Using Cad-PyRx inside BricsCAD opens up the entire Python ecosystem, making it possible to use advanced optimization libraries that simple LISP cannot match.

While Google's OR-Tools is the gold standard for complex optimization, it might be "overkill" unless you have high-level constraints (like weight distribution or pick-pathing). For "fitting the most racks in a room," 2D Bin Packing libraries are often more direct.

Here is a breakdown of how you can use Python via PyRx to solve this.
1. Recommended Python Libraries
Google OR-Tools (Constraint Programming)

    Best For: When you have a list of different rack sizes or need to respect complex "rules" (e.g., specific racks must be near certain doors).

    The Approach: Use the Constraint Solver to define the room as a grid and each rack as a variable that cannot overlap with walls or other racks.

    Installation: You can install it directly within BricsCAD if PyRx is loaded using: PYPIP install ortools

HyperPack (2D Bin Packing)

    Best For: Purely maximizing the number of racks within a boundary.

    Why it's better for CAD: It is designed specifically for "Rectangles in a Container." It can handle irregular shapes if you decompose your room into a series of rectangles.

    Installation: PYPIP install hyperpack

Shapely (Geometry Logic)

    Best For: Calculating if a rack is "inside" or "outside" your polyline boundary.

    Usage: You will likely use this alongside OR-Tools. Shapely can take your BricsCAD Polyline coordinates and perform a "Point-in-Polygon" test to ensure racks stay 100% inside the room.

2. The PyRx Workflow

To use these tools, your Python script in BricsCAD would follow this logical flow:

    Extract Geometry: Use PyRx (specifically PyDb.Polyline) to get the vertex coordinates of your room boundary.

    Define Obstacles: Identify "No-Go" zones (pillars, doors) as internal polylines.

    Run Optimization: Pass these coordinates and your Rack/Aisle dimensions into OR-Tools or HyperPack.

    Insert Blocks: The solver returns a list of (x,y) coordinates. Your script then loops through these coordinates and uses the PyDb.BlockReference class to insert your "Nestainer" blocks at those exact spots.

3. Example Conceptual Code (PyRx + OR-Tools)

This is a simplified look at how you might structure the placement logic within a PyRx command:
Python

import PyRx as Rx
import PyDb as Db
import PyGe as Ge
from ortools.sat.python import cp_model

def PyRxCmd_PackRacks():
    # 1. Get the Room Boundary from BricsCAD
    # (Code to select polyline and get bounding box)
    room_w, room_h = 20000, 30000 # Example mm
    rack_w, rack_h = 1350, 1200
    aisle = 3000

    # 2. Setup OR-Tools Model
    model = cp_model.CpModel()
    
    # Define variables for X and Y positions
    # model.NewIntVar(...) 
    
    # 3. Add Constraints
    # - Racks cannot overlap
    # - Racks must be > 'aisle' distance from certain lines
    
    # 4. Solve and Place
    # Use PyRx to insert blocks
    db = Db.curDb()
    model_space = Db.BlockTableRecord(db.modelSpaceId(), Db.OpenMode.kForWrite)
    
    # Example insertion:
    # new_rack = Db.BlockReference(Ge.Point3d(x, y, 0), block_id)
    # model_space.appendAcDbEntity(new_rack)

4. A Key "Pythonic" Shortcut

If the "Packing Problem" math is too complex to code from scratch, you can use a Grid-Sampling approach with Python:

    Generate a dense grid of points across the room.

    Use Shapely to delete any points that are:

        Outside the room.

        Too close to the walls (Clearance).

        Inside the "Aisle" zones (Rectangles you draw to represent the 6m main lane).

    Place a rack block at every remaining point.

    Count: print(f"Total Racks Placed: {len(remaining_points)}")

Would you like me to help you write a script that specifically uses Shapely to filter out coordinates that are too close to your walls?