add content

marimo_california.py

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+import marimo
+
+__generated_with = "0.9.20"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md("""# Marimo + Blender""")
+    return
+
+
+@app.cell
+def __():
+    #run with
+    #cd california_housing
+    #uv run marimo edit marimo_california.py 
+    import marimo as mo
+    import numpy as np
+    import polars as pl
+    import quak
+    import bpy
+
+    blend_file_path = "housing_data_igor.blend"
+    bpy.ops.wm.open_mainfile(filepath=blend_file_path)
+
+    bpy.context.scene.render.engine = "BLENDER_EEVEE_NEXT"
+    bpy.context.scene.render.resolution_x = 800
+    bpy.context.scene.render.resolution_y = 500
+
+    df = pl.read_csv("a_df.csv")
+    reference_frame = pl.read_csv("b_reference_frame.csv")
+
+    vertices = [(row["longitude_normalized"], row["latitude_normalized"], 0) for row in reference_frame.iter_rows(named=True)]
+
+    mesh = bpy.data.meshes.new("NormalizedMesh")
+    obj = bpy.data.objects.new("CaliforninaNormalizedObject", mesh)
+    bpy.context.collection.objects.link(obj)
+
+    mesh.from_pydata(vertices, [], [])
+    mesh.update()
+    obj.modifiers.new(name="GeometryNodes", type='NODES').node_group = bpy.data.node_groups["geo_house"]
+    bpy.context.view_layer.objects.active = obj
+    obj.select_set(True)
+    reference_frame.head()
+
+    def render_result():
+
+        bpy.ops.render.render()
+        bpy.data.images['Render Result'].save_render(filepath="img.png")
+        return mo.image(src="img.png")
+    return (
+        blend_file_path,
+        bpy,
+        df,
+        mesh,
+        mo,
+        np,
+        obj,
+        pl,
+        quak,
+        reference_frame,
+        render_result,
+        vertices,
+    )
+
+
+@app.cell
+def __(df, mo, quak):
+    widget = mo.ui.anywidget(quak.Widget(df))
+    widget
+    return (widget,)
+
+
+@app.cell
+def __(np, obj, pl, reference_frame, render_result, widget):
+    widget_df = widget.data().pl()
+
+    plotting_frame = reference_frame.with_columns(
+        pl.lit(0).alias("custom_plotting")
+    ).join(
+        widget_df.select(["short_id", "median_house_value"]), 
+        on="short_id", 
+        how="left"
+    ).with_columns(
+        pl.when(pl.col("median_house_value").is_not_null())
+        .then(pl.col("median_house_value"))
+        .otherwise(pl.col("custom_plotting"))
+        .alias("custom_plotting")
+    ).select(["short_id", "custom_plotting"])
+
+    custom_plotting_list = plotting_frame["custom_plotting"].to_list()
+    normalized_values = list(np.interp(custom_plotting_list, 
+                                       (min(custom_plotting_list), max(custom_plotting_list)), 
+                                       (0.1, 3)))
+
+    attr_name = 'median_house_value'
+    attr = obj.data.attributes.get(attr_name) or obj.data.attributes.new(
+        name=attr_name,
+        type='FLOAT',
+        domain='POINT'
+    )
+    attr.data.foreach_set('value', normalized_values)
+    obj.data.update()
+
+    render_result()
+    return (
+        attr,
+        attr_name,
+        custom_plotting_list,
+        normalized_values,
+        plotting_frame,
+        widget_df,
+    )
+
+
+@app.cell
+def __():
+    return
+
+
+if __name__ == "__main__":
+    app.run()