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candspipe.py
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candspipe.py
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# Functions process candidate pkl files from leanpipedt
# Assumes structure of FRB search: multiple pkls per scan/ms file
# Batch output writes candidtes per chunk of ints, but after merging, there is only one entry for all cands.
# Also, a "noise" file is named "noise" instead of "cands" and contains one entry per chunk of ints.
import numpy as n
#import pylab as p # requires display before saving fig...
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
#from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
import multiprocessing as mp
import string, ctypes, glob, types, os
import cPickle as pickle
import leanpipedt as leanpipedt
import leanpipedt_cython as lib
import scipy.special as sp
def mergecands(pkls, outname=''):
""" Converts output of batch processing into single pkl file for visualization below.
Takes string to select list or list of pkl file names and merges them into a single pkl file with two entries instead of one per scan with cands.
Assumes all pkl files are part of same basic dmarr.
In the future, could also merge across scans.
"""
if isinstance(pkls, types.ListType):
pkllist = pkls
elif isinstance(pkls, types.StringType):
pkllist = glob.glob(pkls)
pkllist = [pkllist[i] for i in range(len(pkllist)) if 'merge' not in pkllist[i]]
pkllist.sort(key=lambda i: int(i.rstrip('.pkl').split('_')[3][1:])) # assumes filename structure
if len(pkllist) == 0:
return
if not outname:
outname = pkllist[0].split('_s')[0] + '_merge.pkl'
if os.path.exists(outname):
print 'Outname %s exists. Not overwriting.' % outname
return
print 'Iterating over list %s. Writing to %s' % (pkllist, outname)
for pklfile in pkllist:
if 'merge' in pklfile:
return
# collect construction dicts and merge chunks in time
dd = {}
masterdmarr = []; mastercandloc = []; mastercandprop = []
for i in range(len(pkllist)):
pkl = open(pkllist[i], 'r')
dd[i] = pickle.load(pkl)
candloc = []; candprop = []
for cands in pickle_iter(pkl):
loc, prop = cands
candloc = candloc+loc
candprop = candprop+prop
pkl.close()
print 'Merged file %s over %d/%d (loc/prop) chunks.' % (pkllist[i], len(candloc), len(candprop))
masterdmarr = masterdmarr + list(dd[i]['dmarr'])
dd['loc'+str(i)] = candloc
dd['prop'+str(i)] = candprop
# regrid dmarr
masterdmarr.sort()
masterdmarr = list(n.unique(masterdmarr))
masterdataloc = []
for i in range(len(pkllist)):
dmbin0 = masterdmarr.index(dd[i]['dmarr'][0])
scani = int(pkllist[i].rstrip('.pkl').split('_')[3][1:]) # assumes scan name structure
masterdataloc = masterdataloc + [scani]*len(dd['loc'+str(i)])
mastercandloc = mastercandloc + [(cand[0], cand[1], cand[2], cand[3]+dmbin0) for cand in dd['loc'+str(i)]]
mastercandprop = mastercandprop + dd['prop'+str(i)]
# print 'Master dmarr grid', masterdmarr
# define master dictionary
masterd = dd[i]
masterd['pkllist'] = pkllist
masterd['dmarr'] = n.array(masterdmarr)
outputpkl = open(outname, 'wb')
pickle.dump(masterd, outputpkl)
pickle.dump((masterdataloc,mastercandloc,mastercandprop), outputpkl) # merge file has "dataloc" an index to d['pkllist'] to say which file the cand was in
def mergecands_plotall(rootname, saveroot=''):
""" Wrapper for mergecands that takes argument 'cands*pkl' and makes all merge files, less any extant merge files.
Then generates summary plots (image, dmt) for all candidates.
"""
pkllist = glob.glob(rootname)
scanlist = n.unique([pkllist[i].rstrip('.pkl').split('_')[3] for i in range(len(pkllist))])
for scan in scanlist:
sublist = [pklfile for pklfile in glob.glob('cands*_'+scan+'_*pkl') if 'merge' not in pklfile] # filter out merge files
mergecands(sublist)
if saveroot:
# make summary plots
cands = filter_candidates('cands*merge*pkl', saveroot=saveroot) # summary plot for all
if len(cands) == 2:
candsloc, candsprop = cands
elif len(cands) == 3:
dataloc, candsloc, candsprop = cands
if len(candsprop[0]) > 1: # for long form of cand info
compresscands(glob.glob('cands*merge*pkl'), rootname.split('*')[0]+'_summary.pkl')
# plot all candidates
for pklfile in glob.glob('cands*merge*pkl'):
candsloc, candsprop = filter_candidates(pklfile, saveroot='', threshold=7.) # only plot cands for snr>7
for i in range(len(candsloc)):
singleplot(pklfile, candsloc, candsprop, i, save=1)
def mergeremove(mergefile, outfile, remove={}):
""" Function writes new merge file without specified ranges of scan/ints (containing RFI)
remove is a dict to define data ranges to excise from candidate set: remove = {scan0: [int0,int1,int2,int3]}
where int0-int1 defines first range in that scan and int2-int3 is next, etc...
"""
pkl = open(mergefile,'r')
d = pickle.load(pkl)
cands = pickle.load(pkl)
if len(remove) == 0:
print 'No times to remove.'
else:
print 'Removing %s' % remove
dataloc, candloc, candprop = cands
dataloc2 = []; candloc2 = []; candprop2 = []
for i in xrange(len(dataloc)):
if dataloc[i] in remove.keys():
nranges = len(remove[dataloc[i]])
escape = 0
for first in range(0,nranges,2):
badrange0 = remove[dataloc[i]][first]
badrange1 = remove[dataloc[i]][first+1]
if ((candloc[i][2] > badrange0) & (candloc[i][2] < badrange1)):
escape = 1
if escape:
continue
dataloc2.append(dataloc[i])
candloc2.append(candloc[i])
candprop2.append(candprop[i])
# if the scan has any candidates, add nints to count
goodintcount = 0
for scani in n.unique(dataloc):
if len(n.where(dataloc == scani)[0]):
goodintcount += d['nints']
# correct int count by removed range
for scan in remove.keys():
goodintcount -= remove[scan][1] - remove[scan][0]
# write filtered output
d['remove'] = remove
d['goodintcount'] = goodintcount
outputpkl = open(outfile, 'wb')
pickle.dump(d, outputpkl)
pickle.dump((dataloc2,candloc2,candprop2), outputpkl)
def pickle_iter(infile):
while 1:
try:
yield pickle.load(infile)
except EOFError:
break
def filter_candidates(pklroot, threshold=-999, saveroot=''):
""" Quick ingestion and filtering of candidate pkl file.
Returns candidate location and property arrays.
"""
locs = []; snrs = []; props = []
# read in pickle file of candidates
pkllist = glob.glob(pklroot)
for pklfile in pkllist:
print 'Filling from first data in %s...' % pklfile
pkl = open(pklfile, 'rb')
d = pickle.load(pkl)
cands = pickle.load(pkl)
if len(cands) == 2:
loc, prop = cands
elif len(cands) == 3:
dataloc, loc, prop = cands
snrcol = parsepropinfo(prop)
pkl.close()
locs = locs + loc
snrs = snrs + [prop[i][snrcol] for i in range(len(prop))]
props = props + [prop[i] for i in range(len(prop))]
candsloc = n.array(locs).astype(int)
snrs = n.array(snrs)
ww = n.where(snrs>threshold)[0].astype(int)
candsloc = candsloc[ww]
candsprop = [props[i] for i in ww]
print 'Found %d candidates above threshold %.1f' % (len(candsloc), threshold)
if saveroot and len(candsprop):
dmtplot(d, candsloc, snrs, saveroot=saveroot)
if len(candsprop[0]) > 1: # then long form of cand info
impeakplot(d, candsprop, saveroot=saveroot)
snrhist(snrs, saveroot=saveroot)
if len(cands) == 2:
return candsloc, candsprop
elif len(cands) == 3:
return n.array(dataloc)[ww], candsloc, candsprop
def parsepropinfo(prop):
""" Function to find columns of interest
"""
if len(prop[0]) == 6:
snr0col = 0; x0col = 1; x0col = 2; snrcol = 3; xcol = 4; ycol = 5
elif len(prop[0]) == 3:
snrcol = 0; imcol = 1; speccol = 2
return snrcol
def quickcands(pklname):
pkl = open(pklname,'r')
candloc = []; snr = []
d = pickle.load(pkl)
for cands in pickle_iter(pkl):
if len(cands) == 2:
loc, prop = cands
elif len(cands) == 3:
dataloc, loc, prop = cands
candloc = candloc + loc
snr = snr + [prop[i][0] for i in range(len(prop))]
return n.array(candloc), n.array(snr)
def compresscands(pkllist, outname):
# pkllist = glob.glob(pklroot)
loclist = []; snrlist = n.array([]); fnlist = n.array([])
for pklname in pkllist:
print 'Processing %s (%d/%d)' % (pklname, pkllist.index(pklname)+1, len(pkllist))
# using quickcands doesn't give im peak pixel
# loc,snr = quickcands(pklname)
# for ll in loc:
# loclist.append(ll)
# using filter_candidates allows full "loc" definition
cands = filter_candidates(pklname)
if len(cands) == 2:
candsloc, candsprop = cands
elif len(cands) == 3:
dataloc, candsloc, candsprop = cands
for i in range(len(loc)):
peakx, peaky = n.where(prop[i][1] == prop[i][1].max())
loclist.append(n.concatenate( (loc[i], peakx, peaky) ))
snr = n.array([prop[i][0] for i in range(len(prop))])
snrlist = n.append(snrlist, snr)
fn = n.array([pklname] * len(loc))
fnlist = n.append(fnlist, fn)
pkl = open(outname, 'w')
pickle.dump((fnlist, n.array(loclist), snrlist), pkl)
pkl.close()
def reproducecand(pklname, threshold=-1, candnum=-1, cand = [], **kwargs):
""" Takes merged pickle file and reproduces candidate candnum from list above thresh.
Optionally takes candidate params as list [dt, int, dm] (dt/dm are values not indexes)
kwargs used to modify parameters of search.
"""
# get run metadata and basic candidate info
pkl = open(pklname, 'r')
d = pickle.load(pkl)
pkl.close()
d['savecands'] = False
# overwrite d with kwargs. supports values of type int and string so far.
for key in kwargs.keys():
print 'Overwriting key %s to %s' % (key, kwargs[key])
d[key] = kwargs[key]
if 'excludeants' not in d.keys():
d['excludeants'] = []
window = 200
maxiter = 400
# define time and iteration parameters to get candidate
if (len(cand) == 0) and (candnum != -1): # if not forced, use cand from pkl file
dataloc, locs, props = filter_candidates(pklname, threshold)
snrcol = parsepropinfo(props)
canddmind = locs[candnum][3]
canddtind = locs[candnum][1]
dtarr = [d['dtarr'][canddtind]]
dmarr = [d['dmarr'][canddmind]]
nints = d['datadelay'][canddmind] + window
if 'cand' in d['filename']:
filename = d['filename']
nskip = 0
else:
scani = [int(name.rstrip('.pkl').split('_')[3][1:]) for name in d['pkllist']]
filesplit = d['pkllist'][scani.index(dataloc[candnum])].rstrip('.pkl').split('_')
filename = '_'.join(filesplit[1:4]) + '.ms'
nskip = locs[candnum][2] - (d['datadelay'][canddmind] + window/2)
# method 1
# iterint = nints/(((nints-n.mod(nints,maxiter))/maxiter)+2) # keeps iterint under maxiter
# method 2
iterint = nints-1
print 'Reproducing candidate %d with SNR = %.1f at (%d,%d,%d) in file %s' % (candnum, props[candnum][snrcol], canddtind, nskip, canddmind, filename)
elif len(cand) == 3:
print 'Reproducing provided candidate with values %s' % (cand)
filename = d['filename']
candint = cand[1]
canddm = cand[2]
canddtind = d['dtarr'].index(cand[0])
canddmind = min(range(len(d['dmarr'])), key = lambda i: abs(d['dmarr'][i] - canddm))
dtarr = [d['dtarr'][canddtind]]
dmarr = [d['dmarr'][canddmind]]
nskip = candint - (d['datadelay'][canddmind] + window/2)
nints = d['datadelay'][canddmind] + window
# method 1
# iterint = nints/(((nints-n.mod(nints,maxiter))/maxiter)+2) # keeps iterint under maxiter
# method 2
iterint = nints-1
elif (len(cand) == 0) and (('nskip' in kwargs.keys()) or ('nints' in kwargs.keys()) or ('iterint' in kwargs.keys())):
filename = d['filename']
print 'Reading data at given location for file %s' % d['filename']
nskip = d['nskip']
nints = d['nints']
iterint = d['iterint']
dtarr = d['dtarr']
dmarr = d['dmarr']
else:
print 'Not given proper cand or data definition.'
return
print 'Running new search with %d ints, %d iterint, starting at %d.' % (nints, iterint, nskip)
dd = leanpipedt.pipe_thread(filename=filename, nints=nints, iterint=iterint, nskip=nskip, spw=d['spw'], chans=d['chans'], scan=d['scan'], dmarr=dmarr, dtarr=dtarr, selectpol=d['selectpol'], datacol=d['datacol'], size=d['size'], res=d['res'], sigma_image=d['sigma_image'], searchtype=d['searchtype'], gainfile=d['gainfile'], bpfile=d['bpfile'], filtershape=d['filtershape'], secondaryfilter=d['secondaryfilter'], savecands=d['savecands'], flagmode=d['flagmode'], excludeants=d['excludeants'], nthreads=1)
return dd
def reproducecanddata(d, candint=-1, dmind=0, dtind=0, twindow=30, **kwargs):
""" Runs imaging as in initial detection. Needs to have dictionary and leanpipedt.data populated by run of reproducecand()
Default images all with 'sizex/y'. Given integration, it will image once with 'full_sizex/y'.
Assumes that entire dm track contained in data and datatrim is not needed.
"""
import qimg_cython as qimg
for key in kwargs.keys():
print 'Overwriting key %s to %s' % (key, kwargs[key])
d[key] = kwargs[key]
if 'excludeants' not in d.keys():
d['excludeants'] = []
data0 = leanpipedt.dataprep(d, dmind, dtind, usetrim=False)[d['datadelay'][dmind]/d['dtarr'][dtind]:] # returns masked array of dedispersed, ignores times with self-added data
if candint == -1:
print 'Imaging all integrations and thresholding.'
ims,snr,candints = qimg.imgallfullfilterxy(n.outer(leanpipedt.u[d['iterint']/2], d['freq']/d['freq_orig'][0]), n.outer(leanpipedt.v[d['iterint']/2], d['freq']/d['freq_orig'][0]), data0.data, d['sizex'], d['sizey'], d['res'], d['sigma_image'])
return ims,snr,candints
else:
print 'Imaging int %d.' % candint
im = qimg.imgonefullxy(n.outer(leanpipedt.u[candint], d['freq']/d['freq_orig'][0]), n.outer(leanpipedt.v[candint], d['freq']/d['freq_orig'][0]), data0.data[candint], d['full_sizex'], d['full_sizey'], d['res'])
# measure spectrum
print 'Full image peak SNR=', im.max()/im.std()
peakl, peakm = n.where(im == im.max()) # assumes new style u->u and v->v gridding
l1 = (float((d['full_sizex'])/d['res'])/2. - peakl[0])/d['full_sizex']
m1 = (float((d['full_sizey'])/d['res'])/2. - peakm[0])/d['full_sizey']
# trim interesting int out
minint = max(candint-twindow/2, 0)
maxint = min(candint+twindow/2, len(data0))
data0 = data0[minint:maxint].copy()
lib.phaseshift_threaded(data0, d, l1, m1, leanpipedt.u[candint], leanpipedt.v[candint])
if candint == -1:
return ims,snr,candints
else:
return im, data0.mean(axis=1)
def extractms(pklname, threshold, candnum=-1):
""" Takes summary pickle file and produces MS with snippet of data for each candidate with snr>threshold.
Default is to extract for all cands above threshold. Otherwise take candnum (0-based count).
"""
try:
from casa import ms, split
from casa import quanta as qa
print 'Imported CASA'
incasapy = True
except ImportError:
import tasklib as tl
import casautil
print 'Imported CASA sans casapy'
ms = casautil.tools.ms()
qa = casautil.tools.quanta()
incasapy = False
secondsinday = 24.*60*60
daypersecond = 1.0/secondsinday
# get run metadata and basic candidate info
d = pickle.load(open(pklname, 'r'))
dataloc, locs, props = filter_candidates(pklname, threshold)
scani = [int(name.rstrip('.pkl').split('_')[3][1:]) for name in d['pkllist']]
filesplit = d['pkllist'][scani.index(dataloc[candnum])].split('_')
filename = '_'.join(filesplit[1:4]) + '.ms'
snrcol = parsepropinfo(props)
canddt = locs[candnum][1]
candint = locs[candnum][2]
canddm = locs[candnum][3]
window = d['datadelay'][canddm] # minimally clip data
outms = filename[:-3] + '_cand' + str(canddt) + '-' + str(candint) + '-' + str(canddm) + '.ms'
if not os.path.exists(outms):
print 'Extracting candidate %d with SNR = %.1f at bins %s in file %s into %s' % (candnum, props[candnum][snrcol], locs[candnum], filename, outms)
ms.open(filename)
t0 = ms.range(items=['time'])['time'][0]/(24*3600)
dt = d['inttime']/secondsinday # int time in days
ms.done()
minint = max(0, candint - window - 100)
maxint = candint + 100 # 100 is largest iterint in overall search so it represents high end of candint location error. include extra 15 for plotting window and fft time filter protection(?)
candt0 = t0 + minint*dt
candt1 = t0 + maxint*dt
candt0q = qa.time(qa.quantity(candt0, 'd'), form='ymd', prec=9)[0]
candt1q = qa.time(qa.quantity(candt1, 'd'), form='ymd', prec=9)[0]
print 'Splitting out int %d -- %d and times %s -- %s' % (minint, maxint, candt0q, candt1q)
if incasapy: # if in casapy
split(vis=filename, outputvis=outms, datacolumn='data', timerange=candt0q+'~'+candt1q)
else: # if in python
cfg = tl.SplitConfig() # configure split
cfg.vis = filename
cfg.out = outms
cfg.col = 'data'
cfg.timerange=candt0q+'~'+candt1q
tl.split(cfg) # run task
else:
print 'File %s exists. Not overwriting.' % outms
return outms
def mergeplot(mergepkl, outroot=''):
""" Take merge file and produces comprehensive candidate screening plots.
Starts as dm-t plots, includes dt and peak pixel location.
"""
if not outroot:
outname = mergepkl[:-4] + '_dmt.png'
outname2 = mergepkl[:-4] + '_dmcount.png'
outname3 = mergepkl[:-4] + '_normprob.png'
outname4 = mergepkl[:-4] + '_impeak.png'
outname5 = mergepkl[:-4] + '_noise.png'
else:
outname = outroot + '_dmt.png'
outname2 = outroot + '_dmcount.png'
outname3 = outroot + '_normprob.png'
outname4 = outroot + '_impeak.png'
outname5 = outroot + '_noise.png'
print 'Plotting to %s, %s, %s, %s, %s.' % (outname, outname2, outname3, outname4, outname5)
pkl = open(mergepkl, 'r')
d = pickle.load(pkl)
stuff = pickle.load(pkl)
if len(stuff) == 3: # new format for merged pkl file
dataloc = n.array(stuff[0])
candsloc = n.array(stuff[1])
candsprop = n.array(stuff[2])
if 'remove' in d.keys():
remove = d['remove']
else:
remove = {}
# measure scan change points for labels
changepoints = [dataloc[0]]
for i in range(len(dataloc)-1):
if (dataloc[i+1] - dataloc[i]):
changepoints.append(dataloc[i+1])
changepoints = n.array(changepoints)
shifts = dataloc * d['nints']
dts = n.unique(candsloc[:,1])
tt = d['inttime']*(candsloc[:,2]+shifts)
mint = tt.min(); maxt = tt.max()
dd = n.array(d['dmarr'])[candsloc[:,3]]
mindm = dd.min(); maxdm = dd.max()
snrs = candsprop[:,3]
snrmin = 0.9*snrs.min()
# fig = plt.Figure(figsize=(8,8))
fig = plt.Figure(figsize=(15,10))
ax = {}
for dtind in range(len(dts)):
# for dtind in [0]:
# dmt plot
ax[dtind] = fig.add_subplot(str(len(dts)) + '1' + str(dtind+1))
# ax[dtind] = fig.add_subplot('111')
good = n.where(candsloc[:,1] == dtind)[0]
times = d['inttime']*(candsloc[good,2]+shifts[good])
dms = n.array(d['dmarr'])[candsloc[good,3]]
# sizes = (snrs[good]-0.8*snrs[good].min())
sizes = (snrs[good]-snrmin)**5 # set scaling to give nice visual sense of SNR
ax[dtind].scatter(times, dms, s=sizes, facecolor='none', alpha=0.3, clip_on=False)
ax[dtind].axis( (mint, maxt, mindm, maxdm) )
ax[dtind].set_ylabel('DM (pc/cm3)')
ax[dtind].text(0.9*maxt, 0.9*maxdm, 'dt='+str(d['dtarr'][dtind]))
if dtind == dts[-1]:
plt.setp(ax[dtind].get_xticklabels(), visible=True)
elif dtind == dts[0]:
ax[dtind].xaxis.set_label_position('top')
ax[dtind].xaxis.set_ticks_position('top')
ax[dtind].set_xticks(changepoints[::2]*d['inttime']*d['nints'])
ax[dtind].set_xticklabels(changepoints[::2])
plt.setp( ax[dtind].xaxis.get_majorticklabels(), rotation=90)
else:
plt.setp( ax[dtind].get_xticklabels(), visible=False)
ax[dtind].set_xlabel('Time (s)', fontsize=20)
# ax[dtind].set_xlabel('Scan number', fontsize=20)
ax[dtind].set_ylabel('DM (pc/cm3)', fontsize=20)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(outname)
# dmcount plot
fig2 = plt.Figure(figsize=(15,10))
ax2 = {}
for dtind in range(len(dts)):
good = n.where(candsloc[:,1] == dtind)[0]
times = d['inttime']*(candsloc[good,2]+shifts[good])
times = n.concatenate( (n.array([0]), times))
bins = n.round(times).astype('int')
counts = n.bincount(bins)
ax2[dtind] = fig2.add_subplot(str(len(dts)) + '1' + str(dtind+1))
ax2[dtind].scatter(range(len(counts)), counts, facecolor='none', alpha=0.5, clip_on=False)
ax2[dtind].axis( (mint, maxt, 0, 1.1*counts.max()) )
# label high points
high = n.where(counts > n.median(counts) + 20*counts.std())[0]
for ii in high:
print '%d candidates for dt=%d at %d s' % (counts[ii], d['dtarr'][dtind], ii)
ww = n.where(bins == ii)[0]
print '\tFlag these:', zip(dataloc[good][ww], candsloc[good,2][ww])
print
if dtind == dts[-1]:
plt.setp(ax2[dtind].get_xticklabels(), visible=True)
elif (dtind == dts[0]) or (dtind == len(dts)/2):
ax2[dtind].xaxis.set_label_position('top')
ax2[dtind].xaxis.set_ticks_position('top')
ax2[dtind].set_xticks(changepoints[::2]*d['nints']*d['inttime'])
ax2[dtind].set_xticklabels(changepoints[::2])
plt.setp( ax2[dtind].xaxis.get_majorticklabels(), rotation=90, size='small')
else:
plt.setp( ax2[dtind].get_xticklabels(), visible=False)
ax2[dtind].set_xlabel('Time (s)')
ax2[dtind].set_ylabel('Count')
canvas2 = FigureCanvasAgg(fig2)
canvas2.print_figure(outname2)
# DM SNR norm prob plot
if 'goodintcount' in d.keys():
Z = lambda quan: n.sqrt(2)*sp.erfinv( 2*quan - 1)
quan = lambda ntrials, i: (ntrials + 1/2. - i)/ntrials
snrsort = n.array(sorted(snrs, reverse=True)) # high-res snr
# snrsort = n.array(sorted(candsprop[:,0], reverse=True)) # low-res snr
npix = (d['full_sizex']/d['res']) * (d['full_sizey']/d['res'])
nints = d['goodintcount']
ndms = len(d['dmarr'])
ntrials = npix*nints*ndms*(1 + 1/2. + 1/4. + 1/8.)
Zsort = n.array([Z(quan(ntrials, j+1)) for j in range(len(snrsort))])
fig3 = plt.Figure(figsize=(10,10))
ax3 = fig3.add_subplot(111)
ax3.plot(snrsort, Zsort, 'r.')
refl = n.linspace(min(snrsort.min(), Zsort.min()), max(snrsort.max(), Zsort.max()), 2)
ax3.plot(refl, refl, 'k--')
ax3.set_xlabel('SNR')
ax3.set_ylabel('Normal quantile SNR')
canvas = FigureCanvasAgg(fig3)
canvas.print_figure(outname3)
fig4 = plt.Figure(figsize=(10,10))
ax4 = fig4.add_subplot(111)
sizes = (snrs-0.9*snrs.min())**5 # set scaling to give nice visual sense of SNR
xarr = 60*n.degrees(candsprop[:,4]); yarr = 60*n.degrees(candsprop[:,5])
ax4.scatter(xarr, yarr, s=sizes, facecolor='none', alpha=0.5, clip_on=False)
ax4.set_xlabel('Dec Offset (amin)')
ax4.set_ylabel('RA Offset (amin)')
fov = n.degrees(1./d['res'])*60.
ax4.set_xlim(fov/2, -fov/2)
ax4.set_ylim(-fov/2, fov/2)
canvas4 = FigureCanvasAgg(fig4)
canvas4.print_figure(outname4)
noisehists('noise*pkl', outname5, remove=remove)
def calc_ntrials(candsdir):
""" Function to read config dict from start of cand pickle file and calculate the total number of trials (nint * ndm/nresample * npix)
Expects directory in which to look for cands*merge*pkl files.
Returns total number of trials over all cand files.
"""
pkllist = glob.glob(candsdir + 'cands*merge*pkl')
ntot = 0
print 'Counting over %d cands in directory %s...' % (len(pkllist), candsdir)
for pkl in pkllist:
d = pickle.load(open(pkl, 'r'))
if 'resamplearr' in d.keys(): # some resamplearrs have only 1s, due to bug in merge step. if key exists, it should be filled with this.
resamplearr = n.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
else: # for the few v6 processing files, resamplearr is effectively all 1s.
resamplearr = n.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
n_int_dm = (d['nints']*n.ones(len(d['dmarr']), dtype='int64')/resamplearr).sum()
n_pix = d['sizex']/d['res'] * d['sizey']/d['res']
n_trials = n_int_dm * n_pix
print 'For file %s: %d trials' % (pkl, n_trials)
ntot += n_trials
print 'For sig=6.5, tail prob=4.016e-11 and false pos rate = ', 4.016e-11 * ntot
return ntot
def impeakplot(d, candsprop, saveroot=''):
""" Take images of all candidates and shows peak pixel location relative to each other on summed map.
"""
imlist = [candsprop[i][1] for i in range(len(candsprop))]
lenx = 0; leny = 0
for im in imlist:
sh = im.shape
if sh[0] > leny:
leny = sh[0]
if sh[1] > lenx:
lenx = sh[1]
imsum = n.zeros((leny, lenx), dtype=n.dtype(candsprop[0][1][0,0]))
impeak = []
for im in imlist:
sh = im.shape
imsum[:sh[0],:sh[1]] = imsum[:sh[0],:sh[1]] + im
peaky, peakx = n.where(im == im.max())
impeak = impeak + [[peaky[0],peakx[0]]]
impeak = n.array(impeak)
if saveroot:
savefile = saveroot + '_imsum.png'
print 'Plotting to %s.' % savefile
fov = n.degrees(1./d['res'])*60.
ypix = len(candsprop[i][1])
xpix = len(candsprop[i][1][0])
if len(candsprop)> 0:
fig = plt.Figure()
ax = fig.add_subplot(111)
im = ax.imshow(imsum, aspect='auto', origin='lower', interpolation='nearest', extent=[fov/2, -fov/2, -fov/2, fov/2])
ax.scatter(((ypix/2-impeak[:,1]))*fov/ypix, (impeak[:,0]-xpix/2)*fov/xpix, s=80, marker='o', facecolor='none')
ax.set_xlabel('Dec Offset (amin)')
ax.set_ylabel('RA Offset (amin)')
ax.set_xlim(fov/2, -fov/2)
ax.set_ylim(-fov/2, fov/2)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(savefile)
else:
print 'No candidates to plot.'
return impeak
def dmtplot(d, candsloc, snrs, saveroot=''):
""" Take loc and snr lists to plot dm vs time with symbol size related to snr.
"""
if saveroot:
savefile = saveroot + '_dmt.png'
print 'Plotting to %s.' % savefile
fig = plt.Figure()
ax = fig.add_subplot(111)
if len(candsloc)> 0:
ax.scatter(d['inttime']*candsloc[:,2], n.array(d['dmarr'])[candsloc[:,3]], s=2*snrs**2, facecolor='none', alpha=0.5, clip_on=False)
ax.set_xlabel('Time (s)')
ax.set_ylabel('DM (pc/cm3)')
canvas = FigureCanvasAgg(fig)
canvas.print_figure(savefile)
else:
print 'No candidates to plot.'
def locpluspeak(pklroot):
""" Get all candidates from pklroot selection for compiling location with peak pixel (beam, dt, dm, time, x, y)
"""
# need to get dictionary for imaging detail
pkllist = glob.glob(pklroot)
pkl = open(pkllist[0], 'rb')
d = pickle.load(pkl)
# get candidates and image peaks
cands = filter_candidates(pklroot)
if len(cands) == 2:
candsloc, candsprop = cands
elif len(cands) == 3:
dataloc, candsloc, candsprop = cands
impeaks = impeakplot(d, candsprop, saveroot='')
candslocplus = n.concatenate((candsloc,impeaks), axis=1)
return candslocplus
def snrhist(snr, saveroot=''):
""" Plot SNR histogram of candidates. Expects snr list.
"""
if saveroot:
savefile = saveroot + '_snrhist.png'
print 'Plotting to %s.' % savefile
fig = plt.Figure()
ax = fig.add_subplot(111)
ax.hist(snr, bins=15, normed=True, histtype='step', alpha=0.5)
ax.set_xlabel('SNR')
ax.set_ylabel('Number')
canvas = FigureCanvasAgg(fig)
canvas.print_figure(savefile)
return snr
def dmtpixplot(pklroot, mind, maxd):
""" Get all candidates from pklroot (expected to include multiple scans and days) and look for dm-pix neighbors.
mind and maxd define range of distances in dm,pix space to return.
"""
locpl = locpluspeak(pklroot)
# fill difference array
diff = n.zeros( (len(locpl)**2,4), dtype='int')
for i in range(len(locpl)):
print 'Filling diff array for index ', i
diff[len(locpl)*i:len(locpl)*(i+1)] = locpl[i,2:] - locpl[:,2:] # include int,dm,x,y
dist = n.sqrt(diff[:,1]**2 + diff[:,2]**2 + diff[:,3]**2) # not really cartesian, but whatever
ij = n.where((dist > mind) & (dist < maxd))[0]
j = n.mod(ij, len(locpl)) # index of second
i = (ij - j)/len(locpl) # index of first
print 'Candidates:'
for ind in range(len(j)):
print '*-%d-%d-*png and *-%d-%d-*png' % (locpl[i[ind],2], locpl[i[ind],3], locpl[j[ind],2], locpl[j[ind],3])
def singleplot(pklfile, candsloc, candsprop, i, save=1):
""" Plot a single candidate out of a list.
"""
candsloc = n.array(candsloc).astype(int)
print 'Plotting for candidate at: ', candsloc[i]
pkl = open(pklfile, 'rb')
d = pickle.load(pkl)
pkl.close()
snr = n.array([cand[0] for cand in candsprop])
fig = plt.Figure(figsize=(8.5,8))
# text description of candidate
ax = fig.add_subplot(221, axisbg='white')
# ax.set_title('Candidate @ Tier 1')
# first plot dm-t distribution beneath
ax.scatter(d['inttime']*candsloc[:,2], n.array(d['dmarr'])[candsloc[:,3]], s=(snr-6)**2, facecolor='none', linewidth=0.05, clip_on=False)
ax.scatter(d['inttime']*candsloc[i,2], n.array(d['dmarr'])[candsloc[i,3]], s=(snr[i]-6)**2, marker='x', facecolor='none', linewidth=2, clip_on=False)
ax.set_ylim(0, 4000)
ax.set_xlabel('Time (s)')
ax.set_ylabel('DM (pc/cm3)')
# then add annotating info
beamra = n.round(d['delaycenters'][candsloc[i,0]][0], 1)
beamdec = n.round(d['delaycenters'][candsloc[i,0]][1], 1)
fov = n.degrees(1./d['res'])*60.
xpix,ypix = candsprop[i][1].shape
srcra,srcdec = n.where(candsprop[i][1] == candsprop[i][1].max())
ax.text(0.1, 0.9, d['filename'], fontname='sans-serif', transform = ax.transAxes)
ax.text(0.1, 0.8, 'dt %d, Int %d, DM %.1f' % (d['dtarr'][candsloc[i,1]], candsloc[i,2], d['dmarr'][candsloc[i,3]]), fontname='sans-serif', transform = ax.transAxes)
ax.text(0.1, 0.7, 'Peak: (' + str(n.round((xpix/2-srcra[0])*fov/xpix, 1)) + '\' ,' + str(n.round((ypix/2-srcdec[0])*fov/ypix, 1)) + '\'), SNR: ' + str(n.round(snr[i], 1)), fontname='sans-serif', transform = ax.transAxes)
# plot dynamic spectra
left, width = 0.6, 0.2
bottom, height = 0.2, 0.7
rect_dynsp = [left, bottom, width, height]
rect_lc = [left, bottom-0.1, width, 0.1]
rect_sp = [left+width, bottom, 0.1, height]
ax_dynsp = fig.add_axes(rect_dynsp)
ax_lc = fig.add_axes(rect_lc)
ax_sp = fig.add_axes(rect_sp)
spectra = n.swapaxes(candsprop[i][2].data.real,0,1) # seems that latest pickle actually contains complex values in spectra...
dd = n.concatenate( (spectra[...,0], n.zeros_like(spectra[...,0]), spectra[...,1]), axis=1) # make array for display with white space between two pols
im = ax_dynsp.imshow(dd, origin='lower', interpolation='nearest', aspect='auto', cmap=plt.get_cmap('Greys'))
ax_dynsp.text(0.5, 0.95, 'RR LL', horizontalalignment='center', verticalalignment='center', fontsize=16, color='w', transform = ax_dynsp.transAxes)
ax_dynsp.set_yticks(range(0,len(d['freq']),30))
ax_dynsp.set_yticklabels(d['freq'][::30])
ax_dynsp.set_ylabel('Freq (GHz)')
spectrum = spectra[:,len(spectra[0])/2].mean(axis=1) # assume pulse in middle bin. get stokes I spectrum. **this is wrong in a minority of cases.**
ax_sp.plot(spectrum, range(len(spectrum)), 'k.')
ax_sp.plot(n.zeros(len(spectrum)), range(len(spectrum)), 'k:')
ax_sp.set_ylim(0, len(spectrum))
ax_sp.set_yticklabels([])
xmin,xmax = ax_sp.get_xlim()
ax_sp.set_xticks(n.linspace(xmin,xmax,3).round(2))
# ax_sp.set_xticklabels() # set labels to be rounded?
ax_sp.set_xlabel('Flux (Jy)')
# lc = spectra.mean(axis=2).mean(axis=0) # to show stokes I
lc = dd.mean(axis=0)
lenlc = n.where(lc == 0)[0][0]
ax_lc.plot(range(0,lenlc)+range(2*lenlc,3*lenlc), list(lc)[:lenlc] + list(lc)[-lenlc:], 'k.')
ax_lc.plot(range(0,lenlc)+range(2*lenlc,3*lenlc), list(n.zeros(lenlc)) + list(n.zeros(lenlc)), 'k:')
ax_lc.set_xlabel('Integration')
ax_lc.set_ylabel('Flux (Jy)')
ax_lc.set_xticks([0,0.5*lenlc,lenlc,1.5*lenlc,2*lenlc,2.5*lenlc,3*lenlc])
ax_lc.set_xticklabels(['0',str(lenlc/2),str(lenlc),'','0',str(lenlc/2),str(lenlc)])
ymin,ymax = ax_lc.get_ylim()
ax_lc.set_yticks(n.linspace(ymin,ymax,3).round(2))
# ax.plot(d['freq'], spectrum, '.')
# ax.set_xlabel('Frequency (GHz)')
# ax.set_ylabel('Flux density (Jy)')
# sm = n.sqrt( ((spectrum**2).mean() - spectrum.mean()**2) / spectrum.mean()**2 )
# ax.text(0.05, 0.05, 'specmod =' + str(n.round(sm, 1)), transform = ax.transAxes)
# dm-time of candidates in provided list
ax = fig.add_subplot(223)
ax.scatter(((xpix/2-srcra[0])-0.05*xpix)*fov/xpix, (ypix/2-srcdec[0])*fov/ypix, s=80, marker='<', facecolor='none')
ax.scatter(((xpix/2-srcra[0])+0.05*xpix)*fov/xpix, (ypix/2-srcdec[0])*fov/ypix, s=80, marker='>', facecolor='none')
im = ax.imshow(candsprop[i][1].transpose(), aspect='equal', origin='upper', interpolation='nearest', extent=[fov/2, -fov/2, -fov/2, fov/2], cmap=plt.get_cmap('Greys'), vmin=0, vmax=0.5*candsprop[i][1].max())
# ax.scatter(srcra[0]+20, srcdec[0], s=70, marker='<', facecolor='none')
# ax.imshow(candsprop[i][1], aspect='auto', origin='lower', interpolation='nearest')
# im.colorbar()
ax.set_xlabel('RA Offset (arcmin)')
ax.set_ylabel('Dec Offset (arcmin)')
if save:
candslocstr = '_tier1-%d-%d-%d-%d' % (candsloc[i,0],candsloc[i,1],candsloc[i,2],candsloc[i,3])
canvas = FigureCanvasAgg(fig)
canvas.print_figure(string.join(pklfile.split('.')[:-1], '.') + str(candslocstr) + '.png')
def singleplot2(pklfile, threshold, candnum, im, data, savefile=''):
""" Plot a single candidate with image and data from reproducecanddata
"""
pkl = open(pklfile, 'rb')
d = pickle.load(pkl)
dataloc0, candsloc0, candsprop0 = pickle.load(pkl)
pkl.close()
dataloc0 = n.array(dataloc0); candsloc0 = n.array(candsloc0); candsprop0 = n.array(candsprop0)
dataloc, candsloc, candsprop = filter_candidates(pklfile, threshold)
dataloc = n.array(dataloc); candsloc = n.array(candsloc); candsprop = n.array(candsprop)
scani = [int(name.rstrip('.pkl').split('_')[3][1:]) for name in d['pkllist']]
filesplit = d['pkllist'][scani.index(dataloc[candnum])].split('_')
filename = '_'.join(filesplit[1:4]) + '.ms'
snrcol = parsepropinfo(candsprop)
snr0 = n.array([prop[snrcol] for prop in candsprop0])
snr = n.array([prop[snrcol] for prop in candsprop])
changepoints = []
for i in range(len(dataloc0)-1):
if (dataloc0[i+1] - dataloc0[i]):
changepoints.append(dataloc0[i+1])
changepoints = n.array(changepoints)
fig = plt.Figure(figsize=(8.5,8))
# text description of candidate
ax = fig.add_subplot(221, axisbg='white')
# ax.set_title('Candidate @ Tier 1')
# first plot dm-t distribution beneath
times0 = d['inttime']*(candsloc0[:,2] + d['nints']*dataloc0)
times = d['inttime']*(candsloc[candnum,2] + d['nints']*dataloc[candnum])
ax.scatter(times0, n.array(d['dmarr'])[candsloc0[:,3]], s=(snr0-0.9*snr0.min())**5, facecolor='none', linewidth=0.05, clip_on=False)
ax.scatter(times, n.array(d['dmarr'])[candsloc[candnum,3]], s=(snr-0.9*snr0.min())**5, marker='x', facecolor='none', linewidth=2, clip_on=False)
ax.set_ylim(0, 4500)
ax.set_xlim(times0.min(), times0.max())
ax.set_xlabel('Time (s)')
ax.set_ylabel('DM (pc/cm3)')
ax.set_xticks(n.linspace(times0.min().astype('int'), times0.max().astype('int'), 2))
# ax.set_xticklabels()
# then add annotating info
beamra = n.round(d['delaycenters'][candsloc[candnum,0]][0], 1)
beamdec = n.round(d['delaycenters'][candsloc[candnum,0]][1], 1)
fov = n.degrees(1./d['res'])*60.
xpix,ypix = im.shape
srcra,srcdec = n.where(im == im.max())
ax.text(0.1, 0.9, filename, fontname='sans-serif', transform = ax.transAxes)
ax.text(0.1, 0.8, 'dt %d, Int %d, DM %.1f' % (d['dtarr'][candsloc[candnum,1]], candsloc[candnum,2], d['dmarr'][candsloc[candnum,3]]), fontname='sans-serif', transform = ax.transAxes)
ax.text(0.1, 0.7, 'Peak: (' + str(n.round((xpix/2-srcra[0])*fov/xpix, 1)) + '\' ,' + str(n.round((ypix/2-srcdec[0])*fov/ypix, 1)) + '\'), SNR: ' + str(n.round(snr[candnum], 1)), fontname='sans-serif', transform = ax.transAxes)
# plot dynamic spectra
left, width = 0.6, 0.2
bottom, height = 0.2, 0.7
rect_dynsp = [left, bottom, width, height]
rect_lc = [left, bottom-0.1, width, 0.1]
rect_sp = [left+width, bottom, 0.1, height]
ax_dynsp = fig.add_axes(rect_dynsp)
ax_lc = fig.add_axes(rect_lc)
ax_sp = fig.add_axes(rect_sp)
spectra = n.swapaxes(data.real,0,1) # seems that latest pickle actually contains complex values in spectra...
dd = n.concatenate( (spectra[...,0], n.zeros_like(spectra[...,0]), spectra[...,1]), axis=1) # make array for display with white space between two pols
impl = ax_dynsp.imshow(dd, origin='lower', interpolation='nearest', aspect='auto', cmap=plt.get_cmap('Greys'))
ax_dynsp.text(0.5, 0.95, 'RR LL', horizontalalignment='center', verticalalignment='center', fontsize=16, color='w', transform = ax_dynsp.transAxes)
ax_dynsp.set_yticks(range(0,len(d['freq']),30))
ax_dynsp.set_yticklabels(d['freq'][::30])
ax_dynsp.set_ylabel('Freq (GHz)')
ax_dynsp.set_xlabel('Integration (rel)')
spectrum = spectra[:,len(spectra[0])/2].mean(axis=1) # assume pulse in middle bin. get stokes I spectrum. **this is wrong in a minority of cases.**
ax_sp.plot(spectrum, range(len(spectrum)), 'k.')
ax_sp.plot(n.zeros(len(spectrum)), range(len(spectrum)), 'k:')
ax_sp.set_ylim(0, len(spectrum))
ax_sp.set_yticklabels([])
xmin,xmax = ax_sp.get_xlim()
ax_sp.set_xticks(n.linspace(xmin,xmax,3).round(2))
# ax_sp.set_xticklabels() # set labels to be rounded?
ax_sp.set_xlabel('Flux (Jy)')
# lc = spectra.mean(axis=2).mean(axis=0) # to show stokes I
lc = dd.mean(axis=0)
lenlc = n.where(lc == 0)[0][0]
ax_lc.plot(range(0,lenlc)+range(2*lenlc,3*lenlc), list(lc)[:lenlc] + list(lc)[-lenlc:], 'k.')
ax_lc.plot(range(0,lenlc)+range(2*lenlc,3*lenlc), list(n.zeros(lenlc)) + list(n.zeros(lenlc)), 'k:')
ax_lc.set_xlabel('Integration')
ax_lc.set_ylabel('Flux (Jy)')
ax_lc.set_xticks([0,0.5*lenlc,lenlc,1.5*lenlc,2*lenlc,2.5*lenlc,3*lenlc])
ax_lc.set_xticklabels(['0',str(lenlc/2),str(lenlc),'','0',str(lenlc/2),str(lenlc)])
ymin,ymax = ax_lc.get_ylim()
ax_lc.set_yticks(n.linspace(ymin,ymax,3).round(2))
# ax.plot(d['freq'], spectrum, '.')
# ax.set_xlabel('Frequency (GHz)')
# ax.set_ylabel('Flux density (Jy)')
# sm = n.sqrt( ((spectrum**2).mean() - spectrum.mean()**2) / spectrum.mean()**2 )
# ax.text(0.05, 0.05, 'specmod =' + str(n.round(sm, 1)), transform = ax.transAxes)
# dm-time of candidates in provided list
ax = fig.add_subplot(223)
ax.scatter(((xpix/2-srcra[0])-0.05*xpix)*fov/xpix, (ypix/2-srcdec[0])*fov/ypix, s=80, marker='<', facecolor='none')
ax.scatter(((xpix/2-srcra[0])+0.05*xpix)*fov/xpix, (ypix/2-srcdec[0])*fov/ypix, s=80, marker='>', facecolor='none')
impl = ax.imshow(im.transpose(), aspect='equal', origin='upper', interpolation='nearest', extent=[fov/2, -fov/2, -fov/2, fov/2], cmap=plt.get_cmap('Greys'), vmin=0, vmax=0.5*im.max())
ax.set_xlabel('RA Offset (arcmin)')
ax.set_ylabel('Dec Offset (arcmin)')
if savefile:
canvas = FigureCanvasAgg(fig)
canvas.print_figure(savefile)
def readnoise(noisefile):
f = open(noisefile,'r')
itercount = []; noiseperbl = []; flagfrac = []; imnoise = []
while True: #for each file it loops through to get 'y' column data
try:
value=pickle.load(f)
itercount.append(value[0])
noiseperbl.append(value[1])
flagfrac.append(value[2])
imnoise.append(value[3]) # fixed to image pix
except EOFError: #when hits EOFError it breaks
f.close()
break
return (n.array(itercount), n.array(noiseperbl), n.array(flagfrac), n.array(imnoise))
def noisehists(pklroot, savefile='', remove={}):
""" Cumulative hist of image noise levels.
"""
pkllist = glob.glob(pklroot)
noises = []; noisec = []
print 'Reading %d noise files' % len(pkllist)
for pkl in pkllist:
nn = readnoise(pkl)
ii = nn[0]
nn = list(nn[3])
scani = int(pkl.split('_s')[1].split('.')[0]) # assumes scan name structure
if scani in remove.keys():
print 'Removing some noise measurements from ', pkl
nranges = len(remove[scani])
wwa = []
for first in range(0,nranges,2):
badrange0 = remove[scani][first]
badrange1 = remove[scani][first+1]
ww = list(n.where( (ii > badrange0) & (ii < badrange1) )[0])
if len(ww):
wwa += ww
for i in wwa[::-1]:
junk = nn.pop(i)
noises.append(nn)
noisec = noisec + nn
bins = n.linspace(min(noisec), max(noisec), 50)
fig = plt.Figure(figsize=(10,10))
ax = fig.add_subplot(211, axisbg='white')
stuff = ax.hist(noises, bins=bins, histtype='bar', lw='none', ec='none')