add starting datetime argument to build_timeline

bw_timex/timeline_builder.py

@@ -28,6 +28,7 @@ class TimelineBuilder:
     def __init__(
         self,
         slca: LCA,
+        starting_datetime: datetime,
         edge_filter_function: Callable,
         database_date_dict: dict,
         database_date_dict_static_only: dict,
@@ -45,6 +46,8 @@ def __init__(
         ----------
         slca: LCA
             A static LCA object.
+        starting_datetime: datetime | str, optional
+            Point in time when the demand occurs.
         edge_filter_function: Callable
             A callable that filters edges. If not provided, a function that always returns False is used.
         database_date_dict: dict
@@ -65,6 +68,7 @@ def __init__(
             Keyword arguments passed to the EdgeExtractor which inherits from TemporalisLCA.
         """
         self.slca = slca
+        self.starting_datetime = starting_datetime
         self.edge_filter_function = edge_filter_function
         self.database_date_dict = database_date_dict
         self.database_date_dict_static_only = database_date_dict_static_only
@@ -90,6 +94,7 @@ def __init__(
 
         self.edge_extractor = EdgeExtractor(
             slca,
+            starting_datetime=self.starting_datetime,
             *args,
             edge_filter_function=edge_filter_function,
             cutoff=self.cutoff,

bw_timex/timex_lca.py

@@ -163,6 +163,7 @@ def __init__(
 
     def build_timeline(
         self,
+        starting_datetime: datetime | str = "now",
         temporal_grouping: str = "year",
         interpolation_type: str = "linear",
         edge_filter_function: Callable = None,
@@ -178,6 +179,9 @@ def build_timeline(
 
         Parameters
         ----------
+        starting_datetime: datetime | str, optional
+            Point in time when the demand occurs. This is the initial starting point of the 
+            timeline. Something like `"now"` or `"2023-01-01"`. Default is `"now"`.
         temporal_grouping : str, optional
             Time resolution for grouping exchanges over time in the timeline. Default is 'year',
             other options are 'month', 'day', 'hour'.
@@ -223,6 +227,7 @@ def build_timeline(
         else:
             self.edge_filter_function = edge_filter_function
 
+        self.starting_datetime = starting_datetime
         self.temporal_grouping = temporal_grouping
         self.interpolation_type = interpolation_type
         self.cutoff = cutoff
@@ -237,6 +242,7 @@ def build_timeline(
         # with the TimelineBuilder.build_timeline() method.
         self.timeline_builder = TimelineBuilder(
             self.base_lca,
+            self.starting_datetime,
             self.edge_filter_function,
             self.database_date_dict,
             self.database_date_dict_static_only,

tests/test_starting_datetime.py

@@ -0,0 +1,117 @@
+import math
+from datetime import datetime
+
+import bw2data as bd
+import pytest
+from bw2data.tests import bw2test
+
+from bw_timex import TimexLCA
+
+@pytest.fixture
+@bw2test
+def ab_db():
+    bd.Database("bio").write(
+        {
+            ("bio", "CO2"): {
+                "type": "biosphere",
+                "name": "carbon dioxide",
+            },
+        },
+    )
+
+    bd.Database("db_2030").write(
+        {
+            ("db_2030", "B"): {
+                "name": "B",
+                "location": "somewhere",
+                "reference product": "B",
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": ("db_2030", "B"),
+                    },
+                    {
+                        "amount": 10,
+                        "input": ("bio", "CO2"),
+                        "type": "biosphere",
+                    },
+                ],
+            },
+        },
+    )
+
+    bd.Database("db_2020").write(
+        {
+            ("db_2020", "B"): {
+                "name": "B",
+                "location": "somewhere",
+                "reference product": "B",
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": ("db_2020", "B"),
+                    },
+                    {
+                        "amount": 15,
+                        "input": ("bio", "CO2"),
+                        "type": "biosphere",
+                    },
+                ],
+            },
+        },
+    )
+
+    bd.Database("foreground").write(
+        {
+            ("foreground", "A"): {
+                "name": "A",
+                "location": "somewhere",
+                "reference product": "A",
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": ("foreground", "A"),
+                    },
+                    {
+                        "amount": 1,
+                        "input": ("db_2020", "B"),
+                        "type": "technosphere",
+                    },
+                ],
+            },
+        }
+    )
+
+    bd.Method(("GWP", "example")).write(
+        [
+            (("bio", "CO2"), 1),
+        ]
+    )
+
+def test_starting_datetime(ab_db):
+    method = ("GWP", "example")
+    database_date_dict = {
+        "db_2020": datetime.strptime("2020", "%Y"),
+        "db_2030": datetime.strptime("2030", "%Y"),
+        "foreground": "dynamic",
+    }
+    fu = ("foreground", "A")
+    tlca = TimexLCA({fu: 1}, method, database_date_dict)
+
+    tlca.build_timeline(starting_datetime="2020-01-01")
+    tlca.lci()
+    tlca.static_lcia()
+    assert math.isclose(tlca.static_score, 15, rel_tol=1e-9)
+
+    tlca.build_timeline(starting_datetime="2030-01-01")
+    tlca.lci()
+    tlca.static_lcia()
+    assert math.isclose(tlca.static_score, 10, rel_tol=1e-9)
+
+    tlca.build_timeline(starting_datetime="2025-01-01")
+    tlca.lci()
+    tlca.static_lcia()
+    assert math.isclose(tlca.static_score, 12.5, rel_tol=1e-3)