diff --git a/ntrfc/postprocessing/timeseries/stationary_signal_check.py b/ntrfc/postprocessing/timeseries/stationary_signal_check.py
index 3a6c7f9c30322db7beab213f0deb34be6d001faa..e9db44f0ff986cf1299244fa816e2222fa87a844 100644
--- a/ntrfc/postprocessing/timeseries/stationary_signal_check.py
+++ b/ntrfc/postprocessing/timeseries/stationary_signal_check.py
@@ -20,14 +20,10 @@ def parsed_timeseries_analysis(timesteps, signal, resolvechunks=20, verbose=True
signal_type, stationarity, stationarity_timestep, timescale, lengthscale = check_signal_stationarity(resolvechunks, signal, timesteps)
scales = (timescale,lengthscale)
if stationarity==True:
- plt.figure()
- plt.plot(timesteps, signal,color="black")
- plt.vlines(stationarity_timestep, ymin=-10, ymax=10, linewidth=4, color="k", linestyles="dashed")
- plt.axvspan(stationarity_timestep, max(timesteps), facecolor='grey', alpha=0.5)
-
- plt.xlim(0, timesteps[-1])
- plt.ylim(-10, 10)
- plt.show() #
+ csig=signal
+ ctime=timesteps
+ plot_stationarity_analisys(csig, ctime, signal, stationarity_timestep, timesteps)
+
return stationarity, timescale, stationarity_timestep
for i in range(min_chunk, resolvechunks + 1):
@@ -47,25 +43,35 @@ def parsed_timeseries_analysis(timesteps, signal, resolvechunks=20, verbose=True
# when no further stationarity found, return status
# when done, return last status
- plt.figure()
- plt.plot(timesteps, signal)
- plt.plot(ctime, csig, color="black", linewidth=0.1)
- plt.vlines(stationarity_timestep, ymin=-10, ymax=10, linewidth=4, color="k", linestyles="dashed")
- plt.axvspan(stationarity_timestep, max(timesteps), facecolor='grey', alpha=0.5)
+ plot_stationarity_analisys(csig, ctime, signal, stationarity_timestep, timesteps)
- plt.xlim(0, timesteps[-1])
- plt.ylim(-10, 10)
- plt.show() #
return stationarity, scales, stationarity_timestep
+ plot_stationarity_analisys(csig, ctime, signal, stationarity_timestep, timesteps)
+ return stationarity, scales, stationarity_timestep
+
+
+def plot_stationarity_analisys(csig, ctime, signal, stationarity_timestep, timesteps):
plt.figure()
plt.plot(timesteps, signal)
plt.plot(ctime, csig, color="black", linewidth=4)
- plt.vlines(stationarity_timestep, ymin=0, ymax=2, linewidth=4, color="k", linestyles="dashed")
plt.xlim(0, timesteps[-1])
- plt.ylim(-10, 10)
+ sts = stationarity_timestep if stationarity_timestep>=0 else None
+ ymin,ymax = min(signal),max(signal)
+ if ymax-ymin<0.01:
+ ymax=0.5+np.mean(signal)
+ ymin =-0.5+np.mean(signal)
+ if sts==0.0 or sts:
+ plt.vlines(sts, ymin=ymin, ymax=ymax,
+ linewidth=4, color="k", linestyles="dashed")
+ plt.axvspan(sts, timesteps[-1],
+ facecolor='green', alpha=0.5)
+ else:
+ plt.axvspan(0, timesteps[-1],
+ facecolor='red', alpha=0.5)
+
+ plt.ylim(ymin, ymax)
plt.show() #
- return stationarity, scales, stationarity_timestep
def check_signal_stationarity(resolvechunks, signal, timesteps, verbose = True):
@@ -79,17 +85,17 @@ def check_signal_stationarity(resolvechunks, signal, timesteps, verbose = True):
# a correlating signal has a time and length scale, a mean, a constant variation and autocorrelation
mean = np.mean(signal)
- means = np.mean(checksigchunks, axis=1)
+ means = np.array([np.mean(i) for i in checksigchunks]) #np.mean(checksigchunks, axis=1)
var = np.std(signal)
- vars = np.std(checksigchunks, axis=1)
+ vars = np.array([np.std(i) for i in checksigchunks])#np.std(checksigchunks, axis=1)
# todo: now it is only mean, val and var that is being investigated.
# it makes sense to also investigate the behaviour of the autocorrelation
# but as the signal is divided into chunks, one has to
- const_mean = np.allclose(mean, means,rtol=0.06)
- const_val = np.allclose(mean, signal,rtol=0.06)
- const_var = np.allclose(var,vars,rtol=2)
+ const_mean = np.allclose(mean, means,rtol=0.05)
+ const_val = np.allclose(mean, signal,rtol=0.05)
+ const_var = np.allclose(var,vars,rtol=0.4)
#
if const_mean and const_var:
timescale, lengthscale = integralscales(signal, timesteps)
diff --git a/tests/test_ntrfc_postprocessing.py b/tests/test_ntrfc_postprocessing.py
index 3355a89b3173b5ec12a279cc51ad5bccf3412514..9c4fe5779c0a7d7de0eff6f32f20be2ff116b677 100644
--- a/tests/test_ntrfc_postprocessing.py
+++ b/tests/test_ntrfc_postprocessing.py
@@ -62,7 +62,7 @@ def tanh_signal(numtimesteps, time):
def tanh_sin_signal(numtimesteps, time):
timesteps = np.linspace(0, time, numtimesteps)
- stationary = time/3
+ stationary = time/4
tanhsignal = np.tanh(timesteps * stationary ** -1)
timesteps = np.linspace(0, 1, numtimesteps) * time
@@ -102,9 +102,9 @@ def tanh_sin_noise_signal(numtimesteps, time):
def sine_abate_signal(numtimesteps, time):
timesteps = np.linspace(0, time, numtimesteps)
- stationary = time/4
+ stationary = time/24
abate = np.e ** (-timesteps * stationary ** -1)
- omega = 1000
+ omega = 50
sinesignal = abate / max(abate) * np.sin(timesteps * omega)*0.4+1
return timesteps, sinesignal
@@ -156,7 +156,7 @@ def test_analize_stationarity(verbose=True):
"tanh":tanh_signal(10000,20),
"tanh_sin":tanh_sin_signal(10000,10),
"tanh_sin_noise": tanh_sin_noise_signal(10000, 2.8),
- "sine_abate":sine_abate_signal(40000, 100),
+ "sine_abate":sine_abate_signal(20000, 4),
"complex":complex_signal(40000,60)
}
@@ -165,11 +165,11 @@ def test_analize_stationarity(verbose=True):
"rampconst": (True, (0, 0), [0.45,0.55]),
"sine": (True, (0.0005, 0.0005), [0,0]),
"noise": (True, (0.0005, 0.0005), [0,0]),
- "convergentsine_signal": (True, (0.0005, 0.0005), [1.2,1.8]),
+ "convergentsine_signal": (True, (0.0005, 0.0005), [5,5.5]),
"tanh":(True,(0,0),[3.5,4.5]),
- "tanh_sin":(True,(0.0005,0.0005),[4.5,5.5]),
+ "tanh_sin":(True,(0.0005,0.0005),[4,4.75]),
"tanh_sin_noise":(True,(0.0005,0.0005),[0.8,1.2]),
- "sine_abate":(True,(0.00025,0.00025),[40,60]),
+ "sine_abate":(True,(0.00025,0.00025),[0.35,1]),
"complex":(True,(0.00025,0.0025),[20,26])
}