def create_signal(self):
"""
Retrieves all paramters from the synthesizer controls
and calls the ssg. All line edits have validators set,
however we have to check for intermediate empty inputs..
"""
# the signal components
components = []
weights = []
# number of sample points
if not self.Nt_edit.hasAcceptableInput():
self.OutOfBounds = MessageWindow(
"Minimum number of sample points is 10!", "Value Error")
return
self.Nt = int(self.Nt_edit.text())
if self.debug:
print("Nt changed to:", self.Nt)
self.set_initial_periods(force=True)
self.set_initial_T_c(force=True)
T_edits = [self.T11_edit, self.T12_edit, self.T21_edit, self.T22_edit]
# check for valid inputs
for T_e in T_edits:
if self.debug:
print(f"Is enabled: {T_e.isEnabled()}")
print(f"Checking T_edits: {T_e.text()}")
print(f"Validator output: {T_e.hasAcceptableInput()}")
if not T_e.isEnabled():
continue
if not T_e.hasAcceptableInput():
self.OutOfBounds = MessageWindow(
"All periods must be greater than 0!", "Value Error")
return
# envelope before chirp creation
if self.env_box.isChecked():
if not self.tau_edit.hasAcceptableInput():
self.OutOfBounds = MessageWindow("Missing envelope parameter!",
"Value Error")
return
tau = float(self.tau_edit.text()) / self.dt
env = ssg.create_exp_envelope(tau, self.Nt)
if self.debug:
print(f"Creating the envelope with tau = {tau}")
else:
env = 1 # no envelope
if self.chirp1_box.isChecked():
if not self.A1_edit.hasAcceptableInput():
self.OutOfBounds = MessageWindow(
"Set an amplitude for oscillator1!", "Value Error")
return
# the periods
T11 = float(self.T11_edit.text()) / self.dt
T12 = float(self.T12_edit.text()) / self.dt
A1 = float(self.A1_edit.text())
chirp1 = ssg.create_chirp(T11, T12, self.Nt)
components.append(env * chirp1)
weights.append(A1)
if self.chirp2_box.isChecked():
if not self.A2_edit.hasAcceptableInput():
self.OutOfBounds = MessageWindow(
"Set an amplitude for oscillator2!", "Value Error")
return
T21 = float(self.T21_edit.text()) / self.dt
T22 = float(self.T22_edit.text()) / self.dt
A2 = float(self.A2_edit.text())
chirp2 = ssg.create_chirp(T21, T22, self.Nt)
components.append(env * chirp2)
weights.append(A2)
# noise
if self.noise_box.isChecked():
# QDoubleValidator is a screw up..
alpha = float(self.alpha_edit.text())
if not 0 < alpha < 1:
self.OutOfBounds = MessageWindow(
"AR1 parameter must be smaller than 1!", "Value Error")
return
if not self.d_edit.hasAcceptableInput():
self.OutOfBounds = MessageWindow("Missing noise parameters!",
"Value Error")
return
d = float(self.d_edit.text())
noise = ssg.ar1_sim(alpha, self.Nt)
components.append(noise)
weights.append(d)
if len(components) == 0:
self.OutOfBounds = MessageWindow(
"Activate at least one signal component!", "No Signal")
return
signal = ssg.assemble_signal(components, weights)
# ----------------------------------------
self.raw_signal = signal
self.tvec = self.dt * np.arange(self.Nt)
# ---------------------------------------
if self.debug:
print("created synth. signal:", self.raw_signal[:10])
# plot right away
self.set_initial_periods()
self.set_initial_T_c()
self.doPlot()
dt = 1
wAn = WAnalyzer(periods, dt)
# create a bunch of chirp signals
# with diverging period over time
Nsignals = 50 # times 2
Tstart = 30 # initial period
Tmax = 50 # slowest signal
Nt = 500 # number of samples per signal
signals = [
ssg.create_noisy_chirp(T1=Tstart, T2=Tend, Nt=Nt, eps=1)
for Tend in np.linspace(Tstart, Tmax, Nsignals)
]
# add the same number of pure noise signals
noisy_ones = [ssg.ar1_sim(alpha=0.5, Nt=Nt) for i in range(Nsignals)]
# signals ids are just column numbers here
signals = pd.DataFrame(signals + noisy_ones).T
# get the the individual ridge readouts
ridge_results = {}
# store the individual time averaged Wavelet spectra
df_fouriers = pd.DataFrame(index=wAn.periods)
for ID in signals:
wAn.compute_spectrum(signals[ID], do_plot=False)
rd = wAn.get_maxRidge(smoothing_wsize=11)
ridge_results[ID] = rd
import pandas as pd
from pyboat import WAnalyzer, ssg
from pyboat import plotting as pl
from pyboat.core import ar1_powerspec
import matplotlib.pyplot as ppl
# set up analyzing instance
periods = np.linspace(5, 80, 100)
dt = 1
wAn = WAnalyzer(periods, dt, p_max=20)
# create an ensemble of short AR(1) realizations
# in a real life scenario this would be the 'non-oscillatory'
# test signals
alpha = 0.7
signals = [ssg.ar1_sim(alpha, Nt=120) for i in range(100)]
# store the individual time averaged Wavelet spectra
df_fouriers = pd.DataFrame(index=wAn.periods)
# the individual Fourier estimates
for i, sig in enumerate(signals):
wAn.compute_spectrum(sig, do_plot=False)
df_fouriers[i] = wAn.get_averaged_spectrum()
# plot the Fourier spectra distribution
pl.Fourier_distribution(df_fouriers)
# compare to the theoretical spectrum,
# note the finite size effects suppressing the power for higher periods!