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leanpipet.py
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##########################################
# functional style, uses multiprocessing #
# this version threads within processing #
##########################################
import numpy as n
import pylab as p
import applytelcal, applycals
from scipy import signal
from math import ceil
import multiprocessing as mp
import string, os, ctypes, types
import cPickle as pickle
import time as timestamp
import leanpipe_cython as lib
#import leanpipe_external as lib
import qimg_cython as qimg
#import qimg
#import cProfile
#from numpy import fft as fft
import pyfftw.interfaces.NUMPY_fft as fft
# set up libraries for reading and imaging visibility data
try:
# as a backup, casa stuff can be imported if running casapy
# from casac import casac; # Recommended by Sanjay B. (Don't ask me why this is so! :)
# ms = casac.ms();
from casa import ms
from casa import quanta as qa
print 'Imported CASA'
except ImportError:
print 'No CASA or pyrap available. Can\'t read MS data.'
def calc_hexcenters(fwhmsurvey, fwhmfield, show=0):
""" Tile a large circular area with a small circular areas. sizes are assumed to be fwhm. assumes flat sky.
"""
large = fwhmsurvey
small = fwhmfield
centers = []
(l0,m0) = (0.,0.)
centers.append((l0,m0))
l1 = l0-(small/2.)*n.cos(n.radians(60))
m1 = m0-(small/2.)*n.sin(n.radians(60))
ii = 0
while ( n.sqrt((l1-l0)**2+(m1-m0)**2) < large/2.):
l1 = l1+((-1)**ii)*(small/2.)*n.cos(n.radians(60))
m1 = m1+(small/2.)*n.sin(n.radians(60))
l2 = l1+small/2
m2 = m1
while ( n.sqrt((l2-l0)**2+(m2-m0)**2) < large/2.):
centers.append((l2,m2))
l2 = l2+small/2
l2 = l1-small/2
m2 = m1
while ( n.sqrt((l2-l0)**2+(m2-m0)**2) < large/2.):
centers.append((l2,m2))
l2 = l2-small/2
ii = ii+1
l1 = l0
m1 = m0
ii = 0
while ( n.sqrt((l1-l0)**2+(m1-m0)**2) < large/2.):
l1 = l1-((-1)**ii)*(small/2.)*n.cos(n.radians(60))
m1 = m1-(small/2.)*n.sin(n.radians(60))
l2 = l1
m2 = m1
while ( n.sqrt((l2-l0)**2+(m2-m0)**2) < large/2.):
centers.append((l2,m2))
l2 = l2+small/2
l2 = l1-small/2
m2 = m1
while ( n.sqrt((l2-l0)**2+(m2-m0)**2) < large/2.):
centers.append((l2,m2))
l2 = l2-small/2
ii = ii+1
delaycenters = n.array(centers)
if len(delaycenters) == 1:
plural = ''
else:
plural = 's'
print 'For a search area of %.3f and delay beam of %.3f, we will use %d delay beam%s' % (fwhmsurvey, fwhmfield, len(delaycenters), plural)
return delaycenters
def numpyview(arr, datatype, shape):
""" Takes mp.Array and returns numpy array with shape of data in MS.
"""
# return n.frombuffer(arr.get_obj()).view(n.dtype(datatype)).reshape((iterint, nbl, nchan, npol))
return n.frombuffer(arr.get_obj(), dtype=n.dtype(datatype)).view(n.dtype(datatype)).reshape(shape)
def spec(data0, startint, endint):
""" Function generates spectrogram data from startint to endint
"""
return data0[startint:endint].mean(axis=1).mean(axis=2).real
def detect_bispectra(ba, d, sigma=5., tol=1.3, Q=0, show=0, save=0, verbose=0):
""" Function to detect transient in bispectra
sigma gives the threshold for SNR_bisp (apparent).
tol gives the amount of tolerance in the sigma_b cut for point-like sources (rfi filter).
Q is noise per baseline and can be input. Otherwise estimated from data.
save=0 is no saving, save=1 is save with default name, save=<string>.png uses custom name (must include .png).
"""
# using s=S/Q
mu = lambda s: 1. # for bispectra formed from visibilities
sigbQ3 = lambda s: n.sqrt((1 + 3*mu(s)**2) + 3*(1 + mu(s)**2)*s**2 + 3*s**4) # from kulkarni 1989, normalized by Q**3, also rogers et al 1995
s = lambda basnr, ntr: (2.*basnr/n.sqrt(ntr))**(1/3.) # see rogers et al. 1995 for factor of 2
# measure SNR_bl==Q from sigma clipped times with normal mean and std of bispectra. put into time,dm order
bamean = ba.real.mean(axis=1)
bastd = ba.real.std(axis=1)
(meanmin,meanmax) = lib.sigma_clip(bamean) # remove rfi to estimate noise-like parts
(stdmin,stdmax) = lib.sigma_clip(bastd)
clipped = n.where((bamean > meanmin) & (bamean < meanmax) & (bastd > stdmin) & (bastd < stdmax) & (bamean != 0.0))[0] # remove rfi and zeros
bameanstd = ba[clipped].real.mean(axis=1).std()
basnr = bamean/bameanstd # = S**3/(Q**3 / n.sqrt(n_tr)) = s**3 * n.sqrt(n_tr)
if Q and verbose:
print 'Using given Q =', Q
else:
Q = ((bameanstd/2.)*n.sqrt(d['ntr']))**(1/3.)
if verbose:
print 'Estimating noise per baseline from data. Q (per DM) =', Q
# detect
cands = n.where( (bastd/Q**3 < tol*sigbQ3(s(basnr, d['ntr']))) & (basnr > sigma) ) # get compact sources with high snr
if show or save:
p.figure()
ax = p.axes()
p.subplot(211)
p.title(str(d['nskip']) + ' nskip, ' + str(len(cands))+' candidates', transform = ax.transAxes)
p.plot(basnr, 'b.')
if len(cands[0]) > 0:
p.plot(cands, basnr[cands], 'r*')
p.ylim(-2*basnr[cands].max(),2*basnr[cands].max())
p.xlabel('Integration',fontsize=12,fontweight="bold")
p.ylabel('SNR_b',fontsize=12,fontweight="bold")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 20))
ax.spines['left'].set_position(('outward', 30))
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
p.subplot(212)
p.plot(bastd/Q**3, basnr, 'b.')
# plot reference theory lines
smax = s(basnr.max(), d['nants'])
sarr = smax*n.arange(0,101)/100.
p.plot(sigbQ3(sarr), 1/2.*sarr**3*n.sqrt(d['ntr']), 'k')
p.plot(tol*sigbQ3(sarr), 1/2.*sarr**3*n.sqrt(d['ntr']), 'k--')
if len(cands[0]) > 0:
p.plot(bastd[cands]/Q**3, basnr[cands], 'r*')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 20))
ax.spines['left'].set_position(('outward', 30))
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
if len(cands[0]) > 0:
p.axis([0, tol*sigbQ3(s(basnr[cands].max(), d['nants'])), -0.5*basnr[cands].max(), 1.1*basnr[cands].max()])
# show spectral modulation next to each point
for candint in cands:
sm = n.single(round(specmod(data, d, candint),1))
p.text(bastd[candint]/Q**3, basnr[candint], str(sm), horizontalalignment='right', verticalalignment='bottom')
p.xlabel('sigma_b/Q^3',fontsize=12,fontweight="bold")
p.ylabel('SNR_b',fontsize=12,fontweight="bold")
if save:
if save == 1:
savename = d['filename'].split('.')[:-1]
savename.append(str(d['nskip']) + '_bisp.png')
savename = string.join(savename,'.')
elif isinstance(save, types.StringType):
savename = save
print 'Saving file as ', savename
p.savefig(self.pathout+savename)
return cands[0], basnr, bastd, Q
def estimate_noiseperbl(data0):
""" Takes large data array and sigma clips it to find noise per bl for input to detect_bispectra.
Takes mean across pols and channels for now, as in detect_bispectra.
"""
# define noise per baseline for data seen by detect_bispectra or image
data0mean = data0.mean(axis=3).mean(axis=2).imag # use imaginary part to estimate noise without calibrated, on-axis signal
(data0meanmin, data0meanmax) = lib.sigma_clip(data0mean.flatten())
good = n.where( (data0mean>data0meanmin) & (data0mean<data0meanmax) )
noiseperbl = data0mean[good].std() # measure single noise for input to detect_bispectra
print 'Sigma clip of %.3f to %.3f keeps %d%% of data' % (data0meanmin, data0meanmax, (100.*len(good[0]))/len(data0mean.flatten()))
print 'Estimate of noise per baseline: %.3f' % noiseperbl
return noiseperbl
def imagecands(data0, u0, v0, d, cands, clean=False, show=0, save=0):
""" Image list of candidates returned by detect_bispectra.
Assumes globals data, u, v w already defined.
"""
cands2 = []
for ii in cands:
print
print '**Imaging candidate at int_rel=%d, int_abs=%d**' % (ii, d['nskip']+d['itercount']+ii)
# make image of field
im = imageint(data0, d, ii, size=d['size'], res=d['res'], clean=clean, show=show)
imsize = float(d['size'])/d['res']
pixelscale = 1./d['size']
peakm, peakl = n.where(im == im.max())
l0 = d['l0']
m0 = d['m0']
m1 = -(imsize/2. - peakm[0])*pixelscale # indexed starting at top...
l1 = -(peakl[0] - imsize/2.)*pixelscale # indexed starting at left.
# logic on whether imaged cand is good
centerpix = d['size']/d['res']/2
peak = n.where(n.max(im) == im)
print 'Image peak of %e at (%d,%d). Center pixel %d' % (n.max(im), peak[0][0], peak[1][0], centerpix)
tmp = im[centerpix-centerpix/2:centerpix+centerpix/2, centerpix-centerpix/2:centerpix+centerpix/2]
im_std = tmp[n.where(tmp <= 0.9*tmp.max())].std() # estimate std in center half without image peak
print 'Peak/RMS (central half) = %e' % (im.max()/im_std)
# phase shift to center for spectral analysis
lib.phaseshift(data0, d, l1, m1, u0, v0)
sm = specmod(data0, d, ii)
lib.phaseshift(data0, d, l0, m0, u0, v0) # shift back to original location
print 'Spectral modulation = %.1f. Max expected = %.1f' % (sm, n.sqrt(d['nchan']/d['sigma_image']**2 + d['specmodfilter']))
if im.max()/im_std > d['sigma_image']:
if sm < n.sqrt(d['nchan']/d['sigma_image']**2 + d['specmodfilter']):
print 'Imaging shows significant point source. Spectral modulation suggests broad band signal. Saving.'
cands2.append(ii)
elif not d['specmodfilter']: # if no application of filter, fall back on imaging filter
print 'Image has SNR higher than bisp threshold. Saving'
cands2.append(ii)
# optionally plot
if show:
p.figure()
p.clf()
ax = p.axes()
# text description of candidate
p.subplot(221, axisbg='white')
p.title('Candidate @ Tier 2')
p.xticks([])
p.yticks([])
p.text(0.1, 0.8, d['filename'], fontname='sans-serif')
beamra = n.round(beam, 3)[0]
beamdec = n.round(beam, 3)[1]
p.text(0.1, 0.6, 'Beam: ' + str(beamra) + ', ' + str(beamdec) + ', dt: ' + str(d['timescales'][cand[0]]), fontname='sans-serif')
p.text(0.1, 0.4, 'Integration: ' + str(cand[1]) + ', DM: ' + str(d['dmarr'][cand[2]]), fontname='sans-serif')
p.text(0.1, 0.2, 'SNR: ' + str(n.round(candsnr, 1)), fontname='sans-serif')
# image of dispersed visibilities for candidate
p.subplot(222)
fov = n.degrees(1./d['res'])*3600. # field of view in arcseconds
p.imshow(im, aspect='auto', origin='lower', interpolation='nearest', extent=[fov/2, -fov/2, -fov/2, fov/2])
p.colorbar()
p.xlabel('RA/l Offset (arcsec)')
p.ylabel('Dec/m Offset (arcsec)')
p.subplot(223)
dataph = spec(data0, 0, len(data0))
p.plot(d['freq'], dataph[ii], '.')
p.text(0.05, 0.05, 'specmod =' + str(sm), transform = ax.transAxes)
p.xlabel('Frequency (GHz)')
p.ylabel('Flux density (Jy)')
p.subplot(224)
dataph = n.rot90(dataph)
sh = dataph.shape
im = p.imshow(dataph, aspect='auto', origin='upper', interpolation='nearest', extent=(0, sh[1], 0, sh[0]))
p.colorbar()
# p.plot(self.obs.dmtrack0[dmind][0], self.obs.dmtrack0[dmind][1],'k.') # reference dispersed track
p.xlabel('Integration')
p.ylabel('Channel')
if save:
p.savefig(string.join(d['filename'].split('.')[:-1], '.') + '_sc' + str(d['scan']) + 'sp' + str(d['spw']) + 'i' + str(cand[1]) + '_tier2_.png')
return cands2
def save(d, cands, verbose=0):
""" Save all candidates in pkl file for later aggregation and filtering.
"""
loclist = []
proplist = []
for cand in cands:
(beamnum, dtind, i, dmind, snr, img, specpol) = cand
loclist.append( [beamnum, dtind, d['nskip']+d['itercount']+i, dmind] )
proplist.append( (snr, img, specpol) ) # only one property for now. could do more by storing as list for each cand
if verbose:
print loclist, proplist
# save candidate info in pickle file
pkl = open(d['candsfile'], 'a')
pickle.dump((loclist, proplist), pkl)
pkl.close()
def imgallloop(d, dtind, dmind, beamnum):
""" Parallelizable function for imaging a chunk of data for a single dm.
runs cython qimg library for image, then filters results based on spectral modulation of candidate.
"""
# THIS ONE does the uv gridding and searches for the candidates
# NOTE, the qimg_cython.pyx defines all the different imaging algorithms Casey has written.
twindow = 15 # window to save for plotting data in pickle
# dedisperse using global 'data'
data0 = dataprep(d, dmind) # returns masked array of dedispersed and stitched data
ims,snr,candints = qimg.imgallfullfilterxy(n.outer(u[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(v[d['iterint']/2], d['freq']/d['freq_orig'][0]), data0.data, d['sizex'], d['sizey'], d['res'], d['sigma_image'])
# ims,snr,candints = qimg.imgallfullfilterxy(n.outer(u[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(v[d['iterint']/2], d['freq']/d['freq_orig'][0]), data[dmind], d['sizex'], d['sizey'], d['res'], d['sigma_image'])
# ims = qimg.imgallfullw(n.outer(u[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(v[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(w[d['iterint']/2], d['freq']/d['freq_orig'][0]), data0, d['size'], d['res'], d['wplanes'])
# data0 = n.ma.masked_array(data[dmind], data[dmind] == 0j)
# IF WE FOUND CANDIDATES, MAKE THEIR CANDIDATE PLOTS
if len(candints) > 0:
spectra = []
goodcandints = []
for i in xrange(len(candints)):
# phase shift to get dynamic spectrum
peakl, peakm = n.where(ims[i] == ims[i].max()) # assumes new style u->u and v->v gridding
l1 = (float((d['sizex'])/d['res'])/2. - peakl[0])/d['sizex']
m1 = (float((d['sizey'])/d['res'])/2. - peakm[0])/d['sizey']
# return spectrogram per pol
minint = max(candints[i]-twindow, 0)
maxint = min(candints[i]+twindow, len(data0))
data0_snip = data0[minint:maxint].copy() # get candidate data segment
lib.phaseshift_threaded(data0_snip, d, l1, m1, u[candints[i]], v[candints[i]])
snipint = min(candints[i],twindow) # correct for edge effects
# print i, candints[i], minint, maxint, peakl, peakm, snipint, data0_snip.mean()
bfspec = data0_snip[snipint].mean(axis=0).mean(axis=1).real # mean over bl and pol for known int
bflc = data0_snip.mean(axis=3).mean(axis=2).mean(axis=1).real # mean over ch, bl and pol
snrlc = bflc[snipint] / bflc[range(0,snipint-1)+range(snipint+2,twindow)].std() # lc snr of event. more accurate than img snr
sm = n.sqrt( ((bfspec**2).mean() - bfspec.mean()**2) / bfspec.mean()**2 )
if sm < n.sqrt(d['nchan']/snr[i]**2 + d['specmodfilter']):
print 'Got one! Int=%d, DM=%d, SNR_im=%.1f @ (%d,%d). SNR_lc=%.1f., SM=%.1f < %.1f, so keeping candidate.' % (d['nskip']+d['itercount']+candints[i], d['dmarr'][dmind], snr[i], peakl, peakm, snrlc, sm, n.sqrt(d['nchan']/snr[i]**2 + d['specmodfilter']))
spectra.append(data0_snip.mean(axis=1)) # get real part of spectrogram of candidate
goodcandints.append(i)
else:
print 'Almost... Int=%d, DM=%d, SNR_im=%.1f @ (%d,%d). SNR_lc=%.1f., SM=%.1f > %.1f, so rejecting candidate.' % (d['nskip']+d['itercount']+candints[i], d['dmarr'][dmind], snr[i], peakl, peakm, snrlc, sm, n.sqrt(d['nchan']/snr[i]**2 + d['specmodfilter']))
# return data coods (delta_t, int, dm) and properties (snr, image, spectrum*pol)
# return [(beamnum, dtind, candints[goodcandints[i]], dmind, snr[goodcandints[i]], ims[goodcandints[i]], spectra[i]) for i in xrange(len(goodcandints))] # bug fixed
return [(beamnum, dtind, goodcandints[i], dmind, snr[goodcandints[i]], ims[goodcandints[i]], spectra[i]) for i in xrange(len(goodcandints))] # bug in writing candints!
else:
print 'No candidates.'
return ( n.empty( (0,7) ) )
def time_filter(data0, d, width, show=0):
""" Replaces data array with filtered version via convolution in time. Note that this has trouble with zeroed data.
kernel specifies the convolution kernel. 'm' for mexican hat (a.k.a. ricker, effectively does bg subtraction), 'g' for gaussian. 't' for a tophat. 'b' is a tophat with bg subtraction (or square 'm'). 'w' is a tophat with width that varies with channel, as kept in 'twidth[dmind]'.
width is the kernel width with length nchan. should be tuned to expected pulse width in each channel.
bgwindow is used by 'b' only.
An alternate design for this method would be to make a new data array after filtering, so this can be repeated for many assumed widths without reading data in anew. That would require more memory, so going with repalcement for now.
"""
kernel = d['filtershape']
bgwindow = d['bgwindow']
if not isinstance(width, types.ListType):
width = [width] * len(d['chans'])
# time filter by convolution. functions have different normlizations. m has central peak integral=1 and total is 0. others integrate to 1, so they don't do bg subtraction.
kernelset = {} # optionally could make set of kernels. one per width needed. (used only by 'w' for now).
if kernel == 't':
print 'Applying tophat time filter.'
for w in n.unique(width):
kernel = n.zeros(len(data0)) # tophat.
onrange = range(len(kernel)/2 - w/2, len(kernel)/2 + int(ceil(w/2.)))
kernel[onrange] = 1.
kernelset[w] = kernel/n.where(kernel>0, kernel, 0).sum() # normalize to have peak integral=1, thus outside integral=-1.
elif kernel == 'b':
print 'Applying tophat time filter with bg subtraction (square mexican hat) total width=%d.' % (bgwindow)
for w in n.unique(width):
kernel = n.zeros(len(data0)) # tophat.
onrange = range(len(kernel)/2 - w/2, len(kernel)/2 + int(ceil(w/2.)))
kernel[onrange] = 1.
offrange = range(len(kernel)/2 - (bgwindow+w)/2, len(kernel)/2-w/2) + range(len(kernel)/2 + int(ceil(w/2.)), len(kernel)/2 + int(ceil((w+bgwindow)/2.)))
kernel[offrange] = -1.
posnorm = n.where(kernel>0, kernel, 0).sum() # find normalization of positive
negnorm = n.abs(n.where(kernel<0, kernel, 0).sum()) # find normalization of negative
kernelset[w] = n.where(kernel>0, kernel/posnorm, kernel/negnorm) # pos and neg both sum to 1/-1, so total integral=0
elif kernel == 'g':
print 'Applying gaussian time filter. Note that effective width is much larger than equivalent tophat width.'
for w in n.unique(width):
kernel = signal.gaussian(len(data0), w) # gaussian. peak not quite at 1 for widths less than 3, so it is later renormalized.
kernelset[w] = kernel / (w * n.sqrt(2*n.pi)) # normalize to pdf, not peak of 1.
elif kernel == 'w':
print 'Applying tophat time filter that varies with channel.'
for w in n.unique(width):
kernel = n.zeros(len(data0)) # tophat.
onrange = range(len(kernel)/2 - w/2, len(kernel)/2 + int(ceil(w/2.)))
kernel[onrange] = 1.
kernelset[w] = kernel/n.where(kernel>0, kernel, 0).sum() # normalize to have peak integral=1, thus outside integral=-1.
elif kernel == None:
print 'Applying no time filter.'
return data0
if show:
for kernel in kernelset.values():
p.plot(kernel,'.')
p.title('Time filter kernel')
p.show()
# take ffts (in time)
datafft = fft.fft(data0, axis=0)
# kernelsetfft = {}
# for w in n.unique(width):
# kernelsetfft[w] = fft.fft(n.roll(kernelset[w], len(data0)/2)) # seemingly need to shift kernel to have peak centered near first bin if convolving complex array (but not for real array?)
# **take first kernel. assumes single width in hacky way**
kernelsetfft = fft.fft(n.roll(kernelset[kernelset.keys()[0]], len(data0)/2)) # seemingly need to shift kernel to have peak centered near first bin if convolving complex array (but not for real array?)
# filter by product in fourier space
# for i in range(d['nbl']): # **can't find matrix product I need, so iterating over nbl, chans, npol**
# for j in range(len(d['chans'])):
# for k in range(d['npol']):
# datafft[:,i,j,k] = datafft[:,i,j,k]*kernelsetfft[width[j]] # index fft kernel by twidth
datafft = datafft * kernelsetfft[:,None,None,None]
# ifft to restore time series
# return n.ma.masked_array(fft.ifft(datafft, axis=0), self.flags[:self.nints,:, self.chans,:] == 0)
return fft.ifft(datafft, axis=0)
def time_filter2(data0, d, width, show=0):
data0c = n.zeros_like(data0)
for i in range(1,len(data0c)-1):
data0c[i] = data0[i] - (data0[i-1]+data0[i+1])/2
return data0c
def specmod(data0, d, ii):
"""Calculate spectral modulation for given track.
Spectral modulation is basically the standard deviation of a spectrum.
This helps quantify whether the flux is located in a narrow number of channels or across all channels.
Broadband signal has small modulation (<sqrt(nchan)/SNR) while RFI has larger values.
See Spitler et al 2012 for details.
"""
bfspec = data0[ii].mean(axis=0).mean(axis=1).real # mean over bl and pol
sm = n.sqrt( ((bfspec**2).mean() - bfspec.mean()**2) / bfspec.mean()**2 )
return sm
def imageint(data0, d, i, mode='split', size=48000, res=500, clean=True, gain=0.01, tol=1e-4, newbeam=0, save=0, show=0):
""" Use apiy to image integration
mode defines how frequency dependence is handled. 'split' means separate uv and data points in frequency (but not mfs). 'mean' means mean vis across frequency.
int is used to select uvw coordinates for track. default is first int.
pol can be 'i' for a Stokes I image (mean over pol dimension) or a pol index.
default params size and res are good for 1.4 GHz VLA, C-config image.
clean determines if image is cleaned and beam corrected. gain/tol are cleaning params.
newbeam forces the calculation of a new beam for restoring the cleaned image.
save=0 is no saving, save=1 is save with default name, save=<string>.png uses custom name (must include .png).
"""
td = data0[i].mean(axis=2) # take single integration and mean over pol axis
# apply w phase rotation. generally this is done externally (e.g., by data writing software) and is not needed here.
# wrot = lambda w: n.exp(-2j*n.pi*n.outer(w, self.freq/self.freq_orig[0]))
# td = td*wrot(self.w[i])
# define handling of freq axis
if mode == 'split':
td = td.flatten()
uu = n.outer(u[i], d['freq']/d['freq_orig'][0]).flatten()
vv = n.outer(v[i], d['freq']/d['freq_orig'][0]).flatten()
ww = n.outer(w[i], d['freq']/d['freq_orig'][0]).flatten()
# uu = n.outer(d['u0'][i], d['freq']/d['freq_orig'][0]).flatten()
# vv = n.outer(d['v0'][i], d['freq']/d['freq_orig'][0]).flatten()
# ww = n.outer(d['w0'][i], d['freq']/d['freq_orig'][0]).flatten()
elif mode == 'mean':
td = td.mean(axis=1)
uu = u[i]
vv = v[i]
ww = w[i]
# uu = d['u0'][i]
# vv = d['v0'][i]
# ww = d['w0'][i]
else:
print 'Mode must be \'mean\' or \'split\'.'
return 0
fov = n.degrees(1./res)*3600. # field of view in arcseconds
# make image
ai = aipy.img.Img(size=size, res=res)
uvw_new, td_new = ai.append_hermitian( (uu, vv, ww), td)
ai.put(uvw_new, td_new)
image = ai.image(center = (size/res/2, size/res/2))
image_final = image
# optionally clean image
if clean:
print 'Cleaning image...'
beam = ai.bm_image()
beamgain = aipy.img.beam_gain(beam[0])
(cleanim, dd) = aipy.deconv.clean(image, beam[0], verbose=True, gain=gain, tol=tol)
try:
import gaussfitter
print 'Restoring image with fit to beam shape...'
beam_centered = ai.bm_image(center=(size/res/2, size/res/2))
peak = n.where(beam_centered[0] >= 0.1*beam_centered[0].max(), beam_centered[0], 0.)
beam_params = gaussfitter.gaussfit(peak)
kernel = n.roll(n.roll(gaussfitter.twodgaussian(beam_params, shape=n.shape(beam[0])), size/res/2, axis=0), size/res/2, axis=1) # fit to beam at center, then roll to corners for later convolution step
except ImportError:
print 'Restoring image with peak of beam...'
kernel = n.where(beam[0] >= 0.4*beam[0].max(), beam[0], 0.) # take only peak (gaussian part) pixels of beam image
restored = aipy.img.convolve2d(cleanim, kernel)
image_restored = (restored + dd['res']).real/beamgain
image_final = image_restored
if show or save:
p.figure()
ax = p.axes()
ax.set_position([0.2,0.2,0.7,0.7])
# im = p.imshow(image_final, aspect='auto', origin='upper', interpolation='nearest', extent=[-fov/2, fov/2, -fov/2, fov/2])
im = p.imshow(image_final, aspect='auto', origin='lower', interpolation='nearest', extent=[fov/2, -fov/2, -fov/2, fov/2])
cb = p.colorbar(im)
cb.set_label('Flux Density (Jy)',fontsize=12,fontweight="bold")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 20))
ax.spines['left'].set_position(('outward', 30))
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
p.xlabel('RA/l Offset (arcsec)',fontsize=12,fontweight="bold")
p.ylabel('Dec/m Offset (arcsec)',fontsize=12,fontweight="bold")
centerpix = size/res/2
peak = n.where(n.max(image_final) == image_final)
print 'Image peak of %e at (%d,%d)' % (n.max(image_final), peak[0][0], peak[1][0])
print 'Center pixel coord: %d' % (centerpix)
tmp = image_final[centerpix-centerpix/2:centerpix+centerpix/2, centerpix-centerpix/2:centerpix+centerpix/2]
image_std = tmp[n.where(tmp <= 0.9*tmp.max())].std() # estimate std in center half without image peak
# print 'Peak/RMS = %e' % (tmpimage.max()/image_std)
print 'Peak/RMS (central half) = %e' % (image_final.max()/image_std)
if save:
if save == 1:
savename = d['filename'].split('.')[:-1]
savename.append(str(d['nskip']) + '_im.png')
savename = string.join(savename,'.')
elif isinstance(save, string):
savename = save
print 'Saving file as ', savename
p.savefig(savename)
return image_final
def readprep(d):
""" Prepare to read data
"""
filename = d['filename']; spw = d['spw']; iterint = d['iterint']; datacol = d['datacol']; selectpol = d['selectpol']
scan = d['scan']; d['npol'] = len(d['selectpol']); nints = d['nints']; nskip = d['nskip']
# read metadata either from pickle or ms file
pklname = string.join(filename.split('.')[:-1], '.') + '_init.pkl'
if os.path.exists(pklname):
print 'Pickle of initializing info found. Loading...'
pkl = open(pklname, 'r')
try:
(d['npol_orig'], d['nbl'], d['blarr'], d['inttime'], spwinfo, scansummary) = pickle.load(pkl)
except EOFError:
print 'Bad pickle file. Exiting...'
return 1
scanlist = sorted(scansummary.keys())
starttime_mjd = scansummary[scanlist[scan]]['0']['BeginTime']
else:
print 'No pickle of initializing info found. Making anew...'
pkl = open(pklname, 'wb')
ms.open(filename)
spwinfo = ms.getspectralwindowinfo()
scansummary = ms.getscansummary()
ms.selectinit(datadescid=0) # reset select params for later data selection
selection = {'uvdist': [1., 1e10]} # exclude auto-corrs
ms.select(items = selection)
ms.selectpolarization(selectpol)
scanlist = sorted(scansummary.keys())
starttime_mjd = scansummary[scanlist[scan]]['0']['BeginTime']
d['inttime'] = scansummary[scanlist[scan]]['0']['IntegrationTime']
print 'Initializing integration time (s):', d['inttime']
ms.iterinit(['TIME'], iterint*d['inttime'])
ms.iterorigin()
da = ms.getdata([datacol, 'axis_info'], ifraxis=True)
ms.close()
d['nbl'] = da[datacol].shape[2]
bls = da['axis_info']['ifr_axis']['ifr_shortname']
d['blarr'] = n.array([[int(bls[i].split('-')[0]),int(bls[i].split('-')[1])] for i in xrange(len(bls))])
d['npol'] = len(selectpol)
d['npol_orig'] = da[datacol].shape[0]
print 'Initializing %d polarizations' % (d['npol'])
pickle.dump((d['npol_orig'], d['nbl'], d['blarr'], d['inttime'], spwinfo, scansummary), pkl)
pkl.close()
# set ants
d['ants'] = n.unique(d['blarr'])
d['nants'] = len(n.unique(d['blarr']))
print 'Initializing nants:', d['nants']
print 'Initializing nbl:', d['nbl']
# define list of spw keys (may not be in order!)
freqs = []
for i in spwinfo.keys():
freqs.append(spwinfo[i]['Chan1Freq'])
d['spwlist'] = n.array(sorted(zip(freqs, spwinfo.keys())))[:,1][spw].astype(int) # spwlist defines order of spw to iterate in freq order
d['freq_orig'] = n.array([])
for spw in d['spwlist']:
nch = spwinfo[str(spw)]['NumChan']
ch0 = spwinfo[str(spw)]['Chan1Freq']
chw = spwinfo[str(spw)]['ChanWidth']
d['freq_orig'] = n.concatenate( (d['freq_orig'], (ch0 + chw * n.arange(nch)) * 1e-9) ).astype('float32')
d['freq'] = d['freq_orig'][d['chans']]
d['nchan'] = len(d['chans'])
print 'Initializing nchan:', d['nchan']
# set requested time range based on given parameters
timeskip = d['inttime']*nskip
starttime = qa.getvalue(qa.convert(qa.time(qa.quantity(starttime_mjd+timeskip/(24.*60*60),'d'),form=['ymd'], prec=9)[0], 's'))[0]
stoptime = qa.getvalue(qa.convert(qa.time(qa.quantity(starttime_mjd+(timeskip+(nints+1)*d['inttime'])/(24.*60*60), 'd'), form=['ymd'], prec=9)[0], 's'))[0] # nints+1 to be avoid buffer running out and stalling iteration
print 'First integration of scan:', qa.time(qa.quantity(starttime_mjd,'d'),form=['ymd'],prec=9)[0]
print
print 'Reading scan', str(scanlist[scan]) ,'for times', qa.time(qa.quantity(starttime_mjd+timeskip/(24.*60*60),'d'),form=['hms'], prec=9)[0], 'to', qa.time(qa.quantity(starttime_mjd+(timeskip+(nints+1)*d['inttime'])/(24.*60*60), 'd'), form=['hms'], prec=9)[0]
# read data into data structure
ms.open(filename)
if len(d['spwlist']) == 1:
ms.selectinit(datadescid=d['spwlist'][0])
else:
ms.selectinit(datadescid=0, reset=True) # reset includes spw in iteration over time
selection = {'time': [starttime, stoptime], 'uvdist': [1., 1e10]} # exclude auto-corrs
ms.select(items = selection)
ms.selectpolarization(selectpol)
ms.iterinit(['TIME'], iterint*d['inttime'], 0, adddefaultsortcolumns=False) # read with a bit of padding to get at least nints
iterstatus = ms.iterorigin()
d['itercount1'] = 0
d['l0'] = 0.; d['m0'] = 0.
# dynamically set a uvgrid for whole run
if d['res'] == 0:
d['res'] = n.round(25./(3e-1/d['freq'][len(d['freq'])/2])/2).astype('int') # full field of view. assumes freq in GHz
if d['size'] == 0:
da = ms.getdata(['u','v','w'])
uu = n.outer(da['u'], d['freq']).flatten() * (1e9/3e8)
vv = n.outer(da['v'], d['freq']).flatten() * (1e9/3e8)
d['sizex'] = n.round(uu.max() - uu.min()).astype('int')
d['sizey'] = n.round(vv.max() - vv.min()).astype('int')
print 'Ideal uvgrid size=(%d,%d) and res=%d' % (d['sizex'], d['sizey'], d['res'])
powers = n.fromfunction(lambda i,j: 2**i*3**j, (12,8), dtype='int') # power array for 2**i * 3**j
largerx = n.where(powers-d['sizex']/d['res'] > 0, powers, powers[-1,-1])
p2x, p3x = n.where(largerx == largerx.min())
d['sizex'] = ((2**p2x * 3**p3x)*d['res'])[0]
largery = n.where(powers-d['sizey']/d['res'] > 0, powers, powers[-1,-1])
p2y, p3y = n.where(largery == largery.min())
d['sizey'] = ((2**p2y * 3**p3y)*d['res'])[0]
print 'Using uvgrid size=(%d,%d) (2**(%d,%d)*3**(%d,%d) = (%d,%d)) and res=%d' % (d['sizex'], d['sizey'], p2x, p2y, p3x, p3y, 2**p2x*3**p3x, 2**p2y*3**p3y, d['res'])
d['size'] = max(d['sizex'], d['sizey'])
print 'Image memory usage for %d threads is %d GB' % (d['nthreads'], 8 * d['sizex']/d['res'] * d['sizey']/d['res'] * d['iterint'] * d['nthreads']/1024**3)
return iterstatus
def readiter(d):
""" Iterates over ms.
Returns everything needed for analysis as tuple.
"""
da = ms.getdata([d['datacol'],'axis_info','u','v','w','flag','data_desc_id'], ifraxis=True)
# spws = n.unique(da['data_desc_id']) # spw in use
# good = n.where((da['data_desc_id']) == spws[0])[0] # take first spw
good = n.where((da['data_desc_id']) == d['spwlist'][0])[0] # take first spw
time0 = da['axis_info']['time_axis']['MJDseconds'][good]
data0 = n.transpose(da[d['datacol']], axes=[3,2,1,0])[good]
if d['telcalfile']: # apply telcal solutions
if len(d['spwlist']) > 1:
spwbin = d['spwlist'][0]
else:
spwbin = 0
chanfreq = da['axis_info']['freq_axis']['chan_freq'][:,spwbin]
sols = applytelcal.solutions(d['telcalfile'], chanfreq)
for i in range(len(d['selectpol'])):
try:
sols.setselection(d['telcalcalibrator'], time0[0]/(24*3600), d['selectpol'][i], verbose=0) # chooses solutions closest in time that match pol and source name
sols.apply(data0, d['blarr'], i)
print 'Applied cal for spw %d and pol %s' % (spwbin, d['selectpol'][i])
except:
pass
flag0 = n.transpose(da['flag'], axes=[3,2,1,0])[good]
u0 = da['u'].transpose()[good] * d['freq_orig'][0] * (1e9/3e8) # uvw are in m, so divide by wavelength of first chan to set in lambda
v0 = da['v'].transpose()[good] * d['freq_orig'][0] * (1e9/3e8)
w0 = da['w'].transpose()[good] * d['freq_orig'][0] * (1e9/3e8)
if len(d['spwlist']) > 1:
for spw in d['spwlist'][1:]:
good = n.where((da['data_desc_id']) == spw)[0]
data1 = n.transpose(da[d['datacol']], axes=[3,2,1,0])[good]
if d['telcalfile']: # apply telcal solutions
chanfreq = da['axis_info']['freq_axis']['chan_freq'][:,spw]
sols = applytelcal.solutions(d['telcalfile'], chanfreq)
for i in range(len(d['selectpol'])):
try:
sols.setselection(d['telcalcalibrator'], time0[0]/(24*3600), d['selectpol'][i], verbose=0) # chooses solutions closest in time that match pol and source name
sols.apply(data1, d['blarr'], i)
print 'Applied cal for spw %d and pol %s' % (spw, d['selectpol'][i])
except:
pass
data0 = n.concatenate( (data0, data1), axis=2 )
flag0 = n.concatenate( (flag0, n.transpose(da['flag'], axes=[3,2,1,0])[good]), axis=2 )
del da
data0 = data0[:,:,d['chans'],:]
if d['gainfile']:
sols = applycals.solutions(d['gainfile'], fluxfile=d['fluxfile'], flagants=d['flagantsol'])
sols.parsebp(d['bpfile'])
# sols.setselection(time0[0]/(24*3600.), d['freq']*1e9, d['spw'], d['selectpol'])
sols.setselection(time0[0]/(24*3600.), d['freq']*1e9, [0,1], [0,1]) # only dualpol, 2sb mode implemented
sols.apply(data0, d['blarr'])
d['iterstatus1'] = ms.iternext()
return data0.astype('complex64'), flag0[:,:,d['chans'],:], u0.astype('float32'), v0.astype('float32'), w0.astype('float32'), time0.astype('float32')
def dataprep(d, dmind):
""" Takes most recent data read and dedisperses with white space. also adds previously trimmed data.
data2 is next iteration of data of size iterint by ...
"""
if d['datadelay'][dmind] > 0: # if doing dedispersion...
# data2 = n.concatenate( (n.zeros_like(datatrim[dmind]), data1), axis=0) # prepend with zeros of length=dm delay
data2 = n.concatenate( (n.zeros( (d['datadelay'][dmind],d['nbl'],d['nchan'],d['npol']), dtype='complex64'), data), axis=0) # prepend with zeros of length=dm delay
lib.dedisperse_resample(data2, d['freq'], d['inttime'], d['dmarr'][dmind], verbose=0) # dedisperses data. data0dm=data0 for now
for i in xrange(len(datatrim[dmind])):
data2[i] = data2[i] + datatrim[dmind][i]
datatrim[dmind][:] = data2[d['iterint']/d['resamplearr'][dmind]:(d['datadelay'][dmind] + d['iterint'])/d['resamplearr'][dmind]]
else: # if no dedispersion
data2 = data
return n.ma.masked_array(data2[:d['iterint']/d['resamplearr'][dmind]], data2[:d['iterint']/d['resamplearr'][dmind]] == 0j)
# def dataprep(d, dmind, datatrim, data1): # original version blocks for large dedisp
# """ Takes most recent data read and dedisperses with white space. also adds previously trimmed data.
# """
# if d['datadelay'][dmind] > 0: # if doing dedispersion...
# data2 = n.concatenate( (n.zeros_like(datatrim[dmind]), data1), axis=0) # prepend with zeros of length=dm delay
# lib.dedisperse_threaded(data2, d, d['dmarr'][dmind], verbose=0) # dedisperses data. data0dm=data0 for now
# for i in xrange(len(datatrim[dmind])):
# data2[i] = data2[i] + datatrim[dmind][i]
# datatrim[dmind][:] = data2[-d['datadelay'][dmind]:]
# else: # if no dedispersion
# data2 = data1.copy()
# return n.ma.masked_array(data2[:d['iterint']], data2[:d['iterint']] == 0j)
def readloop(d, eproc, emove):
""" Data generating stage of parallel data function.
data is either read into 'data' buffer, when ready
this keeps data reading bottleneck to 1x the read time.
"""
def dataflag(sigma=3., specmod=2., mode='blstd'):
print 'Flaggging bad visibilities within pol/spw groups...'
chperspw = len(d['freq_orig'])/len(d['spw'])
for spw in range(len(d['spw'])):
freqs = d['freq_orig'][spw*chperspw:(spw+1)*chperspw] # find chans for spw. only works for 2 or more sb
chans = n.array([i for i in range(len(d['freq'])) if d['freq'][i] in freqs])
for pol in range(len(d['selectpol'])):
if mode == 'blstd':
blstd = data1[:,:,chans,pol].std(axis=1) # high std over baseline as fcn of time,freq selects RFI well. also bright transients off axis...
blstd = n.ma.masked_array(blstd, blstd==0j)
ww = n.where(blstd > n.median(blstd) + sigma*blstd.std())
flagfrac = float(len(ww[0])*d['nbl'])/data1.size
print 'Blstd flagging for (spw %d, pol %d), %d sigma flag fraction %3.5f %%' % (spw,pol,sigma,flagfrac*100)
for i in xrange(len(ww[0])):
data1[ww[0][i],:,chans[ww[1][i]],pol] = 0j # must be a better way of doing this!?
elif mode == 'badchan':
blstdmean = data1[:,:,chans,pol].std(axis=1).mean(axis=0)
blstdmean = n.ma.masked_array(blstdmean, blstdmean==0j)
ww = n.where(blstdmean > blstdmean.mean() + sigma * blstdmean.std())
flagfrac = float(len(ww[0])*len(chans)*len(data1))/data1.shape[2]
print 'Badchan flagging for (spw %d, pol %d), %d sigma flag fraction %3.5f %%' % (spw,pol,sigma,flagfrac*100)
data1[:,:,chans[list(ww[0])],pol] = 0j # must be a better way of doing this!?
elif mode == 'scrapebox':
meanamp = n.abs(data1[:,:,chans,pol]).mean(axis=1)
meanamp = n.ma.masked_array(meanamp, meanamp==0j)
sh = meanamp.shape
sx = 7; sy = 7
flagged = 0
for i in range(sh[0]/sx):
for j in range(sh[1]/sy):
mm = n.median(meanamp[i*sx:(i+1)*sx, j*sy:(j+1)*sy])
if mm > n.median(meanamp)*sigma:
data1[i*sx:(i+1)*sx,:,chans[j*sy:(j+1)*sy],pol] = 0j
flagged += sx*sy*len(data1[0])
flagfrac = float(flagged)/data1.size
print 'Scrape flagging for (spw %d, pol %d), %.1f sigma flag fraction %3.5f %%' % (spw,pol,sigma,flagfrac*100)
elif mode == 'medcht':
meanamp = n.abs(data1[:,:,chans,pol]).mean(axis=1)
meanamp = n.ma.masked_array(meanamp, meanamp==0j)
medall = n.median(meanamp)
medch = n.median(meanamp, axis=0)
medt = n.median(meanamp, axis=1)
badch = n.where(medch > sigma*medall)
if len(badch[0]):
data1[:,:,chans[badch],pol] = 0j
badt = n.where(medt > sigma*medall)
if len(badt[0]):
data1[:,:,chans,pol][badt] = 0j
flagfrac = d['nbl']*float(len(chans)*len(badt[0]) + len(data1)*len(badch[0]))/data1.size
print 'Median flagging of %d chans and %d ints for (spw %d, pol %d). %.1f sigma flag fraction %3.5f %%' % (len(badch[0]), len(badt[0]), spw,pol,sigma,flagfrac*100)
elif mode == 'badbp':
bpa = n.abs(data1[:,:,chans,pol]).mean(axis=0).mean(axis=1)
bpa_ant = n.array([ (bpa[n.where(n.any(d['blarr'] == i, axis=1))[0]]).mean() for i in n.unique(d['blarr']) ])
bpa_ant = n.ma.masked_array(bpa_ant, bpa_ant==0)
ww = n.unique(d['blarr'])[n.where(bpa_ant > bpa_ant.mean() + sigma * bpa_ant.std())[0]]
print 'Bad basepol flagging for (spw %d, pol %d) on ants %s' % (spw,pol,ww)
badants = n.where([n.any(a1 in ww) or n.any(a2 in ww) for (a1,a2) in d['blarr']])[0]
for badant in badants:
data1[:,badant,chans,pol] = 0j # must be a better way of doing this!?
elif mode == 'ring':
# find bls with ringing due to strong RFI. sigma=2 seems to work well.
spfft = n.abs(n.fft.ifft(data1[...,chans,pol].mean(axis=0), axis=1)) # delay spectrum of mean data in time
spfft = n.ma.masked_array(spfft, spfft = 0)
badbls = n.where(spfft[:,3*len(chans)/8:len(chans)/2].mean(axis=1) > sigma*n.median(spfft[:,1:], axis=1))[0] # find bls with spectral power at large delay. ignore dc in case this is cal scan.
print 'Ringing flagging for (spw %d, pol %d) on bls %s' % (spw,pol,badbls)
if len(badbls) > d['nbl']/3: # if many bls affected, flag all
data1[:,:,chans,pol] = 0j
else:
for badbl in badbls:
data1[:,badbl,chans,pol] = 0j
else:
print 'Not flagging based on data quality.'
# now start main work of readloop
iterint = d['iterint']; nbl = d['nbl']; nchan = d['nchan']; npol = d['npol']
# data1_mem = mp.sharedctypes.RawArray(ctypes.c_float, (d['iterint']*d['nbl']*d['nchan']*d['npol']*2)) # x2 to store complex values in single array
data1_mem = mp.Array(ctypes.c_float, (d['iterint']*d['nbl']*d['nchan']*d['npol']*2)) # x2 to store complex values in single array
data1 = numpyview(data1_mem, 'complex64', (d['iterint'], d['nbl'], d['nchan'], d['npol']))
datacap, flagcap, ucap, vcap, wcap, timecap = readiter(d) # read "cap", a hack to make sure any single iteration has enough integrations (artifact of irregular inttime)
print 'Read first iteration with shape', datacap.shape
while 1:
# name = mp.current_process().name
# print '%s: filling buffer' % name
datanext, flagnext, unext, vnext, wnext, timenext = readiter(d)
print 'Read next %d ints from iter %d' % (len(datanext), d['itercount1']+iterint)
datanext = n.vstack((datacap,datanext))
flagnext = n.vstack((flagcap,flagnext))
unext = n.vstack((ucap,unext))
vnext = n.vstack((vcap,vnext))
wnext = n.vstack((wcap,wnext))
timenext = n.concatenate((timecap,timenext))
if ((len(datanext) < iterint) and d['iterstatus1']): # read once more if data buffer is too small. don't read if no data! iterator gets confused.
datanext2, flagnext2, unext2, vnext2, wnext2, timenext2 = readiter(d)
print 'Read another %d ints for iter %d' % (len(datanext2), d['itercount1']+iterint)
datanext = n.vstack((datanext,datanext2))
flagnext = n.vstack((flagnext,flagnext2))
unext = n.vstack((unext,unext2))
vnext = n.vstack((vnext,vnext2))
wnext = n.vstack((wnext,wnext2))
timenext = n.concatenate((timenext,timenext2))
del datanext2, flagnext2, unext2, vnext2, wnext2, timenext2 # clean up
# select just the next iteration's worth of data and metadata. leave rest for next iteration's buffer cap.
if len(datanext) >= iterint:
data1[:] = datanext[:iterint]
datacap = datanext[iterint:] # save rest for next iteration
flag1 = flagnext[:iterint]
flagcap = flagnext[iterint:]
u1 = unext[:iterint]
ucap = unext[iterint:]
v1 = vnext[:iterint]
vcap = vnext[iterint:]
w1 = wnext[:iterint]
wcap = wnext[iterint:]
time1 = timenext[:iterint]
timecap = timenext[iterint:]
# optionally can insert transient here
# lib.phaseshift(data1, d, n.radians(0.1), n.radians(0.), u, v) # phase shifts data in place
# data1[100] = data1[100] + 10+0j
# lib.phaseshift(data1, d, n.radians(0.), n.radians(0.1), u, v) # phase shifts data in place
# flag data before moving into place
# bg subtract in time
if d['filtershape']:
if d['filtershape'] == 'z': # 'z' means do zero-mean subtraction in time
pass
else: # otherwise do fft convolution
data1[:] = time_filter(data1, d, 1) # assumes pulse width of 1 integration
# flag data
if d['flagmode'] == 'scrape1.5badchan2badbp32':
dataflag(sigma=1.5, mode='scrapebox')
dataflag(sigma=2., mode='badchan')
dataflag(sigma=3., mode='badbp')
dataflag(sigma=2., mode='badbp')
elif d['flagmode'] == 'medcht1.5badbp2':
dataflag(sigma=1.5, mode='medcht')
dataflag(sigma=2., mode='badbp')
elif d['flagmode'] == 'medcht1.5badbp2blstd3':
dataflag(sigma=1.5, mode='medcht')
dataflag(sigma=2., mode='badbp')
dataflag(sigma=3., mode='blstd')
elif d['flagmode'] == 'ring2medcht1.5badbp2blstd3':
dataflag(sigma=2., mode='ring')
dataflag(sigma=1.5, mode='medcht')
dataflag(sigma=2., mode='badbp')
dataflag(sigma=3., mode='blstd')
elif d['flagmode'] == 'badchan32badbp32':
dataflag(sigma=3., mode='badbp')
dataflag(sigma=2., mode='badbp')
dataflag(sigma=3., mode='badchan')
dataflag(sigma=2., mode='badchan')
else:
print 'No real-time flagging.'
if d['filtershape'] == 'z':
datamean = n.ma.masked_array(data1, data1 == 0j).mean(axis=0)
data1[:] = data1 - datamean
# write noise pkl with: itercount, noiseperbl, zerofrac, imstd_midtdm0
noiseperbl = estimate_noiseperbl(data1)
if d['savecands']:
imstd = qimg.imgonefullxy(n.outer(u1[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(v1[d['iterint']/2], d['freq']/d['freq_orig'][0]), data1[d['iterint']/2], d['sizex'], d['sizey'], d['res']).std()
zerofrac = float(len(n.where(data1 == 0j)[0]))/data1.size
noisefile = 'noise_' + string.join(d['candsfile'].split('_')[1:-1], '_') + '.pkl'
pkl = open(noisefile,'a')
pickle.dump( (d['itercount1'], noiseperbl, zerofrac, imstd), pkl )
pkl.close()
# emove is THE MOVE EVENT THAT WAITS FOR PROCESSOR TO TELL IT TO GO
# wait for signal to move everything to processing buffers
print 'Ready to move data into place for itercount ', d['itercount1']
emove.wait()
emove.clear()
# pool = mp.Pool(processes=d['nthreads'])
# resultlist = []
# if n.any(data1): # doesn't parallelize well. blocking during memory access?
# for dmind in range(len(d['dmarr'])):
# print 'Dedispersing DM = %d (max %d)' % (d['dmarr'][dmind], d['dmarr'][-1])
# result = pool.apply_async(dataprep, [d, dmind, data1])
# # result = pool.apply_async(dataprep, [d, dmind, datatrim[dmind].copy(), data1.copy()])
# resultlist.append(result)
# else:
# print 'Read data is zeros. Moving on...'
# for dmind in range(len(resultlist)):
# # data[dmind][:], datatrim[dmind] = resultlist[dmind].get()
# data[dmind][:] = resultlist[dmind].get()
#
# pool.close()
# pool.join()
data[:] = data1[:]
# flag[:] = flag1[:]
u[:] = u1[:]
v[:] = v1[:]
w[:] = w1[:]
time[:] = time1[:]
d['itercount'] = d['itercount1']
d['iterstatus'] = d['iterstatus1']
d['itercount1'] += iterint
eproc.set() # reading buffer filled, start processing
# NOW MAKE SURE ALL ENDS GRACEFULLY
else:
print 'End of data (in buffer)'
d['iterstatus'] = False # to force processloop to end
eproc.set()
ms.iterend()
ms.close()
break
if not d['iterstatus']: # using iterstatus1 is more conservative here. trying to get around hangup on darwin.