def downloadModels(self):
"""Respond to Download button (Model manager tab)."""
global INSTALLED_MODELS
# Ask for confirmation...
num_models = len(self.selectedModels)
message = "Your are about to download %i language model@p. " + \
"This may take up to several minutes depending on your " + \
"internet connection. Do you want to proceed?"
message = message % num_models
buttonReply = QMessageBox.question(self, "Textable",
pluralize(message, num_models),
QMessageBox.Ok | QMessageBox.Cancel)
if buttonReply == QMessageBox.Cancel:
return
# Download models...
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=num_models)
for model_idx in reversed(self.selectedModels):
model = self.downloadableModelLabels[model_idx]
download_spacy_model(AVAILABLE_MODELS[model])
del self.downloadableModelLabels[model_idx]
progressBar.advance()
# Update GUI...
self.downloadableModelLabels = self.downloadableModelLabels
self.selectedModels = list()
progressBar.finish()
self.controlArea.setDisabled(False)
message = "Downloaded %i language model@p, please restart " + \
"Orange for changes to take effect."
message = message % num_models
QMessageBox.information(None, "Textable",
pluralize(message, num_models), QMessageBox.Ok)
def sendData(self):
"""Compute result of widget processing and send to output"""
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input", "warning")
del self.headerList[:]
self.headerList = self.headerList
self.send("CSV Segmentation", None, self)
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.inputSeg))
# Treat...
for segment in self.csvSeg:
pass
progressBar.advance()
# Set status to OK and report data size...
outputSeg = Segmentation(self.csvSeg, label=self.captionTitle)
if len(self.contentIsNone) == 0 :
message = "%i segment@p sent to output." % len(outputSeg)
message = pluralize(message, len(outputSeg))
self.infoBox.setText(message)
# message if one or more segments has no content and has been ignored
elif len(self.contentIsNone) == 1:
message = "%i segment@p sent to output. (ignored %i segment with \
no content)" % (len(outputSeg), len(self.contentIsNone))
message = pluralize(message, len(outputSeg))
self.infoBox.setText(message)
else :
message = "%i segment@p sent to output. (ignored %i segments with \
no content)" % (len(outputSeg), len(self.contentIsNone))
message = pluralize(message, len(outputSeg))
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Send data to output...
self.send("CSV Segmentation", outputSeg, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
if not self.file:
self.infoBox.setText(u"Please select input file.", "warning")
self.send('Text data', None, self)
return
# Clear created Inputs.
self.clearCreatedInputs()
# Get transcription
try:
transcription = self.get_large_audio_transcription(
self.file,
language=self.language,
set_silence_len=self.selected_dur,
set_silence_threshold=self.selected_vol)
except speech_recognition.UnknownValueError as err:
self.infoBox.setText(
u"You seem to have overuseed the built-in API key, refer to the documentation for further informations.",
"warning")
self.send('Text data', None, self)
return
# Checks if there is a transcription
if transcription is None:
self.infoBox.setText(u"You must use mp3 or wav audio files.",
"warning")
self.send('Text data', None, self)
return
# Regex to get the name of the input file
title = self.file
regex = re.compile("[^(/\\)]+[mp3|wav]$")
match = re.findall(regex, title)
if self.selected_seg:
for chunk in transcription:
new_input = Input(chunk, label=match)
self.createdInputs.append(new_input)
else:
new_input = Input(transcription, label=match)
self.createdInputs.append(new_input)
# Concatenates the segmentations in the output segmentation
self.segmentation = Segmenter.concatenate(
segmentations=self.createdInputs,
label=self.captionTitle,
copy_annotations=False,
import_labels_as="")
#Sending segments length
message = " Succesfully transcripted ! % i segment@p sent to output" % len(
self.segmentation)
message = pluralize(message, len(self.segmentation))
# Send token...
self.send("Text data", self.segmentation, self)
self.infoBox.setText(message)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output."""
# Check that there's a model
if not self.model:
self.noLanguageModelWarning()
return
# Check that there's an input
if self.inputSeg is None:
self.infoBox.setText("Widget needs input.", "warning")
self.send('Summary', None, self)
self.send('HTML_Summary', None, self)
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
# Type of segmentation (per segment or per segmentation)
segments = list()
html_segments = list()
if self.typeSeg == "Summarize each segments individually":
# Process each segment separately, then create segmentation
for segment in self.inputSeg:
content = segment.get_content()
resume, html_resume = self.summarize(self.cv, content)
segments.append(Segment(str_index=resume[0].str_index, ))
html_segments.append(
Segment(str_index=html_resume[0].str_index, ))
elif self.typeSeg == "Summarize all segments as one":
merged_seg = " ".join(
[segment.get_content() for segment in self.inputSeg])
resume, html_resume = self.summarize(self.cv, merged_seg)
segments.append(Segment(str_index=resume[0].str_index, ))
html_segments.append(Segment(str_index=html_resume[0].str_index, ))
# Create segmentation from segment() and assign it to the output
self.outputSeg = Segmentation(segments, self.captionTitle)
self.html_outputSeg = Segmentation(html_segments, self.captionTitle)
# Send segmentation to output channels
self.send("Summary", self.outputSeg, self)
self.send('HTML_Summary', self.html_outputSeg, self)
# Set message to sent
message = "%i segment@p sent to output " % len(self.outputSeg)
message = pluralize(message, len(self.outputSeg))
self.infoBox.setText(message)
self.sendButton.resetSettingsChangedFlag()
self.controlArea.setDisabled(False)
def sendData(self):
"""Compute result of widget processing and send to output"""
# An input is needed
if self.inputSeg == None:
self.infoBox.setText(
"A segmentation input is needed.",
"warning"
)
self.send("Segmentation with annotations", None, self)
return
# Skip if no list is selected
if self.titleLabels == None:
self.infoBox.setText(
"You need to define at least one lexical list.",
"error"
)
self.send("Segmentation with annotations", None, self)
return
# A list must have been selected
if len(self.selectedFields) == 0:
self.infoBox.setText(
"Please select one or more lexical lists.",
"warning"
)
self.send("Segmentation with annotations", None, self)
return
# A annotation key must have been defined
"""
if self.labelName == "":
self.infoBox.setText(
"An annotation key is needed.",
"warning"
)
self.send("Segmentation with annotations", None, self)
return
"""
self.huntTheLexic()
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.outputSeg)
message = pluralize(message, len(self.outputSeg))
# Segmentation go to outputs...
self.send("Segmentation with annotations", self.outputSeg, self)
self.infoBox.setText(message)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input", "warning")
self.send("Linguistically analyzed data", None, self)
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.inputSeg))
# Basic NLP analysis for dev purposes...
analyzedSegments = list()
for segment in self.inputSeg:
analyzedString = ""
doc = self.nlp(segment.get_content())
for token in doc:
analyzedString += "%s\t%s\n" % (token.text, token.pos_)
analyzedSegments.append(Input(analyzedString))
progressBar.advance()
outputSeg = LTTL.Segmenter.concatenate(
analyzedSegments,
import_labels_as=None,
label=self.captionTitle,
)
# Set status to OK and report data size...
message = "%i segment@p sent to output." % len(outputSeg)
message = pluralize(message, len(outputSeg))
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Send data to output...
self.send("Linguistically analyzed data", outputSeg, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Skip if title list is empty:
if self.titleLabels == list():
return
# Check that something has been selected...
if len(self.selectedTitles) == 0:
self.infoBox.setText("Please select one or more titles.",
"warning")
self.send("XML-TEI data", None, self)
return
# Clear created Inputs.
self.clearCreatedInputs()
# Initialize progress bar.
progressBar = gui.ProgressBar(self,
iterations=len(self.selectedTitles))
# Attempt to connect to ECP and retrieve plays...
xml_contents = list()
annotations = list()
try:
for title in self.selectedTitles:
doc_url = self.document_base_url + \
self.filteredTitleSeg[title].annotations["url"]
print(doc_url)
url = re.sub(r"/([^/]+)\.shtml", r"/\1/\1.xml", doc_url)
print(url)
response = urllib.request.urlopen(url)
xml_contents.append(response.read().decode('utf-8'))
source_annotations = \
self.filteredTitleSeg[title].annotations.copy()
#source_annotations["url"] = source_annotations["href"]
#del source_annotations["href"]
annotations.append(source_annotations)
progressBar.advance() # 1 tick on the progress bar...
# If an error occurs (e.g. http error, or memory error)...
except:
#Set Info box and widget to "error" state.
self.infoBox.setText("Couldn't download data from ECP website.",
"error")
# Reset output channel.
self.send("XML-TEI data", None, self)
return
# Store downloaded XML in input objects...
for xml_content_idx in range(len(xml_contents)):
newInput = Input(xml_contents[xml_content_idx], self.captionTitle)
self.createdInputs.append(newInput)
# If there"s only one play, the widget"s output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget"s output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle,
import_labels_as=None,
)
# Annotate segments...
for idx, segment in enumerate(self.segmentation):
segment.annotations.update(annotations[idx])
self.segmentation[idx] = segment
# Store imported URLs as setting.
self.importedURLs = [
self.filteredTitleSeg[self.selectedTitles[0]].annotations["url"]
]
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += "(%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
progressBar.finish()
# Clear progress bar.
progressBar.finish()
# Send token...
self.send("XML-TEI data", self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
# Clear created Inputs...
self.clearCreatedInputs()
if not self.TreetaggerPath:
self.infoBox.setText(self.noTreetaggerPathWarning, "warning")
self.send("Tagged data", None)
return
elif not self.getAvailableLanguages():
self.infoBox.setText(self.noLanguageParameterWarning, "warning")
self.send("Tagged data", None)
return
elif not self.segmentation:
self.infoBox.setText(u"Widget needs input", "warning")
self.send("Tagged data", None)
return
# Initialize progress bar.
self.infoBox.setText(u"Processing, please wait...", "warning")
self.controlArea.setDisabled(True)
self.progressBar = ProgressBar(self, iterations=5)
# Create a copy of input seg, storing annotations in temp attr...
copy_of_input_seg = Segmentation()
copy_of_input_seg.label = self.segmentation.label
for seg_idx, segment in enumerate(self.segmentation):
attr = " ".join([
"%s=%s" % (
''.join(c for c in unicodedata.normalize('NFD', item[0])
if unicodedata.category(c) != 'Mn'),
quoteattr(str(item[1])),
) for item in segment.annotations.items()
])
segment.annotations["tt_ax"] = attr
copy_of_input_seg.append(segment)
self.progressBar.advance()
# Dump segmentation in unique string to avoid multiple calls to TT...
concatenated_text = copy_of_input_seg.to_string(
formatting="<ax_tt %(tt_ax)s>%(__content__)s</ax_tt>",
display_all=True,
)
self.progressBar.advance()
# Tag the segmentation contents...
tagopt = '-token -lemma -sgml -quiet'
if self.replaceUnknown:
tagopt += " -no-unknown"
tagger = treetaggerwrapper.TreeTagger(
TAGLANG=pycountry.languages.get(name=self.language).alpha_2,
TAGOPT=tagopt,
TAGDIR=self.TreetaggerPath,
)
tagged_lines = tagger.tag_text(
concatenated_text,
notagurl=True,
notagemail=True,
notagip=True,
notagdns=True,
)
tagged_input = Input("\n".join(tagged_lines))
self.createdInputs.append(tagged_input)
# Replace <unknown> with [unknown] and " with " then
# re-segment to match the original segmentation structure.
tagged_segmentation, _ = Segmenter.recode(
tagged_input,
substitutions=[
(re.compile(r"<unknown>"), "[unknown]"),
(re.compile(r'"""'), '"""'),
],
)
tagged_segmentation = Segmenter.import_xml(tagged_segmentation,
"ax_tt")
self.progressBar.advance()
# Place each output line of Treetagger in an xml tag with annotations..
xml_segmentation, _ = Segmenter.recode(
tagged_segmentation,
substitutions=[
(re.compile(r"(.+)\t(.+)\t(.+?)(?=[\r\n])"),
'<w lemma="&3" pos-tag="&2">&1</w>'),
(re.compile(r'^\n|\n$'), ''),
],
)
# Segment into individual tokens if XML output option is disabled...
if self.outputFormat == "add XML tags":
output_segmentation = xml_segmentation
else:
try:
output_segmentation = Segmenter.import_xml(
xml_segmentation, "w")
except ValueError:
self.infoBox.setText(
"Please check that either the input contains well-formed "
"XML, or it doesn't contain instances of '<' and '\x3e'",
"error")
self.send("Tagged data", None)
self.progressBar.finish()
self.controlArea.setDisabled(False)
return
self.progressBar.finish()
self.controlArea.setDisabled(False)
output_segmentation.label = self.captionTitle
message = u'%i segment@p sent to output.' % len(output_segmentation)
message = pluralize(message, len(output_segmentation))
self.infoBox.setText(message)
self.send('Tagged data', output_segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Send data from website springfieldspringfield"""
# Skip if title list is empty:
if self.myBasket == list():
self.infoBox.setText(
"Your corpus is empty, please add some movies first",
"warning"
)
self.segmentation = None
self.send("Movie transcripts", self.segmentation, self)
return
# Clear created Inputs.
self.clearCreatedInputs()
annotations = list()
script_list = list()
annotations_dict = dict()
self.controlArea.setDisabled(True)
# Initialize progress bar.
progressBar = ProgressBar(self, iterations=len(self.myBasket))
# This part of code is what fetches the actual script
try:
for movie in self.myBasket:
# Each movie that is in the corpus is split into title and year
# (rsplit makes sure to only split last occurence) which will
# become annotations
b = copy.copy(movie)
future_annotation = b.rsplit('(', 1)
movie_title = future_annotation[0]
movie_year = future_annotation[-1]
movie_year = movie_year[:-1]
annotations_dict["Movie Title"] = movie_title
annotations_dict["Year of release"] = movie_year
# It is important to make a copy of dictionary, otherwise each
# iteration will replace every element of the annotations list
annotations.append(annotations_dict.copy())
# link_end and page_url are the two variables that will have to
# be changed in case scripts need to be taken from elsewhere
link_end = self.path_storage[movie]
page_url = "https://www.springfieldspringfield.co.uk/" + \
"movie_script.php?movie=" + link_end
page = urllib.request.urlopen(page_url)
soup = BeautifulSoup(page, 'html.parser')
# This is what grabs the movie script
script = soup.find("div", {"class":"movie_script"})
script_list.append(script.text)
# 1 tick on the progress bar of the widget
progressBar.advance()
except:
self.infoBox.setText(
"Couldn't download data from SpringfieldSpringfield website.",
"error"
)
self.controlArea.setDisabled(False)
return
# Store downloaded script strings in input objects...
for script in script_list:
newInput = Input(script, self.captionTitle)
self.createdInputs.append(newInput)
# If there's only one play, the widget"s output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget"s output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle,
import_labels_as=None,
)
# Annotate segments...
for idx, segment in enumerate(self.segmentation):
segment.annotations.update(annotations[idx])
self.segmentation[idx] = segment
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += "(%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send("Movie transcripts", self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Skip if title list is empty:
if self.myBasket == list():
self.infoBox.setText(
"Your corpus is empty, please add some books first", "warning")
return
# Clear created Inputs.
self.clearCreatedInputs()
self.controlArea.setDisabled(True)
# Initialize progress bar.
progressBar = ProgressBar(
self,
iterations=len(self.myBasket),
)
text_content = list()
annotations = list()
try:
# Retrieve selected texts from gutenberg
for text in self.myBasket:
gutenberg_id = text[2]
# Get the text with Gutenbergpy
gutenberg_text = gutenbergpy.textget.strip_headers(
gutenbergpy.textget.get_text_by_id(gutenberg_id)).decode(
"utf-8")
text_content.append(gutenberg_text)
# populate the annotation list
annotations.append([text[0], text[1], text[3]])
progressBar.advance()
# If an error occurs (e.g. http error, or memory error)...
except Exception as exc:
# Set Info box and widget to "error" state.
self.infoBox.setText("Couldn't download data from Gutenberg",
"error")
self.controlArea.setDisabled(False)
print(exc)
return
# Store downloaded text strings in input objects...
for text in text_content:
newInput = Input(text, self.captionTitle)
self.createdInputs.append(newInput)
# If there's only one text, the widget's output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget"s output is a concatenation.
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle,
import_labels_as=None,
)
# Annotate segments with book metadata
for idx, segment in enumerate(self.segmentation):
segment.annotations.update({"title": annotations[idx][0]})
segment.annotations.update({"author": annotations[idx][1]})
segment.annotations.update({"language": annotations[idx][2]})
self.segmentation[idx] = segment
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += "(%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send("Gutenberg importation", self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Skip if title list is empty:
if self.myBasket == list():
self.infoBox.setText(
"Your corpus is empty, please add some books first", "warning")
return
# Clear created Inputs.
self.clearCreatedInputs()
self.controlArea.setDisabled(True)
# Initialize progress bar.
progressBar = ProgressBar(
self,
iterations=len(self.myBasket),
)
selectedTexts = list()
text_content = list()
annotations = list()
# get the Gutenberg cache
cache = GutenbergCache.get_cache()
try:
# TODO: Retrieve selected texts from gutenberg
for text in self.myBasket:
# Get the id of the text
query_id = cache.native_query(
sql_query=
"select gutenbergbookid from books where id == {selected_id}"
.format(selected_id=text[2]))
gutenberg_id = list(query_id)
# Get the text with Gutenbergpy
gutenberg_text = gutenbergpy.textget.strip_headers(
gutenbergpy.textget.get_text_by_id(gutenberg_id[0][0]))
text_content.append(gutenberg_text)
annotations.append(text[1])
progressBar.advance()
# If an error occurs (e.g. http error, or memory error)...
except Exception:
# Set Info box and widget to "error" state.
self.infoBox.setText("Couldn't download data from Gutenberg",
"error")
self.controlArea.setDisabled(False)
return
# TODO: send gutenberg texts as output
# Store downloaded lyrics strings in input objects...
for text in text_content:
newInput = Input(text, self.captionTitle)
self.createdInputs.append(newInput)
# If there"s only one play, the widget"s output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget"s output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle,
import_labels_as=None,
)
# TODO: annotate with book metadata
# Annotate segments...
for idx, segment in enumerate(self.segmentation):
segment.annotations.update({"title": annotations[idx]})
self.segmentation[idx] = segment
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += "(%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send("Gutenberg importation", self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Load folders, create and send segmentation"""
# Check that there's something on input...
if (self.displayAdvancedSettings
and not self.folders) or not (self.rootFolderPath
or self.displayAdvancedSettings):
self.infoBox.setText(u'Please select input folder.', 'warning')
self.send('Text data', None, self)
return
# Check that autoNumberKey is not empty (if necessary)...
if self.displayAdvancedSettings and self.autoNumber:
if self.autoNumberKey:
autoNumberKey = self.autoNumberKey
else:
self.infoBox.setText(
u'Please enter an annotation key for auto-numbering.',
'warning')
self.send('Text data', None, self)
return
else:
autoNumberKey = None
# Clear created Inputs...
self.clearCreatedInputs()
annotations = list()
counter = 1
if self.displayAdvancedSettings:
myFolders = self.folders
else:
myFolders = [self.folder]
# Annotations...
allFileListContent = list()
for myFolder in myFolders:
myFiles = myFolder['fileList']
for myFile in myFiles:
annotation = dict()
annotation['file name'] = myFile['fileName']
annotation['file depth level'] = myFile['depthLvl']
annotation['file path'] = myFile['absoluteFilePath']
try:
annotation['file encoding, confidence'] = myFile[
'encoding'] + ", " + str(myFile['encodingConfidence'])
except TypeError:
annotation['file encoding, confidence'] = "unknown"
depths = [k for k in myFile.keys() if k.startswith('depth_')]
for depth in depths:
annotation[depth] = myFile[depth]
annotations.append(annotation)
allFileListContent.append(myFile['fileContent'])
# Create an LTTL.Input for each files...
if len(allFileListContent) == 1:
label = self.captionTitle
else:
label = None
for index in range(len(allFileListContent)):
myInput = Input(allFileListContent[index], label)
segment = myInput[0]
segment.annotations.update(annotations[index])
myInput[0] = segment
self.createdInputs.append(myInput)
# If there's only one file, the widget's output is the created Input.
if len(allFileListContent) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget's output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
segmentations=self.createdInputs,
label=self.captionTitle,
copy_annotations=True,
import_labels_as=None,
sort=False,
auto_number_as=None,
merge_duplicates=False,
progress_callback=None,
)
message = u'%i segment@p sent to output ' % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += u'(%i character@p).' % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send('Text data', self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output."""
# Check that there's a model...
if not self.model:
self.infoBox.setText(
"Please download a language model first.",
"warning",
)
self.tabs.setCurrentIndex(1)
return
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input.", "warning")
for channel in [c.name for c in self.outputs]:
self.send(channel, None, self)
return
# Check max length and adjust if needed...
inputLength = sum(len(s.get_content()) for s in self.inputSeg)
if self.maxLen != "no limit":
maxNumChar = int(self.maxLen.split()[0]) * 1000000
if inputLength > maxNumChar:
self.infoBox.setText(
"Input exceeds max number of characters set by user.",
"warning",
)
for channel in [c.name for c in self.outputs]:
self.send(channel, None, self)
return
else:
if inputLength > self.nlp.max_length:
maxNumChar = inputLength
# Load components if needed...
disabled, enabled = self.getComponentStatus()
if self.mustLoad or not(
self.nlp and set(enabled) <= set(self.loadedComponents)
):
self.loadModel()
self.nlp.max_length = maxNumChar
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.inputSeg))
tokenSegments = list()
entitySegments = list()
chunkSegments = list()
sentenceSegments = list()
# Process each input segment...
for segment in self.inputSeg:
# NLP analysis...
disabled, _ = self.getComponentStatus()
disabled = [c for c in disabled if c in set(self.loadedComponents)]
with self.nlp.disable_pipes(*disabled):
doc = self.nlp(segment.get_content())
# Get token segments...
tokenSegments.extend(spacyItemsToSegments(doc, segment))
# Get named entity segments...
if self.segmentEntities:
entitySegments.extend(spacyItemsToSegments(doc.ents, segment))
# Get noun chunk segments...
if self.segmentChunks:
chunkSegments.extend(
spacyItemsToSegments(doc.noun_chunks, segment),
)
# Get sentences segments...
if self.segmentSentences:
sentenceSegments.extend(
spacyItemsToSegments(doc.sents, segment),
)
progressBar.advance()
# Build segmentations and send them to output...
tokenSeg = Segmentation(tokenSegments, self.captionTitle + "_tokens")
self.send("Tokenized text", tokenSeg, self)
if self.segmentChunks:
chunkSeg = Segmentation(
chunkSegments,
self.captionTitle + "_chunks",
)
self.send("Noun chunks", chunkSeg, self)
if self.segmentEntities:
entitySeg = Segmentation(
entitySegments,
self.captionTitle + "_entities",
)
self.send("Named entities", entitySeg, self)
if self.segmentSentences:
sentenceSeg = Segmentation(
sentenceSegments,
self.captionTitle + "_sentences",
)
self.send("Sentences", sentenceSeg, self)
# Set status to OK and report data size...
message = "%i token@p" % len(tokenSeg)
message = pluralize(message, len(tokenSeg))
if self.segmentChunks:
message += ", %i chunk@p" % len(chunkSeg)
message = pluralize(message, len(chunkSeg))
if self.segmentEntities:
message += ", %i " % len(entitySeg)
message += "entity" if len(entitySeg) == 1 else "entities"
if self.segmentSentences:
message += ", %i sentence@p" % len(sentenceSeg)
message = pluralize(message, len(sentenceSeg))
message += " sent to output."
last_comma_idx = message.rfind(",")
if last_comma_idx > -1:
message = message[:last_comma_idx] + " and" + \
message[last_comma_idx+1:]
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
if not self.importedCorpora:
self.infoBox.setText("Please add a corpus to the selection.",
"warning")
self.send("Files", None, self)
self.send("Utterances", None, self)
return
# Clear created Inputs and initialize progress bar...
self.clearCreatedInputs()
numberOfSteps = 2 if self.outputUtterances else 1
numberOfSteps += 2 if self.outputWords else 0
self.infoBox.setText(
"(1/%i) Retrieving data, please wait..." % numberOfSteps,
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.importedCorpora))
annotations = list()
# Iterate over corpora...
for importedCorpus in self.importedCorpora:
corpus = importedCorpus.split("/")[-1]
# Try to retrieve corpus from cache...
try:
basepath = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
corpusFilepath = os.path.normpath(
os.path.join(
basepath,
self.__class__.cachedFoldername,
importedCorpus[len(self.__class__.baseUrl):],
))
myZip = zipfile.ZipFile(corpusFilepath)
except IOError:
# Else try to download (and cache) requested zip file...
try:
response = requests.get(importedCorpus)
myZip = zipfile.ZipFile(io.BytesIO(response.content))
corpusFolderpath = os.path.dirname(corpusFilepath)
try:
os.makedirs(corpusFolderpath)
except OSError:
pass
try:
outputFile = open(corpusFilepath, "wb")
outputFile.write(response.content)
outputFile.close()
except IOError:
pass
# If an error occurs (e.g. connection error)...
except:
# Set Info box and widget to "error" state.
self.infoBox.setText(
"Couldn't download corpus %s from CHILDES website." %
corpus, "error")
# Reset output channel.
self.send("Files", None, self)
self.send("Utterances", None, self)
progressBar.finish()
self.controlArea.setDisabled(False)
return
# Create Input for each zipped file and store annotations...
for file in myZip.infolist():
file_content = myZip.read(file).decode('utf-8')
# If word segmentation is requested...
if self.outputWords:
# Implement replacements.
file_content = re.sub(
r"<w.+?(<replacement.+</replacement>).*?</w>",
r"\1",
file_content,
)
# Prepend pre-clitics.
file_content, n = re.subn(
r"(<mor .+?)(<mor-pre>.+</mor-pre>)",
r"\2\1",
file_content,
)
# Move <gra> into <mw>.
file_content, n = re.subn(
r"(</mw>)(<gra.+?/>)",
r"\2\1",
file_content,
)
newInput = Input(file_content, self.captionTitle + "_files")
self.createdInputs.append(newInput)
chatSeg = Segmenter.import_xml(newInput, "CHAT")
annotations.append(dict())
annotations[-1]["file_path"] = file.filename
for key in ["Corpus", "Lang", "PID"]:
try:
annotations[-1][key.lower()] = \
chatSeg[0].annotations[key]
except KeyError:
pass
participantListSeg = Segmenter.import_xml(
newInput, "Participants")
recodedInput, _ = Segmenter.recode(
participantListSeg,
[(re.compile("/>"), "> </participant>")])
participantSeg = Segmenter.import_xml(recodedInput,
"participant")
targetChildData = list()
for participant in participantSeg:
if participant.annotations["role"] != "Target_Child":
continue
targetChildData.append(dict())
if "age" in participant.annotations:
targetChildData[-1]["target_child_age"] = \
participant.annotations["age"]
age_parse = re.search(
r"(\d+)Y(\d+)M(\d+)D",
participant.annotations["age"],
)
if age_parse:
targetChildData[-1]["target_child_years"] = \
age_parse.group(1)
months = int(age_parse.group(2)) \
+ 12 * int(age_parse.group(1))
targetChildData[-1]["target_child_months"] = \
'%02d' % months
days = int(age_parse.group(3)) \
+ 30 * months
targetChildData[-1]["target_child_days"] = \
'%02d' % days
if "id" in participant.annotations:
targetChildData[-1]["target_child_id"] = \
participant.annotations["id"]
if "sex" in participant.annotations:
targetChildData[-1]["target_child_sex"] = \
participant.annotations["sex"]
if len(targetChildData) == 1:
annotations[-1].update(targetChildData[0])
progressBar.advance()
# If there's only one file, the widget's output is the created Input...
if len(self.createdInputs) == 1:
self.fileSegmentation = self.createdInputs[0]
# Otherwise the widget's output is a concatenation...
else:
self.fileSegmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle + "_files",
import_labels_as=None,
)
# Annotate segments...
for idx, segment in enumerate(self.fileSegmentation):
segment.annotations.update(annotations[idx])
self.fileSegmentation[idx] = segment
# Terminate progress bar...
progressBar.finish()
message = "%i file@p" % len(self.fileSegmentation)
message = pluralize(message, len(self.fileSegmentation))
self.send("Files", self.fileSegmentation, self)
# Build utterance segmentation if needed...
if self.outputUtterances:
self.infoBox.setText(
"(2/%i) Building utterance segmentation, please wait..." \
% numberOfSteps,
"warning",
)
progressBar = ProgressBar(self,
iterations=len(self.fileSegmentation))
self.utteranceSegmentation = Segmenter.import_xml(
self.fileSegmentation,
"u",
progress_callback=progressBar.advance,
label=self.captionTitle + "_utterances",
)
progressBar.finish()
message += " and " if not self.outputWords else ", "
message += "%i utterance@p" % len(self.utteranceSegmentation)
message = pluralize(message, len(self.utteranceSegmentation))
self.send("Utterances", self.utteranceSegmentation, self)
else:
self.send("Utterances", None, self)
# Build word segmentation if needed...
if self.outputWords:
self.infoBox.setText(
"(%i/%i) Building word segmentation, please wait..." \
% (2 + (1 if self.outputUtterances else 0), numberOfSteps),
"warning",
)
try:
baseSegmentation = self.utteranceSegmentation
except:
baseSegmentation = self.fileSegmentation
progressBar = ProgressBar(self,
iterations=2 * len(baseSegmentation))
wordSegmentation = Segmenter.import_xml(
baseSegmentation,
"w",
progress_callback=progressBar.advance,
)
mwSegmentation = Segmenter.import_xml(
baseSegmentation,
"mw",
progress_callback=progressBar.advance,
)
# Analyze words to extract annotations...
self.infoBox.setText(
"(%i/%i) Extracting word annotations, please wait..." \
% (3 + (1 if self.outputUtterances else 0), numberOfSteps),
"warning",
)
progressBar.finish()
progressBar = ProgressBar(self, iterations=len(wordSegmentation))
wordSegments = list()
for word in wordSegmentation:
mws = word.get_contained_segments(mwSegmentation)
if mws:
for mw in mws:
wordSegment = word.deepcopy()
wordSegment.annotations.update(
self.extractWordAnnotations(mw))
wordSegments.append(wordSegment)
else:
wordSegments.append(word)
progressBar.advance()
self.wordSegmentation = Segmentation(
wordSegments,
label=self.captionTitle + "_words",
)
message += " and %i word@p" % len(self.wordSegmentation)
message = pluralize(message, len(self.wordSegmentation))
self.send("Words", self.wordSegmentation, self)
else:
self.send("Words", None, self)
# Set status to OK and report data size...
message += " sent to output."
message = pluralize(message, len(self.fileSegmentation))
self.infoBox.setText(message)
progressBar.finish()
self.controlArea.setDisabled(False)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Load folders, create and send segmentation"""
# Check that there's something on input...
if (
(self.displayAdvancedSettings and not self.folders) or
not (self.rootFolderPath or self.displayAdvancedSettings)
):
self.infoBox.setText(u'Please select input folder.', 'warning')
self.send('Text data', None, self)
return
# Check that autoNumberKey is not empty (if necessary)...
if self.displayAdvancedSettings and self.autoNumber:
if self.autoNumberKey:
autoNumberKey = self.autoNumberKey
else:
self.infoBox.setText(
u'Please enter an annotation key for auto-numbering.',
'warning'
)
self.send('Text data', None, self)
return
else:
autoNumberKey = None
# Clear created Inputs...
self.clearCreatedInputs()
fileContents = list()
annotations = list()
counter = 1
if self.displayAdvancedSettings:
myFolders = self.folders
else:
myFolders = [[self.rootFolderPath]]
progressBar = gui.ProgressBar(
self,
iterations=len(myFolders)
)
# Walk through each folder and open each files successively...
fileContents = self.fileContents
# Annotations...
myFolders = self.folders
for myFolder in myFolders:
myFiles = myFolder['fileList']
for myFile in myFiles:
# print(myFile)
annotation = dict()
if self.importFileNameKey:
annotation[self.importFileNameKey] = myFile['fileName']
if self.importFolderNameKey:
annotation[self.importFolderNameKey] = myFile['folderName']
if self.FolderDepth1Key:
annotation[self.FolderDepth1Key] = myFile['depth1']
if self.FolderDepth2Key:
annotation[self.FolderDepth2Key] = myFile['depth2']
if self.FolderDepthLvl:
annotation[self.FolderDepthLvl] = myFile['depthLvl']
annotations.append(annotation)
# progressBar.advance()
# Create an LTTL.Input for each files...
if len(fileContents) == 1:
label = self.captionTitle
else:
label = None
for index in range(len(fileContents)):
myInput = Input(fileContents[index], label)
segment = myInput[0]
segment.annotations.update(annotations[index])
myInput[0] = segment
self.createdInputs.append(myInput)
# If there's only one file, the widget's output is the created Input.
if len(fileContents) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget's output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
segmentations=self.createdInputs,
label=self.captionTitle,
copy_annotations=True,
import_labels_as=None,
sort=False,
auto_number_as=None,
merge_duplicates=False,
progress_callback=None,
)
message = u'%i segment@p sent to output ' % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += u'(%i character@p).' % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
progressBar.finish()
self.send('Text data', self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Clear morphology...
self.morphology = dict()
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input", "warning")
self.send("Morphologically analyzed data", None, self)
self.updateGUI()
return
# Perform morphological analysis...
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait (word count)...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=100)
# Word count...
wordCounts = collections.Counter(
[segment.get_content() for segment in self.inputSeg])
self.morphology["wordCounts"] = wordCounts
self.infoBox.setText(
u"Processing, please wait (signature extraction)...",
"warning",
)
progressBar.advance(5) # 5 ticks on the progress bar...
# Learn signatures...
try:
lxa5crab.crab_nebula.MIN_STEM_LEN = self.minStemLen
signatures, stems, suffixes = lxa5crab.find_signatures(wordCounts)
self.morphology["signatures"] = signatures
self.morphology["stems"] = stems
self.morphology["suffixes"] = suffixes
except ValueError as e:
self.infoBox.setText(e.__str__(), "warning")
self.send("Morphologically analyzed data", None, self)
self.controlArea.setDisabled(False)
progressBar.finish() # Clear progress bar.
self.morphology = dict()
self.updateGUI()
return
self.infoBox.setText(
u"Processing, please wait (word parsing)...",
"warning",
)
progressBar.advance(80)
# Parse words...
parser = lxa5crab.build_parser(wordCounts, signatures, stems, suffixes)
self.morphology["parser"] = parser
newSegments = list()
num_analyzed_words = 0
for segment in self.inputSeg:
parses = parser[segment.get_content()]
newSegment = segment.deepcopy()
if parses[0].signature:
num_analyzed_words += 1
newSegment.annotations.update(
{
"stem": parses[0].stem,
"suffix": parses[0].suffix \
if len(parses[0].suffix) else "NULL",
"signature": parses[0].signature
}
)
newSegments.append(newSegment)
self.send(
"Morphologically analyzed data",
Segmentation(newSegments, self.captionTitle),
self,
)
self.updateGUI()
progressBar.advance(15)
# Set status to OK and report data size...
message = "%i segment@p sent to output (%.2f%% analyzed)." % (len(
self.inputSeg), (num_analyzed_words / len(self.inputSeg) * 100))
message = pluralize(message, len(self.inputSeg))
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
self.sendButton.resetSettingsChangedFlag()
def treat_input(self):
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input", "warning")
del self.headerList[:]
self.headerList = self.headerList
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.inputSeg))
# clear lists
del self.csvSeg[:]
del self.contentIsNone[:]
# Process each input segment...
for segment in self.inputSeg:
# Input segment attributes...
inputContent = segment.get_content()
if not self.deleteQuotes == False :
inputContent = inputContent.replace('"',"")
inputAnnotations = segment.annotations
inputStrIdx = segment.str_index
inputStart = segment.start or 0
inputEnd = segment.end or len(inputContent)
#Call data processing
csv_stream = io.StringIO(inputContent)
dialect = sniffer.sniff(csv_stream.readline())
dialect.quoting=csv.QUOTE_NONE
csv_stream.seek(0)
my_reader = csv.reader(csv_stream, dialect)
position = 0
# Process each seg in inputContent
for seg in inputContent:
segAnnotations = inputAnnotations.copy()
# This will launch if sniffer detects a header in the content.
if sniffer.has_header(inputContent) == True:
# go back to the start otherwise we're going to start from the
# second row
csv_stream.seek(0)
# the header row is defined here.
if self.isRenamed == False :
self.dict_keys = next(my_reader)
for key in self.dict_keys:
# this is position of first content
# TODO : separator length (if not 1)
position += (len(key) + 1)
else :
input_keys = next(my_reader)
for key in input_keys:
# this is position of first content
# TODO : separator length (if not 1)
position += (len(key) + 1)
# This will launch if sniffer does not detect a header
# in the content.
if sniffer.has_header(inputContent) == False:
# go back to the start otherwise we're going to start from the
# second row. we do this here even though we don't really care
# about the first row simply because in general we consider the
# first row to not have any missing values
csv_stream.seek(0)
first_row = next(my_reader)
n_cols = len(first_row)
if self.isRenamed == False :
self.dict_keys = list()
for item in range(1, n_cols+1):
self.dict_keys.append(str(item))
csv_stream.seek(0)
# clear the list before appending
del self.headerList[:]
for key in self.dict_keys:
# appends the headers to the gui list
if self.dict_keys.index(key) == self.content_column:
self.headerList.append(str(key)+"(*content)")
self.headerList = self.headerList
else :
self.headerList.append(str(key))
self.headerList = self.headerList
for idx, row in enumerate(my_reader, start=2):
# Get old annotations in new dictionary
oldAnnotations = inputAnnotations.copy()
segAnnotations = dict()
# initiate next row starting position
next_position = position
for key in oldAnnotations.keys():
segAnnotations[key] = oldAnnotations[key]
# This is the main part where we transform our data into
# annotations.
for key in self.dict_keys:
# segAnnotations["length"] = position
# segAnnotations["row"] = str(row)
# if column is content (first column (0) by default)
if self.dict_keys.index(key) == self.content_column:
# put value as content
content = row[self.dict_keys.index(key)]
# else we put value in annotation
else:
# only if value is not None
if len(row[self.dict_keys.index(key)]) != 0 :
segAnnotations[key] = row[self.dict_keys.index(key)]
# implement position and next_position depending on
# content column
if self.dict_keys.index(key) < self.content_column:
position += len(row[self.dict_keys.index(key)]) + 1
next_position += len(row[self.dict_keys.index(key)]) + 1
if self.dict_keys.index(key) >= self.content_column:
next_position += len(row[self.dict_keys.index(key)]) + 1
if len(content) != 0:
self.csvSeg.append(
Segment(
str_index = inputStrIdx,
start = position,
end = position + len(content),
annotations = segAnnotations
)
)
else :
# if no content, add idx of the row and do not append
# TODO : something with contentIsNone
self.contentIsNone.append(idx)
# initiate new row starting position
position = next_position
progressBar.advance()
unSeg = len(self.csvSeg)
# Set status to OK and report segment analyzed...
message = "%i segment@p analyzed." % unSeg
message = pluralize(message, unSeg)
message += " (Ignored %i segment@p with no content)" % \
len(self.contentIsNone)
message = pluralize(message, len(self.contentIsNone))
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
self.sendButton.resetSettingsChangedFlag()
self.sendButton.sendIf()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Skip if title list is empty:
if self.myBasket == list():
self.infoBox.setText(
"Your corpus is empty, please add some songs first", "warning")
return
# Clear created Inputs.
self.clearCreatedInputs()
self.controlArea.setDisabled(True)
# Initialize progress bar.
progressBar = ProgressBar(self, iterations=len(self.myBasket))
# Attempt to connect to Genius and retrieve lyrics...
selectedSongs = list()
song_content = list()
annotations = list()
try:
for song in self.myBasket:
# song is a dict {'idx1':{'title':'song1'...},
# 'idx2':{'title':'song2'...}}
page_url = "http://genius.com" + song['path']
lyrics = self.html_to_text(page_url)
song_content.append(lyrics)
annotations.append(song.copy())
# 1 tick on the progress bar of the widget
progressBar.advance()
# If an error occurs (e.g. http error, or memory error)...
except:
# Set Info box and widget to "error" state.
self.infoBox.setText("Couldn't download data from Genius website.",
"error")
self.controlArea.setDisabled(False)
return
# Store downloaded lyrics strings in input objects...
for song in song_content:
newInput = Input(song, self.captionTitle)
self.createdInputs.append(newInput)
# If there"s only one play, the widget"s output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget"s output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
self.captionTitle,
import_labels_as=None,
)
# Annotate segments...
for idx, segment in enumerate(self.segmentation):
segment.annotations.update(annotations[idx])
self.segmentation[idx] = segment
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Set status to OK and report data size...
message = "%i segment@p sent to output " % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += "(%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send("Lyrics importation", self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Skip if title list is empty:
if self.myBasket == list():
self.infoBox.setText(
"Your corpus is empty, please add some movies first",
"warning")
return
# Clear created Inputs.
self.clearCreatedInputs()
self.controlArea.setDisabled(True)
# Initialize progress bar.
progressBar = ProgressBar(self, iterations=len(self.myBasket))
# Connect to imdb and add elements in lists
list_review = list()
list_annotation = list()
annotations = list()
try:
for item in self.myBasket:
movie = self.ia.get_movie_reviews(item['id'])
movie_annotations = self.ia.get_movie(item['id'])
list_review.append(movie)
list_annotation.append(movie_annotations)
# 1 tick on the progress bar of the widget
progressBar.advance()
# If an error occurs (e.g. http error, or memory error)...
except:
# Set Info box and widget to "error" state.
self.infoBox.setText("Couldn't download data from imdb", "error")
self.controlArea.setDisabled(False)
return
# Store movie critics strings in input objects...
for movie in list_review:
data = movie.get('data', "")
reviews_data = data.get('reviews')
for review in reviews_data:
reviews = review.get('content')
newInput = Input(reviews)
self.createdInputs.append(newInput)
for item in list_annotation:
print(item)
# Store the annotation as dicts in a separate list
annotations_dict = {"title": item, "year": item["year"]}
annot_dict_copy = annotations_dict.copy()
for i in range(25):
annotations.append(annot_dict_copy)
print(annotations)
# If there's only one item, the widget's output is the created Input.
if len(self.createdInputs) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget's output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
self.createdInputs,
import_labels_as=None,
)
# Annotate segments...
for idx, segment in enumerate(self.segmentation):
segment.annotations.update(annotations[idx])
self.segmentation[idx] = segment
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Set status to OK and report data size...
message = f"{len(self.segmentation)} segment@p sent to output"
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += " (%i character@p)." % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
self.send('Segmentation', self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output."""
# Check that there's a model...
if not self.model:
self.noLanguageModelWarning()
self.sendNoneToOutputs()
return
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input.", "warning")
self.sendNoneToOutputs()
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
# Disable control area and initialize progress bar...
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.char_df))
# Get start and end pos of concatenated input segments...
startPositions = [0]
endPositions = list()
numSegments = len(self.inputSeg)
for idx in range(1, numSegments):
prevSegLen = len(self.inputSeg[idx-1].get_content())
startPositions.append(startPositions[-1] + prevSegLen + 1)
endPositions.append(startPositions[-1] - 1)
endPositions.append(startPositions[-1] +
len(self.inputSeg[-1].get_content()) + 1)
# Get or update character aliases...
find_pairs = sys.modules['charnetto.find_pairs']
characters = [entry.split(", ") for entry in self.characters]
find_pairs.map_names(self.char_df, characters)
# Initializations...
charSegments = list()
currentSegmentIdx = 0
# For each character token in Charnetto's output...
for index, charToken in self.char_df.iterrows():
# Skip non-PER named entities.
if charToken["tag"] != "PER":
continue
# Get index of containing segment...
while charToken["end_pos"] > endPositions[currentSegmentIdx]:
currentSegmentIdx += 1
# Create segment for char with its actual coordinates...
strIndex = self.inputSeg[currentSegmentIdx].str_index
start = charToken["start_pos"]-startPositions[currentSegmentIdx]
end = charToken["end_pos"]-startPositions[currentSegmentIdx]
annotations = {"id": charToken["alias"]}
charSegments.append(Segment(strIndex, start, end, annotations))
progressBar.advance()
# Send output...
outputSegmentation = Segmentation(charSegments,
label=self.captionTitle)
self.send("Character segmentation", outputSegmentation, self)
print(outputSegmentation.to_string())
# Set status to OK and report data size...
message = "%i segment@p sent to output." % len(outputSegmentation)
message = pluralize(message, len(outputSegmentation))
self.infoBox.setText(message)
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Load files, create and send segmentation"""
# Check that there's something on input...
if ((self.displayAdvancedSettings and not self.files)
or not (self.file or self.displayAdvancedSettings)):
self.infoBox.setText(u'Please select input file.', 'warning')
self.send('Text data', None, self)
return
# Check that autoNumberKey is not empty (if necessary)...
if self.displayAdvancedSettings and self.autoNumber:
if self.autoNumberKey:
autoNumberKey = self.autoNumberKey
else:
self.infoBox.setText(
u'Please enter an annotation key for auto-numbering.',
'warning')
self.send('Text data', None, self)
return
else:
autoNumberKey = None
# Clear created Inputs...
self.clearCreatedInputs()
fileContents = list()
annotations = list()
counter = 1
if self.displayAdvancedSettings:
myFiles = self.files
else:
myFiles = [[self.file, self.encoding, "", "", "", "eng", False]]
self.infoBox.setText(u"Processing, please wait...", "warning")
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(myFiles))
# Open and process each file successively...
for myFile in myFiles:
filePath = myFile[0]
encoding = myFile[1]
encoding = re.sub(r"[ ]\(.+", "", encoding)
annotation_key = myFile[2]
annotation_value = myFile[3]
pdf_password = myFile[4] # SuperTextFiles
ocr_languages = myFile[5] # SuperTextFiles
ocr_force = myFile[6] # SuperTextFiles
myFiletype = filetype.guess(myFile[0]) # SuperTextFiles
# Try to open the file...
self.error()
# Start SuperTextFiles
try:
if myFiletype is None:
fileContent = self.extract_raw_text(filePath, encoding)
elif myFiletype.extension == "pdf":
if ocr_force is True:
fileContent = self.get_pdf_content(
filePath,
ocr_languages,
)
else:
if self.is_textual_pdf_file(filePath) is True:
fileContent = self.extract_text_from_pdf(filePath)
else:
fileContent = self.get_pdf_content(
filePath,
ocr_languages,
)
elif myFiletype.extension in IMG_FILETYPES:
fileContent = self.ocrize(filePath, ocr_languages)
if fileContent == -1:
message = u"Couldn't open file."
self.infoBox.setText(message, 'error')
self.send('Text data', None, self)
self.controlArea.setDisabled(False)
return
# End SuperTextFiles
except IOError as e:
if "tesseract" in str(e):
QMessageBox.warning(None, 'Textable', str(e),
QMessageBox.Ok)
progressBar.finish()
if len(myFiles) > 1:
message = u"Couldn't open file '%s'." % filePath
else:
message = u"Couldn't open file."
self.infoBox.setText(message, 'error')
self.send('Text data', None, self)
self.controlArea.setDisabled(False)
return
# Remove utf-8 BOM if necessary...
if encoding == u'utf-8':
fileContent = fileContent.lstrip(
codecs.BOM_UTF8.decode('utf-8'))
# Normalize text (canonical decomposition then composition)...
fileContent = normalize('NFC', fileContent)
fileContents.append(fileContent)
# Annotations...
annotation = dict()
if self.displayAdvancedSettings:
if annotation_key and annotation_value:
annotation[annotation_key] = annotation_value
if self.importFilenames and self.importFilenamesKey:
filename = os.path.basename(filePath)
annotation[self.importFilenamesKey] = filename
if self.autoNumber and self.autoNumberKey:
annotation[self.autoNumberKey] = counter
counter += 1
annotations.append(annotation)
progressBar.advance()
# Create an LTTL.Input for each file...
if len(fileContents) == 1:
label = self.captionTitle
else:
label = None
for index in range(len(fileContents)):
myInput = Input(fileContents[index], label)
segment = myInput[0]
segment.annotations.update(annotations[index])
myInput[0] = segment
self.createdInputs.append(myInput)
# If there's only one file, the widget's output is the created Input.
if len(fileContents) == 1:
self.segmentation = self.createdInputs[0]
# Otherwise the widget's output is a concatenation...
else:
self.segmentation = Segmenter.concatenate(
segmentations=self.createdInputs,
label=self.captionTitle,
copy_annotations=True,
import_labels_as=None,
sort=False,
auto_number_as=None,
merge_duplicates=False,
progress_callback=None,
)
message = u'%i segment@p sent to output ' % len(self.segmentation)
message = pluralize(message, len(self.segmentation))
numChars = 0
for segment in self.segmentation:
segmentLength = len(Segmentation.get_data(segment.str_index))
numChars += segmentLength
message += u'(%i character@p).' % numChars
message = pluralize(message, numChars)
self.infoBox.setText(message)
progressBar.finish()
self.controlArea.setDisabled(False)
self.send('Text data', self.segmentation, self)
self.sendButton.resetSettingsChangedFlag()
def sendData(self):
"""Compute result of widget processing and send to output"""
# Check that there's an input...
if self.inputSeg is None:
self.infoBox.setText("Widget needs input", "warning")
self.send("Linguistically analyzed data", None, self)
return
# Initialize progress bar.
self.infoBox.setText(
u"Processing, please wait...",
"warning",
)
self.controlArea.setDisabled(True)
progressBar = ProgressBar(self, iterations=len(self.inputSeg))
tokenizedSegments = list()
# Process each input segment...
for segment in self.inputSeg:
# Input segment attributes...
inputContent = segment.get_content()
inputAnnotations = segment.annotations
inputString = segment.str_index
inputStart = segment.start or 0
inputEnd = segment.end or len(inputContent)
# NLP analysis...
doc = self.nlp(inputContent)
# Process each token in input segment...
for token in doc:
tokenAnnotations = inputAnnotations.copy()
tokenAnnotations.update({
k: getattr(token, k)
for k in RELEVANT_KEYS if getattr(token, k) is not None
})
tokenStart = inputStart + token.idx
tokenizedSegments.append(
Segment(
str_index=inputString,
start=tokenStart,
end=tokenStart + len(token),
annotations=tokenAnnotations,
))
progressBar.advance()
outputSeg = Segmentation(tokenizedSegments, self.captionTitle)
# Set status to OK and report data size...
message = "%i segment@p sent to output." % len(outputSeg)
message = pluralize(message, len(outputSeg))
self.infoBox.setText(message)
print(outputSeg.to_string())
# Clear progress bar.
progressBar.finish()
self.controlArea.setDisabled(False)
# Send data to output...
self.send("Linguistically analyzed data", outputSeg, self)
self.sendButton.resetSettingsChangedFlag()
