Add colab to reproduce the *upcoming* video of Biomaker CA.

Add default methods for stringifying classes. Useful for storing metadata.
Add utility functions for saving and loading dnas.
Add a successful dna for the experiment performed in the colab.

PiperOrigin-RevId: 575125094

self_organising_systems/biomakerca/agent_logic.py

@@ -32,6 +32,7 @@
 from self_organising_systems.biomakerca.env_logic import SpawnOpData
 from self_organising_systems.biomakerca.env_logic import ParallelInterface
 from self_organising_systems.biomakerca.environments import EnvConfig
+from self_organising_systems.biomakerca.utils import stringify_class
 
 
 class AgentLogic(ABC):
@@ -93,6 +94,9 @@ def repr_f(self, key: KeyType, perc: PerceivedData, params: AgentProgramType
     """
     pass
 
+  def __str__(self):
+    return stringify_class(self)
+
 
 def clip_residual(s, ds, clip_val):
   """Correct ds so that the final value of s + ds is within [-clip_val,clip_val].
@@ -190,6 +194,9 @@ def __init__(self, config: EnvConfig, perceive_ids=True, minimal_net=False):
         axis=-1,
     )
 
+  def __str__(self):
+    return stringify_class(self, include_list=["perceive_ids", "minimal_net"])
+
   def split_params_f(
       self, params: AgentProgramType
       ) -> tuple[AgentProgramType, AgentProgramType, AgentProgramType]:

self_organising_systems/biomakerca/dnalib/persistence_1697720583799231923.txt

@@ -0,0 +1,8 @@
+dna: persistence_1697720583799231923
+EnvConfig: {agent_state_size: 2, etd: DefaultTypeDef: {materials_list: ['VOID', 'AIR', 'EARTH', 'IMMOVABLE', 'SUN', 'OUT_OF_BOUNDS'], types: {'VOID': 0, 'AIR': 1, 'EARTH': 2, 'IMMOVABLE': 3, 'SUN': 4, 'OUT_OF_BOUNDS': 5, 'AGENT_UNSPECIALIZED': 6, 'AGENT_ROOT': 7, 'AGENT_LEAF': 8, 'AGENT_FLOWER': 9}, specialization_idxs: {'AGENT_UNSPECIALIZED': 0, 'AGENT_ROOT': 1, 'AGENT_LEAF': 2, 'AGENT_FLOWER': 3}, agent_types: [6 7 8 9], intangible_mats: [0 1], gravity_mats: [2 6 7 8 9], structural_mats: [6 7 8 9], propagate_structure_mats: [2 3 6 7 8 9], agent_spawnable_mats: [0 1 2], structure_decay_mats: [-1 -1  1  0 -1 -1  5  5  5  5], aging_mats: [6 7 8 9], dissipation_rate_per_spec: [[0.5 0.5],  [1.  1. ],  [1.  1. ],  [1.2 1.2]]}, env_state_size: 6, struct_integrity_cap: 200, absorbtion_amounts: [0.25 0.25], dissipation_per_step: [0.01 0.01], spawn_cost: [0.75 0.75], reproduce_cost: [1. 1.], specialize_cost: [0.02 0.02], reproduce_min_dist: 15, reproduce_max_dist: 35, n_reproduce_per_step: 2, nutrient_cap: [10. 10.], material_nutrient_cap: [10. 10.], max_lifetime: 10000}
+BasicAgentLogic: {perceive_ids: True, minimal_net: False}
+BasicMutator: {sd: 0.0001, change_perc: 0.2}
+env_h: 16
+env_w: 28
+author: oteret
+notes: This dna was trained with petri meta evolution. So, no mutator was used. But I tested and it definitely works with BasicMutators and sd of 1e-4. In some cases, 1e-3. See the notebook evolving_in_small_worlds.ipynb for more context.

self_organising_systems/biomakerca/environments.py

@@ -29,6 +29,7 @@
 
 from self_organising_systems.biomakerca.utils import dotdict
 from self_organising_systems.biomakerca.utils import vmap2
+from self_organising_systems.biomakerca.utils import stringify_class
 
 ### Environment
 # This is the state of any given environment. It is made of 3 grids:
@@ -210,6 +211,9 @@ def is_agent_fn(self, env_type):
     """Return true if the cell is an agent. Works for any input dimensionality.
     """
     return (env_type[..., None] == self.agent_types).any(axis=-1)
+
+  def __str__(self):
+    return stringify_class(self, exclude_list=["type_color_map"])
 
 
 DEFAULT_MATERIALS = [
@@ -451,6 +455,8 @@ def __init__(self,
     self.material_nutrient_cap = material_nutrient_cap
     self.max_lifetime = max_lifetime
 
+  def __str__(self):
+    return stringify_class(self)
 
 
 ### Helpers for making environments.