Fixed line-too-long pylint message in probnum and probnum.problems subpackage

src/probnum/_pnmethod/_stopping_criterion.py

@@ -47,7 +47,8 @@ class StoppingCriterion(abc.ABC):
     ...     def __call__(self, solver_state) -> bool:
     ...         return solver_state.rtol < self.rtol
 
-    Now let's combine them by stopping when the solver has reached an absolute and relative tolerance, or a maximum number of iterations.
+    Now let's combine them by stopping when the solver has reached
+    an absolute and relative tolerance, or a maximum number of iterations.
 
     >>> stopcrit = MaxIterations(maxiters=100) | (
     ...     AbsoluteResidualTolerance(atol=1e-6)
@@ -57,7 +58,8 @@ class StoppingCriterion(abc.ABC):
     >>> stopcrit(state)
     False
 
-    Now let's modify the state such that the solver has reached a maximum number of iterations.
+    Now let's modify the state such that the solver has reached
+    a maximum number of iterations.
 
     >>> state.iters = 1000
     >>> stopcrit(state)
@@ -66,8 +68,10 @@ class StoppingCriterion(abc.ABC):
     See Also
     --------
     LambdaStoppingCriterion : Stopping criterion defined via an anonymous function.
-    ~probnum.linalg.solvers.stopping_criteria.LinearSolverStoppingCriterion : Stopping criterion of a probabilistic linear solver.
-    ~probnum.filtsmooth.optim.FiltSmoothStoppingCriterion : Stopping criterion of filters and smoothers.
+    ~probnum.linalg.solvers.stopping_criteria.LinearSolverStoppingCriterion : Stopping
+        criterion of a probabilistic linear solver.
+    ~probnum.filtsmooth.optim.FiltSmoothStoppingCriterion : Stopping criterion of
+        filters and smoothers.
     """
 
     @abc.abstractmethod

src/probnum/problems/_problems.py

@@ -16,7 +16,8 @@
 class TimeSeriesRegressionProblem:
     r"""Time series regression problem.
 
-    Fit a stochastic process to data, given a likelihood (realised by a :obj:`NonlinearGaussian` transition).
+    Fit a stochastic process to data, given a likelihood
+    (realised by a :obj:`NonlinearGaussian` transition).
     Solved by filters and smoothers in :mod:`probnum.filtsmooth`.
 
     Parameters
@@ -28,7 +29,8 @@ class TimeSeriesRegressionProblem:
     measurement_models
         Measurement models.
     solution
-        Array containing solution to the problem at ``locations``. Used for testing and benchmarking.
+        Array containing solution to the problem at ``locations``.
+        Used for testing and benchmarking.
 
     Examples
     --------
@@ -70,7 +72,8 @@ class TimeSeriesRegressionProblem:
     measurement_models: Union[Sequence, np.ndarray]
 
     # For testing and benchmarking
-    # The solution here is a Sequence or array of the values of the truth at the location.
+    # The solution here is a Sequence or
+    # array of the values of the truth at the location.
     solution: Optional[Union[Sequence, np.ndarray]] = None
 
     def __post_init__(self):
@@ -81,8 +84,9 @@ def __post_init__(self):
         2. Check that all inputs have the same length.
         """
 
-        # If a single measurement model has been provided, transform it into a list of models
-        # to ensure that there is a measurement model for every (t, y) combination.
+        # If a single measurement model has been provided,
+        # transform it into a list of models to ensure that
+        # there is a measurement model for every (t, y) combination.
         if not isinstance(self.measurement_models, abc.Iterable):
             self.measurement_models = [self.measurement_models] * len(self.locations)
 
@@ -135,11 +139,14 @@ class InitialValueProblem:
     y0
         Initial value of the solution.
     df
-        Jacobian of the ODE vector-field :math:`f=f(t,y)` with respect to the :math:`y` variable.
+        Jacobian of the ODE vector-field :math:`f=f(t,y)`
+        with respect to the :math:`y` variable.
     ddf
-        Hessian of the ODE vector-field :math:`f=f(t,y)` with respect to the :math:`y` variable.
+        Hessian of the ODE vector-field :math:`f=f(t,y)`
+        with respect to the :math:`y` variable.
     solution
-        Closed form, analytic solution to the problem. Used for testing and benchmarking.
+        Closed form, analytic solution to the problem.
+        Used for testing and benchmarking.
     dy0_all
         All initial derivatives up to some order.
 
@@ -236,7 +243,8 @@ class QuadratureProblem:
     output_dim
         Output dimension of the integrand.
     solution
-        Closed form, analytic solution to the problem. Used for testing and benchmarking.
+        Closed form, analytic solution to the problem.
+        Used for testing and benchmarking.
 
     Examples
     --------

src/probnum/problems/zoo/diffeq/_ivp_examples.py

@@ -18,7 +18,8 @@
 def threebody(t0=0.0, tmax=17.0652165601579625588917206249, y0=None):
     r"""Initial value problem (IVP) based on a three-body problem.
 
-    For the initial conditions :math:`y = (y_1, y_2, \dot{y}_1, \dot{y}_2)^T`, this function implements the second-order three-body problem as a system of
+    For the initial conditions :math:`y = (y_1, y_2, \dot{y}_1, \dot{y}_2)^T`,
+    this function implements the second-order three-body problem as a system of
     first-order ODEs, which is defined as follows: [1]_
 
     .. math::
@@ -39,21 +40,25 @@ def threebody(t0=0.0, tmax=17.0652165601579625588917206249, y0=None):
         d_1 &= ((y_1 + \mu)^2 + y_2^2)^{\frac{3}{2}} \\
         d_2 &= ((y_1 - (1 - \mu))^2 + y_2^2)^{\frac{3}{2}}
 
-    and a constant parameter  :math:`\mu = 0.012277471` denoting the standardized moon mass.
+    and a constant parameter  :math:`\mu = 0.012277471`
+    denoting the standardized moon mass.
 
     Parameters
     ----------
     t0
         Initial time.
     tmax
-        Final time. Default is ``17.0652165601579625588917206249`` which is the period of the solution.
+        Final time. Default is ``17.0652165601579625588917206249``
+        which is the period of the solution.
     y0
-        *(shape=(4, ))* -- Initial value. Default is ``[0.994, 0, 0, -2.00158510637908252240537862224]``.
+        *(shape=(4, ))* -- Initial value.
+        Default is ``[0.994, 0, 0,-2.00158510637908252240537862224]``.
 
     Returns
     -------
     InitialValueProblem
-        InitialValueProblem object describing a three-body problem IVP with the prescribed configuration.
+        InitialValueProblem object describing a three-body problem IVP
+        with the prescribed configuration.
 
     References
     ----------
@@ -111,8 +116,8 @@ def vanderpol(t0=0.0, tmax=30, y0=None, params=1e1):
     Returns
     -------
     InitialValueProblem
-        InitialValueProblem object describing the Van der Pol Oscillator IVP with the prescribed
-        configuration.
+        InitialValueProblem object describing the Van der Pol Oscillator
+        IVP with the prescribed configuration.
     """
 
     if y0 is None:
@@ -170,8 +175,8 @@ def rigidbody(t0=0.0, tmax=20.0, y0=None, params=(-2.0, 1.25, -0.5)):
     Returns
     -------
     InitialValueProblem
-        InitialValueProblem object describing the rigid body dynamics IVP with the prescribed
-        configuration.
+        InitialValueProblem object describing the rigid body dynamics IVP
+        with the prescribed configuration.
     """
     if y0 is None:
         y0 = np.array([1.0, 0.0, 0.9])
@@ -286,7 +291,8 @@ def fitzhughnagumo(t0=0.0, tmax=20.0, y0=None, params=(0.2, 0.2, 3.0, 1.0)):
     Returns
     -------
     InitialValueProblem
-        InitialValueProblem object describing the FitzHugh-Nagumo model with the prescribed configuration.
+        InitialValueProblem object describing the FitzHugh-Nagumo model
+        with the prescribed configuration.
     """
     if y0 is None:
         y0 = np.array([1.0, -1.0])
@@ -335,7 +341,8 @@ def lotkavolterra(t0=0.0, tmax=20.0, y0=None, params=(0.5, 0.05, 0.5, 0.05)):
     Returns
     -------
     InitialValueProblem
-        InitialValueProblem object describing the Lotka-Volterra system with the prescribed configuration.
+        InitialValueProblem object describing the Lotka-Volterra system
+        with the prescribed configuration.
     """
     if y0 is None:
         y0 = np.array([20.0, 20.0])
@@ -499,10 +506,12 @@ def lorenz96(t0=0.0, tmax=30.0, y0=None, num_variables=5, params=(8.0,)):
     tmax
         Final time.
     y0
-        *(shape=(N, ))* -- Initial value. Default is ``[1/F, ..., 1/F]``. `N` is the number of variables in the model.
+        *(shape=(N, ))* -- Initial value. Default is ``[1/F, ..., 1/F]``.
+        `N` is the number of variables in the model.
     num_variables
-        Number of variables in the model. If `y0` is specified, this argument is ignored
-        (and the number of variables is inferred from the dimension of the initial value).
+        Number of variables in the model. If `y0` is specified,
+        this argument is ignored (and the number of variables
+        is inferred from the dimension of the initial value).
     params
         Parameter(s) of the Lorenz96 model. Default is ``(8,)``.
 
@@ -521,7 +530,8 @@ def lorenz96(t0=0.0, tmax=30.0, y0=None, num_variables=5, params=(8.0,)):
         y0 = np.ones(num_variables) * constant_forcing
     elif len(y0) < 4:
         raise ValueError(
-            "The number of variables (i.e. the length of the initial vector) must be at least 4."
+            "The number of variables (i.e. the length of the initial vector)",
+            "must be at least 4.",
         )
 
     def lorenz96_f_vec(t, y, c=constant_forcing):

src/probnum/problems/zoo/diffeq/_ivp_examples_jax.py

@@ -33,7 +33,8 @@ def threebody_jax(tmax=17.0652165601579625588917206249):
         d_1 &= ((y_1 + \mu)^2 + y_2^2)^{\frac{3}{2}} \\
         d_2 &= ((y_1 - (1 - \mu))^2 + y_2^2)^{\frac{3}{2}}
 
-    and a constant parameter  :math:`\mu = 0.012277471` denoting the standardized moon mass.
+    and a constant parameter  :math:`\mu = 0.012277471`
+    denoting the standardized moon mass.
 
     Parameters
     ----------
@@ -106,7 +107,8 @@ def _import_jax():
 
 
 def vanderpol_jax(t0=0.0, tmax=30, y0=None, params=1e1):
-    r"""Initial value problem (IVP) based on the Van der Pol Oscillator, implemented in `jax`.
+    r"""Initial value problem (IVP) based on the Van der Pol Oscillator,
+    implemented in `jax`.
 
     This function implements the second-order Van-der-Pol Oscillator as a system
     of first-order ODEs.

src/probnum/problems/zoo/filtsmooth/_filtsmooth_problems.py

@@ -441,10 +441,11 @@ def benes_daum(
 
     Notes
     -----
-    In order to generate observations for the returned ``TimeSeriesRegressionProblem`` object,
-    the non-linear Beneš SDE has to be linearized.
-    Here, a ``ContinuousEKFComponent`` is used, which corresponds to a first-order
-    linearization as used in the extended Kalman filter.
+    In order to generate observations for the returned
+    ``TimeSeriesRegressionProblem`` object, the non-linear Beneš SDE
+    has to be linearized. Here, a ``ContinuousEKFComponent`` is used,
+    which corresponds to a first-order linearization as used
+    in the extended Kalman filter.
     """
 
     def f(t, x):
@@ -538,7 +539,8 @@ def logistic_ode(
     ek0_or_ek1
         See :py:class:`probnum.diffeq.ODEFilter`
     exclude_initial_condition
-        Whether the resulting regression problem should exclude (i.e. not contain) the initial condition of the ODE.
+        Whether the resulting regression problem should exclude
+        (i.e. not contain) the initial condition of the ODE.
         Optional. Default is True, which means that the initial condition is omitted.
     order
         Order of integration for the Integrated Brownian Motion prior of the solver.

src/probnum/problems/zoo/linalg/_random_linear_system.py

@@ -24,17 +24,20 @@ def random_linear_system(
 ) -> problems.LinearSystem:
     """Random linear system.
 
-    Generate a random linear system from a (random) matrix. If ``matrix`` is a callable instead of a matrix or
-    linear operator, the system matrix is sampled by passing the random generator instance ``rng``. The solution
-    of the linear system is set to a realization from ``solution_rv``. If ``None`` the solution is drawn from a
-    standard normal distribution with iid components.
+    Generate a random linear system from a (random) matrix.
+    If ``matrix`` is a callable instead of a matrix or linear operator,
+    the system matrix is sampled by passing the random generator
+    instance ``rng``. The solution of the linear system is set
+    to a realization from ``solution_rv``. If ``None`` the solution
+    is drawn from a standard normal distribution with iid components.
 
     Parameters
     ----------
     rng
         Random number generator.
     matrix
-        Matrix, linear operator or callable returning either for a given random number generator instance.
+        Matrix, linear operator or callable returning either
+        for a given random number generator instance.
     solution_rv
         Random variable from which the solution of the linear system is sampled.
     kwargs
@@ -43,7 +46,8 @@ def random_linear_system(
     See Also
     --------
     random_spd_matrix : Generate a random symmetric positive-definite matrix.
-    random_sparse_spd_matrix : Generate a random sparse symmetric positive-definite matrix.
+    random_sparse_spd_matrix : Generate a random sparse symmetric
+        positive-definite matrix.
 
     Examples
     --------
@@ -65,13 +69,15 @@ def random_linear_system(
     >>> linsys_spd = random_linear_system(rng, random_spd_matrix, dim=2)
     >>> linsys_spd
     LinearSystem(A=array([[ 9.62543582,  3.14955953],
-           [ 3.14955953, 13.28720426]]), b=array([-2.7108139 ,  1.10779288]), solution=array([-0.33488503,  0.16275307]))
+            [ 3.14955953, 13.28720426]]), b=array([-2.7108139 ,  1.10779288]),
+            solution=array([-0.33488503,  0.16275307]))
 
 
     Linear system with random sparse matrix.
 
     >>> import scipy.sparse
-    >>> random_sparse_matrix = lambda rng,m,n: scipy.sparse.random(m=m, n=n, random_state=rng)
+    >>> random_sparse_matrix = lambda rng,m,n: scipy.sparse.random(m=m, n=n,\
+            random_state=rng)
     >>> linsys_sparse = random_linear_system(rng, random_sparse_matrix, m=4, n=2)
     >>> isinstance(linsys_sparse.A, scipy.sparse.spmatrix)
     True
@@ -89,7 +95,8 @@ def random_linear_system(
     else:
         if A.shape[1] != solution_rv.shape[0]:
             raise ValueError(
-                f"Shape of the system matrix: {A.shape} must match shape of the solution: {solution_rv.shape}."
+                f"Shape of the system matrix: {A.shape} must match shape \
+                of the solution: {solution_rv.shape}."
             )
         x = solution_rv.sample(size=(), rng=rng)
 

src/probnum/problems/zoo/linalg/_random_spd_matrix.py

@@ -34,7 +34,8 @@ def random_spd_matrix(
 
     See Also
     --------
-    random_sparse_spd_matrix : Generate a random sparse symmetric positive definite matrix.
+    random_sparse_spd_matrix : Generate a random
+        sparse symmetric positive definite matrix.
 
     Examples
     --------

src/probnum/problems/zoo/linalg/_suitesparse_matrix.py

@@ -56,9 +56,11 @@ def suitesparse_matrix(
         import requests  # pylint: disable=import-outside-toplevel
     except ImportError as err:
         raise ImportError(
-            "Cannot query SuiteSparse Matrix collection without optional dependency ",
-            "`requests`. Install ProbNum with optional dependencies for the problem zoo via",
-            " `pip install probnum[zoo]` or install requests directly: `pip install requests`.",
+            "Cannot query SuiteSparse Matrix collection without",
+            "optional dependency `requests`. Install ProbNum with",
+            "optional dependencies for the problem zoo via",
+            "`pip install probnum[zoo]` or install requests",
+            "directly: `pip install requests`.",
         ) from err
     response = requests.get(SUITESPARSE_INDEX_URL, "r")
     line_gen = response.iter_lines()
@@ -198,7 +200,11 @@ def _download(
             import requests  # pylint: disable=import-outside-toplevel
         except ImportError as err:
             raise ImportError(
-                "Cannot query SuiteSparse Matrix collection without optional dependency `requests`. Install ProbNum with optional dependencies for the problem zoo via `pip install probnum[zoo]` or install requests directly: `pip install requests`."
+                "Cannot query SuiteSparse Matrix collection without",
+                "optional dependency `requests`. Install ProbNum with",
+                "optional dependencies for the problem zoo via",
+                "`pip install probnum[zoo]` or install requests",
+                "directly: `pip install requests`.",
             ) from err
 
         url = SUITESPARSE_ROOT_URL + f"/MM/{self.group}/{self.name}.tar.gz"
@@ -286,7 +292,8 @@ def _to_html_row(self) -> str:
                     f"{self.psym:.2}",
                     f"{self.nsym:.2}",
                     self.kind,
-                    f'<img src="{SUITESPARSE_ROOT_URL}/files/{self.group}/{self.name}.png">',
+                    f'<img src="{SUITESPARSE_ROOT_URL}/files/\
+                    {self.group}/{self.name}.png">',
                 ]
             )
             + "</tr>"