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# .. _pandas: https://pandas.pydata.org
#
# .. _xarray: http://xarray.pydata.org/en/stable/
#
# .. _seaborn: https://seaborn.pydata.org
#
# .. _ug_1dplots:
#
# 1D plotting commands
# ====================
#
# UltraPlot adds :ref:`several new features <why_plotting>` to matplotlib's
# plotting commands using the intermediate :class:`~ultraplot.axes.PlotAxes` class.
# For the most part, these additions represent a *superset* of matplotlib -- if
# you are not interested, you can use the plotting commands just like you would
# in matplotlib. This section documents the features added for 1D plotting commands
# like :func:`~ultraplot.axes.PlotAxes.plot`, :func:`~ultraplot.axes.PlotAxes.scatter`,
# and :func:`~ultraplot.axes.PlotAxes.bar`.


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_1dstd:
#
# Data arguments
# --------------
#
# The treatment of data arguments passed to the 1D :class:`~ultraplot.axes.PlotAxes`
# commands is standardized. For each command, you can optionally omit
# the dependent variable coordinates, in which case they are inferred from the data
# (see :ref:`xarray and pandas integration <ug_1dintegration>`), or pass
# 2D dependent or independent variable coordinates, in which case the
# plotting command is called for each column of the 2D array(s). If coordinates
# are string labels, they are converted to indices and tick labels using
# :class:`~ultraplot.ticker.IndexLocator` and :class:`~ultraplot.ticker.IndexFormatter`.
# If coordinates are descending and the axis limits are unset, the axis
# direction is automatically reversed. All positional arguments can also be
# specified as keyword arguments (see the documentation for each plotting command).
#
# .. note::
#
#    By default, when choosing the *x* or *y* axis limits,
#    UltraPlot ignores out-of-bounds data along the other axis if it was explicitly
#    fixed by :py:meth:`~matplotlib.axes.Axes.set_xlim` or :py:meth:`~matplotlib.axes.Axes.set_ylim` (or,
#    equivalently, by passing `xlim` or `ylim` to :func:`ultraplot.axes.CartesianAxes.format`).
#    This can be useful if you wish to restrict the view along a "dependent" variable
#    axis within a large dataset. To disable this feature, pass ``inbounds=False`` to
#    the plotting command or set :rcraw:`axes.inbounds` to ``False`` (see also
#    the :rcraw:`cmap.inbounds` setting and the :ref:`user guide <ug_2dstd>`).

# %%
import numpy as np

import ultraplot as uplt

N = 5
state = np.random.RandomState(51423)
with uplt.rc.context({"axes.prop_cycle": uplt.Cycle("Grays", N=N, left=0.3)}):
    # Sample data
    x = np.linspace(-5, 5, N)
    y = state.rand(N, 5)
    fig = uplt.figure(share=False, suptitle="Standardized input demonstration")

    # Plot by passing both x and y coordinates
    ax = fig.subplot(121, title="Manual x coordinates")
    ax.area(x, -1 * y / N, stack=True)
    ax.bar(x, y, linewidth=0, alpha=1, width=0.8)
    ax.plot(x, y + 1, linewidth=2)
    ax.scatter(x, y + 2, marker="s", markersize=5**2)

    # Plot by passing just y coordinates
    # Default x coordinates are inferred from DataFrame,
    # inferred from DataArray, or set to np.arange(0, y.shape[0])
    ax = fig.subplot(122, title="Auto x coordinates")
    ax.area(-1 * y / N, stack=True)
    ax.bar(y, linewidth=0, alpha=1)
    ax.plot(y + 1, linewidth=2)
    ax.scatter(y + 2, marker="s", markersize=5**2)
    fig.format(xlabel="xlabel", ylabel="ylabel")

# %%
import numpy as np

import ultraplot as uplt

# Sample data
cycle = uplt.Cycle("davos", right=0.8)
state = np.random.RandomState(51423)
N, M = 400, 20
xmax = 20
x = np.linspace(0, 100, N)
y = 100 * (state.rand(N, M) - 0.42).cumsum(axis=0)

# Plot the data
fig = uplt.figure(refwidth=2.2, share=False)
axs = fig.subplots([[0, 1, 1, 0], [2, 2, 3, 3]], wratios=(2, 1, 1, 2))
axs[0].axvspan(
    0,
    xmax,
    zorder=3,
    edgecolor="red",
    facecolor=uplt.set_alpha("red", 0.2),
)
for i, ax in enumerate(axs):
    inbounds = i == 1
    title = f"Restricted xlim inbounds={inbounds}"
    title += " (default)" if inbounds else ""
    ax.format(
        xmax=(None if i == 0 else xmax),
        title=("Default xlim" if i == 0 else title),
    )
    ax.plot(x, y, cycle=cycle, inbounds=inbounds)
fig.format(
    xlabel="xlabel",
    ylabel="ylabel",
    suptitle="Default ylim restricted to in-bounds data",
)

# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_1dintegration:
#
# Pandas and xarray integration
# -----------------------------
#
# The 1D :class:`~ultraplot.axes.PlotAxes` commands recognize `pandas`_
# and `xarray`_ data structures. If you omit dependent variable coordinates,
# the commands try to infer them from the :class:`pandas.Series`, :class:`pandas.DataFrame`,
# or :class:`xarray.DataArray`. If you did not explicitly set the *x* or *y* axis label
# or :ref:`legend or colorbar <ug_guides_loc>` label(s), the commands
# try to retrieve them from the :class:`pandas.DataFrame` or :class:`xarray.DataArray`.
# The commands also recognize :class:`pint.Quantity` structures and apply
# unit string labels with formatting specified by :rc:`unitformat`.
#
# These features restore some of the convenience you get with the builtin
# `pandas`_ and `xarray`_ plotting functions. They are also *optional* --
# installation of pandas and xarray are not required to use UltraPlot. The
# automatic labels can be disabled by setting :rcraw:`autoformat` to ``False``
# or by passing ``autoformat=False`` to any plotting command.
#
# .. note::
#
#    For every plotting command, you can pass a :class:`~xarray.Dataset`, :class:`~pandas.DataFrame`,
#    or `dict` to the `data` keyword with strings as data arguments instead of arrays
#    -- just like matplotlib. For example, ``ax.plot('y', data=dataset)`` and
#    ``ax.plot(y='y', data=dataset)`` are translated to ``ax.plot(dataset['y'])``.
#    This is the preferred input style for most `seaborn`_ plotting commands.
#    Also, if you pass a :class:`pint.Quantity` or :class:`~xarray.DataArray`
#    containing a :class:`pint.Quantity`, UltraPlot will automatically call
#    :func:`~pint.UnitRegistry.setup_matplotlib` so that the axes become unit-aware.

# %%
import numpy as np
import pandas as pd
import xarray as xr

# DataArray
state = np.random.RandomState(51423)
data = np.sin(np.linspace(0, 2 * np.pi, 20))[:, None] + state.rand(20, 8).cumsum(axis=1)
coords = {
    "x": xr.DataArray(
        np.linspace(0, 1, 20),
        dims=("x",),
        attrs={"long_name": "distance", "units": "km"},
    ),
    "num": xr.DataArray(
        np.arange(0, 80, 10), dims=("num",), attrs={"long_name": "parameter"}
    ),
}
da = xr.DataArray(
    data, dims=("x", "num"), coords=coords, name="energy", attrs={"units": "kJ"}
)

# DataFrame
data = (np.cos(np.linspace(0, 2 * np.pi, 20)) ** 4)[:, None] + state.rand(20, 5) ** 2
ts = pd.date_range("1/1/2000", periods=20)
df = pd.DataFrame(data, index=ts, columns=["foo", "bar", "baz", "zap", "baf"])
df.name = "data"
df.index.name = "date"
df.columns.name = "category"

# %%
import ultraplot as uplt

fig = uplt.figure(share=False, suptitle="Automatic subplot formatting")

# Plot DataArray
cycle = uplt.Cycle("dark blue", space="hpl", N=da.shape[1])
ax = fig.subplot(121)
ax.scatter(da, cycle=cycle, lw=3, colorbar="t", colorbar_kw={"locator": 20})

# Plot Dataframe
cycle = uplt.Cycle("dark green", space="hpl", N=df.shape[1])
ax = fig.subplot(122)
ax.plot(df, cycle=cycle, lw=3, legend="t", legend_kw={"frame": False})


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_apply_cycle:
#
# Changing the property cycle
# ---------------------------
#
# It is often useful to create custom `property cycles
# <https://matplotlib.org/stable/tutorials/intermediate/color_cycle.html#sphx-glr-tutorials-intermediate-color-cycle-py>`__
# on-the-fly and use different property cycles for different plot elements.
# You can do so using the `cycle` and `cycle_kw` keywords, available
# with most 1D :class:`~ultraplot.axes.PlotAxes` commands. `cycle` and `cycle_kw` are
# passed to the :class:`~ultraplot.constructor.Cycle` :ref:`constructor function
# <why_constructor>`, and the resulting property cycle is used for the plot. You
# can specify `cycle` once with 3D input data (in which case each column is
# plotted in succession according to the property cycle) or call a plotting
# command multiple times with the same `cycle` argument (the property
# cycle is not reset). You can also disable property cycling with ``cycle=False``,
# ``cycle='none'``, or ``cycle=()`` and re-enable the default property cycle with
# ``cycle=True`` (note that as usual, you can also simply override the property cycle
# with relevant artist keywords like `color`). For more information on property cycles,
# see the :ref:`color cycles section <ug_cycles>` and `this matplotlib tutorial
# <https://matplotlib.org/stable/tutorials/intermediate/color_cycle.html#sphx-glr-tutorials-intermediate-color-cycle-py>`__.

# %%
import numpy as np

import ultraplot as uplt

# Sample data
M, N = 50, 5
state = np.random.RandomState(51423)
data1 = (state.rand(M, N) - 0.48).cumsum(axis=1).cumsum(axis=0)
data2 = (state.rand(M, N) - 0.48).cumsum(axis=1).cumsum(axis=0) * 1.5
data1 += state.rand(M, N)
data2 += state.rand(M, N)

with uplt.rc.context({"lines.linewidth": 3}):
    # Use property cycle for columns of 2D input data
    fig = uplt.figure(share=False)
    ax = fig.subplot(121, title="Single plot call")
    ax.plot(
        2 * data1 + data2,
        cycle="black",  # cycle from monochromatic colormap
        cycle_kw={"ls": ("-", "--", "-.", ":")},
    )

    # Use property cycle with successive plot() calls
    ax = fig.subplot(122, title="Multiple plot calls")
    for i in range(data1.shape[1]):
        ax.plot(data1[:, i], cycle="Reds", cycle_kw={"N": N, "left": 0.3})
    for i in range(data1.shape[1]):
        ax.plot(data2[:, i], cycle="Blues", cycle_kw={"N": N, "left": 0.3})
    fig.format(xlabel="xlabel", ylabel="ylabel", suptitle="On-the-fly property cycles")

# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_lines:
#
# Line plots
# ----------
#
# Line plots can be drawn with :func:`~ultraplot.axes.PlotAxes.plot` or
# :func:`~ultraplot.axes.PlotAxes.plotx` (or their aliases, :func:`~ultraplot.axes.PlotAxes.line`
# or :func:`~ultraplot.axes.PlotAxes.linex`). For the ``x`` commands, positional
# arguments are interpreted as *x* coordinates or (*y*, *x*) pairs. This is analogous
# to :func:`~ultraplot.axes.PlotAxes.barh` and :func:`~ultraplot.axes.PlotAxes.fill_betweenx`.
# Also, the default *x* bounds for lines drawn with :func:`~ultraplot.axes.PlotAxes.plot`
# and *y* bounds for lines drawn with :func:`~ultraplot.axes.PlotAxes.plotx` are now
# "sticky", i.e. there is no padding between the lines and axes edges by default.
#
# Step and stem plots can be drawn with :func:`~ultraplot.axes.PlotAxes.step`,
# :func:`~ultraplot.axes.PlotAxes.stepx`, :func:`~ultraplot.axes.PlotAxes.stem`, and
# :func:`~ultraplot.axes.PlotAxes.stemx`. Plots of parallel vertical and horizontal
# lines can be drawn with :func:`~ultraplot.axes.PlotAxes.vlines` and
# :func:`~ultraplot.axes.PlotAxes.hlines`. You can have different colors for "negative" and
# "positive" lines using ``negpos=True`` (see :ref:`below <ug_negpos>` for details).

# %%
import numpy as np

import ultraplot as uplt

state = np.random.RandomState(51423)
gs = uplt.GridSpec(nrows=3, ncols=2)
fig = uplt.figure(refwidth=2.2, span=False, share="labels")

# Vertical vs. horizontal
data = (state.rand(10, 5) - 0.5).cumsum(axis=0)
ax = fig.subplot(gs[0], title="Dependent x-axis")
ax.line(data, lw=2.5, cycle="seaborn")
ax = fig.subplot(gs[1], title="Dependent y-axis")
ax.linex(data, lw=2.5, cycle="seaborn")

# Vertical lines
gray = "gray7"
data = state.rand(20) - 0.5
ax = fig.subplot(gs[2], title="Vertical lines")
ax.area(data, color=gray, alpha=0.2)
ax.vlines(data, negpos=True, lw=2)

# Horizontal lines
ax = fig.subplot(gs[3], title="Horizontal lines")
ax.areax(data, color=gray, alpha=0.2)
ax.hlines(data, negpos=True, lw=2)

# Step
ax = fig.subplot(gs[4], title="Step plot")
data = state.rand(20, 4).cumsum(axis=1).cumsum(axis=0)
cycle = ("gray6", "blue7", "red7", "gray4")
ax.step(data, cycle=cycle, labels=list("ABCD"), legend="ul", legend_kw={"ncol": 2})

# Stems
ax = fig.subplot(gs[5], title="Stem plot")
data = state.rand(20)
ax.stem(data)
fig.format(suptitle="Line plots demo", xlabel="xlabel", ylabel="ylabel")


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_scatter:
#
# Scatter plots
# -------------
#
# The :func:`~ultraplot.axes.PlotAxes.scatter` command now permits omitting *x*
# coordinates and accepts 2D *y* coordinates, just like :func:`~ultraplot.axes.PlotAxes.plot`.
# As with :func:`~ultraplot.axes.PlotAxes.plotx`, the :func:`~ultraplot.axes.PlotAxes.scatterx`
# command is just like :func:`~ultraplot.axes.PlotAxes.scatter`, except positional
# arguments are interpreted as *x* coordinates and (*y*, *x*) pairs.
# :func:`~ultraplot.axes.PlotAxes.scatter` also now accepts keywords
# that look like :func:`~ultraplot.axes.PlotAxes.plot` keywords (e.g., `color` instead of
# `c` and `markersize` instead of `s`). This way, :func:`~ultraplot.axes.PlotAxes.scatter`
# can be used to simply "plot markers, not lines" without changing the input
# arguments relative to :func:`~ultraplot.axes.PlotAxes.plot`.
#
# The property cycler used by :func:`~ultraplot.axes.PlotAxes.scatter` can be changed
# using the `cycle` keyword argument, and unlike matplotlib it can include
# properties like `marker` and `markersize`. The colormap `cmap` and normalizer
# `norm` used with the optional `c` color array are now passed through the
# :class:`~ultraplot.constructor.Colormap` and :class:`~ultraplot.constructor.Norm` constructor
# functions.

# .. important::
#
#    In matplotlib, arrays passed to the marker size keyword `s` always represent the
#    area in units ``points ** 2``. In UltraPlot, arrays passed to `s` are scaled so
#    that the minimum data value has the area ``1`` while the maximum data value
#    has the area :rcraw:`lines.markersize` squared. These minimum and maximum marker
#    sizes can also be specified manually with the `smin` and `smax` keywords,
#    analogous to `vmin` and `vmax` used to scale the color array `c`. This feature
#    can be disabled by passing ``absolute_size=True`` to :func:`~ultraplot.axes.PlotAxes.scatter`
#    or :func:`~ultraplot.axes.PlotAxes.scatterx`. This is done automatically when `seaborn`_
#    calls :func:`~ultraplot.axes.PlotAxes.scatter` internally.

# %%
import numpy as np
import pandas as pd

import ultraplot as uplt

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

# Figure
gs = uplt.GridSpec(ncols=2, nrows=2)
fig = uplt.figure(refwidth=2.2, share="labels", span=False)

# Vertical vs. horizontal
ax = fig.subplot(gs[0], title="Dependent x-axis")
ax.scatter(data, cycle="538")
ax = fig.subplot(gs[1], title="Dependent y-axis")
ax.scatterx(data, cycle="538")

# Scatter plot with property cycler
ax = fig.subplot(gs[2], title="With property cycle")
obj = ax.scatter(
    x,
    data,
    legend="ul",
    legend_kw={"ncols": 2},
    cycle="Set2",
    cycle_kw={"m": ["x", "o", "x", "o"], "ms": [5, 10, 20, 30]},
)

# Scatter plot with colormap
ax = fig.subplot(gs[3], title="With colormap")
data = state.rand(2, 100)
obj = ax.scatter(
    *data,
    s=state.rand(100),
    smin=6,
    smax=60,
    marker="o",
    c=data.sum(axis=0),
    cmap="maroon",
    colorbar="lr",
    colorbar_kw={"label": "label"},
)
fig.format(suptitle="Scatter plot demo", xlabel="xlabel", ylabel="ylabel")

# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_parametric:
#
# Parametric plots
# ----------------
#
# Parametric plots can be drawn using the new :func:`~ultraplot.axes.PlotAxes.parametric`
# command. This creates :class:`~matplotlib.collections.LineCollection`\ s that map
# individual line segments to individual colors, where each segment represents a
# "parametric" coordinate (e.g., time). The parametric coordinates are specified with
# a third positional argument or with the keywords `c`, `color`, `colors` or `values`.
# Representing parametric coordinates with colors instead of text labels can be
# cleaner. The below example makes a simple :func:`~ultraplot.axes.PlotAxes.parametric`
# plot with a colorbar indicating the parametric coordinate.

# %%
import numpy as np
import pandas as pd

import ultraplot as uplt

gs = uplt.GridSpec(ncols=2, wratios=(2, 1))
fig = uplt.figure(figwidth="16cm", refaspect=(2, 1), share=False)
fig.format(suptitle="Parametric plots demo")
cmap = "IceFire"

# Sample data
state = np.random.RandomState(51423)
N = 50
x = (state.rand(N) - 0.52).cumsum()
y = state.rand(N)
c = np.linspace(-N / 2, N / 2, N)  # color values
c = pd.Series(c, name="parametric coordinate")

# Parametric line with smooth gradations
ax = fig.subplot(gs[0])
m = ax.parametric(
    x,
    y,
    c,
    interp=10,
    capstyle="round",
    joinstyle="round",
    lw=7,
    cmap=cmap,
    colorbar="b",
    colorbar_kw={"locator": 5},
)
ax.format(xlabel="xlabel", ylabel="ylabel", title="Line with smooth gradations")

# Sample data
N = 12
radii = np.linspace(1, 0.2, N + 1)
angles = np.linspace(0, 4 * np.pi, N + 1)
x = radii * np.cos(1.4 * angles)
y = radii * np.sin(1.4 * angles)
c = np.linspace(-N / 2, N / 2, N + 1)

# Parametric line with stepped gradations
ax = fig.subplot(gs[1])
m = ax.parametric(x, y, c, cmap=cmap, lw=15)
ax.format(
    xlim=(-1, 1),
    ylim=(-1, 1),
    title="Step gradations",
    xlabel="cosine angle",
    ylabel="sine angle",
)
ax.colorbar(m, loc="b", locator=2, label="parametric coordinate")


# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_bar:
#
# Bar plots and area plots
# ------------------------
#
# The :func:`~ultraplot.axes.PlotAxes.bar` and :func:`~ultraplot.axes.PlotAxes.barh` commands
# apply default *x* or *y* coordinates if you failed to provide them explicitly
# and can *group* or *stack* successive columns of data if you pass 2D arrays instead
# of 1D arrays -- just like `pandas`_. When bars are grouped, their widths and
# positions are adjusted according to the number of bars in the group. Grouping
# is the default behavior and stacking can be enabled with ``stack=True``
# or ``stacked=True``.
#
# The :func:`~ultraplot.axes.PlotAxes.fill_between` and :func:`~ultraplot.axes.PlotAxes.fill_betweenx`
# commands have the new shorthands :func:`~ultraplot.axes.PlotAxes.area`
# and :func:`~ultraplot.axes.PlotAxes.areax`. Similar to :func:`~ultraplot.axes.PlotAxes.bar` and
# :func:`~ultraplot.axes.PlotAxes.barh`, they apply default *x* coordinates if you failed
# to provide them explicitly, and can *overlay* or *stack* successive columns of
# data if you pass 2D arrays instead of 1D arrays -- just like `pandas`_. Overlaying
# is the default behavior but stacking can be enabled with ``stack=True`` or
# ``stacked=True``. Also note the default *x* bounds for shading drawn with
# :func:`~ultraplot.axes.PlotAxes.area` and *y* bounds for shading drawn with
# :func:`~ultraplot.axes.PlotAxes.areax` is now "sticky", i.e. there is no padding
# between the shading and axes edges by default.

# .. important::
#
#    In matplotlib, bar widths for horizontal :func:`~matplotlib.axes.Axes.barh` plots
#    are expressed with the `height` keyword. In UltraPlot, bar widths are always
#    expressed with the `width` keyword. Note that bar widths can also be passed
#    as a third positional argument.
#    Additionally, matplotlib bar widths are always expressed in data units,
#    while UltraPlot bar widths are expressed in step size-relative units by
#    default. For example, ``width=1`` with a dependent coordinate step
#    size of ``2`` fills 100% of the space between each bar rather than 50%. This
#    can be disabled by passing ``absolute_width=True`` to :func:`~ultraplot.axes.PlotAxes.bar`
#    or :func:`~ultraplot.axes.PlotAxes.barh`. This is done automatically when `seaborn`_ calls
#    :func:`~ultraplot.axes.PlotAxes.bar` or :func:`~ultraplot.axes.PlotAxes.barh` internally.

# %%
import numpy as np
import pandas as pd

import ultraplot as uplt

# Sample data
state = np.random.RandomState(51423)
data = state.rand(5, 5).cumsum(axis=0).cumsum(axis=1)[:, ::-1]
data = pd.DataFrame(
    data,
    columns=pd.Index(np.arange(1, 6), name="column"),
    index=pd.Index(["a", "b", "c", "d", "e"], name="row idx"),
)

# Figure
uplt.rc.abc = "a."
uplt.rc.titleloc = "l"
gs = uplt.GridSpec(nrows=2, hratios=(3, 2))
fig = uplt.figure(refaspect=2, refwidth=4.8, share=False)

# Side-by-side bars
ax = fig.subplot(gs[0], title="Side-by-side")
obj = ax.bar(
    data, cycle="Reds", edgecolor="red9", colorbar="ul", colorbar_kw={"frameon": False}
)
ax.format(xlocator=1, xminorlocator=0.5, ytickminor=False)

# Stacked bars
ax = fig.subplot(gs[1], title="Stacked")
obj = ax.barh(
    data.iloc[::-1, :],
    cycle="Blues",
    edgecolor="blue9",
    legend="ur",
    stack=True,
)
fig.format(grid=False, suptitle="Bar plot demo")
uplt.rc.reset()

# %%
import numpy as np

import ultraplot as uplt

# Sample data
state = np.random.RandomState(51423)
data = state.rand(5, 3).cumsum(axis=0)
cycle = ("gray3", "gray5", "gray7")

# Figure
uplt.rc.abc = "a."
uplt.rc.titleloc = "l"
fig = uplt.figure(refwidth=2.3, share=False)

# Overlaid area patches
ax = fig.subplot(121, title="Fill between columns")
ax.area(
    np.arange(5),
    data,
    data + state.rand(5)[:, None],
    cycle=cycle,
    alpha=0.7,
    legend="uc",
    legend_kw={"center": True, "ncols": 2, "labels": ["z", "y", "qqqq"]},
)

# Stacked area patches
ax = fig.subplot(122, title="Stack between columns")
ax.area(
    np.arange(5),
    data,
    stack=True,
    cycle=cycle,
    alpha=0.8,
    legend="ul",
    legend_kw={"center": True, "ncols": 2, "labels": ["z", "y", "qqqq"]},
)
fig.format(grid=False, xlabel="xlabel", ylabel="ylabel", suptitle="Area plot demo")
uplt.rc.reset()

# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_negpos:
#
# Negative and positive colors
# ----------------------------
#
# You can use different colors for "negative" and
# "positive" data by passing ``negpos=True`` to any of the
# :func:`~ultraplot.axes.PlotAxes.fill_between`, :func:`~ultraplot.axes.PlotAxes.fill_betweenx`
# (shorthands :func:`~ultraplot.axes.PlotAxes.area`, :func:`~ultraplot.axes.PlotAxes.areax`),
# :func:`~ultraplot.axes.PlotAxes.vlines`, :func:`~ultraplot.axes.PlotAxes.hlines`,
# :func:`~ultraplot.axes.PlotAxes.bar`, or :func:`~ultraplot.axes.PlotAxes.barh` commands.
# The default negative and positive colors are controlled with :rcraw:`negcolor` and
# :rcraw:`poscolor` but the colors can be modified for particular plots by passing
# ``negcolor=color`` and ``poscolor=color`` to the :class:`~ultraplot.axes.PlotAxes` commands.

# %%
import numpy as np

import ultraplot as uplt

# Sample data
state = np.random.RandomState(51423)
data = 4 * (state.rand(40) - 0.5)

# Figure
uplt.rc.abc = "a."
uplt.rc.titleloc = "l"
fig, axs = uplt.subplots(nrows=3, refaspect=2, figwidth=5)
axs.format(
    xmargin=0,
    xlabel="xlabel",
    ylabel="ylabel",
    grid=True,
    suptitle="Positive and negative colors demo",
)
for ax in axs:
    ax.axhline(0, color="k", linewidth=1)  # zero line

# Line plot
ax = axs[0]
ax.vlines(data, linewidth=3, negpos=True)
ax.format(title="Line plot")

# Bar plot
ax = axs[1]
ax.bar(data, width=1, negpos=True, edgecolor="k")
ax.format(title="Bar plot")

# Area plot
ax = axs[2]
ax.area(data, negpos=True, lw=0.5, edgecolor="k")
ax.format(title="Area plot")

# Reset title styles changed above
uplt.rc.reset()
