lisc.Counts1D

class lisc.Counts1D

A class for collecting counts data for specified terms.

Attributes

counts1d array

The number of articles found for each term.

meta_dataMetaData

Meta data information about the data collection.

__init__()

Initialize LISC Counts1D object.

Methods

__init__()	Initialize LISC Counts1D object.
add_labels(labels[, directory, ...])	Add the given list of strings as labels for the terms.
add_terms(terms[, term_type, directory, ...])	Add terms to the object.
check_counts()	Check how many articles were found for each term.
check_terms([term_type])	Print out the current list of terms.
check_top()	Check the term with the most articles.
copy()	Return a copy of the current object.
drop_data(n_articles)	Drop terms based on number of article results.
drop_term(label)	Drop specified term(s) from the object.
get_index(label)	Get the index for a specified search term.
get_term(label)	Get a search term from the object.
make_search_term(label)	Create the combined search term for a selected term.
run_collection([db, field, api_key, ...])	Collect counts data.
unload_labels([verbose])	Unload labels from the object.
unload_terms([term_type, reset, verbose])	Completely unload terms from the object.

Attributes

has_data	Indicator for if the object has collected data.
has_terms	Indicator for if the object has terms.
labels	The labels for each term.
n_terms	How many terms are included in the object.

add_labels(labels, directory=None, check_consistency=True)

Add the given list of strings as labels for the terms.

Parameters

labelslist of str or str

Labels for each term to add to the object. If list, is assumed to be labels. If str, is assumed to be a file name to load from.

directorySCDB or str, optional

Folder or database object specifying the file location, if loading from file.

check_consistencybool, optional, default: True

Whether to check the object for consistency after adding labels.

add_terms(terms, term_type=None, directory=None, append=False, check_consistency=True)

Add terms to the object.

Parameters

termslist or dict or str

Terms to add to the object. If list, assumed to be terms, which can be a list of str or a list of list of str. If dict, each key should reflect a term_type, and values the corresponding terms. If str, assumed to be a file name to load from.

term_type{‘terms’, ‘inclusions’, ‘exclusions’}

Which type of terms to are being added.

directorySCDB or str, optional

Folder or database object specifying the file location, if loading from file.

appendboolean, optional, default: False

Whether to append the new term(s) to any existing terms. If False, any prior terms are cleared prior to adding current term(s).

check_consistencybool, optional, default: True

Whether to check the object for consistency after adding terms.

Examples

Add search terms, from a list:

>>> base = Base()
>>> base.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])

Add inclusion terms, from a list:

>>> base.add_terms([[], ['brain'], [], []], term_type='inclusions')

Add exclusion terms, from a list:

>>> base.add_terms([['prefrontal'], [], [], []], term_type='exclusions')

check_counts()

Check how many articles were found for each term.

check_terms(term_type='terms')

Print out the current list of terms.

Examples

Check added terms:

>>> base = Base()
>>> base.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])
>>> base.check_terms() 
List of terms used:

frontal lobe    : frontal lobe
temporal lobe   : temporal lobe
parietal lobe   : parietal lobe
occipital lobe  : occipital lobe

Attributes

term_type{‘terms’, ‘inclusions’, ‘exclusions’}

Which type of terms to use.

check_top()

Check the term with the most articles.

copy()

Return a copy of the current object.

drop_data(n_articles)

Drop terms based on number of article results.

Parameters

n_articlesint

Minimum number of articles required to keep each term.

drop_term(label)

Drop specified term(s) from the object.

Parameters

labelstr or int or list

The label of the term to drop. If str, is the label of the term. If int, is used as the index of the term. If list, drops each element of the list.

get_index(label)

Get the index for a specified search term.

Parameters

labelstr

The label of the search term.

Returns

indint

The index of the requested search term.

Raises

IndexError

If the requested term label is not found.

get_term(label)

Get a search term from the object.

Parameters

labelstr or int

The requested term. If str, is the label of the term. If int, is used as the index of the term.

Returns

termTerm

The full search term definition.

property has_data

Indicator for if the object has collected data.

property has_terms

Indicator for if the object has terms.

property labels

The labels for each term.

make_search_term(label)

Create the combined search term for a selected term.

Parameters

labelstr or int

The requested term. If str, is the label of the term. If int, is used as the index of the term.

property n_terms

How many terms are included in the object.

run_collection(db='pubmed', field='TIAB', api_key=None, logging=None, directory=None, verbose=False, **eutils_kwargs)

Collect counts data.

Parameters

dbstr, optional, default: ‘pubmed’

Which database to access from EUtils.

fieldstr, optional, default: ‘TIAB’

Field to search for term in. Defaults to ‘TIAB’, which is Title/Abstract.

api_keystr, optional

An API key for a NCBI account.

logging{None, ‘print’, ‘store’, ‘file’}, optional

What kind of logging, if any, to do for requested URLs.

directorystr or SCDB, optional

Folder or database object specifying the save location.

verbosebool, optional, default: False

Whether to print out updates.

**eutils_kwargs

Additional settings for the EUtils API.

Examples

Collect counts data from added terms:

>>> counts = Counts1D()
>>> counts.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])
>>> counts.run_collection() 

unload_labels(verbose=True)

Unload labels from the object.

unload_terms(term_type='terms', reset=True, verbose=True)

Completely unload terms from the object.

Examples

Unload added terms:

>>> base = Base()
>>> base.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])
>>> base.unload_terms()
Unloading terms.

Attributes

term_type{‘terms’, ‘inclusions’, ‘exclusions’, ‘labels’, ‘all’}

Which type of terms to unload.

resetbool, optional, default: True

Whether to reset in/exclusions to empty lists.

verbosebool, optional

Whether to be verbose in printing out any changes.