ms762wu/EMG_Biometrics_2021
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chore: fft func now operates with np arrays

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Skudalen 2021-06-24 09:48:30 +02:00
parent 9e60922875
commit 996ded965e
1 changed files with 9 additions and 5 deletions
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14
Signal_prep.py
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@ -1,7 +1,7 @@
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from pandas.core.frame import DataFrame from pandas.core.frame import DataFrame
from scipy.fft import fft from scipy.fft import fft, fftfreq
import Handle_emg_data as Handler import Handle_emg_data as Handler
@ -96,12 +96,13 @@ def load_user_emg_data():
return csv_handler.data_container_dict return csv_handler.data_container_dict
# Takes in a df and outputs np arrays for x and y values
def prep_df_for_trans(df:DataFrame): def prep_df_for_trans(df:DataFrame):
sample_rate = SAMPLE_RATE sample_rate = SAMPLE_RATE
min, duration = Handler.get_min_max_timestamp(df) min, duration = Handler.get_min_max_timestamp(df)
x = np.linspace(0, duration, sample_rate * duration, endpoint=False) x = np.linspace(0, duration, sample_rate * duration, endpoint=False)
y = np.array(df.iloc(1)) y = np.array(df.iloc(1))
return x, y return x, y, duration
def normalize_wave(y_values): def normalize_wave(y_values):
y = np.int16((y_values / y_values.max()) * 32767) y = np.int16((y_values / y_values.max()) * 32767)
@ -109,9 +110,12 @@ def normalize_wave(y_values):
def transformed_df(df:DataFrame): def transformed_df(df:DataFrame):
x, y = prep_df_for_trans(df) x_values, y_values, duration = prep_df_for_trans(df)
y_values = fft(y) N = SAMPLE_RATE * duration
return None norm = normalize_wave(y_values)
x_f = fftfreq(N, 1 / SAMPLE_RATE)
y_f = fft(norm)
return x_f, y_f
def plot_df(df:DataFrame): def plot_df(df:DataFrame):
lines = df.plot.line(x='timestamp') lines = df.plot.line(x='timestamp')