# plastid.plotting.colors module¶

Utilities for manipulating colors and converting between representations as RGB hex strings, tuples of floats from 0.0 to 1.0, and tuples of ints from 0.0 to 255.

plastid.plotting.colors.darken(data, amt=0.1, is255=False)[source]

Darken a vector of colors by fraction amt of current intensity.

Parameters: data : matplotlib colorspec or sequence of colorspecs input color(s) amt : float, optional Percentage by which to darken r, g, and b. a remains unchanged (Default: 0.10) numpy.ndarray Lightened version of data
plastid.plotting.colors.get_rgb255(inp)[source]

Fetch r,g,b values where r,g,b are integers ranging from 0 to 255

Parameters: inp : RGB or RGBA sequence or str Can be color hex string, like “#007ADF”, a matplotlib color letter like “r”, a matplotlib color name like “black, et c.” See matplotlib.colors.colorConverter.to_rgb() :class:numpy.ndarray Numpy array of r,g,b tuples where r,g,b take integer values from 0 to 255
plastid.plotting.colors.get_str_from_rgb(inp)[source]

Converts RGB tuples of floats from between 0.0 and 1.0 to RGB hex strings of type #RRGGBB

Parameters: input : tuple Tuple of r,g,b values in range from 0.0 to 1.0 str RGB hex string of form ‘#NNNNNN’ ValueError if values are out of range
plastid.plotting.colors.get_str_from_rgb255(inp)[source]

Converts RGB tuples of ints from between 0 and 255 to RGB hex strings of type #RRGGBB

Parameters: input : tuple Tuple of r,g,b values in range from 0 to 255 str RGB hex string of form ‘#NNNNNN’ ValueError if values are out of range
plastid.plotting.colors.lighten(data, amt=0.1, is255=False)[source]

Lighten a vector of colors by fraction amt of remaining possible intensity.

New colors are calculated as:

>>> new_colors = data + amt*(1.0-data)
>>> new_colors[:,-1] = 1 # keep all alpha at 1.0

Parameters: data : matplotlib colorspec or sequence of colorspecs input color(s) amt : float, optional Percentage by which to lighten r, g, and b. a remains unchanged (Default: 0.10) is255 : bool, optional If True, rgb values in data are assumed to be tween 0 and 255 rather than 0.0 and 1.0. In this case, return values will also be between 0 and 255. numpy.ndarray Lightened version of data