{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.0"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03 — Neighbourhood Prioritization & Targeting\n\nRank postal codes by normalized thermal intensity within structure type groups. Identify priority neighbourhoods for retrofit incentive targeting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 — Load Normalized Results"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.set_theme(style='whitegrid', palette='colorblind')\n\ndf = pd.read_csv('data/ces_normalized_results.csv')\nprint(f'Loaded {len(df)} qualified postal codes')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 — Within-Group Ranking and Priority Quartile"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Rank within each structure type group\ndf['group_rank'] = df.groupby('structure_type')['thermal_intensity'].rank(\n    ascending=False, method='min').astype(int)\ndf['group_size'] = df.groupby('structure_type')['postal_code'].transform('count')\ndf['group_percentile'] = 1 - (df.group_rank - 1) / df.group_size\n\n# Priority: top quartile within group\ndf['priority'] = df.group_percentile >= 0.75\n\nn_priority = df.priority.sum()\nprint(f'Priority postal codes (top quartile): {n_priority} of {len(df)}')\n\n# Summary by structure type\npriority_summary = df.groupby('structure_type').agg(\n    total=('postal_code', 'count'),\n    priority=('priority', 'sum'),\n    mean_intensity=('thermal_intensity', 'mean'),\n    p75_intensity=('thermal_intensity', lambda x: x.quantile(0.75)),\n).round(6)\nprint('\\nPriority summary by structure type:')\npriority_summary"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 — Priority Postal Code Map\n\nVisualize which postal codes are flagged as priority vs. non-priority."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 6))\n\n# Sort by thermal intensity for visual clarity\nplot_df = df.sort_values('thermal_intensity', ascending=False).reset_index(drop=True)\n\ncolors = ['#e74c3c' if p else '#95a5a6' for p in plot_df.priority]\nax.bar(range(len(plot_df)), plot_df.thermal_intensity, color=colors, width=1.0)\nax.set_xlabel('Postal Code (sorted by thermal intensity)')\nax.set_ylabel('Normalized Thermal Intensity')\nax.set_title('All Postal Codes — Red = Priority Quartile')\n\n# Custom legend\nfrom matplotlib.patches import Patch\nax.legend(handles=[Patch(color='#e74c3c', label='Priority (top 25%)'),\n                    Patch(color='#95a5a6', label='Non-priority')],\n          loc='upper right')\nplt.tight_layout()\nplt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 — Priority Targeting Table"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "target_table = (df[df.priority]\n    .sort_values('thermal_intensity', ascending=False)\n    [['postal_code', 'structure_type', 'avg_storeys', 'avg_footprint_m2',\n      'basement_fraction', 'property_count', 'thermal_intensity',\n      'r_squared', 'group_rank']]\n    .reset_index(drop=True))\n\ntarget_table.index += 1\ntarget_table.index.name = 'priority_rank'\n\nprint(f'Top 15 priority postal codes:')\ntarget_table.head(15)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 — Thermal Intensity Distribution: Priority vs. Non-Priority"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n\n# By priority flag\nfor label, color in [('Priority', '#e74c3c'), ('Non-priority', '#95a5a6')]:\n    subset = df[df.priority == (label == 'Priority')]\n    axes[0].hist(subset.thermal_intensity, bins=20, alpha=0.6, color=color, label=label)\naxes[0].set_xlabel('Normalized Thermal Intensity')\naxes[0].set_ylabel('Postal Codes')\naxes[0].set_title('Distribution by Priority Status')\naxes[0].legend()\n\n# By structure type within priority\nfor stype in df.structure_type.unique():\n    subset = df[(df.priority) & (df.structure_type == stype)]\n    if len(subset) > 0:\n        axes[1].hist(subset.thermal_intensity, bins=10, alpha=0.5, label=stype)\naxes[1].set_xlabel('Normalized Thermal Intensity')\naxes[1].set_title('Priority Postal Codes by Structure Type')\naxes[1].legend()\n\nplt.tight_layout()\nplt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 — Sensitivity Analysis: Normalization Parameters\n\nHow sensitive is the ranking to the assumed basement heating fraction and floor height?"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from scipy.stats import spearmanr\n\n# Baseline ranking\nbaseline_rank = df.thermal_intensity.rank(ascending=False)\n\nsensitivities = []\nfor bsmt_mult in [0.0, 0.25, 0.5, 0.75, 1.0]:\n    for floor_h in [2.4, 2.7, 3.0]:\n        above = df.avg_footprint_m2 * df.avg_storeys * floor_h\n        below = df.avg_footprint_m2 * bsmt_mult * 2.4\n        vol = above + below\n        intensity = (df.slope / df.property_count) / vol\n        rho, _ = spearmanr(baseline_rank, intensity.rank(ascending=False))\n        sensitivities.append({\n            'bsmt_fraction': bsmt_mult,\n            'floor_height': floor_h,\n            'rank_correlation': rho\n        })\n\nsens_df = pd.DataFrame(sensitivities)\nprint('Rank stability under parameter variation:')\nprint(f'Min Spearman rho: {sens_df.rank_correlation.min():.3f}')\nprint(f'Max Spearman rho: {sens_df.rank_correlation.max():.3f}')\n\npivot = sens_df.pivot(index='bsmt_fraction', columns='floor_height', values='rank_correlation')\nfig, ax = plt.subplots(figsize=(8, 5))\nsns.heatmap(pivot, annot=True, fmt='.3f', cmap='YlGn', ax=ax, vmin=0.9, vmax=1.0)\nax.set_title('Rank Stability: Spearman ρ vs. Baseline')\nax.set_xlabel('Floor Height (m)')\nax.set_ylabel('Basement Heating Fraction')\nplt.tight_layout()\nplt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7 — Year-Over-Year Monitoring Potential\n\nIf the screening is refreshed annually, changes in postal code thermal intensity can serve as a macro-level M&V signal for neighbourhood-scale retrofit programs."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Simulate year 2 with slight improvement in priority postal codes\ndf['intensity_yr2'] = df.thermal_intensity * np.where(\n    df.priority, np.random.uniform(0.90, 0.98, len(df)),\n    np.random.uniform(0.97, 1.03, len(df)))\n\ndf['pct_change'] = (df.intensity_yr2 - df.thermal_intensity) / df.thermal_intensity * 100\n\nfig, ax = plt.subplots(figsize=(10, 5))\nax.scatter(df[~df.priority].thermal_intensity, df[~df.priority].pct_change,\n           alpha=0.4, s=20, color='#95a5a6', label='Non-priority')\nax.scatter(df[df.priority].thermal_intensity, df[df.priority].pct_change,\n           alpha=0.6, s=30, color='#e74c3c', label='Priority')\nax.axhline(0, color='black', linewidth=0.5)\nax.set_xlabel('Baseline Thermal Intensity')\nax.set_ylabel('Year-over-Year Change (%)')\nax.set_title('Simulated Annual Refresh — Priority Areas Show Improvement')\nax.legend()\nplt.tight_layout()\nplt.show()\n\nprint(f'Priority postal codes mean change: {df[df.priority].pct_change.mean():.1f}%')\nprint(f'Non-priority mean change: {df[~df.priority].pct_change.mean():.1f}%')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.8 — Export Targeting Output"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Full output\ncols = ['postal_code', 'structure_type', 'avg_storeys', 'avg_footprint_m2',\n        'basement_fraction', 'property_count', 'slope', 'r_squared',\n        'thermal_intensity', 'group_rank', 'group_percentile', 'priority']\noutput = df[cols]\noutput.to_csv('data/ces_targeting_output.csv', index=False)\n\n# Priority-only\ntarget_only = output[output.priority].sort_values('thermal_intensity', ascending=False)\ntarget_only.to_csv('data/ces_priority_postal_codes.csv', index=False)\n\nprint(f'Exported {len(output)} postal codes ({len(target_only)} priority)')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n**Summary:** The screening identified the top quartile of postal codes by normalized thermal intensity within each structure type group. These neighbourhoods represent the strongest candidates for targeted retrofit incentive programs — where building envelopes are underperforming relative to their building stock characteristics, and where incentive dollars are most likely to produce measurable gas savings."
   ]
  }
 ]
}