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