Home Backend Development Python Tutorial Listen for realtime location updates from pulsetracker on your backend

Listen for realtime location updates from pulsetracker on your backend

Nov 15, 2024 am 03:23 AM

Introduction

Pulsetracker is a powerful, scalable, flexible location-tracking solution for developers seeking real-time updates without being bound to a proprietary client SDK. With Pulsetracker, you have the freedom to integrate location data into your own backend system using WebSockets or APIs, handling real-time tracking with battery-efficient technology.
This guide will walk you through setting up a Python client (listener) to connect to the Pulsetracker backend and listen for location updates.

Getting Started with PulseTracker

Pulsetracker's backend is capable of processing thousands of location changes per second and allows you to decide how to handle and store these updates.
This flexibility is a major advantage for developers who want to maintain control over their data and integration setup.

Here, we’ll connect to the Pulsetracker real-time update service (which is basically a pusher server) using a Python script that listens to a specific device’s location updates.

Setting Up the Python Client

Below is the code for a simple Python client that connects to the PulseTracker Pusher server, subscribes to a location update channel, and processes real-time location updates.

Prerequisites

To run the Python client, you’ll need:

  • A Pulsetracker account with an API token.
  • In Pulsestracker dashboard or API you can create new App and copy App key
  • Python installed on your machine.
  • The pysher library, a Python client for Pusher.

You can install pysher using pip:

pip install pysher
Copy after login

Python Code to Listen for Location Updates

Here is the Python client code, followed by a detailed explanation:

#!/usr/bin/env python

import sys
import pysher
import time

# Define global variable for Pusher client
global pusher

# Callback function to process location updates
def channel_callback(data):
    print("Channel Callback: %s" % data)
    # Todo: Pass the update to your queue server or to your database ... 

# Handler for connection establishment
def connect_handler(data):
    channel = pusher.subscribe("private-apps.YOUR_APP_KEY")
    channel.bind('App\Events\DeviceLocationUpdated', channel_callback)

if __name__ == '__main__':
    # Set your app key and auth endpoint here
    appkey = "YOUR_APP_KEY"
    auth_endpoint = "https://www.pulsestracker.com/api/broadcasting/auth"

    # Initialize Pusher client with custom host and authentication
    pusher = pysher.Pusher(
        key=appkey,
        auth_endpoint_headers={            
            "Authorization" : "Bearer YOUR_ACCESS_TOKEN"
        },
        auth_endpoint=auth_endpoint,
        custom_host="pusher.pulsestracker.com",
        secure=True,
    )
    pusher.connection.ping_interval = 30
    pusher.connect()

    # Bind the connection handler
    pusher.connection.bind('pusher:connection_established', connect_handler)

    while True:
        time.sleep(1)
Copy after login

Explanation of the Code

  1. Imports and Setup:

    • We import necessary modules and define a global pusher variable, which will be used to manage the connection.
  2. Defining the channel_callback Function:

    • This function will handle incoming location updates. Here, it simply prints the received data, but you can modify it to forward the data to a database, messaging queue, or any storage solution of your choice.
  3. Setting the connect_handler:

    • This function subscribes the client to a specific channel and binds the channel_callback function to the event that transmits location updates, App\Events\DeviceLocationUpdated. This event is triggered whenever a new location update is available.
  4. Initializing the Pusher Client:

    • The main script initializes the Pusher client with your specific app key and authentication endpoint.
    • The auth_endpoint_headers includes a Bearer token, which should be replaced with your actual PulseTracker API token.
    • custom_host is set to pusher.pulsestracker.com, which is the host for PulseTracker’s Pusher service.
    • The connection is configured to be secure (secure=True), and a ping interval is set to keep the connection alive.
  5. Starting the Connection:

    • pusher.connect() establishes the connection with the server, and pusher.connection.bind binds the connect_handler to execute once the connection is successful.
  6. Loop to Keep the Client Running:

    • Finally, a simple infinite loop ensures that the script stays active, listening for location updates indefinitely.

Next Steps

With the client running, it will receive real-time location updates from PulseTracker. You can further modify this script to:

  • Save updates to a database.
  • Forward the data to another API.
  • Analyze the incoming data in real time.

Results

Listen for realtime location updates from pulsetracker on your backend

Conclusion

Pulsetracker provides an effective solution for developers to manage and integrate real-time location tracking into their own systems. With this Python client, you can seamlessly receive and handle location updates, enabling you to build custom, high-performance location-based applications without being locked into a specific client SDK or backend solution.

Happy tracking with Pulsetracker!

The above is the detailed content of Listen for realtime location updates from pulsetracker on your backend. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: The Power of Versatile Programming Python: The Power of Versatile Programming Apr 17, 2025 am 12:09 AM

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

See all articles