Fix marginal modelling when all shares are NanS

polca · Sep 11, 2023 · a3b9067 · a3b9067
1 parent d0b97c6
commit a3b9067
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Showing 3 changed files with 7 additions and 2 deletions.
diff --git a/premise/__init__.py b/premise/__init__.py
@@ -1,5 +1,5 @@
 __all__ = ("NewDatabase", "clear_cache", "get_regions_definition")
-__version__ = (1, 6, 7)
+__version__ = (1, 6, 8)
 
 from pathlib import Path
 

diff --git a/premise/marginal_mixes.py b/premise/marginal_mixes.py
@@ -197,6 +197,10 @@ def consequential_method(data: xr.DataArray, year: int, args: dict) -> xr.DataAr
             region=region, year=year
         ).sum(dim="variables")
 
+        # if shares contains only NaNs, we give its elements the value 1
+        if shares.isnull().all():
+            shares = xr.ones_like(shares)
+
         time_parameters = {
             (False, False, False, False): {
                 "start": year,
@@ -288,6 +292,7 @@ def consequential_method(data: xr.DataArray, year: int, args: dict) -> xr.DataAr
                 (bool(range_time), bool(duration), foresight, lead_time)
             ]["end_avg"]
 
+
         except KeyError:
             print(
                 f"The combination of range_time, duration, foresight, and lead_time {range_time, duration, foresight, lead_time} "

diff --git a/setup.py b/setup.py
@@ -32,7 +32,7 @@ def package_files(directory):
 
 setup(
     name="premise",
-    version="1.6.7",
+    version="1.6.8",
     python_requires=">=3.9,<3.11",
     packages=packages,
     author="Romain Sacchi <[email protected]>, Alois Dirnaichner <[email protected]>, Chris Mutel "