normalize list/array handling in data classes

Establish one invariant across the Drawable hierarchy: x is always a
numpy ndarray, y is always a list of per-channel ndarrays, and empty
data is the same types with zero length.

- Drawable: x starts as empty ndarray; min/max helpers return 0 on
  empty data instead of raising; drop contradictory isinstance(self.y,
  list) branches
- ADC_Result/Accumulation: empty constructors build ndarray x; full
  constructors coerce x/y/y_square with numpy.asarray; Accumulation
  get_ydata/get_yerr return empty ndarrays and no longer crash on
  empty statistics-less accumulations
- MeasurementResult: fix get_lineplotdata to return the accumulated
  means (previously the never-populated self.y, so the GUI line plot
  could never draw); remove dead float/int branches in CSV and HDF
  writers
- Error_Result: get_xdata/get_ydata return ndarrays

Add unittest coverage for the invariant, empty-state accessors,
constructor coercion, line plot data, CSV output and HDF round-trip.

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

tests/test_Error_Result.py

@@ -0,0 +1,23 @@
+import unittest
+
+import numpy as np
+
+from damaris.data.Error_Result import Error_Result
+
+
+class TestErrorResult(unittest.TestCase):
+    def test_plotdata_are_ndarrays(self):
+        """get_xdata/get_ydata must return numpy arrays like every Drawable."""
+        er = Error_Result("core failed")
+        self.assertIsInstance(er.get_xdata(), np.ndarray)
+        self.assertIsInstance(er.get_ydata(), np.ndarray)
+        np.testing.assert_array_equal(er.get_xdata(), [0.0])
+        np.testing.assert_array_equal(er.get_ydata(), [0.0])
+
+    def test_add_returns_self(self):
+        er = Error_Result("msg")
+        self.assertIs(er + 1, er)
+
+
+if __name__ == "__main__":
+    unittest.main()