import os, shutil import numpy import numpy.fft as FFT import math from asap.scantable import scantable from asap.parameters import rcParams from asap.logging import asaplog, asaplog_post_dec from asap.selector import selector from asap.asapgrid import asapgrid2 #from asap._asap import sidebandsep class sbseparator: """ The sbseparator class is defined to separate SIGNAL and IMAGE sideband spectra observed by frequency-switching technique. It also helps supressing emmission of IMAGE sideband. *** WARNING *** THIS MODULE IS EXPERIMENTAL Known issues: - Frequencies of IMAGE sideband cannot be reconstructed from information in scantable in sideband sparation. Frequency setting of SIGNAL sideband is stored in output table for now. - Flag information (stored in FLAGTRA) is ignored. Example: # Create sideband separator instance whith 3 input data sbsep = sbseparator(['test1.asap', 'test2.asap', 'test3.asap']) # Set reference IFNO and tolerance to select data sbsep.set_frequency(5, 30, frame='TOPO') # Set direction tolerance to select data in unit of radian sbsep.set_dirtol(1.e-5) # Set rejection limit of solution sbsep.set_limit(0.2) # Solve image sideband as well sbsep.set_both(True) # Invoke sideband separation sbsep.separate('testout.asap', overwrite = True) """ def __init__(self, infiles): self.intables = None self.signalShift = [] self.imageShift = [] self.dsbmode = True self.getboth = False self.rejlimit = 0.2 self.baseif = -1 self.freqtol = 10. self.freqframe = "" self.solveother = False self.dirtol = [1.e-5, 1.e-5] # direction tolerance in rad (2 arcsec) self.tables = [] self.nshift = -1 self.nchan = -1 self.set_data(infiles) #self.separator = sidebandsep() @asaplog_post_dec def set_data(self, infiles): """ Set data to be processed. infiles : a list of filenames or scantables """ if not (type(infiles) in (list, tuple, numpy.ndarray)): infiles = [infiles] if isinstance(infiles[0], scantable): # a list of scantable for stab in infiles: if not isinstance(stab, scantable): asaplog.post() raise TypeError, "Input data is not a list of scantables." #self.separator._setdata(infiles) self._reset_data() self.intables = infiles else: # a list of filenames for name in infiles: if not os.path.exists(name): asaplog.post() raise ValueError, "Could not find input file '%s'" % name #self.separator._setdataname(infiles) self._reset_data() self.intables = infiles asaplog.push("%d files are set to process" % len(self.intables)) def _reset_data(self): del self.intables self.intables = None self.signalShift = [] #self.imageShift = [] self.tables = [] self.nshift = -1 self.nchan = -1 @asaplog_post_dec def set_frequency(self, baseif, freqtol, frame=""): """ Set IFNO and frequency tolerance to select data to process. Parameters: - reference IFNO to process in the first table in the list - frequency tolerance from reference IF to select data frame : frequency frame to select IF """ self._reset_if() self.baseif = baseif if isinstance(freqtol,dict) and freqtol["unit"] == "Hz": if freqtol['value'] > 0.: self.freqtol = freqtol else: asaplog.post() asaplog.push("Frequency tolerance should be positive value.") asaplog.post("ERROR") return else: # torelance in channel unit if freqtol > 0: self.freqtol = float(freqtol) else: asaplog.post() asaplog.push("Frequency tolerance should be positive value.") asaplog.post("ERROR") return self.freqframe = frame def _reset_if(self): self.baseif = -1 self.freqtol = 0 self.freqframe = "" self.signalShift = [] #self.imageShift = [] self.tables = [] self.nshift = 0 self.nchan = -1 @asaplog_post_dec def set_dirtol(self, dirtol=[1.e-5,1.e-5]): """ Set tolerance of direction to select data """ # direction tolerance in rad if not (type(dirtol) in [list, tuple, numpy.ndarray]): dirtol = [dirtol, dirtol] if len(dirtol) == 1: dirtol = [dirtol[0], dirtol[0]] if len(dirtol) > 1: self.dirtol = dirtol[0:2] else: asaplog.post() asaplog.push("Invalid direction tolerance. Should be a list of float in unit radian") asaplog.post("ERROR") return asaplog.post("Set direction tolerance [%f, %f] (rad)" % \ (self.dirtol[0], self.dirtol[1])) @asaplog_post_dec def set_shift(self, mode="DSB", imageshift=None): """ Set shift mode and channel shift of image band. mode : shift mode ['DSB'|'SSB'] When mode='DSB', imageshift is assumed to be equal to the shift of signal sideband but in opposite direction. imageshift : a list of number of channel shift in image band of each scantable. valid only mode='SSB' """ if mode.upper().startswith("S"): if not imageshift: raise ValueError, "Need to set shift value of image sideband" self.dsbmode = False self.imageShift = imageshift asaplog.push("Image sideband shift is set manually: %s" % str(self.imageShift)) else: # DSB mode self.dsbmode = True self.imageShift = [] @asaplog_post_dec def set_both(self, flag=False): """ Resolve both image and signal sideband when True. """ self.getboth = flag if self.getboth: asaplog.push("Both signal and image sidebands are solved and output as separate tables.") else: asaplog.push("Only signal sideband is solved and output as an table.") @asaplog_post_dec def set_limit(self, threshold=0.2): """ Set rejection limit of solution. """ #self.separator._setlimit(abs(threshold)) self.rejlimit = threshold asaplog.push("The threshold of rejection is set to %f" % self.rejlimit) @asaplog_post_dec def set_solve_other(self, flag=False): """ Calculate spectra by subtracting the solution of the other sideband when True. """ self.solveother = flag if flag: asaplog.push("Expert mode: solution are obtained by subtraction of the other sideband.") @asaplog_post_dec def separate(self, outname="", overwrite=False): """ Invoke sideband separation. outname : a name of output scantable overwrite : overwrite existing table """ # List up valid scantables and IFNOs to convolve. #self.separator._separate() self._setup_shift() #self._preprocess_tables() nshift = len(self.tables) signaltab = self._grid_outtable(self.tables[0].copy()) if self.getboth: imagetab = signaltab.copy() rejrow = [] for irow in xrange(signaltab.nrow()): currpol = signaltab.getpol(irow) currbeam = signaltab.getbeam(irow) currdir = signaltab.get_directionval(irow) spec_array, tabidx = self._get_specarray(polid=currpol,\ beamid=currbeam,\ dir=currdir) #if not spec_array: if len(tabidx) == 0: asaplog.post() asaplog.push("skipping row %d" % irow) rejrow.append(irow) continue signal = self._solve_signal(spec_array, tabidx) signaltab.set_spectrum(signal, irow) if self.getboth: image = self._solve_image(spec_array, tabidx) imagetab.set_spectrum(image, irow) # TODO: Need to remove rejrow form scantables here signaltab.flag_row(rejrow) if self.getboth: imagetab.flag_row(rejrow) if outname == "": outname = "sbsepareted.asap" signalname = outname + ".signalband" if os.path.exists(signalname): if not overwrite: raise Exception, "Output file '%s' exists." % signalname else: shutil.rmtree(signalname) signaltab.save(signalname) if self.getboth: # Warnings asaplog.post() asaplog.push("Saving IMAGE sideband.") asaplog.push("Note, frequency information of IMAGE sideband cannot be properly filled so far. (future development)") asaplog.push("Storing frequency setting of SIGNAL sideband in FREQUENCIES table for now.") asaplog.post("WARN") imagename = outname + ".imageband" if os.path.exists(imagename): if not overwrite: raise Exception, "Output file '%s' exists." % imagename else: shutil.rmtree(imagename) imagetab.save(imagename) def _solve_signal(self, data, tabidx=None): if not tabidx: tabidx = range(len(data)) tempshift = [] dshift = [] if self.solveother: for idx in tabidx: tempshift.append(-self.imageShift[idx]) dshift.append(self.signalShift[idx] - self.imageShift[idx]) else: for idx in tabidx: tempshift.append(-self.signalShift[idx]) dshift.append(self.imageShift[idx] - self.signalShift[idx]) shiftdata = numpy.zeros(data.shape, numpy.float) for i in range(len(data)): shiftdata[i] = self._shiftSpectrum(data[i], tempshift[i]) ifftdata = self._Deconvolution(shiftdata, dshift, self.rejlimit) result_image = self._combineResult(ifftdata) if not self.solveother: return result_image result_signal = self._subtractOtherSide(shiftdata, dshift, result_image) return result_signal def _solve_image(self, data, tabidx=None): if not tabidx: tabidx = range(len(data)) tempshift = [] dshift = [] if self.solveother: for idx in tabidx: tempshift.append(-self.signalShift[idx]) dshift.append(self.imageShift[idx] - self.signalShift[idx]) else: for idx in tabidx: tempshift.append(-self.imageShift[idx]) dshift.append(self.signalShift[idx] - self.imageShift[idx]) shiftdata = numpy.zeros(data.shape, numpy.float) for i in range(len(data)): shiftdata[i] = self._shiftSpectrum(data[i], tempshift[i]) ifftdata = self._Deconvolution(shiftdata, dshift, self.rejlimit) result_image = self._combineResult(ifftdata) if not self.solveother: return result_image result_signal = self._subtractOtherSide(shiftdata, dshift, result_image) return result_signal @asaplog_post_dec def _grid_outtable(self, table): # Generate gridded table for output (Just to get rows) gridder = asapgrid2(table) gridder.setIF(self.baseif) cellx = str(self.dirtol[0])+"rad" celly = str(self.dirtol[1])+"rad" dirarr = numpy.array(table.get_directionval()).transpose() mapx = dirarr[0].max() - dirarr[0].min() mapy = dirarr[1].max() - dirarr[1].min() nx = max(1, numpy.ceil(mapx/self.dirtol[0])) ny = max(1, numpy.ceil(mapy/self.dirtol[0])) asaplog.push("Regrid output scantable with cell = [%s, %s]" % \ (cellx, celly)) gridder.defineImage(nx=nx, ny=ny, cellx=cellx, celly=celly) gridder.setFunc(func='box', convsupport=1) gridder.setWeight(weightType='uniform') gridder.grid() return gridder.getResult() @asaplog_post_dec def _get_specarray(self, polid=None, beamid=None, dir=None): ntable = len(self.tables) spec_array = numpy.zeros((ntable, self.nchan), numpy.float) nspec = 0 asaplog.push("Start data selection by POL=%d, BEAM=%d, direction=[%f, %f]" % (polid, beamid, dir[0], dir[1])) tabidx = [] for itab in range(ntable): tab = self.tables[itab] # Select rows by POLNO and BEAMNO try: tab.set_selection(pols=[polid], beams=[beamid]) if tab.nrow() > 0: tabidx.append(itab) except: # no selection asaplog.post() asaplog.push("table %d - No spectrum ....skipping the table" % (itab)) asaplog.post("WARN") continue # Select rows by direction spec = numpy.zeros(self.nchan, numpy.float) selrow = [] for irow in range(tab.nrow()): currdir = tab.get_directionval(irow) if (abs(currdir[0] - dir[0]) > self.dirtol[0]) or \ (abs(currdir[1] - dir[1]) > self.dirtol[1]): continue selrow.append(irow) if len(selrow) == 0: asaplog.post() asaplog.push("table %d - No spectrum ....skipping the table" % (itab)) asaplog.post("WARN") continue else: seltab = tab.copy() seltab.set_selection(selector(rows=selrow)) if tab.nrow() > 1: asaplog.push("table %d - More than a spectrum selected. averaging rows..." % (itab)) tab = seltab.average_time(scanav=False, weight="tintsys") else: tab = seltab spec_array[nspec] = tab._getspectrum() nspec += 1 if nspec != ntable: asaplog.post() #asaplog.push("Some tables has no spectrum with POL=%d BEAM=%d. averaging rows..." % (polid, beamid)) asaplog.push("Could not find corresponding rows in some tables.") asaplog.push("Number of spectra selected = %d (table: %d)" % (nspec, ntable)) if nspec < 2: asaplog.push("At least 2 spectra are necessary for convolution") asaplog.post("ERROR") return False, tabidx return spec_array[0:nspec], tabidx @asaplog_post_dec def _setup_shift(self): ### define self.tables, self.signalShift, and self.imageShift if not self.intables: asaplog.post() raise RuntimeError, "Input data is not defined." #if self.baseif < 0: # asaplog.post() # raise RuntimeError, "Reference IFNO is not defined." byname = False #if not self.intables: if isinstance(self.intables[0], str): # A list of file name is given if not os.path.exists(self.intables[0]): asaplog.post() raise RuntimeError, "Could not find '%s'" % self.intables[0] stab = scantable(self.intables[0],average=False) ntab = len(self.intables) byname = True else: stab = self.intables[0] ntab = len(self.intables) if len(stab.getbeamnos()) > 1: asaplog.post() asaplog.push("Mult-beam data is not supported by this module.") asaplog.post("ERROR") return valid_ifs = stab.getifnos() if self.baseif < 0: self.baseif = valid_ifs[0] asaplog.post() asaplog.push("IFNO is not selected. Using the first IF in the first scantable. Reference IFNO = %d" % (self.baseif)) if not (self.baseif in valid_ifs): asaplog.post() errmsg = "IF%d does not exist in the first scantable" % \ self.baseif raise RuntimeError, errmsg asaplog.push("Start selecting tables and IFNOs to solve.") asaplog.push("Cheching frequency of the reference IF") unit_org = stab.get_unit() coord = stab._getcoordinfo() frame_org = coord[1] stab.set_unit("Hz") if len(self.freqframe) > 0: stab.set_freqframe(self.freqframe) stab.set_selection(ifs=[self.baseif]) spx = stab._getabcissa() stab.set_selection() basech0 = spx[0] baseinc = spx[1]-spx[0] self.nchan = len(spx) if isinstance(self.freqtol, float): vftol = abs(baseinc * self.freqtol) self.freqtol = dict(value=vftol, unit="Hz") else: vftol = abs(self.freqtol['value']) inctol = abs(baseinc/float(self.nchan)) asaplog.push("Reference frequency setup (Table = 0, IFNO = %d): nchan = %d, chan0 = %f Hz, incr = %f Hz" % (self.baseif, self.nchan, basech0, baseinc)) asaplog.push("Allowed frequency tolerance = %f Hz ( %f channels)" % (vftol, vftol/baseinc)) poltype0 = stab.poltype() self.tables = [] self.signalShift = [] if self.dsbmode: self.imageShift = [] for itab in range(ntab): asaplog.push("Table %d:" % itab) tab_selected = False if itab > 0: if byname: stab = scantable(self.intables[itab],average=False) else: stab = self.intables[itab] unit_org = stab.get_unit() coord = stab._getcoordinfo() frame_org = coord[1] stab.set_unit("Hz") if len(self.freqframe) > 0: stab.set_freqframe(self.freqframe) # Check POLTYPE should be equal to the first table. if stab.poltype() != poltype0: asaplog.post() raise Exception, "POLTYPE should be equal to the first table." # Multiple beam data may not handled properly if len(stab.getbeamnos()) > 1: asaplog.post() asaplog.push("table contains multiple beams. It may not be handled properly.") asaplog.push("WARN") for ifno in stab.getifnos(): stab.set_selection(ifs=[ifno]) spx = stab._getabcissa() if (len(spx) != self.nchan) or \ (abs(spx[0]-basech0) > vftol) or \ (abs(spx[1]-spx[0]-baseinc) > inctol): continue tab_selected = True seltab = stab.copy() seltab.set_unit(unit_org) seltab.set_freqframe(frame_org) self.tables.append(seltab) self.signalShift.append((spx[0]-basech0)/baseinc) if self.dsbmode: self.imageShift.append(-self.signalShift[-1]) asaplog.push("- IF%d selected: sideband shift = %16.12e channels" % (ifno, self.signalShift[-1])) stab.set_selection() stab.set_unit(unit_org) stab.set_freqframe(frame_org) if not tab_selected: asaplog.post() asaplog.push("No data selected in Table %d" % itab) asaplog.post("WARN") asaplog.push("Total number of IFs selected = %d" % len(self.tables)) if len(self.tables) < 2: asaplog.post() raise RuntimeError, "At least 2 IFs are necessary for convolution!" if not self.dsbmode and len(self.imageShift) != len(self.signalShift): asaplog.post() errmsg = "User defined channel shift of image sideband has %d elements, while selected IFNOs are %d" % (len(self.imageShift), len(self.signalShift)) raise RuntimeError, errmsg self.signalShift = numpy.array(self.signalShift) self.imageShift = numpy.array(self.imageShift) self.nshift = len(self.tables) @asaplog_post_dec def _preprocess_tables(self): ### temporary method to preprocess data ### Do time averaging for now. for itab in range(len(self.tables)): self.tables[itab] = self.tables[itab].average_time(scanav=False, weight="tintsys") # def save(self, outfile, outform="ASAP", overwrite=False): # if not overwrite and os.path.exists(outfile): # raise RuntimeError, "Output file '%s' already exists" % outfile # # #self.separator._save(outfile, outform) # def done(self): # self.close() # def close(self): # pass # #del self.separator ######################################################################## def _Deconvolution(self, data_array, shift, threshold=0.00000001): FObs = [] Reject = 0 nshift, nchan = data_array.shape nspec = nshift*(nshift-1)/2 ifftObs = numpy.zeros((nspec, nchan), numpy.float) for i in range(nshift): F = FFT.fft(data_array[i]) FObs.append(F) z = 0 for i in range(nshift): for j in range(i+1, nshift): Fobs = (FObs[i]+FObs[j])/2.0 dX = (shift[j]-shift[i])*2.0*math.pi/float(self.nchan) #print 'dX,i,j=',dX,i,j for k in range(1,self.nchan): if math.fabs(math.sin(dX*k)) > threshold: Fobs[k] += ((FObs[i][k]-FObs[j][k])/2.0/(1.0-math.cos(dX*k))*math.sin(dX*k))*1.0j else: Reject += 1 ifftObs[z] = FFT.ifft(Fobs) z += 1 print 'Threshold=%s Reject=%d' % (threshold, Reject) return ifftObs def _combineResult(self, ifftObs): nspec = len(ifftObs) sum = ifftObs[0] for i in range(1,nspec): sum += ifftObs[i] return(sum/float(nspec)) def _subtractOtherSide(self, data_array, shift, Data): sum = numpy.zeros(len(Data), numpy.float) numSP = len(data_array) for i in range(numSP): SPsub = data_array[i] - Data sum += self._shiftSpectrum(SPsub, -shift[i]) return(sum/float(numSP)) def _shiftSpectrum(self, data, Shift): Out = numpy.zeros(self.nchan, numpy.float) w2 = Shift % 1 w1 = 1.0 - w2 for i in range(self.nchan): c1 = int((Shift + i) % self.nchan) c2 = (c1 + 1) % self.nchan Out[c1] += data[i] * w1 Out[c2] += data[i] * w2 return Out.copy()