Expose numeric trend segment formulas from native reconstruction models

python/set_devices/signal_reconstruction.py

@@ -14,6 +14,7 @@ class Reconstruction:
     input_count: int
     unique_count: int
     rmse: float
+    pieces: tuple = ()
 
 @lru_cache(maxsize=1)
 def library():
@@ -29,7 +30,7 @@ def library():
     except (AttributeError, OSError, RuntimeError) as error:
         raise ValueError("Пересоберите SETProtocol с set_signal.c и set_wavegen.c") from error
 
-def reconstruct(points, method="pchip", output_count=1000, degree=2, *, endpoint=True):
+def reconstruct(points, method="pchip", output_count=1000, degree=2, *, endpoint=True, with_model=False):
     if method not in METHODS or type(output_count) is not int or not 2 <= output_count <= 10000:
         raise ValueError("Неизвестный метод или число выходных точек вне 2…10000")
     count = len(points)
@@ -44,7 +45,14 @@ def reconstruct(points, method="pchip", output_count=1000, degree=2, *, endpoint
         raise ValueError({1: "Недостаточно точек для выбранной степени или неверные параметры",
                           2: "Нужны конечные значения и минимум две различные временные точки",
                           3: "Неустойчивая аппроксимация: уменьшите степень"}.get(code, "Ошибка расчёта"))
-    return Reconstruction(list(zip(ox, oy)), int(meta[0]), int(meta[1]), meta[2])
+    pieces = ()
+    if with_model:
+        from .plot_formula import reconstruction_pieces
+        if method == 'polynomial' and output_count <= degree:
+            pieces = reconstruct(points, method, degree + 1, degree, with_model=True).pieces
+        else:
+            pieces = reconstruction_pieces(work, count, int(meta[1]), method, degree, oy, endpoint)
+    return Reconstruction(list(zip(ox, oy)), int(meta[0]), int(meta[1]), meta[2], pieces)
 
 def dac12(volts, vref=3.3):
     data = (C.c_double * len(volts))(*volts)