in-progress detect saturation rough.

JoeZiminski · JoeZiminski · commit 017b7ce72a51 · 2026-01-06T15:52:58.000+01:00
diff --git a/src/spikeinterface/preprocessing/saturation_working/detect_saturation.py b/src/spikeinterface/preprocessing/saturation_working/detect_saturation.py
@@ -0,0 +1,266 @@
+from __future__ import annotations
+
+import numpy as np
+
+from spikeinterface.core.core_tools import define_function_handling_dict_from_class
+from spikeinterface.preprocessing.silence_periods import SilencedPeriodsRecording
+from spikeinterface.preprocessing.rectify import RectifyRecording
+from spikeinterface.preprocessing.common_reference import CommonReferenceRecording
+from spikeinterface.preprocessing.filter_gaussian import GaussianFilterRecording
+from spikeinterface.core.job_tools import split_job_kwargs, fix_job_kwargs
+from spikeinterface.core.recording_tools import get_noise_levels
+from spikeinterface.core.node_pipeline import PeakDetector, base_peak_dtype
+import numpy as np
+
+
+class DetectSaturation(PeakDetector):
+
+    name = "detect_saturation"
+    preferred_mp_context = None
+
+    def __init__(
+        self,
+        recording,
+        saturation_threshold=5,  # TODO: FIX,   max_voltage = max_voltage if max_voltage is not None else sr.range_volts[:-1]
+        noise_levels=None,  # TODO: REMOVE?
+        seed=None,
+        noise_levels_kwargs=dict(),
+    ):
+
+        # TODO: fix name
+        # TODO: review this
+        EVENT_VECTOR_TYPE = [
+            ('start_sample_index', 'int64'),
+            ('stop_sample_index', 'int64'),
+            ('segment_index', 'int64'),
+            ('channel_x_start', 'float64'),
+            ('channel_x_stop', 'float64'),
+            ('channel_y_start', 'float64'),
+            ('channel_y_stop', 'float64'),
+            ('method_id', 'U128')
+        ]
+        self.saturation_threshold = saturation_threshold
+        self._dtype = np.dtype(base_peak_dtype + [("front", "bool")])
+
+    def get_trace_margin(self):  # TODO: add margin
+        return 0
+
+    def get_dtype(self):
+        return self._dtype
+
+    def compute(self, traces, start_frame, end_frame, segment_index, max_margin):  # TODO: required arguments
+        """
+        Computes
+        :param data: [nc, ns]: voltage traces array
+        :param max_voltage: maximum value of the voltage: scalar or array of size nc (same units as data)
+        :param v_per_sec: maximum derivative of the voltage in V/s (or units/s)
+        :param fs: sampling frequency Hz (defaults to 30kHz)
+        :param proportion: 0 < proportion <1  of channels above threshold to consider the sample as saturated (0.2)
+        :param mute_window_samples=7: number of samples for the cosine taper applied to the saturation
+        :return:
+            saturation [ns]: boolean array indicating the saturated samples
+            mute [ns]: float array indicating the mute function to apply to the data [0-1]
+        """
+        import scipy  # TODO: handle import
+        max_voltage = self.saturation_threshold
+        data = traces.T  # TODO: handle
+
+        # first computes the saturated samples
+        max_voltage = np.atleast_1d(max_voltage)[:, np.newaxis]
+        saturation = np.mean(np.abs(data) > max_voltage * 0.98, axis=0)
+
+        # then compute the derivative of the voltage saturation
+        n_diff_saturated = np.mean(np.abs(np.diff(data, axis=-1)) / fs >= v_per_sec, axis=0)
+        n_diff_saturated = np.r_[n_diff_saturated, 0]
+
+        # if either of those reaches more than the proportion of channels labels the sample as saturated
+        saturation = np.logical_or(saturation > proportion, n_diff_saturated > proportion)
+
+        # apply a cosine taper to the saturation to create a mute function
+        win = scipy.signal.windows.cosine(mute_window_samples)
+        mute = np.maximum(0, 1 - scipy.signal.convolve(saturation, win, mode="same"))
+        return saturation, mute
+
+
+
+        #z = np.median(traces / self.abs_thresholds, 1)
+        #threshold_mask = np.diff((z > 1) != 0, axis=0)
+        #indices = np.flatnonzero(threshold_mask)
+        #threshold_crossings = np.zeros(indices.size, dtype=self._dtype)
+        #threshold_crossings["sample_index"] = indices
+        #threshold_crossings["front"][::2] = True
+        #threshold_crossings["front"][1::2] = False
+        #return (threshold_crossings,)
+
+
+def detect_period_artifacts_by_envelope(
+    recording,
+    detect_threshold=5,
+    min_duration_ms=50,
+    freq_max=20.0,
+    seed=None,
+    noise_levels=None,
+    **noise_levels_kwargs,
+):
+    """
+    Docstring for detect_period_artifacts. Function to detect putative artifact periods as threshold crossings of
+    a global envelope of the channels.
+
+    Parameters
+    ----------
+    recording : RecordingExtractor
+        The recording extractor to detect putative artifacts
+    detect_threshold : float, default: 5
+        The threshold to detect artifacts. The threshold is computed as `detect_threshold * noise_level`
+    freq_max : float, default: 20
+        The maximum frequency for the low pass filter used
+    min_duration_ms : float, default: 50
+        The minimum duration for a threshold crossing to be considered as an artefact.
+    noise_levels : array
+        Noise levels if already computed
+    seed : int | None, default: None
+        Random seed for `get_noise_levels`.
+        If none, `get_noise_levels` uses `seed=0`.
+    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
+
+    """
+
+    envelope = RectifyRecording(recording)
+    envelope = GaussianFilterRecording(envelope, freq_min=None, freq_max=freq_max)
+    envelope = CommonReferenceRecording(envelope)
+
+    from spikeinterface.core.node_pipeline import (
+        run_node_pipeline,
+    )
+
+    _, job_kwargs = split_job_kwargs(noise_levels_kwargs)
+    job_kwargs = fix_job_kwargs(job_kwargs)
+
+    node0 = DetectThresholdCrossing(
+        recording, detect_threshold=detect_threshold, noise_levels=noise_levels, seed=seed, **noise_levels_kwargs
+    )
+
+    threshold_crossings = run_node_pipeline(
+        recording,
+        [node0],
+        job_kwargs,
+        job_name="detect threshold crossings",
+    )
+
+    order = np.lexsort((threshold_crossings["sample_index"], threshold_crossings["segment_index"]))
+    threshold_crossings = threshold_crossings[order]
+
+    periods = []
+    fs = recording.sampling_frequency
+    max_duration_samples = int(min_duration_ms * fs / 1000)
+    num_seg = recording.get_num_segments()
+
+    for seg_index in range(num_seg):
+        sub_periods = []
+        mask = threshold_crossings["segment_index"] == seg_index
+        sub_thr = threshold_crossings[mask]
+        if len(sub_thr) > 0:
+            local_thr = np.zeros(1, dtype=np.dtype(base_peak_dtype + [("front", "bool")]))
+            if not sub_thr["front"][0]:
+                local_thr["sample_index"] = 0
+                local_thr["front"] = True
+                sub_thr = np.hstack((local_thr, sub_thr))
+            if sub_thr["front"][-1]:
+                local_thr["sample_index"] = recording.get_num_samples(seg_index)
+                local_thr["front"] = False
+                sub_thr = np.hstack((sub_thr, local_thr))
+
+            indices = np.flatnonzero(np.diff(sub_thr["front"]))
+            for i, j in zip(indices[:-1], indices[1:]):
+                if sub_thr["front"][i]:
+                    start = sub_thr["sample_index"][i]
+                    end = sub_thr["sample_index"][j]
+                    if end - start > max_duration_samples:
+                        sub_periods.append((start, end))
+
+        periods.append(sub_periods)
+
+    return periods, envelope
+
+
+class SilencedArtifactsRecording(SilencedPeriodsRecording):
+    """
+    Silence user-defined periods from recording extractor traces. The code will construct
+    an enveloppe of the recording (as a low pass filtered version of the traces) and detect
+    threshold crossings to identify the periods to silence. The periods are then silenced either
+    on a per channel basis or across all channels by replacing the values by zeros or by
+    adding gaussian noise with the same variance as the one in the recordings
+
+    Parameters
+    ----------
+    recording : RecordingExtractor
+        The recording extractor to silence putative artifacts
+    detect_threshold : float, default: 5
+        The threshold to detect artifacts. The threshold is computed as `detect_threshold * noise_level`
+    freq_max : float, default: 20
+        The maximum frequency for the low pass filter used
+    min_duration_ms : float, default: 50
+        The minimum duration for a threshold crossing to be considered as an artefact.
+    noise_levels : array
+        Noise levels if already computed
+    seed : int | None, default: None
+        Random seed for `get_noise_levels` and `NoiseGeneratorRecording`.
+        If none, `get_noise_levels` uses `seed=0` and `NoiseGeneratorRecording` generates a random seed using `numpy.random.default_rng`.
+    mode : "zeros" | "noise", default: "zeros"
+        Determines what periods are replaced by. Can be one of the following:
+
+        - "zeros": Artifacts are replaced by zeros.
+
+        - "noise": The periods are filled with a gaussion noise that has the
+                   same variance that the one in the recordings, on a per channel
+                   basis
+    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
+
+    Returns
+    -------
+    silenced_recording : SilencedArtifactsRecording
+        The recording extractor after silencing detected artifacts
+    """
+
+    _precomputable_kwarg_names = ["list_periods"]
+
+    def __init__(
+        self,
+        recording,
+        detect_threshold=5,
+        verbose=False,
+        freq_max=20.0,
+        min_duration_ms=50,
+        mode="zeros",
+        noise_levels=None,
+        seed=None,
+        list_periods=None,
+        **noise_levels_kwargs,
+    ):
+
+        if list_periods is None:
+            list_periods, _ = detect_period_artifacts_by_envelope(
+                recording,
+                detect_threshold=detect_threshold,
+                min_duration_ms=min_duration_ms,
+                freq_max=freq_max,
+                seed=seed,
+                noise_levels=noise_levels,
+                **noise_levels_kwargs,
+            )
+
+            if verbose:
+                for i, periods in enumerate(list_periods):
+                    total_time = np.sum([end - start for start, end in periods])
+                    percentage = 100 * total_time / recording.get_num_samples(i)
+                    print(f"{percentage}% of segment {i} has been flagged as artifactual")
+
+        SilencedPeriodsRecording.__init__(
+            self, recording, list_periods, mode=mode, noise_levels=noise_levels, seed=seed, **noise_levels_kwargs
+        )
+
+
+# function for API
+silence_artifacts = define_function_handling_dict_from_class(
+    source_class=SilencedArtifactsRecording, name="silence_artifacts"
+)
