Gradio LinePlot Docs

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LinePlot

gradio.LinePlot(···)

Description

Create a line plot.

Behavior

As input: this component does *not* accept input.

As output: expects a pandas dataframe with the data to plot.

Initialization

Parameter	Description
`value` pd.DataFrame \| Callable \| None default: None	The pandas dataframe containing the data to display in a scatter plot.
`x` str \| None default: None	Column corresponding to the x axis.
`y` str \| None default: None	Column corresponding to the y axis.
`color` str \| None default: None	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` str \| None default: None	The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
`overlay_point` bool \| None default: None	Whether to draw a point on the line for each (x, y) coordinate pair.
`title` str \| None default: None	The title to display on top of the chart.
`tooltip` list[str] \| str \| None default: None	The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
`x_title` str \| None default: None	The title given to the x axis. By default, uses the value of the x parameter.
`y_title` str \| None default: None	The title given to the y axis. By default, uses the value of the y parameter.
`color_legend_title` str \| None default: None	The title given to the color legend. By default, uses the value of color parameter.
`stroke_dash_legend_title` str \| None default: None	The title given to the stroke_dash legend. By default, uses the value of the stroke_dash parameter.
`color_legend_position` Literal['left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none'] \| None default: None	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` Literal['left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none'] \| None default: None	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` int \| None default: None	The height of the plot in pixels.
`width` int \| None default: None	The width of the plot in pixels.
`x_lim` list[int] \| None default: None	A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
`y_lim` list[int] \| None default: None	A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
`caption` str \| None default: None	The (optional) caption to display below the plot.
`interactive` bool \| None default: True	Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
`label` str \| None default: None	The (optional) label to display on the top left corner of the plot.
`show_label` bool default: True	Whether the label should be displayed.
`container` bool default: True
`scale` int \| None default: None
`min_width` int default: 160
`every` float \| None default: None	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` bool default: True	Whether the plot should be visible.
`elem_id` str \| None default: None	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` list[str] \| str \| None default: None	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.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.LinePlot`	"lineplot"	Uses default values

Demos

import gradio as gr
from vega_datasets import data

stocks = data.stocks()
gapminder = data.gapminder()
gapminder = gapminder.loc[
    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
]
climate = data.climate()
seattle_weather = data.seattle_weather()

## Or generate your own fake data, here's an example for stocks:
#
# import pandas as pd
# import random
#
# stocks = pd.DataFrame(
#     {
#         "symbol": [
#             random.choice(
#                 [
#                     "MSFT",
#                     "AAPL",
#                     "AMZN",
#                     "IBM",
#                     "GOOG",
#                 ]
#             )
#             for _ in range(120)
#         ],
#         "date": [
#             pd.Timestamp(year=2000 + i, month=j, day=1)
#             for i in range(10)
#             for j in range(1, 13)
#         ],
#         "price": [random.randint(10, 200) for _ in range(120)],
#     }
# )


def line_plot_fn(dataset):
    if dataset == "stocks":
        return gr.LinePlot.update(
            stocks,
            x="date",
            y="price",
            color="symbol",
            color_legend_position="bottom",
            title="Stock Prices",
            tooltip=["date", "price", "symbol"],
            height=300,
            width=500,
        )
    elif dataset == "climate":
        return gr.LinePlot.update(
            climate,
            x="DATE",
            y="HLY-TEMP-NORMAL",
            y_lim=[250, 500],
            title="Climate",
            tooltip=["DATE", "HLY-TEMP-NORMAL"],
            height=300,
            width=500,
        )
    elif dataset == "seattle_weather":
        return gr.LinePlot.update(
            seattle_weather,
            x="date",
            y="temp_min",
            tooltip=["weather", "date"],
            overlay_point=True,
            title="Seattle Weather",
            height=300,
            width=500,
        )
    elif dataset == "gapminder":
        return gr.LinePlot.update(
            gapminder,
            x="year",
            y="life_expect",
            color="country",
            title="Life expectancy for countries",
            stroke_dash="cluster",
            x_lim=[1950, 2010],
            tooltip=["country", "life_expect"],
            stroke_dash_legend_title="Country Cluster",
            height=300,
            width=500,
        )


with gr.Blocks() as line_plot:
    with gr.Row():
        with gr.Column():
            dataset = gr.Dropdown(
                choices=["stocks", "climate", "seattle_weather", "gapminder"],
                value="stocks",
            )
        with gr.Column():
            plot = gr.LinePlot()
    dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
    line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)


if __name__ == "__main__":
    line_plot.launch()

import math

import pandas as pd

import gradio as gr
import datetime
import numpy as np


def get_time():
    return datetime.datetime.now()


plot_end = 2 * math.pi


def get_plot(period=1):
    global plot_end
    x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
    y = np.sin(2 * math.pi * period * x)
    update = gr.LinePlot.update(
        value=pd.DataFrame({"x": x, "y": y}),
        x="x",
        y="y",
        title="Plot (updates every second)",
        width=600,
        height=350,
    )
    plot_end += 2 * math.pi
    if plot_end > 1000:
        plot_end = 2 * math.pi
    return update


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            c_time2 = gr.Textbox(label="Current Time refreshed every second")
            gr.Textbox(
                "Change the value of the slider to automatically update the plot",
                label="",
            )
            period = gr.Slider(
                label="Period of plot", value=1, minimum=0, maximum=10, step=1
            )
            plot = gr.LinePlot(show_label=False)
        with gr.Column():
            name = gr.Textbox(label="Enter your name")
            greeting = gr.Textbox(label="Greeting")
            button = gr.Button(value="Greet")
            button.click(lambda s: f"Hello {s}", name, greeting)

    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
    dep = demo.load(get_plot, None, plot, every=1)
    period.change(get_plot, period, plot, every=1, cancels=[dep])

if __name__ == "__main__":
    demo.queue().launch()

Methods

change

gradio.LinePlot.change(fn, ···)

Description

This listener is triggered when the component's value changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input. This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None \| Literal[False] default: None	Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when This listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.LinePlot.clear(fn, ···)

Description

This listener is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None \| Literal[False] default: None	Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when This listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

LinePlot

Description
Behavior
Initialization
Shortcuts
Demos
Methods
change
clear

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