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papervis.py
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#! /usr/bin/env python
"""papervis.py - Script to load and visualize paper data
claw, 10may2012
"""
import sys, string, os, shutil
#import cProfile
from os.path import join
#import mirtask
#from mirtask import uvdat, keys, util
from mirtask import util
from mirexec import TaskInvert, TaskClean, TaskRestore, TaskImFit, TaskCgDisp, TaskImStat, TaskUVFit
import miriad, pickle
import numpy as n
import pylab as p
import scipy.optimize as opt
import scipy.stats.morestats as morestats
#from threading import Thread
#from matplotlib.font_manager import fontManager, FontProperties
#font= FontProperties(size='x-small');
class paper:
def __init__(self, file, nints=1, nskip=0, nocal=False, nopass=False):
# initialize
self.nchan = 256
nchan = self.nchan
li = range(nchan)
self.chans = n.array(li)
self.nants = 64
self.nbl = 2080
nbl = self.nbl
initsize = nints*self.nbl # number of integrations to read in a single chunk
self.approxuvw = True # flag to make template visibility file to speed up writing of dm track data
self.pulsewidth = 0.0 * n.ones(len(self.chans)) # pulse width of crab and m31 candidates
self.dmarr = [0.] # crab
self.nskip = int(nskip*self.nbl) # number of iterations to skip (for reading in different parts of buffer)
nskip = int(self.nskip)
self.file = file
# load data
vis = miriad.VisData(file,)
da = n.zeros((initsize,nchan),dtype='complex64')
fl = n.zeros((initsize,nchan),dtype='bool')
pr = n.zeros((initsize,5),dtype='float64')
# read data into python arrays
i = 0
for inp, preamble, data, flags in vis.readLowlevel ('dsl3', False, nocal=nocal, nopass=nopass):
# Loop to skip some data and read shifted data into original data arrays
if i == 0:
# get few general variables
self.nants0 = inp.getScalar ('nants', 0)
self.inttime0 = inp.getScalar ('inttime', 10.0)
self.nspect0 = inp.getScalar ('nspect', 0)
self.nwide0 = inp.getScalar ('nwide', 0)
self.sdf0 = inp.getScalar ('sdf', self.nspect0)
self.nschan0 = inp.getScalar ('nschan', self.nspect0)
self.ischan0 = inp.getScalar ('ischan', self.nspect0)
self.sfreq0 = inp.getScalar ('sfreq', self.nspect0)
self.restfreq0 = inp.getScalar ('restfreq', self.nspect0)
self.pol0 = inp.getScalar ('pol')
self.sfreq = self.sfreq0
self.sdf = self.sdf0
if i < nskip:
i = i+1
continue
if (i-nskip) < initsize:
da[i-nskip] = data
fl[i-nskip] = flags
pr[i-nskip] = preamble
else:
break # stop at initsize
if not (i % (nbl*50)):
print 'Read integration ', str(i/nbl)
# if i == nbl*50:
# print pr[:nbl,4]
# if i > 0:
# if pr[:nbl,4][0] != pr[i-nbl:i,4][0]:
# print "Whoops! dropped one..."
i = i+1
if i < initsize:
print 'Array smaller than initialized array. Trimming.'
da = da[0:i-nskip]
fl = fl[0:i-nskip]
pr = pr[0:i-nskip]
try:
self.rawdata = da.reshape((i-nskip)/nbl,nbl,nchan)
self.flags = fl.reshape((i-nskip)/nbl,nbl,nchan)
self.preamble = pr
time = self.preamble[::nbl,3]
self.reltime = 24*3600*(time - time[0]) # relative time array in seconds. paper times change...?
print
print 'Data read!'
print 'Shape of raw data, flags, time:'
print self.rawdata.shape, self.flags.shape, self.reltime.shape
print 'First time (s from day): ', (time[0]-int(time[0]))*24*3600
print
except ValueError:
print 'Could not reshape data arrays. Incomplete read?'
def prep(self):
"""
Reshapes data for usage by other functions.
Note that it assumes that any integration with bad data has an entire baseline flagged.
"""
rawdata = self.rawdata
flags = self.flags
data = (rawdata * flags) #[:,:,self.chans]
# totallen = data[flags[:,:,self.chans]].shape[0]
# tlen = data.shape[0]
# chlen = len(self.chans)
# self.data = n.reshape(data[flags[:,:,self.chans]], (tlen, totallen/(tlen*chlen), chlen)) # data is what is typically needed
# self.rawdata = (rawdata * flags)
self.data = data
self.rawdata = rawdata
self.dataph = (self.data.mean(axis=1)).real #dataph is summed and detected to form TP beam at phase center
print 'Data flagged, trimmed in channels, and averaged across baselines.'
print 'New rawdata, data, dataph shapes:'
print self.rawdata.shape, self.data.shape, self.dataph.shape
def spec(self, save=0):
chans = self.chans
reltime = self.reltime
mean = self.dataph.mean()
std = self.dataph.std()
# abs = (self.dataph - mean)/std
abs = self.dataph
print 'Data mean, std: %f, %f' % (mean, std)
p.figure(1)
# ax = p.imshow(n.rot90(abs), aspect='auto', origin='upper', interpolation='nearest', extent=(min(reltime),max(reltime),0,len(chans)), vmin=-4, vmax=4)
ax = p.imshow(n.rot90(abs), aspect='auto', origin='upper', interpolation='nearest', extent=(0,len(reltime),0,len(chans)))
p.colorbar(ax)
p.yticks(n.arange(0,len(self.chans),16), (self.chans[(n.arange(0,len(self.chans), 16))]))
p.xlabel('Time (integration)')
p.ylabel('Channel') # (flagged data removed)')
if save:
savename = self.file.split('.')[:-1]
savename.append(str(self.nskip/self.nbl) + '.spec.png')
savename = string.join(savename,'.')
print 'Saving file as ', savename
p.savefig(savename)
p.clf()
else:
p.show()
def fitspec(self, obsrms=0, save=0):
"""
Fits a powerlaw to the mean spectrum at the phase center.
Returns fit parameters.
"""
# logname = self.file.split('_')[0:3]
# logname.append('fitsp.txt')
# logname = string.join(logname,'_')
# log = open(logname,'a')
freq = self.sfreq + self.chans * self.sdf # freq array in GHz
# estimage of vis rms per channel from spread in imag space at phase center
if obsrms == 0:
print 'estimating obsrms from imaginary part of data...'
# obsrms = n.std((((self.data).mean(axis=1)).mean(axis=0)).imag)/n.sqrt(2) # sqrt(2) scales it to an amplitude error. indep of signal.
obsrms = n.std((((self.data).mean(axis=1)).mean(axis=0)).imag) # std of imag part is std of real part
# spec = n.abs((((self.data).mean(axis=1))).mean(axis=0))
spec = ((((self.data).mean(axis=1))).mean(axis=0)).real
print 'obsrms = %.2f' % (obsrms)
plaw = lambda a, b, x: a * (x/x[0]) ** b
# fitfunc = lambda p, x, rms: n.sqrt(plaw(p[0], p[1], x)**2 + rms**2) # for ricean-biased amplitudes
# errfunc = lambda p, x, y, rms: ((y - fitfunc(p, x, rms))/rms)**2
fitfunc = lambda p, x: plaw(p[0], p[1], x) # for real part of data
errfunc = lambda p, x, y, rms: ((y - fitfunc(p, x))/rms)**2
p0 = [100.,-5.]
p1, success = opt.leastsq(errfunc, p0[:], args = (freq, spec, obsrms))
print 'Fit results: ', p1
chisq = errfunc(p1, freq, spec, obsrms).sum()/(len(freq) - 2)
print 'Reduced chisq: ', chisq
p.figure(2)
p.errorbar(freq, spec, yerr=obsrms*n.ones(len(spec)), fmt='.')
p.plot(freq, fitfunc(p1, freq), label='Fit: %.1f, %.2f. Noise: %.1f, $\chi^2$: %.1f' % (p1[0], p1[1], obsrms, chisq))
p.xlabel('Frequency')
p.ylabel('Flux Density (Jy)')
p.legend()
if save == 1:
savename = self.file.split('.')[:-1]
savename.append(str(self.nskip/self.nbl) + '.fitsp.png')
savename = string.join(savename,'.')
print 'Saving file as ', savename
p.savefig(savename)
# print >> log, savename, 'Fit results: ', p1, '. obsrms: ', obsrms, '. $\Chi^2$: ', chisq
else:
pass
# print >> log, self.file, 'Fit results: ', p1, '. obsrms: ', obsrms, '. $\Chi^2$: ', chisq
def dmtrack(self, dm = 0., t0 = 0., show=0):
"""Takes dispersion measure in pc/cm3 and time offset from first integration in seconds.
t0 defined at first (unflagged) channel. Need to correct by flight time from there to freq=0 for true time.
Returns an array of (timebin, channel) to select from the data array.
"""
reltime = self.reltime
chans = self.chans
tint = self.reltime[1] - self.reltime[0]
# tint = self.inttime0 # could do this instead...?
freq = self.sfreq + chans * self.sdf # freq array in GHz
# given freq, dm, dfreq, calculate pulse time and duration
pulset_firstchan = 4.2e-3 * dm * freq[len(freq)-1]**(-2) # used to start dmtrack at highest-freq unflagged channel
pulset = 4.2e-3 * dm * freq**(-2) + t0 - pulset_firstchan # time in seconds
pulsedt = n.sqrt( (8.3e-6 * dm * (1000*self.sdf) * freq**(-3))**2 + self.pulsewidth**2) # dtime in seconds
timebin = []
chanbin = []
for ch in range(len(chans)):
# ontime = n.where(((pulset[ch] + pulsedt[ch]/2.) >= reltime) & ((pulset[ch] - pulsedt[ch]/2.) <= reltime))
ontime = n.where(((pulset[ch] + pulsedt[ch]/2.) >= reltime - tint/2.) & ((pulset[ch] - pulsedt[ch]/2.) <= reltime + tint/2.))
timebin = n.concatenate((timebin, ontime[0]))
chanbin = n.concatenate((chanbin, (ch * n.ones(len(ontime[0]), dtype=int))))
track = (list(timebin), list(chanbin))
# print 'timebin, chanbin: ', timebin, chanbin
if show:
p.plot(track[1], track[0])
return track
def tracksub(self, dmbin, tbin, bgwindow = 0):
"""Reads data along dmtrack and optionally subtracts background like writetrack method.
Returns the difference of the data in the on and off tracks as a single integration with all bl and chans.
Nominally identical to writetrack, but gives visibilities values off at the 0.01 (absolute) level. Good enough for now.
"""
data = self.data
trackon = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[tbin], show=0)
if ((trackon[1][0] != 0) | (trackon[1][len(trackon[1])-1] != len(self.chans)-1)):
# print 'Track does not span all channels. Skipping.'
return [0]
dataon = data[trackon[0], :, trackon[1]]
# set up bg track
if bgwindow:
# measure max width of pulse (to avoid in bgsub)
twidths = []
for k in trackon[1]:
twidths.append(len(n.array(trackon)[0][list(n.where(n.array(trackon[1]) == k)[0])]))
bgrange = range( max(-bgwindow/2 - max(twidths) + tbin, 0), -max(twidths) + tbin) + range(max(twidths) + tbin, max(twidths) + bgwindow/2 + tbin + 1)
# ** includes negative times!
# print 'bgrange = ', bgrange
for k in bgrange: # build up super track for background subtraction
if bgrange.index(k) == 0: # first time through
trackoff = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[k], show=0)
else: # then extend arrays by next iterations
tmp = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[k], show=0)
trackoff[0].extend(tmp[0])
trackoff[1].extend(tmp[1])
dataoff = data[trackoff[0], :, trackoff[1]]
# compress time axis, then subtract on and off tracks
for ch in n.unique(trackon[1]):
indon = n.where(trackon[1] == ch)
if bgwindow:
indoff = n.where(trackoff[1] == ch)
datadiff = dataon[indon].mean(axis=0) - dataoff[indoff].mean(axis=0)
else:
datadiff = dataon[indon].mean(axis=0)
if ch == 0:
datadiffarr = [datadiff]
else:
datadiffarr = n.append(datadiffarr, [datadiff], axis=0)
datadiffarr = n.array([datadiffarr.transpose()])
return datadiffarr
def writetrack(self, dmbin, tbin, tshift=0, bgwindow=0, show=0):
"""Writes data from track out as miriad visibility file.
Optional background subtraction bl-by-bl over bgwindow integrations. Note that this is bgwindow *dmtracks* so width is bgwindow+track width
Optional spectrum plot with source and background dmtracks
"""
# prep data and track
rawdatatrim = self.rawdata[:,:,self.chans]
track = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[tbin-tshift], show=0)
if ((track[1][0] != 0) | (track[1][len(track[1])-1] != len(self.chans)-1)):
# print 'Track does not span all channels. Skipping.'
return 0
twidths = []
for i in track[1]:
twidths.append(len(n.array(track)[0][list(n.where(n.array(track[1]) == i)[0])]))
if bgwindow > 0:
bgrange = range(-bgwindow/2 - max(twidths) + tbin - tshift, -max(twidths) + tbin - tshift) + range(max(twidths) + tbin - tshift, max(twidths) + bgwindow/2 + tbin - tshift + 1)
# bgrange = range(int(-bgwindow/2.) + tbin - tshift, int(bgwindow/2.) + tbin - tshift + 1)
# bgrange.remove(tbin - tshift); bgrange.remove(tbin - tshift + 1); bgrange.remove(tbin - tshift - 1); bgrange.remove(tbin - tshift + 2); bgrange.remove(tbin - tshift - 2); bgrange.remove(tbin - tshift + 3); bgrange.remove(tbin - tshift - 3)
for i in bgrange: # build up super track for background subtraction
if bgrange.index(i) == 0: # first time through
trackbg = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[i], show=0)
else: # then extend arrays by next iterations
tmp = self.dmtrack(dm=self.dmarr[dmbin], t0=self.reltime[i], show=0)
trackbg[0].extend(tmp[0])
trackbg[1].extend(tmp[1])
else:
print 'Not doing any background subtraction.'
if show:
# show source and background tracks on spectrum
p.figure(1)
p.plot(self.reltime[track[0]], track[1], 'w.')
if bgwindow > 0:
p.plot(self.reltime[trackbg[0]], trackbg[1], 'r.')
self.spec(save=0)
# define input metadata source and output visibility file names
outname = string.join(self.file.split('.')[:-1], '.') + '.' + str(self.nskip/self.nbl) + '-' + 'dm' + str(dmbin) + 't' + str(tbin) + '.mir'
shutil.rmtree(outname, ignore_errors=True)
vis = miriad.VisData(self.file,)
i = 0
int0 = int(self.nskip + (track[0][len(track[0])/2] + tshift) * self.nbl) # choose integration at center of dispersed track
for inp, preamble, data, flags in vis.readLowlevel ('dsl3', False, nocal=True, nopass=True):
if i == 0:
# create output vis file
shutil.rmtree(outname, ignore_errors=True)
out = miriad.VisData(outname)
dOut = out.open ('c')
# set variables
dOut.setPreambleType ('uvw', 'time', 'baseline')
dOut.writeVarInt ('nants', self.nants0)
dOut.writeVarFloat ('inttime', self.inttime0)
dOut.writeVarInt ('nspect', self.nspect0)
dOut.writeVarDouble ('sdf', self.sdf0)
dOut.writeVarInt ('nwide', self.nwide0)
dOut.writeVarInt ('nschan', self.nschan0)
dOut.writeVarInt ('ischan', self.ischan0)
dOut.writeVarDouble ('sfreq', self.sfreq0)
dOut.writeVarDouble ('restfreq', self.restfreq0)
dOut.writeVarInt ('pol', self.pol0)
inp.copyHeader (dOut, 'history')
inp.initVarsAsInput (' ') # ???
inp.copyLineVars (dOut)
if i < int0: # need to grab only integration at pulse+intoff
i = i+1
continue
elif i < int0 + self.nbl:
# write out track, if not flagged
if n.any(flags):
bgarr = []
for j in range(self.nchan):
if j in self.chans:
matches = n.where( j == n.array(self.chans[track[1]]) )[0] # hack, since chans are from 0-64, but track is in trimmed chan space
raw = rawdatatrim[track[0], i-int0, track[1]][matches] # all baselines for the known pulse
raw = raw.mean(axis=0) # create spectrum for each baseline by averaging over time
if bgwindow > 0: # same as above, but for bg
matchesbg = n.where( j == n.array(self.chans[trackbg[1]]) )[0]
rawbg = rawdatatrim[trackbg[0], i-int0, trackbg[1]][matchesbg]
rawbg = rawbg.mean(axis=0)
bgarr.append(rawbg)
data[j] = raw - rawbg
else:
data[j] = raw
else:
flags[j] = False
# ants = util.decodeBaseline (preamble[4])
# print preamble[3], ants
# dOut.write (self.preamble[i], data, flags) # not working right here...?
dOut.write (preamble, data, flags)
i = i+1 # essentially a baseline*int number
elif i >= int0 + self.nbl:
break
dOut.close ()
return 1
def writetrack2(self, dmbin, tbin, tshift=0, bgwindow=0, show=0):
"""Writes data from track out as miriad visibility file.
Alternative to writetrack that uses stored, approximate preamble used from start of pulse, not middle.
Optional background subtraction bl-by-bl over bgwindow integrations. Note that this is bgwindow *dmtracks* so width is bgwindow+track width
"""
# create bgsub data
datadiffarr = self.tracksub(dmbin, tbin, bgwindow=bgwindow)
if n.shape(datadiffarr) == n.shape([0]): # if track doesn't cross band, ignore this iteration
return 0
data = n.zeros(self.nchan, dtype='complex64') # default data array. gets overwritten.
data0 = n.zeros(self.nchan, dtype='complex64') # zero data array for flagged bls
flags = n.zeros(self.nchan, dtype='bool')
# define output visibility file names
outname = string.join(self.file.split('.')[:-1], '.') + '.' + str(self.nskip/self.nbl) + '-' + 'dm' + str(dmbin) + 't' + str(tbin) + '.mir'
print outname
vis = miriad.VisData(self.file,)
int0 = int((tbin + tshift) * self.nbl)
flags0 = []
i = 0
for inp, preamble, data, flags in vis.readLowlevel ('dsl3', False, nocal=True, nopass=True):
if i == 0:
# prep for temp output vis file
shutil.rmtree(outname, ignore_errors=True)
out = miriad.VisData(outname)
dOut = out.open ('c')
# set variables
dOut.setPreambleType ('uvw', 'time', 'baseline')
dOut.writeVarInt ('nants', self.nants0)
dOut.writeVarFloat ('inttime', self.inttime0)
dOut.writeVarInt ('nspect', self.nspect0)
dOut.writeVarDouble ('sdf', self.sdf0)
dOut.writeVarInt ('nwide', self.nwide0)
dOut.writeVarInt ('nschan', self.nschan0)
dOut.writeVarInt ('ischan', self.ischan0)
dOut.writeVarDouble ('sfreq', self.sfreq0)
dOut.writeVarDouble ('restfreq', self.restfreq0)
dOut.writeVarInt ('pol', self.pol0)
inp.copyHeader (dOut, 'history')
inp.initVarsAsInput (' ') # ???
inp.copyLineVars (dOut)
if i < self.nbl:
flags0.append(flags.copy())
i = i+1
else:
break
l = 0
for i in range(len(flags0)): # iterate over baselines
# write out track, if not flagged
if n.any(flags0[i]):
k = 0
for j in range(self.nchan):
if j in self.chans:
data[j] = datadiffarr[0, l, k]
# flags[j] = flags0[i][j]
k = k+1
else:
data[j] = 0 + 0j
# flags[j] = False
l = l+1
else:
data = data0
# flags = n.zeros(self.nchan, dtype='bool')
dOut.write (self.preamble[int0 + i], data, flags0[i])
dOut.close ()
return 1
def makedmt0(self):
"""Integrates data at dmtrack for each pair of elements in dmarr, time.
Not threaded. Uses dmthread directly.
Stores mean of detected signal after dmtrack, effectively forming beam at phase center.
"""
dmarr = self.dmarr
# reltime = n.arange(2*len(self.reltime))/2. # danger!
reltime = self.reltime
chans = self.chans
dmt0arr = n.zeros((len(dmarr),len(reltime)), dtype='float64')
for i in range(len(dmarr)):
for j in range(len(reltime)):
dmtrack = self.dmtrack(dm=dmarr[i], t0=reltime[j])
if ((dmtrack[1][0] == 0) & (dmtrack[1][len(dmtrack[1])-1] == len(self.chans)-1)): # use only tracks that span whole band
# dmt0arr[i,j] = n.abs((((self.data).mean(axis=1))[dmtrack[0],dmtrack[1]]).mean())
dmt0arr[i,j] = ((((self.data).mean(axis=1))[dmtrack[0],dmtrack[1]]).mean()).real # use real part to detect on axis, but keep gaussian dis'n
print 'dedispersed for ', dmarr[i]
self.dmt0arr = dmt0arr
def plotdmt0(self, save=0):
"""calculates rms noise in dmt0 space, then plots circles for each significant point
save=1 means plot to file.
"""
dmarr = self.dmarr
arr = self.dmt0arr
reltime = self.reltime
peaks = self.peaks
tbuffer = 7 # number of extra iterations to trim from edge of dmt0 plot
# Trim data down to where dmt0 array is nonzero
arreq0 = n.where(arr == 0)
trimt = arreq0[1].min()
arr = arr[:,:trimt - tbuffer]
reltime = reltime[:trimt - tbuffer]
print 'dmt0arr/time trimmed to new shape: ',n.shape(arr), n.shape(reltime)
mean = arr.mean()
std = arr.std()
arr = (arr - mean)/std
peakmax = n.where(arr == arr.max())
# Plot
# p.clf()
ax = p.imshow(arr, aspect='auto', origin='lower', interpolation='nearest', extent=(min(reltime),max(reltime),min(dmarr),max(dmarr)))
p.colorbar()
if len(peaks[0]) > 0:
print 'Peak of %f at DM=%f, t0=%f' % (arr.max(), dmarr[peakmax[0][0]], reltime[peakmax[1][0]])
for i in range(len(peaks[1])):
ax = p.imshow(arr, aspect='auto', origin='lower', interpolation='nearest', extent=(min(reltime),max(reltime),min(dmarr),max(dmarr)))
p.axis((min(reltime),max(reltime),min(dmarr),max(dmarr)))
p.plot([reltime[peaks[1][i]]], [dmarr[peaks[0][i]]], 'o', markersize=2*arr[peaks[0][i],peaks[1][i]], markerfacecolor='white', markeredgecolor='blue', alpha=0.5)
p.xlabel('Time (s)')
p.ylabel('DM (pc/cm3)')
p.title('Summed Spectra in DM-t0 space')
if save:
savename = self.file.split('.')[:-1]
savename.append(str(self.nskip/self.nbl) + '.dmt0.png')
savename = string.join(savename,'.')
p.savefig(savename)
def peakdmt0(self, sig=5.):
""" Method to find peaks in dedispersed data (in dmt0 space).
Clips noise, also.
"""
arr = self.dmt0arr
reltime = self.reltime
tbuffer = 7 # number of extra iterations to trim from edge of dmt0 plot
# Trim data down to where dmt0 array is nonzero
arreq0 = n.where(arr == 0)
trimt = arreq0[1].min()
arr = arr[:,:trimt - tbuffer]
reltime = reltime[:trimt - tbuffer]
print 'dmt0arr/time trimmed to new shape: ',n.shape(arr), n.shape(reltime)
# single iteration of sigma clip to find mean and std, skipping zeros
mean = arr.mean()
std = arr.std()
print 'initial mean, std: ', mean, std
cliparr = n.where((arr < mean + 5*std) & (arr > mean - 5*std))
mean = arr[cliparr].mean()
std = arr[cliparr].std()
print 'final mean, sig, std: ', mean, sig, std
# Recast arr as significance array
# arr = n.sqrt((arr-mean)**2 - std**2)/std # PROBABLY WRONG
arr = (arr-mean)/std # for real valued trial output (gaussian dis'n)
# Detect peaks
self.peaks = n.where(arr > sig)
peakmax = n.where(arr == arr.max())
print 'peaks: ', self.peaks
return self.peaks,arr[self.peaks]
def imagedmt0(self, dmbin, t0bin, tshift=0, bgwindow=10, show=0, clean=1, mode='dirty'):
""" Makes and fits an background subtracted image for a given dmbin and t0bin.
tshift can shift the actual t0bin earlier to allow reading small chunks of data relative to pickle.
"""
# set up
outroot = string.join(self.file.split('.')[:-1], '.') + '.' + str(self.nskip/self.nbl) + '-dm' + str(dmbin) + 't' + str(t0bin)
shutil.rmtree (outroot+'.map', ignore_errors=True); shutil.rmtree (outroot+'.beam', ignore_errors=True); shutil.rmtree (outroot+'.clean', ignore_errors=True); shutil.rmtree (outroot+'.restor', ignore_errors=True)
if self.approxuvw:
status = self.writetrack2(dmbin, t0bin, tshift=tshift, bgwindow=bgwindow) # output file at dmbin, trelbin
else:
status = self.writetrack(dmbin, t0bin, tshift=tshift, bgwindow=bgwindow) # output file at dmbin, trelbin
if not status: # if writetrack fails, exit this iteration
return 0
try:
# make image, clean, restor, fit point source
print
print 'Making dirty image for nskip=%d, dm[%d]=%.1f, and trel[%d] = %.3f.' % (self.nskip/self.nbl, dmbin, self.dmarr[dmbin], t0bin-tshift, self.reltime[t0bin-tshift])
txt = TaskInvert (vis=outroot+'.mir', map=outroot+'.map', beam=outroot+'.beam', mfs=True, double=True).snarf() # good for m31 search
if show: txt = TaskCgDisp (in_=outroot+'.map', device='/xs', wedge=True, beambl=True).snarf ()
txt = TaskImStat (in_=outroot+'.map').snarf() # get dirty image stats
if mode == 'clean':
# print 'cleaning image'
thresh = 2*float(txt[0][10][41:47]) # set thresh to 2*noise level in dirty image. hack! OMG!!
txt = TaskClean (beam=outroot+'.beam', map=outroot+'.map', out=outroot+'.clean', cutoff=thresh, region='relpix,boxes(-10,-10,10,10)').snarf () # targeted clean
# txt = TaskClean (beam=outroot+'.beam', map=outroot+'.map', out=outroot+'.clean', cutoff=thresh).snarf ()
print 'Cleaned to %.2f Jy after %d iterations' % (thresh, int(txt[0][-4][19:]))
txt = TaskRestore (beam=outroot+'.beam', map=outroot+'.map', model=outroot+'.clean', out=outroot+'.restor').snarf ()
if show: txt = TaskCgDisp (in_=outroot+'.restor', device='/xs', wedge=True, beambl=True, labtyp='hms,dms', region='relpix,boxes(-100,-100,100,100)').snarf ()
txt = TaskImFit (in_=outroot+'.restor', object='point').snarf ()
# parse output of imfit
# print '012345678901234567890123456789012345678901234567890123456789'
peak = float(txt[0][14][30:36])
epeak = float(txt[0][14][44:])
off_ra = float(txt[0][15][28:39])
eoff_ra = float(txt[0][16][30:39])
off_dec = float(txt[0][15][40:])
eoff_dec = float(txt[0][16][40:])
print 'Fit cleaned image peak %.2f +- %.2f' % (peak, epeak)
return peak, epeak, off_ra, eoff_ra, off_dec, eoff_dec
elif mode == 'dirty':
# print 'stats of dirty image'
peak = float(txt[0][10][50:57]) # get peak of dirty image
epeak = float(txt[0][10][41:47]) # note that epeak is biased by any true flux
print 'Individual dirty image peak %.2f +- %.2f' % (peak, epeak)
if clean:
shutil.rmtree (outroot + '.mir', ignore_errors=True)
# shutil.rmtree (outroot+'.map', ignore_errors=True)
shutil.rmtree (outroot+'.beam', ignore_errors=True); shutil.rmtree (outroot+'.clean', ignore_errors=True); shutil.rmtree (outroot+'.restor', ignore_errors=True)
return peak, epeak
except:
print 'Something broke with imaging!'
return 0
def imsearch(self, dmind, tind, nints, sig=5., show=0, edge=0, mode='dirty'):
"""
Reproduce search result of pulse_search_image.
"""
bgwindow = 10 # where bg subtraction is made
if mode == 'dirty':
# define typical dirty image noise level for this dm
print 'For DM = %.1f, measuring median image noise level' % (self.dmarr[dmind])
bgpeak = []; bgepeak = []
for bgi in range(bgwindow, nints-bgwindow, nints/15):
print 'Measuring noise in integration %d' % (bgi)
outname = string.join(self.file.split('.')[:-1], '.') + '.' + str(self.nskip/self.nbl) + '-' + 'dm' + str(dmind) + 't' + str(bgi) + '.mir'
shutil.rmtree (outname, ignore_errors=True); shutil.rmtree (outname+'.map', ignore_errors=True); shutil.rmtree (outname+'.beam', ignore_errors=True)
status = self.writetrack2(dmind, bgi, bgwindow=bgwindow) # output file at dmbin, trelbin
try:
txt = TaskInvert (vis=outname, map=outname+'.map', beam=outname+'.beam', mfs=True, double=True, cell=80, imsize=250).snarf()
txt = TaskImStat (in_=outname+'.map').snarf() # get dirty image stats
bgpeak.append(float(txt[0][10][51:61])) # get peak of dirty image
bgepeak.append(float(txt[0][10][41:51])) # note that epeak is biased by any true flux
shutil.rmtree (outname, ignore_errors=True)
shutil.rmtree (outname+'.map', ignore_errors=True)
shutil.rmtree (outname+'.beam', ignore_errors=True)
except:
pass
print 'Dirty image noises and their median', bgepeak, n.median(bgepeak)
# now make dirty image
results = self.imagedmt0(dmind, tind, show=show, bgwindow=bgwindow, clean=1, mode=mode)
if mode == 'clean':
peak, epeak, off_ra, eoff_ra, off_dec, eoff_dec = results
elif mode == 'dirty': # need to develop more... needs to be ~10ms processing per int!
peak, epeak = results
epeak = n.median(bgepeak)
if peak/epeak >= sig:
print '\tDetection!'
if mode == 'clean':
print self.nskip/self.nbl, nints, (dmind, tind), 'Peak, (sig), RA, Dec: ', peak, epeak, '(', peak/epeak, ') ', off_ra, eoff_ra, off_dec, eoff_dec
elif mode == 'dirty':
print self.nskip/self.nbl, nints, (dmind, tind), 'Peak, (sig): ', peak, epeak, '(', peak/epeak, ')'
def uvfitdmt0(self, dmbin, t0bin, bgwindow=10, tshift=0, show=1, mode='fit'):
""" Makes and fits a point source to background subtracted visibilities for a given dmbin and t0bin.
tshift can shift the actual t0bin earlier to allow reading small chunks of data relative to pickle.
"""
# set up
outroot = string.join(self.file.split('.')[:-1], '.') + '.' + str(self.nskip/self.nbl) + '-dm' + str(dmbin) + 't' + str(t0bin)
shutil.rmtree (outroot + '.mir', ignore_errors=True)
if self.approxuvw:
status = self.writetrack2(dmbin, t0bin, tshift=tshift, bgwindow=bgwindow) # output file at dmbin, trelbin
else:
status = self.writetrack(dmbin, t0bin, tshift=tshift, bgwindow=bgwindow) # output file at dmbin, trelbin
if not status: # if writetrack fails, exit this iteration
return 0
if mode == 'fit':
try:
# fit point source model to visibilities
print
print 'UVfit for nskip=%d, dm[%d] = %.1f, and trel[%d] = %.3f.' % (self.nskip/self.nbl, dmbin, self.dmarr[dmbin], t0bin-tshift, self.reltime[t0bin-tshift])
txt = TaskUVFit (vis=outroot+'.mir', object='point', select='-auto').snarf()
# parse output of imfit
# print '012345678901234567890123456789012345678901234567890123456789'
peak = float(txt[0][8][30:38])
epeak = float(txt[0][8][46:])
off_ra = float(txt[0][9][30:38])
eoff_ra = float(txt[0][10][31:42])
off_dec = float(txt[0][9][40:])
eoff_dec = float(txt[0][10][42:])
print 'Fit peak %.2f +- %.2f' % (peak, epeak)
shutil.rmtree (outroot + '.mir', ignore_errors=True)
return peak, epeak, off_ra, eoff_ra, off_dec, eoff_dec
except:
print 'Something broke in/after uvfit!'
shutil.rmtree (outroot + '.mir', ignore_errors=True)
return 0
elif mode == 'grid':
llen = 150
mlen = 150
linspl = n.linspace(-4000,4000,llen)
linspm = n.linspace(-4000,4000,mlen)
snrarr = n.zeros((llen, mlen))
for i in range(llen):
print 'Starting loop ', i
for j in range(mlen):
try:
txt = TaskUVFit (vis=outroot+'.mir', object='point', select='-auto', spar='0,'+str(linspl[i])+','+str(linspm[j]), fix='xy').snarf()
# print txt[0][8:10]
peak = float(txt[0][8][30:38])
epeak = float(txt[0][8][46:])
off_ra = float(txt[0][9][30:38])
off_dec = float(txt[0][9][40:])
snrarr[i, j] = peak/epeak
except:
print 'Something broke in/after uvfit!'
peak = n.where(snrarr == snrarr.max())
print 'Peak: ', snrarr[peak]
print 'Location: ', peak, (linspl[peak[0]], linspm[peak[1]])
print
print 'Center SNR: ', snrarr[llen/2,llen/2]
log = open('log.txt','a')
print >> log, outroot, snrarr[peak], peak, (linspl[peak[0]], linspm[peak[1]])
log.close()
ax = p.imshow(snrarr, aspect='auto', origin='lower', interpolation='nearest')
p.colorbar()
p.savefig(outroot + '.png')
def uvfitdmt02 (self, dmbin, t0bin, bgwindow=10, tshift=0, show=1, mode='default'):
"""Experimental alternative function to do uvfitting of visibilities.
Reads in data from dispersion track, then fits uv model of point source in python.
mode defines the optimization approach.
"""
datadiffarr = self.tracksub(dmbin, t0bin, bgwindow=bgwindow)
int0 = int((t0bin + tshift) * self.nbl)
meanfreq = n.mean(self.sfreq + self.sdf * self.chans )
datadiffarr2 = self.tracksub(dmbin, t0bin+bgwindow-20, bgwindow=bgwindow) # arbitrary offset to measure typical noise in bg
datadiffarr3 = self.tracksub(dmbin, t0bin+bgwindow-15, bgwindow=bgwindow) # arbitrary offset to measure typical noise in bg
datadiffarr4 = self.tracksub(dmbin, t0bin+bgwindow+15, bgwindow=bgwindow) # arbitrary offset to measure typical noise in bg
datadiffarr5 = self.tracksub(dmbin, t0bin+bgwindow+20, bgwindow=bgwindow) # arbitrary offset to measure typical noise in bg
datadiffarr6 = self.tracksub(dmbin, t0bin+bgwindow+25, bgwindow=bgwindow) # arbitrary offset to measure typical noise in bg
obsrms2 = n.std((datadiffarr2[0].mean(axis=1)).imag) # std of imag part is std of real part
obsrms3 = n.std((datadiffarr3[0].mean(axis=1)).imag) # std of imag part is std of real part
obsrms4 = n.std((datadiffarr4[0].mean(axis=1)).imag) # std of imag part is std of real part
obsrms5 = n.std((datadiffarr5[0].mean(axis=1)).imag) # std of imag part is std of real part
obsrms6 = n.std((datadiffarr6[0].mean(axis=1)).imag) # std of imag part is std of real part
obsrms = n.median([obsrms2, obsrms3, obsrms4, obsrms5, obsrms6])
print 'obsrms:', obsrms
p.plot(datadiffarr[0].mean(axis=1).real, datadiffarr[0].mean(axis=1).imag, '.')
p.show()
# get flags to help select good ants
vis = miriad.VisData(self.file,)
flags0 = []
i = 0
for inp, preamble, data, flags in vis.readLowlevel ('dsl3', False, nocal=True, nopass=True):
if i < self.nbl:
flags0.append(flags.copy())
i = i+1
else:
break
goodants = n.where( n.any(n.array(flags0), axis=1) == True)
u = []; v = []; w = []
for i in range(self.nbl):
u.append(self.preamble[int0 + i][0]) # in units of ns
v.append(self.preamble[int0 + i][1]) # in units of ns
w.append(self.preamble[int0 + i][2]) # in units of ns
u = n.array(u); v = n.array(v); w = n.array(w)
u = meanfreq * u[goodants]; v = meanfreq * v[goodants]; w = meanfreq * w[goodants]
data = (datadiffarr[0]).mean(axis=1)
print
print 'UVfit2 for nskip=%d, dm[%d] = %.1f, and trel[%d] = %.3f.' % (self.nskip/self.nbl, dmbin, self.dmarr[dmbin], t0bin-tshift, self.reltime[t0bin-tshift])
vi = lambda a, l, m, u, v, w: a * n.exp(-2j * n.pi * (u*l + v*m)) # + w*n.sqrt(1 - l**2 - m**2))) # ignoring w term
def errfunc (p, u, v, w, y, obsrms):
a, l, m = p
err = (y - vi(a, l, m, u, v, w))/obsrms
return err.real + err.imag
def errfunc2 (p, l, m, u, v, w, y):
a = p
err = (y - vi(a, l, m, u, v, w))
return err.real + err.imag
def ltoa (l):
a = n.arcsin(l) * 180 / n.pi * 3600
return a
if mode == 'default':
p0 = [100.,0.,0.]
out = opt.leastsq(errfunc, p0, args = (u, v, w, data, obsrms), full_output=1)
p1 = out[0]
covar = out[1]
print 'Fit results (Jy, arcsec, arcsec):', p1[0], ltoa(p1[1]), ltoa(p1[2])
print 'Change in sumsq: %.2f to %.2f' % (n.sum(errfunc(p0, u, v, w, data, obsrms)**2), n.sum(errfunc(p1, u, v, w, data, obsrms)**2))
print 'here\'s some stuff...'
print covar
print n.sqrt(covar[0][0])
print n.sqrt(covar[1][1])
elif mode == 'map':
p0 = [100.,0.,0.]
out = opt.leastsq(errfunc, p0, args = (u, v, w, data, obsrms), full_output=1)
p1 = out[0]
covar = out[1]
llen = 20
mlen = 20
sumsq = n.zeros((llen, mlen))
linspl = n.linspace(-0.005,0.005,llen) # dl=0.026 => 1.5 deg (half ra width of andromeda)
linspm = n.linspace(-0.005,0.005,mlen) # dl=0.01 => 0.57 deg (half dec width of andromeda)
for i in range(llen):
for j in range(mlen):
sumsq[i, j] = n.sum(errfunc([p1[0], linspl[i], linspm[j]], u, v, w, data, obsrms)**2)
mins = n.where(sumsq < 1.01 * sumsq.min())
print 'Best fit: ', p1
print 'Red. chisq: ', sumsq[mins][0]/(9-3)
print 'Location: ', mins, (ltoa(linspl[mins[0]])[0], ltoa(linspm[mins[1]])[0])
p.plot(mins[1], mins[0], 'w.')
p.imshow(sumsq)
p.colorbar()
p.show()
elif mode == 'lsgrid':
llen = 30
mlen = 30
linspl = n.linspace(-0.005,0.005,llen) # dl=0.026 => 1.5 deg (half ra width of andromeda)
linspm = n.linspace(-0.005,0.005,mlen) # dl=0.01 => 0.57 deg (half dec width of andromeda)
amparr = n.zeros((llen, mlen))
p0 = [100.]
for i in range(llen):
for j in range(mlen):
out = opt.leastsq(errfunc2, p0, args = (linspl[i], linspm[j], u, v, w, data), full_output=1)
amparr[i, j] = out[0]
maxs = n.where(amparr >= 0.9*amparr.max())
print 'Peak: ', amparr.max()
print 'Location: ', maxs, (ltoa(linspl[maxs[0]]), ltoa(linspm[maxs[1]]))
print 'SNR: ', amparr.max()/(obsrms/n.sqrt(9))
print
print 'Center: ', amparr[15,15]
print 'Center SNR: ', amparr[15,15]/(obsrms/n.sqrt(9))
ax = p.imshow(amparr, aspect='auto', origin='lower', interpolation='nearest')
p.colorbar()
p.show()
def tripgen(self, a1=0):
"""Calculates and returns data indexes (i,j,k) for all closed triples.
a1 defines first antenna in triple. only uses that antenna if nonzero.
"""
if a1 == 0:
amin = 1
amax = self.nants+1
else:
amin = a1
amax = a1+1
blarr = []
for bl in self.preamble [0:self.nbl,4]:
blarr.append(util.decodeBaseline (bl))
blarr = n.array(blarr)
# first make triples indexes in antenna numbering
anttrips = []
# for i in range(1,self.nants+1):
for i in range(amin,amax):
for j in range(i+1,self.nants+1):
for k in range(j+1,self.nants+1):
anttrips.append([i,j,k])
# next return data indexes for triples
bltrips = []
for (ant1, ant2, ant3) in anttrips:
bl1 = n.where( (blarr[:,0] == ant1) & (blarr[:,1] == ant2) )[0][0]
bl2 = n.where( (blarr[:,0] == ant2) & (blarr[:,1] == ant3) )[0][0]
bl3 = n.where( (blarr[:,0] == ant1) & (blarr[:,1] == ant3) )[0][0]
bltrips.append([bl1, bl2, bl3])
return n.array(bltrips)
def bisplc(self, chan=-1, dmbin=0, bgwindow=2, a1=0, save=0):
"""Generate lightcurve of all bispectra.
Use a1 to use subset of triples starting with antenna a1.
"""
if chan == -1:
chans = self.chans
else:
chans = n.array([chan])
bisp = lambda d,i,j,k: n.complex(d[i] * d[j] * n.conj(d[k])) # bispectrum w/o normalization
# bisp = lambda d,i,j,k: d[i] * d[j] * n.conj(d[k]) # bispectrum w/o normalization
# theoretical relations for std of bispectra (on pulse) and snr of mean bispectrum
sigb = lambda S, Q: n.sqrt(4 + 6*(S/Q)**2 + 3*(S/Q)**4) * Q**3 # kulkarni 1989
mu = lambda s: s / (3*(1+s))
sigt = lambda S, Q, num: sigb(S,Q) * n.sqrt( (1 + 3*(num-3)*mu(S/Q)) / (num*(num-1)*(num-2)/6.))
sigt0toQ = lambda sigt0, num: (sigt0*n.sqrt(num*(num-1)*(num-2)/(6.*4)))**(1/3.)
triples = self.tripgen(a1=a1)
dibi = n.zeros((len(self.data)-bgwindow, len(triples)))
for ii in range(bgwindow+1, len(self.data)-(bgwindow+1)):
diff = self.tracksub(dmbin, ii, bgwindow=bgwindow)
if len(n.shape(diff)) == 1: # no track
continue
diffmean = diff[0, :, chans].mean(axis=0)
for trip in range(len(triples)):
i, j, k = triples[trip]
dibi[ii, trip] = bisp(diffmean, i, j, k).real
# mean and std are primary products of each trial
dibimean = dibi.mean(axis=1)
dibistd = dibi.std(axis=1)
if save:
good = n.where( (dibistd > 0.5*n.median(dibistd)) & (dibistd < 2.*n.median(dibistd)) )
dibimeanstd = dibimean[good].std()
Q = n.median( (dibistd/2.)**(1/3.) ) # first estimate of Q
# Q = sigt0toQ(dibimeanstd, self.nants) # alternately for Q...
print 'Q1 =', n.median( (dibistd/2.)**(1/3.) )
print 'Q2 =', sigt0toQ(dibimeanstd, self.nants)
# plot snrb lc and expected snr vs. sigb relation
p.figure(1)
p.subplot(211)
p.plot(dibimean/dibimeanstd, 'b.')
p.xlabel('Integation')
p.ylabel('SNR$_{bisp}$')
savename = self.file.split('.')[:-1]
savename.append(str(self.nskip/self.nbl) + '.bisplc.png')
savename = string.join(savename,'.')
p.subplot(212)
p.plot(dibistd, dibimean/dibimeanstd, 'b.')
smax = (dibimean.max())**(1/3.)
sarr = smax*n.arange(0,11)/10.
p.plot(sigb(sarr, Q), sarr**3/dibimeanstd, 'b')
p.xlabel('$\sigma_b$')
p.ylabel('SNR$_{bisp}$')
p.savefig(savename)
p.clf()
return dibimean, dibistd