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Creating Real-Time Graphs with Dash Framework in Python

Introduction to Live Graphs and Dash

If you are looking for a powerful solution to create real-time graphs, then Dash Framework is an excellent choice. Built on top of Flask, Plotly, and ReactJS, it makes it easy to create dynamic visualizations and dashboards using Python.

With its live reloading feature, you can see your changes instantly, allowing for efficient development. In this article, we will dive into how to use the Dash Framework to create live graphs in Python.

We will explore how to install the necessary dependencies, initialize an application and data, create the layout, and update the live graph dynamically.

Creating Live Graphs Using Dash and Plotly

Installing Dependencies

First, you need to install the required dependencies. You can use pip to install dash and plotly.

“`

pip install dash

pip install plotly

“`

Imports

To start using Dash, you need to import the necessary libraries. “` python

import dash

from collections import deque

import random

import plotly.graph_objs as go

from dash.dependencies import Input,

Output

import dash_core_components as dcc

import dash_html_components as html

“`

Initializing the Application and Data

Next, you initialize your application and data. This code creates an empty dataset with 200 points for the scatter plot.

“` python

app = dash.Dash(__name__)

app.layout = html.Div(

[

dcc.Graph(id=”live-graph”, animate=True),

dcc.Interval(id=”graph-update”, interval=1000),

]

)

X = deque(maxlen=200)

Y = deque(maxlen=200)

X.append(1)

Y.append(1)

“`

Dashboard Layout

Now, we create the layout for our dashboard. The HTML component “div” is a container for other HTML components.

In this case, it contains a “graph” component with an ID of “live-graph” and an “interval” component with an ID of “graph-update”.

“` python

app.layout = html.Div(

[

dcc.Graph(id=”live-graph”, animate=True),

dcc.Interval(id=”graph-update”, interval=1000),

]

)

“`

Update the Live Graph

The next step is to update the live graph. You can use the `@app.callback` decorator to trigger the function every time the interval updates.

“` python

@app.callback(

Output(“live-graph”, “figure”), [Input(“graph-update”, “n_intervals”)])

def updateGraph(n):

global X, Y

X.append(X[-1] + 1)

Y.append(Y[-1] + Y[-1] * random.uniform(-0.1, 0.1))

data = plotly.graph_objs.Scatter(

x=list(X),

y=list(Y),

name=”Scatter”,

mode=”lines+markers”

)

return {“data”: [data], “layout”: go.Layout(xaxis=dict(range=[min(X), max(X)]), yaxis=dict(range=[min(Y), max(Y)]))}

“`

Run the Server

Finally, you can run the server using the `run_server` method. “` python

if __name__ == “__main__”:

app.run_server(debug=True)

“`

Conclusion

In conclusion, with Dash Framework and Plotly, you can create powerful and dynamic live graphs using Python. Using an easy-to-understand Python API, it is quick and easy to build customizable, interactive web applications in real-time.

With this tutorial, you have learned how to create a live graph step-by-step, from installing dependencies to updating the graph. With that knowledge, you can go on to create even more sophisticated graphs and gain a deeper understanding of data visualization.

Creating Live Graphs Using Dash and Plotly –

Source Code and

Output

In this section, we will provide the complete source code for the live graph application we have built in the previous section. We will also show you the output of the application by running it on the local server.

Source Code

“` python

import dash

from collections import deque

import random

import plotly.graph_objs as go

from dash.dependencies import Input,

Output

import dash_core_components as dcc

import dash_html_components as html

X = deque(maxlen=200)

Y = deque(maxlen=200)

X.append(1)

Y.append(1)

app = dash.Dash(__name__)

app.layout = html.Div(

[

dcc.Graph(id=”live-graph”, animate=True),

dcc.Interval(id=”graph-update”, interval=1000),

]

)

@app.callback(

Output(“live-graph”, “figure”), [Input(“graph-update”, “n_intervals”)])

def updateGraph(n):

global X, Y

X.append(X[-1] + 1)

Y.append(Y[-1] + Y[-1] * random.uniform(-0.1, 0.1))

data = go.Scatter(

x=list(X),

y=list(Y),

name=”Scatter”,

mode=”lines+markers”

)

return {“data”: [data], “layout”: go.Layout(xaxis=dict(range=[min(X), max(X)]), yaxis=dict(range=[min(Y), max(Y)]))}

if __name__ == “__main__”:

app.run_server(debug=True)

“`

This code will create a scatter plot that is updated every second. The plot contains 200 data points, which are stored in the `X` and `Y` deques.

Output

To run the code, save it as a python file (for example, “live_graph.py”) and execute it from the terminal by running the following command:

“`

python live_graph.py

“`

You will see the output similar to the screenshot below. ![

Output of Live Graph](https://i.imgur.com/2QaXSwi.png)

If you open the URL provided in your browser, you will see the live graph updating every second with new data.

Using the Dash Framework, we have created a dynamic and customizable live graph that updates in real-time using Python. With the use of the `@app.callback` function, we were able to update the graph’s data on every interval update, ensuring accurate and timely visualization of real-time data.

In this article, we learned how to create live graphs using Dash and Plotly. We covered the key points in the creation process, such as installing dependencies, initializing the application and data, creating the dashboard layout, and updating the live graph dynamically.

By providing complete source code and output, readers can see the entire process and results of creating live graphs using Python. Live graphs are crucial in visualizing real-time data, and using Dash Framework allows easy and efficient development of dynamic visualizations and dashboards.

With the knowledge gained from this article, developers can create powerful, customizable, and interactive web applications in real-time, providing accurate and timely visualization of real-time data.

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