scripts/visual_metrics.py - webm/contributor-guide - Git at Google

 #!/usr/bin/python
 #
 # Copyright 2010 Google Inc.
 # All Rights Reserved.

 """Converts video encoding result data from text files to visualization
 data source."""

 __author__ = "jzern@google.com (James Zern),"
 __author__ += "jimbankoski@google.com (Jim Bankoski)"

 import fnmatch
 import os
 import string
 import sys
 from os.path import basename, splitext

 def HasMetrics(line):
   if line[0:1] != "B":
     return True
   return False

 def FileBetter(file_name_1, file_name_2, metric_column):
   """
   Compares two data files and determines which is better and by how
   much. Also produces a histogram of how much better, by PSNR.
   metric_column is the metric.
   """
   # Store and parse our two files into lists of unique tuples.

   # Read the two files, parsing out lines starting with bitrate.
   metric_set1 = set([])
   metric_file = open(file_name_1, "r")
   for line in metric_file:
     metrics = string.split(line)
     if HasMetrics(line):
       tuple = float(metrics[0]), float(metrics[metric_column])
       metric_set1.add(tuple)
   metric_set1_sorted = sorted(metric_set1)

   metric_set2 = set([])
   metric_file = open(file_name_2, "r")
   for line in metric_file:
     metrics = string.split(line)
     if line[0:1] != "B":
       tuple = float(metrics[0]), float(metrics[metric_column])
       metric_set2.add(tuple)
   metric_set2_sorted = sorted(metric_set2)

   def GraphBetter(metric_set1_sorted, metric_set2_sorted, base_is_set_2):
     """
     Search through the sorted metric file for metrics on either side of
     the metric from file 1.  Since both lists are sorted we really
     should not have to search through the entire range, but these
     are small files."""
     total_bitrate_difference_ratio = 0.0
     count = 0
     for bitrate, metric in metric_set1_sorted:
       for i in range(len(metric_set2_sorted) - 1):
         s2_bitrate_0, s2_metric_0 = metric_set2_sorted[i]
         s2_bitrate_1, s2_metric_1 = metric_set2_sorted[i + 1]
         # We have a point on either side of our metric range.
         if metric > s2_metric_0 and metric <= s2_metric_1:

           # Calculate a slope.
           if s2_metric_1 - s2_metric_0 != 0:
             metric_slope = ((s2_bitrate_1 - s2_bitrate_0) /
                             (s2_metric_1 - s2_metric_0))
           else:
             metric_slope = 0

           estimated_s2_bitrate = (s2_bitrate_0 + (metric - s2_metric_0) *
                                   metric_slope)

           # Calculate percentage difference as given by base.
           if base_is_set_2 == 0:
             bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
                                         bitrate)
           else:
             bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
                                         estimated_s2_bitrate)

           total_bitrate_difference_ratio += bitrate_difference_ratio
           count += 1
           break


     # Calculate the average improvement between graphs.
     if count != 0:
       avg = total_bitrate_difference_ratio / count

     else:
       avg = 0.0

     return avg

   # Be fair to both graphs by testing all the points in each.
   avg_improvement = (GraphBetter(metric_set1_sorted, metric_set2_sorted, 1) -
                      GraphBetter(metric_set2_sorted, metric_set1_sorted, 0)) / 2


   return avg_improvement


 def HandleFiles(variables):
   """FIXME!
   """
   file_pattern = variables[1]
   baseline_dir = variables[2]
   print """
 <!DOCTYPE html>
 <html lang="en">
 <head>
 <meta charset="utf-8">
 <title>VP8 Results</title>
 <style type="text/css">
 body {
   font-family: verdana;
 }
 .bold-font {
   font-weight: bold;
   font-size: 14px;
 }

 .small-font {
   font-size: 14px;
 }

 .header-style {
   border: 2px solid white;
   background-color: #cccccc;
 }

 .orange-background {
   background-color: orange;
 }

 .small-margin {
   margin: 0;
   padding: 0;
 }

 .light-gray-background {
   background-color: #f0f0f0;
 }

 .invisible {
   font-size: 1px;
   background-color: white;
   color: white;
   height: 0px;
 }

 #indented {
   position: absolute;
   left: 150px;
 }

 #midcol {
   margin: 0 0px;
 }
 </style>
 <script type="text/javascript" src="http://www.google.com/jsapi"></script>
 <script type="text/javascript"
   src="http://danvk.org/dygraphs/dygraph-combined.js"></script>
 <script type="text/javascript">
 var chart_height=600;
 var chart_width=500;
 var filestable =[];
 var snrs =[];
 """
   # Go through each metric in the list.
   for column in range(1, 6):

     # Dirs is directories after the base to compare to the base.
     dirs = variables[3:len(variables)]

     # Find the metric files in the baseline directory.
     dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))

     print ("filestable[" + str(column) +
            "] =  { cols : [{id:'file',label:'File',type:'string'},")

     for directory in dirs:

       print ( "{id:'" + basename(directory) + "', label:'" +
               basename(directory) + "',type:'number'}"),
       if directory == dirs[len(dirs) - 1]:
         print "],"
       else:
         print ","

     print "rows : ["

     sumoverall = {}
     for directory in dirs:
       sumoverall[directory] = 0

     countoverall = 0
     for filename in dir_list:
       print "{c:[",
       print "{f:'" + splitext(basename(filename))[0] + "'},",
       baseline_file_name = baseline_dir + "/" + filename

       for directory in dirs:
         metric_file_name = directory + "/" + filename
         if os.path.isfile(metric_file_name):
           overall = FileBetter(baseline_file_name, metric_file_name, column)
           print "{v:" + str(100 * overall) + "}",
           if directory == dirs[len(dirs) - 1]:
             print "]",
           else:
             print ",",

           sumoverall[directory] += overall
       countoverall += 1
       print "}, "

     print "{c:[",
     print "{f:'OVERALL'},",

     for directory in dirs:
       print "{v:" + str(100 * sumoverall[directory] / countoverall) + "}",
       if directory == dirs[len(dirs) - 1]:
         print "]",
       else:
         print ",",
     print "}"
     print "]"
     print "}"
     print "snrs[" + str(column) + "] =  ["
     line_count = 0
     for filename in dir_list:
       print '"' + "{ cols : [{id:'datarate',label:'Datarate',type:'number'},",
       print ("{ id : '" + splitext(basename(filename))[0] + ":" +
              basename(baseline_dir) + "', label:'" + basename(baseline_dir) +
              "', type:'number' },"),
       for directory in dirs:
         print ("{id:'" + basename(directory) + "', label:'" +
                basename(directory) + "',type:'number'}"),
         if directory == dirs[len(dirs) - 1]:
           print "],",
         else:
           print ",",

       print "rows : [ ",

       prec = ","
       if len(dirs) > 1:
         postc = ",,,,,,,,,,,,,,,,,,,"[:len(dirs) - 1]
       else:
         postc = ""

       for directory in dirs:
         metric_file_name = directory + "/" + filename
         metric_file = open(metric_file_name, "r")
         for line in metric_file:
           metrics = string.split(line)
           if HasMetrics(line):
             print "{c:[{v:" + metrics[0] + "},",
             print prec,
             print "{v:" + metrics[column] + "}",
             print postc + "]},",
         prec += ","
         postc = postc[1:]

       baseline_file_name = baseline_dir + "/" + filename
       postc = ",,,,,,,,,,,,,,,,,,,,"[:len(dirs)]
       baseline_metric_file = open(baseline_file_name, "r")
       metric_file_lines = baseline_metric_file.readlines()
       for i in range(len(metric_file_lines)):
         line = metric_file_lines[i]
         metrics = string.split(line)
         if HasMetrics(line):
           print ("{c:[{v:" + metrics[0] + "},{v:" + metrics[column] + "}" +
                  postc + "]}"),
           if i < len(metric_file_lines) - 1:
             print ",",

       print "]" + '}"',
       line_count += 1
       if line_count < len(dir_list):
         print ","

     print "]"


   print """
 var selected = 0
 var imagestr = '';
 var bettertable=0;
 var chart=0;
 var better=0;
 var metricdata=0;
 var metricview=0;
 var column=1;
 var formatter=0;
 function changeColumn(col){
   column = col;
   draw_files();
 }
 function setup_vis(){
   chart = new google.visualization.ScatterChart(
       document.getElementById("metricgraph"));

   bettertable = new google.visualization.Table(
       document.getElementById("bettertable"));

   formatter = new google.visualization.NumberFormat(
       {fractionDigits: 1, suffix:"%"});

   draw_files();
 }
 function draw_files(){
   var cssClassNames = {
       'headerRow': 'blue-font small-font bold-font small-margin',
       'tableRow': 'small-font small-margin',
       'oddTableRow': 'light-gray-background small-font small-margin',
       'selectedTableRow': 'orange-background small-font',
       'hoverTableRow': 'small-font header-style',
       'headerCell': 'header-style small-margin',
       'tableCell': 'small-margin'};

   var options = {'allowHtml': true,'cssClassNames': cssClassNames};
   if (better != 0) delete better;
   better = new google.visualization.DataTable(filestable[column])
 """
   for i in range(len(dirs)):
     print "  formatter.format(better," + str(1 + i) + ");"

   print """
   bettertable.draw(better,options);
   google.visualization.events.addListener(bettertable, 'select',
                                           selectBetterHandler);
   query_file()

 }
 function query_file() {
   imagestr = better.getFormattedValue(selected, 0)
   var metricjson = eval('(' + snrs[column][selected] + ')');
   metricdata = new google.visualization.DataTable(metricjson, 0.6);
   if( metricview != 0 ) delete metricview;
   metricview = new google.visualization.DataView(metricdata);

   chart.draw(metricview, {curveType:'function',
       chartArea:{left:40, top:10, width:chart_width, height:chart_height - 110},
       hAxis:{title:"datarate in kbps"}, vAxis:{title:"quality in decibels"},
       legend:{position:"in"}, title:imagestr, pointSize:2, lineWidth:1,
       width:chart_width, height:chart_height - 50});

   google.visualization.events.addListener(chart, 'select', chartSelect);
   google.visualization.events.addListener(chart, 'onmouseover', chartMouseOver);
   google.visualization.events.addListener(chart, 'onmouseout', chartMouseOut);
 }
 function chartMouseOut(e){
   statusbar = document.getElementById('status');
   statusbar.style.display = 'none';

 }
 function chartMouseOver(e){
   pointDifference(e.row, e.column)
 }
 function pointDifference(row, col){

   if(!row || !col)
     return;

   var cols = metricdata.getNumberOfColumns();
   var rows = metricdata.getNumberOfRows();

   var sel_bitrate = metricview.getValue(row, 0 );
   var sel_metric = metricview.getValue(row, col);

   var message = "At " + sel_metric.toFixed(2) + " decibels, "
   message = message + metricdata.getColumnLabel(col) + " is <ul>"

   // col 0 is datarate
   for( var i=1;i<cols;++i){

     var metric_greatest_thats_less = 0;
     var rate_greatest_thats_less = 0;
     var metric_smallest_thats_greater = 999;
     var rate_smallest_thats_greater = 0;

     if(i==col)
       continue;

     // find the lowest metric for this column thats greater than sel_metric and
     // the highest metric for this column thats less than the metric
     for(var line_count = 0; line_count < rows; ++line_count) {
       this_metric = metricdata.getValue(line_count, i)
       this_rate = metricdata.getValue(line_count, 0)
       if(!this_metric)
         continue;

       if(this_metric > metric_greatest_thats_less &&
          this_metric < sel_metric) {
         metric_greatest_thats_less = this_metric;
         rate_greatest_thats_less = this_rate;
       }
       if(this_metric < metric_smallest_thats_greater &&
         this_metric > sel_metric) {
         metric_smallest_thats_greater = this_metric;
         rate_smallest_thats_greater = this_rate;
       }
     }

     metric_slope = ( rate_smallest_thats_greater - rate_greatest_thats_less) /
         ( metric_smallest_thats_greater - metric_greatest_thats_less);

     projected_rate = ( sel_metric - metric_greatest_thats_less) * metric_slope +
         rate_greatest_thats_less;

     difference = 100 * (projected_rate / sel_bitrate - 1);


     if (difference > 0)
       message = message + "<li>  " + difference.toFixed(2) + "% smaller than " +
                 metricdata.getColumnLabel(i) + "</li> "
     else
       message = message + "<li>  " + -difference.toFixed(2) + "% bigger than " +
                 metricdata.getColumnLabel(i) + "</li> "

   }
   message = message + "</ul>"
   statusbar = document.getElementById('status');
   statusbar.innerHTML = "<p>" + message + "</p>";
   statusbar.style.top = "50%";
   statusbar.style.left = "50%";
   statusbar.style.display = 'block';
 }

 function chartSelect(){
   var selection = chart.getSelection();
   var message = '';
   var min = metricview.getFormattedValue(selection[0].row, 0);
   var max = metricview.getFormattedValue(selection[selection.length-1].row, 0);
   var val = metricview.getFormattedValue(selection[0].row,selection[0].column);

   pointDifference(selection[0].row, selection[0].column)
   min = min / 3
   max = max * 3
   metricview.setRows(metricdata.getFilteredRows(
       [{column: 0,minValue: min, maxValue:max}]));

   chart.draw(metricview, {curveType:'function',
       chartArea:{left:40, top:10, width:chart_width, height:chart_height - 110},
       hAxis:{title:"datarate in kbps"}, vAxis:{title:"quality in decibels"},
       legend:{position:"in"}, title:imagestr, pointSize:2, lineWidth:1,
       width:chart_width, height:chart_height - 50});
 }
 function selectBetterHandler() {
   var selection = bettertable.getSelection();
   for (var i = 0; i < selection.length; i++) {
     item = selection[i];
   }
   selected = item.row
   query_file()
 }

 google.load('visualization', '1', {'packages' : ['corechart','table']});
 google.setOnLoadCallback(setup_vis);
 </script>
 </head>

 <body>

   <form name="myform"> Average size reduction to get the same quality
     <input type="radio" checked name="column" value="1"
         onClick="changeColumn('1')" />Average PSNR
     <input type="radio" name="column" value="2"
         onClick="changeColumn('2')" />Global PSNR
     <input type="radio" name="column" value="5"
         onClick="changeColumn('5')" />SSIM
   </form>

   <div id="bettertable" style="float: left"></div>
   <div id="metricgraph" style="float: left"></div>

   <div id="status"
     style='width: 300px; background-color: white; position: absolute;
     top:'50%'; left:'50%'; z-index: 1; display: none;'>
   </div>

 </body>
 </html>

 """
   return

 if len(sys.argv) < 3:
   print """
 This script creates html for displaying visually metric data produced
 in a video stats file,  as created by the WEBM project when enable_psnr
 is turned on:

 Usage: visual_metrics.py statfile_pattern baseline_dir sub_dir [ sub_dir2 ...]

 the script parses each metrics file [see below] that matches the
 statfile_pattern  in the baseline directory and looks for the file that matches
 that same file in each of the sub_dirs, and compares the resultant metrics
 bitrate, avg psnr, glb psnr, and ssim. "

 It provides a table in which each row is a file in the line directory,
 and a column for each subdir, with the cells representing how that clip compares
 to baseline for that subdir.   A graph is given for each which compares filesize
 to that metric.  If you click on a point in the graph it zooms in on that point.

 a SAMPLE metrics file:

 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
  25.911   38.242   38.104   38.258   38.121   75.790    14103
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
  49.982   41.264   41.129   41.255   41.122   83.993    19817
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
  74.967   42.911   42.767   42.899   42.756   87.928    17332
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 100.012   43.983   43.838   43.881   43.738   89.695    25389
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 149.980   45.338   45.203   45.184   45.043   91.591    25438
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 199.852   46.225   46.123   46.113   45.999   92.679    28302
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 249.922   46.864   46.773   46.777   46.673   93.334    27244
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 299.998   47.366   47.281   47.317   47.220   93.844    27137
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 349.769   47.746   47.677   47.722   47.648   94.178    32226
 Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
 399.773   48.032   47.971   48.013   47.946   94.362    36203

 sample use:
 visual_metrics.py "*stt" vp8_20101201 vp8_teststatic vp8_teststatic2 > metrics.html
   """
 else:
   HandleFiles(sys.argv)
	#!/usr/bin/python
	#
	# Copyright 2010 Google Inc.
	# All Rights Reserved.

	"""Converts video encoding result data from text files to visualization
	data source."""

	__author__ = "jzern@google.com (James Zern),"
	__author__ += "jimbankoski@google.com (Jim Bankoski)"

	import fnmatch
	import os
	import string
	import sys
	from os.path import basename, splitext

	def HasMetrics(line):
	if line[0:1] != "B":
	return True
	return False

	def FileBetter(file_name_1, file_name_2, metric_column):
	"""
	Compares two data files and determines which is better and by how
	much. Also produces a histogram of how much better, by PSNR.
	metric_column is the metric.
	"""
	# Store and parse our two files into lists of unique tuples.

	# Read the two files, parsing out lines starting with bitrate.
	metric_set1 = set([])
	metric_file = open(file_name_1, "r")
	for line in metric_file:
	metrics = string.split(line)
	if HasMetrics(line):
	tuple = float(metrics[0]), float(metrics[metric_column])
	metric_set1.add(tuple)
	metric_set1_sorted = sorted(metric_set1)

	metric_set2 = set([])
	metric_file = open(file_name_2, "r")
	for line in metric_file:
	metrics = string.split(line)
	if line[0:1] != "B":
	tuple = float(metrics[0]), float(metrics[metric_column])
	metric_set2.add(tuple)
	metric_set2_sorted = sorted(metric_set2)

	def GraphBetter(metric_set1_sorted, metric_set2_sorted, base_is_set_2):
	"""
	Search through the sorted metric file for metrics on either side of
	the metric from file 1. Since both lists are sorted we really
	should not have to search through the entire range, but these
	are small files."""
	total_bitrate_difference_ratio = 0.0
	count = 0
	for bitrate, metric in metric_set1_sorted:
	for i in range(len(metric_set2_sorted) - 1):
	s2_bitrate_0, s2_metric_0 = metric_set2_sorted[i]
	s2_bitrate_1, s2_metric_1 = metric_set2_sorted[i + 1]
	# We have a point on either side of our metric range.
	if metric > s2_metric_0 and metric <= s2_metric_1:

	# Calculate a slope.
	if s2_metric_1 - s2_metric_0 != 0:
	metric_slope = ((s2_bitrate_1 - s2_bitrate_0) /
	(s2_metric_1 - s2_metric_0))
	else:
	metric_slope = 0

	estimated_s2_bitrate = (s2_bitrate_0 + (metric - s2_metric_0) *
	metric_slope)

	# Calculate percentage difference as given by base.
	if base_is_set_2 == 0:
	bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
	bitrate)
	else:
	bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
	estimated_s2_bitrate)

	total_bitrate_difference_ratio += bitrate_difference_ratio
	count += 1
	break


	# Calculate the average improvement between graphs.
	if count != 0:
	avg = total_bitrate_difference_ratio / count

	else:
	avg = 0.0

	return avg

	# Be fair to both graphs by testing all the points in each.
	avg_improvement = (GraphBetter(metric_set1_sorted, metric_set2_sorted, 1) -
	GraphBetter(metric_set2_sorted, metric_set1_sorted, 0)) / 2


	return avg_improvement


	def HandleFiles(variables):
	"""FIXME!
	"""
	file_pattern = variables[1]
	baseline_dir = variables[2]
	print """
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<title>VP8 Results</title>
	<style type="text/css">
	body {
	font-family: verdana;
	}
	.bold-font {
	font-weight: bold;
	font-size: 14px;
	}

	.small-font {
	font-size: 14px;
	}

	.header-style {
	border: 2px solid white;
	background-color: #cccccc;
	}

	.orange-background {
	background-color: orange;
	}

	.small-margin {
	margin: 0;
	padding: 0;
	}

	.light-gray-background {
	background-color: #f0f0f0;
	}

	.invisible {
	font-size: 1px;
	background-color: white;
	color: white;
	height: 0px;
	}

	#indented {
	position: absolute;
	left: 150px;
	}

	#midcol {
	margin: 0 0px;
	}
	</style>
	<script type="text/javascript" src="http://www.google.com/jsapi"></script>
	<script type="text/javascript"
	src="http://danvk.org/dygraphs/dygraph-combined.js"></script>
	<script type="text/javascript">
	var chart_height=600;
	var chart_width=500;
	var filestable =[];
	var snrs =[];
	"""
	# Go through each metric in the list.
	for column in range(1, 6):

	# Dirs is directories after the base to compare to the base.
	dirs = variables[3:len(variables)]

	# Find the metric files in the baseline directory.
	dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))

	print ("filestable[" + str(column) +
	"] = { cols : [{id:'file',label:'File',type:'string'},")

	for directory in dirs:

	print ( "{id:'" + basename(directory) + "', label:'" +
	basename(directory) + "',type:'number'}"),
	if directory == dirs[len(dirs) - 1]:
	print "],"
	else:
	print ","

	print "rows : ["

	sumoverall = {}
	for directory in dirs:
	sumoverall[directory] = 0

	countoverall = 0
	for filename in dir_list:
	print "{c:[",
	print "{f:'" + splitext(basename(filename))[0] + "'},",
	baseline_file_name = baseline_dir + "/" + filename

	for directory in dirs:
	metric_file_name = directory + "/" + filename
	if os.path.isfile(metric_file_name):
	overall = FileBetter(baseline_file_name, metric_file_name, column)
	print "{v:" + str(100 * overall) + "}",
	if directory == dirs[len(dirs) - 1]:
	print "]",
	else:
	print ",",

	sumoverall[directory] += overall
	countoverall += 1
	print "}, "

	print "{c:[",
	print "{f:'OVERALL'},",

	for directory in dirs:
	print "{v:" + str(100 * sumoverall[directory] / countoverall) + "}",
	if directory == dirs[len(dirs) - 1]:
	print "]",
	else:
	print ",",
	print "}"
	print "]"
	print "}"
	print "snrs[" + str(column) + "] = ["
	line_count = 0
	for filename in dir_list:
	print '"' + "{ cols : [{id:'datarate',label:'Datarate',type:'number'},",
	print ("{ id : '" + splitext(basename(filename))[0] + ":" +
	basename(baseline_dir) + "', label:'" + basename(baseline_dir) +
	"', type:'number' },"),
	for directory in dirs:
	print ("{id:'" + basename(directory) + "', label:'" +
	basename(directory) + "',type:'number'}"),
	if directory == dirs[len(dirs) - 1]:
	print "],",
	else:
	print ",",

	print "rows : [ ",

	prec = ","
	if len(dirs) > 1:
	postc = ",,,,,,,,,,,,,,,,,,,"[:len(dirs) - 1]
	else:
	postc = ""

	for directory in dirs:
	metric_file_name = directory + "/" + filename
	metric_file = open(metric_file_name, "r")
	for line in metric_file:
	metrics = string.split(line)
	if HasMetrics(line):
	print "{c:[{v:" + metrics[0] + "},",
	print prec,
	print "{v:" + metrics[column] + "}",
	print postc + "]},",
	prec += ","
	postc = postc[1:]

	baseline_file_name = baseline_dir + "/" + filename
	postc = ",,,,,,,,,,,,,,,,,,,,"[:len(dirs)]
	baseline_metric_file = open(baseline_file_name, "r")
	metric_file_lines = baseline_metric_file.readlines()
	for i in range(len(metric_file_lines)):
	line = metric_file_lines[i]
	metrics = string.split(line)
	if HasMetrics(line):
	print ("{c:[{v:" + metrics[0] + "},{v:" + metrics[column] + "}" +
	postc + "]}"),
	if i < len(metric_file_lines) - 1:
	print ",",

	print "]" + '}"',
	line_count += 1
	if line_count < len(dir_list):
	print ","

	print "]"


	print """
	var selected = 0
	var imagestr = '';
	var bettertable=0;
	var chart=0;
	var better=0;
	var metricdata=0;
	var metricview=0;
	var column=1;
	var formatter=0;
	function changeColumn(col){
	column = col;
	draw_files();
	}
	function setup_vis(){
	chart = new google.visualization.ScatterChart(
	document.getElementById("metricgraph"));

	bettertable = new google.visualization.Table(
	document.getElementById("bettertable"));

	formatter = new google.visualization.NumberFormat(
	{fractionDigits: 1, suffix:"%"});

	draw_files();
	}
	function draw_files(){
	var cssClassNames = {
	'headerRow': 'blue-font small-font bold-font small-margin',
	'tableRow': 'small-font small-margin',
	'oddTableRow': 'light-gray-background small-font small-margin',
	'selectedTableRow': 'orange-background small-font',
	'hoverTableRow': 'small-font header-style',
	'headerCell': 'header-style small-margin',
	'tableCell': 'small-margin'};

	var options = {'allowHtml': true,'cssClassNames': cssClassNames};
	if (better != 0) delete better;
	better = new google.visualization.DataTable(filestable[column])
	"""
	for i in range(len(dirs)):
	print " formatter.format(better," + str(1 + i) + ");"

	print """
	bettertable.draw(better,options);
	google.visualization.events.addListener(bettertable, 'select',
	selectBetterHandler);
	query_file()

	}
	function query_file() {
	imagestr = better.getFormattedValue(selected, 0)
	var metricjson = eval('(' + snrs[column][selected] + ')');
	metricdata = new google.visualization.DataTable(metricjson, 0.6);
	if( metricview != 0 ) delete metricview;
	metricview = new google.visualization.DataView(metricdata);

	chart.draw(metricview, {curveType:'function',
	chartArea:{left:40, top:10, width:chart_width, height:chart_height - 110},
	hAxis:{title:"datarate in kbps"}, vAxis:{title:"quality in decibels"},
	legend:{position:"in"}, title:imagestr, pointSize:2, lineWidth:1,
	width:chart_width, height:chart_height - 50});

	google.visualization.events.addListener(chart, 'select', chartSelect);
	google.visualization.events.addListener(chart, 'onmouseover', chartMouseOver);
	google.visualization.events.addListener(chart, 'onmouseout', chartMouseOut);
	}
	function chartMouseOut(e){
	statusbar = document.getElementById('status');
	statusbar.style.display = 'none';

	}
	function chartMouseOver(e){
	pointDifference(e.row, e.column)
	}
	function pointDifference(row, col){

	if(!row \|\| !col)
	return;

	var cols = metricdata.getNumberOfColumns();
	var rows = metricdata.getNumberOfRows();

	var sel_bitrate = metricview.getValue(row, 0 );
	var sel_metric = metricview.getValue(row, col);

	var message = "At " + sel_metric.toFixed(2) + " decibels, "
	message = message + metricdata.getColumnLabel(col) + " is <ul>"

	// col 0 is datarate
	for( var i=1;i<cols;++i){

	var metric_greatest_thats_less = 0;
	var rate_greatest_thats_less = 0;
	var metric_smallest_thats_greater = 999;
	var rate_smallest_thats_greater = 0;

	if(i==col)
	continue;

	// find the lowest metric for this column thats greater than sel_metric and
	// the highest metric for this column thats less than the metric
	for(var line_count = 0; line_count < rows; ++line_count) {
	this_metric = metricdata.getValue(line_count, i)
	this_rate = metricdata.getValue(line_count, 0)
	if(!this_metric)
	continue;

	if(this_metric > metric_greatest_thats_less &&
	this_metric < sel_metric) {
	metric_greatest_thats_less = this_metric;
	rate_greatest_thats_less = this_rate;
	}
	if(this_metric < metric_smallest_thats_greater &&
	this_metric > sel_metric) {
	metric_smallest_thats_greater = this_metric;
	rate_smallest_thats_greater = this_rate;
	}
	}

	metric_slope = ( rate_smallest_thats_greater - rate_greatest_thats_less) /
	( metric_smallest_thats_greater - metric_greatest_thats_less);

	projected_rate = ( sel_metric - metric_greatest_thats_less) * metric_slope +
	rate_greatest_thats_less;

	difference = 100 * (projected_rate / sel_bitrate - 1);


	if (difference > 0)
	message = message + "<li> " + difference.toFixed(2) + "% smaller than " +
	metricdata.getColumnLabel(i) + "</li> "
	else
	message = message + "<li> " + -difference.toFixed(2) + "% bigger than " +
	metricdata.getColumnLabel(i) + "</li> "

	}
	message = message + "</ul>"
	statusbar = document.getElementById('status');
	statusbar.innerHTML = "<p>" + message + "</p>";
	statusbar.style.top = "50%";
	statusbar.style.left = "50%";
	statusbar.style.display = 'block';
	}

	function chartSelect(){
	var selection = chart.getSelection();
	var message = '';
	var min = metricview.getFormattedValue(selection[0].row, 0);
	var max = metricview.getFormattedValue(selection[selection.length-1].row, 0);
	var val = metricview.getFormattedValue(selection[0].row,selection[0].column);

	pointDifference(selection[0].row, selection[0].column)
	min = min / 3
	max = max * 3
	metricview.setRows(metricdata.getFilteredRows(
	[{column: 0,minValue: min, maxValue:max}]));

	chart.draw(metricview, {curveType:'function',
	chartArea:{left:40, top:10, width:chart_width, height:chart_height - 110},
	hAxis:{title:"datarate in kbps"}, vAxis:{title:"quality in decibels"},
	legend:{position:"in"}, title:imagestr, pointSize:2, lineWidth:1,
	width:chart_width, height:chart_height - 50});
	}
	function selectBetterHandler() {
	var selection = bettertable.getSelection();
	for (var i = 0; i < selection.length; i++) {
	item = selection[i];
	}
	selected = item.row
	query_file()
	}

	google.load('visualization', '1', {'packages' : ['corechart','table']});
	google.setOnLoadCallback(setup_vis);
	</script>
	</head>

	<body>

	<form name="myform"> Average size reduction to get the same quality
	<input type="radio" checked name="column" value="1"
	onClick="changeColumn('1')" />Average PSNR
	<input type="radio" name="column" value="2"
	onClick="changeColumn('2')" />Global PSNR
	<input type="radio" name="column" value="5"
	onClick="changeColumn('5')" />SSIM
	</form>

	<div id="bettertable" style="float: left"></div>
	<div id="metricgraph" style="float: left"></div>

	<div id="status"
	style='width: 300px; background-color: white; position: absolute;
	top:'50%'; left:'50%'; z-index: 1; display: none;'>
	</div>

	</body>
	</html>

	"""
	return

	if len(sys.argv) < 3:
	print """
	This script creates html for displaying visually metric data produced
	in a video stats file, as created by the WEBM project when enable_psnr
	is turned on:

	Usage: visual_metrics.py statfile_pattern baseline_dir sub_dir [ sub_dir2 ...]

	the script parses each metrics file [see below] that matches the
	statfile_pattern in the baseline directory and looks for the file that matches
	that same file in each of the sub_dirs, and compares the resultant metrics
	bitrate, avg psnr, glb psnr, and ssim. "

	It provides a table in which each row is a file in the line directory,
	and a column for each subdir, with the cells representing how that clip compares
	to baseline for that subdir. A graph is given for each which compares filesize
	to that metric. If you click on a point in the graph it zooms in on that point.

	a SAMPLE metrics file:

	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	25.911 38.242 38.104 38.258 38.121 75.790 14103
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	49.982 41.264 41.129 41.255 41.122 83.993 19817
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	74.967 42.911 42.767 42.899 42.756 87.928 17332
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	100.012 43.983 43.838 43.881 43.738 89.695 25389
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	149.980 45.338 45.203 45.184 45.043 91.591 25438
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	199.852 46.225 46.123 46.113 45.999 92.679 28302
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	249.922 46.864 46.773 46.777 46.673 93.334 27244
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	299.998 47.366 47.281 47.317 47.220 93.844 27137
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	349.769 47.746 47.677 47.722 47.648 94.178 32226
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	399.773 48.032 47.971 48.013 47.946 94.362 36203

	sample use:
	visual_metrics.py "*stt" vp8_20101201 vp8_teststatic vp8_teststatic2 > metrics.html
	"""
	else:
	HandleFiles(sys.argv)