{ "cells": [ { "cell_type": "markdown", "id": "43099c02967edd47", "metadata": {}, "source": [ "## Imports / init" ] }, { "cell_type": "code", "execution_count": 1, "id": "56a7fd5e", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:16.045529Z", "start_time": "2024-10-29T15:42:14.253680Z" } }, "outputs": [], "source": [ "# Those two lines are for dev only : they watch imported libraries for changes\n", "#%load_ext autoreload\n", "#%autoreload 2\n", "\n", "import brightway2 as bw\n", "import os \n", "\n", "import lca_algebraic as agb\n", "\n", "from sympy import init_printing\n", "import bw2io\n", "from dotenv import load_dotenv\n", "\n", "# Pretty print for Sympy\n", "init_printing()" ] }, { "cell_type": "markdown", "id": "9b3cb21d", "metadata": {}, "source": [ "# Init brightway2 and databases" ] }, { "cell_type": "code", "execution_count": 2, "id": "b1cd4f628163aa53", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:17.374044Z", "start_time": "2024-10-29T15:42:17.355722Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial setup already done, skipping\n" ] } ], "source": [ "# Set the current project\n", "# Can be any name\n", "bw.projects.set_current('MyProject')\n", "\n", "# It's better to not leave credential in the code.\n", "# Create a file named .env, that you will not share /commit, and contains the following :\n", "# ECOINVENT_LOGIN=\n", "# ECOINVENT_PASSWORD=\n", "\n", "# This load .env file into os.environ\n", "load_dotenv()\n", "\n", "# This downloads ecoinvent and installs biopshere + technosphere + LCIA methods\n", "if len(bw.databases) > 0:\n", " print(\"Initial setup already done, skipping\")\n", "else:\n", " # This is now the prefered method to init an Brightway2 with Ecoinvent\n", " # It is not more tied to a specific version of bw2io\n", " bw2io.import_ecoinvent_release(\n", " version=\"3.9\",\n", " system_model=\"cutoff\",\n", " username=os.environ[\"ECOINVENT_LOGIN\"], # Read for .env file\n", " password=os.environ[\"ECOINVENT_PASSWORD\"], # Read from .env file\n", " use_mp=True)" ] }, { "cell_type": "code", "execution_count": 3, "id": "50ed7cab", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:19.418859Z", "start_time": "2024-10-29T15:42:19.372097Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[WARNING] Db MyForeground was here. Reseting it\n" ] }, { "data": { "text/html": [ "
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backendnb_activitiestype
name
ecoinvent-3.9-biospheresqlite4709biosphere
ecoinvent-3.9-cutoffsqlite21255background
MyForegroundsqlite0foreground
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" ], "text/plain": [ " backend nb_activities type\n", "name \n", "ecoinvent-3.9-biosphere sqlite 4709 biosphere\n", "ecoinvent-3.9-cutoff sqlite 21255 background\n", "MyForeground sqlite 0 foreground" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We use a separate DB for defining our foreground model / activities\n", "# Choose any name\n", "USER_DB = 'MyForeground'\n", "\n", "# This is better to cleanup the whole foreground model each time, and redefine it in the notebook (or a python file)\n", "# instead of relying on a state or previous run.\n", "# Any persistent state is prone to errors.\n", "agb.resetDb(USER_DB)\n", "\n", "# Parameters are stored at project level : \n", "# Reset them also\n", "# You may remove this line if you import a project and parameters from an external source (see loadParam(..))\n", "agb.resetParams()\n", "\n", "# Overview of the databases\n", "agb.list_databases()" ] }, { "cell_type": "markdown", "id": "774f5943", "metadata": {}, "source": [ "# Introduction to Numpy\n", "\n", "Numpy is a python libray for symbolic calculus. \n", "\n", "You write Sympy expression as you write **standard python expressions**, using **sympy symbols** in them. \n", "\n", "The result is then a **symbolic expression that can be manipulated**, instead of a **numeric value**." ] }, { "cell_type": "code", "execution_count": 4, "id": "f7ff05b4", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:21.309671Z", "start_time": "2024-10-29T15:42:21.100157Z" } }, "outputs": [ { "data": { "image/png": "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", "text/latex": [ "$\\displaystyle 2 x + 4$" ], "text/plain": [ "2⋅x + 4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sympy import symbols \n", "\n", "# create sympy symbol\n", "x = symbols(\"x\")\n", "\n", "# Expressions are not directly evaluated \n", "f = x * 2 + 4 \n", "f" ] }, { "cell_type": "code", "execution_count": 5, "id": "727f1ede", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:22.146321Z", "start_time": "2024-10-29T15:42:22.107804Z" } }, "outputs": [ { "data": { "image/png": "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", "text/latex": [ "$\\displaystyle 10$" ], "text/plain": [ "10" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# symbols can be replaced by values afterwards \n", "f.subs(dict(x=3))" ] }, { "cell_type": "markdown", "id": "4f7bc29d", "metadata": {}, "source": [ "In practice, you don't need to care about Sympy. Just remember that : \n", "* The parameters defined below are **instances of sympy symbols**\n", "* Any **valid python expression** containing a **sympy symbol** will create a **sympy symbolic expression**" ] }, { "cell_type": "markdown", "id": "0cd069ad", "metadata": {}, "source": [ "# Define input parameters\n", "\n", "First, we define the input parameters of the model together with their distribution.\n", "\n", "The numeric parameters are **instances of sympy 'Symbol'**. \n", "\n", "Thus, any python arithmetic expression composed of parameters will result in a **symbolic expression** to be used later in the definition of the model, rather than a static numeric result." ] }, { "cell_type": "code", "execution_count": 6, "id": "139e432e", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:23.404388Z", "start_time": "2024-10-29T15:42:23.347593Z" } }, "outputs": [], "source": [ "# Example of 'float' parameters\n", "a = agb.newFloatParam(\n", " 'a', \n", " default=0.5, min=0.2, max=2, \n", " distrib=agb.DistributionType.TRIANGLE, # Distribution type, linear by default\n", " description=\"hello world\",\n", " label=\"extended label for a\")\n", "\n", "b = agb.newFloatParam(\n", " 'b',\n", " default=0.5, # Fixed if no min /max provided\n", " distrib=agb.DistributionType.FIXED,\n", " description=\"foo bar\")\n", "\n", "share_recycled_aluminium = agb.newFloatParam(\n", " 'share_recycled_aluminium', \n", " default=0.6, \n", " min=0, max=1, std=0.2, \n", " distrib=agb.DistributionType.NORMAL, # Normal distrib, with std dev\n", " description=\"Share of reycled aluminium\")\n", "\n", "c = agb.newFloatParam(\n", " 'c', \n", " default=0.6, std=0.2, \n", " distrib=agb.DistributionType.LOGNORMAL)\n", "\n", "beta = agb.newFloatParam(\n", " 'beta', \n", " default=0.6, std=0.2, a=2, b=5, \n", " distrib=agb.DistributionType.BETA)\n", "\n", "# You can define boolean parameters, taking only discrete values 0 or 1\n", "bool_param = agb.newBoolParam(\n", " 'bool_param', \n", " default=1)\n", "\n", "# Example 'enum' parameter, acting like a switch between several possibilities\n", "# Enum parameters are not Symbol themselves\n", "# They are a facility to represent many boolean parameters at once '_' \n", "# and should be used with the 'newSwitchAct' method \n", "elec_switch_param = agb.newEnumParam(\n", " 'elec_switch_param', \n", " values=[\"us\", \"eu\"], # If provided as list, all possibilities have te same probability\n", " default=\"us\", \n", " description=\"Switch on electricty mix\")\n", "\n", "# Another example enum param\n", "techno_param = agb.newEnumParam(\n", " 'techno_param', \n", " values={\n", " \"technoA\":0.4, \n", " \"technoB\":0.1,\n", " \"technoC\":0.5}, # You can provide a statistical weight for each value, by using a dict\n", " default=\"technoA\", \n", " description=\"Choice of technology\")" ] }, { "cell_type": "markdown", "id": "9a890ab5", "metadata": {}, "source": [ "## Persistance of parameters\n", "\n", "By default, new parameters are kept in memory but also persisted in the project (unless save=False).\n", "\n", "You can persist parameters afterwards with `persistParams()`.\n", "\n", "You can load also load parameters from an existing database with `loadParams()`.\n", "\n", "The persistance of parameters and the distribution is compatible with **Brightway2** and **Activity Browser** [see documentation of stat_arrays](https://stats-arrays.readthedocs.io/en/latest/)" ] }, { "cell_type": "code", "execution_count": 7, "id": "a4604f03", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:24.342499Z", "start_time": "2024-10-29T15:42:24.331733Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[WARNING] [ParamRegistry] Param a was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param a was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param b was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param b was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param share_recycled_aluminium was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param share_recycled_aluminium was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param c was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param c was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param beta was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param beta was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param bool_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param bool_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param elec_switch_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param elec_switch_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param techno_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param techno_param was already defined in '' : overriding.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load parameters previously persisted in the dabatase.\n", "agb.loadParams()" ] }, { "cell_type": "markdown", "id": "07063f99", "metadata": {}, "source": [ "# Manage several databases\n", "\n", "lca_algebraic supports several foreground / background datasets. Background datasets are considered static / non parametrized by the library : they use standard LCA method of **Brightway2**. \n", "\n", "Foreground databases are considered parametric and their activities are developped as functions of parameters and background activities.\n", "\n", "## Set status of a database\n", "\n", "The functions **setForeground(...)** and **setBackground(...)** change the status of a database." ] }, { "cell_type": "code", "execution_count": 8, "id": "58b8ffe4", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:24.708526Z", "start_time": "2024-10-29T15:42:24.699193Z" } }, "outputs": [ { "data": { "text/html": [ "
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backendnb_activitiestype
name
ecoinvent-3.9-biospheresqlite4709biosphere
ecoinvent-3.9-cutoffsqlite21255background
MyForegroundsqlite0foreground
\n", "
" ], "text/plain": [ " backend nb_activities type\n", "name \n", "ecoinvent-3.9-biosphere sqlite 4709 biosphere\n", "ecoinvent-3.9-cutoff sqlite 21255 background\n", "MyForeground sqlite 0 foreground" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agb.setForeground(USER_DB)\n", "agb.list_databases()" ] }, { "cell_type": "markdown", "id": "27e76092", "metadata": {}, "source": [ "## Import / export\n", "\n", "`lca_algebraic` extends [BW2Package](https://2.docs.brightway.dev/technical/bw2io.html), adding persistence of parameters." ] }, { "cell_type": "code", "execution_count": 9, "id": "92c34396", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:25.608081Z", "start_time": "2024-10-29T15:42:25.603446Z" } }, "outputs": [], "source": [ "# Save database and parameters as Bzipped JSON\n", "agb.export_db(USER_DB, \"tmp/db.bw2\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "fd67ea18", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:25.829110Z", "start_time": "2024-10-29T15:42:25.790445Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[WARNING] [ParamRegistry] Param a was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param a was already defined in '' : overriding.\n", "[WARNING] Variable 'a' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param b was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param b was already defined in '' : overriding.\n", "[WARNING] Variable 'b' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param share_recycled_aluminium was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param share_recycled_aluminium was already defined in '' : overriding.\n", "[WARNING] Variable 'share_recycled_aluminium' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param c was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param c was already defined in '' : overriding.\n", "[WARNING] Variable 'c' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param beta was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param beta was already defined in '' : overriding.\n", "[WARNING] Variable 'beta' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param bool_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param bool_param was already defined in '' : overriding.\n", "[WARNING] Variable 'bool_param' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param elec_switch_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param elec_switch_param was already defined in '' : overriding.\n", "[WARNING] Variable 'elec_switch_param' was already defined : overidding it with param.\n", "[WARNING] [ParamRegistry] Param techno_param was already defined in '' : overriding.\n", "[WARNING] [ParamRegistry] Param techno_param was already defined in '' : overriding.\n", "[WARNING] Variable 'techno_param' was already defined : overidding it with param.\n" ] }, { "data": { "text/plain": [ "Brightway2 SQLiteBackend: MyForeground" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reimport DB\n", "agb.import_db(\"tmp/db.bw2\")" ] }, { "cell_type": "markdown", "id": "cca17704", "metadata": {}, "source": [ "## Freeze \n", "\n", "A foreground database can be \"frozen\" to be used as a background database for a specific scenario : the parametrized amounts in the exhanges are computed for a given configuration of the parameters, and replaced by their value. The formulas are still stored in the database and not lost : the database can still be used as a foreground database until its status is changed with `setBackground(...)`.\n", "\n", "This feature is useful for studies requiring several datasets to be used as background by other ones. It also enables to use standard Brightway2 tools, not aware of parametrization. \n" ] }, { "cell_type": "code", "execution_count": 11, "id": "4f69859b", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:26.133506Z", "start_time": "2024-10-29T15:42:26.131121Z" } }, "outputs": [], "source": [ "agb.freezeParams(\n", " USER_DB, # Name of database to freeze\n", " \n", " a=1, b=2) # custom parameter values" ] }, { "cell_type": "markdown", "id": "a9da7045", "metadata": {}, "source": [ "# Get references to background activities\n", "\n", "We provide two functions for easy and fast (indexed) search of activities in reference databases : \n", "* **findBioAct** : Search activity in **biosphere3** db\n", "* **findTechAct** : Search activity in **ecoinvent** db\n", "\n", "Those methods are **faster** and **safer** than using traditionnal \"list-comprehension\" search : \n", "They will **fail with an error** if **more than one activity** matches, preventing the model to be based on a random selection of one activity.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "1f0a23c5", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:27.280100Z", "start_time": "2024-10-29T15:42:26.451342Z" } }, "outputs": [], "source": [ "# Biosphere activities\n", "ground_occupuation = agb.findBioAct('Occupation, industrial area') # Search by name\n", "heat = agb.findBioAct('Heat, waste', categories=['air']) # Add category selector\n", "\n", "# Technosphere activities\n", "\n", "# You can add an optionnal location selector\n", "alu = agb.findTechAct(\"aluminium alloy production, AlMg3\", loc=\"RER\")\n", "alu_scrap = agb.findTechAct('aluminium scrap, new, Recycled Content cut-off')\n", "\n", "# Elec \n", "eu_elec = agb.findTechAct(\"market group for electricity, medium voltage\", 'ENTSO-E')\n", "us_elec = agb.findTechAct(\"market group for electricity, medium voltage\", 'US')\n", "\n", "chromium = agb.findTechAct(\"market for chromium oxide, flakes\")" ] }, { "cell_type": "markdown", "id": "3c488aeb", "metadata": {}, "source": [ "# Define the model\n", "\n", "The model is defined as a nested combination of background activities with amounts.\n", "\n", "Amounts are defined either as constant float values or algebric formulas implying the parameters defined above." ] }, { "cell_type": "markdown", "id": "9a378204", "metadata": {}, "source": [ "## Create new activities" ] }, { "cell_type": "code", "execution_count": 13, "id": "df05b063", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:27.361108Z", "start_time": "2024-10-29T15:42:27.299750Z" } }, "outputs": [], "source": [ "# Create a new activity\n", "activity1 = agb.newActivity(USER_DB, # We define foreground activities in our own DB\n", " \"first foreground activity\", # Name of the activity\n", " \"kg\", # Unit\n", " exchanges= { # We define exhanges as a dictionarry of 'activity : amount'\n", " ground_occupuation:3 * b, # Amount can be a fixed value \n", " heat: b + 0.2 # Amount can be a Sympy expression (any arithmetic expression of Parameters)\n", " })\n", "\n", "# You can create a virtual \"switch\" activity combining several activities with an Enum parameter\n", "elec_mix = agb.newSwitchAct(USER_DB, \n", " \"elect mix\", # Name\n", " elec_switch_param, # Sith parameter\n", " { # Dictionnary of enum values / activities\n", " \"us\" : us_elec, # By default associated amount is 1\n", " \"eu\" : (eu_elec, 0.8) # You can also provide custom amout or formula with a tuple \n", " })" ] }, { "cell_type": "markdown", "id": "ec37d45c", "metadata": {}, "source": [ "## Copy and update existing activity\n", "\n", "You can copy and update an existing background activity.\n", "\n", "Several new helper methods have been added to the class **Activity** for easy update of exchanges." ] }, { "cell_type": "code", "execution_count": 14, "id": "41c81245", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:27.508438Z", "start_time": "2024-10-29T15:42:27.383339Z" } }, "outputs": [], "source": [ "alu2 = agb.copyActivity(\n", " USER_DB, # The copy of a background activity is done in our own DB, so that we can safely update it \n", " alu, # Initial activity : won't be altered\n", " \"Aluminium 2\") # New name\n", "\n", "# Update exchanges by their name \n", "alu2.updateExchanges({\n", " \n", " # Update amount : the special symbol *old_amount* references the previous amount of this exchange\n", " \"aluminium, cast alloy\": agb.old_amount * (1 - share_recycled_aluminium),\n", " \n", " # Update input activity. Note also that you can use '*' wildcard in exchange name\n", " \"electricity*\": elec_mix,\n", " \n", " # Update both input activity and amount. \n", " # Note that you can use '#' for specifying the location of exchange (useful for duplicate exchange names)\n", " \"chromium#GLO\" : dict(amount=4.0, input=chromium)\n", "}) \n", "\n", "# Add exchanges \n", "alu2.addExchanges({alu_scrap : 12})" ] }, { "cell_type": "markdown", "id": "3b4c0990", "metadata": {}, "source": [ "## Final model\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "b31f672f", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:27.630821Z", "start_time": "2024-10-29T15:42:27.595178Z" } }, "outputs": [], "source": [ "total_inventory = agb.newActivity(USER_DB, \"total_inventory\", \"kg\", {\n", " activity1 : b * 5 + a + 1, # Reference the activity we just created\n", " alu2: 3 * share_recycled_aluminium, \n", " alu:0.4 * a})" ] }, { "cell_type": "markdown", "id": "0b76c855", "metadata": {}, "source": [ "## Or load existing model /activities from database\n", "\n", "Alternatively, you may not define the model again, but load it from the USER DB." ] }, { "cell_type": "code", "execution_count": 16, "id": "f230219c", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:27.943300Z", "start_time": "2024-10-29T15:42:27.924542Z" } }, "outputs": [], "source": [ "activity1 = agb.findActivity(\"first foreground activity\", db_name=USER_DB)\n", "total_inventory = agb.findActivity(\"total_inventory\", db_name=USER_DB)\n", "alu2 = agb.findActivity(\"Aluminium 2\", db_name=USER_DB)" ] }, { "cell_type": "markdown", "id": "1759e93c", "metadata": {}, "source": [ "## Display activities\n", "\n", "**printAct** displays the list of all exchanges of an activity.\n", "\n", "Note that symbolic expressions have not been evaluated at this stage" ] }, { "cell_type": "code", "execution_count": 17, "id": "12ab41203d7978be", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:28.313257Z", "start_time": "2024-10-29T15:42:28.299549Z" } }, "outputs": [ { "data": { "text/html": [ "
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first foreground activity (1.000000 kg)
inputamountunit
Heat, wasteHeat, wasteb + 0.2megajoule
Occupation, industrial areaOccupation, industrial area3*bsquare meter-year
\n", "
" ], "text/plain": [ " first foreground activity (1.000000 kg) \\\n", " input amount \n", "Heat, waste Heat, waste b + 0.2 \n", "Occupation, industrial area Occupation, industrial area 3*b \n", "\n", " \n", " unit \n", "Heat, waste megajoule \n", "Occupation, industrial area square meter-year " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print_act displays activities as tables\n", "agb.printAct(activity1) " ] }, { "cell_type": "code", "execution_count": 18, "id": "46ae9abf", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:28.480385Z", "start_time": "2024-10-29T15:42:28.467161Z" } }, "outputs": [ { "data": { "text/html": [ "
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total_inventory (1.000000 kg)
inputamountunit
Aluminium 2Aluminium 2[RER]{FG}3*share_recycled_aluminiumkilogram
aluminium alloy production, AlMg3aluminium alloy production, AlMg3[RER]0.4*akilogram
first foreground activityfirst foreground activity{FG}a + 5*b + 1kg
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" ], "text/plain": [ " total_inventory (1.000000 kg) \\\n", " input \n", "Aluminium 2 Aluminium 2[RER]{FG} \n", "aluminium alloy production, AlMg3 aluminium alloy production, AlMg3[RER] \n", "first foreground activity first foreground activity{FG} \n", "\n", " \n", " amount unit \n", "Aluminium 2 3*share_recycled_aluminium kilogram \n", "aluminium alloy production, AlMg3 0.4*a kilogram \n", "first foreground activity a + 5*b + 1 kg " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agb.printAct(total_inventory)" ] }, { "cell_type": "code", "execution_count": 19, "id": "c08a45dc", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:28.663717Z", "start_time": "2024-10-29T15:42:28.651689Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
first foreground activity (1.000000 kg)
inputamountunit
Heat, wasteHeat, waste1.70000000000000megajoule
Occupation, industrial areaOccupation, industrial area4.50000000000000square meter-year
\n", "
" ], "text/plain": [ " first foreground activity (1.000000 kg) \\\n", " input \n", "Heat, waste Heat, waste \n", "Occupation, industrial area Occupation, industrial area \n", "\n", " \n", " amount unit \n", "Heat, waste 1.70000000000000 megajoule \n", "Occupation, industrial area 4.50000000000000 square meter-year " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You can also compute amounts by replacing parameters with a float value \n", "agb.printAct(activity1, b=1.5)" ] }, { "cell_type": "code", "execution_count": 20, "id": "960585e2", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:28.883542Z", "start_time": "2024-10-29T15:42:28.820656Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 aluminium alloy production, AlMg3[RER] (1.000000 kilogram)Aluminium 2[RER] (1.000000 kilogram)
 inputamountunitinputamountunit
aluminium scrap, new, Recycled Content cut-offnannannanaluminium scrap, new, Recycled Content cut-off12kilogram
aluminium, cast alloymarket for aluminium, cast alloy0.965000kilogrammarket for aluminium, cast alloy0.965 - 0.965*share_recycled_aluminiumkilogram
cast ironmarket for cast iron0.004060kilogrammarket for cast iron0.004060kilogram
chromiummarket for chromium0.003050kilogrammarket for chromium oxide, flakes4.000000kilogram
copper, cathodemarket for copper, cathode0.001020kilogrammarket for copper, cathode0.001020kilogram
electricity, medium voltagemarket group for electricity, medium voltage[RER]1.590000kilowatt hourelect mix{FG}1.590000kilowatt hour
magnesiummarket for magnesium0.030500kilogrammarket for magnesium0.030500kilogram
manganesemarket for manganese0.005080kilogrammarket for manganese0.005080kilogram
silicon, metallurgical grademarket for silicon, metallurgical grade0.004060kilogrammarket for silicon, metallurgical grade0.004060kilogram
zincmarket for zinc0.002030kilogrammarket for zinc0.002030kilogram
\n" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You can print several activities at once to compare them\n", "agb.printAct(alu, alu2)" ] }, { "cell_type": "markdown", "id": "3d6cb156", "metadata": {}, "source": [ "# Select the impacts to consider" ] }, { "cell_type": "code", "execution_count": 21, "id": "6007c86f", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:29.166910Z", "start_time": "2024-10-29T15:42:29.163235Z" } }, "outputs": [ { "data": { "text/plain": [ "[('EF v3.0', 'climate change', 'global warming potential (GWP100)'),\n", " ('EF v3.0', 'climate change: biogenic', 'global warming potential (GWP100)'),\n", " ('EF v3.0', 'climate change: fossil', 'global warming potential (GWP100)'),\n", " ('EF v3.0',\n", " 'climate change: land use and land use change',\n", " 'global warming potential (GWP100)')]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# List of impacts to consider\n", "impacts = agb.findMethods(\"climate change\", mainCat=\"EF v3.0\")\n", "impacts" ] }, { "cell_type": "markdown", "id": "5096a57f", "metadata": {}, "source": [ "# Impacts\n", "\n", "## Define functional unit\n", "\n", "The functional unit is a quantity that can be parametrized\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "fd1ec460", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:29.559542Z", "start_time": "2024-10-29T15:42:29.557654Z" } }, "outputs": [], "source": [ "functional_value = a + 5" ] }, { "cell_type": "markdown", "id": "ff55eb26", "metadata": {}, "source": [ "## Compute impacts" ] }, { "cell_type": "code", "execution_count": 23, "id": "a35f64d35ec07e1b", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:31.310978Z", "start_time": "2024-10-29T15:42:30.075440Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Db changed recently, clearing cache expr\n", "[INFO] Db changed recently, clearing cache lcia\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
climate change - global warming potential (GWP100)[kg CO2-Eq]climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq]climate change: fossil - global warming potential (GWP100)[kg CO2-Eq]climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq]
total_inventory6.49280.01437646.472430.00598809
\n", "
" ], "text/plain": [ " climate change - global warming potential (GWP100)[kg CO2-Eq] \\\n", "total_inventory 6.4928 \n", "\n", " climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq] \\\n", "total_inventory 0.0143764 \n", "\n", " climate change: fossil - global warming potential (GWP100)[kg CO2-Eq] \\\n", "total_inventory 6.47243 \n", "\n", " climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq] \n", "total_inventory 0.00598809 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agb.compute_impacts(\n", " \n", " # Root activity of our inventory\n", " total_inventory, \n", " \n", " # list of impacts to consider\n", " impacts, \n", " \n", " # The impaxts will be divided by the functional unit\n", " functional_unit=functional_value,\n", " \n", " # Parameters of the model\n", " a=1.0,\n", " elec_switch_param=\"us\",\n", " share_recycled_aluminium=0.4)" ] }, { "cell_type": "code", "execution_count": 24, "id": "a65d5217", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:31.412645Z", "start_time": "2024-10-29T15:42:31.358641Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Db changed recently, clearing cache expr\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
climate change - global warming potential (GWP100)[kg CO2-Eq]climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq]climate change: fossil - global warming potential (GWP100)[kg CO2-Eq]climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq]
aluminium alloy production, AlMg3[RER]7.309130.0158937.275950.0172853
Aluminium 2[RER]30.59440.067633330.50110.0256965
\n", "
" ], "text/plain": [ " climate change - global warming potential (GWP100)[kg CO2-Eq] \\\n", "aluminium alloy production, AlMg3[RER] 7.30913 \n", "Aluminium 2[RER] 30.5944 \n", "\n", " climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq] \\\n", "aluminium alloy production, AlMg3[RER] 0.015893 \n", "Aluminium 2[RER] 0.0676333 \n", "\n", " climate change: fossil - global warming potential (GWP100)[kg CO2-Eq] \\\n", "aluminium alloy production, AlMg3[RER] 7.27595 \n", "Aluminium 2[RER] 30.5011 \n", "\n", " climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq] \n", "aluminium alloy production, AlMg3[RER] 0.0172853 \n", "Aluminium 2[RER] 0.0256965 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You can compute several LCAs at a time and compare them:\n", "agb.compute_impacts(\n", " [alu, alu2], # The models\n", " \n", " impacts, # Impacts\n", " \n", " # Parameters of the model\n", " share_recycled_aluminium=0.3,\n", " elec_switch_param=\"us\")" ] }, { "cell_type": "markdown", "id": "261d2a3d", "metadata": {}, "source": [ "## Fast computation of many parameter values" ] }, { "cell_type": "code", "execution_count": 25, "id": "3fd8f2a8", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:31.795303Z", "start_time": "2024-10-29T15:42:31.461114Z" } }, "outputs": [ { "data": { "text/html": [ "
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climate change - global warming potential (GWP100)[kg CO2-Eq]climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq]climate change: fossil - global warming potential (GWP100)[kg CO2-Eq]climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq]
a
113.63930.032360313.59790.00906581
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310.96040.025859610.9260.00852789
410.06740.023692610.03540.00834858
59.353030.02195919.322870.00820513
...............
999952.924290.006358762.911020.00691425
999962.924290.006358762.911020.00691425
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99999 rows × 4 columns

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" ], "text/plain": [ " climate change - global warming potential (GWP100)[kg CO2-Eq] \\\n", "a \n", "1 13.6393 \n", "2 12.1085 \n", "3 10.9604 \n", "4 10.0674 \n", "5 9.35303 \n", "... ... \n", "99995 2.92429 \n", "99996 2.92429 \n", "99997 2.92429 \n", "99998 2.92429 \n", "99999 2.92429 \n", "\n", " climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq] \\\n", "a \n", "1 0.0323603 \n", "2 0.0286456 \n", "3 0.0258596 \n", "4 0.0236926 \n", "5 0.0219591 \n", "... ... \n", "99995 0.00635876 \n", "99996 0.00635876 \n", "99997 0.00635876 \n", "99998 0.00635876 \n", "99999 0.00635876 \n", "\n", " climate change: fossil - global warming potential (GWP100)[kg CO2-Eq] \\\n", "a \n", "1 13.5979 \n", "2 12.0711 \n", "3 10.926 \n", "4 10.0354 \n", "5 9.32287 \n", "... ... \n", "99995 2.91102 \n", "99996 2.91102 \n", "99997 2.91102 \n", "99998 2.91102 \n", "99999 2.91102 \n", "\n", " climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq] \n", "a \n", "1 0.00906581 \n", "2 0.00875842 \n", "3 0.00852789 \n", "4 0.00834858 \n", "5 0.00820513 \n", "... ... \n", "99995 0.00691425 \n", "99996 0.00691425 \n", "99997 0.00691425 \n", "99998 0.00691425 \n", "99999 0.00691425 \n", "\n", "[99999 rows x 4 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fast computation for millions of separate samples\n", "agb.compute_impacts(\n", " total_inventory, # The model \n", " impacts, # Impacts\n", " functional_unit = functional_value,\n", " \n", " # Parameters of the model\n", " a=list(range(1, 100000)), # All lists should have the same size\n", " share_recycled_aluminium=1, # Those parameters are fixed\n", " elec_switch_param=\"eu\")" ] }, { "cell_type": "markdown", "id": "56637741", "metadata": {}, "source": [ "## Split impacts along axis\n", "\n", "It is possible to **tag** activities and then ventilate the impacts according to the value of this \"tag\".\n", "This is useful to split impact by *phase* or *sub module*." ] }, { "cell_type": "code", "execution_count": 26, "id": "714dddc7", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:34.506328Z", "start_time": "2024-10-29T15:42:34.483132Z" } }, "outputs": [], "source": [ "# Tag activities with a custom attribute : 'phase' in this case\n", "alu2.updateMeta(phase= \"phase a\")\n", "activity1.updateMeta(phase= \"phase b\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "7ae836dd", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:35.267517Z", "start_time": "2024-10-29T15:42:34.804655Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Db changed recently, clearing cache expr\n", "[INFO] Db changed recently, clearing cache lcia\n" ] }, { "data": { "text/html": [ "
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phase
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" ], "text/plain": [ " climate change - global warming potential (GWP100)[kg CO2-Eq] \\\n", "phase \n", "_other_ 0.487275 \n", "phase_a 6.00552 \n", "phase_b 0 \n", "*sum* 6.4928 \n", "\n", " climate change: biogenic - global warming potential (GWP100)[kg CO2-Eq] \\\n", "phase \n", "_other_ 0.00105953 \n", "phase_a 0.0133169 \n", "phase_b 0 \n", "*sum* 0.0143764 \n", "\n", " climate change: fossil - global warming potential (GWP100)[kg CO2-Eq] \\\n", "phase \n", "_other_ 0.485063 \n", "phase_a 5.98737 \n", "phase_b 0 \n", "*sum* 6.47243 \n", "\n", " climate change: land use and land use change - global warming potential (GWP100)[kg CO2-Eq] \n", "phase \n", "_other_ 0.00115235 \n", "phase_a 0.00483573 \n", "phase_b 0 \n", "*sum* 0.00598809 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Provide the name of the custom attribute as 'axis'\n", "# The impacts are split between those\n", "agb.compute_impacts(\n", " total_inventory, # The model\n", " impacts, # Impacts\n", " \n", " functional_unit = functional_value,\n", " axis=\"phase\",\n", "\n", " \n", " # Parameters\n", " a=1.0,\n", " elec_switch_param=\"us\",\n", " share_recycled_aluminium=0.4)" ] }, { "cell_type": "markdown", "id": "acd51994", "metadata": {}, "source": [ " # Sensitivity analysis \n", " \n", " ## One at a time \n", " \n", " We provide several functions for computing **statistics** for **local variations** of parameters (one at a time).\n", " \n", " ### oat_matrix(model, impacts)\n", " \n", " Shows a **matrix of impacts x parameters** colored according to the variation of the impact in the bounds of the parameter.\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "eb6d99af", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:37.479760Z", "start_time": "2024-10-29T15:42:37.171371Z" } }, "outputs": [ { "data": { "image/png": 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36++//5YkRUdHq1WrVjYnBmCV33//XQMHDlRRUZF69+6t3r1767777lNkZKRee+017d+/X99//72WLVumWrVq6dlnn1VYWJjdsQFYYMCAAZo7d+4pH2o4iucKgerh0KFDSkxM1N69e1WnTh117dpVc+bMUUhIiPr27av9+/frl19+UU5OjmrVqqXevXvbHRkAzhhnTgAAUE7vvvuufv/9dzVv3lzbt2/Xzz//LOlII2L58uX666+/tGPHDvXv319ZWVnq1auXkpOTbU4NwAqvv/66ioqKdPvtt+vLL7/UiBEjJEkhISEaNmyYnn76aS1dulRff/218vPz9dFHH2ngwIE2pwZQ2WbNmqU5c+aoRo0amjdvnvLy8iRJdevWldvt1p49ezRt2jQ1a9ZMtWrVUlJSkkpKSmxODcAKb7zxhvbu3auOHTsqJSVFs2bNkiRFRkZqxowZWrx4sdLS0vT4448rIyNDISEhmjZtms2pARs5/PlThQ8FpzkBAEA5LViwQIZh6MUXX1S9evWOW9OwYUPNmjVL/fv319NPP62kpCSLUwKww7Jly2QYhp566qmT1l199dV67bXXtGbNGr3xxhvWhANgm+nTp8swDE2cOFE33XSTQkJCvD9zOByKi4vTkCFDtH79ep1zzjm64YYbvG9hAji7ff311zIMQy+88IJCQ0OPWxMWFqaXX35ZI0eO1Pvvv6+5c+danBIAKgZnTgAAUE5RUVHKzs5Wfn6+goKCJB25sRATE6ODBw+Wqt2xY4eaNm2q6667Tp9//rkNaQFYKTQ0VB6PR0VFRd4xPz8/1axZU4cOHSpVm5+fr8jISLVu3Vrr1q2zOioAC9WpU0cZGRk6dOiQatasKenI2iE2Nlb79+8vVbtq1Sp17txZd955pz744AMb0gKwUmRkpPLy8lRYWCh//yNPQzscDkVHRysjI6NU7YEDBxQXF6fExEQtWbLEjriA/f76yu4EvuG8PnYnOCM0JwAAKKeQkBCFhYWVulgIDQ2VaZoqKCg4pj4qKkohISFKS0uzMiYAG9SsWVMBAQGlGpXHu+lwVHR0tEpKSpSVlWVxUgBWCgoKUkhISKn/Xw8KClJgYKBycnKOqQ8PD1ft2rW1Y8cOC1MCsENoaKjCwsJKrR3Cw8PlcrlKPexwVHR0tAICAnTgwAErYwK+g+bEEVW0OcG2TgAAlFOdOnWOuVCoXbu2iouLtWfPnlLjHo9HeXl5xzwxDeDsFBcXp+zs7FJ7xTdq1EimaerXX38tVZuZmamsrCwVFxdbHROAxWJiYmQYRqmxmjVrKj8//4TNyf99owLA2SkuLk75+fmlxurUqSO3263t27eXGne5XMrOzpbT6bQyIgBUGJoTAACUU3x8vPLz85Wenu4da9OmjaQj51H808KFC+V2uxUbG2tlRAA2Oe+88+R2u/Xnn396xzp37izTNDVp0qRStc8884wkqXnz5pZmBGC9+vXrKzs7W7m5ud6xFi1aSJKSk5NL1a5fv175+fkn3HsewNmlYcOGKiwsLPWQU4cOHSRJ//nPf0rVTp8+XSUlJapfv76lGQGfYvdB1L7yp4qiOQEAQDl16tRJkrRixQrv2C233CLTNPXUU0/p1Vdf1ZIlSzRp0iQNHTpUhmHo6quvtisuAAv16NFDpmnqu+++846NGDFCDodDc+bM0YUXXqjbbrtNrVu31pQpU2QYhoYNG2ZjYgBWaNeunSRpzZo13rHevXvLNE099thjWrNmjVwul9auXashQ4bIMAx17tzZrrgALNSlSxdJ0rJly7xjgwYNkmmaeu6553T//ffrgw8+0AMPPKAHHnhAhmHohhtusCcsAJQTZ04AAFBOv/zyizp16lTqkGvTNNWjRw8tW7as1LYNpmmqbt26Wrt2reLi4mxKDMAqBw4c0JNPPqlzzz1XTz/9tHf8nXfe0cMPPyyPx1Oq/tZbb9Unn3xidUwAFlu4cKFuuOEGjRgxQv/6178kSbm5uWrRooX27t17zNohICBAy5cvV8eOHe2KDMAiW7ZsUZ8+fdStWzdNmzbNOz5w4EDNmjXrmPmhRYsWWrlypSIjI+2IC9jv7+9OXVMdNOtld4IzQnMCAIBKUlBQoOeee06zZ8/W7t27FRkZqV69eum5555TfHy83fEA2OzPP//UvHnzSs0PiYmJdscCYAG3262ffvpJoaGh3u1aJGnbtm264447tGrVKu9YfHy83n33XfXu3duOqAB8hGma+vDDD4+5tnj00UdpTKB6ozlxBM0JAAAAAABQXnv27PHefGzRosUxh2cDAID/H82JI6poc6LqnpYBAAAAAMBZqEGDBmrQoIHdMQAA8H1V+DBocCA2AAAAAAAAAACwGK0lAABOQ2pqqiQpICBA9erVKzV2ujh3Aji7TJgwQZJUq1Yt3XfffaXGTtfYsWMrLBcAAPBNy5cvlySFhoaqffv2pcZOV0JCQoXlAgCrcOYEAACnwc/PT5J0/vnna8uWLaXGTodhGHK73RWaDYC9HA6HDMNQ8+bN9fvvv5caO10ej6ei4wGwSZMmTSRJzZo10+LFi0uNnQ7DMJSSklKh2QDY6+g64Z/XFmeyduDaAtXa9u/tTuAbmvS0O8EZ4c0JAABOw9Ge/j97+2fS5+fZAODsk5CQIMMwSr0VdXQMQPW1c+dOSVJwcPAxY6eDuQQ4O5mmqZKSkmPGTvc7AKAqojkBAMBp2LFjh6Qj2zr97xiA6m3ZsmVlGgNQvUybNk2SFBkZecwYgOrtf5sSJxoDcBIciF2lsa0TAAAAAAAAAKDq2bnM7gS+oVE3uxOcEYfdAQAAAICz1fLly/Xzzz+XuX716tVnfBAmAAAAAFQlNCcAACinJk2a6NJLLy1zfZcuXdS0adNKTATAV3Tr1k19+/Ytc/0tt9yixMTESkwEwBcMGzZMjzzySJnrR40apTvvvLMSEwHwFQ6HQ/Xr1y9zfePGjeXvz7Y2AKommhMAAJTTzp07lZqaWub6PXv2nNFBmACqJg61BPC/pk+frlmzZpW5fu7cuZo+fXrlBQLgU1g7AKguaE4AAGAxt9sth4N/ggEcKy8vTwEBAXbHAOBjuPEI4ESKi4u5tgBQZfHeFwAAFiooKFB6eroiIiLsjgLAx2zdulUZGRlq0KCB3VEA+JiMjAyFhobaHQOAj8nKylJ6erqioqLsjgLYx8Ht7aqM/3oAAJym1NTUY7ZlKi4u1ooVK074ZKNpmsrKytInn3wil8ulVq1aWZAUgNW++OILffHFF6XGnE6nhg0bdsLPHJ0fVqxYIcMw1KVLl8qOCaCKcDqd+vDDD5Wfn6/WrVvbHQdAJdi0aZM2btxYaqygoEAzZsw44WeOrh3mzZunkpIStW3btpJTAkDloDkBAMBpmjZtmiZMmFBqLDMzU926dTvlZ03TlGEYuueeeyopHQA7bdy4UdOnT5dhGN5mZUFBQZn3iq9du7bGjRtXiQkB2GH8+PHHrB0OHDggPz+/Mn3eMAz17du3MqIBsNmCBQuOmR+ys7M1dOjQU3726LXFI488UlnxAKBS0ZwAAOAM/PMNiX/ehDwRwzBUo0YNXXjhhRoxYoQGDhxY2REB2KBNmzYaMmSI9+8fffSRQkJC1L9//xN+xuFweOeHvn37qmbNmhYkBWC10107HBUYGKhBgwbpySefrKxoAGxUs2ZNxcfHe/++a9cuORyOk27z+M+1w/Dhw3nrEkCVZZicrAUAQLk4HA7VrVtXaWlpdkcB4GOYHwBIR242Ht0S0jRNJSYmKjo6WvPnzz/hZ47efDzvvPMUEhJiUVIAdmPtAJym1B/tTuAb4i+3O8EZ4c0JAADKafDgwTzpDOC4kpOTFRgYaHcMADZr2LChGjZs6P17fHy86tSpo65du9qYCoAvGjdunMLDw+2OAVQdHIhdpfHmBAAAAAAAAACg6tnzs90JfEODS+1OcEYcdgcAAAAAAAAAAADVC++9AABQQXJycvTVV19p06ZNOnz4sFwu1wlrDcPQ1KlTLUwHwC6maeqjjz7SzJkzvfOD2+0+Yb1hGCf9OYCzS1pamn777bdTrh2kI1tJAqgeUlJSNGfOnDJfWyQlJVmYDgAqBts6AQBQAaZPn66RI0cqNzfXO3a8f2INw5BpmjIMQx6Px8qIAGxQVFSk3r17Kzk5+bhzwvEwPwDVw2+//ab/+7//04oVK8pUT+MSqD7Gjx+v5557TiUlJWVaP7B2QLXGtk5HVNFtnXhzAgCAclq0aJHuvPNOmaap4OBgderUSXFxcfL3559ZoLp7+eWXtXTpUknSTTfdpOuvv575AYC2bt2qLl26KCcnR6ZpKjAwULVr12ZuAKBPPvlE48ePlyTFxcXpqquuYu0AnAwHYldp/NcDAKCcXnnlFZmmqU6dOumLL75QrVq17I4EwEfMmjVLhmFo7NixGjdunN1xAPiIZ599VtnZ2YqLi9OUKVN09dVXy8/Pz+5YAHzAu+++K0m67rrrNGfOHAUGBtqcCAAqD9s6AQBQTjVr1lROTo7+/PNPnXvuuXbHAeBDQkJC5HK5lJWVpfDwcLvjAPARdevW1cGDB7VkyRIlJibaHQeAD6lRo4by8vK0e/duxcXF2R0H8H1pa+1O4Bvi2tud4IzQnAAAoJzCw8Pl5+cnp9NpdxQAPiY2NlYej0eHDh2yOwoAHxISEiLDMJSbmyuHw2F3HAA+JDIyUg6HQ5mZmXZHAaoGmhNHVNHmBKsgAADKqWnTpioqKuIQOgDH6NChg5xOpw4fPmx3FAA+pF69evLz86MxAeAY559/vvLz81VUVGR3FACodKyEAAAop9tvv10ul0vffvut3VEA+JhHHnlEpmnq9ddftzsKAB9y7bXXKj8/Xxs2bLA7CgAfc9ddd8nlcmnu3Ll2RwGqBoc/f6rwoeBs6wQAQDm5XC516dJFaWlpSkpK4twJAKVMmjRJTz31lMaOHatHH31UoaGhdkcCYLP09HS1atVK559/vhYvXqygoCC7IwHwITfccIOWL1+uzz//XAkJCXbHAXzb/o12J/ANddvYneCM0JwAAKCcZsyYoZycHI0dO1YFBQW6+eab1bFjR0VERJz0c4MHD7YoIQC7HD3oduPGjXI6nQoKClLLli1POj8YhqGkpCSrIgKwQWpqqjZv3qxBgwapbt26euyxx3TJJZeccu0QHx9vUUIAdpkwYYI8Ho/eeecdZWVlqXPnzmW6thg7dqxFCQEfQ3PiCJoTAABUTw6HQ4ZhSJJM0/T+75MxDENut7uyowGw2ZnsJ28YBmfYAGc5Pz+/0/4MawegevjfawtJZbq+YO2AaovmxBFVtDlRdTekAgDAR8THx5fpggFA9TNu3Di7IwDwQWfyjCDPFQLVQ0JCAtcWAKoN3pwAAAAAAMBCu3btOqPPNWzYsIKTAABQxaVvtjuBb4i90O4EZ4Q3JwAAAAAAsBBNBgAAAOn0N8EFAAAAAAAAAAAoB96cAACgAmVkZCg5OVm7du1Sfn6+xo4da3ckAD4gPz9fH374oRYtWqRdu3apoKBAKSkp3p87nU59/fXXMgxDt956q41JAdjh4MGD3rVDQkKC3XEA+IhNmzaVWjtMnTrV+zOXy6WDBw/KMAzVq1fPxpQAcOY4cwIAgArgdrv1xBNP6F//+peKi4u94x6Px/u/MzMz1aRJExUUFOjPP/9Uo0aNbEgKwGobN27U9ddfrz179ngPtDUMo9T8YJqmWrZsqa1bt2rJkiVKTEy0Ky4ACy1cuFDPPvusfv31V0lH5ga32+39eWZmprdhOXv2bEVGRtqSE4C1nE6nhg0bps8//1zSkXXC/64d8vPz1bBhQ2VmZurXX39Vy5YtbUoL2IwzJ46oomdOsK0TAAAVoF+/fnrjjTdUXFysli1byt//2JcTo6KiNHDgQBUXF2vOnDk2pARgtUOHDql3797avXu32rVrp0mTJqlGjRrH1BmGoTvvvFOmaWrhwoU2JAVgtZdeekk33nijNm7cKNM0vX/+KSoqSiEhIVqyZInmzZtnU1IAVnK5XLr66qv1+eefKzQ0VL1791ZwcPAxdaGhoRo6dKhKSkqYH1C9Ofz546i6myPRnAAAoJxmzZqlL774QrGxsVq7dq02bdqk6Ojo49b269dPkpScnGxlRAA2ef3117Vv3z716NFDv/zyix555BGFhIQct7Z3796SpFWrVlkZEYANfv75Z40ePVr+/v56/fXXlZGRoTp16hy39vbbb5dpmlqyZInFKQHYYerUqfr555/VpEkTbd26VQsXLjzhW1N9+/aVJC1fvtzKiABQYapuWwUAAB8xbdo0GYahV199VW3btj1p7SWXXCLDMPT7779blA6Anb788ksZhqFXXnlFDsfJnwtq3ry5AgICSp1FAeDs9Oabb0qSnnrqKY0cOfKktV27dpUkbdiwodJzAbDfzJkzZRiGXn/9dcXFxZ20tm3btnI4HPrzzz8tSgcAFYszJwAAKKfY2FgdOnRIubm53iei69Wrp/T09FL7wh4VHR2tgoICFRQUWB0VgMUiIiLkdruVn58vwzAknXx+qF27tpxOZ6mzawCcfeLj47V3714dOHBAtWrVknTyuSEiIkKSlJOTY2lOANaLjo5Wdna28vPzFRgYKOnk80NMTIzy8vJUWFhodVTAN2TQnJMk1Trf7gRnhG2dAAAoJ6fTqcjIyBNu1fK/SkpKvDcpAZzdSkpK5O/vX6b/nzdNU7m5uQoLC7MgGQA7paenKyIiwtuYOJWgoCCalkA1kZ+fr4iICG9j4lRcLtdxz7sDgKqA2QsAgHKKiorSwYMHVVhYeNzD6v5p3759ys7OVsOGDS1KB8BO9evXV0pKitLT0xUbG3vS2jVr1qioqEgtWrSwKB0Au4SFhSknJ0cej0d+fn4nrc3NzVVWVpZq165tUToAdqpVq5b27dun3NxchYeHn7R2x44dys3NVbNmzSxKB/igKnwYNHhzAgCAcmvXrp2ksh1y/f/+3/+TJHXq1KlSMwHwDd26dZN05GyaUxk/frwMw9AVV1xRyakA2K158+byeDzatGnTKWs///xzlZSUqE2bNpUfDIDtOnbsKEn6+uuvT1n79ttvS5K6dOlSqZkAoLLQnAAAoJxuu+02maapMWPGKDc394R13333nSZOnCjDMDRkyBALEwKwy8iRI2UYhl544QV9//33x605cOCAbrvtNn377bcKDAzU/fffb3FKAFa77rrrZJqmXnzxxZPW7dmzR08++aQMw1Dfvn0tSgfATsOGDfNeW6SlpZ2w7v3339ebb74pwzA0fPhwCxMCQMXhQGwAAMrJNE1169ZNK1as0AUXXKARI0Zo3LhxysrK0nfffaedO3fqyy+/1DfffKOSkhJde+21+uKLL+yODcAir7zyivfmYtu2bfX777+rqKhIt9xyi3bt2qV169bJ5XLJNE1NmTKFGwxANZCbm6sWLVooLS1Nt912m0aNGqUrrrhC6enpKiws9K4dXn75ZR08eFDNmzfXpk2bFBAQYHd0ABbo16+f5s+fr7p162rgwIH68MMPlZOTo/fee0+7du3SV199pc2bN8s0Td199916//337Y4M2Ofw33Yn8A3RVXN7N5oTAABUgMzMTN14441avnz5CQ++NU1TPXv21GeffXbK/WMBnF2mTp2qxx57TE6n0ztmGIaOLsVr1qypN954Q4MHD7YrIgCLbdy4UVdddZUOHjx40rVDXFyckpKS1Lx5c4sTArBLYWGh7r77bn3yySfHnR+Orh+GDRumKVOmcCA2qjeaE0fQnAAAoHorKSnRJ598oqlTp+qXX35RUVGRJMnf31+XXHKJhg8frttvv10OB7sqAtVRbm6u5s+fr59++klpaWnyeDyqW7euOnfurH79+ikyMtLuiAAstn//fo0ePVozZ85UYWFhqZ8FBgZq4MCBeuGFF1S3bl2bEgKw048//qgPP/xQK1euPGbtMHz4cCUkJNgdEbBf1k67E/iGmo3sTnBGaE4AAFAJSkpKdPjwYXk8HsXExPA0EwAAOKGioiKtW7eu1M3HDh06KDQ01O5oAAD4NpoTR9CcAAAAAPBPy5cvV2BgoC699NIy1a9evVqFhYU8CQkAAACUBc2JI2hOAABQPTVp0kSxsbH6+eefy1TfpUsXpaWlKSUlpZKTAbCbw+FQvXr1tHfv3jLVN27cWLt375bb7a7kZADsNGzYMNWsWVOTJ08uU/2oUaN06NAhTZ06tZKTAbAbawfgNNGcOKKKNifY9BoAgHLauXOnUlNTy1y/Z88e7dy5s/ICAfApp/ssEM8OAWe/6dOna9asWWWunzt3rqZPn155gQD4FNYOAKoLNsAGAMBibrebQ7EBHFdeXp4CAgLsjgHAx3DjEcCJFBcXc22B6s3B7e2qjNkLAAALFRQUKD09XREREXZHAeBjtm7dqoyMDMXGxtodBYCPycjI4HBsAMfIyspSenq6oqKi7I4CAGeE1hIAAKcpNTX1mG2ZiouLtWLFihM+2WiaprKysvTJJ5/I5XKpVatWFiQFYLUvvvhCX3zxRakxp9OpYcOGnfAzR+eHFStWyDAMdenSpbJjAqginE6nPvzwQ+Xn56t169Z2xwFQCTZt2qSNGzeWGisoKNCMGTNO+Jmja4d58+appKREbdu2reSUAFA5aE4AAHCapk2bpgkTJpQay8zMVLdu3U75WdM0ZRiG7rnnnkpKB8BOGzdu1PTp02UYhrdZWVBQUOa94mvXrq1x48ZVYkIAdhg/fvwxa4cDBw7Iz8+vTJ83DEN9+/atjGgAbLZgwYJj5ofs7GwNHTr0lJ89em3xyCOPVFY8AKhUNCcAADgD/3xD4p83IU/EMAzVqFFDF154oUaMGKGBAwdWdkQANmjTpo2GDBni/ftHH32kkJAQ9e/f/4SfcTgc3vmhb9++qlmzpgVJAVjtdNcORwUGBmrQoEF68sknKysaABvVrFlT8fHx3r/v2rVLDodDDRo0OOFn/rl2GD58OG9dAqiyDJOTtQAAKBeHw6G6desqLS3N7igAfAzzAwDpyM3Go1tCmqapxMRERUdHa/78+Sf8zNGbj+edd55CQkIsSgrAbqwdgNOUvcfuBL6hxokbmr6MNycAACinwYMH86QzgONKTk5WYGCg3TEA2Kxhw4Zq2LCh9+/x8fGqU6eOunbtamMqAL5o3LhxCg8PtzsGUHU4uL1dlfHmBAAAAAAAAACg6sndb3cC3xBe1+4EZ4TWEgAAFSwnJ0fr169Xenq6JCk2Nlbt2rVTRESEzckA2OnAgQOaN2+e1q5dW2p+6NChg/r27as6derYnBCAnTwejw4fPixJio6OLvNh2QDOblu2bDnu2uGCCy6wORkAlB9vTgAAUEF+++03jR49Wt9++61KSkpK/czhcKh3796aOHGiWrVqZVNCAHbweDwaM2aMJk+eLJfLJem/B+MahiFJCggI0KOPPqoJEyZwQxKoRvLz8zVlyhTNnDlTmzZtktvtliT5+/vroosu0sCBAzV8+HCFhobanBSA1RYtWqRRo0Zp8+bNx/15q1at9Morr+jKK6+0OBngY3hz4ogq+uYEzQkAACrAZ599pttvv11FRUU60T+thmEoKChIn3zyiW688UaLEwKwy2233aZZs2bJNE0FBQWpffv2atDgyIF1e/bs0dq1a1VUVCTDMDRw4EB9/PHHNicGYIWtW7fq2muvVUpKyknXDs2aNdOXX36p8847z+KEAOzyzjvv6KGHHpJpmjJNU35+fqpVq5Yk6dChQ95GpmEYeuutt3T//ffbGRewF82JI2hOAABQPe3YsUMXXHCBioqK1KhRI40aNUpXXHFFqZuPS5Ys0auvvqodO3YoODhYW7ZsUePGjW1ODqCyff7557rpppskSY888oieeeYZ1axZs1SN0+nU888/r0mTJskwDC1YsEDXXXedDWkBWCUnJ0cXXnihdu/eLX9/f910003HrB2+//57zZ8/X263Ww0bNtR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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agb.oat_matrix(\n", " total_inventory, \n", " impacts, \n", " functional_unit=functional_value)" ] }, { "cell_type": "markdown", "id": "cba8d13d", "metadata": {}, "source": [ "### oat_dashboard_matrix\n", "\n", "This functions draws a dashboard showing :\n", "* A dropdown list, for choosing a parameter\n", "* Several graphs of evolution of impacts for this parameter\n", "* Full table of data\n", "* A graph of \"bars\" representing the variation of each impact for this parameter (similar to the information given in oat_matrix) " ] }, { "cell_type": "code", "execution_count": 29, "id": "53b48995", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:40.950059Z", "start_time": "2024-10-29T15:42:40.427411Z" }, "jupyter": { "is_executing": true } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8831df6f628477586343218ec90ceef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='param', options=('elec_switch_param', 'a', 'share_recycled_alumini…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agb.oat_dashboard(\n", " total_inventory, \n", " impacts, \n", " functional_unit=functional_value,\n", " \n", " # Optionnal layout parameters\n", " figspace=(0.5,0.5),\n", " figsize=(15, 15),\n", " sharex=True)" ] }, { "cell_type": "markdown", "id": "7ddbb95e", "metadata": {}, "source": [ "## Monte-carlo methods & Sobol indices\n", "\n", "Here we leverage fast computation of monte-carlo approches. \n", "\n", "We compute **global sensivity analysis** (GSA).\n", "Not only local ones.\n", "\n", "### Sobol Matrix \n", "\n", "Similar to OAT matrix, we compute Sobol indices. they represent the ratio between the variance due to a given parameter and the total variance.\n", "\n", "for easier comparison, we translate those relative sobol indices into \"deviation / mean\" importance :\n", "\n", "$$RelativeDeviation = \\frac{\\sqrt{sobol(param) \\times totalVariance(impact))}}{mean(impact)}$$\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "0c190cc4", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:42:51.753213Z", "start_time": "2024-10-29T15:42:51.307338Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n", "Processing Sobol indices ...\n", "Processing sobol for ('EF v3.0', 'climate change', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: biogenic', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: fossil', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: land use and land use change', 'global warming potential (GWP100)')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/SALib/util/__init__.py:274: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n", " names = list(pd.unique(groups))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e0397f447dc141818fac4d62f05ccd87", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='indice', options=('s1', 'st'), value='s1'), Dropdown(description='…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show sobol indices \n", "agb.incer_stochastic_matrix(\n", " total_inventory, \n", " impacts, \n", " functional_unit=functional_value)" ] }, { "cell_type": "markdown", "id": "04040150", "metadata": {}, "source": [ "### Graphs of impacts and their distribution\n", "\n", "We provide a dashboard showing **violin graphs** : the exact probabilistic distribution for each impact. Together with medians of the impacts." ] }, { "cell_type": "code", "execution_count": 31, "id": "5b3f1adb", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:43:00.475808Z", "start_time": "2024-10-29T15:42:59.475391Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[WARNING] Param 'b' is marked as FIXED, but passed in parameters : ignored\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n" ] }, { "data": { "image/png": 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climate change - global warming potential (GWP100) [kg CO2-Eq / ]climate change: biogenic - global warming potential (GWP100) [kg CO2-Eq / ]climate change: fossil - global warming potential (GWP100) [kg CO2-Eq / ]climate change: land use and land use change - global warming potential (GWP100) [kg CO2-Eq / ]
median9.210980.0209869.182330.00761474
std2.629450.006165652.622030.00134992
p[4.655495077080512, 13.302401655380397][0.010480454593969825, 0.03072703029999731][4.639872398432455, 13.263358094633025][0.004697215673802231, 0.008893207315576242]
mean9.123450.02084559.095290.00731336
var0.2882080.2957780.2882840.184583
\n", "
" ], "text/plain": [ " climate change - global warming potential (GWP100) [kg CO2-Eq / ] \\\n", "median 9.21098 \n", "std 2.62945 \n", "p [4.655495077080512, 13.302401655380397] \n", "mean 9.12345 \n", "var 0.288208 \n", "\n", " climate change: biogenic - global warming potential (GWP100) [kg CO2-Eq / ] \\\n", "median 0.020986 \n", "std 0.00616565 \n", "p [0.010480454593969825, 0.03072703029999731] \n", "mean 0.0208455 \n", "var 0.295778 \n", "\n", " climate change: fossil - global warming potential (GWP100) [kg CO2-Eq / ] \\\n", "median 9.18233 \n", "std 2.62203 \n", "p [4.639872398432455, 13.263358094633025] \n", "mean 9.09529 \n", "var 0.288284 \n", "\n", " climate change: land use and land use change - global warming potential (GWP100) [kg CO2-Eq / ] \n", "median 0.00761474 \n", "std 0.00134992 \n", "p [0.004697215673802231, 0.008893207315576242] \n", "mean 0.00731336 \n", "var 0.184583 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { 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oaGhWZw1ADuWuDZ5+/fplmzJw8ODBCggI0CuvvOK0/J43b55KlSqlxx9/XHPmzFGhQoXUtm1bderUSeXKldPy5cs1bNgwlS9fXh988IGV7p577pGvr68uXLigjRs3Ot32okWLrH+7+mHGtky9evUUEBDgML9169aKjo5WdHS0mjVrposXL2r8+PGqXbu2Jk2aZLfs0qVL1a9fP3322WdauXKlYmJiUnuYtHz5clWvXl3fffedQkJC1LlzZ4WEhOi7775TtWrVtGrVKqfpTp48qdq1a2vUqFG6dOmSOnTooMqVK+v3339XRESEPv30U4c0pUqV0iOPPKLJkydryZIlqc4jAADAncgdf2zLbp2Pnn/+eV29elVvvfWW0/kbN25UxYoV9cADD+jXX39V7ty51bJlS3Xt2lXVqlXTpk2b9Prrr6tq1ap65plnrHRlypRR8eLFZYxxWa9NTZtg8eLFMsaoePHiKlOmjMP8hg0bWm2Ctm3bKiEhQZMmTVKTJk3s2iiStGfPHvXp00cffPCBlixZokuXLqV0eCy7d+9WtWrV9Pnnn8vT01OdOnVSiRIlNGXKFNWrV0/Tpk1zmu7q1atq1qyZhg4dqoMHD6pNmzaqW7euli9fru7du+u5555zSBMcHKxhw4ZpzZo1+v7771Odx8S805UqA3Tu3Fn169dXcHBwVmUBcGn69OmKi4vT8OHD9eabb2Z1dlJUtGhR7dixQz4+PlmdFSBVvv/+e129elXh4eFZnRWkUeLyu0CBAm7z7JkyZYqWL1+u559/XoUKFXKYP2vWLHXq1Enx8fHq16+f3njjDYWFhdktExcXp5kzZ+qtt97S7t27rekBAQGqX7++/vrrLy1atEi1a9e2SxcbG6uVK1eqSpUq2r17txYvXqyXX37ZIQ+2SmyzZs2c7sPQoUPtGisXLlxQ9+7dNW/ePD388MNq2bKl8ubNK0kqXLiwBg4cqJo1a6pmzZpav369Hn300RSP09WrV9WjRw9dvXpVw4YNs6vYDx8+XKNGjVKPHj20a9cuhyD7I488ot27d6tFixaaOXOmAgMDJUlz5sxRZGSknn76aTVp0kTVqlWzS/fyyy9r7NixeuaZZ1LszQEAAO4co0aN0tChQ1WkSJGszgpu0Y4dO7I6C5KktWvX6ueff1b37t1VtWpVh/nr16/Xvffeq6tXr6p9+/b64IMPVK5cObtlEhIStGDBAo0aNcphv5o1a6bvv/9eixYtUrdu3RzWv3jxYhUsWFB+fn7JBrBt63JmwIABdp2arl27poEDB+r777/XCy+8oPbt26t8+fKSpDx58qhfv36qWbOmatSoofPnz6t9+/auDo/FGKOePXvq5MmTeuCBB/Ttt9/Ky8tLkjR27FgNHDhQDz74oPbs2ePQkXP48OFasWKFqlatqoULF6pAgQKSbh7bpk2b6v3331fTpk0d8vHEE0/onXfe0bBhw9SzZ0/5+fmlmM/EsqwHdnBwsCpWrMiDCm7p4MGDkuTwIHNXPj4+qlixotNf7wB3FB4erooVK1oBMGQfictvd3r2fPjhh5Kk/v37O8w7c+aM7r//fsXHx+uZZ57RN9984xC8lm4+S7t27ao1a9bo4Ycftptnq2Am7lVhs3r1asXExKhNmzaqU6eOVqxYodjYWLtl4uPjrdf+XFVWkwoODtbYsWMlSRcvXtTcuXOteQ0aNNCXX36pRx55RLVr1051BXD8+PE6evSoypcvrzfeeMNu3htvvKHy5cvr0KFDDj0jtm/frhkzZsjLy0vjxo2zu3fbtWunvn37KiEhQaNGjXLYZmhoqNq1a6eNGzfqr7/+SlU+AQBAzlekSBFVrFiRjo05QMWKFVWxYsWszoY++ugjSc7bBHFxcerevbuuXr2qbt26acaMGU5jPp6enmrZsqUWLlyo119/3W5ecm2CQ4cO6d9//1WTJk3UpEkT7d27V4cOHXJYzpY2tW0Cf39/ff7558qVK5fi4+M1depUa16ZMmX0zTff6IknnlDDhg2VK1euVK3z999/18aNGxUSEqIvvvjCCl5LNzuttGjRQpcvX9bHH39sl+7cuXMaM2aMJGnMmDFW8FqSatWqpRdffFGSnHYE9ff3V+/evXXs2DFNnDgxVflMLF0B7KtXr+qjjz5So0aNlDdvXvn5+alEiRLq0KGDfv7551Stw9Wr0olfl7h+/bpee+01lS9fXv7+/goPD9eLL76oa9euSbrZM+m5555T6dKl5e/vr5IlS2rkyJG6ceOGw/ZOnTqlTz75RO3atVOpUqUUEBCgoKAg1a5dW6NHj7bWmTR/Bw4ckHTzFdjE49Ak/SXl6NGjevbZZ3XXXXcpMDBQefLkUZ06dfTZZ585zU9qrF+/XtHR0SpVqpT8/f2VL18+Va9eXc8//7yVr6Ti4uI0evRoVa5cWQEBAcqfP7+6dOni8tew+fPna/Dgwbr77rtVoEABaxzMqKgorV271mmaxGP9njp1SoMGDVLx4sXl6+ur4sWLa/DgwTp//rzTtMYYffPNN6pdu7YCAwOVP39+tW3bVitWrEjxVZnMOMau9u3bb7+VdPPVeds5T5qvnTt3ql+/fipRooT8/PyUL18+tWjRwuE1b5uEhASNHTtWDRs2VEhIiHx8fFSoUCFVr15dgwcPdnhF/9ixY3rqqaes6z8wMFDFixdXixYt9N5779kt625jHt7KeXbl8OHDGjx4sMqVKyd/f38FBwerYcOG+uqrr1yOSWtz5swZDRo0SOHh4dbz6plnntG5c+ecLj916lQNGDBAVapUUd68eeXv769SpUrpoYce0q5du9KUb1e2bNkiDw8Ph56KkvTOO+9Y19327dvt5h08eFAeHh4OPZfXrFmjF154QXXr1lVoaKh8fX1VuHBhdejQQfPnz3eah8TP4bNnz+rpp59WmTJl5OfnZ52fxPf70aNHNWDAAIWFhSkgIEBVqlTRuHHjrPXt3LlTvXv3VmhoqPz9/VW9enWXBZOr19ASj4O3b98+PfDAAwoNDZWfn5/KlCmjl19+WdevX3e6zhs3buj9999XlSpV5O/vr0KFCql79+7avn17uobnSHytXr16VcOHD1fZsmXl7++vsLAw9e/fX0eOHHGZPi3PiJIlS6pfv36SpO+++86uvHF2r0yZMkVt2rRRwYIF5evrq6JFi+r+++93uF4k++fDlStX1KNHD+XOndturOVGjRrZld/jx4/Xd999Z/0/aRnobAzspOX3I488onz58snT01NeXl7y8vJSoUKF1KFDB82YMcNp+e3sXk5N+b1x40atWLFC9evX18qVKx3K7wIFCujChQuSpLZt29qt31nZYltP4rLFVsFctmyZQ5lju46bNm2qJk2aKCYmRpGRkXbld8WKFXXp0iX5+fmpQYMGDvspSb/88otD+R0TE6N8+fJZ59ImafltC7hv3bo12fJ70KBBkqQOHTpo8ODBduX3U089pY4dO0qSXcU48f8DAgJ01113OTzXbc+C3377TXFxcQ7HOCEhQZLUvHnzTCm/U3LgwAGNHj1azZs3t8qCkJAQNWrUSF999ZWVv8QSX9/GGI0dO1a1atVSrly5FBwcrFatWmnlypUut7l9+3Z1795dBQoUsJ6Z7733XopllispDWvnahzR69ev691331WtWrWUJ08e+fr6KjQ0VHXq1NELL7ygs2fPOqwrJiZG77//vurXr6+QkBD5+/urQoUKeuGFF3TmzJk05z095eqtlgcfffSRqlatKn9/fxUsWFBdu3bV1q1b05z3lNxq+XvlyhUNGzZMZcuWtcbejI6OTrZ8mTVrlpo0aaI8efIoODhYjRs31owZM9KV/9TUIV1de2mpq9rQZsoZbaa0mDFjhho3bmxdr02aNNHs2bPT3X45e/ashg8frsqVK1v7V6tWLb3zzjspDqeV1vpces57Wp0/f15eXl7KmzevQ1k0adIkq941Z84cu3nXr19XYGCg/P397fZ7+/btGjFihBo2bKiiRYvK19dX+fPnV0REhMt2atI676uvvmpdP7bzk/i5deHCBT377LMqWbKk/P39Va5cOY0ePdrK/5EjRzRw4EAVL15cfn5+qlChgtNhxiTXZVdW3T/OJL5Wb9y4oXfeecd6dhQoUEA9evTQzp07XaZPSzu2adOmVp1zyZIldnVvZ/fKggUL1KVLFxUpUkS+vr4qVKiQOnfu7LJ+knic6l9//VWNGjVSUFCQcuXKpYYNGzpcZ7bjZRsuo1mzZnZ5SnzeXI2BnZ5rMr1OnDihKVOmKCwsTC1btnSY/9NPP2nfvn3y8/PTF198IU/PlEOiderUsfu/7fzs2LFDJ06csJuXtE2QeFriPNrKmtQGsCUpd+7cqlChgiRlyPCOtuFBIiMjlTt3bof5vXv3luTYJpgzZ45iY2MVHh6uhg0buky3atUqHT161GG+rT3++eefpz3TJo0OHjxoKlWqZCSZwMBA07JlS9OzZ0/TuHFjExwcbEqUKGG3fHR0tJFkvv32W7vp3377rZFkoqOj7aYvWrTISDINGjQwTZo0MUFBQSYyMtK0b9/eBAcHG0mmffv25syZM6ZChQqmYMGCpmvXrqZVq1bG39/fSDKPPvqoQ75/+OEHI8kULVrUNGnSxPTs2dO0aNHC5M6d29retWvXrOWXLl1qoqOjTa5cuYwk07VrVxMdHW397dixw1p2yZIlJm/evEaSKVmypImMjDStW7e2prVq1crExsam6Ti/8847xtPT00gy5cuXNz169DAdOnQwd911l8PxtB2ze+65x0RERJjAwEDTpk0b07VrV1O8eHEjyYSEhJh9+/Y5bKdMmTLG19fX1KhRw0RGRpouXbpY59fb29tMmTLFIc2IESOMJPPQQw+ZYsWKmcKFC5suXbqYdu3aWeeoTp06Tvf5scceM5KMp6endR4qV65svLy8zJAhQ4wk06RJE4d06T3Gkowks2jRolQd92nTppno6GhTpkwZI8k0bNjQOuejRo2ylps1a5Z1vVWoUMH07NnTNG/e3Hh5eVnHJql+/foZScbf399ERESYXr16mdatW5ty5coZSWbatGnWsseOHTNhYWFGkgkPDzcdO3Y0UVFRpnHjxiZfvnwmODjYbt379u0zkhzuP2OMKVGihJHk9PxnlvSc5+T2Yc2aNSZfvnzW8YiKijJt2rSxzkHr1q3N9evX7dLYnjGRkZGmTJkyJiQkxHTq1Ml07tzZum4qVKhgTp486bA9Ly8vExgYaGrXrm26dOliIiMjTenSpY0kkytXLrN8+XKHNLb7YsSIEak6RgkJCaZQoULGw8PDnDhxwm5ey5YtrWv3448/tps3btw4I8n069fPbnqLFi2Mp6enqVq1qmnXrp3p3r27qVmzprWejz76yCEPtmN03333mVKlSpm8efOayMhI0717d9OnTx+7/erXr58JDQ014eHhpkePHqZZs2bW9f7ee++ZlStXmjx58lj3Q4MGDaxtT5gwwWHbTZo0cXpv2sqMp556ygQFBZkSJUqYHj16mIiICBMQEGAkmU6dOjmsLz4+3rRv395IMr6+vqZVq1YmKirKlC5d2gQGBponnnjCaZmTnMTlUf369U1gYKB1bIsUKWIkmdDQULN7926HtGl9RgwZMsQ0bNjQSDJlypSxK28SP3vi4uJMjx49jCTj5+dn7rnnHtO9e3dTvXp1I8kEBASY33//3W7dtnuraNGi1jPa09PTFC5c2OTJk8c6T6GhoVaapUuXWs/BpGWgLZ/R0dF2923S8tu2r3ny5DHe3t5GkgkKCrLWmTt3bofyOzIy0uH5kJrye/jw4UaSefnll52W37Z7/q677kp3+X3t2jUrn6tWrbI7xrZze/78eTNgwABrHxOX34UKFTKSTMWKFR2uMdufv7+/0/Lb19fXSDIffPCBlTZp+V2rVi1rPSmV35JMgQIFnJbftjKpYMGCdmltz0APDw+Xz3Xb37Zt25weY0nGy8vLtGrVKsPLbxtXZd7rr79uJJlSpUqZFi1amJ49e5omTZpYx7ZLly4mISHBLk3i6zs6Otr4+PiY5s2bmx49epjy5ctb92HS68GYm/eQ7RosXbq06dmzp4mIiDA+Pj6ma9eu6SqbU0rjrM4dHx9vWrRoYd1/bdu2Nb169TIRERHW+jZu3Gi3niNHjpiqVasaSSZfvnwmIiLCdO7c2Vq+ZMmSZv/+/anOtzHpK1dvpTzo1KmTQ3lQsmRJ4+/vbx5//PE0lwfJ1VFupfzt1KmTqVatmgkJCTEdOnQwHTt2tJ4VJUqUMOfPn3dI+8EHH1jrrlu3runVq5epXbu2kWSeffZZl/lMz77ZOLv20lpXNYY2kzu3mTLL6NGjreu1Xr16plevXqZOnTpGknnhhRdcXnu2NEnt3bvXuh5tcYDIyEirPlOzZk1z9uxZuzS3Up9Lz3l3FedIju2YrF692m76ww8/bB2LZ555xm7eggULjCTTrFkzu+n9+/e36hutW7c2UVFRpkGDBtZ9knQ9iY9RvXr1TJ06dUyuXLlM27ZtTVRUlImIiLDbr44dO5q77rrLFCpUyKrH2Z7JTzzxhPnnn39MaGioKV68uEOb4e2333bYtqt4UVbcP64kfk526dLF+Pj4mIiICNOzZ0+rLMudO7dZsWKFQ9q0tmNHjRplWrdubSSZwoUL27UJhgwZYrdu2754enqaunXrmu7du5t69eoZDw8P4+XlZb755huH/Niup1dffdV4eHiYhg0bmqioKKst4eHhYaZOnWotv2PHDhMdHW0KFy5s5TdxnpYuXeqw7qTSc02mpmxy5ptvvjGSzP333+90vq1+EBkZmab1JlWqVCkjObZzH3roISPJbN261ezatctIju32CRMmWHXSpGzPt6T3g03ZsmWNJPPkk0+6zJvtfi5Tpkyy+2BrO3zyySdO52/evNm6Ji5fvmxNt113Xbp0cblu2zU/e/Zsp/MLFixoJJmjR48mm8ek0hTAjo+PtypIrVq1cgj8xMTEOGQwvQFsW6Xs9OnT1rz9+/dbhW/VqlVNhw4dzJUrV6z5a9euNd7e3sbT09McOHDAbr3bt283K1eudNins2fPmlatWhlJ5p133nGYn1Jj4dixYyZ//vzGw8PDfPHFFyY+Pt6ad/r0adO8eXMjybz22mtO0zszY8YMqyE7ceJEh/nbtm0z27dvt/6f+JjVqFHDHDt2zJoXExNjPQAfeeQRh3VNmzbNoZC3Tff29jb58+c3V69etZuXuAHct29fu8D/wYMHTdGiRY0k8/PPPzvdr9y5czs0VN5//31rnUkLk1s5xultALu6bo0x5vjx41ah+cYbb9g1eNeuXWtdo2PHjrWmHzhwwEgyxYoVszs/Ntu3b7e7Zl977TXrnCVtUMfGxpr58+fbTXOnAHZ6z7Orfbh27Zq1D48++qhdJWXv3r2mZMmSRpIZPny4XTrbM0aSqV+/vjlz5ow179y5c+aee+4xkkzPnj0d9mHChAl2D2ljbgacP//8cyPJVK5c2eG8pDWAbYwxvXr1MpLMTz/9ZLe/AQEBpnLlysbT09N06NAhxTTGGDNnzhynBcCKFStMUFCQ8fHxMYcPH7abl/gYtWjRwly4cMEhfeL7/dFHHzVxcXHWvJkzZxrpZoCyRIkSDvfDRx99ZCSZsmXLOqw3pQC2JPPSSy+ZGzduWPO2bt1qBYSSVhA//vhjI8kUKVLE7Ny505p+48YN89RTT1nrTE8A27YPie/RmJgY07VrV+v6Siw9zwhjUtfgsQVq69WrZ/7991+7eZMnTzZeXl4mb9685ty5c9Z0271l+2vUqJFVft+4ccOqaNWoUcNufYnPReLnR+J8Ogtg28rvCRMmWNdk4vK7TJkyxtvb23h7e1vXZOLyO+nzITXlt+05kLgOYntu7Nmzx1rvDz/8YM1PT9lim5b4R4Xr16+bgIAAU6tWLev5Z3tOJNamTRsjyQwePNialjSA/euvv1rzEpfftr+FCxda85OW37bzUq1aNZfl99ChQ611RUZGuiy/bX+252Di/Xrqqafs1pn4uW5roM6aNcvpMbYFRZcuXZpp5berMm/NmjVm69atDssfOXLEarRNmjTJbl7ie6dEiRJm165d1rzE906rVq3s0sXExFjBsKefftruObZ582ZToEABp/dWevfNxlndZcmSJdb9ffHiRYc0a9eutatrJyQkWD9S9e/f3y5NXFyc1WhJGjBJSXrK1fSWB5999pmRbjb8E9eX4+LirKBGWsuD5OpZt1r+tm7d2q78PXv2rLn77ruNJPPWW2/Zpdu8ebPx8vIynp6eZvLkyXbzfvzxR+Ph4eEyn+nZNxtn115a66q0mdy7zZQZNmzYYLy8vIyXl5ddQMwYYyZNmmSVzWkJYNerV88qwxI/U06ePGn9cNS7d2+7NOmtzxmTvvOengD2sGHDjCTz5ptv2k0vVaqUCQsLM/nz5zdVq1ZNVZrFixebvXv3Omxj586dplixYkZyDJQnPkbVqlVz2l5N/NxKGodZv369VY+rVKmSQ5th+vTpRrr5Q2ridMakHMC+XfdPchLXBwoUKGA2b95szbtx44YZPHiwdS0nzmd627G285FcHseOHWtd04nzY8zNsj9PnjzG19fX4YcZ236EhIQ4/ABvO+bly5d32J6rtpuzdSeVnmsyvQHs+++/30gyn3/+udP5tvrZ66+/nqb1JmWrAw4cONBueunSpU3BggWtcjE0NNQhUP3oo48ayXmnx+QC2Js3b7aem85+nLBJbQDbFmSePn260/lnz561zunff/9tTe/SpYtVx3WlWrVqRpL57LPPnM63dVpK3DZLjTQFsG0PniJFiphLly6lKk16A9geHh5OGxpPPvmk9UBK2mvRGGM6dOhgJJnvvvsu1ftl+2WkTp06DvNSaiy8+OKLRrr5a6Mzhw8fNj4+PnYXcUpsldb3338/VcsnPmabNm1ymL9q1Soj3ewBlBa2QFnSHyVsD7ZixYo5FEDGGPP22287vSFtlaZhw4Y53Z7tl+ekD+pbOcYVKlQwFSpUcHggpiS5ALatF1etWrWcpn3vvfeMdLMXm82aNWusylZq2HoHJa3sueJOAez0nmdX+2DrfRkWFmZXIbCZMmWKkW4GUWNiYqzpiStZSXuXGWPMli1bjIeHh/H09DSHDh1K9f7ZehYn7mFoTPoC2Lbe1H379rWm2XpTjBgxwtSpU8fkyZPHqgAmJCSYwoULGw8PD3P8+PFUb8dWwU1akNuOkY+Pj9NKReL9Cg8Ptzu+NrbCqW7dug73X1xcnFUwJv1RMaUAdq1atZw+M20F/n/+8x+76bbeD1999ZVDmuvXr1uV3PQGsJ0V7CdOnDCBgYFGkl0FOT3PCGNSbvCcOXPGBAQEGH9/f4dgiI3t2fHpp59a0xJXuvPly+dQfh87dsxIN3uSJq5YpzeAnZry2/bvxNekrfxOS6PCVn7bKnOJg/q2Z9/atWut9f7xxx/W/MRly2effWbXmyQ6Otp069bNoWyxndvEAUtbgHDIkCFW+R0eHm78/f2tZ1ZcXJzVYzxxT5WkAezE98OpU6fMf/7zH2ve3XffbReMSMp2Xlq0aOGy/Lb1zpRktmzZ4rAOW/lt+7MF5WzPdUnm66+/dkhne67bejPbGpNJy29bvmxvlmRG+Z2eMm/u3LlGkunevbvd9MT3zsyZMx3Subp3fvzxRyPJFC9e3GnPsA8//NDpvXWr++as7jJp0iQjJd9TJ7Hff//dut4SBx9s4uPjTZUqVYwkp/d5ergqV9NbHth6J40ZM8YhTUxMjAkNDU1zeZDehnRK5W+uXLmcBr9tvbOaN29uN932hkdUVJTT7XXs2DHN+UxvADutdVXaTO7dZsoMtgBPr169nM7v1q2by2vP9oxMbOnSpUa6+Ra4s3rwunXrrDpB4rp9eutzKXF13tMTwLbV/5s2bWpN27t3r7We7t27G0l2++2q13ZyvvrqKyPJPP/883bTEx+jv/76y2la2365isPYAlKu2gy2H7GXLFliNz2lAPbtun+Sk7g+4OytmmvXrlltjcSdjNLbjk0pgB0fH2+9AbNu3Tqny7zzzjtW/TQx234463V77do1qxPOwYMH7ebdSgA7Oa6uyfSWu5UrVzaSfaePxGxvC3z55ZdO50+YMMGhTRAdHW1OnTplt5zt3CYO9ts6LXbt2tWaFhUVZSTZvblWoUIFIzkP3joLYJ8/f97Mnj3bejs2LCzMoVNAYqkNYPv4+BhJZt68eU7nx8bGWuc0cWcB29viL730kst12zoLJv0h3sZWP3LW+z453kqDP/74Q9LNMU2cjZGSkcLDw1WlShWH6bYB1mvVqqVChQq5nO9srJX4+HgtXrxYK1as0LFjxxQTEyNzM4gvSeka23b27NmSpKioKKfzixYtqnLlymn79u3as2eP9aVQV44fP65NmzbJ09PT6aDzyQkPD1f16tUdpt91112S5HJsr6NHj2r27NnauXOnLly4YI2Ntm3bNkk3j0u7du0c0rVo0cLpB9icbe/GjRtasWKFJKlPnz5O89G7d2+nY4ndyjFObiyq9LKNYRQdHe10fv/+/fXcc89pz549Onr0qMLCwlSxYkXlyZNHc+bM0ZtvvqnevXurVKlSLrdRt25dffHFFxo6dKiMMWrVqlWm33MZ4VbOsyu24+3qK7VdunRR3rx5de7cOa1fv95hHKbq1avr7rvvdkhXtWpV1ahRQxs2bNBff/1ljdVk888//+iPP/7QP//8o0uXLlnjk9nGudq1a5cqVaqU6v1wJiIiQtLNsctsbONltmzZUrGxsVq7dq1Wr16thg0bauvWrTpx4oSqVq2qwoULO6zvzJkzmj17tv7++2+dO3fOGod2z549Vp6dqVGjhkqXLp1sXps1ayZ/f3+H6eXKldOWLVvUtm1bhzHPvL29VbJkSZ09e1ZHjx51GLc7Oe3bt3c6hpqz58vhw4f177//SpLDeZQkX19fdevWzeEDFKkVEhKiyMhIh+mFChVSmzZtNHXqVC1evFj33HOPpPQ9I1Jj0aJFiomJUYsWLVS0aFGnyzRt2lRffPGFVqxYoSeeeMJhfnR0tMOzJDQ01LqHzpw54/CV6bRKXH4nviZt932uXLm0bt06SfbXZHIfzU2p/LaNuZg/f/5U5zNx2fLRRx/p119/dVimUqVKdmWLbZy65cuXKy4uTj4+Ptb5rlatmt5//315enqqc+fO+vjjj7Vq1So1adJE69at0+XLlxUYGKi6deu6zJOrcfA8PDw0ffp0h3H6EpfftuO7fft2nTp1SpLr8lu6OZZ1Urb7K7HEz3VXUlt+286P7Tl6u8vv69ev688//9TatWt18uRJXb9+XcYYXbp0SZLrZ6S3t7fatGnjMN3VvWO7Jnr06CEfHx+HdNHR0XrmmWcyaK+SV7NmTXl5eembb75R+fLlrXEyXbGds65du8rb27GZ4OnpqXvvvVd///23VqxY4bSu7kp6y9W0lAdHjhzRP//8I0m6//77HdL4+/urR48e+uSTT1Kd79RIb/lbu3Ztp+fDVd3ddm052zfp5rWV3rGw0yqtdVXaTO7dZsoMtjFzXe1Hnz59NGXKlFSvz3b9t2nTxmk9uFatWqpevbo2b96sJUuWOGw3rfU5m/Se97Ro2LChAgICtHLlSl29elWBgYF2bYIrV65o8uTJmj9/vvr06aPz589r/fr1CgkJUe3atR3Wd/nyZesDbadPn7Y+LH3s2DErz84UKlRIjRs3TjavKcVhkmszbN261WmcJjm36/5JLWf1ez8/P0VFRemDDz7Q4sWLrfbIrbZjXdm4caOOHj2qMmXKqFatWk6XsY3x7aoO16FDB6f7Ubp0aW3cuFFHjhxR8eLFU5Wf1EjvNZlWtjpFWtoEia1du9buO0A2I0eOtPtYoa3Ovnv3bh07dkxFihSxzrdt7GvbvydOnKjFixcrOjpax48ft/Y1ufGv+/XrZ30fKbEyZcro119/TfWHGt1V0jZBaqUpgG37CMbt+LKoq0CHrWLkan6ePHkkyeGjjHv27FHnzp2tgsaZixcvpjmftqBJSg966eaHqFKqKBw8eFDSza/xpvVLvK6OSVBQkCQ5/dDNa6+9pjfffNPhg0uJuTouKW0v8Tk4ffq09X9XH+pwNT2jj/GtshWSrgLQISEhypcvn86ePavDhw8rLCxMefLk0bfffqt+/frp5Zdf1ssvv6wiRYqofv36atOmjcOPQg888IDmzZunn376SV27dpWXl5cqVaqkRo0aqVu3bmrevHmm7d/06dM1ffp0h+kDBgxQo0aNkk17K+fZlZSOt4eHh0qVKqVz5845bXAk90NBqVKltGHDBh0+fNiaFh8fryeeeEJfffWVFRxzJj3Pi6TCw8NVrlw57dmzR7t27VKFChU0f/58BQUFqV69erp+/bpGjRql+fPnq2HDhlZF1hb4Tuzrr7/WM888oytXrqQ5z6k5Jxn9TE7v9pw9X2znr0CBAi4bz7fygVPbx6ucsV1fia+h9DwjUsP2LFywYIHL/NjYAphJufrRJSgoSOfOnUvzeXLGdu5cXZMnTpywKiuJr0nbtZJUasrv5NYREhIiDw8PGWPsjktqyhbbRzFtZUvdunWVK1cuXblyRWvXrtU999yjxYsXy9PTU8WKFZN0s/xu1aqVPv74Yy1evFhNmjSxKrQNGzaUr6+vy+21bt3aCoL6+fkpLCxMI0eOlDFGJUqUsFvWVfl97NgxqzGQ9J5PvG1nzwrb/ZX4/4mf667S2e4vW0DSth5Xx/itt97SW2+9ZTcts8vvVatWKSoqyqpnOePqGVmkSBGngWjJ+b1jex64egbkzZtXwcHB1odFM1OZMmX04Ycf6vnnn9cTTzyhJ554QiVKlFCDBg3Uvn17de/e3e66sJ2zV155Ra+88kqy63b1nEnqVsvVjC4PkqsXpMetlL9p2Tcp5Wsro/ctOWmtq9Jmcr09d20zLVu2TP/9738dpnfq1EmdOnVKcRu26/V2tQmkm8+8zZs3O20TpLU+J93aeU8LPz8/NWrUSPPmzdPSpUvVunVrzZ8/Xx4eHoqIiLCeL7YA9sKFC5WQkKBmzZo5/Lj922+/qV+/fsl+cDentgkyoy2aWEhIiEJCQpzOS0+bIKV2rCu2e33v3r3pbhOktfy5FbdyTaaVrW6VtE5rU6BAAR06dMjlcXnvvffsPkLs7e3t9OPbth8E9+zZo0WLFql37952H3C0Sfwhx+joaGuZcuXKueyQJN1sM5QtW1aSrI9z2mJHzjoYpEeePHl09uxZl/WXy5cvW/9OfDxt93Jy9R5bWlfnwTb93Llzacpzxux5Jkjpa6Cp+VpoYt26ddO2bdvUvn17vfDCC6pUqZKCgoLk4+Oj2NhYp7+IpYat11e3bt1S/BUkvb8CpVZaj8nUqVM1cuRI5c6dW5999pmaN2+usLAwBQQEyMPDQ8OHD9eoUaNcNjbSur2UuHr4utMxvhVdu3ZVRESEZs6cqaVLl2r58uWaNm2apk2bpldffVXz5s1T1apVJd08tj/++KOGDx+u2bNna/ny5Vq+fLnGjBmjMWPGqEOHDpo2bZq8vLwyPJ+bNm1y+qtj06ZNUwxgp0ZKhWxWSHyNf/zxx/ryyy8VGhqqDz74QPfcc48KFy5s9STo3bu3fvnll2Qb4WkRERGhPXv2aN68eSpcuLDWr1+v9u3by9vb2+qNMW/ePI0YMcJlAHv9+vUaOHCgvLy8NHr0aHXo0EHh4eEKDAyUh4eHxo4dq4EDB7rMs7OemEll9DP5VrfnTHLXVmZfdxl1PSTH9iwsW7Zsij00bscPza54eno6vSb/+usvPfroo4qOjtY999yT7DWZWFrK70uXLjlUlLy9vVWtWjVt3rxZ69ats3ouuipbrly5YvUIs/WysZUtPj4+atiwof78808tWrRItWrV0qpVq3T33XfbBcsaNWokLy8vLVq0SCNGjNCiRYskpfyl8aFDh9pVeqWbPT6SclZ+L1iwQI8++qhatGihunXrOi2/Ex+rgwcPOu2BaFOgQAHlypXL6p2cOF1StvvLVsG3NQ6THuPVq1dr586dqlq1qmrWrGm3jswsv69evapOnTrpxIkT6tevnx577DGVLVtWQUFB8vLy0u7du1WhQoXbVt/JLLbjndTgwYPVo0cPzZw5U8uWLdOyZcs0YcIETZgwQSNGjNDSpUutXsC2dTRq1EhlypRJdnuVK1dOVb5utVx15+N/q+WvO+9bYs6urbTWVd2pPk+bKXXH+J9//nHaJihZsmSqAtg2rvLr7m2CWz3vaRUREaF58+Zp3rx5atWqlRYuXGj31mWpUqWstoCrNsGRI0cUFRWlmJgYvfDCC+rTp49Kliyp3Llzy9PTU3/++adat26d49sEyclJbYLQ0FC1bt062WUT9xpO7HaVP7d6TaZVSEiITp065TIgXrNmTR06dMh6G/RWNGvWzCGAnT9/fru30ypVqqSCBQtabYHUtgkGDBigvn373nIek2N7U9pV545Dhw5JunnPJO5IY6vnJ9cpJKUfMG0/NOTNmzdNeU5TANv2K01mvNaZmXbu3KktW7aoUKFCmjZtmsMvFrbX+9KjePHi2rNnj1588UWnr++kle0YHzt2TBcuXEhzj4K0mDRpkiTpzTff1COPPOIw/1aOS1L58+eXn5+frl+/rgMHDjjtBbh//36naTP6GN+qokWLaufOndYvn0lduHBBZ8+etZZNLDg4WA888IAeeOABSTcfCoMHD9aMGTP0xBNPWK/a2VSqVEmVKlXS888/L2OMFi5cqN69e+u3337T999/7/S1kls1cuRIpwGT1LiV8+yK7Ri6Ot6StG/fPrtlnc1zxpYXW89J6X/3xVdffeX0NcOMvC+kmxXPMWPGaP78+QoLC1NCQoJVGbX1xli0aJHOnDmjv/76Sz4+PnavJUnS5MmTZYzR4MGD9cILL2R6nt2N7byfOnVKV65ccdpoS+t1l9q0zq6hW3lGJMf2Gl+FChU0fvz4VKdL7HaV386uydWrV1vzU3tNprX8PnPmjNNf+iMjI7V582ZNmjRJ7777rnx8fFyWLYcPH7YC2M6Oc7NmzawAduPGjRUTE6MmTZrYld/GGN19991atWqVLl26pOXLl1tpM4Kz8nvZsmXW/NQc33Xr1jl9fdTGFmBO/Fy3pUsq8T2SK1cuq1df0mPcq1cv7dy5UwMGDNCTTz6ZYh4zyl9//aUTJ06oZs2a+uabbxzmZ/Qz0nZfu3p2nD9/Pl29r209pZP+qGBje1PSmcKFC+vhhx/Www8/LOnmvfXQQw9p5cqVGjp0qBWksj1nOnbsqOeeey7NeXTmdpartmN/+vRpXb582Wkv7FspD5K63eVv0aJFtXfvXu3fv9/pDwjp2beUrqu4uDjrrQ5nUltXpc2UOu7UZurbt+8tBVCKFi2qf//9V/v3779tbQLbPGf1q7TW527neZf+F4yeP3++Nm7cqDNnztgNVxEREaGvv/5aO3fudBnA/u233xQTE6POnTtr9OjRmZ5nd5MZbdHEzp8/r/Pnzzvthe2qTSClvx3riq2szp8/f7rbBLfL7b4mCxUqpFOnTrns7R0ZGakZM2Zo7ty5On36tMsAf2o0a9ZMY8eO1aJFi3Tw4EHt27dPnTt3dviR5N5779Wvv/6q/fv3Wz2wM6pNcCtq1qypDRs2uAzm26aXK1fOrj5layO4Svfvv/9abd0aNWo4XcZ2fpwNB5WcNP3sYhv/75dffkm2u7i7sR28sLAwp93tf/zxR5dpbZU621hXSbVt21bS/wq4WxUaGqrq1asrISHBaSMrI9mOS9LXkiXp5MmTmjdvXoZty8fHRw0aNJAk/fzzz06X+eWXX5xOz+hjfKtsveOc9UiQZJ23lF4LkW4WPq+99pqkmz2fk+Ph4aEWLVpYY2qltHxWuJXz7IrteE+cONHpq0zTpk3TuXPnlCdPHqdjgG3ZskVbtmxxmL5t2zZt2LDBGs/TJrn7Ytu2bRl+3Js3by5PT08tWrRIc+fOlXRzrDubiIgI3bhxQ2+//bauXLmi+vXrOwRok8vztWvXnI7tm5MUL17c+nXX2fUVGxt7S8fg/Pnz+u233xymnzp1yvo2ROJes+l9RqRU3rRo0UK+vr5avHixTp48meb9kNJffrvKkyvJXZPx8fGpPh9pLb9tQ35I9sfzySefVFBQkI4dO6aXXnpJUvrLFluFc8WKFfrzzz8l3TznScvvJk2a6Pr16/rkk0905coV5c6dO8N+hE3u+MbGxqaq/J4wYYJDr8rE/+/SpYsk++e6dHP876Q9LhLfdx06dLCG20h6jP/++29JcjleY2axHS9Xr8smVw9MD9uPjJMmTXL62vn333+frvXanhc7duxwmHf8+HFt2LAh1euqWLGiXnzxRUn29QnbObMFZjPC7SxXixUrZn3TwVk95Pr165o8eXKGbe92l7+2a+unn35yOj8911bBggXl6+urs2fPOi1b5s6dm+oyILm6Km2m1MlJbSZb/drVfria7oqtfvXHH384HTd148aN1rjoiev2Nmmtz93O8y7dDPTkz59fW7ZssY5N0jaBJI0bN0579uxR8eLFHYaBSS7Pxpg0H/PsJjPaokn98MMPDtNiY2M1ceJESc7bBGltx6bUJqhTp44KFCig7du3p2qIvYyQUp5cud3XpC24mrhNkNj999+vEiVK6Nq1axo0aNAt1XVs53fv3r1WXTLpm5SSfdm9e/dul8vdbp07d5YkzZw502n70HZubG0Cm3bt2snX11cHDx60Ouk4S1e/fn2XQ2Wmt02QpgB2ZGSkatSooaNHj6p79+4Ov2pcu3ZNv//+e5oycDuUL19eXl5e2rp1q/WLh81vv/2mDz/80GVa2y9orh4Mzz//vEJCQvTBBx/o/ffftwajT2zfvn1pahyNGDFCkvTSSy85rfhu377daeMlrWwfPhg7dqxdvi9cuKDo6OgMH5vR1tvqk08+0apVq+zmffzxx3a98xK7lWNcsWJFVaxYUWvWrMmAPbjp4YcfVlBQkDZs2KC33nrL7qG3ceNGvfHGG1a+E0+fOHGiYmJiHNZnq0glfqh///33Wr9+vcOyly5dsq5hZ4WAO0jveXale/fuCg8P19GjR/Xss8/aFZr79u3TkCFDJN18RdrZB0OMMXrsscfsxle6cOGCHnvsMRlj1LVrV7sPVNjui88//9wumHPs2DE9+OCDaS60UxISEqJatWrp4sWL+v7771WsWDG74R9sldXPPvvM7v+J2fL83Xff2fWgunbtmh5//PFke6HnFLbrbsSIEVbFQLoZkBs2bJj1ClR6DRkyxG5Mu+vXr2vQoEG6cuWK6tatazekR3qeEdL/yhtXFa7ChQtr8ODBunLlijp06KCtW7c6LHP9+nXNnDnTaU9rX19fl+V3SpW3tFaOXV2T0s2xiFN7Taa1/F65cqX178Tld4ECBfT999/L09NT7777rh5++GE9+OCDDmWLMcauIuasbKlVq5by5MmjmJgYjRkzxq6hnLj8tgVxP/jgA0k3xyTdvXt3ppbf0s3rJ6XyO3fu3Nq9e7fDGMe2IJCvr68efPBBa7rt/rKNA9i/f3+rPEv6XB82bJj178Tl9xtvvKFt27Ypd+7cdh+yvB3lt+14LViwwOH+Gjt2rNXozCjdunVT0aJFdfDgQQ0bNsyuLPn777+tZ0Ba2Z7/o0eP1vnz563pp06d0oMPPmg3VqHNwoULNWfOHIdAujFGs2bNkmRfn+jYsaPq1KmjNWvWqF+/fk7HiDx37py+/PLLVJeHt7tcffrppyXdfKMs8bMwPj5ezz33XJo/IJac213+Dh48WF5eXpo0aZKmTZtmN2/ChAlOv2GSEh8fH+sZ9vLLL9udo82bNzv9ILCU9roqbabUy4o2U2Z44okn5OnpqQkTJjh8XHTq1Klp/oGnUaNGqlevnmJiYjRw4EBdvXrVmnf69GkNHDhQ0s0P5rn6+Fxa6nO3+7x7eHioefPmMsbo888/l6+vr10gvkWLFvLw8EhVm2DKlCl2b07Ex8fr1VdfTfGjzDlBRrdFk3r99det4Jt0s63x4osv6vDhwypevLi6du1qzUtvO9ZWh92zZ4/TH8J9fHw0YsQIGWPUuXNnu7fwbOLj47Vw4UKHY5BeKcXFXLnd16Sto0niNkFivr6+mjx5svz9/TVp0iR17tzZ+vhzUitWrEi2jRQaGmrt3/vvvy8p+QC2rU1w1113Wd+8yUpt27ZVjRo1dP78eT3++ON2Y32PHTtWCxYsUO7cufXUU0/ZpcubN68ee+wxSdLjjz9u167csGGD1dPe1mnIGdv5SfO33Uwa7d+/31SoUMFIMoGBgaZVq1amV69e5t577zXBwcGmRIkSdstHR0cbSebbb7+1m/7tt98aSSY6Otpu+qJFi4wk06RJE6fbd5XOZsSIEUaSGTFihN30p556ykgynp6epkmTJqZXr16mZs2aRpJ5+eWXjSTj7HB89tlnRpLJnTu36dKli+nfv7/p37+/2blzp7XMkiVLTIECBYwkU6hQIdO8eXPTp08f0759e1OmTBkjydSrV89pfl158803jYeHh5FkKlasaKKiokxkZKSpVKmSw/FM6ZgZY5zu37///mtCQkKMJFO0aFHTtWtXExkZaYKDg02RIkXMQw895PRYujrGqcnPI488YiQZLy8v07RpU9OrVy9TpUoV4+XlZZ555hkjybRs2dIhXXqPsW2/Fy1a5PLYOOPqurX57bffjL+/v3V+evXqZVq0aGG8vb2NJNOvXz+75adNm2YkmYCAANOwYUPTs2dP061bN+te8vX1Nb///ru1fMeOHY0kExYWZtq1a2f69Olj2rVrZ4KDg40kU6VKFXPx4kVr+X379hlJDvefMcaUKFHCSDL79u1L0zG4Fek5z8ntw5o1a0y+fPms+VFRUaZdu3bWOWjdurW5fv26XRrbsyIyMtKULl3ahISEmM6dO5suXbpY6ypXrpw5ceKEXbpVq1YZX19fI8mULVvW9OjRw7Rp08YEBASYypUrm86dOzu9NlK6L5IzbNgw61pNeu0kJCSY/PnzW/OXL1/ukP7cuXPWec6fP7/p1KmT6dq1qylUqJDJkyeP9fxL+txM6Xmamv1K6V5p0qSJ03vQ1fSU1ucqzzdu3DBt27Y1koyfn59p06aN6dmzpylTpowJCAgwjz/+uJFkHn74YZf7mpTtWdagQQNTr149ExgYaNq3b2969OhhwsLCrOdR4vLAJq3PCGOMuX79urXeGjVqmAcffND079/fvPPOO9YycXFxpnfv3lZ5VqNGDdO1a1cTFRVlGjZsaHLlymUk2T1PbPdW0aJFXZbftvIm8XPCdi6SloGNGze2zkHi+zbxs9/ZNVmrVi0jyXh7ezu9Jm3XWtLyI7XltyRTt25dK52z8rt169Ymb9681vOpfPny1v3u6+tr/VuSyZMnj8uypV27dtZyd999t928xOV34r/ChQu7LL+TK6tSKr99fHxM3rx5TWBgoJFkPDw8TMGCBa1zPmvWLIdj3K9fP2v5KlWqmJ49e5oqVapY26pRo4ZDPmzPddufv7+/VSbZ/ipUqOCQLnH5bbtnMrP8dlXm2cpVX19f06pVK9OzZ09TsWJF4+HhYV566SWn5U9y5VJK21u8eLF1jMuUKWN69uxpWrZsaXx8fEyXLl3SVTYnvq8KFSpkOnbsaCIiIkxwcLCpWrWq6dSpk8M19uGHHxpJJigoyDRt2tT07t3bdO7c2VpPcHCw2bhxo912jhw5Yu6++24jyeTKlcvcc889pmfPnqZLly7m7rvvNl5eXkaSiYmJSVW+01uuprc8iI+PNx06dLDOd+vWrU3Pnj1NqVKljL+/v3nsscdSLPuScnUtZFb5m9y1984771j3R7169Uzv3r1NnTp1jCSrjpXcNetM4nNUvnx5061bN9OgQQPj4+NjoqOjnV6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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### Alternatively, graphs can be shown horizontally, together with a box of statistical outcomes\n", "agb.distrib(\n", " total_inventory, impacts,\n", " functional_unit=functional_value,\n", " \n", " # Optionnal layout parameters\n", " height=7, width=15,\n", " nb_cols=2,\n", " percentiles=[5, 95])" ] }, { "cell_type": "markdown", "id": "d967b6ef", "metadata": {}, "source": [ "### Full dashboard\n", "\n", "A dashboard groups all this information in a single interface with tabs.\n", "\n", "It also shows total variation of impacts. This last graph could be improved by showing stacked colored bars with the contribution of each parameter to this variation, according to Sobol indices. " ] }, { "cell_type": "code", "execution_count": 33, "id": "fd3cabcc", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:43:17.073782Z", "start_time": "2024-10-29T15:43:15.164157Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[WARNING] Param 'b' is marked as FIXED, but passed in parameters : ignored\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n", "Processing Sobol indices ...\n", "Processing sobol for ('EF v3.0', 'climate change', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: biogenic', 'global warming potential (GWP100)')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/SALib/util/__init__.py:274: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n", " names = list(pd.unique(groups))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing sobol for ('EF v3.0', 'climate change: fossil', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: land use and land use change', 'global warming potential (GWP100)')\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7160950430fd4d2990074f7ad0e8dc03", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Tab(children=(Output(), Output(), Output(), Output()), selected_index=0, titles=('Violin graphs', 'Impact vari…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agb.incer_stochastic_dashboard(\n", " model=total_inventory, \n", " methods=impacts,\n", " functional_unit=functional_value)" ] }, { "cell_type": "markdown", "id": "db110219", "metadata": {}, "source": [ "# Producing simplified models \n", "\n", "One of te outcome of the statisticall analysis above would be to identify main input parameters and produce simplidied models, fixing the minor ones.\n", "\n", "We provide several functions for doing this.\n", "\n", "## Explore initial algrebraic model" ] }, { "cell_type": "code", "execution_count": 34, "id": "335a028b", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:43:21.806501Z", "start_time": "2024-10-29T15:43:21.549765Z" } }, "outputs": [ { "data": { "image/png": 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4RNj/l0eb2B+OYaBu7mDt3P8PCZ/nVRT0ayxS7IOxTaG1UNW1sU5/CRYJsgm3APcWkcsK5bwttOklwLsnzJMNyGJAmsuLEakDsknttMLxbGA9AnikiJwR6j0Iu97dsEGzq4icGM7dGovQuYOq3qZAbmUDNTu2PfBoEVl1TlW/AjwDexvJjxrKGcL1ORyLAqq6DzYB3gEbP9thRsDBuTR3Bw7CDNhdilHUiwsnrOalRwLbRYz1NlyZjelHAzuIyEmFvO8Hnheu499F5MZwfF9MULxYVd8tIueU3ReHA0BVHwQ8DHM09FnP2sCLw9djIrO10ThtxtoQdUZVYD/XGQ6HwxGQOrcEh8oLMUfsxzqq53DS9VjZvNOmnHcCr8feLLWH5N78oqrrA3eJvUbH4NDlnJuqjWptkICsD58aynoT1nd/Gdr061zapL4tIl9U1TtibzT5lIh8pOFeORwOx6yxIeZLz+M3qvoSEflB+H55+LxfSf77h8+1w/+/zJ1L1QdlGqNNOa4xFie6tuk71RiqmqIvIKFfu75wOBwDQdv1fOdv52/HQNCwntblnp1U+yHb9HYttq6zWaHdPwSeJSJ/Likvxp6ZFjrd98RA72PinoTU/QjQPc/7fgRHlzgF609HTLneLvmlTVnJeVrs3WzTrra8tyTXL1ybzidajKUqDMGe8/HicCw+zJ1GaYHO6ojYHzRYznX/mWMRYtb8BcQ9G9JBe1N8Z85DzkOOccyaK9rWnzLuk+sQkb+GB8wPBc5R1a8DfwEeADwVOA54RUlZg+WYALcvHdPGLDmmVd0D9vn6fgTnGMfSxNzxaBka/EUT1ZNgc85Sp+Xr74JH/bmfhZjJ+raqroftBTk3v4e/gDb9ZSpIGD9NaLOn6eTwuUOhTXX3tDdfuZY/x5o9u/9cVd25NCNsEz6LwdHaPHsa3VeWyLgeKlL6+z7ArYAPiMhvGsr9bfi8Q+F4yvNCKWkniVtxT2AnEflxrvz3Aa8FXoddN3Tfp6tieXweG/sHiMi7cvUINo++XVU/KCJXFMpJif3hGAZK544Ma+b+zwbS3yoKyo7fMbLit2NCcU+xiMK1EJGrix0/HP8D8Hvgzh3kWQZcA5wLoKp3BjYmFyW6gGxgVwXD2TMTyKHevwMXAWsB+2YCOZy7HvgVsAawXkl5VzM+WWfRNF+SH3AB54bP29SVM6Drq4WqHqyqx1V9n0eo6itVtejU6aOe/1HVr/VdT0Xdc/87tcA+gABvwIyAY4DdCs7ITbAxcl5xAz2AiFxeOJQfb41jvQ1X5up4fYlRvQ2wN3CkiOyfCfBQ5g2sjiC8DQNHV30ydfxOcyykjvkpc8TOwFmh3/SJg7GFgaNF5DtNiQOSNc4EY21oOqNMY8AS0hmO4aEL3pyW1nKAqr5cVS9U1RtV9SNVx+YcqXPLc7A54xgR+W1D2sZ6JtBjxXknuRxVXQbshzlpn190LIf58OKEa4xCFxolcEnZW0Udq9HZnNvS/wIlNkghzd+By1X1SOAQzGG5TX5xb8IxAgu1yNxgmnw7axs/hRdm6Qtpi6J2madrmHXfWCL4JLALtpC2HrA59qbDjYFvq+qWId1R4fNNwR4DQFVvBWiuvDvlzrXh0DGN0aacWWmMULfrjH7RqU3fg8aI0hcw0fiAOdYXsHQ0xsB9qZ3ANcaSRNv1fOdvQyV/L0ZubMMJ88QjfWBK+xbq1tO63LOTaj/cLXzui21c2x64HbAFcCy24ehLJfXE2jPTQtf7noZ6H1P2JCTtRwj5O+P5xbQfAWa3xuVzxxh+AWyn9pDNNNElv7Qpq02epL2bLetoy3tLef1iUNq0DyxCTZk6lqowc3vOx0t7DNFnPE9jLaatbfcBFftAqm5aBD6qedQoqeiyjqb9QUPm3CXlP1uq6Gu+acuxPWPW/JUh5tmQSepI9fk4DzkPTYx50okRmDVXtMmTOu5bXaOIrMAe5l4beDnwFuDZ2MP2R4jIn0rKGjLHZGkWvX3pGBRmyTFt6x6qz9f3IxhmyjFFDVCy9r2YNMKSg9t1nfFoGer8RZPWE2tzzlKn5eufiEcn2BswSB7tELNa374X1nf+UJWgZX+ZFmLHTx1a7WkSkb9hQY7vWzhVd0/79JWPPceqqmuwetzsCryg4u+BIc2qwH5tx+kE+myu99vPGaL7e+hDu4evn40oOwu6t2o/miY8L5SYdtK4Fa+VXFC/gCwg5qaFOrvs0wtieajqk4GnA1+SXFC/UM+fgW8B6+TKhhaxP7qav2apN1OvYYjruzVzB2AOvM4RSP0A4JC8UGzIcyfgNcCTsKi0t2c88GAxamZSHlW9DfBQ4BQRuSkczjp5MdJshuXAn0Tk97ly1sOi4V4oImVva9sI+CvwfxXn/i4ifykp76cicks4tj7wIOBi4OiScu4fPi+oK2cI1xeJEbCy5vs8YkSH16Cq/wVsISKPK5x6G9BLEC1VfSg2hn4pIqOSJCNy16iqxwOPAd4lImNv/VDVbwOPBz4qIntF1v9q7C1BG4dDZwP/ISJHVWayfBdhfbEM31fV4rFPicieMW0SkQ1DHXcHHoUZ7aep6pNzk+PZmMB+qareFfgccKysjpSbb2vGSxeKyLdLqiwb60lcmRvTf6I8ovtrMeP9n6p6YMn57M0qa5acGxpGdDPuUsvpqt4YpI753jiiBFsAl/ZZgaq+DngzFoX6RT3XlTrWBqczKrTBUtQZSwa5uRjgRsx5+x4R+ejMGlWOEZPz5oIyVHWELSDuCGyAOclOwe7ByXSEGl022LLblquqmwAfxDZ7nAT8vexYRd6L6EmTdYmWc0umZz/cUT3Jeqxi3mmj694Yvr9FSh4A7RFjGqVlHx0x/3Zjb+jBd5Hif2myQfJ9+E+YU/NuwMtF5GMlydvaLMuwfnZmWRuGjli+7RAjpuPHqEKK7dIFh0wbI8bv7zRttUkxYtz/M7P7rao7YIu0y7E3Kb1ERI6IyHcRA9YlIlJswFnAK1X1akw/HIgtnH0B0xGPw964fSTm+H8scA/srYz3BW7OlZXEoRUaI7kcZqcxwHVGb+jDpu9SYyTqC2inMeZaX8CS0xgT+VJdY/SOEQPRGGUYun5IgfM30MDf886NNWjDCfPEI5WYgFNG9L9voff1tIBU+yEbMzcCTxWRi8L3M1V1d2xD+I6qum1hs3isPVOJBs4tw+dE5IUJ6SfBUO9j1J6ENvsRQr4ufYGLaT8C9LTGNaV6YzAPc8el2CbkjYHzqxItJj3XBm32brZEMu/5+sXgtGlrzGI/YxO6HvtTHEtV9ft4Gc54GTE8n/E82W8xbR2ReI8r+sCXE8tJrndSLHWdMis07Q8aMucuUf/ZokaNjhrRDydNXO5i5a7IZ0MmKT/a5+M85DzUIRaNTTaP6MJnHgNV/TfgP4H3Ae8HLsNeCPMu4HOqOhKRf8ulHyzHhDRLyb50tMRS55ih+nx9PwIwHI4paoAR87unqBWCrl9TRP6Q/3/W7eoII9yu6xwtnyeLRozNOUudVqi/Cx7t5bmfGfJoV4ha34bOx2kWiGtBbItcfcmxfXps7xi68NlMaJ/9Fbh74VjjPe0JxedYH4QFEjxJRLYtyxB+278C12B7jDK0Gqct+spg9tt3uTd5yHNpYn+/R/i7kTh98cjwmR97Kc8LpaRtE7diHSxo328pD1SYzZO3yuXprE9rRSwPVo/dd1dcS9autUI5ybE/Arqav0bMzzMfI4a3dg/lcwcwHtgvi/R6h7KEueNX1tWkFjH401g0yX+PaZ2qZhH77w78HNsc+xeMDO4HvBg4fcI8W2LXmyeM5eFzQcROVb0/9lbtohDeEhuUC96GpaobhzxflcIb1sNAeiBQjPCZlZdv1wibFI8rDN4My7Df6zcN5Qzh+mIwwvpM1feZQFXXllyk4USMgG902JxHAD8sHiwLFtch3gccCuyjqrcumShHjP9Oy7CJYvN8IlV9KhYd9gaqo+OW4VIs+u6vsT65B/B1VV0uuajzJVjBwojVI+BpWKToiwrnVia0CQAR+SPwNVX9BcZ1nyaIVRG5XFW3wyKBPxF4CnCTWtTXtxYMhoyXjq2oamyst+FKVo/po0TkZhZit/D5Lw2XfXHViQnHSpcY0Q13jEgbv13V24jUMd8zRxRxH6qjyU8MVX0NcDhwDrCLiPw1IXuSxplgrA1NZ5RpA1h6OiMKA+KySbAMW6z7JBap/WXAh1X1FBGpdaRNGSMm580ROa5W1T2xyPmfBp6LGeEbY8bvK4HOAvtRocsGXnbbcp8KnCUi+Td3LThWgRX0rMkmRZu5RVUfhjmEL6XcQdKmnjZ6rGzeaVPO4zCH8vcb8nSKEo3Spo+OsAdWp445mTM6m3Nb+l/qbJAszdrYfHVP4LM1mySS+3bwTW0OnCMi1zXki8IMfvdYvq1FQrtHdGvXRI3rrH0ptktHHDJtjMhplynbapNixHjfmOX9Xh9bYPo0af11BQPXJRX4ELaItgOAiNykqk8B3gS8EPOVXQucADwTe1gNbPNEhlQOLdMYbcqZicaA+dYZc6AxOrXpe9AYKfoCEvt1H/oiK3feNEZim0dMWWO00RfgGmMGGDEcjVGGFQxTP7RZz3f+bubveefGUrThhDnjkTq05ZQR/e9bqFtP62TPDrSyH7IyT8s9oJOV9U9V/Q7wr9i9jXlYaMyeacAFoW2x+H3Nuc7uIQz3PibsSUjajwC98PzE+xFCu4ZiL43oeI1rivU2Yk7mjqvC572pf/BhBd3quS75pU1Z0Xna7N2coF3Z/ym8t2TXL6alTbO6psCbyfsZp9CuFXQ09icYS1WYqT3n46Uakf1yRKLPuO/+Pk/2W2RbR6RrpLI9LSPSdFNq+i6wgu50ytxolIj6qzBxHZH7g4bMuYvCf5bVNRDbrhU6bH+V72pEP2uUIyZ/rmUFi8vGGkPdsyFd1VFAmc/Hech5qBPMWCeuYHFxRZf1V/nMk+tQ1Z2wh8K/JiJvyqX9hVrQrfOAN6vqh0TkwnBuyByTpVkq9qWjPVaweDgm9Rm3Ift8fT/CQPYjlGiAETmfxzz5ktog/BbnAzup6p+z/4FOA/up6gXAYSLy/jZtnGCeGOF2Xb6safmLOrnGBptzljotX38XPNrXcz+z4tGuELu+Dd2O02vC523KTrbsL322txQRPps2iNnTdFtW38MMdfe0F195xXOsW4fPugCHWXC0lSJyU+54m3Ga1Ff62m8/Abrcm7yCYe4rrkNZf98gfP69iRtVdQ3gBeHrV3OnUp4XSknbJm7F5ljQvu9UzGUbh89LQjld9+lsLi2OyR2By0SkKr7SPfLtol3sjy7nrxFTiI+VR9tnPuhgfbcnH07Z3AGMB/b7Vfh8cEUhDwqf5zVUtn6ujGt1YWRRgI+q6keBw0XkDcBnMBLbWUROyCdU1YPCv8Womql5sgko3/G3Cp9lE9cTw+dpheNl5WSoJIpQ1xoldWXlnVZyrIxwbofd3x8UBmRZOTO/PlV9G/AcLALoNcBRwKtE5JpwfkOM9FaWfQ/HHoo5nXcErgO+COwrIteo6qXAoSJyaC795ljwlmUico6qbgD8Gdts/XyMOC8BXiMix4Y898YCwPwLFgDmUcDeqnpiVd25+kbAYVi02fMx59TmwEHh/GMxsbS+hOB4ufoeKiK/DMfuBfwH8ASsb58f2vwtbDLZIdzPc0Vk04oy6u5Vlv7pwKuA7bG38rxMRMYmQlV9BjaBvAfYH4tSm/9Nir/bA0KbVwAvyaVbBwsO+BHMcXhq7tzycO4R2Gb9vYAfALuKyAkiciTjeKuqvgrYFqgM7CciK4rH1IL9PA04osgXk0BELlbVc4CRqm4gIpeH42cBz1bVW2NCZy8sGvzDVfVeuUk7day34cqsjp+V1HEb4K7AD0Vkx5hrrhorwCdCH34XFp14LeB4YO9gOGX5q/r5J2gYyw35z2Ahd8S0Z0TN+A1pKnmsZCw0jvfc9+cBrwh1/xoLRLU+Ni62xoIVPEtELmko5+mUjOmS9L21LWC9kLdzqOobsN/pLMxR9qf6HAuQqnEmGWtD0hll2qC2jnnSGcEg3Bcbt/fF3rbxIxF5Zi5NFWecQzWXNWmXFF3xdCrm3CaOUtWTMMfre8L3jwMvBe4hIpep6u2B3wFPEZETcnPxMSJyWcjzYSxS+cMIv8UkXC0ix0Zqr7r8Rd6cmKtVdVssqN++InIYq3EJ8ENVvXOurNZaKczrV1Oiy5qupaOyy/ryzdRwO3BhQ7l19+M8Aj+q6i3A17G+NHZMRHanBNPUZG0wwdySvSnh4wXHZqt62uixgDE+banrsjwrazbL5NNX8m4KZ2H9N7aPNmm/61X1aIKji3GebeTrkK6J96tsxXtG5kvWVQ18EmVf0q2maOt/WWCDlKR5M/As4IWqemqROyYYI5tiiyWlCxVN9miD3VPJnaHs2t8IuJxmLbGAg0Vk94i6W9lrFb6YSey7Jj9GcTwdS7yPJNNxtRzSpFmwBdLOx2g4P6Jeu0zFVlPVPwLvkNzmkdCHTgceISIrc8eq9MCqvhGhV5r4tHbcxdxbETk63CtU9QgiMXRdUoM/h8/1sgNiGzPeTeGtTYEvHwRcLiK/yR1L5dAF80dqOV1qjHC+c50RabP0qjMaODtWn6RwQgzXNNnZXa+NdK0xovRFKL/N+KjVF6HcXjRG5O8X469I1hht9UWoI1lj1GiAEygZ09gmk4n0RRj7jbY09ja+meiLkGZEpMbANoH15adu1BgRujVKYzTxYShrYo1RhgHrhzbr+c7f9fZh19yYujbeZENezRTWlCry9LnmVcnJ4XzTfSzVLeE3acUpJb9N5/sWwuG69bSu9uwAafZDru4rK4rLNk/dNqZuSuyZmnbuEllmDDq9hzDc+yhxexLarAl2xvMtebvOJ/sdBrTGlauvtZ8opInmp565edBzRw7Zgw/rU4Me9FyX/NKmrJQ865O+d3PSdl1ZkaeM95by+kXn2jSkbbLdq3itD81VpmXbrLU0clAZOh77bcdSFWZtz/l4od14IW5tuq3fr6t5u0+N0HRPn4UFoXqwiGQPtR8OPDnch1vl2xHOj2jWSE06q2xPy6tYqNfq/PRFO3A94OPYOH2GiFwU0Y7G/WtFdMxV86RR2mKiOjR+f9CQOXfu/GchbZ1tV+cXaio3Zj0kdi0vZr9fksYK5ZZyD3AT1TqqcY0y8v6MmJBjy7AIbaxSSMWzIV3WEVDm83Eemg4P/aeauG+znjfxulWkv6W1/piRr2YVFiFXdFl/la+3TR1PDp8L7GOxoFs/B3bH+CEL7DdkjsmnmXf7sjeOifWVRPBQV/VMZCu5HklOP2Sfr+9HmNJ+BFX9JPEaoOjzGDsfjjXZNTFtfDoJ/tu2UNV3Ax8VkfNrkj0cuAF7dm6b3P9dtmNN7Dc5LUH3FX/H2mdQQj0j3K5rKmta/qKu93CU2Zyz1GnQEY+25FCYLo9uQGHfR8A7sBgYSWvloU21XBoQtb4NnY/TrF/fpeJ8m/4yhh54pa6uKp9NG9TuaQp8f0cKwbOov6d9+crLnmNNCey36jecYJym9pWmcZ363Hzna9oh3xDm0mmgrL9fGT7vqKrrisg/a/I/H3um/FzgSBjrS43PC6WkDZgkbsVFFWU+KXxm3Nxpn87Vv6pdavvybk9FQFdVXQubw/7Eav5I3udX4+d7OvXz14gKvRnpT6zzT5Y+F0DaMx/R67tN6Qvtr1rvqfNvNPqEQ7qquQOwwF0Zsh9it5ApX8jtgEcD/wROKisoh+uwhe2yv6wz/jh8P1FV7wNsAZxQ0vHvyOogYfmHCZPzsFrA5gfMJsAN2WaCXBnrYA60YnoomchK6ig7V0Uiy0qOZ2mrylmDhQOyrJwhXN/a2KB/GNbRdwXekDs/wjrwr8q+q0U8PRH4JWZcPwNzQmekeGI4nscK4GOyenP8KHy+DNgP6ztnAJ9X1cyJtGX43A94b2jvBQ11o6oPwgzun2Fk9RbMAFiX1WSwFRaB9fpcG7fCxtN5oZx7hzLuFOrZDPgvjLS2DXm2wSKvPjrX5nwZTfcqu8Y3hWvcEjM8V21aDuXcNpzfR0SuxN54vxXjGDH+uy0Hrscmso2CaAIz1v6MPXh/A3BmqGMTjHN+hvW5twD/h/WX7L7l27SWqj4PM1x+Wjw/Y9wzfC4I7CIi14vId0XkORjv3QWbKDLEGNZZsJY2vAf1Y3qN8LlBybkqlI2VI1X1ftj4/x2wHfZGiw2wSM5ZO6v6+VVEjOWG/CPGuSOmPTHjF+p5bKxeIsZ77h6+ClBsbK8DHIEF0zwAG/cbYOOVXL6ycqrGdDF9n20Di0R9azqGqu6HicOVmEiucpTVIVrjTDDWhqgzyrRBPu2864x9gT0x8bwJ9vaN43LtqOOMUi4Lx2K0C8TpitLxGcNR2ILX+iH93TDn51/D9QDsAZyf66fLw7WdHvLcI9R9c3bfOuBqaODriPwjAm92yNWHAD+T8aB+qyDhrTkdaKUbqdBlEdcySdl197SJ2+vKbbof24Uy3hby7VFxbO7Qdm4JjqQXYbrv4x3V00aPwcJ5p005WZ67Raav490UzsprlLZ9fxTyvBq7x2W2TZamjq+hmfer5ozYfEm6KoJPYjQddDTnttRGdXWXteF5If8hakHm82g7Rkbhc8GCfKQ9WmX3NHEnNP9GWdvq+uYCvo2su629NiLdnmrrx1jQPhb6N5p0XCOH0Gxj9jJGI7XLtGy1M7EFhDzeBfyfrA7q19SvRqzuG3X3G2p4MWbcRWrkpYZHhs8La1MZnofZ5P+bO9aGQ8ts29RyutQY0LHOiLRZoH+dMYld2oYTYrim6bfozKbvSWPE6gtoNz5G4bN0w1/PGiPm98va17XGaO0PJlFjNIzPqnmoC30BcRpjJvoipEnVGH36gms1RqRuHRGnMWr5cIlqjDbr+c7fhqoN251xYziWujbeNF6ntaZUlqcXLongZGi+j1W6pTWnMJ19C1C/ntbVnp0mlNkPxwO3AJsW6w7YLHyWbjwqQYo90yWmdQ9hIPexYU9C9H4E6IXn2/r2ynj2dAa0xhXq68JPBGn8NKT9CGV5+t6TAKs59IaSc32iS35pU1ZKnutI2Ls5Ybva8N5SXr8Yhc8utSlU2+5NvNaH5srzwiRrLVH7HHtG27FUhZnZcz5eJh4vWVvqfMZt+3tX83Zf9lvMPf0K5i96W8izD8Ypjw8PPBT9dI0aKdKfU7Z/ZcS4XQeRez9V9SHAzzGOe7RYUL+YdjT51fvGPGmUtmhdR+L+oCFz7jz6z6CcG4+nRp8k+Jehfj2kMU3Cfr9UjQXV3FOno7I2V843TfenQ46dBmbNX3Uoezak6zrKfD7OQ9PhoQvCsTZ+iYnXrWiwdTrQH7Pw1fSJWXNFl/VX+Xrb1LFO+LxrRV3Z8fzvPmSOKbZhnu3L3jiGCF9JJA91Uc9EXOV6pFX6Ift8fT+CYRr7EVI0wIhxH0nSPqqENvbuv1ULOvLShqB+AI/BAp3cXPi/S9wK46670XKfIM2a0u26fng0fz7FX9SH76toc85SpzXVX2zDVJ/7CeiSRz8Yysj+DsGCHX2aFmvlkT4imN369h+w+B4PKZ6YoL/MGmU+mzZo2ovzEKxPrywcr7yn9MAXWv0ca0xgv4wj8mM7eZy27Cuj8Fk1rlOeDYEe1rQHNJdOAwv6u1iQ+d9hfWLXqoyq+mDgA9i9/Nectkp5Xij12aJNaB+34o7FwlT1zlggt99i/v0++nRWf75d12F2VZXvZk+sT34yF6ivbNySOze2zy+gzfzVpDcnfe6oauxFPfMRkBLbpyl9/r60eR6mqewMVXPHqkIAEJEL1KIZ7oYtCP13Lp1iUTg/LCL/WHVQ9QGYMXKB2FuqCZEFX1ZWmaoeiP1onxKRj4VjWWe8v6reKitHVe8CfB64N/bj5S/g2hZ5lmED4OzcseuBW6nqg0Tk16GM9bDIypmToTjAloV8Z5VcYt2m2LIBmR2/Fgt8VqzjjJo6YsqZ+fWJyIG5rxer6lHY4MwwAs7MRQkufv8o8BUR+bfw/TxV/QAWvXNfTHzvnRWmqk/H+thzCnXcBOwuIpnzYT8seugm2D0YYffv2ZmRr6qfb6gb4P3AUSLylvD9/NCGp4rI73P1r2QcWwFn5SawD2MbkXfPkW/WjidjUUpPlvE3pWf3Kiuj6V6NMIPguSJyWSj7y9gDV3nsB/xYRLK+cTarJ5xi3dnvtDxcz4WqejmwmapeghH5Ltib4c8Weys8wPuA43JtPVdVnwPsEIIJEtq3OfYb3wYLDrC7iJzJFBFExx9F5G+F42tikdjvBvxURK5Q1a2Aq0TkgkLaB2Jj7hLg0typjJfqxls2ptrwXlbH9YSginmEqLRnAFuo6jNE5Ksl178dcGJhjI6NlZDu81jE7wNyx94B5Mus6+cxY7ku/1sY75MfimhPzPht4rERCzlsJeMojvcR8DdsLP4xtO04zLGxiYj8JRz7ASZWyOU7s1BO3ZguS99X28CMqdvTIVT13zGHyqnAbllwrpr0C3QJpGkcVV03HG8z1oamM8q0Qb6OedcZjwe+LSLHh+8XM+7YqOOMt1LCZRCtXWJ0Rd34jOGoK1n9Jo5XA18L5d5ZLZr53oy/JWR5SH9VmKNui93rN+ceGoypt/K+BTTxdVP+EYE3VXVirg5G5LaYQdSEibSSiNysFjCxTJc1XcskZdf15dfSwO015Tbdj6uwKPI/ybX5+uKxIaNsXkidWwp4NuYk/JaI/Lah7qh6WuoxKMw7bcoJec7CtPtzROSLhfQPxu5dVm8d715JPGeNyGmUln3/LZhmeU6NbTOima9jeb9szojJ10ZXxfDJSsZR1HTQ3Zzb1v9SaoMU0lwD/DLMCU/G+tNnVfUxInISTDRGsjcgFRfFIM4eHVFu9xxNs59gRM1vpLbBoKlvlnFwE2/XtbvJXhuRbk+19WOUtS+rP8ZH8lbiOKRJs4zoZ4zG+ommYaudSe5FDar6KIzL8wuJMfpoVd+oud9NfBoz7mL63dyiyl5Ve2PgJZLzuYfjG2P9CeCzueO3F5GrCmlH2EaAK4CDs+MtOXSBbZtaTscaAzrWGar6WZptlmnojBGT2aWpnDCimWuafosubfo+NEaUvoDW46NOX0C/GmNE3FzRh8aoanOMP3hEmsao9SlUjOmq9mV1N+qLQlvr+GNW+gISNUa4/pWMoytfcJPGiNWtjRojgg8XtcYo0w/SYj0f5+8m/u6MG0MbD8x9rZrHo9eVwniexppSVZ7KtuXSpLaviZNj7mOlbpmAU7Lr73PfAtSsp7UZ41W2RjiXYj9crKrfxDZZvR7byJ7l2Q14HKbNj8kdT7JnpoGWPDlX91HT9iSk7EeAjnm+JW9D+f6d7zCgNa7wvQs/URI/9azzsnqGOHfkkXHoFSXnekOX/NKmrBZ5ovduTnKNbXiPpb1+0Yc2hWrb/ShqeK0PzcVC23SStZaYfY69QRL3QefOTbx3KXfcx8sAxovGr0239ft1NW+vLNyjLubgxnsqIreo6gHAUap6ARaoYhcJe8lK2hqjkWL8OWV9IKtrlbZs4LoR1sefju13e4eIrMilj2lHk1+9V8yhRpnmNabuDxoy586j/wzKbbtafUK8fzlm/2ZTmpj9OckaC6q5R+r3CI5onm+a7k9XHNs72oztrnSWJjwbMmF7U31nzkPT4aHn0dIvEWFDxa4XT7q/vE5/jJi+r6Y3zJvWaTHu217jj4DXAHup6odF5He5up6APSx9LfDTXJ4hc0yWZu7ty545ZkSzr6SRhzqqZ1KuWvJ6pEX6Vn6qltx3sab5fH0/gqH3/QiqmqoByta+Y/dRdfKcehjbDwx/DwGOBb6OPb9+f+A/RORdIe2DsX2gO2Hrm6/GgmB9Abi1ql6LrYVuiK3Tbgn8PrThVGBn4Buh6lX/q+rXgO+JyH+H78cBnxaRz6jqwViQqAdigTruge1VfTfwRMw2uAFbf30Qxl9/wAIqtdonSPMzKG7X9cCjufqT/EUtrzHV5pylTsvXPxGPtuRQmPK+Lswnk/mJ/gXYSUTOV9Xnkr5W3rj/KWBW69u3qOoPgWeq6gML96Jtf+kVbXw24fwCbmljn+XwyPD5/fzBunvapf2aw4LnWMO92Aobt2dTjYwjTsm1sc04bdNXmsb1lSQ8Nx9hw7RZ0x7EXNoGHfb3w7AAZ4eq6sk5nZHlfTLwSazv/quIrAogLgnPC6WkDYfaxK3I5stnqeqBOT/N+lg/vQuwl4hcGzQldNunl1GIvyEi16vqz4FtVPWxIvLd3DXvAhwOXMT4vJLNyWeU1FG2zw/a7fVqitkwYoLnjqRiHUTjn/lIje3T+vlwjXgeJqLsDKVzR4a1C9/3xhx37wsd4lwsCuLOWITDtxbSHw9sBNwP6zjJEJE/q+r3sCjoP1PV72IG0BNCo2/GIjpe2zaPqt4aG6inFya172DRmH8YDLT1sSBkZ2AG1rrkoo+qRfLcFDhDxiNMZlgOXCzB0VZAJl7PLSlvpYjcmFAHjL81e0E5A7m++2AieGfgXlhU6XWwt4xkGDE+sFd9V3sD4iNY6Hi6jtVvmDkJi+59Z+Af2ARyUKGNI+CbEhZmA4rEuSVwtKx2pjbWHa5vt5AujxsK17QV5szII3+dG2GG/sMLoiCf//SSc1uSdq+2xEj2stz5BzL+YNzGwOtYHXUUzCDcmnGsan/AMlb3ydOw6KyvBb4kIier6ruy8+G+7crCYIHXYUZMHr8K6e6ARVz+lKruJCJlRmpfeCLwLlX9MfY2kb9gb7jfETMILwNeHtK+FthTVU/CxO7VIc1Tw/mXyuoHA6t4KcPYWG/DlRFjGmyMHgV8JZR5BrAmNmaXA7cSkfvm0o+NlVDPRthY2F5VX5dLuxYWITemn9eO5Yj8I8bHVFN7osZvBI+NSBjvAVtihtQfc8fuC3y1wF33BU4u5CuWUzemi+n7bBvY26WKXLEKQdQ9PXzdMHxuq6pHhP8vF5F9cun3wBxlN2GLj69T1WKxF4nIEbnvdbokSuO0HGuD0xlV2mAx6QzMyX6Iqm4JfAlzyF0e6mrijAVcFvLFapcYXVE6PmM4KuAK4HZqb1Z4ZcjzPswZs0v4/N9c+mXAx7AFitsD/4m9mWBF7p5MytVQw9eR+UfAyg65eoGTqQxdaSVKdFnkbzpJ2U06sYnby8qNuR+bMf7GqKpjU0HqPBIwNi+0nFvy2Ct8fqShran1JOmxmnknVdeBBeL+BvCF0O6zsbdTbAXcW0TyG0cqeZc0zirTKKl9f4Rxcd2YytJU8nUk75fp39h8Sboqkk8ax32Xc24L/0ujDZLrw6dlTjQRuUxVnwj8BPimqm6bu+dt+namZd6pqpthc9bZ2DwWY4+W/e4x3AnNv9GIZi0xxrcJdbey10i3pybxYyzQYIz7N2J03MqIupr8RZ2P0QQ/0bRstTOBF+TOvQf4HwlvLorsV8V2lN7vOl6M8QMlaOTBoIU2qbJXnwu8WW2R92Js8eIBwJOwF10cjfXfDMep6jWYv+7vwEND2muAp0hhQY0EDm3wVaVycVcaAzrUGQk2yzR0xiR2aRtOiOGaOju7U5u+a43RQl9Aer8u1Rci8qUEn3dbjRHz+43oR2O01RdjbYyYR2PGZ9k81IW+GEtfU9fU9UVI00Zj9OkLrtQYCbq12JYyu7CWD+dNY3Th18gdj17Pd/4Gavg7HO+EG0PeNmvjTeN1WmtKZXk655IYTo68j3UaMplTKq5t1ffIfhGzbwEa1tPods9Oqv3wauz+HaqqT8LW/e+H8ddNwMtkfPNsqj0zLaTeQ5iv+xi1J6HBxoOSNcE+fIG08+2N8WxH/iroaI0rpk2xGq4FPw1pP0JZnr73JMDqN2xfUHKub3TJL23KapMnFW3qiOY9X7/oRZtCte1eyWt9aK5cW1bWtCvlmpo4aKiYeO8S+HhhQOOFOJ9x2/7e1bzdh/0We08RkWNV9WTgPzCNXNrWGI2U4M8p278yds0RXDfCHsr+BPawyg9yeWPb0bTGMQ3MnUZp4StLqkMT9+0MmXPn2H8G5bZdnT5J8S83rYfUpokc423mjBjuqdJRWZtL55um+9Mxx04LXfrBUspKeTZkkvZG+3ych6an/Wipv5rGtqatF0+6v7xOf8zCV9M35knrtPWZp7bry8B3gcdigYS+hnHXQ4EnA2sAb5HVz2INlmNCmkVjX9ITx+TKrvSVxPBQR/VMxFWuRyZK3xa9+Xy75BdwHUPDfgTSNMCIhfN91oYubLNY/+2eWP/bHfutvoPNkdsDewAvxrT5puH69gKegfXBN4rIdqp6DHCCiLxPLajLt0LbTgaOBB6vFizmUcAbQr/I/l8L6+tvCdd2GywA7ovVAgftie1PfRZwmYj8M9gJR4S27A9sLyK7hvyPAd4Wjo9K7k/TPsEYTel2XU88muovmqBd0TbnLHVaTP1T0GkwXR7Nrmt/jGd2zvmOktbKY3xEOdwxfM5iffsrwDOxwLyr7keb/jIltPXZlHHLJHuadsO44siSc6X3NKDr/Up7hc+P5I5tgj3Hf1qNjbU+tvZxNfDLwumkcdqyrzSN6yuIfDYk0oZJ3Uc4mLlUu9tX3Ka/H4bpmRcCv1TVI7FgxnfF9NKm2JjbXUS+WdL8lOeFUtKmxq1YG4trdDoWj+gMVf0G1k+ehgVx3l9CIMuu+7RWx98AG/PfAb6lql/EglJvifHHxcCukraXJt+WDKnzV8x+tomeO8qVkfzMR66N0bF9EnwdbeIjxZSdoW7uYM38F7E3Om+NGR7bAG/GBu3hwCMrxF8XeB7wGeA+wKtCnW/CApKsSXmgjpQ8m2ORR39RKOOdGNHfghlhm2LC/F8w8ltZ6CybhXKKHT774e5ScW4dzEF6hoxHks7K+0Xh2NoV1ww26K7GJvC6cmZ6fWpRSU8O9eyDGdtbY5EsV+ayj2q+b4Z13nMZx6asjv59KhZ5dGvgDVgE1P8ppB+xMPLqo0JbfpVL84Pc+Zi6twppTi+kWcZqI+G2wIMZv0awCSXLNwrtXnBva9qfHc/KjWnvCHsDeR5bFdp2GCZ4fqOqN6rqjdjbNbYIzoqyusGuOet/p2FG0RMwJ0V2/tTc/zeyMFrsQwtlIiLXi8j5InKqiOwfzr+R6eK7wMcxIfIMjHyfCfwVUOBhInJOSHsk8DlsrOyJ9f1HYhF6t5TVUWehmpcylI31VK6sHNMZRORYbDx8JbTpdcCLsN/ju8BLCllGjI8VsMnsKmCLcD772xzrY1m+un7eNJab8o8Y3xja1J6Y8RvDYyPSxnuW56RCmq1YOD63ZHzs568x+143pqfZNrB7tSnVGGFO1z0w0QlmTGfHnlVIf7/wuRbWJ6Tkb8+a+saQqHFSx9oQdUaVNlgUOgNALGDdQ7A3Pe0NXKAWWR7iOGOMyxK1S4yuqBqfMRwFq9888EIskvnpId+dsLfsfUhErsulX4a9ceJ8EfkF5tTYW1U3D+e74Gqo5+uY/CPi70MjV2OGOFi/rUNXWmnEwt8/9t62KXtExT1N5PZiubH342JZ/caoqmPTwoi0eaQMreeWwC/bAZdizqzO6mmhx0rnnRblICJHYW81+zawLaa1n4I5SfcppF1BNe9eSTxnjZig7+fOtxlTq/g6kffzG/hT8qXqqlo+SRj3XfsuUrRRow3C6j489vsEu+oZmCP126q6QTjepm//EHvI+R/hU7B7GWuPjlho9zRyZ4IPIEZL5Pk2hrer2h07T61MTN/Wj1FsX7H+mLJXFr6X1RVjY3Y6RonTLmPX0LOtdiZwV1W9i6o+DXub5DtzaWP1QLHtY/c7ghdjxl2sRh4SRkyuTcAWf76FcevzMX7dEfhxKOfJhYWZLwO3w+bcN2H37CPAppJ74CxDIodW+qpSubhDjQHd6owRcTbLNHTGiMns0mhOiOWaht+ia30B3WqMJH0RzqX26yp9AT1qjAltvy40RlmbY3l7RDzXj4gbn2W2w6T6opi+qq5Z6IusjGiNMQVfcJ3GSNGtxbavqiOSD+dNY4zoRjuk+rqdv+v5GzrixjZr4wk25DTWlNq0rU37RtRwcux9jFgfSOWULF/V9672LUDDelrHe3ZS7YdLsXWi92MbPF+P6fpvAo8Wka8UsqTaM1NBD/uehnYfY/ckVNp4AWVrgtCxL7CNb4+FPDu0Na6YNnW1H2FVvQPcjzCWZ0p7EsA49IIe9zBWokt+aVPWNPZ1tmxXCu8t6fWLnrRplq84J42oXmfuXHMV6l2Z+z95rSWXt4mD5g4ztud8vKzOFz1ecudj5sQ2/X3EhPN2j3Nw7D3NHmreEgsg8sdC+lVtJc7PleJ7LNvTsjK0KdZP/1WsL9+50KaodkSscfSOOdUoIxJ8ZS3qSN0fNGTOnVf/WZbvB4XvdXybUm7Mekhdmlg7M2nOSOCeKh1VN9803Z8uOXYqmCF/pTwbMkl7U3w+zkPT1X5J+qvDdaus/on2l0fYaitDu6flq+kVc6Z1WvnMU9slIjdjAS/eCJyDBUt6M+YnPhp4nIgcnssyZI6BxWdf9sExWdl1WqGWhzqsZ1Kucj3SQ91dtivkifX5+n6EKe1HCHWtIEIDRHwfMbltNqLBX6aqm2CBZJ4f+tS6wCki8uZwP9Zm9Trme7EANUdjgcoOAS4JgVoegwVEQUT+gc2tDwM+je1N/Rk2B/5dRM4q/P8I4G8iktmK2wO/EZE/YL/v7bGXVJwt9iKw+4TrOC6c2w74YrieB2AcehUt9glG8rDbdf3yaKq/qG09KTbnLHVabf0Bveq0kGdqPAqgqm/DgvrtJOMvhBiR/ux+0/6CfNtmsr6N/RZ/wgKpFtEmtk/faOWzqUAr+0xV74AFWvuWiPy2pNzKe9olJ9Y8x7p1+KzaCwQ2V6yJBf+7udDGNnt4kvpKxLi+kohnQ3pc0x7SXDpiNs8kISI3i8iLQh0/Bh4P/BsWDO8voYyHSHlQv9TnhaLTkh634mFYoLUTscBq5wH/ivXrczEfycGFOrrs09lcWvYM1vFYUMKfYpz2GuDewDuALWQ8wN1mpMf+gPT5q1ZvdvTcUdaO4tgb0fDMR4vYPim+jmLdtVyQUHbM3MHaxQMhYRnpLoCIbByTLpf+QODAkuN/plwUgC30l5UVnUdETi0rRyxS5uvDXxFrlqQvLSecu7jm3HXYgGwsr66OcH7Bgv9Ary+L4PrcjBxDBNX1WT1Q1sWcO6eVfceiwa6JRa68MaS5O/ACQjTtMDGfhpH3Hpihf0PWCLVovQ8pud43A18Qi6K/Hka4ecJsrBubENbCyP76kGZ7TGytCGkeENL8OtemHTDSXRkO3YCNxdux8I1xYKTw7fyBXJuzMmrbm0tfJMCtsI0pqOpu2CS9PLuegE2wSKn3Ay4s+d3uh21qyQyQ07CJ+80i8idVvT8mprLzN4V7si42gaGqy7HovYeWXH8ea1J422cMxKLkH5GaL+Q9C5soY9IeSUUU1ZK0bcZ6Elc21ZFLdzIR4q5irID14fWwN1NcXZG9tp83jeW6/CXcEdOemPFby2Ml9TaO97KxGCb2+xSO3QdzvJxWyFdZTsBWwFdLOKK3tuXwfWADVd1ASt46W6UBqpCaPuTZuOF8lMbpaqzNch6uadNi0RnZufOB96rqCsxBswVmbNVxRhWXxWiXFF1RNefGcBSYUboF5izeLxz7G2YY7Aq8IndN2Vy8arFYRC4KvPoibF6emKtDuXV8XZu/wJt3imhPDFefFT53BP6vrE4R+ScdaKWABbqMhnvbQdlV9zRGZ1aVG6N1Ryw0wMuORWNCTXYgE84LbcrI5T2XCF3Vtp5YPRbS1vFpdDm5PD/C3jgVk7aKd6M4q0SjQGLfL9FgGfK2TQxfP4tm3i+bM2Lmi7a6qolPNiNi3Pfgu5jY/xKbRkS+j3FT8Xibvv1+bFPJKqjqk2mwR2u0Qgx31nJzTN8M30eMj5PGutvaay3tqUn8GL+oOJ5db4yOW5k7XMYhtZqlxzG6JQ3aZcq22tnY24I2xxYYDhaRv+aa3KSPyvi27H438eJDaPYDxWrkzjCJLgn5DyTNxt244vgPqNg8UJH+v4D/ik0f8kRxaIS9mMTFHWkM6FZnpNgseXSqMya0S9twQqztUPlb9GTTd6Yx2uiLcC61Xy/QFwGNPu8JNEbMXNGLxpjEH5yqMYLWrfVJUBjTXeiLivQL6oLZ6IuARv9I4Rr69gXXaYwY3RqjMRr5kLi1pk41xqz9GoVzsb5u529q+Ru648aYeTxpXWlaa0pleZraVlVuRPseSD3fx4x/oFZDJnNK8bcp+a0m3reQQ+16Wiirkz07Le2HP2Mb0F4bkTbJnpkmUu5hSL9xzblB3UeJ3JPQZk0wHO/UFxjSRfN2Bc8ObY2rsU0xfiLS+Wkw+xHK8vTZvgKW0fwCplJM6g8KZXTJL0lltc1TyH8gDbq0ZbuieM/XL7rVpuF7ne1exWt9aK4xXphkrSWGg1LQxdgvKfNAKsZS3bgP52diz/l4aTdeIn3Grfp7V/M2/c3BG9e1P5dnS+Br2BzwJOBdhIePStoasw8o1p8zypVb9lvVcl0u/YswO+0zqrqD2MtUU9rRtMYRhQ7WreZKo7T0laXYyknlD5lz59F/Fr5X2XZ1dltMuTHrdDFpmmy6NhoL4nRWmY5qnG9ovj9dcmwUpm1jdaizop8NaVtHSBvt83Eemg4PTeCX6GTdKsLWmUh/qOolzMZXU4ul5I+ZxGfe4hpvwPh9RUTawXJMRPvmxr7sk2MifSVNPNRVPZPaSlfF5o/BvOqRtukryjiQGfl8u+aXXL1LVsfU2CJZXY0aoGTtu+ifmcg2S/DfPhY4XlYHHXssIUBfwG7AJ1R1DSzQyyZiAZFQex79YixI35USAvOp6suxIECfCH9PxQLR7I/5d8ACAWb/74oF6cvwRCxoUdaeI2U8oMo1WCCbE4E/A4eJyEfDuZ2xIIJt9wnGaEq363rk0Tb+qJb1pMQjmJlOa6o/on2d6LSQZyo8qqpvx/TcjhnfhOPJa+VE7E/IofX6Nkw2TkXk+jBn/KeqbiUip+XOJfWXhDqPYArxPAr5Ni451tY+ezHG1++tqKvynobzndivUvEcq4h8GgtuW1fmT8ry5s6nap3kvtIwrqOeDaGnNW0GNJe2XCvZuOTYJP6Ir2CBHtvk/RHxzwtFpZXEuBUYx4IFsvw18ISIOjrr0xFz6Q8wfdrUpuR9fi3nrya92ehPzLWpbn207TMfseu7bXwqqc/bZvNBIwfRMHdASWA/h6Mj/AXrlE9X1TMxEjwAE82ZsbtF+Dyj4vvPQjkHq+rhwL0wQvgu44FbTsTI+TgR+VahHZthJPYvqno8FgX5bdhDA88p1Lsyly+m7lOA6zDCeQ8WAO8DhbIuxwhua+BSVd0a+Gg4lr/OK4APqeo7MeN+O+BksSjDawObqOo9gX+KRRZPvVePLF5jcBBnD4bcCov4fIis3piSpbsk/LsVcGFJ3csZf8vAl0O92YNcywrns/v2X6p6CEaeh5W072DgKOC3mHHzfMxJ8yQcs0LZWAF7o9AV2OamgzBD9P5YZNnXikX0burnUD+WK/MDtw1psj4W056Y8dvEY9sU6o0Z72X3cBTacnbh2NU08+WqcvJjuiR9n20DQESuUtUvYeP0yzgcPUJV98Pe8PxzbI7ZAzMiTghJ6jhj3ZBmZaHYGO3SSlcUxufFNHMU2JsHdgR+x2rn3VXAXsCXRCT/huvl2APVxTdOHIdFcv83uuNqqObrpvx5blonoj2NXC0iJ6vqt4H/VosG/xOM25YBLwcUW6iZSCvlrrFMl9XeW5r7RF3ZdX35jzRze125TVp3xOpFK2qOOZYAInj3SuI4q6hRIL3vF7Vf2ZiK4esUmzUrd5J8I5p1VROfxGg6Rz1i7NEquyeGO5t+o5i+CQv5NqbuVvYaC8dlF5qhzo9RbF+Zf6NJxzVxSIYqzdLXGI2xM6dmq4k9kHAhFnDn9thbi/Jo6ldFmxfK73cTL/6d5nEX0+9Q1fWx8QK2KHNfVR0BfxWRzH/lmCNEaAzoUGeQZrNkbexDZyywPyLz5a8zn3dEPSfcjQauifwtHNXoU2Pcnea5oi+NUdVXY3g7SWMQ55MojulskbrYvmh9UTX2i3XlNMZU9UWCLzt/DbenR19wg8ZI0a11fB3DhzHjLkpjOJY8RnTDjW3WxpvsgWmtKbVpW2m5Ee37C/Wc3HgfI3RLG06Zxr4FwNfTHI4IlHHL0Na4GttEN/sR8vejb5035LmjiB2ICJrpcMwpRnS3NlDna+9cc5XsZ9yypl3J11Sxvu5Y2hgxnfESszbd1u9XZl+OSJ+3+5qD/9jQflR1I+yhjENE5BOq+nPgDFXdSUROKGlrjEaK9eeMGO8Dxbpi9n7eApwV9h5tAnxTVR8hIr+LaYf71R1LCCO64dwmuyum3Jj1kJg0yfv9Iq8hxs6LeS6kbL5puj8b0h3HOhxDw4hueKitX6KrdauJ95c36I9Z+WocjnnHiOFzTIyvpImHHtpRPZPaStc35cfhWFwY0cF+hEQNELP2PYltFvvs1a7AN3LfdwFeGdLfBguE9wIRuUVVrwWeFup7LPA/WBC+xwDnqOrtgDsDHwTuEeo6DPixiFyrqjsDnw315P9/BHC+2rNmr8SCRWV24WNZuF5/NPBO4IMhqEwej8ECBrbdJ5iiKd2uczhWY0Q3PPo24HVYQNB/qOqG4dSVtFsrj92fALNf3z4M48CDsJcgOioQ5ov9ga+IyI9rkvo9nQxXEvdsSF9r2j6XLi4sC5/FYHZLAW3mrya9eSXdPHfU9pmP1Ng+bZ+/guZ1opiyo+eONatOOBwT4mjgw8CngJ9iBurngDOyiJSYoP61iPyz7LuI/A14GrAtcGYo60jgObkywAbRzcCbStoxwiKCCvC/GCnfCdheRC4LabYM9a6KpBlTt4j8AYua/HjgLOCtWETdfwLnhTSXYQP046r6W+CNwBeAC7L6ROQvmHDbCCOAk4DnYoRGKPd5wKXY2yzzbY69VwuuEQvUdwMWBOj1wF1Z+JA3InIVcFm4l6vuae53Ww6cIxblHRG5UUQuzwmX7Py1uXuyB+ZcOQNziBwB/FlELsxVvSHmRPkVcDzwcOAJIlKMjOyYHsr6ESJyBTYZ3QFzUK3EIspemvWDiH4ONWO5If+I8fEQ057G8UszjxXrbRzvuXv4j9zlbYVtELuxcK9Pz42jsTFP85guckSfbcvjQCwIp8PRN9bBovGfgo3PLYFdMqO9gTNKuYwE7UKiriA3PmM4KuAKTPSvyNX/t+xYoe3LsfFcXDg4DniQqj6sQ66GCr6OyD8K9+afHXI1wO4h75tCOSdj/eNErI90oZUyLNBlEdcySdmV9zSS26vKrb0fam+92oJxI37BMceSQi3vEs9ZRU0DiX2f1VxS1Cz5MTWima9jeL9s/KbkS9JVTXySMO4dFYi0R6vsnhg/QdNvNKKhb5bxbaR/pK29NiLdnprEj1Hsq0XbJUbH1XJIDisptzH7GqMx2mXattqZWL95u4hck78JEf1qRNz9ruXFmHGXoJG3xsbNadjDdBr+PwjHvKJJY0CHOiPBZulbZ0xilyZzQiTXxPwWjgr0qTEif78R/WiM1v5gEjVGpE+iOA91oS8WpK+oK8NKpqgvQpokjTEFfQEVGiNSt45ovt+NfNixxnAsUXTJjbRYG48Yr23HapHXYvyTU7FVIjg55j426ZZkTmE6+xbyOBBfT3M4qrCAszryV2VYyYRrXDFtitRwSfzUs84b7NyRO4aqPhj4nYisxOFYZOhhbaCO1/rQXPm2/LOmXW2vqWx93bFEMeXxMqLZZ9y2v3cyb/dovzXtK7kzcAzwTRE5KLTlLOBLVO9zjtlz3aj9KvavjBjXM7F7PzPf1tuxl4l+Q1XXjfQruV/dsejRJec22V0p/mXq1+ka00SM8TZzBsTprDIdNaJhvmm6P11xrMMxNHSs/drqr07WrSratGqsd6A/pu6rcTjmHfPCMRX1FbVCEw91Vc9EXOV6xLGU0PF+hGgNwEIfSVEjTGqbNXKFqq6NvejuuPD9nsB9sGe8ALYHzhORy8P3V2Ja5UpgH+ApInIecAkWqO+nofw/YPbNO7AXYp0aAmlsA3wv/38o92jg5eE6bsHssJNU9VZYoK1VwcJCmzcCDgWuUdXrVfV7ai8kJ1zP9yfQfTGa0u06hyOHrng0lLMvcBfMB/yH3N+jWcijMZooan/CENa3xZ43fhFwiqquN6t2zAk2Bj6CzUWV8Hs6Ma4g7tmQXta0fS5ddFiGBZc7c9YNmQHazF+1ejPSnxizPtrqmQ8SY/tEpK+6LzFcEFM2RM4da9xyyy115x2OwUNVj8OI4tUl594P3E1EnjP9ljliEIyiYzBC33vW7XHMDnVj2TE/UNWDgU+KyK9m3RaHo2u4rjA4XzscjqHD+doRi2nbo943pwvXLMOE+4Ec8w7nckcMXGMsXri+GC5cYzgcjiEgZZ7w9TSHYzZwPTffUNX3Ah8SkfMbEzscDofD4XA4HI5Fh5j1EF8zcTgcQ4CvWzkcjlnDecjhcDjSoKqfwQINHYAFh9kEC1byKBH5eU0+51uHw1EJX992OByO/qCqawJXAReJyGazbo/DUYe1Z90Ah6MNAtHeFdgT2AyoWnwdYYaxYyBQ1e2ADYFfYBHO34j9Ti+ZYbMcM0LCWHbMD94O/Luqvr0QcdjhWAwYsUR1hfO1w+GYM4xYonztqMcA7NER3jd7hWuW4WEA487h6BojnMsdBQyA60Z4v+wNri+GiQGMO4fD4QAmmid8Pc3hmBJczy0OqOo2wM/8oQeHw+FwOBwOh2NJY0TzekhMGofD4egUvm7lcDhmDechh8PhaA9VXQ94IfAIEblaVe+FrSeehfFqPq3zrcPhiIKvbzscDke/EJGbgfVn3Q6HIwZrzroBDkdL7AD8Adt4+0wRuaKYIES73wI4Y7pNczRgQ+DdwDnAV4GbgOUi8vuZtsoxKzSOZcd8QUSuBw4D7jbrtjgcXcJ1hfO1w+GYDzhfOxowM3vU++bU4JpleHA/kGPRwLncUQPXGIsbri+GCdcYDodjKGg1T/h6msMxVbieWxz4m4h8adaNcDgcDofD4XA4HLNBzHqIr5k4HI4ZwtetHA7HrOE85HA4HC0hIv8APgN8X1WvAL4FXAfsJCI3FpI73zocjlj4+rbD4XA4HA4A1rjlFn/5t8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcbfH/l7H+gvX+Z0IAAAAASUVORK5CYII=", "text/latex": [ "$\\displaystyle 0.4 a aluminium_{alloy production AlMg3} + 3 share_{recycled aluminium} \\left(12.0 aluminium_{scrap new Recycled Content cut off} + 1.272 elec_{switch param eu} market_{group for electricity medium voltage1} + 1.59 elec_{switch param us} market_{group for electricity medium voltage} + 1.0 market_{for aluminium cast alloy} \\left(0.965 - 0.965 share_{recycled aluminium}\\right) + 0.00406 market_{for cast iron} + 4.0 market_{for chromium oxide flakes} + 0.00102 market_{for copper cathode} + 0.0305 market_{for magnesium} + 0.00508 market_{for manganese} + 0.00406 market_{for silicon \\mathcal{metallurgi} grade} + 0.00203 market_{for zinc}\\right) + \\left(a + 3.5\\right) \\left(0.7 Heat_{waste} + 1.5 Occupation_{industrial area}\\right)$" ], "text/plain": [ "0.4⋅a⋅aluminium_alloy_production_AlMg3 + 3⋅share_recycled_aluminium⋅(12.0⋅alum ↪\n", "\n", "↪ inium_scrap_new_Recycled_Content_cut_off + 1.272⋅elec_switch_param_eu⋅market ↪\n", "\n", "↪ _group_for_electricity_medium_voltage1 + 1.59⋅elec_switch_param_us⋅market_gr ↪\n", "\n", "↪ oup_for_electricity_medium_voltage + 1.0⋅market_for_aluminium_cast_alloy⋅(0. ↪\n", "\n", "↪ 965 - 0.965⋅share_recycled_aluminium) + 0.00406⋅market_for_cast_iron + 4.0⋅m ↪\n", "\n", "↪ arket_for_chromium_oxide_flakes + 0.00102⋅market_for_copper_cathode + 0.0305 ↪\n", "\n", "↪ ⋅market_for_magnesium + 0.00508⋅market_for_manganese + 0.00406⋅market_for_si ↪\n", "\n", "↪ licon_metallurgical_grade + 0.00203⋅market_for_zinc) + (a + 3.5)⋅(0.7⋅Heat_w ↪\n", "\n", "↪ aste + 1.5⋅Occupation_industrial_area)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First, let's look at the full expression defining our model\n", "expr, _ = agb.actToExpression(total_inventory)\n", "expr" ] }, { "cell_type": "markdown", "id": "6290903d", "metadata": {}, "source": [ "## Compute simplified models\n", "\n", "We provide some method to automatically select a subset of parameters, based on the **sobol indices**, and then compute simplified models for it.\n", "\n", "We also round numerical expression to 3 digits, and we remove terms in sums that are less than 1% of total." ] }, { "cell_type": "code", "execution_count": 36, "id": "a2cadc8f", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:43:42.101888Z", "start_time": "2024-10-29T15:43:40.461761Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/scipy/stats/_qmc.py:958: UserWarning: The balance properties of Sobol' points require n to be a power of 2.\n", " sample = self._random(n, workers=workers)\n", "[WARNING] Param 'b' is marked as FIXED, but passed in parameters : ignored\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n", "Processing sobol for ('EF v3.0', 'climate change', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: biogenic', 'global warming potential (GWP100)')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/SALib/util/__init__.py:274: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n", " names = list(pd.unique(groups))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing sobol for ('EF v3.0', 'climate change: fossil', 'global warming potential (GWP100)')\n", "Processing sobol for ('EF v3.0', 'climate change: land use and land use change', 'global warming potential (GWP100)')\n", "> Method : climate change - global warming potential (GWP100)\n", "S1: 0.9930526859439568\n", "S2: 0.051434250646092555\n", "ST: 1.0062348752524026\n", "Selected params : ['share_recycled_aluminium'] explains: 0.9683778519635373\n" ] }, { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle - 0.511 ShareRecycledAluminium \\left(5.67 ShareRecycledAluminium - 32.1\\right) + 0.434$" ], "text/plain": [ "-0.511⋅ShareRecycledAluminium⋅(5.67⋅ShareRecycledAluminium - 32.1) + 0.434" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "> Method : climate change: biogenic - global warming potential (GWP100)\n", "S1: 0.9937696441331528\n", "S2: 0.0382551727844193\n", "ST: 1.0068989491538092\n", "Selected params : ['share_recycled_aluminium'] explains: 0.9668512669476762\n" ] }, { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle - 0.516 ShareRecycledAluminium \\left(0.0105 ShareRecycledAluminium - 0.0719\\right) + 0.000886$" ], "text/plain": [ "-0.516⋅ShareRecycledAluminium⋅(0.0105⋅ShareRecycledAluminium - 0.0719) + 0.000 ↪\n", "\n", "↪ 886" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "> Method : climate change: fossil - global warming potential (GWP100)\n", "S1: 0.9930493545236683\n", "S2: 0.05147801968954844\n", "ST: 1.0062360583470846\n", "Selected params : ['share_recycled_aluminium'] explains: 0.968343555520936\n" ] }, { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle - 0.514 ShareRecycledAluminium \\left(5.64 ShareRecycledAluminium - 32.0\\right) + 0.417$" ], "text/plain": [ "-0.514⋅ShareRecycledAluminium⋅(5.64⋅ShareRecycledAluminium - 32.0) + 0.417" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "> Method : climate change: land use and land use change - global warming potential (GWP100)\n", "S1: 0.9901255684648551\n", "S2: 0.06999477531502071\n", "ST: 1.006650555242752\n", "Selected params : ['share_recycled_aluminium'] explains: 0.9836306171832606\n" ] }, { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle - 0.517 ShareRecycledAluminium \\left(0.0152 ShareRecycledAluminium - 0.0306\\right) + 0.000961$" ], "text/plain": [ "-0.517⋅ShareRecycledAluminium⋅(0.0152⋅ShareRecycledAluminium - 0.0306) + 0.000 ↪\n", "\n", "↪ 961" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simplified = agb.sobol_simplify_model(\n", " total_inventory, # The model\n", " impacts, # Impacts to consider\n", " functional_unit=functional_value,\n", " \n", " n=1000, # For large model, you may test other value and ensure ST and sum(S1) are close to 1.0 \n", " fixed_mode = agb.FixedParamMode.MEDIAN, # We replace minor parameters by median by default,\n", " min_ratio=0.8, # Min ratio of variability to explain\n", " num_digits=3)" ] }, { "cell_type": "code", "execution_count": 37, "id": "1993def5", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:43:53.734926Z", "start_time": "2024-10-29T15:43:53.668741Z" } }, "outputs": [ { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle - 0.511 share_{recycled aluminium} \\left(5.67 share_{recycled aluminium} - 32.1\\right) + 0.434$" ], "text/plain": [ "-0.511⋅share_recycled_aluminium⋅(5.67⋅share_recycled_aluminium - 32.1) + 0.434" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at the expression for first impact again \n", "# much simpler ! \n", "simplified[0].expr" ] }, { "cell_type": "markdown", "id": "7a757701", "metadata": {}, "source": [ "## Compare simplified model with full model\n", "\n", "Finally, we can compare the distribution of those simplified model against the full model. We provide a function for graphical display of it, and compuation of de R-Square score.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b903c7e6", "metadata": { "ExecuteTime": { "start_time": "2024-10-29T15:43:54.858Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Required param 'elec_switch_param' was missing, replacing by default value : us\n", "[INFO] Required param 'a' was missing, replacing by default value : 0.5\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/lca_algebraic/stats.py:1349: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " \"R² : %0.3g\" % r_value,\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Required param 'elec_switch_param' was missing, replacing by default value : us\n", "[INFO] Required param 'a' was missing, replacing by default value : 0.5\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/lca_algebraic/stats.py:1349: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " \"R² : %0.3g\" % r_value,\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Required param 'elec_switch_param' was missing, replacing by default value : us\n", "[INFO] Required param 'a' was missing, replacing by default value : 0.5\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/lca_algebraic/stats.py:1349: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " \"R² : %0.3g\" % r_value,\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating samples ...\n", "Transforming samples ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO] Required param 'elec_switch_param' was missing, replacing by default value : us\n", "[INFO] Required param 'a' was missing, replacing by default value : 0.5\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "[INFO] Required param 'share_recycled_aluminium' was missing, replacing by default value : 0.6\n", "/home/rjolivet/lca_algebraic/.tox/py311/lib/python3.11/site-packages/lca_algebraic/stats.py:1349: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " \"R² : %0.3g\" % r_value,\n" ] } ], "source": [ "agb.compare_simplified(\n", " total_inventory, \n", " impacts, \n", " simplified,\n", " functional_unit=functional_value)" ] }, { "cell_type": "code", "execution_count": 37, "id": "030d3fae", "metadata": { "ExecuteTime": { "end_time": "2024-10-29T15:38:33.124809100Z", "start_time": "2024-04-16T14:26:34.577210Z" } }, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "Rmd,ipynb", "notebook_metadata_filter": "-all" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }