improve explanation of TemporalDistribution

notebooks/getting_started_minimal.ipynb

@@ -214,7 +214,7 @@
    "source": [
     "Temporal information on the exchange level is carried by [`TemporalDistribution`](https://docs.brightway.dev/projects/bw-temporalis/en/stable/content/api/bw_temporalis/temporal_distribution/index.html#bw_temporalis.temporal_distribution.TemporalDistribution) objects, originally introduced by [`bw_temporalis`](https://github.com/brightway-lca/bw_temporalis). \n",
     "\n",
-    "A `TemporalDistribution` tells you what share of an exchange happens at what point in time. \n"
+    "A `TemporalDistribution` tells you what share of an exchange (*amount array*) happens at what point in time (*dates array*). \n"
    ]
   },
   {
@@ -571,7 +571,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But there's more. Because a `TimexLCA` retains temporal information in the inventory, it allows for dynamic characterization. To help with that, we created another cool package, simply called [`dynamic_characterization`](https://dynamic-characterization.readthedocs.io/en/latest/). Here's a quick demo:"
+    "But there's more: Because a `TimexLCA` retains temporal information in the inventory, it also allows for dynamic characterization. To help with that, we created another package, simply called [`dynamic_characterization`](https://dynamic-characterization.readthedocs.io/en/latest/). This is not today's topic, but here's a quick demo:"
    ]
   },
   {
@@ -595,13 +595,15 @@
     }
    ],
    "source": [
-    "from dynamic_characterization.timex import characterize_co2\n",
+    "from dynamic_characterization.timex import characterize_co2 # <- dynamic characterization function\n",
     "emission_id = bd.get_activity((\"biosphere\", \"CO2\")).id\n",
     "\n",
+    "# defining which characterization function to use for which emission\n",
     "characterization_function_dict = {\n",
     "    emission_id: characterize_co2,\n",
     "}\n",
     "\n",
+    "# characterizing the inventory\n",
     "tlca.dynamic_lcia(\n",
     "    metric=\"radiative_forcing\",\n",
     "    time_horizon=100,\n",
@@ -647,6 +649,7 @@
     "    time_horizon=100,\n",
     "    characterization_function_dict=characterization_function_dict,\n",
     ")\n",
+    "\n",
     "plot_characterized_inventory_as_waterfall(tlca)\n"
    ]
   },