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Commit 0eef74d1 authored by Ryan Everett's avatar Ryan Everett
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gstlal_inspiral_summary_page: uses files compliant with T00517 standard

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gstlal-inspiral/bin/gstlal_inspiral_summary_page
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......@@ -21,11 +21,13 @@
import sys, os, socket, re
import glob, math, shutil
import numpy
from glue import cbcwebpage
from glue import lal
from glue import segments
from optparse import OptionParser
from gstlal import inspiral_pipe
from pylal import git_version
__author__ = "Chad Hanna <channa@ligo.caltech.edu>"
__version__ = "git id %s" % git_version.id
......@@ -36,17 +38,15 @@ def parse_command_line():
parser = OptionParser(version = git_version.verbose_msg, usage = "%prog [options] [file ...]", description = "%prog summary web page")
parser.add_option("--webserver-dir", help = "Set the directory for the webserver. Required. Example /archive/home/channa/public_html/highmass_months_23-24_summary_page")
parser.add_option("--open-box", action = "store_true", help = "Produce open box page")
parser.add_option("--output-name-tag",default = "", metavar = "name", help = "Set the basename for image search")
parser.add_option("--output-user-tag", action = "append", metavar = "name", help = "Set the basename for image search")
parser.add_option("--title", default="gstlal_inspiral offline analysis")
parser.add_option("--glob-path", help = "This is the path to search for images")
opts, filenames = parser.parse_args()
return opts, filenames
# PARSE THE COMMAND LINE
opts, files = parse_command_line()
base_name = opts.output_name_tag
file_base = os.path.split(base_name)[1]
search = base_name.replace('_',' ')
# INITIALIZE THE PAGE OBJECT
page = cbcwebpage.cbcpage(title=opts.title)
......@@ -65,14 +65,16 @@ Summary information: This page provides information on the number of triggers, l
""")
# number of triggers
tab, name = cbcwebpage.wiki_table_parse(base_name+'num_trigs_table.txt')
page.subpages["summary"].add_table(tab[0], "Coinc events summary", "This table gives the number of coincident events")
tab, name = cbcwebpage.wiki_table_parse(os.path.join(opts.glob_path, '%snum_trigs_table.txt' % opts.output_user_tag[0]))
if tab:
page.subpages["summary"].add_table(tab[0], "Coinc events summary", "This table gives the number of coincident events")
# livetime
tab, name = cbcwebpage.wiki_table_parse(base_name+'live_time_table.txt')
page.subpages["summary"].add_table(tab[0], "Live time summary", "This table gives the summary of the live time")
tab, name = cbcwebpage.wiki_table_parse(os.path.join(opts.glob_path, '%slive_time_table.txt' % opts.output_user_tag[0]))
if tab:
page.subpages["summary"].add_table(tab[0], "Live time summary", "This table gives the summary of the live time")
imgtable = cbcwebpage.image_glob(base_name+'horizon.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*HORIZON*.png'))
page.subpages["summary"].add_table(imgtable, "Horizon Distance", "This is the distance at which you should see a 1.4 1.4 Msun binary at SNR 8. The first panel shows the time evolution of the horizon distance and the second panel shows the same information in histogram form. ")
### INJ PARAM PAGE ###
......@@ -91,23 +93,23 @@ are shown together.
""")
page.subpages["injection_params"].add_section("m1_m2","Injected Parameters m1 and m2")
imgtable = cbcwebpage.image_glob(base_name+'6_sim_dist_m1_m2*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_m1_m2*.png'))
page.subpages["injection_params"].sections["m1_m2"].add_table(imgtable, "Injected mass1, mass2", "Above are the injected mass parameters for the simulations done. Typically injections are done uniformally in component mass unless the waveform approximant cannot support certain mass ratios.")
page.subpages["injection_params"].add_section("time_dist","Injected Parameters time and distance")
imgtable = cbcwebpage.image_glob(base_name+'6_sim_dist_time_distance*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_time_distance*.png'))
page.subpages["injection_params"].sections["time_dist"].add_table(imgtable, "Injection time and distance", "Unless doing a directed search injections are uniform in time. Although the correct distance distribution would be uniform in volume typically logarithmic injections are done")
page.subpages["injection_params"].add_section("ra_dec","Injected Parameters RA and DEC")
imgtable = cbcwebpage.image_glob(base_name+'6_sim_dist_ra_dec*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_ra_dec*.png'))
page.subpages["injection_params"].sections["ra_dec"].add_table(imgtable, "Injection RA and DEC", "Unless doing a directed search injections are uniform on the sky.")
page.subpages["injection_params"].add_section("inc_pol","Injected Parameters inclination and polarization")
imgtable = cbcwebpage.image_glob(base_name+'6_sim_dist_inc_pol*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_inc_pol*.png'))
page.subpages["injection_params"].sections["inc_pol"].add_table(imgtable, "Injection Inclination and polarization", "Unless doing a directed search injections are uniform in sin(inclination) and polarizations.")
page.subpages["injection_params"].add_section("spin","Injected Parameters Z component spin")
imgtable = cbcwebpage.image_glob(base_name+'6_sim_dist_spin1z_spin2z*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_spin1z_spin2z*.png'))
page.subpages["injection_params"].sections["spin"].add_table(imgtable, "Injection spin 1z and spin2z", "The Z component spins of the injection set. Often injections are done without spin, so don't be surprised if these are zero.")
......@@ -121,19 +123,19 @@ are often recovered better than mass parameters for the high mass search.
""")
page.subpages["injection_params_acc"].add_section("mchirp_param_acc","Mchirp Parameter Accuracy")
imgtable = cbcwebpage.image_glob(base_name+'2_mchirp_acc_frac_*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*2_mchirp_acc_frac_*.png'))
page.subpages["injection_params_acc"].sections["mchirp_param_acc"].add_table(imgtable, "Mchirp Accuracy", "Accuracy of chirp mass.")
page.subpages["injection_params_acc"].add_section("eta_param_acc","Eta Parameter Accuracy")
imgtable = cbcwebpage.image_glob(base_name+'2_eta_acc_frac_*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*2_eta_acc_frac_*.png'))
page.subpages["injection_params_acc"].sections["eta_param_acc"].add_table(imgtable, "Eta Accuracy", "Accuracy of eta")
page.subpages["injection_params_acc"].add_section("time_param_acc","Time Parameter Accuracy")
imgtable = cbcwebpage.image_glob(base_name+'2_t_acc_*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*2_t_acc_*.png'))
page.subpages["injection_params_acc"].sections["time_param_acc"].add_table(imgtable, "Time Accuracy", "Accuracy of time")
page.subpages["injection_params_acc"].add_section("deff_param_acc","Effective Distance Accuracy")
imgtable = cbcwebpage.image_glob(base_name+'2_deff_acc_frac*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*2_deff_acc_frac*.png'))
page.subpages["injection_params_acc"].sections["deff_param_acc"].add_table(imgtable, "Effective Distance Accuracy", "Accuracy of effective distance")
page.subpages["injection_params_acc"].add_section("spin_param_acc","Effective Spin Accuracy")
imgtable = cbcwebpage.image_glob(base_name+'2_chi_acc*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*2_chi_acc*.png'))
page.subpages["injection_params_acc"].sections["spin_param_acc"].add_table(imgtable, "Effective Spin Accuracy", "Accuracy of mass-weighted spin")
### MISSED FOUND ###
......@@ -151,29 +153,31 @@ than what the "time" refers to. For example, an injection's geocentric end time
but that might occur near a boundary where H2 was also on. Thus one could find an H1L1 injection in
H1H2L1 time.
""")
tab,name = cbcwebpage.wiki_table_parse(base_name+'injection_summary.txt')
page.subpages["missed_found"].add_table(tab[0], "Injection Summary Table", "Summary of missed and found injections broken up by detector time")
page.subpages["missed_found"].add_section("found_missed_mchirp","Found / Missed Chirp Mass")
imgtable = cbcwebpage.image_glob(base_name+'1_d_vs_mchirp_*.png')
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Distance vs Mchirp", "Distance of found and missed injections as a function of chirp mass")
imgtable = cbcwebpage.image_glob(base_name+'1_deff_vs_mchirp_*.png')
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Effective Distance vs Mchirp", "Effective distance of found and missed injections as a function of chirp mass")
imgtable = cbcwebpage.image_glob(base_name+'1_chirpdist_vs_mchirp_*.png')
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Chirp Distance vs Mchirp", "Chirp distance of found and missed injections as a function of chirp mass. For low mass systems the chirp mass scales out of the expected amplitude. The chirp distance utilizes this and rescales the distance to be appropriate for a NS-NS binary. At low total mass the missed/found barrier should be flat on this plot.")
page.subpages["missed_found"].add_section("found_missed_mtotal","Found / Missed Total Mass")
imgtable = cbcwebpage.image_glob(base_name+'1_deff_vs_mtotal_*.png')
page.subpages["missed_found"].sections["found_missed_mtotal"].add_table(imgtable, "Missed Found Effective Distance vs M total", "Effective distance of found and missed injections as a function of total mass")
page.subpages["missed_found"].add_section("found_missed_time","Found / Missed Time")
page.subpages["missed_found"].add_section("found_missed_chi","Found / Missed Spin")
for tag in opts.output_user_tag:
tab,name = cbcwebpage.wiki_table_parse(os.path.join(opts.glob_path, '%sinjection_summary.txt' % tag))
page.subpages["missed_found"].add_table(tab[0], "Injection Summary Table: %s" % tag, "Summary of missed and found injections broken up by detector time")
page.subpages["missed_found"].add_section("found_missed_time","Found / Missed Time")
imgtable = cbcwebpage.image_glob(base_name+'1_deff_vs_t_*.png')
page.subpages["missed_found"].sections["found_missed_time"].add_table(imgtable, "Missed Found vs Time", "Effective distance of found and missed injections as a function of time")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_d_vs_mchirp_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Distance vs Mchirp: %s" % tag, "Distance of found and missed injections as a function of chirp mass")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_mchirp_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Effective Distance vs Mchirp: %s" % tag, "Effective distance of found and missed injections as a function of chirp mass")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_chirpdist_vs_mchirp_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_mchirp"].add_table(imgtable, "Missed Found Chirp Distance vs Mchirp: %s" % tag, "Chirp distance of found and missed injections as a function of chirp mass. For low mass systems the chirp mass scales out of the expected amplitude. The chirp distance utilizes this and rescales the distance to be appropriate for a NS-NS binary. At low total mass the missed/found barrier should be flat on this plot.")
page.subpages["missed_found"].add_section("found_missed_chi","Found / Missed Spin")
imgtable = cbcwebpage.image_glob(base_name+'1_deff_vs_chi_*.png')
page.subpages["missed_found"].sections["found_missed_chi"].add_table(imgtable, "Missed Found vs Spin", "Effective distance of found and missed injections as a function of mass-weighted spin")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_mtotal_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_mtotal"].add_table(imgtable, "Missed Found Effective Distance vs M total: %s" % tag, "Effective distance of found and missed injections as a function of total mass")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_t_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_time"].add_table(imgtable, "Missed Found vs Time: %s" % tag, "Effective distance of found and missed injections as a function of time")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_chi_*.png' % tag))
page.subpages["missed_found"].sections["found_missed_chi"].add_table(imgtable, "Missed Found vs Spin: %s" % tag, "Effective distance of found and missed injections as a function of mass-weighted spin")
# SEARCH SENSITIVITY PLOTS #
page.add_subpage("search_sensitivity","Search Sensitivity", "Search Sensitivity")
......@@ -182,17 +186,19 @@ page.subpages["search_sensitivity"].div("""
This page shows plots that characterize the overall sensitivity of the search. The detection rate is directly proportional to the observed sensitive volume, which is a function of the significance threshold used to identify candidates. These plots are useful for determining how sensitive the search was to the input injection population. Since the search sensitivity is a strong function of the intrinsic parameters of the target source, we break down the sensitive into bins of these parameters. Here we show the sensitivity as a function of total mass and component mass.
""")
page.subpages["search_sensitivity"].add_section("total_mass","Binned by total mass")
imgtable = cbcwebpage.image_glob(base_name+'*BINNED_BY_TOTAL_MASS*.png')
page.subpages["search_sensitivity"].sections["total_mass"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance", "Binned by total mass")
page.subpages["search_sensitivity"].add_section("mass1_mass2","Binned by mass1-mass2")
imgtable = cbcwebpage.image_glob(base_name+'*BINNED_BY_MASS1_MASS2*.png')
page.subpages["search_sensitivity"].sections["mass1_mass2"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance", "Binned by component mass")
page.subpages["search_sensitivity"].add_section("aligned_spin","Binned by aligned spin")
imgtable = cbcwebpage.image_glob(base_name+'*BINNED_BY_ALIGNED_SPIN*.png')
page.subpages["search_sensitivity"].sections["aligned_spin"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance", "Binned by aligned spin")
page.subpages["search_sensitivity"].add_section("mass_ratio","Binned by mass ratio")
imgtable = cbcwebpage.image_glob(base_name+'*BINNED_BY_MASS_RATIO*.png')
page.subpages["search_sensitivity"].sections["mass_ratio"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance", "Binned by mass ratio")
for tag in opts.output_user_tag:
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*BINNED_BY_TOTAL_MASS*.png' % tag))
page.subpages["search_sensitivity"].sections["total_mass"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance: %s" % tag, "Binned by total mass")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*BINNED_BY_MASS1_MASS2*.png' % tag))
page.subpages["search_sensitivity"].sections["mass1_mass2"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance: %s" % tag, "Binned by component mass")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*BINNED_BY_ALIGNED_SPIN*.png' % tag))
page.subpages["search_sensitivity"].sections["aligned_spin"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance: %s" % tag, "Binned by aligned spin")
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*%s*BINNED_BY_MASS_RATIO*.png' % tag))
page.subpages["search_sensitivity"].sections["mass_ratio"].add_table(imgtable, "Sensitive volume (V*T) and mean sensitive distance: %s" % tag, "Binned by mass ratio")
# BACKGROUND#
......@@ -203,12 +209,12 @@ This page shows the ingredients that go into ranking events and determining the
""")
page.subpages["background"].add_section("background","SNR / chi")
page.subpages["background"].sections["background"].div("This section gives the SNR / Chisq background statistics.")
imgtable = cbcwebpage.image_glob(base_name+'*_snrchi2*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*snrchi2*.png'))
page.subpages["background"].sections["background"].add_table(imgtable, "","")
page.subpages["background"].add_section("likelihood","Likelihood")
page.subpages["background"].sections["likelihood"].div("This section gives the likekihood distribution in noise")
imgtable = cbcwebpage.image_glob(base_name+'*_likelihood_ratio_*.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*likelihood_ratio_*.png'))
page.subpages["background"].sections["likelihood"].add_table(imgtable, "","")
......@@ -225,15 +231,15 @@ if not opts.open_box:
The chi-squared test checks that the snr accumulated matches expectation.
""")
page.subpages["chisq"].add_section("chisq","Chi-squared vs SNR")
imgtable = cbcwebpage.image_glob(base_name+'3_chi2_vs_rho_*closedbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'))
page.subpages["chisq"].sections["chisq"].add_table(imgtable, "Chi-squared Vs SNR", "Chi-squared vs snr for single detectors after coincidence. Blue points are full data zero lag, red are software injections and black are time slides.")
page.subpages["chisq"].add_section("snr","Signal-to-Noise Ratio")
imgtable = cbcwebpage.image_glob(base_name+'4_rho_*_vs_*closedbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*closedbox*.png'))
page.subpages["chisq"].sections["snr"].add_table(imgtable, "Signal-to-Noise Ratio", "Comparison of SNR in pairs of detectors.")
page.subpages["chisq"].add_section("deff","Effective Distance")
imgtable = cbcwebpage.image_glob(base_name+'4_deff_*_vs_*closedbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*closedbox*.png'))
page.subpages["chisq"].sections["deff"].add_table(imgtable, "Effective Distance", "Comparison of effective distance in pairs of detectors.")
### Money Plots ###
......@@ -243,10 +249,10 @@ The chi-squared test checks that the snr accumulated matches expectation.
This section provides the detection statistic plots and a summary of the loudest event.
""")
page.subpages["money"].add_section("ifar","Rate vs. IFAR Threshold")
imgtable = cbcwebpage.image_glob(base_name+'5_count_vs_ifar*closedbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_ifar*closedbox*.png'))
page.subpages["money"].sections["ifar"].add_table(imgtable, "Rate vs. IFAR Threshold", "Comparison of observed zero-lag event rate to event rate expected from background as a function of inverse false-alarm rate (IFAR) threshold.")
page.subpages["money"].add_section("table","Loudest Table")
tab,name = cbcwebpage.wiki_table_parse(base_name+'bgsummary_table.txt')
tab,name = cbcwebpage.wiki_table_parse(os.path.join(opts.glob_path, '%sbgsummary_table.txt' % opts.output_user_tag[0]))
page.subpages["money"].sections["table"].add_table(tab[0], "Closed Box Summary Table", "Loudest Events")
#
......@@ -264,15 +270,15 @@ The chi-squared test checks that the snr accumulated matches expectation.
FIXME
""")
page.subpages["chisq"].add_section("chisq","Chi-squared vs SNR")
imgtable = cbcwebpage.image_glob(base_name+'3_chi2_vs_rho_*openbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'))
page.subpages["chisq"].sections["chisq"].add_table(imgtable, "Chi-squared Vs SNR", "Chi-squared vs snr for single detectors after coincidence. Blue points are full data zero lag, red are software injections and black are time slides.")
page.subpages["chisq"].add_section("snr","Signal-to-Noise Ratio")
imgtable = cbcwebpage.image_glob(base_name+'4_rho_*_vs_*openbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*openbox*.png'))
page.subpages["chisq"].sections["snr"].add_table(imgtable, "Signal-to-Noise Ratio", "Comparison of SNR in pairs of detectors.")
page.subpages["chisq"].add_section("deff","Effective Distance")
imgtable = cbcwebpage.image_glob(base_name+'4_deff_*_vs_*openbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*openbox*.png'))
page.subpages["chisq"].sections["deff"].add_table(imgtable, "Effective Distance", "Comparison of effective distance in pairs of detectors.")
### Money Plots ###
......@@ -282,10 +288,10 @@ FIXME
This section provides the detection statistic plots and a summary of the loudest event.
""")
page.subpages["money"].add_section("ifar","Rate vs. IFAR Threshold")
imgtable = cbcwebpage.image_glob(base_name+'5_count_vs_ifar*openbox.png')
imgtable = cbcwebpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_ifar*openbox*.png'))
page.subpages["money"].sections["ifar"].add_table(imgtable, "Rate vs. IFAR Threshold", "Comparison of observed zero-lag event rate to event rate expected from background as a function of inverse false-alarm rate (IFAR) threshold.")
page.subpages["money"].add_section("table","Loudest Table")
tab,name = cbcwebpage.wiki_table_parse(base_name+'summary_table.txt')
tab,name = cbcwebpage.wiki_table_parse(os.path.join(opts.glob_path, '%ssummary_table.txt' % opts.output_user_tag[0]))
page.subpages["money"].sections["table"].add_table(tab[0], "Summary Table", "Loudest Events")
### UPPER LIMIT PLOTS ###
......@@ -327,10 +333,12 @@ This section provides the detection statistic plots and a summary of the loudest
#imgtable = [[cbcwebpage._imagelinkcpy(base_name+'upper_limit.png'), cbcwebpage._imagelinkcpy(base_name+'posterior.png')]]
#page.subpages["ul"].sections["cul"].add_table(imgtable, "Combined 90% Upper limit", "Combined 90% upper limit")
if opts.open_box: page.write(file_base+"open_box")
else: page.write(file_base+"closed_box")
if opts.open_box:
page.write("%s_openbox" % opts.output_user_tag[0])
else:
page.write("%s_closebox" % opts.output_user_tag[0])
# copy the output
if opts.webserver_dir:
for f in page.external_frames:
......
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