import pandas_datareader.data as web
from datetime import datetime

# Set the date range
start = datetime(1939, 1, 1)
end = datetime(2024, 1, 1)

# Fetch AHEMAN data from FRED
aheman_data = web.DataReader('AHEMAN', 'fred', start, end)

# Display the first few rows to verify
print(aheman_data.head())

# Fetch AWHAE data from FRED
awhae_data = web.DataReader('AWHAE', 'fred', start, end)

# Display the first few rows to verify
print(awhae_data.head())

import pandas as pd

# Merge the AHEMAN and AWHAE data on the date column
merged_data = pd.merge(aheman_data, awhae_data, left_index=True, right_index=True, suffixes=('_AHEMAN', '_AWHAE'))

# Display the merged data
print(merged_data.head())

import matplotlib.pyplot as plt

# Plot the dual-axis line graph
fig, ax1 = plt.subplots(figsize=(10, 6))

# Plot AHEMAN on the first axis
ax1.plot(merged_data.index, merged_data['AHEMAN'], color='blue', label='AHEMAN')
ax1.set_xlabel('Date')
ax1.set_ylabel('AHEMAN (Dollars per Hour)', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')

# Create a second y-axis for AWHAE
ax2 = ax1.twinx()
ax2.plot(merged_data.index, merged_data['AWHAE'], color='orange', label='AWHAE')
ax2.set_ylabel('AWHAE (Dollars per Hour)', color='orange')
ax2.tick_params(axis='y', labelcolor='orange')

# Set the title and show the plot
plt.title('AHEMAN vs. AWHAE Over Time')
fig.tight_layout()  # Adjust layout to prevent overlap
plt.show()

# Calculate the Pearson correlation coefficient
correlation = merged_data['AHEMAN'].corr(merged_data['AWHAE'])
print(f'Pearson Correlation Coefficient: {correlation}')

import statsmodels.api as sm

# Define the independent variable (AHEMAN) and dependent variable (AWHAE)
X = merged_data['AHEMAN']
y = merged_data['AWHAE']

# Add a constant to the independent variable (required for OLS regression)
X = sm.add_constant(X)

# Perform the OLS regression
model = sm.OLS(y, X).fit()

# Print the summary of the regression
print(model.summary())


import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from IPython.display import HTML
import textwrap

# Generate predictions from the regression model
y_pred = model.predict(X)

# Create a figure and axes for the dashboard
fig, axs = plt.subplots(2, 2, figsize=(18, 14), facecolor='#000000')
plt.subplots_adjust(hspace=0.4, wspace=0.4)

# Plot 1: Dual Y-Axis Line Graph - AHEMAN vs AWHAE (Index)
ax1 = axs[0, 0]
ax1.plot(merged_data.index, merged_data['AHEMAN'], color='#00FFFF', label='AHEMAN (Hourly Earnings)', zorder=3)
ax1.set_xlabel('Date', fontsize=18, color='white')
ax1.set_ylabel('AHEMAN (Dollars per Hour)', fontsize=18, color='white')
ax1.tick_params(axis='both', labelsize=14, colors='white')
ax1.set_facecolor('#000000')
ax1.spines['left'].set_color('white')
ax1.spines['bottom'].set_color('white')
ax1.set_title('AHEMAN vs AWHAE Over Time', fontsize=22, color='white', pad=35)

# Create a second y-axis for AWHAE
ax2 = ax1.twinx()
ax2.plot(merged_data.index, merged_data['AWHAE'], color='#FF00FF', label='AWHAE (Index)', zorder=3)
ax2.set_ylabel('AWHAE (Index of Aggregate Weekly Hours)', fontsize=18, color='white')
ax2.tick_params(axis='y', labelcolor='#FF00FF')
ax2.spines['right'].set_color('#FF00FF')

# Legends for both axes
ax1.legend(loc='upper left', fontsize=14, facecolor='#181818', edgecolor='white', labelcolor='#00FFFF')
ax2.legend(loc='upper right', fontsize=14, facecolor='#181818', edgecolor='white', labelcolor='#FF00FF')

# Plot 2: Scatter Plot with Regression Line
axs[0, 1].scatter(merged_data['AHEMAN'], merged_data['AWHAE'], color='#FF00FF', label='Actual Data', edgecolor='white')
axs[0, 1].plot(merged_data['AHEMAN'], y_pred, color='#00FFFF', label='Regression Line', zorder=4)
axs[0, 1].set_facecolor('#000000')
axs[0, 1].set_xlabel('AHEMAN (Hourly Earnings)', fontsize=18, color='white')
axs[0, 1].set_ylabel('AWHAE (Index of Aggregate Weekly Hours)', fontsize=18, color='white')
axs[0, 1].tick_params(axis='both', labelsize=14, colors='white')
axs[0, 1].spines['left'].set_color('white')
axs[0, 1].spines['bottom'].set_color('white')
axs[0, 1].set_title('Scatter Plot of AHEMAN vs AWHAE', fontsize=22, color='white', pad=35)
axs[0, 1].legend(loc='upper left', fontsize=14, facecolor='#181818', edgecolor='white', labelcolor='white')

# Plot 3: Pearson Correlation Coefficient (Cyberpunk Theme)
correlation = merged_data['AHEMAN'].corr(merged_data['AWHAE'])
axs[1, 0].text(0.5, 0.5, f'Pearson Correlation Coefficient (Adjusted Data): {correlation:.2f}',
               transform=axs[1, 0].transAxes, fontsize=20, verticalalignment='center',
               horizontalalignment='center', bbox=dict(boxstyle='round', facecolor='#000000',
               edgecolor='#FF00FF'), color='white')  # Font is white, outline is cyberpunk pink
axs[1, 0].axis('off')

# Plot 4: Linear Regression Summary (Text wrapping and resizing for better fit)
summary_text = model.summary().as_text()

# Wrapping the OLS summary for smaller width
wrapped_summary = "\n".join(textwrap.fill(line, width=55) for line in summary_text.splitlines())  # Adjust width to fit

axs[1, 1].text(0.5, 0.5, f'Linear Regression Summary:\n\n{wrapped_summary}',
               transform=axs[1, 1].transAxes, fontsize=10, color='white',
               horizontalalignment='center', verticalalignment='center',
               bbox=dict(facecolor='#000000', edgecolor='#FF00FF', boxstyle='round,pad=0.5'))
axs[1, 1].axis('off')

# Adjust layout to prevent overlap
plt.tight_layout()

# Show the complete dashboard
plt.show()