Fitness Tracker

Requirements

Python: Make sure you have Python installed (version 3.6 or above).
Libraries: Install the following libraries using pip:bashCopy codepip install matplotlib numpy

Step 1: Set Up the Data Structure

We’ll use a simple dictionary to store the fitness data.

import random
import time

fitness_data = {
    "steps": 0,
    "heart_rate": 0,
    "calories_burned": 0
}

Step 2: Simulate Fitness Tracking

We’ll create functions to simulate updating steps, heart rate, and calories burned.

def update_steps():
    # Simulate step counting
    steps = random.randint(0, 100)
    fitness_data["steps"] += steps
    return steps

def update_heart_rate():
    # Simulate heart rate measurement
    heart_rate = random.randint(60, 100)
    fitness_data["heart_rate"] = heart_rate
    return heart_rate

def update_calories_burned(steps):
    # Simple formula for calories burned (approx.)
    calories = steps * 0.04
    fitness_data["calories_burned"] += calories
    return calories

Step 3: Main Loop to Collect Data

Now we’ll create a loop to collect data at regular intervals.

def collect_data(duration=10):
    start_time = time.time()
    while (time.time() - start_time) < duration:
        steps = update_steps()
        heart_rate = update_heart_rate()
        calories = update_calories_burned(steps)
        
        print(f"Steps: {fitness_data['steps']}, Heart Rate: {heart_rate} bpm, Calories Burned: {fitness_data['calories_burned']:.2f}")
        
        time.sleep(1)  # Collect data every second

Step 4: Visualizing the Data

To visualize the data, we can create a simple graph using matplotlib.

import matplotlib.pyplot as plt

def plot_data():
    # Data for plotting
    steps = []
    heart_rates = []
    calories = []

    for _ in range(10):
        steps.append(fitness_data["steps"])
        heart_rates.append(fitness_data["heart_rate"])
        calories.append(fitness_data["calories_burned"])
        time.sleep(1)  # Simulate time passing

    # Plotting
    plt.figure(figsize=(10, 5))
    
    plt.subplot(1, 3, 1)
    plt.plot(steps, label='Steps', color='blue')
    plt.title('Steps Over Time')
    plt.xlabel('Time (s)')
    plt.ylabel('Steps')
    
    plt.subplot(1, 3, 2)
    plt.plot(heart_rates, label='Heart Rate', color='red')
    plt.title('Heart Rate Over Time')
    plt.xlabel('Time (s)')
    plt.ylabel('Heart Rate (bpm)')
    
    plt.subplot(1, 3, 3)
    plt.plot(calories, label='Calories Burned', color='green')
    plt.title('Calories Burned Over Time')
    plt.xlabel('Time (s)')
    plt.ylabel('Calories')

    plt.tight_layout()
    plt.show()

Step 5: Putting It All Together

Finally, we’ll run the data collection and plotting functions.

if __name__ == "__main__":
    collect_data(duration=10)  # Collect data for 10 seconds
    plot_data()  # Plot the collected data

Running the Tracker

Save your script as fitness_tracker.py.
Run it using:bashCopy codepython fitness_tracker.py

This will simulate tracking fitness data and visualize the results after collecting data for 10 seconds.