print '<td>' x $_ for 2, map { scalar @{ $versions{$_} } } @browsers;
print "</thead>\n";
-sub featurerank {
+sub featurescore {
# relative amount of support for given feature
state $statspts = { y=>10, 'y x'=>9, a=>5, j=>2, p=>1 };
- my ($id) = @_;
my $rank = 0;
- if (my $row = $caniuse->{data}->{$id}->{stats}) {
+ if (my $row = shift) {
while (my ($browser, $vercols) = each %versions) {
my $div = 0; # multiplier exponent (decreased to lower value)
my @vers = map { $row->{$browser}->{$_} } @$vercols;
}
for my $id (sort {
- featurerank($b) <=> featurerank($a)
+ featurescore($caniuse->{data}->{$b}->{stats})
+ <=> featurescore($caniuse->{data}->{$a}->{stats})
} keys %{ $caniuse->{data} }) {
my $row = $caniuse->{data}->{$id};
my $data = $row->{stats} or next; # skip metadata [summary]
$prev = $ver;
}
}
- print '<td>', int featurerank($id);
+ state $maxscore = featurescore({ # yes for every possible version
+ map { $_ => { map {$_ => 'y'} @{$versions{$_}} } } keys %versions
+ });
+ print '<td>', int featurescore($caniuse->{data}->{$id}->{stats}) / $maxscore * 100;
}
print '</table>';