issues: 1157140667

Is your feature request related to a problem?

I'm trying to fit a bunch of exponential decays to a dataset of dimensions (Time, experimental_index), along the Time dimension. This works nicely... except in some cases, scipy.optimize raises an error such as RuntimeError: Optimal parameters not found: The maximum number of function evaluations is exceeded.. That's, of course, reasonable, except in my use case I'm fine with a bunch of experimental_index failing to fit, and I'd still like to access the fits that made it through.

Describe the solution you'd like

I'm proposing adding a flag for xarray.curvefit, for example allow_failures: bool = False, that would take this part:

https://github.com/pydata/xarray/blob/f9037c41e36254bfe7efa9feaedd3eae5512bd11/xarray/core/dataset.py#L7738-L7752

and, if active, wrap popt, pcov = curve_fit(func, x, y, **kwargs) in a try/except block that would output nan just as the not len(y) conditional clause does if scipy.optimize.curve_fit fails with a RuntimeError.

Describe alternatives you've considered

At the moment, I sidestep the issue like this:

```python fits = [] for experimental_index in timetraces.experimental_index: trace = traces.sel(experimental_index=experimental_index)

try:
    fit = trace.curvefit(
        "Time",
        fitfunc,
        p0=(...),
        bounds={...},
    )
except (RuntimeError, TypeError) as e:
    logger.debug(...)
    continue

fits.append(fit)

fits = xarray.concat(fits, dim="experimental_index", coords="all") ``` but that seems like a pretty ugly hack...

Additional context

I would of course be happy to implement this!