' + + '' + + _("Hide Search Matches") + + "
" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/main/imspy/genindex.html b/main/imspy/genindex.html new file mode 100644 index 00000000..3b6e2310 --- /dev/null +++ b/main/imspy/genindex.html @@ -0,0 +1,1453 @@ + + + + + + +| + | + |
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Bases: Callback
Called at the end of an epoch.
+Subclasses should override for any actions to run. This function should +only be called during TRAIN mode.
+epoch – Integer, index of epoch.
logs – Dict, metric results for this training epoch, and for the +validation epoch if validation is performed. Validation result +keys are prefixed with val_. For training epoch, the values of +the Model’s metrics are returned. Example: +{‘loss’: 0.2, ‘accuracy’: 0.7}.
Called at the beginning of training.
+Subclasses should override for any actions to run.
+logs – Dict. Currently no data is passed to this argument for this +method but that may change in the future.
+Called at the end of training.
+Subclasses should override for any actions to run.
+logs – Dict. Currently the output of the last call to +on_epoch_end() is passed to this argument for this method but +that may change in the future.
+Get the path to a pretrained model
+model_name (str) – The name of the model to load
Traversable
The path to the pretrained model
+Load a tokenizer from resources
+Tokenizer
The pretrained tokenizer
+Convert CCS to reduced ion mobility (1/k0).
+ccs – collision cross-section
mz – mass-over-charge of the ion
charge – charge state of the ion
mass_gas – mass of drift gas
temp – temperature of the drift gas in C°
t_diff – factor to translate from C° to K
reduced ion mobility
+float
+Convert reduced ion mobility (1/k0) to CCS.
+one_over_k0 – reduced ion mobility
mz – mass-over-charge of the ion
charge – charge state of the ion
mass_gas – mass of drift gas
temp – temperature of the drift gas in C°
t_diff – factor to translate from C° to K
collision cross-section
+float
+Calculate m/z value.
+mass (float) – Mass.
charge (int) – Charge.
m/z value.
+float
+Calculate transmission dependent fragment ion isotope distribution.
+target_spec (MzSpectrum) – Target spectrum.
complement_spec (MzSpectrum) – Complement spectrum.
transmitted_isotopes (MzSpectrum) – Transmitted isotopes.
max_isotope (int) – Maximum isotope.
Transmission dependent fragment ion isotope distribution.
+Bases: RustWrapperObject
Calculate the isotopic spectrum of the peptide ion.
+mass_tolerance (float) – The mass tolerance for the isotopic spectrum calculation.
abundance_threshold (float) – The abundance threshold for the isotopic spectrum calculation.
max_result (int) – The maximum number of results to return.
intensity_min (float) – The minimum intensity of the isotopic spectrum.
The isotopic spectrum of the peptide ion.
+Calculate the isotopic spectrum of the peptide ion.
+mass_tolerance (float) – The mass tolerance for the isotopic spectrum calculation.
abundance_threshold (float) – The abundance threshold for the isotopic spectrum calculation.
max_result (int) – The maximum number of results to return.
intensity_min (float) – The minimum intensity of the isotopic spectrum.
The isotopic spectrum of the peptide ion.
+str
Bases: RustWrapperObject
Calculate the isotope distribution of the product ion.
+mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.
abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.
max_result (int) – The maximum number of results to return.
intensity_min (float) – The minimum intensity of the isotope distribution.
List[Tuple[float, float]]
The isotope distribution of the product ion.
+str
Bases: RustWrapperObject
str
Bases: RustWrapperObject
Find the product ion series with the given charge.
+charge (int) – The charge of the product ion series.
Optional[PeptideProductIonSeries]
The product ion series with the given charge, or None if not found.
+Calculate the isotope distribution of the product ion series collection.
+mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.
abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.
max_result (int) – The maximum number of results to return.
intensity_min (float) – The minimum intensity of the isotope distribution.
The isotope distribution of the product ion series collection.
+Calculate the isotope distribution of the product ion series collection.
+mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.
abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.
max_result (int) – The maximum number of results to return.
intensity_min (float) – The minimum intensity of the isotope distribution.
The isotope distribution of the product ion series collection.
+str
Bases: RustWrapperObject
Associate the peptide sequence with predicted intensities from Prosit intensity prediction.
+flat_intensities (List[float]) – The flat intensities.
TODO (#) – check how charge should be handled
charge (int) – The charge of the product ions.
fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.
normalize (bool) – Whether to normalize the intensities.
half_charge_one (bool) – Whether to use half charge one.
The b and y product ion series of the peptide sequence.
+Calculate the isotopic product ion spectrum of the peptide sequence.
+intensity_min (float)
max_result (int)
abundance_threshold (float)
mass_tolerance (float)
charge (int) – The charge of the product ions.
fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.
The isotopic product ion spectrum of the peptide sequence.
+Calculate the mono-isotopic product ion spectrum of the peptide sequence.
+charge (int) – The charge of the product ions.
fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.
The mono-isotopic product ion spectrum of the peptide sequence.
+Calculate the mono-isotopic product ion spectrum of the peptide sequence.
+charge (int) – The charge of the product ions.
fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.
The mono-isotopic product ion spectrum of the peptide sequence.
+Calculate the b and y product ion series of the peptide sequence.
+charge (int) – The charge of the product ions.
fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.
Tuple[List[PeptideProductIon], List[PeptideProductIon]]
The b and y product ion series of the peptide sequence.
+str
Tuple[str, List[float]]
List[str]
Bases: RustWrapperObject
Data.
+Data.
+pd.DataFrame
+Filter the spectrum for a given m/z range and intensity range.
+mz_min (float) – Minimum m/z value.
mz_max (float) – Maximum m/z value.
intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.
intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.
Filtered spectrum.
+Create a IndexedMzSpectrum from a PyIndexedMzSpectrum.
+spec (pims.PyIndexedMzSpectrum) – PyIndexedMzSpectrum to create the IndexedMzSpectrum from.
+IndexedMzSpectrum created from the PyIndexedMzSpectrum.
+Get the spec_ptr.
+spec_ptr.
+pims.PyIndexedMzSpectrum
+Index.
+Index.
+NDArray[np.int32]
+Intensity.
+Intensity.
+NDArray[np.float64]
+m/z.
+m/z.
+NDArray[np.float64]
+Bases: RustWrapperObject
Add normal noise to the intensity values of the spectrum.
+noise_ppm (float) – Noise ppm, noise will be sampled from a normal distribution with mean 0 and std noise_ppm / 3.0.
+Spectrum with added noise.
+Add uniform noise to the m/z values of the spectrum.
+noise_ppm (float) – Noise ppm, noise will be sampled from a uniform distribution with mean 0 and std noise_ppm.
right_drag (bool, optional) – If true, the noise will be shifted to the right. Defaults to False.
Spectrum with added noise.
+Data.
+Data.
+pd.DataFrame
+Filter the spectrum for a given m/z range and intensity range.
+mz_min (float) – Minimum m/z value.
mz_max (float) – Maximum m/z value.
intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.
intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.
Filtered spectrum.
+Generates a convoluted mass spectrum by adding all spectra in the given list.
+spectra_list (List[MzSpectrum]) – List of mass spectra.
resolution (int) – Desired resolution of returned spectrum.
Convoluted spectrum.
+Create a MzSpectrum from a PyMzSpectrum.
+spec (pims.PyMzSpectrum) – PyMzSpectrum to create the MzSpectrum from.
+MzSpectrum created from the PyMzSpectrum.
+Get the spec_ptr.
+spec_ptr.
+pims.PyMzSpectrum
+Intensity.
+Intensity.
+NDArray[np.float64]
+m/z.
+m/z.
+NDArray[np.float64]
+Convert the spectrum to a centroided spectrum.
+Centroided spectrum.
+generates json string representation of MzSpectrum
+str
Bins the spectrum’s m/z values to a +given resolution and sums the intensities.
+resolution (int) – Negative decadic logarithm of bin size.
+A new MzSpectrum where m/z values are binned according to the given resolution.
+Convert the spectrum to a list of windows.
+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.
List of windows.
+Tuple[NDArray, List[MzSpectrum]]
+Convert the spectrum to a vectorized spectrum.
+resolution (int, optional) – Resolution. Defaults to 2.
+Vectorized spectrum.
+Bases: RustWrapperObject
Create a MzSpectrum from a PyMzSpectrum.
+spec (pims.PyMzSpectrum) – PyMzSpectrum to create the MzSpectrum from.
+MzSpectrum created from the PyMzSpectrum.
+Get the spec_ptr.
+spec_ptr.
+pims.PyMzSpectrumVectorized
+m/z.
+m/z.
+NDArray[np.float64]
+Resolution.
+Resolution.
+float
+Convert the spectrum to a centroided spectrum.
+Centroided spectrum.
+Intensity.
+Intensity.
+NDArray[np.float64]
+Bases: RustWrapperObject
Data.
+Data.
+pd.DataFrame
+Filter the spectrum for a given m/z range and intensity range.
+mz_min (float) – Minimum m/z value.
mz_max (float) – Maximum m/z value.
intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.
intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.
Filtered spectrum.
+Frame ID.
+Frame ID.
+int
+Create a TimsSpectrum from a PyTimsSpectrum.
+spec (pims.PyTimsSpectrum) – PyTimsSpectrum to create the TimsSpectrum from.
+TimsSpectrum created from the PyTimsSpectrum.
+Get the spec_ptr.
+spec_ptr.
+pims.PyTimsSpectrum
+Index.
+Index.
+NDArray[np.int32]
+Intensity.
+Intensity.
+NDArray[np.float64]
+Inverse mobility.
+Inverse mobility.
+float
+MS type.
+MS type.
+str
+m/z.
+m/z.
+NDArray[np.float64]
+Get the MzSpectrum.
+Spectrum.
+Retention time.
+Retention time.
+float
+Scan.
+Scan.
+int
+Calculates the integral of the peaks in a spectrum.
+peaks (NDArray[np.int32]) – Peak indices.
peak_info (dict) – Peak info.
Peak integrals.
+NDArray[np.float64]
+Bases: RustWrapperObject
PyContributionSource
Bases: RustWrapperObject
PyMzSpectrumAnnotated
Bases: RustWrapperObject
PyPeakAnnotation
Bases: ABC
Bases: RustWrapperObject
PySignalAttributes
Bases: RustWrapperObject
PySourceType
Bases: RustWrapperObject
PyTimsFrameAnnotated
Bases: object
DataFrame
Bases: SyntheticExperimentDataHandle, ABC
Bases: RustWrapperObject
Build a frame.
+frame_id (int) – Frame ID.
fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,
fragmentation. (otherwise quadrupole isolation will still be applied but no)
mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.
mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.
precursor_noise_ppm (float) – PPM of the precursor noise.
mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.
fragment_noise_ppm (float) – PPM of the fragment noise.
right_drag (bool) – if true, the noise will be shifted to the right.
Frame.
+Build a frame. The frame will be annotated.
+frame_id (int) – Frame ID.
fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,
fragmentation. (otherwise quadrupole isolation will still be applied but no)
mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.
mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.
precursor_noise_ppm (float) – PPM of the precursor noise.
mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.
fragment_noise_ppm (float) – PPM of the fragment noise.
right_drag (bool) – if true, the noise will be shifted to the right.
Frame.
+Build frames.
+frame_ids (List[int]) – Frame IDs.
fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,
fragmentation. (otherwise quadrupole isolation will still be applied but no)
mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.
mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.
precursor_noise_ppm (float) – PPM of the precursor noise.
mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.
fragment_noise_ppm (float) – PPM of the fragment noise.
right_drag (bool) – if true, the noise will be shifted to the right.
num_threads (int) – Number of threads.
Frames.
+List[TimsFrame]
+Build frames. The frames will be annotated.
+frame_ids (List[int]) – Frame IDs.
fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,
fragmentation. (otherwise quadrupole isolation will still be applied but no)
mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.
mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.
precursor_noise_ppm (float) – PPM of the precursor noise.
mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.
fragment_noise_ppm (float) – PPM of the fragment noise.
right_drag (bool) – if true, the noise will be shifted to the right.
num_threads (int) – Number of threads.
Frames.
+List[TimsFrameAnnotated]
+(int, int)
List[Tuple[int, int]]
Create a TimsTofSyntheticFrameBuilderDIA from a PyTimsTofSyntheticsFrameBuilderDIA.
+py_ptr (ims.PyTimsTofSyntheticsFrameBuilderDIA) – PyTimsTofSyntheticsFrameBuilderDIA.
+TimsTofSyntheticFrameBuilderDIA.
+List[float]
float
ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
PyTimsTofSyntheticsFrameBuilderDIA
Bases: RustWrapperObject
List[TimsFrameAnnotated]
PyTimsTofSyntheticsPrecursorFrameBuilder
This module contains several noise models, +such as detection noise, shot noise and baseline noise.
+signal (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]])
method (str)
custom_mu_function (Optional[Callable])
custom_sigma_function (Optional[Callable])
Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]
Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]
Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]
Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]
Bases: object
Bases: object
Build a row for the frame meta data table from a TimsFrame object.
+:type frame: TimsFrame
+:param frame: TimsFrame object
+:type scan_mode: int
+:param scan_mode: int
+:type frame_start_pos: int
+:param frame_start_pos: int
+:type only_frame_one: bool
+:param only_frame_one: bool
Compress a single frame using zstd.
+:type frame: TimsFrame
+:param frame: TimsFrame object
+:type only_frame_one: bool
+:param only_frame_one: bool
compressed data
+bytes
+DataFrame
Convert inverse mobility values to scan values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.
+Convert m/z values to TOF values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.
+Convert scan values to inverse mobility values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.
+Convert TOF values to m/z values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.
+None
None
Write a single frame to the binary file.
+:type frame: TimsFrame
+:param frame: TimsFrame object
+:type scan_mode: int
+:param scan_mode: int
+:type only_frame_one: bool
+:param only_frame_one: bool
None
None
Calculate the accumulated intensity between two points using the custom CDF.
+Calculate the m/z value of a b or y fragment.
+:type sequence: str
+:param sequence: the peptide sequence
+:type modifications: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
+:param modifications: potential modifications
+:type is_y: bool
+:param is_y: is the fragment a y ion
+:type charge: int
+:param charge: the charge state of the peptide precursor
float
m/z value of the fragment
+Calculate the b and y ion series for a given peptide sequence.
+:type sequence: str
+:param sequence: the peptide sequence
+:type modifications: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
+:param modifications: potential modifications
+:type charge: int
+:param charge: the charge state of the peptide precursor
Tuple[List, List]
b ion series, y ion series
+Calculate the bounds of a normal distribution for a given z-score.
+mean (float) – Mean of the normal distribution
std (float) – Standard deviation of the normal distribution
z_score (float) – Z-score of the normal distribution
Lower bound of the normal distribution
+float
+Calculate the amount of mobility that will be occupied by a single scan
+mobility_min (float) – Minimum mobility value
mobility_max (float) – Maximum mobility value
num_scans (int) – Number of scans that will be taken during the acquisition
Mobility spacing
+float
+Calculate the number of frames that will be taken during the acquisition
+gradient_length (float) – Length of the gradient in seconds
rt_cycle_length (float) – Length of the RT cycle in seconds
Number of frames that will be taken during the acquisition
+int
+Custom implementation of the CDF for a normal distribution.
+Get the path to a pretrained model
+acquisition_mode (str) – The name of the model to load
Traversable
The path to the pretrained model
+Dict[str, float]
Check if the provided fasta path is a folder or file, if its a folder, check if it exists and return all fasta +:type fasta_path: +:param fasta_path: Path to the fasta file or folder containing fasta files
+Dict[str, str]
List of fasta file paths
+Get the frames that will be acquired for a given retention time value. +:type rt_value: +:param rt_value: Retention time value +:type rt_value: float +:type times_array: +:param times_array: Array of retention times +:type times_array: NDArray +:type std_rt: +:param std_rt: Standard deviation of the retention time +:type std_rt: float +:type z_score: +:param z_score: Z-score of the normal distribution +:type z_score: float
+Array of frame indices
+NDArray
+Get the path to a pretrained model
+Traversable
The path to the pretrained model
+Get the path to a pretrained model
+ds_name (str) – The name of the dataset to load
str
The path to the pretrained model
+Get the scans that will be acquired for a given ion mobility value.
+Scale an array of values from the original range (min_val, max_val) to a new range (new_min, new_max).
+irt (NDArray) – Array of values to be scaled.
new_min (float) – Minimum value of the new range.
new_max (float) – Maximum value of the new range.
Array of scaled values.
+NDArray
+str
Dict[str, Any]
Simulate ion intensities for a peptide sequence, charge, and collision energy.
+:type sequence: str
+:param sequence: Peptide sequence
+:type charge: int
+:param charge: Peptide charge
+:type intensities_flat: List[float]
+:param intensities_flat: List of intensities
+:type normalized: bool
+:param normalized: Whether to normalize the intensities
+:type half_charge_one: bool
+:param half_charge_one: Whether to divide the intensity by 2 if the charge is 1
Array of ion intensities
+NDArray
+translate a peptide sequence given as python string into a numpy array of characters with a fixed length
+:type sequence: str
+:param sequence: the peptide sequence
+:type max_length: int
+:param max_length: the maximum length a sequence can have
ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
numpy array of characters
+Simulate ion intensities for a list of peptide sequences, charges, and collision energies.
+:type sequences: List[str]
+:param sequences: List of peptide sequences
+:type charges: List[int]
+:param charges: List of peptide charges
+:type intensities_flat: List[List[float]]
+:param intensities_flat: List of intensities
+:type normalized: bool
+:param normalized: Whether to normalize the intensities
+:type half_charge_one: bool
+:param half_charge_one: Whether to divide the intensity by 2 if the charge is 1
+:type num_threads: int
+:param num_threads: Number of threads to use for the calculation
Array of ion intensities
+NDArray
+Sets a given percentage of the non-zero elements of a numpy vector to zero, +where the probability of being set to 0 is inversely proportional to the element’s value +relative to other values in the vector.
+row (np.ndarray) – Input vector of arbitrary length
percentage (float) – Percentage of non-zero elements to set to zero (between 0 and 1)
Modified vector with the specified percentage of non-zero elements set to zero
+np.ndarray
+check_path()Simulate frame distributions for peptides.
+peptides (DataFrame) – Peptide DataFrame.
frames (DataFrame) – Frame DataFrame.
z_score (float) – Z-score.
std_rt (float) – Standard deviation of retention time.
rt_cycle_length (float) – Retention time cycle length in seconds.
verbose (bool) – Verbosity.
add_noise (bool) – Add noise.
normalize (bool) – Normalize frame abundance.
Peptide DataFrame with frame distributions.
+pd.DataFrame
+Simulate frame distributions for peptides.
+peptides (DataFrame) – Peptide DataFrame.
frames (DataFrame) – Frame DataFrame.
mean_std_rt (float) – mean retention time.
variance_std_rt (float) – variance retention time.
mean_scewness (float) – mean scewness.
variance_scewness (float) – variance scewness.
target_p (float) – target p.
step_size (float) – step size.
rt_cycle_length (float) – Retention time cycle length in seconds.
verbose (bool) – Verbosity.
add_noise (bool) – Add noise.
normalize (bool) – Normalize frame abundance.
n_steps (int) – number of steps.
num_threads (int) – number of threads.
from_existing (bool) – Use existing parameters.
sigmas (ndarray) – sigmas.
lambdas (ndarray) – lambdas.
Peptide DataFrame with frame distributions.
+pd.DataFrame
+Simulate peptide occurrences.
+peptides (DataFrame) – Peptides DataFrame.
intensity_mean (float) – Intensity mean.
intensity_min (float) – Intensity minimum.
intensity_max (float) – Intensity maximum.
verbose (bool) – Verbosity.
sample_occurrences (bool) – Sample occurrences.
intensity_value (float) – Intensity value.
mixture_contribution (float) – Mixture contribution.
Peptides DataFrame.
+pd.DataFrame
+Simulate scan distributions for ions.
+ions (DataFrame) – Ions DataFrame.
scans (DataFrame) – Scan DataFrame.
z_score (float) – Z-score.
mean_std_im (float) – Standard deviation of ion mobility.
variance_std_im (float) – Variance of standard deviation of ion mobility.
verbose (bool) – Verbosity.
add_noise (bool) – Add noise.
normalize (bool) – Normalize scan abundance.
from_existing (bool) – Use existing parameters.
std_means (ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) – Standard deviations.
Ions DataFrame with scan distributions.
+pd.DataFrame
+str
Bases: RustWrapperObject
Get an AcquisitionMode from a pointer.
+ptr (pims.AcquisitionMode) – Pointer to an acquisition mode.
+Acquisition mode.
+Get the acquisition mode.
+Acquisition mode.
+str
+Bases: ABC
Get the acquisition mode.
+Acquisition mode.
+str
+Get the acquisition mode as a numerical value.
+Acquisition mode as a numerical value.
+int
+Convert bytes to scan, tof, and intensity values.
+values (NDArray[np.uint8]) – Bytes.
+Scan values. +NDArray[np.int32]: TOF values. +NDArray[np.float64]: Intensity values.
+NDArray[np.int32]
+Compress a collection of frames.
+frames (List[TimsFrame]) – List of frames.
num_threads (int) – Number of threads to use.
List of compressed bytes.
+List[NDArray[np.uint8]]
+Compress values using ZSTD.
+values (NDArray[np.float64]) – Values to compress.
+Compressed values.
+NDArray[np.uint8]
+Decompress values using ZSTD.
+values (NDArray[np.float64]) – Values to decompress.
ignore_first_n (int) – Number of bytes to ignore.
Decompressed values.
+NDArray[np.uint8]
+Get the number of frames.
+Number of frames.
+int
+Get a table.
+table_name (str) – Table name.
+Table.
+pd.DataFrame
+Get a TimsFrame.
+frame_id (int) – Frame ID.
+TimsFrame.
+Get a TimsFrame.
+frame_ids (int) – Frame ID.
num_threads (int) – Number of threads.
TimsFrame.
+Convert scan and intensity values to bytes.
+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.
Bytes.
+NDArray[np.uint8]
+Convert inverse mobility values to scan values.
+frame_id (int) – Frame ID.
im_values (NDArray[np.float64]) – Inverse mobility values.
Scan values.
+NDArray[np.int32]
+Convert m/z values to TOF values.
+frame_id (int) – Frame ID.
mz_values (NDArray[np.float64]) – m/z values.
TOF values.
+NDArray[np.int32]
+Get the number of scans.
+Number of scans.
+int
+Convert scan values to inverse mobility values.
+frame_id (int) – Frame ID.
scan_values (NDArray[np.int32]) – Scan values.
Inverse mobility values.
+NDArray[np.float64]
+Convert TOF values to m/z values.
+frame_id (int) – Frame ID.
tof_values (NDArray[np.int32]) – TOF values.
m/z values.
+NDArray[np.float64]
+Bases: TimsDataset, RustWrapperObject
Get DIA MS/MS info.
+DIA MS/MS info.
+pd.DataFrame
+Get PASEF meta data for DIA.
+PASEF meta data.
+pd.DataFrame
+Read compressed data.
+Compressed data.
+List[bytes]
+Sample fragment signal.
+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.
Frame.
+Sample precursor signal.
+num_frames (int) – Number of frames.
max_intensity (float) – Maximum intensity.
take_probability (float) – Probability to take signals from sampled frames.
Frame.
+Bases: RustWrapperObject
Data as a pandas DataFrame.
+Data.
+pd.DataFrame
+Filter the frame for a given m/z range, scan range and intensity range.
+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.
Filtered frame.
+Frame ID.
+Frame ID.
+int
+Create a TimsFrame from a PyTimsFrame.
+frame (pims.PyTimsFrame) – PyTimsFrame to create the TimsFrame from.
+TimsFrame created from the PyTimsFrame.
+Create a TimsFrame from a list of TimsSpectrum.
+spectra (List[TimsSpectrum]) – List of TimsSpectrum.
+TimsFrame created from the TimsSpectrum.
+Create a TimsFrame from a list of windows.
+windows (List[TimsSpectrum]) – List of windows.
+TimsFrame created from the windows.
+Get the inverse mobility along the scan marginal.
+Inverse mobility.
+float
+Get the mean and variance of the inverse mobility.
+Mean and variance of the inverse mobility.
+Tuple[float, float]
+Intensity.
+Intensity.
+NDArray[np.float64]
+Inverse mobility.
+Inverse mobility.
+NDArray[np.float64]
+MS type.
+MS type.
+int
+MS type.
+MS type.
+int
+m/z.
+m/z.
+NDArray[np.float64]
+Randomly subsample the frame.
+Args: +take_probability (float): Take probability.
+Returns: +TimsFrame: Subsampled frame.
+Retention time.
+Retention time.
+float
+Scan.
+Scan.
+NDArray[np.int32]
+ndarray[Any, dtype[float64]]
Convert the frame to an IndexedMzSpectrum.
+IndexedMzSpectrum.
+Convert the frame to a noise annotated frame.
+Noise annotated frame.
+Convert the frame to a given resolution.
+resolution (int) – Resolution.
+Frame with the given resolution.
+Convert the frame to a list of TimsSpectrum.
+List of TimsSpectrum.
+List[TimsSpectrum]
+Convert the frame to a list of windows.
+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.
List of windows.
+List[MzSpectrum]
+Time of flight.
+Time of flight.
+NDArray[np.int32]
+Convert the frame to a vectorized frame.
+resolution (int, optional) – Resolution. Defaults to 2.
+Vectorized frame.
+Bases: RustWrapperObject
Data as a pandas DataFrame.
+Data.
+pd.DataFrame
+Filter the frame for a given m/z range, scan range and intensity range.
+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.
Filtered frame.
+Frame ID.
+Frame ID.
+int
+Create a TimsFrameVectorized from a PyTimsFrameVectorized.
+frame (pims.PyTimsFrameVectorized) – PyTimsFrameVectorized to create the TimsFrameVectorized from.
+TimsFrameVectorized created from the PyTimsFrameVectorized.
+Get the arrays at a given index.
+index (int) – Index.
+Arrays at the index.
+NDArray[np.float64]
+Indices.
+Indices.
+NDArray[np.int32]
+Intensity.
+Intensity.
+NDArray[np.float64]
+Inverse mobility.
+Inverse mobility.
+NDArray[np.float64]
+MS type.
+MS type.
+int
+Retention time.
+Retention time.
+float
+Scan.
+Scan.
+NDArray[np.int32]
+Time of flight.
+Time of flight.
+NDArray[np.int32]
+Bases: object
Create a TimsPlane from a PyTimsPlane.
+plane (pims.PyTimsPlane) – PyTimsPlane to create the TimsPlane from.
+TimsPlane created from the PyTimsPlane.
+Bases: object
Get the data as a pandas DataFrame.
+Data.
+pd.DataFrame
+Filter the slice by m/z, scan and intensity.
+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.
Filtered slice.
+Filter the slice by m/z, scan and intensity, for MS1 and MS2 with different ranges.
+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.
Filtered slice.
+First frame ID.
+First frame ID.
+int
+Get the frames.
+Frames.
+List[TimsFrame]
+Create a TimsSlice from a list of TimsFrames.
+ +Create a TimsSlice from a PyTimsSlice.
+tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.
+TimsSlice created from the PyTimsSlice.
+List[TimsPlane]
Last frame ID.
+Last frame ID.
+int
+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)]]]]
Convert the slice to a given resolution.
+resolution (int) – Resolution.
num_threads (int, optional) – Number of threads to use. Defaults to 4.
Slice with given resolution.
+Convert the slice to a list of windows.
+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.
List of windows.
+List[MzSpectrum]
+Get a vectorized version of the slice.
+resolution (int, optional) – Resolution. Defaults to 2.
num_threads (int, optional) – Number of threads to use. Defaults to 4.
Vectorized version of the slice.
+Bases: object
Get the data as a pandas DataFrame.
+Data.
+pd.DataFrame
+Filter the slice by m/z, scan and intensity.
+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.
Filtered slice.
+First frame ID.
+First frame ID.
+int
+Get the frames.
+Frames.
+List[TimsFrame]
+Create a TimsSlice from a PyTimsSlice.
+tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.
+TimsSlice created from the PyTimsSlice.
+Last frame ID.
+Last frame ID.
+int
+Tokenizes a sequence of modified amino acids.
+:type unimod_sequence: str
+:param unimod_sequence: A string representing the sequence of amino acids with modifications.
List[str]
A list of tokenized amino acids.
+Bases: object
Bases: object
Bases: object
Exponential function
+:type x:
+:param x:
+:type λ: float
+:param λ:
+:return:
laplacian distribution with exponential decay
+:type x:
+:param x:
+:type μ: float
+:param μ:
+:type σ: float
+:param σ:
+:type λ: float
+:param λ:
+:return:
Gaussian function
+:type x:
+:param x:
+:type μ: float
+:param μ:
+:type σ: float
+:param σ:
+:return:
get number of amino acids in sequence
+sequence (str) – proforma formatted aa sequence
+Number of amino acids
+int
+Tests if char is end of unimod +bracket
+char (str) – Character of a proForma formatted aa sequence
+Whether char is end of unimod bracket
+bool
+Tests if char is start of unimod +bracket
+char (str) – Character of a proForma formatted aa sequence
+Whether char is start of unimod bracket
+bool
+x (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]])
mass (float)
s (float)
inv_sqrt_2pi (float)
normalize (bool)
Re-index indices, i.e. replace gaps in indices with consecutive numbers. +Can be used, e.g., to re-index frame IDs from precursors for visualization. +:type ids: +:param ids: Indices.
+Indices.
+Tokenize a ProForma formatted sequence string.
+sequence (str) – Sequence string (ProForma formatted)
+List of tokens
+List
+load a pre-fit tokenizer from a json file
+:type path: str
+:param path: path to tokenizer as json file
+:return: a keras tokenizer loaded from json
save a fit keras tokenizer to json for later use
+:type tokenizer: Tokenizer
+:param tokenizer: fit keras tokenizer to save
+:type path: str
+:param path: path to save json to
The imspy package contains the following submodules:
++ Searching for multiple words only shows matches that contain + all words. +
+ + + + + + + + +