[2022 Fall] Final Exam - Question 4¶

Course: AP3021

In [1]:

import matplotlib.pyplot as plt
import numpy as np

import matplotlib.pyplot as plt import numpy as np

In [2]:

stnno,yyyymmddhh,Pressure,Rain,Temp,Wind = np.loadtxt("./data/ground_station_data.txt",unpack="true")

stnno,yyyymmddhh,Pressure,Rain,Temp,Wind = np.loadtxt("./data/ground_station_data.txt",unpack="true")

In [3]:

fig = plt.figure(figsize=(16,12))
plt.plot(Temp)
plt.title("Month Temperature")
plt.ylabel("Temperature")
plt.xlabel("Data_perhour")
plt.legend(['Original'])
plt.grid()

plt.show()

fig = plt.figure(figsize=(16,12)) plt.plot(Temp) plt.title("Month Temperature") plt.ylabel("Temperature") plt.xlabel("Data_perhour") plt.legend(['Original']) plt.grid() plt.show()

The given data is the hourly surface data of February 2019. Please apply FFT to remove the diurnal component of this data set.¶

In [4]:

from scipy.fft import fft, fftfreq, ifft

from scipy.fft import fft, fftfreq, ifft

Temperature¶

In [5]:

xs = np.arange(0, 672, 0.1)
Y = np.fft.fft(Temp)

fig = plt.figure(figsize=(16,12))
plt.plot(Temp)
plt.plot(Y)
plt.title("Month Temperature")
plt.ylabel("Temperature")
plt.xlabel("Data_perhour")
plt.legend(['Original', 'with FFT'])
plt.grid()

plt.show()

xs = np.arange(0, 672, 0.1) Y = np.fft.fft(Temp) fig = plt.figure(figsize=(16,12)) plt.plot(Temp) plt.plot(Y) plt.title("Month Temperature") plt.ylabel("Temperature") plt.xlabel("Data_perhour") plt.legend(['Original', 'with FFT']) plt.grid() plt.show()

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/matplotlib/cbook/__init__.py:1340: ComplexWarning: Casting complex values to real discards the imaginary part
  return np.asarray(x, float)

Pressure¶

In [6]:

xs = np.arange(0, 672, 1)
ys = Pressure[xs]
Y = np.fft.fft((ys))

fig = plt.figure(figsize=(16,12))
plt.plot(xs, ys)
plt.plot(Y)
plt.title("Month Pressure")
plt.ylabel("Pressure")
plt.xlabel("Data_perhour")
plt.legend(['Original', 'with FFT'])
plt.grid()

plt.show()

xs = np.arange(0, 672, 1) ys = Pressure[xs] Y = np.fft.fft((ys)) fig = plt.figure(figsize=(16,12)) plt.plot(xs, ys) plt.plot(Y) plt.title("Month Pressure") plt.ylabel("Pressure") plt.xlabel("Data_perhour") plt.legend(['Original', 'with FFT']) plt.grid() plt.show()

Rain¶

In [7]:

xs = np.arange(0, 672, 1)
ys = Rain[xs]
Y = np.fft.fft((ys))

fig = plt.figure(figsize=(16,12))
plt.plot(xs, ys)
plt.plot(Y)
plt.title("Month Rain")
plt.ylabel("Rain")
plt.xlabel("Data_perhour")
plt.legend(['Original', 'with FFT'])
plt.grid()

plt.show()

xs = np.arange(0, 672, 1) ys = Rain[xs] Y = np.fft.fft((ys)) fig = plt.figure(figsize=(16,12)) plt.plot(xs, ys) plt.plot(Y) plt.title("Month Rain") plt.ylabel("Rain") plt.xlabel("Data_perhour") plt.legend(['Original', 'with FFT']) plt.grid() plt.show()

Wind¶

In [8]:

xs = np.arange(0, 672, 1)
ys = Wind[xs]
Y = np.fft.fft((ys))

fig = plt.figure(figsize=(16,12))
plt.plot(xs, ys)
plt.plot(Y)
plt.title("Month Wind")
plt.ylabel("Wind")
plt.xlabel("Data_perhour")
plt.legend(['Original', 'with FFT'])
plt.grid()

plt.show()

xs = np.arange(0, 672, 1) ys = Wind[xs] Y = np.fft.fft((ys)) fig = plt.figure(figsize=(16,12)) plt.plot(xs, ys) plt.plot(Y) plt.title("Month Wind") plt.ylabel("Wind") plt.xlabel("Data_perhour") plt.legend(['Original', 'with FFT']) plt.grid() plt.show()

In [ ]:

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Last update: 2024-04-27