The word scale derives from the Latin scala, or ladder. The Latin meaning is particularly apt for graphics. The visual representation of a scale — an axis with ticks — looks like a ladder. Scales are the types of functions we use to map varsets to dimensions. At first glance, it would seem that constructing a scale is simply a matter of selecting a range for our numbers and intervals to mark ticks. There is more involved, however. Scales measure the contents of a frame. They determine how we perceive the size, shape, and location of graphics. Choosing a scale (even a default decimal interval scale) requires us to think about what we are measuring and the meaning of our measurements. Ultimately, that choice determines how we interpret a graphic.
<script src="./Scale.js"></script>for now it's require nothing, but it's used almost in any visualization you want to use.
var line = Scale
.Domain([0,450])
.Range([0,60])
.Reverse()
.Clamp(true)
.Linear()- Continuous (Linear, Power, Log, Identity, Time)
- Sequential
- Quantize
- Quantile
- Threshold
- Ordinal (Band, Point, Category)
Continuous scales map a continuous, quantitative input domain to a continuous output range. If the range is also numeric, the mapping may be inverted. A continuous scale is not constructed directly; instead, try a linear, power, log, identity, time or sequential color scale.
Given a value from the domain, returns the corresponding value from the range. If the given value is outside the domain, and clamping is not enabled, the mapping may be extrapolated such that the returned value is outside the range. For example, to apply a position encoding:
var x = Scale
.domain([10, 130])
.range([0, 960])
.Linear();
x['scale'](20); // 80
x['scale'](50); // 320# continuous.domain([domain]) <>
# continuous.range([range]) <>
# continuous.rangeRound([range]) <>
# continuous.ticks([count])
# continuous.tickFormat([count[, specifier]]) <>
Constructs a new continuous scale with the unit domain [0, 1], the unit range [0, 1], the default interpolator and clamping disabled. Linear scales are a good default choice for continuous quantitative data because they preserve proportional differences. Each range value y can be expressed as a function of the domain value x: y = mx + b.
