From 2b7ea47deedcee67118c48857e44a96131bbebed Mon Sep 17 00:00:00 2001
From: Alex Lee <lee3742@purdue.edu>
Date: Fri, 24 Jul 2026 08:49:01 +0000
Subject: [PATCH 1/3] Add initial notebook for bill and BAT analysis

---
 reports/templates/cairo_run_results.ipynb | 1245 +++++++++++++++++++++
 1 file changed, 1245 insertions(+)
 create mode 100644 reports/templates/cairo_run_results.ipynb

diff --git a/reports/templates/cairo_run_results.ipynb b/reports/templates/cairo_run_results.ipynb
new file mode 100644
index 0000000..fc8f488
--- /dev/null
+++ b/reports/templates/cairo_run_results.ipynb
@@ -0,0 +1,1245 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "f2f50c7d",
+      "metadata": {},
+      "source": [
+        "# CAIRO baseline run review\n",
+        "\n",
+        "This state-agnostic notebook loads the post-processed master tables produced from the first four CAIRO runs and provides a compact quality and results review.\n",
+        "\n",
+        "- **Upgrade 00 (runs 1+2):** baseline building loads, combining delivery-only and delivery-plus-supply runs.\n",
+        "- **Upgrade 02 (runs 3+4):** heat-pump upgrade loads, combining delivery-only and delivery-plus-supply runs.\n",
+        "\n",
+        "The notebook reads `comb_bills_year_target/` and `cross_subsidization_BAT_values/` from the cross-utility master-table directory on S3. Change only `STATE` and `BATCH` below to review another completed batch with the same output layout."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9f05f559",
+      "metadata": {},
+      "source": [
+        "## Parameters\n",
+        "\n",
+        "Use a lowercase state abbreviation. `BATCH` must exist under `s3://data.sb/switchbox/cairo/outputs/hp_rates/<state>/all_utilities/` and contain `run_1+2/` and `run_3+4/`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "b01ccd7f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Reviewing NY batch: ny_20260307_r1-8_gascalcfix\n"
+          ]
+        }
+      ],
+      "source": [
+        "from __future__ import annotations\n",
+        "\n",
+        "import math\n",
+        "from typing import cast\n",
+        "\n",
+        "import polars as pl\n",
+        "from IPython.display import display\n",
+        "from lib.plotnine import SB_COLORS, theme_switchbox\n",
+        "from plotnine import (\n",
+        "    aes,\n",
+        "    facet_wrap,\n",
+        "    geom_col,\n",
+        "    geom_hline,\n",
+        "    ggplot,\n",
+        "    labs,\n",
+        "    position_dodge,\n",
+        "    scale_fill_manual,\n",
+        "    scale_x_discrete,\n",
+        "    scale_y_continuous,\n",
+        "    theme,\n",
+        ")\n",
+        "\n",
+        "STATE = \"ny\"\n",
+        "BATCH = \"ny_20260307_r1-8_gascalcfix\"\n",
+        "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n",
+        "\n",
+        "RUN_PAIRS = {\n",
+        "    \"Upgrade 00\": \"run_1+2\",\n",
+        "    \"Upgrade 02\": \"run_3+4\",\n",
+        "}\n",
+        "DATASETS = {\n",
+        "    \"bills\": \"comb_bills_year_target\",\n",
+        "    \"bat\": \"cross_subsidization_BAT_values\",\n",
+        "}\n",
+        "\n",
+        "BLDG_ID = \"bldg_id\"\n",
+        "UTILITY_COL = \"sb.electric_utility\"\n",
+        "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n",
+        "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
+        "SCENARIO_ORDER = list(RUN_PAIRS)\n",
+        "\n",
+        "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c8913da7",
+      "metadata": {},
+      "source": [
+        "## Load the two master-table scenarios\n",
+        "\n",
+        "The master tables already combine each delivery-only run with its matching delivery-plus-supply run. Reading the Hive-partitioned dataset root preserves the electric utility partition as a column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "afabf4fd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loaded both scenarios.\n"
+          ]
+        }
+      ],
+      "source": [
+        "def master_path(run_pair: str, dataset: str) -> str:\n",
+        "    return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n",
+        "\n",
+        "\n",
+        "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n",
+        "    path = master_path(run_pair, dataset)\n",
+        "    return cast(\n",
+        "        pl.DataFrame,\n",
+        "        pl.scan_parquet(path, hive_partitioning=True).collect(),\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "required_bill_cols = {\n",
+        "    BLDG_ID,\n",
+        "    UTILITY_COL,\n",
+        "    \"month\",\n",
+        "    \"weight\",\n",
+        "    \"elec_fixed_charge\",\n",
+        "    \"elec_delivery_bill\",\n",
+        "    \"elec_supply_bill\",\n",
+        "    \"elec_total_bill\",\n",
+        "    \"gas_total_bill\",\n",
+        "    \"propane_total_bill\",\n",
+        "    \"oil_total_bill\",\n",
+        "    \"energy_total_bill\",\n",
+        "}\n",
+        "required_bat_cols = {\n",
+        "    BLDG_ID,\n",
+        "    UTILITY_COL,\n",
+        "    \"weight\",\n",
+        "    \"BAT_percustomer_delivery\",\n",
+        "    \"BAT_percustomer_supply\",\n",
+        "    \"BAT_percustomer_total\",\n",
+        "    \"annual_bill_total\",\n",
+        "    \"economic_burden_total\",\n",
+        "    \"residual_share_total\",\n",
+        "}\n",
+        "\n",
+        "bills_by_scenario: dict[str, pl.DataFrame] = {}\n",
+        "bat_by_scenario: dict[str, pl.DataFrame] = {}\n",
+        "\n",
+        "for scenario, run_pair in RUN_PAIRS.items():\n",
+        "    bills = load_master_table(run_pair, DATASETS[\"bills\"])\n",
+        "    bat = load_master_table(run_pair, DATASETS[\"bat\"])\n",
+        "\n",
+        "    missing_bill_cols = required_bill_cols - set(bills.columns)\n",
+        "    missing_bat_cols = required_bat_cols - set(bat.columns)\n",
+        "    if missing_bill_cols or missing_bat_cols:\n",
+        "        raise ValueError(\n",
+        "            f\"{scenario} has an unexpected schema. \"\n",
+        "            f\"Missing bill columns: {sorted(missing_bill_cols)}; \"\n",
+        "            f\"missing BAT columns: {sorted(missing_bat_cols)}\"\n",
+        "        )\n",
+        "\n",
+        "    bills_by_scenario[scenario] = bills\n",
+        "    bat_by_scenario[scenario] = bat\n",
+        "\n",
+        "print(\"Loaded both scenarios.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5786020",
+      "metadata": {},
+      "source": [
+        "## Inspect the imported data\n",
+        "\n",
+        "Each BAT row represents one building. Each bills table should contain one row per building-month plus an `Annual` row. The samples below make the input shape and columns visible before any transformations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "c2e14a35",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Upgrade 00 bills: (439270, 32)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 32)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>in.representative_income</th><th>in.hvac_cooling_partial_space_conditioning</th><th>month</th><th>weight</th><th>elec_fixed_charge</th><th>elec_delivery_bill</th><th>elec_supply_bill</th><th>elec_total_bill</th><th>gas_fixed_charge</th><th>gas_volumetric_bill</th><th>gas_total_bill</th><th>propane_total_bill</th><th>oil_total_bill</th><th>energy_total_bill</th><th>elec_total_bill_lmi_100</th><th>gas_total_bill_lmi_100</th><th>applied_discount_100</th><th>lmi_tier</th><th>is_lmi</th><th>elec_total_bill_lmi_40</th><th>gas_total_bill_lmi_40</th><th>applied_discount_40</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>bool</td><td>i32</td><td>bool</td><td>f64</td><td>f64</td><td>bool</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Jan&quot;</td><td>229.15313</td><td>22.0</td><td>121.68581</td><td>73.695127</td><td>217.380936</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.394526</td><td>289.775463</td><td>217.380936</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>217.380936</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Feb&quot;</td><td>229.15313</td><td>22.0</td><td>111.893841</td><td>67.764933</td><td>201.658775</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>65.576914</td><td>267.235689</td><td>201.658775</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>201.658775</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Mar&quot;</td><td>229.15313</td><td>22.0</td><td>116.385498</td><td>70.485162</td><td>208.87066</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.757213</td><td>281.627873</td><td>208.87066</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>208.87066</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 32)\n",
+              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ elec_total ┆ gas_total ┆ applied_d ┆ sb.electr │\n",
+              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ _bill_lmi_ ┆ _bill_lmi ┆ iscount_4 ┆ ic_utilit │\n",
+              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ 40         ┆ _40       ┆ 0         ┆ y         │\n",
+              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
+              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ bool      ┆ str       │\n",
+              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 217.380936 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 201.658775 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 208.87066  ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Upgrade 00 BAT: (33790, 29)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 29)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>weight</th><th>BAT_vol_delivery</th><th>BAT_vol_supply</th><th>BAT_vol_total</th><th>BAT_peak_delivery</th><th>BAT_peak_supply</th><th>BAT_peak_total</th><th>BAT_percustomer_delivery</th><th>BAT_percustomer_supply</th><th>BAT_percustomer_total</th><th>annual_bill_delivery</th><th>annual_bill_supply</th><th>annual_bill_total</th><th>economic_burden_delivery</th><th>economic_burden_supply</th><th>economic_burden_total</th><th>residual_share_delivery</th><th>residual_share_supply</th><th>residual_share_total</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>229.15313</td><td>-49.358676</td><td>19.912506</td><td>-29.44617</td><td>10.28216</td><td>-56.906923</td><td>-46.624763</td><td>121.999531</td><td>37.462471</td><td>159.462003</td><td>1673.990339</td><td>853.915644</td><td>2527.905983</td><td>77.214392</td><td>665.411239</td><td>742.625631</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr><tr><td>433835</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>56.031757</td><td>4.081806</td><td>60.113563</td><td>-163.948855</td><td>66.901791</td><td>-97.047064</td><td>-315.298461</td><td>-33.94866</td><td>-349.247121</td><td>1209.152636</td><td>572.401526</td><td>1781.554162</td><td>49.674681</td><td>455.308252</td><td>504.982934</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr><tr><td>442580</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>123.213955</td><td>-12.961722</td><td>110.252234</td><td>-334.516728</td><td>83.608165</td><td>-250.908563</td><td>-702.440525</td><td>-97.522641</td><td>-799.963166</td><td>820.00292</td><td>336.725422</td><td>1156.728342</td><td>47.66703</td><td>283.206129</td><td>330.873159</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 29)\n",
+              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ residual_s ┆ residual_ ┆ residual_ ┆ sb.electr │\n",
+              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ hare_deliv ┆ share_sup ┆ share_tot ┆ ic_utilit │\n",
+              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ ery        ┆ ply       ┆ al        ┆ y         │\n",
+              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
+              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ f64       ┆ str       │\n",
+              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
+              "│ 433835  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
+              "│ 442580  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
+              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Upgrade 02 bills: (439270, 32)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 32)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>in.representative_income</th><th>in.hvac_cooling_partial_space_conditioning</th><th>month</th><th>weight</th><th>elec_fixed_charge</th><th>elec_delivery_bill</th><th>elec_supply_bill</th><th>elec_total_bill</th><th>gas_fixed_charge</th><th>gas_volumetric_bill</th><th>gas_total_bill</th><th>propane_total_bill</th><th>oil_total_bill</th><th>energy_total_bill</th><th>elec_total_bill_lmi_100</th><th>gas_total_bill_lmi_100</th><th>applied_discount_100</th><th>lmi_tier</th><th>is_lmi</th><th>elec_total_bill_lmi_40</th><th>gas_total_bill_lmi_40</th><th>applied_discount_40</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>bool</td><td>i32</td><td>bool</td><td>f64</td><td>f64</td><td>bool</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Jan&quot;</td><td>229.15313</td><td>22.0</td><td>121.68581</td><td>73.695127</td><td>217.380936</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.394526</td><td>289.775463</td><td>217.380936</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>217.380936</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Feb&quot;</td><td>229.15313</td><td>22.0</td><td>111.893841</td><td>67.764933</td><td>201.658775</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>65.576914</td><td>267.235689</td><td>201.658775</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>201.658775</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Mar&quot;</td><td>229.15313</td><td>22.0</td><td>116.385498</td><td>70.485162</td><td>208.87066</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.757213</td><td>281.627873</td><td>208.87066</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>208.87066</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 32)\n",
+              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ elec_total ┆ gas_total ┆ applied_d ┆ sb.electr │\n",
+              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ _bill_lmi_ ┆ _bill_lmi ┆ iscount_4 ┆ ic_utilit │\n",
+              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ 40         ┆ _40       ┆ 0         ┆ y         │\n",
+              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
+              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ bool      ┆ str       │\n",
+              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 217.380936 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 201.658775 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 208.87066  ┆ 0.0       ┆ false     ┆ cenhud    │\n",
+              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Upgrade 02 BAT: (33790, 29)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 29)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>weight</th><th>BAT_vol_delivery</th><th>BAT_vol_supply</th><th>BAT_vol_total</th><th>BAT_peak_delivery</th><th>BAT_peak_supply</th><th>BAT_peak_total</th><th>BAT_percustomer_delivery</th><th>BAT_percustomer_supply</th><th>BAT_percustomer_total</th><th>annual_bill_delivery</th><th>annual_bill_supply</th><th>annual_bill_total</th><th>economic_burden_delivery</th><th>economic_burden_supply</th><th>economic_burden_total</th><th>residual_share_delivery</th><th>residual_share_supply</th><th>residual_share_total</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>229.15313</td><td>-2.6833e6</td><td>892.826396</td><td>-2.6824e6</td><td>-3.3456e6</td><td>-69323.056256</td><td>-3.4149e6</td><td>-3.6903e6</td><td>1156.973687</td><td>-3.6891e6</td><td>1673.990339</td><td>853.915644</td><td>2527.905983</td><td>77.214392</td><td>665.411239</td><td>742.625631</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr><tr><td>433835</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>-2.4715e6</td><td>807.830577</td><td>-2.4707e6</td><td>-2.5670e6</td><td>76790.184085</td><td>-2.4902e6</td><td>-3.6904e6</td><td>1127.572706</td><td>-3.6892e6</td><td>1562.694389</td><td>786.512804</td><td>2349.207193</td><td>42.450795</td><td>627.40938</td><td>669.860174</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr><tr><td>442580</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>-2.1562e6</td><td>665.603985</td><td>-2.1556e6</td><td>-2.3781e6</td><td>223157.024908</td><td>-2.1550e6</td><td>-3.6905e6</td><td>1068.087687</td><td>-3.6895e6</td><td>1397.053079</td><td>686.197431</td><td>2083.25051</td><td>41.364609</td><td>586.579027</td><td>627.943636</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 29)\n",
+              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ residual_s ┆ residual_ ┆ residual_ ┆ sb.electr │\n",
+              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ hare_deliv ┆ share_sup ┆ share_tot ┆ ic_utilit │\n",
+              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ ery        ┆ ply       ┆ al        ┆ y         │\n",
+              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
+              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ f64       ┆ str       │\n",
+              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
+              "│ 433835  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
+              "│ 442580  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
+              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
+              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "for scenario in SCENARIO_ORDER:\n",
+        "    print(f\"\\n{scenario} bills: {bills_by_scenario[scenario].shape}\")\n",
+        "    display(bills_by_scenario[scenario].head(3))\n",
+        "    print(f\"{scenario} BAT: {bat_by_scenario[scenario].shape}\")\n",
+        "    display(bat_by_scenario[scenario].head(3))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b3a50de2",
+      "metadata": {},
+      "source": [
+        "## Structural quality checks\n",
+        "\n",
+        "These checks catch incomplete post-processing, duplicated building rows, unmatched bill and BAT populations, broken bill-component arithmetic, and BAT identities that do not reconcile."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "8441c408",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (2, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>bill rows</th><th>BAT rows</th><th>buildings in annual bills</th><th>buildings in BAT</th><th>annual bill duplicate IDs</th><th>BAT duplicate IDs</th><th>bill IDs absent from BAT</th><th>BAT IDs absent from bills</th><th>max electric component error</th><th>max energy component error</th><th>max BAT identity error</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.8190e-12</td><td>1.1369e-12</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.7337e-12</td><td>2.3283e-10</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (2, 12)\n",
+              "┌───────────┬───────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+              "│ scenario  ┆ bill rows ┆ BAT rows ┆ buildings ┆ … ┆ BAT IDs   ┆ max       ┆ max       ┆ max BAT   │\n",
+              "│ ---       ┆ ---       ┆ ---      ┆ in annual ┆   ┆ absent    ┆ electric  ┆ energy    ┆ identity  │\n",
+              "│ str       ┆ i64       ┆ i64      ┆ bills     ┆   ┆ from      ┆ component ┆ component ┆ error     │\n",
+              "│           ┆           ┆          ┆ ---       ┆   ┆ bills     ┆ error     ┆ error     ┆ ---       │\n",
+              "│           ┆           ┆          ┆ i64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ f64       │\n",
+              "│           ┆           ┆          ┆           ┆   ┆ i64       ┆ f64       ┆ f64       ┆           │\n",
+              "╞═══════════╪═══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ Upgrade   ┆ 439270    ┆ 33790    ┆ 33790     ┆ … ┆ 0         ┆ 9.0949e-1 ┆ 1.8190e-1 ┆ 1.1369e-1 │\n",
+              "│ 00        ┆           ┆          ┆           ┆   ┆           ┆ 3         ┆ 2         ┆ 2         │\n",
+              "│ Upgrade   ┆ 439270    ┆ 33790    ┆ 33790     ┆ … ┆ 0         ┆ 9.0949e-1 ┆ 1.7337e-1 ┆ 2.3283e-1 │\n",
+              "│ 02        ┆           ┆          ┆           ┆   ┆           ┆ 3         ┆ 2         ┆ 0         │\n",
+              "└───────────┴───────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "All structural checks passed.\n"
+          ]
+        }
+      ],
+      "source": [
+        "qa_rows: list[dict[str, object]] = []\n",
+        "\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    bills = bills_by_scenario[scenario]\n",
+        "    bat = bat_by_scenario[scenario]\n",
+        "    annual = bills.filter(pl.col(\"month\") == \"Annual\")\n",
+        "\n",
+        "    bill_components_error = annual.select(\n",
+        "        (\n",
+        "            pl.col(\"elec_total_bill\")\n",
+        "            - pl.col(\"elec_fixed_charge\")\n",
+        "            - pl.col(\"elec_delivery_bill\")\n",
+        "            - pl.col(\"elec_supply_bill\")\n",
+        "        ).abs().max()\n",
+        "    ).item()\n",
+        "    energy_components_error = annual.select(\n",
+        "        (\n",
+        "            pl.col(\"energy_total_bill\")\n",
+        "            - pl.col(\"elec_total_bill\")\n",
+        "            - pl.col(\"gas_total_bill\")\n",
+        "            - pl.col(\"propane_total_bill\")\n",
+        "            - pl.col(\"oil_total_bill\")\n",
+        "        ).abs().max()\n",
+        "    ).item()\n",
+        "    bat_identity_error = bat.select(\n",
+        "        (\n",
+        "            pl.col(\"BAT_percustomer_total\")\n",
+        "            - pl.col(\"annual_bill_total\")\n",
+        "            + pl.col(\"economic_burden_total\")\n",
+        "            + pl.col(\"residual_share_total\")\n",
+        "        ).abs().max()\n",
+        "    ).item()\n",
+        "\n",
+        "    qa_rows.append(\n",
+        "        {\n",
+        "            \"scenario\": scenario,\n",
+        "            \"bill rows\": bills.height,\n",
+        "            \"BAT rows\": bat.height,\n",
+        "            \"buildings in annual bills\": annual[BLDG_ID].n_unique(),\n",
+        "            \"buildings in BAT\": bat[BLDG_ID].n_unique(),\n",
+        "            \"annual bill duplicate IDs\": annual.height - annual[BLDG_ID].n_unique(),\n",
+        "            \"BAT duplicate IDs\": bat.height - bat[BLDG_ID].n_unique(),\n",
+        "            \"bill IDs absent from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+        "            \"BAT IDs absent from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+        "            \"max electric component error\": float(bill_components_error or 0),\n",
+        "            \"max energy component error\": float(energy_components_error or 0),\n",
+        "            \"max BAT identity error\": float(bat_identity_error or 0),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "qa = pl.DataFrame(qa_rows)\n",
+        "display(qa)\n",
+        "\n",
+        "assert qa[\"annual bill duplicate IDs\"].sum() == 0\n",
+        "assert qa[\"BAT duplicate IDs\"].sum() == 0\n",
+        "assert qa[\"bill IDs absent from BAT\"].sum() == 0\n",
+        "assert qa[\"BAT IDs absent from bills\"].sum() == 0\n",
+        "assert cast(float, qa[\"max electric component error\"].max()) < 0.01\n",
+        "assert cast(float, qa[\"max energy component error\"].max()) < 0.01\n",
+        "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01\n",
+        "print(\"All structural checks passed.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "09d12883",
+      "metadata": {},
+      "source": [
+        "## Shared classifications and weighted statistics\n",
+        "\n",
+        "Customer groups are defined from the upgrade 00 heating system and carried into upgrade 02. This preserves the meaningful comparison: what happened to homes that started with fossil fuel, electric resistance, or an existing heat pump?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d829d8ae",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Baseline heating groups:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (4, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>baseline_heating_type</th><th>sample buildings</th><th>weighted households</th></tr><tr><td>str</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6.349536e6</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368.0</td></tr><tr><td>&quot;Other&quot;</td><td>248</td><td>49530.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (4, 3)\n",
+              "┌───────────────────────┬──────────────────┬─────────────────────┐\n",
+              "│ baseline_heating_type ┆ sample buildings ┆ weighted households │\n",
+              "│ ---                   ┆ ---              ┆ ---                 │\n",
+              "│ str                   ┆ u32              ┆ f64                 │\n",
+              "╞═══════════════════════╪══════════════════╪═════════════════════╡\n",
+              "│ Fossil fuel           ┆ 29554            ┆ 6.349536e6          │\n",
+              "│ Electric resistance   ┆ 3070             ┆ 654504.0            │\n",
+              "│ Existing heat pump    ┆ 918              ┆ 198368.0            │\n",
+              "│ Other                 ┆ 248              ┆ 49530.0             │\n",
+              "└───────────────────────┴──────────────────┴─────────────────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n",
+        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n",
+        "    weight_sum = cast(float, valid[weight].sum())\n",
+        "    if valid.is_empty() or weight_sum == 0:\n",
+        "        return float(\"nan\")\n",
+        "    weighted_sum = cast(float, (valid[value] * valid[weight]).sum())\n",
+        "    return weighted_sum / weight_sum\n",
+        "\n",
+        "\n",
+        "def weighted_quantile(\n",
+        "    df: pl.DataFrame,\n",
+        "    value: str,\n",
+        "    q: float,\n",
+        "    weight: str = \"weight\",\n",
+        ") -> float:\n",
+        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n",
+        "    weight_sum = cast(float, valid[weight].sum())\n",
+        "    if valid.is_empty() or weight_sum == 0:\n",
+        "        return float(\"nan\")\n",
+        "    cutoff = weight_sum * q\n",
+        "    return float(valid.filter(pl.col(weight).cum_sum() >= cutoff)[value][0])\n",
+        "\n",
+        "\n",
+        "def add_baseline_heating_group(df: pl.DataFrame) -> pl.DataFrame:\n",
+        "    return df.with_columns(\n",
+        "        pl.when(pl.col(HEATING_TYPE_COL) == \"fossil_fuel\")\n",
+        "        .then(pl.lit(\"Fossil fuel\"))\n",
+        "        .when(pl.col(HEATING_TYPE_COL) == \"electrical_resistance\")\n",
+        "        .then(pl.lit(\"Electric resistance\"))\n",
+        "        .when(pl.col(HEATING_TYPE_COL) == \"heat_pump\")\n",
+        "        .then(pl.lit(\"Existing heat pump\"))\n",
+        "        .otherwise(pl.lit(\"Other\"))\n",
+        "        .alias(\"baseline_heating_type\")\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "baseline_bills = add_baseline_heating_group(\n",
+        "    bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n",
+        ")\n",
+        "baseline_groups = baseline_bills.select(BLDG_ID, \"baseline_heating_type\")\n",
+        "GROUP_ORDER = [\n",
+        "    group\n",
+        "    for group in [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n",
+        "    if group in baseline_bills[\"baseline_heating_type\"].unique().to_list()\n",
+        "]\n",
+        "\n",
+        "print(\"Baseline heating groups:\")\n",
+        "display(\n",
+        "    baseline_bills.group_by(\"baseline_heating_type\").agg(\n",
+        "        pl.len().alias(\"sample buildings\"),\n",
+        "        pl.col(\"weight\").sum().round(0).alias(\"weighted households\"),\n",
+        "    ).sort(\"weighted households\", descending=True)\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4e3a8cea",
+      "metadata": {},
+      "source": [
+        "## Annual bill changes from upgrade 00 to upgrade 02\n",
+        "\n",
+        "The joined table compares the same building before and after the heat-pump upgrade. Negative changes are savings; positive changes are bill increases. Delivered-fuel bills include gas, propane, and oil."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "81d0b011",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (4, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>baseline heating type</th><th>sample buildings</th><th>weighted households</th><th>mean bill before</th><th>mean bill after</th><th>mean annual change</th><th>median annual change</th><th>households saving</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6349536</td><td>3460.63129</td><td>3356.87054</td><td>-103.76075</td><td>1.258823</td><td>0.470479</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504</td><td>3514.585157</td><td>2245.904915</td><td>-1268.680242</td><td>-844.206978</td><td>0.971394</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368</td><td>3537.259437</td><td>2745.8309</td><td>-791.428537</td><td>-420.135112</td><td>0.989821</td></tr><tr><td>&quot;Other&quot;</td><td>248</td><td>49530</td><td>1571.728023</td><td>1577.898763</td><td>6.17074</td><td>59.589828</td><td>0.426295</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (4, 8)\n",
+              "┌────────────┬────────────┬────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n",
+              "│ baseline   ┆ sample     ┆ weighted   ┆ mean bill ┆ mean bill ┆ mean      ┆ median    ┆ household │\n",
+              "│ heating    ┆ buildings  ┆ households ┆ before    ┆ after     ┆ annual    ┆ annual    ┆ s saving  │\n",
+              "│ type       ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ change    ┆ change    ┆ ---       │\n",
+              "│ ---        ┆ i64        ┆ i64        ┆ f64       ┆ f64       ┆ ---       ┆ ---       ┆ f64       │\n",
+              "│ str        ┆            ┆            ┆           ┆           ┆ f64       ┆ f64       ┆           │\n",
+              "╞════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+              "│ Fossil     ┆ 29554      ┆ 6349536    ┆ 3460.6312 ┆ 3356.8705 ┆ -103.7607 ┆ 1.258823  ┆ 0.470479  │\n",
+              "│ fuel       ┆            ┆            ┆ 9         ┆ 4         ┆ 5         ┆           ┆           │\n",
+              "│ Electric   ┆ 3070       ┆ 654504     ┆ 3514.5851 ┆ 2245.9049 ┆ -1268.680 ┆ -844.2069 ┆ 0.971394  │\n",
+              "│ resistance ┆            ┆            ┆ 57        ┆ 15        ┆ 242       ┆ 78        ┆           │\n",
+              "│ Existing   ┆ 918        ┆ 198368     ┆ 3537.2594 ┆ 2745.8309 ┆ -791.4285 ┆ -420.1351 ┆ 0.989821  │\n",
+              "│ heat pump  ┆            ┆            ┆ 37        ┆           ┆ 37        ┆ 12        ┆           │\n",
+              "│ Other      ┆ 248        ┆ 49530      ┆ 1571.7280 ┆ 1577.8987 ┆ 6.17074   ┆ 59.589828 ┆ 0.426295  │\n",
+              "│            ┆            ┆            ┆ 23        ┆ 63        ┆           ┆           ┆           │\n",
+              "└────────────┴────────────┴────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "annual_u2 = bills_by_scenario[\"Upgrade 02\"].filter(pl.col(\"month\") == \"Annual\")\n",
+        "\n",
+        "bill_comparison = (\n",
+        "    baseline_bills.select(\n",
+        "        BLDG_ID,\n",
+        "        UTILITY_COL,\n",
+        "        \"baseline_heating_type\",\n",
+        "        \"weight\",\n",
+        "        pl.col(\"elec_total_bill\").alias(\"electric_before\"),\n",
+        "        (\n",
+        "            pl.col(\"gas_total_bill\")\n",
+        "            + pl.col(\"propane_total_bill\")\n",
+        "            + pl.col(\"oil_total_bill\")\n",
+        "        ).alias(\"delivered_fuel_before\"),\n",
+        "        pl.col(\"energy_total_bill\").alias(\"energy_before\"),\n",
+        "    )\n",
+        "    .join(\n",
+        "        annual_u2.select(\n",
+        "            BLDG_ID,\n",
+        "            pl.col(\"elec_total_bill\").alias(\"electric_after\"),\n",
+        "            (\n",
+        "                pl.col(\"gas_total_bill\")\n",
+        "                + pl.col(\"propane_total_bill\")\n",
+        "                + pl.col(\"oil_total_bill\")\n",
+        "            ).alias(\"delivered_fuel_after\"),\n",
+        "            pl.col(\"energy_total_bill\").alias(\"energy_after\"),\n",
+        "        ),\n",
+        "        on=BLDG_ID,\n",
+        "        how=\"inner\",\n",
+        "        validate=\"1:1\",\n",
+        "    )\n",
+        "    .with_columns(\n",
+        "        (pl.col(\"electric_after\") - pl.col(\"electric_before\")).alias(\"electric_change\"),\n",
+        "        (pl.col(\"delivered_fuel_after\") - pl.col(\"delivered_fuel_before\")).alias(\"delivered_fuel_change\"),\n",
+        "        (pl.col(\"energy_after\") - pl.col(\"energy_before\")).alias(\"energy_change\"),\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "bill_summary_rows: list[dict[str, object]] = []\n",
+        "for group in GROUP_ORDER:\n",
+        "    group_df = bill_comparison.filter(pl.col(\"baseline_heating_type\") == group)\n",
+        "    total_weight = float(group_df[\"weight\"].sum())\n",
+        "    saving_weight = float(group_df.filter(pl.col(\"energy_change\") < 0)[\"weight\"].sum())\n",
+        "    bill_summary_rows.append(\n",
+        "        {\n",
+        "            \"baseline heating type\": group,\n",
+        "            \"sample buildings\": group_df.height,\n",
+        "            \"weighted households\": round(total_weight),\n",
+        "            \"mean bill before\": weighted_mean(group_df, \"energy_before\"),\n",
+        "            \"mean bill after\": weighted_mean(group_df, \"energy_after\"),\n",
+        "            \"mean annual change\": weighted_mean(group_df, \"energy_change\"),\n",
+        "            \"median annual change\": weighted_quantile(group_df, \"energy_change\", 0.5),\n",
+        "            \"households saving\": saving_weight / total_weight if total_weight else float(\"nan\"),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "bill_summary = pl.DataFrame(bill_summary_rows)\n",
+        "display(bill_summary)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "9c25159c",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
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+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x7841617e2360>"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {
+            "image/png": {
+              "height": 450,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "component_rows: list[dict[str, object]] = []\n",
+        "for group in GROUP_ORDER:\n",
+        "    group_df = bill_comparison.filter(pl.col(\"baseline_heating_type\") == group)\n",
+        "    for scenario, suffix in [(\"Upgrade 00\", \"before\"), (\"Upgrade 02\", \"after\")]:\n",
+        "        component_rows.extend(\n",
+        "            [\n",
+        "                {\n",
+        "                    \"baseline_heating_type\": group,\n",
+        "                    \"scenario\": scenario,\n",
+        "                    \"component\": \"Electricity\",\n",
+        "                    \"weighted_mean_bill\": weighted_mean(group_df, f\"electric_{suffix}\"),\n",
+        "                },\n",
+        "                {\n",
+        "                    \"baseline_heating_type\": group,\n",
+        "                    \"scenario\": scenario,\n",
+        "                    \"component\": \"Gas, oil, and propane\",\n",
+        "                    \"weighted_mean_bill\": weighted_mean(group_df, f\"delivered_fuel_{suffix}\"),\n",
+        "                },\n",
+        "            ]\n",
+        "        )\n",
+        "\n",
+        "component_plot_data = pl.DataFrame(component_rows).with_columns(\n",
+        "    pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
+        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+        "    # Plotnine stacks the first level on top, so list delivered fuel first.\n",
+        "    pl.col(\"component\").cast(pl.Enum([\"Gas, oil, and propane\", \"Electricity\"])),\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(\n",
+        "        component_plot_data,\n",
+        "        aes(x=\"baseline_heating_type\", y=\"weighted_mean_bill\", fill=\"component\"),\n",
+        "    )\n",
+        "    + geom_col(width=0.7)\n",
+        "    + facet_wrap(\"scenario\", ncol=2)\n",
+        "    + scale_fill_manual(\n",
+        "        values={\n",
+        "            \"Electricity\": SB_COLORS[\"sky\"],\n",
+        "            \"Gas, oil, and propane\": SB_COLORS[\"carrot\"],\n",
+        "        }\n",
+        "    )\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Baseline heating type\",\n",
+        "        y=\"Weighted mean annual bill\",\n",
+        "        fill=\"Bill component\",\n",
+        "        title=\"Mean household energy bills before and after upgrade 02\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "e0b45c69",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x7841607e2dd0>"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {
+            "image/png": {
+              "height": 800,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "BILL_CHANGE_BIN = 100\n",
+        "bill_change_lo = math.floor(\n",
+        "    weighted_quantile(bill_comparison, \"energy_change\", 0.01) / BILL_CHANGE_BIN\n",
+        ") * BILL_CHANGE_BIN\n",
+        "bill_change_hi = math.ceil(\n",
+        "    weighted_quantile(bill_comparison, \"energy_change\", 0.99) / BILL_CHANGE_BIN\n",
+        ") * BILL_CHANGE_BIN\n",
+        "\n",
+        "bill_change_hist = (\n",
+        "    bill_comparison.with_columns(\n",
+        "        (\n",
+        "            (pl.col(\"energy_change\") / BILL_CHANGE_BIN).floor() * BILL_CHANGE_BIN\n",
+        "            + BILL_CHANGE_BIN / 2\n",
+        "        ).alias(\"bin_center\"),\n",
+        "        pl.when(pl.col(\"energy_change\") < 0)\n",
+        "        .then(pl.lit(\"Savings\"))\n",
+        "        .otherwise(pl.lit(\"Increase\"))\n",
+        "        .alias(\"direction\"),\n",
+        "    )\n",
+        "    .filter(pl.col(\"bin_center\").is_between(bill_change_lo, bill_change_hi))\n",
+        "    .group_by(\"baseline_heating_type\", \"bin_center\", \"direction\")\n",
+        "    .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n",
+        "    .with_columns(\n",
+        "        pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
+        "        pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(\n",
+        "        bill_change_hist,\n",
+        "        aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"),\n",
+        "    )\n",
+        "    + geom_col(width=BILL_CHANGE_BIN * 0.9)\n",
+        "    + facet_wrap(\"baseline_heating_type\", ncol=1, scales=\"free_y\")\n",
+        "    + scale_fill_manual(\n",
+        "        values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]}\n",
+        "    )\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Annual energy bill change ($)\",\n",
+        "        y=\"Weighted households\",\n",
+        "        fill=\"Outcome\",\n",
+        "        title=\"Distribution of annual bill changes after upgrade 02 (1st–99th percentile)\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(\n",
+        "        figure_size=(10.5, max(4.5, 2.0 * len(GROUP_ORDER))),\n",
+        "        legend_position=\"top\",\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "552bca1a",
+      "metadata": {},
+      "source": [
+        "## Bill Alignment Test results\n",
+        "\n",
+        "BAT equals the annual electric bill minus marginal cost and residual cost allocation. Positive values indicate overpayment relative to cost of service; negative values indicate underpayment. Upgrade 02 rows inherit each building's upgrade 00 heating type."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "d9f6d31d",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (8, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>baseline heating type</th><th>weighted mean bill</th><th>weighted mean cost of service</th><th>weighted mean BAT</th><th>weighted median BAT</th><th>households overpaying</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Fossil fuel&quot;</td><td>1515.319009</td><td>1647.096553</td><td>-131.777544</td><td>-208.776876</td><td>0.306486</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Electric resistance&quot;</td><td>3289.7773</td><td>2288.785412</td><td>1000.991888</td><td>502.5949</td><td>0.748892</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Existing heat pump&quot;</td><td>3343.8258</td><td>2370.142712</td><td>973.683087</td><td>461.537772</td><td>0.694386</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Other&quot;</td><td>1096.794794</td><td>1330.485112</td><td>-233.690318</td><td>-257.072923</td><td>0.128254</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Fossil fuel&quot;</td><td>2748.230564</td><td>968811.602916</td><td>-966063.372352</td><td>-649778.388567</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Electric resistance&quot;</td><td>2021.011555</td><td>932681.172989</td><td>-930660.161434</td><td>-649956.358602</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Existing heat pump&quot;</td><td>2552.52301</td><td>1.1092e6</td><td>-1.1066e6</td><td>-650312.923435</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Other&quot;</td><td>1102.965534</td><td>363950.734142</td><td>-362847.768609</td><td>-324834.913154</td><td>0.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (8, 7)\n",
+              "┌────────────┬──────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+              "│ scenario   ┆ baseline     ┆ weighted     ┆ weighted    ┆ weighted    ┆ weighted    ┆ households  │\n",
+              "│ ---        ┆ heating type ┆ mean bill    ┆ mean cost   ┆ mean BAT    ┆ median BAT  ┆ overpaying  │\n",
+              "│ str        ┆ ---          ┆ ---          ┆ of service  ┆ ---         ┆ ---         ┆ ---         │\n",
+              "│            ┆ str          ┆ f64          ┆ ---         ┆ f64         ┆ f64         ┆ f64         │\n",
+              "│            ┆              ┆              ┆ f64         ┆             ┆             ┆             │\n",
+              "╞════════════╪══════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+              "│ Upgrade 00 ┆ Fossil fuel  ┆ 1515.319009  ┆ 1647.096553 ┆ -131.777544 ┆ -208.776876 ┆ 0.306486    │\n",
+              "│ Upgrade 00 ┆ Electric     ┆ 3289.7773    ┆ 2288.785412 ┆ 1000.991888 ┆ 502.5949    ┆ 0.748892    │\n",
+              "│            ┆ resistance   ┆              ┆             ┆             ┆             ┆             │\n",
+              "│ Upgrade 00 ┆ Existing     ┆ 3343.8258    ┆ 2370.142712 ┆ 973.683087  ┆ 461.537772  ┆ 0.694386    │\n",
+              "│            ┆ heat pump    ┆              ┆             ┆             ┆             ┆             │\n",
+              "│ Upgrade 00 ┆ Other        ┆ 1096.794794  ┆ 1330.485112 ┆ -233.690318 ┆ -257.072923 ┆ 0.128254    │\n",
+              "│ Upgrade 02 ┆ Fossil fuel  ┆ 2748.230564  ┆ 968811.6029 ┆ -966063.372 ┆ -649778.388 ┆ 0.0         │\n",
+              "│            ┆              ┆              ┆ 16          ┆ 352         ┆ 567         ┆             │\n",
+              "│ Upgrade 02 ┆ Electric     ┆ 2021.011555  ┆ 932681.1729 ┆ -930660.161 ┆ -649956.358 ┆ 0.0         │\n",
+              "│            ┆ resistance   ┆              ┆ 89          ┆ 434         ┆ 602         ┆             │\n",
+              "│ Upgrade 02 ┆ Existing     ┆ 2552.52301   ┆ 1.1092e6    ┆ -1.1066e6   ┆ -650312.923 ┆ 0.0         │\n",
+              "│            ┆ heat pump    ┆              ┆             ┆             ┆ 435         ┆             │\n",
+              "│ Upgrade 02 ┆ Other        ┆ 1102.965534  ┆ 363950.7341 ┆ -362847.768 ┆ -324834.913 ┆ 0.0         │\n",
+              "│            ┆              ┆              ┆ 42          ┆ 609         ┆ 154         ┆             │\n",
+              "└────────────┴──────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "bat_frames: list[pl.DataFrame] = []\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    bat_scenario = bat_by_scenario[scenario].join(\n",
+        "        baseline_groups,\n",
+        "        on=BLDG_ID,\n",
+        "        how=\"inner\",\n",
+        "        validate=\"1:1\",\n",
+        "    ).with_columns(\n",
+        "        pl.lit(scenario).alias(\"scenario\"),\n",
+        "        (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service_total\"),\n",
+        "    )\n",
+        "    bat_frames.append(bat_scenario)\n",
+        "\n",
+        "bat_long = pl.concat(bat_frames, how=\"diagonal_relaxed\")\n",
+        "\n",
+        "bat_summary_rows: list[dict[str, object]] = []\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    for group in GROUP_ORDER:\n",
+        "        group_df = bat_long.filter(\n",
+        "            (pl.col(\"scenario\") == scenario)\n",
+        "            & (pl.col(\"baseline_heating_type\") == group)\n",
+        "        )\n",
+        "        if group_df.is_empty():\n",
+        "            continue\n",
+        "        total_weight = float(group_df[\"weight\"].sum())\n",
+        "        overpay_weight = float(\n",
+        "            group_df.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum()\n",
+        "        )\n",
+        "        bat_summary_rows.append(\n",
+        "            {\n",
+        "                \"scenario\": scenario,\n",
+        "                \"baseline heating type\": group,\n",
+        "                \"weighted mean bill\": weighted_mean(group_df, \"annual_bill_total\"),\n",
+        "                \"weighted mean cost of service\": weighted_mean(group_df, \"cost_of_service_total\"),\n",
+        "                \"weighted mean BAT\": weighted_mean(group_df, \"BAT_percustomer_total\"),\n",
+        "                \"weighted median BAT\": weighted_quantile(group_df, \"BAT_percustomer_total\", 0.5),\n",
+        "                \"households overpaying\": overpay_weight / total_weight,\n",
+        "            }\n",
+        "        )\n",
+        "\n",
+        "bat_summary = pl.DataFrame(bat_summary_rows)\n",
+        "display(bat_summary)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "636b0805",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
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+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x784150152eb0>"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {
+            "image/png": {
+              "height": 450,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "bat_plot_data = bat_summary.rename(\n",
+        "    {\n",
+        "        \"baseline heating type\": \"baseline_heating_type\",\n",
+        "        \"weighted mean BAT\": \"mean_bat\",\n",
+        "    }\n",
+        ").with_columns(\n",
+        "    pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
+        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(\n",
+        "        bat_plot_data,\n",
+        "        aes(x=\"baseline_heating_type\", y=\"mean_bat\", fill=\"scenario\"),\n",
+        "    )\n",
+        "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
+        "    + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n",
+        "    + scale_fill_manual(\n",
+        "        values={\n",
+        "            \"Upgrade 00\": SB_COLORS[\"sky\"],\n",
+        "            \"Upgrade 02\": SB_COLORS[\"carrot\"],\n",
+        "        }\n",
+        "    )\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Baseline heating type\",\n",
+        "        y=\"Weighted mean BAT ($/year)\",\n",
+        "        fill=\"Scenario\",\n",
+        "        title=\"Mean total BAT by baseline heating type\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "811dbd56",
+      "metadata": {},
+      "source": [
+        "## Monthly bill pattern\n",
+        "\n",
+        "Monthly weighted means show when the heat-pump upgrade raises electric bills and when reduced fossil-fuel use offsets those increases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "131431f7",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (24, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>month</th><th>weighted_mean_energy_bill</th></tr><tr><td>enum</td><td>enum</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Jan&quot;</td><td>520.340206</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Feb&quot;</td><td>371.189947</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Mar&quot;</td><td>393.665096</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Apr&quot;</td><td>295.411291</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;May&quot;</td><td>177.300088</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Aug&quot;</td><td>205.836428</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Sep&quot;</td><td>170.945828</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Oct&quot;</td><td>199.186401</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Nov&quot;</td><td>299.757518</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Dec&quot;</td><td>386.499711</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (24, 3)\n",
+              "┌────────────┬───────┬───────────────────────────┐\n",
+              "│ scenario   ┆ month ┆ weighted_mean_energy_bill │\n",
+              "│ ---        ┆ ---   ┆ ---                       │\n",
+              "│ enum       ┆ enum  ┆ f64                       │\n",
+              "╞════════════╪═══════╪═══════════════════════════╡\n",
+              "│ Upgrade 00 ┆ Jan   ┆ 520.340206                │\n",
+              "│ Upgrade 00 ┆ Feb   ┆ 371.189947                │\n",
+              "│ Upgrade 00 ┆ Mar   ┆ 393.665096                │\n",
+              "│ Upgrade 00 ┆ Apr   ┆ 295.411291                │\n",
+              "│ Upgrade 00 ┆ May   ┆ 177.300088                │\n",
+              "│ …          ┆ …     ┆ …                         │\n",
+              "│ Upgrade 02 ┆ Aug   ┆ 205.836428                │\n",
+              "│ Upgrade 02 ┆ Sep   ┆ 170.945828                │\n",
+              "│ Upgrade 02 ┆ Oct   ┆ 199.186401                │\n",
+              "│ Upgrade 02 ┆ Nov   ┆ 299.757518                │\n",
+              "│ Upgrade 02 ┆ Dec   ┆ 386.499711                │\n",
+              "└────────────┴───────┴───────────────────────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x784150152970>"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {
+            "image/png": {
+              "height": 450,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "monthly_rows: list[dict[str, object]] = []\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n",
+        "    for month in MONTH_ORDER:\n",
+        "        month_df = monthly.filter(pl.col(\"month\") == month)\n",
+        "        monthly_rows.append(\n",
+        "            {\n",
+        "                \"scenario\": scenario,\n",
+        "                \"month\": month,\n",
+        "                \"weighted_mean_energy_bill\": weighted_mean(month_df, \"energy_total_bill\"),\n",
+        "            }\n",
+        "        )\n",
+        "\n",
+        "monthly_summary = pl.DataFrame(monthly_rows).with_columns(\n",
+        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+        "    pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n",
+        ")\n",
+        "display(monthly_summary)\n",
+        "\n",
+        "(\n",
+        "    ggplot(\n",
+        "        monthly_summary,\n",
+        "        aes(x=\"month\", y=\"weighted_mean_energy_bill\", fill=\"scenario\"),\n",
+        "    )\n",
+        "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
+        "    + scale_fill_manual(\n",
+        "        values={\n",
+        "            \"Upgrade 00\": SB_COLORS[\"sky\"],\n",
+        "            \"Upgrade 02\": SB_COLORS[\"carrot\"],\n",
+        "        }\n",
+        "    )\n",
+        "    + scale_x_discrete(limits=MONTH_ORDER)\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Month\",\n",
+        "        y=\"Weighted mean household energy bill\",\n",
+        "        fill=\"Scenario\",\n",
+        "        title=\"Monthly energy bills before and after upgrade 02\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6ed1859",
+      "metadata": {},
+      "source": [
+        "## Utility-level summary\n",
+        "\n",
+        "For multi-utility states, this table quickly identifies territories with unusual bill changes or bill alignment. It remains valid for single-utility states such as Rhode Island."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "c08507b0",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (14, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>utility</th><th>scenario</th><th>sample buildings</th><th>weighted households</th><th>weighted mean total BAT</th><th>weighted median total BAT</th><th>weighted mean bill change</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;cenhud&quot;</td><td>&quot;Upgrade 00&quot;</td><td>1182</td><td>270859</td><td>3.1255e-12</td><td>-227.248726</td><td>0.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>&quot;Upgrade 02&quot;</td><td>1182</td><td>270859</td><td>-3.6886e6</td><td>-3.6887e6</td><td>-733.581717</td></tr><tr><td>&quot;coned&quot;</td><td>&quot;Upgrade 00&quot;</td><td>15435</td><td>3064038</td><td>-2.1219e-11</td><td>-178.654389</td><td>0.0</td></tr><tr><td>&quot;coned&quot;</td><td>&quot;Upgrade 02&quot;</td><td>15435</td><td>3064038</td><td>-324178.202264</td><td>-324436.69905</td><td>-148.40005</td></tr><tr><td>&quot;nimo&quot;</td><td>&quot;Upgrade 00&quot;</td><td>6791</td><td>1532294</td><td>-2.4499e-11</td><td>-150.003962</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;or&quot;</td><td>&quot;Upgrade 02&quot;</td><td>829</td><td>211011</td><td>-4.7359e6</td><td>-4.7360e6</td><td>-312.592456</td></tr><tr><td>&quot;psegli&quot;</td><td>&quot;Upgrade 00&quot;</td><td>4427</td><td>1029955</td><td>-1.3687e-11</td><td>-116.113917</td><td>0.0</td></tr><tr><td>&quot;psegli&quot;</td><td>&quot;Upgrade 02&quot;</td><td>4427</td><td>1029955</td><td>-967458.790291</td><td>-967503.544667</td><td>-577.154952</td></tr><tr><td>&quot;rge&quot;</td><td>&quot;Upgrade 00&quot;</td><td>1444</td><td>351481</td><td>-5.5909e-13</td><td>-171.684586</td><td>0.0</td></tr><tr><td>&quot;rge&quot;</td><td>&quot;Upgrade 02&quot;</td><td>1444</td><td>351481</td><td>-2.8426e6</td><td>-2.8427e6</td><td>-13.503974</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (14, 7)\n",
+              "┌─────────┬────────────┬───────────┬────────────┬────────────────┬────────────────┬────────────────┐\n",
+              "│ utility ┆ scenario   ┆ sample    ┆ weighted   ┆ weighted mean  ┆ weighted       ┆ weighted mean  │\n",
+              "│ ---     ┆ ---        ┆ buildings ┆ households ┆ total BAT      ┆ median total   ┆ bill change    │\n",
+              "│ str     ┆ str        ┆ ---       ┆ ---        ┆ ---            ┆ BAT            ┆ ---            │\n",
+              "│         ┆            ┆ i64       ┆ i64        ┆ f64            ┆ ---            ┆ f64            │\n",
+              "│         ┆            ┆           ┆            ┆                ┆ f64            ┆                │\n",
+              "╞═════════╪════════════╪═══════════╪════════════╪════════════════╪════════════════╪════════════════╡\n",
+              "│ cenhud  ┆ Upgrade 00 ┆ 1182      ┆ 270859     ┆ 3.1255e-12     ┆ -227.248726    ┆ 0.0            │\n",
+              "│ cenhud  ┆ Upgrade 02 ┆ 1182      ┆ 270859     ┆ -3.6886e6      ┆ -3.6887e6      ┆ -733.581717    │\n",
+              "│ coned   ┆ Upgrade 00 ┆ 15435     ┆ 3064038    ┆ -2.1219e-11    ┆ -178.654389    ┆ 0.0            │\n",
+              "│ coned   ┆ Upgrade 02 ┆ 15435     ┆ 3064038    ┆ -324178.202264 ┆ -324436.69905  ┆ -148.40005     │\n",
+              "│ nimo    ┆ Upgrade 00 ┆ 6791      ┆ 1532294    ┆ -2.4499e-11    ┆ -150.003962    ┆ 0.0            │\n",
+              "│ …       ┆ …          ┆ …         ┆ …          ┆ …              ┆ …              ┆ …              │\n",
+              "│ or      ┆ Upgrade 02 ┆ 829       ┆ 211011     ┆ -4.7359e6      ┆ -4.7360e6      ┆ -312.592456    │\n",
+              "│ psegli  ┆ Upgrade 00 ┆ 4427      ┆ 1029955    ┆ -1.3687e-11    ┆ -116.113917    ┆ 0.0            │\n",
+              "│ psegli  ┆ Upgrade 02 ┆ 4427      ┆ 1029955    ┆ -967458.790291 ┆ -967503.544667 ┆ -577.154952    │\n",
+              "│ rge     ┆ Upgrade 00 ┆ 1444      ┆ 351481     ┆ -5.5909e-13    ┆ -171.684586    ┆ 0.0            │\n",
+              "│ rge     ┆ Upgrade 02 ┆ 1444      ┆ 351481     ┆ -2.8426e6      ┆ -2.8427e6      ┆ -13.503974     │\n",
+              "└─────────┴────────────┴───────────┴────────────┴────────────────┴────────────────┴────────────────┘"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "utility_rows: list[dict[str, object]] = []\n",
+        "for utility in sorted(bill_comparison[UTILITY_COL].unique().to_list()):\n",
+        "    utility_bills = bill_comparison.filter(pl.col(UTILITY_COL) == utility)\n",
+        "    for scenario in SCENARIO_ORDER:\n",
+        "        utility_bat = bat_long.filter(\n",
+        "            (pl.col(UTILITY_COL) == utility) & (pl.col(\"scenario\") == scenario)\n",
+        "        )\n",
+        "        utility_rows.append(\n",
+        "            {\n",
+        "                \"utility\": utility,\n",
+        "                \"scenario\": scenario,\n",
+        "                \"sample buildings\": utility_bat.height,\n",
+        "                \"weighted households\": round(float(utility_bat[\"weight\"].sum())),\n",
+        "                \"weighted mean total BAT\": weighted_mean(utility_bat, \"BAT_percustomer_total\"),\n",
+        "                \"weighted median total BAT\": weighted_quantile(utility_bat, \"BAT_percustomer_total\", 0.5),\n",
+        "                \"weighted mean bill change\": (\n",
+        "                    0.0 if scenario == \"Upgrade 00\" else weighted_mean(utility_bills, \"energy_change\")\n",
+        "                ),\n",
+        "            }\n",
+        "        )\n",
+        "\n",
+        "utility_summary = pl.DataFrame(utility_rows)\n",
+        "display(utility_summary)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1d617d8e",
+      "metadata": {},
+      "source": [
+        "## Interpretation checklist\n",
+        "\n",
+        "Before using these results in a report or policy analysis:\n",
+        "\n",
+        "1. Confirm that every structural assertion passes.\n",
+        "2. Check that weighted household totals are stable across scenarios and plausible for the modeled service territories.\n",
+        "3. Investigate heating groups or utilities with extreme bill changes before summarizing statewide results.\n",
+        "4. Confirm that BAT signs and magnitudes are consistent with the intended residual allocation method.\n",
+        "5. Treat the 1st–99th percentile histogram as a visualization choice only; inspect excluded tails separately when diagnosing outliers.\n",
+        "6. Change `STATE` and `BATCH`, restart the kernel, and run all cells to review another batch."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

From 34063ae32b8b3643979dbbb15612ef244d19de9c Mon Sep 17 00:00:00 2001
From: Alex Lee <lee3742@purdue.edu>
Date: Sat, 25 Jul 2026 01:57:52 +0000
Subject: [PATCH 2/3] Add some more analysis

---
 reports/templates/cairo_run_results.ipynb | 3044 +++++++++++++--------
 1 file changed, 1866 insertions(+), 1178 deletions(-)

diff --git a/reports/templates/cairo_run_results.ipynb b/reports/templates/cairo_run_results.ipynb
index fc8f488..a6623fe 100644
--- a/reports/templates/cairo_run_results.ipynb
+++ b/reports/templates/cairo_run_results.ipynb
@@ -1,1245 +1,1933 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a0000001",
+   "metadata": {},
+   "source": [
+    "# CAIRO run results review\n",
+    "\n",
+    "This **state-agnostic** notebook reviews the outputs from the first four CAIRO simulation\n",
+    "runs for any given state and batch. Run it after a new batch is complete to sanity-check\n",
+    "inputs, verify bill arithmetic, and get an initial read on cross-subsidization and\n",
+    "bill-change patterns.\n",
+    "\n",
+    "The first four runs (covering two upgrade levels × two supply configurations) produce\n",
+    "the master tables this notebook reads:\n",
+    "\n",
+    "| Run pair | Upgrade | What it represents |\n",
+    "|----------|---------|--------------------|\n",
+    "| 1+2 | Upgrade 00 (baseline) | Current HVAC; delivery-only + delivery+supply combined |\n",
+    "| 3+4 | Upgrade 02 (HP upgrade) | All buildings given heat pumps; same two supply configs |\n",
+    "\n",
+    "> **Demonstration state**: This notebook currently uses NY batch\n",
+    "> `ny_20260417a_r1-36` as a worked example.  \n",
+    "> **To review a different state, change `STATE` and `BATCH` in the Parameters cell\n",
+    "> and restart the kernel.**\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## How to use for a new state\n",
+    "\n",
+    "1. Change `STATE` (lowercase 2-letter abbreviation, e.g. `\"ri\"`, `\"ct\"`) and `BATCH`\n",
+    "   in the *Parameters* cell below.\n",
+    "2. Verify that `run_1+2` and `run_3+4` exist under\n",
+    "   `s3://data.sb/switchbox/cairo/outputs/hp_rates/<STATE>/all_utilities/<BATCH>/`.\n",
+    "3. Restart the kernel and run all cells (`Kernel → Restart & Run All`).\n",
+    "4. No other code changes are needed — all paths and column references are derived\n",
+    "   from `STATE` and `BATCH`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Notebook sections\n",
+    "\n",
+    "| Section | What it covers |\n",
+    "|---------|---------------|\n",
+    "| **1 — Load data** | Read master bills and BAT tables from S3 for both run pairs |\n",
+    "| **2 — Input data quality checks (EDA)** | Utility assignments, heating-type composition, gas and electric spending distributions |\n",
+    "| **3 — Structural quality checks** | Schema validation, row counts, bill arithmetic identities |\n",
+    "| **4 — Baseline analysis (upgrade 00)** | Cross-subsidy table, BAT distribution, monthly bill pattern |\n",
+    "| **5 — Bill change analysis (upgrade 00 → 02)** | Quadrant bar chart, savings breakdown by heating type |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000002",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "Change `STATE` and `BATCH` here to switch states. Everything else derives from these\n",
+    "two values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a0000003",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import math\n",
+    "from typing import cast\n",
+    "\n",
+    "import polars as pl\n",
+    "from IPython.display import display\n",
+    "from plotnine import (\n",
+    "    aes,\n",
+    "    coord_flip,\n",
+    "    facet_wrap,\n",
+    "    geom_col,\n",
+    "    geom_hline,\n",
+    "    geom_text,\n",
+    "    ggplot,\n",
+    "    guides,\n",
+    "    labs,\n",
+    "    position_dodge,\n",
+    "    position_stack,\n",
+    "    scale_fill_manual,\n",
+    "    scale_x_discrete,\n",
+    "    scale_y_continuous,\n",
+    "    theme,\n",
+    ")\n",
+    "\n",
+    "from lib.plotnine import SB_COLORS, theme_switchbox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a0000004",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "f2f50c7d",
-      "metadata": {},
-      "source": [
-        "# CAIRO baseline run review\n",
-        "\n",
-        "This state-agnostic notebook loads the post-processed master tables produced from the first four CAIRO runs and provides a compact quality and results review.\n",
-        "\n",
-        "- **Upgrade 00 (runs 1+2):** baseline building loads, combining delivery-only and delivery-plus-supply runs.\n",
-        "- **Upgrade 02 (runs 3+4):** heat-pump upgrade loads, combining delivery-only and delivery-plus-supply runs.\n",
-        "\n",
-        "The notebook reads `comb_bills_year_target/` and `cross_subsidization_BAT_values/` from the cross-utility master-table directory on S3. Change only `STATE` and `BATCH` below to review another completed batch with the same output layout."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reviewing NY batch: ny_20260417a_r1-36\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── Change these two values to review a different state or batch ─────────────\n",
+    "STATE = \"ny\"  # lowercase state abbreviation, e.g. \"ri\", \"ct\", \"ma\"\n",
+    "BATCH = \"ny_20260417a_r1-36\"\n",
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "\n",
+    "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n",
+    "RUN_PAIRS: dict[str, str] = {\n",
+    "    \"Upgrade 00\": \"run_1+2\",\n",
+    "    \"Upgrade 02\": \"run_3+4\",\n",
+    "}\n",
+    "DATASETS: dict[str, str] = {\n",
+    "    \"bills\": \"comb_bills_year_target\",\n",
+    "    \"bat\": \"cross_subsidization_BAT_values\",\n",
+    "}\n",
+    "\n",
+    "BLDG_ID = \"bldg_id\"\n",
+    "UTILITY_COL = \"sb.electric_utility\"\n",
+    "GAS_UTILITY_COL = \"sb.gas_utility\"\n",
+    "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n",
+    "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
+    "SCENARIO_ORDER = list(RUN_PAIRS)\n",
+    "\n",
+    "# Human-readable labels for postprocess_group.heating_type values\n",
+    "HEATING_TYPE_LABELS: dict[str, str] = {\n",
+    "    \"fossil_fuel\": \"Fossil fuel\",\n",
+    "    \"electrical_resistance\": \"Electric resistance\",\n",
+    "    \"heat_pump\": \"Existing heat pump\",\n",
+    "}\n",
+    "# Display order for heating groups in charts and tables\n",
+    "HEATING_ORDER = [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n",
+    "\n",
+    "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000005",
+   "metadata": {},
+   "source": [
+    "## Section 1: Load data\n",
+    "\n",
+    "We load `comb_bills_year_target/` (monthly bills per building) and\n",
+    "`cross_subsidization_BAT_values/` (annual BAT metrics per building) for both run pairs.\n",
+    "Both datasets are Hive-partitioned on `sb.electric_utility`.\n",
+    "\n",
+    "Each row in the **bills** table represents one building in one month (plus an `\"Annual\"`\n",
+    "summary row). Each row in the **BAT** table represents one building (annual only).\n",
+    "Schema details are in the AGENTS.md master-table documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a0000006",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "9f05f559",
-      "metadata": {},
-      "source": [
-        "## Parameters\n",
-        "\n",
-        "Use a lowercase state abbreviation. `BATCH` must exist under `s3://data.sb/switchbox/cairo/outputs/hp_rates/<state>/all_utilities/` and contain `run_1+2/` and `run_3+4/`."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded both scenarios.\n",
+      "  Upgrade 00: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n",
+      "  Upgrade 02: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n"
+     ]
+    }
+   ],
+   "source": [
+    "def master_path(run_pair: str, dataset: str) -> str:\n",
+    "    return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n",
+    "\n",
+    "\n",
+    "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n",
+    "    return cast(\n",
+    "        pl.DataFrame,\n",
+    "        pl.scan_parquet(master_path(run_pair, dataset), hive_partitioning=True).collect(),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "bills_by_scenario: dict[str, pl.DataFrame] = {}\n",
+    "bat_by_scenario: dict[str, pl.DataFrame] = {}\n",
+    "\n",
+    "for scenario, run_pair in RUN_PAIRS.items():\n",
+    "    bills_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bills\"])\n",
+    "    bat_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bat\"])\n",
+    "\n",
+    "print(\"Loaded both scenarios.\")\n",
+    "for scenario in SCENARIO_ORDER:\n",
+    "    b = bills_by_scenario[scenario]\n",
+    "    bt = bat_by_scenario[scenario]\n",
+    "    months_present = sorted(b.filter(pl.col(\"month\") != \"Annual\")[\"month\"].unique().to_list())\n",
+    "    print(f\"  {scenario}: bills {b.shape}  |  BAT {bt.shape}  |  months: {months_present}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a0000007",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "b01ccd7f",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Reviewing NY batch: ny_20260307_r1-8_gascalcfix\n"
-          ]
-        }
-      ],
-      "source": [
-        "from __future__ import annotations\n",
-        "\n",
-        "import math\n",
-        "from typing import cast\n",
-        "\n",
-        "import polars as pl\n",
-        "from IPython.display import display\n",
-        "from lib.plotnine import SB_COLORS, theme_switchbox\n",
-        "from plotnine import (\n",
-        "    aes,\n",
-        "    facet_wrap,\n",
-        "    geom_col,\n",
-        "    geom_hline,\n",
-        "    ggplot,\n",
-        "    labs,\n",
-        "    position_dodge,\n",
-        "    scale_fill_manual,\n",
-        "    scale_x_discrete,\n",
-        "    scale_y_continuous,\n",
-        "    theme,\n",
-        ")\n",
-        "\n",
-        "STATE = \"ny\"\n",
-        "BATCH = \"ny_20260307_r1-8_gascalcfix\"\n",
-        "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n",
-        "\n",
-        "RUN_PAIRS = {\n",
-        "    \"Upgrade 00\": \"run_1+2\",\n",
-        "    \"Upgrade 02\": \"run_3+4\",\n",
-        "}\n",
-        "DATASETS = {\n",
-        "    \"bills\": \"comb_bills_year_target\",\n",
-        "    \"bat\": \"cross_subsidization_BAT_values\",\n",
-        "}\n",
-        "\n",
-        "BLDG_ID = \"bldg_id\"\n",
-        "UTILITY_COL = \"sb.electric_utility\"\n",
-        "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n",
-        "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
-        "SCENARIO_ORDER = list(RUN_PAIRS)\n",
-        "\n",
-        "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bills schema:\n",
+      "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'in.representative_income': Float64, 'in.hvac_cooling_partial_space_conditioning': String, 'month': String, 'weight': Float64, 'elec_fixed_charge': Float64, 'elec_delivery_bill': Float64, 'elec_supply_bill': Float64, 'elec_total_bill': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'gas_fixed_charge': Float64, 'gas_volumetric_bill': Float64, 'gas_total_bill': Float64, 'propane_total_bill': Float64, 'oil_total_bill': Float64, 'energy_total_bill': Float64, 'sb.electric_utility': String})\n",
+      "\n",
+      "BAT schema:\n",
+      "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'weight': Float64, 'BAT_vol_delivery': Float64, 'BAT_vol_supply': Float64, 'BAT_vol_total': Float64, 'BAT_percustomer_delivery': Float64, 'BAT_percustomer_supply': Float64, 'BAT_percustomer_total': Float64, 'BAT_epmc_delivery': Float64, 'BAT_epmc_supply': Float64, 'BAT_epmc_total': Float64, 'annual_bill_delivery': Float64, 'annual_bill_supply': Float64, 'annual_bill_total': Float64, 'economic_burden_delivery': Float64, 'economic_burden_supply': Float64, 'economic_burden_total': Float64, 'residual_share_delivery': Float64, 'residual_share_supply': Float64, 'residual_share_total': Float64, 'residual_share_epmc_delivery': Float64, 'residual_share_epmc_supply': Float64, 'residual_share_epmc_total': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'sb.electric_utility': String})\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick schema peek — useful when working with an unfamiliar batch\n",
+    "print(\"Bills schema:\")\n",
+    "print(bills_by_scenario[SCENARIO_ORDER[0]].schema)\n",
+    "print(\"\\nBAT schema:\")\n",
+    "print(bat_by_scenario[SCENARIO_ORDER[0]].schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000008",
+   "metadata": {},
+   "source": [
+    "## Section 2: Input data quality checks (EDA)\n",
+    "\n",
+    "Before looking at bills and the BAT, we examine the raw inputs: how buildings are\n",
+    "distributed across utility territories, what their baseline heating systems are, and\n",
+    "what their gas and electric spending looks like. These checks catch assignment errors\n",
+    "and calibration anomalies early.\n",
+    "\n",
+    "All EDA in this section uses upgrade 00 (`\"Annual\"` rows)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000009",
+   "metadata": {},
+   "source": [
+    "### 2a: Utility assignment\n",
+    "\n",
+    "What fraction of buildings (and weighted households) are assigned to each electric and\n",
+    "gas utility? We expect the partition to align with each utility's approximate share of\n",
+    "the state's residential customers.\n",
+    "\n",
+    "A large fraction of **null gas assignments** is normal — buildings that heat with\n",
+    "electricity, propane, or oil are not assigned a gas utility. Investigate if the null\n",
+    "fraction is surprisingly low in a territory with extensive gas infrastructure, or\n",
+    "surprisingly high in a gas-dense market."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a0000010",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "c8913da7",
-      "metadata": {},
-      "source": [
-        "## Load the two master-table scenarios\n",
-        "\n",
-        "The master tables already combine each delivery-only run with its matching delivery-plus-supply run. Reading the Hive-partitioned dataset root preserves the electric utility partition as a column."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Upgrade 00 — Annual rows: 33,790 sample buildings, 7,251,938 weighted households\n",
+      "\n",
+      "Electric utility assignment:\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "afabf4fd",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loaded both scenarios.\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (8, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>42.3</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>21.1</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>14.2</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>10.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>4.8</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>3.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>2.9</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.251938e6</td><td>99.9</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "def master_path(run_pair: str, dataset: str) -> str:\n",
-        "    return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n",
-        "\n",
-        "\n",
-        "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n",
-        "    path = master_path(run_pair, dataset)\n",
-        "    return cast(\n",
-        "        pl.DataFrame,\n",
-        "        pl.scan_parquet(path, hive_partitioning=True).collect(),\n",
-        "    )\n",
-        "\n",
-        "\n",
-        "required_bill_cols = {\n",
-        "    BLDG_ID,\n",
-        "    UTILITY_COL,\n",
-        "    \"month\",\n",
-        "    \"weight\",\n",
-        "    \"elec_fixed_charge\",\n",
-        "    \"elec_delivery_bill\",\n",
-        "    \"elec_supply_bill\",\n",
-        "    \"elec_total_bill\",\n",
-        "    \"gas_total_bill\",\n",
-        "    \"propane_total_bill\",\n",
-        "    \"oil_total_bill\",\n",
-        "    \"energy_total_bill\",\n",
-        "}\n",
-        "required_bat_cols = {\n",
-        "    BLDG_ID,\n",
-        "    UTILITY_COL,\n",
-        "    \"weight\",\n",
-        "    \"BAT_percustomer_delivery\",\n",
-        "    \"BAT_percustomer_supply\",\n",
-        "    \"BAT_percustomer_total\",\n",
-        "    \"annual_bill_total\",\n",
-        "    \"economic_burden_total\",\n",
-        "    \"residual_share_total\",\n",
-        "}\n",
-        "\n",
-        "bills_by_scenario: dict[str, pl.DataFrame] = {}\n",
-        "bat_by_scenario: dict[str, pl.DataFrame] = {}\n",
-        "\n",
-        "for scenario, run_pair in RUN_PAIRS.items():\n",
-        "    bills = load_master_table(run_pair, DATASETS[\"bills\"])\n",
-        "    bat = load_master_table(run_pair, DATASETS[\"bat\"])\n",
-        "\n",
-        "    missing_bill_cols = required_bill_cols - set(bills.columns)\n",
-        "    missing_bat_cols = required_bat_cols - set(bat.columns)\n",
-        "    if missing_bill_cols or missing_bat_cols:\n",
-        "        raise ValueError(\n",
-        "            f\"{scenario} has an unexpected schema. \"\n",
-        "            f\"Missing bill columns: {sorted(missing_bill_cols)}; \"\n",
-        "            f\"missing BAT columns: {sorted(missing_bat_cols)}\"\n",
-        "        )\n",
-        "\n",
-        "    bills_by_scenario[scenario] = bills\n",
-        "    bat_by_scenario[scenario] = bat\n",
-        "\n",
-        "print(\"Loaded both scenarios.\")"
+      "text/plain": [
+       "shape: (8, 4)\n",
+       "┌──────────────────┬───────────┬─────────────────────┬──────────────┐\n",
+       "│ electric_utility ┆ buildings ┆ weighted_households ┆ pct_of_total │\n",
+       "│ ---              ┆ ---       ┆ ---                 ┆ ---          │\n",
+       "│ str              ┆ u32       ┆ f64                 ┆ f64          │\n",
+       "╞══════════════════╪═══════════╪═════════════════════╪══════════════╡\n",
+       "│ coned            ┆ 15435     ┆ 3.0640e6            ┆ 42.3         │\n",
+       "│ nimo             ┆ 6791      ┆ 1.532294e6          ┆ 21.1         │\n",
+       "│ psegli           ┆ 4427      ┆ 1.0300e6            ┆ 14.2         │\n",
+       "│ nyseg            ┆ 3682      ┆ 792300.0            ┆ 10.9         │\n",
+       "│ rge              ┆ 1444      ┆ 351481.0            ┆ 4.8          │\n",
+       "│ cenhud           ┆ 1182      ┆ 270859.0            ┆ 3.7          │\n",
+       "│ or               ┆ 829       ┆ 211011.0            ┆ 2.9          │\n",
+       "│ **TOTAL**        ┆ 33790     ┆ 7.251938e6          ┆ 99.9         │\n",
+       "└──────────────────┴───────────┴─────────────────────┴──────────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "f5786020",
-      "metadata": {},
-      "source": [
-        "## Inspect the imported data\n",
-        "\n",
-        "Each BAT row represents one building. Each bills table should contain one row per building-month plus an `Annual` row. The samples below make the input shape and columns visible before any transformations."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Gas utility assignment (null = no gas service / unassigned):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "c2e14a35",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Upgrade 00 bills: (439270, 32)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (3, 32)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>in.representative_income</th><th>in.hvac_cooling_partial_space_conditioning</th><th>month</th><th>weight</th><th>elec_fixed_charge</th><th>elec_delivery_bill</th><th>elec_supply_bill</th><th>elec_total_bill</th><th>gas_fixed_charge</th><th>gas_volumetric_bill</th><th>gas_total_bill</th><th>propane_total_bill</th><th>oil_total_bill</th><th>energy_total_bill</th><th>elec_total_bill_lmi_100</th><th>gas_total_bill_lmi_100</th><th>applied_discount_100</th><th>lmi_tier</th><th>is_lmi</th><th>elec_total_bill_lmi_40</th><th>gas_total_bill_lmi_40</th><th>applied_discount_40</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>bool</td><td>i32</td><td>bool</td><td>f64</td><td>f64</td><td>bool</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Jan&quot;</td><td>229.15313</td><td>22.0</td><td>121.68581</td><td>73.695127</td><td>217.380936</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.394526</td><td>289.775463</td><td>217.380936</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>217.380936</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Feb&quot;</td><td>229.15313</td><td>22.0</td><td>111.893841</td><td>67.764933</td><td>201.658775</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>65.576914</td><td>267.235689</td><td>201.658775</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>201.658775</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Mar&quot;</td><td>229.15313</td><td>22.0</td><td>116.385498</td><td>70.485162</td><td>208.87066</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.757213</td><td>281.627873</td><td>208.87066</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>208.87066</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (3, 32)\n",
-              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ elec_total ┆ gas_total ┆ applied_d ┆ sb.electr │\n",
-              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ _bill_lmi_ ┆ _bill_lmi ┆ iscount_4 ┆ ic_utilit │\n",
-              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ 40         ┆ _40       ┆ 0         ┆ y         │\n",
-              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ bool      ┆ str       │\n",
-              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 217.380936 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 201.658775 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 208.87066  ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Upgrade 00 BAT: (33790, 29)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (3, 29)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>weight</th><th>BAT_vol_delivery</th><th>BAT_vol_supply</th><th>BAT_vol_total</th><th>BAT_peak_delivery</th><th>BAT_peak_supply</th><th>BAT_peak_total</th><th>BAT_percustomer_delivery</th><th>BAT_percustomer_supply</th><th>BAT_percustomer_total</th><th>annual_bill_delivery</th><th>annual_bill_supply</th><th>annual_bill_total</th><th>economic_burden_delivery</th><th>economic_burden_supply</th><th>economic_burden_total</th><th>residual_share_delivery</th><th>residual_share_supply</th><th>residual_share_total</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>229.15313</td><td>-49.358676</td><td>19.912506</td><td>-29.44617</td><td>10.28216</td><td>-56.906923</td><td>-46.624763</td><td>121.999531</td><td>37.462471</td><td>159.462003</td><td>1673.990339</td><td>853.915644</td><td>2527.905983</td><td>77.214392</td><td>665.411239</td><td>742.625631</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr><tr><td>433835</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>56.031757</td><td>4.081806</td><td>60.113563</td><td>-163.948855</td><td>66.901791</td><td>-97.047064</td><td>-315.298461</td><td>-33.94866</td><td>-349.247121</td><td>1209.152636</td><td>572.401526</td><td>1781.554162</td><td>49.674681</td><td>455.308252</td><td>504.982934</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr><tr><td>442580</td><td>null</td><td>0</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>123.213955</td><td>-12.961722</td><td>110.252234</td><td>-334.516728</td><td>83.608165</td><td>-250.908563</td><td>-702.440525</td><td>-97.522641</td><td>-799.963166</td><td>820.00292</td><td>336.725422</td><td>1156.728342</td><td>47.66703</td><td>283.206129</td><td>330.873159</td><td>1474.776416</td><td>151.041933</td><td>1625.818349</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (3, 29)\n",
-              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ residual_s ┆ residual_ ┆ residual_ ┆ sb.electr │\n",
-              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ hare_deliv ┆ share_sup ┆ share_tot ┆ ic_utilit │\n",
-              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ ery        ┆ ply       ┆ al        ┆ y         │\n",
-              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ f64       ┆ str       │\n",
-              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 418089  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
-              "│ 433835  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
-              "│ 442580  ┆ null       ┆ 0       ┆ false      ┆ … ┆ 1474.77641 ┆ 151.04193 ┆ 1625.8183 ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆ 6          ┆ 3         ┆ 49        ┆           │\n",
-              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Upgrade 02 bills: (439270, 32)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (3, 32)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>in.representative_income</th><th>in.hvac_cooling_partial_space_conditioning</th><th>month</th><th>weight</th><th>elec_fixed_charge</th><th>elec_delivery_bill</th><th>elec_supply_bill</th><th>elec_total_bill</th><th>gas_fixed_charge</th><th>gas_volumetric_bill</th><th>gas_total_bill</th><th>propane_total_bill</th><th>oil_total_bill</th><th>energy_total_bill</th><th>elec_total_bill_lmi_100</th><th>gas_total_bill_lmi_100</th><th>applied_discount_100</th><th>lmi_tier</th><th>is_lmi</th><th>elec_total_bill_lmi_40</th><th>gas_total_bill_lmi_40</th><th>applied_discount_40</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>bool</td><td>i32</td><td>bool</td><td>f64</td><td>f64</td><td>bool</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Jan&quot;</td><td>229.15313</td><td>22.0</td><td>121.68581</td><td>73.695127</td><td>217.380936</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.394526</td><td>289.775463</td><td>217.380936</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>217.380936</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Feb&quot;</td><td>229.15313</td><td>22.0</td><td>111.893841</td><td>67.764933</td><td>201.658775</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>65.576914</td><td>267.235689</td><td>201.658775</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>201.658775</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>168960.0</td><td>&quot;20% Conditioned&quot;</td><td>&quot;Mar&quot;</td><td>229.15313</td><td>22.0</td><td>116.385498</td><td>70.485162</td><td>208.87066</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>72.757213</td><td>281.627873</td><td>208.87066</td><td>0.0</td><td>false</td><td>0</td><td>false</td><td>208.87066</td><td>0.0</td><td>false</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (3, 32)\n",
-              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ elec_total ┆ gas_total ┆ applied_d ┆ sb.electr │\n",
-              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ _bill_lmi_ ┆ _bill_lmi ┆ iscount_4 ┆ ic_utilit │\n",
-              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ 40         ┆ _40       ┆ 0         ┆ y         │\n",
-              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ bool      ┆ str       │\n",
-              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 217.380936 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 201.658775 ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 208.87066  ┆ 0.0       ┆ false     ┆ cenhud    │\n",
-              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Upgrade 02 BAT: (33790, 29)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (3, 29)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bldg_id</th><th>sb.gas_utility</th><th>upgrade</th><th>postprocess_group.has_hp</th><th>postprocess_group.heating_type</th><th>heats_with_electricity</th><th>heats_with_natgas</th><th>heats_with_oil</th><th>heats_with_propane</th><th>weight</th><th>BAT_vol_delivery</th><th>BAT_vol_supply</th><th>BAT_vol_total</th><th>BAT_peak_delivery</th><th>BAT_peak_supply</th><th>BAT_peak_total</th><th>BAT_percustomer_delivery</th><th>BAT_percustomer_supply</th><th>BAT_percustomer_total</th><th>annual_bill_delivery</th><th>annual_bill_supply</th><th>annual_bill_total</th><th>economic_burden_delivery</th><th>economic_burden_supply</th><th>economic_burden_total</th><th>residual_share_delivery</th><th>residual_share_supply</th><th>residual_share_total</th><th>sb.electric_utility</th></tr><tr><td>i64</td><td>str</td><td>i32</td><td>bool</td><td>str</td><td>bool</td><td>bool</td><td>bool</td><td>bool</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>418089</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>false</td><td>false</td><td>229.15313</td><td>-2.6833e6</td><td>892.826396</td><td>-2.6824e6</td><td>-3.3456e6</td><td>-69323.056256</td><td>-3.4149e6</td><td>-3.6903e6</td><td>1156.973687</td><td>-3.6891e6</td><td>1673.990339</td><td>853.915644</td><td>2527.905983</td><td>77.214392</td><td>665.411239</td><td>742.625631</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr><tr><td>433835</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>-2.4715e6</td><td>807.830577</td><td>-2.4707e6</td><td>-2.5670e6</td><td>76790.184085</td><td>-2.4902e6</td><td>-3.6904e6</td><td>1127.572706</td><td>-3.6892e6</td><td>1562.694389</td><td>786.512804</td><td>2349.207193</td><td>42.450795</td><td>627.40938</td><td>669.860174</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr><tr><td>442580</td><td>null</td><td>2</td><td>false</td><td>&quot;fossil_fuel&quot;</td><td>false</td><td>false</td><td>true</td><td>false</td><td>229.15313</td><td>-2.1562e6</td><td>665.603985</td><td>-2.1556e6</td><td>-2.3781e6</td><td>223157.024908</td><td>-2.1550e6</td><td>-3.6905e6</td><td>1068.087687</td><td>-3.6895e6</td><td>1397.053079</td><td>686.197431</td><td>2083.25051</td><td>41.364609</td><td>586.579027</td><td>627.943636</td><td>3.6919e6</td><td>-968.469282</td><td>3.6909e6</td><td>&quot;cenhud&quot;</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (3, 29)\n",
-              "┌─────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ bldg_id ┆ sb.gas_uti ┆ upgrade ┆ postproces ┆ … ┆ residual_s ┆ residual_ ┆ residual_ ┆ sb.electr │\n",
-              "│ ---     ┆ lity       ┆ ---     ┆ s_group.ha ┆   ┆ hare_deliv ┆ share_sup ┆ share_tot ┆ ic_utilit │\n",
-              "│ i64     ┆ ---        ┆ i32     ┆ s_hp       ┆   ┆ ery        ┆ ply       ┆ al        ┆ y         │\n",
-              "│         ┆ str        ┆         ┆ ---        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│         ┆            ┆         ┆ bool       ┆   ┆ f64        ┆ f64       ┆ f64       ┆ str       │\n",
-              "╞═════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 418089  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
-              "│ 433835  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
-              "│ 442580  ┆ null       ┆ 2       ┆ false      ┆ … ┆ 3.6919e6   ┆ -968.4692 ┆ 3.6909e6  ┆ cenhud    │\n",
-              "│         ┆            ┆         ┆            ┆   ┆            ┆ 82        ┆           ┆           │\n",
-              "└─────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (11, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;(null — no gas service)&quot;</td><td>8212</td><td>1.8038e6</td><td>24.9</td></tr><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>19.1</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>17.6</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>9.2</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>8.5</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>4.8</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>4.1</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>2.2</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>2.1</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.2519e6</td><td>100.0</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "for scenario in SCENARIO_ORDER:\n",
-        "    print(f\"\\n{scenario} bills: {bills_by_scenario[scenario].shape}\")\n",
-        "    display(bills_by_scenario[scenario].head(3))\n",
-        "    print(f\"{scenario} BAT: {bat_by_scenario[scenario].shape}\")\n",
-        "    display(bat_by_scenario[scenario].head(3))"
+      "text/plain": [
+       "shape: (11, 4)\n",
+       "┌─────────────────────────┬───────────┬─────────────────────┬──────────────┐\n",
+       "│ gas_utility             ┆ buildings ┆ weighted_households ┆ pct_of_total │\n",
+       "│ ---                     ┆ ---       ┆ ---                 ┆ ---          │\n",
+       "│ str                     ┆ u32       ┆ f64                 ┆ f64          │\n",
+       "╞═════════════════════════╪═══════════╪═════════════════════╪══════════════╡\n",
+       "│ (null — no gas service) ┆ 8212      ┆ 1.8038e6            ┆ 24.9         │\n",
+       "│ coned                   ┆ 6991      ┆ 1.3887e6            ┆ 19.1         │\n",
+       "│ kedny                   ┆ 6418      ┆ 1.2747e6            ┆ 17.6         │\n",
+       "│ kedli                   ┆ 2874      ┆ 667484.048047       ┆ 9.2          │\n",
+       "│ nimo                    ┆ 2746      ┆ 617985.707991       ┆ 8.5          │\n",
+       "│ …                       ┆ …         ┆ …                   ┆ …            │\n",
+       "│ nyseg                   ┆ 1581      ┆ 348155.122257       ┆ 4.8          │\n",
+       "│ rge                     ┆ 1240      ┆ 297109.964156       ┆ 4.1          │\n",
+       "│ cenhud                  ┆ 713       ┆ 162382.688203       ┆ 2.2          │\n",
+       "│ or                      ┆ 594       ┆ 150773.083876       ┆ 2.1          │\n",
+       "│ **TOTAL**               ┆ 33790     ┆ 7.2519e6            ┆ 100.0        │\n",
+       "└─────────────────────────┴───────────┴─────────────────────┴──────────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "annual_u0 = bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n",
+    "total_bldgs = annual_u0.height\n",
+    "total_weighted = cast(float, annual_u0[\"weight\"].sum())\n",
+    "\n",
+    "print(f\"Upgrade 00 — Annual rows: {total_bldgs:,} sample buildings, {total_weighted:,.0f} weighted households\\n\")\n",
+    "\n",
+    "# Electric utility assignment\n",
+    "elec_assign = (\n",
+    "    annual_u0.with_columns(pl.col(UTILITY_COL).fill_null(\"(null)\"))\n",
+    "    .group_by(UTILITY_COL)\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"buildings\"),\n",
+    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+    "    )\n",
+    "    .with_columns(\n",
+    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+    "    )\n",
+    "    .sort(\"weighted_households\", descending=True)\n",
+    "    .rename({UTILITY_COL: \"electric_utility\"})\n",
+    ")\n",
+    "# Add summary row for electric (cast to match schema)\n",
+    "elec_assign_with_total = pl.concat(\n",
+    "    [\n",
+    "        elec_assign,\n",
+    "        pl.DataFrame(\n",
+    "            {\n",
+    "                \"electric_utility\": [\"**TOTAL**\"],\n",
+    "                \"buildings\": [elec_assign[\"buildings\"].sum()],\n",
+    "                \"weighted_households\": [elec_assign[\"weighted_households\"].sum()],\n",
+    "                \"pct_of_total\": [elec_assign[\"pct_of_total\"].sum()],\n",
+    "            },\n",
+    "            schema=elec_assign.schema,\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "print(\"Electric utility assignment:\")\n",
+    "display(elec_assign_with_total)\n",
+    "\n",
+    "# Gas utility assignment\n",
+    "gas_assign = (\n",
+    "    annual_u0.with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null — no gas service)\"))\n",
+    "    .group_by(GAS_UTILITY_COL)\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"buildings\"),\n",
+    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+    "    )\n",
+    "    .with_columns(\n",
+    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+    "    )\n",
+    "    .sort(\"weighted_households\", descending=True)\n",
+    "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
+    ")\n",
+    "# Add summary row for gas (cast to match schema)\n",
+    "gas_assign_with_total = pl.concat(\n",
+    "    [\n",
+    "        gas_assign,\n",
+    "        pl.DataFrame(\n",
+    "            {\n",
+    "                \"gas_utility\": [\"**TOTAL**\"],\n",
+    "                \"buildings\": [gas_assign[\"buildings\"].sum()],\n",
+    "                \"weighted_households\": [gas_assign[\"weighted_households\"].sum()],\n",
+    "                \"pct_of_total\": [gas_assign[\"pct_of_total\"].sum()],\n",
+    "            },\n",
+    "            schema=gas_assign.schema,\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "print(\"\\nGas utility assignment (null = no gas service / unassigned):\")\n",
+    "display(gas_assign_with_total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000010a",
+   "metadata": {},
+   "source": [
+    "### Zero gas usage verification\n",
+    "\n",
+    "Cross-check the gas utility assignment against ResStock annual gas consumption.\n",
+    "Buildings with zero annual gas consumption should align closely with the\n",
+    "\"(null — no gas service)\" category above, since gas utility assignment is gated\n",
+    "on `has_natgas_connection` (derived from nonzero gas consumption in\n",
+    "`load_curve_annual`).\n",
+    "\n",
+    "Both metrics below are **unweighted building counts** for apples-to-apples\n",
+    "comparison. Small differences can arise from sampling variance or buildings with\n",
+    "minimal gas usage that round to zero in annual totals but are flagged as connected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "a0000010b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ResStock annual load curves from:\n",
+      "  s3://data.sb/nrel/resstock/res_2024_amy2018_2/load_curve_annual/state=NY/upgrade=00/NY_upgrade00_metadata_and_annual_results.parquet\n",
+      "\n",
+      "Buildings with zero annual gas consumption: 8,212 / 33,790 (24.3%)\n",
+      "Buildings with nonzero annual gas consumption: 25,578 / 33,790 (75.7%)\n",
+      "\n",
+      "For comparison, unweighted null gas utility assignment: 8,212 / 33,790 (24.3%)\n",
+      "Difference (zero gas usage - null assignment): 0.0 percentage points\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load ResStock metadata to check gas consumption\n",
+    "RESSTOCK_RELEASE = \"res_2024_amy2018_2\"\n",
+    "RESSTOCK_S3_BASE = \"s3://data.sb/nrel/resstock\"\n",
+    "GAS_CONSUMPTION_COL = \"out.natural_gas.total.energy_consumption.kwh\"\n",
+    "\n",
+    "# Construct path to load_curve_annual for this state and upgrade 00\n",
+    "# Filename pattern: {STATE}_upgrade{UPGRADE}_metadata_and_annual_results.parquet\n",
+    "upgrade_padded = \"00\"\n",
+    "annual_filename = f\"{STATE.upper()}_upgrade{upgrade_padded}_metadata_and_annual_results.parquet\"\n",
+    "annual_path = f\"{RESSTOCK_S3_BASE}/{RESSTOCK_RELEASE}/load_curve_annual/state={STATE.upper()}/upgrade={upgrade_padded}/{annual_filename}\"\n",
+    "\n",
+    "print(f\"Loading ResStock annual load curves from:\\n  {annual_path}\\n\")\n",
+    "\n",
+    "# Load annual data and check for gas consumption column\n",
+    "annual_lf = pl.scan_parquet(annual_path)\n",
+    "\n",
+    "# Select just bldg_id and gas consumption\n",
+    "gas_check = annual_lf.select([BLDG_ID, GAS_CONSUMPTION_COL]).collect()\n",
+    "\n",
+    "# Count buildings with zero gas consumption\n",
+    "zero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) == 0).height\n",
+    "nonzero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) > 0).height\n",
+    "total_bldgs_annual = gas_check.height\n",
+    "\n",
+    "pct_zero_gas = zero_gas / total_bldgs_annual * 100\n",
+    "pct_nonzero_gas = nonzero_gas / total_bldgs_annual * 100\n",
+    "\n",
+    "print(f\"Buildings with zero annual gas consumption: {zero_gas:,} / {total_bldgs_annual:,} ({pct_zero_gas:.1f}%)\")\n",
+    "print(\n",
+    "    f\"Buildings with nonzero annual gas consumption: {nonzero_gas:,} / {total_bldgs_annual:,} ({pct_nonzero_gas:.1f}%)\"\n",
+    ")\n",
+    "\n",
+    "# Compare to null gas utility assignment (unweighted building count)\n",
+    "try:\n",
+    "    null_gas_bldgs = gas_assign.filter(pl.col(\"gas_utility\") == \"(null — no gas service)\")[\"buildings\"][0]\n",
+    "    pct_null_gas_unweighted = null_gas_bldgs / total_bldgs * 100\n",
+    "    print(\n",
+    "        f\"\\nFor comparison, unweighted null gas utility assignment: {null_gas_bldgs:,} / {total_bldgs:,} ({pct_null_gas_unweighted:.1f}%)\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Difference (zero gas usage - null assignment): {pct_zero_gas - pct_null_gas_unweighted:.1f} percentage points\"\n",
+    "    )\n",
+    "except (NameError, IndexError):\n",
+    "    print(\"\\n(Run the utility assignment cell above first to compare with null gas assignment)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000011",
+   "metadata": {},
+   "source": [
+    "### 2b: Heating type and fuel composition\n",
+    "\n",
+    "What fraction of buildings heat with each fuel or technology under upgrade 00\n",
+    "(baseline)? This breakdown drives cross-subsidy and bill-change patterns throughout\n",
+    "the analysis.\n",
+    "\n",
+    "The `postprocess_group.heating_type` column classifies buildings into three groups\n",
+    "used by CAIRO's post-processing:\n",
+    "\n",
+    "- **Fossil fuel** — primary heat source is natural gas, oil, or propane\n",
+    "- **Electric resistance** — primary heat source is electric resistance\n",
+    "- **Existing heat pump** — already has a heat pump in the baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a0000012",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline heating-type breakdown (upgrade 00):\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "b3a50de2",
-      "metadata": {},
-      "source": [
-        "## Structural quality checks\n",
-        "\n",
-        "These checks catch incomplete post-processing, duplicated building rows, unmatched bill and BAT populations, broken bill-component arithmetic, and BAT identities that do not reconcile."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+      "(Deprecated in version 1.0.0)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "8441c408",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (2, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>bill rows</th><th>BAT rows</th><th>buildings in annual bills</th><th>buildings in BAT</th><th>annual bill duplicate IDs</th><th>BAT duplicate IDs</th><th>bill IDs absent from BAT</th><th>BAT IDs absent from bills</th><th>max electric component error</th><th>max energy component error</th><th>max BAT identity error</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.8190e-12</td><td>1.1369e-12</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.7337e-12</td><td>2.3283e-10</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (2, 12)\n",
-              "┌───────────┬───────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-              "│ scenario  ┆ bill rows ┆ BAT rows ┆ buildings ┆ … ┆ BAT IDs   ┆ max       ┆ max       ┆ max BAT   │\n",
-              "│ ---       ┆ ---       ┆ ---      ┆ in annual ┆   ┆ absent    ┆ electric  ┆ energy    ┆ identity  │\n",
-              "│ str       ┆ i64       ┆ i64      ┆ bills     ┆   ┆ from      ┆ component ┆ component ┆ error     │\n",
-              "│           ┆           ┆          ┆ ---       ┆   ┆ bills     ┆ error     ┆ error     ┆ ---       │\n",
-              "│           ┆           ┆          ┆ i64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ f64       │\n",
-              "│           ┆           ┆          ┆           ┆   ┆ i64       ┆ f64       ┆ f64       ┆           │\n",
-              "╞═══════════╪═══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ Upgrade   ┆ 439270    ┆ 33790    ┆ 33790     ┆ … ┆ 0         ┆ 9.0949e-1 ┆ 1.8190e-1 ┆ 1.1369e-1 │\n",
-              "│ 00        ┆           ┆          ┆           ┆   ┆           ┆ 3         ┆ 2         ┆ 2         │\n",
-              "│ Upgrade   ┆ 439270    ┆ 33790    ┆ 33790     ┆ … ┆ 0         ┆ 9.0949e-1 ┆ 1.7337e-1 ┆ 2.3283e-1 │\n",
-              "│ 02        ┆           ┆          ┆           ┆   ┆           ┆ 3         ┆ 2         ┆ 0         │\n",
-              "└───────────┴───────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "All structural checks passed.\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (4, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>heating_label</th><th>buildings</th><th>weighted_households</th><th>heats_natgas</th><th>heats_oil</th><th>heats_propane</th><th>heats_electricity</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6.3495e6</td><td>20201</td><td>7067</td><td>1280</td><td>0</td><td>87.6</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504.118573</td><td>0</td><td>0</td><td>0</td><td>3070</td><td>9.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368.027554</td><td>0</td><td>0</td><td>0</td><td>918</td><td>2.7</td></tr><tr><td>null</td><td>248</td><td>49529.864887</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "qa_rows: list[dict[str, object]] = []\n",
-        "\n",
-        "for scenario in SCENARIO_ORDER:\n",
-        "    bills = bills_by_scenario[scenario]\n",
-        "    bat = bat_by_scenario[scenario]\n",
-        "    annual = bills.filter(pl.col(\"month\") == \"Annual\")\n",
-        "\n",
-        "    bill_components_error = annual.select(\n",
-        "        (\n",
-        "            pl.col(\"elec_total_bill\")\n",
-        "            - pl.col(\"elec_fixed_charge\")\n",
-        "            - pl.col(\"elec_delivery_bill\")\n",
-        "            - pl.col(\"elec_supply_bill\")\n",
-        "        ).abs().max()\n",
-        "    ).item()\n",
-        "    energy_components_error = annual.select(\n",
-        "        (\n",
-        "            pl.col(\"energy_total_bill\")\n",
-        "            - pl.col(\"elec_total_bill\")\n",
-        "            - pl.col(\"gas_total_bill\")\n",
-        "            - pl.col(\"propane_total_bill\")\n",
-        "            - pl.col(\"oil_total_bill\")\n",
-        "        ).abs().max()\n",
-        "    ).item()\n",
-        "    bat_identity_error = bat.select(\n",
-        "        (\n",
-        "            pl.col(\"BAT_percustomer_total\")\n",
-        "            - pl.col(\"annual_bill_total\")\n",
-        "            + pl.col(\"economic_burden_total\")\n",
-        "            + pl.col(\"residual_share_total\")\n",
-        "        ).abs().max()\n",
-        "    ).item()\n",
-        "\n",
-        "    qa_rows.append(\n",
-        "        {\n",
-        "            \"scenario\": scenario,\n",
-        "            \"bill rows\": bills.height,\n",
-        "            \"BAT rows\": bat.height,\n",
-        "            \"buildings in annual bills\": annual[BLDG_ID].n_unique(),\n",
-        "            \"buildings in BAT\": bat[BLDG_ID].n_unique(),\n",
-        "            \"annual bill duplicate IDs\": annual.height - annual[BLDG_ID].n_unique(),\n",
-        "            \"BAT duplicate IDs\": bat.height - bat[BLDG_ID].n_unique(),\n",
-        "            \"bill IDs absent from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
-        "            \"BAT IDs absent from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
-        "            \"max electric component error\": float(bill_components_error or 0),\n",
-        "            \"max energy component error\": float(energy_components_error or 0),\n",
-        "            \"max BAT identity error\": float(bat_identity_error or 0),\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "qa = pl.DataFrame(qa_rows)\n",
-        "display(qa)\n",
-        "\n",
-        "assert qa[\"annual bill duplicate IDs\"].sum() == 0\n",
-        "assert qa[\"BAT duplicate IDs\"].sum() == 0\n",
-        "assert qa[\"bill IDs absent from BAT\"].sum() == 0\n",
-        "assert qa[\"BAT IDs absent from bills\"].sum() == 0\n",
-        "assert cast(float, qa[\"max electric component error\"].max()) < 0.01\n",
-        "assert cast(float, qa[\"max energy component error\"].max()) < 0.01\n",
-        "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01\n",
-        "print(\"All structural checks passed.\")"
+      "text/plain": [
+       "shape: (4, 8)\n",
+       "┌────────────┬───────────┬────────────┬────────────┬───────────┬───────────┬───────────┬───────────┐\n",
+       "│ heating_la ┆ buildings ┆ weighted_h ┆ heats_natg ┆ heats_oil ┆ heats_pro ┆ heats_ele ┆ pct_of_to │\n",
+       "│ bel        ┆ ---       ┆ ouseholds  ┆ as         ┆ ---       ┆ pane      ┆ ctricity  ┆ tal       │\n",
+       "│ ---        ┆ u32       ┆ ---        ┆ ---        ┆ i32       ┆ ---       ┆ ---       ┆ ---       │\n",
+       "│ str        ┆           ┆ f64        ┆ i32        ┆           ┆ i32       ┆ i32       ┆ f64       │\n",
+       "╞════════════╪═══════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+       "│ Fossil     ┆ 29554     ┆ 6.3495e6   ┆ 20201      ┆ 7067      ┆ 1280      ┆ 0         ┆ 87.6      │\n",
+       "│ fuel       ┆           ┆            ┆            ┆           ┆           ┆           ┆           │\n",
+       "│ Electric   ┆ 3070      ┆ 654504.118 ┆ 0          ┆ 0         ┆ 0         ┆ 3070      ┆ 9.0       │\n",
+       "│ resistance ┆           ┆ 573        ┆            ┆           ┆           ┆           ┆           │\n",
+       "│ Existing   ┆ 918       ┆ 198368.027 ┆ 0          ┆ 0         ┆ 0         ┆ 918       ┆ 2.7       │\n",
+       "│ heat pump  ┆           ┆ 554        ┆            ┆           ┆           ┆           ┆           │\n",
+       "│ null       ┆ 248       ┆ 49529.8648 ┆ 0          ┆ 0         ┆ 0         ┆ 0         ┆ 0.7       │\n",
+       "│            ┆           ┆ 87         ┆            ┆           ┆           ┆           ┆           │\n",
+       "└────────────┴───────────┴────────────┴────────────┴───────────┴───────────┴───────────┴───────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "09d12883",
-      "metadata": {},
-      "source": [
-        "## Shared classifications and weighted statistics\n",
-        "\n",
-        "Customer groups are defined from the upgrade 00 heating system and carried into upgrade 02. This preserves the meaningful comparison: what happened to homes that started with fossil fuel, electric resistance, or an existing heat pump?"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x70812ad8ecf0>"
       ]
+     },
+     "execution_count": 44,
+     "metadata": {
+      "image/png": {
+       "height": 400,
+       "width": 1050
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def add_heating_label(df: pl.DataFrame) -> pl.DataFrame:\n",
+    "    \"\"\"Map postprocess_group.heating_type codes to human-readable labels.\"\"\"\n",
+    "    return df.with_columns(\n",
+    "        pl.col(HEATING_TYPE_COL)\n",
+    "        .fill_null(\"Other\")\n",
+    "        .replace(HEATING_TYPE_LABELS, default=pl.col(HEATING_TYPE_COL))\n",
+    "        .alias(\"heating_label\")\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "heating_df = add_heating_label(annual_u0)\n",
+    "heating_stats = (\n",
+    "    heating_df.group_by(\"heating_label\")\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"buildings\"),\n",
+    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+    "        pl.col(\"heats_with_natgas\").cast(pl.Int32).sum().alias(\"heats_natgas\"),\n",
+    "        pl.col(\"heats_with_oil\").cast(pl.Int32).sum().alias(\"heats_oil\"),\n",
+    "        pl.col(\"heats_with_propane\").cast(pl.Int32).sum().alias(\"heats_propane\"),\n",
+    "        pl.col(\"heats_with_electricity\").cast(pl.Int32).sum().alias(\"heats_electricity\"),\n",
+    "    )\n",
+    "    .with_columns(\n",
+    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+    "    )\n",
+    "    .sort(\"weighted_households\", descending=True)\n",
+    ")\n",
+    "print(\"Baseline heating-type breakdown (upgrade 00):\")\n",
+    "display(heating_stats)\n",
+    "\n",
+    "avail_order = [h for h in HEATING_ORDER if h in heating_df[\"heating_label\"].unique().to_list()]\n",
+    "chart_data = (\n",
+    "    heating_stats.filter(pl.col(\"heating_label\").is_in(avail_order))\n",
+    "    .with_columns(pl.col(\"heating_label\").cast(pl.Enum(avail_order)))\n",
+    "    .sort(\"heating_label\")\n",
+    ")\n",
+    "(\n",
+    "    ggplot(chart_data, aes(x=\"heating_label\", y=\"pct_of_total\"))\n",
+    "    + geom_col(fill=SB_COLORS[\"sky\"], width=0.6)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"pct_of_total\"),\n",
+    "        format_string=\"{:.1f}%\",\n",
+    "        nudge_y=0.4,\n",
+    "        size=10,\n",
+    "        va=\"bottom\",\n",
+    "    )\n",
+    "    + scale_y_continuous(expand=(0, 0, 0.12, 0))\n",
+    "    + labs(\n",
+    "        x=\"\",\n",
+    "        y=\"% of weighted households\",\n",
+    "        title=\"Baseline heating type distribution (upgrade 00)\",\n",
+    "    )\n",
+    "    + theme_switchbox()\n",
+    "    + theme(figure_size=(10.5, 4))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000013",
+   "metadata": {},
+   "source": [
+    "### 2c: Annual gas spending by gas utility\n",
+    "\n",
+    "Summary of annual gas bills (`gas_total_bill`, in dollars) for buildings with non-zero\n",
+    "gas bills, grouped by assigned gas utility.\n",
+    "\n",
+    "> **Note**: `gas_total_bill` is a bill amount (dollars), not volumetric consumption\n",
+    "> (kWh or therms). For actual consumption data, cross-reference the ResStock monthly\n",
+    "> load curves column `out.natural_gas.total.energy_consumption`.\n",
+    "\n",
+    "Implausibly wide min/max ranges, or extreme mean-to-median ratios, often trace to\n",
+    "incorrect tariff calibration or misassigned buildings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a0000014",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annual gas spending stats by gas utility\n",
+      "(buildings with gas_total_bill > 0; unweighted mean/median):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "d829d8ae",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Baseline heating groups:\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (4, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>baseline_heating_type</th><th>sample buildings</th><th>weighted households</th></tr><tr><td>str</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6.349536e6</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368.0</td></tr><tr><td>&quot;Other&quot;</td><td>248</td><td>49530.0</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (4, 3)\n",
-              "┌───────────────────────┬──────────────────┬─────────────────────┐\n",
-              "│ baseline_heating_type ┆ sample buildings ┆ weighted households │\n",
-              "│ ---                   ┆ ---              ┆ ---                 │\n",
-              "│ str                   ┆ u32              ┆ f64                 │\n",
-              "╞═══════════════════════╪══════════════════╪═════════════════════╡\n",
-              "│ Fossil fuel           ┆ 29554            ┆ 6.349536e6          │\n",
-              "│ Electric resistance   ┆ 3070             ┆ 654504.0            │\n",
-              "│ Existing heat pump    ┆ 918              ┆ 198368.0            │\n",
-              "│ Other                 ┆ 248              ┆ 49530.0             │\n",
-              "└───────────────────────┴──────────────────┴─────────────────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (9, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings_with_gas_bill</th><th>weighted_households</th><th>min_gas_bill</th><th>max_gas_bill</th><th>mean_gas_bill</th><th>median_gas_bill</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>400.0</td><td>12166.0</td><td>1362.0</td><td>927.0</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>251.0</td><td>7240.0</td><td>1462.0</td><td>1257.0</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>302.0</td><td>6189.0</td><td>1792.0</td><td>1756.0</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>265.0</td><td>4005.0</td><td>1063.0</td><td>998.0</td></tr><tr><td>&quot;nfg&quot;</td><td>2421</td><td>540904.956926</td><td>238.0</td><td>3337.0</td><td>1007.0</td><td>957.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>201.0</td><td>4667.0</td><td>1091.0</td><td>1005.0</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>249.0</td><td>3639.0</td><td>1164.0</td><td>1098.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>325.0</td><td>7328.0</td><td>1374.0</td><td>968.0</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>278.0</td><td>5743.0</td><td>1658.0</td><td>1449.0</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n",
-        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n",
-        "    weight_sum = cast(float, valid[weight].sum())\n",
-        "    if valid.is_empty() or weight_sum == 0:\n",
-        "        return float(\"nan\")\n",
-        "    weighted_sum = cast(float, (valid[value] * valid[weight]).sum())\n",
-        "    return weighted_sum / weight_sum\n",
-        "\n",
-        "\n",
-        "def weighted_quantile(\n",
-        "    df: pl.DataFrame,\n",
-        "    value: str,\n",
-        "    q: float,\n",
-        "    weight: str = \"weight\",\n",
-        ") -> float:\n",
-        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n",
-        "    weight_sum = cast(float, valid[weight].sum())\n",
-        "    if valid.is_empty() or weight_sum == 0:\n",
-        "        return float(\"nan\")\n",
-        "    cutoff = weight_sum * q\n",
-        "    return float(valid.filter(pl.col(weight).cum_sum() >= cutoff)[value][0])\n",
-        "\n",
-        "\n",
-        "def add_baseline_heating_group(df: pl.DataFrame) -> pl.DataFrame:\n",
-        "    return df.with_columns(\n",
-        "        pl.when(pl.col(HEATING_TYPE_COL) == \"fossil_fuel\")\n",
-        "        .then(pl.lit(\"Fossil fuel\"))\n",
-        "        .when(pl.col(HEATING_TYPE_COL) == \"electrical_resistance\")\n",
-        "        .then(pl.lit(\"Electric resistance\"))\n",
-        "        .when(pl.col(HEATING_TYPE_COL) == \"heat_pump\")\n",
-        "        .then(pl.lit(\"Existing heat pump\"))\n",
-        "        .otherwise(pl.lit(\"Other\"))\n",
-        "        .alias(\"baseline_heating_type\")\n",
-        "    )\n",
-        "\n",
-        "\n",
-        "baseline_bills = add_baseline_heating_group(\n",
-        "    bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n",
-        ")\n",
-        "baseline_groups = baseline_bills.select(BLDG_ID, \"baseline_heating_type\")\n",
-        "GROUP_ORDER = [\n",
-        "    group\n",
-        "    for group in [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n",
-        "    if group in baseline_bills[\"baseline_heating_type\"].unique().to_list()\n",
-        "]\n",
-        "\n",
-        "print(\"Baseline heating groups:\")\n",
-        "display(\n",
-        "    baseline_bills.group_by(\"baseline_heating_type\").agg(\n",
-        "        pl.len().alias(\"sample buildings\"),\n",
-        "        pl.col(\"weight\").sum().round(0).alias(\"weighted households\"),\n",
-        "    ).sort(\"weighted households\", descending=True)\n",
-        ")"
+      "text/plain": [
+       "shape: (9, 7)\n",
+       "┌─────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ gas_utility ┆ buildings_wi ┆ weighted_ho ┆ min_gas_bil ┆ max_gas_bil ┆ mean_gas_bi ┆ median_gas_ │\n",
+       "│ ---         ┆ th_gas_bill  ┆ useholds    ┆ l           ┆ l           ┆ ll          ┆ bill        │\n",
+       "│ str         ┆ ---          ┆ ---         ┆ ---         ┆ ---         ┆ ---         ┆ ---         │\n",
+       "│             ┆ u32          ┆ f64         ┆ f64         ┆ f64         ┆ f64         ┆ f64         │\n",
+       "╞═════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ coned       ┆ 6991         ┆ 1.3887e6    ┆ 400.0       ┆ 12166.0     ┆ 1362.0      ┆ 927.0       │\n",
+       "│ kedny       ┆ 6418         ┆ 1.2747e6    ┆ 251.0       ┆ 7240.0      ┆ 1462.0      ┆ 1257.0      │\n",
+       "│ kedli       ┆ 2874         ┆ 667484.0480 ┆ 302.0       ┆ 6189.0      ┆ 1792.0      ┆ 1756.0      │\n",
+       "│             ┆              ┆ 47          ┆             ┆             ┆             ┆             │\n",
+       "│ nimo        ┆ 2746         ┆ 617985.7079 ┆ 265.0       ┆ 4005.0      ┆ 1063.0      ┆ 998.0       │\n",
+       "│             ┆              ┆ 91          ┆             ┆             ┆             ┆             │\n",
+       "│ nfg         ┆ 2421         ┆ 540904.9569 ┆ 238.0       ┆ 3337.0      ┆ 1007.0      ┆ 957.0       │\n",
+       "│             ┆              ┆ 26          ┆             ┆             ┆             ┆             │\n",
+       "│ nyseg       ┆ 1581         ┆ 348155.1222 ┆ 201.0       ┆ 4667.0      ┆ 1091.0      ┆ 1005.0      │\n",
+       "│             ┆              ┆ 57          ┆             ┆             ┆             ┆             │\n",
+       "│ rge         ┆ 1240         ┆ 297109.9641 ┆ 249.0       ┆ 3639.0      ┆ 1164.0      ┆ 1098.0      │\n",
+       "│             ┆              ┆ 56          ┆             ┆             ┆             ┆             │\n",
+       "│ cenhud      ┆ 713          ┆ 162382.6882 ┆ 325.0       ┆ 7328.0      ┆ 1374.0      ┆ 968.0       │\n",
+       "│             ┆              ┆ 03          ┆             ┆             ┆             ┆             │\n",
+       "│ or          ┆ 594          ┆ 150773.0838 ┆ 278.0       ┆ 5743.0      ┆ 1658.0      ┆ 1449.0      │\n",
+       "│             ┆              ┆ 76          ┆             ┆             ┆             ┆             │\n",
+       "└─────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gas_bill_stats = (\n",
+    "    annual_u0.filter(pl.col(\"gas_total_bill\") > 0)\n",
+    "    .with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null)\"))\n",
+    "    .group_by(GAS_UTILITY_COL)\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"buildings_with_gas_bill\"),\n",
+    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+    "        pl.col(\"gas_total_bill\").min().round(0).alias(\"min_gas_bill\"),\n",
+    "        pl.col(\"gas_total_bill\").max().round(0).alias(\"max_gas_bill\"),\n",
+    "        pl.col(\"gas_total_bill\").mean().round(0).alias(\"mean_gas_bill\"),\n",
+    "        pl.col(\"gas_total_bill\").median().round(0).alias(\"median_gas_bill\"),\n",
+    "    )\n",
+    "    .sort(\"buildings_with_gas_bill\", descending=True)\n",
+    "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
+    ")\n",
+    "print(\"Annual gas spending stats by gas utility\\n(buildings with gas_total_bill > 0; unweighted mean/median):\")\n",
+    "display(gas_bill_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000015",
+   "metadata": {},
+   "source": [
+    "### 2d: Annual electric bill by electric utility\n",
+    "\n",
+    "Summary of annual total electric bills (`elec_total_bill = fixed + delivery volumetric\n",
+    "+ supply`), grouped by electric utility. The decomposition into fixed charge, delivery,\n",
+    "and supply components helps identify which tariff component is driving outliers.\n",
+    "\n",
+    "Cross-check median values against published tariff rates for a rough sanity check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "a0000016",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "4e3a8cea",
-      "metadata": {},
-      "source": [
-        "## Annual bill changes from upgrade 00 to upgrade 02\n",
-        "\n",
-        "The joined table compares the same building before and after the heat-pump upgrade. Negative changes are savings; positive changes are bill increases. Delivered-fuel bills include gas, propane, and oil."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "81d0b011",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (4, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>baseline heating type</th><th>sample buildings</th><th>weighted households</th><th>mean bill before</th><th>mean bill after</th><th>mean annual change</th><th>median annual change</th><th>households saving</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6349536</td><td>3460.63129</td><td>3356.87054</td><td>-103.76075</td><td>1.258823</td><td>0.470479</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504</td><td>3514.585157</td><td>2245.904915</td><td>-1268.680242</td><td>-844.206978</td><td>0.971394</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368</td><td>3537.259437</td><td>2745.8309</td><td>-791.428537</td><td>-420.135112</td><td>0.989821</td></tr><tr><td>&quot;Other&quot;</td><td>248</td><td>49530</td><td>1571.728023</td><td>1577.898763</td><td>6.17074</td><td>59.589828</td><td>0.426295</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (4, 8)\n",
-              "┌────────────┬────────────┬────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n",
-              "│ baseline   ┆ sample     ┆ weighted   ┆ mean bill ┆ mean bill ┆ mean      ┆ median    ┆ household │\n",
-              "│ heating    ┆ buildings  ┆ households ┆ before    ┆ after     ┆ annual    ┆ annual    ┆ s saving  │\n",
-              "│ type       ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ change    ┆ change    ┆ ---       │\n",
-              "│ ---        ┆ i64        ┆ i64        ┆ f64       ┆ f64       ┆ ---       ┆ ---       ┆ f64       │\n",
-              "│ str        ┆            ┆            ┆           ┆           ┆ f64       ┆ f64       ┆           │\n",
-              "╞════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ Fossil     ┆ 29554      ┆ 6349536    ┆ 3460.6312 ┆ 3356.8705 ┆ -103.7607 ┆ 1.258823  ┆ 0.470479  │\n",
-              "│ fuel       ┆            ┆            ┆ 9         ┆ 4         ┆ 5         ┆           ┆           │\n",
-              "│ Electric   ┆ 3070       ┆ 654504     ┆ 3514.5851 ┆ 2245.9049 ┆ -1268.680 ┆ -844.2069 ┆ 0.971394  │\n",
-              "│ resistance ┆            ┆            ┆ 57        ┆ 15        ┆ 242       ┆ 78        ┆           │\n",
-              "│ Existing   ┆ 918        ┆ 198368     ┆ 3537.2594 ┆ 2745.8309 ┆ -791.4285 ┆ -420.1351 ┆ 0.989821  │\n",
-              "│ heat pump  ┆            ┆            ┆ 37        ┆           ┆ 37        ┆ 12        ┆           │\n",
-              "│ Other      ┆ 248        ┆ 49530      ┆ 1571.7280 ┆ 1577.8987 ┆ 6.17074   ┆ 59.589828 ┆ 0.426295  │\n",
-              "│            ┆            ┆            ┆ 23        ┆ 63        ┆           ┆           ┆           │\n",
-              "└────────────┴────────────┴────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (7, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>min_elec_total</th><th>max_elec_total</th><th>mean_elec_total</th><th>median_elec_total</th><th>median_fixed</th><th>median_delivery</th><th>median_supply</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>266.0</td><td>26915.0</td><td>1597.0</td><td>1251.0</td><td>255.0</td><td>608.0</td><td>388.0</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>259.0</td><td>17161.0</td><td>1679.0</td><td>1408.0</td><td>215.0</td><td>578.0</td><td>615.0</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>212.0</td><td>14787.0</td><td>2090.0</td><td>1900.0</td><td>197.0</td><td>841.0</td><td>858.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>286.0</td><td>19078.0</td><td>2042.0</td><td>1714.0</td><td>239.0</td><td>755.0</td><td>721.0</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>332.0</td><td>11209.0</td><td>1710.0</td><td>1399.0</td><td>288.0</td><td>510.0</td><td>604.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>368.0</td><td>17252.0</td><td>2388.0</td><td>2031.0</td><td>264.0</td><td>1064.0</td><td>702.0</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>349.0</td><td>14370.0</td><td>2045.0</td><td>1872.0</td><td>289.0</td><td>863.0</td><td>716.0</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "annual_u2 = bills_by_scenario[\"Upgrade 02\"].filter(pl.col(\"month\") == \"Annual\")\n",
-        "\n",
-        "bill_comparison = (\n",
-        "    baseline_bills.select(\n",
-        "        BLDG_ID,\n",
-        "        UTILITY_COL,\n",
-        "        \"baseline_heating_type\",\n",
-        "        \"weight\",\n",
-        "        pl.col(\"elec_total_bill\").alias(\"electric_before\"),\n",
-        "        (\n",
-        "            pl.col(\"gas_total_bill\")\n",
-        "            + pl.col(\"propane_total_bill\")\n",
-        "            + pl.col(\"oil_total_bill\")\n",
-        "        ).alias(\"delivered_fuel_before\"),\n",
-        "        pl.col(\"energy_total_bill\").alias(\"energy_before\"),\n",
-        "    )\n",
-        "    .join(\n",
-        "        annual_u2.select(\n",
-        "            BLDG_ID,\n",
-        "            pl.col(\"elec_total_bill\").alias(\"electric_after\"),\n",
-        "            (\n",
-        "                pl.col(\"gas_total_bill\")\n",
-        "                + pl.col(\"propane_total_bill\")\n",
-        "                + pl.col(\"oil_total_bill\")\n",
-        "            ).alias(\"delivered_fuel_after\"),\n",
-        "            pl.col(\"energy_total_bill\").alias(\"energy_after\"),\n",
-        "        ),\n",
-        "        on=BLDG_ID,\n",
-        "        how=\"inner\",\n",
-        "        validate=\"1:1\",\n",
-        "    )\n",
-        "    .with_columns(\n",
-        "        (pl.col(\"electric_after\") - pl.col(\"electric_before\")).alias(\"electric_change\"),\n",
-        "        (pl.col(\"delivered_fuel_after\") - pl.col(\"delivered_fuel_before\")).alias(\"delivered_fuel_change\"),\n",
-        "        (pl.col(\"energy_after\") - pl.col(\"energy_before\")).alias(\"energy_change\"),\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "bill_summary_rows: list[dict[str, object]] = []\n",
-        "for group in GROUP_ORDER:\n",
-        "    group_df = bill_comparison.filter(pl.col(\"baseline_heating_type\") == group)\n",
-        "    total_weight = float(group_df[\"weight\"].sum())\n",
-        "    saving_weight = float(group_df.filter(pl.col(\"energy_change\") < 0)[\"weight\"].sum())\n",
-        "    bill_summary_rows.append(\n",
-        "        {\n",
-        "            \"baseline heating type\": group,\n",
-        "            \"sample buildings\": group_df.height,\n",
-        "            \"weighted households\": round(total_weight),\n",
-        "            \"mean bill before\": weighted_mean(group_df, \"energy_before\"),\n",
-        "            \"mean bill after\": weighted_mean(group_df, \"energy_after\"),\n",
-        "            \"mean annual change\": weighted_mean(group_df, \"energy_change\"),\n",
-        "            \"median annual change\": weighted_quantile(group_df, \"energy_change\", 0.5),\n",
-        "            \"households saving\": saving_weight / total_weight if total_weight else float(\"nan\"),\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "bill_summary = pl.DataFrame(bill_summary_rows)\n",
-        "display(bill_summary)"
+      "text/plain": [
+       "shape: (7, 10)\n",
+       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
+       "│ electric_ ┆ buildings ┆ weighted_ ┆ min_elec_ ┆ … ┆ median_el ┆ median_fi ┆ median_de ┆ median_s │\n",
+       "│ utility   ┆ ---       ┆ household ┆ total     ┆   ┆ ec_total  ┆ xed       ┆ livery    ┆ upply    │\n",
+       "│ ---       ┆ u32       ┆ s         ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
+       "│ str       ┆           ┆ ---       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
+       "│           ┆           ┆ f64       ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
+       "│ coned     ┆ 15435     ┆ 3.0640e6  ┆ 266.0     ┆ … ┆ 1251.0    ┆ 255.0     ┆ 608.0     ┆ 388.0    │\n",
+       "│ nimo      ┆ 6791      ┆ 1.532294e ┆ 259.0     ┆ … ┆ 1408.0    ┆ 215.0     ┆ 578.0     ┆ 615.0    │\n",
+       "│           ┆           ┆ 6         ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ psegli    ┆ 4427      ┆ 1.0300e6  ┆ 212.0     ┆ … ┆ 1900.0    ┆ 197.0     ┆ 841.0     ┆ 858.0    │\n",
+       "│ nyseg     ┆ 3682      ┆ 792300.0  ┆ 286.0     ┆ … ┆ 1714.0    ┆ 239.0     ┆ 755.0     ┆ 721.0    │\n",
+       "│ rge       ┆ 1444      ┆ 351481.0  ┆ 332.0     ┆ … ┆ 1399.0    ┆ 288.0     ┆ 510.0     ┆ 604.0    │\n",
+       "│ cenhud    ┆ 1182      ┆ 270859.0  ┆ 368.0     ┆ … ┆ 2031.0    ┆ 264.0     ┆ 1064.0    ┆ 702.0    │\n",
+       "│ or        ┆ 829       ┆ 211011.0  ┆ 349.0     ┆ … ┆ 1872.0    ┆ 289.0     ┆ 863.0     ┆ 716.0    │\n",
+       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elec_bill_stats = (\n",
+    "    annual_u0.group_by(UTILITY_COL)\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"buildings\"),\n",
+    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+    "        pl.col(\"elec_total_bill\").min().round(0).alias(\"min_elec_total\"),\n",
+    "        pl.col(\"elec_total_bill\").max().round(0).alias(\"max_elec_total\"),\n",
+    "        pl.col(\"elec_total_bill\").mean().round(0).alias(\"mean_elec_total\"),\n",
+    "        pl.col(\"elec_total_bill\").median().round(0).alias(\"median_elec_total\"),\n",
+    "        pl.col(\"elec_fixed_charge\").median().round(0).alias(\"median_fixed\"),\n",
+    "        pl.col(\"elec_delivery_bill\").median().round(0).alias(\"median_delivery\"),\n",
+    "        pl.col(\"elec_supply_bill\").median().round(0).alias(\"median_supply\"),\n",
+    "    )\n",
+    "    .sort(\"buildings\", descending=True)\n",
+    "    .rename({UTILITY_COL: \"electric_utility\"})\n",
+    ")\n",
+    "print(\"Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\")\n",
+    "display(elec_bill_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000017",
+   "metadata": {},
+   "source": [
+    "## Section 3: Structural quality checks\n",
+    "\n",
+    "These checks catch common post-processing errors before any analysis proceeds:\n",
+    "\n",
+    "- **Duplicate building IDs** — indicate a failed post-processing merge or join.\n",
+    "- **ID mismatches** — buildings that made it into one table but not the other.\n",
+    "- **Electric component arithmetic**:\n",
+    "  `elec_total_bill ≈ elec_fixed_charge + elec_delivery_bill + elec_supply_bill`\n",
+    "- **Energy total arithmetic**:\n",
+    "  `energy_total_bill ≈ elec_total_bill + gas_total_bill + propane_total_bill + oil_total_bill`\n",
+    "- **BAT identity**:\n",
+    "  `BAT_percustomer_total ≈ annual_bill_total − economic_burden_total − residual_share_total`\n",
+    "\n",
+    "All assertions must pass before continuing. A failed assertion points to a specific\n",
+    "post-processing step to investigate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a0000018",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "9c25159c",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
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QGRmpL7/8MsUGrcuXL+udd96xaxgtVKiQChcurCNHjtgFbiQkJOi9995Tnjx59Mgjj5iW9+OPP2rcuHEO04OCghQcHKxjx47p0qVLtum3bt3S8OHDlTVrVj366KNO65qQkKCPP/7Y9n/OnDlVunRpxcTE6Pjx47p165Zt3q+//qrq1auradOmDuUYhqGxY8faBTN4e3urcOHCCgoKUlxcnM6ePavz58/b5oeFhWn48OH67LPPnL7R99dffyk2Ntb2f+HChVW4cGGdOXPG1hNH8vWeO3eucubM6XS9cW9KSEiwNdoXL1483YLC1q1bpylTpkiSqlSposcee0yFCxdWVFSU9u7dq/nz5+vmzZuaMGGCSpcu7fDme0REhC0wqnjx4mrXrp2KFSumW7du6dChQ1q6dKnOnj2rCRMm2IaxSS8xMTEaMWKETp48KS8vLz3++OOqUqWKChYsqHPnzmn79u36448/tG/fPo0YMULjxo1z2kvF3RYXF2cLZsidO7datWqlBx98UNmyZVNkZKR+//13hYeHa8eOHZo5c6bpW8PJLVq0SFeuXFH37t1VunRpSbd7XPj555918+ZN/fLLL6pRo4ZpzzgrVqzQrFmzJEk5cuRQmzZtVLZsWfn7++v06dP6559/NGHCBOXOnTvN633jxg198cUXCg0N1aOPPqoiRYro2rVrOnDggBYsWKCbN2/q22+/VUBAgBo2bGhZzvr163Xz5k099dRTevDBBxUYGChfX18FBATY0mzfvl1jxoxRYmKi8uXLpzZt2qhUqVLKli2bTpw4oWXLlunQoUP68ccfJUnPPfec5fISEhL0+eefKzg4WI8//rhtPz9y5Ijmz5+vy5cva9GiRcqXL5+efvppu7yRkZF69913FR0draJFi6p169YqWbKkEhMTdeLECc2dO1dnz57VnDlzVLZs2RQDliZPnqyCBQvqpZdeUnBwsK5fv64tW7Zo8eLFiomJ0cSJE/XQQw8pR44cDnmTBzMEBwfriSeeUKlSpRQdHa1du3ZpwYIFOnPmjIYPH64vvvgixcA7ZD7DMDRu3Dhb0Fu5cuXUtGlTlShRQleuXNG2bdu0bNkyHTx4UKNGjdInn3zi0Lh/+fJlDRs2TFevXpWPj49atmypBx54QEFBQTp//rxWrlypdevWad26dcqePbv69esnSXr33XeVkJCgAwcO2O6P33jjDVuvUWb7YJJp06YpZ86cateunYoXL668efO61PNQWuvsTEBAgL766itJt4P9li5dKkm2aZJswXGlS5dWcHCwTp06pdWrVzsNaEgKNHn44YfTPbACAADAGQIaAAAAADcNHDhQTz31VGZXI9UOHTpk6yY3ybPPPquePXvaPfDfunWrxo4dq6NHj0qSjhw5okWLFtk1cMTExGjIkCF2wQzlypXTyJEjVaFCBVtj5bVr1zR58mT9/PPPtnQ//vijateurQYNGjitr2EYCgkJ0dixY20PlmNiYjRu3DjNnTtX0u0326Tbwy8MHDjQbmiF1atXa+jQobaG/d9//11Dhw512jtE8rdWmzVrpv79+yswMNBWn0OHDumdd97R3r17beneeecd1ahRw6GhbP/+/Q7BI02bNlX//v1VoEABW5kREREaM2aMNm3aZEv39ttv66GHHrIt20pcXJzy5MmjAQMGqHnz5rZ1v379usaNG6dFixbZ0k6fPt00oGHt2rV26Tp27KgXX3zRocvvvXv36quvvtL69esl3X7b/p9//jEtM0lSMEPVqlU1YsQIu27Rd+3apVGjRtmGt7h48aIWLlxoa5CrWLGivvvuO7vyxowZo3379km6PfTLsGHDHJaZ1m7lkTEuXbpke8O8WLFi6Vaun5+fsmfPrqeeekrPPfecXaBE5cqVVaVKFQ0ZMkQ3b97U0qVL1aVLF7v827dvV3x8vPz8/PTee+/ZvfkbGhqqpk2b6rPPPlPNmjXTNZhBkn744QedPHlS3t7eev/99/XAAw/Y5pUpU0b16tVT48aNNWzYMJ08eVKzZ892qH9mSkxMVGBgoK5du6YxY8bYBYuULVtW9evX17vvvqvNmzdr0aJF6tSpk9PGwejoaH322Wd2QU2VKlVStWrV1K9fPyUmJurHH3/U6NGj7fLFxsZq2rRpkm4HoI0ZM8bu3Fm2bFk1atRIv/32my1QLi3OnTunl19+We3bt7ebXr16dTVr1kzDhg3T2bNnNXXqVNWtW9ey8S8mJkaffPKJ5TBDsbGxGj9+vBITE1WuXDmNHj3abttUqFBBTZo00TfffKOlS5fq559/Vt26dS2HfYiJiVFoaKh69uzpcJw0a9ZMo0eP1v79+/Xjjz+qSZMmdj2CGIah/Pnzq1ChQhoyZIhdYE2lSpXUoEED9e7dW5cuXdKvv/6aYkBDkSJF1K9fP7t6VKtWTcHBwZo0aZKuXr2qpUuXOgRWHDx40BbMUK9ePb355pt2dalevboaNGigwYMH6+LFi/ruu+/Uq1cvp3VB5lu/fr0tmKFVq1bq2rWr3dASderUUc2aNfXOO+/o6NGjWrBggcO+8e233+rq1avy9fXV2LFj7YZ9KV26tOrUqWM7B6xfv14vvvii8ufPb7vXuXz5si19gQIFnA7/laRUqVIaOnRoisNgWEltnZ3JkiWLre7+/v626Wbr4+XlpcaNG+uHH37Qv//+qx49epjeIx87dsx2r9a4cePUrCoAAECqWb9CAwAAAMDUmDFjNGrUKMXHx2d2VVLlm2++sRv2YNCgQRowYIDD24vVqlXTxIkT7R6Erly50i7N3Llz7XpyqFWrlmbMmKGKFSvaNVD4+/tr4MCBevvtt+3yf/XVV06HvJBuBykkD2aQbjdeDhw40KEb6eeff15PP/203bIbNmyo559/3vZ/bGysXa8AznTp0sWhUczLy0tly5bV9OnT7Rprrl+/rl9//dWhjClTptj19NC9e3eNHTvWFsyQVGaxYsU0ceJEtW7d2jb95s2bLje8ffTRR2rRooXduufOnVsjR45UpUqVbNMOHDjgME60dPuBepLnnntOgwYNMh2/vGLFiho3bpzdG3xJ46E7ExISookTJzoEGoSGhmrs2LF2PTysXbvW9nfOnDlVqVIlu0/yxtAcOXI4zK9UqRI9PHiopJ5VJOdvvN66dUsRERGWnztVq1ZNEyZMcAhmSFKhQgVbI9GRI0cc5if1IhMcHGzajXmuXLk0cuRINW/ePMV1dMeNGzf0xx9/SJKeeuopu2CG5CpVqmQb+3zZsmUOww5kpmzZsmnIkCEaO3asQ88X0u3zW6tWrWz/Hzt2zGl5TzzxhOnxGxISYht+ICnQLrkNGzbYhkF47bXXLAPB2rZtqzx58jitgyvy5s2rdu3amc4LDAzUq6++Kkm6cuWKNm7caFlOvXr1nDaarly5UpcuXZK3t7def/11023j4+Ojzp07KzAwUImJiZo/f75leT4+Pnr55ZdNj5NcuXKpd+/e8vb2VlxcnFasWGE3v1ChQvr444/Vv39/015CcuXKZeup6Pjx406HWJLkcL1O8thjj9kCKcy+66T18/f3V69evUzrUrp0adv3888//ygqKsppXeC6n376SU8++WSKn/DwcLfKTQpSKVq0qLp06WIaIFCjRg3bPrZ48WK7fezKlSu2+4d27drZBQYk16ZNG3Xr1k3ffPNNioEBrmjfvn2qgxkyq853atSokaTb1+ht27aZpknqnaFQoUIqX758utcBAADAGQIaAAAAcN8rWLCgvvvuO9PPzJkzNW7cOPXr10/BwcG2PEuWLNG7776bibVOnaioKLughPLlyzu8XZpcwYIF1adPH7Vq1UqtWrVSoUKF7B4eL1u2zPZ30lAczsZHf+KJJ1SzZk3b/wcOHEixcat8+fJ2wQzJl1e9enW7acmDAZK7cwzuEydOOF2mdPvN0a5du1p2iZ81a1YNGDDA7i22f/75xy7N9evX7RrnQ0JC9Morr1iW6e3trTfeeMOuse3PP/+0C4gw06BBA4dtkbzMjh072k07dOiQ3f8XLlywG6++XLly2rNnj+Xn4MGDdkES4eHhdr1amHnttdcs941y5crZjcV8Z/3w3xETE+NSuhMnTqhXr16WHzNBQUFOh7AoVKiQJJkGNCX1FnHq1CldvHjRpTqmh/DwcNs2Selt9qTzWHR0tHbv3p3hdXOHj4+PChYsaDk/adtL5ts/uTsD1ZJL6nXgypUrDuecw4cPS5KyZ8+uKlWqWJbh7e3t0lvXKQkODna6v1WtWtXW7XxS3cyk1Di5efNmSbeH6nHWO4ifn5/tPLp582bL60b+/PmdBhOFhITYto9ZvXPlyuV0+IakQLi4uLgUj6Xk91XJZcmSxXZM3hnAlJCQoK1bt0q6fUwkD7q8U9LQYLGxsU6/A2S+a9eu2YYjaty4sdPhDJK+10uXLtkNg7Vr1y5bsLGz86mPj4+efPLJdBl6Rkr5GHYms+p8p0KFCtmG8Vm9erXD/MTERNv0Rx55JN2GiwIAAHAVQ04AAADgvufr62vXOGulY8eOGj58uC0gYMmSJWrfvr0eeuihDK5h+tm+fbtdQEKbNm1SfKusbdu2atu2rcP069ev2w25UL9+fRUvXtxpWV5eXnrmmWfshlUICwtz2kjjrMw7G9Cs0t751vClS5ec1lOSnnzySfn5+TlNExISorp162rNmjWSbg8vERUVZWvs2bp1q932fvrpp1McczhPnjxq2bKlbSz0ixcv6siRIypTpoxlnooVK6ZYz+SuXr1q9//27dvt/r+zK/eUJCQkKDIyUqVKlbJMk9IxVrx4cVvD3ZUrV9xaPu4d7owrnhaGYej69eu2N/Yl2YadMVOzZk19//33ioqK0sCBA9W6dWtVqlRJxYsXz9A6Jw/eSamRPfkQHQcPHrQLDvM0sbGxunz5sm04IneCRJw1lCVvOIyKirLrreDs2bOSbm/HlBrb0qMxLqUyvL29FRwcrEOHDrncK5CZpH3EqvE/uaR96Nq1azp//rxljxmulHPixAmX6n3z5k1duXLFFkCR/PqSUg9MrnzXdwauREZG2qaldM+R/Jg6ffq0Kleu7DQ9XNOyZUu7XlesJO+JKiXJe85x53s9deqULWAqeW8ezgKjPIkn1blx48bat2+fNm7cqJiYGLt74H379tmCR5J6cwAAALibCGgAAAAAXJQtWzYNGzZMGzZssL1R+/PPP99TAQ13Nk5YjbHtinPnztk1Vrg6rvyd6ZIPWWHGbBzfJMmHKXCW9s50rnBnfZICGgzD0IULF2wBDXeuW1q2kbOAhuRvP5u58+3BO4dLuXDhgkv1ciZ5w7GZlOoYEBBg+zulbspx70r+ZrezXj3Kli2rhQsX2k2bPXu20+FNLly4oD/++EOrV6/W+fPn3RoWqECBAho2bJjGjRunCxcu2A3BUqxYMT344IN65JFH0r2b7eTHjbO33iXZvZl7Z1BSZktISNCWLVu0dOlSHTx40G5okfTkLAAvKaDBbPiBzJLUIJhUt9RI2kdS2j8kx33ELKDBFc7qHR0drZUrV+qvv/5SRESEy72uuMvqup1835o6daqmTp3qUnlJw8og7fz9/dOll5Pkkn+vY8aMcTnfqVOnbD1UJS/Dk84DznhSnRs0aKBvvvlGMTExCgsLU8OGDW3zkoK5K1SokOmBFwAA4P5EQAMAAADghvz586tMmTLatWuXpNtvyd5L7mx0dtZFeErubLBKqcHaKl1GNXyllasNQXeuT/JxulO7jZK67LYq504pBWyk9EZueowtnlJjW2qCSvDfkz9/fmXPnl3R0dEOXcmnRVhYmD788ENbjwCpERoaqsmTJ2v16tUKDw/XwYMHdebMGZ08eVInT57U0qVL9dRTT6lr167pVu//gtjYWL3zzjt2w9bAXko9FXiqO+t95swZDR8+PF2C4O629LjOwfPwvaYff39/Va9eXWFhYVq9erUtoCE+Pl7//vuvpNu9OAAAAGQGAhoAAAAANyUPAkhLN9KZIXn33NLtN//vHI7AVXeO4+vqG6h3brOMGg84rc6ePavQ0NAU0zlbH7Nt5EqvGKdPn7YsMyPcOQb5Dz/8kO5vogPS7TfsK1SooG3btun48eMyDMPl7v+tegGJi4vT+PHjFRcXJ19fXz3zzDOqXbu28ufPr5w5c9rKHz9+vP755x+ny8iaNauaNGmiJk2aSLrdkHPgwAHNmTNHmzdv1oIFC/TAAw+oTp06bqy1teTH3vXr150e68kDm/LkyZMuy08PS5YssQUzhIaGqm3btipVqpT8/f1tQ+xERkaqe/fuGVqPQoUK6ejRoxnWY0BqJNXlziA1d/j7++vChQsuNdwm30fuPK+7w6reU6dOtQUzPPbYY2ratKkKFy6sXLly2XrP+Pvvv/X555+netkpSX6MvPDCC6pXr55L+XLkyJFRVUI6SP699uvXz+V7kOT7efLAylu3bmV6jweu8LQ6N27cWGFhYdq6davtmrRt2zZdv35dPj4+atCgQabWDwAA3L8IaAAAAADclPyt4nvtAfmdvQ4cO3ZMtWrVSnVZXl5etjc4k49/7Ezy8YIl13stuNvurKcr6by8vBQYGGj7/851O3LkiEsNoXd7G5ntFwQ0IKPUq1dP27Zt06VLl7R7926XAock6x5xjh8/bmvsfemll/Tkk0+mW12zZMmiSpUqafjw4erdu7dOnTqlsLCwdAtoSD6UzKlTp1ShQgXLtCdPnjTNl9mSeizKkyePRo8ebQtiuNuSzpOnTp1KMVAmMTExzctLqYzExETb/UJazuGlS5fWhQsXXBoyIWl5uXPntuyByZV1T9rXktc7MTFRu3fvliTVqVNHvXv3TrGcjBAUFKScOXPqxo0bio2NTfehD5A5SpUqZfs7ISEhVd9r8uG6Tp8+7fIwX5nJ0+pcs2ZNWy9K69at0+OPP65Vq1ZJkqpXr56mQCkAAIC0oM9PAAAAwA2nT5+2a2y+1x6kV61a1a6RZ8GCBUpISHCa57333lPt2rVVu3ZtNW7cWLdu3ZJ0+62yihUr2tKtW7dOJ06ccFqWYRj6+eef7aalNqAioy1cuDDFN32PHz+uDRs22P6vUKGCXS8Y1apVsxvzfc6cOSl2iX/lyhX9/vvvtv8DAgLsHvRnhCpVqtjtF2vXrk0xz9atW7VmzRqtWbNGmzdvzsjq4T+mcePGtrdxJ02apOjo6BTzHDx40DJoKjIy0vZ38eLFLcuw6uFBun1u2rdvn+V8Hx8fFStWLMVy3BUaGmp7I3f9+vVO0yada/z8/FwOArkbkrZ/kSJFLIMZ0nObWUk6T0ZHR2vbtm2W6RISEnT8+PE0L+/EiRNOgwO2bdtmu4aULl061cupWbOmpNu9ATkLtIuJidHWrVslSTVq1LAc5ufixYtOe3s4duyYLXgieb2joqJ08+ZNSak/ztKDj4+PqlWrJun2dcjZPYxhGNqxY8c9O+TH/cTf31/lypWTpBTvKeLj422BVMmFhoba7rlSOp8uWrTI6XnibvG0OmfLls3W60lYWJhu3bqlsLAwSdIjjzySocsGAABwhoAGAAAAwEU3btzQu+++a9cg7WpXxylJChJI77R3yps3r2rXrm37f//+/Zo7d65l+mPHjumPP/5QQkKCEhISVLduXbvucFu0aGH7Oy4uLsUx7JcsWWL3oLpChQqpHvIio505c0bTpk2zbAiJi4vTxx9/rPj4eNu0pG7qk+TKlUsPP/yw7f8TJ05o+vTplmUmJibq888/19WrV23Tmjdv7nKX/KmVJ08euzfOf//9d6cPzcPDw/Xqq6/qzTff1Jtvvml7ey8zMH72vSdbtmzq0aOHpNvHxKhRo3Tx4kXL9KdOndLYsWMtG2iTB5ZZBSUcOHDANizCnW7evKkxY8Zo0KBB+u2330wbqaOiorR//35JcggwMgxDn332mTp06KABAwboypUrlutyp1y5cql58+aSbgeYJb0Bf6c9e/Zo4cKFkm6fd9O7d6ArV65owIAB6tChgz777DO3GoCTtv+JEyd048YNh/kJCQmaN29eutXVSp06dWxvD0+aNMk2NMKd5s2bZ1pPd0VFRVmu14ULFzR58mRJt8+vya+77mrUqJHy58+vxMRETZw40XIbz5gxQxcuXJC3t7fatGljWZ5hGJo1a5bpdxwVFaUvv/xSiYmJypIli10DZu7cuW2BSHv37jXNf/XqVf3555+pWEv3PPXUU5KkQ4cO6YcffjA9ZpMCKEeOHKn33nuPoIZ7QNJ+u2HDBi1btsz0O4uPj9ekSZM0bNgwTZo0yW5evnz5bEMizJ0717JXn40bN2ratGkaNWqU9u7dm74r4SZPrHPjxo0lSTt37tTGjRsVExOjnDlz2oKrAAAAMgNDTgAAAOC+Fxsbqz179pjOMwxDFy5c0JEjR/TLL7/o/Pnztnk5c+ZU+/btU73c5I3U+/bt09KlS1WiRAlJtxvM/Pz8HNJJt4MCihcvrly5cilbtmxuv/nZrVs3u14FPvzwQ0VEROjVV1+1NZIZhqG1a9fqgw8+sHt7OqkRIUnbtm31/fff297Q3bhxo15++WWNGjVK5cqVs9U9KipKkydP1o8//miXv2fPnhneWJ8W3377rU6dOqUBAwYof/78km5vmyNHjmj06NF2+03u3LlN94cePXpozZo1trdIv/nmGx07dkz9+/e3G54iIiJCY8aMsb0JJ90e0uTFF1/MqNWz07VrV7u3A/v06aM+ffqoffv2ypLl9k/HxMRErVq1Su+8846tocHHx0fPP//8Xaljkjx58tj+PnDggGbMmKGqVavavR0eEhJi11sGPEujRo10+PBhzZ8/X/v27dOrr76qxo0bq1q1aipQoIASEhJ09uxZ7dixQytWrJB0O7hn6dKlDmUVLVpUwcHBOnXqlH766SdFR0fbus2+dOmStmzZouXLl6tkyZLav3+/4uPjFRERIT8/PwUGBsrX19cWmDRz5kytW7dOzZo1U4kSJeTj46OTJ09q3rx5unz5srJmzeoQyHbkyBFbHQ8cOKBVq1Y5nCudef7557VlyxZFRERo+PDheuyxx1S1alUVLFhQ586d07Zt2/Tnn38qMTFRxYoV03PPPef29k7eU0zS+Tpfvnzy9fWVJK1atUoHDhyQJK1YsUJPPfWUyz3D1K5dW+vWrdONGzc0bNgwtWvXTkWLFtWtW7d04sQJLVu2THFxcbZuzC9duqSIiAgVLFjQtvz0kC1bNnXr1k3jxo3T2bNn1adPHz311FMqW7as/P39FRkZqRUrVujIkSMKCgqy69kjNUqXLq0///xTe/bs0SOPPKKgoCBdu3ZNBw8e1Pz58229GbzyyitpWs9s2bKpX79+Gj16tA4cOKDXX39dbdu2VYkSJZQtWzZFRETo999/tzWEduzY0Wm39fnz59exY8c0dOhQPf744woODlZ0dLSOHDmi+fPn6/Lly5KkZ555xnbdk27fi9SpU0d//fWXwsPDNXbsWD322GPKnz+/rl27pv379+v3339XgQIFbHnOnj2r+Ph4BQcHp3r9zZQrV05t2rTR/PnzNWfOHO3YsUPNmjVTsWLFlDVrVp08eVJ//fWX7Rpdo0YNj77XuNdcu3bNbvg1Kz4+PipcuLDd/0nMzkP169dXvXr1tG7dOn311Vdav369GjZsaNt/jh49qmXLltl6KjFrYH/llVe0fft2Xb16VYMGDVKrVq1UqVIlFSxYUJcuXdLatWu1atUqJSYmqlq1ag7D/CS/j7h8+bKtnrlz586wYebSWueUJD//JD/vBQQE2O7vkgsNDVX+/Pl16dIlTZ8+XdLt7yY9z9cAAADuIqABAAAA971z587ppZdecjvfyJEjlTdv3lQvNyAgwPZ3QkKC/ve//9n+/+GHH1S+fHlJtx/2JhceHq5evXpJuv1Qf/bs2W4tt0qVKnr55Zc1c+ZMu+X9+OOPKlGihPLkyaMTJ044vDHdpUsXh3Hj/fz89OG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-            "text/plain": [
-              "<plotnine.ggplot.ggplot object at 0x7841617e2360>"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {
-            "image/png": {
-              "height": 450,
-              "width": 1050
-            }
-          },
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (2, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>bill rows</th><th>BAT rows</th><th>annual bill bldgs</th><th>BAT bldgs</th><th>duplicate annual IDs</th><th>duplicate BAT IDs</th><th>bill IDs missing from BAT</th><th>BAT IDs missing from bills</th><th>max elec component error</th><th>max energy component error</th><th>max BAT identity error</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.8190e-12</td><td>1.1369e-12</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.7053e-12</td><td>4.6566e-10</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "component_rows: list[dict[str, object]] = []\n",
-        "for group in GROUP_ORDER:\n",
-        "    group_df = bill_comparison.filter(pl.col(\"baseline_heating_type\") == group)\n",
-        "    for scenario, suffix in [(\"Upgrade 00\", \"before\"), (\"Upgrade 02\", \"after\")]:\n",
-        "        component_rows.extend(\n",
-        "            [\n",
-        "                {\n",
-        "                    \"baseline_heating_type\": group,\n",
-        "                    \"scenario\": scenario,\n",
-        "                    \"component\": \"Electricity\",\n",
-        "                    \"weighted_mean_bill\": weighted_mean(group_df, f\"electric_{suffix}\"),\n",
-        "                },\n",
-        "                {\n",
-        "                    \"baseline_heating_type\": group,\n",
-        "                    \"scenario\": scenario,\n",
-        "                    \"component\": \"Gas, oil, and propane\",\n",
-        "                    \"weighted_mean_bill\": weighted_mean(group_df, f\"delivered_fuel_{suffix}\"),\n",
-        "                },\n",
-        "            ]\n",
-        "        )\n",
-        "\n",
-        "component_plot_data = pl.DataFrame(component_rows).with_columns(\n",
-        "    pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
-        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
-        "    # Plotnine stacks the first level on top, so list delivered fuel first.\n",
-        "    pl.col(\"component\").cast(pl.Enum([\"Gas, oil, and propane\", \"Electricity\"])),\n",
-        ")\n",
-        "\n",
-        "(\n",
-        "    ggplot(\n",
-        "        component_plot_data,\n",
-        "        aes(x=\"baseline_heating_type\", y=\"weighted_mean_bill\", fill=\"component\"),\n",
-        "    )\n",
-        "    + geom_col(width=0.7)\n",
-        "    + facet_wrap(\"scenario\", ncol=2)\n",
-        "    + scale_fill_manual(\n",
-        "        values={\n",
-        "            \"Electricity\": SB_COLORS[\"sky\"],\n",
-        "            \"Gas, oil, and propane\": SB_COLORS[\"carrot\"],\n",
-        "        }\n",
-        "    )\n",
-        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
-        "    + labs(\n",
-        "        x=\"Baseline heating type\",\n",
-        "        y=\"Weighted mean annual bill\",\n",
-        "        fill=\"Bill component\",\n",
-        "        title=\"Mean household energy bills before and after upgrade 02\",\n",
-        "    )\n",
-        "    + theme_switchbox()\n",
-        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
-        ")"
+      "text/plain": [
+       "shape: (2, 12)\n",
+       "┌────────────┬───────────┬──────────┬────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ scenario   ┆ bill rows ┆ BAT rows ┆ annual ┆ … ┆ BAT IDs    ┆ max elec   ┆ max       ┆ max BAT   │\n",
+       "│ ---        ┆ ---       ┆ ---      ┆ bill   ┆   ┆ missing    ┆ component  ┆ energy    ┆ identity  │\n",
+       "│ str        ┆ i64       ┆ i64      ┆ bldgs  ┆   ┆ from bills ┆ error      ┆ component ┆ error     │\n",
+       "│            ┆           ┆          ┆ ---    ┆   ┆ ---        ┆ ---        ┆ error     ┆ ---       │\n",
+       "│            ┆           ┆          ┆ i64    ┆   ┆ i64        ┆ f64        ┆ ---       ┆ f64       │\n",
+       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ f64       ┆           │\n",
+       "╞════════════╪═══════════╪══════════╪════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ Upgrade 00 ┆ 439270    ┆ 33790    ┆ 33790  ┆ … ┆ 0          ┆ 9.0949e-13 ┆ 1.8190e-1 ┆ 1.1369e-1 │\n",
+       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ 2         ┆ 2         │\n",
+       "│ Upgrade 02 ┆ 439270    ┆ 33790    ┆ 33790  ┆ … ┆ 0          ┆ 9.0949e-13 ┆ 1.7053e-1 ┆ 4.6566e-1 │\n",
+       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ 2         ┆ 0         │\n",
+       "└────────────┴───────────┴──────────┴────────┴───┴────────────┴────────────┴───────────┴───────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "e0b45c69",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<plotnine.ggplot.ggplot object at 0x7841607e2dd0>"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {
-            "image/png": {
-              "height": 800,
-              "width": 1050
-            }
-          },
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "BILL_CHANGE_BIN = 100\n",
-        "bill_change_lo = math.floor(\n",
-        "    weighted_quantile(bill_comparison, \"energy_change\", 0.01) / BILL_CHANGE_BIN\n",
-        ") * BILL_CHANGE_BIN\n",
-        "bill_change_hi = math.ceil(\n",
-        "    weighted_quantile(bill_comparison, \"energy_change\", 0.99) / BILL_CHANGE_BIN\n",
-        ") * BILL_CHANGE_BIN\n",
-        "\n",
-        "bill_change_hist = (\n",
-        "    bill_comparison.with_columns(\n",
-        "        (\n",
-        "            (pl.col(\"energy_change\") / BILL_CHANGE_BIN).floor() * BILL_CHANGE_BIN\n",
-        "            + BILL_CHANGE_BIN / 2\n",
-        "        ).alias(\"bin_center\"),\n",
-        "        pl.when(pl.col(\"energy_change\") < 0)\n",
-        "        .then(pl.lit(\"Savings\"))\n",
-        "        .otherwise(pl.lit(\"Increase\"))\n",
-        "        .alias(\"direction\"),\n",
-        "    )\n",
-        "    .filter(pl.col(\"bin_center\").is_between(bill_change_lo, bill_change_hi))\n",
-        "    .group_by(\"baseline_heating_type\", \"bin_center\", \"direction\")\n",
-        "    .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n",
-        "    .with_columns(\n",
-        "        pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
-        "        pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "(\n",
-        "    ggplot(\n",
-        "        bill_change_hist,\n",
-        "        aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"),\n",
-        "    )\n",
-        "    + geom_col(width=BILL_CHANGE_BIN * 0.9)\n",
-        "    + facet_wrap(\"baseline_heating_type\", ncol=1, scales=\"free_y\")\n",
-        "    + scale_fill_manual(\n",
-        "        values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]}\n",
-        "    )\n",
-        "    + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n",
-        "    + labs(\n",
-        "        x=\"Annual energy bill change ($)\",\n",
-        "        y=\"Weighted households\",\n",
-        "        fill=\"Outcome\",\n",
-        "        title=\"Distribution of annual bill changes after upgrade 02 (1st–99th percentile)\",\n",
-        "    )\n",
-        "    + theme_switchbox()\n",
-        "    + theme(\n",
-        "        figure_size=(10.5, max(4.5, 2.0 * len(GROUP_ORDER))),\n",
-        "        legend_position=\"top\",\n",
-        "    )\n",
-        ")"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All structural checks passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "qa_rows: list[dict[str, object]] = []\n",
+    "\n",
+    "for scenario in SCENARIO_ORDER:\n",
+    "    bills = bills_by_scenario[scenario]\n",
+    "    bat = bat_by_scenario[scenario]\n",
+    "    annual = bills.filter(pl.col(\"month\") == \"Annual\")\n",
+    "\n",
+    "    elec_err = cast(\n",
+    "        float,\n",
+    "        annual.select(\n",
+    "            (\n",
+    "                pl.col(\"elec_total_bill\")\n",
+    "                - pl.col(\"elec_fixed_charge\")\n",
+    "                - pl.col(\"elec_delivery_bill\")\n",
+    "                - pl.col(\"elec_supply_bill\")\n",
+    "            )\n",
+    "            .abs()\n",
+    "            .max()\n",
+    "        ).item()\n",
+    "        or 0,\n",
+    "    )\n",
+    "    energy_err = cast(\n",
+    "        float,\n",
+    "        annual.select(\n",
+    "            (\n",
+    "                pl.col(\"energy_total_bill\")\n",
+    "                - pl.col(\"elec_total_bill\")\n",
+    "                - pl.col(\"gas_total_bill\")\n",
+    "                - pl.col(\"propane_total_bill\")\n",
+    "                - pl.col(\"oil_total_bill\")\n",
+    "            )\n",
+    "            .abs()\n",
+    "            .max()\n",
+    "        ).item()\n",
+    "        or 0,\n",
+    "    )\n",
+    "    bat_err = cast(\n",
+    "        float,\n",
+    "        bat.select(\n",
+    "            (\n",
+    "                pl.col(\"BAT_percustomer_total\")\n",
+    "                - pl.col(\"annual_bill_total\")\n",
+    "                + pl.col(\"economic_burden_total\")\n",
+    "                + pl.col(\"residual_share_total\")\n",
+    "            )\n",
+    "            .abs()\n",
+    "            .max()\n",
+    "        ).item()\n",
+    "        or 0,\n",
+    "    )\n",
+    "\n",
+    "    qa_rows.append(\n",
+    "        {\n",
+    "            \"scenario\": scenario,\n",
+    "            \"bill rows\": bills.height,\n",
+    "            \"BAT rows\": bat.height,\n",
+    "            \"annual bill bldgs\": annual[BLDG_ID].n_unique(),\n",
+    "            \"BAT bldgs\": bat[BLDG_ID].n_unique(),\n",
+    "            \"duplicate annual IDs\": annual.height - annual[BLDG_ID].n_unique(),\n",
+    "            \"duplicate BAT IDs\": bat.height - bat[BLDG_ID].n_unique(),\n",
+    "            \"bill IDs missing from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+    "            \"BAT IDs missing from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+    "            \"max elec component error\": elec_err,\n",
+    "            \"max energy component error\": energy_err,\n",
+    "            \"max BAT identity error\": bat_err,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "qa = pl.DataFrame(qa_rows)\n",
+    "display(qa)\n",
+    "\n",
+    "assert qa[\"duplicate annual IDs\"].sum() == 0, \"Duplicate annual bill IDs found\"\n",
+    "assert qa[\"duplicate BAT IDs\"].sum() == 0, \"Duplicate BAT IDs found\"\n",
+    "assert qa[\"bill IDs missing from BAT\"].sum() == 0, \"Bill IDs missing from BAT table\"\n",
+    "assert qa[\"BAT IDs missing from bills\"].sum() == 0, \"BAT IDs missing from bills table\"\n",
+    "assert cast(float, qa[\"max elec component error\"].max()) < 0.01, \"Electric component arithmetic mismatch\"\n",
+    "assert cast(float, qa[\"max energy component error\"].max()) < 0.01, \"Energy total arithmetic mismatch\"\n",
+    "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01, \"BAT identity check failed\"\n",
+    "print(\"All structural checks passed.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000019",
+   "metadata": {},
+   "source": [
+    "## Section 4: Baseline analysis (upgrade 00, runs 1+2)\n",
+    "\n",
+    "This section uses the upgrade 00 run pair (runs 1+2), representing the current state:\n",
+    "each building is on its existing heating system with today's electric and gas rates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000020",
+   "metadata": {},
+   "source": [
+    "### 4a: Weighted statistics helpers\n",
+    "\n",
+    "We define weighted mean and weighted quantile here so they are available to all\n",
+    "subsequent cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "a0000021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n",
+    "    \"\"\"Weighted arithmetic mean.\"\"\"\n",
+    "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n",
+    "    wsum = cast(float, valid[weight].sum())\n",
+    "    if valid.is_empty() or wsum == 0:\n",
+    "        return float(\"nan\")\n",
+    "    return cast(float, (valid[value] * valid[weight]).sum()) / wsum\n",
+    "\n",
+    "\n",
+    "def weighted_quantile(\n",
+    "    df: pl.DataFrame,\n",
+    "    value: str,\n",
+    "    q: float,\n",
+    "    weight: str = \"weight\",\n",
+    ") -> float:\n",
+    "    \"\"\"Weighted quantile via cumulative weight sort.\"\"\"\n",
+    "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n",
+    "    wsum = cast(float, valid[weight].sum())\n",
+    "    if valid.is_empty() or wsum == 0:\n",
+    "        return float(\"nan\")\n",
+    "    return float(valid.filter(pl.col(weight).cum_sum() >= wsum * q)[value][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000022",
+   "metadata": {},
+   "source": [
+    "### 4b: Cross-subsidization table\n",
+    "\n",
+    "The **Bill Alignment Test (BAT)** measures whether each customer pays more or less than\n",
+    "their cost of service. A positive BAT means the customer **overpays** (they\n",
+    "cross-subsidize others). A negative BAT means they **underpay** (they receive a\n",
+    "cross-subsidy from others).\n",
+    "\n",
+    "The table below shows each heating-type group's share of customers, electric delivery\n",
+    "revenue, cost of service, and total overpayment. Under flat volumetric rates, heat pump\n",
+    "and electric resistance customers typically overpay because they use more electricity\n",
+    "(and therefore pay more delivery cost per unit) than fossil fuel customers, whose\n",
+    "delivery cost does not scale with their gas consumption.\n",
+    "\n",
+    "Column definitions:\n",
+    "\n",
+    "- **% of delivery revenue** — group's weighted share of annual delivery bill revenue\n",
+    "- **% of cost of service** — group's weighted share of annual cost of service\n",
+    "  (`economic_burden_total + residual_share_total`)\n",
+    "- **Total overpayment** — weighted sum of positive BAT values within the group\n",
+    "- **% of system overpayment** — group's share of the system's total cross-subsidy\n",
+    "- **Mean BAT** — weighted mean BAT per customer (positive = overpaying)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "a0000023",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+      "(Deprecated in version 1.0.0)\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "552bca1a",
-      "metadata": {},
-      "source": [
-        "## Bill Alignment Test results\n",
-        "\n",
-        "BAT equals the annual electric bill minus marginal cost and residual cost allocation. Positive values indicate overpayment relative to cost of service; negative values indicate underpayment. Upgrade 02 rows inherit each building's upgrade 00 heating type."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross-subsidization by customer group (upgrade 00, annual, weighted):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "d9f6d31d",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (8, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>baseline heating type</th><th>weighted mean bill</th><th>weighted mean cost of service</th><th>weighted mean BAT</th><th>weighted median BAT</th><th>households overpaying</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Fossil fuel&quot;</td><td>1515.319009</td><td>1647.096553</td><td>-131.777544</td><td>-208.776876</td><td>0.306486</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Electric resistance&quot;</td><td>3289.7773</td><td>2288.785412</td><td>1000.991888</td><td>502.5949</td><td>0.748892</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Existing heat pump&quot;</td><td>3343.8258</td><td>2370.142712</td><td>973.683087</td><td>461.537772</td><td>0.694386</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Other&quot;</td><td>1096.794794</td><td>1330.485112</td><td>-233.690318</td><td>-257.072923</td><td>0.128254</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Fossil fuel&quot;</td><td>2748.230564</td><td>968811.602916</td><td>-966063.372352</td><td>-649778.388567</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Electric resistance&quot;</td><td>2021.011555</td><td>932681.172989</td><td>-930660.161434</td><td>-649956.358602</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Existing heat pump&quot;</td><td>2552.52301</td><td>1.1092e6</td><td>-1.1066e6</td><td>-650312.923435</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Other&quot;</td><td>1102.965534</td><td>363950.734142</td><td>-362847.768609</td><td>-324834.913154</td><td>0.0</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (8, 7)\n",
-              "┌────────────┬──────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-              "│ scenario   ┆ baseline     ┆ weighted     ┆ weighted    ┆ weighted    ┆ weighted    ┆ households  │\n",
-              "│ ---        ┆ heating type ┆ mean bill    ┆ mean cost   ┆ mean BAT    ┆ median BAT  ┆ overpaying  │\n",
-              "│ str        ┆ ---          ┆ ---          ┆ of service  ┆ ---         ┆ ---         ┆ ---         │\n",
-              "│            ┆ str          ┆ f64          ┆ ---         ┆ f64         ┆ f64         ┆ f64         │\n",
-              "│            ┆              ┆              ┆ f64         ┆             ┆             ┆             │\n",
-              "╞════════════╪══════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-              "│ Upgrade 00 ┆ Fossil fuel  ┆ 1515.319009  ┆ 1647.096553 ┆ -131.777544 ┆ -208.776876 ┆ 0.306486    │\n",
-              "│ Upgrade 00 ┆ Electric     ┆ 3289.7773    ┆ 2288.785412 ┆ 1000.991888 ┆ 502.5949    ┆ 0.748892    │\n",
-              "│            ┆ resistance   ┆              ┆             ┆             ┆             ┆             │\n",
-              "│ Upgrade 00 ┆ Existing     ┆ 3343.8258    ┆ 2370.142712 ┆ 973.683087  ┆ 461.537772  ┆ 0.694386    │\n",
-              "│            ┆ heat pump    ┆              ┆             ┆             ┆             ┆             │\n",
-              "│ Upgrade 00 ┆ Other        ┆ 1096.794794  ┆ 1330.485112 ┆ -233.690318 ┆ -257.072923 ┆ 0.128254    │\n",
-              "│ Upgrade 02 ┆ Fossil fuel  ┆ 2748.230564  ┆ 968811.6029 ┆ -966063.372 ┆ -649778.388 ┆ 0.0         │\n",
-              "│            ┆              ┆              ┆ 16          ┆ 352         ┆ 567         ┆             │\n",
-              "│ Upgrade 02 ┆ Electric     ┆ 2021.011555  ┆ 932681.1729 ┆ -930660.161 ┆ -649956.358 ┆ 0.0         │\n",
-              "│            ┆ resistance   ┆              ┆ 89          ┆ 434         ┆ 602         ┆             │\n",
-              "│ Upgrade 02 ┆ Existing     ┆ 2552.52301   ┆ 1.1092e6    ┆ -1.1066e6   ┆ -650312.923 ┆ 0.0         │\n",
-              "│            ┆ heat pump    ┆              ┆             ┆             ┆ 435         ┆             │\n",
-              "│ Upgrade 02 ┆ Other        ┆ 1102.965534  ┆ 363950.7341 ┆ -362847.768 ┆ -324834.913 ┆ 0.0         │\n",
-              "│            ┆              ┆              ┆ 42          ┆ 609         ┆ 154         ┆             │\n",
-              "└────────────┴──────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (4, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Customer group</th><th>% of customers</th><th>% of delivery revenue</th><th>% of cost of service</th><th>Total overpayment ($M/yr)</th><th>% of system overpayment</th><th>Mean BAT ($/yr)</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;All customers&quot;</td><td>100.0</td><td>100.0</td><td>100.0</td><td>1760.0</td><td>100.0</td><td>-0.0</td></tr><tr><td>&quot;Fossil fuel&quot;</td><td>87.6</td><td>77.0</td><td>83.3</td><td>867.8</td><td>49.3</td><td>-127.0</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>9.0</td><td>17.2</td><td>12.3</td><td>684.9</td><td>38.9</td><td>970.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>2.7</td><td>5.3</td><td>3.9</td><td>205.7</td><td>11.7</td><td>935.0</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "bat_frames: list[pl.DataFrame] = []\n",
-        "for scenario in SCENARIO_ORDER:\n",
-        "    bat_scenario = bat_by_scenario[scenario].join(\n",
-        "        baseline_groups,\n",
-        "        on=BLDG_ID,\n",
-        "        how=\"inner\",\n",
-        "        validate=\"1:1\",\n",
-        "    ).with_columns(\n",
-        "        pl.lit(scenario).alias(\"scenario\"),\n",
-        "        (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service_total\"),\n",
-        "    )\n",
-        "    bat_frames.append(bat_scenario)\n",
-        "\n",
-        "bat_long = pl.concat(bat_frames, how=\"diagonal_relaxed\")\n",
-        "\n",
-        "bat_summary_rows: list[dict[str, object]] = []\n",
-        "for scenario in SCENARIO_ORDER:\n",
-        "    for group in GROUP_ORDER:\n",
-        "        group_df = bat_long.filter(\n",
-        "            (pl.col(\"scenario\") == scenario)\n",
-        "            & (pl.col(\"baseline_heating_type\") == group)\n",
-        "        )\n",
-        "        if group_df.is_empty():\n",
-        "            continue\n",
-        "        total_weight = float(group_df[\"weight\"].sum())\n",
-        "        overpay_weight = float(\n",
-        "            group_df.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum()\n",
-        "        )\n",
-        "        bat_summary_rows.append(\n",
-        "            {\n",
-        "                \"scenario\": scenario,\n",
-        "                \"baseline heating type\": group,\n",
-        "                \"weighted mean bill\": weighted_mean(group_df, \"annual_bill_total\"),\n",
-        "                \"weighted mean cost of service\": weighted_mean(group_df, \"cost_of_service_total\"),\n",
-        "                \"weighted mean BAT\": weighted_mean(group_df, \"BAT_percustomer_total\"),\n",
-        "                \"weighted median BAT\": weighted_quantile(group_df, \"BAT_percustomer_total\", 0.5),\n",
-        "                \"households overpaying\": overpay_weight / total_weight,\n",
-        "            }\n",
-        "        )\n",
-        "\n",
-        "bat_summary = pl.DataFrame(bat_summary_rows)\n",
-        "display(bat_summary)"
+      "text/plain": [
+       "shape: (4, 7)\n",
+       "┌────────────────┬───────────┬──────────┬───────────────┬───────────────┬───────────────┬──────────┐\n",
+       "│ Customer group ┆ % of      ┆ % of     ┆ % of cost of  ┆ Total         ┆ % of system   ┆ Mean BAT │\n",
+       "│ ---            ┆ customers ┆ delivery ┆ service       ┆ overpayment   ┆ overpayment   ┆ ($/yr)   │\n",
+       "│ str            ┆ ---       ┆ revenue  ┆ ---           ┆ ($M/yr)       ┆ ---           ┆ ---      │\n",
+       "│                ┆ f64       ┆ ---      ┆ f64           ┆ ---           ┆ f64           ┆ f64      │\n",
+       "│                ┆           ┆ f64      ┆               ┆ f64           ┆               ┆          │\n",
+       "╞════════════════╪═══════════╪══════════╪═══════════════╪═══════════════╪═══════════════╪══════════╡\n",
+       "│ All customers  ┆ 100.0     ┆ 100.0    ┆ 100.0         ┆ 1760.0        ┆ 100.0         ┆ -0.0     │\n",
+       "│ Fossil fuel    ┆ 87.6      ┆ 77.0     ┆ 83.3          ┆ 867.8         ┆ 49.3          ┆ -127.0   │\n",
+       "│ Electric       ┆ 9.0       ┆ 17.2     ┆ 12.3          ┆ 684.9         ┆ 38.9          ┆ 970.0    │\n",
+       "│ resistance     ┆           ┆          ┆               ┆               ┆               ┆          │\n",
+       "│ Existing heat  ┆ 2.7       ┆ 5.3      ┆ 3.9           ┆ 205.7         ┆ 11.7          ┆ 935.0    │\n",
+       "│ pump           ┆           ┆          ┆               ┆               ┆               ┆          │\n",
+       "└────────────────┴───────────┴──────────┴───────────────┴───────────────┴───────────────┴──────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bat_u0 = add_heating_label(bat_by_scenario[\"Upgrade 00\"]).with_columns(\n",
+    "    (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service\"),\n",
+    ")\n",
+    "\n",
+    "_total_customers = cast(float, bat_u0[\"weight\"].sum())\n",
+    "_total_revenue = cast(float, (bat_u0[\"annual_bill_total\"] * bat_u0[\"weight\"]).sum())\n",
+    "_total_cos = cast(float, (bat_u0[\"cost_of_service\"] * bat_u0[\"weight\"]).sum())\n",
+    "_total_overpay = cast(\n",
+    "    float,\n",
+    "    bat_u0.filter(pl.col(\"BAT_percustomer_total\") > 0)\n",
+    "    .select((pl.col(\"BAT_percustomer_total\") * pl.col(\"weight\")).sum())\n",
+    "    .item()\n",
+    "    or 0,\n",
+    ")\n",
+    "\n",
+    "_avail_groups = [h for h in HEATING_ORDER if h in bat_u0[\"heating_label\"].unique().to_list()]\n",
+    "_groups: dict[str, pl.DataFrame] = {\"All customers\": bat_u0} | {\n",
+    "    label: bat_u0.filter(pl.col(\"heating_label\") == label) for label in _avail_groups\n",
+    "}\n",
+    "\n",
+    "cs_rows: list[dict[str, object]] = []\n",
+    "for name, gdf in _groups.items():\n",
+    "    gdf_over = gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)\n",
+    "    g_cust = cast(float, gdf[\"weight\"].sum())\n",
+    "    g_rev = cast(float, (gdf[\"annual_bill_total\"] * gdf[\"weight\"]).sum())\n",
+    "    g_cos = cast(float, (gdf[\"cost_of_service\"] * gdf[\"weight\"]).sum())\n",
+    "    g_over = cast(\n",
+    "        float,\n",
+    "        gdf_over.select((pl.col(\"BAT_percustomer_total\") * pl.col(\"weight\")).sum()).item() or 0,\n",
+    "    )\n",
+    "    cs_rows.append(\n",
+    "        {\n",
+    "            \"Customer group\": name,\n",
+    "            \"% of customers\": round(g_cust / _total_customers * 100, 1),\n",
+    "            \"% of delivery revenue\": round(g_rev / _total_revenue * 100, 1),\n",
+    "            \"% of cost of service\": round(g_cos / _total_cos * 100, 1),\n",
+    "            \"Total overpayment ($M/yr)\": round(g_over / 1e6, 1),\n",
+    "            \"% of system overpayment\": (round(g_over / _total_overpay * 100, 1) if _total_overpay else float(\"nan\")),\n",
+    "            \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "cs_table = pl.DataFrame(cs_rows)\n",
+    "print(\"Cross-subsidization by customer group (upgrade 00, annual, weighted):\")\n",
+    "display(cs_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000024",
+   "metadata": {},
+   "source": [
+    "### 4c: Delivery BAT by heating type (upgrade 00 vs. upgrade 02)\n",
+    "\n",
+    "The grouped bar chart shows weighted mean total BAT across both upgrade scenarios.\n",
+    "Under upgrade 00 (current rates, baseline HVAC), the direction and magnitude of the\n",
+    "BAT for each heating group set the baseline. Under upgrade 02 (same rates, all\n",
+    "buildings now have heat pumps), the BAT distribution shifts as load profiles change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a0000025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weighted mean total BAT ($/yr) by heating type and scenario:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+      "(Deprecated in version 1.0.0)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "636b0805",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<plotnine.ggplot.ggplot object at 0x784150152eb0>"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {
-            "image/png": {
-              "height": 450,
-              "width": 1050
-            }
-          },
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (6, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>heating_label</th><th>mean_bat_total</th><th>mean_bat_delivery</th><th>mean_bat_supply</th><th>median_bat_total</th><th>pct_overpaying</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Fossil fuel&quot;</td><td>-127.147981</td><td>-114.601156</td><td>-12.546825</td><td>-216.614061</td><td>31.387175</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Electric resistance&quot;</td><td>970.407277</td><td>872.593116</td><td>97.814161</td><td>488.418388</td><td>73.5572</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Existing heat pump&quot;</td><td>935.496001</td><td>854.704502</td><td>80.7915</td><td>465.815976</td><td>67.941825</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Fossil fuel&quot;</td><td>-965993.860424</td><td>-967161.62557</td><td>1167.765146</td><td>-649682.46791</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Electric resistance&quot;</td><td>-930564.235766</td><td>-931658.265235</td><td>1094.02947</td><td>-649856.981097</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Existing heat pump&quot;</td><td>-1.1065e6</td><td>-1.1077e6</td><td>1165.278772</td><td>-650202.31266</td><td>0.0</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "bat_plot_data = bat_summary.rename(\n",
-        "    {\n",
-        "        \"baseline heating type\": \"baseline_heating_type\",\n",
-        "        \"weighted mean BAT\": \"mean_bat\",\n",
-        "    }\n",
-        ").with_columns(\n",
-        "    pl.col(\"baseline_heating_type\").cast(pl.Enum(GROUP_ORDER)),\n",
-        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
-        ")\n",
-        "\n",
-        "(\n",
-        "    ggplot(\n",
-        "        bat_plot_data,\n",
-        "        aes(x=\"baseline_heating_type\", y=\"mean_bat\", fill=\"scenario\"),\n",
-        "    )\n",
-        "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
-        "    + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n",
-        "    + scale_fill_manual(\n",
-        "        values={\n",
-        "            \"Upgrade 00\": SB_COLORS[\"sky\"],\n",
-        "            \"Upgrade 02\": SB_COLORS[\"carrot\"],\n",
-        "        }\n",
-        "    )\n",
-        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
-        "    + labs(\n",
-        "        x=\"Baseline heating type\",\n",
-        "        y=\"Weighted mean BAT ($/year)\",\n",
-        "        fill=\"Scenario\",\n",
-        "        title=\"Mean total BAT by baseline heating type\",\n",
-        "    )\n",
-        "    + theme_switchbox()\n",
-        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
-        ")"
+      "text/plain": [
+       "shape: (6, 7)\n",
+       "┌────────────┬──────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ scenario   ┆ heating_labe ┆ mean_bat_tot ┆ mean_bat_de ┆ mean_bat_su ┆ median_bat_ ┆ pct_overpay │\n",
+       "│ ---        ┆ l            ┆ al           ┆ livery      ┆ pply        ┆ total       ┆ ing         │\n",
+       "│ str        ┆ ---          ┆ ---          ┆ ---         ┆ ---         ┆ ---         ┆ ---         │\n",
+       "│            ┆ str          ┆ f64          ┆ f64         ┆ f64         ┆ f64         ┆ f64         │\n",
+       "╞════════════╪══════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ Upgrade 00 ┆ Fossil fuel  ┆ -127.147981  ┆ -114.601156 ┆ -12.546825  ┆ -216.614061 ┆ 31.387175   │\n",
+       "│ Upgrade 00 ┆ Electric     ┆ 970.407277   ┆ 872.593116  ┆ 97.814161   ┆ 488.418388  ┆ 73.5572     │\n",
+       "│            ┆ resistance   ┆              ┆             ┆             ┆             ┆             │\n",
+       "│ Upgrade 00 ┆ Existing     ┆ 935.496001   ┆ 854.704502  ┆ 80.7915     ┆ 465.815976  ┆ 67.941825   │\n",
+       "│            ┆ heat pump    ┆              ┆             ┆             ┆             ┆             │\n",
+       "│ Upgrade 02 ┆ Fossil fuel  ┆ -965993.8604 ┆ -967161.625 ┆ 1167.765146 ┆ -649682.467 ┆ 0.0         │\n",
+       "│            ┆              ┆ 24           ┆ 57          ┆             ┆ 91          ┆             │\n",
+       "│ Upgrade 02 ┆ Electric     ┆ -930564.2357 ┆ -931658.265 ┆ 1094.02947  ┆ -649856.981 ┆ 0.0         │\n",
+       "│            ┆ resistance   ┆ 66           ┆ 235         ┆             ┆ 097         ┆             │\n",
+       "│ Upgrade 02 ┆ Existing     ┆ -1.1065e6    ┆ -1.1077e6   ┆ 1165.278772 ┆ -650202.312 ┆ 0.0         │\n",
+       "│            ┆ heat pump    ┆              ┆             ┆             ┆ 66          ┆             │\n",
+       "└────────────┴──────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "811dbd56",
-      "metadata": {},
-      "source": [
-        "## Monthly bill pattern\n",
-        "\n",
-        "Monthly weighted means show when the heat-pump upgrade raises electric bills and when reduced fossil-fuel use offsets those increases."
+     "data": {
+      "image/png": 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cnGpZZzdIU353pa3kMikTIeB52F7/L947aXt11caNG62JqjVq1EhXGwAAAHAv+6tfAAAAAHAXK1q0qD766CMtX75cc+bM0ZtvvqnHHntMjRs3VsmSJU2TApYsWaK33nrL5nXU4eHh2r9/v/V7ixYtnPbxXLFiRQ0YMECdO3dW586dlTdvXuu4hIQErV+/3vq9atWqOnjwoPbv32/6OXDggKpVq2Ytf+7cOetbDhzp1auXw64UChUqpNatW1u/nzhxQjdv3rQr079/f+snNDTU4bRSPiF55swZp3El69y5s0vlUrNq1SqbriR69+5tdwMrpUKFCumVV16xGfbnn39mSizIuXr37m1zczSZn5+fXnjhBWs/78uXL7cZf/PmTWu3LPfee6/pE9ZVq1a1Jkfd3i3K7dN68MEHncbZvXt3TZw40ebmaEodO3a07tPM+pZPvjkaEBCgF1980fTpcx8fH/Xq1cthDImJiVq8eLEkqVatWjY3R1Pq0KGD9e0wyeVdlXyj09fXN9WyKefh9kSElElsjp60N2srvTdakT3YXv/PnbS9uiL5rVLJbT300ENpbgMAAADuxxsaAAAAAMCEl5eXKlasqIoVK9oMT+53e/r06dq3b591+ObNm7Vq1Sq1adNGkrR161abel27dk11mn369DEdfujQIevbESRp4sSJmjhxosvzIkmnTp1S2bJlHY6vWrWq0/plypSx+X7lyhW7NzBIt5Idfv31Vx05ckTh4eE6d+5cmuJ0pEiRIpnSzrZt22y+p3yrhCMdO3bU559/rmvXrpm2gbuPo/7rpVtPHVerVk3bt2/X0aNHbcYlJCRYE5/MbrBKt95kkjzO7C0syfLnz+9SlytFixZ1OM7Pz08FChRQVFSUaXJRcvzVq1d3mPAk3erH3pHjx49b33DSunVrh2+KsVgsatiwof79918dPXpU8fHxLncpEx0d7VK52yVv046+p7cdeBa2V1t3yvaaGsMwNGnSJGt3Ez169LDp4gMAAAA5B29oAAAAAIA0yJ8/v1q2bKnvv//e7mnLtWvXWv++/WaDs2SC1Fy4cCHddZOldiPA2U0Uyb6v64SEBLsyS5YsUffu3TVr1ixt3bo105IZJNee5HRFyphCQkJs3oThiMViUbly5UzbwN0ptf7ik28Ynj171mZ4QECANRFo//791m5kUoqIiLC+KaBSpUqZEO3/SUxMVFRUlE6dOmX9JMdgFkvyuu7shrDkfHn8999/1r9vT4y6XfLbW5KSktK0naXs7ictbt/+XdkfuNIOPAvbq607ZXt1xjAM/fjjj1q5cqWkW2/Jevjhh9M1XQAAALgfb2gAAAAAgHTw8vLSG2+8oWXLlllv7qe8EXB7AkFG3jAQExOT7rrJAgICnI539mSplPoNoWPHjmnUqFGmiQ7J8ufPr5CQEMXExLjc1URmS/l/SS2JI6WiRYvqn3/+kSRdvXo10+NC5kr5pLDZjb/UZDSBJvk16VevXlVMTIzN9tetWzdNmjRJBw4c0Lhx4/Twww+rePHiunnzpg4ePKhp06ZJkvz9/dW0adMMxSHd2i/9/vvv2rlzpy5fvmzTNY4zMTEx1u3Fle4XHEm5zb366qsu1zt16pRN9zTOBAcH6/LlyzbdyTiSsruc2xO1Uj7VfvPmzVTnO7mt4OBgl+KEObbX/8P2asvZ9upIUlKSvvvuOy1ZskSS1KBBAw0cODDV8xgAAAB4LhIaAAAAANzV1q5dq7/++sv6/cknn1S1atVcqhsQEKBy5crpyJEjkqTw8HCbcSmdO3fO5Yv9t8uXL5/N988//1wtW7ZMV1tZ5csvv1R8fLz1e40aNfTII4+oVKlSKlKkiAoWLGi96TR58mRNnTrVLXGmfMIzLUkVKcve/v+A50l5g9nV15QnJ6p4e3tn6WvJH3jgAZ06dUq//fabVq9erdWrV9uV8fHx0auvvpqmpBszM2bM0Ny5c9NVNz03ljNTWhK5khMRLl26lGrZixcvWv++PREh5fdLly6luvyTp0dCQ8awvd7C9mrP2fZqJj4+Xl988YU2bNgg6dabGV599VWHXYYAAAAgZ+BsDgAAAMBd7eLFi1q2bJn1e5kyZVxOaDAMw+Ziu7+/v/Xv5NdUJzt+/Hi6Expuf7vD8ePH09VOVjEMQ/v27bN+r1WrliZPnuzwBoK73s4g3VqW+/fvl3SrK4+rV6+mmqCQlJSksLAw6/eM3rRC1kt54yvlNupMcrmCBQum+saS1CQ/VZw3b1675CaLxaLnnntOefPm1U8//WQzLigoSDVr1lTPnj0zvJ4dOXLEenO0cOHCevLJJ1WjRg0FBQXZPMHdr18/RUZG2tUPDAxU3rx5de3aNZunpNMqZRLRiBEjFBIS4lK9tCQJJD+57cr/2tkN0pRPgF+8eDHV/0FyWyQ0ZAzbK9urI2lJaIiJidGoUaO0d+9eSbfervHMM89keP0AAACA+5HQAAAAAOCuVrFiRZvv69atU+/evV16mi88PFxRUVHW78n9cEtS3bp1bcouWLBAzZo1c9reSy+9pO3bt0u61Tf1vHnzZLFYVKFCBeXPn19XrlyRJP3999/q1auX09cn79+/3xqbxWJR06ZNs+x1y1euXLHGJkmtW7d2uPwMw9ChQ4eyJA5XNGjQwOYJ259//lnPPfec0zpLly5VdHS09Xv9+vWzLD5kDl9fXwUGBio6OtqlBKCkpCRrOVdea56YmChvb2+H40+ePClJKlasmN246OhojRkzRocOHVLfvn3Vtm1bRUdHKyAgINWuYdIiZZLRO++8o3LlyqW5jSJFiujatWuKiIhwWi4xMdHhuAoVKlj/9vLySndilzOVKlXSypUrdebMGZ0+fVrFixd3WHbnzp2Sbj1Vf/syKVeunLy9vZWYmKhdu3Y5TW6LiIjQ2bNnJUmhoaGZMBd3L7ZXtldHnG2vKUVFRWnEiBE6fvy4vLy8NGDAAHXo0CHTYwcAAIB7kKIKAAAA4K5WvXp1mzcg7N+/X19++WWq9W7cuKF3333XZljKbiBCQ0NVtmxZ6/e1a9daX4FsZvfu3dqyZYsSExOVmJioli1bWhMQvLy81Lp1a2vZXbt22bxV4nYRERF6/vnn9frrr+v111/XwoULs7Tv6MDAQJs+zJNv8pn5448/rF10uMP9999v87Tr9OnTdezYMYflL1y4oK+++spmWPv27bMsPmSe5Btze/bssekOxsz27dut622lSpVSbfvEiRMOx126dMn6FpDy5cvbjf/pp5+0Z88ePfnkk+rSpYv8/f1VuHDhTL05Kt3q5iZZ6dKlTcskJibaJOvcLnkZ/vvvv05fD+/sJnTZsmWtN52TE7YciYmJSdf+oX79+tZ93IoVKxyWS0xM1MqVKyVJ99xzj81bdaRbXQXdc889kqS//vrL6Y3f5OlYLBaSnDIB2yvb6+1S216TnTx5UkOHDtXx48eVJ08evffeeyQzAAAA3GFIaAAAAABwV/Py8tILL7xgM+ynn37S888/r23btikpKclmXFJSktasWaPevXvr4MGD1uHBwcHq2rWrTbt9+/a1qTt48GD98MMPiouLsw5LTEzU77//rtdff906zMfHRx07drSp+8wzzyhXrlzW7yNGjNDkyZN148YN6zDDMLR9+3b179/fZvjTTz/t0rJILx8fH5s3XcydO1d//fWXTZ/eCQkJmjVrlkaNGmVT9+rVq9q9e3e2JTkUKlRI3bt3t36/efOmevXqpQULFtj8XxISErR69Wo9+eSTNm/haNOmDU9j5xApE4y++eYbxcbGmpaLiorStGnTrN9btWqVatuzZs0yvdl98+ZNTZw40Tqtdu3a2ZVJfh16VnddkvLJ6gMHDpiWWbZsmcPlIv1f/DExMfrmm29MX2WfmJioWbNmOWzD29tbDz30kCRpyZIlDm+S3rhxQ//73//0xhtv6Ndff3XYnpmQkBA1b95ckvTbb7/p6NGjdmUMw9CMGTOsr7Dv1q2baVvJw6OiojRz5kyb/ViyI0eO6LfffpN0az0rWLBgmuKFPbZXtteUXN1eDxw4oLfeekvnz59XgQIFNHr0aLs3ZAEAACDno8sJAAAAAHe9jh07atu2bfr999+tw3bu3KkXXnhBfn5+KlasmEJCQhQVFaWIiAi7mwTe3t764IMPbPqelqQOHTpo/fr1Wr58uaRbNxK++uorTZw4URUqVFDu3LkVHh5u012DJA0dOtTmtc/SrZsdgwcP1ieffGJta+rUqfr+++9VpkwZFShQQBERETpz5oxNvUceeUS1atXK2AJyQevWra1PucbHx+utt95SkSJFVLJkSUVHR+vEiRO6fv26JClfvnwyDEPXrl1TTEyM+vXrp2LFimnJkiVZHqckDRgwQLt27bK+4vv69esaOXKkxowZo/Lly8vHx0f//fef3f+5RIkSevvtt7MlRmRcy5YttXTpUh05ckT79+/XgAED1KRJE5UuXVoFChTQuXPndPLkSa1fv966bnbo0MFu2zPj5+en119/XZ06dVLp0qWtr8BfsmSJTp8+LUlq27atXZc20q3kp+PHj+unn37S9evXVaZMGZu3hvj7+yt//vxOX5Hvijp16sjX11dxcXEaM2aMHnvsMYWGhsrLy0vnzp3T2rVrtWfPHpUtW1bHjx9XbGysTp06pfz581v3ZZUqVVK7du20fPlybd68Wa+99po6d+6ssmXLysvLS+Hh4Vq6dKnDJ6eTPfTQQ9q0aZMOHTqkDz/8UPfdd5/q16+vEiVKKD4+XocOHdKyZct09uxZ+fj4qE6dOmme32eeeUa7d+/WlStX9NZbb+nBBx9U3bp15e/vr7Nnz2r58uXW19e3aNHC4X6xVq1aat68udatW6dff/1Vx48fV9u2bVW0aFHFxsZqx44dWrJkieLi4hQUFKSePXumOVbYY3tle03r9nry5Em999571m3xtddek6+vr06dOuV02n5+fgoJCUlzzAAAAHAfi2GWag4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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x70812ad8e6d0>"
       ]
-    },
+     },
+     "execution_count": 50,
+     "metadata": {
+      "image/png": {
+       "height": 450,
+       "width": 1050
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bat_summary_rows: list[dict[str, object]] = []\n",
+    "for scenario in SCENARIO_ORDER:\n",
+    "    bat_s = add_heating_label(bat_by_scenario[scenario])\n",
+    "    avail = [h for h in HEATING_ORDER if h in bat_s[\"heating_label\"].unique().to_list()]\n",
+    "    for group in avail:\n",
+    "        gdf = bat_s.filter(pl.col(\"heating_label\") == group)\n",
+    "        total_w = cast(float, gdf[\"weight\"].sum())\n",
+    "        bat_summary_rows.append(\n",
+    "            {\n",
+    "                \"scenario\": scenario,\n",
+    "                \"heating_label\": group,\n",
+    "                \"mean_bat_total\": weighted_mean(gdf, \"BAT_percustomer_total\"),\n",
+    "                \"mean_bat_delivery\": weighted_mean(gdf, \"BAT_percustomer_delivery\"),\n",
+    "                \"mean_bat_supply\": weighted_mean(gdf, \"BAT_percustomer_supply\"),\n",
+    "                \"median_bat_total\": weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5),\n",
+    "                \"pct_overpaying\": (\n",
+    "                    cast(\n",
+    "                        float,\n",
+    "                        gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
+    "                    )\n",
+    "                    / total_w\n",
+    "                    * 100\n",
+    "                ),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "bat_summary = pl.DataFrame(bat_summary_rows)\n",
+    "print(\"Weighted mean total BAT ($/yr) by heating type and scenario:\")\n",
+    "display(\n",
+    "    bat_summary.select(\n",
+    "        \"scenario\",\n",
+    "        \"heating_label\",\n",
+    "        \"mean_bat_total\",\n",
+    "        \"mean_bat_delivery\",\n",
+    "        \"mean_bat_supply\",\n",
+    "        \"median_bat_total\",\n",
+    "        \"pct_overpaying\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "avail_bat = [h for h in HEATING_ORDER if h in bat_summary[\"heating_label\"].unique().to_list()]\n",
+    "bat_plot_data = bat_summary.with_columns(\n",
+    "    pl.col(\"heating_label\").cast(pl.Enum(avail_bat)),\n",
+    "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+    ")\n",
+    "\n",
+    "(\n",
+    "    ggplot(bat_plot_data, aes(x=\"heating_label\", y=\"mean_bat_total\", fill=\"scenario\"))\n",
+    "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
+    "    + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n",
+    "    + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n",
+    "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+    "    + labs(\n",
+    "        x=\"Baseline heating type\",\n",
+    "        y=\"Weighted mean total BAT ($/year)\",\n",
+    "        fill=\"Scenario\",\n",
+    "        title=\"Mean total BAT by baseline heating type and upgrade scenario\",\n",
+    "    )\n",
+    "    + theme_switchbox()\n",
+    "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000026",
+   "metadata": {},
+   "source": [
+    "### 4d: Monthly bill pattern\n",
+    "\n",
+    "Weighted-mean household energy bills by month, before (upgrade 00) and after (upgrade 02)\n",
+    "the heat pump retrofit. Winter months show the largest divergence: gas-heated homes have\n",
+    "high gas bills under upgrade 00, while after the retrofit (upgrade 02) gas bills\n",
+    "effectively drop to zero and electric bills increase due to heat pump load."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "a0000027",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "131431f7",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (24, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>month</th><th>weighted_mean_energy_bill</th></tr><tr><td>enum</td><td>enum</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Jan&quot;</td><td>520.340206</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Feb&quot;</td><td>371.189947</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Mar&quot;</td><td>393.665096</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Apr&quot;</td><td>295.411291</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;May&quot;</td><td>177.300088</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Aug&quot;</td><td>205.836428</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Sep&quot;</td><td>170.945828</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Oct&quot;</td><td>199.186401</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Nov&quot;</td><td>299.757518</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Dec&quot;</td><td>386.499711</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (24, 3)\n",
-              "┌────────────┬───────┬───────────────────────────┐\n",
-              "│ scenario   ┆ month ┆ weighted_mean_energy_bill │\n",
-              "│ ---        ┆ ---   ┆ ---                       │\n",
-              "│ enum       ┆ enum  ┆ f64                       │\n",
-              "╞════════════╪═══════╪═══════════════════════════╡\n",
-              "│ Upgrade 00 ┆ Jan   ┆ 520.340206                │\n",
-              "│ Upgrade 00 ┆ Feb   ┆ 371.189947                │\n",
-              "│ Upgrade 00 ┆ Mar   ┆ 393.665096                │\n",
-              "│ Upgrade 00 ┆ Apr   ┆ 295.411291                │\n",
-              "│ Upgrade 00 ┆ May   ┆ 177.300088                │\n",
-              "│ …          ┆ …     ┆ …                         │\n",
-              "│ Upgrade 02 ┆ Aug   ┆ 205.836428                │\n",
-              "│ Upgrade 02 ┆ Sep   ┆ 170.945828                │\n",
-              "│ Upgrade 02 ┆ Oct   ┆ 199.186401                │\n",
-              "│ Upgrade 02 ┆ Nov   ┆ 299.757518                │\n",
-              "│ Upgrade 02 ┆ Dec   ┆ 386.499711                │\n",
-              "└────────────┴───────┴───────────────────────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<plotnine.ggplot.ggplot object at 0x784150152970>"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {
-            "image/png": {
-              "height": 450,
-              "width": 1050
-            }
-          },
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "monthly_rows: list[dict[str, object]] = []\n",
-        "for scenario in SCENARIO_ORDER:\n",
-        "    monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n",
-        "    for month in MONTH_ORDER:\n",
-        "        month_df = monthly.filter(pl.col(\"month\") == month)\n",
-        "        monthly_rows.append(\n",
-        "            {\n",
-        "                \"scenario\": scenario,\n",
-        "                \"month\": month,\n",
-        "                \"weighted_mean_energy_bill\": weighted_mean(month_df, \"energy_total_bill\"),\n",
-        "            }\n",
-        "        )\n",
-        "\n",
-        "monthly_summary = pl.DataFrame(monthly_rows).with_columns(\n",
-        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
-        "    pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n",
-        ")\n",
-        "display(monthly_summary)\n",
-        "\n",
-        "(\n",
-        "    ggplot(\n",
-        "        monthly_summary,\n",
-        "        aes(x=\"month\", y=\"weighted_mean_energy_bill\", fill=\"scenario\"),\n",
-        "    )\n",
-        "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
-        "    + scale_fill_manual(\n",
-        "        values={\n",
-        "            \"Upgrade 00\": SB_COLORS[\"sky\"],\n",
-        "            \"Upgrade 02\": SB_COLORS[\"carrot\"],\n",
-        "        }\n",
-        "    )\n",
-        "    + scale_x_discrete(limits=MONTH_ORDER)\n",
-        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
-        "    + labs(\n",
-        "        x=\"Month\",\n",
-        "        y=\"Weighted mean household energy bill\",\n",
-        "        fill=\"Scenario\",\n",
-        "        title=\"Monthly energy bills before and after upgrade 02\",\n",
-        "    )\n",
-        "    + theme_switchbox()\n",
-        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
-        ")"
+     "data": {
+      "image/png": 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Dhw9X586dy2tXUMGVRVuEY7mZ33Rl2d4yMzO1evVqBQcHKywsTElJSfLx8ZGvr68uXLigp556SnfeeeeN7yhsIiUlxRgSrDS9WtarV88ItBZV3sqVK7Vnzx5FREQoIyNDvr6+6tKli0aMGKH69esXua6lvfbs2VNTpkzRoUOH9Mcff+j06dNKTU2Vj4+PunTpolGjRhVbB1Q8GzduVEpKiiRp/PjxpXpTvlOnThowYIA2btyoDRs26B//+Ic8PT0LLHfp0iWtXLlSBw4cUExMjEwmk+rUqaP27dtrxIgR8vf3N5bN+1vPIjo62jhHStKsWbPUoUOHG91V2BFPT0/de++9+uqrr7R8+XLddtttatKkyQ2Xl5iYqBUrVigoKEhRUVHKzMyUn5+fOnXqpGHDhhXai/vChQu1ZMkS1ahRQ998802xw9jv3r1bs2bNkpOTkxYuXKgaNWrccF1h33r27KkuXbpo//79+vPPPzVu3LhCnyFfunRJK1as0IEDB3T58mU5OzvL399fvXr10vDhwws9J+ZlNpu1a9cubdy4UadOnVJiYqJq1Kghf39/DRgwQIMGDeLZtZWV25AT7u7u6tatm7p166YJEyYoPT1dBw4cUHBwsIKDg3XixIkCY8RaenQICQnRqVOn9P3338vV1VWdOnUyAg4BAQHlVWWgwjp//rxmzZqlyMjIfN+HhoYqNDRU27Zt06BBgzRp0qQiH+RZJCcna968eTp//rzx3aVLl3Tp0iXt2rVL48eP5yIchpSUFL3zzjvGGwsW8fHxxvm+W7dueuWVV1SlSpViy7pw4YKmTJmixMRE47vExETt3btXe/fuVb9+/fTcc8/J1dW1XPYF1tevXz8tXrxYFy9e1MKFC/Xee+/lO0dlZ2fr22+/lZR7Md6hQ4diH/SlpKTo3Xff1f79+/N9bzmHBQcHa+nSpZoxY0aJ3ZFlZmZq6tSpOnLkyE3sIexVWFiYzpw5I0nGW3y1atVS27ZtdezYMW3evLnYm9957dmzR++8844yMzON72JiYrRx40Zt3rxZDz/8sEaPHl1iOXPnztWWLVtuYG9gz+zlPOfl5aWuXbsqKChIW7ZsKTbQkJGRoZ07d0rKfRMRuBGZmZmaPn260Q28lPt2YXR0tPbs2aN7771X//jHP2xYQwCVQVn+prsZly5d0ltvvVXgnkxkZKTx3cWLF8u9Hih7NWrUkIeHh9LT0/PdJ7sRhw8f1jvvvKOkpKR830dERCgiIkLr1q3T+PHjNXTo0BLLWrNmjT799NN8vSNHRUVpzZo12rBhg5599lkNGDDgpuoL+7Fjxw5Jub0zXM9QOgMHDtTGjRt19epV7du3zzhPWqxYsUJfffVVgR5kzp07p3PnzmnVqlV67rnnaEsO7K677tKKFSsUHR2tzz77TP/5z39u6GWzPXv2aO7cuUpLS8v3veU6du3atbrvvvs0duzYfOX369dPS5YsUWJiog4dOlTsUD6WgGPnzp0JM1RyJpNJAwcO1P79+5WSkqITJ04UeHlk6dKl+uabbwq8ZH/27FmdPXtWq1at0pQpU9S6detCt5GWlqZ33nmnwL2X2NhYxcbG6ujRo1q2bJmmTZumWrVqle0OokjlFmi4loeHh3r37q3evXtLyr0Zt3//fgUFBSk4OFinTp0ylrWctMxmszIyMoyHYv/9739Vo0YNrV+/3lrVBuxeSkqKpk2bppiYGPn5+WnEiBFq3bq1nJ2dFR4erqVLl+rcuXPauHGjmjVrprvvvrvY8n744Qc5OTnpmWeeUaNGjXT16lUdPXpUv/76q7KysvS///1PgYGBql27tpX2EPYqKytL06dPN94q7dKli/r376+GDRsqOjpaO3fu1NatWxUcHKw5c+bojTfeKPZH78cffyxvb2+NGzdOjRo1Uk5OjkJDQ7Vs2TJFRkZq69at8vT01FNPPWWtXUQ5c3Z21sMPP6yZM2fq9OnT2rFjh/r27WvM37Bhg8LCwmQymfTII48UW5bZbNYHH3yg/fv3y83NTcOGDVNgYKA8PT0VHR2tTZs2aefOnTp37pw+/vhjTZ48udj2uGrVKmVmZmrs2LFq3bq1fHx8CNNUIpYL3dq1a+e7eOnfv7+OHTumQ4cOKTY2Vr6+vsWWk5qaqo8//ljt27fXrbfeqnr16ikpKUkhISFaunSp0tLS9PXXX8vX17fAzaO8du7cqbS0NN19993q0KGD/Pz85ObmVuL2Yf/s6Tw3YMAABQUF6ciRI8W27+DgYKWlpcnJyUn9+vUrk+MAx7N8+XIlJCToiSeeMHp4O3nypH7++WelpaXpl19+Ubdu3XhhAcBNKavfdDcjIyND77zzjiIjI+Xu7q67775bHTt2VLVq1XT58mWtW7dOwcHBWrFihdq0acPf1grGyclJAQEB2r9/v9avX68uXbqoT58+113OmTNn9NZbbykzM1Ourq4aMWKE2rZtKy8vL507d05Lly7VpUuX9Mknn8jT0zPf78VrRUVF6csvv1SfPn3Ut29f1a5dW7GxsTp+/LiWL1+uzMxMzZs3zwj3oGLLzs5WSEiIJKlRo0bX9TC5UaNGxvSJEyfyXZNu2LDBGMaidu3aGjFihJo2bSo3NzedPXtWy5cvV3h4uObOnSsfHx916NBBrVq10ieffCJJ+vbbb7Vr1y75+Pho5syZRrk82KtcXF1d9eCDD+r999/XsWPHtHHjRt16663XVcaBAwc0e/Zs5eTkyNvbW6NGjVKzZs3k7u6uCxcuaM2aNTp9+rQxrMX9999vrNu0aVM1aNBAYWFh2rJlS5GBhrS0NO3Zs0eSir33gsojb69JISEh+QINq1at0pdffilJ8vHx0d13363mzZvLbDbr5MmTWrJkiRISEvTmm2/qgw8+UN26dfOVnZOTo1mzZunw4cOSpF69eqlXr15q2LChUlNTtXfvXq1evVphYWF688039dFHH3HP2EqsFmi4lqenp/r162f8kE9ISNC+ffsUFBSkvXv36ty5c5Jyww1506Z539wFkHuC9fPzU40aNTRt2rR8b+O1bt1a/fv314svvqhz585p8eLFGjlyZLE/ft3d3TVr1qx8J+FOnTqpdevWmjZtmjIzM/Xrr7/q6aefLtf9gv1bvny5EWZ45JFHNHr0aKNttWrVSn379lX79u316aefKjg4WFu3bi32R6W/v7+ef/75fG+utm/fXkOGDNG7776rPXv2aPXq1Ro6dGix41aiYunevbs6dOigw4cP67vvvlOvXr3k4uKiq1evGuM83nrrrSUOd5OTkyMfHx9Vq1ZNs2bNUrNmzYx5zZs3V+/evfXZZ59p1apV2r17ty5evJjv4v5azs7Oev/994sdlxIVk9lsNm5+9+/fP9/fxFtuuUVffPGFcnJytHXrVo0aNarYsqKjo/Xwww9rzJgx+b4PDAzUkCFDNHnyZEVGRup///ufevfuXeQFzpUrV/Tee++VqhtbVDz2cp7r2bOn8Ybhtm3bigy5Wv7/6NSpk3x8fG529+Gg0tPTNW/ePFWrVs34rm3bturatasmTZqknJwc/fTTT5o2bZoNawmgIivL33Q3I+89zIkTJ2rgwIHGvObNm6tHjx566623dODAAX3++efq3bu3XFxsdjsWN+DJJ5/U888/r/T0dL399tvq0KGD+vbtq06dOqlu3bolPmDOycnRBx98oMzMTHl5eWn27Nn5fqMFBARo0KBBmjFjhg4fPqxPP/1UgYGB8vDwKLS80NBQvfTSS/nur7Rs2VK9evXSbbfdpldffVWpqan64osvNG/ePIburODi4uKUnp4uSWrYsOF1rWu5dkhNTc3XS0xKSooRZujQoYOmTJmSbxiLgIAADRw4UG+88YZOnTqlVatWqUOHDnJ3dzeuWS3Lu7i4cB1byfXr108rVqzQyZMn9fXXX6tHjx4ldtVvkZGRoQ8++EA5OTlq1aqVpk2blu/6ICAgQLfddpsWLFigVatW6eeff1bv3r2NoS1MJpP69eunn376Sbt27dLTTz9d6H2VXbt2KSMjQ66urtfViwkqrrznnUuXLhnTMTEx+uqrryRJbdq00b///W9Vr17dmG8Zjuell15SYmKiPvvsswLXpH/++acRZnjyySc1bNiwfH9LO3furN69e2vKlCmKiIjQ/v371aNHj3LZT+RXfN/zVlSzZk3deuutevXVV/XLL79o8eLFGjJkiMxms0wmEz++gCJ4eXlp5syZeuONNwrtQt3FxcUYIiIpKUlxcXHFljd69OhCfxh07dpVLVq0kJTbNQ8ql0WLFmnkyJHF/jNr1ixj+ezsbC1btkxS7g+BvGGGvIYOHap27dpJkrF8UR599NFCh0Rxd3fXxIkTjYv5tWvX3vB+wv6YTCb985//lCSjm00pt2uwuLg4ubu7l6pLamdnZz311FOaN29evod8eQ0fPtyYLuk8NmTIEMIMldTJkyeNrn+vDVnVqFHDSPxbbpAXp2bNmrrnnnsKnefn56cnn3xSUm5wd/fu3UWW06dPH24CVWL2cp5zd3c3essrqn2npqYqODhYkvI9kAGu11133ZXvZqVF48aNjZuMlgeAAHAjyvI33c3I2+NsYTeznZyc1L17d0m592QYeqLiqVevnt566y3jYbIldDBhwgQ9+OCDevvtt7V582alpKQUuv6BAweM4Soee+yxQoP1VapU0dNPPy1nZ2clJydr06ZNRdanVatWRb4s0qhRIz300EOScn8Lnj59+np2FXYoOTnZmM4bOigNk8lk/B7LW86GDRuUnp4uZ2dnPf3004WWW7VqVT3zzDN67bXX9PLLL99g7VEZODk5acKECXJyclJiYqK+//77Uq+7adMmxcXFycnJSRMnTiz0+sDZ2VmPPPKI/Pz8lJOToz/++CPffMsL0ZY34wtj+VvfvXv36/7/BBVT3tBfamqqMb1y5Uoj3DJp0qR8YQaLOnXq6OGHH5Yk7d+/v8Bvs5UrV0qS2rVrVyDMYNG2bVs9++yzmj9/PmEGK7KbQMOVK1e0a9cuzZ8/Xw8//LDuu+8+/fnnnwQZgFJwc3Mr9g06f39/YzoiIqLYsurVq1fo9yaTyUhHhoeHX38lUamEhoYqNjZWknTbbbcVea42mUzq2bOnJOn06dP5xpjPq2rVqsW2YW9vbyMYwQV55dO8eXNjnPZFixYpOjpav/32myTpnnvuKXUXsSaTKd/57lp5uxAr6VxIV/+Vl+VCt1GjRsbftbwsNwfPnDmjsLCwYsuqX79+sb9Vu3TpYlxkWcZ3LgxvwVd+9nKes4x/e/r06XxvMVjs2LFDmZmZcnd3580W3JSirikkGefehISEfDefAOB6lOVvupuRkZFhTBfV80Le7wsL8cP+tWnTRh9++KGeeeYZBQYGGv9Nk5KStGPHDr3//vt66KGH9OOPPxa472F5AOfq6lpsr5X169c3Qs6W3jCLWq44eYfE4P5JxXflypUyKSctLc2YPnDggKTc82dx7alp06bq06cP5y2oefPmGjZsmCRpzZo1xd7fyMsSlq9bt26xve1WqVJFXbt2NdbJyckx5jVo0MAI9G/durXAuvHx8Tp48KAkhptwVJZebKT/+5vbtm3bYq9JLc8rpPx/c+Pj443gfe/evYu95zdo0CBeTrIym/VxlpWVpcOHDysoKEjBwcE6fPiwsrOzjfmWnhnyDjdRs2ZNdevWzRbVBSqUq1evKi4uzvh/Km+vDHn/nypMcSdpywMXbjxWPsOGDcv3VmdhqlSpYkzn/eHauHHjYtez/GHPyclRVFRUoX/oSxNea9iwoYKDg423cFC5PPjgg9q+fbsSEhL0yiuvKC0tTT4+Pho9evQNl5mZmamEhARdvXq1wLySzoWonLKzs7Vt2zZJRV/o9uzZU25ubsrIyNCmTZuKfXO+pHOXk5OT6tevr9OnT5cYokHlZw/nuY4dO8rb21vx8fHasmWLxo4dm2/+li1bJOVeuBfVzTFQGqW5ppByuzwu7E0tAChOWf+muxl5H9AcPXq00PG9jx49Kin3mpob3xWXi4uLbr/9dt1+++1KT0/XiRMndPLkSe3fv18nT55UVlaWFi1apKCgIL3zzjtyc3OT9H+hggYNGsjZ2bnYbTRo0EDnz58v9kWikq5BatSoIS8vLyUlJXH/pBLIey/uZuR9a93ysK64h33AtR544AFt27ZNCQkJ+vTTTzVnzpwSwy6W819JQSzp/+4fJyUl6fLly6pTp44xr3///jp79qz27NmjK1eu5Pv/Ytu2bcrJyZGHh4cCAwNvZNdQwVnuXWRmZho9IpX0vKJ69eqqUaOGEhMT873sERoaakxzjrQ/Vgs05OTk6Pjx40aA4cCBA/lSzIXdcPPw8FDXrl3VvXt3de/eXS1atKDHBqAQmZmZ2rFjh1avXq3z58+XW+CARG7l5eXldV03VvJ2VTdp0qRSrxcWFnbDN3Dc3d0l5f6wTU1N5eZ3JePn56dRo0bpl19+MUJYDz744HVdvJvNZh05ckQrV67U0aNHlZiYWF7VRQV14MABo114e3sbY+Jdq0mTJgoJCdGWLVs0bty4m/r9aWnD3EyEPZznnJ2d1b9/fy1dulSbN2/WfffdZ7Tv2NhYHTp0SJKM3iSA8lDSwxwAKIktftMVpV+/fvrhhx8UExOjDz/8UI8++qg6duyoatWq6fLly1q7dq0RGBwyZAjnwErCw8NDXbp0UZcuXTR27FhdvnxZCxYs0K5du3TmzBn99NNPRnfWlvsn586d08iRI0tV/s32jFqlShUlJSURqq4EPD09jem8vSyUhtlsNu4R5y3H0iYt99mA0vD09NSjjz6qefPmKSQkROvXr9cdd9xR7DpJSUnGuiXJOzRAYmJivkBDv3799M033+jq1avavXu30fOg9H89NvXq1Ys27UDy9spgeUaQkpJiPGdetmxZicNfW+T9m5v3mQftyf6Ua6Dh9OnTCgoKUlBQkPbt25fvj25hAQY3Nzd17NjRCDC0adOmyO7aAORKSkrSv//973zpMcBe0bsHinPPPfdo7dq1SkxMVLNmza7rgZrZbNbcuXPLfYxcVGx528fHH39c4vKRkZE6efKkAgICbnrb9AoCyT7OcwMHDtTSpUsVHh6us2fPqnnz5pJy32wxm83y9vZWx44db2obAACUJ1v+pruWm5ubpk6dqqlTpyouLk7vv/9+ocu1adNGjzzySJlvH/ahVq1aevXVV/Xqq68qJCREmzdvNgINN4J7J7Dw9fWVh4eH0tPTC4zzXpK4uDijLTVq1Kg8qgcHM3DgQK1bt05Hjx7V999/r379+uXr/aO81KpVS23atNHx48e1c+dOI9Bw+fJlhYSESGK4CUeTdzixm+1JISUl5WarAysp87TAkiVLFBwcrODg4HxvDBV2E9fJyUlt27Y1AgwdO3Yk9QIUoagHIT/99JMRZujVq5eGDx+uhg0bysvLy0j+Hz58WFOmTLFWVeEA8qZmp02bplq1apVqPW9v7xvepqU77erVq9M7QyVVtWpVjRs3Tp988okee+yx6+oVZufOncaNzSZNmujee+9V69atVaNGDaOrT0mlfiMGlc+VK1e0a9eu615v06ZNN3Xz2zLmad26dW+4DFQe9nCea9asmRo0aKCwsDBt2bLFCDRYyu7fvz9vj1ZSpQlW5R2vFigvtEXcjPL4TXez7a1x48aaM2eOJk+erOjoaON7FxcX1a9fXyNHjtStt97K39dKztnZWb1791ZISIhiYmKMniUt909atGihF154wSp1sVyD+Pv7W2V7KD/Ozs5q1aqVDh48qPPnzxvDdJfGhQsXjOm857/q1asrLi6u0GHrgOKYTCY99dRTmjRpkhITE/Xbb78VO6STl5eXYmJiSvXAOO+b8V5eXgXm9+vXT8ePH9e+fft09epVubu7a/fu3cbynTp1uoE9QkWVN9DQqlUrSfl7Arnzzjs1YsSIUpWV95l03mcenCPtT5kHGt5++22ZTKYiL1BbtGhhBBi6dOlSqu5mAEeQlpYmV1dXubq6Fjrf0kXTtd0SHzlyRJLUvHlzvfbaawwLAauwPPyQcsNpNzsOaGluIFmS6DwUrNyGDBmimJgYdejQ4brWs3Qz6+TkpJkzZxZ68QPHtnv3buPG3owZM0q82H3ttdd07Ngxbdu2TY8//nihvYaVdO7KyckxLrK4mQgLW5/nTCaTBg4cqO+//15btmzRww8/rIiICGN804EDB95QubC9G72ekP6vm874+Pgiy6enGZQWbRHlqSx/05VVe9u6das+/vhjtWzZUlOmTJGvr6+uXLkiX19fQgyVRE5OTqnut+W9PrC0nebNm+v48eNKSEi46Xsn126jMImJicZ5lmuQyqF37946ePCg4uLitHPnTvXp06dU623atElSbk8yXbt2Nb5v0qSJ4uLi8o0bD5RWo0aNNHLkSP3+++/6448/dOeddxa5bPPmzRUTE1OqYXQs906qV6+u2rVrF5h/yy236H//+5+uXLmiQ4cOqXv37kbA8ZZbbqGndwdiNpuN85unp6cR2HJ1dVXjxo11/vx5paen39Df3CZNmhjTnCPtT7k9+bQkBU0mk3r16qX//Oc/WrdunX766Se98MIL6tevH2EGQLld2kyaNEn333+/tm3bVugyV69eNcbebtq0ab55UVFRkqQGDRoUeXFluZABykqTJk3k4+MjSQoODi522dTUVJ06darYZdLT0/O9yXKtuLg4HT16VFL+MAUqH2dn52LT3UWxnAu9vb2LfMjHudCxWd4+9/HxUfv27Utc3tJdYVJSkg4cOFDoMhcuXCj2huL+/fuNG+6cu2BhD+c5SxedsbGxOnv2rLZu3SpJatiwoZo1a3bddYNt3ez1hCTVr19fUu55LSsrq9AyrrebYzge2iKsoSx/05VFe4uNjdWHH34oNzc3vfnmm2ratKm8vLxUu3ZtwgyVQFZWlr744gt98MEHpXoRw/LSkbe3txGYCQwMlCTFxMTo3Llzxa5/6tSpEt9kPn/+fLHzt2/fbkxzDVI5DBo0yGhPCxYsyDesd1EOHjxonC9vvfXWfM9hunTpIin33Ffcg+arV6/q888/V1xc3M1UH5XQ2LFj5evrq4yMDP3www9FLte9e3dJUkRERLHnvytXrmjfvn2SpG7duhX6jMPb29t4KWDXrl1KTk42zrkMN+FYdu/erf3790uSBg8enK+HhW7duknK/XtsuR9XlMOHDys7Ozvfd97e3kaoYefOncUGWw8ePKjly5cXKAPlp1xf5bb01LB7927Nnz9fn3zyidavX19s8hlwNJ6ennJzc5PZbNaiRYsK/VG6dOlSZWZmSpJat26db54laXb69GljmbyuXr2q5cuXl0PN4cicnZ2NLq2XL19eZKjhypUrmjt3rl566SUtWbKk2DK//fbbQm8QXL16VZ988onS09MlSbfffvtN1h6VkeVcGBsbazz0y8tsNuvXX3+1drVgJ5KSkoyLndJ2p9+3b19jubzjNOeVkpKi33//vdB5MTEx+vzzzyVJNWrUUM+ePW+k6oChLM9zderUUdu2bSVJ+/bt044dOyTl3iwtbRe2sB83ez0h/d+D5fj4eK1Zs6bA/JycHP6OokS0RZS3sv5NVxbtLSQkRBkZGfLx8WEY3Urol19+0YoVK7Rp0ya9/fbb+YZXvtbatWvztU/Lb6ouXbqoUaNGkqS5c+cWWcaZM2c0depUPfXUU/mGCrjWuXPntGXLlkLnXbhwQd99952k3KFQLN1wo2Lz8PDQ+PHjJeVeC8yYMaPY5yt79+7V22+/rZycHHl7excIU996663y8PBQdna2Pvnkk0L/XmdlZWn+/PlauXKlpkyZwlBQyMfDw0OPP/64JOmvv/4qMhgzYMAA+fj4KCcnR//973+VmppaYJns7Gx98803iomJkZOTk0aNGlXkdi3BhT179mjPnj3KycmRn5+f2rRpc/M7hQphx44d+vjjjyXlhg/+9re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+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x708129d95630>"
       ]
+     },
+     "execution_count": 51,
+     "metadata": {
+      "image/png": {
+       "height": 450,
+       "width": 1050
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_rows: list[dict[str, object]] = []\n",
+    "for scenario in SCENARIO_ORDER:\n",
+    "    monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n",
+    "    for month in MONTH_ORDER:\n",
+    "        mdf = monthly.filter(pl.col(\"month\") == month)\n",
+    "        if mdf.is_empty():\n",
+    "            continue\n",
+    "        monthly_rows.append(\n",
+    "            {\n",
+    "                \"scenario\": scenario,\n",
+    "                \"month\": month,\n",
+    "                \"energy_bill\": weighted_mean(mdf, \"energy_total_bill\"),\n",
+    "                \"elec_bill\": weighted_mean(mdf, \"elec_total_bill\"),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "monthly_df = pl.DataFrame(monthly_rows).with_columns(\n",
+    "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+    "    pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n",
+    ")\n",
+    "\n",
+    "(\n",
+    "    ggplot(monthly_df, aes(x=\"month\", y=\"energy_bill\", fill=\"scenario\"))\n",
+    "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
+    "    + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n",
+    "    + scale_x_discrete(limits=MONTH_ORDER)\n",
+    "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+    "    + labs(\n",
+    "        x=\"Month\",\n",
+    "        y=\"Weighted mean household energy bill\",\n",
+    "        fill=\"Scenario\",\n",
+    "        title=\"Monthly household energy bills — upgrade 00 vs. upgrade 02\",\n",
+    "    )\n",
+    "    + theme_switchbox()\n",
+    "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000028",
+   "metadata": {},
+   "source": [
+    "## Section 5: Bill change analysis (upgrade 00 → upgrade 02)\n",
+    "\n",
+    "This section compares each building's annual energy bill **before** (upgrade 00) and\n",
+    "**after** (upgrade 02) the heat pump retrofit.\n",
+    "\n",
+    "Bill change δ = `bill_after − bill_before`; negative δ means savings.\n",
+    "\n",
+    "Buildings are classified by their **upgrade 00** heating type throughout — i.e., by\n",
+    "the baseline fuel they would be replacing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "a0000029",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bill delta computed for 33,790 buildings.  54.1% have negative delta (savings).\n",
+      "Heating groups: ['Fossil fuel', 'Electric resistance', 'Existing heat pump']\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "c6ed1859",
-      "metadata": {},
-      "source": [
-        "## Utility-level summary\n",
-        "\n",
-        "For multi-utility states, this table quickly identifies territories with unusual bill changes or bill alignment. It remains valid for single-utility states such as Rhode Island."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+      "(Deprecated in version 1.0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── Quadrant definitions (from analysis.qmd) ──────────────────────────────────\n",
+    "\n",
+    "QUADRANT_COLORS: dict[str, str] = {\n",
+    "    \"savings > $1k\": \"#1b5e20\",\n",
+    "    \"savings $0-1k\": \"#81c784\",\n",
+    "    \"losses $0-1k\": \"#ef9a9a\",\n",
+    "    \"losses > $1k\": \"#b71c1c\",\n",
+    "}\n",
+    "QUADRANT_ORDER = list(QUADRANT_COLORS.keys())\n",
+    "QUADRANT_LABELS: dict[str, str] = {\n",
+    "    \"losses > $1k\": \"LOSE > $1K\",\n",
+    "    \"losses $0-1k\": \"LOSE $0-1K\",\n",
+    "    \"savings $0-1k\": \"SAVE $0-1K\",\n",
+    "    \"savings > $1k\": \"SAVE > $1K\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def quadrant_pcts(df: pl.DataFrame) -> dict[str, float]:\n",
+    "    \"\"\"Weighted % of households in each bill-change quadrant (df must have 'delta' + 'weight').\"\"\"\n",
+    "    total = cast(float, df[\"weight\"].sum())\n",
+    "    return {\n",
+    "        \"savings > $1k\": cast(float, df.filter(pl.col(\"delta\") < -1000)[\"weight\"].sum()) / total * 100,\n",
+    "        \"savings $0-1k\": cast(\n",
+    "            float,\n",
+    "            df.filter((pl.col(\"delta\") >= -1000) & (pl.col(\"delta\") < 0))[\"weight\"].sum(),\n",
+    "        )\n",
+    "        / total\n",
+    "        * 100,\n",
+    "        \"losses $0-1k\": cast(\n",
+    "            float,\n",
+    "            df.filter((pl.col(\"delta\") >= 0) & (pl.col(\"delta\") < 1000))[\"weight\"].sum(),\n",
+    "        )\n",
+    "        / total\n",
+    "        * 100,\n",
+    "        \"losses > $1k\": cast(float, df.filter(pl.col(\"delta\") >= 1000)[\"weight\"].sum()) / total * 100,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# ── Bill change dataframe ──────────────────────────────────────────────────────\n",
+    "\n",
+    "bill_delta = (\n",
+    "    add_heating_label(\n",
+    "        bills_by_scenario[\"Upgrade 00\"]\n",
+    "        .filter(pl.col(\"month\") == \"Annual\")\n",
+    "        .select(\n",
+    "            BLDG_ID,\n",
+    "            UTILITY_COL,\n",
+    "            \"weight\",\n",
+    "            HEATING_TYPE_COL,\n",
+    "            \"heats_with_natgas\",\n",
+    "            \"heats_with_oil\",\n",
+    "            \"heats_with_propane\",\n",
+    "            pl.col(\"energy_total_bill\").alias(\"bill_before\"),\n",
+    "        )\n",
+    "    )\n",
+    "    .join(\n",
+    "        bills_by_scenario[\"Upgrade 02\"]\n",
+    "        .filter(pl.col(\"month\") == \"Annual\")\n",
+    "        .select(BLDG_ID, pl.col(\"energy_total_bill\").alias(\"bill_after\")),\n",
+    "        on=BLDG_ID,\n",
+    "        how=\"inner\",\n",
+    "        validate=\"1:1\",\n",
+    "    )\n",
+    "    .with_columns((pl.col(\"bill_after\") - pl.col(\"bill_before\")).alias(\"delta\"))\n",
+    ")\n",
+    "\n",
+    "avail_heating = [h for h in HEATING_ORDER if h in bill_delta[\"heating_label\"].unique().to_list()]\n",
+    "n_save = bill_delta.filter(pl.col(\"delta\") < 0).height\n",
+    "pct_save = n_save / bill_delta.height * 100\n",
+    "print(f\"Bill delta computed for {bill_delta.height:,} buildings.  {pct_save:.1f}% have negative delta (savings).\")\n",
+    "print(f\"Heating groups: {avail_heating}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000030",
+   "metadata": {},
+   "source": [
+    "### 5a: Quadrant bar chart by heating type\n",
+    "\n",
+    "Each bar shows the percentage of weighted households in four outcome bins:\n",
+    "\n",
+    "| Bin | Definition |\n",
+    "|-----|------------|\n",
+    "| SAVE > $1K | Annual energy bill decreases by more than $1,000 |\n",
+    "| SAVE $0-1K | Annual energy bill decreases by $0–$1,000 |\n",
+    "| LOSE $0-1K | Annual energy bill increases by $0–$1,000 |\n",
+    "| LOSE > $1K | Annual energy bill increases by more than $1,000 |\n",
+    "\n",
+    "A bar leaning heavily green indicates that most households in that group would save\n",
+    "money by switching to a heat pump under current rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a0000031",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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zplP3gf4mKSlJf/31V+ZvMzQ0VKVLl1a9evXUokULn3VPf+rUKc2bN0979uzR2bNnVbp0aTVp0kSXXHJJnpZx5swZLVq0SHv37tWRI0dUtGhRlSpVSk2bNlXDhg0L9fealpamDRs2aO3atTp27JhSU1NVqVIl1axZU61atbpou/5PS0vT8uXLtWHDBh09elTR0dEqX768OnTooHLlyhV28wpNQZ63+ctxqqD2LxcTY4w2b96slStX6vDhwzLGqHz58mrYsKGaNWuW68/W4XBoy5YtWrVqlY4cOaLExERVqFBB1apV86vja34eT40xWrdundavX6/Dhw8rPT1d5cqVU/v27f3musDX51gXyueeG/54bZEf1/wZ9W7evFmrV69WXFycEhMTVapUKVWsWFEdOnRQsWLFcl33hXwOm5O0tDQtW7ZMmzdvVlxcnIoWLZp5PV21atXCbp5HEhIS9Pvvvys2NlapqamqXLmyunXrZhm4aIzRypUrM68HixUrppo1a6pTp04qWrRontuSX8ennJa5Zs0al/TLL7/8gjqHcDgcWr16debxJzQ0VGXLltUll1yiOnXq5HrbXSjXGnFxcVqzZo1iY2MVHx+vwMBAp/PfmJiYXNednp6ulStXavPmzTp8+LCCg4NVvnx5derUSZUqVfLZOjgcDq1Zs0YbN27U4cOHlZaWptKlS/v8HmaGWbNmWaZfccUVHvUOFR4ergceeEBPPvmkZd333nuvU9rq1at1+PBhl7wxMTEaMmSIR21+7LHH9P3337ukz549Ww6Hw2UYvILUrl07/frrr7r88ssLtKcGh8OhX3/91XKep0MxDB48WE8//bROnTrllH7w4EGtXbtWLVq0yHM7AwIC1KZNGy1dutRl3sGDBz2q49JLL1VISIhSU1Od0tesWaPbbrstz20EfMoAgB8ZM2aMkeQylSpVyiQnJ3tV13PPPWduueUWl2n69OlO+ebNm2e5zNtvv93Mnj3bNG7c2HK+JNOrVy+zcuVKj9uUkJBgRo0aZWrWrGlbpyQTFRVl7r77bnPw4MEc67Qqv2nTJrNmzRrTtWtX22U0bNjQfPPNN8bhcHjU9tTUVPPhhx+aunXr2tYZGRlpbrvtNrN7925jjDHp6enm0UcfNSVKlHDKN2/ePMtlOBwOM2vWLNOpUycTGBhou5yqVaua1157zZw+fdrjbZ+T22+/3XJZdm0tqO1up0uXLm6/Q55MEyZMcLuMlStXmmuvvdYUKVLEto4iRYqYa665xqxYscK2HrvfmDdT1apVbevftGmTufXWW03RokXd1lG3bl0zZswYk5aW5na9J0yYYFn+ueeec1vOW8ePHzfDhw835cuXd9vuUqVKmccff9ycOnXKbX07d+60LJ+YmGjmzZtnmjdvbruMdu3amd9//z3HNlv9ToYPH27i4+PNY489ZiIiImz3aU888YQ5efKk1/VLMjt37vS6jN1vN8OsWbPMZZdd5nZfI8l07drV/Pbbb7naNp60Izc2bNhgbrjhBtvtLcnExMSYe++9N3N/7I7V/mTMmDHm8OHD5o477jAhISGWyyhdurR59dVXTVJSklftX7Jkibnyyitt65VkypYta5588klz5MiRHOurWrWqS/lZs2aZtLQ08+WXX5p27dqZkiVLOs2/6qqrLOvK+F2WKlXKtm0VK1Y0I0aMMGfPnjXGGHPs2DHTp08fl31lSkqKqVKlimUdV155ZY7rtX//fhMUFGRZfuzYsV5tc3fbKqOtr7/+uildurTb/cQvv/zi0fHLm/14bva57s7dfMlX5212+wdvpttvvz3fj6fGFNz+xeFwmB9//NF069bNlC1b1ml+06ZNc/Fp2Z8b5aaM1XHnueeec8l3ww03mNTUVPPpp5+a6tWr226zChUqmJdeesmcOXPG4/VJSEgwI0eONJUrV7att2TJkub+++83x44dM8YYk5iYaG677TYTHR1tuz5262GMMWvXrjXXX3+9qVatmsvx8cSJE5bt9PXxNKuzZ8+aUaNGmUqVKtnW27RpU/Pjjz/a/j7s9iXefPYFcY6VIb8+95xcSNcWuVGQ1/zGGBMfH29GjRrl9nMMCgoyXbt2Nb/++qtX14a++M0VxPHEHbtjw/Hjx82TTz5pihUrZrvcJk2amMmTJ5v09HS3y7A7v3D3PfXmnMRuv5CQkGDeffddExkZ6TIvODjYDBo0yOk+xh9//GF7jyU8PNw8/PDDOV5DFfTxyROxsbGWy3vnnXe8qsfq3DWn757d+a4Vu+vbhIQEM3r0aJdzlKxTzZo1zYcffmhSUlI8Xh9fXmt4sh7GGPPnn3+a3r17m4oVKzrNL1asmGUbHQ6HmTx5smnfvr3bfUBQUJDp06eP1/vHY8eOmWHDhrndBp07dzaLFi3K1e84w5EjR8ywYcPcXt+Ehoaavn37mqVLl3q1Du7cdNNNlsv68MMPPa5j/fr1lnVUqlTJJe8nn3ximfe6667zeHnp6em2n8f27ds9ric/LVmyxERFRbm0r1mzZubo0aM+X96WLVsst0eZMmVyPP5kNWDAAMt6xo0b57O23nrrrZbLeP311z2uo0mTJi7lu3Xr5rM2Ar5CQAMAv3LLLbdYHoSvvfbafFum3cW8uwuXrFNISIhHJyJbtmwxNWrU8KjOjCkmJsb89ddfbuu1KvfOO++4fVCUdRoyZEiOD3iPHDli2rRp43G7o6OjzbRp08z06dMt51s94Dtx4oTp3bu3V9unQoUKOW4fT/kioMHX292d/LzpeObMmVw9fLn11lstb4Tk5w2zjz76yONtnjH16NHD7Q2bgghoWLJkidsLa6upcuXKZuvWrbZ12t1U+/DDDz1exvPPP++23Vbfi7vuuss0bdrUo/rr1Knjdh0KIqAhNTXV3HHHHV5/B5944gm3N5sLIqAhPT3djBgxwgQEBHjc7tDQUPPpp5+6rddqf/LEE0+4vQmfdWrTpo05fPhwju1PTEw0gwcP9mq7FytWzPz4449u67W6afnLL7+YoUOH2tZrFdCwbNkyU65cOY/bVqdOHbNp0ybz8MMPW843xph3333Xcl5AQIDZsWOH2/V6/vnnLcuWK1fOJCYm5ri9Pd1Wksyll17q8Xpfc801Od70tip3oQU0+PK87UIIaCjI/cuYMWPMyJEjbeu9kAIaevfubS6//HKPt1mlSpU8uvG/fft2U6dOHY/rLV++vFm0aJF55513clwfuwdf8+bNc3tOlT2gIb+OpxlWrVrl8XFIkrnsssss0/MzoMGX51j5/bnn5EK6tsiNgrrmN+bcw0Nvzickmf79++cYvOzL35w/BjT873//M9WqVfN4+Z06dTJxcXG2yyisgIaHHnoox7Y3a9bMnD171vaFnuxTvXr1zL59+2zbXZDHJ09NmzbNcjm//vqrV/UURkDDoEGDTIsWLTzedg0bNszxvN6Y/LnWcLcew4cPN5MmTbKt3yqg4fTp06ZXr15e7QsCAwM9flg/Z84cU7x4cY/qDQoKMj169PD6d2yMMd9//73lw2930z333OP1i3RW6tevb1m/N0ETDofDhIeHW9aTPeD/3nvvtcz36quvetVuu/3Fd99951U9+Wnx4sUFFtQwdepUy+3Rq1cvr+p55ZVXLOu5//77fdLOQ4cOubxEmDFNmTLF43puuOEGl/LFixf3SRsBXyKgAYBfadmypeVBeOTIkfm2TF9czAcEBLi9MDt69KipUKFCruouUaKE2wv1vLZdkhkxYoRt/SkpKbafi7spIiLC3HjjjZbzsj/gO3bsmKlXr16u2h4aGmr++OMPrz/37HwR0ODL7Z6T/LrpeOzYMcvIXE+nRo0auVxI5NcNs8mTJ+e6vltvvdV22+Z3QMPWrVst39jxZKpdu7btg0y7m2reTu4eTvnioVy9evVMfHy8V/X7MqDh7rvvznXbx48f77N2eMvhcNjuUz2ZXn75Zdu6fbE/6dKli0lNTbVdRlJSkuncuXOu6g4ICDATJ060rdvqpuUjjzzits7sAQ2xsbEmJibG67bVr1/ftG3b1nKeMece4mTvHSJjevzxx23XKTU11fa84ZVXXrEtlxO7G7zeTq1btzYJCQm2y7EqcyEFNPj6vM3fAxoKev9id2M+Y7qQAhpyM0VERJiFCxfatunkyZO2vbu4m8qWLWsbHJxTQMO1117rthcEyTWgIb+Op8YYs2jRIrdv8Xsz5WdAg7eTu3Os/P7cc3IhXVvkRkFc8xtjzIwZM0xwcHCu6m/atKnbAA5f/ub8MaAhN1ONGjVsg2oLK6DB06lr16459rKRderYsaPtW8EFdXzyhl2wxq5du7yqpzACGnIzlSpVymzevNm2Tfl1reFuPe6++27bh+KSa0CDw+HwOpgh65RTb0Tffvutbe9z3k7ufscfffRRruu9/PLL8/TikTHGdpt7c0w2xtgGdS5fvtwpn91n5kkvFlnZveU/evRor+rJb4sXL7a8n+broIZXX33Vcnt4e705fvx4y3o86bExJ2vXrnX7ctGBAwc8rsvuOOKLIB/AlwpvABwAsHD8+HHL9CpVqhRwS7xjjNG9996rtLQ0y/lPP/20Dhw4kKu6jx8/rjfffDMvzcvRK6+8ou3bt1vOGzt2rFauXOmSHhYWpuuvv16PPfaY+vXr5zKmWmJior7++uscl+1wOHTzzTdr8+bNuWp7SkqKbrzxxlxv38LkbrsXhtTUVF177bVat25druvYsGGDrr32Wpex13zt7NmzeuCBB3Jd/ssvv9SmTZt82CLPPfjggzpz5kyuym7btk2ff/65bxuUzaOPPqqTJ0/mW/2bN2/O932anaVLl+qTTz7Jdfmnn35aDofDhy3y3CuvvOLRPtXOM888ozlz5viwRc4WLFigSZMm2c5/4IEHtHDhwlzVbYzRXXfd5dW+6auvvvJqGY8//rjl975cuXK688479fDDD6tt27Yu8zdt2mQ5XmWGokWL2u6rxo8fr6SkJMt5M2fOtDyuRUZG6p577rFdXkFZvny5HnzwwcJuRr7x9/M2Xyvo/cvkyZNzvayLQWJiom644QbFxcVZzn/55Ze1Z88el/To6GgNHDhQjz76qHr27Oky//Dhw/r5559z1aaFCxdq3759HufPz+NpXFycrr/+eiUkJOS6fn/l7hyrMD73/HYhXVt4Iqdr/s2bN2vgwIG283Oydu1a3X333ZbzLuRz2PwUGxurW2+99YJct/nz53vV7kWLFmnmzJn52KKcj0/eiI+Pt0yvUKFCnuv2R0ePHtV1111ne+zKr2sNd6ZNm2Z7rWHlm2++0ezZs3O1LEkaMWKE7bzNmzfrjjvuUHp6eq7r98TChQvzdJ/o119/1XPPPZfr8ikpKbbbvHTp0l7VVaZMGcv07L8tu99afi2vsLVv316//vqrIiMjndLXrFmjnj172j5X8Ja/btf9+/dr3Lhx6t69u5o2baq1a9da5uvTp4/Kly/vcb12+2Z/+/wBAhoA+JUTJ05YphcrVqyAW+KsatWq6tKli6pWrWqbJzY2Vj/++KNLekJCgu1DnvDwcLVt21Y9e/ZUkyZNbOu2qtdTpUqVUqdOnVS/fn0FBQVZ5nE4HHr//fct53355ZcuaTExMVq3bp2mTp2q119/XTNmzNCff/6pkJAQr9v3/fff69dff7WcV6tWLd18880aNmyYBg4cqPr161vmO3LkiEaOHOn1svNTXrd7YRgzZozmzZtnOz8kJEQtWrRQixYt3H7W8+fP15gxY/KjiZl++OEHHTt2zHJexYoV1aVLF3Xt2tXtDZOffvopv5pna/fu3bY3CaKjo9W+fXv16NFD9erVs60jL/uD8uXLq0uXLqpVq5YCAgIs88THx2vChAm5qj80NFTNmjVT27ZtFR0dbZvv/fffz/XN3rz49NNPbefVr19fPXr0ULt27WzbfvDgQf3999/51Txbe/fu1QsvvGA5r0SJErr66qv16KOPaujQoerYsaNlPmOMHn300Vzd8C1SpIhatWqlli1bqkiRIrb53n77bcv05cuX2277ihUr6vrrr9ewYcN0xx136JJLLrHMl5SUpCeffNLjNntzIzY+Pl4zZsxwSW/btq22bt2q8ePH6+2339aSJUv0+uuve1xvhvvvv99yux07dkzffPONZZmPPvrIMv3uu+9WTEyM123wRrFixdSxY0fVqFHDbb5x48ZZBjxe6Ar7vK2gFcb+xRcPSvxVUFCQmjRpopYtWyosLMw238GDB/XSSy+5pBtjLL9/1atX19atW/Xll1/qjTfe0G+//aZvv/3WZ+329jPJz+PpCy+84DagKDQ0VC1atFDz5s1zde2RX/JyjlVYn3t+u5CuLaS8XfNL5wJWTp8+7ZIeEBCgrl276p577tEjjzyiq666yvb+xldffaUVK1a4pF+o57B5UaRIEbVr10716tWz/U1J0pw5czRt2rQCbJlnIiMj1aFDB9WqVcuj/FWrVlWnTp1UsmRJ2zy5vUaT8n588pbVQ7CwsDC/2m97KjQ0VC1btlSTJk1s7+1I0vr16y33Vfl9rWHH22P72LFjLdMDAgLUtGlT9ezZU61bt1ZERIRlvmXLltku89FHH3X7UkdERITatGmjBg0auLws5SmHw6H/+7//swyaCA4O1uWXX677779fDz74oHr37q3w8HDLel5//XWvgjyzsjoGSOf2Z0WLFvWqrrwGNNiVz+vyvHXttdcqICDAp1OHDh0sv0++DGrwl+3qcDi0cOFCDR8+XE2bNlWlSpU0dOhQt+dXYWFhGjVqlFftzB4gkuHUqVNe1QPku0LsHQIAXNh1P/bbb7/l2zLddbdYtWpVs2jRIqf8P/30k4mOjrbMf++997rUv2jRIsu87du3dxknc+3atZZ1FylSxLb9dm0PDw83n376qdN4mRs3bjSNGjWyzF+/fn3L+q3GJ3vyySct89p1Vzx16lSzYsWKzClrd/NWXZAGBASYsWPHunT1lp6ebr766ivL70lYWJg5duyY7XbKia+GnPDVds/J5s2bnbbpihUrbLub/fjjj13yrlixwmnsvbNnz5rSpUtblg8NDTVvvvmmU1djycnJ5o033jChoaGWZUqXLm3Onj1rjDEmPj7eZdkzZsywLNeiRQvLtq5du9Zp/YcMGWJZ/s0333Ta9mlpaeall16yzPt///d/lts2P4ecsBvDcsCAASYpKckp7++//27ZZW2DBg0s63bX7WnJkiXNjBkznPIvWbLEtnvpK664wnIZ7rrkvP76651+gwkJCeb++++3zb9s2TKP6/fVkBO1atVyyRcSEmIWL17slO/06dOmb9++lvV+8803eW6Htx588EHLuu+++27L8ZZXrFhh+9n+/PPPLvnddfv74IMPZv6WjTHm+PHj5tprr7XNf+jQIZf6+/XrZ5n3hRdecPneOxwOM2fOHNsxT//55x+X+t0No3DLLbeYb7/91ixdutRpn7Jt27bM8n///bdlWatupR0Oh+XYt+XLlzcLFixwWkZWduMpt2nTxmUZmzdvtswbHBxs9u7d65LfG+62Vfny5c3s2bOd9qEHDx40V111lW2ZAQMGWC7HKu+FMuREfpy3xcbGuhzXhg4darmcZ5991vI4GBsbmy/H08Lcv1x55ZVmypQpZsmSJU5t3LBhQ64+u8IcciIgIMA88cQTTkOxpKSkmPfff9+26+Hw8HCX4UmOHTtmmfeTTz6xbL9VN9RhYWFmzpw5Tts0677W3XrUq1fPjB071mV/tmLFCqdhhfLreHr48GETFhZmmd/qfDQpKcm8+uqrJiQkxHad8nvICV+cYxXE556TC+naIjfy+5p/1apVlnnr1q1r1q1b55L/2LFj5qabbrIsc91117nk9/VvLj+OJ95wd2yIjo42kyZNcroXcPLkSXPXXXfZlrnkkktcllGYQ0707NnTHD9+PDPvp59+aptXkvnwww8zz78SExPNwIEDLfMVL17c6TwtQ0Ecn7z1wAMPWO4vvVWYQ06Ehoaat956y2lflZiYaJ555hnb4ULKlSvn0kV7QVxruFuPtm3bmokTJ5pFixY5lV+1alVm+dTUVMvjb8mSJc2mTZuclnXkyBHbYWmzD4dgzLnzZXe/9y+//NLp937mzJkchw+0+h3/8MMPlnnbt29vYmNjXfLv27fP9OjRw7LMsGHDXPJ7Ys+ePZb1VahQweu67I4RX375pVO+GjVqWObbunWrV8v75JNPLOsZPHiw123PasCAAW4/y/yYmjdvnqf7w8YYc+edd1rWPXbsWK/q2bJli2U9NWvWdFvuwIEDZtSoUbafr90UHBxsJk2a5PX6zpw507K+rPsJwB8Q0ADAr9iNKZf9JpEvubu5kf3GRobRo0db5u/YsaNL3p07d5ovv/zSZdqyZYtl3XYPau3YtX3kyJGW+ZctW2ZbJiUlxSV/0aJFXfK98847lnXbPbCxExsba5nf3bjixhjz9NNPW5b74Ycf3JZzx1cBDb7a7gWxDllNnz7dtn2zZs2yLffzzz/blnP3u7W7AdSlSxeP1vX333+3/F1lvdmeIS0tzfLi3O7BV34GNKxfv96y3fv27bPM37NnT5d22N3AcXdT7auvvrIs880331jmr1SpkmV+u+9Y/fr1Lb/HaWlptsE848aN87h+XwU0fPXVVy7b/pdffrGs1+6hpt1N0PwKaHA4HJZjardr187t+J6///67ZXsefPBBl7x2N5UvvfRSy5um8fHxpkyZMpZlso+devbsWcvf3/XXX+92vceNG2dZ/1tvveWS1+6m5aOPPup2GRlWrFhhWX7NmjWW+a3Gqcxp37V7927bMbVXrlzplPfhhx+2zOeLh/V22yoqKsp2P+RwOGxvrEdERFg+MLPKe6EENOT3eVsGu4cP3o53m5fjaWHuXwYMGGC5f8mLwgxoeO+992yX8dtvv9mWmzhxolPeI0eOWOabPn26Zd1WAUc5PeixW4/KlSubM2fOuC2bIb+Op59//rnttrKr3xhjvv/+e9ty+R3Q4ItzrIL43HPjQrq2yEl+X/M/++yzlnmtAjEzJCYmmpo1a7qUiYmJcdkH5+c5bIa8Xp95w+73FxgY6Pbhid19AElmz549TnkLK6AhKCjI7N692yV/+/btLfNfdtllLnlPnDhhG2xw+vRpl/wFcXzyltXDQLtrTHcKM6Bh5syZtsuwu1aRZBYuXOiUtyCuNezWo1WrVm7P6TIkJCRYnv9mX5cMdi9qWB0fnn/+edvfirvAqDfffNN2G1v9jm+77TaXfFFRUZYB9xmOHDliihUr5lKuSZMmOW4zK0ePHrVsr7uX1OxcccUVlnVlv+9pd7/FKrjEnddee82yHqvze28URkBDQECA+f777/PUbruXc0aPHu1VPXb3gu2+Yw6Hw3zwwQemSJEiXq93yZIlc33/ye548Oeff+aqPiC/BAsA/EiJEiUsx5UrjC6OGjVqpA4dOljO69evnx5//HGX9CNHjrikVatWTdWqVXNJN8ZYdpFVqlQp7xtrwW78zdatW6tcuXI6dOiQy7xjx46pXLlyTmmNGjXSsmXLnNJmz56tBx980Knrx9TUVP3xxx8udbpbH6v80rnt684VV1yhl19+2SV90aJFuvrqq92WzW++2u4FbdasWZbp/fv3V69evWzLXXnllerXr5/leJ6zZs3K8bPMrR49elimp6amWv6uypQpo7179+ZLW7zRqFEjNWrUyCXd4XDYtjuvoqKidNNNN1nO69Onj2W61b7MncGDB1t2HxoUFKQ+ffpow4YNeV6GL9x8882W6UlJSS5DYOR3t/6e2rFjh+WY2r1793bb5WmXLl0UERGhxMREp/RFixZ5vOy7777bsovfqKgode/eXV9//bXLvOyf65IlS5ScnOySz5P9vJVFixbpkUcecVs2g93+OLvatWsrJCTEZXzuWbNmqWnTpk5pe/bs0caNG13qyGkszSpVqujmm2/WxIkTXeZ99NFHGj9+vKRzwx3YdSf82GOPuV1GXowYMUIVK1a0nBcQEKDXXntNP/zwg8u4wImJiVq6dKm6dOmSb20raIV13lYY/HH/ciFq2LCh7r33Xtv5PXv21NVXX23ZJfrcuXN16623Zv5fsmRJlS1bVocPH3bKN2vWLF111VVOaadOndJff/3lUqe3Y/tmuPXWWz3uDjm/jqd23ededdVVtscFSbr66qvVs2dP/f777x4vyxd8dY5VmJ97frlQri18dc1vdV1bq1YtNWjQwHbZ4eHh6t69u3bs2OGUfvLkSW3cuFGNGzfOTLsQz2Fz46677lLz5s1t5z/55JOaMGGC9u/f7zJv7ty5uv322/OzeR6pVauWqlSp4pJ+ySWXaMmSJS7p3bp1c0mLiYlR7dq1tX79epd5x48ft+0aPDtfHp+8ZXU8yX7e4M969eqlvn372s6/4447NGbMGMuhXObOnatOnTpl/l8Q1xp2hg4d6vacLkNERIQGDhxoOS8xMdFlGIfixYt73Aa7Y/v999/vdgi3Bx54QOPGjdOmTZs8Wo7VfrhNmzYqW7asbZlSpUqpdevW+u2335zS161bp1OnTnk9/HFUVJRlekJCgs6ePevVsBN2w3dkH1rIbqghb4cc8XR5/i4gIEDjx4/XNddck6d6Cmu7Dh8+3OuhZ8LDwzVw4ECNGDHC8jrWE3b7Z2+HSgHyGwENAPxKiRIlFBsb65Ke/SK/INStW9d2nt0JQlJSkm2ZkydP6vPPP9fChQu1Y8cO7dixQ2fPns1rMy2VLFnS7diL1apVs3ywbtX+wYMHWwY0DB06VMOGDVPlypW1fft2Pf/885YPLe3GQ5ek3bt3W6bbjdGcE3fj7RYEX273grZ161bLdE8CRK6++mrLm47btm3Lc7tysm7dOn322Wf6559/tGPHDu3evdvjMbwL08GDBzV+/HgtW7ZMO3bsUGxsrOVDX1+oU6eO7UOjiIgIy0Abb9vi6/1lfnI4HJozZ46mTp2qbdu2aceOHZa/S39ht598+umn9fTTT3tdnzf7SV98rnbtHzhwoO1NM3e8ab+7MbCzKlasmK677jpNnjzZKf25555TaGiobrjhBkVFRWnFihV6+OGHXW5GSu6PdRmGDRtmGdAwZcoUvfHGGypevLimTJliGcTZu3dvy2AoX8n+wCy7ChUqqHXr1po/f77LvNjY2IsqoCFDQZ63FZbC3L94+vu8EPTp00fBwe5vrVx11VWWD4yyX/cEBARo8ODBLuPefvLJJypZsqSGDBmiMmXKaP369Xr88cctb5J6sj+y4u1nkh/HU6vrQEk53pQOCAhQ//79CzygwVfnWIX5ueeXC+XawlfnsFb70+3bt+c6cOvAgQNOAQ3ShXcOmxs5nY8UKVJEl156qT7//HOXeXb7j4Jm9/DU7gGwt/m9udb15fHJW1YP66wCQ/1VTt/FwMBA9e3b1zKgIfu2K6hrDSveHttTU1M1ffp0TZ8+PfP89+jRo7ladobcHttDQkLUu3dvjwIaUlNTLQOdfv/991zvhw8dOuR1QENoaKjCw8MtjxNHjhwp0IAGb18gya+Ahttuu01t27bNUx3ZpaWlafTo0Tpx4oRTekBAgCZMmOCT4LbC2K7fffedx8EMAQEBat26tfr376/BgwfnObD19OnTHrcTKEwENADwK7Vq1dLKlStd0q0uEnJiFUUsnTvBDA0NzbG8u6j3sLAwr9ryyy+/aNCgQQX2RnJOEfvetH/w4MH6+uuvNXfuXKf08ePHZ75R6s4DDzxgO8/X2yOvF1p55cvtXtCyvxGWoX79+jmWtXvzKD9vsKWlpel///uf3n///XxbRn75/PPPdf/99xfYg7GC+F76cn+Znw4fPqybb77ZZX/mzwpzP+mLz7Uw2+/JsT7Dm2++qblz5zrtt1JSUvToo4/q0UcfdVs2KirKo5smjRo1Ut++ffXjjz86pScmJmrChAl65JFHNGbMGMuyVm+I+kpwcLBq1qyZY74GDRpYBjQURm8r+a2gz9sKy4Xy+/R39erVyzGP3bmS1WcwYsQITZ8+3eUNzVGjRrk88M4uKCjI7du47njzmeTX8dTuO5SX89H85MtzrML63PPLhXJt4atz2Pzen16I57C54ev9aWGwe3jqq3RvFOb2tHoIlpycrNTUVMue/fyNr7ddQVxrWPHm2L5t2zZdf/31WrNmTa6WZacgju3Hjh3zqk2eOHr0qNugNzvVqlXT5s2bXdLj4uI8fnveGGP7G6xevbrL8qx425OAp8vzlq97VkpNTdUtt9xiGczw+eef67bbbvPJcgp6u6anp+d4zV+sWDFddtll6tOnj6644gqf9s5lF3DmbVAPkN8CC7sBAJBV586dLdMXLFjg1cM/Y4waNmyoqKgolyl7VHR+W7Rokfr37+83F9jeCgwM1LRp0zx62JHdAw88oN69e9vOz+ltAW8FBnJY+6946KGHLshghm+++UZ33HHHRfeW74UgOTlZvXr1uuBuBF/o+8kLpf3lypXTL7/84vVD1qCgII0bN07ly5f3KP/w4cMt08eMGaPly5dbBnC2adPGqdta5K8L/bzNGxfK7/O/pkiRIvr1119VokQJr8uOHj3apftqX7tQj6f+zt8/d7iXn/tTfnO4ENm91VvYvWoWloK61sito0ePqkePHj4PZigovt4HS7k/r7UbNsebF/U2btxo2ctDpUqVXIa788XyHA6HbX53wwAVtIxghm+//dYpPTAwUBMnTvRZMINkv96rVq3yqqccq5c2reqfO3eudu7caZm3Tp06mjp1quLi4vTNN9/otttu8/lQY3b7ZnpogL+hhwYAfsWuy+LTp09nPgj0xMaNG21PBOzGyMwPxhg98MADll3GSeeivmvXrq3ixYurRIkSKl68uBYvXqw5c+YUWBtzsmjRIg0aNMirYT8qVaqkJ598Msc3hXx9AhYREeHT+v5LypQpYzlW46ZNm9SqVSu3Za3KSfbdZ+bVmjVr9NFHH1nOK1KkiJo2baoKFSpk/qaKFy9u2R1dQUtKStKDDz5oOS8oKEgNGzZU9erVnfYHM2fO1IoVKwq4pf7H3RAYnnZd+vHHH9veoClVqpSaNm2q0qVLZ35njDF65ZVXctNcn7rQ95MXSvs//vhjPfHEE0pJSfG4TKtWrfTKK6+oR48eHpfp0KGDOnbsqEWLFjmlb9++XYMGDbIsM3z4cJ+8pWcnLS1NO3bsyPFNNLt9vb+N254XF8N5mzculN9nXiUnJ9u+ae2L7q+t3sLLzpvfz7Rp0/R///d/On78uMdtqFu3rl588UVdf/31HpfJrfw8nma/SZ8hL+ejFwp//9y9dSFdW/hC6dKlfRqwnHV/eqGew+bG5s2bc3yDOa/nI764rrhQ+Pr45I1y5cpZpm/ZsiXPw07lNDyir47tXbt2dZvHm21XUNcauTVy5Ejt3bvXcl7FihXVqFEjlSxZMvMc+OjRo7Y9y2VXqlQpy7o3bdqU4/fM02N7iRIlFBgY6NPhR3N7Xtu8eXNNmTLFJf3HH3/0uDeln376yTK9WbNmlsuz8ttvvykpKUnh4eE5Lm/FihWWwdxRUVF57qHBV1JTU3XzzTfru+++c0rPCGa45ZZbfLq8WrVqKTIy0mV/cvjwYa1cuVKtW7fOsY6kpCTb4dCyf5YLFy60zFe1alWtWLEi3wMLrI4XxYsXv6h61cPFgYAGAH6lfv36qlevnuWBdMSIEbr66qsVExOTYz3vvvuuZXr16tVVq1atvDbTY1u3brW8+VCzZk3NnDnTsvu0+++/329ujG/cuFGXX365EhIS3OYrXbq0qlevrurVq+uyyy7TwIEDPTrpqVOnjmX6woULeRu1gNWuXduyK/Hp06fnGOVsNeZmRp35YerUqZbpd911l958802XrmPT09Nz7Kq3ICxYsMCy+922bdtq6tSpqlKlisu8pUuXFkTT/IbdA6c9e/bYdvdoN0Zzdl9//bVLWkBAgD755BPdeeedCgoKcpq3cuVKv7gZbPc7evHFF/XMM88UcGu8Z7efnzhxom699dYCbo21L774IsebS4GBgapYsaKqV6+umjVr6uabb1aPHj1yFWgwfPhwl4AGyfomQp06dXzeTaeVGTNmuA1oOHjwoJYvX245z19ucvnChX7e5q0Lff+SnbtjiNW6GmM8Poa489NPP+nll192+3bgjBkzLNOz/37++OMPXXfddZbD5mVVvnz5zHPvq6++WldffXWB9ZCRn8fTatWq6c8//3RJnzZtmtvzUWOM7Ta+EFwIn7u3LqRrC1+oU6eOdu3a5ZRWo0YNbd++Pc9BiRfqOWxuzJgxQ7169bKdn5CQoN9++81yXvb9qbtjgh1fHBP8iS+PT96y6zVm06ZNuuyyyzyux+pzPHz4sG2w4vHjx30y/MCMGTN0zz332M53OBwuw8hlyL7tCvpaw1vGGMv9THh4uKZOnaq+ffu6tOO7777zOKChWrVqlgEN06ZNs+2lVzr3APvnn3/2aBmBgYGqVauWy2+4e/fu+uOPPzyqw1d69eplOXTArFmztH79ejVu3Nht+eTkZNveSK+44gqXtGbNmqlMmTIuQyGcOHFC48eP13333Zdjm9944w3L9F69evnFeUZqaqpuuukmff/9907pgYGB+vLLL3XzzTf7fJmBgYG67LLL9MMPP7jMe/311116ibAyfvx4nTx50iW9XLlyLvtIu+PPnXfeWSC9JGzatMkljd6/4I8Kf48EAFkEBARo2LBhlvMOHjyoAQMG5PjmwzfffKNPP/3Uct7QoUML5IIgg1008X333Wd5Uzw5Odn2Ar0wvPrqqy7BDPfee6/i4+NljMmc4uLitGzZMn399de68847PY7g7N69u2X6hx9+KGOMbbm///5bs2fPdpn+q90X5uT06dM55rG6MJLOXWTOnj3bttwvv/yimTNnelWnO5601e539eqrr1qOg/vnn396VG9OEhMT81Tert2PP/64ZTDDiRMntHjx4jwt80JTsmRJy3S7Gxl//fWX1q9f71HdVtu/cePGGjp0qMuNYMn+rQhv5fV7U6FCBcvjxcSJExUfH29bbufOnZb7yQ0bNuSpPd5q1aqV5QX4J5984vYNpY0bN1q235vegjxhjLF8cPv5558rNTU18ziXnp6uPXv2aMGCBfrss8/Us2fPXJ9PXHnllWrYsKFHeR977DHL76evjRw5Uvv377ecZ4zR448/bhncGB4errZt2+ZLm3Lz28mv/fSFct6WlSfHvQt9/5Kdt8eQqVOn+uT84J9//nF7U//333+3fUCb/Vz4hRdecHmo/fLLLysxMdHp3PvAgQNavHixJk2apAEDBhTozeb8PJ7aXRtMnz5ds2bNsi03bdo0v/0teuJC+NyzupCuLQpKz549XdJiY2PdrmtKSorlvvTXX391uhYurHPYDL7YT3pq7Nixbru8f+WVV2zPV7p16+b0v90x4ZdffrF8i/vUqVOWD3UvZL48PnmrWrVqltcAy5Yt86oeq8/RGGN7TBg7dqxX9duZPXu2bcCCJE2YMMG2i/6s264wrjW8dfLkSR06dMgl/bLLLlO/fv0s2+GLY/sHH3ygdevW2ZZ7//33LR+y2rHaDy9YsMDt/YJTp05Z7oft3pb3ROPGjW17Tbjtttvc3tPO6C3Oaj8XGhqqG2+80SU9JCTEtneCp556KsdtOHnyZJdeDzLcfvvtbssWhNTUVN1www2WwQyTJk3Kl2CGDHbr/9133+U4nPWmTZv01FNPWc4bOHCgS6CZVeCDdK4H5Px2+vRpy++JPw03AmQyAOBnkpKSTKVKlYwky6lu3bpm+vTpJjk52anc0aNHzX333WcCAwMty0VHR5vDhw+7LG/evHmW+W+//Xa37bQqU7VqVac8U6ZMsczXsWNHc+LECae827dvN71797Zd76NHj+a6Hdl16dLFstzOnTud8jVs2NAlz/Lly93W7a2uXbtatmXQoEFm165dTnlTUlLMxx9/bIKDg13yh4aGmoMHD+a6HbfffrtlO+bNm2eZPz+3u6/XoU2bNmb27Nlm+fLlZsWKFZnTkSNHMsuePn3alCxZ0rJ8WFiYefvtt01KSkpm/uTkZPPWW2+Z0NBQyzIlS5Y0Z86csW3rzp07bb/rb775plmyZIlTW9euXZtZ9vLLL7ctl56enpkvPT3d/PTTT6Z06dKW+fv27euUP8OECRMs89eqVcvMnDkzs03eeuWVVyzrve6660xCQoJT3jVr1pg2bdrYbtvExESPt2mXLl3ctqtq1aqW5ax4+zsxxn57Pvfccy55J02aZJk3JCTETJkyxTgcjsy8S5cuNdWrV7f9HmVvU1hYmOV3+88//3TKd/bsWfP222/bHkveeOMNr7bNlVdeaebNm2dWrFhhNm/ebLud3Hn++ect627btq1ZunSpU16Hw2H++OMPU6FCBcsy06ZNc6k/N/um5557zrLMhAkTXPIOGjTIMm/v3r3NP//845Q3PT3dfPfddyYqKsqyzKpVq1zq9+Y7nN2RI0dcyhUtWtRy3+BLEydOtP3uZkxly5a1/K3nhd22kmTKly9vfv31V6ff2aFDh0z//v1ty/Tv399yOVZ57Y6Rudnn2p27lSxZ0nzzzTeZZZKSkrzaPgVx3pbBm9+QO3k5nhrjn/uX3Hr55Zctl1W8eHHz+++/O+X9+eefTalSpWy3nVX77D4zSSYgIMA8+eSTTsfzlJQU88EHH5jw8HDLMqGhoebQoUNOyyhatKhLvri4OJ9up7x+9/LzeHro0CHbc0u789HRo0ebkJAQ28/G6nzDGO++m/l9jlUQn3tuXEjXFjnJz2t+Y84dE6y+60WLFjXjx493OZ4fPHjQ9vh61VVXOeXN73PYDHk9nnjD7vcnnbtvM3nyZJOWlpaZ/9SpU+buu++2LdOsWTOXZezdu9c2/2OPPeZ0P2n//v2mZ8+eXu1HvN0veLvv9WYfVRDHp9zo3LmzS90lSpRw+mxzMmTIENvf4d9//52Zz+FwmM8//9xERETYbgsrdvu5jO3wzjvvOO2rEhMTzbPPPmv7OytTpozTd6ugrjVyc52e4eDBg5ZlS5cu7XKtdvz4cfPkk0/abrPvvvvOpf7Vq1fb5i9WrJiZNGmS03fizJkz5tFHH7UtY/e7mT9/vmXecuXKme+++86kpqY65d+2bZvp2LGjZZmHHnrIo+1uZ8yYMbZtb9y4sVmwYIHLd2Dbtm3mmmuusS1388032y5vw4YNJiAgwLJcyZIlzRdffOFyHDp69Kh55plnTFBQkGW5ypUru2yzgpacnGyuvvpql7YFBQWZr7/+Ot+Xn5KSYvt8IigoyDzzzDMu13yJiYlm4sSJtudAgYGBZuPGjS7LstvnP/vss07HXm+m7PcZ7fzwww+Wy/7iiy98sh0BXyKgAYBfWrBgge1JVcYUHR1t2rRpY7p37+72Bn3G9NFHH1kuKz9vbixevNi2PeHh4aZNmzamR48epl69erYnnzmduHvSjuw8vTi+8sorXfLcc889Jj4+3m393vj9999t1zUwMNDUrl3bdOvWzXTo0MHExMTY5h0yZEie2nExBDQMHz7co++Q3XfpzTffdJs/NDTUtGjRwrRo0cL2ZmPG9NZbb7lta0JCgldtzbpthw4dapuvQoUKpmvXrqZDhw6mbNmyHtef1eeff+51GU989dVXtnVFRUWZDh06mG7dupmaNWt63O6s38+LIaBh//79bveFlStXNl27dvVoG2VvU+3atW3z1qtXz/Ts2dO0bNnS9kF6TtvV7qG9N5+FnWPHjrltV6VKlTK/P5UrV7bNV6dOHcubZ/kd0LB582bbm36STI0aNUznzp1N586dTZkyZWzz9ezZ07IteQloSE1Ntdy2EyZMyNcbOCkpKaZKlSpuvy+jRo3y+XI9OV+KiYkxHTt29Oh39tdff1kuxyqv3TEyN/tcuxuW3nyHrRTEeVsGXwU05OV4aox/7l9ya+nSpW7XvWbNmqZr165u18Nd+9w9MMqYgoODTdOmTU2rVq0sH0Jmne655x6XZTRo0MAl30svveTT4Ka8fvfy+3jq7qGldO589JJLLjEtWrRwG8iQMV0IAQ0F8bnnxoV0bZGT/A5oMMaYgQMH2rY/KirKNGvWzFx66aWmWbNmbs+LFi1a5FRvfv/mMuT1eOINdwENGVPRokVNu3btTP369d1uL0m2D7bq1atnW6ZEiRKmQ4cOpkmTJjnuSy7kgIaMKa/Hp9x4/PHHLevPHozjztdff23bzsDAQFO/fn3TpUsXt9cQGZMVdwENGVNYWJhp2bKladq0qeXLNVmnV1991an+grrWyEtAQ1pamm1wiyTTpEkT07NnT9OsWTO3+bJO2fetdi+lZExFihQxbdu2NY0aNcrx9273u3E4HKZDhw5uf/MtW7Y0PXv2tDzuZkxBQUEuL1d5KzU11bRo0cLtOpQvX9506tTJdO/e3dSpU8dt3sjISLN37163y3zooYfc1hEVFWXatGmTeRzK6bs8Y8aMPG0DX7Bap6CgIDN16tQCa4Pdw/6MKTg4OPP43qZNmxyPxY888ojlcjw5Lno7rV+/3qN1vPPOOy3Lb9q0yZebEvAJAhoA+K3Ro0f77CB+7bXX2kZA5+fNjdTUVNu3w3M7FWRAQ043oaymwMBAU7VqVdO5c2dz//33m61bt7pti8PhMLfeemuetkl4eLjZsmWL2+Xk5GIIaPj000/z9F1KSkqyjVD3ZurQoYNHb8VWrFjR4zqzbtuZM2f69DclOZ8OzZkzx+synjh69GiOF43eThdbQIMxxlx33XU+3zbGGPO///3Pp9s++3Z96qmnvC7jjfHjx+e5zV9++aVl3fkd0GCMMc8880ye2h4QEGAWLFhgWXdeAhqMMaZv375etycsLMzUrVvXXHbZZeall14yx48f93h5Gd59913b+iMjI3NVZ048CWjwdLrttttsl2OV3+4YmZt97tatWz0q4+3xtSDO2zL4KqDBmNwfTzP42/4ltxwOh2nVqpVPPrfcPjDydCpbtqw5cOCAyzIeeOABr+sKCQkxNWvWNN27dzfDhw83+/fvd7ud8vrdy+/j6YEDB7wKSs1puhACGgric8+NC+3awp2CCGjYt29fnr+7PXr0cKk3v39zWeX1eOIpXz646datm+0b/x9++KFPlnExBDR4Otkdn3Jj5cqVlsu48cYbPa4jKSnJtmcobycrngQ0eDrVr1/fsieZgrjWyEtAgzHGbc8AuZmy71vXr1/vtvcMbye7383atWtNkSJF8lT34MGDPdpmOfn7779zDB7ydPr4449zXF58fLypUaOGT5bnzW80P+3bt88pqC8oKMh88803Bd6O66+/3ifbtWbNmub06dOWyyisgIYjR45Y/jabNGni680I+EThDLgHAB4YNmyYPvjggzyPHd2nTx9NmjSpUMYYDQ4O1ssvv+x1OauxBgvDAw88oEsvvdSrMg6HQ7t379bChQv1wQcfqF69eho8eLDtWOkBAQH65JNP1KFDh1y1MSAgQOPGjVOdOnVyVf5i0qNHD4WHh+e6fFhYmKZNm2Y5nran6tevr2nTpiksLCzHvH369MnVMnr37q327dt7Xc7T31W9evW8rtsTJUuW1LBhw7wu5y/7g4Ly6quvqlixYj6vd9iwYYqJifG6nKfbv379+l7X7Y0777xT//vf/3Jd/u6777YdW7MgPP/887r22mtzXX7UqFHq3LmzD1t03kcffeT12JTJycnasmWL5syZo2eeeUZVqlTRe++951UdgwcPVokSJSzn3XXXXSpevLhX9RWkFi1a6KOPPvJJXbnZ51atWjVPxzs7F+p5W26Ppxku9P1LhoCAAL333nsKCQkp7Ka4FR4erilTpqh8+fIu815++WU1adLEq/pSU1O1Y8cOzZ07V6+99pqqV6+uJ598UsYYXzXZSX4fT8uXL69vvvlGERERXi/jQuWvn/uFdm1R2CpWrKjvv/9ekZGRuSpfpUoVTZo0ySU9v39zWeX1eFLQqlatqsmTJ9veMxo8eLBatmxZwK26cLk7PuVGixYtLO/TfPPNN9q+fbtHdYSFhen999/3SXvyU/HixfXdd9+paNGiLvMK61rDGy+88IKCg4O9KhMUFOTxsbpRo0b67LPP8v2+bJMmTTRx4kSv1yVD8+bN9e677/qkLS1atNDPP/+sIkWK5Kme1157TXfffXeO+aKiojRv3jzVqlUrT8vr37+/Pv/88zzV4SsVK1bUvHnzVLt2bQUFBWnq1Km67rrrCrwdEydOVL9+/fJUR506dTR//vxcnyPklw8++ECJiYku6TfffHMhtAbIGQENAPzafffdp99++02NGzf2umxoaKhGjx6tGTNmFOoNkKFDh2r48OEe5y9evLjmzp3rFyc5gYGBatu2bZ7qcDgc+uyzz/Twww/b5omIiNDvv/+uQYMGeVV3dHS0vv32W7+4ie4PqlevrhdffDFPdZQqVUrLli3TTTfd5HXZG264QcuWLVPp0qU9yj9q1CivL+ylc9/LH374wasbv7169dKHH37oUd7KlSvr3nvv9bpdnnjxxRe9+r5WrVpVv/32W6EEZBWWGjVqaPr06R4HNbRu3dqjG7blypXTzz//7NUN4RdffNHjC7kbbrghV8cqb7zxxhteP6wLDAzUU089pY8++kgBAQH52Lqc2/H111/riSee8KodGTcxn3jiiXxrW2RkpNcPkrI7c+aMHnroIX3zzTcel0lOTrYM9gsODnZ7zPS15557zjawwkrfvn01f/58yxu2uZGbfW5oaKheeOEFnyw/uwvxvC23x9OsLuT9S1Zt27bVxIkTFRoa6lH+yy67LM/L7Natm8fH6bJly2revHnq1q2b5fzQ0NA8P3xLSUnRq6++qtdffz1P9djJ7+OpJHXu3FkLFizw6qFaq1atdNttt3mc35/46+d+oV1b+IMOHTror7/+8vqBUuvWrbVkyRKVK1fOZV5B/OYy+OJ4khfevEzRpk0bLV261HKbZcgIqmnUqJFHdZYpUybP54T+xJfHp9wICAiw/B46HA7de++9cjgcHtVzzTXX6M033/T4XMMXx/aePXt6nLd27dpaunSpbfBWYV1reKNRo0b65ptvPL53GhgYqPHjx6tLly4eL+PGG2/Ujz/+6FWwVe/evXXFFVd4nF+SBgwYoN9//11lypTxqtyVV16pefPm+ewaRzoXGDh37lw1bdrU67JlypTRl19+qccff9zjMlWqVNHChQvVv39/r5cXGhqqJ554wqvvQUHICGqYMWOGBgwYUChtCAsL03fffafHH3/c42uMrK655hotWLCgUI+vVjZv3qzRo0dbzsvNeRtQIAq7iwgA8ER6err59ttvzeWXX55jN2UVK1b0qtvNguh+0phzXSq763IzLCzMDBw4MLN7v++++85yjN+CGnLC4XCYO+64w2ddXQUFBZmzZ8+6bZcxxixZssT07t3b7TiqMTEx5oEHHjAHDx7MsT5PXQxDTmRYvHixueqqq0zdunXddreXU9fCf/31l7nqqqvcjpMYHh5u+vXrZ5YsWZKrtsbHx5sXX3zRtG7d2pQrV852THKrbZuYmGheeuklt11QVqxY0bz33nsmLS3NpKammjvvvNNERka65MsuOTnZfPTRR+aSSy4xpUqVsqw7txwOh5kyZYpp2rSpbbsjIyPNAw88YE6ePGmMMeaDDz6wHBf0YhxyIsP27dvNjTfeaDuGZnR0tHnmmWdMQkKCZfe+dm06cOCAGTJkiClatKjt9m/evLmZM2eOMeZcN4ddunRxGVvXarseOXLEDBs2zDRo0MBy7MS8DDmR1a5du8xdd91lYmJibNchODjYDBgwwKxcuTLH+gpiyImsNmzYYG688Ua3+6eIiAhz++235zhskTF5G3IiPj7eNGnSxLYd3k7t27f3aLnGGPPss89a1uFuKIe8stpWs2bNMvv27TN33XWX23Os1q1bmxkzZhiHw5HjcqzKuztG5mafm56ebiZPnmzat29vypUr5/V3OCf5dd6WwZdDThiTt+NpVv6wf/GF1atXm169etmuQ9myZc1bb71lzpw543H73H1m8+fPN23btnW7vGeeecacOnXKts0pKSnmsssu89n+qEKFCpbL8dV3Lz+Ppxni4+PNCy+8YPsbl86NQf3GG2+YhIQE8+ijj7rM9/chJwrqc8+LC+nawk5BXfNnSEpKMh999JGpV6+e28+rcePGZuzYsbZDJmRVEL85Y3x3PHHH7vcXGxtrxo0bZ6pUqWK7jvXr1zcTJkzwaJtlXacnn3zS8jpQOnev4tZbbzW7d+82N9xwg0f7kQthyAlfHZ/y4vDhwyY6Otpy2S+99JJXdc2bN8+0a9fOdl1q1KhhvvjiC7Nx48Yc970Z3F3fTps2zTRo0MB2eVWrVjVvvfWW2yFxCupaI69DTmTYunWrue6661z2G1mnzp07mxUrVhhjzl3XtWjRwuWa3d2+NS4uzjz88MOmePHitsuoWbOmGT9+vElNTTUDBgzw+HeT1enTp82oUaNyHHKvffv25ttvv/XoGie30tLSzPjx403nzp1NUFCQ2/Y0atTIPP/883n+Tc6bN89cffXVtvu9jKlChQrmrrvuMjt27PDR2l7ctm/fbu666y5Tvnx5t9s1MjLSXH311Wb+/Pke1VvQQ06cPHnSNGvWzLLcLbfc4qvNBfhcgDH51A8hAOST5ORkLV++XHv37tXRo0cVHx+v4sWLq1SpUmrRooVq1arlN2+JWYmLi9P27dsVGxurvXv3qlixYqpcubK6du2qqKiowm5epnnz5ql79+4u6e+++67uuOMO27bGxsZq5MiR+uyzz1zmLVq0yOOhJc6cOaNFixZlfs7BwcEqUaKEmjRpoqZNm+YqKha5k5SUpL/++ks7d+7U0aNHJZ1726p69epq165dvnT77Y309HTFxsYqNjZWO3fu1MmTJ1W2bFk1aNBArVq18tveDYwx2r9/f2bbDxw4oJIlS6patWrq0qVLoW9Xf3HixAktWLBAe/bs0enTp1W8eHE1aNBA7dq1y9ObAwkJCdq+fbt27typ2NhYpaenq3z58mrTpk2eu2ksKGlpaVq+fLm2bt2qI0eOKC0tTTExMapTp45atWpV6N3g5yQ5OVlLlizRzp07FRcXp4CAAMXExKhhw4Zq0aJFnrvn9MRzzz3n8vZpdHS0fvjhB3Xs2NHyO+ZwOPT333/r/vvv1/Lly53mhYeHKz4+Pse33A8dOqT69evr5MmTLvPWr1/v8ZuEvpaYmKhFixZpx44dOnbsmKKjo1WuXDl17NjRZ90PX2gulPM2X7vQ9y8ZDh06pIULF2r//v1KSEhQyZIl1axZM7Vs2dLr7oiff/55y55BJkyYkNnL2M6dO7Vo0SIdPHhQkjLPRy655JIcz0cmTJigO++80yktODhYX331lfr06WO5TzTGaOPGjXriiSf0008/uczft2+fKlas6Okq5kpBHE8dDofWrFmjDRs26PDhw3I4HCpTpoyaNWumpk2b+u25nicu1M89L/z92sLXdu3apWXLlikuLk6nTp1SVFSUypcvr1atWqlatWpe37u4GM5hu3btqgULFrik79y5U9WqVZPD4dDq1au1atUqHTlyRBERESpXrpxat26tmjVr5nq5ycnJWrx4sbZs2aLjx48rMjIy89orN8N6+Iv8Pj7l1RtvvGE79OKwYcM0cuRIr3qI2rVrlxYtWqTDhw8rJSVFpUuXVqtWrdS4cWOv12XQoEH64osvXNLnzZunrl27yhijTZs2aenSpYqLi1NISIjKlCmj5s2bq2HDhjn+fgvrWiOvTp8+rW3btmnnzp3auXOngoKCVKFCBXXs2NFnx5eMc80tW7YoLi5OQUFBKlOmjFq3bq26dev67L6uMUZbtmzRqlWrFBcXpzNnzmSez7du3VoVKlTwyXI8dfLkSS1btkyHDx9WXFyc0tLSVKpUqczzmipVqvh0eSkpKVq2bJn27dunI0eO6PTp0ypRooRKly6t2rVrq0mTJn59D91fGWO0du1abd++XUeOHNHx48cVFRWl0qVLq1KlSmrTpo3f3rfevXu3+vXrp3Xr1rnMi4iI0NatW/2uNwkgAwENAABLw4YN0xtvvOGUVqJECR07dizHsmvXrlWzZs1c0qdNm5arrs8AAMgPrVq10sqVK53SrrnmGn3//fc5ln333Xcth4Y4fvy4ihcvLkk6evSodu3alTnPGKNt27bpjTfe0OrVq13K9u7d2/LhFIDC58kDo7y47rrr9N133zmltWjRQn///XeOZWfMmGF5jr169WrLc3L4Dz53/BflFNAA7+T38SmvkpOT1bhxY23bts1yftWqVXXvvfeqadOmat68ucqWLVtgbcspoCGv8vtaAwA8sW7dOm3atElz587V559/bjn0pSS98MILevbZZwu4dYDnLtwwdgBAvrIaz/D48eN67733dOjQIcsyxhi3Y3A1bNjQp20EACAvrI518+fP148//qizZ89alklPT9fixYv1+eefu8yrUKGC0w3Gn376Sa1atcqcWrdurVtuucUymEGSV2OkAri4WO2PNmzYoEmTJunEiROWZYwxWr16tT744AOXeYGBgapbt67P2wnf4nMHcLELCwvT9OnTbXvB2L17t5544gldccUVmjJlSsE2Lp/l97UGAHjitttu04033qixY8faBjP06dNHTz31VAG3DPCOd30sAgD+M1q0aGGZ/tBDD+mhhx7yur6SJUuqRo0aeW0WAAA+06JFC61atcop7fjx4+rXr1+u6mvdunWu29KmTRt16tQp1+UBXNhatGihH374wSktJSVFt956a67qa9KkiSIiInzRNOQjPncA/wUNGjTQzJkzdemllyo5Obmwm1Ng/OlaAwDstGnTRlOnTvV6SD6goNFDAwDAUv/+/dWgQQOf1ffCCy8oKCjIZ/UBAJBXjzzyiOX45LkRGhqqZ55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fH7Vq1UrVq1e32Wf+/PnGyBFl6bbbbjMNqZ4ztJDl3LlzCgsLM63LyMjQtm3bci2fs57swYmsfceMGWMKUDg5OSk4OFjt2rVT06ZNValSJdM+q1ev1rRp0wo8p7wcOXJE7777rqljvkKFCmrRooWaNm1qGpli5cqVmj9/fpHq37lzp/G4YsWKat26terUqWMzzcRHH32kAwcOFPMs8hYZGamXXnpJly5dMtZ5eHiofv36at++verXr286x4yMDE2bNk1r167Ntb49e/ZozJgxNh2zXl5eatmypWrWrGmss1qtmjlzZgmfUaaYmBgjHOPg4KDg4GC1adNGAQEBpnKxsbEaO3ZsnsGLmJgYDR48WEePHjWtd3V1VdOmTdWgQQOb12r79u16+eWXCx1quR7JyckaN26cEhISjHVOTk5q0KCB2rdvr0aNGsnV1dXYZrFY9M4775hCXFlOnTql4cOHm76DvL291bBhQ916662qU6eOaWqV5ORkvfHGG9qzZ0+B7dy7d68RLHJ3d1ezZs1sPj+StGXLFtOILjmtX79eY8eOtXlu/fz81KZNG1WpUsW0PiMjQzNnztT3339fYBsLYrFYNG7cuFy/v4KDg9WyZUt5eXkZ6/bt21dg4KMkXz8AAADgn44pJwAAAAAA+P8cHBzUq1cv9erVK88yycnJGjlypDEKwGuvvaZbb721VNqTnJys1157zXRHdMOGDfXmm2+qcePGxpQJcXFx+vTTT03TMHz//ffq0KGDOnfuLEnq0qWLzd38U6ZM0ZEjRyRJjRs31tixY23akLOjNz/u7u66/fbbtWHDBkmZd+5brVabqR3yCjps3bpVd999t83633//3XhctWpVm6HiFy1aZIQZHBwcNHDgQPXv398UYrBardq1a5c+/PBDHTp0yNgvJCSkUFMl5LRo0SLT3d0vvfSSHn/8cWPKkYSEBM2ePVs//fSTcW4uLi6aOXOmfH19JanAKQ2CgoI0btw43XbbbcZzGBERocmTJxvPodVq1VdffWUKZ+R8nX/55Rf98ssveW6XbF/n9957T5GRkZIygxojRozQXXfdZeq4TktL02+//aaZM2cqKipKkjRt2jR16dJFHh4epnJvvvmm6Q77GjVqaPz48WrZsqUcHTPvt4mPj9fcuXP1448/ltid83np0aOHxowZI39/f2PdH3/8oYkTJxrnfeLECW3evNkmQCNlfnYuXLhgLFesWFGvvfaaunfvbkyNkJKSohUrVmjGjBlKTk6WJB08eFBz587Vyy+/XIpnJ23bts00Ukn79u01YcIE01QecXFxmj59ulasWCEpc1Sa9evXq3///kYZq9WqCRMmGCN0VK9eXcOHD1eXLl1MU0AkJiZqxYoVmjVrlpKTk5Wenq6pU6fqu+++M17fvLi5uemll17Sww8/bIRAUlJS9Pnnn+vLL780yn3zzTd69NFHbab1iYyM1Pjx403r2rVrp7Fjx6pmzZrGZycyMlLTp0/Xr7/+apSbNWuW2rVrp4YNG+bbxvwsWbLE9B0lSY8//rgGDhwob29vSZmBg9DQUE2cOFEXL14s8P1dUq8fAAAAcCNghAYAAAAAAArJYrFo/PjxRof4E088oQceeKDUjvfzzz/bdGp99dVXatKkiSkk4Ovrq9GjR2vChAmm/T/55BOj46xixYpq2rSp6b/sd0F7enrabG/atGmRAg2SeTqIiIgImzvYJXOgIXt45I8//rAZVj45Odk08kL37t1N556enm7qoB85cqReeOEFmxEZHBwc1K5dO3388cemIEFB0yTk5fDhw8bjHj166OmnnzZ1tHp5eWnUqFGm8IW/v79uu+0247nNb4oSV1dXffzxx7r99ttN5xsQEKCpU6ea7jj/888/TaGXnK9h9k773LbnfJ3PnDmj9evXG8sff/yx7rvvPlOYQZJcXFzUq1cvffTRR8a6qKgorVmzxlRu9erVppEeGjZsqO+++06tW7c2dXb7+Pho9OjRuQZrSlK7du303nvv2TwvHTt21BtvvGFal7OjWsocnSP781O5cmX98MMP6tmzp6mT383NTQ8++KDmz59veu4WLlxohCZKy9mzZ03Lb775pqkzXMr83nj99ddN79vTp0+byvz111/G952Xl5c+/fRT9ejRw3SeUub3R//+/TV58mRj3YkTJ0wjjeTl9ddf12OPPWYa0cLNzU0vvvii7rzzTmNddHS00Zbsvv76a9OUKCEhIfrkk08UHBxs+uz4+/trypQpeuGFF4x1GRkZ+uyzzwpsY14yMjL01VdfmdZNnDhRI0aMMMIMkuTo6Kh27drpu+++U61atQqst6RePwAAAOBGwAgNAAAAAAAU0pw5c7Ru3TpJUteuXTV06FBJmZ3qq1ev1t69e3Xs2DEFBgaqSZMm6t27t4KCgop9vNWrVxuPXVxc9Nprr5mGGc+pT58+WrFihXbs2CFJOnbsmM6cOaM6deoUuw1F1aVLFzk5ORnBhK1bt6px48bG9sTERIWGhhrLzz33nLZv366rV68qJiZGhw4d0i233GJs37Vrl2kY+TvuuMN0vMOHD5tCH7Vr18610zO7xo0b6+LFi5JkCksUxZkzZ4zHbdq0ybWMk5OT2rRpk2uooyB9+/bNs+PT3d1d/fr106effiopM/Rx4cKFEpv2ZOPGjcbjqlWrymKxFPicVqtWzXhO//rrL/Xr18/YlvWZyfLqq6/aTCmQXb9+/bR06dJSmUpDkoYNG2YzakiWTp06qXr16sboC8ePH7cpkzOwMXToUJtwRHZ16tTRM888o7lz50rKHH1g48aN+r//+7/inkKBcnZ+5wwKZXF3dze93jmnycge3KhXr56ioqKM0ThyU7lyZbm5uRmf2b/++kvt27fPs3y9evXUp0+fPLc/8sgjpvfP8ePH1bJlS2PZYrGYvicrVKig4cOH5zkqhIODg5555hmtWbPGCA1s2bJF165dMwUQCuvo0aOmkTrat2+f7wg/3t7eGjlyZIEjdJTU6wcAAADcCAg0AAAAAABQCMuXLzeGP2/YsKEmTZokJycnnTt3Tv/5z3+0f/9+o+zBgwe1YcMGffPNNxozZky+HVx5iY+PN40C0KlTJwUHB+e7j4ODgx555BEj0CBldtiXZaDBx8dHt956q/78809JmXe4P//886b2ZI0mEBwcrNq1a6tz585asmSJpMwARPZAQ/bRHCpVqqQWLVqYjrd7927TclbIpLCuXr2qpKQk0xQJheHr66uIiAhJmXeO5yV756+Pj0+h62/atGm+23O+F2JiYgpdd0GyP6eXLl3Sk08+WaT9s3fwWq1WU30NGzZUq1at8t3f0dFR//d//1cqgQYnJyc1aNAgz+0ODg4KDg42ziE2NtamTPYQjI+PT74d8lkeeeQRI9CQVUdpBhqaN29uWn711Vc1dOhQtW7dWu7u7qZtOUfeyG7Pnj3G43379l3XeyE3OUebySnn+zzn63Hy5EnT5693796qUKFCvsd0cXHRQw89pBkzZkjKDAvs3r1bXbp0yXe/3GQPZ0nSww8/nO/5SLahmdyU1OsHAAAA3AiYcgIAAAAAgALs3r1bkyZNkpR5B/KMGTPk6emppKQkvfTSS6YwQ3bXrl3Tm2++qT/++KPIxwwPDzfNs17YUELOctlHLygr3bt3Nx4fOHDA1OGYPaDQtWtXSVLnzp1z3W61Wk1D/nfr1s3mDuSSGLo/Pj6+yPvcfvvtxuOlS5fmGmo4e/asNm/ebCwXJVhS0MgelStXNi2np6cXuu6CXO9zmv35TExMVEJCgrFcr169QtVRWiEcf39/0xD9ufHz8zMe5/a8Zv9M1alTp8AObCkz+JB9FIfS/lwGBwdrwIABxvKxY8c0dOhQde3aVffdd5+ee+45vf766/rmm2+0f//+PEcAuN73QlxcXL7br/d9nvN5rFu3bqHalbNccV+PrFBTlsK+vwsaTaWkXj8AAADgRsAIDQAAAAAA5OP8+fMaNWqU0tPT5ebmphkzZhidcHPnzjXusq1bt65GjhypZs2aKSIiQt9//71++eUXSdI777yjhQsXFmkUgJyd7IWduiJnueJ01l+v7t27a+rUqZIyQwl//PGH+vTpYxNQyAoytG/fXi4uLkpLS9PRo0cVERGhgIAAnT592pjGIKvenLJ3lheXl5dXkfd59tlntWbNGqWkpCgyMlL/+te/9Pjjj6tBgwbKyMjQoUOHtGDBAiUlJRn7FGWkjsJ0kpeW631Osz+f165dM20r7vu4pOQ1FUF2BT332T9TRWln1apVjYBAWXwuhwwZInd3d3399ddKTU2VlDlFw+XLl3X58mVJ0q+//iopc0SQsWPHmqaHkUr2vZCbgp7rorwWUvHfXwUFL/KS8/1dpUqVYh0/NyXx+gEAAAA3AgINAAAAAADkIT4+XsOHDzeGOX/rrbfUrFkzSZkd9StXrpQkVaxYUZ999pkqVqwoKfNu7HHjxik9PV3Lli3TpUuXtGfPHtNd/QXJOT1BVgdWQS5dupRvPWXB399fLVq00L59+yRljrrQp08fI6wgZc4lnzX1gJeXl9q0aaO//vpLUuY0Ff369TON1uDl5aX27dvbHMvX19e0vH79ept1paFGjRqaPn26Ro0apeTkZEVEROiDDz7Is3zXrl3VqVOnUm9XScj+/DVs2FDfffddsevK+f7L+f7MS2HLlQcfHx9jio/Cfi4lmcI5BX0us4/OkhuLxVLg8VxcXDRo0CD169dP27dv1759+3Tu3DldvXpVERERps74Q4cOafDgwfrhhx9Mne3Zz7Vbt26aPn16gcctSyX1/irud0Zu39MFTQ2U2/FzUxKvHwAAAHAjYMoJAAAAAABykZ6ertdee01nzpyRlHm3bM+ePY3tV69eNaYZ6N27txFmyO6xxx4zHh89erRIx69SpYrp7uRTp04Var/Tp0+blsurcyv7aAp//vmn0tPTTQGFjh07ytn57/sscpt2Imd5V1dXm+PkvCP67Nmz1932wti+fbvmzp2r5OTkfMs5ODjogQce0OTJk8t11IWiyP6cnj17tlCd53nx8PCQt7e3sVzY93HW584eZf9MnT59ulDPT1xcnK5evZprHZJsplLJChHkpShTJFSpUkUhISEaN26c5s6dq59++kkbNmzQ4sWL9dBDDxnlrl27po8//thm3yz2+JrkfB5zfv/lJef7sLjfkwEBAfnWm5eiPJfX8/oBAAAANwICDQAAAAAA5GC1WvX+++8bIwb06dNHzzzzjKlMeHi48bhGjRq51pN9ffbyheHt7a0mTZoYy3/88YfCwsIKbPePP/5oWpfbqAZloUePHsbj+Ph47du3zzTdRJcuXUzlswca/vrrL0VFRWnv3r3GujvuuCPX47Rp08a0nD0EkRur1apt27Zpy5Yt2rJli/bv31/wyeSwc+dOvfjiizpw4ECu2729vdW8eXOFhIRo/vz5GjduXJGmGylv2Z/TlJQU7dy5M9/y165dM57PLVu2mN6nDg4Oat26tbF87Ngx7d69O9/6LBaLfvrpp2K2vvTdeuutxuP4+HhjpJb8/PDDD3nWIUkVKlQwLZ87dy7f+g4fPlzgMfPj4OCgGjVq6LXXXjNGnZGkgwcPmsplfy+cO3euwI74yMhI03uhqN97RVW3bl35+fkZyytXrjRG1MlLWlqa/ve//xnLTk5OxmgxRdW2bVvT8sKFCwscXWPr1q2m0TqKo7CvHwAAAHAjINAAAAAAAEAOP/zwg9Hh1apVK40bN87m7vratWsb686fP59rPdnX161bt8jt6NWrl/E4LS1N7777rtLS0vIsv2LFClPnc+PGjVWrVq0iH7ck1KxZU/Xr1zeWV6xYYQQAHB0dbabfqFmzptHWpKQkzZkzRxkZGZIyh17v2LFjrsepX7++ateubSzPnz8/3+DHb7/9pqFDh2rEiBEaMWJEnqGE/Pzvf/8zOi3vu+8+rVixQjt27NDOnTu1c+dObdy4UV999ZXGjx9vl3PaZx+qPjc9evQwjRjw/vvvKzU1Nc/ys2fPNp7PESNGKCkpybT9zjvvNC2///77SkxMzLO+xYsX23XH7L333mta/uijjxQZGZln+dOnT+vrr782lt3c3EwjmEiSu7u7qlevbiz/9ttveXbM79u3T1u2bMm3jU8++aQefvhhPfzww3r33XfzLOfg4GCaNiH7KBKSdPfddxuPLRaL3nvvvTxHpLBYLJo0aZLxPhgzZozNyBMlzdHRUffcc4+xHBsbqw8++CDfUTO+/PJL03dE165dTaOI5CYlJSXX9Q0aNDAF17Zv365Vq1blWc+1a9c0c+bMfI8lldzrBwAAANwICDQAAAAAAJDN1q1bjQ6n6tWra9q0ablOdeDp6Wl0wK9YsSLXIeK/++4743FxOrYfeOAB05Dvf/31l5566ikdPXrUdBfwtWvXNH36dE2YMMG0/+DBg8t1moPsozQsXbrUaHOLFi1ynaIj+ygNixcvNh536NBBXl5euR7D0dFRzz33nLGcmpqqZ599VmvWrDF1aqalpWnx4sUaP368sc7X11d9+/Yt8nllH23h4sWLOnz4sA4dOpTrf8ePH1dCQkKRj1GSct79/+6772rXrl2mdkZERBjbg4KCFBISYiyfPn1aTz31lA4dOmR638XGxmr69OlatGiRsa5Dhw5q1KiR6Xj33HOPqlWrZiwfO3ZMjz/+uPbs2WN6jbLex1OmTLn+ky5FjRo1MoU0rl69qkcffVTr169Xenq6sT7rPffkk0+aOsQffvhh+fv729SbPeQTHx+vMWPG6PLly8Y6q9WqTZs2adSoUQW2MTU1VadOndKpU6e0ZMkS7dq1K9dye/bsMW3LGby65ZZbTKO8bN++XYMHD7YJDYWHh+uNN94wjZDSp08fVa5cucC2Xq+nnnpKnp6exvKyZcv04osv2gTNrl69qrFjx+qzzz4z1jk5OemFF14o8BgbN27U1q1bdejQIR05csS0/9NPP20q+5///EezZs0yfe4tFotCQ0P1+OOPF2panJJ6/QAAAIAbgXPBRQAAAAAAuDkcP35cY8eOlcVikbe3tz744INcO96zDBo0SK+//rpiY2M1cOBAjRgxQk2bNlVkZKR++OEHLV++XJLUqVMn07DgheXu7q53331XAwcONEZmyOoM9vX1Vb169RQZGZnr8PSPPfaYKSBQHrp376558+ZJkqkjPOd0E9nXL1iwwKZ8zrvZc7r33nu1efNm/frrr5KkmJgYjRs3Tm+//bbq1q0rFxcXnTlzxuaO91dffdXUEVpYISEhWrFihTIyMhQaGqrQ0NAC96lSpYoGDBighx56SC4uLkU+5vWoWbOmaXnVqlU2d5EPHDhQgwYNMpaHDx+u0NBQo+P6+PHjevLJJ1WhQgXVrVtXKSkpOnnypKmj3tfXV6+88orN8V1cXDRx4kQNGjTIGHXj3Llzev755+Xl5aUGDRooKirK1Enu6Ogob29vxcXFXf8TUApef/11HTlyRBcuXJCU+Z579dVX5ebmpnr16ik9PV2nTp0yBRwkqVmzZho8eHCudT722GNasmSJ8VnfuXOn+vbtq1q1aqlixYo6depUrsGp3ISEhBjBrNTUVA0ZMkS33XabESaKj4/XwYMHtXnzZuM1kaT+/fvb1PWf//xHTzzxhHHs0NBQPfjgg/L391etWrUUHx+vkydPmuqpVq2a6f1Umvz9/fXWW29p9OjRxrodO3aoX79+qly5smrVqqWLFy+awiFZhg8frgYNGuRab+XKlY0RD8LCwjR8+HBJmVPKbNy40Sh3//33a8OGDaYpdebPn69vv/1WNWvWVKVKlXTixAlTwKFSpUqKjo7O85xK8vUDAAAA/ukYoQEAAAAAAGXO/T5ixAglJibKyclJU6dOVZ06dfLdp2fPnurZs6ekzLvYhw0bpp49e+rRRx81RhioUKGCXn/99WKPlNC8eXN99NFHNnfZx8XFaffu3bmGGQYMGGB0vpWnhg0bmu7Mz5JXoKFVq1Y2IzE4OjqqW7du+R7HwcFB48eP11133WVan5ycrEOHDmnv3r02YYZRo0bZTB1QWK1btzZ1nhbGlStXNG3aNI0aNcoU1igLHTp0UMOGDYu0j7e3tz755BPTtCFS5qgMu3fv1qFDh0xhBnd3d33wwQd53iHeqlUrvfvuu3JzczOtT0hI0J49e2zu+B85cmSBn7/yVLFiRX366ac2o1GkpKTo0KFDOnbsmE2YoX379po1a5bNc5ClVq1aevXVV03rMjIydOrUKYWGhprCDI899li+wZhHHnnE9DnLyMjQ77//rjlz5uidd97R7NmztWHDBpvO8OzT3GQJCgrSnDlzTKPFSJnfmbt27dKxY8dM9fj5+Wn27Nm5jkJRWnr06KEpU6bYPLdXr15VaGioTZjB0dFRI0aM0KOPPppnnTmnxcmLo6OjpkyZYjMtjtVqVVhYmPbu3WsKMwQHBxf4/VGSrx8AAADwT0egAQAAAABw00tOTtaoUaOMTq/Ro0frtttuK3A/BwcHTZ48WS+//HKunYsdO3bUjz/+qKCgoOtqX7t27fTjjz/qkUceMU13kFObNm00d+5cvfzyy6U+d31hODg4mKadkDKn8ciro9rZ2dmmE7FVq1aqVKlSgcdyd3fXlClTNHny5Hw7wtu3b6+vvvoq347MguzZs0cfffRRsfb9/ffftWTJkmIfuzicnZ314Ycfqn///goODs6zQz2noKAgffnllxoyZEieI5U4OTmpX79+WrRokVq0aJFvfd27d9eCBQvUo0cPOTrm/pNUvXr19Mknn1zX61NWgoKC9MUXX2jEiBEKCAjIs1zNmjX15ptv6sMPP8x3xBcpc5qZ6dOn24yqkcXHx0ejR4/WiBEj8g1JOTs76/3339dbb72lpk2b5nvM9u3b68MPP9Srr76aZ50NGjTQggULNGDAgDxHNXFzc9OAAQO0cOFCBQcH53vM0nD33Xfrhx9+UO/evfMMezg6OqpLly765ptv9Pjjj+f7HI4YMUIPPPCAAgMD83y/ZvHy8tKMGTP02muvKTAwMNcybm5u6t+/v+bPn28TUMuppF8/AAAA4J/MwVrWtwUAAAAAAHADiouL0+HDh3X8+HEFBASoSZMmqlmzZol3MKWmpmrv3r0KCwtTTEyMPD09FRAQoFatWpXpHdH2zGq16uzZszpw4ICioqLk7Oys4OBg1a5dWzVq1LiuutPT09W3b19duXJFUmaI5OWXX1a9evXk7u5uUz4xMVHbt2/X+PHjjbu0e/bsqalTp15XO8paRkaGDh48qFOnTik6OloVKlRQcHCw6tatKz8/vyLXFxMTo507dyo8PFypqany9/dX48aNVa9evX9kp6zFYtGxY8d09OhRRUdHy9HRUX5+fmrevLlq1apV5HPKyMjQ4cOHdeTIEcXGxsrLy0u1a9dW27ZtizxlSdbn4cKFC4qIiFB0dLQqVaqkoKAgBQcH5zqKSn7S0tKMUTViY2NVuXJlBQcHq169evL19S1SXaUlKSlJoaGhunjxouLi4uTl5aWgoCC1adOm1NtosVh06NAhHT9+XNHR0caxb7311mJNcVPSrx8AAADwT0OgAQAAAAAAoJB27typwYMHG8srVqywGYo/N++9954WLlwoSapbt67xGAAAAAAA5I0pJwAAAAAAAAopOjratJyWllao/S5dumQ8rly5com2CQAAAACAG5VzeTcAAAAAAADgn6JWrVqm5ZEjR+qpp55SjRo1cp0KID4+XuvWrdOWLVuMdXXq1Cn1dgIAAAAAcCNgygkAAAAAAIBCSktL06OPPqqzZ88Wa38XFxctXLhQNWvWLOGWAQAAAABw42HKCQAAAAAAgEJycXHRpEmT5OPjU6x9X3nlFcIMAAAAAAAUEiM0AAAAAAAAFFFsbKy++eYb7d69WxcuXNDVq1dzLefh4aFatWqpbt26euaZZ5huAgAAAACAIiDQAAAAAAAAAAAAAAAA7A5TTgAAAAAAAAAAAAAAALtDoAEAAAAAAAAAAAAAANgdAg0AAAAAAAAAAAAAAMDuEGgAAAAAAAAAAAAAAAB2h0ADAAAAAAAAAAAAAACwOwQaAAAAAAAAAAAAAACA3SHQAAAAAAAAAAAAAAAA7A6BBgAAAAAAAAAAAAAAYHcINAAAAAAAAAAAAAAAALtDoAEAAAAAAAAAAAAAANgdAg0AAAAAAAAAAAAAAMDuEGgAAAAAAAAAAAAAAAB2h0ADAAAAAAAAAAAAAACwOwQaAAAAAAAAAAAAAACA3SHQAAAAAAAAAAAAAAAA7A6BBgAAAAAAAAAAAAAAYHcINAAAAAAAAAAAAAAAALtDoAEAAAAAAAAAAAAAANgdAg0AAAAAAAAAAAAAAMDu/D+bPKmiipayPAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x708129d95a90>"
       ]
+     },
+     "execution_count": 53,
+     "metadata": {
+      "image/png": {
+       "height": 519,
+       "width": 1050
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qb_records: list[dict[str, object]] = []\n",
+    "for group in avail_heating:\n",
+    "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
+    "    pct = quadrant_pcts(gdf)\n",
+    "    for q in QUADRANT_ORDER:\n",
+    "        qb_records.append({\"heating_label\": group, \"quadrant\": q, \"pct\": pct[q]})\n",
+    "\n",
+    "qb_plot = pl.DataFrame(qb_records).with_columns(\n",
+    "    pl.col(\"heating_label\").cast(pl.Enum(list(reversed(avail_heating)))),\n",
+    "    pl.col(\"quadrant\").cast(pl.Enum(QUADRANT_ORDER)),\n",
+    ")\n",
+    "\n",
+    "(\n",
+    "    ggplot(qb_plot, aes(x=\"heating_label\", y=\"pct\", fill=\"quadrant\"))\n",
+    "    + geom_col(position=\"stack\", width=0.55)\n",
+    "    + geom_text(\n",
+    "        mapping=aes(label=\"pct\"),\n",
+    "        data=qb_plot.filter(pl.col(\"pct\") >= 3),\n",
+    "        position=position_stack(vjust=0.5),\n",
+    "        format_string=\"{:.1f}%\",\n",
+    "        color=\"white\",\n",
+    "        size=11,\n",
+    "        fontweight=\"bold\",\n",
+    "    )\n",
+    "    + scale_fill_manual(values=QUADRANT_COLORS, breaks=QUADRANT_ORDER)\n",
+    "    + scale_y_continuous(expand=(0, 0, 0.02, 0))\n",
+    "    + coord_flip()\n",
+    "    + guides(fill=False)\n",
+    "    + labs(\n",
+    "        x=\"\",\n",
+    "        y=\"% of weighted households\",\n",
+    "        title=\"Change in total annual energy bill after upgrading to heat pump (upgrade 00 → 02)\",\n",
+    "    )\n",
+    "    + theme_switchbox()\n",
+    "    + theme(figure_size=(10.5, max(3.5, 1.0 + 1.4 * len(avail_heating))))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000032",
+   "metadata": {},
+   "source": [
+    "### 5b: Savings breakdown table\n",
+    "\n",
+    "The table below shows the fraction of weighted households in each savings/loss bin,\n",
+    "broken out by baseline heating type, plus weighted mean and median bill changes.\n",
+    "The bin boundaries ($0, $1k, $2k) are the same as in the quadrant bars above, with\n",
+    "an additional `Save > $2k/yr` bin to capture large savings from oil/propane retrofits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "a0000033",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "c08507b0",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (14, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>utility</th><th>scenario</th><th>sample buildings</th><th>weighted households</th><th>weighted mean total BAT</th><th>weighted median total BAT</th><th>weighted mean bill change</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;cenhud&quot;</td><td>&quot;Upgrade 00&quot;</td><td>1182</td><td>270859</td><td>3.1255e-12</td><td>-227.248726</td><td>0.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>&quot;Upgrade 02&quot;</td><td>1182</td><td>270859</td><td>-3.6886e6</td><td>-3.6887e6</td><td>-733.581717</td></tr><tr><td>&quot;coned&quot;</td><td>&quot;Upgrade 00&quot;</td><td>15435</td><td>3064038</td><td>-2.1219e-11</td><td>-178.654389</td><td>0.0</td></tr><tr><td>&quot;coned&quot;</td><td>&quot;Upgrade 02&quot;</td><td>15435</td><td>3064038</td><td>-324178.202264</td><td>-324436.69905</td><td>-148.40005</td></tr><tr><td>&quot;nimo&quot;</td><td>&quot;Upgrade 00&quot;</td><td>6791</td><td>1532294</td><td>-2.4499e-11</td><td>-150.003962</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;or&quot;</td><td>&quot;Upgrade 02&quot;</td><td>829</td><td>211011</td><td>-4.7359e6</td><td>-4.7360e6</td><td>-312.592456</td></tr><tr><td>&quot;psegli&quot;</td><td>&quot;Upgrade 00&quot;</td><td>4427</td><td>1029955</td><td>-1.3687e-11</td><td>-116.113917</td><td>0.0</td></tr><tr><td>&quot;psegli&quot;</td><td>&quot;Upgrade 02&quot;</td><td>4427</td><td>1029955</td><td>-967458.790291</td><td>-967503.544667</td><td>-577.154952</td></tr><tr><td>&quot;rge&quot;</td><td>&quot;Upgrade 00&quot;</td><td>1444</td><td>351481</td><td>-5.5909e-13</td><td>-171.684586</td><td>0.0</td></tr><tr><td>&quot;rge&quot;</td><td>&quot;Upgrade 02&quot;</td><td>1444</td><td>351481</td><td>-2.8426e6</td><td>-2.8427e6</td><td>-13.503974</td></tr></tbody></table></div>"
-            ],
-            "text/plain": [
-              "shape: (14, 7)\n",
-              "┌─────────┬────────────┬───────────┬────────────┬────────────────┬────────────────┬────────────────┐\n",
-              "│ utility ┆ scenario   ┆ sample    ┆ weighted   ┆ weighted mean  ┆ weighted       ┆ weighted mean  │\n",
-              "│ ---     ┆ ---        ┆ buildings ┆ households ┆ total BAT      ┆ median total   ┆ bill change    │\n",
-              "│ str     ┆ str        ┆ ---       ┆ ---        ┆ ---            ┆ BAT            ┆ ---            │\n",
-              "│         ┆            ┆ i64       ┆ i64        ┆ f64            ┆ ---            ┆ f64            │\n",
-              "│         ┆            ┆           ┆            ┆                ┆ f64            ┆                │\n",
-              "╞═════════╪════════════╪═══════════╪════════════╪════════════════╪════════════════╪════════════════╡\n",
-              "│ cenhud  ┆ Upgrade 00 ┆ 1182      ┆ 270859     ┆ 3.1255e-12     ┆ -227.248726    ┆ 0.0            │\n",
-              "│ cenhud  ┆ Upgrade 02 ┆ 1182      ┆ 270859     ┆ -3.6886e6      ┆ -3.6887e6      ┆ -733.581717    │\n",
-              "│ coned   ┆ Upgrade 00 ┆ 15435     ┆ 3064038    ┆ -2.1219e-11    ┆ -178.654389    ┆ 0.0            │\n",
-              "│ coned   ┆ Upgrade 02 ┆ 15435     ┆ 3064038    ┆ -324178.202264 ┆ -324436.69905  ┆ -148.40005     │\n",
-              "│ nimo    ┆ Upgrade 00 ┆ 6791      ┆ 1532294    ┆ -2.4499e-11    ┆ -150.003962    ┆ 0.0            │\n",
-              "│ …       ┆ …          ┆ …         ┆ …          ┆ …              ┆ …              ┆ …              │\n",
-              "│ or      ┆ Upgrade 02 ┆ 829       ┆ 211011     ┆ -4.7359e6      ┆ -4.7360e6      ┆ -312.592456    │\n",
-              "│ psegli  ┆ Upgrade 00 ┆ 4427      ┆ 1029955    ┆ -1.3687e-11    ┆ -116.113917    ┆ 0.0            │\n",
-              "│ psegli  ┆ Upgrade 02 ┆ 4427      ┆ 1029955    ┆ -967458.790291 ┆ -967503.544667 ┆ -577.154952    │\n",
-              "│ rge     ┆ Upgrade 00 ┆ 1444      ┆ 351481     ┆ -5.5909e-13    ┆ -171.684586    ┆ 0.0            │\n",
-              "│ rge     ┆ Upgrade 02 ┆ 1444      ┆ 351481     ┆ -2.8426e6      ┆ -2.8427e6      ┆ -13.503974     │\n",
-              "└─────────┴────────────┴───────────┴────────────┴────────────────┴────────────────┴────────────────┘"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Baseline heating</th><th>Buildings</th><th>Weighted hholds</th><th>Save &gt; $2k/yr</th><th>Save $1k-$2k/yr</th><th>Save $0-$1k/yr</th><th>Lose $0-$1k/yr</th><th>Lose &gt; $1k/yr</th><th>Mean change ($/yr)</th><th>Median change ($/yr)</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6349536</td><td>&quot;5.5%&quot;</td><td>&quot;8.4%&quot;</td><td>&quot;34.2%&quot;</td><td>&quot;43.0%&quot;</td><td>&quot;8.9%&quot;</td><td>&quot;$-113&quot;</td><td>&quot;$0&quot;</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504</td><td>&quot;20.3%&quot;</td><td>&quot;24.7%&quot;</td><td>&quot;52.2%&quot;</td><td>&quot;2.9%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-1,311&quot;</td><td>&quot;$-879&quot;</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368</td><td>&quot;10.3%&quot;</td><td>&quot;18.0%&quot;</td><td>&quot;70.9%&quot;</td><td>&quot;0.8%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-817&quot;</td><td>&quot;$-428&quot;</td></tr></tbody></table></div>"
       ],
-      "source": [
-        "utility_rows: list[dict[str, object]] = []\n",
-        "for utility in sorted(bill_comparison[UTILITY_COL].unique().to_list()):\n",
-        "    utility_bills = bill_comparison.filter(pl.col(UTILITY_COL) == utility)\n",
-        "    for scenario in SCENARIO_ORDER:\n",
-        "        utility_bat = bat_long.filter(\n",
-        "            (pl.col(UTILITY_COL) == utility) & (pl.col(\"scenario\") == scenario)\n",
-        "        )\n",
-        "        utility_rows.append(\n",
-        "            {\n",
-        "                \"utility\": utility,\n",
-        "                \"scenario\": scenario,\n",
-        "                \"sample buildings\": utility_bat.height,\n",
-        "                \"weighted households\": round(float(utility_bat[\"weight\"].sum())),\n",
-        "                \"weighted mean total BAT\": weighted_mean(utility_bat, \"BAT_percustomer_total\"),\n",
-        "                \"weighted median total BAT\": weighted_quantile(utility_bat, \"BAT_percustomer_total\", 0.5),\n",
-        "                \"weighted mean bill change\": (\n",
-        "                    0.0 if scenario == \"Upgrade 00\" else weighted_mean(utility_bills, \"energy_change\")\n",
-        "                ),\n",
-        "            }\n",
-        "        )\n",
-        "\n",
-        "utility_summary = pl.DataFrame(utility_rows)\n",
-        "display(utility_summary)"
+      "text/plain": [
+       "shape: (3, 10)\n",
+       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
+       "│ Baseline  ┆ Buildings ┆ Weighted  ┆ Save >    ┆ … ┆ Lose      ┆ Lose >    ┆ Mean      ┆ Median   │\n",
+       "│ heating   ┆ ---       ┆ hholds    ┆ $2k/yr    ┆   ┆ $0-$1k/yr ┆ $1k/yr    ┆ change    ┆ change   │\n",
+       "│ ---       ┆ i64       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ($/yr)    ┆ ($/yr)   │\n",
+       "│ str       ┆           ┆ i64       ┆ str       ┆   ┆ str       ┆ str       ┆ ---       ┆ ---      │\n",
+       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆ str       ┆ str      │\n",
+       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
+       "│ Fossil    ┆ 29554     ┆ 6349536   ┆ 5.5%      ┆ … ┆ 43.0%     ┆ 8.9%      ┆ $-113     ┆ $0       │\n",
+       "│ fuel      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ Electric  ┆ 3070      ┆ 654504    ┆ 20.3%     ┆ … ┆ 2.9%      ┆ 0.0%      ┆ $-1,311   ┆ $-879    │\n",
+       "│ resistanc ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ e         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ Existing  ┆ 918       ┆ 198368    ┆ 10.3%     ┆ … ┆ 0.8%      ┆ 0.0%      ┆ $-817     ┆ $-428    │\n",
+       "│ heat pump ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "SAVE_BINS: list[tuple[float, float, str]] = [\n",
+    "    (-float(\"inf\"), -2000, \"Save > $2k/yr\"),\n",
+    "    (-2000, -1000, \"Save $1k-$2k/yr\"),\n",
+    "    (-1000, 0, \"Save $0-$1k/yr\"),\n",
+    "    (0, 1000, \"Lose $0-$1k/yr\"),\n",
+    "    (1000, float(\"inf\"), \"Lose > $1k/yr\"),\n",
+    "]\n",
+    "\n",
+    "savings_rows: list[dict[str, object]] = []\n",
+    "for group in avail_heating:\n",
+    "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
+    "    total_w = cast(float, gdf[\"weight\"].sum())\n",
+    "    row: dict[str, object] = {\n",
+    "        \"Baseline heating\": group,\n",
+    "        \"Buildings\": gdf.height,\n",
+    "        \"Weighted hholds\": round(total_w),\n",
+    "    }\n",
+    "    for lo, hi, label in SAVE_BINS:\n",
+    "        if lo == -float(\"inf\"):\n",
+    "            filt = gdf.filter(pl.col(\"delta\") < hi)\n",
+    "        elif hi == float(\"inf\"):\n",
+    "            filt = gdf.filter(pl.col(\"delta\") >= lo)\n",
+    "        else:\n",
+    "            filt = gdf.filter((pl.col(\"delta\") >= lo) & (pl.col(\"delta\") < hi))\n",
+    "        row[label] = f\"{cast(float, filt['weight'].sum()) / total_w * 100:.1f}%\"\n",
+    "    row[\"Mean change ($/yr)\"] = f\"${weighted_mean(gdf, 'delta'):,.0f}\"\n",
+    "    row[\"Median change ($/yr)\"] = f\"${weighted_quantile(gdf, 'delta', 0.5):,.0f}\"\n",
+    "    savings_rows.append(row)\n",
+    "\n",
+    "savings_df = pl.DataFrame(savings_rows)\n",
+    "print(\"Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\")\n",
+    "display(savings_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000034",
+   "metadata": {},
+   "source": [
+    "### 5c: Bill change histogram by heating type\n",
+    "\n",
+    "Distribution of annual bill changes, trimmed to the 1st-99th percentile, faceted by\n",
+    "baseline heating type. Green bars are savings; orange bars are increases. A well-behaved\n",
+    "batch produces a smooth, roughly unimodal distribution centered left of zero for\n",
+    "fossil-fuel homes and a broader, more symmetric distribution for electric resistance\n",
+    "homes (which lose the gas bill but gain less electric savings)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "a0000035",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "1d617d8e",
-      "metadata": {},
-      "source": [
-        "## Interpretation checklist\n",
-        "\n",
-        "Before using these results in a report or policy analysis:\n",
-        "\n",
-        "1. Confirm that every structural assertion passes.\n",
-        "2. Check that weighted household totals are stable across scenarios and plausible for the modeled service territories.\n",
-        "3. Investigate heating groups or utilities with extreme bill changes before summarizing statewide results.\n",
-        "4. Confirm that BAT signs and magnitudes are consistent with the intended residual allocation method.\n",
-        "5. Treat the 1st–99th percentile histogram as a visualization choice only; inspect excluded tails separately when diagnosing outliers.\n",
-        "6. Change `STATE` and `BATCH`, restart the kernel, and run all cells to review another batch."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x70812824cbb0>"
       ]
+     },
+     "execution_count": 55,
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1050
+      }
+     },
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": ".venv",
-      "language": "python",
-      "name": "python3"
+   ],
+   "source": [
+    "BIN_WIDTH = 100\n",
+    "_lo = math.floor(weighted_quantile(bill_delta, \"delta\", 0.01) / BIN_WIDTH) * BIN_WIDTH\n",
+    "_hi = math.ceil(weighted_quantile(bill_delta, \"delta\", 0.99) / BIN_WIDTH) * BIN_WIDTH\n",
+    "\n",
+    "hist_data = (\n",
+    "    bill_delta.filter(pl.col(\"heating_label\").is_in(avail_heating))\n",
+    "    .with_columns(\n",
+    "        ((pl.col(\"delta\") / BIN_WIDTH).floor() * BIN_WIDTH + BIN_WIDTH / 2).alias(\"bin_center\"),\n",
+    "        pl.when(pl.col(\"delta\") < 0).then(pl.lit(\"Savings\")).otherwise(pl.lit(\"Increase\")).alias(\"direction\"),\n",
+    "    )\n",
+    "    .filter(pl.col(\"bin_center\").is_between(_lo, _hi))\n",
+    "    .group_by(\"heating_label\", \"bin_center\", \"direction\")\n",
+    "    .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n",
+    "    .with_columns(\n",
+    "        pl.col(\"heating_label\").cast(pl.Enum(avail_heating)),\n",
+    "        pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "(\n",
+    "    ggplot(hist_data, aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"))\n",
+    "    + geom_col(width=BIN_WIDTH * 0.9)\n",
+    "    + facet_wrap(\"heating_label\", ncol=1, scales=\"free_y\")\n",
+    "    + scale_fill_manual(values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]})\n",
+    "    + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n",
+    "    + labs(\n",
+    "        x=\"Annual energy bill change ($/year)\",\n",
+    "        y=\"Weighted households\",\n",
+    "        fill=\"Outcome\",\n",
+    "        title=\"Distribution of annual bill changes after upgrade 02 (1st-99th percentile)\",\n",
+    "    )\n",
+    "    + theme_switchbox()\n",
+    "    + theme(\n",
+    "        figure_size=(10.5, max(4.5, 2.5 * len(avail_heating))),\n",
+    "        legend_position=\"top\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000036",
+   "metadata": {},
+   "source": [
+    "### 5d: Utility-level summary\n",
+    "\n",
+    "Per-utility summary of bill-change and BAT results. Helps identify whether one\n",
+    "utility's results are driving state-level patterns, and flags utilities with\n",
+    "implausible outliers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a0000037",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Per-utility summary (upgrade 00 BAT + upgrade 00→02 bill change):\n"
+     ]
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.13.13"
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (7, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>utility</th><th>buildings</th><th>weighted_households</th><th>mean_bill_change</th><th>median_bill_change</th><th>pct_saving</th><th>mean_bat_total_u0</th><th>pct_overpaying_u0</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859</td><td>-721.0</td><td>-471.0</td><td>78.2</td><td>0.0</td><td>39.1</td></tr><tr><td>&quot;coned&quot;</td><td>15435</td><td>3064038</td><td>-156.0</td><td>-69.0</td><td>59.1</td><td>-0.0</td><td>33.2</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1532294</td><td>-44.0</td><td>150.0</td><td>36.0</td><td>-0.0</td><td>36.9</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300</td><td>-242.0</td><td>0.0</td><td>47.6</td><td>0.0</td><td>35.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011</td><td>-352.0</td><td>-124.0</td><td>63.8</td><td>0.0</td><td>41.7</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1029955</td><td>-720.0</td><td>-332.0</td><td>70.8</td><td>-0.0</td><td>42.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481</td><td>29.0</td><td>236.0</td><td>26.4</td><td>-0.0</td><td>32.8</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (7, 8)\n",
+       "┌─────────┬───────────┬────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐\n",
+       "│ utility ┆ buildings ┆ weighted_h ┆ mean_bill_ ┆ median_bil ┆ pct_saving ┆ mean_bat_t ┆ pct_overp │\n",
+       "│ ---     ┆ ---       ┆ ouseholds  ┆ change     ┆ l_change   ┆ ---        ┆ otal_u0    ┆ aying_u0  │\n",
+       "│ str     ┆ i64       ┆ ---        ┆ ---        ┆ ---        ┆ f64        ┆ ---        ┆ ---       │\n",
+       "│         ┆           ┆ i64        ┆ f64        ┆ f64        ┆            ┆ f64        ┆ f64       │\n",
+       "╞═════════╪═══════════╪════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡\n",
+       "│ cenhud  ┆ 1182      ┆ 270859     ┆ -721.0     ┆ -471.0     ┆ 78.2       ┆ 0.0        ┆ 39.1      │\n",
+       "│ coned   ┆ 15435     ┆ 3064038    ┆ -156.0     ┆ -69.0      ┆ 59.1       ┆ -0.0       ┆ 33.2      │\n",
+       "│ nimo    ┆ 6791      ┆ 1532294    ┆ -44.0      ┆ 150.0      ┆ 36.0       ┆ -0.0       ┆ 36.9      │\n",
+       "│ nyseg   ┆ 3682      ┆ 792300     ┆ -242.0     ┆ 0.0        ┆ 47.6       ┆ 0.0        ┆ 35.7      │\n",
+       "│ or      ┆ 829       ┆ 211011     ┆ -352.0     ┆ -124.0     ┆ 63.8       ┆ 0.0        ┆ 41.7      │\n",
+       "│ psegli  ┆ 4427      ┆ 1029955    ┆ -720.0     ┆ -332.0     ┆ 70.8       ┆ -0.0       ┆ 42.9      │\n",
+       "│ rge     ┆ 1444      ┆ 351481     ┆ 29.0       ┆ 236.0      ┆ 26.4       ┆ -0.0       ┆ 32.8      │\n",
+       "└─────────┴───────────┴────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "util_bat_u0 = bat_by_scenario[\"Upgrade 00\"]\n",
+    "util_rows: list[dict[str, object]] = []\n",
+    "\n",
+    "for utility in sorted(annual_u0[UTILITY_COL].drop_nulls().unique().to_list()):\n",
+    "    delta_u = bill_delta.filter(pl.col(UTILITY_COL) == utility)\n",
+    "    bat_u = util_bat_u0.filter(pl.col(UTILITY_COL) == utility)\n",
+    "    if delta_u.is_empty() or bat_u.is_empty():\n",
+    "        continue\n",
+    "    util_rows.append(\n",
+    "        {\n",
+    "            \"utility\": utility,\n",
+    "            \"buildings\": delta_u.height,\n",
+    "            \"weighted_households\": round(cast(float, delta_u[\"weight\"].sum())),\n",
+    "            \"mean_bill_change\": round(weighted_mean(delta_u, \"delta\"), 0),\n",
+    "            \"median_bill_change\": round(weighted_quantile(delta_u, \"delta\", 0.5), 0),\n",
+    "            \"pct_saving\": round(\n",
+    "                cast(float, delta_u.filter(pl.col(\"delta\") < 0)[\"weight\"].sum())\n",
+    "                / cast(float, delta_u[\"weight\"].sum())\n",
+    "                * 100,\n",
+    "                1,\n",
+    "            ),\n",
+    "            \"mean_bat_total_u0\": round(weighted_mean(bat_u, \"BAT_percustomer_total\"), 0),\n",
+    "            \"pct_overpaying_u0\": round(\n",
+    "                cast(\n",
+    "                    float,\n",
+    "                    bat_u.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
+    "                )\n",
+    "                / cast(float, bat_u[\"weight\"].sum())\n",
+    "                * 100,\n",
+    "                1,\n",
+    "            ),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "util_df = pl.DataFrame(util_rows)\n",
+    "print(\"Per-utility summary (upgrade 00 BAT + upgrade 00→02 bill change):\")\n",
+    "display(util_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0000038",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Interpretation checklist\n",
+    "\n",
+    "Before using these results in a report or policy analysis:\n",
+    "\n",
+    "1. **Structural checks passed** — All assertions in Section 3 must pass cleanly. A\n",
+    "   failure indicates a specific post-processing step to investigate.\n",
+    "\n",
+    "2. **Utility coverage looks right** — Confirm that weighted household totals per\n",
+    "   utility (Section 2a) are stable across scenarios and plausible for the service\n",
+    "   territory.\n",
+    "\n",
+    "3. **Heating-type breakdown is plausible** — The upgrade 00 heating-type composition\n",
+    "   (Section 2b) should reflect the state's known building stock. A suspiciously low\n",
+    "   fossil-fuel share or high heat-pump share may indicate a ResStock metadata issue.\n",
+    "\n",
+    "4. **Gas null fraction makes sense** — High gas-null share is expected in all-electric\n",
+    "   territories; investigate if it is unexpectedly low in a gas-dense market.\n",
+    "\n",
+    "5. **BAT signs are correct** — Under flat default rates, fossil-fuel customers should\n",
+    "   show negative BAT (they underpay); HP/ER customers should show positive BAT\n",
+    "   (they overpay). If this pattern is reversed, check the residual allocation\n",
+    "   configuration in the CAIRO scenario YAML.\n",
+    "\n",
+    "6. **Cross-subsidy magnitude is reasonable** — Compare against prior state runs at\n",
+    "   similar utility scales to identify implausible outliers.\n",
+    "\n",
+    "7. **Bill change signs match fuel-cost logic** — Fossil-fuel homes should generally\n",
+    "   save in warmer states (low heating loads) and see more mixed results in cold\n",
+    "   states with high gas demand. Oil/propane homes typically show larger savings.\n",
+    "\n",
+    "8. **To review another state or batch** — Change `STATE` and `BATCH` in the\n",
+    "   Parameters cell, restart the kernel, and run all cells."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

From e3b6179bc4df1de5d20fb797e460f6962267c7e6 Mon Sep 17 00:00:00 2001
From: Alex Lee <lee3742@purdue.edu>
Date: Sat, 25 Jul 2026 05:30:21 +0000
Subject: [PATCH 3/3] Add some more figures

---
 reports/templates/cairo_run_results.ipynb | 3807 +++++++++++----------
 1 file changed, 1953 insertions(+), 1854 deletions(-)

diff --git a/reports/templates/cairo_run_results.ipynb b/reports/templates/cairo_run_results.ipynb
index a6623fe..3f4e7af 100644
--- a/reports/templates/cairo_run_results.ipynb
+++ b/reports/templates/cairo_run_results.ipynb
@@ -1,1933 +1,2032 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a0000001",
-   "metadata": {},
-   "source": [
-    "# CAIRO run results review\n",
-    "\n",
-    "This **state-agnostic** notebook reviews the outputs from the first four CAIRO simulation\n",
-    "runs for any given state and batch. Run it after a new batch is complete to sanity-check\n",
-    "inputs, verify bill arithmetic, and get an initial read on cross-subsidization and\n",
-    "bill-change patterns.\n",
-    "\n",
-    "The first four runs (covering two upgrade levels × two supply configurations) produce\n",
-    "the master tables this notebook reads:\n",
-    "\n",
-    "| Run pair | Upgrade | What it represents |\n",
-    "|----------|---------|--------------------|\n",
-    "| 1+2 | Upgrade 00 (baseline) | Current HVAC; delivery-only + delivery+supply combined |\n",
-    "| 3+4 | Upgrade 02 (HP upgrade) | All buildings given heat pumps; same two supply configs |\n",
-    "\n",
-    "> **Demonstration state**: This notebook currently uses NY batch\n",
-    "> `ny_20260417a_r1-36` as a worked example.  \n",
-    "> **To review a different state, change `STATE` and `BATCH` in the Parameters cell\n",
-    "> and restart the kernel.**\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## How to use for a new state\n",
-    "\n",
-    "1. Change `STATE` (lowercase 2-letter abbreviation, e.g. `\"ri\"`, `\"ct\"`) and `BATCH`\n",
-    "   in the *Parameters* cell below.\n",
-    "2. Verify that `run_1+2` and `run_3+4` exist under\n",
-    "   `s3://data.sb/switchbox/cairo/outputs/hp_rates/<STATE>/all_utilities/<BATCH>/`.\n",
-    "3. Restart the kernel and run all cells (`Kernel → Restart & Run All`).\n",
-    "4. No other code changes are needed — all paths and column references are derived\n",
-    "   from `STATE` and `BATCH`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Notebook sections\n",
-    "\n",
-    "| Section | What it covers |\n",
-    "|---------|---------------|\n",
-    "| **1 — Load data** | Read master bills and BAT tables from S3 for both run pairs |\n",
-    "| **2 — Input data quality checks (EDA)** | Utility assignments, heating-type composition, gas and electric spending distributions |\n",
-    "| **3 — Structural quality checks** | Schema validation, row counts, bill arithmetic identities |\n",
-    "| **4 — Baseline analysis (upgrade 00)** | Cross-subsidy table, BAT distribution, monthly bill pattern |\n",
-    "| **5 — Bill change analysis (upgrade 00 → 02)** | Quadrant bar chart, savings breakdown by heating type |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000002",
-   "metadata": {},
-   "source": [
-    "## Parameters\n",
-    "\n",
-    "Change `STATE` and `BATCH` here to switch states. Everything else derives from these\n",
-    "two values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "a0000003",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import math\n",
-    "from typing import cast\n",
-    "\n",
-    "import polars as pl\n",
-    "from IPython.display import display\n",
-    "from plotnine import (\n",
-    "    aes,\n",
-    "    coord_flip,\n",
-    "    facet_wrap,\n",
-    "    geom_col,\n",
-    "    geom_hline,\n",
-    "    geom_text,\n",
-    "    ggplot,\n",
-    "    guides,\n",
-    "    labs,\n",
-    "    position_dodge,\n",
-    "    position_stack,\n",
-    "    scale_fill_manual,\n",
-    "    scale_x_discrete,\n",
-    "    scale_y_continuous,\n",
-    "    theme,\n",
-    ")\n",
-    "\n",
-    "from lib.plotnine import SB_COLORS, theme_switchbox"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "a0000004",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reviewing NY batch: ny_20260417a_r1-36\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ── Change these two values to review a different state or batch ─────────────\n",
-    "STATE = \"ny\"  # lowercase state abbreviation, e.g. \"ri\", \"ct\", \"ma\"\n",
-    "BATCH = \"ny_20260417a_r1-36\"\n",
-    "# ─────────────────────────────────────────────────────────────────────────────\n",
-    "\n",
-    "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n",
-    "RUN_PAIRS: dict[str, str] = {\n",
-    "    \"Upgrade 00\": \"run_1+2\",\n",
-    "    \"Upgrade 02\": \"run_3+4\",\n",
-    "}\n",
-    "DATASETS: dict[str, str] = {\n",
-    "    \"bills\": \"comb_bills_year_target\",\n",
-    "    \"bat\": \"cross_subsidization_BAT_values\",\n",
-    "}\n",
-    "\n",
-    "BLDG_ID = \"bldg_id\"\n",
-    "UTILITY_COL = \"sb.electric_utility\"\n",
-    "GAS_UTILITY_COL = \"sb.gas_utility\"\n",
-    "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n",
-    "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
-    "SCENARIO_ORDER = list(RUN_PAIRS)\n",
-    "\n",
-    "# Human-readable labels for postprocess_group.heating_type values\n",
-    "HEATING_TYPE_LABELS: dict[str, str] = {\n",
-    "    \"fossil_fuel\": \"Fossil fuel\",\n",
-    "    \"electrical_resistance\": \"Electric resistance\",\n",
-    "    \"heat_pump\": \"Existing heat pump\",\n",
-    "}\n",
-    "# Display order for heating groups in charts and tables\n",
-    "HEATING_ORDER = [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n",
-    "\n",
-    "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000005",
-   "metadata": {},
-   "source": [
-    "## Section 1: Load data\n",
-    "\n",
-    "We load `comb_bills_year_target/` (monthly bills per building) and\n",
-    "`cross_subsidization_BAT_values/` (annual BAT metrics per building) for both run pairs.\n",
-    "Both datasets are Hive-partitioned on `sb.electric_utility`.\n",
-    "\n",
-    "Each row in the **bills** table represents one building in one month (plus an `\"Annual\"`\n",
-    "summary row). Each row in the **BAT** table represents one building (annual only).\n",
-    "Schema details are in the AGENTS.md master-table documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "a0000006",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded both scenarios.\n",
-      "  Upgrade 00: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n",
-      "  Upgrade 02: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n"
-     ]
-    }
-   ],
-   "source": [
-    "def master_path(run_pair: str, dataset: str) -> str:\n",
-    "    return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n",
-    "\n",
-    "\n",
-    "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n",
-    "    return cast(\n",
-    "        pl.DataFrame,\n",
-    "        pl.scan_parquet(master_path(run_pair, dataset), hive_partitioning=True).collect(),\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "bills_by_scenario: dict[str, pl.DataFrame] = {}\n",
-    "bat_by_scenario: dict[str, pl.DataFrame] = {}\n",
-    "\n",
-    "for scenario, run_pair in RUN_PAIRS.items():\n",
-    "    bills_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bills\"])\n",
-    "    bat_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bat\"])\n",
-    "\n",
-    "print(\"Loaded both scenarios.\")\n",
-    "for scenario in SCENARIO_ORDER:\n",
-    "    b = bills_by_scenario[scenario]\n",
-    "    bt = bat_by_scenario[scenario]\n",
-    "    months_present = sorted(b.filter(pl.col(\"month\") != \"Annual\")[\"month\"].unique().to_list())\n",
-    "    print(f\"  {scenario}: bills {b.shape}  |  BAT {bt.shape}  |  months: {months_present}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "a0000007",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "a0000001",
+      "metadata": {},
+      "source": [
+        "# CAIRO run results review\n",
+        "\n",
+        "This **state-agnostic** notebook reviews the outputs from the first four CAIRO simulation\n",
+        "runs for any given state and batch. Run it after a new batch is complete to sanity-check\n",
+        "inputs, verify bill arithmetic, and get an initial read on cross-subsidization and\n",
+        "bill-change patterns.\n",
+        "\n",
+        "The first four runs (covering two upgrade levels \u00d7 two supply configurations) produce\n",
+        "the master tables this notebook reads:\n",
+        "\n",
+        "| Run pair | Upgrade | What it represents |\n",
+        "|----------|---------|--------------------|\n",
+        "| 1+2 | Upgrade 00 (baseline) | Current HVAC; delivery-only + delivery+supply combined |\n",
+        "| 3+4 | Upgrade 02 (HP upgrade) | All buildings given heat pumps; same two supply configs |\n",
+        "\n",
+        "> **Demonstration state**: This notebook currently uses NY batch\n",
+        "> `ny_20260417a_r1-36` as a worked example.  \n",
+        "> **To review a different state, change `STATE` and `BATCH` in the Parameters cell\n",
+        "> and restart the kernel.**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## How to use for a new state\n",
+        "\n",
+        "1. Change `STATE` (lowercase 2-letter abbreviation, e.g. `\"ri\"`, `\"ct\"`) and `BATCH`\n",
+        "   in the *Parameters* cell below.\n",
+        "2. Verify that `run_1+2` and `run_3+4` exist under\n",
+        "   `s3://data.sb/switchbox/cairo/outputs/hp_rates/<STATE>/all_utilities/<BATCH>/`.\n",
+        "3. Restart the kernel and run all cells (`Kernel \u2192 Restart & Run All`).\n",
+        "4. No other code changes are needed \u2014 all paths and column references are derived\n",
+        "   from `STATE` and `BATCH`.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Notebook sections\n",
+        "\n",
+        "| Section | What it covers |\n",
+        "|---------|---------------|\n",
+        "| **1 \u2014 Load data** | Read master bills and BAT tables from S3 for both run pairs |\n",
+        "| **2 \u2014 Input data quality checks (EDA)** | Utility assignments, heating-type composition, gas and electric spending distributions |\n",
+        "| **3 \u2014 Structural quality checks** | Schema validation, row counts, bill arithmetic identities |\n",
+        "| **4 \u2014 Baseline analysis (upgrade 00)** | Cross-subsidy table, BAT distribution, monthly bill pattern |\n",
+        "| **5 \u2014 Bill change analysis (upgrade 00 \u2192 02)** | Quadrant bar chart, savings breakdown by heating type |"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bills schema:\n",
-      "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'in.representative_income': Float64, 'in.hvac_cooling_partial_space_conditioning': String, 'month': String, 'weight': Float64, 'elec_fixed_charge': Float64, 'elec_delivery_bill': Float64, 'elec_supply_bill': Float64, 'elec_total_bill': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'gas_fixed_charge': Float64, 'gas_volumetric_bill': Float64, 'gas_total_bill': Float64, 'propane_total_bill': Float64, 'oil_total_bill': Float64, 'energy_total_bill': Float64, 'sb.electric_utility': String})\n",
-      "\n",
-      "BAT schema:\n",
-      "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'weight': Float64, 'BAT_vol_delivery': Float64, 'BAT_vol_supply': Float64, 'BAT_vol_total': Float64, 'BAT_percustomer_delivery': Float64, 'BAT_percustomer_supply': Float64, 'BAT_percustomer_total': Float64, 'BAT_epmc_delivery': Float64, 'BAT_epmc_supply': Float64, 'BAT_epmc_total': Float64, 'annual_bill_delivery': Float64, 'annual_bill_supply': Float64, 'annual_bill_total': Float64, 'economic_burden_delivery': Float64, 'economic_burden_supply': Float64, 'economic_burden_total': Float64, 'residual_share_delivery': Float64, 'residual_share_supply': Float64, 'residual_share_total': Float64, 'residual_share_epmc_delivery': Float64, 'residual_share_epmc_supply': Float64, 'residual_share_epmc_total': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'sb.electric_utility': String})\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Quick schema peek — useful when working with an unfamiliar batch\n",
-    "print(\"Bills schema:\")\n",
-    "print(bills_by_scenario[SCENARIO_ORDER[0]].schema)\n",
-    "print(\"\\nBAT schema:\")\n",
-    "print(bat_by_scenario[SCENARIO_ORDER[0]].schema)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000008",
-   "metadata": {},
-   "source": [
-    "## Section 2: Input data quality checks (EDA)\n",
-    "\n",
-    "Before looking at bills and the BAT, we examine the raw inputs: how buildings are\n",
-    "distributed across utility territories, what their baseline heating systems are, and\n",
-    "what their gas and electric spending looks like. These checks catch assignment errors\n",
-    "and calibration anomalies early.\n",
-    "\n",
-    "All EDA in this section uses upgrade 00 (`\"Annual\"` rows)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000009",
-   "metadata": {},
-   "source": [
-    "### 2a: Utility assignment\n",
-    "\n",
-    "What fraction of buildings (and weighted households) are assigned to each electric and\n",
-    "gas utility? We expect the partition to align with each utility's approximate share of\n",
-    "the state's residential customers.\n",
-    "\n",
-    "A large fraction of **null gas assignments** is normal — buildings that heat with\n",
-    "electricity, propane, or oil are not assigned a gas utility. Investigate if the null\n",
-    "fraction is surprisingly low in a territory with extensive gas infrastructure, or\n",
-    "surprisingly high in a gas-dense market."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "a0000010",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "a0000002",
+      "metadata": {},
+      "source": [
+        "## Parameters\n",
+        "\n",
+        "Change `STATE` and `BATCH` here to switch states. Everything else derives from these\n",
+        "two values."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Upgrade 00 — Annual rows: 33,790 sample buildings, 7,251,938 weighted households\n",
-      "\n",
-      "Electric utility assignment:\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 45,
+      "id": "a0000003",
+      "metadata": {},
+      "outputs": [
+        {
+          "ename": "ImportError",
+          "evalue": "cannot import name 'coord_polar' from 'plotnine' (/ebs/home/lee_switch_box/reports2/.venv/lib/python3.13/site-packages/plotnine/__init__.py)",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[45]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpolars\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpl\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m display\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplotnine\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m      9\u001b[39m     aes,\n\u001b[32m     10\u001b[39m     coord_flip,\n\u001b[32m     11\u001b[39m     coord_polar,\n\u001b[32m     12\u001b[39m     element_text,\n\u001b[32m     13\u001b[39m     facet_wrap,\n\u001b[32m     14\u001b[39m     geom_bar,\n\u001b[32m     15\u001b[39m     geom_col,\n\u001b[32m     16\u001b[39m     geom_hline,\n\u001b[32m     17\u001b[39m     geom_text,\n\u001b[32m     18\u001b[39m     ggplot,\n\u001b[32m     19\u001b[39m     guides,\n\u001b[32m     20\u001b[39m     labs,\n\u001b[32m     21\u001b[39m     position_dodge,\n\u001b[32m     22\u001b[39m     position_stack,\n\u001b[32m     23\u001b[39m     scale_fill_manual,\n\u001b[32m     24\u001b[39m     scale_x_discrete,\n\u001b[32m     25\u001b[39m     scale_y_continuous,\n\u001b[32m     26\u001b[39m     theme,\n\u001b[32m     27\u001b[39m )\n\u001b[32m     29\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mplotnine\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SB_COLORS, theme_switchbox\n",
+            "\u001b[31mImportError\u001b[39m: cannot import name 'coord_polar' from 'plotnine' (/ebs/home/lee_switch_box/reports2/.venv/lib/python3.13/site-packages/plotnine/__init__.py)"
+          ]
+        }
+      ],
+      "source": [
+        "from __future__ import annotations\n",
+        "\n",
+        "import math\n",
+        "from typing import cast\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import polars as pl\n",
+        "from IPython.display import display\n",
+        "from plotnine import (\n",
+        "    aes,\n",
+        "    coord_flip,\n",
+        "    facet_wrap,\n",
+        "    geom_col,\n",
+        "    geom_hline,\n",
+        "    geom_text,\n",
+        "    ggplot,\n",
+        "    guides,\n",
+        "    labs,\n",
+        "    position_dodge,\n",
+        "    position_stack,\n",
+        "    scale_fill_manual,\n",
+        "    scale_x_discrete,\n",
+        "    scale_y_continuous,\n",
+        "    theme,\n",
+        ")\n",
+        "\n",
+        "from lib.plotnine import SB_COLORS, theme_switchbox"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (8, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>42.3</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>21.1</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>14.2</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>10.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>4.8</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>3.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>2.9</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.251938e6</td><td>99.9</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000004",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Reviewing NY batch: ny_20260417a_r1-36\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "shape: (8, 4)\n",
-       "┌──────────────────┬───────────┬─────────────────────┬──────────────┐\n",
-       "│ electric_utility ┆ buildings ┆ weighted_households ┆ pct_of_total │\n",
-       "│ ---              ┆ ---       ┆ ---                 ┆ ---          │\n",
-       "│ str              ┆ u32       ┆ f64                 ┆ f64          │\n",
-       "╞══════════════════╪═══════════╪═════════════════════╪══════════════╡\n",
-       "│ coned            ┆ 15435     ┆ 3.0640e6            ┆ 42.3         │\n",
-       "│ nimo             ┆ 6791      ┆ 1.532294e6          ┆ 21.1         │\n",
-       "│ psegli           ┆ 4427      ┆ 1.0300e6            ┆ 14.2         │\n",
-       "│ nyseg            ┆ 3682      ┆ 792300.0            ┆ 10.9         │\n",
-       "│ rge              ┆ 1444      ┆ 351481.0            ┆ 4.8          │\n",
-       "│ cenhud           ┆ 1182      ┆ 270859.0            ┆ 3.7          │\n",
-       "│ or               ┆ 829       ┆ 211011.0            ┆ 2.9          │\n",
-       "│ **TOTAL**        ┆ 33790     ┆ 7.251938e6          ┆ 99.9         │\n",
-       "└──────────────────┴───────────┴─────────────────────┴──────────────┘"
+      "source": [
+        "# \u2500\u2500 Change these two values to review a different state or batch \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+        "STATE = \"ny\"  # lowercase state abbreviation, e.g. \"ri\", \"ct\", \"ma\"\n",
+        "BATCH = \"ny_20260417a_r1-36\"\n",
+        "# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+        "\n",
+        "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n",
+        "RUN_PAIRS: dict[str, str] = {\n",
+        "    \"Upgrade 00\": \"run_1+2\",\n",
+        "    \"Upgrade 02\": \"run_3+4\",\n",
+        "}\n",
+        "DATASETS: dict[str, str] = {\n",
+        "    \"bills\": \"comb_bills_year_target\",\n",
+        "    \"bat\": \"cross_subsidization_BAT_values\",\n",
+        "}\n",
+        "\n",
+        "BLDG_ID = \"bldg_id\"\n",
+        "UTILITY_COL = \"sb.electric_utility\"\n",
+        "GAS_UTILITY_COL = \"sb.gas_utility\"\n",
+        "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n",
+        "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
+        "SCENARIO_ORDER = list(RUN_PAIRS)\n",
+        "\n",
+        "# Human-readable labels for postprocess_group.heating_type values\n",
+        "HEATING_TYPE_LABELS: dict[str, str] = {\n",
+        "    \"fossil_fuel\": \"Fossil fuel\",\n",
+        "    \"electrical_resistance\": \"Electric resistance\",\n",
+        "    \"heat_pump\": \"Existing heat pump\",\n",
+        "}\n",
+        "# Display order for heating groups in charts and tables\n",
+        "HEATING_ORDER = [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n",
+        "\n",
+        "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Gas utility assignment (null = no gas service / unassigned):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000005",
+      "metadata": {},
+      "source": [
+        "## Section 1: Load data\n",
+        "\n",
+        "We load `comb_bills_year_target/` (monthly bills per building) and\n",
+        "`cross_subsidization_BAT_values/` (annual BAT metrics per building) for both run pairs.\n",
+        "Both datasets are Hive-partitioned on `sb.electric_utility`.\n",
+        "\n",
+        "Each row in the **bills** table represents one building in one month (plus an `\"Annual\"`\n",
+        "summary row). Each row in the **BAT** table represents one building (annual only).\n",
+        "Schema details are in the AGENTS.md master-table documentation."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (11, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;(null — no gas service)&quot;</td><td>8212</td><td>1.8038e6</td><td>24.9</td></tr><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>19.1</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>17.6</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>9.2</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>8.5</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>4.8</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>4.1</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>2.2</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>2.1</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.2519e6</td><td>100.0</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000006",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loaded both scenarios.\n",
+            "  Upgrade 00: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n",
+            "  Upgrade 02: bills (439270, 27)  |  BAT (33790, 35)  |  months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "shape: (11, 4)\n",
-       "┌─────────────────────────┬───────────┬─────────────────────┬──────────────┐\n",
-       "│ gas_utility             ┆ buildings ┆ weighted_households ┆ pct_of_total │\n",
-       "│ ---                     ┆ ---       ┆ ---                 ┆ ---          │\n",
-       "│ str                     ┆ u32       ┆ f64                 ┆ f64          │\n",
-       "╞═════════════════════════╪═══════════╪═════════════════════╪══════════════╡\n",
-       "│ (null — no gas service) ┆ 8212      ┆ 1.8038e6            ┆ 24.9         │\n",
-       "│ coned                   ┆ 6991      ┆ 1.3887e6            ┆ 19.1         │\n",
-       "│ kedny                   ┆ 6418      ┆ 1.2747e6            ┆ 17.6         │\n",
-       "│ kedli                   ┆ 2874      ┆ 667484.048047       ┆ 9.2          │\n",
-       "│ nimo                    ┆ 2746      ┆ 617985.707991       ┆ 8.5          │\n",
-       "│ …                       ┆ …         ┆ …                   ┆ …            │\n",
-       "│ nyseg                   ┆ 1581      ┆ 348155.122257       ┆ 4.8          │\n",
-       "│ rge                     ┆ 1240      ┆ 297109.964156       ┆ 4.1          │\n",
-       "│ cenhud                  ┆ 713       ┆ 162382.688203       ┆ 2.2          │\n",
-       "│ or                      ┆ 594       ┆ 150773.083876       ┆ 2.1          │\n",
-       "│ **TOTAL**               ┆ 33790     ┆ 7.2519e6            ┆ 100.0        │\n",
-       "└─────────────────────────┴───────────┴─────────────────────┴──────────────┘"
+      "source": [
+        "def master_path(run_pair: str, dataset: str) -> str:\n",
+        "    return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n",
+        "\n",
+        "\n",
+        "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n",
+        "    return cast(\n",
+        "        pl.DataFrame,\n",
+        "        pl.scan_parquet(master_path(run_pair, dataset), hive_partitioning=True).collect(),\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "bills_by_scenario: dict[str, pl.DataFrame] = {}\n",
+        "bat_by_scenario: dict[str, pl.DataFrame] = {}\n",
+        "\n",
+        "for scenario, run_pair in RUN_PAIRS.items():\n",
+        "    bills_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bills\"])\n",
+        "    bat_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bat\"])\n",
+        "\n",
+        "print(\"Loaded both scenarios.\")\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    b = bills_by_scenario[scenario]\n",
+        "    bt = bat_by_scenario[scenario]\n",
+        "    months_present = sorted(b.filter(pl.col(\"month\") != \"Annual\")[\"month\"].unique().to_list())\n",
+        "    print(f\"  {scenario}: bills {b.shape}  |  BAT {bt.shape}  |  months: {months_present}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "annual_u0 = bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n",
-    "total_bldgs = annual_u0.height\n",
-    "total_weighted = cast(float, annual_u0[\"weight\"].sum())\n",
-    "\n",
-    "print(f\"Upgrade 00 — Annual rows: {total_bldgs:,} sample buildings, {total_weighted:,.0f} weighted households\\n\")\n",
-    "\n",
-    "# Electric utility assignment\n",
-    "elec_assign = (\n",
-    "    annual_u0.with_columns(pl.col(UTILITY_COL).fill_null(\"(null)\"))\n",
-    "    .group_by(UTILITY_COL)\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"buildings\"),\n",
-    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
-    "    )\n",
-    "    .with_columns(\n",
-    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
-    "    )\n",
-    "    .sort(\"weighted_households\", descending=True)\n",
-    "    .rename({UTILITY_COL: \"electric_utility\"})\n",
-    ")\n",
-    "# Add summary row for electric (cast to match schema)\n",
-    "elec_assign_with_total = pl.concat(\n",
-    "    [\n",
-    "        elec_assign,\n",
-    "        pl.DataFrame(\n",
-    "            {\n",
-    "                \"electric_utility\": [\"**TOTAL**\"],\n",
-    "                \"buildings\": [elec_assign[\"buildings\"].sum()],\n",
-    "                \"weighted_households\": [elec_assign[\"weighted_households\"].sum()],\n",
-    "                \"pct_of_total\": [elec_assign[\"pct_of_total\"].sum()],\n",
-    "            },\n",
-    "            schema=elec_assign.schema,\n",
-    "        ),\n",
-    "    ]\n",
-    ")\n",
-    "print(\"Electric utility assignment:\")\n",
-    "display(elec_assign_with_total)\n",
-    "\n",
-    "# Gas utility assignment\n",
-    "gas_assign = (\n",
-    "    annual_u0.with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null — no gas service)\"))\n",
-    "    .group_by(GAS_UTILITY_COL)\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"buildings\"),\n",
-    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
-    "    )\n",
-    "    .with_columns(\n",
-    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
-    "    )\n",
-    "    .sort(\"weighted_households\", descending=True)\n",
-    "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
-    ")\n",
-    "# Add summary row for gas (cast to match schema)\n",
-    "gas_assign_with_total = pl.concat(\n",
-    "    [\n",
-    "        gas_assign,\n",
-    "        pl.DataFrame(\n",
-    "            {\n",
-    "                \"gas_utility\": [\"**TOTAL**\"],\n",
-    "                \"buildings\": [gas_assign[\"buildings\"].sum()],\n",
-    "                \"weighted_households\": [gas_assign[\"weighted_households\"].sum()],\n",
-    "                \"pct_of_total\": [gas_assign[\"pct_of_total\"].sum()],\n",
-    "            },\n",
-    "            schema=gas_assign.schema,\n",
-    "        ),\n",
-    "    ]\n",
-    ")\n",
-    "print(\"\\nGas utility assignment (null = no gas service / unassigned):\")\n",
-    "display(gas_assign_with_total)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000010a",
-   "metadata": {},
-   "source": [
-    "### Zero gas usage verification\n",
-    "\n",
-    "Cross-check the gas utility assignment against ResStock annual gas consumption.\n",
-    "Buildings with zero annual gas consumption should align closely with the\n",
-    "\"(null — no gas service)\" category above, since gas utility assignment is gated\n",
-    "on `has_natgas_connection` (derived from nonzero gas consumption in\n",
-    "`load_curve_annual`).\n",
-    "\n",
-    "Both metrics below are **unweighted building counts** for apples-to-apples\n",
-    "comparison. Small differences can arise from sampling variance or buildings with\n",
-    "minimal gas usage that round to zero in annual totals but are flagged as connected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "a0000010b",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading ResStock annual load curves from:\n",
-      "  s3://data.sb/nrel/resstock/res_2024_amy2018_2/load_curve_annual/state=NY/upgrade=00/NY_upgrade00_metadata_and_annual_results.parquet\n",
-      "\n",
-      "Buildings with zero annual gas consumption: 8,212 / 33,790 (24.3%)\n",
-      "Buildings with nonzero annual gas consumption: 25,578 / 33,790 (75.7%)\n",
-      "\n",
-      "For comparison, unweighted null gas utility assignment: 8,212 / 33,790 (24.3%)\n",
-      "Difference (zero gas usage - null assignment): 0.0 percentage points\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load ResStock metadata to check gas consumption\n",
-    "RESSTOCK_RELEASE = \"res_2024_amy2018_2\"\n",
-    "RESSTOCK_S3_BASE = \"s3://data.sb/nrel/resstock\"\n",
-    "GAS_CONSUMPTION_COL = \"out.natural_gas.total.energy_consumption.kwh\"\n",
-    "\n",
-    "# Construct path to load_curve_annual for this state and upgrade 00\n",
-    "# Filename pattern: {STATE}_upgrade{UPGRADE}_metadata_and_annual_results.parquet\n",
-    "upgrade_padded = \"00\"\n",
-    "annual_filename = f\"{STATE.upper()}_upgrade{upgrade_padded}_metadata_and_annual_results.parquet\"\n",
-    "annual_path = f\"{RESSTOCK_S3_BASE}/{RESSTOCK_RELEASE}/load_curve_annual/state={STATE.upper()}/upgrade={upgrade_padded}/{annual_filename}\"\n",
-    "\n",
-    "print(f\"Loading ResStock annual load curves from:\\n  {annual_path}\\n\")\n",
-    "\n",
-    "# Load annual data and check for gas consumption column\n",
-    "annual_lf = pl.scan_parquet(annual_path)\n",
-    "\n",
-    "# Select just bldg_id and gas consumption\n",
-    "gas_check = annual_lf.select([BLDG_ID, GAS_CONSUMPTION_COL]).collect()\n",
-    "\n",
-    "# Count buildings with zero gas consumption\n",
-    "zero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) == 0).height\n",
-    "nonzero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) > 0).height\n",
-    "total_bldgs_annual = gas_check.height\n",
-    "\n",
-    "pct_zero_gas = zero_gas / total_bldgs_annual * 100\n",
-    "pct_nonzero_gas = nonzero_gas / total_bldgs_annual * 100\n",
-    "\n",
-    "print(f\"Buildings with zero annual gas consumption: {zero_gas:,} / {total_bldgs_annual:,} ({pct_zero_gas:.1f}%)\")\n",
-    "print(\n",
-    "    f\"Buildings with nonzero annual gas consumption: {nonzero_gas:,} / {total_bldgs_annual:,} ({pct_nonzero_gas:.1f}%)\"\n",
-    ")\n",
-    "\n",
-    "# Compare to null gas utility assignment (unweighted building count)\n",
-    "try:\n",
-    "    null_gas_bldgs = gas_assign.filter(pl.col(\"gas_utility\") == \"(null — no gas service)\")[\"buildings\"][0]\n",
-    "    pct_null_gas_unweighted = null_gas_bldgs / total_bldgs * 100\n",
-    "    print(\n",
-    "        f\"\\nFor comparison, unweighted null gas utility assignment: {null_gas_bldgs:,} / {total_bldgs:,} ({pct_null_gas_unweighted:.1f}%)\"\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Difference (zero gas usage - null assignment): {pct_zero_gas - pct_null_gas_unweighted:.1f} percentage points\"\n",
-    "    )\n",
-    "except (NameError, IndexError):\n",
-    "    print(\"\\n(Run the utility assignment cell above first to compare with null gas assignment)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000011",
-   "metadata": {},
-   "source": [
-    "### 2b: Heating type and fuel composition\n",
-    "\n",
-    "What fraction of buildings heat with each fuel or technology under upgrade 00\n",
-    "(baseline)? This breakdown drives cross-subsidy and bill-change patterns throughout\n",
-    "the analysis.\n",
-    "\n",
-    "The `postprocess_group.heating_type` column classifies buildings into three groups\n",
-    "used by CAIRO's post-processing:\n",
-    "\n",
-    "- **Fossil fuel** — primary heat source is natural gas, oil, or propane\n",
-    "- **Electric resistance** — primary heat source is electric resistance\n",
-    "- **Existing heat pump** — already has a heat pump in the baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "a0000012",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000007",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Bills schema:\n",
+            "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'in.representative_income': Float64, 'in.hvac_cooling_partial_space_conditioning': String, 'month': String, 'weight': Float64, 'elec_fixed_charge': Float64, 'elec_delivery_bill': Float64, 'elec_supply_bill': Float64, 'elec_total_bill': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'gas_fixed_charge': Float64, 'gas_volumetric_bill': Float64, 'gas_total_bill': Float64, 'propane_total_bill': Float64, 'oil_total_bill': Float64, 'energy_total_bill': Float64, 'sb.electric_utility': String})\n",
+            "\n",
+            "BAT schema:\n",
+            "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'weight': Float64, 'BAT_vol_delivery': Float64, 'BAT_vol_supply': Float64, 'BAT_vol_total': Float64, 'BAT_percustomer_delivery': Float64, 'BAT_percustomer_supply': Float64, 'BAT_percustomer_total': Float64, 'BAT_epmc_delivery': Float64, 'BAT_epmc_supply': Float64, 'BAT_epmc_total': Float64, 'annual_bill_delivery': Float64, 'annual_bill_supply': Float64, 'annual_bill_total': Float64, 'economic_burden_delivery': Float64, 'economic_burden_supply': Float64, 'economic_burden_total': Float64, 'residual_share_delivery': Float64, 'residual_share_supply': Float64, 'residual_share_total': Float64, 'residual_share_epmc_delivery': Float64, 'residual_share_epmc_supply': Float64, 'residual_share_epmc_total': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'sb.electric_utility': String})\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Quick schema peek \u2014 useful when working with an unfamiliar batch\n",
+        "print(\"Bills schema:\")\n",
+        "print(bills_by_scenario[SCENARIO_ORDER[0]].schema)\n",
+        "print(\"\\nBAT schema:\")\n",
+        "print(bat_by_scenario[SCENARIO_ORDER[0]].schema)"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Baseline heating-type breakdown (upgrade 00):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000008",
+      "metadata": {},
+      "source": [
+        "## Section 2: Input data quality checks (EDA)\n",
+        "\n",
+        "Before looking at bills and the BAT, we examine the raw inputs: how buildings are\n",
+        "distributed across utility territories, what their baseline heating systems are, and\n",
+        "what their gas and electric spending looks like. These checks catch assignment errors\n",
+        "and calibration anomalies early.\n",
+        "\n",
+        "All EDA in this section uses upgrade 00 (`\"Annual\"` rows)."
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
-      "(Deprecated in version 1.0.0)\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000009",
+      "metadata": {},
+      "source": [
+        "### 2a: Utility assignment\n",
+        "\n",
+        "What fraction of buildings (and weighted households) are assigned to each electric and\n",
+        "gas utility? We expect the partition to align with each utility's approximate share of\n",
+        "the state's residential customers.\n",
+        "\n",
+        "A large fraction of **null gas assignments** is normal \u2014 buildings that heat with\n",
+        "electricity, propane, or oil are not assigned a gas utility. Investigate if the null\n",
+        "fraction is surprisingly low in a territory with extensive gas infrastructure, or\n",
+        "surprisingly high in a gas-dense market."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (4, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>heating_label</th><th>buildings</th><th>weighted_households</th><th>heats_natgas</th><th>heats_oil</th><th>heats_propane</th><th>heats_electricity</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6.3495e6</td><td>20201</td><td>7067</td><td>1280</td><td>0</td><td>87.6</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504.118573</td><td>0</td><td>0</td><td>0</td><td>3070</td><td>9.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368.027554</td><td>0</td><td>0</td><td>0</td><td>918</td><td>2.7</td></tr><tr><td>null</td><td>248</td><td>49529.864887</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000010",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Upgrade 00 \u2014 Annual rows: 33,790 sample buildings, 7,251,938 weighted households\n",
+            "\n",
+            "Electric utility assignment:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (8, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>42.3</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>21.1</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>14.2</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>10.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>4.8</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>3.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>2.9</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.251938e6</td><td>99.9</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (8, 4)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 electric_utility \u2506 buildings \u2506 weighted_households \u2506 pct_of_total \u2502\n",
+              "\u2502 ---              \u2506 ---       \u2506 ---                 \u2506 ---          \u2502\n",
+              "\u2502 str              \u2506 u32       \u2506 f64                 \u2506 f64          \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 coned            \u2506 15435     \u2506 3.0640e6            \u2506 42.3         \u2502\n",
+              "\u2502 nimo             \u2506 6791      \u2506 1.532294e6          \u2506 21.1         \u2502\n",
+              "\u2502 psegli           \u2506 4427      \u2506 1.0300e6            \u2506 14.2         \u2502\n",
+              "\u2502 nyseg            \u2506 3682      \u2506 792300.0            \u2506 10.9         \u2502\n",
+              "\u2502 rge              \u2506 1444      \u2506 351481.0            \u2506 4.8          \u2502\n",
+              "\u2502 cenhud           \u2506 1182      \u2506 270859.0            \u2506 3.7          \u2502\n",
+              "\u2502 or               \u2506 829       \u2506 211011.0            \u2506 2.9          \u2502\n",
+              "\u2502 **TOTAL**        \u2506 33790     \u2506 7.251938e6          \u2506 99.9         \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Gas utility assignment (null = no gas service / unassigned):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (11, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings</th><th>weighted_households</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;(null \u2014 no gas service)&quot;</td><td>8212</td><td>1.8038e6</td><td>24.9</td></tr><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>19.1</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>17.6</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>9.2</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>8.5</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>4.8</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>4.1</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>2.2</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>2.1</td></tr><tr><td>&quot;**TOTAL**&quot;</td><td>33790</td><td>7.2519e6</td><td>100.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (11, 4)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 gas_utility             \u2506 buildings \u2506 weighted_households \u2506 pct_of_total \u2502\n",
+              "\u2502 ---                     \u2506 ---       \u2506 ---                 \u2506 ---          \u2502\n",
+              "\u2502 str                     \u2506 u32       \u2506 f64                 \u2506 f64          \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 (null \u2014 no gas service) \u2506 8212      \u2506 1.8038e6            \u2506 24.9         \u2502\n",
+              "\u2502 coned                   \u2506 6991      \u2506 1.3887e6            \u2506 19.1         \u2502\n",
+              "\u2502 kedny                   \u2506 6418      \u2506 1.2747e6            \u2506 17.6         \u2502\n",
+              "\u2502 kedli                   \u2506 2874      \u2506 667484.048047       \u2506 9.2          \u2502\n",
+              "\u2502 nimo                    \u2506 2746      \u2506 617985.707991       \u2506 8.5          \u2502\n",
+              "\u2502 \u2026                       \u2506 \u2026         \u2506 \u2026                   \u2506 \u2026            \u2502\n",
+              "\u2502 nyseg                   \u2506 1581      \u2506 348155.122257       \u2506 4.8          \u2502\n",
+              "\u2502 rge                     \u2506 1240      \u2506 297109.964156       \u2506 4.1          \u2502\n",
+              "\u2502 cenhud                  \u2506 713       \u2506 162382.688203       \u2506 2.2          \u2502\n",
+              "\u2502 or                      \u2506 594       \u2506 150773.083876       \u2506 2.1          \u2502\n",
+              "\u2502 **TOTAL**               \u2506 33790     \u2506 7.2519e6            \u2506 100.0        \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (4, 8)\n",
-       "┌────────────┬───────────┬────────────┬────────────┬───────────┬───────────┬───────────┬───────────┐\n",
-       "│ heating_la ┆ buildings ┆ weighted_h ┆ heats_natg ┆ heats_oil ┆ heats_pro ┆ heats_ele ┆ pct_of_to │\n",
-       "│ bel        ┆ ---       ┆ ouseholds  ┆ as         ┆ ---       ┆ pane      ┆ ctricity  ┆ tal       │\n",
-       "│ ---        ┆ u32       ┆ ---        ┆ ---        ┆ i32       ┆ ---       ┆ ---       ┆ ---       │\n",
-       "│ str        ┆           ┆ f64        ┆ i32        ┆           ┆ i32       ┆ i32       ┆ f64       │\n",
-       "╞════════════╪═══════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-       "│ Fossil     ┆ 29554     ┆ 6.3495e6   ┆ 20201      ┆ 7067      ┆ 1280      ┆ 0         ┆ 87.6      │\n",
-       "│ fuel       ┆           ┆            ┆            ┆           ┆           ┆           ┆           │\n",
-       "│ Electric   ┆ 3070      ┆ 654504.118 ┆ 0          ┆ 0         ┆ 0         ┆ 3070      ┆ 9.0       │\n",
-       "│ resistance ┆           ┆ 573        ┆            ┆           ┆           ┆           ┆           │\n",
-       "│ Existing   ┆ 918       ┆ 198368.027 ┆ 0          ┆ 0         ┆ 0         ┆ 918       ┆ 2.7       │\n",
-       "│ heat pump  ┆           ┆ 554        ┆            ┆           ┆           ┆           ┆           │\n",
-       "│ null       ┆ 248       ┆ 49529.8648 ┆ 0          ┆ 0         ┆ 0         ┆ 0         ┆ 0.7       │\n",
-       "│            ┆           ┆ 87         ┆            ┆           ┆           ┆           ┆           │\n",
-       "└────────────┴───────────┴────────────┴────────────┴───────────┴───────────┴───────────┴───────────┘"
+      "source": [
+        "annual_u0 = bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n",
+        "total_bldgs = annual_u0.height\n",
+        "total_weighted = cast(float, annual_u0[\"weight\"].sum())\n",
+        "\n",
+        "print(f\"Upgrade 00 \u2014 Annual rows: {total_bldgs:,} sample buildings, {total_weighted:,.0f} weighted households\\n\")\n",
+        "\n",
+        "# Electric utility assignment\n",
+        "elec_assign = (\n",
+        "    annual_u0.with_columns(pl.col(UTILITY_COL).fill_null(\"(null)\"))\n",
+        "    .group_by(UTILITY_COL)\n",
+        "    .agg(\n",
+        "        pl.len().alias(\"buildings\"),\n",
+        "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+        "    )\n",
+        "    .with_columns(\n",
+        "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+        "    )\n",
+        "    .sort(\"weighted_households\", descending=True)\n",
+        "    .rename({UTILITY_COL: \"electric_utility\"})\n",
+        ")\n",
+        "# Add summary row for electric (cast to match schema)\n",
+        "elec_assign_with_total = pl.concat(\n",
+        "    [\n",
+        "        elec_assign,\n",
+        "        pl.DataFrame(\n",
+        "            {\n",
+        "                \"electric_utility\": [\"**TOTAL**\"],\n",
+        "                \"buildings\": [elec_assign[\"buildings\"].sum()],\n",
+        "                \"weighted_households\": [elec_assign[\"weighted_households\"].sum()],\n",
+        "                \"pct_of_total\": [elec_assign[\"pct_of_total\"].sum()],\n",
+        "            },\n",
+        "            schema=elec_assign.schema,\n",
+        "        ),\n",
+        "    ]\n",
+        ")\n",
+        "print(\"Electric utility assignment:\")\n",
+        "display(elec_assign_with_total)\n",
+        "\n",
+        "# Gas utility assignment\n",
+        "gas_assign = (\n",
+        "    annual_u0.with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null \u2014 no gas service)\"))\n",
+        "    .group_by(GAS_UTILITY_COL)\n",
+        "    .agg(\n",
+        "        pl.len().alias(\"buildings\"),\n",
+        "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+        "    )\n",
+        "    .with_columns(\n",
+        "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+        "    )\n",
+        "    .sort(\"weighted_households\", descending=True)\n",
+        "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
+        ")\n",
+        "# Add summary row for gas (cast to match schema)\n",
+        "gas_assign_with_total = pl.concat(\n",
+        "    [\n",
+        "        gas_assign,\n",
+        "        pl.DataFrame(\n",
+        "            {\n",
+        "                \"gas_utility\": [\"**TOTAL**\"],\n",
+        "                \"buildings\": [gas_assign[\"buildings\"].sum()],\n",
+        "                \"weighted_households\": [gas_assign[\"weighted_households\"].sum()],\n",
+        "                \"pct_of_total\": [gas_assign[\"pct_of_total\"].sum()],\n",
+        "            },\n",
+        "            schema=gas_assign.schema,\n",
+        "        ),\n",
+        "    ]\n",
+        ")\n",
+        "print(\"\\nGas utility assignment (null = no gas service / unassigned):\")\n",
+        "display(gas_assign_with_total)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
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",
-      "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x70812ad8ecf0>"
+      "cell_type": "markdown",
+      "id": "a0000010a",
+      "metadata": {},
+      "source": [
+        "### Zero gas usage verification\n",
+        "\n",
+        "Cross-check the gas utility assignment against ResStock annual gas consumption.\n",
+        "Buildings with zero annual gas consumption should align closely with the\n",
+        "\"(null \u2014 no gas service)\" category above, since gas utility assignment is gated\n",
+        "on `has_natgas_connection` (derived from nonzero gas consumption in\n",
+        "`load_curve_annual`).\n",
+        "\n",
+        "Both metrics below are **unweighted building counts** for apples-to-apples\n",
+        "comparison. Small differences can arise from sampling variance or buildings with\n",
+        "minimal gas usage that round to zero in annual totals but are flagged as connected."
       ]
-     },
-     "execution_count": 44,
-     "metadata": {
-      "image/png": {
-       "height": 400,
-       "width": 1050
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def add_heating_label(df: pl.DataFrame) -> pl.DataFrame:\n",
-    "    \"\"\"Map postprocess_group.heating_type codes to human-readable labels.\"\"\"\n",
-    "    return df.with_columns(\n",
-    "        pl.col(HEATING_TYPE_COL)\n",
-    "        .fill_null(\"Other\")\n",
-    "        .replace(HEATING_TYPE_LABELS, default=pl.col(HEATING_TYPE_COL))\n",
-    "        .alias(\"heating_label\")\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "heating_df = add_heating_label(annual_u0)\n",
-    "heating_stats = (\n",
-    "    heating_df.group_by(\"heating_label\")\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"buildings\"),\n",
-    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
-    "        pl.col(\"heats_with_natgas\").cast(pl.Int32).sum().alias(\"heats_natgas\"),\n",
-    "        pl.col(\"heats_with_oil\").cast(pl.Int32).sum().alias(\"heats_oil\"),\n",
-    "        pl.col(\"heats_with_propane\").cast(pl.Int32).sum().alias(\"heats_propane\"),\n",
-    "        pl.col(\"heats_with_electricity\").cast(pl.Int32).sum().alias(\"heats_electricity\"),\n",
-    "    )\n",
-    "    .with_columns(\n",
-    "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
-    "    )\n",
-    "    .sort(\"weighted_households\", descending=True)\n",
-    ")\n",
-    "print(\"Baseline heating-type breakdown (upgrade 00):\")\n",
-    "display(heating_stats)\n",
-    "\n",
-    "avail_order = [h for h in HEATING_ORDER if h in heating_df[\"heating_label\"].unique().to_list()]\n",
-    "chart_data = (\n",
-    "    heating_stats.filter(pl.col(\"heating_label\").is_in(avail_order))\n",
-    "    .with_columns(pl.col(\"heating_label\").cast(pl.Enum(avail_order)))\n",
-    "    .sort(\"heating_label\")\n",
-    ")\n",
-    "(\n",
-    "    ggplot(chart_data, aes(x=\"heating_label\", y=\"pct_of_total\"))\n",
-    "    + geom_col(fill=SB_COLORS[\"sky\"], width=0.6)\n",
-    "    + geom_text(\n",
-    "        aes(label=\"pct_of_total\"),\n",
-    "        format_string=\"{:.1f}%\",\n",
-    "        nudge_y=0.4,\n",
-    "        size=10,\n",
-    "        va=\"bottom\",\n",
-    "    )\n",
-    "    + scale_y_continuous(expand=(0, 0, 0.12, 0))\n",
-    "    + labs(\n",
-    "        x=\"\",\n",
-    "        y=\"% of weighted households\",\n",
-    "        title=\"Baseline heating type distribution (upgrade 00)\",\n",
-    "    )\n",
-    "    + theme_switchbox()\n",
-    "    + theme(figure_size=(10.5, 4))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000013",
-   "metadata": {},
-   "source": [
-    "### 2c: Annual gas spending by gas utility\n",
-    "\n",
-    "Summary of annual gas bills (`gas_total_bill`, in dollars) for buildings with non-zero\n",
-    "gas bills, grouped by assigned gas utility.\n",
-    "\n",
-    "> **Note**: `gas_total_bill` is a bill amount (dollars), not volumetric consumption\n",
-    "> (kWh or therms). For actual consumption data, cross-reference the ResStock monthly\n",
-    "> load curves column `out.natural_gas.total.energy_consumption`.\n",
-    "\n",
-    "Implausibly wide min/max ranges, or extreme mean-to-median ratios, often trace to\n",
-    "incorrect tariff calibration or misassigned buildings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "a0000014",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000010b",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading ResStock annual load curves from:\n",
+            "  s3://data.sb/nrel/resstock/res_2024_amy2018_2/load_curve_annual/state=NY/upgrade=00/NY_upgrade00_metadata_and_annual_results.parquet\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Buildings with zero annual gas consumption: 8,212 / 33,790 (24.3%)\n",
+            "Buildings with nonzero annual gas consumption: 25,578 / 33,790 (75.7%)\n",
+            "\n",
+            "For comparison, unweighted null gas utility assignment: 8,212 / 33,790 (24.3%)\n",
+            "Difference (zero gas usage - null assignment): 0.0 percentage points\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load ResStock metadata to check gas consumption\n",
+        "RESSTOCK_RELEASE = \"res_2024_amy2018_2\"\n",
+        "RESSTOCK_S3_BASE = \"s3://data.sb/nrel/resstock\"\n",
+        "GAS_CONSUMPTION_COL = \"out.natural_gas.total.energy_consumption.kwh\"\n",
+        "\n",
+        "# Construct path to load_curve_annual for this state and upgrade 00\n",
+        "# Filename pattern: {STATE}_upgrade{UPGRADE}_metadata_and_annual_results.parquet\n",
+        "upgrade_padded = \"00\"\n",
+        "annual_filename = f\"{STATE.upper()}_upgrade{upgrade_padded}_metadata_and_annual_results.parquet\"\n",
+        "annual_path = f\"{RESSTOCK_S3_BASE}/{RESSTOCK_RELEASE}/load_curve_annual/state={STATE.upper()}/upgrade={upgrade_padded}/{annual_filename}\"\n",
+        "\n",
+        "print(f\"Loading ResStock annual load curves from:\\n  {annual_path}\\n\")\n",
+        "\n",
+        "# Load annual data and check for gas consumption column\n",
+        "annual_lf = pl.scan_parquet(annual_path)\n",
+        "\n",
+        "# Select just bldg_id and gas consumption\n",
+        "gas_check = cast(pl.DataFrame, annual_lf.select([BLDG_ID, GAS_CONSUMPTION_COL]).collect())\n",
+        "\n",
+        "# Count buildings with zero gas consumption\n",
+        "zero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) == 0).height\n",
+        "nonzero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) > 0).height\n",
+        "total_bldgs_annual = gas_check.height\n",
+        "\n",
+        "pct_zero_gas = zero_gas / total_bldgs_annual * 100\n",
+        "pct_nonzero_gas = nonzero_gas / total_bldgs_annual * 100\n",
+        "\n",
+        "print(f\"Buildings with zero annual gas consumption: {zero_gas:,} / {total_bldgs_annual:,} ({pct_zero_gas:.1f}%)\")\n",
+        "print(\n",
+        "    f\"Buildings with nonzero annual gas consumption: {nonzero_gas:,} / {total_bldgs_annual:,} ({pct_nonzero_gas:.1f}%)\"\n",
+        ")\n",
+        "\n",
+        "# Compare to null gas utility assignment (unweighted building count)\n",
+        "try:\n",
+        "    null_gas_bldgs = gas_assign.filter(pl.col(\"gas_utility\") == \"(null \u2014 no gas service)\")[\"buildings\"][0]\n",
+        "    pct_null_gas_unweighted = null_gas_bldgs / total_bldgs * 100\n",
+        "    print(\n",
+        "        f\"\\nFor comparison, unweighted null gas utility assignment: {null_gas_bldgs:,} / {total_bldgs:,} ({pct_null_gas_unweighted:.1f}%)\"\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Difference (zero gas usage - null assignment): {pct_zero_gas - pct_null_gas_unweighted:.1f} percentage points\"\n",
+        "    )\n",
+        "except (NameError, IndexError):\n",
+        "    print(\"\\n(Run the utility assignment cell above first to compare with null gas assignment)\")"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Annual gas spending stats by gas utility\n",
-      "(buildings with gas_total_bill > 0; unweighted mean/median):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000011",
+      "metadata": {},
+      "source": [
+        "### 2b: Heating type and fuel composition\n",
+        "\n",
+        "What fraction of buildings heat with each fuel or technology under upgrade 00\n",
+        "(baseline)? This breakdown drives cross-subsidy and bill-change patterns throughout\n",
+        "the analysis.\n",
+        "\n",
+        "The `postprocess_group.heating_type` column classifies buildings into three groups\n",
+        "used by CAIRO's post-processing:\n",
+        "\n",
+        "- **Fossil fuel** \u2014 primary heat source is natural gas, oil, or propane\n",
+        "- **Electric resistance** \u2014 primary heat source is electric resistance\n",
+        "- **Existing heat pump** \u2014 already has a heat pump in the baseline"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (9, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings_with_gas_bill</th><th>weighted_households</th><th>min_gas_bill</th><th>max_gas_bill</th><th>mean_gas_bill</th><th>median_gas_bill</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>400.0</td><td>12166.0</td><td>1362.0</td><td>927.0</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>251.0</td><td>7240.0</td><td>1462.0</td><td>1257.0</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>302.0</td><td>6189.0</td><td>1792.0</td><td>1756.0</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>265.0</td><td>4005.0</td><td>1063.0</td><td>998.0</td></tr><tr><td>&quot;nfg&quot;</td><td>2421</td><td>540904.956926</td><td>238.0</td><td>3337.0</td><td>1007.0</td><td>957.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>201.0</td><td>4667.0</td><td>1091.0</td><td>1005.0</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>249.0</td><td>3639.0</td><td>1164.0</td><td>1098.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>325.0</td><td>7328.0</td><td>1374.0</td><td>968.0</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>278.0</td><td>5743.0</td><td>1658.0</td><td>1449.0</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000012",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Baseline heating-type breakdown (upgrade 00):\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+            "(Deprecated in version 1.0.0)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (4, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>heating_label</th><th>buildings</th><th>weighted_households</th><th>heats_natgas</th><th>heats_oil</th><th>heats_propane</th><th>heats_electricity</th><th>pct_of_total</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6.3495e6</td><td>20201</td><td>7067</td><td>1280</td><td>0</td><td>87.6</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504.118573</td><td>0</td><td>0</td><td>0</td><td>3070</td><td>9.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368.027554</td><td>0</td><td>0</td><td>0</td><td>918</td><td>2.7</td></tr><tr><td>null</td><td>248</td><td>49529.864887</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (4, 8)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 heating_la \u2506 buildings \u2506 weighted_h \u2506 heats_natg \u2506 heats_oil \u2506 heats_pro \u2506 heats_ele \u2506 pct_of_to \u2502\n",
+              "\u2502 bel        \u2506 ---       \u2506 ouseholds  \u2506 as         \u2506 ---       \u2506 pane      \u2506 ctricity  \u2506 tal       \u2502\n",
+              "\u2502 ---        \u2506 u32       \u2506 ---        \u2506 ---        \u2506 i32       \u2506 ---       \u2506 ---       \u2506 ---       \u2502\n",
+              "\u2502 str        \u2506           \u2506 f64        \u2506 i32        \u2506           \u2506 i32       \u2506 i32       \u2506 f64       \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Fossil     \u2506 29554     \u2506 6.3495e6   \u2506 20201      \u2506 7067      \u2506 1280      \u2506 0         \u2506 87.6      \u2502\n",
+              "\u2502 fuel       \u2506           \u2506            \u2506            \u2506           \u2506           \u2506           \u2506           \u2502\n",
+              "\u2502 Electric   \u2506 3070      \u2506 654504.118 \u2506 0          \u2506 0         \u2506 0         \u2506 3070      \u2506 9.0       \u2502\n",
+              "\u2502 resistance \u2506           \u2506 573        \u2506            \u2506           \u2506           \u2506           \u2506           \u2502\n",
+              "\u2502 Existing   \u2506 918       \u2506 198368.027 \u2506 0          \u2506 0         \u2506 0         \u2506 918       \u2506 2.7       \u2502\n",
+              "\u2502 heat pump  \u2506           \u2506 554        \u2506            \u2506           \u2506           \u2506           \u2506           \u2502\n",
+              "\u2502 null       \u2506 248       \u2506 49529.8648 \u2506 0          \u2506 0         \u2506 0         \u2506 0         \u2506 0.7       \u2502\n",
+              "\u2502            \u2506           \u2506 87         \u2506            \u2506           \u2506           \u2506           \u2506           \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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z2NBE8+bN873vfS9jxozJjjvuWDo/atSovPjii0mSr33ta4v9nTdq1CgnnXRSXnrppcyYMSOPPvpojjvuuCU+DwDK7/PMvxo2bJizzz47Z555ZqZMmZJbbrklP/zhD/P666/n/vvvT5KcfPLJi92edGkq8f2gHj161AgzVGvUqFG+9rWv5fLLL09VVVVGjhy5SKBh2LBhSZKWLVtmk002WWSMNdZYI1tvvXU+/PDDDB8+PDNnzlyp1T+33XbbGmGGak2bNs2hhx6aX//615k1a1bGjh1bIyxQG6/xrVu3zhVXXJHp06cvdguQRo0aZf/998/NN9+cqVOnZuLEiTU+2K8rRx111GJ/lg0bNszxxx+fl156KePGjcs///nPOv9gf88991zsc+7Zs2c23XTTvP3225k/f34OOeSQRVZNaNCgQQ455JDSCrgffPDBUgMNlfS8gcpXP5sAAUAdGTlyZMaPH58k2WmnnZbatvp6sVgsTa6rVf8j6Y033kixWFzu+7dq1aqU7F94f+AvmuqfZ+PGjZcYKkmS9dZbr/SzHjx4cL3UBgCrgqlTp2bIkCFJFryRuLRvCFa/oTdx4sTSPChJ3nrrrcybNy/J0udFDRs2zMEHH1wrYYYkK7xawsLqq+Zl1Vgbc5nq88OGDcvMmTNXqs7Fqc/5ZsOGDRcbZqjWqVOn0vGYMWOWOtbSPhjq2rVrkgWBlhkzZtS49tprryVJNthgg6y33npLHKNbt27Zeeeda+xvvfAcf5999lli39atW5c+CHnnnXeW2A6A1UPHjh1zyimnJEmefvrp9O/fv7TCwK677povf/nLKzxmJb4ftDyvvcn/hTAXNmfOnCQLPtRf0vL+jRot+H7s51n+f2mv7QvXOGrUqBrXaus1fo011ljqvHBF5jq1ZWk/kyZNmmTbbbdNkrz77rt1XsvS/g6tv/76peOFvyS2pDajR49e6r0q6XkDlc8KDQBUjAMOOCB9+/ZdapumTZvWeFydIE+Wvcxbu3bt0rx588ycOXORyfBee+2VYcOG5bnnnssll1ySvfbaKz169EjHjh2X+o3DQqGQvfbaK/fdd18eeOCBTJo0Kbvuums23HDDtG/fvsYbrKuz6p9n586dl/kNzc6dO2fkyJGL/Qc0AFS65ZmvJP+3/dPyev/990vHi1sdYGELz3k++uij0huvC3/bfUW/ZVgulVJzbcxldttttzz44IOZMGFCvv/976dv377p1atX1ltvvcWu2LC8yjnfnDNnTiZNmpS5c+cmSWkp6uWxtA87Fv4gYfr06TVWcqj+O7Eyfx+qf48tWrRYZoil+k30Zb3ZDkD51cb8a88998yLL76YgQMHpl+/fkkWrJ70ne98Z6U+oK/E94NW5LX3s7p165bnnnsukydPzujRoxd5Ha6qqsqgQYOSLJgLrczqDMuycI2fDTzW1Wv87NmzM3HixMyfPz/JgsBwtRX5wtPnsay/f9UhgcmTJ2fWrFl1un3r0mqpDvAsrV2bNm1Kx5/9Ha7IvZL6fd5A5RNoAKBitG7deoX3nps2bVrpuGXLlkttWygU0rJly8ycOTNTpkypce2AAw7IxIkTc9999+XVV1/Nq6++mmRBcrtXr1750pe+lP3333+x3wr81re+lalTp+aJJ57If//73/z3v/9NkjRr1iybbbZZttxyy+y3336f643sSlf9exg+fHgOPvjg5eoj0ADAqmhl5ivLY+E5zYpsS/DRRx9lm222WWSMVWXeUSk118ZcZqONNsq5556bm2++OaNGjcott9ySZMEctHv37tliiy2yzz771Pjm2vKqr/nm/Pnz8/LLL+eRRx7JsGHDavx+atPSQiPV91yZ51Ldd8aMGTnkkEOWq0/1KhGL2x4DgMpQG/OvQqGQ73znO3nnnXdKAb3TTz99me8lLc2q9H7QsgKb++23X/7xj39k1qxZueKKK/Ltb387vXr1SpMmTTJ69Oj84x//KK1Ote+++9ZJjUsLgdTWa/zcuXPzzDPPpH///hk5cuQyP3SvBAt/uevjjz9e7JYd9WF5Qjoruw3d4lTK8wYqg0ADAGTBP2yPPfbY7LvvvnnyySczePDgDB8+PBMnTswbb7yRN954Iw888EB+8IMf5Etf+lKNvg0bNsyZZ56ZQw45JAMHDszgwYMzcuTITJkyJS+99FJeeumlPPjgg/nxj39cY/m8L7pV4R+NAFDpFvcNO+rH4uYyu+yyS3r37p0nn3wygwYNyrBhwzJu3Li89957ee+99/LQQw/luOOOW+434qvVx3xzzpw5ueyyyypm2ez69NlVIgBYPc2cOTOffvpp6fFLL72U3r17r/R4q9P7QW3bts2FF16Yq666KqNGjcoVV1yx2Ha77rrrcoc/K8HCr/FTp07Nj3/844wYMaK8RQGwwgQaAFilLbxiwvTp09OxY8clti0Wi6VEd+vWrRfbpmPHjjnyyCNLj6dNm5aXXnopt99+eyZOnJjrr78+/fr1W+wyZ127dq3xD9SJEyfm2WefzZ/+9KeMGzcuN9xwQ2688cbPtddgXauqqlqpftW/h4022ijnnHNObZYEAF8IC89pzjzzzPTs2XO5+i08p1n4G4azZ8+uiG8DLkul1Fybc5nmzZvngAMOyAEHHJBkQVBg0KBBueuuuzJkyJD8/ve/z6abbpoePXqs8Nh1Od98+OGHS2GGzTffPIcddli6d++e1q1bp3HjxkmSsWPH5qSTTlrhsVdEq1atMnHixMyePXul+iYLPpS58sorl7vfWmuttcL3AmDVMn/+/Fx33XWZMWNGWrZsmenTp+fBBx/M9ttvny233PJzjb06vB+UJF/60pdy5ZVX5sc//nGN4GaTJk3SpUuXfPWrX80OO+xQludRG6/xf/7zn0thhh133DF9+/bN+uuvn9atW5dWFnjzzTdz4YUX1l7htWDhEM6S3vdc2ffzKtnyPG/gi0OgAYBV2kYbbVQ6HjVqVDbccMMltp04cWJmzZqVJMv9BnKrVq3Sp0+fdOzYMeeff34mTpyY999/P5ttttky+7Zr1y4HHnhgmjdvnhtuuCHDhw/PhAkTVnhP7bq28AcJC+8V+FlL2ztwww03zDvvvJPJkyfXyTLcALC66969e+l4/vz5K/V6uvAyrKNHj14llmWtlJrrci6zxhprpHfv3unZs2dOPPHETJ8+PS+//PJKBRo+qzbnm2+99VaSBfsjX3rppaUQQ33r2rVrJk6cuFz7Xn/WhhtumKeeeipTpkxJhw4dssYaa9RBhQCsiu6555688847WWONNfLTn/40t912W15//fXcdNNN+cUvflGrK/WsKu8Hfdb999+fP/zhD9lhhx1y3HHHpVmzZpk3b17atWu3XNsN1KXaeI2vnutsuOGGOf/888v+nKotK4zw4YcfJlkwR2vevHmNa9Xv6S3t/bxk6e/plcvned7AF09l/B8bAFZS165d0759+yTJs88+u9S21dcLhUK23nrrGtcmT5681DdNF07aT506tca1MWPGZNKkSSvVtxKss846pePhw4cvsV31PyQWp3rv7gkTJix1jCQZNmyY5bEB4DNat26djTfeOP+vvXsPirr6/zj+UlCBQllFbiLlJaHMqBBNxFJCbDQ17Q+zmZomtVEz8JI0gzqYyh/1xVshYebUaGMz1lheUEiRQRRUDDVFMTF1XRVQuQ4QcvH3h8P+QBZvIG7xfPzF7J7PZ8+ZzzLnvZ/P+7yPdLv88d1UV1ebb8jW9/zzz5tXl90rLtq+fbuOHj36kL1tOdbS55aIZaqrq3X27Nkmj3NwcJCLi4uk/98H+n61RryZl5cnSfLw8GgymaE1Ytm60t9Go1GXL19usl1lZaXWrl3b4AZ+3XWsra2953fFaDSa91AHAPy3ZWVlafPmzZKkDz74QF5eXvr444/VqVMnXb9+XevWrXuo8/7b7wfVl5OTo/Xr18vT01Ph4eFyd3eXk5OTnJ2dreLBf0vM8XWxjqenZ5NjehzX6eLFi02+V1lZaf5tYGkRl4eHhyTp2rVrTcaX5eXlVhnzNGfcANqexz8TAQDQDO3bt9eECRMkSWlpadq3b5/FdiaTST/++KMkaciQIeaAX5Kys7M1e/ZsRURE6Ny5cxaP/+OPP8x/119BefjwYc2dO1eLFi3S1atXLR6bmZkpSbK1tZWXl9cDjK512NnZqUePHpKkxMREiz9yrl27ppSUlCbP8dJLL5nHtmLFChUXF1tsd+7cOUVGRmrGjBkyGo0t0HsAAP473nrrLUnSwYMHlZCQYHElVXV1teLi4hQREaG4uLgG7xkMBgUGBkqStmzZ0uTD9UOHDmn9+vWKjIzU6dOnW3YQD8ha+tzcWKagoEAREREKDw9XcnKyxWuXn59vThCtH0/eS2vFm3WVKYxGY4My03Vqamr066+/PtS5H0RQUJDs7e1VU1Oj2NhYlZeXN2pTXV2tmJgYxcfHa8GCBeYVfj179tTAgQMlSd98841yc3MtfkZubq6WLl2qmTNn6vjx449uMACAx660tFTLly9XbW2t/Pz8NGbMGEmSm5ub3n//fUnS3r1775lYeaf/wv2g+rKysiTdLu1vDQkMd2qJOb4u1snJyVFVVVWjYysrK7V9+/YW7vm9bd682VxRtr6amhp9//33ys/PlySFhIQ0alNX3aympsactHOnLVu2WOWWFM0ZN4C2hy0nAAD/eqNHj9bBgwd14sQJRUdH69ChQxoyZIjc3NxUWFioU6dOadu2baqqqlKXLl300UcfNTjewcFBlZWVKisr07x58zRixAi98sorcnZ2VkVFhf7880/99ttvkqTnnnvOvLJOup0MUFlZKaPRqNDQUAUHB2vgwIEyGAwqLS1VRkaGduzYIUkaOnToA5furVuxWFNTY84kd3Z2Nr/eUt59913973//U0VFhebMmaNJkyapV69eqq6uVnZ2tnbs2CFXV9cmS9i1b99es2fPVnh4uC5evKjp06dr3Lhx6tu3r7p27arCwkJlZGRo9+7dqq6ulo+PjzmJ4s7zSLf3ycvLy5ONjY25AgcAANagpKREJpPpnu1sbGwaVEGqe61O3bxuMBjMJXOHDh2qgIAApaWlKTY2Vunp6Xr11VfNc+b58+eVkJBgriDg7+/f6HM//PBDHTt2TMXFxQoPD9ebb75pjl8KCgq0f/9+paSkqLa2Vi+//LJ8fHwaHF8/ViksLDT309HR8ZGVem1un1tCc2MZOzs7VVVVqaamRitXrtTevXsVFBSkHj166NatWzp37py2bNmiqqoqde7c2VyF4H60RLxZvyxz3TWVbu8rbWt7+9bQ4MGDlZaWprKyMkVERGjixIny9PQ0f3ZCQoKqqqpkb2+viooKFRQUyGQytfjWDo6Ojpo2bZq++uornThxQmFhYRo3bpyefvppdezYURcvXlR8fLz5/2Dy5MkNHrzMmDFDYWFhKigo0KxZszRmzBj5+PjIxcVFpaWlOnr0qBITE1VeXi43N7cW2foDAPBoPWz8devWLcXExOj69evq0qWLQkND1a5dO/P7o0ePVmpqqrKzsxUbG6tnn31WTk5O99Wn1pqfW0vduDMzM/XLL7/I29tbBoPB/L6tra0MBoM6derUqv2qr7lz/ODBg5WTk6PLly8rMjJSY8eOlaurq8rKyvT3339r165dDbZlvXbtmkwmk9zd3RvE8S193dzd3RUaGqqxY8eqd+/esrW1ldFoVGJiojnZ19fXV0OGDGl0bGBgoH7++WcZjUZt3bpVhYWFCgwMVNeuXZWfn6/k5GTl5uaa4zdr0pxxA2h7SGgAAPzr2djYaMGCBYqOjtaRI0eUmpqq1NTURu08PDy0cOFCde3atcHrXl5eioqKUnR0tEwmk5KSkpSUlNToeFdXV82fP7/Bj98XXnhBkZGRWrlypQoKChQfH6/4+PhGx/bt21czZ8584LH16dNHu3fv1o0bNzRt2jRJ0rp16+Tq6vrA57qbwMBAHTlyRMnJySoqKtLatWsbvd+vXz+dOXOmyXP07dtXkZGR+vLLL1VaWqqffvrJYjt/f3/NmTPHYlJGnz59lJubq7S0NKWlpcnFxUXfffdd8wYHAEAL2rlzp3bu3HnPdpbmsPrlUuvm9aioKA0YMEDS7W2x5s6dKxsbG6Wmpuro0aMWS+ra29srLCzMXHq3PoPBoKioKC1ZskT5+fnaunWrtm7d2qjdwIEDG8U10u3Vb3U3PKOjo82vz5o165Gtjmpun1tKc2IZBwcHff7551q9erUyMjJ0/Phxiyv/7ezs9OmnnzaKR++mJeJNb29v89913z1JiouLM1cuGz58uDIzM5WSkqLz589r+fLlDc5hb2+vJUuWaNu2bUpNTdXGjRu1ceNGrVq16oEqTtyP4OBglZWV6YcfflBeXp7FUuDt27fX9OnT9eqrrzZ4vXv37oqKitKyZct07dq1JqtKeHt7Kzw8nD2ZAeBf4GHjr8TERHPlhU8++aTBA3rp9v2k0NBQhYWFqbi4WGvWrFFERMR9xRqtNT+3Fl9fX3Xr1k03btzQhg0bmmzn6OiogIAAjR8/3lzxoLU0d45/++23dezYMWVlZenkyZONtnAzGAxavHixli9frjNnzmjVqlWSpE2bNjVIdGjp6zZ58mStWbOmyftf/fr102effWbxe2ljY6NZs2YpKipKxcXF2rdvX4PqtXZ2dlq6dKkWL178wP161JozbgBtDwkNAID/BAcHBy1atEgZGRlKSkrSmTNnVFxcLAcHBz311FMKDAzUyJEjm6yQ0Lt3b61cudIc+F++fFlFRUUyGAxyc3NTQECAQkJCLB7v6+urmJgYJScna//+/crLy1NJSYmcnZ3l5uamoKAgDRs27KGqKoSEhOjChQs6cODAA++1/CDatWunOXPmyN/fX3v27NGFCxdUUVEhDw8PjRw5UiEhIRZ/mN/J19dXcXFxio+P1+HDh3X16lVVVVWpe/fu6t27t4KDg+Xr69tk+cIpU6aovLxcWVlZunnzZksPEwCAx8rPz0/vvPOOEhISVFRUZLFNx44dNX/+fAUHB2vPnj06deqUiouL9eSTT8rFxUWDBw9WcHBwoxvy9Xl5eWnNmjVKSEhQenq6Ll26pIqKCjk7O8vLy0ujRo2Sn5+fxfn4iSee0Pz587V+/XpduXLF4tYJj0Jz+tySmhPLdO7cWQsXLlRmZqaSkpKUk5OjgoIC2dvby83NTf3799eECRPUpUuXh+pXc+LNAQMG6L333lN8fHyTFbfqEmoCAgK0c+dOXblyRYWFhXJyctKLL75ortgwdepU/fPPPzp58uQjXek3fvx4+fn5aceOHTp+/LiuX78uGxsbubi4aMCAARozZkyTDw169eql2NhY7dq1S+np6TKZTA2+T0FBQRo0aFCrr34FALQeo9FoflD6xhtvaNCgQRbbeXp6avLkydqwYYMOHTqkvXv36vXXX7+vz2iN+bm1ODk5aerUqfriiy/u2q60tFSJiYnav3+/oqOjLVbffJSaM8fb2tpq2bJlSkpKUnJysq5evarS0lJ169ZN/v7+mjhxorp166bZs2crJiZGZ8+etXhvqqWvW4cOHcxJo3UVFTp06KCePXtq+PDhGjVq1F3vKfr4+Ojrr7/Wpk2b9Ndff8lkMsnJyUne3t6aNGmS1W530txxA2hb2t1qrbsTAAAAAAAAAAAAsBpVVVVasWKF0tPTNWXKFI0ePbrRg+SamhqVlJQoKytL3377rYqKivTaa69p3rx5j6nX/25JSUlavXq1pEdTidVatdVxA2i+R7u0AQAAAAAAAAAAAFbp999/14EDB9S/f3+NHTvW4qp4GxsbGQwGBQYGasKECZKkCxcutHJPAQBtFQkNAAAAAAAAAAAAbZDJZJIkFRUVqaqq6q5ta2trlZ2dLen29q8AALQGNgsEAAAAAAAAAABog5555hlJ0qVLl7Rw4UKNHj1a7u7usre3V7t27VRdXa2ysjKZTCalpKTo5MmTkqQRI0Y8zm4DANoQEhoAAAAAAAAAAADaoGHDhikzM1P79u3T6dOndfr06XseM378eI0aNaoVegcAAAkNAAAAAAAAAAAAbVKHDh00d+5cjRo1Sunp6crOzlZJSYlKS0t18+ZNOTo6qnPnzjIYDBo4cKACAgLUvXv3x91tAEAb0u7WrVu3HncnAAAAAAAAAAAAAAAA6mv/uDsAAAAAAAAAAAAAAABwJxIaAAAAAAAAAAAAAACA1SGhAQAAAAAAAAAAAAAAWB0SGgAAAAAAAAAAAAAAgNUhoQEAAAAAAAAAAAAAAFgdEhoAAAAAAAAAAAAAAIDVIaEBAAAAAAAAAAAAAABYHRIaAAAAAAAAAAAAAACA1SGhAQAAAAAAAAAAAAAAWB0SGgAAAAAAAAAAAAAAgNUhoQEAAAAAAAAAAAAAAFgdEhoAAAAAAAAAAAAAAIDVIaEBAAAAAAAAAAAAAABYHRIaAAAAAAAAAAAAAACA1SGhAQAAAAAAAAAAAAAAWB0SGgAAAAAAAAAAAAAAgNUhoQEAAAAAAAAAAAAAAFgdEhoAAAAAAAAAAAAAAIDVIaEBAAAAAAAAAAAAAABYHRIaAAAAAAAAAAAAAACA1fk/4tCjG6U5974AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x736fad6bacf0>"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {
+            "image/png": {
+              "height": 400,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "shape: (9, 7)\n",
-       "┌─────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-       "│ gas_utility ┆ buildings_wi ┆ weighted_ho ┆ min_gas_bil ┆ max_gas_bil ┆ mean_gas_bi ┆ median_gas_ │\n",
-       "│ ---         ┆ th_gas_bill  ┆ useholds    ┆ l           ┆ l           ┆ ll          ┆ bill        │\n",
-       "│ str         ┆ ---          ┆ ---         ┆ ---         ┆ ---         ┆ ---         ┆ ---         │\n",
-       "│             ┆ u32          ┆ f64         ┆ f64         ┆ f64         ┆ f64         ┆ f64         │\n",
-       "╞═════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-       "│ coned       ┆ 6991         ┆ 1.3887e6    ┆ 400.0       ┆ 12166.0     ┆ 1362.0      ┆ 927.0       │\n",
-       "│ kedny       ┆ 6418         ┆ 1.2747e6    ┆ 251.0       ┆ 7240.0      ┆ 1462.0      ┆ 1257.0      │\n",
-       "│ kedli       ┆ 2874         ┆ 667484.0480 ┆ 302.0       ┆ 6189.0      ┆ 1792.0      ┆ 1756.0      │\n",
-       "│             ┆              ┆ 47          ┆             ┆             ┆             ┆             │\n",
-       "│ nimo        ┆ 2746         ┆ 617985.7079 ┆ 265.0       ┆ 4005.0      ┆ 1063.0      ┆ 998.0       │\n",
-       "│             ┆              ┆ 91          ┆             ┆             ┆             ┆             │\n",
-       "│ nfg         ┆ 2421         ┆ 540904.9569 ┆ 238.0       ┆ 3337.0      ┆ 1007.0      ┆ 957.0       │\n",
-       "│             ┆              ┆ 26          ┆             ┆             ┆             ┆             │\n",
-       "│ nyseg       ┆ 1581         ┆ 348155.1222 ┆ 201.0       ┆ 4667.0      ┆ 1091.0      ┆ 1005.0      │\n",
-       "│             ┆              ┆ 57          ┆             ┆             ┆             ┆             │\n",
-       "│ rge         ┆ 1240         ┆ 297109.9641 ┆ 249.0       ┆ 3639.0      ┆ 1164.0      ┆ 1098.0      │\n",
-       "│             ┆              ┆ 56          ┆             ┆             ┆             ┆             │\n",
-       "│ cenhud      ┆ 713          ┆ 162382.6882 ┆ 325.0       ┆ 7328.0      ┆ 1374.0      ┆ 968.0       │\n",
-       "│             ┆              ┆ 03          ┆             ┆             ┆             ┆             │\n",
-       "│ or          ┆ 594          ┆ 150773.0838 ┆ 278.0       ┆ 5743.0      ┆ 1658.0      ┆ 1449.0      │\n",
-       "│             ┆              ┆ 76          ┆             ┆             ┆             ┆             │\n",
-       "└─────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+      "source": [
+        "def add_heating_label(df: pl.DataFrame) -> pl.DataFrame:\n",
+        "    \"\"\"Map postprocess_group.heating_type codes to human-readable labels.\"\"\"\n",
+        "    return df.with_columns(\n",
+        "        pl.col(HEATING_TYPE_COL)\n",
+        "        .fill_null(\"Other\")\n",
+        "        .replace(HEATING_TYPE_LABELS, default=pl.col(HEATING_TYPE_COL))\n",
+        "        .alias(\"heating_label\")\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "heating_df = add_heating_label(annual_u0)\n",
+        "heating_stats = (\n",
+        "    heating_df.group_by(\"heating_label\")\n",
+        "    .agg(\n",
+        "        pl.len().alias(\"buildings\"),\n",
+        "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+        "        pl.col(\"heats_with_natgas\").cast(pl.Int32).sum().alias(\"heats_natgas\"),\n",
+        "        pl.col(\"heats_with_oil\").cast(pl.Int32).sum().alias(\"heats_oil\"),\n",
+        "        pl.col(\"heats_with_propane\").cast(pl.Int32).sum().alias(\"heats_propane\"),\n",
+        "        pl.col(\"heats_with_electricity\").cast(pl.Int32).sum().alias(\"heats_electricity\"),\n",
+        "    )\n",
+        "    .with_columns(\n",
+        "        (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n",
+        "    )\n",
+        "    .sort(\"weighted_households\", descending=True)\n",
+        ")\n",
+        "print(\"Baseline heating-type breakdown (upgrade 00):\")\n",
+        "display(heating_stats)\n",
+        "\n",
+        "avail_order = [h for h in HEATING_ORDER if h in heating_df[\"heating_label\"].unique().to_list()]\n",
+        "chart_data = (\n",
+        "    heating_stats.filter(pl.col(\"heating_label\").is_in(avail_order))\n",
+        "    .with_columns(pl.col(\"heating_label\").cast(pl.Enum(avail_order)))\n",
+        "    .sort(\"heating_label\")\n",
+        ")\n",
+        "\n",
+        "# Create pie chart\n",
+        "colors = {\n",
+        "    \"Fossil fuel\": SB_COLORS[\"carrot\"],\n",
+        "    \"Electric resistance\": SB_COLORS[\"saffron\"],\n",
+        "    \"Existing heat pump\": SB_COLORS[\"sky\"],\n",
+        "    \"Other\": \"#CCCCCC\",\n",
+        "}\n",
+        "labels = chart_data[\"heating_label\"].to_list()\n",
+        "sizes = chart_data[\"pct_of_total\"].to_list()\n",
+        "pie_colors = [colors.get(label, \"#CCCCCC\") for label in labels]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8, 6))\n",
+        "result = ax.pie(\n",
+        "    sizes,\n",
+        "    labels=labels,\n",
+        "    colors=pie_colors,\n",
+        "    autopct=\"%.1f%%\",\n",
+        "    textprops={\"fontsize\": 11},\n",
+        "    pctdistance=0.7,\n",
+        ")\n",
+        "autotexts = result[2]  # type: ignore[index-out-of-bounds]\n",
+        "for t in autotexts:\n",
+        "    t.set_color(\"white\")\n",
+        "    t.set_fontweight(\"bold\")\n",
+        "ax.set_title(\"Baseline heating type distribution (upgrade 00)\", fontsize=15, fontweight=\"bold\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gas_bill_stats = (\n",
-    "    annual_u0.filter(pl.col(\"gas_total_bill\") > 0)\n",
-    "    .with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null)\"))\n",
-    "    .group_by(GAS_UTILITY_COL)\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"buildings_with_gas_bill\"),\n",
-    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
-    "        pl.col(\"gas_total_bill\").min().round(0).alias(\"min_gas_bill\"),\n",
-    "        pl.col(\"gas_total_bill\").max().round(0).alias(\"max_gas_bill\"),\n",
-    "        pl.col(\"gas_total_bill\").mean().round(0).alias(\"mean_gas_bill\"),\n",
-    "        pl.col(\"gas_total_bill\").median().round(0).alias(\"median_gas_bill\"),\n",
-    "    )\n",
-    "    .sort(\"buildings_with_gas_bill\", descending=True)\n",
-    "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
-    ")\n",
-    "print(\"Annual gas spending stats by gas utility\\n(buildings with gas_total_bill > 0; unweighted mean/median):\")\n",
-    "display(gas_bill_stats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000015",
-   "metadata": {},
-   "source": [
-    "### 2d: Annual electric bill by electric utility\n",
-    "\n",
-    "Summary of annual total electric bills (`elec_total_bill = fixed + delivery volumetric\n",
-    "+ supply`), grouped by electric utility. The decomposition into fixed charge, delivery,\n",
-    "and supply components helps identify which tariff component is driving outliers.\n",
-    "\n",
-    "Cross-check median values against published tariff rates for a rough sanity check."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "a0000016",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000013",
+      "metadata": {},
+      "source": [
+        "### 2c: Annual gas spending by gas utility\n",
+        "\n",
+        "Summary of annual gas bills (`gas_total_bill`, in dollars) for buildings with non-zero\n",
+        "gas bills, grouped by assigned gas utility.\n",
+        "\n",
+        "> **Note**: `gas_total_bill` is a bill amount (dollars), not volumetric consumption\n",
+        "> (kWh or therms). For actual consumption data, cross-reference the ResStock monthly\n",
+        "> load curves column `out.natural_gas.total.energy_consumption`.\n",
+        "\n",
+        "Implausibly wide min/max ranges, or extreme mean-to-median ratios, often trace to\n",
+        "incorrect tariff calibration or misassigned buildings."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (7, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>min_elec_total</th><th>max_elec_total</th><th>mean_elec_total</th><th>median_elec_total</th><th>median_fixed</th><th>median_delivery</th><th>median_supply</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>266.0</td><td>26915.0</td><td>1597.0</td><td>1251.0</td><td>255.0</td><td>608.0</td><td>388.0</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>259.0</td><td>17161.0</td><td>1679.0</td><td>1408.0</td><td>215.0</td><td>578.0</td><td>615.0</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>212.0</td><td>14787.0</td><td>2090.0</td><td>1900.0</td><td>197.0</td><td>841.0</td><td>858.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>286.0</td><td>19078.0</td><td>2042.0</td><td>1714.0</td><td>239.0</td><td>755.0</td><td>721.0</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>332.0</td><td>11209.0</td><td>1710.0</td><td>1399.0</td><td>288.0</td><td>510.0</td><td>604.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>368.0</td><td>17252.0</td><td>2388.0</td><td>2031.0</td><td>264.0</td><td>1064.0</td><td>702.0</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>349.0</td><td>14370.0</td><td>2045.0</td><td>1872.0</td><td>289.0</td><td>863.0</td><td>716.0</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000014",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Annual gas spending stats by gas utility\n",
+            "(buildings with gas_total_bill > 0; unweighted mean/median):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (9, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gas_utility</th><th>buildings_with_gas_bill</th><th>weighted_households</th><th>min_gas_bill</th><th>max_gas_bill</th><th>mean_gas_bill</th><th>median_gas_bill</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>6991</td><td>1.3887e6</td><td>400.0</td><td>12166.0</td><td>1362.0</td><td>927.0</td></tr><tr><td>&quot;kedny&quot;</td><td>6418</td><td>1.2747e6</td><td>251.0</td><td>7240.0</td><td>1462.0</td><td>1257.0</td></tr><tr><td>&quot;kedli&quot;</td><td>2874</td><td>667484.048047</td><td>302.0</td><td>6189.0</td><td>1792.0</td><td>1756.0</td></tr><tr><td>&quot;nimo&quot;</td><td>2746</td><td>617985.707991</td><td>265.0</td><td>4005.0</td><td>1063.0</td><td>998.0</td></tr><tr><td>&quot;nfg&quot;</td><td>2421</td><td>540904.956926</td><td>238.0</td><td>3337.0</td><td>1007.0</td><td>957.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>1581</td><td>348155.122257</td><td>201.0</td><td>4667.0</td><td>1091.0</td><td>1005.0</td></tr><tr><td>&quot;rge&quot;</td><td>1240</td><td>297109.964156</td><td>249.0</td><td>3639.0</td><td>1164.0</td><td>1098.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>713</td><td>162382.688203</td><td>325.0</td><td>7328.0</td><td>1374.0</td><td>968.0</td></tr><tr><td>&quot;or&quot;</td><td>594</td><td>150773.083876</td><td>278.0</td><td>5743.0</td><td>1658.0</td><td>1449.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (9, 7)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 gas_utility \u2506 buildings_wi \u2506 weighted_ho \u2506 min_gas_bil \u2506 max_gas_bil \u2506 mean_gas_bi \u2506 median_gas_ \u2502\n",
+              "\u2502 ---         \u2506 th_gas_bill  \u2506 useholds    \u2506 l           \u2506 l           \u2506 ll          \u2506 bill        \u2502\n",
+              "\u2502 str         \u2506 ---          \u2506 ---         \u2506 ---         \u2506 ---         \u2506 ---         \u2506 ---         \u2502\n",
+              "\u2502             \u2506 u32          \u2506 f64         \u2506 f64         \u2506 f64         \u2506 f64         \u2506 f64         \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 coned       \u2506 6991         \u2506 1.3887e6    \u2506 400.0       \u2506 12166.0     \u2506 1362.0      \u2506 927.0       \u2502\n",
+              "\u2502 kedny       \u2506 6418         \u2506 1.2747e6    \u2506 251.0       \u2506 7240.0      \u2506 1462.0      \u2506 1257.0      \u2502\n",
+              "\u2502 kedli       \u2506 2874         \u2506 667484.0480 \u2506 302.0       \u2506 6189.0      \u2506 1792.0      \u2506 1756.0      \u2502\n",
+              "\u2502             \u2506              \u2506 47          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 nimo        \u2506 2746         \u2506 617985.7079 \u2506 265.0       \u2506 4005.0      \u2506 1063.0      \u2506 998.0       \u2502\n",
+              "\u2502             \u2506              \u2506 91          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 nfg         \u2506 2421         \u2506 540904.9569 \u2506 238.0       \u2506 3337.0      \u2506 1007.0      \u2506 957.0       \u2502\n",
+              "\u2502             \u2506              \u2506 26          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 nyseg       \u2506 1581         \u2506 348155.1222 \u2506 201.0       \u2506 4667.0      \u2506 1091.0      \u2506 1005.0      \u2502\n",
+              "\u2502             \u2506              \u2506 57          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 rge         \u2506 1240         \u2506 297109.9641 \u2506 249.0       \u2506 3639.0      \u2506 1164.0      \u2506 1098.0      \u2502\n",
+              "\u2502             \u2506              \u2506 56          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 cenhud      \u2506 713          \u2506 162382.6882 \u2506 325.0       \u2506 7328.0      \u2506 1374.0      \u2506 968.0       \u2502\n",
+              "\u2502             \u2506              \u2506 03          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2502 or          \u2506 594          \u2506 150773.0838 \u2506 278.0       \u2506 5743.0      \u2506 1658.0      \u2506 1449.0      \u2502\n",
+              "\u2502             \u2506              \u2506 76          \u2506             \u2506             \u2506             \u2506             \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (7, 10)\n",
-       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-       "│ electric_ ┆ buildings ┆ weighted_ ┆ min_elec_ ┆ … ┆ median_el ┆ median_fi ┆ median_de ┆ median_s │\n",
-       "│ utility   ┆ ---       ┆ household ┆ total     ┆   ┆ ec_total  ┆ xed       ┆ livery    ┆ upply    │\n",
-       "│ ---       ┆ u32       ┆ s         ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
-       "│ str       ┆           ┆ ---       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
-       "│           ┆           ┆ f64       ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-       "│ coned     ┆ 15435     ┆ 3.0640e6  ┆ 266.0     ┆ … ┆ 1251.0    ┆ 255.0     ┆ 608.0     ┆ 388.0    │\n",
-       "│ nimo      ┆ 6791      ┆ 1.532294e ┆ 259.0     ┆ … ┆ 1408.0    ┆ 215.0     ┆ 578.0     ┆ 615.0    │\n",
-       "│           ┆           ┆ 6         ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "│ psegli    ┆ 4427      ┆ 1.0300e6  ┆ 212.0     ┆ … ┆ 1900.0    ┆ 197.0     ┆ 841.0     ┆ 858.0    │\n",
-       "│ nyseg     ┆ 3682      ┆ 792300.0  ┆ 286.0     ┆ … ┆ 1714.0    ┆ 239.0     ┆ 755.0     ┆ 721.0    │\n",
-       "│ rge       ┆ 1444      ┆ 351481.0  ┆ 332.0     ┆ … ┆ 1399.0    ┆ 288.0     ┆ 510.0     ┆ 604.0    │\n",
-       "│ cenhud    ┆ 1182      ┆ 270859.0  ┆ 368.0     ┆ … ┆ 2031.0    ┆ 264.0     ┆ 1064.0    ┆ 702.0    │\n",
-       "│ or        ┆ 829       ┆ 211011.0  ┆ 349.0     ┆ … ┆ 1872.0    ┆ 289.0     ┆ 863.0     ┆ 716.0    │\n",
-       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
+      "source": [
+        "gas_bill_stats = (\n",
+        "    annual_u0.filter(pl.col(\"gas_total_bill\") > 0)\n",
+        "    .with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null)\"))\n",
+        "    .group_by(GAS_UTILITY_COL)\n",
+        "    .agg(\n",
+        "        pl.len().alias(\"buildings_with_gas_bill\"),\n",
+        "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+        "        pl.col(\"gas_total_bill\").min().round(0).alias(\"min_gas_bill\"),\n",
+        "        pl.col(\"gas_total_bill\").max().round(0).alias(\"max_gas_bill\"),\n",
+        "        pl.col(\"gas_total_bill\").mean().round(0).alias(\"mean_gas_bill\"),\n",
+        "        pl.col(\"gas_total_bill\").median().round(0).alias(\"median_gas_bill\"),\n",
+        "    )\n",
+        "    .sort(\"buildings_with_gas_bill\", descending=True)\n",
+        "    .rename({GAS_UTILITY_COL: \"gas_utility\"})\n",
+        ")\n",
+        "print(\"Annual gas spending stats by gas utility\\n(buildings with gas_total_bill > 0; unweighted mean/median):\")\n",
+        "display(gas_bill_stats)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "elec_bill_stats = (\n",
-    "    annual_u0.group_by(UTILITY_COL)\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"buildings\"),\n",
-    "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
-    "        pl.col(\"elec_total_bill\").min().round(0).alias(\"min_elec_total\"),\n",
-    "        pl.col(\"elec_total_bill\").max().round(0).alias(\"max_elec_total\"),\n",
-    "        pl.col(\"elec_total_bill\").mean().round(0).alias(\"mean_elec_total\"),\n",
-    "        pl.col(\"elec_total_bill\").median().round(0).alias(\"median_elec_total\"),\n",
-    "        pl.col(\"elec_fixed_charge\").median().round(0).alias(\"median_fixed\"),\n",
-    "        pl.col(\"elec_delivery_bill\").median().round(0).alias(\"median_delivery\"),\n",
-    "        pl.col(\"elec_supply_bill\").median().round(0).alias(\"median_supply\"),\n",
-    "    )\n",
-    "    .sort(\"buildings\", descending=True)\n",
-    "    .rename({UTILITY_COL: \"electric_utility\"})\n",
-    ")\n",
-    "print(\"Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\")\n",
-    "display(elec_bill_stats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000017",
-   "metadata": {},
-   "source": [
-    "## Section 3: Structural quality checks\n",
-    "\n",
-    "These checks catch common post-processing errors before any analysis proceeds:\n",
-    "\n",
-    "- **Duplicate building IDs** — indicate a failed post-processing merge or join.\n",
-    "- **ID mismatches** — buildings that made it into one table but not the other.\n",
-    "- **Electric component arithmetic**:\n",
-    "  `elec_total_bill ≈ elec_fixed_charge + elec_delivery_bill + elec_supply_bill`\n",
-    "- **Energy total arithmetic**:\n",
-    "  `energy_total_bill ≈ elec_total_bill + gas_total_bill + propane_total_bill + oil_total_bill`\n",
-    "- **BAT identity**:\n",
-    "  `BAT_percustomer_total ≈ annual_bill_total − economic_burden_total − residual_share_total`\n",
-    "\n",
-    "All assertions must pass before continuing. A failed assertion points to a specific\n",
-    "post-processing step to investigate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "a0000018",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0000015",
+      "metadata": {},
+      "source": [
+        "### 2d: Annual electric bill by electric utility\n",
+        "\n",
+        "Summary of annual total electric bills (`elec_total_bill = fixed + delivery volumetric\n",
+        "+ supply`), grouped by electric utility. The decomposition into fixed charge, delivery,\n",
+        "and supply components helps identify which tariff component is driving outliers.\n",
+        "\n",
+        "Cross-check median values against published tariff rates for a rough sanity check."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (2, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>bill rows</th><th>BAT rows</th><th>annual bill bldgs</th><th>BAT bldgs</th><th>duplicate annual IDs</th><th>duplicate BAT IDs</th><th>bill IDs missing from BAT</th><th>BAT IDs missing from bills</th><th>max elec component error</th><th>max energy component error</th><th>max BAT identity error</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.8190e-12</td><td>1.1369e-12</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.7053e-12</td><td>4.6566e-10</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000016",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (7, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>electric_utility</th><th>buildings</th><th>weighted_households</th><th>min_elec_total</th><th>max_elec_total</th><th>mean_elec_total</th><th>median_elec_total</th><th>median_fixed</th><th>median_delivery</th><th>median_supply</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;coned&quot;</td><td>15435</td><td>3.0640e6</td><td>266.0</td><td>26915.0</td><td>1597.0</td><td>1251.0</td><td>255.0</td><td>608.0</td><td>388.0</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1.532294e6</td><td>259.0</td><td>17161.0</td><td>1679.0</td><td>1408.0</td><td>215.0</td><td>578.0</td><td>615.0</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1.0300e6</td><td>212.0</td><td>14787.0</td><td>2090.0</td><td>1900.0</td><td>197.0</td><td>841.0</td><td>858.0</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300.0</td><td>286.0</td><td>19078.0</td><td>2042.0</td><td>1714.0</td><td>239.0</td><td>755.0</td><td>721.0</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481.0</td><td>332.0</td><td>11209.0</td><td>1710.0</td><td>1399.0</td><td>288.0</td><td>510.0</td><td>604.0</td></tr><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859.0</td><td>368.0</td><td>17252.0</td><td>2388.0</td><td>2031.0</td><td>264.0</td><td>1064.0</td><td>702.0</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011.0</td><td>349.0</td><td>14370.0</td><td>2045.0</td><td>1872.0</td><td>289.0</td><td>863.0</td><td>716.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (7, 10)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 electric_ \u2506 buildings \u2506 weighted_ \u2506 min_elec_ \u2506 \u2026 \u2506 median_el \u2506 median_fi \u2506 median_de \u2506 median_s \u2502\n",
+              "\u2502 utility   \u2506 ---       \u2506 household \u2506 total     \u2506   \u2506 ec_total  \u2506 xed       \u2506 livery    \u2506 upply    \u2502\n",
+              "\u2502 ---       \u2506 u32       \u2506 s         \u2506 ---       \u2506   \u2506 ---       \u2506 ---       \u2506 ---       \u2506 ---      \u2502\n",
+              "\u2502 str       \u2506           \u2506 ---       \u2506 f64       \u2506   \u2506 f64       \u2506 f64       \u2506 f64       \u2506 f64      \u2502\n",
+              "\u2502           \u2506           \u2506 f64       \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 coned     \u2506 15435     \u2506 3.0640e6  \u2506 266.0     \u2506 \u2026 \u2506 1251.0    \u2506 255.0     \u2506 608.0     \u2506 388.0    \u2502\n",
+              "\u2502 nimo      \u2506 6791      \u2506 1.532294e \u2506 259.0     \u2506 \u2026 \u2506 1408.0    \u2506 215.0     \u2506 578.0     \u2506 615.0    \u2502\n",
+              "\u2502           \u2506           \u2506 6         \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u2502 psegli    \u2506 4427      \u2506 1.0300e6  \u2506 212.0     \u2506 \u2026 \u2506 1900.0    \u2506 197.0     \u2506 841.0     \u2506 858.0    \u2502\n",
+              "\u2502 nyseg     \u2506 3682      \u2506 792300.0  \u2506 286.0     \u2506 \u2026 \u2506 1714.0    \u2506 239.0     \u2506 755.0     \u2506 721.0    \u2502\n",
+              "\u2502 rge       \u2506 1444      \u2506 351481.0  \u2506 332.0     \u2506 \u2026 \u2506 1399.0    \u2506 288.0     \u2506 510.0     \u2506 604.0    \u2502\n",
+              "\u2502 cenhud    \u2506 1182      \u2506 270859.0  \u2506 368.0     \u2506 \u2026 \u2506 2031.0    \u2506 264.0     \u2506 1064.0    \u2506 702.0    \u2502\n",
+              "\u2502 or        \u2506 829       \u2506 211011.0  \u2506 349.0     \u2506 \u2026 \u2506 1872.0    \u2506 289.0     \u2506 863.0     \u2506 716.0    \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (2, 12)\n",
-       "┌────────────┬───────────┬──────────┬────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
-       "│ scenario   ┆ bill rows ┆ BAT rows ┆ annual ┆ … ┆ BAT IDs    ┆ max elec   ┆ max       ┆ max BAT   │\n",
-       "│ ---        ┆ ---       ┆ ---      ┆ bill   ┆   ┆ missing    ┆ component  ┆ energy    ┆ identity  │\n",
-       "│ str        ┆ i64       ┆ i64      ┆ bldgs  ┆   ┆ from bills ┆ error      ┆ component ┆ error     │\n",
-       "│            ┆           ┆          ┆ ---    ┆   ┆ ---        ┆ ---        ┆ error     ┆ ---       │\n",
-       "│            ┆           ┆          ┆ i64    ┆   ┆ i64        ┆ f64        ┆ ---       ┆ f64       │\n",
-       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ f64       ┆           │\n",
-       "╞════════════╪═══════════╪══════════╪════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
-       "│ Upgrade 00 ┆ 439270    ┆ 33790    ┆ 33790  ┆ … ┆ 0          ┆ 9.0949e-13 ┆ 1.8190e-1 ┆ 1.1369e-1 │\n",
-       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ 2         ┆ 2         │\n",
-       "│ Upgrade 02 ┆ 439270    ┆ 33790    ┆ 33790  ┆ … ┆ 0          ┆ 9.0949e-13 ┆ 1.7053e-1 ┆ 4.6566e-1 │\n",
-       "│            ┆           ┆          ┆        ┆   ┆            ┆            ┆ 2         ┆ 0         │\n",
-       "└────────────┴───────────┴──────────┴────────┴───┴────────────┴────────────┴───────────┴───────────┘"
+      "source": [
+        "elec_bill_stats = (\n",
+        "    annual_u0.group_by(UTILITY_COL)\n",
+        "    .agg(\n",
+        "        pl.len().alias(\"buildings\"),\n",
+        "        pl.col(\"weight\").sum().alias(\"weighted_households\"),\n",
+        "        pl.col(\"elec_total_bill\").min().round(0).alias(\"min_elec_total\"),\n",
+        "        pl.col(\"elec_total_bill\").max().round(0).alias(\"max_elec_total\"),\n",
+        "        pl.col(\"elec_total_bill\").mean().round(0).alias(\"mean_elec_total\"),\n",
+        "        pl.col(\"elec_total_bill\").median().round(0).alias(\"median_elec_total\"),\n",
+        "        pl.col(\"elec_fixed_charge\").median().round(0).alias(\"median_fixed\"),\n",
+        "        pl.col(\"elec_delivery_bill\").median().round(0).alias(\"median_delivery\"),\n",
+        "        pl.col(\"elec_supply_bill\").median().round(0).alias(\"median_supply\"),\n",
+        "    )\n",
+        "    .sort(\"buildings\", descending=True)\n",
+        "    .rename({UTILITY_COL: \"electric_utility\"})\n",
+        ")\n",
+        "print(\"Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\")\n",
+        "display(elec_bill_stats)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All structural checks passed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "qa_rows: list[dict[str, object]] = []\n",
-    "\n",
-    "for scenario in SCENARIO_ORDER:\n",
-    "    bills = bills_by_scenario[scenario]\n",
-    "    bat = bat_by_scenario[scenario]\n",
-    "    annual = bills.filter(pl.col(\"month\") == \"Annual\")\n",
-    "\n",
-    "    elec_err = cast(\n",
-    "        float,\n",
-    "        annual.select(\n",
-    "            (\n",
-    "                pl.col(\"elec_total_bill\")\n",
-    "                - pl.col(\"elec_fixed_charge\")\n",
-    "                - pl.col(\"elec_delivery_bill\")\n",
-    "                - pl.col(\"elec_supply_bill\")\n",
-    "            )\n",
-    "            .abs()\n",
-    "            .max()\n",
-    "        ).item()\n",
-    "        or 0,\n",
-    "    )\n",
-    "    energy_err = cast(\n",
-    "        float,\n",
-    "        annual.select(\n",
-    "            (\n",
-    "                pl.col(\"energy_total_bill\")\n",
-    "                - pl.col(\"elec_total_bill\")\n",
-    "                - pl.col(\"gas_total_bill\")\n",
-    "                - pl.col(\"propane_total_bill\")\n",
-    "                - pl.col(\"oil_total_bill\")\n",
-    "            )\n",
-    "            .abs()\n",
-    "            .max()\n",
-    "        ).item()\n",
-    "        or 0,\n",
-    "    )\n",
-    "    bat_err = cast(\n",
-    "        float,\n",
-    "        bat.select(\n",
-    "            (\n",
-    "                pl.col(\"BAT_percustomer_total\")\n",
-    "                - pl.col(\"annual_bill_total\")\n",
-    "                + pl.col(\"economic_burden_total\")\n",
-    "                + pl.col(\"residual_share_total\")\n",
-    "            )\n",
-    "            .abs()\n",
-    "            .max()\n",
-    "        ).item()\n",
-    "        or 0,\n",
-    "    )\n",
-    "\n",
-    "    qa_rows.append(\n",
-    "        {\n",
-    "            \"scenario\": scenario,\n",
-    "            \"bill rows\": bills.height,\n",
-    "            \"BAT rows\": bat.height,\n",
-    "            \"annual bill bldgs\": annual[BLDG_ID].n_unique(),\n",
-    "            \"BAT bldgs\": bat[BLDG_ID].n_unique(),\n",
-    "            \"duplicate annual IDs\": annual.height - annual[BLDG_ID].n_unique(),\n",
-    "            \"duplicate BAT IDs\": bat.height - bat[BLDG_ID].n_unique(),\n",
-    "            \"bill IDs missing from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
-    "            \"BAT IDs missing from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
-    "            \"max elec component error\": elec_err,\n",
-    "            \"max energy component error\": energy_err,\n",
-    "            \"max BAT identity error\": bat_err,\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "qa = pl.DataFrame(qa_rows)\n",
-    "display(qa)\n",
-    "\n",
-    "assert qa[\"duplicate annual IDs\"].sum() == 0, \"Duplicate annual bill IDs found\"\n",
-    "assert qa[\"duplicate BAT IDs\"].sum() == 0, \"Duplicate BAT IDs found\"\n",
-    "assert qa[\"bill IDs missing from BAT\"].sum() == 0, \"Bill IDs missing from BAT table\"\n",
-    "assert qa[\"BAT IDs missing from bills\"].sum() == 0, \"BAT IDs missing from bills table\"\n",
-    "assert cast(float, qa[\"max elec component error\"].max()) < 0.01, \"Electric component arithmetic mismatch\"\n",
-    "assert cast(float, qa[\"max energy component error\"].max()) < 0.01, \"Energy total arithmetic mismatch\"\n",
-    "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01, \"BAT identity check failed\"\n",
-    "print(\"All structural checks passed.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000019",
-   "metadata": {},
-   "source": [
-    "## Section 4: Baseline analysis (upgrade 00, runs 1+2)\n",
-    "\n",
-    "This section uses the upgrade 00 run pair (runs 1+2), representing the current state:\n",
-    "each building is on its existing heating system with today's electric and gas rates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000020",
-   "metadata": {},
-   "source": [
-    "### 4a: Weighted statistics helpers\n",
-    "\n",
-    "We define weighted mean and weighted quantile here so they are available to all\n",
-    "subsequent cells."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "a0000021",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n",
-    "    \"\"\"Weighted arithmetic mean.\"\"\"\n",
-    "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n",
-    "    wsum = cast(float, valid[weight].sum())\n",
-    "    if valid.is_empty() or wsum == 0:\n",
-    "        return float(\"nan\")\n",
-    "    return cast(float, (valid[value] * valid[weight]).sum()) / wsum\n",
-    "\n",
-    "\n",
-    "def weighted_quantile(\n",
-    "    df: pl.DataFrame,\n",
-    "    value: str,\n",
-    "    q: float,\n",
-    "    weight: str = \"weight\",\n",
-    ") -> float:\n",
-    "    \"\"\"Weighted quantile via cumulative weight sort.\"\"\"\n",
-    "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n",
-    "    wsum = cast(float, valid[weight].sum())\n",
-    "    if valid.is_empty() or wsum == 0:\n",
-    "        return float(\"nan\")\n",
-    "    return float(valid.filter(pl.col(weight).cum_sum() >= wsum * q)[value][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000022",
-   "metadata": {},
-   "source": [
-    "### 4b: Cross-subsidization table\n",
-    "\n",
-    "The **Bill Alignment Test (BAT)** measures whether each customer pays more or less than\n",
-    "their cost of service. A positive BAT means the customer **overpays** (they\n",
-    "cross-subsidize others). A negative BAT means they **underpay** (they receive a\n",
-    "cross-subsidy from others).\n",
-    "\n",
-    "The table below shows each heating-type group's share of customers, electric delivery\n",
-    "revenue, cost of service, and total overpayment. Under flat volumetric rates, heat pump\n",
-    "and electric resistance customers typically overpay because they use more electricity\n",
-    "(and therefore pay more delivery cost per unit) than fossil fuel customers, whose\n",
-    "delivery cost does not scale with their gas consumption.\n",
-    "\n",
-    "Column definitions:\n",
-    "\n",
-    "- **% of delivery revenue** — group's weighted share of annual delivery bill revenue\n",
-    "- **% of cost of service** — group's weighted share of annual cost of service\n",
-    "  (`economic_burden_total + residual_share_total`)\n",
-    "- **Total overpayment** — weighted sum of positive BAT values within the group\n",
-    "- **% of system overpayment** — group's share of the system's total cross-subsidy\n",
-    "- **Mean BAT** — weighted mean BAT per customer (positive = overpaying)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "a0000023",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "a0000017",
+      "metadata": {},
+      "source": [
+        "## Section 3: Structural quality checks\n",
+        "\n",
+        "These checks catch common post-processing errors before any analysis proceeds:\n",
+        "\n",
+        "- **Duplicate building IDs** \u2014 indicate a failed post-processing merge or join.\n",
+        "- **ID mismatches** \u2014 buildings that made it into one table but not the other.\n",
+        "- **Electric component arithmetic**:\n",
+        "  `elec_total_bill \u2248 elec_fixed_charge + elec_delivery_bill + elec_supply_bill`\n",
+        "- **Energy total arithmetic**:\n",
+        "  `energy_total_bill \u2248 elec_total_bill + gas_total_bill + propane_total_bill + oil_total_bill`\n",
+        "- **BAT identity**:\n",
+        "  `BAT_percustomer_total \u2248 annual_bill_total \u2212 economic_burden_total \u2212 residual_share_total`\n",
+        "\n",
+        "All assertions must pass before continuing. A failed assertion points to a specific\n",
+        "post-processing step to investigate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000018",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (2, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>bill rows</th><th>BAT rows</th><th>annual bill bldgs</th><th>BAT bldgs</th><th>duplicate annual IDs</th><th>duplicate BAT IDs</th><th>bill IDs missing from BAT</th><th>BAT IDs missing from bills</th><th>max elec component error</th><th>max energy component error</th><th>max BAT identity error</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.8190e-12</td><td>1.1369e-12</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>439270</td><td>33790</td><td>33790</td><td>33790</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.0949e-13</td><td>1.7053e-12</td><td>4.6566e-10</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (2, 12)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 scenario   \u2506 bill rows \u2506 BAT rows \u2506 annual \u2506 \u2026 \u2506 BAT IDs    \u2506 max elec   \u2506 max       \u2506 max BAT   \u2502\n",
+              "\u2502 ---        \u2506 ---       \u2506 ---      \u2506 bill   \u2506   \u2506 missing    \u2506 component  \u2506 energy    \u2506 identity  \u2502\n",
+              "\u2502 str        \u2506 i64       \u2506 i64      \u2506 bldgs  \u2506   \u2506 from bills \u2506 error      \u2506 component \u2506 error     \u2502\n",
+              "\u2502            \u2506           \u2506          \u2506 ---    \u2506   \u2506 ---        \u2506 ---        \u2506 error     \u2506 ---       \u2502\n",
+              "\u2502            \u2506           \u2506          \u2506 i64    \u2506   \u2506 i64        \u2506 f64        \u2506 ---       \u2506 f64       \u2502\n",
+              "\u2502            \u2506           \u2506          \u2506        \u2506   \u2506            \u2506            \u2506 f64       \u2506           \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Upgrade 00 \u2506 439270    \u2506 33790    \u2506 33790  \u2506 \u2026 \u2506 0          \u2506 9.0949e-13 \u2506 1.8190e-1 \u2506 1.1369e-1 \u2502\n",
+              "\u2502            \u2506           \u2506          \u2506        \u2506   \u2506            \u2506            \u2506 2         \u2506 2         \u2502\n",
+              "\u2502 Upgrade 02 \u2506 439270    \u2506 33790    \u2506 33790  \u2506 \u2026 \u2506 0          \u2506 9.0949e-13 \u2506 1.7053e-1 \u2506 4.6566e-1 \u2502\n",
+              "\u2502            \u2506           \u2506          \u2506        \u2506   \u2506            \u2506            \u2506 2         \u2506 0         \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "All structural checks passed.\n"
+          ]
+        }
+      ],
+      "source": [
+        "qa_rows: list[dict[str, object]] = []\n",
+        "\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    bills = bills_by_scenario[scenario]\n",
+        "    bat = bat_by_scenario[scenario]\n",
+        "    annual = bills.filter(pl.col(\"month\") == \"Annual\")\n",
+        "\n",
+        "    elec_err = cast(\n",
+        "        float,\n",
+        "        annual.select(\n",
+        "            (\n",
+        "                pl.col(\"elec_total_bill\")\n",
+        "                - pl.col(\"elec_fixed_charge\")\n",
+        "                - pl.col(\"elec_delivery_bill\")\n",
+        "                - pl.col(\"elec_supply_bill\")\n",
+        "            )\n",
+        "            .abs()\n",
+        "            .max()\n",
+        "        ).item()\n",
+        "        or 0,\n",
+        "    )\n",
+        "    energy_err = cast(\n",
+        "        float,\n",
+        "        annual.select(\n",
+        "            (\n",
+        "                pl.col(\"energy_total_bill\")\n",
+        "                - pl.col(\"elec_total_bill\")\n",
+        "                - pl.col(\"gas_total_bill\")\n",
+        "                - pl.col(\"propane_total_bill\")\n",
+        "                - pl.col(\"oil_total_bill\")\n",
+        "            )\n",
+        "            .abs()\n",
+        "            .max()\n",
+        "        ).item()\n",
+        "        or 0,\n",
+        "    )\n",
+        "    bat_err = cast(\n",
+        "        float,\n",
+        "        bat.select(\n",
+        "            (\n",
+        "                pl.col(\"BAT_percustomer_total\")\n",
+        "                - pl.col(\"annual_bill_total\")\n",
+        "                + pl.col(\"economic_burden_total\")\n",
+        "                + pl.col(\"residual_share_total\")\n",
+        "            )\n",
+        "            .abs()\n",
+        "            .max()\n",
+        "        ).item()\n",
+        "        or 0,\n",
+        "    )\n",
+        "\n",
+        "    qa_rows.append(\n",
+        "        {\n",
+        "            \"scenario\": scenario,\n",
+        "            \"bill rows\": bills.height,\n",
+        "            \"BAT rows\": bat.height,\n",
+        "            \"annual bill bldgs\": annual[BLDG_ID].n_unique(),\n",
+        "            \"BAT bldgs\": bat[BLDG_ID].n_unique(),\n",
+        "            \"duplicate annual IDs\": annual.height - annual[BLDG_ID].n_unique(),\n",
+        "            \"duplicate BAT IDs\": bat.height - bat[BLDG_ID].n_unique(),\n",
+        "            \"bill IDs missing from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+        "            \"BAT IDs missing from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n",
+        "            \"max elec component error\": elec_err,\n",
+        "            \"max energy component error\": energy_err,\n",
+        "            \"max BAT identity error\": bat_err,\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "qa = pl.DataFrame(qa_rows)\n",
+        "display(qa)\n",
+        "\n",
+        "assert qa[\"duplicate annual IDs\"].sum() == 0, \"Duplicate annual bill IDs found\"\n",
+        "assert qa[\"duplicate BAT IDs\"].sum() == 0, \"Duplicate BAT IDs found\"\n",
+        "assert qa[\"bill IDs missing from BAT\"].sum() == 0, \"Bill IDs missing from BAT table\"\n",
+        "assert qa[\"BAT IDs missing from bills\"].sum() == 0, \"BAT IDs missing from bills table\"\n",
+        "assert cast(float, qa[\"max elec component error\"].max()) < 0.01, \"Electric component arithmetic mismatch\"\n",
+        "assert cast(float, qa[\"max energy component error\"].max()) < 0.01, \"Energy total arithmetic mismatch\"\n",
+        "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01, \"BAT identity check failed\"\n",
+        "print(\"All structural checks passed.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0000019",
+      "metadata": {},
+      "source": [
+        "## Section 4: Baseline analysis (upgrade 00, runs 1+2)\n",
+        "\n",
+        "This section uses the upgrade 00 run pair (runs 1+2), representing the current state:\n",
+        "each building is on its existing heating system with today's electric and gas rates."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0000020",
+      "metadata": {},
+      "source": [
+        "### 4a: Weighted statistics helpers\n",
+        "\n",
+        "We define weighted mean and weighted quantile here so they are available to all\n",
+        "subsequent cells."
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
-      "(Deprecated in version 1.0.0)\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000021",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n",
+        "    \"\"\"Weighted arithmetic mean.\"\"\"\n",
+        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n",
+        "    wsum = cast(float, valid[weight].sum())\n",
+        "    if valid.is_empty() or wsum == 0:\n",
+        "        return float(\"nan\")\n",
+        "    return cast(float, (valid[value] * valid[weight]).sum()) / wsum\n",
+        "\n",
+        "\n",
+        "def weighted_quantile(\n",
+        "    df: pl.DataFrame,\n",
+        "    value: str,\n",
+        "    q: float,\n",
+        "    weight: str = \"weight\",\n",
+        ") -> float:\n",
+        "    \"\"\"Weighted quantile via cumulative weight sort.\"\"\"\n",
+        "    valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n",
+        "    wsum = cast(float, valid[weight].sum())\n",
+        "    if valid.is_empty() or wsum == 0:\n",
+        "        return float(\"nan\")\n",
+        "    return float(valid.filter(pl.col(weight).cum_sum() >= wsum * q)[value][0])"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cross-subsidization by customer group (upgrade 00, annual, weighted):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000022",
+      "metadata": {},
+      "source": [
+        "### 4b: Cross-subsidization table\n",
+        "\n",
+        "The **Bill Alignment Test (BAT)** measures whether each customer pays more or less than\n",
+        "their cost of service. A positive BAT means the customer **overpays** (they\n",
+        "cross-subsidize others). A negative BAT means they **underpay** (they receive a\n",
+        "cross-subsidy from others).\n",
+        "\n",
+        "The table below shows each heating-type group's share of customers, electric delivery\n",
+        "revenue, cost of service, and total overpayment. Under flat volumetric rates, heat pump\n",
+        "and electric resistance customers typically overpay because they use more electricity\n",
+        "(and therefore pay more delivery cost per unit) than fossil fuel customers, whose\n",
+        "delivery cost does not scale with their gas consumption.\n",
+        "\n",
+        "Column definitions:\n",
+        "\n",
+        "- **% of delivery revenue** \u2014 group's weighted share of annual delivery bill revenue\n",
+        "- **% of cost of service** \u2014 group's weighted share of annual cost of service\n",
+        "  (`economic_burden_total + residual_share_total`)\n",
+        "- **Net cross-subsidy** \u2014 sum of weighted BAT for all customers in the group.\n",
+        "  Positive = group overpays (cross-subsidizes others); negative = group underpays\n",
+        "  (receives a cross-subsidy). By definition, the net across all groups sums to ~$0.\n",
+        "- **Mean BAT** \u2014 weighted mean BAT per customer (positive = overpaying)"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (4, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Customer group</th><th>% of customers</th><th>% of delivery revenue</th><th>% of cost of service</th><th>Total overpayment ($M/yr)</th><th>% of system overpayment</th><th>Mean BAT ($/yr)</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;All customers&quot;</td><td>100.0</td><td>100.0</td><td>100.0</td><td>1760.0</td><td>100.0</td><td>-0.0</td></tr><tr><td>&quot;Fossil fuel&quot;</td><td>87.6</td><td>77.0</td><td>83.3</td><td>867.8</td><td>49.3</td><td>-127.0</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>9.0</td><td>17.2</td><td>12.3</td><td>684.9</td><td>38.9</td><td>970.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>2.7</td><td>5.3</td><td>3.9</td><td>205.7</td><td>11.7</td><td>935.0</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000023",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Cross-subsidization by customer group (upgrade 00, annual, weighted):\n",
+            "System-wide check: sum of net cross-subsidy = $13.4M (should be ~$0M)\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+            "(Deprecated in version 1.0.0)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Customer group</th><th>% of customers</th><th>% of delivery revenue</th><th>% of cost of service</th><th>Net cross-subsidy ($M/yr)</th><th>Mean BAT ($/yr)</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>87.6</td><td>77.0</td><td>83.3</td><td>-807.3</td><td>-127.0</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>9.0</td><td>17.2</td><td>12.3</td><td>635.1</td><td>970.0</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>2.7</td><td>5.3</td><td>3.9</td><td>185.6</td><td>935.0</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 6)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 Customer group       \u2506 % of      \u2506 % of delivery \u2506 % of cost of \u2506 Net cross-subsidy   \u2506 Mean BAT \u2502\n",
+              "\u2502 ---                  \u2506 customers \u2506 revenue       \u2506 service      \u2506 ($M/yr)             \u2506 ($/yr)   \u2502\n",
+              "\u2502 str                  \u2506 ---       \u2506 ---           \u2506 ---          \u2506 ---                 \u2506 ---      \u2502\n",
+              "\u2502                      \u2506 f64       \u2506 f64           \u2506 f64          \u2506 f64                 \u2506 f64      \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Fossil fuel          \u2506 87.6      \u2506 77.0          \u2506 83.3         \u2506 -807.3              \u2506 -127.0   \u2502\n",
+              "\u2502 Electric resistance  \u2506 9.0       \u2506 17.2          \u2506 12.3         \u2506 635.1               \u2506 970.0    \u2502\n",
+              "\u2502 Existing heat pump   \u2506 2.7       \u2506 5.3           \u2506 3.9          \u2506 185.6               \u2506 935.0    \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (4, 7)\n",
-       "┌────────────────┬───────────┬──────────┬───────────────┬───────────────┬───────────────┬──────────┐\n",
-       "│ Customer group ┆ % of      ┆ % of     ┆ % of cost of  ┆ Total         ┆ % of system   ┆ Mean BAT │\n",
-       "│ ---            ┆ customers ┆ delivery ┆ service       ┆ overpayment   ┆ overpayment   ┆ ($/yr)   │\n",
-       "│ str            ┆ ---       ┆ revenue  ┆ ---           ┆ ($M/yr)       ┆ ---           ┆ ---      │\n",
-       "│                ┆ f64       ┆ ---      ┆ f64           ┆ ---           ┆ f64           ┆ f64      │\n",
-       "│                ┆           ┆ f64      ┆               ┆ f64           ┆               ┆          │\n",
-       "╞════════════════╪═══════════╪══════════╪═══════════════╪═══════════════╪═══════════════╪══════════╡\n",
-       "│ All customers  ┆ 100.0     ┆ 100.0    ┆ 100.0         ┆ 1760.0        ┆ 100.0         ┆ -0.0     │\n",
-       "│ Fossil fuel    ┆ 87.6      ┆ 77.0     ┆ 83.3          ┆ 867.8         ┆ 49.3          ┆ -127.0   │\n",
-       "│ Electric       ┆ 9.0       ┆ 17.2     ┆ 12.3          ┆ 684.9         ┆ 38.9          ┆ 970.0    │\n",
-       "│ resistance     ┆           ┆          ┆               ┆               ┆               ┆          │\n",
-       "│ Existing heat  ┆ 2.7       ┆ 5.3      ┆ 3.9           ┆ 205.7         ┆ 11.7          ┆ 935.0    │\n",
-       "│ pump           ┆           ┆          ┆               ┆               ┆               ┆          │\n",
-       "└────────────────┴───────────┴──────────┴───────────────┴───────────────┴───────────────┴──────────┘"
+      "source": [
+        "bat_u0 = add_heating_label(bat_by_scenario[\"Upgrade 00\"]).with_columns(\n",
+        "    (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service\"),\n",
+        ")\n",
+        "\n",
+        "_total_customers = cast(float, bat_u0[\"weight\"].sum())\n",
+        "_total_revenue = cast(float, (bat_u0[\"annual_bill_total\"] * bat_u0[\"weight\"]).sum())\n",
+        "_total_cos = cast(float, (bat_u0[\"cost_of_service\"] * bat_u0[\"weight\"]).sum())\n",
+        "\n",
+        "_avail_groups = [h for h in HEATING_ORDER if h in bat_u0[\"heating_label\"].unique().to_list()]\n",
+        "_groups: dict[str, pl.DataFrame] = {label: bat_u0.filter(pl.col(\"heating_label\") == label) for label in _avail_groups}\n",
+        "\n",
+        "cs_rows: list[dict[str, object]] = []\n",
+        "for name, gdf in _groups.items():\n",
+        "    g_cust = cast(float, gdf[\"weight\"].sum())\n",
+        "    g_rev = cast(float, (gdf[\"annual_bill_total\"] * gdf[\"weight\"]).sum())\n",
+        "    g_cos = cast(float, (gdf[\"cost_of_service\"] * gdf[\"weight\"]).sum())\n",
+        "\n",
+        "    # Net cross-subsidy: sum of weighted BAT for the group.\n",
+        "    # Positive = group overpays (cross-subsidizes others).\n",
+        "    # Negative = group underpays (receives a cross-subsidy).\n",
+        "    g_net = cast(float, (gdf[\"BAT_percustomer_total\"] * gdf[\"weight\"]).sum())\n",
+        "\n",
+        "    cs_rows.append(\n",
+        "        {\n",
+        "            \"Customer group\": name,\n",
+        "            \"% of customers\": round(g_cust / _total_customers * 100, 1),\n",
+        "            \"% of delivery revenue\": round(g_rev / _total_revenue * 100, 1),\n",
+        "            \"% of cost of service\": round(g_cos / _total_cos * 100, 1),\n",
+        "            \"Net cross-subsidy ($M/yr)\": round(g_net / 1e6, 1),\n",
+        "            \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "cs_table = pl.DataFrame(cs_rows)\n",
+        "print(\"Cross-subsidization by customer group (upgrade 00, annual, weighted):\")\n",
+        "print(\n",
+        "    f\"System-wide check: sum of net cross-subsidy = ${sum(r['Net cross-subsidy ($M/yr)'] for r in cs_rows):.1f}M (should be ~$0M)\"\n",
+        ")\n",
+        "display(cs_table)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bat_u0 = add_heating_label(bat_by_scenario[\"Upgrade 00\"]).with_columns(\n",
-    "    (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service\"),\n",
-    ")\n",
-    "\n",
-    "_total_customers = cast(float, bat_u0[\"weight\"].sum())\n",
-    "_total_revenue = cast(float, (bat_u0[\"annual_bill_total\"] * bat_u0[\"weight\"]).sum())\n",
-    "_total_cos = cast(float, (bat_u0[\"cost_of_service\"] * bat_u0[\"weight\"]).sum())\n",
-    "_total_overpay = cast(\n",
-    "    float,\n",
-    "    bat_u0.filter(pl.col(\"BAT_percustomer_total\") > 0)\n",
-    "    .select((pl.col(\"BAT_percustomer_total\") * pl.col(\"weight\")).sum())\n",
-    "    .item()\n",
-    "    or 0,\n",
-    ")\n",
-    "\n",
-    "_avail_groups = [h for h in HEATING_ORDER if h in bat_u0[\"heating_label\"].unique().to_list()]\n",
-    "_groups: dict[str, pl.DataFrame] = {\"All customers\": bat_u0} | {\n",
-    "    label: bat_u0.filter(pl.col(\"heating_label\") == label) for label in _avail_groups\n",
-    "}\n",
-    "\n",
-    "cs_rows: list[dict[str, object]] = []\n",
-    "for name, gdf in _groups.items():\n",
-    "    gdf_over = gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)\n",
-    "    g_cust = cast(float, gdf[\"weight\"].sum())\n",
-    "    g_rev = cast(float, (gdf[\"annual_bill_total\"] * gdf[\"weight\"]).sum())\n",
-    "    g_cos = cast(float, (gdf[\"cost_of_service\"] * gdf[\"weight\"]).sum())\n",
-    "    g_over = cast(\n",
-    "        float,\n",
-    "        gdf_over.select((pl.col(\"BAT_percustomer_total\") * pl.col(\"weight\")).sum()).item() or 0,\n",
-    "    )\n",
-    "    cs_rows.append(\n",
-    "        {\n",
-    "            \"Customer group\": name,\n",
-    "            \"% of customers\": round(g_cust / _total_customers * 100, 1),\n",
-    "            \"% of delivery revenue\": round(g_rev / _total_revenue * 100, 1),\n",
-    "            \"% of cost of service\": round(g_cos / _total_cos * 100, 1),\n",
-    "            \"Total overpayment ($M/yr)\": round(g_over / 1e6, 1),\n",
-    "            \"% of system overpayment\": (round(g_over / _total_overpay * 100, 1) if _total_overpay else float(\"nan\")),\n",
-    "            \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "cs_table = pl.DataFrame(cs_rows)\n",
-    "print(\"Cross-subsidization by customer group (upgrade 00, annual, weighted):\")\n",
-    "display(cs_table)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000024",
-   "metadata": {},
-   "source": [
-    "### 4c: Delivery BAT by heating type (upgrade 00 vs. upgrade 02)\n",
-    "\n",
-    "The grouped bar chart shows weighted mean total BAT across both upgrade scenarios.\n",
-    "Under upgrade 00 (current rates, baseline HVAC), the direction and magnitude of the\n",
-    "BAT for each heating group set the baseline. Under upgrade 02 (same rates, all\n",
-    "buildings now have heat pumps), the BAT distribution shifts as load profiles change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "a0000025",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Weighted mean total BAT ($/yr) by heating type and scenario:\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "a0000023b",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Per-customer cross-subsidization by fuel type (upgrade 00, annual, weighted):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (5, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Customer group</th><th>Weighted households</th><th>% of customers</th><th>Mean annual bill ($/yr)</th><th>Mean cost of service ($/yr)</th><th>Mean BAT ($/yr)</th><th>Median BAT ($/yr)</th><th>% overpaying</th></tr><tr><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Natural gas&quot;</td><td>4321804</td><td>59.6</td><td>1496.0</td><td>1642.0</td><td>-146.0</td><td>-236.0</td><td>29.3</td></tr><tr><td>&quot;Oil&quot;</td><td>1525306</td><td>21.0</td><td>1736.0</td><td>1816.0</td><td>-80.0</td><td>-180.0</td><td>35.5</td></tr><tr><td>&quot;Propane&quot;</td><td>282199</td><td>3.9</td><td>1716.0</td><td>1798.0</td><td>-81.0</td><td>-128.0</td><td>39.1</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>654504</td><td>9.0</td><td>3404.0</td><td>2433.0</td><td>970.0</td><td>488.0</td><td>73.6</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>198368</td><td>2.7</td><td>3450.0</td><td>2514.0</td><td>935.0</td><td>466.0</td><td>67.9</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (5, 8)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 Customer    \u2506 Weighted    \u2506 % of        \u2506 Mean   \u2506 Mean cost  \u2506 Mean BAT   \u2506 Median \u2506 %          \u2502\n",
+              "\u2502 group       \u2506 households  \u2506 customers   \u2506 annual \u2506 of service \u2506 ($/yr)     \u2506 BAT    \u2506 overpaying \u2502\n",
+              "\u2502 ---         \u2506 ---         \u2506 ---         \u2506 bill   \u2506 ($/yr)     \u2506 ---        \u2506 ($/yr) \u2506 ---        \u2502\n",
+              "\u2502 str         \u2506 i64         \u2506 f64         \u2506 ($/yr) \u2506 ---        \u2506 f64        \u2506 ---    \u2506 f64        \u2502\n",
+              "\u2502             \u2506             \u2506             \u2506 ---    \u2506 f64        \u2506            \u2506 f64    \u2506            \u2502\n",
+              "\u2502             \u2506             \u2506             \u2506 f64    \u2506            \u2506            \u2506        \u2506            \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Natural gas \u2506 4321804     \u2506 59.6        \u2506 1496.0 \u2506 1642.0     \u2506 -146.0     \u2506 -236.0 \u2506 29.3       \u2502\n",
+              "\u2502 Oil         \u2506 1525306     \u2506 21.0        \u2506 1736.0 \u2506 1816.0     \u2506 -80.0      \u2506 -180.0 \u2506 35.5       \u2502\n",
+              "\u2502 Propane     \u2506 282199      \u2506 3.9         \u2506 1716.0 \u2506 1798.0     \u2506 -81.0      \u2506 -128.0 \u2506 39.1       \u2502\n",
+              "\u2502 Electric    \u2506 654504      \u2506 9.0         \u2506 3404.0 \u2506 2433.0     \u2506 970.0      \u2506 488.0  \u2506 73.6       \u2502\n",
+              "\u2502 resistance  \u2506             \u2506             \u2506        \u2506            \u2506            \u2506        \u2506            \u2502\n",
+              "\u2502 Existing    \u2506 198368      \u2506 2.7         \u2506 3450.0 \u2506 2514.0     \u2506 935.0      \u2506 466.0  \u2506 67.9       \u2502\n",
+              "\u2502 heat pump   \u2506             \u2506             \u2506        \u2506            \u2506            \u2506        \u2506            \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Per-customer cross-subsidy table, with fossil fuel broken out by fuel type\n",
+        "FUEL_GROUPS: list[tuple[str, pl.Expr]] = [\n",
+        "    (\"Natural gas\", pl.col(\"heats_with_natgas\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n",
+        "    (\"Oil\", pl.col(\"heats_with_oil\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n",
+        "    (\"Propane\", pl.col(\"heats_with_propane\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n",
+        "    (\"Electric resistance\", pl.col(\"heating_label\") == \"Electric resistance\"),\n",
+        "    (\"Existing heat pump\", pl.col(\"heating_label\") == \"Existing heat pump\"),\n",
+        "]\n",
+        "\n",
+        "per_cust_rows: list[dict[str, object]] = []\n",
+        "for name, filt_expr in FUEL_GROUPS:\n",
+        "    gdf = bat_u0.filter(filt_expr)\n",
+        "    if gdf.is_empty():\n",
+        "        continue\n",
+        "    g_cust = cast(float, gdf[\"weight\"].sum())\n",
+        "\n",
+        "    per_cust_rows.append(\n",
+        "        {\n",
+        "            \"Customer group\": name,\n",
+        "            \"Weighted households\": round(g_cust),\n",
+        "            \"% of customers\": round(g_cust / _total_customers * 100, 1),\n",
+        "            \"Mean annual bill ($/yr)\": round(weighted_mean(gdf, \"annual_bill_total\"), 0),\n",
+        "            \"Mean cost of service ($/yr)\": round(weighted_mean(gdf, \"cost_of_service\"), 0),\n",
+        "            \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n",
+        "            \"Median BAT ($/yr)\": round(weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5), 0),\n",
+        "            \"% overpaying\": round(\n",
+        "                cast(float, gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum()) / g_cust * 100, 1\n",
+        "            ),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "per_cust_table = pl.DataFrame(per_cust_rows)\n",
+        "print(\"Per-customer cross-subsidization by fuel type (upgrade 00, annual, weighted):\")\n",
+        "display(per_cust_table)"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
-      "(Deprecated in version 1.0.0)\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000024",
+      "metadata": {},
+      "source": [
+        "### 4c: Delivery BAT by heating type (upgrade 00 vs. upgrade 02)\n",
+        "\n",
+        "The grouped bar chart shows weighted mean total BAT across both upgrade scenarios.\n",
+        "Under upgrade 00 (current rates, baseline HVAC), the direction and magnitude of the\n",
+        "BAT for each heating group set the baseline. Under upgrade 02 (same rates, all\n",
+        "buildings now have heat pumps), the BAT distribution shifts as load profiles change."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (6, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>scenario</th><th>heating_label</th><th>mean_bat_total</th><th>mean_bat_delivery</th><th>mean_bat_supply</th><th>median_bat_total</th><th>pct_overpaying</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Fossil fuel&quot;</td><td>-127.147981</td><td>-114.601156</td><td>-12.546825</td><td>-216.614061</td><td>31.387175</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Electric resistance&quot;</td><td>970.407277</td><td>872.593116</td><td>97.814161</td><td>488.418388</td><td>73.5572</td></tr><tr><td>&quot;Upgrade 00&quot;</td><td>&quot;Existing heat pump&quot;</td><td>935.496001</td><td>854.704502</td><td>80.7915</td><td>465.815976</td><td>67.941825</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Fossil fuel&quot;</td><td>-965993.860424</td><td>-967161.62557</td><td>1167.765146</td><td>-649682.46791</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Electric resistance&quot;</td><td>-930564.235766</td><td>-931658.265235</td><td>1094.02947</td><td>-649856.981097</td><td>0.0</td></tr><tr><td>&quot;Upgrade 02&quot;</td><td>&quot;Existing heat pump&quot;</td><td>-1.1065e6</td><td>-1.1077e6</td><td>1165.278772</td><td>-650202.31266</td><td>0.0</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": 47,
+      "id": "a0000025",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Weighted mean total BAT ($/yr) by heating type (Upgrade 00 baseline only):\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+            "(Deprecated in version 1.0.0)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>heating_label</th><th>mean_bat_total</th><th>mean_bat_delivery</th><th>mean_bat_supply</th><th>median_bat_total</th><th>pct_overpaying</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>-127.147981</td><td>-114.601156</td><td>-12.546825</td><td>-216.614061</td><td>31.387175</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>970.407277</td><td>872.593116</td><td>97.814161</td><td>488.418388</td><td>73.5572</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>935.496001</td><td>854.704502</td><td>80.7915</td><td>465.815976</td><td>67.941825</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 6)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 heating_label  \u2506 mean_bat_total \u2506 mean_bat_deliv \u2506 mean_bat_supp \u2506 median_bat_to \u2506 pct_overpayin \u2502\n",
+              "\u2502 ---            \u2506 ---            \u2506 ery            \u2506 ly            \u2506 tal           \u2506 g             \u2502\n",
+              "\u2502 str            \u2506 f64            \u2506 ---            \u2506 ---           \u2506 ---           \u2506 ---           \u2502\n",
+              "\u2502                \u2506                \u2506 f64            \u2506 f64           \u2506 f64           \u2506 f64           \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Fossil fuel    \u2506 -127.147981    \u2506 -114.601156    \u2506 -12.546825    \u2506 -216.614061   \u2506 31.387175     \u2502\n",
+              "\u2502 Electric       \u2506 970.407277     \u2506 872.593116     \u2506 97.814161     \u2506 488.418388    \u2506 73.5572       \u2502\n",
+              "\u2502 resistance     \u2506                \u2506                \u2506               \u2506               \u2506               \u2502\n",
+              "\u2502 Existing heat  \u2506 935.496001     \u2506 854.704502     \u2506 80.7915       \u2506 465.815976    \u2506 67.941825     \u2502\n",
+              "\u2502 pump           \u2506                \u2506                \u2506               \u2506               \u2506               \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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iRQuT7CAJCQlau3atli9frvT0dN24cUMLFy7UokWLTOr773//a3wuXbq0PvzwQ/n5+ZnUdeXKFY0fP97I4hEYGKiMjAyjzLBhw/Tiiy8a5WNjY02mNRg2bJjatWtnctwyZcoU7kszIzU1VRUqVNC4ceP07LPPGu1JSkrSggULTKa0+OKLL9SpU6ccdezatcskg0mnTp307rvvGlN+pKWlyd/fX++9957S09O1d+9eSZn/7po2bSpJqlChQoHa265dO33zzTcm60aPHm1k4/Dz89Prr7+eY7+s+93Pz0+enp5GVpht27ZpzJgxOQJismTPziBJAwYMyLN9We2oVq2a/v3vf6tZs2bGPX/jxg2tWLHCJPhl9+7dCggIUPfu3U3qKe57FAAAAAAKiwwNAAAAAABk895772nq1Km5TiVRrlw5DRs2TMOGDdPw4cM1bNgwk0H0rDJjx441eRv+9OnTOerKGsyUpObNm2vAgAEmwQxS5gDisGHDjOW0tDRFRkaalPnqq6+Mt6s7dOig2bNn5whmkKQaNWpoxowZ6tChg7Huhx9+yPXN7AfF19dXc+fONQlmkKQ2bdpo8uTJJut+//33HPv/+eefxoB8+fLltXz5cnXo0MHsdXjhhRc0e/ZsY93Zs2cVFBRULOfRo0cPTZo0yQhmkDKzTnTr1k3//Oc/TcpmDZ5nFxAQoNjYWEmSm5ubli5dqpYtW+aY6qRixYoaOnSoxowZY1LfvfeBl5eXcW9OnjxZ7dq1y1GXq6urRowYYSwnJyfr0qVLxrK7u7saNGhg/Fe3bl2T/atVq2ay3VyZopo7d66ee+45kzY7ODhoypQpatCggbHu9OnTZqdeOXHihPG5SpUqmjZtmhHMIGVm0ujdu7deeuklk/06dOhgnIu7u3uB2pqVoSD7f9n/7Zrbnr1MqVKlTIISUlNTtW7dOrPHSkhI0NatW43lxo0bq1GjRvm2sWbNmlq1apV8fX1NAnjs7e01evRoTZs2zaT8l19+mSMjSnHfowAAAABQWGRoAAAAAAD8pbm4uGjevHlmt2VkZOjq1asKDw/XTz/9ZKRoDwgIkCRNnz49xz5t2rRRmzZt8j2unZ2dnJ2ddeXKFUlSdHS02bZlTXWRnp5u8qZ8dt27d9ezzz5rLGd/I/7u3bvas2ePsVy/fn2dPHkyz7Y1aNBAO3fulJT5xn54eLhq1qyZ7zkVl7ffftvseUpS27Zt5e7ublyLM2fO5CizY8cO47OXl5fi4+MVHx+f6/EqV64sW1tbI2X+n3/+qZYtW97PKUjKPI/c9O3bVwsWLDACY8ydR9Y1kDIDVy5evJjn8WrUqGGy/Mcff+j55583lrN/zouHh4fJcnR0dIEH8h8UPz8/NW/e3Oy2UqVK6cUXXzQZgD979qxcXFxMymX9W5Kkhg0bys7Ozmx9zZs31/fff3/fbb5f3bp109KlS5WQkCBJWrt2rQYPHmwSCCVJ/v7+JtM93DtdRW7GjBljEmxj7vj+/v46ePCgpMwMLufPn1edOnWMMsV9jwIAAABAYRHQAAAAAAD4S7OxsTF5uzs3L774oiZNmqRdu3ZJygxq6Nevnxo3bpyjbHp6un777Tdt375d4eHhCg8PN5mGoqAaNWqk/fv3S5IOHTqkOXPm6OWXX1aNGjVM3qi2trbOMciZ5dSpU7p586axvGTJEi1ZsqRQ7YiMjCyxgAZra+s83+i3srKSp6enEdCQmJiYo8zhw4eNz0ePHtXgwYML1Yasuu+Hm5tbrtMDSJlTPri7uys8PFxSzvPIyMjQoUOHjOWdO3eaDB4XhLnzuH37ttavX6+goCCFh4fr4sWLSk1NLVS9D0P9+vXz3H7vQLm5+8LR0dH4fO3atVzrujf4Jfv0LSXJzs5OL7zwglasWCFJunz5svbu3aunnnrKKJOenq6ffvrJWHZ3dzfZnht3d/d8A6+srKz04osvGgENkhQcHGwENDyoexQAAAAACoOABgAAAAAAJNna2mrixIn6448/dPv2bUnSjz/+mCOg4c6dO5owYYLZKQQKa9CgQdqyZYsx6Ld27VqtXbtWdnZ2cnNzk4uLi9zd3dW4cWM1b95cbm5uOeqIi4u773YkJSXddx0F5ezsbJJhwpxKlSoZn81N/XG/53z9+vX72l+Sqlatmm+ZypUrGwEN955HcnKycZ8V1b3XLSoqSm+99ZbJVCaPivy+z+z3hGT+vnjiiSe0ZcsWSdKxY8cUHBysZs2amZRJSUnR6tWrjeUqVarkmcXgQXv++ef19ddf686dO5KkNWvWmAQsBAYGmgQF9O/fP9fgpuxq165doOPfWy77VB4P4h4FAAAAgMIqlX8RAAAAAAD+N1SqVMkk3bq5aQK++OKLPIMZrK2t5erqqoYNG8rGxibP49nb2+uTTz7JMf3B7du3FRYWpj///FM///yzpk2bpl69emnevHk5MkEUJTPEvcqXL3/fdRRU9swTucltOoos93vOxXG++bUxP8V93dLT0zV9+vQ8gxns7Ozk4eGRbzaEhyG/+6Ig33eXLl3k5eUlKfP7eOutt7R48WL99ttvCgoK0k8//aT+/fvr7Nmzxj7PPvvsfV/L+1G5cmV17drVWP7jjz9MruGaNWuMzw4ODurZs2eB6nV1dS1QuXsDSbIHIDxq/28BAAAA8NdEhgYAAAAAALJxcXExPkdHR5tsi4mJ0ZdffmksW1tbq0+fPmrVqpWqVasmFxcXVahQwRic7d69uy5fvpzn8Tw8PPTJJ5/o6NGjOnz4sEJDQxUTE6OrV68qJibGeBM9PT1dP/zwg65cuaK5c+cag7DZ0+xL0rx589S+ffsin/+jwMHBQQkJCZKkp556SvPnz3+4DSqCe69b//79NXbs2CLXt2PHDgUHBxvLFSpUUP/+/dWgQQO5ubmpSpUqKl++vKysrBQVFaVevXoV+ViWqkyZMlq4cKHefPNNRUREKCUlRStXrtTKlSvNlndzc9OQIUNKtpFmDBgwQOvWrTOW16xZo7FjxyoyMlKBgYHG+r59+6pcuXIFqjO//+9kuff/cdnvy+K+RwEAAACgKAhoAAAAAAAgm8jISOPzvYOHp06dUlpamrE8fvx4Pf/882brSU1NVWxsbIGOaWVlpSZNmqhJkyYm6+/cuaMjR45owYIFRraInTt36sCBA0ZWh3vfxL5w4UKBjvkoc3V1NQIaHtXzLVu2rBwdHY3pL+73PI4fP258tra21vLly02yjWR37yD2X0VUVJQWLlxoMkVDbpo3b64pU6aoQoUKJdCyvNWqVUtt27bV77//Lkny9/fXiBEjtHbtWmVkZEjKvKYvvvhigesMCwsrULnz58+bLGcP6CruexQAAAAAioKABgAAAAAA/r9Lly6ZDAR6eHiYbL93QO+ZZ57Jta5z586ZBD8UhY2NjVq0aKH33ntPL7zwgrH++PHjRkCDl5eXKlSooMTEREnS77//rldffTXPNPqhoaG6evWqpMxgirZt2z7UtPuF1axZM506dUqSdPHiRV24cEE1a9bMtXxcXJxOnDhhLHt7e5sM3D4szZo1065duyRJx44dU0JCgipWrJhr+aioKJMB6MaNGxsD8tnvTS8vr1yDGSQZ391fSUJCgoYMGaJr166Z3W5jYyNPT0/VrFlTnTt3VseOHS3qnh84cKAR0HDjxg2tX79eGzZsMLZ36dKlwNNISJn3SmBgoNq0aZNrmYyMDK1evdpkXdOmTU2Wi/MeBQAAAICiIKABAAAAAABlzhc/c+ZMpaamGuvuHQx0dnY2Wb58+bIcHBxy1JWRkaGPPvooz+Pt27dPCxcuNJbHjBmj1q1bmy177zHi4+ONz6VKldLTTz+tn3/+WZJ06NAhbd68WV27djVbV1RUlF5//XXdvn1bktS+fXv5+fnl2daUlJQ8t5e0Ll266Pvvv5eUORXH3Llz9fHHHxtTfWSXnp6uWbNmae/evZIyB7b9/f1LtL256dy5szFYfOPGDX300UeaMmWK2bKpqakaP368Tp8+LUmqXLmyyYB39nsza6qS0qVzPvZJSEjQN998U+Q237hxo8j7Pkjbtm0zghnq1q2r999/X9WrV5e1tfVDaU9hvydfX1/Vq1fPuL7/+c9/dOfOHWP7gAEDCt2GBQsWqHHjxrK3tze7PSAgwGSakpo1a8rLy8ukTHHeowAAAABQFDn/0gcAAAAA4C/kzp07Cg0NNftfSEiIdu/erS+//FLPP/+8Dhw4YOxXvnx59evXz6SuevXqmSzPmDHDZIoKKTPI4Z133tGff/5psv7s2bMKCQkxpqFwcnLS+fPnjf+WLl1qdhA0IyNDn3/+ucm6WrVqmSwPHjxYZcqUMZanT5+u5cuXG0ELWfUEBQXpjTfeMFk/aNCgHMe89831lStX6siRIwoNDdXly5dzlC9pjRo1MjJUSNL+/fs1fPhwRUREmJSLiYnR5MmTjWAGSerWrZsqV65cYm3NS6dOnVSjRg1jef369ZowYYLi4uJMykVEROjtt982Bool6aWXXpKtra2xnP3eTEhI0OzZs5WUlGRSz5EjRzRixAgjO0eWo0ePKjQ0VMnJyTna6OjoaLL8ww8/aOfOnQoJCTH+HWVNh/IwlS1b1vickJCgkJAQnThxwuy/+5MnT+ratWvGdA7FJft39fvvv2vt2rU6duyYybGzBylkZ2VlpYEDBxrL2cv5+vrKx8en0O25cOGCBgwYoODgYKWnpxvrk5OTtXjxYk2bNs2k/JAhQ3L82y/OexQAAAAAioIMDQAAAACAv7SYmBgNHjy40PtNmTIlR2r1mjVrqnbt2kZK9RMnTqhv377y8vJSxYoVdenSJUVHRxuDh+7u7oqKipIk7dy5Uzt37tSwYcP0xhtvyNvbW3Xr1jUGg0NCQtS7d289/fTTql69uuzs7BQTE6Ndu3aZpHCvWLGiOnfubNIuDw8PjR07VnPmzJEkpaWlacWKFfriiy9Uo0YNVapUSVFRUYqOjjb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+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x736fa0c66890>"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {
+            "image/png": {
+              "height": 450,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "shape: (6, 7)\n",
-       "┌────────────┬──────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-       "│ scenario   ┆ heating_labe ┆ mean_bat_tot ┆ mean_bat_de ┆ mean_bat_su ┆ median_bat_ ┆ pct_overpay │\n",
-       "│ ---        ┆ l            ┆ al           ┆ livery      ┆ pply        ┆ total       ┆ ing         │\n",
-       "│ str        ┆ ---          ┆ ---          ┆ ---         ┆ ---         ┆ ---         ┆ ---         │\n",
-       "│            ┆ str          ┆ f64          ┆ f64         ┆ f64         ┆ f64         ┆ f64         │\n",
-       "╞════════════╪══════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-       "│ Upgrade 00 ┆ Fossil fuel  ┆ -127.147981  ┆ -114.601156 ┆ -12.546825  ┆ -216.614061 ┆ 31.387175   │\n",
-       "│ Upgrade 00 ┆ Electric     ┆ 970.407277   ┆ 872.593116  ┆ 97.814161   ┆ 488.418388  ┆ 73.5572     │\n",
-       "│            ┆ resistance   ┆              ┆             ┆             ┆             ┆             │\n",
-       "│ Upgrade 00 ┆ Existing     ┆ 935.496001   ┆ 854.704502  ┆ 80.7915     ┆ 465.815976  ┆ 67.941825   │\n",
-       "│            ┆ heat pump    ┆              ┆             ┆             ┆             ┆             │\n",
-       "│ Upgrade 02 ┆ Fossil fuel  ┆ -965993.8604 ┆ -967161.625 ┆ 1167.765146 ┆ -649682.467 ┆ 0.0         │\n",
-       "│            ┆              ┆ 24           ┆ 57          ┆             ┆ 91          ┆             │\n",
-       "│ Upgrade 02 ┆ Electric     ┆ -930564.2357 ┆ -931658.265 ┆ 1094.02947  ┆ -649856.981 ┆ 0.0         │\n",
-       "│            ┆ resistance   ┆ 66           ┆ 235         ┆             ┆ 097         ┆             │\n",
-       "│ Upgrade 02 ┆ Existing     ┆ -1.1065e6    ┆ -1.1077e6   ┆ 1165.278772 ┆ -650202.312 ┆ 0.0         │\n",
-       "│            ┆ heat pump    ┆              ┆             ┆             ┆ 66          ┆             │\n",
-       "└────────────┴──────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+      "source": [
+        "bat_summary_rows: list[dict[str, object]] = []\n",
+        "# Only analyze BAT for baseline (Upgrade 00) - cross-subsidization only applies to current rates\n",
+        "bat_s = add_heating_label(bat_by_scenario[\"Upgrade 00\"])\n",
+        "avail = [h for h in HEATING_ORDER if h in bat_s[\"heating_label\"].unique().to_list()]\n",
+        "for group in avail:\n",
+        "    gdf = bat_s.filter(pl.col(\"heating_label\") == group)\n",
+        "    total_w = cast(float, gdf[\"weight\"].sum())\n",
+        "    bat_summary_rows.append(\n",
+        "        {\n",
+        "            \"heating_label\": group,\n",
+        "            \"mean_bat_total\": weighted_mean(gdf, \"BAT_percustomer_total\"),\n",
+        "            \"mean_bat_delivery\": weighted_mean(gdf, \"BAT_percustomer_delivery\"),\n",
+        "            \"mean_bat_supply\": weighted_mean(gdf, \"BAT_percustomer_supply\"),\n",
+        "            \"median_bat_total\": weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5),\n",
+        "            \"pct_overpaying\": (\n",
+        "                cast(\n",
+        "                    float,\n",
+        "                    gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
+        "                )\n",
+        "                / total_w\n",
+        "                * 100\n",
+        "            ),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "bat_summary = pl.DataFrame(bat_summary_rows)\n",
+        "print(\"Weighted mean total BAT ($/yr) by heating type (Upgrade 00 baseline only):\")\n",
+        "display(\n",
+        "    bat_summary.select(\n",
+        "        \"heating_label\",\n",
+        "        \"mean_bat_total\",\n",
+        "        \"mean_bat_delivery\",\n",
+        "        \"mean_bat_supply\",\n",
+        "        \"median_bat_total\",\n",
+        "        \"pct_overpaying\",\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "avail_bat = [h for h in HEATING_ORDER if h in bat_summary[\"heating_label\"].unique().to_list()]\n",
+        "bat_plot_data = bat_summary.with_columns(\n",
+        "    pl.col(\"heating_label\").cast(pl.Enum(avail_bat)),\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(bat_plot_data, aes(x=\"heating_label\", y=\"mean_bat_total\"))\n",
+        "    + geom_col(fill=SB_COLORS[\"sky\"], width=0.6)\n",
+        "    + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Baseline heating type\",\n",
+        "        y=\"Weighted mean total BAT ($/year)\",\n",
+        "        title=\"Mean total BAT by baseline heating type (Upgrade 00)\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, 4.5))\n",
+        ")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x70812ad8e6d0>"
+      "cell_type": "markdown",
+      "id": "a0000026",
+      "metadata": {},
+      "source": [
+        "### 4d: Monthly bill pattern\n",
+        "\n",
+        "Weighted-mean household energy bills by month, before (upgrade 00) and after (upgrade 02)\n",
+        "the heat pump retrofit. Winter months show the largest divergence: gas-heated homes have\n",
+        "high gas bills under upgrade 00, while after the retrofit (upgrade 02) gas bills\n",
+        "effectively drop to zero and electric bills increase due to heat pump load."
       ]
-     },
-     "execution_count": 50,
-     "metadata": {
-      "image/png": {
-       "height": 450,
-       "width": 1050
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bat_summary_rows: list[dict[str, object]] = []\n",
-    "for scenario in SCENARIO_ORDER:\n",
-    "    bat_s = add_heating_label(bat_by_scenario[scenario])\n",
-    "    avail = [h for h in HEATING_ORDER if h in bat_s[\"heating_label\"].unique().to_list()]\n",
-    "    for group in avail:\n",
-    "        gdf = bat_s.filter(pl.col(\"heating_label\") == group)\n",
-    "        total_w = cast(float, gdf[\"weight\"].sum())\n",
-    "        bat_summary_rows.append(\n",
-    "            {\n",
-    "                \"scenario\": scenario,\n",
-    "                \"heating_label\": group,\n",
-    "                \"mean_bat_total\": weighted_mean(gdf, \"BAT_percustomer_total\"),\n",
-    "                \"mean_bat_delivery\": weighted_mean(gdf, \"BAT_percustomer_delivery\"),\n",
-    "                \"mean_bat_supply\": weighted_mean(gdf, \"BAT_percustomer_supply\"),\n",
-    "                \"median_bat_total\": weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5),\n",
-    "                \"pct_overpaying\": (\n",
-    "                    cast(\n",
-    "                        float,\n",
-    "                        gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
-    "                    )\n",
-    "                    / total_w\n",
-    "                    * 100\n",
-    "                ),\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "bat_summary = pl.DataFrame(bat_summary_rows)\n",
-    "print(\"Weighted mean total BAT ($/yr) by heating type and scenario:\")\n",
-    "display(\n",
-    "    bat_summary.select(\n",
-    "        \"scenario\",\n",
-    "        \"heating_label\",\n",
-    "        \"mean_bat_total\",\n",
-    "        \"mean_bat_delivery\",\n",
-    "        \"mean_bat_supply\",\n",
-    "        \"median_bat_total\",\n",
-    "        \"pct_overpaying\",\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "avail_bat = [h for h in HEATING_ORDER if h in bat_summary[\"heating_label\"].unique().to_list()]\n",
-    "bat_plot_data = bat_summary.with_columns(\n",
-    "    pl.col(\"heating_label\").cast(pl.Enum(avail_bat)),\n",
-    "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
-    ")\n",
-    "\n",
-    "(\n",
-    "    ggplot(bat_plot_data, aes(x=\"heating_label\", y=\"mean_bat_total\", fill=\"scenario\"))\n",
-    "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
-    "    + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n",
-    "    + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n",
-    "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
-    "    + labs(\n",
-    "        x=\"Baseline heating type\",\n",
-    "        y=\"Weighted mean total BAT ($/year)\",\n",
-    "        fill=\"Scenario\",\n",
-    "        title=\"Mean total BAT by baseline heating type and upgrade scenario\",\n",
-    "    )\n",
-    "    + theme_switchbox()\n",
-    "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000026",
-   "metadata": {},
-   "source": [
-    "### 4d: Monthly bill pattern\n",
-    "\n",
-    "Weighted-mean household energy bills by month, before (upgrade 00) and after (upgrade 02)\n",
-    "the heat pump retrofit. Winter months show the largest divergence: gas-heated homes have\n",
-    "high gas bills under upgrade 00, while after the retrofit (upgrade 02) gas bills\n",
-    "effectively drop to zero and electric bills increase due to heat pump load."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "a0000027",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x708129d95630>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000027",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x736fac0cda90>"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {
+            "image/png": {
+              "height": 450,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "monthly_rows: list[dict[str, object]] = []\n",
+        "for scenario in SCENARIO_ORDER:\n",
+        "    monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n",
+        "    for month in MONTH_ORDER:\n",
+        "        mdf = monthly.filter(pl.col(\"month\") == month)\n",
+        "        if mdf.is_empty():\n",
+        "            continue\n",
+        "        monthly_rows.append(\n",
+        "            {\n",
+        "                \"scenario\": scenario,\n",
+        "                \"month\": month,\n",
+        "                \"energy_bill\": weighted_mean(mdf, \"energy_total_bill\"),\n",
+        "                \"elec_bill\": weighted_mean(mdf, \"elec_total_bill\"),\n",
+        "            }\n",
+        "        )\n",
+        "\n",
+        "monthly_df = pl.DataFrame(monthly_rows).with_columns(\n",
+        "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
+        "    pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(monthly_df, aes(x=\"month\", y=\"energy_bill\", fill=\"scenario\"))\n",
+        "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
+        "    + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n",
+        "    + scale_x_discrete(limits=MONTH_ORDER)\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Month\",\n",
+        "        y=\"Weighted mean household energy bill\",\n",
+        "        fill=\"Scenario\",\n",
+        "        title=\"Monthly household energy bills \u2014 upgrade 00 vs. upgrade 02\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
+        ")"
       ]
-     },
-     "execution_count": 51,
-     "metadata": {
-      "image/png": {
-       "height": 450,
-       "width": 1050
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "monthly_rows: list[dict[str, object]] = []\n",
-    "for scenario in SCENARIO_ORDER:\n",
-    "    monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n",
-    "    for month in MONTH_ORDER:\n",
-    "        mdf = monthly.filter(pl.col(\"month\") == month)\n",
-    "        if mdf.is_empty():\n",
-    "            continue\n",
-    "        monthly_rows.append(\n",
-    "            {\n",
-    "                \"scenario\": scenario,\n",
-    "                \"month\": month,\n",
-    "                \"energy_bill\": weighted_mean(mdf, \"energy_total_bill\"),\n",
-    "                \"elec_bill\": weighted_mean(mdf, \"elec_total_bill\"),\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "monthly_df = pl.DataFrame(monthly_rows).with_columns(\n",
-    "    pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n",
-    "    pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n",
-    ")\n",
-    "\n",
-    "(\n",
-    "    ggplot(monthly_df, aes(x=\"month\", y=\"energy_bill\", fill=\"scenario\"))\n",
-    "    + geom_col(position=position_dodge(width=0.8), width=0.7)\n",
-    "    + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n",
-    "    + scale_x_discrete(limits=MONTH_ORDER)\n",
-    "    + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n",
-    "    + labs(\n",
-    "        x=\"Month\",\n",
-    "        y=\"Weighted mean household energy bill\",\n",
-    "        fill=\"Scenario\",\n",
-    "        title=\"Monthly household energy bills — upgrade 00 vs. upgrade 02\",\n",
-    "    )\n",
-    "    + theme_switchbox()\n",
-    "    + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000028",
-   "metadata": {},
-   "source": [
-    "## Section 5: Bill change analysis (upgrade 00 → upgrade 02)\n",
-    "\n",
-    "This section compares each building's annual energy bill **before** (upgrade 00) and\n",
-    "**after** (upgrade 02) the heat pump retrofit.\n",
-    "\n",
-    "Bill change δ = `bill_after − bill_before`; negative δ means savings.\n",
-    "\n",
-    "Buildings are classified by their **upgrade 00** heating type throughout — i.e., by\n",
-    "the baseline fuel they would be replacing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "a0000029",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bill delta computed for 33,790 buildings.  54.1% have negative delta (savings).\n",
-      "Heating groups: ['Fossil fuel', 'Electric resistance', 'Existing heat pump']\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000028",
+      "metadata": {},
+      "source": [
+        "## Section 5: Bill change analysis (upgrade 00 \u2192 upgrade 02)\n",
+        "\n",
+        "This section compares each building's annual energy bill **before** (upgrade 00) and\n",
+        "**after** (upgrade 02) the heat pump retrofit.\n",
+        "\n",
+        "Bill change \u03b4 = `bill_after \u2212 bill_before`; negative \u03b4 means savings.\n",
+        "\n",
+        "Buildings are classified by their **upgrade 00** heating type throughout \u2014 i.e., by\n",
+        "the baseline fuel they would be replacing."
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ebs/tmp/ipykernel_6500/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
-      "(Deprecated in version 1.0.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ── Quadrant definitions (from analysis.qmd) ──────────────────────────────────\n",
-    "\n",
-    "QUADRANT_COLORS: dict[str, str] = {\n",
-    "    \"savings > $1k\": \"#1b5e20\",\n",
-    "    \"savings $0-1k\": \"#81c784\",\n",
-    "    \"losses $0-1k\": \"#ef9a9a\",\n",
-    "    \"losses > $1k\": \"#b71c1c\",\n",
-    "}\n",
-    "QUADRANT_ORDER = list(QUADRANT_COLORS.keys())\n",
-    "QUADRANT_LABELS: dict[str, str] = {\n",
-    "    \"losses > $1k\": \"LOSE > $1K\",\n",
-    "    \"losses $0-1k\": \"LOSE $0-1K\",\n",
-    "    \"savings $0-1k\": \"SAVE $0-1K\",\n",
-    "    \"savings > $1k\": \"SAVE > $1K\",\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def quadrant_pcts(df: pl.DataFrame) -> dict[str, float]:\n",
-    "    \"\"\"Weighted % of households in each bill-change quadrant (df must have 'delta' + 'weight').\"\"\"\n",
-    "    total = cast(float, df[\"weight\"].sum())\n",
-    "    return {\n",
-    "        \"savings > $1k\": cast(float, df.filter(pl.col(\"delta\") < -1000)[\"weight\"].sum()) / total * 100,\n",
-    "        \"savings $0-1k\": cast(\n",
-    "            float,\n",
-    "            df.filter((pl.col(\"delta\") >= -1000) & (pl.col(\"delta\") < 0))[\"weight\"].sum(),\n",
-    "        )\n",
-    "        / total\n",
-    "        * 100,\n",
-    "        \"losses $0-1k\": cast(\n",
-    "            float,\n",
-    "            df.filter((pl.col(\"delta\") >= 0) & (pl.col(\"delta\") < 1000))[\"weight\"].sum(),\n",
-    "        )\n",
-    "        / total\n",
-    "        * 100,\n",
-    "        \"losses > $1k\": cast(float, df.filter(pl.col(\"delta\") >= 1000)[\"weight\"].sum()) / total * 100,\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "# ── Bill change dataframe ──────────────────────────────────────────────────────\n",
-    "\n",
-    "bill_delta = (\n",
-    "    add_heating_label(\n",
-    "        bills_by_scenario[\"Upgrade 00\"]\n",
-    "        .filter(pl.col(\"month\") == \"Annual\")\n",
-    "        .select(\n",
-    "            BLDG_ID,\n",
-    "            UTILITY_COL,\n",
-    "            \"weight\",\n",
-    "            HEATING_TYPE_COL,\n",
-    "            \"heats_with_natgas\",\n",
-    "            \"heats_with_oil\",\n",
-    "            \"heats_with_propane\",\n",
-    "            pl.col(\"energy_total_bill\").alias(\"bill_before\"),\n",
-    "        )\n",
-    "    )\n",
-    "    .join(\n",
-    "        bills_by_scenario[\"Upgrade 02\"]\n",
-    "        .filter(pl.col(\"month\") == \"Annual\")\n",
-    "        .select(BLDG_ID, pl.col(\"energy_total_bill\").alias(\"bill_after\")),\n",
-    "        on=BLDG_ID,\n",
-    "        how=\"inner\",\n",
-    "        validate=\"1:1\",\n",
-    "    )\n",
-    "    .with_columns((pl.col(\"bill_after\") - pl.col(\"bill_before\")).alias(\"delta\"))\n",
-    ")\n",
-    "\n",
-    "avail_heating = [h for h in HEATING_ORDER if h in bill_delta[\"heating_label\"].unique().to_list()]\n",
-    "n_save = bill_delta.filter(pl.col(\"delta\") < 0).height\n",
-    "pct_save = n_save / bill_delta.height * 100\n",
-    "print(f\"Bill delta computed for {bill_delta.height:,} buildings.  {pct_save:.1f}% have negative delta (savings).\")\n",
-    "print(f\"Heating groups: {avail_heating}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000030",
-   "metadata": {},
-   "source": [
-    "### 5a: Quadrant bar chart by heating type\n",
-    "\n",
-    "Each bar shows the percentage of weighted households in four outcome bins:\n",
-    "\n",
-    "| Bin | Definition |\n",
-    "|-----|------------|\n",
-    "| SAVE > $1K | Annual energy bill decreases by more than $1,000 |\n",
-    "| SAVE $0-1K | Annual energy bill decreases by $0–$1,000 |\n",
-    "| LOSE $0-1K | Annual energy bill increases by $0–$1,000 |\n",
-    "| LOSE > $1K | Annual energy bill increases by more than $1,000 |\n",
-    "\n",
-    "A bar leaning heavily green indicates that most households in that group would save\n",
-    "money by switching to a heat pump under current rates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "a0000031",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000029",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Bill delta computed for 33,790 buildings.  54.1% have negative delta (savings).\n",
+            "Heating groups: ['Fossil fuel', 'Electric resistance', 'Existing heat pump']\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n",
+            "(Deprecated in version 1.0.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# \u2500\u2500 Quadrant definitions (from analysis.qmd) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+        "\n",
+        "QUADRANT_COLORS: dict[str, str] = {\n",
+        "    \"savings > $1k\": \"#1b5e20\",\n",
+        "    \"savings $0-1k\": \"#81c784\",\n",
+        "    \"losses $0-1k\": \"#ef9a9a\",\n",
+        "    \"losses > $1k\": \"#b71c1c\",\n",
+        "}\n",
+        "QUADRANT_ORDER = list(QUADRANT_COLORS.keys())\n",
+        "QUADRANT_LABELS: dict[str, str] = {\n",
+        "    \"losses > $1k\": \"LOSE > $1K\",\n",
+        "    \"losses $0-1k\": \"LOSE $0-1K\",\n",
+        "    \"savings $0-1k\": \"SAVE $0-1K\",\n",
+        "    \"savings > $1k\": \"SAVE > $1K\",\n",
+        "}\n",
+        "\n",
+        "\n",
+        "def quadrant_pcts(df: pl.DataFrame) -> dict[str, float]:\n",
+        "    \"\"\"Weighted % of households in each bill-change quadrant (df must have 'delta' + 'weight').\"\"\"\n",
+        "    total = cast(float, df[\"weight\"].sum())\n",
+        "    return {\n",
+        "        \"savings > $1k\": cast(float, df.filter(pl.col(\"delta\") < -1000)[\"weight\"].sum()) / total * 100,\n",
+        "        \"savings $0-1k\": cast(\n",
+        "            float,\n",
+        "            df.filter((pl.col(\"delta\") >= -1000) & (pl.col(\"delta\") < 0))[\"weight\"].sum(),\n",
+        "        )\n",
+        "        / total\n",
+        "        * 100,\n",
+        "        \"losses $0-1k\": cast(\n",
+        "            float,\n",
+        "            df.filter((pl.col(\"delta\") >= 0) & (pl.col(\"delta\") < 1000))[\"weight\"].sum(),\n",
+        "        )\n",
+        "        / total\n",
+        "        * 100,\n",
+        "        \"losses > $1k\": cast(float, df.filter(pl.col(\"delta\") >= 1000)[\"weight\"].sum()) / total * 100,\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "# \u2500\u2500 Bill change dataframe \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+        "\n",
+        "bill_delta = (\n",
+        "    add_heating_label(\n",
+        "        bills_by_scenario[\"Upgrade 00\"]\n",
+        "        .filter(pl.col(\"month\") == \"Annual\")\n",
+        "        .select(\n",
+        "            BLDG_ID,\n",
+        "            UTILITY_COL,\n",
+        "            \"weight\",\n",
+        "            HEATING_TYPE_COL,\n",
+        "            \"heats_with_natgas\",\n",
+        "            \"heats_with_oil\",\n",
+        "            \"heats_with_propane\",\n",
+        "            pl.col(\"energy_total_bill\").alias(\"bill_before\"),\n",
+        "        )\n",
+        "    )\n",
+        "    .join(\n",
+        "        bills_by_scenario[\"Upgrade 02\"]\n",
+        "        .filter(pl.col(\"month\") == \"Annual\")\n",
+        "        .select(BLDG_ID, pl.col(\"energy_total_bill\").alias(\"bill_after\")),\n",
+        "        on=BLDG_ID,\n",
+        "        how=\"inner\",\n",
+        "        validate=\"1:1\",\n",
+        "    )\n",
+        "    .with_columns((pl.col(\"bill_after\") - pl.col(\"bill_before\")).alias(\"delta\"))\n",
+        ")\n",
+        "\n",
+        "# Only analyze heating types that would actually be upgrading to a heat pump\n",
+        "# (exclude \"Existing heat pump\" - they already have heat pumps)\n",
+        "avail_heating = [\n",
+        "    h for h in HEATING_ORDER if h in bill_delta[\"heating_label\"].unique().to_list() and h != \"Existing heat pump\"\n",
+        "]\n",
+        "n_save = bill_delta.filter(pl.col(\"delta\") < 0).height\n",
+        "pct_save = n_save / bill_delta.height * 100\n",
+        "print(f\"Bill delta computed for {bill_delta.height:,} buildings.  {pct_save:.1f}% have negative delta (savings).\")\n",
+        "print(f\"Heating groups: {avail_heating}\")"
+      ]
+    },
     {
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s9KQkFalWTRFZxsxOOnxYa+6917WnBDeyDguRIaZFC8u8xZo0ceqdQXIOiDi9ebNTDw4Vr7tOB2fMyPw7gyM1VWf+HSajxn33qViTJpnz9k6ZoqPz53vcfgDAxSUgLEzhrc9H6KefOKHkDRtyXV94q1bnAg+CghQYGekyZEP66dOWQ0J4IjAmxul/k2Qf8Z996AhJCixWTOlxcefacfSoAquc61IxKCZGIdWrK3XfPoVnOUZKUvqxY5llw1udH8fYkZCgxMWLc7UeAAAAsHdj3eslSWFBYSoZXkKRoc7Dg8ae2qn5+xZalr2yeq/Mt0TTHGmavatwhtYMDQxVxUjnMc1Pp5xRQpahJNIcaTqScESli5x7UFQtupqqRFXW4YQ4p6AMSZnDV1SNqqK25c+fu8clHLHdFgAA77wz431t2rtZ7971ltbErtW7Mz7wSb1DLr9THRu0V0hwqMoVL6u6Fes4zT9++rge+Phhy54VcnLy7CmVLnY++KBe5XqKCI1wqSsiNEL1KtdzSssaDJGUkqSt+7dl9vjQvn5btanbWpv2btLQywc7lduwe6MkqW3dNhpy+Z2Z6Vv2b9Vb09/xeh0AiYAG+CljjFauXKm5c+dq8+bNOnXqlIoWLaqqVauqQ4cO6tmzp0KydOWbncPh0PLlyzV79mwdOHBAR48eVWRkpCpVqqSOHTvq0ksvtS2fmpqqhQsXau7cuTp06JBOnDihYsWKqWrVquratas6d+6swH+7W87qjz/+0LvvvitJ+vTTT1U2y1uMefHUU09pQ7aHBiNGjMj8u3v37nr44YclSevXr8+cN3LkSDVu3NiyTk/beubMGf38889avny5Dh48qJSUFJUsWVLNmzdX3759VTHLg/v/CpOSorX3369DP/+suk89pSJVqyq4aFHFXHKJS96dY8dq55gxSjnqXZdTCbt2KTU+XiFZAhNKdemiakOHatf48dK/XVsXqVZNDV991aV8WpY3UZMPHdKeL75QtcHnTiqK1qypLn+5Ro3u+eILJR8+rKgGDVTzoYcy0xP37dPmF1+UAgNVunt3FWvcWEVr11bqiRM6vWWLjvzxh5L27/dq/QAAF5bw1q0VmGUopMTFi12i9r0RXKaMgitUcElPP3lSqTt2KGn9esmLQMBMISGZQ2Kcr9TNGJYWw1wEZFnPpL//VkiV82NEFu3Z0yW/4+xZJW/cKAUEnBtqIkuQYcKiRTLJyQosXlzBFSooqEQJBQQHK/3ECaUfPqy0gwe9WDkAAABkqFO8tmX6rvjd2nD0H62KW205v3GpRqoWfX6IssUHluhk8sn8aGKOOlRop/Bg5+FGNx937Up87t4FuqHutZKkoMAgDWp4m0ueg2cPafPxLQoJDFG/mueHAD431MSPSjfpqlC0gqpGV1GZf4Mj4hKOaHf8bh04yzkpAHjj11W/qc8L/XUq4ZQcbocG8lyDyvXVs1kPl/T9x/brp+Wz9MmssYo7dSRXda/dudap7piixfT64Ff15OdP63TiuV6noyKi9OqgkYop6vzCyZkk5x4vR3//hsY9+IkkKTQ4VN8/NdVleet3bdCslbNVJKyI3hwyOjM9LT1Nj4x9TMmpKWpWo6na1m2jepXP9XK9ee8W/bV5qdbutO5dCZAIaIAfSkxM1Jtvvqnly5c7pZ86dUrr1q3TunXr9OOPP+rpp59WBaub4enpevnll/X33387pZ88eVInT57Uhg0bNGvWLI0YMULlyjl3J5eYmKinnnpKO3bscEo/duyYjh07plWrVmn27Nl66qmnFJ3HYQH83fr16/Xaa68pPj7eKf3gwYM6ePCg5syZo7vuuku9evUqpBYWnoiKFVWqUyeF5RC0UqxxY0U3auR17wYmLU37Jk9W9XvucUqv9+yzqnb33UqIjVVwdLQi69RR8hHXE5mUf98UzbD97bcVWbu2SnXubLm8owsWaPs77yggJOTcUBNZgn02DBumoLAwtZo8WTHNm7uUdTz7rDY+84z2TZni1ToCAC4MQWXLKqze+Qj9lO3b8/VBvHHk/mZAgJtg19zUkX70qBKXLlV4q1YKCApyyetISFDC/PlSWprCmjRxGpIjZds2pe3erfBLLlFY8+Yu40lKUuru3To7d65lYAUAAABywRilG+uA1rCgMF1a5fwDnRNJJ7R4v++7CfdEneJ11LFiB6e0pLQkLbDoSWHLiS3668BStS3fxvKc8ljiMc3c8aOMjHpW6aHi4cUz5y3av1iHzh5Snxq91aJMM8u2/H14tX7ZOUtGuQ9YBoD/mu0Hd+ScyUdS0lx7l/TGxLlfuQRL9G/bTz2adtOWfVsVEBCgOhVrKygwSCfPnFRMZExmvmPxx53Kzf57jj6ZNU5DL7/T8qXf2EM79b9xj8thHBpxwxOqWub8SyIf/PiR/tmzUa/f+apu6nKDZVu/mj9FT33xjNIdbl5OwX8WAQ3wK+np6Ro5cqTWrTsXidWpUye1adNG5cuX14kTJ/TPP//op59+0r59+/TEE0/o3XffVfHixZ3qmDp1amYwQ7du3dSlSxcVK1ZMx48f199//605c+Zo9+7dGj16tN58802ni4FPPvlEO3bsUGBgoHr37q3WrVsrMjJScXFx+uuvvzR//nxt3LhRH374oZ588skC2SaPPPKIkpOTtXXrVr3zzjuSpIcfflh16pzrdqhItnGcfWHHjh16/vnnlZqaqpCQEPXt21cNGjRQdHS0du7cqRkzZujAgQP66KOPFBkZqY4dO/q8Df6qWLNmuuSLLxSabYxuKyXatVOJdu207Y03tOPfHjE8te3NN1X2iitUpGpVp/TwsmUVnuWBycFp01Tjvvuc8sRn69Ej7dQprbzlFpW/6iqV6NBBkTVrSpLO7NihY4sW6dDMmZKkWo89pugGDTLL7Zk4UccWLVLTDz6wDGaQpMCwMDV85RWdWLFCZ7dv92odAQB+LiBARTqcv9FqUlKUuGxZvi0uKCZGEa1aKbR2bZ2dM0eOU6fybVmeSl6/Xql79yq0bl0FxcQooEgROeLjlX78uFL++UcmJUWBxYsrvGXLzDKOs2eVuGSJgitVUrjNkFGSFFK1qsKbNVPSypUFsSoAAAAXvWrFqqlasWqqX6Kevt82TamO871+da/c1Wloil93/aY0U/CBpXWL19G1ta9xCU74dfdvTsNNZPXbnj+06fhmNSzZUKUiSioiOELHko7p4NlDWnnob6WZNFWPrqZW5c73Hnro7GEt3L9Izcs0sw1mkKRLyjbXgbMHtDpujS9WDwDgQxVLVtQDfe/T9Z2u05B379bq2DVe1zF37TxNXzpT/dv2c0qPiohSy9rnjxvj50zQLV1vcsqzYbfrcKMvfT1SP6/4Rf3b9lPNCjUVU7SYdh7aqXW7NmjiH18qKTVZHRt00O09bs0ss3HPJr078wPd2Pl622AGSbql601aG7tOkxd87fV64uJHQAP8yqxZszKDGR577DF1zvZGeevWrXXppZfqscce08mTJzV27FgNHz7cKc/Kf28Kd+zYUY888khmes2aNdWqVSt16tRJX3zxhR566CGni4eMYS4kqX///ho0aJBT2Xbt2qlt27b6+eefdU+2N+fzU+nS57qCO3HihFNapUqV8mV5DodD77zzjlJTUxUdHa1Ro0apSpbuluvVq6du3brppZde0vr16zVmzBhdcsklioiIyJf2+JPA8HA1++QTp2CGlJMntXXkSB2ZP19pZ84oukED1Rk+XMWzjDVe+7HHdGrtWq96anAkJWnVkCFq+v77iqpXzzLPjg8+UFiZMi7pJ7L1bpLh4IwZOjhjhuW86CZNnAIjEnbv1paRIxXVoIHKX3VVZvqR+fO17sEHzwV2fP65AgIDFRAUpNrDhmnN3Xd7vH4AAP8X1rChgkqWzPw/adUqmQTrm6zeOPPzz+f+CA5WYFSUQuvWVXiWYbKCYmJUpEsXnfk34M5TJjfDVHhQh+PkSSXZBXJkDDWRpQeHhD//lElJUXirVufrTUnRmV9+kePsWUX26pW5XcMaNVLyP//IJHo/DiYAAMB/1YtLR0qSIoIjVDqilDpUaK/axWtlzq9TvLY6V+yoP/bOkySVL1peLcuef2iz5fhWbT25rWAbLalJqcbqV7OPAgOc32r9bffvWnvEfTfb+87s174z1kN+hgWFOQ01ke5I14wdMxWoQHWp1Ckz/VRyvCZt+kqSNLD+LSoWdq732S6VOmv9kQ2FEuABAJD+N26Y/jdumMJCQlWxZCX1b9tX9/W5V2EhYZKksjFl9NH/va9uT/ZUUqr3PTY8PfFZhQWH6oqW1r1tL9n0l7bs26rwUOehkJZtXWGZf9WO1Vq1w3p4p6iIKL055LXM/1PSUvTQ2EcVFBioR/qfH+r6wLEDunH0uaCHrx//UhVKnuuN/X9XP6QflkzL1Xr+1x06dKiwm5Ape+/4vuDaJwjgA19//bX69evndho5cqRTGYfDoWnTpkmS2rdv7xLMkKFSpUoaOHCgJGnJkiU6mK3b4/37z53cN8jypnlWDRs21OjRo1WxYkWn9NOnT2cOr2BXtn379ho5cqRLrxAXkzVr1mj37t2SpMGDBzsFM2QIDw/X//3f/ykoKEinT5/WfC+HVLhQVbzuOkVkG+Zk44gR2vf110o+dEjpZ87oxPLl+vuOO1yGfaj50EPy1pnNm7Wkd29tGTVKR+bPV3JcnFJOnNDxZcu0auhQxb7/vspdcYVTmdObNnndU0JgWJgav/WWAv8d99s4HFr/6KNKT0hQ6W7dnPLu+uQTpZ44oaPz5ulklmFdSnfv7vX6AQD8V0BoqMIvOX/TN/3ECSVvcI3Mz5O0NDlOnFDS0qVKWrvWaVZw2bIK9jZ4MzXVdcgKi6Eizi/ENbbbJHt3wRzWrJmC/w0+laTkzZuVtnevAiIiFFyq1Pmm7d6t9CNHZBISlPzPP5npASEhCi5f3qtlAgAA4JzEtETtOb1XX2/5RntP73Oa16Z8a4UFnXsQdFnVnpkvNaU50vTr7t8KvK3ty7dV/1r9LIIZ/tBfB/PWC9plVXuqWNj5cc8X7v9ThxPiVK5oWUWFRmWmrzuyTseSjutY0nGtO7o+Mz06NEpli7q+MAMAKFjJqSmKPRSrt6a/q+cnv+Q0r3LpSrq244Bc1Xvy7CkNff9e3fPh/fp5xSztOrxLZ5POav2uDRo59VUNfGOQ+rXp41QmNS1Vs//+1etlPXfTCFUsef7Z2zsz3temvZvUoEoDlSt+vufpbxf/oNhDsYo9FKvvFk/LTC9XvJzqV66fi7WEw+Hwmyk/0EMD/Mbu3bt15MgRSVK7du3c5m3Xrp3Gjh0rY4z+/vtv9elzfmdbqVIlbdu2TevWrVPv3r0tx5ezEhUVpWLFiunUqVNat26dWmd5w/6/JGO4jpCQENugEkmqWLGiKlWqpN27d2vz5s26ItuD9YtRdJa3RzNY9bqQFh+vE3//rbKXXXa+bKNGUmCg5OXO3KSkaOeYMdo5ZozLvAajRik4Ksopbe/kyV7VL0m1/vc/RdWtm/n/7s8+04l/30SNyBbQkrj//NsASQcOZP4dFB6usDJllBwX5/XyAQD+JyA0VAGhoZn/BxUvrpghQ3IsF1qnjkL/HRbr7Jw5Sv03SDInqbGxCm/a1CktqGRJpe3bZ1PCmuPkSQVl6UkpIDzcNm9AWJhleU8FlijhNKSE48wZJS5dem5edLRzvWfOnP/77FnnerIdywEAAOAdI6PNx7eoctT5gNjgwGCVjiilfWf2Z/ZEkJH+YPP7rKpxEhMWo2fbjpAkLT24XHPyEARxWdWealu+jVNaanqqpm2foc0ntuS6XkmqFVNTzbMMKXHgzEEtPvCXpHPrkNWplPjzfyc7D+8WExaj/WcOCADgH35eMUuv3P6yU1qjqg3zVOdPy3/WT8t/dknv27q3Ojbs4JT22+rfdeTUUa/q796kq27MMqTE2p3r9NHPH0uSqpZxfs5w4Nj5Y87+Y849EFUpUzlXw2v81wUGXtx9GBDQgHxx5ZVXOgUZWAnPdoN527bzXb3lNJxCiRIlVKRIESUkJGh7trfRu3fvrm3btmnp0qV6/vnn1b17d9WuXVtly5ZVkJu39AICAtS9e3dNmzZNM2fO1IkTJ9SxY0fVrFlTpUqVuuh3BhkytmelSpXcbq+MPLt3787sFeNil713BklKT0qyzOvI1nV0UHi4QkuWVMq/QTt5VaprV1W59VantMQDB7Tva+/Gl4pp0ULVswwVcTY2VltfO98lVHi2dc7aFbcjJcVpXniFCgQ0AAAshTVrJpOYqJQtNjdsjXFJys2D/vTjx50DGgIDFRAZKZMloCBDkEXQgcc9NAQGqmjXrgrIcn6YsGCB9O9xMrBoUef8WQMa09Odq4qMFAAAAOyFBIboqpp9NX/fQh1NtH64YozrCyTFworZDtNQEIICgnRVzb5qVMr5AdSp5FOauuU7HUrIW9fQ4UHh6lOjd+b/aY40zdgxU45/t0XWQA5JSjfpln+fy1tMAICCc32na1WpZEW9Nf1dy/kOR7pLWqWSFS1y5k3ZmDIugRMOh0Nvz3jfq3qKFYnW6Dtfyfw/OTVZj3z6mNLSzw1nVKGEc++UKWkpln9LUsUSrs9hkLP8GObBnxDQgHwRHR2dY1BCdqdPn878OzKHG7sBAQGKjIxUQkKCTp1yjii+8sordfz4cU2bNk2rV6/W6tXnxvIJDQ1VvXr11KRJE11xxRWKsrhJfttttyk+Pl5z587Vn3/+qT///FOSFBERoYYNG6pp06bq1auXwize6LtYZHwOO3fuVL9+/Twq818JaEg5ccIlLbpBA51as8Y5MSBAURbDlqRmfFcDA1X3qadUvn9/BYaGKu7XX7Xp2WeV7uH42eHly6vRG2+4pG995RU5bAIsrASGh6vx229njvtt0tO1/pFHnOpIPX7cqUxAlu65A7IFvFhtHwDAf1xgoIp06qTQOnXkSEpS2oEDcmQ558sQbBE0aCyOi2FNmiisQQMFhIUpdf9+JS5aJJP1uLVnj0Jr1XIqE1q9upLXr3dKCypVyiVgInXPHo9XK7x5cwWVLJn5f/LGjUrL0nORyX48zhoYm633MJe8AAAAyBQZEqkb616vCpHlFRUaqS83TlaaSXPJVzW6qktaQmpCvrSpXfm2alWupcKDwrTz1C79vHOWEtKclxUaGKrr616rGsWqO6Xvjt+jb7d+75I/N3pVu0zRWYaUmL93gY5kCfhISHU+nw7MMvp0YLaRqPNrWwEAnAUEBOjxAY/qgb73KSklWXPXzdea2LUu+drVa+uSduz0cZe0a9r31//6P6TSxUpr1Y7VeuLzEdod59n9jdDgUL1z15uKiYxxSv964TfatHeTZyv0rxcHPq9yxc8/UH/jh7e1df/5l5iPn3F+dhAcFGz5t1VeQCKgARehgIAA3Xrrrbrssss0f/58bd68WTt37tTx48e1bt06rVu3TjNnztTjjz+uJk2aOJUNCgrSQw89pKuuukqLFi3S5s2btXv3bp06dUr/z959h9lV1fsD/swkk947SQipJJTQAgKhSS+hexWwewX1+vNeFBU1iCgI6tVL8SoWsKCg6FWREooQOglCqKGFkN4nIZn0MpPk90fMSU5m0iDAVt73eXyes9Zea++1Z4Jzyud815gxYzJmzJjcfvvt+frXv57evXu/MzdYQEs3KZ38r2rB3/+e7qefXta3++WX55nzzittv1BRVZX+F1yQVgMGlM8dMyZr/1HRoPsZZ5RVReh59tlZPn16JlzTcBpzY8179swBN9+cZl27lvVPu+mmzPrrX7frfnb9ylfSsm/fUnvSz36WmqefLhuzbNq0snaTzp2zbPLkJEnTLhv2V1y7enVWvEuCLQDvBmtXr96mD/gbdemSyo2qbq1ZsiSr/xGGW7N8eVqddFIa77QuhV/ZrFlaDhuW5Q8/XPbhf+Pu3cu2blivbtas8mt165bmB24o1dukT5+sXbUqyx9+uNRXO3Vq1q5aVbZdRrP99kvd7NlZ/Y8qSRXNmqXFEUfUu96qTap+bfaeO3VK0332KbVXL1qU5X8v3/d409BGRfPmGx63aLHFsQAArNOleed8cNDZafOPSgM7t945Zw/6QEZMvCsLVq77sKMiFXlPt/0zsMOuZXNrV9eWqjNMWjglLau2XDGzY7MO6dh8Q2C1dk1tJi2cnCRlVSF2ad0rx+5ydKm9W8dBWbF6RW6fuKGEd8uqljln4Fnp3qr826hj5jyVuyf/rVRB4c0Y2H7X7NV5w9ao0xZPz+hZ5c9Ja1bWlLVbNWm50eNWWxwLwI5X1agqP/zMVTnlPeuq6zRr0jS/ueCX+eqvL8qdY+4ujduj1+655IMX15s/6uXRZe1duvTKled+vxQIOGyPQ/O9j1+Rs//7w1tdS9OqJrnuP3+aw/Y4tKz/pakv55KbLt2u+zp+v2PzvkPOKLXHjH8qP7vrurIxU+eWf87QuW3n0uMubbuUj93GQAbvLgINFMbGFROWLFmSrpt8YLuxtWvXlioJtNmkXPB6Xbt2zVlnbdivZ/HixRkzZkxuuOGGzJ8/P1deeWV+8pOfpPlGbzCv17t377LAwvz58zN69OjceOONqa6uzlVXXZWrr746FZt8w65I1qx5Yy+O1v8e+vfvnwsuuGBHLumf3qzbbsuAL30pTTb6RmbbvfbK4Q8/nMXjxmX1smVp2b9/mnbqVG/ulF/9qvS43ZAh9Y431Lep5jvvnPf84Q9pvvPOZf01Tz+dl77xje25lbQ/8MD03mgv9MXjxuW1K6+sN27R88+XtXuedVZqnnwyLfv2TfsDDtgw7qWXsrau/jckAPjntHb58iy9556tjms1bFgqN6quUDdz5rqtF5J1VQkal7/caNS6dVoNG5Y1ixdn9eLFqWzZMo3a1i9vWzdvXr1AQ+MGnhs27lL+oje1tVnx9NNpftCGbzJUNGmS1qefntU1NVlbW5tGHTuWbRWRJLVTpmT1nDlbvd80apQWDW01scnfwDVLlmTN8uWp/MfzzKpevbKiefOsXbEiTTYJPdbN2749KQEA3i1WralNk0ZNyvr6tu2T/7fPZzJv+etZVrcsHZt1TOsm9Su9Pl39TGrXrNsO7PaJd2z1Wkf0PCxH9Dy81F5auzQ3j/tjvXE9W9cv992zdXmV2v7t+tULM6ysW5k2TdrkA7v+2xbXUb2sOvdPe3CLY5o3bp5hfU4stWvX1ObWCbdnbcq3catePjd1a+rSuHLdc/I9Ou6ex2c98Y/Hu5XG1a2pS/WyHbNFKsC/uk5tOuUHn/xuvf7uDWyT8NX3X5gFG1UbeGX6uDRpXP53rUPrDvn5f/4ksxfMyaTZk9K+dfsM6N4/jSrLqyPPXjA7dzx5Z1nf3n32qlfdYP8BW/+coVlV01x//s/z3sGHl/XXLKnJuT/8dJav2rZK0knSvlX7fPfjV5TaK1atyBeu+3K98N646a9mZe3KNK1aV/381ANPznV3/yIVFRU55cAN2yetrF2ZcRtVdoD1BBoojP4blQeePn16+vXrt9mx8+fPz/J/lCEesMmbwpvTunXrHHnkkenatWu++tWvZv78+Zk4cWL22GOPrc7t0KFDhg0blhYtWuSqq67KpEmTMm/evHTu3Hmrc99OG2/VMX9+/fJD661tYJ/q9fr165eXX345NTU1271tyL+6ukWLMvaLX8y+11+fyo0+oKls2jRtN6n2sbGZf/lLZt9225ZPvpVwTJu9985+112XZjuVvyBeMWtWnvn0p0vVH7ZFoxYtMvh//qfUXlNXl7Ff/GLWNLBvePW992bh2LFpO3hd6r/nWWel89FHp6pt21RWVZXGNRSGAOBdbs2aLLvvvrQcNiyNNgmgVrZuXW/Lh9K0FSuy7MEHky08Xylp4O/nyhdfTOOePVO1yfOYRu3aNXy9JUuy7LHHtn6tJM2GDEmj9u03XGvs2Kye3cDex2vXZsUzz6TF0KFJkso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fH7Vq1UrVq1e32Wf+/PnGyBFl6bbbbjMNqZ4ztJDl3LlzCgsLM63LyMjQtm3bci2fs57swYmsfceMGWMKUDg5OSk4OFjt2rVT06ZNValSJdM+q1ev1rRp0wo8p7wcOXJE7777rqljvkKFCmrRooWaNm1qGpli5cqVmj9/fpHq37lzp/G4YsWKat26terUqWMzzcRHH32kAwcOFPMs8hYZGamXXnpJly5dMtZ5eHiofv36at++verXr286x4yMDE2bNk1r167Ntb49e/ZozJgxNh2zXl5eatmypWrWrGmss1qtmjlzZgmfUaaYmBgjHOPg4KDg4GC1adNGAQEBpnKxsbEaO3ZsnsGLmJgYDR48WEePHjWtd3V1VdOmTdWgQQOb12r79u16+eWXCx1quR7JyckaN26cEhISjHVOTk5q0KCB2rdvr0aNGsnV1dXYZrFY9M4775hCXFlOnTql4cOHm76DvL291bBhQ916662qU6eOaWqV5ORkvfHGG9qzZ0+B7dy7d68RLHJ3d1ezZs1sPj+StGXLFtOILjmtX79eY8eOtXlu/fz81KZNG1WpUsW0PiMjQzNnztT3339fYBsLYrFYNG7cuFy/v4KDg9WyZUt5eXkZ6/bt21dg4KMkXz8AAADgn44pJwAAAAAA+P8cHBzUq1cv9erVK88yycnJGjlypDEKwGuvvaZbb721VNqTnJys1157zXRHdMOGDfXmm2+qcePGxpQJcXFx+vTTT03TMHz//ffq0KGDOnfuLEnq0qWLzd38U6ZM0ZEjRyRJjRs31tixY23akLOjNz/u7u66/fbbtWHDBkmZd+5brVabqR3yCjps3bpVd999t83633//3XhctWpVm6HiFy1aZIQZHBwcNHDgQPXv398UYrBardq1a5c+/PBDHTp0yNgvJCSkUFMl5LRo0SLT3d0vvfSSHn/8cWPKkYSEBM2ePVs//fSTcW4uLi6aOXOmfH19JanAKQ2CgoI0btw43XbbbcZzGBERocmTJxvPodVq1VdffWUKZ+R8nX/55Rf98ssveW6XbF/n9957T5GRkZIygxojRozQXXfdZeq4TktL02+//aaZM2cqKipKkjRt2jR16dJFHh4epnJvvvmm6Q77GjVqaPz48WrZsqUcHTPvt4mPj9fcuXP1448/ltid83np0aOHxowZI39/f2PdH3/8oYkTJxrnfeLECW3evNkmQCNlfnYuXLhgLFesWFGvvfaaunfvbkyNkJKSohUrVmjGjBlKTk6WJB08eFBz587Vyy+/XIpnJ23bts00Ukn79u01YcIE01QecXFxmj59ulasWCEpc1Sa9evXq3///kYZq9WqCRMmGCN0VK9eXcOHD1eXLl1MU0AkJiZqxYoVmjVrlpKTk5Wenq6pU6fqu+++M17fvLi5uemll17Sww8/bIRAUlJS9Pnnn+vLL780yn3zzTd69NFHbab1iYyM1Pjx403r2rVrp7Fjx6pmzZrGZycyMlLTp0/Xr7/+apSbNWuW2rVrp4YNG+bbxvwsWbLE9B0lSY8//rgGDhwob29vSZmBg9DQUE2cOFEXL14s8P1dUq8fAAAAcCNghAYAAAAAAArJYrFo/PjxRof4E088oQceeKDUjvfzzz/bdGp99dVXatKkiSkk4Ovrq9GjR2vChAmm/T/55BOj46xixYpq2rSp6b/sd0F7enrabG/atGmRAg2SeTqIiIgImzvYJXOgIXt45I8//rAZVj45Odk08kL37t1N556enm7qoB85cqReeOEFmxEZHBwc1K5dO3388cemIEFB0yTk5fDhw8bjHj166OmnnzZ1tHp5eWnUqFGm8IW/v79uu+0247nNb4oSV1dXffzxx7r99ttN5xsQEKCpU6ea7jj/888/TaGXnK9h9k773LbnfJ3PnDmj9evXG8sff/yx7rvvPlOYQZJcXFzUq1cvffTRR8a6qKgorVmzxlRu9erVppEeGjZsqO+++06tW7c2dXb7+Pho9OjRuQZrSlK7du303nvv2TwvHTt21BtvvGFal7OjWsocnSP781O5cmX98MMP6tmzp6mT383NTQ8++KDmz59veu4WLlxohCZKy9mzZ03Lb775pqkzXMr83nj99ddN79vTp0+byvz111/G952Xl5c+/fRT9ejRw3SeUub3R//+/TV58mRj3YkTJ0wjjeTl9ddf12OPPWYa0cLNzU0vvvii7rzzTmNddHS00Zbsvv76a9OUKCEhIfrkk08UHBxs+uz4+/trypQpeuGFF4x1GRkZ+uyzzwpsY14yMjL01VdfmdZNnDhRI0aMMMIMkuTo6Kh27drpu+++U61atQqst6RePwAAAOBGwAgNAAAAAAAU0pw5c7Ru3TpJUteuXTV06FBJmZ3qq1ev1t69e3Xs2DEFBgaqSZMm6t27t4KCgop9vNWrVxuPXVxc9Nprr5mGGc+pT58+WrFihXbs2CFJOnbsmM6cOaM6deoUuw1F1aVLFzk5ORnBhK1bt6px48bG9sTERIWGhhrLzz33nLZv366rV68qJiZGhw4d0i233GJs37Vrl2kY+TvuuMN0vMOHD5tCH7Vr18610zO7xo0b6+LFi5JkCksUxZkzZ4zHbdq0ybWMk5OT2rRpk2uooyB9+/bNs+PT3d1d/fr106effiopM/Rx4cKFEpv2ZOPGjcbjqlWrymKxFPicVqtWzXhO//rrL/Xr18/YlvWZyfLqq6/aTCmQXb9+/bR06dJSmUpDkoYNG2YzakiWTp06qXr16sboC8ePH7cpkzOwMXToUJtwRHZ16tTRM888o7lz50rKHH1g48aN+r//+7/inkKBcnZ+5wwKZXF3dze93jmnycge3KhXr56ioqKM0ThyU7lyZbm5uRmf2b/++kvt27fPs3y9evXUp0+fPLc/8sgjpvfP8ePH1bJlS2PZYrGYvicrVKig4cOH5zkqhIODg5555hmtWbPGCA1s2bJF165dMwUQCuvo0aOmkTrat2+f7wg/3t7eGjlyZIEjdJTU6wcAAADcCAg0AAAAAABQCMuXLzeGP2/YsKEmTZokJycnnTt3Tv/5z3+0f/9+o+zBgwe1YcMGffPNNxozZky+HVx5iY+PN40C0KlTJwUHB+e7j4ODgx555BEj0CBldtiXZaDBx8dHt956q/78809JmXe4P//886b2ZI0mEBwcrNq1a6tz585asmSJpMwARPZAQ/bRHCpVqqQWLVqYjrd7927TclbIpLCuXr2qpKQk0xQJheHr66uIiAhJmXeO5yV756+Pj0+h62/atGm+23O+F2JiYgpdd0GyP6eXLl3Sk08+WaT9s3fwWq1WU30NGzZUq1at8t3f0dFR//d//1cqgQYnJyc1aNAgz+0ODg4KDg42ziE2NtamTPYQjI+PT74d8lkeeeQRI9CQVUdpBhqaN29uWn711Vc1dOhQtW7dWu7u7qZtOUfeyG7Pnj3G43379l3XeyE3OUebySnn+zzn63Hy5EnT5693796qUKFCvsd0cXHRQw89pBkzZkjKDAvs3r1bXbp0yXe/3GQPZ0nSww8/nO/5SLahmdyU1OsHAAAA3AiYcgIAAAAAgALs3r1bkyZNkpR5B/KMGTPk6emppKQkvfTSS6YwQ3bXrl3Tm2++qT/++KPIxwwPDzfNs17YUELOctlHLygr3bt3Nx4fOHDA1OGYPaDQtWtXSVLnzp1z3W61Wk1D/nfr1s3mDuSSGLo/Pj6+yPvcfvvtxuOlS5fmGmo4e/asNm/ebCwXJVhS0MgelStXNi2np6cXuu6CXO9zmv35TExMVEJCgrFcr169QtVRWiEcf39/0xD9ufHz8zMe5/a8Zv9M1alTp8AObCkz+JB9FIfS/lwGBwdrwIABxvKxY8c0dOhQde3aVffdd5+ee+45vf766/rmm2+0f//+PEcAuN73QlxcXL7br/d9nvN5rFu3bqHalbNccV+PrFBTlsK+vwsaTaWkXj8AAADgRsAIDQAAAAAA5OP8+fMaNWqU0tPT5ebmphkzZhidcHPnzjXusq1bt65GjhypZs2aKSIiQt9//71++eUXSdI777yjhQsXFmkUgJyd7IWduiJnueJ01l+v7t27a+rUqZIyQwl//PGH+vTpYxNQyAoytG/fXi4uLkpLS9PRo0cVERGhgIAAnT592pjGIKvenLJ3lheXl5dXkfd59tlntWbNGqWkpCgyMlL/+te/9Pjjj6tBgwbKyMjQoUOHtGDBAiUlJRn7FGWkjsJ0kpeW631Osz+f165dM20r7vu4pOQ1FUF2BT332T9TRWln1apVjYBAWXwuhwwZInd3d3399ddKTU2VlDlFw+XLl3X58mVJ0q+//iopc0SQsWPHmqaHkUr2vZCbgp7rorwWUvHfXwUFL/KS8/1dpUqVYh0/NyXx+gEAAAA3AgINAAAAAADkIT4+XsOHDzeGOX/rrbfUrFkzSZkd9StXrpQkVaxYUZ999pkqVqwoKfNu7HHjxik9PV3Lli3TpUuXtGfPHtNd/QXJOT1BVgdWQS5dupRvPWXB399fLVq00L59+yRljrrQp08fI6wgZc4lnzX1gJeXl9q0aaO//vpLUuY0Ff369TON1uDl5aX27dvbHMvX19e0vH79ept1paFGjRqaPn26Ro0apeTkZEVEROiDDz7Is3zXrl3VqVOnUm9XScj+/DVs2FDfffddsevK+f7L+f7MS2HLlQcfHx9jio/Cfi4lmcI5BX0us4/OkhuLxVLg8VxcXDRo0CD169dP27dv1759+3Tu3DldvXpVERERps74Q4cOafDgwfrhhx9Mne3Zz7Vbt26aPn16gcctSyX1/irud0Zu39MFTQ2U2/FzUxKvHwAAAHAjYMoJAAAAAABykZ6ertdee01nzpyRlHm3bM+ePY3tV69eNaYZ6N27txFmyO6xxx4zHh89erRIx69SpYrp7uRTp04Var/Tp0+blsurcyv7aAp//vmn0tPTTQGFjh07ytn57/sscpt2Imd5V1dXm+PkvCP67Nmz1932wti+fbvmzp2r5OTkfMs5ODjogQce0OTJk8t11IWiyP6cnj17tlCd53nx8PCQt7e3sVzY93HW584eZf9MnT59ulDPT1xcnK5evZprHZJsplLJChHkpShTJFSpUkUhISEaN26c5s6dq59++kkbNmzQ4sWL9dBDDxnlrl27po8//thm3yz2+JrkfB5zfv/lJef7sLjfkwEBAfnWm5eiPJfX8/oBAAAANwICDQAAAAAA5GC1WvX+++8bIwb06dNHzzzzjKlMeHi48bhGjRq51pN9ffbyheHt7a0mTZoYy3/88YfCwsIKbPePP/5oWpfbqAZloUePHsbj+Ph47du3zzTdRJcuXUzlswca/vrrL0VFRWnv3r3GujvuuCPX47Rp08a0nD0EkRur1apt27Zpy5Yt2rJli/bv31/wyeSwc+dOvfjiizpw4ECu2729vdW8eXOFhIRo/vz5GjduXJGmGylv2Z/TlJQU7dy5M9/y165dM57PLVu2mN6nDg4Oat26tbF87Ngx7d69O9/6LBaLfvrpp2K2vvTdeuutxuP4+HhjpJb8/PDDD3nWIUkVKlQwLZ87dy7f+g4fPlzgMfPj4OCgGjVq6LXXXjNGnZGkgwcPmsplfy+cO3euwI74yMhI03uhqN97RVW3bl35+fkZyytXrjRG1MlLWlqa/ve//xnLTk5OxmgxRdW2bVvT8sKFCwscXWPr1q2m0TqKo7CvHwAAAHAjINAAAAAAAEAOP/zwg9Hh1apVK40bN87m7vratWsb686fP59rPdnX161bt8jt6NWrl/E4LS1N7777rtLS0vIsv2LFClPnc+PGjVWrVq0iH7ck1KxZU/Xr1zeWV6xYYQQAHB0dbabfqFmzptHWpKQkzZkzRxkZGZIyh17v2LFjrsepX7++ateubSzPnz8/3+DHb7/9pqFDh2rEiBEaMWJEnqGE/Pzvf/8zOi3vu+8+rVixQjt27NDOnTu1c+dObdy4UV999ZXGjx9vl3PaZx+qPjc9evQwjRjw/vvvKzU1Nc/ys2fPNp7PESNGKCkpybT9zjvvNC2///77SkxMzLO+xYsX23XH7L333mta/uijjxQZGZln+dOnT+vrr782lt3c3EwjmEiSu7u7qlevbiz/9ttveXbM79u3T1u2bMm3jU8++aQefvhhPfzww3r33XfzLOfg4GCaNiH7KBKSdPfddxuPLRaL3nvvvTxHpLBYLJo0aZLxPhgzZozNyBMlzdHRUffcc4+xHBsbqw8++CDfUTO+/PJL03dE165dTaOI5CYlJSXX9Q0aNDAF17Zv365Vq1blWc+1a9c0c+bMfI8lldzrBwAAANwICDQAAAAAAJDN1q1bjQ6n6tWra9q0ablOdeDp6Wl0wK9YsSLXIeK/++4743FxOrYfeOAB05Dvf/31l5566ikdPXrUdBfwtWvXNH36dE2YMMG0/+DBg8t1moPsozQsXbrUaHOLFi1ynaIj+ygNixcvNh536NBBXl5euR7D0dFRzz33nLGcmpqqZ599VmvWrDF1aqalpWnx4sUaP368sc7X11d9+/Yt8nllH23h4sWLOnz4sA4dOpTrf8ePH1dCQkKRj1GSct79/+6772rXrl2mdkZERBjbg4KCFBISYiyfPn1aTz31lA4dOmR638XGxmr69OlatGiRsa5Dhw5q1KiR6Xj33HOPqlWrZiwfO3ZMjz/+uPbs2WN6jbLex1OmTLn+ky5FjRo1MoU0rl69qkcffVTr169Xenq6sT7rPffkk0+aOsQffvhh+fv729SbPeQTHx+vMWPG6PLly8Y6q9WqTZs2adSoUQW2MTU1VadOndKpU6e0ZMkS7dq1K9dye/bsMW3LGby65ZZbTKO8bN++XYMHD7YJDYWHh+uNN94wjZDSp08fVa5cucC2Xq+nnnpKnp6exvKyZcv04osv2gTNrl69qrFjx+qzzz4z1jk5OemFF14o8BgbN27U1q1bdejQIR05csS0/9NPP20q+5///EezZs0yfe4tFotCQ0P1+OOPF2panJJ6/QAAAIAbgXPBRQAAAAAAuDkcP35cY8eOlcVikbe3tz744INcO96zDBo0SK+//rpiY2M1cOBAjRgxQk2bNlVkZKR++OEHLV++XJLUqVMn07DgheXu7q53331XAwcONEZmyOoM9vX1Vb169RQZGZnr8PSPPfaYKSBQHrp376558+ZJkqkjPOd0E9nXL1iwwKZ8zrvZc7r33nu1efNm/frrr5KkmJgYjRs3Tm+//bbq1q0rFxcXnTlzxuaO91dffdXUEVpYISEhWrFihTIyMhQaGqrQ0NAC96lSpYoGDBighx56SC4uLkU+5vWoWbOmaXnVqlU2d5EPHDhQgwYNMpaHDx+u0NBQo+P6+PHjevLJJ1WhQgXVrVtXKSkpOnnypKmj3tfXV6+88orN8V1cXDRx4kQNGjTIGHXj3Llzev755+Xl5aUGDRooKirK1Enu6Ogob29vxcXFXf8TUApef/11HTlyRBcuXJCU+Z579dVX5ebmpnr16ik9PV2nTp0yBRwkqVmzZho8eHCudT722GNasmSJ8VnfuXOn+vbtq1q1aqlixYo6depUrsGp3ISEhBjBrNTUVA0ZMkS33XabESaKj4/XwYMHtXnzZuM1kaT+/fvb1PWf//xHTzzxhHHs0NBQPfjgg/L391etWrUUHx+vkydPmuqpVq2a6f1Umvz9/fXWW29p9OjRxrodO3aoX79+qly5smrVqqWLFy+awiFZhg8frgYNGuRab+XKlY0RD8LCwjR8+HBJmVPKbNy40Sh3//33a8OGDaYpdebPn69vv/1WNWvWVKVKlXTixAlTwKFSpUqKjo7O85xK8vUDAAAA/ukYoQEAAAAAAGXO/T5ixAglJibKyclJU6dOVZ06dfLdp2fPnurZs6ekzLvYhw0bpp49e+rRRx81RhioUKGCXn/99WKPlNC8eXN99NFHNnfZx8XFaffu3bmGGQYMGGB0vpWnhg0bmu7Mz5JXoKFVq1Y2IzE4OjqqW7du+R7HwcFB48eP11133WVan5ycrEOHDmnv3r02YYZRo0bZTB1QWK1btzZ1nhbGlStXNG3aNI0aNcoU1igLHTp0UMOGDYu0j7e3tz755BPTtCFS5qgMu3fv1qFDh0xhBnd3d33wwQd53iHeqlUrvfvuu3JzczOtT0hI0J49e2zu+B85cmSBn7/yVLFiRX366ac2o1GkpKTo0KFDOnbsmE2YoX379po1a5bNc5ClVq1aevXVV03rMjIydOrUKYWGhprCDI899li+wZhHHnnE9DnLyMjQ77//rjlz5uidd97R7NmztWHDBpvO8OzT3GQJCgrSnDlzTKPFSJnfmbt27dKxY8dM9fj5+Wn27Nm5jkJRWnr06KEpU6bYPLdXr15VaGioTZjB0dFRI0aM0KOPPppnnTmnxcmLo6OjpkyZYjMtjtVqVVhYmPbu3WsKMwQHBxf4/VGSrx8AAADwT0egAQAAAABw00tOTtaoUaOMTq/Ro0frtttuK3A/BwcHTZ48WS+//HKunYsdO3bUjz/+qKCgoOtqX7t27fTjjz/qkUceMU13kFObNm00d+5cvfzyy6U+d31hODg4mKadkDKn8ciro9rZ2dmmE7FVq1aqVKlSgcdyd3fXlClTNHny5Hw7wtu3b6+vvvoq347MguzZs0cfffRRsfb9/ffftWTJkmIfuzicnZ314Ycfqn///goODs6zQz2noKAgffnllxoyZEieI5U4OTmpX79+WrRokVq0aJFvfd27d9eCBQvUo0cPOTrm/pNUvXr19Mknn1zX61NWgoKC9MUXX2jEiBEKCAjIs1zNmjX15ptv6sMPP8x3xBcpc5qZ6dOn24yqkcXHx0ejR4/WiBEj8g1JOTs76/3339dbb72lpk2b5nvM9u3b68MPP9Srr76aZ50NGjTQggULNGDAgDxHNXFzc9OAAQO0cOFCBQcH53vM0nD33Xfrhx9+UO/evfMMezg6OqpLly765ptv9Pjjj+f7HI4YMUIPPPCAAgMD83y/ZvHy8tKMGTP02muvKTAwMNcybm5u6t+/v+bPn28TUMuppF8/AAAA4J/MwVrWtwUAAAAAAHADiouL0+HDh3X8+HEFBASoSZMmqlmzZol3MKWmpmrv3r0KCwtTTEyMPD09FRAQoFatWpXpHdH2zGq16uzZszpw4ICioqLk7Oys4OBg1a5dWzVq1LiuutPT09W3b19duXJFUmaI5OWXX1a9evXk7u5uUz4xMVHbt2/X+PHjjbu0e/bsqalTp15XO8paRkaGDh48qFOnTik6OloVKlRQcHCw6tatKz8/vyLXFxMTo507dyo8PFypqany9/dX48aNVa9evX9kp6zFYtGxY8d09OhRRUdHy9HRUX5+fmrevLlq1apV5HPKyMjQ4cOHdeTIEcXGxsrLy0u1a9dW27ZtizxlSdbn4cKFC4qIiFB0dLQqVaqkoKAgBQcH5zqKSn7S0tKMUTViY2NVuXJlBQcHq169evL19S1SXaUlKSlJoaGhunjxouLi4uTl5aWgoCC1adOm1NtosVh06NAhHT9+XNHR0caxb7311mJNcVPSrx8AAADwT0OgAQAAAAAAoJB27typwYMHG8srVqywGYo/N++9954WLlwoSapbt67xGAAAAAAA5I0pJwAAAAAAAAopOjratJyWllao/S5dumQ8rly5com2CQAAAACAG5VzeTcAAAAAAADgn6JWrVqm5ZEjR+qpp55SjRo1cp0KID4+XuvWrdOWLVuMdXXq1Cn1dgIAAAAAcCNgygkAAAAAAIBCSktL06OPPqqzZ88Wa38XFxctXLhQNWvWLOGWAQAAAABw42HKCQAAAAAAgEJycXHRpEmT5OPjU6x9X3nlFcIMAAAAAAAUEiM0AAAAAAAAFFFsbKy++eYb7d69WxcuXNDVq1dzLefh4aFatWqpbt26euaZZ5huAgAAAACAIiDQAAAAAAAAAAA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-      "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x708129d95a90>"
+      "cell_type": "markdown",
+      "id": "a0000030",
+      "metadata": {},
+      "source": [
+        "### 5a: Quadrant bar chart by heating type\n",
+        "\n",
+        "Each bar shows the percentage of weighted households in four outcome bins:\n",
+        "\n",
+        "| Bin | Definition |\n",
+        "|-----|------------|\n",
+        "| SAVE > $1K | Annual energy bill decreases by more than $1,000 |\n",
+        "| SAVE $0-1K | Annual energy bill decreases by $0\u2013$1,000 |\n",
+        "| LOSE $0-1K | Annual energy bill increases by $0\u2013$1,000 |\n",
+        "| LOSE > $1K | Annual energy bill increases by more than $1,000 |\n",
+        "\n",
+        "A bar leaning heavily green indicates that most households in that group would save\n",
+        "money by switching to a heat pump under current rates."
       ]
-     },
-     "execution_count": 53,
-     "metadata": {
-      "image/png": {
-       "height": 519,
-       "width": 1050
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qb_records: list[dict[str, object]] = []\n",
-    "for group in avail_heating:\n",
-    "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
-    "    pct = quadrant_pcts(gdf)\n",
-    "    for q in QUADRANT_ORDER:\n",
-    "        qb_records.append({\"heating_label\": group, \"quadrant\": q, \"pct\": pct[q]})\n",
-    "\n",
-    "qb_plot = pl.DataFrame(qb_records).with_columns(\n",
-    "    pl.col(\"heating_label\").cast(pl.Enum(list(reversed(avail_heating)))),\n",
-    "    pl.col(\"quadrant\").cast(pl.Enum(QUADRANT_ORDER)),\n",
-    ")\n",
-    "\n",
-    "(\n",
-    "    ggplot(qb_plot, aes(x=\"heating_label\", y=\"pct\", fill=\"quadrant\"))\n",
-    "    + geom_col(position=\"stack\", width=0.55)\n",
-    "    + geom_text(\n",
-    "        mapping=aes(label=\"pct\"),\n",
-    "        data=qb_plot.filter(pl.col(\"pct\") >= 3),\n",
-    "        position=position_stack(vjust=0.5),\n",
-    "        format_string=\"{:.1f}%\",\n",
-    "        color=\"white\",\n",
-    "        size=11,\n",
-    "        fontweight=\"bold\",\n",
-    "    )\n",
-    "    + scale_fill_manual(values=QUADRANT_COLORS, breaks=QUADRANT_ORDER)\n",
-    "    + scale_y_continuous(expand=(0, 0, 0.02, 0))\n",
-    "    + coord_flip()\n",
-    "    + guides(fill=False)\n",
-    "    + labs(\n",
-    "        x=\"\",\n",
-    "        y=\"% of weighted households\",\n",
-    "        title=\"Change in total annual energy bill after upgrading to heat pump (upgrade 00 → 02)\",\n",
-    "    )\n",
-    "    + theme_switchbox()\n",
-    "    + theme(figure_size=(10.5, max(3.5, 1.0 + 1.4 * len(avail_heating))))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000032",
-   "metadata": {},
-   "source": [
-    "### 5b: Savings breakdown table\n",
-    "\n",
-    "The table below shows the fraction of weighted households in each savings/loss bin,\n",
-    "broken out by baseline heating type, plus weighted mean and median bill changes.\n",
-    "The bin boundaries ($0, $1k, $2k) are the same as in the quadrant bars above, with\n",
-    "an additional `Save > $2k/yr` bin to capture large savings from oil/propane retrofits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "a0000033",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000031",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x736fac0cdef0>"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {
+            "image/png": {
+              "height": 519,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "qb_records: list[dict[str, object]] = []\n",
+        "for group in avail_heating:\n",
+        "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
+        "    pct = quadrant_pcts(gdf)\n",
+        "    for q in QUADRANT_ORDER:\n",
+        "        qb_records.append({\"heating_label\": group, \"quadrant\": q, \"pct\": pct[q]})\n",
+        "\n",
+        "qb_plot = pl.DataFrame(qb_records).with_columns(\n",
+        "    pl.col(\"heating_label\").cast(pl.Enum(list(reversed(avail_heating)))),\n",
+        "    pl.col(\"quadrant\").cast(pl.Enum(QUADRANT_ORDER)),\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(qb_plot, aes(x=\"heating_label\", y=\"pct\", fill=\"quadrant\"))\n",
+        "    + geom_col(position=\"stack\", width=0.55)\n",
+        "    + geom_text(\n",
+        "        mapping=aes(label=\"pct\"),\n",
+        "        data=qb_plot.filter(pl.col(\"pct\") >= 3),\n",
+        "        position=position_stack(vjust=0.5),\n",
+        "        format_string=\"{:.1f}%\",\n",
+        "        color=\"white\",\n",
+        "        size=11,\n",
+        "        fontweight=\"bold\",\n",
+        "    )\n",
+        "    + scale_fill_manual(values=QUADRANT_COLORS, breaks=QUADRANT_ORDER)\n",
+        "    + scale_y_continuous(expand=(0, 0, 0.02, 0))\n",
+        "    + coord_flip()\n",
+        "    + guides(fill=False)\n",
+        "    + labs(\n",
+        "        x=\"\",\n",
+        "        y=\"% of weighted households\",\n",
+        "        title=\"Change in total annual energy bill after upgrading to heat pump (upgrade 00 \u2192 02)\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(figure_size=(10.5, max(3.5, 1.0 + 1.4 * len(avail_heating))))\n",
+        ")"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\n"
-     ]
+      "cell_type": "markdown",
+      "id": "a0000032",
+      "metadata": {},
+      "source": [
+        "### 5b: Savings breakdown table\n",
+        "\n",
+        "The table below shows the fraction of weighted households in each savings/loss bin,\n",
+        "broken out by baseline heating type, plus weighted mean and median bill changes.\n",
+        "The bin boundaries ($0, $1k, $2k) are the same as in the quadrant bars above, with\n",
+        "an additional `Save > $2k/yr` bin to capture large savings from oil/propane retrofits."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (3, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Baseline heating</th><th>Buildings</th><th>Weighted hholds</th><th>Save &gt; $2k/yr</th><th>Save $1k-$2k/yr</th><th>Save $0-$1k/yr</th><th>Lose $0-$1k/yr</th><th>Lose &gt; $1k/yr</th><th>Mean change ($/yr)</th><th>Median change ($/yr)</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6349536</td><td>&quot;5.5%&quot;</td><td>&quot;8.4%&quot;</td><td>&quot;34.2%&quot;</td><td>&quot;43.0%&quot;</td><td>&quot;8.9%&quot;</td><td>&quot;$-113&quot;</td><td>&quot;$0&quot;</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504</td><td>&quot;20.3%&quot;</td><td>&quot;24.7%&quot;</td><td>&quot;52.2%&quot;</td><td>&quot;2.9%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-1,311&quot;</td><td>&quot;$-879&quot;</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368</td><td>&quot;10.3%&quot;</td><td>&quot;18.0%&quot;</td><td>&quot;70.9%&quot;</td><td>&quot;0.8%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-817&quot;</td><td>&quot;$-428&quot;</td></tr></tbody></table></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000033",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (3, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Baseline heating</th><th>Buildings</th><th>Weighted hholds</th><th>Save &gt; $2k/yr</th><th>Save $1k-$2k/yr</th><th>Save $0-$1k/yr</th><th>Lose $0-$1k/yr</th><th>Lose &gt; $1k/yr</th><th>Mean change ($/yr)</th><th>Median change ($/yr)</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;Fossil fuel&quot;</td><td>29554</td><td>6349536</td><td>&quot;5.5%&quot;</td><td>&quot;8.4%&quot;</td><td>&quot;34.2%&quot;</td><td>&quot;43.0%&quot;</td><td>&quot;8.9%&quot;</td><td>&quot;$-113&quot;</td><td>&quot;$0&quot;</td></tr><tr><td>&quot;Electric resistance&quot;</td><td>3070</td><td>654504</td><td>&quot;20.3%&quot;</td><td>&quot;24.7%&quot;</td><td>&quot;52.2%&quot;</td><td>&quot;2.9%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-1,311&quot;</td><td>&quot;$-879&quot;</td></tr><tr><td>&quot;Existing heat pump&quot;</td><td>918</td><td>198368</td><td>&quot;10.3%&quot;</td><td>&quot;18.0%&quot;</td><td>&quot;70.9%&quot;</td><td>&quot;0.8%&quot;</td><td>&quot;0.0%&quot;</td><td>&quot;$-817&quot;</td><td>&quot;$-428&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (3, 10)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 Baseline  \u2506 Buildings \u2506 Weighted  \u2506 Save >    \u2506 \u2026 \u2506 Lose      \u2506 Lose >    \u2506 Mean      \u2506 Median   \u2502\n",
+              "\u2502 heating   \u2506 ---       \u2506 hholds    \u2506 $2k/yr    \u2506   \u2506 $0-$1k/yr \u2506 $1k/yr    \u2506 change    \u2506 change   \u2502\n",
+              "\u2502 ---       \u2506 i64       \u2506 ---       \u2506 ---       \u2506   \u2506 ---       \u2506 ---       \u2506 ($/yr)    \u2506 ($/yr)   \u2502\n",
+              "\u2502 str       \u2506           \u2506 i64       \u2506 str       \u2506   \u2506 str       \u2506 str       \u2506 ---       \u2506 ---      \u2502\n",
+              "\u2502           \u2506           \u2506           \u2506           \u2506   \u2506           \u2506           \u2506 str       \u2506 str      \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 Fossil    \u2506 29554     \u2506 6349536   \u2506 5.5%      \u2506 \u2026 \u2506 43.0%     \u2506 8.9%      \u2506 $-113     \u2506 $0       \u2502\n",
+              "\u2502 fuel      \u2506           \u2506           \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u2502 Electric  \u2506 3070      \u2506 654504    \u2506 20.3%     \u2506 \u2026 \u2506 2.9%      \u2506 0.0%      \u2506 $-1,311   \u2506 $-879    \u2502\n",
+              "\u2502 resistanc \u2506           \u2506           \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u2502 e         \u2506           \u2506           \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u2502 Existing  \u2506 918       \u2506 198368    \u2506 10.3%     \u2506 \u2026 \u2506 0.8%      \u2506 0.0%      \u2506 $-817     \u2506 $-428    \u2502\n",
+              "\u2502 heat pump \u2506           \u2506           \u2506           \u2506   \u2506           \u2506           \u2506           \u2506          \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (3, 10)\n",
-       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-       "│ Baseline  ┆ Buildings ┆ Weighted  ┆ Save >    ┆ … ┆ Lose      ┆ Lose >    ┆ Mean      ┆ Median   │\n",
-       "│ heating   ┆ ---       ┆ hholds    ┆ $2k/yr    ┆   ┆ $0-$1k/yr ┆ $1k/yr    ┆ change    ┆ change   │\n",
-       "│ ---       ┆ i64       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ($/yr)    ┆ ($/yr)   │\n",
-       "│ str       ┆           ┆ i64       ┆ str       ┆   ┆ str       ┆ str       ┆ ---       ┆ ---      │\n",
-       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆ str       ┆ str      │\n",
-       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-       "│ Fossil    ┆ 29554     ┆ 6349536   ┆ 5.5%      ┆ … ┆ 43.0%     ┆ 8.9%      ┆ $-113     ┆ $0       │\n",
-       "│ fuel      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "│ Electric  ┆ 3070      ┆ 654504    ┆ 20.3%     ┆ … ┆ 2.9%      ┆ 0.0%      ┆ $-1,311   ┆ $-879    │\n",
-       "│ resistanc ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "│ e         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "│ Existing  ┆ 918       ┆ 198368    ┆ 10.3%     ┆ … ┆ 0.8%      ┆ 0.0%      ┆ $-817     ┆ $-428    │\n",
-       "│ heat pump ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
+      "source": [
+        "SAVE_BINS: list[tuple[float, float, str]] = [\n",
+        "    (-float(\"inf\"), -2000, \"Save > $2k/yr\"),\n",
+        "    (-2000, -1000, \"Save $1k-$2k/yr\"),\n",
+        "    (-1000, 0, \"Save $0-$1k/yr\"),\n",
+        "    (0, 1000, \"Lose $0-$1k/yr\"),\n",
+        "    (1000, float(\"inf\"), \"Lose > $1k/yr\"),\n",
+        "]\n",
+        "\n",
+        "savings_rows: list[dict[str, object]] = []\n",
+        "for group in avail_heating:\n",
+        "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
+        "    total_w = cast(float, gdf[\"weight\"].sum())\n",
+        "    row: dict[str, object] = {\n",
+        "        \"Baseline heating\": group,\n",
+        "        \"Buildings\": gdf.height,\n",
+        "        \"Weighted hholds\": round(total_w),\n",
+        "    }\n",
+        "    for lo, hi, label in SAVE_BINS:\n",
+        "        if lo == -float(\"inf\"):\n",
+        "            filt = gdf.filter(pl.col(\"delta\") < hi)\n",
+        "        elif hi == float(\"inf\"):\n",
+        "            filt = gdf.filter(pl.col(\"delta\") >= lo)\n",
+        "        else:\n",
+        "            filt = gdf.filter((pl.col(\"delta\") >= lo) & (pl.col(\"delta\") < hi))\n",
+        "        row[label] = f\"{cast(float, filt['weight'].sum()) / total_w * 100:.1f}%\"\n",
+        "    row[\"Mean change ($/yr)\"] = f\"${weighted_mean(gdf, 'delta'):,.0f}\"\n",
+        "    row[\"Median change ($/yr)\"] = f\"${weighted_quantile(gdf, 'delta', 0.5):,.0f}\"\n",
+        "    savings_rows.append(row)\n",
+        "\n",
+        "savings_df = pl.DataFrame(savings_rows)\n",
+        "print(\"Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\")\n",
+        "display(savings_df)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "SAVE_BINS: list[tuple[float, float, str]] = [\n",
-    "    (-float(\"inf\"), -2000, \"Save > $2k/yr\"),\n",
-    "    (-2000, -1000, \"Save $1k-$2k/yr\"),\n",
-    "    (-1000, 0, \"Save $0-$1k/yr\"),\n",
-    "    (0, 1000, \"Lose $0-$1k/yr\"),\n",
-    "    (1000, float(\"inf\"), \"Lose > $1k/yr\"),\n",
-    "]\n",
-    "\n",
-    "savings_rows: list[dict[str, object]] = []\n",
-    "for group in avail_heating:\n",
-    "    gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n",
-    "    total_w = cast(float, gdf[\"weight\"].sum())\n",
-    "    row: dict[str, object] = {\n",
-    "        \"Baseline heating\": group,\n",
-    "        \"Buildings\": gdf.height,\n",
-    "        \"Weighted hholds\": round(total_w),\n",
-    "    }\n",
-    "    for lo, hi, label in SAVE_BINS:\n",
-    "        if lo == -float(\"inf\"):\n",
-    "            filt = gdf.filter(pl.col(\"delta\") < hi)\n",
-    "        elif hi == float(\"inf\"):\n",
-    "            filt = gdf.filter(pl.col(\"delta\") >= lo)\n",
-    "        else:\n",
-    "            filt = gdf.filter((pl.col(\"delta\") >= lo) & (pl.col(\"delta\") < hi))\n",
-    "        row[label] = f\"{cast(float, filt['weight'].sum()) / total_w * 100:.1f}%\"\n",
-    "    row[\"Mean change ($/yr)\"] = f\"${weighted_mean(gdf, 'delta'):,.0f}\"\n",
-    "    row[\"Median change ($/yr)\"] = f\"${weighted_quantile(gdf, 'delta', 0.5):,.0f}\"\n",
-    "    savings_rows.append(row)\n",
-    "\n",
-    "savings_df = pl.DataFrame(savings_rows)\n",
-    "print(\"Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\")\n",
-    "display(savings_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000034",
-   "metadata": {},
-   "source": [
-    "### 5c: Bill change histogram by heating type\n",
-    "\n",
-    "Distribution of annual bill changes, trimmed to the 1st-99th percentile, faceted by\n",
-    "baseline heating type. Green bars are savings; orange bars are increases. A well-behaved\n",
-    "batch produces a smooth, roughly unimodal distribution centered left of zero for\n",
-    "fossil-fuel homes and a broader, more symmetric distribution for electric resistance\n",
-    "homes (which lose the gas bill but gain less electric savings)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "a0000035",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x70812824cbb0>"
+      "cell_type": "markdown",
+      "id": "a0000034",
+      "metadata": {},
+      "source": [
+        "### 5c: Bill change histogram by heating type\n",
+        "\n",
+        "Distribution of annual bill changes, trimmed to the 1st-99th percentile, faceted by\n",
+        "baseline heating type. Green bars are savings; orange bars are increases. A well-behaved\n",
+        "batch produces a smooth, roughly unimodal distribution centered left of zero for\n",
+        "fossil-fuel homes and a broader, more symmetric distribution for electric resistance\n",
+        "homes (which lose the gas bill but gain less electric savings)."
       ]
-     },
-     "execution_count": 55,
-     "metadata": {
-      "image/png": {
-       "height": 750,
-       "width": 1050
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BIN_WIDTH = 100\n",
-    "_lo = math.floor(weighted_quantile(bill_delta, \"delta\", 0.01) / BIN_WIDTH) * BIN_WIDTH\n",
-    "_hi = math.ceil(weighted_quantile(bill_delta, \"delta\", 0.99) / BIN_WIDTH) * BIN_WIDTH\n",
-    "\n",
-    "hist_data = (\n",
-    "    bill_delta.filter(pl.col(\"heating_label\").is_in(avail_heating))\n",
-    "    .with_columns(\n",
-    "        ((pl.col(\"delta\") / BIN_WIDTH).floor() * BIN_WIDTH + BIN_WIDTH / 2).alias(\"bin_center\"),\n",
-    "        pl.when(pl.col(\"delta\") < 0).then(pl.lit(\"Savings\")).otherwise(pl.lit(\"Increase\")).alias(\"direction\"),\n",
-    "    )\n",
-    "    .filter(pl.col(\"bin_center\").is_between(_lo, _hi))\n",
-    "    .group_by(\"heating_label\", \"bin_center\", \"direction\")\n",
-    "    .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n",
-    "    .with_columns(\n",
-    "        pl.col(\"heating_label\").cast(pl.Enum(avail_heating)),\n",
-    "        pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "(\n",
-    "    ggplot(hist_data, aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"))\n",
-    "    + geom_col(width=BIN_WIDTH * 0.9)\n",
-    "    + facet_wrap(\"heating_label\", ncol=1, scales=\"free_y\")\n",
-    "    + scale_fill_manual(values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]})\n",
-    "    + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n",
-    "    + labs(\n",
-    "        x=\"Annual energy bill change ($/year)\",\n",
-    "        y=\"Weighted households\",\n",
-    "        fill=\"Outcome\",\n",
-    "        title=\"Distribution of annual bill changes after upgrade 02 (1st-99th percentile)\",\n",
-    "    )\n",
-    "    + theme_switchbox()\n",
-    "    + theme(\n",
-    "        figure_size=(10.5, max(4.5, 2.5 * len(avail_heating))),\n",
-    "        legend_position=\"top\",\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000036",
-   "metadata": {},
-   "source": [
-    "### 5d: Utility-level summary\n",
-    "\n",
-    "Per-utility summary of bill-change and BAT results. Helps identify whether one\n",
-    "utility's results are driving state-level patterns, and flags utilities with\n",
-    "implausible outliers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "a0000037",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Per-utility summary (upgrade 00 BAT + upgrade 00→02 bill change):\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000035",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<plotnine.ggplot.ggplot object at 0x736fa6cd51d0>"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {
+            "image/png": {
+              "height": 750,
+              "width": 1050
+            }
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "BIN_WIDTH = 100\n",
+        "_lo = math.floor(weighted_quantile(bill_delta, \"delta\", 0.01) / BIN_WIDTH) * BIN_WIDTH\n",
+        "_hi = math.ceil(weighted_quantile(bill_delta, \"delta\", 0.99) / BIN_WIDTH) * BIN_WIDTH\n",
+        "\n",
+        "hist_data = (\n",
+        "    bill_delta.filter(pl.col(\"heating_label\").is_in(avail_heating))\n",
+        "    .with_columns(\n",
+        "        ((pl.col(\"delta\") / BIN_WIDTH).floor() * BIN_WIDTH + BIN_WIDTH / 2).alias(\"bin_center\"),\n",
+        "        pl.when(pl.col(\"delta\") < 0).then(pl.lit(\"Savings\")).otherwise(pl.lit(\"Increase\")).alias(\"direction\"),\n",
+        "    )\n",
+        "    .filter(pl.col(\"bin_center\").is_between(_lo, _hi))\n",
+        "    .group_by(\"heating_label\", \"bin_center\", \"direction\")\n",
+        "    .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n",
+        "    .with_columns(\n",
+        "        pl.col(\"heating_label\").cast(pl.Enum(avail_heating)),\n",
+        "        pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "(\n",
+        "    ggplot(hist_data, aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"))\n",
+        "    + geom_col(width=BIN_WIDTH * 0.9)\n",
+        "    + facet_wrap(\"heating_label\", ncol=1, scales=\"free_y\")\n",
+        "    + scale_fill_manual(values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]})\n",
+        "    + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n",
+        "    + labs(\n",
+        "        x=\"Annual energy bill change ($/year)\",\n",
+        "        y=\"Weighted households\",\n",
+        "        fill=\"Outcome\",\n",
+        "        title=\"Distribution of annual bill changes after upgrade 02 (1st-99th percentile)\",\n",
+        "    )\n",
+        "    + theme_switchbox()\n",
+        "    + theme(\n",
+        "        figure_size=(10.5, max(4.5, 2.5 * len(avail_heating))),\n",
+        "        legend_position=\"top\",\n",
+        "    )\n",
+        ")"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (7, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>utility</th><th>buildings</th><th>weighted_households</th><th>mean_bill_change</th><th>median_bill_change</th><th>pct_saving</th><th>mean_bat_total_u0</th><th>pct_overpaying_u0</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859</td><td>-721.0</td><td>-471.0</td><td>78.2</td><td>0.0</td><td>39.1</td></tr><tr><td>&quot;coned&quot;</td><td>15435</td><td>3064038</td><td>-156.0</td><td>-69.0</td><td>59.1</td><td>-0.0</td><td>33.2</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1532294</td><td>-44.0</td><td>150.0</td><td>36.0</td><td>-0.0</td><td>36.9</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300</td><td>-242.0</td><td>0.0</td><td>47.6</td><td>0.0</td><td>35.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011</td><td>-352.0</td><td>-124.0</td><td>63.8</td><td>0.0</td><td>41.7</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1029955</td><td>-720.0</td><td>-332.0</td><td>70.8</td><td>-0.0</td><td>42.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481</td><td>29.0</td><td>236.0</td><td>26.4</td><td>-0.0</td><td>32.8</td></tr></tbody></table></div>"
+      "cell_type": "markdown",
+      "id": "a0000036",
+      "metadata": {},
+      "source": [
+        "### 5d: Utility-level summary\n",
+        "\n",
+        "Per-utility summary of bill-change and BAT results. Helps identify whether one\n",
+        "utility's results are driving state-level patterns, and flags utilities with\n",
+        "implausible outliers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0000037",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Per-utility summary (upgrade 00 BAT + upgrade 00\u219202 bill change):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (7, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>utility</th><th>buildings</th><th>weighted_households</th><th>mean_bill_change</th><th>median_bill_change</th><th>pct_saving</th><th>mean_bat_total_u0</th><th>pct_overpaying_u0</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;cenhud&quot;</td><td>1182</td><td>270859</td><td>-721.0</td><td>-471.0</td><td>78.2</td><td>0.0</td><td>39.1</td></tr><tr><td>&quot;coned&quot;</td><td>15435</td><td>3064038</td><td>-156.0</td><td>-69.0</td><td>59.1</td><td>-0.0</td><td>33.2</td></tr><tr><td>&quot;nimo&quot;</td><td>6791</td><td>1532294</td><td>-44.0</td><td>150.0</td><td>36.0</td><td>-0.0</td><td>36.9</td></tr><tr><td>&quot;nyseg&quot;</td><td>3682</td><td>792300</td><td>-242.0</td><td>0.0</td><td>47.6</td><td>0.0</td><td>35.7</td></tr><tr><td>&quot;or&quot;</td><td>829</td><td>211011</td><td>-352.0</td><td>-124.0</td><td>63.8</td><td>0.0</td><td>41.7</td></tr><tr><td>&quot;psegli&quot;</td><td>4427</td><td>1029955</td><td>-720.0</td><td>-332.0</td><td>70.8</td><td>-0.0</td><td>42.9</td></tr><tr><td>&quot;rge&quot;</td><td>1444</td><td>351481</td><td>29.0</td><td>236.0</td><td>26.4</td><td>-0.0</td><td>32.8</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (7, 8)\n",
+              "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
+              "\u2502 utility \u2506 buildings \u2506 weighted_h \u2506 mean_bill_ \u2506 median_bil \u2506 pct_saving \u2506 mean_bat_t \u2506 pct_overp \u2502\n",
+              "\u2502 ---     \u2506 ---       \u2506 ouseholds  \u2506 change     \u2506 l_change   \u2506 ---        \u2506 otal_u0    \u2506 aying_u0  \u2502\n",
+              "\u2502 str     \u2506 i64       \u2506 ---        \u2506 ---        \u2506 ---        \u2506 f64        \u2506 ---        \u2506 ---       \u2502\n",
+              "\u2502         \u2506           \u2506 i64        \u2506 f64        \u2506 f64        \u2506            \u2506 f64        \u2506 f64       \u2502\n",
+              "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n",
+              "\u2502 cenhud  \u2506 1182      \u2506 270859     \u2506 -721.0     \u2506 -471.0     \u2506 78.2       \u2506 0.0        \u2506 39.1      \u2502\n",
+              "\u2502 coned   \u2506 15435     \u2506 3064038    \u2506 -156.0     \u2506 -69.0      \u2506 59.1       \u2506 -0.0       \u2506 33.2      \u2502\n",
+              "\u2502 nimo    \u2506 6791      \u2506 1532294    \u2506 -44.0      \u2506 150.0      \u2506 36.0       \u2506 -0.0       \u2506 36.9      \u2502\n",
+              "\u2502 nyseg   \u2506 3682      \u2506 792300     \u2506 -242.0     \u2506 0.0        \u2506 47.6       \u2506 0.0        \u2506 35.7      \u2502\n",
+              "\u2502 or      \u2506 829       \u2506 211011     \u2506 -352.0     \u2506 -124.0     \u2506 63.8       \u2506 0.0        \u2506 41.7      \u2502\n",
+              "\u2502 psegli  \u2506 4427      \u2506 1029955    \u2506 -720.0     \u2506 -332.0     \u2506 70.8       \u2506 -0.0       \u2506 42.9      \u2502\n",
+              "\u2502 rge     \u2506 1444      \u2506 351481     \u2506 29.0       \u2506 236.0      \u2506 26.4       \u2506 -0.0       \u2506 32.8      \u2502\n",
+              "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "shape: (7, 8)\n",
-       "┌─────────┬───────────┬────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐\n",
-       "│ utility ┆ buildings ┆ weighted_h ┆ mean_bill_ ┆ median_bil ┆ pct_saving ┆ mean_bat_t ┆ pct_overp │\n",
-       "│ ---     ┆ ---       ┆ ouseholds  ┆ change     ┆ l_change   ┆ ---        ┆ otal_u0    ┆ aying_u0  │\n",
-       "│ str     ┆ i64       ┆ ---        ┆ ---        ┆ ---        ┆ f64        ┆ ---        ┆ ---       │\n",
-       "│         ┆           ┆ i64        ┆ f64        ┆ f64        ┆            ┆ f64        ┆ f64       │\n",
-       "╞═════════╪═══════════╪════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡\n",
-       "│ cenhud  ┆ 1182      ┆ 270859     ┆ -721.0     ┆ -471.0     ┆ 78.2       ┆ 0.0        ┆ 39.1      │\n",
-       "│ coned   ┆ 15435     ┆ 3064038    ┆ -156.0     ┆ -69.0      ┆ 59.1       ┆ -0.0       ┆ 33.2      │\n",
-       "│ nimo    ┆ 6791      ┆ 1532294    ┆ -44.0      ┆ 150.0      ┆ 36.0       ┆ -0.0       ┆ 36.9      │\n",
-       "│ nyseg   ┆ 3682      ┆ 792300     ┆ -242.0     ┆ 0.0        ┆ 47.6       ┆ 0.0        ┆ 35.7      │\n",
-       "│ or      ┆ 829       ┆ 211011     ┆ -352.0     ┆ -124.0     ┆ 63.8       ┆ 0.0        ┆ 41.7      │\n",
-       "│ psegli  ┆ 4427      ┆ 1029955    ┆ -720.0     ┆ -332.0     ┆ 70.8       ┆ -0.0       ┆ 42.9      │\n",
-       "│ rge     ┆ 1444      ┆ 351481     ┆ 29.0       ┆ 236.0      ┆ 26.4       ┆ -0.0       ┆ 32.8      │\n",
-       "└─────────┴───────────┴────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘"
+      "source": [
+        "util_bat_u0 = bat_by_scenario[\"Upgrade 00\"]\n",
+        "util_rows: list[dict[str, object]] = []\n",
+        "\n",
+        "for utility in sorted(annual_u0[UTILITY_COL].drop_nulls().unique().to_list()):\n",
+        "    delta_u = bill_delta.filter(pl.col(UTILITY_COL) == utility)\n",
+        "    bat_u = util_bat_u0.filter(pl.col(UTILITY_COL) == utility)\n",
+        "    if delta_u.is_empty() or bat_u.is_empty():\n",
+        "        continue\n",
+        "    util_rows.append(\n",
+        "        {\n",
+        "            \"utility\": utility,\n",
+        "            \"buildings\": delta_u.height,\n",
+        "            \"weighted_households\": round(cast(float, delta_u[\"weight\"].sum())),\n",
+        "            \"mean_bill_change\": round(weighted_mean(delta_u, \"delta\"), 0),\n",
+        "            \"median_bill_change\": round(weighted_quantile(delta_u, \"delta\", 0.5), 0),\n",
+        "            \"pct_saving\": round(\n",
+        "                cast(float, delta_u.filter(pl.col(\"delta\") < 0)[\"weight\"].sum())\n",
+        "                / cast(float, delta_u[\"weight\"].sum())\n",
+        "                * 100,\n",
+        "                1,\n",
+        "            ),\n",
+        "            \"mean_bat_total_u0\": round(weighted_mean(bat_u, \"BAT_percustomer_total\"), 0),\n",
+        "            \"pct_overpaying_u0\": round(\n",
+        "                cast(\n",
+        "                    float,\n",
+        "                    bat_u.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
+        "                )\n",
+        "                / cast(float, bat_u[\"weight\"].sum())\n",
+        "                * 100,\n",
+        "                1,\n",
+        "            ),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "util_df = pl.DataFrame(util_rows)\n",
+        "print(\"Per-utility summary (upgrade 00 BAT + upgrade 00\u219202 bill change):\")\n",
+        "display(util_df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0000038",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "\n",
+        "## Interpretation checklist\n",
+        "\n",
+        "Before using these results in a report or policy analysis:\n",
+        "\n",
+        "1. **Structural checks passed** \u2014 All assertions in Section 3 must pass cleanly. A\n",
+        "   failure indicates a specific post-processing step to investigate.\n",
+        "\n",
+        "2. **Utility coverage looks right** \u2014 Confirm that weighted household totals per\n",
+        "   utility (Section 2a) are stable across scenarios and plausible for the service\n",
+        "   territory.\n",
+        "\n",
+        "3. **Heating-type breakdown is plausible** \u2014 The upgrade 00 heating-type composition\n",
+        "   (Section 2b) should reflect the state's known building stock. A suspiciously low\n",
+        "   fossil-fuel share or high heat-pump share may indicate a ResStock metadata issue.\n",
+        "\n",
+        "4. **Gas null fraction makes sense** \u2014 High gas-null share is expected in all-electric\n",
+        "   territories; investigate if it is unexpectedly low in a gas-dense market.\n",
+        "\n",
+        "5. **BAT signs are correct** \u2014 Under flat default rates, fossil-fuel customers should\n",
+        "   show negative BAT (they underpay); HP/ER customers should show positive BAT\n",
+        "   (they overpay). If this pattern is reversed, check the residual allocation\n",
+        "   configuration in the CAIRO scenario YAML.\n",
+        "\n",
+        "6. **Cross-subsidy magnitude is reasonable** \u2014 Compare against prior state runs at\n",
+        "   similar utility scales to identify implausible outliers.\n",
+        "\n",
+        "7. **Bill change signs match fuel-cost logic** \u2014 Fossil-fuel homes should generally\n",
+        "   save in warmer states (low heating loads) and see more mixed results in cold\n",
+        "   states with high gas demand. Oil/propane homes typically show larger savings.\n",
+        "\n",
+        "8. **To review another state or batch** \u2014 Change `STATE` and `BATCH` in the\n",
+        "   Parameters cell, restart the kernel, and run all cells."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "util_bat_u0 = bat_by_scenario[\"Upgrade 00\"]\n",
-    "util_rows: list[dict[str, object]] = []\n",
-    "\n",
-    "for utility in sorted(annual_u0[UTILITY_COL].drop_nulls().unique().to_list()):\n",
-    "    delta_u = bill_delta.filter(pl.col(UTILITY_COL) == utility)\n",
-    "    bat_u = util_bat_u0.filter(pl.col(UTILITY_COL) == utility)\n",
-    "    if delta_u.is_empty() or bat_u.is_empty():\n",
-    "        continue\n",
-    "    util_rows.append(\n",
-    "        {\n",
-    "            \"utility\": utility,\n",
-    "            \"buildings\": delta_u.height,\n",
-    "            \"weighted_households\": round(cast(float, delta_u[\"weight\"].sum())),\n",
-    "            \"mean_bill_change\": round(weighted_mean(delta_u, \"delta\"), 0),\n",
-    "            \"median_bill_change\": round(weighted_quantile(delta_u, \"delta\", 0.5), 0),\n",
-    "            \"pct_saving\": round(\n",
-    "                cast(float, delta_u.filter(pl.col(\"delta\") < 0)[\"weight\"].sum())\n",
-    "                / cast(float, delta_u[\"weight\"].sum())\n",
-    "                * 100,\n",
-    "                1,\n",
-    "            ),\n",
-    "            \"mean_bat_total_u0\": round(weighted_mean(bat_u, \"BAT_percustomer_total\"), 0),\n",
-    "            \"pct_overpaying_u0\": round(\n",
-    "                cast(\n",
-    "                    float,\n",
-    "                    bat_u.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n",
-    "                )\n",
-    "                / cast(float, bat_u[\"weight\"].sum())\n",
-    "                * 100,\n",
-    "                1,\n",
-    "            ),\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "util_df = pl.DataFrame(util_rows)\n",
-    "print(\"Per-utility summary (upgrade 00 BAT + upgrade 00→02 bill change):\")\n",
-    "display(util_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0000038",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "## Interpretation checklist\n",
-    "\n",
-    "Before using these results in a report or policy analysis:\n",
-    "\n",
-    "1. **Structural checks passed** — All assertions in Section 3 must pass cleanly. A\n",
-    "   failure indicates a specific post-processing step to investigate.\n",
-    "\n",
-    "2. **Utility coverage looks right** — Confirm that weighted household totals per\n",
-    "   utility (Section 2a) are stable across scenarios and plausible for the service\n",
-    "   territory.\n",
-    "\n",
-    "3. **Heating-type breakdown is plausible** — The upgrade 00 heating-type composition\n",
-    "   (Section 2b) should reflect the state's known building stock. A suspiciously low\n",
-    "   fossil-fuel share or high heat-pump share may indicate a ResStock metadata issue.\n",
-    "\n",
-    "4. **Gas null fraction makes sense** — High gas-null share is expected in all-electric\n",
-    "   territories; investigate if it is unexpectedly low in a gas-dense market.\n",
-    "\n",
-    "5. **BAT signs are correct** — Under flat default rates, fossil-fuel customers should\n",
-    "   show negative BAT (they underpay); HP/ER customers should show positive BAT\n",
-    "   (they overpay). If this pattern is reversed, check the residual allocation\n",
-    "   configuration in the CAIRO scenario YAML.\n",
-    "\n",
-    "6. **Cross-subsidy magnitude is reasonable** — Compare against prior state runs at\n",
-    "   similar utility scales to identify implausible outliers.\n",
-    "\n",
-    "7. **Bill change signs match fuel-cost logic** — Fossil-fuel homes should generally\n",
-    "   save in warmer states (low heating loads) and see more mixed results in cold\n",
-    "   states with high gas demand. Oil/propane homes typically show larger savings.\n",
-    "\n",
-    "8. **To review another state or batch** — Change `STATE` and `BATCH` in the\n",
-    "   Parameters cell, restart the kernel, and run all cells."
-   ]
-  }
- ],
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 }