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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/urllib3/request.py | python | RequestMethods.request_encode_body | (
self,
method,
url,
fields=None,
headers=None,
encode_multipart=True,
multipart_boundary=None,
**urlopen_kw
) | return self.urlopen(method, url, **extra_kw) | Make a request using :meth:`urlopen` with the ``fields`` encoded in
the body. This is useful for request methods like POST, PUT, PATCH, etc.
When ``encode_multipart=True`` (default), then
:meth:`urllib3.filepost.encode_multipart_formdata` is used to encode
the payload with the appropriate content type. Otherwise
:meth:`urllib.urlencode` is used with the
'application/x-www-form-urlencoded' content type.
Multipart encoding must be used when posting files, and it's reasonably
safe to use it in other times too. However, it may break request
signing, such as with OAuth.
Supports an optional ``fields`` parameter of key/value strings AND
key/filetuple. A filetuple is a (filename, data, MIME type) tuple where
the MIME type is optional. For example::
fields = {
'foo': 'bar',
'fakefile': ('foofile.txt', 'contents of foofile'),
'realfile': ('barfile.txt', open('realfile').read()),
'typedfile': ('bazfile.bin', open('bazfile').read(),
'image/jpeg'),
'nonamefile': 'contents of nonamefile field',
}
When uploading a file, providing a filename (the first parameter of the
tuple) is optional but recommended to best mimic behavior of browsers.
Note that if ``headers`` are supplied, the 'Content-Type' header will
be overwritten because it depends on the dynamic random boundary string
which is used to compose the body of the request. The random boundary
string can be explicitly set with the ``multipart_boundary`` parameter. | Make a request using :meth:`urlopen` with the ``fields`` encoded in
the body. This is useful for request methods like POST, PUT, PATCH, etc. | [
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] | def request_encode_body(
self,
method,
url,
fields=None,
headers=None,
encode_multipart=True,
multipart_boundary=None,
**urlopen_kw
):
"""
Make a request using :meth:`urlopen` with the ``fields`` encoded in
the body. This is useful for request methods like POST, PUT, PATCH, etc.
When ``encode_multipart=True`` (default), then
:meth:`urllib3.filepost.encode_multipart_formdata` is used to encode
the payload with the appropriate content type. Otherwise
:meth:`urllib.urlencode` is used with the
'application/x-www-form-urlencoded' content type.
Multipart encoding must be used when posting files, and it's reasonably
safe to use it in other times too. However, it may break request
signing, such as with OAuth.
Supports an optional ``fields`` parameter of key/value strings AND
key/filetuple. A filetuple is a (filename, data, MIME type) tuple where
the MIME type is optional. For example::
fields = {
'foo': 'bar',
'fakefile': ('foofile.txt', 'contents of foofile'),
'realfile': ('barfile.txt', open('realfile').read()),
'typedfile': ('bazfile.bin', open('bazfile').read(),
'image/jpeg'),
'nonamefile': 'contents of nonamefile field',
}
When uploading a file, providing a filename (the first parameter of the
tuple) is optional but recommended to best mimic behavior of browsers.
Note that if ``headers`` are supplied, the 'Content-Type' header will
be overwritten because it depends on the dynamic random boundary string
which is used to compose the body of the request. The random boundary
string can be explicitly set with the ``multipart_boundary`` parameter.
"""
if headers is None:
headers = self.headers
extra_kw = {"headers": {}}
if fields:
if "body" in urlopen_kw:
raise TypeError(
"request got values for both 'fields' and 'body', can only specify one."
)
if encode_multipart:
body, content_type = encode_multipart_formdata(
fields, boundary=multipart_boundary
)
else:
body, content_type = (
urlencode(fields),
"application/x-www-form-urlencoded",
)
extra_kw["body"] = body
extra_kw["headers"] = {"Content-Type": content_type}
extra_kw["headers"].update(headers)
extra_kw.update(urlopen_kw)
return self.urlopen(method, url, **extra_kw) | [
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zhaoweicai/cascade-rcnn | 2252f46158ea6555868ca6fa5c221ea71d9b5e6c | scripts/cpp_lint.py | python | CheckCheck | (filename, clean_lines, linenum, error) | Checks the use of CHECK and EXPECT macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks the use of CHECK and EXPECT macros. | [
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] | def CheckCheck(filename, clean_lines, linenum, error):
"""Checks the use of CHECK and EXPECT macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Decide the set of replacement macros that should be suggested
lines = clean_lines.elided
check_macro = None
start_pos = -1
for macro in _CHECK_MACROS:
i = lines[linenum].find(macro)
if i >= 0:
check_macro = macro
# Find opening parenthesis. Do a regular expression match here
# to make sure that we are matching the expected CHECK macro, as
# opposed to some other macro that happens to contain the CHECK
# substring.
matched = Match(r'^(.*\b' + check_macro + r'\s*)\(', lines[linenum])
if not matched:
continue
start_pos = len(matched.group(1))
break
if not check_macro or start_pos < 0:
# Don't waste time here if line doesn't contain 'CHECK' or 'EXPECT'
return
# Find end of the boolean expression by matching parentheses
(last_line, end_line, end_pos) = CloseExpression(
clean_lines, linenum, start_pos)
if end_pos < 0:
return
if linenum == end_line:
expression = lines[linenum][start_pos + 1:end_pos - 1]
else:
expression = lines[linenum][start_pos + 1:]
for i in xrange(linenum + 1, end_line):
expression += lines[i]
expression += last_line[0:end_pos - 1]
# Parse expression so that we can take parentheses into account.
# This avoids false positives for inputs like "CHECK((a < 4) == b)",
# which is not replaceable by CHECK_LE.
lhs = ''
rhs = ''
operator = None
while expression:
matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||'
r'==|!=|>=|>|<=|<|\()(.*)$', expression)
if matched:
token = matched.group(1)
if token == '(':
# Parenthesized operand
expression = matched.group(2)
(end, _) = FindEndOfExpressionInLine(expression, 0, 1, '(', ')')
if end < 0:
return # Unmatched parenthesis
lhs += '(' + expression[0:end]
expression = expression[end:]
elif token in ('&&', '||'):
# Logical and/or operators. This means the expression
# contains more than one term, for example:
# CHECK(42 < a && a < b);
#
# These are not replaceable with CHECK_LE, so bail out early.
return
elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'):
# Non-relational operator
lhs += token
expression = matched.group(2)
else:
# Relational operator
operator = token
rhs = matched.group(2)
break
else:
# Unparenthesized operand. Instead of appending to lhs one character
# at a time, we do another regular expression match to consume several
# characters at once if possible. Trivial benchmark shows that this
# is more efficient when the operands are longer than a single
# character, which is generally the case.
matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression)
if not matched:
matched = Match(r'^(\s*\S)(.*)$', expression)
if not matched:
break
lhs += matched.group(1)
expression = matched.group(2)
# Only apply checks if we got all parts of the boolean expression
if not (lhs and operator and rhs):
return
# Check that rhs do not contain logical operators. We already know
# that lhs is fine since the loop above parses out && and ||.
if rhs.find('&&') > -1 or rhs.find('||') > -1:
return
# At least one of the operands must be a constant literal. This is
# to avoid suggesting replacements for unprintable things like
# CHECK(variable != iterator)
#
# The following pattern matches decimal, hex integers, strings, and
# characters (in that order).
lhs = lhs.strip()
rhs = rhs.strip()
match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$'
if Match(match_constant, lhs) or Match(match_constant, rhs):
# Note: since we know both lhs and rhs, we can provide a more
# descriptive error message like:
# Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42)
# Instead of:
# Consider using CHECK_EQ instead of CHECK(a == b)
#
# We are still keeping the less descriptive message because if lhs
# or rhs gets long, the error message might become unreadable.
error(filename, linenum, 'readability/check', 2,
'Consider using %s instead of %s(a %s b)' % (
_CHECK_REPLACEMENT[check_macro][operator],
check_macro, operator)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/optimize/_dual_annealing.py | python | dual_annealing | (func, bounds, args=(), maxiter=1000,
local_search_options={}, initial_temp=5230.,
restart_temp_ratio=2.e-5, visit=2.62, accept=-5.0,
maxfun=1e7, seed=None, no_local_search=False,
callback=None, x0=None) | return res | Find the global minimum of a function using Dual Annealing.
Parameters
----------
func : callable
The objective function to be minimized. Must be in the form
``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
and ``args`` is a tuple of any additional fixed parameters needed to
completely specify the function.
bounds : sequence, shape (n, 2)
Bounds for variables. ``(min, max)`` pairs for each element in ``x``,
defining bounds for the objective function parameter.
args : tuple, optional
Any additional fixed parameters needed to completely specify the
objective function.
maxiter : int, optional
The maximum number of global search iterations. Default value is 1000.
local_search_options : dict, optional
Extra keyword arguments to be passed to the local minimizer
(`minimize`). Some important options could be:
``method`` for the minimizer method to use and ``args`` for
objective function additional arguments.
initial_temp : float, optional
The initial temperature, use higher values to facilitates a wider
search of the energy landscape, allowing dual_annealing to escape
local minima that it is trapped in. Default value is 5230. Range is
(0.01, 5.e4].
restart_temp_ratio : float, optional
During the annealing process, temperature is decreasing, when it
reaches ``initial_temp * restart_temp_ratio``, the reannealing process
is triggered. Default value of the ratio is 2e-5. Range is (0, 1).
visit : float, optional
Parameter for visiting distribution. Default value is 2.62. Higher
values give the visiting distribution a heavier tail, this makes
the algorithm jump to a more distant region. The value range is (0, 3].
accept : float, optional
Parameter for acceptance distribution. It is used to control the
probability of acceptance. The lower the acceptance parameter, the
smaller the probability of acceptance. Default value is -5.0 with
a range (-1e4, -5].
maxfun : int, optional
Soft limit for the number of objective function calls. If the
algorithm is in the middle of a local search, this number will be
exceeded, the algorithm will stop just after the local search is
done. Default value is 1e7.
seed : {int or `numpy.random.RandomState` instance}, optional
If `seed` is not specified the `numpy.random.RandomState` singleton is
used.
If `seed` is an int, a new ``RandomState`` instance is used,
seeded with `seed`.
If `seed` is already a ``RandomState`` instance, then that
instance is used.
Specify `seed` for repeatable minimizations. The random numbers
generated with this seed only affect the visiting distribution
function and new coordinates generation.
no_local_search : bool, optional
If `no_local_search` is set to True, a traditional Generalized
Simulated Annealing will be performed with no local search
strategy applied.
callback : callable, optional
A callback function with signature ``callback(x, f, context)``,
which will be called for all minima found.
``x`` and ``f`` are the coordinates and function value of the
latest minimum found, and ``context`` has value in [0, 1, 2], with the
following meaning:
- 0: minimum detected in the annealing process.
- 1: detection occured in the local search process.
- 2: detection done in the dual annealing process.
If the callback implementation returns True, the algorithm will stop.
x0 : ndarray, shape(n,), optional
Coordinates of a single n-dimensional starting point.
Returns
-------
res : OptimizeResult
The optimization result represented as a `OptimizeResult` object.
Important attributes are: ``x`` the solution array, ``fun`` the value
of the function at the solution, and ``message`` which describes the
cause of the termination.
See `OptimizeResult` for a description of other attributes.
Notes
-----
This function implements the Dual Annealing optimization. This stochastic
approach derived from [3]_ combines the generalization of CSA (Classical
Simulated Annealing) and FSA (Fast Simulated Annealing) [1]_ [2]_ coupled
to a strategy for applying a local search on accepted locations [4]_.
An alternative implementation of this same algorithm is described in [5]_
and benchmarks are presented in [6]_. This approach introduces an advanced
method to refine the solution found by the generalized annealing
process. This algorithm uses a distorted Cauchy-Lorentz visiting
distribution, with its shape controlled by the parameter :math:`q_{v}`
.. math::
g_{q_{v}}(\\Delta x(t)) \\propto \\frac{ \\
\\left[T_{q_{v}}(t) \\right]^{-\\frac{D}{3-q_{v}}}}{ \\
\\left[{1+(q_{v}-1)\\frac{(\\Delta x(t))^{2}} { \\
\\left[T_{q_{v}}(t)\\right]^{\\frac{2}{3-q_{v}}}}}\\right]^{ \\
\\frac{1}{q_{v}-1}+\\frac{D-1}{2}}}
Where :math:`t` is the artificial time. This visiting distribution is used
to generate a trial jump distance :math:`\\Delta x(t)` of variable
:math:`x(t)` under artificial temperature :math:`T_{q_{v}}(t)`.
From the starting point, after calling the visiting distribution
function, the acceptance probability is computed as follows:
.. math::
p_{q_{a}} = \\min{\\{1,\\left[1-(1-q_{a}) \\beta \\Delta E \\right]^{ \\
\\frac{1}{1-q_{a}}}\\}}
Where :math:`q_{a}` is a acceptance parameter. For :math:`q_{a}<1`, zero
acceptance probability is assigned to the cases where
.. math::
[1-(1-q_{a}) \\beta \\Delta E] < 0
The artificial temperature :math:`T_{q_{v}}(t)` is decreased according to
.. math::
T_{q_{v}}(t) = T_{q_{v}}(1) \\frac{2^{q_{v}-1}-1}{\\left( \\
1 + t\\right)^{q_{v}-1}-1}
Where :math:`q_{v}` is the visiting parameter.
.. versionadded:: 1.2.0
References
----------
.. [1] Tsallis C. Possible generalization of Boltzmann-Gibbs
statistics. Journal of Statistical Physics, 52, 479-487 (1998).
.. [2] Tsallis C, Stariolo DA. Generalized Simulated Annealing.
Physica A, 233, 395-406 (1996).
.. [3] Xiang Y, Sun DY, Fan W, Gong XG. Generalized Simulated
Annealing Algorithm and Its Application to the Thomson Model.
Physics Letters A, 233, 216-220 (1997).
.. [4] Xiang Y, Gong XG. Efficiency of Generalized Simulated
Annealing. Physical Review E, 62, 4473 (2000).
.. [5] Xiang Y, Gubian S, Suomela B, Hoeng J. Generalized
Simulated Annealing for Efficient Global Optimization: the GenSA
Package for R. The R Journal, Volume 5/1 (2013).
.. [6] Mullen, K. Continuous Global Optimization in R. Journal of
Statistical Software, 60(6), 1 - 45, (2014). DOI:10.18637/jss.v060.i06
Examples
--------
The following example is a 10-dimensional problem, with many local minima.
The function involved is called Rastrigin
(https://en.wikipedia.org/wiki/Rastrigin_function)
>>> from scipy.optimize import dual_annealing
>>> func = lambda x: np.sum(x*x - 10*np.cos(2*np.pi*x)) + 10*np.size(x)
>>> lw = [-5.12] * 10
>>> up = [5.12] * 10
>>> ret = dual_annealing(func, bounds=list(zip(lw, up)), seed=1234)
>>> print("global minimum: xmin = {0}, f(xmin) = {1:.6f}".format(
... ret.x, ret.fun))
global minimum: xmin = [-4.26437714e-09 -3.91699361e-09 -1.86149218e-09 -3.97165720e-09
-6.29151648e-09 -6.53145322e-09 -3.93616815e-09 -6.55623025e-09
-6.05775280e-09 -5.00668935e-09], f(xmin) = 0.000000 | Find the global minimum of a function using Dual Annealing. | [
"Find",
"the",
"global",
"minimum",
"of",
"a",
"function",
"using",
"Dual",
"Annealing",
"."
] | def dual_annealing(func, bounds, args=(), maxiter=1000,
local_search_options={}, initial_temp=5230.,
restart_temp_ratio=2.e-5, visit=2.62, accept=-5.0,
maxfun=1e7, seed=None, no_local_search=False,
callback=None, x0=None):
"""
Find the global minimum of a function using Dual Annealing.
Parameters
----------
func : callable
The objective function to be minimized. Must be in the form
``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
and ``args`` is a tuple of any additional fixed parameters needed to
completely specify the function.
bounds : sequence, shape (n, 2)
Bounds for variables. ``(min, max)`` pairs for each element in ``x``,
defining bounds for the objective function parameter.
args : tuple, optional
Any additional fixed parameters needed to completely specify the
objective function.
maxiter : int, optional
The maximum number of global search iterations. Default value is 1000.
local_search_options : dict, optional
Extra keyword arguments to be passed to the local minimizer
(`minimize`). Some important options could be:
``method`` for the minimizer method to use and ``args`` for
objective function additional arguments.
initial_temp : float, optional
The initial temperature, use higher values to facilitates a wider
search of the energy landscape, allowing dual_annealing to escape
local minima that it is trapped in. Default value is 5230. Range is
(0.01, 5.e4].
restart_temp_ratio : float, optional
During the annealing process, temperature is decreasing, when it
reaches ``initial_temp * restart_temp_ratio``, the reannealing process
is triggered. Default value of the ratio is 2e-5. Range is (0, 1).
visit : float, optional
Parameter for visiting distribution. Default value is 2.62. Higher
values give the visiting distribution a heavier tail, this makes
the algorithm jump to a more distant region. The value range is (0, 3].
accept : float, optional
Parameter for acceptance distribution. It is used to control the
probability of acceptance. The lower the acceptance parameter, the
smaller the probability of acceptance. Default value is -5.0 with
a range (-1e4, -5].
maxfun : int, optional
Soft limit for the number of objective function calls. If the
algorithm is in the middle of a local search, this number will be
exceeded, the algorithm will stop just after the local search is
done. Default value is 1e7.
seed : {int or `numpy.random.RandomState` instance}, optional
If `seed` is not specified the `numpy.random.RandomState` singleton is
used.
If `seed` is an int, a new ``RandomState`` instance is used,
seeded with `seed`.
If `seed` is already a ``RandomState`` instance, then that
instance is used.
Specify `seed` for repeatable minimizations. The random numbers
generated with this seed only affect the visiting distribution
function and new coordinates generation.
no_local_search : bool, optional
If `no_local_search` is set to True, a traditional Generalized
Simulated Annealing will be performed with no local search
strategy applied.
callback : callable, optional
A callback function with signature ``callback(x, f, context)``,
which will be called for all minima found.
``x`` and ``f`` are the coordinates and function value of the
latest minimum found, and ``context`` has value in [0, 1, 2], with the
following meaning:
- 0: minimum detected in the annealing process.
- 1: detection occured in the local search process.
- 2: detection done in the dual annealing process.
If the callback implementation returns True, the algorithm will stop.
x0 : ndarray, shape(n,), optional
Coordinates of a single n-dimensional starting point.
Returns
-------
res : OptimizeResult
The optimization result represented as a `OptimizeResult` object.
Important attributes are: ``x`` the solution array, ``fun`` the value
of the function at the solution, and ``message`` which describes the
cause of the termination.
See `OptimizeResult` for a description of other attributes.
Notes
-----
This function implements the Dual Annealing optimization. This stochastic
approach derived from [3]_ combines the generalization of CSA (Classical
Simulated Annealing) and FSA (Fast Simulated Annealing) [1]_ [2]_ coupled
to a strategy for applying a local search on accepted locations [4]_.
An alternative implementation of this same algorithm is described in [5]_
and benchmarks are presented in [6]_. This approach introduces an advanced
method to refine the solution found by the generalized annealing
process. This algorithm uses a distorted Cauchy-Lorentz visiting
distribution, with its shape controlled by the parameter :math:`q_{v}`
.. math::
g_{q_{v}}(\\Delta x(t)) \\propto \\frac{ \\
\\left[T_{q_{v}}(t) \\right]^{-\\frac{D}{3-q_{v}}}}{ \\
\\left[{1+(q_{v}-1)\\frac{(\\Delta x(t))^{2}} { \\
\\left[T_{q_{v}}(t)\\right]^{\\frac{2}{3-q_{v}}}}}\\right]^{ \\
\\frac{1}{q_{v}-1}+\\frac{D-1}{2}}}
Where :math:`t` is the artificial time. This visiting distribution is used
to generate a trial jump distance :math:`\\Delta x(t)` of variable
:math:`x(t)` under artificial temperature :math:`T_{q_{v}}(t)`.
From the starting point, after calling the visiting distribution
function, the acceptance probability is computed as follows:
.. math::
p_{q_{a}} = \\min{\\{1,\\left[1-(1-q_{a}) \\beta \\Delta E \\right]^{ \\
\\frac{1}{1-q_{a}}}\\}}
Where :math:`q_{a}` is a acceptance parameter. For :math:`q_{a}<1`, zero
acceptance probability is assigned to the cases where
.. math::
[1-(1-q_{a}) \\beta \\Delta E] < 0
The artificial temperature :math:`T_{q_{v}}(t)` is decreased according to
.. math::
T_{q_{v}}(t) = T_{q_{v}}(1) \\frac{2^{q_{v}-1}-1}{\\left( \\
1 + t\\right)^{q_{v}-1}-1}
Where :math:`q_{v}` is the visiting parameter.
.. versionadded:: 1.2.0
References
----------
.. [1] Tsallis C. Possible generalization of Boltzmann-Gibbs
statistics. Journal of Statistical Physics, 52, 479-487 (1998).
.. [2] Tsallis C, Stariolo DA. Generalized Simulated Annealing.
Physica A, 233, 395-406 (1996).
.. [3] Xiang Y, Sun DY, Fan W, Gong XG. Generalized Simulated
Annealing Algorithm and Its Application to the Thomson Model.
Physics Letters A, 233, 216-220 (1997).
.. [4] Xiang Y, Gong XG. Efficiency of Generalized Simulated
Annealing. Physical Review E, 62, 4473 (2000).
.. [5] Xiang Y, Gubian S, Suomela B, Hoeng J. Generalized
Simulated Annealing for Efficient Global Optimization: the GenSA
Package for R. The R Journal, Volume 5/1 (2013).
.. [6] Mullen, K. Continuous Global Optimization in R. Journal of
Statistical Software, 60(6), 1 - 45, (2014). DOI:10.18637/jss.v060.i06
Examples
--------
The following example is a 10-dimensional problem, with many local minima.
The function involved is called Rastrigin
(https://en.wikipedia.org/wiki/Rastrigin_function)
>>> from scipy.optimize import dual_annealing
>>> func = lambda x: np.sum(x*x - 10*np.cos(2*np.pi*x)) + 10*np.size(x)
>>> lw = [-5.12] * 10
>>> up = [5.12] * 10
>>> ret = dual_annealing(func, bounds=list(zip(lw, up)), seed=1234)
>>> print("global minimum: xmin = {0}, f(xmin) = {1:.6f}".format(
... ret.x, ret.fun))
global minimum: xmin = [-4.26437714e-09 -3.91699361e-09 -1.86149218e-09 -3.97165720e-09
-6.29151648e-09 -6.53145322e-09 -3.93616815e-09 -6.55623025e-09
-6.05775280e-09 -5.00668935e-09], f(xmin) = 0.000000
"""
if x0 is not None and not len(x0) == len(bounds):
raise ValueError('Bounds size does not match x0')
lu = list(zip(*bounds))
lower = np.array(lu[0])
upper = np.array(lu[1])
# Check that restart temperature ratio is correct
if restart_temp_ratio <= 0. or restart_temp_ratio >= 1.:
raise ValueError('Restart temperature ratio has to be in range (0, 1)')
# Checking bounds are valid
if (np.any(np.isinf(lower)) or np.any(np.isinf(upper)) or np.any(
np.isnan(lower)) or np.any(np.isnan(upper))):
raise ValueError('Some bounds values are inf values or nan values')
# Checking that bounds are consistent
if not np.all(lower < upper):
raise ValueError('Bounds are not consistent min < max')
# Checking that bounds are the same length
if not len(lower) == len(upper):
raise ValueError('Bounds do not have the same dimensions')
# Wrapper for the objective function
func_wrapper = ObjectiveFunWrapper(func, maxfun, *args)
# Wrapper fot the minimizer
minimizer_wrapper = LocalSearchWrapper(
bounds, func_wrapper, **local_search_options)
# Initialization of RandomState for reproducible runs if seed provided
rand_state = check_random_state(seed)
# Initialization of the energy state
energy_state = EnergyState(lower, upper, callback)
energy_state.reset(func_wrapper, rand_state, x0)
# Minimum value of annealing temperature reached to perform
# re-annealing
temperature_restart = initial_temp * restart_temp_ratio
# VisitingDistribution instance
visit_dist = VisitingDistribution(lower, upper, visit, rand_state)
# Strategy chain instance
strategy_chain = StrategyChain(accept, visit_dist, func_wrapper,
minimizer_wrapper, rand_state, energy_state)
# Run the search loop
need_to_stop = False
iteration = 0
message = []
t1 = np.exp((visit - 1) * np.log(2.0)) - 1.0
while(not need_to_stop):
for i in range(maxiter):
# Compute temperature for this step
s = float(i) + 2.0
t2 = np.exp((visit - 1) * np.log(s)) - 1.0
temperature = initial_temp * t1 / t2
if iteration >= maxiter:
message.append("Maximum number of iteration reached")
need_to_stop = True
break
# Need a re-annealing process?
if temperature < temperature_restart:
energy_state.reset(func_wrapper, rand_state)
break
# starting strategy chain
val = strategy_chain.run(i, temperature)
if val is not None:
message.append(val)
need_to_stop = True
break
# Possible local search at the end of the strategy chain
if not no_local_search:
val = strategy_chain.local_search()
if val is not None:
message.append(val)
need_to_stop = True
break
iteration += 1
# Return the OptimizeResult
res = OptimizeResult()
res.x = energy_state.xbest
res.fun = energy_state.ebest
res.nit = iteration
res.nfev = func_wrapper.nfev
res.njev = func_wrapper.ngev
res.nhev = func_wrapper.nhev
res.message = message
return res | [
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geemaple/leetcode | 68bc5032e1ee52c22ef2f2e608053484c487af54 | leetcode/53.maximum-subarray.py | python | Solution2.maxSubArray | (self, nums) | return largest | :type nums: List[int]
:rtype: int | :type nums: List[int]
:rtype: int | [
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] | def maxSubArray(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
# suppose sumTo[i] = a[0] + a[1] + ... [i - 1], sumTo(0) = 0
# the subarray a[i:j] = sumTo[j] - sumTo[i]
largest = float('-inf')
smallest = 0
sumTo = 0
for num in nums:
sumTo += num
largest = max(largest, sumTo - smallest)
smallest = min(smallest, sumTo)
return largest | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/ccompiler.py | python | get_default_compiler | (osname=None, platform=None) | return 'unix' | Determine the default compiler to use for the given platform.
osname should be one of the standard Python OS names (i.e. the
ones returned by os.name) and platform the common value
returned by sys.platform for the platform in question.
The default values are os.name and sys.platform in case the
parameters are not given. | Determine the default compiler to use for the given platform. | [
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] | def get_default_compiler(osname=None, platform=None):
""" Determine the default compiler to use for the given platform.
osname should be one of the standard Python OS names (i.e. the
ones returned by os.name) and platform the common value
returned by sys.platform for the platform in question.
The default values are os.name and sys.platform in case the
parameters are not given.
"""
if osname is None:
osname = os.name
if platform is None:
platform = sys.platform
if osname == "nt" and sys.version.find('GCC') >= 0:
return 'mingw32'
for pattern, compiler in _default_compilers:
if re.match(pattern, platform) is not None or \
re.match(pattern, osname) is not None:
return compiler
# Default to Unix compiler
return 'unix' | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/Tkinter.py | python | Text.edit | (self, *args) | return self.tk.call(self._w, 'edit', *args) | Internal method
This method controls the undo mechanism and
the modified flag. The exact behavior of the
command depends on the option argument that
follows the edit argument. The following forms
of the command are currently supported:
edit_modified, edit_redo, edit_reset, edit_separator
and edit_undo | Internal method | [
"Internal",
"method"
] | def edit(self, *args):
"""Internal method
This method controls the undo mechanism and
the modified flag. The exact behavior of the
command depends on the option argument that
follows the edit argument. The following forms
of the command are currently supported:
edit_modified, edit_redo, edit_reset, edit_separator
and edit_undo
"""
return self.tk.call(self._w, 'edit', *args) | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/plugins/MetaSearch/pavement.py | python | test_default_csw_connections | () | test that the default CSW connections work | test that the default CSW connections work | [
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"""test that the default CSW connections work"""
relpath = 'resources%sconnections-default.xml' % os.sep
csw_connections_xml = options.base.plugin / relpath
conns = etree.parse(csw_connections_xml)
for conn in conns.findall('csw'):
try:
csw = CatalogueServiceWeb(conn.attrib.get('url')) # spellok
info('Success: %s', csw.identification.title)
csw.getrecords2()
except Exception as err:
raise ValueError('ERROR: %s', err) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/nntplib.py | python | NNTP.newgroups | (self, date, time, file=None) | return self.longcmd('NEWGROUPS ' + date + ' ' + time, file) | Process a NEWGROUPS command. Arguments:
- date: string 'yymmdd' indicating the date
- time: string 'hhmmss' indicating the time
Return:
- resp: server response if successful
- list: list of newsgroup names | Process a NEWGROUPS command. Arguments:
- date: string 'yymmdd' indicating the date
- time: string 'hhmmss' indicating the time
Return:
- resp: server response if successful
- list: list of newsgroup names | [
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"""Process a NEWGROUPS command. Arguments:
- date: string 'yymmdd' indicating the date
- time: string 'hhmmss' indicating the time
Return:
- resp: server response if successful
- list: list of newsgroup names"""
return self.longcmd('NEWGROUPS ' + date + ' ' + time, file) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TChA.ChangeCh | (self, *args) | return _snap.TChA_ChangeCh(self, *args) | ChangeCh(TChA self, char const & SrcCh, char const & DstCh)
Parameters:
SrcCh: char const &
DstCh: char const & | ChangeCh(TChA self, char const & SrcCh, char const & DstCh) | [
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] | def ChangeCh(self, *args):
"""
ChangeCh(TChA self, char const & SrcCh, char const & DstCh)
Parameters:
SrcCh: char const &
DstCh: char const &
"""
return _snap.TChA_ChangeCh(self, *args) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/fft/_pocketfft.py | python | ihfft | (a, n=None, axis=-1, norm=None) | return output * (1 / (sqrt(n) if unitary else n)) | Compute the inverse FFT of a signal that has Hermitian symmetry.
Parameters
----------
a : array_like
Input array.
n : int, optional
Length of the inverse FFT, the number of points along
transformation axis in the input to use. If `n` is smaller than
the length of the input, the input is cropped. If it is larger,
the input is padded with zeros. If `n` is not given, the length of
the input along the axis specified by `axis` is used.
axis : int, optional
Axis over which to compute the inverse FFT. If not given, the last
axis is used.
norm : {None, "ortho"}, optional
Normalization mode (see `numpy.fft`). Default is None.
.. versionadded:: 1.10.0
Returns
-------
out : complex ndarray
The truncated or zero-padded input, transformed along the axis
indicated by `axis`, or the last one if `axis` is not specified.
The length of the transformed axis is ``n//2 + 1``.
See also
--------
hfft, irfft
Notes
-----
`hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
opposite case: here the signal has Hermitian symmetry in the time
domain and is real in the frequency domain. So here it's `hfft` for
which you must supply the length of the result if it is to be odd:
* even: ``ihfft(hfft(a, 2*len(a) - 2) == a``, within roundoff error,
* odd: ``ihfft(hfft(a, 2*len(a) - 1) == a``, within roundoff error.
Examples
--------
>>> spectrum = np.array([ 15, -4, 0, -1, 0, -4])
>>> np.fft.ifft(spectrum)
array([1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 3.+0.j, 2.+0.j]) # may vary
>>> np.fft.ihfft(spectrum)
array([ 1.-0.j, 2.-0.j, 3.-0.j, 4.-0.j]) # may vary | Compute the inverse FFT of a signal that has Hermitian symmetry. | [
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"the",
"inverse",
"FFT",
"of",
"a",
"signal",
"that",
"has",
"Hermitian",
"symmetry",
"."
] | def ihfft(a, n=None, axis=-1, norm=None):
"""
Compute the inverse FFT of a signal that has Hermitian symmetry.
Parameters
----------
a : array_like
Input array.
n : int, optional
Length of the inverse FFT, the number of points along
transformation axis in the input to use. If `n` is smaller than
the length of the input, the input is cropped. If it is larger,
the input is padded with zeros. If `n` is not given, the length of
the input along the axis specified by `axis` is used.
axis : int, optional
Axis over which to compute the inverse FFT. If not given, the last
axis is used.
norm : {None, "ortho"}, optional
Normalization mode (see `numpy.fft`). Default is None.
.. versionadded:: 1.10.0
Returns
-------
out : complex ndarray
The truncated or zero-padded input, transformed along the axis
indicated by `axis`, or the last one if `axis` is not specified.
The length of the transformed axis is ``n//2 + 1``.
See also
--------
hfft, irfft
Notes
-----
`hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
opposite case: here the signal has Hermitian symmetry in the time
domain and is real in the frequency domain. So here it's `hfft` for
which you must supply the length of the result if it is to be odd:
* even: ``ihfft(hfft(a, 2*len(a) - 2) == a``, within roundoff error,
* odd: ``ihfft(hfft(a, 2*len(a) - 1) == a``, within roundoff error.
Examples
--------
>>> spectrum = np.array([ 15, -4, 0, -1, 0, -4])
>>> np.fft.ifft(spectrum)
array([1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 3.+0.j, 2.+0.j]) # may vary
>>> np.fft.ihfft(spectrum)
array([ 1.-0.j, 2.-0.j, 3.-0.j, 4.-0.j]) # may vary
"""
a = asarray(a)
if n is None:
n = a.shape[axis]
unitary = _unitary(norm)
output = conjugate(rfft(a, n, axis))
return output * (1 / (sqrt(n) if unitary else n)) | [
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vslavik/poedit | f7a9daa0a10037e090aa0a86f5ce0f24ececdf6a | deps/boost/tools/litre/cplusplus.py | python | _caller | (up=0) | return ('', 0, '', None) | Get file name, line number, function name and
source text of the caller's caller as 4-tuple:
(file, line, func, text).
The optional argument 'up' allows retrieval of
a caller further back up into the call stack.
Note, the source text may be None and function
name may be '?' in the returned result. In
Python 2.3+ the file name may be an absolute
path. | Get file name, line number, function name and
source text of the caller's caller as 4-tuple:
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":",
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] | def _caller(up=0):
'''Get file name, line number, function name and
source text of the caller's caller as 4-tuple:
(file, line, func, text).
The optional argument 'up' allows retrieval of
a caller further back up into the call stack.
Note, the source text may be None and function
name may be '?' in the returned result. In
Python 2.3+ the file name may be an absolute
path.
'''
try: # just get a few frames'
f = traceback.extract_stack(limit=up+2)
if f:
return f[0]
except:
pass
# running with psyco?
return ('', 0, '', None) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/python/op_hint.py | python | _LiteOperand.aggregate_and_return_name_for_input | (self, out_graphdef) | This adds the node(s) to out_graphdef and returns the input node name.
Args:
out_graphdef: A graphdef that is ready to have this input added.
Returns:
The output that the stub should use as an input for this operand.
Raises:
RuntimeError: if the method is not implemented. | This adds the node(s) to out_graphdef and returns the input node name. | [
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"""This adds the node(s) to out_graphdef and returns the input node name.
Args:
out_graphdef: A graphdef that is ready to have this input added.
Returns:
The output that the stub should use as an input for this operand.
Raises:
RuntimeError: if the method is not implemented.
"""
del out_graphdef
raise RuntimeError("Unimplemented abstract method.") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | Display.GetFromPoint | (*args, **kwargs) | return _misc_.Display_GetFromPoint(*args, **kwargs) | GetFromPoint(Point pt) -> int
Find the display where the given point lies, return wx.NOT_FOUND if it
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"""
GetFromPoint(Point pt) -> int
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"""
return _misc_.Display_GetFromPoint(*args, **kwargs) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/PyChop/ISISDisk.py | python | ISISDisk.getMultiWidths | (self, Ei_in=None, frequency=None) | return {"Eis":Eis, "Moderator":tmod, "Chopper":tchp, "Energy":res_el} | Returns the time widths contributing to the calculated energy width for all reps | Returns the time widths contributing to the calculated energy width for all reps | [
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"""
Returns the time widths contributing to the calculated energy width for all reps
"""
Ei = self.Ei if Ei_in is None else Ei_in
if not Ei:
raise ValueError('Incident energy has not been specified')
if frequency:
oldfreq = self.freq
self.setFrequency(frequency)
Eis, _, res_el, percent, _, chop_width, mod_width = self.__LETgetResolution(False, 0., Ei)
if any([iname in self.instname for iname in ['MERLIN', 'MAPS', 'MARI']]):
chopper_inst = ISISFermi(self.instname, self.variant, self.freq[-1])
tchp = []
tmod = []
for ee in Eis:
res_el.append(chopper_inst.getResolution(0., Ei))
v_van, mod_width, chop_width = chopper_inst.getVanVar(ee)
tchp.append(chop_width * 1.e6)
tmod.append(mod_width * 1.e6)
else:
tchp = chop_width
tmod = mod_width
if frequency:
self.setFrequency(oldfreq)
return {"Eis":Eis, "Moderator":tmod, "Chopper":tchp, "Energy":res_el} | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/command/sdist.py | python | sdist.read_manifest | (self) | Read the manifest file (named by 'self.manifest') and use it to
fill in 'self.filelist', the list of files to include in the source
distribution. | Read the manifest file (named by 'self.manifest') and use it to
fill in 'self.filelist', the list of files to include in the source
distribution. | [
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"""Read the manifest file (named by 'self.manifest') and use it to
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"""
log.info("reading manifest file '%s'", self.manifest)
manifest = open(self.manifest)
for line in manifest:
# ignore comments and blank lines
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manifest.close() | [
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | scripts/cpp_lint.py | python | _NestingState.InnermostClass | (self) | return None | Get class info on the top of the stack.
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"""Get class info on the top of the stack.
Returns:
A _ClassInfo object if we are inside a class, or None otherwise.
"""
for i in range(len(self.stack), 0, -1):
classinfo = self.stack[i - 1]
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NVIDIA/DALI | bf16cc86ba8f091b145f91962f21fe1b6aff243d | dali/python/nvidia/dali/pipeline.py | python | Pipeline.share_outputs | (self) | Returns the outputs of the pipeline.
Main difference to :meth:`outputs`
is that share_outputs doesn't release returned buffers, release_outputs
need to be called for that. If the pipeline is executed asynchronously,
this function blocks until the results become available. It provides
the user with better control about when he wants to run the pipeline, when he wants
to obtain the resulting buffers and when they can be returned to DALI pool when the
results have been consumed.
Needs to be used together with :meth:`release_outputs`
and :meth:`schedule_run`
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"""Returns the outputs of the pipeline.
Main difference to :meth:`outputs`
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need to be called for that. If the pipeline is executed asynchronously,
this function blocks until the results become available. It provides
the user with better control about when he wants to run the pipeline, when he wants
to obtain the resulting buffers and when they can be returned to DALI pool when the
results have been consumed.
Needs to be used together with :meth:`release_outputs`
and :meth:`schedule_run`
Should not be mixed with :meth:`run` in the same pipeline.
:return:
A list of `TensorList` objects for respective pipeline outputs
"""
with self._check_api_type_scope(types.PipelineAPIType.SCHEDULED):
if self._batches_to_consume == 0 or self._gpu_batches_to_consume == 0:
raise StopIteration
self._batches_to_consume -= 1
self._gpu_batches_to_consume -= 1
return self._pipe.ShareOutputs() | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/labeled_tensor/python/ops/core.py | python | _find_consistent_ordering | (a, b) | return ordering | Find the left-most consistent ordering between two lists of unique items.
A consistent ordering combines all elements in both a and b while keeping all
elements in their original order in both inputs. The left-most consistent
ordering orders elements from `a` not found in `b` before elements in `b` not
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Args:
a: list with unique elements.
b: list with unique elements.
Returns:
List containing all elements in either a or b, or None, if no consistent
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"""Find the left-most consistent ordering between two lists of unique items.
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elements in their original order in both inputs. The left-most consistent
ordering orders elements from `a` not found in `b` before elements in `b` not
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b: list with unique elements.
Returns:
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"""
a_set = set(a)
b_set = set(b)
i = 0
j = 0
ordering = []
while i < len(a) and j < len(b):
if a[i] not in b_set:
ordering.append(a[i])
i += 1
elif b[j] not in a_set:
ordering.append(b[j])
j += 1
elif a[i] == b[j]:
ordering.append(a[i])
i += 1
j += 1
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ordering.extend(a[i:])
ordering.extend(b[j:])
return ordering | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/reinforcement-learning/a3c/launcher.py | python | submit | (args) | Submit function of local jobs. | Submit function of local jobs. | [
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gpus = args.gpus.strip().split(',')
"""Submit function of local jobs."""
def mthread_submit(nworker, nserver, envs):
"""
customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional parameters in input
Parameters
----------
nworker: number of slave process to start up
nserver: number of server nodes to start up
envs: enviroment variables to be added to the starting programs
"""
procs = {}
for i, gpu in enumerate(gpus):
for j in range(args.num_threads):
procs[i] = Thread(target=exec_cmd, args=(args.command + ['--gpus=%s'%gpu], 'worker', i*args.num_threads+j, envs))
procs[i].setDaemon(True)
procs[i].start()
for i in range(len(gpus)*args.num_threads, len(gpus)*args.num_threads + nserver):
procs[i] = Thread(target=exec_cmd, args=(args.command, 'server', i, envs))
procs[i].setDaemon(True)
procs[i].start()
# call submit, with nslave, the commands to run each job and submit function
tracker.submit(args.num_threads*len(gpus), args.num_servers, fun_submit=mthread_submit,
pscmd=(' '.join(args.command))) | [
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devsisters/libquic | 8954789a056d8e7d5fcb6452fd1572ca57eb5c4e | src/third_party/protobuf/python/google/protobuf/internal/well_known_types.py | python | Timestamp.ToMilliseconds | (self) | return (self.seconds * _MILLIS_PER_SECOND +
self.nanos // _NANOS_PER_MILLISECOND) | Converts Timestamp to milliseconds since epoch. | Converts Timestamp to milliseconds since epoch. | [
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] | def ToMilliseconds(self):
"""Converts Timestamp to milliseconds since epoch."""
return (self.seconds * _MILLIS_PER_SECOND +
self.nanos // _NANOS_PER_MILLISECOND) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/dataview.py | python | DataViewCtrl.SetExpanderColumn | (*args, **kwargs) | return _dataview.DataViewCtrl_SetExpanderColumn(*args, **kwargs) | SetExpanderColumn(self, DataViewColumn col) | SetExpanderColumn(self, DataViewColumn col) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | net/tools/dafsa/make_dafsa.py | python | reverse | (dafsa) | return sink | Generates a new DAFSA that is reversed, so that the old sink node becomes
the new source node. | Generates a new DAFSA that is reversed, so that the old sink node becomes
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"""Generates a new DAFSA that is reversed, so that the old sink node becomes
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"""
sink = []
nodemap = {}
def dfs(node, parent):
"""Creates reverse nodes.
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"""
if not node:
sink.append(parent)
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nodemap[id(node)] = (node[0][::-1], [parent])
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dfs(node, None)
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physercoe/starquant | c00cad64d1de2da05081b3dc320ef264c6295e08 | cppsrc/log4cplus-2.0.4/catch/.conan/build.py | python | BuilderSettings.channel | (self) | return os.getenv("CONAN_CHANNEL", "testing") | Default Conan package channel when not stable | Default Conan package channel when not stable | [
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""" Default Conan package channel when not stable
"""
return os.getenv("CONAN_CHANNEL", "testing") | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/polynomial/_polybase.py | python | ABCPolyBase.integ | (self, m=1, k=[], lbnd=None) | return self.__class__(coef, self.domain, self.window) | Integrate.
Return a series instance that is the definite integral of the
current series.
Parameters
----------
m : non-negative int
The number of integrations to perform.
k : array_like
Integration constants. The first constant is applied to the
first integration, the second to the second, and so on. The
list of values must less than or equal to `m` in length and any
missing values are set to zero.
lbnd : Scalar
The lower bound of the definite integral.
Returns
-------
new_series : series
A new series representing the integral. The domain is the same
as the domain of the integrated series. | Integrate. | [
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] | def integ(self, m=1, k=[], lbnd=None):
"""Integrate.
Return a series instance that is the definite integral of the
current series.
Parameters
----------
m : non-negative int
The number of integrations to perform.
k : array_like
Integration constants. The first constant is applied to the
first integration, the second to the second, and so on. The
list of values must less than or equal to `m` in length and any
missing values are set to zero.
lbnd : Scalar
The lower bound of the definite integral.
Returns
-------
new_series : series
A new series representing the integral. The domain is the same
as the domain of the integrated series.
"""
off, scl = self.mapparms()
if lbnd is None:
lbnd = 0
else:
lbnd = off + scl*lbnd
coef = self._int(self.coef, m, k, lbnd, 1./scl)
return self.__class__(coef, self.domain, self.window) | [
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xiaohaoChen/rrc_detection | 4f2b110cd122da7f55e8533275a9b4809a88785a | python/caffe/io.py | python | Transformer.set_transpose | (self, in_, order) | Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions | Set the input channel order for e.g. RGB to BGR conversion
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"""
Set the input channel order for e.g. RGB to BGR conversion
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Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions
"""
self.__check_input(in_)
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Cisco-Talos/moflow | ed71dfb0540d9e0d7a4c72f0881b58958d573728 | BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/google/protobuf/internal/containers.py | python | RepeatedCompositeFieldContainer.MergeFrom | (self, other) | Appends the contents of another repeated field of the same type to this
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/control_flow_ops.py | python | group | (*inputs, **kwargs) | Create an op that groups multiple operations.
When this op finishes, all ops in `inputs` have finished. This op has no
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See also @{tf.tuple$tuple} and
@{tf.control_dependencies$control_dependencies}.
Args:
*inputs: Zero or more tensors to group.
name: A name for this operation (optional).
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"""Create an op that groups multiple operations.
When this op finishes, all ops in `inputs` have finished. This op has no
output.
See also @{tf.tuple$tuple} and
@{tf.control_dependencies$control_dependencies}.
Args:
*inputs: Zero or more tensors to group.
name: A name for this operation (optional).
Returns:
An Operation that executes all its inputs.
Raises:
ValueError: If an unknown keyword argument is provided.
"""
if context.in_eager_mode():
return None
name = kwargs.pop("name", None)
if kwargs:
raise ValueError("Unknown keyword arguments: " + ", ".join(kwargs.keys()))
with ops.name_scope(name, "group_deps", inputs) as name:
# Grouping no inputs means do nothing
if not inputs:
return no_op(name=name)
# Sorts *inputs according to their devices.
ops_on_device = {} # device -> operations specified on the device.
for inp in nest.flatten(inputs):
if not hasattr(inp, "device"):
raise TypeError("Expected tf.group() expected Tensor arguments not "
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if not hasattr(inp, "device"):
if isinstance(inp, list):
raise TypeError("To call tf.group() with a list, use "
"tf.group(*[...]) not tf.group([...]).")
raise TypeError("Expected tf.group() expected Tensor arguments not "
"'%s' with type '%s'" % (inp, type(inp)))
dev = inp.device
if dev in ops_on_device:
ops_on_device[dev].append(inp)
else:
ops_on_device[dev] = [inp]
if len(ops_on_device) == 1:
# 1-level tree. The root node is the returned NoOp node.
(dev, deps), = ops_on_device.items()
return _GroupControlDeps(dev, deps, name=name)
# 2-level tree. The root node is the returned NoOp node.
# deps contains 1 NoOp node for each device.
deps = []
def device_key(dev):
"""A sort key that allows None to be compared to strings."""
return "" if dev is None else dev
for dev in sorted(six.iterkeys(ops_on_device), key=device_key):
deps.append(_GroupControlDeps(dev, ops_on_device[dev]))
with ops.control_dependencies(deps):
return no_op(name=name) | [
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cmu-db/peloton | 484d76df9344cb5c153a2c361c5d5018912d4cf4 | script/formatting/formatter.py | python | format_dir | (dir_path, update_header, clang_format_code) | Formats all the files in the dir passed as argument. | Formats all the files in the dir passed as argument. | [
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"""Formats all the files in the dir passed as argument."""
for subdir, _, files in os.walk(dir_path): # _ is for directories.
for file in files:
#print os.path.join(subdir, file)
file_path = subdir + os.path.sep + file
if file_path.endswith(".h") or file_path.endswith(".cpp"):
format_file(file_path, None, update_header, clang_format_code) | [
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cmu-db/noisepage | 79276e68fe83322f1249e8a8be96bd63c583ae56 | build-support/cpplint.py | python | ParseArguments | (args) | return filenames | Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint. | Parses the command line arguments. | [
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"""Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint.
"""
try:
(opts, filenames) = getopt.getopt(args, '', ['help', 'output=', 'verbose=',
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'counting=',
'filter=',
'root=',
'repository=',
'linelength=',
'extensions=',
'exclude=',
'recursive',
'headers=',
'quiet'])
except getopt.GetoptError:
PrintUsage('Invalid arguments.')
verbosity = _VerboseLevel()
output_format = _OutputFormat()
filters = ''
quiet = _Quiet()
counting_style = ''
recursive = False
for (opt, val) in opts:
if opt == '--help':
PrintUsage(None)
if opt == '--version':
PrintVersion()
elif opt == '--output':
if val not in ('emacs', 'vs7', 'eclipse', 'junit'):
PrintUsage('The only allowed output formats are emacs, vs7, eclipse '
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output_format = val
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quiet = True
elif opt == '--verbose' or opt == '--v':
verbosity = int(val)
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filters = val
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if val not in ('total', 'toplevel', 'detailed'):
PrintUsage('Valid counting options are total, toplevel, and detailed')
counting_style = val
elif opt == '--root':
global _root
_root = val
elif opt == '--repository':
global _repository
_repository = val
elif opt == '--linelength':
global _line_length
try:
_line_length = int(val)
except ValueError:
PrintUsage('Line length must be digits.')
elif opt == '--exclude':
global _excludes
if not _excludes:
_excludes = set()
_excludes.update(glob.glob(val))
elif opt == '--extensions':
ProcessExtensionsOption(val)
elif opt == '--headers':
ProcessHppHeadersOption(val)
elif opt == '--recursive':
recursive = True
if not filenames:
PrintUsage('No files were specified.')
if recursive:
filenames = _ExpandDirectories(filenames)
if _excludes:
filenames = _FilterExcludedFiles(filenames)
_SetOutputFormat(output_format)
_SetQuiet(quiet)
_SetVerboseLevel(verbosity)
_SetFilters(filters)
_SetCountingStyle(counting_style)
return filenames | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/NTableWidget.py | python | NTableWidget.get_cell_value | (self, row_index, col_index) | return return_value | Purpose: Get cell value
Requirements: row index and column index are integer and within range.
Guarantees: the cell value with correct type is returned
:param row_index:
:param col_index:
:return: | Purpose: Get cell value
Requirements: row index and column index are integer and within range.
Guarantees: the cell value with correct type is returned
:param row_index:
:param col_index:
:return: | [
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"""
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Requirements: row index and column index are integer and within range.
Guarantees: the cell value with correct type is returned
:param row_index:
:param col_index:
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"""
# check
assert isinstance(row_index, int), 'Row index {0} must be an integer'.format(row_index)
assert isinstance(col_index, int), 'Column index {0} must be an integer'.format(col_index)
if not 0 <= row_index < self.rowCount():
raise RuntimeError('Row index {0} is out of range [0, {1})'
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if not 0 <= col_index < self.columnCount():
raise RuntimeError('Column index {0} is out of range [0, {1})'
''.format(col_index, self.columnCount()))
# get cell type
cell_data_type = self._myColumnTypeList[col_index]
if cell_data_type == 'checkbox':
# Check box
cell_i_j = self.cellWidget(row_index, col_index)
# PyQt5 compatible issue!
assert isinstance(cell_i_j, QCheckBox), 'Cell {0} {1} must be of type QCheckBox but not a {2}' \
''.format(row_index, col_index, type(cell_i_j))
return_value = cell_i_j.isChecked()
else:
# Regular cell for int, float or string
item_i_j = self.item(row_index, col_index)
assert isinstance(item_i_j, QTableWidgetItem), 'Cell {0} {1} must be of type QTableWidgetItem but not a ' \
'{2}'.format(row_index, col_index, type(item_i_j))
# get the string of the cell
return_value = str(item_i_j.text()).strip()
# cast to supported
if return_value == 'None' or len(return_value) == 0:
# None case
return_value = None
elif cell_data_type.startswith('str'):
# case as str of string
pass
elif cell_data_type.startswith('int'):
# integer
try:
return_value = int(return_value)
except ValueError as val_err:
raise RuntimeError('Unable to convert cell ({0}, {1}) with value "{2}" to integer due to {3}.'
''.format(row_index, col_index, return_value, val_err))
elif cell_data_type == 'float' or cell_data_type == 'double':
# float or double
try:
return_value = float(return_value)
except ValueError as val_err:
raise RuntimeError('Unable to convert cell ({0}, {1}) with value "{2}" to float due to {3}.'
''.format(row_index, col_index, return_value, val_err))
# END-IF-ELSE
# END-IF-ELSE
return return_value | [
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yuxng/PoseCNN | 9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04 | lib/datasets/sym.py | python | sym._load_sym_annotation | (self, index) | return {'image': image_path,
'depth': depth_path,
'label': label_path,
'meta_data': metadata_path,
'class_colors': self._class_colors,
'class_weights': self._class_weights,
'cls_index': -1,
'flipped': False} | Load class name and meta data | Load class name and meta data | [
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] | def _load_sym_annotation(self, index):
"""
Load class name and meta data
"""
# image path
image_path = self.image_path_from_index(index)
# depth path
depth_path = self.depth_path_from_index(index)
# label path
label_path = self.label_path_from_index(index)
# metadata path
metadata_path = self.metadata_path_from_index(index)
return {'image': image_path,
'depth': depth_path,
'label': label_path,
'meta_data': metadata_path,
'class_colors': self._class_colors,
'class_weights': self._class_weights,
'cls_index': -1,
'flipped': False} | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pdb.py | python | Pdb.do_undisplay | (self, arg) | undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current frame. | undisplay [expression] | [
"undisplay",
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] | def do_undisplay(self, arg):
"""undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current frame.
"""
if arg:
try:
del self.displaying.get(self.curframe, {})[arg]
except KeyError:
self.error('not displaying %s' % arg)
else:
self.displaying.pop(self.curframe, None) | [
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | python/ikfast_sympy0_6.py | python | IKFastSolver.GetSolvers | () | return {'transform6d':IKFastSolver.solveFullIK_6D,
'rotation3d':IKFastSolver.solveFullIK_Rotation3D,
'translation3d':IKFastSolver.solveFullIK_Translation3D,
'direction3d':IKFastSolver.solveFullIK_Direction3D,
'ray4d':IKFastSolver.solveFullIK_Ray4D,
'lookat3d':IKFastSolver.solveFullIK_Lookat3D,
'translationdirection5d':IKFastSolver.solveFullIK_TranslationDirection5D,
'translationxy2d':IKFastSolver.solveFullIK_TranslationXY2D,
'translationxyorientation3d':IKFastSolver.solveFullIK_TranslationXYOrientation3D,
'translationxaxisangle4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D,
'translationyaxisangle4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D,
'translationzaxisangle4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D,
'translationxaxisangleznorm4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D,
'translationyaxisanglexnorm4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D,
'translationzaxisangleynorm4d':IKFastSolver.solveFullIK_TranslationAxisAngle4D
} | Returns a dictionary of all the supported solvers and their official identifier names | Returns a dictionary of all the supported solvers and their official identifier names | [
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"""Returns a dictionary of all the supported solvers and their official identifier names"""
return {'transform6d':IKFastSolver.solveFullIK_6D,
'rotation3d':IKFastSolver.solveFullIK_Rotation3D,
'translation3d':IKFastSolver.solveFullIK_Translation3D,
'direction3d':IKFastSolver.solveFullIK_Direction3D,
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/gather/policy_json.py | python | PolicyJson._AddMessages | (self) | Processed and adds the 'messages' section to the output. | Processed and adds the 'messages' section to the output. | [
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] | def _AddMessages(self):
'''Processed and adds the 'messages' section to the output.'''
self._AddNontranslateableChunk(" 'messages': {\n")
for name, message in self.data['messages'].iteritems():
self._AddNontranslateableChunk(" '%s': {\n" % name)
self._AddNontranslateableChunk(" 'text': '''")
self._ParseMessage(message['text'], message['desc'])
self._AddNontranslateableChunk("'''\n")
self._AddNontranslateableChunk(" },\n")
self._AddNontranslateableChunk(" },\n") | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | clang/bindings/python/clang/cindex.py | python | SourceRange.end | (self) | return conf.lib.clang_getRangeEnd(self) | Return a SourceLocation representing the last character within a
source range. | Return a SourceLocation representing the last character within a
source range. | [
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"""
Return a SourceLocation representing the last character within a
source range.
"""
return conf.lib.clang_getRangeEnd(self) | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/otci/otci/otci.py | python | OTCI.get_eidcache | (self) | return cache | Get the EID-to-RLOC cache entries. | Get the EID-to-RLOC cache entries. | [
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"""Get the EID-to-RLOC cache entries."""
output = self.execute_command('eidcache')
cache = {}
for line in output:
ip, rloc16, _ = line.split(" ", 2)
cache[Ip6Addr(ip)] = Rloc16(rloc16, 16)
return cache | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/httplib2/upload-diffs.py | python | GetSubversionPropertyChanges | (filename) | return None | Return a Subversion's 'Property changes on ...' string, which is used in
the patch file.
Args:
filename: filename whose property might be set by [auto-props] config.
Returns:
A string like 'Property changes on |filename| ...' if given |filename|
matches any entries in [auto-props] section. None, otherwise. | Return a Subversion's 'Property changes on ...' string, which is used in
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PyMesh/PyMesh | 384ba882b7558ba6e8653ed263c419226c22bddf | python/pymesh/predicates.py | python | orient_2D | (p1, p2, p3) | return PyMesh.orient2d(p1, p2, p3) | Determine the orientation 2D points p1, p2, p3
Args:
p1,p2,p3: 2D points.
Returns:
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Args:
p1,p2,p3: 2D points.
Returns:
positive if (p1, p2, p3) is in counterclockwise order.
negative if (p1, p2, p3) is in clockwise order.
0.0 if they are collinear.
"""
return PyMesh.orient2d(p1, p2, p3) | [
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/AI/MilitaryAI.py | python | Allocator._jump2_threat | (self) | return get_system_jump2_threat(self.sys_id) | Military rating of enemies present 2 jumps away from the system. | Military rating of enemies present 2 jumps away from the system. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | DC.DrawRectanglePointSize | (*args, **kwargs) | return _gdi_.DC_DrawRectanglePointSize(*args, **kwargs) | DrawRectanglePointSize(self, Point pt, Size sz)
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return _gdi_.DC_DrawRectanglePointSize(*args, **kwargs) | [
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Studio3T/robomongo | 2411cd032e2e69b968dadda13ac91ca4ef3483b0 | src/third-party/qscintilla-2.8.4/sources/Python/configure.py | python | ModuleConfiguration.inform_user | (self, target_configuration) | Inform the user about module specific configuration information.
target_configuration is the target configuration. | Inform the user about module specific configuration information.
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target_configuration is the target configuration.
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inform("QScintilla %s is being used." %
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inform("The QScintilla .sip files will be installed in %s." %
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | FilePickerCtrl.GetTextCtrlValue | (*args, **kwargs) | return _controls_.FilePickerCtrl_GetTextCtrlValue(*args, **kwargs) | GetTextCtrlValue(self) -> String | GetTextCtrlValue(self) -> String | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/lib/debug_data.py | python | DebugTensorDatum.debug_op | (self) | return self._debug_op | Name of the debug op.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/rexec.py | python | RExec.r_reload | (self, m) | return self.importer.reload(m) | Reload the module object, re-parsing and re-initializing it.
This method is implicitly called by code executing in the
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/cloudsearch/layer1.py | python | Layer1.describe_domains | (self, domain_names=None) | return self.get_response(doc_path, 'DescribeDomains',
params, verb='POST',
list_marker='DomainStatusList') | Describes the domains (optionally limited to one or more
domains by name) owned by this account.
:type domain_names: list
:param domain_names: Limits the response to the specified domains.
:raises: BaseException, InternalException | Describes the domains (optionally limited to one or more
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"""
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:param domain_names: Limits the response to the specified domains.
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doc_path = ('describe_domains_response',
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params = {}
if domain_names:
for i, domain_name in enumerate(domain_names, 1):
params['DomainNames.member.%d' % i] = domain_name
return self.get_response(doc_path, 'DescribeDomains',
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list_marker='DomainStatusList') | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/kvstore.py | python | KVStore.load_optimizer_states | (self, fname) | Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file. | Loads the optimizer (updater) state from the file. | [
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"""Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file.
"""
assert self._updater is not None, "Cannot load states for distributed training"
self._updater.set_states(open(fname, 'rb').read()) | [
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/treegen/cnf_train.py | python | evaluate | (hparams) | Evaluate a model under training repeatedly. | Evaluate a model under training repeatedly. | [
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"""Evaluate a model under training repeatedly."""
data_iterator, clause_metadata = load_data(random_start=False)
if FLAGS.model_type == 'tree':
m = cnf_model.CNFTreeModel(data_iterator, hparams, clause_metadata)
else:
m = cnf_model.CNFSequenceModel(data_iterator, hparams, clause_metadata)
all_metrics = [m.loss] + m.metrics.values()
mean_values, mean_updates = zip(*(metrics.streaming_mean(v)
for v in all_metrics))
tf.contrib.deprecated.scalar_summary('loss', mean_values[0])
for i, metric_name in enumerate(m.metrics.iterkeys()):
tf.contrib.deprecated.scalar_summary('metric/' + metric_name,
mean_values[i + 1])
num_evals = (FLAGS.eval_lines - 1) // hparams.batch_size + 1
slim.evaluation.evaluation_loop(
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# A py_func must return something convertible to a tensor; reset() returns
# None, and reset() or "" returns "".
final_op=tf.py_func(lambda: data_iterator.reset() or '', [], [tf.string])) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/eclib/ctrlbox.py | python | SegmentBar.GetSegmentLabel | (self, index) | return self._buttons[index].Label | Get the label of the given segment
@param index: segment index
@return: string | Get the label of the given segment
@param index: segment index
@return: string | [
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"""Get the label of the given segment
@param index: segment index
@return: string
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/graph_editor/select.py | python | filter_ts_from_regex | (ops, regex) | return filter_ts(ops, positive_filter=lambda op: regex_obj.search(op.name)) | r"""Get all the tensors linked to ops that match the given regex.
Args:
ops: an object convertible to a list of tf.Operation.
regex: a regular expression matching the tensors' name.
For example, "^foo(/.*)?:\d+$" will match all the tensors in the "foo"
scope.
Returns:
A list of tf.Tensor.
Raises:
TypeError: if ops cannot be converted to a list of tf.Operation. | r"""Get all the tensors linked to ops that match the given regex. | [
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r"""Get all the tensors linked to ops that match the given regex.
Args:
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regex: a regular expression matching the tensors' name.
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ops = util.make_list_of_op(ops)
regex_obj = make_regex(regex)
return filter_ts(ops, positive_filter=lambda op: regex_obj.search(op.name)) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/modtool/core/info.py | python | ModToolInfo._get_base_dir | (self, start_dir) | return None | Figure out the base dir (where the top-level cmake file is) | Figure out the base dir (where the top-level cmake file is) | [
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base_dir = os.path.abspath(start_dir)
if self._check_directory(base_dir):
return base_dir
else:
(up_dir, this_dir) = os.path.split(base_dir)
if os.path.split(up_dir)[1] == 'include':
up_dir = os.path.split(up_dir)[0]
if self._check_directory(up_dir):
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/email/_parseaddr.py | python | mktime_tz | (data) | Turn a 10-tuple as returned by parsedate_tz() into a POSIX timestamp. | Turn a 10-tuple as returned by parsedate_tz() into a POSIX timestamp. | [
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"""Turn a 10-tuple as returned by parsedate_tz() into a POSIX timestamp."""
if data[9] is None:
# No zone info, so localtime is better assumption than GMT
return time.mktime(data[:8] + (-1,))
else:
t = calendar.timegm(data)
return t - data[9] | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/psutil/psutil/__init__.py | python | Process.get_threads | (self) | return self._platform_impl.get_process_threads() | Return threads opened by process as a list of namedtuples
including thread id and thread CPU times (user/system). | Return threads opened by process as a list of namedtuples
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return self._platform_impl.get_process_threads() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/pkg_resources/_vendor/pyparsing.py | python | _makeTags | (tagStr, xml) | return openTag, closeTag | Internal helper to construct opening and closing tag expressions, given a tag name | Internal helper to construct opening and closing tag expressions, given a tag name | [
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"""Internal helper to construct opening and closing tag expressions, given a tag name"""
if isinstance(tagStr,basestring):
resname = tagStr
tagStr = Keyword(tagStr, caseless=not xml)
else:
resname = tagStr.name
tagAttrName = Word(alphas,alphanums+"_-:")
if (xml):
tagAttrValue = dblQuotedString.copy().setParseAction( removeQuotes )
openTag = Suppress("<") + tagStr("tag") + \
Dict(ZeroOrMore(Group( tagAttrName + Suppress("=") + tagAttrValue ))) + \
Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">")
else:
printablesLessRAbrack = "".join(c for c in printables if c not in ">")
tagAttrValue = quotedString.copy().setParseAction( removeQuotes ) | Word(printablesLessRAbrack)
openTag = Suppress("<") + tagStr("tag") + \
Dict(ZeroOrMore(Group( tagAttrName.setParseAction(downcaseTokens) + \
Optional( Suppress("=") + tagAttrValue ) ))) + \
Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">")
closeTag = Combine(_L("</") + tagStr + ">")
openTag = openTag.setResultsName("start"+"".join(resname.replace(":"," ").title().split())).setName("<%s>" % resname)
closeTag = closeTag.setResultsName("end"+"".join(resname.replace(":"," ").title().split())).setName("</%s>" % resname)
openTag.tag = resname
closeTag.tag = resname
return openTag, closeTag | [
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/python/ycm/extra_conf_store.py | python | _RandomName | () | return ''.join( random.choice( string.ascii_lowercase ) for x in range( 15 ) ) | Generates a random module name. | Generates a random module name. | [
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"""Generates a random module name."""
return ''.join( random.choice( string.ascii_lowercase ) for x in range( 15 ) ) | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xpathParserContext.xpathPopBoolean | (self) | return ret | Pops a boolean from the stack, handling conversion if
needed. Check error with #xmlXPathCheckError. | Pops a boolean from the stack, handling conversion if
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"""Pops a boolean from the stack, handling conversion if
needed. Check error with #xmlXPathCheckError. """
ret = libxml2mod.xmlXPathPopBoolean(self._o)
return ret | [
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mysql/mysql-workbench | 2f35f9034f015cbcd22139a60e1baa2e3e8e795c | res/scripts/python/grt_python_debugger.py | python | PyDebugger.do_clear | (self, bp_number) | Handle how a breakpoint must be removed when it is a temporary one. | Handle how a breakpoint must be removed when it is a temporary one. | [
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"""Handle how a breakpoint must be removed when it is a temporary one."""
#self.ui_print("user_clear: %s\n" % arg)
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/environment.py | python | Template.get_corresponding_lineno | (self, lineno) | return 1 | Return the source line number of a line number in the
generated bytecode as they are not in sync. | Return the source line number of a line number in the
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"""Return the source line number of a line number in the
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"""
for template_line, code_line in reversed(self.debug_info):
if code_line <= lineno:
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/training/python/training/evaluation.py | python | evaluate_repeatedly | (checkpoint_dir,
master='',
scaffold=None,
eval_ops=None,
feed_dict=None,
final_ops=None,
final_ops_feed_dict=None,
eval_interval_secs=60,
hooks=None,
config=None,
max_number_of_evaluations=None,
timeout=None,
timeout_fn=None) | return final_ops_hook.final_ops_values | Repeatedly searches for a checkpoint in `checkpoint_dir` and evaluates it.
During a single evaluation, the `eval_ops` is run until the session is
interrupted or requested to finish. This is typically requested via a
`tf.contrib.training.StopAfterNEvalsHook` which results in `eval_ops` running
the requested number of times.
Optionally, a user can pass in `final_ops`, a single `Tensor`, a list of
`Tensors` or a dictionary from names to `Tensors`. The `final_ops` is
evaluated a single time after `eval_ops` has finished running and the fetched
values of `final_ops` are returned. If `final_ops` is left as `None`, then
`None` is returned.
One may also consider using a `tf.contrib.training.SummaryAtEndHook` to record
summaries after the `eval_ops` have run. If `eval_ops` is `None`, the
summaries run immediately after the model checkpoint has been restored.
Note that `evaluate_once` creates a local variable used to track the number of
evaluations run via `tf.contrib.training.get_or_create_eval_step`.
Consequently, if a custom local init op is provided via a `scaffold`, the
caller should ensure that the local init op also initializes the eval step.
Args:
checkpoint_dir: The directory where checkpoints are stored.
master: The address of the TensorFlow master.
scaffold: An tf.train.Scaffold instance for initializing variables and
restoring variables. Note that `scaffold.init_fn` is used by the function
to restore the checkpoint. If you supply a custom init_fn, then it must
also take care of restoring the model from its checkpoint.
eval_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`, which is run until the session is requested to stop,
commonly done by a `tf.contrib.training.StopAfterNEvalsHook`.
feed_dict: The feed dictionary to use when executing the `eval_ops`.
final_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`.
final_ops_feed_dict: A feed dictionary to use when evaluating `final_ops`.
eval_interval_secs: The minimum number of seconds between evaluations.
hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
evaluation loop.
config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
max_number_of_evaluations: The maximum times to run the evaluation. If left
as `None`, then evaluation runs indefinitely.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
Returns:
The fetched values of `final_ops` or `None` if `final_ops` is `None`. | Repeatedly searches for a checkpoint in `checkpoint_dir` and evaluates it. | [
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master='',
scaffold=None,
eval_ops=None,
feed_dict=None,
final_ops=None,
final_ops_feed_dict=None,
eval_interval_secs=60,
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summaries after the `eval_ops` have run. If `eval_ops` is `None`, the
summaries run immediately after the model checkpoint has been restored.
Note that `evaluate_once` creates a local variable used to track the number of
evaluations run via `tf.contrib.training.get_or_create_eval_step`.
Consequently, if a custom local init op is provided via a `scaffold`, the
caller should ensure that the local init op also initializes the eval step.
Args:
checkpoint_dir: The directory where checkpoints are stored.
master: The address of the TensorFlow master.
scaffold: An tf.train.Scaffold instance for initializing variables and
restoring variables. Note that `scaffold.init_fn` is used by the function
to restore the checkpoint. If you supply a custom init_fn, then it must
also take care of restoring the model from its checkpoint.
eval_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`, which is run until the session is requested to stop,
commonly done by a `tf.contrib.training.StopAfterNEvalsHook`.
feed_dict: The feed dictionary to use when executing the `eval_ops`.
final_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`.
final_ops_feed_dict: A feed dictionary to use when evaluating `final_ops`.
eval_interval_secs: The minimum number of seconds between evaluations.
hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
evaluation loop.
config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
max_number_of_evaluations: The maximum times to run the evaluation. If left
as `None`, then evaluation runs indefinitely.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
Returns:
The fetched values of `final_ops` or `None` if `final_ops` is `None`.
"""
eval_step = get_or_create_eval_step()
# Prepare the run hooks.
hooks = hooks or []
if eval_ops is not None:
update_eval_step = state_ops.assign_add(eval_step, 1)
for h in hooks:
if isinstance(h, StopAfterNEvalsHook):
h._set_evals_completed_tensor(update_eval_step) # pylint: disable=protected-access
if isinstance(eval_ops, dict):
eval_ops['update_eval_step'] = update_eval_step
elif isinstance(eval_ops, (tuple, list)):
eval_ops = list(eval_ops) + [update_eval_step]
else:
eval_ops = [eval_ops, update_eval_step]
final_ops_hook = basic_session_run_hooks.FinalOpsHook(final_ops,
final_ops_feed_dict)
hooks.append(final_ops_hook)
num_evaluations = 0
for checkpoint_path in checkpoints_iterator(
checkpoint_dir,
min_interval_secs=eval_interval_secs,
timeout=timeout,
timeout_fn=timeout_fn):
session_creator = monitored_session.ChiefSessionCreator(
scaffold=scaffold,
checkpoint_filename_with_path=checkpoint_path,
master=master,
config=config)
with monitored_session.MonitoredSession(
session_creator=session_creator, hooks=hooks) as session:
logging.info('Starting evaluation at ' + time.strftime(
'%Y-%m-%d-%H:%M:%S', time.gmtime()))
if eval_ops is not None:
while not session.should_stop():
session.run(eval_ops, feed_dict)
logging.info('Finished evaluation at ' + time.strftime(
'%Y-%m-%d-%H:%M:%S', time.gmtime()))
num_evaluations += 1
if (max_number_of_evaluations is not None and
num_evaluations >= max_number_of_evaluations):
return final_ops_hook.final_ops_values
return final_ops_hook.final_ops_values | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/core/fromnumeric.py | python | argsort | (a, axis=-1, kind='quicksort', order=None) | return argsort(axis, kind, order) | Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified
by the `kind` keyword. It returns an array of indices of the same shape as
`a` that index data along the given axis in sorted order.
Parameters
----------
a : array_like
Array to sort.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If None,
the flattened array is used.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
order : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
Returns
-------
index_array : ndarray, int
Array of indices that sort `a` along the specified axis.
In other words, ``a[index_array]`` yields a sorted `a`.
See Also
--------
sort : Describes sorting algorithms used.
lexsort : Indirect stable sort with multiple keys.
ndarray.sort : Inplace sort.
Notes
-----
See `sort` for notes on the different sorting algorithms.
As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
nan values. The enhanced sort order is documented in `sort`.
Examples
--------
One dimensional array:
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])
Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
>>> np.argsort(x, axis=0)
array([[0, 1],
[1, 0]])
>>> np.argsort(x, axis=1)
array([[0, 1],
[0, 1]])
Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
dtype=[('x', '<i4'), ('y', '<i4')])
>>> np.argsort(x, order=('x','y'))
array([1, 0])
>>> np.argsort(x, order=('y','x'))
array([0, 1]) | Returns the indices that would sort an array. | [
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] | def argsort(a, axis=-1, kind='quicksort', order=None):
"""
Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified
by the `kind` keyword. It returns an array of indices of the same shape as
`a` that index data along the given axis in sorted order.
Parameters
----------
a : array_like
Array to sort.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If None,
the flattened array is used.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
order : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
Returns
-------
index_array : ndarray, int
Array of indices that sort `a` along the specified axis.
In other words, ``a[index_array]`` yields a sorted `a`.
See Also
--------
sort : Describes sorting algorithms used.
lexsort : Indirect stable sort with multiple keys.
ndarray.sort : Inplace sort.
Notes
-----
See `sort` for notes on the different sorting algorithms.
As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
nan values. The enhanced sort order is documented in `sort`.
Examples
--------
One dimensional array:
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])
Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
>>> np.argsort(x, axis=0)
array([[0, 1],
[1, 0]])
>>> np.argsort(x, axis=1)
array([[0, 1],
[0, 1]])
Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
dtype=[('x', '<i4'), ('y', '<i4')])
>>> np.argsort(x, order=('x','y'))
array([1, 0])
>>> np.argsort(x, order=('y','x'))
array([0, 1])
"""
try:
argsort = a.argsort
except AttributeError:
return _wrapit(a, 'argsort', axis, kind, order)
return argsort(axis, kind, order) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/distutils/misc_util.py | python | Configuration.add_library | (self,name,sources,**build_info) | Add library to configuration.
Parameters
----------
name : str
Name of the extension.
sources : sequence
List of the sources. The list of sources may contain functions
(called source generators) which must take an extension instance
and a build directory as inputs and return a source file or list of
source files or None. If None is returned then no sources are
generated. If the Extension instance has no sources after
processing all source generators, then no extension module is
built.
build_info : dict, optional
The following keys are allowed:
* depends
* macros
* include_dirs
* extra_compiler_args
* extra_f77_compiler_args
* extra_f90_compiler_args
* f2py_options
* language | Add library to configuration. | [
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] | def add_library(self,name,sources,**build_info):
"""
Add library to configuration.
Parameters
----------
name : str
Name of the extension.
sources : sequence
List of the sources. The list of sources may contain functions
(called source generators) which must take an extension instance
and a build directory as inputs and return a source file or list of
source files or None. If None is returned then no sources are
generated. If the Extension instance has no sources after
processing all source generators, then no extension module is
built.
build_info : dict, optional
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* depends
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* include_dirs
* extra_compiler_args
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* extra_f90_compiler_args
* f2py_options
* language
"""
self._add_library(name, sources, None, build_info)
dist = self.get_distribution()
if dist is not None:
self.warn('distutils distribution has been initialized,'\
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/mfg/games/crowd_modelling.py | python | MFGCrowdModellingState.__init__ | (self, game) | Constructor; should only be called by Game.new_initial_state. | Constructor; should only be called by Game.new_initial_state. | [
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"""Constructor; should only be called by Game.new_initial_state."""
super().__init__(game)
self._is_chance_init = True # is true for the first state of the game.
self._player_id = pyspiel.PlayerId.CHANCE
self._x = None
self._t = 0
# We initialize last_action to the neutral action. This makes sure
# that the first reward does not include any displacement penalty.
self._last_action = self._NEUTRAL_ACTION
self.size = game.size
self.horizon = game.horizon
self.return_value = 0.0
# Represents the current probability distribution over game states.
# Initialized with a uniform distribution.
self._distribution = [1. / self.size for i in range(self.size)] | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | Examples/Image/Detection/FastRCNN/BrainScript/cntk_helpers.py | python | computeAveragePrecision | (recalls, precisions, use_07_metric=False) | return ap | ap = voc_ap(recalls, precisions, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False). | ap = voc_ap(recalls, precisions, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False). | [
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""" ap = voc_ap(recalls, precisions, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
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"""
if use_07_metric:
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ap = 0.
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p = 0
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p = np.max(precisions[recalls >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrecalls = np.concatenate(([0.], recalls, [1.]))
mprecisions = np.concatenate(([0.], precisions, [0.]))
# compute the precision envelope
for i in range(mprecisions.size - 1, 0, -1):
mprecisions[i - 1] = np.maximum(mprecisions[i - 1], mprecisions[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrecalls[1:] != mrecalls[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrecalls[i + 1] - mrecalls[i]) * mprecisions[i + 1])
return ap | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/functions/Examples/ExamplePeakFunction.py | python | ExamplePeakFunction.setActiveParameter | (self, index, value) | Called by the fitting framework when a parameter value is updated.
Only required if the fitting is done over a different parameter
set than that declared | Called by the fitting framework when a parameter value is updated.
Only required if the fitting is done over a different parameter
set than that declared | [
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"""
Called by the fitting framework when a parameter value is updated.
Only required if the fitting is done over a different parameter
set than that declared
"""
param_value = value
if index == 2:
param_value = math.sqrt(math.fabs(1.0/value))
else:
param_value = value
# Final explicit arugment is required to be false here by framework
self.setParameter(index, param_value, False)
param_value = self.getParameterValue(index)
if index == 2: # Sigma. Actually fit to 1/(sigma^2) for stability
return math.pow(1./param_value, 2)
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return param_value | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/v8/third_party/jinja2/filters.py | python | do_map | (*args, **kwargs) | Applies a filter on a sequence of objects or looks up an attribute.
This is useful when dealing with lists of objects but you are really
only interested in a certain value of it.
The basic usage is mapping on an attribute. Imagine you have a list
of users but you are only interested in a list of usernames:
.. sourcecode:: jinja
Users on this page: {{ users|map(attribute='username')|join(', ') }}
Alternatively you can let it invoke a filter by passing the name of the
filter and the arguments afterwards. A good example would be applying a
text conversion filter on a sequence:
.. sourcecode:: jinja
Users on this page: {{ titles|map('lower')|join(', ') }}
.. versionadded:: 2.7 | Applies a filter on a sequence of objects or looks up an attribute.
This is useful when dealing with lists of objects but you are really
only interested in a certain value of it. | [
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"""Applies a filter on a sequence of objects or looks up an attribute.
This is useful when dealing with lists of objects but you are really
only interested in a certain value of it.
The basic usage is mapping on an attribute. Imagine you have a list
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.. sourcecode:: jinja
Users on this page: {{ users|map(attribute='username')|join(', ') }}
Alternatively you can let it invoke a filter by passing the name of the
filter and the arguments afterwards. A good example would be applying a
text conversion filter on a sequence:
.. sourcecode:: jinja
Users on this page: {{ titles|map('lower')|join(', ') }}
.. versionadded:: 2.7
"""
context = args[0]
seq = args[1]
if len(args) == 2 and 'attribute' in kwargs:
attribute = kwargs.pop('attribute')
if kwargs:
raise FilterArgumentError('Unexpected keyword argument %r' %
next(iter(kwargs)))
func = make_attrgetter(context.environment, attribute)
else:
try:
name = args[2]
args = args[3:]
except LookupError:
raise FilterArgumentError('map requires a filter argument')
func = lambda item: context.environment.call_filter(
name, item, args, kwargs, context=context)
if seq:
for item in seq:
yield func(item) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/pkg_resources/_vendor/pyparsing.py | python | pyparsing_common.stripHTMLTags | (s, l, tokens) | return pyparsing_common._html_stripper.transformString(tokens[0]) | Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page' | Parse action to remove HTML tags from web page HTML source | [
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"from",
"web",
"page",
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] | def stripHTMLTags(s, l, tokens):
"""
Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page'
"""
return pyparsing_common._html_stripper.transformString(tokens[0]) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | ColourData.SetCustomColour | (*args, **kwargs) | return _windows_.ColourData_SetCustomColour(*args, **kwargs) | SetCustomColour(self, int i, Colour colour)
Sets the i'th custom colour for the colour dialog. i should be an
integer between 0 and 15. The default custom colours are all invalid colours. | SetCustomColour(self, int i, Colour colour) | [
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"""
SetCustomColour(self, int i, Colour colour)
Sets the i'th custom colour for the colour dialog. i should be an
integer between 0 and 15. The default custom colours are all invalid colours.
"""
return _windows_.ColourData_SetCustomColour(*args, **kwargs) | [
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koth/kcws | 88efbd36a7022de4e6e90f5a1fb880cf87cfae9f | third_party/setuptools/pkg_resources.py | python | safe_name | (name) | return re.sub('[^A-Za-z0-9.]+', '-', name) | Convert an arbitrary string to a standard distribution name
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"""Convert an arbitrary string to a standard distribution name
Any runs of non-alphanumeric/. characters are replaced with a single '-'.
"""
return re.sub('[^A-Za-z0-9.]+', '-', name) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/clique.py | python | CustomType.ValidateAndModify | (self, lang, translation) | Returns true if the translation (a tclib.Translation object) is valid,
otherwise false. The language is also passed in. This method may modify
the translation that is passed in, if it so wishes. | Returns true if the translation (a tclib.Translation object) is valid,
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] | def ValidateAndModify(self, lang, translation):
'''Returns true if the translation (a tclib.Translation object) is valid,
otherwise false. The language is also passed in. This method may modify
the translation that is passed in, if it so wishes.
'''
raise NotImplementedError() | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/framework/tensor_shape.py | python | TensorShape.assert_is_fully_defined | (self) | Raises an exception if `self` is not fully defined in every dimension.
Raises:
ValueError: If `self` does not have a known value for every dimension. | Raises an exception if `self` is not fully defined in every dimension. | [
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"""Raises an exception if `self` is not fully defined in every dimension.
Raises:
ValueError: If `self` does not have a known value for every dimension.
"""
if not self.is_fully_defined():
raise ValueError("Shape %s is not fully defined" % self) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/tools/stats-viewer.py | python | Counter.__init__ | (self, data, offset) | Create a new instance.
Args:
data: the shared data access object containing the counter
offset: the byte offset of the start of this counter | Create a new instance. | [
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data: the shared data access object containing the counter
offset: the byte offset of the start of this counter
"""
self.data = data
self.offset = offset | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | uCSIsBopomofo | (code) | return ret | Check whether the character is part of Bopomofo UCS Block | Check whether the character is part of Bopomofo UCS Block | [
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"""Check whether the character is part of Bopomofo UCS Block """
ret = libxml2mod.xmlUCSIsBopomofo(code)
return ret | [
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kclyu/rpi-webrtc-streamer | e109e418aa9023009b3b59c95eec2de4721125be | tools/telegramBot.py | python | load_config | (config_filename) | If a config file is specified, load the config file.
Even though the config file is not specified, we actually use
the contents of default config for the _PROG_CONFIG global variable. | If a config file is specified, load the config file. | [
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""" If a config file is specified, load the config file.
Even though the config file is not specified, we actually use
the contents of default config for the _PROG_CONFIG global variable.
"""
global _PROG_CONFIG, _LOADED_NOTI_SCHEDULE_DAY, _LOADED_NOTI_SCHEDULE_HOUR
with open(config_filename) as fp:
loaded_config = yaml.load(fp)
for key, value in loaded_config.items():
_PROG_CONFIG[key] = value
_LOADED_NOTI_SCHEDULE_HOUR = get_noti_schedule(
_PROG_CONFIG[PROG_CONFIG_KEY_NOTI_HOUR_SCHEDULE],
PROG_NOTI_HOUR_SCHEDULE)
_LOADED_NOTI_SCHEDULE_DAY = get_noti_schedule(
_PROG_CONFIG[PROG_CONFIG_KEY_NOTI_DAY_SCHEDULE],
PROG_NOTI_DAY_SCHEDULE)
logger.debug("Notification Day Schedule: %s" % _LOADED_NOTI_SCHEDULE_DAY )
logger.debug("Notification Hour Schedule: %s" % _LOADED_NOTI_SCHEDULE_HOUR ) | [
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calamares/calamares | 9f6f82405b3074af7c99dc26487d2e46e4ece3e5 | src/modules/initcpiocfg/main.py | python | get_host_initcpio | () | return mklins | Reads the host system mkinitcpio.conf and returns all
the lines from that file, or an empty list if it does
not exist. | Reads the host system mkinitcpio.conf and returns all
the lines from that file, or an empty list if it does
not exist. | [
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"""
Reads the host system mkinitcpio.conf and returns all
the lines from that file, or an empty list if it does
not exist.
"""
hostfile = "/etc/mkinitcpio.conf"
try:
with open(hostfile, "r") as mkinitcpio_file:
mklins = [x.strip() for x in mkinitcpio_file.readlines()]
except FileNotFoundError:
libcalamares.utils.debug("Could not open host file '%s'" % hostfile)
mklins = []
return mklins | [
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OpenMined/PyDP | a88ee73053aa2bdc1be327a77109dd5907ab41d6 | src/pydp/ml/mechanisms/laplace.py | python | Laplace.check_inputs | (self, value) | return True | Checks that all parameters of the mechanism have been initialised correctly, and that the mechanism is ready
to be used.
Parameters
----------
value : float
The value to be checked
Returns
-------
True if the mechanism is ready to be used.
Raises
------
Exception
If parameters have not been set correctly, or if `value` falls outside the domain of the mechanism. | Checks that all parameters of the mechanism have been initialised correctly, and that the mechanism is ready
to be used.
Parameters
----------
value : float
The value to be checked
Returns
-------
True if the mechanism is ready to be used.
Raises
------
Exception
If parameters have not been set correctly, or if `value` falls outside the domain of the mechanism. | [
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value : float
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True if the mechanism is ready to be used.
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super().check_inputs(value)
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return True | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/nvmserialupdi.py | python | NvmAccessProviderSerial.read | (self, memory_info, offset, numbytes, max_read_chunk=None) | return data | Read the memory in chunks
:param memory_info: dictionary for the memory as provided by the DeviceMemoryInfo class
:param offset: relative offset in the memory type
:param numbytes: number of bytes to read
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:param offset: relative offset in the memory type
:param numbytes: number of bytes to read
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:return: array of bytes read
"""
offset += memory_info[DeviceMemoryInfoKeys.ADDRESS]
# if reading from flash, we want to read words if it would reduce number of USB serial transactions.
# this function is called for everything though, so be careful not to use it for memories read one byte at a time, like fuses
data = []
if max_read_chunk is None:
read_chunk_size = 0x100
else:
read_chunk_size = max_read_chunk
use_word_access = False
memtype_string = memory_info[DeviceMemoryInfoKeys.NAME]
if memtype_string in (MemoryNames.FLASH):
if numbytes > 0x100 and max_read_chunk is None:
use_word_access = True
read_chunk_size = 0x200
elif max_read_chunk is not None:
if max_read_chunk > 256:
use_word_access = True
# SACRIFICES SPEED FOR COMPATIBILITY - above line should happen whenever --limitreadsize=1 command line parameter is not passed, so we can only turn it on for specific tools -> programmer options that have this weird limitation. I couldn't propagate it through this mess!
n_chunk = math.ceil(numbytes/read_chunk_size)
bar = progress_bar.ProgressBar(n_chunk, hide=n_chunk == 1)
while numbytes:
if numbytes < read_chunk_size:
read_chunk_size = numbytes
self.logger.debug("Reading %d bytes from address 0x%06X", read_chunk_size, offset)
if use_word_access:
data += self.avr.read_data_words(offset, read_chunk_size>> 1)
else:
data += self.avr.read_data(offset, read_chunk_size)
offset += read_chunk_size
numbytes -= read_chunk_size
bar.step()
return data | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | dev/archery/archery/utils/source.py | python | ArrowSources.archive | (self, path, dereference=False, compressor=None, revision=None) | Saves a git archive at path. | Saves a git archive at path. | [
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] | def archive(self, path, dereference=False, compressor=None, revision=None):
""" Saves a git archive at path. """
if not self.git_backed:
raise ValueError("{} is not backed by git".format(self))
rev = revision if revision else "HEAD"
archive = git.archive("--prefix=apache-arrow/", rev,
git_dir=self.path)
# TODO(fsaintjacques): fix dereference for
if compressor:
archive = compressor(archive)
with open(path, "wb") as archive_fd:
archive_fd.write(archive) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/sns/connection.py | python | SNSConnection.unsubscribe | (self, subscription) | return self._make_request('Unsubscribe', params) | Allows endpoint owner to delete subscription.
Confirmation message will be delivered.
:type subscription: string
:param subscription: The ARN of the subscription to be deleted. | Allows endpoint owner to delete subscription.
Confirmation message will be delivered. | [
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] | def unsubscribe(self, subscription):
"""
Allows endpoint owner to delete subscription.
Confirmation message will be delivered.
:type subscription: string
:param subscription: The ARN of the subscription to be deleted.
"""
params = {'SubscriptionArn': subscription}
return self._make_request('Unsubscribe', params) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/context.py | python | _Context.set_env_config_path | (self, env_config_path) | Check and set env_config_path. | Check and set env_config_path. | [
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] | def set_env_config_path(self, env_config_path):
"""Check and set env_config_path."""
if not self._context_handle.enable_dump_ir():
raise ValueError("For 'context.set_context', the argument 'env_config_path' is not supported, please "
"enable ENABLE_DUMP_IR with '-D on' and recompile source firstly.")
env_config_path = os.path.realpath(env_config_path)
if not os.path.isfile(env_config_path):
raise ValueError("For 'context.set_context', the 'env_config_path' file %r is not exists, "
"please check whether 'env_config_path' is correct." % env_config_path)
try:
with open(env_config_path, 'r') as f:
json.load(f)
except (TypeError, ValueError) as exo:
raise ValueError(str(exo) + "\nFor 'context.set_context', open or load the 'env_config_path' file {} "
"failed, please check whether 'env_config_path' is json file and correct, or may not "
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self.set_param(ms_ctx_param.env_config_path, env_config_path) | [
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paranoidninja/Pandoras-Box | 91316052a337c3a91da0c6e69f3ba0076436a037 | mingw/share/gcc-6.3.0/python/libstdcxx/v6/printers.py | python | SingleObjContainerPrinter._recognize | (self, type) | return gdb.types.apply_type_recognizers(gdb.types.get_type_recognizers(),
type) or str(type) | Return TYPE as a string after applying type printers | Return TYPE as a string after applying type printers | [
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] | def _recognize(self, type):
"""Return TYPE as a string after applying type printers"""
global _use_type_printing
if not _use_type_printing:
return str(type)
return gdb.types.apply_type_recognizers(gdb.types.get_type_recognizers(),
type) or str(type) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/abseil-cpp-master/abseil-cpp/absl/abseil.podspec.gen.py | python | write_podspec_map | (f, cur_map, depth) | Writes podspec from rule map recursively. | Writes podspec from rule map recursively. | [
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] | def write_podspec_map(f, cur_map, depth):
"""Writes podspec from rule map recursively."""
for key, value in sorted(cur_map.items()):
indent = " " * (depth + 1)
f.write("{indent}{var0}.subspec '{key}' do |{var1}|\n".format(
indent=indent,
key=key,
var0=get_spec_var(depth),
var1=get_spec_var(depth + 1)))
if isinstance(value, dict):
write_podspec_map(f, value, depth + 1)
else:
write_podspec_rule(f, value, depth + 1)
f.write("{indent}end\n".format(indent=indent)) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/profiler/parser/integrator.py | python | AscendTimelineGenerator._get_merged_time_list | (self, time_list, get_interval_time=False, display_name="computation_op") | return merged_res_list, interval_display_list, merged_display_list | Get merged time segment list.
The process of merge is, for example, there is a list [[1,5], [2,6], [7,8]],
each items in this list contains a start_time and end_time,
the merged result is [[1,6], [7,8]]. | Get merged time segment list. | [
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] | def _get_merged_time_list(self, time_list, get_interval_time=False, display_name="computation_op"):
"""
Get merged time segment list.
The process of merge is, for example, there is a list [[1,5], [2,6], [7,8]],
each items in this list contains a start_time and end_time,
the merged result is [[1,6], [7,8]].
"""
time_merged_segment_list = []
tid = self._tid_dict[display_name][0]
pid = self._tid_dict[display_name][1]
for time_item in time_list:
time_segment = list(map(float, time_item[self._start_time_idx:self._duration_idx + 1]))
time_segment[1] += time_segment[0]
if not time_merged_segment_list or \
time_segment[0] > time_merged_segment_list[-1]:
time_merged_segment_list.extend(time_segment)
else:
time_merged_segment_list[-1] = max(
time_merged_segment_list[-1],
time_segment[1]
)
# merged_display_list data used for ui page.
merged_display_list = [
[display_name, tid, time_merged_segment_list[i * 2],
time_merged_segment_list[i * 2 + 1] - time_merged_segment_list[i * 2], pid]
for i in range(len(time_merged_segment_list) // 2)
]
if get_interval_time:
time_merged_segment_list = time_merged_segment_list[1:-1]
# merged_res_list data used to compute overlap with other time_list.
merged_res_list = [
[display_name, tid, time_merged_segment_list[i * 2], time_merged_segment_list[i * 2 + 1], pid]
for i in range(len(time_merged_segment_list) // 2)
]
# interval_display_list is interval time used for ui page.
interval_display_list = [
[display_name, tid, time_merged_segment_list[i * 2],
time_merged_segment_list[i * 2 + 1] - time_merged_segment_list[i * 2], pid]
for i in range(len(time_merged_segment_list) // 2)
]
return merged_res_list, interval_display_list, merged_display_list | [
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LisaAnne/lisa-caffe-public | 49b8643ddef23a4f6120017968de30c45e693f59 | tools/extra/parse_log.py | python | get_line_type | (line) | return line_type | Return either 'test' or 'train' depending on line type | Return either 'test' or 'train' depending on line type | [
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"""Return either 'test' or 'train' depending on line type
"""
line_type = None
if line.find('Train') != -1:
line_type = 'train'
elif line.find('Test') != -1:
line_type = 'test'
return line_type | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.GetViewWhiteSpace | (*args, **kwargs) | return _stc.StyledTextCtrl_GetViewWhiteSpace(*args, **kwargs) | GetViewWhiteSpace(self) -> int
Are white space characters currently visible?
Returns one of SCWS_* constants. | GetViewWhiteSpace(self) -> int | [
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"""
GetViewWhiteSpace(self) -> int
Are white space characters currently visible?
Returns one of SCWS_* constants.
"""
return _stc.StyledTextCtrl_GetViewWhiteSpace(*args, **kwargs) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | build/android/android_commands.py | python | AndroidCommands.RunShellCommand | (self, command, timeout_time=20, log_result=True) | return result | Send a command to the adb shell and return the result.
Args:
command: String containing the shell command to send. Must not include
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timeout_time: Number of seconds to wait for command to respond before
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log_result: Boolean to indicate whether we should log the result of the
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"""Send a command to the adb shell and return the result.
Args:
command: String containing the shell command to send. Must not include
the single quotes as we use them to escape the whole command.
timeout_time: Number of seconds to wait for command to respond before
retrying, used by AdbInterface.SendShellCommand.
log_result: Boolean to indicate whether we should log the result of the
shell command.
Returns:
list containing the lines of output received from running the command
"""
logging.info('>>> $' + command)
if "'" in command: logging.warning(command + " contains ' quotes")
result = self._adb.SendShellCommand("'%s'" % command,
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if log_result:
logging.info('\n>>> '.join(result))
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/bijectors/affine_linear_operator.py | python | AffineLinearOperator.__init__ | (self,
shift=None,
scale=None,
validate_args=False,
name="affine_linear_operator") | Instantiates the `AffineLinearOperator` bijector.
Args:
shift: Floating-point `Tensor`.
scale: Subclass of `LinearOperator`. Represents the (batch) positive
definite matrix `M` in `R^{k x k}`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
Raises:
TypeError: if `scale` is not a `LinearOperator`.
TypeError: if `shift.dtype` does not match `scale.dtype`.
ValueError: if not `scale.is_non_singular`. | Instantiates the `AffineLinearOperator` bijector. | [
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] | def __init__(self,
shift=None,
scale=None,
validate_args=False,
name="affine_linear_operator"):
"""Instantiates the `AffineLinearOperator` bijector.
Args:
shift: Floating-point `Tensor`.
scale: Subclass of `LinearOperator`. Represents the (batch) positive
definite matrix `M` in `R^{k x k}`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
Raises:
TypeError: if `scale` is not a `LinearOperator`.
TypeError: if `shift.dtype` does not match `scale.dtype`.
ValueError: if not `scale.is_non_singular`.
"""
self._graph_parents = []
self._name = name
self._validate_args = validate_args
graph_parents = []
with self._name_scope("init", values=[shift]):
# In the absence of `loc` and `scale`, we'll assume `dtype` is `float32`.
dtype = dtypes.float32
if shift is not None:
shift = ops.convert_to_tensor(shift, name="shift")
graph_parents += [shift]
dtype = shift.dtype.base_dtype
self._shift = shift
if scale is not None:
if (shift is not None and
shift.dtype.base_dtype != scale.dtype.base_dtype):
raise TypeError(
"shift.dtype({}) is incompatible with scale.dtype({}).".format(
shift.dtype, scale.dtype))
if not isinstance(scale, linear_operator.LinearOperator):
raise TypeError("scale is not an instance of tf.LinearOperator")
if validate_args and not scale.is_non_singular:
raise ValueError("Scale matrix must be non-singular.")
graph_parents += scale.graph_parents
if scale.tensor_rank is not None:
batch_ndims = scale.tensor_rank - 2
else:
batch_ndims = scale.tensor_rank_tensor() - 2
graph_parents += [batch_ndims]
if scale.dtype is not None:
dtype = scale.dtype.base_dtype
else:
batch_ndims = 0 # We won't need shape inference when scale is None.
self._scale = scale
self._shaper = _DistributionShape(
batch_ndims=batch_ndims,
event_ndims=1,
validate_args=validate_args)
super(AffineLinearOperator, self).__init__(
forward_min_event_ndims=1,
graph_parents=graph_parents,
is_constant_jacobian=True,
dtype=dtype,
validate_args=validate_args,
name=name) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/layers/python/layers/feature_column.py | python | sparse_column_with_hash_bucket | (column_name,
hash_bucket_size,
combiner="sum") | return _SparseColumnHashed(column_name, hash_bucket_size, combiner) | Creates a _SparseColumn with hashed bucket configuration.
Use this when your sparse features are in string format, but you don't have a
vocab file that maps each string to an integer ID.
output_id = Hash(input_feature_string) % bucket_size
Args:
column_name: A string defining sparse column name.
hash_bucket_size: An int that is > 1. The number of buckets.
combiner: A string specifying how to reduce if the sparse column is
multivalent. Currently "mean", "sqrtn" and "sum" are supported, with
"sum" the default:
* "sum": do not normalize features in the column
* "mean": do l1 normalization on features in the column
* "sqrtn": do l2 normalization on features in the column
For more information: `tf.embedding_lookup_sparse`.
Returns:
A _SparseColumn with hashed bucket configuration
Raises:
ValueError: hash_bucket_size is not greater than 2. | Creates a _SparseColumn with hashed bucket configuration. | [
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"a",
"_SparseColumn",
"with",
"hashed",
"bucket",
"configuration",
"."
] | def sparse_column_with_hash_bucket(column_name,
hash_bucket_size,
combiner="sum"):
"""Creates a _SparseColumn with hashed bucket configuration.
Use this when your sparse features are in string format, but you don't have a
vocab file that maps each string to an integer ID.
output_id = Hash(input_feature_string) % bucket_size
Args:
column_name: A string defining sparse column name.
hash_bucket_size: An int that is > 1. The number of buckets.
combiner: A string specifying how to reduce if the sparse column is
multivalent. Currently "mean", "sqrtn" and "sum" are supported, with
"sum" the default:
* "sum": do not normalize features in the column
* "mean": do l1 normalization on features in the column
* "sqrtn": do l2 normalization on features in the column
For more information: `tf.embedding_lookup_sparse`.
Returns:
A _SparseColumn with hashed bucket configuration
Raises:
ValueError: hash_bucket_size is not greater than 2.
"""
return _SparseColumnHashed(column_name, hash_bucket_size, combiner) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/internal/python_message.py | python | _AddPropertiesForField | (field, cls) | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing. | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field. | [
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"""Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing.
"""
# Catch it if we add other types that we should
# handle specially here.
assert _FieldDescriptor.MAX_CPPTYPE == 10
constant_name = field.name.upper() + '_FIELD_NUMBER'
setattr(cls, constant_name, field.number)
if field.label == _FieldDescriptor.LABEL_REPEATED:
_AddPropertiesForRepeatedField(field, cls)
elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
_AddPropertiesForNonRepeatedCompositeField(field, cls)
else:
_AddPropertiesForNonRepeatedScalarField(field, cls) | [
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amd/OpenCL-caffe | 638543108517265366c18ae5821f3096cf5cf34a | scripts/cpp_lint.py | python | ParseNolintSuppressions | (filename, raw_line, linenum, error) | Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler. | Updates the global list of error-suppressions. | [
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"""Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler.
"""
# FIXME(adonovan): "NOLINT(" is misparsed as NOLINT(*).
matched = _RE_SUPPRESSION.search(raw_line)
if matched:
if matched.group(1) == '_NEXT_LINE':
linenum += 1
category = matched.group(2)
if category in (None, '(*)'): # => "suppress all"
_error_suppressions.setdefault(None, set()).add(linenum)
else:
if category.startswith('(') and category.endswith(')'):
category = category[1:-1]
if category in _ERROR_CATEGORIES:
_error_suppressions.setdefault(category, set()).add(linenum)
else:
error(filename, linenum, 'readability/nolint', 5,
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GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/projects/compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | _CppLintState.ResetErrorCounts | (self) | Sets the module's error statistic back to zero. | Sets the module's error statistic back to zero. | [
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"""Sets the module's error statistic back to zero."""
self.error_count = 0
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/ttk.py | python | OptionMenu.__init__ | (self, master, variable, default=None, *values, **kwargs) | Construct a themed OptionMenu widget with master as the parent,
the resource textvariable set to variable, the initially selected
value specified by the default parameter, the menu values given by
*values and additional keywords.
WIDGET-SPECIFIC OPTIONS
style: stylename
Menubutton style.
direction: 'above', 'below', 'left', 'right', or 'flush'
Menubutton direction.
command: callback
A callback that will be invoked after selecting an item. | Construct a themed OptionMenu widget with master as the parent,
the resource textvariable set to variable, the initially selected
value specified by the default parameter, the menu values given by
*values and additional keywords. | [
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"""Construct a themed OptionMenu widget with master as the parent,
the resource textvariable set to variable, the initially selected
value specified by the default parameter, the menu values given by
*values and additional keywords.
WIDGET-SPECIFIC OPTIONS
style: stylename
Menubutton style.
direction: 'above', 'below', 'left', 'right', or 'flush'
Menubutton direction.
command: callback
A callback that will be invoked after selecting an item.
"""
kw = {'textvariable': variable, 'style': kwargs.pop('style', None),
'direction': kwargs.pop('direction', None)}
Menubutton.__init__(self, master, **kw)
self['menu'] = Tkinter.Menu(self, tearoff=False)
self._variable = variable
self._callback = kwargs.pop('command', None)
if kwargs:
raise Tkinter.TclError('unknown option -%s' % (
kwargs.iterkeys().next()))
self.set_menu(default, *values) | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/losses/__init__.py | python | fmeasure | (output, target, beta=1) | return 1 - (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall) | This operation computes the f-measure between the output and target. If beta is set as one,
its called the f1-scorce or dice similarity coefficient. f1-scorce is monotonic in jaccard distance.
f-measure = (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall)
This loss function is frequently used in semantic segmentation of images. Works with imbalanced classes, for
balanced classes you should prefer cross_entropy instead.
This operation works with both binary and multiclass classification.
Args:
output: the output values from the network
target: it is usually a one-hot vector where the hot bit corresponds to the label index
beta: greater than one weights recall higher than precision, less than one for the opposite.
Commonly chosen values are 0.5, 1 or 2.
Returns:
:class:`~cntk.ops.functions.Function` | This operation computes the f-measure between the output and target. If beta is set as one,
its called the f1-scorce or dice similarity coefficient. f1-scorce is monotonic in jaccard distance. | [
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] | def fmeasure(output, target, beta=1):
"""
This operation computes the f-measure between the output and target. If beta is set as one,
its called the f1-scorce or dice similarity coefficient. f1-scorce is monotonic in jaccard distance.
f-measure = (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall)
This loss function is frequently used in semantic segmentation of images. Works with imbalanced classes, for
balanced classes you should prefer cross_entropy instead.
This operation works with both binary and multiclass classification.
Args:
output: the output values from the network
target: it is usually a one-hot vector where the hot bit corresponds to the label index
beta: greater than one weights recall higher than precision, less than one for the opposite.
Commonly chosen values are 0.5, 1 or 2.
Returns:
:class:`~cntk.ops.functions.Function`
"""
assert len(target.shape) == len(output.shape)
if len(output.shape) == 3:
axis = (1, 2) # assumes that the first axis is the class axis
else:
axis = None
correct_predictions = C.reduce_sum(output * target, axis=axis)
precision = correct_predictions / C.reduce_sum(output, axis=axis)
recall = correct_predictions / C.reduce_sum(target, axis=axis)
return 1 - (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | TextAttr.Apply | (*args, **kwargs) | return _controls_.TextAttr_Apply(*args, **kwargs) | Apply(self, TextAttr style, TextAttr compareWith=None) -> bool | Apply(self, TextAttr style, TextAttr compareWith=None) -> bool | [
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"""Apply(self, TextAttr style, TextAttr compareWith=None) -> bool"""
return _controls_.TextAttr_Apply(*args, **kwargs) | [
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etodd/lasercrabs | 91484d9ac3a47ac38b8f40ec3ff35194714dad8e | assets/script/etodd_blender_fbx/fbx_utils.py | python | elem_props_template_init | (templates, template_type) | return ret | Init a writing template of given type, for *one* element's properties. | Init a writing template of given type, for *one* element's properties. | [
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] | def elem_props_template_init(templates, template_type):
"""
Init a writing template of given type, for *one* element's properties.
"""
ret = OrderedDict()
tmpl = templates.get(template_type)
if tmpl is not None:
written = tmpl.written[0]
props = tmpl.properties
ret = OrderedDict((name, [val, ptype, anim, written]) for name, (val, ptype, anim) in props.items())
return ret | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plat-mac/gensuitemodule.py | python | processfile | (fullname, output=None, basepkgname=None,
edit_modnames=None, creatorsignature=None, dump=None,
verbose=None) | Ask an application for its terminology and process that | Ask an application for its terminology and process that | [
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edit_modnames=None, creatorsignature=None, dump=None,
verbose=None):
"""Ask an application for its terminology and process that"""
if not is_scriptable(fullname) and verbose:
print >>verbose, "Warning: app does not seem scriptable: %s" % fullname
if verbose:
print >>verbose, "\nASKING FOR aete DICTIONARY IN", repr(fullname)
try:
aedescobj, launched = OSATerminology.GetAppTerminology(fullname)
except MacOS.Error, arg:
if arg[0] in (-1701, -192): # errAEDescNotFound, resNotFound
if verbose:
print >>verbose, "GetAppTerminology failed with errAEDescNotFound/resNotFound, trying manually"
aedata, sig = getappterminology(fullname, verbose=verbose)
if not creatorsignature:
creatorsignature = sig
else:
raise
else:
if launched:
if verbose:
print >>verbose, "Launched", fullname
raw = aetools.unpack(aedescobj)
if not raw:
if verbose:
print >>verbose, 'Unpack returned empty value:', raw
return
if not raw[0].data:
if verbose:
print >>verbose, 'Unpack returned value without data:', raw
return
aedata = raw[0]
aete = decode(aedata.data, verbose)
if dump:
dumpaetelist([aete], dump)
return
compileaete(aete, None, fullname, output=output, basepkgname=basepkgname,
creatorsignature=creatorsignature, edit_modnames=edit_modnames,
verbose=verbose) | [
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tiny-dnn/tiny-dnn | c0f576f5cb7b35893f62127cb7aec18f77a3bcc5 | third_party/cpplint.py | python | FilesBelongToSameModule | (filename_cc, filename_h) | return files_belong_to_same_module, common_path | Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the source (e.g. .cc) file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file. | Check if these two filenames belong to the same module. | [
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] | def FilesBelongToSameModule(filename_cc, filename_h):
"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the source (e.g. .cc) file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
fileinfo_cc = FileInfo(filename_cc)
if not fileinfo_cc.Extension().lstrip('.') in GetNonHeaderExtensions():
return (False, '')
fileinfo_h = FileInfo(filename_h)
if not fileinfo_h.Extension().lstrip('.') in GetHeaderExtensions():
return (False, '')
filename_cc = filename_cc[:-(len(fileinfo_cc.Extension()))]
matched_test_suffix = Search(_TEST_FILE_SUFFIX, fileinfo_cc.BaseName())
if matched_test_suffix:
filename_cc = filename_cc[:-len(matched_test_suffix.group(1))]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
filename_h = filename_h[:-(len(fileinfo_h.Extension()))]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path | [
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] | https://github.com/tiny-dnn/tiny-dnn/blob/c0f576f5cb7b35893f62127cb7aec18f77a3bcc5/third_party/cpplint.py#L5571-L5626 | |
apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/symbol.py | python | Symbol.expm1 | (self, *args, **kwargs) | return op.expm1(self, *args, **kwargs) | Convenience fluent method for :py:func:`expm1`.
The arguments are the same as for :py:func:`expm1`, with
this array as data. | Convenience fluent method for :py:func:`expm1`. | [
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] | def expm1(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`expm1`.
The arguments are the same as for :py:func:`expm1`, with
this array as data.
"""
return op.expm1(self, *args, **kwargs) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/operator_schemas.py | python | normalize_module | (
root: torch.nn.Module, target: str, args: Tuple[Any], kwargs : Optional[Dict[str, Any]] = None,
normalize_to_only_use_kwargs : bool = False) | return None | Returns normalized arguments to PyTorch modules. This means that
`args/kwargs` will be matched up to the functional's
signature and return exclusively kwargs in positional order if
`normalize_to_only_use_kwargs` is True.
Also populates default values. Does not support positional-only
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Args:
root (nn.Module): root module upon which we query modules
target (Callable): Function that we are normalizing
args (Tuple[Any]): Tuple of args to the function
kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function
normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
Returns:
Returns normalized_args_and_kwargs, or `None` if not successful. | Returns normalized arguments to PyTorch modules. This means that
`args/kwargs` will be matched up to the functional's
signature and return exclusively kwargs in positional order if
`normalize_to_only_use_kwargs` is True.
Also populates default values. Does not support positional-only
parameters or varargs parameters (*args, **kwargs). | [
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] | def normalize_module(
root: torch.nn.Module, target: str, args: Tuple[Any], kwargs : Optional[Dict[str, Any]] = None,
normalize_to_only_use_kwargs : bool = False) -> Optional[ArgsKwargsPair]:
"""
Returns normalized arguments to PyTorch modules. This means that
`args/kwargs` will be matched up to the functional's
signature and return exclusively kwargs in positional order if
`normalize_to_only_use_kwargs` is True.
Also populates default values. Does not support positional-only
parameters or varargs parameters (*args, **kwargs).
Args:
root (nn.Module): root module upon which we query modules
target (Callable): Function that we are normalizing
args (Tuple[Any]): Tuple of args to the function
kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function
normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
Returns:
Returns normalized_args_and_kwargs, or `None` if not successful.
"""
try:
submod = root.get_submodule(target)
except AttributeError:
raise RuntimeError(f"Tried to normalize node with target {target} but root did not "
f"have that target!")
if hasattr(submod.__class__, '__name__'):
classname = submod.__class__.__name__
if getattr(torch.nn, classname, None) == submod.__class__:
sig = inspect.signature(inspect.unwrap(submod.forward))
if kwargs is None:
kwargs = {}
new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs(sig, args, kwargs,
normalize_to_only_use_kwargs)
return new_args_and_kwargs
return None | [
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/i-pi/ipi/inputs/normalmodes.py | python | InputNormalModes.__init__ | (self, help=None, dimension=None, default=None, dtype=None) | Initializes InputNormalModes.
Just calls the parent initialization function with appropriate arguments. | Initializes InputNormalModes. | [
"Initializes",
"InputNormalModes",
"."
] | def __init__(self, help=None, dimension=None, default=None, dtype=None):
""" Initializes InputNormalModes.
Just calls the parent initialization function with appropriate arguments.
"""
super(InputNormalModes,self).__init__(help=help, default=default, dtype=float, dimension="frequency") | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/terminal/ipapp.py | python | TerminalIPythonApp._classes_default | (self) | return [
InteractiveShellApp, # ShellApp comes before TerminalApp, because
self.__class__, # it will also affect subclasses (e.g. QtConsole)
TerminalInteractiveShell,
HistoryManager,
ProfileDir,
PlainTextFormatter,
IPCompleter,
ScriptMagics,
LoggingMagics,
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] | This has to be in a method, for TerminalIPythonApp to be available. | This has to be in a method, for TerminalIPythonApp to be available. | [
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] | def _classes_default(self):
"""This has to be in a method, for TerminalIPythonApp to be available."""
return [
InteractiveShellApp, # ShellApp comes before TerminalApp, because
self.__class__, # it will also affect subclasses (e.g. QtConsole)
TerminalInteractiveShell,
HistoryManager,
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PlainTextFormatter,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/urllib3/util/connection.py | python | is_connection_dropped | (conn) | Returns True if the connection is dropped and should be closed.
:param conn:
:class:`http.client.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us. | Returns True if the connection is dropped and should be closed. | [
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] | def is_connection_dropped(conn): # Platform-specific
"""
Returns True if the connection is dropped and should be closed.
:param conn:
:class:`http.client.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us.
"""
sock = getattr(conn, "sock", False)
if sock is False: # Platform-specific: AppEngine
return False
if sock is None: # Connection already closed (such as by httplib).
return True
try:
# Returns True if readable, which here means it's been dropped
return wait_for_read(sock, timeout=0.0)
except NoWayToWaitForSocketError: # Platform-specific: AppEngine
return False | [
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