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322,000 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.Anderson | from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
from numpy import asarray, dot, vdot
import numpy as np
class Anderson(GenericBroyden):
"""
Find a root of a function, using (extended) Anderson mixing.
The Jacobian is formed by for a 'best' solution in the space
spanned by last `M` vect... |
class Anderson(GenericBroyden):
'''
Find a root of a function, using (extended) Anderson mixing.
The Jacobian is formed by for a 'best' solution in the space
spanned by last `M` vectors. As a result, only a MxM matrix
inversions and MxN multiplications are required. [Ey]_
Parameters
-------... | 5 | 1 | 19 | 3 | 16 | 0 | 5 | 0.95 | 1 | 2 | 0 | 0 | 4 | 7 | 4 | 12 | 150 | 27 | 63 | 32 | 58 | 60 | 62 | 32 | 57 | 7 | 2 | 3 | 19 |
322,001 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.BroydenFirst | from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
import numpy as np
from numpy import asarray, dot, vdot
class BroydenFirst(GenericBroyden):
"""
Find a root of a function, using Broyden's first Jacobian approximation.
This method is also known as "Broyden's good method".
Parameters
... |
class BroydenFirst(GenericBroyden):
'''
Find a root of a function, using Broyden's first Jacobian approximation.
This method is also known as "Broyden's good method".
Parameters
----------
%(params_basic)s
%(broyden_params)s
%(params_extra)s
See Also
--------
root : Interfac... | 9 | 1 | 6 | 1 | 6 | 0 | 2 | 0.87 | 1 | 3 | 1 | 1 | 8 | 4 | 8 | 16 | 110 | 27 | 45 | 18 | 36 | 39 | 41 | 18 | 32 | 6 | 2 | 1 | 14 |
322,002 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.BroydenSecond | class BroydenSecond(BroydenFirst):
"""
Find a root of a function, using Broyden's second Jacobian approximation.
This method is also known as "Broyden's bad method".
Parameters
----------
%(params_basic)s
%(broyden_params)s
%(params_extra)s
See Also
--------
root : Interfa... | class BroydenSecond(BroydenFirst):
'''
Find a root of a function, using Broyden's second Jacobian approximation.
This method is also known as "Broyden's bad method".
Parameters
----------
%(params_basic)s
%(broyden_params)s
%(params_extra)s
See Also
--------
root : Interface ... | 2 | 1 | 7 | 1 | 6 | 1 | 1 | 5.29 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 17 | 58 | 15 | 7 | 5 | 5 | 37 | 7 | 5 | 5 | 1 | 3 | 0 | 1 |
322,003 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.DiagBroyden | import numpy as np
class DiagBroyden(GenericBroyden):
"""
Find a root of a function, using diagonal Broyden Jacobian approximation.
The Jacobian approximation is derived from previous iterations, by
retaining only the diagonal of Broyden matrices.
.. warning::
This algorithm may be useful... |
class DiagBroyden(GenericBroyden):
'''
Find a root of a function, using diagonal Broyden Jacobian approximation.
The Jacobian approximation is derived from previous iterations, by
retaining only the diagonal of Broyden matrices.
.. warning::
This algorithm may be useful for specific problems... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 1.53 | 1 | 0 | 0 | 0 | 8 | 2 | 8 | 16 | 66 | 18 | 19 | 11 | 10 | 29 | 19 | 11 | 10 | 1 | 2 | 0 | 8 |
322,004 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.ExcitingMixing | import numpy as np
class ExcitingMixing(GenericBroyden):
"""
Find a root of a function, using a tuned diagonal Jacobian approximation.
The Jacobian matrix is diagonal and is tuned on each iteration.
.. warning::
This algorithm may be useful for specific problems, but whether
it will wo... |
class ExcitingMixing(GenericBroyden):
'''
Find a root of a function, using a tuned diagonal Jacobian approximation.
The Jacobian matrix is diagonal and is tuned on each iteration.
.. warning::
This algorithm may be useful for specific problems, but whether
it will work may depend strongly... | 9 | 1 | 3 | 0 | 3 | 0 | 1 | 0.83 | 1 | 0 | 0 | 0 | 8 | 3 | 8 | 16 | 57 | 13 | 24 | 13 | 15 | 20 | 24 | 13 | 15 | 1 | 2 | 0 | 8 |
322,005 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.GenericBroyden | from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
class GenericBroyden(Jacobian):
def setup(self, x0, f0, func):
Jacobian.setup(self, x0, f0, func)
self.last_f = f0
self.last_x = x0
if hasattr(self, 'alpha') and self.alpha is None:
normf0 = norm(f0)
... |
class GenericBroyden(Jacobian):
def setup(self, x0, f0, func):
pass
def _update(self, x, f, dx, df, dx_norm, df_norm):
pass
def update(self, x, f):
pass | 4 | 0 | 7 | 0 | 6 | 1 | 2 | 0.11 | 1 | 1 | 0 | 5 | 3 | 3 | 3 | 8 | 24 | 3 | 19 | 10 | 15 | 2 | 18 | 10 | 14 | 3 | 1 | 2 | 5 |
322,006 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.InverseJacobian | class InverseJacobian:
"""
A simple wrapper that inverts the Jacobian using the `solve` method.
.. legacy:: class
See the newer, more consistent interfaces in :mod:`scipy.optimize`.
Parameters
----------
jacobian : Jacobian
The Jacobian to invert.
Attributes
---------... | class InverseJacobian:
'''
A simple wrapper that inverts the Jacobian using the `solve` method.
.. legacy:: class
See the newer, more consistent interfaces in :mod:`scipy.optimize`.
Parameters
----------
jacobian : Jacobian
The Jacobian to invert.
Attributes
----------
... | 6 | 1 | 4 | 0 | 4 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 3 | 5 | 3 | 3 | 37 | 7 | 15 | 11 | 9 | 15 | 13 | 9 | 9 | 3 | 0 | 1 | 5 |
322,007 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.Jacobian | class Jacobian:
"""
Common interface for Jacobians or Jacobian approximations.
The optional methods come useful when implementing trust region
etc., algorithms that often require evaluating transposes of the
Jacobian.
Methods
-------
solve
Returns J^-1 * v
update
Up... | class Jacobian:
'''
Common interface for Jacobians or Jacobian approximations.
The optional methods come useful when implementing trust region
etc., algorithms that often require evaluating transposes of the
Jacobian.
Methods
-------
solve
Returns J^-1 * v
update
Upda... | 7 | 1 | 5 | 0 | 5 | 0 | 2 | 1.23 | 0 | 3 | 1 | 3 | 5 | 3 | 5 | 5 | 70 | 12 | 26 | 12 | 19 | 32 | 25 | 12 | 18 | 5 | 0 | 2 | 12 |
322,008 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.KrylovJacobian | import scipy.sparse.linalg
from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
import scipy.sparse
import warnings
from inspect import signature
from difflib import get_close_matches
import numpy as np
class KrylovJacobian(Jacobian):
"""
Find a root of a function, using Krylov approximation for inv... |
class KrylovJacobian(Jacobian):
'''
Find a root of a function, using Krylov approximation for inverse Jacobian.
This method is suitable for solving large-scale problems.
Parameters
----------
%(params_basic)s
rdiff : float, optional
Relative step size to use in numerical differentia... | 7 | 1 | 20 | 2 | 15 | 3 | 4 | 1.11 | 1 | 4 | 0 | 0 | 6 | 8 | 6 | 11 | 226 | 32 | 92 | 26 | 84 | 102 | 65 | 24 | 58 | 8 | 1 | 3 | 21 |
322,009 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.LinearMixing | import numpy as np
class LinearMixing(GenericBroyden):
"""
Find a root of a function, using a scalar Jacobian approximation.
.. warning::
This algorithm may be useful for specific problems, but whether
it will work may depend strongly on the problem.
Parameters
----------
%(par... |
class LinearMixing(GenericBroyden):
'''
Find a root of a function, using a scalar Jacobian approximation.
.. warning::
This algorithm may be useful for specific problems, but whether
it will work may depend strongly on the problem.
Parameters
----------
%(params_basic)s
alpha ... | 8 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 7 | 1 | 7 | 15 | 44 | 12 | 16 | 9 | 8 | 16 | 16 | 9 | 8 | 1 | 2 | 0 | 7 |
322,010 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.LowRankMatrix | from numpy import asarray, dot, vdot
from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
from scipy.linalg import get_blas_funcs
import numpy as np
import warnings
from scipy._lib._util import copy_if_needed
class LowRankMatrix:
"""
A matrix represented as
.. math:: \\alpha I + \\sum_{n=0}^{n=... |
class LowRankMatrix:
'''
A matrix represented as
.. math:: \alpha I + \sum_{n=0}^{n=M} c_n d_n^\dagger
However, if the rank of the matrix reaches the dimension of the vectors,
full matrix representation will be used thereon.
'''
def __init__(self, alpha, n, dtype):
pass
@static... | 16 | 10 | 14 | 2 | 9 | 3 | 3 | 0.35 | 0 | 3 | 0 | 0 | 11 | 6 | 13 | 13 | 202 | 40 | 120 | 44 | 104 | 42 | 112 | 42 | 98 | 6 | 0 | 2 | 38 |
322,011 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.NoConvergence | class NoConvergence(Exception):
"""Exception raised when nonlinear solver fails to converge within the specified
`maxiter`."""
pass | class NoConvergence(Exception):
'''Exception raised when nonlinear solver fails to converge within the specified
`maxiter`.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 4 | 0 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 3 | 0 | 0 |
322,012 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_nonlin.py | scipy.optimize._nonlin.TerminationCondition | import numpy as np
from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
class TerminationCondition:
"""
Termination condition for an iteration. It is terminated if
- |F| < f_rtol*|F_0|, AND
- |F| < f_tol
AND
- |dx| < x_rtol*|x|, AND
- |dx| < x_tol
"""
def __init__(se... |
class TerminationCondition:
'''
Termination condition for an iteration. It is terminated if
- |F| < f_rtol*|F_0|, AND
- |F| < f_tol
AND
- |dx| < x_rtol*|x|, AND
- |dx| < x_tol
'''
def __init__(self, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None, iter=None, norm=maxnorm):
... | 3 | 1 | 22 | 5 | 17 | 1 | 5 | 0.29 | 0 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 58 | 14 | 34 | 15 | 30 | 10 | 30 | 14 | 27 | 5 | 0 | 1 | 9 |
322,013 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_numdiff.py | scipy.optimize._numdiff._Fun_Wrapper | import numpy as np
from scipy._lib._array_api import array_namespace
class _Fun_Wrapper:
def __init__(self, fun, x0, args, kwargs):
self.fun = fun
self.x0 = x0
self.args = args
self.kwargs = kwargs
def __call__(self, x):
xp = array_namespace(self.x0)
if xp.isdt... |
class _Fun_Wrapper:
def __init__(self, fun, x0, args, kwargs):
pass
def __call__(self, x):
pass | 3 | 0 | 9 | 1 | 7 | 1 | 2 | 0.2 | 0 | 1 | 0 | 0 | 2 | 4 | 2 | 2 | 21 | 3 | 15 | 9 | 12 | 3 | 14 | 9 | 11 | 3 | 0 | 1 | 4 |
322,014 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize.BracketError | class BracketError(RuntimeError):
pass | class BracketError(RuntimeError):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,015 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize.Brent | import numpy as np
class Brent:
def __init__(self, func, args=(), tol=1.48e-08, maxiter=500, full_output=0, disp=0):
self.func = func
self.args = args
self.tol = tol
self.maxiter = maxiter
self._mintol = 1e-11
self._cg = 0.381966
self.xmin = None
sel... |
class Brent:
def __init__(self, func, args=(), tol=1.48e-08, maxiter=500, full_output=0, disp=0):
pass
def set_bracket(self, brack=None):
pass
def get_bracket_info(self):
pass
def optimize(self):
pass
def get_result(self, full_output=False):
pass | 6 | 0 | 34 | 2 | 29 | 5 | 6 | 0.18 | 0 | 1 | 0 | 0 | 5 | 12 | 5 | 5 | 179 | 13 | 148 | 42 | 141 | 26 | 122 | 41 | 116 | 20 | 0 | 5 | 31 |
322,016 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize.MemoizeJac | import numpy as np
class MemoizeJac:
"""Decorator that caches the return values of a function returning ``(fun, grad)``
each time it is called."""
def __init__(self, fun):
self.fun = fun
self.jac = None
self._value = None
self.x = None
def _compute_if_needed(self, x, *... |
class MemoizeJac:
'''Decorator that caches the return values of a function returning ``(fun, grad)``
each time it is called.'''
def __init__(self, fun):
pass
def _compute_if_needed(self, x, *args):
pass
def __call__(self, x, *args):
''' returns the function value '''
... | 5 | 2 | 5 | 0 | 4 | 0 | 1 | 0.17 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 25 | 4 | 18 | 10 | 13 | 3 | 18 | 10 | 13 | 2 | 0 | 1 | 5 |
322,017 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize.OptimizeResult | from scipy._lib._util import MapWrapper, check_random_state, _RichResult, _call_callback_maybe_halt, _transition_to_rng
class OptimizeResult(_RichResult):
"""
Represents the optimization result.
Attributes
----------
x : ndarray
The solution of the optimization.
success : bool
... |
class OptimizeResult(_RichResult):
'''
Represents the optimization result.
Attributes
----------
x : ndarray
The solution of the optimization.
success : bool
Whether or not the optimizer exited successfully.
status : int
Termination status of the optimizer. Its value... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 19 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 30 | 43 | 3 | 2 | 1 | 1 | 38 | 2 | 1 | 1 | 0 | 3 | 0 | 0 |
322,018 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize.OptimizeWarning | class OptimizeWarning(UserWarning):
"""General warning for :mod:`scipy.optimize`."""
pass | class OptimizeWarning(UserWarning):
'''General warning for :mod:`scipy.optimize`.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
322,019 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize._Brute_Wrapper | import numpy as np
class _Brute_Wrapper:
"""
Object to wrap user cost function for optimize.brute, allowing picklability
"""
def __init__(self, f, args):
self.f = f
self.args = [] if args is None else args
def __call__(self, x):
return self.f(np.asarray(x).flatten(), *self... |
class _Brute_Wrapper:
'''
Object to wrap user cost function for optimize.brute, allowing picklability
'''
def __init__(self, f, args):
pass
def __call__(self, x):
pass | 3 | 1 | 3 | 0 | 3 | 1 | 2 | 0.67 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 12 | 2 | 6 | 5 | 3 | 4 | 6 | 5 | 3 | 2 | 0 | 0 | 3 |
322,020 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_optimize.py | scipy.optimize._optimize._MaxFuncCallError | class _MaxFuncCallError(RuntimeError):
pass | class _MaxFuncCallError(RuntimeError):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,021 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_root_scalar.py | scipy.optimize._root_scalar.MemoizeDer | class MemoizeDer:
"""Decorator that caches the value and derivative(s) of function each
time it is called.
This is a simplistic memoizer that calls and caches a single value
of ``f(x, *args)``.
It assumes that `args` does not change between invocations.
It supports the use case of a root-finder... | class MemoizeDer:
'''Decorator that caches the value and derivative(s) of function each
time it is called.
This is a simplistic memoizer that calls and caches a single value
of ``f(x, *args)``.
It assumes that `args` does not change between invocations.
It supports the use case of a root-finder ... | 6 | 4 | 5 | 0 | 4 | 1 | 2 | 0.52 | 0 | 0 | 0 | 0 | 5 | 4 | 5 | 5 | 40 | 5 | 23 | 11 | 17 | 12 | 23 | 11 | 17 | 2 | 0 | 1 | 8 |
322,022 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo.py | scipy.optimize._shgo.LMap | class LMap:
def __init__(self, v):
self.v = v
self.x_l = None
self.lres = None
self.f_min = None
self.lbounds = [] | class LMap:
def __init__(self, v):
pass | 2 | 0 | 6 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 1 | 1 | 7 | 0 | 7 | 7 | 5 | 0 | 7 | 7 | 5 | 1 | 0 | 0 | 1 |
322,023 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo.py | scipy.optimize._shgo.LMapCache | import numpy as np
class LMapCache:
def __init__(self):
self.cache = {}
self.v_maps = []
self.xl_maps = []
self.xl_maps_set = set()
self.f_maps = []
self.lbound_maps = []
self.size = 0
def __getitem__(self, v):
try:
v = np.ndarray.to... |
class LMapCache:
def __init__(self):
pass
def __getitem__(self, v):
pass
def add_res(self, v, lres, bounds=None):
pass
def sort_cache_result(self):
'''
Sort results and build the global return object
'''
pass | 5 | 1 | 16 | 2 | 12 | 3 | 2 | 0.27 | 0 | 5 | 1 | 0 | 4 | 7 | 4 | 4 | 69 | 11 | 48 | 15 | 43 | 13 | 48 | 15 | 43 | 3 | 0 | 1 | 6 |
322,024 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo.py | scipy.optimize._shgo.SHGO | import sys
import numpy as np
from collections import namedtuple
from scipy.optimize._minimize import standardize_constraints
from scipy import spatial
import time
from scipy._lib._util import _FunctionWrapper
from scipy.optimize import OptimizeResult, minimize, Bounds
import logging
from scipy.optimize._shgo_lib._comp... |
class SHGO:
def __init__(self, func, bounds, args=(), constraints=None, n=None, iters=None, callback=None, minimizer_kwargs=None, options=None, sampling_method='simplicial', workers=1):
pass
def _restrict_to_keys(dictionary, goodkeys):
'''Remove keys from dictionary if not in good... | 35 | 20 | 30 | 4 | 18 | 9 | 5 | 0.5 | 0 | 19 | 7 | 0 | 32 | 61 | 32 | 32 | 1,039 | 157 | 610 | 137 | 572 | 308 | 509 | 135 | 473 | 23 | 0 | 4 | 165 |
322,025 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_complex.py | scipy.optimize._shgo_lib._complex.Complex | import itertools
from ._vertex import VertexCacheField, VertexCacheIndex
import logging
import numpy as np
import copy
from functools import cache
import decimal
class Complex:
"""
Base class for a simplicial complex described as a cache of vertices
together with their connections.
Important methods:
... |
class Complex:
'''
Base class for a simplicial complex described as a cache of vertices
together with their connections.
Important methods:
Domain triangulation:
Complex.triangulate, Complex.split_generation
Triangulating arbitrary points (must be traingulable,
... | 15 | 9 | 78 | 8 | 51 | 27 | 9 | 0.65 | 0 | 18 | 2 | 0 | 14 | 20 | 14 | 14 | 1,213 | 140 | 711 | 169 | 694 | 465 | 658 | 165 | 643 | 28 | 0 | 5 | 120 |
322,026 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.ConstraintWrapper | import numpy as np
class ConstraintWrapper:
"""Object to wrap constraints to pass to `multiprocessing.Pool`."""
def __init__(self, g_cons, g_cons_args):
self.g_cons = g_cons
self.g_cons_args = g_cons_args
def gcons(self, v_x_a):
vfeasible = True
for g, args in zip(self.g_c... |
class ConstraintWrapper:
'''Object to wrap constraints to pass to `multiprocessing.Pool`.'''
def __init__(self, g_cons, g_cons_args):
pass
def gcons(self, v_x_a):
pass | 3 | 1 | 6 | 0 | 5 | 1 | 2 | 0.18 | 0 | 1 | 0 | 0 | 2 | 2 | 2 | 2 | 14 | 1 | 11 | 7 | 8 | 2 | 11 | 7 | 8 | 3 | 0 | 2 | 4 |
322,027 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.FieldWrapper | import numpy as np
class FieldWrapper:
"""Object to wrap field to pass to `multiprocessing.Pool`."""
def __init__(self, field, field_args):
self.field = field
self.field_args = field_args
def func(self, v_x_a):
try:
v_f = self.field(v_x_a, *self.field_args)
exc... |
class FieldWrapper:
'''Object to wrap field to pass to `multiprocessing.Pool`.'''
def __init__(self, field, field_args):
pass
def func(self, v_x_a):
pass | 3 | 1 | 6 | 1 | 6 | 0 | 2 | 0.08 | 0 | 1 | 0 | 0 | 2 | 2 | 2 | 2 | 15 | 2 | 12 | 6 | 9 | 1 | 12 | 6 | 9 | 3 | 0 | 1 | 4 |
322,028 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexBase | import numpy as np
from abc import ABC, abstractmethod
class VertexBase(ABC):
"""
Base class for a vertex.
"""
def __init__(self, x, nn=None, index=None):
"""
Initiation of a vertex object.
Parameters
----------
x : tuple or vector
The geometric loc... |
class VertexBase(ABC):
'''
Base class for a vertex.
'''
def __init__(self, x, nn=None, index=None):
'''
Initiation of a vertex object.
Parameters
----------
x : tuple or vector
The geometric location (domain).
nn : list, optional
... | 9 | 3 | 9 | 1 | 5 | 4 | 2 | 0.87 | 1 | 3 | 0 | 3 | 6 | 6 | 6 | 26 | 64 | 10 | 30 | 15 | 21 | 26 | 24 | 13 | 17 | 3 | 4 | 1 | 9 |
322,029 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexCacheBase | import collections
class VertexCacheBase:
"""Base class for a vertex cache for a simplicial complex."""
def __init__(self):
self.cache = collections.OrderedDict()
self.nfev = 0
self.index = -1
def __iter__(self):
for v in self.cache:
yield self.cache[v]
... |
class VertexCacheBase:
'''Base class for a vertex cache for a simplicial complex.'''
def __init__(self):
pass
def __iter__(self):
pass
def size(self):
'''Returns the size of the vertex cache.'''
pass
def print_out(self):
pass | 5 | 2 | 5 | 0 | 4 | 1 | 2 | 0.17 | 0 | 1 | 0 | 2 | 4 | 3 | 4 | 4 | 24 | 4 | 18 | 11 | 13 | 3 | 18 | 11 | 13 | 2 | 0 | 1 | 6 |
322,030 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexCacheField | import numpy as np
from scipy._lib._util import MapWrapper
class VertexCacheField(VertexCacheBase):
def __init__(self, field=None, field_args=(), g_cons=None, g_cons_args=(), workers=1):
"""
Class for a vertex cache for a simplicial complex with an associated
field.
Parameters
... |
class VertexCacheField(VertexCacheBase):
def __init__(self, field=None, field_args=(), g_cons=None, g_cons_args=(), workers=1):
'''
Class for a vertex cache for a simplicial complex with an associated
field.
Parameters
----------
field : callable
Scalar ... | 14 | 9 | 13 | 1 | 9 | 4 | 3 | 0.47 | 1 | 10 | 4 | 0 | 13 | 16 | 13 | 17 | 181 | 21 | 116 | 52 | 101 | 54 | 108 | 51 | 94 | 4 | 1 | 2 | 35 |
322,031 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexCacheIndex | class VertexCacheIndex(VertexCacheBase):
def __init__(self):
"""
Class for a vertex cache for a simplicial complex without an associated
field. Useful only for building and visualising a domain complex.
Parameters
----------
"""
super().__init__()
se... | class VertexCacheIndex(VertexCacheBase):
def __init__(self):
'''
Class for a vertex cache for a simplicial complex without an associated
field. Useful only for building and visualising a domain complex.
Parameters
----------
'''
pass
def __getitem__(self... | 3 | 1 | 11 | 1 | 6 | 5 | 2 | 0.75 | 1 | 3 | 1 | 0 | 2 | 1 | 2 | 6 | 23 | 2 | 12 | 5 | 9 | 9 | 12 | 5 | 9 | 2 | 1 | 1 | 3 |
322,032 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexCube | class VertexCube(VertexBase):
"""Vertex class to be used for a pure simplicial complex with no associated
differential geometry (single level domain that exists in R^n)"""
def __init__(self, x, nn=None, index=None):
super().__init__(x, nn=nn, index=index)
def connect(self, v):
if v is ... | class VertexCube(VertexBase):
'''Vertex class to be used for a pure simplicial complex with no associated
differential geometry (single level domain that exists in R^n)'''
def __init__(self, x, nn=None, index=None):
pass
def connect(self, v):
pass
def disconnect(self, v):
... | 4 | 1 | 3 | 0 | 3 | 0 | 2 | 0.18 | 1 | 1 | 0 | 0 | 3 | 0 | 3 | 29 | 15 | 2 | 11 | 4 | 7 | 2 | 11 | 4 | 7 | 2 | 5 | 1 | 5 |
322,033 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexScalarField | class VertexScalarField(VertexBase):
"""
Add homology properties of a scalar field f: R^n --> R associated with
the geometry built from the VertexBase class
"""
def __init__(self, x, field=None, nn=None, index=None, field_args=(), g_cons=None, g_cons_args=()):
"""
Parameters
... | class VertexScalarField(VertexBase):
'''
Add homology properties of a scalar field f: R^n --> R associated with
the geometry built from the VertexBase class
'''
def __init__(self, x, field=None, nn=None, index=None, field_args=(), g_cons=None, g_cons_args=()):
'''
Parameters
... | 6 | 5 | 16 | 2 | 6 | 8 | 2 | 1.31 | 1 | 1 | 0 | 0 | 5 | 4 | 5 | 31 | 88 | 14 | 32 | 11 | 25 | 42 | 31 | 10 | 25 | 2 | 5 | 1 | 9 |
322,034 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_shgo_lib/_vertex.py | scipy.optimize._shgo_lib._vertex.VertexVectorField | class VertexVectorField(VertexBase):
"""
Add homology properties of a scalar field f: R^n --> R^m associated with
the geometry built from the VertexBase class.
"""
def __init__(self, x, sfield=None, vfield=None, field_args=(), vfield_args=(), g_cons=None, g_cons_args=(), nn=None, index=None):
... | class VertexVectorField(VertexBase):
'''
Add homology properties of a scalar field f: R^n --> R^m associated with
the geometry built from the VertexBase class.
'''
def __init__(self, x, sfield=None, vfield=None, field_args=(), vfield_args=(), g_cons=None, g_cons_args=(), nn=None, index=None):
... | 2 | 1 | 6 | 1 | 5 | 0 | 1 | 0.67 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 27 | 12 | 2 | 6 | 4 | 2 | 4 | 4 | 2 | 2 | 1 | 5 | 0 | 1 |
322,035 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_spectral.py | scipy.optimize._spectral._NoConvergence | class _NoConvergence(Exception):
pass | class _NoConvergence(Exception):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 3 | 0 | 0 |
322,036 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion.py | scipy.optimize._trustregion.BaseQuadraticSubproblem | import math
import scipy.linalg
import numpy as np
class BaseQuadraticSubproblem:
"""
Base/abstract class defining the quadratic model for trust-region
minimization. Child classes must implement the ``solve`` method.
Values of the objective function, Jacobian and Hessian (if provided) at
the curre... |
class BaseQuadraticSubproblem:
'''
Base/abstract class defining the quadratic model for trust-region
minimization. Child classes must implement the ``solve`` method.
Values of the objective function, Jacobian and Hessian (if provided) at
the current iterate ``x`` are evaluated on demand and then st... | 14 | 6 | 7 | 0 | 5 | 2 | 2 | 0.44 | 0 | 1 | 0 | 3 | 9 | 11 | 9 | 9 | 86 | 11 | 52 | 32 | 38 | 23 | 46 | 28 | 36 | 2 | 0 | 1 | 14 |
322,037 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py | scipy.optimize._trustregion_constr.canonical_constraint.CanonicalConstraint | import scipy.sparse as sps
import numpy as np
class CanonicalConstraint:
"""Canonical constraint to use with trust-constr algorithm.
It represents the set of constraints of the form::
f_eq(x) = 0
f_ineq(x) <= 0
where ``f_eq`` and ``f_ineq`` are evaluated by a single function, see
bel... |
class CanonicalConstraint:
'''Canonical constraint to use with trust-constr algorithm.
It represents the set of constraints of the form::
f_eq(x) = 0
f_ineq(x) <= 0
where ``f_eq`` and ``f_ineq`` are evaluated by a single function, see
below.
The class is supposed to be instantiated ... | 41 | 4 | 12 | 2 | 10 | 0 | 2 | 0.18 | 0 | 4 | 1 | 0 | 1 | 6 | 8 | 8 | 323 | 60 | 222 | 118 | 181 | 41 | 195 | 111 | 161 | 6 | 0 | 1 | 50 |
322,038 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py | scipy.optimize._trustregion_constr.minimize_trustregion_constr.HessianLinearOperator | from scipy.sparse.linalg import LinearOperator
class HessianLinearOperator:
"""Build LinearOperator from hessp"""
def __init__(self, hessp, n):
self.hessp = hessp
self.n = n
def __call__(self, x, *args):
def matvec(p):
return self.hessp(x, p, *args)
return Lin... |
class HessianLinearOperator:
'''Build LinearOperator from hessp'''
def __init__(self, hessp, n):
pass
def __call__(self, x, *args):
pass
def matvec(p):
pass | 4 | 1 | 3 | 0 | 3 | 0 | 1 | 0.13 | 0 | 1 | 1 | 0 | 2 | 2 | 2 | 2 | 11 | 2 | 8 | 6 | 4 | 1 | 8 | 6 | 4 | 1 | 0 | 0 | 3 |
322,039 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py | scipy.optimize._trustregion_constr.minimize_trustregion_constr.LagrangianHessian | import numpy as np
from scipy.sparse.linalg import LinearOperator
class LagrangianHessian:
"""The Hessian of the Lagrangian as LinearOperator.
The Lagrangian is computed as the objective function plus all the
constraints multiplied with some numbers (Lagrange multipliers).
"""
def __init__(self, ... |
class LagrangianHessian:
'''The Hessian of the Lagrangian as LinearOperator.
The Lagrangian is computed as the objective function plus all the
constraints multiplied with some numbers (Lagrange multipliers).
'''
def __init__(self, n, objective_hess, constraints_hess):
pass
def __call_... | 4 | 1 | 5 | 1 | 5 | 0 | 1 | 0.31 | 0 | 1 | 1 | 0 | 2 | 3 | 2 | 2 | 21 | 4 | 13 | 9 | 9 | 4 | 13 | 9 | 9 | 2 | 0 | 1 | 4 |
322,040 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/report.py | scipy.optimize._trustregion_constr.report.BasicReport | class BasicReport(ReportBase):
COLUMN_NAMES = ['niter', 'f evals', 'CG iter', 'obj func', 'tr radius', 'opt', 'c viol']
COLUMN_WIDTHS = [7, 7, 7, 13, 10, 10, 10]
ITERATION_FORMATS = ['^7', '^7', '^7', '^+13.4e', '^10.2e', '^10.2e', '^10.2e'] | class BasicReport(ReportBase):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 6 | 0 | 6 | 4 | 5 | 0 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
322,041 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/report.py | scipy.optimize._trustregion_constr.report.IPReport | class IPReport(ReportBase):
COLUMN_NAMES = ['niter', 'f evals', 'CG iter', 'obj func', 'tr radius', 'opt', 'c viol', 'penalty', 'barrier param', 'CG stop']
COLUMN_WIDTHS = [7, 7, 7, 13, 10, 10, 10, 10, 13, 7]
ITERATION_FORMATS = ['^7', '^7', '^7', '^+13.4e', '^10.2e', '^10.2e', '^10.2e', '^10.2e', '^13.2e',... | class IPReport(ReportBase):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 6 | 0 | 6 | 4 | 5 | 0 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
322,042 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/report.py | scipy.optimize._trustregion_constr.report.ReportBase | class ReportBase:
COLUMN_NAMES: list[str] = NotImplemented
COLUMN_WIDTHS: list[int] = NotImplemented
ITERATION_FORMATS: list[str] = NotImplemented
@classmethod
def print_header(cls):
fmt = '|' + '|'.join([f'{{:^{x}}}' for x in cls.COLUMN_WIDTHS]) + '|'
separators = ['-' * x for x in... | class ReportBase:
@classmethod
def print_header(cls):
pass
@classmethod
def print_iteration(cls, *args):
pass
@classmethod
def print_footer(cls):
pass | 7 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 3 | 23 | 3 | 20 | 14 | 13 | 0 | 15 | 11 | 11 | 1 | 0 | 0 | 3 |
322,043 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/report.py | scipy.optimize._trustregion_constr.report.SQPReport | class SQPReport(ReportBase):
COLUMN_NAMES = ['niter', 'f evals', 'CG iter', 'obj func', 'tr radius', 'opt', 'c viol', 'penalty', 'CG stop']
COLUMN_WIDTHS = [7, 7, 7, 13, 10, 10, 10, 10, 7]
ITERATION_FORMATS = ['^7', '^7', '^7', '^+13.4e', '^10.2e', '^10.2e', '^10.2e', '^10.2e', '^7'] | class SQPReport(ReportBase):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 6 | 0 | 6 | 4 | 5 | 0 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
322,044 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py | scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem | from scipy.sparse.linalg import LinearOperator
import numpy as np
import scipy.sparse as sps
class BarrierSubproblem:
"""
Barrier optimization problem:
minimize fun(x) - barrier_parameter*sum(log(s))
subject to: constr_eq(x) = 0
constr_ineq(x) + s = 0
"""
def __in... |
class BarrierSubproblem:
'''
Barrier optimization problem:
minimize fun(x) - barrier_parameter*sum(log(s))
subject to: constr_eq(x) = 0
constr_ineq(x) + s = 0
'''
def __init__(self, x0, s0, fun, grad, lagr_hess, n_vars, n_ineq, n_eq, constr, jac, barrier_parameter... | 19 | 9 | 13 | 1 | 9 | 4 | 2 | 0.55 | 0 | 4 | 2 | 0 | 16 | 22 | 16 | 16 | 255 | 25 | 148 | 84 | 123 | 82 | 121 | 78 | 102 | 5 | 0 | 3 | 27 |
322,045 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_dogleg.py | scipy.optimize._trustregion_dogleg.DoglegSubproblem | from ._trustregion import _minimize_trust_region, BaseQuadraticSubproblem
import scipy.linalg
import numpy as np
class DoglegSubproblem(BaseQuadraticSubproblem):
"""Quadratic subproblem solved by the dogleg method"""
def cauchy_point(self):
"""
The Cauchy point is minimal along the direction o... |
class DoglegSubproblem(BaseQuadraticSubproblem):
'''Quadratic subproblem solved by the dogleg method'''
def cauchy_point(self):
'''
The Cauchy point is minimal along the direction of steepest descent.
'''
pass
def newton_point(self):
'''
The Newton point is... | 4 | 4 | 27 | 3 | 10 | 14 | 2 | 1.43 | 1 | 0 | 0 | 0 | 3 | 2 | 3 | 12 | 85 | 12 | 30 | 17 | 26 | 43 | 29 | 17 | 25 | 3 | 1 | 1 | 7 |
322,046 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_exact.py | scipy.optimize._trustregion_exact.IterativeSubproblem | import numpy as np
from scipy.linalg import norm, get_lapack_funcs, solve_triangular, cho_solve
from ._trustregion import _minimize_trust_region, BaseQuadraticSubproblem
class IterativeSubproblem(BaseQuadraticSubproblem):
"""Quadratic subproblem solved by nearly exact iterative method.
Notes
-----
Thi... |
class IterativeSubproblem(BaseQuadraticSubproblem):
'''Quadratic subproblem solved by nearly exact iterative method.
Notes
-----
This subproblem solver was based on [1]_, [2]_ and [3]_,
which implement similar algorithms. The algorithm is basically
that of [1]_ but ideas from [2]_ and [3]_ were... | 4 | 3 | 75 | 16 | 39 | 22 | 5 | 0.7 | 1 | 1 | 0 | 0 | 3 | 13 | 3 | 12 | 253 | 54 | 120 | 43 | 115 | 84 | 90 | 41 | 86 | 12 | 1 | 4 | 16 |
322,047 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_trustregion_ncg.py | scipy.optimize._trustregion_ncg.CGSteihaugSubproblem | import math
import numpy as np
import scipy.linalg
from ._trustregion import _minimize_trust_region, BaseQuadraticSubproblem
class CGSteihaugSubproblem(BaseQuadraticSubproblem):
"""Quadratic subproblem solved by a conjugate gradient method"""
def solve(self, trust_radius):
"""
Solve the subpro... |
class CGSteihaugSubproblem(BaseQuadraticSubproblem):
'''Quadratic subproblem solved by a conjugate gradient method'''
def solve(self, trust_radius):
'''
Solve the subproblem using a conjugate gradient method.
Parameters
----------
trust_radius : float
We are... | 2 | 2 | 83 | 10 | 40 | 33 | 7 | 0.83 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 10 | 85 | 10 | 41 | 21 | 39 | 34 | 40 | 21 | 38 | 7 | 1 | 3 | 7 |
322,048 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_zeros_py.py | scipy.optimize._zeros_py.RootResults | from ._optimize import OptimizeResult
class RootResults(OptimizeResult):
"""Represents the root finding result.
Attributes
----------
root : float
Estimated root location.
iterations : int
Number of iterations needed to find the root.
function_calls : int
Number of time... |
class RootResults(OptimizeResult):
'''Represents the root finding result.
Attributes
----------
root : float
Estimated root location.
iterations : int
Number of iterations needed to find the root.
function_calls : int
Number of times the function was called.
converge... | 2 | 1 | 10 | 0 | 10 | 0 | 2 | 1.45 | 1 | 0 | 0 | 0 | 1 | 6 | 1 | 31 | 30 | 3 | 11 | 8 | 9 | 16 | 10 | 8 | 8 | 2 | 4 | 1 | 2 |
322,049 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_zeros_py.py | scipy.optimize._zeros_py.TOMS748Solver | import numpy as np
import warnings
class TOMS748Solver:
"""Solve f(x, *args) == 0 using Algorithm748 of Alefeld, Potro & Shi.
"""
_MU = 0.5
_K_MIN = 1
_K_MAX = 100
def __init__(self):
self.f = None
self.args = None
self.function_calls = 0
self.iterations = 0
... |
class TOMS748Solver:
'''Solve f(x, *args) == 0 using Algorithm748 of Alefeld, Potro & Shi.
'''
def __init__(self):
pass
def configure(self, xtol, rtol, maxiter, disp, k):
pass
def _callf(self, x, error=True):
'''Call the user-supplied function, update book-keeping'''
... | 10 | 7 | 21 | 2 | 16 | 3 | 5 | 0.21 | 0 | 6 | 0 | 0 | 9 | 15 | 9 | 9 | 205 | 27 | 150 | 55 | 139 | 31 | 144 | 54 | 134 | 15 | 0 | 3 | 41 |
322,050 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_czt.py | scipy.signal._czt.CZT | import cmath
from scipy.fft import fft, ifft, next_fast_len
import numpy as np
from numpy import pi, arange
class CZT:
"""
Create a callable chirp z-transform function.
Transform to compute the frequency response around a spiral.
Objects of this class are callables which can compute the
chirp z-tr... |
class CZT:
'''
Create a callable chirp z-transform function.
Transform to compute the frequency response around a spiral.
Objects of this class are callables which can compute the
chirp z-transform on their inputs. This object precalculates the constant
chirps used in the given transform.
... | 4 | 3 | 19 | 2 | 10 | 7 | 2 | 3.29 | 0 | 2 | 0 | 1 | 3 | 9 | 3 | 3 | 158 | 26 | 31 | 16 | 27 | 102 | 29 | 16 | 25 | 2 | 0 | 1 | 5 |
322,051 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_czt.py | scipy.signal._czt.ZoomFFT | from scipy.fft import fft, ifft, next_fast_len
import cmath
import numpy as np
from numpy import pi, arange
class ZoomFFT(CZT):
"""
Create a callable zoom FFT transform function.
This is a specialization of the chirp z-transform (`CZT`) for a set of
equally-spaced frequencies around the unit circle, u... |
class ZoomFFT(CZT):
'''
Create a callable zoom FFT transform function.
This is a specialization of the chirp z-transform (`CZT`) for a set of
equally-spaced frequencies around the unit circle, used to calculate a
section of the FFT more efficiently than calculating the entire FFT and
truncating... | 2 | 1 | 33 | 7 | 26 | 0 | 4 | 2.63 | 1 | 2 | 0 | 0 | 1 | 12 | 1 | 4 | 117 | 19 | 27 | 18 | 25 | 71 | 24 | 18 | 22 | 4 | 1 | 1 | 4 |
322,052 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_filter_design.py | scipy.signal._filter_design.BadCoefficients | class BadCoefficients(UserWarning):
"""Warning about badly conditioned filter coefficients"""
pass | class BadCoefficients(UserWarning):
'''Warning about badly conditioned filter coefficients'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
322,053 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.Bunch | class Bunch:
def __init__(self, **kwds):
self.__dict__.update(kwds) | class Bunch:
def __init__(self, **kwds):
pass | 2 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 3 | 0 | 3 | 2 | 1 | 0 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,054 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.LinearTimeInvariant | class LinearTimeInvariant:
def __new__(cls, *system, **kwargs):
"""Create a new object, don't allow direct instances."""
if cls is LinearTimeInvariant:
raise NotImplementedError('The LinearTimeInvariant class is not meant to be used directly, use `lti` or `dlti` instead.')
retur... | class LinearTimeInvariant:
def __new__(cls, *system, **kwargs):
'''Create a new object, don't allow direct instances.'''
pass
def __init__(self):
'''
Initialize the `lti` baseclass.
The heavy lifting is done by the subclasses.
'''
pass
@property
... | 14 | 8 | 8 | 1 | 4 | 3 | 2 | 0.69 | 0 | 5 | 3 | 5 | 9 | 3 | 9 | 9 | 84 | 13 | 42 | 17 | 28 | 29 | 32 | 13 | 22 | 2 | 0 | 1 | 14 |
322,055 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.StateSpace | from scipy import linalg
import numpy as np
import copy
from numpy import real, atleast_1d, squeeze, asarray, zeros, dot, transpose, ones, linspace
from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
class StateSpace(LinearTimeInvariant):
"""
Linear Tim... |
class StateSpace(LinearTimeInvariant):
'''
Linear Time Invariant system in state-space form.
Represents the system as the continuous-time, first order differential
equation :math:`\dot{x} = A x + B u` or the discrete-time difference
equation :math:`x[k+1] = A x[k] + B u[k]`. `StateSpace` systems
... | 33 | 17 | 12 | 1 | 6 | 4 | 2 | 1.07 | 1 | 11 | 4 | 2 | 24 | 6 | 24 | 33 | 402 | 68 | 161 | 58 | 128 | 173 | 123 | 50 | 98 | 6 | 1 | 2 | 43 |
322,056 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.StateSpaceContinuous | from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
class StateSpaceContinuous(StateSpace, lti):
"""
Continuous-time Linear Time Invariant system in state-space form.
Represents the system as the continuous-time, first order differential
equati... |
class StateSpaceContinuous(StateSpace, lti):
'''
Continuous-time Linear Time Invariant system in state-space form.
Represents the system as the continuous-time, first order differential
equation :math:`\dot{x} = A x + B u`.
Continuous-time `StateSpace` systems inherit additional functionality
f... | 2 | 2 | 15 | 2 | 6 | 7 | 1 | 7.57 | 2 | 0 | 0 | 0 | 1 | 4 | 1 | 42 | 72 | 12 | 7 | 3 | 5 | 53 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
322,057 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.StateSpaceDiscrete | class StateSpaceDiscrete(StateSpace, dlti):
"""
Discrete-time Linear Time Invariant system in state-space form.
Represents the system as the discrete-time difference equation
:math:`x[k+1] = A x[k] + B u[k]`.
`StateSpace` systems inherit additional functionality from the `dlti`
class.
Para... | class StateSpaceDiscrete(StateSpace, dlti):
'''
Discrete-time Linear Time Invariant system in state-space form.
Represents the system as the discrete-time difference equation
:math:`x[k+1] = A x[k] + B u[k]`.
`StateSpace` systems inherit additional functionality from the `dlti`
class.
Parame... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 24.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 60 | 9 | 2 | 1 | 1 | 49 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
322,058 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.TransferFunction | import copy
from numpy import real, atleast_1d, squeeze, asarray, zeros, dot, transpose, ones, linspace
from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
from ._filter_design import tf2zpk, zpk2tf, normalize, freqs, freqz, freqs_zpk, freqz_zpk
import numpy as ... |
class TransferFunction(LinearTimeInvariant):
'''Linear Time Invariant system class in transfer function form.
Represents the system as the continuous-time transfer function
:math:`H(s)=\sum_{i=0}^N b[N-i] s^i / \sum_{j=0}^M a[M-j] s^j` or the
discrete-time transfer function
:math:`H(z)=\sum_{i=0}^N... | 20 | 12 | 12 | 1 | 5 | 5 | 2 | 1.73 | 1 | 5 | 4 | 2 | 11 | 4 | 13 | 22 | 245 | 43 | 74 | 25 | 54 | 128 | 51 | 19 | 37 | 4 | 1 | 2 | 22 |
322,059 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.TransferFunctionContinuous | from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
class TransferFunctionContinuous(TransferFunction, lti):
"""
Continuous-time Linear Time Invariant system in transfer function form.
Represents the system as the transfer function
:math:`H(s)=... |
class TransferFunctionContinuous(TransferFunction, lti):
'''
Continuous-time Linear Time Invariant system in transfer function form.
Represents the system as the transfer function
:math:`H(s)=\sum_{i=0}^N b[N-i] s^i / \sum_{j=0}^M a[M-j] s^j`, where
:math:`b` are elements of the numerator `num`, :m... | 2 | 2 | 15 | 2 | 6 | 7 | 1 | 7.71 | 2 | 0 | 0 | 0 | 1 | 2 | 1 | 31 | 75 | 14 | 7 | 3 | 5 | 54 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
322,060 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.TransferFunctionDiscrete | class TransferFunctionDiscrete(TransferFunction, dlti):
"""
Discrete-time Linear Time Invariant system in transfer function form.
Represents the system as the transfer function
:math:`H(z)=\\sum_{i=0}^N b[N-i] z^i / \\sum_{j=0}^M a[M-j] z^j`, where
:math:`b` are elements of the numerator `num`, :ma... | class TransferFunctionDiscrete(TransferFunction, dlti):
'''
Discrete-time Linear Time Invariant system in transfer function form.
Represents the system as the transfer function
:math:`H(z)=\sum_{i=0}^N b[N-i] z^i / \sum_{j=0}^M a[M-j] z^j`, where
:math:`b` are elements of the numerator `num`, :math:... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 25 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 63 | 11 | 2 | 1 | 1 | 50 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
322,061 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.ZerosPolesGain | from ._filter_design import tf2zpk, zpk2tf, normalize, freqs, freqz, freqs_zpk, freqz_zpk
from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
from numpy import real, atleast_1d, squeeze, asarray, zeros, dot, transpose, ones, linspace
import copy
class ZerosPole... |
class ZerosPolesGain(LinearTimeInvariant):
'''
Linear Time Invariant system class in zeros, poles, gain form.
Represents the system as the continuous- or discrete-time transfer function
:math:`H(s)=k \prod_i (s - z[i]) / \prod_j (s - p[j])`, where :math:`k` is
the `gain`, :math:`z` are the `zeros` ... | 20 | 11 | 9 | 1 | 5 | 3 | 1 | 1.38 | 1 | 5 | 4 | 2 | 13 | 5 | 13 | 22 | 200 | 38 | 68 | 24 | 48 | 94 | 45 | 18 | 31 | 4 | 1 | 2 | 18 |
322,062 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.ZerosPolesGainContinuous | from ._lti_conversion import tf2ss, abcd_normalize, ss2tf, zpk2ss, ss2zpk, cont2discrete, _atleast_2d_or_none
class ZerosPolesGainContinuous(ZerosPolesGain, lti):
"""
Continuous-time Linear Time Invariant system in zeros, poles, gain form.
Represents the system as the continuous time transfer function
... |
class ZerosPolesGainContinuous(ZerosPolesGain, lti):
'''
Continuous-time Linear Time Invariant system in zeros, poles, gain form.
Represents the system as the continuous time transfer function
:math:`H(s)=k \prod_i (s - z[i]) / \prod_j (s - p[j])`, where :math:`k` is
the `gain`, :math:`z` are the `... | 2 | 2 | 16 | 2 | 7 | 7 | 1 | 6 | 2 | 0 | 0 | 0 | 1 | 3 | 1 | 31 | 68 | 12 | 8 | 3 | 6 | 48 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
322,063 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.ZerosPolesGainDiscrete | class ZerosPolesGainDiscrete(ZerosPolesGain, dlti):
"""
Discrete-time Linear Time Invariant system in zeros, poles, gain form.
Represents the system as the discrete-time transfer function
:math:`H(z)=k \\prod_i (z - q[i]) / \\prod_j (z - p[j])`, where :math:`k` is
the `gain`, :math:`q` are the `zer... | class ZerosPolesGainDiscrete(ZerosPolesGain, dlti):
'''
Discrete-time Linear Time Invariant system in zeros, poles, gain form.
Represents the system as the discrete-time transfer function
:math:`H(z)=k \prod_i (z - q[i]) / \prod_j (z - p[j])`, where :math:`k` is
the `gain`, :math:`q` are the `zeros`... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 27.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 68 | 11 | 2 | 1 | 1 | 55 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
322,064 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.dlti | class dlti(LinearTimeInvariant):
"""
Discrete-time linear time invariant system base class.
Parameters
----------
*system: arguments
The `dlti` class can be instantiated with either 2, 3 or 4 arguments.
The following gives the number of arguments and the corresponding
discre... | class dlti(LinearTimeInvariant):
'''
Discrete-time linear time invariant system base class.
Parameters
----------
*system: arguments
The `dlti` class can be instantiated with either 2, 3 or 4 arguments.
The following gives the number of arguments and the corresponding
discret... | 12 | 9 | 10 | 1 | 4 | 5 | 1 | 3.14 | 1 | 5 | 3 | 3 | 9 | 1 | 9 | 18 | 189 | 36 | 37 | 15 | 25 | 116 | 28 | 13 | 18 | 5 | 1 | 2 | 13 |
322,065 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_ltisys.py | scipy.signal._ltisys.lti | class lti(LinearTimeInvariant):
"""
Continuous-time linear time invariant system base class.
Parameters
----------
*system : arguments
The `lti` class can be instantiated with either 2, 3 or 4 arguments.
The following gives the number of arguments and the corresponding
conti... | class lti(LinearTimeInvariant):
'''
Continuous-time linear time invariant system base class.
Parameters
----------
*system : arguments
The `lti` class can be instantiated with either 2, 3 or 4 arguments.
The following gives the number of arguments and the corresponding
contin... | 9 | 9 | 11 | 1 | 4 | 6 | 2 | 3.22 | 1 | 6 | 3 | 3 | 8 | 0 | 8 | 17 | 165 | 30 | 32 | 10 | 23 | 103 | 24 | 10 | 15 | 5 | 1 | 2 | 12 |
322,066 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_short_time_fft.py | scipy.signal._short_time_fft.ShortTimeFFT | from scipy.signal._signaltools import detrend
from functools import cache, lru_cache, partial
import scipy.fft as fft_lib
from collections.abc import Generator, Callable
import numpy as np
from typing import get_args, Literal
from scipy.signal.windows import get_window
class ShortTimeFFT:
"""Provide a parametrized... |
class ShortTimeFFT:
'''Provide a parametrized discrete Short-time Fourier transform (stft)
and its inverse (istft).
.. currentmodule:: scipy.signal.ShortTimeFFT
The `~ShortTimeFFT.stft` calculates sequential FFTs by sliding a
window (`win`) over an input signal by `hop` increments. It can be used t... | 84 | 49 | 34 | 4 | 9 | 22 | 3 | 2.65 | 0 | 16 | 0 | 0 | 46 | 0 | 49 | 49 | 1,959 | 255 | 477 | 212 | 354 | 1,263 | 359 | 131 | 309 | 16 | 0 | 2 | 157 |
322,067 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/signal/_upfirdn.py | scipy.signal._upfirdn._UpFIRDn | import numpy as np
from ._upfirdn_apply import _output_len, _apply, mode_enum
class _UpFIRDn:
"""Helper for resampling."""
def __init__(self, h, x_dtype, up, down):
h = np.asarray(h)
if h.ndim != 1 or h.size == 0:
raise ValueError('h must be 1-D with non-zero length')
self.... |
class _UpFIRDn:
'''Helper for resampling.'''
def __init__(self, h, x_dtype, up, down):
pass
def apply_filter(self, x, axis=-1, mode='constant', cval=0):
'''Apply the prepared filter to the specified axis of N-D signal x.'''
pass | 3 | 2 | 15 | 0 | 13 | 2 | 2 | 0.19 | 0 | 2 | 0 | 0 | 2 | 5 | 2 | 2 | 33 | 2 | 26 | 11 | 23 | 5 | 23 | 11 | 20 | 3 | 0 | 1 | 4 |
322,068 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_base.py | scipy.sparse._base.SparseEfficiencyWarning | class SparseEfficiencyWarning(SparseWarning):
"""The warning emitted when the operation is
inefficient for sparse matrices.
"""
pass | class SparseEfficiencyWarning(SparseWarning):
'''The warning emitted when the operation is
inefficient for sparse matrices.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 5 | 0 | 2 | 1 | 1 | 3 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
322,069 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_base.py | scipy.sparse._base.SparseFormatWarning | class SparseFormatWarning(SparseWarning):
pass | class SparseFormatWarning(SparseWarning):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
322,070 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_base.py | scipy.sparse._base.SparseWarning | class SparseWarning(Warning):
"""General warning for :mod:`scipy.sparse`."""
pass | class SparseWarning(Warning):
'''General warning for :mod:`scipy.sparse`.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 11 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,071 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_base.py | scipy.sparse._base._spbase | import math
import numpy as np
import operator
from ._sputils import asmatrix, check_reshape_kwargs, check_shape, get_sum_dtype, isdense, isscalarlike, _todata, matrix, validateaxis, getdtype, is_pydata_spmatrix
from scipy._lib._sparse import SparseABC, issparse
from warnings import warn
class _spbase(SparseABC):
... |
class _spbase(SparseABC):
''' This class provides a base class for all sparse arrays. It
cannot be instantiated. Most of the work is provided by subclasses.
'''
@property
def ndim(self) -> int:
pass
@property
def _shape_as_2d(self):
pass
@property
def _bsr_cont... | 125 | 41 | 12 | 1 | 6 | 5 | 2 | 0.79 | 1 | 21 | 9 | 3 | 103 | 3 | 105 | 125 | 1,470 | 254 | 686 | 207 | 553 | 540 | 571 | 187 | 456 | 22 | 5 | 3 | 259 |
322,072 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_base.py | scipy.sparse._base.sparray | class sparray:
"""A namespace class to separate sparray from spmatrix"""
@classmethod
def __class_getitem__(cls, arg, /):
"""
Return a parametrized wrapper around the `~scipy.sparse.sparray` type.
.. versionadded:: 1.16.0
Returns
-------
alias : types.Gener... | class sparray:
'''A namespace class to separate sparray from spmatrix'''
@classmethod
def __class_getitem__(cls, arg, /):
'''
Return a parametrized wrapper around the `~scipy.sparse.sparray` type.
.. versionadded:: 1.16.0
Returns
-------
alias : types.GenericA... | 3 | 2 | 21 | 4 | 3 | 14 | 1 | 3 | 0 | 0 | 0 | 7 | 0 | 0 | 1 | 1 | 25 | 5 | 5 | 4 | 1 | 15 | 4 | 3 | 1 | 1 | 0 | 0 | 1 |
322,073 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_bsr.py | scipy.sparse._bsr._bsr_base | from ._data import _data_matrix, _minmax_mixin
from ._sputils import isshape, getdtype, getdata, to_native, upcast, check_shape
from ._sparsetools import bsr_matvec, bsr_matvecs, csr_matmat_maxnnz, bsr_matmat, bsr_transpose, bsr_sort_indices, bsr_tocsr
from . import _sparsetools
from scipy._lib._util import copy_if_nee... |
class _bsr_base(_cs_matrix, _minmax_mixin):
def __init__(self, arg1, shape=None, dtype=None, copy=False, blocksize=None, *, maxprint=None):
pass
def check_format(self, full_check=True):
'''Check whether the array/matrix respects the BSR format.
Parameters
----------
fu... | 28 | 10 | 21 | 3 | 15 | 3 | 3 | 0.21 | 2 | 9 | 1 | 2 | 26 | 8 | 26 | 249 | 607 | 116 | 410 | 117 | 381 | 88 | 309 | 112 | 282 | 25 | 8 | 4 | 88 |
322,074 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_bsr.py | scipy.sparse._bsr.bsr_array | from ._base import issparse, _formats, _spbase, sparray
class bsr_array(_bsr_base, sparray):
"""
Block Sparse Row format sparse array.
This can be instantiated in several ways:
bsr_array(D, [blocksize=(R,C)])
where D is a 2-D ndarray.
bsr_array(S, [blocksize=(R,C)])
... |
class bsr_array(_bsr_base, sparray):
'''
Block Sparse Row format sparse array.
This can be instantiated in several ways:
bsr_array(D, [blocksize=(R,C)])
where D is a 2-D ndarray.
bsr_array(S, [blocksize=(R,C)])
with another sparse array or matrix S (equivalent to S.t... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 88 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 250 | 109 | 20 | 1 | 1 | 0 | 88 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,075 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_bsr.py | scipy.sparse._bsr.bsr_matrix | from ._matrix import spmatrix
class bsr_matrix(spmatrix, _bsr_base):
"""
Block Sparse Row format sparse matrix.
This can be instantiated in several ways:
bsr_matrix(D, [blocksize=(R,C)])
where D is a 2-D ndarray.
bsr_matrix(S, [blocksize=(R,C)])
with another sparse... |
class bsr_matrix(spmatrix, _bsr_base):
'''
Block Sparse Row format sparse matrix.
This can be instantiated in several ways:
bsr_matrix(D, [blocksize=(R,C)])
where D is a 2-D ndarray.
bsr_matrix(S, [blocksize=(R,C)])
with another sparse array or matrix S (equivalent t... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 88 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 270 | 109 | 20 | 1 | 1 | 0 | 88 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,076 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_compressed.py | scipy.sparse._compressed._cs_matrix | from ._index import IndexMixin
from ._data import _data_matrix, _minmax_mixin
import numpy as np
from scipy._lib._util import _prune_array, copy_if_needed
from ._sparsetools import get_csr_submatrix, csr_sample_offsets, csr_todense, csr_sample_values, csr_row_index, csr_row_slice, csr_column_index1, csr_column_index2, ... |
class _cs_matrix(_data_matrix, _minmax_mixin, IndexMixin):
'''
base array/matrix class for compressed row- and column-oriented arrays/matrices
'''
def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
pass
def _getnnz(self, axis=None):
pass
def c... | 54 | 24 | 24 | 3 | 18 | 4 | 4 | 0.26 | 3 | 14 | 2 | 3 | 48 | 8 | 48 | 223 | 1,267 | 193 | 866 | 266 | 812 | 221 | 744 | 259 | 694 | 27 | 7 | 4 | 213 |
322,077 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_coo.py | scipy.sparse._coo._coo_base | from ._sparsetools import coo_tocsr, coo_todense, coo_todense_nd, coo_matvec, coo_matvec_nd, coo_matmat_dense, coo_matmat_dense_nd
import math
from ._data import _data_matrix, _minmax_mixin
from warnings import warn
import numpy as np
from ._sputils import upcast_char, to_native, isshape, getdtype, getdata, downcast_in... |
class _coo_base(_data_matrix, _minmax_mixin):
def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
pass
@property
def row(self):
pass
@row.setter
def row(self):
pass
@property
def col(self):
pass
@col.setter
def col(self):
... | 47 | 10 | 26 | 3 | 17 | 6 | 5 | 0.37 | 2 | 17 | 5 | 2 | 42 | 5 | 42 | 193 | 1,181 | 191 | 734 | 236 | 685 | 272 | 640 | 229 | 595 | 23 | 7 | 4 | 192 |
322,078 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_coo.py | scipy.sparse._coo.coo_array | from ._base import issparse, SparseEfficiencyWarning, _spbase, sparray
class coo_array(_coo_base, sparray):
"""
A sparse array in COOrdinate format.
Also known as the 'ijv' or 'triplet' format.
This can be instantiated in several ways:
coo_array(D)
where D is an ndarray
c... |
class coo_array(_coo_base, sparray):
'''
A sparse array in COOrdinate format.
Also known as the 'ijv' or 'triplet' format.
This can be instantiated in several ways:
coo_array(D)
where D is an ndarray
coo_array(S)
with another sparse array or matrix S (equivalent ... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 90 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 194 | 109 | 18 | 1 | 1 | 0 | 90 | 1 | 1 | 0 | 0 | 8 | 0 | 0 |
322,079 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_coo.py | scipy.sparse._coo.coo_matrix | from ._matrix import spmatrix
class coo_matrix(spmatrix, _coo_base):
"""
A sparse matrix in COOrdinate format.
Also known as the 'ijv' or 'triplet' format.
This can be instantiated in several ways:
coo_matrix(D)
where D is a 2-D ndarray
coo_matrix(S)
with anot... |
class coo_matrix(spmatrix, _coo_base):
'''
A sparse matrix in COOrdinate format.
Also known as the 'ijv' or 'triplet' format.
This can be instantiated in several ways:
coo_matrix(D)
where D is a 2-D ndarray
coo_matrix(S)
with another sparse array or matrix S (equ... | 2 | 1 | 6 | 0 | 4 | 2 | 2 | 19 | 2 | 0 | 0 | 0 | 1 | 0 | 1 | 215 | 119 | 19 | 5 | 2 | 3 | 95 | 5 | 2 | 3 | 2 | 8 | 1 | 2 |
322,080 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csc.py | scipy.sparse._csc._csc_base | import numpy as np
from ._base import _spbase, sparray
from ._compressed import _cs_matrix
from ._sputils import upcast
from ._sparsetools import csr_tocsc, expandptr
class _csc_base(_cs_matrix):
_format = 'csc'
def transpose(self, axes=None, copy=False):
if axes is not None and axes != (1, 0):
... |
class _csc_base(_cs_matrix):
def transpose(self, axes=None, copy=False):
pass
def __iter__(self):
pass
def tocsc(self, copy=False):
pass
def tocsr(self, copy=False):
pass
def nonzero(self):
pass
def _getrow(self, i):
'''Returns a copy of row... | 16 | 3 | 8 | 1 | 6 | 1 | 2 | 0.16 | 1 | 3 | 0 | 2 | 13 | 5 | 14 | 237 | 133 | 26 | 92 | 36 | 76 | 15 | 77 | 32 | 62 | 3 | 8 | 1 | 23 |
322,081 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csc.py | scipy.sparse._csc.csc_array | from ._base import _spbase, sparray
class csc_array(_csc_base, sparray):
"""
Compressed Sparse Column array.
This can be instantiated in several ways:
csc_array(D)
where D is a 2-D ndarray
csc_array(S)
with another sparse array or matrix S (equivalent to S.tocsc())... |
class csc_array(_csc_base, sparray):
'''
Compressed Sparse Column array.
This can be instantiated in several ways:
csc_array(D)
where D is a 2-D ndarray
csc_array(S)
with another sparse array or matrix S (equivalent to S.tocsc())
csc_array((M, N), [dtype])
... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 76 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 238 | 93 | 16 | 1 | 1 | 0 | 76 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,082 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csc.py | scipy.sparse._csc.csc_matrix | from ._matrix import spmatrix
class csc_matrix(spmatrix, _csc_base):
"""
Compressed Sparse Column matrix.
This can be instantiated in several ways:
csc_matrix(D)
where D is a 2-D ndarray
csc_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsc())
... |
class csc_matrix(spmatrix, _csc_base):
'''
Compressed Sparse Column matrix.
This can be instantiated in several ways:
csc_matrix(D)
where D is a 2-D ndarray
csc_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtyp... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 76 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 258 | 93 | 16 | 1 | 1 | 0 | 76 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,083 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csr.py | scipy.sparse._csr._csr_base | from ._sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, get_csr_submatrix, csr_sample_values
from ._base import _spbase, sparray
import numpy as np
from ._compressed import _cs_matrix
from ._sputils import upcast
class _csr_base(_cs_matrix):
_format = 'csr'
_allow_nd = (1, 2)
def transpose(self,... |
class _csr_base(_cs_matrix):
def transpose(self, axes=None, copy=False):
pass
def tolil(self, copy=False):
pass
def tocsr(self, copy=False):
pass
def tocsc(self, copy=False):
pass
def tobsr(self, blocksize=None, copy=True):
pass
@staticmethod
def... | 22 | 3 | 12 | 1 | 10 | 1 | 3 | 0.06 | 1 | 8 | 1 | 2 | 19 | 6 | 20 | 243 | 267 | 49 | 206 | 80 | 183 | 12 | 178 | 76 | 156 | 7 | 8 | 3 | 56 |
322,084 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csr.py | scipy.sparse._csr.csr_array | from ._base import _spbase, sparray
class csr_array(_csr_base, sparray):
"""
Compressed Sparse Row array.
This can be instantiated in several ways:
csr_array(D)
where D is a 2-D ndarray
csr_array(S)
with another sparse array or matrix S (equivalent to S.tocsr())
... |
class csr_array(_csr_base, sparray):
'''
Compressed Sparse Row array.
This can be instantiated in several ways:
csr_array(D)
where D is a 2-D ndarray
csr_array(S)
with another sparse array or matrix S (equivalent to S.tocsr())
csr_array((M, N), [dtype])
... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 100 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 244 | 121 | 20 | 1 | 1 | 0 | 100 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,085 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_csr.py | scipy.sparse._csr.csr_matrix | from ._matrix import spmatrix
class csr_matrix(spmatrix, _csr_base):
"""
Compressed Sparse Row matrix.
This can be instantiated in several ways:
csr_matrix(D)
where D is a 2-D ndarray
csr_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsr())
... |
class csr_matrix(spmatrix, _csr_base):
'''
Compressed Sparse Row matrix.
This can be instantiated in several ways:
csr_matrix(D)
where D is a 2-D ndarray
csr_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsr())
csr_matrix((M, N), [dtype])... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 100 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 264 | 121 | 20 | 1 | 1 | 0 | 100 | 1 | 1 | 0 | 0 | 9 | 0 | 0 |
322,086 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_data.py | scipy.sparse._data._data_matrix | from ._sputils import isscalarlike, validateaxis
from ._base import _spbase, sparray, _ufuncs_with_fixed_point_at_zero
import numpy as np
class _data_matrix(_spbase):
def __init__(self, arg1, *, maxprint=None):
_spbase.__init__(self, arg1, maxprint=maxprint)
@property
def dtype(self):
ret... |
class _data_matrix(_spbase):
def __init__(self, arg1, *, maxprint=None):
pass
@property
def dtype(self):
pass
@dtype.setter
def dtype(self):
pass
def _deduped_data(self):
pass
def __abs__(self):
pass
def __round__(self, ndigits=0):
pas... | 19 | 1 | 6 | 0 | 4 | 1 | 2 | 0.25 | 1 | 1 | 0 | 3 | 16 | 0 | 16 | 141 | 116 | 23 | 76 | 22 | 57 | 19 | 62 | 20 | 45 | 4 | 6 | 1 | 27 |
322,087 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_data.py | scipy.sparse._data._minmax_mixin | import math
from ._base import _spbase, sparray, _ufuncs_with_fixed_point_at_zero
from ._sputils import isscalarlike, validateaxis
import numpy as np
class _minmax_mixin:
"""Mixin for min and max methods.
These are not implemented for dia_matrix, hence the separate class.
"""
def _min_or_max_axis(sel... |
class _minmax_mixin:
'''Mixin for min and max methods.
These are not implemented for dia_matrix, hence the separate class.
'''
def _min_or_max_axis(self, axis, min_or_max, explicit):
pass
def _min_or_max_axis(self, axis, min_or_max, explicit):
pass
def _argminmax_axis(sel... | 11 | 7 | 39 | 7 | 13 | 19 | 5 | 1.42 | 0 | 3 | 1 | 3 | 10 | 1 | 10 | 10 | 402 | 81 | 134 | 44 | 123 | 190 | 122 | 43 | 111 | 18 | 0 | 4 | 46 |
322,088 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dia.py | scipy.sparse._dia._dia_base | from ._sputils import isdense, isscalarlike, isshape, upcast_char, getdtype, get_sum_dtype, validateaxis, check_shape
import numpy as np
from ._base import issparse, _formats, _spbase, sparray
from .._lib._util import copy_if_needed
from ._sparsetools import dia_matmat, dia_matvec, dia_matvecs
from ._data import _data_... |
class _dia_base(_data_matrix):
def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
pass
def __repr__(self):
pass
def _data_mask(self):
'''Returns a mask of the same shape as self.data, where
mask[i,j] is True when data[i,j] corresponds to a sto... | 22 | 2 | 20 | 2 | 17 | 2 | 4 | 0.1 | 1 | 8 | 1 | 2 | 21 | 5 | 21 | 162 | 472 | 72 | 365 | 114 | 343 | 37 | 301 | 111 | 279 | 17 | 7 | 4 | 80 |
322,089 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dia.py | scipy.sparse._dia.dia_array | from ._base import issparse, _formats, _spbase, sparray
class dia_array(_dia_base, sparray):
"""
Sparse array with DIAgonal storage.
This can be instantiated in several ways:
dia_array(D)
where D is a 2-D ndarray
dia_array(S)
with another sparse array or matrix S (... |
class dia_array(_dia_base, sparray):
'''
Sparse array with DIAgonal storage.
This can be instantiated in several ways:
dia_array(D)
where D is a 2-D ndarray
dia_array(S)
with another sparse array or matrix S (equivalent to S.todia())
dia_array((M, N), [dtype]... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 62 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 163 | 74 | 11 | 1 | 1 | 0 | 62 | 1 | 1 | 0 | 0 | 8 | 0 | 0 |
322,090 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dia.py | scipy.sparse._dia.dia_matrix | from ._matrix import spmatrix
class dia_matrix(spmatrix, _dia_base):
"""
Sparse matrix with DIAgonal storage.
This can be instantiated in several ways:
dia_matrix(D)
where D is a 2-D ndarray
dia_matrix(S)
with another sparse array or matrix S (equivalent to S.todia... |
class dia_matrix(spmatrix, _dia_base):
'''
Sparse matrix with DIAgonal storage.
This can be instantiated in several ways:
dia_matrix(D)
where D is a 2-D ndarray
dia_matrix(S)
with another sparse array or matrix S (equivalent to S.todia())
dia_matrix((M, N), [... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 62 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 183 | 74 | 11 | 1 | 1 | 0 | 62 | 1 | 1 | 0 | 0 | 8 | 0 | 0 |
322,091 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dok.py | scipy.sparse._dok._dok_base | from ._sputils import isdense, getdtype, isshape, isintlike, isscalarlike, upcast, upcast_scalar, check_shape
import itertools
from ._index import IndexMixin
from ._base import _spbase, sparray, issparse
import numpy as np
class _dok_base(_spbase, IndexMixin, dict):
_format = 'dok'
_allow_nd = (1, 2)
def ... |
class _dok_base(_spbase, IndexMixin, dict):
def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
pass
def update(self, val):
pass
def _getnnz(self, axis=None):
pass
def count_nonzero(self, axis=None):
pass
def __len__(self):
pa... | 57 | 1 | 7 | 0 | 7 | 0 | 3 | 0.08 | 3 | 18 | 0 | 2 | 54 | 4 | 55 | 231 | 490 | 78 | 385 | 137 | 328 | 30 | 355 | 134 | 299 | 10 | 6 | 4 | 140 |
322,092 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dok.py | scipy.sparse._dok.dok_array | from ._base import _spbase, sparray, issparse
class dok_array(_dok_base, sparray):
"""
Dictionary Of Keys based sparse array.
This is an efficient structure for constructing sparse
arrays incrementally.
This can be instantiated in several ways:
dok_array(D)
where D is a 2-D nd... |
class dok_array(_dok_base, sparray):
'''
Dictionary Of Keys based sparse array.
This is an efficient structure for constructing sparse
arrays incrementally.
This can be instantiated in several ways:
dok_array(D)
where D is a 2-D ndarray
dok_array(S)
with anot... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 40 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 232 | 51 | 10 | 1 | 1 | 0 | 40 | 1 | 1 | 0 | 0 | 7 | 0 | 0 |
322,093 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_dok.py | scipy.sparse._dok.dok_matrix | from ._matrix import spmatrix
class dok_matrix(spmatrix, _dok_base):
"""
Dictionary Of Keys based sparse matrix.
This is an efficient structure for constructing sparse
matrices incrementally.
This can be instantiated in several ways:
dok_matrix(D)
where D is a 2-D ndarray
... |
class dok_matrix(spmatrix, _dok_base):
'''
Dictionary Of Keys based sparse matrix.
This is an efficient structure for constructing sparse
matrices incrementally.
This can be instantiated in several ways:
dok_matrix(D)
where D is a 2-D ndarray
dok_matrix(S)
wi... | 7 | 2 | 4 | 0 | 4 | 0 | 2 | 1.78 | 2 | 0 | 0 | 0 | 6 | 1 | 6 | 258 | 81 | 17 | 23 | 10 | 16 | 41 | 22 | 10 | 15 | 2 | 7 | 1 | 9 |
322,094 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_index.py | scipy.sparse._index.IndexMixin | from ._sputils import isintlike
import numpy as np
from ._base import sparray, issparse
class IndexMixin:
"""
This class provides common dispatching and validation logic for indexing.
"""
def __getitem__(self, key):
index, new_shape = self._validate_indices(key)
if len(index) == 1:
... |
class IndexMixin:
'''
This class provides common dispatching and validation logic for indexing.
'''
def __getitem__(self, key):
pass
def __setitem__(self, key, x):
pass
def _validate_indices(self, key):
'''Returns two tuples: (index tuple, requested shape tuple)''... | 25 | 5 | 15 | 1 | 13 | 2 | 5 | 0.14 | 0 | 13 | 1 | 3 | 24 | 0 | 24 | 24 | 396 | 47 | 310 | 66 | 285 | 42 | 268 | 63 | 243 | 32 | 0 | 3 | 110 |
322,095 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_lil.py | scipy.sparse._lil._lil_base | from bisect import bisect_left
from ._index import IndexMixin, INT_TYPES, _broadcast_arrays
from . import _csparsetools
from ._base import _spbase, sparray, issparse
from ._sputils import getdtype, isshape, isscalarlike, upcast_scalar, check_shape, check_reshape_kwargs
import numpy as np
class _lil_base(_spbase, Index... |
class _lil_base(_spbase, IndexMixin):
def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
pass
def __iadd__(self, other):
pass
def __isub__(self, other):
pass
def __imul__(self, other):
pass
def __itruediv__(self, other):
pass... | 36 | 3 | 10 | 1 | 9 | 1 | 3 | 0.13 | 2 | 13 | 1 | 2 | 35 | 5 | 35 | 184 | 415 | 66 | 311 | 97 | 275 | 39 | 274 | 95 | 238 | 10 | 6 | 3 | 88 |
322,096 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_lil.py | scipy.sparse._lil.lil_array | from ._base import _spbase, sparray, issparse
class lil_array(_lil_base, sparray):
"""
Row-based LIst of Lists sparse array.
This is a structure for constructing sparse arrays incrementally.
Note that inserting a single item can take linear time in the worst case;
to construct the array efficientl... |
class lil_array(_lil_base, sparray):
'''
Row-based LIst of Lists sparse array.
This is a structure for constructing sparse arrays incrementally.
Note that inserting a single item can take linear time in the worst case;
to construct the array efficiently, make sure the items are pre-sorted by
in... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 51 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 185 | 63 | 11 | 1 | 1 | 0 | 51 | 1 | 1 | 0 | 0 | 7 | 0 | 0 |
322,097 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_lil.py | scipy.sparse._lil.lil_matrix | from ._matrix import spmatrix
class lil_matrix(spmatrix, _lil_base):
"""
Row-based LIst of Lists sparse matrix.
This is a structure for constructing sparse matrices incrementally.
Note that inserting a single item can take linear time in the worst case;
to construct the matrix efficiently, make su... |
class lil_matrix(spmatrix, _lil_base):
'''
Row-based LIst of Lists sparse matrix.
This is a structure for constructing sparse matrices incrementally.
Note that inserting a single item can take linear time in the worst case;
to construct the matrix efficiently, make sure the items are pre-sorted by
... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 51 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 205 | 63 | 11 | 1 | 1 | 0 | 51 | 1 | 1 | 0 | 0 | 7 | 0 | 0 |
322,098 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/_matrix.py | scipy.sparse._matrix.spmatrix | class spmatrix:
"""This class provides a base class for all sparse matrix classes.
It cannot be instantiated. Most of the work is provided by subclasses.
"""
_allow_nd = (2,)
@property
def _bsr_container(self):
from ._bsr import bsr_matrix
return bsr_matrix
@property
... | class spmatrix:
'''This class provides a base class for all sparse matrix classes.
It cannot be instantiated. Most of the work is provided by subclasses.
'''
@property
def _bsr_container(self):
pass
@property
def _coo_container(self):
pass
@property
def _csc_cont... | 30 | 12 | 6 | 0 | 2 | 3 | 1 | 1.11 | 0 | 8 | 7 | 7 | 20 | 1 | 21 | 21 | 169 | 34 | 64 | 43 | 25 | 71 | 55 | 35 | 24 | 1 | 0 | 0 | 21 |
322,099 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py | scipy.sparse.linalg._dsolve.linsolve.MatrixRankWarning | class MatrixRankWarning(UserWarning):
"""Warning for exactly singular matrices."""
pass | class MatrixRankWarning(UserWarning):
'''Warning for exactly singular matrices.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
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