def randn(shape):
""""""
values = np.random.randn(*shape)
coords = []
dims = []
for ix in range(len(shape)):
dims.append(str(ix))
coords.append(np.arange(shape[ix]))
return nddata.nddata_core(values, dims, coords)
def ones(shape, dtype=None):
""""""
values = np.ones(shape, dtype=dtype)
coords = []
dims = []
for ix in range(len(shape)):
dims.append(str(ix))
coords.append(np.arange(shape[ix]))
return nddata.nddata_core(values, dims, coords)
dt = data.coords[index][1] - data.coords[index][0]
n_pts = zero_fill_factor * len(data.coords[index])
f = (1. / (n_pts * dt)) * np.r_[0:n_pts]
if shift == True:
f -= (1. / (2 * dt))
# if convert_to_ppm:
# nmr_frequency = data.attrs['nmr_frequency']
# f /= (nmr_frequency / 1.e6)
data.values = np.fft.fft(data.values, n=n_pts, axis=index)
if shift:
data.values = np.fft.fftshift(data.values, axes=index)
data.coords[index] = f
return data
if __name__ == '__main__':
x = np.r_[0:10]
y = np.r_[0:20]
z = np.r_[0:15]
data = nddata.nddata_core(
np.array(range(len(x) * len(y) * len(z))).reshape(
len(x), len(y), len(z)), ['x', 'y', 'z'], [x, y, z])
out = fourier_transform(data, {'dim': 'x'})
print(out)
index = data.dims.index(dim)
dt = data.coords[index][1] - data.coords[index][0]
n_pts = zero_fill_factor * len(data.coords[index])
f = (1.0 / (n_pts * dt)) * np.r_[0:n_pts]
if shift == True:
f -= 1.0 / (2 * dt)
data.values = np.fft.fft(data.values, n=n_pts, axis=index)
if shift:
data.values = np.fft.fftshift(data.values, axes=index)
data.coords[index] = f
return data
if __name__ == "__main__":
x = np.r_[0:10]
y = np.r_[0:20]
z = np.r_[0:15]
data = nddata.nddata_core(
np.array(range(len(x) * len(y) * len(z))).reshape(
len(x), len(y), len(z)),
["x", "y", "z"],
[x, y, z],
)
out = fourier_transform(data, {"dim": "x"})
print(out)