naive implementation of physical_centres_from

[rhayes777]

Pretty sure this does what you want as it uses the model passed to the samples for each grid search run. For any prior which is a grid prior this new function would give you the mean of that prior for each model

[rhayes777]

Obviously requires more testing and generalisation to support tuple input; I just wanted to check I've understood correctly

[Jammy2211]

I have tested the implementation by using the following code in the relevent part of the PyAutoLens source code:

    @property
    def y(self) -> List[float]:
        """
        The y coordinates of the physical values of the subhalo grid, where each value is the centre of a grid cell.

        These are the `centre` coordinates of the dark matter subhalo priors.
        """
        return self.physical_centres_lists_from(
            path="galaxies.subhalo.mass.centre.centre_0"
        )

    @property
    def x(self) -> List[float]:
        """
        The x coordinates of the physical values of the subhalo grid, where each value is the centre of a grid cell.

        These are the `centre` coordinates of the dark matter subhalo priors.
        """
        return self.physical_centres_lists_from(
            path="galaxies.subhalo.mass.centre.centre_1"
        )

This PR gives the desired behaviour, in that it recovers the values for y and x correctly and can be used for other quantities in the model.

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All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 80.60%. Comparing base (4e2d1e7) to head (d29e8b2). Report is 76 commits behind head on main.

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```diff @@ Coverage Diff @@ ## main #1034 +/- ## ========================================== - Coverage 80.77% 80.60% -0.17% ========================================== Files 198 199 +1 Lines 14983 15241 +258 ========================================== + Hits 12102 12285 +183 - Misses 2881 2956 +75 ```

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