line_plot.py

"""gr.LinePlot() component"""

from __future__ import annotations

from typing import Callable, Literal

import altair as alt
import pandas as pd
from gradio_client.documentation import document, set_documentation_group

from gradio.components.base import _Keywords
from gradio.components.plot import AltairPlot, Plot

set_documentation_group("component")


@document()
class LinePlot(Plot):
    """
    Create a line plot.

    Preprocessing: this component does *not* accept input.
    Postprocessing: expects a pandas dataframe with the data to plot.

    Demos: line_plot, live_dashboard
    """

    def __init__(
        self,
        value: pd.DataFrame | Callable | None = None,
        x: str | None = None,
        y: str | None = None,
        *,
        color: str | None = None,
        stroke_dash: str | None = None,
        overlay_point: bool | None = None,
        title: str | None = None,
        tooltip: list[str] | str | None = None,
        x_title: str | None = None,
        y_title: str | None = None,
        color_legend_title: str | None = None,
        stroke_dash_legend_title: str | None = None,
        color_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        stroke_dash_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        height: int | None = None,
        width: int | None = None,
        x_lim: list[int] | None = None,
        y_lim: list[int] | None = None,
        caption: str | None = None,
        interactive: bool | None = True,
        label: str | None = None,
        show_label: bool | None = None,
        container: bool = True,
        scale: int | None = None,
        min_width: int = 160,
        every: float | None = None,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
    ):
        """
        Parameters:
            value: The pandas dataframe containing the data to display in a scatter plot.
            x: Column corresponding to the x axis.
            y: Column corresponding to the y axis.
            color: The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
            stroke_dash: The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
            overlay_point: Whether to draw a point on the line for each (x, y) coordinate pair.
            title: The title to display on top of the chart.
            tooltip: The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
            x_title: The title given to the x axis. By default, uses the value of the x parameter.
            y_title: The title given to the y axis. By default, uses the value of the y parameter.
            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
            stroke_dash_legend_title: The title given to the stroke_dash legend. By default, uses the value of the stroke_dash parameter.
            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
            stroke_dash_legend_position: The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
            height: The height of the plot in pixels.
            width: The width of the plot in pixels.
            x_lim: A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
            caption: The (optional) caption to display below the plot.
            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
            label: The (optional) label to display on the top left corner of the plot.
            show_label: Whether the label should be displayed.
            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            visible: Whether the plot should be visible.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
        """
        self.x = x
        self.y = y
        self.color = color
        self.stroke_dash = stroke_dash
        self.tooltip = tooltip
        self.title = title
        self.x_title = x_title
        self.y_title = y_title
        self.color_legend_title = color_legend_title
        self.stroke_dash_legend_title = stroke_dash_legend_title
        self.color_legend_position = color_legend_position
        self.stroke_dash_legend_position = stroke_dash_legend_position
        self.overlay_point = overlay_point
        self.x_lim = x_lim
        self.y_lim = y_lim
        self.caption = caption
        self.interactive_chart = interactive
        self.width = width
        self.height = height
        super().__init__(
            value=value,
            label=label,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            every=every,
        )

    def get_config(self):
        config = super().get_config()
        config["caption"] = self.caption
        return config

    def get_block_name(self) -> str:
        return "plot"

    @staticmethod
    def update(
        value: pd.DataFrame | dict | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
        x: str | None = None,
        y: str | None = None,
        color: str | None = None,
        stroke_dash: str | None = None,
        overlay_point: bool | None = None,
        title: str | None = None,
        tooltip: list[str] | str | None = None,
        x_title: str | None = None,
        y_title: str | None = None,
        color_legend_title: str | None = None,
        stroke_dash_legend_title: str | None = None,
        color_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        stroke_dash_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        height: int | None = None,
        width: int | None = None,
        x_lim: list[int] | None = None,
        y_lim: list[int] | None = None,
        interactive: bool | None = None,
        caption: str | None = None,
        label: str | None = None,
        show_label: bool | None = None,
        container: bool | None = None,
        scale: int | None = None,
        min_width: int | None = None,
        visible: bool | None = None,
    ):
        """Update an existing plot component.

        If updating any of the plot properties (color, size, etc) the value, x, and y parameters must be specified.

        Parameters:
            value: The pandas dataframe containing the data to display in a scatter plot.
            x: Column corresponding to the x axis.
            y: Column corresponding to the y axis.
            color: The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
            stroke_dash: The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
            overlay_point: Whether to draw a point on the line for each (x, y) coordinate pair.
            title: The title to display on top of the chart.
            tooltip: The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
            x_title: The title given to the x axis. By default, uses the value of the x parameter.
            y_title: The title given to the y axis. By default, uses the value of the y parameter.
            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
            stroke_dash_legend_title: The title given to the stroke legend. By default, uses the value of stroke parameter.
            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation
            stroke_dash_legend_position: The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation
            height: The height of the plot in pixels.
            width: The width of the plot in pixels.
            x_lim: A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
            caption: The (optional) caption to display below the plot.
            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
            label: The (optional) label to display in the top left corner of the plot.
            show_label: Whether the label should be displayed.
            visible: Whether the plot should be visible.
        """
        properties = [
            x,
            y,
            color,
            stroke_dash,
            overlay_point,
            title,
            tooltip,
            x_title,
            y_title,
            color_legend_title,
            stroke_dash_legend_title,
            color_legend_position,
            stroke_dash_legend_position,
            height,
            width,
            x_lim,
            y_lim,
            interactive,
        ]
        if any(properties):
            if not isinstance(value, pd.DataFrame):
                raise ValueError(
                    "In order to update plot properties the value parameter "
                    "must be provided, and it must be a Dataframe. Please pass a value "
                    "parameter to gr.LinePlot.update."
                )
            if x is None or y is None:
                raise ValueError(
                    "In order to update plot properties, the x and y axis data "
                    "must be specified. Please pass valid values for x an y to "
                    "gr.LinePlot.update."
                )
            chart = LinePlot.create_plot(value, *properties)
            value = {"type": "altair", "plot": chart.to_json(), "chart": "line"}

        updated_config = {
            "label": label,
            "show_label": show_label,
            "container": container,
            "scale": scale,
            "min_width": min_width,
            "visible": visible,
            "value": value,
            "caption": caption,
            "__type__": "update",
        }
        return updated_config

    @staticmethod
    def create_plot(
        value: pd.DataFrame,
        x: str,
        y: str,
        color: str | None = None,
        stroke_dash: str | None = None,
        overlay_point: bool | None = None,
        title: str | None = None,
        tooltip: list[str] | str | None = None,
        x_title: str | None = None,
        y_title: str | None = None,
        color_legend_title: str | None = None,
        stroke_dash_legend_title: str | None = None,
        color_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        stroke_dash_legend_position: Literal[
            "left",
            "right",
            "top",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
            "none",
        ]
        | None = None,
        height: int | None = None,
        width: int | None = None,
        x_lim: list[int] | None = None,
        y_lim: list[int] | None = None,
        interactive: bool | None = None,
    ):
        """Helper for creating the scatter plot."""
        interactive = True if interactive is None else interactive
        encodings = {
            "x": alt.X(
                x,  # type: ignore
                title=x_title or x,  # type: ignore
                scale=AltairPlot.create_scale(x_lim),  # type: ignore
            ),
            "y": alt.Y(
                y,  # type: ignore
                title=y_title or y,  # type: ignore
                scale=AltairPlot.create_scale(y_lim),  # type: ignore
            ),
        }
        properties = {}
        if title:
            properties["title"] = title
        if height:
            properties["height"] = height
        if width:
            properties["width"] = width

        if color:
            domain = value[color].unique().tolist()
            range_ = list(range(len(domain)))
            encodings["color"] = {
                "field": color,
                "type": "nominal",
                "scale": {"domain": domain, "range": range_},
                "legend": AltairPlot.create_legend(
                    position=color_legend_position, title=color_legend_title or color
                ),
            }

        highlight = None
        if interactive and any([color, stroke_dash]):
            highlight = alt.selection(
                type="single",  # type: ignore
                on="mouseover",
                fields=[c for c in [color, stroke_dash] if c],
                nearest=True,
            )

        if stroke_dash:
            stroke_dash = {
                "field": stroke_dash,  # type: ignore
                "legend": AltairPlot.create_legend(  # type: ignore
                    position=stroke_dash_legend_position,  # type: ignore
                    title=stroke_dash_legend_title or stroke_dash,  # type: ignore
                ),  # type: ignore
            }  # type: ignore
        else:
            stroke_dash = alt.value(alt.Undefined)  # type: ignore

        if tooltip:
            encodings["tooltip"] = tooltip

        chart = alt.Chart(value).encode(**encodings)  # type: ignore

        points = chart.mark_point(clip=True).encode(
            opacity=alt.value(alt.Undefined) if overlay_point else alt.value(0),
        )
        lines = chart.mark_line(clip=True).encode(strokeDash=stroke_dash)

        if highlight:
            points = points.add_selection(highlight)

            lines = lines.encode(
                size=alt.condition(highlight, alt.value(4), alt.value(1)),
            )

        chart = (lines + points).properties(background="transparent", **properties)
        if interactive:
            chart = chart.interactive()

        return chart

    def postprocess(self, y: pd.DataFrame | dict | None) -> dict[str, str] | None:
        # if None or update
        if y is None or isinstance(y, dict):
            return y
        if self.x is None or self.y is None:
            raise ValueError("No value provided for required parameters `x` and `y`.")
        chart = self.create_plot(
            value=y,
            x=self.x,
            y=self.y,
            color=self.color,
            overlay_point=self.overlay_point,
            title=self.title,
            tooltip=self.tooltip,
            x_title=self.x_title,
            y_title=self.y_title,
            color_legend_title=self.color_legend_title,  # type: ignore
            color_legend_position=self.color_legend_position,  # type: ignore
            stroke_dash_legend_title=self.stroke_dash_legend_title,
            stroke_dash_legend_position=self.stroke_dash_legend_position,  # type: ignore
            x_lim=self.x_lim,
            y_lim=self.y_lim,
            stroke_dash=self.stroke_dash,
            interactive=self.interactive_chart,
            height=self.height,
            width=self.width,
        )

        return {"type": "altair", "plot": chart.to_json(), "chart": "line"}