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totalgood/nlpia | src/nlpia/loaders.py | normalize_column_names | def normalize_column_names(df):
r""" Clean up whitespace in column names. See better version at `pugnlp.clean_columns`
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
['hello_world', 'not_here']
"""
columns = df.columns if hasattr(df, ... | python | def normalize_column_names(df):
r""" Clean up whitespace in column names. See better version at `pugnlp.clean_columns`
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
['hello_world', 'not_here']
"""
columns = df.columns if hasattr(df, ... | [
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>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
['hello_world', 'not_here'] | [
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totalgood/nlpia | src/nlpia/loaders.py | clean_column_values | def clean_column_values(df, inplace=True):
r""" Convert dollar value strings, numbers with commas, and percents into floating point values
>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year ... | python | def clean_column_values(df, inplace=True):
r""" Convert dollar value strings, numbers with commas, and percents into floating point values
>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year ... | [
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>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year 10/2017-3/2018
Presiden... | [
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totalgood/nlpia | src/nlpia/loaders.py | isglove | def isglove(filepath):
""" Get the first word vector in a GloVE file and return its dimensionality or False if not a vector
>>> isglove(os.path.join(DATA_PATH, 'cats_and_dogs.txt'))
False
"""
with ensure_open(filepath, 'r') as f:
header_line = f.readline()
vector_line = f.readline(... | python | def isglove(filepath):
""" Get the first word vector in a GloVE file and return its dimensionality or False if not a vector
>>> isglove(os.path.join(DATA_PATH, 'cats_and_dogs.txt'))
False
"""
with ensure_open(filepath, 'r') as f:
header_line = f.readline()
vector_line = f.readline(... | [
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totalgood/nlpia | src/nlpia/loaders.py | nlp | def nlp(texts, lang='en', linesep=None, verbose=True):
r""" Use the SpaCy parser to parse and tag natural language strings.
Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is ... | python | def nlp(texts, lang='en', linesep=None, verbose=True):
r""" Use the SpaCy parser to parse and tag natural language strings.
Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is ... | [
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Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is None
True
>>> doc = nlp("Domo arigatto Mr. Roboto."... | [
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totalgood/nlpia | src/nlpia/talk.py | get_decoder | def get_decoder(libdir=None, modeldir=None, lang='en-us'):
""" Create a decoder with the requested language model """
modeldir = modeldir or (os.path.join(libdir, 'model') if libdir else MODELDIR)
libdir = os.path.dirname(modeldir)
config = ps.Decoder.default_config()
config.set_string('-hmm', os.pa... | python | def get_decoder(libdir=None, modeldir=None, lang='en-us'):
""" Create a decoder with the requested language model """
modeldir = modeldir or (os.path.join(libdir, 'model') if libdir else MODELDIR)
libdir = os.path.dirname(modeldir)
config = ps.Decoder.default_config()
config.set_string('-hmm', os.pa... | [
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totalgood/nlpia | src/nlpia/talk.py | transcribe | def transcribe(decoder, audio_file, libdir=None):
""" Decode streaming audio data from raw binary file on disk. """
decoder = get_decoder()
decoder.start_utt()
stream = open(audio_file, 'rb')
while True:
buf = stream.read(1024)
if buf:
decoder.process_raw(buf, False, Fal... | python | def transcribe(decoder, audio_file, libdir=None):
""" Decode streaming audio data from raw binary file on disk. """
decoder = get_decoder()
decoder.start_utt()
stream = open(audio_file, 'rb')
while True:
buf = stream.read(1024)
if buf:
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | pre_process_data | def pre_process_data(filepath):
"""
This is dependent on your training data source but we will try to generalize it as best as possible.
"""
positive_path = os.path.join(filepath, 'pos')
negative_path = os.path.join(filepath, 'neg')
pos_label = 1
neg_label = 0
dataset = []
for fil... | python | def pre_process_data(filepath):
"""
This is dependent on your training data source but we will try to generalize it as best as possible.
"""
positive_path = os.path.join(filepath, 'pos')
negative_path = os.path.join(filepath, 'neg')
pos_label = 1
neg_label = 0
dataset = []
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | pad_trunc | def pad_trunc(data, maxlen):
""" For a given dataset pad with zero vectors or truncate to maxlen """
new_data = []
# Create a vector of 0's the length of our word vectors
zero_vector = []
for _ in range(len(data[0][0])):
zero_vector.append(0.0)
for sample in data:
if len(sampl... | python | def pad_trunc(data, maxlen):
""" For a given dataset pad with zero vectors or truncate to maxlen """
new_data = []
# Create a vector of 0's the length of our word vectors
zero_vector = []
for _ in range(len(data[0][0])):
zero_vector.append(0.0)
for sample in data:
if len(sampl... | [
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | clean_data | def clean_data(data):
""" Shift to lower case, replace unknowns with UNK, and listify """
new_data = []
VALID = 'abcdefghijklmnopqrstuvwxyz123456789"\'?!.,:; '
for sample in data:
new_sample = []
for char in sample[1].lower(): # Just grab the string, not the label
if char in... | python | def clean_data(data):
""" Shift to lower case, replace unknowns with UNK, and listify """
new_data = []
VALID = 'abcdefghijklmnopqrstuvwxyz123456789"\'?!.,:; '
for sample in data:
new_sample = []
for char in sample[1].lower(): # Just grab the string, not the label
if char in... | [
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | char_pad_trunc | def char_pad_trunc(data, maxlen):
""" We truncate to maxlen or add in PAD tokens """
new_dataset = []
for sample in data:
if len(sample) > maxlen:
new_data = sample[:maxlen]
elif len(sample) < maxlen:
pads = maxlen - len(sample)
new_data = sample + ['PAD']... | python | def char_pad_trunc(data, maxlen):
""" We truncate to maxlen or add in PAD tokens """
new_dataset = []
for sample in data:
if len(sample) > maxlen:
new_data = sample[:maxlen]
elif len(sample) < maxlen:
pads = maxlen - len(sample)
new_data = sample + ['PAD']... | [
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | create_dicts | def create_dicts(data):
""" Modified from Keras LSTM example"""
chars = set()
for sample in data:
chars.update(set(sample))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
return char_indices, indices_char | python | def create_dicts(data):
""" Modified from Keras LSTM example"""
chars = set()
for sample in data:
chars.update(set(sample))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
return char_indices, indices_char | [
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totalgood/nlpia | src/nlpia/book/examples/ch09.py | onehot_encode | def onehot_encode(dataset, char_indices, maxlen):
"""
One hot encode the tokens
Args:
dataset list of lists of tokens
char_indices dictionary of {key=character, value=index to use encoding vector}
maxlen int Length of each sample
Return:
np array of shape (samples, ... | python | def onehot_encode(dataset, char_indices, maxlen):
"""
One hot encode the tokens
Args:
dataset list of lists of tokens
char_indices dictionary of {key=character, value=index to use encoding vector}
maxlen int Length of each sample
Return:
np array of shape (samples, ... | [
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totalgood/nlpia | src/nlpia/book/examples/ch04_sklearn_pca_source.py | _fit_full | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round... | python | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round... | [
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totalgood/nlpia | src/nlpia/clean_alice.py | extract_aiml | def extract_aiml(path='aiml-en-us-foundation-alice.v1-9'):
""" Extract an aiml.zip file if it hasn't been already and return a list of aiml file paths """
path = find_data_path(path) or path
if os.path.isdir(path):
paths = os.listdir(path)
paths = [os.path.join(path, p) for p in paths]
e... | python | def extract_aiml(path='aiml-en-us-foundation-alice.v1-9'):
""" Extract an aiml.zip file if it hasn't been already and return a list of aiml file paths """
path = find_data_path(path) or path
if os.path.isdir(path):
paths = os.listdir(path)
paths = [os.path.join(path, p) for p in paths]
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totalgood/nlpia | src/nlpia/clean_alice.py | create_brain | def create_brain(path='aiml-en-us-foundation-alice.v1-9.zip'):
""" Create an aiml_bot.Bot brain from an AIML zip file or directory of AIML files """
path = find_data_path(path) or path
bot = Bot()
num_templates = bot._brain.template_count
paths = extract_aiml(path=path)
for path in paths:
... | python | def create_brain(path='aiml-en-us-foundation-alice.v1-9.zip'):
""" Create an aiml_bot.Bot brain from an AIML zip file or directory of AIML files """
path = find_data_path(path) or path
bot = Bot()
num_templates = bot._brain.template_count
paths = extract_aiml(path=path)
for path in paths:
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totalgood/nlpia | src/nlpia/transcoders.py | minify_urls | def minify_urls(filepath, ext='asc', url_regex=None, output_ext='.urls_minified', access_token=None):
""" Use bitly or similar minifier to shrink all URLs in text files within a folder structure.
Used for the NLPIA manuscript directory for Manning Publishing
bitly API: https://dev.bitly.com/links.html
... | python | def minify_urls(filepath, ext='asc', url_regex=None, output_ext='.urls_minified', access_token=None):
""" Use bitly or similar minifier to shrink all URLs in text files within a folder structure.
Used for the NLPIA manuscript directory for Manning Publishing
bitly API: https://dev.bitly.com/links.html
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totalgood/nlpia | src/nlpia/transcoders.py | delimit_slug | def delimit_slug(slug, sep=' '):
""" Return a str of separated tokens found within a slugLike_This => 'slug Like This'
>>> delimit_slug("slugLike_ThisW/aTLA's")
'slug Like This W a TLA s'
>>> delimit_slug('slugLike_ThisW/aTLA', '|')
'slug|Like|This|W|a|TLA'
"""
hyphenated_slug = re.sub(CRE_... | python | def delimit_slug(slug, sep=' '):
""" Return a str of separated tokens found within a slugLike_This => 'slug Like This'
>>> delimit_slug("slugLike_ThisW/aTLA's")
'slug Like This W a TLA s'
>>> delimit_slug('slugLike_ThisW/aTLA', '|')
'slug|Like|This|W|a|TLA'
"""
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totalgood/nlpia | src/nlpia/transcoders.py | clean_asciidoc | def clean_asciidoc(text):
r""" Transform asciidoc text into ASCII text that NL parsers can handle
TODO:
Tag lines and words with meta data like italics, underlined, bold, title, heading 1, etc
>>> clean_asciidoc('**Hello** _world_!')
'"Hello" "world"!'
"""
text = re.sub(r'(\b|^)[\[_*]{1,... | python | def clean_asciidoc(text):
r""" Transform asciidoc text into ASCII text that NL parsers can handle
TODO:
Tag lines and words with meta data like italics, underlined, bold, title, heading 1, etc
>>> clean_asciidoc('**Hello** _world_!')
'"Hello" "world"!'
"""
text = re.sub(r'(\b|^)[\[_*]{1,... | [
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totalgood/nlpia | src/nlpia/transcoders.py | split_sentences_regex | def split_sentences_regex(text):
""" Use dead-simple regex to split text into sentences. Very poor accuracy.
>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson']
"""
parts = regex.split(r'([a-zA-Z0-9][.?!])[\s$]', text)
sen... | python | def split_sentences_regex(text):
""" Use dead-simple regex to split text into sentences. Very poor accuracy.
>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson']
"""
parts = regex.split(r'([a-zA-Z0-9][.?!])[\s$]', text)
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totalgood/nlpia | src/nlpia/transcoders.py | split_sentences_spacy | def split_sentences_spacy(text, language_model='en'):
r""" You must download a spacy language model with python -m download 'en'
The default English language model for spacy tends to be a lot more agressive than NLTK's punkt:
>>> split_sentences_nltk("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) -... | python | def split_sentences_spacy(text, language_model='en'):
r""" You must download a spacy language model with python -m download 'en'
The default English language model for spacy tends to be a lot more agressive than NLTK's punkt:
>>> split_sentences_nltk("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) -... | [
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totalgood/nlpia | src/nlpia/transcoders.py | segment_sentences | def segment_sentences(path=os.path.join(DATA_PATH, 'book'), splitter=split_sentences_nltk, **find_files_kwargs):
""" Return a list of all sentences and empty lines.
TODO:
1. process each line with an aggressive sentence segmenter, like DetectorMorse
2. process our manuscript to create a complet... | python | def segment_sentences(path=os.path.join(DATA_PATH, 'book'), splitter=split_sentences_nltk, **find_files_kwargs):
""" Return a list of all sentences and empty lines.
TODO:
1. process each line with an aggressive sentence segmenter, like DetectorMorse
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totalgood/nlpia | src/nlpia/transcoders.py | fix_hunspell_json | def fix_hunspell_json(badjson_path='en_us.json', goodjson_path='en_us_fixed.json'):
"""Fix the invalid hunspellToJSON.py json format by inserting double-quotes in list of affix strings
Args:
badjson_path (str): path to input json file that doesn't properly quote
goodjson_path (str): path to output ... | python | def fix_hunspell_json(badjson_path='en_us.json', goodjson_path='en_us_fixed.json'):
"""Fix the invalid hunspellToJSON.py json format by inserting double-quotes in list of affix strings
Args:
badjson_path (str): path to input json file that doesn't properly quote
goodjson_path (str): path to output ... | [
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totalgood/nlpia | src/nlpia/book/examples/ch12_retrieval.py | format_ubuntu_dialog | def format_ubuntu_dialog(df):
""" Print statements paired with replies, formatted for easy review """
s = ''
for i, record in df.iterrows():
statement = list(split_turns(record.Context))[-1] # <1>
reply = list(split_turns(record.Utterance))[-1] # <2>
s += 'Statement: {}\n'.format(s... | python | def format_ubuntu_dialog(df):
""" Print statements paired with replies, formatted for easy review """
s = ''
for i, record in df.iterrows():
statement = list(split_turns(record.Context))[-1] # <1>
reply = list(split_turns(record.Utterance))[-1] # <2>
s += 'Statement: {}\n'.format(s... | [
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totalgood/nlpia | src/nlpia/regexes.py | splitext | def splitext(filepath):
""" Like os.path.splitext except splits compound extensions as one long one
>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.bash_profile', '')
"""
exts = getattr(CRE_FILENAME_EXT.search(filepath... | python | def splitext(filepath):
""" Like os.path.splitext except splits compound extensions as one long one
>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.bash_profile', '')
"""
exts = getattr(CRE_FILENAME_EXT.search(filepath... | [
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>>> splitext('~/.bash_profile')
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totalgood/nlpia | src/nlpia/plots.py | offline_plotly_scatter3d | def offline_plotly_scatter3d(df, x=0, y=1, z=-1):
""" Plot an offline scatter plot colored according to the categories in the 'name' column.
>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df)
"""
data = []
# clusters = []
colors = ... | python | def offline_plotly_scatter3d(df, x=0, y=1, z=-1):
""" Plot an offline scatter plot colored according to the categories in the 'name' column.
>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df)
"""
data = []
# clusters = []
colors = ... | [
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totalgood/nlpia | src/nlpia/plots.py | offline_plotly_data | def offline_plotly_data(data, filename=None, config=None, validate=True,
default_width='100%', default_height=525, global_requirejs=False):
r""" Write a plotly scatter plot to HTML file that doesn't require server
>>> from nlpia.loaders import get_data
>>> df = get_data('etpinard') ... | python | def offline_plotly_data(data, filename=None, config=None, validate=True,
default_width='100%', default_height=525, global_requirejs=False):
r""" Write a plotly scatter plot to HTML file that doesn't require server
>>> from nlpia.loaders import get_data
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>>> df.columns = [eval(c) if c[0] in '"\'' else str(c) for c in df.columns]
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totalgood/nlpia | src/nlpia/plots.py | normalize_etpinard_df | def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),
category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):
"""Reformat a dataframe in etpinard's format for use in plot functions and sklearn models"""... | python | def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),
category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):
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totalgood/nlpia | src/nlpia/plots.py | offline_plotly_scatter_bubble | def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',
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filename=None,
config={'displaylogo': False},
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filename=None,
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totalgood/nlpia | src/nlpia/data_utils.py | format_hex | def format_hex(i, num_bytes=4, prefix='0x'):
""" Format hexidecimal string from decimal integer value
>>> format_hex(42, num_bytes=8, prefix=None)
'0000002a'
>>> format_hex(23)
'0x0017'
"""
prefix = str(prefix or '')
i = int(i or 0)
return prefix + '{0:0{1}x}'.format(i, num_bytes) | python | def format_hex(i, num_bytes=4, prefix='0x'):
""" Format hexidecimal string from decimal integer value
>>> format_hex(42, num_bytes=8, prefix=None)
'0000002a'
>>> format_hex(23)
'0x0017'
"""
prefix = str(prefix or '')
i = int(i or 0)
return prefix + '{0:0{1}x}'.format(i, num_bytes) | [
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totalgood/nlpia | src/nlpia/data_utils.py | is_up_url | def is_up_url(url, allow_redirects=False, timeout=5):
r""" Check URL to see if it is a valid web page, return the redirected location if it is
Returns:
None if ConnectionError
False if url is invalid (any HTTP error code)
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r""" Check URL to see if it is a valid web page, return the redirected location if it is
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None if ConnectionError
False if url is invalid (any HTTP error code)
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totalgood/nlpia | src/nlpia/data_utils.py | get_markdown_levels | def get_markdown_levels(lines, levels=set((0, 1, 2, 3, 4, 5, 6))):
r""" Return a list of 2-tuples with a level integer for the heading levels
>>> get_markdown_levels('paragraph \n##bad\n# hello\n ### world\n')
[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bul... | python | def get_markdown_levels(lines, levels=set((0, 1, 2, 3, 4, 5, 6))):
r""" Return a list of 2-tuples with a level integer for the heading levels
>>> get_markdown_levels('paragraph \n##bad\n# hello\n ### world\n')
[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
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totalgood/nlpia | src/nlpia/data_utils.py | iter_lines | def iter_lines(url_or_text, ext=None, mode='rt'):
r""" Return an iterator over the lines of a file or URI response.
>>> len(list(iter_lines('cats_and_dogs.txt')))
263
>>> len(list(iter_lines(list('abcdefgh'))))
8
>>> len(list(iter_lines('abc\n def\n gh\n')))
3
>>> len(list(iter_lines('a... | python | def iter_lines(url_or_text, ext=None, mode='rt'):
r""" Return an iterator over the lines of a file or URI response.
>>> len(list(iter_lines('cats_and_dogs.txt')))
263
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totalgood/nlpia | src/nlpia/data_utils.py | parse_utf_html | def parse_utf_html(url=os.path.join(DATA_PATH, 'utf8_table.html')):
""" Parse HTML table UTF8 char descriptions returning DataFrame with `ascii` and `mutliascii` """
utf = pd.read_html(url)
utf = [df for df in utf if len(df) > 1023 and len(df.columns) > 2][0]
utf = utf.iloc[:1024] if len(utf) == 1025 el... | python | def parse_utf_html(url=os.path.join(DATA_PATH, 'utf8_table.html')):
""" Parse HTML table UTF8 char descriptions returning DataFrame with `ascii` and `mutliascii` """
utf = pd.read_html(url)
utf = [df for df in utf if len(df) > 1023 and len(df.columns) > 2][0]
utf = utf.iloc[:1024] if len(utf) == 1025 el... | [
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totalgood/nlpia | src/nlpia/data_utils.py | clean_csvs | def clean_csvs(dialogpath=None):
""" Translate non-ASCII characters to spaces or equivalent ASCII characters """
dialogdir = os.dirname(dialogpath) if os.path.isfile(dialogpath) else dialogpath
filenames = [dialogpath.split(os.path.sep)[-1]] if os.path.isfile(dialogpath) else os.listdir(dialogpath)
for ... | python | def clean_csvs(dialogpath=None):
""" Translate non-ASCII characters to spaces or equivalent ASCII characters """
dialogdir = os.dirname(dialogpath) if os.path.isfile(dialogpath) else dialogpath
filenames = [dialogpath.split(os.path.sep)[-1]] if os.path.isfile(dialogpath) else os.listdir(dialogpath)
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totalgood/nlpia | src/nlpia/data_utils.py | unicode2ascii | def unicode2ascii(text, expand=True):
r""" Translate UTF8 characters to ASCII
>> unicode2ascii("żółw")
zozw
utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \''
"""
translate = UTF8_TO_ASCII if not expand else UTF8_TO_MULTIASCII
output = ''
f... | python | def unicode2ascii(text, expand=True):
r""" Translate UTF8 characters to ASCII
>> unicode2ascii("żółw")
zozw
utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \''
"""
translate = UTF8_TO_ASCII if not expand else UTF8_TO_MULTIASCII
output = ''
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totalgood/nlpia | src/nlpia/data_utils.py | clean_df | def clean_df(df, header=None, **read_csv_kwargs):
""" Convert UTF8 characters in a CSV file or dataframe into ASCII
Args:
df (DataFrame or str): DataFrame or path or url to CSV
"""
df = read_csv(df, header=header, **read_csv_kwargs)
df = df.fillna(' ')
for col in df.columns:
df[co... | python | def clean_df(df, header=None, **read_csv_kwargs):
""" Convert UTF8 characters in a CSV file or dataframe into ASCII
Args:
df (DataFrame or str): DataFrame or path or url to CSV
"""
df = read_csv(df, header=header, **read_csv_kwargs)
df = df.fillna(' ')
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totalgood/nlpia | src/nlpia/book_parser.py | get_acronyms | def get_acronyms(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript')):
""" Find all the 2 and 3-letter acronyms in the manuscript and return as a sorted list of tuples """
acronyms = []
for f, lines in get_lines(manuscript):
for line in lines:
matches = CRE_ACRONYM.finditer(lin... | python | def get_acronyms(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript')):
""" Find all the 2 and 3-letter acronyms in the manuscript and return as a sorted list of tuples """
acronyms = []
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totalgood/nlpia | src/nlpia/book_parser.py | write_glossary | def write_glossary(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript'), linesep=None):
""" Compose an asciidoc string with acronyms culled from the manuscript """
linesep = linesep or os.linesep
lines = ['[acronyms]', '== Acronyms', '', '[acronyms,template="glossary",id="terms"]']
acronyms = g... | python | def write_glossary(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript'), linesep=None):
""" Compose an asciidoc string with acronyms culled from the manuscript """
linesep = linesep or os.linesep
lines = ['[acronyms]', '== Acronyms', '', '[acronyms,template="glossary",id="terms"]']
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totalgood/nlpia | src/nlpia/book_parser.py | infer_url_title | def infer_url_title(url):
""" Guess what the page title is going to be from the path and FQDN in the URL
>>> infer_url_title('https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html')
'the what if tool code free probing of'
"""
meta = get_url_filemeta(url)
title = ''
if... | python | def infer_url_title(url):
""" Guess what the page title is going to be from the path and FQDN in the URL
>>> infer_url_title('https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html')
'the what if tool code free probing of'
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totalgood/nlpia | src/nlpia/book_parser.py | translate_book | def translate_book(translators=(HyperlinkStyleCorrector().translate, translate_line_footnotes),
book_dir=BOOK_PATH, dest=None, include_tags=None,
ext='.nlpiabak', skip_untitled=True):
""" Fix any style corrections listed in `translate` list of translation functions
>>> len... | python | def translate_book(translators=(HyperlinkStyleCorrector().translate, translate_line_footnotes),
book_dir=BOOK_PATH, dest=None, include_tags=None,
ext='.nlpiabak', skip_untitled=True):
""" Fix any style corrections listed in `translate` list of translation functions
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totalgood/nlpia | src/nlpia/book_parser.py | filter_lines | def filter_lines(input_file, output_file, translate=lambda line: line):
""" Translate all the lines of a single file """
filepath, lines = get_lines([input_file])[0]
return filepath, [(tag, translate(line=line, tag=tag)) for (tag, line) in lines] | python | def filter_lines(input_file, output_file, translate=lambda line: line):
""" Translate all the lines of a single file """
filepath, lines = get_lines([input_file])[0]
return filepath, [(tag, translate(line=line, tag=tag)) for (tag, line) in lines] | [
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totalgood/nlpia | src/nlpia/book_parser.py | filter_tagged_lines | def filter_tagged_lines(tagged_lines, include_tags=None, exclude_tags=None):
r""" Return iterable of tagged lines where the tags all start with one of the include_tags prefixes
>>> filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')])
<generator object filter_ta... | python | def filter_tagged_lines(tagged_lines, include_tags=None, exclude_tags=None):
r""" Return iterable of tagged lines where the tags all start with one of the include_tags prefixes
>>> filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')])
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totalgood/nlpia | src/nlpia/book/examples/ch04_catdog_lsa_sorted.py | accuracy_study | def accuracy_study(tdm=None, u=None, s=None, vt=None, verbosity=0, **kwargs):
""" Reconstruct the term-document matrix and measure error as SVD terms are truncated
"""
smat = np.zeros((len(u), len(vt)))
np.fill_diagonal(smat, s)
smat = pd.DataFrame(smat, columns=vt.index, index=u.index)
if verbo... | python | def accuracy_study(tdm=None, u=None, s=None, vt=None, verbosity=0, **kwargs):
""" Reconstruct the term-document matrix and measure error as SVD terms are truncated
"""
smat = np.zeros((len(u), len(vt)))
np.fill_diagonal(smat, s)
smat = pd.DataFrame(smat, columns=vt.index, index=u.index)
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totalgood/nlpia | src/nlpia/anki.py | get_anki_phrases | def get_anki_phrases(lang='english', limit=None):
""" Retrieve as many anki paired-statement corpora as you can for the requested language
If `ankis` (requested languages) is more than one, then get the english texts associated with those languages.
TODO: improve modularity: def function that takes a sing... | python | def get_anki_phrases(lang='english', limit=None):
""" Retrieve as many anki paired-statement corpora as you can for the requested language
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totalgood/nlpia | src/nlpia/anki.py | get_anki_phrases_english | def get_anki_phrases_english(limit=None):
""" Return all the English phrases in the Anki translation flashcards
>>> len(get_anki_phrases_english(limit=100)) > 700
True
"""
texts = set()
for lang in ANKI_LANGUAGES:
df = get_data(lang)
phrases = df.eng.str.strip().values
... | python | def get_anki_phrases_english(limit=None):
""" Return all the English phrases in the Anki translation flashcards
>>> len(get_anki_phrases_english(limit=100)) > 700
True
"""
texts = set()
for lang in ANKI_LANGUAGES:
df = get_data(lang)
phrases = df.eng.str.strip().values
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totalgood/nlpia | src/nlpia/anki.py | get_vocab | def get_vocab(docs):
""" Build a DataFrame containing all the words in the docs provided along with their POS tags etc
>>> doc = nlp("Hey Mr. Tangerine Man!")
<BLANKLINE>
...
>>> get_vocab([doc])
word pos tag dep ent_type ent_iob sentiment
0 ! PUNCT . pun... | python | def get_vocab(docs):
""" Build a DataFrame containing all the words in the docs provided along with their POS tags etc
>>> doc = nlp("Hey Mr. Tangerine Man!")
<BLANKLINE>
...
>>> get_vocab([doc])
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totalgood/nlpia | src/nlpia/anki.py | get_word_vectors | def get_word_vectors(vocab):
""" Create a word2vec embedding matrix for all the words in the vocab """
wv = get_data('word2vec')
vectors = np.array(len(vocab), len(wv['the']))
for i, tok in enumerate(vocab):
word = tok[0]
variations = (word, word.lower(), word.lower()[:-1])
for w... | python | def get_word_vectors(vocab):
""" Create a word2vec embedding matrix for all the words in the vocab """
wv = get_data('word2vec')
vectors = np.array(len(vocab), len(wv['the']))
for i, tok in enumerate(vocab):
word = tok[0]
variations = (word, word.lower(), word.lower()[:-1])
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totalgood/nlpia | src/nlpia/anki.py | get_anki_vocab | def get_anki_vocab(lang=['eng'], limit=None, filename='anki_en_vocabulary.csv'):
""" Get all the vocab words+tags+wordvectors for the tokens in the Anki translation corpus
Returns a DataFrame of with columns = word, pos, tag, dep, ent, ent_iob, sentiment, vectors
"""
texts = get_anki_phrases(lang=lang,... | python | def get_anki_vocab(lang=['eng'], limit=None, filename='anki_en_vocabulary.csv'):
""" Get all the vocab words+tags+wordvectors for the tokens in the Anki translation corpus
Returns a DataFrame of with columns = word, pos, tag, dep, ent, ent_iob, sentiment, vectors
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totalgood/nlpia | src/nlpia/scripts/lsa_tweets.py | lsa_twitter | def lsa_twitter(cased_tokens):
""" Latent Sentiment Analyis on random sampling of twitter search results for words listed in cased_tokens """
# Only 5 of these tokens are saved for a no_below=2 filter:
# PyCons NLPS #PyCon2016 #NaturalLanguageProcessing #naturallanguageprocessing
if cased_tokens is N... | python | def lsa_twitter(cased_tokens):
""" Latent Sentiment Analyis on random sampling of twitter search results for words listed in cased_tokens """
# Only 5 of these tokens are saved for a no_below=2 filter:
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totalgood/nlpia | src/nlpia/futil.py | wc | def wc(f, verbose=False, nrows=None):
r""" Count lines in a text file
References:
https://stackoverflow.com/q/845058/623735
>>> with open(os.path.join(DATA_PATH, 'dictionary_fda_drug_names.txt')) as fin:
... print(wc(fin) == wc(fin) == 7037 == wc(fin.name))
True
>>> wc(fin.name)
... | python | def wc(f, verbose=False, nrows=None):
r""" Count lines in a text file
References:
https://stackoverflow.com/q/845058/623735
>>> with open(os.path.join(DATA_PATH, 'dictionary_fda_drug_names.txt')) as fin:
... print(wc(fin) == wc(fin) == 7037 == wc(fin.name))
True
>>> wc(fin.name)
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totalgood/nlpia | src/nlpia/futil.py | normalize_filepath | def normalize_filepath(filepath):
r""" Lowercase the filename and ext, expanding extensions like .tgz to .tar.gz.
>>> normalize_filepath('/Hello_World.txt\n')
'hello_world.txt'
>>> normalize_filepath('NLPIA/src/nlpia/bigdata/Goog New 300Dneg\f.bIn\n.GZ')
'NLPIA/src/nlpia/bigdata/goog new 300dneg.bi... | python | def normalize_filepath(filepath):
r""" Lowercase the filename and ext, expanding extensions like .tgz to .tar.gz.
>>> normalize_filepath('/Hello_World.txt\n')
'hello_world.txt'
>>> normalize_filepath('NLPIA/src/nlpia/bigdata/Goog New 300Dneg\f.bIn\n.GZ')
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totalgood/nlpia | src/nlpia/futil.py | find_filepath | def find_filepath(
filename,
basepaths=(os.path.curdir, DATA_PATH, BIGDATA_PATH, BASE_DIR, '~', '~/Downloads', os.path.join('/', 'tmp'), '..')):
""" Given a filename or path see if it exists in any of the common places datafiles might be
>>> p = find_filepath('iq_test.csv')
>>> p == expand_... | python | def find_filepath(
filename,
basepaths=(os.path.curdir, DATA_PATH, BIGDATA_PATH, BASE_DIR, '~', '~/Downloads', os.path.join('/', 'tmp'), '..')):
""" Given a filename or path see if it exists in any of the common places datafiles might be
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>>> p[-len('iq_test.csv'):]
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neo4j/neo4j-python-driver | neo4j/__init__.py | Driver.close | def close(self):
""" Shut down, closing any open connections in the pool.
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self._pool.close()
self._pool = None | python | def close(self):
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neo4j/neo4j-python-driver | neo4j/types/spatial.py | hydrate_point | def hydrate_point(srid, *coordinates):
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neo4j/neo4j-python-driver | neo4j/types/spatial.py | dehydrate_point | def dehydrate_point(value):
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""" Dehydrator for Point data.
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:type value: Point
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if dim == 2:
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | PackStreamDehydrator.dehydrate | def dehydrate(self, values):
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.get | def get(self, key, default=None):
""" Obtain a value from the record by key, returning a default
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:param key:
:param default:
:return:
"""
try:
index = self.__keys.index(str(key))
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""" Obtain a value from the record by key, returning a default
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.index | def index(self, key):
""" Return the index of the given item.
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""" Return the index of the given item.
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.value | def value(self, key=0, default=None):
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:param key:
:param default:
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.values | def values(self, *keys):
""" Return the values of the record, optionally filtering to
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:param keys: indexes or keys of the items to include; if none
are provided, all values will be included
:return: list of values
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""" Return the values of the record, optionally filtering to
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.items | def items(self, *keys):
""" Return the fields of the record as a list of key and value tuples
:return:
"""
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for key in keys:
try:
i = self.index(key)
except KeyError:
d.append(... | python | def items(self, *keys):
""" Return the fields of the record as a list of key and value tuples
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neo4j/neo4j-python-driver | neo4j/blocking.py | _make_plan | def _make_plan(plan_dict):
""" Construct a Plan or ProfiledPlan from a dictionary of metadata values.
:param plan_dict:
:return:
"""
operator_type = plan_dict["operatorType"]
identifiers = plan_dict.get("identifiers", [])
arguments = plan_dict.get("args", [])
children = [_make_plan(chil... | python | def _make_plan(plan_dict):
""" Construct a Plan or ProfiledPlan from a dictionary of metadata values.
:param plan_dict:
:return:
"""
operator_type = plan_dict["operatorType"]
identifiers = plan_dict.get("identifiers", [])
arguments = plan_dict.get("args", [])
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neo4j/neo4j-python-driver | neo4j/blocking.py | unit_of_work | def unit_of_work(metadata=None, timeout=None):
""" This function is a decorator for transaction functions that allows
extra control over how the transaction is carried out.
For example, a timeout (in seconds) may be applied::
@unit_of_work(timeout=25.0)
def count_people(tx):
re... | python | def unit_of_work(metadata=None, timeout=None):
""" This function is a decorator for transaction functions that allows
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For example, a timeout (in seconds) may be applied::
@unit_of_work(timeout=25.0)
def count_people(tx):
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.close | def close(self):
""" Close the session. This will release any borrowed resources,
such as connections, and will roll back any outstanding transactions.
"""
from neobolt.exceptions import ConnectionExpired, CypherError, ServiceUnavailable
try:
if self.has_transaction()... | python | def close(self):
""" Close the session. This will release any borrowed resources,
such as connections, and will roll back any outstanding transactions.
"""
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try:
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.run | def run(self, statement, parameters=None, **kwparameters):
""" Run a Cypher statement within an auto-commit transaction.
The statement is sent and the result header received
immediately but the :class:`.StatementResult` content is
fetched lazily as consumed by the client application.
... | python | def run(self, statement, parameters=None, **kwparameters):
""" Run a Cypher statement within an auto-commit transaction.
The statement is sent and the result header received
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.send | def send(self):
""" Send all outstanding requests.
"""
from neobolt.exceptions import ConnectionExpired
if self._connection:
try:
self._connection.send()
except ConnectionExpired as error:
raise SessionExpired(*error.args) | python | def send(self):
""" Send all outstanding requests.
"""
from neobolt.exceptions import ConnectionExpired
if self._connection:
try:
self._connection.send()
except ConnectionExpired as error:
raise SessionExpired(*error.args) | [
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.fetch | def fetch(self):
""" Attempt to fetch at least one more record.
:returns: number of records fetched
"""
from neobolt.exceptions import ConnectionExpired
if self._connection:
try:
detail_count, _ = self._connection.fetch()
except Connection... | python | def fetch(self):
""" Attempt to fetch at least one more record.
:returns: number of records fetched
"""
from neobolt.exceptions import ConnectionExpired
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.detach | def detach(self, result, sync=True):
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.detach | def detach(self, sync=True):
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"""
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""" Detach this result from its parent session by fetching the
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.keys | def keys(self):
""" The keys for the records in this result.
:returns: tuple of key names
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""" The keys for the records in this result.
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.records | def records(self):
""" Generator for records obtained from this result.
:yields: iterable of :class:`.Record` objects
"""
records = self._records
next_record = records.popleft
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attached = self.attached
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.summary | def summary(self):
""" Obtain the summary of this result, buffering any remaining records.
:returns: The :class:`.ResultSummary` for this result
"""
self.detach()
if self._summary is None:
self._summary = BoltStatementResultSummary(**self._metadata)
return se... | python | def summary(self):
""" Obtain the summary of this result, buffering any remaining records.
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self.detach()
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.single | def single(self):
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:returns: the next :class:`.Record` or :const:`None` if none remain
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.peek | def peek(self):
""" Obtain the next record from this result without consuming it.
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:returns: the next :class:`.Record` or :const:`None` if none remain
"""
records = self._records
if records:
return r... | python | def peek(self):
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neo4j/neo4j-python-driver | neo4j/blocking.py | BoltStatementResult.value | def value(self, item=0, default=None):
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:param default: default value, used if the index of key is unavailable
:returns: list of individual values
"""
return... | python | def value(self, item=0, default=None):
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neo4j/neo4j-python-driver | neo4j/pipelining.py | Pipeline.pull | def pull(self):
"""Returns a generator containing the results of the next query in the pipeline"""
# n.b. pull is now somewhat misleadingly named because it doesn't do anything
# the connection isn't touched until you try and iterate the generator we return
lock_acquired = self._pull_loc... | python | def pull(self):
"""Returns a generator containing the results of the next query in the pipeline"""
# n.b. pull is now somewhat misleadingly named because it doesn't do anything
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neo4j/neo4j-python-driver | neo4j/meta.py | deprecated | def deprecated(message):
""" Decorator for deprecating functions and methods.
::
@deprecated("'foo' has been deprecated in favour of 'bar'")
def foo(x):
pass
"""
def f__(f):
def f_(*args, **kwargs):
from warnings import warn
warn(message, ca... | python | def deprecated(message):
""" Decorator for deprecating functions and methods.
::
@deprecated("'foo' has been deprecated in favour of 'bar'")
def foo(x):
pass
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neo4j/neo4j-python-driver | neo4j/meta.py | experimental | def experimental(message):
""" Decorator for tagging experimental functions and methods.
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def foo(x):
pass
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""" Decorator for tagging experimental functions and methods.
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | hydrate_time | def hydrate_time(nanoseconds, tz=None):
""" Hydrator for `Time` and `LocalTime` values.
:param nanoseconds:
:param tz:
:return: Time
"""
seconds, nanoseconds = map(int, divmod(nanoseconds, 1000000000))
minutes, seconds = map(int, divmod(seconds, 60))
hours, minutes = map(int, divmod(min... | python | def hydrate_time(nanoseconds, tz=None):
""" Hydrator for `Time` and `LocalTime` values.
:param nanoseconds:
:param tz:
:return: Time
"""
seconds, nanoseconds = map(int, divmod(nanoseconds, 1000000000))
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_time | def dehydrate_time(value):
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if isinstance(value, Time):
nanoseconds = int(value.ticks * 1000000000)
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | hydrate_datetime | def hydrate_datetime(seconds, nanoseconds, tz=None):
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:param tz:
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""" Hydrator for `DateTime` and `LocalDateTime` values.
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:param tz:
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_datetime | def dehydrate_datetime(value):
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | hydrate_duration | def hydrate_duration(months, days, seconds, nanoseconds):
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:param nanoseconds:
:return: `duration` namedtuple
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return Duration(months=months, days=days, seconds=seconds, nanoseconds=nanoseconds) | python | def hydrate_duration(months, days, seconds, nanoseconds):
""" Hydrator for `Duration` values.
:param months:
:param days:
:param seconds:
:param nanoseconds:
:return: `duration` namedtuple
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_duration | def dehydrate_duration(value):
""" Dehydrator for `duration` values.
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:return:
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return Structure(b"E", value.months, value.days, value.seconds, int(1000000000 * value.subseconds)) | python | def dehydrate_duration(value):
""" Dehydrator for `duration` values.
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_timedelta | def dehydrate_timedelta(value):
""" Dehydrator for `timedelta` values.
:param value:
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:return:
"""
months = 0
days = value.days
seconds = value.seconds
nanoseconds = 1000 * value.microseconds
return Structure(b"E", months, days, seconds, nanoseconds) | python | def dehydrate_timedelta(value):
""" Dehydrator for `timedelta` values.
:param value:
:type value: timedelta
:return:
"""
months = 0
days = value.days
seconds = value.seconds
nanoseconds = 1000 * value.microseconds
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.zoom | def zoom(self, locator, percent="200%", steps=1):
"""
Zooms in on an element a certain amount.
"""
driver = self._current_application()
element = self._element_find(locator, True, True)
driver.zoom(element=element, percent=percent, steps=steps) | python | def zoom(self, locator, percent="200%", steps=1):
"""
Zooms in on an element a certain amount.
"""
driver = self._current_application()
element = self._element_find(locator, True, True)
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.scroll | def scroll(self, start_locator, end_locator):
"""
Scrolls from one element to another
Key attributes for arbitrary elements are `id` and `name`. See
`introduction` for details about locating elements.
"""
el1 = self._element_find(start_locator, True, True)
... | python | def scroll(self, start_locator, end_locator):
"""
Scrolls from one element to another
Key attributes for arbitrary elements are `id` and `name`. See
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"""
el1 = self._element_find(start_locator, True, True)
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.scroll_up | def scroll_up(self, locator):
"""Scrolls up to element"""
driver = self._current_application()
element = self._element_find(locator, True, True)
driver.execute_script("mobile: scroll", {"direction": 'up', 'element': element.id}) | python | def scroll_up(self, locator):
"""Scrolls up to element"""
driver = self._current_application()
element = self._element_find(locator, True, True)
driver.execute_script("mobile: scroll", {"direction": 'up', 'element': element.id}) | [
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.long_press | def long_press(self, locator, duration=1000):
""" Long press the element with optional duration """
driver = self._current_application()
element = self._element_find(locator, True, True)
action = TouchAction(driver)
action.press(element).wait(duration).release().perform() | python | def long_press(self, locator, duration=1000):
""" Long press the element with optional duration """
driver = self._current_application()
element = self._element_find(locator, True, True)
action = TouchAction(driver)
action.press(element).wait(duration).release().perform() | [
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.click_a_point | def click_a_point(self, x=0, y=0, duration=100):
""" Click on a point"""
self._info("Clicking on a point (%s,%s)." % (x,y))
driver = self._current_application()
action = TouchAction(driver)
try:
action.press(x=float(x), y=float(y)).wait(float(duration)).release(... | python | def click_a_point(self, x=0, y=0, duration=100):
""" Click on a point"""
self._info("Clicking on a point (%s,%s)." % (x,y))
driver = self._current_application()
action = TouchAction(driver)
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_touch.py | _TouchKeywords.click_element_at_coordinates | def click_element_at_coordinates(self, coordinate_X, coordinate_Y):
""" click element at a certain coordinate """
self._info("Pressing at (%s, %s)." % (coordinate_X, coordinate_Y))
driver = self._current_application()
action = TouchAction(driver)
action.press(x=coordinate_X,... | python | def click_element_at_coordinates(self, coordinate_X, coordinate_Y):
""" click element at a certain coordinate """
self._info("Pressing at (%s, %s)." % (coordinate_X, coordinate_Y))
driver = self._current_application()
action = TouchAction(driver)
action.press(x=coordinate_X,... | [
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_waiting.py | _WaitingKeywords.wait_until_element_is_visible | def wait_until_element_is_visible(self, locator, timeout=None, error=None):
"""Waits until element specified with `locator` is visible.
Fails if `timeout` expires before the element is visible. See
`introduction` for more information about `timeout` and its
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_waiting.py | _WaitingKeywords.wait_until_page_contains | def wait_until_page_contains(self, text, timeout=None, error=None):
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Fails if `timeout` expires before the text appears. See
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_waiting.py | _WaitingKeywords.wait_until_page_does_not_contain | def wait_until_page_does_not_contain(self, text, timeout=None, error=None):
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Fails if `timeout` expires before the `text` disappears. See
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`error` can be... | python | def wait_until_page_does_not_contain(self, text, timeout=None, error=None):
"""Waits until `text` disappears from current page.
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_waiting.py | _WaitingKeywords.wait_until_page_contains_element | def wait_until_page_contains_element(self, locator, timeout=None, error=None):
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Fails if `timeout` expires before the element appears. See
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_waiting.py | _WaitingKeywords.wait_until_page_does_not_contain_element | def wait_until_page_does_not_contain_element(self, locator, timeout=None, error=None):
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... | Waits until element specified with `locator` disappears from current page.
Fails if `timeout` expires before the element disappears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See al... | [
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] | 91c808cf0602af6be8135ac529fa488fded04a85 | https://github.com/serhatbolsu/robotframework-appiumlibrary/blob/91c808cf0602af6be8135ac529fa488fded04a85/AppiumLibrary/keywords/_waiting.py#L90-L112 | train |
serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_android_utils.py | _AndroidUtilsKeywords.set_network_connection_status | def set_network_connection_status(self, connectionStatus):
"""Sets the network connection Status.
Android only.
Possible values:
| =Value= | =Alias= | =Data= | =Wifi= | =Airplane Mode= |
| 0 | (None) | 0 | 0 | 0 |
... | python | def set_network_connection_status(self, connectionStatus):
"""Sets the network connection Status.
Android only.
Possible values:
| =Value= | =Alias= | =Data= | =Wifi= | =Airplane Mode= |
| 0 | (None) | 0 | 0 | 0 |
... | [
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] | Sets the network connection Status.
Android only.
Possible values:
| =Value= | =Alias= | =Data= | =Wifi= | =Airplane Mode= |
| 0 | (None) | 0 | 0 | 0 |
| 1 | (Airplane Mode) | 0 | 0 | 1 ... | [
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] | 91c808cf0602af6be8135ac529fa488fded04a85 | https://github.com/serhatbolsu/robotframework-appiumlibrary/blob/91c808cf0602af6be8135ac529fa488fded04a85/AppiumLibrary/keywords/_android_utils.py#L21-L35 | train |
serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_android_utils.py | _AndroidUtilsKeywords.pull_file | def pull_file(self, path, decode=False):
"""Retrieves the file at `path` and return it's content.
Android only.
- _path_ - the path to the file on the device
- _decode_ - True/False decode the data (base64) before returning it (default=False)
"""
driver = self._curre... | python | def pull_file(self, path, decode=False):
"""Retrieves the file at `path` and return it's content.
Android only.
- _path_ - the path to the file on the device
- _decode_ - True/False decode the data (base64) before returning it (default=False)
"""
driver = self._curre... | [
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Android only.
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serhatbolsu/robotframework-appiumlibrary | AppiumLibrary/keywords/_android_utils.py | _AndroidUtilsKeywords.pull_folder | def pull_folder(self, path, decode=False):
"""Retrieves a folder at `path`. Returns the folder's contents zipped.
Android only.
- _path_ - the path to the folder on the device
- _decode_ - True/False decode the data (base64) before returning it (default=False)
"""
dri... | python | def pull_folder(self, path, decode=False):
"""Retrieves a folder at `path`. Returns the folder's contents zipped.
Android only.
- _path_ - the path to the folder on the device
- _decode_ - True/False decode the data (base64) before returning it (default=False)
"""
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Android only.
- _path_ - the path to the folder on the device
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