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>

src/damaris/data/ADC_Result.py

@@ -67,7 +67,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
             self.cont_data = False
             self.sampling_rate = 0
             self.index = []
-            self.x = []
+            self.x = numpy.array([], dtype="float32")
             self.y = []
         # using all arguments for initialization
         elif (x is not None) and (y is not None) and (index is not None) and (sampl_freq is not None) and (desc is not None) and (job_id is not None) and (job_date is not None):
@@ -75,8 +75,8 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
             #for ch in y:
             #    if not numpy.issubdtype(ch.dtype, numpy.integer):
             #        raise TypeError("TypeError: ADC_Result y data must be a list with integer type channels")
-            self.x = x
-            self.y = y
+            self.x = numpy.asarray(x)
+            self.y = [numpy.asarray(ch) for ch in y]
             self.index = index
             self.sampling_rate = sampl_freq
             self.cont_data = True

src/damaris/data/Accumulation.py

@@ -78,7 +78,7 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
         if self.uses_statistics():
             if (y_2 is not None):
                 # y_square is always kept as a list of per-channel arrays
-                self.y_square: _YChannels = y_2 if isinstance(y_2, list) else list(y_2)
+                self.y_square: _YChannels = [numpy.asarray(ch) for ch in y_2]
             elif (y_2 is None) :
                 self.y_square = []
             else:
@@ -93,12 +93,12 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
             
             self.cont_data = False
             self.index = []
-            self.x = []
+            self.x = numpy.array([], dtype="float32")
             self.y = []
 
         elif (x is not None) and (y is not None) and (index is not None) and (sampl_freq is not None) and (n is not None):
-            self.x = x
-            self.y = y
+            self.x = numpy.asarray(x)
+            self.y = [numpy.asarray(ch) for ch in y]
             self.sampling_rate = sampl_freq
 
             self.n = n
@@ -179,15 +179,15 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
 
     # external interface  --------------------------------------------------------------------
 
-    def get_yerr(self, channel: int) -> numpy.ndarray | list:
+    def get_yerr(self, channel: int) -> numpy.ndarray:
         """
         return error (std.dev/sqrt(n)) of mean
         """
 
+        if not self.contains_data():
+            return numpy.array([], dtype="float32")
         if not self.uses_statistics():
             return numpy.zeros((len(self.y[0]),),dtype="float32")
-        if not self.contains_data():
-            return []
 
         self.lock.acquire()
         if self.n < 2:
@@ -203,13 +203,13 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
         # sample standard deviation / sqrt(n)
         return numpy.nan_to_num(numpy.sqrt(variance_over_n))
 
-    def get_ydata(self, channel: int = 0) -> numpy.ndarray | list:
+    def get_ydata(self, channel: int = 0) -> numpy.ndarray:
         """
         return mean data
         """
 
         if not self.contains_data():
-            return []
+            return numpy.array([], dtype="float32")
         self.lock.acquire()
         try:
             tmp_y = self.y[channel] / self.n

src/damaris/data/Drawable.py

@@ -17,8 +17,11 @@ class Drawable:
     def __init__(self):
 
         # Will be set correctly in one of the subclasses
-        self.x: numpy.ndarray | list = []
-        self.y: list = []
+        # x is always a numpy.ndarray, y is always a list of numpy.ndarray
+        # (one array per channel); empty data is represented by zero-length
+        # arrays of the same types
+        self.x: numpy.ndarray = numpy.array([], dtype="float64")
+        self.y: list[numpy.ndarray] = []
 
         self.styles: dict = { }
 
@@ -37,7 +40,7 @@ class Drawable:
         self.ymax = 0
 
 
-    def get_xdata(self) -> numpy.ndarray | list:
+    def get_xdata(self) -> numpy.ndarray:
         "Returns a reference to the x-Plotdata (array)"
         return self.x
 
@@ -50,7 +53,7 @@ class Drawable:
             raise
 
 
-    def get_ydata(self, channel: int = 0) -> numpy.ndarray | list:
+    def get_ydata(self, channel: int = 0) -> numpy.ndarray:
         "Returns the y-Plotdata of channel n (array)"
         try:
             return self.y[channel]
@@ -140,12 +143,16 @@ class Drawable:
 
     def get_xmin(self) -> float:
         "Returns minimun of x"
+        if self.x.size == 0:
+            return 0
         return self.x.min()
 
 
     def get_xminpos(self) -> float:
         "Returns smallest positive value of x"
         mask = self.x > 0
+        if not numpy.any(mask):
+            return 0
         return self.x[mask].min()
 
 
@@ -156,6 +163,8 @@ class Drawable:
 
     def get_xmax(self) -> float:
         "Returns maximum of x"
+        if self.x.size == 0:
+            return 0
         return self.x.max()
 
 
@@ -166,24 +175,21 @@ class Drawable:
 
     def get_ymin(self) -> float | int:
         "Returns minimum of y"
-        if isinstance(self.y, list):
-            return min([yarr.min() for yarr in self.y])
-        else:
-            return self.y.min()
+        if len(self.y) == 0:
+            return 0
+        return min([yarr.min() for yarr in self.y])
 
 
     def get_yminpos(self) -> float | int:
         "Returns smallest positive value of y"
-        if isinstance(self.y, list):
-            ymins = []
-            for ys in self.y:
-                mask = ys > 0
+        ymins = []
+        for ys in self.y:
+            mask = ys > 0
+            if numpy.any(mask):
                 ymins.append(ys[mask].min())
-            ymin = min(ymins)
-        else:
-            mask = self.y > 0
-            ymin = self.y[mask].min()
-        return ymin
+        if len(ymins) == 0:
+            return 0
+        return min(ymins)
 
 
     def set_ymin(self, ymin: float) -> None:
@@ -193,10 +199,9 @@ class Drawable:
 
     def get_ymax(self) -> float | int:
         "Returns maximimum of y"
-        if isinstance(self.y, list):
-            return max([yarr.max() for yarr in self.y])
-        else:
-            return self.y.max()
+        if len(self.y) == 0:
+            return 0
+        return max([yarr.max() for yarr in self.y])
 
 
     def set_ymax(self, ymax: float) -> None:

src/damaris/data/Error_Result.py

@@ -2,6 +2,7 @@
 
 from .Resultable import Resultable
 from .Drawable import Drawable
+import numpy
 
 #############################################################################
 #                                                                           #
@@ -47,11 +48,11 @@ class Error_Result(Resultable, Drawable):
 
     # Nothing to plot
     def get_ydata(self, channel=0):
-        return [0.0]
+        return numpy.array([0.0])
 
     # Nothing to plot
     def get_xdata(self):
-        return [0.0]
+        return numpy.array([0.0])
 
     #overload of operators und built-ins -------------------------------------------------------
 

src/damaris/data/MeasurementResult.py

@@ -195,9 +195,7 @@ class MeasurementResult(Drawable.Drawable, collections.UserDict):
         return [k,v,e]
 
     def get_lineplotdata(self) -> list[numpy.ndarray]:
-        k=self.get_xdata()
-        v=numpy.array(self.y, dtype="float64") 
-        return [k, v]
+        return self.get_xydata()
 
     def uses_statistics(self) -> bool:
         """
@@ -222,16 +220,13 @@ class MeasurementResult(Drawable.Drawable, collections.UserDict):
         dest.write("# x y ysigma n\n")
         for x in self.get_xdata():
             y = self.data[x]
-            if isinstance(y, (float, int)):
-                dest.write("%e%s%e%s0%s1\n" % (x, delimiter, y, delimiter, delimiter))
-            else:
-                dest.write("%e%s%e%s%e%s%d\n" % (x,
-                                                 delimiter,
-                                                 y.mean(),
-                                                 delimiter,
-                                                 y.mean_error(),
-                                                 delimiter,
-                                                 y.n))
+            dest.write("%e%s%e%s%e%s%d\n" % (x,
+                                             delimiter,
+                                             y.mean(),
+                                             delimiter,
+                                             y.mean_error(),
+                                             delimiter,
+                                             y.n))
 
 
     def write_to_hdf(self, hdffile: tables.File, where: str, name: str, title: str, complib: str | None = None, complevel: int | None = None) -> None:
@@ -260,17 +255,12 @@ class MeasurementResult(Drawable.Drawable, collections.UserDict):
             row=mr_table.row
             xdata=self.get_xdata()
             if xdata.shape[0]!=0:
-                for x in self.get_xdata():
+                for x in xdata:
                     y=self.data[x]
                     row["x"]=x
-                    if type(y) in [float, int, int]:
-                        row["y"]=y
-                        row["y_err"]=0.0
-                        row["n"]=1
-                    else:
-                        row["y"]=y.mean()
-                        row["y_err"]=y.mean_error()
-                        row["n"]=y.n
+                    row["y"]=y.mean()
+                    row["y_err"]=y.mean_error()
+                    row["n"]=y.n
                     row.append()
 
         finally:

tests/test_ADC_Result.py

@@ -316,5 +316,31 @@ class TestADCResult(unittest.TestCase):
         self.assertRaises(ValueError, adc1.__rfloordiv__, adc2)
         self.assertRaises(ValueError, adc1.__pow__, adc2)
 
+    def test_empty_state_types(self):
+        """Empty ADC_Result must hold an ndarray x and a list-of-ndarray y."""
+        adc = ADC_Result()
+        self.assertIsInstance(adc.x, np.ndarray)
+        self.assertIsInstance(adc.y, list)
+        self.assertEqual(adc.x.size, 0)
+        self.assertEqual(adc.get_xmin(), 0)
+        self.assertEqual(adc.get_xmax(), 0)
+        self.assertEqual(adc.get_xminpos(), 0)
+        self.assertEqual(adc.get_ymin(), 0)
+        self.assertEqual(adc.get_ymax(), 0)
+        self.assertEqual(adc.get_yminpos(), 0)
+
+    def test_constructor_coerces_lists(self):
+        """Plain lists passed to the constructor must become numpy arrays."""
+        x = [0.0, 1.0, 2.0]
+        y = [[10, 20, 30], [15, 25, 35]]
+        index = [(0, 2)]
+        adc = ADC_Result(x, y, index, 1000.0, {"key": "value"}, 1, datetime(2023, 1, 1))
+        self.assertIsInstance(adc.x, np.ndarray)
+        np.testing.assert_array_equal(adc.x, [0.0, 1.0, 2.0])
+        for ch in adc.y:
+            self.assertIsInstance(ch, np.ndarray)
+        np.testing.assert_array_equal(adc.y[0], [10, 20, 30])
+        np.testing.assert_array_equal(adc.y[1], [15, 25, 35])
+
 if __name__ == "__main__":
     unittest.main()

tests/test_Accumulation.py

@@ -523,5 +523,42 @@ class TestAccumulation(unittest.TestCase):
         self.assertTrue(accu.lock.acquire(timeout=1), "lock leaked after ValueError in write_to_tecmag")
         accu.lock.release()
 
+    def test_empty_state_accessors(self):
+        """Empty Accumulation accessors must return ndarrays, min/max helpers 0."""
+        accu = Accumulation()
+        self.assertIsInstance(accu.x, np.ndarray)
+        self.assertEqual(accu.x.size, 0)
+        self.assertIsInstance(accu.get_ydata(), np.ndarray)
+        self.assertEqual(accu.get_ydata().size, 0)
+        self.assertIsInstance(accu.get_yerr(0), np.ndarray)
+        self.assertEqual(accu.get_yerr(0).size, 0)
+        self.assertEqual(accu.get_xmin(), 0)
+        self.assertEqual(accu.get_xmax(), 0)
+        self.assertEqual(accu.get_ymin(), 0)
+        self.assertEqual(accu.get_ymax(), 0)
+
+    def test_get_yerr_without_statistics_on_empty(self):
+        """get_yerr on an empty accumulation without statistics must not crash."""
+        accu = Accumulation(error=False)
+        err = accu.get_yerr(0)
+        self.assertIsInstance(err, np.ndarray)
+        self.assertEqual(err.size, 0)
+
+    def test_constructor_coerces_lists(self):
+        """Plain lists passed to the constructor must become numpy arrays."""
+        x = [0.0, 1.0, 2.0]
+        y = [[10, 20, 30], [15, 25, 35]]
+        y_2 = [[100.0, 400.0, 900.0], [225.0, 625.0, 1225.0]]
+        accu = Accumulation(x=x, y=y, y_2=y_2, n=1,
+                            index=[(0, 2)], sampl_freq=1000.0, error=True)
+        self.assertIsInstance(accu.x, np.ndarray)
+        np.testing.assert_array_equal(accu.x, [0.0, 1.0, 2.0])
+        for ch in accu.y:
+            self.assertIsInstance(ch, np.ndarray)
+        for ch in accu.y_square:
+            self.assertIsInstance(ch, np.ndarray)
+        np.testing.assert_array_equal(accu.y[0], [10, 20, 30])
+        np.testing.assert_array_equal(accu.y_square[0], [100.0, 400.0, 900.0])
+
 if __name__ == "__main__":
     unittest.main()

tests/test_Drawable.py

@@ -0,0 +1,54 @@
+import unittest
+
+import numpy as np
+
+from damaris.data.Drawable import Drawable
+
+
+class TestDrawable(unittest.TestCase):
+    def test_initial_types(self):
+        """x must start as an empty ndarray, y as a list of arrays."""
+        d = Drawable()
+        self.assertIsInstance(d.x, np.ndarray)
+        self.assertEqual(d.x.size, 0)
+        self.assertIsInstance(d.y, list)
+
+    def test_empty_min_max_helpers(self):
+        """min/max helpers must return 0 instead of raising on empty data."""
+        d = Drawable()
+        self.assertEqual(d.get_xmin(), 0)
+        self.assertEqual(d.get_xmax(), 0)
+        self.assertEqual(d.get_xminpos(), 0)
+        self.assertEqual(d.get_ymin(), 0)
+        self.assertEqual(d.get_ymax(), 0)
+        self.assertEqual(d.get_yminpos(), 0)
+
+    def test_min_max_helpers_with_data(self):
+        d = Drawable()
+        d.x = np.array([-1.0, 0.0, 2.0, 5.0])
+        d.y = [np.array([1.0, -2.0, 3.0]), np.array([0.5, 4.0, -0.5])]
+        self.assertEqual(d.get_xmin(), -1.0)
+        self.assertEqual(d.get_xmax(), 5.0)
+        self.assertEqual(d.get_xminpos(), 2.0)
+        self.assertEqual(d.get_ymin(), -2.0)
+        self.assertEqual(d.get_ymax(), 4.0)
+        self.assertEqual(d.get_yminpos(), 0.5)
+
+    def test_yminpos_without_positive_values(self):
+        d = Drawable()
+        d.x = np.array([1.0])
+        d.y = [np.array([-1.0, -2.0])]
+        self.assertEqual(d.get_yminpos(), 0)
+
+    def test_set_xdata_set_ydata(self):
+        d = Drawable()
+        d.x = np.zeros(3)
+        d.y = [np.zeros(3)]
+        d.set_xdata(1, 5.0)
+        d.set_ydata(0, 1, 7.0)
+        self.assertEqual(d.get_xdata()[1], 5.0)
+        self.assertEqual(d.get_ydata(0)[1], 7.0)
+
+
+if __name__ == "__main__":
+    unittest.main()

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()

tests/test_MeasurementResult.py

@@ -1,8 +1,12 @@
+import io
+import os
+import tempfile
 import unittest
 
 import numpy as np
+import tables
 
-from damaris.data.MeasurementResult import MeasurementResult
+from damaris.data.MeasurementResult import MeasurementResult, read_from_hdf
 
 
 class TestMeasurementResult(unittest.TestCase):
@@ -30,6 +34,53 @@ class TestMeasurementResult(unittest.TestCase):
         mr = MeasurementResult("q")
         self.assertRaises(Exception, mr.__add__, 1)
 
+    def test_get_lineplotdata_returns_means(self):
+        """get_lineplotdata must return the accumulated means, not self.y."""
+        mr = MeasurementResult("test_quantity")
+        for i in range(3):
+            mr[float(i)] = float(i * i)
+        k, v = mr.get_lineplotdata()
+        self.assertIsInstance(k, np.ndarray)
+        self.assertIsInstance(v, np.ndarray)
+        self.assertEqual(k.shape, v.shape)
+        np.testing.assert_array_equal(v, mr.get_ydata())
+        np.testing.assert_array_equal(v, [0.0, 1.0, 4.0])
+
+    def test_get_lineplotdata_empty(self):
+        """get_lineplotdata of an empty result must return empty arrays."""
+        mr = MeasurementResult("q")
+        k, v = mr.get_lineplotdata()
+        self.assertEqual(k.shape[0], 0)
+        self.assertEqual(v.shape[0], 0)
+
+    def test_write_to_csv(self):
+        mr = MeasurementResult("q")
+        mr[1.0] = 5.0
+        mr[2.0] = 7.0
+        buf = io.StringIO()
+        mr.write_to_csv(buf)
+        lines = buf.getvalue().splitlines()
+        self.assertEqual(lines[0], "# quantity:q")
+        self.assertEqual(lines[1], "# x y ysigma n")
+        self.assertEqual(len(lines), 4)
+        self.assertEqual(lines[2].split()[3], "1")
+        self.assertEqual(lines[3].split()[3], "1")
+
+    def test_write_to_hdf_roundtrip(self):
+        mr = MeasurementResult("q")
+        mr[1.0] = 5.0
+        mr[2.0] = 7.0
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, "test.h5")
+            with tables.open_file(path, mode="w") as h5file:
+                mr.write_to_hdf(h5file, "/", "mr", "test result")
+            with tables.open_file(path, mode="r") as h5file:
+                mr2 = read_from_hdf(h5file.get_node("/mr"))
+        self.assertIsNotNone(mr2)
+        self.assertEqual(mr2.quantity_name, "q")
+        np.testing.assert_array_equal(mr2.get_xdata(), mr.get_xdata())
+        np.testing.assert_array_equal(mr2.get_ydata(), mr.get_ydata())
+
 
 if __name__ == "__main__":
     unittest.main()