def spectral_analysis_noisy(df, column, bins = False):
if bins == 'Default':
bins = len(df)
timestep = df.index[1]
else:
timestep = df.index[1]
sampling_freq = 1/timestep
spec = stats_lib.fft(df[column].to_numpy(), int(sampling_freq), int(bins))
frequency = np.fft.fftfreq(int(bins), d=timestep)
return spec, frequency
def spectral_analysis(df, column, bins = False):
if bins == 'Default':
bins = len(df)
timestep = df.index[1]
else:
timestep = df.index[1]
sampling_freq = 1/timestep
spec = stats_lib.fft(df[column].to_numpy(), int(sampling_freq), int(bins))
frequency = np.fft.fftfreq(int(bins), d=timestep)
#call on the smoothing function. pass on the spectral analysis values
s1 = (5) #define the first moving average window. these are hard coded into the program. future versions of the dashboard, we can adjust this.
s2 = (69) #define the second moving average window.
Fxdata, Fydata, Spans = logmovav(s1, s2, frequency, spec)
#return spec, frequency
return Fydata, Fxdata
df['time'] = t
#rename index to time values
df = df.set_index('time')
# %% Spectral Analysis
rotor_freq = mean_val[0]/60
path_fft = 'Results/fft/'
os.makedirs(path_fft)
bins = (run_time*sampling_freq)
timestep = df.index[1]
for column in df:
if column == 'RPM':
pass
else:
spec = stats_lib.fft(df[column].to_numpy(), sampling_freq, bins)
frequency = np.fft.fftfreq(bins, d=timestep)
stats_lib.fft_plot(spec, column, frequency, rotor_freq, units, path_fft)
# plt.loglog(frequency[0:int(bins/2)], spec)
# %% Rolling Averages
#create new data frame that holds the rolling mean for each property
import stats_lib
'''
Mean
'''