all tests pass. need to add more tests for test_cavity, etc.

openptv_python/correspondences.py

@@ -8,7 +8,7 @@
 from .epi import Coord2d, epi_mm
 from .find_candidate import find_candidate
 from .parameters import ControlPar, VolumePar
-from .tracking_frame_buf import Frame, Target, TargetArray, n_tupel
+from .tracking_frame_buf import Frame, Target, n_tupel
 
 # @dataclass
 # class Correspond:
@@ -53,7 +53,7 @@ def safely_allocate_target_usage_marks(
     # Check if any of the allocations failed.
     for cam in range(num_cams):
         if tusage[cam] is None:
-            return None
+            return []  # was None
 
     return tusage
 
@@ -74,27 +74,27 @@ def safely_allocate_adjacency_lists(
     num_cams: int, target_counts: List[int]
 ) -> List[List[List[Correspond]]]:
     """Allocate adjacency lists."""
-    lists = [[None for _ in range(num_cams)] for _ in range(num_cams)]
+    lists = [[[] for _ in range(num_cams)] for _ in range(num_cams)]
     error = 0
 
     for c1 in range(num_cams - 1):
         for c2 in range(c1 + 1, num_cams):
             if error == 0:
                 lists[c1][c2] = [Correspond() for _ in range(target_counts[c1])]  # type: ignore
-                if lists[c1][c2] is None:
+                if not lists[c1][c2]:
                     error = 1
-                    lists[c1][c2] = None
+                    lists[c1][c2] = []
 
                 for edge in range(target_counts[c1]):
                     lists[c1][c2][edge].n = 0
                     lists[c1][c2][edge].p1 = 0
             else:
-                lists[c1][c2] = None
+                lists[c1][c2] = []
 
     if error == 0:
         return lists
 
-    return False
+    return []
 
 
 def four_camera_matching(
@@ -471,7 +471,7 @@ def take_best_candidates(
 
 
 def py_correspondences(
-    img_pts: List[List[TargetArray]],  # num_cams * num_targets[cam]
+    img_pts: List[List[Target]],  # num_cams * num_targets[cam]
     flat_coords: List[List[Coord2d]],
     calib: List[Calibration],
     vparam: VolumePar,

openptv_python/image_processing.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 from scipy import ndimage
-from scipy.ndimage import map_coordinates, uniform_filter
+from scipy.ndimage import uniform_filter
 
 from .parameters import ControlPar
 
@@ -41,20 +41,20 @@ def fast_box_blur(filt_span: int, src: np.ndarray, cpar: ControlPar) -> np.ndarr
     return row_accum
 
 
-def split(img: np.ndarray, half_selector: int, cpar: ControlPar) -> np.ndarray:
-    """Split image into two halves."""
-    cond_offs = cpar.imx if half_selector % 2 else 0
+# def split(img: np.ndarray, half_selector: int, cpar: ControlPar) -> np.ndarray:
+#     """Split image into two halves."""
+#     cond_offs = cpar.imx if half_selector % 2 else 0
 
-    if half_selector == 0:
-        return
+#     if half_selector == 0:
+#         return
 
-    coords_x, coords_y = np.meshgrid(np.arange(cpar.imx), np.arange(cpar.imy // 2))
+#     coords_x, coords_y = np.meshgrid(np.arange(cpar.imx), np.arange(cpar.imy // 2))
 
-    coords_x = coords_x.flatten()
-    coords_y = coords_y.flatten() * 2 + cond_offs
+#     coords_x = coords_x.flatten()
+#     coords_y = coords_y.flatten() * 2 + cond_offs
 
-    new_img = map_coordinates(img, [coords_y, coords_x], mode="constant", cval=0)
-    return new_img
+#     new_img = map_coordinates(img, [coords_y, coords_x], mode="constant", cval=0)
+#     return new_img
 
 
 def subtract_img(img1: np.ndarray, img2: np.ndarray, img_new: np.ndarray) -> None:
@@ -86,7 +86,7 @@ def prepare_image(
     cpar: ControlPar,
     dim_lp: int = 1,
     filter_hp: int = 1,
-    filter_file: str | None = None,
+    filter_file: str = "",
 ):
     """Prepare an image for particle detection: an averaging (smoothing).
 
@@ -99,7 +99,7 @@ def prepare_image(
     img = img.reshape((cpar.imy, cpar.imx))  # Reshape to 2D image
     img_lp = ndimage.uniform_filter(
         img,
-        size=(dim_lp * 2 + 1, dim_lp * 2 + 1),
+        size=dim_lp * 2 + 1,
         mode="constant",
         cval=0.0,
     )
@@ -111,7 +111,7 @@ def prepare_image(
     if filter_hp == 1:
         img_hp = ndimage.uniform_filter(
             img_hp.reshape((cpar.imy, cpar.imx)),
-            size=(3, 3),
+            size=3,
             mode="constant",
             cval=0.0,
         ).flatten()

openptv_python/orientation.py

@@ -2,6 +2,7 @@
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
+import scipy
 
 from openptv_python.constants import COORD_UNUSED
 
@@ -188,7 +189,7 @@ def orient(
     cal: Calibration,
     cpar: ControlPar,
     nfix: int,
-    fix: List[np.ndarray],
+    fix: np.ndarray,
     pix: List[Target],
     flags: OrientPar,
     sigmabeta: np.ndarray,
@@ -486,7 +487,13 @@ def orient(
         )
 
         # Interpret the results
-        # print(f"Coefficients (beta): {beta} Residuals: {residuals}")
+        print(
+            f"Coefficients (beta): {beta} \
+                Residuals: {residuals} \
+                singular_values: {singular_values} \
+                rank: {rank} \
+            "
+        )
 
         # stopflag
         stopflag = True
@@ -620,11 +627,15 @@ def raw_orient(
         # matmul(beta, XPX, XPy, 6, 6, 1, 6, 6)
 
         # Solve the linear system
-        beta, residuals, rank, singular_values = np.linalg.lstsq(X, y, rcond=None)
+        beta, residuals, rank, singular_values = scipy.linalg.lstsq(
+            X, y
+        )  # , rcond=None)
 
         # Interpret the results
         print("Coefficients (beta):", beta)
         print("Residuals:", residuals)
+        print("rank:", rank)
+        print("singular_values:", singular_values)
 
         stopflag = True
         for i in range(6):