def concordance(self, *args, **kwargs):
"""
A concordance method for Tregex queries, CoreNLP dependencies,
tokenised data or plaintext.
>>> wv = ['want', 'need', 'feel', 'desire']
>>> corpus.concordance({'l': wv, 'f': 'root'})
Arguments are the same as :func:`~corpkit.interrogation.Interrogation.interrogate`, plus:
:param only_format_match: if True, left and right window will just be words, regardless of what is in 'show'
:type only_format_match: bool
:param random: randomise lines
:type random: bool
:param only_unique: only unique lines
:type only_unique: bool
:returns: A :class:`corpkit.interrogation.Concordance` instance
"""
from corpkit.interrogator import interrogator
return interrogator(self, conc = True, *args, **kwargs)
def get_stats(self, *args):
"""
Get some basic stats from the corpus, and store as :py:attr:`~corpkit.corpus.Corpus.features`
>>> corpus.get_stats()
:returns: None
"""
from corpkit import interrogator
self.features = interrogator(self.path, 's', 'any').results
print '\nFeatures defined. See .features attribute ...'
def interroplot(path, search, **kwargs):
"""Demo function for interrogator/plotter.
1. Interrogates path with a query
2. Gets relative frequencies
3. Plots the top seven results
:param path: path to corpus
:type path: str
:param search: search
"""
import corpkit
from corpkit import interrogator
if type(search) == str:
search = {'t': search}
kwargs['show'] = kwargs.pop('show', 'w')
quickstart = interrogator(path, search = search, **kwargs)
edited = quickstart.edit('%', 'self', print_info = False)
edited.plot(str(path))
def interroplot(path, query):
"""Demo function for interrogator/plotter.
1. Interrogates path with Tregex query,
2. Gets relative frequencies
3. Plots the top seven results
:param path: path to corpus
:type path: str
:param query: Tregex query
:type query: str
"""
import corpkit
from corpkit import interrogator, editor, plotter
quickstart = interrogator(path, 'words', query, show = ['w'])
edited = editor(quickstart.results, '%', quickstart.totals, print_info = False)
plotter(str(path), edited.results)
def interroplot(path, search, **kwargs):
"""Demo function for interrogator/plotter.
1. Interrogates path with a query
2. Gets relative frequencies
3. Plots the top seven results
:param path: path to corpus
:type path: str
:param search: search
"""
import corpkit
from corpkit import interrogator
if type(search) == str:
search = {'t': search}
kwargs['show'] = kwargs.pop('show', 'w')
quickstart = interrogator(path, search=search, **kwargs)
edited = quickstart.edit('%', 'self', print_info=False)
edited.plot(str(path))
# <markdowncell> # Next, we perform interrogations with *interrogator()*. Its most important arguments are: # # 1. **path to corpus** (the *path* variable) # # 2. Tregex **options**: # * **'t'**: return only words # * **'c'**: return a count of matches # # 3. the **Tregex query** # We only need to count tokens, so we can use the **count** option (it's often faster than getting lists of matching tokens). The cell below will run *interrogator()* over each annual subcorpus and count the number of matches for the query. # <codecell> allwords = interrogator(path, 'count', allwords_query) # <markdowncell> # When the interrogation has finished, we can view the total counts by getting the *totals* branch of the *allwords* interrogation: # <codecell> # from the allwords results, print the totals print allwords.totals # <markdowncell> # If you want to see the query and options that created the results, you can print the *query* branch. # <codecell> print allwords.query # <headingcell level=3>
# 1. **path to corpus** # # 2. Tregex **options**: # * **'t/w/words'**: return only words # * **'c/count'**: return a count of matches # * **'p/pos'**: return only the tag # * **'b/both'**: return tag and word together # # 3. a **Tregex query** # We only need to count tokens, so we can use the `'count'` option (it's often faster than getting lists of matching tokens). The cell below will run `interrogator()` over each annual subcorpus and count the number of matches for the query. # Some common Tregex patterns have been predefined. Searching for `'any'` will find any word in the corpus and count it. # <codecell> allwords = interrogator(annual_trees, 'count', 'any') # <markdowncell> # When the interrogation has finished, we can view our results: # <codecell> # from the allwords results, print the totals print allwords.totals # <markdowncell> # If you want to see the query and options that created the results, you can use: # <codecell> print allwords.query # <markdowncell>
def concordance(self, *args, **kwargs):
"""interrogate the corpus using :func:`~corpkit.corpus.Corpus.concordance`"""
from corpkit import interrogator
return interrogator([s for s in self], conc = True, *args, **kwargs)
def interrogate(self, *args, **kwargs):
"""interrogate the corpus using :func:`~corpkit.corpus.Corpus.interrogate`"""
from corpkit import interrogator
return interrogator([s for s in self], *args, **kwargs)
def interrogate(self, search, *args, **kwargs):
"""Interrogate a corpus of texts for a lexicogrammatical phenomenon
>>> # show lemma form of nouns ending in 'ing'
>>> q = {'w': r'ing$', 'p': r'^N'}
>>> data = corpus.interrogate(q, show = 'l')
:param search: What query should be matching
- t/tregex
- w/word
- l/lemma
- f/function
- g/governor
- d/dependent
- p/pos
- i/index
- n/ngrams
- s/general stats
:type search: str, or, for dependencies, a dict like ``{'w': 'help', 'p': r'^V'}``
:param searchmode: Return results matching any/all criteria
:type searchmode: str ('any'/'all')
:param exclude: The inverse of `search`, removing results from search
:type exclude: dict -- ``{'l': 'be'}``
:param excludemode: Exclude results matching any/all criteria
:type excludemode: str ('any'/'all')
:param query: A search query for the interrogation
:type query: str -- regex/Tregex pattern; dict -- ``{name: pattern}``; list -- word list to match
:param show: What to output. If multiple strings are passed, results will be colon-separated, in order
- t/tree
- w/word
- l/lemma
- g/governor
- d/dependent
- f/function
- p/pos
- i/index
- a/distance from root
:type show: list of strings
:param lemmatise: Force lemmatisation on results
:type lemmatise: bool
:param lemmatag: Explicitly pass a pos to lemmatiser (generally when data is unparsed)
:type lemmatag: False/'n'/'v'/'a'/'r'
:param spelling: Convert all to U.S. or U.K. English
:type spelling: False/'US'/'UK'
:param dep_type: The kind of Stanford CoreNLP dependency parses you want to use
:type dep_type: str -- 'basic-dependencies'/'a', 'collapsed-dependencies'/'b', 'collapsed-ccprocessed-dependencies'/'c'
:param quicksave: Save result as pickle to ```saved_interrogations/str``` on completion
:type quicksave: str
:param gramsize: size of ngrams (default 2)
:type gramsize: int
:param split_contractions: make ``"don't"`` et al into two tokens
:type split_contractions: bool
:param multiprocess: how many parallel processes to run
:type multiprocess: int / bool (to determine automatically)
:param files_as_subcorpora: treat each file as a subcorpus
:type files_as_subcorpora: bool
:returns: A :class:`corpkit.interrogation.Interrogation` object, with ``.query``, ``.results``, ``.totals`` attributes. If multiprocessing is \
invoked, result may be a :class:`corpkit.interrogation.Interrodict` containing corpus names, queries or speakers as keys.
"""
from corpkit import interrogator
par = kwargs.pop('multiprocess', None)
if par and self.subcorpora:
if type(par) == int:
kwargs['multiprocess'] = par
return interrogator(self.subcorpora, search, *args, **kwargs)
else:
return interrogator(self, search, *args, **kwargs)
