colorschemes

Color schemes and gradients.

dms_struct.colorschemes.CBBPALETTE = ('#000000', '#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7')

color-blind safe palette with black.

From http://bconnelly.net/2013/10/creating-colorblind-friendly-figures

Type

tuple

dms_struct.colorschemes.CBPALETTE = ('#999999', '#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7')

color-blind safe palette with gray.

From http://bconnelly.net/2013/10/creating-colorblind-friendly-figures

Type

tuple

class dms_struct.colorschemes.ValueToColorMap(minvalue, maxvalue, cmap='viridis')

Bases: object

Map numerical values to color gradient.

Parameters

• minvalue (float) – Color map starts at this value.

• maxvalue (float) – Color map ends at this value.

• cmap (str or matplotlib.colors.Colormap) – Name of matplotlib colormap, or an actual Colormap object. You can also use the wider set of color maps from palettable, such as by providing palettable.sequential.cmocean.Dense_20.mpl_colormap.

cmap

Color map.

Type

matplotlib.colors.Colormap

minvalue

Color map starts at this value.

Type

float

maxvalue

Color map ends at this value.

Type

float

Examples

Make a data frame with some values, and two color maps (one with default ‘viridis’ and another with ‘cividis’) covering value range in data frame:

>>> import pandas as pd
>>> from dms_struct.colorschemes import ValueToColorMap
>>>
>>> df = pd.DataFrame({'value': [0, 1, 2, 1, 3, 0]})
>>> map1 = ValueToColorMap(df['value'].min(),
...                        df['value'].max())
>>> map2 = ValueToColorMap(df['value'].min(),
...                        df['value'].max(),
...                        cmap='cividis')

Map values to colors using ValueToColorMap.val_to_color():

>>> df = (df
...       .assign(color=lambda x: x['value'].apply(map1.val_to_color),
...               color2=lambda x: x['value'].apply(map2.val_to_color),
...               )
...       )
>>> df
   value    color   color2
0      0  #440154  #00224d
1      1  #30678d  #575d6d
2      2  #35b778  #a59b73
3      1  #30678d  #575d6d
4      3  #fde724  #fde737
5      0  #440154  #00224d

Draw scale bars:

>>> fig1, ax1 = map1.scale_bar(orientation='horizontal',
...                          label='viridis scale')

()

>>> fig2, ax2 = map2.scale_bar(orientation='vertical',
...                          label='cividis scale')

()

scale_bar(*, orientation='vertical', ax=None, label=None, axisfontscale=1, low_high_ticks_only=False)

Draw a scale bar for the value-to-color map.

Parameters

• orientation ({'horizontal', 'vertical'}) – Direction that scale bar drawn is drawn.

• ax (None or matplotlib.axes.Axes) – If specified, draw scale bar on this axis. Otherwise create new axes.

• label (None or str) – Label for scale bar.

• axisfontscale (float) – Scale font size by this much.

• low_high_ticks_only (bool) – Rather than showing numerical ticks, just indicate low and high.

Returns

Figure and axis on which the color bar is drawn.

Return type

(matplotlib.figure.Figure, matplotlib.axes.Axes)

val_to_color(values, *, return_color_as='rgb_hex_code')

Map numerical values between minvalue and maxvalue to colors.

Parameters

• values (number or array-like of numbers) – Values to map to colors

• return_color_as ({'rgb_hex_code', 'rgb_triple'}) – Return color as RGB hex code (e.g., ‘#FF0000’) or triple of numbers (e.g., [255, 0, 0]).

Returns

Either str or length-3 arrays depending on return_color_as. If values is single value, return single value; otherwise array.

Return type

single str or length-3 numpy array, or array of them