python数据可视化插值法折线图补全时间序列可调横坐标间距不改变节点数拟合/插值合二为一

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python数据可视化插值法折线图补全时间序列可调横坐标间距不改变节点数

1.优点

 1. 利用插值算法，补全时间序列
 2. 不均匀分布（原始数据）——>均匀（处理后的数据）
 3. 横坐标间距可调
 4. 曲线圆润，配合回归方程预测趋势

代码1

import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
import time

#显示中文标签
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

# 原数据
x = [1611121375, 1611121460, 1611121546, 1611121631, 1611121717, 1611121802, 1611121888, 1611121974, 1611122060, 1611122145, 1611122231, 1611122316, 1611122402, 1611122488, 1611122574, 1611122660, 1611122746, 1611122832, 1611122917, 1611123003, 1611123089, 1611123174, 1611123260, 1611123346, 1611123432, 1611123517, 1611123603, 1611123688, 1611123774, 1611123860, 1611123946, 1611124031, 1611124117, 1611124203, 1611124289, 1611124374, 1611124460, 1611124546, 1611124631, 1611124717, 1611124802, 1611124888, 1611124974, 1611125059, 1611125145, 1611125231, 1611125316, 1611125402, 1611125487, 1611125573, 1611125658, 1611125744, 1611125829, 1611125915, 1611126001, 1611126086, 1611126172, 1611126258, 1611126343, 1611126429, 1611126516, 1611126601, 1611126687, 1611126773, 1611126858, 1611126944, 1611127030, 1611127116, 1611127201, 1611127287, 1611127373, 1611127458, 1611127544, 1611127630, 1611127715, 1611127801, 1611127887, 1611127972, 1611128058, 1611128143, 1611128229, 1611128316, 1611128401, 1611128487, 1611128573, 1611128659, 1611128744, 1611128832, 1611128918, 1611129004, 1611129089, 1611129175, 1611129261, 1611129346]
y = [84, 84, 84, 84, 85, 84, 85, 85, 85, 86, 87, 87, 87, 88, 88, 88, 88, 89, 89, 90, 90, 91, 91, 91, 91, 92, 92, 92, 92, 93, 93, 93, 93, 93, 94, 94, 94, 95, 95, 95, 95, 95, 96, 96, 97, 97, 98, 98, 99, 99, 99, 100, 100, 101, 102, 102, 103, 103, 103, 103, 103, 104, 105, 106, 106, 106, 106, 107, 108, 108, 109, 109, 110, 110, 110, 111, 112, 113, 113, 114, 114, 114, 114, 114, 116, 96, 76, 59, 59, 58, 58, 58, 58, 58]

# 设置时间粒度，原数据是时间戳，单位是秒，转换为分钟；这个数也可以用于控制插入值的数量，从而影响折线平滑度，越小越平滑越耗时
min = 0.5
# 生成x的矩阵
xnew = np.arange(x[0], x[-1], 60*min, int)
# 生成转换函数，这里参数是三维的'cubic'，根据不同数据可做修改
func = interpolate.interp1d(x, y, kind='cubic')
# 计算新y值
ynew = func(xnew)
# 将时间戳转换为时间
xnew = [time.strftime('%m%d %H:%M', time.localtime(i)) for i in xnew]

# 设置x,y轴代表意思
plt.xlabel("时间")
plt.ylabel("热度")
plt.plot(xnew, ynew)

# 要显示的x轴，不会改变数据，只会改变x的标识，传入一个列表和风格参数
plt.xticks(xnew[0:-1:10],rotation=90)

# 显示
plt.show()

运行结果1
插值法折线图

2.利用插入后的数据进行拟合分析（只修改虚线内）

代码2

import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
import time

#显示中文标签
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

# 原数据
x = [1611121375, 1611121460, 1611121546, 1611121631, 1611121717, 1611121802, 1611121888, 1611121974, 1611122060, 1611122145, 1611122231, 1611122316, 1611122402, 1611122488, 1611122574, 1611122660, 1611122746, 1611122832, 1611122917, 1611123003, 1611123089, 1611123174, 1611123260, 1611123346, 1611123432, 1611123517, 1611123603, 1611123688, 1611123774, 1611123860, 1611123946, 1611124031, 1611124117, 1611124203, 1611124289, 1611124374, 1611124460, 1611124546, 1611124631, 1611124717, 1611124802, 1611124888, 1611124974, 1611125059, 1611125145, 1611125231, 1611125316, 1611125402, 1611125487, 1611125573, 1611125658, 1611125744, 1611125829, 1611125915, 1611126001, 1611126086, 1611126172, 1611126258, 1611126343, 1611126429, 1611126516, 1611126601, 1611126687, 1611126773, 1611126858, 1611126944, 1611127030, 1611127116, 1611127201, 1611127287, 1611127373, 1611127458, 1611127544, 1611127630, 1611127715, 1611127801, 1611127887, 1611127972, 1611128058, 1611128143, 1611128229, 1611128316, 1611128401, 1611128487, 1611128573, 1611128659, 1611128744, 1611128832, 1611128918, 1611129004, 1611129089, 1611129175, 1611129261, 1611129346]
y = [84, 84, 84, 84, 85, 84, 85, 85, 85, 86, 87, 87, 87, 88, 88, 88, 88, 89, 89, 90, 90, 91, 91, 91, 91, 92, 92, 92, 92, 93, 93, 93, 93, 93, 94, 94, 94, 95, 95, 95, 95, 95, 96, 96, 97, 97, 98, 98, 99, 99, 99, 100, 100, 101, 102, 102, 103, 103, 103, 103, 103, 104, 105, 106, 106, 106, 106, 107, 108, 108, 109, 109, 110, 110, 110, 111, 112, 113, 113, 114, 114, 114, 114, 114, 116, 96, 76, 59, 59, 58, 58, 58, 58, 58]

# 设置时间粒度，原数据是时间戳，单位是秒，转换为分钟；这个数也可以用于控制插入值的数量，从而影响折线平滑度，越小越平滑越耗时
min = 0.5
# 生成x的矩阵
xnew = np.arange(x[0], x[-1], 60*min, int)
# 生成转换函数，这里参数是三维的'cubic'，根据不同数据可做修改
func = interpolate.interp1d(x, y, kind='cubic')
# 计算新y值
ynew = func(xnew)

'''--------------------------------------------------------------------------------'''
# 线性拟合y值，3次多项式，可根据需要调整幂
z1 = np.polyfit(xnew, ynew, 3)  # 曲线拟合，返回值为多项式的各项系数
p1 = np.poly1d(z1)  # 返回值为多项式的表达式，也就是函数式子
yvals = p1(xnew)  # 也可以使用yvals=np.polyval(z1,x)
'''--------------------------------------------------------------------------------'''

# 将时间戳转换为时间
xnew = [time.strftime('%m%d %H:%M', time.localtime(i)) for i in xnew]

# 设置x,y轴代表意思
plt.xlabel("时间")
plt.ylabel("热度")

plt.plot(xnew, ynew, '*')
plt.plot(xnew, yvals)

# 要显示的x轴，不会改变数据，只会改变x的标识，传入一个列表和风格参数
plt.xticks(xnew[0:-1:10],rotation=90)

# 显示
plt.show()