Refactor simulation options

notebooks/simulation.toml

@@ -1,9 +1,9 @@
 [general]
 date_start = 1994-05-01
-date_end = 2000-06-30
+date_end = 2010-06-30
 seed = 1163078255
 second_year_draws = true
-ruptures = false
+ruptures = true
 stake = true
 select_trunk = 0
 mechanics = true
@@ -12,6 +12,7 @@ stride_number = 5
 pruning = false
 convergence_steps = 2
 
+# relative to vmapplet/lpy
 [input.lpy_files]
 axiom = 'axiom.lpy'
 parameters = 'parameters.lpy'
@@ -20,21 +21,22 @@ statistics = 'statistics.lpy'
 structure = 'structure.lpy'
 interpretation = 'interpretation.lpy'
 
+# relative to vmapplet/data/markov
+[input.markov_files]
+terminal_fate = 'fuji/terminal_fate.toml'
+models = 'fuji/models'
+
 [output]
 dates = [
     { day = 1, month = 6 },
     { day = 1, month = 11 },
 ]
 
+# All of those attributes will be available in the MTGs
 [output.attributes]
 tree = [
     'growth_units'
 ]
-# FIX: mtg display error when GUs are exported
-growth_unit = [
-     'year',
-     'inflorescence'
-]
 metamer = [
     'number',
     'closest_apex',
@@ -100,48 +102,10 @@ terminal_expansion_rate = 2e-5
 minimum_size = 0.00075
 maximum_size = 0.003
 
-[markov]
+[shoot]
 maximum_length = 70
 minimum_length = 4
 
-# order matters: index = year_no - 1
-[[markov.terminal_fate]]
-large = [0.500, 0.167, 0.000, 0.333]
-medium = [0.000, 0.000, 0.000, 1.000]
-small = [0.100, 0.100, 0.300, 0.500]
-floral = [0.100, 0.300, 0.600, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.246, 0.185, 0.000, 0.569]
-medium = [0.016, 0.238, 0.032, 0.714]
-small = [0.066, 0.067, 0.317, 0.550]
-floral = [0.317, 0.250, 0.433, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.351, 0.106, 0.010, 0.533]
-medium = [0.123, 0.148, 0.063, 0.666]
-small = [0.015, 0.094, 0.453, 0.438]
-floral = [0.182, 0.249, 0.569, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.213, 0.082, 0.000, 0.705]
-medium = [0.027, 0.046, 0.016, 0.911]
-small = [0.000, 0.024, 0.205, 0.771]
-floral = [0.003, 0.413, 0.584, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.100, 0.050, 0.000, 0.850]
-medium = [0.000, 0.020, 0.130, 0.850]
-small = [0.000, 0.000, 0.375, 0.625]
-floral = [0.008, 0.325, 0.667, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.000, 0.100, 0.000, 0.900]
-medium = [0.000, 0.050, 0.050, 0.900]
-small = [0.000, 0.000, 0.350, 0.650]
-floral = [0.000, 0.200, 0.800, 0.000]
-
-
 [fruit]
 flower_duration = 10.0
 max_relative_growth_rate = 0.167

vmapplet/data/markov/fuji/terminal_fate.toml

@@ -0,0 +1,36 @@
+# order matters: index = year_no - 1
+[[terminal_fate]]
+large = [0.500, 0.167, 0.000, 0.333]
+medium = [0.000, 0.000, 0.000, 1.000]
+small = [0.100, 0.100, 0.300, 0.500]
+floral = [0.100, 0.300, 0.600, 0.000]
+
+[[terminal_fate]]
+large = [0.246, 0.185, 0.000, 0.569]
+medium = [0.016, 0.238, 0.032, 0.714]
+small = [0.066, 0.067, 0.317, 0.550]
+floral = [0.317, 0.250, 0.433, 0.000]
+
+[[terminal_fate]]
+large = [0.351, 0.106, 0.010, 0.533]
+medium = [0.123, 0.148, 0.063, 0.666]
+small = [0.015, 0.094, 0.453, 0.438]
+floral = [0.182, 0.249, 0.569, 0.000]
+
+[[terminal_fate]]
+large = [0.213, 0.082, 0.000, 0.705]
+medium = [0.027, 0.046, 0.016, 0.911]
+small = [0.000, 0.024, 0.205, 0.771]
+floral = [0.003, 0.413, 0.584, 0.000]
+
+[[terminal_fate]]
+large = [0.100, 0.050, 0.000, 0.850]
+medium = [0.000, 0.020, 0.130, 0.850]
+small = [0.000, 0.000, 0.375, 0.625]
+floral = [0.008, 0.325, 0.667, 0.000]
+
+[[terminal_fate]]
+large = [0.000, 0.100, 0.000, 0.900]
+medium = [0.000, 0.050, 0.050, 0.900]
+small = [0.000, 0.000, 0.350, 0.650]
+floral = [0.000, 0.200, 0.800, 0.000]

vmapplet/data/simulation.toml

@@ -12,6 +12,7 @@ stride_number = 5
 pruning = false
 convergence_steps = 2
 
+# relative to vmapplet/lpy
 [input.lpy_files]
 axiom = 'axiom.lpy'
 parameters = 'parameters.lpy'
@@ -20,21 +21,22 @@ statistics = 'statistics.lpy'
 structure = 'structure.lpy'
 interpretation = 'interpretation.lpy'
 
+# relative to vmapplet/data/markov
+[input.markov_files]
+terminal_fate = 'fuji/terminal_fate.toml'
+models = 'fuji/models'
+
 [output]
 dates = [
     { day = 1, month = 6 },
     { day = 1, month = 11 },
 ]
 
+# All of those attributes will be available in the MTGs
 [output.attributes]
 tree = [
     'growth_units'
 ]
-# FIX: mtg display error when GUs are exported
-# growth_unit = [
-#     'year',
-#     'inflorescence'
-# ]
 metamer = [
     'number',
     'closest_apex',
@@ -100,47 +102,10 @@ terminal_expansion_rate = 2e-5
 minimum_size = 0.00075
 maximum_size = 0.003
 
-[markov]
+[shoot]
 maximum_length = 70
 minimum_length = 4
 
-# order matters: index = year_no - 1
-[[markov.terminal_fate]]
-large = [0.500, 0.167, 0.000, 0.333]
-medium = [0.000, 0.000, 0.000, 1.000]
-small = [0.100, 0.100, 0.300, 0.500]
-floral = [0.100, 0.300, 0.600, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.246, 0.185, 0.000, 0.569]
-medium = [0.016, 0.238, 0.032, 0.714]
-small = [0.066, 0.067, 0.317, 0.550]
-floral = [0.317, 0.250, 0.433, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.351, 0.106, 0.010, 0.533]
-medium = [0.123, 0.148, 0.063, 0.666]
-small = [0.015, 0.094, 0.453, 0.438]
-floral = [0.182, 0.249, 0.569, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.213, 0.082, 0.000, 0.705]
-medium = [0.027, 0.046, 0.016, 0.911]
-small = [0.000, 0.024, 0.205, 0.771]
-floral = [0.003, 0.413, 0.584, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.100, 0.050, 0.000, 0.850]
-medium = [0.000, 0.020, 0.130, 0.850]
-small = [0.000, 0.000, 0.375, 0.625]
-floral = [0.008, 0.325, 0.667, 0.000]
-
-[[markov.terminal_fate]]
-large = [0.000, 0.100, 0.000, 0.900]
-medium = [0.000, 0.050, 0.050, 0.900]
-small = [0.000, 0.000, 0.350, 0.650]
-floral = [0.000, 0.200, 0.800, 0.000]
-
 [fruit]
 flower_duration = 10.0
 max_relative_growth_rate = 0.167

vmapplet/lpy/axiom.lpy

@@ -8,7 +8,7 @@ module tree(): scale=0
 axiom:
     parameters = {
         **options.apex,
-        **options.markov
+        **options.shoot
     }
     produce tree(tree_data) root() apex(ApexData(hlu=tree_data.initial_hlu, observation=Observation.TRUNK, **parameters))
 

vmapplet/lpy/statistics.lpy

@@ -60,7 +60,7 @@ apex(a):
         elif a.get_observation() == Observation.FLORAL:
             a.set_observation(Observation.DORMANT)
         else:
-            a.set_observation(terminal_fate(simulation.year_no, a.get_observation(), options.markov.terminal_fate))
+            a.set_observation(terminal_fate(simulation.year_no, a.get_observation(), options.input.terminal_fate))
         a.radius = 0
         a.max_terminal_radius_target()
         tree_data.growth_units += 1

vmapplet/lpy/structure.lpy

@@ -105,9 +105,9 @@ metamer(m) > metamer(mr) apex(a):
         hlu = rotate_frame_at_branch(m.hlu, m.branching_angle, m.phyllotactic_angle);
         apex_parameters = {
             **options.apex,
-            **options.markov
+            **options.shoot
         }
-        sylleptic_apex = ApexData(hlu, terminal_fate(simulation.year_no, Observation.FLORAL, options.markov.terminal_fate), **apex_parameters)
+        sylleptic_apex = ApexData(hlu, terminal_fate(simulation.year_no, Observation.FLORAL, options.input.terminal_fate), **apex_parameters)
         sylleptic_apex.parent_observation = Observation.FLORAL
         if m.parent_fbr_id == 0:
             tree_data.first_branches += 1
@@ -138,7 +138,7 @@ metamer(m):
         hlu = rotate_frame_at_branch(m.hlu, m.branching_angle, m.phyllotactic_angle)
         apex_parameters = {
             **options.apex,
-            **options.markov
+            **options.shoot
         }
         a = ApexData(hlu, m.observation, **apex_parameters)
         if m.parent_fbr_id == 0:

vmapplet/options.py

@@ -3,6 +3,7 @@
 import dataclasses as dc
 import pathlib
 from datetime import date
+import io
 
 import toml
 
@@ -57,12 +58,37 @@ class OptionsOutput(OptionsBase):
     attributes: Dict[str, List[str]] = dc.field(default_factory=lambda: dict())
 
 
+markov_files = dict(terminal_fate="fuji/terminal_fate.toml", models="fuji/models")
+
+
 @dc.dataclass
 class OptionsInput(OptionsBase):
     lpy_files: LsystemPaths = dc.field(default_factory=lambda: dict())
     lpy_path: str = dc.field(
         default_factory=lambda: str(pathlib.Path(__file__).parent.joinpath("lpy"))
     )
+    markov_files: dict[str, str] = dc.field(default_factory=lambda: markov_files)
+    markov_path: str = dc.field(
+        default_factory=lambda: str(
+            pathlib.Path(__file__).parent.joinpath("data/markov")
+        )
+    )
+    terminal_fate: Dict[Any, Any] = dc.field(default_factory=lambda: dict())
+
+    def __post_init__(self):
+        # create the expected structure/types from toml input
+        # i.e. a list of dicts with observations as keys
+        terminal_fate = {}
+        if "terminal_fate" in self.markov_files:
+            path = pathlib.Path(self.markov_path) / self.markov_files["terminal_fate"]
+            with io.open(path) as file:
+                terminal_fate = toml.loads(file.read())["terminal_fate"]
+                for i, item in enumerate(terminal_fate):
+                    for observation_str, probabilities in item.items():
+                        # start with year_no = 1
+                        self.terminal_fate[
+                            (i + 1, Observation(observation_str.upper()))
+                        ] = probabilities
 
 
 @dc.dataclass
@@ -126,23 +152,9 @@ class OptionsApex(OptionsBase):
 
 
 @dc.dataclass
-class OptionsMarkov(OptionsBase):
+class OptionsShoot(OptionsBase):
     maximum_length: int = 70  # < 100
     minimum_length: int = 4
-    terminal_fate: Dict[Any, Any] = dc.field(default_factory=lambda: dict())
-
-    def __post_init__(self):
-        # create the expected structure/types from toml input
-        # i.e. a list of dicts with observations as keys
-        terminal_fate = {}
-        if self.terminal_fate is not None:
-            for i, item in enumerate(self.terminal_fate):
-                for observation_str, probabilities in item.items():
-                    # start with year_no = 1
-                    terminal_fate[
-                        (i + 1, Observation(observation_str.upper()))
-                    ] = probabilities
-        self.terminal_fate = terminal_fate
 
 
 @dc.dataclass
@@ -176,7 +188,7 @@ class Options(OptionsBase):
     wood: OptionsWood = dc.field(default_factory=lambda: OptionsWood())
     internode: OptionsInternode = dc.field(default_factory=lambda: OptionsInternode())
     apex: OptionsApex = dc.field(default_factory=lambda: OptionsApex())
-    markov: OptionsMarkov = dc.field(default_factory=lambda: OptionsMarkov())
+    shoot: OptionsShoot = dc.field(default_factory=lambda: OptionsShoot())
     fruit: OptionsFruit = dc.field(default_factory=lambda: OptionsFruit())
     leaf: OptionsLeaf = dc.field(default_factory=lambda: OptionsLeaf())
 
@@ -189,7 +201,7 @@ def __post_init__(self):
         self.wood = _make_dataclass(OptionsWood, self.wood)
         self.internode = _make_dataclass(OptionsInternode, self.internode)
         self.apex = _make_dataclass(OptionsApex, self.apex)
-        self.markov = _make_dataclass(OptionsMarkov, self.markov)
+        self.shoot = _make_dataclass(OptionsShoot, self.shoot)
         self.fruit = _make_dataclass(OptionsFruit, self.fruit)
         self.leaf = _make_dataclass(OptionsLeaf, self.leaf)
 

vmapplet/organs/tree.py

@@ -51,7 +51,6 @@ class Tree:
     inflorescence_death_probability: float = 0.2
 
     def __post_init__(self):
-
         self.angle_unit = "radians"
         self.phyllotactic_angle = self.phyllotactic_angle / 180.0 * pi
         self.branching_angle = self.branching_angle / 180.0 * pi

vmapplet/simulation.py

@@ -78,13 +78,17 @@ def __init__(self, options: Union[str, Options], output_path: Optional[str] = No
 
         self._markov = Markov(
             generator=self._rng,
-            minimum_length=self.options.markov.minimum_length,
-            maximum_length=self.options.markov.maximum_length,
+            minimum_length=self.options.shoot.minimum_length,
+            maximum_length=self.options.shoot.maximum_length,
         )
 
         self._markov_models = {}
-        for path in os.listdir(get_shared_data_path("markov")):
-            path = pathlib.Path(get_shared_data_path("markov")) / path
+        markov_models_dir = (
+            pathlib.Path(get_shared_data_path("markov"))
+            / self.options.input.markov_files["models"]
+        )
+        for path in os.listdir(markov_models_dir):
+            path = markov_models_dir / path
             if path.is_file() and path.suffix == ".toml":
                 with io.open(path) as file:
                     model = MarkovModel(**toml.loads(file.read()))