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321,800 | 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/cluster/vq.py | scipy.cluster.vq.ClusterError | class ClusterError(Exception):
pass | class ClusterError(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 |
321,801 | 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/constants/_codata.py | scipy.constants._codata.ConstantWarning | class ConstantWarning(DeprecationWarning):
"""Accessing a constant no longer in current CODATA data set"""
pass | class ConstantWarning(DeprecationWarning):
'''Accessing a constant no longer in current CODATA data set'''
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 |
321,802 | 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/fft/_backend.py | scipy.fft._backend._ScipyBackend | from . import _realtransforms_backend
from . import _fftlog_backend
from . import _basic_backend
class _ScipyBackend:
"""The default backend for fft calculations
Notes
-----
We use the domain ``numpy.scipy`` rather than ``scipy`` because ``uarray``
treats the domain as a hierarchy. This means the ... |
class _ScipyBackend:
'''The default backend for fft calculations
Notes
-----
We use the domain ``numpy.scipy`` rather than ``scipy`` because ``uarray``
treats the domain as a hierarchy. This means the user can install a single
backend for ``numpy`` and have it implement ``numpy.scipy.fft`` as w... | 3 | 1 | 10 | 1 | 9 | 0 | 4 | 0.58 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 22 | 3 | 12 | 5 | 9 | 7 | 11 | 4 | 9 | 4 | 0 | 1 | 4 |
321,803 | 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/fft/_debug_backends.py | scipy.fft._debug_backends.EchoBackend | class EchoBackend:
"""Backend that just prints the __ua_function__ arguments"""
__ua_domain__ = 'numpy.scipy.fft'
@staticmethod
def __ua_function__(method, args, kwargs):
print(method, args, kwargs, sep='\n') | class EchoBackend:
'''Backend that just prints the __ua_function__ arguments'''
@staticmethod
def __ua_function__(method, args, kwargs):
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 7 | 1 | 5 | 4 | 2 | 1 | 4 | 3 | 2 | 1 | 0 | 0 | 1 |
321,804 | 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/fft/_debug_backends.py | scipy.fft._debug_backends.NumPyBackend | import numpy as np
class NumPyBackend:
"""Backend that uses numpy.fft"""
__ua_domain__ = 'numpy.scipy.fft'
@staticmethod
def __ua_function__(method, args, kwargs):
kwargs.pop('overwrite_x', None)
fn = getattr(np.fft, method.__name__, None)
return NotImplemented if fn is None el... |
class NumPyBackend:
'''Backend that uses numpy.fft'''
@staticmethod
def __ua_function__(method, args, kwargs):
pass | 3 | 1 | 6 | 1 | 5 | 0 | 2 | 0.13 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 11 | 2 | 8 | 5 | 5 | 1 | 6 | 4 | 4 | 2 | 0 | 0 | 2 |
321,805 | 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/integrate/_bvp.py | scipy.integrate._bvp.BVPResult | from scipy.optimize import OptimizeResult
class BVPResult(OptimizeResult):
pass |
class BVPResult(OptimizeResult):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,806 | 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/integrate/_cubature.py | scipy.integrate._cubature.CubatureRegion | from dataclasses import dataclass, field
from types import ModuleType
@dataclass
class CubatureRegion:
estimate: Array
error: Array
a: Array
b: Array
_xp: ModuleType = field(repr=False)
def __lt__(self, other):
this_err = self._xp.max(self._xp.abs(self.error))
other_err = self.... | @dataclass
class CubatureRegion:
def __lt__(self, other):
pass | 3 | 0 | 9 | 2 | 4 | 3 | 1 | 0.3 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 16 | 3 | 10 | 5 | 8 | 3 | 10 | 5 | 8 | 1 | 0 | 0 | 1 |
321,807 | 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/integrate/_cubature.py | scipy.integrate._cubature.CubatureResult | from dataclasses import dataclass, field
@dataclass
class CubatureResult:
estimate: Array
error: Array
status: str
regions: list[CubatureRegion]
subdivisions: int
atol: float
rtol: float | @dataclass
class CubatureResult:
pass | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 8 | 1 | 7 | 0 | 8 | 1 | 7 | 0 | 0 | 0 | 0 |
321,808 | 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/integrate/_cubature.py | scipy.integrate._cubature._InfiniteLimitsTransform | from scipy._lib._array_api import array_namespace, xp_size, xp_copy, xp_promote
import math
class _InfiniteLimitsTransform(_VariableTransform):
"""
Transformation for handling infinite limits.
Assuming ``a = [a_1, ..., a_n]`` and ``b = [b_1, ..., b_n]``:
If :math:`a_i = -\\infty` and :math:`b_i = \\i... |
class _InfiniteLimitsTransform(_VariableTransform):
'''
Transformation for handling infinite limits.
Assuming ``a = [a_1, ..., a_n]`` and ``b = [b_1, ..., b_n]``:
If :math:`a_i = -\infty` and :math:`b_i = \infty`, the i-th integration variable
will use the transformation :math:`x = \frac{1-|t|}{t}`... | 8 | 1 | 23 | 5 | 14 | 4 | 1 | 0.46 | 1 | 0 | 0 | 0 | 5 | 7 | 5 | 9 | 141 | 33 | 74 | 33 | 66 | 34 | 49 | 31 | 43 | 2 | 1 | 1 | 6 |
321,809 | 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/integrate/_cubature.py | scipy.integrate._cubature._VariableTransform | class _VariableTransform:
"""
A transformation that can be applied to an integral.
"""
@property
def transformed_limits(self):
"""
New limits of integration after applying the transformation.
"""
raise NotImplementedError
@property
def points(self):
... | class _VariableTransform:
'''
A transformation that can be applied to an integral.
'''
@property
def transformed_limits(self):
'''
New limits of integration after applying the transformation.
'''
pass
@property
def points(self):
'''
Any problem... | 7 | 5 | 11 | 2 | 2 | 6 | 1 | 2.55 | 0 | 1 | 0 | 1 | 4 | 0 | 4 | 4 | 52 | 13 | 11 | 7 | 4 | 28 | 9 | 5 | 4 | 1 | 0 | 0 | 4 |
321,810 | 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/integrate/_ivp/base.py | scipy.integrate._ivp.base.ConstantDenseOutput | import numpy as np
class ConstantDenseOutput(DenseOutput):
"""Constant value interpolator.
This class used for degenerate integration cases: equal integration limits
or a system with 0 equations.
"""
def __init__(self, t_old, t, value):
super().__init__(t_old, t)
self.value = valu... |
class ConstantDenseOutput(DenseOutput):
'''Constant value interpolator.
This class used for degenerate integration cases: equal integration limits
or a system with 0 equations.
'''
def __init__(self, t_old, t, value):
pass
def _call_impl(self, t):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.36 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 5 | 17 | 2 | 11 | 5 | 8 | 4 | 10 | 5 | 7 | 2 | 1 | 1 | 3 |
321,811 | 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/integrate/_ivp/base.py | scipy.integrate._ivp.base.DenseOutput | import numpy as np
class DenseOutput:
"""Base class for local interpolant over step made by an ODE solver.
It interpolates between `t_min` and `t_max` (see Attributes below).
Evaluation outside this interval is not forbidden, but the accuracy is not
guaranteed.
Attributes
----------
t_min... |
class DenseOutput:
'''Base class for local interpolant over step made by an ODE solver.
It interpolates between `t_min` and `t_max` (see Attributes below).
Evaluation outside this interval is not forbidden, but the accuracy is not
guaranteed.
Attributes
----------
t_min, t_max : float
... | 4 | 2 | 8 | 1 | 4 | 4 | 1 | 1.54 | 0 | 2 | 0 | 6 | 3 | 4 | 3 | 3 | 39 | 6 | 13 | 8 | 9 | 20 | 13 | 8 | 9 | 2 | 0 | 1 | 4 |
321,812 | 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/integrate/_ivp/base.py | scipy.integrate._ivp.base.OdeSolver | import numpy as np
class OdeSolver:
"""Base class for ODE solvers.
In order to implement a new solver you need to follow the guidelines:
1. A constructor must accept parameters presented in the base class
(listed below) along with any other parameters specific to a solver.
2. A con... |
class OdeSolver:
'''Base class for ODE solvers.
In order to implement a new solver you need to follow the guidelines:
1. A constructor must accept parameters presented in the base class
(listed below) along with any other parameters specific to a solver.
2. A constructor must accept ... | 11 | 3 | 12 | 1 | 8 | 2 | 2 | 1.58 | 0 | 4 | 1 | 4 | 6 | 15 | 6 | 6 | 203 | 25 | 69 | 32 | 57 | 109 | 60 | 30 | 50 | 5 | 0 | 3 | 19 |
321,813 | 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/integrate/_ivp/bdf.py | scipy.integrate._ivp.bdf.BDF | from scipy.optimize._numdiff import group_columns
import numpy as np
from .base import OdeSolver, DenseOutput
from scipy.sparse.linalg import splu
from .common import validate_max_step, validate_tol, select_initial_step, norm, EPS, num_jac, validate_first_step, warn_extraneous
from scipy.sparse import issparse, csc_mat... |
class BDF(OdeSolver):
'''Implicit method based on backward-differentiation formulas.
This is a variable order method with the order varying automatically from
1 to 5. The general framework of the BDF algorithm is described in [1]_.
This class implements a quasi-constant step size as explained in [2]_.
... | 12 | 1 | 25 | 4 | 21 | 1 | 3 | 0.59 | 1 | 7 | 2 | 0 | 4 | 23 | 4 | 10 | 382 | 58 | 204 | 79 | 190 | 120 | 178 | 77 | 166 | 16 | 1 | 4 | 36 |
321,814 | 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/integrate/_ivp/bdf.py | scipy.integrate._ivp.bdf.BdfDenseOutput | from .base import OdeSolver, DenseOutput
import numpy as np
class BdfDenseOutput(DenseOutput):
def __init__(self, t_old, t, h, order, D):
super().__init__(t_old, t)
self.order = order
self.t_shift = self.t - h * np.arange(self.order)
self.denom = h * (1 + np.arange(self.order))
... |
class BdfDenseOutput(DenseOutput):
def __init__(self, t_old, t, h, order, D):
pass
def _call_impl(self, t):
pass | 3 | 0 | 11 | 1 | 10 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 5 | 23 | 3 | 20 | 10 | 17 | 0 | 18 | 10 | 15 | 3 | 1 | 1 | 4 |
321,815 | 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/integrate/_ivp/common.py | scipy.integrate._ivp.common.OdeSolution | import numpy as np
from itertools import groupby
class OdeSolution:
"""Continuous ODE solution.
It is organized as a collection of `DenseOutput` objects which represent
local interpolants. It provides an algorithm to select a right interpolant
for each given point.
The interpolants cover the rang... |
class OdeSolution:
'''Continuous ODE solution.
It is organized as a collection of `DenseOutput` objects which represent
local interpolants. It provides an algorithm to select a right interpolant
for each given point.
The interpolants cover the range between `t_min` and `t_max` (see
Attributes b... | 4 | 2 | 27 | 4 | 18 | 5 | 4 | 0.85 | 0 | 3 | 0 | 0 | 3 | 8 | 3 | 3 | 121 | 19 | 55 | 24 | 51 | 47 | 52 | 24 | 48 | 6 | 0 | 1 | 12 |
321,816 | 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/integrate/_ivp/ivp.py | scipy.integrate._ivp.ivp.OdeResult | from scipy.optimize import OptimizeResult
class OdeResult(OptimizeResult):
pass |
class OdeResult(OptimizeResult):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,817 | 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/integrate/_ivp/lsoda.py | scipy.integrate._ivp.lsoda.LSODA | from .common import validate_tol, validate_first_step, warn_extraneous
from .base import OdeSolver, DenseOutput
from scipy.integrate import ode
import numpy as np
class LSODA(OdeSolver):
"""Adams/BDF method with automatic stiffness detection and switching.
This is a wrapper to the Fortran solver from ODEPACK ... |
class LSODA(OdeSolver):
'''Adams/BDF method with automatic stiffness detection and switching.
This is a wrapper to the Fortran solver from ODEPACK [1]_. It switches
automatically between the nonstiff Adams method and the stiff BDF method.
The method was originally detailed in [2]_.
Parameters
-... | 4 | 1 | 30 | 5 | 17 | 8 | 3 | 2.4 | 1 | 4 | 2 | 0 | 3 | 6 | 3 | 9 | 202 | 24 | 53 | 21 | 47 | 127 | 43 | 18 | 39 | 5 | 1 | 1 | 9 |
321,818 | 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/integrate/_ivp/lsoda.py | scipy.integrate._ivp.lsoda.LsodaDenseOutput | import numpy as np
from .base import OdeSolver, DenseOutput
class LsodaDenseOutput(DenseOutput):
def __init__(self, t_old, t, h, order, yh):
super().__init__(t_old, t)
self.h = h
self.yh = yh
self.p = np.arange(order + 1)
def _call_impl(self, t):
if t.ndim == 0:
... |
class LsodaDenseOutput(DenseOutput):
def __init__(self, t_old, t, h, order, yh):
pass
def _call_impl(self, t):
pass | 3 | 0 | 6 | 1 | 6 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 3 | 2 | 5 | 14 | 2 | 12 | 7 | 9 | 0 | 11 | 7 | 8 | 2 | 1 | 1 | 3 |
321,819 | 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/integrate/_ivp/radau.py | scipy.integrate._ivp.radau.Radau | from scipy.sparse import csc_matrix, issparse, eye
from .common import validate_max_step, validate_tol, select_initial_step, norm, num_jac, EPS, warn_extraneous, validate_first_step
from scipy.optimize._numdiff import group_columns
from scipy.sparse.linalg import splu
from scipy.linalg import lu_factor, lu_solve
import... |
class Radau(OdeSolver):
'''Implicit Runge-Kutta method of Radau IIA family of order 5.
The implementation follows [1]_. The error is controlled with a
third-order accurate embedded formula. A cubic polynomial which satisfies
the collocation conditions is used for the dense output.
Parameters
--... | 13 | 1 | 23 | 4 | 18 | 0 | 3 | 0.54 | 1 | 5 | 2 | 0 | 5 | 25 | 5 | 11 | 368 | 60 | 200 | 76 | 185 | 108 | 176 | 74 | 163 | 17 | 1 | 4 | 38 |
321,820 | 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/integrate/_ivp/radau.py | scipy.integrate._ivp.radau.RadauDenseOutput | from .base import OdeSolver, DenseOutput
import numpy as np
class RadauDenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, Q):
super().__init__(t_old, t)
self.h = t - t_old
self.Q = Q
self.order = Q.shape[1] - 1
self.y_old = y_old
def _call_impl(self, t):
... |
class RadauDenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, Q):
pass
def _call_impl(self, t):
pass | 3 | 0 | 11 | 1 | 10 | 1 | 2 | 0.05 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 5 | 24 | 2 | 21 | 10 | 18 | 1 | 19 | 10 | 16 | 3 | 1 | 1 | 4 |
321,821 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.DOP853 | from . import dop853_coefficients
import numpy as np
class DOP853(RungeKutta):
"""Explicit Runge-Kutta method of order 8.
This is a Python implementation of "DOP853" algorithm originally written
in Fortran [1]_, [2]_. Note that this is not a literal translation, but
the algorithmic core and coefficien... |
class DOP853(RungeKutta):
'''Explicit Runge-Kutta method of order 8.
This is a Python implementation of "DOP853" algorithm originally written
in Fortran [1]_, [2]_. Note that this is not a literal translation, but
the algorithmic core and coefficients are the same.
Can be applied in the complex dom... | 5 | 1 | 11 | 1 | 10 | 0 | 2 | 1.45 | 1 | 4 | 1 | 0 | 4 | 3 | 4 | 15 | 143 | 14 | 53 | 38 | 46 | 77 | 47 | 35 | 42 | 2 | 2 | 1 | 6 |
321,822 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.Dop853DenseOutput | from .base import OdeSolver, DenseOutput
import numpy as np
class Dop853DenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, F):
super().__init__(t_old, t)
self.h = t - t_old
self.F = F
self.y_old = y_old
def _call_impl(self, t):
x = (t - self.t_old) / self.h
... |
class Dop853DenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, F):
pass
def _call_impl(self, t):
pass | 3 | 0 | 12 | 2 | 10 | 0 | 3 | 0 | 1 | 3 | 0 | 0 | 2 | 3 | 2 | 5 | 25 | 4 | 21 | 9 | 18 | 0 | 19 | 9 | 16 | 4 | 1 | 2 | 5 |
321,823 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.RK23 | import numpy as np
class RK23(RungeKutta):
"""Explicit Runge-Kutta method of order 3(2).
This uses the Bogacki-Shampine pair of formulas [1]_. The error is controlled
assuming accuracy of the second-order method, but steps are taken using the
third-order accurate formula (local extrapolation is done).... |
class RK23(RungeKutta):
'''Explicit Runge-Kutta method of order 3(2).
This uses the Bogacki-Shampine pair of formulas [1]_. The error is controlled
assuming accuracy of the second-order method, but steps are taken using the
third-order accurate formula (local extrapolation is done). A cubic Hermite
... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 108 | 8 | 16 | 9 | 15 | 84 | 9 | 9 | 8 | 0 | 2 | 0 | 0 |
321,824 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.RK45 | import numpy as np
class RK45(RungeKutta):
"""Explicit Runge-Kutta method of order 5(4).
This uses the Dormand-Prince pair of formulas [1]_. The error is controlled
assuming accuracy of the fourth-order method accuracy, but steps are taken
using the fifth-order accurate formula (local extrapolation is... |
class RK45(RungeKutta):
'''Explicit Runge-Kutta method of order 5(4).
This uses the Dormand-Prince pair of formulas [1]_. The error is controlled
assuming accuracy of the fourth-order method accuracy, but steps are taken
using the fifth-order accurate formula (local extrapolation is done).
A quarti... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2.82 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 112 | 5 | 28 | 9 | 27 | 79 | 9 | 9 | 8 | 0 | 2 | 0 | 0 |
321,825 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.RkDenseOutput | from .base import OdeSolver, DenseOutput
import numpy as np
class RkDenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, Q):
super().__init__(t_old, t)
self.h = t - t_old
self.Q = Q
self.order = Q.shape[1] - 1
self.y_old = y_old
def _call_impl(self, t):
... |
class RkDenseOutput(DenseOutput):
def __init__(self, t_old, t, y_old, Q):
pass
def _call_impl(self, t):
pass | 3 | 0 | 11 | 1 | 10 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 5 | 23 | 2 | 21 | 10 | 18 | 0 | 19 | 10 | 16 | 3 | 1 | 1 | 4 |
321,826 | 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/integrate/_ivp/rk.py | scipy.integrate._ivp.rk.RungeKutta | from .common import validate_max_step, validate_tol, select_initial_step, norm, warn_extraneous, validate_first_step
import numpy as np
from .base import OdeSolver, DenseOutput
class RungeKutta(OdeSolver):
"""Base class for explicit Runge-Kutta methods."""
C: np.ndarray = NotImplemented
A: np.ndarray = Not... |
class RungeKutta(OdeSolver):
'''Base class for explicit Runge-Kutta methods.'''
def __init__(self, fun, t0, y0, t_bound, max_step=np.inf, rtol=0.001, atol=1e-06, vectorized=False, first_step=None, **extraneous):
pass
def _estimate_error(self, K, h):
pass
def _estimate_error_norm(self... | 6 | 1 | 18 | 3 | 15 | 0 | 3 | 0.01 | 1 | 2 | 1 | 3 | 5 | 12 | 5 | 11 | 107 | 22 | 84 | 42 | 76 | 1 | 71 | 40 | 65 | 9 | 1 | 3 | 14 |
321,827 | 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/integrate/_ode.py | scipy.integrate._ode.IntegratorBase | class IntegratorBase:
runner = None
success = None
istate = None
supports_run_relax = None
supports_step = None
supports_solout = False
integrator_classes = []
scalar = float
def acquire_new_handle(self):
self.__class__.active_global_handle += 1
self.handle = self.__... | class IntegratorBase:
def acquire_new_handle(self):
pass
def check_handle(self):
pass
def reset(self, n, has_jac):
'''Prepare integrator for call: allocate memory, set flags, etc.
n - number of equations.
has_jac - if user has supplied function for evaluating Jacob... | 7 | 4 | 5 | 0 | 3 | 2 | 1 | 0.64 | 0 | 2 | 1 | 3 | 6 | 1 | 6 | 6 | 44 | 6 | 25 | 16 | 18 | 16 | 22 | 16 | 15 | 2 | 0 | 1 | 7 |
321,828 | 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/integrate/_ode.py | scipy.integrate._ode.IntegratorConcurrencyError | class IntegratorConcurrencyError(RuntimeError):
"""
Failure due to concurrent usage of an integrator that can be used
only for a single problem at a time.
"""
def __init__(self, name):
msg = f'Integrator `{name}` can be used to solve only a single problem at a time. If you want to integrat... | class IntegratorConcurrencyError(RuntimeError):
'''
Failure due to concurrent usage of an integrator that can be used
only for a single problem at a time.
'''
def __init__(self, name):
pass | 2 | 1 | 5 | 0 | 5 | 0 | 1 | 0.67 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 12 | 12 | 2 | 6 | 3 | 4 | 4 | 4 | 3 | 2 | 1 | 4 | 0 | 1 |
321,829 | 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/integrate/_ode.py | scipy.integrate._ode.complex_ode | from numpy import asarray, array, zeros, isscalar, real, imag, vstack
class complex_ode(ode):
"""
A wrapper of ode for complex systems.
This functions similarly as `ode`, but re-maps a complex-valued
equation system to a real-valued one before using the integrators.
Parameters
----------
... |
class complex_ode(ode):
'''
A wrapper of ode for complex systems.
This functions similarly as `ode`, but re-maps a complex-valued
equation system to a real-valued one before using the integrators.
Parameters
----------
f : callable ``f(t, y, *f_args)``
Rhs of the equation. t is a sc... | 10 | 5 | 15 | 1 | 6 | 7 | 2 | 1.57 | 1 | 2 | 0 | 0 | 8 | 3 | 8 | 18 | 154 | 23 | 51 | 21 | 41 | 80 | 47 | 20 | 38 | 3 | 1 | 1 | 13 |
321,830 | 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/integrate/_ode.py | scipy.integrate._ode.dop853 | from numpy import asarray, array, zeros, isscalar, real, imag, vstack
from . import _dop
class dop853(dopri5):
runner = getattr(_dop, 'dop853', None)
name = 'dop853'
def __init__(self, rtol=1e-06, atol=1e-12, nsteps=500, max_step=0.0, first_step=0.0, safety=0.9, ifactor=6.0, dfactor=0.3, beta=0.0, method=... |
class dop853(dopri5):
def __init__(self, rtol=1e-06, atol=1e-12, nsteps=500, max_step=0.0, first_step=0.0, safety=0.9, ifactor=6.0, dfactor=0.3, beta=0.0, method=None, verbosity=-1):
pass
def reset(self, n, has_jac):
pass | 3 | 0 | 15 | 0 | 15 | 1 | 1 | 0.06 | 1 | 2 | 0 | 0 | 2 | 4 | 2 | 13 | 35 | 2 | 33 | 22 | 19 | 2 | 20 | 11 | 17 | 1 | 2 | 0 | 2 |
321,831 | 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/integrate/_ode.py | scipy.integrate._ode.dopri5 | from numpy import asarray, array, zeros, isscalar, real, imag, vstack
import warnings
from . import _dop
class dopri5(IntegratorBase):
runner = getattr(_dop, 'dopri5', None)
name = 'dopri5'
supports_solout = True
messages = {1: 'computation successful', 2: 'computation successful (interrupted by solout... |
class dopri5(IntegratorBase):
def __init__(self, rtol=1e-06, atol=1e-12, nsteps=500, max_step=0.0, first_step=0.0, safety=0.9, ifactor=10.0, dfactor=0.2, beta=0.0, method=None, verbosity=-1):
pass
def set_solout(self, solout, complex=False):
pass
def reset(self, n, has_jac):
pass... | 6 | 0 | 13 | 0 | 13 | 0 | 2 | 0.03 | 1 | 2 | 0 | 1 | 5 | 18 | 5 | 11 | 82 | 6 | 76 | 43 | 59 | 2 | 53 | 32 | 47 | 3 | 1 | 2 | 9 |
321,832 | 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/integrate/_ode.py | scipy.integrate._ode.lsoda | from numpy import asarray, array, zeros, isscalar, real, imag, vstack
import warnings
from . import _lsoda
class lsoda(IntegratorBase):
runner = getattr(_lsoda, 'lsoda', None)
active_global_handle = 0
messages = {2: 'Integration successful.', -1: 'Excess work done on this call (perhaps wrong Dfun type).', ... |
class lsoda(IntegratorBase):
def __init__(self, with_jacobian=False, rtol=1e-06, atol=1e-12, lband=None, uband=None, nsteps=500, max_step=0.0, min_step=0.0, first_step=0.0, ixpr=0, max_hnil=0, max_order_ns=12, max_order_s=5, method=None):
pass
def reset(self, n, has_jac):
pass
def run(se... | 6 | 0 | 25 | 1 | 22 | 1 | 4 | 0.06 | 1 | 2 | 0 | 0 | 5 | 19 | 5 | 11 | 142 | 13 | 125 | 55 | 106 | 7 | 92 | 42 | 86 | 12 | 1 | 3 | 19 |
321,833 | 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/integrate/_ode.py | scipy.integrate._ode.ode | import warnings
from numpy import asarray, array, zeros, isscalar, real, imag, vstack
class ode:
"""
A generic interface class to numeric integrators.
Solve an equation system :math:`y'(t) = f(t,y)` with (optional) ``jac = df/dy``.
*Note*: The first two arguments of ``f(t, y, ...)`` are in the
op... |
class ode:
'''
A generic interface class to numeric integrators.
Solve an equation system :math:`y'(t) = f(t,y)` with (optional) ``jac = df/dy``.
*Note*: The first two arguments of ``f(t, y, ...)`` are in the
opposite order of the arguments in the system definition function used
by `scipy.integ... | 12 | 9 | 21 | 2 | 7 | 12 | 2 | 4.28 | 0 | 3 | 0 | 1 | 10 | 8 | 10 | 10 | 469 | 74 | 75 | 25 | 63 | 321 | 65 | 23 | 54 | 4 | 0 | 2 | 21 |
321,834 | 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/integrate/_ode.py | scipy.integrate._ode.vode | from . import _vode
import re
from numpy import asarray, array, zeros, isscalar, real, imag, vstack
import warnings
class vode(IntegratorBase):
runner = getattr(_vode, 'dvode', None)
messages = {-1: 'Excess work done on this call. (Perhaps wrong MF.)', -2: 'Excess accuracy requested. (Tolerances too small.)', ... |
class vode(IntegratorBase):
def __init__(self, method='adams', with_jacobian=False, rtol=1e-06, atol=1e-12, lband=None, uband=None, order=12, nsteps=500, max_step=0.0, min_step=0.0, first_step=0.0):
pass
def _determine_mf_and_set_bands(self, has_jac):
'''
Determine the `MF` parameter ... | 7 | 1 | 30 | 4 | 22 | 6 | 5 | 0.24 | 1 | 3 | 0 | 1 | 6 | 17 | 6 | 12 | 200 | 28 | 145 | 54 | 128 | 35 | 105 | 44 | 98 | 12 | 1 | 3 | 30 |
321,835 | 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/integrate/_ode.py | scipy.integrate._ode.zvode | from numpy import asarray, array, zeros, isscalar, real, imag, vstack
from . import _vode
class zvode(vode):
runner = getattr(_vode, 'zvode', None)
supports_run_relax = 1
supports_step = 1
scalar = complex
active_global_handle = 0
def reset(self, n, has_jac):
mf = self._determine_mf_an... |
class zvode(vode):
def reset(self, n, has_jac):
pass | 2 | 0 | 58 | 7 | 51 | 1 | 16 | 0.02 | 1 | 2 | 0 | 0 | 1 | 6 | 1 | 13 | 66 | 9 | 57 | 20 | 55 | 1 | 44 | 20 | 42 | 16 | 2 | 1 | 16 |
321,836 | 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/integrate/_odepack_py.py | scipy.integrate._odepack_py.ODEintWarning | class ODEintWarning(Warning):
"""Warning raised during the execution of `odeint`."""
pass | class ODEintWarning(Warning):
'''Warning raised during the execution of `odeint`.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,837 | 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/integrate/_quad_vec.py | scipy.integrate._quad_vec.DoubleInfiniteFunc | import sys
class DoubleInfiniteFunc:
"""
Argument transform from (-oo, oo) to (-1, 1)
"""
def __init__(self, func):
self._func = func
self._tmin = sys.float_info.min ** 0.5
def get_t(self, x):
s = -1 if x < 0 else 1
return s / (abs(x) + 1)
def __call__(self, t... |
class DoubleInfiniteFunc:
'''
Argument transform from (-oo, oo) to (-1, 1)
'''
def __init__(self, func):
pass
def get_t(self, x):
pass
def __call__(self, t):
pass | 4 | 1 | 5 | 0 | 4 | 0 | 2 | 0.29 | 0 | 0 | 0 | 0 | 3 | 2 | 3 | 3 | 21 | 3 | 14 | 9 | 10 | 4 | 13 | 9 | 9 | 2 | 0 | 1 | 5 |
321,838 | 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/integrate/_quad_vec.py | scipy.integrate._quad_vec.LRUDict | import collections
class LRUDict(collections.OrderedDict):
def __init__(self, max_size):
self.__max_size = max_size
def __setitem__(self, key, value):
existing_key = key in self
super().__setitem__(key, value)
if existing_key:
self.move_to_end(key)
elif len... |
class LRUDict(collections.OrderedDict):
def __init__(self, max_size):
pass
def __setitem__(self, key, value):
pass
def update(self, other):
pass | 4 | 0 | 4 | 0 | 4 | 0 | 2 | 0.08 | 1 | 2 | 0 | 0 | 3 | 1 | 3 | 53 | 15 | 2 | 12 | 6 | 8 | 1 | 11 | 6 | 7 | 3 | 3 | 1 | 5 |
321,839 | 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/integrate/_quad_vec.py | scipy.integrate._quad_vec.SemiInfiniteFunc | import numpy as np
import sys
class SemiInfiniteFunc:
"""
Argument transform from (start, +-oo) to (0, 1)
"""
def __init__(self, func, start, infty):
self._func = func
self._start = start
self._sgn = -1 if infty < 0 else 1
self._tmin = sys.float_info.min ** 0.5
def... |
class SemiInfiniteFunc:
'''
Argument transform from (start, +-oo) to (0, 1)
'''
def __init__(self, func, start, infty):
pass
def get_t(self, x):
pass
def __call__(self, t):
pass | 4 | 1 | 7 | 0 | 6 | 1 | 2 | 0.28 | 0 | 0 | 0 | 0 | 3 | 4 | 3 | 3 | 26 | 3 | 18 | 11 | 14 | 5 | 17 | 11 | 13 | 2 | 0 | 1 | 6 |
321,840 | 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/integrate/_quad_vec.py | scipy.integrate._quad_vec._Bunch | class _Bunch:
def __init__(self, **kwargs):
self.__keys = kwargs.keys()
self.__dict__.update(**kwargs)
def __repr__(self):
key_value_pairs = ', '.join((f'{k}={repr(self.__dict__[k])}' for k in self.__keys))
return f'_Bunch({key_value_pairs})' | class _Bunch:
def __init__(self, **kwargs):
pass
def __repr__(self):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 10 | 1 | 9 | 5 | 6 | 0 | 7 | 5 | 4 | 1 | 0 | 0 | 2 |
321,841 | 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/integrate/_quadpack_py.py | scipy.integrate._quadpack_py.IntegrationWarning | class IntegrationWarning(UserWarning):
"""
Warning on issues during integration.
"""
pass | class IntegrationWarning(UserWarning):
'''
Warning on issues during integration.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 5 | 0 | 2 | 1 | 1 | 3 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
321,842 | 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/integrate/_quadpack_py.py | scipy.integrate._quadpack_py._NQuad | from functools import partial
class _NQuad:
def __init__(self, func, ranges, opts, full_output):
self.abserr = 0
self.func = func
self.ranges = ranges
self.opts = opts
self.maxdepth = len(ranges)
self.full_output = full_output
if self.full_output:
... |
class _NQuad:
def __init__(self, func, ranges, opts, full_output):
pass
def integrate(self, *args, **kwargs):
pass | 3 | 0 | 24 | 1 | 20 | 3 | 5 | 0.12 | 0 | 3 | 0 | 0 | 2 | 7 | 2 | 2 | 49 | 3 | 41 | 21 | 38 | 5 | 37 | 21 | 34 | 8 | 0 | 2 | 10 |
321,843 | 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/integrate/_quadpack_py.py | scipy.integrate._quadpack_py._OptFunc | class _OptFunc:
def __init__(self, opt):
self.opt = opt
def __call__(self, *args):
"""Return stored dict."""
return self.opt | class _OptFunc:
def __init__(self, opt):
pass
def __call__(self, *args):
'''Return stored dict.'''
pass | 3 | 1 | 3 | 0 | 2 | 1 | 1 | 0.2 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 7 | 1 | 5 | 4 | 2 | 1 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
321,844 | 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/integrate/_quadpack_py.py | scipy.integrate._quadpack_py._RangeFunc | class _RangeFunc:
def __init__(self, range_):
self.range_ = range_
def __call__(self, *args):
"""Return stored value.
*args needed because range_ can be float or func, and is called with
variable number of parameters.
"""
return self.range_ | class _RangeFunc:
def __init__(self, range_):
pass
def __call__(self, *args):
'''Return stored value.
*args needed because range_ can be float or func, and is called with
variable number of parameters.
'''
pass | 3 | 1 | 5 | 1 | 2 | 2 | 1 | 0.8 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 11 | 2 | 5 | 4 | 2 | 4 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
321,845 | 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/integrate/_rules/_base.py | scipy.integrate._rules._base.FixedRule | from scipy._lib._array_api import array_namespace, xp_size
class FixedRule(Rule):
"""
A rule implemented as the weighted sum of function evaluations at fixed nodes.
Attributes
----------
nodes_and_weights : (ndarray, ndarray)
A tuple ``(nodes, weights)`` of nodes at which to evaluate ``f``... |
class FixedRule(Rule):
'''
A rule implemented as the weighted sum of function evaluations at fixed nodes.
Attributes
----------
nodes_and_weights : (ndarray, ndarray)
A tuple ``(nodes, weights)`` of nodes at which to evaluate ``f`` and the
corresponding weights. ``nodes`` should be ... | 5 | 2 | 15 | 3 | 3 | 10 | 1 | 5.64 | 1 | 1 | 0 | 2 | 3 | 1 | 3 | 5 | 89 | 16 | 11 | 7 | 6 | 62 | 10 | 6 | 6 | 2 | 1 | 1 | 4 |
321,846 | 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/integrate/_rules/_base.py | scipy.integrate._rules._base.NestedFixedRule | from scipy._lib._array_api import array_namespace, xp_size
class NestedFixedRule(FixedRule):
"""
A cubature rule with error estimate given by the difference between two underlying
fixed rules.
If constructed as ``NestedFixedRule(higher, lower)``, this will use::
estimate(f, a, b) := higher.es... |
class NestedFixedRule(FixedRule):
'''
A cubature rule with error estimate given by the difference between two underlying
fixed rules.
If constructed as ``NestedFixedRule(higher, lower)``, this will use::
estimate(f, a, b) := higher.estimate(f, a, b)
estimate_error(f, a, b) := \|higher.e... | 7 | 2 | 15 | 2 | 6 | 7 | 2 | 2.11 | 1 | 1 | 0 | 3 | 4 | 3 | 4 | 9 | 105 | 21 | 27 | 14 | 20 | 57 | 21 | 12 | 16 | 2 | 2 | 1 | 7 |
321,847 | 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/integrate/_rules/_base.py | scipy.integrate._rules._base.ProductNestedFixed | from scipy._lib._array_api import array_namespace, xp_size
from functools import cached_property
class ProductNestedFixed(NestedFixedRule):
"""
Find the n-dimensional cubature rule constructed from the Cartesian product of 1-D
`NestedFixedRule` quadrature rules.
Given a list of N 1-dimensional quadrat... |
class ProductNestedFixed(NestedFixedRule):
'''
Find the n-dimensional cubature rule constructed from the Cartesian product of 1-D
`NestedFixedRule` quadrature rules.
Given a list of N 1-dimensional quadrature rules which support error estimation
using NestedFixedRule, this will find the N-dimension... | 6 | 1 | 13 | 2 | 11 | 0 | 2 | 1 | 1 | 1 | 0 | 0 | 3 | 2 | 3 | 12 | 88 | 16 | 36 | 13 | 30 | 36 | 19 | 11 | 15 | 3 | 3 | 2 | 7 |
321,848 | 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/integrate/_rules/_base.py | scipy.integrate._rules._base.Rule | class Rule:
"""
Base class for numerical integration algorithms (cubatures).
Finds an estimate for the integral of ``f`` over the region described by two arrays
``a`` and ``b`` via `estimate`, and find an estimate for the error of this
approximation via `estimate_error`.
If a subclass does not... | class Rule:
'''
Base class for numerical integration algorithms (cubatures).
Finds an estimate for the integral of ``f`` over the region described by two arrays
``a`` and ``b`` via `estimate`, and find an estimate for the error of this
approximation via `estimate_error`.
If a subclass does not i... | 3 | 3 | 42 | 7 | 4 | 31 | 2 | 12.89 | 0 | 1 | 0 | 2 | 2 | 0 | 2 | 2 | 146 | 21 | 9 | 6 | 6 | 116 | 9 | 6 | 6 | 2 | 0 | 1 | 3 |
321,849 | 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/integrate/_rules/_gauss_kronrod.py | scipy.integrate._rules._gauss_kronrod.GaussKronrodQuadrature | from scipy._lib._array_api import np_compat, array_namespace
from functools import cached_property
from ._base import NestedFixedRule
from ._gauss_legendre import GaussLegendreQuadrature
class GaussKronrodQuadrature(NestedFixedRule):
"""
Gauss-Kronrod quadrature.
Gauss-Kronrod rules consist of two quadrat... |
class GaussKronrodQuadrature(NestedFixedRule):
'''
Gauss-Kronrod quadrature.
Gauss-Kronrod rules consist of two quadrature rules, one higher-order and one
lower-order. The higher-order rule is used as the estimate of the integral and the
difference between them is used as an estimate for the error.... | 6 | 1 | 39 | 2 | 36 | 1 | 2 | 0.57 | 1 | 2 | 1 | 0 | 3 | 3 | 3 | 12 | 194 | 21 | 110 | 11 | 104 | 63 | 18 | 9 | 14 | 3 | 3 | 1 | 7 |
321,850 | 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/integrate/_rules/_gauss_legendre.py | scipy.integrate._rules._gauss_legendre.GaussLegendreQuadrature | from scipy.special import roots_legendre
from scipy._lib._array_api import array_namespace, np_compat
from functools import cached_property
from ._base import FixedRule
class GaussLegendreQuadrature(FixedRule):
"""
Gauss-Legendre quadrature.
Parameters
----------
npoints : int
Number of no... |
class GaussLegendreQuadrature(FixedRule):
'''
Gauss-Legendre quadrature.
Parameters
----------
npoints : int
Number of nodes for the higher-order rule.
xp : array_namespace, optional
The namespace for the node and weight arrays. Default is None, where NumPy is
used.
... | 4 | 1 | 10 | 2 | 8 | 1 | 2 | 1.53 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 7 | 53 | 10 | 17 | 7 | 13 | 26 | 11 | 6 | 8 | 3 | 2 | 1 | 4 |
321,851 | 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/integrate/_rules/_genz_malik.py | scipy.integrate._rules._genz_malik.GenzMalikCubature | import itertools
from scipy.integrate._rules import NestedFixedRule
from functools import cached_property
from scipy._lib._array_api import array_namespace, np_compat
import math
class GenzMalikCubature(NestedFixedRule):
"""
Genz-Malik cubature.
Genz-Malik is only defined for integrals of dimension >= 2.
... |
class GenzMalikCubature(NestedFixedRule):
'''
Genz-Malik cubature.
Genz-Malik is only defined for integrals of dimension >= 2.
Parameters
----------
ndim : int
The spatial dimension of the integrand.
xp : array_namespace, optional
The namespace for the node and weight arrays... | 6 | 1 | 38 | 6 | 28 | 3 | 2 | 0.55 | 1 | 6 | 0 | 0 | 3 | 4 | 3 | 12 | 166 | 30 | 88 | 34 | 82 | 48 | 44 | 32 | 40 | 4 | 3 | 1 | 6 |
321,852 | 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/interpolate/_bary_rational.py | scipy.interpolate._bary_rational.AAA | import operator
import scipy
import warnings
import numpy as np
class AAA(_BarycentricRational):
"""
AAA real or complex rational approximation.
As described in [1]_, the AAA algorithm is a greedy algorithm for approximation by
rational functions on a real or complex set of points. The rational approx... |
class AAA(_BarycentricRational):
'''
AAA real or complex rational approximation.
As described in [1]_, the AAA algorithm is a greedy algorithm for approximation by
rational functions on a real or complex set of points. The rational approximation is
represented in a barycentric form from which the r... | 9 | 2 | 30 | 4 | 18 | 8 | 3 | 2.09 | 1 | 6 | 0 | 0 | 6 | 9 | 6 | 13 | 401 | 67 | 108 | 55 | 97 | 226 | 90 | 50 | 83 | 6 | 1 | 2 | 15 |
321,853 | 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/interpolate/_bary_rational.py | scipy.interpolate._bary_rational.FloaterHormannInterpolator | import operator
import numpy as np
class FloaterHormannInterpolator(_BarycentricRational):
"""Floater-Hormann barycentric rational interpolator (C∞ smooth on real axis).
As described in [1]_, the method of Floater and Hormann computes weights for a
barycentric rational interpolant with no poles on the rea... |
class FloaterHormannInterpolator(_BarycentricRational):
'''Floater-Hormann barycentric rational interpolator (C∞ smooth on real axis).
As described in [1]_, the method of Floater and Hormann computes weights for a
barycentric rational interpolant with no poles on the real axis.
Parameters
---------... | 4 | 1 | 6 | 1 | 5 | 0 | 2 | 4.75 | 1 | 3 | 0 | 0 | 3 | 0 | 3 | 10 | 117 | 25 | 16 | 8 | 12 | 76 | 16 | 8 | 12 | 3 | 1 | 2 | 6 |
321,854 | 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/interpolate/_bary_rational.py | scipy.interpolate._bary_rational._BarycentricRational | import numpy as np
import scipy
class _BarycentricRational:
"""Base class for barycentric representation of a rational function."""
def __init__(self, x, y, **kwargs):
z = np.asarray(x)
f = np.asarray(y)
self._input_validation(z, f, **kwargs)
to_keep = np.logical_and.reduce((np... |
class _BarycentricRational:
'''Base class for barycentric representation of a rational function.'''
def __init__(self, x, y, **kwargs):
pass
def _input_validation(self, x, y, **kwargs):
pass
def _compute_weights(z, f, **kwargs):
pass
def __call__(self, z):
'''Eva... | 8 | 5 | 21 | 3 | 13 | 6 | 2 | 0.48 | 0 | 3 | 0 | 2 | 7 | 7 | 7 | 7 | 158 | 25 | 90 | 34 | 82 | 43 | 73 | 34 | 65 | 5 | 0 | 2 | 17 |
321,855 | 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/interpolate/_bsplines.py | scipy.interpolate._bsplines.BSpline | from scipy.special import poch
from math import prod
from scipy.sparse import csr_array
import operator
from . import _fitpack_impl
from . import _dierckx
from scipy._lib._util import normalize_axis_index
import numpy as np
class BSpline:
"""Univariate spline in the B-spline basis.
.. math::
S(x) = \... |
class BSpline:
'''Univariate spline in the B-spline basis.
.. math::
S(x) = \sum_{j=0}^{n-1} c_j B_{j, k; t}(x)
where :math:`B_{j, k; t}` are B-spline basis functions of degree `k`
and knots `t`.
Parameters
----------
t : ndarray, shape (n+k+1,)
knots
c : ndarray, shape... | 18 | 12 | 59 | 11 | 20 | 29 | 5 | 1.94 | 0 | 14 | 2 | 1 | 8 | 5 | 12 | 12 | 868 | 164 | 240 | 73 | 221 | 465 | 205 | 68 | 191 | 11 | 0 | 2 | 59 |
321,856 | 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/interpolate/_cubic.py | scipy.interpolate._cubic.Akima1DInterpolator | from typing import Literal
import numpy as np
class Akima1DInterpolator(CubicHermiteSpline):
"""Akima "visually pleasing" interpolator (C1 smooth).
Fit piecewise cubic polynomials, given vectors x and y. The interpolation
method by Akima uses a continuously differentiable sub-spline built from
piecewi... |
class Akima1DInterpolator(CubicHermiteSpline):
'''Akima "visually pleasing" interpolator (C1 smooth).
Fit piecewise cubic polynomials, given vectors x and y. The interpolation
method by Akima uses a continuously differentiable sub-spline built from
piecewise cubic polynomials. The resultant curve passe... | 7 | 1 | 17 | 2 | 13 | 3 | 2 | 1.89 | 1 | 5 | 0 | 0 | 2 | 1 | 4 | 19 | 189 | 33 | 54 | 23 | 46 | 102 | 42 | 20 | 37 | 6 | 3 | 2 | 9 |
321,857 | 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/interpolate/_cubic.py | scipy.interpolate._cubic.CubicHermiteSpline | import numpy as np
from . import PPoly
class CubicHermiteSpline(PPoly):
"""Piecewise cubic interpolator to fit values and first derivatives (C1 smooth).
The result is represented as a `PPoly` instance.
Parameters
----------
x : array_like, shape (n,)
1-D array containing values of the ind... |
class CubicHermiteSpline(PPoly):
'''Piecewise cubic interpolator to fit values and first derivatives (C1 smooth).
The result is represented as a `PPoly` instance.
Parameters
----------
x : array_like, shape (n,)
1-D array containing values of the independent variable.
Values must be... | 2 | 1 | 18 | 4 | 14 | 0 | 2 | 3.93 | 1 | 1 | 0 | 3 | 1 | 1 | 1 | 15 | 86 | 12 | 15 | 8 | 13 | 59 | 15 | 8 | 13 | 2 | 2 | 1 | 2 |
321,858 | 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/interpolate/_cubic.py | scipy.interpolate._cubic.CubicSpline | from scipy.linalg import solve, solve_banded
import numpy as np
class CubicSpline(CubicHermiteSpline):
"""Piecewise cubic interpolator to fit values (C2 smooth).
Interpolate data with a piecewise cubic polynomial which is twice
continuously differentiable [1]_. The result is represented as a `PPoly`
i... |
class CubicSpline(CubicHermiteSpline):
'''Piecewise cubic interpolator to fit values (C2 smooth).
Interpolate data with a piecewise cubic polynomial which is twice
continuously differentiable [1]_. The result is represented as a `PPoly`
instance with breakpoints matching the given data.
Parameters
... | 4 | 2 | 118 | 17 | 81 | 24 | 16 | 1.18 | 1 | 5 | 0 | 0 | 1 | 1 | 2 | 17 | 405 | 56 | 163 | 32 | 159 | 192 | 128 | 30 | 125 | 16 | 3 | 4 | 31 |
321,859 | 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/interpolate/_cubic.py | scipy.interpolate._cubic.PchipInterpolator | import numpy as np
class PchipInterpolator(CubicHermiteSpline):
"""PCHIP shape-preserving interpolator (C1 smooth).
``x`` and ``y`` are arrays of values used to approximate some function f,
with ``y = f(x)``. The interpolant uses monotonic cubic splines
to find the value of new points. (PCHIP stands f... |
class PchipInterpolator(CubicHermiteSpline):
'''PCHIP shape-preserving interpolator (C1 smooth).
``x`` and ``y`` are arrays of values used to approximate some function f,
with ``y = f(x)``. The interpolant uses monotonic cubic splines
to find the value of new points. (PCHIP stands for Piecewise Cubic
... | 6 | 1 | 24 | 4 | 14 | 6 | 2 | 1.7 | 1 | 3 | 0 | 1 | 1 | 1 | 3 | 18 | 154 | 30 | 46 | 24 | 40 | 78 | 42 | 22 | 38 | 3 | 3 | 1 | 6 |
321,860 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.BivariateSpline | import numpy as np
from . import _dfitpack as dfitpack
class BivariateSpline(_BivariateSplineBase):
"""
Base class for bivariate splines.
This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
of data points ``(x, y, z)... |
class BivariateSpline(_BivariateSplineBase):
'''
Base class for bivariate splines.
This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
of data points ``(x, y, z)``.
This class is meant to be subclassed, not instan... | 5 | 3 | 33 | 5 | 8 | 20 | 3 | 3.6 | 1 | 1 | 0 | 3 | 2 | 0 | 3 | 9 | 137 | 22 | 25 | 7 | 20 | 90 | 22 | 6 | 18 | 7 | 1 | 2 | 9 |
321,861 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.InterpolatedUnivariateSpline | import numpy as np
from numpy import zeros, concatenate, ravel, diff, array
from . import _dfitpack as dfitpack
class InterpolatedUnivariateSpline(UnivariateSpline):
"""
1-D interpolating spline for a given set of data points.
.. legacy:: class
Specifically, we recommend using `make_interp_spline... |
class InterpolatedUnivariateSpline(UnivariateSpline):
'''
1-D interpolating spline for a given set of data points.
.. legacy:: class
Specifically, we recommend using `make_interp_spline` instead.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data.
Spline function passes th... | 2 | 1 | 13 | 2 | 10 | 1 | 2 | 7.09 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | 17 | 105 | 16 | 11 | 5 | 8 | 78 | 8 | 4 | 6 | 2 | 1 | 1 | 2 |
321,862 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.LSQBivariateSpline | from numpy import zeros, concatenate, ravel, diff, array
from . import _dfitpack as dfitpack
import warnings
class LSQBivariateSpline(BivariateSpline):
"""
Weighted least-squares bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order ... |
class LSQBivariateSpline(BivariateSpline):
'''
Weighted least-squares bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
tx, ty : array_like
Strictly ordered 1-D sequences of knots coordinates.
w ... | 2 | 1 | 43 | 3 | 35 | 6 | 5 | 1.5 | 1 | 2 | 0 | 0 | 1 | 3 | 1 | 10 | 98 | 9 | 36 | 15 | 33 | 54 | 28 | 14 | 26 | 5 | 2 | 2 | 5 |
321,863 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.LSQSphereBivariateSpline | import numpy as np
from . import _dfitpack as dfitpack
from numpy import zeros, concatenate, ravel, diff, array
class LSQSphereBivariateSpline(SphereBivariateSpline):
"""
Weighted least-squares bivariate spline approximation in spherical
coordinates.
Determines a smoothing bicubic spline according to ... |
class LSQSphereBivariateSpline(SphereBivariateSpline):
'''
Weighted least-squares bivariate spline approximation in spherical
coordinates.
Determines a smoothing bicubic spline according to a given
set of knots in the `theta` and `phi` directions.
.. versionadded:: 0.11.0
Parameters
---... | 3 | 1 | 22 | 4 | 19 | 0 | 6 | 2.34 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 10 | 153 | 26 | 38 | 10 | 35 | 89 | 36 | 10 | 33 | 9 | 2 | 2 | 11 |
321,864 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.LSQUnivariateSpline | import numpy as np
from . import _dfitpack as dfitpack
from numpy import zeros, concatenate, ravel, diff, array
class LSQUnivariateSpline(UnivariateSpline):
"""
1-D spline with explicit internal knots.
.. legacy:: class
Specifically, we recommend using `make_lsq_spline` instead.
Fits a spli... |
class LSQUnivariateSpline(UnivariateSpline):
'''
1-D spline with explicit internal knots.
.. legacy:: class
Specifically, we recommend using `make_lsq_spline` instead.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `t`
specifies the internal knots of the spline
... | 2 | 1 | 26 | 2 | 23 | 1 | 6 | 3.92 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | 17 | 144 | 26 | 24 | 9 | 21 | 94 | 21 | 8 | 19 | 6 | 1 | 2 | 6 |
321,865 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.RectBivariateSpline | from numpy import zeros, concatenate, ravel, diff, array
import numpy as np
from . import _dfitpack as dfitpack
class RectBivariateSpline(BivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters
----------
... |
class RectBivariateSpline(BivariateSpline):
'''
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters
----------
x,y : array_like
1-D arrays of coordinates in strictly ascending order.
Evaluated points outside t... | 2 | 1 | 31 | 3 | 28 | 0 | 8 | 1.76 | 1 | 1 | 0 | 0 | 1 | 3 | 1 | 10 | 90 | 10 | 29 | 8 | 27 | 51 | 26 | 8 | 24 | 8 | 2 | 1 | 8 |
321,866 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.RectSphereBivariateSpline | import numpy as np
from . import _dfitpack as dfitpack
class RectSphereBivariateSpline(SphereBivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh on a sphere.
Can be used for smoothing data.
.. versionadded:: 0.11.0
Parameters
----------
u : array_like
1-D... |
class RectSphereBivariateSpline(SphereBivariateSpline):
'''
Bivariate spline approximation over a rectangular mesh on a sphere.
Can be used for smoothing data.
.. versionadded:: 0.11.0
Parameters
----------
u : array_like
1-D array of colatitude coordinates in strictly ascending ord... | 3 | 1 | 42 | 7 | 35 | 0 | 10 | 1.76 | 1 | 3 | 0 | 0 | 2 | 4 | 2 | 10 | 235 | 39 | 71 | 13 | 67 | 125 | 58 | 12 | 55 | 19 | 2 | 1 | 20 |
321,867 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.SmoothBivariateSpline | from . import _dfitpack as dfitpack
from numpy import zeros, concatenate, ravel, diff, array
import warnings
class SmoothBivariateSpline(BivariateSpline):
"""
Smooth bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not import... |
class SmoothBivariateSpline(BivariateSpline):
'''
Smooth bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
w : array_like, optional
Positive 1-D sequence of weights, of same length as `x`, `y` and `z... | 2 | 1 | 27 | 3 | 24 | 2 | 5 | 2.52 | 1 | 1 | 0 | 0 | 1 | 3 | 1 | 10 | 97 | 11 | 25 | 9 | 22 | 63 | 20 | 8 | 18 | 5 | 2 | 2 | 5 |
321,868 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.SmoothSphereBivariateSpline | import numpy as np
from . import _dfitpack as dfitpack
class SmoothSphereBivariateSpline(SphereBivariateSpline):
"""
Smooth bivariate spline approximation in spherical coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (... |
class SmoothSphereBivariateSpline(SphereBivariateSpline):
'''
Smooth bivariate spline approximation in spherical coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be give... | 3 | 1 | 20 | 4 | 16 | 1 | 5 | 2.59 | 1 | 1 | 0 | 0 | 2 | 3 | 2 | 10 | 140 | 25 | 32 | 8 | 29 | 83 | 29 | 8 | 26 | 8 | 2 | 2 | 10 |
321,869 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.SphereBivariateSpline | import numpy as np
class SphereBivariateSpline(_BivariateSplineBase):
"""
Bivariate spline s(x,y) of degrees 3 on a sphere, calculated from a
given set of data points (theta,phi,r).
.. versionadded:: 0.11.0
See Also
--------
bisplrep :
a function to find a bivariate B-spline repre... |
class SphereBivariateSpline(_BivariateSplineBase):
'''
Bivariate spline s(x,y) of degrees 3 on a sphere, calculated from a
given set of data points (theta,phi,r).
.. versionadded:: 0.11.0
See Also
--------
bisplrep :
a function to find a bivariate B-spline representation of a surfac... | 3 | 3 | 72 | 15 | 5 | 53 | 2 | 12.2 | 1 | 1 | 0 | 3 | 2 | 0 | 2 | 8 | 165 | 33 | 10 | 3 | 7 | 122 | 9 | 3 | 6 | 2 | 1 | 1 | 3 |
321,870 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2.UnivariateSpline | from . import _fitpack_impl
import numpy as np
from . import _dfitpack as dfitpack
import warnings
from numpy import zeros, concatenate, ravel, diff, array
class UnivariateSpline:
"""
1-D smoothing spline fit to a given set of data points.
.. legacy:: class
Specifically, we recommend using `make_... |
class UnivariateSpline:
'''
1-D smoothing spline fit to a given set of data points.
.. legacy:: class
Specifically, we recommend using `make_splrep` instead.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `s`
specifies the number of knots by specifying a smoothing co... | 19 | 12 | 25 | 4 | 9 | 12 | 3 | 2.23 | 0 | 5 | 2 | 3 | 14 | 5 | 16 | 16 | 583 | 99 | 150 | 50 | 130 | 335 | 126 | 44 | 109 | 14 | 0 | 2 | 44 |
321,871 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2._BivariateSplineBase | from . import _dfitpack as dfitpack
import numpy as np
class _BivariateSplineBase:
""" Base class for Bivariate spline s(x,y) interpolation on the rectangle
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
See Also
--------
bisplrep :
a function to find a bivariat... |
class _BivariateSplineBase:
''' Base class for Bivariate spline s(x,y) interpolation on the rectangle
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
See Also
--------
bisplrep :
a function to find a bivariate B-spline representation of a surface
bisplev :
... | 8 | 7 | 32 | 5 | 14 | 14 | 4 | 1.17 | 0 | 2 | 1 | 3 | 5 | 0 | 6 | 6 | 216 | 36 | 83 | 22 | 75 | 97 | 73 | 21 | 66 | 13 | 0 | 3 | 23 |
321,872 | 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/interpolate/_fitpack2.py | scipy.interpolate._fitpack2._DerivedBivariateSpline | class _DerivedBivariateSpline(_BivariateSplineBase):
"""Bivariate spline constructed from the coefficients and knots of another
spline.
Notes
-----
The class is not meant to be instantiated directly from the data to be
interpolated or smoothed. As a result, its ``fp`` attribute and
``get_re... | class _DerivedBivariateSpline(_BivariateSplineBase):
'''Bivariate spline constructed from the coefficients and knots of another
spline.
Notes
-----
The class is not meant to be instantiated directly from the data to be
interpolated or smoothed. As a result, its ``fp`` attribute and
``get_res... | 4 | 1 | 2 | 0 | 2 | 0 | 1 | 0.91 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 8 | 25 | 4 | 11 | 5 | 7 | 10 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
321,873 | 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/interpolate/_fitpack_repro.py | scipy.interpolate._fitpack_repro.Bunch | class Bunch:
def __init__(self, **kwargs):
self.__dict__.update(**kwargs) | class Bunch:
def __init__(self, **kwargs):
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 |
321,874 | 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/interpolate/_fitpack_repro.py | scipy.interpolate._fitpack_repro.F | from ._bsplines import _not_a_knot, make_interp_spline, BSpline, fpcheck, _lsq_solve_qr
from . import _dierckx
import numpy as np
class F:
""" The r.h.s. of ``f(p) = s``.
Given scalar `p`, we solve the system of equations in the LSQ sense:
| A | @ | c | = | y |
| B / p | | 0 | | 0 |... |
class F:
''' The r.h.s. of ``f(p) = s``.
Given scalar `p`, we solve the system of equations in the LSQ sense:
| A | @ | c | = | y |
| B / p | | 0 | | 0 |
where `A` is the matrix of b-splines and `b` is the discontinuity matrix
(the jumps of the k-th derivatives of b-spline bas... | 3 | 1 | 37 | 8 | 20 | 10 | 4 | 0.9 | 0 | 3 | 1 | 0 | 2 | 12 | 2 | 2 | 100 | 23 | 41 | 29 | 38 | 37 | 41 | 29 | 38 | 6 | 0 | 1 | 7 |
321,875 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate.BPoly | import scipy.special as spec
from . import _ppoly
import numpy as np
from scipy.special import comb
class BPoly(_PPolyBase):
"""Piecewise polynomial in the Bernstein basis.
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
Bernstein polynomial basis::
S = sum(c[a, i] * b(a, k; x)... |
class BPoly(_PPolyBase):
'''Piecewise polynomial in the Bernstein basis.
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
Bernstein polynomial basis::
S = sum(c[a, i] * b(a, k; x) for a in range(k+1)),
where ``k`` is the degree of the polynomial, and::
b(a, k; x) = bin... | 14 | 8 | 51 | 9 | 19 | 23 | 5 | 1.55 | 1 | 8 | 1 | 1 | 5 | 1 | 9 | 15 | 555 | 110 | 175 | 62 | 161 | 271 | 150 | 56 | 140 | 12 | 1 | 3 | 47 |
321,876 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate.NdPPoly | from ._interpnd import _ndim_coords_from_arrays
import scipy.special as spec
from math import prod
import numpy as np
from . import _ppoly
class NdPPoly:
"""
Piecewise tensor product polynomial
The value at point ``xp = (x', y', z', ...)`` is evaluated by first
computing the interval indices `i` such ... |
class NdPPoly:
'''
Piecewise tensor product polynomial
The value at point ``xp = (x', y', z', ...)`` is evaluated by first
computing the interval indices `i` such that::
x[0][i[0]] <= x' < x[0][i[0]+1]
x[1][i[1]] <= y' < x[1][i[1]+1]
...
and then computing::
S = sum(... | 13 | 9 | 32 | 6 | 15 | 12 | 3 | 1.07 | 0 | 11 | 1 | 0 | 10 | 3 | 11 | 11 | 431 | 86 | 167 | 53 | 154 | 178 | 148 | 52 | 136 | 7 | 0 | 2 | 35 |
321,877 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate.PPoly | from ._bsplines import make_interp_spline, BSpline
from scipy.special import comb
from . import _fitpack_py
from math import prod
from . import _ppoly
import scipy.special as spec
import numpy as np
class PPoly(_PPolyBase):
"""Piecewise polynomial in the power basis.
The polynomial between ``x[i]`` and ``x[i ... |
class PPoly(_PPolyBase):
'''Piecewise polynomial in the power basis.
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
local power basis::
S = sum(c[m, i] * (xp - x[i])**(k-m) for m in range(k+1))
where ``k`` is the degree of the polynomial.
Parameters
----------
c ... | 11 | 8 | 49 | 8 | 15 | 26 | 4 | 2.01 | 1 | 11 | 2 | 2 | 6 | 1 | 8 | 14 | 461 | 83 | 126 | 39 | 115 | 253 | 100 | 36 | 91 | 6 | 1 | 2 | 29 |
321,878 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate._PPolyBase | import numpy as np
from math import prod
class _PPolyBase:
"""Base class for piecewise polynomials."""
__slots__ = ('c', 'x', 'extrapolate', 'axis')
def __init__(self, c, x, extrapolate=None, axis=0):
self.c = np.asarray(c)
self.x = np.ascontiguousarray(x, dtype=np.float64)
if extr... |
class _PPolyBase:
'''Base class for piecewise polynomials.'''
def __init__(self, c, x, extrapolate=None, axis=0):
pass
def _get_dtype(self, dtype):
pass
@classmethod
def construct_fast(cls, c, x, extrapolate=None, axis=0):
'''
Construct the piecewise polynomial wit... | 8 | 5 | 35 | 4 | 21 | 11 | 7 | 0.52 | 0 | 5 | 0 | 2 | 5 | 4 | 6 | 6 | 222 | 31 | 126 | 24 | 118 | 65 | 108 | 23 | 101 | 16 | 0 | 2 | 39 |
321,879 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate.interp1d | import numpy as np
from ._polyint import _Interpolator1D
from scipy._lib._util import copy_if_needed
from numpy import array, asarray, intp, poly1d, searchsorted
from ._bsplines import make_interp_spline, BSpline
class interp1d(_Interpolator1D):
"""
Interpolate a 1-D function (legacy).
.. legacy:: class
... |
class interp1d(_Interpolator1D):
'''
Interpolate a 1-D function (legacy).
.. legacy:: class
For a guide to the intended replacements for `interp1d` see
:ref:`tutorial-interpolate_1Dsection`.
`x` and `y` are arrays of values used to approximate some function f:
``y = f(x)``. This cla... | 15 | 6 | 25 | 3 | 15 | 7 | 4 | 0.91 | 1 | 5 | 0 | 0 | 12 | 17 | 12 | 20 | 417 | 62 | 187 | 66 | 170 | 171 | 158 | 62 | 145 | 23 | 1 | 4 | 44 |
321,880 | 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/interpolate/_interpolate.py | scipy.interpolate._interpolate.interp2d | class interp2d:
"""
interp2d(x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None)
Class for 2D interpolation (deprecated and removed)
.. versionremoved:: 1.14.0
`interp2d` has been removed in SciPy 1.14.0.
For legacy code, nearly bug-for-bug compatible... | class interp2d:
'''
interp2d(x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None)
Class for 2D interpolation (deprecated and removed)
.. versionremoved:: 1.14.0
`interp2d` has been removed in SciPy 1.14.0.
For legacy code, nearly bug-for-bug compatible rep... | 2 | 1 | 3 | 0 | 3 | 0 | 1 | 3.5 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 24 | 6 | 4 | 3 | 1 | 14 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
321,881 | 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/interpolate/_ndbspline.py | scipy.interpolate._ndbspline.NdBSpline | import numpy as np
from . import _dierckx
from scipy.sparse import csr_array
class NdBSpline:
"""Tensor product spline object.
The value at point ``xp = (x1, x2, ..., xN)`` is evaluated as a linear
combination of products of one-dimensional b-splines in each of the ``N``
dimensions::
c[i1, i2,... |
class NdBSpline:
'''Tensor product spline object.
The value at point ``xp = (x1, x2, ..., xN)`` is evaluated as a linear
combination of products of one-dimensional b-splines in each of the ``N``
dimensions::
c[i1, i2, ..., iN] * B(x1; i1, t1) * B(x2; i2, t2) * ... * B(xN; iN, tN)
Here ``B(x... | 9 | 3 | 32 | 5 | 17 | 11 | 3 | 1.08 | 0 | 6 | 1 | 0 | 4 | 6 | 5 | 5 | 217 | 37 | 88 | 33 | 79 | 95 | 63 | 30 | 57 | 7 | 0 | 2 | 16 |
321,882 | 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/interpolate/_ndgriddata.py | scipy.interpolate._ndgriddata.NearestNDInterpolator | from ._interpnd import LinearNDInterpolator, NDInterpolatorBase, CloughTocher2DInterpolator, _ndim_coords_from_arrays
from scipy.spatial import cKDTree
import numpy as np
class NearestNDInterpolator(NDInterpolatorBase):
"""Nearest-neighbor interpolator in N > 1 dimensions.
Methods
-------
__call__
... |
class NearestNDInterpolator(NDInterpolatorBase):
'''Nearest-neighbor interpolator in N > 1 dimensions.
Methods
-------
__call__
Parameters
----------
x : (npoints, ndims) 2-D ndarray of floats
Data point coordinates.
y : (npoints, ) 1-D ndarray of float or complex
Data v... | 3 | 2 | 37 | 6 | 16 | 16 | 3 | 2.64 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 2 | 143 | 23 | 33 | 14 | 30 | 87 | 28 | 14 | 25 | 4 | 1 | 1 | 6 |
321,883 | 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/interpolate/_polyint.py | scipy.interpolate._polyint.BarycentricInterpolator | import numpy as np
from scipy._lib._util import _asarray_validated, float_factorial, check_random_state, _transition_to_rng
class BarycentricInterpolator(_Interpolator1DWithDerivatives):
"""Barycentric (Lagrange with improved stability) interpolator (C∞ smooth).
Constructs a polynomial that passes through a g... | null | 9 | 5 | 34 | 4 | 15 | 14 | 4 | 2.16 | 1 | 4 | 0 | 0 | 7 | 8 | 7 | 18 | 413 | 67 | 110 | 34 | 101 | 238 | 100 | 33 | 92 | 10 | 2 | 3 | 27 |
321,884 | 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/interpolate/_polyint.py | scipy.interpolate._polyint.KroghInterpolator | from scipy._lib._util import _asarray_validated, float_factorial, check_random_state, _transition_to_rng
import warnings
import numpy as np
class KroghInterpolator(_Interpolator1DWithDerivatives):
"""Krogh interpolator (C∞ smooth).
The polynomial passes through all the pairs ``(xi, yi)``. One may
addition... |
class KroghInterpolator(_Interpolator1DWithDerivatives):
'''Krogh interpolator (C∞ smooth).
The polynomial passes through all the pairs ``(xi, yi)``. One may
additionally specify a number of derivatives at each point `xi`;
this is done by repeating the value `xi` and specifying the
derivatives as s... | 4 | 1 | 23 | 3 | 20 | 0 | 5 | 0.95 | 1 | 3 | 0 | 0 | 3 | 5 | 3 | 14 | 145 | 26 | 61 | 26 | 57 | 58 | 58 | 25 | 54 | 7 | 2 | 3 | 14 |
321,885 | 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/interpolate/_polyint.py | scipy.interpolate._polyint._Interpolator1D | import numpy as np
from scipy._lib._util import _asarray_validated, float_factorial, check_random_state, _transition_to_rng
class _Interpolator1D:
"""
Common features in univariate interpolation
Deal with input data type and interpolation axis rolling. The
actual interpolator can assume the y-data is ... |
class _Interpolator1D:
'''
Common features in univariate interpolation
Deal with input data type and interpolation axis rolling. The
actual interpolator can assume the y-data is of shape (n, r) where
`n` is the number of x-points, and `r` the number of variables,
and use self.dtype as the y-dat... | 9 | 5 | 10 | 1 | 7 | 3 | 2 | 0.82 | 0 | 4 | 0 | 2 | 8 | 3 | 8 | 8 | 124 | 20 | 57 | 21 | 48 | 47 | 52 | 21 | 43 | 5 | 0 | 2 | 17 |
321,886 | 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/interpolate/_polyint.py | scipy.interpolate._polyint._Interpolator1DWithDerivatives | class _Interpolator1DWithDerivatives(_Interpolator1D):
def derivatives(self, x, der=None):
"""
Evaluate several derivatives of the polynomial at the point `x`
Produce an array of derivatives evaluated at the point `x`.
Parameters
----------
x : array_like
... | class _Interpolator1DWithDerivatives(_Interpolator1D):
def derivatives(self, x, der=None):
'''
Evaluate several derivatives of the polynomial at the point `x`
Produce an array of derivatives evaluated at the point `x`.
Parameters
----------
x : array_like
... | 4 | 3 | 32 | 4 | 6 | 21 | 1 | 3.37 | 1 | 3 | 0 | 2 | 3 | 0 | 3 | 11 | 98 | 15 | 19 | 11 | 15 | 64 | 17 | 11 | 13 | 2 | 1 | 1 | 4 |
321,887 | 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/interpolate/_rbf.py | scipy.interpolate._rbf.Rbf | from scipy.special import xlogy
from scipy.spatial.distance import cdist, pdist, squareform
from scipy import linalg
import numpy as np
class Rbf:
"""
Rbf(*args, **kwargs)
Class for radial basis function interpolation of functions from
N-D scattered data to an M-D domain (legacy).
.. legacy:: cla... |
class Rbf:
'''
Rbf(*args, **kwargs)
Class for radial basis function interpolation of functions from
N-D scattered data to an M-D domain (legacy).
.. legacy:: class
`Rbf` is legacy code, for new usage please use `RBFInterpolator`
instead.
Parameters
----------
*args : arr... | 14 | 1 | 11 | 1 | 9 | 1 | 3 | 0.92 | 0 | 3 | 0 | 0 | 12 | 11 | 12 | 12 | 236 | 33 | 107 | 42 | 93 | 98 | 87 | 41 | 74 | 10 | 0 | 2 | 30 |
321,888 | 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/interpolate/_rbfinterp.py | scipy.interpolate._rbfinterp.RBFInterpolator | from scipy.spatial import KDTree
import warnings
import numpy as np
from ._rbfinterp_pythran import _build_system, _build_evaluation_coefficients, _polynomial_matrix
class RBFInterpolator:
"""Radial basis function interpolator in N ≥ 1 dimensions.
Parameters
----------
y : (npoints, ndims) array_like
... |
class RBFInterpolator:
'''Radial basis function interpolator in N ≥ 1 dimensions.
Parameters
----------
y : (npoints, ndims) array_like
2-D array of data point coordinates.
d : (npoints, ...) array_like
N-D array of data values at `y`. The length of `d` along the first
axis ... | 4 | 3 | 87 | 8 | 58 | 21 | 9 | 1.06 | 0 | 8 | 0 | 0 | 3 | 13 | 3 | 3 | 417 | 56 | 176 | 56 | 159 | 186 | 99 | 43 | 95 | 15 | 0 | 2 | 26 |
321,889 | 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/interpolate/_rgi.py | scipy.interpolate._rgi.RegularGridInterpolator | import scipy.sparse.linalg as ssl
from ._cubic import PchipInterpolator
import itertools
import numpy as np
from ._bsplines import make_interp_spline
from ._interpnd import _ndim_coords_from_arrays
from ._ndbspline import make_ndbspl
from ._rgi_cython import evaluate_linear_2d, find_indices
class RegularGridInterpolat... |
class RegularGridInterpolator:
'''Interpolator of specified order on a rectilinear grid in N ≥ 1 dimensions.
The data must be defined on a rectilinear grid; that is, a rectangular
grid with even or uneven spacing. Linear, nearest-neighbor, spline
interpolations are supported. After setting up the inter... | 19 | 2 | 19 | 2 | 12 | 5 | 3 | 1.19 | 0 | 10 | 1 | 0 | 14 | 7 | 16 | 16 | 546 | 98 | 205 | 80 | 185 | 244 | 157 | 77 | 140 | 13 | 0 | 3 | 54 |
321,890 | 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/io/_fast_matrix_market/__init__.py | scipy.io._fast_matrix_market._TextToBytesWrapper | import io
class _TextToBytesWrapper(io.BufferedReader):
"""
Convert a TextIOBase string stream to a byte stream.
"""
def __init__(self, text_io_buffer, encoding=None, errors=None, **kwargs):
super().__init__(text_io_buffer, **kwargs)
self.encoding = encoding or text_io_buffer.encoding ... |
class _TextToBytesWrapper(io.BufferedReader):
'''
Convert a TextIOBase string stream to a byte stream.
'''
def __init__(self, text_io_buffer, encoding=None, errors=None, **kwargs):
pass
def __del__(self):
pass
def _encoding_call(self, method_name, *args, **kwargs):
pa... | 8 | 1 | 4 | 0 | 3 | 1 | 1 | 0.52 | 0 | 1 | 0 | 0 | 7 | 2 | 7 | 7 | 42 | 7 | 23 | 12 | 15 | 12 | 21 | 12 | 13 | 2 | 0 | 1 | 8 |
321,891 | 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/io/_fortran.py | scipy.io._fortran.FortranEOFError | class FortranEOFError(TypeError, OSError):
"""Indicates that the file ended properly.
This error descends from TypeError because the code used to raise
TypeError (and this was the only way to know that the file had
ended) so users might have ``except TypeError:``.
"""
pass | class FortranEOFError(TypeError, OSError):
'''Indicates that the file ended properly.
This error descends from TypeError because the code used to raise
TypeError (and this was the only way to know that the file had
ended) so users might have ``except TypeError:``.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 9 | 2 | 2 | 1 | 1 | 5 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,892 | 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/io/_fortran.py | scipy.io._fortran.FortranFile | import warnings
import numpy as np
class FortranFile:
"""
A file object for unformatted sequential files from Fortran code.
Parameters
----------
filename : file or str
Open file object or filename.
mode : {'r', 'w'}, optional
Read-write mode, default is 'r'.
header_dtype :... |
class FortranFile:
'''
A file object for unformatted sequential files from Fortran code.
Parameters
----------
filename : file or str
Open file object or filename.
mode : {'r', 'w'}, optional
Read-write mode, default is 'r'.
header_dtype : dtype, optional
Data type o... | 10 | 6 | 26 | 6 | 9 | 12 | 3 | 2.19 | 0 | 5 | 2 | 0 | 9 | 2 | 9 | 9 | 322 | 73 | 78 | 23 | 68 | 171 | 69 | 23 | 59 | 12 | 0 | 3 | 27 |
321,893 | 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/io/_fortran.py | scipy.io._fortran.FortranFormattingError | class FortranFormattingError(TypeError, OSError):
"""Indicates that the file ended mid-record.
Descends from TypeError for backward compatibility.
"""
pass | class FortranFormattingError(TypeError, OSError):
'''Indicates that the file ended mid-record.
Descends from TypeError for backward compatibility.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 7 | 2 | 2 | 1 | 1 | 3 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,894 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.BadFortranFormat | class BadFortranFormat(SyntaxError):
pass | class BadFortranFormat(SyntaxError):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
321,895 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.ExpFormat | import numpy as np
class ExpFormat:
@classmethod
def from_number(cls, n, min=None):
"""Given a float number, returns a "reasonable" ExpFormat instance to
represent any number between -n and n.
Parameters
----------
n : float
max number one wants to be able ... |
class ExpFormat:
@classmethod
def from_number(cls, n, min=None):
'''Given a float number, returns a "reasonable" ExpFormat instance to
represent any number between -n and n.
Parameters
----------
n : float
max number one wants to be able to represent
... | 9 | 2 | 13 | 1 | 6 | 6 | 2 | 0.78 | 0 | 3 | 0 | 0 | 4 | 4 | 5 | 5 | 71 | 7 | 36 | 20 | 27 | 28 | 33 | 17 | 27 | 3 | 0 | 1 | 10 |
321,896 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.FortranFormatParser | import threading
class FortranFormatParser:
"""Parser for Fortran format strings. The parse method returns a *Format
instance.
Notes
-----
Only ExpFormat (exponential format for floating values) and IntFormat
(integer format) for now.
"""
def __init__(self):
self.tokenizer = t... |
class FortranFormatParser:
'''Parser for Fortran format strings. The parse method returns a *Format
instance.
Notes
-----
Only ExpFormat (exponential format for floating values) and IntFormat
(integer format) for now.
'''
def __init__(self):
pass
def parse(self, s):
... | 7 | 1 | 13 | 2 | 12 | 0 | 3 | 0.1 | 0 | 8 | 4 | 0 | 6 | 1 | 6 | 6 | 93 | 15 | 71 | 15 | 64 | 7 | 61 | 14 | 54 | 8 | 0 | 3 | 20 |
321,897 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.IntFormat | class IntFormat:
@classmethod
def from_number(cls, n, min=None):
"""Given an integer, returns a "reasonable" IntFormat instance to represent
any number between 0 and n if n > 0, -n and n if n < 0
Parameters
----------
n : int
max number one wants to be able ... | class IntFormat:
@classmethod
def from_number(cls, n, min=None):
'''Given an integer, returns a "reasonable" IntFormat instance to represent
any number between 0 and n if n > 0, -n and n if n < 0
Parameters
----------
n : int
max number one wants to be able t... | 9 | 1 | 10 | 1 | 6 | 4 | 2 | 0.59 | 0 | 1 | 0 | 0 | 4 | 3 | 5 | 5 | 58 | 7 | 32 | 16 | 23 | 19 | 29 | 13 | 23 | 3 | 0 | 1 | 10 |
321,898 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.Token | class Token:
def __init__(self, type, value, pos):
self.type = type
self.value = value
self.pos = pos
def __str__(self):
return f'''Token('{self.type}', "{self.value}")'''
def __repr__(self):
return self.__str__() | class Token:
def __init__(self, type, value, pos):
pass
def __str__(self):
pass
def __repr__(self):
pass | 4 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 11 | 2 | 9 | 7 | 5 | 0 | 9 | 7 | 5 | 1 | 0 | 0 | 3 |
321,899 | 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/io/_harwell_boeing/_fortran_format_parser.py | scipy.io._harwell_boeing._fortran_format_parser.Tokenizer | import re
class Tokenizer:
def __init__(self):
self.tokens = list(TOKENS.keys())
self.res = [re.compile(TOKENS[i]) for i in self.tokens]
def input(self, s):
self.data = s
self.curpos = 0
self.len = len(s)
def next_token(self):
curpos = self.curpos
... |
class Tokenizer:
def __init__(self):
pass
def input(self, s):
pass
def next_token(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 2 | 0 | 0 | 4 | 1 | 0 | 3 | 5 | 3 | 3 | 25 | 3 | 22 | 12 | 18 | 0 | 18 | 12 | 14 | 4 | 0 | 3 | 6 |
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