#Load time series in small chunks (to allow for real time analysis). for ti in xrange(nsamp_down): t.display() #Just to calculate the remaining computation time. t.increase() lapse=ti*1./sampr #Lapse of time between samples (in sec). gulpi=int(1./fsamp) #Number of samples (of the full data) to extract. starti=gulpi*ti #Number of the sample to start. hctmat,vctmat=move(hc0mat,vc0mat,lapse) #Load data from each beam. for beami in xrange(numbeams): datafile=namedir+'BEAM_%.3i/' %int(beami+1) + utc + '.fil' #Name of the data file. data=FilReader(datafile) #This is defined in class Filterbank, in file Filterbank.py. ts=data.makeTS(start=starti,gulp=gulpi,nsamps=gulpi) #Time series. value=np.mean(ts) #Mean value of the data acquired with this beam in this lapse of time. beam_left=hcb_ini+beami*hcb_width #Left border of this beam. beam_right=hcb_ini+(beami+1)*hcb_width #Right border of this beam. beam_upper=vcb_ini #Upper border of this beam. beam_lower=vcb_ini-vcb_height #Lower border of this beam. #Find the indices of the initial sky matrix such that after this lapse of time would fall in the current beam. #indices=np.where((beam_left<=hctmat) & (hctmat<=beam_right) & (beam_lower<=vctmat) & (vctmat<=beam_upper)) #image[indices]+=value #print image print
def main(sampr=samprdef,inputdir=inputdirdef,utc=utcdef,numbeams=numbeamsdef,maxnsamp=maxnsampdef):
'''Main operation within the script.'''
#Some parameters about the beam.
hcres=4.4 #Angular resolution of the horizontal coordinate of the fan beam.
vcres=2. #Angular resolution of the vertical coordinate of the fan beam.
hclen=10. #Angular size (horizontal coordinate) of the sky matrix in degrees.
vclen=10. #Angular size (vertical coordinate) of the sky matrix in degrees.
hpix=100 #Number of pixels on the horizontal axis. WHAT WOULD BE THE CORRECT ONE?
vpix=100 #Number of pixels on the vertical axis.
#Load information about this observation (for example from the first beam).
datafile=inputdir+'/'+utc+'/'+'BEAM_001/'+utc+'.fil'
data=FilReader(datafile)
ra=data.header['ra_rad'] #Right ascension in rad.
dec=data.header['dec_rad'] #Declination in rad.
tobs=data.header['tobs'] #Observing time in sec.
nsamp_full=data.header['nsamples'] #Number of samples of original time series.
#Derive some quantities that will be needed.
nsamp_down=int(tobs*sampr) #Number of samples after downsampling.
fsamp=nsamp_down*1./nsamp_full #Fraction of samples before and after downsampling.
print 'Number of samples: %i ' %nsamp_down
print 'Observing time: %.4e h' %(tobs*1./3600.)
print
#raw_input('Enter to proceed')
#Define a matrix of initial sky position of objects in the sky, centered at the tracked object.
hct=ra #The horizontal coordinate of the tracked object will be the RA.
vct=dec #The vertical coordinate of the tracked object will be the DEC.
hcmin=hct-hclen*0.5 #Left border of the sky matrix.
hcmax=hct+hclen*0.5 #Right border of the sky matrix.
vcmin=vct-vclen*0.5 #Bottom border of the sky matrix.
vcmax=vct+vclen*0.5 #Top border of the sky matrix.
hc0mat=np.tile(np.linspace(hcmin,hcmax,hpix),(vpix,1)) #Vector of borders of the horizontal pixels.
vc0mat=np.tile(np.linspace(vcmin,vcmax,vpix)[::-1],(hpix,1)).T #Vector of borders of the vertical pixels. I want it to start from up to bottom.
#If hc0vec and vc0vec are not of the same size, I could create a matrix with them, so that the 'where' condition below works.
#Define a matrix that will contain the final image (in principle it can be of the size of the sky matrix).
image=np.zeros((hpix,vpix))
hcb_ini=hct-hcres*0.5 #Left border of the primary beam.
vcb_ini=vct+vcres*0.5 #Upper border of the primary beam.
hcb_width=hcres*1./numbeams #Width of each of the beams.
vcb_height=vcres #Height of each of the beams.
t=time_estimate(min(nsamp_down,maxnsamp)) #A class that prints estimated computation time.
#Load time series in small chunks (to allow for real time analysis).
for ti in xrange(min(nsamp_down,maxnsamp)):
t.display() #Just to calculate the remaining computation time.
t.increase()
lapse=ti*1./sampr #Lapse of time between samples (in sec).
gulpi=int(1./fsamp) #Number of samples (of the full data) to extract.
starti=gulpi*ti #Number of the sample to start.
hctmat,vctmat=move_oned(hc0mat,vc0mat,lapse)
#Load data from each beam.
for beami in xrange(numbeams):
datafile=inputdir+'/'+utc+'/'+'BEAM_%.3i/' %int(beami+1) + utc + '.fil' #Name of the data file.
data=FilReader(datafile) #This is defined in class Filterbank, in file Filterbank.py.
ts=data.makeTS(start=starti,gulp=gulpi,nsamps=gulpi) #Time series.
value=np.mean(ts) #Mean value of the data acquired with this beam in this lapse of time.
beam_left=hcb_ini+beami*hcb_width #Left border of this beam.
beam_right=hcb_ini+(beami+1)*hcb_width #Right border of this beam.
beam_upper=vcb_ini #Upper border of this beam.
beam_lower=vcb_ini-vcb_height #Lower border of this beam.
#Find the indices of the initial sky matrix such that after this lapse of time would fall in the current beam.
indices=np.where((beam_left<=hctmat) & (hctmat<=beam_right) & (beam_lower<=vctmat) & (vctmat<=beam_upper))
image[indices]+=value
#print image
print
py.ion()
py.matshow(image,cmap='bone',extent=[hcb_ini,hcb_ini+hclen,vcb_ini-vclen,vcb_ini])
#py.imshow(image,interpolation='nearest',origin='lower',aspect='auto',cmap='bone',extent=[hcb_ini,hcb_ini+hclen,vcb_ini-vclen,vcb_ini])
py.xlabel('Horizontal coordinate')
py.ylabel('Vertical coordinate')
#py.imshow(image.T,origin='lower')
raw_input('Next time sample')
