tandem - X!Tandem output file reader¶

Summary¶

X!Tandem is an open-source proteomic search engine with a very simple, sophisticated application programming interface (API): it simply takes an XML file of instructions on its command line, and outputs the results into an XML file, which has been specified in the input XML file. The output format is described here (PDF).

This module provides a minimalistic way to extract information from X!Tandem output files. You can use the old functional interface (read()) or the new object-oriented interface (TandemXML) to iterate over entries in <group> elements, i.e. identifications for a certain spectrum.

Data access¶

TandemXML - a class representing a single X!Tandem output file. Other data access functions use this class internally.

read() - iterate through peptide-spectrum matches in an X!Tandem output file. Data from a single PSM are converted to a human-readable dict.

chain() - read multiple files at once.

chain.from_iterable() - read multiple files at once, using an iterable of files.

DataFrame() - read X!Tandem output files into a pandas.DataFrame.

Target-decoy approach¶

filter() - iterate through peptide-spectrum matches in a chain of X!Tandem output files, yielding only top PSMs and keeping false discovery rate (FDR) at the desired level. The FDR is estimated using the target-decoy approach (TDA).

filter.chain() - chain a series of filters applied independently to several files.

filter.chain.from_iterable() - chain a series of filters applied independently to an iterable of files.

filter_df() - filter X!Tandem output files and return a pandas.DataFrame.

is_decoy() - determine if a PSM is from the decoy database.

fdr() - estimate the FDR in a data set using TDA.

qvalues() - get an array of scores and local FDR values for a PSM set using the target-decoy approach.

Deprecated functions¶

iterfind() - iterate over elements in an X!Tandem file. You can just call the corresponding method of the TandemXML object.

Dependencies¶

This module requires lxml and numpy.

pyteomics.tandem.chain(*sources, **kwargs)¶

Chain sequence_maker() for several sources into a single iterable. Positional arguments should be sources like file names or file objects. Keyword arguments are passed to the sequence_maker() function.

pyteomics.tandem.sources¶

Sources for creating new sequences from, such as paths or file-like objects

Type:	`Iterable`

pyteomics.tandem.kwargs¶

Additional arguments used to instantiate each sequence

Type:	`Mapping`

chain.from_iterable(files, **kwargs)¶

Chain read() for several files. Keyword arguments are passed to the read() function.

Parameters:	files – Iterable of file names or file objects.

pyteomics.tandem.filter(*args, **kwargs)¶

Read args and yield only the PSMs that form a set with estimated false discovery rate (FDR) not exceeding fdr.

Requires numpy and, optionally, pandas.

Parameters:	args (positional) – Files to read PSMs from. All positional arguments are treated as files. The rest of the arguments must be named. fdr (float, keyword only, 0 <= fdr <= 1) – Desired FDR level. key (callable / array-like / iterable / str, keyword only, optional) – A function used for sorting of PSMs. Should accept exactly one argument (PSM) and return a number (the smaller the better). The default is a function that tries to extract e-value from the PSM. Warning The default function may not work with your files, because format flavours are diverse. reverse (bool, keyword only, optional) – If `True`, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is `False`. is_decoy (callable / array-like / iterable / str, keyword only, optional) – A function used to determine if the PSM is decoy or not. Should accept exactly one argument (PSM) and return a truthy value if the PSM should be considered decoy. Warning The default function may not work with your files, because format flavours are diverse. decoy_prefix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name prefix to use to detect decoy matches. If you provide your own is_decoy, or if you specify decoy_suffix, this parameter has no effect. Default is “DECOY_”. decoy_suffix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name suffix to use to detect decoy matches. If you provide your own is_decoy, this parameter has no effect. Mutually exclusive with decoy_prefix. remove_decoy (bool, keyword only, optional) – Defines whether decoy matches should be removed from the output. Default is `True`. Note If set to `False`, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of `fdr()` for math; basically, if remove_decoy is `True`, then formula 1 is used to control output FDR, otherwise it’s formula 2. This can be changed by overriding the formula argument. formula (int, keyword only, optional) – Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1 if remove_decoy is `True`, else 2 (see `fdr()` for definitions). ratio (float, keyword only, optional) – The size ratio between the decoy and target databases. Default is 1. In theory, the “size” of the database is the number of theoretical peptides eligible for assignment to spectra that are produced by in silico cleavage of that database. correction (int or float, keyword only, optional) – Possible values are 0, 1 and 2, or floating point numbers between 0 and 1. 0 (default): no correction; 1: enable “+1” correction. This accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2: this also corrects that probability for finite size of the sample, so the correction will be slightly less than “+1”. If a floating point number is given, then instead of the expectation value for the number of false PSMs, the confidence value is used. The value of correction is then interpreted as desired confidence level. E.g., if correction=0.95, then the calculated q-values do not exceed the “real” q-values with 95% probability. See this paper for further explanation. pep (callable / array-like / iterable / str, keyword only, optional) – If callable, a function used to determine the posterior error probability (PEP). Should accept exactly one argument (PSM) and return a float. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `DataFrame`). Note If this parameter is given, then PEP values will be used to calculate q-values. Otherwise, decoy PSMs will be used instead. This option conflicts with: is_decoy, remove_decoy, formula, ratio, correction. key can still be provided. Without key, PSMs will be sorted by PEP. full_output (bool, keyword only, optional) – If `True`, then an array of PSM objects is returned. Otherwise, an iterator / context manager object is returned, and the files are parsed twice. This saves some RAM, but is ~2x slower. Default is `True`. Note The name for the parameter comes from the fact that it is internally passed to `qvalues()`. q_label (str, optional) – Field name for q-value in the output. Default is `'q'`. score_label (str, optional) – Field name for score in the output. Default is `'score'`. decoy_label (str, optional) – Field name for the decoy flag in the output. Default is `'is decoy'`. pep_label (str, optional) – Field name for PEP in the output. Default is `'PEP'`. **kwargs (passed to the `chain()` function.) –
Returns:	out
Return type:	iterator or `numpy.ndarray` or `pandas.DataFrame`

filter.chain(*files, **kwargs)¶: Chain filter() for several files. Positional arguments should be file names or file objects. Keyword arguments are passed to the filter() function.

filter.chain.from_iterable(*files, **kwargs)¶

Chain filter() for several files. Keyword arguments are passed to the filter() function.

Parameters:	files – Iterable of file names or file objects.

pyteomics.tandem.fdr(psms=None, formula=1, is_decoy=None, ratio=1, correction=0, pep=None, decoy_prefix='DECOY_', decoy_suffix=None)¶

Estimate FDR of a data set using TDA or given PEP values. Two formulas can be used. The first one (default) is:

$FDR = \frac{N_{decoy}}{N_{target} * ratio}$

The second formula is:

$FDR = \frac{N_{decoy} * (1 + \frac{1}{ratio})}{N_{total}}$

Note

This function is less versatile than qvalues(). To obtain FDR, you can call qvalues() and take the last q-value. This function can be used (with correction = 0 or 1) when numpy is not available.

Parameters:	psms (iterable, optional) – An iterable of PSMs, e.g. as returned by `read()`. Not needed if is_decoy is an iterable. formula (int, optional) – Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1. is_decoy (callable, iterable, or str, optional) – If callable, should accept exactly one argument (PSM) and return a truthy value if the PSM is considered decoy. Default is `is_decoy()`. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `pandas.DataFrame`). Warning The default function may not work with your files, because format flavours are diverse. decoy_prefix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name prefix to use to detect decoy matches. If you provide your own is_decoy, or if you specify decoy_suffix, this parameter has no effect. Default is “DECOY_”. decoy_suffix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name suffix to use to detect decoy matches. If you provide your own is_decoy, this parameter has no effect. Mutually exclusive with decoy_prefix. pep (callable, iterable, or str, optional) – If callable, a function used to determine the posterior error probability (PEP). Should accept exactly one argument (PSM) and return a float. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `pandas.DataFrame`). Note If this parameter is given, then PEP values will be used to calculate FDR. Otherwise, decoy PSMs will be used instead. This option conflicts with: is_decoy, formula, ratio, correction. ratio (float, optional) – The size ratio between the decoy and target databases. Default is 1. In theory, the “size” of the database is the number of theoretical peptides eligible for assignment to spectra that are produced by in silico cleavage of that database. correction (int or float, optional) – Possible values are 0, 1 and 2, or floating point numbers between 0 and 1. 0 (default): no correction; 1: enable “+1” correction. This accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2: this also corrects that probability for finite size of the sample, so the correction will be slightly less than “+1”. If a floating point number is given, then instead of the expectation value for the number of false PSMs, the confidence value is used. The value of correction is then interpreted as desired confidence level. E.g., if correction=0.95, then the calculated q-values do not exceed the “real” q-values with 95% probability. See this paper for further explanation. Note Requires `numpy`, if correction is a float or 2. Note Correction is only needed if the PSM set at hand was obtained using TDA filtering based on decoy counting (as done by using `filter()` without correction).
Returns:	out – The estimation of FDR, (roughly) between 0 and 1.
Return type:	float

pyteomics.tandem.qvalues(*args, **kwargs)¶

Read args and return a NumPy array with scores and q-values. q-values are calculated either using TDA or based on provided values of PEP.

Requires numpy (and optionally pandas).

Parameters:

Parameters:	args (positional) – Files to read PSMs from. All positional arguments are treated as files. The rest of the arguments must be named. key (callable / array-like / iterable / str, keyword only, optional) – If callable, a function used for sorting of PSMs. Should accept exactly one argument (PSM) and return a number (the smaller the better). If array-like, should contain scores for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `DataFrame`). Warning The default function may not work with your files, because format flavours are diverse. reverse (bool, keyword only, optional) – If `True`, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is `False`. is_decoy (callable / array-like / iterable / str, keyword only, optional) – If callable, a function used to determine if the PSM is decoy or not. Should accept exactly one argument (PSM) and return a truthy value if the PSM should be considered decoy. If array-like, should contain boolean values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `DataFrame`). Warning The default function may not work with your files, because format flavours are diverse. decoy_prefix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name prefix to use to detect decoy matches. If you provide your own is_decoy, or if you specify decoy_suffix, this parameter has no effect. Default is “DECOY_”. decoy_suffix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name suffix to use to detect decoy matches. If you provide your own is_decoy, this parameter has no effect. Mutually exclusive with decoy_prefix. pep (callable / array-like / iterable / str, keyword only, optional) – If callable, a function used to determine the posterior error probability (PEP). Should accept exactly one argument (PSM) and return a float. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a `DataFrame`). Note If this parameter is given, then PEP values will be used to calculate q-values. Otherwise, decoy PSMs will be used instead. This option conflicts with: is_decoy, remove_decoy, formula, ratio, correction. key can still be provided. Without key, PSMs will be sorted by PEP. remove_decoy (bool, keyword only, optional) – Defines whether decoy matches should be removed from the output. Default is `False`. Note If set to `False`, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of `fdr()` for math; basically, if remove_decoy is `True`, then formula 1 is used to control output FDR, otherwise it’s formula 2. This can be changed by overriding the formula argument. formula (int, keyword only, optional) – Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1 if remove_decoy is `True`, else 2 (see `fdr()` for definitions). ratio (float, keyword only, optional) – The size ratio between the decoy and target databases. Default is 1. In theory, the “size” of the database is the number of theoretical peptides eligible for assignment to spectra that are produced by in silico cleavage of that database. correction (int or float, keyword only, optional) – Possible values are 0, 1 and 2, or floating point numbers between 0 and 1. 0 (default): no correction; 1: enable “+1” correction. This accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2: this also corrects that probability for finite size of the sample, so the correction will be slightly less than “+1”. If a floating point number is given, then instead of the expectation value for the number of false PSMs, the confidence value is used. The value of correction is then interpreted as desired confidence level. E.g., if correction=0.95, then the calculated q-values do not exceed the “real” q-values with 95% probability. See this paper for further explanation. q_label (str, optional) – Field name for q-value in the output. Default is `'q'`. score_label (str, optional) – Field name for score in the output. Default is `'score'`. decoy_label (str, optional) – Field name for the decoy flag in the output. Default is `'is decoy'`. pep_label (str, optional) – Field name for PEP in the output. Default is `'PEP'`. full_output (bool, keyword only, optional) – If `True`, then the returned array has PSM objects along with scores and q-values. Default is `False`. **kwargs (passed to the `chain()` function.) –
Returns:	out – A sorted array of records with the following fields: ’score’: `np.float64` ’is decoy’: `np.bool_` ’q’: `np.float64` ’psm’: `np.object_` (if full_output is `True`)
Return type:	numpy.ndarray

args (positional) – Files to read PSMs from. All positional arguments are treated as files. The rest of the arguments must be named.
key (callable / array-like / iterable / str, keyword only, optional) –
If callable, a function used for sorting of PSMs. Should accept exactly one argument (PSM) and return a number (the smaller the better). If array-like, should contain scores for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a DataFrame).

Warning

The default function may not work with your files, because format flavours are diverse.
reverse (bool, keyword only, optional) – If True, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is False.
is_decoy (callable / array-like / iterable / str, keyword only, optional) –
If callable, a function used to determine if the PSM is decoy or not. Should accept exactly one argument (PSM) and return a truthy value if the PSM should be considered decoy. If array-like, should contain boolean values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a DataFrame).

Warning

The default function may not work with your files, because format flavours are diverse.
decoy_prefix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name prefix to use to detect decoy matches. If you provide your own is_decoy, or if you specify decoy_suffix, this parameter has no effect. Default is “DECOY_”.
decoy_suffix (str, optional) – If the default is_decoy function works for you, this parameter specifies which protein name suffix to use to detect decoy matches. If you provide your own is_decoy, this parameter has no effect. Mutually exclusive with decoy_prefix.
pep (callable / array-like / iterable / str, keyword only, optional) –
If callable, a function used to determine the posterior error probability (PEP). Should accept exactly one argument (PSM) and return a float. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a DataFrame).

Note

If this parameter is given, then PEP values will be used to calculate q-values. Otherwise, decoy PSMs will be used instead. This option conflicts with: is_decoy, remove_decoy, formula, ratio, correction. key can still be provided. Without key, PSMs will be sorted by PEP.
remove_decoy (bool, keyword only, optional) –
Defines whether decoy matches should be removed from the output. Default is False.

Note

If set to False, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of fdr() for math; basically, if remove_decoy is True, then formula 1 is used to control output FDR, otherwise it’s formula 2. This can be changed by overriding the formula argument.
formula (int, keyword only, optional) – Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1 if remove_decoy is True, else 2 (see fdr() for definitions).
ratio (float, keyword only, optional) – The size ratio between the decoy and target databases. Default is 1. In theory, the “size” of the database is the number of theoretical peptides eligible for assignment to spectra that are produced by in silico cleavage of that database.
correction (int or float, keyword only, optional) –
Possible values are 0, 1 and 2, or floating point numbers between 0 and 1.

0 (default): no correction;

1: enable “+1” correction. This accounts for the probability that a false positive scores better than the first excluded decoy PSM;

2: this also corrects that probability for finite size of the sample, so the correction will be slightly less than “+1”.

If a floating point number is given, then instead of the expectation value for the number of false PSMs, the confidence value is used. The value of correction is then interpreted as desired confidence level. E.g., if correction=0.95, then the calculated q-values do not exceed the “real” q-values with 95% probability.

See this paper for further explanation.
q_label (str, optional) – Field name for q-value in the output. Default is 'q'.
score_label (str, optional) – Field name for score in the output. Default is 'score'.
decoy_label (str, optional) – Field name for the decoy flag in the output. Default is 'is decoy'.
pep_label (str, optional) – Field name for PEP in the output. Default is 'PEP'.
full_output (bool, keyword only, optional) – If True, then the returned array has PSM objects along with scores and q-values. Default is False.
**kwargs (passed to the chain() function.) –

Returns:

out – A sorted array of records with the following fields:

’score’: np.float64
’is decoy’: np.bool_
’q’: np.float64
’psm’: np.object_ (if full_output is True)

Return type:

numpy.ndarray

pyteomics.tandem.iterfind(source, path, **kwargs)[source]¶

Parse source and yield info on elements with specified local name or by specified “XPath”.

Note

This function is provided for backward compatibility only. If you do multiple iterfind() calls on one file, you should create a TandemXML object and use its iterfind() method.

Parameters:	source (str or file) – File name or file-like object. path (str) – Element name or XPath-like expression. Only local names separated with slashes are accepted. An asterisk () means any element. You can specify a single condition in the end, such as: `"/path/to/element[some_value>1.5]"` Note: you can do much more powerful filtering using plain Python. The path can be absolute or “free”. Please don’t specify namespaces. recursive* (bool, optional) – If `False`, subelements will not be processed when extracting info from elements. Default is `True`. iterative (bool, optional) – Specifies whether iterative XML parsing should be used. Iterative parsing significantly reduces memory usage and may be just a little slower. When retrieve_refs is `True`, however, it is highly recommended to disable iterative parsing if possible. Default value is `True`.
Returns:	out
Return type:	iterator

pyteomics.tandem.DataFrame(*args, **kwargs)[source]¶

Read X!Tandem output files into a pandas.DataFrame.

Requires pandas.

Parameters:	sep (str or None, optional) – Some values related to PSMs (such as protein information) are variable-length lists. If sep is a `str`, they will be packed into single string using this delimiter. If sep is `None`, they are kept as lists. Default is `None`. pd_kwargs (dict, optional) – Keyword arguments passed to the `pandas.DataFrame` constructor. args – Passed to `chain()`. *kwargs – Passed to `chain()`.
Returns:	out
Return type:	pandas.DataFrame

class pyteomics.tandem.TandemXML(*args, **kwargs)[source]¶

Bases: pyteomics.xml.XML

Parser class for TandemXML files.

__init__(*args, **kwargs)[source]¶

Create an XML parser object.

Parameters:

Parameters:	source (str or file) – File name or file-like object corresponding to an XML file. read_schema (bool, optional) – Defines whether schema file referenced in the file header should be used to extract information about value conversion. Default is `False`. iterative (bool, optional) – Defines whether an `ElementTree` object should be constructed and stored on the instance or if iterative parsing should be used instead. Iterative parsing keeps the memory usage low for large XML files. Default is `True`. build_id_cache (bool, optional) – Defines whether a dictionary mapping IDs to XML tree elements should be built and stored on the instance. It is used in `XML.get_by_id()`, e.g. when using `pyteomics.mzid.MzIdentML` with `retrieve_refs=True`. huge_tree (bool, optional) – This option is passed to the lxml parser and defines whether security checks for XML tree depth and node size should be disabled. Default is `False`. Enable this option for trusted files to avoid XMLSyntaxError exceptions (e.g. XMLSyntaxError: xmlSAX2Characters: huge text node).

source (str or file) – File name or file-like object corresponding to an XML file.
read_schema (bool, optional) – Defines whether schema file referenced in the file header should be used to extract information about value conversion. Default is False.
iterative (bool, optional) – Defines whether an ElementTree object should be constructed and stored on the instance or if iterative parsing should be used instead. Iterative parsing keeps the memory usage low for large XML files. Default is True.
build_id_cache (bool, optional) – Defines whether a dictionary mapping IDs to XML tree elements should be built and stored on the instance. It is used in XML.get_by_id(), e.g. when using pyteomics.mzid.MzIdentML with retrieve_refs=True.
huge_tree (bool, optional) – This option is passed to the lxml parser and defines whether security checks for XML tree depth and node size should be disabled. Default is False. Enable this option for trusted files to avoid XMLSyntaxError exceptions (e.g. XMLSyntaxError: xmlSAX2Characters: huge text node).

build_id_cache()¶: Construct a cache for each element in the document, indexed by id attribute

build_tree()¶: Build and store the ElementTree instance for the underlying file

clear_id_cache()¶: Clear the element ID cache

clear_tree()¶: Remove the saved ElementTree.

get_by_id(elem_id, **kwargs)¶

Parse the file and return the element with id attribute equal to elem_id. Returns None if no such element is found.

Parameters:	elem_id (str) – The value of the id attribute to match.
Returns:	out
Return type:	`dict` or `None`

iterfind(path, **kwargs)¶

Parse the XML and yield info on elements with specified local name or by specified “XPath”.

Parameters:	path (str) – Element name or XPath-like expression. The path is very close to full XPath syntax, but local names should be used for all elements in the path. They will be substituted with local-name() checks, up to the (first) predicate. The path can be absolute or “free”. Please don’t specify namespaces. **kwargs (passed to `self._get_info_smart()`.) –
Returns:	out
Return type:	iterator

reset()¶: Resets the iterator to its initial state.

pyteomics.tandem.filter_df(*args, **kwargs)[source]¶

Read X!Tandem output files or DataFrames and return a DataFrame with filtered PSMs. Positional arguments can be X!Tandem output files or DataFrames.

Requires pandas.

Parameters:	key (str / iterable / callable, optional) – Default is ‘expect’. is_decoy (str / iterable / callable, optional) – Default is to check if all strings in the “protein” column start with ‘DECOY_’ args – Passed to `auxiliary.filter()` and/or `DataFrame()`. *kwargs – Passed to `auxiliary.filter()` and/or `DataFrame()`.
Returns:	out
Return type:	pandas.DataFrame

pyteomics.tandem.is_decoy(psm, prefix='DECOY_')¶

Given a PSM dict, return True if all protein names for the PSM start with prefix, and False otherwise.

Parameters:	psm (dict) – A dict, as yielded by `read()`. prefix (str, optional) – A prefix used to mark decoy proteins. Default is ‘DECOY_’.
Returns:	out
Return type:	bool

pyteomics.tandem.iterfind(source, path, **kwargs)[source]

Parse source and yield info on elements with specified local name or by specified “XPath”.

Note

This function is provided for backward compatibility only. If you do multiple iterfind() calls on one file, you should create a TandemXML object and use its iterfind() method.

Parameters:	source (str or file) – File name or file-like object. path (str) – Element name or XPath-like expression. Only local names separated with slashes are accepted. An asterisk () means any element. You can specify a single condition in the end, such as: `"/path/to/element[some_value>1.5]"` Note: you can do much more powerful filtering using plain Python. The path can be absolute or “free”. Please don’t specify namespaces. recursive* (bool, optional) – If `False`, subelements will not be processed when extracting info from elements. Default is `True`. iterative (bool, optional) – Specifies whether iterative XML parsing should be used. Iterative parsing significantly reduces memory usage and may be just a little slower. When retrieve_refs is `True`, however, it is highly recommended to disable iterative parsing if possible. Default value is `True`.
Returns:	out
Return type:	iterator

pyteomics.tandem.read(source, iterative=True, **kwargs)[source]¶

Parse source and iterate through peptide-spectrum matches.

Parameters:	source (str or file) – A path to a target X!Tandem output file or the file object itself. iterative (bool, optional) – Defines whether iterative parsing should be used. It helps reduce memory usage at almost the same parsing speed. Default is `True`.
Returns:	out – An iterator over dicts with PSM properties.
Return type:	iterator

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