Index: /branches/sj_ippTests_branch_20080929/ippTests/compIPPphoto.py =================================================================== --- /branches/sj_ippTests_branch_20080929/ippTests/compIPPphoto.py (revision 20141) +++ /branches/sj_ippTests_branch_20080929/ippTests/compIPPphoto.py (revision 20142) @@ -27,6 +27,4 @@ # + magdiff vs. sky # + magdiff vs. skydiff -# + magdiff vs. stellar density -# + magdiff vs. presence of bright stars # + something about PSF sizes; use Michigan moments for photo - # M_rr_cc_psf is the size of the reconstructed PSF, and m_rr_cc is the size of the object @@ -38,10 +36,27 @@ # + histograms of everything - recovery fractions, means, medians, quartiles # + Trends with field number, seeing etc. - +# + magdiff vs. stellar density +# + magdiff vs. presence of bright stars (e.g. number of bright +# stars above some typical S/N or counts threshold - 50% +# saturation?) +# +# XXX Todo: +# +# - Merge identical plots into single .eps file +# +# - Make html page with jpeg/gif versions of plots +# +# - Need mechanism to specify default plotting ranges, since outliers +# mess up axis ranges. Different quantities need different +# ranges. Comparing different plots is also easier if the plot +# ranges are the same. +# +# - second tlist for summary plots (same or different plot routine?) +# +# - towards generality, want to read these things and colname_hash and +# label_l from config files +# # Convention: for scatter, plot second against first; for histogram, plot both histograms -# -# XXX: Need mechanism to specify default plotting ranges, since -# outliers mess up axis ranges. E.g. 0.1 and 0.9 percentiles? -plotcol_tlist = [ +plotcol_1frame_tlist = [ (['d_sky'],['d_mag'],'scatter') ,(['d_x'],['d_y'],'scatter') @@ -56,5 +71,14 @@ ] -def compIPPphoto(summaryTable,mode,plotcol_tlist=plotcol_tlist,skip=False): +plotcol_summary_tlist = [ + (['d_mag_median'],['d_sky_median'],'scatter') + ,(['d_x_median'],['d_y_median'],'scatter') + ,(['d_mag_median'],['N_SDSS'],'scatter') + ,(['d_mag_median','d_mag_mean'],['field','field'],'scatter') + ] + + +def compIPPphoto(summaryTable,mode,plotcol_1frame_tlist=plotcol_1frame_tlist,\ + plotcol_summary_tlist=plotcol_summary_tlist,skip=True): """summaryTable: .fits table for output mode: new or append for creating summaryTable new or appending current run's output to it. @@ -93,5 +117,5 @@ vallist,keylist = valuesKeysSortedByKeys(stats_hash) rowtuple_list.append(vallist) - plotStatsOnefile(column_hash,goodrow_hash,matchtable,plotcol_tlist,bandindex) + plotStatsOnefile(column_hash,goodrow_hash,matchtable,plotcol_1frame_tlist,bandindex) # return column_hash,goodrow_hash newrows = numpy.rec.array(rowtuple_list,names=keylist) @@ -99,7 +123,24 @@ if mode == 'new': tabhdu.writeto(summaryTable,clobber=True) + nrows = tabhdu.header['NAXIS2'] + summaryhash = hashFromPyfitsTable(tabhdu) else: - appendFitsTable(summaryTable,tabhdu) + newtabhdu = appendFitsTable(summaryTable,tabhdu) + nrows = newtabhdu.header['NAXIS2'] + summaryhash = hashFromPyfitsTable(newtabhdu) + # Fake a goodrows hash + sumgoodrows_hash = summaryhash.fromkeys(summaryhash.keys(),numpy.ones((nrows,),bool)) + plotStatsOnefile(summaryhash,sumgoodrows_hash,summaryTable,plotcol_summary_tlist,bandindex) return column_hash,goodrow_hash + +def hashFromPyfitsTable(pyfitsTableHDU): + """Return a hash wrapping the table data so a column can be + addressed as hash[colname] instead of tabledata.field(colname)""" + from numpy import array + outhash = {} + for col in pyfitsTableHDU.columns.names: + # Column names are uppercase, we want lowercase hash keys + outhash[col.lower()] = array(pyfitsTableHDU.data.field(col)) + return outhash def getOutnameStatsOnefile(matchtable,kind,col1_l,col2_l=None,format='eps'): @@ -124,4 +165,6 @@ histo_min_ntile = 0.03 histo_max_ntile = 1-histo_min_ntile + angle_l = [0,45,0,45,0,90] + ptype_l = [ 41, 41, 40, 40, 30,30] for troika in plotcol_tlist: col1name_l = troika[0] @@ -131,9 +174,10 @@ smOpenPlot(outname,format=format) firstplot = True + i = 0 for col1name,col2name in zip(col1name_l,col2name_l): - values1 = values_hash[col1name] - goodrows1 = goodrow_hash[col1name] - values2 = values_hash[col2name] - goodrows2 = goodrow_hash[col2name] + values1 = values_hash[col1name.lower()] + goodrows1 = goodrow_hash[col1name.lower()] + values2 = values_hash[col2name.lower()] + goodrows2 = goodrow_hash[col2name.lower()] # Slice out depth for current filter if necessary if len(values1.shape) > 1: @@ -150,6 +194,9 @@ # Avoid plotting outliers if not re.search('objc',col2name): - [xmin,xmax] = stats_med(values1[goodrows],[histo_min_ntile,histo_max_ntile]) - [ymin,ymax] = stats_med(values2[goodrows],[histo_min_ntile,histo_max_ntile]) + try: + [xmin,xmax] = stats_med(values1[goodrows],[histo_min_ntile,histo_max_ntile]) + [ymin,ymax] = stats_med(values2[goodrows],[histo_min_ntile,histo_max_ntile]) + except IndexError: + print col1name, goodrows, type(goodrows), len(goodrows), len(values1) # print "Huhu", outname, min(values1),max(values2),xmin,xmax smScatterPlot(values1,values2,logical=goodrows,xlab=col1name,ylab=col2name,\ @@ -159,6 +206,6 @@ smScatterPlot(values1,values2,logical=goodrows,xlab=col1name,ylab=col2name) else: - angle(45) - smScatterPlot(values1,values2,logical=goodrows,append=True) + angle(angle_l[i%len(angle_l)]) + smScatterPlot(values1,values2,ptype=ptype_l[i%len(ptype_l)],logical=goodrows,append=True) angle(0) if plottype == 'histogram': @@ -177,4 +224,5 @@ ltype=2) firstplot = False + i+=1 smClosePlot() @@ -196,5 +244,6 @@ newtabhdu.data.field(col)[nrows_old:] = tabHDU.data.field(col) oldtabhdulist.close() - return newtabhdu.writeto(fitsfile,clobber=True) + newtabhdu.writeto(fitsfile,clobber=True) + return newtabhdu @@ -247,5 +296,6 @@ outl = [] for ntile in ntiles: - outl.append(sortarr[ntile*(l-1)]) + idx = max(int(ntile*(l-1)),0) + outl.append(sortarr[idx]) return outl