imspy.timstof package

Subpackages

• imspy.timstof.dbsearch package
  • Submodules
  • imspy.timstof.dbsearch.imspy_dda module
    • create_database()
    • load_config()
    • main()
  • imspy.timstof.dbsearch.imspy_rescore_sage module
    • main()
  • imspy.timstof.dbsearch.sage_output_utility module
    • PatternReplacer
      • PatternReplacer.apply()
    • break_into_equal_size_sets()
    • cosim_from_dict()
    • fragments_to_dict()
    • generate_training_data()
    • plot_summary()
    • re_score_psms()
    • remove_substrings()
    • row_to_fragment()
    • split_dataframe_randomly()
  • imspy.timstof.dbsearch.utility module
    • check_memory()
    • extract_timstof_dda_data()
    • generate_balanced_im_dataset()
    • generate_balanced_rt_dataset()
    • generate_training_data()
    • get_searchable_spec()
    • linear_map()
    • list_to_semicolon_string()
    • map_to_domain()
    • merge_dicts_with_merge_dict()
    • parse_string_list()
    • parse_to_tims2rescore()
    • peptide_length()
    • sanitize_charge()
    • sanitize_mz()
    • split_fasta()
    • split_psms()
    • transform_psm_to_pin()
    • write_psms_binary()
  • Module contents

Submodules

imspy.timstof.collision module

class imspy.timstof.collision.TimsTofCollisionEnergy

Bases: object

abstractmethod get_collision_energies()

Return type:

list[float]

abstractmethod get_collision_energy()

Return type:

float

class imspy.timstof.collision.TimsTofCollisionEnergyDIA(frame, frame_window_group, window_group, scan_start, scan_end, collision_energy)

Bases: TimsTofCollisionEnergy

get_collision_energies(frame_ids, scan_ids)

Return type:

list[float]

get_collision_energy(frame_id, scan_id)

Return type:

float

imspy.timstof.data module

class imspy.timstof.data.AcquisitionMode(mode)

Bases: RustWrapperObject

classmethod from_py_ptr(ptr)

Get an AcquisitionMode from a pointer.

Parameters:

ptr (pims.AcquisitionMode) – Pointer to an acquisition mode.

Returns:

Acquisition mode.

Return type:

AcquisitionMode

get_py_ptr()

property mode: str

Get the acquisition mode.

Returns:

Acquisition mode.

Return type:

str

class imspy.timstof.data.TimsDataset(data_path, in_memory=False, use_bruker_sdk=True)

Bases: ABC

property acquisition_mode: str

Get the acquisition mode.

Returns:

Acquisition mode.

Return type:

str

property acquisition_mode_numeric: int

Get the acquisition mode as a numerical value.

Returns:

Acquisition mode as a numerical value.

Return type:

int

property average_cycle_length: float

bytes_to_indexed_values(values)

Convert bytes to scan, tof, and intensity values.

Parameters:

values (NDArray[np.uint8]) – Bytes.

Returns:

Scan values. NDArray[np.int32]: TOF values. NDArray[np.float64]: Intensity values.

Return type:

NDArray[np.int32]

compress_frames(frames, num_threads=4)

Compress a collection of frames.

Parameters:

• frames (List[TimsFrame]) – List of frames.

• num_threads (int) – Number of threads to use.

Returns:

List of compressed bytes.

Return type:

List[NDArray[np.uint8]]

compress_zstd(values)

Compress values using ZSTD.

Parameters:

values (NDArray[np.float64]) – Values to compress.

Returns:

Compressed values.

Return type:

NDArray[np.uint8]

decompress_zstd(values, ignore_first_n=8)

Decompress values using ZSTD.

Parameters:

• values (NDArray[np.float64]) – Values to decompress.

• ignore_first_n (int) – Number of bytes to ignore.

Returns:

Decompressed values.

Return type:

NDArray[np.uint8]

property description: str

property frame_count: int

Get the number of frames.

Returns:

Number of frames.

Return type:

int

get_table(table_name)

Get a table.

Parameters:

table_name (str) – Table name.

Returns:

Table.

Return type:

pd.DataFrame

get_tims_frame(frame_id)

Get a TimsFrame.

Parameters:

frame_id (int) – Frame ID.

Returns:

TimsFrame.

Return type:

TimsFrame

get_tims_slice(frame_ids, num_threads=8)

Get a TimsFrame.

Parameters:

• frame_ids (int) – Frame ID.

• num_threads (int) – Number of threads.

Returns:

TimsFrame.

Return type:

TimsFrame

property im_lower

property im_upper

indexed_values_to_compressed_bytes(scan_values, tof_values, intensity_values, total_scans)

Convert scan and intensity values to bytes.

Parameters:

• scan_values (NDArray[np.int32]) – Scan values.

• tof_values (NDArray[np.int32]) – TOF values.

• intensity_values (NDArray[np.float64]) – Intensity values.

• total_scans (int) – Total number of scans.

Returns:

Bytes.

Return type:

NDArray[np.uint8]

inverse_mobility_to_scan(frame_id, im_values)

Convert inverse mobility values to scan values.

Parameters:

• frame_id (int) – Frame ID.

• im_values (NDArray[np.float64]) – Inverse mobility values.

Returns:

Scan values.

Return type:

NDArray[np.int32]

property mz_lower

mz_to_tof(frame_id, mz_values)

Convert m/z values to TOF values.

Parameters:

• frame_id (int) – Frame ID.

• mz_values (NDArray[np.float64]) – m/z values.

Returns:

TOF values.

Return type:

NDArray[np.int32]

property mz_upper

property num_scans: int

Get the number of scans.

Returns:

Number of scans.

Return type:

int

scan_to_inverse_mobility(frame_id, scan_values)

Convert scan values to inverse mobility values.

Parameters:

• frame_id (int) – Frame ID.

• scan_values (NDArray[np.int32]) – Scan values.

Returns:

Inverse mobility values.

Return type:

NDArray[np.float64]

tof_to_mz(frame_id, tof_values)

Convert TOF values to m/z values.

Parameters:

• frame_id (int) – Frame ID.

• tof_values (NDArray[np.int32]) – TOF values.

Returns:

m/z values.

Return type:

NDArray[np.float64]

imspy.timstof.data.is_amd64()

imspy.timstof.dda module

class imspy.timstof.dda.FragmentDDA(frame_id, precursor_id, collision_energy, selected_fragment)

Bases: RustWrapperObject

property collision_energy: float

property frame_id: int

classmethod from_py_ptr(fragment)

get_py_ptr()

property precursor_id: int

property selected_fragment: TimsFrame

class imspy.timstof.dda.PrecursorDDA(frame_id, precursor_id, highest_intensity_mz, average_mz, inverse_ion_mobility, collision_energy, precuror_total_intensity, isolation_mz, isolation_width, mono_mz=None, charge=None)

Bases: RustWrapperObject

property average_mz: float

property charge: int | None

property collision_energy: float

property frame_id: int

classmethod from_py_ptr(precursor)

get_py_ptr()

property highest_intensity_mz: float

property inverse_ion_mobility: float

property isolation_mz: float

property isolation_width: float

property mono_mz: float | None

property precuror_total_intensity: float

property precursor_id: int

to_sage_precursor()

Return type:

Precursor

class imspy.timstof.dda.TimsDatasetDDA(data_path, in_memory=False, use_bruker_sdk=True)

Bases: TimsDataset, RustWrapperObject

classmethod from_py_ptr(ptr)

get_pasef_fragments(num_threads=1)

Get PASEF fragments.

Args: num_threads (int, optional): Number of threads. Defaults to 1. CAUTION: As long as connection to datasets is established via bruker so / dll, using multiple threads is unstable.

Returns:

List of PASEF fragments.

Return type:

List[FragmentDDA]

get_precursor_frames(min_intensity=75, max_peaks=500, num_threads=4)

Get precursor frames. :type min_intensity: float :param min_intensity: minimum intensity a peak must have to be considered :type max_peaks: int :param max_peaks: maximum number of peaks to consider, frames will be sorted by intensity and only the top max_peaks will be considered :type num_threads: int :param num_threads: number of threads to use for processing

Returns:

List of all precursor frames

Return type:

List[TimsFrame]

get_py_ptr()

get_sage_processed_precursors(min_intensity=75, max_peaks=5000, file_id=0, num_threads=16)

Return type:

List[ProcessedSpectrum]

get_selected_precursors()

Get meta data for all selected precursors :returns: List of all selected precursors :rtype: List[PrecursorDDA]

imspy.timstof.dia module

class imspy.timstof.dia.TimsDatasetDIA(data_path, in_memory=False, use_bruker_sdk=True)

Bases: TimsDataset, RustWrapperObject

property dia_ms_ms_info

Get DIA MS/MS info.

Returns:

DIA MS/MS info.

Return type:

pd.DataFrame

property dia_ms_ms_windows

Get PASEF meta data for DIA.

Returns:

PASEF meta data.

Return type:

pd.DataFrame

classmethod from_py_ptr(obj)

get_py_ptr()

read_compressed_data_full()

Read compressed data.

Returns:

Compressed data.

Return type:

List[bytes]

sample_fragment_signal(num_frames, window_group, max_intensity=25.0, take_probability=0.5)

Sample fragment signal.

Parameters:

• num_frames (int) – Number of frames.

• window_group (int) – Window group to take frames from.

• max_intensity (float) – Maximum intensity.

• take_probability (float) – Probability to take signals from sampled frames.

Returns:

Frame.

Return type:

TimsFrame

sample_precursor_signal(num_frames, max_intensity=25.0, take_probability=0.5)

Sample precursor signal.

Parameters:

• num_frames (int) – Number of frames.

• max_intensity (float) – Maximum intensity.

• take_probability (float) – Probability to take signals from sampled frames.

Returns:

Frame.

Return type:

TimsFrame

imspy.timstof.frame module

class imspy.timstof.frame.TimsFrame(frame_id, ms_type, retention_time, scan, mobility, tof, mz, intensity)

Bases: RustWrapperObject

property df: DataFrame

Data as a pandas DataFrame.

Returns:

Data.

Return type:

pd.DataFrame

filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0)

Filter the frame for a given m/z range, scan range and intensity range.

Parameters:

• mz_min (float) – Minimum m/z value.

• mz_max (float) – Maximum m/z value.

• scan_min (int, optional) – Minimum scan value. Defaults to 0.

• scan_max (int, optional) – Maximum scan value. Defaults to 1000.

• mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

• mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

• intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

• intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

Returns:

Filtered frame.

Return type:

TimsFrame

property frame_id: int

Frame ID.

Returns:

Frame ID.

Return type:

int

classmethod from_py_ptr(frame)

Create a TimsFrame from a PyTimsFrame.

Parameters:

frame (pims.PyTimsFrame) – PyTimsFrame to create the TimsFrame from.

Returns:

TimsFrame created from the PyTimsFrame.

Return type:

TimsFrame

classmethod from_tims_spectra(spectra)

Create a TimsFrame from a list of TimsSpectrum.

Parameters:

spectra (List[TimsSpectrum]) – List of TimsSpectrum.

Returns:

TimsFrame created from the TimsSpectrum.

Return type:

TimsFrame

classmethod from_windows(windows)

Create a TimsFrame from a list of windows.

Parameters:

windows (List[TimsSpectrum]) – List of windows.

Returns:

TimsFrame created from the windows.

Return type:

TimsFrame

get_inverse_mobility_along_scan_marginal()

Get the inverse mobility along the scan marginal.

Returns:

Inverse mobility.

Return type:

float

get_mobility_mean_and_variance()

Get the mean and variance of the inverse mobility.

Returns:

Mean and variance of the inverse mobility.

Return type:

Tuple[float, float]

get_py_ptr()

property intensity: ndarray[Any, dtype[float64]]

Intensity.

Returns:

Intensity.

Return type:

NDArray[np.float64]

property mobility: ndarray[Any, dtype[float64]]

Inverse mobility.

Returns:

Inverse mobility.

Return type:

NDArray[np.float64]

property ms_type: int

MS type.

Returns:

MS type.

Return type:

int

property ms_type_as_string: str

MS type.

Returns:

MS type.

Return type:

int

property mz: ndarray[Any, dtype[float64]]

m/z.

Returns:

m/z.

Return type:

NDArray[np.float64]

random_subsample_frame(take_probability)

Randomly subsample the frame.

Args: take_probability (float): Take probability.

Returns: TimsFrame: Subsampled frame.

Return type:

TimsFrame

property retention_time: float

Retention time.

Returns:

Retention time.

Return type:

float

property scan: ndarray[Any, dtype[int32]]

Scan.

Returns:

Scan.

Return type:

NDArray[np.int32]

to_dense_windows(window_length=10, resolution=1, overlapping=True, min_num_peaks=5, min_intensity=0.0)

Return type:

ndarray[Any, dtype[float64]]

to_indexed_mz_spectrum()

Convert the frame to an IndexedMzSpectrum.

Returns:

IndexedMzSpectrum.

Return type:

IndexedMzSpectrum

to_noise_annotated_tims_frame()

Convert the frame to a noise annotated frame.

Returns:

Noise annotated frame.

Return type:

TimsFrameAnnotated

to_resolution(resolution)

Convert the frame to a given resolution.

Parameters:

resolution (int) – Resolution.

Returns:

Frame with the given resolution.

Return type:

TimsFrame

to_tims_spectra()

Convert the frame to a list of TimsSpectrum.

Returns:

List of TimsSpectrum.

Return type:

List[TimsSpectrum]

to_windows(window_length=10, overlapping=True, min_num_peaks=5, min_intensity=1)

Convert the frame to a list of windows.

Parameters:

• window_length (float, optional) – Window length. Defaults to 10.

• overlapping (bool, optional) – Whether the windows should overlap. Defaults to True.

• min_num_peaks (int, optional) – Minimum number of peaks in a window. Defaults to 5.

• min_intensity (float, optional) – Minimum intensity of a peak in a window. Defaults to 1.

Returns:

List of windows.

Return type:

List[MzSpectrum]

property tof: ndarray[Any, dtype[int32]]

Time of flight.

Returns:

Time of flight.

Return type:

NDArray[np.int32]

vectorized(resolution=2)

Convert the frame to a vectorized frame.

Parameters:

resolution (int, optional) – Resolution. Defaults to 2.

Returns:

Vectorized frame.

Return type:

TimsFrameVectorized

class imspy.timstof.frame.TimsFrameVectorized(frame_id, ms_type, retention_time, scan, mobility, tof, indices, intensity)

Bases: RustWrapperObject

property df: DataFrame

Data as a pandas DataFrame.

Returns:

Data.

Return type:

pd.DataFrame

filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0)

Filter the frame for a given m/z range, scan range and intensity range.

Parameters:

• mz_min (float) – Minimum m/z value.

• mz_max (float) – Maximum m/z value.

• scan_min (int, optional) – Minimum scan value. Defaults to 0.

• scan_max (int, optional) – Maximum scan value. Defaults to 1000.

• mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

• mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

• intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

• intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

Returns:

Filtered frame.

Return type:

TimsFrameVectorized

property frame_id: int

Frame ID.

Returns:

Frame ID.

Return type:

int

classmethod from_py_ptr(frame)

Create a TimsFrameVectorized from a PyTimsFrameVectorized.

Parameters:

frame (pims.PyTimsFrameVectorized) – PyTimsFrameVectorized to create the TimsFrameVectorized from.

Returns:

TimsFrameVectorized created from the PyTimsFrameVectorized.

Return type:

TimsFrameVectorized

get_arrays_at_index(index)

Get the arrays at a given index.

Parameters:

index (int) – Index.

Returns:

Arrays at the index.

Return type:

NDArray[np.float64]

get_py_ptr()

get_tensor_repr(dense=True, zero_indexed=True, re_index=True, scan_max=None, index_max=None)

property indices: ndarray[Any, dtype[int32]]

Indices.

Returns:

Indices.

Return type:

NDArray[np.int32]

property intensity: ndarray[Any, dtype[float64]]

Intensity.

Returns:

Intensity.

Return type:

NDArray[np.float64]

property mobility: ndarray[Any, dtype[float64]]

Inverse mobility.

Returns:

Inverse mobility.

Return type:

NDArray[np.float64]

property ms_type: str

MS type.

Returns:

MS type.

Return type:

int

property retention_time: float

Retention time.

Returns:

Retention time.

Return type:

float

property scan: ndarray[Any, dtype[int32]]

Scan.

Returns:

Scan.

Return type:

NDArray[np.int32]

property tof: ndarray[Any, dtype[int32]]

Time of flight.

Returns:

Time of flight.

Return type:

NDArray[np.int32]

imspy.timstof.quadrupole module

class imspy.timstof.quadrupole.TimsTofQuadrupoleDIA(frame, frame_window_group, window_group, scan_start, scan_end, isolation_mz, isolation_width, k=None)

Bases: object

all_transmitted(frame_id, scan_id, mz, min_proba=None)

Return type:

bool

any_transmitted(frame_id, scan_id, mz, min_proba=None)

Return type:

bool

apply_transmission(frame_id, scan_id, mz)

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

frame_to_window_group(frame_id)

Return type:

int

get_transmission_set(frame_id, scan_id, mz, min_proba=None)

Return type:

set[int]

is_precursor(frame_id)

Return type:

bool

is_transmitted(frame_id, scan_id, mz, min_proba=None)

Return type:

bool

isotopes_transmitted(frame_id, scan_id, mz_mono, mz, min_proba=None)

Get the transmission probability for a list of isotopes :type frame_id: int :param frame_id: :type scan_id: int :param scan_id: :type mz_mono: float :param mz_mono: :type mz: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] :param mz: :type min_proba: float | None :param min_proba:

Returns:

Return type:

tuple[float, list[tuple[float, float]]]

transmit_frame(frame, min_probability=None)

Return type:

TimsFrame

transmit_ion(frame_ids, scan_ids, spectrum, min_probability)

Return type:

List[List[MzSpectrum]]

transmit_spectrum(frame_id, scan_id, spectrum, min_probability=None)

Return type:

MzSpectrum

imspy.timstof.slice module

class imspy.timstof.slice.TimsPlane

Bases: object

property df

property frame_ids

classmethod from_py_tims_plane(plane)

Create a TimsPlane from a PyTimsPlane.

Parameters:

plane (pims.PyTimsPlane) – PyTimsPlane to create the TimsPlane from.

Returns:

TimsPlane created from the PyTimsPlane.

Return type:

TimsPlane

property intensities

property mobilities

property mz_mean

property mz_std

property num_points

property retention_times

property scans

property tof_mean

property tof_std

class imspy.timstof.slice.TimsSlice(frame_id, scan, tof, retention_time, mobility, mz, intensity)

Bases: object

property df: DataFrame

Get the data as a pandas DataFrame.

Returns:

Data.

Return type:

pd.DataFrame

filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0, num_threads=4)

Filter the slice by m/z, scan and intensity.

Parameters:

• mz_min (float) – Minimum m/z value.

• mz_max (float) – Maximum m/z value.

• scan_min (int, optional) – Minimum scan value. Defaults to 0.

• scan_max (int, optional) – Maximum scan value. Defaults to 1000.

• mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

• mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

• intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

• intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

• num_threads (int, optional) – Number of threads to use. Defaults to 4.

Returns:

Filtered slice.

Return type:

TimsSlice

filter_by_type(mz_min_ms1=0, mz_max_ms1=2000, scan_min_ms1=0, scan_max_ms1=1000, inv_mob_min_ms1=0, inv_mob_max_ms1=2, intensity_min_ms1=0, intensity_max_ms1=1000000000.0, mz_min_ms2=0, mz_max_ms2=2000, scan_min_ms2=0, scan_max_ms2=1000, inv_mob_min_ms2=0, inv_mob_max_ms2=2, intensity_min_ms2=0, intensity_max_ms2=1000000000.0, num_threads=4)

Filter the slice by m/z, scan and intensity, for MS1 and MS2 with different ranges.

Parameters:

• mz_min_ms1 (float, optional) – Minimum m/z value for MS1. Defaults to 0.

• mz_max_ms1 (float, optional) – Maximum m/z value for MS1. Defaults to 2000.

• scan_min_ms1 (int, optional) – Minimum scan value for MS1. Defaults to 0.

• scan_max_ms1 (int, optional) – Maximum scan value for MS1. Defaults to 1000.

• inv_mob_min_ms1 (float, optional) – Minimum inverse mobility value for MS1. Defaults to 0.

• inv_mob_max_ms1 (float, optional) – Maximum inverse mobility value for MS1. Defaults to 2.

• intensity_min_ms1 (float, optional) – Minimum intensity value for MS1. Defaults to 0.

• intensity_max_ms1 (float, optional) – Maximum intensity value for MS1. Defaults to 1e9.

• mz_min_ms2 (float, optional) – Minimum m/z value for MS2. Defaults to 0.

• mz_max_ms2 (float, optional) – Maximum m/z value for MS2. Defaults to 2000.

• scan_min_ms2 (int, optional) – Minimum scan value for MS2. Defaults to 0.

• scan_max_ms2 (int, optional) – Maximum scan value for MS2. Defaults to 1000.

• inv_mob_min_ms2 (float, optional) – Minimum inverse mobility value for MS2. Defaults to 0.

• inv_mob_max_ms2 (float, optional) – Maximum inverse mobility value for MS2. Defaults to 2.

• intensity_min_ms2 (float, optional) – Minimum intensity value for MS2. Defaults to 0.

• intensity_max_ms2 (float, optional) – Maximum intensity value for MS2. Defaults to 1e9.

• num_threads (int, optional) – Number of threads to use. Defaults to 4.

Returns:

Filtered slice.

Return type:

TimsSlice

property first_frame_id: int

First frame ID.

Returns:

First frame ID.

Return type:

int

property fragments

property frames: List[TimsFrame]

Get the frames.

Returns:

Frames.

Return type:

List[TimsFrame]

classmethod from_frames(frames)

Create a TimsSlice from a list of TimsFrames.

Parameters:

frames (List[TimsFrame]) – List of TimsFrames.

Returns:

TimsSlice created from the list of TimsFrames.

Return type:

TimsSlice

classmethod from_py_tims_slice(tims_slice)

Create a TimsSlice from a PyTimsSlice.

Parameters:

tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.

Returns:

TimsSlice created from the PyTimsSlice.

Return type:

TimsSlice

get_tims_planes(tof_max_value=400000, num_chunks=7, num_threads=4)

Return type:

List[TimsPlane]

property last_frame_id: int

Last frame ID.

Returns:

Last frame ID.

Return type:

int

property precursors

to_dense_windows(window_length=10, resolution=1, overlapping=True, min_num_peaks=5, min_intensity=0.0, num_theads=4)

Return type:

tuple[list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]], list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]], list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]]

to_resolution(resolution, num_threads=4)

Convert the slice to a given resolution.

Parameters:

• resolution (int) – Resolution.

• num_threads (int, optional) – Number of threads to use. Defaults to 4.

Returns:

Slice with given resolution.

Return type:

TimsSlice

to_windows(window_length=10, overlapping=True, min_num_peaks=5, min_intensity=1, num_threads=4)

Convert the slice to a list of windows.

Parameters:

• window_length (float, optional) – Window length. Defaults to 10.

• overlapping (bool, optional) – Whether the windows should overlap. Defaults to True.

• min_num_peaks (int, optional) – Minimum number of peaks in a window. Defaults to 5.

• min_intensity (float, optional) – Minimum intensity of a peak in a window. Defaults to 1.

• num_threads (int, optional) – Number of threads to use. Defaults to 1.

Returns:

List of windows.

Return type:

List[MzSpectrum]

vectorized(resolution=2, num_threads=4)

Get a vectorized version of the slice.

Parameters:

• resolution (int, optional) – Resolution. Defaults to 2.

• num_threads (int, optional) – Number of threads to use. Defaults to 4.

Returns:

Vectorized version of the slice.

Return type:

TimsSliceVectorized

class imspy.timstof.slice.TimsSliceVectorized

Bases: object

property df: DataFrame

Get the data as a pandas DataFrame.

Returns:

Data.

Return type:

pd.DataFrame

filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0, num_threads=4)

Filter the slice by m/z, scan and intensity.

Parameters:

• mz_min (float) – Minimum m/z value.

• mz_max (float) – Maximum m/z value.

• scan_min (int, optional) – Minimum scan value. Defaults to 0.

• scan_max (int, optional) – Maximum scan value. Defaults to 1000.

• mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

• mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

• intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

• intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

• num_threads (int, optional) – Number of threads to use. Defaults to 4.

Returns:

Filtered slice.

Return type:

TimsSlice

property first_frame_id: int

First frame ID.

Returns:

First frame ID.

Return type:

int

property fragments

property frames: List[TimsFrameVectorized]

Get the frames.

Returns:

Frames.

Return type:

List[TimsFrame]

classmethod from_vectorized_py_tims_slice(tims_slice)

Create a TimsSlice from a PyTimsSlice.

Parameters:

tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.

Returns:

TimsSlice created from the PyTimsSlice.

Return type:

TimsSlice

get_py_ptr()

get_tensor_repr(dense=True, zero_index=True, re_index=True, frame_max=None, scan_max=None, index_max=None)

property last_frame_id: int

Last frame ID.

Returns:

Last frame ID.

Return type:

int

property precursors

Module contents