Readers

High-level entry points for opening timsTOF .d acquisitions.

tdfpy.get_acquisition_type

get_acquisition_type(
    analysis_dir: str,
) -> Literal["DDA", "DIA", "PRM", "Unknown"]

Determine the acquisition type (DDA or DIA) of a .d folder by examining the MsMsType values in the Frames table.

Parameters:

Name	Type	Description	Default
analysis_dir	str	Path to the .d folder	required

Returns:

Type	Description
Literal['DDA', 'DIA', 'PRM', 'Unknown']	"DDA" if DDA acquisition detected
Literal['DDA', 'DIA', 'PRM', 'Unknown']	"DIA" if DIA acquisition detected
Literal['DDA', 'DIA', 'PRM', 'Unknown']	"PRM" if PRM acquisition detected
Literal['DDA', 'DIA', 'PRM', 'Unknown']	"Unknown" if type cannot be determined

Raises:

Type	Description
FileNotFoundError	If analysis.tdf does not exist

Source code in src/tdfpy/reader.py

def get_acquisition_type(analysis_dir: str) -> Literal["DDA", "DIA", "PRM", "Unknown"]:
    """
    Determine the acquisition type (DDA or DIA) of a .d folder by examining
    the MsMsType values in the Frames table.

    Args:
        analysis_dir: Path to the .d folder

    Returns:
        "DDA" if DDA acquisition detected
        "DIA" if DIA acquisition detected
        "PRM" if PRM acquisition detected
        "Unknown" if type cannot be determined

    Raises:
        FileNotFoundError: If analysis.tdf does not exist
    """
    analysis_tdf_path = Path(analysis_dir) / "analysis.tdf"
    if not analysis_tdf_path.exists():
        raise FileNotFoundError(f"analysis.tdf not found at {analysis_tdf_path}")

    pandas_tdf = PandasTdf(str(analysis_tdf_path))
    frames_df = pandas_tdf.frames

    # Get unique MsMsType values
    msms_types = set(frames_df["MsMsType"].unique())

    # Check for DDA (MS2 type 8)
    if MsMsType.DDA_MS2.value in msms_types:
        return "DDA"

    # Check for DIA (MS2 type 9)
    if MsMsType.DIA_MS2.value in msms_types:
        return "DIA"

    # PRM shows as MsMsType 2
    if 2 in msms_types:
        return "PRM"

    return "Unknown"

tdfpy.DDA

DDA(analysis_dir: str)

Bases: _DFolder

Open a DDA (Data-Dependent Acquisition) .d folder.

Use as a context manager to ensure the TimsData connection is closed when done. Exposes MS1 frames via ms1 and precursors via precursors.

Parameters:

Name	Type	Description	Default
analysis_dir	str	Path to the .d folder containing analysis.tdf and analysis.tdf_bin.	required

Raises:

Type	Description
FileNotFoundError	If the .d folder or required files are missing.

Example

with DDA("/path/to/data.d") as dda:
    for frame in dda.ms1:
        print(frame.frame_id, frame.time)

Source code in src/tdfpy/reader.py

def __init__(self, analysis_dir: str):
    super().__init__(analysis_dir)

    self._precursor_df = PandasTdf(str(self.analysis_tdf_path)).precursors
    self._frames_df = PandasTdf(str(self.analysis_tdf_path)).frames
    self._pasef_frame_msms_info_df = PandasTdf(
        str(self.analysis_tdf_path)
    ).pasef_frame_msms_info

    frame_id_to_rt = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        time = float(row["Time"])
        frame_id_to_rt[frame_id] = time

    frame_to_polarity: dict[int, str] = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        polarity = str(row["Polarity"])
        frame_to_polarity[frame_id] = polarity

    self._pasef_msms_infos: dict[int, list[PasefFrameMsmsInfo]] = {}
    for _, row in self._pasef_frame_msms_info_df.iterrows():
        frame_id = int(row["Frame"])
        polarity = frame_to_polarity[frame_id]
        pasef_info = PasefFrameMsmsInfo(
            _timsdata=self.timsdata,
            frame_id=frame_id,
            scan_num_begin=int(row["ScanNumBegin"]),
            scan_num_end=int(row["ScanNumEnd"]),
            isolation_mz=float(row["IsolationMz"]),
            isolation_width=float(row["IsolationWidth"]),
            collision_energy=float(row["CollisionEnergy"]),
            precursor=int(row["Precursor"])
            if not pd.isna(row["Precursor"])
            else None,
            rt=frame_id_to_rt[frame_id],
            polarity=Polarity.from_str(polarity),
        )

        if pasef_info.precursor is None:
            raise ValueError(
                f"PASEF MS/MS info with null precursor found for frame {pasef_info.frame_id}. All PASEF MS/MS info must have a valid precursor ID."
            )

        if pasef_info.precursor not in self._pasef_msms_infos:
            self._pasef_msms_infos[pasef_info.precursor] = []
        self._pasef_msms_infos[pasef_info.precursor].append(pasef_info)

    self._precursors: dict[int, Precursor] = {}
    self._frame_to_precursors: dict[int, list[Precursor]] = {}
    for _, row in self._precursor_df.iterrows():
        precursor_id = int(row["Id"])
        frame_id = int(row["Parent"])

        precursor = Precursor(
            _timsdata=self.timsdata,
            precursor_id=precursor_id,
            largest_peak_mz=float(row["LargestPeakMz"]),
            average_mz=float(row["AverageMz"]),
            monoisotopic_mz=float(row["MonoisotopicMz"])
            if not pd.isna(row["MonoisotopicMz"])
            else None,
            charge=int(row["Charge"]) if not pd.isna(row["Charge"]) else None,
            scan_number=int(row["ScanNumber"]),
            intensity=float(row["Intensity"]),
            parent_frame=int(row["Parent"]),
            pasef_frame_msms_infos=tuple(
                self._pasef_msms_infos.get(precursor_id, [])
            ),
            rt=frame_id_to_rt[frame_id],
        )
        self._precursors[precursor_id] = precursor
        if frame_id not in self._frame_to_precursors:
            self._frame_to_precursors[frame_id] = []
        self._frame_to_precursors[frame_id].append(precursor)

    self._precursor_lookup = PrecursorLookup(self._precursors)

    self._frames: dict[int, Frame] = {}
    self._ms1_frames: dict[int, DDAMs1Frame] = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        msms_type = int(row["MsMsType"])
        if msms_type == MsMsType.MS1.value:
            precursors_for_frame: list[Precursor] = self._frame_to_precursors.get(
                frame_id, []
            )
            frame = DDAMs1Frame(
                _timsdata=self.timsdata,
                frame_id=frame_id,
                time=float(row["Time"]),
                polarity=Polarity.from_str(str(row["Polarity"])),
                scan_mode=int(row["ScanMode"]),
                msms_type=msms_type,
                tims_id=int(row["TimsId"]),
                max_intensity=int(row["MaxIntensity"]),
                summed_intensities=int(row["SummedIntensities"]),
                num_scans=int(row["NumScans"]),
                num_peaks=int(row["NumPeaks"]),
                mz_calibration=int(row["MzCalibration"]),
                t1=float(row["T1"]),
                t2=float(row["T2"]),
                tims_calibration=int(row["TimsCalibration"]),
                property_group=int(row["PropertyGroup"]),
                accumulation_time=float(row["AccumulationTime"]),
                ramp_time=float(row["RampTime"]),
                precursors=tuple(precursors_for_frame),
            )
            self._ms1_frames[frame_id] = frame
            self._frames[frame_id] = frame
        elif msms_type == MsMsType.DDA_MS2.value:
            pass
        else:
            raise ValueError(f"Unknown MsMsType {msms_type} for frame {frame_id}")

    self._ms1_frames_lookup = Ms1FrameLookup(self._ms1_frames)

    # remove uneeded dataframes to save memory
    del self._precursor_df
    del self._frames_df
    del self._pasef_frame_msms_info_df

metadata property

metadata: MetaData

Global metadata about the acquisition.

calibration property

calibration: Calibration

Calibration information.

ms1 property

ms1: Ms1FrameLookup[DDAMs1Frame]

Lookup for MS1 frames. Supports indexing by frame ID and .query().

precursors property

precursors: PrecursorLookup

Lookup for all precursors. Supports indexing by precursor ID and .query().

close

close() -> None

Close the TimsData connection.

Source code in src/tdfpy/reader.py

def close(self) -> None:
    """Close the TimsData connection."""
    if not self._closed:
        self.timsdata.close()
        self._closed = True

tdfpy.DIA

DIA(analysis_dir: str)

Bases: _DFolder

Open a DIA (Data-Independent Acquisition) .d folder.

Use as a context manager to ensure the TimsData connection is closed when done. Exposes MS1 frames via ms1, individual windows via windows, and window groups via window_groups.

Parameters:

Name	Type	Description	Default
analysis_dir	str	Path to the .d folder containing analysis.tdf and analysis.tdf_bin.	required

Raises:

Type	Description
FileNotFoundError	If the .d folder or required files are missing.

Example

with DIA("/path/to/data.d") as dia:
    for group in dia.window_groups:
        for window in group.windows:
            print(window.isolation_mz)

Source code in src/tdfpy/reader.py

def __init__(self, analysis_dir: str):
    super().__init__(analysis_dir)

    # frames
    self._frames_df = PandasTdf(str(self.analysis_tdf_path)).frames

    frame_id_to_rt = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        time = float(row["Time"])
        frame_id_to_rt[frame_id] = time

    frame_id_to_polarity = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        polarity = str(row["Polarity"])
        frame_id_to_polarity[frame_id] = polarity

    # window groups
    self._dia_frame_msms_windows_df = PandasTdf(
        str(self.analysis_tdf_path)
    ).dia_frame_msms_windows

    # frame to window groups
    self._dia_frame_msms_info = PandasTdf(
        str(self.analysis_tdf_path)
    ).dia_frame_msms_info

    self._dia_window_groups: dict[int, list[DiaWindowGroup]] = {}
    for key, row in self._dia_frame_msms_windows_df.iterrows():
        window_id = int(key)  # type: ignore
        window = DiaWindowGroup(
            window_index=window_id,
            window_group=int(row["WindowGroup"]),
            scan_num_begin=int(row["ScanNumBegin"]),
            scan_num_end=int(row["ScanNumEnd"]),
            isolation_mz=float(row["IsolationMz"]),
            isolation_width=float(row["IsolationWidth"]),
            collision_energy=float(row["CollisionEnergy"]),
        )
        if window.window_group not in self._dia_window_groups:
            self._dia_window_groups[window.window_group] = []
        self._dia_window_groups[window.window_group].append(window)

    # now we need to create DiaWindow objects which have an additional frame_id
    # create dia windows
    self._dia_windows: dict[int, list[DiaWindow]] = {}
    self._all_dia_windows: list[DiaWindow] = []
    for _, row in self._dia_frame_msms_info.iterrows():
        frame_id = int(row["Frame"])
        window_group_id = int(row["WindowGroup"])
        # each frame can have multiple window groups
        window_groups = self._dia_window_groups.get(window_group_id, [])
        if frame_id not in self._dia_windows:
            self._dia_windows[frame_id] = []
        for window_group in window_groups:
            dia_window = DiaWindow(
                _timsdata=self.timsdata,
                frame_id=frame_id,
                window_index=window_group.window_index,
                window_group=window_group.window_group,
                scan_num_begin=window_group.scan_num_begin,
                scan_num_end=window_group.scan_num_end,
                isolation_mz=window_group.isolation_mz,
                isolation_width=window_group.isolation_width,
                collision_energy=window_group.collision_energy,
                rt=frame_id_to_rt[frame_id],
                polarity=Polarity.from_str(frame_id_to_polarity[frame_id]),
            )
            self._dia_windows[frame_id].append(dia_window)
            self._all_dia_windows.append(dia_window)

    self._dia_windows_lookup = DiaWindowLookup(self._all_dia_windows)

    self._frames: dict[int, Frame] = {}
    self._ms1_frames: dict[int, DIAMs1Frame] = {}
    for _, row in self._frames_df.iterrows():
        frame_id = int(row["Id"])
        msms_type = int(row["MsMsType"])
        if msms_type == MsMsType.MS1.value:
            frame = DIAMs1Frame(
                _timsdata=self.timsdata,
                frame_id=frame_id,
                time=float(row["Time"]),
                polarity=Polarity.from_str(str(row["Polarity"])),
                scan_mode=int(row["ScanMode"]),
                msms_type=msms_type,
                tims_id=int(row["TimsId"]) if not pd.isna(row["TimsId"]) else None,
                max_intensity=int(row["MaxIntensity"]),
                summed_intensities=int(row["SummedIntensities"]),
                num_scans=int(row["NumScans"]),
                num_peaks=int(row["NumPeaks"]),
                mz_calibration=int(row["MzCalibration"]),
                t1=int(row["T1"]),
                t2=int(row["T2"]),
                tims_calibration=int(row["TimsCalibration"]),
                property_group=int(row["PropertyGroup"])
                if not pd.isna(row["PropertyGroup"])
                else None,
                accumulation_time=float(row["AccumulationTime"]),
                ramp_time=float(row["RampTime"]),
                dia_windows=tuple(self._dia_windows.get(frame_id, [])),
            )
            self._ms1_frames[frame_id] = frame
            self._frames[frame_id] = frame
        elif msms_type == MsMsType.DIA_MS2.value:
            pass
        else:
            raise ValueError(f"Unknown MsMsType {msms_type} for frame {frame_id}")

    self._ms1_frames_lookup = Ms1FrameLookup(self._ms1_frames)

    # remove uneeded dataframes to save memory
    del self._frames_df
    del self._dia_frame_msms_windows_df
    del self._dia_frame_msms_info

metadata property

metadata: MetaData

Global metadata about the acquisition.

calibration property

calibration: Calibration

Calibration information.

ms1 property

ms1: Ms1FrameLookup[DIAMs1Frame]

Lookup for MS1 frames. Supports indexing by frame ID and .query().

windows property

windows: DiaWindowLookup

Lookup for all DIA windows. Supports indexing by window index and .query().

window_groups property

window_groups: Generator[DiaWindowGroup, None, None]

Iterate over all DiaWindowGroup objects across all window groups.

close

close() -> None

Close the TimsData connection.

Source code in src/tdfpy/reader.py

def close(self) -> None:
    """Close the TimsData connection."""
    if not self._closed:
        self.timsdata.close()
        self._closed = True

tdfpy.PRM

PRM(analysis_dir: str)

Bases: _DFolder

PRM (Parallel Reaction Monitoring) acquisition mode.

Note

Not yet implemented. Raises NotImplementedError on instantiation.

Source code in src/tdfpy/reader.py

def __init__(self, analysis_dir: str):
    raise NotImplementedError("PRM acquisition mode is not yet supported.")

metadata property

metadata: MetaData

Global metadata about the acquisition.

calibration property

calibration: Calibration

Calibration information.

close

close() -> None

Close the TimsData connection.

Source code in src/tdfpy/reader.py

def close(self) -> None:
    """Close the TimsData connection."""
    if not self._closed:
        self.timsdata.close()
        self._closed = True