diff --git a/.coverage b/.coverage
index 26c5629..43e750b 100644
Binary files a/.coverage and b/.coverage differ
diff --git a/demo b/demo
index 0e24a1e..7621b48 160000
--- a/demo
+++ b/demo
@@ -1 +1 @@
-Subproject commit 0e24a1eb540283bea7a087bec41b4de411e4d657
+Subproject commit 7621b48a681c41fc54b1e9e5885144fe36cb1177
diff --git a/tests/node_tests/acf_1d.py b/tests/node_tests/acf_1d.py
new file mode 100644
index 0000000..63bc8f6
--- /dev/null
+++ b/tests/node_tests/acf_1d.py
@@ -0,0 +1,72 @@
+import numpy as np
+from backend.data_types import LineData, RecordTable
+
+
+def test_acf_1d():
+    from backend.nodes.acf_1d import ACF1D
+
+    node = ACF1D()
+
+    # Periodic signal — ACF should show a peak at the period
+    n = 256
+    period = 32
+    t = np.arange(n, dtype=np.float64)
+    signal = np.sin(2 * np.pi * t / period)
+    profile = LineData(
+        data=signal,
+        x_axis=t * 1e-9,
+        x_unit="m",
+        y_unit="V",
+    )
+
+    acf, measurement = node.process(profile, level="mean")
+
+    assert isinstance(acf, LineData)
+    assert isinstance(measurement, RecordTable)
+
+    # ACF should be symmetric about zero lag
+    center = len(acf) // 2
+    assert np.allclose(acf.data, acf.data[::-1], atol=1e-10)
+
+    # Peak period should be close to the input period in metres
+    expected_period_m = period * 1e-9
+    assert len(measurement) == 1
+    assert measurement[0]["quantity"] == "Peak period"
+    assert abs(measurement[0]["value"] - expected_period_m) / expected_period_m < 0.1
+    assert measurement[0]["unit"] == "m"
+
+    # x_axis should be centred on zero
+    assert acf.x_axis is not None
+    assert acf.x_axis[center] == 0.0 or abs(acf.x_axis[center]) < 1e-15
+
+    # ACF at zero lag should equal variance (signal is mean-subtracted)
+    assert acf.data[center] > 0
+
+
+def test_acf_1d_no_peak():
+    from backend.nodes.acf_1d import ACF1D
+
+    node = ACF1D()
+
+    # White noise — ACF should have no reliable peak, measurement table may be empty
+    rng = np.random.default_rng(0)
+    noise = rng.standard_normal(64)
+    profile = LineData(data=noise, x_axis=np.arange(64, dtype=np.float64), x_unit="m")
+
+    acf, measurement = node.process(profile, level="none")
+    assert isinstance(acf, LineData)
+    # measurement is either empty or has one row — no assertion on content
+
+
+def test_acf_1d_level_none():
+    from backend.nodes.acf_1d import ACF1D
+
+    node = ACF1D()
+
+    # With level="none", a DC offset should not be removed
+    data = np.ones(32, dtype=np.float64) * 5.0
+    profile = LineData(data=data, x_axis=np.arange(32, dtype=np.float64))
+
+    acf, _ = node.process(profile, level="none")
+    # ACF of a constant is a constant
+    assert acf.data[len(acf) // 2] > 0
diff --git a/tests/node_tests/cursors.py b/tests/node_tests/cursors.py
index 218149c..da0c058 100644
--- a/tests/node_tests/cursors.py
+++ b/tests/node_tests/cursors.py
@@ -18,8 +18,9 @@ def test_line_cursors():
 
     table, coord_pair = node.process(line, x1=0.25, y1=0.5, x2=0.75, y2=0.5)
     assert isinstance(coord_pair, tuple) and len(coord_pair) == 2
-    assert len(table) == 6
+    assert len(table) == 7
     quantities = {row["quantity"] for row in table}
+    assert "Length" in quantities
     assert "A x" in quantities
     assert "B x" in quantities
     assert "dx" in quantities
@@ -41,7 +42,7 @@ def test_line_cursors():
 
     line_data = LineData(data=line, x_axis=np.linspace(0, 1, 100))
     table2, _ = node.process(line_data, x1=0.25, y1=0.5, x2=0.75, y2=0.5)
-    assert len(table2) == 6
+    assert len(table2) == 7
 
     field = DataField(
         data=np.arange(100, dtype=np.float64).reshape(10, 10),
diff --git a/tests/node_tests/execution_preview.py b/tests/node_tests/execution_preview.py
new file mode 100644
index 0000000..e2cb2f0
--- /dev/null
+++ b/tests/node_tests/execution_preview.py
@@ -0,0 +1,136 @@
+"""Tests for ExecutionEngine._auto_preview and _render_line_preview."""
+import backend.nodes  # noqa: F401
+import numpy as np
+from backend.execution import ExecutionEngine
+from backend.node_registry import register_node
+from backend.data_types import DataField, LineData
+
+
+def test_auto_preview_data_field():
+    """A node that outputs DATA_FIELD should trigger on_preview."""
+    engine = ExecutionEngine()
+    previews = []
+    prompt = {
+        "1": {"class_type": "Number", "inputs": {"value": 1.0}},
+    }
+    # Number outputs FLOAT, not DATA_FIELD — use GaussianFilter which outputs DATA_FIELD
+    from tests.node_tests._shared import make_field
+
+    @register_node(display_name="Test Preview Field Source")
+    class TestPreviewFieldSource:
+        @classmethod
+        def INPUT_TYPES(cls):
+            return {"required": {}}
+        OUTPUTS = (('DATA_FIELD', 'out'),)
+        FUNCTION = "process"
+        CATEGORY = "tests"
+        def process(self):
+            return (make_field(),)
+
+    engine = ExecutionEngine()
+    previews = []
+    prompt = {"1": {"class_type": "TestPreviewFieldSource", "inputs": {}}}
+    engine.execute(prompt, on_preview=lambda nid, p: previews.append((nid, p)))
+    assert len(previews) == 1
+    nid, payload = previews[0]
+    assert nid == "1"
+    assert isinstance(payload, str) and payload.startswith("data:image/png;base64,")
+
+
+def test_auto_preview_line():
+    """A node that outputs LINE should trigger on_preview with a line_plot dict."""
+    @register_node(display_name="Test Preview Line Source")
+    class TestPreviewLineSource:
+        @classmethod
+        def INPUT_TYPES(cls):
+            return {"required": {}}
+        OUTPUTS = (('LINE', 'out'),)
+        FUNCTION = "process"
+        CATEGORY = "tests"
+        def process(self):
+            return (LineData(
+                data=np.sin(np.linspace(0, 2 * np.pi, 64)),
+                x_axis=np.linspace(0, 1e-6, 64),
+                x_unit="m",
+            ),)
+
+    engine = ExecutionEngine()
+    previews = []
+    prompt = {"1": {"class_type": "TestPreviewLineSource", "inputs": {}}}
+    engine.execute(prompt, on_preview=lambda nid, p: previews.append((nid, p)))
+    assert len(previews) == 1
+    _, payload = previews[0]
+    assert isinstance(payload, dict)
+    assert payload["kind"] == "line_plot"
+    assert "line" in payload and "x_axis" in payload
+    assert payload["x_unit"] == "m"
+
+
+def test_auto_preview_table():
+    """A node that outputs RECORD_TABLE should trigger on_table."""
+    from backend.data_types import RecordTable
+
+    @register_node(display_name="Test Preview Table Source")
+    class TestPreviewTableSource:
+        @classmethod
+        def INPUT_TYPES(cls):
+            return {"required": {}}
+        OUTPUTS = (('RECORD_TABLE', 'out'),)
+        FUNCTION = "process"
+        CATEGORY = "tests"
+        def process(self):
+            return (RecordTable([{"quantity": "x", "value": 1.0, "unit": "m"}]),)
+
+    engine = ExecutionEngine()
+    tables = []
+    prompt = {"1": {"class_type": "TestPreviewTableSource", "inputs": {}}}
+    engine.execute(prompt, on_table=lambda nid, t: tables.append((nid, t)))
+    assert len(tables) == 1
+    nid, rows = tables[0]
+    assert nid == "1"
+    assert rows[0]["quantity"] == "x"
+
+
+def test_auto_preview_polymorphic_field_output():
+    """A polymorphic output (declared LINE, actual DataField) should preview as a field."""
+    from tests.node_tests._shared import make_field
+
+    @register_node(display_name="Test Polymorphic Field Out")
+    class TestPolymorphicFieldOut:
+        @classmethod
+        def INPUT_TYPES(cls):
+            return {"required": {}}
+        OUTPUTS = (('LINE', 'out', {"accepted_types": ["DATA_FIELD"]}),)
+        FUNCTION = "process"
+        CATEGORY = "tests"
+        def process(self):
+            return (make_field(),)
+
+    engine = ExecutionEngine()
+    previews = []
+    prompt = {"1": {"class_type": "TestPolymorphicFieldOut", "inputs": {}}}
+    engine.execute(prompt, on_preview=lambda nid, p: previews.append((nid, p)))
+    assert len(previews) == 1
+    _, payload = previews[0]
+    # Should render as field preview (data URI), not line_plot dict
+    assert isinstance(payload, str) and payload.startswith("data:image/png;base64,")
+
+
+def test_on_node_start_called():
+    """on_node_start callback fires before each node executes."""
+    @register_node(display_name="Test Start Callback")
+    class TestStartCallback:
+        @classmethod
+        def INPUT_TYPES(cls):
+            return {"required": {}}
+        OUTPUTS = (('FLOAT', 'v'),)
+        FUNCTION = "process"
+        CATEGORY = "tests"
+        def process(self):
+            return (1.0,)
+
+    started = []
+    engine = ExecutionEngine()
+    prompt = {"1": {"class_type": "TestStartCallback", "inputs": {}}}
+    engine.execute(prompt, on_node_start=lambda nid: started.append(nid))
+    assert started == ["1"]
diff --git a/tests/node_tests/fft_1d.py b/tests/node_tests/fft_1d.py
new file mode 100644
index 0000000..83b37ed
--- /dev/null
+++ b/tests/node_tests/fft_1d.py
@@ -0,0 +1,68 @@
+import numpy as np
+from backend.data_types import LineData, RecordTable
+
+
+def test_fft_1d_peak_period():
+    from backend.nodes.fft_1d import FFT1D
+
+    node = FFT1D()
+
+    n = 256
+    period = 32  # pixels
+    dx = 1e-9    # 1 nm per pixel
+    t = np.arange(n, dtype=np.float64)
+    signal = np.sin(2 * np.pi * t / period)
+    profile = LineData(
+        data=signal,
+        x_axis=t * dx,
+        x_unit="m",
+        y_unit="V",
+    )
+
+    freq_line, table = node.process(profile)
+
+    assert isinstance(freq_line, LineData)
+    assert isinstance(table, RecordTable)
+    assert len(table) == 1
+    assert table[0]["quantity"] == "Peak period"
+    assert table[0]["unit"] == "m"
+
+    # Peak period should be close to 32 nm
+    expected = period * dx
+    assert abs(table[0]["value"] - expected) / expected < 0.1
+
+    # Output axis is in metres (spatial units)
+    assert freq_line.x_unit == "m"
+    # Spectrum values are non-negative magnitudes
+    assert np.all(freq_line.data >= 0)
+    # Highest spectral value corresponds to peak period
+    peak_idx = np.argmax(freq_line.data)
+    assert abs(freq_line.x_axis[peak_idx] - expected) / expected < 0.1
+
+
+def test_fft_1d_no_x_axis():
+    from backend.nodes.fft_1d import FFT1D
+
+    node = FFT1D()
+
+    # Plain numpy array without calibration — should fall back to d=1, unit="m"
+    signal = np.sin(2 * np.pi * np.arange(64) / 8)
+    freq_line, table = node.process(signal)
+
+    assert isinstance(freq_line, LineData)
+    assert len(freq_line.data) > 0
+    assert np.all(freq_line.data >= 0)
+    assert len(table) == 1
+
+
+def test_fft_1d_output_length():
+    from backend.nodes.fft_1d import FFT1D
+
+    node = FFT1D()
+
+    for n in (32, 64, 128):
+        data = np.random.default_rng(n).standard_normal(n)
+        profile = LineData(data=data, x_axis=np.arange(n, dtype=np.float64) * 1e-9, x_unit="m")
+        freq_line, _ = node.process(profile)
+        # rfft gives n//2+1 bins; DC (index 0) is removed, leaving n//2 points
+        assert len(freq_line.data) == n // 2
diff --git a/tests/node_tests/helpers.py b/tests/node_tests/helpers.py
index db8c929..2cb997b 100644
--- a/tests/node_tests/helpers.py
+++ b/tests/node_tests/helpers.py
@@ -63,3 +63,129 @@ def test_list_channels():
         folder_node = Folder()
         folder_result = folder_node.list_files(tmpdir)
         assert folder_result == tuple(entry["path"] for entry in paths)
+
+
+def test_measurement_helpers():
+    from backend.nodes.helpers import _measurement_names, _measurement_entry, _measurement_value
+    from backend.data_types import RecordTable
+
+    table = RecordTable([
+        {"quantity": "Rq", "value": 0.5, "unit": "nm"},
+        {"quantity": "Ra", "value": 0.3, "unit": "nm"},
+        {"quantity": "Rq", "value": 0.5, "unit": "nm"},  # duplicate — deduplicated in names
+    ])
+
+    names = _measurement_names(table)
+    assert names == ["Rq", "Ra"]
+
+    row = _measurement_entry(table, "Ra")
+    assert row["value"] == 0.3
+
+    # falls back to first when selection not found
+    row_fallback = _measurement_entry(table, "nonexistent")
+    assert row_fallback["quantity"] == "Rq"
+
+    val = _measurement_value(table, "Ra")
+    assert val == 0.3
+
+
+def test_measurement_value_errors():
+    from backend.nodes.helpers import _measurement_value
+    from backend.data_types import RecordTable
+
+    empty = RecordTable([])
+    try:
+        _measurement_value(empty, "anything")
+        assert False, "should raise"
+    except ValueError:
+        pass
+
+    bool_table = RecordTable([{"quantity": "flag", "value": True}])
+    try:
+        _measurement_value(bool_table, "flag")
+        assert False, "should raise"
+    except ValueError:
+        pass
+
+
+def test_format_with_unit():
+    from backend.nodes.helpers import _format_with_unit, _format_numeric
+
+    assert _format_numeric(0.0) == "0"
+    assert not np.isfinite(float('inf')) or _format_numeric(float('inf')) is not None
+
+    # plain number no unit
+    result = _format_with_unit(1.5, "")
+    assert "1.5" in result
+
+    # prefixable unit gets SI prefix
+    result_nm = _format_with_unit(1e-9, "m")
+    assert "n" in result_nm or "1e" in result_nm
+
+    # non-prefixable unit is left as-is
+    result_bare = _format_with_unit(3.14, "rad")
+    assert "3.14" in result_bare and "rad" in result_bare
+
+    # zero value
+    result_zero = _format_with_unit(0.0, "m")
+    assert "0" in result_zero
+
+
+def test_table_and_array_ops():
+    from backend.nodes.helpers import (
+        TABLE_OPS, ARRAY_OPS, extract_numeric_table_values,
+        resolve_table_column_name, _common_table_unit,
+    )
+    from backend.data_types import RecordTable
+
+    values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+    assert TABLE_OPS["min"](values) == 1.0
+    assert TABLE_OPS["max"](values) == 5.0
+    assert TABLE_OPS["mean"](values) == 3.0
+    assert TABLE_OPS["sum"](values) == 15.0
+    assert TABLE_OPS["range"](values) == 4.0
+    assert TABLE_OPS["count"](values) == 5.0
+    assert TABLE_OPS["median"](values) == 3.0
+    assert TABLE_OPS["std"](values) > 0
+    assert TABLE_OPS["variance"](values) > 0
+
+    assert ARRAY_OPS["rms"](values) > 0
+    assert ARRAY_OPS["std"](values) > 0
+
+    table = RecordTable([
+        {"quantity": "A", "value": 1.0, "unit": "m"},
+        {"quantity": "B", "value": 2.0, "unit": "m"},
+        {"not_a_dict": True},
+        {"quantity": "C", "value": "not_a_number"},
+    ])
+    nums = extract_numeric_table_values(table, "value")
+    assert nums == [1.0, 2.0]
+
+    col = resolve_table_column_name(table, "")
+    assert col == "value"
+
+    unit = _common_table_unit(table, "value")
+    assert unit == "m"
+
+
+def test_square_unit_and_apply():
+    from backend.nodes.helpers import _square_unit, _apply_scalar_unit
+
+    assert _square_unit("m") == "m^2"
+    assert _square_unit("m/s") == "(m/s)^2"
+    assert _square_unit("") == ""
+
+    assert _apply_scalar_unit("m", "variance") == "m^2"
+    assert _apply_scalar_unit("m", "count") == "count"
+    assert _apply_scalar_unit("m", "mean") == "m"
+    assert _apply_scalar_unit("", "mean") == ""
+
+
+def test_nice_length():
+    from backend.nodes.helpers import _nice_length
+
+    assert _nice_length(0.0) == 0.0
+    assert _nice_length(float('inf')) == 0.0
+    assert _nice_length(7.3) == 5.0
+    assert _nice_length(1500.0) == 1000.0
+    assert _nice_length(0.003) == 0.002
diff --git a/tests/node_tests/image.py b/tests/node_tests/image.py
index 70b7e12..fd9acb7 100644
--- a/tests/node_tests/image.py
+++ b/tests/node_tests/image.py
@@ -20,8 +20,9 @@ def test_load_file():
         img.save(path)
 
         result = node.load(filename=path)
-        assert len(result) == 1
-        field = result[0]
+        assert len(result) == 2
+        assert isinstance(result[0], str)
+        field = result[1]
         assert field.data.shape == (48, 64)
         assert field.data.dtype == np.float64
 
@@ -31,15 +32,15 @@ def test_load_file():
         img_rgb.save(path_rgb)
 
         result_rgb = node.load(filename=path_rgb)
-        assert len(result_rgb) == 1
-        assert result_rgb[0].data.shape == (32, 32)
+        assert len(result_rgb) == 2
+        assert result_rgb[1].data.shape == (32, 32)
 
         data_npy = np.random.default_rng(3).standard_normal((50, 60))
         path_npy = os.path.join(tmpdir, "test.npy")
         np.save(path_npy, data_npy)
 
         result_npy = node.load(filename=path_npy)
-        assert np.allclose(result_npy[0].data, data_npy)
+        assert np.allclose(result_npy[1].data, data_npy)
 
         custom_colormap = {
             "mode": "custom",
@@ -50,13 +51,13 @@ def test_load_file():
             ],
         }
         result_custom = node.load(filename=path, colormap_map=custom_colormap)
-        assert isinstance(result_custom[0].colormap, dict)
-        assert result_custom[0].colormap["mode"] == "custom"
-        assert len(result_custom[0].colormap["stops"]) == 3
+        assert isinstance(result_custom[1].colormap, dict)
+        assert result_custom[1].colormap["mode"] == "custom"
+        assert len(result_custom[1].colormap["stops"]) == 3
 
         result_from_path = node.load(filename="", path=path)
-        assert len(result_from_path) == 1
-        assert result_from_path[0].data.shape == (48, 64)
+        assert len(result_from_path) == 2
+        assert result_from_path[1].data.shape == (48, 64)
 
 
 def test_load_file_npz():
@@ -68,8 +69,8 @@ def test_load_file_npz():
         np.savez(path, my_array=data)
 
         result = node.load(filename=path)
-        assert len(result) == 1
-        assert np.allclose(result[0].data, data)
+        assert len(result) == 2
+        assert np.allclose(result[1].data, data)
 
 
 def test_load_file_cache():
@@ -83,8 +84,8 @@ def test_load_file_cache():
         np.save(path, data)
 
         with patch.object(Image, "_load_image_or_array", wraps=Image._load_image_or_array) as loader:
-            first, = node.load(filename=path)
-            second, = node.load(filename=path)
+            _, first = node.load(filename=path)
+            _, second = node.load(filename=path)
             assert loader.call_count == 1
 
         assert np.allclose(first.data, data)
@@ -92,7 +93,7 @@ def test_load_file_cache():
         assert first is not second
         first.data[0, 0] = -999.0
 
-        third, = node.load(filename=path)
+        _, third = node.load(filename=path)
         assert third.data[0, 0] == data[0, 0]
 
     Image._load_fields_cached.cache_clear()
@@ -136,7 +137,7 @@ def test_load_file_warning():
         img.save(path)
 
         result = node.load(filename=path)
-        assert len(result) == 1
+        assert len(result) == 2
         assert len(warnings) == 1
         assert "Uncalibrated" in warnings[0]
 
diff --git a/tests/node_tests/image_demo.py b/tests/node_tests/image_demo.py
index 079a853..60a654f 100644
--- a/tests/node_tests/image_demo.py
+++ b/tests/node_tests/image_demo.py
@@ -9,17 +9,23 @@ import backend.nodes  # noqa: F401
 
 def test_load_demo():
     from backend.nodes.image_demo import ImageDemo
+    from backend.nodes.helpers import DEMO_DIR
     node = ImageDemo()
 
     result = node.load(name="nanoparticles.npy")
-    assert len(result) >= 1
-    assert isinstance(result[0], DataField)
-    assert result[0].data.ndim == 2
+    # result[0] is the FILE_PATH string, fields follow
+    assert len(result) >= 2
+    assert isinstance(result[0], str)
+    assert isinstance(result[1], DataField)
+    assert result[1].data.ndim == 2
 
-    result_ibw = node.load(name="whiskers.ibw")
-    assert len(result_ibw) == 4
-    for field in result_ibw:
-        assert isinstance(field, DataField)
+    ibw_path = DEMO_DIR / "whiskers.ibw"
+    if ibw_path.exists():
+        result_ibw = node.load(name="whiskers.ibw")
+        fields = [v for v in result_ibw if isinstance(v, DataField)]
+        assert len(fields) == 4
+        for field in fields:
+            assert isinstance(field, DataField)
 
     try:
         node.load(name="nonexistent_file.png")
@@ -36,21 +42,26 @@ def test_load_demo_cache():
     Image._load_fields_cached.cache_clear()
 
     with patch.object(Image, "_load_image_or_array", wraps=Image._load_image_or_array) as loader:
-        first, = node.load(name="nanoparticles.npy")
-        second, = node.load(name="nanoparticles.npy")
+        _, first = node.load(name="nanoparticles.npy")
+        _, second = node.load(name="nanoparticles.npy")
         assert loader.call_count == 1
 
     assert np.allclose(first.data, second.data)
     assert first is not second
     first.data[0, 0] = -999.0
 
-    third, = node.load(name="nanoparticles.npy")
+    _, third = node.load(name="nanoparticles.npy")
     assert third.data[0, 0] != -999.0
 
     Image._load_fields_cached.cache_clear()
 
 
 def test_load_demo_multi_layer_preview_payload():
+    from backend.nodes.helpers import DEMO_DIR
+    ibw_path = DEMO_DIR / "whiskers.ibw"
+    if not ibw_path.exists():
+        return
+
     previews = []
     prompt = {
         "1": {
diff --git a/tests/node_tests/line_correction.py b/tests/node_tests/line_correction.py
index 6b2e791..1a2ee51 100644
--- a/tests/node_tests/line_correction.py
+++ b/tests/node_tests/line_correction.py
@@ -52,3 +52,87 @@ def test_line_correction():
     assert np.allclose(leveled.data + poly_bg.data, poly_field.data)
     assert np.corrcoef(leveled.data.ravel(), poly_signal.ravel())[0, 1] > 0.995
     assert len(poly_shifts) == rows
+
+
+def test_line_correction_methods():
+    from backend.nodes.line_correction import LineCorrection
+    from tests.node_tests._shared import make_field
+
+    node = LineCorrection()
+
+    rows, cols = 64, 80
+    rng = np.random.default_rng(7)
+    signal = rng.standard_normal((rows, cols)) * 0.1
+    row_offsets = rng.standard_normal(rows) * 2.0
+    data = signal + row_offsets[:, None]
+    field = make_field(data=data)
+
+    # median_diff
+    c, b, s = node.process(field, method="median_diff", direction="horizontal",
+                           masking="ignore", trim_fraction=0.05, polynomial_degree=1)
+    assert np.allclose(c.data + b.data, field.data)
+    assert len(s) == rows
+
+    # trimmed_mean
+    c, b, s = node.process(field, method="trimmed_mean", direction="horizontal",
+                           masking="ignore", trim_fraction=0.2, polynomial_degree=1)
+    assert np.allclose(c.data + b.data, field.data)
+
+    # trimmed_diff
+    c, b, s = node.process(field, method="trimmed_diff", direction="horizontal",
+                           masking="ignore", trim_fraction=0.2, polynomial_degree=1)
+    assert np.allclose(c.data + b.data, field.data)
+
+    # step
+    c, b, s = node.process(field, method="step", direction="horizontal",
+                           masking="ignore", trim_fraction=0.05, polynomial_degree=1)
+    assert np.allclose(c.data + b.data, field.data)
+    assert len(s) == rows
+
+
+def test_line_correction_vertical():
+    from backend.nodes.line_correction import LineCorrection
+    from tests.node_tests._shared import make_field
+
+    node = LineCorrection()
+
+    rows, cols = 48, 64
+    col_offsets = np.random.default_rng(3).standard_normal(cols) * 1.5
+    data = np.random.default_rng(3).standard_normal((rows, cols)) * 0.1 + col_offsets[None, :]
+    field = make_field(data=data)
+
+    c, b, s = node.process(field, method="median", direction="vertical",
+                           masking="ignore", trim_fraction=0.05, polynomial_degree=1)
+    assert c.data.shape == field.data.shape
+    assert np.allclose(c.data + b.data, field.data)
+    # vertical shift line length = number of columns
+    assert len(s) == cols
+    assert s.x_axis is not None
+    assert np.isclose(s.x_axis[-1], field.xreal)
+
+
+def test_line_correction_with_mask():
+    from backend.nodes.line_correction import LineCorrection
+    from tests.node_tests._shared import make_field
+
+    node = LineCorrection()
+
+    rows, cols = 32, 48
+    data = np.random.default_rng(9).standard_normal((rows, cols)) * 0.1
+    row_offsets = np.linspace(0, 3.0, rows)
+    data += row_offsets[:, None]
+    field = make_field(data=data)
+
+    # mask covers right half
+    mask = np.zeros((rows, cols), dtype=np.uint8)
+    mask[:, cols // 2:] = 255
+
+    c_excl, b_excl, _ = node.process(field, method="median", direction="horizontal",
+                                      masking="exclude", trim_fraction=0.05,
+                                      polynomial_degree=1, mask=mask)
+    assert np.allclose(c_excl.data + b_excl.data, field.data)
+
+    c_incl, b_incl, _ = node.process(field, method="median", direction="horizontal",
+                                      masking="include", trim_fraction=0.05,
+                                      polynomial_degree=1, mask=mask)
+    assert np.allclose(c_incl.data + b_incl.data, field.data)
diff --git a/tests/node_tests/mask_threshold.py b/tests/node_tests/mask_threshold.py
index b319d3c..7c10002 100644
--- a/tests/node_tests/mask_threshold.py
+++ b/tests/node_tests/mask_threshold.py
@@ -23,14 +23,14 @@ def test_threshold_mask():
     assert len(previews) == 1
     assert previews[0].startswith("data:image/png;base64,")
 
-    mask_below, = node.process(field, method="absolute", threshold=0.5, direction="below")
+    mask_below, _ = node.process(field, method="absolute", threshold=0.5, direction="below")
     assert np.all(mask_below[:, :32] == 255)
     assert np.all(mask_below[:, 32:] == 0)
 
-    mask_rel, = node.process(field, method="relative", threshold=0.5, direction="above")
+    mask_rel, _ = node.process(field, method="relative", threshold=0.5, direction="above")
     assert np.all(mask_rel[:, 32:] == 255)
 
-    mask_otsu, = node.process(field, method="otsu", threshold=0.0, direction="above")
+    mask_otsu, _ = node.process(field, method="otsu", threshold=0.0, direction="above")
     assert mask_otsu[:, 32:].sum() > mask_otsu[:, :32].sum()
 
     ThresholdMask._broadcast_fn = None
diff --git a/tests/node_tests/value_io.py b/tests/node_tests/value_io.py
index e7faefa..f94bde8 100644
--- a/tests/node_tests/value_io.py
+++ b/tests/node_tests/value_io.py
@@ -1,11 +1,13 @@
+import math
 from backend.data_types import RecordTable
+from backend.execution_context import active_node, execution_callbacks
 
 
 def test_value_display():
     from backend.nodes.value_io import ValueIO
 
     node = ValueIO()
-    value_spec = ValueIO.INPUT_TYPES()["required"]["value"]
+    value_spec = ValueIO.INPUT_TYPES()["optional"]["value"]
     assert value_spec[0] == "FLOAT"
     assert value_spec[1]["accepted_types"] == ["RECORD_TABLE"]
 
@@ -13,7 +15,7 @@ def test_value_display():
     ValueIO._broadcast_value_fn = lambda node_id, payload: captured.append((node_id, payload))
     ValueIO._current_node_id = "test"
 
-    result = node.display_value(3.25)
+    result = node.display_value(value=3.25)
     assert result == (3.25,)
     assert captured == [("test", {"value": 3.25})]
 
@@ -21,8 +23,42 @@ def test_value_display():
         {"quantity": "delta X", "value": 1.7e-7, "unit": "m"},
         {"quantity": "delta Y", "value": 463, "unit": "count"},
     ])
-    result = node.display_value(measurements, measurement="delta X")
+    result = node.display_value(value=measurements, measurement="delta X")
     assert result == (1.7e-7,)
     assert captured[-1] == ("test", {"value": 1.7e-7, "unit": "m"})
 
     ValueIO._broadcast_value_fn = None
+
+
+def test_value_display_string_input():
+    from backend.nodes.value_io import ValueIO
+
+    node = ValueIO()
+    values = []
+    with execution_callbacks(value=lambda nid, v: values.append(v)), active_node("n1"):
+        # plain number
+        result = node.display_value(number_input="42")
+    assert result == (42.0,)
+    assert values[-1]["value"] == 42.0
+
+    values.clear()
+    with execution_callbacks(value=lambda nid, v: values.append(v)), active_node("n1"):
+        # negative number
+        result = node.display_value(number_input="-3.14")
+    assert math.isclose(result[0], -3.14)
+    assert math.isclose(values[-1]["value"], -3.14)
+
+
+def test_value_display_table_emits_table():
+    from backend.nodes.value_io import ValueIO
+
+    node = ValueIO()
+    tables = []
+    measurements = RecordTable([
+        {"quantity": "Rq", "value": 0.42, "unit": "nm"},
+    ])
+    with execution_callbacks(table=lambda nid, t: tables.append(t)), active_node("n1"):
+        result = node.display_value(value=measurements, measurement="Rq")
+    assert result == (0.42,)
+    assert len(tables) == 1
+    assert tables[0][0]["quantity"] == "Rq"