Adapt codebase for compatibility with NumPy >= 2.0

docs/tutorial-code/7-min-max-timeslice-constraints/visualise-min-service.ipynb

@@ -83,26 +83,11 @@
    "outputs": [],
    "source": [
     "grouped_by = frame.groupby([\"year\", \"technology\", \"region\"]).sum().reset_index()\n",
-    "grouped_by\n",
     "\n",
     "g = sns.FacetGrid(data=grouped_by, row=\"region\")\n",
     "g.map(sns.lineplot, \"year\", \"capacity\", \"technology\")\n",
     "g.add_legend()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

pyproject.toml

@@ -24,7 +24,7 @@ classifiers = [
     "License :: OSI Approved :: GNU General Public License v3 (GPLv3)"
 ]
 dependencies = [
-    "numpy<2.0",
+    "numpy>=2.0",
     "scipy>=1.13",
     "pandas>=2.2",
     "xarray>=2024.6",
@@ -47,7 +47,6 @@ dev = [
     "nbconvert",
     "nbformat",
     "mypy",
-    "numpy>=1.17",
     "pytest-xdist",
     "bump2version",
     "pyinstaller",

src/muse/mca.py

@@ -372,7 +372,7 @@ def calibrate_legacy_sectors(self):
         from copy import deepcopy
         from logging import getLogger
 
-        from numpy import clip, where
+        from numpy import where
 
         hist_years = []
         if len([s for s in self.sectors if "LegacySector" in str(type(s))]) == 0:
@@ -405,12 +405,9 @@ def calibrate_legacy_sectors(self):
 
                 dims = {i: sector_market[i] for i in sector_market.consumption.dims}
 
-                sector_market.consumption.loc[dims] = clip(
-                    sector_market.consumption.loc[dims]
-                    - sector_market.supply.loc[dims],
-                    0.0,
-                    None,
-                )
+                sector_market.consumption.loc[dims] = (
+                    sector_market.consumption.loc[dims] - sector_market.supply.loc[dims]
+                ).clip(min=0.0, max=None)
                 new_market.consumption.loc[dims] += sector_market.consumption
 
                 dims = {i: sector_market[i] for i in sector_market.supply.dims}
@@ -450,8 +447,6 @@ def single_year_iteration(
     """
     from copy import deepcopy
 
-    from numpy import clip
-
     from muse.commodities import is_enduse
 
     sectors = deepcopy(sectors)
@@ -469,11 +464,9 @@ def single_year_iteration(
 
         dims = {i: sector_market[i] for i in sector_market.consumption.dims}
 
-        sector_market.consumption.loc[dims] = clip(
-            sector_market.consumption.loc[dims] - sector_market.supply.loc[dims],
-            0.0,
-            None,
-        )
+        sector_market.consumption.loc[dims] = (
+            sector_market.consumption.loc[dims] - sector_market.supply.loc[dims]
+        ).clip(min=0.0, max=None)
 
         market.consumption.loc[dims] += sector_market.consumption
 

tests/test_carbon_budget.py

@@ -155,7 +155,10 @@ def test_refine_new_price(market):
     price = market.prices.sel(year=future, commodity=commodities).mean(
         ["region", "commodity"]
     )
-    sample = np.linspace(price, 4 * price, 4)
+
+    # ensure price is a scalar before using it in np.linspace
+    price_value = price.item() if isinstance(price, xr.DataArray) else price
+    sample = np.linspace(price_value, 4 * price_value, 4)
 
     carbon_price = market.prices.sel(
         year=market.year < future, commodity=commodities