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_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))
def _get_avg_data(self):
data = normalize_multi(self.data,
self.background,
self.variance,
norm=self.norm,
scale=self.scale,
mask=self.mask)
t, data = log_merge(*data)
avg_data = np.nanmean(data, axis=-2)
return t, avg_data
def calculate(binning=1):
out = None
for i in range(NRUN):
print("Run {}/{}".format(i + 1, NRUN))
importlib.reload(dual_video_simulator) #recreates iterator
#reset seed... because we use seed(0) in dual_video_simulator
seed(i)
t1, t2 = dual_video_simulator.t1, dual_video_simulator.t2
video = multiply(dual_video_simulator.video, window_video)
#: if the intesity of light source flickers you can normalize each frame to the intensity of the frame
#video = normalize_video(video)
#: perform rfft2 and crop results, to take only first kimax and first kjmax wavenumbers.
fft = rfft2(video, kimax=51, kjmax=0)
#: 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)
#: now perform auto correlation calculation with default parameters and show live
data, bg, var = iccorr_multi(fft,
t1,
t2,
level_size=16,
binning=binning,
period=PERIOD,
auto_background=True)
#perform normalization and merge data
#5 and 7 are redundand, but we are calulating it for easier indexing
for norm in (1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15):
fast, slow = normalize_multi(data, bg, var, norm=norm, scale=True)
#we merge with binning (averaging) of linear data enabled/disabled
x, y = log_merge(fast, slow, binning=binning)
if out is None:
out = np.empty(shape=(NRUN, 16) + y.shape, dtype=y.dtype)
out[0, norm] = y
else:
out[i, norm] = y
return x, out
$ auto_correlate_multi.py
"""
from cddm.multitau import log_merge
import matplotlib.pyplot as plt
import os.path as p
from examples.conf import DATA_PATH
#: load the normalized data to numpy files
import numpy as np
lin_data = np.load(p.join(DATA_PATH,"auto_correlate_multi_raw_fast.npy"))
multi_level = np.load(p.join(DATA_PATH,"auto_correlate_multi_raw_slow.npy"))
(i,j) = (4,12)
x,y = log_merge(lin_data, multi_level)
plt.semilogx(x[1:],y[i,j,1:],"k-", label = "merged")
x = np.arange(lin_data.shape[-1])
plt.semilogx(x[1:], lin_data[i,j,1:], "o", fillstyle = "none", label = "linear (level 0)")
for n,data in enumerate(multi_level):
x = x * 2
plt.semilogx(x[1:], data[i,j,1:], "o", fillstyle = "none", label = "multi (level {})".format(n+1))
plt.title("Multilevel data @ k = ({},{})".format(i,j))
plt.xlabel("t")
plt.ylabel("G / Var")
plt.legend()
plt.show()
#: 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)
if __name__ == "__main__":
import os.path as p
#: now perform auto correlation calculation with default parameters using iterative algorithm
data, bg, var = iacorr_multi(fft, count=NFRAMES)
#: inspect the data
viewer = MultitauViewer(scale=True)
viewer.set_data(data, bg, var)
viewer.set_mask(k=25, angle=0, sector=30)
viewer.plot()
viewer.show()
#perform normalization and merge data
fast, slow = normalize_multi(data, bg, var, scale=True)
#: save the normalized raw data to numpy files
np.save(p.join(DATA_PATH, "auto_correlate_multi_raw_fast.npy"), fast)
np.save(p.join(DATA_PATH, "auto_correlate_multi_raw_slow.npy"), slow)
x, y = log_merge(fast, slow)
#: save the normalized merged data to numpy files
np.save(p.join(DATA_PATH, "auto_correlate_multi_t.npy"), x)
np.save(p.join(DATA_PATH, "auto_correlate_multi_data.npy"), y)
import numpy as np #: norm = 2 data normalized with scale = True lin_5 = np.load(p.join(DATA_PATH,"cross_correlate_multi_raw_fast_norm_5.npy")) multi_5 = np.load(p.join(DATA_PATH,"cross_correlate_multi_raw_slow_norm_5.npy")) #: norm = 3 data normalized with scale = True lin_6 = np.load(p.join(DATA_PATH,"cross_correlate_multi_raw_fast_norm_6.npy")) multi_6 = np.load(p.join(DATA_PATH,"cross_correlate_multi_raw_slow_norm_6.npy")) #: take (i,j) k value (i,j) = (12,2) f, (ax1, ax2) = plt.subplots(2, 1, sharex=True) x,y = log_merge(lin_6, multi_6) #: denoised data, used as a estimator for the weight yd = denoise(decreasing(np.clip(denoise(y),0,1))) x,y = log_merge(lin_5, multi_5) ax2.semilogx(x,y[i,j], label = "norm 5") x,y = log_merge(lin_6, multi_6) ax2.semilogx(x,y[i,j], label = "norm 6") ax1.semilogx(x,yd[i,j], label = "g1") #: now calculate weights and do weighted sum. #: x values for the interpolator
#: norm = 2 data normalized with scale = True
lin_2 = np.load(p.join(DATA_PATH, "cross_correlate_multi_raw_fast_norm_2.npy"))
multi_2 = np.load(
p.join(DATA_PATH, "cross_correlate_multi_raw_slow_norm_2.npy"))
#: norm = 3 data normalized with scale = True
lin_3 = np.load(p.join(DATA_PATH, "cross_correlate_multi_raw_fast_norm_3.npy"))
multi_3 = np.load(
p.join(DATA_PATH, "cross_correlate_multi_raw_slow_norm_3.npy"))
#: take (i,j) k value
(i, j) = (12, 2)
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
x, y = log_merge(lin_3, multi_3)
#: denoised data, used as a estimator for the weight
yd = denoise(decreasing(np.clip(denoise(y), 0, 1)))
x, y = log_merge(lin_2, multi_2)
ax2.semilogx(x, y[i, j], label="norm 2")
x, y = log_merge(lin_3, multi_3)
ax2.semilogx(x, y[i, j], label="norm 3")
ax1.semilogx(x, yd[i, j], label="g1")
#: now calculate weights and do weighted sum.
#: x values for the interpolator