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Addressed copilot comments
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‎docs/search_details.md‎

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@@ -214,7 +214,7 @@ Basic search scales sub-linearly because vectorized regex amortises overhead acr
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Per-string median search time (ms) across string sizes. Tag counts: tiny = 1, small = 5, medium = 10, large = 25, xlarge = 50, xxlarge = 100. Larger strings also contain nested groups (e.g. large = 5 groups at depth 2). The table below is filtered to the `bare_term` query (`Event` / `@Event`) to isolate parse and tree-walk cost from query complexity; see the query heatmap image below for all 12 queries.
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(Example 10-tag string: `Gentalia, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen`)
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(Example 10-tag string: `Human-agent, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen`)
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| String size | Object search (ms) | String search (ms) | Basic search (ms) |
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| ------------------ | -----------------: | -----------------: | ----------------: |
@@ -400,7 +400,7 @@ Search over 200 rows of the `eeg_ds003645s_hed` BIDS test dataset.
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Number of tags in the HED string (1 to 100), query `Event`. Basic search time is dominated by regex compilation overhead and stays roughly constant; tree-based engines scale linearly with the number of nodes to traverse.
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(Example 10-tag string: `Gentalia, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen`)
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(Example 10-tag string: `Human-agent, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen`)
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| Tags | Object search (ms) | String search (ms) | Basic search (ms) |
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| ---: | -----------------: | -----------------: | ----------------: |
@@ -468,7 +468,7 @@ More top-level parenthesised groups increase the number of children the tree mus
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![Group count sweep](_static/images/benchmark_sweep_group_count.png)
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**Query complexity** (1-clause bare term → 8-clause composite. Example string: `Gentalia, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen, (FireBrick, (Flex, Move-body)), (Categorical-value, (Eyelid, Comatose)), (Robotic-agent, (Catamenial, Background-subtask)), (Keyboard, (Cough, River)), (ForestGreen, (Green-color, Locked-in))`):
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**Query complexity** (1-clause bare term → 8-clause composite. Example string: `Human-agent, Move, Computed-feature, Age, Aroused, 3D-shape, Little-toe, To-right-of, Brain-region, DarkSeaGreen, (FireBrick, (Flex, Move-body)), (Categorical-value, (Eyelid, Comatose)), (Robotic-agent, (Catamenial, Background-subtask)), (Keyboard, (Cough, River)), (ForestGreen, (Green-color, Locked-in))`):
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| Complexity | Object search (ms) | String search (ms) | Basic search |
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| --------------------- | -----------------: | -----------------: | ------------ |

‎hed/models/model_constants.py‎

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@@ -8,6 +8,11 @@ class TopTagReturnType(IntEnum):
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Pass one of these constants as the ``include_groups`` argument to control
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whether the method returns anchor tags, containing groups, or (tag, group) pairs.
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Attributes:
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TAGS: Return only the anchor :class:`~hed.models.HedTag` objects.
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GROUPS: Return only the :class:`~hed.models.HedGroup` objects that contain each anchor tag.
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BOTH: Return ``(tag, group)`` tuples pairing each anchor tag with its containing group.
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"""
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TAGS = 0

‎hed/models/string_search.py‎

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@@ -418,5 +418,5 @@ def string_search(strings, query, schema_lookup=None):
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"""
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handler = StringQueryHandler(query)
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return [
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bool(handler.search(s, schema_lookup=schema_lookup)) if s and isinstance(s, str) else False for s in strings
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bool(handler.search(s, schema_lookup=schema_lookup)) if isinstance(s, str) and s else False for s in strings
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]

‎tests/models/test_string_search.py‎

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@@ -1079,6 +1079,15 @@ def test_none_rows(self):
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self.assertFalse(mask[2])
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self.assertFalse(mask[3])
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def test_pandas_na_row(self):
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import pandas as pd
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data = ["A, B", pd.NA, "B, C"]
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mask = string_search(data, "A")
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self.assertTrue(mask[0])
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self.assertFalse(mask[1])
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self.assertFalse(mask[2])
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def test_empty_string_row(self):
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data = ["A", "", "B"]
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mask = string_search(data, "A")

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