normalized code

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import stumpy
import numpy as np

data = pd.read_csv('https://docs.google.com/spreadsheets/d/1SQfhz7_FLaA7aADKdMrf2SL1p2TgQQfnH-earg1eOCY/export?gid=0&format=csv')

def min_max_scaling(column):
    min_val = column.min()
    max_val = column.max()
    normalized_column = (column - min_val) / (max_val - min_val)
    return normalized_column

data['price'] = min_max_scaling(data['price'])
data['Market Cap'] = min_max_scaling(data['Market Cap'])



first_data = data["price"]
second_data = data["Market Cap"]

m = 30
Q = first_data.tail(30)
T = second_data

distance_profile = stumpy.mass(Q, T, normalize=True)
idx = np.argmin(distance_profile)


time_series = first_data

# Number of latest points to highlight
highlight_points = 30

# Range to mark
start_range = idx
end_range = idx + m

# Create the first plot with the gray rectangle for the latest 30 values
plt.figure(figsize=(10, 6))
plt.subplot(2, 1, 1)
plt.plot(time_series, label='Time Series')
latest_data = time_series[-highlight_points:]
x_coordinates = np.arange(len(time_series) - highlight_points, len(time_series))
y_min = np.min(time_series)
y_max = np.max(time_series)
rect = plt.Rectangle(
    (x_coordinates[0], y_min),
    highlight_points,
    y_max - y_min,
    fill=True,
    color='gray',
    alpha=0.5,
)
plt.gca().add_patch(rect)
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('Time Series with Gray Rectangle for Latest 30 Values (first_data)')
plt.legend()

# Create the second plot with the gray rectangle for the specified range
plt.subplot(2, 1, 2)
plt.plot(second_data, label='Time Series')
plt.axvspan(start_range, end_range, color='gray', alpha=0.5)
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('The other graph in which I have to find the similar pattern (second_data)')
plt.legend()

# Adjust layout
plt.tight_layout()

# Show the plots
plt.show()