Geotiff raster saving broken with numpy >= 2.4.0

Start in lib/iris/experimental/raster.py around the _gdal_write_array call at line 210, then reproduce the issue with the provided Cube example under numpy >=2.4. Ensure the x/y minimum and step values passed to GDAL are scalar numeric values, and verify that export_geotiff creates the GeoTIFF without raising TypeError.

🐛 Bug Report

The function iris.experimental.raster.export_geotiff fails with numpy>=2.4.0 with the following GDAL error:

  File " ... /site-packages/osgeo/gdal.py", line 4468, in SetGeoTransform
    return _gdal.Dataset_SetGeoTransform(self, *args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: not a number

This is due to how the parameters are passed to the ...

How To Reproduce

Steps to reproduce the behaviour:

from iris.cube import Cube
from iris.coords import DimCoord
from iris.experimental.raster import export_geotiff
import numpy as np

lats = DimCoord(np.arange(5), standard_name="latitude", units="degrees")
lats.guess_bounds()

lons = DimCoord(np.arange(5), standard_name="longitude", units="degrees")
lons.guess_bounds()

c = Cube(
    data=np.arange(25, dtype=np.float64).reshape(5,5),
    standard_name="depth",
    dim_coords_and_dims=((lats, 0), (lons,1))
)

export_geotiff(c, '/tmp/geo.tiff') # throws a TypeError

Expected behaviour

Does not throw a TypeError!

Environment

• OS & Version: RHEL10
• Iris Version: 3.15.0

Proposed Fix

The issue appears to be with the values passed to _gdal_write_array in experimental/raster.py:
https://github.com/SciTools/iris/blob/6d9ef5e7af7dfb0e26bac398650606827c883a07/lib/iris/experimental/raster.py#L210

The [xy]_min and [xy]_step values should be plain python floats, or numpy floats, but this routine passes in the following:

x_min=np.float64(-0.5), x_step=array([1.]), y_max=np.float64(4.5), y_step=array([-1.])

which is a mixture of scalar and 1D array numpy values. This appeared to silently work up until numpy==2.4.0, but is now failing.

The fix should be as simple as ensuring those values are plain python floats, or a numpy scalar, perhaps by using .item() which is safe to use on scalars and 1D arrays (and will throw an error if the arrays is greater than length 1).