if APPLY_WINDOW == False:
#we still need window for form_factor calculation
window[...] = 1.
#: we must create a video of windows for multiplication
window_video = ((window, window), ) * NFRAMES
#: determines optimal weight factor
from examples.paper.form_factor import g1, bg1, bg2
from cddm.norm import weight_from_g, weight_prime_from_g
bg1 = bg1(KIMAX, 0)
bg2 = bg2(KIMAX, 0)
delta = 0.
g1 = g1(np.arange(NFRAMES), KIMAX, 0)
w = weight_from_g(g1, delta)
wp = weight_prime_from_g(g1, delta, bg1, bg2)
def calculate():
out = None
bgs = []
vars = []
for i in range(NRUN):
print("Run {}/{}".format(i + 1, NRUN))
seed(i)
from examples.paper.form_factor import g1
import os.path as path
SAVEFIG = True
colors = ["C{}".format(i) for i in range(10)]
LABELS = {0: "B'", 1: "C'", 2: "B", 3: "C", 4: "W'", 6: "W"}
MARKERS = {0: "1", 1: "2", 2: "3", 3: "4", 4: "+", 6: "x"}
#which method... either fast, standard, full, random or dual
METHOD = "dual"
#theoretical amplitude of the signal.
amp = rfft2_crop(g1(0), KIMAX, KJMAX)
def _g1(x, f, a, b):
"""g1: exponential decay"""
return a * np.exp(-f * x) + b
def _fit_k(x, ys, p0):
for y in ys:
try:
popt, pcov = curve_fit(_g1, x, y, p0=p0)
yield popt, np.diag(pcov)
except:
yield (np.nan, ) * 3, (np.nan, ) * 3
#: create window for multiplication...
window = blackman(SHAPE)
if APPLY_WINDOW == False:
window[...] = 1.
#: we must create a video of windows for multiplication
window_video = ((window, ), ) * NFRAMES_STANDARD
#: determines optimal weight factor
from examples.paper.form_factor import g1, bg1, bg2
from cddm.norm import weight_from_g, weight_prime_from_g
bg1 = bg1(KIMAX, 0)
bg2 = bg2(KIMAX, 0)
delta = 0.
g1 = g1(np.arange(NFRAMES_STANDARD) * DT_STANDARD, KIMAX, 0)
w = weight_from_g(g1, delta)
wp = weight_prime_from_g(g1, delta, bg1, bg2)
def calculate():
out = None
bgs = []
vars = []
for i in range(NRUN):
print("Run {}/{}".format(i + 1, NRUN))
seed(i)
level_size=16,
binning=1)
#get eefective count in aveariging...
x, n = log_merge_count(clin, cmulti, binning=1)
i, j = (K, 0)
x = np.arange(NFRAMES)
#delta parameter for weight model
delta = 0.
bg1 = bg1(51, 0)[..., i, j]
bg2 = bg2(51, 0)[..., i, j] if CROSS else bg1
g = g1(x, 51, 0, cross=CROSS)[..., i, j]
wp = weight_prime_from_g(g, delta, bg1, bg2)
w = weight_from_g(g, delta)
#error estimators using a simple model of independent data (delta = 0).
err1 = sigma_prime_weighted(0., g + 0j, delta, bg1, bg2) #/n**0.5
err2 = sigma_prime_weighted(1., g, delta, bg1, bg2) #/n**0.5
err3 = sigma_prime_weighted(wp, g, delta, bg1, bg2) #/n**0.5
err5 = sigma_weighted(0., g, delta) #/n**0.5
err6 = sigma_weighted(1., g, delta) #/n**0.5
err7 = sigma_weighted(w, g, delta) #/n**0.5
ax1 = plt.subplot(121)
#: create window for multiplication...
window = blackman(SHAPE)
if APPLY_WINDOW == False:
window[...] = 1.
#: we must create a video of windows for multiplication
window_video = ((window, ), ) * NFRAMES_RANDOM
#: determines optimal weight factor
from examples.paper.form_factor import g1, bg1, bg2
from cddm.norm import weight_from_g, weight_prime_from_g
bg1 = bg1(KIMAX, 0)
bg2 = bg2(KIMAX, 0)
delta = 0.
g1 = g1(np.arange(NFRAMES), KIMAX, 0, cross=False)
w = weight_from_g(g1, delta)
wp = weight_prime_from_g(g1, delta, bg1, bg2)
def calculate():
out = None
bgs = []
vars = []
for i in range(NRUN):
print("Run {}/{}".format(i + 1, NRUN))
seed(i)
