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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import LogLocator, NullFormatter
def plot_boxplot(merged_data, protein_name):
"""
Create a boxplot of the intensity of 4 Scleroderma categories with a logarithmic scale.
"""
# Filter data by conditions
conditions = ["Healthy", "VEDOSS", "SSC_low", "SSC_high"]
custom_palette = {
"Healthy": "green",
"VEDOSS": "violet",
"SSC_low": "cyan",
"SSC_high": "red"
}
# Extract intensity values for each condition
data = [merged_data[merged_data['condition'] == condition]["Intensity"] for condition in conditions]
# Ensure no zeros or negatives for logarithmic scale
for i, condition_data in enumerate(data):
if (condition_data <= 0).any():
raise ValueError(f"Condition '{conditions[i]}' contains zero or negative values, which are invalid for a logarithmic scale.")
# Create the boxplot
fig, ax = plt.subplots(figsize=(12, 8))
bp = ax.boxplot(data, patch_artist=True)
# Set colors for the boxes
for patch, condition in zip(bp['boxes'], conditions):
patch.set_facecolor(custom_palette[condition])
# Set median line colors
for median in bp['medians']:
median.set_color('black')
# Set logarithmic y-axis
ax.set_yscale("log")
y_min = min([d.min() for d in data]) * 0.8
y_max = max([d.max() for d in data]) * 1.2
ax.set_ylim(bottom=y_min, top=y_max)
# Configure ticks and formatters
ax.yaxis.set_major_locator(LogLocator(base=10.0, subs=None, numticks=10))
ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1, numticks=10))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{int(x):g}" if x >= 1 else f"{x:.1g}"))
ax.yaxis.set_minor_formatter(NullFormatter())
ax.set_title(f"Box Plot for {protein_name}", fontsize=16)
ax.set_xticks(range(1, 5))
ax.set_xticklabels(conditions)
ax.set_ylabel("Intensity (Logarithmic Scale)", fontsize=12)
ax.grid(visible=True, linestyle="--", alpha=0.6)
plt.tight_layout()
# Plot graph
plt.show()
return plt
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