From 9cf783aeb2c89d52abde5c44b5b345983f549aea Mon Sep 17 00:00:00 2001
From: Wolfgang Kastaun <wolfgang.kastaun@aei.mpg.de>
Date: Fri, 20 Dec 2024 19:33:29 +0100
Subject: [PATCH] Changed definition of shift sign to the one used in
dsp.timeshift.
Now a positive shift generally means a shift to the right, i.e. a delay is a negative shift.
---
lisainstrument/dynamic_delay_dask.py | 16 ++++++++--------
lisainstrument/dynamic_delay_dsp.py | 14 +++++++-------
lisainstrument/dynamic_delay_numpy.py | 20 ++++++++++----------
lisainstrument/fixed_shift_dask.py | 8 ++++----
lisainstrument/fixed_shift_dsp.py | 8 ++++----
lisainstrument/fixed_shift_numpy.py | 17 ++++++++---------
lisainstrument/regular_interpolators.py | 10 ++++------
7 files changed, 45 insertions(+), 48 deletions(-)
diff --git a/lisainstrument/dynamic_delay_dask.py b/lisainstrument/dynamic_delay_dask.py
index 5b8d662..1304b7f 100644
--- a/lisainstrument/dynamic_delay_dask.py
+++ b/lisainstrument/dynamic_delay_dask.py
@@ -28,8 +28,8 @@ class DynamicShiftDask:
This allows to interpolate samples in a dask array to locations specified
by a shift given by another dask array of same size. The shift is specified in
units of the array index, i.e. there is no separate coordinate array.
- A positive shift refers to values left of a given sample, negative shifts
- to values on the right.
+ A positive shift refers to values right of a given sample, negative shifts
+ to values on the left.
The boundary treatment can be specified for each boundary in terms of
DynShiftBC enums.
@@ -79,8 +79,8 @@ class DynamicShiftDask:
The shift and sample arrays need to have the same size, and each shift provides
the interpolation location relative to the sample with the same index.
- Shifts are floating point values. A shift of +1 refers to the sample on the left,
- -1 the sample on the right, etc. All arrays have to be 1D.
+ Shifts are floating point values. A shift of +1 refers to the sample on the right,
+ -1 the sample on the left, etc. All arrays have to be 1D.
Arguments:
samples: 1D dask array with data samples
@@ -156,8 +156,8 @@ def make_dynamic_shift_linear_dask(
"""Set up DynamicShiftDask instance with linear interpolation method.
Arguments:
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
@@ -180,8 +180,8 @@ def make_dynamic_shift_lagrange_dask(
Arguments:
length: Number of lagrange polynomials (of order length - 1)
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
diff --git a/lisainstrument/dynamic_delay_dsp.py b/lisainstrument/dynamic_delay_dsp.py
index bd0ae14..3c394ef 100644
--- a/lisainstrument/dynamic_delay_dsp.py
+++ b/lisainstrument/dynamic_delay_dsp.py
@@ -67,7 +67,7 @@ class RegularInterpDSP(RegularInterpCore):
) -> np.ndarray:
"""Interpolate regularly spaced data to location in index-space"""
doff = int_offsets - np.arange(locations.shape[0])
- shift = -locations + doff[0] - doff
+ shift = locations - doff[0] + doff
shift_offset = doff[0]
return self.apply_shift(samples, shift, shift_offset)
@@ -79,7 +79,7 @@ class RegularInterpDSP(RegularInterpCore):
shift_offset: int,
) -> np.ndarray:
"""Iterpolate to location specified in terms of shifts instead absolute locations"""
- shift_tot = shift - shift_offset
+ shift_tot = shift + shift_offset
npad_right = 0
@@ -90,7 +90,7 @@ class RegularInterpDSP(RegularInterpCore):
msg = "DSPWrapper: insufficient samples for interpolation"
raise RuntimeError(msg)
- res = dsp.timeshift(samples, -shift_tot, self._order)
+ res = dsp.timeshift(samples, shift_tot, self._order)
if npad_right > 0:
res = res[:-npad_right]
@@ -121,8 +121,8 @@ def make_dynamic_shift_dsp_dask(
Arguments:
order: Lagrange interpolation order (must be odd)
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
@@ -146,8 +146,8 @@ def make_dynamic_shift_dsp_numpy(
Arguments:
order: Lagrange interpolation order (must be odd)
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
diff --git a/lisainstrument/dynamic_delay_numpy.py b/lisainstrument/dynamic_delay_numpy.py
index 639a312..bb5f2f6 100644
--- a/lisainstrument/dynamic_delay_numpy.py
+++ b/lisainstrument/dynamic_delay_numpy.py
@@ -38,8 +38,8 @@ class DynShiftCfg:
"""Config class for dynamic shift interpolation
Attributes:
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
"""
@@ -71,8 +71,8 @@ class DynamicShiftNumpy:
This allows to interpolate samples in a numpy array to locations specified
by a shift given by another numpy array of same size. The shift is specified in
units of the array index, i.e. there is no separate coordinate array.
- A positive shift refers to values left of a given sample, negative shifts
- to values on the right.
+ A positive shift refers to values right of a given sample, negative shifts
+ to values on the left.
The boundary treatment can be specified for each boundary in terms of
DynShiftBC enums.
@@ -121,8 +121,8 @@ class DynamicShiftNumpy:
The shift and sample arrays need to have the same size, and each shift provides
the interpolation location relative to the sample with the same index.
- Shifts are floating point values.A shift of +1 refers to the sample on the left,
- -1 the sample on the right, etc. All arrays have to be 1D.
+ Shifts are floating point values. A shift of +1 refers to the sample on the right,
+ -1 the sample on the left, etc. All arrays have to be 1D.
Arguments:
@@ -187,8 +187,8 @@ def make_dynamic_shift_lagrange_numpy(
Arguments:
length: Number of lagrange polynomials (of order length - 1)
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
@@ -209,8 +209,8 @@ def make_dynamic_shift_linear_numpy(
"""Set up DynamicShiftNumpy instance with linear interpolation method.
Arguments:
- min_delay: Assume that any shift < -min_delay
- max_delay: Assume that any shift > -max_delay
+ min_delay: Assume that any shift > -max_delay
+ max_delay: Assume that any shift < -min_delay
left_bound: Treatment of left boundary
right_bound: Treatment of right boundary
diff --git a/lisainstrument/fixed_shift_dask.py b/lisainstrument/fixed_shift_dask.py
index 471a713..e6e2100 100644
--- a/lisainstrument/fixed_shift_dask.py
+++ b/lisainstrument/fixed_shift_dask.py
@@ -23,7 +23,7 @@ class FixedShiftDask: # pylint: disable=too-few-public-methods
This allows to interpolate samples in a Dask array to locations specified
by a fixed shift. The shift is specified in units of the array index, i.e.
there is no separate coordinate array. A positive shift refers to values
- left of a given sample, negative shifts to values on the right.
+ right of a given sample, negative shifts to values on the left.
The boundary treatment can be specified for each boundary in terms of
DynShiftBC enums.
@@ -98,7 +98,7 @@ class FixedShiftDask: # pylint: disable=too-few-public-methods
Denoting the input data as $y_i$ with $i=0 \ldots N-1$, and the interpolated
input data as $y(t)$, such that $y(i)=y_i$, the output $z_k$ is given by
- $z_k = y(k-s), k=0 \ ldots N - 1$.
+ $z_k = y(k + s), k=0 \ ldots N - 1$.
Required data outside the provided samples is created if the specified
boundary condition allows it, or an exception is raised if the BC disallows it.
@@ -113,11 +113,11 @@ class FixedShiftDask: # pylint: disable=too-few-public-methods
Dask array with interpolated samples
"""
- loc = -shift
+ loc = float(shift)
loc_int = int(np.floor(loc))
loc_frac = loc - loc_int
- interp = self._interp_fac(-loc_frac)
+ interp = self._interp_fac(loc_frac)
margin_left = interp.margin_left - loc_int
margin_right = interp.margin_right + loc_int
diff --git a/lisainstrument/fixed_shift_dsp.py b/lisainstrument/fixed_shift_dsp.py
index c25597d..39afef8 100644
--- a/lisainstrument/fixed_shift_dsp.py
+++ b/lisainstrument/fixed_shift_dsp.py
@@ -44,8 +44,8 @@ class FixedShiftWrapDSP(FixedShiftCore):
for each interpolation point. The order of the interpolating polynomials
is also the order of plynomials that are interpolated with zero error.
- The shift is given as a delay, i.e. positive values refer to locations
- to the left of the output sample at hand.
+ The shift sign is defined such that positive values refer to locations
+ to the right of the output sample at hand.
Arguments:
length: Size of the interpolation stencil
@@ -60,7 +60,7 @@ class FixedShiftWrapDSP(FixedShiftCore):
msg = "FixedShiftWrapDSP: even Lagrange polynomial order not supported by dsp.timeshift"
raise ValueError(msg)
- loc = -float(shift)
+ loc = float(shift)
loc_int = int(np.floor(loc))
# ~ loc_frac = loc - loc_int
@@ -97,7 +97,7 @@ class FixedShiftWrapDSP(FixedShiftCore):
Shifted input excluding margins
"""
a = make_numpy_array_1d_float(samples)
- res = dsp.timeshift(a, -self.shift, self._order)
+ res = dsp.timeshift(a, self.shift, self._order)
if self.margin_left > 0:
res = res[self.margin_left :]
diff --git a/lisainstrument/fixed_shift_numpy.py b/lisainstrument/fixed_shift_numpy.py
index cf8d47d..77eab13 100644
--- a/lisainstrument/fixed_shift_numpy.py
+++ b/lisainstrument/fixed_shift_numpy.py
@@ -64,10 +64,9 @@ class FixedShiftCore(Protocol):
r"""Apply the fractional shift to a 1D numpy array.
The output is the shifted input with left and right margins removed.
- The shift $s$ should be bounded $-1 < s < 1$
Denoting the input data as $y_i$ with $i=0 \ldots N-1$, and the interpolated
input data as $y(t)$, such that $y(i)=y_i$, the output $z_k$ is given by
- $z_k = y(k+M_L-s), k=0 \ ldots N - 1 - M_L - M_R$, where $M_L$ and $M_R$
+ $z_k = y(k + M_L + s), k=0 \ ldots N - 1 - M_L - M_R$, where $M_L$ and $M_R$
are the left and right margin sizes.
Arguments:
@@ -166,15 +165,15 @@ class FixedShiftLagrange(FixedShiftCore):
for each interpolation point. The order of the interpolating polynomials
is also the order of plynomials that are interpolated with zero error.
- The shift is given as a delay, i.e. positive values refer to locations
- to the left of the output sample at hand.
+ The shift sign is defined such that positive values refer to locations
+ to the right of the output sample at hand.
Arguments:
length: Size of the interpolation stencil
shift: The constant shift
"""
- loc = -float(shift)
+ loc = float(shift)
if length % 2 == 0:
loc_int = int(np.floor(loc))
else:
@@ -248,7 +247,7 @@ class FixedShiftNumpy: # pylint: disable=too-few-public-methods
This allows to interpolate samples in a numpy array to locations specified
by a fixed shift. The shift is specified in units of the array index, i.e.
there is no separate coordinate array. A positive shift refers to values
- left of a given sample, negative shifts to values on the right.
+ right of a given sample, negative shifts to values on the left.
The boundary treatment can be specified for each boundary in terms of
DynShiftBC enums.
@@ -323,7 +322,7 @@ class FixedShiftNumpy: # pylint: disable=too-few-public-methods
Denoting the input data as $y_i$ with $i=0 \ldots N-1$, and the interpolated
input data as $y(t)$, such that $y(i)=y_i$, the output $z_k$ is given by
- $z_k = y(k-s), k=0 \ ldots N - 1$.
+ $z_k = y(k + s), k=0 \ ldots N - 1$.
Required data outside the provided samples is created if the specified
boundary condition allows it, or an exception is raised if the BC disallows it.
@@ -339,12 +338,12 @@ class FixedShiftNumpy: # pylint: disable=too-few-public-methods
"""
# pylint: disable=duplicate-code
- loc = -shift
+ loc = float(shift)
loc_int = int(np.floor(loc))
loc_frac = loc - loc_int
# pylint: enable=duplicate-code
- interp = self._interp_fac(-loc_frac)
+ interp = self._interp_fac(loc_frac)
margin_left = interp.margin_left - loc_int
margin_right = interp.margin_right + loc_int
diff --git a/lisainstrument/regular_interpolators.py b/lisainstrument/regular_interpolators.py
index 4f4cc69..cc54d10 100644
--- a/lisainstrument/regular_interpolators.py
+++ b/lisainstrument/regular_interpolators.py
@@ -92,12 +92,12 @@ class RegularInterpCore(Protocol):
The locations are specified via an array s of real-valued shifts. For the element s[i] of
the shift array with array index i, the absolute location within the index space of the
- input samples is given by i - s[i] + ofs, where ofs is a constant integer offset. A zero
+ input samples is given by i + s[i] + ofs, where ofs is a constant integer offset. A zero
shift means the output sample with index i is the input sample with index i+ofs.
The offset can be positive or negative. Shift values that would require samples not
in the input are not allowed. The output should be the same as for
- apply(samples, -shift, shift_offset + np.arange(shift.shape[0]))
+ apply(samples, shift, shift_offset + np.arange(shift.shape[0]))
Arguments:
samples: 1D numpy array with sampled data
@@ -266,10 +266,9 @@ class RegularInterpLagrange(RegularInterpCore):
Returns:
Interpolated samples
"""
- loc = -shift
offsets = shift_offset + np.arange(shift.shape[0])
return self.apply(
- samples, make_numpy_array_1d(loc), make_numpy_array_1d(offsets)
+ samples, make_numpy_array_1d(shift), make_numpy_array_1d(offsets)
)
@@ -343,10 +342,9 @@ class RegularInterpLinear(RegularInterpCore):
Returns:
Interpolated samples
"""
- loc = -shift
offsets = shift_offset + np.arange(shift.shape[0])
return self.apply(
- samples, make_numpy_array_1d(loc), make_numpy_array_1d(offsets)
+ samples, make_numpy_array_1d(shift), make_numpy_array_1d(offsets)
)
--
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