repository_name stringlengths 5 67 | func_path_in_repository stringlengths 4 234 | func_name stringlengths 0 314 | whole_func_string stringlengths 52 3.87M | language stringclasses 6
values | func_code_string stringlengths 52 3.87M | func_documentation_string stringlengths 1 47.2k | func_code_url stringlengths 85 339 |
|---|---|---|---|---|---|---|---|
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.from_other | def from_other(cls, ori, **kwargs):
""" Creates a new instance with an existing one as a template.
Parameters
----------
ori : SymbolicSys instance
\\*\\*kwargs:
Keyword arguments used to create the new instance.
Returns
-------
A new instanc... | python | def from_other(cls, ori, **kwargs):
""" Creates a new instance with an existing one as a template.
Parameters
----------
ori : SymbolicSys instance
\\*\\*kwargs:
Keyword arguments used to create the new instance.
Returns
-------
A new instanc... | Creates a new instance with an existing one as a template.
Parameters
----------
ori : SymbolicSys instance
\\*\\*kwargs:
Keyword arguments used to create the new instance.
Returns
-------
A new instance of the class. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L466-L508 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.from_other_new_params | def from_other_new_params(cls, ori, par_subs, new_pars, new_par_names=None,
new_latex_par_names=None, **kwargs):
""" Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other`` but first it replaces some parameters according to ``p... | python | def from_other_new_params(cls, ori, par_subs, new_pars, new_par_names=None,
new_latex_par_names=None, **kwargs):
""" Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other`` but first it replaces some parameters according to ``p... | Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other`` but first it replaces some parameters according to ``par_subs``
and (optionally) introduces new parameters given in ``new_pars``.
Parameters
----------
ori : SymbolicSys instan... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L511-L557 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.from_other_new_params_by_name | def from_other_new_params_by_name(cls, ori, par_subs, new_par_names=(), **kwargs):
""" Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other_new_params`` but first it creates the new instances from user provided
callbacks generating the expressions ... | python | def from_other_new_params_by_name(cls, ori, par_subs, new_par_names=(), **kwargs):
""" Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other_new_params`` but first it creates the new instances from user provided
callbacks generating the expressions ... | Creates a new instance with an existing one as a template (with new parameters)
Calls ``.from_other_new_params`` but first it creates the new instances from user provided
callbacks generating the expressions the parameter substitutions.
Parameters
----------
ori : SymbolicSys i... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L560-L587 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_jac | def get_jac(self):
""" Derives the jacobian from ``self.exprs`` and ``self.dep``. """
if self._jac is True:
if self.sparse is True:
self._jac, self._colptrs, self._rowvals = self.be.sparse_jacobian_csc(self.exprs, self.dep)
elif self.band is not None: # Banded
... | python | def get_jac(self):
""" Derives the jacobian from ``self.exprs`` and ``self.dep``. """
if self._jac is True:
if self.sparse is True:
self._jac, self._colptrs, self._rowvals = self.be.sparse_jacobian_csc(self.exprs, self.dep)
elif self.band is not None: # Banded
... | Derives the jacobian from ``self.exprs`` and ``self.dep``. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L637-L650 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_jtimes | def get_jtimes(self):
""" Derive the jacobian-vector product from ``self.exprs`` and ``self.dep``"""
if self._jtimes is False:
return False
if self._jtimes is True:
r = self.be.Dummy('r')
v = tuple(self.be.Dummy('v_{0}'.format(i)) for i in range(self.ny))
... | python | def get_jtimes(self):
""" Derive the jacobian-vector product from ``self.exprs`` and ``self.dep``"""
if self._jtimes is False:
return False
if self._jtimes is True:
r = self.be.Dummy('r')
v = tuple(self.be.Dummy('v_{0}'.format(i)) for i in range(self.ny))
... | Derive the jacobian-vector product from ``self.exprs`` and ``self.dep`` | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L652-L664 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.jacobian_singular | def jacobian_singular(self):
""" Returns True if Jacobian is singular, else False. """
cses, (jac_in_cses,) = self.be.cse(self.get_jac())
if jac_in_cses.nullspace():
return True
else:
return False | python | def jacobian_singular(self):
""" Returns True if Jacobian is singular, else False. """
cses, (jac_in_cses,) = self.be.cse(self.get_jac())
if jac_in_cses.nullspace():
return True
else:
return False | Returns True if Jacobian is singular, else False. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L666-L672 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_dfdx | def get_dfdx(self):
""" Calculates 2nd derivatives of ``self.exprs`` """
if self._dfdx is True:
if self.indep is None:
zero = 0*self.be.Dummy()**0
self._dfdx = self.be.Matrix(1, self.ny, [zero]*self.ny)
else:
self._dfdx = self.be.Ma... | python | def get_dfdx(self):
""" Calculates 2nd derivatives of ``self.exprs`` """
if self._dfdx is True:
if self.indep is None:
zero = 0*self.be.Dummy()**0
self._dfdx = self.be.Matrix(1, self.ny, [zero]*self.ny)
else:
self._dfdx = self.be.Ma... | Calculates 2nd derivatives of ``self.exprs`` | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L674-L684 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_f_ty_callback | def get_f_ty_callback(self):
""" Generates a callback for evaluating ``self.exprs``. """
cb = self._callback_factory(self.exprs)
lb = self.lower_bounds
ub = self.upper_bounds
if lb is not None or ub is not None:
def _bounds_wrapper(t, y, p=(), be=None):
... | python | def get_f_ty_callback(self):
""" Generates a callback for evaluating ``self.exprs``. """
cb = self._callback_factory(self.exprs)
lb = self.lower_bounds
ub = self.upper_bounds
if lb is not None or ub is not None:
def _bounds_wrapper(t, y, p=(), be=None):
... | Generates a callback for evaluating ``self.exprs``. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L689-L709 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_j_ty_callback | def get_j_ty_callback(self):
""" Generates a callback for evaluating the jacobian. """
j_exprs = self.get_jac()
if j_exprs is False:
return None
cb = self._callback_factory(j_exprs)
if self.sparse:
from scipy.sparse import csc_matrix
def spars... | python | def get_j_ty_callback(self):
""" Generates a callback for evaluating the jacobian. """
j_exprs = self.get_jac()
if j_exprs is False:
return None
cb = self._callback_factory(j_exprs)
if self.sparse:
from scipy.sparse import csc_matrix
def spars... | Generates a callback for evaluating the jacobian. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L711-L726 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_dfdx_callback | def get_dfdx_callback(self):
""" Generate a callback for evaluating derivative of ``self.exprs`` """
dfdx_exprs = self.get_dfdx()
if dfdx_exprs is False:
return None
return self._callback_factory(dfdx_exprs) | python | def get_dfdx_callback(self):
""" Generate a callback for evaluating derivative of ``self.exprs`` """
dfdx_exprs = self.get_dfdx()
if dfdx_exprs is False:
return None
return self._callback_factory(dfdx_exprs) | Generate a callback for evaluating derivative of ``self.exprs`` | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L728-L733 |
bjodah/pyodesys | pyodesys/symbolic.py | SymbolicSys.get_jtimes_callback | def get_jtimes_callback(self):
""" Generate a callback fro evaluating the jacobian-vector product."""
jtimes = self.get_jtimes()
if jtimes is False:
return None
v, jtimes_exprs = jtimes
return _Callback(self.indep, tuple(self.dep) + tuple(v), self.params,
... | python | def get_jtimes_callback(self):
""" Generate a callback fro evaluating the jacobian-vector product."""
jtimes = self.get_jtimes()
if jtimes is False:
return None
v, jtimes_exprs = jtimes
return _Callback(self.indep, tuple(self.dep) + tuple(v), self.params,
... | Generate a callback fro evaluating the jacobian-vector product. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L735-L742 |
bjodah/pyodesys | pyodesys/symbolic.py | TransformedSys.from_callback | def from_callback(cls, cb, ny=None, nparams=None, dep_transf_cbs=None,
indep_transf_cbs=None, roots_cb=None, **kwargs):
"""
Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
... | python | def from_callback(cls, cb, ny=None, nparams=None, dep_transf_cbs=None,
indep_transf_cbs=None, roots_cb=None, **kwargs):
"""
Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
... | Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
Signature ``rhs(x, y[:], p[:]) -> f[:]``
ny : int
length of y
nparams : int
length of p
dep_transf_cbs : i... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L885-L935 |
bjodah/pyodesys | pyodesys/symbolic.py | ScaledSys.from_callback | def from_callback(cls, cb, ny=None, nparams=None, dep_scaling=1, indep_scaling=1,
**kwargs):
"""
Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
Signature rhs(x, y[... | python | def from_callback(cls, cb, ny=None, nparams=None, dep_scaling=1, indep_scaling=1,
**kwargs):
"""
Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
Signature rhs(x, y[... | Create an instance from a callback.
Analogous to :func:`SymbolicSys.from_callback`.
Parameters
----------
cb : callable
Signature rhs(x, y[:], p[:]) -> f[:]
ny : int
length of y
nparams : int
length of p
dep_scaling : number (... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L1086-L1122 |
bjodah/pyodesys | pyodesys/symbolic.py | PartiallySolvedSystem.from_linear_invariants | def from_linear_invariants(cls, ori_sys, preferred=None, **kwargs):
""" Reformulates the ODE system in fewer variables.
Given linear invariant equations one can always reduce the number
of dependent variables in the system by the rank of the matrix describing
this linear system.
... | python | def from_linear_invariants(cls, ori_sys, preferred=None, **kwargs):
""" Reformulates the ODE system in fewer variables.
Given linear invariant equations one can always reduce the number
of dependent variables in the system by the rank of the matrix describing
this linear system.
... | Reformulates the ODE system in fewer variables.
Given linear invariant equations one can always reduce the number
of dependent variables in the system by the rank of the matrix describing
this linear system.
Parameters
----------
ori_sys : :class:`SymbolicSys` instance
... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/symbolic.py#L1275-L1335 |
bjodah/pyodesys | pyodesys/core.py | integrate_auto_switch | def integrate_auto_switch(odes, kw, x, y0, params=(), **kwargs):
""" Auto-switching between formulations of ODE system.
In case one has a formulation of a system of ODEs which is preferential in
the beginning of the integration, this function allows the user to run the
integration with this system wher... | python | def integrate_auto_switch(odes, kw, x, y0, params=(), **kwargs):
""" Auto-switching between formulations of ODE system.
In case one has a formulation of a system of ODEs which is preferential in
the beginning of the integration, this function allows the user to run the
integration with this system wher... | Auto-switching between formulations of ODE system.
In case one has a formulation of a system of ODEs which is preferential in
the beginning of the integration, this function allows the user to run the
integration with this system where it takes a user-specified maximum number
of steps before switching ... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L820-L911 |
bjodah/pyodesys | pyodesys/core.py | chained_parameter_variation | def chained_parameter_variation(subject, durations, y0, varied_params, default_params=None,
integrate_kwargs=None, x0=None, npoints=1, numpy=None):
""" Integrate an ODE-system for a serie of durations with some parameters changed in-between
Parameters
----------
subject ... | python | def chained_parameter_variation(subject, durations, y0, varied_params, default_params=None,
integrate_kwargs=None, x0=None, npoints=1, numpy=None):
""" Integrate an ODE-system for a serie of durations with some parameters changed in-between
Parameters
----------
subject ... | Integrate an ODE-system for a serie of durations with some parameters changed in-between
Parameters
----------
subject : function or ODESys instance
If a function: should have the signature of :meth:`pyodesys.ODESys.integrate`
(and resturn a :class:`pyodesys.results.Result` object).
... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L917-L996 |
bjodah/pyodesys | pyodesys/core.py | ODESys.pre_process | def pre_process(self, xout, y0, params=()):
""" Transforms input to internal values, used internally. """
for pre_processor in self.pre_processors:
xout, y0, params = pre_processor(xout, y0, params)
return [self.numpy.atleast_1d(arr) for arr in (xout, y0, params)] | python | def pre_process(self, xout, y0, params=()):
""" Transforms input to internal values, used internally. """
for pre_processor in self.pre_processors:
xout, y0, params = pre_processor(xout, y0, params)
return [self.numpy.atleast_1d(arr) for arr in (xout, y0, params)] | Transforms input to internal values, used internally. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L286-L290 |
bjodah/pyodesys | pyodesys/core.py | ODESys.post_process | def post_process(self, xout, yout, params):
""" Transforms internal values to output, used internally. """
for post_processor in self.post_processors:
xout, yout, params = post_processor(xout, yout, params)
return xout, yout, params | python | def post_process(self, xout, yout, params):
""" Transforms internal values to output, used internally. """
for post_processor in self.post_processors:
xout, yout, params = post_processor(xout, yout, params)
return xout, yout, params | Transforms internal values to output, used internally. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L292-L296 |
bjodah/pyodesys | pyodesys/core.py | ODESys.adaptive | def adaptive(self, y0, x0, xend, params=(), **kwargs):
""" Integrate with integrator chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
x0 : float
Initial value of the in... | python | def adaptive(self, y0, x0, xend, params=(), **kwargs):
""" Integrate with integrator chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
x0 : float
Initial value of the in... | Integrate with integrator chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
x0 : float
Initial value of the independent variable.
xend : float
Final value of... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L298-L321 |
bjodah/pyodesys | pyodesys/core.py | ODESys.predefined | def predefined(self, y0, xout, params=(), **kwargs):
""" Integrate with user chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
xout : array_like
params : array_like
... | python | def predefined(self, y0, xout, params=(), **kwargs):
""" Integrate with user chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
xout : array_like
params : array_like
... | Integrate with user chosen output.
Parameters
----------
integrator : str
See :meth:`integrate`.
y0 : array_like
See :meth:`integrate`.
xout : array_like
params : array_like
See :meth:`integrate`.
\*\*kwargs:
See :m... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L323-L345 |
bjodah/pyodesys | pyodesys/core.py | ODESys.integrate | def integrate(self, x, y0, params=(), atol=1e-8, rtol=1e-8, **kwargs):
""" Integrate the system of ordinary differential equations.
Solves the initial value problem (IVP).
Parameters
----------
x : array_like or pair (start and final time) or float
if float:
... | python | def integrate(self, x, y0, params=(), atol=1e-8, rtol=1e-8, **kwargs):
""" Integrate the system of ordinary differential equations.
Solves the initial value problem (IVP).
Parameters
----------
x : array_like or pair (start and final time) or float
if float:
... | Integrate the system of ordinary differential equations.
Solves the initial value problem (IVP).
Parameters
----------
x : array_like or pair (start and final time) or float
if float:
make it a pair: (0, x)
if pair or length-2 array:
... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L347-L449 |
bjodah/pyodesys | pyodesys/core.py | ODESys._integrate_scipy | def _integrate_scipy(self, intern_xout, intern_y0, intern_p,
atol=1e-8, rtol=1e-8, first_step=None, with_jacobian=None,
force_predefined=False, name=None, **kwargs):
""" Do not use directly (use ``integrate('scipy', ...)``).
Uses `scipy.integrate.ode <h... | python | def _integrate_scipy(self, intern_xout, intern_y0, intern_p,
atol=1e-8, rtol=1e-8, first_step=None, with_jacobian=None,
force_predefined=False, name=None, **kwargs):
""" Do not use directly (use ``integrate('scipy', ...)``).
Uses `scipy.integrate.ode <h... | Do not use directly (use ``integrate('scipy', ...)``).
Uses `scipy.integrate.ode <http://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html>`_
Parameters
----------
\*args :
See :meth:`integrate`.
name : str (default: 'lsoda'/'dopri5' when jacobia... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L455-L567 |
bjodah/pyodesys | pyodesys/core.py | ODESys._integrate_gsl | def _integrate_gsl(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='gsl')``).
Uses `GNU Scientific Library <http://www.gnu.org/software/gsl/>`_
(via `pygslodeiv2 <https://pypi.python.org/pypi/pygslodeiv2>`_)
to integrate the ODE system.
Paramete... | python | def _integrate_gsl(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='gsl')``).
Uses `GNU Scientific Library <http://www.gnu.org/software/gsl/>`_
(via `pygslodeiv2 <https://pypi.python.org/pypi/pygslodeiv2>`_)
to integrate the ODE system.
Paramete... | Do not use directly (use ``integrate(..., integrator='gsl')``).
Uses `GNU Scientific Library <http://www.gnu.org/software/gsl/>`_
(via `pygslodeiv2 <https://pypi.python.org/pypi/pygslodeiv2>`_)
to integrate the ODE system.
Parameters
----------
\*args :
see ... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L665-L691 |
bjodah/pyodesys | pyodesys/core.py | ODESys._integrate_odeint | def _integrate_odeint(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='odeint')``).
Uses `Boost.Numeric.Odeint <http://www.odeint.com>`_
(via `pyodeint <https://pypi.python.org/pypi/pyodeint>`_) to integrate
the ODE system.
"""
import pyo... | python | def _integrate_odeint(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='odeint')``).
Uses `Boost.Numeric.Odeint <http://www.odeint.com>`_
(via `pyodeint <https://pypi.python.org/pypi/pyodeint>`_) to integrate
the ODE system.
"""
import pyo... | Do not use directly (use ``integrate(..., integrator='odeint')``).
Uses `Boost.Numeric.Odeint <http://www.odeint.com>`_
(via `pyodeint <https://pypi.python.org/pypi/pyodeint>`_) to integrate
the ODE system. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L693-L705 |
bjodah/pyodesys | pyodesys/core.py | ODESys._integrate_cvode | def _integrate_cvode(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='cvode')``).
Uses CVode from CVodes in
`SUNDIALS <https://computation.llnl.gov/casc/sundials/>`_
(via `pycvodes <https://pypi.python.org/pypi/pycvodes>`_)
to integrate the ODE s... | python | def _integrate_cvode(self, *args, **kwargs):
""" Do not use directly (use ``integrate(..., integrator='cvode')``).
Uses CVode from CVodes in
`SUNDIALS <https://computation.llnl.gov/casc/sundials/>`_
(via `pycvodes <https://pypi.python.org/pypi/pycvodes>`_)
to integrate the ODE s... | Do not use directly (use ``integrate(..., integrator='cvode')``).
Uses CVode from CVodes in
`SUNDIALS <https://computation.llnl.gov/casc/sundials/>`_
(via `pycvodes <https://pypi.python.org/pypi/pycvodes>`_)
to integrate the ODE system. | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L707-L725 |
bjodah/pyodesys | pyodesys/core.py | ODESys.plot_phase_plane | def plot_phase_plane(self, indices=None, **kwargs):
""" Plots a phase portrait from last integration.
This method will be deprecated. Please use :meth:`Result.plot_phase_plane`.
See :func:`pyodesys.plotting.plot_phase_plane`
"""
return self._plot(plot_phase_plane, indices=indice... | python | def plot_phase_plane(self, indices=None, **kwargs):
""" Plots a phase portrait from last integration.
This method will be deprecated. Please use :meth:`Result.plot_phase_plane`.
See :func:`pyodesys.plotting.plot_phase_plane`
"""
return self._plot(plot_phase_plane, indices=indice... | Plots a phase portrait from last integration.
This method will be deprecated. Please use :meth:`Result.plot_phase_plane`.
See :func:`pyodesys.plotting.plot_phase_plane` | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L756-L762 |
bjodah/pyodesys | pyodesys/core.py | ODESys.stiffness | def stiffness(self, xyp=None, eigenvals_cb=None):
""" [DEPRECATED] Use :meth:`Result.stiffness`, stiffness ration
Running stiffness ratio from last integration.
Calculate sittness ratio, i.e. the ratio between the largest and
smallest absolute eigenvalue of the jacobian matrix. The user... | python | def stiffness(self, xyp=None, eigenvals_cb=None):
""" [DEPRECATED] Use :meth:`Result.stiffness`, stiffness ration
Running stiffness ratio from last integration.
Calculate sittness ratio, i.e. the ratio between the largest and
smallest absolute eigenvalue of the jacobian matrix. The user... | [DEPRECATED] Use :meth:`Result.stiffness`, stiffness ration
Running stiffness ratio from last integration.
Calculate sittness ratio, i.e. the ratio between the largest and
smallest absolute eigenvalue of the jacobian matrix. The user may
supply their own routine for calculating the eige... | https://github.com/bjodah/pyodesys/blob/0034a6165b550d8d9808baef58678dca5a493ab7/pyodesys/core.py#L769-L805 |
neon-jungle/wagtailnews | wagtailnews/views/editor.py | build_dummy_request | def build_dummy_request(newsitem):
"""
Construct a HttpRequest object that is, as far as possible,
representative of ones that would receive this page as a response. Used
for previewing / moderation and any other place where we want to
display a view of this page in the admin interface without going... | python | def build_dummy_request(newsitem):
"""
Construct a HttpRequest object that is, as far as possible,
representative of ones that would receive this page as a response. Used
for previewing / moderation and any other place where we want to
display a view of this page in the admin interface without going... | Construct a HttpRequest object that is, as far as possible,
representative of ones that would receive this page as a response. Used
for previewing / moderation and any other place where we want to
display a view of this page in the admin interface without going
through the regular page routing logic. | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/views/editor.py#L225-L268 |
neon-jungle/wagtailnews | wagtailnews/permissions.py | user_can_edit_news | def user_can_edit_news(user):
"""
Check if the user has permission to edit any of the registered NewsItem
types.
"""
newsitem_models = [model.get_newsitem_model()
for model in NEWSINDEX_MODEL_CLASSES]
if user.is_active and user.is_superuser:
# admin can edit news ... | python | def user_can_edit_news(user):
"""
Check if the user has permission to edit any of the registered NewsItem
types.
"""
newsitem_models = [model.get_newsitem_model()
for model in NEWSINDEX_MODEL_CLASSES]
if user.is_active and user.is_superuser:
# admin can edit news ... | Check if the user has permission to edit any of the registered NewsItem
types. | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/permissions.py#L21-L38 |
neon-jungle/wagtailnews | wagtailnews/permissions.py | user_can_edit_newsitem | def user_can_edit_newsitem(user, NewsItem):
"""
Check if the user has permission to edit a particular NewsItem type.
"""
for perm in format_perms(NewsItem, ['add', 'change', 'delete']):
if user.has_perm(perm):
return True
return False | python | def user_can_edit_newsitem(user, NewsItem):
"""
Check if the user has permission to edit a particular NewsItem type.
"""
for perm in format_perms(NewsItem, ['add', 'change', 'delete']):
if user.has_perm(perm):
return True
return False | Check if the user has permission to edit a particular NewsItem type. | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/permissions.py#L41-L49 |
neon-jungle/wagtailnews | wagtailnews/models.py | get_date_or_404 | def get_date_or_404(year, month, day):
"""Try to make a date from the given inputs, raising Http404 on error"""
try:
return datetime.date(int(year), int(month), int(day))
except ValueError:
raise Http404 | python | def get_date_or_404(year, month, day):
"""Try to make a date from the given inputs, raising Http404 on error"""
try:
return datetime.date(int(year), int(month), int(day))
except ValueError:
raise Http404 | Try to make a date from the given inputs, raising Http404 on error | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/models.py#L29-L34 |
neon-jungle/wagtailnews | wagtailnews/models.py | NewsIndexMixin.respond | def respond(self, request, view, newsitems, extra_context={}):
"""A helper that takes some news items and returns an HttpResponse"""
context = self.get_context(request, view=view)
context.update(self.paginate_newsitems(request, newsitems))
context.update(extra_context)
template =... | python | def respond(self, request, view, newsitems, extra_context={}):
"""A helper that takes some news items and returns an HttpResponse"""
context = self.get_context(request, view=view)
context.update(self.paginate_newsitems(request, newsitems))
context.update(extra_context)
template =... | A helper that takes some news items and returns an HttpResponse | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/models.py#L80-L86 |
neon-jungle/wagtailnews | wagtailnews/views/chooser.py | get_newsitem_model | def get_newsitem_model(model_string):
"""
Get the NewsItem model from a model string. Raises ValueError if the model
string is invalid, or references a model that is not a NewsItem.
"""
try:
NewsItem = apps.get_model(model_string)
assert issubclass(NewsItem, AbstractNewsItem)
exc... | python | def get_newsitem_model(model_string):
"""
Get the NewsItem model from a model string. Raises ValueError if the model
string is invalid, or references a model that is not a NewsItem.
"""
try:
NewsItem = apps.get_model(model_string)
assert issubclass(NewsItem, AbstractNewsItem)
exc... | Get the NewsItem model from a model string. Raises ValueError if the model
string is invalid, or references a model that is not a NewsItem. | https://github.com/neon-jungle/wagtailnews/blob/4cdec7013cca276dcfc658d3c986444ba6a42a84/wagtailnews/views/chooser.py#L119-L129 |
geometalab/pyGeoTile | pygeotile/point.py | Point.from_latitude_longitude | def from_latitude_longitude(cls, latitude=0.0, longitude=0.0):
"""Creates a point from lat/lon in WGS84"""
assert -180.0 <= longitude <= 180.0, 'Longitude needs to be a value between -180.0 and 180.0.'
assert -90.0 <= latitude <= 90.0, 'Latitude needs to be a value between -90.0 and 90.0.'
... | python | def from_latitude_longitude(cls, latitude=0.0, longitude=0.0):
"""Creates a point from lat/lon in WGS84"""
assert -180.0 <= longitude <= 180.0, 'Longitude needs to be a value between -180.0 and 180.0.'
assert -90.0 <= latitude <= 90.0, 'Latitude needs to be a value between -90.0 and 90.0.'
... | Creates a point from lat/lon in WGS84 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/point.py#L12-L16 |
geometalab/pyGeoTile | pygeotile/point.py | Point.from_pixel | def from_pixel(cls, pixel_x=0, pixel_y=0, zoom=None):
"""Creates a point from pixels X Y Z (zoom) in pyramid"""
max_pixel = (2 ** zoom) * TILE_SIZE
assert 0 <= pixel_x <= max_pixel, 'Point X needs to be a value between 0 and (2^zoom) * 256.'
assert 0 <= pixel_y <= max_pixel, 'Point Y nee... | python | def from_pixel(cls, pixel_x=0, pixel_y=0, zoom=None):
"""Creates a point from pixels X Y Z (zoom) in pyramid"""
max_pixel = (2 ** zoom) * TILE_SIZE
assert 0 <= pixel_x <= max_pixel, 'Point X needs to be a value between 0 and (2^zoom) * 256.'
assert 0 <= pixel_y <= max_pixel, 'Point Y nee... | Creates a point from pixels X Y Z (zoom) in pyramid | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/point.py#L19-L27 |
geometalab/pyGeoTile | pygeotile/point.py | Point.from_meters | def from_meters(cls, meter_x=0.0, meter_y=0.0):
"""Creates a point from X Y Z (zoom) meters in Spherical Mercator EPSG:900913"""
assert -ORIGIN_SHIFT <= meter_x <= ORIGIN_SHIFT, \
'Meter X needs to be a value between -{0} and {0}.'.format(ORIGIN_SHIFT)
assert -ORIGIN_SHIFT <= meter_y... | python | def from_meters(cls, meter_x=0.0, meter_y=0.0):
"""Creates a point from X Y Z (zoom) meters in Spherical Mercator EPSG:900913"""
assert -ORIGIN_SHIFT <= meter_x <= ORIGIN_SHIFT, \
'Meter X needs to be a value between -{0} and {0}.'.format(ORIGIN_SHIFT)
assert -ORIGIN_SHIFT <= meter_y... | Creates a point from X Y Z (zoom) meters in Spherical Mercator EPSG:900913 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/point.py#L30-L39 |
geometalab/pyGeoTile | pygeotile/point.py | Point.pixels | def pixels(self, zoom=None):
"""Gets pixels of the EPSG:4326 pyramid by a specific zoom, converted from lat/lon in WGS84"""
meter_x, meter_y = self.meters
pixel_x = (meter_x + ORIGIN_SHIFT) / resolution(zoom=zoom)
pixel_y = (meter_y - ORIGIN_SHIFT) / resolution(zoom=zoom)
return ... | python | def pixels(self, zoom=None):
"""Gets pixels of the EPSG:4326 pyramid by a specific zoom, converted from lat/lon in WGS84"""
meter_x, meter_y = self.meters
pixel_x = (meter_x + ORIGIN_SHIFT) / resolution(zoom=zoom)
pixel_y = (meter_y - ORIGIN_SHIFT) / resolution(zoom=zoom)
return ... | Gets pixels of the EPSG:4326 pyramid by a specific zoom, converted from lat/lon in WGS84 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/point.py#L46-L51 |
geometalab/pyGeoTile | pygeotile/point.py | Point.meters | def meters(self):
"""Gets the XY meters in Spherical Mercator EPSG:900913, converted from lat/lon in WGS84"""
latitude, longitude = self.latitude_longitude
meter_x = longitude * ORIGIN_SHIFT / 180.0
meter_y = math.log(math.tan((90.0 + latitude) * math.pi / 360.0)) / (math.pi / 180.0)
... | python | def meters(self):
"""Gets the XY meters in Spherical Mercator EPSG:900913, converted from lat/lon in WGS84"""
latitude, longitude = self.latitude_longitude
meter_x = longitude * ORIGIN_SHIFT / 180.0
meter_y = math.log(math.tan((90.0 + latitude) * math.pi / 360.0)) / (math.pi / 180.0)
... | Gets the XY meters in Spherical Mercator EPSG:900913, converted from lat/lon in WGS84 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/point.py#L54-L60 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.from_quad_tree | def from_quad_tree(cls, quad_tree):
"""Creates a tile from a Microsoft QuadTree"""
assert bool(re.match('^[0-3]*$', quad_tree)), 'QuadTree value can only consists of the digits 0, 1, 2 and 3.'
zoom = len(str(quad_tree))
offset = int(math.pow(2, zoom)) - 1
google_x, google_y = [re... | python | def from_quad_tree(cls, quad_tree):
"""Creates a tile from a Microsoft QuadTree"""
assert bool(re.match('^[0-3]*$', quad_tree)), 'QuadTree value can only consists of the digits 0, 1, 2 and 3.'
zoom = len(str(quad_tree))
offset = int(math.pow(2, zoom)) - 1
google_x, google_y = [re... | Creates a tile from a Microsoft QuadTree | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L16-L24 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.from_tms | def from_tms(cls, tms_x, tms_y, zoom):
"""Creates a tile from Tile Map Service (TMS) X Y and zoom"""
max_tile = (2 ** zoom) - 1
assert 0 <= tms_x <= max_tile, 'TMS X needs to be a value between 0 and (2^zoom) -1.'
assert 0 <= tms_y <= max_tile, 'TMS Y needs to be a value between 0 and (2... | python | def from_tms(cls, tms_x, tms_y, zoom):
"""Creates a tile from Tile Map Service (TMS) X Y and zoom"""
max_tile = (2 ** zoom) - 1
assert 0 <= tms_x <= max_tile, 'TMS X needs to be a value between 0 and (2^zoom) -1.'
assert 0 <= tms_y <= max_tile, 'TMS Y needs to be a value between 0 and (2... | Creates a tile from Tile Map Service (TMS) X Y and zoom | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L27-L32 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.from_google | def from_google(cls, google_x, google_y, zoom):
"""Creates a tile from Google format X Y and zoom"""
max_tile = (2 ** zoom) - 1
assert 0 <= google_x <= max_tile, 'Google X needs to be a value between 0 and (2^zoom) -1.'
assert 0 <= google_y <= max_tile, 'Google Y needs to be a value betw... | python | def from_google(cls, google_x, google_y, zoom):
"""Creates a tile from Google format X Y and zoom"""
max_tile = (2 ** zoom) - 1
assert 0 <= google_x <= max_tile, 'Google X needs to be a value between 0 and (2^zoom) -1.'
assert 0 <= google_y <= max_tile, 'Google Y needs to be a value betw... | Creates a tile from Google format X Y and zoom | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L35-L40 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.for_point | def for_point(cls, point, zoom):
"""Creates a tile for given point"""
latitude, longitude = point.latitude_longitude
return cls.for_latitude_longitude(latitude=latitude, longitude=longitude, zoom=zoom) | python | def for_point(cls, point, zoom):
"""Creates a tile for given point"""
latitude, longitude = point.latitude_longitude
return cls.for_latitude_longitude(latitude=latitude, longitude=longitude, zoom=zoom) | Creates a tile for given point | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L43-L46 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.for_pixels | def for_pixels(cls, pixel_x, pixel_y, zoom):
"""Creates a tile from pixels X Y Z (zoom) in pyramid"""
tms_x = int(math.ceil(pixel_x / float(TILE_SIZE)) - 1)
tms_y = int(math.ceil(pixel_y / float(TILE_SIZE)) - 1)
return cls(tms_x=tms_x, tms_y=(2 ** zoom - 1) - tms_y, zoom=zoom) | python | def for_pixels(cls, pixel_x, pixel_y, zoom):
"""Creates a tile from pixels X Y Z (zoom) in pyramid"""
tms_x = int(math.ceil(pixel_x / float(TILE_SIZE)) - 1)
tms_y = int(math.ceil(pixel_y / float(TILE_SIZE)) - 1)
return cls(tms_x=tms_x, tms_y=(2 ** zoom - 1) - tms_y, zoom=zoom) | Creates a tile from pixels X Y Z (zoom) in pyramid | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L49-L53 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.for_meters | def for_meters(cls, meter_x, meter_y, zoom):
"""Creates a tile from X Y meters in Spherical Mercator EPSG:900913"""
point = Point.from_meters(meter_x=meter_x, meter_y=meter_y)
pixel_x, pixel_y = point.pixels(zoom=zoom)
return cls.for_pixels(pixel_x=pixel_x, pixel_y=pixel_y, zoom=zoom) | python | def for_meters(cls, meter_x, meter_y, zoom):
"""Creates a tile from X Y meters in Spherical Mercator EPSG:900913"""
point = Point.from_meters(meter_x=meter_x, meter_y=meter_y)
pixel_x, pixel_y = point.pixels(zoom=zoom)
return cls.for_pixels(pixel_x=pixel_x, pixel_y=pixel_y, zoom=zoom) | Creates a tile from X Y meters in Spherical Mercator EPSG:900913 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L56-L60 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.for_latitude_longitude | def for_latitude_longitude(cls, latitude, longitude, zoom):
"""Creates a tile from lat/lon in WGS84"""
point = Point.from_latitude_longitude(latitude=latitude, longitude=longitude)
pixel_x, pixel_y = point.pixels(zoom=zoom)
return cls.for_pixels(pixel_x=pixel_x, pixel_y=pixel_y, zoom=zoo... | python | def for_latitude_longitude(cls, latitude, longitude, zoom):
"""Creates a tile from lat/lon in WGS84"""
point = Point.from_latitude_longitude(latitude=latitude, longitude=longitude)
pixel_x, pixel_y = point.pixels(zoom=zoom)
return cls.for_pixels(pixel_x=pixel_x, pixel_y=pixel_y, zoom=zoo... | Creates a tile from lat/lon in WGS84 | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L63-L67 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.quad_tree | def quad_tree(self):
"""Gets the tile in the Microsoft QuadTree format, converted from TMS"""
value = ''
tms_x, tms_y = self.tms
tms_y = (2 ** self.zoom - 1) - tms_y
for i in range(self.zoom, 0, -1):
digit = 0
mask = 1 << (i - 1)
if (tms_x & ma... | python | def quad_tree(self):
"""Gets the tile in the Microsoft QuadTree format, converted from TMS"""
value = ''
tms_x, tms_y = self.tms
tms_y = (2 ** self.zoom - 1) - tms_y
for i in range(self.zoom, 0, -1):
digit = 0
mask = 1 << (i - 1)
if (tms_x & ma... | Gets the tile in the Microsoft QuadTree format, converted from TMS | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L75-L88 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.google | def google(self):
"""Gets the tile in the Google format, converted from TMS"""
tms_x, tms_y = self.tms
return tms_x, (2 ** self.zoom - 1) - tms_y | python | def google(self):
"""Gets the tile in the Google format, converted from TMS"""
tms_x, tms_y = self.tms
return tms_x, (2 ** self.zoom - 1) - tms_y | Gets the tile in the Google format, converted from TMS | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L91-L94 |
geometalab/pyGeoTile | pygeotile/tile.py | Tile.bounds | def bounds(self):
"""Gets the bounds of a tile represented as the most west and south point and the most east and north point"""
google_x, google_y = self.google
pixel_x_west, pixel_y_north = google_x * TILE_SIZE, google_y * TILE_SIZE
pixel_x_east, pixel_y_south = (google_x + 1) * TILE_S... | python | def bounds(self):
"""Gets the bounds of a tile represented as the most west and south point and the most east and north point"""
google_x, google_y = self.google
pixel_x_west, pixel_y_north = google_x * TILE_SIZE, google_y * TILE_SIZE
pixel_x_east, pixel_y_south = (google_x + 1) * TILE_S... | Gets the bounds of a tile represented as the most west and south point and the most east and north point | https://github.com/geometalab/pyGeoTile/blob/b1f44271698f5fc4d18c2add935797ed43254aa6/pygeotile/tile.py#L97-L105 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedAllPairs.fit | def fit(self, X, C):
"""
Fit one classifier comparing each pair of classes
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predictin... | python | def fit(self, X, C):
"""
Fit one classifier comparing each pair of classes
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predictin... | Fit one classifier comparing each pair of classes
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label for each observation (more m... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L98-L123 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedAllPairs.decision_function | def decision_function(self, X, method='most-wins'):
"""
Calculate a 'goodness' distribution over labels
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'pre... | python | def decision_function(self, X, method='most-wins'):
"""
Calculate a 'goodness' distribution over labels
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'pre... | Calculate a 'goodness' distribution over labels
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'predict_proba'
method - by taking into account also the margins of ... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L137-L184 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedAllPairs.predict | def predict(self, X, method = 'most-wins'):
"""
Predict the less costly class for a given observation
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'predi... | python | def predict(self, X, method = 'most-wins'):
"""
Predict the less costly class for a given observation
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'predi... | Predict the less costly class for a given observation
Note
----
Predictions can be calculated either by counting which class wins the most
pairwise comparisons (as in [1] and [2]), or - for classifiers with a 'predict_proba'
method - by taking into account also the margi... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L186-L228 |
david-cortes/costsensitive | costsensitive/__init__.py | FilterTree.fit | def fit(self, X, C):
"""
Fit a filter tree classifier
Note
----
Shifting the order of the classes within the cost array will produce different
results, as it will build a different binary tree comparing different classes
at each node.
Par... | python | def fit(self, X, C):
"""
Fit a filter tree classifier
Note
----
Shifting the order of the classes within the cost array will produce different
results, as it will build a different binary tree comparing different classes
at each node.
Par... | Fit a filter tree classifier
Note
----
Shifting the order of the classes within the cost array will produce different
results, as it will build a different binary tree comparing different classes
at each node.
Parameters
----------
X : ar... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L387-L462 |
david-cortes/costsensitive | costsensitive/__init__.py | FilterTree.predict | def predict(self, X):
"""
Predict the less costly class for a given observation
Note
----
The implementation here happens in a Python loop rather than in some
NumPy array operations, thus it will be slower than the other algorithms
here, even though in th... | python | def predict(self, X):
"""
Predict the less costly class for a given observation
Note
----
The implementation here happens in a Python loop rather than in some
NumPy array operations, thus it will be slower than the other algorithms
here, even though in th... | Predict the less costly class for a given observation
Note
----
The implementation here happens in a Python loop rather than in some
NumPy array operations, thus it will be slower than the other algorithms
here, even though in theory it implies fewer comparisons.
... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L464-L494 |
david-cortes/costsensitive | costsensitive/__init__.py | CostProportionateClassifier.fit | def fit(self, X, y, sample_weight=None):
"""
Fit a binary classifier with sample weights to data.
Note
----
Examples at each sample are accepted with probability = weight/Z,
where Z = max(weight) + extra_rej_const.
Larger values for extra_rej_const ensure... | python | def fit(self, X, y, sample_weight=None):
"""
Fit a binary classifier with sample weights to data.
Note
----
Examples at each sample are accepted with probability = weight/Z,
where Z = max(weight) + extra_rej_const.
Larger values for extra_rej_const ensure... | Fit a binary classifier with sample weights to data.
Note
----
Examples at each sample are accepted with probability = weight/Z,
where Z = max(weight) + extra_rej_const.
Larger values for extra_rej_const ensure that no example gets selected in
every single sample... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L551-L588 |
david-cortes/costsensitive | costsensitive/__init__.py | CostProportionateClassifier.decision_function | def decision_function(self, X, aggregation = 'raw'):
"""
Calculate how preferred is positive class according to classifiers
Note
----
If passing aggregation = 'raw', it will output the proportion of the classifiers
that voted for the positive class.
If pa... | python | def decision_function(self, X, aggregation = 'raw'):
"""
Calculate how preferred is positive class according to classifiers
Note
----
If passing aggregation = 'raw', it will output the proportion of the classifiers
that voted for the positive class.
If pa... | Calculate how preferred is positive class according to classifiers
Note
----
If passing aggregation = 'raw', it will output the proportion of the classifiers
that voted for the positive class.
If passing aggregation = 'weighted', it will output the average predicted prob... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L594-L631 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedOneVsRest.fit | def fit(self, X, C):
"""
Fit one weighted classifier per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label... | python | def fit(self, X, C):
"""
Fit one weighted classifier per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label... | Fit one weighted classifier per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label for each observation (more means worse). | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L722-L741 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedOneVsRest.decision_function | def decision_function(self, X):
"""
Calculate a 'goodness' distribution over labels
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict the cost of each label.
Returns
-------
pred : array (n_samp... | python | def decision_function(self, X):
"""
Calculate a 'goodness' distribution over labels
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict the cost of each label.
Returns
-------
pred : array (n_samp... | Calculate a 'goodness' distribution over labels
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict the cost of each label.
Returns
-------
pred : array (n_samples, n_classes)
A goodness score (more i... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L757-L791 |
david-cortes/costsensitive | costsensitive/__init__.py | WeightedOneVsRest.predict | def predict(self, X):
"""
Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost label.
Returns
-------
y_hat : array (n_samples,)
... | python | def predict(self, X):
"""
Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost label.
Returns
-------
y_hat : array (n_samples,)
... | Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost label.
Returns
-------
y_hat : array (n_samples,)
Label with expected minimum cos... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L802-L817 |
david-cortes/costsensitive | costsensitive/__init__.py | RegressionOneVsRest.fit | def fit(self, X, C):
"""
Fit one regressor per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label for each ... | python | def fit(self, X, C):
"""
Fit one regressor per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label for each ... | Fit one regressor per class
Parameters
----------
X : array (n_samples, n_features)
The data on which to fit a cost-sensitive classifier.
C : array (n_samples, n_classes)
The cost of predicting each label for each observation (more means worse). | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L854-L870 |
david-cortes/costsensitive | costsensitive/__init__.py | RegressionOneVsRest.decision_function | def decision_function(self, X, apply_softmax = True):
"""
Get cost estimates for each observation
Note
----
If called with apply_softmax = False, this will output the predicted
COST rather than the 'goodness' - meaning, more is worse.
If called w... | python | def decision_function(self, X, apply_softmax = True):
"""
Get cost estimates for each observation
Note
----
If called with apply_softmax = False, this will output the predicted
COST rather than the 'goodness' - meaning, more is worse.
If called w... | Get cost estimates for each observation
Note
----
If called with apply_softmax = False, this will output the predicted
COST rather than the 'goodness' - meaning, more is worse.
If called with apply_softmax = True, it will output one minus the softmax on the cost... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L875-L910 |
david-cortes/costsensitive | costsensitive/__init__.py | RegressionOneVsRest.predict | def predict(self, X):
"""
Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost labels.
Returns
-------
y_hat : array (n_samples,)
... | python | def predict(self, X):
"""
Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost labels.
Returns
-------
y_hat : array (n_samples,)
... | Predict the less costly class for a given observation
Parameters
----------
X : array (n_samples, n_features)
Data for which to predict minimum cost labels.
Returns
-------
y_hat : array (n_samples,)
Label with expected minimum co... | https://github.com/david-cortes/costsensitive/blob/355fbf20397ce673ce9e22048b6c52dbeeb354cc/costsensitive/__init__.py#L915-L930 |
IAMconsortium/pyam | pyam/read_ixmp.py | read_ix | def read_ix(ix, **kwargs):
"""Read timeseries data from an ixmp object
Parameters
----------
ix: ixmp.TimeSeries or ixmp.Scenario
this option requires the ixmp package as a dependency
kwargs: arguments passed to ixmp.TimeSeries.timeseries()
"""
if not isinstance(ix, ixmp.TimeSeries)... | python | def read_ix(ix, **kwargs):
"""Read timeseries data from an ixmp object
Parameters
----------
ix: ixmp.TimeSeries or ixmp.Scenario
this option requires the ixmp package as a dependency
kwargs: arguments passed to ixmp.TimeSeries.timeseries()
"""
if not isinstance(ix, ixmp.TimeSeries)... | Read timeseries data from an ixmp object
Parameters
----------
ix: ixmp.TimeSeries or ixmp.Scenario
this option requires the ixmp package as a dependency
kwargs: arguments passed to ixmp.TimeSeries.timeseries() | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/read_ixmp.py#L8-L24 |
IAMconsortium/pyam | pyam/utils.py | requires_package | def requires_package(pkg, msg, error_type=ImportError):
"""Decorator when a function requires an optional dependency
Parameters
----------
pkg : imported package object
msg : string
Message to show to user with error_type
error_type : python error class
"""
def _requires_package... | python | def requires_package(pkg, msg, error_type=ImportError):
"""Decorator when a function requires an optional dependency
Parameters
----------
pkg : imported package object
msg : string
Message to show to user with error_type
error_type : python error class
"""
def _requires_package... | Decorator when a function requires an optional dependency
Parameters
----------
pkg : imported package object
msg : string
Message to show to user with error_type
error_type : python error class | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L36-L52 |
IAMconsortium/pyam | pyam/utils.py | write_sheet | def write_sheet(writer, name, df, index=False):
"""Write a pandas DataFrame to an ExcelWriter,
auto-formatting column width depending on maxwidth of data and colum header
Parameters
----------
writer: pandas.ExcelWriter
an instance of a pandas ExcelWriter
name: string
name of th... | python | def write_sheet(writer, name, df, index=False):
"""Write a pandas DataFrame to an ExcelWriter,
auto-formatting column width depending on maxwidth of data and colum header
Parameters
----------
writer: pandas.ExcelWriter
an instance of a pandas ExcelWriter
name: string
name of th... | Write a pandas DataFrame to an ExcelWriter,
auto-formatting column width depending on maxwidth of data and colum header
Parameters
----------
writer: pandas.ExcelWriter
an instance of a pandas ExcelWriter
name: string
name of the sheet to be written
df: pandas.DataFrame
... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L70-L96 |
IAMconsortium/pyam | pyam/utils.py | read_pandas | def read_pandas(fname, *args, **kwargs):
"""Read a file and return a pd.DataFrame"""
if not os.path.exists(fname):
raise ValueError('no data file `{}` found!'.format(fname))
if fname.endswith('csv'):
df = pd.read_csv(fname, *args, **kwargs)
else:
xl = pd.ExcelFile(fname)
... | python | def read_pandas(fname, *args, **kwargs):
"""Read a file and return a pd.DataFrame"""
if not os.path.exists(fname):
raise ValueError('no data file `{}` found!'.format(fname))
if fname.endswith('csv'):
df = pd.read_csv(fname, *args, **kwargs)
else:
xl = pd.ExcelFile(fname)
... | Read a file and return a pd.DataFrame | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L99-L110 |
IAMconsortium/pyam | pyam/utils.py | read_file | def read_file(fname, *args, **kwargs):
"""Read data from a file saved in the standard IAMC format
or a table with year/value columns
"""
if not isstr(fname):
raise ValueError('reading multiple files not supported, '
'please use `pyam.IamDataFrame.append()`')
logger()... | python | def read_file(fname, *args, **kwargs):
"""Read data from a file saved in the standard IAMC format
or a table with year/value columns
"""
if not isstr(fname):
raise ValueError('reading multiple files not supported, '
'please use `pyam.IamDataFrame.append()`')
logger()... | Read data from a file saved in the standard IAMC format
or a table with year/value columns | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L113-L125 |
IAMconsortium/pyam | pyam/utils.py | format_data | def format_data(df, **kwargs):
"""Convert a `pd.Dataframe` or `pd.Series` to the required format"""
if isinstance(df, pd.Series):
df = df.to_frame()
# Check for R-style year columns, converting where necessary
def convert_r_columns(c):
try:
first = c[0]
second = ... | python | def format_data(df, **kwargs):
"""Convert a `pd.Dataframe` or `pd.Series` to the required format"""
if isinstance(df, pd.Series):
df = df.to_frame()
# Check for R-style year columns, converting where necessary
def convert_r_columns(c):
try:
first = c[0]
second = ... | Convert a `pd.Dataframe` or `pd.Series` to the required format | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L128-L255 |
IAMconsortium/pyam | pyam/utils.py | sort_data | def sort_data(data, cols):
"""Sort `data` rows and order columns"""
return data.sort_values(cols)[cols + ['value']].reset_index(drop=True) | python | def sort_data(data, cols):
"""Sort `data` rows and order columns"""
return data.sort_values(cols)[cols + ['value']].reset_index(drop=True) | Sort `data` rows and order columns | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L258-L260 |
IAMconsortium/pyam | pyam/utils.py | find_depth | def find_depth(data, s='', level=None):
"""
return or assert the depth (number of `|`) of variables
Parameters
----------
data : pd.Series of strings
IAMC-style variables
s : str, default ''
remove leading `s` from any variable in `data`
level : int or str, default None
... | python | def find_depth(data, s='', level=None):
"""
return or assert the depth (number of `|`) of variables
Parameters
----------
data : pd.Series of strings
IAMC-style variables
s : str, default ''
remove leading `s` from any variable in `data`
level : int or str, default None
... | return or assert the depth (number of `|`) of variables
Parameters
----------
data : pd.Series of strings
IAMC-style variables
s : str, default ''
remove leading `s` from any variable in `data`
level : int or str, default None
if None, return depth (number of `|`); else, ret... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L263-L304 |
IAMconsortium/pyam | pyam/utils.py | pattern_match | def pattern_match(data, values, level=None, regexp=False, has_nan=True):
"""
matching of model/scenario names, variables, regions, and meta columns to
pseudo-regex (if `regexp == False`) for filtering (str, int, bool)
"""
matches = np.array([False] * len(data))
if not isinstance(values, collecti... | python | def pattern_match(data, values, level=None, regexp=False, has_nan=True):
"""
matching of model/scenario names, variables, regions, and meta columns to
pseudo-regex (if `regexp == False`) for filtering (str, int, bool)
"""
matches = np.array([False] * len(data))
if not isinstance(values, collecti... | matching of model/scenario names, variables, regions, and meta columns to
pseudo-regex (if `regexp == False`) for filtering (str, int, bool) | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L307-L329 |
IAMconsortium/pyam | pyam/utils.py | _escape_regexp | def _escape_regexp(s):
"""escape characters with specific regexp use"""
return (
str(s)
.replace('|', '\\|')
.replace('.', '\.') # `.` has to be replaced before `*`
.replace('*', '.*')
.replace('+', '\+')
.replace('(', '\(')
.replace(')', '\)')
.r... | python | def _escape_regexp(s):
"""escape characters with specific regexp use"""
return (
str(s)
.replace('|', '\\|')
.replace('.', '\.') # `.` has to be replaced before `*`
.replace('*', '.*')
.replace('+', '\+')
.replace('(', '\(')
.replace(')', '\)')
.r... | escape characters with specific regexp use | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L332-L343 |
IAMconsortium/pyam | pyam/utils.py | years_match | def years_match(data, years):
"""
matching of year columns for data filtering
"""
years = [years] if isinstance(years, int) else years
dt = datetime.datetime
if isinstance(years, dt) or isinstance(years[0], dt):
error_msg = "`year` can only be filtered with ints or lists of ints"
... | python | def years_match(data, years):
"""
matching of year columns for data filtering
"""
years = [years] if isinstance(years, int) else years
dt = datetime.datetime
if isinstance(years, dt) or isinstance(years[0], dt):
error_msg = "`year` can only be filtered with ints or lists of ints"
... | matching of year columns for data filtering | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L346-L355 |
IAMconsortium/pyam | pyam/utils.py | hour_match | def hour_match(data, hours):
"""
matching of days in time columns for data filtering
"""
hours = [hours] if isinstance(hours, int) else hours
return data.isin(hours) | python | def hour_match(data, hours):
"""
matching of days in time columns for data filtering
"""
hours = [hours] if isinstance(hours, int) else hours
return data.isin(hours) | matching of days in time columns for data filtering | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L372-L377 |
IAMconsortium/pyam | pyam/utils.py | datetime_match | def datetime_match(data, dts):
"""
matching of datetimes in time columns for data filtering
"""
dts = dts if islistable(dts) else [dts]
if any([not isinstance(i, datetime.datetime) for i in dts]):
error_msg = (
"`time` can only be filtered by datetimes"
)
raise Ty... | python | def datetime_match(data, dts):
"""
matching of datetimes in time columns for data filtering
"""
dts = dts if islistable(dts) else [dts]
if any([not isinstance(i, datetime.datetime) for i in dts]):
error_msg = (
"`time` can only be filtered by datetimes"
)
raise Ty... | matching of datetimes in time columns for data filtering | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L422-L432 |
IAMconsortium/pyam | pyam/utils.py | to_int | def to_int(x, index=False):
"""Formatting series or timeseries columns to int and checking validity.
If `index=False`, the function works on the `pd.Series x`; else,
the function casts the index of `x` to int and returns x with a new index.
"""
_x = x.index if index else x
cols = list(map(int, _... | python | def to_int(x, index=False):
"""Formatting series or timeseries columns to int and checking validity.
If `index=False`, the function works on the `pd.Series x`; else,
the function casts the index of `x` to int and returns x with a new index.
"""
_x = x.index if index else x
cols = list(map(int, _... | Formatting series or timeseries columns to int and checking validity.
If `index=False`, the function works on the `pd.Series x`; else,
the function casts the index of `x` to int and returns x with a new index. | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L435-L449 |
IAMconsortium/pyam | pyam/utils.py | concat_with_pipe | def concat_with_pipe(x, cols=None):
"""Concatenate a `pd.Series` separated by `|`, drop `None` or `np.nan`"""
cols = cols or x.index
return '|'.join([x[i] for i in cols if x[i] not in [None, np.nan]]) | python | def concat_with_pipe(x, cols=None):
"""Concatenate a `pd.Series` separated by `|`, drop `None` or `np.nan`"""
cols = cols or x.index
return '|'.join([x[i] for i in cols if x[i] not in [None, np.nan]]) | Concatenate a `pd.Series` separated by `|`, drop `None` or `np.nan` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L452-L455 |
IAMconsortium/pyam | pyam/utils.py | reduce_hierarchy | def reduce_hierarchy(x, depth):
"""Reduce the hierarchy (depth by `|`) string to the specified level"""
_x = x.split('|')
depth = len(_x) + depth - 1 if depth < 0 else depth
return '|'.join(_x[0:(depth + 1)]) | python | def reduce_hierarchy(x, depth):
"""Reduce the hierarchy (depth by `|`) string to the specified level"""
_x = x.split('|')
depth = len(_x) + depth - 1 if depth < 0 else depth
return '|'.join(_x[0:(depth + 1)]) | Reduce the hierarchy (depth by `|`) string to the specified level | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/utils.py#L458-L462 |
IAMconsortium/pyam | pyam/core.py | _aggregate | def _aggregate(df, by):
"""Aggregate `df` by specified column(s), return indexed `pd.Series`"""
by = [by] if isstr(by) else by
cols = [c for c in list(df.columns) if c not in ['value'] + by]
return df.groupby(cols).sum()['value'] | python | def _aggregate(df, by):
"""Aggregate `df` by specified column(s), return indexed `pd.Series`"""
by = [by] if isstr(by) else by
cols = [c for c in list(df.columns) if c not in ['value'] + by]
return df.groupby(cols).sum()['value'] | Aggregate `df` by specified column(s), return indexed `pd.Series` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1334-L1338 |
IAMconsortium/pyam | pyam/core.py | _check_rows | def _check_rows(rows, check, in_range=True, return_test='any'):
"""Check all rows to be in/out of a certain range and provide testing on
return values based on provided conditions
Parameters
----------
rows: pd.DataFrame
data rows
check: dict
dictionary with possible values of '... | python | def _check_rows(rows, check, in_range=True, return_test='any'):
"""Check all rows to be in/out of a certain range and provide testing on
return values based on provided conditions
Parameters
----------
rows: pd.DataFrame
data rows
check: dict
dictionary with possible values of '... | Check all rows to be in/out of a certain range and provide testing on
return values based on provided conditions
Parameters
----------
rows: pd.DataFrame
data rows
check: dict
dictionary with possible values of 'up', 'lo', and 'year'
in_range: bool, optional
check if val... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1345-L1385 |
IAMconsortium/pyam | pyam/core.py | _apply_criteria | def _apply_criteria(df, criteria, **kwargs):
"""Apply criteria individually to every model/scenario instance"""
idxs = []
for var, check in criteria.items():
_df = df[df['variable'] == var]
for group in _df.groupby(META_IDX):
grp_idxs = _check_rows(group[-1], check, **kwargs)
... | python | def _apply_criteria(df, criteria, **kwargs):
"""Apply criteria individually to every model/scenario instance"""
idxs = []
for var, check in criteria.items():
_df = df[df['variable'] == var]
for group in _df.groupby(META_IDX):
grp_idxs = _check_rows(group[-1], check, **kwargs)
... | Apply criteria individually to every model/scenario instance | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1388-L1397 |
IAMconsortium/pyam | pyam/core.py | _make_index | def _make_index(df, cols=META_IDX):
"""Create an index from the columns of a dataframe"""
return pd.MultiIndex.from_tuples(
pd.unique(list(zip(*[df[col] for col in cols]))), names=tuple(cols)) | python | def _make_index(df, cols=META_IDX):
"""Create an index from the columns of a dataframe"""
return pd.MultiIndex.from_tuples(
pd.unique(list(zip(*[df[col] for col in cols]))), names=tuple(cols)) | Create an index from the columns of a dataframe | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1400-L1403 |
IAMconsortium/pyam | pyam/core.py | validate | def validate(df, criteria={}, exclude_on_fail=False, **kwargs):
"""Validate scenarios using criteria on timeseries values
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.validate()` for details
kwargs: passed to `df.filter()`
"""
fdf = df.filter(**kwargs)
if ... | python | def validate(df, criteria={}, exclude_on_fail=False, **kwargs):
"""Validate scenarios using criteria on timeseries values
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.validate()` for details
kwargs: passed to `df.filter()`
"""
fdf = df.filter(**kwargs)
if ... | Validate scenarios using criteria on timeseries values
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.validate()` for details
kwargs: passed to `df.filter()` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1406-L1419 |
IAMconsortium/pyam | pyam/core.py | require_variable | def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,
**kwargs):
"""Check whether all scenarios have a required variable
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.require_variable()` for details
kwargs: passed to `df.f... | python | def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,
**kwargs):
"""Check whether all scenarios have a required variable
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.require_variable()` for details
kwargs: passed to `df.f... | Check whether all scenarios have a required variable
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.require_variable()` for details
kwargs: passed to `df.filter()` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1422-L1437 |
IAMconsortium/pyam | pyam/core.py | categorize | def categorize(df, name, value, criteria,
color=None, marker=None, linestyle=None, **kwargs):
"""Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.categorize()` ... | python | def categorize(df, name, value, criteria,
color=None, marker=None, linestyle=None, **kwargs):
"""Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.categorize()` ... | Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
df: IamDataFrame instance
args: see `IamDataFrame.categorize()` for details
kwargs: passed to `df.filter()` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1440-L1459 |
IAMconsortium/pyam | pyam/core.py | check_aggregate | def check_aggregate(df, variable, components=None, exclude_on_fail=False,
multiplier=1, **kwargs):
"""Check whether the timeseries values match the aggregation
of sub-categories
Parameters
----------
df: IamDataFrame instance
args: see IamDataFrame.check_aggregate() for deta... | python | def check_aggregate(df, variable, components=None, exclude_on_fail=False,
multiplier=1, **kwargs):
"""Check whether the timeseries values match the aggregation
of sub-categories
Parameters
----------
df: IamDataFrame instance
args: see IamDataFrame.check_aggregate() for deta... | Check whether the timeseries values match the aggregation
of sub-categories
Parameters
----------
df: IamDataFrame instance
args: see IamDataFrame.check_aggregate() for details
kwargs: passed to `df.filter()` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1462-L1479 |
IAMconsortium/pyam | pyam/core.py | filter_by_meta | def filter_by_meta(data, df, join_meta=False, **kwargs):
"""Filter by and join meta columns from an IamDataFrame to a pd.DataFrame
Parameters
----------
data: pd.DataFrame instance
DataFrame to which meta columns are to be joined,
index or columns must include `['model', 'scenario']`
... | python | def filter_by_meta(data, df, join_meta=False, **kwargs):
"""Filter by and join meta columns from an IamDataFrame to a pd.DataFrame
Parameters
----------
data: pd.DataFrame instance
DataFrame to which meta columns are to be joined,
index or columns must include `['model', 'scenario']`
... | Filter by and join meta columns from an IamDataFrame to a pd.DataFrame
Parameters
----------
data: pd.DataFrame instance
DataFrame to which meta columns are to be joined,
index or columns must include `['model', 'scenario']`
df: IamDataFrame instance
IamDataFrame from which meta... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1482-L1534 |
IAMconsortium/pyam | pyam/core.py | compare | def compare(left, right, left_label='left', right_label='right',
drop_close=True, **kwargs):
"""Compare the data in two IamDataFrames and return a pd.DataFrame
Parameters
----------
left, right: IamDataFrames
the IamDataFrames to be compared
left_label, right_label: str, default... | python | def compare(left, right, left_label='left', right_label='right',
drop_close=True, **kwargs):
"""Compare the data in two IamDataFrames and return a pd.DataFrame
Parameters
----------
left, right: IamDataFrames
the IamDataFrames to be compared
left_label, right_label: str, default... | Compare the data in two IamDataFrames and return a pd.DataFrame
Parameters
----------
left, right: IamDataFrames
the IamDataFrames to be compared
left_label, right_label: str, default `left`, `right`
column names of the returned dataframe
drop_close: bool, default True
remov... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1537-L1556 |
IAMconsortium/pyam | pyam/core.py | concat | def concat(dfs):
"""Concatenate a series of `pyam.IamDataFrame`-like objects together"""
if isstr(dfs) or not hasattr(dfs, '__iter__'):
msg = 'Argument must be a non-string iterable (e.g., list or tuple)'
raise TypeError(msg)
_df = None
for df in dfs:
df = df if isinstance(df, I... | python | def concat(dfs):
"""Concatenate a series of `pyam.IamDataFrame`-like objects together"""
if isstr(dfs) or not hasattr(dfs, '__iter__'):
msg = 'Argument must be a non-string iterable (e.g., list or tuple)'
raise TypeError(msg)
_df = None
for df in dfs:
df = df if isinstance(df, I... | Concatenate a series of `pyam.IamDataFrame`-like objects together | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L1559-L1572 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.variables | def variables(self, include_units=False):
"""Get a list of variables
Parameters
----------
include_units: boolean, default False
include the units
"""
if include_units:
return self.data[['variable', 'unit']].drop_duplicates()\
.res... | python | def variables(self, include_units=False):
"""Get a list of variables
Parameters
----------
include_units: boolean, default False
include the units
"""
if include_units:
return self.data[['variable', 'unit']].drop_duplicates()\
.res... | Get a list of variables
Parameters
----------
include_units: boolean, default False
include the units | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L161-L173 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.append | def append(self, other, ignore_meta_conflict=False, inplace=False,
**kwargs):
"""Append any castable object to this IamDataFrame.
Columns in `other.meta` that are not in `self.meta` are always merged,
duplicate region-variable-unit-year rows raise a ValueError.
Parameters... | python | def append(self, other, ignore_meta_conflict=False, inplace=False,
**kwargs):
"""Append any castable object to this IamDataFrame.
Columns in `other.meta` that are not in `self.meta` are always merged,
duplicate region-variable-unit-year rows raise a ValueError.
Parameters... | Append any castable object to this IamDataFrame.
Columns in `other.meta` that are not in `self.meta` are always merged,
duplicate region-variable-unit-year rows raise a ValueError.
Parameters
----------
other: pyam.IamDataFrame, ixmp.TimeSeries, ixmp.Scenario,
pd.DataFra... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L175-L246 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.pivot_table | def pivot_table(self, index, columns, values='value',
aggfunc='count', fill_value=None, style=None):
"""Returns a pivot table
Parameters
----------
index: str or list of strings
rows for Pivot table
columns: str or list of strings
colu... | python | def pivot_table(self, index, columns, values='value',
aggfunc='count', fill_value=None, style=None):
"""Returns a pivot table
Parameters
----------
index: str or list of strings
rows for Pivot table
columns: str or list of strings
colu... | Returns a pivot table
Parameters
----------
index: str or list of strings
rows for Pivot table
columns: str or list of strings
columns for Pivot table
values: str, default 'value'
dataframe column to aggregate or count
aggfunc: str or ... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L248-L290 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.interpolate | def interpolate(self, year):
"""Interpolate missing values in timeseries (linear interpolation)
Parameters
----------
year: int
year to be interpolated
"""
df = self.pivot_table(index=IAMC_IDX, columns=['year'],
values='value', ... | python | def interpolate(self, year):
"""Interpolate missing values in timeseries (linear interpolation)
Parameters
----------
year: int
year to be interpolated
"""
df = self.pivot_table(index=IAMC_IDX, columns=['year'],
values='value', ... | Interpolate missing values in timeseries (linear interpolation)
Parameters
----------
year: int
year to be interpolated | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L292-L310 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.as_pandas | def as_pandas(self, with_metadata=False):
"""Return this as a pd.DataFrame
Parameters
----------
with_metadata : bool, default False or dict
if True, join data with all meta columns; if a dict, discover
meaningful meta columns from values (in key-value)
"""... | python | def as_pandas(self, with_metadata=False):
"""Return this as a pd.DataFrame
Parameters
----------
with_metadata : bool, default False or dict
if True, join data with all meta columns; if a dict, discover
meaningful meta columns from values (in key-value)
"""... | Return this as a pd.DataFrame
Parameters
----------
with_metadata : bool, default False or dict
if True, join data with all meta columns; if a dict, discover
meaningful meta columns from values (in key-value) | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L312-L331 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame._discover_meta_cols | def _discover_meta_cols(self, **kwargs):
"""Return the subset of `kwargs` values (not keys!) matching
a `meta` column name"""
cols = set(['exclude'])
for arg, value in kwargs.items():
if isstr(value) and value in self.meta.columns:
cols.add(value)
retu... | python | def _discover_meta_cols(self, **kwargs):
"""Return the subset of `kwargs` values (not keys!) matching
a `meta` column name"""
cols = set(['exclude'])
for arg, value in kwargs.items():
if isstr(value) and value in self.meta.columns:
cols.add(value)
retu... | Return the subset of `kwargs` values (not keys!) matching
a `meta` column name | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L333-L340 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.timeseries | def timeseries(self, iamc_index=False):
"""Returns a pd.DataFrame in wide format (years or timedate as columns)
Parameters
----------
iamc_index: bool, default False
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all `data` columns
... | python | def timeseries(self, iamc_index=False):
"""Returns a pd.DataFrame in wide format (years or timedate as columns)
Parameters
----------
iamc_index: bool, default False
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all `data` columns
... | Returns a pd.DataFrame in wide format (years or timedate as columns)
Parameters
----------
iamc_index: bool, default False
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all `data` columns | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L342-L361 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.set_meta | def set_meta(self, meta, name=None, index=None):
"""Add metadata columns as pd.Series, list or value (int/float/str)
Parameters
----------
meta: pd.Series, list, int, float or str
column to be added to metadata
(by `['model', 'scenario']` index if possible)
... | python | def set_meta(self, meta, name=None, index=None):
"""Add metadata columns as pd.Series, list or value (int/float/str)
Parameters
----------
meta: pd.Series, list, int, float or str
column to be added to metadata
(by `['model', 'scenario']` index if possible)
... | Add metadata columns as pd.Series, list or value (int/float/str)
Parameters
----------
meta: pd.Series, list, int, float or str
column to be added to metadata
(by `['model', 'scenario']` index if possible)
name: str, optional
meta column name (default... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L367-L429 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.categorize | def categorize(self, name, value, criteria,
color=None, marker=None, linestyle=None):
"""Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
name: str
category column name
v... | python | def categorize(self, name, value, criteria,
color=None, marker=None, linestyle=None):
"""Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
name: str
category column name
v... | Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
name: str
category column name
value: str
category identifier
criteria: dict
dictionary with variables mapped to app... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L431-L472 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame._new_meta_column | def _new_meta_column(self, name):
"""Add a column to meta if it doesn't exist, set to value `np.nan`"""
if name is None:
raise ValueError('cannot add a meta column `{}`'.format(name))
if name not in self.meta:
self.meta[name] = np.nan | python | def _new_meta_column(self, name):
"""Add a column to meta if it doesn't exist, set to value `np.nan`"""
if name is None:
raise ValueError('cannot add a meta column `{}`'.format(name))
if name not in self.meta:
self.meta[name] = np.nan | Add a column to meta if it doesn't exist, set to value `np.nan` | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L474-L479 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.require_variable | def require_variable(self, variable, unit=None, year=None,
exclude_on_fail=False):
"""Check whether all scenarios have a required variable
Parameters
----------
variable: str
required variable
unit: str, default None
name of unit ... | python | def require_variable(self, variable, unit=None, year=None,
exclude_on_fail=False):
"""Check whether all scenarios have a required variable
Parameters
----------
variable: str
required variable
unit: str, default None
name of unit ... | Check whether all scenarios have a required variable
Parameters
----------
variable: str
required variable
unit: str, default None
name of unit (optional)
year: int or list, default None
years (optional)
exclude_on_fail: bool, default ... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L481-L519 |
IAMconsortium/pyam | pyam/core.py | IamDataFrame.validate | def validate(self, criteria={}, exclude_on_fail=False):
"""Validate scenarios using criteria on timeseries values
Parameters
----------
criteria: dict
dictionary with variable keys and check values
('up' and 'lo' for respective bounds, 'year' for years)
ex... | python | def validate(self, criteria={}, exclude_on_fail=False):
"""Validate scenarios using criteria on timeseries values
Parameters
----------
criteria: dict
dictionary with variable keys and check values
('up' and 'lo' for respective bounds, 'year' for years)
ex... | Validate scenarios using criteria on timeseries values
Parameters
----------
criteria: dict
dictionary with variable keys and check values
('up' and 'lo' for respective bounds, 'year' for years)
exclude_on_fail: bool, default False
flag scenarios faili... | https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/core.py#L521-L541 |
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