时间序列的傅里叶结果解释Python

Question

我已经传递了我的时间序列数据，它本质上是通过傅里叶变换从传感器测量压力的测量值，类似于 https://towardsdatascience.com/fourier-transform-for-time-series-292eb887b101。 使用的文件可以在这里找到： https://docs.google.com/spreadsheets/d/1MLETSU5Trl5gLGO6pv32rxBsR8xZNkbK/edit?usp=sharing&ouid=110574180158524908052&rtpof=true&sd=true 相关代码是这样的：

import pandas as pd
import numpy as np
file='test.xlsx'
df=pd.read_excel(file,header=0)
#df=pd.read_csv(file,header=0)
df.head()
df.tail()
# drop ID
df=df[['JSON_TIMESTAMP','ADH_DEL_CURTAIN_DELIVERY~ADH_DEL_AVERAGE_ADH_WEIGHT_FB','ADH_DEL_CURTAIN_DELIVERY~ADH_DEL_ADH_COATWEIGHT_SP']]
# extract year month 
df["year"] = df["JSON_TIMESTAMP"].str[:4]
df["month"] = df["JSON_TIMESTAMP"].str[5:7]
df["day"] = df["JSON_TIMESTAMP"].str[8:10]
df= df.sort_values( ['year', 'month','day'],
          ascending = [True, True,True])
df['JSON_TIMESTAMP'] = df['JSON_TIMESTAMP'].astype('datetime64[ns]')
df.sort_values(by='JSON_TIMESTAMP', ascending=True)
df1=df.copy()
df1 = df1.set_index('JSON_TIMESTAMP')
df1 = df1[["ADH_DEL_CURTAIN_DELIVERY~ADH_DEL_AVERAGE_ADH_WEIGHT_FB"]]
import matplotlib.pyplot as plt
#plt.figure(figsize=(15,7))
plt.rcParams["figure.figsize"] = (25,8)
df1.plot()
#df.plot(style='k. ')
plt.show()
df1.hist(bins=20)
    from scipy.fft import rfft,rfftfreq

## https://towardsdatascience.com/fourier-transform-for-time-series-292eb887b101

# convert into x and y
x = list(range(len(df1.index)))
y = df1['ADH_DEL_CURTAIN_DELIVERY~ADH_DEL_AVERAGE_ADH_WEIGHT_FB']

# apply fast fourier transform and take absolute values
f=abs(np.fft.fft(df1))

# get the list of frequencies
num=np.size(x)
freq = [i / num for i in list(range(num))]

# get the list of spectrums
spectrum=f.real*f.real+f.imag*f.imag
nspectrum=spectrum/spectrum[0]

# plot nspectrum per frequency, with a semilog scale on nspectrum
plt.semilogy(freq,nspectrum)
nspectrum
type(freq)
freq= np.array(freq)
freq
type(nspectrum)
nspectrum = nspectrum.flatten()
# improve the plot by adding periods in number of days rather than  frequency
import pandas as pd
results = pd.DataFrame({'freq': freq, 'nspectrum': nspectrum})
results['period'] = results['freq'] / (1/365)
plt.semilogy(results['period'], results['nspectrum'])
# improve the plot by convertint the data into grouped per day  to avoid peaks
results['period_round'] = results['period'].round()
grouped_day = results.groupby('period_round')['nspectrum'].sum()
plt.semilogy(grouped_day.index, grouped_day)
#plt.xticks([1, 13, 26, 39, 52])

我的最终结果是这样的： 数据傅里叶变换的结果

我的问题是，这最终对我们的数据显示什么，以及直观上，最后一节的尖峰意味着什么？我能用这样的结果做什么？ 预先感谢大家！