full_dia.alphatims package

Submodules

full_dia.alphatims.bruker module

This module provides functions to handle Bruker data. It primarily implements the TimsTOF class, that acts as an in-memory container for Bruker data accession and storage.

exception full_dia.alphatims.bruker.PrecursorFloatError

Bases: TypeError

Used to indicate that a precursor value is not an int but a float.

class full_dia.alphatims.bruker.TimsTOF(bruker_d_folder_name, *, mz_estimation_from_frame=1, mobility_estimation_from_frame=1, slice_as_dataframe=True, use_calibrated_mz_values_as_default=0, use_hdf_if_available=True, mmap_detector_events=True, drop_polarity=True, convert_polarity_to_int=True)

Bases: object

A class that stores Bruker TimsTOF data in memory for fast access.

Data can be read directly from a Bruker .d folder. All OS’s are supported, but reading mz_values and mobility_values from a .d folder requires Windows or Linux due to availability of Bruker libraries. On MacOS, they are estimated based on metadata, but these values are not guaranteed to be correct. Often they fall within 0.02 Th, but errors up to 6 Th have already been observed!

A TimsTOF object can also be exported to HDF for subsequent access. This file format is portable to all OS’s. As such, initial reading on Windows with correct mz_values and mobility_values can be done and the resulting HDF file can safely be read on MacOS. This HDF file also provides improved accession times for subsequent use.

After reading, data can be accessed with traditional Python slices. As TimsTOF data is 5-dimensional, the data can be sliced in 5 dimensions as well. These dimensions follows the design of the TimsTOF Pro:

1 LC: rt_values, frame_indices

The first dimension allows to slice retention_time values or frames indices. These values and indices have a one-to-one relationship.

2 TIMS: mobility_values, scan_indices

The second dimension allows to slice mobility values or scan indices (i.e. a single push). These values and indices have a one-to-one relationship.

3 QUAD: quad_mz_values, precursor_indices

The third dimension focusses on the quadrupole and indirectly on the collision cell. It allows to slice lower and upper quadrupole mz values (e.g. the m/z of unfragmented ions / precursors). If set to -1, the quadrupole and collision cell are assumed to be inactive, i.e. precursor ions are detected instead of fragments. Equally, this dimension allows to slice precursor indices. Precursor index 0 defaults to all precusors (i.e. quad mz values equal to -1). In DDA, precursor indices larger than 0 point to ddaPASEF MSMS spectra. In DIA, precursor indices larger than 0 point to windows, i.e. all scans in a frame with equal quadrupole and collision settings that is repeated once per full cycle. Note that these values do not have a one-to-one relationship.

4 TOF: mz_values, tof_indices

The fourth dimension allows to slice (fragment) mz_values or tof indices. Note that the quadrupole dimension determines if precursors are detected or fragments. These values and indices have a one-to-one relationship.

5 DETECTOR: intensity_values

The fifth dimension allows to slice intensity values.

Note that all dimensions except for the detector have both (float) values and (integer) indices. For each dimension, slices can be provided in several different ways:

• int:

  A single int can be used to select a single index. If used in the fifth dimension, it still allows to select intensity_values

• float:

  A single float can be used to select a single value. As the values arrays are discrete, the smallest index with a value equal to or larger than this value is actually selected. For intensity_value slicing, the exact value is used.

• slice:

  A Python slice with start, stop and step can be provided. Start and stop values can independently be set to int or float. If a float is provided it conversed to an int as previously described. The step always needs to be provided as an int. Since there is not one-to-one relation from values to indices for QUAD and DETECTOR, the step value is ignored in these cases and only start and stop can be used.

  IMPORTANT NOTE: negative start, step and stop integers are not supported!

• iterable:

  An iterable with (mixed) floats and ints can also be provided, in a similar fashion as Numpy’s fancy indexing.

  IMPORTANT NOTE: The resulting integers after float->int conversion need to be sorted in ascending order!

• np.ndarray:

  Multiple slicing is supported by providing either a np.int64[:, 3] array, where each row is assumed to be a (start, stop, step) tuple or np.float64[:, 2] where each row is assumed to be a (start, stop) tuple.

  IMPORTANT NOTE: These arrays need to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

Alternatively, a dictionary can be used to define filters for each dimension (see examples).

The result of such slicing is a pd.DataFrame with the following columns:

• raw_indices

• frame_indices

• scan_indices

• precursor_indices

• tof_indices

• rt_values

• mobility_values

• quad_low_mz_values

• quad_high_mz_values

• mz_values

• intensity_values

Instead of returning a pd.DataFrame, raw indices can be returned by setting the last slice element to “raw”.

Examples

>>> data[:100.0]
# Return all datapoints with rt_values < 100.0 seconds

>>> data[:, 450]
# Return all datapoints with scan_index = 450

>>> data[:, :, 700.: 710.]
# Return all datapoints with 700.0 <= quad_mz_values < 710.0

>>> data[:, :, :, 621.9: 191000]
# Return all datapoints with 621.9 <= mz_values and
# tof_indices < 191000

>>> data[[1, 8, 10], :, 0, 621.9: np.inf]
# Return all datapoints from frames 1, 8 and 10, which are unfragmented
# (precursor_index = 0) and with 621.9 <= mz_values < np.inf

>>> data[:, :, 999]
# Return all datapoints from precursor 999
# (for diaPASEF this is a traditional MSMS spectrum)

>>> scan_slices = np.array([[10, 20, 1], [100, 200, 10]])
>>> data[:, scan_slices, :, :, :]
# Return all datapoints with scan_indices in range(10, 20) or
# range(100, 200, 10)

>>> df = data[
...     {
...         "frame_indices": [1, 191],
...         "scan_indices": slice(300, 800, 10),
...         "mz_values": slice(None, 400.5),
...         "intensity_values": 50,
...     }
... ]
# Slice by using a dictionary

>>> data[:, :, 999, "raw"]
# Return the raw indices of datapoints from precursor 999

property accumulation_times

np.ndarray : The accumulation times of all frames.

property acquisition_mode

str : The acquisition mode.

as_dataframe(indices, *, raw_indices=True, frame_indices=True, scan_indices=True, quad_indices=False, tof_indices=True, precursor_indices=True, rt_values=True, rt_values_min=True, mobility_values=True, quad_mz_values=True, push_indices=True, mz_values=True, intensity_values=True, corrected_intensity_values=True, raw_indices_sorted=False)

Convert raw indices to a pd.DataFrame.

Parameters:

• indices (np.int64[:]) – The raw indices for which coordinates need to be retrieved.

• raw_indices (bool) – If True, include “raw_indices” in the dataframe. Default is True.

• frame_indices (bool) – If True, include “frame_indices” in the dataframe. Default is True.

• scan_indices (bool) – If True, include “scan_indices” in the dataframe. Default is True.

• quad_indices (bool) – If True, include “quad_indices” in the dataframe. Default is False.

• tof_indices (bool) – If True, include “tof_indices” in the dataframe. Default is True.

• precursor_indices (bool) – If True, include “precursor_indices” in the dataframe. Default is True.

• rt_values (bool) – If True, include “rt_values” in the dataframe. Default is True.

• rt_values_min (bool) – If True, include “rt_values_min” in the dataframe. Default is True.

• mobility_values (bool) – If True, include “mobility_values” in the dataframe. Default is True.

• quad_mz_values (bool) – If True, include “quad_low_mz_values” and “quad_high_mz_values” in the dict. Default is True.

• push_indices (bool) – If True, include “push_indices” in the dataframe. Default is True.

• mz_values (bool) – If True, include “mz_values” in the dataframe. Default is True.

• intensity_values (bool) – If True, include “intensity_values” in the dataframe. Default is True.

• corrected_intensity_values (bool) – If True, include “corrected_intensity_values” in the dataframe. Default is True.

• raw_indices_sorted (bool) – If True, raw_indices are assumed to be sorted, resulting in a faster conversion. Default is False.

Returns:

A dataframe with all requested columns.

Return type:

pd.DataFrame

bin_intensities(indices, axis)

Sum and project the intensities of the indices along 1 or 2 axis.

Parameters:

• indices (np.int64[:]) – The selected indices whose coordinates need to be summed along the selected axis.

• axis (tuple) – Must be length 1 or 2 and can only contain the elements “rt_values”, “mobility_values” and “mz_values”.

Returns:

np.float64[ – An array or heatmap that express the summed intensity along the selected axis.

Return type:

], np.float64[:, 2]

calculate_global_calibrated_mz_values(calibrant1=(922.009798, 1.1895, slice(0, 1, None)), calibrant2=(1221.990637, 1.382, slice(0, 1, None)), mz_tolerance=10, mobility_tolerance=0.1)

Calculate global calibrated_mz_values based on two calibrant ions.

Parameters:

• calibrant1 (tuple) – The first calibrant ion. This is a tuple with (mz, mobility, precursor_slice) foat values. Default is (922.009798, 1.1895, slice(0, 1)).

• calibrant2 (tuple) – The first calibrant ion. This is a tuple with (mz, mobility, precursor_slice) foat values. Default is (1221.990637, 1.3820, slice(0, 1)).

• mz_tolerance (float) – The tolerance window (in Th) with respect to the uncalibrated mz_values. If this is too large, the calibrant ion might not be the most intense ion anymore. If this is too small, the calibrant ion might not be contained. Default is 10.

• mobility_tolerance (float) – The tolerance window with respect to the uncalibrated mobility_values. If this is too large, the calibrant ion might not be the most intense ion anymore. If this is too small, the calibrant ion might not be contained. Default is 0.1.

Return type:

None

property calibrated_mz_max_value

float : The maximum calibrated mz value.

property calibrated_mz_min_value

float : The minimum calibrated mz value.

property calibrated_mz_values

np.ndarray : np.float64[:] : The global calibrated mz values.

convert_from_indices(raw_indices, *, frame_indices=None, quad_indices=None, scan_indices=None, tof_indices=None, return_raw_indices=False, return_frame_indices=False, return_scan_indices=False, return_quad_indices=False, return_tof_indices=False, return_precursor_indices=False, return_rt_values=False, return_rt_values_min=False, return_mobility_values=False, return_quad_mz_values=False, return_push_indices=False, return_mz_values=False, return_intensity_values=False, return_corrected_intensity_values=False, raw_indices_sorted=False)

Convert selected indices to a dict.

Parameters:

• raw_indices (np.int64[:], None) – The raw indices for which coordinates need to be retrieved.

• frame_indices (np.int64[:], None) – The frame indices for which coordinates need to be retrieved.

• quad_indices (np.int64[:], None) – The quad indices for which coordinates need to be retrieved.

• scan_indices (np.int64[:], None) – The scan indices for which coordinates need to be retrieved.

• tof_indices (np.int64[:], None) – The tof indices for which coordinates need to be retrieved.

• return_raw_indices (bool) – If True, include “raw_indices” in the dict. Default is False.

• return_frame_indices (bool) – If True, include “frame_indices” in the dict. Default is False.

• return_scan_indices (bool) – If True, include “scan_indices” in the dict. Default is False.

• return_quad_indices (bool) – If True, include “quad_indices” in the dict. Default is False.

• return_tof_indices (bool) – If True, include “tof_indices” in the dict. Default is False.

• return_precursor_indices (bool) – If True, include “precursor_indices” in the dict. Default is False.

• return_rt_values (bool) – If True, include “rt_values” in the dict. Default is False.

• return_rt_values_min (bool) – If True, include “rt_values_min” in the dict. Default is False.

• return_mobility_values (bool) – If True, include “mobility_values” in the dict. Default is False.

• return_quad_mz_values (bool) – If True, include “quad_low_mz_values” and “quad_high_mz_values” in the dict. Default is False.

• return_push_indices (bool) – If True, include “push_indices” in the dict. Default is False.

• return_mz_values (bool) – If True, include “mz_values” in the dict. Default is False.

• return_intensity_values (bool) – If True, include “intensity_values” in the dict. Default is False.

• return_corrected_intensity_values (bool) – If True, include “corrected_intensity_values” in the dict. Default is False.

• raw_indices_sorted (bool) – If True, raw_indices are assumed to be sorted, resulting in a faster conversion. Default is False.

Returns:

A dict with all requested columns.

Return type:

dict

convert_to_indices(values, *, return_frame_indices=False, return_scan_indices=False, return_tof_indices=False, side='left', return_type='')

Convert selected values to an array in the requested dimension.

Parameters:

• values (float, np.float64[...], iterable) – The raw values for which indices need to be retrieved.

• return_frame_indices (bool) – If True, convert the values to “frame_indices”. Default is False.

• return_scan_indices (bool) – If True, convert the values to “scan_indices”. Default is False.

• return_tof_indices (bool) – If True, convert the values to “tof_indices”. Default is False.

• side (str) – If there is an exact match between the values and reference array, which index should be chosen. See also np.searchsorted. Options are “left” or “right”. Default is “left”.

• return_type (str) – Alternative way to define the return type. Options are “frame_indices”, “scan_indices” or “tof_indices”. Default is “”.

Returns:

An array with the same shape as values or iterable or an int which corresponds to the requested value.

Return type:

np.int64[…], int

Raises:

PrecursorFloatError – When trying to convert a quad float other than np.inf or -np.inf to precursor index.

property cycle

np.ndarray : np.float64[:,:,:,:] : The quad values.

property dia_mz_cycle

np.ndarray : np.float64[:, 2] : The mz_values of a DIA cycle.

property dia_precursor_cycle

np.ndarray : np.int64[:] : The precursor indices of a DIA cycle.

property directory

str : The directory of this TimsTOF object.

estimate_strike_count(frame_slices, scan_slices, precursor_slices, tof_slices, quad_slices)

Estimate the number of detector events, given a set of slices.

Parameters:

• frame_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(frame_slices[:, :2].ravel()) >= 0) = True).

• scan_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(scan_slices[:, :2].ravel()) >= 0) = True).

• precursor_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

• tof_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(tof_slices[:, :2].ravel()) >= 0) = True).

• quad_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(quad_slices.ravel()) >= 0) = True).

Returns:

The estimated number of detector events given these slices.

Return type:

int

property fragment_frames

pd.DataFrame : The fragment frames table.

property frame_max_index

int : The maximum frame index.

property frames

pd.DataFrame : The frames table of the analysis.tdf SQL.

index_precursors(centroiding_window=0, keep_n_most_abundant_peaks=-1)

Retrieve all MS2 spectra acquired with DDA.

IMPORTANT NOTE: This function is intended for DDA samples. While it in theory works for DIA sample too, this probably has little value.

Parameters:

• centroiding_window (int) – The centroiding window to use. If 0, no centroiding is performed. Default is 0.

• keep_n_most_abundant_peaks (int) – Keep the n most abundant peaks. If -1, all peaks are retained. Default is -1.

Returns:

The spectrum_indptr array, spectrum_tof_indices array and spectrum_intensity_values array.

Return type:

tuple (np.int64[:], np.uint32[:], np.uint32[:])

property intensity_corrections

np.ndarray : The intensity_correction per frame.

property intensity_max_value

float : The maximum intensity value.

property intensity_min_value

float : The minimum intensity value.

property intensity_values

np.ndarray : np.uint16[:] : The intensity values.

property is_compressed

bool : HDF array is compressed or not.

property max_accumulation_time

float : The maximum accumulation time of all frames.

property meta_data

dict : The metadata for the acquisition.

property mobility_max_value

float : The maximum mobility value.

property mobility_min_value

float : The minimum mobility value.

property mobility_values

np.ndarray : np.float64[:] : The mobility values.

property mz_max_value

float : The maximum mz value.

property mz_min_value

float : The minimum mz value.

property mz_values

np.ndarray : np.float64[:] : The mz values.

property precursor_indices

np.ndarray : np.int64[:] : The precursor indices.

property precursor_max_index

int : The maximum precursor index.

property precursors

pd.DataFrame : The precursor table.

property push_indptr

np.ndarray : np.int64[:] : The tof indptr.

property quad_indptr

np.ndarray : np.int64[:] : The quad indptr (tof_indices).

property quad_mz_max_value

float : The maximum quad mz value.

property quad_mz_min_value

float : The minimum quad mz value.

property quad_mz_values

np.ndarray : np.float64[:, 2] : The (low, high) quad mz values.

property raw_quad_indptr

np.ndarray : np.int64[:] : The raw quad indptr (push indices).

property rt_max_value

float : The maximum rt value.

property rt_values

np.ndarray : np.float64[:] : The rt values.

property sample_name

str : The sample name of this TimsTOF object.

save_as_hdf(directory=None, file_name=None, overwrite=False, compress=False, return_as_bytes_io=False)

Save the TimsTOF object as an hdf file.

Parameters:

• directory (str) – The directory where to save the HDF file. Ignored if return_as_bytes_io == True. Default is None, meaning it is parsed from input file.

• file_name (str) – The file name of the HDF file. Ignored if return_as_bytes_io == True. Default is None, meaning it is parsed from input file.

• overwrite (bool) – If True, an existing file is truncated. If False, the existing file is appended to only if the original group, array or property does not exist yet. Default is False.

• compress (bool) – If True, compression is used. This roughly halves files sizes (on-disk), at the cost of taking 3-6 longer accession times. See also alphatims.utils.create_hdf_group_from_dict. If False, no compression is used Default is False.

• return_as_bytes_io (bool) – If True, the HDF file is only created in memory and returned as a bytes stream. If False, the file is written to disk. Default is False.

Returns:

The full file name or a bytes stream containing the HDF file.

Return type:

str, io.BytesIO

save_as_spectra(directory, file_name, overwrite=False, centroiding_window=5, keep_n_most_abundant_peaks=-1, mgf=True)

Save profile spectra from this TimsTOF object as an spectrum file.

Parameters:

• directory (str) – The directory where to save the spectrum file.

• file_name (str) – The file name of the spectrum file.

• overwrite (bool) – If True, an existing file is truncated. If False, nothing happens if a file already exists. Default is False.

• centroiding_window (int) – The centroiding window to use. If 0, no centroiding is performed. Default is 5.

• keep_n_most_abundant_peaks (int) – Keep the n most abundant peaks. If -1, all peaks are retained. Default is -1.

Returns:

The full file name of the spectrum file.

Return type:

str

property scan_max_index

int : The maximum scan index.

set_cycle()

Set the quad cycle for diaPASEF data.

Return type:

None

property tof_indices

np.ndarray : np.uint32[:] : The tof indices.

property tof_max_index

int : The maximum tof index.

use_calibrated_mz_values_as_default(use_calibrated_mz_values)

Override the default mz_values with the global calibrated_mz_values.

Calibrated_mz_values will be calculated if they do not exist yet.

Parameters:

use_calibrated_mz_values (int) – If not 0, the mz_values are overwritten with global calibrated_mz_values. If 1, calibration at the MS1 level is performed. If 2, calibration at the MS2 level is performed.

Return type:

None

property version

str : AlphaTims version used to create this TimsTOF object.

property zeroth_frame

bool : A blank zeroth frame is present so frames are 1-indexed.

full_dia.alphatims.bruker.add_intensity_to_bin(query_index, intensities, parsed_indices, intensity_bins)

Add the intensity of a query to the appropriate bin.

IMPORTANT NOTE: This function is decorated with alphatims.utils.pjit. The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

Parameters:

• query_index (int) – The query whose intensity needs to be binned The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

• intensities (np.float64[:]) – An array with intensities that need to be binned.

• parsed_indices (np.int64[:], np.int64[:, :]) – Description of parameter parsed_indices.

• intensity_bins (np.float64[:]) – A buffer with intensity bins to which the current query will be added.

Return type:

None

full_dia.alphatims.bruker.calculate_dia_cycle_mask(dia_mz_cycle, quad_slices, dia_precursor_cycle=None, precursor_slices=None)

Calculate a boolean mask for cyclic push indices satisfying queries.

Parameters:

• dia_mz_cycle (np.float64[:, 2]) – An array with (upper, lower) mz values of a DIA cycle (per push).

• quad_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(quad_slices.ravel()) >= 0) = True).

• dia_precursor_cycle (np.int64[:]) – An array with precursor indices of a DIA cycle (per push).

• precursor_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

Returns:

A mask that determines if a cyclic push index is valid given the requested slices.

Return type:

np.bool_[:]

full_dia.alphatims.bruker.centroid_spectra(index, spectrum_indptr, spectrum_counts, spectrum_tof_indices, spectrum_intensity_values, window_size)

Smoothen and centroid a profile spectrum (inplace operation).

IMPORTANT NOTE: This function will overwrite all input arrays.

IMPORTANT NOTE: This function is decorated with alphatims.utils.pjit. The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

Parameters:

• index (int) – The push index whose intensity_values and tof_indices will be centroided.

• spectrum_indptr (np.int64[:]) – An index pointer array defining the (untrimmed) spectrum boundaries.

• spectrum_counts (np. int64[:]) – The original array defining how many distinct tof indices each spectrum has.

• spectrum_tof_indices (np.uint32[:]) – The original array containing tof indices.

• spectrum_intensity_values (np.float64[:]) – The original array containing intensity values.

• window_size (int) – The window size to use for smoothing and centroiding peaks.

full_dia.alphatims.bruker.convert_slice_key_to_float_array(key)

Convert a key to a slice float array.

NOTE: This function should only be used for QUAD or DETECTOR dimensions.

Parameters:

key (slice, int, float, None, iterable) – The key that needs to be converted.

Returns:

Each row represent a a (start, stop) slice.

Return type:

np.float64[:, 2]

Raises:

ValueError – When the key is an np.ndarray with more than 2 columns.

full_dia.alphatims.bruker.convert_slice_key_to_int_array(data, key, dimension)

Convert a key of a data dimension to a slice integer array.

Parameters:

• data (alphatims.bruker.TimsTOF) – The TimsTOF objext for which to get slices.

• key (slice, int, float, None, iterable) – The key that needs to be converted.

• dimension (str) – The dimension for which the key needs to be retrieved

Returns:

Each row represent a a (start, stop, step) slice.

Return type:

np.int64[:, 3]

Raises:

• ValueError – When the key contains elements other than int or float.

• PrecursorFloatError – When trying to convert a quad float to precursor index.

full_dia.alphatims.bruker.filter_indices(frame_slices, scan_slices, precursor_slices, tof_slices, quad_slices, intensity_slices, frame_max_index, scan_max_index, push_indptr, precursor_indices, quad_mz_values, quad_indptr, tof_indices, intensities)

Filter raw indices by slices from all dimensions.

Parameters:

• frame_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(frame_slices[:, :2].ravel()) >= 0) = True).

• scan_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(scan_slices[:, :2].ravel()) >= 0) = True).

• precursor_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

• tof_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(tof_slices[:, :2].ravel()) >= 0) = True).

• quad_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(quad_slices.ravel()) >= 0) = True).

• intensity_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(intensity_slices.ravel()) >= 0) = True).

• frame_max_index (int) – The maximum frame index of a TimsTOF object.

• scan_max_index (int) – The maximum scan index of a TimsTOF object.

• push_indptr (np.int64[:]) – The self.push_indptr array of a TimsTOF object.

• precursor_indices (np.int64[:]) – The self.precursor_indices array of a TimsTOF object.

• quad_mz_values (np.float64[:, 2]) – The self.quad_mz_values array of a TimsTOF object.

• quad_indptr (np.int64[:]) – The self.quad_indptr array of a TimsTOF object.

• tof_indices (np.uint32[:]) – The self.tof_indices array of a TimsTOF object.

• intensities (np.uint16[:]) – The self.intensity_values array of a TimsTOF object.

Returns:

The raw indices that satisfy all the slices.

Return type:

np.int64[:]

full_dia.alphatims.bruker.filter_spectra_by_abundant_peaks(index, spectrum_indptr, spectrum_counts, spectrum_tof_indices, spectrum_intensity_values, keep_n_most_abundant_peaks)

Filter a spectrum to retain only the most abundant peaks.

IMPORTANT NOTE: This function will overwrite all input arrays.

IMPORTANT NOTE: This function is decorated with alphatims.utils.pjit. The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

Parameters:

• index (int) – The push index whose intensity_values and tof_indices will be centroided.

• spectrum_indptr (np.int64[:]) – An index pointer array defining the (untrimmed) spectrum boundaries.

• spectrum_counts (np. int64[:]) – The original array defining how many distinct tof indices each spectrum has.

• spectrum_tof_indices (np.uint32[:]) – The original array containing tof indices.

• spectrum_intensity_values (np.float64[:]) – The original array containing intensity values.

• keep_n_most_abundant_peaks (int) – Keep only this many abundant peaks.

full_dia.alphatims.bruker.filter_tof_to_csr(tof_slices, push_indices, tof_indices, push_indptr)

Get a CSR-matrix with raw indices satisfying push indices and tof slices.

Parameters:

• tof_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(tof_slices[:, :2].ravel()) >= 0) = True).

• push_indices (np.int64[:]) – The push indices from where to retrieve the TOF slices.

• tof_indices (np.uint32[:]) – The self.tof_indices array of a TimsTOF object.

• push_indptr (np.int64[:]) – The self.push_indptr array of a TimsTOF object.

Returns:

(np.int64[ – An (indptr, values, columns) tuple, where indptr are push indices, values raw indices, and columns the tof_slices.

Return type:

], np.int64[:], np.int64[:],)

full_dia.alphatims.bruker.get_dia_push_indices(frame_slices, scan_slices, quad_slices, scan_max_index, dia_mz_cycle, dia_precursor_cycle=None, precursor_slices=None, zeroth_frame=True)

Filter DIA push indices by slices from LC, TIMS and QUAD.

Parameters:

• frame_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(frame_slices[:, :2].ravel()) >= 0) = True).

• scan_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(scan_slices[:, :2].ravel()) >= 0) = True).

• quad_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(quad_slices.ravel()) >= 0) = True).

• scan_max_index (int) – The maximum scan index of a TimsTOF object.

• dia_mz_cycle (np.float64[:, 2]) – An array with (upper, lower) mz values of a DIA cycle (per push).

• dia_precursor_cycle (np.int64[:]) – An array with precursor indices of a DIA cycle (per push).

• precursor_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

• zeroth_frame (bool) – Indicates if a zeroth frame was used before a DIA cycle.

Returns:

The raw push indices that satisfy all the slices.

Return type:

np.int64[:]

full_dia.alphatims.bruker.indptr_lookup(targets, queries, momentum_amplifier=2)

Find the indices of queries in targets.

This function is equivalent to “np.searchsorted(targets, queries, “right”) - 1”. By utilizing the fact that queries are also sorted, it is significantly faster though.

Parameters:

• targets (np.int64[:]) – A sorted list of index pointers where queries needs to be looked up.

• queries (np.int64[:]) – A sorted list of queries whose index pointers needs to be looked up.

• momentum_amplifier (int) – Factor to add momentum to linear searching, attempting to quickly discard empty range without hits. Invreasing it can speed up searching of queries if they are sparsely spread out in targets.

Returns:

The indices of queries in targets.

Return type:

np.int64[:]

full_dia.alphatims.bruker.init_bruker_dll(bruker_dll_file_name='/home/docs/checkouts/readthedocs.org/user_builds/full-dia/checkouts/latest/full_dia/alphatims/ext/timsdata.so')

Open a bruker.dll in Python.

Five functions are defined for this dll:

• tims_open: [c_char_p, c_uint32] -> c_uint64

• tims_close: [c_char_p, c_uint32] -> c_uint64

• tims_read_scans_v2: [c_uint64, c_int64, c_uint32, c_uint32, c_void_p, c_uint32] -> c_uint32

• tims_index_to_mz: [c_uint64, c_int64, POINTER(c_double), POINTER(c_double), c_uint32] -> None

• tims_scannum_to_oneoverk0: Same as “tims_index_to_mz”

Parameters:

bruker_dll_file_name (str) – The absolute path to the timsdata.dll. Default is alphatims.utils.BRUKER_DLL_FILE_NAME.

Returns:

The Bruker dll library.

Return type:

ctypes.cdll

full_dia.alphatims.bruker.open_bruker_d_folder(bruker_d_folder_name, bruker_dll_file_name='/home/docs/checkouts/readthedocs.org/user_builds/full-dia/checkouts/latest/full_dia/alphatims/ext/timsdata.so')

A context manager for a bruker dll connection to a .d folder.

Parameters:

• bruker_d_folder_name (str) – The name of a Bruker .d folder.

• bruker_dll_file_name (str, ctypes.cdll) – The path to Bruker’ timsdata.dll library. Alternatively, the library itself can be passed as argument. Default is alphatims.utils.BRUKER_DLL_FILE_NAME, which in itself is dependent on the OS.

Returns:

The opened bruker dll and identifier of the .d folder.

Return type:

tuple (ctypes.cdll, int).

full_dia.alphatims.bruker.parse_decompressed_bruker_binary_type1(decompressed_bytes, scan_indices_, tof_indices_, intensities_, scan_start, scan_index)

Parse a Bruker binary scan buffer into tofs and intensities.

Parameters:

• decompressed_bytes (bytes) – A Bruker scan binary buffer that is already decompressed with lzf.

• scan_indices (np.ndarray) – The scan_indices_ buffer array.

• tof_indices (np.ndarray) – The tof_indices_ buffer array.

• intensities (np.ndarray) – The intensities_ buffer array.

• scan_start (int) – The offset where to start new tof_indices and intensity_values.

• scan_index (int) – The scan index.

Returns:

The number of peaks in this scan.

Return type:

int

full_dia.alphatims.bruker.parse_decompressed_bruker_binary_type2(decompressed_bytes)

Parse a Bruker binary frame buffer into scans, tofs and intensities.

Parameters:

decompressed_bytes (bytes) – A Bruker frame binary buffer that is already decompressed with pyzstd.

Returns:

The scan_indices, tof_indices and intensities present in this binary array

Return type:

tuple (np.uint32[:], np.uint32[:], np.uint32[:]).

full_dia.alphatims.bruker.parse_keys(data, keys)

Convert different keys to a key dict with defined types.

NOTE: Negative slicing is not supported and all indiviudal keys are assumed to be sorted, disjunct and strictly increasing

Parameters:

• data (alphatims.bruker.TimsTOF) – The TimsTOF objext for which to get slices.

• keys (tuple) – A tuple of at most 5 elemens, containing slices, ints, floats, Nones, and/or iterables. See alphatims.bruker.convert_slice_key_to_int_array and alphatims.bruker.convert_slice_key_to_float_array for more details.

Returns:

The resulting dict always has the following items:

• ”frame_indices”: np.int64[:, 3]

• ”scan_indices”: np.int64[:, 3]

• ”tof_indices”: np.int64[:, 3]

• ”precursor_indices”: np.int64[:, 3]

• ”quad_values”: np.float64[:, 2]

• ”intensity_values”: np.float64[:, 2]

Return type:

dict

full_dia.alphatims.bruker.process_frame(frame_id, tdf_bin_file_name, tims_offset_values, scan_indptr, intensities, tof_indices, frame_indptr, max_scan_count, compression_type, max_peaks_per_scan)

Read and parse a frame directly from a Bruker .d.analysis.tdf_bin.

Parameters:

• frame_id (int) – The frame number that should be processed. Note that this is interpreted as 1-indixed instead of 0-indexed, so that it is compatible with Bruker.

• tdf_bin_file_name (str) – The full file name of the SQL database “analysis.tdf_bin” in a Bruker .d folder.

• tims_offset_values (np.int64[:]) – The offsets that indicate the starting indices of each frame in the binary. These are contained in the “TimsId” column of the frames table in “analysis.tdf_bin”.

• scan_indptr (np.int64[:]) – A buffer containing zeros that can store the cumulative number of detections per scan. The size should be equal to max_scan_count * len(frames) + 1. A dummy 0-indexed frame is required to be present for len(frames). The last + 1 allows to explicitly interpret the end of a scan as the start of a subsequent scan.

• intensities (np.uint16[:]) – A buffer that can store the intensities of all detections. It’s size can be determined by summing the “NumPeaks” column from the frames table in “analysis.tdf_bin”.

• tof_indices (np.uint32[:]) – A buffer that can store the tof indices of all detections. It’s size can be determined by summing the “NumPeaks” column from the frames table in “analysis.tdf_bin”.

• frame_indptr (np.int64[:]) – The cumulative sum of the number of detections per frame. The size should be equal to len(frames) + 1. A dummy 0-indexed frame is required to be present for len(frames). The last + 1 allows to explicitly interpret the end of a frame as the start of a subsequent frame.

• max_scan_count (int) – The maximum number of scans a single frame can have.

• compression_type (int) – The compression type. This must be either 1 or 2. Should be treieved from the global metadata.

• max_peaks_per_scan (int) – The maximum number of peaks per scan. Should be retrieved from the global metadata.

Return type:

None

full_dia.alphatims.bruker.read_bruker_binary(frames, bruker_d_folder_name, compression_type, max_peaks_per_scan, mmap_detector_events=None)

Read all data from an “analysis.tdf_bin” of a Bruker .d folder.

Parameters:

• frames (pd.DataFrame) – The frames from the “analysis.tdf” SQL database of a Bruker .d folder. These can be acquired with e.g. alphatims.bruker.read_bruker_sql.

• bruker_d_folder_name (str) – The full path to a Bruker .d folder.

• compression_type (int) – The compression type. This must be either 1 or 2.

• max_peaks_per_scan (int) – The maximum number of peaks per scan. Should be treieved from the global metadata.

• mmap_detector_events (bool) – Do not save the intensity_values and tof_indices in memory, but use an mmap instead. Default is True

Returns:

The scan_indptr, tof_indices and intensities.

Return type:

tuple (np.int64[:], np.uint32[:], np.uint16[:]).

full_dia.alphatims.bruker.read_bruker_sql(bruker_d_folder_name, add_zeroth_frame=True, drop_polarity=True, convert_polarity_to_int=True)

Read metadata, (fragment) frames and precursors from a Bruker .d folder.

Parameters:

• bruker_d_folder_name (str) – The name of a Bruker .d folder.

• add_zeroth_frame (bool) – Bruker uses 1-indexing for frames. If True, a zeroth frame is added without any TOF detections to make Python simulate this 1-indexing. If False, frames are 0-indexed. Default is True.

• drop_polarity (bool) – The polarity column of the frames table contains “+” or “-” and is not numerical. If True, the polarity column is dropped from the frames table. this ensures a fully numerical pd.DataFrame. If False, this column is kept, resulting in a pd.DataFrame with dtype=object. Default is True.

• convert_polarity_to_int (bool) – Convert the polarity to int (-1 or +1). This allows to keep it in numerical form. This is ignored if the polarity is dropped. Default is True.

Returns:

(str, dict, pd.DataFrame, pd.DataFrame, pd.DataFrame). The acquisition_mode, global_meta_data, frames, fragment_frames and precursors. For diaPASEF, precursors is None.

Return type:

tuple

full_dia.alphatims.bruker.save_as_mgf(full_file_name, spectrum_indptr, intensities, mobilities, average_mzs, mono_mzs, charges, rtinseconds, spectrum_tof_indices, spectrum_intensity_values, precursor_max_index, mz_values)

full_dia.alphatims.bruker.save_as_spectra(full_file_name, spectrum_indptr, intensities, mobilities, average_mzs, mono_mzs, charges, rtinseconds, spectrum_tof_indices, spectrum_intensity_values, mz_values)

full_dia.alphatims.bruker.set_precursor(precursor_index, offset_order, precursor_offsets, quad_indptr, tof_indices, intensities, spectrum_tof_indices, spectrum_intensity_values, spectrum_indptr, spectrum_counts)

Sum the intensities of all pushes belonging to a single precursor.

IMPORTANT NOTE: This function is decorated with alphatims.utils.pjit. The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

Parameters:

• precursor_index (int) – The precursor index indicating which MS2 spectrum to determine.

• offset_order (np.int64[:]) – The order of self.precursor_indices, obtained with np.argsort.

• precursor_offsets (np.int64[:]) – An index pointer array for precursor offsets.

• quad_indptr (np.int64[:]) – The self.quad_indptr array of a TimsTOF object.

• tof_indices (np.uint32[:]) – The self.tof_indices array of a TimsTOF object.

• intensities (np.uint16[:]) – The self.intensity_values array of a TimsTOF object.

• spectrum_tof_indices (np.uint32[:]) – A buffer array to store tof indices of the new spectrum.

• spectrum_intensity_values (np.float64[:]) – A buffer array to store intensity values of the new spectrum.

• spectrum_indptr (np.int64[:]) – An index pointer array defining the original spectrum boundaries.

• spectrum_counts (np. int64[:]) – An buffer array defining how many distinct tof indices the new spectrum has.

Return type:

None

full_dia.alphatims.bruker.trim_spectra(index, spectrum_tof_indices, spectrum_intensity_values, spectrum_indptr, trimmed_spectrum_tof_indices, trimmed_spectrum_intensity_values, new_spectrum_indptr)

Trim remaining bytes after merging of multiple pushes.

IMPORTANT NOTE: This function is decorated with alphatims.utils.pjit. The first argument is thus expected to be provided as an iterable containing ints instead of a single int.

Parameters:

• index (int) – The push index whose intensity_values and tof_indices will be trimmed.

• spectrum_tof_indices (np.uint32[:]) – The original array containing tof indices.

• spectrum_intensity_values (np.float64[:]) – The original array containing intensity values.

• spectrum_indptr (np.int64[:]) – An index pointer array defining the original spectrum boundaries.

• trimmed_spectrum_tof_indices (np.uint32[:]) – A buffer array to store new tof indices.

• trimmed_spectrum_intensity_values (np.float64[:]) – A buffer array to store new intensity values.

• new_spectrum_indptr (np.int64[:]) – An index pointer array defining the trimmed spectrum boundaries.

Return type:

None

full_dia.alphatims.bruker.valid_precursor_index(precursor_index, precursor_slices)

Check if a precursor index is included in the slices.

Parameters:

• precursor_index (int) – The precursor index to validate.

• precursor_slices (np.int64[:, 3]) – Each row of the array is assumed to be a (start, stop, step) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(precursor_slices[:, :2].ravel()) >= 0) = True).

Returns:

True if the precursor index is present in any of the slices. False otherwise.

Return type:

bool

full_dia.alphatims.bruker.valid_quad_mz_values(low_mz_value, high_mz_value, quad_slices)

Check if the low and high quad mz values are included in the slices.

NOTE: Just a part of the quad range needs to overlap with a part of a single slice.

Parameters:

• low_mz_value (float) – The lower mz value of the current quad selection.

• high_mz_value (float) – The upper mz value of the current quad selection.

• quad_slices (np.float64[:, 2]) – Each row of the array is assumed to be (lower_mz, upper_mz) tuple. This array is assumed to be sorted, disjunct and strictly increasing (i.e. np.all(np.diff(quad_slices.ravel()) >= 0) = True).

Returns:

True if some part of the quad overlaps with some part of some slice. False if there is no overlap in the range.

Return type:

bool

full_dia.alphatims.utils module

This module provides generic utilities. These utilities primarily focus on:

• logging

• compilation

• parallelization

• generic io

class full_dia.alphatims.utils.Global_Stack(all_available_options)

Bases: object

A stack that holds multiple option stacks.

The current value of each option stack can be retrieved by indexing, i.e. option_value = self[option_key].

property current_values: dict

dict : A dict with (option_key: option_value) mapping.

property is_locked

bool : A flag to check if this stack is modifiable

lock()

A context manager to lock this stack and prevent modification.

redo()

Increase the stack pointer with 1.

Returns:

(“”, None) if the pointer was already at the maximum. Otherwise (option_name, new_value) if the pointer was increased.

Return type:

tuple

property size

int : The size of this stack without the initial value.

trim()

Remove all elements above of the current stack pointer

Returns:

True if something was removed, i.e. if stack pointer was not at the top. False if nothing could be deleted, i.e. the stack pointer was at the top.

Return type:

bool

undo()

Reduce the stack pointer with 1.

Returns:

(“”, None) if the pointer was already at the maximum. Otherwise (option_name, new_value) if the pointer was reduced.

Return type:

tuple

update(option_key, option_value)

Update an option stack with a value.

Parameters:

• option_key (str) – The name of the option stack to update.

• option_value (type) – An value to add to this stack. Can be any object that supports the “!=” operator.

Returns:

(“”, None) if the pointer was not updated, i.e. the latest update was equal to the current update. Otherwise (option_name, new_value).

Return type:

tuple

class full_dia.alphatims.utils.Option_Stack(option_name, option_initial_value)

Bases: object

A stack with the option to redo and undo.

property current_value

type : The current value of this stack.

property option_name: str

str : The name of this stack.

redo()

Increase the stack pointer with 1.

Returns:

None if the pointer was already at the maximum. Otherwise the new value if the pointer was increased.

Return type:

type

property size: int

int : The size of this stack without the initial value.

trim()

Remove all elements above of the current stack pointer

Returns:

True if something was removed, i.e. if stack pointer was not at the top. False if nothing could be deleted, i.e. the stack pointer was at the top.

Return type:

bool

undo()

Reduce the stack pointer with 1.

Returns:

None if the pointer was already at the maximum. Otherwise the new value if the pointer was reduced.

Return type:

type

update(option_value)

Update this stack with the value.

Parameters:

option_value (type) – An value to add to this stack. Can be any object that supports the “!=” operator.

Returns:

True if the stack was updated. False if the provided value equald the current value of this stack.

Return type:

bool

full_dia.alphatims.utils.check_github_version(silent=False)

Checks and logs the current version of AlphaTims.

Check if the local version equals the AlphaTims GitHub master branch. This is only possible with an active internet connection and if no credentials are required for GitHub.

Parameters:

silent (str) – Use the logger to display the obtained conclusion. Default is False.

Returns:

The version on the AlphaTims GitHub master branch. “” if no version can be found on GitHub

Return type:

str

full_dia.alphatims.utils.class_njit(_func=None)

full_dia.alphatims.utils.create_dict_from_hdf_group(hdf_group, mmap_arrays=None, parent_file_name=None)

Convert the contents of an HDF group and return as normal Python dict.

Parameters:

• hdf_group (h5py.File.group) – An open and readable HDF group.

• mmap_arrays (iterable) – These array will be mmapped instead of pre-loaded. Default is None

• parent_file_name (str) – The parent_file_name. This is required when mmap_arrays is not None. Default is None.

Returns:

A Python dict. Keys of the dict are names of arrays, attrs and subgroups. Values are corresponding arrays and attrs. Subgroups are converted to subdicts. If a subgroup has an “is_pd_dataframe=True” attr, it is automatically converted to a pd.dataFrame.

Return type:

dict

Raises:

ValueError – When an attr value in the HDF group is not an int, float, str or bool.

full_dia.alphatims.utils.create_hdf_group_from_dict(hdf_group, data_dict, *, overwrite=False, compress=False, recursed=False, chunked=False)

Save a dict to an open hdf group.

Parameters:

• hdf_group (h5py.File.group) – An open and writable HDF group.

• data_dict (dict) –

  A dict that needs to be written to HDF. Keys always need to be strings. Values are stored as follows:

  • subdicts -> subgroups.

  • np.array -> array

  • pd.dataframes -> subdicts with “is_pd_dataframe: True” attribute.

  • bool, int, float and str -> attrs.

  • None values are skipped and not stored explicitly.

• overwrite (bool) – If True, existing subgroups, arrays and attrs are fully truncated/overwritten. If False, the existing value in HDF remains unchanged. Default is False.

• compress (bool) – If True, all arrays are compressed with binary shuffle and “lzf” compression. If False, arrays are saved as provided. On average, compression halves file sizes, at the cost of 2-10 time longer accession times. Default is False.

• recursed (bool) – If False, the default progress callback is added while itereating over the keys of the data_dict. If True, no callback is added, allowing subdicts to not trigger callback. Default is False.

• chunked (bool) – If True, all arrays are chunked. If False, arrays are saved as provided. Default is False.

Raises:

• ValueError – When a value of data_dict cannot be converted to an HDF value (see data_dict).

• KeyError – When a key of data_dict is not a string.

Return type:

None

full_dia.alphatims.utils.load_parameters(parameter_file_name)

Load a parameter dict from a file.

Parameters:

parameter_file_name (str) – A file name that contains parameters in .json format.

Returns:

A dict with parameters.

Return type:

dict

full_dia.alphatims.utils.njit(_func=None, *args, **kwargs)

A wrapper for the numba.njit decorator.

This can be overriden with kwargs.

Parameters:

• _func (callable, None) – The function to decorate.

• *args – See numba.njit decorator.

• **kwargs – See numba.njit decorator.

Returns:

A numba.njit decorated function.

Return type:

function

full_dia.alphatims.utils.pjit(_func=None, *, thread_count=None, include_progress_callback=True, **kwargs)

A decorator that parallelizes the numba.njit decorator with threads.

The first argument of the decorated function need to be an iterable. A range-object will be most performant as iterable. The original function should accept a single element of this iterable as its first argument. The original function cannot return values, instead it should store results in e.g. one if its input arrays that acts as a buffer array. The original function needs to be numba.njit compatible. Numba argument “nogil” is always set to True.

Parameters:

• _func (callable, None) – The function to decorate.

• thread_count (int, None) – The number of threads to use. This is always parsed with alphatims.utils.set_threads. Not possible as positional arguments, it always needs to be an explicit keyword argument. Default is None.

• include_progress_callback (bool) – If True, the default progress callback will be used as callback. (See “progress_callback” function.) If False, no callback is added. See set_progress_callback for callback styles. Default is True.

Returns:

A parallelized numba.njit decorated function.

Return type:

function

full_dia.alphatims.utils.progress_callback(iterable, include_progress_callback=True, total=-1)

A generator that adds progress callback to an iterable.

Parameters:

• iterable – An iterable.

• include_progress_callback (bool) – If True, the default progress callback will be used as callback. If False, no callback is added. See set_progress_callback for callback styles. Default is True.

• total (int) – The length of the iterable. If -1, this will be read as len(iterable), if __len__ is implemented. Default is -1.

Returns:

A generator over the iterable with added callback.

Return type:

iterable

full_dia.alphatims.utils.save_parameters(parameter_file_name, paramaters)

Save parameters to a parameter file.

IMPORTANT NOTE: This overwrites any existing file.

Parameters:

• parameter_file_name (str) – The file name to where the parameters are written.

• paramaters (dict) – A dictionary with parameters.

Return type:

None

full_dia.alphatims.utils.set_logger(*, log_file_name='', stream=True, log_level=20, overwrite=False)

Set the log stream and file.

All previously set handlers will be disabled with this command.

Parameters:

• log_file_name (str, None) – The file name to where the log is written. Folders are automatically created if needed. This is relative to the current path. When an empty string is provided, a log is written to the AlphaTims “logs” folder with the name “log_yymmddhhmmss” (reversed timestamp year to seconds). If None, no log file is saved. Default is “”.

• stream (bool) – If False, no log data is sent to stream. If True, all logging can be tracked with stdout stream. Default is True.

• log_level (int) – The logging level. Usable values are defined in Python’s “logging” module. Default is logging.INFO.

• overwrite (bool) – If True, overwrite the log_file if one exists. If False, append to this log file. Default is False.

Returns:

The file name to where the log is written.

Return type:

str

full_dia.alphatims.utils.set_progress_callback(progress_callback)

Set the global progress callback.

Parameters:

progress_callback –

The new global progress callback. Options are:

• None, no progress callback will be used

• True, a textual progress callback (tqdm) will be enabled

• Any object that supports a max and value variable.

full_dia.alphatims.utils.set_threads(threads, set_global=True)

Parse and set the (global) number of threads.

Parameters:

• threads (int) – The number of threads. If larger than available cores, it is trimmed to the available maximum. If 0, it is set to the maximum cores available. If negative, it indicates how many cores NOT to use.

• set_global (bool) – If False, the number of threads is only parsed to a valid value. If True, the number of threads is saved as a global variable. Default is True.

Returns:

The number of threads.

Return type:

int

full_dia.alphatims.utils.show_python_info()

Log all Python information.

This is done in the following format:

• [timestamp]> Python information:

• [timestamp]> alphatims - [current_version]

• [timestamp]> [required package] - [current_version]

• …

• [timestamp]> [required package] - [current_version]

Return type:

None

full_dia.alphatims.utils.threadpool(_func=None, *, thread_count=None, include_progress_callback=True, return_results=False)

A decorator that parallelizes a function with threads and callback.

The original function should accept a single element as its first argument. If the caller function provides an iterable as first argument, the function is applied to each element of this iterable in parallel.

Parameters:

• _func (callable, None) – The function to decorate.

• thread_count (int, None) – The number of threads to use. This is always parsed with alphatims.utils.set_threads. Not possible as positional arguments, it always needs to be an explicit keyword argument. Default is None.

• include_progress_callback (bool) – If True, the default progress callback will be used as callback. (See “progress_callback” function.) If False, no callback is added. See set_progress_callback for callback styles. Default is True.

• return_results (bool) – If True, it returns the results in the same order as the iterable. This can be much slower than not returning results. Iti is better to store them in a buffer results array instead (be carefull to avoid race conditions). If the iterable is not an iterable but a single index, a result is always returned. Default is False.

Returns:

A parallelized decorated function.

Return type:

function

Module contents