diff --git "a/src/current_er_model.ipynb" "b/src/current_er_model.ipynb" new file mode 100644--- /dev/null +++ "b/src/current_er_model.ipynb" @@ -0,0 +1,3980 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Emissions Reduction Nigeria Mangroves\n", + "This notebook demonstrates a step-by-step model to estimate Emissions Reductions (ER) for the Nigeria mangrove project over 30 years. \n", + "\n", + "We will:\n", + "1. Input species data, densities, and mortality rates.\n", + "2. Combine these with a planting schedule to get the surviving trees each year.\n", + "3. Apply the Chapman-Richards growth equation to estimate diameter at each year since planting\n", + "4. Convert diameter growth to biomass using species-specific allometric equations and root:shoot ratios.\n", + "5. Convert biomass to carbon (t CO2) using known conversion factors.\n", + "6. Sum up all species’ contributions, apply a buffer, leakage, baseline deductions, and implement a 1-year β€œswift” shift. Round down to nearest hundred. Provide milestone totals at 5, 10, 15, 20, 25, 30 years.\n", + "**Note that for now we are not adding in SOC. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# General Instructions\n", + "\n", + "πŸ“ TODO: \n", + "This denotes where key instructions are stored for items that need to be changed. Most important thing to pay attention to.\n", + "\n", + "πŸ› οΈ FUTURE: \n", + "Denotes a future improvement or addition.\n", + "\n", + "πŸ“š SOURCES: \n", + "Where to add sources for the defaults or data being used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Option pre-step 0.1: Determine tree density/ha" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Planting Density Calculation:\n", + "Species 1:\n", + "- Area per tree: 1.56 square meters\n", + "- Trees per hectare: 4000 trees/ha\n", + "- Total trees for species 1: 4000 trees/ha\n", + "\n", + "Species 2:\n", + "- Area per tree: 1.56 square meters\n", + "- Trees per hectare: 444 trees/ha\n", + "- Total trees for species 2: 444 trees/ha\n", + "\n", + "Total trees per hectare: 4444 trees/ha\n" + ] + } + ], + "source": [ + "# πŸ“ TODO: Use this to figure out densities of trees per hectares from planting spacing, if needed\n", + "\n", + "# 0.1 Pre-step - Figure out tree density\n", + "# This is to compute trees per hectare densities from planting spacings, to input into the next cell\n", + "\n", + "def calculate_species_density(row_spacing, col_spacing, hectare_percentage):\n", + " \"\"\"\n", + " Calculate trees per hectare for a species based on its spacing and percentage of hectare\n", + " \n", + " Parameters:\n", + " row_spacing: spacing between rows in meters\n", + " col_spacing: spacing between trees in the same row in meters\n", + " hectare_percentage: percentage of hectare allocated to this species (0-100)\n", + " \"\"\"\n", + " area_per_tree = row_spacing * col_spacing\n", + " hectare_area = 10000\n", + " species_area = hectare_area * (hectare_percentage / 100)\n", + " trees_per_hectare = species_area / area_per_tree\n", + " return trees_per_hectare\n", + "\n", + "# Example: Calculate for two species with different spacings and percentages\n", + "# e.g. Species 1: Rhizophora 90% of hectare, 1.5m Γ— 1.5m spacing\n", + "species1_density = calculate_species_density(\n", + " row_spacing=1.5,\n", + " col_spacing=1.5,\n", + " hectare_percentage=90\n", + ")\n", + "\n", + "# e.g. Species 2: Avicennia 10% of hectare, 1.2m Γ— 1.2m spacing\n", + "species2_density = calculate_species_density(\n", + " row_spacing=1.5,\n", + " col_spacing=1.5,\n", + " hectare_percentage=10\n", + ")\n", + "\n", + "print(\"Planting Density Calculation:\")\n", + "print(f\"Species 1:\")\n", + "print(f\"- Area per tree: {1.25 * 1.25:.2f} square meters\") #Change to match above\n", + "print(f\"- Trees per hectare: {species1_density:.0f} trees/ha\")\n", + "print(f\"- Total trees for species 1: {species1_density:.0f} trees/ha\")\n", + "\n", + "print(f\"\\nSpecies 2:\")\n", + "print(f\"- Area per tree: {1.25 * 1.25:.2f} square meters\") #Change to match above\n", + "print(f\"- Trees per hectare: {species2_density:.0f} trees/ha\")\n", + "print(f\"- Total trees for species 2: {species2_density:.0f} trees/ha\")\n", + "\n", + "print(f\"\\nTotal trees per hectare: {species1_density + species2_density:.0f} trees/ha\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1\n", + "In this cell, we will:\n", + "- Define the species we are using.\n", + "- Define the planting density (trees per hectare) for each species.\n", + "- Optional pre-step of figuring out densities.\n", + "- Define a mortality rate (%) over the 30 years\n", + "\n", + "We will store these in Python dictionaries or dataframes for convenient access.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1 Output. Species planted per hectare and amount that would survive per year:\n", + "\n", + "Species and Planting Densities:\n", + "Rhizophora spp.: 4000 trees/ha\n", + "Avicennia germinans: 444 trees/ha\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Note that species planted at the same densities are overlapping lines and will not be visible in the plot\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "\n", + "# -----------------------------------------------------------\n", + "# Step 1: Define species, friendly names, densities, mortality\n", + "# -----------------------------------------------------------\n", + "\n", + "# πŸ“ TODO: Add different/more species to this list with a letter (Species_E, Species_F, etc), or remove species as needed\n", + "# πŸ“ TODO: Make sure the species_list and the species_names match\n", + "species_list = [\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"]\n", + "\n", + "# 1) Friendly names dictionary\n", + "species_names = {\n", + " \"Species_A\": \"Rhizophora spp.\",\n", + " \"Species_B\": \"Avicennia germinans\",\n", + "# \"Species_C\": \"Null\",\n", + "# \"Species_D\": \"Null\",\n", + "# \"Species_E\": \"Null\"\n", + "}\n", + "\n", + "# We'll use the short codes (keys) for internal processing\n", + "species_list = list(species_names.keys())\n", + "\n", + "# πŸ“ TODO: Add your species densities here, you can figure them out in the pre-step if needed\n", + "# 2) Densities (trees per hectare), assume 1 hectare for demo\n", + "densities = {\n", + " \"Species_A\": 4000,\n", + " \"Species_B\": 444,\n", + "# \"Species_C\": 50,\n", + "# \"Species_D\": 50,\n", + "# \"Species_E\": 0\n", + "}\n", + "\n", + "# πŸ“ TODO: Add your mortalities here\n", + "# πŸ› οΈ FUTURE: Mortalities are the same for all species for now, this may be changed in the future.\n", + "# 3) Here a unique mortality rate is provided for years 1..10, then constant from year 11..30\n", + "# Change these as needed, remember to change the constant for later years below the list as well \n", + "# List sources here if possible\n", + "# In this version, the same mortality rate is used for all species, would need to revise code to change this\n", + "mortality_years_1_10 = [\n", + " 0.15, # Year 1\n", + " 0.12, # Year 2\n", + " 0.06, # Year 3\n", + " 0.05, # Year 4\n", + " 0.05, # Year 5\n", + " 0.05, # Year 6\n", + " 0.05, # Year 7\n", + " 0.05, # Year 8\n", + " 0.05, # Year 9\n", + " 0.05 # Year 10\n", + "]\n", + "constant_rate_after_10 = 0.015 # for years 11..30\n", + "full_mortality = mortality_years_1_10 + [constant_rate_after_10]*20 # total 30 elements\n", + "\n", + "# πŸ“š SOURCES: for mortality rates used\n", + "# [Input sources here]\n", + "\n", + "# 4) We'll apply the same pattern to all species for this demo\n", + "mortality_rates = {sp: full_mortality[:] for sp in species_list}\n", + "\n", + "# 5) Demonstration of survival if all trees are planted at once in Year 1 (1 hectare each)\n", + "survival_data = {}\n", + "years = range(1, 31)\n", + "\n", + "for sp in species_list:\n", + " initial_trees = densities[sp]\n", + " surviving_each_year = []\n", + " \n", + " number_remaining = initial_trees\n", + " for y in range(30):\n", + " mort_rate = mortality_rates[sp][y]\n", + " number_remaining = number_remaining * (1 - mort_rate)\n", + " surviving_each_year.append(number_remaining)\n", + " \n", + " survival_data[sp] = surviving_each_year\n", + "\n", + "df_survival_demo = pd.DataFrame(survival_data, index=years)\n", + "df_survival_demo.index.name = \"Year\"\n", + "\n", + "# -----------------------------------------------------------\n", + "# Visualization\n", + "# -----------------------------------------------------------\n", + "\n", + "# 6) Rename columns to friendly names for display\n", + "df_survival_demo_friendly = df_survival_demo.rename(columns=species_names)\n", + "\n", + "# Display the species and densities planted, and surviving number of trees per hectare\n", + "print(\"Step 1 Output. Species planted per hectare and amount that would survive per year:\")\n", + "print(\"\\nSpecies and Planting Densities:\")\n", + "for code in species_list:\n", + " print(f\"{species_names[code]}: {densities[code]} trees/ha\")\n", + "display(df_survival_demo_friendly.style.format(\"{:.0f}\"))\n", + "\n", + "# 7) Visualization: Surviving Trees Over Time with Friendly Names\n", + "plt.figure(figsize=(8, 5))\n", + "for friendly_col in df_survival_demo_friendly.columns:\n", + " plt.plot(df_survival_demo_friendly.index,\n", + " df_survival_demo_friendly[friendly_col],\n", + " marker='o', \n", + " label=friendly_col)\n", + " \n", + "plt.title(\"Surviving Trees Per Year\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Surviving Trees\")\n", + "plt.grid(True)\n", + "plt.legend() # legend uses friendly names\n", + "plt.show()\n", + "\n", + "print(\"**Note that species planted at the same densities are overlapping lines and will not be visible in the plot\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2\n", + "In this cell, we will:\n", + "- Define a planting schedule\n", + "- Differentiate between total hectares planted, all hectares planted and replanted, and specifically new hectares planted as these will be needed for summary statistics later\n", + "- For each species, in each planting year, we plant a certain number of hectares.\n", + "- For each subsequent year, we apply the mortality rate *for that cohort’s first year* to the year the cohort is in. \n", + " - For example, a planting in Year 5 means the β€œcohort’s Year 1” is the calendar Year 5 of the project.\n", + " - The mortality for β€œyear 2” of that cohort is the calendar Year 6 of the project, etc.\n", + "- Create a dataframe with the number of surviving trees in each of the 30 years (sum across all cohorts).\n", + "- Round down to the nearest whole number.\n", + "\n", + "Key outputs for later steps:\n", + "- df_cohort (the surviving trees per project year based on their planting cohort) will be used in step 5 combined with the biomass per tree to get total biomass numbers for the project area " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 2 Output\n", + "\n", + "Planting Schedule:\n", + "Year_1: 2,500 hectares\n", + "Year_2: 2,875 hectares\n", + "Year_3: 375 hectares\n", + "\n", + "Detailed Breakdown of Surviving Trees per Planting Cohort, 1 Table per Species\n", + "-- Survival Table for Rhizophora spp. --\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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 Cohort_1Cohort_2Cohort_3Cohort_4Total_Surviving_Trees
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------\n", + "# Step 2: Planting schedule & survivors\n", + "# -------------------------------------\n", + "\n", + "# πŸ“ TODO: Change all three planting schedule code items here : total, planting_schedule, and new_hectares_schedule\n", + "\n", + "# Define total hectares planted in this project\n", + "total_hectares_planted = 5000\n", + "\n", + "# New hectares planted each year (excluding replanting)\n", + "new_hectares_schedule = {\n", + " \"Year_1\": 2500, # all hectares are new in year 1\n", + " \"Year_2\": 2500, # new hectares only\n", + " \"Year_3\": 0, # new hectares only\n", + "}\n", + "\n", + "# Planting schedule --- hectares planted AND/OR replanted each year\n", + "planting_schedule = {\n", + " \"Year_1\": 2500, # hectares\n", + " \"Year_2\": 2875, # new and replanted hectares\n", + " \"Year_3\": 375, # new and replanted hectares\n", + "# \"Year_4\": 0,\n", + "# \"Year_5\": 0\n", + "}\n", + "\n", + "# Initialize a dictionary to hold survival dataframes for each species\n", + "survival_dfs = {}\n", + "\n", + "for sp in species_list:\n", + " # For convenience, create a dataframe with rows = Year (1 to 30),\n", + " # columns = planting cohort (1 to 5).\n", + " # Each cell will hold the number of surviving trees in that year from that cohort.\n", + " \n", + " df_survival = pd.DataFrame(0.0, index=range(1, 31), columns=[f\"Cohort_{i}\" for i in range(1, 5)])\n", + " \n", + " # We'll plant for 5 years, so we have 5 cohorts\n", + " for cohort_idx, year_str in enumerate(planting_schedule.keys(), start=1):\n", + " planting_year = int(year_str.split(\"_\")[1]) # e.g. \"Year_3\" -> 3\n", + " hectares_planted = planting_schedule[year_str]\n", + " \n", + " # Number of trees initially planted for this species in that cohort\n", + " initial_trees = densities[sp] * hectares_planted\n", + " \n", + " # For each of the 30 project years, figure out how old that cohort is:\n", + " # Age in \"cohort years\" = (calendar_year - planting_year + 1)\n", + " # Mortality rate used = mortality_rates[sp][cohort_age-1] if 1 <= cohort_age <= 30\n", + " # If cohort_age < 1 or > 30 => 0 trees or no mortality to apply\n", + " alive_trees = initial_trees\n", + " for proj_year in range(1, 31):\n", + " cohort_age = proj_year - planting_year + 1\n", + " if 1 <= cohort_age <= 30:\n", + " mort_rate = mortality_rates[sp][cohort_age - 1]\n", + " alive_trees = alive_trees * (1 - mort_rate)\n", + " df_survival.loc[proj_year, f\"Cohort_{cohort_idx}\"] = alive_trees\n", + " else:\n", + " df_survival.loc[proj_year, f\"Cohort_{cohort_idx}\"] = 0\n", + " \n", + " df_survival[\"Total_Surviving_Trees\"] = df_survival.sum(axis=1)\n", + " survival_dfs[sp] = df_survival\n", + "\n", + "# Merge \"Total_Surviving_Trees\" from each species side-by-side into a single DataFrame\n", + "df_survival_all_species = pd.DataFrame(index=range(1, 31))\n", + "df_survival_all_species.index.name = \"Year\"\n", + "\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " df_survival_all_species[friendly] = survival_dfs[sp][\"Total_Surviving_Trees\"]\n", + "\n", + "print(\"Step 2 Output\")\n", + "print(\"\\nPlanting Schedule:\")\n", + "for year, hectares in planting_schedule.items():\n", + " print(f\"{year}: {hectares:,} hectares\")\n", + "\n", + "print(\"\\nDetailed Breakdown of Surviving Trees per Planting Cohort, 1 Table per Species\")\n", + "\n", + "for sp in species_list:\n", + " friendly_name = species_names[sp] \n", + " \n", + " # survival_dfs[sp] should have columns: Cohort_1, Cohort_2, ... plus Total_Surviving_Trees\n", + " df_cohort = survival_dfs[sp].copy()\n", + " \n", + " print(f\"-- Survival Table for {friendly_name} --\")\n", + " # Display the first 15 rows (or as many as you prefer).\n", + " # Use .style.format(\"{:.0f}\") to hide decimal places if you have float-based survival.\n", + " display(\n", + " df_cohort.head(15).style.format(\"{:.0f}\")\n", + " )\n", + "\n", + "# Quick plot of each species on the same figure\n", + "plt.figure(figsize=(8,5))\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " plt.plot(df_survival_all_species.index, df_survival_all_species[friendly], label=friendly, marker='o')\n", + "\n", + "plt.title(\"Surviving Trees Over Time (All Species)\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Surviving Trees\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3 : Chapman-Richards\n", + "\n", + "1. We input literature-based Chapman-Richards parameters (asymptote, c factor) and a literature-based maturity age to determine the growth rate (b) parameter for our Chapman-Richards curve\n", + "2. We apply Chapman-Richards growth function to our surviving trees\n", + "2a. We first apply it to dbh\n", + "2b. We then apply it to height" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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52.9817954.148831
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species_A Heightspecies_B Height
Age
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10.6254710.590557
20.9743080.842327
31.4825721.209161
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52.7632222.133456
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116.6696344.952867
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137.6903745.689574
148.1375746.012336
158.5445346.306055
168.9133796.572265
179.2465376.812718
189.5465887.029276
199.8161537.223832
2010.0578167.398250
2110.2740707.554329
2210.4672807.693776
2310.6396667.818194
2410.7932907.929070
2510.9300538.027777
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2811.2558168.262893
2911.3410378.324401
3011.4166398.378966
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Saved growth table β†’ cr_growth_projections.csv\n", + "Saved parameters β†’ cr_parameters.csv\n", + "\n", + "Final parameters:\n", + "species_a_dbh_a: 11.0700\n", + "species_a_dbh_b: 0.1237\n", + "species_a_dbh_c: 2.1000\n", + "species_a_height_a: 12.0000\n", + "species_a_height_b: 0.1237\n", + "species_a_height_c: 2.1000\n", + "species_b_dbh_a: 17.0000\n", + "species_b_dbh_b: 0.1237\n", + "species_b_dbh_c: 2.1000\n", + "species_b_height_a: 8.8000\n", + "species_b_height_b: 0.1237\n", + "species_b_height_c: 2.1000\n" + ] + } + ], + "source": [ + "# ------------------------------------------------------------\n", + "# Required imports\n", + "# ------------------------------------------------------------\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. SPECIES AND PARAMETER SETTINGS\n", + "# ------------------------------------------------------------\n", + "# Initial values for DBH and height for each species\n", + "start_params = {\n", + " \"species_A\": {\"dbh\": 1.0, \"height\": 0.5}, # cm, m\n", + " \"species_B\": {\"dbh\": 1.0, \"height\": 0.5} # cm, m\n", + "}\n", + "\n", + "# Growth parameters for each species and dimension\n", + "growth_params = {\n", + " \"species_A\": {\n", + " \"dbh\": dict(a=11.07, Tm=6, c=2.1), # cm, yr, –\n", + " \"height\": dict(a=12.0, Tm=6, c=2.1) # m, yr, –\n", + " },\n", + " \"species_B\": {\n", + " \"dbh\": dict(a=17.0, Tm=6, c=2.1), # cm, yr, –\n", + " \"height\": dict(a=8.8, Tm=6, c=2.1) # m, yr, –\n", + " }\n", + "}\n", + "\n", + "project_years = 30 # simulation horizon\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. GROWTH FUNCTIONS\n", + "# ------------------------------------------------------------\n", + "def chapman_richards(t, a, c, Tm, start_value):\n", + " \"\"\"Basic Chapman-Richards growth function with start value adjustment\"\"\"\n", + " b = np.log(c) / Tm\n", + " t = np.asarray(t)\n", + " return start_value + (a - start_value) * (1 - np.exp(-b * t))**c\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. GENERATE GROWTH DATA\n", + "# ------------------------------------------------------------\n", + "# Create time series\n", + "ages = np.arange(0, project_years + 1)\n", + "\n", + "# Container for results\n", + "rows = []\n", + "final_params = {}\n", + "\n", + "for sp in [\"species_A\", \"species_B\"]:\n", + " for dim in [\"dbh\", \"height\"]:\n", + " # Get parameters\n", + " p = growth_params[sp][dim]\n", + " a, c, Tm = p[\"a\"], p[\"c\"], p[\"Tm\"]\n", + " start_value = start_params[sp][dim]\n", + " \n", + " # Calculate b parameter\n", + " b = np.log(c) / Tm\n", + " \n", + " # Store parameters\n", + " param_prefix = f\"{sp.lower()}_{dim}\"\n", + " final_params[f\"{param_prefix}_a\"] = a\n", + " final_params[f\"{param_prefix}_b\"] = b\n", + " final_params[f\"{param_prefix}_c\"] = c\n", + " \n", + " # Calculate growth\n", + " size = chapman_richards(ages, a, c, Tm, start_value)\n", + " increment = np.diff(size) # annual increment\n", + " increment = np.insert(increment, 0, 0) # add 0 for first year\n", + " \n", + " # Store results\n", + " for t, s, i in zip(ages, size, increment):\n", + " rows.append({\n", + " 'Species': sp,\n", + " 'Dimension': dim,\n", + " 'Age': t,\n", + " 'Size': s,\n", + " 'Annual_increment': i\n", + " })\n", + "\n", + "# Create dataframe\n", + "df = pd.DataFrame(rows)\n", + "\n", + "# ------------------------------------------------------------\n", + "# 4. VISUALIZATIONS\n", + "# ------------------------------------------------------------\n", + "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n", + "plt.style.use('default') # Reset to default style\n", + "\n", + "dimensions = [\"dbh\", \"height\"]\n", + "units = {\"dbh\": \"cm\", \"height\": \"m\"}\n", + "colors = {\"species_A\": \"green\", \"species_B\": \"red\"}\n", + "\n", + "for i, dim in enumerate(dimensions):\n", + " # Size plot\n", + " for sp in [\"species_A\", \"species_B\"]:\n", + " mask = (df['Species'] == sp) & (df['Dimension'] == dim)\n", + " axes[0,i].plot(df[mask]['Age'], \n", + " df[mask]['Size'],\n", + " label=sp,\n", + " color=colors[sp])\n", + " \n", + " axes[0,i].set_title(f\"{dim.upper()} Growth\")\n", + " axes[0,i].set_xlabel(\"Age (years)\")\n", + " axes[0,i].set_ylabel(f\"Size ({units[dim]})\")\n", + " axes[0,i].grid(True)\n", + " axes[0,i].legend()\n", + "\n", + " # Increment plot\n", + " for sp in [\"species_A\", \"species_B\"]:\n", + " mask = (df['Species'] == sp) & (df['Dimension'] == dim)\n", + " axes[1,i].plot(df[mask]['Age'],\n", + " df[mask]['Annual_increment'],\n", + " label=sp,\n", + " color=colors[sp])\n", + " \n", + " axes[1,i].set_title(f\"{dim.upper()} Annual Increment\")\n", + " axes[1,i].set_xlabel(\"Age (years)\")\n", + " axes[1,i].set_ylabel(f\"Annual increment ({units[dim]}/year)\")\n", + " axes[1,i].grid(True)\n", + " axes[1,i].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# TABLES\n", + "# Create separate tables for DBH and Height by year\n", + "# Reshape the data for DBH\n", + "dbh_table = df[df['Dimension'] == 'dbh'].pivot(\n", + " index='Age', \n", + " columns='Species', \n", + " values='Size'\n", + ")\n", + "dbh_table.columns = [f\"{col} DBH\" for col in dbh_table.columns]\n", + "\n", + "# Reshape the data for Height\n", + "height_table = df[df['Dimension'] == 'height'].pivot(\n", + " index='Age', \n", + " columns='Species', \n", + " values='Size'\n", + ")\n", + "height_table.columns = [f\"{col} Height\" for col in height_table.columns]\n", + "\n", + "# Display the tables\n", + "print(\"DBH by year (cm):\")\n", + "display(dbh_table)\n", + "print(\"\\nHeight by year (m):\")\n", + "display(height_table)\n", + "\n", + "# Additional single plot showing both DBH and Height trajectories\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot DBH\n", + "for sp in [\"species_A\", \"species_B\"]:\n", + " mask = (df['Species'] == sp) & (df['Dimension'] == 'dbh')\n", + " plt.plot(df[mask]['Age'], \n", + " df[mask]['Size'],\n", + " label=f\"{sp} DBH (cm)\",\n", + " linestyle='-',\n", + " color=colors[sp])\n", + " \n", + " # Plot Height with dashed lines\n", + " mask = (df['Species'] == sp) & (df['Dimension'] == 'height')\n", + " plt.plot(df[mask]['Age'], \n", + " df[mask]['Size'],\n", + " label=f\"{sp} Height (m)\",\n", + " linestyle='--',\n", + " color=colors[sp])\n", + "\n", + "plt.title(\"Growth Trajectories - DBH and Height\")\n", + "plt.xlabel(\"Age (years)\")\n", + "plt.ylabel(\"Size (cm for DBH, m for Height)\")\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# ------------------------------------------------------------\n", + "# 5. SAVE RESULTS\n", + "# ------------------------------------------------------------\n", + "# Save growth curves\n", + "df.to_csv(\"cr_growth_projections.csv\", index=False)\n", + "print(\"\\nSaved growth table β†’ cr_growth_projections.csv\")\n", + "\n", + "# Save parameters\n", + "params_df = pd.DataFrame([final_params])\n", + "params_df.to_csv(\"cr_parameters.csv\", index=False)\n", + "print(\"Saved parameters β†’ cr_parameters.csv\")\n", + "\n", + "# Print final parameters for reference\n", + "print(\"\\nFinal parameters:\")\n", + "for param, value in final_params.items():\n", + " print(f\"{param}: {value:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Determining biomass per tree\n", + "We will:\n", + "- Bring in an allometric equation for each species \n", + "- Bring in a root to shoot ratio (R2S) for each species\n", + " - Note that sources for both should be stored as comments in the code\n", + "- Bring in our diameters per year since planting from step 3 as (d) for our allometric equations\n", + " - Doing so, we calculate the Aboveground Biomass (AGB) at each year since planting for each species\n", + "- Multiply the AGB by the root to shoot ratio to get Belowground Biomass (BGB)\n", + "- Add ABG and BGB to get total biomass per tree (in kg), which is then converted to tonnes \n", + "\n", + "Output for later stages:\n", + "- df_biomass_per_tree_all_species = the total (AGB+BGB) biomass per tree per species\n", + "\n", + "Visualization outputs:\n", + "- A table of the biomass per year since planting per species, provided in kg for better interpretation\n", + "- A plot of the biomass per year since planting per species, in tonnes " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 4: Allometric equations, R2S, & total biomass per species\n", + "\n", + "Allometric Equations Used:\n", + "Rhizophora spp. (Species_A) uses equation: Total = 1.938 Γ— (DBHΒ² H)^0.67628\n", + "Avicennia germinans (Species_B) uses equation: Total = 1.486 Γ— (DBHΒ² H)^0.55864\n", + "\n", + "Root to Shoot Ratios Used:\n", + " Rhizophora spp. (Species_A): 0.0\n", + " Avicennia germinans (Species_B): 0.0\n", + "\n", + "Table: Total kg per tree for all species (year since planting)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Rhizophora spp.Avicennia germinans
Year  
11.621.33
23.052.38
35.864.33
410.417.25
516.8911.12
625.2715.81
735.3421.15
846.8326.95
959.3633.03
1072.5939.21
1186.1645.38
1299.7951.41
13113.2157.22
14126.2462.75
15138.7367.96
16150.5572.83
17161.6677.34
18172.0081.50
19181.5885.30
20190.3888.78
21198.4491.93
22205.7894.79
23212.4497.37
24218.4799.69
25223.91101.78
26228.81103.65
27233.20105.32
28237.14106.81
29240.67108.15
30243.82109.34
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 Rhizophora spp.Avicennia germinans
Year  
11.621.33
21.421.05
32.811.95
44.562.93
56.483.87
68.374.69
710.085.34
811.485.80
912.536.07
1013.226.19
1113.576.16
1213.636.03
1313.435.81
1413.035.53
1512.485.21
1611.834.87
1711.114.51
1810.344.16
199.573.81
208.803.47
218.063.16
227.342.86
236.662.58
246.032.32
255.442.09
264.901.87
274.401.67
283.941.49
293.521.33
303.151.19
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------\n", + "# Step 4: Allometric equations & biomass (AGB & BGB) per tree \n", + "# -------------------------------------------------------\n", + "\n", + "# Allometric equations with corrected lambda functions that take both DBH and height\n", + "allometric_info = {\n", + " \"Species_A\": {\n", + " \"equation_str\": \"Total = 1.938 Γ— (DBHΒ² H)^0.67628\",\n", + " \"func\": lambda d, h: 1.938 * (d**2 * h)**0.67628\n", + " },\n", + " # Species A source: Zanvo et al. 2023 - Benin study\n", + " \"Species_B\": {\n", + " \"equation_str\": \"Total = 1.486 Γ— (DBHΒ² H)^0.55864\",\n", + " \"func\": lambda d, h: 1.486 * (d**2 * h)**0.55864\n", + " },\n", + " # Species B source: Zanvo et al. 2023 - Benin study\n", + "}\n", + "\n", + "# Root-to-shoot ratios\n", + "# Note that these are not used here as Zanvo allometrics are total (above & belowground) bimass eqns\n", + "root_shoot_ratios = {\n", + " \"Species_A\": 0.0,\n", + " \"Species_B\": 0.0\n", + "}\n", + "\n", + "biomass_dfs = {} # dictionary to store DataFrames for each species\n", + "\n", + "# Map the species names between the dataframes\n", + "species_mapping = {\n", + " \"Species_A\": \"species_A\",\n", + " \"Species_B\": \"species_B\"\n", + "}\n", + "\n", + "for sp in species_list:\n", + " # Create an empty DataFrame with the columns you want\n", + " df_bio = pd.DataFrame(index=range(1, 31), columns=[\n", + " \"DBH\",\n", + " \"Height\",\n", + " \"Aboveground_kg_per_tree\",\n", + " \"Belowground_kg_per_tree\",\n", + " \"Total_Biomass_kg_per_tree\",\n", + " \"Total_Biomass_tonnes_per_tree\",\n", + " \"Yearly_Biomass_Change_kg_per_tree\"\n", + " ], dtype=float)\n", + " \n", + " df_species = species_mapping[sp]\n", + " \n", + " for t in range(1, 31):\n", + " # Get DBH and Height from our growth data\n", + " dbh_mask = (df['Species'] == df_species) & (df['Dimension'] == 'dbh') & (df['Age'] == t)\n", + " height_mask = (df['Species'] == df_species) & (df['Dimension'] == 'height') & (df['Age'] == t)\n", + " \n", + " d = df[dbh_mask]['Size'].values[0] # DBH in cm\n", + " h = df[height_mask]['Size'].values[0] # Height in m\n", + " \n", + " # Store DBH and Height for reference\n", + " df_bio.loc[t, \"DBH\"] = d\n", + " df_bio.loc[t, \"Height\"] = h\n", + " \n", + " # Calculate aboveground biomass using both DBH and height\n", + " agb_kg = allometric_info[sp][\"func\"](d, h)\n", + " \n", + " # Calculate belowground biomass\n", + " r2s = root_shoot_ratios[sp]\n", + " bgb_kg = agb_kg * r2s\n", + " \n", + " # Calculate total biomass\n", + " total_kg_per_tree = agb_kg + bgb_kg\n", + " total_tonnes_per_tree = total_kg_per_tree / 1000.0\n", + " \n", + " # Store all values in the dataframe\n", + " df_bio.loc[t, \"Aboveground_kg_per_tree\"] = agb_kg\n", + " df_bio.loc[t, \"Belowground_kg_per_tree\"] = bgb_kg\n", + " df_bio.loc[t, \"Total_Biomass_kg_per_tree\"] = total_kg_per_tree\n", + " df_bio.loc[t, \"Total_Biomass_tonnes_per_tree\"] = total_tonnes_per_tree\n", + " \n", + " # Calculate yearly biomass change\n", + " df_bio[\"Yearly_Biomass_Change_kg_per_tree\"] = df_bio[\"Total_Biomass_kg_per_tree\"].diff()\n", + " df_bio.loc[1, \"Yearly_Biomass_Change_kg_per_tree\"] = df_bio.loc[1, \"Total_Biomass_kg_per_tree\"]\n", + " \n", + " biomass_dfs[sp] = df_bio\n", + "\n", + "# Create summary DataFrames\n", + "df_biomass_per_tree_all_species = pd.DataFrame(index=range(1, 31))\n", + "df_biomass_per_tree_all_species.index.name = \"Year\"\n", + "\n", + "df_total_kg_per_tree = pd.DataFrame(index=range(1, 31))\n", + "df_total_kg_per_tree.index.name = \"Year\"\n", + "\n", + "df_yearly_change_kg = pd.DataFrame(index=range(1, 31))\n", + "df_yearly_change_kg.index.name = \"Year\"\n", + "\n", + "# Populate summary DataFrames\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " df_biomass_per_tree_all_species[friendly] = biomass_dfs[sp][\"Total_Biomass_tonnes_per_tree\"]\n", + " df_total_kg_per_tree[friendly] = biomass_dfs[sp][\"Total_Biomass_kg_per_tree\"]\n", + " df_yearly_change_kg[friendly] = biomass_dfs[sp][\"Yearly_Biomass_Change_kg_per_tree\"]\n", + "\n", + "# Convert the Yearly Biomass Change to Tonnes\n", + "df_yearly_change_tonnes = df_yearly_change_kg / 1000.0\n", + "\n", + "# Display results\n", + "print(\"Step 4: Allometric equations, R2S, & total biomass per species\")\n", + "print(\"\\nAllometric Equations Used:\")\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " eq_str = allometric_info[sp][\"equation_str\"]\n", + " print(f\"{friendly} ({sp}) uses equation: {eq_str}\")\n", + "\n", + "print(\"\\nRoot to Shoot Ratios Used:\")\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " r2s = root_shoot_ratios[sp]\n", + " print(f\" {friendly} ({sp}): {r2s}\")\n", + "\n", + "print(\"\\nTable: Total kg per tree for all species (year since planting)\")\n", + "display(df_total_kg_per_tree.style.format(\"{:.2f}\"))\n", + "\n", + "print(\"\\nYearly Biomass Change (kg) per Tree:\")\n", + "display(df_yearly_change_kg.style.format(\"{:.2f}\"))\n", + "\n", + "# Visualization\n", + "plt.figure(figsize=(12, 6))\n", + "for sp in species_list:\n", + " plt.plot(\n", + " biomass_dfs[sp].index,\n", + " biomass_dfs[sp][\"Total_Biomass_tonnes_per_tree\"],\n", + " marker='o',\n", + " label=f\"{species_names[sp]} - Total Biomass\"\n", + " )\n", + "\n", + "plt.title(\"Total Biomass per Tree\")\n", + "plt.xlabel(\"Year since planting\")\n", + "plt.ylabel(\"Biomass (tonnes per tree)\")\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5 : Calculating the total project biomass and biomass change\n", + "\n", + "We combined previous per tree values (biomass) with our planting cohorts and remaining trees after mortality. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 5 completed. \n", + "Now we have total biomass & yearly change across all trees in project area \n", + "from combining the per-tree biomass/change (tonnes) with the surviving trees per project year from each planting cohort.\n", + "\n", + "=== Total Biomass across Project Area (tonnes) [All species, all planting cohorts] ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Rhizophora spp.Avicennia germinans
Year  
1138101250
2386663413
3694555836
41203259558
519333814527
628599620399
739364826791
851040033327
963017339681
1074747045603
1187261251782
12100806258406
13114044464720
14126575670546
15138231075839
16148900880578
17158525584762
18167085888405
19174593491532
20181083294171
21186606496359
22191224598132
23195005499527
241980196100581
252003375101329
262020281101804
272031571102038
282037864102059
292039735101894
302037715101565
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Total Yearly Biomass Change across Project Area (tonnes) [All species, all planting cohorts] ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Rhizophora spp.Avicennia germinans
Year  
1138101250
2248572162
3307892423
4508693722
5730134969
6926585873
71076526392
81167526535
91197736354
101172975922
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "See also the breakdown of each species' biomass change per planting cohort (first 10 years):\n", + "\n", + "--- Cohort-Based Total Biomass for Rhizophora spp. (Species_A) ---\n", + "First 10 rows, with integer-like display:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Cohort_1Cohort_2Cohort_3Total_Biomass_tonnesYearly_Biomass_Change_tonnes
Project_Year     
113810001381013810
2227851588103866624857
3411812620320716945530789
46954947358341812032550869
510717979981617719333873013
61523081232561043228599692658
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Cohort-Based Total Biomass for Avicennia germinans (Species_B) ---\n", + "First 10 rows, with integer-like display:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Cohort_1Cohort_2Cohort_3Total_Biomass_tonnesYearly_Biomass_Change_tonnes
Project_Year     
112500012501250
219751438034132162
33377227118858362423
45378388429695583722
578356185507145274969
6105829011807203995873
713447121691175267916392
816276154641587333276535
918947187172017396816354
1021373217892441456035922
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ---------------------------------------------------------\n", + "# Step 5: The total biomass and yearly biomass change for project area\n", + "# [Across all surviving trees from all planting cohorts per species]\n", + "# ---------------------------------------------------------\n", + "\n", + "# We are now matching planting, project year, and mortality through cohorts\n", + "# A cohort is the grouping of trees by age, not by project year\n", + "# As such, hectares that are replanted infall into the same cohort as hectares that are originally planted in that year, as they are both the same age\n", + "# Take care with matching the right number of cohorts here \n", + "\n", + "# πŸ“ TODO: Change the cohort_cols range to be 1 greater than your number of cohorts (keep in mind this includes years that just have replanting)\n", + "# The cohort columns needs to be adjusted so that the range is one more than the number of cohorts\n", + "# So if you only have two planting / replanting years, you use e.g. range(1,3)\n", + "cohort_cols = [f\"Cohort_{i}\" for i in range(1, 4)]\n", + "\n", + "# πŸ“ TODO: Add or comment out cohorts based on your planting_schedule, remove final comma\n", + "planting_years = {\n", + " \"Cohort_1\": 1,\n", + " \"Cohort_2\": 2,\n", + " \"Cohort_3\": 3\n", + "# \"Cohort_4\": 4 #remember to add commas or remove final comma\n", + "# \"Cohort_5\": 5\n", + "}\n", + "\n", + "# Dictionary to store the final DataFrames: total biomass (tonnes) across cohorts\n", + "# for each species, plus yearly biomass change\n", + "total_biomass_cohorts_dfs = {}\n", + "\n", + "for sp in species_list:\n", + " # 1) Prepare an empty DataFrame indexed by project year (1..30)\n", + " # There is one column per cohort, plus \"Total_Biomass_tonnes\"\n", + " # and \"Yearly_Biomass_Change_tonnes\"\n", + " df_total = pd.DataFrame(\n", + " 0.0,\n", + " index=range(1, 31),\n", + " columns=cohort_cols + [\"Total_Biomass_tonnes\", \"Yearly_Biomass_Change_tonnes\"]\n", + " )\n", + " df_total.index.name = \"Project_Year\"\n", + " \n", + " prev_total = 0.0 # track last year's total for differencing\n", + " \n", + " for proj_year in range(1, 31):\n", + " sum_this_year = 0.0\n", + " \n", + " # 2) Loop over each cohort to accumulate total biomass\n", + " for cohort in cohort_cols:\n", + " # planting year for this cohort\n", + " p_year = planting_years[cohort] \n", + " \n", + " # 3) Cohort age in the current project year\n", + " # e.g. if planting_year=2 and proj_year=3, age=2\n", + " age = proj_year - p_year + 1\n", + " \n", + " if 1 <= age <= 30:\n", + " # 4) Per-tree biomass from Step 4 (indexed by \"age\")\n", + " per_tree_tonnes = biomass_dfs[sp].loc[age, \"Total_Biomass_tonnes_per_tree\"]\n", + " \n", + " # 5) Surviving trees from Step 2\n", + " surviving_trees = survival_dfs[sp].loc[proj_year, cohort]\n", + " \n", + " # 6) Cohort biomass in the current project year\n", + " cohort_biomass = per_tree_tonnes * surviving_trees\n", + " else:\n", + " cohort_biomass = 0.0\n", + " \n", + " df_total.loc[proj_year, cohort] = cohort_biomass\n", + " sum_this_year += cohort_biomass\n", + " \n", + " # 7) Sum across cohorts for this species & project year\n", + " df_total.loc[proj_year, \"Total_Biomass_tonnes\"] = sum_this_year\n", + " \n", + " # 8) Compute yearly change\n", + " yearly_change = sum_this_year - prev_total\n", + " df_total.loc[proj_year, \"Yearly_Biomass_Change_tonnes\"] = yearly_change\n", + " \n", + " prev_total = sum_this_year\n", + " \n", + " # 9) Avoid NaN in year 1: set year 1's change to the total in year 1\n", + " # (assuming \"Year 0\" is zero biomass)\n", + " first_year_val = df_total.loc[1, \"Total_Biomass_tonnes\"]\n", + " df_total.loc[1, \"Yearly_Biomass_Change_tonnes\"] = first_year_val\n", + " \n", + " # 10) Store in the final dictionary\n", + " total_biomass_cohorts_dfs[sp] = df_total\n", + "\n", + "# Create combined dataframes\n", + "# 1) Create a DataFrame showing Total_Biomass_tonnes for each species\n", + "df_total_biomass_all_species = pd.DataFrame(index=range(1, 31))\n", + "df_total_biomass_all_species.index.name = \"Year\"\n", + "\n", + "# 2) Create a DataFrame showing Yearly_Biomass_Change_tonnes for each species\n", + "df_yearly_change_all_species = pd.DataFrame(index=range(1, 31))\n", + "df_yearly_change_all_species.index.name = \"Year\"\n", + "\n", + "# Loop over each species, pull the two columns from total_biomass_cohorts_dfs\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " df_cohort_biomass = total_biomass_cohorts_dfs[sp]\n", + " \n", + " # \"Total_Biomass_tonnes\" column\n", + " df_total_biomass_all_species[friendly] = df_cohort_biomass[\"Total_Biomass_tonnes\"]\n", + " \n", + " # \"Yearly_Biomass_Change_tonnes\" column\n", + " df_yearly_change_all_species[friendly] = df_cohort_biomass[\"Yearly_Biomass_Change_tonnes\"]\n", + "\n", + "#########################\n", + "# Visualization section #\n", + "#########################\n", + "print(\"Step 5 completed. \\nNow we have total biomass & yearly change across all trees in project area \\nfrom combining the per-tree biomass/change (tonnes) with the surviving trees per project year from each planting cohort.\")\n", + "\n", + "# Display Total Biomass and Yearly Change tables across all species\n", + "print(\"\\n=== Total Biomass across Project Area (tonnes) [All species, all planting cohorts] ===\")\n", + "display(df_total_biomass_all_species.style.format(\"{:.0f}\"))\n", + "\n", + "print(\"\\n=== Total Yearly Biomass Change across Project Area (tonnes) [All species, all planting cohorts] ===\")\n", + "display(df_yearly_change_all_species.head(10).style.format(\"{:.0f}\"))\n", + "\n", + "print(\"\\nSee also the breakdown of each species' biomass change per planting cohort (first 10 years):\")\n", + "for sp in species_list:\n", + " friendly = species_names[sp]\n", + " df_cohort_biomass = total_biomass_cohorts_dfs[sp].copy() # columns = Cohort_1..n, plus total columns\n", + " \n", + " print(f\"\\n--- Cohort-Based Total Biomass for {friendly} ({sp}) ---\")\n", + " print(\"First 10 rows, with integer-like display:\\n\")\n", + " display(df_cohort_biomass.head(10).style.format(\"{:.0f}\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6: Computing the CO2e sequestered and the project's yearly CO2 change\n", + "\n", + "- Default factors for converting tree biomass to carbon and then Carbon to CO2 are input\n", + "- These are combined with biomass output from the previous section to get CO2 totals for the project area\n", + "- Summary statistics are generated" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 6. Total CO2e sequestered results\n", + "\n", + "=== Per species and Total tCO2 Change & tCO2 per Cumulative Hectares\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Rhizophora spp.Avicennia germinansTotal_Yearly_tCO2_ChangeTotal_HectaresCumulative_HectarestCO2_per_ha
Year      
123820215725977500025005
242875373046605500050009
3531084179572875000500011
4877456421941665000500019
512594085701345115000500027
6159827101301699565000500034
7185689110261967155000500039
8201385112732126585000500043
9206596109612175575000500044
10202326102152125415000500043
11215857106582265145000500045
12233639114262450645000500049
13228345108912392365000500048
14216152100492262015000500045
1520104391292101735000500042
1618404481741922185000500038
1716601672181732345000500035
1814765662841539405000500031
1912949953921348915000500027
2011194345531164965000500023
21952693774990435000500020
22796583058827165000500017
23652172406676235000500014
24519911818538095000500011
2539982129141273500050008
262916182029981500050006
271947440419878500050004
28108553610891500050002
293228-2862942500050001
30-3486-568-405350005000-1
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Summary Stats per Species ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Species_NameTotal_30yrs_tCO2Annual_Avg_tCO2Avg_tCO2_per_Tree
Species_Code    
Species_ARhizophora spp.35148541171620.15
Species_BAvicennia germinans17518958400.07
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Final Summary (All Species Combined) ===\n", + "Total 30-year tCO2 Change: 3,690,043 tonnes CO2\n", + "Average Yearly tCO2 Change: 123,001 tonnes CO2/yr\n", + "Avg Annual per Hectare (over 30 yrs): 25 tonnes CO2/ha/yr (Option A)\n", + "Total sequestered by 1 ha over project period (30 yrs) = 738 tCO2/ha\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------\n", + "# Step 6: Total CO2e sequestered and yearly CO2e change for project area\n", + "# ---------------------------------------------------------\n", + "\n", + "# -----------------------------------------------------\n", + "# 1) Convert biomass change (tonnes) -> tCO2\n", + "# -----------------------------------------------------\n", + "# Constants\n", + "carbon_fraction = 0.47 # fraction of biomass that is carbon\n", + "tCO2_tC_fraction = 3.67 # tonnes CO2 per tonne of C\n", + "\n", + "df_yearly_tCO2_change = pd.DataFrame(index=df_yearly_change_all_species.index)\n", + "df_yearly_tCO2_change.index.name = \"Year\"\n", + "\n", + "for col in df_yearly_change_all_species.columns:\n", + " # col is the friendly species name\n", + " # Convert to tCO2\n", + " df_yearly_tCO2_change[col] = (\n", + " df_yearly_change_all_species[col]\n", + " * carbon_fraction\n", + " * tCO2_tC_fraction\n", + " )\n", + "\n", + "# ---------------------------------\n", + "# 2) Summary statistics \n", + "# ---------------------------------\n", + "summary_stats_species = {}\n", + "\n", + "# For example, if you have 4 planting years:\n", + "# total trees for species = sum of (densities[sp] * planting_schedule[year])\n", + "# Then the \"average change per tree\" = \n", + "# ( sum over 30 yrs of that species' tCO2 change ) / ( total trees for that species )\n", + "\n", + "for sp_short in species_list:\n", + " sp_name = species_names[sp_short] # friendly name used as column\n", + " # Sum all 30 yrs\n", + " total_30yrs = df_yearly_tCO2_change[sp_name].sum()\n", + " annual_avg = df_yearly_tCO2_change[sp_name].mean()\n", + " \n", + " # total trees planted for this species\n", + " # we use planting_schedule here to include new plantings and replantings as its total trees planted ever\n", + " # e.g. sum of planting_schedule values\n", + " total_hectares_planted = sum(planting_schedule.values())\n", + " \n", + " # total trees (density * hectares) across the planting schedule, new & replanting\n", + " total_trees_planted = 0\n", + " for year_key, hectares in planting_schedule.items():\n", + " # e.g. \"Year_1\" -> 1\n", + " y_int = int(year_key.split(\"_\")[1])\n", + " total_trees_planted += densities[sp_short] * hectares\n", + "\n", + " avg_per_tree = (total_30yrs / total_trees_planted) if total_trees_planted else 0\n", + " \n", + " summary_stats_species[sp_short] = {\n", + " \"Species_Name\": sp_name,\n", + " \"Total_30yrs_tCO2\": total_30yrs,\n", + " \"Annual_Avg_tCO2\": annual_avg,\n", + " \"Avg_tCO2_per_Tree\": avg_per_tree\n", + " }\n", + "\n", + "# 3 Compute the total carbon sequestered across all species (sum of Total_Yearly_tCO2_Change)\n", + "df_yearly_tCO2_change[\"Total_Yearly_tCO2_Change\"] = df_yearly_tCO2_change.sum(axis=1)\n", + "\n", + "# Statistics based off of new hectares (total project hectares) planted\n", + "# Also computes the cumulative new hectares planted per project year, e.g. 1000 ha in year 1 + 2000 in year 2 = 3000 by year 3\n", + "max_year_planted = max(int(y.split(\"_\")[1]) for y in new_hectares_schedule.keys()) \n", + "cumulative_ha_by_year = []\n", + "\n", + "running_sum = 0\n", + "for y in range(1, 31):\n", + " if y <= max_year_planted:\n", + " # Add the hectares from new_hectares_schedule if it exists\n", + " key = f\"Year_{y}\"\n", + " if key in new_hectares_schedule:\n", + " running_sum += new_hectares_schedule[key]\n", + " # If y > max_year_planted, we keep the same running_sum\n", + " cumulative_ha_by_year.append(running_sum)\n", + "\n", + "# Convert to a Pandas Series indexed by year\n", + "s_cumulative_ha = pd.Series(cumulative_ha_by_year, index=range(1,31), name=\"Cumulative_Hectares\")\n", + "# Add to df_yearly_tCO2_change\n", + "\n", + "# Calculate total new hectares planted (not including replantings)\n", + "total_new_hectares = sum(new_hectares_schedule.values())\n", + "df_yearly_tCO2_change[\"Total_Hectares\"] = total_new_hectares\n", + "\n", + "df_yearly_tCO2_change[\"Cumulative_Hectares\"] = s_cumulative_ha\n", + "\n", + "# Create a new column: tCO2 per hectare (using total hectares instead of cumulative)\n", + "df_yearly_tCO2_change[\"tCO2_per_ha\"] = df_yearly_tCO2_change.apply(\n", + " lambda row: (row[\"Total_Yearly_tCO2_Change\"] / row[\"Total_Hectares\"]) \n", + " if row[\"Total_Hectares\"] > 0 else 0,\n", + " axis=1\n", + ")\n", + "\n", + "# -----------------------------\n", + "# Visualization section \n", + "# -----------------------------\n", + "print(\"Step 6. Total CO2e sequestered results\")\n", + "# The table with the results \n", + "print(\"\\n=== Per species and Total tCO2 Change & tCO2 per Cumulative Hectares\")\n", + "display(\n", + " df_yearly_tCO2_change.style.format(\"{:.0f}\")\n", + ")\n", + "\n", + "##SUMMARY STATS TABLES##\n", + "\n", + "df_summary_species = pd.DataFrame(summary_stats_species).T\n", + "df_summary_species.index.name = \"Species_Code\"\n", + "\n", + "print(\"\\n=== Summary Stats per Species ===\")\n", + "display(\n", + " df_summary_species.style.format({\n", + " \"Total_30yrs_tCO2\": \"{:.0f}\",\n", + " \"Annual_Avg_tCO2\": \"{:.0f}\",\n", + " \"Avg_tCO2_per_Tree\": \"{:.2f}\"\n", + " })\n", + ")\n", + "\n", + "# SUMMARY stats for total CO2e\n", + "# 1) sum over 30 yrs\n", + "total_30yr_tCO2 = df_yearly_tCO2_change[\"Total_Yearly_tCO2_Change\"].sum()\n", + "\n", + "# 2) average yearly (just the mean across the 30 rows)\n", + "avg_yearly_tCO2 = df_yearly_tCO2_change[\"Total_Yearly_tCO2_Change\"].mean()\n", + "\n", + "# 3) total hectares planted (not including replantings)\n", + "total_new_hectares_all = sum(new_hectares_schedule.values())\n", + "\n", + "# 3.1) Average annual change per total hectares (not including replantings)\n", + "avg_annual_per_ha = total_30yr_tCO2 / total_new_hectares_all / 30.0\n", + "\n", + "# 3.2) Total sequestered by 1 ha over project period (30 yrs)\n", + "avg_total_per_ha = total_30yr_tCO2 / total_new_hectares_all\n", + "\n", + "print(f\"\\n=== Final Summary (All Species Combined) ===\")\n", + "print(f\"Total 30-year tCO2 Change: {total_30yr_tCO2:,.0f} tonnes CO2\")\n", + "print(f\"Average Yearly tCO2 Change: {avg_yearly_tCO2:,.0f} tonnes CO2/yr\")\n", + "print(f\"Avg Annual per Hectare (over 30 yrs): {avg_annual_per_ha:,.0f} tonnes CO2/ha/yr (Option A)\")\n", + "print(f\"Total sequestered by 1 ha over project period (30 yrs) = {avg_total_per_ha:,.0f} tCO2/ha\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7: Final Credit Calculations\n", + "\n", + "- The CO2 sequestration results from the previous step are taken\n", + "- Placeholders for soil carbon (null), baseline, and leakage are input\n", + "- A 20% buffer is input \n", + "- The final Emissions Reductions (credits) are calculated\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 7 completed. Final data with placeholders, buffer, swift shift, and rounding to nearest 100.\n", + "\n", + "Final Emissions Reductions Table:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Sum_Species_tCO2Soil_tCO2Gross_tCO2Baseline_Leakage_DeductionNet_tCO2Post_Buffer_tCO2Swift_tCO2Rounded_Swift_tCO2
Year        
1259770259770259772078200
246605046605046605372842078220700
357287057287057287458303728437200
494166094166094166753324583045800
5134511013451101345111076097533275300
616995601699560169956135965107609107600
719671501967150196715157372135965135900
821265802126580212658170126157372157300
921755702175570217557174045170126170100
1021254102125410212541170033174045174000
1122651402265140226514181211170033170000
1224506402450640245064196051181211181200
1323923602392360239236191388196051196000
1422620102262010226201180961191388191300
1521017302101730210173168138180961180900
1619221801922180192218153774168138168100
1717323401732340173234138587153774153700
1815394001539400153940123152138587138500
1913489101348910134891107913123152123100
201164960116496011649693197107913107900
2199043099043099043792349319793100
2282716082716082716661737923479200
2367623067623067623540986617366100
2453809053809053809430475409854000
2541273041273041273330184304743000
2629981029981029981239853301833000
2719878019878019878159022398523900
281089101089101089187131590215900
2929420294202942235487138700
30-40530-40530-4053-324223542300
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total 30-year Credits (Swift, rounded to hundred) = 2,953,800 tCO2\n", + "Annual average = 98,460 tCO2/yr\n", + "Average per hectare (30 yrs) = 514 tCO2/ha\n", + "Average per hectare per year = 17 tCO2/ha\n", + "\n", + "Milestone totals (Swift, nearest 100):\n", + " Year 5: 179,000 tCO2\n", + " Year 10: 923,900 tCO2\n", + " Year 15: 1,843,300 tCO2\n", + " Year 20: 2,534,600 tCO2\n", + " Year 25: 2,870,000 tCO2\n", + " Year 30: 2,953,800 tCO2\n" + ] + }, + { + "data": { + "image/png": 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N6tWrA3cPn+fWvXMwLfNXx40bh6Ojo83P8OHDAXUOJqjzON3d3TPcZ2bb8mL16tUMHDiQL7/8kubNm1OxYkUGDBiQbem0SpUq4erqmuPzP3PmDK6urlSsWBGAxx9/nCeffNI6vcNsNrN8+XK6detmbZOX18SisKprWGJMz1JC7N5toCbQoPbdnTt3mDt3bobn1LFjRyDjc0rP8v50dnbOss3WrVs5cOAAP/74I8888wy7du3ipZdesu7Pbh6wp6dntlN5cpLT589y37l5/166dIlr167h5OSU4bVKSEiw+TxkdnsHB4cM8eSW0Wikb9++zJkzh3379vHrr79SpUoV3nzzTa5duwZAixYtcHV1ZevWrfzxxx+cOXPGmkjv27eP69evs3XrVh5++GFq1ap1X3Hkt7x+X+WmveX8AS8vrwz7Dh06REBAAI8++ihbtmyxvh/u5ezsjKIo1s+JKJmk/J0QRcCGDRu4ceMG69ato0aNGtbtcXFx93V/TZo0oWLFinz33XfWk+5y4952lSpVAmD8+PEEBwdnehtvb29ATVYyS24ftFZwpUqVmD17NrNnz+bs2bN8//33vP7661y+fDnTE+ZAHQn39/cnIiKC8+fPZzpP+vz588TGxtKhQweb0crBgwczfPhwjh07xp9//kl8fDyDBw+2iQdy95pY5GWeuhYqVKiAXq8nNDSUESNGZNomu6TLkhj+888/WbZp0KCB9bULCAggKCiIzz//nKFDh/Lkk09a7yOz+b8XL1603hbUBOfek3Uh+2Q/O5bHzs37t1KlSjz00ENZvvfKli2b4T6rVq1q3X/nzp0H+qcgvccff5w+ffowe/ZsTpw4wVNPPYWTkxOtWrVi69atVKtWDXd3d+rVq8fDDz8MwI4dO9i2bRudO3fOlxjyQ16/rwICAvj888/ZsGEDr7/+eqZtNmzYgIODQ4aTUg8dOkS7du2oUaMGkZGRGI3GLB/nn3/+wWAwUKZMmTw/J1F8yIi0EEWA5Q9H+pERRVH44osv7uv+HB0dee211/j999955513Mm1z+fJlfv7552zvx9vbm0cffZT//e9/GUbhLT+WxMHf359t27bZVGEwm8020ySycu8IYVaqV6/OyJEjCQgI4Jdffsm27fjx41EUheHDh2dYcMFsNvPiiy+iKArjx4+32de3b1+cnZ1ZsmQJS5YsoWrVqgQGBt7Xa1JUuLq64u/vz6FDh6hfv36mzym7UVQnJycefvhhTp06lavH0+l0zJ8/H71ez1tvvQVA8+bNcXFxYfny5TZtz58/z08//UTbtm2t22rWrMmJEydsqp9cvXrVOp0hr7y9vfHw8ODrr7+2TgkAderHvffZuXNnrl69itlszvR1svwTZUngVqxYYXP7b775Js9TBa5evUpqamqm+37//XcAm6k77dq1IzY2lrVr11qnb5QuXZpmzZoxd+5cLl68eN/TOnL7Wc3LbfP6fdWjRw/q1q3Le++9l+m0t9WrVxMZGclzzz1nc0QgLi6Odu3aUa1aNaKioqhQoUK28f7555/UrVs3T89RFD8yIi1EERAQEICTkxN9+/bl1Vdf5fbt23z66ad5Prs/vVdeeYVjx44xceJE9u/fT0hIiHWBg127dvH5558zefJkWrZsme39fPbZZ3To0IGgoCAGDRpE1apV+eeffzh27Bi//PIL3377LQBvvfUW33//PU8//TRvv/02rq6uzJ8/P1erg5UtW5YaNWrw3Xff0bZtWypWrEilSpWoUKEC/v7+hISEULt2bcqWLcuBAweIiIjIcjTYomXLlsyePZsxY8bQqlUrRo4cSfXq1a0Lsuzbt4/Zs2dnmANbvnx5evTowZIlS7h27Rrjxo2zlhfL62tSlMyZM4dWrVrRunVrXnzxRWrWrMm///7LH3/8wcaNGzNUr7iXn58fP/zwQ64f79FHH+X555/nk08+Yc+ePbRq1YoJEybwxhtvMGDAAPr27cvVq1eZPHkyzs7OTJw40Xrb0NBQPvvsM/r3709YWBhXr17lgw8+oFy5cvf13EuVKsU777zDc889R48ePQgLC+PatWtMmjQpw9SMPn36sGLFCjp27Mjo0aN56qmncHR05Pz582zfvp1u3brRo0cP6tSpQ//+/Zk9ezaOjo60a9eOI0eOMGPGjDzHuX37dkaPHk2/fv1o0aIFDz30EJcvX+brr78mIiKCAQMG2Bx1adu2LWazmW3bttnMQ2/Xrh0TJ05Ep9Px9NNP39dr5ePjA8Dnn39O2bJlcXZ2platWrmarpLdbfPyfaXX61m7di0BAQE0b96csWPH0rx5c1JSUti4cSOff/45vr6+zJw50/rYx48ft/7zMHXqVE6ePGlTCeaRRx6xluEEtTrL/v37GTp06H29TqIY0fBERyGKlZwWyMiuase9i1FkVnVh48aNSoMGDRRnZ2elatWqyiuvvGI98zz9We55XSDiu+++Uzp16qRUrlxZcXBwUCpUqKD4+/srCxYsUFJSUmxi//DDDzO9j//9739Kr169FDc3N8XR0VFxd3dXnn766Qxn2f/8889Ks2bNFIPBoLi7uyuvvPKK8vnnn+dYtUNRFGXr1q1Kw4YNFYPBYK1qcPv2beWFF15Q6tevr5QrV05xcXFRvL29lYkTJ+Z6oYTo6GilZ8+eSpUqVRQHBwfFzc1NCQ4OVvbu3ZvlbSIjI63VEU6cOHHfr8mDLKpyP1U77q2woCjq4h+dOnXKsB1QRowYYbPt9OnTypAhQ5SqVasqjo6OSuXKlZUWLVoo7777bo7xbtu2TQGU/fv322zP6jOgKIpy6dIlpUyZMoq/v79125dffqnUr19fcXJyUoxGo9KtWzflt99+y3DbpUuXKnXq1FGcnZ2VunXrKqtXr86yakdm72syqRLz5ZdfKo8++qji5OSkPPbYY8qiRYsy/byZTCZlxowZ1s9rmTJllNq1ayvDhg1TTp48aW2XkpKijB07VnFzc1OcnZ2VZs2aKdHR0XlekOXcuXPKW2+9pbRs2VJxd3dXHBwclLJlyypNmzZV5s6da1ORRFEUJS0tTalUqZIC2FRr+fnnnxVAadSoUYbHyG3VDkVRlNmzZyu1atVS9Hp9hu+8nOR029x8X1n8/fffyuuvv67Url3b2g9PPfWUMm/evAzVh7KrupRZHJb3c2xsbK6fmyiedIqS7jiVEEIIUUDq169Py5Yt+fTTT7UORYgHEhoayp9//pnj9DdR/EkiLYQQolBERETQo0cPTp48meVCOELYu1OnTlGnTh1++ukn62qKouSSRFoIIUShmTdvHg0aNKB169Zah1Ik5HTiYalSpTLM0bc3iqJkOKH3Xnq93u6r11hs376dkydP8vzzz2sdirADkkgLIYQQdiqn5HLgwIGZLjtvTyzLq2dn+/btGUrRCVEUSCIthBBC2KmDBw9mu79SpUrUrFmzcIK5T1evXs1xMRVvb+8iVxZSCJBEWgghhBBCiPti3xOrhBBCCCGEsFOyIEshS0tL4+LFi5QtW7bInFghhBBCCFGSKIrCv//+i6enZ7Yn9EoiXcguXryIl5eX1mEIIYQQQogcnDt3LttynZJIFzLLyRTnzp3LdBlYk8lEZGQkgYGBODo6FnZ4AukDeyH9oD3pA+1JH2hP+kB7WvRBcnIyXl5eOZ4EK4l0IbNM5yhXrlyWibSrqyvlypWTD6xGpA/sg/SD9qQPtCd9oD3pA+1p2Qc5TcOVkw2FEEIIIYS4D5JICyGEEEIIcR8kkRZCCCGEEOI+aDpHevr06axbt47ff/8dFxcXWrRowfvvv4+3t7e1jaIoTJ48mc8//5zExESaNm3K/Pnzefzxx61tUlJSGDduHF9//TW3bt2ibdu2fPLJJzZnWSYmJjJq1Ci+//57ALp27crcuXMpX768tc3Zs2cZMWIEP/30Ey4uLoSEhDBjxgycnJysbQ4fPszIkSPZv38/FStWZNiwYUyYMEFK2QkhhCjyFEXhzp07mM1mrUOxGyaTCQcHB27fvi2vi0YKog/0ej0ODg4PnL9pmkjv3LmTESNG8OSTT3Lnzh3efPNNAgMDOXr0KKVLlwbggw8+YNasWSxZsoTHHnuMd999l4CAAI4fP249k3LMmDFs3LiRVatW8dBDDzF27Fg6d+5MbGwser0egJCQEM6fP09ERAQAzz//PKGhoWzcuBEAs9lMp06dqFy5Mnv27OHq1asMHDgQRVGYO3cuoJ7BGRAQgL+/PwcOHODEiRMMGjSI0qVLM3bs2MJ++YQQQoh8k5qaSnx8PDdv3tQ6FLuiKAru7u6cO3dOBs00UlB94OrqioeHh82AaV5pmkhbklqLxYsX4+bmRmxsLG3atEFRFGbPns2bb75JcHAwAEuXLqVKlSqsXLmSYcOGkZSUxMKFC1m2bBnt2rUDYPny5Xh5ebF161aCgoI4duwYERERxMTE0LRpUwC++OILmjdvzvHjx/H29iYyMpKjR49y7tw5PD09AZg5cyaDBg1i6tSplCtXjhUrVnD79m2WLFmCwWDAx8eHEydOMGvWLMLDw+UDJoQQokhKS0vj9OnT6PV6PD09cXJykr9p/0lLS+P69euUKVMm24U5RMHJ7z5QFIXU1FSuXLnC6dOnefTRR+/7fu2q/F1SUhIAFStWBOD06dMkJCQQGBhobWMwGPD19WXv3r0MGzaM2NhYTCaTTRtPT098fHzYu3cvQUFBREdHYzQarUk0QLNmzTAajezduxdvb2+io6Px8fGxJtEAQUFBpKSkEBsbi7+/P9HR0fj6+mIwGGzajB8/njNnzlCrVq0MzyklJYWUlBTr9eTkZEA9TGEymTK0t2zLbJ8oHNIH9kH6QXvSB9orrD5ISUnBbDZTtWpVXF1dC/SxihpL0mUwGOSfC40URB8YDAb0ej1nz57l5s2bNrkd5P4zZzeJtKIohIeH06pVK3x8fABISEgAoEqVKjZtq1Spwl9//WVt4+TkRIUKFTK0sdw+ISEBNze3DI/p5uZm0+bex6lQoQJOTk42bWrWrJnhcSz7Mkukp0+fzuTJkzNsj4yMzPbLKioqKst9onBIH9gH6QftSR9or6D7wMHBAXd3d27evMmdO3cK9LGKqn///VfrEEq8/O6D1NRUbt26xc6dOzO873M7xcluEumRI0fy66+/smfPngz77v3vQ1GUHP8jubdNZu3zo42iKFneFmD8+PGEh4dbr1tWygkMDMxyQZaoqCgCAgKk8LtGpA/sg/SD9qQPtFdYfXD79m3OnTtHmTJlcHZ2LrDHKYoUReHff/+lbNmyMiKtkYLqg9u3b+Pi4kKbNm0yvO8tMwhyYheJ9EsvvcT333/Prl27bCptuLu7A+por4eHh3X75cuXrSPB7u7upKamkpiYaDMqffnyZVq0aGFtc+nSpQyPe+XKFZv72bdvn83+xMRETCaTTRvL6HT6x4GMo+YWBoMhw+ECAEdHx2y/FHPaLwqe9IF9KM79YDbD7t0QHw8eHtC6Nfx3frRdKc59UFQUdB+YzWZ0Oh2lSpWSecD3SEtLA7C+PqLwFVQflCpVCp1Ol+nnK7efN03fEYqiMHLkSNatW8dPP/2UYWpErVq1cHd3tzmklZqays6dO61JcuPGjXF0dLRpEx8fz5EjR6xtmjdvTlJSEvv377e22bdvH0lJSTZtjhw5Qnx8vLVNZGQkBoOBxo0bW9vs2rWL1NRUmzaenp4ZpnwIIUR21q2DmjXB3x9CQtTfNWuq24Uoysxm2LEDvv5a/V3SK8YNGjSI7t27P9B97NixA51Ox7Vr1/IlptyoWbMms2fPtl5PSEggICCA0qVL25QOLuk0TaRHjBjB8uXLWblyJWXLliUhIYGEhARu3boFqP95jBkzhmnTprF+/XqOHDnCoEGDcHV1JSQkBACj0cjQoUMZO3Ys27Zt49ChQ/Tv35969epZq3jUqVOH9u3bExYWRkxMDDExMYSFhdG5c2drzerAwEDq1q1LaGgohw4dYtu2bYwbN46wsDDrFIyQkBAMBgODBg3iyJEjrF+/nmnTpknFDiFEnqxbBz17wvnzttsvXFC3SzItiiot/kEcNGgQOp0OnU6Hg4MD1atX58UXXyQxMbHgHtQOHTp0iM6dO+Pm5oazszM1a9akd+/e/P333/d1fwcOHOD555+3Xv/oo4+Ij48nLi6OEydO5Dq5X7JkSZaJ9x9//MHgwYOpVq0aBoOBWrVq0bdvXw4ePGjTbtOmTXTu3Bmj0YirqytPPvkkS5YssWnzv//9j759++Ll5YWLiwt16tRhzpw59/PU80TTRPrTTz8lKSkJPz8/PDw8rD+rV6+2tnn11VcZM2YMw4cPp0mTJly4cIHIyEhrDWlQO7d79+706tWLli1b4urqysaNG601pAFWrFhBvXr1CAwMJDAwkPr167Ns2TLrfr1ez+bNm3F2dqZly5b06tWL7t27M2PGDGsbo9FIVFQU58+fp0mTJgwfPpzw8HCbOdBCCJEdsxlGj4b/Tq+wYdk2ZoyM4omiR8t/ENu3b098fDxnzpzhyy+/ZOPGjQwfPrzgHtDOXL58mXbt2lGpUiV+/PFHjh07xqJFi/Dw8LjvuuCVK1e2KYpw6tQpGjduzKOPPpppAYe8OnjwII0bN+bEiRN89tlnHD16lPXr11O7dm2btTnmzp1Ljx49eOqpp4iOjubXX3+lT58+vPDCC4wbN87aLjY2lsqVK7N8+XJ+++033nzzTcaPH8+8efMeONZsKaJQJSUlKYCSlJSU6f7U1FRlw4YNSmpqaiFHJiykD+xDce2H7dsVRU2Zs//Zvl3rSItvHxQlhdUHt27dUo4eParcunXLui0tTVGuX8/dT1KSolStmvX7WadTlGrV1Ha5ub+0tNzHPnDgQKVbt24228LDw5WKFStar5vNZmXy5MlK1apVFScnJ6VBgwbKDz/8YN2/fft2BVASExOt2w4dOqQAyqlTp5TExERl4cKFitFoVCIiIpTatWsrpUuXVoKCgpSLFy9ab3Pnzh3l5ZdfVoxGo1KxYkXllVdeUQYMGGATX1pamvL+++8rtWrVUpydnZX69esr3377rU38mzdvVh599FHF2dlZ8fPzUxYvXpwhvvTWr1+vODg4KCaTKcvXqVGjRsqMGTOs17t166bo9XprPhIfH68Ayu+//64oiqLUqFFD+eijj6yXAevPwIEDba5btt3L8rqm/5k4caKSlpamPP7440rjxo0Vs9mc4XaW53n27FnF0dFRefnll5XExESbth9//LECKDExMVk+5+HDhyv+/v5Z7s/sfW+RU75mIbPmhRCiEKU7DSNf2glRUG7ehDJlcvdjNKojz1lRFHWk2mjM3f09yOKKf/75JxERETYni82ZM4eZM2cyY8YMfv31V4KCgujatSsnT57M033fvHmTGTNmsGzZMnbt2sXZs2dtRkVnzpzJokWLWLhwIXv27OGff/5h/fr1Nvfx1ltvsXjxYj799FN+++03Xn75Zfr378/OnTsBOHfuHMHBwXTs2JG4uDiee+45Xn/99Wzjcnd3586dO6xfv95aTexefn5+7NixA1DPUdu9ezcVKlSwVkvbvn077u7u1imv6R04cID27dvTq1cv4uPjmTNnDmvXrgXg+PHj1m33atGiBbNnz6ZcuXLEx8cTHx/PuHHjiIuL47fffmPs2LGZnjxomQqyZs0aTCZTpqtHDxs2jDJlyvD1119n+bokJSVZ1yYpKJJICyFEIUpXgChbW7bAxYsFG4sQxcWmTZsoU6YMLi4uPPLIIxw9epTXXnvNun/GjBm89tpr9OnTB29vb95//32eeOIJm5PpcsNkMrFgwQKaNGlCo0aNGDlyJNu2bbPunz17NuPHj+eZZ56hTp06LFiwAKPRaN1/48YNZs2axaJFiwgKCuLhhx9m0KBB9O/fn88++wxQp70+/PDDfPTRR3h7e9OvXz8GDRqUbVzNmjXjjTfeICQkhEqVKtGhQwc+/PBDm4plfn5+7N69m7S0NH799Vf0ej2hoaHW5HrHjh34+vpmev+VK1fGYDDg4uKCu7s7RqPRmqC6ublZt93LyckJo9GITqfD3d0dd3d3ypQpY/0Hpnbt2tk+rxMnTmA0Gm0qt6W/74cffpgTJ05ketvo6Gi++eYbhg0blu1jPChJpIUQohC1bp27ZHr5cqhRQz1hKyam4OMS4l6urnD9eu5+tmzJ3X1u2ZK7+8vr4or+/v7ExcWxb98+XnrpJYKCgnjppZcAtR7wxYsXadmypc1tWrZsybFjx/L0OK6urjzyyCPW6x4eHtYyuElJScTHx9O8eXPrfgcHB5o0aWK9fvToUW7fvk1AQABlypSx/nz11VecOnUKgGPHjtGsWTObIgbp7zMrU6dOJSEhgQULFlC3bl0WLFhA7dq1OXz4MABt2rTh33//5dChQ+zcuRNfX1/8/f2tI+HZJdL5TclhDY683E9m9/Hbb7/RrVs33n77bQICAh7oMXIiibQQQhQiRYGsKkfpdOrPuHFqwn3njlpCrHlzaNoUVqyAdNU3hShQOh2ULp27n8BAqFZNvU1W9+XlpbbLzf3lNb8qXbo0//d//0f9+vX5+OOPSUlJybCqcHaLu1mmF6SfFpHZEtH31hbW6XRZTqXIjKUe8ubNm4mLi7P+HD16lDVr1mSIIa8eeughnn32WWbOnMmxY8fw9PS0Fk0wGo088cQT7Nixg507d+Ln50fr1q2Ji4vj5MmTnDhxAj8/v/t+7Lx47LHHAHL8R+axxx4jKSmJi5kcnktNTeXPP//k0Ucftdl+9OhRnn76acLCwnjrrbfyL+gsSCIthBCF6J134NgxcHaG/9acsqpWDdasgQ8/hF27IDYWBg0CJyfYvx/691dHqadMgXvXmJLavUJLej1YpsjemwRbrs+eXXgLDk2cOJEZM2Zw8eJFypUrh6enZ4aVk/fu3UudOnUAdeoCYLOWRFxcXJ4e0zIFISbdIaQ7d+4QGxtrvV63bl0MBgNnz57l//7v/2x+vLy8rG1i7jkMde/13HBycuKRRx7hxo0b1m1+fn5s376dXbt24efnR/ny5albty7vvvsubm5u1tcjt/cP6mI+ObW7t80TTzxB3bp1mTlzpvWfi/QsJfWeeeYZHBwcmDVrVoY2CxYs4MaNG/Tt29e67bfffsPf35+BAwcyderUXD+XByGJtBBCFJLt29VEGmDRIvXkq+3bYeVK9ffp0xAcfLd9o0aweDGcO6cmzx4ekJAAEydC9epqkv3LL7K4i7APwcHqP4JVq9put/yDmP69XdD8/Px4/PHHmTZtGgCvvPIK77//PqtXr+b48eO8/vrrxMXFMXr0aABrIjtp0iROnDjB5s2bmTlzZp4fd/To0bz33nusX7+e33//neHDh9vUWS5btizjxo3j5ZdfZunSpZw6dYpDhw4xf/58li5dCsALL7zAqVOnCA8P5/jx46xcuTJDzeR7bdq0if79+7Np0yZOnDjB8ePHmTFjBlu2bKFbt242r0tERAQ6nY66detat61YsSLP0zpq1KiBTqdj06ZNXLlyhevXr2farmbNmly/fp1t27bx999/c/PmTXQ6HYsXL+bEiRO0adOGLVu28Oeff/Lrr78ydepUa8zVq1fngw8+YM6cObzzzjv8/vvvnDp1ilmzZvHqq68yduxYmjZtCtxNogMCAggPD7euTXLlypU8Pa88y7amh8h3Uv7O/kkf2Ifi1g+XLyuKp6daCmzIkPu7j5QURVmxQlGaNs25fJ5Op/6sXXv/MRe3PiiKtCx/d7/u3FHLN65cqf6+c+eB7zJbmZW/UxRFWbFiheLk5KScPXvWpvydo6NjhvJ3iqIoe/bsUerVq6c4OzsrrVu3Vr799ttMy9+lt379eiV9KmUymZTRo0cr5cqVU8qXL6+Eh4dnWv5uzpw5ire3t+Lo6KhUrlxZCQoKUnbu3Glts3HjRuX//u//FIPBoLRu3VpZtGhRtuXvTp06pYSFhSmPPfaY4uLiopQvX1558sknlcWLF9u0u3btmqLX65WePXtmeA7z5s2zaZu+/J2iqOXy7i1xN2XKFMXd3V3R6XSZlr+zeOGFF5SHHnrIWv7O4vjx48qAAQMUT09PxcnJSalRo4bSt29f5ZdffrG5/fr165XmzZsrpUuXVpydnZXGjRsrixYtsmkzceLEDKX2AKVGjRpZxpUf5e90ivIAk3FEniUnJ2M0GklKSrKumJieyWRiy5YtdOzYMdfrvIv8JX1gH4pTP6SlQZcu6olWtWvDwYPqPNAHsW+feqh81aqs2+h06mjg6dP3d0i9OPVBUVVYfXD79m1Onz5NrVq1cHZ2LrDHKYrS0tJITk6mXLlymZZqEwWvoPogu/d9TvmahbwjhBCigM2erSbRBgOsXv3gSTSoJx/mVNVJUdRpIbt3P/jjCSGEyEgSaSGEKEAHDoBlLYXZs6F+/fy7b1ncRQghtCWJtBBCFJDkZOjTB0wmeOaZnEeQ8yq3i7vktp0QQoi8kURaCCEKgKKoifOff6ol6774Iu+1cXPSunX2tXsBHB0hkxV/hRBC5ANJpIUQogAsWqSeCKjXq7WdK1TI/8fITe1ek0kth3fhQv4/vih+pP6AKEny4/0uibQQQuSzo0fhv9WJmTpVXZmwoGRXu3fuXHU1uePH1dHr06cLLg5RtFkqgty8eVPjSIQoPJb3+4NUxHHIr2CEEELArVvQu7f6OyAAXnml4B8zOBi6dVOrc8THq3OiW7dWR6y7dIG2beHUKXXbtm0y1UNkpNfrKV++PJcvXwbA1dU1w5LaJVVaWhqpqancvn1byt9pJL/7QFEUbt68yeXLlylfvjz6B1hyUxJpIYTIRy+/DEeOQJUqsGwZFNbfXb0e/Pwybq9RQ02w27VTR8rbtIGoqPytHiKKB/f/1qy3JNNCpSgKt27dwsXFRf650EhB9UH58uWt7/v7JYm0EELkk2+/hc8+U+cnL1umJtP2wMMDduyAoCA4dEhNuH/8EZ58UuvIhD3R6XR4eHjg5uaGyWTSOhy7YTKZ2LVrF23atJGFiTRSEH3g6Oj4QCPRFpJICyFEPjh9GsLC1Muvv65O67AnlSvDTz9Bx44QHa1O99i8WZ3uIUR6er0+XxKM4kKv13Pnzh2cnZ0lkdaIPfeBTPYRQogHZDJB376QlKSeWDh5stYRZa58eYiMVKt4/PuvOkIdFaV1VEIIUXRJIi2EEA9owgTYt09NVFeuVGs326syZdSR6A4d1BMiO3eG77/XOiohhCiaJJEWQogH8OOP8P776uUvv4SaNTUNJ1dcXGD9erXaR2qq+nvVKq2jEkKIokcSaSGEuE/x8RAaql5+8UV1GfCiwmCA1auhf38wmyEkBBYv1joqIYQoWiSRFkKIPDCb1QoYK1ao0yKuXFFLyc2apXVkeefgAEuXwvPPq0uaDxkC8+er+8xm2LlTx65dVdm5U4fZrG2sQghhj6RqhxBC5NK6dTB6NJw/f3ebTgdDh4Kzs3ZxPYhSpWDBAnB1hdmzYeRIdb739u1w/rwD0IRZs9SVEufMUaeBCCGEUMmItBBC5MK6ddCzp20SDepI7pgx6v6iSqdTR9Tfeku9vmxZxud54YL6/Ivy8xRCiPwmibQQQuTAbFZHohUl6zZjxlCkpz/odDBpEhiNme+3PPei/jyFECI/SSIthBA52L074whteooC586p7Yqy3bvVWthZKS7PUwgh8osk0kIIkYP4+PxtZ69KyvMUQoj8Iom0EELk4KGHctfOw6Ng4yhouY2/qD9PIYTIL5JICyFENlJT4eOPs2+j04GXF7RuXTgxFZTWrdXqHDpd5vuLy/MUQoj8Iom0EEJkwWSCvn3VJbUty37fm2Rars+eDXp9oYaX7/R6tcQdZJ5MK4q6HHpRf55CCJFfJJEWQohMmM0wYIBa7s3JCTZtgrVroWpV23bVqsGaNcWnvnJwsPp87n2eDv+tOvDpp3DjRuHHJYQQ9kgWZBFCiHukpamr/K1apY5Er1sHgYHqvm7d1KoV8fHqXOHWrYvfCG1wsPo8t2+/ww8/xNGhwxPUrOlAixZw6BD066f+U1HcnrcQQuSVJNJCCJFOWhoMGwZffaUmiqtXQ6dOd/fr9eDnp1l4hUavB19fhRs3LuDr2wBHR9iwAZ5+Gr77Dl5/HT78UOsohRBCW5pO7di1axddunTB09MTnU7Hhg0bbPbrdLpMfz5M9+3t5+eXYX+fPn1s7icxMZHQ0FCMRiNGo5HQ0FCuXbtm0+bs2bN06dKF0qVLU6lSJUaNGkVqaqpNm8OHD+Pr64uLiwtVq1ZlypQpKNmt0CCEKFIUBUaNgi+/VJfOXrkSevTQOir70aIFLF6sXp4xA774Qtt4hBBCa5om0jdu3KBBgwbMmzcv0/3x8fE2P4sWLUKn0/HMM8/YtAsLC7Np99lnn9nsDwkJIS4ujoiICCIiIoiLiyM0NNS632w206lTJ27cuMGePXtYtWoVa9euZezYsdY2ycnJBAQE4OnpyYEDB5g7dy4zZsxg1qxZ+fiKCCFAnZ+8c6eOXbuqsnOnrlBW0lMUGDcO5s9XT7RbuhR69Sr4xy1q+vaFyZPVy8OHw9at2sYjhBBa0nRqR4cOHejQoUOW+93d3W2uf/fdd/j7+/Pwww/bbHd1dc3Q1uLYsWNEREQQExND06ZNAfjiiy9o3rw5x48fx9vbm8jISI4ePcq5c+fw9PQEYObMmQwaNIipU6dSrlw5VqxYwe3bt1myZAkGgwEfHx9OnDjBrFmzCA8PR5dVvSghRJ6sW6cux33+vAPQhFmz1BP65swpuBP6FAXefBMs/xd/8QX0718wj1UcTJgAJ0/C8uXQsydER0OdOlpHJYQQha/IzJG+dOkSmzdvZunSpRn2rVixguXLl1OlShU6dOjAxIkTKVu2LADR0dEYjUZrEg3QrFkzjEYje/fuxdvbm+joaHx8fKxJNEBQUBApKSnExsbi7+9PdHQ0vr6+GAwGmzbjx4/nzJkz1KpVK9O4U1JSSElJsV5PTk4GwGQyYTKZMrS3bMtsnygc0gfaWb9eR58+etQZU3f/Ob1wQaFnT1i1ykyPHvk/nerdd0sxfbp65tzHH5sZMCAN6f7sPwuffgqnT+v5+edSdOqksGfPHSpXLuwIiz/5PtKe9IH2tOiD3D5WkUmkly5dStmyZQm+Z0iqX79+1KpVC3d3d44cOcL48eP53//+R1RUFAAJCQm4ublluD83NzcSEhKsbapUqWKzv0KFCjg5Odm0qVmzpk0by20SEhKyTKSnT5/OZMtx0HQiIyNxdXXN8vla4hfakT4oXGYzDB8eiKLoSZ9EAyiKDlAYMSIVB4eofK0WsW7d//HVV48DMGTIYapX/5MtW/Lv/ouDrD4Lw4Y58ccfbTh9ujRt2yYzZcpenJzSCjm6kkG+j7QnfaC9wuyDmzdv5qpdkUmkFy1aRL9+/XB2drbZHhYWZr3s4+PDo48+SpMmTfjll19o1KgRQKbTLhRFsdl+P20sJxpmN61j/PjxhIeHW68nJyfj5eVFYGAg5cqVy9DeZDIRFRVFQEAAjpYVIEShkj7QxqpVOq5eze4rScfff7tSrlwnfH3zZ1R6zpxSfPWVmpVPnWrmlVdqA7Xz5b6Lg9x8Fp54Alq3Vvj994dYt64TS5eas1wZUeSdfB9pT/pAe1r0gWUGQU6KRCK9e/dujh8/zurVq3Ns26hRIxwdHTl58iSNGjXC3d2dS5cuZWh35coV64iyu7s7+/bts9mfmJiIyWSyaWMZnba4fPkyQIbR7PQMBoPNdBALR0fHbN8MOe0XBU/6oOBdvQrffgsrVsCePbm7zZIlDvj4QBanReTaJ5/AK6+olydPhjfe0ANSGDkz2X0W6tVTa0q3bw+rVpWidu1STJxYyAGWAPJ9pD3pA+0VZh/k9nGKxMqGCxcupHHjxjRo0CDHtr/99hsmkwkPDw8AmjdvTlJSEvv377e22bdvH0lJSbRo0cLa5siRI8THx1vbREZGYjAYaNy4sbXNrl27bEriRUZG4unpmWHKhxAljdkMO3bA11+rv7OrsnH7trpyXvfu6oImL76Y+yQa1KS7alXo2FFdMOXWrbzHu3AhjBihXh4/Xj15Tty/tm3VOdMAkyapZQOFEKIk0DSRvn79OnFxccTFxQFw+vRp4uLiOHv2rLVNcnIy3377Lc8991yG2586dYopU6Zw8OBBzpw5w5YtW3j22Wdp2LAhLVu2BKBOnTq0b9+esLAwYmJiiImJISwsjM6dO+Pt7Q1AYGAgdevWJTQ0lEOHDrFt2zbGjRtHWFiYdfpFSEgIBoOBQYMGceTIEdavX8+0adOkYoco8datg5o1wd8fQkLU3zVrqtst0tJg+3YYOhSqVIFnn1UX9TCZ1KkBH34If/2lVufI6uOk00GFCtCsmXp/P/yglmJzd4fnnoNdu9Tt97o3yV+6FCwzwsLDYerUrB9T5N5zz90d4R88GH7+Wdt4hBCiUCga2r59uwJk+Bk4cKC1zWeffaa4uLgo165dy3D7s2fPKm3atFEqVqyoODk5KY888ogyatQo5erVqzbtrl69qvTr108pW7asUrZsWaVfv35KYmKiTZu//vpL6dSpk+Li4qJUrFhRGTlypHL79m2bNr/++qvSunVrxWAwKO7u7sqkSZOUtLS0PD3npKQkBVCSkpIy3Z+amqps2LBBSU1NzdP9ivwjfZB7a9cqik6nKGoBubs/Op36M3OmorzyiqJUq2a738tLUV5/XVEOH878/u69T8u2tWvVdidOKMpbbylKjRq27WrVUpS331aUkyfv3t+9j235GTlSUfL48S1x8vpZMJsVpXt39fWtVElRTp0q4ABLAPk+0p70gfa06IOc8jULnaLI0nyFKTk5GaPRSFJSUpYnG27ZsoWOHTvKXCyNSB/kjtmsjjyfP5+79uXLqyPR/ftDq1bqyoGZuVtH+u42Ly+YPTtjHem0NNi9W13O+9tv4d9/7+7z9objx7OO59tv1RrIImv381m4cQN8fSE2FmrXVmtMly9fsHEWZ/J9pD3pA+1p0Qc55WsWRWKOtBDC/uzenbskunVrNTlOSIDPP4c2bbJOokFNls+cgaioO4SHHyQq6g6nT2e+GEupUmrStnChev8rV6onvel02SfROp06raMwVkwsaUqXhu+/V6fp/P67+s/K7du5n0MvhBBFiSTSQoj7ku7c3Gy9+CL06AGZFK/Jkl4Pvr4KbdpcwNdXyVXdaFdXdc70Dz+oo83ZURQ4d079Z0DkP09P2LhRTaq3bYNKlbKfQy+EEEWVJNJCiPvyX2GcfGuXn9IV18lWbv8ZEHn3xBMwapR6+cYN230XLqgj1ZJMCyGKOkmkhRD3pXVrdeQxKzqdOre5devCi8nCnpP8ksJshmXLMt9nOTNnzBiZ5iGEKNokkRZC3Je0NHjoocz3WcrJzZ5Nvi7nnVutW+dcSk+rJL+kyGkOvUyvEUIUB5JICyHyTFHUBU0OH1bnPt+7uGe1auqiK5mdIFgY9HqYM0e9fG8yrXWSX1LkdtqMTK8RQhRlkkgLIfJs1iz44gu1asaaNeqc1+3b1aoZ27eTZZWNwhQcrMZWtartdq2T/JJCptcIIUoCB60DEEIULRs23F3BbuZM6NxZveznp1VEWQsOhm7d1OkD8fFq0ta6tYxEFwbL9JoLF+7Oib6XTK8RQhR1kkgLIXLtl1+gXz81MXrxRXXhFHun19tnkl/cWabX9OypTqfJLJkeNUr+qRFCFG0ytUMIkSvnz0OXLnDzJgQGwscfZ30ynxCQ9fQaZ2f196efQlJS4cclhBD5RRJpIUSOrl9Xk+iLF6FuXfjmG3CQ41kiFywrVaafQ3/unLooy59/wpAhWU/9EEIIeyd/CoUQ2TKb1RXp4uLAzQ02bwajUeuoRFGS2fSab76Bli3VRVnmzr27eIsQQhQlMiIthMjWq6+qyz0bDPDdd+pIohAP6skn1ZNVAcaNg/37tY1HCCHuhyTSQogsLViglroDWLoUmjXTNh5RvIwcqZ6MaDJBr17wzz9aRySEEHkjibQQIlORkWqiA/DOO9C7t7bxiOJHp4Mvv4RHHoG//oJBg2S+tBCiaJFEWgiRwdGj8Oyz6vzo0FB4802tIxLFldEI336rTh3auPHudA8hhCgKJJEWQti4fBk6dYLkZHWxjC++kDJ3omA1bHh3SffXX4eff9Y2HiGEyC1JpIUQVrdvQ/fuarmyRx5RKyoYDFpHJUqC55+Hvn3VoyC9e8OVK1pHJIQQOZNEWggBqHNTBw+G6GgoX14tc1epktZRiZJCp4PPPgNvb3VZ8dBQSEvTOiohhMieJNJClFBmM+zYAV9/rf5++21YtUpdaGXdOjWhEaIwlS2rzpd2cYEff4T33tM6IiGEyJ4syCJECbRuHYwerS77fa/PPgN//8KPSQiAevVg/nx1xcMJE6BFi4yLuQghhL2QEWkhSph169TavZkl0aBO6xBCS4MHw8CB6tSOvn3h0iWtIxJCiMxJIi1ECWI2qyPRWdXq1elgzBi1nRBamj8fHn8cEhKgXz95Twoh7JMk0kKUILt3Zz0SDWqCfe6c2k4ILZUurc6XLl0atm1TFwUSQgh7I4m0ECVIfHz+thOiINWpoy5TDzBlCmzdqm08QghxL0mkhShBPDzyt50QBa1/fwgLU4+WhITAxYtaRySEEHdJIi1ECdK6NVSrlvV+nQ68vNR2QtiLOXOgQQN1kZa+fSElxbZ0o8yfFkJoRRJpIUoQvT7ruaaWZcBnz1bbCWEvXFzU+dJly8KuXVC5slqiMSRE/V2zplqNRgghCpsk0kKUML//rv52dLTdXq0arFkDwcGFH5MQOXn0UXWKB8C//9ruu3BBLekoybQQorDJgixClCCXLsHcuerlb78Fo1E9sdDDQ53OISPRwl6ZzfDNN5nvU5S7pRu7dZP3sRCi8EgiLUQJ8sEHcPMmPPkkdO16dzqHEPYuL6UbZSVEIURhkakdQpQQ8fHwySfq5SlTJIkWRYuUbhRC2CNJpIUoId57D27fhhYtIChI62iEyBsp3SiEsEeaJtK7du2iS5cueHp6otPp2LBhg83+QYMGodPpbH6aNWtm0yYlJYWXXnqJSpUqUbp0abp27cr5e47/JSYmEhoaitFoxGg0EhoayrVr12zanD17li5dulC6dGkqVarEqFGjSE1NtWlz+PBhfH19cXFxoWrVqkyZMgUlq7WWhbAj58/DZ5+pl2U0WhRFltKNWb13pXSjEEILmibSN27coEGDBsybNy/LNu3btyc+Pt76s2XLFpv9Y8aMYf369axatYo9e/Zw/fp1OnfujDldYdGQkBDi4uKIiIggIiKCuLg4QkNDrfvNZjOdOnXixo0b7Nmzh1WrVrF27VrGjh1rbZOcnExAQACenp4cOHCAuXPnMmPGDGbNmpWPr4gQBWPaNLX2bps28PTTWkcjRN7p9Wo9acg8mVYUKd0ohCh8mp5s2KFDBzp06JBtG4PBgLu7e6b7kpKSWLhwIcuWLaNdu3YALF++HC8vL7Zu3UpQUBDHjh0jIiKCmJgYmjZtCsAXX3xB8+bNOX78ON7e3kRGRnL06FHOnTuHp6cnADNnzmTQoEFMnTqVcuXKsWLFCm7fvs2SJUswGAz4+Phw4sQJZs2aRXh4ODoZ4hN26q+/4Msv1csyGi2KsuBgtUTj6NGZn3h482bhxySEKNnsvmrHjh07cHNzo3z58vj6+jJ16lTc3NwAiI2NxWQyERgYaG3v6emJj48Pe/fuJSgoiOjoaIxGozWJBmjWrBlGo5G9e/fi7e1NdHQ0Pj4+1iQaICgoiJSUFGJjY/H39yc6OhpfX18MBoNNm/Hjx3PmzBlq1aqVafwpKSmkpKRYrycnJwNgMpkwmUwZ2lu2ZbZPFI7i1gfvvKPHZCqFv38aLVqYKSpPq7j1Q1Fkj33QpQt07Ah79uispRt37dLxzjt6hg9XePLJOzz8sNZR5h977IOSRvpAe1r0QW4fy64T6Q4dOvDss89So0YNTp8+zYQJE3j66aeJjY3FYDCQkJCAk5MTFSpUsLldlSpVSEhIACAhIcGaeKfn5uZm06ZKlSo2+ytUqICTk5NNm5o1a2Z4HMu+rBLp6dOnM3ny5AzbIyMjcXV1zfK5R0VFZblPFI7i0AcJCa4sWdIWgMDAn9my5R+NI8q74tAPRZ299kG5cnDjBjzxBNSp04pjxx6iW7d/mTZtD3p98Tp/xV77oCSRPtBeYfbBzVwe4rLrRLp3797Wyz4+PjRp0oQaNWqwefNmgrNZfk1RFJupFplNu8iPNpYTDbOb1jF+/HjCw8Ot15OTk/Hy8iIwMJBy5cplaG8ymYiKiiIgIADHe5eeE4WiOPVBWJges7kUgYFpjB3bLOcb2JHi1A9FVVHqg/r1oUkThePHK/LLL52YODFN65DyRVHqg+JK+kB7WvSBZQZBTuw6kb6Xh4cHNWrU4OTJkwC4u7uTmppKYmKizaj05cuXadGihbXNpUuXMtzXlStXrCPK7u7u7Nu3z2Z/YmIiJpPJpo1ldDr94wAZRrPTMxgMNtNBLBwdHbN9M+S0XxS8ot4HJ0/C8uXq5SlTSuHoWDSrXRb1figOikIf/N//wYIF0LcvTJ+uJyhIX6wqeBSFPijupA+0V5h9kNvHKVJ/Wa9evcq5c+fw+K9QaOPGjXF0dLQZ6o+Pj+fIkSPWRLp58+YkJSWxf/9+a5t9+/aRlJRk0+bIkSPEp6vkHxkZicFgoHHjxtY2u3btsimJFxkZiaenZ4YpH0LYgylT1GWVO3WCdKcICFFs9ekDAwdCWhr07w/3VDkVQoh8p2kiff36deLi4oiLiwPg9OnTxMXFcfbsWa5fv864ceOIjo7mzJkz7Nixgy5dulCpUiV69OgBgNFoZOjQoYwdO5Zt27Zx6NAh+vfvT7169axVPOrUqUP79u0JCwsjJiaGmJgYwsLC6Ny5M97e3gAEBgZSt25dQkNDOXToENu2bWPcuHGEhYVZp1+EhIRgMBgYNGgQR44cYf369UybNk0qdgi7dOwYrFypXs5kir4QxdbcufDII3D2LLz4oloWTwghCoqmifTBgwdp2LAhDRs2BCA8PJyGDRvy9ttvo9frOXz4MN26deOxxx5j4MCBPPbYY0RHR1O2bFnrfXz00Ud0796dXr160bJlS1xdXdm4cSP6dMVEV6xYQb169QgMDCQwMJD69euzbNky6369Xs/mzZtxdnamZcuW9OrVi+7duzNjxgxrG6PRSFRUFOfPn6dJkyYMHz6c8PBwm/nPQtiLKVPUUblu3eC/gypClAhly8KKFWo96VWrIN1XvRBC5DtN50j7+flluzLgjz/+mON9ODs7M3fuXObOnZtlm4oVK7LcMlk0C9WrV2fTpk3ZtqlXrx67du3KMSYhtHTkCKxerV6W0WhREjVtqv4z+eabMGIEtGihzqEWQoj8VqTmSAshcjZpkno4u2dPaNBA62iE0MZrr4GvL1y/Dv36UWTqpwshihZJpIUoRuLiYO1adfXCiRO1jkYI7ej16rSO8uVh/345OiOEKBiSSAtRjEyapP7u3Rt8fDQNRQjNeXnBF1+ol6dNg507tY1HCFH8SCItRDERGwvffQelSslotBAWPXvCkCHqdKf+/SExUeuIhBDFiSTSQhQTb7+t/u7XD2rX1jYWIezJnDnw6KNw/jw8/7yUxBNC5B9JpIUoBmJiYMsWdV7ohAlaRyOEfSlTRq2r7uAAa9bA4sVaRySEKC4kkRaiGLBM5RgwQB15E0LYatIE3n1XvTxqFJw8qW08QojiQRJpIYq4PXsgMlIdbXvrLa2jEcJ+vfIK+PvDjRsQEgKpqVpHJIQo6iSRFqKIs4xGDx4MDz+sbSxC2LNSpeCrr6BiRTh48O55BUIIcb80XdlQCPFgduyAn34CR0cZjRYiN6pVgy+/hOBg+OADaNdOPZoTHw8eHtC6tXqugRBC5IYk0kIUUYpyd0QtLAyqV9c2HiGKih491M/MF19AUBCkpd3dV62aWuUjOFi7+IQQRYdM7RCiiNq2DXbvBoMBxo/XOhohihY/P/V3+iQa4MIFtfb0unWFHpIQogiSRFqIIij9aPSwYeoomhAid8xmeO21zPdZakyPGaO2E0KI7EgiLUQR9OOPEB0Nzs7w+utaRyNE0bJ7t7o4S1YUBc6dU9sJIUR2ZI60EEWE2az+Yb94ESZPVreNGKGeICWEyL34+PxtJ4QouSSRFqIIWLcORo+2HUXT6eDxx7WLSYiiKrf/fMo/qUKInMjUDiHs3Lp16slP9x6KVhQYOlROihIir1q3Vs8r0OmybuPlpbYTQojsSCIthB0zm9WRaMsJUJmRk6KEyBu9Xi1xB1kn01OnSj1pIUTOJJEWwo7JSVFCFIzgYFizBqpWtd1uSZ5//LHwYxJCFD2SSAthx+SkKCEKTnAwnDkD27fDypXq75071WR6xQr4+mutIxRC2Ds52VAIOyYnRQlRsPT6u4uzWEyYAJMmwYsvQosWUKOGFpEJIYoCGZEWwo7ldFKUTicnRQmR3958E5o3h6QkGDBAzkEQQmRNEmkh7JjlpKjMTja0JNezZ8tJUULkJwcHWL4cypSBXbvgww+1jkgIYa8kkRbCzgUHQ6NGGbdXq6aeLBUcXPgxCVHcPfwwzJ2rXp4wAWJjtY1HCGGfZI60EHbu4kWIi1MvL10Kjo7qnOjWrWUkWoiCNHAgbNoEa9dCv37wyy/g6qp1VEIIeyKJtBB2bulSSEuDli3V+ZpCiMKh08Fnn0F0NBw/DmPHwqefah2VEMKeyNQOIeyYosCiRerloUO1jUWIkuihh9R/ZgEWLICNG7WNRwhhXySRFsKO7d4Nf/yhnvT07LNaRyNEydSuHYSHq5eHDoVLl7SNRwhhPySRFsKOWUaje/dWk2khhDamTYP69eHKFRgyJPNKOkKIkkcSaSHsVHIyfPutennIEG1jEaKkMxjU1Q8NBtiyBT75ROuIhBD2QBJpIezU6tVw8ybUrq0uDiGE0Nbjj8MHH6iXx42Do0e1jUcIoT1JpIWwU5ZpHUOGZL2yoRCicL30EgQFwe3bakm8lBStIxJCaEkSaSHs0NGjEBOj1omWkndC2A+dDhYvhkqV1PruEyZoHZEQQkuSSAthhyyj0Z07Q5Uq2sYihLDl4QFffqlenjEDtm/XNh4hhHY0TaR37dpFly5d8PT0RKfTsWHDBus+k8nEa6+9Rr169ShdujSenp4MGDCAixcv2tyHn58fOp3O5qdPnz42bRITEwkNDcVoNGI0GgkNDeXatWs2bc6ePUuXLl0oXbo0lSpVYtSoUaSmptq0OXz4ML6+vri4uFC1alWmTJmCIqdui3yWmgpffaVeltrRQtinbt0gLEyt3jFgACQmah2REEILmibSN27coEGDBsybNy/Dvps3b/LLL78wYcIEfvnlF9atW8eJEyfo2rVrhrZhYWHEx8dbfz777DOb/SEhIcTFxREREUFERARxcXGEhoZa95vNZjp16sSNGzfYs2cPq1atYu3atYwdO9baJjk5mYCAADw9PTlw4ABz585lxowZzJo1Kx9fESFg82a1xJa7O3TooHU0QoiszJoF//d/cP48vPCClMQToiTSdInwDh060CGLTMFoNBIVFWWzbe7cuTz11FOcPXuW6tWrW7e7urri7u6e6f0cO3aMiIgIYmJiaNq0KQBffPEFzZs35/jx43h7exMZGcnRo0c5d+4cnp6eAMycOZNBgwYxdepUypUrx4oVK7h9+zZLlizBYDDg4+PDiRMnmDVrFuHh4ejkbDCRTxYuVH8PGAAOmn5ChRDZKVMGVqyAFi3gm2/UqVghIepCSvHx6hSQ1q3Vcx2EEMVTkfoznZSUhE6no3z58jbbV6xYwfLly6lSpQodOnRg4sSJlC1bFoDo6GiMRqM1iQZo1qwZRqORvXv34u3tTXR0ND4+PtYkGiAoKIiUlBRiY2Px9/cnOjoaX19fDAaDTZvx48dz5swZatWqlWnMKSkppKQ7rTs5ORlQp66YTKYM7S3bMtsnCoeWfXDxIvzwgwOgIzTUREl+G8hnQXvSBzlr2BAmTCjFpEl6nn9e4dVXISHh7sBK1aoKs2aZ6dHj/oarpQ+0J32gPS36ILePVWQS6du3b/P6668TEhJCuXLlrNv79etHrVq1cHd358iRI4wfP57//e9/1tHshIQE3NzcMtyfm5sbCQkJ1jZV7jmjq0KFCjg5Odm0qVmzpk0by20SEhKyTKSnT5/O5MmTM2yPjIzE1dU1y+d772i8KHxa9MGaNY+SllaXOnWucurUHk6dKvQQ7I58FrQnfZC9evWgatWnuXChLAkJtgnzhQvQu7ee1147QPPm8ff9GNIH2pM+0F5h9sHNmzdz1a5IJNImk4k+ffqQlpbGJ/csJxUWFma97OPjw6OPPkqTJk345ZdfaNSoEUCm0y4URbHZfj9tLCcaZjetY/z48YSHh1uvJycn4+XlRWBgoM0/BOmfa1RUFAEBATg6OmZ5v6LgaNUHigLjxqkfyfBwIx07diy0x7ZH8lnQnvRB7pjNMGKEA6AA9/490KHTKaxY8SSTJt3J8zQP6QPtSR9oT4s+sMwgyIndJ9Imk4levXpx+vRpfvrpp0yTz/QaNWqEo6MjJ0+epFGjRri7u3Pp0qUM7a5cuWIdUXZ3d2ffvn02+xMTEzGZTDZtLKPTFpcvXwbIMJqdnsFgsJkOYuHo6JjtmyGn/aLgFXYf7NoFf/yhzrvs08cB6X6VfBa0J32QvZ9/hnv+PNhQFB3nz0NMjCN+fvf3GNIH2pM+0F5h9kFuH8eu60hbkuiTJ0+ydetWHnrooRxv89tvv2EymfDw8ACgefPmJCUlsX//fmubffv2kZSURIsWLaxtjhw5Qnz83cNukZGRGAwGGjdubG2za9cum5J4kZGReHp6ZpjyIcT9sNSO7t1bTaaFEEVDfC5nbOS2nRCi6NA0kb5+/TpxcXHExcUBcPr0aeLi4jh79ix37tyhZ8+eHDx4kBUrVmA2m0lISCAhIcGazJ46dYopU6Zw8OBBzpw5w5YtW3j22Wdp2LAhLVu2BKBOnTq0b9+esLAwYmJiiImJISwsjM6dO+Pt7Q1AYGAgdevWJTQ0lEOHDrFt2zbGjRtHWFiYdQQ8JCQEg8HAoEGDOHLkCOvXr2fatGlSsUPki+Rk+PZb9fKQIdrGIoTIm//GbfKtnRCi6NA0kT548CANGzakYcOGAISHh9OwYUPefvttzp8/z/fff8/58+d54okn8PDwsP7s3bsXACcnJ7Zt20ZQUBDe3t6MGjWKwMBAtm7dij7dRLQVK1ZQr149AgMDCQwMpH79+ixbtsy6X6/Xs3nzZpydnWnZsiW9evWie/fuzJgxw9rGUo7v/PnzNGnShOHDhxMeHm4z/1mI+7V6Ndy8CbVrQ/PmWkcjhMiL1q2hWjV1+fDM6HTg5aW2E0IUL5rOkfbz88t2ZcCcVg308vJi586dOT5OxYoVWb58ebZtqlevzqZNm7JtU69ePXbt2pXj4wmRV5ZpHUOGZP3HWAhhn/R6mDMHevZUP7+Z/emaPVvqSQtRHNn1HGkhSoKjRyEmRv0jO2CA1tEIIe5HcDCsWQNVq2bcV7u2uqS4EKL4kURaCI1ZRqM7d4ZsCsAIIexccDCcOQPbt8PKlbB8OZQuDceOwfTpWkcnhCgIdl/+TojiLDUVvvpKvTx0qLaxCCEenF6PTYm7tDT1SNOkSdCuHTRrplVkQoiCICPSQmho82a4cgXc3aFDB62jEULkt/79oW9fddGWkBC1Qo8QoviQRFoIDS1cqP4eOBAc5PiQEMWOTgeffgo1asDp0zBypNYRCSHykyTSQmjk4kX44Qf18uDB2sYihCg4RiOsWAGlSsGyZer8aSFE8SCJtBAaWbpUnT/ZsiX8tzaQEKKYatkSJkxQL7/4ojo6LYQo+iSRFkIDinK3WoecZChEyfDWW9CihTpPun9/uHNH64iEEA9KEmkhNLB7N/zxB5QpA88+q3U0QojC4OCglsQrVw727oWpU7WOSAjxoCSRFkIDltHo3r3VZFoIUTLUqqWefAgwZQr8/LO28QghHowk0kIUsuRk+PZb9fKQIdrGIoQofCEh6tSOtDTo1w+SkrSOSAhxvySRFqKQrV4NN2+qywY3b651NEIILcyfr45O//UXDB+udTRCiPslibQQhcwyrWPIELXGrBCi5ClXTi2Jp9ffXU5cCFH0SCItRCE6ehRiYtQ/ngMGaB2NEEJLzZvDxInq5eHD4c8/tY1HCJF3kkgLUYgso9GdO0OVKtrGIoTQ3htvQKtW8O+/6nxpk0nriIQQeSGJtBCFJDUVvvpKvSy1o4UQoB6dWr5cXf0wJgbeeUfriIQQeSGJtBCFZPNmuHIF3N2hQwetoxFC2IsaNWDBAvXy1KlqnXkhRNEgibQQhWThQvX3wIHqwgxCCGHRp4/63ZCWppbGu3ZN64iEELkhibQQheDiRfjhB/Xy4MHaxiKEsE9z58Ijj8DZs/DCC6AoWkckhMiJjIsJUYDMZvUw7YIF6khTixbg7a11VEIIe1S2rFoKr2VLtd58UBB4eenYtasqpUvr8PdX51QLIeyHjEgLUUDWrYOaNcHfX/2jCHDsmLpdCCEy89RTMHmyennoUAgIcGDWrCYEBDhQs6Z8fwhhbySRFqIArFsHPXvC+fO2269dU7fLH0MhRFYee0z9fe/UjgsX5PtDCHsjibQQ+cxshtGjM5/faNk2ZozaTggh0jOb4eWXM98n3x9C2B9JpIXIZ7t3ZxyJTk9R4Nw5KXElhMhIvj+EKFokkRYin8XH5287IUTJId8fQhQtkkgLkc88PPK3nRCi5JDvDyGKFkmkhchnrVtDtWqg02W+X6cDLy+1nRBCpJfT9wfI94cQ9iRPiXR8fDzLly9ny5YtpKam2uy7ceMGU6ZMydfghCiK9HqYMyfzkw0tfxxnz5Z6sEKIjCzfH5B1Mj1ihHx/CGEvcp1IHzhwgLp16zJixAh69uyJj48Pv/32m3X/9evXmWwpfilECRccDI0aZdxerRqsWaPuF0KIzAQHq98TVavabndxUX9/9hkkJRV+XEKIjHKdSL/xxhsEBweTmJjIpUuXCAgIwNfXl0OHDhVkfEIUSYmJcPiwennJEnW1su3b4fRpSaKFEDkLDoYzZyAq6g7h4QeJirrDuXPqIk+nT0NYmCwhLoQ9yPUS4bGxscyfP59SpUpRtmxZ5s+fT40aNWjbti0//vgj1atXL8g4hShSNmwAkwnq1YOBA7WORghRFOn14OurcOPGBXx9G+DoCKtWQatW8O23EBCgJtRCCO3kaY707du3ba6/+uqrvPHGGwQGBrJ37958DUyIouybb9TfvXppG4cQonhp2hSmTVMvjxoF6WZYCiE0kOsRaR8fH/bu3Uv9+vVtto8bNw5FUejbt2++BydEUXT1Kmzdql5+9lltYxFCFD9jx8K2bfDjj+o/6wcOgKur1lEJUTLlekR6wIAB/Pzzz5nue+WVV5gyZYpM7xACWL8e7tyBBg3A21vraIQQxU2pUvDVV+DuDkePqkuGCyG0ketE+rnnnmPZsmVZ7n/11Vc5ffp0nh58165ddOnSBU9PT3Q6HRs2bLDZrygKkyZNwtPTExcXF/z8/GwqhQCkpKTw0ksvUalSJUqXLk3Xrl05f8/6qomJiYSGhmI0GjEajYSGhnLt2jWbNmfPnqVLly6ULl2aSpUqMWrUqAwl/g4fPoyvry8uLi5UrVqVKVOmoMjZHuIelmkdvXtrG4cQovhyc4Ply9USeV98AatXax2RECXTfS3I8uuvv7JmzRrWrl3Lr7/+et8PfuPGDRo0aMC8efMy3f/BBx8wa9Ys5s2bx4EDB3B3dycgIIB///3X2mbMmDGsX7+eVatWsWfPHq5fv07nzp0xm83WNiEhIcTFxREREUFERARxcXGEhoZa95vNZjp16sSNGzfYs2cPq1atYu3atYwdO9baJjk5mYCAADw9PTlw4ABz585lxowZzJo1676fvyh+rlyBn35SL8u0DiFEQWrbFt54Q738/PPw55/axiNEiaTkwb59+xQfHx+lVKlSik6nU3Q6nVKqVCmlXr16yv79+/NyVxkAyvr1663X09LSFHd3d+W9996zbrt9+7ZiNBqVBQsWKIqiKNeuXVMcHR2VVatWWdtcuHBBKVWqlBIREaEoiqIcPXpUAZSYmBhrm+joaAVQfv/9d0VRFGXLli1KqVKllAsXLljbfP3114rBYFCSkpIURVGUTz75RDEajcrt27etbaZPn654enoqaWlpuX6eSUlJCmC933ulpqYqGzZsUFJTU3N9nyJ/PUgfLFigKKAojRoVQGAljHwWtCd9oL2c+sBkUpSWLdXvnSefVJSUlEIOsASQz4H2tOiDnPI1i1yfbHj06FHatm1LnTp1WL58OXXq1EFRFI4dO8ZHH31E27ZtiYmJoW7duvmS4J8+fZqEhAQCAwOt2wwGA76+vuzdu5dhw4YRGxuLyWSyaePp6Wk9MTIoKIjo6GiMRiNNmza1tmnWrBlGo5G9e/fi7e1NdHQ0Pj4+eHp6WtsEBQWRkpJCbGws/v7+REdH4+vri8FgsGkzfvx4zpw5Q61atTJ9HikpKaSkpFivJycnA2AymTCZTBnaW7Zltk8Ujgfpg9Wr9UApevY0YzKl5XNkJYt8FrQnfaC93PTB0qXw5JMOHDigY/x4M++9J989+Uk+B9rTog9y+1i5TqQnTpxIQEAAa9euRZdu3dKGDRvSt29fgoODmTRpEt9YJog+oISEBACqVKlis71KlSr89ddf1jZOTk5UqFAhQxvL7RMSEnBzc8tw/25ubjZt7n2cChUq4OTkZNOmZs2aGR7Hsi+rRHr69OmZrvgYGRmJazanWUdFRWW5TxSOvPbBtWsGdu4MAuChh35iy5abBRFWiSOfBe1JH2gvpz4YNsyd995ryqxZekqX3k/jxpcLKbKSQz4H2ivMPrh5M3d/w3OdSO/YsYMffvjBJom20Ol0vPHGG3Ts2DH3EebSvY+nKEqmMWTXJrP2+dFG+e9Ew+ziGT9+POHh4dbrycnJeHl5ERgYSLly5TK0N5lMREVFERAQgKOjY5b3KwrO/fbBZ5+VIi1NR5MmaQwe7Fdg8ZUU8lnQnvSB9nLbBx07QnKymU8+0fPpp804ePAO6Q6yigcgnwPtadEHlhkEOcl1Iv3vv/9mGLVNz93d3eYkwAfl7u4OqKO9Hh4e1u2XL1+2xuHu7k5qaiqJiYk2o9KXL1+mRYsW1jaXLl3KcP9XrlyxuZ99+/bZ7E9MTMRkMtm0sYxOp38cyDhqnp7BYLCZDmLh6OiY7Zshp/2i4OW1D9asUX/37l0KR8f7Oo9XZEI+C9qTPtBebvpg5kzYuxfi4nQMHuxIVJS6OqLIH/I50F5h9kFuHyfXf+1r1qzJ/v37s9y/b98+atSokdu7y1GtWrVwd3e3GcZPTU1l586d1iS5cePGODo62rSJj4/nyJEj1jbNmzcnKSnJJvZ9+/aRlJRk0+bIkSPEx8db20RGRmIwGGjcuLG1za5du2xK4kVGRuLp6ZlhyocoeeLjYdcu9bJU6xBCaMHZWV1CvHRp2L4dpk/XOiIhir9cJ9K9e/cmPDycI0eOZNh3+PBhxo0bR58+ffL04NevXycuLo64uDhAPcEwLi6Os2fPotPpGDNmDNOmTWP9+vUcOXKEQYMG4erqSkhICABGo5GhQ4cyduxYtm3bxqFDh+jfvz/16tWjXbt2ANSpU4f27dsTFhZGTEwMMTExhIWF0blzZ7z/Wy0jMDCQunXrEhoayqFDh9i2bRvjxo0jLCzMOv0iJCQEg8HAoEGDOHLkCOvXr2fatGmEh4fnONVEFH9r14KiQLNmkI//TwohRJ54e8Mnn6iXJ06E3bu1jUeI4i7XUzvGjx/P1q1beeKJJwgICKBOnTqAWs1j69atPPXUU4wfPz5PD37w4EH8/f2t1y1ziQcOHMiSJUt49dVXuXXrFsOHDycxMZGmTZsSGRlJ2bJlrbf56KOPcHBwoFevXty6dYu2bduyZMkS9OmOZ61YsYJRo0ZZq3t07drVpna1Xq9n8+bNDB8+nJYtW+Li4kJISAgzZsywtjEajURFRTFixAiaNGlChQoVCA8Pt5n/LEouyzm2vXppG4cQQgwYAFu3wrJlEBIC//sfVKyodVRCFE+5TqSdnZ3Zvn07H330EV9//TU7d+4E4LHHHuPdd9/l5ZdfznQucHb8/PyyXRlQp9MxadIkJk2alG1cc+fOZe7cuVm2qVixIsuXL882lurVq7Np06Zs29SrV49dluP3QvznwgXYs0e93LOntrEIIQSoo9IxMXDyJAweDBs2qKsgCiHyV64TaQAnJydee+01XnvttYKKR4giZ80adVpHy5bg5aV1NEIIAWXKqMuGN2sG338PH38MDRqo53N4eEDr1nIiohD5IddzpBMTE5k7d26m5UCSkpKy3CdEcSfTOoQQ9qhhQ7DMUBwzBvz91ake/v5QsyasW6dldEIUD7lOpOfNm8euXbsyrX1sNBrZvXt3ttMrhCiOzp1Ty03pdPDMM1pHI4QQtrKqJX3hgjoVTZJpIR5MrhPptWvX8sILL2S5f9iwYayxFNIVooT49lv1d6tWULWqtrEIIUR6ZrM6Ep0Zy+lJY8ao7YQQ9yfXifSpU6d49NFHs9z/6KOPcurUqXwJSoiiwjKto3dvbeMQQoh77d4N589nvV9R1KNqUiJPiPuX60Rar9dz8eLFLPdfvHiRUqVkNTdRcpw5A/v2ybQOIYR9SrfGWL60E0JklOvMt2HDhmzYsCHL/evXr6dhw4b5EZMQRYJlJpOvL/y3or0QQtgND4/8bSeEyCjX5e9GjhxJnz59qFatGi+++KJ1wROz2cwnn3zCRx99xMqVKwssUCHszerV6m+p1iGEsEetW0O1auqJhVkt2VCtmtpOCHF/cj0i/cwzz/Dqq68yatQoKlasSMOGDWnUqBEVK1ZkzJgxhIeH01NWoxAlxJ9/wsGDUKqUTOsQQtgnvR7mzFEvZ7UYS7NmUk9aiAeRp0nNU6dOZd++fQwcOBBPT0/c3d0ZPHgw0dHRTJ8+nbNnzxZUnELYFUu1Dn9/cHPTNhYhhMhKcLA6De3eqkKWJcPXrIEcFvUVQmQjTysbAjRr1oz4+Hjc7skerl69Sq1atTBLHR1RAsi0DiFEUREcDN26qdU50q9sOGYMzJsH/furR9j+7/+0jlSIoifPibSiKOgyOUZ0/fp1nJ2d8yUoIezZyZNw6JB6ODQ4WOtohBAiZ3o9+PnZbps5E375RV1UKjgYYmLA1VWT8IQosnKdSIeHhwOg0+mYMGECruk+bWazmX379vHEE0/ke4BC2BvLtI62baFSJW1jEUKI++XkpH6fNWoEhw/D88/DsmVZz6cWQmSU60T60KFDgDoiffjwYZycnKz7nJycaNCgAePGjcv/CIWwM5ZFWGRahxCiqPP0VL/Tnn4aVqxQTz4cOVLrqIQoOnKdSG/fvh2AwYMHM2fOHMqVK1dgQQlhr44fh//9DxwcoEcPraMRQogH16YNfPghhIfDyy9Dw4bQsqXWUQlRNOR5KcLFixdLEi1KLMtodEDA3bPehRCiqBszBnr3hjt34NlnISFB64iEKBpkTW8h8kCmdQghiiOdDr78EurWVSt79OoFJpPWUQlh/ySRFiKXjh6FI0fA0VEtJSWEEMVJmTKwbh2ULauWynvtNa0jEsL+SSItRC5ZRqMDA6FCBW1jEUKIguDtDUuXqpc/+ghWrdI2HiHsnSTSQuSCotxNpHv31jYWIYQoSD16wOuvq5eHDoXfftM2HiHsmSTSQuTCb7/BsWNq3dWuXbWORgghCta770K7dnDzpppYJyVpHZEQ9kkSaSFywTIa3b49GI3axiKEEAVNr4eVK8HLS13NdeBASEvTOioh7I8k0kLkQFFg9Wr1slTrEEKUFJUrw9q16pG4776DDz7QOiIh7I8k0kLk4Ndf4cQJMBhkWocQomR58kmYN0+9/OabsHWrtvEIYW8kkRYiB5ZpHR07qmWhhBCiJAkLU086TEuDPn3g7FmtIxLCfkgiLUQ20lfrkGkdQoiSat48aNwYrl6FZ56BGzdgxw74+mv1t9msdYRCaMNB6wCEsGeHDsEff4CLC3TurHU0QgihDWdndb50o0Zw8CBUqaIm0xbVqsGcORAcrF2MQmhBRqSFyIZlNLpTJ3XVLyGEKKlq1IARI9TL6ZNogAsXoGdPdWVEIUoSSaSFyIJM6xBCiLvMZli8OPN9iqL+HjNGpnmIkkUSaSGyEBur4/RpcHVVTzQUQoiSbPduOH8+6/2KAufOqe2EKCkkkRYiC2vW6ADo0gVKl9Y4GCGE0Fh8fP62E6I4kERaiEwoCqxZo348ZFqHEEKAh0f+thOiOJBEWoh0zGbYuVPHN988xtmzOlxdoUMHraMSQgjttW6tVufQ6bJu4+WlthOipLD7RLpmzZrodLoMPyP+O3V40KBBGfY1a9bM5j5SUlJ46aWXqFSpEqVLl6Zr166cv2eiV2JiIqGhoRiNRoxGI6GhoVy7ds2mzdmzZ+nSpQulS5emUqVKjBo1itTU1AJ9/qLwrFsHNWtCQIADX39dB1BHpn/4Qdu4hBDCHuj1aok7yDqZ7tdPbSdESWH3ifSBAweIj4+3/kRFRQHw7LPPWtu0b9/eps2WLVts7mPMmDGsX7+eVatWsWfPHq5fv07nzp0xpzu1OCQkhLi4OCIiIoiIiCAuLo7Q0FDrfrPZTKdOnbhx4wZ79uxh1apVrF27lrFjxxbwKyAKw7p1aumme0+kuXVLSjoJIYRFcDCsWQNVq9put5QHnTcPDh8u/LiE0IrdL8hSuXJlm+vvvfcejzzyCL6+vtZtBoMBd3f3TG+flJTEwoULWbZsGe3atQNg+fLleHl5sXXrVoKCgjh27BgRERHExMTQtGlTAL744guaN2/O8ePH8fb2JjIykqNHj3Lu3Dk8PT0BmDlzJoMGDWLq1KmUK1euIJ6+KARmM4wefbd8U2bGjIFu3WSkRQghgoPV78Pdu9UTCz08oFkztd7+Tz+pJ2gfOAD3/PkWoliy+0Q6vdTUVJYvX054eDi6dMeVduzYgZubG+XLl8fX15epU6fi5uYGQGxsLCaTicDAQGt7T09PfHx82Lt3L0FBQURHR2M0Gq1JNECzZs0wGo3s3bsXb29voqOj8fHxsSbRAEFBQaSkpBAbG4u/v3+mMaekpJCSkmK9npycDIDJZMJkMmVob9mW2T5RMHbu1HH+fNYfBUtJp+3b7+Drm022LfKVfBa0J32gPXvug5Ytba+vXAmtWjnwxx86evRIIyLCjMGgTWz5yZ77oKTQog9y+1hFKpHesGED165dY9CgQdZtHTp04Nlnn6VGjRqcPn2aCRMm8PTTTxMbG4vBYCAhIQEnJycqVKhgc19VqlQhISEBgISEBGvinZ6bm5tNmypVqtjsr1ChAk5OTtY2mZk+fTqTJ0/OsD0yMhJXV9csb2eZwiIK3q5dVYEmObb74Yc4bty4UPABCRvyWdCe9IH2ikofjBlThldfbcPPPzvSvfs5Ro6My/bkxKKkqPRBcVaYfXDz5s1ctStSifTChQvp0KGDzahw7969rZd9fHxo0qQJNWrUYPPmzQQHB2d5X4qi2Ixq6zL5pN9Pm3uNHz+e8PBw6/Xk5GS8vLwIDAzMdDqIyWQiKiqKgIAAHB0ds7xfkX9Kl9Yxa1bO7Tp0eAJf3wYFH5AA5LNgD6QPtFcU+6BmTR1duyps21aDoKBqjBmTpnVID6Qo9kFxo0UfWGYQ5KTIJNJ//fUXW7duZV0OZ315eHhQo0YNTp48CYC7uzupqakkJibajEpfvnyZFi1aWNtcunQpw31duXLFOgrt7u7Ovn37bPYnJiZiMpkyjFSnZzAYMGRybMvR0THbN0NO+0X+8fdXSzpduJD5PGmdTt3v7+8gc6Q1IJ8F7UkfaK8o9UGnTjBrlnpuyeuv63n8cX2xWB22KPVBcVWYfZDbx7H7qh0Wixcvxs3NjU6dOmXb7urVq5w7dw6P/yrCN27cGEdHR5vDAfHx8Rw5csSaSDdv3pykpCT2799vbbNv3z6SkpJs2hw5coT4dEs2RUZGYjAYaNy4cb49T1H40pd0upflYMPs2XKioRBC5NaoURAWBmlp0KcP/Pab1hEJUTCKRCKdlpbG4sWLGThwIA4OdwfRr1+/zrhx44iOjubMmTPs2LGDLl26UKlSJXr06AGA0Whk6NChjB07lm3btnHo0CH69+9PvXr1rFU86tSpQ/v27QkLCyMmJoaYmBjCwsLo3Lkz3t7eAAQGBlK3bl1CQ0M5dOgQ27ZtY9y4cYSFhUnFjmIgOBieey7j9mrV1FJP2cwSEkIIcQ+dTi2F5+sL//4LXbvC339rHZUQ+a9ITO3YunUrZ8+eZciQITbb9Xo9hw8f5quvvuLatWt4eHjg7+/P6tWrKVu2rLXdRx99hIODA7169eLWrVu0bduWJUuWoE83xLhixQpGjRplre7RtWtX5s2bZ/NYmzdvZvjw4bRs2RIXFxdCQkKYMWNGAT97UVh+/VX9/cILZlxdD9GhwxMynUMIIe6Tk5M6ENG0Kfz5p1qTPzJS3S5EcVEkEunAwECUTCavuri48OOPP+Z4e2dnZ+bOncvcuXOzbFOxYkWWL1+e7f1Ur16dTZs25RywKHKOH4d9+9TpG2++mUZs7AV8fRtIEi2EEA+gUiX4/nto3hx27oSRI+Gzz7JfZlyIoqRITO0QoqB99ZX6u317yObcUSGEEHn0+OOwapWaPH/xBXz8sdYRCZF/JJEWJV5aGixbpl4eMEDbWIQQojjq2BE+/FC9HB4OuTiYLESRIIm0KPF27FBXLjQa1RNihBBC5L/wcBg8WB286N0bfv9d64iEeHCSSIsSzzKto3dvcHbWNhYhhCiudDr49FNo1QqSkqBLF/jnH62jEuLBSCItSrTr19WzygEGDtQ2FiGEKO4MBli3DmrUgD/+gGefhdu31SODX3+t/jabtY5SiNwrElU7hCgo69fDjRvwyCPqWeVCCCEKVuXKsHEjtGgBP/2kVva4cePu/mrV1EWypH6/KApkRFqUaJZpHQMGSDkmIYQoLPXqqaXwwDaJBrhwQa05vW5d4cclRF5JIi1KrHPnYNs29XJoqLaxCCFESWI2Q1ZLN1iWjRgzRqZ5CPsnibQosVasUL+w27SBWrW0jkYIIUqO3bvh/Pms9yuKOtixe3fhxSTE/ZBEWpRIigJLl6qX5SRDIYQoXPHx+dtOCK1IIi1KpIMH1RqmLi7qXDwhhBCFx8Mjf9sJoRVJpEWJZBmN7tEDypXTNhYhhChpWrdWq3Nkd5J3tWpqOyHsmSTSosRJTVXrlYIsCS6EEFrQ69USd5B1Mu3lVXjxCHG/JJEWJc7mzepqWh4e0K6d1tEIIUTJFBysLohVtart9sqVwcEBoqNh9Oi7VTyEsEeSSIsSx1I7un9/dVRECCGENoKD4cwZ2L4dVq5Uf8fHq1WVdDqYPx/ee0/rKIXImqxsKEqUv/9WR6RBpnUIIYQ90OvBz892W69ekJCgjki/8Qa4u8PgwZqEJ0S2ZERalCirVoHJBI0agY+P1tEIIYTIyqhR8Prr6uWwsLuDIELYE0mkRYmSfklwIYQQ9m3aNPX72myGZ5+Fffu0jkgIW5JIixLj2DE4cEA9iaVvX62jEUIIkROdDr78Etq3h1u3oFMnOH5c66iEuEsSaVFiWEajO3QANzdtYxFCCJE7jo7w7bfw5JNw9SoEBcHFi1pHJYRKEmlRIpjNsGyZelmWBBdCiKKlTBl1jvSjj8Jff6kDIklJWkclhCTSooTYvh0uXIAKFaBzZ62jEUIIkVeVK8OPP6oVPH79Fbp3h5QUraMSJZ0k0qJEsCwJ3qcPGAzaxiKEEOL+1KoFW7ZA2bKwYweEhqpHHIXQiiTSotj7919Yt069LNU6hBCiaGvYEDZsuDt3eswYWf1QaEcSaVHsrVsHN2/CY49B06ZaRyOEEOJBPf303fNe5s2D99/XNh5RckkiLYo9y7SOAQPUUkpCCCGKvt69YfZs9fL48bBkiZbRiJJKlggXxdpff6knGoI6l04IIUTxMXq0Wgrvgw/guefU0qZBQbB7N8THg4cHtG6tLkMuREGQRFoUa8uXq7/9/aF6dW1jEUIIkf/eew8SEtS1Anr0gPLl4fLlu/urVYM5cyA4WLMQRTEmUztEsaUosiS4EEIUd5bVD594AlJTbZNoUEuf9ux596RzIfKTJNKi2Nq3D06cAFdXeOYZraMRQghRUEqVgitXMt9nqegxZoyUyhP5TxJpUWxZRqODg9Wao0IIIYqn3bvVkeesKAqcO6e2EyI/SSItiqWUFFi1Sr0sS4ILIUTxFh+fv+2EyC1JpEWxtGkTJCZC1arqiYZCCCGKLw+P/G0nRG5JIi2KJcu0jtBQKXskhBDFXevWanWO7NYKqFpVbSdEfrLrRHrSpEnodDqbH3d3d+t+RVGYNGkSnp6euLi44Ofnx2+//WZzHykpKbz00ktUqlSJ0qVL07VrV86fP2/TJjExkdDQUIxGI0ajkdDQUK5du2bT5uzZs3Tp0oXSpUtTqVIlRo0aRWpqaoE9d3H/rlyBLVvUy1KtQwghij+9Xi1xB1kn005OkJxceDGJksGuE2mAxx9/nPj4eOvP4cOHrfs++OADZs2axbx58zhw4ADu7u4EBATw77//WtuMGTOG9evXs2rVKvbs2cP169fp3Lkz5nSn7oaEhBAXF0dERAQRERHExcURmm71DrPZTKdOnbhx4wZ79uxh1apVrF27lrFjxxbOiyDy5Ouv4c4dePJJqFNH62iEEEIUhuBgWLNGHXlOz90dypWD06chMBDuGScT4oHY/YIsDg4ONqPQFoqiMHv2bN58802C/6uyvnTpUqpUqcLKlSsZNmwYSUlJLFy4kGXLltGuXTsAli9fjpeXF1u3biUoKIhjx44RERFBTEwMTZs2BeCLL76gefPmHD9+HG9vbyIjIzl69Cjnzp3D09MTgJkzZzJo0CCmTp1KuXLlCunVELmRfklwIYQQJUdwMHTrlnFlw6NH4emn4eBBdeXDyEgwGrWOVhQHdp9Inzx5Ek9PTwwGA02bNmXatGk8/PDDnD59moSEBAIDA61tDQYDvr6+7N27l2HDhhEbG4vJZLJp4+npiY+PD3v37iUoKIjo6GiMRqM1iQZo1qwZRqORvXv34u3tTXR0ND4+PtYkGiAoKIiUlBRiY2Pxz+ZstpSUFFJSUqzXk/87rmQymTCZTBnaW7Zltk/k7MgR+OUXRxwdFZ555g738zJKH9gH6QftSR9oT/rg/rRsefdyWhrUrg0RERAU5MD+/TqCgtLYvNlMbsbBpA+0p0Uf5Pax7DqRbtq0KV999RWPPfYYly5d4t1336VFixb89ttvJCQkAFClShWb21SpUoW//voLgISEBJycnKhQoUKGNpbbJyQk4ObmluGx3dzcbNrc+zgVKlTAycnJ2iYr06dPZ/LkyRm2R0ZG4urqmuXtoqKisr1fkbklS+oCj9KoUQL79+9/oPuSPrAP0g/akz7QnvRB/njzzXJMnNiSffucaNUqkYkTY3BxuZOr20ofaK8w++DmzZu5amfXiXSHDh2sl+vVq0fz5s155JFHWLp0Kc2aNQNAd89ZBYqiZNh2r3vbZNb+ftpkZvz48YSHh1uvJycn4+XlRWBgYKZTQkwmE1FRUQQEBODo6JjtfQtbZjO8+KL6lh47tjIdO3a8r/uRPrAP0g/akz7QnvRB/mvZEtq3V/j994eYO7cDGzeas120S/pAe1r0QXIuz0y160T6XqVLl6ZevXqcPHmS7t27A+posUe6wpCXL1+2jh67u7uTmppKYmKizaj05cuXadGihbXNpUuXMjzWlStXbO5n3759NvsTExMxmUwZRqrvZTAYMBgMGbY7Ojpm+2bIab+4y2xW58NFRKhz4ipUgG7dHHjQl0/6wD5IP2hP+kB70gf556mnICoK2rWDvXtL0b17KbZsgTJlsr+d9IH2CrMPcvs4dl+1I72UlBSOHTuGh4cHtWrVwt3d3WaYPzU1lZ07d1qT5MaNG+Po6GjTJj4+niNHjljbNG/enKSkJJtpAPv27SMpKcmmzZEjR4hPtyRSZGQkBoOBxo0bF+hzFtlbtw5q1lQXXXn/fXVbaqq6IIsQQgiRmcaN755wuHs3dO4MN25oHZUoiuw6kR43bhw7d+7k9OnT7Nu3j549e5KcnMzAgQPR6XSMGTOGadOmsX79eo4cOcKgQYNwdXUlJCQEAKPRyNChQxk7dizbtm3j0KFD9O/fn3r16lmreNSpU4f27dsTFhZGTEwMMTExhIWF0blzZ7y9vQEIDAykbt26hIaGcujQIbZt28a4ceMICwuTih0aWrcOevaEe8qCc+OGun3dOm3iEkIIYf+efBJ+/FEtjbdzJ3TpArmcFiuElV0n0ufPn6dv3754e3sTHByMk5MTMTEx1KhRA4BXX32VMWPGMHz4cJo0acKFCxeIjIykbLrJTh999BHdu3enV69etGzZEldXVzZu3Ig+3XJ3K1asoF69egQGBhIYGEj9+vVZtmyZdb9er2fz5s04OzvTsmVLevXqRffu3ZkxY0bhvRjChtkMo0eDomTdZswYtZ0QQgiRmaZN1WS6bFnYvh26doVbt7SOShQldj1HetWqVdnu1+l0TJo0iUmTJmXZxtnZmblz5zJ37tws21SsWJHly5dn+1jVq1dnk8wXsBu7d2cciU5PUeDcObWdn1+hhSWEEKKIadbMUhoPtm1T61B/9x24uGgdmSgK7HpEWoispJuuni/thBBClFwtWsAPP0Dp0uqJiD16wO3b6lHNnTt17NpVlZ07dXKUU2QgibQoktIVasmXdkIIIUq2Vq1gyxZwdVWne7RoATVqQECAA7NmNSEgwIGaNeX8G2FLEmlRJF26BNmV8NbpwMtLXRpWCCGEyI02bWDzZnBygkOH4MIF2/0XLsjJ7MKWJNKiSLlzB157Dfr0uXui4b0JteX67NmQ7pxSIYQQIketW6tl8TJj+bsjJ7MLC0mkRZHx99/Qvj188IF6fdw4+OYbqFrVtl21arBmDQQHF36MQgghirbdu+HKlaz3pz+ZXQi7rtohhEVsrJoYnz2rngyyaBH06qXuCw5Wv9Di49U50a1by0i0EEKI+yMns4u8kERa2L0lS+CFFyAlBf7v/2D9evDxubtfr5cSd0IIIfKHnMwu8kKmdgi7lZoKw4fD4MFqEt25Mxw4YJtECyGEEPmpdWt1imB2J7Rbjn4KIYm0sEsXL6qjzJ9+qn6ZTZ6sFsgvX17ryIQQQhRnej3MmaNeziqZTk2FkycLLyZhvySRFnZn925o1Aiio9UzpzduhLffhlLybhVCCFEIgoPVk9bvPZndw0PddvWqWnd6/35t4hP2Q1ITYTcUBebOhaefVutE+/jAwYPQqZPWkQkhhChpgoPhzBmIirpDePhBoqLucO4cxMXBk0+qyfTTT8PWrVpHKrQkibTQhNkMO3bA11+rv//9FwYOhFGj1FrRffpATIx6cqEQQgihBb0efH0V2rS5gK+vgl4PlSrBtm3Qrh3cuAEdO8K332odqdCKVO0QhW7dOhg9Gs6fv7vN0RFMJvVL68MP1WL32Z3oIYQQQmilbFnYtAlCQ9UkundvdYT6hRe0jkwUNhmRFoVq3Tp1edX0STSoSTTAhAnw8suSRAshhLBvBoN6VPWFF9SpiS++CO++e3f1Q1EySCItCo3ZrI5EZ/Ulo9PBwoWy7KoQQoiiQa+HTz5RB4FA/T1mDKSlaRqWKESSSItCs3t3xpHo9GTZVSGEEEWNTgdTptwtmffxxzBgwN0jraJ4k0RaFBpZdlUIIURxNWoULF8ODg6wYgV07w43b2odlShokkiLQiPLrgohhCjO+vWD778HFxfYsgUCAiAxUeuoREGSRFoUmtat1QVWsqLTgZeXLLsqhBCi6OrQQa0tXb487N0Lbdqoq/VCxtKvck5Q0SeJtCg0f/yh1tzMjKVKx+zZ6skbQgghRFHVooV6vo+HBxw5Ai1bwrx5ULMm+PtDSIj6u2ZNtZqVKLokkRaFwmyGIUPUxVYaNIBq1Wz3V6umLscaHKxNfEIIIUR+8vFRR6T/7//UFRJfeinjCfcXLqglYSWZLrpkQRZRKObNU79QypRR549Vrar+tx4fr/7H3rq1jEQLIYQoXmrWhJ071d+ZVfFQFPWI7Jgx0K2b/B0siiSRFgXu1CkYP169/OGHUL26etnPT7OQhBBCiEJx4kT2pfDSl36Vv4tFj0ztEAUqLQ2GDoVbt9T5YM8/r3VEQgghROGR0q/FmyTSokAtWKAe1nJ1hS+/hFLyjhNCCFGCSOnX4k3SGlFgzpyBV19VL7/3Hjz8sKbhCCGEEIWudWv1hHpLdarMeHpK6deiShJpUSAUBcLC1HJ3rVrBiBFaRySEEEIUPr3+7vLhWSXTKSlqmTxR9EgiLQrEl1+qBemdnWHRIpnSIYQQouQKDlZLvFatarvd3V2d0nH1qlpr+vvvtYlP3D9Jb0S+O3cOxo5VL7/7Ljz6qLbxCCGEEFoLDlanPG7fDitXqr/Pn4fffoO2bdUjuN27wwcfqEd1RdEgibTIV4oCw4bBv/9Cs2ZqbUwhhBBCqNM8/Pygb1/1t14PFSrADz/A8OHq39DXXoPBg9XpHsL+SSIt8tVXX6lfCAaDOqVDissLIYQQ2XN0hPnz1cXL9HpYulQdpb5yRevIRE4kkRb55uLFuyPQkyZBnTpaRiOEEEIULSNGwJYtYDTCzz/DU0/JSYj2ThJpkS8UBV58Ea5dgyZNYNw4rSMSQgghip7AQIiJgUceUedUN28OmzZpHZXIiiTSIl98/bV6trGjIyxeDA6y+LwQQghxX2rXhn371HnU169D164wa5achGiP7DqRnj59Ok8++SRly5bFzc2N7t27c/z4cZs2gwYNQqfT2fw0a9bMpk1KSgovvfQSlSpVonTp0nTt2pXz58/btElMTCQ0NBSj0YjRaCQ0NJRr167ZtDl79ixdunShdOnSVKpUiVGjRpGamlogz70ouXQJXnpJvTxhAvj4aBuPEEIIUdQ99BD8+KO6JoOiqNWwwsJA0g77YteJ9M6dOxkxYgQxMTFERUVx584dAgMDuXHjhk279u3bEx8fb/3ZsmWLzf4xY8awfv16Vq1axZ49e7h+/TqdO3fGbDZb24SEhBAXF0dERAQRERHExcURGhpq3W82m+nUqRM3btxgz549rFq1irVr1zLWUuetBBs5Ev75Bxo0gNdf1zoaIYQQonhwcoLPPoOPPlLXY1i4EAIC4O+/wWyGHTvUI8I7dqjXReGz6wPwERERNtcXL16Mm5sbsbGxtGnTxrrdYDDg7u6e6X0kJSWxcOFCli1bRrt27QBYvnw5Xl5ebN26laCgII4dO0ZERAQxMTE0bdoUgC+++ILmzZtz/PhxvL29iYyM5OjRo5w7dw5PT08AZs6cyaBBg5g6dSrlypUriJfA7q1Zo/44OKhTOhwdtY5ICCGEKD50OvVEfm9v6N0bdu26e+T30qW77apVU1dQDA7WJMwSy64T6XslJSUBULFiRZvtO3bswM3NjfLly+Pr68vUqVNxc3MDIDY2FpPJRGBgoLW9p6cnPj4+7N27l6CgIKKjozEajdYkGqBZs2YYjUb27t2Lt7c30dHR+Pj4WJNogKCgIFJSUoiNjcXf3z/TmFNSUkhJVwwyOTkZAJPJhMlkytDesi2zffbm779h+HAHQMcrr5jx8UmjCISdo6LUB8WZ9IP2pA+0J32gPXvpg3bt1CQ6KMiBS5d0gALcXXP8wgWFnj1h1SozPXoUr8nUWvRBbh+ryCTSiqIQHh5Oq1at8Ek3CbdDhw48++yz1KhRg9OnTzNhwgSefvppYmNjMRgMJCQk4OTkRIUKFWzur0qVKiQkJACQkJBgTbzTc3Nzs2lTpUoVm/0VKlTAycnJ2iYz06dPZ/LkyRm2R0ZG4urqmuXtoqKistxnL2bObMyVK9WoXj2ZRo12smVLmtYh5aui0AclgfSD9qQPtCd9oD176AOzGe7cCQScSZ9EAyiKmlyPGJGKg0NUsVzHoTD74ObNm7lqV2QS6ZEjR/Lrr7+yZ88em+29e/e2Xvbx8aFJkybUqFGDzZs3E5zN8Q1FUdDp7r4J019+kDb3Gj9+POHh4dbrycnJeHl5ERgYmOl0EJPJRFRUFAEBATja8TyJ77/XsXu3A6VKKaxa5UqTJu21DinfFJU+KO6kH7QnfaA96QPt2VMf7Nyp459/skvddPz9tyvlynXC17f4jEpr0QeWGQQ5KRKJ9EsvvcT333/Prl27qFatWrZtPTw8qFGjBidPngTA3d2d1NRUEhMTbUalL1++TIsWLaxtLqWfaPSfK1euWEeh3d3d2bdvn83+xMRETCZThpHq9AwGAwaDIcN2R0fHbN8MOe0vbGYz7N4N8fFQurRaNB7glVd0NG9eJN5GeWZvfVBSST9oT/pAe9IH2rOHPsjtSodXrjgUy3OWCrMPcvs4dl21Q1EURo4cybp16/jpp5+oVatWjre5evUq586dw8PDA4DGjRvj6OhoczggPj6eI0eOWBPp5s2bk5SUxP79+61t9u3bR1JSkk2bI0eOEB8fb20TGRmJwWCgcePG+fJ87dW6dVCzJvj7Q0gIdOumnuBQtaq6gqEQQgghCt5/qU2OshnfE/nMrhPpESNGsHz5clauXEnZsmVJSEggISGBW7duAXD9+nXGjRtHdHQ0Z86cYceOHXTp0oVKlSrRo0cPAIxGI0OHDmXs2LFs27aNQ4cO0b9/f+rVq2et4lGnTh3at29PWFgYMTExxMTEEBYWRufOnfH29gYgMDCQunXrEhoayqFDh9i2bRvjxo0jLCysWFfsWLcOevaEe8puA3DhgrqUqRBCCCEKXuvWanWObGaUAjBtGmRz+pbIR3adSH/66ackJSXh5+eHh4eH9Wf16tUA6PV6Dh8+TLdu3XjssccYOHAgjz32GNHR0ZQtW9Z6Px999BHdu3enV69etGzZEldXVzZu3Ig+3Uz8FStWUK9ePQIDAwkMDKR+/fosW7bMul+v17N582acnZ1p2bIlvXr1onv37syYMaPwXpBCZjbD6NFZr6RkKckjtSuFEEKIgqfXqyXuIGMybbnu5ATbtqlrO0RGFm58JZFdT25VclgL08XFhR9//DHH+3F2dmbu3LnMnTs3yzYVK1Zk+fLl2d5P9erV2VSCFrzfvTvzkWgLRYFz59R2fn6FFpYQQghRYgUHq+s3jB5t+ze6WjWYPRvq1FHrTR8+DEFB8Npr8M47ss5DQbHrEWmhrXTTwfOlnRBCCCEeXHAwnDkD27fDypXq79On1e116sC+ffDii2rb99+HNm3U9iL/SSItspTbkxpy204IIYQQ+UOvV48G9+2r/k5fN9rFBT75RB25NhohJgaeeEK9LvKXJNIiS5aTGrKi04GXl9pOCCGEEPblmWcgLg6aNYOkJHj2WXWk+r+aDSIfSCItspT+pIZ7WU5qmD2bYrl6khBCCFEc1KypLi3++uvq3+4FC+Cpp+DoUa0jKx4kkRbZyqqyX7Vq6iGibBaPFEIIIYQdcHSE6dPhxx/BzQ2OHIEmTWDRIrVwgNkMO3bA11+rv6UaV+7ZddUOoS1FgTfeUC+PHKkeIoqPV+dEt24tI9FCCCFEURIQAP/7HwwYAFFRMHQoLF4Mf/4JFy/ebVetmnpEWgbLciaJtMjShg1w4IC6JPhbb8lKSUIIIURR5+4OERHw4YfqYNmePRnbXLigLsYmR55zJlM7RKbMZjV5BnXRFUmihRBCiOKhVCkYNw4qVcp8v2UZD1l0LWeSSItMrVihnohQoYL6YRNCCCFE8bF7N1y+nPX+9IuuiaxJIi0ySE2FiRPVy6+/DuXLaxqOEEIIIfKZLLqWPySRFhl88YW6ApKHh3qSoRBCCCGKl9wuppaWVrBxFHWSSAsbN27AO++olydMAFdXbeMRQgghRP6zLLpmWRciK889p1bwkIQ6c5JICxsffwyXLkGtWmpZHCGEEEIUP+kXXbs3mdbp1B8fH7h9Wz3psE0bOHGi0MO0e5JIC6vERPjgA/XylCng5KRtPEIIIYQoOMHBaom7qlVtt1sWXfv1V3UlxDJl4OefoUEDmDlTKnmkJ4m0sPrwQ7h2Tf0PtG9fraMRQgghREELDlbPi9q+HVauVH+fPq1u1+lg2DB1JcSAAHV0etw4aNUKjh3TOnL7IIm0ACAh4e4hnnfflVULhRBCiJJCrwc/P3UQzc8vYw5Qo4a6vPiXX0K5chATAw0bwvvvw507WkRsPySRFvD/7d17cFTl/cfxz8ltuUcukmTNAvGCqIsUiCjUlJuJxpoisVOUinFELQNaQmoFwQpeCqiFgRlEUar1jg7gpQWVzBCCyoghAyOlDOWSTEBhInE0IdEkJOf3x/llMSQQOCX7LLvv18zO7p7zJPsN33yHb84++zyS/vpXqaZGuvZa6Te/MR0NAAAIJZblfHZq1y4pM1OqrXWWyB0xwrliHalopKGSEmnFCufx/Pltf4IXAABEpuRkad066R//cPaZKCqShgxxLsjV1ztjGhqkTZukt9927sN5TjWNNPT4484v/w03SGPGmI4GAACEMsuScnKcq9NZWU4P8eijzrvaixdL/fpJo0dLEyc69/36SWvXmo66fdBIR7j//Ed6/XXn8fz5ZmMBAADnD69X+uAD6Y03pB49pO3bpT/9STp0qPm4r7+Wfvvb8GymaaQj3F/+4iyyPn68dM01pqMBAADnE8uSfv97Z6m8Dh1aH2Pbzn1ubvhN86CRjmBFRc5fh5blrNQBAADgxt69zvJ4p2Lb0sGD0qefBi+mYKCRjmBz5jj3kyZJV15pNhYAAHD+Onz43I47X9BIR6iCAik/X4qNdT5sCAAA4FZS0pmNKy9v3ziCjUY6Atm29MgjzuM//MH5NC0AAIBbaWnO0nhtLaGbm+us5nHyBxLPVzTSEeif/5S2bpU6dToxvQMAAMCt6OgTOySf3ExblnNLT3fu335bGjBAWrDA2djlfEYjHWEaGk40z9OnS4mJZuMBAADhITtbWr1auuii5seTk53jGzZIxcXSL38pVVdLs2dLV10l/etfZuI9F2ikI8yqVc5WnhdcIP35z6ajAQAA4SQ7WyotdT6L9dZbzn1JiXNckgYPdlbueP11Z171/v3Opi6//rX03/8aDd0VGukIUlcnPfaY8/jhh6Xu3c3GAwAAwk90tDRqlHTHHc59dHTz85Yl3XmntGePNHOms/DB+vWS3y/NmiVVVZ0Y29AgFRZa2rz5IhUWWiG3DjWNdAT5+9+lAwekhATpj380HQ0AAIhkXbtKCxc675RnZjpbjT/9tHT55dKbb0pr1jgLIqSnx2jx4lSlp8eE3HbjNNIRoqZGevJJ5/Gjj0qdO5uNBwAAQJL695fWrXMWQ7jkEmet6TvvdLYVD/XtxmmkI8SyZc4vZr9+0v33m44GAADgBMuSbrlF2rXL2W35VMvohdp24zGmA0D7aWhwJvTv33/iavS8eVJcnNGwAAAAWuXxOKt6NDXMrfn5duOjRgUttFZxRdqF5cuXKyUlRR06dNDQoUP1aQhuHL92rXP1efRo6d57pWPHpJgYpnQAAIDQdj5tN04jfZbeeecd5ebmas6cOdq+fbvS0tKUmZmpsrIy06EFrF3b+ryi48el3/0udOYVAQAAnOxMtxs/03HtiUb6LC1evFiTJ0/WvffeqyuuuEJLliyRz+fT888/bzo0Sc50junTT/+WSKjMKwIAADhZW9uNW5bk8znjTGOO9Fmoq6tTcXGxZs2a1ex4RkaGtmzZ0urX1NbWqvZn+19WVlZKkurr61VfX99ifNOx1s6dicJCS4cOnTqtTfOKCgqOa+TI03TbEex/zQHODfJgHjkwjxyYRw7MWLTI0u23R8uyJNs+0VFbltO7/O1vDWpstNXY2D6vf6b5ppE+C0ePHlVDQ4MSEhKaHU9ISNCRI0da/ZoFCxbo8ccfb3F8w4YN6tSp0ylfKz8/31WMmzdfJCm1zXEffbRD1dVfu3qNSOE2Bzi3yIN55MA8cmAeOQguj0d6+OEkrVw5UBUVHQPHe/b8UZMn/1sez2GtX99+r19TU3NG42ikXbBOeq/Btu0Wx5o88sgjysvLCzyvrKyUz+dTRkaGunXr1mJ8fX298vPzlZ6ertjY2LOOrXNnS4sXtz0uM/MXGjly0Fl//0jwv+YA5wZ5MI8cmEcOzCMH5tx8s7Pa2KZNPyk//99KT/dr1KhYRUcPljS4XV+7aQZBW2ikz0KvXr0UHR3d4upzeXl5i6vUTTwejzweT4vjsbGxpy3Its6fyujRzryir79ufZ60ZTnnR4+OabFlJ5pzmwOcW+TBPHJgHjkwjxyYERsrjR0r1dZ+rbFjBwUtB2f6OnzY8CzExcVp6NChLd7eyc/P14gRIwxF1Vx0tLR0qfP45IvkTc+XLGm57z0AAADODo30WcrLy9PKlSv18ssva/fu3ZoxY4bKyso0ZcoU06EFZGdLq1dLF13U/HhysnM8O9tMXAAAAOGEqR1nacKECaqoqNATTzyhw4cPy+/3a/369erbt6/p0JrJzpbGjXN2/Tl82FlrMS2NK9EAAADnCo20C1OnTtXUqVNNh9Gm6GjzW2cCAACEK6Z2AAAAAC7QSAMAAAAu0EgDAAAALtBIAwAAAC7QSAMAAAAu0EgDAAAALtBIAwAAAC7QSAMAAAAu0EgDAAAALtBIAwAAAC6wRXiQ2bYtSaqsrGz1fH19vWpqalRZWanY2Nhghob/Rw5CA3kwjxyYRw7MIwfmmchBU5/W1LedCo10kFVVVUmSfD6f4UgAAABwOlVVVYqPjz/lectuq9XGOdXY2KhvvvlGXbt2lWVZLc5XVlbK5/Pp4MGD6tatm4EIQQ5CA3kwjxyYRw7MIwfmmciBbduqqqqS1+tVVNSpZ0JzRTrIoqKilJyc3Oa4bt26UbCGkYPQQB7MIwfmkQPzyIF5wc7B6a5EN+HDhgAAAIALNNIAAACACzTSIcbj8Wju3LnyeDymQ4lY5CA0kAfzyIF55MA8cmBeKOeADxsCAAAALnBFGgAAAHCBRhoAAABwgUYaAAAAcIFGGgAAAHCBRjrELF++XCkpKerQoYOGDh2qTz/91HRIEWPevHmyLKvZLTEx0XRYYW3z5s3KysqS1+uVZVl6//33m523bVvz5s2T1+tVx44dNWrUKO3atctMsGGqrRzcfffdLeriuuuuMxNsmFqwYIGuueYade3aVb1799att96qPXv2NBtDLbSvM8kBtdD+nn/+eV199dWBjVeGDx+ujz76KHA+FOuARjqEvPPOO8rNzdWcOXO0fft2paWlKTMzU2VlZaZDixhXXXWVDh8+HLjt3LnTdEhhrbq6WoMGDdKyZctaPf/MM89o8eLFWrZsmYqKipSYmKj09HRVVVUFOdLw1VYOJOmmm25qVhfr168PYoThr7CwUNOmTdMXX3yh/Px8HT9+XBkZGaqurg6MoRba15nkQKIW2ltycrIWLlyobdu2adu2bRozZozGjRsXaJZDsg5shIxhw4bZU6ZMaXZswIAB9qxZswxFFFnmzp1rDxo0yHQYEUuS/d577wWeNzY22omJifbChQsDx3766Sc7Pj7efuGFFwxEGP5OzoFt23ZOTo49btw4I/FEqvLycluSXVhYaNs2tWDCyTmwbWrBlO7du9srV64M2TrginSIqKurU3FxsTIyMpodz8jI0JYtWwxFFXn27t0rr9erlJQU3X777Tpw4IDpkCJWSUmJjhw50qwmPB6PRo4cSU0E2aZNm9S7d2/1799f9913n8rLy02HFNZ++OEHSVKPHj0kUQsmnJyDJtRC8DQ0NGjVqlWqrq7W8OHDQ7YOaKRDxNGjR9XQ0KCEhIRmxxMSEnTkyBFDUUWWa6+9Vq+99po++eQTvfTSSzpy5IhGjBihiooK06FFpKbfe2rCrMzMTL355pvauHGjFi1apKKiIo0ZM0a1tbWmQwtLtm0rLy9P119/vfx+vyRqIdhay4FELQTLzp071aVLF3k8Hk2ZMkXvvfeerrzyypCtgxhjr4xWWZbV7Llt2y2OoX1kZmYGHg8cOFDDhw/XJZdcoldffVV5eXkGI4ts1IRZEyZMCDz2+/1KTU1V3759tW7dOmVnZxuMLDw98MAD+uqrr/TZZ5+1OEctBMepckAtBMfll1+uHTt26Pvvv9eaNWuUk5OjwsLCwPlQqwOuSIeIXr16KTo6usVfVeXl5S3++kJwdO7cWQMHDtTevXtNhxKRmlZMoSZCS1JSkvr27UtdtIMHH3xQH374oQoKCpScnBw4Ti0Ez6ly0BpqoX3ExcXp0ksvVWpqqhYsWKBBgwZp6dKlIVsHNNIhIi4uTkOHDlV+fn6z4/n5+RoxYoShqCJbbW2tdu/eraSkJNOhRKSUlBQlJiY2q4m6ujoVFhZSEwZVVFTo4MGD1MU5ZNu2HnjgAa1du1YbN25USkpKs/PUQvtrKwetoRaCw7Zt1dbWhmwdMLUjhOTl5WnSpElKTU3V8OHD9eKLL6qsrExTpkwxHVpEeOihh5SVlaU+ffqovLxcTz31lCorK5WTk2M6tLB17Ngx7du3L/C8pKREO3bsUI8ePdSnTx/l5uZq/vz5uuyyy3TZZZdp/vz56tSpkyZOnGgw6vByuhz06NFD8+bN02233aakpCSVlpZq9uzZ6tWrl8aPH28w6vAybdo0vfXWW/rggw/UtWvXwBW3+Ph4dezYUZZlUQvtrK0cHDt2jFoIgtmzZyszM1M+n09VVVVatWqVNm3apI8//jh068DYeiFo1XPPPWf37dvXjouLs4cMGdJs6R20rwkTJthJSUl2bGys7fV67ezsbHvXrl2mwwprBQUFtqQWt5ycHNu2nWW/5s6daycmJtoej8f+1a9+Ze/cudNs0GHmdDmoqamxMzIy7AsvvNCOjY21+/TpY+fk5NhlZWWmww4rrf37S7JfeeWVwBhqoX21lQNqITjuueeeQA904YUX2mPHjrU3bNgQOB+KdWDZtm0Hs3EHAAAAwgFzpAEAAAAXaKQBAAAAF2ikAQAAABdopAEAAAAXaKQBAAAAF2ikAQAAABdopAEAAAAXaKQBAAAAF2ikAQAAABdopAEAkiTbtnXDDTfoxhtvbHFu+fLlio+PV1lZmYHIACA00UgDACRJlmXplVde0datW7VixYrA8ZKSEs2cOVNLly5Vnz59zulr1tfXn9PvBwDBRCMNAAjw+XxaunSpHnroIZWUlMi2bU2ePFljx47VsGHDdPPNN6tLly5KSEjQpEmTdPTo0cDXfvzxx7r++ut1wQUXqGfPnrrlllu0f//+wPnS0lJZlqV3331Xo0aNUocOHfTGG2+Y+DEB4JywbNu2TQcBAAgtt956q77//nvddtttevLJJ1VUVKTU1FTdd999uuuuu/Tjjz9q5syZOn78uDZu3ChJWrNmjSzL0sCBA1VdXa3HHntMpaWl2rFjh6KiolRaWqqUlBT169dPixYt0uDBg+XxeOT1eg3/tADgDo00AKCF8vJy+f1+VVRUaPXq1dq+fbu2bt2qTz75JDDm0KFD8vl82rNnj/r379/ie3z77bfq3bu3du7cKb/fH2iklyxZounTpwfzxwGAdsHUDgBAC71799b999+vK664QuPHj1dxcbEKCgrUpUuXwG3AgAGSFJi+sX//fk2cOFEXX3yxunXrppSUFElq8QHF1NTU4P4wANBOYkwHAAAITTExMYqJcf6baGxsVFZWlp5++ukW45KSkiRJWVlZ8vl8eumll+T1etXY2Ci/36+6urpm4zt37tz+wQNAENBIAwDaNGTIEK1Zs0b9+vULNNc/V1FRod27d2vFihVKS0uTJH322WfBDhMAgoqpHQCANk2bNk3fffed7rjjDn355Zc6cOCANmzYoHvuuUcNDQ3q3r27evbsqRdffFH79u3Txo0blZeXZzpsAGhXNNIAgDZ5vV59/vnnamho0I033ii/36/p06crPj5eUVFRioqK0qpVq1RcXCy/368ZM2bo2WefNR02ALQrVu0AAAAAXOCKNAAAAOACjTQAAADgAo00AAAA4AKNNAAAAOACjTQAAADgAo00AAAA4AKNNAAAAOACjTQAAADgAo00AAAA4AKNNAAAAOACjTQAAADgwv8B7MQPnaL/q0oAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results exported to: step7_final_results.csv\n" + ] + } + ], + "source": [ + "# ------------------------------------------------------------------\n", + "# Step 7: Final Summation, Placeholders, Buffer, Swift Shift, Rounding\n", + "# + Table Outputs, Plot, and CSV Export\n", + "# ------------------------------------------------------------------\n", + "\n", + "# 1) Create a DataFrame for the final results.\n", + "# We'll assume we have a series or DataFrame column named\n", + "# \"Total_Yearly_tCO2_Change\" that sums across species.\n", + "# If you have a different name or structure, adapt accordingly.\n", + "df_final = pd.DataFrame(index=range(1, 31))\n", + "df_final.index.name = \"Year\"\n", + "\n", + "# Let's say we fill df_final[\"Sum_Species_tCO2\"] from your total tCO2 increments\n", + "# e.g. from: df_yearly_tCO2_change[\"Total_Yearly_tCO2_Change\"]\n", + "df_final[\"Sum_Species_tCO2\"] = df_yearly_tCO2_change[\"Total_Yearly_tCO2_Change\"]\n", + "\n", + "# 2) Soil carbon placeholder = 0\n", + "df_final[\"Soil_tCO2\"] = 0.0\n", + "\n", + "# 3) Gross = sum of species + soil\n", + "df_final[\"Gross_tCO2\"] = df_final[\"Sum_Species_tCO2\"] + df_final[\"Soil_tCO2\"]\n", + "\n", + "# 4) Baseline/Leakage placeholder = 0 for now\n", + "df_final[\"Baseline_Leakage_Deduction\"] = 0.0\n", + "df_final[\"Net_tCO2\"] = df_final[\"Gross_tCO2\"] - df_final[\"Baseline_Leakage_Deduction\"]\n", + "\n", + "# 5) Apply a 20% buffer\n", + "df_final[\"Post_Buffer_tCO2\"] = df_final[\"Net_tCO2\"] * 0.80 # keep 80% after buffer\n", + "\n", + "# 6) Swift shift: year 1 = 0, year 2 = value of year 1, etc.\n", + "swift_values = [0.0]*30\n", + "for i in range(2, 31):\n", + " swift_values[i-1] = df_final.loc[i-1, \"Post_Buffer_tCO2\"]\n", + "df_final[\"Swift_tCO2\"] = swift_values\n", + "\n", + "# 7) Round down to nearest hundred\n", + "df_final[\"Rounded_Swift_tCO2\"] = df_final[\"Swift_tCO2\"].apply(\n", + " lambda x: math.floor(x / 100.0)*100\n", + ")\n", + "\n", + "# Summaries\n", + "total_30yrs = df_final[\"Rounded_Swift_tCO2\"].sum()\n", + "annual_average = df_final[\"Rounded_Swift_tCO2\"].mean()\n", + "total_hectares_planted = sum(planting_schedule.values()) or 1 # avoid /0\n", + "\n", + "avg_per_hectare = total_30yrs / total_hectares_planted\n", + "avg_per_hectare_per_year = total_30yrs / total_hectares_planted / 30\n", + "\n", + "milestones = [5, 10, 15, 20, 25, 30]\n", + "milestone_dict = {m: df_final.loc[1:m, \"Rounded_Swift_tCO2\"].sum() for m in milestones}\n", + "\n", + "print(\"Step 7 completed. Final data with placeholders, buffer, swift shift, and rounding to nearest 100.\\n\")\n", + "\n", + "# --- Display the results\n", + "print(\"Final Emissions Reductions Table:\")\n", + "display(\n", + " df_final.style.format(\"{:.0f}\")\n", + ")\n", + "\n", + "print(f\"\\nTotal 30-year Credits (Swift, rounded to hundred) = {total_30yrs:,.0f} tCO2\")\n", + "print(f\"Annual average = {annual_average:,.0f} tCO2/yr\")\n", + "print(f\"Average per hectare (30 yrs) = {avg_per_hectare:,.0f} tCO2/ha\")\n", + "print(f\"Average per hectare per year = {avg_per_hectare_per_year:,.0f} tCO2/ha\")\n", + "\n", + "print(\"\\nMilestone totals (Swift, nearest 100):\")\n", + "for m in milestones:\n", + " print(f\" Year {m}: {milestone_dict[m]:,.0f} tCO2\")\n", + "\n", + "# --- Plot the Rounded_Swift_tCO2 over time ---\n", + "plt.figure(figsize=(8,5))\n", + "plt.plot(df_final.index, df_final[\"Rounded_Swift_tCO2\"], marker='o', color='blue', label=\"Rounded Swift tCO2\")\n", + "plt.title(\"Final Credits Over Time (Rounded_Swift_tCO2)\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"tCO2\")\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# --- Export to CSV ---\n", + "csv_filename = \"step7_final_results.csv\"\n", + "df_final.to_csv(csv_filename, index=True)\n", + "print(f\"Results exported to: {csv_filename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## End of current version of model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mx-ers", + "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": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}