def main(sampr=samprdef, inputdir=inputdirdef, utc=utcdef, numbeams=numbeamsdef, maxnsamp=maxnsampdef, savenpy=savenpydef, loadnpy=loadnpydef, saveonly=saveonlydef, liveplot=liveplotdef):
'''Main operation within the script.'''
#Decide if .npy input data should be loaded/saved, and create output dir for .npy data (if desired) and plots.
savenpy, samprdir, outputdir=FN.load_and_save_check(inputdir, utc, sampr, numbeams, savenpy, saveonly, maxnsamp)
#Obtain some parameters about the observation ("infodata", either as .npy or .fil file) and save it.
infodata=FN.load_and_save_infodata(inputdir, utc, sampr, savenpy, loadnpy)
ra, dec, tobs, nsamp_full, tsamp, maptype=infodata['ra_rad'], infodata['dec_rad'], infodata['tobs'], infodata['nsamples'], infodata['tsamp'], infodata['maptype'] #A bit redundant, since nsamp_full=tobs/tsamp.+++
old_sampr=1./tsamp #Old number of samples per second.
ra=ra*180./np.pi
dec=dec*180./np.pi
lst_hms=commands.getstatusoutput('mopsr_getlst '+utc)[1].split(' ')[1].split(':') #Local sidereal time in HH:MM:SS.SSS.
lst=(int(lst_hms[0])*3600.+int(lst_hms[1])*60.+float(lst_hms[2]))*360./sidday
if maptype=='drift':
#In the drift scan mode, the RA from the heather is always zero, so I calculate it from the LST.
ra=lst
#Derive or define some quantities that will be needed.
global hpix #This line is needed because to avoid an UnboundLocalError.
if hpix==0:
hpix=tobs
hsize=hres*1./numbeams #Horizontal angular size of each beam, in degrees.
vsize=vres #Vertical angular size of each beam, in degrees.
frac_sampr=int(round(old_sampr*1./sampr)) #Number of old samples per new sample.
nsamp_down=int(round(sampr*tobs))
#tvec=np.arange(0., tobs, 1./sampr) #Vector of time (in seconds).
#tvec=np.linspace(0.,tobs, nsamp_down)
tvec=np.linspace(0.,tobs, nsamp_down-1)#THE -1 IS BECAUSE DATA WILL LOSE ONE SAMPLE!!+++USE THIS FOR DRIFT
#tvec=np.linspace(0.,tobs, nsamp_down-2)#THE -2 IS BECAUSE DATA WILL LOSE TWO SAMPLES!! WHY?+++ USE THIS FOR TRACK
if maxnsamp!=True:
tvec=tvec[0:maxnsamp] #Vector of time (in seconds).
#Define the size of the final map.
ra_max=ra+hres*0.5 #Maximum RAS observed (initial RAS plus half of the fan beam size).
if maptype=='drift':
ra_min=ra-hres*0.5-(tobs*360./sidday) #Minimum RAS observed (left border of the initial fan beam minus the hour angle described after the observation). +++IS THIS CONVERSION CORRECT?
elif maptype=='track':
ra_min=ra-hres*0.5
map_ra=np.linspace(ra_min, ra_max, hpix) #Vector of RAS.
dec_max=dec+vres*0.5 #I impose the same maximum size as the horizontal coordinate.
dec_min=dec-vres*0.5
ra_vec=np.linspace(ra_min, ra_max, hpix) #Vector of RAS.
dec_vec=np.linspace(dec_min, dec_max, vpix) #Vector of DEC.
mapi=np.zeros((vpix, hpix)) #Matrix of values of the map.
f_t_map=np.zeros((numbeams, len(tvec))) #Matrix of fan beam versus time (raw data from the beams).
ras_cor=(ra_vec%360.) #Restrict RAS to the interval [0,360). This will be used for plotting and saving.
ras_mat=np.tile(ras_cor, (len(tvec), 1)).T
dec_mat=np.tile(dec_vec, (len(tvec), 1)).T
#Load data beam by beam.
if not loadnpy:
print 'Reading filterbank files (it will take longer than reading numpy files)...'
print
else:
print 'Reading numpy files if already existing...'
print
if savenpy:
print 'The input data will be saved as numpy files.'
print
t=time_estimate(numbeams) #A class that prints estimated computation time.
for beami in xrange(numbeams):
t.display() #Shows the remaining computation time.
t.increase() #Needed to calculate the remaining computation time.
#Load/save data.
npydatafile=samprdir+'BEAM_%.3i' %int(beami+1) #Name of the .npy file (to be either loaded or saved).
if loadnpy and os.path.isfile(npydatafile+'.npy'): #Load .npy data.
if saveonly: #If the .npy file already exists, move on to the next file.
continue
data=np.load(npydatafile+'.npy')
else: #Load .fil data.
datafile=inputdir+utc+'/'+'BEAM_%.3i/' %int(beami+1) + utc + '.fil' #Name of the data file.
data_raw=FilReader(datafile)
data=(data_raw.collapse()/data_raw.header.nchans).downsample(frac_sampr)*1./frac_sampr
if savenpy: #Save input data as numpy files, if necessary and if desired.
np.save(npydatafile,data)
if saveonly: #Simply load .fil data and save .npy data.
continue
if maxnsamp!=True:
data=data[0:maxnsamp]
f_t_map[beami,:]=data #Stores raw fan beam versus time data.
ras_ini=ra-hres*0.5+hsize*beami #Initial RAS of this particular beam. It will be the RAS of the left-most border of the fan beam.
'''
print np.shape(f_t_map)
mapi=f_t_map.copy()
lefti=np.array([])
righti=np.zeros(len(tvec))
newmapi=np.zeros((numbeams, len(tvec)+len(righti)))
shifti=1
for fi in xrange(numbeams):
lefti=np.zeros(fi)
righti=np.zeros()
newmapi[fi]=np.hstack((lefti,mapi[fi],righti))
lefti=np.concatentae
'''
return tvec, f_t_map
'''
def main(maptype=maptypedef, sampr=samprdef, inputdir=inputdirdef, utc=utcdef, numbeams=numbeamsdef, maxnsamp=maxnsampdef, savenpy=savenpydef, loadnpy=loadnpydef, saveonly=saveonlydef, liveplot=liveplotdef):
'''Main operation within the script.'''
#Decide if .npy input data should be loaded/saved, and create output dir for .npy data (if desired) and plots.
savenpy, samprdir, outputdir=FN.load_and_save_check(inputdir, utc, sampr, numbeams, savenpy, saveonly, maxnsamp)
#Obtain some parameters about the observation ("infodata", either as .npy or .fil file) and save it.
infodata=FN.load_and_save_infodata(inputdir, utc, sampr, savenpy, loadnpy)
ra, dec, tobs, nsamp_full, tsamp=infodata['ra_rad'], infodata['dec_rad'], infodata['tobs'], infodata['nsamples'], infodata['tsamp'] #A bit redundant, since nsamp_full=tobs/tsamp.+++
old_sampr=1./tsamp #Old number of samples per second.
ra=ra*180./np.pi
dec=dec*180./np.pi
lst_hms=commands.getstatusoutput('mopsr_getlst '+utc)[1].split(' ')[1].split(':') #Local sidereal time in HH:MM:SS.SSS.
lst=(int(lst_hms[0])*3600.+int(lst_hms[1])*60.+float(lst_hms[2]))*360./sidday #+++TRIVIAL QUESTION, IS THIS CONVERSION CORRECT?
if maptype=='drift':
#In the drift scan mode, the RA from the heather is always zero, so I calculate it from the LST.
ra=lst
#Derive or define some quantities that will be needed.
global hpix #This line is needed because to avoid an UnboundLocalError.
if hpix==0:
hpix=tobs
hsize=hres*1./numbeams #Horizontal angular size of each beam, in degrees.
vsize=vres #Vertical angular size of each beam, in degrees.
frac_sampr=int(round(old_sampr*1./sampr)) #Number of old samples per new sample.
nsamp_down=int(round(sampr*tobs))
#tvec=np.arange(0., tobs, 1./sampr) #Vector of time (in seconds).
#tvec=np.linspace(0.,tobs, nsamp_down)
tvec=np.linspace(0.,tobs, nsamp_down-1)#THE -1 IS BECAUSE DATA WILL LOSE ONE SAMPLE!!+++USE THIS FOR DRIFT
#tvec=np.linspace(0.,tobs, nsamp_down-2)#THE -2 IS BECAUSE DATA WILL LOSE TWO SAMPLES!! WHY?+++ USE THIS FOR TRACK
#NOT YET IMPLEMENTED.-----------
if maxnsamp!=True:
tvec=tvec[0:maxnsamp] #Vector of time (in seconds).
#NOT YET IMPLEMENTED.-----------
#Define the size of the final map.
ra_max=ra+hres*0.5 #Maximum RAS observed (initial RAS plus half of the fan beam size).
if maptype=='drift':
ra_min=ra-hres*0.5-(tobs*360./sidday) #Minimum RAS observed (left border of the initial fan beam minus the hour angle described after the observation). +++IS THIS CONVERSION CORRECT?
elif maptype=='track':
ra_min=ra-hres*0.5
map_ra=np.linspace(ra_min, ra_max, hpix) #Vector of RAS.
dec_max=dec+vres*0.5 #I impose the same maximum size as the horizontal coordinate.
dec_min=dec-vres*0.5
ra_vec=np.linspace(ra_min, ra_max, hpix) #Vector of RAS.
dec_vec=np.linspace(dec_min, dec_max, vpix) #Vector of DEC.
mapi=np.zeros((vpix, hpix)) #Matrix of values of the map.
f_t_map=np.zeros((numbeams, len(tvec))) #Matrix of fan beam versus time (raw data from the beams).
ras_cor=(ra_vec%360.) #Restrict RAS to the interval [0,360). This will be used for plotting and saving.
ras_mat=np.tile(ras_cor, (len(tvec), 1)).T
dec_mat=np.tile(dec_vec, (len(tvec), 1)).T
#Load data beam by beam.
if not loadnpy:
print 'Reading filterbank files (it will take longer than reading numpy files)...'
print
else:
print 'Reading numpy files if already existing...'
print
if savenpy:
print 'The input data will be saved as numpy files.'
print
t=time_estimate(numbeams) #A class that prints estimated computation time.
for beami in xrange(numbeams):
t.display() #Shows the remaining computation time.
t.increase() #Needed to calculate the remaining computation time.
#Load/save data.
npydatafile=samprdir+'BEAM_%.3i' %int(beami+1) #Name of the .npy file (to be either loaded or saved).
if loadnpy and os.path.isfile(npydatafile+'.npy'): #Load .npy data.
if saveonly: #If the .npy file already exists, move on to the next file.
continue
data=np.load(npydatafile+'.npy')
else: #Load .fil data.
datafile=inputdir+utc+'/'+'BEAM_%.3i/' %int(beami+1) + utc + '.fil' #Name of the data file.
data_raw=FilReader(datafile)
data=(data_raw.collapse()/data_raw.header.nchans).downsample(frac_sampr)*1./frac_sampr
if savenpy: #Save input data as numpy files, if necessary and if desired.
np.save(npydatafile,data)
if saveonly: #Simply load .fil data and save .npy data.
continue
if maxnsamp!=True:
data=data[0:maxnsamp]
f_t_map[beami,:]=data #Stores raw fan beam versus time data.
ras_ini=ra-hres*0.5+hsize*beami #Initial RAS of this particular beam. It will be the RAS of the left-most border of the fan beam.
for dec_i in xrange(len(dec_vec)):
dec_ini=dec_vec[dec_i] #Initial DEC of this particular beam.
print dec_ini
print ras_ini
print
if maptype=='drift':
rvec, dvec=FN.move_drift(ras_ini, dec_ini, tvec) #RAS, DEC of this beam over time.
elif maptype=='track':
rvec, dvec=FN.move_track(lst, Mol_lat, ras_ini, dec_ini, tvec) #RAS, DEC of this beam over time.
rvec_cor=rvec%360.
rvec_cor=rvec###################
print rvec
print dvec
RA_mat=np.tile(rvec_cor, (hpix, 1))
DEC_mat=np.tile(dvec, (vpix, 1))
ra_indices=np.argmin(abs(RA_mat-ras_mat), axis=0)
dec_indices=np.argmin(abs((DEC_mat+90.)-(dec_mat+90.)), axis=0) #I add 90 to assure that DEC is positive, in order to avoid negative DEC to be identified with positive DEC and viceversa.
py.ion()
py.plot(ras_ini, dec_ini, 'o')
py.plot(rvec, dvec,'.')
raw_input('next')
#sorting=rvec.argsort() #In order to use interp, the vectors must be increasing.
#mapi[dec_i, :]+=np.interp(ra_vec, rvec[sorting], data[sorting])
mapi[dec_indices,ra_indices]+=data
if liveplot:
if beami==numbeams or (beami+1)%plotevery==0:
twod_live(ras_cor, dec_vec, tvec, mapi, beami+1, f_t_map) #Plot maps as they are produced.
if not saveonly:
print 'Saving numpy output data.'
print
#RAS,DEC=np.meshgrid(ras_cor,dec_vec)
#hammer_x, hammer_y=hammer(ras_cor, dec_vec) #Hammer projected x, y coordinates.
#hammer_x, hammer_y=hammer(RAS, DEC)
#datadict={'RAS':RAS, 'DEC':DEC, 'MAP': mapi, 'Hammer_x':hammer_x, 'Hammer_y':hammer_y}
datadict={'RAS':ras_cor, 'DEC':dec_vec, 'MAP': mapi}
outputfile=samprdir.replace('input','output')[:-1]
np.save(outputfile,datadict) #Save final map vector as numpy file.
#Save 2D output plot.
print 'Saving and preparing a 2D plot.'
print
outputplotfile=samprdir.replace('data/input','plots/twod')[:-1]
inputplotfile=outputfile+'.npy' #The input file to produce the plots is the numpy file that was just created.
twod_save(ras_cor, dec_vec, mapi, tvec, f_t_map, outputplotfile)
def get_ftmap(sampr=samprdef, inputdir=inputdirdef, utc=utcdef, first_beam=first_beam_def, last_beam=last_beam_def, first_tsamp=first_tsamp_def, last_tsamp=last_tsamp_def, savenpy=savenpydef, loadnpy=loadnpydef, saveonly=saveonlydef, liveplot=liveplotdef):
'''Main operation within the script.'''
#Decide if .npy input data should be loaded/saved, and create output dir for .npy data (if desired) and plots.
numbeams=last_beam-first_beam+1
beamsvec=np.arange(first_beam, last_beam+1)
savenpy, samprdir, outputdir=load_and_save_check(inputdir, utc, sampr, savenpy, saveonly, first_tsamp, last_tsamp)
#Obtain some parameters about the observation ("infodata", either as .npy or .fil file) and save it.
infodata=load_and_save_infodata(inputdir, utc, sampr, savenpy, loadnpy)
ra, dec, tobs, nsamp_full, tsamp, maptype=infodata['ra_rad'], infodata['dec_rad'], infodata['tobs'], infodata['nsamples'], infodata['tsamp'], infodata['maptype'] #A bit redundant, since nsamp_full=tobs/tsamp.+++
old_sampr=1./tsamp #Old number of samples per second.
ra=ra*180./np.pi
dec=dec*180./np.pi
lst_hms=commands.getstatusoutput('mopsr_getlst '+utc)[1].split(' ')[1].split(':') #Local sidereal time in HH:MM:SS.SSS.
lst=(int(lst_hms[0])*3600.+int(lst_hms[1])*60.+float(lst_hms[2]))*360./sidday
if maptype=='drift':
#In the drift scan mode, the RA from the heather is always zero, so I calculate it from the LST.
ra=lst
#Derive or define some quantities that will be needed.
frac_sampr=int(round(old_sampr*1./sampr)) #Number of old samples per new sample.
nsamp_down=int(round(sampr*tobs)) #This is the number of samples that the data should have (if no samples were lost after downsampling).
#tvec=np.arange(0., tobs, 1./sampr) #Vector of time (in seconds).
#tvec=np.linspace(0.,tobs, nsamp_down)
#Load data beam by beam.
if not loadnpy:
print 'Reading filterbank files (it will take longer than reading numpy files)...'
print
else:
print 'Reading numpy files if already existing...'
print
if savenpy:
print 'The input data will be saved as numpy files.'
print
t=time_estimate(numbeams) #A class that prints estimated computation time.
for beami in beamsvec:
t.display() #Shows the remaining computation time.
t.increase() #Needed to calculate the remaining computation time.
#Load/save data.
npydatafile=samprdir+'BEAM_%.3i' %int(beami) #Name of the .npy file (to be either loaded or saved).
if loadnpy and os.path.isfile(npydatafile+'.npy'): #Load .npy data.
if saveonly: #If the .npy file already exists, move on to the next file.
continue
data=np.load(npydatafile+'.npy')
else: #Load .fil data.
datafile=inputdir+utc+'/'+'BEAM_%.3i/' %int(beami) + utc + '.fil' #Name of the data file.
data_raw=FilReader(datafile)
data_raw.setNthreads(1)
data=(data_raw.collapse()/data_raw.header.nchans).downsample(frac_sampr)*1./frac_sampr
if savenpy: #Save input data as numpy files, if necessary and if desired.
np.save(npydatafile,data)
if saveonly: #Simply load .fil data and save .npy data.
continue
if beami==beamsvec[0]:
#if abs(len(data)-nsamp_down)>2:
# print 'Too many samples are missing when downsampling! Why?'
# print
# exit()
tvec=np.linspace(0., tobs, nsamp_down)[0:len(data)]
ftmap=np.zeros((totalnumbeams, len(tvec))) #Matrix of fan beam versus time (raw data from the beams).
if first_tsamp>1 and last_tsamp==True:
tvec=tvec[first_tsamp-1:]
ftmap=ftmap[:,first_tsamp-1:]
elif first_tsamp==1 and last_tsamp!=True:
tvec=tvec[:(last_tsamp-1)]
ftmap=ftmap[:,:(last_tsamp-1)]
elif first_tsamp==1 and last_tsamp==True:
pass
else:
tvec=tvec[(first_tsamp-1):(last_tsamp-1)]
ftmap=ftmap[:,(first_tsamp-1):(last_tsamp-1)]
if first_tsamp>1 and last_tsamp==True:
data=data[first_tsamp-1:]
elif first_tsamp==1 and last_tsamp!=True:
data=data[:(last_tsamp-1)]
elif first_tsamp==1 and last_tsamp==True:
pass
else:
data=data[(first_tsamp-1):(last_tsamp-1)]
ftmap[beami-1,:]=data #Stores raw fan beam versus time data.
return tvec, ftmap, dec
def test_collapse(self, filfile):
myFil = FilReader(filfile)
myTim = myFil.collapse()
np.testing.assert_equal(myTim.dtype, np.float32)
np.testing.assert_equal(myTim.size, myFil.header.nsamples)
np.testing.assert_allclose(myTim.mean(), 104.7, atol=0.1)
