Align wrt pathways

docs/transform.rst

@@ -123,7 +123,7 @@ These  datasets provide 1 kWh of battery capacity, and the technology
 shares are adjusted over time with values found
 under  https://github.com/polca/premise/blob/master/premise/data/battery/scenario.csv.
 
-.. _Degen: https://doi.org/10.1016/j.joule.2023.01.001
+.. _Degen: https://www.nature.com/articles/s41560-023-01355-z
 
 
 Stationary batteries

premise/clean_datasets.py

@@ -40,11 +40,10 @@ def remove_uncertainty(database):
 
     for dataset in database:
         for exchange in dataset["exchanges"]:
-            if not exchange.get("preserve uncertainty", False):
-                exchange["uncertainty type"] = 0
-                exchange["loc"] = exchange["amount"]
-                for key in uncertainty_keys:
-                    exchange[key] = nan_value
+            exchange["uncertainty type"] = 0
+            exchange["loc"] = exchange["amount"]
+            for key in uncertainty_keys:
+                exchange[key] = nan_value
 
     return database
 

premise/data/fuels/hydrogen_efficiency_parameters.yml

@@ -3,7 +3,7 @@ from electrolysis:
   var: hydrogen, from electrolysis
   feedstock name: electricity, low voltage
   feedstock unit: kilowatt hour
-  floor value: 48.0
+  floor value: 48.9
 from SMR of biogas, with CCS:
   name: hydrogen production, steam methane reforming, from biomethane, with CCS
   var: hydrogen, from biogas, with CCS

premise/external.py

@@ -3,6 +3,7 @@
 """
 
 import logging
+import math
 import uuid
 from collections import defaultdict
 from functools import lru_cache
@@ -1461,6 +1462,21 @@ def relink_to_new_datasets(
                 if isinstance(new_loc, str):
                     new_loc = [(new_loc, 1.0)]
 
+                def redefine_loc(e):
+                    if e.get("uncertainty type") in [0, 3, 4]:
+                        return e["amount"] * ratio * share
+
+                    elif e.get("uncertainty type") == 5:
+                        return e.get("loc", 0) * (
+                            e["amount"] * ratio * share / e["amount"]
+                        )
+
+                    elif e.get("uncertainty type") == 2:
+                        return math.log(e["amount"] * ratio * share)
+
+                    else:
+                        return None
+
                 if len(new_loc) > 0:
                     for loc, share in new_loc:
                         # add new exchange
@@ -1472,6 +1488,23 @@ def relink_to_new_datasets(
                                 "location": loc,
                                 "name": new_name,
                                 "product": new_ref,
+                                "uncertainty type": exc.get("uncertainty type", 0),
+                                "loc": redefine_loc(exc),
+                                "scale": (
+                                    exc.get("scale", 0) if "scale" in exc else None
+                                ),
+                                "minimum": (
+                                    exc.get("minimum", 0)
+                                    * (exc["amount"] * ratio * share / exc["amount"])
+                                    if "minimum" in exc
+                                    else None
+                                ),
+                                "maximum": (
+                                    exc.get("maximum", 0)
+                                    * (exc["amount"] * ratio * share / exc["amount"])
+                                    if "maximum" in exc
+                                    else None
+                                ),
                             }
                         )
 

premise/inventory_imports.py

@@ -277,6 +277,18 @@ def check_uncertainty_data(data, filename):
                             ]
                         )
 
+                if exc["uncertainty type"] == 2:
+                    if exc["amount"] < 0:
+                        if exc.get("negative") is not True:
+                            rows.append(
+                                [
+                                    dataset["name"][:30],
+                                    exc["name"][:30],
+                                    exc["uncertainty type"],
+                                    "'negative' should be TRUE",
+                                ]
+                            )
+
                 # if distribution is triangular, make sure that `minimum`
                 # and `maximum` are not equal and are comprising the `loc`
                 if exc["uncertainty type"] == 5: