.. currentmodule:: altair

.. _user-guide-rule-marks:

Rule
~~~~
The ``rule`` mark represents each data point as a line segment. It can be used in two ways. First, as a line segment that spans the complete width or height of a view. Second, a rule can be used to draw a line segment between two positions.

Rule Mark Properties
--------------------
.. altair-plot::
    :hide-code:
    :div_class: properties-example

    import altair as alt
    import pandas as pd

    x_slider = alt.binding_range(min=1, max=100, step=1)
    x_var = alt.param(bind=x_slider, value=35, name="x")

    x2_slider = alt.binding_range(min=1, max=100, step=1)
    x2_var = alt.param(bind=x2_slider, value=75, name="x2")

    y_slider = alt.binding_range(min=1, max=100, step=1)
    y_var = alt.param(bind=y_slider, value=25, name="y")

    y2_slider = alt.binding_range(min=1, max=100, step=1)
    y2_var = alt.param(bind=y2_slider, value=75, name="y2")

    strokeWidth_slider = alt.binding_range(min=0, max=10, step=0.5)
    strokeWidth_var = alt.param(bind=strokeWidth_slider, value=2, name="strokeWidth")

    strokeCap_select = alt.binding_select(options=["butt", "round", "square"])
    strokeCap_var = alt.param(bind=strokeCap_select, value="butt", name="strokeCap")

    strokeDash_select = alt.binding_select(
        options=[[1, 0], [8, 8], [8, 4], [4, 4], [4, 2], [2, 1], [1, 1]]
    )
    strokeDash_var = alt.param(bind=strokeDash_select, value=[1, 0], name="strokeDash")

    alt.Chart().mark_rule(
        color="orange",
        strokeWidth=strokeWidth_var,
        strokeCap=strokeCap_var,
        strokeDash=strokeDash_var,
    ).encode(
        x=alt.datum(x_var, type="quantitative", scale=alt.Scale(domain=[0, 100])),
        y=alt.datum(y_var, type="quantitative", scale=alt.Scale(domain=[0, 100])),
        x2=alt.datum(x2_var),
        y2=alt.datum(y2_var),
    ).add_params(
        x_var,
        x2_var,
        y_var,
        y2_var,
        strokeWidth_var,
        strokeCap_var,
        strokeDash_var,
    )


A ``rule`` mark definition can contain any :ref:`standard mark properties <mark-properties>`.

Examples
--------
Width/Height-Spanning Rules
^^^^^^^^^^^^^^^^^^^^^^^^^^^
If the ``rule`` mark only has ``y`` encoding, the output view produces horizontal rules that spans the complete width. Similarly, if the ``rule`` mark only has ``x`` encoding, the output view produces vertical rules that spans the height.

We can use rules to show the average price of different stocks akin to ``tick`` marks.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.stocks()

    alt.Chart(source).mark_rule().encode(
        y="mean(price):Q",
        size=alt.value(2),
        color="symbol:N"
    )


The fact that rule marks span the width or the height of a single view make them useful as an annotation layer. For example, we can use rules to show average values of different stocks alongside the price curve.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.stocks()

    base = alt.Chart(source).properties(width=550)
    line = base.mark_line().encode(
        x="date",
        y="price",
        color="symbol"
    )
    rule = base.mark_rule().encode(
        y="average(price)",
        color="symbol",
        size=alt.value(2)
    )

    line + rule

We can also use a rule mark to show global mean value over a histogram.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.movies.url

    base = alt.Chart(source)
    bar = base.mark_bar().encode(
        x=alt.X("IMDB_Rating:Q").bin().axis(None),
        y="count()"
    )
    rule = base.mark_rule(color="red").encode(
        x="mean(IMDB_Rating):Q",
        size=alt.value(5),
    )

    bar + rule


Ranged Rules
^^^^^^^^^^^^
To control the spans of horizontal/vertical rules, ``x`` and ``x2``/ ``y`` and ``y2`` channels can be specified.

For example, we can use ``y`` and ``y2`` show the ``"min"`` and ``"max"`` values of horsepowers for cars from different locations.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.cars()

    alt.Chart(source).mark_rule().encode(
        x="Origin",
        y="min(Horsepower)",
        y2="max(Horsepower)",
    )
