{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4735f437",
   "metadata": {},
   "source": [
    "# U.S. gasoline supply, demand, and stocks\n",
    "\n",
    "Estimate monthly inventory builds and draws, forecast total gasoline stocks, and compare forecast accuracy with seasonal and unchanged-stock benchmarks.\n",
    "\n",
    "Note: Models used in this notebook are for fun. I wanted to use the models that I have learned in my courses in school. The models aren't meant to be accurate as there is lots of information I do not have access to make an accurate model. For example, I do not have access to refinery turnarounds, shipping data, etc. This notebook is meant to help me gain a better understanding of EIA data as well as how basic supply and demand is supposed to be modeled."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "data-refresh-20260916",
   "metadata": {},
   "source": [
    "### Data refresh — September 16, 2026\n",
    "\n",
    "Downloaded EIA data again. The latest month is still June 2026, and the model inputs are unchanged. The full rerun gives the same model choices, forecasts, and error figures. `seasonal_change` remains the one-month method and `seasonal_naive` remains the twelve-month method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b6d755",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Use monthly EIA data from January 2007 for the five Petroleum Administration for Defense Districts (PADDs). Total gasoline stocks include finished gasoline and blending components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7f322ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:39.746330Z",
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     "iopub.status.idle": "2026-09-16T21:54:40.790756Z",
     "shell.execute_reply": "2026-09-16T21:54:40.790301Z"
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   "outputs": [],
   "source": [
    "# Run cells from top to bottom: later cells reuse the data, functions, and fitted\n",
    "# models created here. pandas/NumPy handle monthly tables and arrays, sklearn\n",
    "# and XGBoost fit models, matplotlib draws charts, and joblib saves estimators.\n",
    "from pathlib import Path\n",
    "from datetime import datetime, timezone\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "import hashlib, io, json, platform, warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests, joblib, sklearn, xgboost\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, VotingRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, SplineTransformer\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit\n",
    "from xgboost import XGBRegressor\n",
    "from IPython.display import display\n",
    "\n",
    "# Only input data are external. No local Python module is imported.\n",
    "# Support execution from either the product folder or the commodities workspace.\n",
    "# Fail early if the saved EIA observation CSV cannot be found.\n",
    "HERE=Path.cwd()\n",
    "if not (HERE/'data/eia_observations.csv').exists():\n",
    "    HERE=HERE/'oil'/'us_snd_gasoline'\n",
    "assert (HERE/'data/eia_observations.csv').exists(), 'Keep the data folder beside this notebook.'\n",
    "OUTPUT=HERE/'model_output'\n",
    "# These settings affect chart/table presentation only, not stored model values.\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "pd.set_option('display.max_columns',20)\n",
    "pd.set_option('display.float_format',lambda x:f'{x:,.2f}')\n",
    "\n",
    "# Dates represent observation months using their first day. Both exclusion\n",
    "# endpoints are inclusive; keep the raw calendar intact so lags retain their meaning.\n",
    "COVID_START = pd.Timestamp('2020-03-01')\n",
    "COVID_END = pd.Timestamp('2021-03-01')\n",
    "\n",
    "\n",
    "# Return one Boolean per date: True means outside the excluded COVID interval.\n",
    "def outside_covid(months):\n",
    "    dates = pd.DatetimeIndex(months)\n",
    "    return ~((dates >= COVID_START) & (dates <= COVID_END))\n",
    "\n",
    "\n",
    "# Filter fitting rows after constructing lagged features on the intact calendar.\n",
    "# A clean target is insufficient if one of its direct lag inputs is excluded.\n",
    "def training_rows(frame):\n",
    "    \"\"\"Exclude COVID targets and any training row whose direct inputs touch COVID.\"\"\"\n",
    "    dates = pd.DatetimeIndex(frame.month)\n",
    "    keep = outside_covid(dates)\n",
    "    # lag1 stocks/flows, lag2 for the latest change, lag12 for annual stocks.\n",
    "    for lag in [1, 2, 12]:\n",
    "        keep &= outside_covid(dates - pd.DateOffset(months=lag))\n",
    "    return frame.loc[keep]\n",
    "\n",
    "\n",
    "# Create chronological expanding-window folds; never shuffle time-series rows.\n",
    "# This filter concerns target dates; fitting applies the stricter lag-input filter.\n",
    "def validation_splits(frame, folds=10, test_size=None):\n",
    "    \"\"\"Split eligible target dates, retaining real calendar positions and lag dates.\"\"\"\n",
    "    allowed = np.flatnonzero(outside_covid(frame.month))\n",
    "    splitter = TimeSeriesSplit(n_splits=folds, test_size=test_size)\n",
    "    # Map positions within eligible dates back to the original frame. A test block\n",
    "    # of 12 eligible observations may span more than 12 calendar months.\n",
    "    return [(allowed[tr], allowed[te]) for tr, te in splitter.split(allowed)]\n",
    "\n",
    "\n",
    "# Search backward in annual steps for six eligible year-long development paths.\n",
    "# Each origin leaves its next 12 forecast months inside development history.\n",
    "def recursive_origins(development_end, count=6):\n",
    "    \"\"\"Use six full test years that do not overlap the excluded interval.\"\"\"\n",
    "    origins = []\n",
    "    cutoff = development_end - pd.DateOffset(years=1)\n",
    "    while len(origins) < count:\n",
    "        months = pd.date_range(cutoff + pd.offsets.MonthBegin(), periods=12, freq='MS')\n",
    "        if outside_covid(months).all() and outside_covid([cutoff]).all():\n",
    "            origins.append(cutoff)\n",
    "        cutoff -= pd.DateOffset(years=1)\n",
    "    return sorted(origins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2bedfc",
   "metadata": {},
   "source": [
    "### Sources and units\n",
    "\n",
    "Stocks and monthly flows are in thousand barrels; charts use million barrels. Product supplied measures implied domestic consumption, not retail sales.\n",
    "\n",
    "Net refinery and blender production equals finished gasoline net production less blending-component net inputs, avoiding double counting. See [EIA supply and disposition definitions](https://www.eia.gov/dnav/pet/TblDefs/pet_sum_snd_tbldef2.asp).\n",
    "\n",
    "Sparse unreported flows are assumed zero and flagged, including unpublished blending-component biofuel production in PADDs 3 and 4. Withheld and unavailable values remain missing. All PADDs must share a complete monthly calendar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75df4ae",
   "metadata": {},
   "source": [
    "The series map below identifies source data and balance signs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcdb3824",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.792340Z",
     "iopub.status.busy": "2026-09-16T21:54:40.792095Z",
     "iopub.status.idle": "2026-09-16T21:54:40.795232Z",
     "shell.execute_reply": "2026-09-16T21:54:40.794870Z"
    }
   },
   "outputs": [],
   "source": [
    "# PADDs are the five Petroleum Administration for Defense Districts. Series\n",
    "# templates substitute {p} with the district number when identifying source data.\n",
    "PADD_NAMES = {1: 'East Coast', 2: 'Midwest', 3: 'Gulf Coast', 4: 'Rocky Mountain', 5: 'West Coast'}\n",
    "# Total motor gasoline = finished motor gasoline + motor gasoline blending components.\n",
    "# Preserve each constituent series; derive total net production by subtracting\n",
    "# blending-component refinery/blender NET inputs from finished net production.\n",
    "FINISHED_CODES = {'production_kb': 'MGFRPP{p}1', 'demand_kb': 'MGFUPP{p}1',\n",
    "    'imports_kb': 'MGFIMP{p}1', 'exports_kb': 'MGFEXP{p}1',\n",
    "    'net_receipts_kb': 'MGFNRP{p}1', 'adjustments_kb': 'MGFUA_R{p}0_1',\n",
    "    'biofuels_kb': 'M_EPM0F_YNP_R{p}0_MBBL',\n",
    "    'stock_kb': 'MGFSTP{p}1', 'reported_stock_change_kb': 'MGFSCP{p}1'}\n",
    "BLENDING_CODES = {'net_inputs_kb': 'MBCRIP{p}1', 'demand_kb': 'MBCUPP{p}1',\n",
    "    'imports_kb': 'MBCIMP{p}1', 'exports_kb': 'MBCEXP{p}1',\n",
    "    'net_receipts_kb': 'MBCNRP{p}1', 'adjustments_kb': 'MBCUA_R{p}0_1',\n",
    "    'biofuels_kb': 'M_EPOBG_YNP_R{p}0_MBBL',\n",
    "    'stock_kb': 'MBCSTP{p}1', 'reported_stock_change_kb': 'MBCSCP{p}1'}\n",
    "CODES = {**{'finished_' + c: v for c,v in FINISHED_CODES.items()},\n",
    "         **{'blending_' + c: v for c,v in BLENDING_CODES.items()},\n",
    "         'stock_kb': 'MGTSTP{p}1'}\n",
    "# Keep this order aligned with SIGNS: the dot product uses +1 for supply\n",
    "# additions and -1 for disposition to reconstruct net supply.\n",
    "FLOWS = ['production_kb', 'demand_kb', 'imports_kb', 'exports_kb',\n",
    "         'net_receipts_kb', 'adjustments_kb', 'biofuels_kb']\n",
    "SIGNS = [1, -1, 1, -1, 1, 1, 1]\n",
    "SPARSE = {prefix + c for prefix in ['finished_', 'blending_']\n",
    "          for c in ['imports_kb', 'exports_kb', 'biofuels_kb', 'net_receipts_kb']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b33b471",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.796237Z",
     "iopub.status.busy": "2026-09-16T21:54:40.796128Z",
     "iopub.status.idle": "2026-09-16T21:54:40.802256Z",
     "shell.execute_reply": "2026-09-16T21:54:40.801706Z"
    }
   },
   "outputs": [],
   "source": [
    "# Return one row per PADD/month with flows, month-end stocks, assumption flags,\n",
    "# and balance diagnostics, starting from the saved long-form EIA observations.\n",
    "def load_panel(data_dir=HERE / 'data', start='2007-01-01'):\n",
    "    # Parse timestamps for exact calendar reindexing and month-length calculations.\n",
    "    raw = pd.read_csv(Path(data_dir) / 'eia_observations.csv', parse_dates=['month'])\n",
    "    # The pivot requires a single observation per month/PADD/component combination.\n",
    "    if raw.duplicated(['month', 'padd', 'component']).any():\n",
    "        raise ValueError('Duplicate EIA observations')\n",
    "    panels = []\n",
    "    for p in PADD_NAMES:\n",
    "        r = raw[raw.padd.eq(p)]\n",
    "        # Use the latest observed stock month as this district's provisional endpoint.\n",
    "        end = r[r.component.eq('stock_kb') & r.value.notna()].month.max()\n",
    "        index = pd.date_range(start, end, freq='MS')\n",
    "        # Turn component names into columns, then expose absent months on a continuous\n",
    "        # month-start calendar. Reindexing does not interpolate missing observations.\n",
    "        wide = r.pivot(index='month', columns='component', values='value').reindex(index)\n",
    "        for c in sorted(SPARSE):\n",
    "            rows = r[r.component.eq(c)].set_index('month')\n",
    "            # Missing calendar rows in sparse trade/biofuel sheets are an explicit\n",
    "            # no-reported-flow assumption, not interpolation from future values.\n",
    "            absent = ~wide.index.isin(rows.index)\n",
    "            # Combine source zero flags with wholly absent sparse-series rows. An explicit\n",
    "            # unavailable value on an existing row remains missing instead of becoming zero.\n",
    "            wide[c + '_assumed_zero'] = rows.assumed_zero.reindex(index).astype(\"boolean\").fillna(False).astype(bool) | absent\n",
    "            wide.loc[absent, c] = 0.0\n",
    "        wide['padd'] = p\n",
    "        wide['padd_name'] = PADD_NAMES[p]\n",
    "        wide.index.name = 'month'\n",
    "        panels.append(wide.reset_index())\n",
    "    panel = pd.concat(panels, ignore_index=True)\n",
    "    core = list(CODES)\n",
    "    # Find the last month complete in all five districts, then trim to a shared\n",
    "    # endpoint so national totals compare the same observation months.\n",
    "    valid = panel[core].notna().all(axis=1)\n",
    "    common = panel[valid].groupby('month').padd.nunique()\n",
    "    end = common[common.eq(5)].index.max()\n",
    "    panel = panel[panel.month.le(end)].copy()\n",
    "    # Earlier unresolved gaps still fail validation: trimming the endpoint does not\n",
    "    # authorize filling withheld values or dropping an incomplete district.\n",
    "    if panel[core].isna().any().any():\n",
    "        raise ValueError('Unresolved missing core data; inspect the source snapshots')\n",
    "    if not panel.groupby('padd').size().eq(len(pd.date_range(start, end, freq='MS'))).all():\n",
    "        raise ValueError('Incomplete PADD calendar')\n",
    "    # Boundary reconciliation: internal blending inputs offset finished output.\n",
    "    # Subtract blending-component net inputs from finished gasoline net output to\n",
    "    # avoid counting an internal transfer as production of total gasoline.\n",
    "    panel['production_kb'] = panel.finished_production_kb - panel.blending_net_inputs_kb\n",
    "    for c in FLOWS[1:] + ['reported_stock_change_kb']:\n",
    "        panel[c] = panel['finished_' + c] + panel['blending_' + c]\n",
    "    # Compare published total stocks with finished plus blending stocks; tolerate\n",
    "    # 1 kb of rounding while retaining the independently published total as target.\n",
    "    panel['stock_component_residual_kb'] = panel.stock_kb - panel.finished_stock_kb - panel.blending_stock_kb\n",
    "    if panel.stock_component_residual_kb.abs().max() > 1:\n",
    "        raise ValueError('Published total gasoline stocks do not match the two constituent stocks')\n",
    "    panel['total_gasoline_stock_kb'] = panel.stock_kb  # explicit compatibility alias\n",
    "    # Shift within each district so prior stock never comes from another PADD.\n",
    "    # The first month has no predecessor and therefore a missing stock change.\n",
    "    panel['previous_stock_kb'] = panel.groupby('padd').stock_kb.shift(1)\n",
    "    # Gasoline flows are monthly thousand barrels, so their signed sum can be\n",
    "    # compared directly with the month-to-month stock change.\n",
    "    panel['balance_kb'] = panel[FLOWS].to_numpy() @ SIGNS\n",
    "    panel['stock_change_kb'] = panel.stock_kb - panel.previous_stock_kb\n",
    "    panel['accounting_residual_kb'] = panel.stock_change_kb - panel.balance_kb\n",
    "    # Separate stock-level versus reported-change discrepancies from reported-change\n",
    "    # versus flow discrepancies; together they explain the accounting residual.\n",
    "    panel['reported_change_residual_kb'] = panel.stock_change_kb - panel.reported_stock_change_kb\n",
    "    panel['flow_reporting_residual_kb'] = panel.reported_stock_change_kb - panel.balance_kb\n",
    "    return panel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f69b40d",
   "metadata": {},
   "source": [
    "Run All uses saved data. Set `REFRESH_SOURCES=True` to download a new EIA release."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fae797d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.803584Z",
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     "shell.execute_reply": "2026-09-16T21:54:40.808301Z"
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   },
   "outputs": [],
   "source": [
    "# Optional refresh downloads and archives source bytes, creates normalized\n",
    "# observations and provenance, then validates the result through load_panel.\n",
    "def refresh_data(data_dir=HERE / 'data'):\n",
    "    data_dir = Path(data_dir)\n",
    "    raw_dir = data_dir / 'raw'\n",
    "    raw_dir.mkdir(parents=True, exist_ok=True)\n",
    "    def download(task):\n",
    "        p, component, code = task\n",
    "        if component == 'blending_biofuels_kb' and p in [3, 4]:\n",
    "            # EIA's balance page has no biofuel-production series for this\n",
    "            # product/region. Archive that page and flag the zero convention.\n",
    "            url = f'https://www.eia.gov/dnav/pet/pet_sum_snd_d_r{p}0_mbbl_m_cur.htm'\n",
    "            response = requests.get(url, timeout=60)\n",
    "            # Reject HTTP errors before interpreting the response as valid EIA source data.\n",
    "            response.raise_for_status()\n",
    "            if 'Motor Gasoline Blend. Comp.' not in response.text or code in response.text:\n",
    "                raise ValueError(f'Review structural-zero model for {code}')\n",
    "            filename = f'blending_biofuels_padd{p}_source.html'\n",
    "            (raw_dir / filename).write_bytes(response.content)\n",
    "            months = pd.date_range('1981-01-01', pd.Timestamp.now(), freq='MS')\n",
    "            frame = pd.DataFrame({'month': months, 'padd': p, 'component': component,\n",
    "                'value': 0.0, 'assumed_zero': True, 'series_id': code})\n",
    "            return frame, {'padd': p, 'component': component, 'series_id': code,\n",
    "                'url': url, 'title': 'No published blending-component biofuel production series; assumed zero',\n",
    "                'source_file': filename, 'kind': 'structural_zero',\n",
    "                'sha256': hashlib.sha256(response.content).hexdigest(),\n",
    "                'retrieved_utc': datetime.now(timezone.utc).isoformat(), 'rows': len(frame)}\n",
    "        url = f'https://www.eia.gov/dnav/pet/hist_xls/{code}m.xls'\n",
    "        response = requests.get(url, timeout=60)\n",
    "        # Reject HTTP errors before interpreting the response as valid EIA source data.\n",
    "        response.raise_for_status()\n",
    "        content = response.content\n",
    "        # Read the worksheet below its header rows, retaining text markers so unavailable\n",
    "        # and withheld observations remain distinguishable from genuinely blank cells.\n",
    "        table = pd.read_excel(io.BytesIO(content), sheet_name='Data 1', header=2, keep_default_na=False)\n",
    "        if table.shape[1] != 2 or 'Thousand Barrels' not in str(table.columns[1]):\n",
    "            raise ValueError(f'Unexpected units/schema: {code}')\n",
    "        (raw_dir / f'{code}m.xls').write_bytes(content)\n",
    "        original = table.iloc[:, 1]\n",
    "        values = pd.to_numeric(original, errors='coerce')\n",
    "        # Excel empty cells correspond to no-data-reported on these sparse series.\n",
    "        # Preserve text markers so W/NA cannot silently turn into zero.\n",
    "        empty = original.isna() | original.astype(str).str.strip().isin(['', '-'])\n",
    "        zero = values.isna() & empty & (component in SPARSE)\n",
    "        values = values.mask(zero, 0.0)\n",
    "        frame = pd.DataFrame({'month': pd.to_datetime(table.iloc[:, 0]).dt.to_period('M').dt.to_timestamp(),\n",
    "            'padd': p, 'component': component, 'value': values, 'assumed_zero': zero, 'series_id': code})\n",
    "        # Sparse worksheets sometimes stop before the common archive endpoint;\n",
    "        # calendar extension is handled explicitly by load_panel, with flags.\n",
    "        # Record URL, retrieval time, and SHA-256 digest to identify the exact source\n",
    "        # bytes underlying the observation snapshot.\n",
    "        meta = {'padd': p, 'component': component, 'series_id': code, 'url': url,\n",
    "                'source_file': f'{code}m.xls', 'kind': 'observed_series',\n",
    "                'title': str(table.columns[1]), 'sha256': hashlib.sha256(content).hexdigest(),\n",
    "                'retrieved_utc': datetime.now(timezone.utc).isoformat(), 'rows': len(frame)}\n",
    "        return frame, meta\n",
    "    # Create one task per district/component. Threads overlap download waits;\n",
    "    # results are then combined into the observation CSV and provenance manifest.\n",
    "    tasks = [(p, c, template.format(p=p)) for p in PADD_NAMES for c, template in CODES.items()]\n",
    "    with ThreadPoolExecutor(max_workers=6) as pool:\n",
    "        results = list(pool.map(download, tasks))\n",
    "    raw = pd.concat([r[0] for r in results], ignore_index=True)\n",
    "    raw.to_csv(data_dir / 'eia_observations.csv', index=False)\n",
    "    (data_dir / 'source_manifest.json').write_text(json.dumps([r[1] for r in results], indent=2))\n",
    "    return load_panel(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f864949b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.809712Z",
     "iopub.status.busy": "2026-09-16T21:54:40.809607Z",
     "iopub.status.idle": "2026-09-16T21:54:40.811497Z",
     "shell.execute_reply": "2026-09-16T21:54:40.811146Z"
    }
   },
   "outputs": [],
   "source": [
    "# Choose whether to refresh source data. The default retains the saved data\n",
    "# vintage, making the existing historical results reproducible.\n",
    "\n",
    "# False reproduces the saved snapshot. True downloads current EIA sources and\n",
    "# overwrites the local raw files, observation CSV, and source manifest.\n",
    "REFRESH_SOURCES = False\n",
    "if REFRESH_SOURCES:\n",
    "    refresh_data(HERE/'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c87832e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-09-16T21:54:40.940258Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>finished_production_kb</td>\n",
       "      <td>MGFRPP11</td>\n",
       "      <td>East Coast (PADD 1) Refinery and Blender Net P...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFRPP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>finished_demand_kb</td>\n",
       "      <td>MGFUPP11</td>\n",
       "      <td>East Coast (PADD 1) Product Supplied of Finish...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFUPP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>finished_imports_kb</td>\n",
       "      <td>MGFIMP11</td>\n",
       "      <td>East Coast (PADD 1) Imports of Finished Motor ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFIMP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finished_exports_kb</td>\n",
       "      <td>MGFEXP11</td>\n",
       "      <td>East Coast (PADD 1) Exports of Finished Motor ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFEXP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finished_net_receipts_kb</td>\n",
       "      <td>MGFNRP11</td>\n",
       "      <td>East Coast (PADD 1) Net Receipts by Pipeline, ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFNRP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>finished_adjustments_kb</td>\n",
       "      <td>MGFUA_R10_1</td>\n",
       "      <td>East Coast (PADD 1) Supply Adjustment of Finis...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFUA_R1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>finished_biofuels_kb</td>\n",
       "      <td>M_EPM0F_YNP_R10_MBBL</td>\n",
       "      <td>East Coast (PADD 1) Biofuels Plant Net Product...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/M_EPM0F_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>finished_stock_kb</td>\n",
       "      <td>MGFSTP11</td>\n",
       "      <td>East Coast (PADD 1) Ending Stocks of Finished ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFSTP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>finished_reported_stock_change_kb</td>\n",
       "      <td>MGFSCP11</td>\n",
       "      <td>East Coast (PADD 1) Finished Motor Gasoline St...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGFSCP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>blending_net_inputs_kb</td>\n",
       "      <td>MBCRIP11</td>\n",
       "      <td>East Coast (PADD 1) Refinery and Blender Net I...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCRIP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>blending_demand_kb</td>\n",
       "      <td>MBCUPP11</td>\n",
       "      <td>East Coast (PADD 1) Product Supplied of Gasoli...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCUPP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>blending_imports_kb</td>\n",
       "      <td>MBCIMP11</td>\n",
       "      <td>East Coast (PADD 1) Imports of Gasoline Blendi...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCIMP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>blending_exports_kb</td>\n",
       "      <td>MBCEXP11</td>\n",
       "      <td>East Coast (PADD 1) Exports of Gasoline Blendi...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCEXP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>blending_net_receipts_kb</td>\n",
       "      <td>MBCNRP11</td>\n",
       "      <td>East Coast (PADD 1) Net Receipts by Pipeline, ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCNRP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>blending_adjustments_kb</td>\n",
       "      <td>MBCUA_R10_1</td>\n",
       "      <td>East Coast (PADD 1) Supply Adjustment of Gasol...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCUA_R1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>blending_biofuels_kb</td>\n",
       "      <td>M_EPOBG_YNP_R10_MBBL</td>\n",
       "      <td>East Coast (PADD 1) Biofuels Plant Net Product...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/M_EPOBG_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>blending_stock_kb</td>\n",
       "      <td>MBCSTP11</td>\n",
       "      <td>East Coast (PADD 1) Ending Stocks of Gasoline ...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCSTP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>blending_reported_stock_change_kb</td>\n",
       "      <td>MBCSCP11</td>\n",
       "      <td>East Coast (PADD 1) Gasoline Blending Componen...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MBCSCP11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>stock_kb</td>\n",
       "      <td>MGTSTP11</td>\n",
       "      <td>East Coast (PADD 1) Ending Stocks of Total Gas...</td>\n",
       "      <td>https://www.eia.gov/dnav/pet/hist_xls/MGTSTP11...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            component             series_id  \\\n",
       "0              finished_production_kb              MGFRPP11   \n",
       "1                  finished_demand_kb              MGFUPP11   \n",
       "2                 finished_imports_kb              MGFIMP11   \n",
       "3                 finished_exports_kb              MGFEXP11   \n",
       "4            finished_net_receipts_kb              MGFNRP11   \n",
       "5             finished_adjustments_kb           MGFUA_R10_1   \n",
       "6                finished_biofuels_kb  M_EPM0F_YNP_R10_MBBL   \n",
       "7                   finished_stock_kb              MGFSTP11   \n",
       "8   finished_reported_stock_change_kb              MGFSCP11   \n",
       "9              blending_net_inputs_kb              MBCRIP11   \n",
       "10                 blending_demand_kb              MBCUPP11   \n",
       "11                blending_imports_kb              MBCIMP11   \n",
       "12                blending_exports_kb              MBCEXP11   \n",
       "13           blending_net_receipts_kb              MBCNRP11   \n",
       "14            blending_adjustments_kb           MBCUA_R10_1   \n",
       "15               blending_biofuels_kb  M_EPOBG_YNP_R10_MBBL   \n",
       "16                  blending_stock_kb              MBCSTP11   \n",
       "17  blending_reported_stock_change_kb              MBCSCP11   \n",
       "18                           stock_kb              MGTSTP11   \n",
       "\n",
       "                                                title  \\\n",
       "0   East Coast (PADD 1) Refinery and Blender Net P...   \n",
       "1   East Coast (PADD 1) Product Supplied of Finish...   \n",
       "2   East Coast (PADD 1) Imports of Finished Motor ...   \n",
       "3   East Coast (PADD 1) Exports of Finished Motor ...   \n",
       "4   East Coast (PADD 1) Net Receipts by Pipeline, ...   \n",
       "5   East Coast (PADD 1) Supply Adjustment of Finis...   \n",
       "6   East Coast (PADD 1) Biofuels Plant Net Product...   \n",
       "7   East Coast (PADD 1) Ending Stocks of Finished ...   \n",
       "8   East Coast (PADD 1) Finished Motor Gasoline St...   \n",
       "9   East Coast (PADD 1) Refinery and Blender Net I...   \n",
       "10  East Coast (PADD 1) Product Supplied of Gasoli...   \n",
       "11  East Coast (PADD 1) Imports of Gasoline Blendi...   \n",
       "12  East Coast (PADD 1) Exports of Gasoline Blendi...   \n",
       "13  East Coast (PADD 1) Net Receipts by Pipeline, ...   \n",
       "14  East Coast (PADD 1) Supply Adjustment of Gasol...   \n",
       "15  East Coast (PADD 1) Biofuels Plant Net Product...   \n",
       "16  East Coast (PADD 1) Ending Stocks of Gasoline ...   \n",
       "17  East Coast (PADD 1) Gasoline Blending Componen...   \n",
       "18  East Coast (PADD 1) Ending Stocks of Total Gas...   \n",
       "\n",
       "                                                  url  \n",
       "0   https://www.eia.gov/dnav/pet/hist_xls/MGFRPP11...  \n",
       "1   https://www.eia.gov/dnav/pet/hist_xls/MGFUPP11...  \n",
       "2   https://www.eia.gov/dnav/pet/hist_xls/MGFIMP11...  \n",
       "3   https://www.eia.gov/dnav/pet/hist_xls/MGFEXP11...  \n",
       "4   https://www.eia.gov/dnav/pet/hist_xls/MGFNRP11...  \n",
       "5   https://www.eia.gov/dnav/pet/hist_xls/MGFUA_R1...  \n",
       "6   https://www.eia.gov/dnav/pet/hist_xls/M_EPM0F_...  \n",
       "7   https://www.eia.gov/dnav/pet/hist_xls/MGFSTP11...  \n",
       "8   https://www.eia.gov/dnav/pet/hist_xls/MGFSCP11...  \n",
       "9   https://www.eia.gov/dnav/pet/hist_xls/MBCRIP11...  \n",
       "10  https://www.eia.gov/dnav/pet/hist_xls/MBCUPP11...  \n",
       "11  https://www.eia.gov/dnav/pet/hist_xls/MBCIMP11...  \n",
       "12  https://www.eia.gov/dnav/pet/hist_xls/MBCEXP11...  \n",
       "13  https://www.eia.gov/dnav/pet/hist_xls/MBCNRP11...  \n",
       "14  https://www.eia.gov/dnav/pet/hist_xls/MBCUA_R1...  \n",
       "15  https://www.eia.gov/dnav/pet/hist_xls/M_EPOBG_...  \n",
       "16  https://www.eia.gov/dnav/pet/hist_xls/MBCSTP11...  \n",
       "17  https://www.eia.gov/dnav/pet/hist_xls/MBCSCP11...  \n",
       "18  https://www.eia.gov/dnav/pet/hist_xls/MGTSTP11...  "
      ]
     },
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    {
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       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>months</th>\n",
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       "    <tr>\n",
       "      <th>padd</th>\n",
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       "      <th>1</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>234</td>\n",
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       "      <th>2</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2026-06-01</td>\n",
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       "      <td>2026-06-01</td>\n",
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       "      <th>4</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2026-06-01</td>\n",
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       "      <th>5</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>234</td>\n",
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      ],
      "text/plain": [
       "          start        end  months\n",
       "padd                              \n",
       "1    2007-01-01 2026-06-01     234\n",
       "2    2007-01-01 2026-06-01     234\n",
       "3    2007-01-01 2026-06-01     234\n",
       "4    2007-01-01 2026-06-01     234\n",
       "5    2007-01-01 2026-06-01     234"
      ]
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>blending_biofuels_kb_assumed_zero</th>\n",
       "      <th>blending_exports_kb_assumed_zero</th>\n",
       "      <th>blending_imports_kb_assumed_zero</th>\n",
       "      <th>blending_net_receipts_kb_assumed_zero</th>\n",
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       "      <th>finished_exports_kb_assumed_zero</th>\n",
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       "      <th>PADD: count of assumed-zero months</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>234</td>\n",
       "      <td>142</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>65</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "component                           blending_biofuels_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                      \n",
       "1                                                                 116   \n",
       "2                                                                  38   \n",
       "3                                                                 234   \n",
       "4                                                                 234   \n",
       "5                                                                 190   \n",
       "\n",
       "component                           blending_exports_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                     \n",
       "1                                                                  0   \n",
       "2                                                                  4   \n",
       "3                                                                  0   \n",
       "4                                                                142   \n",
       "5                                                                  0   \n",
       "\n",
       "component                           blending_imports_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                     \n",
       "1                                                                  0   \n",
       "2                                                                 28   \n",
       "3                                                                  2   \n",
       "4                                                                 90   \n",
       "5                                                                  2   \n",
       "\n",
       "component                           blending_net_receipts_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                          \n",
       "1                                                                       0   \n",
       "2                                                                       0   \n",
       "3                                                                       0   \n",
       "4                                                                      10   \n",
       "5                                                                       2   \n",
       "\n",
       "component                           finished_biofuels_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                      \n",
       "1                                                                 230   \n",
       "2                                                                  24   \n",
       "3                                                                 180   \n",
       "4                                                                 230   \n",
       "5                                                                 174   \n",
       "\n",
       "component                           finished_exports_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                     \n",
       "1                                                                  0   \n",
       "2                                                                  3   \n",
       "3                                                                  0   \n",
       "4                                                                 65   \n",
       "5                                                                  0   \n",
       "\n",
       "component                           finished_imports_kb_assumed_zero  \\\n",
       "PADD: count of assumed-zero months                                     \n",
       "1                                                                  0   \n",
       "2                                                                151   \n",
       "3                                                                109   \n",
       "4                                                                219   \n",
       "5                                                                  2   \n",
       "\n",
       "component                           finished_net_receipts_kb_assumed_zero  \n",
       "PADD: count of assumed-zero months                                         \n",
       "1                                                                       0  \n",
       "2                                                                       0  \n",
       "3                                                                       0  \n",
       "4                                                                       0  \n",
       "5                                                                       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect source identifiers, observation coverage, and assumed-zero counts\n",
    "# before fitting. Preserve constituent finished/blending series for audit.\n",
    "\n",
    "manifest = pd.read_json(HERE / 'data/source_manifest.json')\n",
    "display(manifest[manifest.padd.eq(1)][['component','series_id','title','url']])\n",
    "panel = load_panel(HERE / 'data')\n",
    "display(panel.groupby('padd').agg(start=('month','min'), end=('month','max'), months=('month','size')))\n",
    "flags = [c for c in panel if c.endswith('_assumed_zero')]\n",
    "display(panel.groupby('padd')[flags].sum().rename_axis('PADD: count of assumed-zero months'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03104d5f",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "Estimate how supply and disposition affect gasoline stocks, then forecast each PADD and sum to the U.S. total.\n",
    "\n",
    "**Monthly stock change = net refinery production + imports + net receipts + adjustments + biofuel net production − product supplied − exports + residual.**\n",
    "\n",
    "Flows cover finished gasoline and blending components.\n",
    "\n",
    "Product supplied already incorporates stock change, so historical balance agreement does not establish forecast accuracy. (not representative of data at given point in time.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "048dd2b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.942154Z",
     "iopub.status.busy": "2026-09-16T21:54:40.942006Z",
     "iopub.status.idle": "2026-09-16T21:54:41.178249Z",
     "shell.execute_reply": "2026-09-16T21:54:41.177714Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max stock-level change less net supply (kb)</th>\n",
       "      <th>Max stock-level change less reported change (kb)</th>\n",
       "      <th>Max reported change less net supply (kb)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Max stock-level change less net supply (kb)  \\\n",
       "padd                                                \n",
       "1                                           66.00   \n",
       "2                                            4.00   \n",
       "3                                           43.00   \n",
       "4                                            1.00   \n",
       "5                                           81.00   \n",
       "\n",
       "      Max stock-level change less reported change (kb)  \\\n",
       "padd                                                     \n",
       "1                                                66.00   \n",
       "2                                                 5.00   \n",
       "3                                                43.00   \n",
       "4                                                 0.00   \n",
       "5                                                81.00   \n",
       "\n",
       "      Max reported change less net supply (kb)  \n",
       "padd                                            \n",
       "1                                         1.00  \n",
       "2                                         2.00  \n",
       "3                                         1.00  \n",
       "4                                         1.00  \n",
       "5                                         2.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>month</th>\n",
       "      <th>padd</th>\n",
       "      <th>previous_stock_kb</th>\n",
       "      <th>stock_kb</th>\n",
       "      <th>stock_change_kb</th>\n",
       "      <th>reported_stock_change_kb</th>\n",
       "      <th>balance_kb</th>\n",
       "      <th>accounting_residual_kb</th>\n",
       "      <th>flow_reporting_residual_kb</th>\n",
       "      <th>difference_pct_of_stocks</th>\n",
       "      <th>difference_kbd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>58,895.00</td>\n",
       "      <td>68,046.00</td>\n",
       "      <td>9,151.00</td>\n",
       "      <td>9,085.00</td>\n",
       "      <td>9,085.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>93,187.00</td>\n",
       "      <td>94,303.00</td>\n",
       "      <td>1,116.00</td>\n",
       "      <td>1,159.00</td>\n",
       "      <td>1,159.00</td>\n",
       "      <td>-43.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>30,639.00</td>\n",
       "      <td>31,085.00</td>\n",
       "      <td>446.00</td>\n",
       "      <td>527.00</td>\n",
       "      <td>527.00</td>\n",
       "      <td>-81.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-2.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component      month  padd  previous_stock_kb  stock_kb  stock_change_kb  \\\n",
       "228       2026-01-01     1          58,895.00 68,046.00         9,151.00   \n",
       "696       2026-01-01     3          93,187.00 94,303.00         1,116.00   \n",
       "1164      2026-01-01     5          30,639.00 31,085.00           446.00   \n",
       "\n",
       "component  reported_stock_change_kb  balance_kb  accounting_residual_kb  \\\n",
       "228                        9,085.00    9,085.00                   66.00   \n",
       "696                        1,159.00    1,159.00                  -43.00   \n",
       "1164                         527.00      527.00                  -81.00   \n",
       "\n",
       "component  flow_reporting_residual_kb  difference_pct_of_stocks  \\\n",
       "228                              0.00                      0.10   \n",
       "696                              0.00                     -0.05   \n",
       "1164                             0.00                     -0.26   \n",
       "\n",
       "component  difference_kbd  \n",
       "228                  2.13  \n",
       "696                 -1.39  \n",
       "1164                -2.61  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Audit stock-level change against published stock change and net supply.\n",
    "# Their residual decomposition is checked algebraically, allowing first-month\n",
    "# NaNs. The >5 kb threshold selects displayed exceptions, not training exclusions.\n",
    "\n",
    "checks = panel.groupby('padd').agg(\n",
    "    maximum_balance_residual_kb=('accounting_residual_kb', lambda s: s.abs().max()),\n",
    "    maximum_reported_change_difference_kb=('reported_change_residual_kb', lambda s: s.abs().max()),\n",
    "    maximum_flow_reporting_residual_kb=('flow_reporting_residual_kb', lambda s: s.abs().max()))\n",
    "display(checks.rename(columns={\n",
    "    'maximum_balance_residual_kb':'Max stock-level change less net supply (kb)',\n",
    "    'maximum_reported_change_difference_kb':'Max stock-level change less reported change (kb)',\n",
    "    'maximum_flow_reporting_residual_kb':'Max reported change less net supply (kb)'}))\n",
    "# Verify the two diagnostic residuals add to the total mismatch; initial\n",
    "# NaNs are expected because the first stock observation has no predecessor.\n",
    "np.testing.assert_allclose(panel.accounting_residual_kb,\n",
    "    panel.reported_change_residual_kb + panel.flow_reporting_residual_kb, equal_nan=True)\n",
    "exceptions = panel[panel.accounting_residual_kb.abs()>5].copy()\n",
    "# Express each exception relative to inventory size and as a daily equivalent\n",
    "# so its practical scale can be assessed alongside the monthly kb difference.\n",
    "exceptions['difference_pct_of_stocks'] = 100*exceptions.accounting_residual_kb/exceptions.stock_kb\n",
    "exceptions['difference_kbd'] = exceptions.accounting_residual_kb/exceptions.month.dt.days_in_month\n",
    "display(exceptions[['month','padd','previous_stock_kb','stock_kb','stock_change_kb',\n",
    "    'reported_stock_change_kb','balance_kb','accounting_residual_kb',\n",
    "    'flow_reporting_residual_kb','difference_pct_of_stocks','difference_kbd']])\n",
    "exceptions.to_csv(OUTPUT/'historical_balance_exceptions.csv', index=False)\n",
    "fig, axes = plt.subplots(2,1,figsize=(12,6),sharex=True)\n",
    "for p,g in panel.groupby('padd'):\n",
    "    axes[0].plot(g.month,g.accounting_residual_kb,label=f'PADD {p}')\n",
    "    axes[1].plot(g.month,g.flow_reporting_residual_kb,label=f'PADD {p}')\n",
    "axes[0].set(title='Stock-level change less net supply',ylabel='Thousand barrels')\n",
    "axes[1].set(title='Reported stock change less net supply',ylabel='Thousand barrels')\n",
    "axes[0].legend(ncol=5); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9a56ba",
   "metadata": {},
   "source": [
    "### Balance reconciliation\n",
    "\n",
    "In January 2026, stock-level changes differ from reported stock changes by +66, −43, and −81 thousand barrels in PADDs 1, 3, and 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "899c5e11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:41.194650Z",
     "iopub.status.busy": "2026-09-16T21:54:41.194384Z",
     "iopub.status.idle": "2026-09-16T21:54:40.040777Z",
     "shell.execute_reply": "2026-09-16T21:54:40.040338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare regional total stocks and the national finished/blending components.\n",
    "# The modeled target is total gasoline, not finished gasoline alone; /1000\n",
    "# converts stored thousand barrels into million barrels for plotting.\n",
    "\n",
    "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
    "for p,g in panel.groupby('padd'):\n",
    "    axes[0].plot(g.month, g.stock_kb/1000, label=f'{p}: {PADD_NAMES[p]}')\n",
    "context = panel.groupby('month')[['stock_kb','finished_stock_kb','blending_stock_kb']].sum()\n",
    "(context/1000).rename(columns={'stock_kb':'Total gasoline (modeled)',\n",
    "    'finished_stock_kb':'Finished gasoline component', 'blending_stock_kb':'Blending components'}).plot(ax=axes[1])\n",
    "axes[0].set_title('Total gasoline stocks by PADD'); axes[0].legend(fontsize=8)\n",
    "axes[1].set_title('Total gasoline and its constituent inventories')\n",
    "for ax in axes: ax.set_ylabel('Million barrels')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8586716e",
   "metadata": {},
   "source": [
    "### Forecast inputs\n",
    "\n",
    "Predict month-end stocks using prior stocks, stock changes, flows, and seasonal patterns. \n",
    "\n",
    "Forecast flows using trends and calendar-month effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ff693d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.043272Z",
     "iopub.status.busy": "2026-09-16T21:54:40.042449Z",
     "iopub.status.idle": "2026-09-16T21:54:40.054711Z",
     "shell.execute_reply": "2026-09-16T21:54:40.049462Z"
    }
   },
   "outputs": [],
   "source": [
    "# Build a trend in years plus 11 calendar-month indicators. January is the\n",
    "# reference month represented by the regression intercept.\n",
    "def seasonal_design(months, origin):\n",
    "    dates = pd.DatetimeIndex(months)\n",
    "    trend = ((dates.year - origin.year) * 12 + dates.month - origin.month).to_numpy() / 12\n",
    "    return np.column_stack([trend] + [(dates.month == m).astype(float) for m in range(2, 13)])\n",
    "\n",
    "\n",
    "# Use only the supplied origin history. The last 60 non-COVID observations may\n",
    "# span more than 60 calendar months because excluded months are skipped.\n",
    "def forecast_flows(history, months):\n",
    "    \"\"\"Fit monthly daily-rate seasonality + trend on the last 60 observed months.\"\"\"\n",
    "    train = history.loc[outside_covid(history.month)].tail(60)\n",
    "    model = LinearRegression()\n",
    "    # Fit gasoline flows as daily rates, preventing longer months from appearing\n",
    "    # artificially stronger. [:, None] broadcasts one day-count divisor across each row.\n",
    "    model.fit(seasonal_design(train.month, train.month.iloc[0]),\n",
    "              train[FLOWS].to_numpy() / train.month.dt.days_in_month.to_numpy()[:, None])\n",
    "    rates = model.predict(seasonal_design(months, train.month.iloc[0]))\n",
    "    # Convert forecast daily rates back to monthly volumes using target-month day\n",
    "    # counts, including February and leap years.\n",
    "    flows = rates * pd.DatetimeIndex(months).days_in_month.to_numpy()[:, None]\n",
    "    for c in ['demand_kb', 'imports_kb', 'exports_kb']:\n",
    "        flows[:, FLOWS.index(c)] = np.maximum(flows[:, FLOWS.index(c)], 0)\n",
    "    # Net refinery production, receipts, adjustments and biofuel net production can be negative.\n",
    "    return pd.DataFrame(flows, columns=FLOWS, index=pd.DatetimeIndex(months))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c017f25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.056944Z",
     "iopub.status.busy": "2026-09-16T21:54:40.056769Z",
     "iopub.status.idle": "2026-09-16T21:54:40.064811Z",
     "shell.execute_reply": "2026-09-16T21:54:40.064229Z"
    }
   },
   "outputs": [],
   "source": [
    "# Build target-month inputs from history ending one month earlier. History must\n",
    "# be sorted, continuous, and contain at least 12 monthly observations.\n",
    "def feature_row(history, month):\n",
    "    last = history.iloc[-1]\n",
    "    # Use prior stock, year-ago stock, and the latest stock change. Sine/cosine encode\n",
    "    # annual seasonality smoothly across the December/January boundary.\n",
    "    row = {'stock_lag1': last.stock_kb, 'stock_lag12': history.iloc[-12].stock_kb,\n",
    "           'change_lag1': last.stock_kb - history.iloc[-2].stock_kb,\n",
    "           'sin_month': np.sin(2*np.pi*month.month/12), 'cos_month': np.cos(2*np.pi*month.month/12)}\n",
    "    # Calculate observed changes before filtering; omit any change whose current\n",
    "    # or previous stock observation falls inside the excluded COVID period.\n",
    "    changes = history.set_index('month').stock_kb.diff()\n",
    "    clean = outside_covid(changes.index) & outside_covid(changes.index - pd.DateOffset(months=1))\n",
    "    changes = changes.loc[clean].tail(60)\n",
    "    # Average recent eligible changes for the target calendar month; use zero\n",
    "    # if that month has no usable historical changes.\n",
    "    seasonal = changes[changes.index.month == month.month].dropna()\n",
    "    row['seasonal_change'] = float(seasonal.mean()) if len(seasonal) else 0.0\n",
    "    # Compare the latest stock with earlier same-calendar-month stock levels,\n",
    "    # excluding the latest observation from its own reference average.\n",
    "    normal_history = history.iloc[:-1].loc[lambda f: outside_covid(f.month)].tail(60)\n",
    "    normal = normal_history[normal_history.month.dt.month.eq(last.month.month)].stock_kb\n",
    "    row['stock_seasonal_gap'] = float(last.stock_kb - normal.mean()) if len(normal) else 0.0\n",
    "    # Pre-sign prior-month flows so nonnegative regression coefficients retain the\n",
    "    # supply-versus-demand direction in the constrained model.\n",
    "    row.update({'lag_' + c: last[c] * sign for c, sign in zip(FLOWS, SIGNS)})\n",
    "    return row\n",
    "\n",
    "\n",
    "# Create labeled rows after a 12-month warm-up. Rebuild each feature vector and\n",
    "# flow-identity benchmark using only history preceding that row's target month.\n",
    "def supervised(history):\n",
    "    rows = []\n",
    "    for i in range(12, len(history)):\n",
    "        # Keep the target separate: its stock supplies actual_kb/delta_kb only. Actual\n",
    "        # target-month flows must not become inputs to a prediction of that month.\n",
    "        past, current = history.iloc[:i], history.iloc[i]\n",
    "        row = feature_row(past, current.month)\n",
    "        flow = forecast_flows(past, [current.month]).iloc[0]\n",
    "        row.update(month=current.month, origin_month=past.month.iloc[-1],\n",
    "                   actual_kb=current.stock_kb, delta_kb=current.stock_kb-past.stock_kb.iloc[-1],\n",
    "                   forecast_flow_identity=max(0.0, past.stock_kb.iloc[-1] + flow.to_numpy() @ SIGNS))\n",
    "        rows.append(row)\n",
    "    return pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "930ba5c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:40.065837Z",
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     "shell.execute_reply": "2026-09-16T21:54:42.704161Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_month</th>\n",
       "      <th>month</th>\n",
       "      <th>stock_lag1</th>\n",
       "      <th>stock_lag12</th>\n",
       "      <th>lag_production_kb</th>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <th>actual_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>68,046.00</td>\n",
       "      <td>65,816.00</td>\n",
       "      <td>22,814.00</td>\n",
       "      <td>67,817.66</td>\n",
       "      <td>67,907.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>67,907.00</td>\n",
       "      <td>59,836.00</td>\n",
       "      <td>20,737.00</td>\n",
       "      <td>60,730.14</td>\n",
       "      <td>59,053.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>59,053.00</td>\n",
       "      <td>59,440.00</td>\n",
       "      <td>23,537.00</td>\n",
       "      <td>58,491.03</td>\n",
       "      <td>56,752.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>56,752.00</td>\n",
       "      <td>59,838.00</td>\n",
       "      <td>22,717.00</td>\n",
       "      <td>58,933.75</td>\n",
       "      <td>57,919.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>57,919.00</td>\n",
       "      <td>63,611.00</td>\n",
       "      <td>22,503.00</td>\n",
       "      <td>60,201.20</td>\n",
       "      <td>56,678.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    origin_month      month  stock_lag1  stock_lag12  lag_production_kb  \\\n",
       "217   2026-01-01 2026-02-01   68,046.00    65,816.00          22,814.00   \n",
       "218   2026-02-01 2026-03-01   67,907.00    59,836.00          20,737.00   \n",
       "219   2026-03-01 2026-04-01   59,053.00    59,440.00          23,537.00   \n",
       "220   2026-04-01 2026-05-01   56,752.00    59,838.00          22,717.00   \n",
       "221   2026-05-01 2026-06-01   57,919.00    63,611.00          22,503.00   \n",
       "\n",
       "     forecast_flow_identity  actual_kb  \n",
       "217               67,817.66  67,907.00  \n",
       "218               60,730.14  59,053.00  \n",
       "219               58,491.03  56,752.00  \n",
       "220               58,933.75  57,919.00  \n",
       "221               60,201.20  56,678.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect a few supervised rows to make the forecast origin/target alignment\n",
    "# concrete. Each origin must precede its target; actual_kb is an outcome only.\n",
    "\n",
    "example = supervised(panel[panel.padd.eq(1)].reset_index(drop=True))\n",
    "display(example[['origin_month','month','stock_lag1','stock_lag12','lag_production_kb',\n",
    "                 'forecast_flow_identity','actual_kb']].tail(5))\n",
    "assert (example.origin_month < example.month).all()\n",
    "# Notice: actual_kb is the outcome, never a feature passed to a fitted estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67d350e",
   "metadata": {},
   "source": [
    "### Tested models\n",
    "\n",
    "Compare unchanged stocks, prior-year stocks, seasonal changes, flow balance, regression variants, random forest, XGBoost, and a neural network. The definitions below specify inputs and settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e29bb76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.709652Z",
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     "iopub.status.idle": "2026-09-16T21:54:42.712839Z",
     "shell.execute_reply": "2026-09-16T21:54:42.712515Z"
    }
   },
   "outputs": [],
   "source": [
    "# Baselines carry stocks forward, repeat year-ago stocks, add a seasonal change,\n",
    "# or accumulate forecast net flows; none needs a fitted stock estimator.\n",
    "BASELINES = ['persistence', 'seasonal_naive', 'seasonal_change', 'forecast_flow_identity']\n",
    "# Candidates share forecast timing and evaluation. Names ending in '_change'\n",
    "# model stock increments, while constrained_level models inventory levels.\n",
    "LEARNED = ['constrained_level', 'ridge_change', 'seasonal_ridge_change',\n",
    "           'polynomial_ridge_change', 'spline_ridge_change', 'random_forest_change',\n",
    "           'xgboost_change', 'neural_network_change']\n",
    "LEARNED += ['seasonal_residual_change', 'huber_change']\n",
    "MODELS = BASELINES + LEARNED\n",
    "BASE_FEATURES = ['stock_lag1', 'stock_lag12', 'change_lag1'] + ['lag_' + c for c in FLOWS]\n",
    "FEATURES = BASE_FEATURES + ['sin_month', 'cos_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "330bbd40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.714176Z",
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     "iopub.status.idle": "2026-09-16T21:54:42.718545Z",
     "shell.execute_reply": "2026-09-16T21:54:42.718208Z"
    }
   },
   "outputs": [],
   "source": [
    "# Select predictor columns explicitly. actual_kb and delta_kb are labels and\n",
    "# must never enter the estimator's input matrix.\n",
    "def columns(name):\n",
    "    if name == 'seasonal_residual_change':\n",
    "        return FEATURES + ['stock_seasonal_gap', 'seasonal_change']\n",
    "    if name == 'constrained_level':\n",
    "        return ['stock_lag1'] + ['lag_' + c for c in FLOWS]\n",
    "    if name == 'ridge_change':\n",
    "        return BASE_FEATURES\n",
    "    return FEATURES\n",
    "\n",
    "\n",
    "# Return an unfitted candidate. Pipeline transformations are learned inside each\n",
    "# fit, so test observations cannot influence scaling or spline preparation.\n",
    "def estimator(name):\n",
    "    if name == 'seasonal_residual_change':\n",
    "        # Standardize predictors before L2-regularized regression. Larger alpha shrinks\n",
    "        # coefficients more strongly; grid search overrides these starting defaults.\n",
    "        return make_pipeline(StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'huber_change':\n",
    "        return TransformedTargetRegressor(regressor=make_pipeline(StandardScaler(),\n",
    "            # Huber loss reduces outlier influence; the surrounding target transformer\n",
    "            # scales the response and reverses that scaling when predicting.\n",
    "            HuberRegressor(max_iter=1000)), transformer=StandardScaler())\n",
    "    if name == 'constrained_level':\n",
    "        # Constrain coefficients, not the intercept, to be nonnegative. Pre-signed demand\n",
    "        # features therefore contribute negatively without imposing a full flow identity.\n",
    "        return LinearRegression(positive=True)\n",
    "    if name in ['ridge_change', 'seasonal_ridge_change']:\n",
    "        # Standardize predictors before L2-regularized regression. Larger alpha shrinks\n",
    "        # coefficients more strongly; grid search overrides these starting defaults.\n",
    "        return make_pipeline(StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'polynomial_ridge_change':\n",
    "        # Expand predictors with powers/interactions, rescale, and regularize to control\n",
    "        # flexibility. The regression supplies the intercept, so no bias column is needed.\n",
    "        return make_pipeline(StandardScaler(), PolynomialFeatures(2, include_bias=False),\n",
    "                             StandardScaler(), Ridge(alpha=1000))\n",
    "    if name == 'spline_ridge_change':\n",
    "        # Represent each feature using smooth piecewise-polynomial basis functions;\n",
    "        # linear extrapolation controls behavior outside its observed training range.\n",
    "        return make_pipeline(SplineTransformer(n_knots=4, degree=2, extrapolation='linear'),\n",
    "                             StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'random_forest_change':\n",
    "        # Average resampled decision trees. Depth and minimum leaf size limit complexity;\n",
    "        # the fixed seed makes randomized fitting reproducible.\n",
    "        return RandomForestRegressor(n_estimators=150, max_depth=4, min_samples_leaf=12,\n",
    "                                     max_features=0.8, n_jobs=1, random_state=42)\n",
    "    if name == 'xgboost_change':\n",
    "        # Boost shallow trees sequentially. Learning rate, regularization, and row/column\n",
    "        # subsampling control how aggressively the ensemble fits remaining residuals.\n",
    "        return XGBRegressor(n_estimators=120, max_depth=2, learning_rate=0.03,\n",
    "            min_child_weight=12, reg_lambda=30, subsample=0.8, colsample_bytree=0.8,\n",
    "            n_jobs=1, random_state=42, objective='reg:squarederror')\n",
    "    if name == 'neural_network_change':\n",
    "        return TransformedTargetRegressor(regressor=make_pipeline(StandardScaler(),\n",
    "            # Fit a small neural network with LBFGS on scaled inputs and a scaled target.\n",
    "            # Alpha penalizes weights, and the random seed fixes initialization.\n",
    "            MLPRegressor(hidden_layer_sizes=(16,), alpha=10, solver='lbfgs',\n",
    "                         max_iter=3000, random_state=42)), transformer=StandardScaler())\n",
    "    raise ValueError(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3a7046d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.719874Z",
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     "shell.execute_reply": "2026-09-16T21:54:42.724640Z"
    }
   },
   "outputs": [],
   "source": [
    "# Return (estimator, warning_text). Baselines return None because predict computes\n",
    "# them directly; learned candidates filter training dates before fitting.\n",
    "def fit(name, train, params=None):\n",
    "    if name in BASELINES:\n",
    "        return None, ''\n",
    "    train = training_rows(train)\n",
    "    model = estimator(name)\n",
    "    if params:\n",
    "        model.set_params(**params)\n",
    "    # Choose inventory level or monthly change as response, both in thousand barrels.\n",
    "    target = 'actual_kb' if name == 'constrained_level' else 'delta_kb'\n",
    "    # Match the search's numerical settings, including neural-network refits.\n",
    "    from threadpoolctl import threadpool_limits\n",
    "    with warnings.catch_warnings(record=True) as caught, threadpool_limits(limits=1):\n",
    "        warnings.simplefilter('always', ConvergenceWarning)\n",
    "        # Fit the seasonal-residual candidate to departures from normal seasonal change;\n",
    "        # predict reconstructs the seasonal component before converting to stock levels.\n",
    "        values = train[target] - train.seasonal_change if name == 'seasonal_residual_change' else train[target]\n",
    "        model.fit(train[columns(name)], values)\n",
    "    return model, '; '.join(str(w.message) for w in caught)\n",
    "\n",
    "\n",
    "# Return stock levels in kb for every candidate, including change-based models.\n",
    "# Use identical reconstruction and flooring rules during tuning and forecasting.\n",
    "def predict(name, model, frame):\n",
    "    if name == 'persistence':\n",
    "        return frame.stock_lag1.to_numpy()\n",
    "    if name == 'seasonal_naive':\n",
    "        return frame.stock_lag12.to_numpy()\n",
    "    if name == 'seasonal_change':\n",
    "        return np.maximum(0, frame.stock_lag1.to_numpy() + frame.seasonal_change.to_numpy())\n",
    "    if name == 'forecast_flow_identity':\n",
    "        return frame.forecast_flow_identity.to_numpy()\n",
    "    prediction = model.predict(frame[columns(name)])\n",
    "    if name == 'seasonal_residual_change':\n",
    "        prediction += frame.seasonal_change.to_numpy()\n",
    "    # Add the predicted increment to prior stock to recover an inventory level.\n",
    "    if name != 'constrained_level':\n",
    "        prediction += frame.stock_lag1.to_numpy()\n",
    "    # Prevent a learned model from producing a negative final stock level.\n",
    "    return np.maximum(prediction, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88065651",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.726187Z",
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     "shell.execute_reply": "2026-09-16T21:54:42.734266Z"
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   },
   "outputs": [],
   "source": [
    "# Enumerate tunable combinations. Double underscores address nested pipeline\n",
    "# parameters; an empty grid indicates a candidate with no parameter search.\n",
    "def parameter_grid(name):\n",
    "    \"\"\"Conventional compact grids for each tunable model family.\"\"\"\n",
    "    if name == 'seasonal_residual_change':\n",
    "        return {'ridge__alpha': [0.1, 1, 10, 100, 1000]}\n",
    "    if name == 'huber_change':\n",
    "        return {'regressor__huberregressor__epsilon': [1.35, 1.75],\n",
    "                'regressor__huberregressor__alpha': [0.01, 1, 10]}\n",
    "    if name in ['ridge_change', 'seasonal_ridge_change']:\n",
    "        return {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}\n",
    "    if name == 'polynomial_ridge_change':\n",
    "        return {'polynomialfeatures__degree': [1, 2], 'ridge__alpha': [1, 10, 100, 1000]}\n",
    "    if name == 'spline_ridge_change':\n",
    "        return {'splinetransformer__n_knots': [3, 5, 7],\n",
    "                'splinetransformer__degree': [2, 3], 'ridge__alpha': [1, 100, 1000]}\n",
    "    if name == 'random_forest_change':\n",
    "        return {'n_estimators': [100, 200], 'max_depth': [3, 5, None],\n",
    "                'min_samples_leaf': [5, 12]}\n",
    "    if name == 'xgboost_change':\n",
    "        return {'n_estimators': [100, 200], 'max_depth': [2, 4],\n",
    "                'learning_rate': [0.01, 0.05, 0.1], 'reg_lambda': [1, 30]}\n",
    "    if name == 'neural_network_change':\n",
    "        return {'regressor__mlpregressor__hidden_layer_sizes': [(16,), (32,), (32, 16)],\n",
    "                'regressor__mlpregressor__alpha': [0.01, 1, 10]}\n",
    "    return {}\n",
    "\n",
    "\n",
    "# Adapt candidates to sklearn's cloning/search interface. Score reconstructed\n",
    "# stock levels, including seasonal adjustments, damping, and the zero floor.\n",
    "class StockRegressor(RegressorMixin, BaseEstimator):\n",
    "    \"\"\"Let GridSearchCV score the final stock-level forecast.\"\"\"\n",
    "    def __init__(self, name, model):\n",
    "        self.name = name\n",
    "        self.model = model\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        from sklearn.base import clone\n",
    "        X = training_rows(X)\n",
    "        target = X.actual_kb if self.name == 'constrained_level' else X.delta_kb\n",
    "        if self.name == 'seasonal_residual_change':\n",
    "            target = target - X.seasonal_change\n",
    "        # Clone an unfitted candidate for each fold so fitted state cannot leak across\n",
    "        # search trials. A trailing underscore conventionally marks fitted attributes.\n",
    "        self.model_ = clone(self.model).fit(X[columns(self.name)], target)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        return predict(self.name, self.model_, X)\n",
    "\n",
    "\n",
    "# Search only the supplied past window; return parameters, all candidate/fold\n",
    "# scores, and convergence warnings. The chosen estimator is fitted separately.\n",
    "def tune(name, train, folds=10, jobs=8, splits=None):\n",
    "    \"\"\"Search past-only folds and retain every candidate's scores.\"\"\"\n",
    "    grid = parameter_grid(name)\n",
    "    if not grid:\n",
    "        return {}, pd.DataFrame(), ''\n",
    "    cv = splits if splits is not None else validation_splits(train, folds)\n",
    "    # Maximize negative MAE, equivalent to minimizing stock error. model__ reaches\n",
    "    # the candidate inside StockRegressor; refit=False avoids an unused final fit.\n",
    "    search = GridSearchCV(StockRegressor(name, estimator(name)),\n",
    "        {'model__' + key: value for key, value in grid.items()},\n",
    "        scoring='neg_mean_absolute_error', cv=cv, n_jobs=jobs,\n",
    "        refit=False, error_score='raise', return_train_score=True)\n",
    "    from joblib import parallel_backend\n",
    "    from threadpoolctl import threadpool_limits\n",
    "    with warnings.catch_warnings(record=True) as caught, threadpool_limits(limits=1), parallel_backend('threading'):\n",
    "        warnings.simplefilter('always', ConvergenceWarning)\n",
    "        search.fit(train, train.actual_kb)\n",
    "    results = pd.DataFrame(search.cv_results_)\n",
    "    results['params'] = results.params.map(\n",
    "        lambda p: json.dumps({k.removeprefix('model__'): v for k,v in p.items()}, sort_keys=True))\n",
    "    # Reverse sklearn's negative-score convention to report positive MAE.\n",
    "    results['mean_test_mae_kb'] = -results.mean_test_score\n",
    "    # Remove the wrapper prefix so fit() can set parameters on the candidate itself.\n",
    "    params = {k.removeprefix('model__'): v for k,v in search.best_params_.items()}\n",
    "    warning = '; '.join(sorted({str(w.message) for w in caught}))\n",
    "    return params, results, warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0ed97d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.736155Z",
     "iopub.status.busy": "2026-09-16T21:54:42.736045Z",
     "iopub.status.idle": "2026-09-16T21:54:42.750270Z",
     "shell.execute_reply": "2026-09-16T21:54:42.749981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>param_grid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{'polynomialfeatures__degree': [1, 2], 'ridge_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{'splinetransformer__n_knots': [3, 5, 7], 'spl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{'n_estimators': [100, 200], 'max_depth': [3, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{'n_estimators': [100, 200], 'max_depth': [2, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{'regressor__mlpregressor__hidden_layer_sizes'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{'ridge__alpha': [0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>{'regressor__huberregressor__epsilon': [1.35, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model                                         param_grid\n",
       "0         constrained_level                                                 {}\n",
       "1              ridge_change    {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}\n",
       "2     seasonal_ridge_change    {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}\n",
       "3   polynomial_ridge_change  {'polynomialfeatures__degree': [1, 2], 'ridge_...\n",
       "4       spline_ridge_change  {'splinetransformer__n_knots': [3, 5, 7], 'spl...\n",
       "5      random_forest_change  {'n_estimators': [100, 200], 'max_depth': [3, ...\n",
       "6            xgboost_change  {'n_estimators': [100, 200], 'max_depth': [2, ...\n",
       "7     neural_network_change  {'regressor__mlpregressor__hidden_layer_sizes'...\n",
       "8  seasonal_residual_change          {'ridge__alpha': [0.1, 1, 10, 100, 1000]}\n",
       "9              huber_change  {'regressor__huberregressor__epsilon': [1.35, ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show each candidate's search space before running the experiment; nested\n",
    "# parameter names identify the relevant estimator or pipeline transformation.\n",
    "\n",
    "display(pd.DataFrame([{'model':name,'param_grid':parameter_grid(name)} for name in LEARNED]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ac54b6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.751794Z",
     "iopub.status.busy": "2026-09-16T21:54:42.751406Z",
     "iopub.status.idle": "2026-09-16T21:54:42.755446Z",
     "shell.execute_reply": "2026-09-16T21:54:42.755109Z"
    }
   },
   "outputs": [],
   "source": [
    "# MAE and RMSE are in kb; RMSE weights large misses more. R-squared is unitless\n",
    "# and may be negative when errors exceed the actual sample's mean-only benchmark.\n",
    "def score(actual, predicted):\n",
    "    return {'mae_kb': mean_absolute_error(actual, predicted),\n",
    "            'rmse_kb': mean_squared_error(actual, predicted)**0.5,\n",
    "            'r2': r2_score(actual, predicted), 'n': len(actual)}\n",
    "\n",
    "\n",
    "# Require one row for every PADD in each group before summing additive columns.\n",
    "# Compute national errors after summation; regional forecast errors can offset.\n",
    "def aggregate(frame, keys, additive):\n",
    "    \"\"\"Reject partial U.S. totals instead of silently summing fewer than five PADDs.\"\"\"\n",
    "    if frame.duplicated(keys + ['padd']).any():\n",
    "        raise ValueError('Duplicate PADD rows in aggregation')\n",
    "    if not frame.groupby(keys).padd.nunique().eq(5).all():\n",
    "        raise ValueError('U.S. aggregation requires all five PADDs')\n",
    "    return frame.groupby(keys, as_index=False)[additive].sum(min_count=5)\n",
    "\n",
    "\n",
    "# Score each group, expanding either a scalar or tuple group key into the named\n",
    "# key columns of the resulting metrics table.\n",
    "def metric_table(predictions, keys):\n",
    "    return pd.DataFrame([{**dict(zip(keys, k if isinstance(k, tuple) else (k,))),\n",
    "        **score(g.actual_kb, g.predicted_kb)} for k,g in predictions.groupby(keys)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82083f4",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "Select models with the lowest development MAE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3633bde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:42.756952Z",
     "iopub.status.busy": "2026-09-16T21:54:42.756644Z",
     "iopub.status.idle": "2026-09-16T21:54:45.348568Z",
     "shell.execute_reply": "2026-09-16T21:54:45.348198Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training targets: 2008-01-01 to 2014-06-01\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_month</th>\n",
       "      <th>month</th>\n",
       "      <th>actual_kb</th>\n",
       "      <th>predicted_kb</th>\n",
       "      <th>absolute_error_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>59,765.00</td>\n",
       "      <td>60,048.29</td>\n",
       "      <td>283.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>57,773.00</td>\n",
       "      <td>58,116.20</td>\n",
       "      <td>343.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>55,712.00</td>\n",
       "      <td>57,668.53</td>\n",
       "      <td>1,956.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>50,685.00</td>\n",
       "      <td>55,592.71</td>\n",
       "      <td>4,907.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>53,624.00</td>\n",
       "      <td>51,854.05</td>\n",
       "      <td>1,769.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>62,085.00</td>\n",
       "      <td>57,592.70</td>\n",
       "      <td>4,492.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>69,032.00</td>\n",
       "      <td>63,100.06</td>\n",
       "      <td>5,931.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>68,142.00</td>\n",
       "      <td>66,421.36</td>\n",
       "      <td>1,720.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>64,542.00</td>\n",
       "      <td>61,938.98</td>\n",
       "      <td>2,603.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>63,272.00</td>\n",
       "      <td>61,243.42</td>\n",
       "      <td>2,028.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>61,203.00</td>\n",
       "      <td>60,052.57</td>\n",
       "      <td>1,150.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>61,350.00</td>\n",
       "      <td>59,171.76</td>\n",
       "      <td>2,178.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   origin_month      month  actual_kb  predicted_kb  absolute_error_kb\n",
       "78   2014-06-01 2014-07-01  59,765.00     60,048.29             283.29\n",
       "79   2014-07-01 2014-08-01  57,773.00     58,116.20             343.20\n",
       "80   2014-08-01 2014-09-01  55,712.00     57,668.53           1,956.53\n",
       "81   2014-09-01 2014-10-01  50,685.00     55,592.71           4,907.71\n",
       "82   2014-10-01 2014-11-01  53,624.00     51,854.05           1,769.95\n",
       "83   2014-11-01 2014-12-01  62,085.00     57,592.70           4,492.30\n",
       "84   2014-12-01 2015-01-01  69,032.00     63,100.06           5,931.94\n",
       "85   2015-01-01 2015-02-01  68,142.00     66,421.36           1,720.64\n",
       "86   2015-02-01 2015-03-01  64,542.00     61,938.98           2,603.02\n",
       "87   2015-03-01 2015-04-01  63,272.00     61,243.42           2,028.58\n",
       "88   2015-04-01 2015-05-01  61,203.00     60,052.57           1,150.43\n",
       "89   2015-05-01 2015-06-01  61,350.00     59,171.76           2,178.24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "mae_kb    2,447.15\n",
       "rmse_kb   2,979.55\n",
       "r2            0.68\n",
       "n            12.00\n",
       "Name: Worked-fold scores, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Work through the first expanding-window fold for one PADD with the constrained\n",
    "# model. This illustrative split uses the raw calendar; the full evaluation\n",
    "# uses validation_splits for COVID-eligible target dates. fit still filters inputs.\n",
    "\n",
    "worked = supervised(panel[panel.padd.eq(1)].reset_index(drop=True))\n",
    "development_example = worked.iloc[:-24]\n",
    "train_idx, test_idx = next(TimeSeriesSplit(n_splits=10, test_size=12).split(development_example))\n",
    "train_example, test_example = development_example.iloc[train_idx], development_example.iloc[test_idx]\n",
    "example_model, example_warning = fit('constrained_level', train_example)\n",
    "example_predictions = predict('constrained_level', example_model, test_example)\n",
    "worked_results = test_example[['origin_month','month','actual_kb']].copy()\n",
    "worked_results['predicted_kb'] = example_predictions\n",
    "worked_results['absolute_error_kb'] = abs(worked_results.actual_kb-worked_results.predicted_kb)\n",
    "print('Training targets:',train_example.month.min().date(),'to',train_example.month.max().date())\n",
    "display(worked_results)\n",
    "display(pd.Series(score(test_example.actual_kb,example_predictions),name='Worked-fold scores'))\n",
    "assert train_example.month.max() < test_example.month.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2637eec1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.349777Z",
     "iopub.status.busy": "2026-09-16T21:54:45.349637Z",
     "iopub.status.idle": "2026-09-16T21:54:45.356163Z",
     "shell.execute_reply": "2026-09-16T21:54:45.355836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost features: ['stock_lag1', 'stock_lag12', 'change_lag1', 'lag_production_kb', 'lag_demand_kb', 'lag_imports_kb', 'lag_exports_kb', 'lag_net_receipts_kb', 'lag_adjustments_kb', 'lag_biofuels_kb', 'sin_month', 'cos_month']\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
      "             enable_categorical=True, eval_metric=None, feature_types=None,\n",
      "             feature_weights=None, gamma=None, grow_policy=None,\n",
      "             importance_type=None, interaction_constraints=None,\n",
      "             learning_rate=0.03, max_bin=None, max_cat_threshold=None,\n",
      "             max_cat_to_onehot=None, max_delta_step=None, max_depth=2,\n",
      "             max_leaves=None, min_child_weight=12, missing=nan,\n",
      "             monotone_constraints=None, multi_strategy=None, n_estimators=120,\n",
      "             n_jobs=1, num_parallel_tree=None, ...)\n",
      "TransformedTargetRegressor(regressor=Pipeline(steps=[('standardscaler',\n",
      "                                                      StandardScaler()),\n",
      "                                                     ('mlpregressor',\n",
      "                                                      MLPRegressor(alpha=10,\n",
      "                                                                   hidden_layer_sizes=(16,),\n",
      "                                                                   max_iter=3000,\n",
      "                                                                   random_state=42,\n",
      "                                                                   solver='lbfgs'))]),\n",
      "                           transformer=StandardScaler())\n"
     ]
    }
   ],
   "source": [
    "# Inspect estimator definitions and feature columns before fitting. Printed\n",
    "# objects describe configuration, not learned coefficients or held-out accuracy.\n",
    "\n",
    "# Inspect any candidate and its exact feature list without opening another notebook.\n",
    "print('XGBoost features:', columns('xgboost_change'))\n",
    "print(estimator('xgboost_change'))\n",
    "print(estimator('neural_network_change'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e1cc65",
   "metadata": {},
   "source": [
    "### Model selection\n",
    "\n",
    "- **One month:** Compare methods on ten chronological development tests, then sum PADD forecasts. The current outlook uses `seasonal_change`, which has the lowest U.S. MAE in July 2024–June 2026.\n",
    "- **Twelve months:** Select a development candidate on six annual paths, then compare it with simple benchmarks on two final-period annual paths. Use the method with the lowest national MAE for the current outlook. Predicted stocks feed later months without actual-data updates.\n",
    "\n",
    "Exclude March 2020–March 2021 and stock-training rows that use those months as inputs. The stock-training exclusions extend through March 2022.\n",
    "\n",
    "Tests use revised EIA data and assume prior-month observations are available. The final 24 months were used to choose the current one-month and twelve-month methods, so their reported errors are retrospective comparisons. It is not an untouched test of that choice.\n",
    "\n",
    "Refit the chosen models on all eligible observations for the outlook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5406604",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.358781Z",
     "iopub.status.busy": "2026-09-16T21:54:45.358596Z",
     "iopub.status.idle": "2026-09-16T21:54:45.368957Z",
     "shell.execute_reply": "2026-09-16T21:54:45.368635Z"
    }
   },
   "outputs": [],
   "source": [
    "# Choose each PADD's one-step candidate on development data, evaluate the fixed\n",
    "# choices on the last 24 months, then refit winners for the live outlook.\n",
    "def evaluate(samples, folds=10, holdout=24, jobs=8):\n",
    "    \"\"\"Choose models only on development CV; score untouched final 24 months.\"\"\"\n",
    "    predictions, fold_metrics, selections, fitted, warning_rows = [], [], [], {}, []\n",
    "    best_parameters, searches = {}, []\n",
    "    for padd, frame in samples.items():\n",
    "        # Reserve the final holdout rows before any tuning or candidate selection.\n",
    "        development = frame.iloc[:-holdout]\n",
    "        if len(development) - folds*12 < 36:\n",
    "            raise ValueError('Need >=36 initial training months plus 10 annual folds and holdout')\n",
    "        splits = validation_splits(development, folds, test_size=12)\n",
    "        for name in MODELS:\n",
    "            params, results, warning = tune(name, development, folds, jobs, splits)\n",
    "            best_parameters[padd, name] = params\n",
    "            if not results.empty:\n",
    "                searches.append(results.assign(padd=padd, model=name, stage='development',\n",
    "                    train_end=development.month.max()))\n",
    "                print(f'PADD {padd} {name}: grid MAE {results.mean_test_mae_kb.min():,.1f}; {params}', flush=True)\n",
    "            if warning:\n",
    "                warning_rows.append(dict(padd=padd, model=name, stage='grid_search', warning=warning))\n",
    "            # Reuse tuning folds for development diagnostics; these are selection scores,\n",
    "            # not independent nested-CV estimates of future generalization.\n",
    "            for fold, (tr, te) in enumerate(splits, 1):\n",
    "                train, test = training_rows(development.iloc[tr]), development.iloc[te]\n",
    "                model, warning = fit(name, train, params)\n",
    "                pred = predict(name, model, test)\n",
    "                fold_metrics.append(dict(padd=padd, model=name, fold=fold,\n",
    "                    train_end=train.month.max(), test_start=test.month.min(), test_end=test.month.max(),\n",
    "                    train_mae_kb=mean_absolute_error(train.actual_kb, predict(name, model, train)),\n",
    "                    **score(test.actual_kb, pred)))\n",
    "                output = test[['month', 'origin_month', 'actual_kb']].copy()\n",
    "                output['predicted_kb'], output['padd'], output['model'] = pred, padd, name\n",
    "                output['split'], output['fold'] = 'cv', fold\n",
    "                predictions.append(output)\n",
    "                if warning:\n",
    "                    warning_rows.append(dict(padd=padd, model=name, stage=f'cv_{fold}', warning=warning))\n",
    "        current = pd.concat(predictions, ignore_index=True)\n",
    "        current = current[current.padd.eq(padd)]\n",
    "        # Pool absolute development errors for this PADD across folds and select\n",
    "        # the lowest mean; no holdout observations enter this comparison.\n",
    "        losses = current.assign(error=lambda x: abs(x.actual_kb-x.predicted_kb)).groupby('model').error.mean()\n",
    "        winner = losses.idxmin()\n",
    "        selections.append(dict(padd=padd, selected_model=winner, cv_mae_kb=losses[winner]))\n",
    "        for name in MODELS:\n",
    "            model, warning = fit(name, development, best_parameters[padd, name])\n",
    "            # This is one-step evaluation: each row uses actual prior-month inputs, while\n",
    "            # estimator parameters remain fitted on development data for the entire block.\n",
    "            test = frame.iloc[-holdout:]\n",
    "            output = test[['month', 'origin_month', 'actual_kb']].copy()\n",
    "            output['predicted_kb'] = predict(name, model, test)\n",
    "            output['padd'], output['model'], output['split'], output['fold'] = padd, name, 'holdout', 0\n",
    "            predictions.append(output)\n",
    "            if warning:\n",
    "                warning_rows.append(dict(padd=padd, model=name, stage='holdout', warning=warning))\n",
    "        # Refit the already chosen winner on all eligible history for future use.\n",
    "        # This final estimator is separate from the one that produced holdout scores.\n",
    "        final, warning = fit(winner, frame, best_parameters[padd, winner])\n",
    "        fitted[padd] = {'name': winner, 'estimator': final}\n",
    "        if warning:\n",
    "            warning_rows.append(dict(padd=padd, model=winner, stage='final', warning=warning))\n",
    "        print(f'PADD {padd}: selected {winner}; CV MAE {losses[winner]:,.1f} kb', flush=True)\n",
    "    return (pd.concat(predictions, ignore_index=True), pd.DataFrame(fold_metrics),\n",
    "            pd.DataFrame(selections), fitted,\n",
    "            pd.DataFrame(warning_rows, columns=['padd','model','stage','warning']),\n",
    "            best_parameters, pd.concat(searches, ignore_index=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46e2998",
   "metadata": {},
   "source": [
    "Annual forecasts carry predicted stocks and flows forward one month at a time. Report the difference between the flow balance and statistical stock path separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a35e6d6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.370811Z",
     "iopub.status.busy": "2026-09-16T21:54:45.370646Z",
     "iopub.status.idle": "2026-09-16T21:54:45.377307Z",
     "shell.execute_reply": "2026-09-16T21:54:45.377034Z"
    }
   },
   "outputs": [],
   "source": [
    "# Produce a recursive path per district plus national sums. After the origin,\n",
    "# features depend on predicted stocks/flows, not future actual observations.\n",
    "def forecast(history, fitted, horizon=12):\n",
    "    all_paths = []\n",
    "    for p, state in fitted.items():\n",
    "        past = history[history.padd.eq(p)].sort_values('month').copy()\n",
    "        origin = past.month.iloc[-1]\n",
    "        months = pd.date_range(origin + pd.offsets.MonthBegin(), periods=horizon, freq='MS')\n",
    "        # Forecast the full flow path once at the origin; do not refit flows each step\n",
    "        # using synthetic observations appended to the stock path.\n",
    "        flows = forecast_flows(past, months)\n",
    "        # Track a separate raw flow-identity path initialized at the last observed stock.\n",
    "        identity_stock = past.stock_kb.iloc[-1]\n",
    "        for h, month in enumerate(months, 1):\n",
    "            previous = past.stock_kb.iloc[-1]\n",
    "            flow = flows.loc[month]\n",
    "            balance = flow.to_numpy() @ SIGNS\n",
    "            # At longer horizons, past contains earlier predicted rows. This is the recursive\n",
    "            # feedback through which forecast errors can propagate along the path.\n",
    "            features = feature_row(past, month)\n",
    "            features['forecast_flow_identity'] = max(0.0, previous + balance)\n",
    "            stock = float(predict(state['name'], state['estimator'], pd.DataFrame([features]))[0])\n",
    "            # Keep the raw flow accumulation unclipped so impossible inventory paths remain\n",
    "            # visible; it can diverge from the statistical stock forecast.\n",
    "            identity_stock += balance\n",
    "            row = dict(month=month, origin_month=origin, horizon=h, padd=p,\n",
    "                padd_name=PADD_NAMES[p], model=state['name'], stock_kb=stock,\n",
    "                previous_stock_kb=previous, stock_change_kb=stock-previous,\n",
    "                identity_stock_unclipped_kb=identity_stock, balance_kb=balance,\n",
    "                # Report statistical stock change minus monthly net supply in kb; this\n",
    "                # discrepancy is not an extra observed EIA adjustment.\n",
    "                model_reconciliation_kb=stock-previous-balance,\n",
    "                supply_kb=flow.production_kb+flow.imports_kb+flow.net_receipts_kb+flow.adjustments_kb+flow.biofuels_kb,\n",
    "                total_demand_kb=flow.demand_kb+flow.exports_kb, **flow.to_dict())\n",
    "            all_paths.append(row)\n",
    "            # Append the predicted row so the next month uses this stock and these flows\n",
    "            # as its most recent history, without seeing future actual values.\n",
    "            past = pd.concat([past, pd.DataFrame([row])], ignore_index=True)\n",
    "    regional = pd.DataFrame(all_paths)\n",
    "    sums = FLOWS + ['stock_kb', 'previous_stock_kb','stock_change_kb', 'identity_stock_unclipped_kb',\n",
    "                   'balance_kb','model_reconciliation_kb','supply_kb','total_demand_kb']\n",
    "    return regional, aggregate(regional, ['month','origin_month','horizon'], sums)\n",
    "\n",
    "\n",
    "# Select the year-ahead family separately from one-step winners, using complete\n",
    "# recursive development paths and their national stock errors.\n",
    "def select_horizon(samples, panel, folds=10, jobs=8):\n",
    "    \"\"\"Select a year-ahead model using full forecast paths on earlier non-COVID years.\"\"\"\n",
    "    predictions, searches, warning_rows = [], [], []\n",
    "    development_end = samples[1].month.iloc[-25]\n",
    "    for cutoff in recursive_origins(development_end):\n",
    "        for name in MODELS:\n",
    "            states = {}\n",
    "            for p in PADD_NAMES:\n",
    "                # Restrict fitting to targets observed by this simulated forecast origin.\n",
    "                train = samples[p][samples[p].month.le(cutoff)]\n",
    "                # Retune using only this origin's past history before generating its full path.\n",
    "                params, results, warning = tune(name, train, folds, jobs)\n",
    "                if not results.empty:\n",
    "                    searches.append(results.assign(padd=p, model=name,\n",
    "                        stage='recursive_development', train_end=cutoff))\n",
    "                if warning:\n",
    "                    warning_rows.append(dict(padd=p, model=name, stage='recursive_grid_search', warning=warning))\n",
    "                model, warning = fit(name, train, params)\n",
    "                if warning:\n",
    "                    warning_rows.append(dict(padd=p, model=name, stage='recursive_cv', warning=warning))\n",
    "                states[p] = {'name':name, 'estimator':model}\n",
    "            path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "            path = path[['padd','month','origin_month','horizon','stock_kb']].rename(columns={'stock_kb':'predicted_kb'})\n",
    "            # Attach actual stocks only after forecasting, for scoring. one_to_one rejects\n",
    "            # duplicate month/PADD observations that could otherwise multiply rows.\n",
    "            path = path.merge(panel[['padd','month','stock_kb']], on=['padd','month'], validate='one_to_one').rename(columns={'stock_kb':'actual_kb'})\n",
    "            predictions.append(path.assign(model=name))\n",
    "        print(f'Recursive development origin {cutoff.date()} complete', flush=True)\n",
    "    predictions = pd.concat(predictions, ignore_index=True)\n",
    "    national = aggregate(predictions, ['month','origin_month','horizon','model'], ['actual_kb','predicted_kb'])\n",
    "    # Compare national error across complete development paths to select one common\n",
    "    # family for all PADDs; individual regional estimators are still fitted separately.\n",
    "    scores = metric_table(national, ['model']).sort_values(['mae_kb','model'])\n",
    "    return scores.iloc[0].model, predictions, national, scores, pd.concat(searches, ignore_index=True), pd.DataFrame(warning_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c34d414",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.378820Z",
     "iopub.status.busy": "2026-09-16T21:54:45.378708Z",
     "iopub.status.idle": "2026-09-16T21:54:45.380805Z",
     "shell.execute_reply": "2026-09-16T21:54:45.380582Z"
    }
   },
   "outputs": [],
   "source": [
    "# Run controls: use ten expanding validation folds and eight search workers.\n",
    "# The worker count controls parallel grid trials; numerical threads are capped\n",
    "# inside fitting to avoid multiplying the number of active CPU threads.\n",
    "\n",
    "data_dir=HERE/'data'\n",
    "output_dir=OUTPUT\n",
    "folds=10\n",
    "jobs=8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23053c6d",
   "metadata": {},
   "source": [
    "### Fit and evaluate\n",
    "\n",
    "Prepare regional training data, compare candidates, and evaluate selected models on the final 24 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32758ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Path(output_dir)\n",
    "# Build chronological supervised samples separately for all five districts,\n",
    "# then run the full development search, one-step holdout evaluation, and refits.\n",
    "# This is a computationally expensive cell; progress and warnings are retained.\n",
    "output.mkdir(parents=True, exist_ok=True)\n",
    "panel = load_panel(data_dir)\n",
    "histories = {p: panel[panel.padd.eq(p)].reset_index(drop=True) for p in PADD_NAMES}\n",
    "samples = {p: supervised(history) for p,history in histories.items()}\n",
    "predictions, fold_metrics, selection, fitted, fit_warnings, best_parameters, grid_results = evaluate(samples, folds, jobs=jobs)\n",
    "\n",
    "display(fold_metrics.groupby('model').agg(folds=('fold','count'),test_mae_kb=('mae_kb','mean')))\n",
    "display(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8558e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assemble the one-step regional-winner model and score national sums. Select\n",
    "# the year-ahead family separately on recursive development paths, then refit\n",
    "# that family for each region to generate the 12-month outlook.\n",
    "\n",
    "# Label the development-selected regional forecasts as a combined model while\n",
    "# retaining every original candidate's predictions for comparison.\n",
    "selected = predictions.merge(selection[['padd','selected_model']], on='padd')\n",
    "selected = selected[selected.model.eq(selected.selected_model)].copy()\n",
    "selected['model'] = 'selected_padd_models'\n",
    "one_month_models = selection.sort_values('padd').set_index('padd').selected_model.to_dict()\n",
    "one_month_model_label = ' + '.join(dict.fromkeys(one_month_models.values()))\n",
    "combined = pd.concat([predictions, selected[predictions.columns]], ignore_index=True)\n",
    "us_predictions = aggregate(combined, ['month','origin_month','model','split','fold'], ['actual_kb','predicted_kb'])\n",
    "padd_metrics = metric_table(combined, ['padd','model','split'])\n",
    "us_metrics = metric_table(us_predictions, ['model','split'])\n",
    "# Use the lowest national final-period MAE for the current one-month outlook.\n",
    "# This makes the final period a selection sample for this deployed choice.\n",
    "one_month_model = (us_metrics[us_metrics.split.eq('holdout')]\n",
    "    .sort_values(['mae_kb','model']).iloc[0].model)\n",
    "if one_month_model != 'selected_padd_models':\n",
    "    one_month_models = {p: one_month_model for p in PADD_NAMES}\n",
    "    one_month_model_label = one_month_model\n",
    "    fitted = {}\n",
    "    for p, frame in samples.items():\n",
    "        model, warning = fit(one_month_model, frame, best_parameters[p, one_month_model])\n",
    "        fitted[p] = {'name':one_month_model, 'estimator':model}\n",
    "        if warning:\n",
    "            fit_warnings.loc[len(fit_warnings)] = [p, one_month_model, 'one_month_final', warning]\n",
    "# Generate the next month using the selected one-step estimators. The separate\n",
    "# year-ahead selection below may choose a different family for the 12-month path.\n",
    "one_step_regional, one_step_national = forecast(panel, fitted, horizon=1)\n",
    "development_year_ahead_model, recursive_cv, us_recursive_cv, horizon_scores, horizon_grids, horizon_warnings = select_horizon(samples, panel, folds, jobs)\n",
    "grid_results = pd.concat([grid_results, horizon_grids], ignore_index=True)\n",
    "fit_warnings = pd.concat([fit_warnings, horizon_warnings], ignore_index=True)\n",
    "# Refit the selected year-ahead family on full eligible history, using parameter\n",
    "# settings chosen before the holdout; then build the forward 12-month outlook.\n",
    "horizon_fitted = {}\n",
    "for p, frame in samples.items():\n",
    "    model, warning = fit(development_year_ahead_model, frame, best_parameters[p, development_year_ahead_model])\n",
    "    horizon_fitted[p] = {'name':development_year_ahead_model, 'estimator':model}\n",
    "    if warning:\n",
    "        fit_warnings.loc[len(fit_warnings)] = [p, development_year_ahead_model, 'horizon_final', warning]\n",
    "\n",
    "display(us_metrics.sort_values(['split','mae_kb']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a4fb5a",
   "metadata": {},
   "source": [
    "Test the selected year-ahead method on two separate annual paths against unchanged stocks and the one-month models rolled forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87366e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test two nonoverlapping 12-month paths in the holdout using the same recursion\n",
    "# as the outlook. Refit at each origin with fixed development-selected settings,\n",
    "# and compare with persistence and the recursively applied one-step model.\n",
    "\n",
    "# Two disjoint 12-month paths in the final holdout: same recursive procedure as outlook.\n",
    "recursive = []\n",
    "recursive_benchmarks = []\n",
    "# Use the two annual blocks within the final 24 months, starting 24 and\n",
    "# 12 months before the final observed date.\n",
    "for offset in [24, 12]:\n",
    "    states = {}\n",
    "    cutoff = panel.month.max() - pd.DateOffset(months=offset)\n",
    "    for p, state in horizon_fitted.items():\n",
    "        # Restrict fitting to targets observed by this simulated forecast origin.\n",
    "        train = samples[p][samples[p].month.le(cutoff)]\n",
    "        model, warning = fit(state['name'], train, best_parameters[p, state['name']])\n",
    "        states[p] = {'name': state['name'], 'estimator': model}\n",
    "        if warning:\n",
    "            fit_warnings.loc[len(fit_warnings)] = [p, state['name'], 'recursive_holdout', warning]\n",
    "    path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "    # Attach actual stocks only after forecasting, for scoring. one_to_one rejects\n",
    "    # duplicate month/PADD observations that could otherwise multiply rows.\n",
    "    path = path.merge(panel[['padd','month','stock_kb']], on=['padd','month'], suffixes=('_forecast','_actual'), validate='one_to_one')\n",
    "    path = path.rename(columns={'stock_kb_forecast':'predicted_kb','stock_kb_actual':'actual_kb'})\n",
    "    recursive.append(path)\n",
    "    for label in dict.fromkeys(['persistence', one_month_model_label, 'seasonal_naive']):\n",
    "        baseline_states = {}\n",
    "        for p in PADD_NAMES:\n",
    "            name = label if label in ('persistence', 'seasonal_naive') else one_month_models[p]\n",
    "            model, warning = fit(name, samples[p][samples[p].month.le(cutoff)], best_parameters[p, name])\n",
    "            baseline_states[p] = {'name':name, 'estimator':model}\n",
    "        baseline, _ = forecast(panel[panel.month.le(cutoff)], baseline_states)\n",
    "        baseline = baseline[['padd','month','origin_month','horizon','stock_kb']].rename(columns={'stock_kb':'predicted_kb'})\n",
    "        baseline = baseline.merge(panel[['padd','month','stock_kb']], on=['padd','month'], validate='one_to_one').rename(columns={'stock_kb':'actual_kb'})\n",
    "        recursive_benchmarks.append(baseline.assign(model=label))\n",
    "recursive = pd.concat(recursive, ignore_index=True)\n",
    "recursive_benchmarks.append(recursive[['padd','month','origin_month','horizon','actual_kb','predicted_kb']]\n",
    "    .assign(model=development_year_ahead_model))\n",
    "recursive_benchmarks = pd.concat(recursive_benchmarks, ignore_index=True)\n",
    "comparison = aggregate(recursive_benchmarks, ['month','origin_month','horizon','model'], ['actual_kb','predicted_kb'])\n",
    "year_ahead_model = metric_table(comparison, ['model']).sort_values(['mae_kb','model']).iloc[0].model\n",
    "if year_ahead_model != development_year_ahead_model:\n",
    "    horizon_fitted = {}\n",
    "    for p, frame in samples.items():\n",
    "        model, warning = fit(year_ahead_model, frame, best_parameters[p, year_ahead_model])\n",
    "        horizon_fitted[p] = {'name':year_ahead_model, 'estimator':model}\n",
    "        if warning:\n",
    "            fit_warnings.loc[len(fit_warnings)] = [p, year_ahead_model, 'horizon_final', warning]\n",
    "    selected_paths = []\n",
    "    for offset in [24, 12]:\n",
    "        cutoff = panel.month.max() - pd.DateOffset(months=offset)\n",
    "        states = {p: {'name':year_ahead_model,\n",
    "            'estimator':fit(year_ahead_model, samples[p][samples[p].month.le(cutoff)],\n",
    "                            best_parameters[p, year_ahead_model])[0]} for p in PADD_NAMES}\n",
    "        path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "        path = path.merge(panel[['padd','month','stock_kb']], on=['padd','month'],\n",
    "            suffixes=('_forecast','_actual'), validate='one_to_one')\n",
    "        selected_paths.append(path.rename(columns={\n",
    "            'stock_kb_forecast':'predicted_kb','stock_kb_actual':'actual_kb'}))\n",
    "    recursive = pd.concat(selected_paths, ignore_index=True)\n",
    "regional, national = forecast(panel, horizon_fitted)\n",
    "us_recursive = aggregate(recursive, ['month','origin_month','horizon'], ['actual_kb','predicted_kb'])\n",
    "\n",
    "display(metric_table(us_recursive, ['horizon']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "867de9ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.493809Z",
     "iopub.status.busy": "2026-09-16T21:54:45.493689Z",
     "iopub.status.idle": "2026-09-16T21:54:45.500941Z",
     "shell.execute_reply": "2026-09-16T21:54:45.500593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>production_kb</th>\n",
       "      <th>demand_kb</th>\n",
       "      <th>imports_kb</th>\n",
       "      <th>exports_kb</th>\n",
       "      <th>net_receipts_kb</th>\n",
       "      <th>adjustments_kb</th>\n",
       "      <th>biofuels_kb</th>\n",
       "      <th>stock_kb</th>\n",
       "      <th>total_gasoline_stock_kb</th>\n",
       "      <th>finished_stock_kb</th>\n",
       "      <th>blending_stock_kb</th>\n",
       "      <th>finished_production_kb</th>\n",
       "      <th>blending_net_inputs_kb</th>\n",
       "      <th>stock_component_residual_kb</th>\n",
       "      <th>balance_kb</th>\n",
       "      <th>accounting_residual_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>258,162.00</td>\n",
       "      <td>273,679.00</td>\n",
       "      <td>17,358.00</td>\n",
       "      <td>24,087.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>776.00</td>\n",
       "      <td>177.00</td>\n",
       "      <td>221,699.00</td>\n",
       "      <td>221,699.00</td>\n",
       "      <td>16,023.00</td>\n",
       "      <td>205,676.00</td>\n",
       "      <td>284,284.00</td>\n",
       "      <td>26,122.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-21,293.00</td>\n",
       "      <td>-3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>273,782.00</td>\n",
       "      <td>269,138.00</td>\n",
       "      <td>20,046.00</td>\n",
       "      <td>28,600.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,399.00</td>\n",
       "      <td>201.00</td>\n",
       "      <td>220,389.00</td>\n",
       "      <td>220,389.00</td>\n",
       "      <td>15,249.00</td>\n",
       "      <td>205,140.00</td>\n",
       "      <td>289,712.00</td>\n",
       "      <td>15,930.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,310.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>272,873.00</td>\n",
       "      <td>267,513.00</td>\n",
       "      <td>19,935.00</td>\n",
       "      <td>28,452.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,011.00</td>\n",
       "      <td>198.00</td>\n",
       "      <td>219,441.00</td>\n",
       "      <td>219,441.00</td>\n",
       "      <td>18,683.00</td>\n",
       "      <td>200,758.00</td>\n",
       "      <td>291,097.00</td>\n",
       "      <td>18,224.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-948.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         month  production_kb  demand_kb  imports_kb  exports_kb  \\\n",
       "231 2026-04-01     258,162.00 273,679.00   17,358.00   24,087.00   \n",
       "232 2026-05-01     273,782.00 269,138.00   20,046.00   28,600.00   \n",
       "233 2026-06-01     272,873.00 267,513.00   19,935.00   28,452.00   \n",
       "\n",
       "     net_receipts_kb  adjustments_kb  biofuels_kb   stock_kb  \\\n",
       "231             0.00          776.00       177.00 221,699.00   \n",
       "232             0.00        2,399.00       201.00 220,389.00   \n",
       "233             0.00        2,011.00       198.00 219,441.00   \n",
       "\n",
       "     total_gasoline_stock_kb  finished_stock_kb  blending_stock_kb  \\\n",
       "231               221,699.00          16,023.00         205,676.00   \n",
       "232               220,389.00          15,249.00         205,140.00   \n",
       "233               219,441.00          18,683.00         200,758.00   \n",
       "\n",
       "     finished_production_kb  blending_net_inputs_kb  \\\n",
       "231              284,284.00               26,122.00   \n",
       "232              289,712.00               15,930.00   \n",
       "233              291,097.00               18,224.00   \n",
       "\n",
       "     stock_component_residual_kb  balance_kb  accounting_residual_kb  \n",
       "231                         0.00  -21,293.00                   -3.00  \n",
       "232                         0.00   -1,310.00                    0.00  \n",
       "233                         0.00     -948.00                    0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>padd_name</th>\n",
       "      <th>month</th>\n",
       "      <th>stock_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>East Coast</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>54,202.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>47,759.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gulf Coast</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>78,365.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rocky Mountain</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>6,957.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>29,120.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>United States (sum of PADDs)</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>216,404.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd                     padd_name      month   stock_kb\n",
       "0     1                    East Coast 2026-07-01  54,202.60\n",
       "1     2                       Midwest 2026-07-01  47,759.80\n",
       "2     3                    Gulf Coast 2026-07-01  78,365.00\n",
       "3     4                Rocky Mountain 2026-07-01   6,957.20\n",
       "4     5                    West Coast 2026-07-01  29,120.20\n",
       "5     0  United States (sum of PADDs) 2026-07-01 216,404.80"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine regional history into national totals and show the next-month\n",
    "# forecast. The latest table contains five PADD rows plus one U.S. reporting row.\n",
    "\n",
    "# Build national historical totals from all five districts. Ratios, when needed,\n",
    "# must be recomputed from their summed numerator and denominator.\n",
    "us_history = aggregate(panel, ['month'], FLOWS+['stock_kb','total_gasoline_stock_kb','finished_stock_kb','blending_stock_kb',\n",
    "    'finished_production_kb','blending_net_inputs_kb','stock_component_residual_kb','balance_kb','accounting_residual_kb'])\n",
    "# Use padd=0 as a reporting label for the U.S. total; it is not a sixth region.\n",
    "latest_us = one_step_national.assign(padd=0, padd_name='United States (sum of PADDs)',\n",
    "                                                   model=one_month_model_label)\n",
    "latest = pd.concat([one_step_regional, latest_us], ignore_index=True)\n",
    "\n",
    "display(us_history.tail(3))\n",
    "display(latest[['padd','padd_name','month','stock_kb']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0147203f",
   "metadata": {},
   "source": [
    "### Save results\n",
    "\n",
    "Export predictions, errors, selected models, forecast paths, and run settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8f24f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save result tables and fitted models for reporting and reuse. CSVs carry\n",
    "# readable diagnostics; joblib preserves estimator state; coefficients expose\n",
    "# the constrained regression's fitted relationship.\n",
    "\n",
    "# Collect inputs, predictions, scores, selections, and audits under stable names.\n",
    "# Each name becomes a CSV stem consumed by later reporting cells.\n",
    "tables = {'padd_monthly_model':panel,'us_monthly_model':us_history,\n",
    "    'padd_predictions':combined,'us_predictions':us_predictions,'padd_model_metrics':padd_metrics,\n",
    "    'us_model_metrics':us_metrics,'fold_metrics':fold_metrics,'selected_models':selection,\n",
    "    'padd_forecast_12m':regional,'us_forecast_12m':national,'latest_forecast':latest,\n",
    "    'recursive_cv_predictions':recursive_cv, 'us_recursive_cv_predictions':us_recursive_cv,\n",
    "    'year_ahead_model_cv_metrics':horizon_scores,\n",
    "    'recursive_benchmark_predictions':recursive_benchmarks,\n",
    "    'recursive_holdout_predictions':recursive,'us_recursive_holdout_predictions':us_recursive,\n",
    "    'recursive_holdout_metrics':metric_table(recursive,['padd']),\n",
    "    'us_recursive_horizon_metrics':metric_table(us_recursive,['horizon']), 'fit_warnings':fit_warnings,\n",
    "    'grid_search_results':grid_results,\n",
    "    'best_parameters':pd.DataFrame([{'padd':p, 'model':name, 'params':json.dumps(params, sort_keys=True)}\n",
    "        for (p,name),params in best_parameters.items()])}\n",
    "# Record target and origin dates plus separate fitting/evaluation eligibility\n",
    "# flags so readers can inspect exactly which dates the exclusion model affects.\n",
    "tables['training_sample_audit'] = pd.concat([frame[['month','origin_month']].assign(\n",
    "    padd=p, training_eligible=frame.index.isin(training_rows(frame).index),\n",
    "    evaluation_eligible=outside_covid(frame.month)) for p, frame in samples.items()], ignore_index=True)\n",
    "# Write named result tables without pandas' row index; rerunning replaces files\n",
    "# with the same names in model_output.\n",
    "for name, frame in tables.items():\n",
    "    frame.to_csv(output/f'{name}.csv', index=False)\n",
    "# Save all final candidate estimators as well as selected models so downstream\n",
    "# analysis can reproduce predictions without repeating parameter searches.\n",
    "all_fitted = {}\n",
    "for p, frame in samples.items():\n",
    "    for name in MODELS:\n",
    "        model, warning = fit(name, frame, best_parameters[p, name])\n",
    "        all_fitted[p, name] = {'name': name, 'estimator': model}\n",
    "joblib.dump(all_fitted, output/'all_fitted_models.joblib')\n",
    "joblib.dump(horizon_fitted, output/'fitted_horizon_models.joblib')\n",
    "joblib.dump(fitted, output/'fitted_models.joblib')\n",
    "# Export an interpretable constrained-regression coefficient table by PADD.\n",
    "# These coefficients apply to pre-signed inputs, not unsigned raw demand flows.\n",
    "coefficients = []\n",
    "for p, frame in samples.items():\n",
    "    model, _ = fit('constrained_level', frame)\n",
    "    coefficients.append({'padd':p,'intercept_kb':model.intercept_,**dict(zip(columns('constrained_level'),model.coef_))})\n",
    "pd.DataFrame(coefficients).to_csv(output/'constrained_model_coefficients.csv',index=False)\n",
    "\n",
    "print('Saved',len(tables),'result tables to',output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b7ca4c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Persist machine-readable run documentation alongside result tables. Source\n",
    "# URLs identify provenance; versions and settings describe the actual run,\n",
    "# including limitations of revised-history and point-only forecasts.\n",
    "\n",
    "# Record scope, units, actual data dates, selection rules, limitations, and\n",
    "# software versions alongside outputs for reproducibility and interpretation.\n",
    "metadata = {'product':'Total motor gasoline: finished motor gasoline plus motor gasoline blending components',\n",
    "    'covid_exclusion': {'start':'2020-03-01', 'end':'2021-03-01', 'inclusive':True,\n",
    "        'method':'Exclude targets and direct lag inputs touching COVID from stock fitting; exclude COVID observations from flow fits and seasonal averages; preserve the full calendar and historical balance.',\n",
    "        'evaluation':'Exclude COVID target months from development selection; full recursive development years avoid COVID; final 24 months unchanged.'},\n",
    "    'stock_units':'thousand barrels at month end', 'flow_units':'thousand barrels per calendar month',\n",
    "    'equation':'S[t] = S[t-1] + (finished refinery/blender net production[t] - blending-component refinery/blender net inputs[t]) + imports[t] + net receipts[t] + adjustments[t] + biofuel net production[t] - product supplied[t] - exports[t] + residual[t]',\n",
    "    'data_start':str(panel.month.min().date()),'data_end':str(panel.month.max().date()),\n",
    "    'cv':f'{folds} expanding folds, 12 eligible test months each; final 24 months excluded from selection',\n",
    "    'holdout_start':str(samples[1].month.iloc[-24].date()),\n",
    "    'forecast_timing':'One month after latest observed EIA month; conditional on prior monthly data being available. Current revised history, not a real-time release-vintage backtest.',\n",
    "    'one_month_models':one_month_models,\n",
    "    'one_month_model':one_month_model,\n",
    "    'one_month_model_label':one_month_model_label,\n",
    "    'development_year_ahead_model':development_year_ahead_model,\n",
    "    'year_ahead_model':year_ahead_model,\n",
    "    'horizon_selection':'Compare the development-selected family with persistence, the current one-month method, and same-month-last-year stocks on two final-period annual paths. Use the lowest national MAE for the current twelve-month outlook; these paths are now part of model choice.',\n",
    "    'selection':'Regional comparison models use lowest pooled development CV MAE per PADD. Current one-month outlook uses the lowest national MAE on the final 24 months; that period is now part of model choice, so its score is descriptive rather than an untouched test of the deployed method.',\n",
    "    'hyperparameter_search':{'method':'GridSearchCV', 'folds':folds,\n",
    "        'scoring':'neg_mean_absolute_error', 'splitter':'TimeSeriesSplit on non-COVID target dates; 12 eligible observations per development block',\n",
    "        'evaluation':'Development scores reuse tuning folds and are selection scores, not nested CV estimates. Final 24 months excluded from every search.',\n",
    "        'recursive':'Holdout and final refits freeze parameters selected before the holdout.'},\n",
    "    'flow_forecast':'Last 60 non-COVID observations, daily rates, linear trend and month fixed effects; net production remains signed',\n",
    "    'components':'Flows sum finished and blending components; production is finished net production minus blending net inputs; stocks use independently published MGTSTP series',\n",
    "    'forecast_stock_floor':0, 'uncertainty':'Point forecasts only; no calibrated prediction intervals',\n",
    "    'recursive_validation':'Two disjoint 12-month holdout paths; refit at each origin; only two errors per horizon',\n",
    "    'versions':{'python':platform.python_version(),'numpy':np.__version__,'pandas':pd.__version__,\n",
    "                'sklearn':sklearn.__version__, 'xgboost':xgboost.__version__},\n",
    "    'sources':['https://www.eia.gov/dnav/pet/pet_sum_snd_d_r10_mbbl_m_cur.htm',\n",
    "               'https://www.eia.gov/dnav/pet/TblDefs/pet_sum_snd_tbldef2.asp']}\n",
    "(output/'model_metadata.json').write_text(json.dumps(metadata,indent=2))\n",
    "# Serialize candidate definitions for inspection; repr describes configuration,\n",
    "# while the joblib files above preserve actual fitted estimator state.\n",
    "settings = {name: {'features': columns(name), 'estimator': repr(estimator(name)),\n",
    "                   'param_grid':parameter_grid(name)}\n",
    "            for name in LEARNED}\n",
    "(output/'candidate_models.json').write_text(json.dumps(settings, indent=2))\n",
    "print(us_metrics[us_metrics.split.eq('holdout')].sort_values('mae_kb').to_string(index=False))\n",
    "\n",
    "print('Data through:',metadata['data_end'])\n",
    "print('Fitting warnings:',len(fit_warnings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "031aca58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.501969Z",
     "iopub.status.busy": "2026-09-16T21:54:45.501864Z",
     "iopub.status.idle": "2026-09-16T21:54:45.510104Z",
     "shell.execute_reply": "2026-09-16T21:54:45.509678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train_end</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold  train_end test_start   test_end   n\n",
       "0     1 2013-05-01 2013-06-01 2014-05-01  12\n",
       "1     2 2014-05-01 2014-06-01 2015-05-01  12\n",
       "2     3 2015-05-01 2015-06-01 2016-05-01  12\n",
       "3     4 2016-05-01 2016-06-01 2017-05-01  12\n",
       "4     5 2017-05-01 2017-06-01 2018-05-01  12\n",
       "5     6 2018-05-01 2018-06-01 2019-05-01  12\n",
       "6     7 2019-05-01 2019-06-01 2021-06-01  12\n",
       "7     8 2020-02-01 2021-07-01 2022-06-01  12\n",
       "8     9 2022-06-01 2022-07-01 2023-06-01  12\n",
       "9    10 2023-06-01 2023-07-01 2024-06-01  12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folds = tables['fold_metrics']\n",
    "# Inspect one representative model/PADD because candidates share split dates.\n",
    "# Here folds is reassigned from the earlier integer setting to the metrics table.\n",
    "# Assertions require training before testing and development before the holdout.\n",
    "schedule = folds.query(\"padd == 1 and model == 'persistence'\")[['fold','train_end','test_start','test_end','n']]\n",
    "display(schedule)\n",
    "assert (pd.to_datetime(schedule.train_end) < pd.to_datetime(schedule.test_start)).all()\n",
    "assert pd.to_datetime(schedule.test_end).max() < pd.Timestamp(metadata['holdout_start'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb22492d",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "Compare one-month forecasts with unchanged stocks and the same month last year over July 2024–June 2026. `seasonal_change` has the lowest national MAE in this period and is used for the current one-month outlook.\n",
    "\n",
    "Mean absolute error (MAE) measures the average miss; root mean squared error (RMSE) gives more weight to large misses. Errors are in thousand barrels unless labeled otherwise. Lower is better. R² measures fit to stock levels, not accuracy in predicting builds and draws.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42e11cbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.511329Z",
     "iopub.status.busy": "2026-09-16T21:54:45.511211Z",
     "iopub.status.idle": "2026-09-16T21:54:45.516541Z",
     "shell.execute_reply": "2026-09-16T21:54:45.516103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>selected_model</th>\n",
       "      <th>cv_mae_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>2,406.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>1,511.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>2,073.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>345.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>1,050.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd            selected_model  cv_mae_kb\n",
       "0     1  seasonal_residual_change   2,406.06\n",
       "1     2     neural_network_change   1,511.81\n",
       "2     3  seasonal_residual_change   2,073.41\n",
       "3     4      random_forest_change     345.96\n",
       "4     5      random_forest_change   1,050.34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare development MAE by candidate and district, then inspect the average\n",
    "# train/test gap. A large gap suggests overfitting or a changing data regime;\n",
    "# these reused selection scores are not an independent performance estimate.\n",
    "\n",
    "display(tables['selected_models'])\n",
    "# Join selected family names to their saved per-PADD parameter dictionaries\n",
    "# so readers can see which settings produced the reported regional winners.\n",
    "selected_parameters = (tables['selected_models']\n",
    "    .merge(tables['best_parameters'], left_on=['padd','selected_model'], right_on=['padd','model'])\n",
    "    [['padd','selected_model','cv_mae_kb','params']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e11cbf-parameters-heading",
   "metadata": {},
   "source": [
    "### Best parameters for the development-selected PADD comparison models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "42e11cbf-parameter-results",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.517658Z",
     "iopub.status.busy": "2026-09-16T21:54:45.517495Z",
     "iopub.status.idle": "2026-09-16T21:54:45.646174Z",
     "shell.execute_reply": "2026-09-16T21:54:45.645765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>selected_model</th>\n",
       "      <th>cv_mae_kb</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>2,406.06</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>1,511.81</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>2,073.41</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>345.96</td>\n",
       "      <td>{\"max_depth\": 5, \"min_samples_leaf\": 12, \"n_estimators\": 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>1,050.34</td>\n",
       "      <td>{\"max_depth\": 5, \"min_samples_leaf\": 5, \"n_estimators\": 100}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd            selected_model  cv_mae_kb  \\\n",
       "0     1  seasonal_residual_change   2,406.06   \n",
       "1     2     neural_network_change   1,511.81   \n",
       "2     3  seasonal_residual_change   2,073.41   \n",
       "3     4      random_forest_change     345.96   \n",
       "4     5      random_forest_change   1,050.34   \n",
       "\n",
       "                                                                                            params  \n",
       "0                                                                            {\"ridge__alpha\": 100}  \n",
       "1  {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}  \n",
       "2                                                                             {\"ridge__alpha\": 10}  \n",
       "3                                    {\"max_depth\": 5, \"min_samples_leaf\": 12, \"n_estimators\": 200}  \n",
       "4                                     {\"max_depth\": 5, \"min_samples_leaf\": 5, \"n_estimators\": 100}  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9128e_row3_col1, #T_9128e_row6_col3, #T_9128e_row6_col4, #T_9128e_row10_col0, #T_9128e_row10_col2 {\n",
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       "</style>\n",
       "<table id=\"T_9128e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >padd</th>\n",
       "      <th id=\"T_9128e_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_9128e_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_9128e_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_9128e_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_9128e_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >model</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row0\" class=\"row_heading level0 row0\" >constrained_level</th>\n",
       "      <td id=\"T_9128e_row0_col0\" class=\"data row0 col0\" >2913.403885</td>\n",
       "      <td id=\"T_9128e_row0_col1\" class=\"data row0 col1\" >2138.861640</td>\n",
       "      <td id=\"T_9128e_row0_col2\" class=\"data row0 col2\" >2350.076713</td>\n",
       "      <td id=\"T_9128e_row0_col3\" class=\"data row0 col3\" >399.682590</td>\n",
       "      <td id=\"T_9128e_row0_col4\" class=\"data row0 col4\" >1191.054700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row1\" class=\"row_heading level0 row1\" >forecast_flow_identity</th>\n",
       "      <td id=\"T_9128e_row1_col0\" class=\"data row1 col0\" >2581.938536</td>\n",
       "      <td id=\"T_9128e_row1_col1\" class=\"data row1 col1\" >1663.697036</td>\n",
       "      <td id=\"T_9128e_row1_col2\" class=\"data row1 col2\" >2617.334390</td>\n",
       "      <td id=\"T_9128e_row1_col3\" class=\"data row1 col3\" >403.372965</td>\n",
       "      <td id=\"T_9128e_row1_col4\" class=\"data row1 col4\" >1244.472820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row2\" class=\"row_heading level0 row2\" >huber_change</th>\n",
       "      <td id=\"T_9128e_row2_col0\" class=\"data row2 col0\" >2823.421747</td>\n",
       "      <td id=\"T_9128e_row2_col1\" class=\"data row2 col1\" >1558.503198</td>\n",
       "      <td id=\"T_9128e_row2_col2\" class=\"data row2 col2\" >2135.227305</td>\n",
       "      <td id=\"T_9128e_row2_col3\" class=\"data row2 col3\" >369.336804</td>\n",
       "      <td id=\"T_9128e_row2_col4\" class=\"data row2 col4\" >1108.711965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row3\" class=\"row_heading level0 row3\" >neural_network_change</th>\n",
       "      <td id=\"T_9128e_row3_col0\" class=\"data row3 col0\" >2709.874511</td>\n",
       "      <td id=\"T_9128e_row3_col1\" class=\"data row3 col1\" >1511.805799</td>\n",
       "      <td id=\"T_9128e_row3_col2\" class=\"data row3 col2\" >2192.544672</td>\n",
       "      <td id=\"T_9128e_row3_col3\" class=\"data row3 col3\" >349.311362</td>\n",
       "      <td id=\"T_9128e_row3_col4\" class=\"data row3 col4\" >1091.024843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row4\" class=\"row_heading level0 row4\" >persistence</th>\n",
       "      <td id=\"T_9128e_row4_col0\" class=\"data row4 col0\" >3303.675000</td>\n",
       "      <td id=\"T_9128e_row4_col1\" class=\"data row4 col1\" >2375.866667</td>\n",
       "      <td id=\"T_9128e_row4_col2\" class=\"data row4 col2\" >2453.091667</td>\n",
       "      <td id=\"T_9128e_row4_col3\" class=\"data row4 col3\" >382.075000</td>\n",
       "      <td id=\"T_9128e_row4_col4\" class=\"data row4 col4\" >1357.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row5\" class=\"row_heading level0 row5\" >polynomial_ridge_change</th>\n",
       "      <td id=\"T_9128e_row5_col0\" class=\"data row5 col0\" >2799.748899</td>\n",
       "      <td id=\"T_9128e_row5_col1\" class=\"data row5 col1\" >1540.444174</td>\n",
       "      <td id=\"T_9128e_row5_col2\" class=\"data row5 col2\" >2121.850360</td>\n",
       "      <td id=\"T_9128e_row5_col3\" class=\"data row5 col3\" >357.564042</td>\n",
       "      <td id=\"T_9128e_row5_col4\" class=\"data row5 col4\" >1098.769342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row6\" class=\"row_heading level0 row6\" >random_forest_change</th>\n",
       "      <td id=\"T_9128e_row6_col0\" class=\"data row6 col0\" >2567.431387</td>\n",
       "      <td id=\"T_9128e_row6_col1\" class=\"data row6 col1\" >1572.129607</td>\n",
       "      <td id=\"T_9128e_row6_col2\" class=\"data row6 col2\" >2352.774531</td>\n",
       "      <td id=\"T_9128e_row6_col3\" class=\"data row6 col3\" >345.962995</td>\n",
       "      <td id=\"T_9128e_row6_col4\" class=\"data row6 col4\" >1050.340472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row7\" class=\"row_heading level0 row7\" >ridge_change</th>\n",
       "      <td id=\"T_9128e_row7_col0\" class=\"data row7 col0\" >2848.496454</td>\n",
       "      <td id=\"T_9128e_row7_col1\" class=\"data row7 col1\" >1709.912984</td>\n",
       "      <td id=\"T_9128e_row7_col2\" class=\"data row7 col2\" >2106.668849</td>\n",
       "      <td id=\"T_9128e_row7_col3\" class=\"data row7 col3\" >374.328005</td>\n",
       "      <td id=\"T_9128e_row7_col4\" class=\"data row7 col4\" >1162.654369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row8\" class=\"row_heading level0 row8\" >seasonal_change</th>\n",
       "      <td id=\"T_9128e_row8_col0\" class=\"data row8 col0\" >2542.861667</td>\n",
       "      <td id=\"T_9128e_row8_col1\" class=\"data row8 col1\" >1603.556667</td>\n",
       "      <td id=\"T_9128e_row8_col2\" class=\"data row8 col2\" >2569.987500</td>\n",
       "      <td id=\"T_9128e_row8_col3\" class=\"data row8 col3\" >388.769583</td>\n",
       "      <td id=\"T_9128e_row8_col4\" class=\"data row8 col4\" >1218.497500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row9\" class=\"row_heading level0 row9\" >seasonal_naive</th>\n",
       "      <td id=\"T_9128e_row9_col0\" class=\"data row9 col0\" >4715.858333</td>\n",
       "      <td id=\"T_9128e_row9_col1\" class=\"data row9 col1\" >2602.416667</td>\n",
       "      <td id=\"T_9128e_row9_col2\" class=\"data row9 col2\" >3130.266667</td>\n",
       "      <td id=\"T_9128e_row9_col3\" class=\"data row9 col3\" >540.133333</td>\n",
       "      <td id=\"T_9128e_row9_col4\" class=\"data row9 col4\" >1608.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row10\" class=\"row_heading level0 row10\" >seasonal_residual_change</th>\n",
       "      <td id=\"T_9128e_row10_col0\" class=\"data row10 col0\" >2406.055688</td>\n",
       "      <td id=\"T_9128e_row10_col1\" class=\"data row10 col1\" >1513.634038</td>\n",
       "      <td id=\"T_9128e_row10_col2\" class=\"data row10 col2\" >2073.408195</td>\n",
       "      <td id=\"T_9128e_row10_col3\" class=\"data row10 col3\" >362.788998</td>\n",
       "      <td id=\"T_9128e_row10_col4\" class=\"data row10 col4\" >1058.705726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row11\" class=\"row_heading level0 row11\" >seasonal_ridge_change</th>\n",
       "      <td id=\"T_9128e_row11_col0\" class=\"data row11 col0\" >2799.748899</td>\n",
       "      <td id=\"T_9128e_row11_col1\" class=\"data row11 col1\" >1540.444174</td>\n",
       "      <td id=\"T_9128e_row11_col2\" class=\"data row11 col2\" >2121.850360</td>\n",
       "      <td id=\"T_9128e_row11_col3\" class=\"data row11 col3\" >357.564042</td>\n",
       "      <td id=\"T_9128e_row11_col4\" class=\"data row11 col4\" >1098.769342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row12\" class=\"row_heading level0 row12\" >spline_ridge_change</th>\n",
       "      <td id=\"T_9128e_row12_col0\" class=\"data row12 col0\" >2648.632413</td>\n",
       "      <td id=\"T_9128e_row12_col1\" class=\"data row12 col1\" >1660.795388</td>\n",
       "      <td id=\"T_9128e_row12_col2\" class=\"data row12 col2\" >2237.021045</td>\n",
       "      <td id=\"T_9128e_row12_col3\" class=\"data row12 col3\" >358.961277</td>\n",
       "      <td id=\"T_9128e_row12_col4\" class=\"data row12 col4\" >1159.631276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9128e_level0_row13\" class=\"row_heading level0 row13\" >xgboost_change</th>\n",
       "      <td id=\"T_9128e_row13_col0\" class=\"data row13 col0\" >2566.385775</td>\n",
       "      <td id=\"T_9128e_row13_col1\" class=\"data row13 col1\" >1593.213574</td>\n",
       "      <td id=\"T_9128e_row13_col2\" class=\"data row13 col2\" >2320.149609</td>\n",
       "      <td id=\"T_9128e_row13_col3\" class=\"data row13 col3\" >347.521720</td>\n",
       "      <td id=\"T_9128e_row13_col4\" class=\"data row13 col4\" >1061.878385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7700e80b38c0>"
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_mae_kb</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>test_minus_train_kb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_residual_change</th>\n",
       "      <td>1,384.94</td>\n",
       "      <td>1,482.92</td>\n",
       "      <td>97.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural_network_change</th>\n",
       "      <td>1,271.29</td>\n",
       "      <td>1,570.91</td>\n",
       "      <td>299.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest_change</th>\n",
       "      <td>1,117.73</td>\n",
       "      <td>1,577.73</td>\n",
       "      <td>459.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost_change</th>\n",
       "      <td>1,131.14</td>\n",
       "      <td>1,577.83</td>\n",
       "      <td>446.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_ridge_change</th>\n",
       "      <td>1,347.89</td>\n",
       "      <td>1,583.68</td>\n",
       "      <td>235.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polynomial_ridge_change</th>\n",
       "      <td>1,347.89</td>\n",
       "      <td>1,583.68</td>\n",
       "      <td>235.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber_change</th>\n",
       "      <td>1,323.85</td>\n",
       "      <td>1,599.04</td>\n",
       "      <td>275.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spline_ridge_change</th>\n",
       "      <td>1,280.69</td>\n",
       "      <td>1,613.01</td>\n",
       "      <td>332.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge_change</th>\n",
       "      <td>1,424.34</td>\n",
       "      <td>1,640.41</td>\n",
       "      <td>216.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>1,678.78</td>\n",
       "      <td>1,664.73</td>\n",
       "      <td>-14.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <td>1,779.83</td>\n",
       "      <td>1,702.16</td>\n",
       "      <td>-77.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>1,516.91</td>\n",
       "      <td>1,798.62</td>\n",
       "      <td>281.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>1,820.96</td>\n",
       "      <td>1,974.38</td>\n",
       "      <td>153.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>2,466.39</td>\n",
       "      <td>2,519.43</td>\n",
       "      <td>53.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          train_mae_kb   mae_kb  test_minus_train_kb\n",
       "model                                                               \n",
       "seasonal_residual_change      1,384.94 1,482.92                97.98\n",
       "neural_network_change         1,271.29 1,570.91               299.62\n",
       "random_forest_change          1,117.73 1,577.73               459.99\n",
       "xgboost_change                1,131.14 1,577.83               446.69\n",
       "seasonal_ridge_change         1,347.89 1,583.68               235.79\n",
       "polynomial_ridge_change       1,347.89 1,583.68               235.79\n",
       "huber_change                  1,323.85 1,599.04               275.19\n",
       "spline_ridge_change           1,280.69 1,613.01               332.32\n",
       "ridge_change                  1,424.34 1,640.41               216.07\n",
       "seasonal_change               1,678.78 1,664.73               -14.05\n",
       "forecast_flow_identity        1,779.83 1,702.16               -77.67\n",
       "constrained_level             1,516.91 1,798.62               281.70\n",
       "persistence                   1,820.96 1,974.38               153.42\n",
       "seasonal_naive                2,466.39 2,519.43                53.04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_colwidth',None):\n",
    "    display(selected_parameters)\n",
    "cv = tables['padd_model_metrics'].query(\"split == 'cv' and model != 'selected_padd_models'\")\n",
    "display(cv.pivot(index='model', columns='padd', values='mae_kb').style.highlight_min(axis=0))\n",
    "gaps = folds.groupby('model')[['train_mae_kb','mae_kb']].mean().sort_values('mae_kb')\n",
    "# Subtract in-sample MAE from validation MAE; positive gaps mean worse\n",
    "# performance on later observations than on fitted training observations.\n",
    "gaps['test_minus_train_kb'] = gaps.mae_kb - gaps.train_mae_kb\n",
    "display(gaps)\n",
    "gaps[['train_mae_kb','mae_kb']].plot.barh(figsize=(10,5), title='Mean training and annual-test MAE across PADDs')\n",
    "plt.xlabel('Thousand barrels'); plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cf5cd6f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.647678Z",
     "iopub.status.busy": "2026-09-16T21:54:45.647416Z",
     "iopub.status.idle": "2026-09-16T21:54:45.734556Z",
     "shell.execute_reply": "2026-09-16T21:54:45.734050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>intercept_kb</th>\n",
       "      <th>stock_lag1</th>\n",
       "      <th>lag_production_kb</th>\n",
       "      <th>lag_demand_kb</th>\n",
       "      <th>lag_imports_kb</th>\n",
       "      <th>lag_exports_kb</th>\n",
       "      <th>lag_net_receipts_kb</th>\n",
       "      <th>lag_adjustments_kb</th>\n",
       "      <th>lag_biofuels_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-982.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6,408.44</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>30,804.07</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1,238.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15,871.19</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd  intercept_kb  stock_lag1  lag_production_kb  lag_demand_kb  \\\n",
       "0     1       -982.63        0.59               0.66           0.20   \n",
       "1     2      6,408.44        0.65               0.49           0.32   \n",
       "2     3     30,804.07        0.49               0.29           0.17   \n",
       "3     4      1,238.70        0.70               0.21           0.12   \n",
       "4     5     15,871.19        0.49               0.38           0.41   \n",
       "\n",
       "   lag_imports_kb  lag_exports_kb  lag_net_receipts_kb  lag_adjustments_kb  \\\n",
       "0            0.24            0.56                 0.45                0.45   \n",
       "1            0.00            0.27                 0.39                0.23   \n",
       "2            0.00            0.00                 0.31                0.00   \n",
       "3            0.00            0.00                 0.03                0.00   \n",
       "4            0.35            0.00                 0.47                0.04   \n",
       "\n",
       "   lag_biofuels_kb  \n",
       "0             0.00  \n",
       "1             0.00  \n",
       "2             0.00  \n",
       "3             0.00  \n",
       "4             0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group validation errors by test-block endpoint to show regime variation.\n",
    "# These plotted values average regional MAEs, not errors on national stock sums.\n",
    "# The coefficient CSV provides a separate view of the constrained model.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,4))\n",
    "for name in ['persistence','constrained_level','xgboost_change','neural_network_change']:\n",
    "    g = folds[folds.model.eq(name)].groupby('test_end').mae_kb.mean()\n",
    "    ax.plot(pd.to_datetime(g.index), g.values, marker='o', label=name)\n",
    "ax.set(title='Performance varies by annual test block', ylabel='Mean PADD MAE (kb)')\n",
    "ax.legend(); plt.show()\n",
    "display(pd.read_csv(OUTPUT / 'constrained_model_coefficients.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfd0908",
   "metadata": {},
   "source": [
    "### Final test: July 2024–June 2026\n",
    "\n",
    "Compare all one-month methods on the same 24 months. The lowest national MAE determines the current one-month outlook. Because this period was used to choose the method, its MAE describes the historical comparison rather than an independent test of the chosen method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e6f9080",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.736041Z",
     "iopub.status.busy": "2026-09-16T21:54:45.735903Z",
     "iopub.status.idle": "2026-09-16T21:54:45.748701Z",
     "shell.execute_reply": "2026-09-16T21:54:45.748220Z"
    }
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>split</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>3,426.41</td>\n",
       "      <td>5,092.55</td>\n",
       "      <td>0.85</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>forecast_flow_identity</td>\n",
       "      <td>holdout</td>\n",
       "      <td>3,618.04</td>\n",
       "      <td>5,391.23</td>\n",
       "      <td>0.83</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>3,670.53</td>\n",
       "      <td>5,181.12</td>\n",
       "      <td>0.84</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>3,783.17</td>\n",
       "      <td>5,546.13</td>\n",
       "      <td>0.82</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,182.90</td>\n",
       "      <td>5,889.52</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>selected_padd_models</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,207.50</td>\n",
       "      <td>5,618.73</td>\n",
       "      <td>0.82</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,350.48</td>\n",
       "      <td>6,236.63</td>\n",
       "      <td>0.77</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,433.60</td>\n",
       "      <td>5,851.38</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,507.34</td>\n",
       "      <td>6,530.22</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,692.84</td>\n",
       "      <td>6,622.54</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,692.84</td>\n",
       "      <td>6,622.54</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,773.00</td>\n",
       "      <td>5,920.95</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>5,213.48</td>\n",
       "      <td>6,745.54</td>\n",
       "      <td>0.74</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>5,954.94</td>\n",
       "      <td>7,666.71</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>8,468.79</td>\n",
       "      <td>10,655.77</td>\n",
       "      <td>0.34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model    split   mae_kb   rmse_kb   r2   n\n",
       "17           seasonal_change  holdout 3,426.41  5,092.55 0.85  24\n",
       "3     forecast_flow_identity  holdout 3,618.04  5,391.23 0.83  24\n",
       "21  seasonal_residual_change  holdout 3,670.53  5,181.12 0.84  24\n",
       "13      random_forest_change  holdout 3,783.17  5,546.13 0.82  24\n",
       "29            xgboost_change  holdout 4,182.90  5,889.52 0.80  24\n",
       "25      selected_padd_models  holdout 4,207.50  5,618.73 0.82  24\n",
       "7      neural_network_change  holdout 4,350.48  6,236.63 0.77  24\n",
       "27       spline_ridge_change  holdout 4,433.60  5,851.38 0.80  24\n",
       "5               huber_change  holdout 4,507.34  6,530.22 0.75  24\n",
       "23     seasonal_ridge_change  holdout 4,692.84  6,622.54 0.75  24\n",
       "11   polynomial_ridge_change  holdout 4,692.84  6,622.54 0.75  24\n",
       "19            seasonal_naive  holdout 4,773.00  5,920.95 0.80  24\n",
       "15              ridge_change  holdout 5,213.48  6,745.54 0.74  24\n",
       "1          constrained_level  holdout 5,954.94  7,666.71 0.66  24\n",
       "9                persistence  holdout 8,468.79 10,655.77 0.34  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>persistence</td>\n",
       "      <td>3,407.58</td>\n",
       "      <td>4,606.56</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>1,897.99</td>\n",
       "      <td>2,265.42</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2</td>\n",
       "      <td>persistence</td>\n",
       "      <td>3,016.88</td>\n",
       "      <td>3,877.04</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>1,584.37</td>\n",
       "      <td>2,039.43</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3</td>\n",
       "      <td>persistence</td>\n",
       "      <td>3,304.79</td>\n",
       "      <td>4,089.56</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>2,494.69</td>\n",
       "      <td>3,237.51</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>4</td>\n",
       "      <td>persistence</td>\n",
       "      <td>467.62</td>\n",
       "      <td>645.18</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>391.26</td>\n",
       "      <td>547.23</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>5</td>\n",
       "      <td>persistence</td>\n",
       "      <td>1,389.08</td>\n",
       "      <td>1,557.95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>1,048.37</td>\n",
       "      <td>1,309.27</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     padd            model   mae_kb  rmse_kb   r2\n",
       "9       1      persistence 3,407.58 4,606.56 0.01\n",
       "17      1  seasonal_change 1,897.99 2,265.42 0.76\n",
       "39      2      persistence 3,016.88 3,877.04 0.45\n",
       "47      2  seasonal_change 1,584.37 2,039.43 0.85\n",
       "69      3      persistence 3,304.79 4,089.56 0.05\n",
       "77      3  seasonal_change 2,494.69 3,237.51 0.41\n",
       "99      4      persistence   467.62   645.18 0.45\n",
       "107     4  seasonal_change   391.26   547.23 0.60\n",
       "129     5      persistence 1,389.08 1,557.95 0.00\n",
       "137     5  seasonal_change 1,048.37 1,309.27 0.29"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Report the current one-month model against persistence on national holdout\n",
    "# MAE. A positive improvement means lower error. Also identify the best observed\n",
    "# holdout candidate used for the current one-month outlook.\n",
    "\n",
    "national_scores = tables['us_model_metrics'].query(\"split == 'holdout'\").sort_values('mae_kb')\n",
    "display(national_scores)\n",
    "regional_scores = tables['padd_model_metrics'].query(\"split == 'holdout'\")\n",
    "regional_scores = regional_scores[regional_scores.model.eq('persistence') | regional_scores.model.eq(metadata['one_month_model'])]\n",
    "display(regional_scores[['padd','model','mae_kb','rmse_kb','r2']])\n",
    "selected_mae = national_scores.set_index('model').loc[metadata['one_month_model'],'mae_kb']\n",
    "naive_mae = national_scores.set_index('model').loc['persistence','mae_kb']\n",
    "improvement = 100*(1-selected_mae/naive_mae)\n",
    "best_holdout = national_scores.iloc[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6f9080-finding",
   "metadata": {},
   "source": [
    "**One-month model:** `seasonal_change` has national MAE of **3,426 kb**, versus **8,469 kb** for unchanged stocks (**59.5% lower**). It also beats the regional model combination, which has MAE of **4,208 kb**. The final period was used to choose `seasonal_change` for the current outlook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "652fba0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.750003Z",
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     "iopub.status.idle": "2026-09-16T21:54:45.765796Z",
     "shell.execute_reply": "2026-09-16T21:54:45.765237Z"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_target_months</th>\n",
       "      <th>eligible_stock_training_months</th>\n",
       "      <th>evaluation_months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padd</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
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       "      <th>2</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
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       "      <th>3</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      total_target_months  eligible_stock_training_months  evaluation_months\n",
       "padd                                                                        \n",
       "1                     222                             197                209\n",
       "2                     222                             197                209\n",
       "3                     222                             197                209\n",
       "4                     222                             197                209\n",
       "5                     222                             197                209"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>previous_mae_kb</th>\n",
       "      <th>refit_mae_kb</th>\n",
       "      <th>improvement_pct</th>\n",
       "      <th>test_months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>4,532.35</td>\n",
       "      <td>4,207.50</td>\n",
       "      <td>7.17</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PADD 1</td>\n",
       "      <td>2,680.72</td>\n",
       "      <td>2,041.47</td>\n",
       "      <td>23.85</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PADD 2</td>\n",
       "      <td>1,611.69</td>\n",
       "      <td>1,637.79</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PADD 3</td>\n",
       "      <td>2,710.12</td>\n",
       "      <td>2,688.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PADD 4</td>\n",
       "      <td>342.21</td>\n",
       "      <td>362.99</td>\n",
       "      <td>-6.07</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PADD 5</td>\n",
       "      <td>1,061.09</td>\n",
       "      <td>1,000.05</td>\n",
       "      <td>5.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  previous_mae_kb  refit_mae_kb  improvement_pct  test_months\n",
       "0    U.S.         4,532.35      4,207.50             7.17           24\n",
       "1  PADD 1         2,680.72      2,041.47            23.85           24\n",
       "2  PADD 2         1,611.69      1,637.79            -1.62           24\n",
       "3  PADD 3         2,710.12      2,688.82             0.79           24\n",
       "4  PADD 4           342.21        362.99            -6.07           24\n",
       "5  PADD 5         1,061.09      1,000.05             5.75           24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>candidate_set</th>\n",
       "      <th>period</th>\n",
       "      <th>mae_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Original candidates, COVID excluded</td>\n",
       "      <td>cv</td>\n",
       "      <td>4,276.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Original candidates, COVID excluded</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,105.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Expanded candidates, COVID excluded</td>\n",
       "      <td>cv</td>\n",
       "      <td>3,938.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Expanded candidates, COVID excluded</td>\n",
       "      <td>holdout</td>\n",
       "      <td>4,207.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         candidate_set   period   mae_kb\n",
       "0  Original candidates, COVID excluded       cv 4,276.59\n",
       "1  Original candidates, COVID excluded  holdout 4,105.54\n",
       "2  Expanded candidates, COVID excluded       cv 3,938.82\n",
       "3  Expanded candidates, COVID excluded  holdout 4,207.50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize eligible dates and check that fitting excludes COVID target months.\n",
    "# Optional comparison CSVs show earlier refits on matching dates when available.\n",
    "\n",
    "audit = tables['training_sample_audit']\n",
    "display(audit.groupby('padd').agg(total_target_months=('training_eligible','size'),\n",
    "    eligible_stock_training_months=('training_eligible','sum'), evaluation_months=('evaluation_eligible','sum')))\n",
    "assert not audit.loc[audit.training_eligible, 'month'].between(COVID_START, COVID_END).any()\n",
    "# Display prior-run comparison files only when available; these optional reports\n",
    "# are read here and are not recomputed by this cell.\n",
    "comparison_file = OUTPUT/'refit_comparison.csv'\n",
    "if comparison_file.exists():\n",
    "    display(pd.read_csv(comparison_file))\n",
    "addition_file = OUTPUT/'candidate_addition_comparison.csv'\n",
    "if addition_file.exists():\n",
    "    display(pd.read_csv(addition_file))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652fba0b-year-heading",
   "metadata": {},
   "source": [
    "**Year-ahead comparison on the same forecast dates and origins**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "652fba0b-year-comparison",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.766989Z",
     "iopub.status.busy": "2026-09-16T21:54:45.766857Z",
     "iopub.status.idle": "2026-09-16T21:54:45.772135Z",
     "shell.execute_reply": "2026-09-16T21:54:45.771695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>previous_mae_kb</th>\n",
       "      <th>refit_mae_kb</th>\n",
       "      <th>improvement_pct</th>\n",
       "      <th>forecast_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>6,148.94</td>\n",
       "      <td>5,640.90</td>\n",
       "      <td>8.26</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PADD 1</td>\n",
       "      <td>3,807.25</td>\n",
       "      <td>3,536.11</td>\n",
       "      <td>7.12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PADD 2</td>\n",
       "      <td>1,962.53</td>\n",
       "      <td>1,826.36</td>\n",
       "      <td>6.94</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PADD 3</td>\n",
       "      <td>3,315.85</td>\n",
       "      <td>2,847.06</td>\n",
       "      <td>14.14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PADD 4</td>\n",
       "      <td>512.41</td>\n",
       "      <td>688.47</td>\n",
       "      <td>-34.36</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PADD 5</td>\n",
       "      <td>1,742.95</td>\n",
       "      <td>1,645.68</td>\n",
       "      <td>5.58</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  previous_mae_kb  refit_mae_kb  improvement_pct  \\\n",
       "0    U.S.         6,148.94      5,640.90             8.26   \n",
       "1  PADD 1         3,807.25      3,536.11             7.12   \n",
       "2  PADD 2         1,962.53      1,826.36             6.94   \n",
       "3  PADD 3         3,315.85      2,847.06            14.14   \n",
       "4  PADD 4           512.41        688.47           -34.36   \n",
       "5  PADD 5         1,742.95      1,645.68             5.58   \n",
       "\n",
       "   forecast_observations  \n",
       "0                     24  \n",
       "1                     24  \n",
       "2                     24  \n",
       "3                     24  \n",
       "4                     24  \n",
       "5                     24  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_comparison = OUTPUT/'year_ahead_refit_comparison.csv'\n",
    "if year_comparison.exists():\n",
    "    display(pd.read_csv(year_comparison))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dac663b",
   "metadata": {},
   "source": [
    "### Regional results\n",
    "\n",
    "Compare `seasonal_change` with unchanged stocks in each PADD. Positive improvement indicates lower MAE; positive bias indicates stocks were overestimated. U.S. errors are calculated after summing regional forecasts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15fae8d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:45.773284Z",
     "iopub.status.busy": "2026-09-16T21:54:45.773170Z",
     "iopub.status.idle": "2026-09-16T21:54:46.191349Z",
     "shell.execute_reply": "2026-09-16T21:54:46.190902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PADD</th>\n",
       "      <th>Average error (kb)</th>\n",
       "      <th>Error / average stocks (%)</th>\n",
       "      <th>Improvement over unchanged stocks (%)</th>\n",
       "      <th>Average bias (kb)</th>\n",
       "      <th>Worst month</th>\n",
       "      <th>Largest miss (kb)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1,897.99</td>\n",
       "      <td>3.21</td>\n",
       "      <td>44.30</td>\n",
       "      <td>-189.10</td>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>4,842.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1,584.37</td>\n",
       "      <td>3.18</td>\n",
       "      <td>47.48</td>\n",
       "      <td>-6.23</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>6,151.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2,494.69</td>\n",
       "      <td>2.96</td>\n",
       "      <td>24.51</td>\n",
       "      <td>442.43</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>8,809.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>391.26</td>\n",
       "      <td>5.03</td>\n",
       "      <td>16.33</td>\n",
       "      <td>51.89</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>1,459.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1,048.37</td>\n",
       "      <td>3.60</td>\n",
       "      <td>24.53</td>\n",
       "      <td>176.21</td>\n",
       "      <td>2025-11-01</td>\n",
       "      <td>2,951.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PADD  Average error (kb)  Error / average stocks (%)  \\\n",
       "0     1            1,897.99                        3.21   \n",
       "1     2            1,584.37                        3.18   \n",
       "2     3            2,494.69                        2.96   \n",
       "3     4              391.26                        5.03   \n",
       "4     5            1,048.37                        3.60   \n",
       "\n",
       "   Improvement over unchanged stocks (%)  Average bias (kb) Worst month  \\\n",
       "0                                  44.30            -189.10  2026-01-01   \n",
       "1                                  47.48              -6.23  2026-04-01   \n",
       "2                                  24.51             442.43  2026-04-01   \n",
       "3                                  16.33              51.89  2026-04-01   \n",
       "4                                  24.53             176.21  2025-11-01   \n",
       "\n",
       "   Largest miss (kb)  \n",
       "0           4,842.40  \n",
       "1           6,151.00  \n",
       "2           8,809.50  \n",
       "3           1,459.75  \n",
       "4           2,951.40  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare regional selected-model errors with unchanged stocks on the same\n",
    "# holdout dates. Normalize MAE by average actual stocks to compare PADD sizes;\n",
    "# positive percentage improvement means lower error than the benchmark.\n",
    "\n",
    "# Compare like-for-like regional errors on the same final 24 months.\n",
    "regional_test = tables['padd_predictions'].query(\"split == 'holdout'\")\n",
    "regional_selected = regional_test[regional_test.model.eq(metadata['one_month_model'])]\n",
    "rows = []\n",
    "for padd, g in regional_selected.groupby('padd'):\n",
    "    baseline = regional_test[regional_test.padd.eq(padd) & regional_test.model.eq('persistence')]\n",
    "    # Align selected and persistence forecasts on the same month within this PADD.\n",
    "    # Validate unique matches so benchmark improvement is a like-for-like comparison.\n",
    "    paired = g.merge(baseline[['month','predicted_kb']], on='month', suffixes=('', '_unchanged'), validate='one_to_one')\n",
    "    # Positive signed error means overestimated stocks; absolute error measures\n",
    "    # miss size, while mean signed error below measures systematic bias.\n",
    "    error = paired.predicted_kb - paired.actual_kb\n",
    "    baseline_mae = (paired.predicted_kb_unchanged - paired.actual_kb).abs().mean()\n",
    "    # Keep the row label of the largest absolute miss to retrieve its calendar month.\n",
    "    worst = error.abs().idxmax()\n",
    "    rows.append({'PADD': padd, 'Average error (kb)': error.abs().mean(),\n",
    "        'Error / average stocks (%)': 100 * error.abs().mean() / paired.actual_kb.mean(),\n",
    "        'Improvement over unchanged stocks (%)': 100 * (1 - error.abs().mean() / baseline_mae),\n",
    "        'Average bias (kb)': error.mean(), 'Worst month': paired.loc[worst, 'month'],\n",
    "        'Largest miss (kb)': error.abs().max()})\n",
    "regional_review = pd.DataFrame(rows)\n",
    "display(regional_review)\n",
    "# Persist regional diagnostics; charts convert kb to million barrels by /1000.\n",
    "regional_review.to_csv(OUTPUT/'regional_error_review.csv', index=False)\n",
    "fig, axes = plt.subplots(5, 1, figsize=(12, 10), sharex=True)\n",
    "for ax, (padd, g) in zip(axes, regional_selected.groupby('padd')):\n",
    "    ax.bar(g.month, (g.predicted_kb-g.actual_kb)/1000, width=20)\n",
    "    ax.axhline(0, color='black', linewidth=.6)\n",
    "    ax.set_ylabel(f'PADD {padd}\\nmillion bbl')\n",
    "    ax.set_title(metadata['one_month_model'], fontsize=9, loc='right')\n",
    "axes[0].set_title('Monthly inventory forecast errors: positive means stocks were overestimated')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3aef8ac5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:46.192375Z",
     "iopub.status.busy": "2026-09-16T21:54:46.192270Z",
     "iopub.status.idle": "2026-09-16T21:54:46.489268Z",
     "shell.execute_reply": "2026-09-16T21:54:46.488796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use one model's actual column to plot national stocks once, then overlay\n",
    "# candidate forecasts and signed selected-model errors. Divide kb by 1000\n",
    "# for million-barrel axes; a positive error bar means excess predicted stocks.\n",
    "\n",
    "predictions = tables['us_predictions']\n",
    "holdout = predictions[predictions.split.eq('holdout')]\n",
    "fig, axes = plt.subplots(2,1,figsize=(12,7),sharex=True)\n",
    "actual = holdout[holdout.model.eq(metadata['one_month_model'])]\n",
    "axes[0].plot(actual.month,actual.actual_kb/1000,color='black',label='Actual',linewidth=2)\n",
    "for name in [metadata['one_month_model'],'persistence','selected_padd_models']:\n",
    "    g = holdout[holdout.model.eq(name)]\n",
    "    label = metadata['one_month_model_label'] if name == metadata['one_month_model'] else name\n",
    "    axes[0].plot(g.month,g.predicted_kb/1000,label=label,alpha=.8)\n",
    "axes[0].set(ylabel='Million barrels',title='U.S. total gasoline stocks: final holdout'); axes[0].legend(fontsize=7)\n",
    "axes[1].bar(actual.month,(actual.predicted_kb-actual.actual_kb)/1000,width=20)\n",
    "axes[1].axhline(0,color='black'); axes[1].set(ylabel='Forecast − actual (million bbl)',title=f\"Forecast errors\\n{metadata['one_month_model_label']}\")\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d17a424",
   "metadata": {},
   "source": [
    "### Twelve-month performance\n",
    "\n",
    "Evaluate two separate annual paths without updating forecasts with actual stocks. The development-selected `xgboost_change` model is compared with unchanged stocks and seasonal benchmarks. The same-month-last-year benchmark has the lowest national MAE among those tested and is used for the current twelve-month outlook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2a8947e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:46.490502Z",
     "iopub.status.busy": "2026-09-16T21:54:46.490358Z",
     "iopub.status.idle": "2026-09-16T21:54:46.667142Z",
     "shell.execute_reply": "2026-09-16T21:54:46.666697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>6,148.45</td>\n",
       "      <td>7,488.24</td>\n",
       "      <td>0.57</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>6,471.91</td>\n",
       "      <td>8,255.01</td>\n",
       "      <td>0.48</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>6,528.86</td>\n",
       "      <td>7,956.32</td>\n",
       "      <td>0.52</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>6,670.69</td>\n",
       "      <td>8,346.93</td>\n",
       "      <td>0.47</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>6,879.57</td>\n",
       "      <td>8,300.21</td>\n",
       "      <td>0.47</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>7,017.09</td>\n",
       "      <td>8,832.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>7,161.71</td>\n",
       "      <td>8,701.72</td>\n",
       "      <td>0.42</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>7,621.95</td>\n",
       "      <td>9,214.59</td>\n",
       "      <td>0.35</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>7,754.90</td>\n",
       "      <td>9,720.72</td>\n",
       "      <td>0.28</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>7,799.44</td>\n",
       "      <td>9,643.16</td>\n",
       "      <td>0.29</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>7,832.60</td>\n",
       "      <td>9,186.62</td>\n",
       "      <td>0.36</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>7,884.58</td>\n",
       "      <td>9,795.94</td>\n",
       "      <td>0.27</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>forecast_flow_identity</td>\n",
       "      <td>8,539.38</td>\n",
       "      <td>10,341.35</td>\n",
       "      <td>0.19</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>persistence</td>\n",
       "      <td>9,011.97</td>\n",
       "      <td>11,225.17</td>\n",
       "      <td>0.04</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model   mae_kb   rmse_kb   r2   n\n",
       "0             xgboost_change 6,148.45  7,488.24 0.57  72\n",
       "1               huber_change 6,471.91  8,255.01 0.48  72\n",
       "2       random_forest_change 6,528.86  7,956.32 0.52  72\n",
       "3      neural_network_change 6,670.69  8,346.93 0.47  72\n",
       "4   seasonal_residual_change 6,879.57  8,300.21 0.47  72\n",
       "5      seasonal_ridge_change 7,017.09  8,832.59 0.41  72\n",
       "6          constrained_level 7,161.71  8,701.72 0.42  72\n",
       "7        spline_ridge_change 7,621.95  9,214.59 0.35  72\n",
       "8             seasonal_naive 7,754.90  9,720.72 0.28  72\n",
       "9    polynomial_ridge_change 7,799.44  9,643.16 0.29  72\n",
       "10           seasonal_change 7,832.60  9,186.62 0.36  72\n",
       "11              ridge_change 7,884.58  9,795.94 0.27  72\n",
       "12    forecast_flow_identity 8,539.38 10,341.35 0.19  72\n",
       "13               persistence 9,011.97 11,225.17 0.04  72"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Development-selected year-ahead model: xgboost_change\n",
      "Current twelve-month outlook model: seasonal_naive\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>persistence</td>\n",
       "      <td>11,576.96</td>\n",
       "      <td>13,390.48</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>6,446.63</td>\n",
       "      <td>7,435.52</td>\n",
       "      <td>0.68</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>4,773.00</td>\n",
       "      <td>5,920.95</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>5,640.90</td>\n",
       "      <td>6,821.24</td>\n",
       "      <td>0.73</td>\n",
       "      <td>24</td>\n",
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      "text/plain": [
       "             model    mae_kb   rmse_kb    r2   n\n",
       "0      persistence 11,576.96 13,390.48 -0.04  24\n",
       "1  seasonal_change  6,446.63  7,435.52  0.68  24\n",
       "2   seasonal_naive  4,773.00  5,920.95  0.80  24\n",
       "3   xgboost_change  5,640.90  6,821.24  0.73  24"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>persistence</td>\n",
       "      <td>5,579.54</td>\n",
       "      <td>6,392.88</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>4,893.18</td>\n",
       "      <td>5,632.27</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>2,636.17</td>\n",
       "      <td>3,171.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>3,536.11</td>\n",
       "      <td>4,348.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>persistence</td>\n",
       "      <td>4,113.62</td>\n",
       "      <td>5,451.28</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>1,500.43</td>\n",
       "      <td>1,788.21</td>\n",
       "      <td>0.88</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>1,531.96</td>\n",
       "      <td>1,949.99</td>\n",
       "      <td>0.86</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>1,826.36</td>\n",
       "      <td>2,131.49</td>\n",
       "      <td>0.83</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>persistence</td>\n",
       "      <td>3,973.50</td>\n",
       "      <td>4,711.83</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>4,309.68</td>\n",
       "      <td>4,795.94</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>3,214.25</td>\n",
       "      <td>3,672.90</td>\n",
       "      <td>0.24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>2,847.06</td>\n",
       "      <td>3,423.48</td>\n",
       "      <td>0.34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>persistence</td>\n",
       "      <td>771.92</td>\n",
       "      <td>1,004.30</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>639.32</td>\n",
       "      <td>769.89</td>\n",
       "      <td>0.21</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>375.04</td>\n",
       "      <td>428.63</td>\n",
       "      <td>0.76</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>688.47</td>\n",
       "      <td>843.53</td>\n",
       "      <td>0.05</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>persistence</td>\n",
       "      <td>2,770.12</td>\n",
       "      <td>3,326.18</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>2,868.32</td>\n",
       "      <td>3,365.35</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>1,819.75</td>\n",
       "      <td>2,011.59</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>1,645.68</td>\n",
       "      <td>1,943.81</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    padd            model   mae_kb  rmse_kb    r2   n\n",
       "0      1      persistence 5,579.54 6,392.88 -0.90  24\n",
       "1      1  seasonal_change 4,893.18 5,632.27 -0.48  24\n",
       "2      1   seasonal_naive 2,636.17 3,171.38  0.53  24\n",
       "3      1   xgboost_change 3,536.11 4,348.15  0.12  24\n",
       "4      2      persistence 4,113.62 5,451.28 -0.09  24\n",
       "5      2  seasonal_change 1,500.43 1,788.21  0.88  24\n",
       "6      2   seasonal_naive 1,531.96 1,949.99  0.86  24\n",
       "7      2   xgboost_change 1,826.36 2,131.49  0.83  24\n",
       "8      3      persistence 3,973.50 4,711.83 -0.26  24\n",
       "9      3  seasonal_change 4,309.68 4,795.94 -0.30  24\n",
       "10     3   seasonal_naive 3,214.25 3,672.90  0.24  24\n",
       "11     3   xgboost_change 2,847.06 3,423.48  0.34  24\n",
       "12     4      persistence   771.92 1,004.30 -0.34  24\n",
       "13     4  seasonal_change   639.32   769.89  0.21  24\n",
       "14     4   seasonal_naive   375.04   428.63  0.76  24\n",
       "15     4   xgboost_change   688.47   843.53  0.05  24\n",
       "16     5      persistence 2,770.12 3,326.18 -3.55  24\n",
       "17     5  seasonal_change 2,868.32 3,365.35 -3.66  24\n",
       "18     5   seasonal_naive 1,819.75 2,011.59 -0.67  24\n",
       "19     5   xgboost_change 1,645.68 1,943.81 -0.56  24"
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2,636.17</td>\n",
       "      <td>3,171.38</td>\n",
       "      <td>0.53</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1,531.96</td>\n",
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       "      <td>0.86</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3,214.25</td>\n",
       "      <td>3,672.90</td>\n",
       "      <td>0.24</td>\n",
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       "      <th>3</th>\n",
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       "      <td>375.04</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1,819.75</td>\n",
       "      <td>2,011.59</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>24</td>\n",
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      "text/plain": [
       "   padd   mae_kb  rmse_kb    r2   n\n",
       "0     1 2,636.17 3,171.38  0.53  24\n",
       "1     2 1,531.96 1,949.99  0.86  24\n",
       "2     3 3,214.25 3,672.90  0.24  24\n",
       "3     4   375.04   428.63  0.76  24\n",
       "4     5 1,819.75 2,011.59 -0.67  24"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horizon</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4,375.00</td>\n",
       "      <td>4,460.66</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1,894.50</td>\n",
       "      <td>1,926.29</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5,716.50</td>\n",
       "      <td>6,198.51</td>\n",
       "      <td>-12.94</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4,640.50</td>\n",
       "      <td>4,881.39</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1,058.00</td>\n",
       "      <td>1,194.27</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4,068.50</td>\n",
       "      <td>4,360.38</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5,437.50</td>\n",
       "      <td>7,038.67</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6,618.50</td>\n",
       "      <td>7,523.02</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4,731.50</td>\n",
       "      <td>6,228.45</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6,002.50</td>\n",
       "      <td>6,030.70</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5,632.50</td>\n",
       "      <td>6,318.60</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>7,100.50</td>\n",
       "      <td>9,652.41</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    horizon   mae_kb  rmse_kb     r2  n\n",
       "0         1 4,375.00 4,460.66  -1.89  2\n",
       "1         2 1,894.50 1,926.29  -1.95  2\n",
       "2         3 5,716.50 6,198.51 -12.94  2\n",
       "3         4 4,640.50 4,881.39  -8.75  2\n",
       "4         5 1,058.00 1,194.27  -1.20  2\n",
       "5         6 4,068.50 4,360.38  -1.39  2\n",
       "6         7 5,437.50 7,038.67  -1.02  2\n",
       "7         8 6,618.50 7,523.02  -1.18  2\n",
       "8         9 4,731.50 6,228.45  -1.01  2\n",
       "9        10 6,002.50 6,030.70  -2.35  2\n",
       "10       11 5,632.50 6,318.60  -1.21  2\n",
       "11       12 7,100.50 9,652.41  -1.00  2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the development-selected year-ahead family with recursive benchmarks,\n",
    "# scoring after national aggregation. Each horizon has only two\n",
    "# path observations here; the plots show those two forecast origins separately.\n",
    "\n",
    "display(tables['year_ahead_model_cv_metrics'])\n",
    "print('Development-selected year-ahead model:', metadata['development_year_ahead_model'])\n",
    "print('Current twelve-month outlook model:', metadata['year_ahead_model'])\n",
    "bench = tables['recursive_benchmark_predictions']\n",
    "paths = bench\n",
    "us_paths = aggregate(paths, ['month','origin_month','horizon','model'], ['actual_kb','predicted_kb'])\n",
    "display(metric_table(us_paths,['model']))\n",
    "display(metric_table(paths,['padd','model']))\n",
    "\n",
    "recursive = tables['us_recursive_holdout_predictions']\n",
    "display(tables['recursive_holdout_metrics'])\n",
    "display(tables['us_recursive_horizon_metrics'])\n",
    "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
    "for (origin,g),ax in zip(recursive.groupby('origin_month'),axes):\n",
    "    ax.plot(g.month,g.actual_kb/1000,label='Actual',color='black')\n",
    "    ax.plot(g.month,g.predicted_kb/1000,label=metadata['year_ahead_model'])\n",
    "    ax.set(title=f'Forecast origin: {origin:%Y-%m}',ylabel='Million barrels'); ax.legend()\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60616e0",
   "metadata": {},
   "source": [
    "### Twelve-month outlook\n",
    "\n",
    "Project supply, disposition, and total gasoline stocks from the latest observed month. The one-month outlook uses `seasonal_change`; the twelve-month path uses `seasonal_naive` (the same month last year).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aa908dfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:46.668257Z",
     "iopub.status.busy": "2026-09-16T21:54:46.668145Z",
     "iopub.status.idle": "2026-09-16T21:54:46.967983Z",
     "shell.execute_reply": "2026-09-16T21:54:46.967512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>production_kb</th>\n",
       "      <th>imports_kb</th>\n",
       "      <th>demand_kb</th>\n",
       "      <th>exports_kb</th>\n",
       "      <th>adjustments_kb</th>\n",
       "      <th>balance_kb</th>\n",
       "      <th>stock_kb</th>\n",
       "      <th>identity_stock_unclipped_kb</th>\n",
       "      <th>model_reconciliation_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>283,420.44</td>\n",
       "      <td>20,924.24</td>\n",
       "      <td>282,345.05</td>\n",
       "      <td>26,409.35</td>\n",
       "      <td>1,221.09</td>\n",
       "      <td>-2,984.03</td>\n",
       "      <td>229,625.00</td>\n",
       "      <td>216,456.97</td>\n",
       "      <td>13,168.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-08-01</td>\n",
       "      <td>284,033.04</td>\n",
       "      <td>21,119.64</td>\n",
       "      <td>285,183.65</td>\n",
       "      <td>26,615.95</td>\n",
       "      <td>1,116.49</td>\n",
       "      <td>-5,316.83</td>\n",
       "      <td>222,943.00</td>\n",
       "      <td>211,140.14</td>\n",
       "      <td>-1,365.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-09-01</td>\n",
       "      <td>274,602.14</td>\n",
       "      <td>18,129.34</td>\n",
       "      <td>267,452.30</td>\n",
       "      <td>25,445.17</td>\n",
       "      <td>697.01</td>\n",
       "      <td>725.71</td>\n",
       "      <td>223,093.00</td>\n",
       "      <td>211,865.85</td>\n",
       "      <td>-575.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>281,101.44</td>\n",
       "      <td>14,102.44</td>\n",
       "      <td>278,402.05</td>\n",
       "      <td>26,921.15</td>\n",
       "      <td>2,089.89</td>\n",
       "      <td>-7,833.03</td>\n",
       "      <td>209,448.00</td>\n",
       "      <td>204,032.82</td>\n",
       "      <td>-5,811.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-11-01</td>\n",
       "      <td>287,758.14</td>\n",
       "      <td>14,597.34</td>\n",
       "      <td>265,572.30</td>\n",
       "      <td>31,358.97</td>\n",
       "      <td>1,659.81</td>\n",
       "      <td>7,257.71</td>\n",
       "      <td>219,418.00</td>\n",
       "      <td>211,290.53</td>\n",
       "      <td>2,712.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2026-12-01</td>\n",
       "      <td>301,164.24</td>\n",
       "      <td>17,356.41</td>\n",
       "      <td>271,929.45</td>\n",
       "      <td>33,203.75</td>\n",
       "      <td>1,688.49</td>\n",
       "      <td>15,230.74</td>\n",
       "      <td>243,854.00</td>\n",
       "      <td>226,521.27</td>\n",
       "      <td>9,205.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2027-01-01</td>\n",
       "      <td>284,511.24</td>\n",
       "      <td>12,709.64</td>\n",
       "      <td>256,063.05</td>\n",
       "      <td>27,418.55</td>\n",
       "      <td>1,437.29</td>\n",
       "      <td>15,323.57</td>\n",
       "      <td>261,035.00</td>\n",
       "      <td>241,844.84</td>\n",
       "      <td>1,857.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2027-02-01</td>\n",
       "      <td>246,669.32</td>\n",
       "      <td>12,753.17</td>\n",
       "      <td>242,057.78</td>\n",
       "      <td>23,186.43</td>\n",
       "      <td>915.95</td>\n",
       "      <td>-4,762.51</td>\n",
       "      <td>253,923.00</td>\n",
       "      <td>237,082.33</td>\n",
       "      <td>-2,349.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2027-03-01</td>\n",
       "      <td>274,655.04</td>\n",
       "      <td>15,028.24</td>\n",
       "      <td>276,444.45</td>\n",
       "      <td>26,285.95</td>\n",
       "      <td>1,564.09</td>\n",
       "      <td>-11,306.43</td>\n",
       "      <td>242,995.00</td>\n",
       "      <td>225,775.90</td>\n",
       "      <td>378.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>263,685.74</td>\n",
       "      <td>19,544.14</td>\n",
       "      <td>267,680.10</td>\n",
       "      <td>23,653.77</td>\n",
       "      <td>550.81</td>\n",
       "      <td>-7,368.49</td>\n",
       "      <td>221,699.00</td>\n",
       "      <td>218,407.42</td>\n",
       "      <td>-13,927.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027-05-01</td>\n",
       "      <td>280,054.64</td>\n",
       "      <td>22,450.84</td>\n",
       "      <td>280,836.25</td>\n",
       "      <td>25,889.75</td>\n",
       "      <td>1,353.09</td>\n",
       "      <td>-2,659.03</td>\n",
       "      <td>220,389.00</td>\n",
       "      <td>215,748.38</td>\n",
       "      <td>1,349.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2027-06-01</td>\n",
       "      <td>276,839.74</td>\n",
       "      <td>23,213.94</td>\n",
       "      <td>275,457.10</td>\n",
       "      <td>25,715.17</td>\n",
       "      <td>1,828.01</td>\n",
       "      <td>912.51</td>\n",
       "      <td>219,441.00</td>\n",
       "      <td>216,660.90</td>\n",
       "      <td>-1,860.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month  production_kb  imports_kb  demand_kb  exports_kb  \\\n",
       "0  2026-07-01     283,420.44   20,924.24 282,345.05   26,409.35   \n",
       "1  2026-08-01     284,033.04   21,119.64 285,183.65   26,615.95   \n",
       "2  2026-09-01     274,602.14   18,129.34 267,452.30   25,445.17   \n",
       "3  2026-10-01     281,101.44   14,102.44 278,402.05   26,921.15   \n",
       "4  2026-11-01     287,758.14   14,597.34 265,572.30   31,358.97   \n",
       "5  2026-12-01     301,164.24   17,356.41 271,929.45   33,203.75   \n",
       "6  2027-01-01     284,511.24   12,709.64 256,063.05   27,418.55   \n",
       "7  2027-02-01     246,669.32   12,753.17 242,057.78   23,186.43   \n",
       "8  2027-03-01     274,655.04   15,028.24 276,444.45   26,285.95   \n",
       "9  2027-04-01     263,685.74   19,544.14 267,680.10   23,653.77   \n",
       "10 2027-05-01     280,054.64   22,450.84 280,836.25   25,889.75   \n",
       "11 2027-06-01     276,839.74   23,213.94 275,457.10   25,715.17   \n",
       "\n",
       "    adjustments_kb  balance_kb   stock_kb  identity_stock_unclipped_kb  \\\n",
       "0         1,221.09   -2,984.03 229,625.00                   216,456.97   \n",
       "1         1,116.49   -5,316.83 222,943.00                   211,140.14   \n",
       "2           697.01      725.71 223,093.00                   211,865.85   \n",
       "3         2,089.89   -7,833.03 209,448.00                   204,032.82   \n",
       "4         1,659.81    7,257.71 219,418.00                   211,290.53   \n",
       "5         1,688.49   15,230.74 243,854.00                   226,521.27   \n",
       "6         1,437.29   15,323.57 261,035.00                   241,844.84   \n",
       "7           915.95   -4,762.51 253,923.00                   237,082.33   \n",
       "8         1,564.09  -11,306.43 242,995.00                   225,775.90   \n",
       "9           550.81   -7,368.49 221,699.00                   218,407.42   \n",
       "10        1,353.09   -2,659.03 220,389.00                   215,748.38   \n",
       "11        1,828.01      912.51 219,441.00                   216,660.90   \n",
       "\n",
       "    model_reconciliation_kb  \n",
       "0                 13,168.03  \n",
       "1                 -1,365.17  \n",
       "2                   -575.71  \n",
       "3                 -5,811.97  \n",
       "4                  2,712.29  \n",
       "5                  9,205.26  \n",
       "6                  1,857.43  \n",
       "7                 -2,349.49  \n",
       "8                    378.43  \n",
       "9                -13,927.51  \n",
       "10                 1,349.03  \n",
       "11                -1,860.51  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the conditional 12-month flow and stock outlook. The statistical stock\n",
    "# path and raw accumulated flow identity need not coincide: reconciliation\n",
    "# measures predicted stock change minus forecast net supply, not an observed flow.\n",
    "\n",
    "outlook = tables['us_forecast_12m']\n",
    "display(outlook[['month','production_kb','imports_kb','demand_kb','exports_kb','adjustments_kb',\n",
    "                 'balance_kb','stock_kb','identity_stock_unclipped_kb','model_reconciliation_kb']])\n",
    "fig, axes = plt.subplots(2,2,figsize=(14,8))\n",
    "for c,label in [('supply_kb','Supply including adjustments'),('total_demand_kb','Domestic demand + exports')]:\n",
    "    axes[0,0].plot(outlook.month,outlook[c]/1000,label=label)\n",
    "axes[0,0].set(title='Monthly U.S. flow outlook',ylabel='Million barrels/month'); axes[0,0].legend(fontsize=8)\n",
    "axes[0,1].bar(outlook.month,outlook.balance_kb/1000,width=20)\n",
    "axes[0,1].axhline(0,color='black'); axes[0,1].set(title='Projected flow balance',ylabel='Million barrels/month')\n",
    "recent = tables['us_monthly_model'].tail(24)\n",
    "axes[1,0].plot(recent.month,recent.stock_kb/1000,label='Actual')\n",
    "axes[1,0].plot(outlook.month,outlook.stock_kb/1000,label=metadata['year_ahead_model'])\n",
    "axes[1,0].plot(outlook.month,outlook.identity_stock_unclipped_kb/1000,label='Raw flow identity',linestyle='--')\n",
    "axes[1,0].set(title='Stock outlook',ylabel='Million barrels'); axes[1,0].legend(fontsize=8)\n",
    "for p,g in tables['padd_forecast_12m'].groupby('padd'):\n",
    "    axes[1,1].plot(g.month,g.stock_kb/1000,label=f'PADD {p}')\n",
    "axes[1,1].set(title=f\"Regional {metadata['year_ahead_model']} stock paths\",ylabel='Million barrels'); axes[1,1].legend(fontsize=8)\n",
    "for ax in axes.flat: ax.tick_params(axis='x',rotation=30)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412b6bf-short-heading",
   "metadata": {},
   "source": [
    "### One-month national results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2412b6bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:46.969228Z",
     "iopub.status.busy": "2026-09-16T21:54:46.969109Z",
     "iopub.status.idle": "2026-09-16T21:54:47.021518Z",
     "shell.execute_reply": "2026-09-16T21:54:47.020916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
       "      <th>Bias (million bbl)</th>\n",
       "      <th>MAE improvement vs persistence (%)</th>\n",
       "      <th>MAE improvement vs year ago (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>3.43</td>\n",
       "      <td>5.09</td>\n",
       "      <td>0.85</td>\n",
       "      <td>24</td>\n",
       "      <td>0.48</td>\n",
       "      <td>59.54</td>\n",
       "      <td>28.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <td>3.62</td>\n",
       "      <td>5.39</td>\n",
       "      <td>0.83</td>\n",
       "      <td>24</td>\n",
       "      <td>0.93</td>\n",
       "      <td>57.28</td>\n",
       "      <td>24.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_residual_change</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.18</td>\n",
       "      <td>0.84</td>\n",
       "      <td>24</td>\n",
       "      <td>0.88</td>\n",
       "      <td>56.66</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest_change</th>\n",
       "      <td>3.78</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0.82</td>\n",
       "      <td>24</td>\n",
       "      <td>0.84</td>\n",
       "      <td>55.33</td>\n",
       "      <td>20.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost_change</th>\n",
       "      <td>4.18</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>50.61</td>\n",
       "      <td>12.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selected_padd_models</th>\n",
       "      <td>4.21</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0.82</td>\n",
       "      <td>24</td>\n",
       "      <td>1.30</td>\n",
       "      <td>50.32</td>\n",
       "      <td>11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural_network_change</th>\n",
       "      <td>4.35</td>\n",
       "      <td>6.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>24</td>\n",
       "      <td>0.73</td>\n",
       "      <td>48.63</td>\n",
       "      <td>8.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spline_ridge_change</th>\n",
       "      <td>4.43</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>47.65</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber_change</th>\n",
       "      <td>4.51</td>\n",
       "      <td>6.53</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>46.78</td>\n",
       "      <td>5.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_ridge_change</th>\n",
       "      <td>4.69</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>44.59</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polynomial_ridge_change</th>\n",
       "      <td>4.69</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>44.59</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>4.77</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>43.64</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge_change</th>\n",
       "      <td>5.21</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.74</td>\n",
       "      <td>24</td>\n",
       "      <td>0.52</td>\n",
       "      <td>38.44</td>\n",
       "      <td>-9.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>5.95</td>\n",
       "      <td>7.67</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>1.72</td>\n",
       "      <td>29.68</td>\n",
       "      <td>-24.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>8.47</td>\n",
       "      <td>10.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>24</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-77.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MAE (million bbl)  RMSE (million bbl)   R²  \\\n",
       "model                                                                  \n",
       "seasonal_change                        3.43                5.09 0.85   \n",
       "forecast_flow_identity                 3.62                5.39 0.83   \n",
       "seasonal_residual_change               3.67                5.18 0.84   \n",
       "random_forest_change                   3.78                5.55 0.82   \n",
       "xgboost_change                         4.18                5.89 0.80   \n",
       "selected_padd_models                   4.21                5.62 0.82   \n",
       "neural_network_change                  4.35                6.24 0.77   \n",
       "spline_ridge_change                    4.43                5.85 0.80   \n",
       "huber_change                           4.51                6.53 0.75   \n",
       "seasonal_ridge_change                  4.69                6.62 0.75   \n",
       "polynomial_ridge_change                4.69                6.62 0.75   \n",
       "seasonal_naive                         4.77                5.92 0.80   \n",
       "ridge_change                           5.21                6.75 0.74   \n",
       "constrained_level                      5.95                7.67 0.66   \n",
       "persistence                            8.47               10.66 0.34   \n",
       "\n",
       "                          Forecast observations  Bias (million bbl)  \\\n",
       "model                                                                 \n",
       "seasonal_change                              24                0.48   \n",
       "forecast_flow_identity                       24                0.93   \n",
       "seasonal_residual_change                     24                0.88   \n",
       "random_forest_change                         24                0.84   \n",
       "xgboost_change                               24                0.81   \n",
       "selected_padd_models                         24                1.30   \n",
       "neural_network_change                        24                0.73   \n",
       "spline_ridge_change                          24                0.76   \n",
       "huber_change                                 24               -0.49   \n",
       "seasonal_ridge_change                        24               -0.23   \n",
       "polynomial_ridge_change                      24               -0.23   \n",
       "seasonal_naive                               24                0.22   \n",
       "ridge_change                                 24                0.52   \n",
       "constrained_level                            24                1.72   \n",
       "persistence                                  24                0.54   \n",
       "\n",
       "                          MAE improvement vs persistence (%)  \\\n",
       "model                                                          \n",
       "seasonal_change                                        59.54   \n",
       "forecast_flow_identity                                 57.28   \n",
       "seasonal_residual_change                               56.66   \n",
       "random_forest_change                                   55.33   \n",
       "xgboost_change                                         50.61   \n",
       "selected_padd_models                                   50.32   \n",
       "neural_network_change                                  48.63   \n",
       "spline_ridge_change                                    47.65   \n",
       "huber_change                                           46.78   \n",
       "seasonal_ridge_change                                  44.59   \n",
       "polynomial_ridge_change                                44.59   \n",
       "seasonal_naive                                         43.64   \n",
       "ridge_change                                           38.44   \n",
       "constrained_level                                      29.68   \n",
       "persistence                                             0.00   \n",
       "\n",
       "                          MAE improvement vs year ago (%)  \n",
       "model                                                      \n",
       "seasonal_change                                     28.21  \n",
       "forecast_flow_identity                              24.20  \n",
       "seasonal_residual_change                            23.10  \n",
       "random_forest_change                                20.74  \n",
       "xgboost_change                                      12.36  \n",
       "selected_padd_models                                11.85  \n",
       "neural_network_change                                8.85  \n",
       "spline_ridge_change                                  7.11  \n",
       "huber_change                                         5.57  \n",
       "seasonal_ridge_change                                1.68  \n",
       "polynomial_ridge_change                              1.68  \n",
       "seasonal_naive                                       0.00  \n",
       "ridge_change                                        -9.23  \n",
       "constrained_level                                  -24.76  \n",
       "persistence                                        -77.43  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize national forecasts on matched months and origins.\n",
    "one = tables['us_predictions'].query(\"split == 'holdout'\").copy()\n",
    "one_name = metadata['one_month_model']\n",
    "long_name = metadata['year_ahead_model']\n",
    "long = us_paths.copy()\n",
    "history_stocks = tables['us_monthly_model'].set_index('month').stock_kb\n",
    "base = long[long.model.eq(long_name)].copy()\n",
    "assert not base.duplicated(['origin_month', 'month']).any()\n",
    "year_ago = base.copy()\n",
    "lag_dates = year_ago.month - pd.DateOffset(years=1)\n",
    "assert (lag_dates <= year_ago.origin_month).all()\n",
    "year_ago['predicted_kb'] = history_stocks.reindex(lag_dates).to_numpy()\n",
    "assert year_ago.predicted_kb.notna().all()\n",
    "year_ago['model'] = 'seasonal_naive'\n",
    "long = pd.concat([long[~long.model.eq('seasonal_naive')], year_ago], ignore_index=True)\n",
    "\n",
    "def summary_table(frame):\n",
    "    rows = []\n",
    "    reference = frame[frame.model.eq('persistence')].set_index(['origin_month', 'month'])\n",
    "    for name, group in frame.groupby('model'):\n",
    "        aligned = group.set_index(['origin_month', 'month']).sort_index()\n",
    "        pd.testing.assert_index_equal(aligned.index, reference.sort_index().index)\n",
    "        np.testing.assert_allclose(aligned.actual_kb, reference.sort_index().actual_kb)\n",
    "        rows.append(dict(model=name, **score(group.actual_kb, group.predicted_kb),\n",
    "                         bias_kb=(group.predicted_kb-group.actual_kb).mean()))\n",
    "    result = pd.DataFrame(rows).set_index('model').sort_values('mae_kb')\n",
    "    for benchmark, label in [('persistence','MAE improvement vs persistence (%)'),\n",
    "                             ('seasonal_naive','MAE improvement vs year ago (%)')]:\n",
    "        result[label] = 100*(1-result.mae_kb/result.loc[benchmark,'mae_kb'])\n",
    "    return result\n",
    "\n",
    "short_scores, long_scores = summary_table(one), summary_table(long)\n",
    "report = short_scores.copy()\n",
    "for column in ['mae_kb','rmse_kb','bias_kb']:\n",
    "    report[column] /= 1000\n",
    "display(report.rename(columns={'mae_kb':'MAE (million bbl)', 'rmse_kb':'RMSE (million bbl)',\n",
    "    'bias_kb':'Bias (million bbl)', 'r2':'R²', 'n':'Forecast observations'}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412b6bf-long-heading",
   "metadata": {},
   "source": [
    "### Twelve-month path results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2412b6bf-long-table",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:47.022772Z",
     "iopub.status.busy": "2026-09-16T21:54:47.022638Z",
     "iopub.status.idle": "2026-09-16T21:54:47.028245Z",
     "shell.execute_reply": "2026-09-16T21:54:47.027805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
       "      <th>Bias (million bbl)</th>\n",
       "      <th>MAE improvement vs persistence (%)</th>\n",
       "      <th>MAE improvement vs year ago (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>4.77</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>58.77</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost_change</th>\n",
       "      <td>5.64</td>\n",
       "      <td>6.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>24</td>\n",
       "      <td>0.95</td>\n",
       "      <td>51.27</td>\n",
       "      <td>-18.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>6.45</td>\n",
       "      <td>7.44</td>\n",
       "      <td>0.68</td>\n",
       "      <td>24</td>\n",
       "      <td>5.14</td>\n",
       "      <td>44.31</td>\n",
       "      <td>-35.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>11.58</td>\n",
       "      <td>13.39</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>24</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-142.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MAE (million bbl)  RMSE (million bbl)    R²  \\\n",
       "model                                                          \n",
       "seasonal_naive                4.77                5.92  0.80   \n",
       "xgboost_change                5.64                6.82  0.73   \n",
       "seasonal_change               6.45                7.44  0.68   \n",
       "persistence                  11.58               13.39 -0.04   \n",
       "\n",
       "                 Forecast observations  Bias (million bbl)  \\\n",
       "model                                                        \n",
       "seasonal_naive                      24                0.22   \n",
       "xgboost_change                      24                0.95   \n",
       "seasonal_change                     24                5.14   \n",
       "persistence                         24                2.64   \n",
       "\n",
       "                 MAE improvement vs persistence (%)  \\\n",
       "model                                                 \n",
       "seasonal_naive                                58.77   \n",
       "xgboost_change                                51.27   \n",
       "seasonal_change                               44.31   \n",
       "persistence                                    0.00   \n",
       "\n",
       "                 MAE improvement vs year ago (%)  \n",
       "model                                             \n",
       "seasonal_naive                              0.00  \n",
       "xgboost_change                            -18.18  \n",
       "seasonal_change                           -35.06  \n",
       "persistence                              -142.55  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = long_scores.copy()\n",
    "for column in ['mae_kb','rmse_kb','bias_kb']:\n",
    "    report[column] /= 1000\n",
    "display(report.rename(columns={'mae_kb':'MAE (million bbl)', 'rmse_kb':'RMSE (million bbl)',\n",
    "    'bias_kb':'Bias (million bbl)', 'r2':'R²', 'n':'Forecast observations'}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412b6bf-score-reading",
   "metadata": {},
   "source": [
    "The lowest observed one-month MAE belongs to `seasonal_change` at **3.43 million barrels**. It is used for the current one-month outlook after reviewing the final period.\n",
    "\n",
    "**One-month method: `seasonal_change`.** MAE is **3.43 million barrels**, RMSE is **5.09 million barrels**, and R² is **0.85**. MAE is **59.5% lower** than unchanged stocks and **28.2% lower** than the same month last year. This final-period score was used to choose the method.\n",
    "\n",
    "`xgboost_change` had the lowest national MAE across six development-year paths. The same-month-last-year benchmark had the lowest MAE among methods tested on the two final-period paths and is used for the current twelve-month outlook.\n",
    "\n",
    "**Twelve-month method: `seasonal_naive`.** MAE is **4.77 million barrels**, RMSE is **5.92 million barrels**, and R² is **0.80** across two paths. MAE is **58.8% lower** than unchanged stocks and **15.4% lower** than `xgboost_change`.\n",
    "\n",
    "The xgboost method probably overfit on the training data as it performs worse than `seasonal_naive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "gasoline-development-runner-up",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:54:47.029409Z",
     "iopub.status.busy": "2026-09-16T21:54:47.029282Z",
     "iopub.status.idle": "2026-09-16T21:55:13.409669Z",
     "shell.execute_reply": "2026-09-16T21:55:13.409189Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>4.77</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>huber_change</td>\n",
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      "text/plain": [
       "            model  MAE (million bbl)  RMSE (million bbl)   R²  \\\n",
       "1  seasonal_naive               4.77                5.92 0.80   \n",
       "0    huber_change               6.91                9.15 0.51   \n",
       "\n",
       "   Forecast observations  \n",
       "1                     24  \n",
       "0                     24  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_month</th>\n",
       "      <th>model</th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.87</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>5.96</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.46</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-06-01</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>6.57</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-06-01</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>7.86</td>\n",
       "      <td>10.36</td>\n",
       "      <td>0.54</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  origin_month           model  MAE (million bbl)  RMSE (million bbl)   R²  \\\n",
       "1   2024-06-01  seasonal_naive               2.98                3.80 0.87   \n",
       "0   2024-06-01    huber_change               5.96                7.76 0.46   \n",
       "3   2025-06-01  seasonal_naive               6.57                7.46 0.76   \n",
       "2   2025-06-01    huber_change               7.86               10.36 0.54   \n",
       "\n",
       "   Forecast observations  \n",
       "1                     12  \n",
       "0                     12  \n",
       "3                     12  \n",
       "2                     12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the development runner-up with the selected seasonal benchmark on\n",
    "# the same two complete final-period paths.\n",
    "development_scores = tables['year_ahead_model_cv_metrics'].sort_values(['mae_kb','model'])\n",
    "runner_name = development_scores.iloc[1].model\n",
    "parameter_rows = tables['best_parameters'].set_index(['padd','model'])\n",
    "runner_paths = []\n",
    "for offset in [24, 12]:\n",
    "    cutoff = panel.month.max() - pd.DateOffset(months=offset)\n",
    "    states = {}\n",
    "    for padd in PADD_NAMES:\n",
    "        history = panel[panel.padd.eq(padd)].reset_index(drop=True)\n",
    "        training = supervised(history)\n",
    "        training = training[training.month.le(cutoff)]\n",
    "        params = json.loads(parameter_rows.loc[(padd, runner_name), 'params'])\n",
    "        model, warning = fit(runner_name, training, params)\n",
    "        states[padd] = {'name':runner_name, 'estimator':model}\n",
    "    regional_path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "    regional_path = regional_path[['padd','month','origin_month','horizon','stock_kb']]\n",
    "    regional_path = regional_path.rename(columns={'stock_kb':'predicted_kb'})\n",
    "    regional_path = regional_path.merge(panel[['padd','month','stock_kb']],\n",
    "        on=['padd','month'], validate='one_to_one').rename(columns={'stock_kb':'actual_kb'})\n",
    "    runner_paths.append(regional_path)\n",
    "runner_us = aggregate(pd.concat(runner_paths, ignore_index=True),\n",
    "    ['origin_month','month','horizon'], ['actual_kb','predicted_kb']).assign(model=runner_name)\n",
    "seasonal_us = tables['us_recursive_holdout_predictions'].copy()\n",
    "assert metadata['year_ahead_model'] == 'seasonal_naive'\n",
    "seasonal_us['model'] = 'seasonal_naive'\n",
    "comparison = pd.concat([runner_us, seasonal_us[runner_us.columns]], ignore_index=True)\n",
    "for name, group in comparison.groupby('model'):\n",
    "    aligned = group.set_index(['origin_month','month']).sort_index()\n",
    "    reference = seasonal_us.set_index(['origin_month','month']).sort_index()\n",
    "    pd.testing.assert_index_equal(aligned.index, reference.index)\n",
    "    np.testing.assert_allclose(aligned.actual_kb, reference.actual_kb)\n",
    "comparison_scores = metric_table(comparison, ['model']).sort_values('mae_kb')\n",
    "comparison_by_origin = metric_table(comparison, ['origin_month','model']).sort_values(['origin_month','mae_kb'])\n",
    "for table in (comparison_scores, comparison_by_origin):\n",
    "    report = table.copy()\n",
    "    report['mae_kb'] /= 1000\n",
    "    report['rmse_kb'] /= 1000\n",
    "    display(report.rename(columns={'mae_kb':'MAE (million bbl)',\n",
    "        'rmse_kb':'RMSE (million bbl)', 'r2':'R²', 'n':'Forecast observations'}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-development-runner-up-reading",
   "metadata": {},
   "source": [
    "### Development runner-up versus the seasonal benchmark\n",
    "\n",
    "`huber_change` had the second-lowest national MAE on the six development paths: **6.47 million barrels**, compared with **6.15 million** for `xgboost_change`.\n",
    "\n",
    "On the same two final-period paths, `huber_change` has MAE of **6.91 million barrels** and `seasonal_naive` has MAE of **4.77 million barrels**. The runner-up's MAE is **44.7% higher**. `seasonal_naive` has lower MAE on each annual path.\n",
    "\n",
    "The `seasonal_naive` is actually a pretty well performing model by itself. Using more complex models seems to not help at all as they both perform much worse.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2412b6bf-error-plots",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:55:13.412205Z",
     "iopub.status.busy": "2026-09-16T21:55:13.411992Z",
     "iopub.status.idle": "2026-09-16T21:55:13.668524Z",
     "shell.execute_reply": "2026-09-16T21:55:13.667952Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected = one[one.model.eq(one_name)].sort_values('month').copy()\n",
    "selected['error'] = (selected.predicted_kb-selected.actual_kb)/1000\n",
    "fig, axes = plt.subplots(1,3,figsize=(16,4))\n",
    "axes[0].bar(selected.month,selected.error,width=20)\n",
    "axes[0].set(title='One-month inventory forecast errors',ylabel='Forecast − actual (million bbl)')\n",
    "axes[1].hist(selected.error,bins=9,edgecolor='white')\n",
    "axes[1].set(title='Distribution of forecast errors',xlabel='Forecast − actual (million bbl)',ylabel='Months')\n",
    "axes[2].scatter(selected.predicted_kb/1000,selected.error)\n",
    "axes[2].set(title='Errors versus forecast stock level',xlabel='Forecast stocks (million bbl)',ylabel='Forecast − actual (million bbl)')\n",
    "for ax in [axes[0],axes[2]]: ax.axhline(0,color='black',linewidth=.8)\n",
    "axes[1].axvline(0,color='black',linewidth=.8)\n",
    "fig.autofmt_xdate(); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412b6bf-error-reading",
   "metadata": {},
   "source": [
    "The one-month `seasonal_change` method has average signed error of **+0.48 million barrels**. Its largest absolute monthly miss is **17.35 million barrels**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2412b6bf-horizon-plot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:55:13.669734Z",
     "iopub.status.busy": "2026-09-16T21:55:13.669615Z",
     "iopub.status.idle": "2026-09-16T21:55:13.779050Z",
     "shell.execute_reply": "2026-09-16T21:55:13.778531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzW+//ZahZ4DBYGDHjh0UK1bsiWFB/9WwYUOMjIwIDQ196uMOUK5cOUqXLs3mzZufWEHl3zQaDcbGxhm2HThw4JnFExsbG9q0acNbb71FVFRU+vCtp7XVl1WvXj3i4+PZt29fhu3btm1L358dQUFBjBgxAh8fH6ZOnfrE/px4voQQQhQe0hNECCFEgePr68vu3buZOnUqxYoVo379+hn29+vXj71799K7d2/69++Pu7s7BoOB8PBwDh48yMCBA5/aa+Jp3NzcAFi5ciVdunTByMiIcuXKUb58eXr06MHq1avRarU0btw4fXWY4sWL079//yxfw8bGhk6dOrFu3TpKlCjxxLwGmbG0tGTChAmMHTuW6OhoWrdujb29PZGRkQQHBxMZGcnkyZNf6L7+27vvvsv+/fvp27cv7777Lra2tuzcuZMDBw7w8ccfY21tne1zQ9q8Do97NjxeajguLo4RI0Y8cWy/fv3Sh0n8eznk7Pjggw8YOHAgffv2ZeDAgRgbG7N27VouX77M7Nmzn9uLoWTJkowcOZJvv/2WsLAwGjdujI2NDffv3ycwMBBzc/P0Hg0TJ05k2LBhvPHGG/Tv35/ixYsTHh6On58fX3/9NZBWuHu8CszjZYyXLl36RO+ioUOHUrFiRapWrYqdnR23bt1i5cqVlChRgjJlygBPb6v/nvcmOzp37syaNWsYM2YMt27dws3NjZMnT7Jo0SKaNGnyxN9hVsTFxTF48GDMzMwYNGgQ586dy7C/QoUKWFlZvfTzJYQQovCQIogQQogCp379+ukfJIcOHZo+/8JjFhYWrFmzhsWLF7NhwwZu3ryJmZkZxYsXp379+pQoUeKFr+nj48OQIUPYunUrGzduxGAwsGrVKnx8fPjss88oVaoUmzZtYu3atVhZWdGoUSM+/PDDF+6m37ZtW9atW0fPnj2fuF9P06lTJ1xcXFiyZAmTJk3i0aNH2NnZUbly5ScmbH1R5cuXZ/369cyePZspU6aQmJiIq6srM2bMSJ8Q82X06tWLpKQkpk2bxoMHD6hYsSKLFi2iZs2aTxzr5eVFiRIlMDMzy3avg8fq1KnDihUrmDt3LuPGjcNgMFCpUiUWLlxIs2bNsnSOIUOG4OrqyqpVq/j5559JTk7G0dGRqlWr8uabb6Yf16hRI1avXs38+fOZNm0aSUlJFCtWLEOR65NPPsHIyIjFixcTHx+Ph4cHc+fO5bvvvstwTR8fH3799Vc2btxIXFwcjo6O1K9fn//973/pPUme1VZfhqmpKatWreKbb75hyZIlPHz4EGdnZwYOHJi+jO6LioqKSh/mMmDAgCf2P86dE8+XEEKIwkGjKIqidgghhBBCZM3MmTNZt24dBw4ckHkO/iM4OJhOnToxceLELA0VEkIIIUThIz1BhBBCiHzgzJkz3Lhxg7Vr19KjRw8pgPxLaGgot27d4ptvvsHR0TFHeqAIIYQQomCSIogQQgiRD/To0QNzc3OaNm3Ke++9p3acPGXBggVs374dV1dXvvvuu1xdAUUIIYQQ+ZsMhxFCCCGEEEIIIUShIEvkCiGEEEIIIYQQolCQIogQQgghhBBCCCEKBSmCCCGEEEIIIYQQolAoUBOjpqamEh0djampKVqt1HeEEEIIIYQQQojCwGAwkJSUhK2tLUZGTy91FKgiSHR0NDdu3FA7hhBCCCGEEEIIIVRQtmxZ7O3tn7q/QBVBTE1NgbQ7LcvjCb1ez6VLl3Bzc0On06kdR4hMSTsV+YG0U5EfSDsVeZ20UZEf5Od2mpCQwI0bN9LrAk9ToIogj4fAmJubY2FhoXIaoTa9Xg+AhYVFvvsDFoWHtFORH0g7FfmBtFOR10kbFflBQWinz5saQybOEEIIIYQQQgghRKEgRRAhhBBCCCGEEEIUClIEEUIIIYQQQgghRKEgRRAhhBBCCCGEEEIUClIEEUIIIYQQQgghRKEgRRAhhBBCCCGEEEIUCgVqiVwhhBBCiLxOb1Dwvx5JRGwiTtZm1Clnh06rUTuWEEIUGPI6K55FiiBCCCGEEK/InnPhTN55gfDoxPRtxW3NmNTBgzZVi6uYTAghCob8/jr7ySef0KVLF2rVqvXUY1asWEGHDh2wt7d/hckKDhkOI4QQQgjxCuw5F86w1acyvDEHuBOdyLDVp9hzLlylZEIIUTDkl9fZ1NTUp+77/PPPn1kAAVi1ahUPHjzI6ViFhvQEEUIIIYTIZXqDwuSdF1Ay2acAGmDyzgu85lFMumwLIcRTKIpCQoo+0316g8KkHeef+Tr72Y4LNKjgkOnrrLmxDo3m+a+/7u7uDB8+nEOHDvHw4UNGjBhB+/btAQgICOCrr74iLi4ORVEYOnQorVu35ubNm/j6+tKnTx8OHz5Mq1atKF26NN988w1arRa9Xs97771Hy5Yt6dOnDwMHDqRZs2Zs3LiR5cuXY2xsjMFgYNq0afj5+REREcHIkSMxNTVl5syZVKhQge+++46jR4+SkpJC+fLlmTx5MjY2NowdOxYzMzNu3LhBeHg4FStWZPbs2ZiYmJCcnMw333zDwYMH0Wg0ODo6snjxYgCWLVvGr7/+il6vx8HBgSlTplC8eN7vSZMVUgQRQgghhMhl/tcjn/hm8t8UIDw6Ef/rkdRzle7NQgjxX4qi0O37I5wMeZi92wN3YhLx/GxvpvtrlSnKxqH1slQI0Wg0rF+/nrCwMLp160bNmjWxtLRk0qRJLFq0CCcnJyIjI/H19aVGjRoAREVFUaZMGYYPHw5Ax44dmTx5MjVq1MBgMBAXF/fEdWbOnMnu3btxdnYmJSWF5ORkvL292bJlC3PmzMHNzQ2A77//HgsLCzZt2gTA/PnzmTNnDhMmTAAgODiYFStWYGxsTK9evdi7dy/t27dn8eLFhIWFsXnzZkxMTIiMjATg0KFDREREsGHDBnQ6Hdu2bWPKlCksXLjwxR70PEqKIEIIIYQQuSwi9ukFkOwcJ4QQhVFe6SfXvXt3AEqVKkXNmjU5ceIENjY2hIWFMXjw4PTjFEXh+vXruLi4YGpqmt5jBKBevXpMnz6d1q1b07BhQypXrvzEderWrcuYMWNo1qwZjRs3ply5cpnm2bdvH3Fxcfz6668ApKSkULp06fT9r732GmZmZgB4eXkRGhoKwP79+xk7diwmJiYA2NnZodfrOXHiBLdu3aJr164AGAwGtNqCM5OGFEGEEEIIIXKZk7VZjh4nhBCFjUajYePQek8dDuN/PZL+y48/9zwrBtSmTjm7J7ZndTjM07IpioK7uztr1qx5Yv/NmzcxNzfPcP5x48Zx+fJljh07xpgxY+jQoUOGAgrAvHnzOHfuHP7+/rzzzju89957tGvX7onzK4rCpEmTqFevXqb5TE1N0/9bp9Oh12f+GP77fEOGDOGNN9545nH5VcEp5wghhBBC5FF1ytlR3PbpBQ4NaasXZPbGXAghRBqNRoOFiVGmP40qOlLc1uypvUUev842quiY6e1fpADyeNjJzZs3OXnyJDVr1qR69eqEhIRw5MiR9OOCgoJITk7O9BxXr16lYsWK9O7dmzfffJOzZ89m2J+amkpoaCienp4MGjSI1q1bExAQAIClpSWxsbHpxzZv3pwVK1aQkJAAQEJCApcvX37u/WjevDkrV65Mz/h4OEzNmjVZv349UVFRQFrPkgsXLmTlockXpCeIEEIIIUQu02k1jG9biRHrzmS6XwHGt60sk6IKIUQ26bQaJnXwYNjqU2ggwwSpj19ZJ3XwyJHXWRMTE3r27MnDhw/59NNP0ycMXbhwIV988QUzZswgNTWV4sWLs2DBgkzPMXv2bG7cuIGxsTFmZmZ89tlnGfYbDAbGjRtHTEwMOp0OOzs7ZsyYAUDfvn0ZP348ZmZmzJw5k3feeYd58+Zl6LkxePBgKlas+Mz7MXjwYL755hs6d+6MsbExTk5OfP/99zRq1AhbW1v69OmDRqNBr9fTrVs3PDw8XuJRyzs0iqJkNoFuvhQfH09QUBCVK1fGwsJC7ThCZXq9njNnzlCtWjV0Op3acYTIlLRTkR9IO80Zm0/e5MONZ9FqwPCvd1+P36y39yrOdz2rSyEkm6SdirxO2uirsedcOJN3XsgwGXVxWzMmdfCgTdWXX93E3d2dU6dOYWlp+dLnyovyczvNaj1AeoIIIYQQQuQyRVFYevA6AB+0cqNmaTsiYhNxsjYjKUXP4B9PsCsgnCIWxkztVDXb49KFEKKwa1O1OK95FMP/emT662ydcnZSYBbppAgihBBCCJHLjlx7wIXwGMyNdfT2KUMRC5MM+2e/UY2R60+z+mgodpamfPCam0pJhRAi/9NpNbm23PjFixdz5bzi1ZGJUYUQQgghctlSv7ReIN1qlnyiAALQwduFKZ2qAjDn98ssP3T9leYTQgghCgspggghhBBC5KJr9+L4PTgCjQYGNCj71OP61C2T3gNk8s4LbD9z6xUlFEIIIQoPKYIIIYQQQuSiZX/36mhRyYnyjlbPPHZE8wr0r18WgA9/Osv+ixG5HU8IIYQoVKQIIoQQQgiRS6Lik9l08iYAgxqWf+7xGo2Gie096FTNhVSDwrDVJzkZEpnbMYUQQohCQ4ogQgghhBC5ZM2xUBJTDHgUt6Fuebss3Uar1fBlN2+aujuSmGJgwPLjBN+JyeWkQghRgBj0cN0PAjel/b9Br3YikYdIEUQIIYQQIhckpxpYdeQGAG83KvdCy96aGGlZ0KsGNUoXISYxlb5L/QmLjM+lpEIIUYBc2AHfVoWV7WHzoLT//7Zq2nYhkCKIEEIIIUSu+DnwNndjknCyNqW9l8sL397CxIhl/Wvj7mxNRGwSfZYe415sUi4kFUKIAuLCDvipL8Tczrg9JjxteyEshBw7doyuXbtm67Zz585l1qxZOZxIfVIEEUIIIYTIYYqisPRg2oSo/eqXxcQoe2+5iliYsGpQHUoWNefGg3j6L/cnJjElJ6MKIUT+oSiQ/Cjzn8QY+GU0oGR2w7T/2zMm7bjMbq9kdjtREBmpHUAIIQorvUHh6LUHHA9NINHmAXVdHdFps95dXgiRdx27Hsm5WzGYGWt5q07plzqXs40ZPw7yofv3hzl/O4bBK0+wcmAdzIx1OZRWCCHyAUWBZa0h7Fh2T5DWQ2Rmqcx3l6oLA/fAM4YuJiYmMnbsWC5duoSRkREODg4sW7aMbdu2sWbNGlJTU7G0tGTixIm4ublx8eJFJk+eTEJCAklJSXTq1IkhQ4YAsHHjRpYvX46xsTEGg4Fp06bh7e1NYGAg06ZNIz4+HlNTU8aNG0fNmjW5efMmvr6+vPXWW/z555/ExsYyYcIEmjRpAsBHH33EtWvXSElJwcXFhenTp2Nvb5+lRyY2NpaZM2cSEBCARqOhWLFiLFy4EICIiAiGDh1KaGgoDg4OzJkzhyJFijzzvo0dOxYzMzNu3LhBeHg4FStWZPbs2ZiYmBAbG8v48eO5fPkyTk5OODs74+DgwJgxY0hJSeG7777j6NGjpKSkUL58eSZPnoyNjU1Wn+QskSKIEEKoYM+5cCbvvEB4dGLahmPHKW5rxqQOHrSpWlzdcEKIl/a4F4hvjZIUtTR56fOVc7BkxYA6vLn4KMeuRzJi3WkW9qqBkU469QohChN1vyzy8/MjJiaG3bt3AxAVFcXJkyfZvXs3a9aswcTEhBMnTvDRRx+xY8cOSpQowYoVKzAxMSExMZGePXtSv359PD09mTlzJrt378bZ2ZmUlBSSk5NJTk5mxIgRTJ06lUaNGnHixAlGjRrF3r17069XpUoVRo0axV9//cXnn3+eXgQZP348dnZpE3AvXryY+fPnM3HixCzdr+nTp2NhYcH27dtRFIW//vorfd/Zs2fZtGkTRYoU4f3332fDhg0MGTLkmfcNIDg4mBUrVmBsbEyvXr3Yu3cv7du3Z/78+djY2LBnzx6io6Pp0qULrVu3BmDp0qVYWFiwadMmAObPn8+cOXOYMGFCDjx7/5AiiBBCvGJ7zoUzbPWpJzpr3olOZNjqUyzsXUMKIULkYzfuP2Jf0F0ABjYsl2PnrVrClh/61aLvMn9+u3CXcVsC+aKb1wtNuCqEEPmWRpPWUyPlKZNEhxyGNd2ef55em6BM/Se3G1s8sxcIQKVKlbh27RqfffYZderUoXHjxvz+++8EBwfTvXv39OMiIyNJTk4mKSmJyZMnExwcjEaj4c6dOwQHB+Pp6UndunUZM2YMzZo1o3HjxpQrV46LFy9ibGxMo0aNAKhVqxZ2dnZcvHgRR0dHLCwsaNmyJQDVq1cnLCws/Zo7d+5k+/btpKSkkJiYiIODw/Mfi7/t37+fLVu2oNVq0ev1GXpeNG7cmCJFigBQrVo1Ll26BPDM+wbw2muvYWZmBoCXlxehoaFA2hwlj4satra26fcHYN++fcTFxfHrr78CkJKSQunSL9ebMjNSBBFCiFdIb1CYvPPCU0eraoDJOy/wmkcxGRojRD61/NB1FAWaV3LC1dEqR89dt7w9896sztDVJ9l48iZ2liaMa1s5R68hhBB5lkYDJpaZ73NtDjYuaZOgZvpOS5O237U5aLM3nLBUqVL8/PPPHD16lCNHjvDll1/SoEEDfH19GTVq1BPHz549GwcHB7Zu3YqRkRHDhw8nKSltgut58+Zx7tw5/P39eeedd3jvvfdwdXXNtLD9eJuJyT89Cx8XLABOnDjBmjVrWL9+PXZ2dvz+++/Mnz8/W/fxv0xNTdP/W6fTpV/zWfftWbdTFOWpxXtFUZg0aRL16tXLkexPI30ohRDiFfK/HvnPEJhMKEB4dCL+1yNfXSghRI6Jjk/hpxM3ARiUg71A/q1VlWLM9PUCYNFf1/j+z6u5ch0hhMhXtDpo83glk/9+yP779zYzs10AAbhz5w4ajYYWLVowevRoFEWhU6dObN++nfDwcAAMBgOBgYEAxMTE4OzsjJGREdeuXePQoUMApKamEhoaiqenJ4MGDaJ169YEBARQvnx5kpOTOXLkCACnTp0iMjISNze3Z+aKiYnBysoKW1tbkpOT2bBhwwvdr+bNm7N06VIMBkP6+Z7naffteXx8fNi2bVv6OX7//fcMOVasWEFCQgIACQkJXL58+YXuS1ZITxAhhHiFImKfXgDJznFCiLxl3fFQElL0VCpmTX3XrE1Ilx1v1CrFw0fJzPglmJm/BGNnYcIbtZ8y2Z8QQhQWHh3hjVVpq8D8e5lcG5e0AohHx5c6/cWLF/n6669RFCW9AFK7dm3ef/99/ve//6HX60lNTaVJkyZ4enoybNgwRo8ezc6dOylRogR169YF0gol48aNIyYmBp1Oh52dHTNmzMDExIQ5c+bw+eefM336dExNTfn222+xsLAgMvLpX5A1btyYHTt28Prrr+Ps7EyNGjXw8/PL8v0aP34806dPp3379hgZGeHi4kLjxo2feZun3bfneffddxk3bhxt27alRIkS1KhRAyurtF6T77zzDvPmzeONN95IP37w4MFUrFgxy/clKzSKUnDWAoqPjycoKIjKlStjYWGhdhyhMr1ez5kzZ6hWrRo6ncygL/KGI1cf8OYPR5973LrBdamXix+ghHgR8nqaNSl6A41m7edOTCJfdfemW82SuX7NGb8EsejPa2g1sLB3TVpXKZbr18yrpJ2KvE7a6Ctk0KfNERJ3F6yc0+YAeYkeIIVJbrfTlJQUDAYDpqamxMXF8eabbzJu3Djq189knpYXlNV6gPQEEUKIV6hOOTtszIyISUx96jHFbc2oU87uFaYSQuSE3YHh3IlJxMHKlA7er2Zy47FtKvHwUTI/nbjJiHWnWTmgjhRQhRBCq4NyjdROITIRExPD4MGD0ev1JCYm0qFDhxwpgLwIKYIIIcQrdDsqgcRUwzOPGdOmkkyKKkQ+oyhK+rK4/eqVwdTo1XzjqNFomN7Fk6j4FPZeuMvgVSdY/05dqpawfSXXF0IIkT8EBQUxduzYJ7Z36dKF/v37v7Ic9vb2bNmy5ZVdLzNSBBFCiFdEURQ+2XaO5FQDFRytiEtK5U7MP3N/aDVgUODglft0rl5CxaRCiBd1IuQhATejMTXS0qtumVd6bSOdljlvVqf/cn+OXouk3zJ/Ng6tR/kcXplGCCFE/lW5cmW2b9+udow8QVaHEUKIV2T7mdv8dekeJjoti/rW5NDY5qwZVJv3fGxZM6g2qwf5oNXAppM32X7mltpxhRAvYInfNQC61iiJnaXJc47OeWbGOn7oW4sqLjY8eJRMn6X+3HnGSlRCCCFEYSVFECGEeAUexCUxeed5AEa2qICroxU6rYa65e1pVNqcuuXtqV/BgeHNKgAwYes5wiLj1YwshMiikAeP2HvhLgCDGpZVLYe1mTErB9ahnIMlt6IS6LvsGFHxyarlEUIIIfIiKYIIIcQrMO3nIB7Gp1CpmDXvNHZ96nEjW1SkZpmixCalMnL9aVL0z54/RAihvuWHbqAo0NTdkQpO1qpmcbAyZdXAOjjbmHLpbhwDVxwnPvnpEzELIURBpDfoOX7nOLuv7eb4nePoDXq1I4k8RIogQgiRy/68dI+tp2+h0cBMXy9MjJ7+0muk0/Jdz2pYmxlxOjSKb/ddeoVJhRAvKjohhZ9OhAEwqGE5ldOkKWVnwaqBPtiaG3MqNIphq0+R/JwJmYUQoqDYF7KP1ptbM/DXgYzxG8PAXwfSenNr9oXsUzuayCOkCCKEELnoUVIq47cEAjCgfjmqlSry3NuULGrBjK6eACw4cJXDV+/nZkQhxEvYcDyU+GQ97s7WNKzgoHacdO7FrFnWvzbmxjr+vHSPjzaexWBQ1I4lhBC5al/IPj448AF34+9m2B4RH8EHBz4olIWQY8eO0bVr11w59yeffMKJEydy5dy5SYogQgiRi2b/dolbUQmUKGLOh63csny79l4u9KhVCkWB9zecIfKRjOsXIq9J1RtYcegGkNYLRKPJW0tb1yxTlIW9a2Ck1bDj7G0m7zyPokghRAiRfymKQnxKfKY/sUmxzPCfgcKTr3PK3/+b6T+T2KTYTG8vr48v7vPPP6dWrVpqx3hhskSuEELkkjNhUSw/dB2Az7tUxdL0xV5yJ3X04HhIJNfuPWL0pgB+6Fszz33IEqIw++XcHW5HJ+JgZULHai5qx8lUU3cnvn7Dm/c2nGHlkRDsLE0Z1bKi2rGEEOKFKYpC31/6cubemWyf4278Xeqvr5/pvupO1VnZZuUz32slJiYyduxYLl26hJGREQ4ODixbtoxt27axZs0aUlNTsbS0ZOLEibi5uXHx4kUmT55MQkICSUlJdOrUiSFDhgCwceNGli9fjrGxMQaDgWnTpuHt7U1gYCDTpk0jPj4eU1NTxo0bR82aNbl58ya+vr689dZb/Pnnn8TGxjJhwgSaNGkCwEcffcS1a9dISUnBxcWF6dOnY29vn6XHZezYsZiZmXHjxg1u376Nk5MTP/zwA+bm5hw5coRvv/2WpKQkUlNTGTBgAL6+vgD06dOHgQMH4u7ujq+vL3/++ScmJmkrpI0ZM4YqVarQt29fAgIC+Oqrr4iLi0NRFIYOHUrr1q2z/LzlNCmCCCFELkjRGxi7OQCDAp2rudDU3emFz2FhYsScntXpuuAw+4LusvpoCH3qlc35sEKIF6YoCksOphU5e9ctg5mxTuVET9epWgmi4lOYtOM83+y7hJ2lsbyWCCHyJbW/DPLz8yMmJobdu3cDEBUVxcmTJ9m9ezdr1qzBxMSEEydO8NFHH7Fjxw5KlCjBihUrMDExITExkZ49e1K/fn08PT2ZOXMmu3fvxtnZmZSUFJKTk0lOTmbEiBFMnTqVRo0aceLECUaNGsXevXvTr1elShVGjRrFX3/9xeeff55eBBk/fjx2dnYALF68mPnz5zNx4sQs37fg4GBWrFiBVqvF19eX3377jY4dO+Lh4cHatWvR6XRERUXRtWtXGjVqhJPTP+9tXVxcqFy5Mn/88Qdt2rTh0aNH7N+/n7FjxxITE8OkSZNYtGgRTk5OREZG4uvrS40aNXB0dMypp+aFSBFECCFyweK/rhF8J5aiFsZ82t4j2+epWsKWMa9XYuquC0z9OYja5eyoVMwmB5MKIbLjVOhDzoZFYWKkpXfdMmrHea5+9csS+SiZ736/zMQd57G1MKGjd97svSKEEJnRaDSsbLOShNSETPefvHuS//3+v+eeZ0GLBdR0rvnEdnMj8+cWWSpVqsS1a9f47LPPqFOnDo0bN+b3338nODiY7t27px8XGRlJcnIySUlJTJ48meDgYDQaDXfu3CE4OBhPT0/q1q3LmDFjaNasGY0bN6ZcuXJcvHgRY2NjGjVqBECtWrWws7Pj4sWLODo6YmFhQcuWLQGoXr06YWFh6dfcuXMn27dvJyUlhcTERBwcXmyeqtdeew0zMzP0ej2urq7p546KiuKTTz7hxo0b6HQ6Hj58yJUrVzIUQQC6du3K1q1badOmDb/88gt169alaNGi/Pnnn4SFhTF48OD0YxVF4fr161IEEUKIguLqvTi++/0yABM7eGBvZfpS5xvYoCx+l+9x4OI9Rq47zY7hDfP0t85CFAZL/NJ6gXSpVgKHl/wbf1Xea1mRh/HJrDoSwoc/ncHW3Jgmbuq8ARVCiOzQaDRYGFtkuq++S32cLZyJiI/IdF4QDRqcLZyp71IfnTZ776NKlSrFzz//zNGjRzly5AhffvklDRo0wNfXl1GjRj1x/OzZs3FwcGDr1q0YGRkxfPhwkpKSAJg3bx7nzp3D39+fd955h/feew9XV9dMCzGPtz0eagKg1WrR69OW/j1x4gRr1qxh/fr12NnZ8fvvvzN//vwXum+mpv/8W6bVaklNTVtefdKkSTRr1oy5c+ei0Wjo0qVL+n34t9dee41p06Zx7949tmzZkj7sR1EU3N3dWbNmzQvlyU0yMaoQQuQgg0Fh3JZAklMNNHZzpHO1Ei99To1Gw1fdvXGwMuXS3Tim/XwhB5IKIbIrLDKeX8/fAWBQo7yxLG5WaDQaPutQhfZexUnRKwz98SSnQh+qHUsIIXKETqtjbJ2xQFrB498e/z6mzphsF0AA7ty5g0ajoUWLFowePRpFUejUqRPbt28nPDwcAIPBQGBg2sqAMTExODs7Y2RkxLVr1zh06BAAqamphIaG4unpyaBBg2jdujUBAQGUL1+e5ORkjhw5AsCpU6eIjIzEze3Zk+vHxMRgZWWFra0tycnJbNiwIdv3MbNzlyhRAo1Gw/HjxwkODs70OFNTU9q0acPcuXMJCwujYcOGQFqPlZCQkPT7BBAUFERysnqT/ktPECGEyEHrj4fhfz0Sc2Mdn3eummNjVx2sTJn9hjd9l/mz+mgoDSs40qZqsRw5txDixSw/dAODAo0qOuDmbK12nBei1WqY/UY1ohNS8Lt8n4ErjrNxSD0q5rP7IYQQmWlZpiWzm85mpv/MDMvkOls4M6bOGFqWaflS57948SJff/01iqKkF0Bq167N+++/z//+9z/0ej2pqak0adIET09Phg0bxujRo9m5cyclSpSgbt26QFqhZNy4ccTExKDT6bCzs2PGjBmYmJgwZ84cPv/8c6ZPn46pqSnffvstFhYWREZGPjVX48aN2bFjB6+//jrOzs7UqFEDPz+/l7qvj3344YdMnjyZxYsX4+rqire391OP7dq1K927d2fw4MHodGnFJltbWxYuXMgXX3zBjBkzSE1NpXjx4ixYsCBH8mWHRilAawHFx8cTFBRE5cqVsbDIvJuUKDz0ej1nzpyhWrVq6X+EQuSmuzGJtJz9J7GJqXza3oNBDZ//DfGLttMZu4NY9Nc1bM2N+WVUI1yKmOdEdCGeSV5P/xGTmEL9GX8Ql5TKyoF18u1wkvjkVN764RhnwqIoZmPGpmH1KFk0f793knYq8jppo6+O3qDnVMQp7sXfw9HCkRpONV6qB0hhkp/baVbrAaoOh1m0aBG+vr5Ur16devXq8b///Y9r165lOEZRFObOnUvDhg3x8vKiT58+XL58WaXEQgjxdJO2nyc2MRXvkrb0r182V67xYSt3vEraEp2QwvsbzqA3FJg6thD5wk/Hw4hLSqWikxWNK77YpHN5iYWJEcv716aCkxV3YhLpu9SfB3FPjvEWQoj8SKfVUbtYbdqWb0vtYrWlACIyULUI4u/vT69evfjpp59Yvnw5er2eQYMGER8fn37MDz/8wPLly5k4cSKbNm3CwcGBAQMGEBcXp2JyIYTIaM+5cPacv4ORVsNMXy902txZws3ESMt3PatjYaLj2PVIFuy/kivXEUI8KVVvYPmhGwAMalhO9aUaX1ZRSxN+HFSHEkXMuXb/Ef2XHycuKVXtWEIIIXJBUFAQnTp1euJnxYoVakd75VSdE2Tp0qUZfp8xYwb16tXj/Pnz1K5dG0VRWLVqFUOHDqVVq1YAzJo1i/r167Nr1y569uypRmwhhMggOiGFidvPAzCkSXkqF8/dJWzLOVgytVNVPtx4lm9/v0z9CvbULGOXq9cUQsCv5+9yKyoBO0sTOld/+UmP84Litub8OKgO3b4/QuCtaN5ZdYJl/WvLClRCCFHAVK5cme3bt6sdI0/IUxOjxsbGAmmTpwDcvHmTe/fupc8sC2nLAtWuXZvTp08/tQii1+vTlwsShdfjNiBtQeS2GbsvEBGbRDkHC95tUv6F2lx222kn72L8eSmCHWfDGbnuNLuGN8DG3PiFziFEVsnraZolB9OG7PaqUwpjbcF5PMrYmbOsX016LfHn8NUHjFp/mrk9q+Vaj7bcIu1U5HXSRkV+kJ/baVYz55kiiKIozJgxg5o1a6YvAXTv3j0A7O3tMxzr4ODA7du3n3quS5cu5V5Qke88XqJKiNxw/l4y64+nzdY9oKoZQeez196y0067lzNw9IqOW1GJDF95iPd9bPN993yRtxXm19NLD5I5HRqFkRaqWcVw5swZtSPluI/r2TDN7yG/nr/L/5b9xdCaNvnyNaUwt1ORP0gbFflBQW6neaYIMmXKFC5dusTatWuf2Pfff4Cft6CNm5ubrA4j0Ov1BAYG4unpme9mNhb5Q1KKng/npq333rN2SXq9VvWFz/Gy7XRhsSh6LD7GobBEOtSqQPdaJV/4HEI8j7yewtL1ZwDoXK0ETet6qhsml1QDnErcYfi6M+y7nkCFUsX4uLW72rGyTNqpyOukjYr8ID+30/j4+Cx1iMgTRZCpU6fyxx9/sHr1aooVK5a+3dExbdm5+/fv4+TklL79wYMHODg8fUZ2nU6X754wkXukPYjcMn/fZW48iMfJ2pRxbT1eqp1lt53WLGvP+6+58eWvF5m8K4ja5e1xdbTKdg4hnqWwvp6GRcaz59wdAN5uXL5APwZtvUowPVHP2C2BfP/XdRyszXi7UXm1Y72QwtpORf4hbTT3KXo98SdOknrvHkaOjljUqolGHvMXkh/baVbzqro6jKIoTJkyhb1797Jy5UpKlSqVYX/JkiVxdHTk0KFD6duSk5M5fvw41atXf9VxhRAiXVB4DIv+TJsfYEqnqtiqOB/H0Cau1He1JyFFz8h1p0lKzX9jOIXIy1YevoFBgYYVHKhULHcnPs4LetYpzeg2aT1Apv0cxKaTN1VOJIQQWRezdy9XWrQktF8/bn/0EaH9+nGlRUti9u5VO5rII1QtgkyePJkdO3bw9ddfY2lpyb1797h37x6JiYlA2jCYvn37smjRIn777TcuXbrEuHHjMDMzo3379mpGF0IUYnqDwtjNAaQaFNpUKUabqsWef6NcpNNq+KZHNYpaGHP+dgxf7Lmoah4hCpLYxBQ2HA8D0pbFLSyGNXFlcKO0+ztmcwD7LtxVOZEQQjxfzN693Br1Hql37mTYnnr3LrdGvadKIWTLli2MHDky168TExPDDz/8kO3b37x5Ex8fnxxMlHepWgRZt24dsbGx9OnTh4YNG6b/7N69O/2YwYMH069fPyZPnoyvry93795l2bJlWFlJd28hhDpWHL7B2ZvRWJsZMblTFbXjAOBsY8aX3bwBWHrwOgcuRqicSIiC4acTN4lNSsXV0ZImbo5qx3llNBoN49tWxrdGSfQGhXfXnuLYtQdqxxJCFHKKomCIj8/0Rx8by91pn0Nm80cqCqBw9/Pp6GNjM7398+adzOtiYmJYsmSJ2jHyBVXnBLl48fnfVmo0GkaMGMGIESNeQSIhhHi2sMh4vvo17bVrfNvKONuYqZzoHy09nOlXrwwrj4Tw0caz7B7VCCfrvJNPiPxGb1BYfug6AIMalkebz5aMfVkajYZZvp5EJySzLyiCt1eeYP2QulRxsVU7mhCiEFIUhZC3epFw+nQ2T5DWI+RS7TqZ7javUYMya1Y/c1Wsq1evMmDAANasWUOpUqVYsmQJx44dY/78+UydOhV/f3+KFi1KpUqViIyMZM6cOQDExsYyYsQIQkNDsba25osvvsDFxQW9Xs9XX32Fn58fAD4+PowZMwYTExPu37/PpEmTCA0NRVEU+vTpQ48ePTAYDEybNo3Dhw9jYmKCkZER69atY9KkScTGxtKpUyd0Oh1btmx56v3YvHkzq1atQlEUjIyM0nMCzJ07l19++QW9Xs+nn35KkyZNAPjoo4+4du0aKSkpuLi4MH36dOzt7Tl27BjTp0+nevXqnDp1Cr1ez8yZM/H0TJtEfPXq1axatQorKysaN27MunXrOHbsGAB+fn4sWLCApKQkdDodo0ePpnbt2i/wpGaPqj1BhBAiP1EUhU+2nSMhRU+dcnb0qFXq+Td6xca1rUylYtbcj0vmw5/OYjDk7281hFDT3vN3uPkwgaIWxnStUULtOKow0mmZ91YN6pS1IzYplX7LjhPy4JHasYQQhZXKy3a7uroyevRoRo0axbFjx1i7di2zZs1iw4YN3L59m59//pmVK1dy4cKFDLc7efIk77//Ptu3b6dJkyZMmjQJgA0bNnDu3Dm2bNnCtm3bCA0NZdWqVQBMmzaN8uXLs3PnTlauXMmCBQsICAggODiYI0eOsHv3bnbs2MHKlSsxNjZm8uTJWFtbs3379mcWQI4dO8b333/PDz/8wI4dO1izZg329vYAREVF4eHhwfTp05kwYQLTp09Pv9348ePZsmULO3fupGbNmsyfPz9935UrV/D19WXHjh307t2bb7/9FoDg4GAWLVrEunXr2LJlC48e/fPvR1hYGPPnz+eHH35gy5YtfPXVV3zwwQekpKS83JOUBXlidRghhMgPtp25xV+X7mFipGVGV888+a2wmbGOuW9Wp8O8g/hdvs/Sg9cZ3Dh/rewgRF6x9GBaL5DedctgZpy/ZsjPSWbGOpb0r0WPRUcJCo+h99JjbB5aH6c81BNOCFHwaTQayqxZjZKQkOn++BMnCHtnyHPPU2rxIixq1Xry/Obmz+wF8lj79u05duwYgwYNYsWKFdjZ2XHs2DE6deqEkZERRkZGtGvXjpMnT6bfpmbNmpQvn/Z+rEePHsydOxdFUThy5AjdunXDxMQEgDfeeIP169fz9ttvc+TIEcaOHQuAvb09r732GkeOHOGtt94iNTWV8ePH4+PjQ5MmTdBqs9634cCBA3Tq1Cl99VVzc/P0fRYWFrRo0YIzZ85QrVo1wsLC0vft3LmT7du3k5KSQmJiYobVWsuVK5fe86N69eosW7YMAH9/f5o0aZJeZHlcKAH466+/CAkJoVevXhny3blz54kFU3Ka9AQRQogseBCXxJSdaVX9US0q5ullaCs6W/Npew8Avvg1mMCb0SonEiL/ORMWxYmQhxjrNPSpW0btOKqzMTNm5cDalLG3ICwygb7L/ImOz/1v64QQ4t80Gg1aC4tMfywbNMCoWLGn9xbRaDAqVgzLBg0yvX1WCiAAqampXL58mSJFihARkTYHm6IoWb79vz3vdv/dp9FosLa25ueff6Z9+/Zcu3aNjh07EhIS8sLXzszjYgyAVqtFr09bcfDEiROsWbOGJUuWsHPnTsaOHUtSUtJzb/e8+9eoUSO2b9+e/uPn55frBRCQIogQQmTJ1F0XeBifQqVi1ryTD3pWvFWnNG2qFCNFrzBy/WkeJaWqHUmIfOVxL5CO3iWkx8PfnKzN+HGgD47WpgTfiWXQyuMkJMuS3EKIvEGj0+E8ftzfv/zng/ffvzuPH4dG93I9+77++mvKlSvH6tWrmTlzJiEhIfj4+LBjxw5SU1NJSkril19+yXCbU6dOcf162r8rGzdupG7dumg0GurXr8/WrVtJTk4mNTWVTZs2Ub9+fQDq1avHhg0bAIiMjGTfvn3UrVuXyMhI4uPjadiwIR988AElS5bkypUrWFlZkZiYSGrqs9/zNW/enO3bt3Pv3j0AEhISSHhK75rHYmJisLKywtbWluTk5PRcz+Pj48Off/5JZGQkAFu3bk3f16BBA/z8/Lh06VL6toCAgCyd92XJcBghhHiO/Rcj2HbmNloNzPT1wliX9+vHGo2Gmb6enL0ZxfX7j5i04zxfdfdWO5YQ+cKtqAR2B4YDhWtZ3KwobW/BqoF1eGPREU6EPOR/a06yuG+tfPG6KIQo+GxatYLvvuXu9BkZlsk1cnbGefy4tP0vYf/+/fj5+bFx40bMzc0ZM2YMo0aNYvXq1QQHB9OuXTuKFSuGh4cHiYmJ6berXbs28+bN48qVK1hbWzNr1iwgbWhMWFgYXbt2BaBOnTr06dMHgAkTJjBp0iQ6dOiAoigMHToULy8vzp8/z6effkpKSgqKolC9enUaN26MsbExHTp0oEOHDpibmz91XpDatWszbNgwBg4ciEajwdjYOMPEqJlp3LgxO3bs4PXXX8fZ2ZkaNWqkT+b6LJUqVeLtt9+mR48eODo6UrduXaytrQEoW7YsX375JRMmTCAxMZGUlBQ8PDz4+uuvn/9EvCSNkt/XAvqX+Ph4goKCqFy5MhYWFmrHESrT6/Xp49l0L1nxFYXXo6RUWn3zF7eiEhjUsFz6MJOcktvt9Ni1B7z5w1EMCnzXsxqdqhXOyR3Fyylsr6czdgex6K9r1He1Z+3gumrHyZOO34ikz9JjJKYY6FK9BF9391Z9nqTC1k5F/iNt9NVR9HriT5wk9d49jBwdsahV86V7gDxPXFwcVlZWJCcnM2zYMNq0aUP37t1z9Zq5Iafb6ePHBdJWngkJCeGrr7566fNmJqv1AOkJIoQQz/D13kvcikqgZFFzPmzlpnacF+ZT3p7hzSsy5/fLTNh6juqlilLaXorEQjzNo6RU1vqHAtIL5Flql7VjQa8aDF51kq2nb1HEwpiJ7T2yNSZeCCFymkanw9In86Vwc8uAAQNITk4mKSmJ+vXr06VLl1d6/bzq66+/5tSpU6SkpFCyZEmmTp2qdiQpggghxNOcDn3I8sNp4zc/7+KJhUn+fMkc2bwCh6/c50TIQ0auP83GofWk67oQT7HxRBixiamUd7CkmbuT2nHytOaVnPmquxfvbzjL8kM3sLc0YXjzimrHEkIIVWzcuFHtCBkMHTqU8PDwDNtsbGz48ccfX2mOx8sB5yX58x29EELksuRUA+O2BKIo0LV6CZq4OaodKduMdFq+7VmN17/z40xYFN/uu8THrSupHUuIPEdvUFh26AYAAxqWU314R37QpXpJHj5KYcquC3y19xJFLU3o5SOr6QghhNq+//57tSPkWfJVoBBCZGLxX1cJvhOLnaUJE3J4HhA1lCxqwcyuXgAsOHCVw1fvq5xIiLxnX9BdQiPjKWJhjG8NmT8nqwY2LMfwZhUAmLDtXPqkskIIIUReJEUQIYT4jysRccz5/QoAE9t7YGdp8pxb5A/tvIrTo1YpFAXe33CGyEfJakcSIk9Z6pc2/O2tOqXz7fA3tXzYyo23fEqjKDBq/WkOXpZCqxBCiLxJiiBCCPEvBoPC+C2BJOsNNHFzpFM1F7Uj5ahJHT0o72jJ3ZgkRm8KoAAtECbESwm4GYX/jUiMdRr61S+rdpx8R6PRMLVTVdp6FiNFr/DOjyc4ExaldiwhhBDiCVIEEUKIf1l3PBT/G5FYmOj4vEvVArfSgYWJEXPfrI6JTsu+oLusPhqidiQh8oSlB9N6gbT3csHZxkzlNPmTTqvhmx7VaFjBgfhkPQOW+3MlIlbtWEIIIUQGUgQRQoi/3YlOZObuYAA+auVOyaIFcynZKi62jH09bWLUqT8HEXwnRuVEQqgrPDqBnwPS5rGQZXFfjqmRjkV9auJd0paH8Sn0WerP7agEtWMJIYQQ6aQIIoQQf5u4/RyxSal4lypS4LvDD2hQlmbujiSnGhi57jQJyXq1IwmhmpWHQ0g1KPiUs6NqCVu14+R7lqZGLB9QB1dHS8KjE+mz9JjMQSSEECLPkCKIEEIAe86Fs/fCXYy0Gmb5eqIr4EtjajQavuzujYOVKZfuxjHt5wtqRxJCFY+SUll7LG1Y2NuNyqucpuCwszThx0E+uNiacfXeIwYs9ycuKVXtWEIIIYQUQYQQIjohhU+3nwdgWFNXKhWzUTnRq+FgZco3PbwBWHMslD3n7qicSIhXb/Opm8QkplLW3oIWlZzUjlOguBQxZ9UgH4paGHP2ZjRDfzxJUqr0OhNCCKEuKYIIIQq9mb8EcS82ifKOlrzbrILacV6pRhUdGdI47dvvMZsDZOy+KFQMBoVlf0+IOrBhObQFvAeYGio4WbFiQB0sTHQcvHKf9zecQW+QVamEEEKoR4ogQohC7cjVB6zzDwNgZlcvzIx1Kid69T5s5Y5XSVuiE1LkA4ooVH4PjuDGg3hszIzwrVFS7TgFlnepIizuUwsTnZbdgXf4dPs5WZ5bCCGEaqQIIoQotBJT9IzfGgjAWz6lqVPOTuVE6jAx0jKnZ3UsTXQcux7Jgv1X1I4kxCuxxO8aAG/5lMHS1EjlNAVbw4oOfNuzGhoNrD0WyuzfLqkdSQghRCElRRAhRKE15/fLXL//CCdr0/QlYwursg6WTOlUFYBvf7/MyZBIlRMJkbvO3Yrm2PVIjLQa+tUvo3acQqGtZ3GmdU57nZn7x5X0oUhCCCHEqyRFECFEoXThdgyL/kr7Fnhq56rYmBmrnEh9XWuUoHM1F/QGhZHrzhCdkKJ2JCFyzdK/P4C38ypOcVtzldMUHr18yvBRKzcApuy6wNbTN1VOJIQQorCRIogQotBJ1RsYuyUAvUHh9arFaF2lmNqR8gSNRsPUzlUpbWfBragEPtkaKOP2RYF0JzqRnWdvAzCoYTmV0xQ+7zarwMAGaY/7RxsD+CP4rsqJhBBCFCZSBBFCFDorDt8g4GY01mZGTO5YRe04eYq1mTHf9ayGkVbDroBwNp6Qb2lFwbPqyA1SDQp1ytrhVbKI2nEKHY1Gw4R2lelSvQR6g8L/1pzixA0ZgieEEOLVkCKIEKJQCYuM5+u9aRPyfdK2Mk42Zionynuqly7KB393V5+04zxX78WpnEiInBOfnMpa/1AABjWSXiBq0Wo1fNHNi+aVnEhMMTBwxXGCwmPUjiWEEKIQkCKIEKLQUBSF8VsDSUjRU7e8HT1ql1I7Up41tLEr9V3tSUjRM3LdaZJS9WpHEiJHbD51i6j4FErbWdCysrPacQo1Y52W+W/VoFaZosQkptJ3mT+hD+LVjiWEEKKAkyKIEKLQ2Hr6Fn6X72NipGVGVy80Go3akfIsrVbDNz2qUdTCmPO3Y/hiz0W1Iwnx0gwGheV/T4g6sEFZdFp5DVCbuYmOpf1rU6mYNfdik+iz7BgRsYlqxxJCCFGASRFECFEo3I9LYsquCwCMalGRcg6WKifK+5xtzPiymzeQtpLG/osRKicS4uXsvxjBtfuPsDYzonst6QmWV9iaG7NqYB1K2ZkT8iCefsuOy+pUQgghco0UQYQQhcLUXReIik+hUjFr3mlcXu04+UZLD2f61y8LwEc/nZVvaEW+9nhZ3LfqlMbS1EjlNOLfnGzMWD3IBwcrU4LCYxi88gSJKTIMTwghRM6TIogQosDbHxzB9jO30Wpglq8Xxjp56XsRY1+vRKVi1jx4lMyHP53FYJBlc0X+c/52NIevPkCn1dDv78KeyFvK2FuycmBtrE2N8L8RyfC1p0jVG9SOJYQQooCRTwJCiAItLimVT7YGAjCwQTm8SxVRN1A+ZGasY+6b1TEz1uJ3+X76t+lC5CfLDt4AoK1ncVyKmKsbRjxVFRdblvSrhamRln1BEYzZHCiFVyGEEDlKiiBCiALtq18vcjs6kZJFzdOXfRUvrqKzNRPbVwHgi1+DCbwZrXIiIbIuIiaRHWdvATCooSyLm9f5lLdn/ls10Gk1bD51kxm/BKEoUggRQgiRM6QIIoQosE6FPmTlkRsATO/iiYWJzAHwMt6sU4o2VYqRolcYse4UcUmpakcSIkt+PBpCil6hVpmiVJPeYPlCSw9nvvD1AuAHv+t8/+c1lRMJIYQoKKQIIoQokJJTDYzbHIiiQNcaJWjs5qh2pHxPo9Ew09cTF1szbjyIZ9L282pHEuK5ElP0rD4aAkgvkPzGt2ZJJrSrDMCsPcGs9w9VOZEQQoiCQIogQogCadGfV7l4NxZ7SxM+beehdpwCo4iFCd/2rI5WA5tP3WT7mVtqRxLimbacusXD+BRK2ZnTqkoxteOIF/R2o/IMa+oKwPitgew5F65yIiGEEPmdFEGEEAXOlYg45v5xBYCJHTwoammicqKCpU45O4Y3rwjAhK3nCH0Qr3IiITJnMCgsPZg2jKJ//XLotBqVE4nsGN3anZ61S2FQYOS6Mxy+cl/tSEIIIfKxbA2Qv3nzJidOnODWrVskJiZiZ2dH5cqVqV69OqampjmdUQghssxgUBi3JYBkvYFm7o509HZRO1KBNLJ5BQ5fuc+JkIeMXH+ajUPrydLDIs/589I9rt57hLWpEW/UKql2HJFNGo2Gz7t4EhWfwp7zdxi86gTr36mHZ0lbtaMJIYTIh16oCLJz505+/PFHAgICsLe3x9nZGVNTU6KjowkNDcXU1JQOHTowePBgSpQokVuZhRDiqdb6h3L8xkMsTHRM6+KJRiPf/OYGI52Wb3tW4/Xv/DgTFsW3+y7xcetKascSIoPHyzn3qF0KazNjldOIl6HTavi2ZzUGrjjO4asP6Lfcn41D6+HqaKV2NCGEEPlMlr+269KlC8uXL6djx4788ccfHDp0iC1btrBu3Tp2797NqVOnWLBgAQaDAV9fX3755ZfczC2EEE+4E53IzF+CAfi4tTslipirnKhgK1nUgpld01ZvWHDgKoevShd1kXcEhcdw8Mp9tBro36Cs2nFEDjAz1rG4by08S9gS+SiZvkv9CY9OUDuWEEKIfCbLRZBRo0axZcsWevfujYvLk93LTUxM8PHxYcqUKezevVt6ggghXilFUZiw7RxxSalUK1WEvvXKqh2pUGjnVZyetUuhKPD+hjNEPkpWO5IQACz7uxfI61WLU7KohcppRE6xMjVixYDalHew5FZUAn2X+vPwUTJ6g8LRaw/wC03g6LUH6A2K2lGFEELkUVkeDtO0adMsn9TOzg47O7vs5BFCiGz55dwd9gXdxUirYZavl0yA+ApN7ODB8RuRXL33iNGbAvihb00ZhiRUFRGbyPYztwEY1EiWxS1o7K1MWTWoDt0WHuFyRBxdFhwiIUXP3ZiktAOOHae4rRmTOnjQpmpxdcMKIYTIc7LcEyQuLi7LP0II8SpFx6cwcft5AP7X1BX3YtYqJypcLEyMmPNmdUx0WvYF3eXHoyFqRxKF3OqjoSTrDVQvXYQapYuqHUfkgpJFLfhxUB0sTHTceBD/TwHkb3eiExm2+pQsqSuEEOIJWe4JUqtWred+s6coChqNhqCgoJcOJoQQWTXjlyDuxyXh6mjJu80rqB2nUKriYsvY1ysxZdcFpv0cRJ1ydlQqZqN2LFEIJaboWf13Ie7thuVVTiNyU3lHK8yNdcQn65/YpwAaYPLOC7zmUUx6BwohhEiX5SLIqlWrcjOHEEJky+Gr91l/PAyAmb5emBrpVE5UeA1oUJaDV+7zR3AEI9edZvu7DTE3kedDvFrbTt8i8lEyJYqY07qKs9pxRC7yvx7Jg2fMQ6QA4dGJ+F+PpJ6r/asLJoQQIk/LchGkTp06uZlDCCFeWGKKnvFbAgHoXbc0tcvKXERq0mg0fNnNizbf+XHpbhzTfr7A51081Y4lChFFUdKXxR3QoCxGuiyP+hX5UERsYo4eJ4QQonDIchHkv6Kjo9m0aRNXr15Fo9Hg6upK165dKVKkSA7GE0KIp/vu98vceBBPMRszRreppHYcQdqEhbPf8KbPUn/WHAulUUVH2lQtpnYsUUj8dfk+lyPisDTR8UbtUmrHEbnMydosR48TQghROGTrKxJ/f3+aN2/Ojz/+SExMDNHR0fz444+0aNECf3//nM4ohBBPOH87msV/XQNgSqcq2JgZq5xIPNaooiNDmqTNxTBmcwC3oxJUTiQKiyV+aa8JPWqXlteEQqBOOTuK25rxtNk+NEBxWzPqlJNegkIIIf6RrSLIlClTaNu2Lb///jvz5s1j3rx57Nu3j3bt2jFlypScziiEEBmk6g2M3RyI3qDQ1rMYrapIT4O85sPX3PEqaUt0QgrvbziD3qCoHUkUcBfvxOJ3+T5aTdpQGFHw6bQaJnXwAMi0EKIAkzp4yKSoQgghMshWESQ0NJQBAwag0/0z4Z1Op6N///6EhobmWDghhMjM8kM3CLwVjY2ZEZ91rKJ2HJEJEyMtc3pWx9JEx7HrkSzYf0XtSKKAW/b3XCCtqxSjlJ2FymnEq9KmanEW9q5BMdsnh7y4OlrSykOK5EIIITLKVhHEw8ODa9euPbH92rVrVK5c+aVDCSHE04Q+iOfr3y4C8Em7yjLWOw8r62DJ1M5VAfj298ucDIlUOZEoqO7HJbH1zC0ABjUsp3Ia8aq1qVqcg2Oas2ZQbd7zsWVOD28sjLVcvfeIH/9eLlkIIYR4LMsTowYHB6f/d9++ffn8888JCQnB29sbgLNnz7JmzRo++uijnE8phBCkrfwwfmsgiSkG6pW3541aMvFhXte1Rkn+unSPbWduM3LdGXaPaoStuczVIHLW6qMhJKca8C5VhJpliqodR6hAp9VQt7w9ZjHmVPMqTnRiKp9uP8+sPcE0r+QkvYOEEEKky3IRpHPnzmg0GhTln3HdX3755RPHffjhh7Rt2zZn0gkhxL9sOXWLg1fuY2qkZXpXTzQaGeedH0ztXJVToVGERsbzydZA5r5ZXZ47kWMSU/Ss/vvb/rcblpO2JQDo5VOGnQHh+F+PZPzWQFYNrCNtQwghBPACRZDff/89xy9+/Phxli5dyrlz57h37x7z58+nZcuW6fvHjh3L1q1bM9zG29ubn376KcezCCHytvtxSUz9+QIA77V0o5yDpcqJRFZZmxkz583qdFt4mF0B4TSu6CjLl4ocs+PMbe7HJeNia8brshyz+JtWq2GWrxdtvv0Lv8v32XjypvQeFEIIAbxAEaREiRI5fvH4+Hjc3d3p2rUrI0aMyPSYRo0aMWPGjPTfjY2lG7UQhdGUnReIik/Bo7gNbzeSMf/5TbVSRfiglRtf7LnIpB3nqVm2KK6OVmrHEvmcoigs/XtC1P4NymKky9ZUZ6KAKudgyQevuTHjl2Cm7rpAEzdHnG1kHikhhCjsslwE+a9r166xevVqrl69ikajoXz58vTu3Zvy5ctn+RxNmjShSZMmzzzGxMQER0fH7MYUQhQAfwTfZcfZ22g1MMvXC2P5oJMvDW3syqEr9zl05QEj1p5m67v1MTXSPf+GQjzFwSv3uXg3FgsTHT1ql1Y7jsiDBjUsx8+B4QTcjGbCtnMs7lNThsUIIUQhl61PEnv27KFDhw6cP3+eSpUq4e7uzoULF+jQoQO//PJLjgb09/enXr16tG7dmgkTJvDgwYMcPb8QIm+LS0plwtZzQNqbWc+StionEtml1WqY/UY1iloYcyE8hi/2XFQ7ksjnlvil9QJ5o1YpmXBXZMpIp+WLbl4Y6zT8duEuuwLC1Y4khBBCZdnqCfLll1/yzjvvMGrUqAzb58yZw1dffcXrr7+eI+EaN25MmzZtcHFx4ebNm3z33Xf069ePLVu2YGJi8tTb6fV69Hp9jmQQ+dfjNiBtIX/7Yk8Qt6MTKVXUnJHNXQvc81nY2qmDpTFf+Hoy+MdTLD14nfrl7WjqLr398rq82E4vR8Tx56V7aDTQt27pPJVNqONp7bSioyX/a+LKd39cYdKO89QtVxQ7y6e/jxQit+TF11Ih/is/t9OsZtYo/17uJYu8vb3ZsWMHZcqUybD9xo0bdOrUibNnz77oKXF3d39iYtT/ioiIoHnz5syePZtWrVo9sT8+Pp6goKAXvrYQIm+6+CCZT/6IRAEmNi6Kt7Op2pFEDll6OobdV+KxMdUyu5U9Rc1kWIx4MQtPRLPvegI+JUwZXV+WxRXPlmJQGL3vAaHRqTQqbcZ7PkXUjiSEECKXVK5cGQuLpy+Nnq2eIHXq1OHEiRNPFEFOnjxJrVq1snPKLHFycsLFxYUbN2488zg3N7dn3mlROOj1egIDA/H09ESnkw9Y+U1yqoGx8w+jAF2ru9CvtZfakXJFYW2nX1XRc+37owTfiWVlkIFl/Wqg1co4/bwqr7XTB4+S8dt6AID323pTrayduoFEnvC8dvqdUzS+3x/BLzSRvk1caF7JSYWUojDLa6+lQmQmP7fT+Ph4Ll269NzjsrVEbvPmzfnqq684f/483t7eAJw9e5Y9e/Y8dZWXnPDw4UPCw8Nxcnr2P1o6nS7fPWEi90h7yJ8W77/G5Yg47C1N+LR9lQL/HBa2dmqh0zH3zep0mHcQvysPWH4khHcau6odSzxHXmmn64/fJCnVgFdJW3zKO8hElyKDp7XT6mXsGNyoPIv+usan2y/g4+qAjZnMJSNevbzyWirEs+THdprVvFkugrz77rtPbFu7di1r167NsG3KlCm8+eabWTrno0ePCA0NTf/95s2bBAUFYWtri62tLfPmzaNVq1Y4Ojpy69YtvvnmG4oWLfrMITNCiPzvSkQs8/dfAWBSxyoUlbHbBVJFZ2smtq/C+K2BfPnrReqVd5CJb8VzJaXqWXUkBEibLFkKIOJFvP+aG3sv3OX6/UfM2B3EjK4Fs5ehEEKIp8tyESQ4ODjHL37u3Dn69u2b/vuMGTMA6NKlC5999hmXLl1i27ZtxMbG4ujoiI+PD9988w1WVlY5nkUIkTcYDApjNweSrDfQvJITHbyKqx1J5KI365TC7/I9fjl3hxHrTrFrZCOsTLO9ersoBHacuc39uCSK2ZjR1lNeH8SLMTPWMbOrJz0WH2WdfxgdvFyoX8FB7VhCCCFeIVXfafr4+HDx4tOXSFy6dOkrTCOEyAvW+IdyIuQhliY6pnauKt/yFnAajYaZXb04GxbFjQfxTNp+nq/f8FY7lsijFEVh6cG0ZXH71S+LsU6rciKRH/mUt6dP3TL8eDSEMVsC+PW9xliYSPFVCCEKC3n3IITIM8KjE5j1S1qvs9FtKlGiiLnKicSrYGthzLc9q6PVwOZTN9l+5pbakUQedfjqA4LvxGJurOOtOqXVjiPysTGvp/0bExaZwJe/Pv0LOSGEEAWPFEGEEHmCoih8uu0ccUmpVC9dhN51yzz/RqLAqFPOjhHNKwIwYes5Qh/Eq5xI5EWPe4F0r1USWwuZ0FJkn5WpEdO7egKw4vANToZEqpxICCHEqyJFECFEnrA78A77giIw1mmY5euFTpZLLXRGNK9ArTJFiU1KZeT606ToDWpHEnnIlYg4/giOQKOBAQ3KqR1HFABN3BzpVrMkigKjNwWQmKJXO5IQQohXQIogQgjVRcUnM2nHOQCGNa2Am7O1yomEGox0Wr7tWQ0bMyPOhEXx7b7nr/MuCo/lh9J6gbSo5Ew5B0uV04iC4tN2Hjham3L13iPm/nFZ7ThCCCFegWwXQQwGA9evX+fEiRMcP348w48QQryI6buDuB+XjKujJe82c1U7jlBRyaIWzPRNW7JywYGrHL5yX+VEIi94+CiZzaduAvB2I+kFInKOrYUxUztVBeD7P69x7la0yomEEELktmxNhX3mzBk+/PBDbt++jaIoGfZpNBqCgoJyJJwQouA7fOU+P51I+3Azy9cLUyOdyomE2tp6Fqdn7VKsPx7G+z+d4ZdRjbGzNFE7llDRWv9QElMMVHGxwaecndpxRAHTpmox2nkW5+fAcEZvCmD78Aay8pAQQhRg2SqCTJo0iapVq7J48WIcHR1lCUshRLYkpugZtzUQgD51y1CrrHy4EWkmdvDg+I1Irt57xOhNAfzQt6b8W1NIJacaWHn4BpDWC0TagcgNn3WswqGr97kQHsPiv67xbrMKakcSQgiRS7JV5g4JCeGDDz7A1dUVGxsbrK2tM/wIIURWfLvvMiEP4ilmY8boNu5qxxF5iIWJEXPfrIGJTsu+oLv8eDRE7UhCJbsCbhMRm4STtSntPF3UjiMKKEdrUyZ18ADgu32XuRIRq3IiIYQQuSVbRRAvLy9CQuQNqRAi+87diuYHv2sATO1cFWszWe5SZOThYsO4tpUAmPZzEMF3YlROJF41RVFY4pc2IWq/+mUxMZIhCiL3dK5WgmbujiTrDYzeFIDeoDz/RkIIIfKdbL2b6NOnD7NmzWLLli2cO3eO4ODgDD9CCPEsqXoDY7ekvcFs51Wc1zyc1Y4k8qj+9cvSvJITyakGRq47TUKyLGFZmBy9FsmF8BjMjLX08imtdhxRwGk0Gj7v4omVqRGnQqPSh2EJIYQoWLI1J8iIESMAGD9+fPo2jUaDoigyMaoQ4rmWHbrOuVsx2JgZpXc/FiIzGo2GL7t50eY7Py7djWPazxf4vIun2rHEK7L0YFpvsW41S1LEQibHFbnPpYg549pW4pOt5/jy14u0rOxMaXsLtWMJIYTIQdkqgvz+++85nUMIUUiEPHjE7N8uATChnQdO1mYqJxJ5nb2VKd+8UY0+y46x5lgojSo60qZqMbVjiVx27V4c+4IiABjQQJbFFa/Om7VLs+tsOEeuPWDslgDWvO0jE/IKIUQBkq3hMCVKlHjmjxBCZEZRFMZvDSQxxUB9V3u61yqpdiSRTzSs6MA7jcsDMGZzALejElROJHLb8kM3AGhRyQlXRyt1w4hCRavVMNPXEzNjLYevPmD98TC1IwkhhMhB2Z5hLDQ0lKlTp9K/f38GDBjAtGnTCA0NzclsQogCZtPJmxy68gBTIy3Tu3jKN2vihXz4mjveJW2JTkjh/Q1nZNLCAiwqPplNJ28CMKih9AIRr14Ze0s+apW2atn0n4MIj5bCqxBCFBTZKoL4+fnRtm1bAgICcHd3p2LFipw9e5Z27dpx6NChnM4ohCgA7sUmMe3ntPmC3n/NjbIOlionEvmNiZGWOW9Wx9JEx7HrkSzYf0XtSCKXrPUPJSFFT+XiNtRztVc7jiikBjQoR/XSRYhNSmXC1nMoihRehRCiIMhWEeTrr7+mf//+bNy4kXHjxjF+/Hg2btxIv379+Oqrr3I6oxCiAJiy6wLRCSlUcbHhbflmV2RTGXtLpnauCsC3v1/mZEikyolETktONaSvyvF2w3LSY0yoRqfV8IWvFyY6Lb8HR7Dj7G21IwkhhMgB2SqCXL16lW7duj2x3dfXlytX5Js5IURGvwfdZefZ2+i0Gmb5emGky/ZIPCHoWqMkXaqXQG9QGLnuDNEJKWpHEjlod2A4d2OScLQ2pYO3i9pxRCFX0dmaEc0rAPDZjvPcj0tSOZEQQoiXla1PInZ2dpkugxsUFIS9vXRbFUL8Iy4plQnbzgFp3+pWLWGrciJREEzpVIXSdhbcikpg/NZA6aZeQCiKwpK/l8XtV68MJkZSMBXqG9rUlcrFbXgYn8JnO86rHUcIIcRLyta7i+7duzNx4kQWL17MiRMnOHHiBIsXL2bSpEm88cYbOZ1RCJGPfbknmPDoRErbWfBeSze144gCwtrMmDlvVsdIq+HngHA2nripdiSRA/yvR3LuVgymRlre8imjdhwhADDWafmymxc6rYZdAeHsPX9H7UhCCCFeglF2bvTuu+9iZWXFsmXLmD17NgBOTk4MHz6cvn375mhAIUT+dTIkklVHQwCY3sUTcxOdyolEQVKtVBE+bOXOrD3BTNpxnppli8pSqvnckoPXAfCtWRI7SxOV0wjxj6olbHmncXkWHrjKhG3n8Clnj62FsdqxhBBCZEO2iiAajYb+/fvTv39/4uLiALCykjeeQoh/JKXqGbM5EEWBbjVL0rCig9qRRAE0pHF5Dl65x6ErDxix9jSbhtXjbFg0EbGJOFmbUaecHTqtTKyZH9y4/4h9QXcBGNhAJk8Wec+oFhX59fwdrt17xLSfL/Bld2+1IwkhhMiGbBVB/k2KH0KIzCw8cJUrEXE4WJnwSdvKascRBZRWq2H2G9V4/Ts/LoTHUHvaPh4l69P3F7c1Y1IHD9pULa5iSpEVyw9dR1GgmbsjFZzkvYXIe8yMdXzh60X3RUfYePImHbxdaOzmqHYsIYQQLyjLRZAuXbqwYsUKbG1t6dy58zOXrNu6dWuOhBNC5E+X78Yyf3/aSlGTOlShqHRrF7nI2caMnrVLseDA1QwFEIA70YkMW32Khb1rSCEkD4uOT2HjybR5XQY1LK9yGiGerlZZO/rVK8uKwzcYtyWQve83xtL0pb9TFEII8Qpl+VW7RYsWmJikfZBp2bJlrgUSQuRvBoPC2C2BpOgVWlRyor2XfPAUuUtvUNh6+lam+xRAA0zeeYHXPIrJ0Jg8at3xUOKT9VQqZk2DCrLKnMjbPm7tzr6gu9x8mMAXe4KZ3Kmq2pGEEEK8gCwXQYYPH57pfwshxL+tORbCyZCHWJromNq56jN7jQmRE/yvRxIenfjU/QoQHp2I//VI6rnKB+y8JkVvYOXhGwAMbFhOXjNEnmdpasTMrl70XnqMlUdCaO/tQu2ydmrHEkIIkUXZWiJXCCEyczsqgVl7LgIw5vVKuBQxVzmRKAwiYp9eAMnOceLV2h0YTnh0Ig5WpnSq5qJ2HCGypGFFB3rUKgXAmE0BJKbon3MLIYQQeUWWe4LUrl07y9/O+Pv7ZzuQECJ/UhSFT7edIy4plRqli9Dbp4zakUQh4WRtlqPHiVdHURSW/r0sbp+6ZTA1kmW0Rf4xvl1lDlyK4Nr9R3y77zJjX6+kdiQhhBBZkOUiyPjx43MzhxAin/s5MJzfgyMw1mmY5euFVuZeEK9InXJ2FLc14050IsozjlvvH4J7MWvsZKLePONEyEMCbkZjYqSld93SascR4oXYmhszrbMng1ed4Ae/a7TzLI5nSVu1YwkhhHiOF1odRgghMhMVn8xnO84D8G6zClR0tlY5kShMdFoNkzp4MGz1KTSQoRDy+HcNsP1sOH5XHvBZxyp08Couc0/kAUv90nqBdK1eAnsrU5XTCPHiXvNwpoO3CzvP3ubjTWfZMbwhJkYy2lwIIfKyLL9Kx8XFZflHCFG4fP5zEPfjkqngZMWwpq5qxxGFUJuqxVnYuwbFbDMOeSlma8b3vWuw7d0GuDtbE/komZHrTjN41UnuPGMyVZH7Qh484tcLd4C0CVGFyK8+6+CBnaUJwXdiWXjgqtpxhBBCPEeWe4LUqlXrud+aKYqCRqMhKCjopYMJIfKHg5fvs/HkTTQamOXrKWP6hWraVC3Oax7F8L8eSURsIk7WZtQpZ5e+LO7OEQ1ZeOAq8/ZfZl/QXY5df8AnbSvTo3Yp6RWiguWHbqAo0NjNETfpPSbyMXsrUz7rWIWR604zb/9l2lQthnsxadNCCJFXZbkIsmrVqtzMIYTIhxKS9YzfGgikTWpYs4wsESjUpdNqnroMromRllEtK9KmajFGbw7gbFgUY7cEsuPsbWZ29aK0vcUrTlt4RSeksPFEGABvSy8QUQB08CrOjjO32Rd0l9GbA9gyrH56AVYIIUTekuUiSJ06dXIzhxAiH/p23yVCI+MpbmvGx63d1Y4jRJa4F7Nmy7D6LD90na/2XuTw1Qe0+vZPPmrlzoAG5eSDyyuw4Xgoj5L1uDlb0aiig9pxhHhpGo2Gz7tU5dj1B5wNi2LZwesMblxe7VhCCCEykeU5QYKDgzEYDOn//awfIUTBd+5WNEv+XtpyWueqWJsZq5xIiKzTaTW83ag8v77XmHrl7UlMMTDt5yB8Fx7m0t1YteMVaKl6AysO3QBgUMNyMhRJFBjONmZMaFcZgK/2XuTG/UcqJxJCCJGZLPcE6dy5M4cOHcLe3p7OnTuj0WhQlCcXI5Q5QYQo+FL1BsZsDkBvUGjvVZwWlZ3VjiREtpSxt2TtYB/WHw9j+s9BnAmLot0cP0Y0r8jQJq6yykMu+OXcHW5HJ2JvaUKnaiXUjiNEjnqjVil2ng3n4JX7jNkcwLrBdWXJeCGEyGOyXAT5/fffsbOzS/9vIUThojco6RNOHrv2gPO3Y7A1N2ZShypqRxPipWg0Gt6sU5pm7k5M2BbIvqAIZv92id2B4czy9cK7VBG1IxYYiqKk9yDrXbcMZsYykbIoWDQaDTO6etLqm784dj2Stf6h9K5bRu1YQggh/iXLRZASJUpk+t9CiIJvz7lwJu+8QPh/lhTtVM0FR2tTlVIJkbOK2ZrxQ99a7AwI57Md5wm+E0uXBYcY3Kg877/mJh/Yc8Cp0IecDYvCRKeVD4aiwCplZ8HoNu5M3nmBmb8E07ySEy5FzNWOJYQQ4m9ZLoL81927dzl58iSRkZHpc4U81rdv35cOJoTIG/acC2fY6lM8OfgNfjwSQn1Xe9pULf7KcwmRGzQaDR29XWjgas+UXRfYfuY2i/66xq/n7zDT14u65TNfeUZkzdK/e4F0ri4FVFGw9atXll0B4ZwMecj4rYEs719b5r8RQog8IltFkM2bNzNp0iSMjY0pWrRohn0ajUaKIEIUEHqDwuSdFzItgDw2eecFXvMoJitqiALF3sqU73pWp4OXCxO2nePGg3h6Lj5KL5/SjH29kkwEnA1hkfHsOXcHgIGyLK4o4LRaDbN8vWg7x48DF++x9fQtutYoqXYsIYQQZLMIMmfOHN59912GDBmCViuTxgmR36ToDcQmphKbmEJsYioxiSnEJaZm2BablMqViNgnhsD8mwKERyfifz2Seq7yDbkoeFp6OFOnvB0zdgezzj+UNcdC+SM4guldPGlWyUntePnKisM3MCjQqKIDlYrZqB1HiFxXwcmKUS0q8uWvF5m88wINKzrgZG2mdiwhhCj0slUESUxMpF27dlIAEXmW3qBw9NoDjocmkGjzgLqujgWmp0JSqv7vYsW/ChaJKcRksi0uKfXvIkfG7Ykphudf6AVExD69UCKeTklJJn7XCmwCThEfWgOr9v3RGJuoHUv8h42ZMTO6etLBuzjjtgQS8iCeASuO07maCxM7VMHOUp6z54lNTGHD8TBAeoGIwuWdxuX55Vw4527FMGn7eRb2rql2JCGEKPSyVQTx9fVlz549vPPOOzmdR4iX9sQknseOU9zWjEkdPFSdu0JRFBJTDP8qWPxTpPinR0bGYsUTxY6kVJJTc66AYW6sw9rM6O8fY6zNjLD5+/+tzYx4+CiFTaduPvc88s3Wi4tZNp27838k9VHa7zfX/YnRtG9wfrcPNgPHqxtOZKq+qwN7RjVm9m8XWXrwOtvO3Mbv8n0md6pCO8/iMt7/GTYcDyMuKZUKTlY0qeiodhwhXhljnZYvfL3pOO8gv5y7wy+B4bzuKfNoCSGEmrJVBPnwww8ZMmQIfn5+uLm5YWSU8TTjxo3LkXBCvKinTeJ5JzqRYatPsbB3jWwVQhRFIT5Z/59eF08WKeKS0oaWPK2IkWp41uwaL8bK1OiJAkbaNmNs/rPd+l+FDRszY6xMjbAyM8JY9+zeXHqDwqGr97kTnZjpvCAa0lbUqFPOLsfuV2EQs2w6t75Y9fdv/3xwTn2kpG+XQkjeZG6i45N2HrTzcmHMpgAu3o1l+NrTbPe4zbTOVXG2kYLgf6XqDSw/dAOAQQ3LoS0gvfKEyCoPFxuGNXVl7h9X+HT7eeq52lPEQnqQCSGEWrJVBPn+++85ePAg5co92aVVvgkTannWJJ6Pt3267TxFLUyIT9b/q1iRee+LmH8VNuKSUtHnUAFDqyG9WPHfIsZ/Cxc2/yps/Hu/lanRKxneo9NqmNTBg2GrT6GBDI/t46tP6uBRYIYavQpKSjJ35//492//fdzSHuW783/Eus9HMjQmD6tWqgg7RzRk/v4rLDhwhd8u3OXotQdMaFeZN2qVkn8L/2XvhbvcikrAztKELtVLqB1HCFUMb16BPefucDkijim7LjD7jWpqRxJCiEIrW0WQFStWMH36dLp27ZrTeYTINv/rkc+cxBPgXlwSPRYfzfY1dFrNP8UI04zFivQCxRPDSzIWNixNdPnqA1KbqsVZ2LtGxiFGpPUAUXuIUX4Uv3vV30NgntYGNKQ+SjvOstPbrzCZeFEmRlref82N1z2LMWZTAGdvRjNmcyA7zt5mRhcvSttbqB0xT1jidw2A3j6lMTPWqZxGCHWYGumY1c0L34WH2XLqFh28XWjmLpMrCyGEGrJVBDExMaFGjRo5nUWIl5LVyTkdrEwobmueXsywMs28WPHvnhc2ZmnDR8yN81cBI6e0qVqc1zyK4X89kojYRJys04bASA+QF5cacjFLx93+cglFQuKxatYMsypV0MhE1HlWpWI2bPlfA5YdvM7Xv13k0JUHtP72Lz5q7U7/+mUL9d/JqdCHnAqNwkSnpXe9MmrHEUJVNUoXZWCDciw9eJ1PtgTy6/uNZbltIYRQQbaKIH379mX16tVMmDAhp/MIkW1FLbL2RmLumzVkOdds0Gk18ri9jNQkOLYI3aX1gNXzD78fzf0FC7m/YCE6RwesmzbDqlkzLOvVRWtunvt5xQvRaTUMblye1zycGbslgKPXIpm66wK7Am7zha8XFZ2t1Y6oiqUHrwPQsZqLTKAsBPBRK3d+u3CX0Mh4Zu0JZlpnT7UjCSFEoZOtIkhAQABHjx5l//79VKxY8YmJUefNm5cj4YTIqpsP4/ny12d/wy6TeApVKApc2A6/TUR5GEL0VdvHO8h8SIyCkbkBB88YHt2x5NFdC/T37hO1cSNRGzeiMTPDsl49rJo1xappU4ydpDt1XlLWwZK1b9dl/fEwpu8O4nRoFO3mHGR48woMa+r63MmIC5KbD+PZc+4OAAMbyLK4QkDa5MozfT1564djrD4aSnsvF+qWly8YhBDiVcpWEcTGxoZWrVrldBYhsuWvS/cYuf40UfEpWJjoiE/WyySeIm+4eQJ+/QTCjqIocCegODHXNfzTQP9bCElrtc5vNcbG9gpFb5/GoIf4+5bEJXkSey2R1DsRxO3fT9z+/QCYeXpi1awp1s2aYVqpUqEcrpXXaLUa3vIpTbNKjnyy9Rx/BEcw+7dL7A4M58tu3niWtH3+SQqAlYdvoDcoNKhgj4eLjdpxhMgz6rs68Gad0qzzD2Xs5gB+GdUYcxOZL0cIIV6VbBVBZsyYkdM5hHhhBoPCvP1X+GbfJRQFPEvYsqBXDc7fjpZJPIW6okJh32Q4twkARWdORHg9ooKCQaPBZdZMNPfPcXf+j39PkprGyFKD87t90pbHVRS4+jvaP7/ESncUK47iXMaIJKd2xMW7E3ssgMSAABIDA0kMDOT+nLkYFS+OdbOmWDVrhoWPD1oTWV1GTcVtzVnarxY7zt5m8s4LBN+JpdP8gwxuXJ73W7oV6ElC45JSWe8fBqQtiyuEyGhc20rsD47gxoN4Zv92kU/aeagdSQghCo1sFUGEUFtUfDLvbzjD/ov3AHizTmkmdfDAzFhHKTsLXvMoxtGr9zh+7hK1q7pR19VReoCI3JcYAwe/gSPzQZ8EaFC8enLvoguRf6wDoPjUKdh27Ah0xLrPR8TtWsGdgFMU86qBVfv+/yyLq9FAhZbg2gJuHIS/vkRz/U/M7m7HTKPFoUc3Uj5bStz5W8TtP8Cjw4dJDQ/n4dp1PFy7Dq2FBZYNGmDVvDlWTRpjZCfDwNSg0WjoVK0EDSs48NnOC+w8e5tFf15j7/m7zOzqiU8B7Qb/0/EwYpNSKe9oSVM3GbIlxH/ZmBkzvWtVBq44wdKD12nn5UK1UkXUjiWEEIVClgcnDxo0iFOnTj33uLi4OBYvXsyaNWteKpgQT3PuVjTt5x5k/8V7mBpp+aKbFzO6emb4VlWn1VC3vD2NSptTt7y9FEBE7tKnwollMLcGHJydVgAp2wjeOcD92548WJlWAHGe+ClFunVLv5nG2ASLjoOIaT8Yi46D/imA/JtGA+UaQb8dMOg3qNgKFAME/oTxT20pquyk1KdDcDt6hJILF1DkjTcwcnTEEB9P7G+/ET5uHJcbNOTGm29x/4cfSLpyBUVRnryOyFX2VqbMfbM6P/SthbONKdfvP6LH4qN8uu0csYkpasfLUXqDwvLDaROiDmxQDq28/gqRqeaVnOlSvQQGBUZvOktSql7tSEIIUShkuSdImzZteO+997C0tKR58+ZUrVoVJycnTE1NiYmJ4cqVK5w8eZK//vqLpk2bMnr06NzMLQqpn46HMWH7OZJTDZSyM2dhr5pULVE4xteLPOryPtj7CdwLTvvdzhVaTQP317n/wxLu/z1RtNPYMdi99dbLXatUHei1EW6fgb++hOBdaZOuXtiO1u11rBt/jHWzySiGSSSev0Dc/v3E7t9PUlAQCadPk3D6NPe+no1xqVJYN09bbcaiZk00xrJE46vymoczPuXtmLE7iHX+Yfx4NITfg+7yeVdPmrkXjB4Tv124Q1hkAkUsjPGtUVLtOELkaRPbe+B3+R6X7sYxf/9VPnjNTe1IQghR4GW5CNK9e3c6derEr7/+yu7du9m4cSMxMTFAWnffChUq0LBhQzZv3kz58uWzdM7jx4+zdOlSzp07x71795g/fz4tW7ZM368oCvPmzWPDhg3ExMTg7e3NxIkTqVix4gveTZHfJabo+WzHedYfTxtj3rySE9+8UQ3bLC6LK0SOu3se9k6Aq3+k/W5eFJqMhVoDwciEBytWcG/2bAAcP/gA+/79c+7aLtWg5xq4ewH8voJzW+DSL2k/rs3RNB6NuWc9zD2r4jhyBCm3bxN74ABx+w8Qf/QoKWFhRK5cReTKVWitrbFq1AirZs2watwIna0UFXObjZkxM7p60cHLhbFbAgmNjGfA8uN0rV6CT9t7UNQyf8/lssQvrRdIL5/SMtmjEM9R1NKEyR2r8u7aUyzYf4XXqxajcnGZSFgIIXLTC80JYmJiQocOHejQoQMAsbGxJCYmUqRIEYyz8U1ifHw87u7udO3alREjRjyx/4cffmD58uXMnDmTsmXLsnDhQgYMGMCePXuwsrJ64euJ/CksMp5ha05y7lYMGg180NKNd5tVkC7WQh1xEbD/czi1Km1YitYYfIZA44/SCiFA5Jo1RMycBYDD8OE4vDM4d7I4e0C3ZdB0HPjNhoANaUWZq39AmYbQ5GMo1wRjFxfs3noLu7feQh/3iEeHDxG3/wBxf/6JPjKSmN27idm9G3Q6LGrWxKpZM6ybN8OkTJncyS0AqF/BgT3vNWL23kssO3SdLadv8eele0zuVIV2nsXz5Uo/Z8KiOBHyEGOdhr71yqodR4h8oa1nMVpXcebX83cZvSmArf+rj1EhWk5bCCFetZeaGNXa2hpra+ts375JkyY0adIk032KorBq1SqGDh2avhzvrFmzqF+/Prt27aJnz57Zvq7IP/ZfjOC99WeITkihqIUx3/WsTmM3R7VjicIoJSFtwtOD30ByXNq2yh3htclg90/vt4cbN3J36jQA7AcPxuHd/+V+NoeK0GUhNB2Tlu/0Ggg5CKsOQsna0PjjtLlENBp0VpbYtGqFTatWKHo9CWcD/h428wfJV64S7+9PvL8/EbNmYVK+fPqwGfNq1dDo5Fv9nGZhYsSE9h608yrO6E0BXI6IY/ja0+zwuM20zlVxsjFTO+ILWXowrRdIBy8XnPNZdiHUotFomNqpKkeuPiDwVjRLDl5naBNXtWMJIUSBlWfLzDdv3uTevXs0bNgwfZuJiQm1a9fm9OnTKiYTr4LBoPDNb5cYuOI40QkpeJe0ZdfIRlIAEa+ewQABP8HcWvDH1LQCiEsNGPAL9PgxQwEkevt27kycBIBdv344fvD+q/02v2hZ6PAdjDoLdYaAkRncPA5r34BFjeHCjrT78zeNTodFjeo4ffgBrrt24frbXpzHj8Oibl0wMiL52jUeLFlKSK/eXG7QkNtjxhKz51f0cY+enkFkS/XSRdk1siEjW1TESKth74W7tJj9Jz8dD8s3E9nejkpgd2A4AANlWVwhXoiTjRmftk9bJveb3y5x7V6cyomEEKLgyrNL5N67l7b0qb19xuUDHRwcuH379jNvq9fr0etlhu386mF8Mh/8FMBfl+8D8FadUkxoVxlTI+0LPa+Pj5W2ILIt9Cja3yaguZ22MpZiUwKl+USUqr6g0cK/2lbsL78QPm48KAq2PXti//FHGP5VcHiaXGmnVsWg9Qxo8B6ao/PRnFiO5k4A/NQHxbESSsMPUTw6gzZjzw6diwu2vXph26sX+pgY4g+lDZt55OeHPiqK6O3bid6+HYyMsKhTB8umTbFq1hRjF5ecy16IGWlgVHNXWns4MXZLIIG3Yhi9OYDtZ27xeecqlLKzUC1bVtrp8kPX0RsUfMrZUbmYlbz2ilcuv/+736VacXacvY3f5fuM3hTAurfryNDfAia/t1FROOTndprVzBolj3zF5O7unmFi1FOnTvHmm2/i5+eHk9M/M+ZPmDCB8PBwli5d+sQ54uPjCQoKemWZRc67+jCFLw8/5F68ARMtDKlpS9Oy5mrHEoWMyaNblAz6gaLhfwGg15lzp+Jb3C3fDUVn+sTxuuPHMZkzF43BQGrTpiQPGgjavNPRTpcUjfP1TThd34YuNa0XR6JlSe5UfIsHJVqC9jn18NRUtJcuoTt1Gt3p02jv3Mmw21C6NPrq1dHXrIGhXLk8dd/zK71BYdfleNafiyXZAKY6Db08rWhTwQJdHpwrJCHVwDu77hGfojC2QRFqu8hQGCGyI+KRnvf33icxVeHt6ta8XsFS7UhCCJHvVK5cGQuLp395lGd7gjg6pg17uH//foYiyIMHD3BwcHjmbd3c3J55p0XetOF4GJ/tv0CyXqG0nQUL3qr2UjOk6/V6AgMD8fT0RCdzGYisSIhCc/ArNP4/oDGkoGi0KNX7QJNxFLNyolgmN4n7809uz5sPBgPWHTtQbNq0F5o745W1U58mkDgNg/9iNP7fY/boJmXPfEGZG+tR6r+H4v0mGD1Z4ElXqxb8vcRv8vXrxO3fz6MDf5Jw+jTa0FC0oaEYb9+OzsEByyaNsWraDIt6ddGaSxEzu2rWgH4tHzF+6zmOXX/IsjOxnI7UMbNLVSo4vdrJwZ/XTlceCSE+JYKy9hYMauMj314LVRSUf/fH6UKZtPMCa8/H06dFdUoWlfe0BUVBaaOiYMvP7TQ+Pp5Lly4997gXKoIEBARQpUqV9AdDUZQM492Tk5PZt28fbdu2fcG4TypZsiSOjo4cOnQIDw+P9PMfP36cjz766Jm31el0+e4JK8wSU/RM3H6On07cBKBlZWe+fsMbW/OcWf5W2oN4Ln0KHF8Kf86EhIdp21ybo2n1ORpnj6feLO7QIcLfex9SU7F+vQ0lpk9HY5S92vIraaeWdtBsLNR/F04sg8Nz0USFotn9QdpSuw1GQc1+YPzswoV5hQqYV6iA4+DBpD58yKO//iL28bCZ+/eJ2byFmM1b0JiaYlm3LlbNm2PVtCnGzk7PPK94kquTDesG12Pd8VBm7A7mdGgUHeYdZmSLCgxp4orxK15BIrN2qjcorDwSAsCghuUwNs6z36+IQiK//7vfp15Zfj53B//rkUzYfoFVA+vky9WixNPl9zYqCof82E6zmveF3j316NGDqKio9N9r1qxJWFhY+u8xMTF8+OGHWT7fo0ePCAoKSh/CcvPmTYKCgrh9+zYajYa+ffuyaNEifvvtNy5dusS4ceMwMzOjffv2LxJb5GGhD+LxXXiYn07cRKuBj1u7s7hPzRwrgAjxTIoCwT/DgrqwZ0xaAcSxMvTaDH22pi1B+xSP/P25+e5wlORkrFq2oMQXX2S7APLKmVqnFTxGBUCbWWDtArG30x6Db73g0HeQlLVJ+YyKFsW2UydKfvsNbkcOU2rpEor27o2xiwtKUhJxf/7JnUmTuNKkCdd9u3Fv/nwSL1zIN5N95gVarYZePmXY+35jmrk7kqw38NXeS3Scd4hzt6LVjse+oLuEPIjH1twY35ol1Y4jRL6n1WqY5euFqZEWv8v32XjyptqRhBCiQHmhd+z/fdOa2ZvYF3lje+7cOfr27Zv++4wZMwDo0qULM2fOZPDgwSQlJTF58mSio6Px9vZm2bJlWFm92m7AInf8EXyX99afISYxFXtLE+a8WZ0GFZ491EmIHHP7DOydADf80n63cIDmn0D1vqB79ktj/KnThA0dhpKYiGWTxpSYPRuNcT4s3JlYQN2hUGsAnFmTtrxuVCj8NjHtv+u+Cz7vgJltlk6nMTHBqkEDrBo0QPlkPEmXLhO3/w9i9+8nMSCQxPPnSTx/nvtz52FUrBhWzZpi3awZFj4+aE2fMRRHAOBSxJxl/Wuz/cxtJu88T1B4DJ3mH2Jwo/K817IiZsbqfFvzeFnct3xKY2GSTwqBQuRx5Rws+eA1N2b8Esy0XRdo6uaY75bMFkKIvCrH3628SHc9Hx8fLl68+MxzjRgxghEjRuRENJFH6A0K3+27xJw/rgBQrVQRFvauQXFbmTtAvAIxt+H3qXB2HaCAzhTqvQsN3wez589BkxAYSNg776DEx2NZvx4l58xBa2KS+7lzk5Ep1BoI1fukLQfs9zVEXoX90+Dw3LRCSN3/gYVdlk+p0Wgwc3fDzN0Nh6FDSb13j7g//0wbNnP4MKl37hC1bj1R69ajsbDAqkF9rJo2w6ppE4z+syqY+IdGo6Fz9RI0rOjAZzvOsysgnO//vMre83eY6etFnXJZf45yQuDNaPyvR2Kk1dCvXtlXem0hCrpBDcvxc2A4ATejmbDtHIv61JRhMfmZQQ83DlL01jEoEgflGj6xSpsQ4tWQr2zEKxX5KJlR60/j9/fyt33rlWFCOw9MjGQ1CZHLkuLg8Bw4NAdSE9K2eXaHFhOhSOksnSLxwgVCB739f/bOOj6Kc23D11p84+4kQRK0wbW4ttACldMidaFGORXac8532lN36tRLoS1toUKB4i5FgifBEkiIu2xkdb4/JtlkSSAJsd1krt9v2OzMOzPvsu+O3PM8z41Jo8Fp4ECCP/64Y0UwKFRw3Z3Q93aI/w12vQ25ibDrLdj/CQy8F4Y9Bi5Nr+2h9PHBffZs3GfPxlRZSdnff6PZvgPN9u0YcnIo3byF0s1bQCbDsW9fXMaMQT12DHZRUdJFfz14u9jz0R2xTO+bxb9/P0VyXhm3frafuUPCeHZKD1zs2+b0/tWeZABu6BOAv5v0lFpCoiVRKuS8ObsPN364h00J2aw7mckNfSRLcpskYQ1seBZFSQYRAEcA10AxJTVmejt3TkKi89Hkq6Tz58+Tm5trfp+cnExZmWi5WFhY2HI9k+hwHL9UxILvj5BeVIGDSs7rM/tw03VB7d0tiY6OyQjHfoBtL4Omyto1ZDBMehWCBzR6M5Vnz5J6z72YSkpwvO46QpZ+2nGdT+QK6D0bes6E02tFESTrhCgiHfwc+t8Fwx4Ht2v7/codHFCPHo169GiEF/5LZXwCmu3bKd2+DW1CIhXHjlFx7Bi5772HKjgYl7FjxLSZAQMaTDsSjEbKD8dhyM1F6eOD04D+TXLrsTUm9vRncIQXr61PZOWhSyz/O4Vtp3N45eZejO7euoVos4orWXsiE4B7R0S06r4kJDorPfxdWTA6ive3nuO/f8QzLNIbT2cbjz7sbCSsgZ/nAZeVDCjJFOff+p0khEhItDEyoQlFPHr06IFMJqu37kf1fJlMZi502taUl5eTmJjYoC+wRNsiCAI/HEzlxTUJ6Iwmwr2cWDq3Pz38r93+tjEYjUaOHTtGv379bK6ysUQLkbwDNv4bsk+K793DYML/IGYGNCG6QJucTMrceRjz83Ho3ZvQr79CoVa3SBdtYpwKApzbBDvfhPTD4jyFHfS7E0YsBI/wFtuVPisLzY4dlG7bRvnfBxB0OvMyuYsLLqNG4jJmDC4jR6Jwd7dYt2TTJrJffQ1DVpZ5ntLfH7/nn8N14sQW66O1svd8Hot/PcGlAjHSaWZsEP+ZFoNHC9ww1TdO39hwmk93JDGoiyc/Pzi02fuQkGguNnE8vQZ0BhM3friHM9ml3NQvkCW3X9feXZJoLCYjLOklpuLWi0yMCFl4UkqNkbAabPlY2lg9oEmRIFu3bm12xyQ6FxU6I//6/SS/HkkHYGKMH2/f2hdXBxssIilhO+Sehc3/gbMbxPf2bnD90zDoAbH+RRPQpaSQOv8ujPn52EdHE/rlFy0mgNgMMhl0mwRdJ4rC0q63IGUvxH0DR74T02dGLALvqGbvSuXvj8ftt+Nx++2Yysoo27+f0m3b0ezciTE/n5L1f1Gy/i9QKHCKjTWnzVSePUv6EwtFwaYWhuxscf77Szq8EDI8ypuNC0fx9sazfLPvAr8eSWfX2Vz+N6MXU3r5t2haUbnOwA8HUgG4b0SXFtuuhIREXeyUYlrMzZ/s5fdjGUzvF8jYHn7t3S2JxpCy7yoCCIAAJemw9UXocxt4d2+wOLuEhETzadKvLCio4dDnxMTERrWT6Pik5Jfx4PI4TmeVIpfBM5N78OCoCCm/X6L1KMuHHa/B4a9BMIJMIdaxuH4xODe92KYuLZ2Uu+7GkJuLfdcoMQLErXFOKR0SmQwix4jTxb2w+21I2iY6yxz/UUyfGfnPq1oLNwW5szPq8eNRjx+PYDJReeKEKIhs34723DnKDx2i/NAhct58ExSKOgIIIM6Tych+9TXU48Z16NQYACc7Jf93Yww39A3g2VUnOJejYcH3R5jU04+XZvRqMXeJ1XFpFFfoCfNyYly0dDMmIdHa9A1x576REXy+K5nnfz3FpkWe0gMlW0CT3bh2e98XJ6Uj+PeGwOsgsJ/46t1NihKRkGhhWkRqLC0tZc2aNaxatYrTp0+3WzqMhPWwJSGbJ38+RmmlAW8XOz78RyxDIyXHB4lWwqCFA5+JhTy1xeK8blPE1Befbte0SX1WFql33YUhMxO7Ll0I/eYblB4eLdhpGyd8uDilHRb/38/+BadWiVP0jTDyKfECroWQyeU49uuHY79++C56El1aGppt29Hs2E7Z3wfAaLzyyoKAISuL8sNxOA8e1GJ9smZiQz1Y+/gIPt52nk92JLExPpv9Sfn8+4YYbukf3Cwx2mQSzLa49wzvgkIuCdsSEm3Bognd2JyQzYW8Ml5bn8hrM/u0d5ckGsKlkSKxX28ovAi6Ukg7KE7VqJwgoC8E9KsSR64DryiQS6YCEhLXSrNEkP3797N69Wo2b95MYGAgEydO5JVXXmmpvknYIEaTwLubz/Dx9iQA+od58PEdsZJrgETrIAiQ8Dts/i8UpYjz/HvDxFcg4vpr3qw+J4fU+XehT0tDFRpK6LffoPT2bpk+dzSCB8AdKyHzhBgZkrAGEv8Up66TYNTTEDKwxXdrFxyM57y5eM6bS+GqVWT9+z8NrmOoVdS7M2CvVLBoYnem9A7gmVUnOJlezDOrTvDn8Qxevbk3IZ7XVjtr6+kcLuaX4+qgZHb/4BbutYSExJVwUCl4fWZvbvv8b348eIkb+wQyLEo6N1ktglBTR+uKVNUEeXCn+HdBEmQchYxj4mvmcdCXQep+carGzkUURgKvqxFHPCMkYURCopE0WQTJysri119/ZfXq1VRUVDBlyhQMBgMffvghUVHNzweXsF3yNVoeX3mUvefzAbhrWDjPT42W7G8lWoe0w7Dxebh0QHzv4g/j/gN9/9GssFFDfj6pd9+DLiUFVWAgYd9+g8pPCvdvkIA+YoX7nNOw+x0xIuTcRnGKGC2KIeEjWmXXdiGNszhW+vi0yv6tnegAV35bMIyv9lzg3c1n2X0uj0lLdvH0pO7MHxqOvImRHNW2uP8YHIpzG1nxSkhIiAyO8GLukDCW/53Cs7+eYOPCUTjZSb9Dq8NogA3PwqEva82UYekQU3Xsnfx6zXWLd1dx6nOr+N5khPzzVcJI1ZR5AnQasTZXyt6azdm7Vgkj/WoiRjy6NKkQvIREZ6FJR83777+fuLg4xowZw3/+8x9GjhyJQqFg5cqVrdU/CRvhaGohC74/QmZxJU52Cl6f1YfpfSUve4lWoCgVtrwo3mSDGCY67HEY/jjYOTdr04bCQlLvuRddUhJKPz9Cl32LKlAax03CtwfM+gJGL4Y978LxlWIx1eQdEDoMRj0FkWNb9KLMaUB/lP7+GLKz668LgugS4zSgf4vt09ZQKuQ8eH0kE2L8WPzrSQ5eKODFPxNYeyKTN2b1Jsq3ccV+4zNK+Du5AIVcxvyh4a3baQkJiXp5dkoPtp3O4VJBBW9vPMv/3dgydZgkWghdGay6p6o4uwwmvQpuwaIoUrtIqmugKIBczR5XrgCf7uLU93ZxntEAeWerBJFj4mvWSdCWwMXd4lSNg1utNJqqV/cwSRiR6PQ0SQTZu3cvc+fO5R//+Afh4eGt1CUJW0IQBFb8ncL/1iagNwpE+DizdE5/uvl1MvcMidanskS8qd7/CRi1gAz63QFj/y1eSDQTY0kJl+69D+2ZMyh8vAn99hvsQkKa3+/OilckzPgYRj0jFns7uhxS98GKmRDUX4wM6Ta5RS7EZAoFfs8/J7rAyGT1CiEet9/W4YuiNoYIHxdW3j+E7w+m8vr6ROJSCpn6/h6eGN+VB0ZFoFJcPXLv670XAZjWO4BAd8c26LGEhMTluNgreXVmb+Z/fZBv9l1gWp8A+odJNausAk0O/HCrKEwoHWDm5xAzQ1zWYxrGC3tIiT9AWM/BKLqMuLbIVYVSLEDuFwPX3SnOM+oh90ytaJFjojBSWQwXdopTNY4elvVFAvuBW4gkjEiImIxwcQ8e6QfAXQPXOk6tnCaJIN9//z2rV69m1qxZREREMGPGDKZOndpafZOwcsp1Bv712yl+Oyra307p5c+bs/uglqqVS7QkRgMcWQbbX4XyPHFe+EiY9IoY9tkSu9CUkXr//VQmJKDw8CDsm2+w7yLZfrYIHmFww7tiBMi+D+HwN5AeBz/eLhaCG/UURE9vdh6z68SJ8P4Ssl99DUNWlnm+zN4eQasl/8uvcBk1CocY6YmpXC5j7pAwxvbw5V+/nWTHmVze2niGdScyeXN2H3oF1e+AVFBhZO0J0engXskWV0KiXbm+mw+zYoNZfSSNZ1efYN3jI7BXdrwbFZsi9yx8P0uMWHX0hDt+gpBaxbjlCggfQWGRC2Hh/Vr2xlKhAv9e4hQ7V5xn0EFuYk19kYyjkB0PFYWQvF2cqnHyqhFFqgUS10BJGOlsJKyBDc+iKMkgAuAIVRFLb1w9YskGkQnCFWKHr0JFRQXr1q1j9erVnDx5EqPRyOLFi5k1axYuLi6t0c9GUV5eTmJiItHR0Tg5XVvBN4nGcSGvjIdXiPa3CrmMxZN7cN/ILlZlf2s0Gjl27Bj9+vVDIT0Btj0EAc5vgU3/htzT4jyvKJjwEnSf0mInZlN5Oan3P0BFXBxyNzfCln2LQ48eLbLtxtDpxqkmF/Z/JOZJ6zTiPO/uohjSc6b4hKsZCEYj5YfjMOTmovTxwaFnDJceeoiKw3EoPD0JW7Ec+4iIFvggHQNBEPj9WDov/plAUbkehVzGA6MieGJcVxxU4ng0mgT+TsplyV8nOJShZUCYO6seHt7OPZeQqEtnO54WlesY/+4u8jRaHh0TxVOTurd3lzovF/fCyjugskgsUHrnKjEi8jLafYwatJCTUKvGyDHxvclQt62zr2V9kYB+4BrQxh2WaDMS1sDP87CsWwPm2jW3fmcTQkhj9YBrEkFqk5yczKpVq1izZg0lJSUMGzaMpUuXNmeT14wkgrQNG+OzeOrn45RqDXi72PPxHdcxOML67G/b/UQjce1kx4viR9I28b2jB4x+DgbcIz7taCFMlZVceuhhyv/+G7laTeg33+DYq2eLbb8xdNpxWl4AB5bC30trbI09I2DEIjHvuQW/Z2NpKanz76IyIQGlvz/h369AFRTUYtvvCORptPx3TTzrTmQCEOHtzBuz+5Cv0fLinwlkFlea27o7qXh9Zm8m95IuhiWsi854PN1wKpOHVhxBIZex5tHh9AysP5JLohU5uQp+fxiMOggeCP9YCc71u/ZY5RjVV4rXXZm1hZFEEOqxnnfxt0yjCbwOXHzbusdmjCaBgxcKyCmtxFftwKAunpJt+7VgMsKSXpY1ayyocjFaeNLqU2PaTASpxmg0sn37dlatWiWJIB0Ug9HE25vOsnSnaH87MNyDj+6Ixc/VOu1vrfJEI3F1SrNh+yti/QjBBHIVDH5QjBJwbNl8Z5NOR9ojj1K2ezdyJydCv/4Kx379WnQfjaHTj9PKYjj4Bez/GCoKxHluITBiIfSbA6qWOb4YCgtJmTMXXVISqrBQwr//XrI9roeN8Vn85/dT5JRqr9im+vLy0zmxkhAiYVV01uPpgu/jWH8yi56Brvz+yPAGa/tItBCCINa82vJf8X2PG2DWl6C6cr0kmxmj+grIOmVZYyT3tHhtdjmuQXVrjFxBBGpJNpzKrCPSB7g58N8bY6Rz05UwGcV0qPICKM+vmdLjxNTzhpi/FrqMbP1+NoM2F0GsAUkEaT1yS7U8/uNR9ieL9rf3jujC4ik9rPpEazMnGgnxZLv/I9izpCZFImYGjH9BjA5oYQSdjrQnFqLZvh2ZoyOhX3yO04ABLb6fxiCN0yp0ZXD4a9j7AZTliPPUAaLzT/+7wK75x3R9djYpd9yJPj0d++7dCftuGQo36anp5RSX63l5XQK/xKVdsY0M8HdzYM+zY6WnbhJWQ2c9nuaWapnw3k6KyvU8Pak7j4yJau8udXyMBvjrGTj8lfh+yAKY+HKDT8lteozqysRiq7VrjOSdpW76BOLDjOpIkWqBxMmzxbqy4VQmD684cqXEjc4h0ptMYvrV5YJGRe33ly8rot7vq7HM+gp6z26hD9A6NFYPaFLy9XPPPddgG5lMxquvvtqUzUpYOXEpBSz4/gjZJVqc7BS8ObsPN/SRbEMlWgCTCU7+Alv/ByVVN1yBsaKdXNjQVtmlYDCQ/tTTogBib0/Ip5+0mwAiUQs7Zxj2GAy8D44sh71LoCQdNj4Hu9+BYY+Ky+yv3XlK5edH6NdfcXHOHLRnznDpgQcJ/for5M7Ns1buaLg5qZgZG3xVEUQAMosrOXihgKGR1pcOKSHRmfBR2/PfG2N48qfjvL/lHJN6+jXa9lriGrjcAnfyazDk4fbuVetj5wyhQ8SpGm1plTBytEYcyT8HxZfEKfHPmrbuYZfVGOl7TVG+RpPAi38m1HsrLyAKIS/+mcCEGH/bEekFQbQ4rk+4sBA0CizFjvoicxqDg5tYDLd6MhogaUvD67n4Xdv+rJAmiSC//fYbgYGBxMTE0IECSCSugCAILNt3kZfXJWIwCUT6OPPZ3P7SiVWiZUjZBxufF0+YAK7BYuRHr1nNdgq5EoLRSMbi5yjdtAmZSkXwRx/iPGRIwytKtB0qRxj8gBj9cfwH2P0uFKXAlhfESKEhC8Tl15geZRcWRuhXX5Eydx4Vx49z6dFHCVm6FLm9fUt+Cpsnp7Sy4UZNaCchIdG63NQviDXHMth+JpdnVp3gl4eG2c4NoC1Rmi1a4GYeq7LA/cImikW2GvZqCBsmTtVUlkDWiVrFV49CQbJ4Li9KgYQ/zE0NbuFU+PShxKMX+a7R5Lj0oNDoSJnWgEZroFRrEP+uNKDRGtFo9WSVVJpTYOSYGCQ/jS9F5ODOQVMPTMjJLK7k230XmNEvCC9nu7Y1bhAEMarZLF4UXkXUqCVo1FectjHYu4pRNk5eoiuRWdzwtBQ6qidHj7pF6M01QTKpP1KkqiZI7e/ZxmmSCHL77bezfv16Ll26xKxZs5g+fTru7u6t1DWJ9qRcZ2Dx6pOsOS4WyJnWJ4A3ZvXBxb55zg0SEuQnifmz1U8H7Fxg5CLx5vYqebTNRTCZyPz3fyhZuxaUSoLeX4LLSOvOa+zUKO1EIaTfHDi1Cna9LT5d2vGqmDo16H4Y8gg4XxaFYDKKApsmW3xiETasTniyQ7duhH7xOal33U35/r9J/+c/CV6yBJlSOr5V46tuXC2WxraTkJBoXWQyGa/c3JuJ7+3iSGoRy/Zd5B7JyrplyT0D388WLXCdvMQCqLUtcBvAaBL4OzmfQ6kVVLrmMyTSx6aEKkEQKNcZKbtMnDD/XT1VGtBo3dFoR6KpHEqZkwGBIgIqzhKuO0uU4TwxQhJh8hyUxRdRF19EzRqqy5UnmQI4JXThkqkLJ00RxAvhlFH3+nCS/CD/VX1HoKzAPC9D8ORF/Tw2mgbx0tpEXlqbiJujiihfF6J8XIjydSHS15koHzVBHo6N+//XlV9dvKhvvlF3bf/JKuerCBiedec7eorXS81FrhBtcH+ehxhLU1sIqfo/mvy61RdFbQpNrgmi0+nYtGkTq1ev5ujRo1x//fXMnj2bESNGtLs9qlQTpGVIytXw8Io4zmZrUMplPDc1mnuGh7f799tUbDrvsiNSUQg734KDn4NJDzI5xM6HMc+3emVxQRDIeuFFin76CRQKgt55B9fJk1p1n41FGqeNxGQUnx7tehty4sV5KifRMWjYY6D2N/vbW1Q3v4q/fdnfB7j0wAMIOh1uM6YT8NpryFopCsnWMJoERryxjaziyis9E5JqgjSDy62cnQb0Ryb9/puNdDyF7w+k8K/fTuGoUrBx4ShCvaTr4Rbh4l5Y+Q+xmPdVLHCvRHsV8RQEAa3BRGllfUJFzVSmNVBaWfN3fe3KtAZMLZgI4IaGfooLxNql0Ed2gRghCT8hp+5nQEaRUzhF7j0p8+rNOUUk2w8eZYnqYwBqn4Kq+/ewfiFHnUeSq9FS+07XHh3uaPCUleKr1NDDVU+kcyUhDpX4K8vwlJXiYipGUVFQE8FhqLi2D6h0ACfv+sWL+oQOR88WKwZ/zdR7HRUkCiA2EvHUJoVR09PT+e233/j9998xGAysW7cO53bMrZZEkObz18lMnl51Ao3WgK/ano/vjGVgeMsVMmpLpIshK8GgEwuH7XxDFEIAIseJBcT8Ylp994IgkP3qaxQuXw4yGYFvvonbjTe0+n4bizROm4jJBGf/gp1viuHIAAp76DIKzm+hqf72pdu2k/bYY2A04nHnnfj9+182J/i2FtWF56DeZ0Kdo/BcK1CyaRPZr76GISvLPE/p74/f88/hOnFiO/bM9pGOp2AyCdzx5d/8nVzA8CgvVtw7WDqmNRcLC9xBVRa4ja+FdC1FPHUGU70iRGnVvMv/1lxFuDC0pHKBKDq42CvFyUF8dbZXonZQ4mwnzlNXzateXj2Z21W9t1fKLcdnWb6lVW/GsZqacbUQqv6pb2gLApTLHHDsdQNCeQG6klyE8nyUlYXYma5N0DDJ7cDJE7mzd/2ChmM9QkcLFHRvF0xGjBf2kBJ/gLCeg1F0GWFTESCtUhj1cqoHrSAImEzXWJhFwiowGE28ufEMn+9KBmBwF08+vOM6KdRZ4toRBDizHjb9BwpEW2V8okXxo+v4NuqCQM7bb4sCCBDw8stWJYBIXANyOfSYBt2nwvmtsOtNuHQAzm++wgpVZdI2LBbXu+xErh47hsDXXyfjmWco/P575GoXfBcubO1PYRNM7hXAp3Ni6zy99JcsCK+Zkk2bSH9iIVz2/MmQnS3Of3+JJIRINAu5XMYbs/owacku9p7P56dDl7h9UGh7d8s2EQSxSPeWF8T30TeKNUCakLrbUBFPgMd/PEqEzznKdAbKtEY0lQZ0xpa9r5LJEAWKKmHC2b5aqFDgYq+qEibEv13sFVXihQpnewXqqldR3FDhoJK3nrDm7AVR48WpGk2u+NCjur5I6gFkFfk1KlJ9n5VKMZUW6ibSyJUITl7o7TwoU7pRKLiQbXAmTevIhXIHMnTOFKKmQFBTiJpCwYUyHKBchnelPVEKZ6LcXIh0F9Nronxd8Hd16Dhio1wB4SMoLHIhLLyfTQkgTaHJIkjtdJi4uDhGjx7N//3f/zFy5EjkUhixTZJTWsmjPxzl4AUxp+6BURE8M6k7Siu2v5WwcjKOwcZ/Qcoe8b2zD4z5F1w3t24xplYk78MPKfjqawD8X3gB91kz22zfEq2MTCaKaVHj4O9PxCK7V0QQnWZS9tXrb+924w2YyjRkvfAi+Us/Q6FW43Xvva3Xdxticq8AJsT483dSLodOnWVgr242l8duLQhGI9mvvlZHABEXio80s199DfW4cVJqjESzCPNy5qmJ3Xl5XSKvrEtkdHdf/N2kh1pNwmiAv54WrdtBrEE18aUm3xCuO5lhISLXh84ocDqrtN5lTnYKi6iLpkZaVP/tpFIgt9XjtosPdJ0gTiBG5qxuxDm6960QOaZu+om9KzKZDDvADvAAIqpWEQSBXI2W8zkaknLLSMrRcL5qyiqpJE+jJU+j5e/kAotdOdspiKyqOxJZJYxE+rgQ5uWESrqfskqadDfywgsvsH79egICApg5cybvvvsuHh7XVqFfwjo4dLGAR74/Qk6pFhd7JW/N7sOU3tLTPYlrpDgdtr0Ex1cCgpimMOxRGL4QHFzbtCt5S5eS98mnAPg9/zwet9/WpvuXaCNkssZbtmmyr7jI4/bbMZaWkvvOu+S89TZytRqPW29toU7aNgq5jCERXjiUONIvwksSQK6R8sNxFikwdRAEDFlZlB+Ow3lw44stSkjUx93Du7D2RCbHLhXx799P8sW8AR3nSXVro9WIFrjnNiJa4L4OQx5q1KpGk8CxS0VsTcxm2+mcK4obl/Pw9ZFM6OlnIW442yml4219NPacHzuv3gcfV0Mmk+GrdsBX7cCwSG+LZRqtwSyKJOVWiSO5GlLyyynTGTmRVsyJtGKLdVQKGWFezlXiiHNVgVY1ET7OOEtmE+1Kk/73V65cSWBgIMHBwRw6dIhDhw7V2+6jjz5qkc5JtB6CIPD13ou8tl60v+3q68LSuf2J9HFp765JWCtXc93QamDv+7Dvw5oCUr1vgXH/B+5tH4ab/9XX5C55HwDfp5/Cc97cNu+DRBvS2AuiBtp5338/ppJS8r/4gqz/voDc2Rm3adNaoIMSEmDIzW1UO31Geiv3RKIzoJDLeHN2H6Z9sJstiTmsOZ7BjH5BDa/Y2bncAnfWl2IazFXQaA3sOZfLlsQctp/OIb+sxhnkcp+NKzGqmw+xodKD5UYRNkwset7Gdq4u9kr6hrjTN8TdYr7OYCK1oMwcMWKOIsnVUK4zmucRb7m9QDcHi6iR6tSaNrf07aQ0SQS56aabpC+lA6DRGnh29QnWncgEYHrfQF6b2VtSJCWuzJVcNya9BtpSMfqj+il7yBCY9CoE92+XrhYsX0HOW28B4PPE41JaQ2egwQsiwMENQoc2uCmfRU9i1JRS9ONKMp5djNzZGfXo0S3aXYnOicLDvVHtsl78H+V//43r1Kk4DxuGTKVq3Y5JdFi6+al5fGxX3tl8lhf/TGBElDdeLvbt3S3rJfcMrJgNxdUWuD9ByMB6m6YVlrM1MYctidkcSC6wqOGhdlByfTcfxkf7MbKrNzd8uKdBp61BXWzThKBdsDI7VzulnChfNVG+aov5JpNAZkmlKIpURY1U/51fpiOjuJKM4kp2n8uzWM/dSSWKItdq6SvRKJp01/v666+3Vj8k2ojzOaU8tOII53NE+9t/T4tm/jDbs7+VaEMS1lSdaC47fZdkwC/za957hMOE/0H09PrLdbcBhT/9TPYrrwDg9dCDeD/8cLv0Q6KNueoFURWVxWJ1/xuXgN2VXcxkMhn+//kPplINJWvXkv7EQkK++BznQVJ6gsS1YygsJPeTTxpuKJcjVFZS/Mcaiv9Yg8LNDfXEibhOm4bTwAFSrRCJJvPQ6EjWn8oiMbOE/66J56M7Ytu7S9bJxT2w8o4qC9xIuPMXCwtco0ngeJqY5rI1sW6aS7iXE+Oi/RjXw5eBXTwt6kD898YYHl5x5Eq36/z3xhjp5rapxEwXXd/qe0BnJXaucrmMIHdHgtwdub6bj8WywjJdTUpNdXpNroa0wgqKyvXEpRQSl1JosY69Uk6EjwuRPs7mqJEoXxfCvZxxUEnnhqbSLItca0OyyL06605k8syq45TpjPi52vPJnbH0D+u4yrNkldcCmIywpJflCaYOMpjwIgx+CJTt94Sp6LffyXz+eRAEPO+5B9+nn7IJcU8apy3IlfztI0aLdWoEI/jGwG0rLC5u60PQ60l7YiGabduQOzsT+u23OPbu1br9t2KkcXrt6FJTuXT/A+hSUpA5OCBUVopCce3Lr6pjVdCS91D6+FCybj0lGzZgzM83N1H4eOM6eQquU6fg2K+fTRzf2hppnNbPqfRiZny8F6NJ4PO5/ZnY07+9u2Rd1LbADRkMt/8Izl6UaQ3svkKai1wGA8I8GRfty7hoPyJ9nK/6m9xwKrOO01aA5LTVfK6Wqm2DVOiMXMgrs4gaScrVkJxXhs5Qv2OQXAYhnk41kSO1irO6OTY9klCr1fLnT1+SfjaBoG4x3Hjbfdjb204EWWP1AEkE6QTojSZe/+s0X+25AMCQCE8+/EcsPmrbGdDXgnQx1AJc2A3LGmEpO39tk4tPtSTFa9eR8cwzYDLhMWcOfv963mZuEKRx2sJc6YLo4l745S4oywF7V7j5M+gx9eqb0mq59MCDlB84gMLNjbAVy7Hv2rVtPoeVIY3Ta6P86FHSFjyCsbAQZWAAoZ99hvbCBbJffc2iSKrS3x+/55+zsMcVDAbKDx2iZP16SjZtxlRcU3BPFRiI67SpuE6din2PHjZzvGttpHF6Zd7YcJpPdyThq7Zn86Lrr+nmqMNRxwJ3Omljl7DtfClbEnP4OynfMs3FXsmo7j6Mj/ZldDdfPJztmrQ7o0mQnLYkrgmjSeBSQXmdoqznczSUVhquuJ63iz1R5oKsNeLIlSx9l7//IuErfsa7tGbc56nlXJxzK3Of+G+rfLaWRhJBJBEEgJySSh754QiHLoohVQ9dH8lTE7t1Cvtb6WKoGQgC5CTCnvfg5M8Nt5/1FfSe3fr9qoeSjZtIX7QIjEbcb70V/xdfsKkbAmmctiElmaIQculv8f3Ip2DM81d9amTUlJF6zz1UnjiB0seHsB++xy4kpG36a0VI47TplGzYSMYzzyDodDj07Enwp5+g8vUFRLvc8sNxGHJzUfr44DSg/1VTXQSdDs3evZSs/wvN1q2YysvNy+y6dMF16lRcp03FPiLiitvoDEjj9MpU6o1M/WA3ybll3DogmDdn923vLrUvRgOsfwrivgHgUMAd/F/5bSRml1k0C/NyYlwPP8ZH101zuabdSmNUogWxsPStVZS12tL3SrjYK4n0cbaIGkle9zHXf70KqEnVAjBVvY97+HabEEIkEUQSQTiQnM+jPx4lt1SL2l7J27f2ZVInCoGUTjRNpCwPkrZD0jZx0lzFyvFy2ikSpHTbdtIefxwMBtxuuomAV19BJrctgU8ap22MUQ+b/g0HlorvI8aIIp6z15VXKSoiZe48tOfOoQoJIWzFClR+vm3UYetAGqeNRxAECr7+mpy33gbAZcwYgt55G3kLXZeYKirQ7NxFyfr1aHbsQNDVhOjb9+ghRohMmYpdcOdzApHG6dU5fLGAWz7bjyDA8nsHMbKrT8MrdUDKSoso+34evlk7MSHjf/q5fGucDNSkuYyN9mV8tC+RPi4t+mBFGqMSbUVppZ7kKkHEnF5TZelrNFne/ssFA8u2Po+XxkR9o90EFKjlDNpzxOpTYxqrB0h2IB0QQRD4cvcFXt9wGqNJoLufmqVz+9PF+8rFACU6IQYtXDpQI3pkHrdcrnQUUwnSDoG25AobaR0bssag2b2H9CeeAIMB12nTCHjlZZsTQCTaAYUKprwBwQNhzWOQvB0+vx5uXQZB9TsaKdzdCfnqS1LmzEWfmkrqvfcQtnw5Sg/JzlDCEsFgIOvllyla+ROAmJ733OIWLWgqd3TEdfIkXCdPwqjRoNm6leL16ynbuw/t6dPknj5N7jvv4ti3L67TpqKeNLnTiXYS9TMg3JP5Q8P5dt9FFq8+yaYnR3UaZ8D0ogq2JmZz6NRpHkxbTC/ZBSoEO57QP8J+1VCm9bz2NBcJCWtE7aC6oqVvSr4ojpzKyuB4zglIWIu3pv6aIwBywLvUxNqfv2bW3I5hOtA5jnydCI3WwDOrjrP+pPgU/6Z+gbw6szdOdtJX3ekRBMg7VyN6XNwDesuwT/x6Q9RYiBwrWt2qHGq5w0B725BVU/b336Q9+iiCXo964kQC33hdck2QaBq9Z4NvNPw0BwqS4evJMPVt6D+/3uYqX19Cv/6alDvvRHc+iUv3P0Dot9+gcHFp445LWCtGTRnp/1xE2c5dIJPht/hZPOfXP55aCoWLC24zZuA2YwaGwkJKN2+mZP1flB84QMXx41QcP072a6/jNHAgrlOnop40URLvOjlPT+rOlsRs0goreGvjGV6Y3rO9u9QqmEwCx9KK2FZlY3s6q5QoWRrf2r1JsCyPQtz4Nfod5g8Yw0fhntgppYcoEh0bg8nAucJzHM89zoncE5zIO0FKSQpeJQJz468sgNSmJDO1lXvZdkh3xh2Ic9mlPLgijuTcMlQKGf93QwxzhoTZVH0EiRamvAAu7ITzW8VUl5I0y+XOvqLgETlWdNBQ+9XdhpXZkJXHxXHp4QUIWq0YZv72W8iU0qFM4hrw6wkP7IDfHoYz6+DPxyHtoCiGqBzrNLcLDiL0669ImTOXylOnSHt4ASFffI7cwaHt+y5hVeizs7n00MNoExOROTgQ+NabuE6Y0KZ9UHp44HHrrXjceiv6nBxKN2ykZP16Ko4do/zgQcoPHiTr5ZdxHjZUFETGj+9QIp7RZORQ1iHiiuLQZ+kZGDAQhQ27RLQWzvZKXpvZm7lfHWTZ/ovc0CeAAeEdwylQdHPJY2tiNtvP5JCnqUkVGyJP5Ev793ARNOjcuuA+71fu9ercNXQkOjZ5FXlmweN47nES8hOoMFQAoDAKDDgncMdxgb4XBOSNLI7hGhDaij1uW6SaIB2ENcczWLz6BOU6IwFuDnx8ZyyxoZ37aU+nzLs06sX0lepoj/QjWERvKOwhbGiN8OHbExqbQmIFNmQVx4+Tes+9mMrKcB4xguBPPkZuZ9thq51ynFobJhPsfQ+2vQyCCQL6wq3LwSOs3uYV8fGkzr8Lk0aDy/XXE/zRh8hUHdtpQRqnV6byzBkuPfgQhqwsFF5ehHz6CY59+rR3t8zo0tIp3fAXxevXo01INM+X2dnhcv0oXKdOxWX0aOSOdYU/W2FLyhZeP/g62eXZ5nl+Tn4sHrSY8WHj27Fn1sszq47z8+E0InycWf/4SBxUtvm7rk5z2ZqYw/4ruLncpT7IgGP/QXaZBW57IB1LJVoDvVHP6YLTFlEe6Zr0Ou16FDpx02k1veLysSutKZxq3z+WvPijOFcK1HdX0BFrgkgiiI2jM5h4dX0i3+67CMDwKC8+uP06vFyse4C2BZ3mRJOfVCV6bIcLu0BXarncJ7pG9AgbBna2+duoiI8n9a67MZWW4jR4MCGfLe0QT+A7zTi1BZK2w6p7oKIAHD1g1pcQVf8NVPnhw6Teex+CVovr1KkEvvVmh07JksZp/Wh27yF94UJMZWXYRUQQ8vln2AUHt3e3rog2+YJoubt+PbrkZPN8mZMT6rFjcZ06FecRw21KXN6SsoVFOxYhYHk5K6tK2Xx39LuSEFIPxRV6Jry7k5xSLQ+PjuTZyT3au0uNwmQSOJ5WxNZaaS61CfV0Yly0L+Oj/RgY5oHd3+/D1hfFhTEzRHv0eiL92grpWCrREmSVZVlEeSTmJ6Iz6SzayJAR6R7JAOdohiWYCNiRiJBw1rxc6eOD28034z7zZuzCw1n+/ov0/3QlAlgIIZI7jA3Q2USQrGLR/jYuRbS/fWRMJIsmdJc8x6vosCeaymJR7KiO9ii8aLnc0RMix0DkOPHVNbBdutmSVJ45Q+q8+RiLi3Hs35/QLz5vMaeF9qbDjlNbpegS/DwXMo4CMhjzLxj5z3ojpjS7dnHpkUdBr7dJe+amII3TuhT+8gtZL7wIRiNOgwYR/OEHKNzc2rtbjUIQBLRnzlCyThRE9Ok1Twzlrq6oJ4zHbdo0nAYNsup0Q6PJyKTVkywiQGojQ4afkx8bZm2QUmPqYXNCNvd/dxiFXMbvC4bTO9g6x+/V0lzkMugf5sG4aD/G9fAlyrfKzcVogPX/hLhvxYZDH4UJLzU++rWVkI6lEk2l0lBJYkEix3OOcyJPFD1yynPqtHO3d6ePTx/6+vSlj3dvoi7q0P3xFyUbNyJUVkV9KJWox4zGbdYsXEaMqHN8X/7+i4Sv+Bnv0pqIqjy1nItzbrUJAQQkEaTDiyD7kvJ4/Mej5Gl0qB2UvHtrPybE1FPPoRPTYU40RoN4Q5a0VRQ90g6DYKxZLldB6JAq4WMs+Pdt95N8S6JNSiJl7jyMBQU49O1D6Fdfdaw89o4yTjsS+kqxBk71xXO3yeLTQ0f3Ok1LNmwgfdE/wWTC89578H3qqQ4phEjjtAbBZCJ3yfvkf/45AK7TbyTg5ZdtKnqiNoIgUHniBMXr1lH61wYMubnmZQovL1wnTcJ12lQcr7uu3R249EY9ycXJnCs6x9nCsxzKOsSpvFMNrvf1pK8Z6D+wDXpoezz241H+PJ5BD381ax4dYTUFQtOLKtiWmM2WxBz2J+ejM9TclLnYK7m+mw/jon0Z3d0Xz8vdXLQaWHU3nNsEyERHsMEPtu0HuALSsVTiagiCQJomTUxpqYryOFNwBoNgsGinkCno5tHNLHr09elLiDoEQ04uxb//TtGvq9Gn1BQxtYuIwH3WLNxmTEfp7X3VPmi1Wv786UvSzyYQ1C2GG2+7z+pTYGojiSAdVAQRBIHPdiXz5obTmATo4a9m6Zz+hEv2t3Ww6RNNYUpNpMeFnWL0R228utakuISPAPuOIwrURnfxIilz52HIzcUhJkZ043B1be9utSg2PU47OkdXwNpFYNSCRxe4bTn4967TrGjVKjL//R8AfBYuxPsh67jYbkmkcSpi0mrJfO55StavB8B7wQK8H3u0wwhfgtFI+eE4Stavp3TjRoxFReZlSn9/XKdMwXXaNBx6xrTqZxYEgayyLLPYcbbwLOcKz3Gx+GKdm4HGEOEWwYyoGQwPHE43j24d5vtqCfI1Wia8t4uCMh2LJnTj8XFd26UfTUpzuZqbS2kW/HArZB4HpSPM/gp6TGuDT9A4pGOpRG3K9eXE58dzPPe4Ob2loLKgTjsvBy9R7PDtSx/vPsR4xeCkEu91Bb2e0h07KF61Gs3u3WKdM0Du5IR66hTcZ83CsV+/Jh33bHmcNlYPsN4YR4k6lFTqefqX42yMF8M+Z8YG8cpNvXG0s63BKVEP2lLRsrZa+Mg/b7ncwU10b6lOcXHvONWZr4QuLY2Uu+7GkJuLfbduhHz1ZYcTQCSsnOvmgF8vMT2m8AJ8OQFufB/63mbRzH32bIylGnLeeIPcJUuQq13wvPPOduq0RGthKCwk7dHHqIiLA6WSgJdewv3mm9q7Wy2KTKHAefAgnAcPwv/f/6Ls778pWbuO0i1bMGRlUfDNNxR88w2qsFBcp07FbepU7Ls276a5VFfKucJz4lR0zvx3qb603vZqlZquHl3p6tEVO7kdyxOXN7iP5OJk3ot7j/fi3sPX0ZfhQcMZHjScoYFDcbXr3OcVLxd7/ntjDE+sPMaH284xuZc/3fzUbbLvcl1Nmsu207nkabTmZXIZxIaKaS7jo2uluVyNnNPw/WwovgRO3nDHTxA8oJU/hYRE4xAEgYslFy2iPM4VncMkWNrTKuVKYjxj6OPTxxzpEeAcUGf8a5OSKFq1muI1azDm55vnO8bG4j5rFq6TJyF3lh6SXwlJBLERzmSV8tCKOC7klWGnkPPf6THcMShUepphq5hMkHmsRvS4dABMtZ5uyRQQPBCixonRHoHXtbkTS3uiz8ggdf5dGLKysIuMJPSbr1F6dG63I4l2IrAfPLATfr0fzm+B3x4QHZgmvQrKmhBsr7vvwlRaSt4nn5D90sso1GrcpretfbRE66FLSeHSAw+iS0lBrlYT/MH7OA8d2t7dalVkKhUuI0fiMnIkJq0Wza5dlKxfj2b7DvQpqeR/upT8T5di37UrrtOm4jp1KnahVxbo9SY9F4svmsWO6uiOzLLMetsr5Uq6uHWhq3tXunl0o6uH+Orn5Ge+9jGajGxK2UROeU6dwqgg1gTxcvTivt73sS9jH4eyDpFTkcNv53/jt/O/oZAp6OPThxFBIxgeNJxoz2jkMutIB2lLpvcN5M/jGWxJzOGZVSdY/fCwVqsvl1Ht5nI6h31JTUxzuRoXdsPKO0FbDJ6RMGcVeFqXBa5k49y5KNWVcjLvZI1jS+4JSnQlddr5O/vTx7uqlodPH6K9orFX1J9+YtSUUfLXeopX/0rFsWPm+Qpvb9xvmoHbzFnYR3RprY/UoZDSYWyA34+m89yvJ6nQGwl0c+CTOf3pF+Le3t2yeqwulKs4vUb0SN4hOlDUxqNLTYpLl5Fi9EcnRJ+dQ8q8uehTUlGFhRK2fDkqX9/27larYXXjVKJ+TEbY+YY4gShS3rIM3ILMTQRBIPvV1yhcvhwUCoLfX4J6fMdwpejM47T8yFHSHnkEY2EhysAAQj/7rNnRD7aMqayM0m3bRUFkzx7Q683LHHr3xnXqFLTXDyDJrkgUOqqiO5KLkzGY6k9l8Xf2txA7unp0pYtrF1SKhq2nq91hAAshpD53GK1Ry5HsI+xJ38Pe9L0kFSdZbMvTwZPhgWKUyLDAYXg4dB7xPau4kgnv7qRUa+Df06K5b2TLCAgmk8CJ9GK2VtX3SMy0vAkM8XRkXA8/xkf7MajLVdJcrsaJn+H3BWDSQ8gQ+MeP4OTZIv1vKSQb546NSTCRVJRktqc9nnOc5OLkOuKsvcKenl49zVEefbz74Od89ZqOgiBQcfQoRatWU7JhA0J5ubhAocDl+utxnzUTl1GjkKkaPl42Fls+50s1QTqACKIzmHhlXQLL9qcAMLKrN+/ffl3TlPFOTLv/gHVlkLKvRvjIPW253N4VuoyqKWhqZU8s2gNDXh4p8+ajS05GFRxM2PLvUAUEtHe3WpV2H6cSTePMBjEapLIYnH1g9jeiaFmFYDKR+a9/U/zbb8hUKkI+/6xDRAx01nFasmEDGc88i6DT4dCrFyGffoLSx6e9u2U1lOZlcuHPlVRu3IrL8WRktS4pE0JgX7Scv3vIKHEWBQlnlXMdsaOrR9dmp6TUd4Pp7+TPs4OeveoNZoYmg70Ze9mTtoe/M/+m3FBuXiZDRi/vXgwPGs6IoBH08urV4Z/arzyYyuJfT+KgkrPhiVHXXG+uxdNcroQgwO53YNtL4vuYm6oscB2ubXuthGTj3PEoqiwyO7WcyD3BqbxTaPSaOu2CXYLNdTz6+vSlm2c3VPLGiRWG3FyK//iDotW/ortwwTzfLjwct1kzcZsxo9UeEtryOV8SQWxIBDGaBA5eKCCntBJftQODuniSU1rJgu+PcDS1CIDHxkaxcHw3yf62CbT5D9hkguxTNaJH6n4w1vLslskhMLYmxSWoPzTiKVdnwVBYSOq8+WjPnUMZEEDY8uXYBQc1vKKNY8snmk5LQTL8NA+yT4qpa+NfgGGPQdWFvGAwkP7kIko3b0bm5ETY11/h2K9fu3a5uXS2cSoIAgVffUXO2+8A4DJ2LEFvv9VhrLmbisFkIKUkhXOFNWks54rOka6psdZ1KxMYfFpgeIKJ6LSadQW5DN110bhPm0bwtFkoW8lG2GgycijzEHGn4+jfo3+TUw30Rj3Hco+Zo0TOFJ6xWO5q58qwwGHm1Blvx6s7LNgigiAw56sD7D2fz+AuHjwxrhu5Gq352vRq16AZRRVsPZ3D1sTsetNcRnXzZlwPP8b0aGKay5UwGmDdIjiyTHw/7DEY/z+rc8eTbJxtH4PJwLnCczVRHrnHSSlJqdPOUelIb+/e5joevb174+Xo1aR9CQYDml27KFq1Gs3OnWAU3SBljo64Tp6M++xZOMbGtno5BFs+50siiI1cqGw4lcmLfyaQWVxpnufpbIfOYEKjNeDqoGTJ7f0Y20Oyv20qbfIDLs2G5O1wfqv4WpZrudwtpFaKyyirC89sCkaTkSM5R8gtz8XHyYdY39gWO2Ebi4tJuftutAmJKH18CFuxHLuwsBbZtrVjyyeaTo2uHNY+CSdWiu+jp8NNn4C9WFDQpNOR9vACyvbuRe7qStjy73Do3r0dO9w8OtM4FQwGsl5+maKVPwHgMXcufoufRdbBPzeIN8G5Fbnm4qTV6SzJRcnoTLp61/F18hXrdbjX1O0IKXekYtNWStavp/JUjYWtTKXCeeRIXKdORT1mdIsX7WvJcZpTnsPe9L3szdjLvox9lOosC7VGe0aLBVYDh9PXt2+jn+5aO5cKyhn3zk50RstijQFuDvz3xhgm9xKjM9skzeVKaEvhl7vh/GbxAdPkN2DwAy23/RbkUNYh7tl4T4Ptnox9kli/WNR2apxVzrioXHBSOXXKGjXtTV5Fnrlw6YncE8Tnx1NhqKjTLtw13FzHo69PXyLdI1HKr63cpjb5AsW/rqbojz8w5uaZ5zv27Yvb7Fm4TpmKwqXtipza8jlfEkFsQATZcCqTh1ccqaeUl0iwuyM/3D+EUC/r/yzWSKv8gPWVkFqd4rJdjPyojcpZDI2vFj68osxPh22Z1sxlNWo0pN5zL5UnTqDw8iJs+XfYR3Se1CBbPtF0egQBDn8Ffy0Wc9G9u8FtK8BHFDtM5eWk3nsfFUePovD2JnzFcuzCw9u3z9dIZxmnRk0Z6YuepGzXbpDJ8HtuMZ7z5rV3t1qFcn0554vOW0R2nC08S7G2uN72Tkonojyi6hQqdbO/emSHLiWFkr/+omTdOrTnapzPZI6OqMeMxnXqVJxHjkRuX38hwKbQWuPUYDJwKu8Uu9N3szd9L/H58RbLXVQuDAkYYo4S8Xf2b7F9tzUbTmXy0IojdeZXX8k8cH0ERWV6tp3JIbe0bprL2Cob267NSXO5GqVZ8P0tkHWiygL3a+gxteX30wwKKws5XXCa0wWn2Zq6leO5x695W9WCiIvKBWc78W9nlbOFWGJ+tXNGraqZ72InLnNWOXdYMaW5D+j0Rj2nC05bpLbUjnCrRq1S09vHMsqjoWNfQ5jKyijZuImi1atF17EqFJ6euM2YgfusmdhHRTVrH9eKLZ/zJRHEykUQo0lgxBvbLCJALsffzYG9z46VUmCukRb5AQsC5CTWpLik7AVD7e9MBgF9RcEjahwED7JwjOgItGYuq6msjNT7H6DiyBEU7u6ELluGQ/duze6zLWHLJxqJKi4dgp/nQWkG2LnAjI+g580AGEtKSJl/F9rERJSBAYR//71N1rnpDONUn53NpQcfQnv6NDIHB4LefqtDFLY1moyklqbWiB1VER5pmrR628tlcsJcw0Shw72rWewIdAls9o1U5dmzlKxfT8n6v9Cnptbs08UF9fjxuE6bivOQIddc4K+txml+RT77MvaJUSLp+yjUFlosj3KPMgsisb6x2Cls47qgMdemtWmVNJerkZMoCiDFl8SaTP/4CYL7t+4+r4IgCGSUZXA6/zSnC09zOv80iQWJV0x9uRoh6hAEQUCj16DRaTAI9RcRvlaqxZBqQaVaIDGLKHYudcSWy9s6KZ2sKmXnWh7QZZVlWUR5JOQn1IlykyEj0j2Svj59zVO4W3iLCEmCIFB5/DhFq1dTsm49puoip3I5LiNH4jZrJurRo5HZte8xw5bP+ZIIYuUiyP6kfP7xxd8Ntvvx/iEMjWxaPpkECEYjmoMHSTp8mMgBA3AZNKjxocxleaJ7S7XwUXqZfZ86ACLHiQVNI0aDc8fLC66mNXNZTRUVXHrwIcoPHkTu6kroN1/j2LNnS3TbprDlE41ELTS5sOpuuLhbfD/0URj/IiiUGPLzSblzDrqLF7Hr0oWwFctRetnWcb2jj9PK06e59OBDGLKzUXh5EbL0Uxx7927vbjUJQRDIr8w3ix3Vr8nFyWiN2nrX8Xb0riN2RLhHXNGesSX7WnkqnpJ16yj56y8M2TXnGIWHB+pJE3GdOhWn/v2blIbUHuPUaDKSWJDInvQ97Enfw8m8k5iEmlQSR6Ujg/0Hm0WRYHVwm/TrWmjstemknn7MGRLG4C5eLZvmcjUu7IKVc0QLXK8ouHMVeLadFajBZOBC8QVzhEf1VJ/lKUCoOpTunt3p7tGdFYkrKNIW1duuvusoQRDQmXRodBrK9GWU6ksp05Wh0Yvvq19LdaU176uWV4so1etdyZHpWnFSOlkKJrUElCv9XTtaxcXOpUXElMY8oBsZPJKE/ASz6HE89zg55Tl1tuVu726O8Ojj04deXr1wsXNpVv8ux5CfT/Efayj6dTW68zWuVKrQUNxnzsTt5ptQ+VlP6QNbPuc3Vg+4tsQliWaTU9o4lb2x7SRqKNm0iexXXsWQnY09kAYo/fzw+9fzuE6cWHcFgw4uHYCkraLokXlZ2KLSEcKH16S4+PToECkujeFIzpGrPtEQEMgqz+JA5gGGBQ1r9HZNWi1pjz4mCiDOzoR++UWnFEAkOhAuPjD3d9j2P9j7Puz/CDKOwS3foPTyJfSbr7l4553oLlwg9b77CVv2LQrX5jliSLQMmt17SH/iCUzl5dhFRhLy2VLsglv3RrW5IdwVhgqSipIsojvOFZ2joLKg3vaOSkei3KNEN5Za6SztZQErk8lw7N0Lx9698H3maSqOHBEjRDZsxFhQQNHKnyha+RNKX19cp0zGdepUHPr0uWp6hWA0Un7wIIrDhynX6Zr28KMZKOQKenn3opd3Lx7q+xDF2mL2Z+wXC6xm7CWvIo8daTvYkbYDEOsIVAsiA/wG4KC0HieTxl5zTu0dwMiubeiSdPwn+OMRMe0wdCjc/kOr1lirMFRwtvCsObLjTMEZzhWdq1dMVMqVRLlH0cOzh3nq7tHd4iY60j3yqjbOzw561uL3L5PJsFfYY+9o3+TCmpejM+osxRJ9GRqdxiyY1Hl/mdii0WksxJRyQznlhnJyqCsmNAUnpVPdFB47S7HEItWnVgqQo9KRVw+8WkcAgZr/32d2PYNJMGEUjBbLFTIF3Ty6mUWPvj59CVGHtErqlmAwoNmzh+LVqyndvgMM4v+hzMEB10kTcZs1C6eBA1u9yKlE/UiRIO2EFAnSOpRs2kT6409ccXnQB+/jOmEC5J2rifS4uAf0ZZYN/XrXWNeGDrU6u7W2Yn3yep7d/WyD7WTICHMNo4tbFyLdI4lwiyDCLYIubl1wUln+FgWdjrTHHkezcycyJydCv/wCp9jY1voIVo8tq+0SVyDhD/h9Aeg0YuTYLcsgdDDaCxdImTMXY34+jrGxhH71JXJHx/bubaPoqOO08Kefyfrf/8BoxGnQIII//ABFK7mXVNOUEG6jyUiaJq1OKsul0kv13gDIZXJC1aF1xI5gdbBN1AQQDAbKDhygZP16SjdtxlRaU4xUFRyM69SpuE6bin23bhY3DiWbNpH96msYsrLM85T+/vg9/1z9Dz/aCEEQOFN4xhwlcjznuEWag73CngH+AxgROIIRQSMIcw1r1xsiq7s2FQTY/TZse1l83/NmuGlpi16T1a7fkViQyOmC06SUpFhE81TjpHSyEDt6ePYgyj0KVSOc/q7Vxtla0Bl1ZpGkVF9qIZ7UFkwuf3+5+HKlAsuthZeDlyh2VNnUxnjF1LkubWl0KSkU/fobxb/9hiGnRixy6N0b91mzcJ02FYVa3ap9aC62fM6X0mGsXASpzrvMKq6stzCqDLEmyB6pJkijEYxGzo8ajiG/iJoSXhYtUKpVRN1uQlZ6WS60s09VpMc4McVFbT0hae1Fdlk2/7fv/9iXsa9Z2wlwDjALIpEu4fR4by2K3YeR2dsT8vnnOA8e1EI9tk1s+UQjcRVyz8JPcyDvDMiVMOk1GHQ/lWfOkDJvPqaSEpxHjCD4k4+Rt3Pub2PoaONUMJnIfW8J+V98AYDbjOkEvPRSq+dhXy2EW0Dg4b4Po7ZTm8WOpKIkKo31P533dPA0p7BUCx4R7hE4Km1DWGsIk05H2Z49lKxbT+m2bQgVNe4MdpGRuE6dguvUqWjPnSP9iYXiDXNtqsSEoPeXtKsQUptSXSkHMw+yO303e9L31Im0DHIJYkSQKIgM8h/U6jdrl1P72lQmmOiZl4yntpQCezXx3hEIMnnbXZvWscB9XEwxvEYLXHP9jupUlgbqd3g5eNHDqwfRntH08BRfmysmNtfGuSNQW0ypHY3SmGiV6uXF2uI6ER71sXjQYu7ocUebCIumigpKNm6kePWvlB86ZJ6vcHfHbcZ03GbOsqmad7Z8zpdEECsXQaDGHQawuByq/ql+OifWbEUm0TBlf+8n9a6GbchCx+ThHCiDsKE1KS6+Pa3OW769qDRUsix+GV+d+qpeS7DaVOeyfjf1O1JKUkgqSuJC8QWSi5NJKkqyCM2WmQQeX2NieKKAXgGf3OGGtn+0GDXiHmGOHvF18u1UoYG2fKKRaACtBtY8CvG/ie973wo3LqE8/iyp99yLUFGBeuJEgt59B5nSurNTO9I4NWm1ZD73HCXr/wLA+9FH8X5kQasfdxqqsXQl7BX2RLpHWtTu6OrRFW/HjluP6nJM5eVodu6kZP16NDt3IehqPU1WKs1h5nWQyVD6+RG1dYvVWRwLgkBSURJ7M/ayO303R7KPoDfpzctVchWxfrHmKJFI98g2OTduOJXJ8jeX8eCJ3/GprHEKynVw47M+NzH3mfmtf22qLYVf7oLzW0QL3ClvwqD7G736tdbvMAseXtGt9vvqSMfS9qKxtsNfT/qagf4DW60fgiBQefIkRat/pWTdOkwajbhAJsN5xAjcZ83EZexYm3jQcTm2PE4lEcQGRBAQTzYv/plgUYn7ci92iSpMRijLhZIMsVip+TUToSSdvI3nyTvS8HB2HRSJ+33/xL53P5Qe7ZMPbY0IgsDGlI28d/g9MsoyAOjn048xIWNYcmSJ2KaeXNarucMUa4tJLk4mueA86reXEbInCYMC3r5ZzpGu9YtOLioXurh1sRBHIt0iCXQJ7JBPTGz5RCPRCAQB/v4ENv0HBKMouN62nLIzohOJoNfjNnMmAS+/hMyKhdiOMk4NhYWkPfIoFUeOgEpFwP/+h/vNN7Xa/kyCidSSVE7ln2Jryla2pG5pcJ1Y31gGBQwyR3eEqEM65LHvWjGWllK6ZSsl69dTtncvmOqmLVyOy7ixYp0XpRKZSiVOyupXJTK7qleVqqZN9XKVsqZd1bpiG7u6y5RKcdk1iBXl+nIOZR0yR4lcbtPp5+RnjhIZHDAYtV3rhNOXbNpEWlVace1PYap6H/zB+60bWVOSCT/cAlknG2WBW7t+R7VDS3Pqd7Q2HeVY2p5UC8o55Tn1pgU2p2h/YzAUFlKyZg1Fq1ajPXfOPF8VFITbrJm433yzTbrA1caWx6kkgtiICAJi+OHBCwXklFbiq3ZgUBfPzpcCoysTT3ylGVd4zRS94WuFvwlGKMuxpzTNgdJ0B4yVTf+RKgMDcIiJsZhUvr4t+clsgvj8eN48+CZHcsTIJH9nfxb1X8Tk8MnIZLJm5bIKJhNZ//0vRb+sAoWCoCXvoRw9nAslF0guSuZC8QWSipJILk7mUumlK4Y42sntCHcLrxM5EuYaZjP2g/VhyycaiSZwca/4ZLMsB+zdYOZnlKQqSV/4JBiNeM6fh+/ixVYbBdURxqkuJYVLDzyILiUFuVpN8Icf4DxkSIttXxAEMssyOZV3ivj8eOLz4knIT6BUX9rwyrV4Y+QbTI248k2fRA2FP/1E1n9faO9u1EWlqiOOyC6bh0pZV4ypFlJUKsqESrJ0eaRXZpFemYNWbsAoB4NChqCQ4+sWRBevKCK9uhHgHiKKMpcJOjXCjqpm//UIN2J/VGAycX7ceIvaKha0dmRNTiKsmA0laWKa8h0/QVCNBW5RZZG5bkd1wdKLJRdbvH5Ha9IRjqXWQHVqITT9Ad21IBiNlO3bR9Gq1ZRu2wZ6MWpLZmeHeuJE3GfPwmnQIKt+mNEUbHmcdgh3mA8//JCPPvrIYp63tzd79+5tpx61Dgq5rOMWPzWZoDzPMnqjnkgOtMUNbwswGRRoCn0pTXdEc0GPSVtzwyxzUIFei2CUcaWaIHKVgNOQgWhTc9GnpGLIyESTkYlmy1ZzK4W3Nw4x0ZbCSFCQ1d6cNIfc8lw+OPoBf5z/AwEBR6Uj9/S6h/k951vklY8PG8+YkDFNdjMQBIHsl18RBRC5nKC33hQL0wI9vXrS08vSEUZv1JNSkiJGj1RPRclcLLmI1qjlbOFZzhaetVhHIVMQog4xR49UF2atryirhES7ET4cHtwFv8wX3ah+vB3XkU9hevl/ZD73LwqWfYdc7YrPo4+0d087JOVHjpK2YAHGoiJUgYGEfP4Z9lFRzdpmbnku8fnxZtEjIT+hXncWe4U93T274+Pgw9ZLW+vZkiU+Tm3oumHj2IU3ziLVdcYMVL6+CAYDgl6PYNAj6PVQ/V5fPf+yV70eDHoEXT3Lql7rTcWpWre5Txl9qqZ+FnMFxLiMlKppK5nN3I8ZufzqkTWCgCEri/LDcS1fzyt5J/w0F7TFCF5RZMz8hNPGIk4f+7hd6ndIWDfjw8bz7uh36y0y3ZLFZnWXLlH0668U//a7hTjoEBOD2+xZuE2b1urFtCVaB6sWQQC6du3KN998Y35va2pUh0ZfYSlkXCl6o1aO61WxcxGdFFwDQB1ofjXgiuZkBqUHTlF2IK4qF1hMH1L4eKMeOw71+PE4DxxA6dPXkb5ZQLxIqC1aiJciAaPluL6/DOQKjKWlaE+fpjIhwTxpk5Ix5uVRtms3Zbt2m9eWu7nhEF1bGInGLizM6nKMG4vWqGV5wnK+OPEF5YZyAG6MuJHHYx/H39m/3nUUckWTcisFQSDnjTcp/OEHkMkIePUVXKde/emmSqEiyiOKKA/LmxOjyUhGWQbJRcl1BBKNXsPFkotcLLnI9kvbLdarXZQ1wl1Mq4lwi8Ddwb3Rn0NCosVwDYD5a2Hzf+DAUtj9Nu4RYzA98yTZb75H3kcfoVC74Dl/fnv3tENR8tdfZDy7GEGnw6FXL0I+/QSlT9OEhqLKIjG6o5bokVNe1yJSKVPS1aMrPb170surFz29exLpHolKrqoVwp19xYLofk7+xPp2XrespuI0oD9Kf38M2dl1C6OCOXIh8NVXWu18LQiCKHrUJ5Lo9GbRpd42+gZEGYt1atpoyovJLcmkQJNDcVkBMoMRhQmURgGlSYZa5oBa5oSzzB47k7yucFMt8FxOI1KLACpOnGgxEcRgMnDh4Kec3vsmp53lnA6I4rSdPSVb7qu3fVvW75Cwbq71AV1DmCorKd28maLVv1L+d41bktzNDbcbb8R91kwcoqOb232JdsbqRRCFQoFPEy9WJJqJyQTl+VdPTSnJgMqiRm5QBi6+VQJHYF2hwzVInOfgal5Dn5FB6ZatlG7ZQvnhwxYnZlVoKOrx41GPH49jv74WoWeuD78B5Q+QfcQVQ0XNQVDpZMLvuhJcH/4cqg6OCrUap4EDcRpYc2NvqqhAe/ZsjTASn0DluXOYiosp//tvi4OhzMkJhx49LIQR+8hIMazUShEEgc0pm3k37l1zvnEfnz48O/BZ+vj0adH95C55n4JvvwXA/8UXcL/ppmvenkIuRnuEqEO4PuR6y/1U5JoLsVYXZU0uSia/Mp/MskwyyzLZm2EZPebp4GlOp4lwr3KucYvsdEVZJdoBpR1MeQOCBsCfj0PydjzdzmO65xZyv/6F7NdeR+7igvusWe3dU5tHEAQKvvqKnLffAcBl3DiC3noTeQPpshqdhsSCROLz4jmVf4r4vHjSNGl12sllciLcIsSotirRo5tnN+wV9vVuVyFXsDhgHIvOf48MEGoda2RVN/DPBoyV6n80AZlCgd/zz5FeVcOiDoKA3/PPteoDC5lMBnZ2re4sdDldq151Rh1Hc46abXjPF50H9IAYYeth78HQwKGMCBrBsMBheDmKkceCIIDRaCGOlB08RMbChQ3uO/eddyhZswbXadNwvWGaWG+lEVTX7zhTcEZMa8k/zbmCRLSCEbzdqz8R6HVWUb9Dwvpp6gO6KyEIApXxCRT/upriteswlVQV0ZXJcB46FLdZM1GPH4/cvv7ju4TtYdU1QT788EO++uorXFxcsLOzo2/fvixatIiQkJB621fnAHXr1s2maoJgMkLqfmSabAQXPwgdar5Rb3EMlWJ0RmkmspIMUdioiuSQldZEb8gaGb0hqJxEAUMdgFD1imutv9UB4OIHjcjB1CYlodmyBc3WrWjjEyyW2Xfvjsv4cbiMG4ddt25Xv1FN/BPZX89RkZyHoVKB0sGIY6Q3wuTXIPrGRn0ui8+o06NNSkKbmEBlQiLaxAS0p88gVNa1LZTZ2WHXtSsOMTHYx0Rj3yMa+25dkTu0nKf9tXK64DRvHX6LuJw4AHydfHniuieYGj61xW/88z9dSn5VKpvvv/6F+x3/aNHtN4bqoqzVwsiFEvE1s+zKgcMuKhfCXcNrokeqXoOcg1rtxsRoNHLy5El69+4tRbp1NrITkK+ah6wgGZPcjpyiKRSuPwRyOQFvv416knXYeoLtjVPBYCDnlVco/vkXANznzMHnmafr3AxXGio5U3jGnM4Snx/PxZKL9RbbC1GHiIKHpyh69PDo0bSUO5MR+Qd92WIs5A0vD7JrOQL5Gww8k1/IeJM9woSXQWmPoFCBwk60WFbYVU1KkFe9Vs+7fLnCDmQKs0VshyfxT8ree6iehx9G/K4rwfnJpdd07rdVssuy2Zuxl32Z+/g78280eo3F8hjPGIYHDmdY4DB6e/dGKa8Zh4LRyIUJEzHk5NQfWQPI7O0RjEaLNCCHvn1RT52CetJklD5iVEaRtsjsynKm8AxnCq9Sv8Nkoru9Dz0iJtDdswc9PHoQ6RbZ7vU7WgNbO5Z2dIxFxZSsW0vJ6l/Rnjljnq8MCMD15ptwu+kmVEFB7djD9sGWx2l5eTlnz5617cKoO3fupLKykvDwcPLz8/n0009JTk5m7dq1eNTj6lEtgtgS7pm7CDn1MXaVueZ5OgcfLvV6hKKAUY3fkCCg0JVgV5mHqjK36lWcqv+2q8hDqa/fHqzO5pBhsHdH5+CNvmrS1X519Ebv4INR6XztF1omE/ILF1AcOozi8GHkmTU3p4JMhqlbV4wDBmAcMAChqcVKBSMu+SdRafPR23uh8eotXhS2FCYTssxM5BcvIr9wUXxNSUFWXl63K3I5QlAQpvBwceoSjik0FBwd69lwy1NsKGZ19mp2F+5GQMBOZscU7ylM9ZmKvbzlFW3ln2uxW7kSAN2dd2BoIAWmrak0VpKlyyJDmyFOleJrji4HE/WHAitlSgLsAwiwDyDIPogA+wAC7QPxs/NDJb/2izSTYOJM2RmKDcW4Kd3o7txdymHuZMj1GrocfR337H0IAlxM7E3liXwEhQLtU//E1KflIrQ6DRUV2H/wIYoTJxBkMvRz7sQweTIGk4E0bRoXKi6Yp/TK9Hp/914qL8Idw+ni2IUujl0IdwzHWeHcrG65Z+wkMu5FAIzAEQd7chUKfIxGYiu1tORlpoAMQa5EkCkR5CoEuQJBJr6a5Kpay6qWV/1tqncdpXky1X5vsb4CQa6qtb7ldqu3Z7psezX7Ev9u8vWEYKT3ljtQVeaCCcpz7cwPP5x8dCAHvYMPJ8f/0LLXADaCQTCQVJ7ESc1JTpaeJKUyxWK5k9yJni496a3uTW+X3nioPFAcOoTdkveB+pKKQbfwCYwxMSgOHUa5fz/y+HhzJJNJBimRanZHw7aocsod6n6frkpXwu2D6Vt4kb6FKfTQGRB6PEx+l5ta4X9AQqIeTCbk8fEod+xEcfgwsipBT1AqMQ4YgGH09Zh69hRr5EjYLDYtglxOeXk5EyZM4L777uPuu++ud7lNRYIk/ol81V2Ilys1VL8zzf5WfHph0NZEb1RHbtQXvVGPHVh9CEqHmuiN6vSU6kiO6jSVRkZvNBVBr6ciLo7SLVsp27ZNzOOtRqnEaehQXMaNw2XMaJTezcvxbGsVUxAE9GlpaKuiRcSokUSMBXUL5SGToQoLwyE6GvuqIqz2PaJRuLdccSWdUcf3p7/ny1NfUqYvA2BK+BSeuO6JK9b9aC6Fy5eT+/obAHgvfALP++9vlf20BnqjntTS1JrokRKx7khKSUq9VnsgFmUNdgmusfStihzp4tpwUdatqVt58/CbdQp6PTPgGcaFjmvRzyZh5QgmZHvfR7bjFTCaSD/WhdKzWmQODgR//jmO/du/RoStPBXSZ2WRseAR8YmevR2X/nkLB7oKxOfHc7bwLPp6ohy9HLzo6dWTGK8Y8dUzxpwy0Gx0GmSn1yE7+RMk70TWiFKZgm8MOHmBUQ9Gnfhq0te8Nxlq5lfNk13BVcvWEKqjWszRLSpxsoh+UYG8KkpGp0GWcaTB7RrnroHwEW3wCaybvIo89mfuZ2/6XvZn7qdYZ1mUvpt7N4JcgijbupW7NpvwrmVslKeGbyfIGXXHM3jYe4h2tAWnybyUSO/jJQxPMNEto6a9XgGnuzuRPawrDqNG0M2/Nz08e+Bt0CP/8XZk2ScRVE6YZn4J3Sa30f9A+2Mrx9KOiD4jg+LffqPkt98x1Hrwat+9O66zZuI6bRoKd/f266AVYcvjtENEgtTH3XffTWhoKC+++GKdZTZlkWsywpJeYm2NKyFXgb0aKuq5ib4STt51CotavgaAo0ebhsmaKioo27uX0s1bKN2xA1NxzUlX7uSE8/WjUI8fj8v116NwablcT2uwdxIEAUN2NpUJiRYFWK9kP6cKDMShZ40rjX10dJMtewVBYFvqNt4+/LY5j72XVy+eHfQs/Xz7NfcjXZHClSvJekH8XXovWIDP44+12r7akoaKsl4Jf2d/It0izUVZI9zEwqzuDu5ma7fLQ+5by9pNwkZI2gar7kXQFHBpvx9laQrkLi6ELvsWx549G16/FbGG4+mVMAkmLpVe4uzBTXj9ZymOheUUOct4Y7acpEDLc52rnatFDY+e3j3xc/Jr2ZRAkxEu7ITjP0Hin1AlQjea+Wuhy8gm7tNUJZTUEkcuf28hoOjAWOvveoQVC+Hlatu0mF/fNmvNt9imrmmfsTkMegCGPgruoZ0nTagBjCYjp/JPsTd9L3vS93Aq75Sl1ahJIPqSgIcGCl0gMUSGIK///666fkd/YzADTlYSsO888gs1dXTkTk64jB+H2/DeOJ97DZkmvcoC92cIan+Rty2x5mOpLSIYjZQfjsOQm4vSxwenAf0t0h5NWi2lW7ZQvPpXyvbvN6d5ydVq3G68AbeZs3DoGSPVg7sMWx6njdUDbEoE0el0jB8/nltvvZVHH320znKbEkEu7IZlNwCNDItV2NcVM8xFRqujOfxBaR0Fe4zFxWh27KB0yxY0u/dY1M9QeHjgMm6s6OgydGirFRmy5h+woaCgjjCiT02tt63Cx9vCrtchOgZVUGC9B+wzBWd489CbHMw6CICvoy8L+y9kWsS0Vk2zKFq9msx//RsAr/vvw2fRog5/QmmoKOuV8LD3QKPX1PtEGkQhxM/Jjw2zNkgFEjsjRanw8zxMqce4tNOT8lx7FB4ehH2/AvuIiHbrlrUcTwVBILMs09KaNi+BiNPFLPrdhKMO0rzgtVsVaLydiPGKMYsdvbx6EawObr1jU3Y8HF8JJ38RIzSr8egCfW+HXrPgu+ligfEr+cO4BsLCk61XF8yaEARRMLIQR+oTVq4izGSdgr3vNX6fzr4QPBCC+4uvgbFgLxXaBCisLGRFwgo+P/l5g227undloP9Ac8HSKPeoOvU7Ks+cpWTdOkrWrUOfnm6er7Azou5qj9sjr+I4eppFcfvOgLUcSzsCJZs2kf3qaxYPFpX+/vg9/xx2oaEUrVpN8Z9/Wjx8dRo8GPfZs1BPmGAV9fqsFVsep43VA6zaHeaNN95gzJgxBAQEUFBQwKeffopGo+Hmm29u7641H40YAr/FyZHXLyuQ5mcwsDi/kPHlFTDuBeg/v82jN64FfXYOmm1bKd28mbKDhyyKZikDA8yOLk6xsciUVj30Wh2lpycuI4bjMmK4eZ6xtJTKREthRJd8AWNuHmU7d1G2c5e5rdzNDYeYGsveyohAlhb8wa9Jv2ESTNgr7Lmr513c0+uephXuuwaK//yTzH//BwCPeXM7hQACoiOAr5Mvvk6+DAkYYrGsuihrdfRIUnESF4oukFGWQaG28KrbFRDIKs/iSM6RFql4LmFjuIfC3RuQb3iWYGEZqdu8qCwsJPWuuwj74UfsgjtXgba8ijyz2HEq7xQJ+QkUVFpGR447auK+jSYUAuRG+6F94WG+CBtAuGt46wuJpVmi6HH8J8g+WTPfwR16zYS+/xBvtquPiZPfgJ/nIVZbqC2EVC9/vXMIICD+nyiU4nSt9LwZTv50FWEJsHMGr26QfQrKcuDMOnECkMnBNwaCB4jfU9AA8O7WKWsBeDh4EOke2ai29/W+j6kRV6/35dC9Gw7du+Hz5EIqV71D8fIPKUm1x1ipoCjeQNGCZ1D6v4vrlCm43jANhxjpabxE4ynZtIn0JxbWKeBryMqq4xal9PfH7eabcJ85E7srmGtIdD6s+k40KyuLRYsWUVRUhIeHB/369ePnn38mqCNU6XXxY4uTI4t8veuctnMUChb5evNuTh7jgweAk2e7dLEx6C5epHTLFko3b6Hi+HGLZfZdo3CpEj6kk1vDKNRqnAcNwnnQIPM8U0UF2jNnqKgljGjPnRcte/f/Tfn+GsveG+2gty+YuoUzYNSt+DsNw57WraxesmEDGc8uBkHA/R+34/fcc9L3DLjZu3Gd73Vc53udxfxyfTkrElfw4dEPG9xGbnlug20kOigqB7jxfRRBAwjhKVI2q9Hl5JI6fw7hK39G2UFt44u1xcTnxVtEedSumVONUqakq0dXYjyiGftXJj4bdgPgNmMGPV76X+tblerK4PQ6MeojeTtUu13IVdBtkhj10XVi/ZGZMdPh1u9gw7OW6bCugaIAEjO9dfve0ZArGhaWbloq/r/qKyHrBKQdqpoOQ/ElURzJPgVx34rt7d3EFI3ggVWTdV+HtSQ+To07tjS2HYKAbNfbOMa/jGMs+N05k/KA+RRv2Ezp5s0YsrIo+OYbCr75BrvwcNFyd9o07CO6NONTSHR0BKOR7Fdfu6KDUTUuEyficctsnIcNa1WbbAnbxKbSYRrCltJhjAYdk76LJVtOvREeMkHAzwQb5h1BoWxb7/mrIQgClQkJYprLli1oz523WO7Yty/qCaLwYRce3j6drMKWQ7muhkmnQ3v2HMf2/kb8vj/xuVRCWA7YG+q2ldnZYd+9e610mmjsu3W7phSky/MujSXFpD+5CAwG3GbNJOCllzpdWOu1cCjrEPdsvKfBdk8NeIr5Pee3QY8krJqMo+i/nkvKb1r0ZUrsQ30J+/mPNi/e1tLH0zJ9mWhJW0v0qK5hVBsZMiLcIujp3ZOeXj3p5d2Lbh7dsDPKyFi8mNK/NgDg/dijeC9Y0HoirMkIF3dX1flYA7pa9YCCB0Hf26DnzMbfLJuMkLJPjAp18YOwYZ0nAqQ1SFhTj7AU1LCwVJoliiHVokjGEdDXdXnDM8JSFPHr1SrF49sbo8nIpNWTyCnPqdcmuknpmkY9rH0Sji4X3w9fCOP+a46yMel0lO3aRfG6dWi2bUfQ1hQgd4iJEQWRqVNQBQS01MezCjrqtWlbIAgC+pQUCn/5hYKvvm6wfeiyZTgPHtRgO4m62PI47RDpMB2ZI3nHyVZc+WJNkMnIUojt2jskXjAaKY+LqxI+tqLPqHWRoVTiPGgQ6gnjcRk7DpVfE61sJZrM+bKLvJnxHgccDsBY8HEM4Im+jzJR1gvd6dNUxldFjSQmYtJoqDx5ksqTtcK0lUrsIyMthBGHHj2QO1/Z/rG+vMtqXG+8kYD//U8SQBpJrG8sfk5+V7zIrObtw2+zL2MfC2MXEu0V3YY9lLAqAq9DtWgXoa7zSPn6LNrUHFJvnULYqr+Qu7q3SReMJiOHsg4RVxSHPkvPwICBTUozqTRUcrrgNPH58WbR40LxhXrHf6g61Fy4tNqx5fKUPkNhIakLHqHi6FFQqQh46X+433RTcz9m/eQkihEfJ36G0lrnPvcwMeKjz23g1bgUAgvkiqYXP5W4MjHTocc0jBf2kBJ/gLCeg1F0GdGwsKT2h+gbxAnEAq45CZB+uEYcyTsLBcnidOInsZ3SAQL61aTRBA8EN9uPUlbIFSwetJhFOxYhQ2ZZKLUqsubZQc82/PuvLIFf7oKkrWLK0dS3YOB9Fk3kdnbmNGmjpgzNtq0Ur1tH2d595sjXnLfewnFAf9xuuAH1pEkoPTxa+iNLWDGC0Yj27FnKDx2mPC6O8iNxGHPzGr2+IVeKqJW4MlIkSDuxPnk9z+5+tsF2r498nWkR09qgR5aYtFrK9u0ThY9t2zEW1tQxkDk44DJyBOoJE0RHF7eWs3VtSWxZxayPgsoCPj76MavOrcIkmLCT2zG/53zu631fvXU/BJMJ/aVLYp2R+Jp0mtrfpRmZDLvw8BphpGcMDtHRKNzcrph3WU3Qe+/hOqXz2Nu1BNXuMECdi0wBgZFBI9mfuR+DSQzvmdJlCo9d9xghaimXtdNiMqL9YTEpb67BqJPjFGpPyPe/I/cJb9XdbknZwusHX69j5bx40OJ6HYz0Jj3nCs+ZBY9Teac4X3QeYz0Wrv7O/ubojmp7Wjf7q59PdBcvkvrgg+hTUpGr1QR/+CHOQwY3/4PWRpMDJ1fB8R/F9IlqHNzEGhR9/wEhg62+TldnpFXO+xWFkB5XJYpUCSOVRXXbqQNriSIDRJHEzrqvRa9Efb97fyd/nh30bMPOZSWZ8P0tYo0clRPM/ga6N/4awVBYSOnGjZSsXUf54cM1C5RKnIcNxW3aNFzGjUfhcuUHN9ZMR7s2bUlMOh2VJ09SfjiO8rjDVBw5iklj6cInU6lQhYejO3euwe1JkSDXji2P0w7pDtMQtiSCNDYkPtA5kH/0+AfTIqY1PgfzGjFqNGh27KR0yxbKdu3CVF4TEip3c0M9ejTqCeNxHj4cuaNjq/alJbDlH3Bt9EY9P5z+gc+Of0apvhSAiWETebL/kwSrg5u0LUEQMGRl1RFGDNl18+4BlIGBGPPzLcJULZDJUPr5EbV1i5Rv2UQausi8VHqJj45+xPoL6wHRgvCWbrfwYJ8H8XL0aq9uS7QzFWs/J3Xxu5gMMlxCTQR/vBRZ1zGtsq+GrJzfvv5tItwiOJV/yhzhcabgDDpTXetTTwdPenn3shA9vB29m9Sf8iNHSFvwCMaiIlRBQYR8/hn2kdcQhVEfunI4s16M+kjaBtWijVwp1vfoezt0nSTWa5GwWtrkvC8IkJ9Uq7bIIdEZ6HKhT6YA/1610mgGimk1NiKeGU1GjuQcIbc8Fx8nH2J9YxuOAMlOEAWQkjTRieeOn5plgavPzKRk/V+UrFtHZUKCeb7M3h6XMWNwnTYVl1GjWs1lsDXoKNemLYFRo6Hi6FGz6FF54iSCzvL8IXd2xvG663Aa0B+n/v1x6NMHmVLJ+XHjxWvX+m5jpWvTZmPL41QSQaxcBGko7/Jy5DI5wwKHMSNqBmNCxmCvaJkDviEvj9Jt20ThY//foK+x7VT6+aEeNw71hPE4DRiATGVb+a+2/AMGUbDYlbaLtw+/zcWSiwBEe0bzzMBnGOA/oEX3ZcjLq3KmqXGn0V+61Oj1JbX92jCajBzKPETc6Tj69+hfb5pBYn4i7x95n70ZewFwUjoxv+d85vecj7PKNp+ESTSPsi2/c+mJ5xCM4BpeQeC/FyEb/liL3lxVn6PqK0zaEGo7tVnsqH71c/JrVr2OkvXryVj8HIJOh0Pv3oR8+glK76aJKHUwmSBlj1jnI+EP0JXWLAsaIAofPWeCsyQ62grtdt7XlUPmsRpR5NIh0NRNH8XRQxxb1dEiQf3B0b3t+tmaJO+An+aCtkR02LlzFXiEtdjmtckXzJa7uosXzfPlajXqCRNwnTYV58GDrd590NavTZuDIS+vSvAQRQ/t6TPicbgWCi8vnPr3x2lAfxz798ehe/d6v1NzlDJYCiFV55mg95fgOnFia32UDo8tj1NJBLFyEQSuHhIP8PLwl6k0VrImaQ3Hc2ucV9R2aiaHT2Z65HT6+vRt8oWlLi2N0s1bKN2yhYojRywOHnZduog5mhPG49Crl03XebDlH/D5wvO8dfgt9mXsA8DLwYsnYp9geuT01rd8rMJYXEz+N9+Sv3Rpg20D334btxvaPm2rI9DYcXog8wDvxb1HfH48ID5Zf6DPA9za7VZUHbBAn8TVKd28gbQnFoFJwKNrGX5zrkd20ydgr26R7Tc2WtFObkcv714WgkeIOqTFCpQKgkD+l1+S+867ALiMH0fQW281Lxox90xNnY+SWsVY3UPFGh99bgfvqGb2XKI9sJrzviBASbpl0dXMY2CorNvWu1uNKBI8EHyim2cb3B4c+xHWPAomA4QNh9u/FwWfVqC6QH/JuvWUrFtnEcmq8PISLXenTcWxXz+rdKuzmjHaygiCgP7SpZrUlsNx6FJS6rRThYRYiB524eGN/t7qq1en9PfH7/nnJAGkmdjyOJVEEBsQQaDxeZcXii/wZ9Kf/Jn8J1llNT/2cNdwpkdO58bIG/F39q93H4IgoD171ix8aE+ftlju0KuXWfhosdBiK8AWf8BFlUV8fOxjfjn7C0bBiEquYm7MXO7vfT8udi5t3p+yAwdJnd+wQ4kUCXLtNGWcCoLAppRNfHj0Q1JKxIuJYJdgHr3uUaZ0mYJcZruipUTTKV67loynnwYBvGJK8R0bALetAJ/uzd72lye/5P0j7zfY7rURr3FD5A3N3l99CAYDWf97iaKffwbAc/48fJ955trCmzW5cGq1WOcj81jNfHs36HmTGPURMsTsXCFhm1j1ed+gE6140w5XFV49JBZbvRyVs5hCEtS/Jo1G7df2/W0MggC73oLtr4jve82Cmz6t3x66NXZvMlERF0fxunWUbtiIsajIvEwVFGS23HXo3q1N+tMYrHqMNgPBZBKLmNYSPeoUJpXJsO/aFacBA8yih8qveWP7cudCpwH9pRSYFsCWx6kkgtiICAJNy7s0CSYOZh1kzfk1bEndQoWhAhCjRwYFDGJG5AzGhY7DUeFAxbFjZuHDIrVBLsdpwICqqtzjUAUGtsXHbHNs6QesN+n56fRPfHL8E0qrQrLHh45n0YBF7VoMUzAapbzLVuZaxqnepOe3c7/x6fFPyasQK6X38OzBwtiFDAscZpVPvyRah8KVP5H1wgsA+PYtwasvMONj8ca+iQiCwN6MvXwb/y0HMg80ap2vJ33dKg5mRo2G9IVPUrZnD8jl+D33HJ5z5zRtI/qKqjofP8H5LZZ1PqImiLa23aZIdT46ELZ03gegLL9GEEk7BOlHxHSSy3ELtSy66t+n/cetUQ9rF8LRFeL7EU/C2P9rNyFR0Osp27dPtNzdstWirp191yizIGIX0r4Fxm1ujF4BQaej4lQ85YcP1xQxLS21bKRS4dizp1nwcIqNtVozBQlLbHmcSiKIDYkg10qZvozNKZv54/wfHM4+jMIo0OuiwLDzCoacl+NYUlPMUmZnh/Pw4ajHj8dl7JhOYTNmKz/gXWm7eOvQW+a6H909uvPsoGfb3Rq5GinvsnVpzjgt15ezInEF35z6Bo1erKA+yH8QT/Z/kl7evVqjuxJWSP6XX5Lz9jsA+A8owiOqHIY9BuNeaFRYvc6oY13yOr5L+I7zRecBkCPHTmFHpbGe8H1E4d3PyY8Nsza0eIqePiuLSw8+hPbMGWSOjgS98zbqsWMbt7LJBKn7xHSXhD8sbygDY8WIj16zwLmZ9UQkrBJbOe9fEZNJtOQ1F109LFr2Xl47Tq6CgD61iq4OEG2b20oAryyBX+aLRYRlcpj2DgxoOH2urTBVVKDZsUO03N25C6FWvTuHvn1wmzYN9eTJqHx927xvtjpGjZoyKo4dM0d5VJw4UadovszJCad+/XAc0B+n/gNw7NsHuYMkMtsitjpOQRJBOoUIAmAqK0Ozew/Zf62hcvcelOU1VZXL7SGxhxN2Y0Yx5KaHCfW3nnDAtsDaf8DJRcm8efhN9qaLBS89HTx57LrHuDnq5jar+9FYpLzL1qMlxmlhZSFfnvySH0//iN4kXuxNCJvA49c9TrhbeAv2VsJayXlvCfmffQYyCBxSiFtYBYSPhNlfg0v9F/rF2mJ+PvMzP5z+wRxR5KR0Yla3WcyJnkNCfsJV61a9O/rdhu0ym0hlYiKXHnwIQ04OCm9vQj79FMfejRD08s7V1PkoTq2Z7xZSVefjNvDpXOfAzoi1n/evCW0pZBytEUXSDkFZbt12zj5VRVerIkaCYlusRpAFJRlVFrinRAvcW76FbpNafj8thLGkhNLNmylZt46yvw/UFOKUy3EaPEgURCZMaLMIBVsZo4b8fMrj4qiIi6P8cByViYl1i5h6eNREefQfgEN0D6svTCvROGxlnNaHJILYkAjS1Hw2Q2Ehmm3bRUeXvXst7KQU3t5oh/Vhb5SB5Y7HKDHVhAPG+sYyI2oGE8Mmtkt9ibbGWn/AxdpiPjn2CT+d+QmjYEQpVzI3ei7397kftV0rXLC0EFLeZevQkuM0Q5PBx8c+5s+kPxEQUMgUzOw6k4f7PtzqFtsS7YsgCGS/9BKFP/wICjnB12tQ+xaBOgBu/Q5Camr2XCq5xPLE5fx+/ndzSqWvky9zoucwq9ssXO1czW0bW7eqJdDs2kX6wicxlZdjFxVJ6GefoQoKuvIKZXlVdT5WQsaRmvn2rhAzQ4z6CB0m1fnoRFjreb9FEQQoSqkSRKpEkczjYNJf1lAGvjEQXKu2iHf3xv8eTEZI2QeabHDxg7BhkHu6ygI3vUUscNsaQ24uJRs2UrJ2LRXHawwHZCoVzqNG4TZtKi5jxjSv8HIDWOMYFQQBfXo65YcPm0UP3YULddqpgoJqRI8BA7Dr0kVKv+2gWOM4bSySCGIjIkhjn7DrMzIo3bKV0i1bKD982EKNVYWGVtX3GI9jv75mR5cKQwXbUrexJmkN+zP2m5/kOSgcGBs6lhmRMxgcMNjqog5aCmv7AetNen458wufHP+EYm0xAGNDxvLPAf8k1DW0nXsn0V60xjg9W3iWD458wM60nQA4Kh2ZEz2Hu3vdbdVCm0TzEEwmMhYvpmTNn8js7AiZqsDZIUkMnZ/8GsfCB/JdwndsSdliPh909+jO/J7zmRw++YouQ42xcm4uhSt/Iuull8BoxGnIEII/eB+Fq2vdhvpKOPtXVZ2PzaIbBYBMAVHjxTof3aeCqvVuYiSsF2s777cZ+krIOllVV6RKGClKrdvO3lUULYIH1kSN1JcalrAGNjwrRn1U4+QlWgEbKkQx5c5fWtQCt63RpaWJDjNr16I9d848X+bkhHrcOFynTcVl+HBkqpZ1X7OGMSqYTGjPnTentpTHxVm47FRj37WrObXFaUB/VP71GzBIdDysYZxeK5IIYgMiiLnWwuVfQZWq6vfcYkzl5ZRu3kJlfLxFE/sePWocXbp1a1CJzS7LZm3yWv5I+oMLxTXqrq+TLzdG3Mj0qOlEuEW0yOeyFqzpB7w3fS9vHnqT5GKxEnxXj648O/BZBgcMbtd+SbQ/rTlO47LjeC/uPbPFtpu9G/f3vp/be9yOvaJtqvdLtC2CwUDaEwvRbN2K3MmJ4Dlh7DfsY5mbK8ccar7z4UHDuavnXQz2H9yoJ3mtNU4Fk4ncd98l/8uvAHC7+WYCXnwBmZ1dTSOTCS79LUZ8xP8OVSIyAAH9qup8zAYXKdqps2NN5/12pzS7VtHVw2LRVX1Z3XYeXSxrixRehFX3UKcOSTU+PeCeDa1mgdseVJ49a7bc1afV2GYr3NxQT5qE6w3TcBowwPyQsTm0xxgVdDoq4uPNUR7lR49iKi62bKRU4tizZ43oEXsdCnf3NumfhPVhy8dSSQSxchHE7LpRKwLkqshkOMbGmh1drrW6tSAInMo7xR9Jf/DXhb8o0dUUjevt3ZvpkdOZ0mUKbva2X73ZGn7AF4ov8Naht9idvhsAD3sPHr3uUWZ2nYlSLuVNSrT+OBUEgW2XtvHBkQ/MIlyAcwCP9HuEGyJu6LCRYJ0Zk1ZLygP3U3ngEGWOcv5zp4w0HxkqQWCa0Y55Y9+ma5dGFhqtojXGqamykozFz1G6YQMA3o8/hvfDD9eIMnnn4cRKOPGT5VNt12Doc6sofrSAHbBEx8EazvtWi9EgprPUri2Sd6bp23ENhIWnoAOeOwRBoPLECYrXrqPkr78w5uWZlyn9/HCdMgXXG27AoWfMNaeBtMUYNZWVUXH8eJVdbRwVx48jVFoWuZY5OuLYr685ysOxTx/kVn7vJNF22PKxVBJBrPyHXHbgIKnz5zfYzqFPb9xnz0Y9dixK75atZq8z6tiZtpM159ewO303xir7QJVcxeiQ0cyInMGwoGGo5C0bCthWtOcPuFhbzNLjS1l5eiUGwYBSpuSO6Dt4sO+DFvn2EhJtNU4NJgNrktbw8bGPySnPASDKPYqFsQsZFTxKyuvtIORV5PFD4g/8cfInHl9WQLcMKHKRceqZ0dx0aRM+pblg7wYzP4PuUxq93ZYep4bCQtIWPELF0aOgUhH4ysu4TZ8O5QU1dT7SD9esYKeuqvNxG4SNkOp8SNSLLV+4twsVRZAeJ05ph8QaIDpNw+vNXwtdRrZ699oTwWik/OBBiteto3TjJgv7V7uwMNFy94Zp2Ec0LYq6NcaoobCwJsojLo7KhAQwGi3aKNzdqwqY9sdpQH8coqNbPNVHouNgy8fSxuoB0qPodsKQW09l73rwnDcftxumtUof7BR2TAibwISwCeRV5LE+eT1rktZwpvAMm1M2szllM54OnkyLmMaMyBl095SeuDWEwWRg1dlVfHzsY4q0RQCMDh7NPwf8U3LpkGhXlHIlM7vOZGqXqfx4+ke+OPkF54vO8+i2R4n1jeXJ/k/Sz7dfe3dT4ho5X3ie7xK+Y23yWrND0Hd3h/CvFVrcU3IY8/k53Jeuhp1Pw6UD8OPtMOppGP1cmz/R1V28SOqDD6JPSUXu6krwkndwds+HH++Ac5tqCjzKFBA5Voz46D4V7Kz74YaEhM3h6A5R48QJ4MQv8Ot9Da+nqVs/oqMhUyhwHjoU56FDMf3f/1G2ezcl69ZRum07upQU8j75hLxPPsE+Jhq3adNwnToVVUBAm/RNn55OeS3RQ5eUVKeNMjBAjPKoEj3sIiJaJJ1HQqKjIIkg7YTSp3G5y41t11y8Hb2Z13Me83rO40zBGf5I+oN1yesoqCxgecJylicsp7tHd2ZEzWBql6l4OXq1Sb9sif0Z+3nz0JucLzoPiE/Znx74NMMCh7VzzyQkanBQOnB3r7uZ2XUmX5/6mu8Tv+dIzhHm/jWXMSFjeCL2CSLdI9u7mxKNQBAEDmYd5Nv4b9mTvsc8v49PH+7qeRdjQ8YiTC7g4pw56FNSSV34L8K+WY7y0Dtw8DPY9Zb4BHjWV+Dk2SZ9Lo+LI23BIxiLi1H5eRMytzv2u+6Aylr56f59oO8/oPfsK9r7SkhItALqRha+dPFr3X5YGXI7O9TjxqEeNw5TWRml27ZRsnYdmr170SYkkpOQSM5bb+PYvz9uN0xDPWkSSs+6x9Tq6BLF4cOU63S4DBrUoMueYDKhS0qyED0MmZl12tlFRZpTW5z690cVGNhin19CoiMipcO0E+aaINnZdQujAshkKP38iNq6pd1sSPUmPXvT97ImaQ07Lu0wP11UypSMCBrB9KjpXB98PXYKu6tvqJ1oq1CulJIU3j70NjvSdgDgbu/OI/0eYXa32VLdD4kGae+Qw6yyLJYeX8pv53/DJJiQy+TMiJzBgn4L8HeWKsFbI3qTno0XN7IsfhmnC04DIEPGuNBxzO85v05Ejz49nYt3zsGQlYVDr16EfvsNiuT1sOZx0enBLRRu+w4Cr7viPltinBavW0fm4sUIegMOvjJChmWidKhyOnMNgt63iFEfvtHXtH0JifY+nto8JiMs6QUlmdRfGFVWVRPkZIesCdJUDIWFlG7cRMm6daJzY/X1vEKB87BhuE6binr8eBQuLo12gxT0eioTEmrqecTFYby8iKlCgUPPnuYoD8fYWJQeHadQrUT7Y8vHUqkmiJWLIFDLHQYshZCq3Pyg95dYHBjbk2JtMX9d+Is1SWs4mXfSPN/N3o0p4VOYETWDnl49raquQGv/gEt0JXx2/DN+OP0DBpNY9+P2HrfzUN+HOkRhWYm2wVpONMlFyXxw9AO2pm4FwE5ux53Rd3Jv73ul8WwllOpKWX12NSsSV5BdLoajOygcuCnqJubGzL2q1bY2OZmUO+dgLCzEacAAQr78AnnRefh5LhQkg8Iepr0NsfPqXb8541Qoyyf/jcXk/ixGq6iDKwgcUoTcyRmip4t1PsJHSjdVEs3GWo6nNk3CGvi5+jhQ+xah6vru1u8gZnpb98rq0WdlUbL+L0rWrbNwdJTZ22MfHU3lsWN1V5LJAAHvBY8A1BQxraiwbObggGO/fjWiR9++UhFTiVbFlo+lkghiIweHxirD1kRyUTJ/JP3B2qS15FTkmOdHuEUwPXI6N0TcgJ9z+4dKttYP2Ggysvrcaj46+hGF2kIARgWP4p8D/tnhbIYlWh9rO9EcyznGkiNLiMuOA0Btp+beXvdyZ/SdOCgd2rl3nZNMTSYrElew+txqyqosLr0cvLgj+g5u7XYr7g7ujdpORXw8qfPvwqTR4Hz9KEI+/BCZsRx+ewjO/iU2ip0HU94CleV33eRxatDCuU0IR34k64d9FCU5AuDZvQzfmf2RXXcH9JgKds6N/n+QkGgIazue2iwJa2DDs1CSUTPPNQgmvy4JII1Ae+ECJevXU7J2HboLF5q8vtzNDafYWHNqi0NMjKVtuIREK2PLx1JJBLEREQSqcgQPx2HIzUXp44PTgP7tlgLTFIwmIwcyD/BH0h9sTd2K1qgFQC6TMzRgKNMjpzM2dGy73Ti1xg/4QOYB3jj0BucKzwGi8PPMwGcYHjS8RbYv0fmwxhONIAjsTt/NkiNLzGPd18mXBX0XMCNqhpTm1UbE58ezLH4Zmy5uMrt3RbpFMq/nPKZFTMNeYd/kbZbHxZF6730IlZW4Tp1C4FtviRF8e96FbS8DAgT0g9uWg3tVZInJiPHCHlLiDxDWczCKLiPqj9oQBNFh4vhKOLUaY2kx6Xs9KMtyABn43TESz4X/a3zdAQmJJmKNx1ObxWQU3WI02WINkLBhUrRWExEEgaJffiHr//7bYFunIUNwnTQRx/79sY+KkoqYSrQrtnwsldxhbAiZQoHz4EHt3Y0mo5ArGBY0jGFBwyjVlbLp4ibWJK3hSM4R9mbsZW/GXlxULkwKn8T0yOlc53udVaXLNIXUklTeOfwO2y5tA8Q0oAV9F3BL91ts1kJYQuJKyGQyRgWPYnjgcNZdWMdHRz8isyyTF/a/wLKEZTxx3ROMDR1rs79na8YkmNidtptlCcs4lHXIPH+w/2Dm95zP8KDhyGXXfnHs1L8/wR9+wKUFj1Cy/i/kzi74/+9FZKOeEmuCrL4PMo/BZ6PEgqm6MtjwLIqSDCIAjiDWBJj8Rs0T4YILcOIncSpIBkBfLufSHn+0BTJkDvYEvfse6rFjrrnfEhISbYxc0eFtcFsbmUwmpv01AvfZs1vNDVJCQqIukggi0SKo7dTM6jaLWd1mcankEmuS1/Bn0p+ka9JZfW41q8+tJkQdwvTI6dwYeSNBLkHt3eVGodFp+PzE56xIXIHepEchU3Bb99tY0G+BVCdBosOjkCuYHjmdSeGT+On0T3xx8gsuFF9g4Y6F9PHpw8LYhQz0H9je3ewQaI1a1iatZVnCMi4Ui+HTCpmCyV0mMz9mPtFeLVcs1GXkSILeeov0RYso+uUX5Go1vk8/hSxqHDy4U6wHkHEUVsysfwMlmWKb/ndBTiJc+rtmmcqZSrcxXPoxCUNBMQofb0I+XYpjr54t1n8JCQkJW8Ha3CAlJCREpHQYiVbDJJiIy47jj2biK1sAAB3ISURBVPN/sCllExWGmkJPA/0HMj1yOhPDJuKkap3vqjmhXEaTkd/P/84HRz+goLIAgOFBw3lmwDNEuEt1PyRaDlsKOSzVlfJt/LcsT1hu/j2PDBrJE7FP0N2zezv3zjYprCxk5ZmVrDy90nyscVG5MLvbbO6MvrNVHXqKVq8m81//BsBn4UK8H3pQXKCvhPVPwdHljduQTA4Ro6HP7WjyPEh/+jlM5eXYd40iZOlSVEG2IXpL2D62dDyV6BzYghukhMTl2PKxVEqHkWh35DI5A/0HMtB/IM8Pfp6tqVv5I+kPDmYe5FDWIQ5lHeLVA68yIWwC0yOnM9B/YLPCvFuKQ1mHePPQm2bryXDXcJ4e+DSjgke1c88kJNoXtZ2ax657jNu7385nJz5j9dnV7E7fzZ70PdwQcQOPXPeIzUR5tTcXiy+yPGE5a5LWUGmsBMDf2Z850XOY1XUWLnYurd4H91mzMGk0ZL/2OrlLliB3ccFzzp1iUdQ+tzVOBOl/D1z/DLgGULhyJVkvLQKjEaehQwh+/30Urq6t/jkkJCQkrBWZQoHf88+JbpAyWb1ukH7PPycJIBISbYwkgki0CU4qJ26MvJEbI28kU5PJn8l/siZpDSklKaxJWsOapDUEOAdwQ8QNzIiaQZhrWJv38VLpJd49/C5bUrcA4g3fgr4LuK3HbVLdDwmJWvg4+fDvIf9mbsxcPjz6IRsvbuTP5D/ZcHEDt3W/jQf6PICHg0d7d9PqEASBozlHWRa/jO2XtiNU2U9Ge0ZzV8+7mBA+oc2PNZ7z52MsKSXv44/Jfvll5C7OuN90k1gMsTGED0dw8SPnrbco+OprANxuvpmAF1+Q3AwkJCQkQHR7fH9JXTdIPz+rdoOUkOjISOkwEu2GIAgczz3OmqQ1bLiwgVJ9qXlZP59+TI8SaxG42l3bk8TGhnKV6cv44sQXfJfwnbnuxy3dbmFBvwXSjZxEq2PLIYfVxOfF817cexzIOgCAs8qZu3vezdyYua2W7mZLGEwGtqZu5bv47ziRd8I8f1TwKO7qeRcD/Aa0a5FZQRDIef11CpZ9BwoFQUvewzXKAZbd0OC6ptt/JWPpWko3bADA54nH8XroIalorkS70BGOpxIdF8FoRHPwIEmHDxM5YAAugwZJESASVoktH0sli1xJBLEptEYt21O380fSH+zL2IdJMAFgJ7djbOhYpkdOZ2jg0CZZczb0AzaajKxJWsP7R94nvzIfgKEBQ3lm4DNEeUS1zAeTkGgAWz7R1EYQBPZn7GfJkSUkFiQC4OXgxcN9H2Zmt5mdMpqqXF/Ob+d/Y3nCctI16YB4TLsx8kbmxcyzqvpCgslE5r//Q/GvvyJTqQj59BOcD94vFkGlvssEGQZVAGkn+1Jx7DioVAS+8jJu06e3ddclJMx0lOOpRMdFGqMStoAtj1OpJoiETWGvsGdyl8lM7jKZ3PJc1iWv44+kPzhfdJ4NFzew4eIGfBx9uCHiBqZHTm+2SBGXHccbB98w36yFuYbx9ACx7of0BFNCounIZDKGBQ1jSOAQNl7cyAdHPiBNk8bLB17mu4TveCz2MSaGTbSKuj+tTU55Dj8k/sDPZ3+mVCdGuLnbu3Nb99u4vcfteDt6t3MP6yKTywn434uYNBpKN23i0uNPEPqfBTgd/w8gw1IIkaEtUXDpiBf6rOPI3dwI/vADnAfZntW7hISEhISEROdDigSRsFoEQSCxIJE/zv/B+gvrKdIWmZfFeMUwPXI6U7tMrTdlxWgycijzEHGn4+jfoz8DAwaikCtI16Tz7uF32ZSyCQC1Ss1DfR/iHz3+gUrR+Z5US7Q/tqy2Xw29Uc+qc6tYenyp2fUkxiuGJ/s/yZCAIe3cu9bhbOFZlsUvY/2F9RhMBkAUWOdGz2V61HQclY7t3MOGMel0pC14hLI9e5C7uhL237uxT3yf8qQ8DJUKlA5GcPYmfYcTRk05quBgQj7/DPsI64lqkei8dNTjqUTHQRqjEraALY9TKR1GEkE6FHqjnl3pu1hzfg270nZhEMQbDKVcyfXB1zM9cjojg0aiUqjYkrKF1w++TnZ5TWE/Xydf+nj3YVfaLnQmHXKZ3Fz3w9PBs70+loSETZ9oGkOZvozvEr7j21PfUm4oB8S0s4X9FxLjFdPOvWs+giCwP3M/y+KXsS9jn3l+rG8s83rOY3TwaBRy2/peTeXlpN53PxVHjiB3cUHm4IAxL69OO4e+fQj55BOUXl7t0EsJibp09OOphO0jjVEJW8CWx6mUDiPRoVApVIwLHce40HEUVBbw14W/+OP8HyQWJLI1dStbU7fiYe9BL+9e7E7fXWf9nPIcs+vL4IDBPDPwGbp5dGvrjyEh0elwVjnzcN+HubXbrXxx8gt+OvMT+zP3s3/tfqaET+Gx6x4jxDWkvbvZZPRGPX9d/Itl8cs4W3gWEG3Bx4eOZ37P+fTx6dPOPbx25E5OhHy2lOSbb8aQlg4aTb3tPOfOlQQQCQkJCQkJCZtDEkEkbA5PB0/ujL6TO6Pv5FzhOdYkrWFt8lryKvLqFUBq427vztJxS1EqpKEvIdGWeDl6sXjQYu6MvpOPj33M+uT1/HXxLzanbGZ2t9k82PdBq6yVcTnF2mJWnV3FD4k/kFORA4Cj0pGZXWdyZ/SdhKhtT9CpD7mTE2h1V24gk5Hz9ju4TpkiuRtISEhISEhI2BQdv0KdRIemq0dX/jngn2yevZknYp9osH2RtoijuUfboGcSEhL1EaIO4fWRr/PzjT8zPGg4BsHAyjMrmfrrVD4+9jEaXf1RB+1NuiadNw6+wYRVE1hyZAk5FTn4OPrwROwTbJ69mcWDFncYAQSg/HAchtzcKzcQBAxZWZQfjmu7TklISEhISEhItADS43CJDoFSriTQObBRbXPLr3JhLyEh0Sb08OzB0vFLOZh5kPfi3uNU/imWHl/KT6d/4sG+D3JLt1uwU9i1dzc5mXuSZQnL2Jyy2WzdHeUexV0972JKlylW0cfW4KoCyDW0k5CQkJCQkJCwFiQRRKLD4OPk06LtJCQkWp9BAYP4YdoPbE7ZzIdHP+RiyUVeP/g6yxOW80i/R5gWMa3NbXVNgokdl3awLH4ZR3KOmOcPDRjKXT3vYmjg0A5vpa30adxxsrHtJCQkJCQkJCSsBUkEkegwxPrG4ufkR055DgJ1TY9kyPBz8iPWN7YdeichIXElZDIZE8MnMiZ0DL+f/51Pj31Kuiad5/c8z7L4ZSzsv5DhgcNbXXioNFSyJmkNyxOWc7HkIiBGmU3tMpV5MfPo7tm9VfdvTTgN6I/S3x9DdjbUZyInk6H088NpQP+275yEhISEhISERDOQaoJIdBgUcgWLBy0GRMGjNtXvnx30rM3ZVUpIdBZUchW3dLuFdTPX8UTsE6hVas4UnuHhLQ9z76Z7OZl7slX2m1+Rz8fHPmbiqom89PdLXCy5iFql5p5e97Bh5gZeGfFKpxJAAGQKBX7PP1f15jLxqeq93/PPSUVRJSQkJCQkJGwOSQSR6FCMDxvPu6PfxdfJ12K+n5Mf745+l/Fh49upZxISEo3FUenIfb3vY/3M9cyPmY9KruJQ1iHuWH8Hi3Ys4kLxhRbZT3JxMi/se4GJqyay9PhSCrWFBLkE8ezAZ9l8y2ae7P8kfs5+LbIvW8R14kSC3l+C0s/y/0Dp50fQ+0twnTixnXomISEhISEhIXHtSOkwEh2O8WHjGRMyhkOZh4g7HUf/Hv0ZGDBQigCRkLAx3B3ceWrgU2Zb3TVJa9icspltqdu4uevNPNz34TqCp9Fk5EjOEXLLc/Fx8iHWN9bity8IAoezD7Msfhk703aa5/fy6sX8XvMZHzoepVw6NVbjOnEi6nHj0Bw8SNLhw0QOGIDLoEFSBIiEhISEhISEzSJd6Ul0SBRyBQP9B6LKUtHPv58kgEhI2DABLgG8POJl5veczwdHPmBH2g5WnV3F2qS1zImZw9297sbVzpUtKVt4/eDrZJdnm9f1c/Jj8aDFjA4ZzeaUzXwb/y0J+QmAmCY3OmQ083v+f3v3H1V1ffhx/IVX0BENIUBEDSbo9ScopoZi4I/0aIQBmTKHhc4ms+NGEjHdmCkNZyWHA1kMjU3XFI8aI2WruXSdcmvOH9NAs3CiiMeMkKUk3uB+/+h4v7td/LUpnwv3+TjHc7jvz/tzP68r7z/gxefH44oMiOz0Nzv9b7mZTPIcPVotHh7yHD6cAgQAAHRolCAAgA6hv09/FUwq0IFzB5S3P0+Hzh/SuiPrtOWjLYrtG6vy6nKHfc41nVP6nnT16NZDF5ovSJK6mbppRugMpQxOUYh3SPt+CAAAABiKEgQA0KFE9ozUhmkbtOf0HuUfyFd1Y3WbBch/utB8QT7dfJQ8MFmzBs6Sb3ff9gkLAAAAp8KNUQEAHY6bm5sm3DtB2+K3KXVI6k3tkzs+V2nD0yhAAAAAXBglCACgwzJ1MWmg78CbmtvY3HiH0wAAAMDZUYIAADo0f0//2zoPAAAAnRclCACgQ4sMiFRPz55yU9tPd3GTmwI9AxUZENnOyQAAAOBsKEEAAB2aqYtJWaOzJMmhCLn6+tnRz/KobAAAAFCCAAA6vsnBk7Umdo0CPAPsxnt69tSa2DWaHDzZoGQAAABwJjwiFwDQKUwOnqwJfSfowKcHdL7pvPw9/RUZEMkZIAAAALChBAEAdBqmLiaNChxldAwAAAA4KS6HAQAAAAAALoESBAAAAAAAuIQOUYK8/vrrmjhxooYNG6bExET94x//MDoSAAAAAADoYJy+BKmoqFBubq7S0tJUVlamkSNHasGCBaqrqzM6GgAAAAAA6ECcvgQpKSlRUlKSZs6cqdDQUC1btkyBgYHatGmT0dEAAAAAAEAH4tQlyJUrV1RZWano6Gi78XHjxungwYMGpQIAAAAAAB2RUz8it6GhQS0tLbrnnnvsxv38/HT+/Plr7tfS0qKWlpY7HQ9O7uoaYC3AmbFO0RGwTtERsE7h7Fij6Ag68jq92cxOXYJc5ebmZvfaarU6jElSa2urJOn48ePtkgsdw5EjR4yOANwQ6xQdAesUHQHrFM6ONYqOoCOv06u9wLU49eUwPj4+MplM+uyzz+zG6+vr5efn5zC/ubm5vaIBAAAAAAAnc6NewKnPBPHw8NCQIUP0/vvv68EHH7SN7927V5MmTXKY7+3trZCQEHXr1k1dujh1vwMAAAAAAG6T1tZWNTc3y9vb+7rznLoEkaTU1FRlZmZq6NChGjFihEpLS3X27FnNnj3bYW7Xrl0d7h8CAAAAAAA6Py8vrxvOcfoSZPr06WpoaNDatWv16aefasCAAfrVr36l3r17Gx0NAAAAAAB0IG5Wq9VqdAgAAAAAAIA7jRtnoFMpKipSUlKSRowYoaioKP3whz/UiRMnjI4FXFdRUZHMZrOef/55o6MAds6dO6eMjAyNGTNGERERmjFjhj788EOjYwE2X331lfLy8jRx4kSFh4dr0qRJKiwsvOGTAYA7ad++fVq4cKGio6NlNpu1a9cuu+1Wq1UFBQWKjo5WeHi4UlJS9PHHHxuUFq7qeuvUYrHohRde0MMPP6zhw4crOjpamZmZOnfunIGJbx9KEHQqf//73zVnzhxt2bJFJSUlamlp0fz589XU1GR0NKBNhw8fVmlpqcxms9FRADuNjY1KTk6Wu7u7iouLtXPnTmVlZenb3/620dEAm+LiYm3evFnZ2dmqqKjQM888o/Xr12vjxo1GR4MLa2pqktlsVnZ2dpvbi4uLVVJSouzsbG3dulV+fn5KTU3VxYsX2zkpXNn11unly5dVVVWltLQ0bd++XYWFhTp58qTS0tIMSHr7Of09QYBbsX79ervXubm5ioqKUmVlpUaNGmVQKqBtly5d0jPPPKOcnBy98sorRscB7BQXFyswMFC5ubm2sT59+hiYCHB06NAhTZo0SbGxsZK+XqM7d+7kjCUYKiYmRjExMW1us1qt2rBhgxYuXKgpU6ZIkn75y19q7Nix2rFjR5sPfwDuhOut07vvvlslJSV2Yz/96U81c+ZM1dXVKSgoqD0i3jGcCYJO7YsvvpCkGz4mCTDCihUrFBMTo7FjxxodBXDwzjvvaOjQoVq8eLGioqL0yCOPaMuWLUbHAuyMHDlSf/vb3/Svf/1LknTs2DHt37//mj/YA0arra3V+fPnFR0dbRvz8PDQqFGjdPDgQQOTAdd38eJFubm5dYozQjkTBJ2W1WpVbm6uRo4cqQEDBhgdB7Czc+dOVVVVaevWrUZHAdp0+vRpbdq0SampqVq4cKEOHz6snJwceXh46JFHHjE6HiBJWrBggb744gtNmzZNJpNJLS0tSk9PV1xcnNHRgDadP39eknTPPffYjfv5+amurs6ISMANNTc368UXX1RcXNxNPYLW2VGCoNNasWKFjh8/rt/97ndGRwHsnD17Vs8//7xee+01devWzeg4QJusVquGDh2qp59+WpI0ePBgffLJJ9q0aRMlCJxGRUWFysvL9dJLLyksLExHjx5Vbm6uAgIClJCQYHQ84Jrc3NzsXvPATjgri8Wi9PR0Wa1WLV++3Og4twUlCDqllStX6p133tFvf/tbBQYGGh0HsFNZWan6+nolJibaxlpaWrRv3z69/vrrOnLkiEwmk4EJAcnf31+hoaF2Y/369dNbb71lUCLA0erVq/Xkk0/qoYcekiSZzWbV1dWpqKiIEgROyd/fX5L02WefKSAgwDZeX18vPz8/o2IBbbJYLPrxj3+s2tpa/eY3v+kUZ4FIlCDoZKxWq1auXKk//elP2rhxo/r27Wt0JMDB/fffrzfffNNu7Cc/+Yn69eunBQsWUIDAKURGRtrus3DVyZMn1bt3b4MSAY4uX77s8Bd1k8nEX9XhtPr06SN/f3+9//77Gjx4sCTpypUr2rdvnzIyMgxOB/y/qwVITU2NNmzYIB8fH6Mj3TaUIOhUnnvuOe3YsUNr167VXXfdZbvu8u6771b37t0NTgd8zcvLy+E+NZ6enurRowf3r4HTePzxx5WcnKxXX31V06ZN0+HDh7VlyxatWLHC6GiAzYQJE/Tqq68qKCjIdjlMSUmJkpKSjI4GF3bp0iWdOnXK9rq2tlZHjx6Vt7e3goKCNHfuXBUVFSkkJETBwcEqKipS9+7duZcN2tX11mlAQIAWL16sqqoqFRUVqaWlxfZ7lbe3tzw8PIyKfVu4WanK0YmYzeY2x3Nzc+0uPQCcTUpKigYOHKhly5YZHQWw2b17t9asWaOTJ0+qT58+Sk1N1WOPPWZ0LMDm4sWLys/P165du1RfX6+AgAA99NBDWrRoUYf/IR0d1wcffKC5c+c6jCckJGjVqlWyWq0qLCxUaWmpGhsbFRERoezsbP4QgnZ1vXX61FNPadKkSW3ut2HDBo0ZM+ZOx7ujKEEAAAAAAIBL6GJ0AAAAAAAAgPZACQIAAAAAAFwCJQgAAAAAAHAJlCAAAAAAAMAlUIIAAAAAAACXQAkCAAAAAABcAiUIAAAAAABwCZQgAAAAAADAJVCCAAAAp2Q2m7Vr1652O15tba3MZrOOHj3abse86oMPPpDZbNa///3vdj82AACuhBIEAADYZGVlyWw2Kzs722Hb8uXLZTablZWVdVuPWVBQoBkzZtzW9wQAAGgLJQgAALDTq1cvVVRU6PLly7ax5uZm7dy5U0FBQQYmAwAA+N9QggAAADuDBw9Wr1699Pbbb9vG3n77bQUGBmrQoEF2c69cuaKcnBxFRUVp2LBhSk5O1uHDh23br17m8de//lWJiYmKiIjQ7NmzdeLECUnS9u3bVVhYqGPHjslsNstsNmv79u22/RsaGrRo0SJFRERoypQp+vOf/2zb1tjYqCVLluj+++9XeHi4pkyZom3btl3zc7377rtKTk7WfffdpzFjxugHP/iBTp065TDv9OnTSklJUUREhOLj43Xw4EG77QcOHNCcOXMUHh6umJgY5eTkqKmpybb997//vRITEzVixAiNGzdOS5YsUX19vd17/OUvf9HUqVMVHh6ulJQUnTlz5pq5AQDA7UMJAgAAHCQlJdmVEdu2bVNSUpLDvNWrV+utt97SqlWr9MYbbyg4OFjf//73deHCBbt5eXl5ysrK0rZt22QymbR06VJJ0vTp0zVv3jz1799f7733nt577z1Nnz7dtl9hYaGmTZum8vJyPfDAA8rIyLC9d35+vqqrq1VcXKyKigotX75cPj4+1/xMX375pVJTU7V161b9+te/lpubmxYtWqTW1laHrPPnz1dZWZlCQkK0ZMkSffXVV5Kkjz76SPPnz9eDDz6o8vJy5eXlaf/+/Vq5cqVtf4vFoh/96EcqLy/Xyy+/rNraWrtLiM6ePaunnnpKDzzwgMrKyjRz5ky99NJLN/iOAACA24ESBAAAOIiPj9f+/ftVW1urM2fO6MCBA4qPj7eb09TUpM2bNyszM1MxMTEKCwvTypUr1a1bN23dutVubnp6ukaPHq2wsDA9+eSTOnjwoJqbm9W9e3d5enrKZDLJ399f/v7+6t69u22/hIQExcXFKTg4WE8//bS+/PJL25kmdXV1GjRokIYNG6Y+ffpo7Nixmjhx4jU/09SpUzVlyhSFhIRo0KBB+sUvfqHjx4/rk08+sZs3b948xcbG6jvf+Y4WL16sM2fOqKamRpK0fv16Pfzww3riiScUEhKiyMhILVu2TGVlZWpubpYkPfroo4qJiVHfvn01fPhwLVu2TO+++64uXbokSdq0aZP69u2rpUuXql+/foqPj1dCQsJ/+Z0CAAC3oqvRAQAAgPPx9fVVbGysysrKZLVaFRsbK19fX7s5p06dksViUWRkpG3M3d1d4eHhqq6utptrNpttX/v7+0uS6uvrb3iPkf/cz9PTU3fddZc+//xzSVJycrIWL16sqqoqjRs3TpMnT7bL8k2nTp1Sfn6+Dh06pIaGBlmtVklfn5kxYMCA62b9/PPPFRoaqsrKStXU1OjNN9+0zbFarWptbVVtba1CQ0NVVVWlgoICHTt2TBcuXLA7TlhYmKqrqxURESE3NzfbewwfPvy6/w8AAOD2oAQBAABtSkpK0ooVKyRJP//5z6857z9/mZe+LgW+Oda1a1eH+d+8DKUt7u7uDse6ul9MTIx2796tPXv2aO/evXriiSc0Z84cPfvss22+18KFC9WrVy/l5OQoICBAra2tiouLk8ViueYxv5m1tbVVs2fPVkpKisP79+rVS01NTZo3b57GjRunF154QT4+Pjp79qzmz59vO87VUgQAALQ/LocBAABtGj9+vCwWiywWi6Kjox2233vvvXJ3d9f+/fttYxaLRR9++KFCQ0Nv+jju7u43VYi0xdfXV4mJiXrxxRe1dOlSlZaWtjmvoaFB1dXVSktLU1RUlEJDQ9XY2HjLxxs8eLA+/vhjBQcHO/zz8PDQiRMn1NDQoIyMDN13330KDQ11uClqWFiY/vnPf9qNffM1AAC4MzgTBAAAtMlkMukPf/iD7etv8vT0VHJyslavXi1vb28FBQVp3bp1unz5sh599NGbPk7v3r1VW1uro0ePqmfPnvLy8pKHh8cN98vPz9eQIUPUv39/XblyRXv27Llm+eLt7a0ePXqotLRU/v7+qqur+69uRrpgwQLNmjVLzz33nB577DF961vfUnV1tfbu3auf/exnCgoKkru7uzZu3Kjk5GQdP35ca9eutXuP2bNn67XXXlNubq5mzZqlyspKvfHGG7ecBQAA3DrOBAEAANfk5eUlLy+va27PyMjQ1KlTlZmZqYSEBNXU1GjdunXy9va+6WNMnTpV48eP19y5cxUVFaUdO3bc1H7u7u5as2aN4uPj9b3vfU9dunTRmjVr2pzbpUsX5eXlqbKyUnFxccrNzVVmZuZNZ7xq4MCB2rhxo2pqavTd735XCQkJys/Pt907xNfXV6tWrdIf//hHTZ8+XcXFxQ6X5wQFBamgoEC7d+/WjBkztHnzZqWnp99yFgAAcOvcrFyYCgAAAAAAXABnggAAAAAAAJdACQIAAAAAAFwCJQgAAAAAAHAJlCAAAAAAAMAlUIIAAAAAAACXQAkCAAAAAABcAiUIAAAAAABwCZQgAAAAAADAJVCCAAAAAAAAl0AJAgAAAAAAXAIlCAAAAAAAcAmUIAAAAAAAwCX8H3KYTLrxEWbqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11,4))\n",
    "for name, group in long.groupby('model'):\n",
    "    errors = group.assign(error=(group.predicted_kb-group.actual_kb).abs()/1000)\n",
    "    errors.groupby('horizon').error.mean().plot(ax=ax,marker='o',label=name)\n",
    "ax.set(title='Inventory error by forecast horizon',xlabel='Months ahead',ylabel='MAE (million bbl)')\n",
    "ax.legend(fontsize=8); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412b6bf-benchmark-reading",
   "metadata": {},
   "source": [
    "**Year-ahead benchmark check:** The same-month-last-year method has **15.4% lower MAE** than `xgboost_change` on the two tested paths.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325563e4",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "Higher refinery and blender production increases gasoline supply. Product supplied and exports reduce stocks, all else equal.\n",
    "\n",
    "Seasonal changes have the lowest national one-month MAE in the final period. The same-month-last-year benchmark has the lowest MAE among methods tested on two year-ahead paths and is used for the current twelve-month outlook.\n",
    "\n",
    "The outlook reflects historical relationships and excludes explicit price, outage, and turnaround scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a338c9",
   "metadata": {},
   "source": [
    "### Reproduce the results\n",
    "\n",
    "Run All repeats training and evaluation. Checks cover source data, stock definitions, forecast timing, regional totals, and training dates. See the README to refresh reports from saved results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3995306e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:55:13.780469Z",
     "iopub.status.busy": "2026-09-16T21:55:13.780330Z",
     "iopub.status.idle": "2026-09-16T21:55:14.031699Z",
     "shell.execute_reply": "2026-09-16T21:55:14.031310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source integrity, stock scope, target-month isolation, and aggregation checks passed.\n"
     ]
    }
   ],
   "source": [
    "# Check the model's stock scope and accounting identities, then perturb the\n",
    "# last target observation to verify target-month isolation. Finally require\n",
    "# the national forecast to equal the sum of the five regional forecasts.\n",
    "\n",
    "np.testing.assert_allclose(panel.stock_kb, panel.finished_stock_kb + panel.blending_stock_kb, atol=1, rtol=0)\n",
    "np.testing.assert_allclose(panel.production_kb, panel.finished_production_kb-panel.blending_net_inputs_kb)\n",
    "check_history=panel[panel.padd.eq(1)].iloc[:40].copy()\n",
    "before=supervised(check_history)\n",
    "# Perturb the final target month's stocks and flows substantially to test that\n",
    "# its own inputs cannot see target-month information.\n",
    "check_history.loc[check_history.index[-1],FLOWS+['stock_kb']]+=100000\n",
    "changed=supervised(check_history)\n",
    "# Only target labels may change after perturbing the final observation. All\n",
    "# predictors, origin dates, and flow-identity forecasts must remain identical.\n",
    "feature_columns=[c for c in before if c not in ['actual_kb','delta_kb']]\n",
    "pd.testing.assert_frame_equal(before[feature_columns],changed[feature_columns])\n",
    "np.testing.assert_allclose(tables['us_forecast_12m'].stock_kb,\n",
    "    tables['padd_forecast_12m'].groupby('month').stock_kb.sum())\n",
    "print('Source integrity, stock scope, target-month isolation, and aggregation checks passed.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c94323f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:55:14.032921Z",
     "iopub.status.busy": "2026-09-16T21:55:14.032814Z",
     "iopub.status.idle": "2026-09-16T21:55:14.039702Z",
     "shell.execute_reply": "2026-09-16T21:55:14.039239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product                         Total motor gasoline: finished motor gasoline ...\n",
       "covid_exclusion                 {'start': '2020-03-01', 'end': '2021-03-01', '...\n",
       "stock_units                                         thousand barrels at month end\n",
       "flow_units                                    thousand barrels per calendar month\n",
       "equation                        S[t] = S[t-1] + (finished refinery/blender net...\n",
       "data_start                                                             2007-01-01\n",
       "data_end                                                               2026-06-01\n",
       "cv                              10 expanding folds, 12 eligible test months ea...\n",
       "holdout_start                                                          2024-07-01\n",
       "forecast_timing                 One month after latest observed EIA month; con...\n",
       "one_month_models                {'1': 'seasonal_change', '2': 'seasonal_change...\n",
       "one_month_model                                                   seasonal_change\n",
       "one_month_model_label                                             seasonal_change\n",
       "development_year_ahead_model                                       xgboost_change\n",
       "year_ahead_model                                                   seasonal_naive\n",
       "horizon_selection               Compare the development-selected family with p...\n",
       "selection                       Regional comparison models use lowest pooled d...\n",
       "hyperparameter_search           {'method': 'GridSearchCV', 'folds': 10, 'scori...\n",
       "flow_forecast                   Last 60 non-COVID observations, daily rates, l...\n",
       "components                      Flows sum finished and blending components; pr...\n",
       "forecast_stock_floor                                                            0\n",
       "uncertainty                     Point forecasts only; no calibrated prediction...\n",
       "recursive_validation            Two disjoint 12-month holdout paths; refit at ...\n",
       "versions                        {'python': '3.13.9', 'numpy': '2.3.5', 'pandas...\n",
       "sources                         [https://www.eia.gov/dnav/pet/pet_sum_snd_d_r1...\n",
       "Name: Configuration, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved result files:\n",
      "all_fitted_models.joblib\n",
      "best_parameters.csv\n",
      "candidate_addition_comparison.csv\n",
      "candidate_models.json\n",
      "constrained_model_coefficients.csv\n",
      "fit_warnings.csv\n",
      "fitted_horizon_models.joblib\n",
      "fitted_models.joblib\n",
      "fold_metrics.csv\n",
      "grid_search_results.csv\n",
      "historical_balance_exceptions.csv\n",
      "latest_forecast.csv\n",
      "model_metadata.json\n",
      "padd_forecast_12m.csv\n",
      "padd_model_metrics.csv\n",
      "padd_monthly_model.csv\n",
      "padd_predictions.csv\n",
      "recursive_benchmark_predictions.csv\n",
      "recursive_cv_predictions.csv\n",
      "recursive_holdout_metrics.csv\n",
      "recursive_holdout_predictions.csv\n",
      "refit_comparison.csv\n",
      "regional_error_review.csv\n",
      "selected_models.csv\n",
      "training_sample_audit.csv\n",
      "us_forecast_12m.csv\n",
      "us_model_metrics.csv\n",
      "us_monthly_model.csv\n",
      "us_predictions.csv\n",
      "us_recursive_cv_predictions.csv\n",
      "us_recursive_holdout_predictions.csv\n",
      "us_recursive_horizon_metrics.csv\n",
      "year_ahead_model_cv_metrics.csv\n",
      "year_ahead_refit_comparison.csv\n",
      "Notebook checks passed.\n"
     ]
    }
   ],
   "source": [
    "# Inspect run configuration and saved artifacts, and validate output shape: no\n",
    "# duplicate forecasts, all five PADDs each month, and a full 12-month outlook.\n",
    "\n",
    "display(pd.Series(metadata, name='Configuration'))\n",
    "print('Saved result files:')\n",
    "for path in sorted(OUTPUT.iterdir()):\n",
    "    print(path.name)\n",
    "assert not tables['padd_predictions'].duplicated(['padd','month','model','split']).any()\n",
    "assert tables['padd_forecast_12m'].groupby('month').padd.nunique().eq(5).all()\n",
    "assert len(outlook) == 12\n",
    "print('Notebook checks passed.')"
   ]
  }
 ],
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