Custom discrete shape scale
Shapes that make up the palette. See
matplotlib.markers. for list of all possible
shapes. The values will be matched with the
of the scale or the
breaks if provided.
If it is a dict then it should map data values to shapes.
Major break points. Alternatively, a callable that takes a tuple of limits and returns a list of breaks. Default is to automatically calculate the breaks.
Multiplicative and additive expansion constants that determine how the scale is expanded. If specified must be of length 2 or 4. Specifically the values are in this order:
(mul, add) (mul_low, add_low, mul_high, add_high)
(0, 0)- Do not expand.
(0, 1)- Expand lower and upper limits by 1 unit.
(1, 0)- Expand lower and upper limits by 100%.
(0, 0, 0, 0)- Do not expand, as
(0, 0, 0, 1)- Expand upper limit by 1 unit.
(0, 1, 0.1, 0)- Expand lower limit by 1 unit and upper limit by 10%.
(0, 0, 0.1, 2)- Expand upper limit by 10% plus 2 units.
If not specified, suitable defaults are chosen.
Name used as the label of the scale. This is what shows up as the axis label or legend title. Suitable defaults are chosen depending on the type of scale.
str. Labels at the breaks.
Alternatively, a callable that takes an array_like of
break points as input and returns a list of strings.
Limits of the scale. Most commonly, these are the min & max values for the scales. For scales that deal with categoricals, these may be a subset or superset of the categories.
Function to map data points onto the scale. Most scales define their own palettes.
str. Aesthetics covered by the
scale. These are defined by each scale and the
user should probably not change them. Have fun.
Whether to drop unused categories from the scale
True translate missing values and show them.
False remove missing values. Default value is
na_translate=True, what aesthetic value should be
assigned to the missing values. This parameter does not
apply to position scales where
nan is always placed
on the right.