Rename grav_index to curve_index (#68)

* Fix documentation

* Import `lengths` in init

* Change gravitropism to curvature

sleap_roots/__init__.py

@@ -6,6 +6,7 @@
 import sleap_roots.convhull
 import sleap_roots.ellipse
 import sleap_roots.networklength
+import sleap_roots.lengths
 import sleap_roots.points
 import sleap_roots.scanline
 import sleap_roots.series
@@ -16,4 +17,4 @@
 
 # Define package version.
 # This is read dynamically by setuptools in pyproject.toml to determine the release version.
-__version__ = "0.0.4"
+__version__ = "0.0.5"

sleap_roots/lengths.py

@@ -111,13 +111,13 @@ def get_root_lengths_max(pts: np.ndarray) -> np.ndarray:
     return max_length
 
 
-def get_grav_index(
+def get_curve_index(
     lengths: Union[float, np.ndarray], base_tip_dists: Union[float, np.ndarray]
 ) -> Union[float, np.ndarray]:
-    """Calculate the gravitropism index of a root.
+    """Calculate the curvature index of a root.
 
-    The gravitropism index quantifies the curviness of the root's growth. A higher
-    gravitropism index indicates a curvier root (less responsive to gravity), while a
+    The curvature index quantifies the curviness of the root's growth. A higher
+    curvature index indicates a curvier root (less responsive to gravity), while a
     lower index indicates a straighter root (more responsive to gravity). The index is
     computed as the difference between the maximum root length and straight-line
     distance from the base to the tip of the root, normalized by the root length.
@@ -129,7 +129,7 @@ def get_grav_index(
             root(s). Can be a scalar or a 1D numpy array of shape `(instances,)`.
 
     Returns:
-       Gravitropism index of the root(s), quantifying its/their curviness. Will be a
+       Curvature index of the root(s), quantifying its/their curviness. Will be a
             scalar if input is scalar, or a 1D numpy array of shape `(instances,)`
             otherwise.
     """
@@ -144,8 +144,8 @@ def get_grav_index(
     if lengths.shape != base_tip_dists.shape:
         raise ValueError("The shapes of lengths and base_tip_dists must match.")
 
-    # Calculate the gravitropism index where possible
-    grav_index = np.where(
+    # Calculate the curvature index where possible
+    curve_index = np.where(
         (~np.isnan(lengths))
         & (~np.isnan(base_tip_dists))
         & (lengths > 0)
@@ -156,6 +156,6 @@ def get_grav_index(
 
     # Return scalar or array based on the input type
     if is_scalar_input:
-        return grav_index.item()
+        return curve_index.item()
     else:
-        return grav_index
+        return curve_index

sleap_roots/scanline.py

@@ -102,7 +102,7 @@ def get_scanline_last_ind(scanline_intersection_counts: np.ndarray):
     Return:
         Scalar of count_scanline_interaction index for the last interaction.
     """
-    # get the first scanline index using scanline_intersection_counts
+    # get the last scanline index using scanline_intersection_counts
     if np.where((scanline_intersection_counts > 0))[0].shape[0] > 0:
         scanline_last_ind = np.where((scanline_intersection_counts > 0))[0][-1]
         return scanline_last_ind

sleap_roots/trait_pipelines.py

@@ -36,7 +36,7 @@
     get_ellipse_b,
     get_ellipse_ratio,
 )
-from sleap_roots.lengths import get_grav_index, get_max_length_pts, get_root_lengths
+from sleap_roots.lengths import get_curve_index, get_max_length_pts, get_root_lengths
 from sleap_roots.networklength import (
     get_bbox,
     get_network_distribution,
@@ -811,13 +811,13 @@ def define_traits(self) -> List[TraitDef]:
                 description="Scalar of base median ratio.",
             ),
             TraitDef(
-                name="grav_index",
-                fn=get_grav_index,
+                name="curve_index",
+                fn=get_curve_index,
                 input_traits=["primary_length", "primary_base_tip_dist"],
                 scalar=True,
                 include_in_csv=True,
                 kwargs={},
-                description="Scalar of primary root gravity index.",
+                description="Scalar of primary root curvature index.",
             ),
             TraitDef(
                 name="base_length_ratio",
@@ -1189,13 +1189,13 @@ def define_traits(self) -> List[TraitDef]:
                 "tip(s) of the main root(s).",
             ),
             TraitDef(
-                name="main_grav_indices",
+                name="main_curve_indices",
                 fn=get_base_tip_dist,
                 input_traits=["main_base_pts", "main_tip_pts"],
                 scalar=False,
                 include_in_csv=True,
                 kwargs={},
-                description="Gravitropism index for each main root.",
+                description="Curvature index for each main root.",
             ),
             TraitDef(
                 name="network_solidity",
@@ -1291,13 +1291,13 @@ def define_traits(self) -> List[TraitDef]:
                 "convex hull.",
             ),
             TraitDef(
-                name="grav_index",
-                fn=get_grav_index,
+                name="curve_index",
+                fn=get_curve_index,
                 input_traits=["primary_length", "primary_base_tip_dist"],
                 scalar=True,
                 include_in_csv=True,
                 kwargs={},
-                description="Scalar of primary root gravity index.",
+                description="Scalar of primary root curvature index.",
             ),
             TraitDef(
                 name="primary_base_tip_dist",

tests/test_lengths.py

@@ -1,5 +1,5 @@
 from sleap_roots.lengths import (
-    get_grav_index,
+    get_curve_index,
     get_root_lengths,
     get_root_lengths_max,
     get_max_length_pts,
@@ -146,8 +146,8 @@ def lengths_all_nan():
     return np.array([np.nan, np.nan, np.nan])
 
 
-# tests for get_grav_index function
-def test_get_grav_index_canola(canola_h5):
+# tests for get_curve_index function
+def test_get_curve_index_canola(canola_h5):
     series = Series.load(
         canola_h5, primary_name="primary_multi_day", lateral_name="lateral_3_nodes"
     )
@@ -158,22 +158,22 @@ def test_get_grav_index_canola(canola_h5):
     bases = get_bases(max_length_pts)
     tips = get_tips(max_length_pts)
     base_tip_dist = get_base_tip_dist(bases, tips)
-    grav_index = get_grav_index(primary_length, base_tip_dist)
-    np.testing.assert_almost_equal(grav_index, 0.08898137324716636)
+    curve_index = get_curve_index(primary_length, base_tip_dist)
+    np.testing.assert_almost_equal(curve_index, 0.08898137324716636)
 
 
-def test_get_grav_index():
+def test_get_curve_index():
     # Test 1: Scalar inputs where length > base_tip_dist
-    # Gravitropism index should be (10 - 8) / 10 = 0.2
-    assert get_grav_index(10, 8) == 0.2
+    # Curvature index should be (10 - 8) / 10 = 0.2
+    assert get_curve_index(10, 8) == 0.2
 
     # Test 2: Scalar inputs where length and base_tip_dist are zero
     # Should return NaN as length is zero
-    assert np.isnan(get_grav_index(0, 0))
+    assert np.isnan(get_curve_index(0, 0))
 
     # Test 3: Scalar inputs where length < base_tip_dist
     # Should return NaN as it's an invalid case
-    assert np.isnan(get_grav_index(5, 10))
+    assert np.isnan(get_curve_index(5, 10))
 
     # Test 4: Array inputs covering various cases
     # Case 1: length > base_tip_dist, should return 0.2
@@ -183,35 +183,35 @@ def test_get_grav_index():
     lengths = np.array([10, 0, 5, 15])
     base_tip_dists = np.array([8, 0, 10, 12])
     expected = np.array([0.2, np.nan, np.nan, 0.2])
-    result = get_grav_index(lengths, base_tip_dists)
+    result = get_curve_index(lengths, base_tip_dists)
     assert np.allclose(result, expected, equal_nan=True)
 
     # Test 5: Mismatched shapes between lengths and base_tip_dists
     # Should raise a ValueError
     with pytest.raises(ValueError):
-        get_grav_index(np.array([10, 20]), np.array([8]))
+        get_curve_index(np.array([10, 20]), np.array([8]))
 
     # Test 6: Array inputs with NaN values
     # Case 1: length > base_tip_dist, should return 0.2
     # Case 2 and 3: either length or base_tip_dist is NaN, should return NaN
     lengths = np.array([10, np.nan, np.nan])
     base_tip_dists = np.array([8, 8, np.nan])
     expected = np.array([0.2, np.nan, np.nan])
-    result = get_grav_index(lengths, base_tip_dists)
+    result = get_curve_index(lengths, base_tip_dists)
     assert np.allclose(result, expected, equal_nan=True)
 
 
-def test_get_grav_index_shape():
+def test_get_curve_index_shape():
     # Check if scalar inputs result in scalar output
-    result = get_grav_index(10, 8)
+    result = get_curve_index(10, 8)
     assert isinstance(
         result, (int, float)
     ), f"Expected scalar output, got {type(result)}"
 
     # Check if array inputs result in array output
     lengths = np.array([10, 15])
     base_tip_dists = np.array([8, 12])
-    result = get_grav_index(lengths, base_tip_dists)
+    result = get_curve_index(lengths, base_tip_dists)
     assert isinstance(
         result, np.ndarray
     ), f"Expected np.ndarray output, got {type(result)}"
@@ -225,7 +225,7 @@ def test_get_grav_index_shape():
     # Check the shape of output for larger array inputs
     lengths = np.array([10, 15, 20, 25])
     base_tip_dists = np.array([8, 12, 18, 22])
-    result = get_grav_index(lengths, base_tip_dists)
+    result = get_curve_index(lengths, base_tip_dists)
     assert (
         result.shape == lengths.shape
     ), f"Output shape {result.shape} does not match input shape {lengths.shape}"
@@ -235,22 +235,22 @@ def test_nan_values():
     lengths = np.array([10, np.nan, 30])
     base_tip_dists = np.array([8, 16, np.nan])
     np.testing.assert_array_equal(
-        get_grav_index(lengths, base_tip_dists), np.array([0.2, np.nan, np.nan])
+        get_curve_index(lengths, base_tip_dists), np.array([0.2, np.nan, np.nan])
     )
 
 
 def test_zero_lengths():
     lengths = np.array([0, 20, 30])
     base_tip_dists = np.array([0, 16, 24])
     np.testing.assert_array_equal(
-        get_grav_index(lengths, base_tip_dists), np.array([np.nan, 0.2, 0.2])
+        get_curve_index(lengths, base_tip_dists), np.array([np.nan, 0.2, 0.2])
     )
 
 
 def test_invalid_scalar_values():
-    assert np.isnan(get_grav_index(np.nan, 8))
-    assert np.isnan(get_grav_index(10, np.nan))
-    assert np.isnan(get_grav_index(0, 8))
+    assert np.isnan(get_curve_index(np.nan, 8))
+    assert np.isnan(get_curve_index(10, np.nan))
+    assert np.isnan(get_curve_index(0, 8))
 
 
 # tests for `get_root_lengths`

tests/test_trait_pipelines.py

@@ -63,14 +63,14 @@ def test_younger_monocot_pipeline(rice_h5, rice_folder):
 
     # Value range assertions for traits
     assert (
-        rice_traits["grav_index"].fillna(0) >= 0
-    ).all(), "grav_index in rice_traits contains negative values"
+        rice_traits["curve_index"].fillna(0) >= 0
+    ).all(), "curve_index in rice_traits contains negative values"
     assert (
-        all_traits["grav_index_median"] >= 0
-    ).all(), "grav_index in all_traits contains negative values"
+        all_traits["curve_index_median"] >= 0
+    ).all(), "curve_index in all_traits contains negative values"
     assert (
-        all_traits["main_grav_indices_mean_median"] >= 0
-    ).all(), "main_grav_indices_mean_median in all_traits contains negative values"
+        all_traits["main_curve_indices_mean_median"] >= 0
+    ).all(), "main_curve_indices_mean_median in all_traits contains negative values"
     assert (
         (0 <= rice_traits["main_angles_proximal_p95"])
         & (rice_traits["main_angles_proximal_p95"] <= 180)