diff --git a/gstlal-inspiral/bin/gstlal_inspiral_summary_page b/gstlal-inspiral/bin/gstlal_inspiral_summary_page
index a7f434feb5288a63bd9698e57dca0fce92003d96..5c0db382d67e61218c353e43cb81fdc656b41706 100755
--- a/gstlal-inspiral/bin/gstlal_inspiral_summary_page
+++ b/gstlal-inspiral/bin/gstlal_inspiral_summary_page
@@ -1,9 +1,64 @@
-#!/usr/bin/python
+#!/usr/bin/env python
+#
+# Copyright (C) 2011 Chad Hanna
+# Copyright (C) 2019 Patrick Godwin
+#
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 2 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+## @file
+# This program makes a summary web page of a gstlal inspiral analysis.
 
 import sys, os, glob, shutil, time
+import re
 import subprocess
-from gstlal import webpage
+import json
+
 from optparse import OptionParser
+from ligo.scald import report
+
+ABOUT_URL = 'https://docs.google.com/document/d/12wJjCz8L1UczqRm86q3-EFnpnIZ2FHPSwKR_kym2L5g/pub?embedded=true'
+SCHEDULE_URL = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vR1XwazhZYoVbMXFZCq_s2rlzyNwcF0xC4kyhnKrCeXFSWte-2jRodL590RU3PsoX4OVypYyAZ7Nl_8/pubhtml?widget=true&headers=false'
+IMAGE_URL = 'https://results.ligo.uwm.edu/~gstlalcbc/share/vis/gstlal.png'
+
+
+def sanitizetext(item):
+	# FIXME: hack to clean up formatted columns downstream,
+	#        should really be handling this at the source
+	item = item.replace('⊙', '')
+	htmlr = re.compile('<.*?>')
+	alphanumr = re.compile('[\W_]+')
+	item = re.sub(htmlr, '', item)
+	return re.sub(alphanumr, '', item)
+
+def read_format_json(path):
+	"""
+	formats tabular summaries into a format readable by ligo-scald
+	FIXME: remove once formatting is changed upstream
+	"""
+	table = ''
+	with open(path, 'r') as f:
+		table = json.loads(f.read())
+
+	### format data
+	# FIXME: hack to clean up formatted columns downstream,
+	#        should really be handling this at the source
+	fields = [{'key': sanitizetext(col['id']), 'label': sanitizetext(col['label']), 'sortable': True} for col in table['cols']]
+	colnames = [field['key'] for field in fields]
+	items = [{col: val['v'] for col, val in zip(colnames, row['c'])} for row in table['rows']]
+
+	return {'fields': fields, 'items': items}
 
 def parse_command_line():
 	parser = OptionParser(usage = "%prog [options] [file ...]", description = "%prog summary web page")
@@ -21,444 +76,379 @@ def parse_command_line():
 # Setup
 #
 
-
 opts, files = parse_command_line()
-# Setup an page and navigation bar
-thispage = webpage.page()
-nav = webpage.tabs()
 
+thisreport = report.Report('gstlal analysis', image=IMAGE_URL)
 
-#
-# Add the page tabs first
-#
-
+try:
+	os.makedirs(opts.webserver_dir)
+except OSError:
+	pass
+try:
+	os.makedirs(opts.webserver_dir+'_lite')
+except OSError:
+	pass
 
-summary = webpage.tab("summary", "Summary information", "Summary information", ["segments_div_wrapper"])
-injparams = webpage.tab("injection_params","Injection Parameters", "Injection Parameters")
-#injacc = webpage.tab("injection_params_acc","Injection Parameters Accuracy", "Injection Accuracy")
-#missed_found = webpage.tab("missed_found","Missed Found", "Missed Found")
-injrecovery = webpage.tab("injection_recovery","Injection Recovery", "Injection Recovery")
-sens = webpage.tab("search_sensitivity","Search Sensitivity", "Search Sensitivity")
-back = webpage.tab("background","Background", "Background")
-#chisq = webpage.tab("chisq","Chi-squared", "Chi-squared")
-money = webpage.tab("money","Money Plots", "Money Plots")
-about_doc = webpage.tab("about", "About", "About")
-sched_doc = webpage.tab("schedule", "Schedule", "Schedule")
 
 #
-# add the sections to the nav bar and the nav bar to the page
+# Add the page tabs
 #
 
-nav+=[webpage.elem("li", [webpage.elem("img", [], """ style="margin-left:-40px; margin-top:5px; width:100" src="https://ldas-jobs.cgca.uwm.edu/~gstlalcbc/share/vis/gstlal.png" """)])]
-nav+=[summary, injparams, injrecovery, sens, back, money, about_doc, sched_doc]
-nav+=[webpage.elem("li", [webpage.elem("div", [time.strftime("%Y-%m-%d %H:%M")], "")])]
-thispage+=nav
-thispage+=webpage.elem("br",[])
+
+summary = report.Tab('Summary')
+injparams = report.Tab('Injection Parameters')
+injrecovery = report.Tab('Injection Recovery')
+sens = report.Tab('Search Sensitivity')
+back = report.Tab('Background')
+money = report.Tab('Money Plots')
+about_doc = report.Tab('About', url=ABOUT_URL)
+sched_doc = report.Tab('Schedule', url=SCHEDULE_URL)
+
 
 #
 # Add summary page content to the summary page tab
 #
 
+summary += report.Header('Summary')
 
-summary_about = webpage.elem("p", ["This page was generated on %s by %s in the working directory <code>%s</code>." % (time.strftime("%Y-%m-%d %H:%M"), os.environ["USER"], os.environ["PWD"])])
-
-
-
-summary_lt = webpage.section("Search Summary")
-thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]), "lt_summary_div"))
-summary_lt += [webpage.elem("div", [], "id=lt_summary_div width=100% height=500")]
-
-#FIXME assumes the zero lag database is the first tag
-summary_seg = webpage.section("Segments")
-thispage.header_content.append(webpage.googleTimelineFromJson(os.path.join(opts.glob_path, '%s_segments.json' % opts.output_user_tag[0]), "segments_div"))
-thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_segment_table.json' % opts.output_user_tag[0]), "segment_table_div", gpscolumns = [2,3,4]))
-summary_seg += [webpage.elem("div", [], "id=segments_div width=100% height=500")]
-summary_seg += [webpage.elem("div", [], "id=segment_table_div width=100% height=500")]
-
-summary_env = webpage.section("Environment")
-#makefile = [f for f in glob.glob("Makefile*") if "Makefile.offline_analysis_rules" not in f][0]
-tmpcontent = ["\n".join(["%s=%s" % (k, os.environ[k]) for k in ("PATH", "PYTHONPATH", "PKG_CONFIG_PATH", "GST_PLUGIN_PATH")])]
-summary_env += [webpage.elem("pre", tmpcontent)]
-
-summary_make = webpage.section("Analysis Makefile")
-#summary_make += [webpage.elem("pre", [webpage.elem("code", [open(makefile).read()])])]
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]))
+summary += report.Table('Search Summary').save(tabledata, opts.webserver_dir)
 
-summary_commands = webpage.section("Commands run")
-#summary_commands += [webpage.elem("pre", [webpage.elem("samp", ["\n".join(["\t %s" % x for x in  subprocess.check_output(["make", "-B", "-n", "-f", makefile]).split("\n") ])])])]
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_segment_table.json' % opts.output_user_tag[0]))
+summary += report.Table('Segments').save(tabledata, opts.webserver_dir)
 
+summary += report.Description("This page was generated on %s by %s in the working directory: %s" % (time.strftime("%Y-%m-%d %H:%M"), os.environ["USER"], os.environ["PWD"]))
 
-summary_div = summary([summary_about, summary_lt, summary_seg, summary_env, summary_make, summary_commands])
-
+thisreport += summary
 
 #
 # Add injection parameter content
 #
 
+injparams += report.Header('Injection Parameters')
+injparams += report.Description("Injections are the addition of simulated signals into the gravitational wave strain data.  They are useful for diagnosing the pipeline and measuring the sensitivity.  Care is taken to ensure that the input distribution is sensible.  The remaining sections describe the input distributions for the injections.  They are not necessarily what is analyzed since the analyzed segments are not used for determining the injection sets (i.e. injections may be scheduled for times when a detector is not even functioning).")
+injparams += report.Description("The following plots are typically produced by injection waveform type.  Several injection runs of the same waveform (with different random seeds) are often done.  Here all runs of the same waveform type are shown together.")
 
-inj_params_about = webpage.elem("p", ["Injections are the addition of simulated signals into the gravitational wave strain data.  They are useful for diagnosing the pipeline and measuring the sensitivity.  Care is taken to ensure that the input distribution is sensible.  The remaining sections describe the input distributions for the injections.  They are not necessarily what is analyzed since the analyzed segments are not used for determining the injection sets (i.e. injections may be scheduled for times when a detector is not even functioning).  <br><br>The following plots are typically produced by injection waveform type.  Several injection runs of the same waveform (with different random seeds) are often done.  Here all runs of the same waveform type are shown together."])
-
-inj_params_m1m2 = webpage.section("Injected Parameters (m1 and m2)")
-inj_params_m1m2 += webpage.image_glob('%s/*6_sim_dist_m1_m2*.png' % opts.glob_path, "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.")
+imgfooter = "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."
+injparams += report.ImageGrid("Injected Parameters (m1 and m2)", grid_size=2, footer=imgfooter).glob(os.path.join(opts.glob_path, '*6_sim_dist_m1_m2*.png'))
 
-inj_params_time_dist = webpage.section("Injected Parameters time and distance")
-inj_params_time_dist += webpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_time_distance*.png'), "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")
+imgfooter = "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"
+injparams += report.ImageGrid("Injected Parameters time and distance", grid_size=2, footer=imgfooter).glob(os.path.join(opts.glob_path, '*6_sim_dist_time_distance*.png'))
 
-inj_params_ra_dec = webpage.section("Injected Parameters RA and DEC")
-inj_params_ra_dec += webpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_ra_dec*.png'), "Unless doing a directed search injections are uniform on the sky.")
+imgfooter = "Unless doing a directed search injections are uniform on the sky."
+injparams += report.ImageGrid("Injected Parameters RA and DEC", grid_size=2, footer=imgfooter).glob(os.path.join(opts.glob_path, '*6_sim_dist_ra_dec*.png'))
 
-inj_params_inc_pol = webpage.section("Injected Parameters inclination and polarization")
-inj_params_inc_pol += webpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_inc_pol*.png'), "Unless doing a directed search injections are uniform in sin(inclination) and polarizations.")
+imgfooter = "Unless doing a directed search injections are uniform in sin(inclination) and polarizations."
+injparams += report.ImageGrid("Injected Parameters inclination and polarization", grid_size=2, footer=imgfooter).glob(os.path.join(opts.glob_path, '*6_sim_dist_inc_pol*.png'))
 
-inj_params_spin1z_spin2z = webpage.section("Injected Parameters Z component spin")
-inj_params_spin1z_spin2z += webpage.image_glob(os.path.join(opts.glob_path, '*6_sim_dist_spin1z_spin2z*.png'), "The Z component spins of the injection set.  Often injections are done without spin, so don't be surprised if these are zero.")
-
-inj_params_div = injparams([inj_params_about, inj_params_m1m2, inj_params_time_dist, inj_params_ra_dec, inj_params_inc_pol, inj_params_spin1z_spin2z])
+imgfooter = "The Z component spins of the injection set.  Often injections are done without spin, so don't be surprised if these are zero."
+injparams += report.ImageGrid("Injected Parameters Z component spin", grid_size=2, footer=imgfooter).glob(os.path.join(opts.glob_path, '*6_sim_dist_spin1z_spin2z*.png'))
 
+thisreport += injparams
 
 #
 # Add injection accuracy content
 #
 
+injrecovery += report.Header("Injection Accuracy")
+injrecovery += report.Description("The accuracy of recovered parameters aids at diagnosing the performance of the pipeline as well as hinting at the possibility of measuring certain parameters.  Mass parameters for the low mass search are often recovered better than mass parameters for the high mass search.")
 
-inj_acc_about = webpage.elem("p", ["The accuracy of recovered parameters aids at diagnosing the performance of the pipeline as well as hinting at the possibility of measuring certain parameters.  Mass parameters for the low mass search are often recovered better than mass parameters for the high mass search."])
-
-inj_acc_mchirp = webpage.section("Mchirp Parameter Accuracy")
-inj_acc_mchirp += webpage.image_glob(os.path.join(opts.glob_path, '*2_mchirp_acc_frac_*.png'), "Accuracy of chirp mass.")
+injrecovery += report.ImageGrid("Mchirp Parameter Accuracy", grid_size=3, footer="Accuracy of chirp mass").glob(os.path.join(opts.glob_path, '*2_mchirp_acc_frac_*.png'))
 
-inj_acc_eta = webpage.section("Eta Parameter Accuracy")
-inj_acc_eta += webpage.image_glob(os.path.join(opts.glob_path, '*2_eta_acc_frac_*.png'), "Accuracy of eta")
+injrecovery += report.ImageGrid("Eta Parameter Accuracy", grid_size=3, footer="Accuracy of eta").glob(os.path.join(opts.glob_path, '*2_eta_acc_frac_*.png'))
 
-inj_acc_mtotal = webpage.section("Total Mass Parameter Accuracy")
-inj_acc_mtotal += webpage.image_glob(os.path.join(opts.glob_path, '*2_mtotal_acc_frac_*.png'), "Accuracy of total mass")
+injrecovery += report.ImageGrid("Total Mass Parameter Accuracy", grid_size=3, footer="Accuracy of total mass").glob(os.path.join(opts.glob_path, '*2_mtotal_acc_frac_*.png'))
 
-inj_acc_time = webpage.section("Time Parameter Accuracy")
-inj_acc_time += webpage.image_glob(os.path.join(opts.glob_path, '*2_t_acc_*.png'), "Accuracy of time")
+injrecovery += report.ImageGrid("Time Parameter Accuracy", grid_size=3, footer="Accuracy of time").glob(os.path.join(opts.glob_path, '*2_t_acc_*.png'))
 
-inj_acc_spin = webpage.section("Effective Spin Accuracy")
-inj_acc_spin += webpage.image_glob(os.path.join(opts.glob_path, '*2_chi_acc_scatter*.png'), "Accuracy of mass-weighted spin")
-
-inj_acc_snr = webpage.section("SNR Recovery")
-inj_acc_snr += webpage.image_glob(os.path.join(opts.glob_path, '*2_snr_*.png'), "Injected expected SNR vs recovered SNR")
+injrecovery += report.ImageGrid("Effective Spin Accuracy", grid_size=3, footer="Accuracy of mass-weighted spin").glob(os.path.join(opts.glob_path, '*2_chi_acc_scatter*.png'))
 
+injrecovery += report.ImageGrid("SNR Recovery", grid_size=3, footer="Injected expected SNR vs recovered SNR").glob(os.path.join(opts.glob_path, '*2_snr_*.png'))
 
+imgfooter = "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."
+injrecovery += report.ImageGrid("Horizon distance", grid_size=1, footer=imgfooter).glob(os.path.join(opts.glob_path, '*HORIZON*.png'))
 
 #
 # Add injection missed / found content
 #
 
+injrecovery += report.Header("Injection Missed/Found")
+injrecovery += report.Description("""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections as a for the various IFO times for coincident triggers. We allow double coincident events so some categories can have multiple types of found injections (for  example H1L1 and H1H2L1 triggers in H1H2L1 time).  Because of ambiguity concerning the time of an injection and the injection window it is occasionally possible to find an injection in more detectors than what the "time" refers to.  For example, an injection's geocentric end time might be in H1L1 time but that might occur near a boundary where H2 was also on.  Thus one could find an H1L1 injection in H1H2L1 time.""")
 
-missed_found_about = webpage.elem("p", ["""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections as a for the various IFO times for coincident triggers. We allow double coincident events so some categories can have multiple types of found injections (for  example H1L1 and H1H2L1 triggers in H1H2L1 time).  Because of ambiguity concerning the time of an injection and the injection window it is occasionally possible to find an injection in more detectors than what the "time" refers to.  For example, an injection's geocentric end time might be in H1L1 time but that might occur near a boundary where H2 was also on.  Thus one could find an H1L1 injection in H1H2L1 time."""])
-
-horizon = webpage.section("Horizon distance")
-fm_tab = webpage.section("Found / Missed Summary Table")
-fm_mc = webpage.section("Found / Missed Chirp Mass")
-fm_mt = webpage.section("Found / Missed Total Mass")
-fm_eta = webpage.section("Found / Missed Eta")
-fm_time = webpage.section("Found / Missed Time")
-fm_spin = webpage.section("Found / Missed Spin")
-fm_snr = webpage.section("Found / Missed Expected SNR")
-fm_mtab = webpage.section("Missed Table")
-
-horizon += webpage.image_glob('%s/*HORIZON*.png' % opts.glob_path, "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.")
-
+injrecovery += report.Header("Found / Missed Summary Table")
 for tag in opts.output_user_tag:
-	thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_injection_summary.json' % tag), "%sinjection_summary" % tag))
-	fm_tab += [webpage.elem("p", ["Summary of missed and found injections broken up by detector time for %s" % tag])]
-	fm_tab += [webpage.elem("div", [], "id=%sinjection_summary" % tag)]
-
-	fm_mc += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_d_vs_mchirp_*.png' % tag), "Distance of found and missed injections as a function of chirp mass: %s" % tag)
-	fm_mc += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_mchirp_*.png' % tag), "Effective distance of found and missed injections as a function of chirp mass: %s" % tag)
-	fm_mc += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_chirpdist_vs_mchirp_*.png' % 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: %s" % tag)
+	tabledata = read_format_json(os.path.join(opts.glob_path, '%s_injection_summary.json' % tag))
+	injrecovery += report.Description("Summary of missed and found injections broken up by detector time for %s" % tag)
+	injrecovery += report.Table('Injection Summary Table for %s' % tag).save(tabledata, opts.webserver_dir)
 
-	fm_mt += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_mtotal_*.png' % tag), "Effective distance of found and missed injections as a function of total mass: %s" % tag)
+injrecovery += report.Header('Found / Missed Plots')
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
 
-	fm_eta += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_chirpdist_vs_eta_*.png' % tag), "Effective distance of found and missed injections as a function of symmetric mass ratio eta: %s" % tag)
+	plots = os.path.join(opts.glob_path, '*%s*1_d_vs_mchirp_*.png' % tag)
+	imgfooter = "Distance of found and missed injections as a function of chirp mass: %s" % tag
+	injrecovery += report.ImageGrid("Distance vs. chirp mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_time += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_t_*.png' % tag), "Effective distance of found and missed injections as a function of time: %s" % tag)
+	plots = os.path.join(opts.glob_path, '*%s*1_deff_vs_mchirp_*.png' % tag)
+	imgfooter = "Effective distance of found and missed injections as a function of chirp mass: %s" % tag
+	injrecovery += report.ImageGrid("Effective distance vs. chirp mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_spin += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_deff_vs_chi_*.png' % tag), "Effective distance of found and missed injections as a function of mass-weighted spin: %s" % tag)
+	plots = os.path.join(opts.glob_path, '*%s*1_chirpdist_vs_mchirp_*.png' % tag)
+	imgfooter = "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: %s" % tag
+	injrecovery += report.ImageGrid("Chirp distance vs. chirp mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % tag), "Expected SNR of found and missed injections as a function of chirp mass.: %s" % tag)
+injrecovery += report.Header("Found / Missed Total Mass")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
+	plots = os.path.join(opts.glob_path, '%s*1_deff_vs_mtotal_*.png' % tag)
+	imgfooter = "Effective distance of found and missed injections as a function of total mass: %s" % tag
+	injrecovery += report.ImageGrid("Effective distance vs. total mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mtotal_*.png' % tag), "Expected SNR of found and missed injections as a function of total mass: %s" % tag)
+injrecovery += report.Header("Found / Missed Eta")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
+	plots = os.path.join(opts.glob_path, '*%s*1_chirpdist_vs_eta_*.png' % tag)
+	imgfooter = "Effective distance of found and missed injections as a function of symmetric mass ratio eta: %s" % tag
+	injrecovery += report.ImageGrid("Effective distance vs. eta: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_eta_*.png' % tag), "Expected SNR of found and missed injections as a function of symmetric mass ratio eta: %s" % tag)
+injrecovery += report.Header("Found / Missed Time")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
+	plots = os.path.join(opts.glob_path, '*%s*1_deff_vs_t_*.png' % tag)
+	imgfooter = "Effective distance of found and missed injections as a function of time: %s" % tag
+	injrecovery += report.ImageGrid("Effective distance vs. time: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_chi_*.png' % tag), "Expected SNR of found and missed injections as a function of mass-weighted spin: %s" % tag)
+injrecovery += report.Header("Found / Missed Spin")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
+	plots = os.path.join(opts.glob_path, '*%s*1_deff_vs_chi_*.png' % tag)
+	imgfooter = "Effective distance of found and missed injections as a function of mass-weighted spin: %s" % tag
+	injrecovery += report.ImageGrid("Effective distance vs. mass-weighted spin: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-	fm_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_t_*.png' % tag), "Expected SNR of found and missed injections as a function of time")
+injrecovery += report.Header("Found / Missed Expected SNR")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
 
-	# FIXME stupid convention to rely on the first tag being all of them. Note, previously we just used opts.output_user_tag[0] here.
-	thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_missed_summary.json' % tag), "%smissed_summary" % tag, gpscolumns = [2,3]))
-	fm_mtab += [webpage.elem("p", ["Missed Injection Table for %s" % tag])]
-	fm_mtab += [webpage.elem("div", [], "id=%smissed_summary" % tag)]
+	plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % tag)
+	imgfooter = "Expected SNR of found and missed injections as a function of chirp mass: %s" % tag
+	injrecovery += report.ImageGrid("Expected SNR vs. chirp mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-inj_recovery_div = injrecovery([inj_acc_about, inj_acc_mchirp, inj_acc_eta, inj_acc_mtotal, inj_acc_time, inj_acc_spin, inj_acc_snr, horizon, missed_found_about, fm_tab, fm_mc, fm_mt, fm_eta, fm_time, fm_spin, fm_snr, fm_mtab])
+	plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mtotal_*.png' % tag)
+	imgfooter = "Expected SNR of found and missed injections as a function of total mass: %s" % tag
+	injrecovery += report.ImageGrid("Expected SNR vs. total mass: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
+	plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_eta_*.png' % tag)
+	imgfooter = "Expected SNR of found and missed injections as a function of symmetric mass ratio eta: %s" % tag
+	injrecovery += report.ImageGrid("Expected SNR vs. eta: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-#
-# Add sensitivity content
-#
+	plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_cgi_*.png' % tag)
+	imgfooter = "Expected SNR of found and missed injections as a function of mass-weighted spin: %s" % tag
+	injrecovery += report.ImageGrid("Expected SNR vs. mass-weighted spin: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-sens_about = webpage.elem("p", ["""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 source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0"""])
+	plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_t_*.png' % tag)
+	imgfooter = "Expected SNR of found and missed injections as a function of time: %s" % tag
+	injrecovery += report.ImageGrid("Expected SNR vs. time: %s" % tag, grid_size=2, footer=imgfooter, visible=visible).glob(plots)
 
-sens_div_list = [sens_about]
+injrecovery += report.Header("Missed Table")
 for tag in opts.output_user_tag:
-	images = webpage.image_glob(os.path.join(opts.glob_path, '*%s*_BINNED_BY_SOURCE_TYPE*.png' % tag), "Sensitive volume (V*T) and mean sensitive distance: %s" % tag)
-	if len(images.content) > 0:
-		sens_section = webpage.section("Binned by SOURCE_TYPE")
-		sens_section += images
-		sens_div_list.append(sens_section)
-
-sens_div = sens(sens_div_list)
+	tabledata = read_format_json(os.path.join(opts.glob_path, '%s_missed_summary.json' % tag))
+	injrecovery += report.Table('Missed Injection Table for %s' % tag).save(tabledata, opts.webserver_dir)
 
+thisreport += injrecovery
 
 #
 # Add sensitivity content
 #
 
+sens += report.Header('Sensitivity')
 
-back_about = webpage.elem("p", ["This page shows the ingredients that go into ranking events and determining the False alarm probability."])
+sens += report.Description("""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 source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0""")
 
-back_snrchi = webpage.section("SNR and chi-squared")
-back_trigs = webpage.section("Background Triggers")
-#back_ratio = webpage.section("SNR ratios")
-back_rate = webpage.section("Rates")
-
-back_snrchi += webpage.image_glob(os.path.join(opts.glob_path, '*BACKGROUND_PDF_SNRCHI2*.png'), "Single Detector Background SNR-chisq Distributions")
-back_snrchi += webpage.image_glob(os.path.join(opts.glob_path, '*INJECTION_PDF_SNRCHI2*.png'), "Single Detector Signal Chisq (Conditional) Distributions")
-back_snrchi += webpage.image_glob(os.path.join(opts.glob_path, '*LR_SNRCHI2*.png'), "Single Detector Likelihood Ratios")
+for tag in opts.output_user_tag:
+	visible = 'ALL_LLOID_COMBINED' in tag
+	plots = os.path.join(opts.glob_path, '*%s*_BINNED_BY_SOURCE_TYPE*.png' % tag)
+	imgfooter = "Binned by source type"
+	sens += report.ImageGrid("Sensitive volume (V*T) and mean sensitive distance: %s" % tag, grid_size=3, footer=imgfooter, visible=visible).glob(plots)
 
-back_trigs += webpage.image_glob(os.path.join(opts.glob_path, '*3_bgparams_mchirp_vs_eta_vs_snr_*.png'), "Single detector triggers coincident in time-shifted analyses.")
-back_trigs += webpage.image_glob(os.path.join(opts.glob_path, '*3_bgparams_mchirp_vs_chieff_vs_snr*.png'), "Single detector triggers coincident in time-shifted analyses.")
-back_trigs += webpage.image_glob(os.path.join(opts.glob_path, '*3_bgparams_tau0_vs_tau3_vs_snr*.png'), "Single detector triggers coincident in time-shifted analyses.")
+thisreport += sens
 
-#back_ratio += webpage.image_glob(os.path.join(opts.glob_path, '*SNR_PDF*.png'), "")
+#
+# Add background content
+#
 
-back_rate +=  webpage.image_glob(os.path.join(opts.glob_path, '*RATES*.png'), "")
+back += report.Header('Background')
+back += report.Description("This page shows the ingredients that go into ranking events and determining the False alarm probability.")
 
+imgfooter = "Single detector triggers coincident in time-shifted analyses."
+back += report.ImageGrid("Background Triggers", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_bgparams_*_vs_snr*.png'))
+back += report.ImageGrid("SNR Ratios", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*SNR_PDF*.png'))
+back += report.ImageGrid("Rates", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*RATES*.png'))
 
+back += report.Header("SNR and chi-squared")
+back += report.ImageGrid("Single Detector Background SNR-chisq Distributions", grid_size=3).glob(os.path.join(opts.glob_path, '*BACKGROUND_PDF_SNRCHI2*.png'))
+back += report.ImageGrid("Single Detector Signal Chisq (Conditional) Distributions", grid_size=3).glob(os.path.join(opts.glob_path, '*INJECTION_PDF_SNRCHI2*.png'))
+back += report.ImageGrid("Single Detector Likelihood Ratios", grid_size=3).glob(os.path.join(opts.glob_path, '*LR_SNRCHI2*.png'))
+if opts.open_box:
+	back += report.ImageGrid("Single Detector Signal Chisq (Conditional) Distributions: Zero lag", grid_size=3).glob(os.path.join(opts.glob_path, '*ZERO_LAG_PDF_SNRCHI2*.png'))
 
 #
 # Add chisq content
 #
 
+back += report.Description("The chi-squared test checks that the snr accumulated matches expectation.")
 
-chisq_about = webpage.elem("p", ["The chi-squared test checks that the snr accumulated matches expectation."])
+imgfooter = "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."
+back += report.ImageGrid("Chi-squared Distributions", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'))
 
-chisq_dist = webpage.section("Chi-squared Distributions")
-chisq_snr = webpage.section("Signal-to-Noise Ratio")
-chisq_deff = webpage.section("Effective Distance")
+imgfooter = "Comparison of SNR in pairs of detectors."
+back += report.ImageGrid("Signal-to-Noise Ratio", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*closedbox*.png'))
 
-chisq_dist += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'), "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.")
-chisq_dist += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_mc_vs_rho_*closedbox*.png'), "Chi-squared vs chirp mass for single detectors after coincidence.")
+imgfooter = "Comparison of effective distance in pairs of detectors."
+back += report.ImageGrid("Effective Distance", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*closedbox*.png'))
 
-#chisq_snr += webpage.image_glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*closedbox*.png'), "Comparison of SNR in pairs of detectors.")
+if opts.open_box:
+	back += report.Header('Open Box Results')
 
-chisq_deff += webpage.image_glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*closedbox*.png'), "Comparison of effective distance in pairs of detectors.")
+	imgfooter = "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."
+	back += report.ImageGrid("Chi-squared Distributions: Zero lag", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'))
 
-#back_div = back([back_about, back_snrchi, back_trigs, back_ratio, back_rate, chisq_about, chisq_dist, chisq_deff])
-back_div = back([back_about, back_snrchi, back_trigs, back_rate, chisq_about, chisq_dist, chisq_deff])
+	imgfooter = "Comparison of SNR in pairs of detectors."
+	back += report.ImageGrid("Signal-to-Noise Ratio: Zero lag", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*openbox*.png'))
 
+	imgfooter = "Comparison of effective distance in pairs of detectors."
+	back += report.ImageGrid("Effective Distance", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*openbox*.png'))
+
+thisreport += back
 
 #
 # Add results content
 #
 
+money += report.Header('Money Plots')
+money += report.Description("This section provides the detection statistic plots and a summary of the loudest event.")
 
-money_about = webpage.elem("p", ["This section provides the detection statistic plots and a summary of the loudest event."])
-money_ifar = webpage.section("Rate vs. Threshold")
-money_table = webpage.section("Loudest Table")
-
-money_ifar += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'), "Comparison of single time slide event rate to event rate expected from background as a function of threshold.")
-thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]), "%sbgsummary_table" % opts.output_user_tag[0], gpscolumns = [5], scinotationcolumns = [1,2]))
-money_table += [webpage.elem("p", ["Closed Box Summary Table"])]
-money_table += [webpage.elem("div", [], "id=%sbgsummary_table" % opts.output_user_tag[0])]
-
-money_div = money([money_about, money_ifar, money_table])
-
-
-#
-# Open Box plots
-#
+money += report.Header('Closed Box Results')
+imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+money += report.ImageGrid("Rate vs. Threshold", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'))
 
+tabledata = read_format_json(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]))
+money += report.Table('Closed Box Summary Table').save(tabledata, opts.webserver_dir)
 
 if opts.open_box:
+	money += report.Header('Open Box Results')
+	imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+	money += report.ImageGrid("Rate vs. Threshold", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'))
+	
+	tabledata = read_format_json(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]))
+	money += report.Table('Open Box Summary Table').save(tabledata, opts.webserver_dir)
 
-	back_snrchi += webpage.image_glob(os.path.join(opts.glob_path, '*ZERO_LAG_PDF_SNRCHI2*.png'), "Single Detector Signal Chisq (Conditional) Distributions for zero lag")
-
-	chisq_dist += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'), "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.")
-
-	chisq_snr += webpage.image_glob(os.path.join(opts.glob_path, '*4_rho_*_vs_*openbox*.png'), "Comparison of SNR in pairs of detectors.")
-
-	chisq_deff += webpage.image_glob(os.path.join(opts.glob_path, '*4_deff_*_vs_*openbox*.png'), "Comparison of effective distance in pairs of detectors.")
-
-	money_ifar += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'), "Comparison of observed event rate to event rate expected from background as a function of threshold.")
-	thispage.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]), "%ssummary_table" % opts.output_user_tag[0], gpscolumns = [5], scinotationcolumns = [1,2]))
-	money_table += [webpage.elem("p", ["Open Box Summary Table"])]
-	money_table += [webpage.elem("div", [], "id=%ssummary_table" % opts.output_user_tag[0])]
+thisreport += money
 
 #
 # Add search documentation
 #
 
-about_search = webpage.section("Search Plan Documentation")
-about_search += [webpage.elem("iframe", [], """src="https://docs.google.com/document/d/12wJjCz8L1UczqRm86q3-EFnpnIZ2FHPSwKR_kym2L5g/pub?embedded=true" width=100%% height=100%% id=search_doc_div""")]
-
-about_div = about_doc([about_search])
+thisreport += about_doc
+thisreport += sched_doc
 
 #
-# Add search documentation
+# write the result
 #
 
-about_shifts = webpage.section("Search Run Schedule")
-about_shifts += [webpage.elem("iframe", [], """width=100% height=100% src="https://docs.google.com/spreadsheets/d/e/2PACX-1vR1XwazhZYoVbMXFZCq_s2rlzyNwcF0xC4kyhnKrCeXFSWte-2jRodL590RU3PsoX4OVypYyAZ7Nl_8/pubhtml?widget=true&amp;headers=false" id=shift_doc_div""")]
-
-shift_div = sched_doc([about_shifts])
+thisreport.save(opts.webserver_dir)
 
 #
-# add all the content from each tab to the pages
+# set up a lite version of the summary page
 #
 
+reportlite = report.Report('gstlal analysis', image=IMAGE_URL)
 
-thispage += [summary_div, inj_params_div, inj_recovery_div, sens_div, back_div, money_div, about_div, shift_div]
-
+summary = report.Tab('Summary')
+about_doc = report.Tab('About', url=ABOUT_URL)
+sched_doc = report.Tab('Schedule', url=SCHEDULE_URL)
 
 #
-# write the result
-#
-
-
-try:
-	os.makedirs(opts.webserver_dir)
-except OSError:
-	pass
-
-f = open("%s/index.html" % opts.webserver_dir, "w")
-thispage.write(f)
-f.close()
-
-shutil.copytree(opts.glob_path, os.path.join(opts.webserver_dir, os.path.split(opts.glob_path)[1]))
-
-##############################################################################
-# FIXME the following code to write a lite version of the page can probably be
-# simplified and parallelized with writing of the full version of the page
-
-# Set up a lite version of the summary page
-thispage_lite = webpage.page()
-nav_lite = webpage.tabs()
-
-summary_lite = webpage.tab("summary_lite", "Summary information", "Summary information")
-about_doc_lite = webpage.tab("about", "About", "About")
-sched_doc_lite = webpage.tab("schedule", "Schedule", "Schedule")
-
 # Summary lite setup
-nav_lite+=[webpage.elem("li", [webpage.elem("img", [], """ style="margin-left:-40px; margin-top:5px; width:100" src="https://ldas-jobs.cgca.uwm.edu/~gstlalcbc/share/vis/gstlal.png" """)])]
-nav_lite+=[summary_lite, about_doc_lite, sched_doc_lite]
-nav_lite+=[webpage.elem("li", [webpage.elem("div", [time.strftime("%Y-%m-%d %H:%M")], "")])]
-thispage_lite+=nav_lite
-thispage_lite+=webpage.elem("br",[])
-
-summary_lite_about = webpage.elem("p", ["This page was generated on %s by %s in the working directory <code>%s</code>." % (time.strftime("%Y-%m-%d %H:%M"), os.environ["USER"], os.environ["PWD"])])
-
-summary_lite_lt = webpage.section("Search Summary")
-thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]), "lt_summary_lite_div"))
-summary_lite_lt += [webpage.elem("div", [], "id=lt_summary_lite_div width=100% height=500")]
+#
 
-# Include horizon distance plots
-horizon_lite = webpage.section("Horizon distance")
-horizon_lite += webpage.image_glob('%s/*HORIZON*.png' % opts.glob_path, "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.")
+summary += report.Header('Summary')
 
-# Include found/missed as function of decisive characteristic SNR
-fm_lite_snr = webpage.section("Found / Missed Expected SNR")
-fm_lite_snr += webpage.elem("p", ["""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections for the various IFO times for coincident triggers."""])
-thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_injection_summary.json' % opts.output_user_tag[0]), "inj_summary_lite_div"))
-fm_lite_snr += [webpage.elem("div", [], "id=inj_summary_lite_div width=100% height=500")]
-fm_lite_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % opts.output_user_tag[0]), "Expected SNR of found and missed injections as a function of chirp mass.: %s" % opts.output_user_tag[0])
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]))
+summary += report.Table('Search Summary').save(tabledata, opts.webserver_dir+'_lite')
 
-# Include missed injection table
-fm_lite_mtab = webpage.section("Missed Table")
-thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_missed_summary.json' % opts.output_user_tag[0]), "missed_summary_lite_div", gpscolumns = [2,3]))
-fm_lite_mtab += [webpage.elem("p", ["Missed Injection Table for %s" % opts.output_user_tag[0]])]
-fm_lite_mtab += [webpage.elem("div", [], "id=missed_summary_lite_div")]
+## Include horizon distance plots
+imgfooter = "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."
+summary += report.ImageGrid("Horizon distance", grid_size=1, footer=imgfooter).glob(os.path.join(opts.glob_path, '*HORIZON*.png'))
 
-# Include search sensitivity binned by source type
-sens_lite = webpage.section("Search Sensitivity")
-sens_lite += webpage.elem("p", ["""These plots 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. Here we show the sensitivity as a function of source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0"""])
-sens_lite += webpage.image_glob(os.path.join(opts.glob_path, '*%s*_FAR_BINNED_BY_SOURCE_TYPE*.png' % opts.output_user_tag[0]), "Sensitive volume (V*T) and mean sensitive distance: %s" % opts.output_user_tag[0])
+## Include found/missed as function of decisive characteristic SNR
+summary += report.Header("Found / Missed Expected SNR")
+summary += report.Description("""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections for the various IFO times for coincident triggers.""")
 
-# Include FAP estimation plots
-back_snrchi_lite = webpage.section("SNR and chi-squared")
-back_snrchi_lite += [webpage.elem("p", ["This page shows the ingredients that go into ranking events and determining the False alarm probability."])]
-back_snrchi_lite += webpage.image_glob(os.path.join(opts.glob_path, '*BACKGROUND_PDF_SNRCHI2*.png'), "Single Detector Background SNR-chisq Distributions")
-back_snrchi_lite += webpage.image_glob(os.path.join(opts.glob_path, '*INJECTION_PDF_SNRCHI2*.png'), "Single Detector Signal Chisq (Conditional) Distributions")
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_injection_summary.json' % opts.output_user_tag[0]))
+footer = "Summary of missed and found injections broken up by detector time for %s" % opts.output_user_tag[0]
+summary += report.Table('Injection Summary Table', footer=footer).save(tabledata, opts.webserver_dir+'_lite')
 
-# Include Chi-square distribution plots
-chisq_dist_lite = webpage.section("Chi-squared Distributions")
-chisq_dist_lite += [webpage.elem("p", ["The chi-squared test checks that the snr accumulated matches expectation."])]
-chisq_dist_lite += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'), "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.")
+plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % opts.output_user_tag[0])
+imgfooter = "Expected SNR of found and missed injections as a function of chirp mass: %s" % opts.output_user_tag[0]
+summary += report.ImageGrid("Expected SNR vs. chirp mass", grid_size=4, footer=imgfooter).glob(plots)
 
-# Include IFAR and lnL plots
-money_ifar_lite = webpage.section("Rate vs. Threshold")
-money_ifar_lite += [webpage.elem("p", ["This section provides the detection statistic plots and a summary of the loudest event."])]
-money_ifar_lite += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'), "Comparison of single time slide event rate to event rate expected from background as a function of threshold.")
+## Include missed injection table
+summary += report.Header("Missed Table")
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_missed_summary.json' % opts.output_user_tag[0]))
+summary += report.Table('Missed Injection Table for %s' % opts.output_user_tag[0]).save(tabledata, opts.webserver_dir+'_lite')
 
-# Include loudest table
-thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]), "bgsummary_table_lite_div", gpscolumns = [5], scinotationcolumns = [1,2]))
-money_ifar_lite += [webpage.elem("p", ["Closed Box Summary Table"])]
-money_ifar_lite += [webpage.elem("div", [], "id=bgsummary_table_lite_div")]
+## Include search sensitivity binned by source type
+summary += report.Header('Search Sensitivity')
+summary += report.Description("""These plots 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. Here we show the sensitivity as a function of source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0""")
 
-#
-# OPEN BOX Lite
-#
+plots = os.path.join(opts.glob_path, '*%s*_BINNED_BY_SOURCE_TYPE*.png' % opts.output_user_tag[0])
+imgfooter = "Binned by source type"
+summary += report.ImageGrid("Sensitive volume (V*T) and mean sensitive distance: %s" % opts.output_user_tag[0], grid_size=4, footer=imgfooter, visible=False).glob(plots)
 
+## Include FAP estimation plots
+summary += report.Header("SNR and chi-squared")
+summary += report.ImageGrid("Single Detector Background SNR-chisq Distributions", grid_size=4).glob(os.path.join(opts.glob_path, '*BACKGROUND_PDF_SNRCHI2*.png'))
+summary += report.ImageGrid("Single Detector Signal Chisq (Conditional) Distributions", grid_size=4).glob(os.path.join(opts.glob_path, '*INJECTION_PDF_SNRCHI2*.png'))
 if opts.open_box:
-	back_snrchi_lite += webpage.image_glob(os.path.join(opts.glob_path, '*ZERO_LAG_PDF_SNRCHI2*.png'), "Single Detector Signal Chisq (Conditional) Distributions for zero lag")
-
-	chisq_dist_lite += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'), "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.")
-
-	money_ifar_lite += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'), "Comparison of observed event rate to event rate expected from background as a function of threshold.")
-	thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]), "zlsummary_table_lite_div", gpscolumns = [5], scinotationcolumns = [1,2]))
-	money_ifar_lite += [webpage.elem("p", ["Open Box Summary Table"])]
-	money_ifar_lite += [webpage.elem("div", [], "id=zlsummary_table_lite_div")]
+	summary += report.ImageGrid("Single Detector Signal Chisq (Conditional) Distributions: Zero lag", grid_size=4).glob(os.path.join(opts.glob_path, '*ZERO_LAG_PDF_SNRCHI2*.png'))
 
-summary_lite_div = summary_lite([summary_lite_about, summary_lite_lt, horizon_lite, fm_lite_snr, fm_lite_mtab, sens_lite, back_snrchi_lite, chisq_dist_lite, money_ifar_lite])
+## Include Chi-square distribution plots
+summary += report.Header("Chi-squared Distributions")
+summary += report.Description("The chi-squared test checks that the snr accumulated matches expectation.")
 
-#
-# Add search documentation to lite section
-#
+imgfooter = "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."
+summary += report.ImageGrid("Chi-squared Distributions", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'))
+if opts.open_box:
+	summary += report.ImageGrid("Chi-squared Distributions: Zero lag", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'))
 
-about_search_lite = webpage.section("Search Plan Documentation")
-about_search_lite += [webpage.elem("iframe", [], """src="https://docs.google.com/document/d/12wJjCz8L1UczqRm86q3-EFnpnIZ2FHPSwKR_kym2L5g/pub?embedded=true" width=100%% height=100%% id=search_doc_div""")]
+## Include IFAR, lnL plots + summary table
+summary += report.Header('Money Plots')
+summary += report.Description("This section provides the detection statistic plots and a summary of the loudest event.")
 
-about_lite_div = about_doc_lite([about_search_lite])
+imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+summary += report.ImageGrid("Rate vs. Threshold", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'))
 
-#
-# Add shift documentation to lite section
-#
+tabledata = read_format_json(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]))
+summary += report.Table('Closed Box Summary Table').save(tabledata, opts.webserver_dir+'_lite')
 
-about_shifts_lite = webpage.section("Search Run Schedule")
-about_shifts_lite += [webpage.elem("iframe", [], """width=100% height=100% src="https://docs.google.com/spreadsheets/d/e/2PACX-1vR1XwazhZYoVbMXFZCq_s2rlzyNwcF0xC4kyhnKrCeXFSWte-2jRodL590RU3PsoX4OVypYyAZ7Nl_8/pubhtml?widget=true&amp;headers=false" id=shift_doc_div""")]
+if opts.open_box:
+	imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+	summary += report.ImageGrid("Rate vs. Threshold: Zero lag", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'))
+	
+	tabledata = read_format_json(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]))
+	summary += report.Table('Open Box Summary Table').save(tabledata, opts.webserver_dir+'_lite')
 
-shift_lite_div = sched_doc_lite([about_shifts_lite])
+reportlite += summary
 
 #
-# Add all the content from each tab to the lite pages
+# Add search documentation
 #
 
-thispage_lite += [summary_lite_div, about_lite_div, shift_lite_div]
+reportlite += about_doc
+reportlite += sched_doc
 
 #
 # write the result
 #
 
-try:
-	os.makedirs(opts.webserver_dir + "_lite")
-except OSError:
-	pass
-
-flite = open("%s/index.html" % (opts.webserver_dir + "_lite", ), "w")
-thispage_lite.write(flite)
-flite.close()
-
-shutil.copytree(opts.glob_path, os.path.join(opts.webserver_dir + "_lite", os.path.split(opts.glob_path)[1]))
+reportlite.save(opts.webserver_dir + "_lite", config_name='report_lite')
diff --git a/gstlal-inspiral/bin/gstlal_inspiral_summary_page_lite b/gstlal-inspiral/bin/gstlal_inspiral_summary_page_lite
index 4cb90493c703ad19e45ee1c5799ff0d3da942491..e8a5a630a3cc2af47396930e9b1899adb1c50c15 100755
--- a/gstlal-inspiral/bin/gstlal_inspiral_summary_page_lite
+++ b/gstlal-inspiral/bin/gstlal_inspiral_summary_page_lite
@@ -1,9 +1,61 @@
-#!/usr/bin/python
+#!/usr/bin/env python
+#
+# Copyright (C) 2018 Chad Hanna
+# Copyright (C) 2019 Patrick Godwin
+#
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 2 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
 import sys, os, glob, shutil, time
+import re
 import subprocess
-from gstlal import webpage
-from optparse import OptionParser
+import json
 
+from optparse import OptionParser
+from ligo.scald import report
+
+ABOUT_URL = 'https://docs.google.com/document/d/12wJjCz8L1UczqRm86q3-EFnpnIZ2FHPSwKR_kym2L5g/pub?embedded=true'
+SCHEDULE_URL = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vR1XwazhZYoVbMXFZCq_s2rlzyNwcF0xC4kyhnKrCeXFSWte-2jRodL590RU3PsoX4OVypYyAZ7Nl_8/pubhtml?widget=true&amp;headers=false'
+IMAGE_URL = 'https://results.ligo.uwm.edu/~gstlalcbc/share/vis/gstlal.png'
+
+
+def sanitizetext(item):
+	# FIXME: hack to clean up formatted columns downstream,
+	#        should really be handling this at the source
+	item = item.replace('&#x2299;', '')
+	htmlr = re.compile('<.*?>')
+	alphanumr = re.compile('[\W_]+')
+	item = re.sub(htmlr, '', item)
+	return re.sub(alphanumr, '', item)
+
+def read_format_json(path):
+	"""
+	formats tabular summaries into a format readable by ligo-scald
+	FIXME: remove once formatting is changed upstream
+	"""
+	table = ''
+	with open(path, 'r') as f:
+		table = json.loads(f.read())
+
+	### format data
+	# FIXME: hack to clean up formatted columns downstream,
+	#        should really be handling this at the source
+	fields = [{'key': sanitizetext(col['id']), 'label': sanitizetext(col['label']), 'sortable': True} for col in table['cols']]
+	colnames = [field['key'] for field in fields]
+	items = [{col: val['v'] for col, val in zip(colnames, row['c'])} for row in table['rows']]
+
+	return {'fields': fields, 'items': items}
 
 def parse_command_line():
 	parser = OptionParser(usage = "%prog [options] [file ...]", description = "%prog summary web page")
@@ -12,10 +64,8 @@ def parse_command_line():
 	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")
-	parser.add_option("--no-navigation", action = "store_true", help = "Do not print a navigation bar")
 
 	opts, filenames = parser.parse_args()
-	print opts.glob_path
 	return opts, filenames
 
 
@@ -24,135 +74,103 @@ def parse_command_line():
 #
 
 opts, files = parse_command_line()
-thispage_lite = webpage.page()
+reportdir = opts.webserver_dir+'_lite'
 
-if not opts.no_navigation:
-	nav_lite = webpage.tabs()
-	summary_lite = webpage.tab("summary_lite", "Summary information", "Summary information")
-	about_doc_lite = webpage.tab("about", "About", "About")
-	sched_doc_lite = webpage.tab("schedule", "Schedule", "Schedule")
+thisreport = report.Report('gstlal analysis', image=IMAGE_URL)
 
-	# Summary lite setup
-	nav_lite+=[webpage.elem("li", [webpage.elem("img", [], """ style="margin-left:-40px; margin-top:5px; width:100" src="https://ldas-jobs.cgca.uwm.edu/~gstlalcbc/share/vis/gstlal.png" """)])]
-	nav_lite+=[summary_lite, about_doc_lite, sched_doc_lite]
-	nav_lite+=[webpage.elem("li", [webpage.elem("div", [time.strftime("%Y-%m-%d %H:%M")], "")])]
-	thispage_lite+=nav_lite
+try:
+	os.makedirs(reportdir)
+except OSError:
+	pass
 
+#
+# Add the page tabs
+#
 
-thispage_lite+=webpage.elem("br",[])
-summary_lite_about = webpage.elem("p", ["This page was generated on %s by %s in the working directory <code>%s</code>." % (time.strftime("%Y-%m-%d %H:%M"), os.environ["USER"], os.environ["PWD"])])
+summary = report.Tab('Summary')
+about_doc = report.Tab('About', url=ABOUT_URL)
+sched_doc = report.Tab('Schedule', url=SCHEDULE_URL)
 
 
 #
-# IFAR, ln L plots and table
+# add page header
 #
-money_ifar_lite = webpage.section("Rate vs. Threshold")
-money_ifar_lite += [webpage.elem("p", ["This section provides the detection statistic plots and a summary of the loudest event."])]
-if not opts.open_box:
-	money_ifar_lite += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'), "Comparison of single time slide event rate to event rate expected from background as a function of threshold.")
-	thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]), "bgsummary_table_lite_div", gpscolumns = [5], scinotationcolumns = [1,2]))
-	money_ifar_lite += [webpage.elem("p", ["Closed Box Summary Table"])]
-	money_ifar_lite += [webpage.elem("div", [], "id=bgsummary_table_lite_div")]
-else:
-	money_ifar_lite += webpage.image_glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'), "Comparison of observed event rate to event rate expected from background as a function of threshold.")
-	thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]), "zlsummary_table_lite_div", gpscolumns = [5], scinotationcolumns = [1,2]))
-	money_ifar_lite += [webpage.elem("p", ["Open Box Summary Table"])]
-	money_ifar_lite += [webpage.elem("div", [], "id=zlsummary_table_lite_div")]
-
-# Include found/missed as function of decisive characteristic SNR
-fm_lite_snr = webpage.section("Found / Missed Expected SNR")
-fm_lite_snr += webpage.elem("p", ["""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections for the various IFO times for coincident triggers."""])
-try:
-	thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_injection_summary.json' % opts.output_user_tag[0]), "inj_summary_lite_div"))
-except IOError:
-	print >> sys.stderr, "Injection table not found, continuing"
-fm_lite_snr += [webpage.elem("div", [], "id=inj_summary_lite_div width=100% height=500")]
-fm_lite_snr += webpage.image_glob(os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % opts.output_user_tag[0]), "Expected SNR of found and missed injections as a function of chirp mass.: %s" % opts.output_user_tag[0])
-
-# Include missed injection table
-fm_lite_mtab = webpage.section("Missed Table")
-try:
-	thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_missed_summary.json' % opts.output_user_tag[0]), "missed_summary_lite_div", gpscolumns = [2,3]))
-except IOError:
-	print >> sys.stderr, "Missed injection table not found, continuing"
-fm_lite_mtab += [webpage.elem("p", ["Missed Injection Table for %s" % opts.output_user_tag[0]])]
-fm_lite_mtab += [webpage.elem("div", [], "id=missed_summary_lite_div")]
 
-# Include search sensitivity binned by source type
-sens_lite = webpage.section("Search Sensitivity")
-sens_lite += webpage.elem("p", ["""These plots 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. Here we show the sensitivity as a function of source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0"""])
-sens_lite += webpage.image_glob(os.path.join(opts.glob_path, '*%s*_FAR_BINNED_BY_SOURCE_TYPE*.png' % opts.output_user_tag[0]), "Sensitive volume (V*T) and mean sensitive distance: %s" % opts.output_user_tag[0])
+summary += report.Header('Summary')
+summary += report.Description("This page was generated on %s by %s in the working directory: %s" % (time.strftime("%Y-%m-%d %H:%M"), os.environ["USER"], os.environ["PWD"]))
 
-# Include horizon distance plots
-horizon_lite = webpage.section("Horizon distance")
-horizon_lite += webpage.image_glob('%s/*HORIZON*.png' % opts.glob_path, "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.")
 
-# Search summary
-summary_lite_lt = webpage.section("Search Summary")
-thispage_lite.header_content.append(webpage.googleTableFromJson(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]), "lt_summary_lite_div"))
-summary_lite_lt += [webpage.elem("div", [], "id=lt_summary_lite_div width=100% height=500")]
+#
+# IFAR, ln L plots and table
+#
+
+summary += report.Header('Rate vs. Threshold')
+summary += report.Description("This section provides the detection statistic plots and a summary of the loudest event.")
 
+summary += report.Header('Closed Box Results')
+imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+summary += report.ImageGrid("Rate vs. Threshold", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_closedbox*.png'))
 
+tabledata = read_format_json(os.path.join(opts.glob_path, '%sbgsummary_table.json' % opts.output_user_tag[0]))
+summary += report.Table('Closed Box Summary Table').save(tabledata, reportdir)
 
-# These don't work, replace them??
-#back_snrchi_lite = webpage.section("SNR and chi-squared")
-#back_snrchi_lite += [webpage.elem("p", ["This page shows the ingredients that go into ranking events and determining the False alarm probability."])]
-#back_snrchi_lite += webpage.image_glob(os.path.join(opts.glob_path, '*BACKGROUND_PDF_SNRCHI2*.png'), "Single Detector Background SNR-chisq Distributions")
-#back_snrchi_lite += webpage.image_glob(os.path.join(opts.glob_path, '*INJECTION_PDF_SNRCHI2*.png'), "Single Detector Signal Chisq (Conditional) Distributions")
+if opts.open_box:
+	summary += report.Header('Open Box Results')
+	imgfooter = "Comparison of single time slide event rate to event rate expected from background as a function of threshold."
+	summary += report.ImageGrid("Rate vs. Threshold", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*5_count_vs_*_openbox*.png'))
+	
+	tabledata = read_format_json(os.path.join(opts.glob_path, '%ssummary_table.json' % opts.output_user_tag[0]))
+	summary += report.Table('Open Box Summary Table').save(tabledata, reportdir)
 
-# Include Chi-square distribution plots
-chisq_dist_lite = webpage.section("Chi-squared Distributions")
-chisq_dist_lite += [webpage.elem("p", ["The chi-squared test checks that the snr accumulated matches expectation."])]
-if not opts.open_box:
-	chisq_dist_lite += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'), "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.")
-else:
-	chisq_dist_lite += webpage.image_glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'), "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.")
+## Include found/missed as function of decisive characteristic SNR
+summary += report.Header("Found / Missed Expected SNR")
+summary += report.Description("""Measuring the found and missed injections as a function of various parameters aids in diagnosing the pipeline as well as providing the expected sensitivity of the pipeline to real signals.  The plots in this section show the missed and found injections for the various IFO times for coincident triggers.""")
 
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_injection_summary.json' % opts.output_user_tag[0]))
+footer = "Summary of missed and found injections broken up by detector time for %s" % opts.output_user_tag[0]
+summary += report.Table('Injection Summary Table', footer=footer).save(tabledata, reportdir)
 
-if not opts.no_navigation:
-	summary_lite_div = summary_lite([summary_lite_about, money_ifar_lite, fm_lite_snr, fm_lite_mtab, sens_lite, horizon_lite, chisq_dist_lite, summary_lite_lt])
-else:
-	summary_lite_div = webpage.elem("div", [summary_lite_about, money_ifar_lite, fm_lite_snr, fm_lite_mtab, sens_lite, horizon_lite, chisq_dist_lite, summary_lite_lt], "class='tabcontent' style='display: block;'")
+plots = os.path.join(opts.glob_path, '*%s*1_dec_expsnr_vs_mchirp_*.png' % opts.output_user_tag[0])
+imgfooter = "Expected SNR of found and missed injections as a function of chirp mass: %s" % opts.output_user_tag[0]
+summary += report.ImageGrid("Expected SNR vs. chirp mass", grid_size=4, footer=imgfooter).glob(plots)
 
-if not opts.no_navigation:
-	#
-	# Add search documentation to lite section
-	#
+## Include missed injection table
+summary += report.Header("Missed Table")
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_missed_summary.json' % opts.output_user_tag[0]))
+summary += report.Table('Missed Injection Table for %s' % opts.output_user_tag[0]).save(tabledata, reportdir)
 
-	about_search_lite = webpage.section("Search Plan Documentation")
-	about_search_lite += [webpage.elem("iframe", [], """src="https://docs.google.com/document/d/12wJjCz8L1UczqRm86q3-EFnpnIZ2FHPSwKR_kym2L5g/pub?embedded=true" width=100%% height=100%% id=search_doc_div""")]
 
-	about_lite_div = about_doc_lite([about_search_lite])
+## Include search sensitivity binned by source type
+summary += report.Header('Search Sensitivity')
+summary += report.Description("""These plots 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. Here we show the sensitivity as a function of source type: BNS mchirp between 0.8 and 2, NSBH between 2 and 4.5, BBH between 4.5 and 45.0, IMBH between 45.0 and 450.0""")
 
-	#
-	# Add shift documentation to lite section
-	#
+plots = os.path.join(opts.glob_path, '*%s*_BINNED_BY_SOURCE_TYPE*.png' % opts.output_user_tag[0])
+imgfooter = "Binned by source type"
+summary += report.ImageGrid("Sensitive volume (V*T) and mean sensitive distance: %s" % opts.output_user_tag[0], grid_size=4, footer=imgfooter, visible=False).glob(plots)
 
-	about_shifts_lite = webpage.section("Search Run Schedule")
-	about_shifts_lite += [webpage.elem("iframe", [], """width=100% height=100% src="https://docs.google.com/spreadsheets/d/e/2PACX-1vR1XwazhZYoVbMXFZCq_s2rlzyNwcF0xC4kyhnKrCeXFSWte-2jRodL590RU3PsoX4OVypYyAZ7Nl_8/pubhtml?widget=true&amp;headers=false" id=shift_doc_div""")]
+## Include horizon distance plots
+imgfooter = "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."
+summary += report.ImageGrid("Horizon distance", grid_size=1, footer=imgfooter).glob(os.path.join(opts.glob_path, '*HORIZON*.png'))
 
-	shift_lite_div = sched_doc_lite([about_shifts_lite])
+## Search summary
+tabledata = read_format_json(os.path.join(opts.glob_path, '%s_plotsummary.json' % opts.output_user_tag[0]))
+summary += report.Table('Search Summary').save(tabledata, reportdir)
 
-	#
-	# Add all the content from each tab to the lite pages
-	#
+## Include Chi-square distribution plots
+summary += report.Header("Chi-squared Distributions")
+summary += report.Description("The chi-squared test checks that the snr accumulated matches expectation.")
 
-	thispage_lite += [summary_lite_div, about_lite_div, shift_lite_div]
-else:
-	thispage_lite += [summary_lite_div]
+imgfooter = "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."
+summary += report.ImageGrid("Chi-squared Distributions", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*closedbox*.png'))
+if opts.open_box:
+	summary += report.ImageGrid("Chi-squared Distributions: Zero lag", grid_size=3, footer=imgfooter).glob(os.path.join(opts.glob_path, '*3_chi2_vs_rho_*openbox*.png'))
 
+thisreport += summary
+thisreport += about_doc
+thisreport += sched_doc
 
 #
 # write the result
 #
 
-try:
-	os.makedirs(opts.webserver_dir + "_lite")
-except OSError:
-	pass
-
-flite = open("%s/index.html" % (opts.webserver_dir + "_lite", ), "w")
-thispage_lite.write(flite)
-flite.close()
-
-shutil.copytree(opts.glob_path, os.path.join(opts.webserver_dir + "_lite", os.path.split(opts.glob_path)[1]))
+thisreport.save(reportdir, config_name='report_lite')