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# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_cycles:
#
# Color cycles
# ============
#
# ProPlot defines **color cycles** or **discrete colormaps** as color palettes
# comprising sets of *distinct colors*. Unlike :ref:`continuous colormaps <ug_cmaps>`,
# interpolation between these colors may not make sense. Generally, color cycles are
# used with line plots, bar plots, and other distinct plot elements. Occasionally,
# they are used as colormaps for qualitative or categorical data. ProPlot's
# color cycles are registered as `~proplot.colors.DiscreteColormap`\ s,
# and can be converted into matplotlib `property cyclers
# <https://matplotlib.org/stable/tutorials/intermediate/color_cycle.html>`__
# for use with distinct plot elements using the `~proplot.constructor.Cycle`
# constructor function. `~proplot.constructor.Cycle` can also
# :ref:`extract colors <ug_cycles_new>` from `~proplot.colors.ContinuousColormap`\ s.
#
# ProPlot :ref:`adds several features <why_colormaps_cycles>` 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.


# %% [raw] raw_mimetype="text/restructuredtext" tags=[]
# .. _ug_cycles_included:
#
# Included color cycles
# ---------------------
#
# Use `~proplot.demos.show_cycles` to generate a table of the color cycles
# registered by default, loaded from the ``cycles``
# `~proplot.config.Configurator.user_folder`, and/or created with the
# `~proplot.constructor.Cycle` constructor function. To retrieve the list
# of colors associated with a registered or on-the-fly color cycle,
# simply use `~proplot.utils.get_colors`.

# %%
import proplot as pplt
fig, axs = pplt.show_cycles()


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_cycles_changing:
#
# Changing the color cycle
# ------------------------
#
# Various plotting commands like `~proplot.axes.PlotAxes.line` and
# `~proplot.axes.PlotAxes.scatter` now accept a `cycle` keyword
# passed to the `~proplot.constructor.Cycle` constructor function
# (see the :ref:`1D plotting section <ug_apply_cycle>`). To save
# your color cycle data and use it every time ProPlot is imported, simply pass
# ``save=True`` to `~proplot.constructor.Cycle`. If you want to change the global
# property cycler, pass a `~proplot.colors.DiscreteColormap` or colormap name
# to :rcraw:`cycle` or pass the result of `~proplot.constructor.Cycle`
# to :rcraw:`axes.prop_cycle` (see the :ref:`configuration guide <ug_config>`).

# %%
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)
fig.format(suptitle='Changing the color cycle')

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

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

# 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)
labels = kwargs['labels']
for i in range(data.shape[1]):
    ax.plot(data[:, i], cycle='qual1', legend='b', label=labels[i], lw=lw)
ax.format(title='Multiple plot calls')


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_cycles_new:
#
# Making color cycles
# -------------------
#
# You can make new color cycles with the `~proplot.constructor.Cycle`
# :ref:`constructor function <why_constructor>`. One great way to make cycles is by
# sampling colormaps! Just pass the colormap name to `~proplot.constructor.Cycle`,
# and optionally specify the number of samples you want to draw as the last
# positional argument -- e.g. ``pplt.Cycle('Blues', 5)``. Using e.g.
# ``ax.plot(data, cycle='Blues')`` will automatically use the same
# number of samples as the number of columns in the dataset.
#
# Positional arguments passed to `~proplot.constructor.Cycle` are interpreted
# by the `~proplot.constructor.Colormap` constructor function, and the resulting
# colormap is sampled at discrete values. To exclude near-white colors on the
# end of a colormap, pass e.g. ``left=x`` to `~proplot.constructor.Cycle`, or
# supply a plotting command with e.g. ``cycle_kw={'left': x}``. See
# the :ref:`colormaps section <ug_cmaps>` for details.
#
# In the below example, several cycles are constructed from scratch, and the
# lines are referenced with colorbars and legends. Note that ProPlot allows
# you to :ref:`generate colorbars from lists of artists <ug_cbars>`.

# %%
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')


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_cycles_other:
#
# Cycles of other properties
# --------------------------
#
# `~proplot.constructor.Cycle` can also generate cyclers that change
# properties other than color. Below, a single-color dash style cycler is
# constructed and applied to the axes locally. To apply it globally, simply
# use ``pplt.rc['axes.prop_cycle'] = cycle``.

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


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_cycles_dl:
#
# Downloading color cycles
# ------------------------
#
# There are plenty of online interactive tools for generating and testing
# color cycles, including
# `i want hue <http://tools.medialab.sciences-po.fr/iwanthue/index.php>`__,
# `coolers <https://coolors.co>`__, and
# `viz palette <https://projects.susielu.com/viz-palette>`__.
#
# To add color cycles downloaded from any of these sources, add a cycle data file
# to the ``cycles`` subfolder inside `~proplot.config.Configurator.user_folder`
# and call `~proplot.config.register_cycles` (or restart your python session). You
# can also use `~proplot.colors.DiscreteColormap.from_file` or manually pass
# discrete colormaps or file paths to `~proplot.config.register_cycles`. See
# `~proplot.colors.DiscreteColormap.from_file` for a table of valid
# data file extensions.
