Exemple #1
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    def test_get_beam_w_fr(self):
        #interps = fringe.get_beam_w_fr(self.aa, (1,4), ref_chan=160)
        #t,firs, frbins, frspace = fringe.get_fringe_rate_kernels(interps, 42.9, 401)
        frp, bins = fringe.aa_to_fr_profile(self.aa, (1,4), 100) 
        print "the bins",len(bins), len(frp)
        timebins, firs = fringe.frp_to_firs(frp, bins, self.aa.get_freqs(), fq0=self.aa.get_freqs()[100])
        print len(timebins), len(firs[100])
        frp_fit = fringe.fir_to_frp(firs[100])
#        print n.ones_like(frspace).sum()
#        print n.sum(frp**2)
        p.subplot(121)
        p.plot(timebins, firs[100])
        p.plot(timebins, n.abs(firs[100]))
        p.subplot(122)
        print len(bins), bins[0], bins[-1]
        p.plot(bins, frp, 'k')
        p.plot(bins, frp_fit, 'b', label='new')
        p.xlim(-.0005,.0020)
        p.ylim(0.0,1.0)
        p.legend()
        p.show()
Exemple #2
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 def test_get_beam_w_fr(self):
     #interps = fringe.get_beam_w_fr(self.aa, (1,4), ref_chan=160)
     #t,firs, frbins, frspace = fringe.get_fringe_rate_kernels(interps, 42.9, 401)
     frp, bins = fringe.aa_to_fr_profile(self.aa, (1, 4), 100)
     print "the bins", len(bins), len(frp)
     timebins, firs = fringe.frp_to_firs(frp,
                                         bins,
                                         self.aa.get_freqs(),
                                         fq0=self.aa.get_freqs()[100])
     print len(timebins), len(firs[100])
     frp_fit = fringe.fir_to_frp(firs[100])
     #        print n.ones_like(frspace).sum()
     #        print n.sum(frp**2)
     p.subplot(121)
     p.plot(timebins, firs[100])
     p.plot(timebins, n.abs(firs[100]))
     p.subplot(122)
     print len(bins), bins[0], bins[-1]
     p.plot(bins, frp, 'k')
     p.plot(bins, frp_fit, 'b', label='new')
     p.xlim(-.0005, .0020)
     p.ylim(0.0, 1.0)
     p.legend()
     p.show()
Exemple #3
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h = a.healpix.HealpixMap(nside=64) #healpix map for the beam
xyz = h.px2crd(n.arange( h.npix() ), ncrd=3)
tx,ty,tz = n.dot(aa._eq2zen, xyz) #rotate the coordinated system to be centered on the array. This is equatorial centered at the array.
_bmx = aa[0].bm_response((tx,ty,tz),pol='x')[0]
_bmy = aa[0].bm_response((tx,ty,tz),pol='y')[0]
bmI = 0.5 * (_bmx**2 + _bmy**2)
bmI = n.where(tz > 0, bmI, 0) # only use beam values above the horizon.

bl = aa.get_baseline(0,26,'r') * .151 #baseline length in frequency.
print aa.get_baseline(0,26,'r')
fng = C.frf_conv.mk_fng(bl, xyz)

#get the fringe rate filter in frf_conv. aa only has one channel in it.
frp, bins = fringe.aa_to_fr_profile(aa, (1,4), 0) #XXX hard-coded
tbins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[0],alietal=True)
frp = fringe.fir_to_frp(firs)
#frf,bins,wgt,(cen,wid) = C.frf_conv.get_optimal_kernel_at_ref(aa, 0, (0,26)) 
#bwfrs = C.frf_conv.get_beam_w_fr(aa, (0,26), ref_chan=0) 
#tbins,firs,frbins,frfs = C.frf_conv.get_fringe_rate_kernels(bwfrs,42.9,403)

#need to renormalize to get the proper scaling. firs are properly normalized.
#firs = firs[0]
#frfs = n.fft.fftshift(n.fft.fft(n.fft.ifftshift(firs), axis=-1))
#get weights.
wgts = scipy.interpolate.interp1d(bins, frp, kind='linear')
fng_wgt = wgts(fng) #gets weightings at fringes on the sky.
fng_bm = bmI * fng_wgt
#flat weighting determined by the maximum possible fringe rate for a baseline 
#and 0.
skypass = n.where(n.abs(bins) < n.max(n.abs(fng)), 1., 0)
# This is the noise level after being filtered and averaged by the application of this filter.
Exemple #4
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                                        ij,
                                        mychan,
                                        bins=frbins,
                                        pol=opts.pol,
                                        bl_scale=opts.bl_scale)
    timebins, firs[sep] = fringe.frp_to_firs(
        frp,
        bins,
        aa.get_afreqs(),
        fq0=aa.get_afreqs()[mychan],
        bl_scale=opts.bl_scale,
        fr_width_scale=opts.fr_width_scale,
        alietal=opts.alietal,
        maxfr=opts.maxfr)

    frp = fringe.fir_to_frp(firs[sep])
    if opts.boxcar:
        print 'Making Boxcar',
        print 'Width {0}s ...'.format(opts.teff)
        top_hat = n.zeros_like(firs[sep])
        l_hat = len(top_hat[0])
        if opts.teff: box_time = opts.teff
        else: box_time = 2232.
        start = n.round(l_hat / 2. - box_time / data_inttime / 2.)
        end = n.round(l_hat / 2. + box_time / data_inttime / 2.)
        diff = n.round(box_time / data_inttime - (end - start))
        if diff != 0: end += diff
        if (end - start) % 2 == 0: end += 1
        top_hat[:, start:end] += 1.

        if opts.frc:
Exemple #5
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#DEFAULT_FRBINS = n.arange(-.01+5e-5/2,.01,5e-5) # Hz

firs = {}
for sep in seps:
    c = 0
    while c != -1:
        ij = map(int, sep2ij[sep].split(',')[c].split('_'))
        bl = a.miriad.ij2bl(*ij)
        if blconj[bl]: c+=1
        else: break
    print mychan,ij,opts.bl_scale
    frp, bins = fringe.aa_to_fr_profile(aa, ij, mychan, bins=frbins,pol=opts.pol,bl_scale=opts.bl_scale)
    timebins, firs[sep] = fringe.frp_to_firs(frp, bins, aa.get_afreqs(), fq0=aa.get_afreqs()[mychan],
         bl_scale=opts.bl_scale, fr_width_scale = opts.fr_width_scale, alietal = opts.alietal,maxfr=opts.maxfr)

    frp = fringe.fir_to_frp(firs[sep])
    if opts.boxcar:
        print 'Making Boxcar',
        print 'Width {0}s ...'.format(opts.teff)
        top_hat = n.zeros_like(firs[sep])
        l_hat =len(top_hat[0])
        if opts.teff: box_time = opts.teff
        else: box_time = 2232.
        start = n.round(l_hat/2. - box_time/data_inttime/2.)
        end = n.round(l_hat/2. + box_time/data_inttime/2.)
        diff = n.round(box_time/data_inttime - ( end - start))
        if diff != 0: end += diff
        if  (end-start) % 2 == 0: end +=1
        top_hat[:,start:end] += 1.

        if opts.frc:
Exemple #6
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            timebins, firs[sep] = fringe.frp_to_firs(frp[scale][frw_scale][sep], bins, aa.get_afreqs(), fq0=aa.get_afreqs()[mychan], limit_xtalk=True, bl_scale = scale, fr_width_scale = frw_scale, maxfr=opts.maxfr)
            timebins*= opts.data_inttime/opts.inttime

            if False and scale ==1:
                delta=prms0[-1]/n.sqrt(1+prms0[-1]**2)
                print 'model fit parameters: ',prms0
                print 'norm is: ', n.sum(frp)
                print 'mean is: ', n.sum(bins*frp)/n.sum(frp)
                mn= n.sum(bins*frp)/n.sum(frp)
                sq= n.sqrt(n.sum((bins-mn)**2*frp)/n.sum(frp))
                sk= n.sum(((bins-mn)/sq)**3*frp)/n.sum(frp)
                ftsk= (4-n.pi)/2.* (delta*n.sqrt(2/n.pi))**3/(1-2*delta**2/n.pi)**(1.5)
                print 'actual skew is: ', sk
                print 'fitted skew is: ', ftsk

            frps[sep], frp_freqs = fringe.fir_to_frp(firs[sep],tbins=timebins)
            baselines = ''.join(sep2ij[sep] for sep in seps)

            if PLOT:
                ax_frp.plot(frp_freqs*1e3,frps[sep][mychan]/n.max(frps[sep][mychan]), 
                                label='{0}'.format(scale),color=cmap(cnt))
                ax_firs.plot(timebins,n.abs(firs[sep][mychan]),'-',
                                label='{0}'.format(scale),color=cmap(cnt),
                                linewidth=2)
                ax_firs.plot(timebins,firs[sep][mychan].real,'--',
                                color=cmap(cnt),alpha=.5)
                ax_firs.plot(timebins,firs[sep][mychan].imag,'-.',
                                color=cmap(cnt),alpha=.5)
                ax_firs.set_xlabel('s')
            envelope = n.abs(firs[sep][mychan])
            envelope /= n.max(envelope)
Exemple #7
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                                                 limit_xtalk=True)

        if False and pad == 1:
            delta = prms0[-1] / n.sqrt(1 + prms0[-1]**2)
            print 'model fit parameters: ', prms0
            print 'norm is: ', n.sum(frp)
            print 'mean is: ', n.sum(bins * frp) / n.sum(frp)
            mn = n.sum(bins * frp) / n.sum(frp)
            sq = n.sqrt(n.sum((bins - mn)**2 * frp) / n.sum(frp))
            sk = n.sum(((bins - mn) / sq)**3 * frp) / n.sum(frp)
            ftsk = (4 - n.pi) / 2. * (delta * n.sqrt(2 / n.pi))**3 / (
                1 - 2 * delta**2 / n.pi)**(1.5)
            print 'actual skew is: ', sk
            print 'fitted skew is: ', ftsk

        frps[sep], frp_freqs = fringe.fir_to_frp(firs[sep], tbins=timebins)
        baselines = ''.join(sep2ij[sep] for sep in seps)

    for sep in seps:
        if PLOT:
            ax_frp.plot(frp_freqs * 1e3,
                        frps[sep][mychan] / n.max(frps[sep][mychan]),
                        label='{0}'.format(pad),
                        color=cmap(cnt))
            ax_firs.plot(timebins,
                         n.abs(firs[sep][mychan]),
                         label='{0}'.format(pad),
                         color=cmap(cnt))
            ax_firs.set_xlabel('s')
        envelope = n.abs(firs[sep][mychan])
        envelope /= n.max(envelope)
Exemple #8
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        if False and pad ==1:
            delta=prms0[-1]/n.sqrt(1+prms0[-1]**2)
            print 'model fit parameters: ',prms0
            print 'norm is: ', n.sum(frp)
            print 'mean is: ', n.sum(bins*frp)/n.sum(frp)
            mn= n.sum(bins*frp)/n.sum(frp)
            sq= n.sqrt(n.sum((bins-mn)**2*frp)/n.sum(frp))
            sk= n.sum(((bins-mn)/sq)**3*frp)/n.sum(frp)
            ftsk= (4-n.pi)/2.* (delta*n.sqrt(2/n.pi))**3/(1-2*delta**2/n.pi)**(1.5)
            print 'actual skew is: ', sk
            print 'fitted skew is: ', ftsk

        frps[sep], frp_freqs = fringe.fir_to_frp(firs[sep],tbins=timebins)
        baselines = ''.join(sep2ij[sep] for sep in seps)

    for sep in seps:
        if PLOT:
            ax_frp.plot(frp_freqs*1e3,frps[sep][mychan]/n.max(frps[sep][mychan]), 
                            label='{0}'.format(pad),color=cmap(cnt))
            ax_firs.plot(timebins,n.abs(firs[sep][mychan]),
                            label='{0}'.format(pad),color=cmap(cnt))
            ax_firs.set_xlabel('s')
        envelope = n.abs(firs[sep][mychan])
        envelope /= n.max(envelope)
        dt = n.sqrt(n.sum(envelope*timebins**2)/n.sum(envelope))
        dt_50 = (timebins[envelope>0.5].max() - timebins[envelope>0.5].min())
        print "pad = ", pad, "variance width = ",sep, 
        print " [s]:",int(n.round(dt)),
Exemple #9
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            timebins *= opts.data_inttime / opts.inttime

            if False and scale == 1:
                delta = prms0[-1] / n.sqrt(1 + prms0[-1]**2)
                print 'model fit parameters: ', prms0
                print 'norm is: ', n.sum(frp)
                print 'mean is: ', n.sum(bins * frp) / n.sum(frp)
                mn = n.sum(bins * frp) / n.sum(frp)
                sq = n.sqrt(n.sum((bins - mn)**2 * frp) / n.sum(frp))
                sk = n.sum(((bins - mn) / sq)**3 * frp) / n.sum(frp)
                ftsk = (4 - n.pi) / 2. * (delta * n.sqrt(2 / n.pi))**3 / (
                    1 - 2 * delta**2 / n.pi)**(1.5)
                print 'actual skew is: ', sk
                print 'fitted skew is: ', ftsk

            frps[sep], frp_freqs = fringe.fir_to_frp(firs[sep], tbins=timebins)
            baselines = ''.join(sep2ij[sep] for sep in seps)

            if PLOT:
                ax_frp.plot(frp_freqs * 1e3,
                            frps[sep][mychan] / n.max(frps[sep][mychan]),
                            label='{0}'.format(scale),
                            color=cmap(cnt))
                ax_firs.plot(timebins,
                             n.abs(firs[sep][mychan]),
                             '-',
                             label='{0}'.format(scale),
                             color=cmap(cnt),
                             linewidth=2)
                ax_firs.plot(timebins,
                             firs[sep][mychan].real,
Exemple #10
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            #firs = n.tile(gaus, (nchan,1))
            #firs = n.zeros_like(firs)
            #num_t = n.shape(firs)[1]
            #firs[:,num_t/2-int(opts.teff/42.9499)/2 : num_t/2+int(opts.teff/42.9499)/2] += 1
            #firs /= n.sqrt(n.sum(n.abs(firs)**2,axis=1).reshape(-1,1)) # normalize so that n.sum(abs(fir)**2) = 1
            #firs = firs[chans]
            top_hat = n.zeros_like(firs)
            l_hat = len(top_hat[0])
            if opts.teff:
                top_hat[:,
                        l_hat / 2 - n.int(opts.teff / 42.9499) / 2.:l_hat / 2 +
                        n.int(opts.teff / 42.9499) / 2.] += 1
            else:
                top_hat = n.ones(n.int(2232 / 42.9499))

            junk = fringe.fir_to_frp(top_hat)
            #junk = n.roll(junk, frp[0].argmax() - junk[0].argmax()  ,axis=-1)
            firs = fringe.frp_to_fir(junk)
            firs /= n.sqrt(n.sum(n.abs(top_hat)**2, axis=-1)).reshape(-1, 1)
            sys.stdout.flush()
    for k in days:
        noise_array[k] = {}
        un_filt_noise_array[k] = {}
        for bl in x[k]:
            noise_shape = list(x[k][bls_master[0]].shape)
            d_size = noise_shape[1]
            n_mult = 50.
            noise_shape[1] *= n_mult
            ns = noise(noise_shape) * NOISE
            ns1 = n.copy(ns)
            #wij = n.transpose( f[days[0]][bls_master[0]], [1,0]) #flags (time,freq)
Exemple #11
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h = a.healpix.HealpixMap(nside=64) #healpix map for the beam
xyz = h.px2crd(n.arange( h.npix() ), ncrd=3)
tx,ty,tz = n.dot(aa._eq2zen, xyz) #rotate the coordinated system to be centered on the array. This is equatorial centered at the array.
_bmx = aa[0].bm_response((tx,ty,tz),pol='x')[0]
_bmy = aa[0].bm_response((tx,ty,tz),pol='y')[0]
bmI = 0.5 * (_bmx**2 + _bmy**2)
bmI = n.where(tz > 0, bmI, 0) # only use beam values above the horizon.

bl = aa.get_baseline(0,26,'r') * .151 #baseline length in frequency.
fng = C.frf_conv.mk_fng(bl, xyz)

#get the fringe rate filter in frf_conv. aa only has one channel in it.
frp, bins = fringe.aa_to_fr_profile(aa, (0,26), 0)
tbins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[0])
frp = fringe.fir_to_frp(firs)
#frf,bins,wgt,(cen,wid) = C.frf_conv.get_optimal_kernel_at_ref(aa, 0, (0,26)) 
#bwfrs = C.frf_conv.get_beam_w_fr(aa, (0,26), ref_chan=0) 
#tbins,firs,frbins,frfs = C.frf_conv.get_fringe_rate_kernels(bwfrs,42.9,403)

#need to renormalize to get the proper scaling. firs are properly normalized.
#firs = firs[0]
#frfs = n.fft.fftshift(n.fft.fft(n.fft.ifftshift(firs), axis=-1))
#get weights.
wgts = scipy.interpolate.interp1d(bins, frp, kind='linear')
fng_wgt = wgts(fng) #gets weightings at fringes on the sky.
fng_bm = bmI * fng_wgt
#flat weighting determined by the maximum possible fringe rate for a baseline 
#and 0.
skypass = n.where(n.abs(bins) < n.max(n.abs(fng)), 1., 0)
# This is the noise level after being filtered and averaged by the application of this filter.
Exemple #12
0
        timebins, firs[sep] = fringe.frp_to_firs(frp, bins, aa.get_afreqs(), fq0=aa.get_afreqs()[mychan], limit_xtalk=True,frpad=pad)


        if False and pad ==1:
            delta=prms0[-1]/n.sqrt(1+prms0[-1]**2)
            print 'model fit parameters: ',prms0
            print 'norm is: ', n.sum(frp)
            print 'mean is: ', n.sum(bins*frp)/n.sum(frp)
            mn= n.sum(bins*frp)/n.sum(frp)
            sq= n.sqrt(n.sum((bins-mn)**2*frp)/n.sum(frp))
            sk= n.sum(((bins-mn)/sq)**3*frp)/n.sum(frp)
            ftsk= (4-n.pi)/2.* (delta*n.sqrt(2/n.pi))**3/(1-2*delta**2/n.pi)**(1.5)
            print 'actual skew is: ', sk
            print 'fitted skew is: ', ftsk

        frps[sep], frp_freqs = fringe.fir_to_frp(firs[sep],
                tbins=timebins*opts.lstbintime/opts.inttime)
        baselines = ''.join(sep2ij[sep] for sep in seps)

    #print the effective integration time for each seperation
    for sep in seps:
        print "sep",sep
        print "   NEBW T_eff = ",
        print 1.2/noise_equivalent_bandwidth(frp_freqs,frps[sep][mychan])

        if NOISE > 0.: # Create a fake EoR signal to inject
            print 'FILTERING WHITE NOSIE at {0} Jy ...'.format(NOISE) ,
            sys.stdout.flush()
            ## this last term is to make the power spectrum equal 
            ## to expected noise line if 21cmSense noise only no filter is run
            ed = noise((nchan,711))  * NOISE 
            ed1 = n.copy(ed)