{ "cells": [ { "cell_type": "markdown", "id": "3ec54aed-d110-49ea-8790-853e398637fc", "metadata": { "tags": [] }, "source": [ "# Running `ws3` and `libcbm` as a two-stage sequential pipeline (from scratch)\n", "\n", "We run `ws3` and `libcbm` in a two-stage sequential software pipeline. This is the _de facto_ standard way to run CBM models (i.e., run a forest estate model and CBM in a two-stage sequential pipeline, where the output from the first stage becomes the input for the second stage). The pipeline stages can be _hard linked_ (i.e., output from stage-1 model directly piped into stage-2 model at runtime, with no intermediate disk-based data drop), or _soft-linked_ (i.e., output from stage-1 model exported to disk in a specific format, then this same data is read and imported to stage-2 model). Either way, the result from running the pipeline should be the same. We demonstrate both approaches in this example.\n", "\n", "This notebook creates the linkages between `ws3` and `libcbm` *from scratch* (i.e., all code required to create these linkages is developed directly in this notebook). A complementary notebook (`031_ws3_libcbm_sequential-builtin`) basically replicates what we do here, but using `ws3` built-in CBM linkage functions. One goals of presenting the *from scratch* linkages is to help users better understand how these linkages are implemented. Note that the linkage implementation presented here is similar to but distinct from implementation `ws3` built-in CBM linkages. The from-scratch implementation show here is in some regards simplified at the expense of generality (i.e., the linkages are only guaranteed to work with our sample dataset we use), in contrast with the built-in linkage functions which should work well for a broad range of `ws3` model datasets (but at the expense of more complexity and less transparency). \n", "\n", "Note that soft-linked version of this pipeline can be implemented using almost any combinination of forest estate model (e.g., ws3, Patchworks, Woodstock, FPS Atlas, Woodlot, etc.) and CBM (e.g., libcbm, CBM-CFS3, GCBM, spadesCBM, etc.), although an intermediary _data munging_ module might need to be included in the middle of the pipeline to link the two main stages if the forest estate model you are using does not include built-in soft-link data export functions that are compatible with the CBM implementation you are using. The data munging module could be a human manually reformatting raw stage-1 output data using a spreadsheet (simple, but yuck), a Jupyter or R markdown notebook that semi-automates (and documents) the data munging process, or a fully-automated software module.\n", "\n", "`ws3` does not currently include built-in functions to export data for soft-link to CBM, so we implement some custom data munging code in this notebook (with examples of both soft- and hard-link approaches). One obvious advantage of selecting `ws3` and `libcbm` as the software modules for this two-stage pipeline is that they are both Python packages, which makes it easy to hard-link them with a bit of custom data munging Python code. \n", "\n", "Note that we plan to extend `ws3` at some point to include built-in `libcbm` soft-link and hard-link functions (similar to those implemented in this notebook). " ] }, { "cell_type": "markdown", "id": "608efce5-d83f-4e32-a476-3c6bbb7820e3", "metadata": {}, "source": [ "## Install `ws3` and `libcbm` packages\n", "\n", "First, make sure we have the correct versions of `ws3` and `libcbm` installed. Both of these packages are relatively new and under active development, it is best we stick to known-working versions of each package from their respective GitHub repos. \n", "\n", "\n", "> We _strongly recommend_ that you run this notebook in venv-sandboxed Python kernel (see `venv_python_kernel_setup` notebook for an example of how to do this). This will ensure that you are working from a fresh Python package environment, and not wasting time debugging random interactions between this notebook and whatever mishmash of packages you have installed on your system in various parts of your Python path. You have been warned. \n" ] }, { "cell_type": "code", "execution_count": 1, "id": "620efbb3-e717-4600-8a29-84cca3aeaf69", "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "id": "a0adec50", "metadata": {}, "source": [ "Optionally, uninstall the `ws3` package and replace it with a pointer to _this local clone of the GitHub repository code_ (useful if you want ot tweak the source code for whatever reason). " ] }, { "cell_type": "code", "execution_count": 2, "id": "5812f84e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing installation: ws3 1.1.0.dev0\n", "Uninstalling ws3-1.1.0.dev0:\n", " Successfully uninstalled ws3-1.1.0.dev0\n", "Note: you may need to restart the kernel to use updated packages.\n", "Obtaining file:///home/gep/tmp/ws3\n", " Installing build dependencies ... \u001b[?25ldone\n", "\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n", "\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n", "\u001b[?25h Installing backend dependencies ... \u001b[?25ldone\n", "\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: dill in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (0.4.0)\n", "Requirement already satisfied: fiona in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.10.1)\n", "Requirement already satisfied: gurobipy in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (12.0.3)\n", "Requirement already satisfied: highspy in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.11.0)\n", "Requirement already satisfied: libcbm in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.8.1)\n", "Requirement already satisfied: matplotlib in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (3.10.5)\n", "Requirement already satisfied: numpy>=1.21 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.2.6)\n", "Requirement already satisfied: pandas>=1.3 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.3.1)\n", "Requirement already satisfied: profilehooks in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.13.0)\n", "Requirement already 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(2.4.0)\n", "Building wheels for collected packages: ws3\n", " Building editable for ws3 (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for ws3: filename=ws3-1.1.0.dev0-py3-none-any.whl size=4136 sha256=5df49d1809d2f15004f3046010971c142390fb47fb3bfa7620b09e11903424a7\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-11t0_8gk/wheels/fa/4b/10/3fe4b92a02fb87987a6fe53a10fad0a22a781bf98cd7b63f17\n", "Successfully built ws3\n", "Installing collected packages: ws3\n", "Successfully installed ws3-1.1.0.dev0\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "clobber_ws3 = True\n", "if clobber_ws3:\n", " %pip uninstall -y ws3\n", " %pip install -e .." ] }, { "cell_type": "markdown", "id": "410e80d1", "metadata": {}, "source": [ "Use `pip` to install Python packages listed in `requirements.txt` (some extra packages needed for example notebooks to run correctly)." ] }, { "cell_type": "code", "execution_count": 3, "id": "58245440", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: seaborn in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 1)) (0.13.2)\n", "Requirement already satisfied: geopandas in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 2)) (1.1.1)\n", "Requirement already satisfied: ipywidgets in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 3)) (8.1.7)\n", "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.2.6)\n", "Requirement already satisfied: pandas>=1.2 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.3.1)\n", "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 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pandas>=1.2->seaborn->-r requirements.txt (line 1)) (2025.2)\n", "Requirement already satisfied: ptyprocess>=0.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.7.0)\n", "Requirement already satisfied: certifi in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pyogrio>=0.7.2->geopandas->-r requirements.txt (line 2)) (2025.8.3)\n", "Requirement already satisfied: six>=1.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.17.0)\n", "Requirement already satisfied: executing>=1.2.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (2.2.0)\n", "Requirement already satisfied: asttokens>=2.1.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (3.0.0)\n", "Requirement already satisfied: pure-eval in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.2.3)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install -r requirements.txt" ] }, { "cell_type": "code", "execution_count": 4, "id": "958a9e67-32cc-41c1-94cb-07fb1418bb88", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['/home/gep/tmp/ws3/.venv/lib/python3.12/site-packages/libcbm']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import libcbm\n", "libcbm.__path__" ] }, { "cell_type": "markdown", "id": "1a9cf39c-0aeb-49c5-a574-c27bdc95378b", "metadata": {}, "source": [ "Create a `ForestModel` instance by loading Woodstock-formatted input files." ] }, { "cell_type": "code", "execution_count": 5, "id": "3d44df5e-a3a1-4e66-9f1b-c623485b5049", "metadata": { "tags": [] }, "outputs": [], "source": [ "import ws3.forest" ] }, { "cell_type": "code", "execution_count": 6, "id": "b1b83350-c093-45a8-8575-b3c167cd0e4a", "metadata": { "tags": [] }, "outputs": [], "source": [ "base_year = 2020\n", "horizon = 10\n", "period_length = 10\n", "max_age = 1000\n", "tvy_name = \"totvol\"\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "1bca82e3-1d37-467e-9938-ce57a574f893", "metadata": { "tags": [] }, "outputs": [], "source": [ "fm = ws3.forest.ForestModel(model_name=\"tsa24_clipped\",\n", " model_path=\"data/woodstock_model_files_tsa24_clipped\",\n", " base_year=base_year,\n", " horizon=horizon,\n", " period_length=period_length,\n", " max_age=max_age)\n", "fm.import_landscape_section()\n", "fm.import_areas_section(convert_periods_to_years=period_length)\n", "fm.import_yields_section(convert_periods_to_years=period_length)\n", "fm.import_actions_section(convert_periods_to_years=period_length)\n", "fm.import_transitions_section(convert_periods_to_years=period_length)\n", "fm.initialize_areas()\n", "fm.add_null_action()\n", "fm.reset_actions()" ] }, { "cell_type": "markdown", "id": "c8820848-3b98-45ed-bbd3-a27474f11be5", "metadata": {}, "source": [ "Schedule some harvesting in our `ws3.ForestModel` instance using the self-parametrising priority queue heuristic defined in the local `util` module (just so we have something interesting to push through `libcbm`)." ] }, { "cell_type": "code", "execution_count": 8, "id": "cf7b9bf3-c9f5-46ad-bd54-ef9f0d31b31d", "metadata": { "tags": [] }, "outputs": [], "source": [ "from util import schedule_harvest_areacontrol" ] }, { "cell_type": "code", "execution_count": 9, "id": "ed17d02d-87c3-4f38-b867-8c3589c8d663", "metadata": { "tags": [] }, "outputs": [], "source": [ "sch = schedule_harvest_areacontrol(fm)" ] }, { "cell_type": "code", "execution_count": 10, "id": "3a1c03d3-36b2-4ad7-ab2b-919976276fb3", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " array([,\n", " ,\n", " ], dtype=object))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from util import compile_scenario, plot_scenario\n", "df = compile_scenario(fm)\n", "plot_scenario(df)" ] }, { "cell_type": "markdown", "id": "7ffd0f12-6c31-4f85-9d0b-059fb4177a61", "metadata": { "tags": [] }, "source": [ "## Hard-link `ForestModel` to `libcbm`\n", "\n", "Next, we compile some of the data from our `ForestModel` instance as `pandas.DataFrame` tables in the form expected by `libcbm.input.sit.sit_cbm_factory`. " ] }, { "cell_type": "code", "execution_count": 11, "id": "95f45b0b-3829-4dd1-ab0e-526b7d4fa672", "metadata": { "tags": [] }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "id": "b08bfe88-3edd-48a9-8ab7-03e548253d5a", "metadata": {}, "source": [ "Show hints on expected data table formatting for the `sit_reader.parse` function." ] }, { "cell_type": "markdown", "id": "4ae19761-188f-40b5-9ac2-cc0f9738b86a", "metadata": {}, "source": [ "### Compile yield data" ] }, { "cell_type": "code", "execution_count": 12, "id": "6ffcfa82-3235-4dce-ad4c-b22bb7d16e35", "metadata": {}, "outputs": [], "source": [ "species_classifier_colname = \"species\"\n", "leading_species_classifier_colname = \"leading_species\"\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "c9871a10-9a0d-4358-b89f-5638004dfe74", "metadata": { "tags": [] }, "outputs": [], "source": [ "nv = 100 # this MIGHT have to match the number of ages classes in sit_age_classes (not sure)" ] }, { "cell_type": "code", "execution_count": 14, "id": "3302bc15-e07f-4021-a151-e3d1babd8ed9", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = {\"theme0\":[], \"theme1\":[], \"theme2\":[], \"theme3\":[], \"theme4\":[],\n", " species_classifier_colname:[], leading_species_classifier_colname:[], \n", " **{\"v%i\" % i:[] for i in range(nv + 1)}}\n" ] }, { "cell_type": "markdown", "id": "b834d2d2-c40e-4763-9895-a6e2e3c5645d", "metadata": {}, "source": [ "Load CANFI species data table." ] }, { "cell_type": "code", "execution_count": 15, "id": "fc19347b-34d6-4e19-8ced-41dc31629c65", "metadata": { "tags": [] }, "outputs": [], "source": [ "canfi_species = pd.read_csv(\"data/canfi_species.csv\")\n", "canfi_species.set_index(\"canfi_species\", inplace=True)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "679673f7-53b3-4e7a-a7d6-ff2a69a798e8", "metadata": { "tags": [] }, "outputs": [], "source": [ "leading_species_from_dtype_key = {} # we will need this later (as leading_species needs to be a \"classifier\" in libcbm\n", "for dtype_key, ytype, curves in fm.yields[:-1]:\n", " if ytype != \"a\": continue\n", " for yname, curve in curves:\n", " for i in range(5): data[\"theme%i\" % i].append(dtype_key[i])\n", " species = \"softwood\" if int(yname[-4:]) < 1200 else \"hardwood\" # CANFI species codes happen to be sorted by softwood/hardwood\n", " leading_species_from_dtype_key[dtype_key] = species\n", " data[species_classifier_colname].append(species)\n", " data[leading_species_classifier_colname].append(species) # just a weird libcbm data model thing...\n", " for i in range(nv + 1): data[\"v%i\" % i].append(curve[i * fm.period_length])\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "764cae99-9d36-4938-9b11-af42dcd68bff", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_yield = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 18, "id": "09ccb8e8-3f63-4d28-bc10-1dda1c538379", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesleading_speciesv0v1v2...v91v92v93v94v95v96v97v98v99v100
0??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
1??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
2??2402000?2402000softwoodsoftwood0.04.013.0...235.0235.0235.0235.0235.0235.0235.0235.0235.0235.0
3??2402000?2422000softwoodsoftwood0.00.00.0...336.0336.0336.0336.0336.0336.0336.0336.0336.0336.0
4??2403000?2403000softwoodsoftwood0.03.015.0...246.0246.0246.0246.0246.0246.0246.0246.0246.0246.0
5??2403000?2423000softwoodsoftwood0.00.00.0...433.0433.0433.0433.0433.0433.0433.0433.0433.0433.0
6??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
7??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
8??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
9??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
10??2403001?2403001softwoodsoftwood0.00.00.0...190.0190.0190.0190.0190.0190.0190.0190.0190.0190.0
11??2403001?2423001softwoodsoftwood0.00.00.0...340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0
12??2401002?2401002softwoodsoftwood0.00.04.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
13??2401002?2421002softwoodsoftwood0.00.00.0...196.0196.0196.0196.0196.0196.0196.0196.0196.0196.0
14??2402002?2402002softwoodsoftwood0.05.018.0...194.0194.0194.0194.0194.0194.0194.0194.0194.0194.0
15??2402002?2422002softwoodsoftwood0.00.00.0...293.0293.0293.0293.0293.0293.0293.0293.0293.0293.0
16??2403002?2403002softwoodsoftwood0.08.029.0...277.0277.0277.0277.0277.0277.0277.0277.0277.0277.0
17??2403002?2423002softwoodsoftwood0.00.00.0...428.0428.0428.0428.0428.0428.0428.0428.0428.0428.0
18??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
19??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
20??2402003?2402003softwoodsoftwood0.00.00.0...215.0215.0215.0215.0215.0215.0215.0215.0215.0215.0
21??2402003?2422003softwoodsoftwood0.00.00.0...358.0358.0358.0358.0358.0358.0358.0358.0358.0358.0
22??2403003?2403003softwoodsoftwood0.06.025.0...270.0270.0270.0270.0270.0270.0270.0270.0270.0270.0
23??2403003?2423003softwoodsoftwood0.00.00.0...476.0476.0476.0476.0476.0476.0476.0476.0476.0476.0
24??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
25??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
26??2402004?2402004softwoodsoftwood0.00.00.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
27??2402004?2422004softwoodsoftwood0.00.00.0...304.0304.0304.0304.0304.0304.0304.0304.0304.0304.0
28??2403004?2403004softwoodsoftwood0.00.00.0...255.0255.0255.0255.0255.0255.0255.0255.0255.0255.0
29??2403004?2423004softwoodsoftwood0.00.00.0...394.0394.0394.0394.0394.0394.0394.0394.0394.0394.0
30??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
31??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
32??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
33??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
34??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
35??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
36??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
37??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
38??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
39??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
40??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
41??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
42??2401007?2401007softwoodsoftwood0.00.00.0...178.0178.0178.0178.0178.0178.0178.0178.0178.0178.0
43??2401007?2421007softwoodsoftwood0.00.00.0...353.0353.0353.0353.0353.0353.0353.0353.0353.0353.0
44??2402007?2402007softwoodsoftwood0.00.02.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
45??2402007?2422007softwoodsoftwood0.00.00.0...415.0415.0415.0415.0415.0415.0415.0415.0415.0415.0
46??2403007?2403007softwoodsoftwood0.06.028.0...269.0269.0269.0269.0269.0269.0269.0269.0269.0269.0
47??2403007?2423007softwoodsoftwood0.00.00.0...535.0535.0535.0535.0535.0535.0535.0535.0535.0535.0
\n", "

48 rows \u00d7 108 columns

\n", "
" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species leading_species v0 \\\n", "0 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "1 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "2 ? ? 2402000 ? 2402000 softwood softwood 0.0 \n", "3 ? ? 2402000 ? 2422000 softwood softwood 0.0 \n", "4 ? ? 2403000 ? 2403000 softwood softwood 0.0 \n", "5 ? ? 2403000 ? 2423000 softwood softwood 0.0 \n", "6 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "7 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "8 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "9 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "10 ? ? 2403001 ? 2403001 softwood softwood 0.0 \n", "11 ? ? 2403001 ? 2423001 softwood softwood 0.0 \n", "12 ? ? 2401002 ? 2401002 softwood softwood 0.0 \n", "13 ? ? 2401002 ? 2421002 softwood softwood 0.0 \n", "14 ? ? 2402002 ? 2402002 softwood softwood 0.0 \n", "15 ? ? 2402002 ? 2422002 softwood softwood 0.0 \n", "16 ? ? 2403002 ? 2403002 softwood softwood 0.0 \n", "17 ? ? 2403002 ? 2423002 softwood softwood 0.0 \n", "18 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "19 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "20 ? ? 2402003 ? 2402003 softwood softwood 0.0 \n", "21 ? ? 2402003 ? 2422003 softwood softwood 0.0 \n", "22 ? ? 2403003 ? 2403003 softwood softwood 0.0 \n", "23 ? ? 2403003 ? 2423003 softwood softwood 0.0 \n", "24 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "25 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "26 ? ? 2402004 ? 2402004 softwood softwood 0.0 \n", "27 ? ? 2402004 ? 2422004 softwood softwood 0.0 \n", "28 ? ? 2403004 ? 2403004 softwood softwood 0.0 \n", "29 ? ? 2403004 ? 2423004 softwood softwood 0.0 \n", "30 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "31 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "32 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "33 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "34 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "35 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "36 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "37 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "38 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "39 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "40 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "41 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "42 ? ? 2401007 ? 2401007 softwood softwood 0.0 \n", "43 ? ? 2401007 ? 2421007 softwood softwood 0.0 \n", "44 ? ? 2402007 ? 2402007 softwood softwood 0.0 \n", "45 ? ? 2402007 ? 2422007 softwood softwood 0.0 \n", "46 ? ? 2403007 ? 2403007 softwood softwood 0.0 \n", "47 ? ? 2403007 ? 2423007 softwood softwood 0.0 \n", "\n", " v1 v2 ... v91 v92 v93 v94 v95 v96 v97 v98 \\\n", "0 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "1 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "2 4.0 13.0 ... 235.0 235.0 235.0 235.0 235.0 235.0 235.0 235.0 \n", "3 0.0 0.0 ... 336.0 336.0 336.0 336.0 336.0 336.0 336.0 336.0 \n", "4 3.0 15.0 ... 246.0 246.0 246.0 246.0 246.0 246.0 246.0 246.0 \n", "5 0.0 0.0 ... 433.0 433.0 433.0 433.0 433.0 433.0 433.0 433.0 \n", "6 0.0 0.0 ... 97.0 97.0 97.0 97.0 97.0 97.0 97.0 97.0 \n", "7 0.0 0.0 ... 97.0 97.0 97.0 97.0 97.0 97.0 97.0 97.0 \n", "8 0.0 0.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "9 0.0 0.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "10 0.0 0.0 ... 190.0 190.0 190.0 190.0 190.0 190.0 190.0 190.0 \n", "11 0.0 0.0 ... 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0 \n", "12 0.0 4.0 ... 127.0 127.0 127.0 127.0 127.0 127.0 127.0 127.0 \n", "13 0.0 0.0 ... 196.0 196.0 196.0 196.0 196.0 196.0 196.0 196.0 \n", "14 5.0 18.0 ... 194.0 194.0 194.0 194.0 194.0 194.0 194.0 194.0 \n", "15 0.0 0.0 ... 293.0 293.0 293.0 293.0 293.0 293.0 293.0 293.0 \n", "16 8.0 29.0 ... 277.0 277.0 277.0 277.0 277.0 277.0 277.0 277.0 \n", "17 0.0 0.0 ... 428.0 428.0 428.0 428.0 428.0 428.0 428.0 428.0 \n", "18 0.0 0.0 ... 150.0 150.0 150.0 150.0 150.0 150.0 150.0 150.0 \n", "19 0.0 0.0 ... 150.0 150.0 150.0 150.0 150.0 150.0 150.0 150.0 \n", "20 0.0 0.0 ... 215.0 215.0 215.0 215.0 215.0 215.0 215.0 215.0 \n", "21 0.0 0.0 ... 358.0 358.0 358.0 358.0 358.0 358.0 358.0 358.0 \n", "22 6.0 25.0 ... 270.0 270.0 270.0 270.0 270.0 270.0 270.0 270.0 \n", "23 0.0 0.0 ... 476.0 476.0 476.0 476.0 476.0 476.0 476.0 476.0 \n", "24 0.0 0.0 ... 158.0 158.0 158.0 158.0 158.0 158.0 158.0 158.0 \n", "25 0.0 0.0 ... 158.0 158.0 158.0 158.0 158.0 158.0 158.0 158.0 \n", "26 0.0 0.0 ... 209.0 209.0 209.0 209.0 209.0 209.0 209.0 209.0 \n", "27 0.0 0.0 ... 304.0 304.0 304.0 304.0 304.0 304.0 304.0 304.0 \n", "28 0.0 0.0 ... 255.0 255.0 255.0 255.0 255.0 255.0 255.0 255.0 \n", "29 0.0 0.0 ... 394.0 394.0 394.0 394.0 394.0 394.0 394.0 394.0 \n", "30 2.0 12.0 ... 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 \n", "31 2.0 12.0 ... 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 \n", "32 8.0 30.0 ... 118.0 118.0 118.0 118.0 118.0 118.0 118.0 118.0 \n", "33 8.0 30.0 ... 118.0 118.0 118.0 118.0 118.0 118.0 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535.0 \n", "\n", "[48 rows x 108 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_yield" ] }, { "cell_type": "markdown", "id": "9ca73cc7-a6ad-49c1-ab69-3144150311dc", "metadata": {}, "source": [ "### Compile inventory. \n", "\n", "`ws3` \"themes\" map 1:1 to `libcbm` \"classifiers\"." ] }, { "cell_type": "markdown", "id": "e959818b-7e3b-4cc6-bb91-5c611b25fa49", "metadata": {}, "source": [ "Start by loading the Woodstock-formatted initial inventory input file." ] }, { "cell_type": "code", "execution_count": 19, "id": "c9d89258-4da0-4b01-8f8e-c269151d30ca", "metadata": { "tags": [] }, "outputs": [], "source": [ "names = [\"_\", \"theme0\", \"theme1\", \"theme2\", \"theme3\", \"theme4\", \"age\", \"area\"]\n", "sit_inventory = pd.read_csv(\"data/woodstock_model_files_tsa24_clipped/tsa24_clipped.are\",\n", " delimiter=\" \", header=None, names=names)\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "2aaff64e-5950-4b59-a48d-88df6f19c7f4", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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_theme0theme1theme2theme3theme4agearea
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" ], "text/plain": [ " _ theme0 theme1 theme2 theme3 theme4 age area\n", "0 *A tsa24_clipped 0 2401000 100 2401000 85 15.182275\n", "1 *A tsa24_clipped 0 2401000 100 2401000 95 20.653789\n", "2 *A tsa24_clipped 0 2401000 100 2401000 105 1.109374\n", "3 *A tsa24_clipped 0 2401000 100 2401000 125 25.731748\n", "4 *A tsa24_clipped 0 2401000 100 2401000 135 62.023828" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_inventory.head()" ] }, { "cell_type": "markdown", "id": "9d23c691-93d0-465c-8daf-58143606cc41", "metadata": {}, "source": [ "Drop useless column." ] }, { "cell_type": "code", "execution_count": 21, "id": "434f1fd9-da22-449b-97c9-c359a3031f06", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_inventory.drop('_', axis=1, inplace=True)" ] }, { "cell_type": "markdown", "id": "3b6f03f7-4660-49ac-be72-a0c5ad6d1b28", "metadata": {}, "source": [ "Add missing columns. First, we define a helper function that we cna use here (and later) to deduce the correct `leading_species` value (i.e., the sixth classifier) from a `pandas.Series` that includes the 5 themes in our `ws3` model." ] }, { "cell_type": "code", "execution_count": 22, "id": "e98d3a32-2ea3-4926-ad5d-10fbe4f492ea", "metadata": { "tags": [] }, "outputs": [], "source": [ "def _leading_species(dtype_key):\n", " for mask, leading_species in leading_species_from_dtype_key.items():\n", " if fm.match_mask(mask, dtype_key):\n", " return leading_species\n", " \n", "def __leading_species(r):\n", " dtype_key = tuple(str(r[\"theme%i\" % i]) for i in range(5))\n", " return _leading_species(dtype_key)" ] }, { "cell_type": "code", "execution_count": 23, "id": "18fc62e6-63d6-4931-ba8d-1cc535f9e170", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_inventory[species_classifier_colname] = sit_inventory.apply(__leading_species, axis=1)\n", "sit_inventory[\"using_age_class\"] = \"FALSE\"\n", "sit_inventory[\"delay\"] = 0\n", "sit_inventory[\"landclass\"] = 0\n", "sit_inventory[\"historic_disturbance\"] = \"fire\"\n", "sit_inventory[\"last_pass_disturbance\"] = sit_inventory.apply(lambda r: \"fire\" if r[\"theme2\"] == r[\"theme4\"] else \"harvest\", axis=1)\n" ] }, { "cell_type": "markdown", "id": "2bf11fb2-f8cf-4a91-9e1a-88d135a48b86", "metadata": {}, "source": [ "The `landclass` column values should contain an integer in the range $[0, 22], which CBM maps to one of 23 UNFCCC land classes (see Table 3-1 in the the CBM-CFS3 user guide, shown below for convenience). We just use a value of 0 for all inventory records (i.e., \"Forest land remaining forest land\").\n", "\n", "The `dist1` and `dist2` values in the `last_pass_disturbance` column correspond to fire and harvesting (and match the values used in the `disturbance_types` table defined a few cells down)." ] }, { "cell_type": "markdown", "id": "12199984-07bf-472f-b2ad-b8a1ca6072f2", "metadata": {}, "source": [ "![UNFCCC land class values](images/cbmcfs3_ug_table3-1.png)" ] }, { "cell_type": "markdown", "id": "c2e4f32d-482f-4302-bc3e-f0ec754268df", "metadata": {}, "source": [ "Shuffle columns to match order expected by `libcbm`." ] }, { "cell_type": "code", "execution_count": 24, "id": "94e6f8ed-6429-4aed-a2ba-9120ca3a88a9", "metadata": { "tags": [] }, "outputs": [], "source": [ "names = [\"theme0\", \"theme1\", \"theme2\", \"theme3\", \"theme4\", species_classifier_colname,\n", " \"using_age_class\", \"age\", \"area\", \"delay\", \"landclass\",\n", " \"historic_disturbance\", \"last_pass_disturbance\"]\n", "sit_inventory = sit_inventory[names]" ] }, { "cell_type": "code", "execution_count": 25, "id": "e20e9fee-0dde-4f4c-93a2-55d2d918e7b7", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesusing_age_classageareadelaylandclasshistoric_disturbancelast_pass_disturbance
0tsa24_clipped024010001002401000softwoodFALSE8515.18227500firefire
1tsa24_clipped024010001002401000softwoodFALSE9520.65378900firefire
2tsa24_clipped024010001002401000softwoodFALSE1051.10937400firefire
3tsa24_clipped024010001002401000softwoodFALSE12525.73174800firefire
4tsa24_clipped024010001002401000softwoodFALSE13562.02382800firefire
5tsa24_clipped024010001002401000softwoodFALSE14545.32229000firefire
6tsa24_clipped024010001002401000softwoodFALSE1553.05280400firefire
7tsa24_clipped0240200512012402005hardwoodFALSE851.81297900firefire
8tsa24_clipped124010022042401002softwoodFALSE78103.76740300firefire
9tsa24_clipped124010022042401002softwoodFALSE804.17314700firefire
10tsa24_clipped124010022042401002softwoodFALSE85282.12963600firefire
11tsa24_clipped124010022042401002softwoodFALSE9173.10215600firefire
12tsa24_clipped124010022042401002softwoodFALSE9328.37956700firefire
13tsa24_clipped124010022042401002softwoodFALSE9594.94676000firefire
14tsa24_clipped124010022042401002softwoodFALSE10532.17541900firefire
15tsa24_clipped124010022042401002softwoodFALSE1134.18482600firefire
16tsa24_clipped124010022042401002softwoodFALSE11550.03081700firefire
17tsa24_clipped124010022042401002softwoodFALSE12578.16612100firefire
18tsa24_clipped124010022042401002softwoodFALSE13572.24421900firefire
19tsa24_clipped124010022042401002softwoodFALSE14596.38442700firefire
20tsa24_clipped124010022042401002softwoodFALSE1539.59146900firefire
21tsa24_clipped124010022042401002softwoodFALSE15534.32629200firefire
22tsa24_clipped124010022042421002softwoodFALSE200.42205400fireharvest
23tsa24_clipped124020001002402000softwoodFALSE1650.63800500firefire
24tsa24_clipped124020022042402002softwoodFALSE7832.64168200firefire
25tsa24_clipped124020022042402002softwoodFALSE9348.21816500firefire
26tsa24_clipped124020022042402002softwoodFALSE9533.89498200firefire
27tsa24_clipped124020022042402002softwoodFALSE1153.19537900firefire
28tsa24_clipped124030001002403000softwoodFALSE9314.81164300firefire
29tsa24_clipped124030022042403002softwoodFALSE732.24399100firefire
30tsa24_clipped124030022042423002softwoodFALSE959.81429100fireharvest
31tsa24_clipped124030022042423002softwoodFALSE1832.36619900fireharvest
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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species using_age_class \\\n", "0 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "1 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "2 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "3 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "4 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "5 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "6 tsa24_clipped 0 2401000 100 2401000 softwood FALSE \n", "7 tsa24_clipped 0 2402005 1201 2402005 hardwood FALSE \n", "8 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "9 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "10 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "11 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "12 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "13 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "14 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "15 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "16 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "17 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "18 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "19 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "20 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "21 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "22 tsa24_clipped 1 2401002 204 2421002 softwood FALSE \n", "23 tsa24_clipped 1 2402000 100 2402000 softwood FALSE \n", "24 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "25 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "26 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "27 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "28 tsa24_clipped 1 2403000 100 2403000 softwood FALSE \n", "29 tsa24_clipped 1 2403002 204 2403002 softwood FALSE \n", "30 tsa24_clipped 1 2403002 204 2423002 softwood FALSE \n", "31 tsa24_clipped 1 2403002 204 2423002 softwood FALSE \n", "\n", " age area delay landclass historic_disturbance \\\n", "0 85 15.182275 0 0 fire \n", "1 95 20.653789 0 0 fire \n", "2 105 1.109374 0 0 fire \n", "3 125 25.731748 0 0 fire \n", "4 135 62.023828 0 0 fire \n", "5 145 45.322290 0 0 fire \n", "6 155 3.052804 0 0 fire \n", "7 85 1.812979 0 0 fire \n", "8 78 103.767403 0 0 fire \n", "9 80 4.173147 0 0 fire \n", "10 85 282.129636 0 0 fire \n", "11 91 73.102156 0 0 fire \n", "12 93 28.379567 0 0 fire \n", "13 95 94.946760 0 0 fire \n", "14 105 32.175419 0 0 fire \n", "15 113 4.184826 0 0 fire \n", "16 115 50.030817 0 0 fire \n", "17 125 78.166121 0 0 fire \n", "18 135 72.244219 0 0 fire \n", "19 145 96.384427 0 0 fire \n", "20 153 9.591469 0 0 fire \n", "21 155 34.326292 0 0 fire \n", "22 20 0.422054 0 0 fire \n", "23 165 0.638005 0 0 fire \n", "24 78 32.641682 0 0 fire \n", "25 93 48.218165 0 0 fire \n", "26 95 33.894982 0 0 fire \n", "27 115 3.195379 0 0 fire \n", "28 93 14.811643 0 0 fire \n", "29 73 2.243991 0 0 fire \n", "30 9 59.814291 0 0 fire \n", "31 18 32.366199 0 0 fire \n", "\n", " last_pass_disturbance \n", "0 fire \n", "1 fire \n", "2 fire \n", "3 fire \n", "4 fire \n", "5 fire \n", "6 fire \n", "7 fire \n", "8 fire \n", "9 fire \n", "10 fire \n", "11 fire \n", "12 fire \n", "13 fire \n", "14 fire \n", "15 fire \n", "16 fire \n", "17 fire \n", "18 fire \n", "19 fire \n", "20 fire \n", "21 fire \n", "22 harvest \n", "23 fire \n", "24 fire \n", "25 fire \n", "26 fire \n", "27 fire \n", "28 fire \n", "29 fire \n", "30 harvest \n", "31 harvest " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_inventory" ] }, { "cell_type": "markdown", "id": "c739952f-be7f-4264-bbff-87c4e814f267", "metadata": {}, "source": [ "### Compile classifiers" ] }, { "cell_type": "code", "execution_count": 26, "id": "84ee15d2-3109-4a24-9122-68a2c8a54deb", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = {\"classifier_id\":[], \"name\":[], \"description\":[]}\n", " data[\"classifier_id\"].append(i+1)\n", " data[\"name\"].append(\"_CLASSIFIER\")\n", " data[\"description\"].append(\"theme%i\" % i)\n", " for v in fm.theme_basecodes(i):\n", " data[\"classifier_id\"].append(i+1)\n", " data[\"name\"].append(v)\n", " data[\"description\"].append(v) # these are not very good descriptions, but will not affect CBM model output\n", "\n", "data[\"classifier_id\"].append(6)\n", "data[\"name\"].append(\"_CLASSIFIER\")\n", "data[\"description\"].append(species_classifier_colname)\n", "\n", "data[\"classifier_id\"].append(6)\n", "data[\"name\"].append(\"softwood\")\n", "data[\"description\"].append(\"softwood\")\n", "\n", "data[\"classifier_id\"].append(6)\n", "data[\"name\"].append(\"hardwood\")\n", "data[\"description\"].append(\"hardwood\")\n", "\n", "sit_classifiers = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 27, "id": "20441985-a4f8-42f6-a805-cf5ddd4a2b87", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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classifier_idnamedescription
01_CLASSIFIERtheme0
11tsa24_clippedtsa24_clipped
22_CLASSIFIERtheme1
3200
4211
............
72524230002423000
73524030012403001
746_CLASSIFIERspecies
756softwoodsoftwood
766hardwoodhardwood
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77 rows \u00d7 3 columns

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" ], "text/plain": [ " classifier_id name description\n", "0 1 _CLASSIFIER theme0\n", "1 1 tsa24_clipped tsa24_clipped\n", "2 2 _CLASSIFIER theme1\n", "3 2 0 0\n", "4 2 1 1\n", ".. ... ... ...\n", "72 5 2423000 2423000\n", "73 5 2403001 2403001\n", "74 6 _CLASSIFIER species\n", "75 6 softwood softwood\n", "76 6 hardwood hardwood\n", "\n", "[77 rows x 3 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_classifiers" ] }, { "cell_type": "markdown", "id": "1222abbe-8787-4007-ae8e-9f1a09bdb257", "metadata": {}, "source": [ "### Compile disturbance types" ] }, { "cell_type": "code", "execution_count": 28, "id": "e77edbd8-f0ec-4ca3-8f85-175b89f9099a", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = {\"id\":[\"harvest\", \"fire\"],\n", " \"name\":[\"harvest\", \"fire\"]}\n", "sit_disturbance_types = pd.DataFrame(data)\n", " \"name\":[\"harvest\", \"fire\"]}\n", "sit_disturbance_types = pd.DataFrame(data)" ] }, { "cell_type": "markdown", "id": "83918f2b-1ae1-4817-b62b-9fc9252e266c", "metadata": {}, "source": [ "### Compile age classes" ] }, { "cell_type": "code", "execution_count": 29, "id": "83c88870-f095-45ef-bd40-c429df86ba61", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = {\"name\":[\"age_0\"],\n", " \"class_size\":[0],\n", " \"start_year\":[0],\n", " \"end_year\":[0]}\n", "for i, ac in enumerate(range(period_length, max_age+period_length, period_length)):\n", " data[\"name\"].append(\"age_%i\" % (i+1))\n", " data[\"class_size\"].append(period_length)\n", " data[\"start_year\"].append(ac - period_length + 1)\n", " data[\"end_year\"].append(ac)\n", "sit_age_classes = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 30, "id": "b71c05a0-6bf4-4b4b-9d5d-bdff293163b0", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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nameclass_sizestart_yearend_year
0age_0000
1age_110110
2age_2101120
3age_3102130
4age_4103140
...............
96age_9610951960
97age_9710961970
98age_9810971980
99age_9910981990
100age_100109911000
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101 rows \u00d7 4 columns

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" ], "text/plain": [ " name class_size start_year end_year\n", "0 age_0 0 0 0\n", "1 age_1 10 1 10\n", "2 age_2 10 11 20\n", "3 age_3 10 21 30\n", "4 age_4 10 31 40\n", ".. ... ... ... ...\n", "96 age_96 10 951 960\n", "97 age_97 10 961 970\n", "98 age_98 10 971 980\n", "99 age_99 10 981 990\n", "100 age_100 10 991 1000\n", "\n", "[101 rows x 4 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_age_classes" ] }, { "cell_type": "markdown", "id": "576b8718-0cf6-4fa8-b9e0-b2d5958243bb", "metadata": {}, "source": [ "### Compile events" ] }, { "cell_type": "code", "execution_count": 31, "id": "b8c11339-0129-4c47-b4bf-a14d47c83333", "metadata": { "tags": [] }, "outputs": [], "source": [ "columns = [\n", " \"theme0\",\n", " \"theme1\",\n", " \"theme2\",\n", " \"theme3\",\n", " \"theme4\",\n", " species_classifier_colname,\n", " \"using_age_class\",\n", " \"min_softwood_age\",\n", " \"max_softwood_age\",\n", " \"min_hardwood_age\",\n", " \"max_hardwood_age\",\n", " \"MinYearsSinceDist\",\n", " \"MaxYearsSinceDist\",\n", " \"LastDistTypeID\",\n", " \"MinTotBiomassC\",\n", " \"MaxTotBiomassC\",\n", " \"MinSWMerchBiomassC\",\n", " \"MaxSWMerchBiomassC\",\n", " \"MinHWMerchBiomassC\",\n", " \"MaxHWMerchBiomassC\",\n", " \"MinTotalStemSnagC\",\n", " \"MaxTotalStemSnagC\",\n", " \"MinSWStemSnagC\",\n", " \"MaxSWStemSnagC\",\n", " \"MinHWStemSnagC\",\n", " \"MaxHWStemSnagC\",\n", " \"MinTotalStemSnagMerchC\",\n", " \"MaxTotalStemSnagMerchC\",\n", " \"MinSWMerchStemSnagC\",\n", " \"MaxSWMerchStemSnagC\",\n", " \"MinHWMerchStemSnagC\",\n", " \"MaxHWMerchStemSnagC\",\n", " \"efficiency\",\n", " \"sort_type\",\n", " \"target_type\",\n", " \"target\",\n", " \"disturbance_type\",\n", " \"disturbance_year\"\n", "]\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "aee310d7-8ce7-4ed3-9a41-f24ac304f96f", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = {c:[] for c in columns}" ] }, { "cell_type": "code", "execution_count": 33, "id": "a13743e9-4d69-4562-b31b-cc9a8be15f2c", "metadata": { "tags": [] }, "outputs": [], "source": [ "for dtype_key, age, area, acode, period, _ in sch:\n", " for i in range(5): data[\"theme%i\" % i].append(dtype_key[i])\n", " data[species_classifier_colname].append(_leading_species(dtype_key))\n", " data[\"using_age_class\"].append(\"FALSE\")\n", " data[\"min_softwood_age\"].append(-1)\n", " data[\"max_softwood_age\"].append(-1)\n", " data[\"min_hardwood_age\"].append(-1)\n", " data[\"max_hardwood_age\"].append(-1)\n", " for c in columns[11:-6]: data[c].append(-1)\n", " data[\"efficiency\"].append(1)\n", " data[\"sort_type\"].append(3) # oldest first (see Table 3-3 in the CBM-CFS3 user guide)\n", " data[\"target_type\"].append(\"A\") # area target\n", " data[\"target\"].append(area)\n", " data[\"disturbance_type\"].append(acode)\n", " data[\"disturbance_year\"].append(period*fm.period_length)\n", "sit_events = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 34, "id": "5675fc47-51f2-424f-afe4-fd17f43c97fe", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesusing_age_classmin_softwood_agemax_softwood_agemin_hardwood_age...MinSWMerchStemSnagCMaxSWMerchStemSnagCMinHWMerchStemSnagCMaxHWMerchStemSnagCefficiencysort_typetarget_typetargetdisturbance_typedisturbance_year
0tsa24_clipped124020022042402002softwoodFALSE-1-1-1...-1-1-1-113A3.195379harvest10
1tsa24_clipped124020022042402002softwoodFALSE-1-1-1...-1-1-1-113A10.057453harvest10
2tsa24_clipped124020001002402000softwoodFALSE-1-1-1...-1-1-1-113A0.058533harvest10
3tsa24_clipped124010022042401002softwoodFALSE-1-1-1...-1-1-1-113A34.326292harvest10
4tsa24_clipped124010022042401002softwoodFALSE-1-1-1...-1-1-1-113A9.591469harvest10
..................................................................
59tsa24_clipped124010022042401002softwoodFALSE-1-1-1...-1-1-1-113A60.164322harvest100
60tsa24_clipped124010022042421002softwoodFALSE-1-1-1...-1-1-1-113A28.271171harvest100
61tsa24_clipped124030022042423002softwoodFALSE-1-1-1...-1-1-1-113A10.632204harvest100
62tsa24_clipped124030001002423000softwoodFALSE-1-1-1...-1-1-1-113A1.497807harvest100
63tsa24_clipped124030001002423000softwoodFALSE-1-1-1...-1-1-1-113A0.166423harvest100
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64 rows \u00d7 38 columns

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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species using_age_class \\\n", "0 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "1 tsa24_clipped 1 2402002 204 2402002 softwood FALSE \n", "2 tsa24_clipped 1 2402000 100 2402000 softwood FALSE \n", "3 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "4 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", ".. ... ... ... ... ... ... ... \n", "59 tsa24_clipped 1 2401002 204 2401002 softwood FALSE \n", "60 tsa24_clipped 1 2401002 204 2421002 softwood FALSE \n", "61 tsa24_clipped 1 2403002 204 2423002 softwood FALSE \n", "62 tsa24_clipped 1 2403000 100 2423000 softwood FALSE \n", "63 tsa24_clipped 1 2403000 100 2423000 softwood FALSE \n", "\n", " min_softwood_age max_softwood_age min_hardwood_age ... \\\n", "0 -1 -1 -1 ... \n", "1 -1 -1 -1 ... \n", "2 -1 -1 -1 ... \n", "3 -1 -1 -1 ... \n", "4 -1 -1 -1 ... \n", ".. ... ... ... ... \n", "59 -1 -1 -1 ... \n", "60 -1 -1 -1 ... \n", "61 -1 -1 -1 ... \n", "62 -1 -1 -1 ... \n", "63 -1 -1 -1 ... \n", "\n", " MinSWMerchStemSnagC MaxSWMerchStemSnagC MinHWMerchStemSnagC \\\n", "0 -1 -1 -1 \n", "1 -1 -1 -1 \n", "2 -1 -1 -1 \n", "3 -1 -1 -1 \n", "4 -1 -1 -1 \n", ".. ... ... ... \n", "59 -1 -1 -1 \n", "60 -1 -1 -1 \n", "61 -1 -1 -1 \n", "62 -1 -1 -1 \n", "63 -1 -1 -1 \n", "\n", " MaxHWMerchStemSnagC efficiency sort_type target_type target \\\n", "0 -1 1 3 A 3.195379 \n", "1 -1 1 3 A 10.057453 \n", "2 -1 1 3 A 0.058533 \n", "3 -1 1 3 A 34.326292 \n", "4 -1 1 3 A 9.591469 \n", ".. ... ... ... ... ... \n", "59 -1 1 3 A 60.164322 \n", "60 -1 1 3 A 28.271171 \n", "61 -1 1 3 A 10.632204 \n", "62 -1 1 3 A 1.497807 \n", "63 -1 1 3 A 0.166423 \n", "\n", " disturbance_type disturbance_year \n", "0 harvest 10 \n", "1 harvest 10 \n", "2 harvest 10 \n", "3 harvest 10 \n", "4 harvest 10 \n", ".. ... ... \n", "59 harvest 100 \n", "60 harvest 100 \n", "61 harvest 100 \n", "62 harvest 100 \n", "63 harvest 100 \n", "\n", "[64 rows x 38 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_events" ] }, { "cell_type": "markdown", "id": "c8a2a52a-bc32-4e91-bde0-e154462bd04d", "metadata": {}, "source": [ "To do: figure out what Woodstock export does to make sure that exported schedules map harvesting events to the correct age class (not just oldest first, which is stupid and inaccurate)." ] }, { "cell_type": "markdown", "id": "a40f4067-a4c5-4c8e-b182-65ba4ea3f934", "metadata": {}, "source": [ "### Compile transitions" ] }, { "cell_type": "code", "execution_count": 35, "id": "7887fa00-da8e-4fb3-8e88-47bcbad17aa7", "metadata": { "tags": [] }, "outputs": [], "source": [ "au_table = pd.read_csv(\"data/au_table.csv\")\n", "au_table1 = au_table.set_index(\"au_id\")\n", "au_table2 = au_table.set_index(\"managed_curve_id\")" ] }, { "cell_type": "code", "execution_count": 36, "id": "1ed47866-f7bb-4b78-89fe-7d6aae18adde", "metadata": { "tags": [] }, "outputs": [], "source": [ "columns = [\"theme0\",\n", " \"theme1\",\n", " \"theme2\",\n", " \"theme3\",\n", " \"theme4\",\n", " species_classifier_colname,\n", " \"using_age_class\",\n", " \"min_softwood_age\",\n", " \"max_softwood_age\",\n", " \"min_hardwood_age\",\n", " \"max_hardwood_age\",\n", " \"disturbance_type\",\n", " \"to_theme0\",\n", " \"to_theme1\",\n", " \"to_theme2\",\n", " \"to_theme3\",\n", " \"to_theme4\",\n", " \"to_%s\" % species_classifier_colname,\n", " \"regen_delay\",\n", " \"reset_age\",\n", " \"percent\"]\n", "data = {c:[] for c in columns}\n", "for acode in fm.transitions:\n", " if acode != \"harvest\": continue\n", " for smask in fm.transitions[acode]:\n", " tmask, tprop, _, _, _, _, _ = fm.transitions[acode][smask][\"\"][0]\n", " for i in range(5): data[\"theme%i\" % i].append(smask[i])\n", " data[species_classifier_colname].append(\"softwood\" if au_table1.loc[int(smask[2])].canfi_species < 1200 else \"hardwood\")\n", " data[\"using_age_class\"].append(\"FALSE\")\n", " data[\"min_softwood_age\"].append(-1)\n", " data[\"max_softwood_age\"].append(-1)\n", " data[\"min_hardwood_age\"].append(-1)\n", " data[\"max_hardwood_age\"].append(-1)\n", " data[\"disturbance_type\"].append(\"harvest\")\n", " for i in range(5): data[\"to_theme%i\" % i].append(tmask[i])\n", " data[\"to_%s\" % species_classifier_colname].append(\"softwood\" if au_table2.loc[int(tmask[4])].canfi_species < 1200 else \"hardwood\")\n", " data[\"regen_delay\"].append(0)\n", " data[\"reset_age\"].append(0)\n", " data[\"percent\"].append(100)\n", "sit_transitions = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 37, "id": "cf931674-a4be-4174-960f-f96c8de5b82b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesusing_age_classmin_softwood_agemax_softwood_agemin_hardwood_age...disturbance_typeto_theme0to_theme1to_theme2to_theme3to_theme4to_speciesregen_delayreset_agepercent
0??2402000??softwoodFALSE-1-1-1...harvest????2422000softwood00100
1??2403000??softwoodFALSE-1-1-1...harvest????2423000softwood00100
2??2403001??softwoodFALSE-1-1-1...harvest????2423001softwood00100
3??2401002??softwoodFALSE-1-1-1...harvest????2421002softwood00100
4??2402002??softwoodFALSE-1-1-1...harvest????2422002softwood00100
5??2403002??softwoodFALSE-1-1-1...harvest????2423002softwood00100
6??2402003??softwoodFALSE-1-1-1...harvest????2422003softwood00100
7??2403003??softwoodFALSE-1-1-1...harvest????2423003softwood00100
8??2402004??softwoodFALSE-1-1-1...harvest????2422004softwood00100
9??2403004??softwoodFALSE-1-1-1...harvest????2423004softwood00100
10??2401007??softwoodFALSE-1-1-1...harvest????2421007softwood00100
11??2402007??softwoodFALSE-1-1-1...harvest????2422007softwood00100
12??2403007??softwoodFALSE-1-1-1...harvest????2423007softwood00100
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13 rows \u00d7 21 columns

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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species using_age_class \\\n", "0 ? ? 2402000 ? ? softwood FALSE \n", "1 ? ? 2403000 ? ? softwood FALSE \n", "2 ? ? 2403001 ? ? softwood FALSE \n", "3 ? ? 2401002 ? ? softwood FALSE \n", "4 ? ? 2402002 ? ? softwood FALSE \n", "5 ? ? 2403002 ? ? softwood FALSE \n", "6 ? ? 2402003 ? ? softwood FALSE \n", "7 ? ? 2403003 ? ? softwood FALSE \n", "8 ? ? 2402004 ? ? softwood FALSE \n", "9 ? ? 2403004 ? ? softwood FALSE \n", "10 ? ? 2401007 ? ? softwood FALSE \n", "11 ? ? 2402007 ? ? softwood FALSE \n", "12 ? ? 2403007 ? ? softwood FALSE \n", "\n", " min_softwood_age max_softwood_age min_hardwood_age ... \\\n", "0 -1 -1 -1 ... \n", "1 -1 -1 -1 ... \n", "2 -1 -1 -1 ... \n", "3 -1 -1 -1 ... \n", "4 -1 -1 -1 ... \n", "5 -1 -1 -1 ... \n", "6 -1 -1 -1 ... \n", "7 -1 -1 -1 ... \n", "8 -1 -1 -1 ... \n", "9 -1 -1 -1 ... \n", "10 -1 -1 -1 ... \n", "11 -1 -1 -1 ... \n", "12 -1 -1 -1 ... \n", "\n", " disturbance_type to_theme0 to_theme1 to_theme2 to_theme3 to_theme4 \\\n", "0 harvest ? ? ? ? 2422000 \n", "1 harvest ? ? ? ? 2423000 \n", "2 harvest ? ? ? ? 2423001 \n", "3 harvest ? ? ? ? 2421002 \n", "4 harvest ? ? ? ? 2422002 \n", "5 harvest ? ? ? ? 2423002 \n", "6 harvest ? ? ? ? 2422003 \n", "7 harvest ? ? ? ? 2423003 \n", "8 harvest ? ? ? ? 2422004 \n", "9 harvest ? ? ? ? 2423004 \n", "10 harvest ? ? ? ? 2421007 \n", "11 harvest ? ? ? ? 2422007 \n", "12 harvest ? ? ? ? 2423007 \n", "\n", " to_species regen_delay reset_age percent \n", "0 softwood 0 0 100 \n", "1 softwood 0 0 100 \n", "2 softwood 0 0 100 \n", "3 softwood 0 0 100 \n", "4 softwood 0 0 100 \n", "5 softwood 0 0 100 \n", "6 softwood 0 0 100 \n", "7 softwood 0 0 100 \n", "8 softwood 0 0 100 \n", "9 softwood 0 0 100 \n", "10 softwood 0 0 100 \n", "11 softwood 0 0 100 \n", "12 softwood 0 0 100 \n", "\n", "[13 rows x 21 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_transitions" ] }, { "cell_type": "markdown", "id": "426ce5da-34d5-4f78-8a65-dcfda524c4f1", "metadata": { "tags": [] }, "source": [ "### Import data into and run `libcbm`" ] }, { "cell_type": "code", "execution_count": 38, "id": "c69bab3c-4217-4e8f-8c36-2d1952b35d86", "metadata": { "tags": [] }, "outputs": [], "source": [ "from libcbm.input.sit import sit_reader\n", "from libcbm.input.sit import sit_cbm_factory " ] }, { "cell_type": "markdown", "id": "fd455b2a-4b6a-4253-b741-2bd973a89b9d", "metadata": {}, "source": [ "Compile the data tables we created into a `sit_data` object." ] }, { "cell_type": "code", "execution_count": 39, "id": "107f9f5b-3237-427f-9572-c6764cab5bdd", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['theme0', 'theme1', 'theme2', 'theme3', 'theme4', 'species',\n", " 'leading_species', 'v0', 'v1', 'v2',\n", " ...\n", " 'v91', 'v92', 'v93', 'v94', 'v95', 'v96', 'v97', 'v98', 'v99', 'v100'],\n", " dtype='object', length=108)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit_data = None\n", "sit_yield.columns" ] }, { "cell_type": "code", "execution_count": 40, "id": "25fbbbe2-02f8-4879-9b85-71b9a3cc0909", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_data = sit_reader.parse(sit_classifiers=sit_classifiers,\n", " sit_disturbance_types=sit_disturbance_types,\n", " sit_age_classes=sit_age_classes,\n", " sit_inventory=sit_inventory,\n", " sit_yield=sit_yield,\n", " sit_events=sit_events,\n", " sit_transitions=sit_transitions,\n", " sit_eligibilities=None)" ] }, { "cell_type": "markdown", "id": "493d0e5b-e3d3-489f-8acf-06735303d75f", "metadata": {}, "source": [ "Create a `config` namespace object that has the same structure we would have if we loaded a `sit_config.json` JSON file into memory using the `json` module." ] }, { "cell_type": "code", "execution_count": 41, "id": "255d17c5-5c52-47f8-a51b-788bcaab1389", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_config = {\n", " \"mapping_config\": {\n", " \"nonforest\": None,\n", " \"species\": {\n", " \"species_classifier\": species_classifier_colname,\n", " \"species_mapping\": [\n", " {\"user_species\": \"softwood\", \"default_species\": \"Softwood forest type\"},\n", " {\"user_species\": \"hardwood\", \"default_species\": \"Hardwood forest type\"}\n", " ]\n", " },\n", " \"spatial_units\": {\n", " \"mapping_mode\": \"SingleDefaultSpatialUnit\",\n", " \"admin_boundary\": \"British Columbia\",\n", " \"eco_boundary\": \"Montane Cordillera\"},\n", " \"disturbance_types\": {\n", " \"disturbance_type_mapping\": [\n", " {\"user_dist_type\": \"harvest\", \"default_dist_type\": \"Clearcut harvesting without salvage\"},\n", " {\"user_dist_type\": \"fire\", \"default_dist_type\": \"Wildfire\"}\n", " ]\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 42, "id": "f76fdd09-29a8-4f17-8f24-9b9e7b71c6ec", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit = sit_cbm_factory.initialize_sit(sit_data=sit_data, config=sit_config)" ] }, { "cell_type": "code", "execution_count": 43, "id": "f2664b7a-a115-41d0-a044-b958b4a12f56", "metadata": { "tags": [] }, "outputs": [], "source": [ "classifiers, inventory = sit_cbm_factory.initialize_inventory(sit)" ] }, { "cell_type": "code", "execution_count": 44, "id": "5d336005-deeb-4f5f-a3f4-571f0ffe21e6", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species\n", "0 1 2 19 29 58 70\n", "1 1 2 19 29 58 70\n", "2 1 2 19 29 58 70\n", "3 1 2 19 29 58 70\n", "4 1 2 19 29 58 70\n", "5 1 2 19 29 58 70\n", "6 1 2 19 29 58 70\n", "7 1 2 20 30 59 71\n", "8 1 3 16 31 54 70\n", "9 1 3 16 31 54 70\n", "10 1 3 16 31 54 70\n", "11 1 3 16 31 54 70\n", "12 1 3 16 31 54 70\n", "13 1 3 16 31 54 70\n", "14 1 3 16 31 54 70\n", "15 1 3 16 31 54 70\n", "16 1 3 16 31 54 70\n", "17 1 3 16 31 54 70\n", "18 1 3 16 31 54 70\n", "19 1 3 16 31 54 70\n", "20 1 3 16 31 54 70\n", "21 1 3 16 31 54 70\n", "22 1 3 16 31 48 70\n", "23 1 3 8 29 39 70\n", "24 1 3 4 31 33 70\n", "25 1 3 4 31 33 70\n", "26 1 3 4 31 33 70\n", "27 1 3 4 31 33 70\n", "28 1 3 25 29 66 70\n", "29 1 3 23 31 63 70\n", "30 1 3 23 31 50 70\n", "31 1 3 23 31 50 70" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifiers.to_pandas()" ] }, { "cell_type": "code", "execution_count": 45, "id": "80a17a0b-7c3d-42bb-beed-5007afeb468d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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inventory_idagespatial_unitafforestation_pre_type_idareadelayland_classhistorical_disturbance_typelast_pass_disturbance_type
018542-1.015.1822750022
129542-1.020.6537890022
2310542-1.01.1093740022
3412542-1.025.7317480022
4513542-1.062.0238280022
5614542-1.045.3222900022
6715542-1.03.0528040022
788542-1.01.8129790022
897842-1.0103.7674030022
9108042-1.04.1731470022
10118542-1.0282.1296360022
11129142-1.073.1021560022
12139342-1.028.3795670022
13149542-1.094.9467600022
141510542-1.032.1754190022
151611342-1.04.1848260022
161711542-1.050.0308170022
171812542-1.078.1661210022
181913542-1.072.2442190022
192014542-1.096.3844270022
202115342-1.09.5914690022
212215542-1.034.3262920022
22232042-1.00.4220540021
232416542-1.00.6380050022
24257842-1.032.6416820022
25269342-1.048.2181650022
26279542-1.033.8949820022
272811542-1.03.1953790022
28299342-1.014.8116430022
29307342-1.02.2439910022
3031942-1.059.8142910021
31321842-1.032.3661990021
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" ], "text/plain": [ " inventory_id age spatial_unit afforestation_pre_type_id area \\\n", "0 1 85 42 -1.0 15.182275 \n", "1 2 95 42 -1.0 20.653789 \n", "2 3 105 42 -1.0 1.109374 \n", "3 4 125 42 -1.0 25.731748 \n", "4 5 135 42 -1.0 62.023828 \n", "5 6 145 42 -1.0 45.322290 \n", "6 7 155 42 -1.0 3.052804 \n", "7 8 85 42 -1.0 1.812979 \n", "8 9 78 42 -1.0 103.767403 \n", "9 10 80 42 -1.0 4.173147 \n", "10 11 85 42 -1.0 282.129636 \n", "11 12 91 42 -1.0 73.102156 \n", "12 13 93 42 -1.0 28.379567 \n", "13 14 95 42 -1.0 94.946760 \n", "14 15 105 42 -1.0 32.175419 \n", "15 16 113 42 -1.0 4.184826 \n", "16 17 115 42 -1.0 50.030817 \n", "17 18 125 42 -1.0 78.166121 \n", "18 19 135 42 -1.0 72.244219 \n", "19 20 145 42 -1.0 96.384427 \n", "20 21 153 42 -1.0 9.591469 \n", "21 22 155 42 -1.0 34.326292 \n", "22 23 20 42 -1.0 0.422054 \n", "23 24 165 42 -1.0 0.638005 \n", "24 25 78 42 -1.0 32.641682 \n", "25 26 93 42 -1.0 48.218165 \n", "26 27 95 42 -1.0 33.894982 \n", "27 28 115 42 -1.0 3.195379 \n", "28 29 93 42 -1.0 14.811643 \n", "29 30 73 42 -1.0 2.243991 \n", "30 31 9 42 -1.0 59.814291 \n", "31 32 18 42 -1.0 32.366199 \n", "\n", " delay land_class historical_disturbance_type last_pass_disturbance_type \n", "0 0 0 2 2 \n", "1 0 0 2 2 \n", "2 0 0 2 2 \n", "3 0 0 2 2 \n", "4 0 0 2 2 \n", "5 0 0 2 2 \n", "6 0 0 2 2 \n", "7 0 0 2 2 \n", "8 0 0 2 2 \n", "9 0 0 2 2 \n", "10 0 0 2 2 \n", "11 0 0 2 2 \n", "12 0 0 2 2 \n", "13 0 0 2 2 \n", "14 0 0 2 2 \n", "15 0 0 2 2 \n", "16 0 0 2 2 \n", "17 0 0 2 2 \n", "18 0 0 2 2 \n", "19 0 0 2 2 \n", "20 0 0 2 2 \n", "21 0 0 2 2 \n", "22 0 0 2 1 \n", "23 0 0 2 2 \n", "24 0 0 2 2 \n", "25 0 0 2 2 \n", "26 0 0 2 2 \n", "27 0 0 2 2 \n", "28 0 0 2 2 \n", "29 0 0 2 2 \n", "30 0 0 2 1 \n", "31 0 0 2 1 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inventory.to_pandas()" ] }, { "cell_type": "code", "execution_count": 46, "id": "4adbd81a-c73c-4074-beea-177bf6f8e0c4", "metadata": { "tags": [] }, "outputs": [], "source": [ "from libcbm.model.cbm.cbm_output import CBMOutput\n", "\n", "cbm_output = CBMOutput(\n", " classifier_map=sit.classifier_value_names,\n", " disturbance_type_map=sit.disturbance_name_map)" ] }, { "cell_type": "code", "execution_count": 47, "id": "7b69eb8f-88f6-45c4-8ded-84bb86b5a9aa", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{0: '', 1: 'harvest', 2: 'fire'}" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit.disturbance_name_map" ] }, { "cell_type": "code", "execution_count": 48, "id": "76f490a3-8163-4f4c-ac27-fe7408cd8e78", "metadata": { "tags": [] }, "outputs": [], "source": [ "from libcbm.storage.backends import BackendType\n", "from libcbm.model.cbm import cbm_simulator" ] }, { "cell_type": "code", "execution_count": 49, "id": "9eea0f79-c3c9-4111-9bf4-53fabfb1bbb9", "metadata": { "tags": [] }, "outputs": [], "source": [ "with sit_cbm_factory.initialize_cbm(sit) as cbm:\n", " # Create a function to apply rule based disturbance events and transition rules based on the SIT input\n", " rule_based_processor = sit_cbm_factory.create_sit_rule_based_processor(sit, cbm)\n", " # The following line of code spins up the CBM inventory and runs it through 200 timesteps.\n", " cbm_simulator.simulate(\n", " cbm,\n", " n_steps = 200,\n", " classifiers = classifiers,\n", " inventory = inventory,\n", " pre_dynamics_func = rule_based_processor.pre_dynamics_func,\n", " reporting_func = cbm_output.append_simulation_result,\n", " backend_type = BackendType.numpy\n", " )" ] }, { "cell_type": "code", "execution_count": 50, "id": "10b5d9da-29b4-47fd-9f70-4bded42f0dad", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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identifiertimesteptheme0theme1theme2theme3theme4species
010tsa24_clipped024010001002401000softwood
120tsa24_clipped024010001002401000softwood
230tsa24_clipped024010001002401000softwood
340tsa24_clipped024010001002401000softwood
450tsa24_clipped024010001002401000softwood
...........................
1331576200tsa24_clipped124020022042422002softwood
1331677200tsa24_clipped124020001002402000softwood
1331778200tsa24_clipped124010022042421002softwood
1331879200tsa24_clipped124030022042423002softwood
1331980200tsa24_clipped124030001002423000softwood
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13320 rows \u00d7 8 columns

\n", "
" ], "text/plain": [ " identifier timestep theme0 theme1 theme2 theme3 theme4 \\\n", "0 1 0 tsa24_clipped 0 2401000 100 2401000 \n", "1 2 0 tsa24_clipped 0 2401000 100 2401000 \n", "2 3 0 tsa24_clipped 0 2401000 100 2401000 \n", "3 4 0 tsa24_clipped 0 2401000 100 2401000 \n", "4 5 0 tsa24_clipped 0 2401000 100 2401000 \n", "... ... ... ... ... ... ... ... \n", "13315 76 200 tsa24_clipped 1 2402002 204 2422002 \n", "13316 77 200 tsa24_clipped 1 2402000 100 2402000 \n", "13317 78 200 tsa24_clipped 1 2401002 204 2421002 \n", "13318 79 200 tsa24_clipped 1 2403002 204 2423002 \n", "13319 80 200 tsa24_clipped 1 2403000 100 2423000 \n", "\n", " species \n", "0 softwood \n", "1 softwood \n", "2 softwood \n", "3 softwood \n", "4 softwood \n", "... ... \n", "13315 softwood \n", "13316 softwood \n", "13317 softwood \n", "13318 softwood \n", "13319 softwood \n", "\n", "[13320 rows x 8 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbm_output.classifiers.to_pandas()" ] }, { "cell_type": "markdown", "id": "e3745cb2-e909-4e4f-aeb8-8d2fa1f601a0", "metadata": {}, "source": [ "#### Pool Results" ] }, { "cell_type": "code", "execution_count": 51, "id": "d50c8bc9-d11c-46d2-99fa-4c1b3df6b3fc", "metadata": {}, "outputs": [], "source": [ "pi = cbm_output.classifiers.to_pandas().merge(cbm_output.pools.to_pandas(), left_on=[\"identifier\", \"timestep\"], right_on=[\"identifier\", \"timestep\"])" ] }, { "cell_type": "code", "execution_count": 52, "id": "3a2fee2d-e487-485a-8c7f-de7db7dbbd9b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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identifiertimesteptheme0theme1theme2theme3theme4speciesInputSoftwoodMerch...BelowGroundSlowSoilSoftwoodStemSnagSoftwoodBranchSnagHardwoodStemSnagHardwoodBranchSnagCO2CH4CONO2Products
010tsa24_clipped024010001002401000softwood15.182275379.954177...1078.72746745.45111521.8936490.00.092677.32858792.128202829.1289830.00.0
120tsa24_clipped024010001002401000softwood20.653789613.944456...1472.10424961.25929531.0140780.00.0126665.441390125.3301271127.9373530.00.0
230tsa24_clipped024010001002401000softwood1.10937438.082569...79.4276923.4564161.7282630.00.06836.0279216.73184260.5847640.00.0
340tsa24_clipped024010001002401000softwood25.7317481108.940724...1865.66168395.73069242.8800010.00.0160128.418096156.1439071405.2530660.00.0
450tsa24_clipped024010001002401000softwood62.0238282920.109288...4532.555956253.780205106.4403930.00.0387938.424985376.3694033387.2231590.00.0
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5 rows \u00d7 35 columns

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" ], "text/plain": [ " identifier timestep theme0 theme1 theme2 theme3 theme4 \\\n", "0 1 0 tsa24_clipped 0 2401000 100 2401000 \n", "1 2 0 tsa24_clipped 0 2401000 100 2401000 \n", "2 3 0 tsa24_clipped 0 2401000 100 2401000 \n", "3 4 0 tsa24_clipped 0 2401000 100 2401000 \n", "4 5 0 tsa24_clipped 0 2401000 100 2401000 \n", "\n", " species Input SoftwoodMerch ... BelowGroundSlowSoil \\\n", "0 softwood 15.182275 379.954177 ... 1078.727467 \n", "1 softwood 20.653789 613.944456 ... 1472.104249 \n", "2 softwood 1.109374 38.082569 ... 79.427692 \n", "3 softwood 25.731748 1108.940724 ... 1865.661683 \n", "4 softwood 62.023828 2920.109288 ... 4532.555956 \n", "\n", " SoftwoodStemSnag SoftwoodBranchSnag HardwoodStemSnag HardwoodBranchSnag \\\n", "0 45.451115 21.893649 0.0 0.0 \n", "1 61.259295 31.014078 0.0 0.0 \n", "2 3.456416 1.728263 0.0 0.0 \n", "3 95.730692 42.880001 0.0 0.0 \n", "4 253.780205 106.440393 0.0 0.0 \n", "\n", " CO2 CH4 CO NO2 Products \n", "0 92677.328587 92.128202 829.128983 0.0 0.0 \n", "1 126665.441390 125.330127 1127.937353 0.0 0.0 \n", "2 6836.027921 6.731842 60.584764 0.0 0.0 \n", "3 160128.418096 156.143907 1405.253066 0.0 0.0 \n", "4 387938.424985 376.369403 3387.223159 0.0 0.0 \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pi.head()" ] }, { "cell_type": "code", "execution_count": 53, "id": "a160c19f-5a1b-4cd3-ae73-18ce5cd1411b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "biomass_pools = [\"SoftwoodMerch\",\"SoftwoodFoliage\", \"SoftwoodOther\", \"SoftwoodCoarseRoots\", \"SoftwoodFineRoots\",\n", " \"HardwoodMerch\", \"HardwoodFoliage\", \"HardwoodOther\", \"HardwoodCoarseRoots\", \"HardwoodFineRoots\"]\n", "\n", "dom_pools = [\"AboveGroundVeryFastSoil\", \"BelowGroundVeryFastSoil\", \"AboveGroundFastSoil\", \"BelowGroundFastSoil\",\n", " \"MediumSoil\", \"AboveGroundSlowSoil\", \"BelowGroundSlowSoil\", \"SoftwoodStemSnag\", \"SoftwoodBranchSnag\",\n", " \"HardwoodStemSnag\", \"HardwoodBranchSnag\"]\n", "\n", "biomass_result = pi[[\"timestep\"]+biomass_pools]\n", "dom_result = pi[[\"timestep\"]+dom_pools]\n", "total_eco_result = pi[[\"timestep\"]+biomass_pools+dom_pools]\n", "\n", "annual_carbon_stocks = pd.DataFrame(\n", " {\n", " \"Year\": pi[\"timestep\"],\n", " \"Biomass\": pi[biomass_pools].sum(axis=1),\n", " \"DOM\": pi[dom_pools].sum(axis=1),\n", " \"Total Ecosystem\": pi[biomass_pools+dom_pools].sum(axis=1)})\n", "\n", "annual_carbon_stocks.groupby(\"Year\").sum().plot(figsize=(10,10),xlim=(0,160),ylim=(0,None))" ] }, { "cell_type": "markdown", "id": "2a0473ed-06fe-419b-969f-dbb3ef399574", "metadata": { "tags": [] }, "source": [ "#### State Variable Results" ] }, { "cell_type": "code", "execution_count": 54, "id": "c29c6b2f-3d21-47bd-b7be-bf24a72a34ee", "metadata": {}, "outputs": [], "source": [ "si = cbm_output.state.to_pandas()" ] }, { "cell_type": "code", "execution_count": 55, "id": "d252cbb5-37f5-4299-a4fb-b54d70e36f7b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " identifier timestep last_disturbance_type last_disturbance_event \\\n", "0 1 0 fire 0 \n", "1 2 0 fire 0 \n", "2 3 0 fire 0 \n", "3 4 0 fire 0 \n", "4 5 0 fire 0 \n", "\n", " time_since_last_disturbance time_since_land_class_change growth_enabled \\\n", "0 85 85 1 \n", "1 95 95 1 \n", "2 105 105 1 \n", "3 125 125 1 \n", "4 135 135 1 \n", "\n", " enabled land_class age growth_multiplier regeneration_delay \n", "0 1 0 85 1.0 0 \n", "1 1 0 95 1.0 0 \n", "2 1 0 105 1.0 0 \n", "3 1 0 125 1.0 0 \n", "4 1 0 135 1.0 0 " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "si.head()" ] }, { "cell_type": "code", "execution_count": 56, "id": "962ef717-981a-4141-b517-7829f762ebff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "state_variables = [\"timestep\", \"time_since_last_disturbance\", \"time_since_land_class_change\",\n", " \"growth_enabled\", \"enabled\", \"land_class\", \"age\", \"growth_multiplier\", \"regeneration_delay\"]\n", "si[state_variables].groupby(\"timestep\").mean().plot(figsize=(10,10))\n" ] }, { "cell_type": "markdown", "id": "0acf437b-2e0d-471a-9cce-89c89c012b98", "metadata": {}, "source": [ "#### Flux Indicators" ] }, { "cell_type": "code", "execution_count": 57, "id": "a78eed4d-7f26-4dec-bf63-d9e485cd32b7", "metadata": {}, "outputs": [], "source": [ "fi = cbm_output.flux.to_pandas()" ] }, { "cell_type": "code", "execution_count": 58, "id": "a8481c91-4556-4367-84fc-df4171d7c6d3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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identifiertimestepDisturbanceCO2ProductionDisturbanceCH4ProductionDisturbanceCOProductionDisturbanceBioCO2EmissionDisturbanceBioCH4EmissionDisturbanceBioCOEmissionDecayDOMCO2EmissionDisturbanceSoftProduction...DisturbanceVFastBGToAirDisturbanceFastAGToAirDisturbanceFastBGToAirDisturbanceMediumToAirDisturbanceSlowAGToAirDisturbanceSlowBGToAirDisturbanceSWStemSnagToAirDisturbanceSWBranchSnagToAirDisturbanceHWStemSnagToAirDisturbanceHWBranchSnagToAir
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" ], "text/plain": [ " identifier timestep DisturbanceCO2Production DisturbanceCH4Production \\\n", "0 1 0 0.0 0.0 \n", "1 2 0 0.0 0.0 \n", "2 3 0 0.0 0.0 \n", "3 4 0 0.0 0.0 \n", "4 5 0 0.0 0.0 \n", "\n", " DisturbanceCOProduction DisturbanceBioCO2Emission \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", " DisturbanceBioCH4Emission DisturbanceBioCOEmission DecayDOMCO2Emission \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "\n", " DisturbanceSoftProduction ... DisturbanceVFastBGToAir \\\n", "0 0.0 ... 0.0 \n", "1 0.0 ... 0.0 \n", "2 0.0 ... 0.0 \n", "3 0.0 ... 0.0 \n", "4 0.0 ... 0.0 \n", "\n", " DisturbanceFastAGToAir DisturbanceFastBGToAir DisturbanceMediumToAir \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "\n", " DisturbanceSlowAGToAir DisturbanceSlowBGToAir DisturbanceSWStemSnagToAir \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "\n", " DisturbanceSWBranchSnagToAir DisturbanceHWStemSnagToAir \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", " DisturbanceHWBranchSnagToAir \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 54 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fi.head()" ] }, { "cell_type": "code", "execution_count": 59, "id": "8c7d9331-30c7-4a67-b0bb-0bc09822527e", "metadata": {}, "outputs": [], "source": [ "annual_process_fluxes = [\n", " \"DecayDOMCO2Emission\",\n", " \"DeltaBiomass_AG\",\n", " \"DeltaBiomass_BG\",\n", " \"TurnoverMerchLitterInput\",\n", " \"TurnoverFolLitterInput\",\n", " \"TurnoverOthLitterInput\",\n", " \"TurnoverCoarseLitterInput\",\n", " \"TurnoverFineLitterInput\",\n", " \"DecayVFastAGToAir\",\n", " \"DecayVFastBGToAir\",\n", " \"DecayFastAGToAir\",\n", " \"DecayFastBGToAir\",\n", " \"DecayMediumToAir\",\n", " \"DecaySlowAGToAir\",\n", " \"DecaySlowBGToAir\",\n", " \"DecaySWStemSnagToAir\",\n", " \"DecaySWBranchSnagToAir\",\n", " \"DecayHWStemSnagToAir\",\n", " \"DecayHWBranchSnagToAir\"\n", "]\n" ] }, { "cell_type": "code", "execution_count": 60, "id": "47cc9984-d674-4c5d-b247-f1578f1110e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fi[[\"timestep\"]+annual_process_fluxes].groupby(\"timestep\").sum().plot(figsize=(15,10))" ] }, { "cell_type": "markdown", "id": "d2f30cde-eba7-42b6-b76f-149d49d9267f", "metadata": {}, "source": [ "#### Disturbance Statistics" ] }, { "cell_type": "code", "execution_count": 61, "id": "4cf1ccff-412d-46a9-b797-a5a9c2420829", "metadata": {}, "outputs": [], "source": [ "rule_based_processor.sit_event_stats_by_timestep[1]" ] }, { "cell_type": "code", "execution_count": 62, "id": "0624b80c-7bb2-47be-aae8-cfb1350a5848", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2tsa24_clipped124020001002402000softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea0.058533harvest1011
3tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea34.326292harvest1011
4tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea9.591469harvest1011
..................................................................
59tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea60.164322harvest10011
60tsa24_clipped124010022042421002softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea28.271171harvest10011
61tsa24_clipped124030022042423002softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea10.632204harvest10011
62tsa24_clipped124030001002423000softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea1.497807harvest10011
63tsa24_clipped124030001002423000softwood-1-1-1.0-1.0...-1.0-1.01.0SORT_BY_SW_AGEArea0.166423harvest10011
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64 rows \u00d7 37 columns

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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species min_age max_age \\\n", "0 tsa24_clipped 1 2402002 204 2402002 softwood -1 -1 \n", "1 tsa24_clipped 1 2402002 204 2402002 softwood -1 -1 \n", "2 tsa24_clipped 1 2402000 100 2402000 softwood -1 -1 \n", "3 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", "4 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", ".. ... ... ... ... ... ... ... ... \n", "59 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", "60 tsa24_clipped 1 2401002 204 2421002 softwood -1 -1 \n", "61 tsa24_clipped 1 2403002 204 2423002 softwood -1 -1 \n", "62 tsa24_clipped 1 2403000 100 2423000 softwood -1 -1 \n", "63 tsa24_clipped 1 2403000 100 2423000 softwood -1 -1 \n", "\n", " MinYearsSinceDist MaxYearsSinceDist ... MinHWMerchStemSnagC \\\n", "0 -1.0 -1.0 ... -1.0 \n", "1 -1.0 -1.0 ... -1.0 \n", "2 -1.0 -1.0 ... -1.0 \n", "3 -1.0 -1.0 ... -1.0 \n", "4 -1.0 -1.0 ... -1.0 \n", ".. ... ... ... ... \n", "59 -1.0 -1.0 ... -1.0 \n", "60 -1.0 -1.0 ... -1.0 \n", "61 -1.0 -1.0 ... -1.0 \n", "62 -1.0 -1.0 ... -1.0 \n", "63 -1.0 -1.0 ... -1.0 \n", "\n", " MaxHWMerchStemSnagC efficiency sort_type target_type target \\\n", "0 -1.0 1.0 SORT_BY_SW_AGE Area 3.195379 \n", "1 -1.0 1.0 SORT_BY_SW_AGE Area 10.057453 \n", "2 -1.0 1.0 SORT_BY_SW_AGE Area 0.058533 \n", "3 -1.0 1.0 SORT_BY_SW_AGE Area 34.326292 \n", "4 -1.0 1.0 SORT_BY_SW_AGE Area 9.591469 \n", ".. ... ... ... ... ... \n", "59 -1.0 1.0 SORT_BY_SW_AGE Area 60.164322 \n", "60 -1.0 1.0 SORT_BY_SW_AGE Area 28.271171 \n", "61 -1.0 1.0 SORT_BY_SW_AGE Area 10.632204 \n", "62 -1.0 1.0 SORT_BY_SW_AGE Area 1.497807 \n", "63 -1.0 1.0 SORT_BY_SW_AGE Area 0.166423 \n", "\n", " disturbance_type time_step disturbance_type_id sort_field \n", "0 harvest 10 1 1 \n", "1 harvest 10 1 1 \n", "2 harvest 10 1 1 \n", "3 harvest 10 1 1 \n", "4 harvest 10 1 1 \n", ".. ... ... ... ... \n", "59 harvest 100 1 1 \n", "60 harvest 100 1 1 \n", "61 harvest 100 1 1 \n", "62 harvest 100 1 1 \n", "63 harvest 100 1 1 \n", "\n", "[64 rows x 37 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rule_based_processor.sit_events" ] }, { "cell_type": "markdown", "id": "581abed2-a5bb-4515-b91a-70ca53040f36", "metadata": {}, "source": [ "#### Appendix" ] }, { "cell_type": "markdown", "id": "aa41c70d-95c9-499f-b345-8d28dcb430c8", "metadata": {}, "source": [ "#### SIT source data" ] }, { "cell_type": "code", "execution_count": 63, "id": "d75c554c-c1ea-4573-add4-6fd4bb2e910f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameclass_sizestart_yearend_year
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classifier_idnamedescription
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............
71524010012401001
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73524030012403001
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71 rows \u00d7 3 columns

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sit_disturbance_type_ididname
01harvestharvest
12firefire
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theme0theme1theme2theme3theme4speciesmin_agemax_ageMinYearsSinceDistMaxYearsSinceDist...MinSWMerchStemSnagCMaxSWMerchStemSnagCMinHWMerchStemSnagCMaxHWMerchStemSnagCefficiencysort_typetarget_typetargetdisturbance_typetime_step
0tsa24_clipped124020022042402002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea3.195379harvest10
1tsa24_clipped124020022042402002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea10.057453harvest10
2tsa24_clipped124020001002402000softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea0.058533harvest10
3tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea34.326292harvest10
4tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea9.591469harvest10
..................................................................
59tsa24_clipped124010022042401002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea60.164322harvest100
60tsa24_clipped124010022042421002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea28.271171harvest100
61tsa24_clipped124030022042423002softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea10.632204harvest100
62tsa24_clipped124030001002423000softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea1.497807harvest100
63tsa24_clipped124030001002423000softwood-1-1-1.0-1.0...-1.0-1.0-1.0-1.01.0SORT_BY_SW_AGEArea0.166423harvest100
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64 rows \u00d7 35 columns

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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species min_age max_age \\\n", "0 tsa24_clipped 1 2402002 204 2402002 softwood -1 -1 \n", "1 tsa24_clipped 1 2402002 204 2402002 softwood -1 -1 \n", "2 tsa24_clipped 1 2402000 100 2402000 softwood -1 -1 \n", "3 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", "4 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", ".. ... ... ... ... ... ... ... ... \n", "59 tsa24_clipped 1 2401002 204 2401002 softwood -1 -1 \n", "60 tsa24_clipped 1 2401002 204 2421002 softwood -1 -1 \n", "61 tsa24_clipped 1 2403002 204 2423002 softwood -1 -1 \n", "62 tsa24_clipped 1 2403000 100 2423000 softwood -1 -1 \n", "63 tsa24_clipped 1 2403000 100 2423000 softwood -1 -1 \n", "\n", " MinYearsSinceDist MaxYearsSinceDist ... MinSWMerchStemSnagC \\\n", "0 -1.0 -1.0 ... -1.0 \n", "1 -1.0 -1.0 ... -1.0 \n", "2 -1.0 -1.0 ... -1.0 \n", "3 -1.0 -1.0 ... -1.0 \n", "4 -1.0 -1.0 ... -1.0 \n", ".. ... ... ... ... \n", "59 -1.0 -1.0 ... -1.0 \n", "60 -1.0 -1.0 ... -1.0 \n", "61 -1.0 -1.0 ... -1.0 \n", "62 -1.0 -1.0 ... -1.0 \n", "63 -1.0 -1.0 ... -1.0 \n", "\n", " MaxSWMerchStemSnagC MinHWMerchStemSnagC MaxHWMerchStemSnagC efficiency \\\n", "0 -1.0 -1.0 -1.0 1.0 \n", "1 -1.0 -1.0 -1.0 1.0 \n", "2 -1.0 -1.0 -1.0 1.0 \n", "3 -1.0 -1.0 -1.0 1.0 \n", "4 -1.0 -1.0 -1.0 1.0 \n", ".. ... ... ... ... \n", "59 -1.0 -1.0 -1.0 1.0 \n", "60 -1.0 -1.0 -1.0 1.0 \n", "61 -1.0 -1.0 -1.0 1.0 \n", "62 -1.0 -1.0 -1.0 1.0 \n", "63 -1.0 -1.0 -1.0 1.0 \n", "\n", " sort_type target_type target disturbance_type time_step \n", "0 SORT_BY_SW_AGE Area 3.195379 harvest 10 \n", "1 SORT_BY_SW_AGE Area 10.057453 harvest 10 \n", "2 SORT_BY_SW_AGE Area 0.058533 harvest 10 \n", "3 SORT_BY_SW_AGE Area 34.326292 harvest 10 \n", "4 SORT_BY_SW_AGE Area 9.591469 harvest 10 \n", ".. ... ... ... ... ... \n", "59 SORT_BY_SW_AGE Area 60.164322 harvest 100 \n", "60 SORT_BY_SW_AGE Area 28.271171 harvest 100 \n", "61 SORT_BY_SW_AGE Area 10.632204 harvest 100 \n", "62 SORT_BY_SW_AGE Area 1.497807 harvest 100 \n", "63 SORT_BY_SW_AGE Area 0.166423 harvest 100 \n", "\n", "[64 rows x 35 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit.sit_data.disturbance_events" ] }, { "cell_type": "code", "execution_count": 69, "id": "ce6566fa-a450-4402-89cf-32a7b4b49402", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesmin_agemax_agedisturbance_typetheme0_trtheme1_trtheme2_trtheme3_trtheme4_trspecies_trregeneration_delayreset_agepercent
0??2402000??softwood-1-1harvest????2422000softwood00100.0
1??2403000??softwood-1-1harvest????2423000softwood00100.0
2??2403001??softwood-1-1harvest????2423001softwood00100.0
3??2401002??softwood-1-1harvest????2421002softwood00100.0
4??2402002??softwood-1-1harvest????2422002softwood00100.0
5??2403002??softwood-1-1harvest????2423002softwood00100.0
6??2402003??softwood-1-1harvest????2422003softwood00100.0
7??2403003??softwood-1-1harvest????2423003softwood00100.0
8??2402004??softwood-1-1harvest????2422004softwood00100.0
9??2403004??softwood-1-1harvest????2423004softwood00100.0
10??2401007??softwood-1-1harvest????2421007softwood00100.0
11??2402007??softwood-1-1harvest????2422007softwood00100.0
12??2403007??softwood-1-1harvest????2423007softwood00100.0
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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species min_age max_age \\\n", "0 ? ? 2402000 ? ? softwood -1 -1 \n", "1 ? ? 2403000 ? ? softwood -1 -1 \n", "2 ? ? 2403001 ? ? softwood -1 -1 \n", "3 ? ? 2401002 ? ? softwood -1 -1 \n", "4 ? ? 2402002 ? ? softwood -1 -1 \n", "5 ? ? 2403002 ? ? softwood -1 -1 \n", "6 ? ? 2402003 ? ? softwood -1 -1 \n", "7 ? ? 2403003 ? ? softwood -1 -1 \n", "8 ? ? 2402004 ? ? softwood -1 -1 \n", "9 ? ? 2403004 ? ? softwood -1 -1 \n", "10 ? ? 2401007 ? ? softwood -1 -1 \n", "11 ? ? 2402007 ? ? softwood -1 -1 \n", "12 ? ? 2403007 ? ? softwood -1 -1 \n", "\n", " disturbance_type theme0_tr theme1_tr theme2_tr theme3_tr theme4_tr \\\n", "0 harvest ? ? ? ? 2422000 \n", "1 harvest ? ? ? ? 2423000 \n", "2 harvest ? ? ? ? 2423001 \n", "3 harvest ? ? ? ? 2421002 \n", "4 harvest ? ? ? ? 2422002 \n", "5 harvest ? ? ? ? 2423002 \n", "6 harvest ? ? ? ? 2422003 \n", "7 harvest ? ? ? ? 2423003 \n", "8 harvest ? ? ? ? 2422004 \n", "9 harvest ? ? ? ? 2423004 \n", "10 harvest ? ? ? ? 2421007 \n", "11 harvest ? ? ? ? 2422007 \n", "12 harvest ? ? ? ? 2423007 \n", "\n", " species_tr regeneration_delay reset_age percent \n", "0 softwood 0 0 100.0 \n", "1 softwood 0 0 100.0 \n", "2 softwood 0 0 100.0 \n", "3 softwood 0 0 100.0 \n", "4 softwood 0 0 100.0 \n", "5 softwood 0 0 100.0 \n", "6 softwood 0 0 100.0 \n", "7 softwood 0 0 100.0 \n", "8 softwood 0 0 100.0 \n", "9 softwood 0 0 100.0 \n", "10 softwood 0 0 100.0 \n", "11 softwood 0 0 100.0 \n", "12 softwood 0 0 100.0 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit.sit_data.transition_rules" ] }, { "cell_type": "code", "execution_count": 70, "id": "0b249781-a749-4c74-abe4-95e64288590a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesleading_speciesv0v1v2...v91v92v93v94v95v96v97v98v99v100
0??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
1??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
2??2402000?2402000softwoodsoftwood0.04.013.0...235.0235.0235.0235.0235.0235.0235.0235.0235.0235.0
3??2402000?2422000softwoodsoftwood0.00.00.0...336.0336.0336.0336.0336.0336.0336.0336.0336.0336.0
4??2403000?2403000softwoodsoftwood0.03.015.0...246.0246.0246.0246.0246.0246.0246.0246.0246.0246.0
5??2403000?2423000softwoodsoftwood0.00.00.0...433.0433.0433.0433.0433.0433.0433.0433.0433.0433.0
6??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
7??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
8??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
9??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
10??2403001?2403001softwoodsoftwood0.00.00.0...190.0190.0190.0190.0190.0190.0190.0190.0190.0190.0
11??2403001?2423001softwoodsoftwood0.00.00.0...340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0
12??2401002?2401002softwoodsoftwood0.00.04.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
13??2401002?2421002softwoodsoftwood0.00.00.0...196.0196.0196.0196.0196.0196.0196.0196.0196.0196.0
14??2402002?2402002softwoodsoftwood0.05.018.0...194.0194.0194.0194.0194.0194.0194.0194.0194.0194.0
15??2402002?2422002softwoodsoftwood0.00.00.0...293.0293.0293.0293.0293.0293.0293.0293.0293.0293.0
16??2403002?2403002softwoodsoftwood0.08.029.0...277.0277.0277.0277.0277.0277.0277.0277.0277.0277.0
17??2403002?2423002softwoodsoftwood0.00.00.0...428.0428.0428.0428.0428.0428.0428.0428.0428.0428.0
18??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
19??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
20??2402003?2402003softwoodsoftwood0.00.00.0...215.0215.0215.0215.0215.0215.0215.0215.0215.0215.0
21??2402003?2422003softwoodsoftwood0.00.00.0...358.0358.0358.0358.0358.0358.0358.0358.0358.0358.0
22??2403003?2403003softwoodsoftwood0.06.025.0...270.0270.0270.0270.0270.0270.0270.0270.0270.0270.0
23??2403003?2423003softwoodsoftwood0.00.00.0...476.0476.0476.0476.0476.0476.0476.0476.0476.0476.0
24??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
25??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
26??2402004?2402004softwoodsoftwood0.00.00.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
27??2402004?2422004softwoodsoftwood0.00.00.0...304.0304.0304.0304.0304.0304.0304.0304.0304.0304.0
28??2403004?2403004softwoodsoftwood0.00.00.0...255.0255.0255.0255.0255.0255.0255.0255.0255.0255.0
29??2403004?2423004softwoodsoftwood0.00.00.0...394.0394.0394.0394.0394.0394.0394.0394.0394.0394.0
30??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
31??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
32??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
33??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
34??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
35??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
36??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
37??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
38??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
39??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
40??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
41??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
42??2401007?2401007softwoodsoftwood0.00.00.0...178.0178.0178.0178.0178.0178.0178.0178.0178.0178.0
43??2401007?2421007softwoodsoftwood0.00.00.0...353.0353.0353.0353.0353.0353.0353.0353.0353.0353.0
44??2402007?2402007softwoodsoftwood0.00.02.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
45??2402007?2422007softwoodsoftwood0.00.00.0...415.0415.0415.0415.0415.0415.0415.0415.0415.0415.0
46??2403007?2403007softwoodsoftwood0.06.028.0...269.0269.0269.0269.0269.0269.0269.0269.0269.0269.0
47??2403007?2423007softwoodsoftwood0.00.00.0...535.0535.0535.0535.0535.0535.0535.0535.0535.0535.0
\n", "

48 rows \u00d7 108 columns

\n", "
" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species leading_species v0 \\\n", "0 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "1 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "2 ? ? 2402000 ? 2402000 softwood softwood 0.0 \n", "3 ? ? 2402000 ? 2422000 softwood softwood 0.0 \n", "4 ? ? 2403000 ? 2403000 softwood softwood 0.0 \n", "5 ? ? 2403000 ? 2423000 softwood softwood 0.0 \n", "6 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "7 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "8 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "9 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "10 ? ? 2403001 ? 2403001 softwood softwood 0.0 \n", "11 ? ? 2403001 ? 2423001 softwood softwood 0.0 \n", "12 ? ? 2401002 ? 2401002 softwood softwood 0.0 \n", "13 ? ? 2401002 ? 2421002 softwood softwood 0.0 \n", "14 ? ? 2402002 ? 2402002 softwood softwood 0.0 \n", "15 ? ? 2402002 ? 2422002 softwood softwood 0.0 \n", "16 ? ? 2403002 ? 2403002 softwood softwood 0.0 \n", "17 ? ? 2403002 ? 2423002 softwood softwood 0.0 \n", "18 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "19 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "20 ? ? 2402003 ? 2402003 softwood softwood 0.0 \n", "21 ? ? 2402003 ? 2422003 softwood softwood 0.0 \n", "22 ? ? 2403003 ? 2403003 softwood softwood 0.0 \n", "23 ? ? 2403003 ? 2423003 softwood softwood 0.0 \n", "24 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "25 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "26 ? ? 2402004 ? 2402004 softwood softwood 0.0 \n", "27 ? ? 2402004 ? 2422004 softwood softwood 0.0 \n", "28 ? ? 2403004 ? 2403004 softwood softwood 0.0 \n", "29 ? ? 2403004 ? 2423004 softwood softwood 0.0 \n", "30 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "31 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "32 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "33 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "34 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "35 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "36 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "37 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "38 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "39 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "40 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "41 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "42 ? ? 2401007 ? 2401007 softwood softwood 0.0 \n", "43 ? ? 2401007 ? 2421007 softwood softwood 0.0 \n", "44 ? ? 2402007 ? 2402007 softwood softwood 0.0 \n", "45 ? ? 2402007 ? 2422007 softwood softwood 0.0 \n", "46 ? ? 2403007 ? 2403007 softwood softwood 0.0 \n", "47 ? ? 2403007 ? 2423007 softwood softwood 0.0 \n", "\n", " v1 v2 ... v91 v92 v93 v94 v95 v96 v97 v98 \\\n", "0 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "1 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "2 4.0 13.0 ... 235.0 235.0 235.0 235.0 235.0 235.0 235.0 235.0 \n", "3 0.0 0.0 ... 336.0 336.0 336.0 336.0 336.0 336.0 336.0 336.0 \n", "4 3.0 15.0 ... 246.0 246.0 246.0 246.0 246.0 246.0 246.0 246.0 \n", "5 0.0 0.0 ... 433.0 433.0 433.0 433.0 433.0 433.0 433.0 433.0 \n", "6 0.0 0.0 ... 97.0 97.0 97.0 97.0 97.0 97.0 97.0 97.0 \n", "7 0.0 0.0 ... 97.0 97.0 97.0 97.0 97.0 97.0 97.0 97.0 \n", "8 0.0 0.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "9 0.0 0.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "10 0.0 0.0 ... 190.0 190.0 190.0 190.0 190.0 190.0 190.0 190.0 \n", "11 0.0 0.0 ... 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0 \n", "12 0.0 4.0 ... 127.0 127.0 127.0 127.0 127.0 127.0 127.0 127.0 \n", "13 0.0 0.0 ... 196.0 196.0 196.0 196.0 196.0 196.0 196.0 196.0 \n", "14 5.0 18.0 ... 194.0 194.0 194.0 194.0 194.0 194.0 194.0 194.0 \n", "15 0.0 0.0 ... 293.0 293.0 293.0 293.0 293.0 293.0 293.0 293.0 \n", "16 8.0 29.0 ... 277.0 277.0 277.0 277.0 277.0 277.0 277.0 277.0 \n", "17 0.0 0.0 ... 428.0 428.0 428.0 428.0 428.0 428.0 428.0 428.0 \n", "18 0.0 0.0 ... 150.0 150.0 150.0 150.0 150.0 150.0 150.0 150.0 \n", "19 0.0 0.0 ... 150.0 150.0 150.0 150.0 150.0 150.0 150.0 150.0 \n", "20 0.0 0.0 ... 215.0 215.0 215.0 215.0 215.0 215.0 215.0 215.0 \n", "21 0.0 0.0 ... 358.0 358.0 358.0 358.0 358.0 358.0 358.0 358.0 \n", "22 6.0 25.0 ... 270.0 270.0 270.0 270.0 270.0 270.0 270.0 270.0 \n", "23 0.0 0.0 ... 476.0 476.0 476.0 476.0 476.0 476.0 476.0 476.0 \n", "24 0.0 0.0 ... 158.0 158.0 158.0 158.0 158.0 158.0 158.0 158.0 \n", "25 0.0 0.0 ... 158.0 158.0 158.0 158.0 158.0 158.0 158.0 158.0 \n", "26 0.0 0.0 ... 209.0 209.0 209.0 209.0 209.0 209.0 209.0 209.0 \n", "27 0.0 0.0 ... 304.0 304.0 304.0 304.0 304.0 304.0 304.0 304.0 \n", "28 0.0 0.0 ... 255.0 255.0 255.0 255.0 255.0 255.0 255.0 255.0 \n", "29 0.0 0.0 ... 394.0 394.0 394.0 394.0 394.0 394.0 394.0 394.0 \n", "30 2.0 12.0 ... 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 \n", "31 2.0 12.0 ... 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 \n", "32 8.0 30.0 ... 118.0 118.0 118.0 118.0 118.0 118.0 118.0 118.0 \n", "33 8.0 30.0 ... 118.0 118.0 118.0 118.0 118.0 118.0 118.0 118.0 \n", "34 10.0 37.0 ... 135.0 135.0 135.0 135.0 135.0 135.0 135.0 135.0 \n", "35 10.0 37.0 ... 135.0 135.0 135.0 135.0 135.0 135.0 135.0 135.0 \n", "36 4.0 17.0 ... 127.0 127.0 127.0 127.0 127.0 127.0 127.0 127.0 \n", "37 4.0 17.0 ... 127.0 127.0 127.0 127.0 127.0 127.0 127.0 127.0 \n", "38 8.0 30.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "39 8.0 30.0 ... 144.0 144.0 144.0 144.0 144.0 144.0 144.0 144.0 \n", "40 9.0 35.0 ... 163.0 163.0 163.0 163.0 163.0 163.0 163.0 163.0 \n", "41 9.0 35.0 ... 163.0 163.0 163.0 163.0 163.0 163.0 163.0 163.0 \n", "42 0.0 0.0 ... 178.0 178.0 178.0 178.0 178.0 178.0 178.0 178.0 \n", "43 0.0 0.0 ... 353.0 353.0 353.0 353.0 353.0 353.0 353.0 353.0 \n", "44 0.0 2.0 ... 209.0 209.0 209.0 209.0 209.0 209.0 209.0 209.0 \n", "45 0.0 0.0 ... 415.0 415.0 415.0 415.0 415.0 415.0 415.0 415.0 \n", "46 6.0 28.0 ... 269.0 269.0 269.0 269.0 269.0 269.0 269.0 269.0 \n", "47 0.0 0.0 ... 535.0 535.0 535.0 535.0 535.0 535.0 535.0 535.0 \n", "\n", " v99 v100 \n", "0 145.0 145.0 \n", "1 145.0 145.0 \n", "2 235.0 235.0 \n", "3 336.0 336.0 \n", "4 246.0 246.0 \n", "5 433.0 433.0 \n", "6 97.0 97.0 \n", "7 97.0 97.0 \n", "8 144.0 144.0 \n", "9 144.0 144.0 \n", "10 190.0 190.0 \n", "11 340.0 340.0 \n", "12 127.0 127.0 \n", "13 196.0 196.0 \n", "14 194.0 194.0 \n", "15 293.0 293.0 \n", "16 277.0 277.0 \n", "17 428.0 428.0 \n", "18 150.0 150.0 \n", "19 150.0 150.0 \n", "20 215.0 215.0 \n", "21 358.0 358.0 \n", "22 270.0 270.0 \n", "23 476.0 476.0 \n", "24 158.0 158.0 \n", "25 158.0 158.0 \n", "26 209.0 209.0 \n", "27 304.0 304.0 \n", "28 255.0 255.0 \n", "29 394.0 394.0 \n", "30 105.0 105.0 \n", "31 105.0 105.0 \n", "32 118.0 118.0 \n", "33 118.0 118.0 \n", "34 135.0 135.0 \n", "35 135.0 135.0 \n", "36 127.0 127.0 \n", "37 127.0 127.0 \n", "38 144.0 144.0 \n", "39 144.0 144.0 \n", "40 163.0 163.0 \n", "41 163.0 163.0 \n", "42 178.0 178.0 \n", "43 353.0 353.0 \n", "44 209.0 209.0 \n", "45 415.0 415.0 \n", "46 269.0 269.0 \n", "47 535.0 535.0 \n", "\n", "[48 rows x 108 columns]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sit.sit_data.yield_table" ] }, { "cell_type": "code", "execution_count": 71, "id": "245029f5-e7b0-4d00-b746-6b7a05d57c27", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 72, "id": "9f85b048-b672-4017-9c3b-319bb0c1d82c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"mapping_config\": {\n", " \"disturbance_types\": {\n", " \"disturbance_type_mapping\": [\n", " {\n", " \"default_dist_type\": \"Clearcut harvesting without salvage\",\n", " \"user_dist_type\": \"harvest\"\n", " },\n", " {\n", " \"default_dist_type\": \"Wildfire\",\n", " \"user_dist_type\": \"fire\"\n", " }\n", " ]\n", " },\n", " \"nonforest\": null,\n", " \"spatial_units\": {\n", " \"admin_boundary\": \"British Columbia\",\n", " \"eco_boundary\": \"Montane Cordillera\",\n", " \"mapping_mode\": \"SingleDefaultSpatialUnit\"\n", " },\n", " \"species\": {\n", " \"species_classifier\": \"species\",\n", " \"species_mapping\": [\n", " {\n", " \"default_species\": \"Softwood forest type\",\n", " \"user_species\": \"softwood\"\n", " },\n", " {\n", " \"default_species\": \"Hardwood forest type\",\n", " \"user_species\": \"hardwood\"\n", " }\n", " ]\n", " }\n", " }\n", "}\n" ] } ], "source": [ "print(json.dumps(sit.config, indent=4, sort_keys=True))" ] }, { "cell_type": "markdown", "id": "c62e475d-5869-4f7a-bdf0-8638dfd9b4e4", "metadata": {}, "source": [ "## Soft-link `ws3` and `libcbm`\n", "\n", "In this section, we will dump the `libcbm` input data we created in above to appropriately named and formatted data files on disk, and load them into `libcbm` using the built-in SIT import functions." ] }, { "cell_type": "markdown", "id": "67357926-1787-4f00-ba45-8ba4588783d3", "metadata": {}, "source": [ "Extend the `sit_config` namespace object to include missing filename information under the `import_config` key." ] }, { "cell_type": "code", "execution_count": 73, "id": "e2d370be-7729-4dac-880b-9126dbbd49fa", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_config.update(\n", " {\n", " \"import_config\":\n", " {\n", " \"classifiers\": {\"type\":\"csv\", \"params\":{\"path\":\"sit_classifiers.csv\"}},\n", " \"disturbance_types\": {\"type\":\"csv\", \"params\":{\"path\":\"sit_disturbance_types.csv\"}},\n", " \"age_classes\": {\"type\": \"csv\", \"params\": {\"path\": \"sit_age_classes.csv\"}},\n", " \"inventory\": {\"type\": \"csv\", \"params\": {\"path\": \"sit_inventory.csv\"}},\n", " \"yield\": {\"type\": \"csv\", \"params\": {\"path\": \"sit_yield.csv\"}},\n", " \"events\": {\"type\": \"csv\", \"params\": {\"path\": \"sit_events.csv\"}},\n", " \"transitions\": \t {\"type\": \"csv\", \"params\": {\"path\": \"sit_transitions.csv\"}}\n", " }\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 74, "id": "65d78c7a-b8b7-47a5-89e7-1b38018ce437", "metadata": { "tags": [] }, "outputs": [], "source": [ "json.dump(sit_config, open(\"data/libcbm_model_files/sit_config.json\", \"w\"), indent=4, sort_keys=True)\n" ] }, { "cell_type": "code", "execution_count": 75, "id": "c212cd29-1554-44b1-8506-a14fff903b97", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'import_config': {'classifiers': {'type': 'csv',\n", " 'params': {'path': 'sit_classifiers.csv'}},\n", " 'disturbance_types': {'type': 'csv',\n", " 'params': {'path': 'sit_disturbance_tyeps.csv'}},\n", " 'age_classes': {'type': 'csv', 'params': {'path': 'sit_age_classes.csv'}},\n", " 'inventory': {'type': 'csv', 'params': {'path': 'sit_inventory.csv'}},\n", " 'yield': {'type': 'csv', 'params': {'path': 'sit_yield.csv'}},\n", " 'events': {'type': 'csv', 'params': {'path': 'sit_events.csv'}},\n", " 'transitions': {'type': 'csv', 'params': {'path': 'sit_ransitions.csv'}}},\n", " 'mapping_config': {'nonforest': None,\n", " 'species': {'species_classifier': 'species',\n", " 'species_mapping': [{'user_species': 'softwood',\n", " 'default_species': 'Softwood forest type'},\n", " {'user_species': 'hardwood', 'default_species': 'Hardwood forest type'}]},\n", " 'spatial_units': {'mapping_mode': 'SingleDefaultSpatialUnit',\n", " 'admin_boundary': 'British Columbia',\n", " 'eco_boundary': 'Montane Cordillera'},\n", " 'disturbance_types': {'disturbance_type_mapping': [{'user_dist_type': 'harvest',\n", " 'default_dist_type': 'Clearcut harvesting without salvage'},\n", " {'user_dist_type': 'fire', 'default_dist_type': 'Wildfire'}]}}}" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "json.load(open(\"data/sit_config.json\"))\n" ] }, { "cell_type": "markdown", "id": "7cf709ae-a5e1-4b9a-8a5a-5c8b6dfe381c", "metadata": {}, "source": [ "Now dump the data tables to CSV files." ] }, { "cell_type": "code", "execution_count": 76, "id": "f9a0c9b1-5c66-4310-ae96-f20c10e8bf9f", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_classifiers.to_csv(\"data/libcbm_model_files/sit_classifiers.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": 77, "id": "6c1385f7-0717-4234-ad00-cc491d43bb68", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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classifier_idnamedescription
01_CLASSIFIERtheme0
11tsa24_clippedtsa24_clipped
22_CLASSIFIERtheme1
3200
4211
............
72524230002423000
73524030012403001
746_CLASSIFIERspecies
756softwoodsoftwood
766hardwoodhardwood
\n", "

77 rows \u00d7 3 columns

\n", "
" ], "text/plain": [ " classifier_id name description\n", "0 1 _CLASSIFIER theme0\n", "1 1 tsa24_clipped tsa24_clipped\n", "2 2 _CLASSIFIER theme1\n", "3 2 0 0\n", "4 2 1 1\n", ".. ... ... ...\n", "72 5 2423000 2423000\n", "73 5 2403001 2403001\n", "74 6 _CLASSIFIER species\n", "75 6 softwood softwood\n", "76 6 hardwood hardwood\n", "\n", "[77 rows x 3 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"data/libcbm_model_files/sit_classifiers.csv\")\n" ] }, { "cell_type": "code", "execution_count": 78, "id": "2e2ea758-070a-4af3-8f22-5d789e7ec4bc", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_disturbance_types.to_csv(\"data/libcbm_model_files/sit_disturbance_types.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": 79, "id": "6972f880-d456-4cde-ade6-d95e630aa2f3", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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idname
0harvestharvest
1firefire
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" ], "text/plain": [ " id name\n", "0 harvest harvest\n", "1 fire fire" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"data/libcbm_model_files/sit_disturbance_types.csv\")\n" ] }, { "cell_type": "code", "execution_count": 80, "id": "a7505523-87b0-4a7c-a376-9c1e46eb90cf", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_age_classes.to_csv(\"data/libcbm_model_files/sit_age_classes.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": 81, "id": "3fe82967-4d91-41fb-b359-f75aae25ff2e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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nameclass_sizestart_yearend_year
0age_0000
1age_110110
2age_2101120
3age_3102130
4age_4103140
...............
96age_9610951960
97age_9710961970
98age_9810971980
99age_9910981990
100age_100109911000
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101 rows \u00d7 4 columns

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" ], "text/plain": [ " name class_size start_year end_year\n", "0 age_0 0 0 0\n", "1 age_1 10 1 10\n", "2 age_2 10 11 20\n", "3 age_3 10 21 30\n", "4 age_4 10 31 40\n", ".. ... ... ... ...\n", "96 age_96 10 951 960\n", "97 age_97 10 961 970\n", "98 age_98 10 971 980\n", "99 age_99 10 981 990\n", "100 age_100 10 991 1000\n", "\n", "[101 rows x 4 columns]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"data/libcbm_model_files/sit_age_classes.csv\")\n" ] }, { "cell_type": "code", "execution_count": 82, "id": "aaf7f738-8021-48ea-8258-17d75ad11ef0", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit_inventory.to_csv(\"data/libcbm_model_files/sit_inventory.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": 83, "id": "3b7e7755-5c2f-46de-a59c-761ac16b6c29", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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theme0theme1theme2theme3theme4speciesleading_speciesv0v1v2...v91v92v93v94v95v96v97v98v99v100
0??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
1??2401000?2401000softwoodsoftwood0.00.01.0...145.0145.0145.0145.0145.0145.0145.0145.0145.0145.0
2??2402000?2402000softwoodsoftwood0.04.013.0...235.0235.0235.0235.0235.0235.0235.0235.0235.0235.0
3??2402000?2422000softwoodsoftwood0.00.00.0...336.0336.0336.0336.0336.0336.0336.0336.0336.0336.0
4??2403000?2403000softwoodsoftwood0.03.015.0...246.0246.0246.0246.0246.0246.0246.0246.0246.0246.0
5??2403000?2423000softwoodsoftwood0.00.00.0...433.0433.0433.0433.0433.0433.0433.0433.0433.0433.0
6??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
7??2401001?2401001softwoodsoftwood0.00.00.0...97.097.097.097.097.097.097.097.097.097.0
8??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
9??2402001?2402001softwoodsoftwood0.00.00.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
10??2403001?2403001softwoodsoftwood0.00.00.0...190.0190.0190.0190.0190.0190.0190.0190.0190.0190.0
11??2403001?2423001softwoodsoftwood0.00.00.0...340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0
12??2401002?2401002softwoodsoftwood0.00.04.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
13??2401002?2421002softwoodsoftwood0.00.00.0...196.0196.0196.0196.0196.0196.0196.0196.0196.0196.0
14??2402002?2402002softwoodsoftwood0.05.018.0...194.0194.0194.0194.0194.0194.0194.0194.0194.0194.0
15??2402002?2422002softwoodsoftwood0.00.00.0...293.0293.0293.0293.0293.0293.0293.0293.0293.0293.0
16??2403002?2403002softwoodsoftwood0.08.029.0...277.0277.0277.0277.0277.0277.0277.0277.0277.0277.0
17??2403002?2423002softwoodsoftwood0.00.00.0...428.0428.0428.0428.0428.0428.0428.0428.0428.0428.0
18??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
19??2401003?2401003softwoodsoftwood0.00.00.0...150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0
20??2402003?2402003softwoodsoftwood0.00.00.0...215.0215.0215.0215.0215.0215.0215.0215.0215.0215.0
21??2402003?2422003softwoodsoftwood0.00.00.0...358.0358.0358.0358.0358.0358.0358.0358.0358.0358.0
22??2403003?2403003softwoodsoftwood0.06.025.0...270.0270.0270.0270.0270.0270.0270.0270.0270.0270.0
23??2403003?2423003softwoodsoftwood0.00.00.0...476.0476.0476.0476.0476.0476.0476.0476.0476.0476.0
24??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
25??2401004?2401004softwoodsoftwood0.00.00.0...158.0158.0158.0158.0158.0158.0158.0158.0158.0158.0
26??2402004?2402004softwoodsoftwood0.00.00.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
27??2402004?2422004softwoodsoftwood0.00.00.0...304.0304.0304.0304.0304.0304.0304.0304.0304.0304.0
28??2403004?2403004softwoodsoftwood0.00.00.0...255.0255.0255.0255.0255.0255.0255.0255.0255.0255.0
29??2403004?2423004softwoodsoftwood0.00.00.0...394.0394.0394.0394.0394.0394.0394.0394.0394.0394.0
30??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
31??2401005?2401005hardwoodhardwood0.02.012.0...105.0105.0105.0105.0105.0105.0105.0105.0105.0105.0
32??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
33??2402005?2402005hardwoodhardwood0.08.030.0...118.0118.0118.0118.0118.0118.0118.0118.0118.0118.0
34??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
35??2403005?2403005hardwoodhardwood0.010.037.0...135.0135.0135.0135.0135.0135.0135.0135.0135.0135.0
36??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
37??2401006?2401006hardwoodhardwood0.04.017.0...127.0127.0127.0127.0127.0127.0127.0127.0127.0127.0
38??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
39??2402006?2402006hardwoodhardwood0.08.030.0...144.0144.0144.0144.0144.0144.0144.0144.0144.0144.0
40??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
41??2403006?2403006hardwoodhardwood0.09.035.0...163.0163.0163.0163.0163.0163.0163.0163.0163.0163.0
42??2401007?2401007softwoodsoftwood0.00.00.0...178.0178.0178.0178.0178.0178.0178.0178.0178.0178.0
43??2401007?2421007softwoodsoftwood0.00.00.0...353.0353.0353.0353.0353.0353.0353.0353.0353.0353.0
44??2402007?2402007softwoodsoftwood0.00.02.0...209.0209.0209.0209.0209.0209.0209.0209.0209.0209.0
45??2402007?2422007softwoodsoftwood0.00.00.0...415.0415.0415.0415.0415.0415.0415.0415.0415.0415.0
46??2403007?2403007softwoodsoftwood0.06.028.0...269.0269.0269.0269.0269.0269.0269.0269.0269.0269.0
47??2403007?2423007softwoodsoftwood0.00.00.0...535.0535.0535.0535.0535.0535.0535.0535.0535.0535.0
\n", "

48 rows \u00d7 108 columns

\n", "
" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species leading_species v0 \\\n", "0 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "1 ? ? 2401000 ? 2401000 softwood softwood 0.0 \n", "2 ? ? 2402000 ? 2402000 softwood softwood 0.0 \n", "3 ? ? 2402000 ? 2422000 softwood softwood 0.0 \n", "4 ? ? 2403000 ? 2403000 softwood softwood 0.0 \n", "5 ? ? 2403000 ? 2423000 softwood softwood 0.0 \n", "6 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "7 ? ? 2401001 ? 2401001 softwood softwood 0.0 \n", "8 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "9 ? ? 2402001 ? 2402001 softwood softwood 0.0 \n", "10 ? ? 2403001 ? 2403001 softwood softwood 0.0 \n", "11 ? ? 2403001 ? 2423001 softwood softwood 0.0 \n", "12 ? ? 2401002 ? 2401002 softwood softwood 0.0 \n", "13 ? ? 2401002 ? 2421002 softwood softwood 0.0 \n", "14 ? ? 2402002 ? 2402002 softwood softwood 0.0 \n", "15 ? ? 2402002 ? 2422002 softwood softwood 0.0 \n", "16 ? ? 2403002 ? 2403002 softwood softwood 0.0 \n", "17 ? ? 2403002 ? 2423002 softwood softwood 0.0 \n", "18 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "19 ? ? 2401003 ? 2401003 softwood softwood 0.0 \n", "20 ? ? 2402003 ? 2402003 softwood softwood 0.0 \n", "21 ? ? 2402003 ? 2422003 softwood softwood 0.0 \n", "22 ? ? 2403003 ? 2403003 softwood softwood 0.0 \n", "23 ? ? 2403003 ? 2423003 softwood softwood 0.0 \n", "24 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "25 ? ? 2401004 ? 2401004 softwood softwood 0.0 \n", "26 ? ? 2402004 ? 2402004 softwood softwood 0.0 \n", "27 ? ? 2402004 ? 2422004 softwood softwood 0.0 \n", "28 ? ? 2403004 ? 2403004 softwood softwood 0.0 \n", "29 ? ? 2403004 ? 2423004 softwood softwood 0.0 \n", "30 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "31 ? ? 2401005 ? 2401005 hardwood hardwood 0.0 \n", "32 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "33 ? ? 2402005 ? 2402005 hardwood hardwood 0.0 \n", "34 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "35 ? ? 2403005 ? 2403005 hardwood hardwood 0.0 \n", "36 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "37 ? ? 2401006 ? 2401006 hardwood hardwood 0.0 \n", "38 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "39 ? ? 2402006 ? 2402006 hardwood hardwood 0.0 \n", "40 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "41 ? ? 2403006 ? 2403006 hardwood hardwood 0.0 \n", "42 ? ? 2401007 ? 2401007 softwood softwood 0.0 \n", "43 ? ? 2401007 ? 2421007 softwood softwood 0.0 \n", "44 ? ? 2402007 ? 2402007 softwood softwood 0.0 \n", "45 ? ? 2402007 ? 2422007 softwood softwood 0.0 \n", "46 ? ? 2403007 ? 2403007 softwood softwood 0.0 \n", "47 ? ? 2403007 ? 2423007 softwood softwood 0.0 \n", "\n", " v1 v2 ... v91 v92 v93 v94 v95 v96 v97 v98 \\\n", "0 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "1 0.0 1.0 ... 145.0 145.0 145.0 145.0 145.0 145.0 145.0 145.0 \n", "2 4.0 13.0 ... 235.0 235.0 235.0 235.0 235.0 235.0 235.0 235.0 \n", "3 0.0 0.0 ... 336.0 336.0 336.0 336.0 336.0 336.0 336.0 336.0 \n", "4 3.0 15.0 ... 246.0 246.0 246.0 246.0 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0tsa24_clipped124020022042402002softwoodFalse-1-1-1...-1-1-1-113A3.195379harvest10
1tsa24_clipped124020022042402002softwoodFalse-1-1-1...-1-1-1-113A10.057453harvest10
2tsa24_clipped124020001002402000softwoodFalse-1-1-1...-1-1-1-113A0.058533harvest10
3tsa24_clipped124010022042401002softwoodFalse-1-1-1...-1-1-1-113A34.326292harvest10
4tsa24_clipped124010022042401002softwoodFalse-1-1-1...-1-1-1-113A9.591469harvest10
..................................................................
59tsa24_clipped124010022042401002softwoodFalse-1-1-1...-1-1-1-113A60.164322harvest100
60tsa24_clipped124010022042421002softwoodFalse-1-1-1...-1-1-1-113A28.271171harvest100
61tsa24_clipped124030022042423002softwoodFalse-1-1-1...-1-1-1-113A10.632204harvest100
62tsa24_clipped124030001002423000softwoodFalse-1-1-1...-1-1-1-113A1.497807harvest100
63tsa24_clipped124030001002423000softwoodFalse-1-1-1...-1-1-1-113A0.166423harvest100
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64 rows \u00d7 38 columns

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theme0theme1theme2theme3theme4speciesusing_age_classmin_softwood_agemax_softwood_agemin_hardwood_age...disturbance_typeto_theme0to_theme1to_theme2to_theme3to_theme4to_speciesregen_delayreset_agepercent
0??2402000??softwoodFalse-1-1-1...harvest????2422000softwood00100
1??2403000??softwoodFalse-1-1-1...harvest????2423000softwood00100
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6??2402003??softwoodFalse-1-1-1...harvest????2422003softwood00100
7??2403003??softwoodFalse-1-1-1...harvest????2423003softwood00100
8??2402004??softwoodFalse-1-1-1...harvest????2422004softwood00100
9??2403004??softwoodFalse-1-1-1...harvest????2423004softwood00100
10??2401007??softwoodFalse-1-1-1...harvest????2421007softwood00100
11??2402007??softwoodFalse-1-1-1...harvest????2422007softwood00100
12??2403007??softwoodFalse-1-1-1...harvest????2423007softwood00100
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13 rows \u00d7 21 columns

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" ], "text/plain": [ " theme0 theme1 theme2 theme3 theme4 species using_age_class \\\n", "0 ? ? 2402000 ? ? softwood False \n", "1 ? ? 2403000 ? ? softwood False \n", "2 ? ? 2403001 ? ? softwood False \n", "3 ? ? 2401002 ? ? softwood False \n", "4 ? ? 2402002 ? ? softwood False \n", "5 ? ? 2403002 ? ? softwood False \n", "6 ? ? 2402003 ? ? softwood False \n", "7 ? ? 2403003 ? ? softwood False \n", "8 ? ? 2402004 ? ? softwood False \n", "9 ? ? 2403004 ? ? softwood False \n", "10 ? ? 2401007 ? ? softwood False \n", "11 ? ? 2402007 ? ? softwood False \n", "12 ? ? 2403007 ? ? softwood False \n", "\n", " min_softwood_age max_softwood_age min_hardwood_age ... \\\n", "0 -1 -1 -1 ... \n", "1 -1 -1 -1 ... \n", "2 -1 -1 -1 ... \n", "3 -1 -1 -1 ... \n", "4 -1 -1 -1 ... \n", "5 -1 -1 -1 ... \n", "6 -1 -1 -1 ... \n", "7 -1 -1 -1 ... \n", "8 -1 -1 -1 ... \n", "9 -1 -1 -1 ... \n", "10 -1 -1 -1 ... \n", "11 -1 -1 -1 ... \n", "12 -1 -1 -1 ... \n", "\n", " disturbance_type to_theme0 to_theme1 to_theme2 to_theme3 to_theme4 \\\n", "0 harvest ? ? ? ? 2422000 \n", "1 harvest ? ? ? ? 2423000 \n", "2 harvest ? ? ? ? 2423001 \n", "3 harvest ? ? ? ? 2421002 \n", "4 harvest ? ? ? ? 2422002 \n", "5 harvest ? ? ? ? 2423002 \n", "6 harvest ? ? ? ? 2422003 \n", "7 harvest ? ? ? ? 2423003 \n", "8 harvest ? ? ? ? 2422004 \n", "9 harvest ? ? ? ? 2423004 \n", "10 harvest ? ? ? ? 2421007 \n", "11 harvest ? ? ? ? 2422007 \n", "12 harvest ? ? ? ? 2423007 \n", "\n", " to_species regen_delay reset_age percent \n", "0 softwood 0 0 100 \n", "1 softwood 0 0 100 \n", "2 softwood 0 0 100 \n", "3 softwood 0 0 100 \n", "4 softwood 0 0 100 \n", "5 softwood 0 0 100 \n", "6 softwood 0 0 100 \n", "7 softwood 0 0 100 \n", "8 softwood 0 0 100 \n", "9 softwood 0 0 100 \n", "10 softwood 0 0 100 \n", "11 softwood 0 0 100 \n", "12 softwood 0 0 100 \n", "\n", "[13 rows x 21 columns]" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"data/libcbm_model_files/sit_transitions.csv\")\n" ] }, { "cell_type": "markdown", "id": "7fdc842c-6c15-4b44-957f-7862b25e4ace", "metadata": {}, "source": [ "Now we can soft-link to `libcbm` using the built-in data file import functions. " ] }, { "cell_type": "code", "execution_count": 90, "id": "6dc651da-499f-4470-b639-f7273127e44e", "metadata": { "tags": [] }, "outputs": [], "source": [ "sit = sit_cbm_factory.load_sit(\"data/libcbm_model_files/sit_config.json\")\n" ] }, { "cell_type": "code", "execution_count": 91, "id": "377ddda8-69f6-49f0-b3cd-61447dc378c4", "metadata": { "tags": [] }, "outputs": [], "source": [ "classifiers, inventory = sit_cbm_factory.initialize_inventory(sit)" ] }, { "cell_type": "code", "execution_count": 92, "id": "4a219fad-8d95-4290-b6ad-d3fc7979c15b", "metadata": { "tags": [] }, "outputs": [], "source": [ "cbm_output = CBMOutput(\n", " classifier_map=sit.classifier_value_names,\n", " disturbance_type_map=sit.disturbance_name_map)" ] }, { "cell_type": "code", "execution_count": 93, "id": "c41b30da-5b22-4190-af9c-435b0349086a", "metadata": { "tags": [] }, "outputs": [], "source": [ "with sit_cbm_factory.initialize_cbm(sit) as cbm:\n", " # Create a function to apply rule based disturbance events and transition rules based on the SIT input\n", " rule_based_processor = sit_cbm_factory.create_sit_rule_based_processor(sit, cbm)\n", " # The following line of code spins up the CBM inventory and runs it through 200 timesteps.\n", " cbm_simulator.simulate(\n", " cbm,\n", " n_steps = 200,\n", " classifiers = classifiers,\n", " inventory = inventory,\n", " pre_dynamics_func = rule_based_processor.pre_dynamics_func,\n", " reporting_func = cbm_output.append_simulation_result,\n", " backend_type = BackendType.numpy\n", " )" ] }, { "cell_type": "code", "execution_count": 94, "id": "6e929f6b-d9c3-4c34-90df-885894ffddb6", "metadata": {}, "outputs": [], "source": [ "pi = cbm_output.classifiers.to_pandas().merge(cbm_output.pools.to_pandas(), left_on=[\"identifier\", \"timestep\"], right_on=[\"identifier\", \"timestep\"])" ] }, { "cell_type": "code", "execution_count": 95, "id": "9e76c8fc-3a95-4105-a584-c3285dd4844e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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identifiertimesteptheme0theme1theme2theme3theme4speciesInputSoftwoodMerch...BelowGroundSlowSoilSoftwoodStemSnagSoftwoodBranchSnagHardwoodStemSnagHardwoodBranchSnagCO2CH4CONO2Products
010tsa24_clipped024010001002401000softwood15.182275379.954177...1078.72746745.45111521.8936490.00.092677.32858792.128202829.1289830.00.0
120tsa24_clipped024010001002401000softwood20.653789613.944456...1472.10424961.25929531.0140780.00.0126665.441390125.3301271127.9373530.00.0
230tsa24_clipped024010001002401000softwood1.10937438.082569...79.4276923.4564161.7282630.00.06836.0279216.73184260.5847640.00.0
340tsa24_clipped024010001002401000softwood25.7317481108.940724...1865.66168395.73069242.8800010.00.0160128.418096156.1439071405.2530660.00.0
450tsa24_clipped024010001002401000softwood62.0238282920.109288...4532.555956253.780205106.4403930.00.0387938.424985376.3694033387.2231590.00.0
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5 rows \u00d7 35 columns

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" ], "text/plain": [ " identifier timestep theme0 theme1 theme2 theme3 theme4 \\\n", "0 1 0 tsa24_clipped 0 2401000 100 2401000 \n", "1 2 0 tsa24_clipped 0 2401000 100 2401000 \n", "2 3 0 tsa24_clipped 0 2401000 100 2401000 \n", "3 4 0 tsa24_clipped 0 2401000 100 2401000 \n", "4 5 0 tsa24_clipped 0 2401000 100 2401000 \n", "\n", " species Input SoftwoodMerch ... BelowGroundSlowSoil \\\n", "0 softwood 15.182275 379.954177 ... 1078.727467 \n", "1 softwood 20.653789 613.944456 ... 1472.104249 \n", "2 softwood 1.109374 38.082569 ... 79.427692 \n", "3 softwood 25.731748 1108.940724 ... 1865.661683 \n", "4 softwood 62.023828 2920.109288 ... 4532.555956 \n", "\n", " SoftwoodStemSnag SoftwoodBranchSnag HardwoodStemSnag HardwoodBranchSnag \\\n", "0 45.451115 21.893649 0.0 0.0 \n", "1 61.259295 31.014078 0.0 0.0 \n", "2 3.456416 1.728263 0.0 0.0 \n", "3 95.730692 42.880001 0.0 0.0 \n", "4 253.780205 106.440393 0.0 0.0 \n", "\n", " CO2 CH4 CO NO2 Products \n", "0 92677.328587 92.128202 829.128983 0.0 0.0 \n", "1 126665.441390 125.330127 1127.937353 0.0 0.0 \n", "2 6836.027921 6.731842 60.584764 0.0 0.0 \n", "3 160128.418096 156.143907 1405.253066 0.0 0.0 \n", "4 387938.424985 376.369403 3387.223159 0.0 0.0 \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pi.head()" ] }, { "cell_type": "code", "execution_count": 96, "id": "6a88ea2b-7071-43f8-9bb9-bbd4c995b0b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "biomass_pools = [\"SoftwoodMerch\",\"SoftwoodFoliage\", \"SoftwoodOther\", \"SoftwoodCoarseRoots\", \"SoftwoodFineRoots\",\n", " \"HardwoodMerch\", \"HardwoodFoliage\", \"HardwoodOther\", \"HardwoodCoarseRoots\", \"HardwoodFineRoots\"]\n", "\n", "dom_pools = [\"AboveGroundVeryFastSoil\", \"BelowGroundVeryFastSoil\", \"AboveGroundFastSoil\", \"BelowGroundFastSoil\",\n", " \"MediumSoil\", \"AboveGroundSlowSoil\", \"BelowGroundSlowSoil\", \"SoftwoodStemSnag\", \"SoftwoodBranchSnag\",\n", " \"HardwoodStemSnag\", \"HardwoodBranchSnag\"]\n", "\n", "biomass_result = pi[[\"timestep\"]+biomass_pools]\n", "dom_result = pi[[\"timestep\"]+dom_pools]\n", "total_eco_result = pi[[\"timestep\"]+biomass_pools+dom_pools]\n", "\n", "annual_carbon_stocks = pd.DataFrame(\n", " {\n", " \"Year\": pi[\"timestep\"],\n", " \"Biomass\": pi[biomass_pools].sum(axis=1),\n", " \"DOM\": pi[dom_pools].sum(axis=1),\n", " \"Total Ecosystem\": pi[biomass_pools+dom_pools].sum(axis=1)\n", " }\n", ")\n", "\n", "annual_carbon_stocks.groupby(\"Year\").sum().plot(figsize=(10,10),xlim=(0,160),ylim=(0,None))" ] }, { "cell_type": "markdown", "id": "fffc3f18-4bfb-4711-bd6c-8ce75a5eb33d", "metadata": {}, "source": [ "Ta da! Too easy! Just kidding. If you actually made it this far without losing all our marbles, you are probably thinking something like, \"Wow. This is intimidatingly complicated. I am not sure if I could easily reproduce this in my model without help.\"\n", "\n", "CBM (both the GUI-driven CBM-CFS3 Windows app, and the code-based `libcbm_py` package, which are functionally identical) is notoriously intolerant of badly-formed SIT input data. Combine that the relative complexity of the SIT format and the relatively obscure error messages returned from CBM when you feed it bogus input data, and what you get is an intimidatingly difficult challenge ahead of you if you want to implement a software linkage between `ws3` and `libcbm_py` (e.g., in support of a fully reproducible and transparent scientific analysis workflow). \n", "\n", "To help make this task more accessible, we have implemented a `to_cbm_sit` method in the `ForestModel` class that fully automates the process of generating a valid CBM input dataset from any `ForestModel` model object. We demonstrate how to use these built-in CBM linkage functions in example notebook `031_ws3_libcbm_sequential-builtin`, so check that out next if you want to see an example of how linking CBM to `ws3` does _not_ have to be as complicated or difficult as what we have shown here." ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }