Talk:Pyronema

q7

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LinearRegression import numpy as np

1. Filter data for Oregon

oregon_data = df[df["state"] == "Oregon"]

1. Visualize the relationship between mean_avg and bad_mental_health_days

plt.figure(figsize=(10, 6)) sns.scatterplot(x="mean_avg", y="bad_mental_health_days", data=oregon_data, color="blue", s=100) sns.regplot(x="mean_avg", y="bad_mental_health_days", data=oregon_data, scatter=False, color="red") plt.title("Relationship Between PM2.5 (mean_avg) and Mental Health in Oregon", fontsize=16) plt.xlabel("PM2.5 Levels (mean_avg)", fontsize=14) plt.ylabel("Bad Mental Health Days", fontsize=14) plt.grid(True) plt.show()

1. Fit a linear regression model

X = oregon_data["mean_avg"].values.reshape(-1, 1) # Independent variable y = oregon_data["bad_mental_health_days"].values # Dependent variable model = LinearRegression() model.fit(X, y)

1. Display the regression results

print(f"Model Coefficient (Slope): {model.coef_[0]:.3f}") print(f"Model Intercept: {model.intercept_:.3f}") print(f"R-squared: {model.score(X, y):.3f}")

1. Hypothesis testing: Permutation test

observed_slope = model.coef_[0] permuted_slopes = []

1. Shuffle mean_avg values and compute slopes

n_permutations = 1000 for _ in range(n_permutations):

   shuffled_mean_avg = np.random.permutation(oregon_data["mean_avg"])
   perm_model = LinearRegression().fit(shuffled_mean_avg.reshape(-1, 1), y)
   permuted_slopes.append(perm_model.coef_[0])

1. Compute p-value

p_value = np.mean(np.abs(permuted_slopes) >= np.abs(observed_slope)) print(f"P-value from permutation test: {p_value:.3f}")

1. Plot the permutation distribution

plt.figure(figsize=(10, 6)) plt.hist(permuted_slopes, bins=30, color="skyblue", edgecolor="black", alpha=0.7) plt.axvline(observed_slope, color="red", linestyle="--", linewidth=2, label="Observed Slope") plt.title("Permutation Test for PM2.5 Effect on Mental Health", fontsize=16) plt.xlabel("Slope from Permutation", fontsize=14) plt.ylabel("Frequency", fontsize=14) plt.legend() plt.grid(True) plt.show() 2601:646:A101:E40:EDFB:7607:3A97:6201 (talk) 01:34, 7 December 2024 (UTC)

q8

import numpy as np

def optimize_child_health(play_start_hour, play_duration, air_quality, lung_health, outdoor_benefit):

   """
   Objective function to optimize children's health based on outdoor activity and air quality.

   Parameters:
   - play_start_hour (int): Hour of the day when outdoor play begins (7 <= hour <= 19).
   - play_duration (float): Duration of outdoor play in hours (0 <= duration <= 2).
   - air_quality (function): A function that takes an hour and returns air quality index (AQI).
   - lung_health (function): A function that computes the impact of air quality on lung health.
     Signature: lung_health(exposure_duration, air_quality).
   - outdoor_benefit (function): A function that computes the health benefit of outdoor play.
     Signature: outdoor_benefit(duration).

   Returns:
   - Total health score (float): A value where higher is better for the child’s health.
   """
   # Constants
   MIN_PLAY_HOUR = 7  # 7 AM
   MAX_PLAY_HOUR = 19  # 7 PM
   MAX_PLAY_DURATION = 2  # Maximum outdoor play time in hours

   # Check constraints
   if not (MIN_PLAY_HOUR <= play_start_hour <= MAX_PLAY_HOUR):
       raise ValueError("Play start hour must be between 7 AM and 7 PM.")
   if not (0 <= play_duration <= MAX_PLAY_DURATION):
       raise ValueError("Play duration must be between 0 and 2 hours.")

   # Calculate the average air quality during playtime
   play_hours = np.arange(play_start_hour, play_start_hour + play_duration, 0.25)
   play_hours = play_hours[play_hours <= MAX_PLAY_HOUR]  # Limit to allowable hours
   avg_air_quality = np.mean([air_quality(hour) for hour in play_hours])

   # Calculate lung health impact based on air quality
   lung_health_impact = lung_health(play_duration, avg_air_quality)

   # Calculate outdoor activity benefits
   activity_benefit = outdoor_benefit(play_duration)

   # Total health score: maximize benefits while minimizing harm
   total_health_score = activity_benefit - lung_health_impact

   return total_health_score

(!!You don't need to include the code below)

1. Example utility functions

def air_quality(hour):

   """Example function returning AQI for a given hour."""
   return 50 + 10 * np.sin((hour - 12) / 4)  # Simulates better air in morning/evening

def lung_health(exposure_duration, avg_air_quality):

   """Example function modeling the impact of air quality on lung health."""
   return exposure_duration * avg_air_quality / 100  # Higher AQI worsens lung health

def outdoor_benefit(duration):

   """Example function modeling the benefit of outdoor play."""
   return duration * (2 - 0.5 * duration)  # Benefits diminish with overplay

1. Example optimization scenario

play_start_hour = 10 play_duration = 1.5

health_score = optimize_child_health(play_start_hour, play_duration, air_quality, lung_health, outdoor_benefit) print(f"Health Score for play start at {play_start_hour} and duration {play_duration} hours: {health_score:.2f}") 2601:646:A101:E40:EDFB:7607:3A97:6201 (talk) 01:39, 7 December 2024 (UTC)