対応のあるデータのグラフのPythonコード

import matplotlib.pyplot as plt
import numpy as np
import random
import matplotlib
from matplotlib import rcParams
import matplotlib.colors as mcolors
rcParams['pdf.fonttype'] = 42
# フォントサイズ
plt.rcParams.update({'font.size': 10})
# 10個の群のデータポイントを直接入力
values_groups = [
    [40, 89, 78, 60],
    [30, 48, 50, 28]
    
]

# x位置を決定
x_positions = list(range(1, 3))

# 色とマーカーのリスト
colors = ['green', 'green']
markers = ['D', 'D']
labels = [
    'Group A', 'Group B'
]

# プロットの準備
plt.figure(figsize=(1.5, 3.5))
# 各群のデータをプロット
for i, values in enumerate(values_groups):
    x_positions_group = [x_positions[i]] * len(values)
    
    # colors[i] を alpha=0.3 に設定
    color_with_alpha = mcolors.to_rgba(colors[i], alpha=0.3)
    
    # 箱ひげ図をプロット（最背面）
    plt.boxplot(values, positions=[x_positions[i]], widths=0.6, patch_artist=True,
                boxprops=dict(facecolor=color_with_alpha, edgecolor='black', linewidth=1),
                medianprops=dict(color='black'),
                whiskerprops=dict(color='black'),
                capprops=dict(color='black'),
                flierprops=dict(marker='o', color='black', alpha=0.5))
    
# プロットされた個別値の座標を書き出し、それらをつなぐ
for n in range(0, len(values_groups) - 1, 2):
    group1_values = values_groups[2 * n]
    group2_values = values_groups[2 * n + 1]
    group1_x = [x_positions[2 * n]] * len(group1_values)
    group2_x = [x_positions[2 * n + 1]] * len(group2_values)
    for idx in range(min(len(group1_values), len(group2_values))):
        x1 = group1_x[idx]
        x2 = group2_x[idx]
        y1 = group1_values[idx]
        y2 = group2_values[idx]
        plt.plot([x1, x2], [y1, y2], color='gray', linestyle='-', linewidth=1, zorder=3)

# 各群のデータをプロット
for i, values in enumerate(values_groups):
    x_positions_group = [x_positions[i]] * len(values)
    




# 各群のデータをプロット（箱ひげ図の後にプロット）
for i, values in enumerate(values_groups):
    x_positions_group = [x_positions[i]] * len(values)

    marker_style = markers[i % len(markers)]
    if (i + 1) % 2 == 0:
        plt.scatter(x_positions_group, values, edgecolors=colors[i], marker=marker_style, facecolors='White', label=labels[i], zorder=4)
    else:
        plt.scatter(x_positions_group, values, color=colors[i],edgecolors='black',linewidth=0.5, marker=marker_style, label=labels[i], zorder=4)

# T検定を行い、p値を求める
from scipy.stats import ttest_rel
p_values = []
for i in range(0, len(values_groups) - 1, 2):
    t_stat, p_value = ttest_rel(values_groups[i], values_groups[i + 1])
    p_values.append((f'Group {i + 1} vs Group {i + 2}', p_value))

# T検定結果の表示
for i, (comparison, p_value) in enumerate(p_values):
    group1 = i * 2 + 1
    group2 = i * 2 + 2
    x_mid = (x_positions[group1 - 1] + x_positions[group2 - 1]) / 2
    y_max = max(max(values_groups[group1 - 1]), max(values_groups[group2 - 1]))
    plt.plot([x_positions[group1 - 1] , x_positions[group2 - 1] ], [y_max + 2.2, y_max + 2.2], color='black',linewidth=0.9)
    if p_value < 0.05:
        if p_value < 0.0001:
            plt.text(x_mid, y_max + 2.2, '***', ha='center', color='black')
        elif p_value < 0.001:
            plt.text(x_mid, y_max + 2.2, '**', ha='center', color='black')
        else:
            plt.text(x_mid, y_max + 2.2, '*', ha='center', color='black')
        
        # P値を適切な桁数で表示
        if p_value < 0.0005:
            formatted_p_value = f'{p_value:.5f}'
        elif p_value < 0.005:
            formatted_p_value = f'{p_value:.4f}'
        elif p_value < 0.05:
            formatted_p_value = f'{p_value:.3f}'
        else:
            formatted_p_value = f'{p_value:.2f}'
        plt.text(x_mid, y_max + 6, f'p = {formatted_p_value}', ha='center', color='black', fontsize=6)
    else:
        plt.text(x_mid, y_max + 3, 'n.s.', ha='center', color='black')
        formatted_p_value = f'{p_value:.3f}'
        plt.text(x_mid, y_max + 6, f'p = {formatted_p_value}', ha='center', color='black', fontsize=6)
        
# 軸の線の太さを設定
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['bottom'].set_linewidth(1)
plt.gca().spines['left'].set_linewidth(1)

# y軸の補助目盛の設定
plt.tick_params(axis='y', which='both', length=4, width=1)
plt.ylim(bottom=25, top=95)
plt.yticks([30, 40 , 50, 60 ,70, 80, 90])

# x軸の補助目盛の設定
plt.tick_params(axis='x', which='both', length=4, width=1)
plt.ylabel('Value')
plt.xticks(
    x_positions,
    ['Goup A', 'Group B'],
    
)
plt.xlabel('Sample')

# 補助線の設定
plt.grid(False)

# 凡例の位置を調整
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
plt.savefig('Sample note.svg',transparent=False, bbox_inches="tight")
plt.show()