这是我的代码：
[
{type:"a", index:0, value:0.1},
{type:"a", index:1, value:0.2},
...
]
并使用此表：
import scipy.stats.stats
import datetime
import numpy
import matplotlib.pyplot as plt
from scipy.stats.stats
import pearsonr
file= u"C:/Users/Nikita/Desktop/file.csv"
with open(file) as f:
rows = [line.strip().split(';') for line in f][:2482]
dates = [row[0] for row in rows]
dates = [datetime.datetime.strptime(d, "%d.%m.%y") for d in dates]
dol = [float(row[1]) for row in rows]
val = [float(row[2]) for row in rows]
plt.plot_date(dates, val, fmt='b-')
plt.plot_date(dates, dol, fmt='r-')
plt.figure()
plt.show()
win=1000
depen = []
for i in range(len(val)):
lo=max(0, i-win)
hi=min(len(val)-1, i+win)
depen.append(scipy.stats.stats.pearsonr(val[i-win:i+win], dol[i-win:i+win])[0])
print (depen)
plt.plot_date(dates, depen, fmt='r-')
plt.figure()
ply.show()
addepend = pearsonr(val, dol)[0]
print (addepend)
print (numpy.corrcoef(val, dol)[0, 1])
有什么问题？
15.06.05 10566.37 28.6521
16.06.05 10578.65 28.6275
17.06.05 10623.07 28.6024
18.06.05 10609.11 28.5841
21.06.05 10599.67 28.4765
22.06.05 10587.93 28.5497
23.06.05 10421.44 28.5528
etc.