def inspect_cand(mergepkl, candnum=-1, scan=0, **kwargs): """ Create detailed plot of a single candidate. Thresholds (as minimum), then provides list of candidates to select with candnum. scan can be used to define scan number with pre-merge pkl file. kwargs passed to rt.set_pipeline """ d = pickle.load(open(mergepkl, "r")) loc, prop = read_candidates(mergepkl) if not os.path.dirname(d["filename"]): d["filename"] = os.path.join(d["workdir"], d["filename"]) # feature columns if "snr2" in d["features"]: snrcol = d["features"].index("snr2") elif "snr1" in d["features"]: snrcol = d["features"].index("snr1") if "l2" in d["features"]: lcol = d["features"].index("l2") elif "l1" in d["features"]: lcol = d["features"].index("l1") if "m2" in d["features"]: mcol = d["features"].index("m2") elif "m1" in d["features"]: mcol = d["features"].index("m1") if not scan: scancol = d["featureind"].index("scan") segmentcol = d["featureind"].index("segment") intcol = d["featureind"].index("int") dtindcol = d["featureind"].index("dtind") dmindcol = d["featureind"].index("dmind") # sort and prep candidate list snrs = n.array([prop[i][snrcol] for i in range(len(prop))]) # if isinstance(snrmin, type(None)): # snrmin = min(snrs) # sortord = snrs.argsort() # snrinds = n.where(snrs[sortord] > snrmin)[0] # loc = loc[sortord][snrinds] # prop = n.array([prop[i][snrcol] for i in range(len(prop))][sortord][snrinds]) # total hack to get prop as list to sort if candnum < 0: for i in range(len(loc)): logger.info("%d %s %s" % (i, str(loc[i]), str(prop[i][snrcol]))) logger.info("Returning candidate (loc, snr) ...") return (loc, n.array([prop[i][snrcol] for i in range(len(prop))])) else: logger.info( "Reproducing and visualizing candidate %d at %s with properties %s." % (candnum, loc[candnum], prop[candnum]) ) if not scan: scan = loc[candnum, scancol] nsegments = len(d["segmenttimesdict"][scan]) else: nsegments = len(d["segmenttimes"]) segment = loc[candnum, segmentcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] candint = loc[candnum, intcol] dmarrorig = d["dmarr"] dtarrorig = d["dtarr"] d2 = rt.set_pipeline( d["filename"], scan, fileroot=d["fileroot"], paramfile="rtparams.py", savecands=False, savenoise=False, nsegments=nsegments, **kwargs ) im, data = rt.pipeline_reproduce( d2, segment, (candint, dmind, dtind) ) # with candnum, pipeline will return cand image and data # calc source location peakl, peakm = n.where(im == im.max()) xpix, ypix = im.shape l1 = (xpix / 2.0 - peakl[0]) / (xpix * d["uvres"]) m1 = (ypix / 2.0 - peakm[0]) / (ypix * d["uvres"]) pt_ra, pt_dec = d["radec"] src_ra, src_dec = source_location(pt_ra, pt_dec, l1, m1) logger.info("Peak (RA, Dec): %.3f, %.3f" % (src_ra, src_dec)) logger.info("Returning candidate d, im, data") return d2, im, data
def plot_cand(candsfile, candloc=[], candnum=-1, threshold=0, savefile=True, returndata=False, outname='', **kwargs): """ Reproduce detection of a single candidate for plotting or inspection. candsfile can be merge or single-scan cands pkl file. Difference defined by presence of scan in d['featureind']. candloc reproduces candidate at given location (scan, segment, integration, dmind, dtind, beamnum). candnum selects one to reproduce from ordered list threshold is min of sbs(SNR) used to filter candidates to select with candnum. savefile/outname define if/how to save png of candidate if returndata, (im, data) returned. kwargs passed to rt.set_pipeline """ # get candidate info loc, prop = pc.read_candidates(candsfile) # define state dict and overload with user prefs d0 = pickle.load(open(candsfile, 'r')) for key in kwargs: logger.info('Setting %s to %s' % (key, kwargs[key])) d0[key] = kwargs[key] d0['logfile'] = False # no need to save log # feature columns if 'snr2' in d0['features']: snrcol = d0['features'].index('snr2') elif 'snr1' in d0['features']: snrcol = d0['features'].index('snr1') if 'l2' in d0['features']: lcol = d0['features'].index('l2') elif 'l1' in d0['features']: lcol = d0['features'].index('l1') if 'm2' in d0['features']: mcol = d0['features'].index('m2') elif 'm1' in d0['features']: mcol = d0['features'].index('m1') try: scancol = d0['featureind'].index('scan') # if merged pkl except ValueError: scancol = -1 # if single-scan pkl segmentcol = d0['featureind'].index('segment') intcol = d0['featureind'].index('int') dtindcol = d0['featureind'].index('dtind') dmindcol = d0['featureind'].index('dmind') # sort and prep candidate list snrs = prop[:, snrcol] select = np.where(np.abs(snrs) > threshold)[0] loc = loc[select] prop = prop[select] times = pc.int2mjd(d0, loc) times = times - times[0] # default case will print cand info if (candnum < 0) and (not len(candloc)): logger.info('Getting candidates...') logger.info('candnum: loc, SNR, DM (pc/cm3), time (s; rel)') for i in range(len(loc)): logger.info("%d: %s, %.1f, %.1f, %.1f" % (i, str(loc[i]), prop[i, snrcol], np.array( d0['dmarr'])[loc[i, dmindcol]], times[i])) else: # if candnum or candloc provided, try to reproduce if (candnum >= 0) and not len(candloc): logger.info( 'Reproducing and visualizing candidate %d at %s with properties %s.' % (candnum, loc[candnum], prop[candnum])) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] if scancol >= 0: # here we have a merge pkl scan = loc[candnum, scancol] else: # a scan-based cands pkl scan = d0['scan'] segment = loc[candnum, segmentcol] candint = loc[candnum, intcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] beamnum = 0 candloc = (scan, segment, candint, dmind, dtind, beamnum) elif len(candloc) and (candnum < 0): assert len( candloc ) == 6, 'candloc should be length 6 ( scan, segment, candint, dmind, dtind, beamnum ).' logger.info('Reproducing and visualizing candidate %d at %s' % (candnum, candloc)) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] scan, segment, candint, dmind, dtind, beamnum = candloc else: raise Exception, 'Provide candnum or candloc, not both' # if working locally, set workdir appropriately. Can also be used in queue system with full path given. if not os.path.dirname(candsfile): d0['workdir'] = os.getcwd() else: d0['workdir'] = os.path.dirname(candsfile) filename = os.path.join(d0['workdir'], os.path.basename(d0['filename'])) # clean up d0 of superfluous keys params = pp.Params() # will be used as input to rt.set_pipeline for key in d0.keys(): if not hasattr(params, key) and 'memory_limit' not in key: _ = d0.pop(key) d0['npix'] = 0 d0['uvres'] = 0 d0['nsegments'] = 0 d0['logfile'] = False # get cand data d = rt.set_pipeline(filename, scan, **d0) im, data = rt.pipeline_reproduce( d, candloc, product='imdata') # removed loc[candnum] # optionally plot if savefile: loclabel = scan, segment, candint, dmind, dtind, beamnum make_cand_plot(d, im, data, loclabel, outname=outname) # optionally return data if returndata: return (im, data)
def plot_cand(mergepkl, candnum=-1, outname="", **kwargs): """ Create detailed plot of a single candidate. Thresholds (as minimum), then provides list of candidates to select with candnum. kwargs passed to rt.set_pipeline """ # if isinstance(pkllist, list): # outroot = '_'.join(pkllist[0].split('_')[1:3]) # merge_cands(pkllist, outroot=outroot) # mergepkl = 'cands_' + outroot + '_merge.pkl' # elif isinstance(pkllist, str): # logger.info('Assuming input is mergepkl') # mergepkl = pkllist d = pickle.load(open(mergepkl, "r")) loc, prop = read_candidates(mergepkl) if not os.path.dirname(d["filename"]): d["filename"] = os.path.join(d["workdir"], d["filename"]) # feature columns if "snr2" in d["features"]: snrcol = d["features"].index("snr2") elif "snr1" in d["features"]: snrcol = d["features"].index("snr1") if "l2" in d["features"]: lcol = d["features"].index("l2") elif "l1" in d["features"]: lcol = d["features"].index("l1") if "m2" in d["features"]: mcol = d["features"].index("m2") elif "m1" in d["features"]: mcol = d["features"].index("m1") scancol = d["featureind"].index("scan") segmentcol = d["featureind"].index("segment") intcol = d["featureind"].index("int") dtindcol = d["featureind"].index("dtind") dmindcol = d["featureind"].index("dmind") # sort and prep candidate list snrs = n.array([prop[i][snrcol] for i in range(len(prop))]) # if isinstance(snrmin, type(None)): # snrmin = min(snrs) # sortord = snrs.argsort() # snrinds = n.where(snrs[sortord] > snrmin)[0] # loc = loc[sortord][snrinds] # prop = n.array([prop[i][snrcol] for i in range(len(prop))][sortord][snrinds]) # total hack to get prop as list to sort # prop = prop[sortord][snrinds] # original if candnum < 0: logger.info("Getting candidates...") for i in range(len(loc)): logger.info("%d %s %s" % (i, str(loc[i]), str(prop[i][snrcol]))) return (loc, n.array([prop[i][snrcol] for i in range(len(prop))])) else: logger.info( "Reproducing and visualizing candidate %d at %s with properties %s." % (candnum, loc[candnum], prop[candnum]) ) scan = loc[candnum, scancol] segment = loc[candnum, segmentcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] candint = loc[candnum, intcol] dmarrorig = d["dmarr"] dtarrorig = d["dtarr"] nsegments = len(d["segmenttimesdict"][scan]) d2 = rt.set_pipeline( d["filename"], scan, fileroot=d["fileroot"], paramfile="rtparams.py", savecands=False, savenoise=False, nsegments=nsegments, **kwargs ) im, data = rt.pipeline_reproduce( d2, segment, (candint, dmind, dtind) ) # with candnum, pipeline will return cand image and data # calc source location peakl, peakm = n.where(im == im.max()) xpix, ypix = im.shape l1 = (xpix / 2.0 - peakl[0]) / (xpix * d["uvres"]) m1 = (ypix / 2.0 - peakm[0]) / (ypix * d["uvres"]) pt_ra, pt_dec = d["radec"] src_ra, src_dec = source_location(pt_ra, pt_dec, l1, m1) logger.info("Peak (RA, Dec): %.3f, %.3f" % (src_ra, src_dec)) # plot it logger.info("Plotting...") fig = plt.Figure(figsize=(8.5, 8)) ax = fig.add_subplot(221, axisbg="white") # # first plot dm-t distribution beneath times0 = int2mjd(d, loc) times0 = times0 - times0[0] times = times0[candnum] dms0 = n.array(dmarrorig)[list(loc[:, dmindcol])] dms = dmarrorig[loc[candnum, dmindcol]] snr0 = prop[:][snrcol] snr = prop[candnum][snrcol] snrmin = 0.8 * min(d["sigma_image1"], d["sigma_image2"]) # plot positive good = n.where(snr0 > 0) ax.scatter( times0[good], dms0[good], s=(snr0[good] - snrmin) ** 5, facecolor="none", linewidth=0.2, clip_on=False ) ax.scatter(times, dms, s=(snr - snrmin) ** 5, facecolor="none", linewidth=2, clip_on=False) # plot negative good = n.where(snr0 < 0) ax.scatter( times0[good], dms0[good], s=(n.abs(snr0)[good] - snrmin) ** 5, marker="x", edgecolors="k", linewidth=0.2, clip_on=False, ) ax.set_ylim(dmarrorig[0], dmarrorig[-1]) ax.set_xlim(times0.min(), times0.max()) ax.set_xlabel("Time (s)") ax.set_ylabel("DM (pc/cm3)") # # then add annotating info ax.text(0.1, 0.9, d["fileroot"] + "_sc" + str(scan), fontname="sans-serif", transform=ax.transAxes) ax.text( 0.1, 0.8, "seg %d, int %d, DM %.1f, dt %d" % (segment, loc[candnum, intcol], dmarrorig[loc[candnum, dmindcol]], dtarrorig[loc[candnum, dtindcol]]), fontname="sans-serif", transform=ax.transAxes, ) ax.text( 0.1, 0.7, "Peak: (" + str(n.round(peakx, 1)) + "' ," + str(n.round(peaky, 1)) + "'), SNR: " + str(n.round(snr, 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_xlabel("Flux (Jy)") 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)) # image ax = fig.add_subplot(223) fov = n.degrees(1.0 / d["uvres"]) * 60.0 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 not outname: outname = os.path.join( d["workdir"], "cands_%s_sc%dseg%di%ddm%ddt%d.png" % (d["fileroot"], scan, segment, loc[candnum, intcol], dmind, dtind), ) canvas = FigureCanvasAgg(fig) canvas.print_figure(outname) return ([], [])
def plot_cand(candsfile, candloc=[], candnum=-1, threshold=0, savefile=True, returndata=False, outname='', newplot=True, returnstate=False, **kwargs): """ Reproduce detection of a single candidate for plotting or inspection. candsfile can be merge or single-scan cands pkl file. Difference defined by presence of scan in d['featureind']. candloc reproduces candidate at given location (scan, segment, integration, dmind, dtind, beamnum). candnum selects one to reproduce from ordered list threshold is min of sbs(SNR) used to filter candidates to select with candnum. savefile/outname define if/how to save png of candidate if returndata, (im, data) returned. kwargs passed to rt.set_pipeline if newplot, then plot with the new candidate plot using bridget's version """ # get candidate info loc, prop, d0 = pc.read_candidates(candsfile, returnstate=True) # define state dict and overload with user prefs for key in kwargs: logger.info('Setting %s to %s' % (key, kwargs[key])) d0[key] = kwargs[key] d0['logfile'] = False # no need to save log # feature columns if 'snr2' in d0['features']: snrcol = d0['features'].index('snr2') elif 'snr1' in d0['features']: snrcol = d0['features'].index('snr1') if 'l2' in d0['features']: lcol = d0['features'].index('l2') elif 'l1' in d0['features']: lcol = d0['features'].index('l1') if 'm2' in d0['features']: mcol = d0['features'].index('m2') elif 'm1' in d0['features']: mcol = d0['features'].index('m1') scancol = d0['featureind'].index('scan') segmentcol = d0['featureind'].index('segment') intcol = d0['featureind'].index('int') dtindcol = d0['featureind'].index('dtind') dmindcol = d0['featureind'].index('dmind') # sort and prep candidate list snrs = prop[:, snrcol] select = np.where(np.abs(snrs) > threshold)[0] loc = loc[select] prop = prop[select] if candnum >= 0 or len(candloc): if (candnum >= 0) and not len(candloc): logger.info( 'Reproducing and visualizing candidate %d at %s with properties %s.' % (candnum, loc[candnum], prop[candnum])) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] scan = loc[candnum, scancol] segment = loc[candnum, segmentcol] candint = loc[candnum, intcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] beamnum = 0 candloc = (scan, segment, candint, dmind, dtind, beamnum) elif len(candloc) and (candnum < 0): assert len( candloc ) == 6, 'candloc should be length 6 ( scan, segment, candint, dmind, dtind, beamnum ).' logger.info('Reproducing and visualizing candidate %d at %s' % (candnum, candloc)) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] scan, segment, candint, dmind, dtind, beamnum = candloc else: raise Exception, 'Provide candnum or candloc, not both' # if working locally, set workdir appropriately. Can also be used in queue system with full path given. if not os.path.dirname(candsfile): d0['workdir'] = os.getcwd() else: d0['workdir'] = os.path.dirname(candsfile) filename = os.path.join(d0['workdir'], os.path.basename(d0['filename'])) if d0.has_key('segmenttimesdict'): # using merged pkl segmenttimes = d0['segmenttimesdict'][scan] else: segmenttimes = d0['segmenttimes'] # clean up d0 of superfluous keys params = pp.Params() # will be used as input to rt.set_pipeline for key in d0.keys(): if not hasattr(params, key): # and 'memory_limit' not in key: _ = d0.pop(key) d0['npix'] = 0 d0['uvres'] = 0 d0['logfile'] = False d0['savenoise'] = False d0['savecands'] = False # this triggers redefinition of segment boundaries. memory optimization changed, so this is a problem. # d0['nsegments'] = 0 # d0['scale_nsegments'] = 1. d0['segmenttimes'] = segmenttimes d0['nsegments'] = len(segmenttimes) # get cand data d = rt.set_pipeline(filename, scan, **d0) (vismem, immem) = rt.calc_memory_footprint(d) if 'memory_limit' in d: assert vismem + immem < d[ 'memory_limit'], 'memory_limit defined, but nsegments must (for now) be set to initial values to properly reproduce candidate' im, data = rt.pipeline_reproduce( d, candloc, product='imdata') # removed loc[candnum] # optionally plot if savefile: loclabel = scan, segment, candint, dmind, dtind, beamnum if newplot: make_cand_plot(d, im, data, loclabel, version=2, snrs=snrs, outname=outname) else: make_cand_plot(d, im, data, loclabel, version=1, outname=outname) # optionally return data if returndata: return (im, data) elif returnstate: return d
def plot_cand(candsfile, candloc=[], candnum=-1, threshold=0, savefile=True, returndata=False, outname='', **kwargs): """ Reproduce detection of a single candidate for plotting or inspection. candsfile can be merge or single-scan cands pkl file. Difference defined by presence of scan in d['featureind']. candloc reproduces candidate at given location (scan, segment, integration, dmind, dtind, beamnum). candnum selects one to reproduce from ordered list threshold is min of sbs(SNR) used to filter candidates to select with candnum. savefile/outname define if/how to save png of candidate if returndata, (im, data) returned. kwargs passed to rt.set_pipeline """ # get candidate info loc, prop = pc.read_candidates(candsfile) # define state dict and overload with user prefs d0 = pickle.load(open(candsfile, 'r')) for key in kwargs: logger.info('Setting %s to %s' % (key, kwargs[key])) d0[key] = kwargs[key] d0['logfile'] = False # no need to save log # feature columns if 'snr2' in d0['features']: snrcol = d0['features'].index('snr2') elif 'snr1' in d0['features']: snrcol = d0['features'].index('snr1') if 'l2' in d0['features']: lcol = d0['features'].index('l2') elif 'l1' in d0['features']: lcol = d0['features'].index('l1') if 'm2' in d0['features']: mcol = d0['features'].index('m2') elif 'm1' in d0['features']: mcol = d0['features'].index('m1') try: scancol = d0['featureind'].index('scan') # if merged pkl except ValueError: scancol = -1 # if single-scan pkl segmentcol = d0['featureind'].index('segment') intcol = d0['featureind'].index('int') dtindcol = d0['featureind'].index('dtind') dmindcol = d0['featureind'].index('dmind') # sort and prep candidate list snrs = prop[:, snrcol] select = np.where(np.abs(snrs) > threshold)[0] loc = loc[select] prop = prop[select] times = pc.int2mjd(d0, loc) times = times - times[0] # default case will print cand info if (candnum < 0) and (not len(candloc)): logger.info('Getting candidates...') logger.info('candnum: loc, SNR, DM (pc/cm3), time (s; rel)') for i in range(len(loc)): logger.info("%d: %s, %.1f, %.1f, %.1f" % (i, str(loc[i]), prop[i, snrcol], np.array(d0['dmarr'])[loc[i,dmindcol]], times[i])) else: # if candnum or candloc provided, try to reproduce if (candnum >= 0) and not len(candloc): logger.info('Reproducing and visualizing candidate %d at %s with properties %s.' % (candnum, loc[candnum], prop[candnum])) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] if scancol >= 0: # here we have a merge pkl scan = loc[candnum, scancol] else: # a scan-based cands pkl scan = d0['scan'] segment = loc[candnum, segmentcol] candint = loc[candnum, intcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] beamnum = 0 candloc = (scan, segment, candint, dmind, dtind, beamnum) elif len(candloc) and (candnum < 0): assert len(candloc) == 6, 'candloc should be length 6 ( scan, segment, candint, dmind, dtind, beamnum ).' logger.info('Reproducing and visualizing candidate %d at %s' % (candnum, candloc)) dmarrorig = d0['dmarr'] dtarrorig = d0['dtarr'] scan, segment, candint, dmind, dtind, beamnum = candloc else: raise Exception, 'Provide candnum or candloc, not both' # if working locally, set workdir appropriately. Can also be used in queue system with full path given. if not os.path.dirname(candsfile): d0['workdir'] = os.getcwd() else: d0['workdir'] = os.path.dirname(candsfile) filename = os.path.join(d0['workdir'], os.path.basename(d0['filename'])) # clean up d0 of superfluous keys params = pp.Params() # will be used as input to rt.set_pipeline for key in d0.keys(): if not hasattr(params, key) and 'memory_limit' not in key: _ = d0.pop(key) d0['npix'] = 0 d0['uvres'] = 0 d0['nsegments'] = 0 d0['logfile'] = False # get cand data d = rt.set_pipeline(filename, scan, **d0) im, data = rt.pipeline_reproduce(d, candloc, product='imdata') # removed loc[candnum] # optionally plot if savefile: loclabel = scan, segment, candint, dmind, dtind, beamnum make_cand_plot(d, im, data, loclabel, outname=outname) # optionally return data if returndata: return (im, data)
def inspect_cand(mergepkl, candnum=-1, scan=0, **kwargs): """ Create detailed plot of a single candidate. Thresholds (as minimum), then provides list of candidates to select with candnum. scan can be used to define scan number with pre-merge pkl file. kwargs passed to rt.set_pipeline """ d = pickle.load(open(mergepkl, 'r')) loc, prop = read_candidates(mergepkl) if not os.path.dirname(d['filename']): d['filename'] = os.path.join(d['workdir'], d['filename']) # feature columns if 'snr2' in d['features']: snrcol = d['features'].index('snr2') elif 'snr1' in d['features']: snrcol = d['features'].index('snr1') if 'l2' in d['features']: lcol = d['features'].index('l2') elif 'l1' in d['features']: lcol = d['features'].index('l1') if 'm2' in d['features']: mcol = d['features'].index('m2') elif 'm1' in d['features']: mcol = d['features'].index('m1') if not scan: scancol = d['featureind'].index('scan') segmentcol = d['featureind'].index('segment') intcol = d['featureind'].index('int') dtindcol = d['featureind'].index('dtind') dmindcol = d['featureind'].index('dmind') # sort and prep candidate list snrs = n.array([prop[i][snrcol] for i in range(len(prop))]) # if isinstance(snrmin, type(None)): # snrmin = min(snrs) # sortord = snrs.argsort() # snrinds = n.where(snrs[sortord] > snrmin)[0] # loc = loc[sortord][snrinds] # prop = n.array([prop[i][snrcol] for i in range(len(prop))][sortord][snrinds]) # total hack to get prop as list to sort if candnum < 0: for i in range(len(loc)): logger.info("%d %s %s" % (i, str(loc[i]), str(prop[i][snrcol]))) logger.info('Returning candidate (loc, snr) ...') return (loc, n.array([prop[i][snrcol] for i in range(len(prop))])) else: logger.info('Reproducing and visualizing candidate %d at %s with properties %s.' % (candnum, loc[candnum], prop[candnum])) if not scan: scan = loc[candnum, scancol] nsegments = len(d['segmenttimesdict'][scan]) else: nsegments = len(d['segmenttimes']) segment = loc[candnum, segmentcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] candint = loc[candnum, intcol] dmarrorig = d['dmarr'] dtarrorig = d['dtarr'] d2 = rt.set_pipeline(d['filename'], scan, fileroot=d['fileroot'], paramfile='rtparams.py', savecands=False, savenoise=False, nsegments=nsegments, **kwargs) im, data = rt.pipeline_reproduce(d2, segment, (candint, dmind, dtind)) # with candnum, pipeline will return cand image and data # calc source location if snrmax > -1*snrmin: l1, m1 = calc_lm(d, im, minmax='max') else: l1, m1 = calc_lm(d, im, minmax='min') pt_ra, pt_dec = d['radec'] src_ra, src_dec = source_location(pt_ra, pt_dec, l1, m1) logger.info('Peak (RA, Dec): %s, %s' % (src_ra, src_dec)) logger.info('Returning candidate d, im, data') return d2, im, data
def plot_cand(mergepkl, candnum=-1, outname='', threshold=0, **kwargs): """ Create detailed plot of a single candidate. threshold is min of sbs(SNR) used to filter candidates to select with candnum. kwargs passed to rt.set_pipeline """ d = pickle.load(open(mergepkl, 'r')) loc, prop = read_candidates(mergepkl) if not os.path.dirname(d['filename']): d['filename'] = os.path.join(d['workdir'], d['filename']) # feature columns if 'snr2' in d['features']: snrcol = d['features'].index('snr2') elif 'snr1' in d['features']: snrcol = d['features'].index('snr1') if 'l2' in d['features']: lcol = d['features'].index('l2') elif 'l1' in d['features']: lcol = d['features'].index('l1') if 'm2' in d['features']: mcol = d['features'].index('m2') elif 'm1' in d['features']: mcol = d['features'].index('m1') scancol = d['featureind'].index('scan') segmentcol = d['featureind'].index('segment') intcol = d['featureind'].index('int') dtindcol = d['featureind'].index('dtind') dmindcol = d['featureind'].index('dmind') # sort and prep candidate list snrs = n.array([prop[i][snrcol] for i in range(len(prop))]) select = n.where(n.abs(snrs) > threshold)[0] loc = loc[select] prop = [prop[i] for i in select] if candnum < 0: logger.info('Getting candidates...') for i in range(len(loc)): logger.info("%d %s %s" % (i, str(loc[i]), str(prop[i][snrcol]))) return (loc, n.array([prop[i][snrcol] for i in range(len(prop))])) else: logger.info('Reproducing and visualizing candidate %d at %s with properties %s.' % (candnum, loc[candnum], prop[candnum])) # get cand info scan = loc[candnum, scancol] segment = loc[candnum, segmentcol] dmind = loc[candnum, dmindcol] dtind = loc[candnum, dtindcol] candint = loc[candnum, intcol] dmarrorig = d['dmarr'] dtarrorig = d['dtarr'] # set up state dict d['starttime_mjd'] = d['starttime_mjddict'][scan] d['scan'] = scan d['nsegments'] = len(d['segmenttimesdict'][scan]) # d2 = rt.set_pipeline(d['filename'], scan, fileroot=d['fileroot'], savecands=False, savenoise=False, nsegments=nsegments, **kwargs) d['segmenttimes'] = d['segmenttimesdict'][scan] d['savecands'] = False; d['savenoise'] = False for key in kwargs.keys(): logger.info('Setting %s to %s' % (key, kwargs[key])) d[key] = kwargs[key] # reproduce im, data = rt.pipeline_reproduce(d, segment, (candint, dmind, dtind)) # with candnum, pipeline will return cand image and data # calc source location snrmin = im.min()/im.std() snrmax = im.max()/im.std() if snrmax > -1*snrmin: l1, m1 = rt.calc_lm(d, im, minmax='max') else: l1, m1 = rt.calc_lm(d, im, minmax='min') pt_ra, pt_dec = d['radec'] src_ra, src_dec = source_location(pt_ra, pt_dec, l1, m1) logger.info('Peak (RA, Dec): %s, %s' % (src_ra, src_dec)) # plot it logger.info('Plotting...') fig = plt.Figure(figsize=(8.5,8)) ax = fig.add_subplot(221, axisbg='white') # # first plot dm-t distribution beneath times0 = int2mjd(d, loc) times0 = times0 - times0[0] times = times0[candnum] dms0 = n.array(dmarrorig)[list(loc[:,dmindcol])] dms = dmarrorig[loc[candnum,dmindcol]] snr0 = [prop[i][snrcol] for i in range(len(prop))] snr = snr0[candnum] snrmin = 0.8 * min(d['sigma_image1'], d['sigma_image2']) # plot positive good = n.where(snr0 > 0)[0] ax.scatter(times0[good], dms0[good], s=(snr0[good]-snrmin)**5, facecolor='none', linewidth=0.2, clip_on=False) ax.scatter(times, dms, s=(snr-snrmin)**5, facecolor='none', linewidth=2, clip_on=False) # plot negative good = n.where(snr0 < 0) ax.scatter(times0[good], dms0[good], s=(n.abs(snr0)[good]-snrmin)**5, marker='x', edgecolors='k', linewidth=0.2, clip_on=False) ax.set_ylim(dmarrorig[0], dmarrorig[-1]) ax.set_xlim(times0.min(), times0.max()) ax.set_xlabel('Time (s)') ax.set_ylabel('DM (pc/cm3)') # # then add annotating info ax.text(0.1, 0.9, d['fileroot']+'_sc'+str(scan), fontname='sans-serif', transform = ax.transAxes) ax.text(0.1, 0.8, 'seg %d, int %d, DM %.1f, dt %d' % (segment, loc[candnum, intcol], dmarrorig[loc[candnum, dmindcol]], dtarrorig[loc[candnum,dtindcol]]), fontname='sans-serif', transform = ax.transAxes) ax.text(0.1, 0.7, 'Peak: (' + str(n.round(l1, 3)) + '\' ,' + str(n.round(m1, 3)) + '\'), SNR: ' + str(n.round(snr, 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_xlabel('Flux (Jy)') 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)) # image ax = fig.add_subplot(223) fov = n.degrees(1./d['uvres'])*60. # 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 not outname: outname = os.path.join(d['workdir'], 'cands_%s_sc%dseg%di%ddm%ddt%d.png' % (d['fileroot'], scan, segment, loc[candnum, intcol], dmind, dtind)) canvas = FigureCanvasAgg(fig) canvas.print_figure(outname) return ([],[])