suplfig2.py

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np

# Load data for the other plots
CSmethod = ""
while CSmethod not in ['SPARTA', 'UCBShift', 'ShiftX', 'consensus']:
    CSmethod = input("What CS prediciton method to use? [SPARTA, UCBShift, ShiftX, consensus]")

data_source_file = './csp_stats_' + CSmethod + '.csv'
df = pd.read_csv(data_source_file)

# Setting up the figure and subplots
fig, axs = plt.subplots(2, 2, figsize=(20, 20), gridspec_kw={'height_ratios': [1, 1], 'width_ratios': [1, 1]})
plt.subplots_adjust(hspace=0.4, wspace=0.4)  # Increase spacing between subplots

# Calculate residuals for each pair
df['residual_2'] = df['consensus_AF3'] - df['consensus']

sns.scatterplot(ax=axs[0, 0], x=df['consensus'], y=df['consensus_AF3'])
axs[0, 0].plot([0, 1], [0, 1], color='red')  # y=x line
axs[0, 0].set_title('NMR vs AF3 CSP_Rank_Scores', fontsize=20)
axs[0, 0].set_xlabel('PDB Score', fontsize=20)
axs[0, 0].set_ylabel('AF3 Score', fontsize=20)
axs[0, 0].text(0.7, 0.1, 'PDB>AF3', color='red', fontweight='bold', fontsize=16, verticalalignment='bottom', horizontalalignment='left', transform=axs[0, 0].transAxes)
axs[0, 0].text(0.1, 0.9, 'AF3>PDB', color='red', fontweight='bold', fontsize=16, verticalalignment='top', horizontalalignment='left', transform=axs[0, 0].transAxes)

# Histogram of residuals for consensus_AF2
data = df['residual_2']
max_abs_value = max(abs(data.min()), abs(data.max()))
symmetric_range = (-max_abs_value, max_abs_value)
bin_width = (symmetric_range[1] - symmetric_range[0]) / 20
bins = np.arange(symmetric_range[0], symmetric_range[1] + bin_width, bin_width)

sns.histplot(ax=axs[0, 1], data=data, kde=True, bins=bins)
axs[0, 1].set_title('Histogram of Residuals for CSP_Rank_Score', fontsize=20)
axs[0, 1].set_xlabel('Residual', fontsize=20)
axs[0, 1].set_ylabel('Frequency', fontsize=20)
axs[0, 1].axvline(x=0.0, color='red', linestyle='-', linewidth=2)
axs[0, 1].text(0.7, 0.7, 'AF3>PDB', color='red', fontweight='bold', fontsize=16, verticalalignment='bottom', horizontalalignment='left', transform=axs[0, 1].transAxes)
axs[0, 1].text(0.05, 0.7, 'PDB>AF3', color='red', fontweight='bold', fontsize=16, verticalalignment='top', horizontalalignment='left', transform=axs[0, 1].transAxes)

df['residual_3'] = df['consensus_AF3'] - df['consensus_AF2']

# Scatter plot for consensus vs consensus_AF2
sns.scatterplot(ax=axs[1, 0], x=df['consensus_AF2'], y=df['consensus_AF3'])
axs[1, 0].plot([0, 1], [0, 1], color='red')  # y=x line
axs[1, 0].set_title('AF2 vs AF3 CSP_Rank_Scores', fontsize=20)
axs[1, 0].set_xlabel('AF2 Score', fontsize=20)
axs[1, 0].set_ylabel('AF3 Score', fontsize=20)
axs[1, 0].text(0.7, 0.1, 'AF2>AF3', color='red', fontweight='bold', fontsize=16, verticalalignment='bottom', horizontalalignment='left', transform=axs[1, 0].transAxes)
axs[1, 0].text(0.1, 0.9, 'AF3>AF2', color='red', fontweight='bold', fontsize=16, verticalalignment='top', horizontalalignment='left', transform=axs[1, 0].transAxes)

# Histogram of residuals for consensus_AF2
data = df['residual_3']
max_abs_value = max(abs(data.min()), abs(data.max()))
symmetric_range = (-max_abs_value, max_abs_value)
bin_width = (symmetric_range[1] - symmetric_range[0]) / 20
bins = np.arange(symmetric_range[0], symmetric_range[1] + bin_width, bin_width)

sns.histplot(ax=axs[1, 1], data=data, kde=True, bins=bins)
axs[1, 1].set_title('Histogram of Residuals for CSP_Rank_Score', fontsize=20)
axs[1, 1].set_xlabel('Residual', fontsize=20)
axs[1, 1].set_ylabel('Frequency', fontsize=20)
axs[1, 1].axvline(x=0.0, color='red', linestyle='-', linewidth=2)
axs[1, 1].text(0.7, 0.7, 'AF3>AF2', color='red', fontweight='bold', fontsize=16, verticalalignment='bottom', horizontalalignment='left', transform=axs[1, 1].transAxes)
axs[1, 1].text(0.05, 0.7, 'AF2>AF3', color='red', fontweight='bold', fontsize=16, verticalalignment='top', horizontalalignment='left', transform=axs[1, 1].transAxes)

# Adjust spacing between subplots
plt.subplots_adjust(hspace=0.4, wspace=0.15)

plt.show()