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Eq_O2.py
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Eq_O2.py
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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
# Read the Excel file
ramp_file = pd.read_excel('ramp_processed.xlsx', sheet_name='2')
# Create subplots
fig, ax = plt.subplots(figsize=(10, 15))
dot_size = 50
# Plot Graph 1 - VE
sns.scatterplot(x='power', y='eq_o2', data=ramp_file, color='blue', label='Eq_O2', s=dot_size)
# Perform clustering on the data
X = ramp_file[['power', 'eq_o2']]
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
ramp_file['cluster'] = kmeans.labels_
# Define colors for regression lines
colors = ['green', 'blue'] # Example colors
# Fit regression lines to each cluster
for cluster in range(2):
cluster_data = ramp_file[ramp_file['cluster'] == cluster]
sns.regplot(x='power', y='eq_o2', data=cluster_data, scatter=False, color=colors[cluster])
ax.set_xlabel('Power (Watt)', fontweight='bold')
ax.set_ylabel('Equivalent O2 (VE/VO2)', fontweight='bold')
#Based on the regression lines, treshold can be visualized using vertical lines and x-ticks
# Draw dashed green line at x=170
plt.axvline(x=160, color='green', linestyle='--', label='Aerobic Treshold')
# Add tick for the Aerobic Treshold
ticks = [160]
# Add highest and lowest x-axis values
ticks.extend([ramp_file['power'].min(), ramp_file['power'].max()])
ax.set_xticks(ticks)
# General title
fig.suptitle("Equivalent O2", fontsize=16)
# Add legend
ax.legend()
# Adjust layout
plt.tight_layout()
# Show plot
plt.show()