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bcbnz/pylabels | labels/sheet.py | Sheet._shade_missing_label | def _shade_missing_label(self):
"""Helper method to shade a missing label. Not intended for external use.
"""
# Start a drawing for the whole label.
label = Drawing(float(self._lw), float(self._lh))
label.add(self._clip_label)
# Fill with a rectangle; the clipping path ... | python | def _shade_missing_label(self):
"""Helper method to shade a missing label. Not intended for external use.
"""
# Start a drawing for the whole label.
label = Drawing(float(self._lw), float(self._lh))
label.add(self._clip_label)
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bcbnz/pylabels | labels/sheet.py | Sheet._shade_remaining_missing | def _shade_remaining_missing(self):
"""Helper method to shade any missing labels remaining on the current
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Note that this will modify the internal _position attribute and should
therefore only be used once all the 'real' labels have been drawn.
... | python | def _shade_remaining_missing(self):
"""Helper method to shade any missing labels remaining on the current
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bcbnz/pylabels | labels/sheet.py | Sheet._draw_label | def _draw_label(self, obj, count):
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"""
# Start a drawing for the whole label.
label = Drawing(float(self._lw), float(self._lh))
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"""Helper method to draw on the current label. Not intended for external use.
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bcbnz/pylabels | labels/sheet.py | Sheet.add_labels | def add_labels(self, objects, count=1):
"""Add multiple labels to the sheet.
Parameters
----------
objects: iterable
An iterable of the objects to add. Each of these will be passed to
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"""Add multiple labels to the sheet.
Parameters
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An iterable of the objects to add. Each of these will be passed to
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bcbnz/pylabels | labels/sheet.py | Sheet.save | def save(self, filelike):
"""Save the file as a PDF.
Parameters
----------
filelike: path or file-like object
The filename or file-like object to save the labels under. Any
existing contents will be overwritten.
"""
# Shade any remaining missing ... | python | def save(self, filelike):
"""Save the file as a PDF.
Parameters
----------
filelike: path or file-like object
The filename or file-like object to save the labels under. Any
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"""
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bcbnz/pylabels | labels/sheet.py | Sheet.preview | def preview(self, page, filelike, format='png', dpi=72, background_colour=0xFFFFFF):
"""Render a preview image of a page.
Parameters
----------
page: positive integer
Which page to render. Must be in the range [1, page_count]
filelike: path or file-like object
... | python | def preview(self, page, filelike, format='png', dpi=72, background_colour=0xFFFFFF):
"""Render a preview image of a page.
Parameters
----------
page: positive integer
Which page to render. Must be in the range [1, page_count]
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bcbnz/pylabels | labels/sheet.py | Sheet.preview_string | def preview_string(self, page, format='png', dpi=72, background_colour=0xFFFFFF):
"""Render a preview image of a page as a string.
Parameters
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page: positive integer
Which page to render. Must be in the range [1, page_count]
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bcbnz/pylabels | labels/specifications.py | Specification._calculate | def _calculate(self):
"""Checks the dimensions of the sheet are valid and consistent.
NB: this is called internally when needed; there should be no need for
user code to call it.
"""
# Check the dimensions are larger than zero.
for dimension in ('_sheet_width', '_sheet_... | python | def _calculate(self):
"""Checks the dimensions of the sheet are valid and consistent.
NB: this is called internally when needed; there should be no need for
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"""
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bcbnz/pylabels | labels/specifications.py | Specification.bounding_boxes | def bounding_boxes(self, mode='fraction', output='dict'):
"""Get the bounding boxes of the labels on a page.
Parameters
----------
mode: 'fraction', 'actual'
If 'fraction', the bounding boxes are expressed as a fraction of the
height and width of the sheet. If 'a... | python | def bounding_boxes(self, mode='fraction', output='dict'):
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estnltk/estnltk | estnltk/wiki/parser.py | templatesCollector | def templatesCollector(text, open, close):
"""leaves related articles and wikitables in place"""
others = []
spans = [i for i in findBalanced(text, open, close)]
spanscopy = copy(spans)
for i in range(len(spans)):
start, end = spans[i]
o = text[start:end]
ol = o.lower()
... | python | def templatesCollector(text, open, close):
"""leaves related articles and wikitables in place"""
others = []
spans = [i for i in findBalanced(text, open, close)]
spanscopy = copy(spans)
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estnltk/estnltk | estnltk/prettyprinter/prettyprinter.py | assert_legal_arguments | def assert_legal_arguments(kwargs):
"""Assert that PrettyPrinter arguments are correct.
Raises
------
ValueError
In case there are unknown arguments or a single layer is mapped to more than one aesthetic.
"""
seen_layers = set()
for k, v in kwargs.items():
if k not in LEGAL_... | python | def assert_legal_arguments(kwargs):
"""Assert that PrettyPrinter arguments are correct.
Raises
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In case there are unknown arguments or a single layer is mapped to more than one aesthetic.
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seen_layers = set()
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Detects which aesthetics are mapped to which layers
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Parameters
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kwargs: dict
The keyword arguments to PrettyPrinter.
Returns
-------
dict, dict
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"""Function that parses PrettyPrinter arguments.
Detects which aesthetics are mapped to which layers
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kwargs: dict
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estnltk/estnltk | estnltk/prettyprinter/prettyprinter.py | PrettyPrinter.css | def css(self):
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return '\n'.join(css_list) | python | def css(self):
"""Returns
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str
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"""
css_list = [DEFAULT_MARK_CSS]
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css_list.extend(get_mark_css(aes, self.values[aes]))
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estnltk/estnltk | estnltk/prettyprinter/prettyprinter.py | PrettyPrinter.render | def render(self, text, add_header=False):
"""Render the HTML.
Parameters
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add_header: boolean (default: False)
If True, add HTML5 header and footer.
Returns
-------
str
The rendered HTML.
"""
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add_header: boolean (default: False)
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estnltk/estnltk | estnltk/estner/crfsuiteutil.py | Trainer.train | def train(self, nerdocs, mode_filename):
"""Train a CRF model using given documents.
Parameters
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nerdocs: list of estnltk.estner.ner.Document.
The documents for model training.
mode_filename: str
The fielname where to save the model.
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estnltk/estnltk | estnltk/estner/crfsuiteutil.py | Tagger.tag | def tag(self, nerdoc):
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nerdoc: estnltk.estner.Document
The document to be tagged.
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labels: list of lists of str
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nerdoc: estnltk.estner.Document
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labels: list of lists of str
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estnltk/estnltk | estnltk/wiki/wikiextra.py | balancedSlicer | def balancedSlicer(text, openDelim='[', closeDelim=']'):
"""
Assuming that text contains a properly balanced expression using
:param openDelim: as opening delimiters and
:param closeDelim: as closing delimiters.
:return: text between the delimiters
"""
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"""
Assuming that text contains a properly balanced expression using
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:param closeDelim: as closing delimiters.
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estnltk/estnltk | estnltk/wiki/convert.py | json_2_text | def json_2_text(inp, out, verbose = False):
"""Convert a Wikipedia article to Text object.
Concatenates the sections in wikipedia file and rearranges other information so it
can be interpreted as a Text object.
Links and other elements with start and end positions are annotated
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"""Convert a Wikipedia article to Text object.
Concatenates the sections in wikipedia file and rearranges other information so it
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estnltk/estnltk | estnltk/grammar/match.py | concatenate_matches | def concatenate_matches(a, b, text, name):
"""Concatenate matches a and b.
All submatches will be copied to result."""
match = Match(a.start, b.end, text[a.start:b.end], name)
for k, v in a.matches.items():
match.matches[k] = v
for k, v in b.matches.items():
match.matches[k] = v
... | python | def concatenate_matches(a, b, text, name):
"""Concatenate matches a and b.
All submatches will be copied to result."""
match = Match(a.start, b.end, text[a.start:b.end], name)
for k, v in a.matches.items():
match.matches[k] = v
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estnltk/estnltk | estnltk/grammar/match.py | Match.dict | def dict(self):
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del res[MATCHES]
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del res[NAME]
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"""Dictionary representing this match and all child symbol matches."""
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estnltk/estnltk | estnltk/vabamorf/morf.py | regex_from_markers | def regex_from_markers(markers):
"""Given a string of characters, construct a regex that matches them.
Parameters
----------
markers: str
The list of string containing the markers
Returns
-------
regex
The regular expression matching the given markers.
"""
return re... | python | def regex_from_markers(markers):
"""Given a string of characters, construct a regex that matches them.
Parameters
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markers: str
The list of string containing the markers
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estnltk/estnltk | estnltk/vabamorf/morf.py | convert | def convert(word):
"""This method converts given `word` to UTF-8 encoding and `bytes` type for the
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if six.PY2:
if isinstance(word, unicode):
return word.encode('utf-8')
else:
return word.decode('utf-8').encode('utf-8') # make sure it is real utf8, ... | python | def convert(word):
"""This method converts given `word` to UTF-8 encoding and `bytes` type for the
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return word.encode('utf-8')
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estnltk/estnltk | estnltk/vabamorf/morf.py | postprocess_result | def postprocess_result(morphresult, trim_phonetic, trim_compound):
"""Postprocess vabamorf wrapper output."""
word, analysis = morphresult
return {
'text': deconvert(word),
'analysis': [postprocess_analysis(a, trim_phonetic, trim_compound) for a in analysis]
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"""Postprocess vabamorf wrapper output."""
word, analysis = morphresult
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estnltk/estnltk | estnltk/vabamorf/morf.py | trim_phonetics | def trim_phonetics(root):
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Parameters
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root: str
The string to remove the phonetic markup.
Returns
-------
str
The string with phonetic markup removed.
"""
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global phonetic_reg... | python | def trim_phonetics(root):
"""Function that trims phonetic markup from the root.
Parameters
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root: str
The string to remove the phonetic markup.
Returns
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str
The string with phonetic markup removed.
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estnltk/estnltk | estnltk/vabamorf/morf.py | get_root | def get_root(root, phonetic, compound):
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root: str
The word root form.
phonetic: boolean
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compound: boolean
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estnltk/estnltk | estnltk/vabamorf/morf.py | get_group_tokens | def get_group_tokens(root):
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Parameters
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root: str
The root form.
Returns
-------
list of (list of str)
List of grouped root tokens.
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root: str
The root form.
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list of (list of str)
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estnltk/estnltk | estnltk/vabamorf/morf.py | fix_spelling | def fix_spelling(words, join=True, joinstring=' '):
"""Simple function for quickly correcting misspelled words.
Parameters
----------
words: list of str or str
Either a list of pretokenized words or a string. In case of a string, it will be splitted using
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estnltk/estnltk | estnltk/vabamorf/morf.py | synthesize | def synthesize(lemma, form, partofspeech='', hint='', guess=True, phonetic=False):
"""Synthesize a single word based on given morphological attributes.
Note that spellchecker does not respect pre-tokenized words and concatenates
token sequences such as "New York".
Parameters
----------
lemma: ... | python | def synthesize(lemma, form, partofspeech='', hint='', guess=True, phonetic=False):
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estnltk/estnltk | estnltk/vabamorf/morf.py | Vabamorf.instance | def instance():
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estnltk/estnltk | estnltk/vabamorf/morf.py | Vabamorf.analyze | def analyze(self, words, **kwargs):
"""Perform morphological analysis and disambiguation of given text.
Parameters
----------
words: list of str or str
Either a list of pretokenized words or a string. In case of a string, it will be splitted using
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estnltk/estnltk | estnltk/vabamorf/morf.py | Vabamorf.disambiguate | def disambiguate(self, words):
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words: list of dict
A sentence of words.
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A sentence of words.
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Parameters
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words: list of str or str
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Either a list of pretokenized words or a string. In case of a string, it will be splitted using
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estnltk/estnltk | estnltk/clausesegmenter.py | ClauseSegmenter.prepare_sentence | def prepare_sentence(self, sentence):
"""Prepare the sentence for segment detection."""
# depending on how the morphological analysis was added, there may be
# phonetic markup. Remove it, if it exists.
for word in sentence:
for analysis in word[ANALYSIS]:
anal... | python | def prepare_sentence(self, sentence):
"""Prepare the sentence for segment detection."""
# depending on how the morphological analysis was added, there may be
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for word in sentence:
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max_index = 0
max_depth = 1
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for token in sentence:
if CLAUSE_ANNOT not in token:
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estnltk/estnltk | estnltk/clausesegmenter.py | ClauseSegmenter.rename_annotations | def rename_annotations(self, sentence):
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annotations = []
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estnltk/estnltk | estnltk/examples/split_large_koondkorpus_files.py | format_time | def format_time( sec ):
''' Re-formats time duration in seconds (*sec*) into more easily readable
form, where (days,) hours, minutes, and seconds are explicitly shown.
Returns the new duration as a formatted string.
'''
import time
if sec < 864000:
# Idea from: http://stackover... | python | def format_time( sec ):
''' Re-formats time duration in seconds (*sec*) into more easily readable
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estnltk/estnltk | estnltk/examples/split_large_koondkorpus_files.py | split_Text | def split_Text( text, file_name, verbose = True ):
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estnltk/estnltk | estnltk/examples/split_large_koondkorpus_files.py | write_Text_into_file | def write_Text_into_file( text, old_file_name, out_dir, suffix='__split', verbose=True ):
''' Based on *old_file_name*, *suffix* and *out_dir*, constructs a new file name and
writes *text* (in the ascii normalised JSON format) into the new file.
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name = os.path.basename( old_file_name )
if ... | python | def write_Text_into_file( text, old_file_name, out_dir, suffix='__split', verbose=True ):
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estnltk/estnltk | estnltk/teicorpus.py | parse_tei_corpora | def parse_tei_corpora(root, prefix='', suffix='.xml', target=['artikkel'], encoding=None):
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estnltk/estnltk | estnltk/teicorpus.py | parse_div | def parse_div(soup, metadata, target):
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estnltk/estnltk | estnltk/teicorpus.py | parse_paragraphs | def parse_paragraphs(soup):
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list of (list of str)
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estnltk/estnltk | estnltk/teicorpus.py | tokenize_documents | def tokenize_documents(docs):
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sep = '\n\n'
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for doc in docs:
text = '\n\n'.join(['\n'.join(para[SENTENCES]) for para in doc[PARAGRAPHS]])
doc[TEXT] = text
del doc[PARAGRAPHS]
texts... | python | def tokenize_documents(docs):
"""Convert the imported documents to :py:class:'~estnltk.text.Text' instances."""
sep = '\n\n'
texts = []
for doc in docs:
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NB! It overwrites the default model, so do not use it unless
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estnltk/estnltk | estnltk/wordnet/wn.py | _get_synset_offsets | def _get_synset_offsets(synset_idxes):
"""Returs pointer offset in the WordNet file for every synset index.
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-----
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synset_idxes : list of ints
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Notes
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synset_idxes : list of ints
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estnltk/estnltk | estnltk/wordnet/wn.py | _get_synsets | def _get_synsets(synset_offsets):
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Notes
-----
Internal function. Do not call directly.
Stores every parsed synset into global synset dictionary under two keys:
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"""Given synset offsets in the WordNet file, parses synset object for every offset.
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estnltk/estnltk | estnltk/wordnet/wn.py | _get_key_from_raw_synset | def _get_key_from_raw_synset(raw_synset):
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estnltk/estnltk | estnltk/wordnet/wn.py | synset | def synset(synset_key):
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synset_key : string
Unique synset identifier in the form of `lemma.pos.sense_no`.
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synset_key : string
Unique synset identifier in the form of `lemma.pos.sense_no`.
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estnltk/estnltk | estnltk/wordnet/wn.py | synsets | def synsets(lemma,pos=None):
"""Returns all synset objects which have lemma as one of the variant literals and fixed pos, if provided.
Notes
-----
Uses lazy initialization - parses only those synsets which are not yet initialized, others are fetched from a dictionary.
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----------
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"""Returns all synset objects which have lemma as one of the variant literals and fixed pos, if provided.
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estnltk/estnltk | estnltk/wordnet/wn.py | all_synsets | def all_synsets(pos=None):
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estnltk/estnltk | estnltk/wordnet/wn.py | lemma | def lemma(lemma_key):
"""Returns the Lemma object with the given key.
Parameters
----------
lemma_key : str
Key of the returned lemma.
Returns
-------
Lemma
Lemma matching the `lemma_key`.
"""
if lemma_key in LEMMAS_DICT:
return LEMMAS_DICT[lemma_key]
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"""Returns the Lemma object with the given key.
Parameters
----------
lemma_key : str
Key of the returned lemma.
Returns
-------
Lemma
Lemma matching the `lemma_key`.
"""
if lemma_key in LEMMAS_DICT:
return LEMMAS_DICT[lemma_key]
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----------
lemma : str
Literal of the sought Lemma objects.
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset._recursive_hypernyms | def _recursive_hypernyms(self, hypernyms):
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Notes
-----
Internal method. Do not call directly.
Parameters
----------
hypernyms : set of Synsets
An set of hypernyms met so far.
Returns
... | python | def _recursive_hypernyms(self, hypernyms):
"""Finds all the hypernyms of the synset transitively.
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hypernyms : set of Synsets
An set of hypernyms met so far.
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset._min_depth | def _min_depth(self):
"""Finds minimum path length from the root.
Notes
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Internal method. Do not call directly.
Returns
-------
int
Minimum path length from the root.
"""
if "min_depth" in self.__dict__:
return self.... | python | def _min_depth(self):
"""Finds minimum path length from the root.
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int
Minimum path length from the root.
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.get_related_synsets | def get_related_synsets(self,relation):
"""Retrieves all the synsets which are related by given relation.
Parameters
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relation : str
Name of the relation via which the sought synsets are linked.
Returns
-------
list of Synsets
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.closure | def closure(self, relation, depth=float('inf')):
"""Finds all the ancestors of the synset using provided relation.
Parameters
----------
relation : str
Name of the relation which is recursively used to fetch the ancestors.
Returns
-------
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relation : str
Name of the relation which is recursively used to fetch the ancestors.
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list of Synsets
Roots via hypernymy relation.
"""
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Returns
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.lch_similarity | def lch_similarity(self, synset):
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Notes
-----
Similarity is calculated using the formula -log( (dist(synset1,synset2)+1) / (2*maximum taxonomy depth) ).
Parameters
----------
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"""Calculates Leacock and Chodorow's similarity between the two synsets.
Notes
-----
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.wup_similarity | def wup_similarity(self, target_synset):
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Notes
-----
Similarity is calculated using the formula ( 2*depth(least_common_subsumer(synset1,synset2)) ) / ( depth(synset1) + depth(synset2) )
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"""Calculates Wu and Palmer's similarity between the two synsets.
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-----
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.definition | def definition(self):
"""Returns the definition of the synset.
Returns
-------
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Definition of the synset as a new-line separated concatenated string from all its variants' definitions.
"""
return '\n'.join([variant.gloss for variant in self.... | python | def definition(self):
"""Returns the definition of the synset.
Returns
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Definition of the synset as a new-line separated concatenated string from all its variants' definitions.
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Returns
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List of its variants' examples.
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List of its variants' examples.
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.lemmas | def lemmas(self):
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Returns
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List of its variations' literals as Lemma objects.
"""
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List of its variations' literals as Lemma objects.
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estnltk/estnltk | estnltk/wordnet/wn.py | Synset.lowest_common_hypernyms | def lowest_common_hypernyms(self,target_synset):
"""Returns the common hypernyms of the synset and the target synset, which are furthest from the closest roots.
Parameters
----------
target_synset : Synset
Synset with which the common hypernyms are sought.
... | python | def lowest_common_hypernyms(self,target_synset):
"""Returns the common hypernyms of the synset and the target synset, which are furthest from the closest roots.
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target_synset : Synset
Synset with which the common hypernyms are sought.
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estnltk/estnltk | estnltk/wordnet/wn.py | Lemma.synset | def synset(self):
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Returns
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Synset into which the given lemma belongs to.
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | convert_vm_json_to_mrf | def convert_vm_json_to_mrf( vabamorf_json ):
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Kolmandaks
kolmandaks+0 //_D_ //
... | python | def convert_vm_json_to_mrf( vabamorf_json ):
''' Converts from vabamorf's JSON output, given as dict, into pre-syntactic mrf
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | load_fs_mrf_to_syntax_mrf_translation_rules | def load_fs_mrf_to_syntax_mrf_translation_rules( rulesFile ):
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | _convert_punctuation | def _convert_punctuation( line ):
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | convert_mrf_to_syntax_mrf | def convert_mrf_to_syntax_mrf( mrf_lines, conversion_rules ):
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | remove_duplicate_analyses | def remove_duplicate_analyses( mrf_lines, allow_to_delete_all = True ):
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estnltk/estnltk | estnltk/syntax/syntax_preprocessing.py | SyntaxPreprocessing.process_vm_json | def process_vm_json( self, json_dict, **kwargs ):
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mrf_lines = convert_Text_to_mrf( text )
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estnltk/estnltk | estnltk/prettyprinter/terminalprettyprinter.py | _preformat | def _preformat( text, layers, markup_settings = None ):
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estnltk/estnltk | estnltk/prettyprinter/templates.py | get_mark_css | def get_mark_css(aes_name, css_value):
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The name of the class.
css_value: str
The value for the CSS property defined by aes_name.
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list of str
The CSS codeblocks
"""
... | python | def get_mark_css(aes_name, css_value):
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estnltk/estnltk | estnltk/mw_verbs/verbchain_nom_vinf_extender.py | VerbChainNomVInfExtender._loadSubcatRelations | def _loadSubcatRelations( self, inputFile ):
''' Laeb sisendfailist (inputFile) verb-nom/adv-vinf rektsiooniseoste mustrid.
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estnltk/estnltk | estnltk/mw_verbs/verbchain_nom_vinf_extender.py | VerbChainNomVInfExtender.tokenMatchesNomAdvVinf | def tokenMatchesNomAdvVinf( self, token, verb, vinf):
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estnltk/estnltk | estnltk/mw_verbs/verbchain_nom_vinf_extender.py | VerbChainNomVInfExtender.extendChainsInSentence | def extendChainsInSentence( self, sentence, foundChains ):
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clauses = getClausesByClauseIDs( sentence )
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estnltk/estnltk | estnltk/mw_verbs/verbchain_nom_vinf_extender.py | VerbChainNomVInfExtender.extendChainsInClause | def extendChainsInClause( self, clause, clauseID, foundChains ):
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estnltk/estnltk | estnltk/wiki/sections.py | sectionsParser | def sectionsParser(text):
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... | train | https://github.com/estnltk/estnltk/blob/28ae334a68a0673072febc318635f04da0dcc54a/estnltk/wiki/sections.py#L13-L144 |
estnltk/estnltk | estnltk/textcleaner.py | TextCleaner.clean | def clean(self, text):
"""Remove all unwanted characters from text."""
return ''.join([c for c in text if c in self.alphabet]) | python | def clean(self, text):
"""Remove all unwanted characters from text."""
return ''.join([c for c in text if c in self.alphabet]) | [
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estnltk/estnltk | estnltk/textcleaner.py | TextCleaner.invalid_characters | def invalid_characters(self, text):
"""Give simple list of invalid characters present in text."""
return ''.join(sorted(set([c for c in text if c not in self.alphabet]))) | python | def invalid_characters(self, text):
"""Give simple list of invalid characters present in text."""
return ''.join(sorted(set([c for c in text if c not in self.alphabet]))) | [
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estnltk/estnltk | estnltk/textcleaner.py | TextCleaner.find_invalid_chars | def find_invalid_chars(self, text, context_size=20):
"""Find invalid characters in text and store information about
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Parameters
----------
context_size: int
How many characters to return as the context.
"""
result = defaultdict(list)
... | python | def find_invalid_chars(self, text, context_size=20):
"""Find invalid characters in text and store information about
the findings.
Parameters
----------
context_size: int
How many characters to return as the context.
"""
result = defaultdict(list)
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estnltk/estnltk | estnltk/textcleaner.py | TextCleaner.compute_report | def compute_report(self, texts, context_size=10):
"""Compute statistics of invalid characters on given texts.
Parameters
----------
texts: list of str
The texts to search for invalid characters.
context_size: int
How many characters to return as the conte... | python | def compute_report(self, texts, context_size=10):
"""Compute statistics of invalid characters on given texts.
Parameters
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texts: list of str
The texts to search for invalid characters.
context_size: int
How many characters to return as the conte... | [
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texts: list of str
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How many characters to return as the context.
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