Tree-1

thingiverse

This appears to be a set of configuration parameters for generating branching shapes, likely using a procedural modeling algorithm. Let's go through some observations and explanations about each setting: * **Geometry and Size** * `start_budding_position` and `stop_budding_position`: These settings indicate the positions along the length of the shape where budding or branching begins. The budding process generates new branches from a node. * `base_shape`, `trunk_radius`, and `trunk_length`: This set defines the trunk of the branch, specifying its initial shape, radius, and length. * **Budding Parameters** * `maximum_branching_iterations` controls how many iterations can occur before no more branches are generated from a given node. A high value allows for longer and potentially more complex branching patterns. ```python start_budding_position = 46 # at what point in the shape budding will begin base_shape = 0 # define what shape the base of the trunk takes trunk_radius = 10 # sets the initial radius of the branch, in a grid trunk_length = 40 # defines how long a single "level" or step of branching goes on maximum_branching_iterations = 5 # controls maximum branching at each iteration. ``` * `branch_cross_section_shape` indicates which shape (1-6) a newly grown branch will have, allowing different styles and looks in branching shapes. ```python max_number_of_branches_from_each_branch = 4 # limits how many new branches that will occur per level of a given starting branch. length_max_ratio=100 branch_twist_angle = 4 min_branching_angle = 40 ``` * `tip_min_ratio` specifies the minimum growth factor along with length. As well as defining tip branching max. ```python tip_max_ratio = 70 root_max_ratio = 60 # controls upper/lower branches maximum radius based on trunk's height at any point length_min_ratio=70 # minimum increase from root branch size for given node, in a grid of sizes. ``` * `branch_diameter_cutoff` and `branches_keep_growing`: Determine whether new branches can be grown further than the initial or set branch diameters. A value below will disallow branching if their diameter meets or exceeds that given number. **Example Python Code Using These Parameters:** ```python import math # Sample Function: def generate_branch(shape_id, current_x=0.0): print(f"Generated branch at X: {current_x} ShapeID={shape_id}") class BranchTreeGenerator: def __init__(self, start_budding_position, stop_budding_position, max_number_of_branches_from_each_branch): # define what happens based off branching iteration self.start_budding = start_budding_position self.stop_bud_position = stop_budding_position # Controls growth iterations from initial branches given the following 8 numbers self.maximum_iterations = 5 self.iterations_count=0 # How many branchs a starting one can give to its kids before being complete and ready for branching of new types, rather than child ones in each iteration: self.max_number_of_branches = max_number_of_branches_from_each_branch def create_branching_tree(self): iterations_completed=0 start_y=self.start_budding while iterations_completed<self.maximum_iterations: # Get a starting shape to draw the next generation based on its 16 unique position for iteration_y in range(start_y, self.stop_bud_position+1): new_start_x=current_X + math.sqrt(200*(0.07**(self.iterations_count*iteration_y))) iterations_completed+=1 for i in range(self.max_number_of_branches): generate_branch(17,(current_X)) # Use branch generator based off values def start_tree(self): self.create_branching_tree() # Create a generator instance and initiate b=BranchTreeGenerator( int("stop_budding_position".split()[-1]), int("start_budding_position".split()[-1]), max(1,int("maximum_branching_iterations".split()[0]))) ``` This sample code shows how to use the parameters provided to create a procedural branching model based off certain rules specified by given numbers, assuming their inputs into Python function calls as `start`/`stop` values. Feel free to modify according to needs of your desired algorithm results.