bw_temporalis.lca
=================

.. py:module:: bw_temporalis.lca


Exceptions
----------

.. autoapisummary::

   bw_temporalis.lca.MultipleTechnosphereExchanges


Classes
-------

.. autoapisummary::

   bw_temporalis.lca.NoExchange
   bw_temporalis.lca.TemporalisLCA


Module Contents
---------------

.. py:exception:: MultipleTechnosphereExchanges

   Bases: :py:obj:`Exception`


   Common base class for all non-exit exceptions.

   Initialize self.  See help(type(self)) for accurate signature.


.. py:class:: NoExchange

   The edge was created dynamically via a datapackage. There is no edge in the database.


.. py:class:: TemporalisLCA(lca_object: bw2calc.LCA, starting_datetime: datetime.datetime | str = 'now', cutoff: float | None = 0.0005, biosphere_cutoff: float | None = 1e-06, max_calc: int | None = 2000, static_activity_indices: set[int] | None = None, skip_coproducts: bool | None = False, functional_unit_unique_id: int | None = -1, graph_traversal: bw_graph_tools.NewNodeEachVisitGraphTraversal | None = NewNodeEachVisitGraphTraversal)

   Calculate an LCA using graph traversal, with edges using temporal distributions.

   Edges with temporal distributions should store this information using `"temporal_distributions"`:

   ```python
       exchange["temporal_distribution"] = bw_temporalis.TemporalDistribution(
           times=numpy.array([-2, -1, 0, 1, 2], dtype="timedelta64[s]"),
           values=numpy.ones(5)
       )
   ```

   Temporal distribution times must always have the data type `timedelta64[s]`. Not all edges need to have temporal distributions.

   Temporal distributions are **not density functions** - their values should sum to the exchange amount.

   As graph traversal is much slower than matrix calculations, we can limit which nodes get traversed in several ways:

   * All activities in a database marked as `static`
   * Any activity ids passed in `static_activity_indices`
   * Any activities whose cumulative impact is below the cutoff score

   The output of a Temporalis LCA calculation is a `bw_temporalis.Timeline`, which can be characterized.

   :param lca_object: The already instantiated and calculated LCA class (i.e. `.lci()` and `.lcia()` have already been done)
   :type lca_object: bw2calc.LCA
   :param starting_datetime: When the functional unit happens. Must be a point in time. Normally something like `"now"` or `"2023-01-01"`.
   :type starting_datetime: datetime.datetime | str
   :param cutoff: The fraction of the total score below which graph traversal should stop. In range `(0, 1)`.
   :type cutoff: float
   :param biosphere_cutoff: The fraction of the total score below which we don't include separate biosphere nodes to be characterized in the `Timeline`. In range `(0, 1)`.
   :type biosphere_cutoff: float
   :param max_calc: Total number of LCA inventory calculations to perform during graph traversal
   :type max_calc: int
   :param static_activity_indices: Activity database node `id` values where graph traversal will stop
   :type static_activity_indices: set[int]
   :param skip_coproducts: Should we also traverse edges for the other products in multioutput activities?
   :type skip_coproducts: bool
   :param functional_unit_unique_id: The unique id of the functional unit. Strongly recommended to leave as default.
   :type functional_unit_unique_id: int
   :param graph_traversal: Optional subclass of `NewNodeEachVisitGraphTraversal` for advanced usage
   :type graph_traversal: bw_graph_tools.NewNodeEachVisitGraphTraversal


   .. py:method:: _exchange_iterator(input_id: int, output_id: int) -> list[bw2data.backends.ExchangeDataset]


   .. py:method:: _exchange_value(exchange: Union[bw2data.backends.ExchangeDataset, NoExchange], row_id: int, col_id: int, matrix_label: str) -> Union[float, bw_temporalis.temporal_distribution.TemporalDistribution]


   .. py:method:: build_timeline(node_timeline: bool | None = False) -> bw_temporalis.timeline.Timeline


   .. py:method:: get_biosphere_exchanges(flow_id: int, activity_id: int) -> collections.abc.Iterable[bw2data.backends.ExchangeDataset]


   .. py:method:: get_technosphere_exchange(input_id: int, output_id: int) -> bw2data.backends.ExchangeDataset


   .. py:attribute:: edge_mapping


   .. py:attribute:: edges


   .. py:attribute:: flow_mapping


   .. py:attribute:: flows


   .. py:attribute:: lca_object


   .. py:attribute:: nodes


   .. py:attribute:: t0


   .. py:attribute:: unique_id
      :value: -1