Attribute

class silvimetric.resources.attribute.Attribute(name: str, dtype)

Bases: object

Represents point data from a PDAL execution that has been binned, and provides the information necessary to transfer that data to the database.

dtype: AttributeDtype

SilviMetric representation of array of numpy dtype

entry_name() → str

Return TileDB attribute name.

static from_dict(data: dict) → Attribute

Make an Attribute from a JSON like object

static from_string(data: str) → Attribute

Create Attribute from string or dict version of it.

Parameters:

data – Stringified or json object of attribute

Raises:

TypeError – Incorrect type of data, must be string or dict

Returns:

Return derived Attribute

make_array(data, copy=False)

Create Pandas Extension array for TileDB compatibility.

name

Name of the attribute, eg. Intensity.

schema() → Attr

Create the tiledb schema for this attribute. :return: TileDB attribute schema

to_json() → object

Metric

class silvimetric.resources.metric.Metric(name: str, dtype: dtype, method: Callable[[DataFrame, Any], DataFrame], dependencies: list[Self] | None = None, filters: List[Callable[[DataFrame, Any | None], DataFrame]] | None = None, attributes: List[Attribute] | None = None, nan_policy: Literal['propagate', 'raise'] = 'propagate')

Bases: object

A Metric is a TileDB entry representing derived cell data. There is a base set of metrics available through Silvimetric, or you can create your own. A Metric object has all the information necessary to facilitate the derivation of data as well as its insertion into the database.

add_filter(fn: Callable[[DataFrame, Any | None], DataFrame])

Add filter method to list of filters to run before calling main method.

do(data: DataFrame, *args) → DataFrame

Run metric and filters. Use previously run metrics to avoid running the same thing multiple times.

dtype

Numpy data type.

entry_name(attr: str) → str

Name for use in TileDB and extract file generation.

filters

List of user-defined filters to perform before performing method.

static from_dict(data: dict) → Metric

static from_string(data: str) → Self

name

Metric name. eg. mean

nan_policy

Describe how metric should handle NaN found in dependencies.

• propagate: if a NaN is present in the dependencies, return NaN for

this metric as well. - raise: if a NaN is present, a ValueError will be raised.

nan_value

Value to denote empty space or invalid values.

run_filters(data: DataFrame) → DataFrame

sanitize_and_run(d, locs, args)

Sanitize arguments, find the indices

schema(attr: Attribute) → Any

Create schema for TileDB creation.

Parameters:

attr – silvimetric.resources.entry.Atttribute

Returns:

TileDB Attribute

to_json() → dict[str, any]