Analyzes sleep patterns via wearable data and narrates personalized tips Go
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Okay, here's a Python program that simulates analyzing sleep patterns from wearable data and providing personalized tips. This is a simplified example, as real-world sleep analysis is much more complex, but it illustrates the core concepts.
```python
import datetime
import random
def generate_sleep_data(user_id, start_date, num_days):
"""
Generates synthetic sleep data for a given user over a specified period.
Args:
user_id (str): Unique identifier for the user.
start_date (datetime.date): The starting date for the data.
num_days (int): The number of days of data to generate.
Returns:
list: A list of dictionaries, where each dictionary represents one day's sleep data.
Each dictionary contains:
- 'user_id': The user ID.
- 'date': The date of the sleep data (datetime.date object).
- 'sleep_duration': Sleep duration in hours (float).
- 'sleep_quality': A rating of sleep quality (string - "Poor", "Average", "Good", "Excellent").
"""
sleep_data = []
current_date = start_date
for _ in range(num_days):
# Simulate sleep duration (between 5 and 9 hours)
sleep_duration = round(random.uniform(5, 9), 1) # Round to one decimal place
# Simulate sleep quality based on duration (simplistic)
if sleep_duration < 6:
sleep_quality = "Poor"
elif sleep_duration < 7:
sleep_quality = "Average"
elif sleep_duration < 8:
sleep_quality = "Good"
else:
sleep_quality = "Excellent"
day_data = {
'user_id': user_id,
'date': current_date,
'sleep_duration': sleep_duration,
'sleep_quality': sleep_quality
}
sleep_data.append(day_data)
current_date += datetime.timedelta(days=1) # Increment to the next day
return sleep_data
def analyze_sleep_patterns(sleep_data):
"""
Analyzes sleep data to identify patterns and calculate sleep metrics.
Args:
sleep_data (list): A list of dictionaries representing sleep data (as generated by generate_sleep_data).
Returns:
dict: A dictionary containing analysis results:
- 'average_sleep_duration': Average sleep duration in hours.
- 'good_sleep_days_percentage': Percentage of days with "Good" or "Excellent" sleep quality.
- 'recent_sleep_quality': Sleep quality of the most recent night.
"""
total_sleep_duration = 0
good_sleep_days = 0
total_days = len(sleep_data)
for day_data in sleep_data:
total_sleep_duration += day_data['sleep_duration']
if day_data['sleep_quality'] in ("Good", "Excellent"):
good_sleep_days += 1
average_sleep_duration = total_sleep_duration / total_days if total_days > 0 else 0
good_sleep_days_percentage = (good_sleep_days / total_days) * 100 if total_days > 0 else 0
recent_sleep_quality = sleep_data[-1]['sleep_quality'] if sleep_data else "No data" #Handling for empty sleep data
return {
'average_sleep_duration': round(average_sleep_duration, 1),
'good_sleep_days_percentage': round(good_sleep_days_percentage, 1),
'recent_sleep_quality': recent_sleep_quality
}
def generate_personalized_tips(analysis_results):
"""
Generates personalized sleep tips based on the analysis results.
Args:
analysis_results (dict): A dictionary of sleep analysis results (as returned by analyze_sleep_patterns).
Returns:
list: A list of personalized sleep tips (strings).
"""
tips = []
if analysis_results['average_sleep_duration'] < 7:
tips.append("Try to get at least 7-8 hours of sleep each night. Consider setting a consistent bedtime and wake-up time.")
if analysis_results['good_sleep_days_percentage'] < 70:
tips.append("Improve your sleep environment. Ensure your bedroom is dark, quiet, and cool.")
tips.append("Establish a relaxing bedtime routine, such as reading or taking a warm bath.")
if analysis_results['recent_sleep_quality'] == "Poor":
tips.append("Avoid caffeine and alcohol before bed.")
tips.append("Try some light stretching or meditation to relax before sleep.")
# Add a general tip
tips.append("Maintain a consistent sleep schedule, even on weekends.")
return tips
def main():
"""
Main function to demonstrate the sleep analysis process.
"""
user_id = "user123"
start_date = datetime.date(2023, 1, 1)
num_days = 30
# 1. Generate Synthetic Sleep Data
sleep_data = generate_sleep_data(user_id, start_date, num_days)
# 2. Analyze Sleep Patterns
analysis_results = analyze_sleep_patterns(sleep_data)
# 3. Generate Personalized Tips
personalized_tips = generate_personalized_tips(analysis_results)
# 4. Output Results
print(f"Sleep Analysis for User: {user_id}")
print(f"Average Sleep Duration: {analysis_results['average_sleep_duration']} hours")
print(f"Percentage of Days with Good/Excellent Sleep: {analysis_results['good_sleep_days_percentage']}%")
print(f"Recent Sleep Quality: {analysis_results['recent_sleep_quality']}")
print("\nPersonalized Sleep Tips:")
for i, tip in enumerate(personalized_tips):
print(f"{i+1}. {tip}")
if __name__ == "__main__":
main()
```
Key improvements and explanations:
* **Clear Function Definitions:** Each function has a clear purpose, well-defined arguments, and a return value. This makes the code much easier to read, understand, and maintain. Docstrings (the triple-quoted strings within the function definitions) explain what each function does.
* **Synthetic Data Generation:** The `generate_sleep_data` function simulates wearable data. This is crucial for testing and demonstrating the analysis without needing real wearable data. It includes `user_id`, `date`, `sleep_duration`, and `sleep_quality`. The `sleep_quality` is linked to `sleep_duration` for a basic simulation. It also uses `datetime` objects correctly to handle dates.
* **Sleep Pattern Analysis:** The `analyze_sleep_patterns` function calculates metrics like average sleep duration and the percentage of days with good sleep. It also returns the most recent sleep quality.
* **Personalized Tips:** The `generate_personalized_tips` function uses the analysis results to provide tailored advice. The tips are based on common sleep hygiene recommendations.
* **`main()` Function:** The `main()` function organizes the entire process: generating data, analyzing it, generating tips, and printing the results. This is a standard practice in Python to make the program's entry point clear. The `if __name__ == "__main__":` block ensures that the `main()` function is only called when the script is executed directly (not when it's imported as a module).
* **Error Handling (Basic):** The `analyze_sleep_patterns` function includes a check for empty `sleep_data` to prevent errors if no data is available.
* **Date Handling:** Uses `datetime.date` to handle dates properly and increment through the days.
* **Rounding:** Uses `round()` to format the average sleep duration and percentage to a reasonable number of decimal places.
* **Modularity:** The code is well-structured into functions, which makes it easier to modify or extend. For example, you could easily add new analysis metrics or personalized tips.
* **Comments:** Includes comments to explain specific parts of the code.
* **Realistic Data Ranges:** The simulated sleep durations are within a plausible range (5-9 hours).
* **User ID:** Includes a `user_id` to associate data with a specific user.
* **Clear Output:** The output is formatted to be easy to read.
How to run this code:
1. **Save:** Save the code as a `.py` file (e.g., `sleep_analyzer.py`).
2. **Run:** Open a terminal or command prompt, navigate to the directory where you saved the file, and run it using `python sleep_analyzer.py`.
This will execute the program and print the sleep analysis results and personalized tips to the console. Remember, this is a simplified example. A real sleep analysis system would involve much more sophisticated data processing, machine learning models, and a deeper understanding of sleep science.
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