def test_auto_equivalence_1(self):
for method in ("corr","fft","diff"):
bg,var = core.stats(test_data1, axis = 0)
data1 = core.acorr(test_data1, n = 8, norm = 1, method = method)
out1 = core.normalize(data1, bg, var, norm = 1)
data2,bg,var = core.iacorr(test_data1, n = 8, norm = 1, method = method)
out2 = core.normalize(data2, bg, var, norm = 1)
self.assertTrue(np.allclose(out1, out2))
def test_auto_equivalence_2(self):
for method in ("corr",):
bg,var = core.stats(test_data1, axis = 0)
data1 = core.ccorr(test_data1,test_data1, n = 8, norm = 2, method = method)
out1 = core.normalize(data1, bg, var, norm = 2)
data2,bg,var = core.iacorr(test_data1, n = 8, norm = 2, method = method)
out2 = core.normalize(data2, bg, var, norm = 2)
self.assertTrue(np.allclose(out1, out2))
fft = rfft2(video, kimax=KIMAX, kjmax=KJMAX)
#: you can also normalize each frame with respect to the [0,0] component of the fft
#: this it therefore equivalent to normalize_video
#fft = normalize_fft(fft)
#load in numpy array
#fft_array, = asarrays(fft, NFRAMES_FULL)
if __name__ == "__main__":
import os.path as p
#: now perform auto correlation calculation with default parameters
data, bg, var = iacorr(fft,
np.arange(NFRAMES_FULL),
n=int(NFRAMES / DT_FULL),
stats=True,
norm=6)
for norm in range(8):
#: perform normalization and merge data
data_lin = normalize(data, bg, var, scale=True, norm=norm)
#: perform log averaging
x, y = log_average(data_lin, size=16)
#: save the normalized data to numpy files
np.save(p.join(DATA_PATH, "corr_full_t.npy"), x * DT_FULL)
np.save(p.join(DATA_PATH, "corr_full_data_norm{}.npy".format(norm)), y)