Python使用SciencePlots库绘制学术图表详解

pip install SciencePlots

import matplotlib.font_manager as fm
import os
# 字体文件夹路径
font_dir = r'C:\Windows\Fonts'  # Windows系统字体路径
# 获取字体文件
font_files = fm.findSystemFonts(fontpaths=[font_dir])
# 添加字体
for font_file in font_files:
    fm.fontManager.addfont(font_file)

import matplotlib.pyplot as plt
import scienceplots
# 基础科学风格
plt.style.use(['science'])
# 科学风格 + 明亮主题 + 中文支持
plt.style.use(['science', 'bright', 'no-latex', 'cjk-sc-font'])
# IEEE期刊风格
plt.style.use(['science', 'ieee'])

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import scienceplots
# 设置绘图风格
plt.style.use(['science', 'bright', 'no-latex','grid', 'cjk-sc-font'])
def create_custom_cmap():
    """创建自定义渐变色映射"""
    # 定义颜色
    color_a_hex = "#C6DFDF"
    color_b_hex = "#6a73cf"
    def hex_to_rgb(hex_color):
        hex_color = hex_color.lstrip('#')
        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
    def interpolate_color(color1, color2, steps):
        delta_r = (color2[0] - color1[0]) / steps
        delta_g = (color2[1] - color1[1]) / steps
        delta_b = (color2[2] - color1[2]) / steps
        colors = []
        for i in range(steps + 1):
            r = color1[0] + delta_r * i
            g = color1[1] + delta_g * i
            b = color1[2] + delta_b * i
            colors.append((r/255.0, g/255.0, b/255.0))
        return colors
    color_a = hex_to_rgb(color_a_hex)
    color_b = hex_to_rgb(color_b_hex)
    colors = interpolate_color(color_a, color_b, 100)
    return ListedColormap(colors)
# 创建示例数据
categories = ['类别A', '类别B', '类别C', '类别D']
values = [4, 3, 5, 2]
# 创建图表
fig, ax = plt.subplots(figsize=(6, 4))
# 创建渐变色映射
cmap = create_custom_cmap()
# 绘制柱状图，为每个柱子设置不同的颜色
bars = ax.bar(categories, values)
# 为每个柱子设置渐变色
for i, bar in enumerate(bars):
    # 根据值的大小计算颜色索引
    color_idx = int((values[i] - min(values)) / (max(values) - min(values)) * 99)
    bar.set_color(cmap(color_idx))
# 设置标签和标题
ax.set_xlabel('分类')
ax.set_ylabel('数值')
ax.set_title('渐变色柱状图示例')
# 调整布局
plt.tight_layout()
# 显示图表
plt.show()

plt.style.use(['science', 'bright', 'no-latex'])
# 创建图表
fig, ax = plt.subplots(figsize=(8, 6))
# 设置边距
plt.tight_layout(pad=1.5)
# 添加网格线
ax.grid(True, linestyle='--', alpha=0.7)
# 设置图例位置
ax.legend(loc='upper right', bbox_to_anchor=(1.15, 1))

# 设置高DPI输出
plt.savefig('figure.png', dpi=300, bbox_inches='tight')