def setSpectrum(settings):
"""Initialize spectrum calculation"""
fft_settings.update(settings)
sp = []
for _ in range(ntracks):
sp.append(spectral(**fft_settings))
return sp
def setSpectrum(new_settings):
"""Initialize spectrum calculation"""
global fft_settings
fft_settings.update(new_settings)
#remove unrequired keys
fft_settings = filterDictByKeysSet(
fft_settings,
inspect.signature(spectral).parameters) #keep required parameters
sp = []
for _ in range(ntracks):
sp.append(spectral(**fft_settings))
return sp
def setSpectrum(new_settings):
"""Initialize spectrum calculation"""
global new_fft_settings
fft_settings_ = copy.deepcopy(fft_settings)
fft_settings_.update(new_settings)
# remove unrequired keys
fft_settings_ = filterDictByKeysSet(fft_settings_, inspect.signature(
spectral).parameters) # keep required parameters
print("fft_settings_", fft_settings_)
# import pdb; pdb.set_trace()
new_fft_settings = fft_settings_
sp = []
for _ in range(ntracks):
sp.append(spectral(**fft_settings_))
return sp
def window_demo(data, fft_settings):
"""Plot window shape, adds up to 5 plots. Returns base64 png image"""
global ax
fft_settings_ = copy.deepcopy(fft_settings)
new_fft_settings = dictKeysStrrep(data["FFTsettings"], "fft-")
winParam_ = filterDictByKeyStr(new_fft_settings, "window-")
winParam_ = dictKeysStrrep(winParam_, "window-")
fft_settings_.update(new_fft_settings)
fft_settings_["winParam"].update(
winParam_) #update initial window parameters with new
#keep required parameters
fft_settings_ = filterDictByKeysSet(fft_settings_,
inspect.signature(spectral).parameters)
sp = spectral(**fft_settings_)
w1 = sp.w
if not ax:
_, ax = plt.subplots()
if len(lines) >= 5: #limit to 5 curves
lines.pop(0)[0].remove()
# recompute the ax.dataLim
ax.relim()
# update ax.viewLim using the new dataLim
ax.autoscale_view()
plt.draw()
#legend: window + winparam if exist
lines.append(
ax.plot(w1,
lw=0.5,
label=fft_settings_['win'] +
('' if winParam_ == {} else str(winParam_))))
ax.legend(prop={'size': 8})
#Matplotlib to base64 picture https://stackoverflow.com/questions/31492525/converting-matplotlib-png-to-base64-for-viewing-in-html-template
figfile = BytesIO()
plt.savefig(figfile, format='png')
figfile.seek(0) # rewind to beginning of file
figdata_png = base64.b64encode(figfile.getvalue())
# base64_string = base64_bytes.decode(figdata_png)
return figdata_png.decode('utf8')
import numpy as np
from matplotlib import pyplot as plt
from spectralc3 import spectral
import calc
fs=100
t=np.arange(0,10,1/fs)
y=np.sin(2*np.pi*7*t)+0.3*np.sin(2*np.pi*14*t+np.pi/2)
plt.figure()
plt.plot(t,y)
plt.xlabel('t(s)')
plt.figure()
#fs, fftlines, overlap=overlap, win='hann', averaging='exp',nAverage=10
sp = spectral(fs, fs*5, overlap=0.75, win='hanning', averaging='overall',nAverage=10,winParam={"sym":False},detrend='constant')
w1 = sp.w
sp.add(y)
Y, f = sp.get()
plt.semilogy(f,Y)
plt.xlabel('f(Hz)')
sp = spectral(fs, fs*5, overlap=0.75, win='general_cosine', averaging='overall',nAverage=10,winParam={"sym":False,"a":[1, 1.942604, 1.340318, 0.440811, 0.043097]},detrend='constant')
w2 = sp.w
sp.add(y)
Y, f = sp.get()
plt.semilogy(f,Y)
plt.legend()