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: