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train | PyTorchWrapperRNN.begin_update | Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass. | thinc/extra/wrappers.py | def begin_update(self, x_data, h_0=None, drop=0.0):
"""Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass.
"""
x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
# Make prediction
out, ... | def begin_update(self, x_data, h_0=None, drop=0.0):
"""Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass.
"""
x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
# Make prediction
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train | layerize | Wrap a function into a layer | thinc/api.py | def layerize(begin_update=None, predict=None, *args, **kwargs):
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if begin_update is not None:
return FunctionLayer(begin_update, predict=predict, *args, **kwargs)
def wrapper(begin_update):
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return wra... | def layerize(begin_update=None, predict=None, *args, **kwargs):
"""Wrap a function into a layer"""
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return FunctionLayer(begin_update, predict=predict, *args, **kwargs)
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train | metalayerize | Wrap a function over a sequence of layers and an input into a layer. | thinc/api.py | def metalayerize(user_func):
"""Wrap a function over a sequence of layers and an input into a layer."""
def returned(layers, *args, **kwargs):
def begin_update(X, *args, **kwargs):
return user_func(layers, X, *args, **kwargs)
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"""Wrap a function over a sequence of layers and an input into a layer."""
def returned(layers, *args, **kwargs):
def begin_update(X, *args, **kwargs):
return user_func(layers, X, *args, **kwargs)
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train | noop | Transform a sequences of layers into a null operation. | thinc/api.py | def noop(*layers):
"""Transform a sequences of layers into a null operation."""
def begin_update(X, drop=0.0):
return X, lambda D, *a, **k: D
return begin_update | def noop(*layers):
"""Transform a sequences of layers into a null operation."""
def begin_update(X, drop=0.0):
return X, lambda D, *a, **k: D
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train | chain | Compose two models `f` and `g` such that they become layers of a single
feed-forward model that computes `g(f(x))`.
Raises exception if their dimensions don't match. | thinc/api.py | def chain(*layers):
"""Compose two models `f` and `g` such that they become layers of a single
feed-forward model that computes `g(f(x))`.
Raises exception if their dimensions don't match.
"""
if len(layers) == 0:
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return layers[0]
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"""Compose two models `f` and `g` such that they become layers of a single
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Raises exception if their dimensions don't match.
"""
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train | clone | Construct `n` copies of a layer, with distinct weights.
i.e. `clone(f, 3)(x)` computes `f(f'(f''(x)))`. | thinc/api.py | def clone(orig, n):
"""Construct `n` copies of a layer, with distinct weights.
i.e. `clone(f, 3)(x)` computes `f(f'(f''(x)))`.
"""
if n == 0:
return layerize(noop())
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for i in range(n - 1):
layers.append(copy.deepcopy(orig))
layers[-1].set_id()
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i.e. `clone(f, 3)(x)` computes `f(f'(f''(x)))`.
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train | concatenate | Compose two or more models `f`, `g`, etc, such that their outputs are
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))` | thinc/api.py | def concatenate(*layers): # pragma: no cover
"""Compose two or more models `f`, `g`, etc, such that their outputs are
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"""
if not layers:
return noop()
ops = layers[0].ops
def begin_update(X, *a, **k):
forwa... | def concatenate(*layers): # pragma: no cover
"""Compose two or more models `f`, `g`, etc, such that their outputs are
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train | split_backward | Separate a sequence of layers' `begin_update` methods into two lists of
functions: one that computes the forward values, and the other that completes
the backward pass. The backward sequence is only populated after the forward
functions have been applied. | thinc/api.py | def split_backward(layers): # pragma: no cover
"""Separate a sequence of layers' `begin_update` methods into two lists of
functions: one that computes the forward values, and the other that completes
the backward pass. The backward sequence is only populated after the forward
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train | sink_return | Transform a function `func` that returns tuples into a function that returns
single values. Call a function `sink` on the unused values. | thinc/api.py | def sink_return(func, sink, splitter=None): # pragma: no cover
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"""
def wrap(*args, **kwargs):
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train | uniqued | Group inputs to a layer, so that the layer only has to compute
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local to each minibatch.
The uniqued wrapper is useful for word inputs, because common ... | thinc/api.py | def uniqued(layer, column=0):
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train | foreach | Map a layer across list items | thinc/api.py | def foreach(layer, drop_factor=1.0):
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lengths = []
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train | foreach_sentence | Map a layer across sentences (assumes spaCy-esque .sents interface) | thinc/api.py | def foreach_sentence(layer, drop_factor=1.0):
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train | func_dump | Serialize user defined function. | thinc/extra/_vendorized/keras_generic_utils.py | def func_dump(func):
'''Serialize user defined function.'''
code = marshal.dumps(func.__code__).decode('raw_unicode_escape')
defaults = func.__defaults__
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train | func_load | Deserialize user defined function. | thinc/extra/_vendorized/keras_generic_utils.py | def func_load(code, defaults=None, closure=None, globs=None):
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train | preprocess | Parse the texts with spaCy. Make one-hot vectors for the labels. | examples/text-pair/glove_mwe_multipool_predict.py | def preprocess(ops, nlp, rows, get_ids):
"""Parse the texts with spaCy. Make one-hot vectors for the labels."""
Xs = []
ys = []
for (text1, text2), label in rows:
Xs.append((get_ids([nlp(text1)])[0], get_ids([nlp(text2)])[0]))
ys.append(label)
return Xs, to_categorical(ys, nb_classes... | def preprocess(ops, nlp, rows, get_ids):
"""Parse the texts with spaCy. Make one-hot vectors for the labels."""
Xs = []
ys = []
for (text1, text2), label in rows:
Xs.append((get_ids([nlp(text1)])[0], get_ids([nlp(text2)])[0]))
ys.append(label)
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train | BiLSTM | Create a bidirectional LSTM layer. Args: number out, number in | thinc/neural/_classes/rnn.py | def BiLSTM(nO, nI):
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train | LSTM | Create an LSTM layer. Args: number out, number in | thinc/neural/_classes/rnn.py | def LSTM(nO, nI):
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gates = LSTM_gates(weights.ops)
return Recurrent(RNN_step(weights, gates)) | def LSTM(nO, nI):
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train | Bidirectional | Stitch two RNN models into a bidirectional layer. | thinc/neural/_classes/rnn.py | def Bidirectional(l2r, r2l):
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nO = l2r.nO
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train | Recurrent | Apply a stepwise model over a sequence, maintaining state. For RNNs | thinc/neural/_classes/rnn.py | def Recurrent(step_model):
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train | RNN_step | Create a step model for an RNN, given weights and gates functions. | thinc/neural/_classes/rnn.py | def RNN_step(weights, gates):
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train | get_file | Downloads a file from a URL if it not already in the cache.
Passing the MD5 hash will verify the file after download as well as if it is already present in the cache.
# Arguments
fname: name of the file
origin: original URL of the file
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Passing the MD5 hash will verify the file after download as well as if it is already present in the cache.
# Arguments
fname: n... | def get_file(fname, origin, untar=False, unzip=False,
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train | validate_file | Validates a file against a MD5 hash
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fpath: path to the file being validated
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fpath: path to the file being validated
md5_hash: the MD5 hash being validated against
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Whether the file is valid
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fpath: path to the file being validated
md5_hash: the MD5 hash being validated against
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train | Orth | Get word forms. | examples/spacy_tagger.py | def Orth(docs, drop=0.0):
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ids = numpy.zeros((sum(len(doc) for doc in docs),), dtype="i")
i = 0
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"""Get word forms."""
ids = numpy.zeros((sum(len(doc) for doc in docs),), dtype="i")
i = 0
for doc in docs:
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ids[i] = token.orth
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train | Shape | Get word shapes. | examples/spacy_tagger.py | def Shape(docs, drop=0.0):
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train | Prefix | Get prefixes. | examples/spacy_tagger.py | def Prefix(docs, drop=0.0):
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train | Suffix | Get suffixes. | examples/spacy_tagger.py | def Suffix(docs, drop=0.0):
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train | track_progress | Print training progress. Called after each epoch. | examples/text-pair/glove_mwe_multipool_siamese.py | def track_progress(**context):
"""Print training progress. Called after each epoch."""
model = context["model"]
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train | preprocess | Parse the texts with spaCy. Make one-hot vectors for the labels. | examples/text-pair/glove_mwe_multipool_siamese.py | def preprocess(ops, nlp, rows, get_ids):
"""Parse the texts with spaCy. Make one-hot vectors for the labels."""
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Xs.append((get_ids([nlp(text1)])[0], get_ids([nlp(text2)])[0]))
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"""Parse the texts with spaCy. Make one-hot vectors for the labels."""
Xs = []
ys = []
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Xs.append((get_ids([nlp(text1)])[0], get_ids([nlp(text2)])[0]))
ys.append(label)
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train | is_cupy_array | Check whether an array is a cupy array | thinc/neural/util.py | def is_cupy_array(arr):
"""Check whether an array is a cupy array"""
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return False
elif isinstance(arr, cupy.ndarray):
return True
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"""Check whether an array is a cupy array"""
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train | prefer_gpu | Use GPU if it's available. Returns True if so, False otherwise. | thinc/neural/util.py | def prefer_gpu():
"""Use GPU if it's available. Returns True if so, False otherwise."""
from ._classes.model import Model
from .ops import CupyOps
if CupyOps.xp is not None:
Model.Ops = CupyOps
Model.ops = CupyOps()
return True
else:
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"""Use GPU if it's available. Returns True if so, False otherwise."""
from ._classes.model import Model
from .ops import CupyOps
if CupyOps.xp is not None:
Model.Ops = CupyOps
Model.ops = CupyOps()
return True
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train | mark_sentence_boundaries | Pad sentence sequences with EOL markers. | thinc/neural/util.py | def mark_sentence_boundaries(sequences, drop=0.0): # pragma: no cover
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(set) List of application names to back up
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Return the list of pretty app names that are available in the database.
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set of str.
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Ask the user if he really want something to happen.
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question(str): What can happen
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(boolean): Confirmed or not
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train | delete | Delete the given file, directory or link.
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Args:
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"""
Delete the given file, directory or link.
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Args:
filepath (str): Absolute full path to a file. e.g. /path/to/file
"""
# Some files have ACLs, let's remove them recursively
remove_acl(filepath)
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Delete the given file, directory or link.
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Args:
filepath (str): Absolute full path to a file. e.g. /path/to/file
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train | link | Create a link to a target file or a folder.
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Also do not include any trailing slash.
e.g. link('/path/to/file', '/path/to/link')
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Also do not include any trailing slash.
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train | chmod | Recursively set the chmod for files to 0600 and 0700 for folders.
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Args:
target (str): Root file or folder | mackup/utils.py | def chmod(target):
"""
Recursively set the chmod for files to 0600 and 0700 for folders.
It's ok unless we need something more specific.
Args:
target (str): Root file or folder
"""
assert isinstance(target, str)
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Recursively set the chmod for files to 0600 and 0700 for folders.
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Args:
target (str): Root file or folder
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assert isinstance(target, str)
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train | error | Throw an error with the given message and immediately quit.
Args:
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"""
Throw an error with the given message and immediately quit.
Args:
message(str): The message to display.
"""
fail = '\033[91m'
end = '\033[0m'
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"""
Throw an error with the given message and immediately quit.
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message(str): The message to display.
"""
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train | get_dropbox_folder_location | Try to locate the Dropbox folder.
Returns:
(str) Full path to the current Dropbox folder | mackup/utils.py | def get_dropbox_folder_location():
"""
Try to locate the Dropbox folder.
Returns:
(str) Full path to the current Dropbox folder
"""
host_db_path = os.path.join(os.environ['HOME'], '.dropbox/host.db')
try:
with open(host_db_path, 'r') as f_hostdb:
data = f_hostdb.read... | def get_dropbox_folder_location():
"""
Try to locate the Dropbox folder.
Returns:
(str) Full path to the current Dropbox folder
"""
host_db_path = os.path.join(os.environ['HOME'], '.dropbox/host.db')
try:
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data = f_hostdb.read... | [
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train | get_google_drive_folder_location | Try to locate the Google Drive folder.
Returns:
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"""
Try to locate the Google Drive folder.
Returns:
(str) Full path to the current Google Drive folder
"""
gdrive_db_path = 'Library/Application Support/Google/Drive/sync_config.db'
yosemite_gdrive_db_path = ('Library/Application Support/Google/Dr... | def get_google_drive_folder_location():
"""
Try to locate the Google Drive folder.
Returns:
(str) Full path to the current Google Drive folder
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train | get_box_folder_location | Try to locate the Box folder.
Returns:
(str) Full path to the current Box folder | mackup/utils.py | def get_box_folder_location():
"""
Try to locate the Box folder.
Returns:
(str) Full path to the current Box folder
"""
box_prefs_path = ('Library/Application Support/Box/Box Sync/'
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box_home = None
box_prefs = os.path.join(os.environ['H... | def get_box_folder_location():
"""
Try to locate the Box folder.
Returns:
(str) Full path to the current Box folder
"""
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train | get_copy_folder_location | Try to locate the Copy folder.
Returns:
(str) Full path to the current Copy folder | mackup/utils.py | def get_copy_folder_location():
"""
Try to locate the Copy folder.
Returns:
(str) Full path to the current Copy folder
"""
copy_settings_path = 'Library/Application Support/Copy Agent/config.db'
copy_home = None
copy_settings = os.path.join(os.environ['HOME'], copy_settings_path)
... | def get_copy_folder_location():
"""
Try to locate the Copy folder.
Returns:
(str) Full path to the current Copy folder
"""
copy_settings_path = 'Library/Application Support/Copy Agent/config.db'
copy_home = None
copy_settings = os.path.join(os.environ['HOME'], copy_settings_path)
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train | get_icloud_folder_location | Try to locate the iCloud Drive folder.
Returns:
(str) Full path to the iCloud Drive folder. | mackup/utils.py | def get_icloud_folder_location():
"""
Try to locate the iCloud Drive folder.
Returns:
(str) Full path to the iCloud Drive folder.
"""
yosemite_icloud_path = '~/Library/Mobile Documents/com~apple~CloudDocs/'
icloud_home = os.path.expanduser(yosemite_icloud_path)
if not os.path.isdi... | def get_icloud_folder_location():
"""
Try to locate the iCloud Drive folder.
Returns:
(str) Full path to the iCloud Drive folder.
"""
yosemite_icloud_path = '~/Library/Mobile Documents/com~apple~CloudDocs/'
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train | is_process_running | Check if a process with the given name is running.
Args:
(str): Process name, e.g. "Sublime Text"
Returns:
(bool): True if the process is running | mackup/utils.py | def is_process_running(process_name):
"""
Check if a process with the given name is running.
Args:
(str): Process name, e.g. "Sublime Text"
Returns:
(bool): True if the process is running
"""
is_running = False
# On systems with pgrep, check if the given process is running... | def is_process_running(process_name):
"""
Check if a process with the given name is running.
Args:
(str): Process name, e.g. "Sublime Text"
Returns:
(bool): True if the process is running
"""
is_running = False
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train | remove_acl | Remove the ACL of the file or folder located on the given path.
Also remove the ACL of any file and folder below the given one,
recursively.
Args:
path (str): Path to the file or folder to remove the ACL for,
recursively. | mackup/utils.py | def remove_acl(path):
"""
Remove the ACL of the file or folder located on the given path.
Also remove the ACL of any file and folder below the given one,
recursively.
Args:
path (str): Path to the file or folder to remove the ACL for,
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"""
# Some fil... | def remove_acl(path):
"""
Remove the ACL of the file or folder located on the given path.
Also remove the ACL of any file and folder below the given one,
recursively.
Args:
path (str): Path to the file or folder to remove the ACL for,
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"""
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train | can_file_be_synced_on_current_platform | Check if the given path can be synced locally.
Check if it makes sense to sync the file at the given path on the current
platform.
For now we don't sync any file in the ~/Library folder on GNU/Linux.
There might be other exceptions in the future.
Args:
(str): Path to the file or folder to ... | mackup/utils.py | def can_file_be_synced_on_current_platform(path):
"""
Check if the given path can be synced locally.
Check if it makes sense to sync the file at the given path on the current
platform.
For now we don't sync any file in the ~/Library folder on GNU/Linux.
There might be other exceptions in the fu... | def can_file_be_synced_on_current_platform(path):
"""
Check if the given path can be synced locally.
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train | Config.fullpath | Full path to the Mackup configuration files.
The full path to the directory when Mackup is storing the configuration
files.
Returns:
str | mackup/config.py | def fullpath(self):
"""
Full path to the Mackup configuration files.
The full path to the directory when Mackup is storing the configuration
files.
Returns:
str
"""
return str(os.path.join(self.path, self.directory)) | def fullpath(self):
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Full path to the Mackup configuration files.
The full path to the directory when Mackup is storing the configuration
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Returns:
str
"""
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train | Config._setup_parser | Configure the ConfigParser instance the way we want it.
Args:
filename (str) or None
Returns:
SafeConfigParser | mackup/config.py | def _setup_parser(self, filename=None):
"""
Configure the ConfigParser instance the way we want it.
Args:
filename (str) or None
Returns:
SafeConfigParser
"""
assert isinstance(filename, str) or filename is None
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SafeConfigParser
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train | Config._warn_on_old_config | Warn the user if an old config format is detected. | mackup/config.py | def _warn_on_old_config(self):
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# Is an old setion is in the config file ?
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for old_section in old_sections:
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train | Config._parse_engine | Parse the storage engine in the config.
Returns:
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"""
Parse the storage engine in the config.
Returns:
str
"""
if self._parser.has_option('storage', 'engine'):
engine = str(self._parser.get('storage', 'engine'))
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assert is... | def _parse_engine(self):
"""
Parse the storage engine in the config.
Returns:
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"""
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train | Config._parse_path | Parse the storage path in the config.
Returns:
str | mackup/config.py | def _parse_path(self):
"""
Parse the storage path in the config.
Returns:
str
"""
if self.engine == ENGINE_DROPBOX:
path = get_dropbox_folder_location()
elif self.engine == ENGINE_GDRIVE:
path = get_google_drive_folder_location()
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Parse the storage path in the config.
Returns:
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train | Config._parse_directory | Parse the storage directory in the config.
Returns:
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"""
Parse the storage directory in the config.
Returns:
str
"""
if self._parser.has_option('storage', 'directory'):
directory = self._parser.get('storage', 'directory')
# Don't allow CUSTOM_APPS_DIR as a storage dir... | def _parse_directory(self):
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Parse the storage directory in the config.
Returns:
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"""
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directory = self._parser.get('storage', 'directory')
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train | Config._parse_apps_to_ignore | Parse the applications to ignore in the config.
Returns:
set | mackup/config.py | def _parse_apps_to_ignore(self):
"""
Parse the applications to ignore in the config.
Returns:
set
"""
# We ignore nothing by default
apps_to_ignore = set()
# Is the "[applications_to_ignore]" in the cfg file ?
section_title = 'applications_to... | def _parse_apps_to_ignore(self):
"""
Parse the applications to ignore in the config.
Returns:
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"""
# We ignore nothing by default
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train | Config._parse_apps_to_sync | Parse the applications to backup in the config.
Returns:
set | mackup/config.py | def _parse_apps_to_sync(self):
"""
Parse the applications to backup in the config.
Returns:
set
"""
# We allow nothing by default
apps_to_sync = set()
# Is the "[applications_to_sync]" section in the cfg file ?
section_title = 'applications_t... | def _parse_apps_to_sync(self):
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Parse the applications to backup in the config.
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train | load_template | Build tabula-py option from template file
Args:
file_like_obj: File like object of Tabula app template
Returns:
`obj`:dict: tabula-py options | tabula/template.py | def load_template(path_or_buffer):
'''Build tabula-py option from template file
Args:
file_like_obj: File like object of Tabula app template
Returns:
`obj`:dict: tabula-py options
'''
from itertools import groupby
from operator import itemgetter
path_or_buffer = _stringif... | def load_template(path_or_buffer):
'''Build tabula-py option from template file
Args:
file_like_obj: File like object of Tabula app template
Returns:
`obj`:dict: tabula-py options
'''
from itertools import groupby
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train | _convert_template_option | Convert Tabula app template to tabula-py option
Args:
template (dict): Tabula app template
Returns:
`obj`:dict: tabula-py option | tabula/template.py | def _convert_template_option(template):
'''Convert Tabula app template to tabula-py option
Args:
template (dict): Tabula app template
Returns:
`obj`:dict: tabula-py option
'''
option = {}
extraction_method = template.get('extraction_method')
if extraction_method == 'guess'... | def _convert_template_option(template):
'''Convert Tabula app template to tabula-py option
Args:
template (dict): Tabula app template
Returns:
`obj`:dict: tabula-py option
'''
option = {}
extraction_method = template.get('extraction_method')
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train | deprecated | This is a decorator which can be used to mark functions
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"""This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emmitted
when the function is used."""
def newFunc(*args, **kwargs):
warnings.warn("Call to deprecated function {}.".format(func.__name__),
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"""This is a decorator which can be used to mark functions
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tabula-py options are translated into tabula-java options, see
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options, as well as an optional path to pass to tabula-java as a regular
argument and an optional encoding to use for any required output sent to
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tabula-py op... | def _run(java_options, options, path=None, encoding='utf-8'):
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train | read_pdf | Read tables in PDF.
Args:
input_path (file like obj):
File like object of tareget PDF file.
output_format (str, optional):
Output format of this function (dataframe or json)
encoding (str, optional):
Encoding type for pandas. Default is 'utf-8'
ja... | tabula/wrapper.py | def read_pdf(input_path,
output_format='dataframe',
encoding='utf-8',
java_options=None,
pandas_options=None,
multiple_tables=False, **kwargs):
'''Read tables in PDF.
Args:
input_path (file like obj):
File like object of tareg... | def read_pdf(input_path,
output_format='dataframe',
encoding='utf-8',
java_options=None,
pandas_options=None,
multiple_tables=False, **kwargs):
'''Read tables in PDF.
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train | read_pdf_with_template | Read tables in PDF.
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input_path (file like obj):
File like object of tareget PDF file.
template_path (file_like_obj):
File like object for Tabula app template.
pandas_options (dict, optional):
Set pandas options like {'header': None}.
encodin... | tabula/wrapper.py | def read_pdf_with_template(
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encoding='utf-8',
java_options=None,
**kwargs):
'''Read tables in PDF.
Args:
input_path (file like obj):
File like object of tareget PDF file.
template_path (f... | def read_pdf_with_template(
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encoding='utf-8',
java_options=None,
**kwargs):
'''Read tables in PDF.
Args:
input_path (file like obj):
File like object of tareget PDF file.
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train | convert_into | Convert tables from PDF into a file.
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input_path (file like obj):
File like object of tareget PDF file.
output_path (str):
File path of output file.
output_format (str, optional):
Output format of this function (csv, json or tsv). Default: csv
... | tabula/wrapper.py | def convert_into(input_path, output_path, output_format='csv', java_options=None, **kwargs):
'''Convert tables from PDF into a file.
Args:
input_path (file like obj):
File like object of tareget PDF file.
output_path (str):
File path of output file.
output_format... | def convert_into(input_path, output_path, output_format='csv', java_options=None, **kwargs):
'''Convert tables from PDF into a file.
Args:
input_path (file like obj):
File like object of tareget PDF file.
output_path (str):
File path of output file.
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train | convert_into_by_batch | Convert tables from PDFs in a directory.
Args:
input_dir (str):
Directory path.
output_format (str, optional):
Output format of this function (csv, json or tsv)
java_options (list, optional):
Set java options like `-Xmx256m`.
kwargs (dict):
... | tabula/wrapper.py | def convert_into_by_batch(input_dir, output_format='csv', java_options=None, **kwargs):
'''Convert tables from PDFs in a directory.
Args:
input_dir (str):
Directory path.
output_format (str, optional):
Output format of this function (csv, json or tsv)
java_option... | def convert_into_by_batch(input_dir, output_format='csv', java_options=None, **kwargs):
'''Convert tables from PDFs in a directory.
Args:
input_dir (str):
Directory path.
output_format (str, optional):
Output format of this function (csv, json or tsv)
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train | _extract_from | Extract tables from json.
Args:
raw_json (list):
Decoded list from tabula-java JSON.
pandas_options (dict optional):
pandas options for `pd.DataFrame()` | tabula/wrapper.py | def _extract_from(raw_json, pandas_options=None):
'''Extract tables from json.
Args:
raw_json (list):
Decoded list from tabula-java JSON.
pandas_options (dict optional):
pandas options for `pd.DataFrame()`
'''
data_frames = []
if pandas_options is None:
... | def _extract_from(raw_json, pandas_options=None):
'''Extract tables from json.
Args:
raw_json (list):
Decoded list from tabula-java JSON.
pandas_options (dict optional):
pandas options for `pd.DataFrame()`
'''
data_frames = []
if pandas_options is None:
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train | _convert_pandas_csv_options | Translate `pd.read_csv()` options into `pd.DataFrame()` especially for header.
Args:
pandas_option (dict):
pandas options like {'header': None}.
columns (list):
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''' Translate `pd.read_csv()` options into `pd.DataFrame()` especially for header.
Args:
pandas_option (dict):
pandas options like {'header': None}.
columns (list):
list of column name.
'''
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''' Translate `pd.read_csv()` options into `pd.DataFrame()` especially for header.
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pandas_option (dict):
pandas options like {'header': None}.
columns (list):
list of column name.
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train | build_options | Build options for tabula-java
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Raw option string for tabula-java.
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Raw option string for tabula-java.
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train | localize_file | Ensure localize target file.
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path (str):
File path or file like object or URL of target file.
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path (str):
File path or file like object or URL of target file.
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filename (str): file name in local storage
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path (str):
File path or file like object or URL of target file.
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train | _stringify_path | Convert path like object to string
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train | receive | I implement 'wormhole receive'. I return a Deferred that fires with
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* WrongPasswordError: the two sides didn't use matching passwords
* Timeout: something didn't happen fast enough for our tastes
* TransferError: the sender rejected the transfe... | src/wormhole/cli/cmd_receive.py | def receive(args, reactor=reactor, _debug_stash_wormhole=None):
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* WrongPasswordError: the two sides didn't use matching passwords
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train | Receiver._extract_file | the zipfile module does not restore file permissions
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"""
the zipfile module does not restore file permissions
so we'll do it manually
"""
out_path = os.path.join(extract_dir, info.filename)
out_path = os.path.abspath(out_path)
if not out_path.startswith(extract_dir):
... | def _extract_file(self, zf, info, extract_dir):
"""
the zipfile module does not restore file permissions
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"""
out_path = os.path.join(extract_dir, info.filename)
out_path = os.path.abspath(out_path)
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train | get_tor | If launch_tor=True, I will try to launch a new Tor process, ask it
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Otherwise if tor_control_port is provided, I will attempt to connect
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timing=None,
stderr=sys.stderr):
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train | find_public_key | This looks for an appropriate SSH key to send, possibly querying
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click.prompt())
Returns a 3-tuple: kind, keyid, pubkey_data | src/wormhole/cli/cmd_ssh.py | def find_public_key(hint=None):
"""
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the user in the meantime. DO NOT CALL after reactor.run as this
(possibly) does blocking stuff like asking the user questions (via
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train | wormhole | Create a Magic Wormhole and communicate through it.
Wormholes are created by speaking the same magic CODE in two
different places at the same time. Wormholes are secure against
anyone who doesn't use the same code. | src/wormhole/cli/cli.py | def wormhole(context, dump_timing, transit_helper, relay_url, appid):
"""
Create a Magic Wormhole and communicate through it.
Wormholes are created by speaking the same magic CODE in two
different places at the same time. Wormholes are secure against
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Create a Magic Wormhole and communicate through it.
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train | _dispatch_command | Internal helper. This calls the given command (a no-argument
callable) with the Config instance in cfg and interprets any
errors for the user. | src/wormhole/cli/cli.py | def _dispatch_command(reactor, cfg, command):
"""
Internal helper. This calls the given command (a no-argument
callable) with the Config instance in cfg and interprets any
errors for the user.
"""
cfg.timing.add("command dispatch")
cfg.timing.add(
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train | send | Send a text message, file, or directory | src/wormhole/cli/cli.py | def send(cfg, **kwargs):
"""Send a text message, file, or directory"""
for name, value in kwargs.items():
setattr(cfg, name, value)
with cfg.timing.add("import", which="cmd_send"):
from . import cmd_send
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train | receive | Receive a text message, file, or directory (from 'wormhole send') | src/wormhole/cli/cli.py | def receive(cfg, code, **kwargs):
"""
Receive a text message, file, or directory (from 'wormhole send')
"""
for name, value in kwargs.items():
setattr(cfg, name, value)
with cfg.timing.add("import", which="cmd_receive"):
from . import cmd_receive
if len(code) == 1:
cfg.co... | def receive(cfg, code, **kwargs):
"""
Receive a text message, file, or directory (from 'wormhole send')
"""
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setattr(cfg, name, value)
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train | ssh_invite | Add a public-key to a ~/.ssh/authorized_keys file | src/wormhole/cli/cli.py | def ssh_invite(ctx, code_length, user, **kwargs):
"""
Add a public-key to a ~/.ssh/authorized_keys file
"""
for name, value in kwargs.items():
setattr(ctx.obj, name, value)
from . import cmd_ssh
ctx.obj.code_length = code_length
ctx.obj.ssh_user = user
return go(cmd_ssh.invite, c... | def ssh_invite(ctx, code_length, user, **kwargs):
"""
Add a public-key to a ~/.ssh/authorized_keys file
"""
for name, value in kwargs.items():
setattr(ctx.obj, name, value)
from . import cmd_ssh
ctx.obj.code_length = code_length
ctx.obj.ssh_user = user
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train | ssh_accept | Send your SSH public-key
In response to a 'wormhole ssh invite' this will send public-key
you specify (if there's only one in ~/.ssh/* that will be sent). | src/wormhole/cli/cli.py | def ssh_accept(cfg, code, key_file, yes, **kwargs):
"""
Send your SSH public-key
In response to a 'wormhole ssh invite' this will send public-key
you specify (if there's only one in ~/.ssh/* that will be sent).
"""
for name, value in kwargs.items():
setattr(cfg, name, value)
from .... | def ssh_accept(cfg, code, key_file, yes, **kwargs):
"""
Send your SSH public-key
In response to a 'wormhole ssh invite' this will send public-key
you specify (if there's only one in ~/.ssh/* that will be sent).
"""
for name, value in kwargs.items():
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train | allocate_tcp_port | Return an (integer) available TCP port on localhost. This briefly
listens on the port in question, then closes it right away. | src/wormhole/transit.py | def allocate_tcp_port():
"""Return an (integer) available TCP port on localhost. This briefly
listens on the port in question, then closes it right away."""
# We want to bind() the socket but not listen(). Twisted (in
# tcp.Port.createInternetSocket) would do several other things:
# non-blocking, cl... | def allocate_tcp_port():
"""Return an (integer) available TCP port on localhost. This briefly
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train | Common._not_forever | If the timer fires first, cancel the deferred. If the deferred fires
first, cancel the timer. | src/wormhole/transit.py | def _not_forever(self, timeout, d):
"""If the timer fires first, cancel the deferred. If the deferred fires
first, cancel the timer."""
t = self._reactor.callLater(timeout, d.cancel)
def _done(res):
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t.cancel()
return res
d.... | def _not_forever(self, timeout, d):
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t = self._reactor.callLater(timeout, d.cancel)
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train | receive | This is a convenience API which returns a Deferred that callbacks
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the wormhole). Under the hood, it's just using an instance
returned from :func:`wormhole.wormhole`. This is similar to the
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:param unicode ... | src/wormhole/xfer_util.py | def receive(reactor,
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use_tor=False,
launch_tor=False,
tor_control_port=None,
on_code=None):
"""
This is a convenience API which returns a Deferred that callbacks
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train | TinyDB.table | Get access to a specific table.
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:param name: The name of the table.
:type name: str
:param cache_size: How many query results to cache.
:param table_cla... | tinydb/database.py | def table(self, name=DEFAULT_TABLE, **options):
"""
Get access to a specific table.
Creates a new table, if it hasn't been created before, otherwise it
returns the cached :class:`~tinydb.Table` object.
:param name: The name of the table.
:type name: str
:param c... | def table(self, name=DEFAULT_TABLE, **options):
"""
Get access to a specific table.
Creates a new table, if it hasn't been created before, otherwise it
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:param name: The name of the table.
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train | TinyDB.purge_table | Purge a specific table from the database. **CANNOT BE REVERSED!**
:param name: The name of the table.
:type name: str | tinydb/database.py | def purge_table(self, name):
"""
Purge a specific table from the database. **CANNOT BE REVERSED!**
:param name: The name of the table.
:type name: str
"""
if name in self._table_cache:
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proxy = StorageProxy(self._storage, n... | def purge_table(self, name):
"""
Purge a specific table from the database. **CANNOT BE REVERSED!**
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:type name: str
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train | Table.process_elements | Helper function for processing all documents specified by condition
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A repeating pattern in TinyDB is to run some code on all documents
that match a condition or are specified by their ID. This is
implemented in this function.
The function passed as ``func`` has to be a c... | tinydb/database.py | def process_elements(self, func, cond=None, doc_ids=None, eids=None):
"""
Helper function for processing all documents specified by condition
or IDs.
A repeating pattern in TinyDB is to run some code on all documents
that match a condition or are specified by their ID. This is
... | def process_elements(self, func, cond=None, doc_ids=None, eids=None):
"""
Helper function for processing all documents specified by condition
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A repeating pattern in TinyDB is to run some code on all documents
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train | Table.insert | Insert a new document into the table.
:param document: the document to insert
:returns: the inserted document's ID | tinydb/database.py | def insert(self, document):
"""
Insert a new document into the table.
:param document: the document to insert
:returns: the inserted document's ID
"""
doc_id = self._get_doc_id(document)
data = self._read()
data[doc_id] = dict(document)
self._wri... | def insert(self, document):
"""
Insert a new document into the table.
:param document: the document to insert
:returns: the inserted document's ID
"""
doc_id = self._get_doc_id(document)
data = self._read()
data[doc_id] = dict(document)
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train | Table.insert_multiple | Insert multiple documents into the table.
:param documents: a list of documents to insert
:returns: a list containing the inserted documents' IDs | tinydb/database.py | def insert_multiple(self, documents):
"""
Insert multiple documents into the table.
:param documents: a list of documents to insert
:returns: a list containing the inserted documents' IDs
"""
doc_ids = []
data = self._read()
for doc in documents:
... | def insert_multiple(self, documents):
"""
Insert multiple documents into the table.
:param documents: a list of documents to insert
:returns: a list containing the inserted documents' IDs
"""
doc_ids = []
data = self._read()
for doc in documents:
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train | Table.remove | Remove all matching documents.
:param cond: the condition to check against
:type cond: query
:param doc_ids: a list of document IDs
:type doc_ids: list
:returns: a list containing the removed document's ID | tinydb/database.py | def remove(self, cond=None, doc_ids=None, eids=None):
"""
Remove all matching documents.
:param cond: the condition to check against
:type cond: query
:param doc_ids: a list of document IDs
:type doc_ids: list
:returns: a list containing the removed document's ID... | def remove(self, cond=None, doc_ids=None, eids=None):
"""
Remove all matching documents.
:param cond: the condition to check against
:type cond: query
:param doc_ids: a list of document IDs
:type doc_ids: list
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train | Table.update | Update all matching documents to have a given set of fields.
:param fields: the fields that the matching documents will have
or a method that will update the documents
:type fields: dict | dict -> None
:param cond: which documents to update
:type cond: query
... | tinydb/database.py | def update(self, fields, cond=None, doc_ids=None, eids=None):
"""
Update all matching documents to have a given set of fields.
:param fields: the fields that the matching documents will have
or a method that will update the documents
:type fields: dict | dict -> N... | def update(self, fields, cond=None, doc_ids=None, eids=None):
"""
Update all matching documents to have a given set of fields.
:param fields: the fields that the matching documents will have
or a method that will update the documents
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train | Table.write_back | Write back documents by doc_id
:param documents: a list of document to write back
:param doc_ids: a list of document IDs which need to be written back
:returns: a list of document IDs that have been written | tinydb/database.py | def write_back(self, documents, doc_ids=None, eids=None):
"""
Write back documents by doc_id
:param documents: a list of document to write back
:param doc_ids: a list of document IDs which need to be written back
:returns: a list of document IDs that have been written
""... | def write_back(self, documents, doc_ids=None, eids=None):
"""
Write back documents by doc_id
:param documents: a list of document to write back
:param doc_ids: a list of document IDs which need to be written back
:returns: a list of document IDs that have been written
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