INPUTS: fn1 : str file to be fixed fn2 : str new filename of fixed file. :OPTIONS: clobber : bool whether to overwrite existing FITS files Other options will be passed to :func:`spexsxd_scatter_model` :OUTPUTS: status : int 0 if a problem, 1 if everything is Okay """ # 2011-11-10 13:34 IJMC: Created try: from astropy.io import fits as pyfits except: import pyfits if kw.has_key('clobber'): clobber = kw.pop('clobber') else: clobber = False try: dat0 = pyfits.getdata(fn1) hdr0 = pyfits.getheader(fn1) except: print "Could not read one of data or header from %s" % fn1 return 0 try: hdr0.update('SCAT_FIX', 1, 'SXD Scattered light fixed by spexsxd_scatter_fix()') except: print "Could not update header properly." return 0 try: if hdr0.has_key('ITIME') and not kw.has_key('itime'): kw['itime'] = hdr0['ITIME'] mod = spexsxd_scatter_model(dat0, **kw) except: print "Could not not model scattered light with spexsxd_scatter_model()" return 0 try: if not isinstance(mod, np.ndarray): mod = mod[0] pyfits.writeto(fn2, dat0 - mod, header=hdr0, clobber=clobber, output_verify='ignore') except: print "Could not write updated file to %s; clobber is %s" % (fn2, clobber) return 0 return 1
[docs]def tophat2(param, x): """Grey-pixel tophat function with set width param: [cen_pix, amplitude, background] newparam: [amplitude, full width, cen_pix, background] x : must be array of ints, arange(0, size-1) returns the model.""" # 2011-11-09 21:37 IJMC: Created import analysis as an oversamp = 100. x2 = np.linspace(x[0], x[-1], (x.size - 1)*oversamp + 1) halfwid = np.round(param[1] / 2.).astype(int) halfwid = param[1] / 2. intpix, fracpix = int(param[2]), param[2] % 1 intwid, fracwid = int(halfwid), halfwid % 1 th2 = 0.0 + param[0] * ((-halfwid <= (x2 - param[2])) * ((x2 - param[2]) < halfwid)) th = an.binarray(np.concatenate((th2, np.zeros(oversamp-1))), oversamp) return th + param[3]
[docs]def tophat_alt(param, x): """Standard tophat function (alternative version). :INPUTS: p : sequence p[0] -- Amplitude p[1] -- full width dispersion p[2] -- central offset (mean location) p[3] -- vertical offset (OPTIONAL) x : scalar or sequence values at which to evaluate function :OUTPUTS: y : scalar or sequence 1.0 where |x| < 0.5, 0.5 where |x| = 0.5, 0.0 otherwise. """ # 2012-04-11 12:50 IJMC: Created if len(param)==3: vertical_offset = 0. elif len(param)>3: vertical_offset = param[3] else: print "Input `param` to function `tophat` requires length > 3." return -1 amplitude, width, center = param[0:3] absx = np.abs((x - center) ) if hasattr(x, '__iter__'): ret = np.zeros(x.shape, float) #ind1 = absx < (0.5*width) #ret[ind1] = 1.0 i0 = np.searchsorted(x, center-width/2) i1 = np.searchsorted(x, center+width/2) ret[i0:i1] = 1. #ind2 = absx ==(0.5*width) #ret[ind2] = 0.5 else: if absx < 0.5: ret = 1.0 elif absx==0.5: ret = 0.5 else: ret = 0. return amplitude * ret + vertical_offset
[docs]def model_resel(param, x): """Model a spectral resolution element. :INPUTS: param : sequence param[0, 1, 2] - amplitude, sigma, and central location of Gaussian line profile (cf. :func:`analysis.gaussian`). param[3, 4, 5] - amplitude, width, and central location of top-hat-like background (cf. :func:`tophat`). param[6::] - additional (constant or polynomial) background components, for evaluation with :func:`numpy.polyval` x : sequence Values at which to evaluate model function (i.e., pixels). Typically 1D. :OUTPUTS: line : NumPy array model of the resolution element, of same shape as `x`. :DESCRIPTION: Model a spectral resolution element along the spatial direction. This consists of a (presumably Gaussian) line profile superimposed on the spectral trace's top-hat-like background, with an additional constant (or polynomial) out-of-echelle-order background component. """ # 2012-04-11 12:57 IJMC: Created # 2012-04-12 13:29 IJMC: Try to be more clever and save time in # the back2 polyval call. lineprofile = gaussian(param[0:3], x) back1 = tophat(param[3:6], x) if hasattr(param[6::], '__iter__') and len(param[6::])>1: back2 = np.polyval(param[6::], x) else: back2 = param[6::] return lineprofile + back1 + back2
[docs]def add_partial_pixel(x0, y0, z0, z): """ :INPUTS: x0 : int or sequence of ints first index of z at which data will be added y0 : int or sequence of ints second index of z at which data will be added z0 : scalar or sequence values which will be added to z z : 2D NumPy array initial data, to which partial-pixels will be added """ # 2012-04-11 14:24 IJMC: Created nx, ny = z.shape[0:2] x = np.arange(nx) y = np.arange(ny) dx, dy = 1, 1 ret = np.array(z, copy=True) if hasattr(x0, '__iter__'): if len(x0)==len(y0) and len(x0)==len(z0): for x00, y00, z00 in zip(x0, y0, z0): ret = add_partial_pixel(x00, y00, z00, ret) else: print "Inputs x0, y0, and z0 must have the same length!" ret = -1 if False: if hasattr(x0, '__iter__'): x0 = np.array(x0, copy=False) y0 = np.array(y0, copy=False) z0 = np.array(z0, copy=False) else: x0 = np.array([x0]) y0 = np.array([y0]) z0 = np.array([z0]) ix0 = np.tile(np.vstack((np.floor(x0), np.floor(x0)+1)).astype(int), (2,1)) iy0 = np.tile(np.vstack((np.floor(y0), np.floor(y0)+1)).astype(int), (2,1)) xfrac = x0 % 1 yfrac = y0 % 1 weights0 = np.vstack([(1. - xfrac) * (1. - yfrac), \ xfrac * (1. - yfrac), \ (1. - xfrac) * yfrac, \ xfrac * yfrac]) for ii in range(ix0.shape[1]): print ii, weights0[:,ii]*z0[ii] ret[ix0[:,ii], iy0[:,ii]] = ret[ix0[:,ii], iy0[:,ii]] + weights0[:,ii]*z0[ii] else: ix0 = map(int, [np.floor(x0), np.floor(x0)+1]*2) iy0 = map(int, [np.floor(y0)]*2 + [np.floor(y0)+1]*2) xfrac = x0 % 1 yfrac = y0 % 1 weights0 = [(1. - xfrac) * (1. - yfrac), \ xfrac * (1. - yfrac), \ (1. - xfrac) * yfrac, \ xfrac * yfrac] ix = [] iy = [] weights = [] for ii in range(4): #print ix0[ii], iy0[ii], weights0[ii] if ix0[ii]>=0 and ix0[ii]<nx and iy0[ii]>=0 and iy0[ii]<ny: ix.append(ix0[ii]), iy.append(iy0[ii]) weights.append(weights0[ii]) npix = len(ix) if npix>0: sumweights = sum(weights) for ii in range(npix): #print ix[ii], iy[ii], weights[ii] ret[ix[ii], iy[ii]] += weights[ii] * z0 return ret
[docs]def modelSpectralTrace(param, shape=None, nscat=None, npw=None, npy=None, noy=None, now=None, ndist=None, x=None, y=None, transpose=False): """Model a raw spectral trace! :INPUTS: NOTE that most inputs should be in the _rectified_ frame. Trace background pedestal level : 1D array Width of background pedestarl level : scalar (for now) Center of trace : 1D array Offset of object spectrum, relative to center : scalar width of 1D PSF : scalar Area of 1D psf : 1D array Distortion (x and y, somehow???) Scattered light background : scalar (for now) """ # 2012-04-11 17:50 IJMC: Created ######################################## # Parse various inputs: ######################################## param = np.array(param, copy=False) nx, ny = shape # Construct pixel vectors: if x is None: x = np.arange(nx) if y is None: y = np.arange(ny) def makevec(input): if not hasattr(input, '__iter__'): ret = [input]*ny #ret = np.polyval([input], y) elif len(input)==1: ret = [input[0]]*ny #ret = np.polyval(input, y) else: ret = np.polyval(input, y) return ret # Define all the parameters: pedestal_level = param[0:ny] obj_flux = param[ny:ny*2] pedestal_width = param[ny*2:ny*2+npw] obj_fwhm = param[ny*2+npw:ny*2+npw+now] spectrum_yparam = param[ny*2+npw+now : ny*2+npw+now+npy] obj_yparam = param[ny*2+npw+now+npy : ny*2+npw+now+npy+noy] disp_distort = param[ny*2+npw+now+npy+noy : ny*2+npw+now+npy+noy+ndist] scat_param = list(param[-ndist:]) pedestal_width = makevec(pedestal_width) obj_fwhm = makevec(obj_fwhm) obj_yloc = makevec(obj_yparam) #, mode='cheby') spectrum_yloc = makevec(spectrum_yparam) #, mode='cheby') ######################################## # Generate spectral model ######################################## rectified_model = np.zeros((nx, ny), float) for ii in range(ny): obj_param = [obj_flux[ii], obj_fwhm[ii], obj_yloc[ii]] bkg_param = [pedestal_level[ii], pedestal_width[ii], spectrum_yloc[ii]] oneDparam = obj_param + bkg_param + scat_param rectified_model[:, ii] = model_resel(oneDparam, x) #ypts = x #xpts = ii + np.polyval(disp_distort, x-obj_yloc[ii]) #distorted_model = spec.add_partial_pixel(ypts, xpts, oneD, model) interp_model = 0*rectified_model if False: # One way to do it: newxcoords = (y - np.polyval(disp_distort, (x.reshape(nx,1)-obj_yloc.reshape(1,ny)))) meshmod = interpolate.RectBivariateSpline(x, y, rectified_model, kx=1, ky=1, s=0.) for ii in range(nx): interp_model[ii,:] = meshmod(x[ii], newxcoords[ii,:]) else: # An alternate (not entirely equivalanet) approach, about equally fast: shifts = np.polyval(disp_distort, x) intshifts = np.floor(shifts) minshift = intshifts.min() shifts -= minshift intshifts -= minshift for ii in range(nx): kern = np.zeros(intshifts.max() - intshifts.min()+2, float) kern[intshifts[ii]] += 1. - (shifts[ii] - intshifts[ii]) kern[intshifts[ii]+1] += (shifts[ii] - intshifts[ii]) interp_model[ii] = np.convolve(rectified_model[ii], kern, 'same') return interp_model
[docs]def makeSpexSlitlessSky(skyfns, scatcen=[980, 150], scatdim=[60, 300]): """ Generate a normalized Sky frame from SpeX slitless spectroscopy data. :INPUTS: skyfns : list of strs Filenames of slitless-spectroscopy sky frames scatcen : 2-sequence center of region to use in median-normalizing the frame. scatdim : 2-sequence full width of region to use in median-normalizing the frame. :OUTPUTS: (sky, skyHeader) """ # 2012-04-19 09:38 IJMC: Created import phot nsky = len(skyfns) skyscat = phot.subreg2(skyfns, center=scatcen, dim=scatdim) normfact = np.median(skyscat.reshape(nsky, np.prod(scatdim)), 1) hdr = pyfits.getheader(skyfns[0]) skyframe = np.zeros((hdr['naxis1'], hdr['naxis2']), float) for skyfn, factor in zip(skyfns, normfact): skyframe += (pyfits.getdata(skyfn) / factor / nsky) hdr.update('SKYRGCEN', str(scatcen)) hdr.update('SKYRGDIM', str(scatdim)) hdr.update('SKYNOTE', 'sky frame, median=1 in SKYRG') return skyframe, hdr
[docs]def resamplespec(w1, w0, spec0, oversamp=100): """ Resample a spectrum while conserving flux density. :INPUTS: w1 : sequence new wavelength grid (i.e., center wavelength of each pixel) w0 : sequence old wavelength grid (i.e., center wavelength of each pixel) spec0 : sequence old spectrum (e.g., flux density or photon counts) oversamp : int factor by which to oversample input spectrum prior to rebinning. The worst fractional precision you achieve is roughly 1./oversamp. :NOTE: Format is the same as :func:`numpy.interp` :REQUIREMENTS: :doc:`tools` """ from tools import errxy # 2012-04-25 18:40 IJMC: Created nlam = len(w0) x0 = np.arange(nlam, dtype=float) x0int = np.arange((nlam-1.)*oversamp + 1., dtype=float)/oversamp w0int = np.interp(x0int, x0, w0) spec0int = np.interp(w0int, w0, spec0)/oversamp # Set up the bin edges for down-binning maxdiffw1 = np.diff(w1).max() w1bins = np.concatenate(([w1[0] - maxdiffw1], .5*(w1[1::] + w1[0:-1]), \ [w1[-1] + maxdiffw1])) # Bin down the interpolated spectrum: junk, spec1, junk2, junk3 = errxy(w0int, spec0int, w1bins, xmode=None, ymode='sum', xerr=None, yerr=None) return spec1 #wcoords = spec.wavelengthMatch(mastersky0, wsky, thissky, ethissky, guess=wcoef1, order=None)
[docs]def wavelengthMatch(spectrum, wtemplate, template, etemplate, guess=None, nthread=1): """ Determine dispersion solution for a spectrum, from a template. :INPUTS: spectrum : 1D sequence Spectrum for which a wavelength solution is desired. wtemplate : 1D sequence Known wavelength grid of a template spectrum. template : 1D sequence Flux (e.g.) levels of the template spectrum with known wavelength solution. etemplate : 1D sequence Uncertainties on the template values. This can be important in a weighted fit! :OPTIONS: guess : sequence Initial guess for the wavelength solution. This is very helpful, if you have it! The guess should be a sequence containing the set of Chebychev polynomial coefficients, followed by a scale factor and DC offset (to help in scaling the template). If guess is None, attempt to fit a simple linear dispersion relation. order : int > 0 NOT YET IMPLEMENTED! BUT EVENTUALLY: if guess is None, this sets the polynomial order of the wavelength solution. FOR THE MOMENT: if guess is None, return a simple linear solution. This is likely to fail entirely for strongly nonlinear dispersion solutions or poorly mismatched template and spectrum. nthread : int > 0 Number of processors to use for MCMC searching. :RETURNS: (wavelength, wavelength_polynomial_coefficients, full_parameter_set) :NOTES: This implementation uses a rather crude MCMC sampling approach to sample parameters space and 'home in' on better solutions. There is probably a way to do this that is both faster and more optimal... Note that if 'spectrum' and 'template' are of different lengths, the longer one will be trimmed at the end to make the lengths match. :REQUIREMENTS: `emcee <http://TBD>`_ """ #2012-04-25 20:53 IJMC: Created # 2012-09-23 20:17 IJMC: Now spectrum & template can be different length. # 2013-03-09 17:23 IJMC: Added nthread option import emcee import phasecurves as pc nlam_s = len(spectrum) nlam_t = len(template) if nlam_s <= nlam_t: template = np.array(template, copy=True)[0:nlam_s] etemplate = np.array(etemplate, copy=True)[0:nlam_s] nlam = nlam_s spectrum_trimmed = False else: # nlam_s > nlam_t: spectrum0 = np.array(spectrum, copy=True) spectrum = spectrum0[0:nlam_t] wtemplate = np.array(wtemplate, copy=True)[0:nlam_t] nlam = nlam_t spectrum_trimmed = True # Create a normalized vector of coordinates for computing # normalized polynomials: dx0 = 1. / (nlam - 1.) x0n = dx0 * np.arange(nlam) - 1. #x0n = 2*np.arange(nlam, dtype=float) / (nlam - 1.) - 1 if guess is None: # Start with a simple linear wavelength solution. guess = [np.diff(wtemplate).mean() * len(template)/2, np.mean(wtemplate), np.median(template)/np.median(spectrum), 1.] # Define arguments for use by fitting routines: fitting_args = (makemodel, x0n, spectrum, wtemplate, template, 1./etemplate**2) # Try to find an initial best fit: bestparams = an.fmin(pc.errfunc, guess, args=fitting_args, disp=False) # Initial fit is likely a local minimum, so explore parameter # space using an MCMC approach. ndim = len(guess) nwalkers = ndim * 50 sampler = emcee.EnsembleSampler(nwalkers, ndim, pc.lnprobfunc, args=fitting_args, threads=nthread) # Initialize the sampler with various starting positions: e_params1 = np.vstack((np.array(guess)/10., np.zeros(ndim) + .01)).max(0) e_params2 = np.vstack((bestparams/10., np.zeros(ndim) + .01)).max(0) p0 = np.vstack(([guess, bestparams], \ [np.random.normal(guess, e_params1) for ii in xrange(nwalkers/2-1)], \ [np.random.normal(bestparams, e_params2) for ii in xrange(nwalkers/2-1)])) # Run the sampler for a while: pos, prob, state = sampler.run_mcmc(p0, 300) # Burn-in bestparams = sampler.flatchain[np.nonzero(sampler.lnprobability.ravel()==sampler.lnprobability.ravel().max())[0][0]] # Optimize the latest set of best parameters. bestparams = an.fmin(pc.errfunc, bestparams, args=fitting_args, disp=False) dispersionSolution = bestparams[0:-2] if spectrum_trimmed: x0n_original = dx0 * np.arange(nlam_s) - 1. wavelengths = np.polyval(dispersionSolution, x0n_original) else: wavelengths = np.polyval(dispersionSolution, x0n) return dispersionSolution, wavelengths, bestparams
[docs]def makemodel(params, xvec, specvec, wtemplate): """Helper function for :func:`wavelengthMatch`: generate a scaled, interpolative model of the template.""" wcoef = params[0:-2] scale, offset = params[-2::] neww = np.polyval(wcoef, xvec) return offset + scale * np.interp(wtemplate, neww, specvec, left=0, right=0)
[docs]def normalizeSpecFlat(flatdat, nspec=1, minsep=50, median_width=51, readnoise=40, badpixelmask=None, traces=None): """Trace and normalize a spectroscopic flat field frame. :INPUTS: flatdat : 2D NumPy array Master, unnormalized flat frame: assumed to be measured in photoelectrons (for computing uncertainties). nspec : int Number of spectral orders to find and normalize minsep : int Minimum separation, in pixels, between spectral orders that will be found. median_width : int Width of median-filter kernel used to compute the low- readnoise : scalar Detector read noise, in electrons. For computing uncertainties. badpixelmask : 2D NumPy array bad pixel mask: 1 at bad pixel locations, 0 elsewhere. traces : 2D NumPy Array (nord, pord) shaped numpy array representing the polynomial coefficients for each order (suitable for use with np.polyval), as produced by :func:`traceorders` """ # 2012-04-28 06:22 IJMC: Created # 2012-07-24 21:04 IJMC: Now, as a final step, all bad indices are set to unity. # 2014-12-17 20:07 IJMC: Added 'traces' option import analysis as an from scipy import signal if badpixelmask is None: badpixelmask = np.zeros(flatdat.shape, bool) # Enforce positivity and de-weight negative flux values: e_flatdat = np.sqrt(flatdat + readnoise**2) badindices = ((flatdat<=0) + badpixelmask).nonzero() e_flatdat[badindices] = flatdat[badindices] * 1e9 flatdat[badindices] = 1. # Find spectral orders, using derivatives (will probably fail if # spec. overlaps the edge!): ordvec = an.meanr(flatdat, axis=1, nsigma=3) filtvec = signal.medfilt(ordvec, 9) dvec1 = np.diff(filtvec) dvec2 = -np.diff(filtvec) dvec1[dvec1<0] = 0. dvec2[dvec2<0] = 0. x1 = np.arange(dvec1.size) available1 = np.ones(dvec1.size, dtype=bool) available2 = np.ones(dvec1.size, dtype=bool) pos1 = [] pos2 = [] for ii in range(nspec): thisx1 = x1[dvec1==dvec1[available1].max()][0] available1[np.abs(x1 - thisx1) < minsep] = False pos1.append(thisx1) thisx2 = x1[dvec2==dvec2[available2].max()][0] available2[np.abs(x1 - thisx2) < minsep] = False pos2.append(thisx2) limits = np.array(zip(np.sort(pos1), np.sort(pos2))) # Generate and normalize the spectral traces: masterflat = np.ones(flatdat.shape, dtype=float) if traces is not None: nx = flatdat.shape[1] xvec = np.arange(nx) ymat = np.tile(np.arange(flatdat.shape[0]), (nx, 1)).T for ii in range(nspec): if traces is None: profvec = np.median(flatdat[limits[ii,0]:limits[ii,1], :], axis=0) e_profvec = np.sqrt(an.wmean(flatdat[limits[ii,0]:limits[ii,1], :], 1./e_flatdat[limits[ii,0]:limits[ii,1], :]**2, axis=0) / np.diff(limits[ii]))[0] e_profvec[e_profvec <= 0] = profvec.max()*1e9 smooth_prof = signal.medfilt(profvec, median_width) masterflat[limits[ii,0]:limits[ii,1], :] = flatdat[limits[ii,0]:limits[ii,1], :] / smooth_prof else: traceloc = np.polyval(traces[ii], xvec) limind = (limits[:,1] > traceloc.mean()).nonzero()[0][0] order_ind_2d = ((ymat - traceloc) > (limits[limind,0] - traceloc.mean())) * ((ymat - traceloc) < (limits[limind,1] - traceloc.mean())) profvec = np.array([np.median(flatdat[order_ind_2d[:,jj], jj]) for jj in xrange(nx)]) smooth_prof = signal.medfilt(profvec, median_width) for jj in xrange(nx): masterflat[order_ind_2d[:,jj],jj] = flatdat[order_ind_2d[:,jj],jj] / smooth_prof[jj] # Ideally, we would do some sort of weighted fitting here. Instead, # for now, just take a running median: masterflat[badindices] = 1. return masterflat
[docs]def optspecextr_idl(frame, gain, readnoise, x1, x2, idlexec, clobber=True, tempframefn='tempframe.fits', specfn='tempspec.fits', scriptfn='temp_specextract.pro', IDLoptions="adjfunc='adjgauss', adjoptions={center:1,centerfit:1,centerdeg:3}, bgdeg=3", inmask=None): """Run optimal spectral extraction in IDL; pass results to Python. :INPUTS: frame : str filename, or 2D Numpy Array, or list of filenames containing frames from which spectra will be extracted. This should be in units of ADU (not electrons) for the noise properties to come out properly. Also, the spectral trace must run vertically across the frame. gain : scalar Detector gain, in electrons / ADU readnoise : scalar Detector read noise, in electrons x1, x2 : ints, or lists of ints Start and stop indices of the spectral trace across the frame. If multiple frames are input and a single x1/x2 is input, the same value will be used for each frame. Note however that multiple x1/x2 can also be input (one for each frame). idlexec : str Path to the IDL executable. OPTSPECEXTR.PRO and its associated files must be in your IDL path. If set to None, then it will be set to: os.popen('which idl').read().strip() :OPTIONS: clobber : bool Whether to overwrite files when writing input data to TEMPFRAMFN. tempframefn : str If input 'frame' is an array, it will be written to this filename in order to pass it to IDL. specfn : str IDL will write the spectral data to this filename in order to pass it back to Python. scriptfn : str Filename in which the short IDL script will be written. IDLoptions : str Options to pass to OPTSPECEXTR.PRO. For example: "adjfunc='adjgauss', adjoptions={center:1,centerfit:1,centerdeg:3}, bgdeg=3" Note that this Python code will break if you _don't_ trace the spectrum (adjoptions, etc.); this is an area for future work if I ever use a spectrograph with straight traces. inmask : None or str Name of the good pixel mask for OPTSPECEXTR.PRO. Equal to 1 for good pixels, and 0 for bad pixels. :OUTPUTS: For each input frame, a list of four items: [0] -- Extracted spectrum, ADU per pixel [1] -- Uncertainty (1 sigma) of extracted spectrum [2] -- Location of trace (in pixels) across the frame [3] -- Width of trace across the frame :NOTES: Note that this more closely follows Horne et al. than does :func:`optimalExtract`, and is faster than both that function and (especially!) :func:`extractSpectralProfiles`. The only downside (if it is one) is that this function requires IDL. :TO-DO: Add options for user input of a variance frame, or of sky variance. Allow more flexibility (tracing, input/output options, etc.) :REQUIREMENTS: IDL `OPTSPECEXTR <http://physics.ucf.edu/~jh/ast/software.html>`_ """ # 2012-08-18 16:36 IJMC: created # 2012-08-19 09:39 IJMC: Added 'inmask' option. import os try: from astropy.io import fits as pyfits except: import pyfits # Put the input frames in the proper format: if isinstance(frame, np.ndarray): frameisfilename = False if frame.ndim==2: frames = [frame] elif frame.ndim==1: print "Input array should be 2D or 3D -- no telling what will happen next!" else: frames = frame else: frameisfilename = True if isinstance(frame, str): frames = [frame] else: frames = frame if not hasattr(x1, '__iter__'): x1 = [x1] * len(frames) if not hasattr(x2, '__iter__'): x2 = [x2] * len(frames) if idlexec is None: idlexec = os.popen('which idl').read().strip() # Loop through all files: specs = [] ii = 0 for frame in frames: if frameisfilename: tempframefn = frame else: pyfits.writeto(tempframefn, frame, clobber=clobber) # Prepare the temporary IDL script: idlcmds = [] idlcmds.append("frame = readfits('%s')\n" % tempframefn) idlcmds.append("gain = %1.3f\n" % gain) idlcmds.append("readnoise = %i\n" % readnoise) idlcmds.append("varim = abs(frame) / gain + readnoise^2\n") idlcmds.append("x1 = %i & x2 = %i\n" % (x1[ii],x2[ii])) if inmask is not None: idlcmds.append("inmask = readfits('%s')\n" % inmask) IDLoptions += ', inmask=inmask' idlcmds.append("spec = optspecextr(frame, varim, readnoise, gain, x1, x2, adjparms=adjparm, opvar=opvar, %s)\n" % IDLoptions) idlcmds.append("spec_err_loc_width = [[spec], [sqrt(opvar)], [adjparm.traceest], [adjparm.widthest]]\n") idlcmds.append("writefits,'%s', spec_err_loc_width\n" % (specfn)) idlcmds.append("exit\n") # Write it to disk, and execute it. f = open(scriptfn, 'w') f.writelines(idlcmds) f.close() os.system('%s %s' % (idlexec, scriptfn)) # Read the spectrum into Python, and iterate. spec = pyfits.getdata(specfn) specs.append(spec) ii += 1 # Clean up after ourselves: if not frameisfilename and os.path.isfile(tempframefn): os.remove(tempframefn) if os.path.isfile(specfn): os.remove(specfn) if os.path.isfile(scriptfn): os.remove(scriptfn) # If only one file was run, we don't need to return a list. if ii==1: specs = specs[0] return specs
[docs]def optimalExtract(*args, **kw): """ Extract spectrum, following Horne 1986. :INPUTS: data : 2D Numpy array Appropriately calibrated frame from which to extract spectrum. Should be in units of ADU, not electrons! variance : 2D Numpy array Variances of pixel values in 'data'. gain : scalar Detector gain, in electrons per ADU readnoise : scalar Detector readnoise, in electrons. :OPTIONS: goodpixelmask : 2D numpy array Equals 0 for bad pixels, 1 for good pixels bkg_radii : 2- or 4-sequence If length 2: inner and outer radii to use in computing background. Note that for this to be effective, the spectral trace should be positions in the center of 'data.' If length 4: start and end indices of both apertures for background fitting, of the form [b1_start, b1_end, b2_start, b2_end] where b1 and b2 are the two background apertures, and the elements are arranged in strictly ascending order. extract_radius : int or 2-sequence radius to use for both flux normalization and extraction. If a sequence, the first and last indices of the array to use for spectral normalization and extraction. dispaxis : bool 0 for horizontal spectrum, 1 for vertical spectrum bord : int >= 0 Degree of polynomial background fit. bsigma : int >= 0 Sigma-clipping threshold for computing background. pord : int >= 0 Degree of polynomial fit to construct profile. psigma : int >= 0 Sigma-clipping threshold for computing profile. csigma : int >= 0 Sigma-clipping threshold for cleaning & cosmic-ray rejection. finite : bool If true, mask all non-finite values as bad pixels. nreject : int > 0 Number of pixels to reject in each iteration. :RETURNS: 3-tuple: [0] -- spectrum flux (in electrons) [1] -- uncertainty on spectrum flux [1] -- background flux :EXAMPLE: :: :SEE_ALSO: :func:`superExtract`. :NOTES: Horne's classic optimal extraction algorithm is optimal only so long as the spectral traces are very nearly aligned with detector rows or columns. It is *not* well-suited for extracting substantially tilted or curved traces, for the reasons described by Marsh 1989, Mukai 1990. For extracting such spectra, see :func:`superExtract`. """ # 2012-08-20 08:24 IJMC: Created from previous, low-quality version. # 2012-09-03 11:37 IJMC: Renamed to replace previous, low-quality # version. Now bkg_radii and extract_radius # can refer to either a trace-centered # coordinate system, or the specific # indices of all aperture edges. Added nreject. from scipy import signal # Parse inputs: frame, variance, gain, readnoise = args[0:4] # Parse options: if kw.has_key('goodpixelmask'): goodpixelmask = np.array(kw['goodpixelmask'], copy=True).astype(bool) else: goodpixelmask = np.ones(frame.shape, dtype=bool) if kw.has_key('dispaxis'): if kw['dispaxis']==1: frame = frame.transpose() variance = variance.transpose() goodpixelmask = goodpixelmask.transpose() if kw.has_key('verbose'): verbose = kw['verbose'] else: verbose = False if kw.has_key('bkg_radii'): bkg_radii = kw['bkg_radii'] else: bkg_radii = [15, 20] if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii)) if kw.has_key('extract_radius'): extract_radius = kw['extract_radius'] else: extract_radius = 10 if verbose: message("Setting option 'extract_radius' to: " + str(extract_radius)) if kw.has_key('bord'): bord = kw['bord'] else: bord = 1 if verbose: message("Setting option 'bord' to: " + str(bord)) if kw.has_key('bsigma'): bsigma = kw['bsigma'] else: bsigma = 3 if verbose: message("Setting option 'bsigma' to: " + str(bsigma)) if kw.has_key('pord'): pord = kw['pord'] else: pord = 2 if verbose: message("Setting option 'pord' to: " + str(pord)) if kw.has_key('psigma'): psigma = kw['psigma'] else: psigma = 4 if verbose: message("Setting option 'psigma' to: " + str(psigma)) if kw.has_key('csigma'): csigma = kw['csigma'] else: csigma = 5 if verbose: message("Setting option 'csigma' to: " + str(csigma)) if kw.has_key('finite'): finite = kw['finite'] else: finite = True if verbose: message("Setting option 'finite' to: " + str(finite)) if kw.has_key('nreject'): nreject = kw['nreject'] else: nreject = 100 if verbose: message("Setting option 'nreject' to: " + str(nreject)) if finite: goodpixelmask *= (np.isfinite(frame) * np.isfinite(variance)) variance[True-goodpixelmask] = frame[goodpixelmask].max() * 1e9 nlam, fitwidth = frame.shape xxx = np.arange(-fitwidth/2, fitwidth/2) xxx0 = np.arange(fitwidth) if len(bkg_radii)==4: # Set all borders of background aperture: backgroundAperture = ((xxx0 > bkg_radii[0]) * (xxx0 <= bkg_radii[1])) + \ ((xxx0 > bkg_radii[2]) * (xxx0 <= bkg_radii[3])) else: # Assume trace is centered, and use only radii. backgroundAperture = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) <= bkg_radii[1]) if hasattr(extract_radius, '__iter__'): extractionAperture = (xxx0 > extract_radius[0]) * (xxx0 <= extract_radius[1]) else: extractionAperture = np.abs(xxx) < extract_radius nextract = extractionAperture.sum() xb = xxx[backgroundAperture] #Step3: Sky Subtraction if bord==0: # faster to take weighted mean: background = an.wmean(frame[:, backgroundAperture], (goodpixelmask/variance)[:, backgroundAperture], axis=1) else: background = 0. * frame for ii in range(nlam): fit = an.polyfitr(xb, frame[ii, backgroundAperture], bord, bsigma, w=(goodpixelmask/variance)[ii, backgroundAperture], verbose=verbose-1) background[ii, :] = np.polyval(fit, xxx) # (my 3a: mask any bad values) badBackground = True - np.isfinite(background) background[badBackground] = 0. if verbose and badBackground.any(): print "Found bad background values at: ", badBackground.nonzero() skysubFrame = frame - background #Step4: Extract 'standard' spectrum and its variance standardSpectrum = nextract * an.wmean(skysubFrame[:, extractionAperture], goodpixelmask[:, extractionAperture], axis=1) varStandardSpectrum = nextract * an.wmean(variance[:, extractionAperture], goodpixelmask[:, extractionAperture], axis=1) # (my 4a: mask any bad values) badSpectrum = True - (np.isfinite(standardSpectrum)) standardSpectrum[badSpectrum] = 1. varStandardSpectrum[badSpectrum] = varStandardSpectrum[True - badSpectrum].max() * 1e9 #Step5: Construct spatial profile; enforce positivity & normalization normData = skysubFrame / standardSpectrum varNormData = variance / standardSpectrum**2 # Iteratively clip outliers newBadPixels = True iter = -1 if verbose: print "Looking for bad pixel outliers in profile construction." xl = np.linspace(-1., 1., nlam) while newBadPixels: iter += 1 if pord==0: # faster to take weighted mean: profile = np.tile(an.wmean(normData, (goodpixelmask/varNormData), axis=0), (nlam,1)) else: profile = 0. * frame for ii in range(fitwidth): fit = an.polyfitr(xl, normData[:, ii], pord, np.inf, w=(goodpixelmask/varNormData)[:, ii], verbose=verbose-1) profile[:, ii] = np.polyval(fit, xl) if profile.min() < 0: profile[profile < 0] = 0. profile /= profile.sum(1).reshape(nlam, 1) #Step6: Revise variance estimates modelData = standardSpectrum * profile + background variance = (np.abs(modelData)/gain + (readnoise/gain)**2) / \ (goodpixelmask + 1e-9) # Avoid infinite variance outlierSigmas = (frame - modelData)**2/variance if outlierSigmas.max() > psigma**2: maxRejectedValue = max(psigma**2, np.sort(outlierSigmas[:, extractionAperture].ravel())[-nreject]) worstOutliers = (outlierSigmas>=maxRejectedValue).nonzero() goodpixelmask[worstOutliers] = False newBadPixels = True numberRejected = len(worstOutliers[0]) else: newBadPixels = False numberRejected = 0 if verbose: print "Rejected %i pixels on this iteration " % numberRejected #Step5: Construct spatial profile; enforce positivity & normalization varNormData = variance / standardSpectrum**2 if verbose: print "%i bad pixels found" % iter # Iteratively clip Cosmic Rays newBadPixels = True iter = -1 if verbose: print "Looking for bad pixel outliers in optimal extraction." while newBadPixels: iter += 1 #Step 8: Extract optimal spectrum and its variance gp = goodpixelmask * profile denom = (gp * profile / variance)[:, extractionAperture].sum(1) spectrum = ((gp * skysubFrame / variance)[:, extractionAperture].sum(1) / denom).reshape(nlam, 1) varSpectrum = (gp[:, extractionAperture].sum(1) / denom).reshape(nlam, 1) #Step6: Revise variance estimates modelData = spectrum * profile + background variance = (np.abs(modelData)/gain + (readnoise/gain)**2) / \ (goodpixelmask + 1e-9) # Avoid infinite variance #Iterate until worse outliers are all identified: outlierSigmas = (frame - modelData)**2/variance if outlierSigmas.max() > csigma**2: maxRejectedValue = max(csigma**2, np.sort(outlierSigmas[:, extractionAperture].ravel())[-nreject]) worstOutliers = (outlierSigmas>=maxRejectedValues).nonzero() goodpixelmask[worstOutliers] = False newBadPixels = True numberRejected = len(worstOutliers[0]) else: newBadPixels = False numberRejected = 0 if verbose: print "Rejected %i pixels on this iteration " % numberRejected if verbose: print "%i bad pixels found" % iter ret = (spectrum, varSpectrum, profile, background, goodpixelmask) return ret
[docs]def superExtract(*args, **kw): """ Optimally extract curved spectra, following Marsh 1989. :INPUTS: data : 2D Numpy array Appropriately calibrated frame from which to extract spectrum. Should be in units of ADU, not electrons! variance : 2D Numpy array Variances of pixel values in 'data'. gain : scalar Detector gain, in electrons per ADU readnoise : scalar Detector readnoise, in electrons. :OPTIONS: trace : 1D numpy array location of spectral trace. If None, :func:`traceorders` is invoked. goodpixelmask : 2D numpy array Equals 0 for bad pixels, 1 for good pixels npoly : int Number of profile polynomials to evaluate (Marsh's "K"). Ideally you should not need to set this -- instead, play with 'polyspacing' and 'extract_radius.' For symmetry, this should be odd. polyspacing : scalar Spacing between profile polynomials, in pixels. (Marsh's "S"). A few cursory tests suggests that the extraction precision (in the high S/N case) scales as S^-2 -- but the code slows down as S^2. pord : int Order of profile polynomials; 1 = linear, etc. bkg_radii : 2-sequence inner and outer radii to use in computing background extract_radius : int radius to use for both flux normalization and extraction dispaxis : bool 0 for horizontal spectrum, 1 for vertical spectrum bord : int >= 0 Degree of polynomial background fit. bsigma : int >= 0 Sigma-clipping threshold for computing background. tord : int >= 0 Degree of spectral-trace polynomial (for trace across frame -- not used if 'trace' is input) csigma : int >= 0 Sigma-clipping threshold for cleaning & cosmic-ray rejection. finite : bool If true, mask all non-finite values as bad pixels. qmode : str ('fast' or 'slow') How to compute Marsh's Q-matrix. Valid inputs are 'fast-linear', 'slow-linear', 'fast-nearest,' 'slow-nearest,' and 'brute'. These select between various methods of integrating the nearest-neighbor or linear interpolation schemes as described by Marsh; the 'linear' methods are preferred for accuracy. Use 'slow' if you are running out of memory when using the 'fast' array-based methods. 'Brute' is both slow and inaccurate, and should not be used. nreject : int Number of outlier-pixels to reject at each iteration. retall : bool If true, also return the 2D profile, background, variance map, and bad pixel mask. :RETURNS: object with fields for: spectrum varSpectrum trace :EXAMPLE: :: import spec import numpy as np import pylab as py def gaussian(p, x): if len(p)==3: p = concatenate((p, [0])) return (p[3] + p[0]/(p[1]*sqrt(2*pi)) * exp(-(x-p[2])**2 / (2*p[1]**2))) # Model some strongly tilted spectral data: nx, nlam = 80, 500 x0 = np.arange(nx) gain, readnoise = 3.0, 30. background = np.ones(nlam)*10 flux = np.ones(nlam)*1e4 center = nx/2. + np.linspace(0,10,nlam) FWHM = 3. model = np.array([gaussian([flux[ii]/gain, FWHM/2.35, center[ii], background[ii]], x0) for ii in range(nlam)]) varmodel = np.abs(model) / gain + (readnoise/gain)**2 observation = np.random.normal(model, np.sqrt(varmodel)) fitwidth = 60 xr = 15 output_spec = spec.superExtract(observation, varmodel, gain, readnoise, polyspacing=0.5, pord=1, bkg_radii=[10,30], extract_radius=5, dispaxis=1) py.figure() py.plot(output_spec.spectrum.squeeze() / flux) py.ylabel('(Measured flux) / (True flux)') py.xlabel('Photoelectrons') :TO_DO: Iterate background fitting and reject outliers; maybe first time would be unweighted for robustness. Introduce even more array-based, rather than loop-based, calculations. For large spectra computing the C-matrix takes the most time; this should be optimized somehow. :SEE_ALSO: """ # 2012-08-25 20:14 IJMC: Created. # 2012-09-21 14:32 IJMC: Added error-trapping if no good pixels # are in a row. Do a better job of extracting # the initial 'standard' spectrum. from scipy import signal from pylab import * from nsdata import imshow, bfixpix # Parse inputs: frame, variance, gain, readnoise = args[0:4] frame = gain * np.array(frame, copy=False) variance = gain**2 * np.array(variance, copy=False) variance[variance<=0.] = readnoise**2 # Parse options: if kw.has_key('verbose'): verbose = kw['verbose'] else: verbose = False if verbose: from time import time if kw.has_key('goodpixelmask'): goodpixelmask = kw['goodpixelmask'] if isinstance(goodpixelmask, str): goodpixelmask = pyfits.getdata(goodpixelmask).astype(bool) else: goodpixelmask = np.array(goodpixelmask, copy=True).astype(bool) else: goodpixelmask = np.ones(frame.shape, dtype=bool) if kw.has_key('dispaxis'): dispaxis = kw['dispaxis'] else: dispaxis = 0 if dispaxis==0: frame = frame.transpose() variance = variance.transpose() goodpixelmask = goodpixelmask.transpose() if kw.has_key('pord'): pord = kw['pord'] else: pord = 2 if kw.has_key('polyspacing'): polyspacing = kw['polyspacing'] else: polyspacing = 1 if kw.has_key('bkg_radii'): bkg_radii = kw['bkg_radii'] else: bkg_radii = [15, 20] if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii)) if kw.has_key('extract_radius'): extract_radius = kw['extract_radius'] else: extract_radius = 10 if verbose: message("Setting option 'extract_radius' to: " + str(extract_radius)) if kw.has_key('npoly'): npoly = kw['npoly'] else: npoly = 2 * int((2.0 * extract_radius) / polyspacing / 2.) + 1 if kw.has_key('bord'): bord = kw['bord'] else: bord = 1 if verbose: message("Setting option 'bord' to: " + str(bord)) if kw.has_key('tord'): tord = kw['tord'] else: tord = 3 if verbose: message("Setting option 'tord' to: " + str(tord)) if kw.has_key('bsigma'): bsigma = kw['bsigma'] else: bsigma = 3 if verbose: message("Setting option 'bsigma' to: " + str(bsigma)) if kw.has_key('csigma'): csigma = kw['csigma'] else: csigma = 5 if verbose: message("Setting option 'csigma' to: " + str(csigma)) if kw.has_key('qmode'): qmode = kw['qmode'] else: qmode = 'fast' if verbose: message("Setting option 'qmode' to: " + str(qmode)) if kw.has_key('nreject'): nreject = kw['nreject'] else: nreject = 100 if verbose: message("Setting option 'nreject' to: " + str(nreject)) if kw.has_key('finite'): finite = kw['finite'] else: finite = True if verbose: message("Setting option 'finite' to: " + str(finite)) if kw.has_key('retall'): retall = kw['retall'] else: retall = False if finite: goodpixelmask *= (np.isfinite(frame) * np.isfinite(variance)) variance[True-goodpixelmask] = frame[goodpixelmask].max() * 1e9 nlam, fitwidth = frame.shape # Define trace (Marsh's "X_j" in Eq. 9) if kw.has_key('trace'): trace = kw['trace'] else: trace = None if trace is None: trace = 5 if not hasattr(trace, '__iter__'): if verbose: print "Tracing not fully tested; dispaxis may need adjustment." #pdb.set_trace() tracecoef = traceorders(frame, pord=trace, nord=1, verbose=verbose-1, plotalot=verbose-1, g=gain, rn=readnoise, badpixelmask=True-goodpixelmask, dispaxis=dispaxis, fitwidth=min(fitwidth, 80)) trace = np.polyval(tracecoef.ravel(), np.arange(nlam)) #xxx = np.arange(-fitwidth/2, fitwidth/2) #backgroundAperture = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) < bkg_radii[1]) #extractionAperture = np.abs(xxx) < extract_radius #nextract = extractionAperture.sum() #xb = xxx[backgroundAperture] xxx = np.arange(fitwidth) - trace.reshape(nlam,1) backgroundApertures = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) <= bkg_radii[1]) extractionApertures = np.abs(xxx) <= extract_radius nextracts = extractionApertures.sum(1) #Step3: Sky Subtraction background = 0. * frame for ii in range(nlam): if goodpixelmask[ii, backgroundApertures[ii]].any(): fit = an.polyfitr(xxx[ii,backgroundApertures[ii]], frame[ii, backgroundApertures[ii]], bord, bsigma, w=(goodpixelmask/variance)[ii, backgroundApertures[ii]], verbose=verbose-1) background[ii, :] = np.polyval(fit, xxx[ii]) else: background[ii] = 0. background_at_trace = np.array([np.interp(0, xxx[j], background[j]) for j in range(nlam)]) # (my 3a: mask any bad values) badBackground = True - np.isfinite(background) background[badBackground] = 0. if verbose and badBackground.any(): print "Found bad background values at: ", badBackground.nonzero() skysubFrame = frame - background # Interpolate and fix bad pixels for extraction of standard # spectrum -- otherwise there can be 'holes' in the spectrum from # ill-placed bad pixels. fixSkysubFrame = bfixpix(skysubFrame, True-goodpixelmask, n=8, retdat=True) #Step4: Extract 'standard' spectrum and its variance standardSpectrum = np.zeros((nlam, 1), dtype=float) varStandardSpectrum = np.zeros((nlam, 1), dtype=float) for ii in range(nlam): thisrow_good = extractionApertures[ii] #* goodpixelmask[ii] * standardSpectrum[ii] = fixSkysubFrame[ii, thisrow_good].sum() varStandardSpectrum[ii] = variance[ii, thisrow_good].sum() spectrum = standardSpectrum.copy() varSpectrum = varStandardSpectrum # Define new indices (in Marsh's appendix): N = pord + 1 mm = np.tile(np.arange(N).reshape(N,1), (npoly)).ravel() nn = mm.copy() ll = np.tile(np.arange(npoly), N) kk = ll.copy() pp = N * ll + mm qq = N * kk + nn jj = np.arange(nlam) # row (i.e., wavelength direction) ii = np.arange(fitwidth) # column (i.e., spatial direction) jjnorm = np.linspace(-1, 1, nlam) # normalized X-coordinate jjnorm_pow = jjnorm.reshape(1,1,nlam) ** (np.arange(2*N-1).reshape(2*N-1,1,1)) # Marsh eq. 9, defining centers of each polynomial: constant = 0. # What is it for??? poly_centers = trace.reshape(nlam, 1) + polyspacing * np.arange(-npoly/2+1, npoly/2+1) + constant # Marsh eq. 11, defining Q_kij (via nearest-neighbor interpolation) # Q_kij = max(0, min(S, (S+1)/2 - abs(x_kj - i))) if verbose: tic = time() if qmode=='fast-nearest': # Array-based nearest-neighbor mode. if verbose: tic = time() Q = np.array([np.zeros((npoly, fitwidth, nlam)), np.array([polyspacing * np.ones((npoly, fitwidth, nlam)), 0.5 * (polyspacing+1) - np.abs((poly_centers - ii.reshape(fitwidth, 1, 1)).transpose(2, 0, 1))]).min(0)]).max(0) elif qmode=='slow-linear': # Code is a mess, but it works. invs = 1./polyspacing poly_centers_over_s = poly_centers / polyspacing xps_mat = poly_centers + polyspacing xms_mat = poly_centers - polyspacing Q = np.zeros((npoly, fitwidth, nlam), dtype=float) for i in range(fitwidth): ip05 = i + 0.5 im05 = i - 0.5 for j in range(nlam): for k in range(npoly): xkj = poly_centers[j,k] xkjs = poly_centers_over_s[j,k] xps = xps_mat[j,k] #xkj + polyspacing xms = xms_mat[j,k] # xkj - polyspacing if (ip05 <= xms) or (im05 >= xps): qval = 0. elif (im05) > xkj: lim1 = im05 lim2 = min(ip05, xps) qval = (lim2 - lim1) * \ (1. + xkjs - 0.5*invs*(lim1+lim2)) elif (ip05) < xkj: lim1 = max(im05, xms) lim2 = ip05 qval = (lim2 - lim1) * \ (1. - xkjs + 0.5*invs*(lim1+lim2)) else: lim1 = max(im05, xms) lim2 = min(ip05, xps) qval = lim2 - lim1 + \ invs * (xkj*(-xkj + lim1 + lim2) - \ 0.5*(lim1*lim1 + lim2*lim2)) Q[k,i,j] = max(0, qval) elif qmode=='fast-linear': # Code is a mess, but it's faster than 'slow' mode invs = 1./polyspacing xps_mat = poly_centers + polyspacing Q = np.zeros((npoly, fitwidth, nlam), dtype=float) for j in range(nlam): xkj_vec = np.tile(poly_centers[j,:].reshape(npoly, 1), (1, fitwidth)) xps_vec = np.tile(xps_mat[j,:].reshape(npoly, 1), (1, fitwidth)) xms_vec = xps_vec - 2*polyspacing ip05_vec = np.tile(np.arange(fitwidth) + 0.5, (npoly, 1)) im05_vec = ip05_vec - 1 ind00 = ((ip05_vec <= xms_vec) + (im05_vec >= xps_vec)) ind11 = ((im05_vec > xkj_vec) * (True - ind00)) ind22 = ((ip05_vec < xkj_vec) * (True - ind00)) ind33 = (True - (ind00 + ind11 + ind22)).nonzero() ind11 = ind11.nonzero() ind22 = ind22.nonzero() n_ind11 = len(ind11[0]) n_ind22 = len(ind22[0]) n_ind33 = len(ind33[0]) if n_ind11 > 0: ind11_3d = ind11 + (np.ones(n_ind11, dtype=int)*j,) lim2_ind11 = np.array((ip05_vec[ind11], xps_vec[ind11])).min(0) Q[ind11_3d] = ((lim2_ind11 - im05_vec[ind11]) * invs * \ (polyspacing + xkj_vec[ind11] - 0.5*(im05_vec[ind11] + lim2_ind11))) if n_ind22 > 0: ind22_3d = ind22 + (np.ones(n_ind22, dtype=int)*j,) lim1_ind22 = np.array((im05_vec[ind22], xms_vec[ind22])).max(0) Q[ind22_3d] = ((ip05_vec[ind22] - lim1_ind22) * invs * \ (polyspacing - xkj_vec[ind22] + 0.5*(ip05_vec[ind22] + lim1_ind22))) if n_ind33 > 0: ind33_3d = ind33 + (np.ones(n_ind33, dtype=int)*j,) lim1_ind33 = np.array((im05_vec[ind33], xms_vec[ind33])).max(0) lim2_ind33 = np.array((ip05_vec[ind33], xps_vec[ind33])).min(0) Q[ind33_3d] = ((lim2_ind33 - lim1_ind33) + invs * \ (xkj_vec[ind33] * (-xkj_vec[ind33] + lim1_ind33 + lim2_ind33) - 0.5*(lim1_ind33*lim1_ind33 + lim2_ind33*lim2_ind33))) elif qmode=='brute': # Neither accurate, nor memory-frugal. oversamp = 4. jj2 = np.arange(nlam*oversamp, dtype=float) / oversamp trace2 = np.interp(jj2, jj, trace) poly_centers2 = trace2.reshape(nlam*oversamp, 1) + polyspacing * np.arange(-npoly/2+1, npoly/2+1, dtype=float) + constant x2 = np.arange(fitwidth*oversamp, dtype=float)/oversamp Q = np.zeros((npoly, fitwidth, nlam), dtype=float) for k in range(npoly): Q[k,:,:] = an.binarray((np.abs(x2.reshape(fitwidth*oversamp,1) - poly_centers2[:,k]) <= polyspacing), oversamp) Q /= oversamp*oversamp*2 else: # 'slow' Loop-based nearest-neighbor mode: requires less memory if verbose: tic = time() Q = np.zeros((npoly, fitwidth, nlam), dtype=float) for k in range(npoly): for i in range(fitwidth): for j in range(nlam): Q[k,i,j] = max(0, min(polyspacing, 0.5*(polyspacing+1) - np.abs(poly_centers[j,k] - i))) if verbose: print '%1.2f s to compute Q matrix (%s mode)' % (time() - tic, qmode) # Some quick math to find out which dat columns are important, and # which contain no useful spectral information: Qmask = Q.sum(0).transpose() > 0 Qind = Qmask.transpose().nonzero() Q_cols = [Qind[0].min(), Qind[0].max()] nQ = len(Qind[0]) Qsm = Q[:,Q_cols[0]:Q_cols[1]+1,:] # Prepar to iteratively clip outliers newBadPixels = True iter = -1 if verbose: print "Looking for bad pixel outliers." while newBadPixels: iter += 1 if verbose: print "Beginning iteration %i" % iter # Compute pixel fractions (Marsh Eq. 5): # (Note that values outside the desired polynomial region # have Q=0, and so do not contribute to the fit) #E = (skysubFrame / spectrum).transpose() invEvariance = (spectrum**2 / variance).transpose() weightedE = (skysubFrame * spectrum / variance).transpose() # E / var_E invEvariance_subset = invEvariance[Q_cols[0]:Q_cols[1]+1,:] # Define X vector (Marsh Eq. A3): if verbose: tic = time() X = np.zeros(N * npoly, dtype=float) X0 = np.zeros(N * npoly, dtype=float) for q in qq: X[q] = (weightedE[Q_cols[0]:Q_cols[1]+1,:] * Qsm[kk[q],:,:] * jjnorm_pow[nn[q]]).sum() if verbose: print '%1.2f s to compute X matrix' % (time() - tic) # Define C matrix (Marsh Eq. A3) if verbose: tic = time() C = np.zeros((N * npoly, N*npoly), dtype=float) buffer = 1.1 # C-matrix computation buffer (to be sure we don't miss any pixels) for p in pp: qp = Qsm[ll[p],:,:] for q in qq: # Check that we need to compute C: if np.abs(kk[q] - ll[p]) <= (1./polyspacing + buffer): if q>=p: # Only compute over non-zero columns: C[q, p] = (Qsm[kk[q],:,:] * qp * jjnorm_pow[nn[q]+mm[p]] * invEvariance_subset).sum() if q>p: C[p, q] = C[q, p] if verbose: print '%1.2f s to compute C matrix' % (time() - tic) ################################################## ################################################## # Just for reference; the following is easier to read, perhaps, than the optimized code: if False: # The SLOW way to compute the X vector: X2 = np.zeros(N * npoly, dtype=float) for n in nn: for k in kk: q = N * k + n xtot = 0. for i in ii: for j in jj: xtot += E[i,j] * Q[k,i,j] * (jjnorm[j]**n) / Evariance[i,j] X2[q] = xtot # Compute *every* element of C (though most equal zero!) C = np.zeros((N * npoly, N*npoly), dtype=float) for p in pp: for q in qq: if q>=p: C[q, p] = (Q[kk[q],:,:] * Q[ll[p],:,:] * (jjnorm.reshape(1,1,nlam)**(nn[q]+mm[p])) / Evariance).sum() if q>p: C[p, q] = C[q, p] ################################################## ################################################## # Solve for the profile-polynomial coefficients (Marsh Eq. A)4: if np.abs(np.linalg.det(C)) < 1e-10: Bsoln = np.dot(np.linalg.pinv(C), X) else: Bsoln = np.linalg.solve(C, X) Asoln = Bsoln.reshape(N, npoly).transpose() # Define G_kj, the profile-defining polynomial profiles (Marsh Eq. 8) Gsoln = np.zeros((npoly, nlam), dtype=float) for n in range(npoly): Gsoln[n] = np.polyval(Asoln[n,::-1], jjnorm) # reorder polynomial coef. # Compute the profile (Marsh eq. 6) and normalize it: if verbose: tic = time() profile = np.zeros((fitwidth, nlam), dtype=float) for i in range(fitwidth): profile[i,:] = (Q[:,i,:] * Gsoln).sum(0) #P = profile.copy() # for debugging if profile.min() < 0: profile[profile < 0] = 0. profile /= profile.sum(0).reshape(1, nlam) profile[True - np.isfinite(profile)] = 0. if verbose: print '%1.2f s to compute profile' % (time() - tic) #Step6: Revise variance estimates modelSpectrum = spectrum * profile.transpose() modelData = modelSpectrum + background variance0 = np.abs(modelData) + readnoise**2 variance = variance0 / (goodpixelmask + 1e-9) # De-weight bad pixels, avoiding infinite variance outlierVariances = (frame - modelData)**2/variance if outlierVariances.max() > csigma**2: newBadPixels = True # Base our nreject-counting only on pixels within the spectral trace: maxRejectedValue = max(csigma**2, np.sort(outlierVariances[Qmask])[-nreject]) worstOutliers = (outlierVariances>=maxRejectedValue).nonzero() goodpixelmask[worstOutliers] = False numberRejected = len(worstOutliers[0]) #pdb.set_trace() else: newBadPixels = False numberRejected = 0 if verbose: print "Rejected %i pixels on this iteration " % numberRejected # Optimal Spectral Extraction: (Horne, Step 8) fixSkysubFrame = bfixpix(skysubFrame, True-goodpixelmask, n=8, retdat=True) spectrum = np.zeros((nlam, 1), dtype=float) #spectrum1 = np.zeros((nlam, 1), dtype=float) varSpectrum = np.zeros((nlam, 1), dtype=float) goodprof = profile.transpose() #* goodpixelmask for ii in range(nlam): thisrow_good = extractionApertures[ii] #* goodpixelmask[ii] denom = (goodprof[ii, thisrow_good] * profile.transpose()[ii, thisrow_good] / variance0[ii, thisrow_good]).sum() if denom==0: spectrum[ii] = 0. varSpectrum[ii] = 9e9 else: spectrum[ii] = (goodprof[ii, thisrow_good] * skysubFrame[ii, thisrow_good] / variance0[ii, thisrow_good]).sum() / denom #spectrum1[ii] = (goodprof[ii, thisrow_good] * modelSpectrum[ii, thisrow_good] / variance0[ii, thisrow_good]).sum() / denom varSpectrum[ii] = goodprof[ii, thisrow_good].sum() / denom #if spectrum.size==1218 and ii>610: # pdb.set_trace() #if spectrum.size==1218: pdb.set_trace() ret = baseObject() ret.spectrum = spectrum ret.raw = standardSpectrum ret.varSpectrum = varSpectrum ret.trace = trace ret.units = 'electrons' ret.background = background_at_trace ret.function_name = 'spec.superExtract' if retall: ret.profile_map = profile ret.extractionApertures = extractionApertures ret.background_map = background ret.variance_map = variance0 ret.goodpixelmask = goodpixelmask ret.function_args = args ret.function_kw = kw return ret
[docs]def spextractor(frame, gain, readnoise, framevar=None, badpixelmask=None, mode='superExtract', trace=None, options=None, trace_options=None, verbose=False): """Extract a spectrum from a frame using one of several methods. :INPUTS: frame : 2D Numpy array or filename Contains a single spectral trace. gain : None or scalar Gain of data contained in 'frame;' i.e., number of collected photoelectrons equals frame * gain. readnoise : None or scalar Read noise of detector, in electrons. framevar : None, 2D Numpy array, or filename Variance of values in 'frame.' If and only if framevar is None, use gain and readnoise to compute variance. badpixelmask : None, 2D Numpy array, or filename Mask of bad pixels in 'frame.' Bad pixels are set to 1, good pixels are set to 0. mode : str Which spectral extraction mode to use. Options are: superExtract -- see :func:`superExtract` optimalExtract -- see :func:`optimalExtract` spline -- see :func:`extractSpectralProfiles` Must also input trace_options. trace : None, or 1D Numpy Array Spectral trace location: fractional pixel index along the entire spectral trace. If None, :func:`traceorders` will be called using the options in 'trace_options.' options : None or dict Keyword options to be passed to the appropriate spectral extraction algorithm. Note that you should be able to pass the same sets of parameters to :func:`superExtract` and :func:`optimalExtract` (the necessary parameter sets overlap, but are not identical). trace_options : None or dict Keyword options to be passed to :func:`traceorders` (if no trace is input, or if mode='spline') :RETURNS: spectrum, error or variance of spectrum, sky background, ... :NOTES: When 'optimalextract' is used: if len(bkg_radii)==2 then the background apertures will be reset based on the median location of the trace. If extract_radius is a singleton, it will be similarly redefined. :EXAMPLE: :: frame = pyfits.getdata('spectral_filename.fits') gain, readnoise = 3.3, 30 output = spec.spextractor(frame, gain, readnoise, mode='superExtract', \ options=dict(bord=2, bkg_radii=[20, 30], extract_radius=15, \ polyspacing=1./3, pord=5, verbose=True, trace=trace, \ qmode='slow-linear')) output2 = spec.spextractor(frame, gain, readnoise, mode='optimalExtract', \ options=dict(bkg_radii=[20,30], extract_radius=15, bord=2, \ bsigma=3, pord=3, psigma=8, csigma=5, verbose=1)) """ # 2012-09-03 11:12 IJMC: Created from tools import array_or_filename # Parse inputs: frame = array_or_filename(frame) framevar = array_or_filename(framevar, noneoutput=np.abs(frame) / gain + (readnoise / gain)**2) if options is None: options = dict(dispaxis=0) if verbose and not options.has_key('verbose'): options['verbose'] = verbose if trace is None: if trace_options is None: trace_options = dict(pord=2) if not trace_options.has_key('dispaxis'): if options.has_key('dispaxis'): trace_options['dispaxis'] = options['dispaxis'] trace_coef = traceorders(frame, **trace_options) trace = np.polyval(trace_coef.ravel(), np.arange(frame.shape[1-options['dispaxis']])) options['trace'] = trace ################################################## # Extract spectrum in one of several ways: ################################################## if mode.lower()=='superextract': ret = superExtract(frame, framevar, gain, readnoise, **options) elif mode.lower()=='spline': # First, set things up: try: trace_coef += 0.0 except: trace_coef = np.polyfit(np.arange(trace.size), trace, trace_options['pord']).reshape(1, trace_options['pord']+1) trace_options['retall'] = True if not trace_options.has_key('bkg_radii'): if options.has_key('bkg_radii') and len(options['bkg_radii'])==2: trace_options['bkg_radii'] = options['bkg_radii'] if not trace_options.has_key('extract_radius'): if options.has_key('extract_radius') and not hasattr(options['extract_radius'], '__iter__'): trace_options['extract_radius'] = options['extract_radius'] prof = makeprofile(frame, trace_coef, **trace_options) ret = extractSpectralProfiles(prof, **trace_options) elif mode.lower()=='optimalextract': # First, re-define bkg_radii and extract_radius (if necessary): options = dict().update(options) # prevents alterations of options if options.has_key('bkg_radii') and len(options['bkg_radii'])==2: t0 = np.median(trace) bkg_radii = [t0-options['bkg_radii'][1], t0-options['bkg_radii'][0], t0+options['bkg_radii'][0], t0+options['bkg_radii'][1]] options['bkg_radii'] = bkg_radii if verbose: print "Re-defining background apertures: ", bkg_radii if options.has_key('extract_radius') and \ (not hasattr(options['extract_radius'], '__iter__') or \ (len(options['extract_radius'])==1)): extract_radius = [t0 - options['extract_radius'], \ t0 + options['extract_radius']] options['extract_radius'] = extract_radius if verbose: print "Re-defining extraction aperture: ", extract_radius ret = optimalExtract(frame, framevar, gain, readnoise, **options) else: print "No valid spectral extraction mode specified!" ret = -1 return ret
[docs]def scaleSpectralSky_pca(frame, skyframes, variance=None, mask=None, badpixelmask=None, npca=3, gain=3.3, readnoise=30): """ Use PCA and blank sky frames to subtract frame : str or NumPy array data frame to subtract sky from. Assumed to be in ADU, not electrons (see gain and readnoise) npca : int number of PCA components to remove f0 = pyfits.getdata(odome.procsci[0]) mask = pyfits.getdata(odome._proc + 'skyframes_samplemask.fits').astype(bool) badpixelmask = pyfits.getdata( odome.badpixelmask).astype(bool) The simplest way to fit sky to a 'frame' containing bright spectral is to include the spectral-trace regions in 'mask' but set the 'variance' of those regions extremely high (to de-weight them in the least-squares fit). To use for multi-object data, consider running multiple times (once per order) Returns the best-fit sky frame as determined from the first 'npca' PCA components. """ # 2012-09-17 16:41 IJMC: Created from scipy import sparse from pcsa import pca # Parse inputs if not isinstance(frame, np.ndarray): frame = pyfits.getdata(frame) if variance is None: variance = np.abs(frame)/gain + (readnoise/gain)**2 else: variance = np.array(variance, copy=False) weights = 1./variance nx, ny = frame.shape n_tot = nx*ny if badpixelmask is None: badpixelmask = np.zeros((nx, ny), dtype=bool) elif isinstance(badpixelmask, str): badpixelmask = pyfits.getdata(badpixelmask).astype(bool) else: badpixelmask = np.array(badpixelmask).astype(bool) weights[badpixelmask] = 0. if mask is None: mask = np.ones(frame.shape, dtype=bool) n_elem = n_tot else: n_elem = mask.astype(bool).sum() maskind = mask.nonzero() if isinstance(skyframes, np.ndarray) and skyframes.ndim==3: pass else: skyframes = np.array([pyfits.getdata(fn) for fn in skyframes]) skyframes_ind = np.array([sf[maskind] for sf in skyframes]) frame_ind = frame[maskind] pcaframes = np.zeros((npca, n_elem), dtype=np.float32) iter = 0 pcas = [] for jj in range(npca): pcas.append(pca(skyframes_ind, None, ord=jj+1)) pcaframes[0] = pcas[0][0][0] for jj in range(1, npca): pcaframes[jj] = (pcas[jj][0] - pcas[jj-1][0])[0] # del pcas svecs = sparse.csr_matrix(pcaframes).transpose() fitcoef, efitcoef = an.lsqsp(svecs, frame_ind, weights[maskind]) skyvalues = (fitcoef.reshape(npca, 1) * pcaframes).sum(0) #eskyvalues = (np.diag(efitcoef).reshape(npca, 1) * pcaframes).sum(0) skyframe = np.zeros((nx, ny), dtype=float) skyframe[maskind] = skyvalues return skyframe
[docs]def scaleSpectralSky_dsa(subframe, variance=None, badpixelmask=None, nk=31, pord=1, nmed=3, gain=3.3, readnoise=30, dispaxis=0, spatial_index=None): """ Use difference-imaging techniques to subtract moderately tilted sky background. Doesn't work so well! subframe : NumPy array data subframe containing sky data to be subtracted (and, perhaps, an object's spectral trace). Assumed to be in ADU, not electrons (see gain and readnoise) variance : None or NumPy array variance of each pixel in 'subframe' nmed : int size of 2D median filter pord : int degree of spectral tilt. Keep this number low! nk : int Number of kernel pixels in :func:`dia.dsa` nmed : int Size of window for 2D median filter (to reject bad pixels, etc.) dispaxis : int set dispersion axis: 0 = horizontal and 1 = vertical gain, readnoise : ints If 'variance' is None, these are used to estimate the uncertainties. spatial_index : None, or 1D NumPy array of type bool Which spatial rows (if dispaxis=0) to use when fitting the tilt of sky lines across the spectrum. If you want to use all, set to None. If you want to ignore some (e.g., because there's a bright object's spectrum there) then set those rows' elements of spatial_index to 'False'. :NOTES: Note that (in my experience!) this approach works better when you set all weights to unity, rather than using the suggested (photon + read noise) variances. Returns the best-fit sky frame. """ # 2012-09-17 16:41 IJMC: Created from scipy import signal import dia # Parse inputs if not isinstance(subframe, np.ndarray): subframe = pyfits.getdata(subframe) if variance is None: variance = np.abs(subframe)/gain + (readnoise/gain)**2 else: variance = np.array(variance, copy=False) weights = 1./variance if badpixelmask is None: badpixelmask = np.zeros(subframe.shape, dtype=bool) elif isinstance(badpixelmask, str): badpixelmask = pyfits.getdata(badpixelmask).astype(bool) else: badpixelmask = np.array(badpixelmask).astype(bool) weights[badpixelmask] = 0. if dispaxis==1: subframe = subframe.transpose() variance = variance.transpose() weights = weights.transpose() badpixelmask = badpixelmask.transpose() sub = subframe if nmed > 1: ss = signal.medfilt2d(sub, nmed) else: ss = sub.copy() ref = np.median(ss, axis=0) n, nlam = ss.shape if spatial_index is None: spatial_index = np.arange(n) else: spatial_index = np.array(spatial_index, copy=False).nonzero() gaussianpar = np.zeros((n, 4)) for ii in range(n): test = dia.dsa(ref, ss[ii], nk, w=weights[ii], noback=True) gaussianpar[ii] = fitGaussian(test[1])[0] position_fit = an.polyfitr(np.arange(n)[spatial_index], gaussianpar[:,2][spatial_index], pord, 3) positions = np.polyval(position_fit, np.arange(n)) width_fit = np.median(gaussianpar[:,1]) skyframe = 0*ss testDC_spec = np.ones(nlam) testx = np.arange(nk, dtype=float) lsqfits = np.zeros((n,2)) for ii in range(n): testgaussian = gaussian([1, width_fit, positions[ii], 0.], testx) testgaussian /= testgaussian.sum() testspectrum = dia.rconvolve1d(ref, testgaussian) skyframe[ii] = testspectrum if dispaxis==1: skyframe = skyframe.transpose() return skyframe
[docs]def scaleSpectralSky_cor(subframe, badpixelmask=None, maxshift=20, fitwidth=2, pord=1, nmed=3, dispaxis=0, spatial_index=None, refpix=None, tord=2): """ Use cross-correlation to subtract tilted sky backgrounds. subframe : NumPy array data subframe containing sky data to be subtracted (and, perhaps, an object's spectral trace). badpixelmask : None or NumPy array A boolean array, equal to zero for good pixels and unity for bad pixels. If this is set, the first step will be a call to :func:`nsdata.bfixpix` to interpolate over these values. nmed : int size of 2D median filter for pre-smoothing. pord : int degree of spectral tilt. Keep this number low! maxshift : int Maximum acceptable shift. NOT YET IMPLEMENTED! fitwidth : int Maximum radius (in pixels) for fitting to the peak of the cross-correlation. nmed : int Size of window for 2D median filter (to reject bad pixels, etc.) dispaxis : int set dispersion axis: 0 = horizontal and 1 = vertical spatial_index : None, or 1D NumPy array of type *bool* Which spatial rows (if dispaxis=0) to use when fitting the tilt of sky lines across the spectrum. If you want to use all, set to None. If you want to ignore some (e.g., because there's a bright object's spectrum there) then set those rows' elements of spatial_index to 'False'. refpix : scalar Pixel along spatial axis to which spectral fits should be aligned; if a spectral trace is present, one should set "refpix" to the location of the trace. tord : int Order of polynomial fits along spatial direction in aligned 2D-spectral frame, to account for misalignments or irregularities of tilt direction. :RETURNS: a model of the sky background, of the same shape as 'subframe.' """ # 2012-09-22 17:04 IJMC: Created # 2012-12-27 09:53 IJMC: Edited to better account for sharp edges # in backgrounds. from scipy import signal from nsdata import bfixpix # Parse inputs if not isinstance(subframe, np.ndarray): subframe = pyfits.getdata(subframe) if badpixelmask is None: pass else: badpixelmask = np.array(badpixelmask).astype(bool) subframe = bfixpix(subframe, badpixelmask, retdat=True) if dispaxis==1: subframe = subframe.transpose() # Define necessary variables and vectors: npix, nlam = subframe.shape if spatial_index is None: spatial_index = np.ones(npix, dtype=bool) else: spatial_index = np.array(spatial_index, copy=False) if refpix is None: refpix = npix/2. lampix = np.arange(nlam) tpix = np.arange(npix) alllags = np.arange(nlam-maxshift*2) - np.floor(nlam/2 - maxshift) # Median-filter the input data: if nmed > 1: ssub = signal.medfilt2d(subframe, nmed) else: ssub = subframe.copy() ref = np.median(ssub, axis=0) #allcor = np.zeros((npix, nlam-maxshift*2)) shift = np.zeros(npix, dtype=float) for ii in tpix: # Cross-correlate to measure alignment at each row: cor = np.correlate(ref[maxshift:-maxshift], signal.medfilt(ssub[ii], nmed)[maxshift:-maxshift], mode='same') # Measure offset of each row: maxind = alllags[(cor==cor.max())].mean() fitind = np.abs(alllags - maxind) <= fitwidth quadfit = np.polyfit(alllags[fitind], cor[fitind], 2) shift[ii] = -0.5 * quadfit[1] / quadfit[0] shift_polyfit = an.polyfitr(tpix[spatial_index], shift[spatial_index], pord, 3) #, w=weights) refpos = np.polyval(shift_polyfit, refpix) #pdb.set_trace() fitshift = np.polyval(shift_polyfit, tpix) - refpos # Interpolate each row to a common frame to create an improved reference: newssub = np.zeros((npix, nlam)) for ii in tpix: newssub[ii] = np.interp(lampix, lampix+fitshift[ii], ssub[ii]) #pdb.set_trace() newref = np.median(newssub[spatial_index,:], axis=0) tfits = np.zeros((nlam, tord+1), dtype=float) newssub2 = np.zeros((npix, nlam)) for jj in range(nlam): tfits[jj] = an.polyfitr(tpix, newssub[:,jj], tord, 3) newssub2[:, jj] = np.polyval(tfits[jj], tpix) # Create the final model of the sky background: skymodel = np.zeros((npix, nlam), dtype=float) shiftmodel = np.zeros((npix, nlam), dtype=float) for ii in tpix: #skymodel[ii] = np.interp(lampix, lampix-fitshift[ii], newref) skymodel[ii] = np.interp(lampix, lampix-fitshift[ii], newssub2[ii]) shiftmodel[ii] = np.interp(lampix, lampix+fitshift[ii], ssub[ii]) #pdb.set_trace() if dispaxis==1: skymodel = skymodel.transpose() return skymodel, shiftmodel, newssub, newssub2
[docs]def defringe_sinusoid(subframe, badpixelmask=None, nmed=5, dispaxis=0, spatial_index=None, period_limits=[20, 100], retall=False, gain=None, readnoise=None, bictest=False, sinonly=False): """ Use simple fitting to subtract fringes and sky background. subframe : NumPy array data subframe containing sky data to be subtracted (and, perhaps, an object's spectral trace). Assumed to be in ADU, not electrons (see gain and readnoise) nmed : int Size of window for 2D median filter (to reject bad pixels, etc.) dispaxis : int set dispersion axis: 0 = horizontal and 1 = vertical spatial_index : None, or 1D NumPy array of type bool Which spatial rows (if dispaxis=0) to use when fitting the tilt of sky lines across the spectrum. If you want to use all, set to None. If you want to ignore some (e.g., because there's a bright object's spectrum there) then set those rows' elements of spatial_index to 'False'. period_limits : 2-sequence Minimum and maximum periods (in pixels) of fringe signals to accept as 'valid.' Resolution elements with best-fit periods outside this range will only be fit by a linear trend. gain : scalar Gain of detector, in electrons per ADU (where 'subframe' is in units of ADUs). readnoise : scalar Readnoise of detector, in electrons (where 'subframe' is in units of ADUs). bictest : bool If True, use 'gain' and 'readnoise' to compute the Bayesian Information Criterion (BIC) for each fit; a sinusoid is only removed if BIC(sinusoid fit) is lower than BIC(constant fit). sinonly : bool If True, the output "model 2D spectrum" will only contain the sinusoidal component. Otherwise, it will contain DC and linear-trend terms. :NOTES: Note that (in my experience!) this approach works better when you set all weights to unity, rather than using the suggested (photon + read noise) variances. :REQUIREMENTS: :doc:`analysis` (for :func:`analysis.fmin`) :doc:`phasecurves` (for :func:`phasecurves.errfunc`) :doc:`lomb` (for Lomb-Scargle periodograms) SciPy 'signal' module (for median-filtering) Returns the best-fit sky frame. """ # 2012-09-19 16:22 IJMC: Created # 2012-12-16 15:11 IJMC: Made some edits to fix bugs, based on outlier indexing. from scipy import signal import lomb from analysis import fmin # scipy.optimize.fmin would also be O.K. from phasecurves import errfunc twopi = np.pi*2 fudgefactor = 1.5 def ripple(params, x): # Sinusoid + constant offset if params[1]==0: ret = 0.0 else: ret = params[0] * np.cos(twopi*x/params[1] - params[2]) + params[3] return ret def linripple(params, x): # Sinusoid + linear trend if params[1]==0: ret = 0.0 else: ret = params[0] * np.cos(twopi*x/params[1] - params[2]) + params[3] + params[4]*x return ret # Parse inputs if not isinstance(subframe, np.ndarray): subframe = pyfits.getdata(subframe) if gain is None: gain = 1 if readnoise is None: readnoise = 1 if badpixelmask is None: badpixelmask = np.zeros(subframe.shape, dtype=bool) elif isinstance(badpixelmask, str): badpixelmask = pyfits.getdata(badpixelmask).astype(bool) else: badpixelmask = np.array(badpixelmask).astype(bool) if dispaxis==1: subframe = subframe.transpose() badpixelmask = badpixelmask.transpose() sub = subframe if nmed > 1: sdat = signal.medfilt2d(sub, nmed) else: sdat = sub.copy() if bictest: var_sdat = np.abs(sdat)/gain + (readnoise/gain)**2 npix, nlam = sdat.shape if spatial_index is None: spatial_index = np.ones(npix, dtype=bool) else: spatial_index = np.array(spatial_index, copy=False).astype(bool) ############################## periods = np.logspace(0.5, np.log10(npix), npix*2) x = np.arange(npix) allfits = np.zeros((nlam, 5)) for jj in range(nlam): vec = sdat[:, jj].copy() this_index = spatial_index * (True - badpixelmask[:, jj]) #if jj==402: pdb.set_trace() if this_index.sum() > 1: # LinFit the data; exclude values inconsistent with a sinusoid: linfit = an.polyfitr(x[this_index], vec[this_index], 1, 3) linmodel = (x) * linfit[0] + linfit[1] vec2 = vec - (linmodel - linfit[1]) maxexcursion = an.dumbconf(vec2[this_index], .683)[0] * (fudgefactor / .88) index = this_index * (np.abs(vec2 - np.median(vec2[this_index])) <= (6*maxexcursion)) # Use Lomb-Scargle to find strongest period: #lsp = lomb.lomb(vec2[index], x[index], twopi/periods) freqs, lsp = lomb.fasper(x[index], vec2[index], 12., 0.5) guess_period = (1./freqs[lsp==lsp.max()]).mean() # If the best-fit period is within our limits, fit it: if (guess_period <= period_limits[1]) and (guess_period >= period_limits[0]): #periods2 = np.arange(guess_period-1, guess_period+1, 0.02) #lsp2 = lomb.lomb(vec2[index], x[index], twopi/periods2) #guess_period = periods2[lsp2==lsp2.max()].mean() guess_dc = np.median(vec2[index]) guess_amp = an.dumbconf(vec2[index], .683, mid=guess_dc)[0] / 0.88 guess = [guess_amp, guess_period, np.pi, guess_dc] if bictest: w = 1./var_sdat[:,jj][index] else: w = np.ones(index.sum()) fit = fmin(errfunc, guess, args=(ripple, x[index], vec2[index], w), full_output=True, disp=False) guess2 = np.concatenate((fit[0], [linfit[0]])) fit2 = fmin(errfunc, guess2, args=(linripple, x[index], vec[index], w), full_output=True, disp=False) if bictest: ripple_model = linripple(fit2[0], x) bic_ripple = (w*(vec - ripple_model)[index]**2).sum() + 6*np.log(index.sum()) bic_linfit = (w*(vec - linmodel)[index]**2).sum() + 2*np.log(index.sum()) #if jj==546: # pdb.set_trace() if bic_ripple >= bic_linfit: fit2 = [[0, 1e9, 0, linfit[1], linfit[0]], 0] else: # Clearly not a valid ripple -- just use the linear fit. fit2 = [[0, 1e11, 0, linfit[1], linfit[0]], 0] else: # *NO* good pixels! #linfit = an.polyfitr(x[spatial_index], vec[spatial_index], 1, 3) fit2 = [[0, 1e11, 0, np.median(vec[spatial_index]), 0], 0] allfits[jj] = fit2[0] # Median filter the resulting coefficients if nmed > 1: # No median-filtering newfits = np.array([signal.medfilt(fits, nmed) for fits in allfits.transpose()]).transpose() newfits[:, 2] = allfits[:,2] # Don't smooth phase else: newfits = allfits # Generate the model sky pattern skymodel = np.zeros((npix, nlam)) for jj in range(nlam): if sinonly: coef = newfits[jj].copy() coef[3:] = 0. else: coef = newfits[jj] skymodel[:, jj] = linripple(coef, x) if dispaxis==1: skymodel = skymodel.transpose() if retall: ret = skymodel, allfits else: ret = skymodel return ret #
[docs]def makexflat(subreg, xord, nsigma=3, minsnr=10, minfrac=0.5, niter=1): """Helper function for XXXX. :INPUTS: subreg : 2D NumPy array spectral subregion, containing spectral background, sky, and/or target flux measurements. xord : scalar or sequence Order of polynomial by which each ROW will be normalized. If niter>0, xord can be a sequence of length (niter+1). A good approach for, e.g., spectral dome flats is to set niter=1 and xord=[15,2]. nsigma : scalar Sigma-clipping level for calculation of column-by-column S/N minsnr : scalar Minimum S/N value to use when selecting 'good' columns for normalization. minfrac : scalar, 0 < minfrac < 1 Fraction of columns to use, selected by highest S/N, when selecting 'good' columns for normalization. niter : int Number of iterations. If set to zero, do not iterate (i.e., run precisely once through.) :NOTES: Helper function for functions XXXX """ # 2013-01-20 14:20 IJMC: Created ny, nx = subreg.shape xall = np.arange(2048.) subreg_new = subreg.copy() iter = 0 if not hasattr(xord, '__iter__'): xord = [xord]*(niter+1) while iter <= niter: snr = an.snr(subreg_new, axis=0, nsigma=nsigma) ind = (snr > np.sort(snr)[-int(minfrac*snr.size)]) * (snr > minsnr) xxx = ind.nonzero()[0] norm_subreg = subreg[:,ind] / np.median(subreg[:,ind], 0) coefs = np.array([an.polyfitr(xxx, row, xord[iter], 3) for row in norm_subreg]) xflat = np.array([np.polyval(coef0, xall) for coef0 in coefs]) iter += 1 subreg_new = subreg / xflat return xflat
[docs]def make_spectral_flats(sky, domeflat, subreg_corners, badpixelmask=None, xord_pix=[15,2], xord_sky=[2,1], yord=2, minsnr=5, minfrac_pix=0.7, minfrac_sky=0.5, locs=None, nsigma=3): """ Construct appropriate corrective frames for multi-object spectrograph data. Specifically: corrections for irregular slit widths, and pixel-by-pixel detector sensitivity variations. sky : 2D NumPy array Master spectral sky frame, e.g. from median-stacking many sky frames or masking-and-stacking dithered science spectra frames. This frame is used to construct a map to correct science frames (taken with the identical slit mask!) for irregular sky backgrounds resulting from non-uniform slit widths (e.g., Keck/MOSFIRE). Note that the dispersion direction should be 'horizontal' (i.e., parallel to rows) in this frames. domeflat : 2D NumPy array Master dome spectral flat, e.g. from median-stacking many dome spectra. This need not be normalized in the dispersion or spatial directions. This frame is used to construct a flat map of the pixel-to-pixel variations in detector sensitivity. Note that the dispersion direction should be 'horizontal' (i.e., parallel to rows) in this frames. subreg_corners : sequence of 2- or 4-sequences Indices (or merely starting- and ending-rows) for each subregion of interest in 'sky' and 'domeflat' frames. Syntax should be that of :func:`tools.extractSubregion`, or such that subreg=sky[subreg_corners[0]:subreg_corners[1]] badpixelmask : 2D NumPy array, or str Nonzero for any bad pixels; these will be interpolated over using :func:`nsdata.bfixpix`. xord_pix : sequence Polynomial orders for normalization in dispersion direction of pixel-based flat (dome flat) on successive iterations; see :func:`makexflat`. xord_sky : sequence Polynomial orders for normalization in dispersion direction of slit-based correction (sky frame) on successive iterations; see :func:`makexflat`. yord : scalar Polynomial order for normalization of pixel-based (dome) flat in spatial direction. locs : None, or sequence Row-index in each subregion of the location of the spectral-trace-of-interest. Only used for rectifying of sky frame; leaving this at None will have at most a mild effect. If not None, should be the same length as subreg_corners. If subreg_corners[0] = [800, 950] then locs[0] might be set to, e.g., 75 if the trace lies in the middle of the subregion. :RETURNS: wideslit_skyflat, narrowslit_domeflat :EXAMPLE: :: try: from astropy.io import fits as pyfits except: import pyfits import spec import ir obs = ir.initobs('20121010') sky = pyfits.getdata(obs._raw + 'masktersky.fits') domeflat = pyfits.getdata(obs._raw + 'mudflat.fits') allinds = [[7, 294], [310, 518], [532, 694], [710, 960], [976, 1360], [1378, 1673], [1689, 2022]] locs = [221, 77, 53, 88, 201, 96, 194] skycorrect, pixcorrect = spec.make_spectral_flats(sky, domeflat, allinds, obs.badpixelmask, locs=locs) """ # 2013-01-20 14:40 IJMC: Created from tools import extractSubregion from nsdata import bfixpix # Check inputs: if not isinstance(sky, np.ndarray): sky = pyfits.getdata(sky) if not isinstance(domeflat, np.ndarray): domeflat = pyfits.getdata(domeflat) ny0, nx0 = sky.shape if badpixelmask is None: badpixelmask = np.zeros((ny0, nx0), dtype=bool) if not isinstance(badpixelmask, np.ndarray): badpixelmask = pyfits.getdata(badpixelmask) # Correct any bad pixels: if badpixelmask.any(): bfixpix(sky, badpixelmask) bfixpix(domeflat, badpixelmask) wideslit_skyflat = np.ones((ny0, nx0), dtype=float) narrowslit_domeflat = np.ones((ny0, nx0), dtype=float) # Loop through all subregions specified: for jj in range(len(subreg_corners)): # Define extraction & alignment indices: specinds = np.array(subreg_corners[jj]).ravel().copy() if len(specinds)==2: specinds = np.concatenate(([0, nx0], specinds)) if locs is None: loc = np.mean(specinds[2:4]) else: loc = locs[jj] skysub = extractSubregion(sky, specinds, retall=False) domeflatsub = extractSubregion(domeflat, specinds, retall=False) badsub = extractSubregion(badpixelmask, specinds, retall=False) ny, nx = skysub.shape yall = np.arange(ny) # Normalize Dome Spectral Flat in X-direction (rows): xflat_dome = makexflat(domeflatsub, xord_pix, minsnr=minsnr, minfrac=minfrac_pix, niter=len(xord_pix)-1, nsigma=nsigma) ymap = domeflatsub*0.0 xsubflat = domeflatsub / np.median(domeflatsub, 0) / xflat_dome # Normalize Dome Spectral Flat in Y-direction (columns): ycoefs = [an.polyfitr(yall, xsubflat[:,ii], yord, nsigma) for ii in range(nx)] yflat = np.array([np.polyval(ycoef0, yall) for ycoef0 in ycoefs]).transpose() pixflat = domeflatsub / (xflat_dome * yflat * np.median(domeflatsub, 0)) bogus = (pixflat< 0.02) + (pixflat > 50) pixflat[bogus] = 1. # Normalize Sky spectral Flat in X-direction (rows): askysub = scaleSpectralSky_cor(skysub/pixflat, badsub, pord=2, refpix=loc, nmed=1) xflat_sky = makexflat(askysub[1], xord_sky, minsnr=minsnr, minfrac=minfrac_sky, niter=len(xord_sky)-1, nsigma=nsigma) wideslit_skyflat[specinds[2]:specinds[3], specinds[0]:specinds[1]] = xflat_sky narrowslit_domeflat[specinds[2]:specinds[3], specinds[0]:specinds[1]] = pixflat print "Done with subregion %i" % (jj+1) return wideslit_skyflat, narrowslit_domeflat
[docs]def calibrate_stared_mosfire_spectra(scifn, outfn, skycorrect, pixcorrect, subreg_corners, **kw): """Correct non-dithered WIDE-slit MOSFIRE spectral frames for: pixel-to-pixel nonuniformities (i.e., traditional flat-fielding) detector nonlinearities tilted spectral lines non-uniform slit widths (which cause non-smooth backgrounds) Note that the dispersion direction should be 'horizontal' (i.e., parallel to rows) in this frame. :INPUTS: scifn : str Filename of raw, uncalibrated science frame (in ADUs, not electrons) outfn : str Name into which final, calibrated file should be written. skycorrect : str or 2D NumPy array Slitloss correction map (i.e., for slits of nonuniform width), such as generated by :func:`make_spectral_flats`. pixcorrect : str or 2D NumPy array Pixel-by-pixel sensitivity correction map (i.e., flat field), such as generated by :func:`make_spectral_flats`. subreg_corners : sequence of 2- or 4-sequences Indices (or merely starting- and ending-rows) for each subregion of interest in 'sky' and 'domeflat' frames. Syntax should be that of :func:`tools.extractSubregion`, or such that subreg=sky[subreg_corners[0]:subreg_corners[1]] linearize : bool Whether to linearity-correct the data. If linearizing: linearity correction is computed & applied AFTER applying the pixel-by-pixel (flatfield) correction, but BEFORE the slitloss (sky) correction. bkg_radii : 2-sequence Inner and outer radius for background computation and removal; measured in pixels from the center of the profile. The values [11,52] seems to work well for MOSFIRE K-band spectra. locs : None, or sequence Row-index in each subregion of the location of the spectral-trace-of-interest. Only used for rectifying of sky frame; leaving this at None will have at most a mild effect. If not None, should be the same length as subreg_corners. If subreg_corners[0] = [800, 950] then locs[0] might be set to, e.g., 75 if the trace lies in the middle of the subregion. gain: scalar Detector gain, in electrons per ADU readnoise: scalar Detector readnoise, in electrons polycoef : str, None, or sequence Polynomial coefficients for detector linearization: see :func:`ir.linearity_correct` and :func:`ir.linearity_mosfire`. unnormalized_flat : str or 2D NumPy array If not 'None', this is used to define the subregion header keywords ('subreg0', 'subreg1', etc.) for each slit's two-dimensional spectrum. These keywords are required by much of my extraction machinery! :EXAMPLE: :: try: from astropy.io import fits as pyfits except: import pyfits import spec import ir obs = ir.initobs('20121010') allinds = [[7, 294], [310, 518], [532, 694], [710, 960], [976, 1360], [1378, 1673], [1689, 2022]] locs = [221, 77, 53, 88, 201, 96, 194] unnorm_flat_fn = obs._raw + 'mudflat.fits' if False: sky = pyfits.getdata(obs._raw + 'masktersky.fits') unnorm_domeflat = pyfits.getdata(unnorm_flat_fn) skycorrect, pixcorrect = spec.make_spectral_flats(sky, unnorm_domeflat, allinds, badpixelmask=obs.badpixelmask, locs=locs) else: skycorrect = obs._raw + 'skycorrect.fits' pixcorrect = obs._raw + 'pixcorrect.fits' linearcoef='/Users/ianc/proj/transit/data/mosfire_linearity/linearity/mosfire_linearity_cnl_coefficients_bic-optimized.fits' rawfn = obs.rawsci outfn = obs.procsci spec.calibrate_stared_mosfire_spectra(rawfn, outfn, skycorrect, pixcorrect, allinds, linearize=True, badpixelmask=obs.badpixelmask, locs=locs, polycoef=linearcoef, verbose=True, clobber=True, unnormalized_flat=unnorm_flat_fn) :NOTES: This routine is *slow*, mostly because of the call to :func:`defringe_sinusoid`. Consider running multiple processes in parallel, to speed things up! :SEE_ALSO: :func:`ir.mosfire_speccal`, :func:`defringe_sinusoid` """ # 2013-01-20 16:17 IJMC: Created. # 2013-01-25 15:59 IJMC: Fixed various bugs relating to # determinations of subregion boundaries. try: from astropy.io import fits as pyfits except: import pyfits from nsdata import bfixpix import ir import os from tools import extractSubregion, findRectangles import sys #pdb.set_trace() if not isinstance(scifn, str) and hasattr(scifn, '__iter__'): for scifn0, outfn0 in zip(scifn, outfn): calibrate_stared_mosfire_spectra(scifn0, outfn0, skycorrect, pixcorrect, subreg_corners, **kw) else: # Set defaults: names = ['linearize', 'bkg_radii', 'gain', 'readnoise', 'polycoef', 'verbose', 'badpixelmask', 'locs', 'clobber', 'unnormalized_flat'] defaults = [False, [11, 52], 2.15, 21.1, None, False, None, None, False, None] for n,d in zip(names, defaults): exec('%s = kw["%s"] if kw.has_key("%s") else d' % (n, n, n)) # Check inputs: scihdr = pyfits.getheader(scifn) sci = pyfits.getdata(scifn) exptime = scihdr['TRUITIME'] ncoadd = scihdr['COADDONE'] nread = scihdr['READDONE'] if not isinstance(pixcorrect, np.ndarray): scihdr.update('SLITFLAT', pixcorrect) pixcorrect = pyfits.getdata(pixcorrect) else: scihdr.update('PIXFLAT', 'User-specified data array') if not isinstance(skycorrect, np.ndarray): scihdr.update('SLITFLAT', skycorrect) skycorrect = pyfits.getdata(skycorrect) else: scihdr.update('SLITFLAT', 'User-specified data array') if unnormalized_flat is not None: if not isinstance(unnormalized_flat, np.ndarray): scihdr.update('MUDFLAT', unnormalized_flat) unnormalized_flat = pyfits.getdata(unnormalized_flat) else: scihdr.update('MUDFLAT', 'User-specified data array') ny0, nx0 = sci.shape if badpixelmask is None: badpixelmask = np.zeros((ny0, nx0), dtype=bool) if not isinstance(badpixelmask, np.ndarray): badpixelmask = pyfits.getdata(badpixelmask) # Correct any bad pixels: if badpixelmask.any(): bfixpix(sci, badpixelmask) if linearize: # If you edit this section, be sure to update the same # section in ir.mosfire_speccal! fmap = ir.makefmap_mosfire() sci_lin, liniter = ir.linearity_correct(sci/pixcorrect, nread-1, ncoadd, 1.45, exptime, fmap, ir.linearity_mosfire, verbose=verbose, linfuncargs=dict(polycoef=polycoef), retall=True) ir.hdradd(scihdr, 'COMMENT', 'Linearity-corrected by calibrate_stared_mosfire_spectra.') ir.hdradd(scihdr, 'LIN_ITER', liniter) if isinstance(polycoef, str): ir.hdradd(scihdr, 'LINPOLY', os.path.split(polycoef)[1]) else: if isinstance(polycoef, np.ndarray) and polycoef.ndim==3: ir.hdradd(scihdr, 'LINPOLY', 'user-input array used for linearity correction.') else: ir.hdradd(scihdr, 'LINPOLY', str(polycoef)) else: sci_lin = sci/pixcorrect ir.hdradd(scihdr, 'COMMENT', 'NOT linearity-corrected.') newframe = np.zeros((ny0, nx0), dtype=float) # Loop through all subregions specified: for jj in range(len(subreg_corners)): # Define extraction & alignment indices: specinds = np.array(subreg_corners[jj]).ravel().copy() if len(specinds)==2: specinds = np.concatenate(([0, nx0], specinds)) if locs is None: loc = np.mean(specinds[2:4]) else: loc = locs[jj] # Prepare various necessities: scisub = extractSubregion(sci, specinds, retall=False) badsub = extractSubregion(badpixelmask, specinds, retall=False) slitflat = extractSubregion(skycorrect, specinds, retall=False) ny, nx = scisub.shape yall = np.arange(ny) # Define subregion boundaries from the flat-field frame. # Ideally, this should go *outside* the loop (no need to # re-calculate this for every file...) if unnormalized_flat is not None: samplemask, boundaries = ir.spectral_subregion_mask(unnormalized_flat) samplemask *= (unnormalized_flat > 500) samplemask_corners = findRectangles(samplemask, minsepy=42, minsepx=150) nspec = samplemask_corners.shape[0] for kk in range(nspec): ir.hdradd(scihdr, 'SUBREG%i' % kk, str(samplemask_corners[kk])) ir.hdradd(scihdr, 'NSUBREG', nspec) # Align the subregion, so that sky lines run along detector columns: aligned_scisub = scaleSpectralSky_cor(scisub/slitflat, badsub, pord=2, refpix=loc, nmed=1) # Model the sky background, using linear trends plus a sinusoid: if locs is None: spatind = np.ones(ny, dtype=bool) else: spatind = (np.abs(np.arange(ny) - loc) > bkg_radii[0]) * (np.abs(np.arange(ny) - loc) < bkg_radii[1]) sscisub = defringe_sinusoid(aligned_scisub[1], badpixelmask=badsub, period_limits=[9,30], gain=gain, readnoise=readnoise, bictest=False, spatial_index=spatind, nmed=1) # Compute the corrected subregion: aligned_flattened_skycorrected_subregion = \ (aligned_scisub[1] - sscisub) * slitflat + aligned_scisub[3] #try2 = (aligned_scisub[1] - sscisub) * slitflat + aligned_scisub newframe[specinds[2]:specinds[3],specinds[0]:specinds[1]] = aligned_flattened_skycorrected_subregion print "Done with subregion %i" % (jj+1) scihdr.update('MOSCAL', 'Calibrated by calibrate_stared_mosfire_spectra') pyfits.writeto(outfn, newframe.astype(float), scihdr, clobber=clobber) return
[docs]def reconstitute_gmos_roi(infile, outfile, **kw): """Convert GMOS frames taken with custom ROIs into standard FITS frames. :INPUTS: in : string or sequence of strings Input filename or filenames. out : string or sequence of strings Output filename or filenames. :OPTIONS: clobber : bool Passed to PyFITS; whether to overwrite existing files. """ # 2013-03-28 21:37 IJMC: Created at the summit of Mauna Kea import ir def trimlims(instr): trimchars = '[]:,' for trimchar in trimchars: instr = instr.replace(trimchar, ' ') return map(float, instr.strip().split()) # Handle recursive case: if not isinstance(infile, str): for thisin, thisout in zip(infile, outfile): reconstitute_gmos_roi(thisin, thisout, **kw) return # Handle non-recursive (single-file) case: file = pyfits.open(infile) hdr = file[0].header nroi = hdr['detnroi'] biny, binx = map(int, file[1].header['ccdsum'].split()) detsize = trimlims(hdr['detsize']) binning = 2 frame = np.zeros(((detsize[3]-detsize[2]+1)/binx, (detsize[1]-detsize[0]+1)/biny), dtype=int) print frame.shape # Read in the coordinates for each ROI: roi_xys = np.zeros((nroi, 4), dtype=int) for ii in range(1, nroi+1): roi_xys[ii-1, 0] = (hdr['detro%ix' % ii] - 1) roi_xys[ii-1, 1] = (hdr['detro%ix' % ii] + hdr['detro%ixs' % ii] - 1) roi_xys[ii-1, 2] = (hdr['detro%iy' % ii] - 1) /binx roi_xys[ii-1, 3] = (hdr['detro%iy' % ii] - 1)/binx + hdr['detro%iys' % ii] - 1 # Read in the C nhdus = len(file) - 1 detsecs = np.zeros((nhdus, 4), dtype=int) datasecs = np.zeros((nhdus, 4), dtype=int) biassecs = np.zeros((nhdus, 4), dtype=int) for ii in range(nhdus): detsecs[ii] = trimlims(file[ii+1].header['detsec']) datasecs[ii] = trimlims(file[ii+1].header['datasec']) biassecs[ii] = trimlims(file[ii+1].header['biassec']) di2 = (detsecs[ii,0]-1)/biny, detsecs[ii,1]/biny, (detsecs[ii,2]-1)/binx, detsecs[ii,3]/binx thisdat = file[ii+1].data #pdb.set_trace() if biassecs[ii,0]==1025: frame[di2[2]:di2[3], di2[0]:di2[1]] = thisdat[:, :-32] elif biassecs[ii,0]==1: frame[di2[2]:di2[3], di2[0]:di2[1]] = thisdat[:, 32:] else: print 'bombed. bummer! I should have written a better function' file.close() hdr = pyfits.getheader(infile) for ii in range(nroi): ir.hdradd(hdr, 'SUBREG%i' % ii, '[%i %i %i %i]' % tuple(roi_xys[ii])) pyfits.writeto(outfile, frame, hdr, **kw) return #detsecs, datasecs, biassecs
[docs]def rotationalProfile(delta_epsilon, delta_lam): """Compute the rotational profile of a star, assuming solid-body rotation and linear limb darkening. This uses Eq. 18.14 of Gray's Photospheres, 2005, 3rd Edition. :INPUTS: delta_epsilon : 2-sequence [0] : delta_Lambda_L = lambda * V * sin(i)/c; the rotational displacement at the stellar limb. [1] : epsilon, the linear limb darkening coefficient, used in the relation I(theta) = I0 + epsilon * (cos(theta) - 1). [2] : OPTIONAL! The central location of the profile (otherwise assumed to be located at delta_lam=0). delta_lam : scalar or sequence Wavelength minus offset: Lambda minus lambda_0. Grid upon which computations will be done. :EXAMPLE: :: import pylab as py import spec dlam = py.np.linspace(-2, 2, 200) # Create wavelength grid profile = spec.rotationalProfile([1, 0.6], dlam) py.figure() py.plot(dlam, profile) """ # 2013-05-26 10:37 IJMC: Created. delta_lambda_L, epsilon = delta_epsilon[0:2] if len(delta_epsilon)>2: # optional lambda_offset lamdel2 = 1. - ((delta_lam - delta_epsilon[2])/delta_lambda_L)**2 else: lamdel2 = 1. - (delta_lam/delta_lambda_L)**2 if not hasattr(delta_lam, '__iter__'): delta_lam = np.array([delta_lam]) ret = (4*(1.-epsilon) * np.sqrt(lamdel2) + np.pi*epsilon*lamdel2) / \ (2*np.pi * delta_lambda_L * (1. - epsilon/3.)) ret[lamdel2<0] = 0. return ret
[docs]def modelline(param, prof2, dv): """Generate a rotational profile, convolve it with a second input profile, normalize it (simply), and return. :INPUTS: param : 1D sequence param[0:3] -- see :func:`rotationalProfile` param[4] -- multiplicative scaling factor dv : velocity grid prof2 : second input profile """ # 2013-08-07 09:55 IJMC: Created nk = dv.size rot_model = rotationalProfile(param[0:3], dv) conv_rot_model = np.convolve(rot_model, prof2, 'same') norm_conv_rot_model = conv_rot_model - (conv_rot_model[0] + (conv_rot_model[-1] - conv_rot_model[0])/(nk - 1.) * np.arange(nk)) return norm_conv_rot_model * param[3]