review: keep BaseViewAgent prompt agent-agnostic, move imports to top

Two review responses bundled:

1. Per review on #1824: BaseViewAgent/main.jinja2 is the base template
   inherited by hvPlotAgent, VegaLiteAgent, and DeckGLAgent. The previous
   gridded block named kind='quadmesh'/'image'/'contourf'/'heatmap' which
   are hvPlot-only concepts. VegaLite uses mark: rect, DeckGL uses layer
   types, so injecting hvPlot kind values into their prompt context is
   misleading. Kept the metadata (xarray source, dims, data_vars) in the
   base block; each agent's own prompt carries the syntax-specific
   guidance. Updated test to assert no hvPlot kinds leak into the base.

2. Style cleanup for lint discipline: moved holoviews/pandas/xarray/
   unittest.mock imports to the top of test_hvplot_gridded.py (was
   inline inside test bodies). Replaced two em dashes in hvPlotAgent
   prompt and test docstring.

Author: ghostiee-11

lumen/ai/prompts/BaseViewAgent/main.jinja2

@@ -14,10 +14,9 @@ Here is the current dataset to use:
 Data originates from an xarray dataset.
 - dimensions: {{ gridded.dims | join(', ') }}
 - data variables: {{ gridded.data_vars | join(', ') }}
-Prefer kind='quadmesh' for this data (it preserves continuous coordinate
-axes). Fall back to 'image' for strictly regular grids, 'contourf' for
-filled contour plots, or 'heatmap' if ordinal/categorical axes are wanted.
-Set x/y to coordinate dimensions and z to the value column.
+Use a 2D gridded encoding with axes on the coordinate dimensions and
+colour/value on the data variable, using whichever syntax this agent
+accepts (see agent-specific instructions below).
 {%- endif %}
 
 {%- if "view" in memory and memory["view"].get("agent") and actor_name.startswith(memory["view"].get("agent")) %}

lumen/ai/prompts/hvPlotAgent/main.jinja2

@@ -8,7 +8,7 @@ the values.
 {%- if gridded is defined and gridded %}
 
 For gridded xarray data:
-- Prefer kind='quadmesh' — it preserves continuous coordinate axes and is
+- Prefer kind='quadmesh'. It preserves continuous coordinate axes and is
   the right default for xarray-derived grids. Use 'image' only for strictly
   regular grids, 'contourf' for filled contours, or 'heatmap' if you
   specifically want ordinal/categorical axes.

lumen/tests/ai/test_gridded.py

@@ -107,7 +107,11 @@ def test_baseview_prompt_includes_gridded_block():
     assert "xarray dataset" in rendered
     assert "lat, lon" in rendered
     assert "air" in rendered
-    assert "kind='quadmesh'" in rendered
+    # The base prompt must stay agent-agnostic: no hvPlot-specific kind
+    # values, since VegaLite and DeckGL inherit this template too.
+    assert "kind=" not in rendered
+    assert "quadmesh" not in rendered
+    assert "heatmap" not in rendered
 
 
 def test_baseview_prompt_no_gridded_block_for_tabular():

lumen/tests/views/test_hvplot_gridded.py

@@ -1,6 +1,10 @@
+from unittest.mock import MagicMock, patch
+
+import holoviews as hv
 import numpy as np
 import pandas as pd
 import pytest
+import xarray as xr
 
 from lumen.pipeline import Pipeline
 from lumen.sources.base import InMemorySource
@@ -37,7 +41,7 @@ def tabular_pipeline(tabular_df):
 # ---- Tests ----
 
 def test_hvplot_z_param_accepted():
-    """z param is a valid Selector on hvPlotBaseView — no AttributeError."""
+    """z param is a valid Selector on hvPlotBaseView (no AttributeError)."""
     assert "z" in hvPlotView.param
 
 
@@ -51,7 +55,6 @@ def test_hvplot_heatmap_from_longform_df(gridded_pipeline):
 
     The public z= param is mapped internally to hvPlot's C= for heatmap.
     """
-    import holoviews as hv
     view = hvPlotView(
         pipeline=gridded_pipeline,
         kind="heatmap",
@@ -72,8 +75,6 @@ def test_hvplot_z_maps_to_C_only_for_heatmap(gridded_pipeline):
     For contourf/quadmesh/image the view pivots to xarray, so we patch the
     pivoted DataArray's hvplot accessor instead.
     """
-    from unittest.mock import MagicMock, patch
-
     heatmap_view = hvPlotView(
         pipeline=gridded_pipeline, kind="heatmap", x="lon", y="lat", z="air",
     )
@@ -113,11 +114,10 @@ def test_hvplot_quadmesh_from_longform_df(gridded_pipeline):
 
     Long-form pandas is the only data format XArraySQLSource produces, so the
     view must pivot it to a 2D array before handing to hvPlot, otherwise
-    hvPlot raises NotImplementedError for quadmesh on pandas. Addresses
-    @ahuang11's review: quadmesh/image are semantically more correct than
+    hvPlot raises NotImplementedError for quadmesh on pandas. Addresses the
+    review on #1823: quadmesh/image are semantically more correct than
     heatmap for continuous-coordinate gridded data.
     """
-    import holoviews as hv
     view = hvPlotView(
         pipeline=gridded_pipeline,
         kind="quadmesh",
@@ -131,7 +131,6 @@ def test_hvplot_quadmesh_from_longform_df(gridded_pipeline):
 
 def test_hvplot_image_from_longform_df(gridded_pipeline):
     """kind=image renders an Image from a long-form pandas DataFrame via pivot."""
-    import holoviews as hv
     view = hvPlotView(
         pipeline=gridded_pipeline,
         kind="image",
@@ -163,7 +162,6 @@ def test_hvplot_gridded_skips_pivot_if_xarray_unavailable(gridded_pipeline):
     Build a view with a non-gridded kind so construction doesn't trigger the
     quadmesh render path, then flip the flag and call _to_gridded directly.
     """
-    import pandas as pd
     view = hvPlotView(
         pipeline=gridded_pipeline,
         kind="scatter",
@@ -178,7 +176,6 @@ def test_hvplot_gridded_skips_pivot_if_xarray_unavailable(gridded_pipeline):
 
 def test_hvplot_gridded_passes_through_non_dataframe(gridded_pipeline):
     """If the pipeline hands over an already-gridded xarray object, skip pivot."""
-    import xarray as xr
     view = hvPlotView(
         pipeline=gridded_pipeline,
         kind="quadmesh",