jb::binning_strategy_concept Class Reference

The interface that must be met by the binning_strategy in jb::histogram. More...

Public Member Functions
sample_type	histogram_min () const
	The minimum value that will be included in the histogram. More...
sample_type	histogram_max () const
	The smallest value that will not be included in the histogram. More...
sample_type	theoretical_min () const
	The minimum value that can be attained by the samples. More...
sample_type	theoretical_max () const
	The minimum value that can be attained by the samples. More...
std::size_t	sample2bin (sample_type const &x) const
	Map a variable type into a bin number. More...
sample_type	bin2sample (std::size_t i) const
	Map a bin number into the start of its range. More...
sample_type	interpolate (sample_type x_a, sample_type x_b, double y_a, double s, double q) const
	Interpolate a x-coordinate given endpoints and y values. More...

Public Attributes
typedef	sample_type
	The type of the samples. More...

Detailed Description

The interface that must be met by the binning_strategy in jb::histogram.

Definition at line 5 of file binning_strategy_concept.dox.

Member Function Documentation

bin2sample()

sample_type jb::binning_strategy_concept::bin2sample

(

std::size_t

i

)

const

Map a bin number into the start of its range.

histogram_max()

sample_type jb::binning_strategy_concept::histogram_max

(

)

const

The smallest value that will not be included in the histogram.

Any sample with such a value or higher is included in the overflow bin.

Notice that the histogram only includes the samples such that: histogram_min() <= sample < histogram_max().

histogram_min()

sample_type jb::binning_strategy_concept::histogram_min

(

)

const

The minimum value that will be included in the histogram.

Any sample with a smaller value is included in the underflow bin.

interpolate()

sample_type jb::binning_strategy_concept::interpolate	(	sample_type	x_a,
		sample_type	x_b,
		double	y_a,
		double	s,
		double	q
	)		const

Interpolate a x-coordinate given endpoints and y values.

Given the two endpoints (x_a,y_a) and (x_b,y_a + s) of a line, and a y value such that y is the [y_a,y_a + s] range, compute the value of \(x\) that most closely satisfies:

\[ {{(x - x_a)} \over {(y - y_a)}} = {{(x_b - x_a)} \over {s}} \]

This function is used to encapsulate rounding differences for different types of samples.

Parameters

x_a	The x coordinate for the a point.
y_a	The y coordinate for the a point.
x_b	The x coordinate for the b point.
s	The difference between y_b and y_a.
q	A value in the [y_a,y_b] range.

Returns

The closest solution to the equation shown above for values in the sample_type domain.

sample2bin()

std::size_t jb::binning_strategy_concept::sample2bin

(

sample_type const &

x

)

const

Map a variable type into a bin number.

theoretical_max()

sample_type jb::binning_strategy_concept::theoretical_max

(

)

const

The minimum value that can be attained by the samples.

For example, 86400 if all samples are "secons within a day", or simply std::numberic_limits<sample_type>::max().

theoretical_min()

sample_type jb::binning_strategy_concept::theoretical_min

(

)

const

The minimum value that can be attained by the samples.

For example, 0 if all samples are known to be positive, or maybe std::numberic_limits<sample_type>::min().

Member Data Documentation

sample_type

typedef jb::binning_strategy_concept::sample_type

The type of the samples.

Must be comparable using operator< and operator<=.

Definition at line 12 of file binning_strategy_concept.dox.

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The documentation for this class was generated from the following file:

• doc/binning_strategy_concept.dox