Handle scalar coercion with more nuance#76
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f62565a handled reading length-1 datasets as scalars (since numpy 2 no longer handled these cases automatically. However, it was a little overzealous and always converted numpy scalar objects (np.generic) into python scalar objects. Two examples where this was a problem were NXData data_scale_factor and data_offset; these used to return a numpy scalar and now returned the inner value (also, the typing for these was _completely wrong_). This fixes those annotations, and returns the numpy scalar - if appropriate - because these fields can also validly contain arrays. Also, slightly tightened up conversion cases in other instances of e.g. str conversion.
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f62565a handled reading length-1 datasets as scalars (since numpy 2 no longer handled these cases automatically. However, it was a little overzealous and always converted numpy scalar objects (np.generic) into python scalar objects.
Two examples where this was a problem were NXData data_scale_factor and data_offset; these used to return a numpy scalar and now returned the inner value (also, the typing for these was completely wrong).
This fixes those annotations, and returns the numpy scalar - if appropriate - because these fields can also validly contain arrays.
Also, slightly tightened up conversion cases in other instances of e.g. str conversion.