Fix test failing due to Numpy 2.0 promotion rules

From Numpy 2.0 adding a numpy.float32 and a Python numeric type returns
a numy.float32 when it previously returned a numpy.float64. This changes
the behavior when using the Python builtin sum function on a
numpy.float32 array as the internal computations now will be performed
as numpy.float32 additions when it used to be numpy.float64.

Changing these lines to calling .sum() on the arrays behaves the same
way as before (doing the internal calculations as float64 and
downcasting the result to a float32).

python/test/segyio_c.py

@@ -540,10 +540,10 @@ def read_line(f, metrics, iline_idx, xline_idx):
     buf = numpy.zeros((len(iline_idx), samples), dtype=numpy.single)
 
     f.getline(xline_trace0, len(iline_idx), xline_stride, offsets, buf)
-    assert sum(sum(buf)) == approx(800.061169624, abs=1e-6)
+    assert buf.sum() == approx(800.061169624, abs=1e-6)
 
     f.getline(iline_trace0, len(xline_idx), iline_stride, offsets, buf)
-    assert sum(sum(buf)) == approx(305.061146736, abs=1e-6)
+    assert buf.sum() == approx(305.061146736, abs=1e-6)
 
     f.close()