flavor_engine

Vape Cart Flavor Engine — 11D gustatory interpolation and NCM coupling.

Implements the 6-phase blend pipeline:

1. Raw weighted vector summation

2. Interaction matrix corrections (suppression/synergy/cross-modal)

3. Temporal phase reconstruction → TDS string

4. NCM cascade computation (Hill saturation + receptor synergies)

5. Nearest-neighbor classification + novelty score

6. Derived metrics (palatability, reward_density, cling, nostalgia)

Also: morph, temperature modification, cascade trigger detection, emergence.

class flavor_engine.FlavorProfile(name, vector, delta_str, temporal, norimaki_inv=None, retronasal=0.5, category='other')

Bases: object

A single flavour from the cart.

Parameters:

• name (str)

• vector (List[float])

• delta_str (str)

• temporal (Dict[str, Any])

• norimaki_inv (List[float] | None)

• retronasal (float)

• category (str)

name: str

vector: List[float]

delta_str: str

temporal: Dict[str, Any]

norimaki_inv: List[float] | None = None

retronasal: float = 0.5

category: str = 'other'

property dominant_axis: str

Dominant axis.

Returns:

Result string.

Return type:

str

class flavor_engine.CompositeResult(vector, raw_vector, ncm_deltas, tds_string, temporal_phases, derived_metrics, nearest_flavor, nearest_similarity, novelty_score, emergence_flags, cascade_triggers, attractor=None, ingredients=<factory>)

Bases: object

Output of a blend/morph computation.

Parameters:

• vector (List[float])

• raw_vector (List[float])

• ncm_deltas (Dict[str, float])

• tds_string (str)

• temporal_phases (List[Dict[str, Any]])

• derived_metrics (Dict[str, float])

• nearest_flavor (str)

• nearest_similarity (float)

• novelty_score (float)

• emergence_flags (List[str])

• cascade_triggers (List[str])

• attractor (str | None)

• ingredients (List[Dict[str, Any]])

vector: List[float]

raw_vector: List[float]

ncm_deltas: Dict[str, float]

tds_string: str

temporal_phases: List[Dict[str, Any]]

derived_metrics: Dict[str, float]

nearest_flavor: str

nearest_similarity: float

novelty_score: float

emergence_flags: List[str]

cascade_triggers: List[str]

attractor: str | None = None

ingredients: List[Dict[str, Any]]

to_dict()

Convert to dict representation.

Returns:

Result dictionary.

Return type:

dict

class flavor_engine.FlavorEngine(yaml_path=None)

Bases: object

11-dimensional gustatory computation engine.

Parameters:

yaml_path (Optional[str])

__init__(yaml_path=None)

Initialize the instance.

Parameters:

yaml_path (Optional[str]) – The yaml path value.

get_flavor(name)

Look up a single flavor by name (case-insensitive).

Return type:

Optional[FlavorProfile]

Parameters:

name (str)

list_flavors()

List flavors.

Returns:

The result.

Return type:

List[str]

blend(recipe)

Run the full 6-phase blend pipeline.

Parameters:

recipe (List[Dict[str, Any]]) – Each dict: {flavor: str, weight: float, prep: str?, temp_c: float?}

Return type:

CompositeResult

morph(flavor_a, flavor_b, t=0.5)

Interpolate between two flavors at point t ∈ [0, 1].

Return type:

CompositeResult

Parameters:

• flavor_a (str)

• flavor_b (str)

• t (float)

apply_temperature(profile, temp_c)

Return a new FlavorProfile with temperature modifications.

Return type:

FlavorProfile

Parameters:

• profile (FlavorProfile)

• temp_c (float)