matplotlib小技巧

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LogNorm

def calculate_density(data, x_divided = 100, y_divided = 100):
    X_min, X_max = data[:,0].min(), data[:,0].max()
    Y_min, Y_max = data[:,1].min(), data[:,1].max()

    # X_min, X_max, Y_min, Y_max = 0, 1, 0, 1
    x_window = (X_max - X_min) / x_divided
    y_window = (Y_max - Y_min) / y_divided
    # print(X_min, X_max)
    x_position = np.arange(X_min, X_max, x_window)
    y_position = np.arange(Y_min, Y_max, y_window)

    # x_position = [1,2,3]
    # y_position = [3,4,5]

    x,y = np.array(np.meshgrid(x_position, y_position, indexing='ij'))
    position = np.column_stack((x.ravel(), y.ravel()))

    print("pos:",position.shape, len(position))
    print(position)
    density = np.zeros(len(position))
    
    for i in range(len(position)):
        x_min, x_max = position[i][0] - x_window/2, position[i][0] + x_window/2
        y_min, y_max = position[i][1] - y_window/2, position[i][1] + y_window/2
        mask = (data[:, 0] >= x_min) & (data[:, 0] <= x_max) & \
               (data[:, 1] >= y_min) & (data[:, 1] <= y_max)
        density[i] = np.sum(mask)
    return position, density

def scatter(data, x_divided = 100, y_divided = 100, s = 5, save = ""):
    # 创建颜色映射
    plt.cla()
    position, density = calculate_density(data, x_divided, y_divided)
    norm_density = density / np.max(density)
    cmap = plt.cm.viridis  # 选择一个颜色映射

    # 绘制散点图

    mask = norm_density==0
    position = position[~mask]
    norm_density = norm_density[~mask]

    plt.scatter(position[:, 0], position[:, 1], c=norm_density, cmap=cmap, alpha=1,s=s)

    # 添加颜色条
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=LogNorm(vmin = 1, vmax = np.max(density)))
    cb = plt.colorbar(sm)

    cb.ax.tick_params(labelsize=12)

    # plt.xlim(0,1)
    # plt.ylim(0,1)

    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)

    plt.tight_layout()

    if save != "":
        print("save_path:", save)
        plt.savefig(save)
    plt.show()

Data = []
for i in range(4):
    mean = np.random.uniform(0.3,0.5)
    std = np.random.uniform(0.2,0.3)
    lent = int(10000*np.random.rand())
    data = np.random.normal(mean, std, lent)
    

    dmin = np.random.uniform(0.0, 0.15)
    dmax = np.random.uniform(0.5, 0.75)
    data = data[data>dmin]
    data = data[data<dmax]

    print(mean, std, data.shape)
    print(data.mean(), data.std())

    Data.append(data)

fig, ax = plt.subplots()

for i, data in enumerate(Data):
    ax.boxplot(data, positions=[i], widths=0.3, 
            showfliers=False,    
            medianprops={'color': f'C{i}', 'linewidth': 2}, 
            whiskerprops={'color': f'C{i}', 'linewidth': 2},  
            capprops={'color': f'C{i}', 'linewidth': 2},      
            boxprops={'color': f'C{i}',  'linewidth': 2})
    

# # 绘制连接线
x = [(0,3),(0,2),(1,3),(1,2),(2,3)]
print(len(x))
y = np.linspace(0.98, 0.75, len(x))


for i in range(len(x)):
    x1, x2 = x[i]
    ax.plot([x1, x2], [y[i], y[i]], color = 'black', linewidth=1)
    ax.plot([x1, x1], [y[i]-0.02, y[i]], color = 'black', linewidth=1)
    ax.plot([x2, x2], [y[i]-0.02, y[i]], color = 'black', linewidth=1)
    ax.text((x1+x2)/2, y[i], "***", horizontalalignment='center')

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.xticks(fontsize=10)
plt.yticks(fontsize=12)
plt.ylim(-0.05, 1.00)

plt.tight_layout()


plt.savefig("box_plot.png")

plt.show()