一、功能介绍
输入：实测x、y数据
输出：必选：x、y散点图
必选：x、y线性拟合直线
可选：相关性、显著性分析结果显示1-1
1-2
二、代码2-1
import random
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
class Linearfitplot:
def __init__(self, x, y, legends=None, labels=None, fsize=(8, 8), show_info=1):
"""
:param x: 数据x列表
:param y: 数据y列表
:param legends: 图例名，默认为 "线性拟合结果", "实测值"
:param labels:坐标轴标题名，默认为 "数据x", "数据y"
:param show_info:是否显示拟合结果信息
"""
if legends is None:
legends = ["线性拟合结果", "实测值"]
if labels is None:
labels = ["数据x", "数据y"]
self.x = x
self.y = y
self.fsize = fsize
self.legends = legends
self.labels = labels
self.show_info = show_info
def change_legend(self, new_legends):
self.legends = new_legends
def change_label(self, new_labels):
self.labels = new_labels
def rsquared(self, show_info_or_not=0):
"""
:param show_info_or_not: 布尔类型，当其为真时显示信息
:param x:x数据序列
:param y:y数据序列
:return:
r: 相关系数
p：显著性
slope：曲线斜率
intercept:截距
"""
ws = 3 # 各参数保留的小数位数
check_p = "不显著"
slope, intercept, *useless = stats.linregress(self.x, self.y)
r, p = stats.pearsonr(self.x, self.y)
if p <= 0.01:
check_p = "非常显著**"
elif p <= 0.05:
check_p = "显著*"
slope, intercept, r, p = round(slope, ws), round(intercept, ws), round(r, ws), round(p, ws)
info = "y = {0}x + {1} r-square = {2} p：{3}；{4}".format(slope, intercept, round(r**2, ws), p, check_p)
if show_info_or_not:
return info
else:
return slope, intercept, r, p
def draw_plot(self, *args):
"""
绘制图像，包含散点图和拟合线
:param args:
:return:
"""
# 设置画布大小
plt.figure(figsize=self.fsize)
# 生成df
x_name, y_name = "x", "y"
dict_data = {"x": self.x,
"y": self.y
}
df = pd.DataFrame(dict_data)
# 计算并绘制拟合曲线
z1 = np.polyfit(x_data, y_data, 1)
p1 = np.poly1d(z1) # 将系数代入方程，得到函式p1
yvals_data = p1(df[x_name])
# 绘制曲线
sns.set_style("darkgrid")
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文
sns.set_context("talk", font_scale=1)
sns.lineplot(x_data, yvals_data, color="r", lw=1, label=self.legends[0])
sns.scatterplot(x=x_name, y=y_name, data=df, *args, label=self.legends[1])
plt.xlabel(self.labels[0])
plt.ylabel(self.labels[1])
info_show = self.rsquared(self.show_info)
plt.text(self.fsize[0] * 0.6, self.fsize[0] * 0.1, info_show, size=self.fsize[0] * 1.8)
plt.tight_layout()
plt.show()
if __name__ == "__main__":
len_data = 10 # 测试数据序列的长度
x_data = list(range(len_data)) # x轴数据序列
y_data = [i*2+1+random.uniform(0, 1) for i in x_data]
plot1 = Linearfitplot(x_data, y_data, fsize=(10, 6.18))
plot1.draw_plot()
