}
} @browsers
}; # fallback hash based on release semantics
-my $usagemax = (max(map { ref $_ eq 'HASH' && sum(values %$_) } values %$canihas) // 1) / 100;
my $usagepct = 1; # score multiplier for 0..100 result
# normalise usage percentage to only include shown browsers
$usagepct = 100.01 / featurescore({ # yes for every possible version
map { $_ => { map {$_ => 'y'} map { @{$_} } @{$versions{$_}} } } keys %versions
});
+$_->{usage} = featurescore($_->{stats}) * $usagepct
+ for values %{ $caniuse->{data} };
print '<table class="mapped">';
print '<col span="3">'; # should match first thead row
sprintf('p%01d', $usage * ($usagepct - .0001) / 10),
sprintf('p%02d', $usage * ($usagepct - .0001)),
),
- sprintf('pp%02d', $usage / $usagemax),
),
scalar @span,
$title,
sub sayusagecol {
my ($id) = @_;
- print '<td>', int featurescore($caniuse->{data}->{$id}->{stats}) * $usagepct;
+ print '<td>', int $caniuse->{data}->{$id}->{usage};
}
say '<tbody>';
for my $id (sort {
- featurescore($caniuse->{data}->{$b}->{stats})
- <=> featurescore($caniuse->{data}->{$a}->{stats})
+ $caniuse->{data}->{$b}->{usage} <=> $caniuse->{data}->{$a}->{usage}
} keys %{ $caniuse->{data} }) {
$caniuse->{data}->{$id}->{stats} or next; # skip metadata [summary]
printf '<tr id="%s">', $id;