def test_add(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
list(add(video, ((bg, bg), ) * 128))
video = fromarrays((vid, ))
out = add(video, ((bg, ), ) * 128)
for frames, true_frame in zip(out, vid_add):
self.assertTrue(np.allclose(frames[0], true_frame))
def test_multiply(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
list(multiply(video, ((window, window), ) * 128))
video = fromarrays((vid, ))
out = multiply(video, ((window, ), ) * 128)
for frames, true_frame in zip(out, vid_multiply):
self.assertTrue(np.allclose(frames[0], true_frame))
def test_normalize(self):
video = fromarrays((self.vid,))
fft = rfft2(video)
fft, = asarrays(normalize_fft(fft),128)
self.assertTrue(np.allclose(fft, self.fft_norm))
video = fromarrays((self.vid,))
fft = rfft2(video)
fft, = asarrays(normalize_fft(fft, inplace = True),128)
self.assertTrue(np.allclose(fft, self.fft_norm))
def test_rfft2_scipy(self):
set_rfft2lib("scipy")
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video),128)
self.assertTrue(np.allclose(fft, self.fft))
for kimax, kjmax in ((5,6), (7,7),(4,4)):
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kimax = kimax, kjmax = kjmax),128)
self.assertTrue(np.allclose(fft[:,0:kimax+1], self.fft[:,0:kimax+1,0:kjmax+1]))
self.assertTrue(np.allclose(fft[:,-kimax:], self.fft[:,-kimax:,0:kjmax+1]))
def test_equivalence_diff_3(self):
norm = 3
bg, var = stats(self.test_data1)
data = multitau.acorr_multi(self.test_data1,
level_size=16,
norm=1,
method="corr",
binning=0)
data = multitau.normalize_multi(data, bg, var, norm=1)
x_, out0 = multitau.log_merge(*data)
data = multitau.ccorr_multi(self.test_data1,
self.test_data1,
level_size=16,
norm=norm,
method="diff",
binning=0)
data = multitau.normalize_multi(data, bg, var, norm=norm)
x_, out = multitau.log_merge(*data)
self.assertTrue(np.allclose(out0, out))
data, bg, var = multitau.iacorr_multi(fromarrays((self.test_data1, )),
count=64,
level_size=16,
norm=1,
method="diff",
binning=0)
data = multitau.normalize_multi(data, bg, var, norm=1)
x_, out = multitau.log_merge(*data)
self.assertTrue(np.allclose(out0, out))
def test_cross_equivalence(self):
for method in ("corr","diff","fft"):
bg,var = core.stats(test_data1, test_data2, axis = 0)
data = core.ccorr(test_data1, test_data2,n = 8, norm = 1, method = method)
out1 = core.normalize(data, bg, var)
vid = fromarrays((test_data1, test_data2))
data,bg,var = core.iccorr(vid, count = len(test_data1),chunk_size = 16,n = 8, norm = 1, method = method)
out2 = core.normalize(data, bg, var)
self.assertTrue(np.allclose(out1, out2))
def test_multiple(self):
video = fromarrays((vid, ))
video = subtract(video, ((bg, ), ) * 128, dtype=FDTYPE)
video = multiply(video, ((window, ), ) * 128, inplace=True)
video = add(video, ((bg, ), ) * 128, inplace=True)
video = subtract(video, ((bg, ), ) * 128, inplace=True)
out = normalize_video(video, inplace=True)
for frames, true_frame in zip(out, vid_multiple):
self.assertTrue(np.allclose(frames[0], true_frame))
def test_rfft2_numpy(self):
set_rfft2lib("numpy")
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video),128)
self.assertTrue(np.allclose(fft, self.fft))
for kimax, kjmax in ((5,6), (7,7),(4,4)):
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kimax = kimax, kjmax = kjmax),128)
self.assertTrue(np.allclose(fft[:,0:kimax+1], self.fft[:,0:kimax+1,0:kjmax+1]))
self.assertTrue(np.allclose(fft[:,-kimax:], self.fft[:,-kimax:,0:kjmax+1]))
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kimax = None, kjmax = 6),128)
self.assertTrue(np.allclose(fft, self.fft[:,:,0:7]))
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kimax = 6),128)
self.assertTrue(np.allclose(fft[:,0:7,:], self.fft[:,0:7,:]))
with self.assertRaises(ValueError):
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kimax = 16),128)
with self.assertRaises(ValueError):
video = fromarrays((self.vid,))
fft, = asarrays(rfft2(video, kjmax = 17),128)
def test_show_matplotlib(self):
set_showlib("matplotlib")
video = fromarrays((vid, vid))
with self.assertRaises(ValueError):
show_fft(video, mode="wrong")
video = show_video(video)
video = show_fft(video, mode="real")
video = show_fft(video, clip=256)
video = show_diff(video)
video = play(video, fps=100)
video = load(video, 128)
def test_equivalence_norm_2(self):
norm = 2
bg, var = stats(self.test_data1)
data= multitau.acorr_multi(self.test_data1, level_size = 16, norm = norm)
data = multitau.normalize_multi(data,bg,var, norm = norm)
x_, out0 = multitau.log_merge(*data)
data = multitau.ccorr_multi(self.test_data1,self.test_data1, level_size = 16, norm = norm)
data = multitau.normalize_multi(data,bg,var, norm = norm)
x_, out = multitau.log_merge(*data)
self.assertTrue(np.allclose(out0,out))
data,bg,var = multitau.iacorr_multi(fromarrays((self.test_data1,)),count = 64, level_size = 16, norm = norm)
data = multitau.normalize_multi(data,bg,var, norm = norm)
x_, out = multitau.log_merge(*data)
self.assertTrue(np.allclose(out0,out))
import matplotlib.pyplot as plt
from cddm import conf
import numpy as np
#setting this to 2 shows progress bar
conf.set_verbose(2)
SHAPE = (512, 512)
vid = np.load("simple_brownian_ddm_video.npy")
nframes = len(vid)
#obtain frames iterator
video = fromarrays((vid, ))
##apply blackman window
window = blackman(SHAPE)
video = apply_window(video, (window, ))
#perform rfft2 and crop data
video = rfft2(video, kisize=64, kjsize=64)
#load all frames into numpy array
#video, = asmemmaps("brownian_single_camera_fft", video, nframes)
#compute and create numpy array
video, = asarrays(video, nframes)
np.save("simple_brownian_ddm_fft.npy", video)
v1 = np.load("simple_brownian_cddm_video_0.npy")
v2 = np.load("simple_brownian_cddm_video_1.npy")
nframes = len(v1)
#v1[3000:] = v1[3000:] + v1[0]
#v2[3000:] = v2[3000:] + v2[-1]
v1 = v1 + np.random.randn(64, 33)
v2 = v2 + np.random.randn(64, 33)
v1 = v1 - v1[:2].mean(axis=0)[None, ...]
v2 = v2 - v2[:2].mean(axis=0)[None, ...]
t1 = np.load("simple_brownian_cddm_t1.npy")
t2 = np.load("simple_brownian_cddm_t2.npy")
nframes = len(v1)
v = fromarrays((v1, v2))
data, bg, var = iccorr_multi(v,
t1,
t2,
level=5,
chunk_size=64,
auto_background=True,
period=PERIOD,
binning=True,
stats=True,
norm=1,
show=True)
#data, bg, var = ccorr_multi(v1,v2 , t1,t2, n=2**5, period = PERIOD, binning = True, norm = 0, stats = True)
#data2 = ccorr_multi(v1,v2 , t1,t2, n=2**4, period = PERIOD, binning = False, norm = 2)
def test_crop(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
list(crop(video, roi=((0, 4), 0, 2)))
out, = asarrays(crop(video, roi=((0, 2), (0, 2))), 128)
self.assertTrue(np.allclose(out[:], vid[:, 0:2, 0:2]))
def test_mask(self):
m = vid[0] > 0.
video = fromarrays((vid, ))
out, = asarrays(mask(video, m), 128)
self.assertTrue(np.allclose(out, vid[:, m]))
def test_load(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
video = load(video)
video = load(video, 128)
video = load(video)
def test_normalize(self):
video = fromarrays((vid, ))
out = normalize_video(video)
for frames, true_frame in zip(out, vid_normalize):
self.assertTrue(np.allclose(frames[0], true_frame))
def test_memmaps(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
video = asmemmaps("deleteme", video)
video = asmemmaps("deleteme", video, 128)
