diff --git a/.github/workflows/python-publish-ezmsg-sigproc.yml b/.github/workflows/python-publish-ezmsg-sigproc.yml index b6ee9d14..21b91ef2 100644 --- a/.github/workflows/python-publish-ezmsg-sigproc.yml +++ b/.github/workflows/python-publish-ezmsg-sigproc.yml @@ -17,8 +17,8 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Install uv - uses: astral-sh/setup-uv@v2 + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 - name: Build Package run: uv build diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml index c642829c..510982a5 100644 --- a/.github/workflows/python-tests.yml +++ b/.github/workflows/python-tests.yml @@ -13,7 +13,7 @@ jobs: build: strategy: matrix: - python-version: ["3.10", "3.11", "3.12"] + python-version: ["3.10.15", "3.11", "3.12", "3.13"] os: - "ubuntu-latest" - "windows-latest" @@ -23,17 +23,11 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Install uv - uses: astral-sh/setup-uv@v2 - with: - enable-cache: true - cache-dependency-glob: "uv.lock" - - - name: Set up Python ${{ matrix.python-version }} - run: uv python install ${{ matrix.python-version }} + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 - name: Install the project - run: uv sync --all-extras --dev + run: uv sync --python ${{ matrix.python-version }} - name: Lint run: diff --git a/.gitignore b/.gitignore index 743bd3c1..2212bfef 100644 --- a/.gitignore +++ b/.gitignore @@ -142,4 +142,5 @@ cython_debug/ # JetBrains .idea/ -src/ezmsg/sigproc/__version__.py \ No newline at end of file +src/ezmsg/sigproc/__version__.py +uv.lock diff --git a/README.md b/README.md index d396c5a6..255d654d 100644 --- a/README.md +++ b/README.md @@ -1,33 +1,48 @@ # ezmsg.sigproc -Timeseries signal processing implementations for ezmsg +## Overview -## Dependencies - -* `ezmsg` -* `numpy` -* `scipy` -* `pywavelets` +ezmsg-sigproc offers timeseries signal‑processing primitives built atop the ezmsg message‑passing framework. Core dependencies include ezmsg, numpy, scipy, pywavelets, and sparse; the project itself is managed through hatchling and uses VCS hooks to populate __version__.py. ## Installation -### Release - Install the latest release from pypi with: `pip install ezmsg-sigproc` (or `uv add ...` or `poetry add ...`). -### Development Version +You can install pre-release versions directly from GitHub: -You can add the development version of `ezmsg-sigproc` to your project's dependencies in one of several ways. +* Using `pip`: `pip install git+https://github.com/ezmsg-org/ezmsg-sigproc.git@dev` +* Using `uv`: `uv add git+https://github.com/ezmsg-org/ezmsg-sigproc --branch dev` +* Using `poetry`: `poetry add "git+https://github.com/ezmsg-org/ezmsg-sigproc.git@dev"` -You can clone it and add its path to your project dependencies. You may wish to do this if you intend to edit `ezmsg-sigproc`. If so, please refer to the [Developers](#developers) section below. +> See the [Development](#development) section below for installing with the intention of developing. -You can also add it directly from GitHub: +## Source layout & key modules +* All source resides under src/ezmsg/sigproc, which contains a suite of processors (for example, filter.py, spectrogram.py, spectrum.py, sampler.py) and math and util subpackages. +* The framework’s backbone is base.py, defining standard protocols—Processor, Producer, Consumer, and Transformer—that enable both stateless and stateful processing chains. +* Filtering is implemented in filter.py, providing settings dataclasses and a stateful transformer that applies supplied coefficients to incoming data. +* Spectral analysis uses a composite spectrogram transformer chaining windowing, spectrum computation, and axis adjustments. -* Using `pip`: `pip install git+https://github.com/ezmsg-org/ezmsg-sigproc.git@dev` -* Using `poetry`: `poetry add "git+https://github.com/ezmsg-org/ezmsg-sigproc.git@dev"` -* Using `uv`: `uv add git+https://github.com/ezmsg-org/ezmsg-sigproc --branch dev` +## Operating styles: Standalone processors vs. ezmsg pipelines +While each processor is designed to be assembled into an ezmsg pipeline, the components are also well‑suited for offline, ad‑hoc analysis. You can instantiate processors directly in scripts or notebooks for quick prototyping or to validate results from other code. The companion Unit wrappers, however, are meant for assembling processors into a full ezmsg pipeline. + +A fully defined ezmsg pipeline shines in online streaming scenarios where message routing, scheduling, and latency handling are crucial. Nevertheless, you can run the same pipeline offline—say, within a Jupyter notebook—if your analysis benefits from ezmsg’s structured execution model. Deciding between a standalone processor and a full pipeline comes down to the trade‑off between simplicity and the operational overhead of the pipeline: + +* Standalone processors: Low overhead, ideal for one‑off or exploratory offline tasks. +* Pipeline + Unit wrappers: Additional setup cost but bring concurrency, standardized interfaces, and automatic message flow—useful when your offline experiment mirrors a live system or when you require fine‑grained pipeline behavior. + +## Documentation & tests +* `docs/ProcessorsBase.md` details the processor hierarchy and generic type patterns, providing a solid foundation for custom components. +* Unit tests (e.g., `tests/unit/test_sampler.py`) offer concrete examples of usage, showcasing sampler generation, windowing, and message handling. + +## Where to learn next +* Study docs/ProcessorsBase.md to master the processor architecture. +* Explore unit tests for hands‑on examples of composing processors and Units. +* Review the ezmsg framework in pyproject.toml to understand the surrounding ecosystem. +* Experiment with the code—try running processors standalone and then integrate them into a small pipeline to observe the trade‑offs firsthand. + +This approach equips newcomers to choose the right level of abstraction—raw processor, Unit wrapper, or full pipeline—based on the demands of their analysis or streaming application. -## Developers +## Development We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for development. It is not strictly required, but if you intend to contribute to ezmsg-sigproc then using `uv` will lead to the smoothest collaboration. @@ -36,4 +51,5 @@ We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for devel 3. Open a terminal and `cd` to the cloned folder. 4. `uv sync` to create a .venv and install dependencies. 5. `uv run pre-commit install` to install pre-commit hooks to do linting and formatting. -6. After editing code and making commits, Run the test suite before making a PR: `uv run pytest tests` +6. Run the test suite before finalizing your edits: `uv run pytest tests` +7. Make a PR against the `dev` branch of the main repo. diff --git a/pyproject.toml b/pyproject.toml index d12b70be..20ccbaad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,9 +20,18 @@ dependencies = [ "sparse>=0.15.4", ] -[project.optional-dependencies] +[dependency-groups] +dev = [ + "typer>=0.12.5", + "pre-commit>=4.2.0", + "jupyter>=1.1.1", + {include-group = "lint"}, + {include-group = "test"}, +] +lint = [ + "ruff" +] test = [ - "flake8>=7.1.1", "frozendict>=2.4.4", "pytest-asyncio>=0.24.0", "pytest-cov>=5.0.0", @@ -42,9 +51,6 @@ version-file = "src/ezmsg/sigproc/__version__.py" [tool.hatch.build.targets.wheel] packages = ["src/ezmsg"] -[tool.uv] -dev-dependencies = ["pre-commit>=3.8.0", "ruff>=0.6.7"] - [tool.pytest.ini_options] norecursedirs = "tests/helpers" addopts = "-p no:warnings" diff --git a/src/ezmsg/sigproc/filter.py b/src/ezmsg/sigproc/filter.py index 6003ca26..9a3a4c54 100644 --- a/src/ezmsg/sigproc/filter.py +++ b/src/ezmsg/sigproc/filter.py @@ -32,17 +32,19 @@ class FilterCoefficients: def _normalize_coefs( - coefs: FilterCoefficients | tuple[npt.NDArray, npt.NDArray] | npt.NDArray, -) -> tuple[str, tuple[npt.NDArray, ...]]: + coefs: FilterCoefficients | tuple[npt.NDArray, npt.NDArray] | npt.NDArray | None, +) -> tuple[str, tuple[npt.NDArray, ...] | None]: coef_type = "ba" if coefs is not None: # scipy.signal functions called with first arg `*coefs`. # Make sure we have a tuple of coefficients. - if isinstance(coefs, npt.NDArray): + if isinstance(coefs, np.ndarray): coef_type = "sos" coefs = (coefs,) # sos funcs just want a single ndarray. elif isinstance(coefs, FilterCoefficients): - coefs = (FilterCoefficients.b, FilterCoefficients.a) + coefs = (coefs.b, coefs.a) + elif not isinstance(coefs, tuple): + coefs = (coefs,) return coef_type, coefs @@ -91,16 +93,20 @@ def _reset_state(self, message: AxisArray) -> None: axis = message.dims[0] if self.settings.axis is None else self.settings.axis axis_idx = message.get_axis_idx(axis) n_tail = message.data.ndim - axis_idx - 1 - coefs = ( - (self.settings.coefs,) - if self.settings.coefs is not None - and not isinstance(self.settings.coefs, tuple) - else self.settings.coefs - ) - zi_func = {"ba": scipy.signal.lfilter_zi, "sos": scipy.signal.sosfilt_zi}[ - self.settings.coef_type - ] - zi = zi_func(*coefs) + _, coefs = _normalize_coefs(self.settings.coefs) + + if self.settings.coef_type == "ba": + b, a = coefs + if len(a) == 1 or np.allclose(a[1:], 0): + # For FIR filters, use lfiltic with zero initial conditions + zi = scipy.signal.lfiltic(b, a, []) + else: + # For IIR filters... + zi = scipy.signal.lfilter_zi(b, a) + else: + # For second-order sections (SOS) filters, use sosfilt_zi + zi = scipy.signal.sosfilt_zi(*coefs) + zi_expand = (None,) * axis_idx + (slice(None),) + (None,) * n_tail n_tile = ( message.data.shape[:axis_idx] + (1,) + message.data.shape[axis_idx + 1 :] @@ -166,12 +172,7 @@ def _process(self, message: AxisArray) -> AxisArray: if message.data.size > 0: axis = message.dims[0] if self.settings.axis is None else self.settings.axis axis_idx = message.get_axis_idx(axis) - coefs = ( - (self.settings.coefs,) - if self.settings.coefs is not None - and not isinstance(self.settings.coefs, tuple) - else self.settings.coefs - ) + _, coefs = _normalize_coefs(self.settings.coefs) filt_func = {"ba": scipy.signal.lfilter, "sos": scipy.signal.sosfilt}[ self.settings.coef_type ] diff --git a/src/ezmsg/sigproc/gaussiansmoothing.py b/src/ezmsg/sigproc/gaussiansmoothing.py new file mode 100644 index 00000000..6d13cfc8 --- /dev/null +++ b/src/ezmsg/sigproc/gaussiansmoothing.py @@ -0,0 +1,93 @@ +from typing import Callable +import warnings + +import numpy as np + +from .filter import ( + FilterBaseSettings, + BACoeffs, + FilterByDesignTransformer, + BaseFilterByDesignTransformerUnit, +) + + +class GaussianSmoothingSettings(FilterBaseSettings): + sigma: float | None = 1.0 + """ + sigma : float + Standard deviation of the Gaussian kernel. + """ + + width: int | None = 4 + """ + width : int + Number of standard deviations covered by the kernel window if kernel_size is not provided. + """ + + kernel_size: int | None = None + """ + kernel_size : int | None + Length of the kernel in samples. If provided, overrides automatic calculation. + """ + + +def gaussian_smoothing_filter_design( + sigma: float = 1.0, + width: int = 4, + kernel_size: int | None = None, +) -> BACoeffs | None: + # Parameter checks + if sigma <= 0: + raise ValueError(f"sigma must be positive. Received: {sigma}") + + if width <= 0: + raise ValueError(f"width must be positive. Received: {width}") + + if kernel_size is not None: + if kernel_size < 1: + raise ValueError(f"kernel_size must be >= 1. Received: {kernel_size}") + else: + kernel_size = int(2 * width * sigma + 1) + + # Warn if kernel_size is smaller than recommended but don't fail + expected_kernel_size = int(2 * width * sigma + 1) + if kernel_size < expected_kernel_size: + ## TODO: Either add a warning or determine appropriate kernel size and raise an error + warnings.warn( + f"Provided kernel_size {kernel_size} is smaller than recommended " + f"size {expected_kernel_size} for sigma={sigma} and width={width}. " + "The kernel may be truncated." + ) + + from scipy.signal.windows import gaussian + + b = gaussian(kernel_size, std=sigma) + b /= np.sum(b) # Ensure normalization + a = np.array([1.0]) + + return b, a + + +class GaussianSmoothingFilterTransformer( + FilterByDesignTransformer[GaussianSmoothingSettings, BACoeffs] +): + def get_design_function( + self, + ) -> Callable[[float], BACoeffs]: + # Create a wrapper function that ignores fs parameter since gaussian smoothing doesn't need it + def design_wrapper(fs: float) -> BACoeffs: + return gaussian_smoothing_filter_design( + sigma=self.settings.sigma, + width=self.settings.width, + kernel_size=self.settings.kernel_size, + ) + + return design_wrapper + + +class GaussianSmoothingFilter( + BaseFilterByDesignTransformerUnit[ + GaussianSmoothingSettings, GaussianSmoothingFilterTransformer + ] +): + SETTINGS = GaussianSmoothingSettings diff --git a/src/ezmsg/sigproc/util/sparse.py b/src/ezmsg/sigproc/util/sparse.py index 8478a68e..1596b591 100644 --- a/src/ezmsg/sigproc/util/sparse.py +++ b/src/ezmsg/sigproc/util/sparse.py @@ -1,11 +1,13 @@ +import numpy as np import sparse -def sliding_win_oneaxis( +def sliding_win_oneaxis_old( s: sparse.SparseArray, nwin: int, axis: int, step: int = 1 ) -> sparse.SparseArray: """ Like `ezmsg.util.messages.axisarray.sliding_win_oneaxis` but for sparse arrays. + This approach is about 4x slower than the version that uses coordinate arithmetic below. Args: s: The input sparse array. @@ -25,5 +27,97 @@ def sliding_win_oneaxis( full_slices[: axis + 1] + (sl,) + full_slices[axis + 2 :] for sl in targ_slices ] result = sparse.concatenate([s[_] for _ in full_slices], axis=axis) - # TODO: Profile this approach vs modifying coords only. return result + + +def sliding_win_oneaxis( + s: sparse.SparseArray, nwin: int, axis: int, step: int = 1 +) -> sparse.SparseArray: + """ + Generates a view-like sparse array using a sliding window of specified length along a specified axis. + Sparse analog of an optimized dense as_strided-based implementation with these properties: + + - Accepts a single `nwin` and a single `axis`. + - Inserts a new 'win' axis immediately BEFORE the original target axis. + Output shape: + s.shape[:axis] + (W,) + (nwin,) + s.shape[axis+1:] + where W = s.shape[axis] - (nwin - 1). + - If `step > 1`, stepping is applied by slicing along the new windows axis (same observable behavior + as doing `slice_along_axis(result, slice(None, None, step), axis)` in the dense version). + + Args: + s: Input sparse array (pydata/sparse COO-compatible). + nwin: Sliding window size (must be > 0). + axis: Axis of `s` along which the window slides (supports negative indexing). + step: Stride between windows. If > 1, applied by slicing the windows axis after construction. + + Returns: + A sparse array with a new windows axis inserted before the original axis. + + Notes: + - Mirrors the dense function’s known edge case: when nwin == shape[axis] + 1, W becomes 0 and + an empty windows axis is returned. + - Built by coordinate arithmetic; no per-window indexing or concatenation. + """ + if -s.ndim <= axis < 0: + axis = s.ndim + axis + if not (0 <= axis < s.ndim): + raise ValueError(f"Invalid axis {axis} for array with {s.ndim} dimensions") + if nwin <= 0: + raise ValueError("nwin must be > 0") + dim = s.shape[axis] + + last_win_start = dim - nwin + win_starts = list(range(0, last_win_start + 1, step)) + n_win_out = len(win_starts) + if n_win_out <= 0: + # Return array with proper shape except empty along windows axis + return sparse.zeros( + s.shape[:axis] + (0,) + (nwin,) + s.shape[axis + 1 :], dtype=s.dtype + ) + + coo = s.asformat("coo") + coords = coo.coords # shape: (ndim, nnz) + data = coo.data # shape: (nnz,) + ia = coords[axis] # indices along sliding axis, shape: (nnz,) + + # We emit contributions for each offset o in [0, nwin-1]. + # For a nonzero at index i, it contributes to window start w = i - o when 0 <= w < W. + out_coords_blocks = [] + out_data_blocks = [] + + # Small speed/memory tweak: reuse dtypes and pre-allocate o-array once per loop. + idx_dtype = coords.dtype + + for win_ix, win_start in enumerate(win_starts): + w = ia - win_start + # Valid window starts are those within [0, nwin] + mask = (w >= 0) & (w < nwin) + if not mask.any(): + continue + + sel = np.nonzero(mask)[0] + w_sel = w[sel] + + # Build new coords with windows axis inserted at `axis` and the original axis + # becoming the next axis with fixed offset value `o`. + # Output ndim = s.ndim + 1 + before = coords[:axis, sel] # unchanged + after_other = coords[axis + 1 :, sel] # dims after original axis + win_idx_row = np.full((1, sel.size), win_ix, dtype=idx_dtype) + + new_coords = np.vstack([before, win_idx_row, w_sel[None, :], after_other]) + + out_coords_blocks.append(new_coords) + out_data_blocks.append(data[sel]) + + if not out_coords_blocks: + return sparse.zeros( + s.shape[:axis] + (n_win_out,) + (nwin,) + s.shape[axis + 1 :], dtype=s.dtype + ) + + out_coords = np.hstack(out_coords_blocks) + out_data = np.hstack(out_data_blocks) + out_shape = s.shape[:axis] + (n_win_out,) + (nwin,) + s.shape[axis + 1 :] + + return sparse.COO(out_coords, out_data, shape=out_shape) diff --git a/tests/unit/test_filter.py b/tests/unit/test_filter.py new file mode 100644 index 00000000..9387ae98 --- /dev/null +++ b/tests/unit/test_filter.py @@ -0,0 +1,17 @@ +import numpy as np +from ezmsg.sigproc.filter import FilterTransformer, FilterSettings, FilterCoefficients +from ezmsg.util.messages.axisarray import AxisArray + + +def test_filter_transformer_accepts_dataclass_coefficients(): + data = np.arange(10.0) + msg = AxisArray( + data=data, + dims=["time"], + axes={"time": AxisArray.TimeAxis(fs=1.0, offset=0.0)}, + key="test", + ) + coefs = FilterCoefficients(b=np.array([1.0]), a=np.array([1.0, 0.0])) + transformer = FilterTransformer(settings=FilterSettings(axis="time", coefs=coefs)) + out = transformer(msg) + assert np.allclose(out.data, data) diff --git a/tests/unit/test_gaussian_smoothing_filter.py b/tests/unit/test_gaussian_smoothing_filter.py new file mode 100644 index 00000000..02a4082b --- /dev/null +++ b/tests/unit/test_gaussian_smoothing_filter.py @@ -0,0 +1,255 @@ +import numpy as np +import pytest +from ezmsg.util.messages.axisarray import AxisArray + +from ezmsg.sigproc.gaussiansmoothing import ( + gaussian_smoothing_filter_design, + GaussianSmoothingSettings, + GaussianSmoothingFilterTransformer, +) + + +@pytest.mark.parametrize( + "axis,sigma,width,kernel_size", + [ + ("time", 1.5, 5, None), + ("time", 2.0, 4, 21), + ], +) +def test_gaussian_smoothing_filter_function(axis, sigma, width, kernel_size): + """Test the gaussian_smoothing_filter convenience function.""" + transformer = GaussianSmoothingFilterTransformer( + axis=axis, + sigma=sigma, + width=width, + kernel_size=kernel_size, + ) + + assert isinstance(transformer, GaussianSmoothingFilterTransformer) + assert transformer.settings.axis == axis + assert transformer.settings.sigma == sigma + assert transformer.settings.width == width + assert transformer.settings.kernel_size == kernel_size + + +def test_gaussian_smoothing_settings_defaults(): + """Test the GaussianSmoothingSettings class with default values.""" + settings = GaussianSmoothingSettings() + assert settings.sigma == 1.0 + assert settings.width == 4 + assert settings.kernel_size is None + + +def test_gaussian_smoothing_settings_custom(): + """Test the GaussianSmoothingSettings class with custom values.""" + settings = GaussianSmoothingSettings( + sigma=2.5, + width=6, + kernel_size=21, + ) + assert settings.sigma == 2.5 + assert settings.width == 6 + assert settings.kernel_size == 21 + + +@pytest.mark.parametrize( + "sigma,width,kernel_size", + [ + (1.0, 4, None), + (2.0, 6, None), + (1.5, 5, 11), + (1.5, 5, 17), # Fixed kernel_size to be >= expected + ], +) +def test_gaussian_smoothing_filter_design_parameters(sigma, width, kernel_size): + """Test gaussian smoothing filter design across multiple parameter configurations.""" + coefs = gaussian_smoothing_filter_design( + sigma=sigma, + width=width, + kernel_size=kernel_size, + ) + assert coefs is not None + assert isinstance(coefs, tuple) + assert len(coefs) == 2 # b and a coefficients + + b, a = coefs + assert b is not None and a is not None + assert isinstance(b, np.ndarray) and isinstance(a, np.ndarray) + assert np.all(b >= 0) # positive + assert np.allclose(b, b[::-1]) # symmetric + assert np.isclose(np.sum(b), 1.0) # normalized + assert b[len(b) // 2] == np.max(b) # center of kernel is peak + assert len(a) == 1 and a[0] == 1.0 # default for gaussian window + + expected_kernel_size = ( + int(2 * width * sigma + 1) if kernel_size is None else kernel_size + ) + assert len(b) == expected_kernel_size + + +def test_gaussian_smoothing_kernel_properties(): + """Test that larger sigma creates wider kernel""" + coefs_small = gaussian_smoothing_filter_design(sigma=1.0) + b_small, _ = coefs_small + coefs_large = gaussian_smoothing_filter_design(sigma=3.0) + b_large, _ = coefs_large + + assert len(b_large) >= len(b_small) # wider kernel + assert b_large[len(b_large) // 2] < b_small[len(b_small) // 2] # lower peak + + +@pytest.mark.parametrize("sigma", [0.0, -1.0]) +@pytest.mark.parametrize("width", [0.0, -1.0]) +@pytest.mark.parametrize("kernel_size", [0, -1]) +def test_gaussian_smoothing_filter_design_invalid_inputs(sigma, width, kernel_size): + """Test the gaussian smoothing filter design function with invalid inputs.""" + with pytest.raises(ValueError): + gaussian_smoothing_filter_design(sigma=sigma) + with pytest.raises(ValueError): + gaussian_smoothing_filter_design(width=width) + with pytest.raises(ValueError): + gaussian_smoothing_filter_design(kernel_size=kernel_size) + + +@pytest.mark.parametrize("data_shape", [(100,), (1, 100), (100, 2), (100, 2, 3)]) +def test_gaussian_smoothing_filter_process(data_shape): + """Test gaussian smoothing filter with different data shapes.""" + # Create test data + data = np.arange(np.prod(data_shape)).reshape(data_shape) + + # Create appropriate dims and axes based on shape + if len(data_shape) == 1: + dims = ["time"] + axes = {"time": AxisArray.TimeAxis(fs=100.0, offset=0)} + elif len(data_shape) == 2: + dims = ["time", "ch"] + axes = { + "time": AxisArray.TimeAxis(fs=100.0, offset=0), + "ch": AxisArray.CoordinateAxis( + data=np.arange(data_shape[1]).astype(str), dims=["ch"] + ), + } + else: + dims = ["freq", "time", "ch"] + axes = { + "freq": AxisArray.LinearAxis(unit="Hz", offset=0.0, gain=1.0), + "time": AxisArray.TimeAxis(fs=100.0, offset=0), + "ch": AxisArray.CoordinateAxis( + data=np.arange(data_shape[2]).astype(str), dims=["ch"] + ), + } + + msg = AxisArray(data=data, dims=dims, axes=axes, key="test_gaussian_smoothing") + + # Instantiate transformer (not unit) + transformer = GaussianSmoothingFilterTransformer( + settings=GaussianSmoothingSettings(axis="time", sigma=2.0, width=4) + ) + + # Process message using __call__ method + output_msg = transformer(msg) + + # Assertions + assert isinstance(output_msg, AxisArray) + assert output_msg.data.shape == data.shape + assert np.isfinite(output_msg.data).all() + + +def test_gaussian_smoothing_edge_cases(): + """Test edge cases for gaussian smoothing filter.""" + # Test with very small sigma + coefs_small = gaussian_smoothing_filter_design(sigma=0.01) + b_small, a_small = coefs_small + assert len(b_small) > 0 + assert np.isclose(np.sum(b_small), 1.0) + + # Test with very large sigma + coefs_large = gaussian_smoothing_filter_design(sigma=100.0) + b_large, a_large = coefs_large + assert len(b_large) > 0 + assert np.isclose(np.sum(b_large), 1.0) + + # Test with very small width + coefs_narrow = gaussian_smoothing_filter_design(width=1) + b_narrow, a_narrow = coefs_narrow + assert len(b_narrow) > 0 + + # Test with very large width + coefs_wide = gaussian_smoothing_filter_design(width=100) + b_wide, a_wide = coefs_wide + assert len(b_wide) > len(b_narrow) # Wider kernel + + +def test_gaussian_smoothing_update_settings(): + """Test the update_settings functionality of the Gaussian smoothing filter.""" + # Setup parameters + fs = 200.0 + dur = 1.0 + n_times = int(dur * fs) + n_chans = 2 + + # Create input data with high frequency noise + t = np.arange(n_times) / fs + # Create a signal with both low and high frequency components + signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.sin(2 * np.pi * 50 * t) + in_dat = np.vstack([signal, signal + np.random.randn(n_times) * 0.1]).T + + # Create message + msg_in = AxisArray( + data=in_dat, + dims=["time", "ch"], + axes={ + "time": AxisArray.TimeAxis(fs=fs, offset=0), + "ch": AxisArray.CoordinateAxis( + data=np.arange(n_chans).astype(str), dims=["ch"] + ), + }, + key="test_gaussian_smoothing_update_settings", + ) + + def _calc_smoothing_effect(msg): + """Calculate the smoothing effect by comparing variance.""" + return np.var(msg.data, axis=0) + + original_variance = _calc_smoothing_effect(msg_in) + + # Initialize filter with small sigma (minimal smoothing) + proc = GaussianSmoothingFilterTransformer( + axis="time", + sigma=0.5, + width=4, + coef_type="ba", + ) + + # Process first message + result1 = proc(msg_in) + variance1 = _calc_smoothing_effect(result1) + + # Small sigma should have minimal effect + assert np.allclose(variance1, original_variance, rtol=0.1) + + # Update settings - change to larger sigma (more smoothing) + proc.update_settings(sigma=3.0) + + # Process the same message with new settings + result2 = proc(msg_in) + variance2 = _calc_smoothing_effect(result2) + + # Larger sigma should reduce variance (more smoothing) + assert np.all(variance2 < variance1) + + # Test 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