# Let's import the basic libraries we need for plotting.
import warnings; warnings.filterwarnings('ignore')
import pandas as pd
import matplotlib.pyplot as plt

# Data setup
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv"
df = pd.read_csv(url)
df.head()

# Check the shape of the data
print('Shape:', df.shape)

# Convert Month to datetime for proper plotting
df['Month'] = pd.to_datetime(df['Month'])
df = df.set_index('Month')

# Plot the basic time series
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'], label='Monthly Passengers')
plt.title('Monthly Airline Passengers')
plt.xlabel('Month')
plt.ylabel('Number of Passengers')
plt.legend()
plt.show()

# Add a simple text annotation
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'], label='Monthly Passengers')
plt.title('Passengers with Text Annotation')
plt.xlabel('Month')
plt.ylabel('Number of Passengers')
plt.legend()
plt.annotate('See big jump',
             xy=(df.index[20], df['Passengers'].iloc[20]),
             xytext=(df.index[10], 250),
             arrowprops=dict(facecolor='black', arrowstyle='->'))
plt.show()

# Highlight the highest point
max_idx = df['Passengers'].idxmax()
max_val = df['Passengers'].max()
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'], label='Monthly Passengers')
plt.scatter([max_idx], [max_val], color='red', zorder=5)
plt.annotate(f'Peak: {max_val}', (max_idx, max_val),
             xytext=(max_idx, max_val+30),
             ha='center', color='red',
             arrowprops=dict(facecolor='red', arrowstyle='->'))
plt.title('Highlight the Peak')
plt.legend()
plt.show()

# Drawing an important time window using a colored span
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
plt.axvspan(df.index[12], df.index[24], color='yellow', alpha=0.3)
plt.title('Highlight a Time Period')
plt.show()

# Highlight values above a certain threshold
threshold = 400
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
plt.axhline(threshold, color='green', linestyle='--', lw=2, label='Threshold')
over_idx = df[df['Passengers'] > threshold].index
over_val = df['Passengers'][df['Passengers'] > threshold]
plt.scatter(over_idx, over_val, color='green')
plt.title('Highlight All Points Above 400 Passengers')
plt.legend()
plt.show()

# Add a rectangle using patches
import matplotlib.patches as patches
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(df.index, df['Passengers'])
rect = patches.Rectangle((df.index[40], 100),
                         df.index[10]-df.index[0], 400,
                         linewidth=2, edgecolor='purple', facecolor='none')
ax.add_patch(rect)
plt.title('Rectangle Highlight for Special Period')
plt.show()

# Highlight weekends over a year (example of iteration and annotation)
import numpy as np
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
for idx in df.index[:12]:
    if idx.month in [6,7,8]:
        plt.axvline(idx, color='pink', alpha=0.3)
plt.title('Highlight Summer Months')
plt.show()

# Annotating with images (advanced, optional)
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
# Example uses a small matrix as an image (no file needed)
arr = np.zeros((10,10))
arr[2:8,2:8] = 1
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(df.index, df['Passengers'])
imagebox = OffsetImage(arr, cmap='Blues', zoom=1)
ab = AnnotationBbox(imagebox, (df.index[30], df['Passengers'].iloc[30]), frameon=False)
ax.add_artist(ab)
plt.title('Add a Simple Icon Annotation')
plt.show()

# Add multiple annotations in a loop (quick summary markers)
highlight_idx = [5, 15, 25]
values = df['Passengers'].iloc[highlight_idx]
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
for i, idx in enumerate(highlight_idx):
    plt.annotate(f'Point {i+1}', (df.index[idx], values.iloc[i]),
                 xytext=(df.index[idx], values.iloc[i]+20),
                 ha='center', arrowprops=dict(arrowstyle='->'))
plt.title('Multiple Highlighted Events')
plt.show()

# Interactive annotation: ask user for a label and point
point = int(input('Which data point (0-143) would you like to annotate? '))
label = input('What label should I use? ')
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
plt.annotate(label, (df.index[point], df['Passengers'].iloc[point]),
             xytext=(df.index[point], df['Passengers'].iloc[point] + 30),
             arrowprops=dict(arrowstyle='->', color='blue'))
plt.title('Your Annotation')
plt.show()

# Challenge: highlight largest year-over-year jump
diff = df['Passengers'].diff(12)
biggest = diff.idxmax()
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
plt.annotate('Largest jump', (biggest, df.loc[biggest, 'Passengers']),
             xytext=(biggest, df.loc[biggest, 'Passengers']+40),
             arrowprops=dict(facecolor='orange', arrowstyle='->'))
plt.title('Challenge: Largest Yearly Increase')
plt.show()

# Troubleshooting: Overlapping labels? Use adjust_text for smart layout
# !pip install adjustText  # Uncomment to install if needed
from adjustText import adjust_text
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
texts = []
for i in [10, 20, 30, 40, 50]:
    texts.append(plt.text(df.index[i], df['Passengers'].iloc[i]+20, f'P{i}', color='crimson'))
adjust_text(texts, arrowprops=dict(arrowstyle='->'))
plt.title('No Overlap: Smart Label Placement')
plt.show()

# Extra tip: Remove an annotation (refresh the plot)
plt.figure(figsize=(10,5))
plt.plot(df.index, df['Passengers'])
plt.title('No Annotation - Clean Slate')
plt.show()