{ "cells": [ { "cell_type": "markdown", "id": "7fb27b941602401d91542211134fc71a", "metadata": {}, "source": [ "# Elfving 2010 growth demo\n", "\n", "This notebook demonstrates a full workflow:\n", "\n", "1. Reconstruct a young stand using NYSKOG.\n", "2. Convert heights to DBH and apply sapling damage mortality.\n", "3. Hand over the tree list to the Elfving (2010) growth model.\n", "4. Update tree height (Söderberg 1992) and volume (Söderberg 1986) during growth.\n", "5. Grow in 5-year steps, apply thinning at age 50, and plot results.\n", "\n", "**Reproducibility:** fixed random seeds and deterministic NYSKOG settings.\n" ] }, { "cell_type": "markdown", "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": {}, "source": [ "## Notebook Objectives\n", "- Show a complete growth workflow around Elfving (2010) stand development equations.\n", "- Provide runnable, copy-safe snippets that work in the docs build environment.\n", "\n", "## Prerequisites\n", "- Python environment with `pyforestry` installed from this repository.\n", "- Execute cells in order; random components should use fixed seeds where shown.\n", "\n", "## Sources\n", "- Elfving (2010) implementations in `pyforestry.sweden domain packages.elfving_2010` and related helpers.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "30391945", "metadata": { "execution": { "iopub.execute_input": "2026-02-19T23:09:55.976960Z", "iopub.status.busy": "2026-02-19T23:09:55.976592Z", "iopub.status.idle": "2026-02-19T23:09:57.383084Z", "shell.execute_reply": "2026-02-19T23:09:57.381043Z" } }, "outputs": [], "source": [ "import copy\n", "import math\n", "import random\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pyforestry.base.helpers import CircularPlot, Stand, Tree\n", "from pyforestry.base.helpers.primitives import Age, SiteBase\n", "from pyforestry.base.helpers.tree_species import TreeSpecies\n", "from pyforestry.sweden.adapters.elfving_1982 import (\n", " HuginMeanHeightModel,\n", " NfiRegion,\n", " NyskogReconstruction,\n", " RegenerationType,\n", " nyskog_indicators_from_site,\n", ")\n", "from pyforestry.sweden.adapters.elfving_2010 import Elfving2010Model\n", "from pyforestry.sweden.mortality.naslund_1986 import (\n", " Naslund1986DamageModel,\n", " SaplingSpeciesGroup,\n", ")\n", "from pyforestry.sweden.systems.nystrom_soderberg_1987 import NystromSoderberg1987\n", "from pyforestry.sweden.height.soderberg_1992 import soderberg_1992_height_tree_age_m\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", "from pyforestry.sweden.volume.soderberg_1986_form_height import soderberg_1986_volume_m3\n", "\n", "\n", "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", "\n", "\n", "random.seed(42)\n", "np.random.seed(42)\n" ] }, { "cell_type": "markdown", "id": "a8c309e8", "metadata": {}, "source": [ "## 1. Define a Swedish site" ] }, { "cell_type": "code", "execution_count": 2, "id": "1f14a826", "metadata": { "execution": { "iopub.execute_input": "2026-02-19T23:09:57.387816Z", "iopub.status.busy": "2026-02-19T23:09:57.386842Z", "iopub.status.idle": "2026-02-19T23:09:57.642471Z", "shell.execute_reply": "2026-02-19T23:09:57.641115Z" } }, "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': ,\n", " 'bottom_layer': ,\n", " 'soil_texture': ,\n", " 'soil_moisture': ,\n", " 'soil_depth': ,\n", " 'soil_water': ,\n", " 'ditched': False}}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "SwedishSiteDemo(latitude=60.5, longitude=15.0, altitude=150.0, field_layer=, bottom_layer=, soil_texture=, soil_moisture=, soil_depth=, soil_water=, aspect=None, incline_percent=None, ditched=False, temperature_sum_odin1983=1215.1999999999998, county=, humidity=np.float64(75.0), distance_to_coast=np.float64(254.68051713105228), climate_zone=, sis_spruce_100=None, sis_pine_100=None, sis_birch_50=SiteIndexValue(19.889499999999984, reference_age=AgeMeasurement(50.0, code=2 [DBH]), species={TreeName(genus=TreeGenus(name='Betula', code='BETULA'), species_name='pendula', code='BPEN'), TreeName(genus=TreeGenus(name='Betula', code='BETULA'), species_name='pubescens', code='BPUB')}, fn=eriksson_1997_height_trajectory_sweden_birch), n_of_limes_norrlandicus=True)" ] }, "execution_count": 2, "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", "site" ] }, { "cell_type": "markdown", "id": "edcbad32", "metadata": {}, "source": [ "## 2. Reconstruct a young stand (NYSKOG)" ] }, { "cell_type": "code", "execution_count": 12, "id": "8534113a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'pine': 197.21557785033062,\n", " 'spruce': 435.7513070153287,\n", " 'contorta': 0.0,\n", " 'larch': 0.0,\n", " 'birch': 1159.603329004732,\n", " 'other_broadleaf': 48.31680537519716}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regen_type = RegenerationType.NATURAL\n", "species_to_plant = TreeSpecies.Sweden.pinus_sylvestris\n", "nfi_region = NfiRegion.REG3\n", "\n", "age_years = 12.0\n", "\n", "mean_height_main_m = HuginMeanHeightModel.mean_height(\n", " age_years=age_years,\n", " species=species_to_plant,\n", " site_index_pine_m=site_index_pine_m,\n", " site_index_spruce_m=site_index_spruce_m,\n", ")\n", "\n", "indicators = nyskog_indicators_from_site(\n", " field_layer=site.field_layer,\n", " soil_moisture=site.soil_moisture,\n", ")\n", "\n", "# NOTE: ASINW is typically derived from the published Elfving regeneration functions (Appendix 2).\n", "asinw = 110.0\n", "q = NyskogReconstruction.production_potential_q(asinw)\n", "ln_q = math.log(q)\n", "ln_si = math.log(site_index_pine_m)\n", "\n", "under_dimension_prob = NyskogReconstruction.udim_probability(q)\n", "height_indicator_dm = max(15.0, 10.0 * mean_height_main_m)\n", "\n", "stem_total = NyskogReconstruction.total_stems(\n", " regeneration_type=regen_type,\n", " mean_height_main_m=mean_height_main_m,\n", " q=q,\n", " ln_q=ln_q,\n", " ln_si=ln_si,\n", " under_dimension_prob=under_dimension_prob,\n", " wet=indicators[\"wet\"],\n", " dry=indicators[\"dry\"],\n", " height_indicator_dm=height_indicator_dm,\n", " deterministic=True,\n", ")\n", "\n", "prop_conifer = NyskogReconstruction.proportion_conifer(\n", " regeneration_type=regen_type,\n", " q=q,\n", " ln_qind=ln_q,\n", " stem_total=stem_total,\n", " ln_si=ln_si,\n", " wet=indicators[\"wet\"],\n", " dry=indicators[\"dry\"],\n", " rich=indicators[\"rich\"],\n", " poor=indicators[\"poor\"],\n", " deterministic=True,\n", ")\n", "\n", "prop_dom_conifer = NyskogReconstruction.dominant_conifer_share(\n", " regeneration_type=regen_type,\n", " qind=q,\n", " ln_si=ln_si,\n", " wet=indicators[\"wet\"],\n", " dry=indicators[\"dry\"],\n", " rich=indicators[\"rich\"],\n", " poor=indicators[\"poor\"],\n", " hwod=indicators[\"hwod\"],\n", " hwd=indicators[\"hwd\"],\n", " shrubs=indicators[\"shrubs\"],\n", " lichen=indicators[\"lichen\"],\n", " deterministic=True,\n", ")\n", "\n", "stems = NyskogReconstruction.stems_per_species(\n", " regeneration_type=regen_type,\n", " species_to_plant=species_to_plant,\n", " stem_total=stem_total,\n", " prop_conifer=prop_conifer,\n", " prop_dom_conifer=prop_dom_conifer,\n", " site_index_m=site_index_pine_m,\n", " nfi_region=nfi_region,\n", ")\n", "\n", "stems" ] }, { "cell_type": "code", "execution_count": 13, "id": "c1bc011c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "199" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def mean_height_secondary(species: TreeSpecies) -> float:\n", " site_index_m = (\n", " site_index_spruce_m\n", " if species\n", " in {\n", " TreeSpecies.Sweden.picea_abies,\n", " TreeSpecies.Sweden.picea_sitchensis,\n", " TreeSpecies.Sweden.picea_mariana,\n", " }\n", " else site_index_pine_m\n", " )\n", "\n", " return NyskogReconstruction.secondary_mean_height(\n", " regeneration_type=regen_type,\n", " secondary_species=species,\n", " site_index_m=site_index_m,\n", " mean_height_main_m=mean_height_main_m,\n", " herb=indicators[\"herb\"],\n", " dry=indicators[\"dry\"],\n", " wet=indicators[\"wet\"],\n", " deterministic=True,\n", " )\n", "\n", "\n", "species_map = {\n", " \"pine\": TreeSpecies.Sweden.pinus_sylvestris,\n", " \"spruce\": TreeSpecies.Sweden.picea_abies,\n", " \"contorta\": TreeSpecies.Sweden.pinus_contorta,\n", " \"larch\": TreeSpecies.Sweden.larix_sibirica,\n", " \"birch\": TreeSpecies.Sweden.betula_pendula,\n", " \"other_broadleaf\": TreeSpecies.Sweden.populus_tremula,\n", "}\n", "\n", "mean_heights = {\n", " \"pine\": mean_height_main_m,\n", " \"spruce\": mean_height_secondary(TreeSpecies.Sweden.picea_abies),\n", " \"contorta\": mean_height_secondary(TreeSpecies.Sweden.pinus_contorta),\n", " \"larch\": mean_height_secondary(TreeSpecies.Sweden.larix_sibirica),\n", " \"birch\": mean_height_secondary(TreeSpecies.Sweden.betula_pendula),\n", " \"other_broadleaf\": mean_height_secondary(TreeSpecies.Sweden.populus_tremula),\n", "}\n", "\n", "sample_n = 200\n", "\n", "total_stems = sum(stems.values())\n", "trees: list[Tree] = []\n", "\n", "for label, stems_per_ha in stems.items():\n", " if stems_per_ha <= 0.0:\n", " continue\n", " share = stems_per_ha / total_stems\n", " n_trees = max(1, int(round(sample_n * share)))\n", "\n", " species = species_map[label]\n", " mean_height = mean_heights[label]\n", " cvh = NyskogReconstruction.height_variation(\n", " species=species,\n", " species_height_m=mean_height,\n", " q=q,\n", " ln_q=ln_q,\n", " self_rejuvenated=1,\n", " deterministic=True,\n", " )\n", " beta, shape = NyskogReconstruction.weibull_parameters(\n", " species=species,\n", " cvh=cvh,\n", " mean_height_m=mean_height,\n", " )\n", "\n", " heights = beta * np.random.weibull(shape, size=n_trees)\n", " weight = stems_per_ha / n_trees\n", "\n", " for height in heights:\n", " trees.append(\n", " Tree(\n", " species=species,\n", " height_m=float(height),\n", " weight_n=weight,\n", " )\n", " )\n", "\n", "base_trees = copy.deepcopy(trees)\n", "len(trees)" ] }, { "cell_type": "markdown", "id": "2d787e7d", "metadata": {}, "source": [ "## 3. Convert heights to DBH and apply sapling damage" ] }, { "cell_type": "code", "execution_count": 14, "id": "27b9a3b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "199" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def stand_metrics(tree_list: list[Tree]) -> dict[str, float]:\n", " heights = np.array([t.height_m or 0.0 for t in tree_list])\n", " weights = np.array([t.weight_n or 0.0 for t in tree_list])\n", " total_weight = weights.sum()\n", " if total_weight <= 0.0:\n", " return {\n", " \"mean_height_m\": 0.0,\n", " \"total_height_sqr_m2_per_100m2\": 0.0,\n", " \"broadleaf_height_sqr_share\": 0.0,\n", " \"h_max_m\": 0.0,\n", " }\n", " mean_height = float((heights * weights).sum() / total_weight)\n", "\n", " height_sqr_sum = float(((heights**2) * weights).sum())\n", " total_height_sqr_m2_per_100m2 = height_sqr_sum * 0.01\n", "\n", " broadleaf_species = {\n", " TreeSpecies.Sweden.betula_pendula,\n", " TreeSpecies.Sweden.betula_pubescens,\n", " TreeSpecies.Sweden.populus_tremula,\n", " }\n", " broadleaf_mask = np.array([t.species in broadleaf_species for t in tree_list])\n", " broadleaf_sqr_sum = float(((heights**2) * weights * broadleaf_mask).sum())\n", " if height_sqr_sum > 0.0:\n", " broadleaf_share = broadleaf_sqr_sum / height_sqr_sum\n", " else:\n", " broadleaf_share = 0.0\n", "\n", " top_heights = sorted(heights, reverse=True)[:3]\n", " h_max_m = float(np.mean(top_heights)) if top_heights else 0.0\n", "\n", " return {\n", " \"mean_height_m\": mean_height,\n", " \"total_height_sqr_m2_per_100m2\": total_height_sqr_m2_per_100m2,\n", " \"broadleaf_height_sqr_share\": broadleaf_share,\n", " \"h_max_m\": h_max_m,\n", " }\n", "\n", "\n", "def sapling_group(species: TreeSpecies) -> SaplingSpeciesGroup:\n", " if species in {\n", " TreeSpecies.Sweden.pinus_sylvestris,\n", " TreeSpecies.Sweden.larix_sibirica,\n", " TreeSpecies.Sweden.larix_decidua,\n", " TreeSpecies.Sweden.larix_europaea_x_leptolepis,\n", " TreeSpecies.Sweden.larix_sukaczewii,\n", " }:\n", " return SaplingSpeciesGroup.PINE\n", " if species == TreeSpecies.Sweden.pinus_contorta:\n", " return SaplingSpeciesGroup.CONTORTA\n", " if species == TreeSpecies.Sweden.picea_abies:\n", " return SaplingSpeciesGroup.SPRUCE\n", " if species in {\n", " TreeSpecies.Sweden.betula_pendula,\n", " TreeSpecies.Sweden.betula_pubescens,\n", " }:\n", " return SaplingSpeciesGroup.BIRCH\n", " if species in {\n", " TreeSpecies.Sweden.populus_tremula,\n", " TreeSpecies.Sweden.populus_tremula_x_tremuloides,\n", " }:\n", " return SaplingSpeciesGroup.ASPEN\n", " return SaplingSpeciesGroup.OTHER_BROADLEAF\n", "\n", "\n", "def apply_dbh(tree_list: list[Tree]) -> None:\n", " metrics = stand_metrics(tree_list)\n", " if metrics[\"mean_height_m\"] <= 0.0:\n", " return\n", " for tree in tree_list:\n", " if tree.height_m is None or tree.species is None:\n", " continue\n", " tree.diameter_cm = NystromSoderberg1987.dbh_from_height(\n", " height_dm=tree.height_m * 10.0,\n", " species=tree.species,\n", " total_height_sqr_m2_per_100m2=metrics[\"total_height_sqr_m2_per_100m2\"],\n", " broadleaf_height_sqr_share=metrics[\"broadleaf_height_sqr_share\"],\n", " natural_regeneration=1,\n", " cleaning_indicator=0,\n", " years_since_cleaning=0,\n", " altitude_m=site.altitude or 0.0,\n", " latitude_deg=site.latitude,\n", " shrubs=indicators[\"shrubs\"],\n", " herb_grass=indicators[\"herb\"],\n", " near_coast=1 if (site.distance_to_coast or 99.0) < 5.0 else 0,\n", " site_index_pine_m=site_index_pine_m,\n", " h_max_m=metrics[\"h_max_m\"],\n", " veg=0,\n", " )\n", "\n", "\n", "def damage_proportions(tree_list: list[Tree]) -> dict[SaplingSpeciesGroup, float]:\n", " stems = {sg: 0.0 for sg in SaplingSpeciesGroup}\n", " height_sums = {sg: 0.0 for sg in SaplingSpeciesGroup}\n", "\n", " for tree in tree_list:\n", " if tree.species is None or tree.height_m is None:\n", " continue\n", " group = sapling_group(tree.species)\n", " stems[group] += tree.weight_n or 0.0\n", " height_sums[group] += (tree.weight_n or 0.0) * tree.height_m\n", "\n", " mean_heights = {\n", " sg: (height_sums[sg] / stems[sg] if stems[sg] > 0.0 else 0.0)\n", " for sg in SaplingSpeciesGroup\n", " }\n", "\n", " return Naslund1986DamageModel.damage_proportions(\n", " stems=stems,\n", " mean_heights=mean_heights,\n", " site_index_pine_m=site_index_pine_m,\n", " site_index_spruce_m=site_index_spruce_m,\n", " latitude_deg=site.latitude,\n", " altitude_m=site.altitude or 0.0,\n", " )\n", "\n", "\n", "def apply_damage_mortality(\n", " tree_list: list[Tree],\n", " damage_props: dict[SaplingSpeciesGroup, float],\n", ") -> None:\n", " for tree in tree_list:\n", " if tree.species is None:\n", " continue\n", " group = sapling_group(tree.species)\n", " damage_prop = damage_props.get(group, 0.0)\n", " damage_prop = min(1.0, max(0.0, damage_prop))\n", " if tree.weight_n is not None:\n", " tree.weight_n *= 1.0 - damage_prop\n", "\n", "\n", "apply_dbh(trees)\n", "apply_damage_mortality(trees, damage_proportions(trees))\n", "\n", "for tree in trees:\n", " tree.age = Age.DBH(age_years)\n", "\n", "len(trees)" ] }, { "cell_type": "markdown", "id": "aa1cf929", "metadata": {}, "source": [ "## 4. Hand over to Elfving 2010" ] }, { "cell_type": "code", "execution_count": 15, "id": "96878c26", "metadata": {}, "outputs": [], "source": [ "plot = CircularPlot(\n", " id=1,\n", " area_m2=10000.0,\n", " trees=trees,\n", ")\n", "stand = Stand(site=site, plots=[plot])\n", "\n", "base_attrs = {\n", " \"site_index_m\": site_index_pine_m,\n", " \"temperature_sum_dd\": 1200.0,\n", " \"latitude_deg\": site.latitude,\n", " \"altitude_m\": site.altitude or 0.0,\n", " \"distance_to_coast_km\": 50.0,\n", " \"field_estimated_basal_area_m2_ha\": 0.0,\n", " \"thinned_0_10_years\": False,\n", " \"thinned_11_25_years\": False,\n", " \"thinned_11_30_years\": False,\n", "}\n", "\n", "# build_context resolves the model's inputs as it builds, so the attributes it\n", "# reads (site index among them) have to be supplied to the call rather than\n", "# set on the context afterwards.\n", "model = Elfving2010Model()\n", "ctx = model.build_context(stand, mode_hint=\"tree_list\", attrs=base_attrs)\n" ] }, { "cell_type": "markdown", "id": "eecd6829", "metadata": {}, "source": [ "## 5. Growth + thinning + Söderberg helpers\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "fb990d01", "metadata": {}, "outputs": [], "source": [ "PINE_SET = {\n", " TreeSpecies.Sweden.pinus_sylvestris,\n", " TreeSpecies.Sweden.pinus_contorta,\n", " TreeSpecies.Sweden.larix_sibirica,\n", " TreeSpecies.Sweden.larix_decidua,\n", " TreeSpecies.Sweden.larix_europaea_x_leptolepis,\n", " TreeSpecies.Sweden.larix_sukaczewii,\n", "}\n", "SPRUCE_SET = {\n", " TreeSpecies.Sweden.picea_abies,\n", " TreeSpecies.Sweden.picea_sitchensis,\n", " TreeSpecies.Sweden.picea_mariana,\n", "}\n", "BIRCH_SET = {\n", " TreeSpecies.Sweden.betula_pendula,\n", " TreeSpecies.Sweden.betula_pubescens,\n", "}\n", "BEECH_SET = {TreeSpecies.Sweden.fagus_sylvatica}\n", "OAK_SET = {\n", " TreeSpecies.Sweden.quercus_robur,\n", " TreeSpecies.Sweden.quercus_petraea,\n", " TreeSpecies.Sweden.quercus_rubra,\n", "}\n", "\n", "\n", "def infer_part_of_sweden(latitude_deg: float) -> str:\n", " if latitude_deg < 57.0:\n", " return \"south\"\n", " if latitude_deg < 59.0:\n", " return \"middle\"\n", " return \"north\"\n", "\n", "\n", "def soderberg_climate_flags() -> tuple[bool, bool]:\n", " climate_zone = getattr(site, \"climate_zone\", None)\n", " if climate_zone is None:\n", " return False, False\n", " label = climate_zone.value.label\n", " return label.startswith(\"M\"), label.startswith(\"K\")\n", "\n", "\n", "def basal_area_m2_ha(tree_list: list[Tree]) -> float:\n", " total = 0.0\n", " for tree in tree_list:\n", " d = tree.diameter_cm or 0.0\n", " w = tree.weight_n or 0.0\n", " total += (math.pi * (d / 200.0) ** 2) * w\n", " return float(total)\n", "\n", "\n", "def soderberg_stand_context(tree_list: list[Tree]) -> dict[str, object]:\n", " ba_total = basal_area_m2_ha(tree_list)\n", " max_diameter = max((t.diameter_cm or 0.0 for t in tree_list), default=0.0)\n", "\n", " species_ba: dict[TreeSpecies, float] = {}\n", " group_ba = {\"pine\": 0.0, \"spruce\": 0.0, \"birch\": 0.0, \"beech\": 0.0, \"oak\": 0.0}\n", "\n", " for tree in tree_list:\n", " if tree.species is None:\n", " continue\n", " d = tree.diameter_cm or 0.0\n", " w = tree.weight_n or 0.0\n", " ba = (math.pi * (d / 200.0) ** 2) * w\n", " if ba <= 0.0:\n", " continue\n", "\n", " species_ba[tree.species] = species_ba.get(tree.species, 0.0) + ba\n", "\n", " if tree.species in PINE_SET:\n", " group_ba[\"pine\"] += ba\n", " elif tree.species in SPRUCE_SET:\n", " group_ba[\"spruce\"] += ba\n", " elif tree.species in BIRCH_SET:\n", " group_ba[\"birch\"] += ba\n", " elif tree.species in BEECH_SET:\n", " group_ba[\"beech\"] += ba\n", " elif tree.species in OAK_SET:\n", " group_ba[\"oak\"] += ba\n", "\n", " dominant_species = (\n", " max(species_ba.items(), key=lambda kv: kv[1])[0]\n", " if species_ba\n", " else TreeSpecies.Sweden.pinus_sylvestris\n", " )\n", " site_index_dominant_m = (\n", " site_index_spruce_m if dominant_species in SPRUCE_SET else site_index_pine_m\n", " )\n", " near_coast = (site.distance_to_coast or 999.0) < 50.0\n", " maritime, continental = soderberg_climate_flags()\n", "\n", " return {\n", " \"stand_basal_area_m2_ha\": ba_total,\n", " \"max_diameter_cm\": max_diameter,\n", " \"dominant_species\": dominant_species,\n", " \"site_index_dominant_m\": site_index_dominant_m,\n", " \"part_of_sweden\": infer_part_of_sweden(site.latitude),\n", " \"near_coast\": near_coast,\n", " \"maritime\": maritime,\n", " \"continental\": continental,\n", " \"prop_pine\": group_ba[\"pine\"] / ba_total if ba_total > 0.0 else 0.0,\n", " \"prop_spruce\": group_ba[\"spruce\"] / ba_total if ba_total > 0.0 else 0.0,\n", " \"prop_birch\": group_ba[\"birch\"] / ba_total if ba_total > 0.0 else 0.0,\n", " \"prop_beech\": group_ba[\"beech\"] / ba_total if ba_total > 0.0 else 0.0,\n", " \"prop_oak\": group_ba[\"oak\"] / ba_total if ba_total > 0.0 else 0.0,\n", " }\n", "\n", "\n", "def apply_soderberg_height_volume(\n", " tree_list: list[Tree],\n", " *,\n", " fallback_age_years: float,\n", ") -> None:\n", " stand_ctx = soderberg_stand_context(tree_list)\n", " max_diameter = float(stand_ctx[\"max_diameter_cm\"])\n", "\n", " if max_diameter <= 0.0:\n", " for tree in tree_list:\n", " tree.height_m = 0.0\n", " tree.volume_m3 = 0.0\n", " return\n", "\n", " for tree in tree_list:\n", " if tree.species is None:\n", " continue\n", "\n", " d = tree.diameter_cm or 0.0\n", " if d <= 0.0:\n", " tree.height_m = 0.0\n", " tree.volume_m3 = 0.0\n", " continue\n", "\n", " age_bh_years = float(tree.age) if tree.age is not None else float(fallback_age_years)\n", " age_bh_years = max(2.0, age_bh_years)\n", "\n", " tree.height_m = soderberg_1992_height_tree_age_m(\n", " species=tree.species,\n", " diameter_cm=d,\n", " tree_age_bh_years=age_bh_years,\n", " max_diameter_cm=max_diameter,\n", " stand_basal_area_m2_ha=float(stand_ctx[\"stand_basal_area_m2_ha\"]),\n", " dominant_species=stand_ctx[\"dominant_species\"],\n", " site_index_dominant_m=float(stand_ctx[\"site_index_dominant_m\"]),\n", " latitude_deg=site.latitude,\n", " altitude_m=site.altitude or 0.0,\n", " prop_pine=float(stand_ctx[\"prop_pine\"]),\n", " prop_spruce=float(stand_ctx[\"prop_spruce\"]),\n", " prop_birch=float(stand_ctx[\"prop_birch\"]),\n", " prop_beech=float(stand_ctx[\"prop_beech\"]),\n", " part_of_sweden=str(stand_ctx[\"part_of_sweden\"]),\n", " maritime=bool(stand_ctx[\"maritime\"]),\n", " continental=bool(stand_ctx[\"continental\"]),\n", " near_coast=bool(stand_ctx[\"near_coast\"]),\n", " south_east=False,\n", " region5=False,\n", " split_plot=False,\n", " )\n", "\n", " tree.volume_m3 = soderberg_1986_volume_m3(\n", " species=tree.species,\n", " diameter_cm=d,\n", " age_bh_years=age_bh_years,\n", " max_diameter_cm=max_diameter,\n", " stand_basal_area_m2_ha=float(stand_ctx[\"stand_basal_area_m2_ha\"]),\n", " dominant_species=stand_ctx[\"dominant_species\"],\n", " site_index_dominant_m=float(stand_ctx[\"site_index_dominant_m\"]),\n", " latitude_deg=site.latitude,\n", " altitude_m=site.altitude or 0.0,\n", " part_of_sweden=str(stand_ctx[\"part_of_sweden\"]),\n", " distance_to_coast_lt_50km=bool(stand_ctx[\"near_coast\"]),\n", " split_plot=False,\n", " crowberry=site.field_layer == Sweden.FieldLayer.CROWBERRY,\n", " south_slope=False,\n", " wet_soil=bool(indicators[\"wet\"]),\n", " fertilized=False,\n", " herbs=bool(indicators[\"herb\"]),\n", " maritime=bool(stand_ctx[\"maritime\"]),\n", " region5=False,\n", " continental=bool(stand_ctx[\"continental\"]),\n", " north_slope=False,\n", " dry_soil=bool(indicators[\"dry\"]),\n", " south_east=False,\n", " groundwater_never=False,\n", " prop_pine=float(stand_ctx[\"prop_pine\"]),\n", " prop_spruce=float(stand_ctx[\"prop_spruce\"]),\n", " prop_birch=float(stand_ctx[\"prop_birch\"]),\n", " prop_beech=float(stand_ctx[\"prop_beech\"]),\n", " prop_oak=float(stand_ctx[\"prop_oak\"]),\n", " )\n", "\n", "\n", "def summarize_stand(tree_list: list[Tree], age: float) -> dict[str, float]:\n", " weights = np.array([t.weight_n or 0.0 for t in tree_list])\n", " diameters = np.array([t.diameter_cm or 0.0 for t in tree_list])\n", " heights = np.array([t.height_m or 0.0 for t in tree_list])\n", " volumes = np.array([getattr(t, \"volume_m3\", 0.0) for t in tree_list])\n", " total_w = float(weights.sum())\n", "\n", " if total_w <= 0.0:\n", " return {\n", " \"age_years\": age,\n", " \"mean_dbh_cm\": 0.0,\n", " \"mean_height_m\": 0.0,\n", " \"qmd_cm\": 0.0,\n", " \"hq_m\": 0.0,\n", " \"basal_area_m2_ha\": 0.0,\n", " \"volume_m3_ha\": 0.0,\n", " \"stems_per_ha\": 0.0,\n", " \"pine_share\": 0.0,\n", " \"spruce_share\": 0.0,\n", " \"birch_share\": 0.0,\n", " \"pine_mean_height_m\": 0.0,\n", " \"spruce_mean_height_m\": 0.0,\n", " \"birch_mean_height_m\": 0.0,\n", " }\n", "\n", " mean_dbh = float((diameters * weights).sum() / total_w)\n", " mean_height = float((heights * weights).sum() / total_w)\n", " ba_total = basal_area_m2_ha(tree_list)\n", " qmd_cm = math.sqrt((40000.0 * ba_total) / (math.pi * total_w)) if ba_total > 0.0 else 0.0\n", " volume_total = float((volumes * weights).sum())\n", "\n", " valid = (diameters > 0.0) & (heights > 0.0) & (weights > 0.0)\n", " if qmd_cm > 0.0 and np.any(valid):\n", " d_bins_cm = np.round(diameters[valid], 1)\n", " h_valid = heights[valid]\n", " w_valid = weights[valid]\n", "\n", " d_unique_cm, inverse = np.unique(d_bins_cm, return_inverse=True)\n", " h_weighted_sum = np.zeros(d_unique_cm.shape, dtype=float)\n", " w_sum = np.zeros(d_unique_cm.shape, dtype=float)\n", " np.add.at(h_weighted_sum, inverse, h_valid * w_valid)\n", " np.add.at(w_sum, inverse, w_valid)\n", "\n", " h_mean_by_bin = np.divide(\n", " h_weighted_sum,\n", " w_sum,\n", " out=np.zeros_like(h_weighted_sum),\n", " where=w_sum > 0.0,\n", " )\n", "\n", " if d_unique_cm.size == 1:\n", " hq_m = float(h_mean_by_bin[0])\n", " else:\n", " q_eval_cm = float(np.clip(qmd_cm, d_unique_cm[0], d_unique_cm[-1]))\n", " hq_m = float(np.interp(q_eval_cm, d_unique_cm, h_mean_by_bin))\n", " else:\n", " hq_m = 0.0\n", "\n", " group_ba = {\"pine\": 0.0, \"spruce\": 0.0, \"birch\": 0.0}\n", " group_height_sum = {\"pine\": 0.0, \"spruce\": 0.0, \"birch\": 0.0}\n", " group_weight = {\"pine\": 0.0, \"spruce\": 0.0, \"birch\": 0.0}\n", " for tree in tree_list:\n", " d = tree.diameter_cm or 0.0\n", " w = tree.weight_n or 0.0\n", " h = tree.height_m or 0.0\n", " ba = (math.pi * (d / 200.0) ** 2) * w\n", " if tree.species in PINE_SET:\n", " group_ba[\"pine\"] += ba\n", " group_height_sum[\"pine\"] += h * w\n", " group_weight[\"pine\"] += w\n", " elif tree.species in SPRUCE_SET:\n", " group_ba[\"spruce\"] += ba\n", " group_height_sum[\"spruce\"] += h * w\n", " group_weight[\"spruce\"] += w\n", " elif tree.species in BIRCH_SET:\n", " group_ba[\"birch\"] += ba\n", " group_height_sum[\"birch\"] += h * w\n", " group_weight[\"birch\"] += w\n", "\n", " def _mean_height(group: str) -> float:\n", " return group_height_sum[group] / group_weight[group] if group_weight[group] > 0 else 0.0\n", "\n", " return {\n", " \"age_years\": age,\n", " \"mean_dbh_cm\": mean_dbh,\n", " \"mean_height_m\": mean_height,\n", " \"qmd_cm\": qmd_cm,\n", " \"hq_m\": hq_m,\n", " \"basal_area_m2_ha\": ba_total,\n", " \"volume_m3_ha\": volume_total,\n", " \"stems_per_ha\": total_w,\n", " \"pine_share\": group_ba[\"pine\"] / ba_total if ba_total > 0 else 0.0,\n", " \"spruce_share\": group_ba[\"spruce\"] / ba_total if ba_total > 0 else 0.0,\n", " \"birch_share\": group_ba[\"birch\"] / ba_total if ba_total > 0 else 0.0,\n", " \"pine_mean_height_m\": _mean_height(\"pine\"),\n", " \"spruce_mean_height_m\": _mean_height(\"spruce\"),\n", " \"birch_mean_height_m\": _mean_height(\"birch\"),\n", " }\n", "def increment_tree_ages(tree_list: list[Tree], dt: float) -> None:\n", " for tree in tree_list:\n", " if tree.age is None:\n", " continue\n", " tree.age = Age.DBH(float(tree.age) + dt)\n", "\n", "\n", "def apply_thinning(\n", " tree_list: list[Tree],\n", " *,\n", " target_ba_reduction: float = 0.30,\n", ") -> None:\n", " total_ba = basal_area_m2_ha(tree_list)\n", " if total_ba <= 0.0 or target_ba_reduction <= 0.0:\n", " return\n", "\n", " target_remove = total_ba * target_ba_reduction\n", " removed = 0.0\n", "\n", " tree_list.sort(key=lambda t: t.diameter_cm or 0.0)\n", "\n", " for tree in tree_list:\n", " d = tree.diameter_cm or 0.0\n", " w = tree.weight_n or 0.0\n", " ba = (math.pi * (d / 200.0) ** 2) * w\n", " if removed >= target_remove:\n", " break\n", " if ba <= 0:\n", " continue\n", " remaining = target_remove - removed\n", " if ba <= remaining:\n", " tree.weight_n = 0.0\n", " removed += ba\n", " else:\n", " keep_ratio = (ba - remaining) / ba\n", " tree.weight_n = w * keep_ratio\n", " removed = target_remove\n", " break\n", "\n", " tree_list[:] = [t for t in tree_list if (t.weight_n or 0.0) > 0.0]\n", "\n", "\n", "def update_thinning_flags(\n", " ctx,\n", " *,\n", " years_since_thinning: float | None,\n", ") -> None:\n", " if years_since_thinning is None:\n", " ctx.attrs[\"thinned_0_10_years\"] = False\n", " ctx.attrs[\"thinned_11_25_years\"] = False\n", " ctx.attrs[\"thinned_11_30_years\"] = False\n", " return\n", "\n", " years = float(years_since_thinning)\n", " ctx.attrs[\"thinned_0_10_years\"] = 0.0 <= years <= 10.0\n", " ctx.attrs[\"thinned_11_25_years\"] = 10.0 < years <= 25.0\n", " ctx.attrs[\"thinned_11_30_years\"] = 10.0 < years <= 30.0\n" ] }, { "cell_type": "markdown", "id": "61a6057e", "metadata": {}, "source": [ "## 6. Run growth with thinning at age 50\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "7f0a3b93", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/csvi0001/projects/pyforestry/src/pyforestry/base/simulation/core.py:178: UserWarning: Elfving growth scaled linearly from 5-year period.\n", " self.model.update_step(self, years) # type: ignore[call-arg]\n" ] }, { "data": { "text/html": [ "
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432.04.6474934.3079886.5500376.1239233.79316916.5406831125.7055780.0999150.2389730.6485983.8518852.6811264.961107
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642.06.0038695.3055768.4581578.6911946.32508332.7690171125.7055780.1086800.2614070.6146694.9405443.6089825.968994
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1165.07.4520935.36959212.71337212.9728499.97268071.515818785.5982280.1657070.3787050.4273406.7789795.0495985.308850
1270.07.9148325.65175313.51694113.64652611.27320084.497446785.5982280.1685770.3837740.4181157.1066455.3593645.562822
1375.08.3558555.91966114.28590914.28423012.59233198.257384785.5982280.1712330.3884100.4096467.4094805.6508175.806817
1480.08.7769726.17442315.02309214.89635813.925443112.724125785.5982280.1737290.3926900.4018027.6898865.9255006.041495
1585.09.1809156.53775515.73098115.78775215.268697129.948252785.5982280.1754750.3969890.3947588.1849446.2782916.389961
1690.09.5470336.76203416.37495416.33613816.544382145.031778785.5982280.1771020.4007630.3885048.4228156.5150596.601206
1795.09.8984846.97566716.99528316.86320817.821620160.548263785.5982280.1787060.4042780.3826238.6434536.7385676.804595
18100.010.2363357.17915017.59365017.35689019.098633176.443178785.5982280.1802920.4075830.3770588.8478336.9497037.000294
19105.010.5615367.37289418.17155917.85585520.373928192.664151785.5982280.1818610.4107060.3717649.0367987.1491857.188432
20110.010.8749377.55724918.73036118.32233321.646250209.161600785.5982280.1834150.4136810.3667079.2111007.3376707.369106
21112.010.9958827.62774918.94665818.48733622.149075215.763640785.5982280.1840420.4148370.3647349.2765197.4095157.438542
\n", "
" ], "text/plain": [ " age_years mean_dbh_cm mean_height_m qmd_cm hq_m \\\n", "0 12.0 1.237350 2.096590 2.017114 3.868016 \n", "1 17.0 2.179986 2.625663 3.197608 4.685135 \n", "2 22.0 3.062327 3.196680 4.361743 4.852382 \n", "3 27.0 3.890355 3.769662 5.500991 6.293038 \n", "4 32.0 4.647493 4.307988 6.550037 6.123923 \n", "5 37.0 5.348931 4.818685 7.533108 7.880787 \n", "6 42.0 6.003869 5.305576 8.458157 8.691194 \n", "7 47.0 6.618503 5.770677 9.330839 9.424115 \n", "8 50.0 5.770843 4.343333 9.836455 10.542922 \n", "9 55.0 6.361744 4.706293 10.843617 11.393093 \n", "10 60.0 6.921236 5.047552 11.801147 12.210791 \n", "11 65.0 7.452093 5.369592 12.713372 12.972849 \n", "12 70.0 7.914832 5.651753 13.516941 13.646526 \n", "13 75.0 8.355855 5.919661 14.285909 14.284230 \n", "14 80.0 8.776972 6.174423 15.023092 14.896358 \n", "15 85.0 9.180915 6.537755 15.730981 15.787752 \n", "16 90.0 9.547033 6.762034 16.374954 16.336138 \n", "17 95.0 9.898484 6.975667 16.995283 16.863208 \n", "18 100.0 10.236335 7.179150 17.593650 17.356890 \n", "19 105.0 10.561536 7.372894 18.171559 17.855855 \n", "20 110.0 10.874937 7.557249 18.730361 18.322333 \n", "21 112.0 10.995882 7.627749 18.946658 18.487336 \n", "\n", " basal_area_m2_ha volume_m3_ha stems_per_ha pine_share spruce_share \\\n", "0 0.359729 0.981899 1125.705578 0.044440 0.211271 \n", "1 0.903993 2.776621 1125.705578 0.065109 0.214388 \n", "2 1.682034 5.849300 1125.705578 0.077748 0.221807 \n", "3 2.675448 10.514759 1125.705578 0.092391 0.226593 \n", "4 3.793169 16.540683 1125.705578 0.099915 0.238973 \n", "5 5.017220 23.974670 1125.705578 0.105323 0.250320 \n", "6 6.325083 32.769017 1125.705578 0.108680 0.261407 \n", "7 7.697613 42.845932 1125.705578 0.111005 0.271049 \n", "8 5.969908 35.903258 785.598228 0.160062 0.361216 \n", "9 7.255021 46.594899 785.598228 0.162178 0.367591 \n", "10 8.592881 58.495043 785.598228 0.164004 0.373418 \n", "11 9.972680 71.515818 785.598228 0.165707 0.378705 \n", "12 11.273200 84.497446 785.598228 0.168577 0.383774 \n", "13 12.592331 98.257384 785.598228 0.171233 0.388410 \n", "14 13.925443 112.724125 785.598228 0.173729 0.392690 \n", "15 15.268697 129.948252 785.598228 0.175475 0.396989 \n", "16 16.544382 145.031778 785.598228 0.177102 0.400763 \n", "17 17.821620 160.548263 785.598228 0.178706 0.404278 \n", "18 19.098633 176.443178 785.598228 0.180292 0.407583 \n", "19 20.373928 192.664151 785.598228 0.181861 0.410706 \n", "20 21.646250 209.161600 785.598228 0.183415 0.413681 \n", "21 22.149075 215.763640 785.598228 0.184042 0.414837 \n", "\n", " birch_share pine_mean_height_m spruce_mean_height_m birch_mean_height_m \n", "0 0.730713 1.304561 0.925812 2.604143 \n", "1 0.708895 1.863024 1.305792 3.187549 \n", "2 0.688992 2.529350 1.745628 3.801178 \n", "3 0.669228 3.225432 2.204157 4.408685 \n", "4 0.648598 3.851885 2.681126 4.961107 \n", "5 0.630466 4.426840 3.148682 5.479041 \n", "6 0.614669 4.940544 3.608982 5.968994 \n", "7 0.601246 5.406512 4.049613 6.437830 \n", "8 0.456496 5.588671 3.924743 4.387771 \n", "9 0.445887 6.021158 4.324665 4.710656 \n", "10 0.436221 6.415930 4.698936 5.016899 \n", "11 0.427340 6.778979 5.049598 5.308850 \n", "12 0.418115 7.106645 5.359364 5.562822 \n", "13 0.409646 7.409480 5.650817 5.806817 \n", "14 0.401802 7.689886 5.925500 6.041495 \n", "15 0.394758 8.184944 6.278291 6.389961 \n", "16 0.388504 8.422815 6.515059 6.601206 \n", "17 0.382623 8.643453 6.738567 6.804595 \n", "18 0.377058 8.847833 6.949703 7.000294 \n", "19 0.371764 9.036798 7.149185 7.188432 \n", "20 0.366707 9.211100 7.337670 7.369106 \n", "21 0.364734 9.276519 7.409515 7.438542 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "end_age = age_years + 100.0\n", "step_years = 5.0\n", "thin_at_age = 50.0\n", "\n", "years_elapsed = 0.0\n", "current_age = age_years\n", "last_thin_age: float | None = None\n", "\n", "ctx.attrs[\"field_estimated_basal_area_m2_ha\"] = basal_area_m2_ha(ctx.plots[0].trees)\n", "update_thinning_flags(ctx, years_since_thinning=None)\n", "\n", "records: list[dict[str, float]] = []\n", "\n", "while True:\n", " apply_soderberg_height_volume(\n", " ctx.plots[0].trees,\n", " fallback_age_years=current_age,\n", " )\n", " records.append(summarize_stand(ctx.plots[0].trees, current_age))\n", "\n", " if current_age >= end_age:\n", " break\n", "\n", " dt = min(step_years, end_age - current_age)\n", " if last_thin_age is None and current_age < thin_at_age < current_age + dt:\n", " dt = thin_at_age - current_age\n", "\n", " # Update local field-estimated BA every growth step (angle-count proxy).\n", " ctx.attrs[\"field_estimated_basal_area_m2_ha\"] = basal_area_m2_ha(ctx.plots[0].trees)\n", " years_since_thinning = None if last_thin_age is None else current_age - last_thin_age\n", " update_thinning_flags(ctx, years_since_thinning=years_since_thinning)\n", "\n", " ctx.update_step(dt)\n", " increment_tree_ages(ctx.plots[0].trees, dt)\n", "\n", " years_elapsed += dt\n", " current_age += dt\n", "\n", " if last_thin_age is None and math.isclose(current_age, thin_at_age):\n", " apply_thinning(ctx.plots[0].trees, target_ba_reduction=0.30)\n", " last_thin_age = current_age\n", "\n", " # Re-estimate local BA after thinning and rebuild context to refresh metrics.\n", " local_ba_after_thin = basal_area_m2_ha(ctx.plots[0].trees)\n", " stand = Stand(site=site, plots=ctx.plots)\n", " ctx = model.build_context(stand, mode_hint=\"tree_list\", attrs=base_attrs)\n", " update_thinning_flags(ctx, years_since_thinning=0.0)\n", " ctx.attrs[\"field_estimated_basal_area_m2_ha\"] = local_ba_after_thin\n", " ctx.state[\"t\"] = years_elapsed\n", " ctx.state[\"years_since_thin\"] = 0.0\n", "\n", "results = pd.DataFrame(records)\n", "results\n" ] }, { "cell_type": "markdown", "id": "8be38d72", "metadata": {}, "source": [ "## 7. Plot summary results\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "104fa480", "metadata": {}, "outputs": [ { "data": { "image/png": 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kW2RZ99FHH1W3pcuLfNsqLU7rpxKtWrVKndDWDywkDeeFF15QJw+NT+pbSlqtSrci4ze9RUVF+PXXX9VcDhJQyLfA8g29dHeRtBLjt+ktIa125VtVObmXzjpGcgIlv59xX7UlOfExjkDI6IvMMSLfnDaVkmKc8E5s2rRJjUQYRzjqb6sEFfJNtAQVJzvBb2sS6EgQJHNrNE4za/w7ywlc45M9+Ra8cdegjiLHqpxYyvMPHTq0RT8raV5yzEhnNAkWjeTxTlVLRmTq+/bbb9Wxawya5T0px66MKtQn3cgkRUpGyeTLBUnNa6qj2MlIiqCMcshxKF3O6pP3pwQd9dOhpKuTpNgZj+3m7vMJEyao97i0aJZRkuaSx5f0z7S0tOPSG09E9pukScmXLhJcyPuMiMyPgQVRJyEne/KNteQxS0tHyXHesmWLOiGVE1X55tJ4Ui0nF5JKJH/85QRJUqWkRaR8SyipOEJykaV9rKTlSBrLpZdeqtqYyiRsjdMwpK2sTOgmJxLy83LCL88vJ78yUZ+02GzuSZycWJx55pnqpEkeQ06IpJWttOM0kpNymadBgg3JI5cgR05wZERAgpAlS5ac9Dnk95bHu+mmm1RLSplLQlI1ZJkEZ81pJStpG5JWIbZv367+lRNO+dZVLnISanT77berIEACL2l/+fbbb6tvZY0/byQBggR4sv/khFy+HZffX3L666evyLfGElxJMCltOOV2/Xaw7T0viexv+ZZXglD5fWT75HiRb69lm+XEVfalh4eHOiYkUJRvleXn5HeWbTbXZIzS+laCZUmbk/eE1BLIKJi8HlKfILVJUr/TFEkNkhNRyeWX94Xk7MtrLileLWlB3PjbegkKZURh/PjxKuXIuK9ORl5v2c4bbrhBjQzJqJAE41JT1Zj8npISJSfTt9xyy39ukzyeHF+SxiafJbKNclzKiIPUQNUnx7S8B+W9LiNxf/zxh6rpkGUSkLRkn8vvLClKUmMkgZbUrkj6ktRCSDpT/c+A+qQuSj7TJGVQ2hvLMSfHoLw/Vq5cqYJ02R4ZPZOWtBdddBGio6PVe0y+yJD3vnzmEFEnYO7qcSKq6/ZyxRVXGHr06KE6ukiHGGlzKt2HGrdSlE5AM2bMUK1FZT1pE3vVVVepLir1SdcZ6fgkrS2lu4y0iJX2lo275Qjp9PPYY4+pjjeyrrEdrLTjNLZ7FI1bUTbuVCTbJS0ppeuTPE7//v1VG8um1pdWp9KyUn4HaY0pXbCee+654zroSNvKpkgrTWnTKc8j++LTTz9VLTzlOf+L8bGbujTeN/KYwcHBajula9LVV1+tuvg0Jh23pFOVtA11dHQ0JCYmqlaljVsHn+y5m9NhyLivpZtVU4/beH8ZOzY17vol3XWkFaxxe6VTj7T1ldaqRkeOHFEtfaVtrHS3kraj0l5Y1pXuPv/1HMZtkn9PRh5LOk2dqAVv45arsl+lK5Z0EZP3i7Qmle5O+/fvN63X1HEuHbGkFar8jPw+8rtJ17PG3ZFasj2yv+SYk30o99XfL40Z98eXX36pWrvKcS8/N2rUKNX1qCnSelbWkX3fHNIGV1qwyvtdXlvprCTva9l2aS1df//06tVLtaOVFq7yHHKcS2c52censs+FtJCWNtPG9WTfNO641VTnNnn/yvtH9rtsc/fu3Q3Tpk0z7Zc9e/YYpkyZopbL/fIZNWTIEMNnn33WrP1CRO1PJ/8zd3BDRNQW5JtL+dZVvtGcNWsWdyo1i6QBPvbYY+pbe3PNYH8yMoonqUq///67qcFBW5BROBlJMNZvERG1FlOhiKhLkgJTmeFbUj4knUNy1CXNRVKqjGljRI1JhyshKULSSUjS7iS1TdKjOltQISlEclxLKqLU89SvCyEi6owYWBBRlyRFs1JbIjnn0qFIisKlYF3620sbSqKmyHEiAagcO9LdSuoIpL2zjFh0NnJsS7H1gAED8Pnnn5+0GxsRUWfAVCgiIiIiImo1TpBHREREREStxsCCiIiIiIhajYEFERERERG1GgMLIiIiIiJqNQYWRERERETUagwsiIiIiIio1RhYEBERERFRqzGwICIiIiKiVmNgQURERERErcbAgoiIiIiIWo2BBRERERERtRoDCyIiIiIiajUGFkRERERE1GoMLIiIiIiIqNUYWBARERERUasxsCAiIiIiolZjYEFERERERK3GwIKIiIiIiFqNgQUREREREbWaHbowvV6P9PR0uLu7Q6fTmXtziIiIiIgsisFgQFFREUJCQmBjY2O5gYUEFeHh4ebeDCIiIiIii5aamoqwsDDLDSxkpML4i3p4eJh7cyyKjAZlZ2fD39//P6NTsg48JojHBPFzgvi3w/oUFhaqL/KN590WG1gY058kqGBg0fYnkeXl5Wq/MrAgHhMaQ1UVcj75VF33ve5a6Oztrfrg4OcE8Zggfk5YD10zyg66dGBBRNTReaZV6emm66zsIiIiqsMcFyIiIiIiajUGFkRERERE1GpWkQpVU1ODqqoqc29Gl8udln0mdRatqbFwcHBgjQYRERGRFbDowEJyoI8ePYr8/Hxzb0qX3HcSXEjf4tbMESJBSVRUlAowiIiIiMhyWXRgYQwqAgIC4OLiwkn0WhhYVFdXw87O7pT3m3ECw4yMDERERHD/ExEREVkwO0tOfzIGFb6+vubeHKsMLITMgyHBhTyWvZW35iTLYOPiYu5NICIi6pQsNrAw1lTISAWZjzEFSgI9BhbU1dk4OCDwoQfNvRlERESdksV3hWrNt+3E/U9EREREVj5iQURERETU1RSWV+FgVjEOZpfgQFYxAtwdce3IKHQFDCy6mKeeegrz58/Hli1bzL0pRFbHUFWF3C+/Utd9pl4FHeuGiIjoVP6eGAzIKqpQgYNcDmZr/8pFltfXN8yTgQWduquvvhqff/65ui7F0+Hh4Zg0aRKefvpp3Hfffbj99tu5e4nM9IegMjnZdJ2JlkREdDJ6vQFH8sqwL7MI+7KKcDCrBAeyi5GUVYyiiuoT/lyghyNiAtzQ3d8NiSGeXWYnc8Sikzr77LMxe/ZsVYS+YsUKXH/99SgpKcEHH3wANzc3c28eEREREdWSL5vSC8q1AOJoEfZlFmN/VhH2ZxajrKqmyf1ka6NDpI8Lov3dVBChBRKu6B7gBg+nrtlJk4FFJ+Xo6IigoCB1/YorrsDSpUtVClRgYGCDVCgZ3ZC2uiNHjsRrr72GyspKXH755XjzzTdNXZhk2WOPPYavv/5arZuYmIgZM2Zg7NixZv0diYiIiLpiCtNeFTxogYOMRMi/xScYgXCwtVHBQo/aiwogAtwQ6esCRztbWBI7azsYThQ1tidne9tWd6dydnY2tdBtTIKO4OBg9e+BAwdw2WWXoV+/frjhhhvU/ddccw2Sk5MxZ84chISEYN68eWpEZPv27ejRo0ertouIiIjIEh0rrqgdfZA0pmLT9cLypgMIOxsdov1d0SPQHT0D3BEb5Kauy6iEna3FN2K1vsBCgoqEJxZ2+PPueuYsuDic+q5et24dvvnmG4wfP77J+729vfHuu+/C1tYWcXFxmDhxIhYvXqwCi4MHD+Lbb7/FkSNHVFAhpE5jwYIFKtXqhRdeOOXtIiIiIurq8koqTcHD/swiNRqxP6sYuSWVTa5vowO6+bmq4KFnoBt6Bsm/7ujm6woHO+sIIE7EqgKLruS3335TtRQyY7WMVFx44YV455138P777x+3bq9evVRQYSSjFzIaITZt2qRGanr27NngZyoqKjgjOREREVlVG1cJHKT+QQsetOvZjbowGUmySYSPC3rUBhCxQe7quoxKONlbVgpTW7GqwEJSkmT0wBzP21Ljxo1ThdpSJyEjDSebtbrxfZJ2pdfr1XX5V4KOjRs3Ngg+BIvAiVqOLWaJiDq36ho9Dh0rwe6jRdidUaguEkhkFJSf8GdCvZy10QdJY6q9SC2EswMDiJawqsBCTrhbk5LUkVxdXRETE9Pqx+nfvz9qamqQlZWFUaNGtcm2EVkrGwcHBD3+mLk3g4iIahWUVZmCB+2i1UFUVGtfsDYW5OGEHrUBRGygu7oudRBujl3j/LCz4160cJICdeWVV2LatGmqa5QEGseOHcOSJUvQu3dvnHvuuebeRCIiIqL/nA/icG7pcUFEWn7ZCbNF4oLdERfkgQT5N9hDBROezl2zjWtXwcDCCkiR9nPPPYd7770XaWlpqrZi+PDhDCqIiIio05G2rXuPFmJXRsNUptLKmhOmMcUHuyM+2MN0kU5MNlJlTR1KZ5DK3i6qsLAQnp6eKCgogIeHR4P7ysvLcejQIURFRcHJycls29hVyWEhheMy83drWuXydbAcUq8jKXUBAQGwsbHOrheGqirkzflOXfe+/DKrr7fgMUGN8ZiglhwTcq4hs1IbRx/Uv0cLcTintMkdKR2XJH2pQRAR5AFPF45CmOt8uzGOWBARNZP8EazYv990nd+FERE1T3lVDfZnNUxjkiCi6ARzQgS4O9YbgXBHQrAHovxcrWY+iK6KgQURERERtWlb1x1pBeqy7UgBtqfmITW/HPomcmTsbXXo7u+mAof6gYSvmyNfkS6IgQURERERnZKC0irsSC/A9jTtIsHEiVKZfFwdtDSmoLogQlq6WvukcpaEgQURERERNWuGamMQIQGE/Jua23RXpjBvZySGeKJXqAdCnfUYHh+OIE/nVtVtUufHwIKIiIiIGsgtqawLICSdKa3ghK1dZXbqxFAPJIZ6oneopwoovF0dGhZvezgxqLACDCyIiIiIrNix4gotiKgNIHamF54wiOjm64JetQGEMYhgVyYyYmBBREREZCWyisprRyEKa4OIAmQUlDe5brSfa20QoY1G9JIgghPM0UkwsCAiaiYbBwcEP/M09xcRdQlZheVaV6baAEL+zSysOG49KXuQIEKNQNReeoV4wN2J80NQyzCwICIiIuriSiqqVeCwJTUfW1Ly1b9HC48fiZDJqKW9qwQRxpSmhBAPuDnylJBaj0cRERERURdSozdgf1aRKYCQy77MouPmiZAgokeAu+rM1LteEOHiwNM/ah88sjoh6Z7w+OOP488//0RmZia8vb3Rt29fPPXUUxg+fLi5N4/IahmqqpD/01x13eviSdDZM02AiNrf0YJyUwCxJTVPdWkqqaw5br1gTyf0C/cyXXqHeTKIoA7FwKITuvjii1FVVYXPP/8c0dHRKrhYvHgxcnNzT/kx5fHseRJE1CoGgwHlu3Zp1yf9D+zGTkTmSmlydbBFnzAv9K0NIvpHeCHQw4kvCJkVA4tOJj8/HytXrsSyZcswZswYtSwyMhJDhgwxrSOTy7z//vv45Zdf1HpBQUF4+eWXcemll6r7k5OTERUVhe+++06tt2bNGnzwwQc4fPgw5s+fjy1btpge680331QX+RmjTz/9FK+99hoOHDgAHx8fFei8++676r6CggLcf//96nHKy8sxaNAgvPHGG2pEhYiIiNonpalnoLsKHrTRCG81Y7Wt3EHUiVhXYGEwAFVNTzPfruxdtJYLzeDm5qYucuI+bNgwODo6NrmepEq99NJLeOutt/Dll19iypQpSExMRHx8vGmdBx98UAUIs2fPVo8za9as/3x+CUDuuecevPjiizjzzDNRUlKCVatWmb6tnThxogo2/vjjD3h6euLDDz/E+PHjsW/fPrWciIiITpbSlIfNtaMRMjJRypQmsiDWFVhIUPFCSMc/7yPpgINrs1a1s7PDZ599hhtuuAEzZ87EgAED1MjF5Zdfjj59+pjWk9GJ66+/Xl1/9tln8ffff+Odd95RIxRGd911FyZNmtSiTX3uuedw77334s4770R1dbXaHuNoydKlS7F9+3ZVA2IMeF599VUVBP3444+48cYbW/RcRERElpzSJK1eZRRia+1oBFOayNJZV2DRRUjqkYwMrFixAqtXr8aCBQtUqtPHH3+Mq6++Wq3TuIhbbtdPcRKSptQSEjCkp6erEYimbNy4EcXFxfD19W2wvKysDAcPHmzRcxEREVkSmal6Q3IuNh7Ow4bkPOw5WsiUJrI61hVYSEqSjB6Y43lbyMnJSaUiyeWJJ55QoxNPPvmkKbBoitRe1Ofq2nCUxMbGRqUzNS7qNnJ2dj7pNun1egQHB6u6jsa8vLz+83ciIiKyBNU1euw5WqQCiQ2H81Qw0dTs1ezSRNbGugILOfFuZkpSZ5OQkKBSjoykIHvatGkNbvfv3/+kj+Hv74+jR4+q4MIYhNQf5XB3d0e3bt1UB6qxY8ce9/OSliU/L+lRsh4REZE1KK6oxuYUbSRCggi53rjdqxRSJwR7YFA3bwyK9MHASG8EebJLE1kX6wosuoCcnBxVP3Httdeqmgo52d+wYYNKhbrwwgtN6/3www8q1WnkyJH4+uuvsW7dOnzyyScnfWwJFrKzs9VjXXLJJSrFSubK8PDwMK0jc2XcfPPNKgiR0RJJc5Li7dtvvx1nnHGGSrm66KKLMGPGDMTGxqrUKSnklmUtTb0i6mpk3orAxx41XSciy5QuaU0yEpGci/UnSGtyd7RD/0gJIrSLtH115ezVZOUYWHQy0hFq6NChqoWr1C1IqlJ4eLgq5n7kkUdM6z399NOYM2cObrnlFtVuVoILGdU4GekYJcXdL7zwgir4llqO++67r0G3qOnTp6s2svL80lbWz89PBSFCRjkkiHj00UdV4CNBijz36NGjERgY2I57hahzkPeAzsHB3JtBRG3c8nV3RqFWG1EbTKQ3kdYU6uVcOxrhjUHdfFT7V7Z7JWpIZ2icdN+FFBYWqpanMrdC/W/dhZwcHzp0SM3nIPUKlnZyM2/ePDVK0F7ksDB2hWpcu9ESlvw6WBupsZEC/4CAAFWvQ8Rjgrri54SkNUmr1w2HtULrzSn5allTaU2SzmRMbWJak+UeE3Tq59uNccSCiKiZDNXVKPjlV3Xd84LzobPjRyhRZ5dRUGaqjVifnKtGJxqnNblJWlOElwogBndjWhPRqeJfRSKiZjLo9SirbXjgcd5EcM5bos4nNbcUS/dmmYIJaQN7srSmgZE+iA1iWhNRW2Bg0QV14ew1IiKidrEvswjvLT2AX7emNxiRsNEBCSEepk5NElAEe568vToRnRoGFkRERNRlbTuSj3eXHMBfuzJNy4ZG+WB4d18M7uaDfuzWRNRhGFgQERFRl7M2KQfvLj2AFfuPqdvSZ+TsXkG4dVwMEkM9zb15RFbJrIGFdB2SeROkVapMvCazOsvM0o899hg7BxAREdFxqcDL92WrlCeZX8LYwenCviH4v7Hd0SPQnXuMyFoDC5lkbebMmfj888/Rq1cvNRHcNddco1pa3XnnnebcNCIiIuok9HqDSnWSgGJ7WoFa5mBrg0sGheHm0d0R4eti7k0kInMHFqtXr1azSU+cOFHd7tatG7799lsVYBAREZF1q67R47dtGXh/2QHsyyxWy5zsbXDFkEjcODqac0sQdTJmDSxGjhypRiz27duHnj17YuvWrVi5ciXefPPNJtevqKhQl/oTdhgnX5FLfXJbhkyNF2o5435rzf4z7v+mXiPqWozvKWt+HQ22tvC//z7TdWveF4LHBLXXMVFRXYN5m9Mxc/lBpOSWmeaamDY8EteMiISvm6Pp+ahz4+dE19eS95lZA4sHH3xQzeIXFxcHW1tb1NTU4Pnnn8eUKVOaXP/FF1/E008/fdzy7OxsNcNzfVVVVWpHSB2HXCxJcnKyCsTWrVuHfv36tctjyB8GeT1Ea2beln0vr0NOTg7s7e1P+XHI/OR1lPerHBucPRVASQmsHY8JautjorxKj593ZOOrjZnILq5SyzydbHH5gEBc0scf7k52qCktQFYp931Xwc+Jrq+oqKhrBBbfffcdvvrqK3zzzTeqxmLLli246667EBISgunTpx+3/sMPP4x77rmnwYhFeHg4/P39j5tiXAIN2RF2dnbq0pVInYnUnRj5+Phg8ODBqialT58+iIqKQnp6Ovz8/E75dzP+3H/tn9YGA/LY8sfF19cXTk5OrXosMv8fBwky5f3GwIJ4TFBbfk4UllfhqzUpmP1vMnJKKtWyQA9HXD8yClOGhMPFoWv9Hac6/NvR9bXk/M2s79T7778fDz30EC6//HJ1u3fv3jh8+LAamWgqsHB0dFSXxuTDq/EHmNyWDzfjpas5++yzMXv2bHVdOmZJp6zzzz8fKSkp6mRdOmidiHG04WQBg3GfnGj/yGPUX+dUGR+/qdeIuh5rfy0N1dUoXLBQXfc4+yzoutiXFu3B2o8Jat0xkVtSidn/HsJnq5JRVK5lF4T7OOPmMd1xycAwONrZchdbAH5OdG0t+Xw361+C0tLS4zZWUqKYM6kFUUFBQeoiqUqSNpaamqrSviSNSd6kMsIjli1bpm4vXLgQgwYNUj+7YsUKtR9llCMmJkYti4iIUKlm9SUlJWHcuHFwcXFB3759VUE9ETXNoNejdN06dZHrRHRqMgvL8dxvuzByxhK8s+SACiq6+7vi9cl9sfTesbhyaCSDCqIuyKxft8k38HKiKye8kgq1efNmvP7667j22mvb5fnkW/iyaq0IrCM52zm36lv/4uJiNdeHBAiSUlRygtzuBx54AK+++iqio6Ph5eWlUsc++ugjvPHGG6pQPiMjA3v27GnwM48++qj6mR49eqjrUt9y4MABFeARERG1pdTcUlWQ/cOGI6is0YLzXiEealI7mdzOxqbrZRgQUScJLN555x08/vjjuOWWW5CVlaVqK2666SY88cQT7fJ8ElQM/WYoOtraK9bCxb5lPbZ/++03uLm5qesSSEjqkyw72XDUM888gzPPPFNdl/qSt956C++++64prax79+4qwKjvvvvuM7X7lcJ4CfAksIiNjW3x70lERNSUA1nF+GDZQczfkoYavdZpcFCkN249PQZje/p3yZRlIupkgYW7u7tqLXui9rLWTNKTPvjgA3U9NzcX77//Ps455xzVxelEJA3KaPfu3ao17/jx40/6PFIMbmSs25Agj4EFERG11s70Ary/9CD+2JEBY+fykTF+uO30GAyN8mFAQWRhrKryUFKSZPTAHM/bUq6urir1yWjgwIFqRnJJbbr++utP+DOm53Ru3nPW7/pk/MaINS5ERNQam1Ly8P6yJCzZk2VadkZ8oAoo+oV7cecSWSirCizkxLmlKUmdraNCWVnzakSkZkKCi8WLF58wECEiImorMqndv/uP4b3F+7DxiNb3XkomJvYJwa3juiMuqGFbeCKyPFYVWHQlksYkbWZFXl6eqpWQIm4peG9uz2HpJCUF3Q4ODjjttNNUR6mdO3fiuuuua+etJyIiSycNUaR24p/9x7ByfzbWJOWirEqbWNXORodJA0Lxf2NjEOVXN5pORJaNgUUntWDBAlPNg9SiyOzkP/zwA8aOHavazTaHFMbLXBZSDC8T6snj3Xzzze285USWS2dvD/+77zJdJ7I2OcUVWHngGFaoYOIYjhaWN7jfz80B42I8cceEXgj3YUBBZG10BvnKoYuSmbel7qCgoKDJmbcPHTqkZqnmjM8tJ4dFdXW1Ckxa062Dr4PlkNobKewPCAjgZGjEY8KK0ps2JuepUYkV+7OxM72wwf0OdjaqCHtUDz+M6uGPngGuanScnxNkxL8dXd/Jzrcb44gFERERmb5U2i/pTfuy1ajE2kM5KK9qOBlkXJA7Rvf0V8HE4G4+cLKvm/eIzT+IrBsDCyKiZjJUV6No8RJ13X386dDZ8SOUur5jxRX498Ax/LPvGFYeyEZmYUWD+/3dHTEqxg+jevrhtBg/BLg7mW1biahz419FIqJmMuj1KPn3X3XdbdxYcEov6orKq2qwITkPKw5kY8W+Y9iV0TC9ydHOBkOifDC6h78KJmID3TnfBBE1CwMLIiIiC09v2ptZpIqtpVZibVIOKqobpjclBHuY6iQGdfNukN5ERNRcFh9YdOHadIvA/U9E1PGyi6R7k1YnIQFFVlHD9KYAd0eM7OGnRiUkvUnSnYioc6nSVyGlMAXV+mrE+sSiK7DYwMI4o3RpaWmzZ6GmtldZWan+tbXlt19ERO3ZvWn9oTzVuUlGJXY3Sm9yspfuTb513ZsC3ZjeRNRJVOurkVqUioP5B3Eg/4Dp3+TCZHXfMO94fHTB9+gKLDawkBNZLy8v1R5TuLi48EO0g9vNSncQaTso+14eh4iI2k5mYTmW7snC4j1Zqvi6tFKbnM6oV4iHaVRiYCTTm4jMrUZfg7TiNFPwsD9/v/o3uSAZlXrti9jGXPR6OOU1b/6yzsCiz/aCgoLUv8bggloWWEhgYGNj06qATH4+IiKCQR0RUSvp9QZsPZJvCiYazykh6UxjatvASnqTnxvTm4jMdQ51rOwY9uftV8GD8d+k/CSU1zScVNLISa9HdFUVYiqrEFNVhe61/wY7B0DXrQ+6CosOLOSEWGablol6qqqqzL05XYoEFTk5OfD19W3VZGgODg6cTI2I6BQVllepzk2L92Ri+d5s5JTUfasp3/n0CfPC6bEBGB8foAqwbWzYq4yoI5VWldYFD3l1gUR+RX6T6zvoDSqA6G4MIirleiVCbd1gExAPdEsAAoyXeMDFp0u9oBYdWNRPi2KOf8sDC6lTkVnLWxNYEFkSnb09/G691XSdqD2+6TyYXYIlezKxZE+Wagtbra9rQuLuaKdawJ4eF4ixsf4clSDq4EJqCRr25e0zBRCS2tQUG4MBEVXV6FFVhR6VlSqA6FFZhXCDDWz9Y4HgegFEYALgEap9W9DFWUVgQUTUVqOg9oEB3JnU5vNKrD2Uq1KcJJhIyS1tcH+0vyvGxwVgXFyAmuna3pZf9hC1Z3CfWZqpBQ/1RiAOFRxSwUVT/Kpr0KOqUgUO2qUS3auq4eQWDAT2B4ISgcDai28MYGu5p9+W+5sRERF14sJrCSKWNFF47WBrg6HRPjg9LkBdIn1dzbqtRJaqsLIQB/IOHFcLUVRZdMJCauPIgwQSPWtTmbx1doCMQoT3bhhEuPrC2jCwICJqJkN1NYr/WaGuu40eBR27nVEz1dQrvF7SROG1zCtxeu2oxMgYP7g68s8zUVupqqlCUkHScbUQR0uONrm+ncGAbiqFSQscjOlMIdU1sHENAIL6aYFDUG8gsBfg1xOwZXqs2nc8bImImseg16N42TJ13XXkaej62bDU3oXX/+zLVoFEU4XXfaXwunZUQlrDtqYDHxFppBuTpDHty92HvXl71eVQ/iFUG6qb3EVB1dWm9CVjKlNUVRUcbOy0gCFCAojEukDCjemwJ8PAgoiIqM0Kr4tNKU5NFV6P7umvRiVYeE3UOlLvIHUPe3P3qhEIFUTk7kVOeU6T67vrDehRWaECh56mOogqeMh71NkHCOoLBNZLZZLUJju2bG4pBhZERESnqKpGj7VJuVi0O7PJwuvu/q6mFCcWXhOdmrzyPFPgoEYj8vapieWaKqbWGYDI6hr0rChHbGUVYisr1b+BNTXQ6WwA3x5ApAQPveoCCfdgi+jI1BkwsCAiImphFydJcVqw8ygW785CQVndyQ0Lr4lOXbW+Ws1CLYGDMY1pf+5+ZJU1PdGxm0GHnhUV6FlZoQUQFdqkcs4GA2DnpI08BPcBgvtqaUzS2tXemS9RO2JgQURE9B8keJDC64U7j2LZ3myUVdV1cfJ1dcAZ8YE4PZ6F10TNVVBRYBqBMI5GyChEpb6uFqm+CL0OsWWlKp3JOBIhxdRqnMHRAwjqXxdEyEVGJiy4rWtnxT1ORETUhOyiCvy9K1ONTKw+eAxVNXX1EqFezpjQKxBn9wrCoG4+sOWM10RNqtHXIKU4BZtKN6lOTCqQyN2r5opoigts0LNaj9jSIlUL0bNSa+vqIqMQagU/IHhwbQBRG0h4dQM4mW+nwMCCiIioVmpuqRqVkMuGw3kwnsuIHgFuOKtXkLokhrKLE1FjZdVlal6I3bm7sSd3j2lEorymvMmdFQp7LYWppFD9KwFEaHU1TFNAeoQB3fo2DCJYD9GpMbAgImommbfC98YbTdfJMjo57cssxoIdWjCxK6Ph/BJ9wzxxVqIWTHT3dzPbdhJ1Nvnl+diTtwd7cvaY/j1UeAh6g/64dZ1gix6wQ2xpsRqJkCBCujO51Y/cfboDUfWCCOnSZIUTzHV1/MtIRNRMOhsbOISFcn91cXq9AVuO5GsjEzuOIjmnrpOTZDQNjfLFWb0CMaFXEEK8WOhJ1k2C74ySDNMohPFyosnlfGwcEa+3RWxxPuJLCxFXUYnw6mrYGlfQ2QIB8UCQsR6ij1Zk7eTRkb8WtRMGFkREZBVtYdcdylUjE3/tOorMwgrTfQ52NhgV46dGJqQI28fVwazbSmTOrkwyN4QEDhJISCqTXC+sbDiSZxRu54a4GhvEFecirigX8ZWV8KvR100eamMPQ1BvlHn2hFP0MNiE9AUCegH2Th35a1EHYmBBRNRMhupqlKxZq667DhvKdKgu0hZ24c5MNc9E/bawbo52am4JGZkYGxugbhNZk9KqUlX/UH8UQiaaa6ork53OFjH2noitAeILcxFXkKmKqt3rpzLJHBH+8UBofyBELgPUXBEGG3sUZmXBKSCABdZWgJ+kRETNZNDrUfTXX+q6y5DBdd/KUZdoCysjEWfGB+LsxCCMiPGFo50pOYPIouWW56oaCOMohPx7uPAwDKgXGNRytXVCrIMX4qoNiCs4hri8I+heWYXjxvGknasEEKEDtH8ltcnB5fgn1x9fc0GWi4EFERF1aWwLS1RXDyFtXHfl7FIXVReRs+eEE8z5OXgizt4L8dLetSAb8ccOIayqqq4rk5FXZMMgQmojnDy526ltA4uKigo4Ojq25iGIiIhaLK+kEn/syMDPW9KxPjm3QVvYmAA3Nb8E28KSpQcR6SXp2J2zWwskcnep6zI60ZRIlyDE2XkirqoKcQVZiMs8CL/qlONXdA+pTWWSQEKCiP7szkTtE1gsXLgQ3377LVasWIGUlBTo9Xq4uLhgwIABmDBhAq655hqEhIS05CGJiIiapbSyGot2Z+HnzWlYvi8b1XpDg7awE2qDCQksiCwtiDhSdEQFD2okIme3Go3Ir8g/bl1bnS26u4cj3s5TFVMn5B9Fz6P74FrVRBDh4qvVQhhHI4L7AR7BHfNLkfUGFvPnz8eDDz6IgoICnHvuubj//vsRGhoKZ2dn5ObmYseOHVi0aBGeffZZXH311epff3//9t96IiKy+G5OK/cfw89b0vDXrkyUVtbVTCQEe+DCfiE4r2+ImgmbyBLIPBAphSkqcDClNOXsRlFV0XHr2tnYoYdHFBIc/RBfVYOEgiz0SN8Jp6R/jn9gRw8gpF/DQMIzHNCxWow6OLB44YUX8Oqrr2LixImwaWLK9MmTJ6t/09LS8NZbb+GLL77Avffe24abSURE1jTPxKaUPJXm9Pv2DOSW1HWpifBxUcHEBX1D0CPQ3azbSdRaNfoaVURtHImQi3RnKqkqOW5dext79PTqgQSXYMTXAAlFOeiRsQcOBxcf/8C2DlodROggIHSgFkj4RLMrE3WOwGLdunXNejAZxXj55Zdbu01ERGSF9h4tUiMTElCk5ZeZlvu5OeC8PiG4oF8I+od7QcdvWKkLzxFhLKo2BhFl1XXHupGjrSNivWMR7x6JXrBHfHEeumcegP22f4CqugkdTby7aUFE2GDtEpQI2LEGljoeu0IRETWTzs4OPtdcbbpOrXckrxS/bpUi7DTsOVqX6uHqYKsmrLuwXyhO6+4LO9vjR8uJOqsqfRWS8pNMoxAyIrEvdx/Ka8qPW9fZzhlxPnGIl9EIG1fElxYhOusA7PZuAgp/O/7BHdy1NCZjECEjEm5MP6fOwe5UCoh+/PFHLF26FFlZWaqAu765c+e25fYREXUaOhsbOEZFmXszujxJbZIUp1+2pGF9cp5puYOtDcbG+qtgYnx8AJzsOc8EdZ10ph05O7Dz2E71r8wVUVFTN7u7kau9qwoiEnwSEO/gg14V5YjMToJtykZg/e+AvrrhD8ikcwEJWvCgAolBgF9PwIbvDbKQwOLOO+/ErFmzMG7cOAQGBnJImoiI/lNJhXR0ylRpTv/U6+gkWU3DonxV3cQ5icHwdLHn3qRO351pZ85O7Di2Q/0rIxKl1cenJ7nbuyPeNx4JvgmId49AQmUVIo4dhk3aRmDr+0BZXVBt4hpQF0DIRWojHFlLRBYcWHz11VdqVEK6QxERWRNDTQ1KN2xU110GDYTOlt8a/ldHpxX7szF/czr+3pXZYBbsxFAPXNg3FOf3DUGQp1O7v3ZEpzrZnIxC1A8kCisLm0xniveJRy+/Xkj0SUAvOCA8R4KITcC+b4Gc/cc/ga2jVmCtAonaEQl2aSJrCyw8PT0RHR3dPltDRNTJA4vC339X153792NgcYKOThsOS0enNPyxPQN5pVWm+yJ9paNTqOroxLkmqLPJKctRgYO61AYTx8qONdmdSaUz+SYg0S8Rvdy7IbooB7ZH1gO7VwFH3gYqj28Nq7oymQqsBwKBvQE7h4755Yg6a2Dx1FNP4emnn8ann36q5rEgIiLanVGo0px+3dq4o5Mjzu8brAIKmcSOHZ2oM5BRB0lhklEI478ZJRlNTjYX4xWjAghjINFD5wR7GYlIWQNseQ3I3AEY9McXWKtRiCFaSpMEFK6+HfcLEnWVwOLSSy9Vs28HBASgW7dusLdvmA+7adOmttw+IqJTJl1Zvtj1BY6WHMULo16Aj5MP92Ybd3T6dl0GFh/ci32Zxablbo52ODsxCBf1C8WwaB92dCKzKq0qVe1djYXVEkhIsXVjOujQzbMbEn0TVUpTL99eiPOMgZOkMaWuA7b9CaQ+BRSmHf8knhFA+BAgYhgQPhQI7MUCa7JKLQ4sZGbtjRs34qqrrmLxNhF1yrzoDZkb8PnOz7H8yHLT8nn75+G63teZddssQXlVDRbsOIrvN6Ri1cGcBh2dTo8LUEXY4+LY0YnM50DeAazPXG9KZ0oqSFKzWTcW5ham1UTUBhJSI+GmrwEkpSllLbBuDnBkI9B4sjqdLRDUuy6IkItnaMf9gkSWFFj8/vvvWLhwIUaOHNk+W0REdIqTTy1KWYTPd3yuvpU0fgMZ5RmlTiwkyGBgcerB2rYjBSqY+GVrOorKq00dnQaGueOSwZE4p3cIPJ3Z0YnMQ97jCw8txMLkhThYcPC4+wNcAkwBhPwraU1ejp5A/mEtiNjwjTYqkblTjviGPyzrhQ8GwiWQGKK1fnV067hfjsiSA4vw8HB4eHi0z9YQEZ1CmsO8A/Pw5a4vkVacZpq19sLuF2JqwlR1fcJPE7A1eyvyyvPg7eTNfdxMOcUVmLc5DT9sOIK9mXXFqGHezpg8KBz/6x8C+8oilRprY8MJ7KhjpRSmYEHyAhVM7Mvb16C4enDQYPTx76PSmeTi7+IP1FQBR7dpgcS/M7V/i482PYu1cSRCRiX84wEe30TtE1i89tpreOCBBzBz5kxVY0FEZA7SreWb3d/gu73fmdo/ejl6YUrcFFwWexl8nesKJWO9Y7E3by9WpK3ABd0v4At2EtWqRewxNToh805U1Wjf3jra2eCcxCBMHhyu5p2wsdGpCVKzsprofkPUTmQOCQkk5CJ1E0Z2OjsMDxmOs7qdhXER4+Dh4KHNE5G6Hlj9gRZEyPwR1XWNBRQbO63lq4xGRNQGE+5BfP2IOiqwkNqK0tJSdO/eHS4uLscVb+fm5p7qthARNSvl4YudX+DXg7+iUl+ploW7h2N6wnRcEHOB6iff2JjwMSqwWJa6rFWBhc7ODt5XXmm6bkkOHSvBDxtS8dOmI8gsrJsxWDo5XTooXM03wVQnMgdpviCBxIJDC0xpjsaOTUODh6pgYnzEeHhKMHFkA7DkReDgUiC7LvAwcfKqHYmoDSJCBgAOLh37CxFZsBb/ZXzzzTfbZ0uIiE6S478paxM+2/EZlh1ZZlrex68Prk68GqeHnw5bmxNPVjc2bCxmbZuFVemrUFlTCQfbU+sdr7OxgVNsT4t5nUorq/H7tgyV6rQuue5LIR9XB/yvfyguHRSGuCCmvlLHyyrNwl/Jf6lUJ0ljNLLR2WBw4GCcFaUFEz6O3lp60z+vATvmAQUpDR/Ip3u9QGIY4NeTaU1EnSmwmD59evtsCRFRIzX6GixOWaw6PG07ts1UkD02fCyu7nU1+gf0b9a8CFKw6efsp9KnNhzdgBGhI6w7SEvJV6MTMudESaU2G7aNDhjT01/VToyPD4SDHWsmqGPJ+/Pvw3+rkYnNWZthqC2ilvf8gMABOLvb2Tgj8gz1XkbWHmDVB8DOuUDOgboHcXADYs8F4s8DIkYAbv58GYk6W2BRUlICV1fXZj9oS9cnIqqvrLoMPx/4Wc1BkVqUqpY52DioVKdpCdNUp6eWkG85R4eNxtz9c9WIx6kGFjLzdtlWLcBx7tunS828nVVUjnmb0lTtxMHsuvaZ3XxdVKrTxQPCEOTpZNZtJOuTW56LRYcXqVQnaRNdvy1sP/9+ODvqbJwZeabq6oScg8D62drIRJZ0b6pl5wT0PAvoNQnoMYGpTUSdPbCIiYnB7bffruawCAkJOeG3YIsWLcLrr7+O0aNH4+GHH27rbSUiC5dTloM5e+dgzp45yK/IV8s8HT1VMbYUZatvKk+RpENJYLE8dTkeHvLwKc0ALYFFwfz56rpTYq9OH1hU1eixdE8Wvt9wBEv3ZqFGr30D7Gxvi4l9gtXoxOBu3pwNmzpUQUWBGomUkYl1R9ehxqCNmonefr1VzYRcglyDgPxUYPMcbWQifXPdg9jYAzHjgcSLgdhzAEd3vopEXSWwWLZsGR577DE8/fTT6NevHwYNGqQCDCcnJ+Tl5WHXrl1YvXq1KuSWgOLGG29s/y0nIouRXJCsRid+OfgLKmq0wuFQt1A1OnFRzEVwsW99ceWwkGGq9Wx6STr25+9HT2/LqZVo7EBWkaqb+GlTGo4V1xViD4jwUsGEBBXuTpxzgjqOdG5bmrJU1UysSV+DaoM2F4qQOSWMwYS871GUCeyYD+yYC6SuaTgxXdRoLZiQVCdnto4m6pKBRWxsLH744QccOXJE/fvPP/9g1apVKCsrg5+fH/r374+PPvoI5557LnuZE1GzSR61FGQvTV1qyqeWyaukIPuMiDNOWpDdUtItSjrI/HPkHzVqYWmBRVF5lSrEllQnqaEw8nNzUGlOUogdE8BvdanjFFcWq9RDmbju3/R/UaWvatAC2hhMRHhEAKW5wO5fgB0/AckrAVNKlA6IHAEkTgLiL2TNBJElFW+HhYXh7rvvVpe2kpaWhgcffBB//vmnClR69uyJTz75BAMHDmyz5yCizlWQLYHEZzs/a9DtRVKVpveajoGBA9stNWdM2BgVWMjJzg19bkBXJymo65Pz8N36VPyxPQNlVVpKia2NDuNiAzB5UBjGxQXA3paF2NRxE1Yuy1iG1btWY2XaSlNLaBHjFYMJ3SaoYCLaMxooLwD2/AHsuAdIWgro60YxEDpIG5nodRHg0XQKNhF1PmZtxC5pVKeddhrGjRunAguZvfXgwYPw8vIy52YRUTsory43FWSnFKWYZsg9v/v5ag6KaK/odt/vElg8i2exPXu76kDTmpoNcxdiS6qTdHZKzik1Le/u76rNiD0gFAHuLMSm9pddmo0t2VuwJWuL+ndXzi5U1wsQunl0UwXYZ0WehRjvGKCyBNi3APjzcWD/X0BNXeCBoN61wcT/tNmviajLMWtgMWPGDISHh2P27NmmZSebzbuiokJdjAoLtdl2ZfZXuVDbkf0p34Zyv1Jrjwnp+vL93u9VUXZeRZ5aJrPiTu45uUFBdkcca/7O/oj3iVcz9ko61P9i/ndK+8B4HR34uSPPuzElH1+uPowFO4+aZsR2dbDFeX2CVapT/3Av02hPR+xPfk5Y32jjgfwDWiCRvUWNOKYVpx23XohzCM6JPkeNTEjKoU7qpg4shmHRc8C+hdBV1QXDBr+eMPSqDSb8etQ9CP+mWwx+TnR9Lfl7YtbA4pdffsFZZ52FSy+9FMuXL0doaChuueUW3HBD0ykKL774oiogbyw7Oxvl5eUdsMXWdRAVFBSokxkbG6ZRUMuPiYzSDPyQ/AMWpi00pUMEOQdhUuQknB16tqp50BfpkVWU1aG7d7D3YBVY/H3wb5zmcVqLftZQWYmy8jJ1PTsrCzqHU5toryXKq/RYuDcXP23Nwr5s7blF72BXXJjoh/E9vVWXJ6BKfRZ2JH5OWLaSqhLsLtiNXfm7sDN/J3bn70ZZTd0xKGxgg27u3dDLq5e6xHvGw6nCCV4ernBKWoPKAy/BMXkRbCqLTT9T7RGO8u4TUR5zDqp9YgEJhuW8JatjPwuoY/BzousrKipq9ro6g/HrNzOQrlLinnvuUcHFunXrcNddd+HDDz/EtGnTmjViISMeklLl4cHZYdv6g0BOUvz9/RlYUIuOiYP5B/Hpzk/x56E/TW0kE3wSVP2EFGTb2Zj1+wzsztmNy/+4HE62Tvjnsn9Up6jmMuj1qNi9W113jI9XM3G3l8M5JfhqbYpKeSos11JLHO1scGG/EEwdFoFeIZ4wN35OWA45FThSfMQ0EiGpTTI6YWyqYORq76pmvJc5JvoF9FPNFtxkUjpRXQ5D8r+o2PQdnA8vgq4sr+7xpU4iYRIMMjIR0l8LJsgq8HOi65PzbW9vb/Xl4n+db7f4L3xKSoo6mW9cXCkfSqmpqYiIiGjRwSata1944QV1W7pL7dy5Ex988EGTgYWjo6O6NCYnOfxWve3Ja8x9S809JnYc24GPtn2EJalLTMuGBw/H9b2vx+CgwZ1mroQEvwQ12VZWaZaakGtU2Kjm/7CNDVx69263bdPrDVi+Lxufr05W/xq/9onwccFVwyJU/YSXS/uPkrQEPye6psqaSlUPYayNkH9zynOOWy/MLUzNcC9BRF//vqoA29StTdIjMrcDB5cCScuAlNUquDA1h3b1BxIuUnUTuvCh6v3TOT4FqKPxc6Jra8k5dosDi6ioKGRkZKhC6/pyc3PVfTU1dRPd/Jfg4GAkJCQ0WBYfH4+ffvqppZtFRObqSnR0PT7a/hHWZNT1m5eRCQkoevn16pR/4KSI+4d9P2D5keUtCyzaSX5ppRqZ+HLNYaTk1uWfj431x7ThkRjTM0B1eiI6VdKsQEYitmZtVW2ed+bsbND+1dhMQeaUMI5GyOW4Bgd5h7UOThJIJC0HynIb3G1wC0JZ2Cg4DboSNlGjAFvzjlASUceyO5UTiaa+eSwuLjalNjWXdITau3dvg2X79u1DZGRkSzeLiDqQ3qBXxc8f7/gY27K3qWW2OltMjJ6I6xKv65AOT60xNnysCiyWpS7Do0MfbfZoiqRCle/SUqGcElqfCrUjrUAVY8/fkoaKaq04zsPJTo1MXDUsEt38XFv1+GS9709JSZQAQoIJ+Te1KPW49XycfNQohHFEQoKK41IDZX6J5BV1oxJ5hxreL2lQ3UYC0WOB6HEw+PZAYXY2nOTLR9bnEVmdZgcWUgch5A/w448/DheXuplwZZRi7dq1albulpD5MEaMGKFSoSZPnqxqLGbNmqUuRNQ5u8L8cegPfLLjE5V/LRxsHDCpxyQ1qZ2aNbcLGBI0RNVYZJZmYk/uHsT7xjfr5wzV1cj//nt1PfCxR0+peLuyWo8/d2Tgi9WHsfFwXQ56fLAHpg+PxIX9QuHs0HYTA5LlK6suUy2UN2VtUilNEuwXVTUsttRBh+5e3bWRCP9+KpgIdz8+rRlV5UDq2toRiaVA+hY58us9kC0QNlgLJLqPA0IHArb1ZnFnNyciq9bswGLz5s2mEYvt27fDod4fVLnet29f3HfffS168sGDB2PevHl4+OGH8cwzz6hUqjfffBNXXnllix6HiNo/H3v+/vn4eNvHyCjLMBVxXhZ7GaYmTO1y80E42TlheMhwNVGfTJbX3MCiNTIKyvDN2hR8uy4Vx4q1JhR2Njqc0ztYBRQDI707TR0KdW755fkqiNiUuUmNRqi5IwzVx800L0XWfQO0EYk+/n1Um+fjGOskJJCQUYnaOokG/ONMIxJqFmwnNksholYGFkuXLlX/XnPNNXjrrbfarAvTeeedpy5E1Dln0ZWUoc93fo7sMq2VqbejN65KuAqXx13e9IlKFyHpUBJYSErX//X9v3Z5DvkiZk1SLr5YnYy/dmWiRq998xvo4YgrhkRiypBwBHhwIjs6+TGUUZKBjZkbVTCxOXMzDhYcPG49aUgwIGCAKa1J5o84YQc2VSchIxLLgEPLgdJGRdtuQbWBRO3FI5gvERG1T41F/cnsiMgyFVQU4Jvd3+DrPV+r68YTl4sjLsa0ftPg5ljbXrILGx02Wv0rRazSIUp+v7ZSXFGNeZvT8OXqZOzLrOvfPzTKB9OGd8OEXoGwt+X8MNR0fYSkGUoAsTFroxqVkJS9xqI9o1UQMTBwIAYEDkCIa8iJR7yMdRLGUYn/qJOAf+3cEkRE7R1YlJSU4KWXXsLixYuRlZV13Gx8SUlJLX1IIuok5AT7i51f4Pt936u8bRHpEYlrE6/FxG4TkZeTBxf7uvqqrkzSt3r79cb2Y9vxz5F/cEnPS1r9mAeyivHVmsP4ceMRFVwImbxu0oBQTB0eibigrjvCQ+2jqqZKBbcyIiFpTXIprCxssI6dzk6l66kRicD+KqCQwusTP2j9OollQPrmltVJEBF1VGBx/fXXq1myp06dqtrFMieYqOuTjjGzd8zG/APzTS0oY71jcX2f63FmxJmqb33jLxEsgbSdlcBC0qFONbCortFj8Z4s1d1p5YFjpuXRfq6qs9PFA8Pg6cyTNtIUVxarTk3G1CaZ/6Wipm7iV1N9hH8fDAzQRiMkAD5pQC8TnmTtAg4s0gKJw1In0XCGbNZJEFGnDCz+/PNP/P7776pVLBF1bfvz9qsOTzJLtqRgCPk2VOagGBU6yuK/OJA6i3e3vIvVGavVCI2c0DVXbnEFvtuaogqy0/K1kzjZXePjAjF9RCRO6+4HG849YfVk/gjjaISkNe3N22t6rxnJ6IO872REQgKJWJ9YNafESZXla0HEgb+BA4uBIq2pggnrJIioKwQWMqW3j89JhmCJqNOTb0w/3v6xmsfB6LTQ03BD7xtUzra1kALXYNdgVRy7NmOtCjROpsqgQ8rgsVi6JxufvfoPymvPD71d7HHZ4AhcOTQC4T6WkSpGp1ZonVKUogIIY9cmud3UbNYSQBgDiW4e3f47iJcRw6Pb6gKJ1HWAod6EtBIUy4R03U9nnQQRdZ3A4tlnn8UTTzyBzz//vMFcFkTUuakORRlr8Mn2T7D26FpTb/szI8/Edb2vU5NjWRvjLNxz9s5RQVZTgUV2UQWW7c3Ckj1ZWLH/WG3thDZxXZ8wT1WMfV6fYDjZc+4JayMjDzLqtyFzg5balLkJOeUNOyzJe0wCWGMgISMTga6BzXuCkhxtLglJcZJgoiSr4f1+sUDMGUCPM4CIEYA9O4wRURcILPr379/g25QDBw4gMDAQ3bp1g719w+HaTZs2tf1WElGrTn6krarMQbEjZ4epGPS87uepouwozyir3rsSTEhgIQXcsq/kRHBneiEW787Ckr1Z2HYkX6WwG/m5OeD0uABcMTQS/cK9zLnpZIYJIiWVacPRDVifuV4FEo0LrSWFSWoijIGEzCPR7LbM+hqt0Hq/jEosAtI2Niy6lu5NUWO0QKL7eMA7so1/QyKiDggsLrroolY+DRF1tGp9taqdkBEKY997mW1azZLd62oEu7E3vRgcNBgudi5qno5bfvgFmw+4IbOwYTFtYqgHTo8LxOk9/RBblq2+aHEMZYcna3gP7c7ZrUYk5CItYBvPaC3HjoxCDAoapAKJXn694Gjr2PwnKc7SRiMkkDi4BCjLbXh/YCIQMx6IORMIHwrYtXy2dyKiThVYPPnkk+2/JUTUJqSr068Hf8WsbbOQVpymlrnZu2FK3BRcGX8lfJ19uacBpOSUYsmeTCzZm42i0mjYuu/AkpRlqCw8Ey4OthgZ46dGJsbFBSCwdhI7fWUlMp97S10PfOxR6Bx4kmdp752dx3ZqgcTRDargurS6tME68l6S0YhBgYPURdrAnnAiuqbUVANH1tfWSiwCMrY2vN/RE+g+VgskJKDwCGmj346IqBPWWBBR5z0p+u3gb/hw24emgEK6zUxNmIrLYi+Du4M7rFlVjR4bD+dh6Z4s1R5W5pwwsvOMg7P7DgQEHcQLFzyKodE+cLRjzYSlq6ypVO2GJYiQYEKaGhjnbzGSNCZTIBE0CHHecar9cosUptfWScioxDKgdtJJk+C+tYHEGdr8Erb800xEVtQVqqnuFbLMyckJMTExuPrqq3HNNde01TYS0X+ka/yW9Bs+3PohjhQfMQUUUj8xOXZyi1qoWhq93oCle7PULNj/7MtGYbk2aZ2wtdFhUKQ3xscHYGBUb1yz5CcU1CSjZ2gNgwoLVV5dbgokpEZiW/a24+aQ8HL0Up3RJEVOgoke3j1go2vhLOnVlUDqmtpaicVA1s6G9zt7azUSPc7Uuji5td2s70REXSqwkI5Qzz//PM455xwMGTJEdZpZv349FixYgFtvvRWHDh3C//3f/6G6uho33HBD+2w1EamA4vek39UIhUxwJxhQaMqralQw8fGKJBzMLjEdLdIWdlyslt40uqd/g4nr+vr3xZbsLWqyvMviLuMRZgFKq0rVKIQxtUmCCuMEkEbynjGORsi/3b26tzyQEPkpdYHEoeVAZXGD3lBqdusetaMSIf2Blo56EBFZYmCxcuVKPPfcc7j55psbLP/www/x119/4aeffkKfPn3w9ttvM7AgaseibAkoDhceVsu8Hb1xTeI1KuXppDP0Wrjckkp8teYwvlidjGPFlWqZu6MdJg8Ox7m9g1UXJxmpaMqY8DEqsFh2ZBkDiy6qpKoEW7K2YP3R9SqYkHqJakPdKJXwd/avCySCBiHKI+rUJoKUDk5SK7H3T2DfAiB7T8P7Xf21IEIuMirhwvmfiMjytTiwWLhwIWbMmHHc8vHjx+Pee+9V188991w89NBDbbOFRGRqdfnHoT9UUXZyYbIpoLg68WpcHnu5VQcUycdK8MnKQ/hhYyrKq7RZ60I8nXDtyChcNjgc7k72/912Nmws3tr0FtZlrFPfdFvz/uwqJLVJAgh5zeTfXTm7UFN/0jgAQa5BpkJrCSQi3CNOfUb5iiJtREICif1/AaX15qzQ2QLhQ+qCiaA+gM0pjHwQEVlTYCGzbv/666+4++67GyyXZcYZuUtKSuDubt2FokRtGVAsSF6AmVtnmgIKyQOf3ms6roi7wqpPgDcezsVH/xzCwl1HTXNN9ArxwI2jo9UIhb1t80/sJAUm1C1UFb6vzliN8RHj22/D6ZRI6q2M0q1MW4mV6StVelPjGgl5DaVGQgIJqZOQ26ccSIi8w1ogISMTySuB+qlUTp5a0XXsOVoHJ6mdICKyYi0OLB5//HFVQ7F06VJVYyEf2OvWrcMff/yBmTNnqnX+/vtvjBkzpj22l8iqAoqFyQsxc9tMHCo4pJZ5OnqqOSikdayrvTb7s7Wp0Rvw966jmPVPEjal5JuWj4v1xw2jozE82veUTiTlZ2SyvK93f63qLJoKLHS2tvCYONF0ndqfjB5JatOKtBX4N+1fU4MCo0CXQIwIGWEqtm71/CyS4iQT0xlTnLJ2Nbzfp7sWSPQ8G4gYBtj+92gYEZG1aHFgIQXZCQkJePfddzF37lz1DVJcXByWL1+OESNGqHWMKVFE1HIy+/NfyX/hg60fIKkgydTy0jhC4Saz71qhssoa/LgxFR+vPITDOdrcAg62NriofwiuHxWNnoGtHyU1BRZHlqvXoXERrwQTrkOHtPp56MTkb4oc92pUIm0lNmZubFBwLXNGyIjEyJCRGBk6Uo00tWpEQlQUa5PTSSCxbyFQeqzuPjkGIoZrgYQEFH49+PIREZ3AKTXLPu2009SFiNo4oDj8F2ZumWmaKVvmnpieMF1NbGetAUV2UQW+XJ2ML9ccRl6pdoIp3ZymDovEtBGRCHDXJq9rCwMDBqoJ0HLLc1UHIekURe2vuLIYa4+uVYGEjEpklGQ0uF/SmSSIkMuQoCFtk/6Xn1ovxWkFUKMV+yuOHlqdhEpxOoOF10REbRlYFBYWwsPDw3T9ZIzrEVHzA4pFhxepEYoD+QfUMnd7d0ztNRVXxV9ltRPbHcgqwscrDmHu5jRUVmsF2eE+zrh+ZDQuHRQGF4e2n0TM3tYep4WeplLQJB2qcWBh0OtReVjrxOUQGQkdi3NPeVRiX94+/Jv+rwomNmdubtC9ycHGQaU2yWshwUQ3j26tH5XQ64H0TXUpTpk7Gt7vHVWX4hQ5gilORESnwK65k+JlZGQgICAAXl5eTX7Ayx8KWV5T07AjBxGdOKBYnLJYBRT78/bXBRQJU3FlwpUq/cnayOfI2kNSkJ2kZsc2kjaxUpB9Vq+gE7aLbStjwsaowELazt4x4I6G21ddjdzZn6nrgY89Cp2DQ7tuiyUprCzEmvQ1plGJrLK611dItybjqIR0b2qTiR0rS4CDS4F9Ekz8BZRkNUxxChsCxJ4N9DwH8I+VQpvWPycRkRVrVmCxZMkSU8cnKdomotYFFEtTluL9re+rb22FpN9IQHFVwlVWGVBU1+jx546j+GhFErYdKVDL5BzvzPhAFVAMjPRu/TfWzTQqdJSqrZBgL704HSFuIR3yvJZ4nO/J3aOCCAkmZKK6+q1gnWydMCR4CE4L0UYlIjwi2uaJC9JqayUWAEnLgfpdo2T0L+Z0LZDoMQFw9W2b5yQiouYHFvU7PLHbE9Gpfxu/JHWJahsrJ1xCOjtJupMEFdLxydoUV1Tju/Wp+HTlIaTll6lljnY2uGRgGK4bGYVo/46vK/Fy8kI//37YlLUJy1KX4Yr4Kzp8G7qq/PJ81arXOCqRU15vngcAUZ5R2qhEyEgMDBoIR1vH1j+ppDhlbKmrlzi6reH9XhFaICEjE5EjATuOMhERtZdTSlJesWKFmmk7KSkJP/zwA0JDQ/Hll18iKioKI0eObPutJOriAYWcoErK0+7c3aaAQgqypyVMs8qA4mhBOT5blYyv1x5GUbmWW+/r6oCpwyNVUbavWxuccLayOxQDi2aOSuTvwdyjc1W9xI5jO9QyIxc7FwwNHqqCCamXkCLsNiGTlkhL2B1zgV3zgcK0enfqgLDBdSlOAfFMcSIi6qyBxU8//YSpU6fiyiuvxKZNm1BRoQ0zFxUV4YUXXlDzWRCRFlBI29L3t7xvCijkRMsYUMg349Zmz9FCNaHdL1vTUFWjzWgX7eeq2sVOGhAKJ/vOMTeEBBavb3wd6zPXq45F1tqRqylFlUVYlb4K/xz5ByuOrEBeRV6D+2O8YlQ6mQQSAwIGqIL4NgsmZDRCgomdc4H8lLr75PXpPq4uxcnNv22ek4iI2jeweO6559REeNOmTcOcOXNMy2UOi2eeeaalD0dkkQGFTOYlAcXOnJ1qmRSiSkAhrWOtLaCQ/bHywDE1od2K/XXzAwzp5qMmtBsfFwCbdi7IbilJ2Yn0iFSzPMtJ9IRuE2DNUgpT1KibBBMyr0T9Dk4SLMsEdcZRiSDXoLZ98sxdWiAhAUWu1oZZkQkiZVQi8WKg+3jAvu3aDhMRUQcFFnv37sXo0aObbDObn183Cy6RNZKTrjc2vqEKVY0BhcySLbNlezt5w9oKsn/bloGZyw9iz9EitUzih3N6B+OGUdGq01NnJt2hvtj1hRp1srbAQiak25K1RbXcld8/uTD5uMBL9o+MTIQYQhASFAKbtmy9e+xAbTDxE5Ct1SMpdk7aiETiJKDHWYBDG8xnQURE5gssgoODceDAAXTr1q3B8pUrVyI6OrrttoyoC5HuTm9tekt9o2vseDMlXgsofJy0jmrWFFD8sjUd7yw5gEPHStQyFwdbTB4Urgqyw326xsmgpENJYCHpPjX6Gtja2Kp5K9wnaEGGpc1hIYXXMtImx7AUXhdVacGgsNPZqWJrCSbkYuzgpNfrkZXVsG3sKctLrktzOrq9brmtgzZJXa9J2giFo3XO60JEZJGBxU033YQ777wTn376qWr/mJ6ejtWrV+O+++7DE0880T5bSdRJSTvS97a8h18P/goDDLDV2eLiHhfj5r43w9/FuvK8mwoovF3sVTBx1bBIeLl0rW48/QL6qckJpYZg27Ft6B/QHzo7O7iNPA2WkqJ2MP+gGpGQYGJL9pYGhdfejt4YFTZKBRLDQ4a3z0SNBUeAnfO1YEKKsY1s7IDosVowETcRcO7co1tERHSKgcUDDzyAgoICjBs3DuXl5SotytHRUQUWt912W0sfjqhLyivPw0fbP8KcPXNU2oiYEDkBt/e/Hd08G47mWUNA8fMWCSj2Izmn1BRQSP3EtOHd4ObY9jNkdwR7G3tVN/DnoT+xNHWpCiy6usqaSqw/ut4UTKQV1++mBPT07qkCidFho9Hbr7capWlzRZlaJycZnUhd03DCum4jtZqJ+AsAF+sa6SMisgSn9Bf/+eefx6OPPopdu3apofCEhAS4ubFrClm+0qpSfLnrS3y28zMUVxWrZUODhuKugXch0S8R1hZQzN+SjncbBRQ3ju6u2sZ21YCivnHh41RgIbUG9wy8Bwa9HlXpGeo++5DgLpEOdazsmErnkmBCCtHLqrX5QoSDjYNqB2sMJoLdgttnI0pygN0/a8FE8koZL6m9QwdEDNdqJhIuBNwC2uf5iYioQ5zyX34XFxcMGjSobbeGqJOSUYm5++aquSiMk37F+8TjrgF3qTSRjpoVutOkPG3WRigO1wYUPq4OaoZsmYPC1QICCiPpciT1BUkFSUgtTEWoUyByZs1S9wU+9ih0Dg6dMsVJ2hurUYnUf7AjZ0eD+/2d/VUQIcGEBBUu9u1U81KWB+z+TUtzkhmw6826reaZkDSnXhcBHpzZnIjIUrT4DKCkpAQvvfQSFi9erIr2ZMSiPpk0j8hSSM75X8l/4Z3N7yClSOubH+YWhjsG3IGzup0FG0nfsKYuTzuP4cuNu3E417IDCiMPBw8MCByAdUfXYdmRZbgy5jJ0RjIKsTZjrSmYyCprWFCd6JuI0eFaMCEBcbsFwuWF2uzX0s3p4BKgNk1QCe5bG0z8D/CObJ/nJyIis2rxmcD111+P5cuXq0nypEOUNX1TS9ZldfpqvLnpTezK2aVuS3cnKcq+pMclbTfpVxcJKOZtTsO7Sw40CChuGh2tirItMaCoT07GJbCQdKjOFFgcLTlqagcr21dRo01WamxzPDx4OMaEay1h27ORgK6qVBuV2DkP2P83UG87ENALSPyfFlD4dm+3bSAios6hxWcEf/75J37//XecdppldEYhakwmtXtz45tYk7HGNAHY1YlXq8nt2i1tpBOqqhdQpNQGFF7Odrh5jNRQdLP4gKJ+29lXNryi5igpqiw0a4rT/vz9WJKyRF2Ms7kbhbiGqEBCAqFBQYPgaOvYfhtTUwUcWAzdtjnw37sANvXqNuDbQ6uZkGAiIK79toGIiDqdFp8ZeHt7w8eH3TrI8sjswpLytCB5gbptZ2OHy2Ivww29b4Cvsy+sKqDYlIZ3lu5Haq52wuirUp6iMCHaGZGhwW07GVonJ3M2yIRwhwoOYVXaavTrwOeW+TNkssXFKYtVMHGk+IjpPknD6+vf11QvEeMV074jyAYDkL4J2PqdlupUekxKr9XF4N0NOgkkJKAITAQ4kk1EZJVaHFg8++yzar6Kzz//XBVwE3V10jVn5taZ+GnfT6g2VEMHHSZGT8St/W5FmHsYrDmg8HOTlKfuuHJYBJzsbNpuMrQuOGohgcU/R5ajH1zb9bkkpUnqJSSYWJa6DLnluQ26OI0IGYHTI05XoxMdMvlifgqw7TstoMjZX7fc1R+GxEuQE3o6fHqNh862HVrTEhGR5QUW/fv3b/BNmMy8HRgYqGbftrdvmGu+adOmtt9KonZQXFmM2Ttnq/axxhacMm+BdHqK9Ym1qoBi7qYjeHfpgSYDChcH7WOicaMGazI2bCxm75it2rXeZhjf5kX7hZWFal4JGZVYmbayQUtYmZhORiTGR4xXQUWHpOOVF2gT10lAcfjfuuV2ztqEdX0vB6LHwaCzQbUEmxyhICKi5gYWF110EXcWWQyZJOy7vd9h1rZZyK/IV8tkMrC7B96NwUGDYW0BhcyUfSSvLqCQGoorh0bC2YHfQBtJypGXoxfyy/KQ2T8cPbx6tHoOi8ySTDXxngQTMmmdjJYZBboEqlEJuQwMHKgm62t3qm5iEbB1jtbZyVSErQOiRgF9LgfizwecPOp+xoqDTSIiOsXA4sknn2zOakSdmuSr/37od7y3+T2kl6SrZd08uuHOAXeqb4OtpcNZZXXdCAUDiuaRGailu9KvSb9iSXghBgwed0r7Pik/CUtSteLr7ce2N7hPaiRkQj45FhN8EzrmeJS6ibRNwLY5tXUT2hwtin880PcyoPelgKf1pAQSEdGps462LmTVpJvOirQVqnXs/jwtRzzAOQC39LsFF8ZcqIq0rSWg+EkCiiUHkJZvHKFwxM1jojlC0QxS0yCBhbR3vW/wfc2eB0UCCGMnp+TCZNN9UssjIyHGkYlIjw6c2yHvMLD9+ybqJgKA3pdoqU5BfZjiRERELWIdZ1RktbZkbcEbG9/ApqxNpnz16xKvwxXxV6he/9YcUPzf2O64YkgEU56a6bQQmYXbFgVph5B0YAOiug9sclShqqZKzSshgYSkOmWXZZvuk5Qmme1aRiWkINzP2Q8dpiwf2PXzf9ZNwJZ/FoiI6NTwLwhZJEk5eWvTWyrtxNhN58r4K3Fd7+vg6egJawkoftx4BO8trQso/N1lhIIBxalwc3DDMN9BiP9jFVL2vopur30BnYODqRHAyvSVWHJ4iRodK64qNv2cq70rRoeOxumRp2NkyEj1OB3mVOomiIiIThEDC7IoUhD7/tb3Mf/AfJWGIt17Lux+oUp7CnINgjXQ6w34ZWs6Xlm4t0FA8X8SUAyNgJM9i7JP1ajwUTiGVcgozkBO2TEsS/5XjUxIe9gqfZVpPX9nf1UvISlO0hDAwVYLQDpEs+omJgOeoR23TUREZBUYWJDFdHr6YtcXqtOTsVXn6eGn444Bd6C7V3dYi3WHcvHc77uw7UiBus2Aom3JZHRzMUOlN53141moqvcJKo0AjPUS0mWsrVvSNqtuYtv3WkCRc6BR3cSlWkDBugkiIupMgUVNTQ0+++wzLF68WE2W1bi3/ZIlWuoJUUeR/v8z1s1ASlGKut3Pvx/uHXQv+gV05BzJ5nXoWAle+nM3Fu7MVLddHWxxy7gYXDcyiiMUbSjELRTejt7Iq8iDAQb09uujBRPhpyPaKxodTtVNzNeKsFNWNaybiD9PS3WKHsu6CSIi6pyBxZ133qkCi4kTJyIxMdFqWnRS55NSmIIZ62eowMKYfiJzUZwXfZ7VHJd5JZV4a/F+fLXmMKr1BtjogClDInDXGT3VaAW1veEhw5FTnoPJk55BkHd4x+9ifQ1wYDGw5Stg74JGdROjtSJsqZtwdO/4bSMiIqvW4sBizpw5+P7773Huuee2zxYR/YfSqlJ8tP0jfL7zc5XXLu1ip8ZPxU19b1KFstagoroGX6w6jHeW7EdhuTax2rhYfzxybjx6BPKEsj3JMSaXANdAdKi8ZGDzV8CWb4DCtLrlAQlAH+N8E6ybICKiLhRYODg4ICYmpn22hug/5qNYkLwAr254FVmlWaYWoA8OeRBRnlFWsw/+2H4UMxbsQUpuqVoWF+SOxyYmYGSPDmxdSh2jqhzY8xuw6Qvg0PK65c7eWppTvyuAoN6cb4KIiLpmYHHvvffirbfewrvvvms16SZkfntz9+KldS9hQ+YGdTvULRQPDH5Add6xluNwU0oenv99NzYezlO3A9wdcd+EWFw8MAy2kgNF7U5nYwPX004zXW83R3dowYTMOVGeb3x2rV5iwDRt3gk7proREVEXDyxWrlyJpUuX4s8//0SvXr1gb2/f4P65c+e25faRlSuoKMB7W97Dd3u/U+1jnWyd1FwUV/e6Gk52TrAGqbmlaoTit20Z6razvS1uHB2tLq6ObOzWkXR2dvA4a0L7PHh5gdYeVgKK9M11yz3CgP5XAv2uBLw7cHZuIiKiFmrxWYmXlxf+97//tfTHiFqkRl+DeQfm4e1Nb6sOPOLMyDNx36D7EOIWYhV7s6CsCu8vPYDZ/yajskYPGZi5ZEAY7p0QiyBP6wiqLJ7MOZGyWgsmds4Halslw8YeiDtXG52Q2bBtOPcIERFZYGAxe/bs9tkSolpbs7fihbUvYFfOLnW7u2d3PDT0IQwLHmYV+6iqRo+v1xxW3Z7ySrVJ106L8VWF2b1CrGPW8M5c41KTr6Um2Xp5nXoaXlEmsPVbYPOXDeec8I8D+k/VOju5smaGiIi6FuZRUKdxrOwY3tj4Bn45+Iu67WbvpmbMvjzuctjLN7hWcNL6965MvPTnHiQdK1HLYgLc8Mi5cRgXG2A1tSSdmaGqCtlvvKmuBz72KHQOLZhRu6YaOLBIG53YtwAw1GjLpZNZ4iRgwHQgbBALsYmIyLoCix9//FG1nE1JSUFlZWWD+zZt2nRKG/Liiy/ikUceUfNkvPmm9oebrIO0jP1m9zf4YOsHKKnSTqgvirkIdw64E37O1vGt7fYjBXj+j11Yk5Srbvu6OuDuM3vi8sHhsLPt4BmcqW3lJtW1iS3S6mSUsCHAgKlAr/9xzgkiIrLOwOLtt9/Go48+iunTp+Pnn3/GNddcg4MHD2L9+vW49dZbT2kj5GdnzZqFPn36nNLPU9e1On216vaUVJCkbif6JuLhoQ+jj791HAvp+WV4deFezN2szUvgYGeD60dG4f/Gdoe7k+WP0rS7khxg0+fA0e1AQDwQ0h8I7ge4+bfv81aVAbt/1UYnklfULXfxBfpO0dKdAuLadxuIiIg6WIsDi/fff18FAVOmTMHnn3+OBx54ANHR0XjiiSeQm6t929oSxcXFuPLKK/HRRx/hueeea/HPU9eUVpyGV9e/ikUpi9Rtb0dv3DXwLjVSYaOz/G/oiyuqMXPZQXy0IgkV1Xq17KJ+Ibj/7DiEejmbe/O6voytwNpZwPYf6mam3omGnZZC+mmBhvwb3B9w9W2b5930JbD9e63Lk6IDYsZrwUTsuYBdC9KniIiILDmwkPSnESNGqOvOzs4oKipS16dOnYphw4ap+S1aQkY5Jk6ciDPOOOM/A4uKigp1MSosLFT/6vV6daG2I/tTcv7ber+WV5fjs52f4dOdn6KipgK2OltM7jkZt/S9BR6OHoABqq2spaqu0eOHjUfwxqL9OFaspREO7uat6ij6hnmp2531WG6vY6LN1FQBe3+Hbu2H0KWuMS02BPeDIXYidDn7gYwtwLH90BUeAeQik88Z1/OKUAGGIbhv3ciGs/aaNN4HxuuQiwQQ23+AbstX0ElgYXw8z3AY+l0F9JsCeIbXfxBYik5/TFCH4zFBPCYsT0s+41scWAQFBSEnJweRkZHqsmbNGvTt2xeHDh0y/cFtrjlz5qiaDEmFam4dxtNPP33c8uzsbJSXl7fouem/D6KCggL1mtq0wURg8jj/Zv2LD/d+iKNlR9WyPt59cFv8bYhyj0J5QTnkP0u2OrkA76w4gqQc7fcM83LEbSNDMaa7dBeqRFaWNpu4tRwTbUVXlguX3d/DZec3sC3JVMsMNnYojz4bpYlXoSqwX4OCaF1lMeyO7YZ99g7Txa4gGbr8FCA/BbrdP5vWrfaIQJV/L1T5J6LaPxGVHj1RVl6mAuCCLb/D5eB8OCUthK52VMRgY4/yqDNRFncJKsOGyyx6gNzVyV9bSzsmyHx4TBCPCctjHERol8Di9NNPx6+//ooBAwbguuuuw913362KuTds2IBJkyY1+3FSU1NVofZff/0FJ6fm9eR/+OGHcc899zQYsQgPD4e/vz88PDxa+qvQf/xxkC5Esm9be8JwqOAQZqyfgdUZq9XtQJdA3DPwHpwVeZZVdDranVGIlxbsxYr9x9RtL2d73H56DK4cGqFqKqzxmGgTGVuhWzdLTSpnOrF39QcGXgPDwKvh6B6MpuemDgDCogFMNC3Ry6hDxjYgYzN0MqqRvgW6vEOwK0xRF+eDf2rr1QCZB2X0wQDv32eZppcwBMTD0H8a0HsyHF18TvC8lqfTHRNkdjwmiMeE5WnuebrQGVo4zGBMO7Kz02IS6Q4ls3HHxMTg5ptvhkMz2y/Onz9fTbRna1s38VNNTY36IyV/oCTlqf59TZHAwtPTU31jxsCibclrLN+gBwQEnPIJQ3FlMWZunYmvd3+NakO1ahkrM2Zf3/t6uNi7wNJlFZbjtb/24YeNqdAbAHtbHaYP74bbT+8BTxd7qzwm2iTdafcvWv1EvXQnhAwAht4M9LoIsGuj0/rSXK1mQgUam9XFkJuCwlStBsajux10fS8GJKAIHWCVbWI7xTFBnQqPCeIxYXlacr7d4hEL+eNR/w/I5MmT1aWlxo8fj+3btzdYJh2m4uLi8OCDD/5nUEGdl9RI/Jb0m5qTQuamEGPCxuCBwQ8gwiMClq6ssgaz/knCh/8cRGmlNlfBub2D8ODZcYj0dTX35nVNxdnAxs+ADZ/UtWyVuU0kkJCAQuZ/aGsuPkD3cdqllq40F54SZFSWaAXZDnw9iYiIWjWPxYoVK/Dhhx+qNrOSBhUaGoovv/wSUVFRGDlyZLMew93dHYmJiQ2Wubq6wtfX97jl1HXIbNkya7bMni0i3CPw4JAHMTpsNKzBol2ZePKXnUjLL1O3+0d44bGJ8RgY6WPuTeua5CReRid2/AjU1M6Z4xoADLoWGHQN4B7UsdsjwYYEFERERNT6wOKnn35SHaCkRezmzZtNXZqksOOFF17AH3/80dKHJAuQX56Ptze/jR/3/QgDDHC2c8aNfW7EtIRpcLC1/PaaqbmlePrXnVi0WyvSDfF0wkPnxuP8PsFWUUfS5ulOu34GpH4idW3d8tBBwNCbgARJdzLPMaU6IJWUqus2ri58bYmIiFoTWEhL2JkzZ2LatGmqq5ORtKB95pln0BrLli1r1c9Tx6vR1+Cn/T+poKKgQuvbf07UObh34L0IdA20+JekoroGH684hHeW7Ed5lR52NjpcPyoad4yPgYvDKQ0IWq/irNp0p08bpjslTgKG3ASEDTT3FsJQVYWsl19W1wMfexS6ZtaUERERWYMWn/ns3bsXo0cfn9YixRz5+flttV3UBUi6k6Q9SfqT6OHdA48MeQSDgtoh370T+vfAMTz+8w4kZZeo28OiffDshYnoEehu7k3rWtI2AWs/BHbOrUt3cgsEBl0HDLwacLf8AJWIiMgqA4vg4GAcOHAA3bp1a7BcOkPJDNxk+XLKcvDmpjcx/8B8ddvN3g239b8Nl8VeBjsby/+WPrOwHM/9vhu/bk1Xt/3cHFUdxYX9Qpga01zVlbXdnT4EjqyrWx42WBudSLiQM1QTERF1MS0+C7zpppvU/BOffvqpOolKT0/H6tWrcd999+GJJ55on62kTqFaX43v9n6H9za/h6IqbbKUC7tfiLsG3gU/Zz9YOpk1+4vVh/H63/tQXFENGx0wdVgk7pkQC0/nrtc+1mzpThtma+lOxUfrpTtdDAy9EQg1f7oTERERdVBg8cADD6g+tuPGjVOzXUtalKOjowosbrvttlPcDOrsNmZuVGlP+/L2qdvxPvF4ZOgj6BfQD9Zg4+FcPDZ/p5rsTvQN98LzFyUiMdTT3JvWNaRt1EYndswF9FXaMrcgYHBtupNbgLm3kIiIiFrplPJWnn/+eTz66KPYtWuXmgwnISEBbm5urd0W6oSyS7Px5uY31bwUwsPBA3cOuBMX97gYtsZphy1YbkklZvy5B99tSFW3ZWRC5qO4fHA4bGTIgv67u9OaD4C0DXXLw4Zo3Z3iL2C6ExERkQU55YR4FxcXDBpkHUW61qhKX4Ufk3/EV0lfoaSqBDrocHHPi3FH/zvg7eQNS6fXG1QwMWPBHuSXat+wTx4UpoIKX7c2mtnZUpXlA5s+10YoCtO0ZdJyWNKdhki60wBzbyERERGZM7C49tprm7We1F5Q17YuYx2eX/s8kgqS1O3efr1V2lOin3VMXLgjrQCPzd+BLalal7O4IHc8d1EiBnXjJHcnlZcMrJkJbP4SqCyum8xuyA3AwGsAN390dTobGzj362e6TkRERKcQWHz22WeIjIxE//791SRRZHmOlhzFqxtexcLkheq2p72nKsye1HMSbHSWfxJVWF6F1//ahy9WJ0NvAFwdbFVh9vThkbCztfzf/5SlrgNWvwvs/hUw6LVlAb2A4bcCvS8B7CxnhEdnZwevSf8z92YQERF17cDi5ptvVhPiJSUlqdGLq666Cj4+/AbXElTVVOHzXZ9j1rZZKKsuU0HEpT0vxeSQyYgJi7H4oEIC5V+2pqsWstlF2kzy5/cNUS1kAz2czL15nVNNNbDnNy2gOLK+bnn38cCI24DocQBnHCciIrIqzQ4s3n//fbzxxhuYO3euSnd6+OGHMXHiRFx33XWYMGEC+/d3UavSVuHFdS8iuTBZ3e4f0F+lPfX06omsrCxYugNZRXh8/k6sTspRt6P9XPHMhYkY2cPy2+eeCp2kOK2dC6z9AMhPqauf6DMZGHYrEJgASw9CZfZtobO35+ceERHRqRZvS1vZKVOmqMvhw4dVetQtt9yCqqoq1SGKnaG6jrTiNLyy/hUsTlmsbvs6+eLeQffivOjz1MmSdPuyZKWV1XhnyQF8vCIJVTUGONrZ4I7xPXD9qCg42ll+t6sWKzgC3ZoP4L/xM9gY6ydcfIHB12sXK2kXK0FF5nPPq+uBjz0KnYODuTeJiIio63eFkpNPucg3eJZ+EmpJKmoqMHvHbHy8/WN13VZniylxU3BLv1vg7uAOSyfH61+7MvHMr7uQll+mlo2PC8BTF/RCuI+LuTev80nfDKx6F9g5DzpDDaTBrsG3B3RSP9H3csDe2dxbSERERF0xsKioqDClQq1cuRLnnXce3n33XZx99tmwYYeUTm956nK8tO4lHCk+om4PChyk0p56ePeANUjJKcVTv+7Ekj1aileol7MKKM5MCDT3pnUu8kXBvgVa/cThf02LDd1GIT/+KngOugQ621P+ToKIiIgsVLPPDiTlSYq3IyIicM0116jrvr6+7bt11CZSC1MxY/0MLD+yXN0OcA7AfYPvw9ndzraKHPGK6hp8uDwJ7y09gIpqPextdbhxdDRuG9cDzg5MezKpLAG2fKNNaJd7UFtmYwckXgIMvwWGwN6okLobCy/mJyIionYOLGbOnKmCiqioKCxfvlxdmiIjGtQ5SIenT7Z/olKfKvWVsNPZYWqvqbi5z81wsbeOtJ9/9mXjyV924tCxEnV7RHdfVZwdE8CZ4k2KjgLrZgEbPgXK8rRlTp7AoGu1Ce08QrRlTHkkIiKitggspk2bZhXfbltKHcGSlCV4ef3LSC9JV8uGBQ/Dw0MfRrRnNKzB0YJyPPv7Lvy+LUPd9nd3xOPnJeD8PsE8jk07aTuw+n1g+w+AXut0BO8oYNgtQL8rAEcGX0RERNROE+RR55dckKzax65KX6VuB7sG44HBD2B8xHirOKGuqtHj81XJeOPvfSiprIGNDpg+ohvuPrMnPJzszb155iejDgcXa/UTScvqlkcM1ya0iz0XsGF6GBEREbUcKzAtRGlVqZrgTia6q9ZXw97GHlf3uho39LkBznbW0blnz9FC3Pv9VuxML1S3B0R44dmLEtErxNPcm2Z+VeXAtu+A1e8Bx/Zqy3S2QMKFwPDbgLCB5t7CLkGCc6cEba4OawjUiYiIWoKBRRenN+jxx6E/8ObGN5FZmqmWjQodhYeGPIQIjwhYg+oaPT78JwlvLtqn5qTwcrHHw+fE4dKB4bCRIQtrVpwNbPgEWPcRUHpMWyZthQdOB4beBHhZxzHSVmRSPO/LLzP3ZhAREXVKDCy6sC1ZW1QdxfZj29XtULdQFVCMCRtjNd+myszZ9/6wDVtT89XtM+ID8cKkRAS4O8GqZe/T0p22zgFqKrRlnuHA0JuBAdMAJw9zbyERERFZGAYWXVB6cTre2PgGFiQvULdd7FxUytNV8VfByc46Tqhr9AZ8uvIQXvlrLyqr9XB3ssNT5/fCpAGhVhNUHcdgAA6vAla9A+z7s255yABgxG1A/IUA558gIiKidsLAogspqSpRM2Z/sfML1T5WBx0m9ZiE2/rfBj9nP1iL5GMluP/HrVifrLVGHd3THzMu7o1gT+uoJTlOTTWw51fg37eB9E21C3VA3EStfiJimBQEmHkjLYO+shKZzz2vrgc+9ihsHBzMvUlERESdBgOLLqBGX4P5B+bjnc3vIKc8Ry0bEjREdXuK9YmFtdDrDfhyzWG89OcelFXVwNXBFo+dl4DLB4db5yhFRTGw5WutIDv/sLZMRqykVeywWwG/GHNvIREREVkRBhad3NqMtXhl/SvYm6d18olwj8C9g+7FuPBxVnUynZpbigd/2oZVB7XAani0L16+pA/Cfaxjor8GijKBdR8C6z8ByrXaEjj7aJPZDb4ecPM39xYSERGRFWJg0Ynno3ht42tYlqrNNeDu4K5mzJ4SNwX2tvZWNdnfd+tT8exvu9S8FM72tnjonDhMHRZpfR2fsvZoBdnSNramUlvmE62lO/WdAjhYYZBFREREnQYDi06moKIAM7fOxJw9c1BtqIatzhaTYyfjlr63wMvJC9ZEZs9+aO42LNubrW4PivTGq5f2RTc/V1hVQXbySq0ge//CuuXhQ4ERt3NCOyIiIuo0GFh0ElX6Kny/93t8sPUDFVwY56O4b9B9iPaKhjWRUYp5m9Pw1C87UVheDQc7G9w/IRbXjoyCrbWMUkhB9u6ftYAifXPtQh0Qfx4w/HYgYqiZN5CIiIioIQYWneAkekXaCry64VUcKjiklsV4xeD+QfdjROgIWJusonI8Om8H/t6lTfbXN8wTr03ui5gAd1hNQfbmL4HV7wMFKfUKsq8Eht8K+HY39xYSERERNYmBhRntz9uvCrNXZ6xWt70dvVXrWGkha2djfS/Nb9vS8fj8HcgrrYK9rQ53ndETN42Ohp2tDSxe0VFg7YfaLNnl2ogVXPxqC7KvA1ytp51wZyYNExx79DBdJyIiojrWd/baCeSU5eC9Le/hp/0/QW/Qw97GXk1uJ5PcSZG2tcktqcTjP+/A79sy1O2EYA81ShEfbAWzQ2ftBlbVFmTrq7RlPt21Ce2kINveSufm6KR09vbwmXqVuTeDiIioU2Jg0YEqayrx9e6vMWvbLBRXFatlZ0aeibsH3I1wj3BYo792HsUj87bjWHGlqp+4dVwMbhsXo+oqLLsge0VtQfZfdcvDhwGn3QH0PAewseDfn4iIiCwSA4sOqqNYlLIIr294HUeKj6hl8T7xaoK7QUGDYI0KSqvw9K87MXdzmrrdM9ANr13aD73DPGHRBdm75gOr3gYyttYryD5f6/AUPsTMG0hERER06hhYtLOdOTtVHcXGzI3qtr+zP+4ccCfO734+bHTW+a30sr1ZarK7zMIKSJOnG0d3x11n9ICTvS0sUkURsOlLYI0UZKdqy+ycgf5XAcP+jwXZXYi+shJZM15W1wMefAA2Dg7m3iQiIqJOg4FFO8kqzcJbm97Crwd/hQEGONk6YXqv6bg28Vq42FvnRGZF5VV4/vfdmLNeO7mO9nPFK5f2xcBIb1ikwgxg7Uxgw2ygol5B9tCbgEFSkO1r7i2kU2Coqq2FISIiogYYWLSxsuoyfLbzM8zeMVtdFxOjJ+KuAXchyDUI1mrVgWO4/8dtSMsvgzTTuWZEFO4/KxbODrYWWpD9DrDt+7qCbN8eWkF2n8tYkE1EREQWiYFFG5HuTr8n/a5GKTJLtTkY+vn3U3UUvf17w1qVVlbjpT/34IvVh9XtcB9nvHpJXwyN9rW8guyU1cC/bwH7FtQtjxih1U/0PJsF2URERGTRGFi0gS1ZWzBj3QzsyNmhboe4huDugXfjrG5nWXWv+/XJubjvh604nFOqbl85NAKPnBsPV0cLOuz0emDvH1pAcWRdw4Ls0+4EwqyzOJ+IiIisjwWd4ZnHYysfw88Hf1bXXexc1FwUMieFk8yWbKXKq2rw6sK9+OTfQ+qL/BBPJ8y4pA9G9fCHxaiu0Oae+PdtIGe/tszWEeg3BRhxBwuyiYiIyOowsGilQNdA6KBTs2XLrNl+ztY9Q/LmlDw1SnEwu0TdnjwoDI+dlwAPJ3tYBJkVe+NnwJoPgCJtQj84emqzYw+9GXAPNPcWEhEREZkFA4tWui7xOkyInIBYn1hYMxmleHvxfsxcfhB6A+Dv7ogZF/fG6XEWcqJddFQLJjZ8ClQUasvcQ4DhtwADpgNOVjBLOKnURodu3dSesOY0RyIioqYwsGglaR1r7UGF1FLIvBRJtaMUF/YLwdMX9IKXiwX0+D+2X5vQbuscoKZSW+YXq82Q3XsyYGcBvyM1m87eHr7XXsM9RkRE1AQGFnTKiiuq8fKCuo5PMkrx7IWJODvRAtrqpq4H/n0T2PO7tHzSloUP0wqy2eGJiIiI6DgMLOiULN2bhUfnbkd6Qbm6fdmgcNXxydOlC9dSSKX5/r+1gOLwv3XLY8/VAoqIYebcOiIiIqJOjYEFtUhuSSWe/W0X5m1OM81L8eL/+mBkjy5ctF5TBez4SWsZm7VLW2ZjD/SZrHV4Cogz9xZSJ6GvrET262+o6/733A0bB6bCERERGTGwoGYxGAz4bVsGnvplJ3JKKmGjA649LQr3TOgJF4cuehhVFAObvgBWvwcUHtGWObgBA68Ght0CeIaaewupE9KXavOyEBERUUNd9IyQOlJGQRken78Di3Znqds9A90w4+I+6B/h3TVfiOJsYN2HwLqPgPJ8bZlrADDsZmDQdYCzl7m3kIiIiKjLYWBBJ6Q3GPDN2hTMWLAXRRXVsLfV4bZxPfB/Y7vDwc6m6+253CRg1bvAlq+Baq02BD7RWrpT3ymAvfVOakhERETUWgwsqEmHjpXgvh/3YXNasbrdP8JLjVL0DHTvenssfYtWkL3rZ8Cg15aFDABG3gXEnQfY2Jp7C4mIiIi6PAYW1EB1jR4frzyEN/7eh4pqPZztbXH/WbGYPqIbbKWwoit1eEpaqhVkJy2rWx5zhtbhqdsomeHMnFtIREREZFEYWJDJzvQCNdHdjjRtZunBEe54dfIARPq5dZ29VFMN7JqvBRRHt2nLdLZA4sXapHZBvc29hUREREQWiYEFobyqBu8s2Y+Zy5NQozfAw8kOj02Mx6gwewT6uHSNPVReCGz+Clj7AZCfoi2zdwEGTNM6PHlHmnsLyQLodDrYh4SYrhMREVEdBhZWbn1yrhqlSMouUbfP7R2Epy7oBT9XB2RlaV2gOjUJItZ+qLWNrdBGWuDsAwy9CRh8A+Dqa+4tJAuis7eH3803mXsziIiIOiUGFlaquKIaLy/Ygy9WH1a3/d0d8eyFvXB2YrC6rdfXFjl3Vkc2AqvfrS3IrtGW+fYAht8K9LkMcOgiIy1EREREFoKBhRVaujcLj87djvQCreXqZYPC8ci58fB0sTf3pp2cvgbY87s2oV3qmrrlUWOA4bdphdk2XbANLhEREZEFMGtg8eKLL2Lu3LnYs2cPnJ2dMWLECMyYMQOxsbHm3CyLlVtSiWd/24V5m9PU7XAfZ7w0qQ9Oi/FDp1ZRBGz+GljzPpCvjbDAxh7ofSkw/BYWZFOHMVRWIvvd99R1/9tuhc7BgXufiIioMwQWy5cvx6233orBgwejuroajz76KCZMmIBdu3bB1dXVnJtmUQwGA37dloGnf9mJnJJKSNfYa0+Lwj0TesLFoRMPWuWnajNkb5T6iQJtmbO3Njv2kBsA9yBzbyFZGYM0HsvPN11n+TYREVEds55VLliwoMHt2bNnIyAgABs3bsTo0aPNtl2WJKOgDI/P34FFu7VC7NhAd8y4pA/6hXuh00qT+on3gJ3z69VPxGjdnWSGbNZPEBEREXU6nerr6oIC7VtpHx+fJu+vqKhQF6PCwkJToXGnLzbuYHq9AXPWp+KlBXtVoba9rQ63ju2Om8d0h4OdzX/uL7lfRjo6bL9K/cS+P6Fb/R509eonDN1GwSABRY8JgK62foKvtVl0+DHRifeB8bq1H4s8JojHBPFzwvLpW/C3rtMEFvLH+p577sHIkSORmJh4wpqMp59++rjl2dnZKC/XCpEJSMkrx4uLDmNzWrHaHYlBrnjkzEhE+zojP/dYsw8iCfTkdbFpx4JoXVUJnPfMhcv2z2FXmKqWGWzsUB4zESV9rka1X4K2YnbztpvaT0cdE529xqKsvExdz87KsvoaCx4T1BiPCeIxYXmKioq6XmBx2223Ydu2bVi5cuUJ13n44YdV8FF/xCI8PBz+/v7w8PCAtauu0eOTf5Px5qL9qKjWw9neFvdN6IlpwyNhK4UVLfzjIBOAyb5tl5PIwjTo1s0CNn0OXbk2UmVw8gIGXgPD4Ovh6BECx7Z/VmqFdj8mugB9ZSWynJzVdf+AANhYefE2jwniMUH8nLB8Tk5OXSuwuP322/HLL7/gn3/+QVhY2AnXc3R0VJfG5CTHWk90jHamF6iJ7nakaelho3r44YX/9UZ4K2bOlpPINt+36Ztr6yfmAfpqbZlPd9XdSafqJ1xZENuJtcsx0ZXY2Jhm3Lbq/VCP1R8TxGOC+Dlh4Wxa8LfOrIGFpFRIUDFv3jwsW7YMUVFR5tycLimrqBxvL96Pb9elokZvgIeTHR4/LwGXDAwznQCZneTm7VugBRSH641IdRulTWjX4yzOP0Fdgryj7Pz9TdeJiIiokwQW0mr2m2++wc8//wx3d3ccPXpULff09FTzWtCJSUH2rH+S8PGKJJRWap2Tzu0dhKcu6IUA9+YPWbWryhJgyzfa/BO5SdoyGzsg8WKtw1NIP3NvIVGLyLwV/rffxr1GRETU2QKLDz74QP07duzY49rOXn311Wbaqs6tqkaPb9elqFGKY8WVapm0jn34nDgMjfZFp1CYDkj9xIbZQLnW8x9OnsCga4EhNwIeIebeQiIiIiJqY2ZPhaLm76vft2fg1YV7kZxTqpZF+bnigbNicXZiUOdIe0rfoo1O7Piprn7CO0pLd5L6CUc3c28hEREREbWTTlG8TSe3+mAOXvpzN7Ye0bon+bk54s4zeuDyweGwtzVz8WhNFbD7F2DtLKDe/BOIPE0LKHqeDdjYmnMLidq03eyxD2ep63433Wj17WaJiIjqY2DRie05WogZf+7B0r3Z6raLgy1uHB2NG0ZFw9XRzC9dcTaw8TNgwydAUUZd/UTCRVpAETrAvNtH1A5kjLU6O9t0vROMExIREXUaDCw6ofT8Mrz+9z78tOkIJFvMzkaHKUMicMf4HvB3N/PsDmmbtPoJSXeq0Wo84BoADLpGzUEBj2Dzbh8RERERmQUDi06koLQK7y87gNmrklFZrU2fPrF3MO47K1bVU5hNdWVtutOHwJF1dctDBwJDbgJ6XQTYcTo7IiIiImvGwKITKK+qwRerk/He0oMoKKtSy4ZG+eDhc+NVxydzsSnNBpZ/Bmz8FCjOrF1oDyRO0gKKsIFm2zYiIiIi6lwYWJiRTGg3f3OaSntKyy9Ty3oGuuGhc+IwLjbAfJ2ejmyEbu0H8N85Hzq9FujALRAYdB0w8GrAPdA820VEREREnRYDCzO1jl22L1sVZu85WqSWBXs64e4ze+LiAWGwtTFDQFFdAeycD6z7EEjbaCpKNYQNhm7ozUD8BYCdQ8dvFxERERF1CQwsOti2I/l48Y89WJ2Uo267O9nhlrExuOa0bnCyN0Nb1qKjwIZPtcnsSrK0ZbYOMPSahJyYS+CTOB46GzO3tCXqJCTgtvXS0hPZEYqIiKghBhYd5HBOCV5ZuBe/bdNaszrY2mD6iEgVVHi7dvBIgLSaOrJeK8beNb9uMjv3YFO6k8HFF9VZtYEGESk6BwcE3HM39wYREVETGFi0s5ziCryz5AC+XnsYVTUGSNnE//qF4p4JPRHm7YIOT3faMRdYOxPI2FK3PHwYMPRGLd3J1l5bpte6UhERERERNQcDi3ZSWlmNj1ccwqx/klBcoY0IjO7pj4fOjkNCiAc6VGE6sP4TbUK70mPaMltHoPelWkAR3Ldjt4eIiIiILA4DizZWXaPHdxtS8eai/cguqlDLEkM98PA58Tgtxg8dmu6UulYbndj9a126k0coMPg6YMB0wLUDt4fIAhiqqpDzyafquu9110JnXzvCR0RERAws2rLT08KdmXh54R4kZZeoZeE+zrj/rDic1zsYNh3V6amqHNjxo1Y/cXRb3fLI04AhNwJx5wG2jCeJTvV9XpWebrrOAm4iIqI6PMNsAxuSc/Hin3uw8XCeuu3j6oDbT4/BlUMj4WDXQR2VCo7UpTuV5WrL7Jxq051uAoJ6d8x2EBEREZFVYmDRSnfN2Yz5W7RvMJ3sbXD9yGjcNCYa7k4dkCJRVQbs+R3Y+i1wcAlgqC249gyvS3dy8Wn/7SAiIiIiq8fAopUifV3VhHaTB4XjrjN6INDDqX0PKunWlLJaCyZ2/QxUFNbd122UNjrR8xymOxERERFRh2Jg0Uo3jI7G+X2DERPgjnaVmwRsnaNd8g/XLfeMAPperl18u7fvNhARERERnQADi1Zyc7Rrv6CiLF+bwG7Lt0DqmrrlDu5ArwuBvlOAiBEAZ8YmIiIiIjNjYNHZ1FRr9RKS6iT1EzVay1robIDosUDfK4C4iYBDB0+uR0SKjQvfe0RERE1hYNFZHN2hBRPbvgdKsuqW+8cD/aZo3Z08Qsy5hURWz8bBAYEPPWj1+4GIiKgpDCzMqThLCySkbiJze91yF18tkJBUJ5kVW8du+URERETUuTGw6Ggygd3eP7TRiQOLAUONttzWAeh5thZM9DgTsOWMvkRERETUdTCw6AgGA5C6VgsmdswDKgrq7gsdpKU69ZrEOSeIOjlDVRVyv/xKXfeZehV09vwCgIiIyIiBRXvKSwa2fqcFFHmH6pZ7hAF9L9NGJ/x6tOsmEFHbMRgMqExONl1nkiIREVEdBhZtrbxQaxErdROH/61bbu8KJFyojU5EjmSLWCIiIiKyKAws2oK+Bkhaqs03sec3oLq89g4dED1GG5mIPx9wcG2TpyMiIiIi6mwYWLTW8leA9R8DxUfrlvn11IKJPpMBz7BWPwURERERUWfHwKK1Co9oQYWzN5B4iZbqFDKALWKJiIiIyKowsGitoTcDMWcAPc4C7Bza5EUhIiIiIupqGFi0VkC8diEiq8AWs0RERE1jYEFE1Ew2Dg4Ievwx7i8iIqIm2DS1kIiIiIiIqCUYWBARERERUasxFYqIqJkMVVXIm/Oduu59+WWstyAiIqqHgQURUTMZDAZU7N9vuq7jniMiIjJhKhQREREREbUaAwsiIiIiImo1BhZERERERNRqDCyIiIiIiKjVGFgQEREREZF1d4WSriyisLDQ3JticfR6PYqKiuDk5AQbG8afxGNCvS8qK1FUUaGuOxcWqpm4rRk/J4jHBPFzwvIV1p5nG8+7T0ZnaM5andSRI0cQHh5u7s0gIiIiIrJoqampCAsLs9zAQr4tS09Ph7u7O3Q6dpRv6+hUgjY5iDw8PNr0salr4jFBPCaInxPEvx3Wx2AwqCyWkJCQ/8xi6dKpUPLL/VfkRK0jQQUDC+IxQfycIP7tIJ5PWC9PT89mrcfkeSIiIiIiajUGFkRERERE1GoMLKhJjo6OePLJJ9W/RDwmiJ8T1Bz820E8Jqxbly7eJiIiIiKizoEjFkRERERE1GoMLIiIiIiIqNUYWBARERERUasxsCAiIiIiolZjYGHFXnzxRQwePFjNXB4QEICLLroIe/fubbCO1PY/9dRTarZFZ2dnjB07Fjt37jTbNlPHHyMyq/1dd91lWsZjwvqkpaXhqquugq+vL1xcXNCvXz9s3LjRdD+PCetSXV2Nxx57DFFRUervQnR0NJ555hno9XrTOjwmLNs///yD888/X50byN+I+fPnN7i/Oa9/RUUFbr/9dvj5+cHV1RUXXHABjhw50sG/CbU1BhZWbPny5bj11luxZs0a/P333+qPxYQJE1BSUmJa5+WXX8brr7+Od999F+vXr0dQUBDOPPNMNbU7WTZ5vWfNmoU+ffo0WM5jwrrk5eXhtNNOg729Pf7880/s2rULr732Gry8vEzr8JiwLjNmzMDMmTPV34Xdu3er1/+VV17BO++8Y1qHx4Rlk/OEvn37qmOgKc15/eULq3nz5mHOnDlYuXIliouLcd5556GmpqYDfxNqc9JulkhkZWVJ62HD8uXL1W29Xm8ICgoyvPTSS6YdVF5ebvD09DTMnDmTO82CFRUVGXr06GH4+++/DWPGjDHceeedajmPCevz4IMPGkaOHHnC+3lMWJ+JEycarr322gbLJk2aZLjqqqvUdR4T1kXOG+bNm2e63ZzXPz8/32Bvb2+YM2eOaZ20tDSDjY2NYcGCBR38G1Bb4ogFmRQUFKh/fXx81L+HDh3C0aNH1ShG/cmPxowZg1WrVnHPWTAZyZo4cSLOOOOMBst5TFifX375BYMGDcKll16qUib79++Pjz76yHQ/jwnrM3LkSCxevBj79u1Tt7du3aq+cT733HPVbR4T1q05r7+kUlZVVTVYR9KmEhMTeX7RxdmZewOoc5AvHe655x71B0Pe2EI+GERgYGCDdeX24cOHzbKd1P5kWHrTpk1q+LoxHhPWJykpCR988IH6fHjkkUewbt063HHHHepEYdq0aTwmrNCDDz6ovoiKi4uDra2tSl15/vnnMWXKFHU/PyesW3Nef1nHwcEB3t7ex61j/HnqmhhYkHLbbbdh27Zt6lunxqQwq3EQ0ngZWYbU1FTceeed+Ouvv+Dk5HTC9XhMWA8pyJURixdeeEHdlhELKcKUYEMCCyMeE9bju+++w1dffYVvvvkGvXr1wpYtW1S+vHzjPH36dNN6PCas26m8/jy/6PqYCkWqK4OkOyxduhRhYWGmPSLFVqLxtwdZWVnHfRNBlkGGp+X1HThwIOzs7NRFivzffvttdd34uvOYsB7BwcFISEhosCw+Ph4pKSnqOj8nrM/999+Phx56CJdffjl69+6NqVOn4u6771Zd5ASPCevWnNdf1qmsrFTNIU60DnVNDCysmHwzICMVc+fOxZIlS1TrwPrktrz5pWOUkXwQyInmiBEjzLDF1N7Gjx+P7du3q28gjRf5tvrKK69U16WtJI8J6yIdoRq3oZbc+sjISHWdnxPWp7S0FDY2DU8fJCXK2G6Wx4R1a87rL19eSae5+utkZGRgx44dPL/o6tq0FJy6lP/7v/9TXRqWLVtmyMjIMF1KS0tN60hXB1ln7ty5hu3btxumTJliCA4ONhQWFpp126nj1O8KJXhMWJd169YZ7OzsDM8//7xh//79hq+//trg4uJi+Oqrr0zr8JiwLtOnTzeEhoYafvvtN8OhQ4fU3wc/Pz/DAw88YFqHx4Tldw7cvHmzusip5Ouvv66uHz58uNmv/80332wICwszLFq0yLBp0ybD6aefbujbt6+hurrajL8ZtRYDCysmHwZNXWbPnt2gbdyTTz6pWsc5OjoaRo8erT4kyHoDCx4T1ufXX381JCYmqs+AuLg4w6xZsxrcz2PCusjJoXwmREREGJycnAzR0dGGRx991FBRUWFah8eEZVu6dGmT5w8SdDb39S8rKzPcdtttBh8fH4Ozs7PhvPPOM6SkpJjpN6K2opP/mXvUhIiIiIiIujbWWBARERERUasxsCAiIiIiolZjYEFERERERK3GwIKIiIiIiFqNgQUREREREbUaAwsiIiIiImo1BhZERERERNRqDCyIiIiIiKjVGFgQEVG7GD16NL755ptOv3cvueQSvP766+beDCKiLo+BBRGRBVu1ahVsbW1x9tlnd+jz/vbbbzh69Cguv/xydHZPPPEEnn/+eRQWFpp7U4iIujQGFkREFuzTTz/F7bffjpUrVyIlJaXDnvftt9/GNddcAxsb8/6Zqays/M91+vTpg27duuHrr7/ukG0iIrJUDCyIiCxUSUkJvv/+e/zf//0fzjvvPHz22WfHrfPLL7+gR48ecHZ2xrhx4/D5559Dp9MhPz+/waiHpDXJOuHh4bjjjjvUY5/IsWPHsGjRIlxwwQWmZddee63ahvqqq6sRFBSkgh9hMBjw8ssvIzo6Wj1X37598eOPP5rWr6mpwXXXXYeoqCh1f2xsLN56660Gj3n11VfjoosuwosvvoiQkBD07NlTLX///ffV7+nk5ITAwECV/lSfbOu3337bgr1LRESNMbAgIrJQ3333nTr5lstVV12F2bNnq5N3o+TkZHWCLSfiW7ZswU033YRHH320wWNs374dZ511FiZNmoRt27apx5TRj9tuu+2Ezyv3u7i4ID4+3rTs+uuvx4IFC5CRkWFa9scff6C4uBiTJ09Wtx977DG1jR988AF27tyJu+++W2338uXL1f16vR5hYWEqWNq1a5dKYXrkkUfU7foWL16M3bt34++//1YpWRs2bFDB0DPPPIO9e/eq7ZBAqb4hQ4Zg3bp1qKioOOX9TURk9QxERGSRRowYYXjzzTfV9aqqKoOfn5/h77//Nt3/4IMPGhITExv8zKOPPiqRhyEvL0/dnjp1quHGG29ssM6KFSsMNjY2hrKysiaf94033jBER0cftzwhIcEwY8YM0+2LLrrIcPXVV6vrxcXFBicnJ8OqVasa/Mx1111nmDJlygl/x1tuucVw8cUXm25Pnz7dEBgYaKioqDAt++mnnwweHh6GwsLCEz7O1q1b1e+dnJx8wnWIiOjkOGJBRGSB5Jt5+QbeWDxtZ2eHyy67zJR2ZFxn8ODBx31zX9/GjRtVCpWbm5vpIiMYMnpw6NChJp+7rKxMpRw1JqMWMiIhsrKy8Pvvv6sUKSEjEOXl5TjzzDMbPNcXX3yBgwcPmh5j5syZGDRoEPz9/dX9H3300XG1I71794aDg4PptjxmZGSkSrGaOnWqqqUoLS1t8DOSWiUaLyciouaza8G6RETURXzyySeqhiE0NNS0TNKg7O3tkZeXB29vb3Vb6inqq58qJSSAkBQpSSVqLCIiosnn9vPzU8/R2LRp0/DQQw9h9erV6iIF06NGjTI9j5Bgo/42C0dHR/WvpDxJetRrr72G4cOHw93dHa+88grWrl3bYH1XV9cGt2W9TZs2YdmyZfjrr79UCtVTTz2F9evXw8vLS62Tm5ur/pWAhYiITg0DCyIiCyMBhXzTLyfgEyZMaHDfxRdfrL6xlxqJuLg4VedQn9Qj1DdgwABV7xATE9Ps5+/fv79qNWsMYIx8fX1VPYeMWkhgIV2jjBISElQAIaMPY8aMafJxV6xYgREjRuCWW24xLas/mnEyMmJzxhlnqMuTTz6pAoolS5ao2hGxY8cOVb8hQREREZ0aBhZERBZGCpblpF46KHl6eja4T4q1ZTRDAgsZiZCJ4R588EG1rhRwGztHGUcy5L5hw4bh1ltvxQ033KBGA4yF0e+8884JAwv55v/ff/89rhOUpEPJMunwNH369AajCvfdd58akZDRi5EjR6p5JaQjlaQ8yboS3EjAtHDhQtUZ6ssvv1SjDnL9v/ZHUlKSKtiWQEeCKXkOKWqvH7Q0DsKIiKhlWGNBRGRhJHCQb+YbBxXGEQsJICQ1SE7IpZ3r3Llz1VwO0o3J2BXKmH4ky6Ur0/79+1XakgQNjz/+OIKDg0/4/DIhn9RONDUvhGyX/KzUaUg72PqeffZZlaYkrWKlo5Ss8+uvv5oCh5tvvlmNMEityNChQ5GTk9Ng9OJEZHRCfsfTTz9dPa7UaUhr2V69eqn7pbZj3rx5KnAiIqJTp5MK7lb8PBERWRCZgVpOvFNTU1v1OJmZmerEXYq/pXDaSIqjJaCQInJjGpK5vffee/j5559V/QUREZ06pkIREVkxmThOOkNJ/YOkLkkx9MnmqGgumYRORk6kZkICC0k9kroLqfuQkZT6k+eZmxS0nyiti4iImo8jFkREVkxqGmTSO+mKJF2epB3rww8/rIqd25JMxicpTVIgLXUc48ePb9PHJyIi82NgQURERERErcbibSIiIiIiajUGFkRERERE1GoMLIiIiIiIqNUYWBARERERUasxsCAiIiIiolZjYEFERERERK3GwIKIiIiIiFqNgQUREREREaG1/h/t+/D/V4DcegAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, 3, figsize=(13, 8), 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[\"volume_m3_ha\"], marker=\"o\")\n", "axes[2].set_ylabel(\"Volume (m³/ha)\")\n", "\n", "axes[3].plot(results[\"age_years\"], results[\"stems_per_ha\"], marker=\"o\")\n", "axes[3].set_ylabel(\"Stems (n/ha)\")\n", "\n", "axes[4].plot(results[\"age_years\"], results[\"hq_m\"], marker=\"o\")\n", "axes[4].set_ylabel(\"HQ (m)\")\n", "\n", "axes[5].plot(results[\"age_years\"], results[\"pine_share\"], label=\"Pine\")\n", "axes[5].plot(results[\"age_years\"], results[\"spruce_share\"], label=\"Spruce\")\n", "axes[5].plot(results[\"age_years\"], results[\"birch_share\"], label=\"Birch\")\n", "axes[5].set_ylabel(\"BA share\")\n", "axes[5].legend(loc=\"best\")\n", "\n", "for ax in axes:\n", " ax.axvline(thin_at_age, color=\"tab:red\", linestyle=\"--\", alpha=0.6)\n", " ax.grid(True, alpha=0.3)\n", "\n", "axes[3].set_xlabel(\"Age (years)\")\n", "axes[4].set_xlabel(\"Age (years)\")\n", "axes[5].set_xlabel(\"Age (years)\")\n", "fig.suptitle(\n", " \"Elfving 2010 growth with Söderberg height/volume and thinning at age 50\",\n", " fontsize=12,\n", ")\n", "fig.tight_layout()\n", "\n", "\n", "fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4))\n", "ax2.plot(results[\"age_years\"], results[\"pine_mean_height_m\"], label=\"Pine\")\n", "ax2.plot(results[\"age_years\"], results[\"spruce_mean_height_m\"], label=\"Spruce\")\n", "ax2.plot(results[\"age_years\"], results[\"birch_mean_height_m\"], label=\"Birch\")\n", "ax2.set_ylabel(\"Mean height (m)\")\n", "ax2.set_xlabel(\"Age (years)\")\n", "ax2.axvline(thin_at_age, color=\"tab:red\", linestyle=\"--\", alpha=0.6)\n", "ax2.grid(True, alpha=0.3)\n", "ax2.legend(loc=\"best\")\n", "fig2.suptitle(\"Söderberg 1992 mean height by species\", fontsize=12)\n", "fig2.tight_layout()\n" ] } ], "metadata": { "kernelspec": { "display_name": "pyforestry", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 }