core.CCOutput

core.CCOutput(
    time,
    state_variables,
    state_variable_names,
    diagnostic_variables,
    solution,
    run_name,
)

Container for the results of one call to CCModel.integrate().

CCOutput carries the full trajectory produced by the solver and exposes output-focused operations (noise addition, pyleoclim export, time resampling). Keeping these on the output rather than on the model means a single model instance can produce multiple independent outputs without them overwriting each other.

Attributes

model_time : ndarray of float

Raw time axis as returned by the solver. Never modified after construction; inspect this to understand the solver’s actual grid.

time : ndarray of float

User-facing time axis. Starts equal to model_time; replaced by reframe_time_axis when a different grid is requested. state_variables is always aligned to time.

state_variables : structured ndarray

Named state variable arrays, indexed by variable name.

state_variable_names : list of str

Ordered list of state variable names.

diagnostic_variables : dict of str → ndarray

Named diagnostic arrays.

solution : object

Raw solver output (retained for dense-output resampling via reframe_time_axis).

run_name : str

Label for this integration run.

Methods

Name	Description
add_noise	Add externally provided noise to an output variable.
get_series_by_name	Return a variable’s array and its storage location.
reframe_time_axis	Resample state variables onto a new time axis.
remove_noise	Restore a variable to its pre-noise values.
to_pyleo	Export one or more variables as pyleoclim Series objects.

add_noise

core.CCOutput.add_noise(var_name, noise_ts)

Add externally provided noise to an output variable.

The unmodified values are saved on the first call so that remove_noise can restore the clean series.

Parameters

var_name : str

Name of a state or diagnostic variable.

noise_ts : array - like

Noise series with the same shape as the target variable.

get_series_by_name

core.CCOutput.get_series_by_name(var_name)

Return a variable’s array and its storage location.

State variables live in a structured numpy array; diagnostic variables live in a dict. Returning the location string lets callers that write back (e.g. add_noise) know which container to update.

Returns

values : ndarray of float

location : {'state', 'diagnostic'}

reframe_time_axis

core.CCOutput.reframe_time_axis(t_eval)

Resample state variables onto a new time axis.

Uses the dense output from solve_ivp when available (accurate polynomial interpolation); falls back to linear interpolation for fixed-step solvers.

After this call, time is replaced by t_eval and state_variables is replaced by the interpolated values. model_time is never modified and always reflects the solver’s original grid.

Parameters

t_eval : array - like

Target time axis.

Returns

reframed : structured ndarray or ndarray

Resampled state variables on t_eval.

Raises

: UserWarning

If this output was produced by a stochastic (SDE) solver without dense output, and t_eval is coarser than the integrated time series. Linearly interpolating between two stochastic path points spaced wider than the original grid invents values that were never realised, discarding Wiener increments and distorting distributional properties. Use si= at integration time to control output spacing instead.

remove_noise

core.CCOutput.remove_noise(var_name)

Restore a variable to its pre-noise values.

Reverses add_noise by replacing the noisy array with the clean copy saved on the first add_noise call.

to_pyleo

core.CCOutput.to_pyleo(var_names=None)

Export one or more variables as pyleoclim Series objects.

Parameters

var_names : str or list of str = None

Name(s) of state or diagnostic variable(s) to export.

Returns

: pyleoclim.Series or pyleoclim.MultipleSeries