Пример #1
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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()
Пример #2
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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
Пример #3
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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]
Пример #4
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"""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)
Пример #5
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 def test_plot(self):
     kmap = k_indexmap(33, 32, angle=0, sector=5, kstep=1.)
     plot_indexmap(kmap)