Merge 6d6dcfa into 625c88f

pyomeca/processing/rototrans.py

@@ -154,7 +154,9 @@ def rototrans_from_markers(
     else:
         raise ValueError("`axis_to_recalculate must be `x`, `y` or `z`")
 
-    rt = caller(np.zeros((4, 4, origin.time.size)))
+    rt = np.zeros((4, 4, origin.time.size))
+    rt[3, 3, :] = 1
+    rt = caller(rt)
     rt[:3, 0, :] = x / np.linalg.norm(x, axis=0)
     rt[:3, 1, :] = y / np.linalg.norm(y, axis=0)
     rt[:3, 2, :] = z / np.linalg.norm(z, axis=0)
@@ -165,7 +167,9 @@ def rototrans_from_markers(
 def rototrans_from_transposed_rototrans(
     caller: Callable, rt: xr.DataArray
 ) -> xr.DataArray:
-    rt_t = caller(np.zeros((4, 4, rt.time.size)))
+    rt_t = np.zeros((4, 4, rt.time.size))
+    rt_t[3, 3, :] = 1
+    rt_t = caller(rt_t)
 
     # the rotation part is just the transposed of the rotation
     rt_t.meca.rotation = rt.meca.rotation.transpose("col", "row", "time")

pyomeca/rototrans.py

@@ -71,8 +71,16 @@ def __new__(
             coords["time"] = time
 
         # Make sure last line reads [0, 0, 0, 1]
-        data[3, :3, :] = 0
-        data[3, 3, :] = 1
+        zeros = data[3, :3, :]
+        ones = data[3, 3, :]
+        if not np.alltrue(zeros == 0) or not np.alltrue(ones == 1):
+            some_zeros = np.random.choice(zeros.ravel(), 5)
+            some_ones = np.random.choice(ones.ravel(), 5)
+            raise ValueError(
+                "Last line does not read [0, 0, 0, 1].\n"
+                f"Here are some values that should be 0: {some_zeros}\n"
+                f"And others that should 1: {some_ones}"
+            )
 
         return xr.DataArray(
             data=data,
@@ -84,7 +92,7 @@ def __new__(
 
     @classmethod
     def from_random_data(
-        cls, distribution: str = "normal", size: tuple = (4, 4, 100), **kwargs
+        cls, distribution: str = "normal", size: tuple = (3, 1, 100), **kwargs
     ) -> xr.DataArray:
         """
         Create random data from a specified distribution (normal by default) using random walk.

tests/data/is_expected_array_val.csv

@@ -65,4 +65,4 @@ idx,shape_val,first_last_val,mean_val,median_val,sum_val,nans_val
 64,"(4, 51, 93)","(733.7041, 1.0)",319.803440918195,303.8114,6067310.8811,0
 65,"(4, 51, 580)","(44.16278839111328, 1.0)",362.29798490932,337.751922607422,42535594.9182787,915
 66,"(1, 38, 11600)","(-0.022051572799682617, 0.0)",-0.001679033595597,0,-740.118008939112,0
-67,"(4, 4, 580)","(744.5535888671875, 1.0)",385.4765160798091,278.7399444580078,3535590.605484009,108
+67,"(4, 4, 580)","(0.306872, 1.0)",107.58354,0.0,975137.207354,216

tests/test_object_creation.py

@@ -68,7 +68,14 @@ def test_rototrans_creation():
     np.testing.assert_array_equal(x=array, y=xr.DataArray(np.eye(4)[..., np.newaxis]))
     assert array.dims == dims
 
-    array = Rototrans(MARKERS_DATA.values, time=MARKERS_DATA.time)
+    data = Markers(MARKERS_DATA.values)
+    array = Rototrans.from_markers(
+        origin=data.isel(channel=[0]),
+        axis_1=data.isel(channel=[0, 1]),
+        axis_2=data.isel(channel=[0, 2]),
+        axes_name="xy",
+        axis_to_recalculate="y",
+    )
     is_expected_array(array, **EXPECTED_VALUES[67])
 
     size = 4, 4, 100

tests/test_processing_rt.py

@@ -66,6 +66,12 @@ def test_construct_rt():
     with pytest.raises(IndexError):
         Rototrans(data=np.zeros(1))
 
+    with pytest.raises(ValueError):
+        Rototrans(data=np.zeros((4, 4, 1)))
+
+    with pytest.raises(ValueError):
+        Rototrans(data=np.ones((4, 4, 1)))
+
     with pytest.raises(IndexError):
         Rototrans.from_euler_angles(
             angles=random_vector[..., :5],