def colorcolor_axes(stardata, axes, colorscale, cmap, vmin, vmax, color_slope=False, colorbar=True, edgecolors='k', **kwargs): colorcolor_columns = stardata.get_colorcolor_columns(max_flag=256) try: plot_trajectory_core(axes, colorcolor_columns['hmk'], colorcolor_columns['jmh'], colorcolor_columns['date'], cmap=cmap, vmin=vmin, vmax=vmax, colorbar=colorbar, edgecolors=edgecolors, linewidths=0.5, **kwargs) # plot boundaries are manually set for readability, if necessary if len(axes.get_xticks()) > 7: xmin = np.floor(colorcolor_columns['hmk'].min() * 0.95 * 20)/20. xmax = np.ceil( colorcolor_columns['hmk'].max() * 1.05 * 20)/20. xticks = np.linspace(xmin, xmax, 6) axes.set_xticks(xticks) if color_slope: colorcolor_slope, jmh_intercept, slope_err = ( slope(colorcolor_columns['hmk'], colorcolor_columns['jmh'], colorcolor_columns['hmk_err'], colorcolor_columns['jmh_err'], verbose=False) ) axes.plot([0, 6], [jmh_intercept, jmh_intercept + 6*colorcolor_slope], '--', scalex=False, scaley=False) axes.get_figure().canvas.draw() except Exception as e: print "Color-color plot broke: {0}".format(e) pass
def statcruncher (table, sid, season=0, rob=True, per=True, graded=False, colorslope=False, flags=0) : """ Calculates several statistical properties for a given star. Will work with "lonely" datapoints (i.e. not all JHK mags are well-defined). Optionally works with graded data, too! Parameters ---------- table : atpy.Table Table with time-series photometry sid : int 13-digit WFCAM source ID of star to plot season : int, optional Which observing season of our dataset (1, 2, 3, or all). Any value that is not the integers (1, 2, or 3) will be treated as "no season", and no time-cut will be made. Note that this is the default behavior. rob : bool, optional Use robust statistics, in addition to normal ones? (takes longer, default True) per : bool, optional Run period-finding? Uses fast chi-squared and lomb-scargle. (takes longer, default True) graded : bool, optional Also calculate Stetson indices using quality grades as weights? Uses stetson_graded; requires that the data has been graded by night_cleanser.null_cleanser_grader(). colorslope : bool, optional Calculate color slopes? Runs them over (JvJ-H, KvH-K, J-HvH-K). Make sure your data has been color-error-corrected! Default False. flags : int, optional Maximum ppErrBit quality flags to use (default 0) Returns ------- ret : data structure Contains the computed values. They can be accessed as attributes (e.g., "ret.j_mean" or "ret.Stetson"). """ s_table = data_cut ( table, sid, season=season) if len(s_table) < 1: print "no data for %d!" % sid return None # First, let's compute single-band statistics. This will require # separate data_cuts on each band. full_jtable = band_cut(s_table, 'j') full_htable = band_cut(s_table, 'h') full_ktable = band_cut(s_table, 'k') j_table = band_cut(s_table, 'j', max_flag=flags) h_table = band_cut(s_table, 'h', max_flag=flags) k_table = band_cut(s_table, 'k', max_flag=flags) jmh_table = band_cut(j_table, 'h', max_flag=flags) hmk_table = band_cut(h_table, 'k', max_flag=flags) # jhk_table used only for colorslope jhk_table = band_cut( jmh_table, 'k', max_flag=flags) # get a date (x-axis) for each jdate = j_table.MEANMJDOBS hdate = h_table.MEANMJDOBS kdate = k_table.MEANMJDOBS jmhdate = jmh_table.MEANMJDOBS hmkdate = hmk_table.MEANMJDOBS # date = s_table.MEANMJDOBS # get a magnitude and magnitude error for each band jcol = j_table.JAPERMAG3; jerr = j_table.JAPERMAG3ERR hcol = h_table.HAPERMAG3; herr = h_table.HAPERMAG3ERR kcol = k_table.KAPERMAG3; kerr = k_table.KAPERMAG3ERR jmhcol= jmh_table.JMHPNT; jmherr = jmh_table.JMHPNTERR hmkcol= hmk_table.HMKPNT; hmkerr = hmk_table.HMKPNTERR # get the RA and DEC columns, checking for sensible values racol= s_table.RA[(s_table.RA > 0) & (s_table.RA < 7)] decol= s_table.DEC[(s_table.DEC > -4) & (s_table.DEC < 4)] # Now let's get some ability to track errorful data. # messy_table_j = band_cut( s_table, 'j') # messy_table_h = band_cut( s_table, 'h') # messy_table_k = band_cut( s_table, 'k') # jppcol = messy_table_j.JPPERRBITS # hppcol = messy_table_h.HPPERRBITS # kppcol = messy_table_k.KPPERRBITS # make an empty data structure and just assign it information, then return # the object itself! then there's no more worrying about indices. class Empty(): pass ret = Empty() # How many nights have observations in each band? ret.N_j = len(j_table) ret.N_h = len(h_table) ret.N_k = len(k_table) # What's the distribution of flags and nights? js = full_jtable.JPPERRBITS hs = full_htable.HPPERRBITS ks = full_ktable.KPPERRBITS ret.N_j_noflag = len(js[js == 0]) ret.N_h_noflag = len(hs[hs == 0]) ret.N_k_noflag = len(ks[ks == 0]) ret.N_j_info = len(js[(js < 256) & (js > 0)]) ret.N_h_info = len(hs[(hs < 256) & (hs > 0)]) ret.N_k_info = len(ks[(ks < 256) & (ks > 0)]) ret.N_j_warn = len(js[ js >= 256 ]) ret.N_h_warn = len(hs[ hs >= 256 ]) ret.N_k_warn = len(ks[ ks >= 256 ]) # Mean position of this source ret.RA = racol.mean() ret.DEC = decol.mean() # Calculate the Stetson index... S, choice, stetson_nights = Stetson_machine (s_table, flags) ret.Stetson = S ret.Stetson_choice = choice ret.Stetson_N = stetson_nights if graded: # Calculate the graded Stetson index... g_S, g_choice, g_stetson_nights = ( graded_Stetson_machine (s_table, flags) ) ret.graded_Stetson = g_S ret.graded_Stetson_choice = g_choice ret.graded_Stetson_N = g_stetson_nights # Calculate PSTAR parameters ret.pstar_mean = s_table.PSTAR.mean() ret.pstar_median = np.median(s_table.PSTAR) ret.pstar_rms = s_table.PSTAR.std() # Create parallel data structures for each band, so we can iterate ret.j = Empty(); ret.j.data = jcol; ret.j.err = jerr; ret.j.date = jdate ret.h = Empty(); ret.h.data = hcol; ret.h.err = herr; ret.h.date = hdate ret.k = Empty(); ret.k.data = kcol; ret.k.err = kerr; ret.k.date = kdate ret.jmh = Empty(); ret.jmh.data=jmhcol; ret.jmh.err = jmherr ret.hmk = Empty(); ret.hmk.data=hmkcol; ret.hmk.err = hmkerr ret.jmh.date = jmhdate; ret.hmk.date = hmkdate ret.j.N = ret.N_j ; ret.h.N = ret.N_h ; ret.k.N = ret.N_k ret.jmh.N = len(jmh_table) ; ret.hmk.N = len(hmk_table) bands = [ ret.j, ret.h, ret.k, ret.jmh, ret.hmk ] for b in bands: # use b.data, b.err # if this band is empty, don't try to do the following assignments if b.N == 0: continue b.rchi2 = reduced_chisq( b.data, b.err ) b.mean = b.data.mean() b.median = np.median(b.data) # dao b.rms = b.data.std() b.min = b.data.min() b.max = b.data.max() b.range = b.max - b.min b.err_mean = b.err.mean() #dao b.err_median = np.median(b.err) #dao b.err_rms = b.err.std() #dao b.err_min = b.err.min() #dao b.err_max = b.err.max() #dao b.err_range = b.err_max - b.err_min #dao # Robust quantifiers simply have an "r" at the end of their names if rob: b.datar, b.indr = rb.removeoutliers(b.data, 3, niter=2, retind=True) b.errr = b.err[b.indr] b.meanr = rb.meanr(b.data) b.medianr = rb.medianr(b.data) # dao b.rmsr = rb.stdr(b.data) b.minr = b.datar.min() b.maxr = b.datar.max() b.ranger = b.maxr - b.minr b.err_meanr = b.errr.mean() # dao b.err_medianr = np.median(b.errr) #dao b.err_rmsr = b.errr.std() #dao b.err_minr = b.errr.min() #dao b.err_maxr = b.errr.max() #dao b.err_ranger = b.err_maxr - b.err_minr #dao # Period finding... is a little dodgy still, and might take forever if per==True and b.N > 2: hifac = lsp_tuning(b.date) b.lsp = lsp(b.date, b.data, 6., hifac) Jmax = lsp_mask(b.lsp[0], b.lsp[1]) b.lsp_per = 1./ b.lsp[0][Jmax] b.lsp_pow = b.lsp[1][Jmax] b.lsp_sig = getSignificance(b.lsp[0], b.lsp[1], b.lsp[2], 6.)[Jmax] best_freq, chimin = test_analyze( b.date, b.data, b.err, ret_chimin=True ) b.fx2_per, b.fx2_chimin = 1./best_freq, chimin if colorslope: # J vs J-H : use jmh_table exclusively (ret.jjh_slope, a, ret.jjh_slope_err) = ( slope( jmh_table.JMHPNT, jmh_table.JAPERMAG3, jmh_table.JMHPNTERR, jmh_table.JAPERMAG3ERR, verbose=False) ) # K vs H-K : use hmk_table exclusively (ret.khk_slope, a, ret.khk_slope_err) = ( slope( hmk_table.HMKPNT, hmk_table.KAPERMAG3, hmk_table.HMKPNTERR, hmk_table.KAPERMAG3ERR, verbose=False) ) # J-H vs H-K : use jhk_table exclusively (ret.jhk_slope, a, ret.jhk_slope_err) = ( slope( jhk_table.HMKPNT, jhk_table.JMHPNT, jhk_table.HMKPNTERR, jhk_table.JMHPNTERR, verbose=False) ) # and the pp_max, using the messy table # (slated for a re-implementation) # ret.jpp_max = jppcol.max() # ret.hpp_max = hppcol.max() # ret.kpp_max = kppcol.max() return ret