def ampmean(amp):
imap = k_indexmap(amp.shape[0],
amp.shape[1],
angle=0,
sector=180,
kstep=1.0)
for i in range(KIMAX):
mask = imap == i
yield amp[mask].mean()
def fit(x, y, label="data"):
imap = k_indexmap(y.shape[0], y.shape[1], angle=0, sector=180, kstep=1.0)
popt, pcov = _fit_data(x, y, imap)
ki, kj = rfft2_grid(y.shape[0], y.shape[1])
k = (ki**2 + kj**2)**0.5
#mask of valid (successfully fitted) data
mask = np.all(np.logical_not(np.isnan(popt)), axis=-1)
return mask, k, popt, pcov
ax1, ax1a = fig1.subplots(1, 2)
fig2 = plt.figure()
ax2, ax2a = fig2.subplots(1, 2)
for i, norm in enumerate((2, 3, 6)):
x = np.load(path.join(DATA_PATH, "corr_{}_t.npy".format(METHOD)))
y = np.load(
path.join(DATA_PATH, "corr_{}_data_norm{}.npy".format(METHOD, norm)))
#time mask for valid data. For a given time, all data at any k value must be valid
mask = np.isnan(y)
mask = np.logical_not(np.all(mask, axis=tuple(range(mask.ndim - 1))))
x, y = x[mask], y[..., mask]
imap = k_indexmap(y.shape[0], y.shape[1], angle=0, sector=180, kstep=1.0)
ki, kj = rfft2_grid(y.shape[0], y.shape[1])
ks = (ki**2 + kj**2)**0.5
if METHOD == "dual":
popt, cov = _fit_data(x, y, imap)
else:
#skip the first element (zero time)
popt, cov = _fit_data(x[1:], y[..., 1:], imap)
m = np.all(np.logical_not(np.isnan(popt)), axis=-1)
popt = popt[m]
cov = cov[m]
k = ks[m]
imap = imap[m]
"""Demonstrates how to create mask array for data masking during computation"""
from cddm.map import k_indexmap, plot_indexmap
from examples.conf import KISIZE, KJSIZE
kmap = k_indexmap(KISIZE, KJSIZE, angle=0, sector=90)
mask = (kmap >= 20) & (kmap <= 30)
if __name__ == "__main__":
plot_indexmap(mask)
def test_plot(self):
kmap = k_indexmap(33, 32, angle=0, sector=5, kstep=1.)
plot_indexmap(kmap)
