# Color cycles¶

Proplot defines color cycles or discrete colormaps as color palettes comprising sets of distinct colors. Unlike continuous colormaps, interpolation between these colors may not make sense. Generally, color cycles are used with distinct plot elements like lines and bars. Occasionally, they are used with categorical data as “qualitative” colormaps. Proplot’s color cycles are registered as DiscreteColormaps, and can be easily converted into property cyclers for use with distinct plot elements using the Cycle constructor function. Cycle can also extract colors from ContinuousColormaps.

Proplot adds several features to help you use color cycles effectively in your figures. This section documents the new registered color cycles, explains how to make and modify color cycles, and shows how to apply them to your plots.

## Included color cycles¶

Use show_cycles to generate a table of registered color cycles. The table includes the default color cycles registered by proplot and “user” color cycles created with the Cycle constructor function or loaded from user_folder. If you need the list of colors associated with a registered or on-the-fly color cycle, simply use get_colors.

[1]:

import proplot as pplt
fig, axs = pplt.show_cycles(rasterized=True)


## Changing the color cycle¶

Most 1D PlotAxes commands like line and scatter accept a cycle keyword (see the 1D plotting section). This can be used to change the color cycle on-the-fly, whether plotting with successive calls to PlotAxes commands or a single call using 2D array(s) (see the 1D plotting section). To change the global property cycler, pass a DiscreteColormap or cycle name to rc.cycle or pass the result of Cycle to rc['axes.prop_cycle'] (see the configuration guide).

[2]:

import proplot as pplt
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data = (state.rand(12, 6) - 0.45).cumsum(axis=0)
kwargs = {'legend': 'b', 'labels': list('abcdef')}

# Figure
lw = 5
pplt.rc.cycle = '538'
fig = pplt.figure(refwidth=1.9, suptitle='Changing the color cycle')

# Modify the default color cycle
ax = fig.subplot(131, title='Global color cycle')
ax.plot(data, lw=lw, **kwargs)

# Pass the cycle to a plotting command
ax = fig.subplot(132, title='Local color cycle')
ax.plot(data, cycle='qual1', lw=lw, **kwargs)

# As above but draw each line individually
# Note that passing cycle=name to successive plot calls does
# not reset the cycle position if the cycle is unchanged
ax = fig.subplot(133, title='Multiple plot calls')
labels = kwargs['labels']
for i in range(data.shape[1]):
ax.plot(data[:, i], cycle='qual1', legend='b', label=labels[i], lw=lw)


## Making color cycles¶

Proplot includes tools for merging color cycles, modifying existing color cycles, making new color cycles, and saving color cycles for future use. Most of these features can be accessed via the Cycle constructor function. This command returns Cycler instances whose color properties are determined by the positional arguments (see below for changing other properties). Note that every PlotAxes command that accepts a cycle keyword passes it through this function (see the 1D plotting section).

Positional arguments passed to Cycle are interpreted by the Colormap constructor function. If the result is a DiscreteColormap, those colors are used for the resulting Cycler. If the result is a ContinuousColormap, the colormap is sampled at N discrete values – for example, pplt.Cycle('Blues', 5) selects 5 evenly-spaced values. When building color cycles on-the-fly, for example with ax.plot(data, cycle='Blues'), proplot automatically selects as many colors as there are columns in the 2D array (i.e., if we are drawing 10 lines using an array with 10 columns, proplot will select 10 evenly-spaced values from the colormap). To exclude near-white colors on the end of a colormap, pass e.g. left=x to Cycle, or supply a plotting command with e.g. cycle_kw={'left': x}. See the colormaps section for details.

In the below example, several color cycles are constructed from scratch, and the lines are referenced with colorbars and legends. Note that proplot permits generating colorbars from lists of artists.

[3]:

import proplot as pplt
import numpy as np
fig = pplt.figure(refwidth=2, share=False)
state = np.random.RandomState(51423)
data = (20 * state.rand(10, 21) - 10).cumsum(axis=0)

# Cycle from on-the-fly monochromatic colormap
ax = fig.subplot(121)
lines = ax.plot(data[:, :5], cycle='plum', lw=5)
fig.colorbar(lines, loc='b', col=1, values=np.arange(0, len(lines)))
fig.legend(lines, loc='b', col=1, labels=np.arange(0, len(lines)))
ax.format(title='Cycle from a single color')

# Cycle from registered colormaps
ax = fig.subplot(122)
cycle = pplt.Cycle('blues', 'reds', 'oranges', 15, left=0.1)
lines = ax.plot(data[:, :15], cycle=cycle, lw=5)
fig.colorbar(lines, loc='b', col=2, values=np.arange(0, len(lines)), locator=2)
fig.legend(lines, loc='b', col=2, labels=np.arange(0, len(lines)), ncols=4)
ax.format(title='Cycle from merged colormaps', suptitle='Color cycles from colormaps')


## Cycles of other properties¶

Cycle can generate Cycler instances that change line and scatter properties other than color. In the below example, a single-color line property cycler is constructed and applied to the axes locally using the line properties lw and dashes (the aliases linewidth or linewidths would also work). The resulting property cycle can be applied globally using pplt.rc['axes.prop_cycle'] = cycle.

[4]:

import proplot as pplt
import numpy as np
import pandas as pd

# Cycle that loops through 'dashes' Line2D property
cycle = pplt.Cycle(lw=3, dashes=[(1, 0.5), (1, 1.5), (3, 0.5), (3, 1.5)])

# Sample data
state = np.random.RandomState(51423)
data = (state.rand(20, 4) - 0.5).cumsum(axis=0)
data = pd.DataFrame(data, columns=pd.Index(['a', 'b', 'c', 'd'], name='label'))

# Plot data
fig, ax = pplt.subplots(refwidth=2.5, suptitle='Plot without color cycle')
obj = ax.plot(
data, cycle=cycle, legend='ll',
legend_kw={'ncols': 2, 'handlelength': 2.5}
)


To add color cycles downloaded from any of these sources, save the color data file to the cycles subfolder inside user_folder, or to a folder named proplot_cycles in the same directory as your python session or an arbitrary parent directory (see local_folders). After adding the file, call register_cycles or restart your python session. You can also use from_file or manually pass DiscreteColormap instances or file paths to register_cycles. See register_cycles for a table of recognized data file extensions.