repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1 value | code stringlengths 75 19.8k | code_tokens listlengths 20 707 | docstring stringlengths 3 17.3k | docstring_tokens listlengths 3 222 | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1 value | idx int64 0 252k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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, 'columns') else df
columns = [c.lower().replace(' ', '_') for c in columns]
return columns | 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, 'columns') else df
columns = [c.lower().replace(' ', '_') for c in columns]
return columns | [
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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 10/2017-3/2018
President's party R
Senate majority party R
House majority party R
Top-bracket marginal income tax rate 38.3
National debt millions 2.10896e+07
National debt millions of 1983 dollars 8.47004e+06
Deficit\n(millions of 1983 dollars) 431443
Surplus string in 1983 dollars NaN
Deficit string in 1983 dollars ($ = $10B) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Net surplus in 1983 dollars ($B) -430
Name: 0, dtype: object
"""
dollars_percents = re.compile(r'[%$,;\s]+')
if not inplace:
df = df.copy()
for c in df.columns:
values = None
if df[c].dtype.char in '<U S O'.split():
try:
values = df[c].copy()
values = values.fillna('')
values = values.astype(str).str.replace(dollars_percents, '')
# values = values.str.strip().str.replace(dollars_percents, '').str.strip()
if values.str.len().sum() > .2 * df[c].astype(str).str.len().sum():
values[values.isnull()] = np.nan
values[values == ''] = np.nan
values = values.astype(float)
except ValueError:
values = None
except: # noqa
logger.error('Error on column {} with dtype {}'.format(c, df[c].dtype))
raise
if values is not None:
if values.isnull().sum() < .6 * len(values) and values.any():
df[c] = values
return df | python | def clean_column_values(df, inplace=True):
r""" Convert dollar value strings, numbers with commas, and percents into floating point values
>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year 10/2017-3/2018
President's party R
Senate majority party R
House majority party R
Top-bracket marginal income tax rate 38.3
National debt millions 2.10896e+07
National debt millions of 1983 dollars 8.47004e+06
Deficit\n(millions of 1983 dollars) 431443
Surplus string in 1983 dollars NaN
Deficit string in 1983 dollars ($ = $10B) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Net surplus in 1983 dollars ($B) -430
Name: 0, dtype: object
"""
dollars_percents = re.compile(r'[%$,;\s]+')
if not inplace:
df = df.copy()
for c in df.columns:
values = None
if df[c].dtype.char in '<U S O'.split():
try:
values = df[c].copy()
values = values.fillna('')
values = values.astype(str).str.replace(dollars_percents, '')
# values = values.str.strip().str.replace(dollars_percents, '').str.strip()
if values.str.len().sum() > .2 * df[c].astype(str).str.len().sum():
values[values.isnull()] = np.nan
values[values == ''] = np.nan
values = values.astype(float)
except ValueError:
values = None
except: # noqa
logger.error('Error on column {} with dtype {}'.format(c, df[c].dtype))
raise
if values is not None:
if values.isnull().sum() < .6 * len(values) and values.any():
df[c] = values
return df | [
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>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year 10/2017-3/2018
President's party R
Senate majority party R
House majority party R
Top-bracket marginal income tax rate 38.3
National debt millions 2.10896e+07
National debt millions of 1983 dollars 8.47004e+06
Deficit\n(millions of 1983 dollars) 431443
Surplus string in 1983 dollars NaN
Deficit string in 1983 dollars ($ = $10B) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Net surplus in 1983 dollars ($B) -430
Name: 0, dtype: object | [
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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()
try:
num_vectors, num_dim = header_line.split()
return int(num_dim)
except (ValueError, TypeError):
pass
vector = vector_line.split()[1:]
if len(vector) % 10:
print(vector)
print(len(vector) % 10)
return False
try:
vector = np.array([float(x) for x in vector])
except (ValueError, TypeError):
return False
if np.all(np.abs(vector) < 12.):
return len(vector)
return False | 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()
try:
num_vectors, num_dim = header_line.split()
return int(num_dim)
except (ValueError, TypeError):
pass
vector = vector_line.split()[1:]
if len(vector) % 10:
print(vector)
print(len(vector) % 10)
return False
try:
vector = np.array([float(x) for x in vector])
except (ValueError, TypeError):
return False
if np.all(np.abs(vector) < 12.):
return len(vector)
return False | [
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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 None
True
>>> doc = nlp("Domo arigatto Mr. Roboto.")
>>> doc.text
'Domo arigatto Mr. Roboto.'
>>> doc.ents
(Roboto,)
>>> docs = nlp("Hey Mr. Tangerine Man!\nPlay a song for me.\n", linesep='\n')
>>> doc = docs[0]
>>> [t for t in doc]
[Hey, Mr., Tangerine, Man, !]
>>> [tok.text for tok in doc]
['Hey', 'Mr.', 'Tangerine', 'Man', '!']
>>> [(tok.text, tok.tag_) for tok in doc]
[('Hey', 'UH'),
('Mr.', 'NNP'),
('Tangerine', 'NNP'),
('Man', 'NN'),
('!', '.')]
>>> [(ent.text, ent.ent_id, ent.has_vector, ent.vector[:3].round(3)) for ent in doc.ents]
[('Tangerine Man', 0, True, array([0.72 , 1.913, 2.675], dtype=float32))]
"""
# doesn't let you load a different model anywhere else in the module
linesep = os.linesep if linesep in ('default', True, 1, 'os') else linesep
tqdm_prog = no_tqdm if (not verbose or (hasattr(texts, '__len__') and len(texts) < 3)) else tqdm
global _parse
if not _parse:
try:
_parse = spacy.load(lang)
except (OSError, IOError):
try:
spacy.cli.download(lang)
except URLError:
logger.warning("Unable to download Spacy language model '{}' so nlp(text) just returns text.split()".format(lang))
parse = _parse or str.split
# TODO: reverse this recursion (str first then sequence) to allow for sequences of sequences of texts
if isinstance(texts, str):
if linesep:
return nlp(texts.split(linesep))
else:
return nlp([texts])
if hasattr(texts, '__len__'):
if len(texts) == 1:
return parse(texts[0])
elif len(texts) > 1:
return [(parse or str.split)(text) for text in tqdm_prog(texts)]
else:
return None
else:
# return generator if sequence of strings doesn't have __len__ which means its an iterable or generator itself
return (parse(text) for text in tqdm_prog(texts)) | 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 None
True
>>> doc = nlp("Domo arigatto Mr. Roboto.")
>>> doc.text
'Domo arigatto Mr. Roboto.'
>>> doc.ents
(Roboto,)
>>> docs = nlp("Hey Mr. Tangerine Man!\nPlay a song for me.\n", linesep='\n')
>>> doc = docs[0]
>>> [t for t in doc]
[Hey, Mr., Tangerine, Man, !]
>>> [tok.text for tok in doc]
['Hey', 'Mr.', 'Tangerine', 'Man', '!']
>>> [(tok.text, tok.tag_) for tok in doc]
[('Hey', 'UH'),
('Mr.', 'NNP'),
('Tangerine', 'NNP'),
('Man', 'NN'),
('!', '.')]
>>> [(ent.text, ent.ent_id, ent.has_vector, ent.vector[:3].round(3)) for ent in doc.ents]
[('Tangerine Man', 0, True, array([0.72 , 1.913, 2.675], dtype=float32))]
"""
# doesn't let you load a different model anywhere else in the module
linesep = os.linesep if linesep in ('default', True, 1, 'os') else linesep
tqdm_prog = no_tqdm if (not verbose or (hasattr(texts, '__len__') and len(texts) < 3)) else tqdm
global _parse
if not _parse:
try:
_parse = spacy.load(lang)
except (OSError, IOError):
try:
spacy.cli.download(lang)
except URLError:
logger.warning("Unable to download Spacy language model '{}' so nlp(text) just returns text.split()".format(lang))
parse = _parse or str.split
# TODO: reverse this recursion (str first then sequence) to allow for sequences of sequences of texts
if isinstance(texts, str):
if linesep:
return nlp(texts.split(linesep))
else:
return nlp([texts])
if hasattr(texts, '__len__'):
if len(texts) == 1:
return parse(texts[0])
elif len(texts) > 1:
return [(parse or str.split)(text) for text in tqdm_prog(texts)]
else:
return None
else:
# return generator if sequence of strings doesn't have __len__ which means its an iterable or generator itself
return (parse(text) for text in tqdm_prog(texts)) | [
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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.")
>>> doc.text
'Domo arigatto Mr. Roboto.'
>>> doc.ents
(Roboto,)
>>> docs = nlp("Hey Mr. Tangerine Man!\nPlay a song for me.\n", linesep='\n')
>>> doc = docs[0]
>>> [t for t in doc]
[Hey, Mr., Tangerine, Man, !]
>>> [tok.text for tok in doc]
['Hey', 'Mr.', 'Tangerine', 'Man', '!']
>>> [(tok.text, tok.tag_) for tok in doc]
[('Hey', 'UH'),
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('Tangerine', 'NNP'),
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('!', '.')]
>>> [(ent.text, ent.ent_id, ent.has_vector, ent.vector[:3].round(3)) for ent in doc.ents]
[('Tangerine Man', 0, True, array([0.72 , 1.913, 2.675], dtype=float32))] | [
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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.path.join(modeldir, lang))
config.set_string('-lm', os.path.join(modeldir, lang + '.lm.bin'))
config.set_string('-dict', os.path.join(modeldir, 'cmudict-' + lang + '.dict'))
print(config)
return ps.Decoder(config) | 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.path.join(modeldir, lang))
config.set_string('-lm', os.path.join(modeldir, lang + '.lm.bin'))
config.set_string('-dict', os.path.join(modeldir, 'cmudict-' + lang + '.dict'))
print(config)
return ps.Decoder(config) | [
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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, False)
else:
break
decoder.end_utt()
return evaluate_results(decoder) | 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:
decoder.process_raw(buf, False, False)
else:
break
decoder.end_utt()
return evaluate_results(decoder) | [
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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 filename in glob.glob(os.path.join(positive_path, '*.txt')):
with open(filename, 'r') as f:
dataset.append((pos_label, f.read()))
for filename in glob.glob(os.path.join(negative_path, '*.txt')):
with open(filename, 'r') as f:
dataset.append((neg_label, f.read()))
shuffle(dataset)
return dataset | 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 = []
for filename in glob.glob(os.path.join(positive_path, '*.txt')):
with open(filename, 'r') as f:
dataset.append((pos_label, f.read()))
for filename in glob.glob(os.path.join(negative_path, '*.txt')):
with open(filename, 'r') as f:
dataset.append((neg_label, f.read()))
shuffle(dataset)
return 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(sample) > maxlen:
temp = sample[:maxlen]
elif len(sample) < maxlen:
temp = sample
additional_elems = maxlen - len(sample)
for _ in range(additional_elems):
temp.append(zero_vector)
else:
temp = sample
new_data.append(temp)
return new_data | 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(sample) > maxlen:
temp = sample[:maxlen]
elif len(sample) < maxlen:
temp = sample
additional_elems = maxlen - len(sample)
for _ in range(additional_elems):
temp.append(zero_vector)
else:
temp = sample
new_data.append(temp)
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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 VALID:
new_sample.append(char)
else:
new_sample.append('UNK')
new_data.append(new_sample)
return new_data | 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 VALID:
new_sample.append(char)
else:
new_sample.append('UNK')
new_data.append(new_sample)
return new_data | [
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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'] * pads
else:
new_data = sample
new_dataset.append(new_data)
return new_dataset | 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'] * pads
else:
new_data = sample
new_dataset.append(new_data)
return new_dataset | [
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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, tokens, encoding length)
"""
X = np.zeros((len(dataset), maxlen, len(char_indices.keys())))
for i, sentence in enumerate(dataset):
for t, char in enumerate(sentence):
X[i, t, char_indices[char]] = 1
return X | 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, tokens, encoding length)
"""
X = np.zeros((len(dataset), maxlen, len(char_indices.keys())))
for i, sentence in enumerate(dataset):
for t, char in enumerate(sentence):
X[i, t, char_indices[char]] = 1
return X | [
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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(2))
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
print(components_.round(2))
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
print(self.components_.round(2))
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V | 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(2))
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
print(components_.round(2))
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
print(self.components_.round(2))
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V | [
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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]
else:
zf = zipfile.ZipFile(path)
paths = []
for name in zf.namelist():
if '.hg/' in name:
continue
paths.append(zf.extract(name, path=BIGDATA_PATH))
return paths | 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]
else:
zf = zipfile.ZipFile(path)
paths = []
for name in zf.namelist():
if '.hg/' in name:
continue
paths.append(zf.extract(name, path=BIGDATA_PATH))
return 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:
if not path.lower().endswith('.aiml'):
continue
try:
bot.learn(path)
except AimlParserError:
logger.error(format_exc())
logger.warning('AIML Parse Error: {}'.format(path))
num_templates = bot._brain.template_count - num_templates
logger.info('Loaded {} trigger-response pairs.\n'.format(num_templates))
print('Loaded {} trigger-response pairs from {} AIML files.'.format(bot._brain.template_count, len(paths)))
return bot | 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:
if not path.lower().endswith('.aiml'):
continue
try:
bot.learn(path)
except AimlParserError:
logger.error(format_exc())
logger.warning('AIML Parse Error: {}'.format(path))
num_templates = bot._brain.template_count - num_templates
logger.info('Loaded {} trigger-response pairs.\n'.format(num_templates))
print('Loaded {} trigger-response pairs from {} AIML files.'.format(bot._brain.template_count, len(paths)))
return bot | [
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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
Args:
path (str): Directory or file path
ext (str): File name extension to filter text files by. default='.asc'
output_ext (str): Extension to append to filenames of altered files default='' (in-place replacement of URLs)
FIXME: NotImplementedError! Untested!
"""
access_token = access_token or secrets.bitly.access_token
output_ext = output_ext or ''
url_regex = regex.compile(url_regex) if isinstance(url_regex, str) else url_regex
filemetas = []
for filemeta in find_files(filepath, ext=ext):
filemetas += [filemeta]
altered_text = ''
with open(filemeta['path'], 'rt') as fin:
text = fin.read()
end = 0
for match in url_regex.finditer(text):
url = match.group()
start = match.start()
altered_text += text[:start]
resp = requests.get('https://api-ssl.bitly.com/v3/shorten?access_token={}&longUrl={}'.format(
access_token, url), allow_redirects=True, timeout=5)
js = resp.json()
short_url = js['shortUrl']
altered_text += short_url
end = start + len(url)
altered_text += text[end:]
with open(filemeta['path'] + (output_ext or ''), 'wt') as fout:
fout.write(altered_text)
return altered_text | 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
Args:
path (str): Directory or file path
ext (str): File name extension to filter text files by. default='.asc'
output_ext (str): Extension to append to filenames of altered files default='' (in-place replacement of URLs)
FIXME: NotImplementedError! Untested!
"""
access_token = access_token or secrets.bitly.access_token
output_ext = output_ext or ''
url_regex = regex.compile(url_regex) if isinstance(url_regex, str) else url_regex
filemetas = []
for filemeta in find_files(filepath, ext=ext):
filemetas += [filemeta]
altered_text = ''
with open(filemeta['path'], 'rt') as fin:
text = fin.read()
end = 0
for match in url_regex.finditer(text):
url = match.group()
start = match.start()
altered_text += text[:start]
resp = requests.get('https://api-ssl.bitly.com/v3/shorten?access_token={}&longUrl={}'.format(
access_token, url), allow_redirects=True, timeout=5)
js = resp.json()
short_url = js['shortUrl']
altered_text += short_url
end = start + len(url)
altered_text += text[end:]
with open(filemeta['path'] + (output_ext or ''), 'wt') as fout:
fout.write(altered_text)
return altered_text | [
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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_SLUG_DELIMITTER, sep, slug)
return hyphenated_slug | 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'
"""
hyphenated_slug = re.sub(CRE_SLUG_DELIMITTER, sep, slug)
return hyphenated_slug | [
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'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|^)[\[_*]{1,2}([a-zA-Z0-9])', r'"\2', text)
text = re.sub(r'([a-zA-Z0-9])[\]_*]{1,2}', r'\1"', text)
return text | 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|^)[\[_*]{1,2}([a-zA-Z0-9])', r'"\2', text)
text = re.sub(r'([a-zA-Z0-9])[\]_*]{1,2}', r'\1"', text)
return text | [
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TODO:
Tag lines and words with meta data like italics, underlined, bold, title, heading 1, etc
>>> clean_asciidoc('**Hello** _world_!')
'"Hello" "world"!' | [
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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$]', text)
sentences = [''.join(s) for s in zip(parts[0::2], parts[1::2])]
return sentences + [parts[-1]] if len(parts) % 2 else sentences | python | def split_sentences_regex(text):
""" Use dead-simple regex to split text into sentences. Very poor accuracy.
>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson']
"""
parts = regex.split(r'([a-zA-Z0-9][.?!])[\s$]', text)
sentences = [''.join(s) for s in zip(parts[0::2], parts[1::2])]
return sentences + [parts[-1]] if len(parts) % 2 else sentences | [
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>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson'] | [
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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. ;) --Watson 2.0")
['Hi Ms. Lovelace.', "I'm a wanna-\nbe human @ I.B.M.", ';) --Watson 2.0']
>>> split_sentences_spacy("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) --Watson 2.0")
['Hi Ms. Lovelace.', "I'm a wanna-", 'be human @', 'I.B.M. ;) --Watson 2.0']
>>> split_sentences_spacy("Hi Ms. Lovelace. I'm at I.B.M. --Watson 2.0")
['Hi Ms. Lovelace.', "I'm at I.B.M. --Watson 2.0"]
>>> split_sentences_nltk("Hi Ms. Lovelace. I'm at I.B.M. --Watson 2.0")
['Hi Ms. Lovelace.', "I'm at I.B.M.", '--Watson 2.0']
"""
doc = nlp(text)
sentences = []
if not hasattr(doc, 'sents'):
logger.warning("Using NLTK sentence tokenizer because SpaCy language model hasn't been loaded")
return split_sentences_nltk(text)
for w, span in enumerate(doc.sents):
sent = ''.join(doc[i].string for i in range(span.start, span.end)).strip()
if len(sent):
sentences.append(sent)
return sentences | 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. ;) --Watson 2.0")
['Hi Ms. Lovelace.', "I'm a wanna-\nbe human @ I.B.M.", ';) --Watson 2.0']
>>> split_sentences_spacy("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) --Watson 2.0")
['Hi Ms. Lovelace.', "I'm a wanna-", 'be human @', 'I.B.M. ;) --Watson 2.0']
>>> split_sentences_spacy("Hi Ms. Lovelace. I'm at I.B.M. --Watson 2.0")
['Hi Ms. Lovelace.', "I'm at I.B.M. --Watson 2.0"]
>>> split_sentences_nltk("Hi Ms. Lovelace. I'm at I.B.M. --Watson 2.0")
['Hi Ms. Lovelace.', "I'm at I.B.M.", '--Watson 2.0']
"""
doc = nlp(text)
sentences = []
if not hasattr(doc, 'sents'):
logger.warning("Using NLTK sentence tokenizer because SpaCy language model hasn't been loaded")
return split_sentences_nltk(text)
for w, span in enumerate(doc.sents):
sent = ''.join(doc[i].string for i in range(span.start, span.end)).strip()
if len(sent):
sentences.append(sent)
return sentences | [
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>>> split_sentences_nltk("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) --Watson 2.0")
['Hi Ms. Lovelace.', "I'm a wanna-\nbe human @ I.B.M.", ';) --Watson 2.0']
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['Hi Ms. Lovelace.', "I'm a wanna-", 'be human @', 'I.B.M. ;) --Watson 2.0']
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>>> split_sentences_nltk("Hi Ms. Lovelace. I'm at I.B.M. --Watson 2.0")
['Hi Ms. Lovelace.', "I'm at I.B.M.", '--Watson 2.0'] | [
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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 complete-sentence and heading training set normalized/simplified
syntax net tree is the input feature set common words and N-grams inserted with their label as additional feature
3. process a training set with a grammar checker and syntax to bootstrap a "complete sentence" labeler.
4. process each 1-3 line window (breaking on empty lines) with syntax net to label them
5. label each 1-3-line window of lines as "complete sentence, partial sentence/phrase, or multi-sentence"
>>> 10000 > len(segment_sentences(path=os.path.join(DATA_PATH, 'book'))) >= 4
True
>>> len(segment_sentences(path=os.path.join(DATA_PATH, 'psychology-scripts.txt'), splitter=split_sentences_nltk))
23
"""
sentences = []
if os.path.isdir(path):
for filemeta in find_files(path, **find_files_kwargs):
with open(filemeta['path']) as fin:
i, batch = 0, []
try:
for i, line in enumerate(fin):
if not line.strip():
sentences.extend(splitter('\n'.join(batch)))
batch = [line] # may contain all whitespace
else:
batch.append(line)
except (UnicodeDecodeError, IOError):
logger.error('UnicodeDecodeError or IOError on line {} in file {} from stat: {}'.format(
i + 1, fin.name, filemeta))
raise
if len(batch):
# TODO: tag sentences with line + filename where they started
sentences.extend(splitter('\n'.join(batch)))
else:
batch = []
for i, line in enumerate(iter_lines(path)):
# TODO: filter out code and meta lines using asciidoc or markdown parser
# split into batches based on empty lines
if not line.strip():
sentences.extend(splitter('\n'.join(batch)))
# first line may contain all whitespace
batch = [line]
else:
batch.append(line)
if len(batch):
# TODO: tag sentences with line + filename where they started
sentences.extend(splitter('\n'.join(batch)))
return sentences | 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
2. process our manuscript to create a complete-sentence and heading training set normalized/simplified
syntax net tree is the input feature set common words and N-grams inserted with their label as additional feature
3. process a training set with a grammar checker and syntax to bootstrap a "complete sentence" labeler.
4. process each 1-3 line window (breaking on empty lines) with syntax net to label them
5. label each 1-3-line window of lines as "complete sentence, partial sentence/phrase, or multi-sentence"
>>> 10000 > len(segment_sentences(path=os.path.join(DATA_PATH, 'book'))) >= 4
True
>>> len(segment_sentences(path=os.path.join(DATA_PATH, 'psychology-scripts.txt'), splitter=split_sentences_nltk))
23
"""
sentences = []
if os.path.isdir(path):
for filemeta in find_files(path, **find_files_kwargs):
with open(filemeta['path']) as fin:
i, batch = 0, []
try:
for i, line in enumerate(fin):
if not line.strip():
sentences.extend(splitter('\n'.join(batch)))
batch = [line] # may contain all whitespace
else:
batch.append(line)
except (UnicodeDecodeError, IOError):
logger.error('UnicodeDecodeError or IOError on line {} in file {} from stat: {}'.format(
i + 1, fin.name, filemeta))
raise
if len(batch):
# TODO: tag sentences with line + filename where they started
sentences.extend(splitter('\n'.join(batch)))
else:
batch = []
for i, line in enumerate(iter_lines(path)):
# TODO: filter out code and meta lines using asciidoc or markdown parser
# split into batches based on empty lines
if not line.strip():
sentences.extend(splitter('\n'.join(batch)))
# first line may contain all whitespace
batch = [line]
else:
batch.append(line)
if len(batch):
# TODO: tag sentences with line + filename where they started
sentences.extend(splitter('\n'.join(batch)))
return sentences | [
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syntax net tree is the input feature set common words and N-grams inserted with their label as additional feature
3. process a training set with a grammar checker and syntax to bootstrap a "complete sentence" labeler.
4. process each 1-3 line window (breaking on empty lines) with syntax net to label them
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>>> 10000 > len(segment_sentences(path=os.path.join(DATA_PATH, 'book'))) >= 4
True
>>> len(segment_sentences(path=os.path.join(DATA_PATH, 'psychology-scripts.txt'), splitter=split_sentences_nltk))
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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 json file with properly quoted strings in list of affixes
Returns:
list of all words with all possible affixes in *.txt format (simplified .dic format)
References:
Syed Faisal Ali 's Hunspell dic parser: https://github.com/SyedFaisalAli/HunspellToJSON
"""
with open(badjson_path, 'r') as fin:
with open(goodjson_path, 'w') as fout:
for i, line in enumerate(fin):
line2 = regex.sub(r'\[(\w)', r'["\1', line)
line2 = regex.sub(r'(\w)\]', r'\1"]', line2)
line2 = regex.sub(r'(\w),(\w)', r'\1","\2', line2)
fout.write(line2)
with open(goodjson_path, 'r') as fin:
words = []
with open(goodjson_path + '.txt', 'w') as fout:
hunspell = json.load(fin)
for word, affixes in hunspell['words'].items():
words += [word]
fout.write(word + '\n')
for affix in affixes:
words += [affix]
fout.write(affix + '\n')
return words | 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 json file with properly quoted strings in list of affixes
Returns:
list of all words with all possible affixes in *.txt format (simplified .dic format)
References:
Syed Faisal Ali 's Hunspell dic parser: https://github.com/SyedFaisalAli/HunspellToJSON
"""
with open(badjson_path, 'r') as fin:
with open(goodjson_path, 'w') as fout:
for i, line in enumerate(fin):
line2 = regex.sub(r'\[(\w)', r'["\1', line)
line2 = regex.sub(r'(\w)\]', r'\1"]', line2)
line2 = regex.sub(r'(\w),(\w)', r'\1","\2', line2)
fout.write(line2)
with open(goodjson_path, 'r') as fin:
words = []
with open(goodjson_path + '.txt', 'w') as fout:
hunspell = json.load(fin)
for word, affixes in hunspell['words'].items():
words += [word]
fout.write(word + '\n')
for affix in affixes:
words += [affix]
fout.write(affix + '\n')
return words | [
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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(statement)
s += 'Reply: {}\n\n'.format(reply)
return 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(statement)
s += 'Reply: {}\n\n'.format(reply)
return 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), 'group', str)()
return (filepath[:(-len(exts) or None)], exts) | 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), 'group', str)()
return (filepath[:(-len(exts) or None)], exts) | [
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>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.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 = ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)']
# df.columns = clean_columns(df.columns)
x = get_array(df, x, default=0)
y = get_array(df, y, default=1)
z = get_array(df, z, default=-1)
for i in range(len(df['name'].unique())):
name = df['Name'].unique()[i]
color = colors[i]
x = x[pd.np.array(df['name'] == name)]
y = y[pd.np.array(df['name'] == name)]
z = z[pd.np.array(df['name'] == name)]
trace = dict(
name=name,
x=x, y=y, z=z,
type="scatter3d",
mode='markers',
marker=dict(size=3, color=color, line=dict(width=0)))
data.append(trace)
layout = dict(
width=800,
height=550,
autosize=False,
title='Iris dataset',
scene=dict(
xaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
yaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
zaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
aspectratio=dict(x=1, y=1, z=0.7),
aspectmode='manual'
),
)
fig = dict(data=data, layout=layout)
# IPython notebook
# plotly.iplot(fig, filename='pandas-3d-iris', validate=False)
url = plotly.offline.plot(fig, filename='pandas-3d-iris', validate=False)
return url | 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 = ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)']
# df.columns = clean_columns(df.columns)
x = get_array(df, x, default=0)
y = get_array(df, y, default=1)
z = get_array(df, z, default=-1)
for i in range(len(df['name'].unique())):
name = df['Name'].unique()[i]
color = colors[i]
x = x[pd.np.array(df['name'] == name)]
y = y[pd.np.array(df['name'] == name)]
z = z[pd.np.array(df['name'] == name)]
trace = dict(
name=name,
x=x, y=y, z=z,
type="scatter3d",
mode='markers',
marker=dict(size=3, color=color, line=dict(width=0)))
data.append(trace)
layout = dict(
width=800,
height=550,
autosize=False,
title='Iris dataset',
scene=dict(
xaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
yaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
zaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
aspectratio=dict(x=1, y=1, z=0.7),
aspectmode='manual'
),
)
fig = dict(data=data, layout=layout)
# IPython notebook
# plotly.iplot(fig, filename='pandas-3d-iris', validate=False)
url = plotly.offline.plot(fig, filename='pandas-3d-iris', validate=False)
return url | [
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>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df) | [
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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') # pd.read_csv('https://plot.ly/~etpinard/191.csv')
>>> df.columns = [eval(c) if c[0] in '"\'' else str(c) for c in df.columns]
>>> data = {'data': [
... Scatter(x=df[continent+', x'],
... y=df[continent+', y'],
... text=df[continent+', text'],
... marker=Marker(size=df[continent+', size'].fillna(10000), sizemode='area', sizeref=131868,),
... mode='markers',
... name=continent) for continent in ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
... ],
... 'layout': Layout(xaxis=XAxis(title='Life Expectancy'), yaxis=YAxis(title='GDP per Capita', type='log'))
... }
>>> html = offline_plotly_data(data, filename=None)
"""
config_default = dict(DEFAULT_PLOTLY_CONFIG)
if config is not None:
config_default.update(config)
with open(os.path.join(DATA_PATH, 'plotly.js.min'), 'rt') as f:
js = f.read()
html, divid, width, height = _plot_html(
data,
config=config_default,
validate=validate,
default_width=default_width, default_height=default_height,
global_requirejs=global_requirejs)
html = PLOTLY_HTML.format(plotlyjs=js, plotlyhtml=html)
if filename and isinstance(filename, str):
with open(filename, 'wt') as f:
f.write(html)
return html | 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
>>> df = get_data('etpinard') # pd.read_csv('https://plot.ly/~etpinard/191.csv')
>>> df.columns = [eval(c) if c[0] in '"\'' else str(c) for c in df.columns]
>>> data = {'data': [
... Scatter(x=df[continent+', x'],
... y=df[continent+', y'],
... text=df[continent+', text'],
... marker=Marker(size=df[continent+', size'].fillna(10000), sizemode='area', sizeref=131868,),
... mode='markers',
... name=continent) for continent in ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
... ],
... 'layout': Layout(xaxis=XAxis(title='Life Expectancy'), yaxis=YAxis(title='GDP per Capita', type='log'))
... }
>>> html = offline_plotly_data(data, filename=None)
"""
config_default = dict(DEFAULT_PLOTLY_CONFIG)
if config is not None:
config_default.update(config)
with open(os.path.join(DATA_PATH, 'plotly.js.min'), 'rt') as f:
js = f.read()
html, divid, width, height = _plot_html(
data,
config=config_default,
validate=validate,
default_width=default_width, default_height=default_height,
global_requirejs=global_requirejs)
html = PLOTLY_HTML.format(plotlyjs=js, plotlyhtml=html)
if filename and isinstance(filename, str):
with open(filename, 'wt') as f:
f.write(html)
return html | [
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>>> from nlpia.loaders import get_data
>>> df = get_data('etpinard') # pd.read_csv('https://plot.ly/~etpinard/191.csv')
>>> df.columns = [eval(c) if c[0] in '"\'' else str(c) for c in df.columns]
>>> data = {'data': [
... Scatter(x=df[continent+', x'],
... y=df[continent+', y'],
... text=df[continent+', text'],
... marker=Marker(size=df[continent+', size'].fillna(10000), sizemode='area', sizeref=131868,),
... mode='markers',
... name=continent) for continent in ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
... ],
... 'layout': Layout(xaxis=XAxis(title='Life Expectancy'), yaxis=YAxis(title='GDP per Capita', type='log'))
... }
>>> html = offline_plotly_data(data, filename=None) | [
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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"""
possible_categories = ['Africa', 'Americas', 'Asia', 'Europe',
'Oceania'] if possible_categories is None else possible_categories
df.columns = clean_columns(df.columns)
df = pd.read_csv(df) if isinstance(df, str) else df
columns = clean_columns(list(columns))
df2 = pd.DataFrame(columns=columns)
df2[category_col] = np.concatenate([np.array([categ] * len(df)) for categ in possible_categories])
columns = zip(columns, [[clean_columns(categ + ', ' + column) for categ in possible_categories] for column in columns])
for col, category_cols in columns:
df2[col] = np.concatenate([df[label].values for label in category_cols])
return df2 | 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']):
"""Reformat a dataframe in etpinard's format for use in plot functions and sklearn models"""
possible_categories = ['Africa', 'Americas', 'Asia', 'Europe',
'Oceania'] if possible_categories is None else possible_categories
df.columns = clean_columns(df.columns)
df = pd.read_csv(df) if isinstance(df, str) else df
columns = clean_columns(list(columns))
df2 = pd.DataFrame(columns=columns)
df2[category_col] = np.concatenate([np.array([categ] * len(df)) for categ in possible_categories])
columns = zip(columns, [[clean_columns(categ + ', ' + column) for categ in possible_categories] for column in columns])
for col, category_cols in columns:
df2[col] = np.concatenate([df[label].values for label in category_cols])
return df2 | [
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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',
category_col='category', possible_categories=None,
filename=None,
config={'displaylogo': False},
xscale=None, yscale='log',
layout={'hovermode': 'closest', 'showlegend': False, 'autosize': True},
marker={'sizemode': 'area'},
min_size=10,
):
r"""Interactive scatterplot of a DataFrame with the size and color of circles linke to two columns
config keys:
fillFrame setBackground displaylogo sendData showLink linkText staticPlot scrollZoom plot3dPixelRatio displayModeBar
showTips workspace doubleClick autosizable editable
layout keys:
angularaxis annotations autosize bargap bargroupgap barmode barnorm boxgap boxgroupgap boxmode calendar
direction dragmode font geo height hiddenlabels hiddenlabelssrc hidesources hovermode images legend
mapbox margin orientation paper_bgcolor plot_bgcolor radialaxis scene separators shapes showlegend sliders smith
ternary title titlefont updatemenus width xaxis yaxis
marker keys:
autocolorscale blend border cauto cmax cmin color colorbar colors colorscale colorsrc colorssrc line maxdisplayed
opacity opacitysrc outliercolor reversescale showscale size sizemax sizemin sizemode sizeref sizesrc symbol symbolsrc
marker['sizeref'] gives the denominator of the circle scaling factor.
Typically it should be about a tenth of the minimum 'size' column value
>>> from nlpia.data.loaders import get_data
>>> df = get_data('cities_us_wordvectors_pca2_meta').iloc[:100]
>>> html = offline_plotly_scatter_bubble(
... df.sort_values('population', ascending=False)[:350].copy().sort_values('population'),
... x='x', y='y',
... size_col='population', text_col='name', category_col='timezone',
... xscale=None, yscale=None, # 'log' or None
... layout={}, marker={'sizeref': 3000})
"""
config_default = dict(DEFAULT_PLOTLY_CONFIG)
marker_default = {
'size': size_col or min_size,
'sizemode': 'area',
'sizeref': int(df[size_col].min() * .8) if size_col else min_size}
marker_default.update(marker)
size_col = marker_default.pop('size')
layout_default = {
'xaxis': XAxis(title=x, type=xscale),
'yaxis': YAxis(title=y, type=yscale),
}
layout_default.update(**layout)
if config is not None:
config_default.update(config)
df.columns = clean_columns(df.columns)
if possible_categories is None and category_col is not None:
if category_col in df.columns:
category_labels = df[category_col]
else:
category_labels = np.array(category_col)
possible_categories = list(set(category_labels))
possible_categories = [None] if possible_categories is None else possible_categories
if category_col and category_col in df:
masks = [np.array(df[category_col] == label) for label in possible_categories]
else:
masks = [np.array([True] * len(df))] * len(possible_categories)
data = {'data': [
Scatter(x=df[x][mask].values,
y=df[y][mask].values,
text=df[text_col][mask].values,
marker=Marker(size=df[size_col][mask] if size_col in df.columns else size_col,
**marker_default),
mode='markers',
name=str(category_name)) for (category_name, mask) in zip(possible_categories, masks)
],
'layout': Layout(**layout_default)
}
return offline_plotly_data(data, filename=filename, config=config_default) | python | def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',
category_col='category', possible_categories=None,
filename=None,
config={'displaylogo': False},
xscale=None, yscale='log',
layout={'hovermode': 'closest', 'showlegend': False, 'autosize': True},
marker={'sizemode': 'area'},
min_size=10,
):
r"""Interactive scatterplot of a DataFrame with the size and color of circles linke to two columns
config keys:
fillFrame setBackground displaylogo sendData showLink linkText staticPlot scrollZoom plot3dPixelRatio displayModeBar
showTips workspace doubleClick autosizable editable
layout keys:
angularaxis annotations autosize bargap bargroupgap barmode barnorm boxgap boxgroupgap boxmode calendar
direction dragmode font geo height hiddenlabels hiddenlabelssrc hidesources hovermode images legend
mapbox margin orientation paper_bgcolor plot_bgcolor radialaxis scene separators shapes showlegend sliders smith
ternary title titlefont updatemenus width xaxis yaxis
marker keys:
autocolorscale blend border cauto cmax cmin color colorbar colors colorscale colorsrc colorssrc line maxdisplayed
opacity opacitysrc outliercolor reversescale showscale size sizemax sizemin sizemode sizeref sizesrc symbol symbolsrc
marker['sizeref'] gives the denominator of the circle scaling factor.
Typically it should be about a tenth of the minimum 'size' column value
>>> from nlpia.data.loaders import get_data
>>> df = get_data('cities_us_wordvectors_pca2_meta').iloc[:100]
>>> html = offline_plotly_scatter_bubble(
... df.sort_values('population', ascending=False)[:350].copy().sort_values('population'),
... x='x', y='y',
... size_col='population', text_col='name', category_col='timezone',
... xscale=None, yscale=None, # 'log' or None
... layout={}, marker={'sizeref': 3000})
"""
config_default = dict(DEFAULT_PLOTLY_CONFIG)
marker_default = {
'size': size_col or min_size,
'sizemode': 'area',
'sizeref': int(df[size_col].min() * .8) if size_col else min_size}
marker_default.update(marker)
size_col = marker_default.pop('size')
layout_default = {
'xaxis': XAxis(title=x, type=xscale),
'yaxis': YAxis(title=y, type=yscale),
}
layout_default.update(**layout)
if config is not None:
config_default.update(config)
df.columns = clean_columns(df.columns)
if possible_categories is None and category_col is not None:
if category_col in df.columns:
category_labels = df[category_col]
else:
category_labels = np.array(category_col)
possible_categories = list(set(category_labels))
possible_categories = [None] if possible_categories is None else possible_categories
if category_col and category_col in df:
masks = [np.array(df[category_col] == label) for label in possible_categories]
else:
masks = [np.array([True] * len(df))] * len(possible_categories)
data = {'data': [
Scatter(x=df[x][mask].values,
y=df[y][mask].values,
text=df[text_col][mask].values,
marker=Marker(size=df[size_col][mask] if size_col in df.columns else size_col,
**marker_default),
mode='markers',
name=str(category_name)) for (category_name, mask) in zip(possible_categories, masks)
],
'layout': Layout(**layout_default)
}
return offline_plotly_data(data, filename=filename, config=config_default) | [
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config keys:
fillFrame setBackground displaylogo sendData showLink linkText staticPlot scrollZoom plot3dPixelRatio displayModeBar
showTips workspace doubleClick autosizable editable
layout keys:
angularaxis annotations autosize bargap bargroupgap barmode barnorm boxgap boxgroupgap boxmode calendar
direction dragmode font geo height hiddenlabels hiddenlabelssrc hidesources hovermode images legend
mapbox margin orientation paper_bgcolor plot_bgcolor radialaxis scene separators shapes showlegend sliders smith
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marker keys:
autocolorscale blend border cauto cmax cmin color colorbar colors colorscale colorsrc colorssrc line maxdisplayed
opacity opacitysrc outliercolor reversescale showscale size sizemax sizemin sizemode sizeref sizesrc symbol symbolsrc
marker['sizeref'] gives the denominator of the circle scaling factor.
Typically it should be about a tenth of the minimum 'size' column value
>>> from nlpia.data.loaders import get_data
>>> df = get_data('cities_us_wordvectors_pca2_meta').iloc[:100]
>>> html = offline_plotly_scatter_bubble(
... df.sort_values('population', ascending=False)[:350].copy().sort_values('population'),
... x='x', y='y',
... size_col='population', text_col='name', category_col='timezone',
... xscale=None, yscale=None, # 'log' or None
... layout={}, marker={'sizeref': 3000}) | [
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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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>>> format_hex(42, num_bytes=8, prefix=None)
'0000002a'
>>> format_hex(23)
'0x0017' | [
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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)
cleaned up URL (following redirects and possibly adding HTTP schema "http://")
>>> is_up_url("duckduckgo.com") # a more private, less manipulative search engine
'https://duckduckgo.com/'
>>> urlisup = is_up_url("totalgood.org")
>>> not urlisup or str(urlisup).startswith('http')
True
>>> urlisup = is_up_url("wikipedia.org")
>>> str(urlisup).startswith('http')
True
>>> 'wikipedia.org' in str(urlisup)
True
>>> bool(is_up_url('8158989668202919656'))
False
>>> is_up_url('invalidurlwithoutadomain')
False
"""
if not isinstance(url, basestring) or '.' not in url:
return False
normalized_url = prepend_http(url)
session = requests.Session()
session.mount(url, HTTPAdapter(max_retries=2))
try:
resp = session.get(normalized_url, allow_redirects=allow_redirects, timeout=timeout)
except ConnectionError:
return None
except:
return None
if resp.status_code in (301, 302, 307) or resp.headers.get('location', None):
return resp.headers.get('location', None) # return redirected URL
elif 100 <= resp.status_code < 400:
return normalized_url # return the original URL that was requested/visited
else:
return False | python | 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)
cleaned up URL (following redirects and possibly adding HTTP schema "http://")
>>> is_up_url("duckduckgo.com") # a more private, less manipulative search engine
'https://duckduckgo.com/'
>>> urlisup = is_up_url("totalgood.org")
>>> not urlisup or str(urlisup).startswith('http')
True
>>> urlisup = is_up_url("wikipedia.org")
>>> str(urlisup).startswith('http')
True
>>> 'wikipedia.org' in str(urlisup)
True
>>> bool(is_up_url('8158989668202919656'))
False
>>> is_up_url('invalidurlwithoutadomain')
False
"""
if not isinstance(url, basestring) or '.' not in url:
return False
normalized_url = prepend_http(url)
session = requests.Session()
session.mount(url, HTTPAdapter(max_retries=2))
try:
resp = session.get(normalized_url, allow_redirects=allow_redirects, timeout=timeout)
except ConnectionError:
return None
except:
return None
if resp.status_code in (301, 302, 307) or resp.headers.get('location', None):
return resp.headers.get('location', None) # return redirected URL
elif 100 <= resp.status_code < 400:
return normalized_url # return the original URL that was requested/visited
else:
return False | [
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Returns:
None if ConnectionError
False if url is invalid (any HTTP error code)
cleaned up URL (following redirects and possibly adding HTTP schema "http://")
>>> is_up_url("duckduckgo.com") # a more private, less manipulative search engine
'https://duckduckgo.com/'
>>> urlisup = is_up_url("totalgood.org")
>>> not urlisup or str(urlisup).startswith('http')
True
>>> urlisup = is_up_url("wikipedia.org")
>>> str(urlisup).startswith('http')
True
>>> 'wikipedia.org' in str(urlisup)
True
>>> bool(is_up_url('8158989668202919656'))
False
>>> is_up_url('invalidurlwithoutadomain')
False | [
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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('- bullet \n##bad\n# hello\n ### world\n')
[(0, '- bullet '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 2)
[(2, 'bad')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 1)
[]
"""
if isinstance(levels, (int, float, basestring, str, bytes)):
levels = [float(levels)]
levels = set([int(i) for i in levels])
if isinstance(lines, basestring):
lines = lines.splitlines()
level_lines = []
for line in lines:
level_line = None
if 0 in levels:
level_line = (0, line)
lstripped = line.lstrip()
for i in range(6, 1, -1):
if lstripped.startswith('#' * i):
level_line = (i, lstripped[i:].lstrip())
break
if level_line and level_line[0] in levels:
level_lines.append(level_line)
return level_lines | 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')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n')
[(0, '- bullet '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 2)
[(2, 'bad')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 1)
[]
"""
if isinstance(levels, (int, float, basestring, str, bytes)):
levels = [float(levels)]
levels = set([int(i) for i in levels])
if isinstance(lines, basestring):
lines = lines.splitlines()
level_lines = []
for line in lines:
level_line = None
if 0 in levels:
level_line = (0, line)
lstripped = line.lstrip()
for i in range(6, 1, -1):
if lstripped.startswith('#' * i):
level_line = (i, lstripped[i:].lstrip())
break
if level_line and level_line[0] in levels:
level_lines.append(level_line)
return level_lines | [
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[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n')
[(0, '- bullet '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 2)
[(2, 'bad')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n', 1)
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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('abc\n def\n gh')))
3
>>> 20000 > len(list(iter_lines(BOOK_PATH))) > 200
True
"""
if url_or_text is None or not url_or_text:
return []
# url_or_text = 'https://www.fileformat.info/info/charset/UTF-8/list.htm'
elif isinstance(url_or_text, (str, bytes, basestring)):
if '\n' in url_or_text or '\r' in url_or_text:
return StringIO(url_or_text)
elif os.path.isfile(os.path.join(DATA_PATH, url_or_text)):
return open(os.path.join(DATA_PATH, url_or_text), mode=mode)
elif os.path.isfile(url_or_text):
return open(os.path.join(url_or_text), mode=mode)
if os.path.isdir(url_or_text):
filepaths = [filemeta['path'] for filemeta in find_files(url_or_text, ext=ext)]
return itertools.chain.from_iterable(map(open, filepaths))
url = looks_like_url(url_or_text)
if url:
for i in range(3):
return requests.get(url, stream=True, allow_redirects=True, timeout=5)
else:
return StringIO(url_or_text)
elif isinstance(url_or_text, (list, tuple)):
# FIXME: make this lazy with chain and map so it doesn't gobble up RAM
text = ''
for s in url_or_text:
text += '\n'.join(list(iter_lines(s, ext=ext, mode=mode))) + '\n'
return iter_lines(text) | 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
>>> len(list(iter_lines(list('abcdefgh'))))
8
>>> len(list(iter_lines('abc\n def\n gh\n')))
3
>>> len(list(iter_lines('abc\n def\n gh')))
3
>>> 20000 > len(list(iter_lines(BOOK_PATH))) > 200
True
"""
if url_or_text is None or not url_or_text:
return []
# url_or_text = 'https://www.fileformat.info/info/charset/UTF-8/list.htm'
elif isinstance(url_or_text, (str, bytes, basestring)):
if '\n' in url_or_text or '\r' in url_or_text:
return StringIO(url_or_text)
elif os.path.isfile(os.path.join(DATA_PATH, url_or_text)):
return open(os.path.join(DATA_PATH, url_or_text), mode=mode)
elif os.path.isfile(url_or_text):
return open(os.path.join(url_or_text), mode=mode)
if os.path.isdir(url_or_text):
filepaths = [filemeta['path'] for filemeta in find_files(url_or_text, ext=ext)]
return itertools.chain.from_iterable(map(open, filepaths))
url = looks_like_url(url_or_text)
if url:
for i in range(3):
return requests.get(url, stream=True, allow_redirects=True, timeout=5)
else:
return StringIO(url_or_text)
elif isinstance(url_or_text, (list, tuple)):
# FIXME: make this lazy with chain and map so it doesn't gobble up RAM
text = ''
for s in url_or_text:
text += '\n'.join(list(iter_lines(s, ext=ext, mode=mode))) + '\n'
return iter_lines(text) | [
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>>> 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')))
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>>> len(list(iter_lines('abc\n def\n gh')))
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>>> 20000 > len(list(iter_lines(BOOK_PATH))) > 200
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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 else utf
utf.columns = 'char name hex'.split()
utf.name = utf.name.str.replace('<control>', 'CONTTROL CHARACTER')
multiascii = [' '] * len(utf)
asc = [' '] * len(utf)
rows = []
for i, name in enumerate(utf.name):
if i < 128 and str.isprintable(chr(i)):
asc[i] = chr(i)
else:
asc[i] = ' '
big = re.findall(r'CAPITAL\ LETTER\ ([a-z0-9A-Z ]+$)', name)
small = re.findall(r'SMALL\ LETTER\ ([a-z0-9A-Z ]+$)', name)
pattern = r'(?P<description>' \
r'(?P<lang>LATIN|GREEK|COPTIC|CYRILLIC)?[\s]*' \
r'(?P<case>CAPITAL|SMALL)?[\s]*' \
r'(?P<length>CHARACTER|LETTER)?[\s]*' \
r'(?P<ukrainian>BYELORUSSIAN-UKRAINIAN)?[\s]*' \
r'(?P<name>[-_><a-z0-9A-Z\s ]+)[\s]*' \
r'\(?(?P<code_point>U\+[- a-fA-F0-9]{4,8})?\)?)[\s]*' # noqa
match = re.match(pattern, name)
gd = match.groupdict()
gd['char'] = chr(i)
gd['suffix'] = None
gd['wordwith'] = None
withprefix = re.match(r'(?P<prefix>DOTLESS|TURNED|SMALL)(?P<name>.*)' +
r'(?P<wordwith>WITH|SUPERSCRIPT|SUBSCRIPT|DIGRAPH)\s+(?P<suffix>[-_><a-z0-9A-Z\s ]+)',
gd['name'])
if withprefix:
gd.update(withprefix.groupdict())
withsuffix = re.match(r'(?P<name>.*)(?P<wordwith>WITH|SUPERSCRIPT|SUBSCRIPT|DIGRAPH)\s+' +
r'(?P<suffix>[-_><a-z0-9A-Z\s ]+)',
gd['name'])
if withsuffix:
gd.update(withsuffix.groupdict())
gd['code_point'] = gd['code_point'] or format_hex(i, num_bytes=4, prefix='U+').upper()
if i < 128:
gd['ascii'] = chr(i)
else:
multiascii = gd['name']
if gd['suffix'] and gd['wordwith']:
multiascii = NAME_ACCENT.get(gd['suffix'], "'")
else:
if big:
m = big[0]
multiascii[i] = m
if len(m) == 1:
asc[i] = m
elif small:
multiascii[i] = small[0].lower()
if len(multiascii[i]) == 1:
asc[i] = small[0].lower()
rows.append(gd)
df = pd.DataFrame(rows)
df.multiascii = df.multiascii.str.strip()
df['ascii'] = df['ascii'].str.strip()
df.name = df.name.str.strip()
return df | 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 else utf
utf.columns = 'char name hex'.split()
utf.name = utf.name.str.replace('<control>', 'CONTTROL CHARACTER')
multiascii = [' '] * len(utf)
asc = [' '] * len(utf)
rows = []
for i, name in enumerate(utf.name):
if i < 128 and str.isprintable(chr(i)):
asc[i] = chr(i)
else:
asc[i] = ' '
big = re.findall(r'CAPITAL\ LETTER\ ([a-z0-9A-Z ]+$)', name)
small = re.findall(r'SMALL\ LETTER\ ([a-z0-9A-Z ]+$)', name)
pattern = r'(?P<description>' \
r'(?P<lang>LATIN|GREEK|COPTIC|CYRILLIC)?[\s]*' \
r'(?P<case>CAPITAL|SMALL)?[\s]*' \
r'(?P<length>CHARACTER|LETTER)?[\s]*' \
r'(?P<ukrainian>BYELORUSSIAN-UKRAINIAN)?[\s]*' \
r'(?P<name>[-_><a-z0-9A-Z\s ]+)[\s]*' \
r'\(?(?P<code_point>U\+[- a-fA-F0-9]{4,8})?\)?)[\s]*' # noqa
match = re.match(pattern, name)
gd = match.groupdict()
gd['char'] = chr(i)
gd['suffix'] = None
gd['wordwith'] = None
withprefix = re.match(r'(?P<prefix>DOTLESS|TURNED|SMALL)(?P<name>.*)' +
r'(?P<wordwith>WITH|SUPERSCRIPT|SUBSCRIPT|DIGRAPH)\s+(?P<suffix>[-_><a-z0-9A-Z\s ]+)',
gd['name'])
if withprefix:
gd.update(withprefix.groupdict())
withsuffix = re.match(r'(?P<name>.*)(?P<wordwith>WITH|SUPERSCRIPT|SUBSCRIPT|DIGRAPH)\s+' +
r'(?P<suffix>[-_><a-z0-9A-Z\s ]+)',
gd['name'])
if withsuffix:
gd.update(withsuffix.groupdict())
gd['code_point'] = gd['code_point'] or format_hex(i, num_bytes=4, prefix='U+').upper()
if i < 128:
gd['ascii'] = chr(i)
else:
multiascii = gd['name']
if gd['suffix'] and gd['wordwith']:
multiascii = NAME_ACCENT.get(gd['suffix'], "'")
else:
if big:
m = big[0]
multiascii[i] = m
if len(m) == 1:
asc[i] = m
elif small:
multiascii[i] = small[0].lower()
if len(multiascii[i]) == 1:
asc[i] = small[0].lower()
rows.append(gd)
df = pd.DataFrame(rows)
df.multiascii = df.multiascii.str.strip()
df['ascii'] = df['ascii'].str.strip()
df.name = df.name.str.strip()
return df | [
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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 filename in filenames:
filepath = os.path.join(dialogdir, filename)
df = clean_df(filepath)
df.to_csv(filepath, header=None)
return filenames | 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)
for filename in filenames:
filepath = os.path.join(dialogdir, filename)
df = clean_df(filepath)
df.to_csv(filepath, header=None)
return filenames | [
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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 = ''
for c in text:
if not c or ord(c) < 128:
output += c
else:
output += translate[c] if c in translate else ' '
return output.strip() | 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 = ''
for c in text:
if not c or ord(c) < 128:
output += c
else:
output += translate[c] if c in translate else ' '
return output.strip() | [
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utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \'' | [
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/data_utils.py#L305-L321 | train | 226,334 |
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[col] = df[col].apply(unicode2ascii)
return df | 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(' ')
for col in df.columns:
df[col] = df[col].apply(unicode2ascii)
return df | [
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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(line)
if matches:
for m in matches:
if m.group('a2'):
acronyms.append((m.group('a2'), m.group('s2')))
elif m.group('a3'):
acronyms.append((m.group('a3'), m.group('s3')))
elif m.group('a4'):
acronyms.append((m.group('a4'), m.group('s4')))
elif m.group('a5'):
acronyms.append((m.group('a5'), m.group('s5')))
return sorted(dict(acronyms).items()) | 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 = []
for f, lines in get_lines(manuscript):
for line in lines:
matches = CRE_ACRONYM.finditer(line)
if matches:
for m in matches:
if m.group('a2'):
acronyms.append((m.group('a2'), m.group('s2')))
elif m.group('a3'):
acronyms.append((m.group('a3'), m.group('s3')))
elif m.group('a4'):
acronyms.append((m.group('a4'), m.group('s4')))
elif m.group('a5'):
acronyms.append((m.group('a5'), m.group('s5')))
return sorted(dict(acronyms).items()) | [
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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 = get_acronyms(manuscript)
for a in acronyms:
lines.append('*{}*:: {} -- '.format(a[0], a[1][0].upper() + a[1][1:]))
return linesep.join(lines) | 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"]']
acronyms = get_acronyms(manuscript)
for a in acronyms:
lines.append('*{}*:: {} -- '.format(a[0], a[1][0].upper() + a[1][1:]))
return linesep.join(lines) | [
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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 meta:
if meta.get('hostname', url) == 'drive.google.com':
title = get_url_title(url)
else:
title = meta.get('filename', meta['hostname']) or meta['hostname']
title, fileext = splitext(title)
else:
logging.error('Unable to retrieve URL: {}'.format(url))
return None
return delimit_slug(title, ' ') | 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'
"""
meta = get_url_filemeta(url)
title = ''
if meta:
if meta.get('hostname', url) == 'drive.google.com':
title = get_url_title(url)
else:
title = meta.get('filename', meta['hostname']) or meta['hostname']
title, fileext = splitext(title)
else:
logging.error('Unable to retrieve URL: {}'.format(url))
return None
return delimit_slug(title, ' ') | [
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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(translate_book(book_dir=BOOK_PATH, dest='cleaned_hyperlinks'))
3
>>> rm_rf(os.path.join(BOOK_PATH, 'cleaned_hyperlinks'))
"""
if callable(translators) or not hasattr(translators, '__len__'):
translators = (translators,)
sections = get_tagged_sections(book_dir=book_dir, include_tags=include_tags)
file_line_maps = []
for fileid, (filepath, tagged_lines) in enumerate(sections):
logger.info('filepath={}'.format(filepath))
destpath = filepath
if not dest:
copyfile(filepath, filepath + '.' + ext.lstrip('.'))
elif os.path.sep in dest:
destpath = os.path.join(dest, os.path.basename(filepath))
else:
destpath = os.path.join(os.path.dirname(filepath), dest, os.path.basename(filepath))
ensure_dir_exists(os.path.dirname(destpath))
with open(destpath, 'w') as fout:
logger.info('destpath={}'.format(destpath))
for lineno, (tag, line) in enumerate(tagged_lines):
if (include_tags is None or tag in include_tags or
any((tag.startswith(t) for t in include_tags))):
for translate in translators:
new_line = translate(line) # TODO: be smarter about writing to files in-place
if line != new_line:
file_line_maps.append((fileid, lineno, filepath, destpath, line, new_line))
line = new_line
fout.write(line)
return file_line_maps | 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
>>> len(translate_book(book_dir=BOOK_PATH, dest='cleaned_hyperlinks'))
3
>>> rm_rf(os.path.join(BOOK_PATH, 'cleaned_hyperlinks'))
"""
if callable(translators) or not hasattr(translators, '__len__'):
translators = (translators,)
sections = get_tagged_sections(book_dir=book_dir, include_tags=include_tags)
file_line_maps = []
for fileid, (filepath, tagged_lines) in enumerate(sections):
logger.info('filepath={}'.format(filepath))
destpath = filepath
if not dest:
copyfile(filepath, filepath + '.' + ext.lstrip('.'))
elif os.path.sep in dest:
destpath = os.path.join(dest, os.path.basename(filepath))
else:
destpath = os.path.join(os.path.dirname(filepath), dest, os.path.basename(filepath))
ensure_dir_exists(os.path.dirname(destpath))
with open(destpath, 'w') as fout:
logger.info('destpath={}'.format(destpath))
for lineno, (tag, line) in enumerate(tagged_lines):
if (include_tags is None or tag in include_tags or
any((tag.startswith(t) for t in include_tags))):
for translate in translators:
new_line = translate(line) # TODO: be smarter about writing to files in-place
if line != new_line:
file_line_maps.append((fileid, lineno, filepath, destpath, line, new_line))
line = new_line
fout.write(line)
return file_line_maps | [
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>>> len(translate_book(book_dir=BOOK_PATH, dest='cleaned_hyperlinks'))
3
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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_tagged_lines at ...>
>>> list(filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')],
... include_tags='natural'))
[('natural', 'Hello.')]
"""
include_tags = (include_tags,) if isinstance(include_tags, str) else include_tags
exclude_tags = (exclude_tags,) if isinstance(exclude_tags, str) else exclude_tags
for tagged_line in tagged_lines:
if (include_tags is None or tagged_line[0] in include_tags or
any((tagged_line[0].startswith(t) for t in include_tags))):
if exclude_tags is None or not any((tagged_line[0].startswith(t) for t in exclude_tags)):
yield tagged_line
else:
logger.debug('skipping tag {} because it starts with one of the exclude_tags={}'.format(
tagged_line[0], exclude_tags))
else:
logger.debug('skipping tag {} because not in {}'.format(tagged_line[0], include_tags)) | 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()')])
<generator object filter_tagged_lines at ...>
>>> list(filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')],
... include_tags='natural'))
[('natural', 'Hello.')]
"""
include_tags = (include_tags,) if isinstance(include_tags, str) else include_tags
exclude_tags = (exclude_tags,) if isinstance(exclude_tags, str) else exclude_tags
for tagged_line in tagged_lines:
if (include_tags is None or tagged_line[0] in include_tags or
any((tagged_line[0].startswith(t) for t in include_tags))):
if exclude_tags is None or not any((tagged_line[0].startswith(t) for t in exclude_tags)):
yield tagged_line
else:
logger.debug('skipping tag {} because it starts with one of the exclude_tags={}'.format(
tagged_line[0], exclude_tags))
else:
logger.debug('skipping tag {} because not in {}'.format(tagged_line[0], include_tags)) | [
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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 verbosity:
print()
print('Sigma:')
print(smat.round(2))
print()
print('Sigma without zeroing any dim:')
print(np.diag(smat.round(2)))
tdm_prime = u.values.dot(smat.values).dot(vt.values)
if verbosity:
print()
print('Reconstructed Term-Document Matrix')
print(tdm_prime.round(2))
err = [np.sqrt(((tdm_prime - tdm).values.flatten() ** 2).sum() / np.product(tdm.shape))]
if verbosity:
print()
print('Error without reducing dimensions:')
print(err[-1])
# 2.3481474529927113e-15
smat2 = smat.copy()
for numdim in range(len(s) - 1, 0, -1):
smat2.iloc[numdim, numdim] = 0
if verbosity:
print('Sigma after zeroing out dim {}'.format(numdim))
print(np.diag(smat2.round(2)))
# d0 d1 d2 d3 d4 d5
# ship 2.16 0.00 0.0 0.0 0.0 0.0
# boat 0.00 1.59 0.0 0.0 0.0 0.0
# ocean 0.00 0.00 0.0 0.0 0.0 0.0
# voyage 0.00 0.00 0.0 0.0 0.0 0.0
# trip 0.00 0.00 0.0 0.0 0.0 0.0
tdm_prime2 = u.values.dot(smat2.values).dot(vt.values)
err += [np.sqrt(((tdm_prime2 - tdm).values.flatten() ** 2).sum() / np.product(tdm.shape))]
if verbosity:
print('Error after zeroing out dim {}'.format(numdim))
print(err[-1])
return err | 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)
if verbosity:
print()
print('Sigma:')
print(smat.round(2))
print()
print('Sigma without zeroing any dim:')
print(np.diag(smat.round(2)))
tdm_prime = u.values.dot(smat.values).dot(vt.values)
if verbosity:
print()
print('Reconstructed Term-Document Matrix')
print(tdm_prime.round(2))
err = [np.sqrt(((tdm_prime - tdm).values.flatten() ** 2).sum() / np.product(tdm.shape))]
if verbosity:
print()
print('Error without reducing dimensions:')
print(err[-1])
# 2.3481474529927113e-15
smat2 = smat.copy()
for numdim in range(len(s) - 1, 0, -1):
smat2.iloc[numdim, numdim] = 0
if verbosity:
print('Sigma after zeroing out dim {}'.format(numdim))
print(np.diag(smat2.round(2)))
# d0 d1 d2 d3 d4 d5
# ship 2.16 0.00 0.0 0.0 0.0 0.0
# boat 0.00 1.59 0.0 0.0 0.0 0.0
# ocean 0.00 0.00 0.0 0.0 0.0 0.0
# voyage 0.00 0.00 0.0 0.0 0.0 0.0
# trip 0.00 0.00 0.0 0.0 0.0 0.0
tdm_prime2 = u.values.dot(smat2.values).dot(vt.values)
err += [np.sqrt(((tdm_prime2 - tdm).values.flatten() ** 2).sum() / np.product(tdm.shape))]
if verbosity:
print('Error after zeroing out dim {}'.format(numdim))
print(err[-1])
return err | [
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/book/examples/ch04_catdog_lsa_sorted.py#L143-L187 | train | 226,342 |
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 single language and call it recursively if necessary
>>> get_anki_phrases('afr')[:2]
["'n Groen piesang is nie ryp genoeg om te eet nie.",
"'n Hond het agter die kat aan gehardloop."]
"""
lang = lang.strip().lower()[:3]
lang = LANG2ANKI[lang[:2]] if lang not in ANKI_LANGUAGES else lang
if lang[:2] == 'en':
return get_anki_phrases_english(limit=limit)
return sorted(get_data(lang).iloc[:, -1].str.strip().values) | python | 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 single language and call it recursively if necessary
>>> get_anki_phrases('afr')[:2]
["'n Groen piesang is nie ryp genoeg om te eet nie.",
"'n Hond het agter die kat aan gehardloop."]
"""
lang = lang.strip().lower()[:3]
lang = LANG2ANKI[lang[:2]] if lang not in ANKI_LANGUAGES else lang
if lang[:2] == 'en':
return get_anki_phrases_english(limit=limit)
return sorted(get_data(lang).iloc[:, -1].str.strip().values) | [
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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
texts = texts.union(set(phrases))
if limit and len(texts) >= limit:
break
return sorted(texts) | 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
texts = texts.union(set(phrases))
if limit and len(texts) >= limit:
break
return sorted(texts) | [
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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 . punct O 0.0
1 Hey INTJ UH intj O 0.0
2 Man NOUN NN ROOT PERSON I 0.0
3 Mr. PROPN NNP compound O 0.0
4 Tangerine PROPN NNP compound PERSON B 0.0
"""
if isinstance(docs, spacy.tokens.doc.Doc):
return get_vocab([docs])
vocab = set()
for doc in tqdm(docs):
for tok in doc:
vocab.add((tok.text, tok.pos_, tok.tag_, tok.dep_, tok.ent_type_, tok.ent_iob_, tok.sentiment))
# TODO: add ent type info and other flags, e.g. like_url, like_email, etc
return pd.DataFrame(sorted(vocab), columns='word pos tag dep ent_type ent_iob sentiment'.split()) | 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])
word pos tag dep ent_type ent_iob sentiment
0 ! PUNCT . punct O 0.0
1 Hey INTJ UH intj O 0.0
2 Man NOUN NN ROOT PERSON I 0.0
3 Mr. PROPN NNP compound O 0.0
4 Tangerine PROPN NNP compound PERSON B 0.0
"""
if isinstance(docs, spacy.tokens.doc.Doc):
return get_vocab([docs])
vocab = set()
for doc in tqdm(docs):
for tok in doc:
vocab.add((tok.text, tok.pos_, tok.tag_, tok.dep_, tok.ent_type_, tok.ent_iob_, tok.sentiment))
# TODO: add ent type info and other flags, e.g. like_url, like_email, etc
return pd.DataFrame(sorted(vocab), columns='word pos tag dep ent_type ent_iob sentiment'.split()) | [
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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 in variations:
if w in wv:
vectors[i, :] = wv[w]
if not np.sum(np.abs(vectors[i])):
logger.warning('Unable to find {}, {}, or {} in word2vec.'.format(*variations))
return vectors | 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])
for w in variations:
if w in wv:
vectors[i, :] = wv[w]
if not np.sum(np.abs(vectors[i])):
logger.warning('Unable to find {}, {}, or {} in word2vec.'.format(*variations))
return vectors | [
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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, limit=limit)
docs = nlp(texts, lang=lang)
vocab = get_vocab(docs)
vocab['vector'] = get_word_vectors(vocab) # TODO: turn this into a KeyedVectors object
if filename:
vocab.to_csv(os.path.join(BIGDATA_PATH, filename))
return vocab | 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
"""
texts = get_anki_phrases(lang=lang, limit=limit)
docs = nlp(texts, lang=lang)
vocab = get_vocab(docs)
vocab['vector'] = get_word_vectors(vocab) # TODO: turn this into a KeyedVectors object
if filename:
vocab.to_csv(os.path.join(BIGDATA_PATH, filename))
return vocab | [
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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 None:
cased_tokens = ('PyConOpenSpaces PyCon PyCon2017 PyCon2018 PyCon2016 PyCon2015 OpenSpace PyconTutorial ' +
'NLP NaturalLanguageProcessing NLPInAction NaturalLanguageProcessingInAction NLPIA Twote Twip'
).split()
cased_tokens += [s + 's' for s in cased_tokens]
cased_tokens += 'TotalGood TotalGoods HobsonLane Hob Hobs TotalGood.com ' \
'www.TotalGood.com http://www.TotalGood.com https://www.TotalGood.com'.split()
allcase_tokens = cased_tokens + [s.lower() for s in cased_tokens]
allcase_tokens += [s.title() for s in cased_tokens]
allcase_tokens += [s.upper() for s in cased_tokens]
KEEP_TOKENS = allcase_tokens + ['#' + s for s in allcase_tokens]
# takes 15 minutes and 10GB of RAM for 500k tweets if you keep all 20M unique tokens/names URLs
vocab_path = os.path.join(BIGDATA_PATH, 'vocab939370.pkl')
if os.path.isfile(vocab_path):
print('Loading vocab: {} ...'.format(vocab_path))
vocab = Dictionary.load(vocab_path)
print(' len(vocab) loaded: {}'.format(len(vocab.dfs)))
else:
tweets_path = os.path.join(BIGDATA_PATH, 'tweets.csv.gz')
print('Loading tweets: {} ...'.format(tweets_path))
tweets = read_csv(tweets_path)
tweets = pd.np.array(tweets.text.str.split())
with gzip.open(os.path.join(BIGDATA_PATH, 'tweets.txt.gz'), 'w') as f:
for tokens in tweets:
f.write((' '.join(tokens) + '\n').encode('utf-8'))
# tweets['text'] = tweets.text.apply(lambda s: eval(s).decode('utf-8'))
# tweets['user'] = tweets.user.apply(lambda s: eval(s).decode('utf-8'))
# tweets.to_csv('tweets.csv.gz', compression='gzip')
print('Computing vocab from {} tweets...'.format(len(tweets)))
vocab = Dictionary(tweets, no_below=NO_BELOW, no_above=NO_ABOVE, keep_tokens=set(KEEP_TOKENS))
vocab.filter_extremes(no_below=NO_BELOW, no_above=NO_ABOVE, keep_n=KEEP_N, keep_tokens=set(KEEP_TOKENS))
print(' len(vocab) after filtering: {}'.format(len(vocab.dfs)))
# no time at all, just a bookeeping step, doesn't actually compute anything
tfidf = TfidfModel(id2word=vocab, dictionary=vocab)
tfidf.save(os.path.join(BIGDATA_PATH, 'tfidf{}.pkl'.format(len(vocab.dfs))))
tweets = [vocab.doc2bow(tw) for tw in tweets]
json.dump(tweets, gzip.open(os.path.join(BIGDATA_PATH, 'tweet_bows.json.gz'), 'w'))
gc.collect()
# LSA is more useful name than LSA
lsa = LsiModel(tfidf[tweets], num_topics=200, id2word=vocab, extra_samples=100, power_iters=2)
return lsa | 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:
# PyCons NLPS #PyCon2016 #NaturalLanguageProcessing #naturallanguageprocessing
if cased_tokens is None:
cased_tokens = ('PyConOpenSpaces PyCon PyCon2017 PyCon2018 PyCon2016 PyCon2015 OpenSpace PyconTutorial ' +
'NLP NaturalLanguageProcessing NLPInAction NaturalLanguageProcessingInAction NLPIA Twote Twip'
).split()
cased_tokens += [s + 's' for s in cased_tokens]
cased_tokens += 'TotalGood TotalGoods HobsonLane Hob Hobs TotalGood.com ' \
'www.TotalGood.com http://www.TotalGood.com https://www.TotalGood.com'.split()
allcase_tokens = cased_tokens + [s.lower() for s in cased_tokens]
allcase_tokens += [s.title() for s in cased_tokens]
allcase_tokens += [s.upper() for s in cased_tokens]
KEEP_TOKENS = allcase_tokens + ['#' + s for s in allcase_tokens]
# takes 15 minutes and 10GB of RAM for 500k tweets if you keep all 20M unique tokens/names URLs
vocab_path = os.path.join(BIGDATA_PATH, 'vocab939370.pkl')
if os.path.isfile(vocab_path):
print('Loading vocab: {} ...'.format(vocab_path))
vocab = Dictionary.load(vocab_path)
print(' len(vocab) loaded: {}'.format(len(vocab.dfs)))
else:
tweets_path = os.path.join(BIGDATA_PATH, 'tweets.csv.gz')
print('Loading tweets: {} ...'.format(tweets_path))
tweets = read_csv(tweets_path)
tweets = pd.np.array(tweets.text.str.split())
with gzip.open(os.path.join(BIGDATA_PATH, 'tweets.txt.gz'), 'w') as f:
for tokens in tweets:
f.write((' '.join(tokens) + '\n').encode('utf-8'))
# tweets['text'] = tweets.text.apply(lambda s: eval(s).decode('utf-8'))
# tweets['user'] = tweets.user.apply(lambda s: eval(s).decode('utf-8'))
# tweets.to_csv('tweets.csv.gz', compression='gzip')
print('Computing vocab from {} tweets...'.format(len(tweets)))
vocab = Dictionary(tweets, no_below=NO_BELOW, no_above=NO_ABOVE, keep_tokens=set(KEEP_TOKENS))
vocab.filter_extremes(no_below=NO_BELOW, no_above=NO_ABOVE, keep_n=KEEP_N, keep_tokens=set(KEEP_TOKENS))
print(' len(vocab) after filtering: {}'.format(len(vocab.dfs)))
# no time at all, just a bookeeping step, doesn't actually compute anything
tfidf = TfidfModel(id2word=vocab, dictionary=vocab)
tfidf.save(os.path.join(BIGDATA_PATH, 'tfidf{}.pkl'.format(len(vocab.dfs))))
tweets = [vocab.doc2bow(tw) for tw in tweets]
json.dump(tweets, gzip.open(os.path.join(BIGDATA_PATH, 'tweet_bows.json.gz'), 'w'))
gc.collect()
# LSA is more useful name than LSA
lsa = LsiModel(tfidf[tweets], num_topics=200, id2word=vocab, extra_samples=100, power_iters=2)
return lsa | [
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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)
7037
"""
tqdm_prog = tqdm if verbose else no_tqdm
with ensure_open(f, mode='r') as fin:
for i, line in tqdm_prog(enumerate(fin)):
if nrows is not None and i >= nrows - 1:
break
# fin.seek(0)
return i + 1 | 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)
7037
"""
tqdm_prog = tqdm if verbose else no_tqdm
with ensure_open(f, mode='r') as fin:
for i, line in tqdm_prog(enumerate(fin)):
if nrows is not None and i >= nrows - 1:
break
# fin.seek(0)
return i + 1 | [
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References:
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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.bin.gz'
"""
filename = os.path.basename(filepath)
dirpath = filepath[:-len(filename)]
cre_controlspace = re.compile(r'[\t\r\n\f]+')
new_filename = cre_controlspace.sub('', filename)
if not new_filename == filename:
logger.warning('Stripping whitespace from filename: {} => {}'.format(
repr(filename), repr(new_filename)))
filename = new_filename
filename = filename.lower()
filename = normalize_ext(filename)
if dirpath:
dirpath = dirpath[:-1] # get rid of the trailing os.path.sep
return os.path.join(dirpath, filename)
return filename | 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')
'NLPIA/src/nlpia/bigdata/goog new 300dneg.bin.gz'
"""
filename = os.path.basename(filepath)
dirpath = filepath[:-len(filename)]
cre_controlspace = re.compile(r'[\t\r\n\f]+')
new_filename = cre_controlspace.sub('', filename)
if not new_filename == filename:
logger.warning('Stripping whitespace from filename: {} => {}'.format(
repr(filename), repr(new_filename)))
filename = new_filename
filename = filename.lower()
filename = normalize_ext(filename)
if dirpath:
dirpath = dirpath[:-1] # get rid of the trailing os.path.sep
return os.path.join(dirpath, filename)
return filename | [
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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
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fullpath = expand_filepath(os.path.join(basedir, filename))
if os.path.isfile(fullpath):
return fullpath
return False | 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
>>> p = find_filepath('iq_test.csv')
>>> p == expand_filepath(os.path.join(DATA_PATH, 'iq_test.csv'))
True
>>> p[-len('iq_test.csv'):]
'iq_test.csv'
>>> find_filepath('exponentially-crazy-filename-2.718281828459045.nonexistent')
False
"""
if os.path.isfile(filename):
return filename
for basedir in basepaths:
fullpath = expand_filepath(os.path.join(basedir, filename))
if os.path.isfile(fullpath):
return fullpath
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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.
"""
if not self._closed:
self._closed = True
if self._pool is not None:
self._pool.close()
self._pool = None | python | def close(self):
""" Shut down, closing any open connections in the pool.
"""
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neo4j/neo4j-python-driver | neo4j/types/spatial.py | hydrate_point | def hydrate_point(srid, *coordinates):
""" Create a new instance of a Point subclass from a raw
set of fields. The subclass chosen is determined by the
given SRID; a ValueError will be raised if no such
subclass can be found.
"""
try:
point_class, dim = __srid_table[srid]
except KeyError:
point = Point(coordinates)
point.srid = srid
return point
else:
if len(coordinates) != dim:
raise ValueError("SRID %d requires %d coordinates (%d provided)" % (srid, dim, len(coordinates)))
return point_class(coordinates) | python | def hydrate_point(srid, *coordinates):
""" Create a new instance of a Point subclass from a raw
set of fields. The subclass chosen is determined by the
given SRID; a ValueError will be raised if no such
subclass can be found.
"""
try:
point_class, dim = __srid_table[srid]
except KeyError:
point = Point(coordinates)
point.srid = srid
return point
else:
if len(coordinates) != dim:
raise ValueError("SRID %d requires %d coordinates (%d provided)" % (srid, dim, len(coordinates)))
return point_class(coordinates) | [
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neo4j/neo4j-python-driver | neo4j/types/spatial.py | dehydrate_point | def dehydrate_point(value):
""" Dehydrator for Point data.
:param value:
:type value: Point
:return:
"""
dim = len(value)
if dim == 2:
return Structure(b"X", value.srid, *value)
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""" Dehydrator for Point data.
:param value:
:type value: Point
:return:
"""
dim = len(value)
if dim == 2:
return Structure(b"X", value.srid, *value)
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return Structure(b"Y", value.srid, *value)
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | PackStreamDehydrator.dehydrate | def dehydrate(self, values):
""" Convert native values into PackStream values.
"""
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except KeyError:
pass
else:
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raise TypeError("Non-string dictionary keys are not supported")
return {key: dehydrate_(value) for key, value in obj.items()}
else:
raise TypeError(obj)
return tuple(map(dehydrate_, values)) | python | def dehydrate(self, values):
""" Convert native values into PackStream values.
"""
def dehydrate_(obj):
try:
f = self.dehydration_functions[type(obj)]
except KeyError:
pass
else:
return f(obj)
if obj is None:
return None
elif isinstance(obj, bool):
return obj
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elif isinstance(obj, str):
return obj
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else:
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elif isinstance(obj, dict):
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return {key: dehydrate_(value) for key, value in obj.items()}
else:
raise TypeError(obj)
return tuple(map(dehydrate_, 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 default:
:return:
"""
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""" Obtain a value from the record by key, returning a default
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:param key:
:param default:
:return:
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try:
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.index | def index(self, key):
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:param key:
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raise TypeError(key) | python | def index(self, key):
""" Return the index of the given item.
:param key:
:return:
"""
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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:
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return self[index] | python | def value(self, key=0, default=None):
""" Obtain a single value from the record by index or key. If no
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.values | def values(self, *keys):
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return list(self) | python | 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
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neo4j/neo4j-python-driver | neo4j/types/__init__.py | Record.items | def items(self, *keys):
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return list((self.__keys[i], super(Record, self).__getitem__(i)) for i in range(len(self))) | 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(child) for child in plan_dict.get("children", [])]
if "dbHits" in plan_dict or "rows" in plan_dict:
db_hits = plan_dict.get("dbHits", 0)
rows = plan_dict.get("rows", 0)
return ProfiledPlan(operator_type, identifiers, arguments, children, db_hits, rows)
else:
return Plan(operator_type, identifiers, arguments, children) | 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", [])
children = [_make_plan(child) for child in plan_dict.get("children", [])]
if "dbHits" in plan_dict or "rows" in plan_dict:
db_hits = plan_dict.get("dbHits", 0)
rows = plan_dict.get("rows", 0)
return ProfiledPlan(operator_type, identifiers, arguments, children, db_hits, rows)
else:
return Plan(operator_type, identifiers, arguments, children) | [
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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):
return tx.run("MATCH (a:Person) RETURN count(a)").single().value()
"""
def wrapper(f):
def wrapped(*args, **kwargs):
return f(*args, **kwargs)
wrapped.metadata = metadata
wrapped.timeout = timeout
return wrapped
return wrapper | python | 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):
return tx.run("MATCH (a:Person) RETURN count(a)").single().value()
"""
def wrapper(f):
def wrapped(*args, **kwargs):
return f(*args, **kwargs)
wrapped.metadata = metadata
wrapped.timeout = timeout
return wrapped
return wrapper | [
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def count_people(tx):
return tx.run("MATCH (a:Person) RETURN count(a)").single().value() | [
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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():
try:
self.rollback_transaction()
except (CypherError, TransactionError, SessionError, ConnectionExpired, ServiceUnavailable):
pass
finally:
self._closed = True
self._disconnect(sync=True) | python | 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():
try:
self.rollback_transaction()
except (CypherError, TransactionError, SessionError, ConnectionExpired, ServiceUnavailable):
pass
finally:
self._closed = True
self._disconnect(sync=True) | [
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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.
If a statement is executed before a previous
:class:`.StatementResult` in the same :class:`.Session` has
been fully consumed, the first result will be fully fetched
and buffered. Note therefore that the generally recommended
pattern of usage is to fully consume one result before
executing a subsequent statement. If two results need to be
consumed in parallel, multiple :class:`.Session` objects
can be used as an alternative to result buffering.
For more usage details, see :meth:`.Transaction.run`.
:param statement: template Cypher statement
:param parameters: dictionary of parameters
:param kwparameters: additional keyword parameters
:returns: :class:`.StatementResult` object
"""
from neobolt.exceptions import ConnectionExpired
self._assert_open()
if not statement:
raise ValueError("Cannot run an empty statement")
if not isinstance(statement, (str, Statement)):
raise TypeError("Statement must be a string or a Statement instance")
if not self._connection:
self._connect()
cx = self._connection
protocol_version = cx.protocol_version
server = cx.server
has_transaction = self.has_transaction()
statement_text = str(statement)
statement_metadata = getattr(statement, "metadata", None)
statement_timeout = getattr(statement, "timeout", None)
parameters = fix_parameters(dict(parameters or {}, **kwparameters), protocol_version,
supports_bytes=server.supports("bytes"))
def fail(_):
self._close_transaction()
hydrant = PackStreamHydrator(protocol_version)
result_metadata = {
"statement": statement_text,
"parameters": parameters,
"server": server,
"protocol_version": protocol_version,
}
run_metadata = {
"metadata": statement_metadata,
"timeout": statement_timeout,
"on_success": result_metadata.update,
"on_failure": fail,
}
def done(summary_metadata):
result_metadata.update(summary_metadata)
bookmark = result_metadata.get("bookmark")
if bookmark:
self._bookmarks_in = tuple([bookmark])
self._bookmark_out = bookmark
self._last_result = result = BoltStatementResult(self, hydrant, result_metadata)
if has_transaction:
if statement_metadata:
raise ValueError("Metadata can only be attached at transaction level")
if statement_timeout:
raise ValueError("Timeouts only apply at transaction level")
else:
run_metadata["bookmarks"] = self._bookmarks_in
cx.run(statement_text, parameters, **run_metadata)
cx.pull_all(
on_records=lambda records: result._records.extend(
hydrant.hydrate_records(result.keys(), records)),
on_success=done,
on_failure=fail,
on_summary=lambda: result.detach(sync=False),
)
if not has_transaction:
try:
self._connection.send()
self._connection.fetch()
except ConnectionExpired as error:
raise SessionExpired(*error.args)
return result | 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
immediately but the :class:`.StatementResult` content is
fetched lazily as consumed by the client application.
If a statement is executed before a previous
:class:`.StatementResult` in the same :class:`.Session` has
been fully consumed, the first result will be fully fetched
and buffered. Note therefore that the generally recommended
pattern of usage is to fully consume one result before
executing a subsequent statement. If two results need to be
consumed in parallel, multiple :class:`.Session` objects
can be used as an alternative to result buffering.
For more usage details, see :meth:`.Transaction.run`.
:param statement: template Cypher statement
:param parameters: dictionary of parameters
:param kwparameters: additional keyword parameters
:returns: :class:`.StatementResult` object
"""
from neobolt.exceptions import ConnectionExpired
self._assert_open()
if not statement:
raise ValueError("Cannot run an empty statement")
if not isinstance(statement, (str, Statement)):
raise TypeError("Statement must be a string or a Statement instance")
if not self._connection:
self._connect()
cx = self._connection
protocol_version = cx.protocol_version
server = cx.server
has_transaction = self.has_transaction()
statement_text = str(statement)
statement_metadata = getattr(statement, "metadata", None)
statement_timeout = getattr(statement, "timeout", None)
parameters = fix_parameters(dict(parameters or {}, **kwparameters), protocol_version,
supports_bytes=server.supports("bytes"))
def fail(_):
self._close_transaction()
hydrant = PackStreamHydrator(protocol_version)
result_metadata = {
"statement": statement_text,
"parameters": parameters,
"server": server,
"protocol_version": protocol_version,
}
run_metadata = {
"metadata": statement_metadata,
"timeout": statement_timeout,
"on_success": result_metadata.update,
"on_failure": fail,
}
def done(summary_metadata):
result_metadata.update(summary_metadata)
bookmark = result_metadata.get("bookmark")
if bookmark:
self._bookmarks_in = tuple([bookmark])
self._bookmark_out = bookmark
self._last_result = result = BoltStatementResult(self, hydrant, result_metadata)
if has_transaction:
if statement_metadata:
raise ValueError("Metadata can only be attached at transaction level")
if statement_timeout:
raise ValueError("Timeouts only apply at transaction level")
else:
run_metadata["bookmarks"] = self._bookmarks_in
cx.run(statement_text, parameters, **run_metadata)
cx.pull_all(
on_records=lambda records: result._records.extend(
hydrant.hydrate_records(result.keys(), records)),
on_success=done,
on_failure=fail,
on_summary=lambda: result.detach(sync=False),
)
if not has_transaction:
try:
self._connection.send()
self._connection.fetch()
except ConnectionExpired as error:
raise SessionExpired(*error.args)
return result | [
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If a statement is executed before a previous
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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 ConnectionExpired as error:
raise SessionExpired(*error.args)
else:
return detail_count
return 0 | python | 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 ConnectionExpired as error:
raise SessionExpired(*error.args)
else:
return detail_count
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neo4j/neo4j-python-driver | neo4j/blocking.py | Session.detach | def detach(self, result, sync=True):
""" Detach a result from this session by fetching and buffering any
remaining records.
:param result:
:param sync:
:returns: number of records fetched
"""
count = 0
if sync and result.attached():
self.send()
fetch = self.fetch
while result.attached():
count += fetch()
if self._last_result is result:
self._last_result = None
if not self.has_transaction():
self._disconnect(sync=False)
result._session = None
return count | python | def detach(self, result, sync=True):
""" Detach a result from this session by fetching and buffering any
remaining records.
:param result:
:param sync:
:returns: number of records fetched
"""
count = 0
if sync and result.attached():
self.send()
fetch = self.fetch
while result.attached():
count += fetch()
if self._last_result is result:
self._last_result = None
if not self.has_transaction():
self._disconnect(sync=False)
result._session = None
return count | [
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neo4j/neo4j-python-driver | neo4j/blocking.py | Transaction.run | def run(self, statement, parameters=None, **kwparameters):
""" Run a Cypher statement within the context of this transaction.
The statement is sent to the server lazily, when its result is
consumed. To force the statement to be sent to the server, use
the :meth:`.Transaction.sync` method.
Cypher is typically expressed as a statement template plus a
set of named parameters. In Python, parameters may be expressed
through a dictionary of parameters, through individual parameter
arguments, or as a mixture of both. For example, the `run`
statements below are all equivalent::
>>> statement = "CREATE (a:Person {name:{name}, age:{age}})"
>>> tx.run(statement, {"name": "Alice", "age": 33})
>>> tx.run(statement, {"name": "Alice"}, age=33)
>>> tx.run(statement, name="Alice", age=33)
Parameter values can be of any type supported by the Neo4j type
system. In Python, this includes :class:`bool`, :class:`int`,
:class:`str`, :class:`list` and :class:`dict`. Note however that
:class:`list` properties must be homogenous.
:param statement: template Cypher statement
:param parameters: dictionary of parameters
:param kwparameters: additional keyword parameters
:returns: :class:`.StatementResult` object
:raise TransactionError: if the transaction is closed
"""
self._assert_open()
return self.session.run(statement, parameters, **kwparameters) | python | def run(self, statement, parameters=None, **kwparameters):
""" Run a Cypher statement within the context of this transaction.
The statement is sent to the server lazily, when its result is
consumed. To force the statement to be sent to the server, use
the :meth:`.Transaction.sync` method.
Cypher is typically expressed as a statement template plus a
set of named parameters. In Python, parameters may be expressed
through a dictionary of parameters, through individual parameter
arguments, or as a mixture of both. For example, the `run`
statements below are all equivalent::
>>> statement = "CREATE (a:Person {name:{name}, age:{age}})"
>>> tx.run(statement, {"name": "Alice", "age": 33})
>>> tx.run(statement, {"name": "Alice"}, age=33)
>>> tx.run(statement, name="Alice", age=33)
Parameter values can be of any type supported by the Neo4j type
system. In Python, this includes :class:`bool`, :class:`int`,
:class:`str`, :class:`list` and :class:`dict`. Note however that
:class:`list` properties must be homogenous.
:param statement: template Cypher statement
:param parameters: dictionary of parameters
:param kwparameters: additional keyword parameters
:returns: :class:`.StatementResult` object
:raise TransactionError: if the transaction is closed
"""
self._assert_open()
return self.session.run(statement, parameters, **kwparameters) | [
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>>> statement = "CREATE (a:Person {name:{name}, age:{age}})"
>>> tx.run(statement, {"name": "Alice", "age": 33})
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.detach | def detach(self, sync=True):
""" Detach this result from its parent session by fetching the
remainder of this result from the network into the buffer.
:returns: number of records fetched
"""
if self.attached():
return self._session.detach(self, sync=sync)
else:
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""" Detach this result from its parent session by fetching the
remainder of this result from the network into the buffer.
:returns: number of records fetched
"""
if self.attached():
return self._session.detach(self, sync=sync)
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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
"""
try:
return self._metadata["fields"]
except KeyError:
if self.attached():
self._session.send()
while self.attached() and "fields" not in self._metadata:
self._session.fetch()
return self._metadata.get("fields") | python | def keys(self):
""" The keys for the records in this result.
:returns: tuple of key names
"""
try:
return self._metadata["fields"]
except KeyError:
if self.attached():
self._session.send()
while self.attached() and "fields" not in self._metadata:
self._session.fetch()
return self._metadata.get("fields") | [
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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
"""
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""" Generator for records obtained from this result.
:yields: iterable of :class:`.Record` objects
"""
records = self._records
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while records:
yield next_record()
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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 self._summary | python | 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)
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neo4j/neo4j-python-driver | neo4j/blocking.py | StatementResult.single | def single(self):
""" Obtain the next and only remaining record from this result.
A warning is generated if more than one record is available but
the first of these is still returned.
:returns: the next :class:`.Record` or :const:`None` if none remain
:warns: if more than one record is available
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records = list(self)
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if size == 0:
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warn("Expected a result with a single record, but this result contains %d" % size)
return records[0] | python | def single(self):
""" Obtain the next and only remaining record from this result.
A warning is generated if more than one record is available but
the first of these is still returned.
:returns: the next :class:`.Record` or :const:`None` if none remain
:warns: if more than one record is available
"""
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return records[0] | [
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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.
This leaves the record in the buffer for further processing.
:returns: the next :class:`.Record` or :const:`None` if none remain
"""
records = self._records
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return None
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return records[0]
return None | python | def peek(self):
""" Obtain the next record from this result without consuming it.
This leaves the record in the buffer for further processing.
:returns: the next :class:`.Record` or :const:`None` if none remain
"""
records = self._records
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return None
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self._session.send()
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neo4j/neo4j-python-driver | neo4j/blocking.py | BoltStatementResult.value | def value(self, item=0, default=None):
""" Return the remainder of the result as a list of values.
:param item: field to return for each remaining record
:param default: default value, used if the index of key is unavailable
:returns: list of individual values
"""
return [record.value(item, default) for record in self.records()] | python | def value(self, item=0, default=None):
""" Return the remainder of the result as a list of values.
:param item: field to return for each remaining record
:param default: default value, used if the index of key is unavailable
:returns: list of individual values
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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_lock.acquire(blocking=False)
if not lock_acquired:
raise PullOrderException()
return self._results_generator() | 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
# the connection isn't touched until you try and iterate the generator we return
lock_acquired = self._pull_lock.acquire(blocking=False)
if not lock_acquired:
raise PullOrderException()
return self._results_generator() | [
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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, category=DeprecationWarning, stacklevel=2)
return f(*args, **kwargs)
f_.__name__ = f.__name__
f_.__doc__ = f.__doc__
f_.__dict__.update(f.__dict__)
return f_
return f__ | python | 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, category=DeprecationWarning, stacklevel=2)
return f(*args, **kwargs)
f_.__name__ = f.__name__
f_.__doc__ = f.__doc__
f_.__dict__.update(f.__dict__)
return f_
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neo4j/neo4j-python-driver | neo4j/meta.py | experimental | def experimental(message):
""" Decorator for tagging experimental functions and methods.
::
@experimental("'foo' is an experimental function and may be "
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def foo(x):
pass
"""
def f__(f):
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return f__ | python | def experimental(message):
""" Decorator for tagging experimental functions and methods.
::
@experimental("'foo' is an experimental function and may be "
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def foo(x):
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"""
def f__(f):
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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(minutes, 60))
seconds = (1000000000 * seconds + nanoseconds) / 1000000000
t = Time(hours, minutes, seconds)
if tz is None:
return t
tz_offset_minutes, tz_offset_seconds = divmod(tz, 60)
zone = FixedOffset(tz_offset_minutes)
return zone.localize(t) | 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))
minutes, seconds = map(int, divmod(seconds, 60))
hours, minutes = map(int, divmod(minutes, 60))
seconds = (1000000000 * seconds + nanoseconds) / 1000000000
t = Time(hours, minutes, seconds)
if tz is None:
return t
tz_offset_minutes, tz_offset_seconds = divmod(tz, 60)
zone = FixedOffset(tz_offset_minutes)
return zone.localize(t) | [
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_time | def dehydrate_time(value):
""" Dehydrator for `time` values.
:param value:
:type value: Time
:return:
"""
if isinstance(value, Time):
nanoseconds = int(value.ticks * 1000000000)
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if value.tzinfo:
return Structure(b"T", nanoseconds, value.tzinfo.utcoffset(value).seconds)
else:
return Structure(b"t", nanoseconds) | python | def dehydrate_time(value):
""" Dehydrator for `time` values.
:param value:
:type value: Time
:return:
"""
if isinstance(value, Time):
nanoseconds = int(value.ticks * 1000000000)
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if value.tzinfo:
return Structure(b"T", nanoseconds, value.tzinfo.utcoffset(value).seconds)
else:
return Structure(b"t", nanoseconds) | [
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | hydrate_datetime | def hydrate_datetime(seconds, nanoseconds, tz=None):
""" Hydrator for `DateTime` and `LocalDateTime` values.
:param seconds:
:param nanoseconds:
:param tz:
:return: datetime
"""
minutes, seconds = map(int, divmod(seconds, 60))
hours, minutes = map(int, divmod(minutes, 60))
days, hours = map(int, divmod(hours, 24))
seconds = (1000000000 * seconds + nanoseconds) / 1000000000
t = DateTime.combine(Date.from_ordinal(UNIX_EPOCH_DATE_ORDINAL + days), Time(hours, minutes, seconds))
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return t
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tz_offset_minutes, tz_offset_seconds = divmod(tz, 60)
zone = FixedOffset(tz_offset_minutes)
else:
zone = timezone(tz)
return zone.localize(t) | python | def hydrate_datetime(seconds, nanoseconds, tz=None):
""" Hydrator for `DateTime` and `LocalDateTime` values.
:param seconds:
:param nanoseconds:
:param tz:
:return: datetime
"""
minutes, seconds = map(int, divmod(seconds, 60))
hours, minutes = map(int, divmod(minutes, 60))
days, hours = map(int, divmod(hours, 24))
seconds = (1000000000 * seconds + nanoseconds) / 1000000000
t = DateTime.combine(Date.from_ordinal(UNIX_EPOCH_DATE_ORDINAL + days), Time(hours, minutes, seconds))
if tz is None:
return t
if isinstance(tz, int):
tz_offset_minutes, tz_offset_seconds = divmod(tz, 60)
zone = FixedOffset(tz_offset_minutes)
else:
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return zone.localize(t) | [
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_datetime | def dehydrate_datetime(value):
""" Dehydrator for `datetime` values.
:param value:
:type value: datetime
:return:
"""
def seconds_and_nanoseconds(dt):
if isinstance(dt, datetime):
dt = DateTime.from_native(dt)
zone_epoch = DateTime(1970, 1, 1, tzinfo=dt.tzinfo)
t = dt.to_clock_time() - zone_epoch.to_clock_time()
return t.seconds, t.nanoseconds
tz = value.tzinfo
if tz is None:
# without time zone
value = utc.localize(value)
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"d", seconds, nanoseconds)
elif hasattr(tz, "zone") and tz.zone:
# with named time zone
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"f", seconds, nanoseconds, tz.zone)
else:
# with time offset
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"F", seconds, nanoseconds, tz.utcoffset(value).seconds) | python | def dehydrate_datetime(value):
""" Dehydrator for `datetime` values.
:param value:
:type value: datetime
:return:
"""
def seconds_and_nanoseconds(dt):
if isinstance(dt, datetime):
dt = DateTime.from_native(dt)
zone_epoch = DateTime(1970, 1, 1, tzinfo=dt.tzinfo)
t = dt.to_clock_time() - zone_epoch.to_clock_time()
return t.seconds, t.nanoseconds
tz = value.tzinfo
if tz is None:
# without time zone
value = utc.localize(value)
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"d", seconds, nanoseconds)
elif hasattr(tz, "zone") and tz.zone:
# with named time zone
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"f", seconds, nanoseconds, tz.zone)
else:
# with time offset
seconds, nanoseconds = seconds_and_nanoseconds(value)
return Structure(b"F", seconds, nanoseconds, tz.utcoffset(value).seconds) | [
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | hydrate_duration | def hydrate_duration(months, days, seconds, nanoseconds):
""" Hydrator for `Duration` values.
:param months:
:param days:
:param seconds:
:param nanoseconds:
:return: `duration` namedtuple
"""
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.
:param value:
:type value: Duration
:return:
"""
return Structure(b"E", value.months, value.days, value.seconds, int(1000000000 * value.subseconds)) | python | def dehydrate_duration(value):
""" Dehydrator for `duration` values.
:param value:
:type value: Duration
:return:
"""
return Structure(b"E", value.months, value.days, value.seconds, int(1000000000 * value.subseconds)) | [
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neo4j/neo4j-python-driver | neo4j/types/temporal.py | dehydrate_timedelta | 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
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
return Structure(b"E", months, days, seconds, nanoseconds) | [
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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)
driver.zoom(element=element, percent=percent, steps=steps) | [
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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)
el2 = self._element_find(end_locator, True, True)
driver = self._current_application()
driver.scroll(el1, el2) | python | 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)
el2 = self._element_find(end_locator, True, True)
driver = self._current_application()
driver.scroll(el1, el2) | [
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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().perform()
except:
assert False, "Can't click on a point at (%s,%s)" % (x,y) | 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)
try:
action.press(x=float(x), y=float(y)).wait(float(duration)).release().perform()
except:
assert False, "Can't click on a point at (%s,%s)" % (x,y) | [
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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, y=coordinate_Y).release().perform() | 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, y=coordinate_Y).release().perform() | [
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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
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`, `Wait Until Page Contains
Element`, `Wait For Condition` and BuiltIn keyword `Wait Until Keyword
Succeeds`.
"""
def check_visibility():
visible = self._is_visible(locator)
if visible:
return
elif visible is None:
return error or "Element locator '%s' did not match any elements after %s" % (locator, self._format_timeout(timeout))
else:
return error or "Element '%s' was not visible in %s" % (locator, self._format_timeout(timeout))
self._wait_until_no_error(timeout, check_visibility) | python | 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
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`, `Wait Until Page Contains
Element`, `Wait For Condition` and BuiltIn keyword `Wait Until Keyword
Succeeds`.
"""
def check_visibility():
visible = self._is_visible(locator)
if visible:
return
elif visible is None:
return error or "Element locator '%s' did not match any elements after %s" % (locator, self._format_timeout(timeout))
else:
return error or "Element '%s' was not visible in %s" % (locator, self._format_timeout(timeout))
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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):
"""Waits until `text` appears on current page.
Fails if `timeout` expires before the text appears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Does Not Contain`,
`Wait Until Page Contains Element`,
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BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
if not error:
error = "Text '%s' did not appear in <TIMEOUT>" % text
self._wait_until(timeout, error, self._is_text_present, text) | python | def wait_until_page_contains(self, text, timeout=None, error=None):
"""Waits until `text` appears on current page.
Fails if `timeout` expires before the text appears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Does Not Contain`,
`Wait Until Page Contains Element`,
`Wait Until Page Does Not Contain Element` and
BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
if not error:
error = "Text '%s' did not appear in <TIMEOUT>" % text
self._wait_until(timeout, error, self._is_text_present, text) | [
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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):
"""Waits until `text` disappears from current page.
Fails if `timeout` expires before the `text` disappears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
`Wait Until Page Contains Element`,
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BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
def check_present():
present = self._is_text_present(text)
if not present:
return
else:
return error or "Text '%s' did not disappear in %s" % (text, self._format_timeout(timeout))
self._wait_until_no_error(timeout, check_present) | python | def wait_until_page_does_not_contain(self, text, timeout=None, error=None):
"""Waits until `text` disappears from current page.
Fails if `timeout` expires before the `text` disappears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
`Wait Until Page Contains Element`,
`Wait Until Page Does Not Contain Element` and
BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
def check_present():
present = self._is_text_present(text)
if not present:
return
else:
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... | Waits until `text` disappears from current page.
Fails if `timeout` expires before the `text` disappears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
`Wait Until Page Contains Element`,
`Wait Until Page Does Not Contain Element` and
BuiltIn keyword `Wait Until Keyword Succeeds`. | [
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] | 91c808cf0602af6be8135ac529fa488fded04a85 | https://github.com/serhatbolsu/robotframework-appiumlibrary/blob/91c808cf0602af6be8135ac529fa488fded04a85/AppiumLibrary/keywords/_waiting.py#L48-L70 | train | 226,394 |
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):
"""Waits until element specified with `locator` appears on current page.
Fails if `timeout` expires before the element appears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
`Wait Until Page Does Not Contain`
`Wait Until Page Does Not Contain Element`
and BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
if not error:
error = "Element '%s' did not appear in <TIMEOUT>" % locator
self._wait_until(timeout, error, self._is_element_present, locator) | python | def wait_until_page_contains_element(self, locator, timeout=None, error=None):
"""Waits until element specified with `locator` appears on current page.
Fails if `timeout` expires before the element appears. See
`introduction` for more information about `timeout` and its
default value.
`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
`Wait Until Page Does Not Contain`
`Wait Until Page Does Not Contain Element`
and BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
if not error:
error = "Element '%s' did not appear in <TIMEOUT>" % locator
self._wait_until(timeout, error, self._is_element_present, locator) | [
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`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
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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):
"""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 also `Wait Until Page Contains`,
`Wait Until Page Does Not Contain`,
`Wait Until Page Contains Element` and
BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
def check_present():
present = self._is_element_present(locator)
if not present:
return
else:
return error or "Element '%s' did not disappear in %s" % (locator, self._format_timeout(timeout))
self._wait_until_no_error(timeout, check_present) | python | def wait_until_page_does_not_contain_element(self, locator, timeout=None, error=None):
"""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 also `Wait Until Page Contains`,
`Wait Until Page Does Not Contain`,
`Wait Until Page Contains Element` and
BuiltIn keyword `Wait Until Keyword Succeeds`.
"""
def check_present():
present = self._is_element_present(locator)
if not present:
return
else:
return error or "Element '%s' did not disappear in %s" % (locator, self._format_timeout(timeout))
self._wait_until_no_error(timeout, check_present) | [
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`error` can be used to override the default error message.
See also `Wait Until Page Contains`,
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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 |
| 1 | (Airplane Mode) | 0 | 0 | 1 |
| 2 | (Wifi only) | 0 | 1 | 0 |
| 4 | (Data only) | 1 | 0 | 0 |
| 6 | (All network on) | 1 | 1 | 0 |
"""
driver = self._current_application()
return driver.set_network_connection(int(connectionStatus)) | 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 |
| 1 | (Airplane Mode) | 0 | 0 | 1 |
| 2 | (Wifi only) | 0 | 1 | 0 |
| 4 | (Data only) | 1 | 0 | 0 |
| 6 | (All network on) | 1 | 1 | 0 |
"""
driver = self._current_application()
return driver.set_network_connection(int(connectionStatus)) | [
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Android only.
Possible values:
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| 0 | (None) | 0 | 0 | 0 |
| 1 | (Airplane Mode) | 0 | 0 | 1 |
| 2 | (Wifi only) | 0 | 1 | 0 |
| 4 | (Data only) | 1 | 0 | 0 |
| 6 | (All network on) | 1 | 1 | 0 | | [
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] | 91c808cf0602af6be8135ac529fa488fded04a85 | https://github.com/serhatbolsu/robotframework-appiumlibrary/blob/91c808cf0602af6be8135ac529fa488fded04a85/AppiumLibrary/keywords/_android_utils.py#L21-L35 | train | 226,397 |
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._current_application()
theFile = driver.pull_file(path)
if decode:
theFile = base64.b64decode(theFile)
return str(theFile) | 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._current_application()
theFile = driver.pull_file(path)
if decode:
theFile = base64.b64decode(theFile)
return str(theFile) | [
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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)
"""
driver = self._current_application()
theFolder = driver.pull_folder(path)
if decode:
theFolder = base64.b64decode(theFolder)
return theFolder | 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)
"""
driver = self._current_application()
theFolder = driver.pull_folder(path)
if decode:
theFolder = base64.b64decode(theFolder)
return theFolder | [
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