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LinePlot

Introduction

LinePlot widget in Supervisely is a widget that allows users to display one or more lines of data in a plot. It provides a canvas area that can be customized with various options, such as line smoothing, axis labels, and displaying legends. LinePlot widget is useful for visualizing time-series data or data with continuous variables.

Function signature

x1 = list(range(10))
y1 = [random.randint(10, 148) for _ in range(10)]

x2 = list(range(30))
y2 = [random.randint(1, 300) for _ in range(30)]

line_plot = LinePlot("My Line Plot")
line_plot.add_series("Line 1", x1, y1)
line_plot.add_series("Line 2", x2, y2)

or

size1 = 10
x1 = list(range(size1))
y1 = np.random.randint(low=10, high=148, size=size1).tolist()
s1 = [{"x": x, "y": y} for x, y in zip(x1, y1)]

size2 = 30
x2 = list(range(size2))
y2 = np.random.randint(low=0, high=300, size=size2).tolist()
s2 = [{"x": x, "y": y} for x, y in zip(x2, y2)]

line_plot = LinePlot(
    title="My Line Plot",
    series=[{"name": "Line 1", "data": s1}, {"name": "Line 2", "data": s2}],
    smoothing_weight=0,
    group_key=None,
    show_legend=True,
    decimals_in_float=2,
    xaxis_decimals_in_float=None,
    yaxis_interval=None,
    widget_id=None,
    yaxis_autorescale=True,
)

Parameters

Parameters
Type
Description

title

str

LinePlot title

series

list

List of input data series

smoothing_weight

int

Smoothing

group_key

str

Synced charts key

show_legend

bool

Show legend on LinePlot

decimals_in_float

int

The number of fractions to display floating values in Y axis

xaxis_decimals_in_float

int

The number of fractions to display floating values in X axis

yaxis_interval

list

Min and max values on Y axis (e.g. [0, 1])

yaxis_autorescale

bool

Set autoscaling of the Y axis

widget_id

str

ID of the widget

title

Determine LinePlot title.

type: str

line_chart = LinePlot(title="Max vs Denis")

series

Determine list of input data series.

type: list

default value: []

size1 = 10
x1 = list(range(size1))
y1 = np.random.randint(low=10, high=148, size=size1).tolist()
s1 = [{"x": x, "y": y} for x, y in zip(x1, y1)]

size2 = 30
x2 = list(range(size2))
y2 = np.random.randint(low=0, high=300, size=size2).tolist()
s2 = [{"x": x, "y": y} for x, y in zip(x2, y2)]

line_chart = LinePlot(
    title="Max vs Denis",
    series=[{"name": "Max", "data": s1}, {"name": "Denis", "data": s2}],
)

smoothing_weight

Determine LinePlot smoothing.

type: int

default value: 0

group_key

Synced LinePlot key.

type: str

default value: None

show_legend

Determine showing legend on LinePlot.

type: bool

default value: True

line_chart = LinePlot(
    title="Max vs Denis",
    series=[{"name": "Max", "data": s1}, {"name": "Denis", "data": s2}],
    show_legend=False,
)

decimals_in_float

The number of fractions to display floating values in Y axis.

type: int

default value: 2

xaxis_decimals_in_float

The number of fractions to display floating values in X axis.

type: int

default value: None

line_chart = LinePlot(
    title="Max vs Denis",
    series=[{"name": "Max", "data": s1}, {"name": "Denis", "data": s2}],
    xaxis_decimals_in_float=44,
)

yaxis_interval

Determine min and max values on Y axis (e.g. [0, 1]).

type: list

default value: None

line_chart = LinePlot(
    title="Max vs Denis",
    series=[{"name": "Max", "data": s1}, {"name": "Denis", "data": s2}],
    yaxis_interval=[0, 200],
)

yaxis_autorescale

Set autoscaling of the Y axis.

type: bool

default value: True

widget_id

ID of the widget.

type: str

default value: None

Methods and attributes

Attributes and Methods
Description

update_y_range(ymin: int, ymax: int)

Update LinePlot data.

add_series(name: str, x: list, y: list, send_changes: bool = True)

Add new series of data in LinePlot.

add_series_batch(series: list)

Add new series of data in LinePlot by batch.

add_to_series(name_or_id: str or int, data: List[tuple] or List[dict] or tuple or dict)

Add data to existing series.

get_series_by_name(name: str)

Return series data by name.

Mini App Example

You can find this example in our GitHub repository:

ui-widgets-demos/charts and plots/005_line_plot/src/main.py

Import libraries

import os
import numpy as np
import supervisely as sly
from supervisely.app.widgets import Card, Container, LinePlot
from dotenv import load_dotenv

Init API client

First, we load environment variables with credentials and init API for communicating with Supervisely Instance:

load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api()

Prepare series for plot

size1 = 10
x1 = list(range(size1))
y1 = np.random.randint(low=10, high=148, size=size1).tolist()
s1 = [{"x": x, "y": y} for x, y in zip(x1, y1)]

size2 = 30
x2 = list(range(size2))
y2 = np.random.randint(low=0, high=300, size=size2).tolist()
s2 = [{"x": x, "y": y} for x, y in zip(x2, y2)]

Initialize LinePlot widget

line_chart = LinePlot(
    title="Max vs Denis",
    series=[{"name": "Max", "data": s1}, {"name": "Denis", "data": s2}],
    show_legend=False,
)

Create app layout

Prepare a layout for app using Card widget with the content parameter and place widget that we've just created in the Container widget.

card = Card(
    title="Line Plot",
    content=line_chart,
)

layout = Container(widgets=[card])

Create app using layout

Create an app object with layout parameter.

app = sly.Application(layout=layout)
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