Merge branch 'docs_getting_started'

bw_timex/timex_lca.py

@@ -1154,6 +1154,39 @@ def create_labelled_dynamic_biosphere_dataframe(self) -> pd.DataFrame:
 
         return df
 
+    def create_labelled_dynamic_inventory_dataframe(self) -> pd.DataFrame:
+        """
+        Returns the dynamic_inventory_df with comprehensible labels for flows and activities instead of ids.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        pd.DataFrame, dynamic inventory matrix as a pandas.DataFrame with comprehensible labels instead of ids.
+        """
+
+        if not hasattr(self, "dynamic_inventory_df"):
+            warnings.warn(
+                "Dynamic inventory not yet calculated. Call TimexLCA.lci(build_dynamic_biosphere=True) first."
+            )
+
+        df = self.dynamic_inventory_df.copy()
+        df["flow"] = df["flow"].apply(lambda x: bd.get_node(id=x)["name"])
+
+        activity_name_cache = {}
+
+        for activity in df["activity"].unique():
+            if activity not in activity_name_cache:
+                activity_name_cache[activity] = resolve_temporalized_node_name(
+                    self.activity_time_mapping_dict_reversed[activity][0][1]
+                )
+
+        df["activity"] = df["activity"].map(activity_name_cache)
+
+        return df
+
     def plot_dynamic_inventory(self, bio_flows, cumulative=False) -> None:
         """
         Simple plot of dynamic inventory of a biosphere flow over time, with optional cumulative plotting.

bw_timex/utils.py

@@ -306,3 +306,34 @@ def plot_characterized_inventory_as_waterfall(
     ax.set_axisbelow(True)
     plt.grid(True)
     plt.show()
+
+def get_exchange(**kwargs) -> Exchange:
+    """
+    Get an exchange from the database.
+    """
+    mapping = {
+        "input_code": ExchangeDataset.input_code,
+        "input_database": ExchangeDataset.input_database,
+        "output_code": ExchangeDataset.output_code,
+        "output_database": ExchangeDataset.output_database,
+    }
+    qs = ExchangeDataset.select()
+    for key, value in kwargs.items():
+        try:
+            qs = qs.where(mapping[key] == value)
+        except KeyError:
+            continue
+
+    candidates = [Exchange(obj) for obj in qs]
+    if len(candidates) > 1:
+        raise MultipleResults(
+            "Found {} results for the given search. Please be more specific or double-check your system model for duplicates.".format(len(candidates))
+        )
+    elif not candidates:
+        raise UnknownObject
+    return candidates[0]
+
+def add_temporal_distribution_to_exchange(temporal_distribution: TemporalDistribution, **kwargs):
+    exchange = get_exchange(**kwargs)
+    exchange["temporal_distribution"] = temporal_distribution
+    exchange.save()