/ 
Term-Fabulous-0.01
/ Term::Fabulous::Chart::Transform

NAME

Term::Fabulous::Chart::Transform - Prepare the data of a chart series: normalize, smooth, accumulate, resample

SYNOPSIS

# In a chart, as the transform of a series (or of all series):
use Term::Fabulous::Widget::LineChart;

my $chart = Term::Fabulous::Widget::LineChart->new(
	series => [
		{ name => 'Raw',      data => \@samples },
		{ name => 'Smoothed', data => \@samples, transform => [ [ 'moving_average', 7, 'center' ] ] },
		{ name => 'Indexed',  data => \@prices,  transform => [ 'sort', [ 'index', 100 ] ] },
	],
);

# On its own:
use Term::Fabulous::Chart::Transform qw(parse_transforms apply_transforms);

my $steps = parse_transforms( 'my program', 'transform', [ [ 'resample', 3600, 'sum' ], 'cumulative' ] );
my ( $xs, $ys ) = apply_transforms( $steps, \@epochs, \@values );

DESCRIPTION

A series can be prepared before it is drawn: smoothed, normalized, summed up, resampled to an interval, thinned out. The transform of a series (or of the whole chart, for every series without one of its own) lists the steps, which run in order on the series' points. The data you give the chart is kept as it is: the steps run again whenever it changes, so live data stays prepared the same way. How the chart uses the prepared points is described in "Preparing data" in Term::Fabulous::Widget::XYChart.

A step is a name, an array of a name and its arguments, or a code reference. Write the list of steps as an array, even for one step with arguments: transform => [ [ 'moving_average', 5 ] ]. A single step without arguments can stand alone: transform => 'cumulative'. transform => [ 'moving_average', 5 ] dies with a message that shows the right form. Unknown names, a wrong number of arguments and invalid arguments die when the transform is given.

In a KDL layout, each transform node is one step, its name and arguments as the node's arguments; repeat the node for several steps (see "KDL PROPERTIES" in Term::Fabulous::Widget::XYChart):

series "visits" {
	data 120 135 160 158 171 190 185
	transform "moving_average" 3 "center"
	transform "index" 100
}

The steps see the x values as numbers: the positions 0, 1, 2, ... of points without x, the category numbers on a category axis, epoch seconds on a time axis. Points without a value (undef, gaps in a line) stay gaps unless a step says otherwise; windows skip them.

Daily visits as a dim raw line with a 7-day moving average and a dashed exponentially smoothed line over it, and below it share and bond prices both indexed to 100 at day 1

The program in the picture is in "Smooth noisy data and index it to 100 (transforms)" in Term::Fabulous::Cookbook::ChartTechniques.

Steps

Each example shows the y values a step makes of the y values before it; the x values are 0, 1, 2, ... unless the example gives them.

normalize, [ 'normalize', $low, $high ]

Scales the values linearly so the smallest becomes $low and the largest $high (default 0 and 1); when all values are equal, all become $low. Use it to compare the shapes of series of different sizes on one axis.

transform => 'normalize'                     # 2, 4, 6, 8, 10  =>  0, 0.25, 0.5, 0.75, 1
transform => [ [ 'normalize', 0, 100 ] ]     # 2, 4, 6, 8, 10  =>  0, 25, 50, 75, 100
share

Each value as a fraction of the total of the absolute values of the series (0 to 1); with the axis format => 'percent' the axis shows percentages.

transform => 'share'                         # 1, 2, 3, 4  =>  0.1, 0.2, 0.3, 0.4
zscore

The number of standard deviations (of the whole series) each value lies from the series mean; when all values are equal, all become 0.

transform => 'zscore'                        # 2, 4, 4, 6  =>  -1.41, 0, 0, 1.41
index, [ 'index', $base ]

Each value relative to the first non-zero value, which becomes $base (default 100). Use it instead of a second axis to compare the growth of series of different sizes: all start at 100.

transform => 'index'                         # 40, 50, 60  =>  100, 125, 150
transform => [ [ 'index', 1 ] ]              # 40, 50, 60  =>  1, 1.25, 1.5
cumulative

The running total.

transform => 'cumulative'                    # 1, 2, undef, 3  =>  1, 3, undef, 6
difference

The change from the previous value; the first point gets none (a gap). After a gap, the change is counted from the last value before it.

transform => 'difference'                    # 5, 7, 4, 10  =>  undef, 2, -3, 6
rate, [ 'rate', $per ]

The change per unit of x from the previous point, times $per (default 1): a counter sampled with epoch seconds becomes a rate per second, with [ 'rate', 60 ] per minute. The first point, and a point at the same x as the one before, get none.

transform => 'rate'                          # x 0, 10, 20; y 100, 160, 190  =>  undef, 6, 3
transform => [ [ 'rate', 60 ] ]              # x 0, 10, 20; y 100, 160, 190  =>  undef, 360, 180
[ 'moving_average', $window ], [ 'moving_average', $window, 'center' ]

The mean of the last $window values (trailing, the default), or of the $window values around each point (center), which does not lag behind. $window is a whole number of points, at least 1. At the ends, where fewer values are at hand, the mean is taken of those there are.

transform => [ [ 'moving_average', 3 ] ]               # 3, 6, 9, 6, 3  =>  3, 4.5, 6, 7, 6
transform => [ [ 'moving_average', 3, 'center' ] ]     # 3, 6, 9, 6, 3  =>  4.5, 6, 7, 6, 4.5
[ 'exponential', $alpha ]

Exponential smoothing: each value moves $alpha (greater than 0, at most 1) of the way from the smoothed value before it towards its own value; the first value stays. Small values smooth more.

transform => [ [ 'exponential', 0.5 ] ]      # 10, 20, 20, 0  =>  10, 15, 17.5, 8.75
[ 'median', $window ]

The median of the $window values around each point (a whole number of points, at least 1): removes single spikes but keeps steps.

transform => [ [ 'median', 3 ] ]             # 1, 1, 9, 1, 1  =>  1, 1, 1, 1, 1
[ 'gaussian', $sigma ]

A Gaussian blur with a standard deviation of $sigma points (a positive number); each value is the weighted mean of the values up to three $sigma away. The smoothest of the smoothing steps.

transform => [ [ 'gaussian', 1 ] ]           # 0, 0, 10, 0, 0  =>  0.77, 2.57, 4.03, 2.57, 0.77
[ 'scale', $factor ], [ 'offset', $amount ]

Multiply by a number or add a number, for example to convert units.

transform => [ [ 'scale', 1000 ] ]           # 1.5, 2, 2.5  =>  1500, 2000, 2500
transform => [ [ 'offset', -273.15 ] ]       # 293.15, 300  =>  20, 26.85
[ 'clip', $low, $high ]

Limits the values to a range; either end may be undef for no limit. $low must not be greater than $high.

transform => [ [ 'clip', 0, 100 ] ]          # -5, 50, 120, 80  =>  0, 50, 100, 80
transform => [ [ 'clip', undef, 100 ] ]      # -5, 50, 120, 80  =>  -5, 50, 100, 80
abs

The absolute values.

transform => 'abs'                           # -3, 0, 2  =>  3, 0, 2
sort

Sorts the points by x. Lines and areas on numeric and time axes are sorted anyway; use it before steps that depend on the order, such as cumulative or index, on bars or points.

transform => 'sort'                          # x 3, 1, 2; y 30, 10, 20  =>  x 1, 2, 3; y 10, 20, 30
[ 'resample', $interval ], [ 'resample', $interval, $aggregate ]

Groups the points into intervals of x (3600 for hours of epoch seconds) and replaces each group by one point at the start of its interval: the mean (default), sum, min, max, first, last value or the count of points. Intervals without points are left out; a group of gaps only is a gap (a count of 0).

transform => [ [ 'resample', 10 ] ]           # x 1, 4, 12, 15, 27; y 1, 3, 5, 7, 9  =>  x 0, 10, 20; y 2, 6, 9
transform => [ [ 'resample', 10, 'sum' ] ]    # same points                          =>  x 0, 10, 20; y 4, 12, 9
transform => [ [ 'resample', 10, 'count' ] ]  # same points                          =>  x 0, 10, 20; y 2, 2, 1
[ 'downsample', $count ]

Thins a long series out to $count points (a whole number, at least 3) with the Largest-Triangle-Three-Buckets method, which keeps peaks and dips. The first and the last point stay; gaps are dropped. A series of $count points or fewer only loses its gaps. Charts draw any number of points; this only makes drawing faster.

transform => [ [ 'downsample', 3 ] ]         # 1, 5, 2, 8, 3, 4  =>  x 0, 3, 5; y 1, 8, 4
regression

The least-squares straight line through the points, at every x of the series (gaps get a value too). A series with fewer than two values becomes gaps only. The series option trend draws such a line in addition to the series.

transform => 'regression'                    # 1, 3, 2, 4  =>  1.3, 2.1, 2.9, 3.7
a code reference

Called with copies of the x and y values as two array references; returns two array references of equal length, the new x and y values. Anything else dies when the frame is drawn.

transform => [ sub ( $xs, $ys ) { return ( $xs, [ map { defined ? $_ * 2 : undef } @$ys ] ) } ]

FUNCTIONS

parse_transforms

my $steps = parse_transforms( $owner, $name, $spec );

Checks a specification and returns its steps. Dies with $owner and $name in the message for unknown names and wrong arguments.

apply_transforms

my ( $xs, $ys ) = apply_transforms( $steps, \@xs, \@ys );

Runs the steps on the values and returns new arrays.

transform_names

The names of all steps, sorted.

SEE ALSO

"Preparing data" in Term::Fabulous::Widget::XYChart, "Series and data" in Term::Fabulous::Manual::Charts, "Smooth noisy data and index it to 100 (transforms)" in Term::Fabulous::Cookbook::ChartTechniques, Term::Fabulous::Chart::Curve.