{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "elfving-2010-preset-1",
   "metadata": {},
   "source": [
    "# Elfving 2010 composite preset demo\n",
    "\n",
    "This notebook demonstrates a reusable simulation-context preset for a composite stand workflow:\n",
    "\n",
    "1. Regeneration quality via the published Elfving regeneration functions (Appendix 2; ASINW).\n",
    "2. Stand creation via NYSKOG.\n",
    "3. Young-stand growth via Nyström (2000 + Nyström/Söderberg 1987) with Näslund (1986) damage index and damage mortality.\n",
    "4. Mortality routing via the Swedish mortality engine each 5-year step.\n",
    "5. Smoothed phase-over from young to mature growth and mature-tree forecasting with Elfving 2010 stand-level correction.\n",
    "6. Standing valuation each 5-year step with Söderberg (1992) bark/height and Mellanskog 2013 prices.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elfving-2010-preset-2",
   "metadata": {},
   "source": [
    "## Handover logic (pyforestry preset)\n",
    "In `Elfving2010Pipeline.step()` the handover is handled as a smooth hybrid, not a hard plot-type switch:\n",
    "\n",
    "1. Trees with `dbh_cm < handover_dbh_cm` are treated as young-phase trees and updated with Nyström-based young-stand growth (`_apply_nystrom_young_growth`).\n",
    "2. The Elfving 2010 mature-tree step is then run through the simulation context (`_ctx.update_step(dt)`).\n",
    "3. For trees that were young at step start, DBH is blended between young and mature trajectories using\n",
    "   `phase_over_weight = sigmoid((mean_height - handover_mean_height_m) / handover_smoothing_width_m)`.\n",
    "4. As stand mean height rises, `phase_over_weight` moves from ~0 to ~1, so influence shifts progressively from young-stand equations to mature-tree equations.\n",
    "\n",
    "`handover_dbh_cm` remains the tree-level split helper, while `handover_mean_height_m` + `handover_smoothing_width_m` control how quickly the stand-level transition occurs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "elfving-2010-preset-3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-19T23:09:59.461601Z",
     "iopub.status.busy": "2026-02-19T23:09:59.461370Z",
     "iopub.status.idle": "2026-02-19T23:10:00.776482Z",
     "shell.execute_reply": "2026-02-19T23:10:00.774403Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pyforestry.base.helpers.primitives import SiteBase\n",
    "from pyforestry.sweden.simulation.presets import (\n",
    "    Elfving2010Pipeline,\n",
    "    Elfving2010PipelineConfig,\n",
    "    build_elfving_2010_pipeline,\n",
    ")\n",
    "from pyforestry.sweden.site import Sweden, SwedishSite\n",
    "from pyforestry.sweden.siteindex.sis.generated_site_category_trees import (\n",
    "    predict_site_categories_county_tree,\n",
    ")\n",
    "from pyforestry.sweden.siteindex.sis.hagglund_lundmark_1977 import Hagglund_Lundmark_1977_SIS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "elfving-2010-preset-4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-19T23:10:00.781335Z",
     "iopub.status.busy": "2026-02-19T23:10:00.780583Z",
     "iopub.status.idle": "2026-02-19T23:10:00.786838Z",
     "shell.execute_reply": "2026-02-19T23:10:00.784925Z"
    }
   },
   "outputs": [],
   "source": [
    "class SwedishSiteDemo(SwedishSite):\n",
    "    \"\"\"Concrete wrapper for notebooks (implements SiteBase abstract method).\"\"\"\n",
    "\n",
    "    def compute_attributes(self) -> None:\n",
    "        SwedishSite.__post_init__(self)\n",
    "\n",
    "    def __post_init__(self) -> None:\n",
    "        SiteBase.__post_init__(self)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "elfving-2010-preset-5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-19T23:10:00.789720Z",
     "iopub.status.busy": "2026-02-19T23:10:00.789455Z",
     "iopub.status.idle": "2026-02-19T23:10:01.074833Z",
     "shell.execute_reply": "2026-02-19T23:10:01.073148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sis_closeness': {'requested_sis': 20.0,\n",
       "  'achieved_sis': 20.698654683125625,\n",
       "  'sis_abs_error': 0.6986546831256248,\n",
       "  'sis_rel_error_pct': 3.493273415628124},\n",
       " 'predicted_site_categories': {'field_layer': <SwedenFieldLayer.LICHEN_FREQUENT: Vegetation(code=17, swedish_name='Lavrik', english_name='Lichen, frequent occurrence', index=-0.5)>,\n",
       "  'bottom_layer': <SwedenBottomLayer.BOGMOSS_TYPE: BottomLayerType(code=4, english_name='Bogmoss type (Sphagnum)', swedish_name='Vitmosstyp')>,\n",
       "  'soil_texture': <SwedenSoilTextureTill.SANDY_MOIG: SoilTextureCategory(code=4, swedish_name='Sandig-moig morän', english_name='Sandy-silty till', short_name='Medium sand')>,\n",
       "  'soil_moisture': <SwedenSoilMoisture.MESIC: SoilMoistureData(code=2, swedish_description='frisk', english_description='Mesic (subsoil water depth = 1-2 m)')>,\n",
       "  'soil_depth': <SwedenSoilDepth.DEEP: SoilDepthCat(code=1, swedish_description='Mäktigt >70 cm. Inga synliga hällar', english_description='Deep >70cm. No visible stone outcrops.')>,\n",
       "  'soil_water': <SwedenSoilWater.SHORTER_PERIODS: SoilWaterCat(code=2, swedish_description='kortare perioder', english_description='Shorter periods')>,\n",
       "  'ditched': False}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pyforestry.sweden.simulation.presets.elfving_2010_pipeline.Elfving2010Pipeline at 0x7a576d99fe00>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_index_pine_m = 20.0\n",
    "site_index_spruce_m = 22.0\n",
    "county = Sweden.County.KOPPARBERG_OVRIGA\n",
    "site_category_species = \"Pinus sylvestris\"\n",
    "requested_sis = site_index_pine_m\n",
    "\n",
    "predicted_site_categories = predict_site_categories_county_tree(\n",
    "    sis_hagglund_1979=requested_sis,\n",
    "    species=site_category_species,\n",
    "    Direktlan=county,\n",
    ")\n",
    "\n",
    "site = SwedishSiteDemo(\n",
    "    latitude=60.5,\n",
    "    longitude=15.0,\n",
    "    altitude=150.0,\n",
    "    field_layer=predicted_site_categories[\"field_layer\"],\n",
    "    bottom_layer=predicted_site_categories[\"bottom_layer\"],\n",
    "    soil_texture=predicted_site_categories[\"soil_texture\"],\n",
    "    soil_moisture=predicted_site_categories[\"soil_moisture\"],\n",
    "    soil_depth=predicted_site_categories[\"soil_depth\"],\n",
    "    soil_water=predicted_site_categories[\"soil_water\"],\n",
    "    ditched=predicted_site_categories[\"ditched\"],\n",
    ")\n",
    "\n",
    "achieved_sis = Hagglund_Lundmark_1977_SIS(\n",
    "    species=site_category_species,\n",
    "    latitude=site.latitude,\n",
    "    altitude=site.altitude or 0.0,\n",
    "    soil_moisture=site.soil_moisture,\n",
    "    ground_layer=site.bottom_layer or Sweden.BottomLayer.FRESH_MOSS,\n",
    "    vegetation=site.field_layer,\n",
    "    soil_texture=site.soil_texture or Sweden.SoilTextureTill.SANDY,\n",
    "    climate_code=site.climate_zone or Sweden.ClimateZone.K1,\n",
    "    lateral_water=site.soil_water or Sweden.SoilWater.SELDOM_NEVER,\n",
    "    soil_depth=site.soil_depth or Sweden.SoilDepth.DEEP,\n",
    "    incline_percent=site.incline_percent or 0.0,\n",
    "    aspect=site.aspect or 0.0,\n",
    "    nfi_adjustments=True,\n",
    "    dlan=site.county or county,\n",
    "    ditched=bool(site.ditched),\n",
    "    peat=False,\n",
    "    gotland=False,\n",
    "    coast=(site.distance_to_coast or 9999.0) < 50.0,\n",
    "    limes_norrlandicus=bool(site.n_of_limes_norrlandicus),\n",
    ")\n",
    "\n",
    "sis_closeness = {\n",
    "    \"requested_sis\": requested_sis,\n",
    "    \"achieved_sis\": float(achieved_sis),\n",
    "    \"sis_abs_error\": abs(float(achieved_sis) - requested_sis),\n",
    "    \"sis_rel_error_pct\": 100.0 * abs(float(achieved_sis) - requested_sis) / requested_sis,\n",
    "}\n",
    "\n",
    "display({\"sis_closeness\": sis_closeness, \"predicted_site_categories\": predicted_site_categories})\n",
    "\n",
    "cube_path = Path(\"/tmp/pyforestry_elfving_2010_valuation_cube.nc\")\n",
    "\n",
    "config = Elfving2010PipelineConfig(\n",
    "    site_index_pine_m=site_index_pine_m,\n",
    "    site_index_spruce_m=site_index_spruce_m,\n",
    "    initial_age_years=12.0,\n",
    "    sample_trees=80,\n",
    "    random_seed=42,\n",
    "    dt_years=5.0,\n",
    "    handover_dbh_cm=10.0,\n",
    "    handover_mean_height_m=7.0,\n",
    "    handover_smoothing_width_m=1.0,\n",
    "    valuation_solution_cube_path=str(cube_path),\n",
    "    valuation_solution_cube_autogenerate_if_missing=True,\n",
    "    valuation_solution_cube_generate_workers=2,\n",
    ")\n",
    "\n",
    "preset = build_elfving_2010_pipeline(config)\n",
    "\n",
    "# Builds the cube on first run, then reuses the cached file.\n",
    "preset.ensure_valuation_solution_cube(\n",
    "    path=str(cube_path),\n",
    "    workers=config.valuation_solution_cube_generate_workers,\n",
    ")\n",
    "preset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "elfving-2010-preset-6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading solution cube from /tmp/pyforestry_elfving_2010_valuation_cube.nc...\n",
      "Pricelist hash verified.\n",
      "Cube loaded successfully.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/csvi0001/projects/pyforestry/src/pyforestry/sweden/simulation/mortality/engine.py:290: UserWarning: Soderberg self-thinning annual probability exceeded [0, 1]; clamped.\n",
      "  ) = calibrate_soderberg(\n"
     ]
    },
    {
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       "      <td>1.0</td>\n",
       "      <td>0.741697</td>\n",
       "      <td>0.494388</td>\n",
       "      <td>146.008405</td>\n",
       "      <td>0.072929</td>\n",
       "      <td>148.779569</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3146.783429</td>\n",
       "      <td>8.924202</td>\n",
       "      <td>8.345823</td>\n",
       "      <td>11.801698</td>\n",
       "      <td>11.622084</td>\n",
       "      <td>34.422760</td>\n",
       "      <td>236.385065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546211</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.812290</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.072778</td>\n",
       "      <td>136.731613</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3021.363742</td>\n",
       "      <td>9.023853</td>\n",
       "      <td>8.379753</td>\n",
       "      <td>12.110298</td>\n",
       "      <td>12.081583</td>\n",
       "      <td>34.801869</td>\n",
       "      <td>238.035797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.539800</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999322</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.072413</td>\n",
       "      <td>125.419687</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2906.295888</td>\n",
       "      <td>9.098639</td>\n",
       "      <td>8.380499</td>\n",
       "      <td>12.396541</td>\n",
       "      <td>12.491660</td>\n",
       "      <td>35.077673</td>\n",
       "      <td>244.748067</td>\n",
       "      <td>...</td>\n",
       "      <td>0.533314</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999587</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.072054</td>\n",
       "      <td>115.067854</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2800.666862</td>\n",
       "      <td>9.151415</td>\n",
       "      <td>8.352931</td>\n",
       "      <td>12.662935</td>\n",
       "      <td>12.872680</td>\n",
       "      <td>35.271186</td>\n",
       "      <td>247.590677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.526664</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.071692</td>\n",
       "      <td>105.629026</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2703.591212</td>\n",
       "      <td>9.184556</td>\n",
       "      <td>8.301068</td>\n",
       "      <td>12.911520</td>\n",
       "      <td>13.228463</td>\n",
       "      <td>35.398564</td>\n",
       "      <td>246.608077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519887</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.071380</td>\n",
       "      <td>97.075650</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2614.279465</td>\n",
       "      <td>9.200093</td>\n",
       "      <td>8.228358</td>\n",
       "      <td>13.143902</td>\n",
       "      <td>13.562037</td>\n",
       "      <td>35.472394</td>\n",
       "      <td>249.753362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512997</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.071133</td>\n",
       "      <td>89.311747</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2532.061160</td>\n",
       "      <td>9.199768</td>\n",
       "      <td>8.137780</td>\n",
       "      <td>13.361280</td>\n",
       "      <td>13.875826</td>\n",
       "      <td>35.502605</td>\n",
       "      <td>252.701641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506009</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>82.218305</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2456.306417</td>\n",
       "      <td>9.185139</td>\n",
       "      <td>8.031921</td>\n",
       "      <td>13.564701</td>\n",
       "      <td>14.164899</td>\n",
       "      <td>35.497099</td>\n",
       "      <td>266.106935</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498996</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.070493</td>\n",
       "      <td>75.754744</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2386.450728</td>\n",
       "      <td>9.157639</td>\n",
       "      <td>7.913153</td>\n",
       "      <td>13.755085</td>\n",
       "      <td>14.451550</td>\n",
       "      <td>35.462464</td>\n",
       "      <td>269.030921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.491883</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999963</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.070124</td>\n",
       "      <td>69.855688</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2321.968301</td>\n",
       "      <td>9.118552</td>\n",
       "      <td>7.783443</td>\n",
       "      <td>13.933291</td>\n",
       "      <td>14.711008</td>\n",
       "      <td>35.404101</td>\n",
       "      <td>273.205946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484678</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.069731</td>\n",
       "      <td>64.482427</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2262.453851</td>\n",
       "      <td>9.069148</td>\n",
       "      <td>7.644760</td>\n",
       "      <td>14.099921</td>\n",
       "      <td>14.967174</td>\n",
       "      <td>35.326692</td>\n",
       "      <td>269.155765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.477341</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999981</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.069115</td>\n",
       "      <td>59.514450</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2207.460168</td>\n",
       "      <td>9.010680</td>\n",
       "      <td>7.498875</td>\n",
       "      <td>14.255772</td>\n",
       "      <td>15.205291</td>\n",
       "      <td>35.234185</td>\n",
       "      <td>267.258407</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470119</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.068537</td>\n",
       "      <td>54.993683</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2156.650399</td>\n",
       "      <td>8.944268</td>\n",
       "      <td>7.347381</td>\n",
       "      <td>14.401359</td>\n",
       "      <td>16.458678</td>\n",
       "      <td>35.129873</td>\n",
       "      <td>272.778502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.462892</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.067718</td>\n",
       "      <td>50.809769</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2109.683038</td>\n",
       "      <td>8.871075</td>\n",
       "      <td>7.191820</td>\n",
       "      <td>14.537374</td>\n",
       "      <td>18.220067</td>\n",
       "      <td>35.017005</td>\n",
       "      <td>279.400934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.455741</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.066793</td>\n",
       "      <td>46.967360</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2066.252009</td>\n",
       "      <td>8.792194</td>\n",
       "      <td>7.033518</td>\n",
       "      <td>14.664480</td>\n",
       "      <td>18.358997</td>\n",
       "      <td>34.898478</td>\n",
       "      <td>279.535170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448618</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.065773</td>\n",
       "      <td>43.431029</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2026.085303</td>\n",
       "      <td>8.708661</td>\n",
       "      <td>6.873721</td>\n",
       "      <td>14.783300</td>\n",
       "      <td>18.494380</td>\n",
       "      <td>34.776863</td>\n",
       "      <td>277.813048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.441590</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.064556</td>\n",
       "      <td>40.166706</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1988.915188</td>\n",
       "      <td>8.621445</td>\n",
       "      <td>6.573785</td>\n",
       "      <td>14.894552</td>\n",
       "      <td>18.167603</td>\n",
       "      <td>34.654613</td>\n",
       "      <td>267.673424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.434616</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.063298</td>\n",
       "      <td>37.170115</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1954.503232</td>\n",
       "      <td>8.531461</td>\n",
       "      <td>6.417318</td>\n",
       "      <td>14.998907</td>\n",
       "      <td>18.293716</td>\n",
       "      <td>34.533892</td>\n",
       "      <td>265.985719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.427698</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.061953</td>\n",
       "      <td>34.411957</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>1922.622201</td>\n",
       "      <td>8.439529</td>\n",
       "      <td>6.265576</td>\n",
       "      <td>15.097052</td>\n",
       "      <td>18.422259</td>\n",
       "      <td>34.416612</td>\n",
       "      <td>267.988473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.420853</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.060679</td>\n",
       "      <td>31.881030</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1893.076160</td>\n",
       "      <td>8.346348</td>\n",
       "      <td>6.117903</td>\n",
       "      <td>15.189606</td>\n",
       "      <td>18.551463</td>\n",
       "      <td>34.304489</td>\n",
       "      <td>274.354293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413918</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.059179</td>\n",
       "      <td>29.546041</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1865.676050</td>\n",
       "      <td>8.252643</td>\n",
       "      <td>5.972450</td>\n",
       "      <td>15.277188</td>\n",
       "      <td>18.678375</td>\n",
       "      <td>34.198964</td>\n",
       "      <td>271.651670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.407033</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.057746</td>\n",
       "      <td>27.400110</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1840.251618</td>\n",
       "      <td>8.158952</td>\n",
       "      <td>5.829582</td>\n",
       "      <td>15.360338</td>\n",
       "      <td>18.802749</td>\n",
       "      <td>34.101118</td>\n",
       "      <td>269.653403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.400060</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>128.502189</td>\n",
       "      <td>0.056119</td>\n",
       "      <td>25.424432</td>\n",
       "      <td>215.948036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    step_index  age_years  years_elapsed  stems_per_ha  mean_dbh_cm  \\\n",
       "0          0.0       12.0            0.0   6280.005000     1.057714   \n",
       "1          1.0       17.0            5.0   5924.097337     3.728514   \n",
       "2          2.0       22.0           10.0   5528.820090     5.168527   \n",
       "3          3.0       27.0           15.0   5136.990965     5.928494   \n",
       "4          4.0       32.0           20.0   4770.469843     6.514196   \n",
       "5          5.0       37.0           25.0   4387.584072     7.024615   \n",
       "6          6.0       42.0           30.0   4032.047405     7.515996   \n",
       "7          7.0       47.0           35.0   3706.805206     7.995235   \n",
       "8          8.0       52.0           40.0   3412.017232     8.464794   \n",
       "9          9.0       57.0           45.0   3146.783429     8.924202   \n",
       "10        10.0       62.0           50.0   3021.363742     9.023853   \n",
       "11        11.0       67.0           55.0   2906.295888     9.098639   \n",
       "12        12.0       72.0           60.0   2800.666862     9.151415   \n",
       "13        13.0       77.0           65.0   2703.591212     9.184556   \n",
       "14        14.0       82.0           70.0   2614.279465     9.200093   \n",
       "15        15.0       87.0           75.0   2532.061160     9.199768   \n",
       "16        16.0       92.0           80.0   2456.306417     9.185139   \n",
       "17        17.0       97.0           85.0   2386.450728     9.157639   \n",
       "18        18.0      102.0           90.0   2321.968301     9.118552   \n",
       "19        19.0      107.0           95.0   2262.453851     9.069148   \n",
       "20        20.0      112.0          100.0   2207.460168     9.010680   \n",
       "21        21.0      117.0          105.0   2156.650399     8.944268   \n",
       "22        22.0      122.0          110.0   2109.683038     8.871075   \n",
       "23        23.0      127.0          115.0   2066.252009     8.792194   \n",
       "24        24.0      132.0          120.0   2026.085303     8.708661   \n",
       "25        25.0      137.0          125.0   1988.915188     8.621445   \n",
       "26        26.0      142.0          130.0   1954.503232     8.531461   \n",
       "27        27.0      147.0          135.0   1922.622201     8.439529   \n",
       "28        28.0      152.0          140.0   1893.076160     8.346348   \n",
       "29        29.0      157.0          145.0   1865.676050     8.252643   \n",
       "30        30.0      162.0          150.0   1840.251618     8.158952   \n",
       "\n",
       "    mean_height_m     qmd_cm       hq_m  basal_area_m2_ha  \\\n",
       "0        1.854133   1.645865   3.035315          1.336099   \n",
       "1        3.112517   5.159541   4.622832         12.386091   \n",
       "2        4.805762   7.031923   6.425653         21.471905   \n",
       "3        5.788569   8.059965   7.989983         26.209880   \n",
       "4        6.096231   8.875290   9.220967         29.513190   \n",
       "5        6.624864   9.503964   9.566627         31.126111   \n",
       "6        7.104746  10.103842  10.026305         32.328726   \n",
       "7        7.548066  10.684001  10.620957         33.232076   \n",
       "8        7.961064  11.249377  11.153601         33.912360   \n",
       "9        8.345823  11.801698  11.622084         34.422760   \n",
       "10       8.379753  12.110298  12.081583         34.801869   \n",
       "11       8.380499  12.396541  12.491660         35.077673   \n",
       "12       8.352931  12.662935  12.872680         35.271186   \n",
       "13       8.301068  12.911520  13.228463         35.398564   \n",
       "14       8.228358  13.143902  13.562037         35.472394   \n",
       "15       8.137780  13.361280  13.875826         35.502605   \n",
       "16       8.031921  13.564701  14.164899         35.497099   \n",
       "17       7.913153  13.755085  14.451550         35.462464   \n",
       "18       7.783443  13.933291  14.711008         35.404101   \n",
       "19       7.644760  14.099921  14.967174         35.326692   \n",
       "20       7.498875  14.255772  15.205291         35.234185   \n",
       "21       7.347381  14.401359  16.458678         35.129873   \n",
       "22       7.191820  14.537374  18.220067         35.017005   \n",
       "23       7.033518  14.664480  18.358997         34.898478   \n",
       "24       6.873721  14.783300  18.494380         34.776863   \n",
       "25       6.573785  14.894552  18.167603         34.654613   \n",
       "26       6.417318  14.998907  18.293716         34.533892   \n",
       "27       6.265576  15.097052  18.422259         34.416612   \n",
       "28       6.117903  15.189606  18.551463         34.304489   \n",
       "29       5.972450  15.277188  18.678375         34.198964   \n",
       "30       5.829582  15.360338  18.802749         34.101118   \n",
       "\n",
       "    standing_volume_m3_per_ha  ...  birch_ba_share  handover_dbh_cm  \\\n",
       "0                    4.235100  ...        0.539395             10.0   \n",
       "1                   43.222614  ...        0.636483             10.0   \n",
       "2                  101.081714  ...        0.604408             10.0   \n",
       "3                  143.699278  ...        0.592802             10.0   \n",
       "4                  170.543845  ...        0.578334             10.0   \n",
       "5                  190.400927  ...        0.571245             10.0   \n",
       "6                  207.490288  ...        0.564895             10.0   \n",
       "7                  220.763792  ...        0.558753             10.0   \n",
       "8                  231.542078  ...        0.552621             10.0   \n",
       "9                  236.385065  ...        0.546211             10.0   \n",
       "10                 238.035797  ...        0.539800             10.0   \n",
       "11                 244.748067  ...        0.533314             10.0   \n",
       "12                 247.590677  ...        0.526664             10.0   \n",
       "13                 246.608077  ...        0.519887             10.0   \n",
       "14                 249.753362  ...        0.512997             10.0   \n",
       "15                 252.701641  ...        0.506009             10.0   \n",
       "16                 266.106935  ...        0.498996             10.0   \n",
       "17                 269.030921  ...        0.491883             10.0   \n",
       "18                 273.205946  ...        0.484678             10.0   \n",
       "19                 269.155765  ...        0.477341             10.0   \n",
       "20                 267.258407  ...        0.470119             10.0   \n",
       "21                 272.778502  ...        0.462892             10.0   \n",
       "22                 279.400934  ...        0.455741             10.0   \n",
       "23                 279.535170  ...        0.448618             10.0   \n",
       "24                 277.813048  ...        0.441590             10.0   \n",
       "25                 267.673424  ...        0.434616             10.0   \n",
       "26                 265.985719  ...        0.427698             10.0   \n",
       "27                 267.988473  ...        0.420853             10.0   \n",
       "28                 274.354293  ...        0.413918             10.0   \n",
       "29                 271.651670  ...        0.407033             10.0   \n",
       "30                 269.653403  ...        0.400060             10.0   \n",
       "\n",
       "    handover_mean_height_m  handover_smoothing_width_m  phase_over_weight  \\\n",
       "0                      7.0                         1.0           0.025843   \n",
       "1                      7.0                         1.0           0.026127   \n",
       "2                      7.0                         1.0           0.089160   \n",
       "3                      7.0                         1.0           0.236639   \n",
       "4                      7.0                         1.0           0.393830   \n",
       "5                      7.0                         1.0           0.397734   \n",
       "6                      7.0                         1.0           0.530882   \n",
       "7                      7.0                         1.0           0.647876   \n",
       "8                      7.0                         1.0           0.741697   \n",
       "9                      7.0                         1.0           0.812290   \n",
       "10                     7.0                         1.0           0.999322   \n",
       "11                     7.0                         1.0           0.999587   \n",
       "12                     7.0                         1.0           0.999740   \n",
       "13                     7.0                         1.0           0.999832   \n",
       "14                     7.0                         1.0           0.999888   \n",
       "15                     7.0                         1.0           0.999924   \n",
       "16                     7.0                         1.0           0.999948   \n",
       "17                     7.0                         1.0           0.999963   \n",
       "18                     7.0                         1.0           0.999974   \n",
       "19                     7.0                         1.0           0.999981   \n",
       "20                     7.0                         1.0           0.999986   \n",
       "21                     7.0                         1.0           0.999989   \n",
       "22                     7.0                         1.0           0.999992   \n",
       "23                     7.0                         1.0           0.999994   \n",
       "24                     7.0                         1.0           0.999995   \n",
       "25                     7.0                         1.0           0.999996   \n",
       "26                     7.0                         1.0           0.999995   \n",
       "27                     7.0                         1.0           0.999996   \n",
       "28                     7.0                         1.0           0.999997   \n",
       "29                     7.0                         1.0           0.999997   \n",
       "30                     7.0                         1.0           0.999998   \n",
       "\n",
       "    young_damage_index_mean  young_damage_mortality_stems_removed_per_ha  \\\n",
       "0                  0.000000                                     0.000000   \n",
       "1                  0.338599                                   355.907662   \n",
       "2                  0.320805                                   293.255050   \n",
       "3                  0.305181                                   226.008770   \n",
       "4                  0.293559                                   185.301565   \n",
       "5                  0.444586                                   201.974752   \n",
       "6                  0.460353                                   183.090480   \n",
       "7                  0.476984                                   164.308754   \n",
       "8                  0.494388                                   146.008405   \n",
       "9                  0.512423                                   128.502189   \n",
       "10                 0.512423                                   128.502189   \n",
       "11                 0.512423                                   128.502189   \n",
       "12                 0.512423                                   128.502189   \n",
       "13                 0.512423                                   128.502189   \n",
       "14                 0.512423                                   128.502189   \n",
       "15                 0.512423                                   128.502189   \n",
       "16                 0.512423                                   128.502189   \n",
       "17                 0.512423                                   128.502189   \n",
       "18                 0.512423                                   128.502189   \n",
       "19                 0.512423                                   128.502189   \n",
       "20                 0.512423                                   128.502189   \n",
       "21                 0.512423                                   128.502189   \n",
       "22                 0.512423                                   128.502189   \n",
       "23                 0.512423                                   128.502189   \n",
       "24                 0.512423                                   128.502189   \n",
       "25                 0.512423                                   128.502189   \n",
       "26                 0.512423                                   128.502189   \n",
       "27                 0.512423                                   128.502189   \n",
       "28                 0.512423                                   128.502189   \n",
       "29                 0.512423                                   128.502189   \n",
       "30                 0.512423                                   128.502189   \n",
       "\n",
       "    mortality_fraction_mean  mortality_stems_removed_per_ha       asinw  \n",
       "0                  0.000000                        0.000000  215.948036  \n",
       "1                  0.000000                        0.000000  215.948036  \n",
       "2                  0.031774                      102.022198  215.948036  \n",
       "3                  0.054707                      165.820355  215.948036  \n",
       "4                  0.064724                      181.219556  215.948036  \n",
       "5                  0.069491                      180.911019  215.948036  \n",
       "6                  0.071683                      172.446187  215.948036  \n",
       "7                  0.072578                      160.933444  215.948036  \n",
       "8                  0.072929                      148.779569  215.948036  \n",
       "9                  0.072778                      136.731613  215.948036  \n",
       "10                 0.072413                      125.419687  215.948036  \n",
       "11                 0.072054                      115.067854  215.948036  \n",
       "12                 0.071692                      105.629026  215.948036  \n",
       "13                 0.071380                       97.075650  215.948036  \n",
       "14                 0.071133                       89.311747  215.948036  \n",
       "15                 0.070860                       82.218305  215.948036  \n",
       "16                 0.070493                       75.754744  215.948036  \n",
       "17                 0.070124                       69.855688  215.948036  \n",
       "18                 0.069731                       64.482427  215.948036  \n",
       "19                 0.069115                       59.514450  215.948036  \n",
       "20                 0.068537                       54.993683  215.948036  \n",
       "21                 0.067718                       50.809769  215.948036  \n",
       "22                 0.066793                       46.967360  215.948036  \n",
       "23                 0.065773                       43.431029  215.948036  \n",
       "24                 0.064556                       40.166706  215.948036  \n",
       "25                 0.063298                       37.170115  215.948036  \n",
       "26                 0.061953                       34.411957  215.948036  \n",
       "27                 0.060679                       31.881030  215.948036  \n",
       "28                 0.059179                       29.546041  215.948036  \n",
       "29                 0.057746                       27.400110  215.948036  \n",
       "30                 0.056119                       25.424432  215.948036  \n",
       "\n",
       "[31 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = preset.run_projection(site=site, n_steps=30)\n",
    "results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "elfving-2010-preset-7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(11, 9), sharex=True)\n",
    "axes = axes.flatten()\n",
    "\n",
    "axes[0].plot(results[\"age_years\"], results[\"qmd_cm\"], marker=\"o\")\n",
    "axes[0].set_ylabel(\"QMD (cm)\")\n",
    "\n",
    "axes[1].plot(results[\"age_years\"], results[\"basal_area_m2_ha\"], marker=\"o\")\n",
    "axes[1].set_ylabel(\"Basal area (m²/ha)\")\n",
    "\n",
    "axes[2].plot(results[\"age_years\"], results[\"standing_volume_m3sk_per_ha\"], marker=\"o\")\n",
    "axes[2].set_ylabel(\"Standing volume (m³/ha)\")\n",
    "\n",
    "axes[3].plot(results[\"age_years\"], results[\"standing_value_sek_per_ha\"], marker=\"o\")\n",
    "axes[3].set_ylabel(\"Standing value (SEK/ha)\")\n",
    "\n",
    "axes[4].plot(results[\"age_years\"], results[\"hq_m\"], marker=\"o\")\n",
    "axes[4].set_ylabel(\"HQ (m)\")\n",
    "axes[4].set_xlabel(\"Age (years)\")\n",
    "\n",
    "axes[5].plot(\n",
    "    results[\"age_years\"],\n",
    "    results[\"mortality_fraction_mean\"],\n",
    "    marker=\"o\",\n",
    "    label=\"Mortality\",\n",
    ")\n",
    "axes[5].plot(\n",
    "    results[\"age_years\"],\n",
    "    results[\"young_damage_index_mean\"],\n",
    "    marker=\"o\",\n",
    "    label=\"Young damage index\",\n",
    ")\n",
    "axes[5].set_ylabel(\"Fraction\")\n",
    "axes[5].set_xlabel(\"Age (years)\")\n",
    "axes[5].legend()\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "fig.suptitle(\"Elfving 2010 composite preset (5-year steps)\", fontsize=12)\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "elfving-2010-preset-8",
   "metadata": {},
   "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>step_index</th>\n",
       "      <th>age_years</th>\n",
       "      <th>stems_per_ha</th>\n",
       "      <th>qmd_cm</th>\n",
       "      <th>hq_m</th>\n",
       "      <th>phase_over_weight</th>\n",
       "      <th>young_damage_index_mean</th>\n",
       "      <th>mortality_fraction_mean</th>\n",
       "      <th>standing_volume_m3_per_ha</th>\n",
       "      <th>standing_value_sek_per_ha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6280.005000</td>\n",
       "      <td>1.645865</td>\n",
       "      <td>3.035315</td>\n",
       "      <td>0.025843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.235100</td>\n",
       "      <td>954.536749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5924.097337</td>\n",
       "      <td>5.159541</td>\n",
       "      <td>4.622832</td>\n",
       "      <td>0.026127</td>\n",
       "      <td>0.338599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.222614</td>\n",
       "      <td>9393.257654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5528.820090</td>\n",
       "      <td>7.031923</td>\n",
       "      <td>6.425653</td>\n",
       "      <td>0.089160</td>\n",
       "      <td>0.320805</td>\n",
       "      <td>0.031774</td>\n",
       "      <td>101.081714</td>\n",
       "      <td>21991.013746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5136.990965</td>\n",
       "      <td>8.059965</td>\n",
       "      <td>7.989983</td>\n",
       "      <td>0.236639</td>\n",
       "      <td>0.305181</td>\n",
       "      <td>0.054707</td>\n",
       "      <td>143.699278</td>\n",
       "      <td>31413.367873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4770.469843</td>\n",
       "      <td>8.875290</td>\n",
       "      <td>9.220967</td>\n",
       "      <td>0.393830</td>\n",
       "      <td>0.293559</td>\n",
       "      <td>0.064724</td>\n",
       "      <td>170.543845</td>\n",
       "      <td>37321.865845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4387.584072</td>\n",
       "      <td>9.503964</td>\n",
       "      <td>9.566627</td>\n",
       "      <td>0.397734</td>\n",
       "      <td>0.444586</td>\n",
       "      <td>0.069491</td>\n",
       "      <td>190.400927</td>\n",
       "      <td>41805.725249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4032.047405</td>\n",
       "      <td>10.103842</td>\n",
       "      <td>10.026305</td>\n",
       "      <td>0.530882</td>\n",
       "      <td>0.460353</td>\n",
       "      <td>0.071683</td>\n",
       "      <td>207.490288</td>\n",
       "      <td>46406.732463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3706.805206</td>\n",
       "      <td>10.684001</td>\n",
       "      <td>10.620957</td>\n",
       "      <td>0.647876</td>\n",
       "      <td>0.476984</td>\n",
       "      <td>0.072578</td>\n",
       "      <td>220.763792</td>\n",
       "      <td>49735.181893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3412.017232</td>\n",
       "      <td>11.249377</td>\n",
       "      <td>11.153601</td>\n",
       "      <td>0.741697</td>\n",
       "      <td>0.494388</td>\n",
       "      <td>0.072929</td>\n",
       "      <td>231.542078</td>\n",
       "      <td>53834.688306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3146.783429</td>\n",
       "      <td>11.801698</td>\n",
       "      <td>11.622084</td>\n",
       "      <td>0.812290</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.072778</td>\n",
       "      <td>236.385065</td>\n",
       "      <td>56019.632797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3021.363742</td>\n",
       "      <td>12.110298</td>\n",
       "      <td>12.081583</td>\n",
       "      <td>0.999322</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.072413</td>\n",
       "      <td>238.035797</td>\n",
       "      <td>56659.620607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2906.295888</td>\n",
       "      <td>12.396541</td>\n",
       "      <td>12.491660</td>\n",
       "      <td>0.999587</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.072054</td>\n",
       "      <td>244.748067</td>\n",
       "      <td>58278.188229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2800.666862</td>\n",
       "      <td>12.662935</td>\n",
       "      <td>12.872680</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.071692</td>\n",
       "      <td>247.590677</td>\n",
       "      <td>59093.888348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2703.591212</td>\n",
       "      <td>12.911520</td>\n",
       "      <td>13.228463</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.071380</td>\n",
       "      <td>246.608077</td>\n",
       "      <td>58718.153219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2614.279465</td>\n",
       "      <td>13.143902</td>\n",
       "      <td>13.562037</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.071133</td>\n",
       "      <td>249.753362</td>\n",
       "      <td>59818.457018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2532.061160</td>\n",
       "      <td>13.361280</td>\n",
       "      <td>13.875826</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>252.701641</td>\n",
       "      <td>60965.486977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2456.306417</td>\n",
       "      <td>13.564701</td>\n",
       "      <td>14.164899</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.070493</td>\n",
       "      <td>266.106935</td>\n",
       "      <td>66227.967767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2386.450728</td>\n",
       "      <td>13.755085</td>\n",
       "      <td>14.451550</td>\n",
       "      <td>0.999963</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.070124</td>\n",
       "      <td>269.030921</td>\n",
       "      <td>68063.269449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2321.968301</td>\n",
       "      <td>13.933291</td>\n",
       "      <td>14.711008</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.069731</td>\n",
       "      <td>273.205946</td>\n",
       "      <td>69949.889228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2262.453851</td>\n",
       "      <td>14.099921</td>\n",
       "      <td>14.967174</td>\n",
       "      <td>0.999981</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.069115</td>\n",
       "      <td>269.155765</td>\n",
       "      <td>68864.192994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2207.460168</td>\n",
       "      <td>14.255772</td>\n",
       "      <td>15.205291</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.068537</td>\n",
       "      <td>267.258407</td>\n",
       "      <td>69023.136480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2156.650399</td>\n",
       "      <td>14.401359</td>\n",
       "      <td>16.458678</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.067718</td>\n",
       "      <td>272.778502</td>\n",
       "      <td>72723.935175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2109.683038</td>\n",
       "      <td>14.537374</td>\n",
       "      <td>18.220067</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.066793</td>\n",
       "      <td>279.400934</td>\n",
       "      <td>77753.842862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2066.252009</td>\n",
       "      <td>14.664480</td>\n",
       "      <td>18.358997</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.065773</td>\n",
       "      <td>279.535170</td>\n",
       "      <td>78606.417698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2026.085303</td>\n",
       "      <td>14.783300</td>\n",
       "      <td>18.494380</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.064556</td>\n",
       "      <td>277.813048</td>\n",
       "      <td>78840.761866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1988.915188</td>\n",
       "      <td>14.894552</td>\n",
       "      <td>18.167603</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.063298</td>\n",
       "      <td>267.673424</td>\n",
       "      <td>76320.921363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1954.503232</td>\n",
       "      <td>14.998907</td>\n",
       "      <td>18.293716</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.061953</td>\n",
       "      <td>265.985719</td>\n",
       "      <td>76812.299191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1922.622201</td>\n",
       "      <td>15.097052</td>\n",
       "      <td>18.422259</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.060679</td>\n",
       "      <td>267.988473</td>\n",
       "      <td>78931.979204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1893.076160</td>\n",
       "      <td>15.189606</td>\n",
       "      <td>18.551463</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.059179</td>\n",
       "      <td>274.354293</td>\n",
       "      <td>83123.053110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1865.676050</td>\n",
       "      <td>15.277188</td>\n",
       "      <td>18.678375</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.057746</td>\n",
       "      <td>271.651670</td>\n",
       "      <td>82922.323674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1840.251618</td>\n",
       "      <td>15.360338</td>\n",
       "      <td>18.802749</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.512423</td>\n",
       "      <td>0.056119</td>\n",
       "      <td>269.653403</td>\n",
       "      <td>82946.816255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    step_index  age_years  stems_per_ha     qmd_cm       hq_m  \\\n",
       "0          0.0       12.0   6280.005000   1.645865   3.035315   \n",
       "1          1.0       17.0   5924.097337   5.159541   4.622832   \n",
       "2          2.0       22.0   5528.820090   7.031923   6.425653   \n",
       "3          3.0       27.0   5136.990965   8.059965   7.989983   \n",
       "4          4.0       32.0   4770.469843   8.875290   9.220967   \n",
       "5          5.0       37.0   4387.584072   9.503964   9.566627   \n",
       "6          6.0       42.0   4032.047405  10.103842  10.026305   \n",
       "7          7.0       47.0   3706.805206  10.684001  10.620957   \n",
       "8          8.0       52.0   3412.017232  11.249377  11.153601   \n",
       "9          9.0       57.0   3146.783429  11.801698  11.622084   \n",
       "10        10.0       62.0   3021.363742  12.110298  12.081583   \n",
       "11        11.0       67.0   2906.295888  12.396541  12.491660   \n",
       "12        12.0       72.0   2800.666862  12.662935  12.872680   \n",
       "13        13.0       77.0   2703.591212  12.911520  13.228463   \n",
       "14        14.0       82.0   2614.279465  13.143902  13.562037   \n",
       "15        15.0       87.0   2532.061160  13.361280  13.875826   \n",
       "16        16.0       92.0   2456.306417  13.564701  14.164899   \n",
       "17        17.0       97.0   2386.450728  13.755085  14.451550   \n",
       "18        18.0      102.0   2321.968301  13.933291  14.711008   \n",
       "19        19.0      107.0   2262.453851  14.099921  14.967174   \n",
       "20        20.0      112.0   2207.460168  14.255772  15.205291   \n",
       "21        21.0      117.0   2156.650399  14.401359  16.458678   \n",
       "22        22.0      122.0   2109.683038  14.537374  18.220067   \n",
       "23        23.0      127.0   2066.252009  14.664480  18.358997   \n",
       "24        24.0      132.0   2026.085303  14.783300  18.494380   \n",
       "25        25.0      137.0   1988.915188  14.894552  18.167603   \n",
       "26        26.0      142.0   1954.503232  14.998907  18.293716   \n",
       "27        27.0      147.0   1922.622201  15.097052  18.422259   \n",
       "28        28.0      152.0   1893.076160  15.189606  18.551463   \n",
       "29        29.0      157.0   1865.676050  15.277188  18.678375   \n",
       "30        30.0      162.0   1840.251618  15.360338  18.802749   \n",
       "\n",
       "    phase_over_weight  young_damage_index_mean  mortality_fraction_mean  \\\n",
       "0            0.025843                 0.000000                 0.000000   \n",
       "1            0.026127                 0.338599                 0.000000   \n",
       "2            0.089160                 0.320805                 0.031774   \n",
       "3            0.236639                 0.305181                 0.054707   \n",
       "4            0.393830                 0.293559                 0.064724   \n",
       "5            0.397734                 0.444586                 0.069491   \n",
       "6            0.530882                 0.460353                 0.071683   \n",
       "7            0.647876                 0.476984                 0.072578   \n",
       "8            0.741697                 0.494388                 0.072929   \n",
       "9            0.812290                 0.512423                 0.072778   \n",
       "10           0.999322                 0.512423                 0.072413   \n",
       "11           0.999587                 0.512423                 0.072054   \n",
       "12           0.999740                 0.512423                 0.071692   \n",
       "13           0.999832                 0.512423                 0.071380   \n",
       "14           0.999888                 0.512423                 0.071133   \n",
       "15           0.999924                 0.512423                 0.070860   \n",
       "16           0.999948                 0.512423                 0.070493   \n",
       "17           0.999963                 0.512423                 0.070124   \n",
       "18           0.999974                 0.512423                 0.069731   \n",
       "19           0.999981                 0.512423                 0.069115   \n",
       "20           0.999986                 0.512423                 0.068537   \n",
       "21           0.999989                 0.512423                 0.067718   \n",
       "22           0.999992                 0.512423                 0.066793   \n",
       "23           0.999994                 0.512423                 0.065773   \n",
       "24           0.999995                 0.512423                 0.064556   \n",
       "25           0.999996                 0.512423                 0.063298   \n",
       "26           0.999995                 0.512423                 0.061953   \n",
       "27           0.999996                 0.512423                 0.060679   \n",
       "28           0.999997                 0.512423                 0.059179   \n",
       "29           0.999997                 0.512423                 0.057746   \n",
       "30           0.999998                 0.512423                 0.056119   \n",
       "\n",
       "    standing_volume_m3_per_ha  standing_value_sek_per_ha  \n",
       "0                    4.235100                 954.536749  \n",
       "1                   43.222614                9393.257654  \n",
       "2                  101.081714               21991.013746  \n",
       "3                  143.699278               31413.367873  \n",
       "4                  170.543845               37321.865845  \n",
       "5                  190.400927               41805.725249  \n",
       "6                  207.490288               46406.732463  \n",
       "7                  220.763792               49735.181893  \n",
       "8                  231.542078               53834.688306  \n",
       "9                  236.385065               56019.632797  \n",
       "10                 238.035797               56659.620607  \n",
       "11                 244.748067               58278.188229  \n",
       "12                 247.590677               59093.888348  \n",
       "13                 246.608077               58718.153219  \n",
       "14                 249.753362               59818.457018  \n",
       "15                 252.701641               60965.486977  \n",
       "16                 266.106935               66227.967767  \n",
       "17                 269.030921               68063.269449  \n",
       "18                 273.205946               69949.889228  \n",
       "19                 269.155765               68864.192994  \n",
       "20                 267.258407               69023.136480  \n",
       "21                 272.778502               72723.935175  \n",
       "22                 279.400934               77753.842862  \n",
       "23                 279.535170               78606.417698  \n",
       "24                 277.813048               78840.761866  \n",
       "25                 267.673424               76320.921363  \n",
       "26                 265.985719               76812.299191  \n",
       "27                 267.988473               78931.979204  \n",
       "28                 274.354293               83123.053110  \n",
       "29                 271.651670               82922.323674  \n",
       "30                 269.653403               82946.816255  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[[\n",
    "    \"step_index\",\n",
    "    \"age_years\",\n",
    "    \"stems_per_ha\",\n",
    "    \"qmd_cm\",\n",
    "    \"hq_m\",\n",
    "    \"phase_over_weight\",\n",
    "    \"young_damage_index_mean\",\n",
    "    \"mortality_fraction_mean\",\n",
    "    \"standing_volume_m3sk_per_ha\",\n",
    "    \"standing_value_sek_per_ha\",\n",
    "]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14a0829",
   "metadata": {},
   "source": [
    "## Genetic algorithm: best thinning routine (perpetuity objective)\n",
    "\n",
    "This section uses a genetic algorithm (GA) to find a thinning routine that maximizes a **perpetuity** objective (NPV/EAV/LEV), while still simulating multiple generations for robust trajectory behavior.\n",
    "\n",
    "Assumptions:\n",
    "- `0-4` thinnings per generation (from below, by basal-area fraction).\n",
    "- Mandatory clear-cut at the end of each rotation.\n",
    "- Replant/regenerate by calling `preset.initialize(site=site)` exactly as at stand instantiation.\n",
    "- No site-index or productivity drift over time (`config` unchanged, deterministic seed).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fc09737",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import io\n",
    "import math\n",
    "import os\n",
    "import warnings\n",
    "from contextlib import redirect_stderr, redirect_stdout\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=\"Soderberg self-thinning annual probability exceeded .*\",\n",
    ")\n",
    "\n",
    "GA_ROTATION_YEARS = 70\n",
    "GA_NUM_GENERATIONS = 4\n",
    "GA_DISCOUNT_RATE = 0.03\n",
    "GA_MAX_THINNINGS = 4\n",
    "GA_REGENERATION_COST_SEK_PER_HA = 0.0\n",
    "\n",
    "GA_DT_YEARS = float(config.dt_years)\n",
    "GA_AGE_MIN = GA_DT_YEARS\n",
    "GA_AGE_MAX = GA_ROTATION_YEARS - GA_DT_YEARS\n",
    "GA_INTENSITY_MIN = 0.10\n",
    "GA_INTENSITY_MAX = 0.50\n",
    "GA_GRADE_MIN = -1.0\n",
    "GA_GRADE_MAX = 1.0\n",
    "GA_SPECIES_WEIGHT_MIN = 0.25\n",
    "GA_SPECIES_WEIGHT_MAX = 3.0\n",
    "GA_REGEN_YEARS = float(max(0.0, config.initial_age_years))\n",
    "\n",
    "if abs((GA_ROTATION_YEARS / GA_DT_YEARS) - round(GA_ROTATION_YEARS / GA_DT_YEARS)) > 1e-9:\n",
    "    raise ValueError(\"GA_ROTATION_YEARS must be divisible by config.dt_years\")\n",
    "\n",
    "\n",
    "def ga_quiet_call(func, /, *args, **kwargs):\n",
    "    with io.StringIO() as stdout_buffer, io.StringIO() as stderr_buffer:\n",
    "        with redirect_stdout(stdout_buffer), redirect_stderr(stderr_buffer):\n",
    "            return func(*args, **kwargs)\n",
    "\n",
    "\n",
    "def ga_tree_basal_area_m2_ha(tree) -> float:\n",
    "    d_cm = float(tree.diameter_cm or 0.0)\n",
    "    w = float(tree.weight_n or 0.0)\n",
    "    if d_cm <= 0.0 or w <= 0.0:\n",
    "        return 0.0\n",
    "    return math.pi * (d_cm / 200.0) ** 2 * w\n",
    "\n",
    "\n",
    "def ga_species_group(tree) -> str:\n",
    "    species = getattr(tree, \"species\", None)\n",
    "    full_name = str(getattr(species, \"full_name\", \"\") or \"\").lower()\n",
    "    if \"picea\" in full_name:\n",
    "        return \"spruce\"\n",
    "    if \"pinus\" in full_name or \"larix\" in full_name:\n",
    "        return \"pine\"\n",
    "    return \"broadleaf\"\n",
    "\n",
    "\n",
    "def ga_parse_thinning_decision(decision) -> dict[str, float]:\n",
    "    if isinstance(decision, (float, int)):\n",
    "        return {\n",
    "            \"intensity\": float(np.clip(float(decision), GA_INTENSITY_MIN, GA_INTENSITY_MAX)),\n",
    "            \"grade\": -1.0,\n",
    "            \"spruce_weight\": 1.0,\n",
    "            \"pine_weight\": 1.0,\n",
    "            \"broadleaf_weight\": 1.0,\n",
    "        }\n",
    "\n",
    "    if len(decision) == 2:\n",
    "        _age_year, intensity = decision\n",
    "        return {\n",
    "            \"intensity\": float(np.clip(float(intensity), GA_INTENSITY_MIN, GA_INTENSITY_MAX)),\n",
    "            \"grade\": -1.0,\n",
    "            \"spruce_weight\": 1.0,\n",
    "            \"pine_weight\": 1.0,\n",
    "            \"broadleaf_weight\": 1.0,\n",
    "        }\n",
    "\n",
    "    if len(decision) != 6:\n",
    "        raise ValueError(\"Thinning decision must be float/int, (age, intensity), or 6-tuple\")\n",
    "\n",
    "    _age_year, intensity, grade, w_spruce, w_pine, w_broadleaf = decision\n",
    "    return {\n",
    "        \"intensity\": float(np.clip(float(intensity), GA_INTENSITY_MIN, GA_INTENSITY_MAX)),\n",
    "        \"grade\": float(np.clip(float(grade), GA_GRADE_MIN, GA_GRADE_MAX)),\n",
    "        \"spruce_weight\": float(\n",
    "            np.clip(float(w_spruce), GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)\n",
    "        ),\n",
    "        \"pine_weight\": float(np.clip(float(w_pine), GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)),\n",
    "        \"broadleaf_weight\": float(\n",
    "            np.clip(float(w_broadleaf), GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)\n",
    "        ),\n",
    "    }\n",
    "\n",
    "\n",
    "def ga_apply_targeted_thinning(preset, thinning_decision) -> dict[str, float]:\n",
    "    decision = ga_parse_thinning_decision(thinning_decision)\n",
    "    frac = float(np.clip(decision[\"intensity\"], 0.0, 0.95))\n",
    "\n",
    "    if frac <= 0.0:\n",
    "        return {\n",
    "            \"harvest_value_sek_per_ha\": 0.0,\n",
    "            \"harvest_volume_m3_per_ha\": 0.0,\n",
    "            \"removed_stems_per_ha\": 0.0,\n",
    "            \"removed_basal_area_m2_ha\": 0.0,\n",
    "            \"removed_spruce_stems_per_ha\": 0.0,\n",
    "            \"removed_pine_stems_per_ha\": 0.0,\n",
    "            \"removed_broadleaf_stems_per_ha\": 0.0,\n",
    "            \"thinning_grade\": float(decision[\"grade\"]),\n",
    "            \"spruce_weight\": float(decision[\"spruce_weight\"]),\n",
    "            \"pine_weight\": float(decision[\"pine_weight\"]),\n",
    "            \"broadleaf_weight\": float(decision[\"broadleaf_weight\"]),\n",
    "        }\n",
    "\n",
    "    candidates = [\n",
    "        t\n",
    "        for t in preset.tree_list\n",
    "        if float(t.weight_n or 0.0) > 0.0 and float(t.diameter_cm or 0.0) > 0.0\n",
    "    ]\n",
    "    if not candidates:\n",
    "        return {\n",
    "            \"harvest_value_sek_per_ha\": 0.0,\n",
    "            \"harvest_volume_m3_per_ha\": 0.0,\n",
    "            \"removed_stems_per_ha\": 0.0,\n",
    "            \"removed_basal_area_m2_ha\": 0.0,\n",
    "            \"removed_spruce_stems_per_ha\": 0.0,\n",
    "            \"removed_pine_stems_per_ha\": 0.0,\n",
    "            \"removed_broadleaf_stems_per_ha\": 0.0,\n",
    "            \"thinning_grade\": float(decision[\"grade\"]),\n",
    "            \"spruce_weight\": float(decision[\"spruce_weight\"]),\n",
    "            \"pine_weight\": float(decision[\"pine_weight\"]),\n",
    "            \"broadleaf_weight\": float(decision[\"broadleaf_weight\"]),\n",
    "        }\n",
    "\n",
    "    total_ba = sum(ga_tree_basal_area_m2_ha(t) for t in candidates)\n",
    "    target_ba = total_ba * frac\n",
    "    if target_ba <= 1e-12:\n",
    "        return {\n",
    "            \"harvest_value_sek_per_ha\": 0.0,\n",
    "            \"harvest_volume_m3_per_ha\": 0.0,\n",
    "            \"removed_stems_per_ha\": 0.0,\n",
    "            \"removed_basal_area_m2_ha\": 0.0,\n",
    "            \"removed_spruce_stems_per_ha\": 0.0,\n",
    "            \"removed_pine_stems_per_ha\": 0.0,\n",
    "            \"removed_broadleaf_stems_per_ha\": 0.0,\n",
    "            \"thinning_grade\": float(decision[\"grade\"]),\n",
    "            \"spruce_weight\": float(decision[\"spruce_weight\"]),\n",
    "            \"pine_weight\": float(decision[\"pine_weight\"]),\n",
    "            \"broadleaf_weight\": float(decision[\"broadleaf_weight\"]),\n",
    "        }\n",
    "\n",
    "    dbh_vals = np.array([float(t.diameter_cm or 0.0) for t in candidates], dtype=float)\n",
    "    dbh_min = float(np.min(dbh_vals))\n",
    "    dbh_max = float(np.max(dbh_vals))\n",
    "    dbh_span = max(1e-6, dbh_max - dbh_min)\n",
    "    grade = float(decision[\"grade\"])\n",
    "    alpha = (grade + 1.0) / 2.0  # -1 => 0 (from below), +1 => 1 (from above)\n",
    "\n",
    "    species_weights = {\n",
    "        \"spruce\": float(decision[\"spruce_weight\"]),\n",
    "        \"pine\": float(decision[\"pine_weight\"]),\n",
    "        \"broadleaf\": float(decision[\"broadleaf_weight\"]),\n",
    "    }\n",
    "\n",
    "    scored_candidates: list[tuple[float, object]] = []\n",
    "    for tree in candidates:\n",
    "        dbh = float(tree.diameter_cm or 0.0)\n",
    "        dbh_norm = float((dbh - dbh_min) / dbh_span)\n",
    "        dbh_priority = (1.0 - alpha) * (1.0 - dbh_norm) + alpha * dbh_norm\n",
    "        sp_group = ga_species_group(tree)\n",
    "        sp_weight = species_weights[sp_group]\n",
    "        priority = sp_weight * (0.5 + dbh_priority)\n",
    "        scored_candidates.append((priority, tree))\n",
    "\n",
    "    scored_candidates.sort(key=lambda item: (item[0], float(item[1].diameter_cm or 0.0)), reverse=True)\n",
    "\n",
    "    removed_records = []\n",
    "    removed_ba = 0.0\n",
    "    removed_stems = 0.0\n",
    "    removed_species_stems = {\"spruce\": 0.0, \"pine\": 0.0, \"broadleaf\": 0.0}\n",
    "\n",
    "    for _priority, tree in scored_candidates:\n",
    "        if removed_ba >= target_ba - 1e-12:\n",
    "            break\n",
    "\n",
    "        tree_ba = ga_tree_basal_area_m2_ha(tree)\n",
    "        weight = float(tree.weight_n or 0.0)\n",
    "        if tree_ba <= 0.0 or weight <= 0.0:\n",
    "            continue\n",
    "\n",
    "        take_ratio = min(1.0, (target_ba - removed_ba) / tree_ba)\n",
    "        take_weight = weight * take_ratio\n",
    "        if take_weight <= 0.0:\n",
    "            continue\n",
    "\n",
    "        removed_tree = copy.copy(tree)\n",
    "        removed_tree.weight_n = take_weight\n",
    "        removed_records.append(removed_tree)\n",
    "\n",
    "        tree.weight_n = weight - take_weight\n",
    "        removed_stems += take_weight\n",
    "        removed_ba += tree_ba * take_ratio\n",
    "        removed_species_stems[ga_species_group(tree)] += take_weight\n",
    "\n",
    "    preset._trees = [t for t in preset.tree_list if float(t.weight_n or 0.0) > 1e-9]\n",
    "\n",
    "    harvest = preset.value_standing_forest(removed_records) if removed_records else {\n",
    "        \"standing_value_sek_per_ha\": 0.0,\n",
    "        \"standing_volume_m3sk_per_ha\": 0.0,\n",
    "    }\n",
    "    return {\n",
    "        \"harvest_value_sek_per_ha\": float(harvest[\"standing_value_sek_per_ha\"]),\n",
    "        \"harvest_volume_m3_per_ha\": float(harvest[\"standing_volume_m3sk_per_ha\"]),\n",
    "        \"removed_stems_per_ha\": float(removed_stems),\n",
    "        \"removed_basal_area_m2_ha\": float(removed_ba),\n",
    "        \"removed_spruce_stems_per_ha\": float(removed_species_stems[\"spruce\"]),\n",
    "        \"removed_pine_stems_per_ha\": float(removed_species_stems[\"pine\"]),\n",
    "        \"removed_broadleaf_stems_per_ha\": float(removed_species_stems[\"broadleaf\"]),\n",
    "        \"thinning_grade\": float(grade),\n",
    "        \"spruce_weight\": float(species_weights[\"spruce\"]),\n",
    "        \"pine_weight\": float(species_weights[\"pine\"]),\n",
    "        \"broadleaf_weight\": float(species_weights[\"broadleaf\"]),\n",
    "    }\n",
    "\n",
    "\n",
    "def ga_decode_genome(genome: list[float]) -> tuple[tuple[int, float, float, float, float, float], ...]:\n",
    "    n_thinnings = int(np.clip(round(genome[0]), 0, GA_MAX_THINNINGS))\n",
    "    entries: list[tuple[int, float, float, float, float, float]] = []\n",
    "\n",
    "    for idx in range(GA_MAX_THINNINGS):\n",
    "        base = 1 + idx * 6\n",
    "        age_gene = float(genome[base])\n",
    "        intensity_gene = float(genome[base + 1])\n",
    "        grade_gene = float(genome[base + 2])\n",
    "        spruce_weight_gene = float(genome[base + 3])\n",
    "        pine_weight_gene = float(genome[base + 4])\n",
    "        broadleaf_weight_gene = float(genome[base + 5])\n",
    "\n",
    "        age_year = int(\n",
    "            np.clip(\n",
    "                round(age_gene / GA_DT_YEARS) * GA_DT_YEARS,\n",
    "                GA_AGE_MIN,\n",
    "                GA_AGE_MAX,\n",
    "            )\n",
    "        )\n",
    "        intensity = float(np.clip(intensity_gene, GA_INTENSITY_MIN, GA_INTENSITY_MAX))\n",
    "        grade = float(np.clip(grade_gene, GA_GRADE_MIN, GA_GRADE_MAX))\n",
    "        w_spruce = float(\n",
    "            np.clip(spruce_weight_gene, GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)\n",
    "        )\n",
    "        w_pine = float(np.clip(pine_weight_gene, GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX))\n",
    "        w_broadleaf = float(\n",
    "            np.clip(broadleaf_weight_gene, GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)\n",
    "        )\n",
    "        entries.append((age_year, intensity, grade, w_spruce, w_pine, w_broadleaf))\n",
    "\n",
    "    entries.sort(key=lambda x: x[0])\n",
    "    selected: list[tuple[int, float, float, float, float, float]] = []\n",
    "    used_ages: set[int] = set()\n",
    "\n",
    "    for age_year, intensity, grade, w_spruce, w_pine, w_broadleaf in entries:\n",
    "        if len(selected) >= n_thinnings:\n",
    "            break\n",
    "        if age_year in used_ages:\n",
    "            continue\n",
    "        selected.append(\n",
    "            (\n",
    "                age_year,\n",
    "                round(intensity, 3),\n",
    "                round(grade, 3),\n",
    "                round(w_spruce, 3),\n",
    "                round(w_pine, 3),\n",
    "                round(w_broadleaf, 3),\n",
    "            )\n",
    "        )\n",
    "        used_ages.add(age_year)\n",
    "\n",
    "    if len(selected) < n_thinnings:\n",
    "        for age_year in range(int(GA_AGE_MIN), int(GA_AGE_MAX) + 1, int(GA_DT_YEARS)):\n",
    "            if len(selected) >= n_thinnings:\n",
    "                break\n",
    "            if age_year in used_ages:\n",
    "                continue\n",
    "            selected.append(\n",
    "                (\n",
    "                    age_year,\n",
    "                    round((GA_INTENSITY_MIN + GA_INTENSITY_MAX) / 2.0, 3),\n",
    "                    -1.0,\n",
    "                    1.0,\n",
    "                    1.0,\n",
    "                    1.0,\n",
    "                )\n",
    "            )\n",
    "            used_ages.add(age_year)\n",
    "\n",
    "    selected.sort(key=lambda x: x[0])\n",
    "    return tuple(selected)\n",
    "\n",
    "\n",
    "GA_PRESET_CACHE: dict[str, object] = {}\n",
    "\n",
    "\n",
    "def ga_get_cached_preset(config_arg):\n",
    "    key = repr(config_arg)\n",
    "    preset = GA_PRESET_CACHE.get(key)\n",
    "    if preset is None:\n",
    "        preset = build_elfving_2010_pipeline(config_arg)\n",
    "        shared_cube = ga_quiet_call(preset._load_solution_cube)\n",
    "        preset._load_solution_cube = lambda: shared_cube  # type: ignore[method-assign]\n",
    "        GA_PRESET_CACHE[key] = preset\n",
    "    return preset\n",
    "\n",
    "\n",
    "def ga_accounting_metrics(\n",
    "    cashflows: pd.DataFrame,\n",
    "    *,\n",
    "    discount_rate: float,\n",
    "    accounting_method: str,\n",
    ") -> dict[str, float | str]:\n",
    "    if cashflows.empty:\n",
    "        return {\n",
    "            \"accounting_method\": accounting_method,\n",
    "            \"discount_rate\": float(discount_rate),\n",
    "            \"objective_value\": 0.0,\n",
    "            \"perpetuity_npv_sek_per_ha\": 0.0,\n",
    "            \"perpetuity_eav_sek_per_ha_year\": 0.0,\n",
    "            \"perpetuity_lev_sek_per_ha\": 0.0,\n",
    "            \"perpetuity_annuity_sek_per_ha_year\": 0.0,\n",
    "            \"horizon_npv_sek_per_ha\": 0.0,\n",
    "            \"horizon_eav_sek_per_ha_year\": 0.0,\n",
    "            \"faustmann_lev_sek_per_ha\": 0.0,\n",
    "            \"faustmann_annuity_sek_per_ha_year\": 0.0,\n",
    "            \"horizon_years\": 0.0,\n",
    "            \"cycle_length_years\": float(GA_ROTATION_YEARS + GA_REGEN_YEARS),\n",
    "            \"cycle_pv_sek_per_ha\": 0.0,\n",
    "        }\n",
    "\n",
    "    rate = float(discount_rate)\n",
    "    if rate < 0.0:\n",
    "        raise ValueError(\"discount_rate must be >= 0\")\n",
    "\n",
    "    horizon_years = float(cashflows[\"year\"].max())\n",
    "    cycle_length_years = float(GA_ROTATION_YEARS + GA_REGEN_YEARS)\n",
    "    first_cycle_end = float(GA_ROTATION_YEARS)\n",
    "    cycle_cashflows = cashflows.loc[cashflows[\"year\"] <= first_cycle_end + 1e-9].copy()\n",
    "    cycle_pv = float(\n",
    "        (\n",
    "            cycle_cashflows[\"cashflow_sek_per_ha\"]\n",
    "            / ((1.0 + rate) ** cycle_cashflows[\"year\"])\n",
    "        ).sum()\n",
    "    )\n",
    "\n",
    "    if rate > 0.0:\n",
    "        cycle_discount = (1.0 + rate) ** (-cycle_length_years)\n",
    "        denom = 1.0 - cycle_discount\n",
    "        perpetuity_npv = float(cycle_pv / denom) if denom > 1e-12 else 0.0\n",
    "        perpetuity_eav = float(perpetuity_npv * rate)\n",
    "    else:\n",
    "        # Zero-discount fallback keeps metrics finite for diagnostics.\n",
    "        perpetuity_npv = float(cycle_pv)\n",
    "        perpetuity_eav = float(cycle_pv / max(1.0, cycle_length_years))\n",
    "\n",
    "    perpetuity_lev = float(perpetuity_npv)\n",
    "\n",
    "    method_key = accounting_method.strip().lower()\n",
    "    if method_key in {\"perpetuity_npv\", \"horizon_npv\", \"faustmann_lev\", \"perpetuity_lev\"}:\n",
    "        objective_value = perpetuity_npv\n",
    "    elif method_key in {\n",
    "        \"perpetuity_eav\",\n",
    "        \"horizon_eav\",\n",
    "        \"faustmann_annuity\",\n",
    "        \"perpetuity_annuity\",\n",
    "    }:\n",
    "        objective_value = perpetuity_eav\n",
    "    else:\n",
    "        raise ValueError(\n",
    "            \"accounting_method must be one of: perpetuity_npv, perpetuity_eav, \"\n",
    "            \"perpetuity_lev, perpetuity_annuity (legacy aliases: horizon_npv, \"\n",
    "            \"horizon_eav, faustmann_lev, faustmann_annuity)\"\n",
    "        )\n",
    "\n",
    "    # Backward-compatible aliases map to perpetuity metrics.\n",
    "    horizon_npv = float(perpetuity_npv)\n",
    "    horizon_eav = float(perpetuity_eav)\n",
    "    faustmann_lev = float(perpetuity_lev)\n",
    "    faustmann_annuity = float(perpetuity_eav)\n",
    "\n",
    "    return {\n",
    "        \"accounting_method\": method_key,\n",
    "        \"discount_rate\": rate,\n",
    "        \"objective_value\": float(objective_value),\n",
    "        \"perpetuity_npv_sek_per_ha\": float(perpetuity_npv),\n",
    "        \"perpetuity_eav_sek_per_ha_year\": float(perpetuity_eav),\n",
    "        \"perpetuity_lev_sek_per_ha\": float(perpetuity_lev),\n",
    "        \"perpetuity_annuity_sek_per_ha_year\": float(perpetuity_eav),\n",
    "        \"horizon_npv_sek_per_ha\": horizon_npv,\n",
    "        \"horizon_eav_sek_per_ha_year\": horizon_eav,\n",
    "        \"faustmann_lev_sek_per_ha\": faustmann_lev,\n",
    "        \"faustmann_annuity_sek_per_ha_year\": faustmann_annuity,\n",
    "        \"horizon_years\": horizon_years,\n",
    "        \"cycle_length_years\": cycle_length_years,\n",
    "        \"cycle_pv_sek_per_ha\": float(cycle_pv),\n",
    "    }\n",
    "\n",
    "\n",
    "def _ga_simulate_with_preset(\n",
    "    preset,\n",
    "    schedule: tuple[tuple[int, float, float, float, float, float], ...],\n",
    "    site_arg,\n",
    "    *,\n",
    "    discount_rate: float = GA_DISCOUNT_RATE,\n",
    "    accounting_method: str = \"perpetuity_npv\",\n",
    "    return_trace: bool = False,\n",
    "    return_metrics: bool = False,\n",
    "):\n",
    "    preset.initialize(site=site_arg)\n",
    "    base_asinw = float(preset._regen_asinw)\n",
    "\n",
    "    steps_per_rotation = int(round(GA_ROTATION_YEARS / GA_DT_YEARS))\n",
    "    thinning_by_step = {\n",
    "        int(round(decision[0] / GA_DT_YEARS)): decision for decision in schedule\n",
    "    }\n",
    "\n",
    "    events: list[dict[str, float | int | str]] = []\n",
    "\n",
    "    for generation in range(1, GA_NUM_GENERATIONS + 1):\n",
    "        for step_idx in range(1, steps_per_rotation + 1):\n",
    "            preset.step(dt_years=GA_DT_YEARS)\n",
    "            thinning_decision = thinning_by_step.get(step_idx)\n",
    "\n",
    "            if thinning_decision is not None:\n",
    "                decision = ga_parse_thinning_decision(thinning_decision)\n",
    "                thinning = ga_apply_targeted_thinning(preset, thinning_decision)\n",
    "                if thinning[\"harvest_value_sek_per_ha\"] > 0.0:\n",
    "                    generation_start_year = (generation - 1) * (GA_ROTATION_YEARS + GA_REGEN_YEARS)\n",
    "                    event_year = generation_start_year + step_idx * GA_DT_YEARS\n",
    "                    events.append(\n",
    "                        {\n",
    "                            \"generation\": generation,\n",
    "                            \"year\": float(event_year),\n",
    "                            \"event\": \"thinning\",\n",
    "                            \"cashflow_sek_per_ha\": float(thinning[\"harvest_value_sek_per_ha\"]),\n",
    "                            \"volume_m3_per_ha\": float(thinning[\"harvest_volume_m3_per_ha\"]),\n",
    "                            \"thinning_intensity\": float(decision[\"intensity\"]),\n",
    "                            \"thinning_grade\": float(decision[\"grade\"]),\n",
    "                            \"spruce_weight\": float(decision[\"spruce_weight\"]),\n",
    "                            \"pine_weight\": float(decision[\"pine_weight\"]),\n",
    "                            \"broadleaf_weight\": float(decision[\"broadleaf_weight\"]),\n",
    "                        }\n",
    "                    )\n",
    "\n",
    "        clearcut = preset.value_standing_forest()\n",
    "        generation_start_year = (generation - 1) * (GA_ROTATION_YEARS + GA_REGEN_YEARS)\n",
    "        clearcut_year = float(generation_start_year + GA_ROTATION_YEARS)\n",
    "        events.append(\n",
    "            {\n",
    "                \"generation\": generation,\n",
    "                \"year\": clearcut_year,\n",
    "                \"event\": \"clearcut\",\n",
    "                \"cashflow_sek_per_ha\": float(clearcut[\"standing_value_sek_per_ha\"]),\n",
    "                \"volume_m3_per_ha\": float(clearcut[\"standing_volume_m3sk_per_ha\"]),\n",
    "            }\n",
    "        )\n",
    "\n",
    "        if GA_REGENERATION_COST_SEK_PER_HA > 0.0:\n",
    "            events.append(\n",
    "                {\n",
    "                    \"generation\": generation,\n",
    "                    \"year\": clearcut_year,\n",
    "                    \"event\": \"regeneration_cost\",\n",
    "                    \"cashflow_sek_per_ha\": -float(GA_REGENERATION_COST_SEK_PER_HA),\n",
    "                    \"volume_m3_per_ha\": 0.0,\n",
    "                }\n",
    "            )\n",
    "\n",
    "        if generation < GA_NUM_GENERATIONS:\n",
    "            preset.initialize(site=site_arg)\n",
    "            if abs(float(preset._regen_asinw) - base_asinw) > 1e-9:\n",
    "                raise RuntimeError(\"Regeneration productivity changed between generations.\")\n",
    "\n",
    "    cashflows = pd.DataFrame(events)\n",
    "    cashflows[\"pv_sek_per_ha\"] = cashflows[\"cashflow_sek_per_ha\"] / (\n",
    "        (1.0 + float(discount_rate)) ** cashflows[\"year\"]\n",
    "    )\n",
    "    metrics = ga_accounting_metrics(\n",
    "        cashflows,\n",
    "        discount_rate=float(discount_rate),\n",
    "        accounting_method=accounting_method,\n",
    "    )\n",
    "    objective_value = float(metrics[\"objective_value\"])\n",
    "\n",
    "    if return_trace and return_metrics:\n",
    "        return objective_value, cashflows, metrics\n",
    "    if return_trace:\n",
    "        return objective_value, cashflows\n",
    "    if return_metrics:\n",
    "        return objective_value, metrics\n",
    "    return objective_value\n",
    "\n",
    "\n",
    "def ga_simulate_schedule(\n",
    "    schedule: tuple[tuple[int, float, float, float, float, float], ...],\n",
    "    *,\n",
    "    config_arg,\n",
    "    site_arg,\n",
    "    discount_rate: float = GA_DISCOUNT_RATE,\n",
    "    accounting_method: str = \"perpetuity_npv\",\n",
    "    return_trace: bool = False,\n",
    "    return_metrics: bool = False,\n",
    "):\n",
    "    preset = ga_get_cached_preset(config_arg)\n",
    "    return ga_quiet_call(\n",
    "        _ga_simulate_with_preset,\n",
    "        preset,\n",
    "        schedule,\n",
    "        site_arg,\n",
    "        discount_rate=float(discount_rate),\n",
    "        accounting_method=accounting_method,\n",
    "        return_trace=return_trace,\n",
    "        return_metrics=return_metrics,\n",
    "    )\n",
    "\n",
    "\n",
    "def _ga_standing_state(preset) -> dict[str, float]:\n",
    "    standing = preset.value_standing_forest()\n",
    "    return {\n",
    "        \"stems_per_ha\": float(sum(max(0.0, float(t.weight_n or 0.0)) for t in preset.tree_list)),\n",
    "        \"basal_area_m2_ha\": float(sum(ga_tree_basal_area_m2_ha(t) for t in preset.tree_list)),\n",
    "        \"standing_volume_m3sk_per_ha\": float(standing[\"standing_volume_m3sk_per_ha\"]),\n",
    "        \"standing_value_sek_per_ha\": float(standing[\"standing_value_sek_per_ha\"]),\n",
    "    }\n",
    "\n",
    "\n",
    "def _ga_build_flow_trajectory_with_preset(\n",
    "    preset,\n",
    "    schedule: tuple[tuple[int, float, float, float, float, float], ...],\n",
    "    site_arg,\n",
    ") -> pd.DataFrame:\n",
    "    preset.initialize(site=site_arg)\n",
    "    base_asinw = float(preset._regen_asinw)\n",
    "\n",
    "    steps_per_rotation = int(round(GA_ROTATION_YEARS / GA_DT_YEARS))\n",
    "    thinning_by_step = {\n",
    "        int(round(decision[0] / GA_DT_YEARS)): decision for decision in schedule\n",
    "    }\n",
    "\n",
    "    rows: list[dict[str, float | int | str]] = []\n",
    "\n",
    "    def record(\n",
    "        *,\n",
    "        generation: int,\n",
    "        year: float,\n",
    "        event: str,\n",
    "        thinning_intensity: float = 0.0,\n",
    "        thinning_grade: float = 0.0,\n",
    "        spruce_weight: float = 0.0,\n",
    "        pine_weight: float = 0.0,\n",
    "        broadleaf_weight: float = 0.0,\n",
    "        removed_spruce_stems_per_ha: float = 0.0,\n",
    "        removed_pine_stems_per_ha: float = 0.0,\n",
    "        removed_broadleaf_stems_per_ha: float = 0.0,\n",
    "        harvest_value_sek_per_ha: float = 0.0,\n",
    "        harvest_volume_m3_per_ha: float = 0.0,\n",
    "    ) -> None:\n",
    "        state = _ga_standing_state(preset)\n",
    "        rows.append(\n",
    "            {\n",
    "                \"generation\": int(generation),\n",
    "                \"year\": float(year),\n",
    "                \"event\": event,\n",
    "                \"thinning_intensity\": float(thinning_intensity),\n",
    "                \"thinning_grade\": float(thinning_grade),\n",
    "                \"spruce_weight\": float(spruce_weight),\n",
    "                \"pine_weight\": float(pine_weight),\n",
    "                \"broadleaf_weight\": float(broadleaf_weight),\n",
    "                \"removed_spruce_stems_per_ha\": float(removed_spruce_stems_per_ha),\n",
    "                \"removed_pine_stems_per_ha\": float(removed_pine_stems_per_ha),\n",
    "                \"removed_broadleaf_stems_per_ha\": float(removed_broadleaf_stems_per_ha),\n",
    "                \"harvest_value_sek_per_ha\": float(harvest_value_sek_per_ha),\n",
    "                \"harvest_volume_m3_per_ha\": float(harvest_volume_m3_per_ha),\n",
    "                **state,\n",
    "            }\n",
    "        )\n",
    "\n",
    "    record(generation=1, year=0.0, event=\"start\")\n",
    "\n",
    "    for generation in range(1, GA_NUM_GENERATIONS + 1):\n",
    "        for step_idx in range(1, steps_per_rotation + 1):\n",
    "            preset.step(dt_years=GA_DT_YEARS)\n",
    "            generation_start_year = (generation - 1) * (GA_ROTATION_YEARS + GA_REGEN_YEARS)\n",
    "            event_year = generation_start_year + step_idx * GA_DT_YEARS\n",
    "            record(generation=generation, year=float(event_year), event=\"growth\")\n",
    "\n",
    "            thinning_decision = thinning_by_step.get(step_idx)\n",
    "            if thinning_decision is None:\n",
    "                continue\n",
    "\n",
    "            decision = ga_parse_thinning_decision(thinning_decision)\n",
    "            thinning = ga_apply_targeted_thinning(preset, thinning_decision)\n",
    "            record(\n",
    "                generation=generation,\n",
    "                year=float(event_year),\n",
    "                event=\"thinning\",\n",
    "                thinning_intensity=float(decision[\"intensity\"]),\n",
    "                thinning_grade=float(decision[\"grade\"]),\n",
    "                spruce_weight=float(decision[\"spruce_weight\"]),\n",
    "                pine_weight=float(decision[\"pine_weight\"]),\n",
    "                broadleaf_weight=float(decision[\"broadleaf_weight\"]),\n",
    "                removed_spruce_stems_per_ha=float(thinning[\"removed_spruce_stems_per_ha\"]),\n",
    "                removed_pine_stems_per_ha=float(thinning[\"removed_pine_stems_per_ha\"]),\n",
    "                removed_broadleaf_stems_per_ha=float(thinning[\"removed_broadleaf_stems_per_ha\"]),\n",
    "                harvest_value_sek_per_ha=float(thinning[\"harvest_value_sek_per_ha\"]),\n",
    "                harvest_volume_m3_per_ha=float(thinning[\"harvest_volume_m3_per_ha\"]),\n",
    "            )\n",
    "\n",
    "        clearcut = preset.value_standing_forest()\n",
    "        generation_start_year = (generation - 1) * (GA_ROTATION_YEARS + GA_REGEN_YEARS)\n",
    "        clearcut_year = float(generation_start_year + GA_ROTATION_YEARS)\n",
    "        record(\n",
    "            generation=generation,\n",
    "            year=clearcut_year,\n",
    "            event=\"clearcut\",\n",
    "            harvest_value_sek_per_ha=float(clearcut[\"standing_value_sek_per_ha\"]),\n",
    "            harvest_volume_m3_per_ha=float(clearcut[\"standing_volume_m3sk_per_ha\"]),\n",
    "        )\n",
    "\n",
    "        if generation < GA_NUM_GENERATIONS:\n",
    "            preset.initialize(site=site_arg)\n",
    "            if abs(float(preset._regen_asinw) - base_asinw) > 1e-9:\n",
    "                raise RuntimeError(\"Regeneration productivity changed between generations.\")\n",
    "            record(\n",
    "                generation=generation + 1,\n",
    "                year=clearcut_year + GA_REGEN_YEARS,\n",
    "                event=\"regenerate\",\n",
    "            )\n",
    "\n",
    "    return pd.DataFrame.from_records(rows)\n",
    "\n",
    "\n",
    "def ga_build_flow_trajectory(\n",
    "    schedule: tuple[tuple[int, float, float, float, float, float], ...],\n",
    "    *,\n",
    "    config_arg,\n",
    "    site_arg,\n",
    ") -> pd.DataFrame:\n",
    "    preset = ga_get_cached_preset(config_arg)\n",
    "    return ga_quiet_call(_ga_build_flow_trajectory_with_preset, preset, schedule, site_arg)\n",
    "\n",
    "\n",
    "def ga_evaluate_population(\n",
    "    population,\n",
    "    *,\n",
    "    config_arg,\n",
    "    site_arg,\n",
    "    discount_rate: float = GA_DISCOUNT_RATE,\n",
    "    accounting_method: str = \"perpetuity_npv\",\n",
    "    parallel=None,\n",
    "):\n",
    "    decoded_schedules = [ga_decode_genome(genome) for genome in population]\n",
    "    unique_schedules = sorted(set(decoded_schedules))\n",
    "\n",
    "    if parallel is None:\n",
    "        fitness_values = [\n",
    "            ga_simulate_schedule(\n",
    "                schedule,\n",
    "                config_arg=config_arg,\n",
    "                site_arg=site_arg,\n",
    "                discount_rate=float(discount_rate),\n",
    "                accounting_method=accounting_method,\n",
    "            )\n",
    "            for schedule in unique_schedules\n",
    "        ]\n",
    "    else:\n",
    "        # Keep parent-side globals picklable when dispatching jobs.\n",
    "        GA_PRESET_CACHE.clear()\n",
    "        fitness_values = parallel(\n",
    "            delayed(ga_simulate_schedule)(\n",
    "                schedule,\n",
    "                config_arg=config_arg,\n",
    "                site_arg=site_arg,\n",
    "                discount_rate=float(discount_rate),\n",
    "                accounting_method=accounting_method,\n",
    "            )\n",
    "            for schedule in unique_schedules\n",
    "        )\n",
    "\n",
    "    schedule_to_fitness = dict(zip(unique_schedules, fitness_values))\n",
    "    fitness = [float(schedule_to_fitness[schedule]) for schedule in decoded_schedules]\n",
    "    return fitness, decoded_schedules\n",
    "\n",
    "\n",
    "def ga_random_genome(rng: np.random.Generator) -> list[float]:\n",
    "    genes = [float(rng.integers(0, GA_MAX_THINNINGS + 1))]\n",
    "    for _ in range(GA_MAX_THINNINGS):\n",
    "        genes.append(float(rng.uniform(GA_AGE_MIN, GA_AGE_MAX)))\n",
    "        genes.append(float(rng.uniform(GA_INTENSITY_MIN, GA_INTENSITY_MAX)))\n",
    "        genes.append(float(rng.uniform(GA_GRADE_MIN, GA_GRADE_MAX)))\n",
    "        genes.append(float(rng.uniform(GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)))\n",
    "        genes.append(float(rng.uniform(GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)))\n",
    "        genes.append(float(rng.uniform(GA_SPECIES_WEIGHT_MIN, GA_SPECIES_WEIGHT_MAX)))\n",
    "    return genes\n",
    "\n",
    "\n",
    "def ga_mutate(genome: list[float], rng: np.random.Generator, *, rate: float) -> list[float]:\n",
    "    out = genome.copy()\n",
    "\n",
    "    if rng.random() < rate:\n",
    "        out[0] = float(np.clip(round(out[0] + rng.integers(-1, 2)), 0, GA_MAX_THINNINGS))\n",
    "\n",
    "    for idx in range(GA_MAX_THINNINGS):\n",
    "        base = 1 + idx * 6\n",
    "        age_idx = base\n",
    "        intensity_idx = base + 1\n",
    "        grade_idx = base + 2\n",
    "        spruce_idx = base + 3\n",
    "        pine_idx = base + 4\n",
    "        broadleaf_idx = base + 5\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[age_idx] = float(np.clip(out[age_idx] + rng.normal(0.0, 8.0), GA_AGE_MIN, GA_AGE_MAX))\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[intensity_idx] = float(\n",
    "                np.clip(\n",
    "                    out[intensity_idx] + rng.normal(0.0, 0.06),\n",
    "                    GA_INTENSITY_MIN,\n",
    "                    GA_INTENSITY_MAX,\n",
    "                )\n",
    "            )\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[grade_idx] = float(\n",
    "                np.clip(out[grade_idx] + rng.normal(0.0, 0.18), GA_GRADE_MIN, GA_GRADE_MAX)\n",
    "            )\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[spruce_idx] = float(\n",
    "                np.clip(\n",
    "                    out[spruce_idx] + rng.normal(0.0, 0.35),\n",
    "                    GA_SPECIES_WEIGHT_MIN,\n",
    "                    GA_SPECIES_WEIGHT_MAX,\n",
    "                )\n",
    "            )\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[pine_idx] = float(\n",
    "                np.clip(\n",
    "                    out[pine_idx] + rng.normal(0.0, 0.35),\n",
    "                    GA_SPECIES_WEIGHT_MIN,\n",
    "                    GA_SPECIES_WEIGHT_MAX,\n",
    "                )\n",
    "            )\n",
    "\n",
    "        if rng.random() < rate:\n",
    "            out[broadleaf_idx] = float(\n",
    "                np.clip(\n",
    "                    out[broadleaf_idx] + rng.normal(0.0, 0.35),\n",
    "                    GA_SPECIES_WEIGHT_MIN,\n",
    "                    GA_SPECIES_WEIGHT_MAX,\n",
    "                )\n",
    "            )\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "def ga_crossover(\n",
    "    parent_a: list[float],\n",
    "    parent_b: list[float],\n",
    "    rng: np.random.Generator,\n",
    "    *,\n",
    "    rate: float,\n",
    ") -> tuple[list[float], list[float]]:\n",
    "    if rng.random() >= rate:\n",
    "        return parent_a.copy(), parent_b.copy()\n",
    "\n",
    "    child_a = parent_a.copy()\n",
    "    child_b = parent_b.copy()\n",
    "\n",
    "    for idx in range(len(child_a)):\n",
    "        if rng.random() < 0.5:\n",
    "            child_a[idx], child_b[idx] = child_b[idx], child_a[idx]\n",
    "\n",
    "    return child_a, child_b\n",
    "\n",
    "\n",
    "def ga_tournament_select(\n",
    "    population: list[list[float]],\n",
    "    fitness: list[float],\n",
    "    rng: np.random.Generator,\n",
    "    *,\n",
    "    n_select: int,\n",
    "    tournament_size: int,\n",
    ") -> list[list[float]]:\n",
    "    selected = []\n",
    "    for _ in range(n_select):\n",
    "        indices = rng.integers(0, len(population), size=tournament_size)\n",
    "        best_index = max(indices, key=lambda idx: fitness[int(idx)])\n",
    "        selected.append(population[int(best_index)].copy())\n",
    "    return selected\n",
    "\n",
    "\n",
    "\n",
    "def ga_optimize_routine(\n",
    "    *,\n",
    "    config_arg,\n",
    "    site_arg,\n",
    "    discount_rate: float,\n",
    "    accounting_method: str,\n",
    "    pop_size: int,\n",
    "    ga_generations: int,\n",
    "    elite: int,\n",
    "    tournament_size: int,\n",
    "    mutation_rate: float,\n",
    "    crossover_rate: float,\n",
    "    workers: int,\n",
    "    seed: int,\n",
    "    verbose: bool = False,\n",
    ") -> dict[str, object]:\n",
    "    if pop_size <= 2:\n",
    "        raise ValueError(\"pop_size must be > 2\")\n",
    "    if not (1 <= elite < pop_size):\n",
    "        raise ValueError(\"elite must be in [1, pop_size)\")\n",
    "\n",
    "    rng = np.random.default_rng(seed)\n",
    "    population = [ga_random_genome(rng) for _ in range(int(pop_size))]\n",
    "    best_score_history: list[float] = []\n",
    "    best_schedule_history: list[tuple[tuple[int, float, float, float, float, float], ...]] = []\n",
    "\n",
    "    parallel = Parallel(n_jobs=int(workers), backend=\"loky\") if int(workers) > 1 else None\n",
    "    if parallel is None:\n",
    "        fitness, decoded = ga_evaluate_population(\n",
    "            population,\n",
    "            config_arg=config_arg,\n",
    "            site_arg=site_arg,\n",
    "            discount_rate=float(discount_rate),\n",
    "            accounting_method=accounting_method,\n",
    "            parallel=None,\n",
    "        )\n",
    "\n",
    "        for generation_idx in range(int(ga_generations) + 1):\n",
    "            best_index = int(np.argmax(fitness))\n",
    "            best_score = float(fitness[best_index])\n",
    "            best_schedule = decoded[best_index]\n",
    "            best_score_history.append(best_score)\n",
    "            best_schedule_history.append(best_schedule)\n",
    "\n",
    "            if verbose:\n",
    "                print(\n",
    "                    f\"GA[{accounting_method}] gen {generation_idx:02d} | \"\n",
    "                    f\"best objective: {best_score:,.2f} | schedule: {best_schedule}\"\n",
    "                )\n",
    "\n",
    "            if generation_idx == int(ga_generations):\n",
    "                break\n",
    "\n",
    "            elite_indices = np.argsort(fitness)[-int(elite):]\n",
    "            next_population = [population[int(i)].copy() for i in elite_indices]\n",
    "            parents = ga_tournament_select(\n",
    "                population,\n",
    "                fitness,\n",
    "                rng,\n",
    "                n_select=int(pop_size) - int(elite),\n",
    "                tournament_size=int(tournament_size),\n",
    "            )\n",
    "\n",
    "            for idx in range(0, len(parents), 2):\n",
    "                parent_a = parents[idx]\n",
    "                parent_b = parents[(idx + 1) % len(parents)]\n",
    "                child_a, child_b = ga_crossover(\n",
    "                    parent_a,\n",
    "                    parent_b,\n",
    "                    rng,\n",
    "                    rate=float(crossover_rate),\n",
    "                )\n",
    "                next_population.append(ga_mutate(child_a, rng, rate=float(mutation_rate)))\n",
    "                if len(next_population) < int(pop_size):\n",
    "                    next_population.append(ga_mutate(child_b, rng, rate=float(mutation_rate)))\n",
    "\n",
    "            population = next_population[: int(pop_size)]\n",
    "            fitness, decoded = ga_evaluate_population(\n",
    "                population,\n",
    "                config_arg=config_arg,\n",
    "                site_arg=site_arg,\n",
    "                discount_rate=float(discount_rate),\n",
    "                accounting_method=accounting_method,\n",
    "                parallel=None,\n",
    "            )\n",
    "    else:\n",
    "        with parallel as parallel_runner:\n",
    "            fitness, decoded = ga_evaluate_population(\n",
    "                population,\n",
    "                config_arg=config_arg,\n",
    "                site_arg=site_arg,\n",
    "                discount_rate=float(discount_rate),\n",
    "                accounting_method=accounting_method,\n",
    "                parallel=parallel_runner,\n",
    "            )\n",
    "\n",
    "            for generation_idx in range(int(ga_generations) + 1):\n",
    "                best_index = int(np.argmax(fitness))\n",
    "                best_score = float(fitness[best_index])\n",
    "                best_schedule = decoded[best_index]\n",
    "                best_score_history.append(best_score)\n",
    "                best_schedule_history.append(best_schedule)\n",
    "\n",
    "                if verbose:\n",
    "                    print(\n",
    "                        f\"GA[{accounting_method}] gen {generation_idx:02d} | \"\n",
    "                        f\"best objective: {best_score:,.2f} | schedule: {best_schedule}\"\n",
    "                    )\n",
    "\n",
    "                if generation_idx == int(ga_generations):\n",
    "                    break\n",
    "\n",
    "                elite_indices = np.argsort(fitness)[-int(elite):]\n",
    "                next_population = [population[int(i)].copy() for i in elite_indices]\n",
    "                parents = ga_tournament_select(\n",
    "                    population,\n",
    "                    fitness,\n",
    "                    rng,\n",
    "                    n_select=int(pop_size) - int(elite),\n",
    "                    tournament_size=int(tournament_size),\n",
    "                )\n",
    "\n",
    "                for idx in range(0, len(parents), 2):\n",
    "                    parent_a = parents[idx]\n",
    "                    parent_b = parents[(idx + 1) % len(parents)]\n",
    "                    child_a, child_b = ga_crossover(\n",
    "                        parent_a,\n",
    "                        parent_b,\n",
    "                        rng,\n",
    "                        rate=float(crossover_rate),\n",
    "                    )\n",
    "                    next_population.append(ga_mutate(child_a, rng, rate=float(mutation_rate)))\n",
    "                    if len(next_population) < int(pop_size):\n",
    "                        next_population.append(ga_mutate(child_b, rng, rate=float(mutation_rate)))\n",
    "\n",
    "                population = next_population[: int(pop_size)]\n",
    "                fitness, decoded = ga_evaluate_population(\n",
    "                    population,\n",
    "                    config_arg=config_arg,\n",
    "                    site_arg=site_arg,\n",
    "                    discount_rate=float(discount_rate),\n",
    "                    accounting_method=accounting_method,\n",
    "                    parallel=parallel_runner,\n",
    "                )\n",
    "\n",
    "    best_schedule = best_schedule_history[-1]\n",
    "    best_objective, best_cashflows, best_metrics = ga_simulate_schedule(\n",
    "        best_schedule,\n",
    "        config_arg=config_arg,\n",
    "        site_arg=site_arg,\n",
    "        discount_rate=float(discount_rate),\n",
    "        accounting_method=accounting_method,\n",
    "        return_trace=True,\n",
    "        return_metrics=True,\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"best_schedule\": best_schedule,\n",
    "        \"best_objective\": float(best_objective),\n",
    "        \"best_score_history\": best_score_history,\n",
    "        \"best_schedule_history\": best_schedule_history,\n",
    "        \"best_cashflows\": best_cashflows,\n",
    "        \"best_metrics\": best_metrics,\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f01b5d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GA generation 00 | best perpetuity objective: 25,869 SEK/ha | schedule: ((5, 0.408, 0.401, 1.733, 0.928, 2.052), (10, 0.3, -1.0, 1.0, 1.0, 1.0), (20, 0.348, -0.092, 0.967, 0.608, 1.201), (30, 0.331, 0.683, 1.424, 2.281, 2.596))\n",
      "GA generation 01 | best perpetuity objective: 30,214 SEK/ha | schedule: ((10, 0.478, 0.649, 0.344, 2.092, 2.531), (15, 0.444, -0.434, 2.201, 0.672, 2.585), (25, 0.191, -0.883, 0.565, 1.408, 1.51))\n",
      "GA generation 02 | best perpetuity objective: 31,765 SEK/ha | schedule: ((5, 0.28, 0.401, 1.122, 0.915, 1.162), (15, 0.5, 0.555, 2.201, 1.228, 2.441), (20, 0.393, -0.623, 0.25, 0.608, 1.674))\n",
      "GA generation 03 | best perpetuity objective: 34,034 SEK/ha | schedule: ((15, 0.5, 0.555, 2.201, 1.228, 2.441), (20, 0.478, 0.899, 0.25, 2.092, 2.531), (25, 0.241, -0.954, 0.565, 1.408, 2.596), (50, 0.28, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 04 | best perpetuity objective: 34,559 SEK/ha | schedule: ((15, 0.5, 0.65, 2.201, 0.546, 2.441), (20, 0.478, 0.993, 0.25, 2.092, 2.457), (25, 0.241, -1.0, 0.565, 1.507, 2.596), (50, 0.324, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 05 | best perpetuity objective: 34,612 SEK/ha | schedule: ((15, 0.5, 0.65, 2.201, 1.014, 2.441), (20, 0.478, 0.899, 0.25, 2.092, 2.821), (25, 0.276, -1.0, 0.565, 1.507, 2.596), (50, 0.324, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 06 | best perpetuity objective: 35,404 SEK/ha | schedule: ((10, 0.5, 0.555, 1.919, 0.425, 2.441), (20, 0.5, 1.0, 0.25, 2.092, 2.648), (25, 0.276, -0.952, 0.565, 1.408, 2.596), (50, 0.322, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 07 | best perpetuity objective: 35,608 SEK/ha | schedule: ((10, 0.5, 0.65, 2.362, 1.618, 2.24), (20, 0.462, 1.0, 0.25, 1.907, 2.648), (25, 0.276, -0.956, 0.565, 2.048, 3.0), (50, 0.322, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 08 | best perpetuity objective: 36,069 SEK/ha | schedule: ((10, 0.5, 0.418, 2.288, 0.425, 2.441), (20, 0.5, 1.0, 0.25, 2.092, 2.821), (25, 0.395, -0.952, 0.25, 1.408, 2.596), (50, 0.322, -0.737, 1.995, 0.915, 1.162))\n",
      "GA generation 09 | best perpetuity objective: 36,389 SEK/ha | schedule: ((10, 0.5, 0.249, 2.201, 0.425, 2.441), (20, 0.479, 1.0, 0.25, 2.092, 2.821), (25, 0.442, -0.952, 0.346, 1.408, 1.86), (50, 0.322, -0.737, 1.995, 0.915, 1.021))\n",
      "GA generation 10 | best perpetuity objective: 36,845 SEK/ha | schedule: ((10, 0.5, 0.529, 2.288, 0.849, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.648), (25, 0.477, -0.952, 0.25, 1.263, 2.596), (45, 0.334, -0.737, 2.272, 0.915, 0.25))\n",
      "GA generation 11 | best perpetuity objective: 37,039 SEK/ha | schedule: ((10, 0.5, 0.529, 1.589, 0.849, 2.441), (20, 0.5, 0.934, 0.351, 2.198, 2.803), (25, 0.477, -0.952, 0.284, 1.408, 2.474), (45, 0.439, -0.982, 2.272, 0.915, 0.961))\n",
      "GA generation 12 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.288, 0.342, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.621), (25, 0.477, -0.807, 0.346, 1.408, 2.596), (45, 0.5, -0.737, 2.272, 0.915, 1.021))\n",
      "GA generation 13 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.288, 0.342, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.621), (25, 0.477, -0.807, 0.346, 1.408, 2.596), (45, 0.5, -0.737, 2.272, 0.915, 1.021))\n",
      "GA generation 14 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.288, 0.342, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.621), (25, 0.477, -0.807, 0.771, 1.408, 2.902), (45, 0.5, -0.737, 2.272, 0.915, 1.021))\n",
      "GA generation 15 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.288, 0.342, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.621), (25, 0.477, -0.807, 0.771, 1.408, 2.902), (45, 0.5, -0.737, 2.272, 0.915, 1.021))\n",
      "GA generation 16 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.529, 2.579, 0.425, 2.89), (20, 0.5, 1.0, 0.25, 2.695, 3.0), (25, 0.477, -0.807, 0.722, 1.367, 2.902), (45, 0.5, -0.737, 2.272, 0.915, 1.021))\n",
      "GA generation 17 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.288, 0.342, 2.441), (20, 0.5, 1.0, 0.648, 3.0, 3.0), (25, 0.477, -1.0, 0.551, 1.408, 2.886), (45, 0.5, -0.737, 2.272, 0.872, 1.021))\n",
      "GA generation 18 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.136, 0.342, 2.686), (20, 0.5, 1.0, 0.278, 2.695, 3.0), (25, 0.477, -0.735, 0.275, 1.408, 2.628), (45, 0.5, -0.737, 2.272, 0.881, 1.021))\n",
      "GA generation 19 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.6, 2.14, 0.509, 2.559), (20, 0.5, 1.0, 0.25, 3.0, 2.621), (25, 0.477, -0.617, 0.771, 1.142, 2.964), (45, 0.5, -0.589, 2.272, 0.444, 0.961))\n",
      "GA generation 20 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.543, 2.574, 0.342, 2.931), (20, 0.5, 0.998, 0.25, 2.695, 2.621), (25, 0.477, -0.807, 0.346, 1.688, 1.598), (45, 0.5, -0.712, 2.264, 0.25, 1.021))\n",
      "GA generation 21 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.146, 2.169, 0.342, 2.496), (20, 0.5, 0.858, 0.25, 3.0, 2.834), (25, 0.477, -0.735, 0.771, 1.408, 2.964), (45, 0.5, -0.589, 2.272, 0.444, 1.021))\n",
      "GA generation 22 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.207, 2.2, 0.32, 2.469), (20, 0.5, 1.0, 0.278, 3.0, 2.648), (25, 0.477, -0.965, 0.535, 1.694, 2.956), (45, 0.5, -0.709, 2.336, 0.912, 0.66))\n",
      "GA generation 23 | best perpetuity objective: 37,143 SEK/ha | schedule: ((10, 0.5, 0.184, 2.36, 0.25, 2.469), (20, 0.5, 0.908, 0.473, 2.363, 2.621), (25, 0.477, -0.746, 0.535, 1.57, 2.905), (45, 0.5, -0.629, 1.985, 0.385, 0.548))\n",
      "GA generation 24 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.257, 2.288, 0.806, 2.764), (20, 0.5, 0.826, 0.25, 1.993, 3.0), (25, 0.477, -0.721, 0.25, 1.331, 1.821), (45, 0.5, -0.733, 2.272, 0.736, 0.966))\n",
      "GA generation 25 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.257, 2.288, 0.806, 2.764), (20, 0.5, 0.826, 0.25, 1.993, 3.0), (25, 0.477, -0.721, 0.25, 1.331, 1.821), (45, 0.5, -0.733, 2.272, 0.736, 0.966))\n",
      "GA generation 26 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.257, 2.288, 0.806, 2.764), (20, 0.5, 0.826, 0.25, 1.993, 3.0), (25, 0.477, -0.721, 0.25, 1.331, 1.821), (45, 0.5, -0.733, 2.272, 0.736, 0.966))\n",
      "GA generation 27 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.257, 2.288, 0.806, 2.764), (20, 0.5, 0.826, 0.25, 1.128, 3.0), (25, 0.477, -0.766, 0.25, 1.331, 1.821), (45, 0.5, -0.733, 2.272, 0.636, 0.966))\n",
      "GA generation 28 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.176, 2.263, 0.665, 2.764), (20, 0.5, 0.826, 0.689, 1.881, 3.0), (25, 0.477, -0.721, 0.25, 1.303, 2.286), (45, 0.5, -0.733, 2.272, 0.736, 1.354))\n",
      "GA generation 29 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.176, 2.288, 0.806, 2.764), (20, 0.5, 0.564, 0.25, 1.128, 3.0), (25, 0.477, -0.721, 0.25, 1.331, 1.821), (45, 0.5, -0.913, 2.272, 0.723, 1.284))\n",
      "GA generation 30 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.257, 2.288, 0.806, 2.764), (20, 0.5, 0.843, 0.25, 1.881, 2.54), (25, 0.477, -0.645, 0.25, 1.303, 1.708), (45, 0.5, -0.733, 2.272, 0.636, 0.966))\n",
      "GA generation 31 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.381, 2.288, 1.041, 2.764), (20, 0.5, 0.759, 0.25, 1.993, 3.0), (25, 0.477, -0.766, 0.25, 1.168, 1.669), (45, 0.5, -0.733, 2.272, 0.736, 0.25))\n",
      "GA generation 32 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.112, 2.062, 0.375, 3.0), (20, 0.5, 0.826, 0.25, 1.881, 3.0), (25, 0.477, -0.888, 0.25, 1.331, 2.419), (45, 0.5, -1.0, 2.455, 0.636, 0.966))\n",
      "GA generation 33 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.381, 1.302, 1.041, 2.686), (20, 0.5, 0.759, 0.25, 2.157, 3.0), (25, 0.477, -1.0, 0.25, 0.995, 1.941), (45, 0.5, -1.0, 2.399, 0.633, 0.25))\n",
      "GA generation 34 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.112, 2.062, 0.583, 2.686), (20, 0.5, 0.811, 0.25, 1.993, 3.0), (25, 0.477, -0.721, 0.25, 1.331, 2.48), (45, 0.5, -0.952, 2.272, 0.736, 2.272))\n",
      "GA generation 35 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.381, 1.302, 1.041, 2.686), (20, 0.5, 0.759, 0.25, 2.157, 3.0), (25, 0.477, -1.0, 0.25, 0.995, 1.941), (45, 0.5, -1.0, 2.399, 0.633, 0.25))\n",
      "GA generation 36 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.112, 2.138, 0.583, 3.0), (20, 0.5, 0.775, 0.25, 1.993, 3.0), (25, 0.477, -0.721, 0.25, 1.549, 2.516), (45, 0.5, -0.952, 2.272, 0.25, 0.722))\n",
      "GA generation 37 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.254, 2.062, 0.632, 3.0), (20, 0.5, 0.564, 0.25, 2.08, 2.994), (25, 0.477, -0.872, 0.25, 1.331, 1.708), (45, 0.5, -0.693, 2.365, 1.384, 1.514))\n",
      "GA generation 38 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.381, 1.89, 0.632, 2.686), (20, 0.5, 0.759, 0.296, 2.181, 2.994), (25, 0.477, -0.673, 0.25, 1.331, 1.821), (45, 0.5, -1.0, 2.007, 0.736, 0.25))\n",
      "GA generation 39 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.206, 1.684, 1.049, 3.0), (20, 0.5, 0.715, 0.538, 1.415, 3.0), (25, 0.477, -0.433, 0.25, 0.924, 2.029), (45, 0.5, -0.887, 2.272, 1.063, 0.966))\n",
      "GA generation 40 | best perpetuity objective: 37,163 SEK/ha | schedule: ((10, 0.5, 0.208, 2.089, 0.525, 2.908), (20, 0.5, 0.73, 0.25, 1.593, 3.0), (25, 0.477, -0.854, 0.25, 1.331, 1.708), (45, 0.5, -0.316, 1.875, 0.975, 0.25))\n",
      "\n",
      "Best perpetuity objective over 4 generations: 37,163 SEK/ha\n",
      "Workers used: 8\n"
     ]
    },
    {
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       "      <th>thinning_no</th>\n",
       "      <th>year_in_rotation</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.208</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.525</td>\n",
       "      <td>2.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.593</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.331</td>\n",
       "      <td>1.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-0.316</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thinning_no  year_in_rotation  basal_area_fraction_removed  thinning_grade  \\\n",
       "0            1                10                        0.500           0.208   \n",
       "1            2                20                        0.500           0.730   \n",
       "2            3                25                        0.477          -0.854   \n",
       "3            4                45                        0.500          -0.316   \n",
       "\n",
       "   spruce_weight  pine_weight  broadleaf_weight  \n",
       "0          2.089        0.525             2.908  \n",
       "1          0.250        1.593             3.000  \n",
       "2          0.250        1.331             1.708  \n",
       "3          1.875        0.975             0.250  "
      ]
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       "      <th></th>\n",
       "      <th>generation</th>\n",
       "      <th>year</th>\n",
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       "      <th>cashflow_sek_per_ha</th>\n",
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       "      <th>spruce_weight</th>\n",
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       "      <td>0.500</td>\n",
       "      <td>0.730</td>\n",
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       "      <td>3.000</td>\n",
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       "      <td>1.708</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5273.816028</td>\n",
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       "      <td>92.0</td>\n",
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       "      <td>11630.655456</td>\n",
       "      <td>58.153277</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.208</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.525</td>\n",
       "      <td>2.908</td>\n",
       "      <td>766.618892</td>\n",
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       "      <td>0.500</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.593</td>\n",
       "      <td>3.000</td>\n",
       "      <td>828.520153</td>\n",
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       "      <td>0.477</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.331</td>\n",
       "      <td>1.708</td>\n",
       "      <td>482.916758</td>\n",
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       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>127.0</td>\n",
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       "      <td>19432.070838</td>\n",
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       "      <td>0.500</td>\n",
       "      <td>-0.316</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.250</td>\n",
       "      <td>455.188779</td>\n",
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       "      <td>0.525</td>\n",
       "      <td>2.908</td>\n",
       "      <td>67.908962</td>\n",
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       "      <td>0.730</td>\n",
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       "      <td>1.593</td>\n",
       "      <td>3.000</td>\n",
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       "      <td>38.154307</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.331</td>\n",
       "      <td>1.708</td>\n",
       "      <td>42.777938</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>19432.070838</td>\n",
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       "      <td>0.500</td>\n",
       "      <td>-0.316</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.250</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.089</td>\n",
       "      <td>0.525</td>\n",
       "      <td>2.908</td>\n",
       "      <td>6.015541</td>\n",
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       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>266.0</td>\n",
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       "      <td>16892.736069</td>\n",
       "      <td>69.785314</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.593</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.501270</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>11414.458672</td>\n",
       "      <td>38.154307</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.331</td>\n",
       "      <td>1.708</td>\n",
       "      <td>3.789374</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>291.0</td>\n",
       "      <td>thinning</td>\n",
       "      <td>19432.070838</td>\n",
       "      <td>61.918890</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-0.316</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.250</td>\n",
       "      <td>3.571796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>316.0</td>\n",
       "      <td>clearcut</td>\n",
       "      <td>41757.136111</td>\n",
       "      <td>104.583413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.665791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    generation   year     event  cashflow_sek_per_ha  volume_m3_per_ha  \\\n",
       "0            1   10.0  thinning         11630.655456         58.153277   \n",
       "1            1   20.0  thinning         16892.736069         69.785314   \n",
       "2            1   25.0  thinning         11414.458672         38.154307   \n",
       "3            1   45.0  thinning         19432.070838         61.918890   \n",
       "4            1   70.0  clearcut         41757.136111        104.583413   \n",
       "5            2   92.0  thinning         11630.655456         58.153277   \n",
       "6            2  102.0  thinning         16892.736069         69.785314   \n",
       "7            2  107.0  thinning         11414.458672         38.154307   \n",
       "8            2  127.0  thinning         19432.070838         61.918890   \n",
       "9            2  152.0  clearcut         41757.136111        104.583413   \n",
       "10           3  174.0  thinning         11630.655456         58.153277   \n",
       "11           3  184.0  thinning         16892.736069         69.785314   \n",
       "12           3  189.0  thinning         11414.458672         38.154307   \n",
       "13           3  209.0  thinning         19432.070838         61.918890   \n",
       "14           3  234.0  clearcut         41757.136111        104.583413   \n",
       "15           4  256.0  thinning         11630.655456         58.153277   \n",
       "16           4  266.0  thinning         16892.736069         69.785314   \n",
       "17           4  271.0  thinning         11414.458672         38.154307   \n",
       "18           4  291.0  thinning         19432.070838         61.918890   \n",
       "19           4  316.0  clearcut         41757.136111        104.583413   \n",
       "\n",
       "    thinning_intensity  thinning_grade  spruce_weight  pine_weight  \\\n",
       "0                0.500           0.208          2.089        0.525   \n",
       "1                0.500           0.730          0.250        1.593   \n",
       "2                0.477          -0.854          0.250        1.331   \n",
       "3                0.500          -0.316          1.875        0.975   \n",
       "4                  NaN             NaN            NaN          NaN   \n",
       "5                0.500           0.208          2.089        0.525   \n",
       "6                0.500           0.730          0.250        1.593   \n",
       "7                0.477          -0.854          0.250        1.331   \n",
       "8                0.500          -0.316          1.875        0.975   \n",
       "9                  NaN             NaN            NaN          NaN   \n",
       "10               0.500           0.208          2.089        0.525   \n",
       "11               0.500           0.730          0.250        1.593   \n",
       "12               0.477          -0.854          0.250        1.331   \n",
       "13               0.500          -0.316          1.875        0.975   \n",
       "14                 NaN             NaN            NaN          NaN   \n",
       "15               0.500           0.208          2.089        0.525   \n",
       "16               0.500           0.730          0.250        1.593   \n",
       "17               0.477          -0.854          0.250        1.331   \n",
       "18               0.500          -0.316          1.875        0.975   \n",
       "19                 NaN             NaN            NaN          NaN   \n",
       "\n",
       "    broadleaf_weight  pv_sek_per_ha  \n",
       "0              2.908    8654.299951  \n",
       "1              3.000    9353.098383  \n",
       "2              1.708    5451.609032  \n",
       "3              0.250    5138.590069  \n",
       "4                NaN    5273.816028  \n",
       "5              2.908     766.618892  \n",
       "6              3.000     828.520153  \n",
       "7              1.708     482.916758  \n",
       "8              0.250     455.188779  \n",
       "9                NaN     467.167422  \n",
       "10             2.908      67.908962  \n",
       "11             3.000      73.392326  \n",
       "12             1.708      42.777938  \n",
       "13             0.250      40.321727  \n",
       "14               NaN      41.382824  \n",
       "15             2.908       6.015541  \n",
       "16             3.000       6.501270  \n",
       "17             1.708       3.789374  \n",
       "18             0.250       3.571796  \n",
       "19               NaN       3.665791  "
      ]
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5924.097337</td>\n",
       "      <td>12.386091</td>\n",
       "      <td>43.222614</td>\n",
       "      <td>9393.257654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5528.820090</td>\n",
       "      <td>21.471905</td>\n",
       "      <td>101.081714</td>\n",
       "      <td>21991.013746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>thinning</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.208</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.525</td>\n",
       "      <td>2.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1039.962157</td>\n",
       "      <td>11630.655456</td>\n",
       "      <td>58.153277</td>\n",
       "      <td>4488.857932</td>\n",
       "      <td>10.735953</td>\n",
       "      <td>42.928437</td>\n",
       "      <td>10360.358290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4185.511091</td>\n",
       "      <td>21.556643</td>\n",
       "      <td>103.499569</td>\n",
       "      <td>24968.284598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4</td>\n",
       "      <td>301.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1529.515839</td>\n",
       "      <td>10.901386</td>\n",
       "      <td>67.980591</td>\n",
       "      <td>24229.928814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4</td>\n",
       "      <td>306.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1527.951363</td>\n",
       "      <td>12.170841</td>\n",
       "      <td>68.684669</td>\n",
       "      <td>24598.970927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>4</td>\n",
       "      <td>311.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1526.396064</td>\n",
       "      <td>13.426721</td>\n",
       "      <td>94.787721</td>\n",
       "      <td>36986.867332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>4</td>\n",
       "      <td>316.0</td>\n",
       "      <td>growth</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1524.849750</td>\n",
       "      <td>14.666107</td>\n",
       "      <td>104.583413</td>\n",
       "      <td>41757.136111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>4</td>\n",
       "      <td>316.0</td>\n",
       "      <td>clearcut</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41757.136111</td>\n",
       "      <td>104.583413</td>\n",
       "      <td>1524.849750</td>\n",
       "      <td>14.666107</td>\n",
       "      <td>104.583413</td>\n",
       "      <td>41757.136111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    generation   year     event  thinning_intensity  thinning_grade  \\\n",
       "0            1    0.0     start                 0.0           0.000   \n",
       "1            1    5.0    growth                 0.0           0.000   \n",
       "2            1   10.0    growth                 0.0           0.000   \n",
       "3            1   10.0  thinning                 0.5           0.208   \n",
       "4            1   15.0    growth                 0.0           0.000   \n",
       "..         ...    ...       ...                 ...             ...   \n",
       "75           4  301.0    growth                 0.0           0.000   \n",
       "76           4  306.0    growth                 0.0           0.000   \n",
       "77           4  311.0    growth                 0.0           0.000   \n",
       "78           4  316.0    growth                 0.0           0.000   \n",
       "79           4  316.0  clearcut                 0.0           0.000   \n",
       "\n",
       "    spruce_weight  pine_weight  broadleaf_weight  removed_spruce_stems_per_ha  \\\n",
       "0           0.000        0.000             0.000                          0.0   \n",
       "1           0.000        0.000             0.000                          0.0   \n",
       "2           0.000        0.000             0.000                          0.0   \n",
       "3           2.089        0.525             2.908                          0.0   \n",
       "4           0.000        0.000             0.000                          0.0   \n",
       "..            ...          ...               ...                          ...   \n",
       "75          0.000        0.000             0.000                          0.0   \n",
       "76          0.000        0.000             0.000                          0.0   \n",
       "77          0.000        0.000             0.000                          0.0   \n",
       "78          0.000        0.000             0.000                          0.0   \n",
       "79          0.000        0.000             0.000                          0.0   \n",
       "\n",
       "    removed_pine_stems_per_ha  removed_broadleaf_stems_per_ha  \\\n",
       "0                         0.0                        0.000000   \n",
       "1                         0.0                        0.000000   \n",
       "2                         0.0                        0.000000   \n",
       "3                         0.0                     1039.962157   \n",
       "4                         0.0                        0.000000   \n",
       "..                        ...                             ...   \n",
       "75                        0.0                        0.000000   \n",
       "76                        0.0                        0.000000   \n",
       "77                        0.0                        0.000000   \n",
       "78                        0.0                        0.000000   \n",
       "79                        0.0                        0.000000   \n",
       "\n",
       "    harvest_value_sek_per_ha  harvest_volume_m3_per_ha  stems_per_ha  \\\n",
       "0                   0.000000                  0.000000   6280.005000   \n",
       "1                   0.000000                  0.000000   5924.097337   \n",
       "2                   0.000000                  0.000000   5528.820090   \n",
       "3               11630.655456                 58.153277   4488.857932   \n",
       "4                   0.000000                  0.000000   4185.511091   \n",
       "..                       ...                       ...           ...   \n",
       "75                  0.000000                  0.000000   1529.515839   \n",
       "76                  0.000000                  0.000000   1527.951363   \n",
       "77                  0.000000                  0.000000   1526.396064   \n",
       "78                  0.000000                  0.000000   1524.849750   \n",
       "79              41757.136111                104.583413   1524.849750   \n",
       "\n",
       "    basal_area_m2_ha  standing_volume_m3_per_ha  standing_value_sek_per_ha  \n",
       "0           1.336099                   4.235100                 954.536749  \n",
       "1          12.386091                  43.222614                9393.257654  \n",
       "2          21.471905                 101.081714               21991.013746  \n",
       "3          10.735953                  42.928437               10360.358290  \n",
       "4          21.556643                 103.499569               24968.284598  \n",
       "..               ...                        ...                        ...  \n",
       "75         10.901386                  67.980591               24229.928814  \n",
       "76         12.170841                  68.684669               24598.970927  \n",
       "77         13.426721                  94.787721               36986.867332  \n",
       "78         14.666107                 104.583413               41757.136111  \n",
       "79         14.666107                 104.583413               41757.136111  \n",
       "\n",
       "[80 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 850x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GA_POP_SIZE = 96\n",
    "GA_GA_GENERATIONS = 40\n",
    "GA_ELITE = 10\n",
    "GA_TOURNAMENT_SIZE = 24\n",
    "GA_MUTATION_RATE = 0.20\n",
    "GA_CROSSOVER_RATE = 0.80\n",
    "GA_WORKERS = min(8, max(1, (os.cpu_count() or 2) - 1))\n",
    "\n",
    "rng = np.random.default_rng(20260218)\n",
    "population = [ga_random_genome(rng) for _ in range(GA_POP_SIZE)]\n",
    "\n",
    "best_npv_history: list[float] = []\n",
    "best_schedule_history: list[tuple[tuple[int, float], ...]] = []\n",
    "\n",
    "parallel = Parallel(n_jobs=GA_WORKERS, backend=\"loky\") if GA_WORKERS > 1 else None\n",
    "\n",
    "if parallel is not None:\n",
    "    context = parallel\n",
    "else:\n",
    "    context = None\n",
    "\n",
    "if context is None:\n",
    "    fitness, decoded = ga_evaluate_population(\n",
    "        population,\n",
    "        config_arg=config,\n",
    "        site_arg=site,\n",
    "        parallel=None,\n",
    "    )\n",
    "\n",
    "    for ga_generation in range(GA_GA_GENERATIONS + 1):\n",
    "        best_index = int(np.argmax(fitness))\n",
    "        best_npv = float(fitness[best_index])\n",
    "        best_schedule = decoded[best_index]\n",
    "\n",
    "        best_npv_history.append(best_npv)\n",
    "        best_schedule_history.append(best_schedule)\n",
    "\n",
    "        print(\n",
    "            f\"GA generation {ga_generation:02d} | best perpetuity objective: {best_npv:,.0f} SEK/ha \"\n",
    "            f\"| schedule: {best_schedule}\"\n",
    "        )\n",
    "\n",
    "        if ga_generation == GA_GA_GENERATIONS:\n",
    "            break\n",
    "\n",
    "        elite_indices = np.argsort(fitness)[-GA_ELITE:]\n",
    "        next_population = [population[int(i)].copy() for i in elite_indices]\n",
    "\n",
    "        parents = ga_tournament_select(\n",
    "            population,\n",
    "            fitness,\n",
    "            rng,\n",
    "            n_select=GA_POP_SIZE - GA_ELITE,\n",
    "            tournament_size=GA_TOURNAMENT_SIZE,\n",
    "        )\n",
    "\n",
    "        for idx in range(0, len(parents), 2):\n",
    "            parent_a = parents[idx]\n",
    "            parent_b = parents[(idx + 1) % len(parents)]\n",
    "            child_a, child_b = ga_crossover(\n",
    "                parent_a,\n",
    "                parent_b,\n",
    "                rng,\n",
    "                rate=GA_CROSSOVER_RATE,\n",
    "            )\n",
    "            next_population.append(ga_mutate(child_a, rng, rate=GA_MUTATION_RATE))\n",
    "            if len(next_population) < GA_POP_SIZE:\n",
    "                next_population.append(ga_mutate(child_b, rng, rate=GA_MUTATION_RATE))\n",
    "\n",
    "        population = next_population[:GA_POP_SIZE]\n",
    "        fitness, decoded = ga_evaluate_population(\n",
    "            population,\n",
    "            config_arg=config,\n",
    "            site_arg=site,\n",
    "            parallel=None,\n",
    "        )\n",
    "else:\n",
    "    with context as parallel_runner:\n",
    "        fitness, decoded = ga_evaluate_population(\n",
    "            population,\n",
    "            config_arg=config,\n",
    "            site_arg=site,\n",
    "            parallel=parallel_runner,\n",
    "        )\n",
    "\n",
    "        for ga_generation in range(GA_GA_GENERATIONS + 1):\n",
    "            best_index = int(np.argmax(fitness))\n",
    "            best_npv = float(fitness[best_index])\n",
    "            best_schedule = decoded[best_index]\n",
    "\n",
    "            best_npv_history.append(best_npv)\n",
    "            best_schedule_history.append(best_schedule)\n",
    "\n",
    "            print(\n",
    "                f\"GA generation {ga_generation:02d} | best perpetuity objective: {best_npv:,.0f} SEK/ha \"\n",
    "                f\"| schedule: {best_schedule}\"\n",
    "            )\n",
    "\n",
    "            if ga_generation == GA_GA_GENERATIONS:\n",
    "                break\n",
    "\n",
    "            elite_indices = np.argsort(fitness)[-GA_ELITE:]\n",
    "            next_population = [population[int(i)].copy() for i in elite_indices]\n",
    "\n",
    "            parents = ga_tournament_select(\n",
    "                population,\n",
    "                fitness,\n",
    "                rng,\n",
    "                n_select=GA_POP_SIZE - GA_ELITE,\n",
    "                tournament_size=GA_TOURNAMENT_SIZE,\n",
    "            )\n",
    "\n",
    "            for idx in range(0, len(parents), 2):\n",
    "                parent_a = parents[idx]\n",
    "                parent_b = parents[(idx + 1) % len(parents)]\n",
    "                child_a, child_b = ga_crossover(\n",
    "                    parent_a,\n",
    "                    parent_b,\n",
    "                    rng,\n",
    "                    rate=GA_CROSSOVER_RATE,\n",
    "                )\n",
    "                next_population.append(ga_mutate(child_a, rng, rate=GA_MUTATION_RATE))\n",
    "                if len(next_population) < GA_POP_SIZE:\n",
    "                    next_population.append(ga_mutate(child_b, rng, rate=GA_MUTATION_RATE))\n",
    "\n",
    "            population = next_population[:GA_POP_SIZE]\n",
    "            fitness, decoded = ga_evaluate_population(\n",
    "                population,\n",
    "                config_arg=config,\n",
    "                site_arg=site,\n",
    "                parallel=parallel_runner,\n",
    "            )\n",
    "\n",
    "best_schedule = best_schedule_history[-1]\n",
    "best_npv, best_cashflows = ga_simulate_schedule(\n",
    "    best_schedule,\n",
    "    config_arg=config,\n",
    "    site_arg=site,\n",
    "    return_trace=True,\n",
    ")\n",
    "\n",
    "best_routine_df = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"thinning_no\": idx + 1,\n",
    "            \"year_in_rotation\": age_year,\n",
    "            \"basal_area_fraction_removed\": intensity,\n",
    "            \"thinning_grade\": grade,\n",
    "            \"spruce_weight\": spruce_weight,\n",
    "            \"pine_weight\": pine_weight,\n",
    "            \"broadleaf_weight\": broadleaf_weight,\n",
    "        }\n",
    "        for idx, (\n",
    "            age_year,\n",
    "            intensity,\n",
    "            grade,\n",
    "            spruce_weight,\n",
    "            pine_weight,\n",
    "            broadleaf_weight,\n",
    "        ) in enumerate(best_schedule)\n",
    "    ]\n",
    ")\n",
    "\n",
    "print(f\"\\nBest perpetuity objective over {GA_NUM_GENERATIONS} generations: {best_npv:,.0f} SEK/ha\")\n",
    "print(f\"Workers used: {GA_WORKERS}\")\n",
    "\n",
    "if best_routine_df.empty:\n",
    "    print(\"Best routine: no thinning before clear-cut.\")\n",
    "else:\n",
    "    display(best_routine_df)\n",
    "\n",
    "display(best_cashflows)\n",
    "\n",
    "best_flow_trajectory = ga_build_flow_trajectory(\n",
    "    best_schedule,\n",
    "    config_arg=config,\n",
    "    site_arg=site,\n",
    ")\n",
    "\n",
    "display(best_flow_trajectory)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "axes[0].plot(range(len(best_npv_history)), best_npv_history, marker=\"o\")\n",
    "axes[0].set_xlabel(\"GA generation\")\n",
    "axes[0].set_ylabel(\"Best perpetuity objective (SEK/ha)\")\n",
    "axes[0].set_title(\"GA convergence\")\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "axes[1].bar(\n",
    "    best_cashflows[\"year\"],\n",
    "    best_cashflows[\"cashflow_sek_per_ha\"],\n",
    "    width=3.5,\n",
    "    alpha=0.8,\n",
    "    label=\"Cashflow\",\n",
    ")\n",
    "axes[1].plot(\n",
    "    best_cashflows[\"year\"],\n",
    "    best_cashflows[\"pv_sek_per_ha\"],\n",
    "    color=\"black\",\n",
    "    marker=\"o\",\n",
    "    label=\"Present value\",\n",
    ")\n",
    "axes[1].set_xlabel(\"Years from start\")\n",
    "axes[1].set_ylabel(\"SEK/ha\")\n",
    "axes[1].set_title(\"Best schedule cashflows over 4 generations\")\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "flow_plot_df = best_flow_trajectory.sort_values([\"year\", \"event\"]).reset_index(drop=True)\n",
    "clearcut_years = sorted(flow_plot_df.loc[flow_plot_df[\"event\"] == \"clearcut\", \"year\"].unique())\n",
    "thinning_points = flow_plot_df.loc[flow_plot_df[\"event\"] == \"thinning\"]\n",
    "\n",
    "fig2, axes2 = plt.subplots(2, 1, figsize=(12, 7), sharex=True)\n",
    "\n",
    "axes2[0].plot(\n",
    "    flow_plot_df[\"year\"],\n",
    "    flow_plot_df[\"basal_area_m2_ha\"],\n",
    "    marker=\"o\",\n",
    "    linewidth=1.2,\n",
    "    color=\"tab:blue\",\n",
    "    label=\"Basal area\",\n",
    ")\n",
    "if not thinning_points.empty:\n",
    "    axes2[0].scatter(\n",
    "        thinning_points[\"year\"],\n",
    "        thinning_points[\"basal_area_m2_ha\"],\n",
    "        color=\"tab:orange\",\n",
    "        s=28,\n",
    "        label=\"After thinning\",\n",
    "        zorder=3,\n",
    "    )\n",
    "axes2[0].set_ylabel(\"Basal area (m²/ha)\")\n",
    "axes2[0].set_title(\"Basal area and standing volume over the full 4-generation flow\")\n",
    "axes2[0].grid(True, alpha=0.3)\n",
    "axes2[0].legend()\n",
    "\n",
    "axes2[1].plot(\n",
    "    flow_plot_df[\"year\"],\n",
    "    flow_plot_df[\"standing_volume_m3sk_per_ha\"],\n",
    "    marker=\"o\",\n",
    "    linewidth=1.2,\n",
    "    color=\"tab:green\",\n",
    "    label=\"Standing volume\",\n",
    ")\n",
    "if not thinning_points.empty:\n",
    "    axes2[1].scatter(\n",
    "        thinning_points[\"year\"],\n",
    "        thinning_points[\"standing_volume_m3sk_per_ha\"],\n",
    "        color=\"tab:orange\",\n",
    "        s=28,\n",
    "        label=\"After thinning\",\n",
    "        zorder=3,\n",
    "    )\n",
    "axes2[1].set_xlabel(\"Years from start\")\n",
    "axes2[1].set_ylabel(\"Standing volume (m³/ha)\")\n",
    "axes2[1].grid(True, alpha=0.3)\n",
    "axes2[1].legend()\n",
    "\n",
    "for clearcut_year in clearcut_years:\n",
    "    for axis in axes2:\n",
    "        axis.axvline(clearcut_year, color=\"tab:red\", linestyle=\"--\", alpha=0.35)\n",
    "\n",
    "fig2.tight_layout()\n",
    "\n",
    "\n",
    "fig3, ax3 = plt.subplots(figsize=(8.5, 6))\n",
    "flow_phase_df = flow_plot_df.loc[flow_plot_df[\"stems_per_ha\"] > 0.0].copy()\n",
    "flow_phase_df[\"log10_stems_per_ha\"] = np.log10(flow_phase_df[\"stems_per_ha\"].clip(lower=1e-6))\n",
    "\n",
    "for generation in sorted(flow_phase_df[\"generation\"].unique()):\n",
    "    generation_df = flow_phase_df.loc[flow_phase_df[\"generation\"] == generation].copy()\n",
    "    generation_df = generation_df.sort_values([\"year\", \"event\"]) \n",
    "    ax3.plot(\n",
    "        generation_df[\"log10_stems_per_ha\"],\n",
    "        generation_df[\"basal_area_m2_ha\"],\n",
    "        marker=\"o\",\n",
    "        linewidth=1.1,\n",
    "        label=f\"Generation {int(generation)}\",\n",
    "        alpha=0.9,\n",
    "    )\n",
    "\n",
    "thinning_phase_df = flow_phase_df.loc[flow_phase_df[\"event\"] == \"thinning\"]\n",
    "if not thinning_phase_df.empty:\n",
    "    ax3.scatter(\n",
    "        thinning_phase_df[\"log10_stems_per_ha\"],\n",
    "        thinning_phase_df[\"basal_area_m2_ha\"],\n",
    "        marker=\"^\",\n",
    "        s=42,\n",
    "        color=\"tab:orange\",\n",
    "        label=\"Thinning points\",\n",
    "        zorder=3,\n",
    "    )\n",
    "\n",
    "clearcut_phase_df = flow_phase_df.loc[flow_phase_df[\"event\"] == \"clearcut\"]\n",
    "if not clearcut_phase_df.empty:\n",
    "    ax3.scatter(\n",
    "        clearcut_phase_df[\"log10_stems_per_ha\"],\n",
    "        clearcut_phase_df[\"basal_area_m2_ha\"],\n",
    "        marker=\"x\",\n",
    "        s=56,\n",
    "        color=\"tab:red\",\n",
    "        label=\"Clear-cut points\",\n",
    "        zorder=3,\n",
    "    )\n",
    "\n",
    "ax3.set_xlabel(\"log10(Stems per ha)\")\n",
    "ax3.set_ylabel(\"Basal area (m²/ha)\")\n",
    "ax3.set_title(\"Stand development path: basal area vs log stem density\")\n",
    "ax3.grid(True, alpha=0.3)\n",
    "ax3.legend()\n",
    "fig3.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53781633",
   "metadata": {},
   "source": [
    "## Rent and Accounting Method Competitions (1%, 2%, 3%, 4%)\n",
    "\n",
    "For each rent rate, we run a GA competition under multiple accounting objectives (including Faustmann) and compare the optimized routines.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff2d8852",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Perpetuity NPV routine per rent rate:\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>rent_rate</th>\n",
       "      <th>accounting_method</th>\n",
       "      <th>perpetuity_npv_sek_per_ha</th>\n",
       "      <th>n_thinnings</th>\n",
       "      <th>best_schedule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>122464.094296</td>\n",
       "      <td>3</td>\n",
       "      <td>((10, 0.5, 0.908, 0.25, 1.078, 2.409), (20, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2%</td>\n",
       "      <td>Perpetuity LEV</td>\n",
       "      <td>58276.200051</td>\n",
       "      <td>3</td>\n",
       "      <td>((10, 0.252, 0.996, 1.642, 0.467, 1.904), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>37702.679331</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.942, 1.878, 2.935, 2.912), (15, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>27759.014563</td>\n",
       "      <td>4</td>\n",
       "      <td>((5, 0.327, 0.971, 2.485, 0.624, 2.296), (10, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rent_rate accounting_method  perpetuity_npv_sek_per_ha  n_thinnings  \\\n",
       "0        1%    Perpetuity NPV              122464.094296            3   \n",
       "1        2%    Perpetuity LEV               58276.200051            3   \n",
       "2        3%    Perpetuity NPV               37702.679331            4   \n",
       "3        4%    Perpetuity NPV               27759.014563            4   \n",
       "\n",
       "                                       best_schedule  \n",
       "0  ((10, 0.5, 0.908, 0.25, 1.078, 2.409), (20, 0....  \n",
       "1  ((10, 0.252, 0.996, 1.642, 0.467, 1.904), (15,...  \n",
       "2  ((10, 0.5, 0.942, 1.878, 2.935, 2.912), (15, 0...  \n",
       "3  ((5, 0.327, 0.971, 2.485, 0.624, 2.296), (10, ...  "
      ]
     },
     "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>rent_rate</th>\n",
       "      <th>accounting_method</th>\n",
       "      <th>objective_value</th>\n",
       "      <th>perpetuity_npv_sek_per_ha</th>\n",
       "      <th>perpetuity_eav_sek_per_ha_year</th>\n",
       "      <th>perpetuity_lev_sek_per_ha</th>\n",
       "      <th>n_thinnings</th>\n",
       "      <th>best_schedule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1%</td>\n",
       "      <td>Perpetuity EAV</td>\n",
       "      <td>1209.022383</td>\n",
       "      <td>120902.238258</td>\n",
       "      <td>1209.022383</td>\n",
       "      <td>120902.238258</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.909, 1.783, 0.594, 2.149), (20, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1%</td>\n",
       "      <td>Perpetuity LEV</td>\n",
       "      <td>118450.299770</td>\n",
       "      <td>118450.299770</td>\n",
       "      <td>1184.502998</td>\n",
       "      <td>118450.299770</td>\n",
       "      <td>4</td>\n",
       "      <td>((15, 0.5, -0.879, 0.428, 0.615, 1.572), (25, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>122464.094296</td>\n",
       "      <td>122464.094296</td>\n",
       "      <td>1224.640943</td>\n",
       "      <td>122464.094296</td>\n",
       "      <td>3</td>\n",
       "      <td>((10, 0.5, 0.908, 0.25, 1.078, 2.409), (20, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2%</td>\n",
       "      <td>Perpetuity EAV</td>\n",
       "      <td>1149.814804</td>\n",
       "      <td>57490.740195</td>\n",
       "      <td>1149.814804</td>\n",
       "      <td>57490.740195</td>\n",
       "      <td>4</td>\n",
       "      <td>((5, 0.409, 0.152, 0.29, 3.0, 2.958), (10, 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2%</td>\n",
       "      <td>Perpetuity LEV</td>\n",
       "      <td>58276.200051</td>\n",
       "      <td>58276.200051</td>\n",
       "      <td>1165.524001</td>\n",
       "      <td>58276.200051</td>\n",
       "      <td>3</td>\n",
       "      <td>((10, 0.252, 0.996, 1.642, 0.467, 1.904), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>53931.598426</td>\n",
       "      <td>53931.598426</td>\n",
       "      <td>1078.631969</td>\n",
       "      <td>53931.598426</td>\n",
       "      <td>3</td>\n",
       "      <td>((15, 0.499, 0.403, 0.25, 1.005, 2.652), (20, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3%</td>\n",
       "      <td>Perpetuity EAV</td>\n",
       "      <td>1104.491789</td>\n",
       "      <td>36816.392969</td>\n",
       "      <td>1104.491789</td>\n",
       "      <td>36816.392969</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.419, 2.215, 1.621, 2.514), (15, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3%</td>\n",
       "      <td>Perpetuity LEV</td>\n",
       "      <td>34412.051678</td>\n",
       "      <td>34412.051678</td>\n",
       "      <td>1032.361550</td>\n",
       "      <td>34412.051678</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.819, 1.06, 0.579, 2.544), (25, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>37702.679331</td>\n",
       "      <td>37702.679331</td>\n",
       "      <td>1131.080380</td>\n",
       "      <td>37702.679331</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.942, 1.878, 2.935, 2.912), (15, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4%</td>\n",
       "      <td>Perpetuity EAV</td>\n",
       "      <td>1029.337837</td>\n",
       "      <td>25733.445914</td>\n",
       "      <td>1029.337837</td>\n",
       "      <td>25733.445914</td>\n",
       "      <td>3</td>\n",
       "      <td>((10, 0.489, 0.254, 1.739, 1.683, 1.685), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4%</td>\n",
       "      <td>Perpetuity LEV</td>\n",
       "      <td>26493.541111</td>\n",
       "      <td>26493.541111</td>\n",
       "      <td>1059.741644</td>\n",
       "      <td>26493.541111</td>\n",
       "      <td>4</td>\n",
       "      <td>((10, 0.5, 0.668, 0.464, 1.202, 1.226), (15, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4%</td>\n",
       "      <td>Perpetuity NPV</td>\n",
       "      <td>27759.014563</td>\n",
       "      <td>27759.014563</td>\n",
       "      <td>1110.360583</td>\n",
       "      <td>27759.014563</td>\n",
       "      <td>4</td>\n",
       "      <td>((5, 0.327, 0.971, 2.485, 0.624, 2.296), (10, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rent_rate accounting_method  objective_value  perpetuity_npv_sek_per_ha  \\\n",
       "0         1%    Perpetuity EAV      1209.022383              120902.238258   \n",
       "1         1%    Perpetuity LEV    118450.299770              118450.299770   \n",
       "2         1%    Perpetuity NPV    122464.094296              122464.094296   \n",
       "3         2%    Perpetuity EAV      1149.814804               57490.740195   \n",
       "4         2%    Perpetuity LEV     58276.200051               58276.200051   \n",
       "5         2%    Perpetuity NPV     53931.598426               53931.598426   \n",
       "6         3%    Perpetuity EAV      1104.491789               36816.392969   \n",
       "7         3%    Perpetuity LEV     34412.051678               34412.051678   \n",
       "8         3%    Perpetuity NPV     37702.679331               37702.679331   \n",
       "9         4%    Perpetuity EAV      1029.337837               25733.445914   \n",
       "10        4%    Perpetuity LEV     26493.541111               26493.541111   \n",
       "11        4%    Perpetuity NPV     27759.014563               27759.014563   \n",
       "\n",
       "    perpetuity_eav_sek_per_ha_year  perpetuity_lev_sek_per_ha  n_thinnings  \\\n",
       "0                      1209.022383              120902.238258            4   \n",
       "1                      1184.502998              118450.299770            4   \n",
       "2                      1224.640943              122464.094296            3   \n",
       "3                      1149.814804               57490.740195            4   \n",
       "4                      1165.524001               58276.200051            3   \n",
       "5                      1078.631969               53931.598426            3   \n",
       "6                      1104.491789               36816.392969            4   \n",
       "7                      1032.361550               34412.051678            4   \n",
       "8                      1131.080380               37702.679331            4   \n",
       "9                      1029.337837               25733.445914            3   \n",
       "10                     1059.741644               26493.541111            4   \n",
       "11                     1110.360583               27759.014563            4   \n",
       "\n",
       "                                        best_schedule  \n",
       "0   ((10, 0.5, 0.909, 1.783, 0.594, 2.149), (20, 0...  \n",
       "1   ((15, 0.5, -0.879, 0.428, 0.615, 1.572), (25, ...  \n",
       "2   ((10, 0.5, 0.908, 0.25, 1.078, 2.409), (20, 0....  \n",
       "3   ((5, 0.409, 0.152, 0.29, 3.0, 2.958), (10, 0.4...  \n",
       "4   ((10, 0.252, 0.996, 1.642, 0.467, 1.904), (15,...  \n",
       "5   ((15, 0.499, 0.403, 0.25, 1.005, 2.652), (20, ...  \n",
       "6   ((10, 0.5, 0.419, 2.215, 1.621, 2.514), (15, 0...  \n",
       "7   ((10, 0.5, 0.819, 1.06, 0.579, 2.544), (25, 0....  \n",
       "8   ((10, 0.5, 0.942, 1.878, 2.935, 2.912), (15, 0...  \n",
       "9   ((10, 0.489, 0.254, 1.739, 1.683, 1.685), (15,...  \n",
       "10  ((10, 0.5, 0.668, 0.464, 1.202, 1.226), (15, 0...  \n",
       "11  ((5, 0.327, 0.971, 2.485, 0.624, 2.296), (10, ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rent_rates = [0.01, 0.02, 0.03, 0.04]\n",
    "accounting_methods = [\n",
    "    (\"Perpetuity NPV\", \"perpetuity_npv\"),\n",
    "    (\"Perpetuity EAV\", \"perpetuity_eav\"),\n",
    "    (\"Perpetuity LEV\", \"perpetuity_lev\"),\n",
    "]\n",
    "\n",
    "# Moderate defaults for multi-scenario runs; increase if you want harder search.\n",
    "ACC_POP_SIZE = 28\n",
    "ACC_GA_GENERATIONS = 10\n",
    "ACC_ELITE = 4\n",
    "ACC_TOURNAMENT_SIZE = 10\n",
    "ACC_MUTATION_RATE = 0.20\n",
    "ACC_CROSSOVER_RATE = 0.80\n",
    "ACC_WORKERS = min(6, max(1, (os.cpu_count() or 2) - 1))\n",
    "\n",
    "competition_rows: list[dict[str, object]] = []\n",
    "competition_histories: dict[tuple[str, float], list[float]] = {}\n",
    "competition_schedules: dict[tuple[str, float], tuple[tuple[int, float], ...]] = {}\n",
    "\n",
    "for method_label, method_key in accounting_methods:\n",
    "    for rate in rent_rates:\n",
    "        seed = 20260218 + int(round(rate * 1000.0)) + abs(hash(method_key)) % 10000\n",
    "\n",
    "        result = ga_optimize_routine(\n",
    "            config_arg=config,\n",
    "            site_arg=site,\n",
    "            discount_rate=rate,\n",
    "            accounting_method=method_key,\n",
    "            pop_size=ACC_POP_SIZE,\n",
    "            ga_generations=ACC_GA_GENERATIONS,\n",
    "            elite=ACC_ELITE,\n",
    "            tournament_size=ACC_TOURNAMENT_SIZE,\n",
    "            mutation_rate=ACC_MUTATION_RATE,\n",
    "            crossover_rate=ACC_CROSSOVER_RATE,\n",
    "            workers=ACC_WORKERS,\n",
    "            seed=seed,\n",
    "            verbose=False,\n",
    "        )\n",
    "\n",
    "        metrics = result[\"best_metrics\"]\n",
    "        best_schedule = result[\"best_schedule\"]\n",
    "\n",
    "        competition_histories[(method_key, rate)] = [float(v) for v in result[\"best_score_history\"]]\n",
    "        competition_schedules[(method_key, rate)] = best_schedule\n",
    "\n",
    "        competition_rows.append(\n",
    "            {\n",
    "                \"rent_rate\": f\"{int(rate * 100)}%\",\n",
    "                \"rent_rate_value\": float(rate),\n",
    "                \"accounting_method\": method_label,\n",
    "                \"method_key\": method_key,\n",
    "                \"objective_value\": float(result[\"best_objective\"]),\n",
    "                \"perpetuity_npv_sek_per_ha\": float(metrics[\"perpetuity_npv_sek_per_ha\"]),\n",
    "                \"perpetuity_eav_sek_per_ha_year\": float(metrics[\"perpetuity_eav_sek_per_ha_year\"]),\n",
    "                \"perpetuity_lev_sek_per_ha\": float(metrics[\"perpetuity_lev_sek_per_ha\"]),\n",
    "                \"perpetuity_annuity_sek_per_ha_year\": float(\n",
    "                    metrics[\"perpetuity_annuity_sek_per_ha_year\"]\n",
    "                ),\n",
    "                \"best_schedule\": str(best_schedule),\n",
    "                \"n_thinnings\": int(len(best_schedule)),\n",
    "            }\n",
    "        )\n",
    "\n",
    "competition_df = pd.DataFrame(competition_rows).sort_values(\n",
    "    [\"rent_rate_value\", \"accounting_method\"]\n",
    ")\n",
    "\n",
    "\n",
    "best_npv_by_rent_df = competition_df.loc[\n",
    "    competition_df.groupby(\"rent_rate_value\")[\"perpetuity_npv_sek_per_ha\"].idxmax()\n",
    "].sort_values(\"rent_rate_value\")\n",
    "\n",
    "print(\"Best Perpetuity NPV routine per rent rate:\")\n",
    "display(\n",
    "    best_npv_by_rent_df[\n",
    "        [\n",
    "            \"rent_rate\",\n",
    "            \"accounting_method\",\n",
    "            \"perpetuity_npv_sek_per_ha\",\n",
    "            \"n_thinnings\",\n",
    "            \"best_schedule\",\n",
    "        ]\n",
    "    ].reset_index(drop=True)\n",
    ")\n",
    "\n",
    "display(\n",
    "    competition_df[\n",
    "        [\n",
    "            \"rent_rate\",\n",
    "            \"accounting_method\",\n",
    "            \"objective_value\",\n",
    "            \"perpetuity_npv_sek_per_ha\",\n",
    "            \"perpetuity_eav_sek_per_ha_year\",\n",
    "            \"perpetuity_lev_sek_per_ha\",\n",
    "            \"n_thinnings\",\n",
    "            \"best_schedule\",\n",
    "        ]\n",
    "    ].reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# Common metric comparison: resulting perpetuity NPV for each optimized routine.\n",
    "npv_pivot = competition_df.pivot(\n",
    "    index=\"rent_rate\",\n",
    "    columns=\"accounting_method\",\n",
    "    values=\"perpetuity_npv_sek_per_ha\",\n",
    ")\n",
    "\n",
    "fig4, axes4 = plt.subplots(1, 2, figsize=(13, 4.8))\n",
    "\n",
    "for method_label, _method_key in accounting_methods:\n",
    "    method_df = competition_df.loc[competition_df[\"accounting_method\"] == method_label]\n",
    "    axes4[0].plot(\n",
    "        method_df[\"rent_rate_value\"] * 100.0,\n",
    "        method_df[\"perpetuity_npv_sek_per_ha\"],\n",
    "        marker=\"o\",\n",
    "        linewidth=1.2,\n",
    "        label=method_label,\n",
    "    )\n",
    "\n",
    "axes4[0].set_xlabel(\"Rent rate (%)\")\n",
    "axes4[0].set_ylabel(\"Perpetuity NPV (SEK/ha)\")\n",
    "axes4[0].set_title(\"Perpetuity NPV of optimized routines\")\n",
    "axes4[0].grid(True, alpha=0.3)\n",
    "axes4[0].legend()\n",
    "\n",
    "for method_label, method_key in accounting_methods:\n",
    "    method_subset = competition_df.loc[competition_df[\"accounting_method\"] == method_label]\n",
    "    best_rate = float(method_subset.loc[method_subset[\"objective_value\"].idxmax(), \"rent_rate_value\"])\n",
    "    history = competition_histories[(method_key, best_rate)]\n",
    "    axes4[1].plot(\n",
    "        range(len(history)),\n",
    "        history,\n",
    "        marker=\"o\",\n",
    "        linewidth=1.1,\n",
    "        label=f\"{method_label} @ {int(best_rate * 100)}%\",\n",
    "    )\n",
    "\n",
    "axes4[1].set_xlabel(\"GA generation\")\n",
    "axes4[1].set_ylabel(\"Objective value\")\n",
    "axes4[1].set_title(\"Example convergence traces\")\n",
    "axes4[1].grid(True, alpha=0.3)\n",
    "axes4[1].legend()\n",
    "\n",
    "fig4.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbe6030",
   "metadata": {},
   "source": [
    "## Site Index Spruce Sweep (12 to 36, step 2)\n",
    "\n",
    "This section re-optimizes the routine for each spruce site index (`SI_spruce = 12, 14, ..., 36`) with the expanded thinning decision:\n",
    "- thinning age,\n",
    "- thinning intensity,\n",
    "- thinning grade (from below to from above),\n",
    "- species targeting weights (spruce/pine/broadleaf).\n",
    "\n",
    "It then compares how optimized behavior and value outcomes change as site index increases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "078c8e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing for spruce SI=12 ...\n",
      "Optimizing for spruce SI=14 ...\n",
      "Optimizing for spruce SI=16 ...\n",
      "Optimizing for spruce SI=18 ...\n",
      "Optimizing for spruce SI=20 ...\n",
      "Optimizing for spruce SI=22 ...\n",
      "Optimizing for spruce SI=24 ...\n",
      "Optimizing for spruce SI=26 ...\n",
      "Optimizing for spruce SI=28 ...\n",
      "Optimizing for spruce SI=30 ...\n",
      "Optimizing for spruce SI=32 ...\n",
      "Optimizing for spruce SI=34 ...\n",
      "Optimizing for spruce SI=36 ...\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>si_spruce_m</th>\n",
       "      <th>best_objective</th>\n",
       "      <th>perpetuity_npv_sek_per_ha</th>\n",
       "      <th>perpetuity_eav_sek_per_ha_year</th>\n",
       "      <th>n_thinnings</th>\n",
       "      <th>first_thinning_year</th>\n",
       "      <th>mean_thinning_intensity</th>\n",
       "      <th>mean_thinning_grade</th>\n",
       "      <th>mean_spruce_weight</th>\n",
       "      <th>mean_pine_weight</th>\n",
       "      <th>mean_broadleaf_weight</th>\n",
       "      <th>removed_spruce_share</th>\n",
       "      <th>removed_pine_share</th>\n",
       "      <th>removed_broadleaf_share</th>\n",
       "      <th>peak_basal_area_m2_ha</th>\n",
       "      <th>peak_standing_volume_m3_per_ha</th>\n",
       "      <th>best_schedule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>30665.801965</td>\n",
       "      <td>30665.801965</td>\n",
       "      <td>919.974059</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.379333</td>\n",
       "      <td>-0.190333</td>\n",
       "      <td>1.417000</td>\n",
       "      <td>1.559000</td>\n",
       "      <td>2.216333</td>\n",
       "      <td>0.155578</td>\n",
       "      <td>0.222214</td>\n",
       "      <td>0.622209</td>\n",
       "      <td>26.518195</td>\n",
       "      <td>185.897136</td>\n",
       "      <td>((15, 0.5, -0.766, 0.51, 1.642, 3.0), (25, 0.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>36302.473027</td>\n",
       "      <td>36302.473027</td>\n",
       "      <td>1089.074191</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.381250</td>\n",
       "      <td>0.365500</td>\n",
       "      <td>1.333750</td>\n",
       "      <td>1.284250</td>\n",
       "      <td>1.649750</td>\n",
       "      <td>0.136672</td>\n",
       "      <td>0.312439</td>\n",
       "      <td>0.550889</td>\n",
       "      <td>25.360309</td>\n",
       "      <td>182.215460</td>\n",
       "      <td>((10, 0.203, 0.402, 0.582, 1.775, 1.977), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
       "      <td>35384.752119</td>\n",
       "      <td>35384.752119</td>\n",
       "      <td>1061.542564</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>-0.059000</td>\n",
       "      <td>1.668750</td>\n",
       "      <td>0.815500</td>\n",
       "      <td>1.941250</td>\n",
       "      <td>0.127659</td>\n",
       "      <td>0.350134</td>\n",
       "      <td>0.522208</td>\n",
       "      <td>26.419193</td>\n",
       "      <td>145.754191</td>\n",
       "      <td>((10, 0.5, 0.854, 2.095, 0.25, 2.159), (25, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>35474.319316</td>\n",
       "      <td>35474.319316</td>\n",
       "      <td>1064.229579</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.401500</td>\n",
       "      <td>0.592500</td>\n",
       "      <td>1.993500</td>\n",
       "      <td>1.285250</td>\n",
       "      <td>2.478000</td>\n",
       "      <td>0.149156</td>\n",
       "      <td>0.276345</td>\n",
       "      <td>0.574499</td>\n",
       "      <td>23.878426</td>\n",
       "      <td>154.844758</td>\n",
       "      <td>((10, 0.5, 0.893, 1.88, 2.322, 2.832), (15, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>34471.069121</td>\n",
       "      <td>34471.069121</td>\n",
       "      <td>1034.132074</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.439500</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>2.144000</td>\n",
       "      <td>1.185000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.140046</td>\n",
       "      <td>0.304411</td>\n",
       "      <td>0.555542</td>\n",
       "      <td>25.092204</td>\n",
       "      <td>134.226021</td>\n",
       "      <td>((10, 0.49, 0.874, 2.155, 1.362, 2.594), (20, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22.0</td>\n",
       "      <td>34624.494557</td>\n",
       "      <td>34624.494557</td>\n",
       "      <td>1038.734837</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.452750</td>\n",
       "      <td>-0.197500</td>\n",
       "      <td>1.090750</td>\n",
       "      <td>1.873250</td>\n",
       "      <td>2.105000</td>\n",
       "      <td>0.132174</td>\n",
       "      <td>0.351754</td>\n",
       "      <td>0.516072</td>\n",
       "      <td>26.209880</td>\n",
       "      <td>143.699278</td>\n",
       "      <td>((15, 0.5, 0.367, 1.012, 1.982, 3.0), (25, 0.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.0</td>\n",
       "      <td>36408.114952</td>\n",
       "      <td>36408.114952</td>\n",
       "      <td>1092.243449</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.385250</td>\n",
       "      <td>0.341000</td>\n",
       "      <td>1.686250</td>\n",
       "      <td>0.579000</td>\n",
       "      <td>1.911250</td>\n",
       "      <td>0.145361</td>\n",
       "      <td>0.289963</td>\n",
       "      <td>0.564676</td>\n",
       "      <td>23.889244</td>\n",
       "      <td>165.942086</td>\n",
       "      <td>((10, 0.498, 0.256, 1.776, 0.958, 2.276), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.0</td>\n",
       "      <td>36185.665307</td>\n",
       "      <td>36185.665307</td>\n",
       "      <td>1085.569959</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.393000</td>\n",
       "      <td>0.313000</td>\n",
       "      <td>1.734333</td>\n",
       "      <td>1.721333</td>\n",
       "      <td>2.036000</td>\n",
       "      <td>0.084444</td>\n",
       "      <td>0.236526</td>\n",
       "      <td>0.679030</td>\n",
       "      <td>32.295198</td>\n",
       "      <td>233.399277</td>\n",
       "      <td>((10, 0.229, 0.781, 1.471, 2.169, 2.007), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28.0</td>\n",
       "      <td>36441.372373</td>\n",
       "      <td>36441.372373</td>\n",
       "      <td>1093.241171</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.444500</td>\n",
       "      <td>0.359000</td>\n",
       "      <td>0.947000</td>\n",
       "      <td>0.788750</td>\n",
       "      <td>1.806250</td>\n",
       "      <td>0.134114</td>\n",
       "      <td>0.341460</td>\n",
       "      <td>0.524427</td>\n",
       "      <td>21.678641</td>\n",
       "      <td>143.365208</td>\n",
       "      <td>((10, 0.5, 0.399, 0.399, 0.763, 2.133), (15, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30.0</td>\n",
       "      <td>36862.452494</td>\n",
       "      <td>36862.452494</td>\n",
       "      <td>1105.873575</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>-0.069250</td>\n",
       "      <td>1.584250</td>\n",
       "      <td>1.477500</td>\n",
       "      <td>2.575250</td>\n",
       "      <td>0.085945</td>\n",
       "      <td>0.314878</td>\n",
       "      <td>0.599176</td>\n",
       "      <td>29.497887</td>\n",
       "      <td>224.679311</td>\n",
       "      <td>((10, 0.239, 0.111, 0.441, 0.883, 1.301), (15,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32.0</td>\n",
       "      <td>34074.210278</td>\n",
       "      <td>34074.210278</td>\n",
       "      <td>1022.226308</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.415000</td>\n",
       "      <td>-0.330500</td>\n",
       "      <td>1.415000</td>\n",
       "      <td>1.898750</td>\n",
       "      <td>1.589500</td>\n",
       "      <td>0.134290</td>\n",
       "      <td>0.347138</td>\n",
       "      <td>0.518571</td>\n",
       "      <td>26.305075</td>\n",
       "      <td>144.223382</td>\n",
       "      <td>((15, 0.5, 0.39, 0.25, 2.383, 2.918), (25, 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.0</td>\n",
       "      <td>39120.724617</td>\n",
       "      <td>39120.724617</td>\n",
       "      <td>1173.621739</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.485750</td>\n",
       "      <td>0.382500</td>\n",
       "      <td>1.117750</td>\n",
       "      <td>1.922250</td>\n",
       "      <td>1.689750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.479458</td>\n",
       "      <td>0.520542</td>\n",
       "      <td>22.548264</td>\n",
       "      <td>149.226408</td>\n",
       "      <td>((10, 0.5, 0.124, 0.25, 0.734, 1.061), (15, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>36.0</td>\n",
       "      <td>35529.966516</td>\n",
       "      <td>35529.966516</td>\n",
       "      <td>1065.898995</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.368250</td>\n",
       "      <td>0.176750</td>\n",
       "      <td>1.314250</td>\n",
       "      <td>1.885750</td>\n",
       "      <td>2.461000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478347</td>\n",
       "      <td>0.521653</td>\n",
       "      <td>28.448793</td>\n",
       "      <td>176.505466</td>\n",
       "      <td>((10, 0.5, 0.104, 0.94, 1.373, 2.867), (25, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    si_spruce_m  best_objective  perpetuity_npv_sek_per_ha  \\\n",
       "0          12.0    30665.801965               30665.801965   \n",
       "1          14.0    36302.473027               36302.473027   \n",
       "2          16.0    35384.752119               35384.752119   \n",
       "3          18.0    35474.319316               35474.319316   \n",
       "4          20.0    34471.069121               34471.069121   \n",
       "5          22.0    34624.494557               34624.494557   \n",
       "6          24.0    36408.114952               36408.114952   \n",
       "7          26.0    36185.665307               36185.665307   \n",
       "8          28.0    36441.372373               36441.372373   \n",
       "9          30.0    36862.452494               36862.452494   \n",
       "10         32.0    34074.210278               34074.210278   \n",
       "11         34.0    39120.724617               39120.724617   \n",
       "12         36.0    35529.966516               35529.966516   \n",
       "\n",
       "    perpetuity_eav_sek_per_ha_year  n_thinnings  first_thinning_year  \\\n",
       "0                       919.974059            3                 15.0   \n",
       "1                      1089.074191            4                 10.0   \n",
       "2                      1061.542564            4                 10.0   \n",
       "3                      1064.229579            4                 10.0   \n",
       "4                      1034.132074            4                 10.0   \n",
       "5                      1038.734837            4                 15.0   \n",
       "6                      1092.243449            4                 10.0   \n",
       "7                      1085.569959            3                 10.0   \n",
       "8                      1093.241171            4                 10.0   \n",
       "9                      1105.873575            4                 10.0   \n",
       "10                     1022.226308            4                 15.0   \n",
       "11                     1173.621739            4                 10.0   \n",
       "12                     1065.898995            4                 10.0   \n",
       "\n",
       "    mean_thinning_intensity  mean_thinning_grade  mean_spruce_weight  \\\n",
       "0                  0.379333            -0.190333            1.417000   \n",
       "1                  0.381250             0.365500            1.333750   \n",
       "2                  0.450000            -0.059000            1.668750   \n",
       "3                  0.401500             0.592500            1.993500   \n",
       "4                  0.439500             0.243750            2.144000   \n",
       "5                  0.452750            -0.197500            1.090750   \n",
       "6                  0.385250             0.341000            1.686250   \n",
       "7                  0.393000             0.313000            1.734333   \n",
       "8                  0.444500             0.359000            0.947000   \n",
       "9                  0.320000            -0.069250            1.584250   \n",
       "10                 0.415000            -0.330500            1.415000   \n",
       "11                 0.485750             0.382500            1.117750   \n",
       "12                 0.368250             0.176750            1.314250   \n",
       "\n",
       "    mean_pine_weight  mean_broadleaf_weight  removed_spruce_share  \\\n",
       "0           1.559000               2.216333              0.155578   \n",
       "1           1.284250               1.649750              0.136672   \n",
       "2           0.815500               1.941250              0.127659   \n",
       "3           1.285250               2.478000              0.149156   \n",
       "4           1.185000               2.200000              0.140046   \n",
       "5           1.873250               2.105000              0.132174   \n",
       "6           0.579000               1.911250              0.145361   \n",
       "7           1.721333               2.036000              0.084444   \n",
       "8           0.788750               1.806250              0.134114   \n",
       "9           1.477500               2.575250              0.085945   \n",
       "10          1.898750               1.589500              0.134290   \n",
       "11          1.922250               1.689750              0.000000   \n",
       "12          1.885750               2.461000              0.000000   \n",
       "\n",
       "    removed_pine_share  removed_broadleaf_share  peak_basal_area_m2_ha  \\\n",
       "0             0.222214                 0.622209              26.518195   \n",
       "1             0.312439                 0.550889              25.360309   \n",
       "2             0.350134                 0.522208              26.419193   \n",
       "3             0.276345                 0.574499              23.878426   \n",
       "4             0.304411                 0.555542              25.092204   \n",
       "5             0.351754                 0.516072              26.209880   \n",
       "6             0.289963                 0.564676              23.889244   \n",
       "7             0.236526                 0.679030              32.295198   \n",
       "8             0.341460                 0.524427              21.678641   \n",
       "9             0.314878                 0.599176              29.497887   \n",
       "10            0.347138                 0.518571              26.305075   \n",
       "11            0.479458                 0.520542              22.548264   \n",
       "12            0.478347                 0.521653              28.448793   \n",
       "\n",
       "    peak_standing_volume_m3_per_ha  \\\n",
       "0                       185.897136   \n",
       "1                       182.215460   \n",
       "2                       145.754191   \n",
       "3                       154.844758   \n",
       "4                       134.226021   \n",
       "5                       143.699278   \n",
       "6                       165.942086   \n",
       "7                       233.399277   \n",
       "8                       143.365208   \n",
       "9                       224.679311   \n",
       "10                      144.223382   \n",
       "11                      149.226408   \n",
       "12                      176.505466   \n",
       "\n",
       "                                        best_schedule  \n",
       "0   ((15, 0.5, -0.766, 0.51, 1.642, 3.0), (25, 0.5...  \n",
       "1   ((10, 0.203, 0.402, 0.582, 1.775, 1.977), (15,...  \n",
       "2   ((10, 0.5, 0.854, 2.095, 0.25, 2.159), (25, 0....  \n",
       "3   ((10, 0.5, 0.893, 1.88, 2.322, 2.832), (15, 0....  \n",
       "4   ((10, 0.49, 0.874, 2.155, 1.362, 2.594), (20, ...  \n",
       "5   ((15, 0.5, 0.367, 1.012, 1.982, 3.0), (25, 0.5...  \n",
       "6   ((10, 0.498, 0.256, 1.776, 0.958, 2.276), (15,...  \n",
       "7   ((10, 0.229, 0.781, 1.471, 2.169, 2.007), (15,...  \n",
       "8   ((10, 0.5, 0.399, 0.399, 0.763, 2.133), (15, 0...  \n",
       "9   ((10, 0.239, 0.111, 0.441, 0.883, 1.301), (15,...  \n",
       "10  ((15, 0.5, 0.39, 0.25, 2.383, 2.918), (25, 0.4...  \n",
       "11  ((10, 0.5, 0.124, 0.25, 0.734, 1.061), (15, 0....  \n",
       "12  ((10, 0.5, 0.104, 0.94, 1.373, 2.867), (25, 0....  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dataclasses import replace\n",
    "\n",
    "SI_SWEEP_VALUES = list(range(12, 38, 2))\n",
    "SI_SWEEP_DISCOUNT_RATE = 0.03\n",
    "SI_SWEEP_ACCOUNTING_METHOD = \"perpetuity_npv\"\n",
    "\n",
    "SI_SWEEP_POP_SIZE = 28\n",
    "SI_SWEEP_GA_GENERATIONS = 12\n",
    "SI_SWEEP_ELITE = 4\n",
    "SI_SWEEP_TOURNAMENT_SIZE = 10\n",
    "SI_SWEEP_MUTATION_RATE = 0.20\n",
    "SI_SWEEP_CROSSOVER_RATE = 0.80\n",
    "SI_SWEEP_WORKERS = min(6, max(1, (os.cpu_count() or 2) - 1))\n",
    "\n",
    "si_rows: list[dict[str, object]] = []\n",
    "si_schedule_map: dict[int, tuple[tuple[int, float, float, float, float, float], ...]] = {}\n",
    "\n",
    "for si_spruce in SI_SWEEP_VALUES:\n",
    "    print(f\"Optimizing for spruce SI={si_spruce} ...\")\n",
    "\n",
    "    config_si = replace(config, site_index_spruce_m=float(si_spruce))\n",
    "    result_si = ga_optimize_routine(\n",
    "        config_arg=config_si,\n",
    "        site_arg=site,\n",
    "        discount_rate=SI_SWEEP_DISCOUNT_RATE,\n",
    "        accounting_method=SI_SWEEP_ACCOUNTING_METHOD,\n",
    "        pop_size=SI_SWEEP_POP_SIZE,\n",
    "        ga_generations=SI_SWEEP_GA_GENERATIONS,\n",
    "        elite=SI_SWEEP_ELITE,\n",
    "        tournament_size=SI_SWEEP_TOURNAMENT_SIZE,\n",
    "        mutation_rate=SI_SWEEP_MUTATION_RATE,\n",
    "        crossover_rate=SI_SWEEP_CROSSOVER_RATE,\n",
    "        workers=SI_SWEEP_WORKERS,\n",
    "        seed=20260400 + int(si_spruce),\n",
    "        verbose=False,\n",
    "    )\n",
    "\n",
    "    best_schedule_si = result_si[\"best_schedule\"]\n",
    "    si_schedule_map[int(si_spruce)] = best_schedule_si\n",
    "\n",
    "    flow_si = ga_build_flow_trajectory(\n",
    "        best_schedule_si,\n",
    "        config_arg=config_si,\n",
    "        site_arg=site,\n",
    "    )\n",
    "\n",
    "    thinning_flow_si = flow_si.loc[flow_si[\"event\"] == \"thinning\"].copy()\n",
    "\n",
    "    if best_schedule_si:\n",
    "        intensities = [float(decision[1]) for decision in best_schedule_si]\n",
    "        grades = [float(decision[2]) for decision in best_schedule_si]\n",
    "        w_spruce = [float(decision[3]) for decision in best_schedule_si]\n",
    "        w_pine = [float(decision[4]) for decision in best_schedule_si]\n",
    "        w_broadleaf = [float(decision[5]) for decision in best_schedule_si]\n",
    "        first_thinning_year = float(min(decision[0] for decision in best_schedule_si))\n",
    "    else:\n",
    "        intensities = []\n",
    "        grades = []\n",
    "        w_spruce = []\n",
    "        w_pine = []\n",
    "        w_broadleaf = []\n",
    "        first_thinning_year = np.nan\n",
    "\n",
    "    removed_spruce = float(thinning_flow_si[\"removed_spruce_stems_per_ha\"].sum())\n",
    "    removed_pine = float(thinning_flow_si[\"removed_pine_stems_per_ha\"].sum())\n",
    "    removed_broadleaf = float(thinning_flow_si[\"removed_broadleaf_stems_per_ha\"].sum())\n",
    "    removed_total = removed_spruce + removed_pine + removed_broadleaf\n",
    "\n",
    "    si_rows.append(\n",
    "        {\n",
    "            \"si_spruce_m\": float(si_spruce),\n",
    "            \"best_objective\": float(result_si[\"best_objective\"]),\n",
    "            \"perpetuity_npv_sek_per_ha\": float(result_si[\"best_metrics\"][\"perpetuity_npv_sek_per_ha\"]),\n",
    "            \"perpetuity_eav_sek_per_ha_year\": float(\n",
    "                result_si[\"best_metrics\"][\"perpetuity_eav_sek_per_ha_year\"]\n",
    "            ),\n",
    "            \"n_thinnings\": int(len(best_schedule_si)),\n",
    "            \"first_thinning_year\": float(first_thinning_year)\n",
    "            if not np.isnan(first_thinning_year)\n",
    "            else np.nan,\n",
    "            \"mean_thinning_intensity\": float(np.mean(intensities)) if intensities else 0.0,\n",
    "            \"mean_thinning_grade\": float(np.mean(grades)) if grades else 0.0,\n",
    "            \"mean_spruce_weight\": float(np.mean(w_spruce)) if w_spruce else 0.0,\n",
    "            \"mean_pine_weight\": float(np.mean(w_pine)) if w_pine else 0.0,\n",
    "            \"mean_broadleaf_weight\": float(np.mean(w_broadleaf)) if w_broadleaf else 0.0,\n",
    "            \"removed_spruce_share\": float(removed_spruce / removed_total) if removed_total > 0 else 0.0,\n",
    "            \"removed_pine_share\": float(removed_pine / removed_total) if removed_total > 0 else 0.0,\n",
    "            \"removed_broadleaf_share\": float(removed_broadleaf / removed_total)\n",
    "            if removed_total > 0\n",
    "            else 0.0,\n",
    "            \"peak_basal_area_m2_ha\": float(flow_si[\"basal_area_m2_ha\"].max()),\n",
    "            \"peak_standing_volume_m3sk_per_ha\": float(flow_si[\"standing_volume_m3sk_per_ha\"].max()),\n",
    "            \"best_schedule\": str(best_schedule_si),\n",
    "        }\n",
    "    )\n",
    "\n",
    "si_df = pd.DataFrame(si_rows).sort_values(\"si_spruce_m\").reset_index(drop=True)\n",
    "display(si_df)\n",
    "\n",
    "fig_si, axes_si = plt.subplots(2, 2, figsize=(14, 9), sharex=True)\n",
    "\n",
    "axes_si[0, 0].plot(si_df[\"si_spruce_m\"], si_df[\"perpetuity_npv_sek_per_ha\"], marker=\"o\")\n",
    "axes_si[0, 0].set_ylabel(\"Perpetuity NPV (SEK/ha)\")\n",
    "axes_si[0, 0].set_title(\"Value response to spruce site index\")\n",
    "axes_si[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "axes_si[0, 1].plot(si_df[\"si_spruce_m\"], si_df[\"n_thinnings\"], marker=\"o\", label=\"n thinnings\")\n",
    "axes_si[0, 1].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"mean_thinning_intensity\"],\n",
    "    marker=\"o\",\n",
    "    label=\"mean intensity\",\n",
    ")\n",
    "axes_si[0, 1].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"mean_thinning_grade\"],\n",
    "    marker=\"o\",\n",
    "    label=\"mean grade\",\n",
    ")\n",
    "axes_si[0, 1].set_title(\"Optimized thinning structure vs site index\")\n",
    "axes_si[0, 1].grid(True, alpha=0.3)\n",
    "axes_si[0, 1].legend()\n",
    "\n",
    "axes_si[1, 0].plot(si_df[\"si_spruce_m\"], si_df[\"mean_spruce_weight\"], marker=\"o\", label=\"spruce weight\")\n",
    "axes_si[1, 0].plot(si_df[\"si_spruce_m\"], si_df[\"mean_pine_weight\"], marker=\"o\", label=\"pine weight\")\n",
    "axes_si[1, 0].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"mean_broadleaf_weight\"],\n",
    "    marker=\"o\",\n",
    "    label=\"broadleaf weight\",\n",
    ")\n",
    "axes_si[1, 0].set_xlabel(\"Spruce site index (m)\")\n",
    "axes_si[1, 0].set_ylabel(\"Mean optimized targeting weight\")\n",
    "axes_si[1, 0].set_title(\"Species targeting preference vs site index\")\n",
    "axes_si[1, 0].grid(True, alpha=0.3)\n",
    "axes_si[1, 0].legend()\n",
    "\n",
    "axes_si[1, 1].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"removed_spruce_share\"],\n",
    "    marker=\"o\",\n",
    "    label=\"removed spruce share\",\n",
    ")\n",
    "axes_si[1, 1].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"removed_pine_share\"],\n",
    "    marker=\"o\",\n",
    "    label=\"removed pine share\",\n",
    ")\n",
    "axes_si[1, 1].plot(\n",
    "    si_df[\"si_spruce_m\"],\n",
    "    si_df[\"removed_broadleaf_share\"],\n",
    "    marker=\"o\",\n",
    "    label=\"removed broadleaf share\",\n",
    ")\n",
    "axes_si[1, 1].set_xlabel(\"Spruce site index (m)\")\n",
    "axes_si[1, 1].set_ylabel(\"Share of removed stems\")\n",
    "axes_si[1, 1].set_title(\"Realized species removal composition\")\n",
    "axes_si[1, 1].grid(True, alpha=0.3)\n",
    "axes_si[1, 1].legend()\n",
    "\n",
    "fig_si.tight_layout()\n"
   ]
  }
 ],
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