diff --git a/dev/generate datapackages.ipynb b/dev/generate datapackages.ipynb
index 9b7498a..4c5c735 100644
--- a/dev/generate datapackages.ipynb	
+++ b/dev/generate datapackages.ipynb	
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a081c764-a942-4627-9197-fcd373e98b62",
    "metadata": {},
    "outputs": [
@@ -236,55 +236,199 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "0% [#                         ] 100% | ETA: 00:00:01"
+      "0% [#                         ] 100% | ETA: 00:00:01/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [###                       ] 100% | ETA: 00:02:10/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[{35812: <xarray.DataArray 'value' ()> Size: 8B\n",
-      "array(6.18299776e+11)\n",
-      "Coordinates:\n",
-      "    pathway    <U10 40B 'SSP2-RCP19'\n",
-      "    region     <U3 12B 'BRA'\n",
-      "    year       int64 8B 2005\n",
-      "    model      <U5 20B 'image'\n",
-      "    variables  <U49 196B 'Industry_Food and Tobacco_Solid biomass'}, {35812: <xarray.DataArray 'value' ()> Size: 8B\n",
-      "array(6.18299776e+11)\n",
-      "Coordinates:\n",
-      "    pathway    <U10 40B 'SSP2-RCP19'\n",
-      "    region     <U3 12B 'BRA'\n",
-      "    year       int64 8B 2005\n",
-      "    model      <U5 20B 'image'\n",
-      "    variables  <U49 196B 'Industry_Food processing_Solid biomass'}, {36217: <xarray.DataArray 'value' ()> Size: 8B\n",
-      "array(5.26656812e+11)\n",
-      "Coordinates:\n",
-      "    pathway    <U10 40B 'SSP2-RCP19'\n",
-      "    region     <U3 12B 'BRA'\n",
-      "    year       int64 8B 2005\n",
-      "    model      <U5 20B 'image'\n",
-      "    variables  <U49 196B 'Transport_Freight (heavy truck)_Liquid fossil'}, {36190: <xarray.DataArray 'value' ()> Size: 8B\n",
-      "array(1.15323356e+12)\n",
-      "Coordinates:\n",
-      "    pathway    <U10 40B 'SSP2-RCP19'\n",
-      "    region     <U3 12B 'BRA'\n",
-      "    year       int64 8B 2005\n",
-      "    model      <U5 20B 'image'\n",
-      "    variables  <U49 196B 'Transport_Passenger_Liquid'}]\n"
+      "0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
      ]
     },
     {
-     "ename": "TypeError",
-     "evalue": "MultiLCA.__init__() got an unexpected keyword argument 'demands'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m dp \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethods\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m#'EF v3.1 - climate change - global warming potential (GWP100)',\u001b[39;49;00m\n\u001b[1;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRELICS - metals extraction - Lithium\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscenarios\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mregion\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[38;5;241;43m!=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWorld\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#regions=[\"WEU\",],\u001b[39;49;00m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscenarios\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSSP2-RCP19\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscenarios\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariables\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43many\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIndustry\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTransport\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHeating\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#years=[2005, 2020, 2050, 2070, 2100],\u001b[39;49;00m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcharacterization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#flows=[\"Lithium - natural resource - in ground - kilogram\",],\u001b[39;49;00m\n\u001b[1;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#data_type=np.float32,\u001b[39;49;00m\n\u001b[1;32m     18\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdemand_cutoff\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:742\u001b[0m, in \u001b[0;36mPathways.calculate\u001b[0;34m(self, methods, models, scenarios, regions, years, variables, characterization, flows, multiprocessing, data_type, demand_cutoff)\u001b[0m\n\u001b[1;32m    739\u001b[0m         bar\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m    740\u001b[0m         \u001b[38;5;66;03m# Iterate over each region\u001b[39;00m\n\u001b[1;32m    741\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 742\u001b[0m             \u001b[43mprocess_region\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    743\u001b[0m \u001b[43m                \u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    744\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    745\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m                    \u001b[49m\u001b[43myear\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mregion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mvars_info\u001b[49m\u001b[43m[\u001b[49m\u001b[43mregion\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscenarios\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    752\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    753\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mdp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    754\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mA_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    755\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mB_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    756\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreverse_classifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    757\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlca_results\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    758\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mflows\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    759\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mdemand_cutoff\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    760\u001b[0m \u001b[43m                \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    761\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    762\u001b[0m         )\n\u001b[1;32m    764\u001b[0m \u001b[38;5;66;03m# Store results in the LCA results xarray\u001b[39;00m\n\u001b[1;32m    765\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n",
-      "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:303\u001b[0m, in \u001b[0;36mprocess_region\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    295\u001b[0m     FU\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m    296\u001b[0m         {\n\u001b[1;32m    297\u001b[0m             idx: demand \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mfloat\u001b[39m(unit_vector),\n\u001b[1;32m    298\u001b[0m         }\n\u001b[1;32m    299\u001b[0m     )\n\u001b[1;32m    301\u001b[0m \u001b[38;5;28mprint\u001b[39m(FU)\n\u001b[0;32m--> 303\u001b[0m lca \u001b[38;5;241m=\u001b[39m \u001b[43mbc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMultiLCA\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdemands\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFU\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_objs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mdp\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    307\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    308\u001b[0m lca\u001b[38;5;241m.\u001b[39mlci()\n\u001b[1;32m    309\u001b[0m lca\u001b[38;5;241m.\u001b[39mlcia()\n",
-      "\u001b[0;31mTypeError\u001b[0m: MultiLCA.__init__() got an unexpected keyword argument 'demands'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [####                      ] 100% | ETA: 00:02:07/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [######                    ] 100% | ETA: 00:02:12/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [########                  ] 100% | ETA: 00:02:05/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [#########                 ] 100% | ETA: 00:01:57/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0% [###########               ] 100% | ETA: 00:01:55/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n"
      ]
     }
    ],
@@ -292,8 +436,8 @@
     "import numpy as np\n",
     "dp = p.calculate(\n",
     "    methods=[\n",
-    "        #'EF v3.1 - climate change - global warming potential (GWP100)',\n",
-    "        'RELICS - metals extraction - Lithium',\n",
+    "        'EF v3.1 - climate change - global warming potential (GWP100)',\n",
+    "        #'RELICS - metals extraction - Lithium',\n",
     "    ],\n",
     "    regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
     "    #regions=[\"WEU\",],\n",
@@ -313,32 +457,173 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "c9786349-3b91-4439-8494-208ba7bc3b4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bw2calc as bc\n",
+    "import bw_processing as bwp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "dedf1bb9-47a2-497c-b788-7fa64986827a",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "20.305023566997583"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lca = bc.LCA(\n",
+    "    demand={1329: 1},\n",
+    "    data_objs=[dp],\n",
+    ")\n",
+    "lca.lci()\n",
+    "lca.lcia()\n",
+    "lca.score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f501423c-de64-4604-9409-2a40c77c32bb",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0mdp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdehydrated_interfaces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mSource:\u001b[0m   \n",
-       "    \u001b[0;32mdef\u001b[0m \u001b[0mdehydrated_interfaces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Return a list of the resource groups which have dehydrated interfaces\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m            \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"group\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresources\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefinedInterface\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      /opt/homebrew/Caskroom/miniforge/base/envs/pathways/lib/python3.11/site-packages/bw_processing/datapackage.py\n",
-       "\u001b[0;31mType:\u001b[0m      method"
+       "[array([(    0,     0), (    0, 11778), (    0, 17244), ...,\n",
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+       " array([1.      , 0.012393, 0.002134, ..., 0.0025  , 0.00165 , 1.      ]),\n",
+       " array([False,  True,  True, ...,  True,  True,  True]),\n",
+       " array([(4461,     0), (4538,     0), (  92,     0), ..., (4681, 36486),\n",
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+       "       dtype=[('row', '<i4'), ('col', '<i4')]),\n",
+       " array([6.67590000e-04, 8.51835000e-04, 7.53360000e-04, ...,\n",
+       "        9.28059118e-07, 2.32558140e-08, 1.30841121e-04]),\n",
+       " array([(2687, 2687), (1572, 1572), (4590, 4590), (4658, 4658),\n",
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+       "        (4531, 4531), (3448, 3448), (2401, 2401), ( 789,  789),\n",
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+       "        (1532, 1532), (2394, 2394), (2180, 2180), ( 630,  630),\n",
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+       "        (3379, 3379), ( 880,  880), (3365, 3365)],\n",
+       "       dtype=[('row', '<i4'), ('col', '<i4')]),\n",
+       " array([ 5.20e-02,  6.00e-03,  6.00e-03,  6.00e-03,  6.00e-03,  6.00e-03,\n",
+       "         1.00e+00,  1.00e+00,  1.00e+00,  1.00e+00,  1.00e+00,  1.00e+00,\n",
+       "         1.00e+00,  1.00e+00,  1.00e+00,  1.00e+00, -1.00e+00, -1.00e+00,\n",
+       "        -1.00e+00, -1.00e+00,  2.06e+01,  2.06e+01,  2.06e+01,  2.06e+01,\n",
+       "         2.06e+01,  2.73e+02,  2.73e+02,  2.73e+02,  2.73e+02,  2.73e+02,\n",
+       "         4.37e-01,  4.37e-01,  4.37e-01,  4.37e-01,  4.37e-01,  1.53e+03,\n",
+       "         1.53e+03,  1.53e+03,  1.53e+03,  1.53e+03,  1.61e+02,  1.61e+02,\n",
+       "         1.61e+02,  1.61e+02,  1.61e+02,  5.81e+03,  5.81e+03,  5.81e+03,\n",
+       "         5.81e+03,  5.81e+03,  6.52e+03,  6.52e+03,  6.52e+03,  6.52e+03,\n",
+       "         6.52e+03,  8.60e+02,  8.60e+02,  8.60e+02,  8.60e+02,  8.60e+02,\n",
+       "         1.64e+02,  1.64e+02,  1.64e+02,  1.64e+02,  1.64e+02,  1.30e+00,\n",
+       "         1.30e+00,  1.30e+00,  1.30e+00,  1.30e+00,  9.43e+03,  9.43e+03,\n",
+       "         9.43e+03,  9.43e+03,  9.43e+03,  2.30e+03,  2.30e+03,  2.30e+03,\n",
+       "         2.30e+03,  2.30e+03,  9.04e+01,  9.04e+01,  9.04e+01,  9.04e+01,\n",
+       "         9.04e+01,  5.97e+02,  5.97e+02,  5.97e+02,  5.97e+02,  5.97e+02,\n",
+       "         9.60e+03,  9.60e+03,  9.60e+03,  9.60e+03,  9.60e+03,  1.24e+04,\n",
+       "         1.24e+04,  1.24e+04,  1.24e+04,  1.24e+04,  3.74e+03,  3.74e+03,\n",
+       "         3.74e+03,  3.74e+03,  3.74e+03,  2.43e+00,  2.43e+00,  2.43e+00,\n",
+       "         2.43e+00,  2.43e+00,  1.93e+03,  1.93e+03,  1.93e+03,  1.93e+03,\n",
+       "         1.93e+03,  7.20e+03,  7.20e+03,  7.20e+03,  7.20e+03,  7.20e+03,\n",
+       "         7.94e+01,  7.94e+01,  7.94e+01,  7.94e+01,  1.96e+03,  1.96e+03,\n",
+       "         1.96e+03,  1.96e+03,  1.96e+03,  1.62e+04,  1.62e+04,  1.62e+04,\n",
+       "         1.62e+04,  1.62e+04,  1.12e+01,  1.12e+01,  1.12e+01,  1.12e+01,\n",
+       "         1.12e+01,  1.25e+04,  1.25e+04,  1.25e+04,  1.25e+04,  1.25e+04,\n",
+       "         1.60e+02,  1.60e+02,  1.60e+02,  1.60e+02,  1.60e+02,  7.71e+02,\n",
+       "         7.71e+02,  7.71e+02,  7.71e+02,  7.71e+02,  2.98e+01,  2.98e+01,\n",
+       "         2.98e+01,  2.98e+01,  2.98e+01,  2.98e+01,  2.98e+01,  2.98e+01,\n",
+       "         2.98e+01,  2.98e+01,  5.54e+00,  5.54e+00,  5.54e+00,  5.54e+00,\n",
+       "         5.54e+00,  2.70e+01,  2.70e+01,  2.70e+01,  2.70e+01,  2.70e+01,\n",
+       "         2.20e+03,  2.20e+03,  2.20e+03,  2.20e+03,  2.20e+03,  7.38e+03,\n",
+       "         7.38e+03,  7.38e+03,  7.38e+03,  7.38e+03,  6.23e+03,  6.23e+03,\n",
+       "         6.23e+03,  6.23e+03,  6.23e+03,  1.46e+04,  1.46e+04,  1.46e+04,\n",
+       "         1.46e+04,  1.46e+04,  4.81e-01,  4.81e-01,  4.81e-01,  4.81e-01,\n",
+       "         1.74e+04,  1.74e+04,  1.74e+04,  1.74e+04,  1.74e+04,  2.00e-02,\n",
+       "         2.00e-02,  2.00e-02,  2.00e-02,  2.00e-02,  2.52e+04,  2.52e+04,\n",
+       "         2.52e+04,  2.52e+04,  2.52e+04,  6.34e+00,  6.34e+00,  6.34e+00,\n",
+       "         6.34e+00,  6.34e+00,  4.40e-02,  4.40e-02,  4.40e-02,  4.40e-02,\n",
+       "         4.40e-02])]"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "dp.dehydrated_interfaces??"
+    "dp.data"
    ]
   },
   {
diff --git a/pathways/lca.py b/pathways/lca.py
index 51c5326..79086c6 100644
--- a/pathways/lca.py
+++ b/pathways/lca.py
@@ -99,7 +99,7 @@ def load_matrix_and_index(
 
     # give `indices_array` a list of tuples of indices
     indices_array = np.array(
-        list(zip(coords[:, 0].astype(int), coords[:, 1].astype(int))),
+        list(zip(coords[:, 1].astype(int), coords[:, 0].astype(int))),
         dtype=bwp.INDICES_DTYPE,
     )
 
@@ -144,7 +144,7 @@ def get_lca_matrices(
         dirpath / "A_matrix.csv",
     )
 
-    b_data, b_indices, b_sign = load_matrix_and_index(
+    b_data, b_indices, _ = load_matrix_and_index(
         dirpath / "B_matrix.csv",
     )
 
@@ -158,29 +158,17 @@ def get_lca_matrices(
         matrix='biosphere_matrix',
         indices_array=b_indices,
         data_array=b_data,
-        flip_array=b_sign,
     )
 
-    # c_data, c_indices, c_sign = fill_characterization_factors_matrix(
-    #     B_inds, methods
-    # )
-    #
-    #
-    # # if c_data is multidimensional
-    # if len(c_data.shape) > 1:
-    #     dp.add_persistent_array(
-    #         matrix='characterization_matrix',
-    #         indices_array=c_indices,
-    #         data_array=c_data,
-    #         flip_array=c_sign,
-    #     )
-    # else:
-    #     dp.add_persistent_vector(
-    #         matrix='characterization_matrix',
-    #         indices_array=c_indices,
-    #         data_array=c_data,
-    #         flip_array=c_sign,
-    #     )
+    c_data, c_indices = fill_characterization_factors_matrix(
+         B_inds, methods
+    )
+
+    dp.add_persistent_vector(
+        matrix='characterization_matrix',
+        indices_array=c_indices,
+        data_array=c_data,
+    )
 
     return dp, A_inds, B_inds
 
@@ -188,7 +176,7 @@ def get_lca_matrices(
 def fill_characterization_factors_matrix(
         biosphere_flows: dict,
         methods
-) -> np.ndarray:
+) -> Tuple[np.ndarray, np.ndarray]:
     """
     Create a characterization matrix based on the list of biosphere flows
     given.
@@ -199,24 +187,24 @@ def fill_characterization_factors_matrix(
 
     lcia_data = get_lcia_methods(methods=methods)
 
-    print(lcia_data)
-
-
-    # create a numpy array filled with zeros
-    # of size equal to biosphere_flows and lcia methods
+    biosphere_flows = {
+        k[:3]:v for k, v in biosphere_flows.items()
+    }
 
-    cf_matrix = np.zeros((len(biosphere_flows), len(methods)))
+    list_flows = []
+    list_cf = []
 
-    # fill the matrix
-    for i, flow in enumerate(biosphere_flows):
-        for j, method in enumerate(methods):
-            try:
-                cf_matrix[i, j] = lcia_data[method][flow[:3]]
-            except KeyError:
-                continue
+    for method, flows in lcia_data.items():
+        for flow, cf in flows.items():
+            flow_idx = int(biosphere_flows.get(flow))
+            if flow_idx:
+                list_flows.append((flow_idx, flow_idx))
+                list_cf.append(cf)
 
-    return cf_matrix
+    data = np.array(list_cf)
+    indices = np.array(list_flows, dtype=bwp.INDICES_DTYPE)
 
+    return data, indices
 def remove_double_counting(A: csr_matrix, vars_info: dict) -> csr_matrix:
     """
     Remove double counting from a technosphere matrix.
diff --git a/pathways/pathways.py b/pathways/pathways.py
index 41c16d5..13d3f41 100644
--- a/pathways/pathways.py
+++ b/pathways/pathways.py
@@ -239,15 +239,15 @@ def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
     act_categories = lca_results_coords["act_category"].values
 
     # if lcia_matrix is not None:
-    #     impact_categories = lca_results_coords["impact_category"].values
-    #     target = np.zeros(
-    #         (
-    #             len(act_categories),
-    #             len(list(vars_idx)),
-    #             len(locations),
-    #             len(impact_categories),
-    #         )
-    #     )
+    impact_categories = lca_results_coords["impact_category"].values
+    target = np.zeros(
+        (
+           len(act_categories),
+           len(list(vars_idx)),
+           len(locations),
+           len(impact_categories),
+        )
+    )
     # else:
     #     if flows is not None:
     #         target = np.zeros(
@@ -260,6 +260,7 @@ def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
 
     FU = []
 
+    vars_list = []
     for v, variable in enumerate(variables):
         idx, dataset = vars_idx[variable]["idx"], vars_idx[variable]["dataset"]
 
@@ -298,57 +299,42 @@ def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
             }
         )
 
-    print(FU)
+        vars_list.append(v)
 
-    lca = bc.MultiLCA(
-        demands=FU,
-        method_config=dp,
+    lca = bc.LCA(
+        demand=FU[0],
         data_objs=[dp],
     )
     lca.lci()
-    lca.lcia()
-    lca.score
 
+    # Populate the result array
+    act_locs = [a[-1] for a in rev_A_index.values()]
 
+    # Initialize the new array with zeros for missing data
+    E = np.zeros((len(A_index), len(locations), len(impact_categories)))
 
-        #
-        #
-        # # Create the demand vector
-        # f = create_demand_vector([idx], A, demand, unit_vector)
-        #
-        # # Solve the inventory
-        # C = solve_inventory(A, B, f)
-        #
-        # if lcia_matrix is not None:
-        #     if lcia_matrix.ndim != 2 or lcia_matrix.shape[0] != B.shape[1]:
-        #         raise ValueError("Incompatible dimensions between B and lcia_matrix")
-        #
-        #     # Solve the LCA problem to get the LCIA scores
-        #     D = characterize_inventory(C, lcia_matrix)
-        #
-        #     # Sum along the first axis of D to get final result
-        #     D = D.sum(axis=1)
-        #
-        #     # Initialize the new array with zeros for missing data
-        #     E = np.zeros((len(A_index), len(locations), len(impact_categories)))
-        #
-        #     # Populate the result array
-        #     act_locs = [a[-1] for a in rev_A_index.values()]
-        #
-        #     for i, act in enumerate(rev_A_index.values()):
-        #         if act[-1] in act_locs:
-        #             loc_idx = location_to_index[act[-1]]
-        #             E[i, loc_idx, :] = D[i, :]
-        #
-        #     acts_idx = generate_A_indices(
-        #         A_index,
-        #         reverse_classifications,
-        #         lca_results_coords,
-        #     )
-        #
-        #     # Sum over the first axis of D,
-        #     # using acts_idx for advanced indexing
-        #     target[:, v] = E[acts_idx, ...].sum(axis=0)
+    acts_idx = generate_A_indices(
+       A_index,
+       reverse_classifications,
+       lca_results_coords,
+    )
+
+    for f, fu in enumerate(FU):
+        print(f)
+        lca.redo_lcia(fu)
+
+        # Sum along the first axis of D to get final result
+        D = lca.characterized_inventory.sum(axis=0)
+
+        for i, act in enumerate(rev_A_index.values()):
+            if act[-1] in act_locs:
+                loc_idx = location_to_index[act[-1]]
+                E[i, loc_idx] = D[:, i]
+
+
+        # Sum over the first axis of D,
+        # using acts_idx for advanced indexing
+        target[:, vars_list[f]] = E[acts_idx, ...].sum(axis=0)
         #
         # else:
         #     # else, just sum the results of the inventory
@@ -389,9 +375,7 @@ def get_indices():
     return {
         "act_category": act_categories,
         "variable": list(vars_idx.keys()),
-        "impact_category": (
-            impact_categories if lcia_matrix is not None else get_indices()
-        ),
+        "impact_category": impact_categories,
         "year": year,
         "region": region,
         "D": target,
@@ -730,7 +714,6 @@ def calculate(
                         # Process each region in parallel
                         with Pool(cpu_count()) as p:
                             results = p.map(process_region, data_for_regions)
-
                     else:
                         results = []
                         # use pyprind
diff --git a/requirements.txt b/requirements.txt
index dd2058f..9e5c453 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,3 +6,5 @@ xarray
 premise
 pyyaml
 scikit-umfpack
+bw_processing
+bw2calc == 2.0.DEV16
diff --git a/setup.py b/setup.py
index 12afebd..060a835 100644
--- a/setup.py
+++ b/setup.py
@@ -49,7 +49,9 @@ def package_files(directory):
         "scipy",
         "premise",
         "pyyaml",
-        "scikit-umfpack"
+        "scikit-umfpack",
+        "bw_processing",
+        "bw2calc==2.0.DEV16"
     ],
     url="https://github.com/polca/premise",
     description="Scenario-level LCA of energy systems and transition pathways",