def subsampInterp_regular(ts, c, axis=-1):
"""Makes a subsample sinc interpolation on an N-D array in a given axis.
This uses sinc interpolation to return ts(t-a), where a is related to
the factor c as described below.
ts : the N-D array with a time series in "axis"
c : the fraction of the sampling interval such that a = c*dt -- note that
c is only useful in the interval (0, 1]
axis : specifies the axis of the time dimension
"""
To = ts.shape[axis]
# put time series in the last dimension
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
## ts_buf = circularize(ts)
ts_buf = ts.copy()
T = ts_buf.shape[-1]
Fn = T / 2 + 1
# make sure the interpolating filter's dtype is complex!
filter_dtype = np.dtype(ts.dtype.char.upper())
phs_shift = np.empty((T,), filter_dtype)
phs_shift[:Fn] = np.exp(-2.0j * np.pi * c * np.arange(Fn) / float(T))
phs_shift[Fn:] = np.conjugate(reverse(phs_shift[1 : T - (Fn - 1)]))
fft(ts_buf, shift=False, inplace=True)
np.multiply(ts_buf, phs_shift, ts_buf)
ifft(ts_buf, shift=False, inplace=True)
## ts[:] = ts_buf[...,To-1:2*To-1]
ts[:] = ts_buf[:]
del ts_buf
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
def subsampInterp_regular(ts, c, axis=-1):
"""Makes a subsample sinc interpolation on an N-D array in a given axis.
This uses sinc interpolation to return ts(t-a), where a is related to
the factor c as described below.
ts : the N-D array with a time series in "axis"
c : the fraction of the sampling interval such that a = c*dt -- note that
c is only useful in the interval (0, 1]
axis : specifies the axis of the time dimension
"""
To = ts.shape[axis]
# put time series in the last dimension
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
## ts_buf = circularize(ts)
ts_buf = ts.copy()
T = ts_buf.shape[-1]
Fn = T / 2 + 1
# make sure the interpolating filter's dtype is complex!
filter_dtype = np.dtype(ts.dtype.char.upper())
phs_shift = np.empty((T, ), filter_dtype)
phs_shift[:Fn] = np.exp(-2.j * np.pi * c * np.arange(Fn) / float(T))
phs_shift[Fn:] = np.conjugate(reverse(phs_shift[1:T - (Fn - 1)]))
fft(ts_buf, shift=False, inplace=True)
np.multiply(ts_buf, phs_shift, ts_buf)
ifft(ts_buf, shift=False, inplace=True)
## ts[:] = ts_buf[...,To-1:2*To-1]
ts[:] = ts_buf[:]
del ts_buf
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
def subsampInterp(ts, c, axis=-1):
"""Makes a subsample sinc interpolation on an N-D array in a given axis.
This uses sinc interpolation to return ts(t-a), where a is related to
the factor c as described below.
ts : the N-D array with a time series in "axis"
c : the fraction of the sampling interval such that a = c*dt -- note that
c is only useful in the interval (0, 1]
axis : specifies the axis of the time dimension
"""
To = ts.shape[axis]
# find ramps and subtract them
ramps = np.empty_like(ts)
rax_shape = [1] * len(ts.shape)
rax_shape[axis] = To
rax = np.arange(To)
rax.shape = rax_shape
(mre, b, r) = lin_regression(ts.real, axis=axis)
ramps.real[:] = rax * mre
if ts.dtype.type in np.sctypes["complex"]:
(mim, b, r) = lin_regression(ts.imag, axis=axis)
ramps.imag[:] = rax * mim
np.subtract(ts, ramps, ts)
# find biases and subtract them
ts_mean = ts.mean(axis=axis)
if len(ts_mean.shape):
mean_shape = list(ts.shape)
mean_shape[axis] = 1
ts_mean.shape = tuple(mean_shape)
np.subtract(ts, ts_mean, ts)
# put time series in the last dimension
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
ts_buf = circularize(ts)
## ts_buf = ts.copy()
T = ts_buf.shape[-1]
Fn = T / 2 + 1
# make sure the interpolating filter's dtype is complex!
filter_dtype = np.dtype(ts.dtype.char.upper())
phs_shift = np.empty((T,), filter_dtype)
phs_shift[:Fn] = np.exp(-2.0j * np.pi * c * np.arange(Fn) / float(T))
phs_shift[Fn:] = np.conjugate(reverse(phs_shift[1 : T - (Fn - 1)]))
fft(ts_buf, shift=False, inplace=True)
np.multiply(ts_buf, phs_shift, ts_buf)
ifft(ts_buf, shift=False, inplace=True)
ts[:] = ts_buf[..., To - 1 : 2 * To - 1]
## ts[:] = ts_buf[:]
del ts_buf
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
# add back biases and analytically interpolated ramps
np.subtract(rax, c, rax)
# np.add(rax, c, rax)
ramps.real[:] = rax * mre
if ts.dtype.type in np.sctypes["complex"]:
ramps.imag[:] = rax * mim
np.add(ts, ramps, ts)
np.add(ts, ts_mean, ts)
def subsampInterp(ts, c, axis=-1):
"""Makes a subsample sinc interpolation on an N-D array in a given axis.
This uses sinc interpolation to return ts(t-a), where a is related to
the factor c as described below.
ts : the N-D array with a time series in "axis"
c : the fraction of the sampling interval such that a = c*dt -- note that
c is only useful in the interval (0, 1]
axis : specifies the axis of the time dimension
"""
To = ts.shape[axis]
# find ramps and subtract them
ramps = np.empty_like(ts)
rax_shape = [1] * len(ts.shape)
rax_shape[axis] = To
rax = np.arange(To)
rax.shape = rax_shape
(mre, b, r) = lin_regression(ts.real, axis=axis)
ramps.real[:] = rax * mre
if ts.dtype.type in np.sctypes['complex']:
(mim, b, r) = lin_regression(ts.imag, axis=axis)
ramps.imag[:] = (rax * mim)
np.subtract(ts, ramps, ts)
# find biases and subtract them
ts_mean = ts.mean(axis=axis)
if len(ts_mean.shape):
mean_shape = list(ts.shape)
mean_shape[axis] = 1
ts_mean.shape = tuple(mean_shape)
np.subtract(ts, ts_mean, ts)
# put time series in the last dimension
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
ts_buf = circularize(ts)
## ts_buf = ts.copy()
T = ts_buf.shape[-1]
Fn = T / 2 + 1
# make sure the interpolating filter's dtype is complex!
filter_dtype = np.dtype(ts.dtype.char.upper())
phs_shift = np.empty((T, ), filter_dtype)
phs_shift[:Fn] = np.exp(-2.j * np.pi * c * np.arange(Fn) / float(T))
phs_shift[Fn:] = np.conjugate(reverse(phs_shift[1:T - (Fn - 1)]))
fft(ts_buf, shift=False, inplace=True)
np.multiply(ts_buf, phs_shift, ts_buf)
ifft(ts_buf, shift=False, inplace=True)
ts[:] = ts_buf[..., To - 1:2 * To - 1]
## ts[:] = ts_buf[:]
del ts_buf
if axis != -1:
ts = np.swapaxes(ts, axis, -1)
# add back biases and analytically interpolated ramps
np.subtract(rax, c, rax)
#np.add(rax, c, rax)
ramps.real[:] = rax * mre
if ts.dtype.type in np.sctypes['complex']:
ramps.imag[:] = rax * mim
np.add(ts, ramps, ts)
np.add(ts, ts_mean, ts)
