pydsstools.core.gridinfo.factory

Factory function for creating grid metadata objects.

This module provides the GridInfoCreate factory function which automatically selects and instantiates the appropriate grid info class based on grid_type.

Functions

GridInfoCreate(**kwargs)

Factory function to create appropriate GridInfo subclass.
pydsstools.core.gridinfo.factory.GridInfoCreate(**kwargs)[source]

Factory function to create appropriate GridInfo subclass.

Automatically selects the correct GridInfo subclass (GridInfo, HrapInfo, AlbersInfo, or SpecifiedInfo) based on the grid_type parameter.

This is the recommended way to create grid metadata objects as it:

  • Provides type validation

  • Ensures correct class selection

  • Validates all required fields

  • Applies default values

Parameters:

**kwargs – Keyword arguments for grid metadata. Must include ‘grid_type’. Other required/optional parameters depend on the grid type.

Returns:

Instance of GridInfo, HrapInfo, AlbersInfo, or SpecifiedInfo.

Return type:

GridInfoBase

Raises:

  • TypeError – If no keyword arguments provided.

  • ValidationError – If required fields are missing or invalid.

Examples

Create HRAP grid metadata:

>>> from pydsstools.core.gridinfo import GridInfoCreate, GridType, DataType
>>> info = GridInfoCreate(
...     grid_type=GridType.hrap,
...     data_type=DataType.per_aver,
...     shape=(100, 150),
...     cell_size=4762.5,
...     data_units="MM",
...     data_source="NEXRAD"
... )
>>> type(info).__name__
'HrapInfo'

Create Albers/SHG grid metadata:

>>> info = GridInfoCreate(
...     grid_type=GridType.albers,
...     data_type=DataType.inst_val,
...     shape=(500, 700),
...     cell_size=2000.0,
...     proj_datum=Datum.nad83,
...     lat_0=23.0,
...     lat_1=29.5,
...     lat_2=45.5,
...     lon_0=-96.0,
...     min_xy=(-1500000, 500000),
...     data_units="M"
... )
>>> type(info).__name__
'AlbersInfo'

Create specified grid with custom CRS:

>>> info = GridInfoCreate(
...     grid_type=GridType.specified,
...     data_type=DataType.per_aver,
...     shape=(200, 300),
...     cell_size=1000.0,
...     crs="EPSG:32610",
...     crs_name="WGS84 / UTM zone 10N",
...     nodata=-9999.0,
...     min_xy=(500000, 4000000),
...     data_units="MM"
... )
>>> type(info).__name__
'SpecifiedInfo'

Create undefined/basic grid:

>>> info = GridInfoCreate(
...     grid_type=GridType.undefined,
...     data_type=DataType.inst_val,
...     shape=(100, 100),
...     cell_size=1000.0,
...     data_units="M"
... )
>>> type(info).__name__
'GridInfo'

With extra fields:

>>> info = GridInfoCreate(
...     grid_type=GridType.hrap,
...     data_type=DataType.per_aver,
...     shape=(100, 150),
...     cell_size=4762.5,
...     data_units="MM",
...     custom_field="custom_value",  # Goes to extra dict
...     another_field=123
... )
>>> info.extra_info['custom_field']
'custom_value'

Notes

The factory uses Pydantic’s discriminated union feature to automatically select the correct class based on grid_type. This provides:

  • Better error messages for invalid fields

  • Type-specific validation

  • Clear separation of grid type concerns

Grid Type Mapping:

  • GridType.undefined or undefined_time → GridInfo

  • GridType.hrap or hrap_time → HrapInfo

  • GridType.albers or albers_time → AlbersInfo

  • GridType.specified or specified_time → SpecifiedInfo