diff --git a/lisainstrument/dynamic_delay_dask.py b/lisainstrument/dynamic_delay_dask.py
index 1962d03daf0bdd4a4b96279227d73d598664c3e9..144c25abd2c000741f97cf76271ac86f9705e9f0 100644
--- a/lisainstrument/dynamic_delay_dask.py
+++ b/lisainstrument/dynamic_delay_dask.py
@@ -5,7 +5,7 @@ Use make_dynamic_shift_lagrange_dask to create a Lagrange interpolator for dask
from __future__ import annotations
-from typing import Any, Callable, Final
+from typing import Any, Final
import dask
import dask.array as da
@@ -195,8 +195,11 @@ def make_dynamic_shift_lagrange_dask(
def numpyfy_dask_multi(
- dafunc: Callable[[*Any], DaskArray1D], chunks: int
-) -> Callable[[*Any], NumpyArray1D]:
+ # ~ dafunc: Callable[[*Any], DaskArray1D],
+ dafunc: Any,
+ chunks: int,
+) -> Any:
+ # ~ ) -> Callable[[*Any], NumpyArray1D]:
"""Convert function operating on 1D dask arrays to work with 1D numpy arrays.
Before calling the dask-based function, all arguments which are numpy
diff --git a/lisainstrument/dynamic_delay_dsp_dask.py b/lisainstrument/dynamic_delay_dsp_dask.py
index a87a675ab46c96533cf6268f8b55f02a72f1f535..bd0ae1410025f608eb0058b95bcd7a2b44ae1885 100644
--- a/lisainstrument/dynamic_delay_dsp_dask.py
+++ b/lisainstrument/dynamic_delay_dsp_dask.py
@@ -2,7 +2,7 @@
There are two implementations of Lagrange interpolation:
-1. dynamic_delay_dsp_dask.make_regular_interpolator_dsp wraps legacy implementation dsp.timeshift
+1. dynamic_delay_dsp_dask.make_regular_interpolator_dsp wraps legacy implementation dsp.timeshift
into new interface
2. dynamic_delay_numpy.make_regular_interpolator_lagrange provides a completely new implementation
of Lagrange interpolation
@@ -19,16 +19,12 @@ import numpy as np
from lisainstrument import dsp
from lisainstrument.dynamic_delay_dask import DynamicShiftDask
from lisainstrument.dynamic_delay_numpy import (
+ DynamicShiftNumpy,
DynShiftBC,
DynShiftCfg,
- RegularInterpCore,
- RegularInterpolator,
- DynamicShiftNumpy,
)
from lisainstrument.fir_filters_numpy import NumpyArray1D
-
-
-
+from lisainstrument.regular_interpolators import RegularInterpCore, RegularInterpolator
class RegularInterpDSP(RegularInterpCore):
@@ -138,6 +134,7 @@ def make_dynamic_shift_dsp_dask(
cfg = DynShiftCfg(min_delay, max_delay, left_bound, right_bound)
return DynamicShiftDask(cfg, interp)
+
def make_dynamic_shift_dsp_numpy(
order: int,
min_delay: float,
diff --git a/lisainstrument/fixed_shift_numpy.py b/lisainstrument/fixed_shift_numpy.py
index 538497b91e7bc696160d8b0a2d6814278a8a0d28..8b98393533d95bd4e2a10ea1ead7622df0bc9f6e 100644
--- a/lisainstrument/fixed_shift_numpy.py
+++ b/lisainstrument/fixed_shift_numpy.py
@@ -1,4 +1,4 @@
-"""Functions for interpolating numpy arrays with 1D regularly spaced data
+"""Functions for interpolating numpy arrays with 1D regularly spaced data
This provides a generic interface RegularInterpolator as well as two interpolation
methods, linear and Lagrange. The latter is written from scratch, see module
@@ -110,16 +110,16 @@ def make_fir_lagrange_fixed(length: int, frac_shift: float) -> DefFilterFIR:
\end{align*}
$$
- The offset is chosen to center the stencil around a shift
+ The offset is chosen to center the stencil around a shift
$s=0$ for odd length and s=$1/2$ for even length. The shift should
not exceed the bounds $-1/2 < s < 1/2$ for odd length or $0 < s < 1$
- for even length significantly, to avoid large overshoots that inherently
+ for even length significantly, to avoid large overshoots that inherently
occur off-center for high order lagrange interpolation.
Arguments:
length: Number of FIR coefficients (=Lagrange order + 1)
frac_shift: The shift $s$
-
+
Returns:
FIR filter definition
"""
diff --git a/lisainstrument/regular_interpolators.py b/lisainstrument/regular_interpolators.py
index a8433c7e0c3f2d9446e581d5aae007b35266f003..4f4cc6930fc529cf5bd5ce2775ac7aa586df7cd8 100644
--- a/lisainstrument/regular_interpolators.py
+++ b/lisainstrument/regular_interpolators.py
@@ -1,4 +1,4 @@
-"""Functions for interpolating numpy arrays with 1D regularly spaced data
+"""Functions for interpolating numpy arrays with 1D regularly spaced data
This provides a generic interface RegularInterpolator as well as two interpolation
methods, linear and Lagrange. The latter is written from scratch, see module
diff --git a/tests/test_dynamic_delay_dask.py b/tests/test_dynamic_delay_dask.py
index 7f3b12f749f33a9c25e33addc5c938364e11ea08..fc147c9a115d0dd2123f31789d03cbcbace34e93 100644
--- a/tests/test_dynamic_delay_dask.py
+++ b/tests/test_dynamic_delay_dask.py
@@ -77,7 +77,6 @@ def test_dynamic_shift_lagrange_dask() -> None:
assert np.all(s_np == s_da)
-
def test_lagrange_polynomials():
length = 11
d = -(length // 2)
diff --git a/tests/test_dynamic_delay_dsp_dask.py b/tests/test_dynamic_delay_dsp_dask.py
index e9f7897227bece530cd3a9d2921b4a97d733c823..8be00af6af83a4ede4ad383031e8b8d8d37360f5 100644
--- a/tests/test_dynamic_delay_dsp_dask.py
+++ b/tests/test_dynamic_delay_dsp_dask.py
@@ -1,34 +1,37 @@
import numpy as np
import pytest
-from lisainstrument.dynamic_delay_dsp_dask import make_dynamic_shift_dsp_dask, make_dynamic_shift_dsp_numpy
-from lisainstrument.dynamic_delay_dask import numpyfy_dask_bivariate
-
+from lisainstrument.dynamic_delay_dask import numpyfy_dask_multi
+from lisainstrument.dynamic_delay_dsp_dask import (
+ make_dynamic_shift_dsp_dask,
+ make_dynamic_shift_dsp_numpy,
+)
from lisainstrument.dynamic_delay_numpy import DynShiftBC
-
-
-
-def test_dynamic_shift_dsp_dask():
+def test_dynamic_shift_dsp_dask() -> None:
order = 5
- length = order + 1
t, dt = np.linspace(-5.345, 10.345, 1003, retstep=True)
def g(x):
- n = x / 10.0
- return 4.32546 + 3.34324 * x + 4.342 * x**2 + 0.46 * x**3 + 1.43598 * x**4 + 3.3456 * x**5
+ return (
+ 4.32546
+ + 3.34324 * x
+ + 4.342 * x**2
+ + 0.46 * x**3
+ + 1.43598 * x**4
+ + 3.3456 * x**5
+ )
y = g(t)
d = (0.93456456 + 0.0235345 * np.cos(4.3354 * t)) / dt
-
op_da = make_dynamic_shift_dsp_dask(
order, d.min(), d.max(), DynShiftBC.FLAT, DynShiftBC.EXCEPTION
)
- op_na = numpyfy_dask_bivariate(op_da, chunks=19)
+ op_na = numpyfy_dask_multi(op_da, chunks=19)
s_da = op_na(y, d)
s_ex = g(np.maximum(t[0], t - d * dt))
@@ -44,4 +47,3 @@ def test_dynamic_shift_dsp_dask():
s_np = op_np(y, d)
assert np.all(s_np == s_da)
-