matplotlib.colors.BoundaryNorm¶

class
matplotlib.colors.
BoundaryNorm
(boundaries, ncolors, clip=False, *, extend='neither')[source]¶ Bases:
matplotlib.colors.Normalize
Generate a colormap index based on discrete intervals.
Unlike
Normalize
orLogNorm
,BoundaryNorm
maps values to integers instead of to the interval 01.Mapping to the 01 interval could have been done via piecewise linear interpolation, but using integers seems simpler, and reduces the number of conversions back and forth between integer and floating point.
Parameters:  boundariesarraylike
Monotonically increasing sequence of boundaries
 ncolorsint
Number of colors in the colormap to be used
 clipbool, optional
If clip is
True
, out of range values are mapped to 0 if they are belowboundaries[0]
or mapped toncolors  1
if they are aboveboundaries[1]
.If clip is
False
, out of range values are mapped to 1 if they are belowboundaries[0]
or mapped to ncolors if they are aboveboundaries[1]
. These are then converted to valid indices byColormap.__call__
. extend{'neither', 'both', 'min', 'max'}, default: 'neither'
Extend the number of bins to include one or both of the regions beyond the boundaries. For example, if
extend
is 'min', then the color to which the region between the first pair of boundaries is mapped will be distinct from the first color in the colormap, and by default aColorbar
will be drawn with the triangle extension on the left or lower end.
Returns:  int16 scalar or array
Notes
boundaries defines the edges of bins, and data falling within a bin is mapped to the color with the same index.
If the number of bins, including any extensions, is less than ncolors, the color index is chosen by linear interpolation, mapping the
[0, nbins  1]
range onto the[0, ncolors  1]
range.
__call__
(self, value, clip=None)[source]¶ Normalize value data in the
[vmin, vmax]
interval into the[0.0, 1.0]
interval and return it.Parameters:  value
Data to normalize.
 clipbool
If
None
, defaults toself.clip
(which defaults toFalse
).
Notes
If not already initialized,
self.vmin
andself.vmax
are initialized usingself.autoscale_None(value)
.

__init__
(self, boundaries, ncolors, clip=False, *, extend='neither')[source]¶ Parameters:  boundariesarraylike
Monotonically increasing sequence of boundaries
 ncolorsint
Number of colors in the colormap to be used
 clipbool, optional
If clip is
True
, out of range values are mapped to 0 if they are belowboundaries[0]
or mapped toncolors  1
if they are aboveboundaries[1]
.If clip is
False
, out of range values are mapped to 1 if they are belowboundaries[0]
or mapped to ncolors if they are aboveboundaries[1]
. These are then converted to valid indices byColormap.__call__
. extend{'neither', 'both', 'min', 'max'}, default: 'neither'
Extend the number of bins to include one or both of the regions beyond the boundaries. For example, if
extend
is 'min', then the color to which the region between the first pair of boundaries is mapped will be distinct from the first color in the colormap, and by default aColorbar
will be drawn with the triangle extension on the left or lower end.
Returns:  int16 scalar or array
Notes
boundaries defines the edges of bins, and data falling within a bin is mapped to the color with the same index.
If the number of bins, including any extensions, is less than ncolors, the color index is chosen by linear interpolation, mapping the
[0, nbins  1]
range onto the[0, ncolors  1]
range.

__module__
= 'matplotlib.colors'¶

__slotnames__
= []¶