Static Visualization API

The static module in biosigio.visualization provides functions for static plotting of biosignal data. These functions are primarily used internally by the Recording class methods but can also be called directly for advanced customization.

Module Documentation

biosigio.visualization.static

Static plotting functions for EMG data.

Recording

Core biosignal recording: signals + channels + events + metadata.

Modality-agnostic container for EEG / EMG / iEEG / MEG / stim / marker data imported from any supported format.

Attributes: signals (pd.DataFrame): Raw signal data with time as index. metadata (dict): Metadata dictionary containing recording information. channels (dict): Channel information including type, unit, sampling frequency. events (pd.DataFrame): Annotations or events associated with the signals, with columns 'onset', 'duration', 'description'.

Source code in biosigio/core/emg.py

class Recording:
    """
    Core biosignal recording: signals + channels + events + metadata.

    Modality-agnostic container for EEG / EMG / iEEG / MEG / stim / marker data
    imported from any supported format.

    Attributes:
        signals (pd.DataFrame): Raw signal data with time as index.
        metadata (dict): Metadata dictionary containing recording information.
        channels (dict): Channel information including type, unit, sampling frequency.
        events (pd.DataFrame): Annotations or events associated with the signals,
                               with columns 'onset', 'duration', 'description'.
    """

    def __init__(self):
        """Initialize an empty recording."""
        self.signals = None
        self.metadata = {}
        self.channels = {}
        # Initialize events as an empty DataFrame with specified columns
        self.events = pd.DataFrame(columns=["onset", "duration", "description"])

    def plot_signals(
        self,
        channels=None,
        time_range=None,
        offset_scale=0.8,
        uniform_scale=True,
        detrend=False,
        grid=True,
        title=None,
        show=True,
        plt_module=None,
    ):
        """
        Plot signals in a single plot with vertical offsets.

        Args:
            channels: List of channels to plot. If None, plot all channels.
            time_range: Tuple of (start_time, end_time) to plot. If None, plot all data.
            offset_scale: Portion of allocated space each signal can use (0.0 to 1.0).
            uniform_scale: Whether to use the same scale for all signals.
            detrend: Whether to remove mean from signals before plotting.
            grid: Whether to show grid lines.
            title: Optional title for the figure.
            show: Whether to display the plot.
            plt_module: Matplotlib pyplot module to use.
        """
        # Delegate to the static plotting function in visualization module
        static_plot_signals(
            rec_object=self,
            channels=channels,
            time_range=time_range,
            offset_scale=offset_scale,
            uniform_scale=uniform_scale,
            detrend=detrend,
            grid=grid,
            title=title,
            show=show,
            plt_module=plt_module,
        )

    @classmethod
    def _infer_importer(cls, filepath: str) -> ImporterName:
        """
        Infer the importer to use based on the file extension.
        """
        # rstrip path separators so a Zarr store passed as a directory with a
        # trailing slash (e.g. "rec.zarr/") still resolves by its ".zarr" suffix.
        extension = os.path.splitext(filepath.rstrip("/\\"))[1].lower()
        if extension in {".edf", ".bdf"}:
            return "edf"
        elif extension in {".set"}:
            return "eeglab"
        elif extension in {".otb", ".otb+"}:
            return "otb"
        elif extension in {".csv", ".txt"}:
            return "csv"
        elif extension in {".hea", ".dat", ".atr"}:
            return "wfdb"
        elif extension in {".xdf", ".xdfz"}:
            return "xdf"
        elif extension in {".fif", ".ds"}:
            return "meg"
        elif extension in {".vhdr"}:
            return "brainvision"
        elif extension in {".parquet", ".feather", ".arrow"}:
            return "tabular"
        elif extension in {
            ".rhd",
            ".rhs",
            ".ns1",
            ".ns2",
            ".ns3",
            ".ns4",
            ".ns5",
            ".ns6",
            ".smr",
            ".smrx",
            ".plx",
            ".pl2",
            ".trc",
            ".ncs",
        }:
            return "neo"
        elif extension == ".zarr":
            return "zarr"
        else:
            raise ValueError(f"Unsupported file extension: {extension}")

    @classmethod
    def from_file(
        cls,
        filepath: str,
        importer: ImporterName | None = None,
        force_csv: bool = False,
        bids_channels: str = "auto",
        **kwargs,
    ) -> "Recording":
        """
        The method to create a Recording object from file.

        Args:
            filepath: Path to the input file
            importer: Name of the importer to use. Can be one of the following:
                - 'trigno': Delsys Trigno EMG system (CSV)
                - 'otb': OTB/OTB+ EMG system (OTB, OTB+)
                - 'eeglab': EEGLAB .set files (SET)
                - 'edf': EDF/EDF+/BDF/BDF+ format (EDF, BDF)
                - 'csv': Generic CSV (or TXT) files with columnar data
                - 'wfdb': Waveform Database (WFDB)
                - 'xdf': XDF format (multi-stream Lab Streaming Layer files)
                - 'meg': MEG via MNE (.fif and CTF .ds; requires the 'meg' extra)
                - 'brainvision': BrainVision .vhdr via MNE (requires the 'meg' extra)
                - 'tabular': biosigIO Parquet/Arrow/Feather (requires the 'arrow' extra)
                - 'neo': proprietary electrophysiology formats via python-neo
                  (Intan, Blackrock, Spike2, Plexon, Micromed, Neuralynx, ...;
                  requires the 'neo' extra)
                - 'zarr': biosigIO Zarr serving store (requires the 'zarr' extra)
                If None, the importer will be inferred from the file extension.
                Automatic import is supported for CSV/TXT files.
            force_csv: If True and importer is 'csv', forces using the generic CSV
                      importer even if the file appears to match a specialized format.
            bids_channels: When 'auto' (default), look for a sibling BIDS
                      _channels.tsv next to the file and apply its per-channel
                      type/units over the importer's inferred values. Pass 'off'
                      to disable.
            **kwargs: Additional arguments passed to the importer.
                For XDF files, useful kwargs include:
                - stream_names: List of stream names to import
                - stream_types: List of stream types to import (e.g., ["EMG", "EXG"])
                - stream_ids: List of stream IDs to import

        Returns:
            Recording: New Recording object with loaded data
        """
        if importer is None:
            importer = cls._infer_importer(filepath)

        importers = {
            "trigno": "TrignoImporter",  # CSV with Delsys Trigno Headers
            "otb": "OTBImporter",  # OTB/OTB+ EMG system data
            "edf": "EDFImporter",  # EDF/EDF+/BDF format
            "eeglab": "EEGLABImporter",  # EEGLAB .set files
            "csv": "CSVImporter",  # Generic CSV/Text files
            "wfdb": "WFDBImporter",  # Waveform Database format
            "xdf": "XDFImporter",  # XDF multi-stream format
            "meg": "MEGImporter",  # MEG via MNE (.fif, CTF .ds)
            "brainvision": "BrainVisionImporter",  # BrainVision via MNE (.vhdr)
            "tabular": "TabularImporter",  # biosigIO Parquet / Arrow / Feather
            "neo": "NeoImporter",  # proprietary ephys via python-neo
            "zarr": "ZarrImporter",  # biosigIO Zarr serving store
        }

        if importer not in importers:
            raise ValueError(
                f"Unsupported importer: {importer}. "
                f"Available importers: {list(importers.keys())}\n"
                "- trigno: Delsys Trigno EMG system\n"
                "- otb: OTB/OTB+ EMG system\n"
                "- edf: EDF/EDF+/BDF format\n"
                "- eeglab: EEGLAB .set files\n"
                "- csv: Generic CSV/Text files\n"
                "- wfdb: Waveform Database\n"
                "- xdf: XDF multi-stream format\n"
                "- meg: MEG via MNE (.fif, CTF .ds)\n"
                "- brainvision: BrainVision via MNE (.vhdr)\n"
                "- tabular: biosigIO Parquet/Arrow/Feather (.parquet, .feather, .arrow)\n"
                "- neo: proprietary electrophysiology formats via python-neo "
                "(Intan, Blackrock, Spike2, Plexon, Micromed, Neuralynx, ...)\n"
                "- zarr: biosigIO Zarr serving store (.zarr)"
            )

        # If using CSV importer and force_csv is set, pass it as force_generic
        if importer == "csv":
            kwargs["force_generic"] = force_csv

        # Import the appropriate importer class
        importer_module = __import__(
            f"biosigio.importers.{importer}", globals(), locals(), [importers[importer]]
        )
        importer_class = getattr(importer_module, importers[importer])

        # Create importer instance and load data
        rec = importer_class().load(filepath, **kwargs)

        # Record provenance: which format this recording came from. setdefault so a
        # re-imported serialization file (tabular/zarr) keeps the ORIGINAL
        # source_format restored from its metadata rather than being relabeled.
        rec.metadata.setdefault("source_format", importer)

        # In a BIDS layout, the sibling _channels.tsv is the authoritative source
        # of per-channel type/units; apply it over the importer's header/label
        # guesses unless explicitly disabled with bids_channels="off".
        if bids_channels != "off":
            from ..bids import apply_channels_tsv, find_channels_tsv

            channels_tsv = find_channels_tsv(filepath)
            if channels_tsv:
                apply_channels_tsv(rec, channels_tsv)

        return rec

    def select_channels(
        self,
        channels: str | list[str] | None = None,
        channel_type: str | None = None,
        inplace: bool = False,
        *,
        modality: str | None = None,
    ) -> "Recording":
        """
        Select specific channels from the data and return a new Recording object.

        Args:
            channels: Channel name or list of channel names to select. If None and
                    channel_type is specified, selects all channels of that type.
            channel_type: Type of channels to select ('EMG', 'ACC', 'GYRO', etc.).
                        If specified with channels, filters the selection to only
                        channels of this type.

        Returns:
            Recording: A new Recording object containing only the selected channels

        Examples:
            # Select specific channels
            new_rec = rec.select_channels(['EMG1', 'ACC1'])

            # Select all EMG channels
            emg_only = rec.select_channels(channel_type='EMG')

            # Select specific EMG channels only, this example does not select ACC channels
            emg_subset = rec.select_channels(['EMG1', 'ACC1'], channel_type='EMG')
        """
        if self.signals is None:
            raise ValueError("No signals loaded")

        if channels is None and channel_type is None and modality is None:
            raise ValueError("Specify at least one of: channels, channel_type, or modality.")

        # If type/modality specified but no channels, select all matching channels
        if channels is None and channel_type is not None:
            channels = self.get_channels_by_type(channel_type)
            if not channels:
                raise ValueError(f"No channels found of type: {channel_type}")
        elif channels is None and modality is not None:
            channels = self.get_channels_by_modality(modality)
            if not channels:
                raise ValueError(f"No channels found of modality: {modality}")
        elif isinstance(channels, str):
            channels = [channels]

        if channels is None:
            raise ValueError("Specify at least one of: channels, channel_type, or modality.")

        # Validate channels exist
        if not all(ch in self.signals.columns for ch in channels):
            missing = [ch for ch in channels if ch not in self.signals.columns]
            raise ValueError(f"Channels not found: {missing}")

        # Filter by type if specified
        if channel_type is not None:
            channels = [ch for ch in channels if self.channels[ch]["channel_type"] == channel_type]
            if not channels:
                raise ValueError(f"None of the selected channels are of type: {channel_type}")

        # Filter by modality if specified
        if modality is not None:
            canonical_modality = validate_modality(modality)
            channels = [
                ch for ch in channels if self.channels[ch].get("modality") == canonical_modality
            ]
            if not channels:
                raise ValueError(f"None of the selected channels are of modality: {modality}")

        # Create new Recording object
        new_rec = Recording()

        # Copy selected signals and channels
        new_rec.signals = self.signals[channels].copy()
        new_rec.channels = {ch: self.channels[ch].copy() for ch in channels}

        # Copy metadata
        new_rec.metadata = self.metadata.copy()

        if not inplace:
            return new_rec
        else:
            self.signals = new_rec.signals
            self.channels = new_rec.channels
            self.metadata = new_rec.metadata
            return self

    def resample(self, target_rate: float) -> "Recording":
        """Return a NEW, anti-aliased down-sampled copy of this recording.

        Low-resolution demos need a smaller, lighter recording; this rebuilds the
        uniform signal grid at ``target_rate`` using a polyphase resampler
        (``scipy.signal.resample_poly``), which applies a Kaiser-windowed sinc
        anti-alias FIR before decimation. A naive stride-decimation would fold
        energy above the new Nyquist back into the band (aliasing); resample_poly
        removes that energy first, so no aliasing occurs.

        Non-destructive: ``self`` is left untouched and a new Recording is returned,
        mirroring ``select_channels``'s copy semantics.

        Resampling factors come from the integer source/target rates:
        ``g = gcd(int(src), int(target)); up = int(target)//g; down = int(src)//g``
        and ``resample_poly(x, up, down)`` runs once, vectorized over all channels
        along ``axis=0``.

        Args:
            target_rate: Desired sampling rate in Hz. Must be <= the source rate
                (this is a DOWN-sampling helper). A target equal to the source
                returns an unchanged copy; a target above it raises ``ValueError``
                rather than silently up-sampling (up-sampling cannot recover
                detail and is out of scope for the low-res pipeline).

        Returns:
            Recording: A new Recording with the resampled signals, each channel's
                ``sample_frequency`` set to the achieved rate (source * up / down,
                which equals ``target_rate`` for integer rates), and channel/recording
                metadata and events preserved. Events are unchanged because their
                onsets/durations are in SECONDS, which stay valid under any rate
                change (only the per-sample grid shrinks, not wall-clock time).

        Raises:
            ValueError: If no signals are loaded, if channels do not share a single
                ``sample_frequency`` (biosigio stores one uniform grid; mixed-rate
                resampling is out of scope), or if ``target_rate`` exceeds the
                source rate.
        """
        from math import gcd

        from scipy.signal import resample_poly

        if self.signals is None:
            raise ValueError("No signals loaded")

        if target_rate <= 0:
            raise ValueError(f"target_rate must be positive, got {target_rate}")

        # biosigio stores all channels on one uniform-length grid; a per-channel rate
        # mix is out of scope here, matching the exporter's single-rate guard.
        distinct_rates = {info["sample_frequency"] for info in self.channels.values()}
        if len(distinct_rates) > 1:
            raise ValueError(
                "Resampling requires a single sampling rate across all channels, but "
                f"multiple were found: {sorted(distinct_rates)} Hz. biosigio stores one "
                "uniform grid; resample each rate group separately."
            )

        source_rate = float(next(iter(distinct_rates)))

        # Down-sampling only: refuse to up-sample/alias; an equal rate is a no-op
        # copy so callers can resample unconditionally without special-casing.
        if target_rate > source_rate:
            raise ValueError(
                f"target_rate {target_rate} Hz exceeds source rate {source_rate} Hz; "
                "resample() only down-samples (low-res). Up-sampling is out of scope."
            )

        new_rec = Recording()
        new_rec.channels = {ch: info.copy() for ch, info in self.channels.items()}
        new_rec.metadata = self.metadata.copy()
        # Onsets/durations are in SECONDS, so they remain valid after the grid
        # changes; copy them through unchanged.
        new_rec.events = self.events.copy() if self.events is not None else self.events

        if target_rate == source_rate:
            # No grid change: copy signals through untouched (fresh RangeIndex for
            # consistency with the resampled path).
            new_rec.signals = self.signals.copy().reset_index(drop=True)
            return new_rec

        # Rational resampling factors from the integer rates.
        src_i = int(round(source_rate))
        tgt_i = int(round(target_rate))
        g = gcd(src_i, tgt_i)
        up = tgt_i // g
        down = src_i // g

        # The achieved rate is exactly source * up / down. Store THAT, not the
        # requested float, so the metadata can never disagree with the data (a
        # non-integer or odd target snaps to the nearest achievable rational rate;
        # warn so the caller knows). This avoids silently writing e.g. 99.5 Hz
        # onto a grid that resample_poly actually produced at 100 Hz.
        actual_rate = source_rate * up / down
        if abs(actual_rate - target_rate) > 1e-9:
            logging.warning(
                "Requested resample to %g Hz; nearest achievable rational rate is "
                "%g Hz, which is what is stored on the channels.",
                target_rate,
                actual_rate,
            )

        columns = list(self.signals.columns)
        data = self.signals.to_numpy(dtype=float)
        # resample_poly over axis=0 resamples every channel column at once with the
        # shared anti-alias FIR.
        resampled = resample_poly(data, up, down, axis=0)

        new_rec.signals = pd.DataFrame(resampled, columns=columns)
        new_rec.signals.index = pd.RangeIndex(len(new_rec.signals))
        for info in new_rec.channels.values():
            info["sample_frequency"] = actual_rate

        return new_rec

    def get_channel_types(self) -> list[str]:
        """
        Get list of unique channel types in the data.

        Returns:
            List of channel types (e.g., ['EMG', 'ACC', 'GYRO'])
        """
        return list({info["channel_type"] for info in self.channels.values()})

    def get_channels_by_type(self, channel_type: str) -> list[str]:
        """
        Get list of channels of a specific type.

        Args:
            channel_type: Type of channels to get ('EMG', 'ACC', 'GYRO', etc.)

        Returns:
            List of channel names of the specified type
        """
        return [ch for ch, info in self.channels.items() if info["channel_type"] == channel_type]

    def get_modalities(self) -> list[str]:
        """
        Get the list of unique modalities present in the data.

        Returns:
            List of modalities (e.g., ['EEG', 'EMG', 'MISC']).
        """
        return list(
            {info.get("modality") for info in self.channels.values() if info.get("modality")}
        )

    def get_channels_by_modality(self, modality: str) -> list[str]:
        """
        Get the channels belonging to a given modality.

        Args:
            modality: Modality to filter by ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC').

        Returns:
            List of channel names of the specified modality.
        """
        canonical_modality = validate_modality(modality)
        return [
            ch for ch, info in self.channels.items() if info.get("modality") == canonical_modality
        ]

    def to_edf(
        self,
        filepath: str,
        method: str = "both",
        fft_noise_range: tuple | None = None,
        svd_rank: int | None = None,
        precision_threshold: float = 0.01,
        format: Literal["auto", "edf", "bdf"] = "auto",
        bypass_analysis: bool | None = None,
        verify: bool = False,
        verify_tolerance: float = 1e-6,
        verify_channel_map: dict[str, str] | None = None,
        verify_plot: bool = False,
        events_df: pd.DataFrame | None = None,
        create_channels_tsv: bool = True,
        clip_outliers: bool | str = "auto",
        **kwargs,
    ) -> dict | None:
        """
        Export the recording to EDF/BDF format, optionally including events.

        Args:
            filepath: Path to save the EDF/BDF file
            method: Method for signal analysis ('svd', 'fft', or 'both')
                'svd': Uses Singular Value Decomposition for noise floor estimation
                'fft': Uses Fast Fourier Transform for noise floor estimation
                'both': Uses both methods and takes the minimum noise floor (default)
            fft_noise_range: Optional tuple (min_freq, max_freq) specifying frequency range for noise in FFT method
            svd_rank: Optional manual rank cutoff for signal/noise separation in SVD method
            precision_threshold: Maximum acceptable precision loss percentage (default: 0.01%)
            format: Format to use ('auto', 'edf', or 'bdf'). Default is 'auto'.
                    If 'edf' or 'bdf' is specified, that format will be used directly.
                    If 'auto', the format (EDF/16-bit or BDF/24-bit) is chosen based
                    on signal analysis to minimize precision loss while preferring EDF
                    if sufficient.
            bypass_analysis: If True, skip signal analysis step when format is explicitly
                             set to 'edf' or 'bdf'. If None (default), analysis is skipped
                             automatically when format is forced. Set to False to force
                             analysis even with a specified format. Ignored if format='auto'.
            verify: If True, reload the exported file and compare signals with the original
                    to check for data integrity loss. Results are printed. (default: False)
            verify_tolerance: Absolute tolerance used when comparing signals during verification. (default: 1e-6)
            verify_channel_map: Optional dictionary mapping original channel names (keys)
                                to reloaded channel names (values) for verification.
                                Used if `verify` is True and channel names might differ.
            verify_plot: If True and verify is True, plots a comparison of original vs reloaded signals.
            events_df: Optional DataFrame with events ('onset', 'duration', 'description').
                      If None, uses self.events. (This provides flexibility)
            create_channels_tsv: If True, create a BIDS-compliant channels.tsv file (default: True)
            clip_outliers: Singularity handling for the per-channel physical window.
                'auto' (default) keeps the full range losslessly but clips rare extreme
                outliers to a robust window only when keeping them would crater the bulk
                signal's resolution at the chosen format (with a warning); True always
                clips to the robust window; False never clips. See EDFExporter.export for
                the advanced ``outlier_sigmas`` / ``min_effective_bits`` knobs.
            **kwargs: Additional arguments for the EDF exporter

        Returns:
            Union[str, None]: If verify is True, returns a string with verification results.
                             Otherwise, returns None.

        Raises:
            ValueError: If no signals are loaded
        """
        from ..exporters.edf import EDFExporter  # Local import

        if self.signals is None:
            raise ValueError("No signals loaded")

        # --- Determine if analysis should be bypassed ---
        final_bypass_analysis = False
        if format.lower() == "auto":
            if bypass_analysis is True:
                logging.warning(
                    "bypass_analysis=True ignored because format='auto'. Analysis is required."
                )
            # Analysis is always needed for 'auto' format
            final_bypass_analysis = False
        elif format.lower() in ["edf", "bdf"]:
            if bypass_analysis is None:
                # Default behaviour: skip analysis if format is forced
                final_bypass_analysis = True
                msg = (
                    f"Format forced to '{format}'. Skipping signal analysis for faster export. "
                    "Set bypass_analysis=False to force analysis."
                )
                logging.log(logging.CRITICAL, msg)
            elif bypass_analysis is True:
                final_bypass_analysis = True
                logging.log(logging.CRITICAL, "bypass_analysis=True set. Skipping signal analysis.")
            else:  # bypass_analysis is False
                final_bypass_analysis = False
                logging.info(
                    f"Format forced to '{format}' but bypass_analysis=False. Performing signal analysis."
                )
        else:
            # Should not happen if Literal type hint works, but good practice
            logging.warning(
                f"Unknown format '{format}'. Defaulting to 'auto' behavior (analysis enabled)."
            )
            format = "auto"
            final_bypass_analysis = False

        # Determine which events DataFrame to use
        if events_df is None:
            events_to_export = self.events
        else:
            events_to_export = events_df

        # Combine parameters
        all_params: dict[str, Any] = {
            "precision_threshold": precision_threshold,
            "method": method,
            "fft_noise_range": fft_noise_range,
            "svd_rank": svd_rank,
            "format": format,
            "bypass_analysis": final_bypass_analysis,
            "events_df": events_to_export,  # Pass the events dataframe
            "create_channels_tsv": create_channels_tsv,
            "clip_outliers": clip_outliers,
            **kwargs,
        }

        EDFExporter.export(self, filepath, **all_params)

        verification_report_dict = None
        if verify:
            logging.info(f"Verification requested. Reloading exported file: {filepath}")
            try:
                # Reload the exported file
                reloaded_rec = Recording.from_file(filepath, importer="edf")

                logging.info("Comparing original signals with reloaded signals...")
                # Compare signals using the imported function
                verification_results = compare_signals(
                    self, reloaded_rec, tolerance=verify_tolerance, channel_map=verify_channel_map
                )

                # Generate and log report using the imported function
                report_verification_results(verification_results, verify_tolerance)
                verification_report_dict = verification_results

                # Plot comparison using imported function if requested
                summary = verification_results.get("channel_summary", {})
                comparison_mode = summary.get("comparison_mode", "unknown")
                compared_count = sum(1 for k in verification_results if k != "channel_summary")

                if verify_plot and compared_count > 0 and comparison_mode != "failed":
                    plot_comparison(self, reloaded_rec, channel_map=verify_channel_map)
                elif verify_plot:
                    logging.warning(
                        "Skipping verification plot: No channels were successfully compared."
                    )

            except Exception as e:
                logging.error(f"Verification failed during reload or comparison: {e}")
                verification_report_dict = {
                    "error": str(e),
                    "channel_summary": {"comparison_mode": "failed"},
                }

        return verification_report_dict

    def to_parquet(self, filepath: str) -> str:
        """Export to a self-describing biosigIO Parquet file.

        Signals are stored as a columnar table (channels = columns, time index
        preserved); channels/events/metadata travel in the file's schema metadata,
        so ``Recording.from_file`` round-trips it losslessly. Great for analytics
        (DuckDB/Polars/pandas/Spark). Requires the ``arrow`` extra (pyarrow).

        Args:
            filepath: Output ``.parquet`` path.

        Returns:
            str: The written file path.
        """
        from ..exporters.tabular import TabularExporter

        return TabularExporter.to_parquet(self, filepath)

    def to_arrow(self, filepath: str) -> str:
        """Export to a biosigIO Arrow/Feather file (fast zero-copy IPC).

        Same self-describing schema as :meth:`to_parquet`; round-trips via
        ``Recording.from_file``. Requires the ``arrow`` extra (pyarrow).

        Args:
            filepath: Output ``.feather`` / ``.arrow`` path.

        Returns:
            str: The written file path.
        """
        from ..exporters.tabular import TabularExporter

        return TabularExporter.to_arrow(self, filepath)

    def to_zarr(self, filepath: str, **kwargs) -> str:
        """Export to a sharded Zarr v3 serving store with a min/max view pyramid.

        Writes one cloud-native store that serves viewing, inference, and training
        from a single conversion: ``level 0`` of each ``(modality, rate)`` group is
        the anti-aliased, per-modality-resampled inference signal, with a min/max
        render pyramid above it (flagged not-for-inference). A derived serving copy,
        not the archival source (BIDS/EDF stay authoritative). Requires the ``zarr``
        extra (zarr v3). See :class:`~biosigio.exporters.zarr.ZarrExporter` for the
        tuning knobs (``modality_rates``, ``dtype``, chunk/shard sizing, ...).

        Args:
            filepath: Output store path (``.zarr`` appended if missing).
            **kwargs: Forwarded to :meth:`ZarrExporter.export`.

        Returns:
            str: The written store path.
        """
        from ..exporters.zarr import ZarrExporter

        # The empty-signal guard lives once, in ZarrExporter.export ("No signals
        # loaded"), matching the tabular path; no duplicate guard here.
        return ZarrExporter.export(self, filepath, **kwargs)

    def set_metadata(self, key: str, value: Any) -> None:
        """
        Set metadata value.

        Args:
            key: Metadata key
            value: Metadata value
        """
        self.metadata[key] = value

    def get_metadata(self, key: str) -> Any:
        """
        Get metadata value.

        Args:
            key: Metadata key

        Returns:
            Value associated with the key
        """
        return self.metadata.get(key)

    def has_metadata(self, key: str) -> bool:
        """Return True if ``key`` is present in the recording metadata."""
        return key in self.metadata

    def get_n_channels(self) -> int:
        """Number of channels in the recording."""
        return len(self.channels)

    def get_n_samples(self) -> int:
        """Number of time samples per channel (0 if no signals are loaded)."""
        return 0 if self.signals is None else len(self.signals)

    def get_sampling_frequency(self) -> float:
        """Sampling frequency in Hz, when all channels share a single rate.

        Raises:
            ValueError: if no channels are loaded, or channels have differing
                sampling frequencies; for a mixed-rate recording read
                ``channels[ch]["sample_frequency"]`` per channel instead.
        """
        if not self.channels:
            raise ValueError("No channels loaded")
        rates = {info["sample_frequency"] for info in self.channels.values()}
        if len(rates) > 1:
            raise ValueError(
                "Channels have differing sampling frequencies; read "
                "channels[ch]['sample_frequency'] per channel instead."
            )
        return float(next(iter(rates)))

    def get_duration(self) -> float:
        """Total recording duration in seconds (n_samples / sampling_frequency).

        Computed from the time index spacing, so it is the full window length
        (one sample period longer than the last sample's timestamp). Returns 0.0
        when fewer than two samples are loaded (a single sample has no inferable
        sample period).
        """
        if self.signals is None or len(self.signals) < 2:
            return 0.0
        index = self.signals.index
        sample_period = float(index[1] - index[0])
        return len(index) * sample_period

    def add_channel(
        self,
        label: str,
        data: np.ndarray,
        sample_frequency: float,
        physical_dimension: str,
        channel_type: str,
        *,
        modality: str | None = None,
        prefilter: str = "n/a",
    ) -> None:
        """
        Add a new channel to the recording.

        Args:
            label: Channel label or name (as per EDF specification)
            data: Channel data
            sample_frequency: Sampling frequency in Hz (as per EDF specification)
            physical_dimension: Physical dimension/unit of measurement (as per EDF specification)
            channel_type: BIDS channel type ('EEG', 'EMG', 'ECG', 'ACC', 'SEEG', ...).
                Required; validated against the modality vocabulary. There is no
                default (a missing type must be explicit, e.g. 'OTHER'/'MISC').
            modality: Coarse modality ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC').
                If None, it is inferred from ``channel_type``.
            prefilter: Pre-filtering applied to the channel (keyword-only).
        """
        canonical_type = validate_channel_type(channel_type)
        canonical_modality = (
            infer_modality_from_channel_type(canonical_type)
            if modality is None
            else validate_modality(modality)
        )

        if self.signals is None:
            # Create DataFrame with time index
            time = np.arange(len(data)) / sample_frequency
            self.signals = pd.DataFrame(index=time)

        self.signals[label] = data
        self.channels[label] = {
            "sample_frequency": sample_frequency,
            "physical_dimension": physical_dimension,
            "prefilter": prefilter,
            "channel_type": canonical_type,
            "modality": canonical_modality,
        }

    def set_channel(
        self,
        label: str,
        *,
        channel_type: str | None = None,
        modality: str | None = None,
        physical_dimension: str | None = None,
        prefilter: str | None = None,
    ) -> None:
        """
        Update metadata of an existing channel (the supported relabel path).

        Args:
            label: Existing channel label.
            channel_type: New BIDS channel type (validated). When given without an
                explicit ``modality``, the modality is re-derived from it.
            modality: New coarse modality (validated).
            physical_dimension: New physical unit.
            prefilter: New prefilter string.

        Raises:
            KeyError: If ``label`` is not an existing channel.
            ValueError: If ``channel_type`` or ``modality`` is not in the
                modality vocabulary.
        """
        if label not in self.channels:
            raise KeyError(f"Channel not found: {label}")
        info = self.channels[label]
        if channel_type is not None:
            info["channel_type"] = validate_channel_type(channel_type)
            if modality is None:
                info["modality"] = infer_modality_from_channel_type(info["channel_type"])
        if modality is not None:
            info["modality"] = validate_modality(modality)
        if physical_dimension is not None:
            info["physical_dimension"] = physical_dimension
        if prefilter is not None:
            info["prefilter"] = prefilter

    def add_event(self, onset: float, duration: float, description: str) -> None:
        """
        Add an event/annotation to the recording.

        Args:
            onset: Event onset time in seconds.
            duration: Event duration in seconds.
            description: Event description string.
        """
        new_event = pd.DataFrame(
            [{"onset": float(onset), "duration": float(duration), "description": description}]
        )
        # Avoid concatenating onto the empty, object-dtype events frame, which
        # would coerce the numeric columns to object. Start from the typed
        # new_event when there are no existing events.
        if self.events is None or self.events.empty:
            self.events = new_event
        else:
            self.events = pd.concat([self.events, new_event], ignore_index=True)
        # Sort events by onset time for consistency
        self.events = self.events.sort_values(by="onset").reset_index(drop=True)

__init__()

Initialize an empty recording.

Source code in biosigio/core/emg.py

def __init__(self):
    """Initialize an empty recording."""
    self.signals = None
    self.metadata = {}
    self.channels = {}
    # Initialize events as an empty DataFrame with specified columns
    self.events = pd.DataFrame(columns=["onset", "duration", "description"])

add_channel(label, data, sample_frequency, physical_dimension, channel_type, *, modality=None, prefilter='n/a')

Add a new channel to the recording.

Args: label: Channel label or name (as per EDF specification) data: Channel data sample_frequency: Sampling frequency in Hz (as per EDF specification) physical_dimension: Physical dimension/unit of measurement (as per EDF specification) channel_type: BIDS channel type ('EEG', 'EMG', 'ECG', 'ACC', 'SEEG', ...). Required; validated against the modality vocabulary. There is no default (a missing type must be explicit, e.g. 'OTHER'/'MISC'). modality: Coarse modality ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC'). If None, it is inferred from channel_type. prefilter: Pre-filtering applied to the channel (keyword-only).

Source code in biosigio/core/emg.py

def add_channel(
    self,
    label: str,
    data: np.ndarray,
    sample_frequency: float,
    physical_dimension: str,
    channel_type: str,
    *,
    modality: str | None = None,
    prefilter: str = "n/a",
) -> None:
    """
    Add a new channel to the recording.

    Args:
        label: Channel label or name (as per EDF specification)
        data: Channel data
        sample_frequency: Sampling frequency in Hz (as per EDF specification)
        physical_dimension: Physical dimension/unit of measurement (as per EDF specification)
        channel_type: BIDS channel type ('EEG', 'EMG', 'ECG', 'ACC', 'SEEG', ...).
            Required; validated against the modality vocabulary. There is no
            default (a missing type must be explicit, e.g. 'OTHER'/'MISC').
        modality: Coarse modality ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC').
            If None, it is inferred from ``channel_type``.
        prefilter: Pre-filtering applied to the channel (keyword-only).
    """
    canonical_type = validate_channel_type(channel_type)
    canonical_modality = (
        infer_modality_from_channel_type(canonical_type)
        if modality is None
        else validate_modality(modality)
    )

    if self.signals is None:
        # Create DataFrame with time index
        time = np.arange(len(data)) / sample_frequency
        self.signals = pd.DataFrame(index=time)

    self.signals[label] = data
    self.channels[label] = {
        "sample_frequency": sample_frequency,
        "physical_dimension": physical_dimension,
        "prefilter": prefilter,
        "channel_type": canonical_type,
        "modality": canonical_modality,
    }

add_event(onset, duration, description)

Add an event/annotation to the recording.

Args: onset: Event onset time in seconds. duration: Event duration in seconds. description: Event description string.

Source code in biosigio/core/emg.py

def add_event(self, onset: float, duration: float, description: str) -> None:
    """
    Add an event/annotation to the recording.

    Args:
        onset: Event onset time in seconds.
        duration: Event duration in seconds.
        description: Event description string.
    """
    new_event = pd.DataFrame(
        [{"onset": float(onset), "duration": float(duration), "description": description}]
    )
    # Avoid concatenating onto the empty, object-dtype events frame, which
    # would coerce the numeric columns to object. Start from the typed
    # new_event when there are no existing events.
    if self.events is None or self.events.empty:
        self.events = new_event
    else:
        self.events = pd.concat([self.events, new_event], ignore_index=True)
    # Sort events by onset time for consistency
    self.events = self.events.sort_values(by="onset").reset_index(drop=True)

from_file(filepath, importer=None, force_csv=False, bids_channels='auto', **kwargs) classmethod

The method to create a Recording object from file.

Args: filepath: Path to the input file importer: Name of the importer to use. Can be one of the following: - 'trigno': Delsys Trigno EMG system (CSV) - 'otb': OTB/OTB+ EMG system (OTB, OTB+) - 'eeglab': EEGLAB .set files (SET) - 'edf': EDF/EDF+/BDF/BDF+ format (EDF, BDF) - 'csv': Generic CSV (or TXT) files with columnar data - 'wfdb': Waveform Database (WFDB) - 'xdf': XDF format (multi-stream Lab Streaming Layer files) - 'meg': MEG via MNE (.fif and CTF .ds; requires the 'meg' extra) - 'brainvision': BrainVision .vhdr via MNE (requires the 'meg' extra) - 'tabular': biosigIO Parquet/Arrow/Feather (requires the 'arrow' extra) - 'neo': proprietary electrophysiology formats via python-neo (Intan, Blackrock, Spike2, Plexon, Micromed, Neuralynx, ...; requires the 'neo' extra) - 'zarr': biosigIO Zarr serving store (requires the 'zarr' extra) If None, the importer will be inferred from the file extension. Automatic import is supported for CSV/TXT files. force_csv: If True and importer is 'csv', forces using the generic CSV importer even if the file appears to match a specialized format. bids_channels: When 'auto' (default), look for a sibling BIDS _channels.tsv next to the file and apply its per-channel type/units over the importer's inferred values. Pass 'off' to disable. **kwargs: Additional arguments passed to the importer. For XDF files, useful kwargs include: - stream_names: List of stream names to import - stream_types: List of stream types to import (e.g., ["EMG", "EXG"]) - stream_ids: List of stream IDs to import

Returns: Recording: New Recording object with loaded data

Source code in biosigio/core/emg.py

@classmethod
def from_file(
    cls,
    filepath: str,
    importer: ImporterName | None = None,
    force_csv: bool = False,
    bids_channels: str = "auto",
    **kwargs,
) -> "Recording":
    """
    The method to create a Recording object from file.

    Args:
        filepath: Path to the input file
        importer: Name of the importer to use. Can be one of the following:
            - 'trigno': Delsys Trigno EMG system (CSV)
            - 'otb': OTB/OTB+ EMG system (OTB, OTB+)
            - 'eeglab': EEGLAB .set files (SET)
            - 'edf': EDF/EDF+/BDF/BDF+ format (EDF, BDF)
            - 'csv': Generic CSV (or TXT) files with columnar data
            - 'wfdb': Waveform Database (WFDB)
            - 'xdf': XDF format (multi-stream Lab Streaming Layer files)
            - 'meg': MEG via MNE (.fif and CTF .ds; requires the 'meg' extra)
            - 'brainvision': BrainVision .vhdr via MNE (requires the 'meg' extra)
            - 'tabular': biosigIO Parquet/Arrow/Feather (requires the 'arrow' extra)
            - 'neo': proprietary electrophysiology formats via python-neo
              (Intan, Blackrock, Spike2, Plexon, Micromed, Neuralynx, ...;
              requires the 'neo' extra)
            - 'zarr': biosigIO Zarr serving store (requires the 'zarr' extra)
            If None, the importer will be inferred from the file extension.
            Automatic import is supported for CSV/TXT files.
        force_csv: If True and importer is 'csv', forces using the generic CSV
                  importer even if the file appears to match a specialized format.
        bids_channels: When 'auto' (default), look for a sibling BIDS
                  _channels.tsv next to the file and apply its per-channel
                  type/units over the importer's inferred values. Pass 'off'
                  to disable.
        **kwargs: Additional arguments passed to the importer.
            For XDF files, useful kwargs include:
            - stream_names: List of stream names to import
            - stream_types: List of stream types to import (e.g., ["EMG", "EXG"])
            - stream_ids: List of stream IDs to import

    Returns:
        Recording: New Recording object with loaded data
    """
    if importer is None:
        importer = cls._infer_importer(filepath)

    importers = {
        "trigno": "TrignoImporter",  # CSV with Delsys Trigno Headers
        "otb": "OTBImporter",  # OTB/OTB+ EMG system data
        "edf": "EDFImporter",  # EDF/EDF+/BDF format
        "eeglab": "EEGLABImporter",  # EEGLAB .set files
        "csv": "CSVImporter",  # Generic CSV/Text files
        "wfdb": "WFDBImporter",  # Waveform Database format
        "xdf": "XDFImporter",  # XDF multi-stream format
        "meg": "MEGImporter",  # MEG via MNE (.fif, CTF .ds)
        "brainvision": "BrainVisionImporter",  # BrainVision via MNE (.vhdr)
        "tabular": "TabularImporter",  # biosigIO Parquet / Arrow / Feather
        "neo": "NeoImporter",  # proprietary ephys via python-neo
        "zarr": "ZarrImporter",  # biosigIO Zarr serving store
    }

    if importer not in importers:
        raise ValueError(
            f"Unsupported importer: {importer}. "
            f"Available importers: {list(importers.keys())}\n"
            "- trigno: Delsys Trigno EMG system\n"
            "- otb: OTB/OTB+ EMG system\n"
            "- edf: EDF/EDF+/BDF format\n"
            "- eeglab: EEGLAB .set files\n"
            "- csv: Generic CSV/Text files\n"
            "- wfdb: Waveform Database\n"
            "- xdf: XDF multi-stream format\n"
            "- meg: MEG via MNE (.fif, CTF .ds)\n"
            "- brainvision: BrainVision via MNE (.vhdr)\n"
            "- tabular: biosigIO Parquet/Arrow/Feather (.parquet, .feather, .arrow)\n"
            "- neo: proprietary electrophysiology formats via python-neo "
            "(Intan, Blackrock, Spike2, Plexon, Micromed, Neuralynx, ...)\n"
            "- zarr: biosigIO Zarr serving store (.zarr)"
        )

    # If using CSV importer and force_csv is set, pass it as force_generic
    if importer == "csv":
        kwargs["force_generic"] = force_csv

    # Import the appropriate importer class
    importer_module = __import__(
        f"biosigio.importers.{importer}", globals(), locals(), [importers[importer]]
    )
    importer_class = getattr(importer_module, importers[importer])

    # Create importer instance and load data
    rec = importer_class().load(filepath, **kwargs)

    # Record provenance: which format this recording came from. setdefault so a
    # re-imported serialization file (tabular/zarr) keeps the ORIGINAL
    # source_format restored from its metadata rather than being relabeled.
    rec.metadata.setdefault("source_format", importer)

    # In a BIDS layout, the sibling _channels.tsv is the authoritative source
    # of per-channel type/units; apply it over the importer's header/label
    # guesses unless explicitly disabled with bids_channels="off".
    if bids_channels != "off":
        from ..bids import apply_channels_tsv, find_channels_tsv

        channels_tsv = find_channels_tsv(filepath)
        if channels_tsv:
            apply_channels_tsv(rec, channels_tsv)

    return rec

get_channel_types()

Get list of unique channel types in the data.

Returns: List of channel types (e.g., ['EMG', 'ACC', 'GYRO'])

Source code in biosigio/core/emg.py

def get_channel_types(self) -> list[str]:
    """
    Get list of unique channel types in the data.

    Returns:
        List of channel types (e.g., ['EMG', 'ACC', 'GYRO'])
    """
    return list({info["channel_type"] for info in self.channels.values()})

get_channels_by_modality(modality)

Get the channels belonging to a given modality.

Args: modality: Modality to filter by ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC').

Returns: List of channel names of the specified modality.

Source code in biosigio/core/emg.py

def get_channels_by_modality(self, modality: str) -> list[str]:
    """
    Get the channels belonging to a given modality.

    Args:
        modality: Modality to filter by ('EEG', 'EMG', 'IEEG', 'MEG', 'BEH', 'MISC').

    Returns:
        List of channel names of the specified modality.
    """
    canonical_modality = validate_modality(modality)
    return [
        ch for ch, info in self.channels.items() if info.get("modality") == canonical_modality
    ]

get_channels_by_type(channel_type)

Get list of channels of a specific type.

Args: channel_type: Type of channels to get ('EMG', 'ACC', 'GYRO', etc.)

Returns: List of channel names of the specified type

Source code in biosigio/core/emg.py

def get_channels_by_type(self, channel_type: str) -> list[str]:
    """
    Get list of channels of a specific type.

    Args:
        channel_type: Type of channels to get ('EMG', 'ACC', 'GYRO', etc.)

    Returns:
        List of channel names of the specified type
    """
    return [ch for ch, info in self.channels.items() if info["channel_type"] == channel_type]

get_duration()

Total recording duration in seconds (n_samples / sampling_frequency).

Computed from the time index spacing, so it is the full window length (one sample period longer than the last sample's timestamp). Returns 0.0 when fewer than two samples are loaded (a single sample has no inferable sample period).

Source code in biosigio/core/emg.py

def get_duration(self) -> float:
    """Total recording duration in seconds (n_samples / sampling_frequency).

    Computed from the time index spacing, so it is the full window length
    (one sample period longer than the last sample's timestamp). Returns 0.0
    when fewer than two samples are loaded (a single sample has no inferable
    sample period).
    """
    if self.signals is None or len(self.signals) < 2:
        return 0.0
    index = self.signals.index
    sample_period = float(index[1] - index[0])
    return len(index) * sample_period

get_metadata(key)

Get metadata value.

Args: key: Metadata key

Returns: Value associated with the key

Source code in biosigio/core/emg.py

def get_metadata(self, key: str) -> Any:
    """
    Get metadata value.

    Args:
        key: Metadata key

    Returns:
        Value associated with the key
    """
    return self.metadata.get(key)

get_modalities()

Get the list of unique modalities present in the data.

Returns: List of modalities (e.g., ['EEG', 'EMG', 'MISC']).

Source code in biosigio/core/emg.py

def get_modalities(self) -> list[str]:
    """
    Get the list of unique modalities present in the data.

    Returns:
        List of modalities (e.g., ['EEG', 'EMG', 'MISC']).
    """
    return list(
        {info.get("modality") for info in self.channels.values() if info.get("modality")}
    )

get_n_channels()

Number of channels in the recording.

Source code in biosigio/core/emg.py

def get_n_channels(self) -> int:
    """Number of channels in the recording."""
    return len(self.channels)

get_n_samples()

Number of time samples per channel (0 if no signals are loaded).

Source code in biosigio/core/emg.py

def get_n_samples(self) -> int:
    """Number of time samples per channel (0 if no signals are loaded)."""
    return 0 if self.signals is None else len(self.signals)

get_sampling_frequency()

Sampling frequency in Hz, when all channels share a single rate.

Raises: ValueError: if no channels are loaded, or channels have differing sampling frequencies; for a mixed-rate recording read channels[ch]["sample_frequency"] per channel instead.

Source code in biosigio/core/emg.py

def get_sampling_frequency(self) -> float:
    """Sampling frequency in Hz, when all channels share a single rate.

    Raises:
        ValueError: if no channels are loaded, or channels have differing
            sampling frequencies; for a mixed-rate recording read
            ``channels[ch]["sample_frequency"]`` per channel instead.
    """
    if not self.channels:
        raise ValueError("No channels loaded")
    rates = {info["sample_frequency"] for info in self.channels.values()}
    if len(rates) > 1:
        raise ValueError(
            "Channels have differing sampling frequencies; read "
            "channels[ch]['sample_frequency'] per channel instead."
        )
    return float(next(iter(rates)))

has_metadata(key)

Return True if key is present in the recording metadata.

Source code in biosigio/core/emg.py

def has_metadata(self, key: str) -> bool:
    """Return True if ``key`` is present in the recording metadata."""
    return key in self.metadata

plot_signals(channels=None, time_range=None, offset_scale=0.8, uniform_scale=True, detrend=False, grid=True, title=None, show=True, plt_module=None)

Plot signals in a single plot with vertical offsets.

Args: channels: List of channels to plot. If None, plot all channels. time_range: Tuple of (start_time, end_time) to plot. If None, plot all data. offset_scale: Portion of allocated space each signal can use (0.0 to 1.0). uniform_scale: Whether to use the same scale for all signals. detrend: Whether to remove mean from signals before plotting. grid: Whether to show grid lines. title: Optional title for the figure. show: Whether to display the plot. plt_module: Matplotlib pyplot module to use.

Source code in biosigio/core/emg.py

def plot_signals(
    self,
    channels=None,
    time_range=None,
    offset_scale=0.8,
    uniform_scale=True,
    detrend=False,
    grid=True,
    title=None,
    show=True,
    plt_module=None,
):
    """
    Plot signals in a single plot with vertical offsets.

    Args:
        channels: List of channels to plot. If None, plot all channels.
        time_range: Tuple of (start_time, end_time) to plot. If None, plot all data.
        offset_scale: Portion of allocated space each signal can use (0.0 to 1.0).
        uniform_scale: Whether to use the same scale for all signals.
        detrend: Whether to remove mean from signals before plotting.
        grid: Whether to show grid lines.
        title: Optional title for the figure.
        show: Whether to display the plot.
        plt_module: Matplotlib pyplot module to use.
    """
    # Delegate to the static plotting function in visualization module
    static_plot_signals(
        rec_object=self,
        channels=channels,
        time_range=time_range,
        offset_scale=offset_scale,
        uniform_scale=uniform_scale,
        detrend=detrend,
        grid=grid,
        title=title,
        show=show,
        plt_module=plt_module,
    )

resample(target_rate)

Return a NEW, anti-aliased down-sampled copy of this recording.

Low-resolution demos need a smaller, lighter recording; this rebuilds the uniform signal grid at target_rate using a polyphase resampler (scipy.signal.resample_poly), which applies a Kaiser-windowed sinc anti-alias FIR before decimation. A naive stride-decimation would fold energy above the new Nyquist back into the band (aliasing); resample_poly removes that energy first, so no aliasing occurs.

Non-destructive: self is left untouched and a new Recording is returned, mirroring select_channels's copy semantics.

Resampling factors come from the integer source/target rates: g = gcd(int(src), int(target)); up = int(target)//g; down = int(src)//g and resample_poly(x, up, down) runs once, vectorized over all channels along axis=0.

Args: target_rate: Desired sampling rate in Hz. Must be <= the source rate (this is a DOWN-sampling helper). A target equal to the source returns an unchanged copy; a target above it raises ValueError rather than silently up-sampling (up-sampling cannot recover detail and is out of scope for the low-res pipeline).

Returns: Recording: A new Recording with the resampled signals, each channel's sample_frequency set to the achieved rate (source * up / down, which equals target_rate for integer rates), and channel/recording metadata and events preserved. Events are unchanged because their onsets/durations are in SECONDS, which stay valid under any rate change (only the per-sample grid shrinks, not wall-clock time).

Raises: ValueError: If no signals are loaded, if channels do not share a single sample_frequency (biosigio stores one uniform grid; mixed-rate resampling is out of scope), or if target_rate exceeds the source rate.

Source code in biosigio/core/emg.py

def resample(self, target_rate: float) -> "Recording":
    """Return a NEW, anti-aliased down-sampled copy of this recording.

    Low-resolution demos need a smaller, lighter recording; this rebuilds the
    uniform signal grid at ``target_rate`` using a polyphase resampler
    (``scipy.signal.resample_poly``), which applies a Kaiser-windowed sinc
    anti-alias FIR before decimation. A naive stride-decimation would fold
    energy above the new Nyquist back into the band (aliasing); resample_poly
    removes that energy first, so no aliasing occurs.

    Non-destructive: ``self`` is left untouched and a new Recording is returned,
    mirroring ``select_channels``'s copy semantics.

    Resampling factors come from the integer source/target rates:
    ``g = gcd(int(src), int(target)); up = int(target)//g; down = int(src)//g``
    and ``resample_poly(x, up, down)`` runs once, vectorized over all channels
    along ``axis=0``.

    Args:
        target_rate: Desired sampling rate in Hz. Must be <= the source rate
            (this is a DOWN-sampling helper). A target equal to the source
            returns an unchanged copy; a target above it raises ``ValueError``
            rather than silently up-sampling (up-sampling cannot recover
            detail and is out of scope for the low-res pipeline).

    Returns:
        Recording: A new Recording with the resampled signals, each channel's
            ``sample_frequency`` set to the achieved rate (source * up / down,
            which equals ``target_rate`` for integer rates), and channel/recording
            metadata and events preserved. Events are unchanged because their
            onsets/durations are in SECONDS, which stay valid under any rate
            change (only the per-sample grid shrinks, not wall-clock time).

    Raises:
        ValueError: If no signals are loaded, if channels do not share a single
            ``sample_frequency`` (biosigio stores one uniform grid; mixed-rate
            resampling is out of scope), or if ``target_rate`` exceeds the
            source rate.
    """
    from math import gcd

    from scipy.signal import resample_poly

    if self.signals is None:
        raise ValueError("No signals loaded")

    if target_rate <= 0:
        raise ValueError(f"target_rate must be positive, got {target_rate}")

    # biosigio stores all channels on one uniform-length grid; a per-channel rate
    # mix is out of scope here, matching the exporter's single-rate guard.
    distinct_rates = {info["sample_frequency"] for info in self.channels.values()}
    if len(distinct_rates) > 1:
        raise ValueError(
            "Resampling requires a single sampling rate across all channels, but "
            f"multiple were found: {sorted(distinct_rates)} Hz. biosigio stores one "
            "uniform grid; resample each rate group separately."
        )

    source_rate = float(next(iter(distinct_rates)))

    # Down-sampling only: refuse to up-sample/alias; an equal rate is a no-op
    # copy so callers can resample unconditionally without special-casing.
    if target_rate > source_rate:
        raise ValueError(
            f"target_rate {target_rate} Hz exceeds source rate {source_rate} Hz; "
            "resample() only down-samples (low-res). Up-sampling is out of scope."
        )

    new_rec = Recording()
    new_rec.channels = {ch: info.copy() for ch, info in self.channels.items()}
    new_rec.metadata = self.metadata.copy()
    # Onsets/durations are in SECONDS, so they remain valid after the grid
    # changes; copy them through unchanged.
    new_rec.events = self.events.copy() if self.events is not None else self.events

    if target_rate == source_rate:
        # No grid change: copy signals through untouched (fresh RangeIndex for
        # consistency with the resampled path).
        new_rec.signals = self.signals.copy().reset_index(drop=True)
        return new_rec

    # Rational resampling factors from the integer rates.
    src_i = int(round(source_rate))
    tgt_i = int(round(target_rate))
    g = gcd(src_i, tgt_i)
    up = tgt_i // g
    down = src_i // g

    # The achieved rate is exactly source * up / down. Store THAT, not the
    # requested float, so the metadata can never disagree with the data (a
    # non-integer or odd target snaps to the nearest achievable rational rate;
    # warn so the caller knows). This avoids silently writing e.g. 99.5 Hz
    # onto a grid that resample_poly actually produced at 100 Hz.
    actual_rate = source_rate * up / down
    if abs(actual_rate - target_rate) > 1e-9:
        logging.warning(
            "Requested resample to %g Hz; nearest achievable rational rate is "
            "%g Hz, which is what is stored on the channels.",
            target_rate,
            actual_rate,
        )

    columns = list(self.signals.columns)
    data = self.signals.to_numpy(dtype=float)
    # resample_poly over axis=0 resamples every channel column at once with the
    # shared anti-alias FIR.
    resampled = resample_poly(data, up, down, axis=0)

    new_rec.signals = pd.DataFrame(resampled, columns=columns)
    new_rec.signals.index = pd.RangeIndex(len(new_rec.signals))
    for info in new_rec.channels.values():
        info["sample_frequency"] = actual_rate

    return new_rec

select_channels(channels=None, channel_type=None, inplace=False, *, modality=None)

Select specific channels from the data and return a new Recording object.

Args: channels: Channel name or list of channel names to select. If None and channel_type is specified, selects all channels of that type. channel_type: Type of channels to select ('EMG', 'ACC', 'GYRO', etc.). If specified with channels, filters the selection to only channels of this type.

Returns: Recording: A new Recording object containing only the selected channels

Examples: # Select specific channels new_rec = rec.select_channels(['EMG1', 'ACC1'])

# Select all EMG channels
emg_only = rec.select_channels(channel_type='EMG')

# Select specific EMG channels only, this example does not select ACC channels
emg_subset = rec.select_channels(['EMG1', 'ACC1'], channel_type='EMG')

Source code in biosigio/core/emg.py

def select_channels(
    self,
    channels: str | list[str] | None = None,
    channel_type: str | None = None,
    inplace: bool = False,
    *,
    modality: str | None = None,
) -> "Recording":
    """
    Select specific channels from the data and return a new Recording object.

    Args:
        channels: Channel name or list of channel names to select. If None and
                channel_type is specified, selects all channels of that type.
        channel_type: Type of channels to select ('EMG', 'ACC', 'GYRO', etc.).
                    If specified with channels, filters the selection to only
                    channels of this type.

    Returns:
        Recording: A new Recording object containing only the selected channels

    Examples:
        # Select specific channels
        new_rec = rec.select_channels(['EMG1', 'ACC1'])

        # Select all EMG channels
        emg_only = rec.select_channels(channel_type='EMG')

        # Select specific EMG channels only, this example does not select ACC channels
        emg_subset = rec.select_channels(['EMG1', 'ACC1'], channel_type='EMG')
    """
    if self.signals is None:
        raise ValueError("No signals loaded")

    if channels is None and channel_type is None and modality is None:
        raise ValueError("Specify at least one of: channels, channel_type, or modality.")

    # If type/modality specified but no channels, select all matching channels
    if channels is None and channel_type is not None:
        channels = self.get_channels_by_type(channel_type)
        if not channels:
            raise ValueError(f"No channels found of type: {channel_type}")
    elif channels is None and modality is not None:
        channels = self.get_channels_by_modality(modality)
        if not channels:
            raise ValueError(f"No channels found of modality: {modality}")
    elif isinstance(channels, str):
        channels = [channels]

    if channels is None:
        raise ValueError("Specify at least one of: channels, channel_type, or modality.")

    # Validate channels exist
    if not all(ch in self.signals.columns for ch in channels):
        missing = [ch for ch in channels if ch not in self.signals.columns]
        raise ValueError(f"Channels not found: {missing}")

    # Filter by type if specified
    if channel_type is not None:
        channels = [ch for ch in channels if self.channels[ch]["channel_type"] == channel_type]
        if not channels:
            raise ValueError(f"None of the selected channels are of type: {channel_type}")

    # Filter by modality if specified
    if modality is not None:
        canonical_modality = validate_modality(modality)
        channels = [
            ch for ch in channels if self.channels[ch].get("modality") == canonical_modality
        ]
        if not channels:
            raise ValueError(f"None of the selected channels are of modality: {modality}")

    # Create new Recording object
    new_rec = Recording()

    # Copy selected signals and channels
    new_rec.signals = self.signals[channels].copy()
    new_rec.channels = {ch: self.channels[ch].copy() for ch in channels}

    # Copy metadata
    new_rec.metadata = self.metadata.copy()

    if not inplace:
        return new_rec
    else:
        self.signals = new_rec.signals
        self.channels = new_rec.channels
        self.metadata = new_rec.metadata
        return self

set_channel(label, *, channel_type=None, modality=None, physical_dimension=None, prefilter=None)

Update metadata of an existing channel (the supported relabel path).

Args: label: Existing channel label. channel_type: New BIDS channel type (validated). When given without an explicit modality, the modality is re-derived from it. modality: New coarse modality (validated). physical_dimension: New physical unit. prefilter: New prefilter string.

Raises: KeyError: If label is not an existing channel. ValueError: If channel_type or modality is not in the modality vocabulary.

Source code in biosigio/core/emg.py

def set_channel(
    self,
    label: str,
    *,
    channel_type: str | None = None,
    modality: str | None = None,
    physical_dimension: str | None = None,
    prefilter: str | None = None,
) -> None:
    """
    Update metadata of an existing channel (the supported relabel path).

    Args:
        label: Existing channel label.
        channel_type: New BIDS channel type (validated). When given without an
            explicit ``modality``, the modality is re-derived from it.
        modality: New coarse modality (validated).
        physical_dimension: New physical unit.
        prefilter: New prefilter string.

    Raises:
        KeyError: If ``label`` is not an existing channel.
        ValueError: If ``channel_type`` or ``modality`` is not in the
            modality vocabulary.
    """
    if label not in self.channels:
        raise KeyError(f"Channel not found: {label}")
    info = self.channels[label]
    if channel_type is not None:
        info["channel_type"] = validate_channel_type(channel_type)
        if modality is None:
            info["modality"] = infer_modality_from_channel_type(info["channel_type"])
    if modality is not None:
        info["modality"] = validate_modality(modality)
    if physical_dimension is not None:
        info["physical_dimension"] = physical_dimension
    if prefilter is not None:
        info["prefilter"] = prefilter

set_metadata(key, value)

Set metadata value.

Args: key: Metadata key value: Metadata value

Source code in biosigio/core/emg.py

def set_metadata(self, key: str, value: Any) -> None:
    """
    Set metadata value.

    Args:
        key: Metadata key
        value: Metadata value
    """
    self.metadata[key] = value

to_arrow(filepath)

Export to a biosigIO Arrow/Feather file (fast zero-copy IPC).

Same self-describing schema as :meth:to_parquet; round-trips via Recording.from_file. Requires the arrow extra (pyarrow).

Args: filepath: Output .feather / .arrow path.

Returns: str: The written file path.

Source code in biosigio/core/emg.py

def to_arrow(self, filepath: str) -> str:
    """Export to a biosigIO Arrow/Feather file (fast zero-copy IPC).

    Same self-describing schema as :meth:`to_parquet`; round-trips via
    ``Recording.from_file``. Requires the ``arrow`` extra (pyarrow).

    Args:
        filepath: Output ``.feather`` / ``.arrow`` path.

    Returns:
        str: The written file path.
    """
    from ..exporters.tabular import TabularExporter

    return TabularExporter.to_arrow(self, filepath)

to_edf(filepath, method='both', fft_noise_range=None, svd_rank=None, precision_threshold=0.01, format='auto', bypass_analysis=None, verify=False, verify_tolerance=1e-06, verify_channel_map=None, verify_plot=False, events_df=None, create_channels_tsv=True, clip_outliers='auto', **kwargs)

Export the recording to EDF/BDF format, optionally including events.

Args: filepath: Path to save the EDF/BDF file method: Method for signal analysis ('svd', 'fft', or 'both') 'svd': Uses Singular Value Decomposition for noise floor estimation 'fft': Uses Fast Fourier Transform for noise floor estimation 'both': Uses both methods and takes the minimum noise floor (default) fft_noise_range: Optional tuple (min_freq, max_freq) specifying frequency range for noise in FFT method svd_rank: Optional manual rank cutoff for signal/noise separation in SVD method precision_threshold: Maximum acceptable precision loss percentage (default: 0.01%) format: Format to use ('auto', 'edf', or 'bdf'). Default is 'auto'. If 'edf' or 'bdf' is specified, that format will be used directly. If 'auto', the format (EDF/16-bit or BDF/24-bit) is chosen based on signal analysis to minimize precision loss while preferring EDF if sufficient. bypass_analysis: If True, skip signal analysis step when format is explicitly set to 'edf' or 'bdf'. If None (default), analysis is skipped automatically when format is forced. Set to False to force analysis even with a specified format. Ignored if format='auto'. verify: If True, reload the exported file and compare signals with the original to check for data integrity loss. Results are printed. (default: False) verify_tolerance: Absolute tolerance used when comparing signals during verification. (default: 1e-6) verify_channel_map: Optional dictionary mapping original channel names (keys) to reloaded channel names (values) for verification. Used if verify is True and channel names might differ. verify_plot: If True and verify is True, plots a comparison of original vs reloaded signals. events_df: Optional DataFrame with events ('onset', 'duration', 'description'). If None, uses self.events. (This provides flexibility) create_channels_tsv: If True, create a BIDS-compliant channels.tsv file (default: True) clip_outliers: Singularity handling for the per-channel physical window. 'auto' (default) keeps the full range losslessly but clips rare extreme outliers to a robust window only when keeping them would crater the bulk signal's resolution at the chosen format (with a warning); True always clips to the robust window; False never clips. See EDFExporter.export for the advanced outlier_sigmas / min_effective_bits knobs. **kwargs: Additional arguments for the EDF exporter

Returns: Union[str, None]: If verify is True, returns a string with verification results. Otherwise, returns None.

Raises: ValueError: If no signals are loaded

Source code in biosigio/core/emg.py

def to_edf(
    self,
    filepath: str,
    method: str = "both",
    fft_noise_range: tuple | None = None,
    svd_rank: int | None = None,
    precision_threshold: float = 0.01,
    format: Literal["auto", "edf", "bdf"] = "auto",
    bypass_analysis: bool | None = None,
    verify: bool = False,
    verify_tolerance: float = 1e-6,
    verify_channel_map: dict[str, str] | None = None,
    verify_plot: bool = False,
    events_df: pd.DataFrame | None = None,
    create_channels_tsv: bool = True,
    clip_outliers: bool | str = "auto",
    **kwargs,
) -> dict | None:
    """
    Export the recording to EDF/BDF format, optionally including events.

    Args:
        filepath: Path to save the EDF/BDF file
        method: Method for signal analysis ('svd', 'fft', or 'both')
            'svd': Uses Singular Value Decomposition for noise floor estimation
            'fft': Uses Fast Fourier Transform for noise floor estimation
            'both': Uses both methods and takes the minimum noise floor (default)
        fft_noise_range: Optional tuple (min_freq, max_freq) specifying frequency range for noise in FFT method
        svd_rank: Optional manual rank cutoff for signal/noise separation in SVD method
        precision_threshold: Maximum acceptable precision loss percentage (default: 0.01%)
        format: Format to use ('auto', 'edf', or 'bdf'). Default is 'auto'.
                If 'edf' or 'bdf' is specified, that format will be used directly.
                If 'auto', the format (EDF/16-bit or BDF/24-bit) is chosen based
                on signal analysis to minimize precision loss while preferring EDF
                if sufficient.
        bypass_analysis: If True, skip signal analysis step when format is explicitly
                         set to 'edf' or 'bdf'. If None (default), analysis is skipped
                         automatically when format is forced. Set to False to force
                         analysis even with a specified format. Ignored if format='auto'.
        verify: If True, reload the exported file and compare signals with the original
                to check for data integrity loss. Results are printed. (default: False)
        verify_tolerance: Absolute tolerance used when comparing signals during verification. (default: 1e-6)
        verify_channel_map: Optional dictionary mapping original channel names (keys)
                            to reloaded channel names (values) for verification.
                            Used if `verify` is True and channel names might differ.
        verify_plot: If True and verify is True, plots a comparison of original vs reloaded signals.
        events_df: Optional DataFrame with events ('onset', 'duration', 'description').
                  If None, uses self.events. (This provides flexibility)
        create_channels_tsv: If True, create a BIDS-compliant channels.tsv file (default: True)
        clip_outliers: Singularity handling for the per-channel physical window.
            'auto' (default) keeps the full range losslessly but clips rare extreme
            outliers to a robust window only when keeping them would crater the bulk
            signal's resolution at the chosen format (with a warning); True always
            clips to the robust window; False never clips. See EDFExporter.export for
            the advanced ``outlier_sigmas`` / ``min_effective_bits`` knobs.
        **kwargs: Additional arguments for the EDF exporter

    Returns:
        Union[str, None]: If verify is True, returns a string with verification results.
                         Otherwise, returns None.

    Raises:
        ValueError: If no signals are loaded
    """
    from ..exporters.edf import EDFExporter  # Local import

    if self.signals is None:
        raise ValueError("No signals loaded")

    # --- Determine if analysis should be bypassed ---
    final_bypass_analysis = False
    if format.lower() == "auto":
        if bypass_analysis is True:
            logging.warning(
                "bypass_analysis=True ignored because format='auto'. Analysis is required."
            )
        # Analysis is always needed for 'auto' format
        final_bypass_analysis = False
    elif format.lower() in ["edf", "bdf"]:
        if bypass_analysis is None:
            # Default behaviour: skip analysis if format is forced
            final_bypass_analysis = True
            msg = (
                f"Format forced to '{format}'. Skipping signal analysis for faster export. "
                "Set bypass_analysis=False to force analysis."
            )
            logging.log(logging.CRITICAL, msg)
        elif bypass_analysis is True:
            final_bypass_analysis = True
            logging.log(logging.CRITICAL, "bypass_analysis=True set. Skipping signal analysis.")
        else:  # bypass_analysis is False
            final_bypass_analysis = False
            logging.info(
                f"Format forced to '{format}' but bypass_analysis=False. Performing signal analysis."
            )
    else:
        # Should not happen if Literal type hint works, but good practice
        logging.warning(
            f"Unknown format '{format}'. Defaulting to 'auto' behavior (analysis enabled)."
        )
        format = "auto"
        final_bypass_analysis = False

    # Determine which events DataFrame to use
    if events_df is None:
        events_to_export = self.events
    else:
        events_to_export = events_df

    # Combine parameters
    all_params: dict[str, Any] = {
        "precision_threshold": precision_threshold,
        "method": method,
        "fft_noise_range": fft_noise_range,
        "svd_rank": svd_rank,
        "format": format,
        "bypass_analysis": final_bypass_analysis,
        "events_df": events_to_export,  # Pass the events dataframe
        "create_channels_tsv": create_channels_tsv,
        "clip_outliers": clip_outliers,
        **kwargs,
    }

    EDFExporter.export(self, filepath, **all_params)

    verification_report_dict = None
    if verify:
        logging.info(f"Verification requested. Reloading exported file: {filepath}")
        try:
            # Reload the exported file
            reloaded_rec = Recording.from_file(filepath, importer="edf")

            logging.info("Comparing original signals with reloaded signals...")
            # Compare signals using the imported function
            verification_results = compare_signals(
                self, reloaded_rec, tolerance=verify_tolerance, channel_map=verify_channel_map
            )

            # Generate and log report using the imported function
            report_verification_results(verification_results, verify_tolerance)
            verification_report_dict = verification_results

            # Plot comparison using imported function if requested
            summary = verification_results.get("channel_summary", {})
            comparison_mode = summary.get("comparison_mode", "unknown")
            compared_count = sum(1 for k in verification_results if k != "channel_summary")

            if verify_plot and compared_count > 0 and comparison_mode != "failed":
                plot_comparison(self, reloaded_rec, channel_map=verify_channel_map)
            elif verify_plot:
                logging.warning(
                    "Skipping verification plot: No channels were successfully compared."
                )

        except Exception as e:
            logging.error(f"Verification failed during reload or comparison: {e}")
            verification_report_dict = {
                "error": str(e),
                "channel_summary": {"comparison_mode": "failed"},
            }

    return verification_report_dict

to_parquet(filepath)

Export to a self-describing biosigIO Parquet file.

Signals are stored as a columnar table (channels = columns, time index preserved); channels/events/metadata travel in the file's schema metadata, so Recording.from_file round-trips it losslessly. Great for analytics (DuckDB/Polars/pandas/Spark). Requires the arrow extra (pyarrow).

Args: filepath: Output .parquet path.

Returns: str: The written file path.

Source code in biosigio/core/emg.py

def to_parquet(self, filepath: str) -> str:
    """Export to a self-describing biosigIO Parquet file.

    Signals are stored as a columnar table (channels = columns, time index
    preserved); channels/events/metadata travel in the file's schema metadata,
    so ``Recording.from_file`` round-trips it losslessly. Great for analytics
    (DuckDB/Polars/pandas/Spark). Requires the ``arrow`` extra (pyarrow).

    Args:
        filepath: Output ``.parquet`` path.

    Returns:
        str: The written file path.
    """
    from ..exporters.tabular import TabularExporter

    return TabularExporter.to_parquet(self, filepath)

to_zarr(filepath, **kwargs)

Export to a sharded Zarr v3 serving store with a min/max view pyramid.

Writes one cloud-native store that serves viewing, inference, and training from a single conversion: level 0 of each (modality, rate) group is the anti-aliased, per-modality-resampled inference signal, with a min/max render pyramid above it (flagged not-for-inference). A derived serving copy, not the archival source (BIDS/EDF stay authoritative). Requires the zarr extra (zarr v3). See :class:~biosigio.exporters.zarr.ZarrExporter for the tuning knobs (modality_rates, dtype, chunk/shard sizing, ...).

Args: filepath: Output store path (.zarr appended if missing). **kwargs: Forwarded to :meth:ZarrExporter.export.

Returns: str: The written store path.

Source code in biosigio/core/emg.py

def to_zarr(self, filepath: str, **kwargs) -> str:
    """Export to a sharded Zarr v3 serving store with a min/max view pyramid.

    Writes one cloud-native store that serves viewing, inference, and training
    from a single conversion: ``level 0`` of each ``(modality, rate)`` group is
    the anti-aliased, per-modality-resampled inference signal, with a min/max
    render pyramid above it (flagged not-for-inference). A derived serving copy,
    not the archival source (BIDS/EDF stay authoritative). Requires the ``zarr``
    extra (zarr v3). See :class:`~biosigio.exporters.zarr.ZarrExporter` for the
    tuning knobs (``modality_rates``, ``dtype``, chunk/shard sizing, ...).

    Args:
        filepath: Output store path (``.zarr`` appended if missing).
        **kwargs: Forwarded to :meth:`ZarrExporter.export`.

    Returns:
        str: The written store path.
    """
    from ..exporters.zarr import ZarrExporter

    # The empty-signal guard lives once, in ZarrExporter.export ("No signals
    # loaded"), matching the tabular path; no duplicate guard here.
    return ZarrExporter.export(self, filepath, **kwargs)

plot_comparison(rec_original, rec_reloaded, channels=None, time_range=None, detrend=False, grid=True, suptitle='Signal Comparison', show=True, channel_map=None, plt_module=plt)

Plot original and reloaded signals overlaid for visual comparison.

Creates subplots for each channel pair.

Args: rec_original: The original Recording object. rec_reloaded: The reloaded Recording object. channels: List of original channels to plot. If None, plot common/mapped channels. time_range: Tuple of (start_time, end_time) to plot. If None, plot all data. detrend: Whether to remove mean from signals before plotting. grid: Whether to show grid lines on subplots. suptitle: Optional main title for the figure. show: Whether to display the plot. channel_map: Optional dictionary mapping original channel names (keys) to reloaded channel names (values). If None, tries exact name match first, then falls back to order-based matching. plt_module: Matplotlib pyplot module to use.

Source code in biosigio/visualization/static.py

def plot_comparison(
    rec_original: "Recording",
    rec_reloaded: "Recording",
    channels: list[str] | None = None,
    time_range: tuple[float, float] | None = None,
    detrend: bool = False,
    grid: bool = True,
    suptitle: str | None = "Signal Comparison",
    show: bool = True,
    channel_map: dict[str, str] | None = None,
    plt_module: Any = plt,
) -> None:
    """
    Plot original and reloaded signals overlaid for visual comparison.

    Creates subplots for each channel pair.

    Args:
        rec_original: The original Recording object.
        rec_reloaded: The reloaded Recording object.
        channels: List of original channels to plot. If None, plot common/mapped channels.
        time_range: Tuple of (start_time, end_time) to plot. If None, plot all data.
        detrend: Whether to remove mean from signals before plotting.
        grid: Whether to show grid lines on subplots.
        suptitle: Optional main title for the figure.
        show: Whether to display the plot.
        channel_map: Optional dictionary mapping original channel names (keys)
                    to reloaded channel names (values). If None, tries exact name
                    match first, then falls back to order-based matching.
        plt_module: Matplotlib pyplot module to use.
    """
    # Removed local import: from biosigio.core.emg import Recording

    if rec_original.signals is None or rec_reloaded.signals is None:
        raise ValueError("Both Recording objects must have signals loaded to plot a comparison")

    original_channel_names = set(rec_original.signals.columns)
    reloaded_channel_names = set(rec_reloaded.signals.columns)

    # --- Channel Matching Logic (adapted from compare_signals) ---
    comparison_mode = "unknown"
    unmatched_original = []
    unmatched_reloaded = []
    channel_pairs = []

    if channel_map is not None:
        comparison_mode = "mapped"
        valid_mappings = {
            orig: mapped
            for orig, mapped in channel_map.items()
            if orig in original_channel_names and mapped in reloaded_channel_names
        }

        # Filter by user-specified channels if provided
        if channels is not None:
            valid_mappings = {
                orig: mapped for orig, mapped in valid_mappings.items() if orig in channels
            }

        channel_pairs = list(valid_mappings.items())

        specified_orig_in_map = set(channel_map.keys())
        specified_reloaded_in_map = set(channel_map.values())

        unmatched_original = list(original_channel_names - specified_orig_in_map)
        unmatched_reloaded = list(reloaded_channel_names - specified_reloaded_in_map)

        if channels is not None:  # Further filter unmatched if specific channels were requested
            unmatched_original = [ch for ch in unmatched_original if ch in channels]

    else:
        # Try exact name matching first
        common_channels = list(original_channel_names.intersection(reloaded_channel_names))
        if channels is not None:  # Filter by user-specified channels
            common_channels = [ch for ch in common_channels if ch in channels]

        if common_channels:
            comparison_mode = "exact_name"
            channel_pairs = [(ch, ch) for ch in common_channels]

            specified_channels_set = set(channels) if channels else original_channel_names
            common_channels_set = set(common_channels)

            unmatched_original = list(specified_channels_set - common_channels_set)
            # Reloaded unmatched are those not in common from the full reloaded set
            unmatched_reloaded = list(reloaded_channel_names - common_channels_set)
        else:
            # Fall back to order-based matching if no exact matches or map provided
            comparison_mode = "order_based"
            original_list = sorted(original_channel_names)
            reloaded_list = sorted(reloaded_channel_names)

            if channels is not None:  # Filter original list by user-specified channels
                original_list = [ch for ch in original_list if ch in channels]

            min_len = min(len(original_list), len(reloaded_list))
            channel_pairs = list(
                zip(original_list[:min_len], reloaded_list[:min_len], strict=False)
            )
            unmatched_original = original_list[min_len:]
            unmatched_reloaded = reloaded_list[min_len:]

    if not channel_pairs:
        print("Warning: No channel pairs found for plotting based on selection criteria.")
        if unmatched_original:
            print(f"  Unmatched original channels: {unmatched_original}")
        if unmatched_reloaded:
            print(f"  Unmatched reloaded channels: {unmatched_reloaded}")
        return

    print(f"Plotting comparison using mode: {comparison_mode}")
    if unmatched_original:
        print(f"  Original channels not plotted: {unmatched_original}")
    if unmatched_reloaded:
        print(f"  Reloaded channels not plotted: {unmatched_reloaded}")

    # --- Plotting ---
    n_pairs = len(channel_pairs)
    fig, axes = plt_module.subplots(
        n_pairs, 1, figsize=(15, 2.5 * n_pairs), sharex=True, squeeze=False
    )
    axes = axes.flatten()  # Ensure axes is always a 1D array

    if suptitle:
        fig.suptitle(suptitle, fontsize=16)

    for i, (orig_ch, reloaded_ch) in enumerate(channel_pairs):
        ax = axes[i]
        sig_orig = rec_original.signals[orig_ch]
        sig_reloaded = rec_reloaded.signals[reloaded_ch]

        # Apply time range
        if time_range:
            start, end = time_range
            # Be robust to time range slightly outside index
            # Break long line 608
            sig_orig_idx_start = sig_orig.index.searchsorted(start)
            sig_orig_idx_end = sig_orig.index.searchsorted(end, side="right")
            sig_orig = sig_orig.loc[sig_orig_idx_start:sig_orig_idx_end]

            sig_reloaded_idx_start = sig_reloaded.index.searchsorted(start)
            sig_reloaded_idx_end = sig_reloaded.index.searchsorted(end, side="right")
            sig_reloaded = sig_reloaded.loc[sig_reloaded_idx_start:sig_reloaded_idx_end]

        time_vector = sig_orig.index  # Assuming time vectors are aligned

        # Detrend if requested
        if detrend:
            sig_orig = sig_orig - sig_orig.mean()
            sig_reloaded = sig_reloaded - sig_reloaded.mean()

        # Plot signals
        ax.plot(time_vector, sig_orig, label="Original", color="blue", linewidth=1.0)
        ax.plot(
            time_vector, sig_reloaded, label="Reloaded", color="red", linestyle="--", linewidth=1.0
        )

        title_str = f"{orig_ch} -> {reloaded_ch}" if orig_ch != reloaded_ch else orig_ch
        ax.set_ylabel(title_str, rotation=0, labelpad=30, ha="right", va="center")
        ax.ticklabel_format(axis="y", style="sci", scilimits=(-3, 3))  # Use scientific notation

        if grid:
            ax.grid(True, linestyle=":", alpha=0.6)

        # Add legend to the first subplot only for clarity
        if i == 0:
            ax.legend(loc="upper right")

    # Set common x-label
    axes[-1].set_xlabel("Time (s)")

    # Adjust layout
    plt_module.tight_layout(rect=[0, 0.03, 1, 0.97])  # Adjust rect for suptitle

    if show:
        plt_module.show()

plot_signals(rec_object, channels=None, time_range=None, offset_scale=0.8, uniform_scale=True, detrend=False, grid=True, title=None, show=True, plt_module=plt)

Plot EMG signals in a single plot with vertical offsets.

Args: rec_object: The Recording object containing the signals and metadata. channels: List of channels to plot. If None, plot all channels. time_range: Tuple of (start_time, end_time) to plot. If None, plot all data. offset_scale: Portion of allocated space each signal can use (0.0 to 1.0). uniform_scale: Whether to use the same scale for all signals. detrend: Whether to remove mean from signals before plotting. grid: Whether to show grid lines. title: Optional title for the figure. show: Whether to display the plot. plt_module: Matplotlib pyplot module to use.

Source code in biosigio/visualization/static.py

def plot_signals(
    rec_object: "Recording",  # Changed first arg to accept Recording object
    channels: list[str] | None = None,
    time_range: tuple[float, float] | None = None,
    offset_scale: float = 0.8,
    uniform_scale: bool = True,
    detrend: bool = False,
    grid: bool = True,
    title: str | None = None,
    show: bool = True,
    plt_module: Any = plt,
) -> None:
    """
    Plot EMG signals in a single plot with vertical offsets.

    Args:
        rec_object: The Recording object containing the signals and metadata.
        channels: List of channels to plot. If None, plot all channels.
        time_range: Tuple of (start_time, end_time) to plot. If None, plot all data.
        offset_scale: Portion of allocated space each signal can use (0.0 to 1.0).
        uniform_scale: Whether to use the same scale for all signals.
        detrend: Whether to remove mean from signals before plotting.
        grid: Whether to show grid lines.
        title: Optional title for the figure.
        show: Whether to display the plot.
        plt_module: Matplotlib pyplot module to use.
    """
    if rec_object.signals is None:
        raise ValueError("Recording object has no signals loaded.")

    signals_df = rec_object.signals

    if channels is None:
        channels = list(signals_df.columns)
    elif not all(ch in signals_df.columns for ch in channels):
        missing = [ch for ch in channels if ch not in signals_df.columns]
        raise ValueError(f"Channels not found: {missing}")

    # Create figure
    fig, ax = plt_module.subplots(figsize=(12, 8))

    # Set figure title if provided
    if title:
        ax.set_title(title, fontsize=14, pad=20)

    # Process signals
    processed_data = {}
    max_range = 0
    min_val = np.inf
    max_val = -np.inf

    for _i, channel in enumerate(channels):
        data = signals_df[channel]
        if time_range:
            start, end = time_range
            # Use searchsorted for robustness if index isn't perfectly aligned
            start_idx = data.index.searchsorted(start)
            end_idx = data.index.searchsorted(end, side="right")
            data = data.iloc[start_idx:end_idx]

        # Detrend if requested
        if detrend:
            data = data - data.mean()

        processed_data[channel] = data
        channel_range = data.max() - data.min()
        if channel_range > 0:
            max_range = max(max_range, channel_range)
        min_val = min(min_val, data.min())
        max_val = max(max_val, data.max())

    # Handle case where all signals are flat
    if max_range == 0:
        max_range = 1.0  # Avoid division by zero

    # Plot each signal with offset
    n_channels = len(channels)
    yticks = []
    yticklabels = []

    for i, channel in enumerate(channels):
        data = processed_data[channel]
        channel_range = data.max() - data.min()

        # Calculate offset and scaling
        offset = (n_channels - 1 - i) * max_range  # Offset based on max_range

        # Scale only if uniform_scale is False and channel_range > 0
        if not uniform_scale and channel_range > 0:
            scaled_data = (data - data.min()) / channel_range * (max_range * offset_scale) + offset
        else:
            # If uniform scale, just apply offset (no scaling needed as offset is based on max_range)
            # Or if channel_range is 0 (flat line)
            scaled_data = data + offset

        # Plot the signal
        ax.plot(data.index, scaled_data, linewidth=1, label=channel)

        # Store tick position and label (use mean value + offset for tick position)
        yticks.append(data.mean() + offset)
        yticklabels.append(f"{channel}")

    # Set axis labels and ticks
    ax.set_xlabel("Time (s)")
    ax.set_yticks(yticks)
    ax.set_yticklabels(yticklabels)

    # Set y-axis limits with some padding based on the overall scaled range
    # Calculate overall min/max of scaled data for limits
    all_scaled_min = np.inf
    all_scaled_max = -np.inf
    for i, channel in enumerate(channels):
        data = processed_data[channel]
        offset = (n_channels - 1 - i) * max_range  # Offset based on max_range
        channel_range = data.max() - data.min()
        if not uniform_scale and channel_range > 0:
            scaled_data = (data - data.min()) / channel_range * (max_range * offset_scale) + offset
        else:
            scaled_data = data + offset
        all_scaled_min = min(all_scaled_min, scaled_data.min())
        all_scaled_max = max(all_scaled_max, scaled_data.max())

    padding = max_range * 0.1  # 10% padding based on max range
    ax.set_ylim(all_scaled_min - padding, all_scaled_max + padding)

    if grid:
        ax.grid(True, linestyle="--", alpha=0.7)

    # Adjust layout
    plt_module.tight_layout()
    if show:
        plt_module.show()

Key Functions Summary

• plot_signals(): Plot EMG signals in a single figure with vertical offsets.
• plot_comparison(): Plot original and reloaded signals overlaid for visual comparison.

Direct Usage Examples

While these functions are typically accessed through the Recording class methods, they can be called directly for advanced use cases:

Plotting Signals Directly

from biosigio import Recording
from biosigio.visualization.static import plot_signals

# Load EMG data
rec = Recording.from_file("data.csv", importer="trigno")

# Plot signals directly with custom parameters
plot_signals(
    rec_object=rec,
    channels=['EMG1', 'EMG2'],
    time_range=(0, 5),
    offset_scale=0.7,
    uniform_scale=False,
    detrend=True,
    grid=True,
    title="Custom EMG Plot",
    show=True
)

Plotting Comparison Directly

from biosigio import Recording
from biosigio.visualization.static import plot_comparison

# Load original and reloaded EMG data
rec_original = Recording.from_file("original.csv", importer="trigno")
rec_reloaded = Recording.from_file("reloaded.edf")

# Create channel mapping
channel_map = {
    'EMG1': 'Channel_1',
    'EMG2': 'Channel_2'
}

# Plot comparison directly
plot_comparison(
    rec_original=rec_original,
    rec_reloaded=rec_reloaded,
    channels=['EMG1', 'EMG2'],
    time_range=(1, 3),
    detrend=True,
    grid=True,
    suptitle="Signal Comparison",
    channel_map=channel_map,
    show=True
)

Customizing Plots

The static plotting functions provide several parameters for customization:

• Channel selection: Display only specific channels
• Time range: Plot a specific time window
• Detrending: Remove mean value for better comparison
• Uniform scaling: Control whether all signals use the same scale
• Offset scale: Control spacing between channels
• Grid lines: Toggle grid visibility
• Titles: Add custom titles to plots

For most use cases, the Recording.plot_signals() method is recommended as it provides a simplified interface that delegates to plot_signals() here. Note that plot_comparison() is a module-level function only; there is no Recording.plot_comparison() method, so call it directly from biosigio.visualization.static (as shown above, or automatically via Recording.to_edf(..., verify=True, verify_plot=True)).