id int32 0 252k | 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 list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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49,500 | arne-cl/discoursegraphs | src/discoursegraphs/readwrite/rst/hilda.py | HILDARSTTree.hildatree2dgparentedtree | def hildatree2dgparentedtree(self):
"""Convert the tree from HILDA's format into a conventional binary tree,
which can be easily converted into output formats like RS3.
"""
def transform(hilda_tree):
"""Transform a HILDA parse tree into a more conventional parse tree.
... | python | def hildatree2dgparentedtree(self):
"""Convert the tree from HILDA's format into a conventional binary tree,
which can be easily converted into output formats like RS3.
"""
def transform(hilda_tree):
"""Transform a HILDA parse tree into a more conventional parse tree.
... | [
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49,501 | jrderuiter/pybiomart | src/pybiomart/server.py | Server.marts | def marts(self):
"""List of available marts."""
if self._marts is None:
self._marts = self._fetch_marts()
return self._marts | python | def marts(self):
"""List of available marts."""
if self._marts is None:
self._marts = self._fetch_marts()
return self._marts | [
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49,502 | jrderuiter/pybiomart | src/pybiomart/server.py | Server.list_marts | def list_marts(self):
"""Lists available marts in a readable DataFrame format.
Returns:
pd.DataFrame: Frame listing available marts.
"""
def _row_gen(attributes):
for attr in attributes.values():
yield (attr.name, attr.display_name)
retu... | python | def list_marts(self):
"""Lists available marts in a readable DataFrame format.
Returns:
pd.DataFrame: Frame listing available marts.
"""
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49,503 | kata198/python-nonblock | nonblock/BackgroundRead.py | bgread | def bgread(stream, blockSizeLimit=65535, pollTime=.03, closeStream=True):
'''
bgread - Start a thread which will read from the given stream in a non-blocking fashion, and automatically populate data in the returned object.
@param stream <object> - A stream on which to read. Socket, file, etc.
... | python | def bgread(stream, blockSizeLimit=65535, pollTime=.03, closeStream=True):
'''
bgread - Start a thread which will read from the given stream in a non-blocking fashion, and automatically populate data in the returned object.
@param stream <object> - A stream on which to read. Socket, file, etc.
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49,504 | kata198/python-nonblock | nonblock/BackgroundRead.py | _do_bgread | def _do_bgread(stream, blockSizeLimit, pollTime, closeStream, results):
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49,505 | texperience/django-bootstrap-ui | bootstrap_ui/views.py | set_theme | def set_theme(request):
"""
Redirect to a given url while setting the chosen theme in the session or cookie. The url and the theme identifier
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Since this view changes how the user will see the rest of the site, it must only be accessed as a POST request. I... | python | def set_theme(request):
"""
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49,506 | codeinn/vcs | vcs/utils/baseui_config.py | make_ui | def make_ui(self, path='hgwebdir.config'):
"""
A funcion that will read python rc files and make an ui from read options
:param path: path to mercurial config file
"""
#propagated from mercurial documentation
sections = [
'alias',
'auth',
'decode/... | python | def make_ui(self, path='hgwebdir.config'):
"""
A funcion that will read python rc files and make an ui from read options
:param path: path to mercurial config file
"""
#propagated from mercurial documentation
sections = [
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49,507 | codeinn/vcs | vcs/backends/hg/changeset.py | MercurialChangeset.status | def status(self):
"""
Returns modified, added, removed, deleted files for current changeset
"""
return self.repository._repo.status(self._ctx.p1().node(),
self._ctx.node()) | python | def status(self):
"""
Returns modified, added, removed, deleted files for current changeset
"""
return self.repository._repo.status(self._ctx.p1().node(),
self._ctx.node()) | [
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49,508 | codeinn/vcs | vcs/backends/hg/changeset.py | MercurialChangeset._fix_path | def _fix_path(self, path):
"""
Paths are stored without trailing slash so we need to get rid off it if
needed. Also mercurial keeps filenodes as str so we need to decode
from unicode to str
"""
if path.endswith('/'):
path = path.rstrip('/')
return saf... | python | def _fix_path(self, path):
"""
Paths are stored without trailing slash so we need to get rid off it if
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"""
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49,509 | codeinn/vcs | vcs/backends/hg/changeset.py | MercurialChangeset.get_nodes | def get_nodes(self, path):
"""
Returns combined ``DirNode`` and ``FileNode`` objects list representing
state of changeset at the given ``path``. If node at the given ``path``
is not instance of ``DirNode``, ChangesetError would be raised.
"""
if self._get_kind(path) != N... | python | def get_nodes(self, path):
"""
Returns combined ``DirNode`` and ``FileNode`` objects list representing
state of changeset at the given ``path``. If node at the given ``path``
is not instance of ``DirNode``, ChangesetError would be raised.
"""
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49,510 | codeinn/vcs | vcs/backends/hg/changeset.py | MercurialChangeset.get_node | def get_node(self, path):
"""
Returns ``Node`` object from the given ``path``. If there is no node at
the given ``path``, ``ChangesetError`` would be raised.
"""
path = self._fix_path(path)
if not path in self.nodes:
if path in self._file_paths:
... | python | def get_node(self, path):
"""
Returns ``Node`` object from the given ``path``. If there is no node at
the given ``path``, ``ChangesetError`` would be raised.
"""
path = self._fix_path(path)
if not path in self.nodes:
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49,511 | codeinn/vcs | vcs/nodes.py | FileNode.content | def content(self):
"""
Returns lazily content of the FileNode. If possible, would try to
decode content from UTF-8.
"""
content = self._get_content()
if bool(content and '\0' in content):
return content
return safe_unicode(content) | python | def content(self):
"""
Returns lazily content of the FileNode. If possible, would try to
decode content from UTF-8.
"""
content = self._get_content()
if bool(content and '\0' in content):
return content
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49,512 | codeinn/vcs | vcs/nodes.py | FileNode.lexer | def lexer(self):
"""
Returns pygment's lexer class. Would try to guess lexer taking file's
content, name and mimetype.
"""
try:
lexer = lexers.guess_lexer_for_filename(self.name, self.content, stripnl=False)
except lexers.ClassNotFound:
lexer = le... | python | def lexer(self):
"""
Returns pygment's lexer class. Would try to guess lexer taking file's
content, name and mimetype.
"""
try:
lexer = lexers.guess_lexer_for_filename(self.name, self.content, stripnl=False)
except lexers.ClassNotFound:
lexer = le... | [
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49,513 | codeinn/vcs | vcs/nodes.py | FileNode.history | def history(self):
"""
Returns a list of changeset for this file in which the file was changed
"""
if self.changeset is None:
raise NodeError('Unable to get changeset for this FileNode')
return self.changeset.get_file_history(self.path) | python | def history(self):
"""
Returns a list of changeset for this file in which the file was changed
"""
if self.changeset is None:
raise NodeError('Unable to get changeset for this FileNode')
return self.changeset.get_file_history(self.path) | [
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49,514 | codeinn/vcs | vcs/nodes.py | FileNode.annotate | def annotate(self):
"""
Returns a list of three element tuples with lineno,changeset and line
"""
if self.changeset is None:
raise NodeError('Unable to get changeset for this FileNode')
return self.changeset.get_file_annotate(self.path) | python | def annotate(self):
"""
Returns a list of three element tuples with lineno,changeset and line
"""
if self.changeset is None:
raise NodeError('Unable to get changeset for this FileNode')
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49,515 | codeinn/vcs | vcs/nodes.py | SubModuleNode.name | def name(self):
"""
Returns name of the node so if its path
then only last part is returned.
"""
org = safe_unicode(self.path.rstrip('/').split('/')[-1])
return u'%s @ %s' % (org, self.changeset.short_id) | python | def name(self):
"""
Returns name of the node so if its path
then only last part is returned.
"""
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49,516 | NICTA/revrand | revrand/glm.py | GeneralizedLinearModel.predict | def predict(self, X, nsamples=200, likelihood_args=()):
"""
Predict target values from Bayesian generalized linear regression.
Parameters
----------
X : ndarray
(N*,d) array query input dataset (N* samples, d dimensions).
nsamples : int, optional
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"""
Predict target values from Bayesian generalized linear regression.
Parameters
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X : ndarray
(N*,d) array query input dataset (N* samples, d dimensions).
nsamples : int, optional
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49,517 | NICTA/revrand | revrand/glm.py | GeneralizedLinearModel.predict_moments | def predict_moments(self, X, nsamples=200, likelihood_args=()):
r"""
Predictive moments, in particular mean and variance, of a Bayesian GLM.
This function uses Monte-Carlo sampling to evaluate the predictive mean
and variance of a Bayesian GLM. The exact expressions evaluated are,
... | python | def predict_moments(self, X, nsamples=200, likelihood_args=()):
r"""
Predictive moments, in particular mean and variance, of a Bayesian GLM.
This function uses Monte-Carlo sampling to evaluate the predictive mean
and variance of a Bayesian GLM. The exact expressions evaluated are,
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Predictive moments, in particular mean and variance, of a Bayesian GLM.
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49,518 | NICTA/revrand | revrand/glm.py | GeneralizedLinearModel.predict_logpdf | def predict_logpdf(self, X, y, nsamples=200, likelihood_args=()):
r"""
Predictive log-probability density function of a Bayesian GLM.
Parameters
----------
X : ndarray
(N*,d) array query input dataset (N* samples, D dimensions).
y : float or ndarray
... | python | def predict_logpdf(self, X, y, nsamples=200, likelihood_args=()):
r"""
Predictive log-probability density function of a Bayesian GLM.
Parameters
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X : ndarray
(N*,d) array query input dataset (N* samples, D dimensions).
y : float or ndarray
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49,519 | NICTA/revrand | revrand/glm.py | GeneralizedLinearModel.predict_cdf | def predict_cdf(self, X, quantile, nsamples=200, likelihood_args=()):
r"""
Predictive cumulative density function of a Bayesian GLM.
Parameters
----------
X : ndarray
(N*,d) array query input dataset (N* samples, D dimensions).
quantile : float
Th... | python | def predict_cdf(self, X, quantile, nsamples=200, likelihood_args=()):
r"""
Predictive cumulative density function of a Bayesian GLM.
Parameters
----------
X : ndarray
(N*,d) array query input dataset (N* samples, D dimensions).
quantile : float
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49,520 | dead-beef/markovchain | markovchain/cli/image.py | cmd_generate | def cmd_generate(args):
"""Generate images.
Parameters
----------
args : `argparse.Namespace`
Command arguments.
"""
check_output_format(args.output, args.count)
markov = load(MarkovImage, args.state, args)
if args.size is None:
if markov.scanner.resize is None:
... | python | def cmd_generate(args):
"""Generate images.
Parameters
----------
args : `argparse.Namespace`
Command arguments.
"""
check_output_format(args.output, args.count)
markov = load(MarkovImage, args.state, args)
if args.size is None:
if markov.scanner.resize is None:
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49,521 | dead-beef/markovchain | markovchain/cli/image.py | cmd_filter | def cmd_filter(args):
"""Filter an image.
Parameters
----------
args : `argparse.Namespace`
Command arguments.
"""
check_output_format(args.output, args.count)
img = Image.open(args.input)
width, height = img.size
if args.state is not None:
markov = load(MarkovImag... | python | def cmd_filter(args):
"""Filter an image.
Parameters
----------
args : `argparse.Namespace`
Command arguments.
"""
check_output_format(args.output, args.count)
img = Image.open(args.input)
width, height = img.size
if args.state is not None:
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49,522 | dead-beef/markovchain | markovchain/text/util.py | lstrip_ws_and_chars | def lstrip_ws_and_chars(string, chars):
"""Remove leading whitespace and characters from a string.
Parameters
----------
string : `str`
String to strip.
chars : `str`
Characters to remove.
Returns
-------
`str`
Stripped string.
Examples
--------
>>>... | python | def lstrip_ws_and_chars(string, chars):
"""Remove leading whitespace and characters from a string.
Parameters
----------
string : `str`
String to strip.
chars : `str`
Characters to remove.
Returns
-------
`str`
Stripped string.
Examples
--------
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49,523 | dead-beef/markovchain | markovchain/text/util.py | capitalize | def capitalize(string):
"""Capitalize a sentence.
Parameters
----------
string : `str`
String to capitalize.
Returns
-------
`str`
Capitalized string.
Examples
--------
>>> capitalize('worD WORD WoRd')
'Word word word'
"""
if not string:
ret... | python | def capitalize(string):
"""Capitalize a sentence.
Parameters
----------
string : `str`
String to capitalize.
Returns
-------
`str`
Capitalized string.
Examples
--------
>>> capitalize('worD WORD WoRd')
'Word word word'
"""
if not string:
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49,524 | dead-beef/markovchain | markovchain/text/util.py | re_flags | def re_flags(flags, custom=ReFlags):
"""Parse regexp flag string.
Parameters
----------
flags: `str`
Flag string.
custom: `IntEnum`, optional
Custom flag enum (default: None).
Returns
-------
(`int`, `int`)
(flags for `re.compile`, custom flags)
Raises
... | python | def re_flags(flags, custom=ReFlags):
"""Parse regexp flag string.
Parameters
----------
flags: `str`
Flag string.
custom: `IntEnum`, optional
Custom flag enum (default: None).
Returns
-------
(`int`, `int`)
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49,525 | dead-beef/markovchain | markovchain/text/util.py | re_flags_str | def re_flags_str(flags, custom_flags):
"""Convert regexp flags to string.
Parameters
----------
flags : `int`
Flags.
custom_flags : `int`
Custom flags.
Returns
-------
`str`
Flag string.
"""
res = ''
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if flags & getattr(r... | python | def re_flags_str(flags, custom_flags):
"""Convert regexp flags to string.
Parameters
----------
flags : `int`
Flags.
custom_flags : `int`
Custom flags.
Returns
-------
`str`
Flag string.
"""
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49,526 | dead-beef/markovchain | markovchain/text/util.py | re_sub | def re_sub(pattern, repl, string, count=0, flags=0, custom_flags=0):
"""Replace regular expression.
Parameters
----------
pattern : `str` or `_sre.SRE_Pattern`
Compiled regular expression.
repl : `str` or `function`
Replacement.
string : `str`
Input string.
count: `i... | python | def re_sub(pattern, repl, string, count=0, flags=0, custom_flags=0):
"""Replace regular expression.
Parameters
----------
pattern : `str` or `_sre.SRE_Pattern`
Compiled regular expression.
repl : `str` or `function`
Replacement.
string : `str`
Input string.
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49,527 | dead-beef/markovchain | markovchain/text/util.py | CharCase.convert | def convert(self, string):
"""Return a copy of string converted to case.
Parameters
----------
string : `str`
Returns
-------
`str`
Examples
--------
>>> CharCase.LOWER.convert('sTr InG')
'str ing'
>>> CharCase.UPPER.conv... | python | def convert(self, string):
"""Return a copy of string converted to case.
Parameters
----------
string : `str`
Returns
-------
`str`
Examples
--------
>>> CharCase.LOWER.convert('sTr InG')
'str ing'
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49,528 | NICTA/revrand | revrand/utils/rand.py | endless_permutations | def endless_permutations(N, random_state=None):
"""
Generate an endless sequence of random integers from permutations of the
set [0, ..., N).
If we call this N times, we will sweep through the entire set without
replacement, on the (N+1)th call a new permutation will be created, etc.
Parameter... | python | def endless_permutations(N, random_state=None):
"""
Generate an endless sequence of random integers from permutations of the
set [0, ..., N).
If we call this N times, we will sweep through the entire set without
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the length of the set
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49,529 | codeinn/vcs | vcs/backends/__init__.py | get_repo | def get_repo(path=None, alias=None, create=False):
"""
Returns ``Repository`` object of type linked with given ``alias`` at
the specified ``path``. If ``alias`` is not given it will try to guess it
using get_scm method
"""
if create:
if not (path or alias):
raise TypeError("I... | python | def get_repo(path=None, alias=None, create=False):
"""
Returns ``Repository`` object of type linked with given ``alias`` at
the specified ``path``. If ``alias`` is not given it will try to guess it
using get_scm method
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if create:
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49,530 | codeinn/vcs | vcs/backends/__init__.py | get_backend | def get_backend(alias):
"""
Returns ``Repository`` class identified by the given alias or raises
VCSError if alias is not recognized or backend class cannot be imported.
"""
if alias not in settings.BACKENDS:
raise VCSError("Given alias '%s' is not recognized! Allowed aliases:\n"
... | python | def get_backend(alias):
"""
Returns ``Repository`` class identified by the given alias or raises
VCSError if alias is not recognized or backend class cannot be imported.
"""
if alias not in settings.BACKENDS:
raise VCSError("Given alias '%s' is not recognized! Allowed aliases:\n"
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49,531 | codeinn/vcs | vcs/utils/helpers.py | get_scms_for_path | def get_scms_for_path(path):
"""
Returns all scm's found at the given path. If no scm is recognized
- empty list is returned.
:param path: path to directory which should be checked. May be callable.
:raises VCSError: if given ``path`` is not a directory
"""
from vcs.backends import get_bac... | python | def get_scms_for_path(path):
"""
Returns all scm's found at the given path. If no scm is recognized
- empty list is returned.
:param path: path to directory which should be checked. May be callable.
:raises VCSError: if given ``path`` is not a directory
"""
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49,532 | codeinn/vcs | vcs/utils/helpers.py | get_repo_paths | def get_repo_paths(path):
"""
Returns path's subdirectories which seems to be a repository.
"""
repo_paths = []
dirnames = (os.path.abspath(dirname) for dirname in os.listdir(path))
for dirname in dirnames:
try:
get_scm(dirname)
repo_paths.append(dirname)
... | python | def get_repo_paths(path):
"""
Returns path's subdirectories which seems to be a repository.
"""
repo_paths = []
dirnames = (os.path.abspath(dirname) for dirname in os.listdir(path))
for dirname in dirnames:
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repo_paths.append(dirname)
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49,533 | codeinn/vcs | vcs/utils/helpers.py | run_command | def run_command(cmd, *args):
"""
Runs command on the system with given ``args``.
"""
command = ' '.join((cmd, args))
p = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
return p.retcode, stdout, stderr | python | def run_command(cmd, *args):
"""
Runs command on the system with given ``args``.
"""
command = ' '.join((cmd, args))
p = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
return p.retcode, stdout, stderr | [
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49,534 | codeinn/vcs | vcs/utils/helpers.py | get_highlighted_code | def get_highlighted_code(name, code, type='terminal'):
"""
If pygments are available on the system
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unchanged content is returned.
"""
import logging
try:
import pygments
pygments
except ImportError:
return code
from p... | python | def get_highlighted_code(name, code, type='terminal'):
"""
If pygments are available on the system
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"""
import logging
try:
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pygments
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49,535 | codeinn/vcs | vcs/utils/helpers.py | parse_datetime | def parse_datetime(text):
"""
Parses given text and returns ``datetime.datetime`` instance or raises
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:param text: string of desired date/datetime or something more verbose,
like *yesterday*, *2weeks 3days*, etc.
"""
text = text.strip().lower()
INPUT_FORMATS = (
... | python | def parse_datetime(text):
"""
Parses given text and returns ``datetime.datetime`` instance or raises
``ValueError``.
:param text: string of desired date/datetime or something more verbose,
like *yesterday*, *2weeks 3days*, etc.
"""
text = text.strip().lower()
INPUT_FORMATS = (
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49,536 | codeinn/vcs | vcs/utils/helpers.py | get_dict_for_attrs | def get_dict_for_attrs(obj, attrs):
"""
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"""
data = {}
for attr in attrs:
data[attr] = getattr(obj, attr)
return data | python | def get_dict_for_attrs(obj, attrs):
"""
Returns dictionary for each attribute from given ``obj``.
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data = {}
for attr in attrs:
data[attr] = getattr(obj, attr)
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49,537 | NICTA/revrand | revrand/likelihoods.py | Bernoulli.loglike | def loglike(self, y, f):
r"""
Bernoulli log likelihood.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
\boldsymbol\Phi \mathbf{w}`)
... | python | def loglike(self, y, f):
r"""
Bernoulli log likelihood.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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49,538 | NICTA/revrand | revrand/likelihoods.py | Binomial.loglike | def loglike(self, y, f, n):
r"""
Binomial log likelihood.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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... | python | def loglike(self, y, f, n):
r"""
Binomial log likelihood.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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49,539 | NICTA/revrand | revrand/likelihoods.py | Binomial.df | def df(self, y, f, n):
r"""
Derivative of Binomial log likelihood w.r.t.\ f.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
\boldsymbol\Phi ... | python | def df(self, y, f, n):
r"""
Derivative of Binomial log likelihood w.r.t.\ f.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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49,540 | NICTA/revrand | revrand/likelihoods.py | Gaussian.loglike | def loglike(self, y, f, var=None):
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Gaussian log likelihood.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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49,541 | NICTA/revrand | revrand/likelihoods.py | Gaussian.df | def df(self, y, f, var):
r"""
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Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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r"""
Derivative of Gaussian log likelihood w.r.t.\ f.
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y: ndarray
array of 0, 1 valued integers of targets
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49,542 | NICTA/revrand | revrand/likelihoods.py | Poisson.loglike | def loglike(self, y, f):
r"""
Poisson log likelihood.
Parameters
----------
y: ndarray
array of integer targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
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Returns
--... | python | def loglike(self, y, f):
r"""
Poisson log likelihood.
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y: ndarray
array of integer targets
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latent function from the GLM prior (:math:`\mathbf{f} =
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logp: ndarray
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49,543 | NICTA/revrand | revrand/likelihoods.py | Poisson.Ey | def Ey(self, f):
r"""
Expected value of the Poisson likelihood.
Parameters
----------
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
\boldsymbol\Phi \mathbf{w}`)
Returns
-------
Ey: ndarray
expected... | python | def Ey(self, f):
r"""
Expected value of the Poisson likelihood.
Parameters
----------
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
\boldsymbol\Phi \mathbf{w}`)
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49,544 | NICTA/revrand | revrand/likelihoods.py | Poisson.df | def df(self, y, f):
r"""
Derivative of Poisson log likelihood w.r.t.\ f.
Parameters
----------
y: ndarray
array of 0, 1 valued integers of targets
f: ndarray
latent function from the GLM prior (:math:`\mathbf{f} =
\boldsymbol\Phi \mat... | python | def df(self, y, f):
r"""
Derivative of Poisson log likelihood w.r.t.\ f.
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----------
y: ndarray
array of 0, 1 valued integers of targets
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latent function from the GLM prior (:math:`\mathbf{f} =
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49,545 | dead-beef/markovchain | markovchain/parser.py | Parser.reset | def reset(self, state_size_changed=False):
"""Reset parser state.
Parameters
----------
state_size_changed : `bool`, optional
`True` if maximum state size changed (default: `False`).
"""
if state_size_changed:
self.state = deque(repeat('', self.st... | python | def reset(self, state_size_changed=False):
"""Reset parser state.
Parameters
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state_size_changed : `bool`, optional
`True` if maximum state size changed (default: `False`).
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49,546 | dead-beef/markovchain | markovchain/image/util.py | convert | def convert(ctype, img, palette_img, dither=False):
"""Convert an image to palette type.
Parameters
----------
ctype : `int`
Conversion type.
img : `PIL.Image`
Image to convert.
palette_img : `PIL.Image`
Palette source image.
dither : `bool`, optional
Enable ... | python | def convert(ctype, img, palette_img, dither=False):
"""Convert an image to palette type.
Parameters
----------
ctype : `int`
Conversion type.
img : `PIL.Image`
Image to convert.
palette_img : `PIL.Image`
Palette source image.
dither : `bool`, optional
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49,547 | codeinn/vcs | vcs/backends/git/config.py | _unescape_value | def _unescape_value(value):
"""Unescape a value."""
def unescape(c):
return {
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"\\\"": "\"",
"\\n": "\n",
"\\t": "\t",
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return re.sub(r"(\\.)", unescape, value) | python | def _unescape_value(value):
"""Unescape a value."""
def unescape(c):
return {
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49,548 | codeinn/vcs | vcs/backends/git/config.py | Config.get_boolean | def get_boolean(self, section, name, default=None):
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:param name: Name of the setting, including section and possible
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49,549 | codeinn/vcs | vcs/backends/git/config.py | ConfigFile.from_file | def from_file(cls, f):
"""Read configuration from a file-like object."""
ret = cls()
section = None
setting = None
for lineno, line in enumerate(f.readlines()):
line = line.lstrip()
if setting is None:
if _strip_comments(line).strip() == ""... | python | def from_file(cls, f):
"""Read configuration from a file-like object."""
ret = cls()
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49,550 | codeinn/vcs | vcs/backends/git/config.py | ConfigFile.from_path | def from_path(cls, path):
"""Read configuration from a file on disk."""
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"""Read configuration from a file on disk."""
f = GitFile(path, 'rb')
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49,551 | codeinn/vcs | vcs/backends/git/config.py | ConfigFile.write_to_path | def write_to_path(self, path=None):
"""Write configuration to a file on disk."""
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f = GitFile(path, 'wb')
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"""Write configuration to a file on disk."""
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49,552 | codeinn/vcs | vcs/backends/git/config.py | ConfigFile.write_to_file | def write_to_file(self, f):
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49,553 | codeinn/vcs | vcs/backends/git/config.py | StackedConfig.default_backends | def default_backends(cls):
"""Retrieve the default configuration.
This will look in the repository configuration (if for_path is
specified), the users' home directory and the system
configuration.
"""
paths = []
paths.append(os.path.expanduser("~/.gitconfig"))
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"""
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paths.append(os.path.expanduser("~/.gitconfig"))
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49,554 | NICTA/revrand | revrand/utils/datasets.py | make_regression | def make_regression(func, n_samples=100, n_features=1, bias=0.0, noise=0.0,
random_state=None):
"""
Make dataset for a regression problem.
Examples
--------
>>> f = lambda x: 0.5*x + np.sin(2*x)
>>> X, y = make_regression(f, bias=.5, noise=1., random_state=1)
>>> X.shape... | python | def make_regression(func, n_samples=100, n_features=1, bias=0.0, noise=0.0,
random_state=None):
"""
Make dataset for a regression problem.
Examples
--------
>>> f = lambda x: 0.5*x + np.sin(2*x)
>>> X, y = make_regression(f, bias=.5, noise=1., random_state=1)
>>> X.shape... | [
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49,555 | NICTA/revrand | revrand/utils/datasets.py | make_polynomial | def make_polynomial(degree=3, n_samples=100, bias=0.0, noise=0.0,
return_coefs=False, random_state=None):
"""
Generate a noisy polynomial for a regression problem
Examples
--------
>>> X, y, coefs = make_polynomial(degree=3, n_samples=200, noise=.5,
... ... | python | def make_polynomial(degree=3, n_samples=100, bias=0.0, noise=0.0,
return_coefs=False, random_state=None):
"""
Generate a noisy polynomial for a regression problem
Examples
--------
>>> X, y, coefs = make_polynomial(degree=3, n_samples=200, noise=.5,
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49,556 | NICTA/revrand | revrand/utils/datasets.py | get_data_home | def get_data_home(data_home=None):
"""
Return the path of the revrand data dir.
This folder is used by some large dataset loaders to avoid
downloading the data several times.
By default the data dir is set to a folder named 'revrand_data'
in the user home folder.
Alternatively, it can be ... | python | def get_data_home(data_home=None):
"""
Return the path of the revrand data dir.
This folder is used by some large dataset loaders to avoid
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49,557 | NICTA/revrand | revrand/utils/datasets.py | fetch_gpml_sarcos_data | def fetch_gpml_sarcos_data(transpose_data=True, data_home=None):
"""
Fetch the SARCOS dataset from the internet and parse appropriately into
python arrays
>>> gpml_sarcos = fetch_gpml_sarcos_data()
>>> gpml_sarcos.train.data.shape
(44484, 21)
>>> gpml_sarcos.train.targets.shape
(44484... | python | def fetch_gpml_sarcos_data(transpose_data=True, data_home=None):
"""
Fetch the SARCOS dataset from the internet and parse appropriately into
python arrays
>>> gpml_sarcos = fetch_gpml_sarcos_data()
>>> gpml_sarcos.train.data.shape
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>>> gpml_sarcos.train.targets.shape
(44484... | [
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python arrays
>>> gpml_sarcos = fetch_gpml_sarcos_data()
>>> gpml_sarcos.train.data.shape
(44484, 21)
>>> gpml_sarcos.train.targets.shape
(44484,)
>>> gpml_sarcos.train.targets.round(2) # doctest: +ELLIPSIS
array... | [
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49,558 | NICTA/revrand | revrand/utils/datasets.py | fetch_gpml_usps_resampled_data | def fetch_gpml_usps_resampled_data(transpose_data=True, data_home=None):
"""
Fetch the USPS handwritten digits dataset from the internet and parse
appropriately into python arrays
>>> usps_resampled = fetch_gpml_usps_resampled_data()
>>> usps_resampled.train.targets.shape
(4649,)
>>> usps... | python | def fetch_gpml_usps_resampled_data(transpose_data=True, data_home=None):
"""
Fetch the USPS handwritten digits dataset from the internet and parse
appropriately into python arrays
>>> usps_resampled = fetch_gpml_usps_resampled_data()
>>> usps_resampled.train.targets.shape
(4649,)
>>> usps... | [
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>>> usps_resampled.train.targets.shape
(4649,)
>>> usps_resampled.train.targets # doctest: +ELLIPSIS
array([6, 0, 1, ..., 9, 2, 7])
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49,559 | dead-beef/markovchain | markovchain/storage/base.py | Storage.split_state | def split_state(self, state):
"""Split state string.
Parameters
----------
state : `str`
Returns
-------
`list` of `str`
"""
if self.state_separator:
return state.split(self.state_separator)
return list(state) | python | def split_state(self, state):
"""Split state string.
Parameters
----------
state : `str`
Returns
-------
`list` of `str`
"""
if self.state_separator:
return state.split(self.state_separator)
return list(state) | [
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49,560 | dead-beef/markovchain | markovchain/storage/base.py | Storage.random_link | def random_link(self, dataset, state, backward=False):
"""Get a random link.
Parameters
----------
dataset : `object`
Dataset from `self.get_dataset()`.
state : `object`
Link source.
backward : `bool`, optional
Link direction.
... | python | def random_link(self, dataset, state, backward=False):
"""Get a random link.
Parameters
----------
dataset : `object`
Dataset from `self.get_dataset()`.
state : `object`
Link source.
backward : `bool`, optional
Link direction.
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49,561 | dead-beef/markovchain | markovchain/storage/base.py | Storage.save | def save(self, fp=None):
"""Update settings JSON data and save to file.
Parameters
----------
fp : `file` or `str`, optional
Output file.
"""
self.settings['storage'] = {
'state_separator': self.state_separator
}
self.do_save(fp) | python | def save(self, fp=None):
"""Update settings JSON data and save to file.
Parameters
----------
fp : `file` or `str`, optional
Output file.
"""
self.settings['storage'] = {
'state_separator': self.state_separator
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49,562 | codeinn/vcs | vcs/utils/termcolors.py | parse_color_setting | def parse_color_setting(config_string):
"""Parse a DJANGO_COLORS environment variable to produce the system palette
The general form of a pallete definition is:
"palette;role=fg;role=fg/bg;role=fg,option,option;role=fg/bg,option,option"
where:
palette is a named palette; one of 'light', '... | python | def parse_color_setting(config_string):
"""Parse a DJANGO_COLORS environment variable to produce the system palette
The general form of a pallete definition is:
"palette;role=fg;role=fg/bg;role=fg,option,option;role=fg/bg,option,option"
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49,563 | NICTA/revrand | revrand/utils/base.py | couple | def couple(f, g):
r"""
Compose a function thate returns two arguments.
Given a pair of functions that take the same arguments, return a
single function that returns a pair consisting of the return values
of each function.
Notes
-----
Equivalent to::
lambda f, g: lambda *args, ... | python | def couple(f, g):
r"""
Compose a function thate returns two arguments.
Given a pair of functions that take the same arguments, return a
single function that returns a pair consisting of the return values
of each function.
Notes
-----
Equivalent to::
lambda f, g: lambda *args, ... | [
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49,564 | NICTA/revrand | revrand/utils/base.py | decouple | def decouple(fn):
"""
Inverse operation of couple.
Create two functions of one argument and one return from a function that
takes two arguments and has two returns
Examples
--------
>>> h = lambda x: (2*x**3, 6*x**2)
>>> f, g = decouple(h)
>>> f(5)
250
>>> g(5)
150
... | python | def decouple(fn):
"""
Inverse operation of couple.
Create two functions of one argument and one return from a function that
takes two arguments and has two returns
Examples
--------
>>> h = lambda x: (2*x**3, 6*x**2)
>>> f, g = decouple(h)
>>> f(5)
250
>>> g(5)
150
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>>> h = lambda x: (2*x**3, 6*x**2)
>>> f, g = decouple(h)
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>>> g(5)
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49,565 | NICTA/revrand | revrand/utils/base.py | nwise | def nwise(iterable, n):
r"""
Sliding window iterator.
Iterator that acts like a sliding window of size `n`; slides over
some iterable `n` items at a time. If iterable has `m` elements,
this function will return an iterator over `m-n+1` tuples.
Parameters
----------
iterable : iterable
... | python | def nwise(iterable, n):
r"""
Sliding window iterator.
Iterator that acts like a sliding window of size `n`; slides over
some iterable `n` items at a time. If iterable has `m` elements,
this function will return an iterator over `m-n+1` tuples.
Parameters
----------
iterable : iterable
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Sliding window iterator.
Iterator that acts like a sliding window of size `n`; slides over
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iterable : iterable
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49,566 | NICTA/revrand | revrand/utils/base.py | scalar_reshape | def scalar_reshape(a, newshape, order='C'):
"""
Reshape, but also return scalars or empty lists.
Identical to `numpy.reshape` except in the case where `newshape` is
the empty tuple, in which case we return a scalar instead of a
0-dimensional array.
Examples
--------
>>> a = np.arange(6... | python | def scalar_reshape(a, newshape, order='C'):
"""
Reshape, but also return scalars or empty lists.
Identical to `numpy.reshape` except in the case where `newshape` is
the empty tuple, in which case we return a scalar instead of a
0-dimensional array.
Examples
--------
>>> a = np.arange(6... | [
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>>> a = np.arange(6)
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49,567 | NICTA/revrand | revrand/utils/base.py | flatten | def flatten(arys, returns_shapes=True, hstack=np.hstack, ravel=np.ravel,
shape=np.shape):
"""
Flatten a potentially recursive list of multidimensional objects.
.. note::
Not to be confused with `np.ndarray.flatten()` (a more befitting
might be `chain` or `stack` or maybe somethin... | python | def flatten(arys, returns_shapes=True, hstack=np.hstack, ravel=np.ravel,
shape=np.shape):
"""
Flatten a potentially recursive list of multidimensional objects.
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49,568 | NICTA/revrand | revrand/utils/base.py | unflatten | def unflatten(ary, shapes, reshape=scalar_reshape):
r"""
Inverse opertation of flatten.
Given a flat (1d) array, and a list of shapes (represented as tuples),
return a list of ndarrays with the specified shapes.
Parameters
----------
ary : a 1d array
A flat (1d) array.
shapes ... | python | def unflatten(ary, shapes, reshape=scalar_reshape):
r"""
Inverse opertation of flatten.
Given a flat (1d) array, and a list of shapes (represented as tuples),
return a list of ndarrays with the specified shapes.
Parameters
----------
ary : a 1d array
A flat (1d) array.
shapes ... | [
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49,569 | NICTA/revrand | revrand/utils/base.py | sumprod | def sumprod(seq):
"""
Product of tuple, or sum of products of lists of tuples.
Parameters
----------
seq : tuple or list
Returns
-------
int :
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Examples
--------
>>> tup = (... | python | def sumprod(seq):
"""
Product of tuple, or sum of products of lists of tuples.
Parameters
----------
seq : tuple or list
Returns
-------
int :
the product of input tuples, or the sum of products of lists of tuples,
recursively.
Examples
--------
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49,570 | NICTA/revrand | revrand/utils/base.py | map_recursive | def map_recursive(fn, iterable, output_type=None):
"""
Apply a function of a potentially nested list of lists.
Parameters
----------
fn : callable
The function to apply to each element (and sub elements) in iterable
iterable : iterable
An iterable, sequence, sequence of sequence... | python | def map_recursive(fn, iterable, output_type=None):
"""
Apply a function of a potentially nested list of lists.
Parameters
----------
fn : callable
The function to apply to each element (and sub elements) in iterable
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49,571 | NICTA/revrand | revrand/utils/base.py | map_indices | def map_indices(fn, iterable, indices):
r"""
Map a function across indices of an iterable.
Notes
-----
Roughly equivalent to, though more efficient than::
lambda fn, iterable, *indices: (fn(arg) if i in indices else arg
for i, arg in enumerate(iterab... | python | def map_indices(fn, iterable, indices):
r"""
Map a function across indices of an iterable.
Notes
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49,572 | codeinn/vcs | vcs/utils/archivers.py | get_archiver | def get_archiver(self, kind):
"""
Returns instance of archiver class specific to given kind
:param kind: archive kind
"""
archivers = {
'tar': TarArchiver,
'tbz2': Tbz2Archiver,
'tgz': TgzArchiver,
'zip': ZipArchiver,
}
return archivers[kind]() | python | def get_archiver(self, kind):
"""
Returns instance of archiver class specific to given kind
:param kind: archive kind
"""
archivers = {
'tar': TarArchiver,
'tbz2': Tbz2Archiver,
'tgz': TgzArchiver,
'zip': ZipArchiver,
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49,573 | NICTA/revrand | revrand/basis_functions.py | slice_init | def slice_init(func):
"""
Decorator for adding partial application functionality to a basis object.
This will add an "apply_ind" argument to a basis object initialiser that
can be used to apply the basis function to only the dimensions specified in
apply_ind. E.g.,
>>> X = np.ones((100, 20))
... | python | def slice_init(func):
"""
Decorator for adding partial application functionality to a basis object.
This will add an "apply_ind" argument to a basis object initialiser that
can be used to apply the basis function to only the dimensions specified in
apply_ind. E.g.,
>>> X = np.ones((100, 20))
... | [
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49,574 | NICTA/revrand | revrand/basis_functions.py | slice_transform | def slice_transform(func, self, X, *vargs, **kwargs):
"""
Decorator for implementing partial application.
This must decorate the ``transform`` and ``grad`` methods of basis objects
if the ``slice_init`` decorator was used.
"""
X = X if self.apply_ind is None else X[:, self.apply_ind]
return... | python | def slice_transform(func, self, X, *vargs, **kwargs):
"""
Decorator for implementing partial application.
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"""
X = X if self.apply_ind is None else X[:, self.apply_ind]
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49,575 | NICTA/revrand | revrand/basis_functions.py | apply_grad | def apply_grad(fun, grad):
"""
Apply a function that takes a gradient matrix to a sequence of 2 or 3
dimensional gradients.
This is partucularly useful when the gradient of a basis concatenation
object is quite complex, eg.
>>> X = np.random.randn(100, 3)
>>> y = np.random.randn(100)
>... | python | def apply_grad(fun, grad):
"""
Apply a function that takes a gradient matrix to a sequence of 2 or 3
dimensional gradients.
This is partucularly useful when the gradient of a basis concatenation
object is quite complex, eg.
>>> X = np.random.randn(100, 3)
>>> y = np.random.randn(100)
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49,576 | NICTA/revrand | revrand/basis_functions.py | Basis.get_dim | def get_dim(self, X):
"""
Get the output dimensionality of this basis.
This makes a cheap call to transform with the initial parameter values
to ascertain the dimensionality of the output features.
Parameters
----------
X : ndarray
(N, d) array of ob... | python | def get_dim(self, X):
"""
Get the output dimensionality of this basis.
This makes a cheap call to transform with the initial parameter values
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49,577 | NICTA/revrand | revrand/basis_functions.py | Basis.params_values | def params_values(self):
"""
Get a list of the ``Parameter`` values if they have a value.
This does not include the basis regularizer.
"""
return [p.value for p in atleast_list(self.params) if p.has_value] | python | def params_values(self):
"""
Get a list of the ``Parameter`` values if they have a value.
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return [p.value for p in atleast_list(self.params) if p.has_value] | [
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49,578 | NICTA/revrand | revrand/basis_functions.py | RadialBasis.transform | def transform(self, X, lenscale=None):
"""
Apply the RBF to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or ndarray, optional
scal... | python | def transform(self, X, lenscale=None):
"""
Apply the RBF to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
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lenscale: scalar or ndarray, optional
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49,579 | NICTA/revrand | revrand/basis_functions.py | SigmoidalBasis.transform | def transform(self, X, lenscale=None):
r"""
Apply the sigmoid basis function to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: float
the le... | python | def transform(self, X, lenscale=None):
r"""
Apply the sigmoid basis function to X.
Parameters
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X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: float
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49,580 | NICTA/revrand | revrand/basis_functions.py | _RandomKernelBasis.transform | def transform(self, X, lenscale=None):
"""
Apply the random basis to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or ndarray, optional
... | python | def transform(self, X, lenscale=None):
"""
Apply the random basis to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or ndarray, optional
... | [
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49,581 | NICTA/revrand | revrand/basis_functions.py | _RandomKernelBasis.grad | def grad(self, X, lenscale=None):
r"""
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Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or... | python | def grad(self, X, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the length scales.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
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49,582 | NICTA/revrand | revrand/basis_functions.py | FastFoodRBF.transform | def transform(self, X, lenscale=None):
"""
Apply the Fast Food RBF basis to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or ndarray, optional
... | python | def transform(self, X, lenscale=None):
"""
Apply the Fast Food RBF basis to X.
Parameters
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X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
lenscale: scalar or ndarray, optional
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49,583 | NICTA/revrand | revrand/basis_functions.py | FastFoodGM.transform | def transform(self, X, mean=None, lenscale=None):
"""
Apply the spectral mixture component basis to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
mean: ndarray,... | python | def transform(self, X, mean=None, lenscale=None):
"""
Apply the spectral mixture component basis to X.
Parameters
----------
X: ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
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49,584 | NICTA/revrand | revrand/basis_functions.py | FastFoodGM.grad | def grad(self, X, mean=None, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the mean and length scales.
Parameters
----------
x: ndarray
(n, d) array of observations where n is the number of samples, and
d is the dimensionality of x.
... | python | def grad(self, X, mean=None, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the mean and length scales.
Parameters
----------
x: ndarray
(n, d) array of observations where n is the number of samples, and
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49,585 | NICTA/revrand | revrand/basis_functions.py | BasisCat.transform | def transform(self, X, *params):
"""
Return the basis function applied to X.
I.e. Phi(X, params), where params can also optionally be used and
learned.
Parameters
----------
X : ndarray
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49,586 | NICTA/revrand | revrand/basis_functions.py | BasisCat.grad | def grad(self, X, *params):
"""
Return the gradient of the basis function for each parameter.
Parameters
----------
X : ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
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... | python | def grad(self, X, *params):
"""
Return the gradient of the basis function for each parameter.
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----------
X : ndarray
(N, d) array of observations where N is the number of samples, and
d is the dimensionality of X.
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49,587 | NICTA/revrand | revrand/basis_functions.py | BasisCat.params | def params(self):
"""
Return a list of all of the ``Parameter`` objects.
Or a just a single ``Parameter`` is there is only one, and single empty
``Parameter`` if there are no parameters.
"""
paramlist = [b.params for b in self.bases if b.params.has_value]
if len... | python | def params(self):
"""
Return a list of all of the ``Parameter`` objects.
Or a just a single ``Parameter`` is there is only one, and single empty
``Parameter`` if there are no parameters.
"""
paramlist = [b.params for b in self.bases if b.params.has_value]
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49,588 | codeinn/vcs | vcs/utils/diffs.py | get_udiff | def get_udiff(filenode_old, filenode_new, show_whitespace=True):
"""
Returns unified diff between given ``filenode_old`` and ``filenode_new``.
"""
try:
filenode_old_date = filenode_old.changeset.date
except NodeError:
filenode_old_date = None
try:
filenode_new_date = fil... | python | def get_udiff(filenode_old, filenode_new, show_whitespace=True):
"""
Returns unified diff between given ``filenode_old`` and ``filenode_new``.
"""
try:
filenode_old_date = filenode_old.changeset.date
except NodeError:
filenode_old_date = None
try:
filenode_new_date = fil... | [
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49,589 | codeinn/vcs | vcs/utils/diffs.py | get_gitdiff | def get_gitdiff(filenode_old, filenode_new, ignore_whitespace=True):
"""
Returns git style diff between given ``filenode_old`` and ``filenode_new``.
:param ignore_whitespace: ignore whitespaces in diff
"""
for filenode in (filenode_old, filenode_new):
if not isinstance(filenode, FileNode):... | python | def get_gitdiff(filenode_old, filenode_new, ignore_whitespace=True):
"""
Returns git style diff between given ``filenode_old`` and ``filenode_new``.
:param ignore_whitespace: ignore whitespaces in diff
"""
for filenode in (filenode_old, filenode_new):
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49,590 | codeinn/vcs | vcs/utils/diffs.py | DiffProcessor.copy_iterator | def copy_iterator(self):
"""
make a fresh copy of generator, we should not iterate thru
an original as it's needed for repeating operations on
this instance of DiffProcessor
"""
self.__udiff, iterator_copy = itertools.tee(self.__udiff)
return iterator_copy | python | def copy_iterator(self):
"""
make a fresh copy of generator, we should not iterate thru
an original as it's needed for repeating operations on
this instance of DiffProcessor
"""
self.__udiff, iterator_copy = itertools.tee(self.__udiff)
return iterator_copy | [
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49,591 | codeinn/vcs | vcs/utils/diffs.py | DiffProcessor._extract_rev | def _extract_rev(self, line1, line2):
"""
Extract the filename and revision hint from a line.
"""
try:
if line1.startswith('--- ') and line2.startswith('+++ '):
l1 = line1[4:].split(None, 1)
old_filename = l1[0].lstrip('a/') if len(l1) >= 1 el... | python | def _extract_rev(self, line1, line2):
"""
Extract the filename and revision hint from a line.
"""
try:
if line1.startswith('--- ') and line2.startswith('+++ '):
l1 = line1[4:].split(None, 1)
old_filename = l1[0].lstrip('a/') if len(l1) >= 1 el... | [
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49,592 | codeinn/vcs | vcs/utils/diffs.py | DiffProcessor._parse_udiff | def _parse_udiff(self):
"""
Parse the diff an return data for the template.
"""
lineiter = self.lines
files = []
try:
line = lineiter.next()
# skip first context
skipfirst = True
while 1:
# continue until we ... | python | def _parse_udiff(self):
"""
Parse the diff an return data for the template.
"""
lineiter = self.lines
files = []
try:
line = lineiter.next()
# skip first context
skipfirst = True
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49,593 | codeinn/vcs | vcs/utils/diffs.py | DiffProcessor._safe_id | def _safe_id(self, idstring):
"""Make a string safe for including in an id attribute.
The HTML spec says that id attributes 'must begin with
a letter ([A-Za-z]) and may be followed by any number
of letters, digits ([0-9]), hyphens ("-"), underscores
("_"), colons (":"), and peri... | python | def _safe_id(self, idstring):
"""Make a string safe for including in an id attribute.
The HTML spec says that id attributes 'must begin with
a letter ([A-Za-z]) and may be followed by any number
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49,594 | codeinn/vcs | vcs/utils/diffs.py | DiffProcessor.raw_diff | def raw_diff(self):
"""
Returns raw string as udiff
"""
udiff_copy = self.copy_iterator()
if self.__format == 'gitdiff':
udiff_copy = self._parse_gitdiff(udiff_copy)
return u''.join(udiff_copy) | python | def raw_diff(self):
"""
Returns raw string as udiff
"""
udiff_copy = self.copy_iterator()
if self.__format == 'gitdiff':
udiff_copy = self._parse_gitdiff(udiff_copy)
return u''.join(udiff_copy) | [
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49,595 | deontologician/restnavigator | restnavigator/halnav.py | APICore.cache | def cache(self, link, nav):
'''Stores a navigator in the identity map for the current
api. Can take a link or a bare uri'''
if link is None:
return # We don't cache navigators without a Link
elif hasattr(link, 'uri'):
self.id_map[link.uri] = nav
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... | python | def cache(self, link, nav):
'''Stores a navigator in the identity map for the current
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if link is None:
return # We don't cache navigators without a Link
elif hasattr(link, 'uri'):
self.id_map[link.uri] = nav
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49,596 | deontologician/restnavigator | restnavigator/halnav.py | APICore.get_cached | def get_cached(self, link, default=None):
'''Retrieves a cached navigator from the id_map.
Either a Link object or a bare uri string may be passed in.'''
if hasattr(link, 'uri'):
return self.id_map.get(link.uri, default)
else:
return self.id_map.get(link, default... | python | def get_cached(self, link, default=None):
'''Retrieves a cached navigator from the id_map.
Either a Link object or a bare uri string may be passed in.'''
if hasattr(link, 'uri'):
return self.id_map.get(link.uri, default)
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49,597 | deontologician/restnavigator | restnavigator/halnav.py | APICore.is_cached | def is_cached(self, link):
'''Returns whether the current navigator is cached. Intended
to be overwritten and customized by subclasses.
'''
if link is None:
return False
elif hasattr(link, 'uri'):
return link.uri in self.id_map
else:
re... | python | def is_cached(self, link):
'''Returns whether the current navigator is cached. Intended
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if link is None:
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49,598 | deontologician/restnavigator | restnavigator/halnav.py | PartialNavigator.expand_uri | def expand_uri(self, **kwargs):
'''Returns the template uri expanded with the current arguments'''
kwargs = dict([(k, v if v != 0 else '0') for k, v in kwargs.items()])
return uritemplate.expand(self.link.uri, kwargs) | python | def expand_uri(self, **kwargs):
'''Returns the template uri expanded with the current arguments'''
kwargs = dict([(k, v if v != 0 else '0') for k, v in kwargs.items()])
return uritemplate.expand(self.link.uri, kwargs) | [
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49,599 | deontologician/restnavigator | restnavigator/halnav.py | PartialNavigator.expand_link | def expand_link(self, **kwargs):
'''Expands with the given arguments and returns a new
untemplated Link object
'''
props = self.link.props.copy()
del props['templated']
return Link(
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) | python | def expand_link(self, **kwargs):
'''Expands with the given arguments and returns a new
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'''
props = self.link.props.copy()
del props['templated']
return Link(
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"Link",
"object"
] | 453b9de4e70e602009d3e3ffafcf77d23c8b07c5 | https://github.com/deontologician/restnavigator/blob/453b9de4e70e602009d3e3ffafcf77d23c8b07c5/restnavigator/halnav.py#L139-L148 |
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