def test_classic_sta_lta_c_python(self):
"""
Test case for ctypes version of recursive_sta_lta
"""
nsta, nlta = 5, 10
c1 = classic_sta_lta(self.data, nsta, nlta)
c2 = classic_sta_lta_py(self.data, nsta, nlta)
self.assertTrue(np.allclose(c1, c2, rtol=1e-10))
ref = np.array([0.38012302, 0.37704431, 0.47674533, 0.67992292])
self.assertTrue(np.allclose(ref, c2[99:103]))
trace.normalize()
trace.plot()
samp_rate = trace.stats.sampling_rate
N = int(samp_rate)
df = pd.DataFrame(trace.data)
# df = pd.DataFrame(np.random.randn(35000))
CMA = df.rolling(N, center=True).mean()
CMA = normalized(CMA)
POW = df.rolling(N, center=True).apply(lambda x: (x**2).mean())
POW = normalized(POW)
STALTA = classic_sta_lta_py(df, int(5 * samp_rate), int(10 * samp_rate))
STALTA = pd.DataFrame(STALTA)
STALTA = normalized(STALTA)
idx = range(len(df))
X = pd.concat([CMA, POW, STALTA], axis=1)
X = X.dropna()
kmeans_fit = cluster.KMeans(n_clusters=3).fit(X)
cluster_labels = kmeans_fit.labels_
fig = plt.figure()
ax = plt.axes(projection="3d")
ax.scatter(CMA,
POW,
STALTA,