_create_lookup_table(N, data, gamma=1.0)
This assumes a mapping $f : [0, 1] \rightarrow [0, 1]$ . The returned data is an array of N values $y = f(x)$ where x is sampled from [0, 1].
By default (gamma = 1) x is equidistantly sampled from [0, 1]. The gamma correction factor $\gamma$ distorts this equidistant sampling by $x \rightarrow x^\gamma$ .
This function is internally used for .LinearSegmentedColormap
.
The number of elements of the created lookup table; at least 1.
Defines the mapping $f$ .
If a (M, 3) array-like, the rows define values (x, y0, y1). The x values must start with x=0, end with x=1, and all x values be in increasing order.
A value between $x_i$ and $x_{i+1}$ is mapped to the range $y^1_{i-1} \ldots y^0_i$ by linear interpolation.
For the simple case of a y-continuous mapping, y0 and y1 are identical.
The two values of y are to allow for discontinuous mapping functions. E.g. a sawtooth with a period of 0.2 and an amplitude of 1 would be:
[(0, 1, 0), (0.2, 1, 0), (0.4, 1, 0), ..., [(1, 1, 0)]
In the special case of N == 1
, by convention the returned value is y0 for x == 1.
If data is a callable, it must accept and return numpy arrays:
data(x : ndarray) -> ndarray
and map values between 0 - 1 to 0 - 1.
Gamma correction factor for input distribution x of the mapping.
See also https://en.wikipedia.org/wiki/Gamma_correction.
The lookup table where lut[x * (N-1)]
gives the closest value for values of x between 0 and 1.
Create an N -element 1D lookup table.
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