laser_measles.biweekly package#

BaseScenario#: alias of BaseBiweeklyScenario

class BiweeklyModel(scenario, params, name='biweekly')[source]#

Bases: BaseLaserModel

A class to represent the biweekly model.

Parameters:

scenario (BaseScenario) – A scenario containing the scenario data, including population, latitude, and longitude.
params (BiweeklyParams) – A set of parameters for the model.
name (str, optional) – The name of the model. Defaults to “biweekly”.

Notes

This class initializes the model with the given scenario and parameters. The scenario must include the following columns:

id (string): The name of the patch or location.

pop (integer): The population count for the patch.

lat (float degrees): The latitude of the patches (e.g., from geographic or population centroid).

lon (float degrees): The longitude of the patches (e.g., from geographic or population centroid).

mcv1 (float): The MCV1 coverage for the patches.

infect(indices, num_infected)[source]#

Infects the given nodes with the given number of infected individuals.

Parameters:

indices (int | np.ndarray) – The indices of the nodes to infect.
num_infected (int | np.ndarray) – The number of infected individuals to infect.

Return type:

None

recover(indices, num_recovered)[source]#

Recovers the given nodes with the given number of recovered individuals. Moves individuals from Infected to Recovered compartment.

Parameters:

indices (int | np.ndarray) – The indices of the nodes to recover.
num_recovered (int | np.ndarray) – The number of recovered individuals.

Return type:

None

scenario_wrapper_class#: alias of BaseBiweeklyScenario

patches: PatchLaserFrame#

pydantic model BiweeklyParams[source]#

Bases: BaseModelParams

Parameters for the biweekly model.

Show JSON schema

{
   "title": "BiweeklyParams",
   "description": "Parameters for the biweekly model.",
   "type": "object",
   "properties": {
      "seed": {
         "default": 20250314,
         "description": "Random seed",
         "title": "Seed",
         "type": "integer"
      },
      "start_time": {
         "default": "2000-01",
         "description": "Initial start time of simulation in YYYY-MM format",
         "title": "Start Time",
         "type": "string"
      },
      "num_ticks": {
         "default": 365,
         "description": "Number of time steps",
         "title": "Num Ticks",
         "type": "integer"
      },
      "verbose": {
         "default": false,
         "description": "Whether to print verbose output",
         "title": "Verbose",
         "type": "boolean"
      },
      "show_progress": {
         "default": true,
         "description": "Whether to show progress bar during simulation",
         "title": "Show Progress",
         "type": "boolean"
      },
      "use_numba": {
         "default": true,
         "description": "Whether to use numba acceleration when available",
         "title": "Use Numba",
         "type": "boolean"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid

Fields:

property states: list[str]#

property time_step_days: int#

Model#: alias of BiweeklyModel

Params#: alias of BiweeklyParams

Subpackages#

Submodules#

laser_measles.biweekly.base module#

Basic classes for biweekly model.

class PatchLaserFrame(*args, **kwargs)[source]#: Bases: WrappedClass

pydantic model BaseScenarioSchema[source]#

Bases: Model

Schema for the scenario data.

Show JSON schema

{
   "title": "BaseScenarioSchema",
   "description": "Schema for the scenario data.",
   "type": "object",
   "properties": {
      "pop": {
         "title": "Pop",
         "type": "integer"
      },
      "lat": {
         "title": "Lat",
         "type": "number"
      },
      "lon": {
         "title": "Lon",
         "type": "number"
      },
      "id": {
         "title": "Id",
         "type": "string"
      },
      "mcv1": {
         "title": "Mcv1",
         "type": "number"
      }
   },
   "required": [
      "pop",
      "lat",
      "lon",
      "id",
      "mcv1"
   ]
}

Fields:

id (str)
lat (float)
lon (float)
mcv1 (float)
pop (int)

field id: str [Required]#

field lat: float [Required]#

field lon: float [Required]#

field mcv1: float [Required]#

field pop: int [Required]#

DataFrame#: alias of BaseScenarioSchemaDataFrame

LazyFrame#: alias of BaseScenarioSchemaLazyFrame

class BaseBiweeklyScenario(df)[source]#: Bases: BaseScenario

BaseScenario#: alias of BaseBiweeklyScenario

laser_measles.biweekly.mixing module#

pairwise_haversine(df)[source]#

Pairwise distances for all (lon, lat) points using the Haversine formula.

Parameters:: df (pl.DataFrame) – Polars DataFrame with ‘lon’ and ‘lat’ columns
Returns:: Pairwise distances in kilometers

init_gravity_diffusion(df, scale, dist_exp)[source]#

Return type:: ndarray

pairwise_haversine_(lon, lat)[source]#

Calculate pairwise distances for all (lon, lat) points using the Haversine formula.

Parameters:

lon (ndarray) – Array of longitude values in degrees
lat (ndarray) – Array of latitude values in degrees

Return type:

ndarray

Returns:

Compressed matrix of pairwise distances in kilometers, where only the upper triangle (excluding diagonal) is stored. The full matrix can be reconstructed using: full_matrix = np.zeros((n, n)) full_matrix[np.triu_indices(n, k=1)] = compressed_matrix full_matrix = full_matrix + full_matrix.T

init_gravity_diffusion_(df, scale, dist_exp)[source]#

Initialize a gravity diffusion matrix for population mixing.

Parameters:

df (DataFrame | tuple[ndarray, ndarray]) – Either a DataFrame with ‘population’, ‘lat’, and ‘lon’ columns, or a tuple of (lon, lat) arrays
scale (float) – Scaling factor for the diffusion matrix
dist_exp (float) – Distance exponent for the gravity model

Return type:

ndarray

Returns:

Normalized diffusion matrix where each row sums to 1

laser_measles.biweekly.model module#

A class to represent the biweekly model.

class BiweeklyModel(scenario, params, name='biweekly')[source]#

Bases: BaseLaserModel

A class to represent the biweekly model.

Parameters:

scenario (BaseScenario) – A scenario containing the scenario data, including population, latitude, and longitude.
params (BiweeklyParams) – A set of parameters for the model.
name (str, optional) – The name of the model. Defaults to “biweekly”.

Notes

This class initializes the model with the given scenario and parameters. The scenario must include the following columns:

id (string): The name of the patch or location.

pop (integer): The population count for the patch.

lat (float degrees): The latitude of the patches (e.g., from geographic or population centroid).

lon (float degrees): The longitude of the patches (e.g., from geographic or population centroid).

mcv1 (float): The MCV1 coverage for the patches.

scenario_wrapper_class#: alias of BaseBiweeklyScenario

patches: PatchLaserFrame#

infect(indices, num_infected)[source]#

Infects the given nodes with the given number of infected individuals.

Parameters:

indices (int | np.ndarray) – The indices of the nodes to infect.
num_infected (int | np.ndarray) – The number of infected individuals to infect.

Return type:

None

recover(indices, num_recovered)[source]#

Recovers the given nodes with the given number of recovered individuals. Moves individuals from Infected to Recovered compartment.

Parameters:

indices (int | np.ndarray) – The indices of the nodes to recover.
num_recovered (int | np.ndarray) – The number of recovered individuals.

Return type:

None

scenario: BaseScenario#

params: BaseModelParams#

instances: list#

phases: list#

metrics: list#

Model#: alias of BiweeklyModel

laser_measles.biweekly.params module#

pydantic model BiweeklyParams[source]#

Bases: BaseModelParams

Parameters for the biweekly model.

Show JSON schema

{
   "title": "BiweeklyParams",
   "description": "Parameters for the biweekly model.",
   "type": "object",
   "properties": {
      "seed": {
         "default": 20250314,
         "description": "Random seed",
         "title": "Seed",
         "type": "integer"
      },
      "start_time": {
         "default": "2000-01",
         "description": "Initial start time of simulation in YYYY-MM format",
         "title": "Start Time",
         "type": "string"
      },
      "num_ticks": {
         "default": 365,
         "description": "Number of time steps",
         "title": "Num Ticks",
         "type": "integer"
      },
      "verbose": {
         "default": false,
         "description": "Whether to print verbose output",
         "title": "Verbose",
         "type": "boolean"
      },
      "show_progress": {
         "default": true,
         "description": "Whether to show progress bar during simulation",
         "title": "Show Progress",
         "type": "boolean"
      },
      "use_numba": {
         "default": true,
         "description": "Whether to use numba acceleration when available",
         "title": "Use Numba",
         "type": "boolean"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid

Fields:

property states: list[str]#

property time_step_days: int#

Params#: alias of BiweeklyParams